bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3855a98ba76d7bac8c20abc3786cfa53\",\n \"score\": \"0.5790455\",\n \"text\": \"def test_register_valid_form(self):\\n\\t\\tdata = {\\n\\t\\t\\t'username': 'henry009',\\n\\t\\t\\t'password1': 'W0rldToUR',\\n\\t\\t\\t'password2': 'W0rldToUR',\\n\\t\\t}\\n\\n\\t\\tform = UserCreationForm(data=data)\\n\\t\\tself.assertTrue(form.is_valid())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9467b041275a5bdff89e8c3b56ab690\",\n \"score\": \"0.5785946\",\n \"text\": \"def test_register_invalid_form(self):\\n\\t\\tdata = {\\n\\t\\t\\t'username': 'henry009',\\n\\t\\t\\t'password1': '',\\n\\t\\t\\t'password2': '',\\n\\t\\t}\\n\\n\\t\\tform = UserCreationForm(data=data)\\n\\t\\tself.assertFalse(form.is_valid())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"606c01d22ad738140d6ad99b0939e7d3\",\n \"score\": \"0.5784133\",\n \"text\": \"def test_if_created_success(self):\\r\\n \\r\\n res = self.client.post(\\r\\n reverse(CREATE_DISH_URL),\\r\\n data=self.create_payload\\r\\n )\\r\\n self.assertEqual(res.status_code,status.HTTP_201_CREATED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4d082f8e5362696ec01940448a58e43\",\n \"score\": \"0.57836413\",\n \"text\": \"def is_valid(self):\\n return self.form_submitted and self.forms[self.form_submitted].is_valid()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44ae1fa8955c282c651e2e664b8985cb\",\n \"score\": \"0.57737565\",\n \"text\": \"def success(self):\\n\\n return not self.failure\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78c2db861a294d338e80eb5d5a4644c7\",\n \"score\": \"0.57730603\",\n \"text\": \"def test_is_valid(self):\\n self.broke = BankAccount(\\\"Kat Dennings\\\")\\n self.broke.set_balance(0)\\n self.closed = BankAccount(\\\"Neth Behrs\\\")\\n self.closed.set_status(\\\"closed\\\")\\n self.assertTrue(self.avi.is_valid())\\n self.assertFalse(self.broke.is_valid())\\n self.assertFalse(self.closed.is_valid())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca6c599c56fe338ec98bd7ab70db6fff\",\n \"score\": \"0.57712144\",\n \"text\": \"def test_branch_creation(self):\\n # Login\\n self.client.login(username=self.admin.username, password=self.password)\\n\\n # submit an empty form\\n response = self.client.post(self.branch_add_url, data=self.empty_form)\\n\\n # check that the same template is displayed (form + errors)\\n self.assertTemplateUsed('admin/change_form.html')\\n \\n # an empty form should return 9 errors for the required fields\\n self.assertContains(response, 'This field is required', count=9)\\n \\n # now submit valid form\\n response = self.client.post(self.branch_add_url, follow=True, data=self.branch_data)\\n \\n # store branch in a variable\\n branch = Branch.objects.get(slug=self.branch_data['slug'])\\n \\n # check that the branch was created successfully, following a redirect\\n self.assertRedirects(response, response.redirect_chain[0][0], response.redirect_chain[0][1])\\n self.assertTemplateUsed('admin/change_list.html')\\n self.assertEqual(response.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb7ace4f2f467d9472bd4f5c48504497\",\n \"score\": \"0.5769032\",\n \"text\": \"def test_create_new_question_failure(self):\\n ## get the number of questions before creating a new question obj\\n questions_total_before = len(Question.query.all())\\n\\n ## load response data\\n response = self.client().post('/questions', json={\\n 'question': 'question', \\n 'answer': 'answer',\\n 'category': 'category',\\n 'difficulty': None,\\n })\\n ## get the number of questions after creating a new question obj\\n questions_total_after = len(Question.query.all())\\n \\n ## check success value, status_code \\n self.assertFalse(json.loads(response.data)['success'])\\n self.assertEqual(response.status_code, 422)\\n ## as no obj was created the questions_total_before should be equal questions_total_after\\n self.assertTrue(questions_total_before == questions_total_after)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"837af7aad702bec5cdd7cb0d0e16ef8c\",\n \"score\": \"0.5765833\",\n \"text\": \"def is_success(self):\\n\\n import os\\n if os.path.exists(self.id):\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68bef48f7919955e26fafc22204e7a28\",\n \"score\": \"0.5758976\",\n \"text\": \"def test_add_modify_trip_empty_form(self):\\n response = self.client.post(self.urls['add_trip'], {})\\n \\n self.assertContains(response, 'This field is required.', count=8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d124a51b1f4cde2f9541f482071fedfb\",\n \"score\": \"0.57574934\",\n \"text\": \"def test_ticket_create_validation_failed(self):\\n res = self.client.post('/tickets/ticket_create', {'title': '', 'text': ''})\\n assert b'This field is required' in res.content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc7f08385dff803227d29f84c222348b\",\n \"score\": \"0.57537276\",\n \"text\": \"def test_new_product_post_fail(self):\\n\\n response = self.client.post(\\n reverse('reports:new_product'),\\n {u'name': u''},\\n follow=True\\n )\\n\\n self.assertEqual(response.status_code, 200)\\n self.assertFalse(response.context['form'].is_valid())\\n self.assertEqual(response.context['form'].errors, {\\n 'name': ['To pole jest wymagane.'],\\n 'unit': ['To pole jest wymagane.'],\\n 'category': ['To pole jest wymagane.']\\n })\\n self.assertTemplateUsed(response, 'reports/new_element.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0c23439c4c4ca79429651a44fe90d55\",\n \"score\": \"0.57519215\",\n \"text\": \"def test_POST_workflow_with_invalid_formdata(self):\\n perm = _makePermission(perm='change_user', model='user', app_label='auth')\\n self.user.user_permissions.add(perm)\\n\\n form = self.app.get(reverse('user.add'), user=self.user).forms['user-form']\\n\\n response = form.submit()\\n\\n response.mustcontain('There are some errors or missing data')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2871e424098704d3b48764efb88a0faa\",\n \"score\": \"0.57426554\",\n \"text\": \"def test_valid_already_success(self, m_apps):\\n m_order = mock.MagicMock()\\n m_order.get_absolute_url.return_value = '/order/url/'\\n m_model = mock.MagicMock()\\n m_model.get_by_order_id.return_value = m_order\\n m_apps.get_model.return_value = m_model\\n\\n self.payment.process()\\n self.payment.complete()\\n process_mock.reset_mock()\\n success_mock.reset_mock()\\n\\n response = self._req(self._get_data(), code=302)\\n if response['Location'].startswith('http://testserver'):\\n response['Location'] = response['Location'][17:]\\n self.assertEqual(response['Location'], '/order/url/')\\n\\n m_apps.get_model.assert_called_once_with(*conf.MODEL)\\n m_model.get_by_order_id.assert_called_once_with(self.payment.order_id)\\n self.assertEqual(m_order.get_absolute_url.call_count, 1)\\n\\n payment = Payment.objects.get(pk=self.payment.id)\\n self.assertTrue(payment.is_completed)\\n self.assertTrue(payment.is_payed)\\n self.assertTrue(payment.is_started)\\n\\n # Проверяем что отправились правильные сигналы\\n self._check_signals(0, 0, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f92840421239c893aa41147d74f5c77f\",\n \"score\": \"0.5741619\",\n \"text\": \"def form_valid(self, form):\\n User.objects.create_user(**form.cleaned_data)\\n return redirect(self.success_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82624d8331db8fef13f2b251b3f6c74e\",\n \"score\": \"0.57408065\",\n \"text\": \"def test_AddRoomFormIsNotValid(self):\\n\\n # Riempimento\\n # Al form manca il campo bedsNumber\\n form_data = {'roomNumber' : 14,\\n 'services' : ['GARAGE', 'WIFI'],\\n 'price' : 120.0 }\\n\\n # Istanziamento form\\n addRoomForm = AddRoomForm(data=form_data)\\n\\n # Verifica\\n self.assertFalse(addRoomForm.is_valid(), addRoomForm.errors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00595624e1b3c7a54d012d0f764dedbb\",\n \"score\": \"0.57391965\",\n \"text\": \"def test_taskname_form_empty(self):\\n\\n # get object\\n form = TasknameForm(data = {})\\n # compare\\n self.assertFalse(form.is_valid())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9616f5f8363291e79d206b1aecd1f36f\",\n \"score\": \"0.5731502\",\n \"text\": \"def test_create_valid_user_success(self):\\n\\n res = self.client.post(CREATE_USER_URL, self.payload)\\n\\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\\n user = get_user_model().objects.get(**res.data)\\n self.assertTrue(user.check_password(self.payload['password']))\\n self.assertNotIn('password', res.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b539ac657a339455a6e8e43472673092\",\n \"score\": \"0.57312447\",\n \"text\": \"def test_unique(self):\\n form = KeyAdminForm({'key': self.key.key})\\n self.assertFalse(form.is_valid())\\n self.assertTrue(\\\"exists\\\" in form.errors['key'][0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef6f7ccc775980e0ffc9b248e044fd2c\",\n \"score\": \"0.57308775\",\n \"text\": \"def import_succeeded(self):\\n return not self.validation_errors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3cbd8d705611ba914824d41b7d04dd6\",\n \"score\": \"0.5730092\",\n \"text\": \"def is_valid(self) -> bool:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a131201eaa6a1fe11759ee250fd34f3\",\n \"score\": \"0.57259554\",\n \"text\": \"def form_valid(self, form):\\n self.object = form.save(commit=False)\\n self.object.uploaded_by = unicode(self.request.user)\\n self.object.save()\\n \\n return self.response_class(\\n request = self.request,\\n template = self.template_name_success,\\n context = {\\\"asset\\\":self.object}\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"154d4a3929fa3deeda52934475e0d67c\",\n \"score\": \"0.5717287\",\n \"text\": \"def test_register_success(self, *_):\\n # Open /logout to ensure user is logout\\n self.open(base_url+'/logout')\\n # Open register page\\n self.open(base_url+'/register')\\n # Enter testuser's email into element #email\\n self.type(\\\"#email\\\", test_user.email)\\n # Enter testuser's name\\\" into element #name\\n self.type(\\\"#name\\\", test_user.name)\\n # Enter testuser's password into element #password\\n self.type(\\\"#password\\\", test_user.raw_password)\\n # Enter testuser's password into element #password2\\n self.type(\\\"#password2\\\", test_user.raw_password)\\n # Click element input[type = “submit”]\\n self.click('input[type=\\\"submit\\\"]')\\n # Validate that the current page has been redirected to /login\\n self.assert_equal(self.get_current_url(), base_url + '/login')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a96d5cc1aacd4b4e0a3bcdad8cfa8b0d\",\n \"score\": \"0.5716532\",\n \"text\": \"def is_failed(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a02e4b1a69d1e19520520b7992f6055f\",\n \"score\": \"0.5715792\",\n \"text\": \"def test_registration_form_tos(self):\\n no_tos = self.__good_data\\n del no_tos['tos']\\n form = forms.RegistrationFormTermsOfService(data=no_tos)\\n self.failIf(form.is_valid())\\n self.assertEqual(form.errors['tos'], [u\\\"You must agree to the terms to register.\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acdd5115a78b710896c99c5c3782d653\",\n \"score\": \"0.5715443\",\n \"text\": \"def is_valid(self):\\n if self._errors is not None and len(self._errors) == 0:\\n self._errors = None\\n self.full_clean()\\n return super(JcropForm, self).is_valid()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4c171cf93a20c7409436f9dbe4708e4\",\n \"score\": \"0.5703319\",\n \"text\": \"def test_successful_post_to_build_form(self):\\n self.client.force_login(self.user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2086ec8d53a92c36235c197db1a4cae1\",\n \"score\": \"0.5702617\",\n \"text\": \"def test_create_user_sucess(self):\\n response = self.client.post(\\\"/user\\\", json=SUCCESS_USER_1)\\n self.assertEqual(response.status_code, 201)\\n self.assertIn(\\\"User was successfully created.\\\", str(response.data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"260befc57a01ebba74af1c7c30ca7852\",\n \"score\": \"0.57006764\",\n \"text\": \"def succeeded(self):\\n return self.success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1047a469bd267ce4e34d54bddf5bbcd\",\n \"score\": \"0.56991297\",\n \"text\": \"def validate_ok(self):\\n unacceptable = [\\n not self.name_is_valid,\\n not self.no_geometry_button.isChecked() and not self.units_are_valid,\\n self.mesh_button.isChecked() and not self.file_is_valid,\\n ] + self.pixel_validator.unacceptable_pixel_states()\\n self.is_valid.emit(not any(unacceptable))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0081db7de0a42fdac42955b77f347c15\",\n \"score\": \"0.5696547\",\n \"text\": \"def test_crear_sucursal(self):\\n sucursal = Sucursal.objects.create(\\n descripcion='FormosaTest1'\\n )\\n\\n return self.assertTrue(sucursal)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fbfd780041a7d9663f030fd0e9f28ed\",\n \"score\": \"0.568844\",\n \"text\": \"def form_valid(self, form):\\n # Get account object creating the character\\n account = self.request.user\\n character = None\\n\\n # Get attributes from the form\\n self.attributes = {k: form.cleaned_data[k] for k in form.cleaned_data.keys()}\\n charname = self.attributes.pop(\\\"db_key\\\")\\n description = self.attributes.pop(\\\"desc\\\")\\n # Create a character\\n character, errors = self.typeclass.create(charname, account, description=description)\\n\\n if errors:\\n # Echo error messages to the user\\n [messages.error(self.request, x) for x in errors]\\n\\n if character:\\n # Assign attributes from form\\n for key, value in self.attributes.items():\\n setattr(character.db, key, value)\\n\\n # Return the user to the character management page, unless overridden\\n messages.success(self.request, \\\"Your character '%s' was created!\\\" % character.name)\\n return HttpResponseRedirect(self.success_url)\\n\\n else:\\n # Call the Django \\\"form failed\\\" hook\\n messages.error(self.request, \\\"Your character could not be created.\\\")\\n return self.form_invalid(form)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993873,"cells":{"query_id":{"kind":"string","value":"fcac08a3fde20e7c1b9f5eec5b4927ac"},"query":{"kind":"string","value":"Checking if a molecular system is composed of specific elements. The function returns True or False depending on whether or not the system is entirely composed of the elements required."},"positive_passages":{"kind":"list like","value":[{"docid":"9367000d4507d11fa32011a52b5c36ee","score":"0.7346343","text":"def is_composed_of(molecular_system, selection='all', syntax='MolSysMT', **kwargs):\n\n\n\n\n from . import get\n\n if len(kwargs):\n\n # molecules in kwargs\n set_molecules = {'n_ions', 'n_waters', 'n_small_molecules', 'n_peptides', 'n_proteins',\n 'n_dnas', 'n_rnas', 'n_lipids', 'n_oligosaccharides', 'n_saccharides'}\n\n if set_molecules & set(kwargs.keys()):\n\n aux_dictionary = get(molecular_system, element=\"atom\", selection=selection, syntax=syntax,\n output_type='dictionary',\n n_ions=True, n_waters=True, n_small_molecules=True, n_peptides=True, n_proteins=True,\n n_dnas=True, n_rnas=True, n_lipids=True, n_oligosaccharides=True, n_saccharides=True)\n\n for key, value in aux_dictionary.items():\n if value:\n if key in kwargs:\n if isinstance(kwargs[key], bool):\n if not kwargs[key]:\n return False\n elif isinstance(kwargs[key], (int, np.int64)):\n if not kwargs[key]==value:\n return False\n else:\n return False\n\n # n_elements in kwargs\n\n set_n_elements = {'n_atoms', 'n_groups', 'n_components', 'n_molecules', 'n_chains',\n 'n_entities'}\n\n if set_n_elements & set(kwargs.keys()):\n\n aux_dictionary = get(molecular_system, element=\"atom\", selection=selection, syntax=syntax,\n output_type='dictionary',\n n_atoms=True, n_groups=True, n_components=True, n_molecules=True, n_chains=True,\n n_entities=True)\n\n for key, value in kwargs.items():\n if key in set_n_elements:\n if isinstance(value, bool):\n if value:\n if aux_dictionary[key]==0:\n return False\n else:\n if aux_dictionary[key]>0:\n return False\n elif isinstance(value, (int, np.int64)):\n if value!=aux_dictionary[key]:\n return False\n\n else:\n\n n_atoms_selection = get(molecular_system, element='atom', selection=selection,\n syntax=syntax, n_atoms=True)\n\n n_atoms = get(molecular_system, element='atom', selection=selection,\n syntax=syntax, n_atoms=True)\n\n if n_atoms!=n_atoms_selection:\n return False\n\n return True","title":""}],"string":"[\n {\n \"docid\": \"9367000d4507d11fa32011a52b5c36ee\",\n \"score\": \"0.7346343\",\n \"text\": \"def is_composed_of(molecular_system, selection='all', syntax='MolSysMT', **kwargs):\\n\\n\\n\\n\\n from . import get\\n\\n if len(kwargs):\\n\\n # molecules in kwargs\\n set_molecules = {'n_ions', 'n_waters', 'n_small_molecules', 'n_peptides', 'n_proteins',\\n 'n_dnas', 'n_rnas', 'n_lipids', 'n_oligosaccharides', 'n_saccharides'}\\n\\n if set_molecules & set(kwargs.keys()):\\n\\n aux_dictionary = get(molecular_system, element=\\\"atom\\\", selection=selection, syntax=syntax,\\n output_type='dictionary',\\n n_ions=True, n_waters=True, n_small_molecules=True, n_peptides=True, n_proteins=True,\\n n_dnas=True, n_rnas=True, n_lipids=True, n_oligosaccharides=True, n_saccharides=True)\\n\\n for key, value in aux_dictionary.items():\\n if value:\\n if key in kwargs:\\n if isinstance(kwargs[key], bool):\\n if not kwargs[key]:\\n return False\\n elif isinstance(kwargs[key], (int, np.int64)):\\n if not kwargs[key]==value:\\n return False\\n else:\\n return False\\n\\n # n_elements in kwargs\\n\\n set_n_elements = {'n_atoms', 'n_groups', 'n_components', 'n_molecules', 'n_chains',\\n 'n_entities'}\\n\\n if set_n_elements & set(kwargs.keys()):\\n\\n aux_dictionary = get(molecular_system, element=\\\"atom\\\", selection=selection, syntax=syntax,\\n output_type='dictionary',\\n n_atoms=True, n_groups=True, n_components=True, n_molecules=True, n_chains=True,\\n n_entities=True)\\n\\n for key, value in kwargs.items():\\n if key in set_n_elements:\\n if isinstance(value, bool):\\n if value:\\n if aux_dictionary[key]==0:\\n return False\\n else:\\n if aux_dictionary[key]>0:\\n return False\\n elif isinstance(value, (int, np.int64)):\\n if value!=aux_dictionary[key]:\\n return False\\n\\n else:\\n\\n n_atoms_selection = get(molecular_system, element='atom', selection=selection,\\n syntax=syntax, n_atoms=True)\\n\\n n_atoms = get(molecular_system, element='atom', selection=selection,\\n syntax=syntax, n_atoms=True)\\n\\n if n_atoms!=n_atoms_selection:\\n return False\\n\\n return True\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"23d46596c5f225108ac9b32c09840b3f","score":"0.6892202","text":"def _is_from_chemical_system(chemical_system, struct):\n chemsys = list(set([sp.symbol for sp in struct.composition]))\n if len(chemsys) != len(chemical_system):\n return False\n for el in chemsys:\n if not el in chemical_system:\n return False\n return True","title":""},{"docid":"afa1fe54fa42619f767173bbce2f56dc","score":"0.66731745","text":"def __contains__(self, element):\n if isinstance(element, BasicCompartmentCompound):\n return element.compartment == self\n elif isinstance(element, BasicCompound):\n return element in self._contained\n elif isinstance(element, BasicReaction):\n return all(cmpd in self for cmpd in element.compounds_iter())\n else:\n raise PyOrganismError(\"unrecognised metabolic component '{0}'\",\n element)","title":""},{"docid":"cc49fb93081497391d2e72c61e872657","score":"0.6550423","text":"def isValid(self):\n return self.has_input_section('rem') and \\\n self.has_input_section('molecule')","title":""},{"docid":"3270faa06de9daff51a11231f6eb9853","score":"0.6218953","text":"def check_multiplicity( self, part ):\n ok = True\n for k,v in self.MULTIPLICITY_LIMITS.items():\n if isinstance( part, k ):\n ok = ok and len( [item for item in self.sub_com if isinstance( item, k )]) < v\n return ok","title":""},{"docid":"528d22cf8b4d48668056f7a352e8e749","score":"0.61732703","text":"def check_multiplicity( self, mod, part ):\n ok = True\n for k,v in self.MULTIPLICITY_LIMITS.items():\n if isinstance( part, k ):\n ok = ok and len( [item for item in mod.sub_com if isinstance( item, k ) ] ) < v\n return ok","title":""},{"docid":"de75d99e180c982eb6c5f7012757c1a5","score":"0.60713637","text":"def can_install(self,part):\n return self.is_legal_sub_com(part) and part.scale <= self.scale and part.volume <= self.free_volume and self.check_multiplicity( part )","title":""},{"docid":"708e7e3d05112c54838b2497f44638f4","score":"0.6057568","text":"def _supports(self, system: Union[ProteinLigandComplex]) -> bool:\n super_checks = super()._supports(system)\n proteins = [c for c in system.components if isinstance(c, self._COMPATIBLE_PROTEIN_TYPES)]\n ligands = [c for c in system.components if isinstance(c, self._COMPATIBLE_LIGAND_TYPES)]\n return all([super_checks, len(proteins) == 1]) and all([super_checks, len(ligands) == 1])","title":""},{"docid":"612e2ba637c170fb239273a092cf1b20","score":"0.5942075","text":"def HasCombination(self, bindings):\n self.program.CreateSolver()\n # Optimization: check the entire combination only if all of the bindings\n # are possible separately.\n return (all(self.program.solver.Solve({b}, self) for b in bindings)\n and self.program.solver.Solve(bindings, self))","title":""},{"docid":"3c160135f8280887e8aa4bd30eaf833c","score":"0.58893836","text":"def contains(self, pool):\n return (pool in self.molecule_design_pools)","title":""},{"docid":"0baefdec0f6c4b4c8d002ee266c0e766","score":"0.5879723","text":"def can_build(self, structure: common.Structure,\n adjacentEnemies: bool) -> bool:\n # Can't possible build if we don't have any such structures left\n # on the board.\n if self.structures[structure] <= 0:\n return False\n cost: common.Resources = self.faction.structure_cost(\n structure, adjacentEnemies)\n remainingResources = self.resources - cost\n if remainingResources.is_valid():\n return True\n return self._can_burn_power_for_missing_resources(remainingResources)","title":""},{"docid":"56ebe0d450f72ce66202b53cec8e6d0f","score":"0.58636296","text":"def _check_structure(self):\n if (not self._xml or self._xml.getroot().tag != self._APPLICATION\n or self._xml.find(self._COMPONENT) is None\n or self._xml.find(self._COMPONENT).tag != self._COMPONENT):\n return False\n return self._xml.find(self._COMPONENT).get(\n self._NAME) == self._PROJECTJDKTABLE","title":""},{"docid":"3e994788be796ccd79cfec369640dd7b","score":"0.58187366","text":"def check(sequence, max_homopolymer=4, max_simple_segment=6, max_content=0.6):\r\n if not homopolymer(sequence, max_homopolymer):\r\n return False\r\n if not repeat(sequence, max_simple_segment):\r\n return False\r\n if not dyad_repeat(sequence, max_simple_segment):\r\n return False\r\n if not inverse_repeat(sequence, max_simple_segment):\r\n return False\r\n if not cg_content(sequence, max_content):\r\n return False\r\n\r\n return True","title":""},{"docid":"46e36f338a39d1383d960d22599ccf2d","score":"0.57574743","text":"def has_beam_elements(self) -> bool:\n return self._mesh._stub.List(self._mesh._message).element_shape_info.has_beam_elements","title":""},{"docid":"1bd01c7f5b664152d8f751e1447dc6d4","score":"0.5750804","text":"def is_valid(self) -> bool:\n gen_elements = {i.element for i in self.items if i.kind is ItemKind.GENERATOR}\n chip_elements = {i.element for i in self.items if i.kind is ItemKind.MICROCHIP}\n return not gen_elements or chip_elements <= gen_elements","title":""},{"docid":"e5eaac51f759d7d198df9ff1c69fb0c8","score":"0.5745781","text":"def containedStructure(containee, container):\n\n if isinstance(containee, dict):\n if isinstance(container, dict):\n # then we have shapes to check\n return (set(containee.keys()).issubset(set(container.keys())) and\n # so the keys are all the same\n all(containedStructure(containee[k], container[k])\n for k in containee.keys()))\n # thus all values will be tested in the same way.\n else:\n return False # containee is a dict, but container isn't\n elif isinstance(containee, list):\n if isinstance(container, list):\n return set(containee).issubset(set(container))\n else:\n return False # containee is a lit, but container isn't\n elif containee is None:\n return True\n else:\n # else they have to be the same type\n return isinstance(container, type(containee))","title":""},{"docid":"490662a449b4a36ad33da430a7c49a7e","score":"0.5743979","text":"def is_concrete(self):\n # 1.\n mp_concrete_ok = all(mp.is_concrete() for mp in self.monomer_patterns)\n # 2.\n compartment_ok = self.compartment is not None and \\\n all(mp.is_site_concrete() for mp in self.monomer_patterns)\n return mp_concrete_ok or compartment_ok","title":""},{"docid":"cb50932784da92c7d9342ef71406fdcc","score":"0.57316095","text":"def is_valid_chemical_structure(cls, chemical_structures):\n try:\n cls._validate_chemical_structures(chemical_structures)\n except ValueError:\n result = False\n else:\n result = True\n return result","title":""},{"docid":"72516625cf07fdf934147620c3a94f2f","score":"0.57044786","text":"def hasUnitElement(self):\n leftUnities = [];\n for left in range(0, self.order):\n isLeftUnity = True;\n for right in range(0, self.order):\n if not self.multiply([left,right]) == right:\n isLeftUnity = False;\n break;\n if isLeftUnity:\n leftUnities.append(left);\n\n for right in leftUnities:\n isRightUnity = True;\n for left in range(0, self.order):\n if not self.multiply([left,right]) == left: \n isRightUnity = False;\n break;\n if isRightUnity:\n return True;\n\n return False;","title":""},{"docid":"5f65c5dcb120296f4f0b149034a93770","score":"0.56872874","text":"def tower_is_constructible(blocks):\n for i in range(len(blocks) - 1):\n # check that each pair of blocks is stably individually\n top = blocks[i+1]\n bottom = blocks[i]\n if not pair_is_stable(bottom, top): return False\n\n return True","title":""},{"docid":"87903b9a18c6d2be5d1c3e892db20559","score":"0.56788063","text":"def tower_is_constructable(self, blocks):\n for i in range(1, len(blocks)+1):\n # check that each pair of blocks is stably individually\n subtower = blocks[:i]\n if not self.tower_is_stable(subtower): return False\n\n return True","title":""},{"docid":"42df8847f4f61a46852745b67aa89a9f","score":"0.5674524","text":"def f2(mol):\n for atoms in filter(lambda x: len(x) <= 4, mol.GetRingInfo().AtomRings()):\n atom_types = Counter((mol.GetAtomWithIdx(atom).GetSymbol() for atom in atoms))\n if atom_types[\"O\"] + atom_types[\"S\"] + atom_types[\"N\"] > 1:\n return True\n\n return False","title":""},{"docid":"8c6f18718716353849364347d69d5b39","score":"0.56671256","text":"def contain(self, element):\n x, y = symbols(\"x, y\")\n a, b = self.basis[0].coeff_e\n c = self.basis[1].coeff_e[1]\n a_, b_ = element.coeff_e\n sol = solve((a * x - a_, b * x + c * y - b_), x, y)\n a__, b__ = sol[x], sol[y]\n\n return a__.is_integer and b__.is_integer","title":""},{"docid":"518c334071f997f897129e4926d5220d","score":"0.5661782","text":"def does_fit_in_room(model) -> bool:\n\n _, structures = walls_and_structures_in_room()\n wall_right_x, wall_bottom_y = wall_coordinates()\n is_valid = True\n min_area_overlap_invalid = 1e-10 # I check are overlap to determine in a configuration is invalid, \n # but I don't want false positives if there is effectively no overlap, but it isn't zero\n components = storage_ring_system_components(model)\n for component, structure in itertools.product(components, structures):\n x, y = component.exterior.xy\n x, y = np.array(x), np.array(y)\n is_out_side_wall = np.any(x > wall_right_x) or np.any(y < wall_bottom_y)\n is_overlapping = structure.intersection(component).area > min_area_overlap_invalid\n if is_out_side_wall or is_overlapping:\n is_valid = False\n break\n return is_valid","title":""},{"docid":"55321fe89aaf97722178227db8f188dd","score":"0.56345063","text":"def has_full(self):\n if self.has_three() and self.has_pair():\n return True\n return False","title":""},{"docid":"eb50c75e0b19d9c33aa7f09477702164","score":"0.56010145","text":"def is_concrete(self):\n # 1.\n sites_ok = self.is_site_concrete()\n # 2.\n compartment_ok = not self.monomer.model().compartments or self.compartment\n return compartment_ok and sites_ok","title":""},{"docid":"f5f4057cf533d969a06362f420ede06d","score":"0.55767643","text":"def contains_named_molecule(self) -> bool:\n return any(c[\"molecule_name\"] for c in self.components.values())","title":""},{"docid":"34c817637295bf694bcc8e11ef2822d4","score":"0.55751926","text":"def __contains__(self, molecule_design):\n return molecule_design in self.molecule_designs","title":""},{"docid":"f06be3f913e94c31e1d5fa7487659dee","score":"0.5558672","text":"def is_complex(self):\n for xf in self.transforms:\n if isinstance(xf, (ComplexTransform, ComplexSimulator)):\n return True\n return False","title":""},{"docid":"64bc786619dc9b9a00ec7bbdb3f6cfd0","score":"0.5553932","text":"def check_backbone_atoms(residue):\n intrp = residue.residue_name_plus_atom_names_interpreter().atom_name_interpretation\n if not intrp: return False\n return not bool(intrp.missing_atom_names().intersection(['N','CA','C','O']))","title":""},{"docid":"23688bd4b34e953f050c88e2bdd1a3bb","score":"0.5536081","text":"def _has_struct(elem):\n return isinstance(elem, np.ndarray) and any(isinstance(\n e, scipy.io.matlab.mio5_params.mat_struct) for e in elem)","title":""},{"docid":"e699b67840c99245d4bf8296713eb6ea","score":"0.55340946","text":"def can_equip(self,part):\n return self.is_legal_inv_com(part) and part.scale == self.scale and not self.inv_com","title":""},{"docid":"45cef083daa1baf03af46e017e392b87","score":"0.5529885","text":"def __contains__(self, element):\r\n is_parent_ok = element.parent is self\r\n is_child_ok = element in self.children\r\n if is_parent_ok:\r\n if is_child_ok:\r\n return True\r\n else:\r\n msg = \"Element is not in the container but parent is that \" \\\r\n \"container.\"\r\n else:\r\n if is_child_ok:\r\n msg = \"Element is in the container but parent is not that \" \\\r\n \"container.\"\r\n else:\r\n return False\r\n raise InconsistentParenthoodError(msg)","title":""},{"docid":"a510c51a2903044be94fab64671dfa2e","score":"0.55290663","text":"def solved_puz(puz):\n # All groups are solved\n for r in puz.rows:\n if not solved_group(r):\n return False\n for c in puz.cols:\n if not solved_group(c):\n return False\n for b in puz.boxs:\n if not solved_group(b):\n return False\n # And no nodes are unsolved\n return len(puz.unsolved) == 0","title":""},{"docid":"fc7bc46bb2882afeaf2d37846a63e665","score":"0.5527124","text":"def check_satisfiability(self) -> bool:\n\t\treturn np.all(self.get_cand_count() >= self.equiv_size_array) # returns False if any cand is 0","title":""},{"docid":"ad71063c8d01021939efcc8df7049342","score":"0.5515364","text":"def _satisfies_intersection_constraint(scheme, solution):\n clusters = construct_clusters(scheme, solution)\n clusters_instersect = False\n for i in range(len(clusters)):\n for j in range(i + 1, len(clusters)):\n clusters_instersect |= bool(clusters[i].machines & clusters[j].machines)\n clusters_instersect |= bool(clusters[i].parts & clusters[j].parts)\n return not clusters_instersect","title":""},{"docid":"99e6023f3b54bb9c5337bd987b430f76","score":"0.54989064","text":"def has_undesirable_elements(mol):\n atomsHC = oechem.OEOrAtom(oechem.OEIsHydrogen(), oechem.OEIsCarbon())\n atomsNO = oechem.OEOrAtom(oechem.OEIsNitrogen(), oechem.OEIsOxygen())\n atomsFS = oechem.OEOrAtom(oechem.OEHasAtomicNum(9), oechem.OEIsSulfur())\n atomsHCNO = oechem.OEOrAtom(atomsHC, atomsNO)\n atomsHCNOFS = oechem.OEOrAtom(atomsHCNO, atomsFS)\n atomsHCNOFSCl = oechem.OEOrAtom(atomsHCNOFS, oechem.OEHasAtomicNum(17))\n atomsHCNOFSClP = oechem.OEOrAtom(atomsHCNOFSCl, oechem.OEIsPhosphorus())\n\n undesirable_atom = mol.GetAtom(oechem.OENotAtom(atomsHCNOFSClP))\n if undesirable_atom is not None:\n return True\n\n return False","title":""},{"docid":"5a524b1f006ed53c2061c69fc7516539","score":"0.5472856","text":"def _check_candidate(subset_types, he, cand):\n is_candidate = False\n if subset_types in [\"all\", \"empirical\"]:\n if len(he) != len(cand):\n is_candidate = True\n elif subset_types == \"immediate\":\n if len(he) == len(cand) - 1 or len(he) - 1 == len(cand):\n is_candidate = True\n return is_candidate","title":""},{"docid":"001c2c67c6c6adb01140c10d2ca4e751","score":"0.54618907","text":"def __contains__(self, element):\n if len(self.formula) == 0:\n LOGGER.warn(\"testing element against empty formula\")\n return element in self.formula","title":""},{"docid":"7fc5c2037ae0580f091395e8d32c8bdd","score":"0.54612345","text":"def atoms_in_subsys(self, alist, fsubsys):\n return all(f in fsubsys for f in map(lambda a: self._mol.fragnumbers[a], alist))","title":""},{"docid":"04c17526696948d8ffeba768e5d7d9b5","score":"0.54607683","text":"def atoms_in_active(self, alist, subsys):\n if subsys == None: return True\n return any(a in subsys for a in alist)","title":""},{"docid":"eda5c11b37024c3964ffadc74f8b1548","score":"0.54542404","text":"def CanHaveCombination(self, bindings):\n goals = set(bindings)\n seen = set()\n stack = [self]\n # TODO(kramm): Take blocked nodes into account, like in Bindings()?\n while stack and goals:\n node = stack.pop()\n if node in seen:\n continue\n seen.add(node)\n goals -= goals & node.bindings\n stack.extend(node.incoming)\n return not goals","title":""},{"docid":"fa61cad4ffbe28e3e1698351d66be96c","score":"0.5446482","text":"def contains_molecule(self, molecule_name: str) -> bool:\n return any(\n c[\"molecule_name\"] == molecule_name for c in self.components.values()\n )","title":""},{"docid":"d816a939fa08cd743cb44021624593b8","score":"0.54320884","text":"def check(self):\n flag = 0\n if len(self.name)!=len(self.connection)+1:\n logging.warning(\"The number of connection and name in Proposition might not match\")\n flag=1\n\n if self.unit == 1 & self.out_negative==1:\n logging.warning(\"There is a conflict in judging unit\")\n flag =1\n\n sign = ['V','^','->','~','<->']\n for i in self.connection:\n if i not in sign:\n logging.warning('there might be invalid sign in Proposition')\n flag = 1\n\n for i in self.name:\n if type(i)==type('a'):\n if i < 'A' or i > 'Z':\n logging.warning('the Proposition should be from A to Z')\n flag=1\n if i == 'V':\n logging.warning('try to not use V in Proposition')\n flag = 1\n\n else:\n if False == i.check():\n flag =1\n\n if self.unit == 1:\n for i in self.name:\n if type(i)!=type('a'):\n logging.warning('the content of unit might not be string')\n flag = 1\n\n\n if flag==0:\n print(show(self.result)+' has been checked without problems')\n return True\n else:\n logging.warning(show(self.result)+' has been checked with problems!!!')\n return False","title":""},{"docid":"fd1d5727be200682530e67297b16f60f","score":"0.54292655","text":"def validate_sudoku(puzzle):\n\tif puzzle is None:\n\t\treturn False\n\n\tcoords, groups, all_units = sudoku_reference()\n\tfull = [str(x) for x in range(1, 10)] # Full set, 1-9 as strings\n\n\t# Checks if all units contain a full set\n\treturn all([sorted([puzzle[cell] for cell in unit]) == full for unit in all_units])","title":""},{"docid":"036b430d80cd0ea431e4d1765dd8e620","score":"0.54260206","text":"def establish_has_single_uom(self):\n\n self.has_single_uom_flag = True\n common_uom = None\n for part in self.parts():\n part_uom = self.uom_for_part(part)\n if common_uom is None:\n common_uom = part_uom\n elif part_uom != common_uom:\n self.has_single_uom_flag = False\n break\n if common_uom is None: self.has_single_uom_flag = True # all uoms are None (probably discrete properties)\n return self.has_single_uom_flag","title":""},{"docid":"bd6a1a9e5c5c5261a3f40e3587658f9d","score":"0.54250455","text":"def something_is_still_afloat(fleet):\n for x in fleet:\n for y in x:\n if(y):\n return True\n return False","title":""},{"docid":"fc6cc1bd26fcf4bfcc9fad17799596c0","score":"0.5420859","text":"def is_enzyme_cmpd(self):\n return any((self.is_product==True, self.is_substrate==True))","title":""},{"docid":"aaf07b4478a84666d83fec632d4c8b93","score":"0.54199886","text":"def can_equip(self,part):\n return self.is_legal_inv_com(part) and part.scale <= self.scale","title":""},{"docid":"aaf07b4478a84666d83fec632d4c8b93","score":"0.54199886","text":"def can_equip(self,part):\n return self.is_legal_inv_com(part) and part.scale <= self.scale","title":""},{"docid":"ca11f85244a474224030fef1d8a4af8b","score":"0.5418681","text":"def can_build_on_map(self, position, game_map):\n x_init, y_init = position.value\n length, height = self.size\n\n # First check if the building will be placed within the boundaries of the map:\n if x_init - (length - 1) < 0:\n print('Sorry, building placement too far west.')\n return False\n if x_init >= game_map.width:\n print('Sorry, building placement too far east.')\n return False\n if y_init < 0:\n print('Sorry, building placement too far south.')\n return False\n if y_init + (height - 1) >= game_map.height:\n print('Sorry, building placement too far north.')\n return False\n\n # Next, check that there are no obstructions. Buildings are allowed to be built\n # only where there are blank spaces or wood.\n x_final = x_init - length\n y_final = y_init + height\n for x in range(x_init, x_final, -1):\n for y in range(y_init, y_final):\n thing_on_map = game_map.bldngs_n_rsrcs[y][x]\n if thing_on_map != ' ' and not isinstance(thing_on_map, Wood):\n print('Sorry, there is {} occupying part of '\n 'that space.'.format(thing_on_map))\n return False\n return True","title":""},{"docid":"8ec1e6a0cd4381d925f7a69295f044dd","score":"0.5410975","text":"def is_partkernel(\n self, op_type: str, ifm_channels: int, ifm_dtype: str, kernel_elements: int\n ) -> bool:\n if op_type != \"ethosu_conv2d\":\n return False\n\n depth_first_utilization = ifm_channels / _round_up(\n ifm_channels, 32 if ifm_dtype == \"int8\" else 16\n )\n part_kernel_first_utilization = (ifm_channels / _round_up(ifm_channels, 8)) * (\n kernel_elements / _round_up(kernel_elements, 4 if ifm_dtype == \"int8\" else 2)\n )\n\n return part_kernel_first_utilization > depth_first_utilization or ifm_channels <= 8","title":""},{"docid":"b3a14f98237eda3d7a83cd756d93c64d","score":"0.5409834","text":"def check(self):\n if not self.W_in.shape == (self.Nr, self.Nu + 1):\n return False \n elif not self.W.shape == (self.Nr, self.Nr):\n return False \n elif not self.W_phi.shape == (self.Nphi, self.Nr):\n return False \n elif not self.W_out.shape == (self.Ny, self.Nphi + 1):\n return False \n else:\n return True","title":""},{"docid":"94f961b27e4a0671acbb4d6790672a4a","score":"0.54080683","text":"def constraints_supported(self):\n for constraint in self.mol.constraints:\n if constraint.desc not in ('position', 'distance', 'hbonds'):\n return False\n else:\n return True","title":""},{"docid":"1d24af7d62d29714df4a2ed9a1c74f23","score":"0.5392581","text":"def has_solid_elements(self) -> bool:\n return self._mesh._stub.List(self._mesh._message).element_shape_info.has_solid_elements","title":""},{"docid":"8541687f374390a1605e73ceb40afcfa","score":"0.5388962","text":"def is_occupied(array):\n return all(array)","title":""},{"docid":"1ba532aeccae07ef470a8b4769433284","score":"0.53881603","text":"def check_atoms(reactants, products):\n\n ratoms = [get_atoms(smiles) for smiles in reactants]\n patoms = [get_atoms(smiles) for smiles in products]\n\n # flatten\n ratoms = sum(ratoms, [])\n patoms = sum(patoms, [])\n\n ratoms.sort()\n patoms.sort()\n\n return ratoms == patoms","title":""},{"docid":"02e71e45e88e45d82a5860d4165d48d7","score":"0.53851956","text":"def check_composition(self):\n\n n_m = 0\n n_f = 0\n\n for student in self.students:\n if student.gender == 'm':\n n_m += 1\n if student.gender == 'f':\n n_f += 1\n\n return not (0 < n_f < n_m)","title":""},{"docid":"09ca5e35d1e3b6b2b21b5e086726ee16","score":"0.5381045","text":"def validate(self, processes=1, fast=False, completeness_only=False):\n\n self._validate_structure()\n self._validate_bagittxt()\n\n self.validate_fetch()\n\n self._validate_contents(\n processes=processes, fast=fast, completeness_only=completeness_only\n )\n\n return True","title":""},{"docid":"1c020d7660f28b4d750b2281caceb6f8","score":"0.53562754","text":"def can_compose(self, obj):\r\n\r\n return False","title":""},{"docid":"bbd3355ff4ad1dbbc14751557fb18218","score":"0.5323333","text":"def is_complex (self):\n\t\treturn not (len (self.subtags) == 1\n\t\t\tor self.grandfathered\n\t\t\tand len (self.subtags[1]) != 3\n\t\t\tand ot.from_bcp_47[self.subtags[0]] == ot.from_bcp_47[self.language])","title":""},{"docid":"6942e76ccd2750378ee17888184b6541","score":"0.53183836","text":"def failsSpeciesConstraints(species):\n \n from rmgpy.rmg.input import getInput\n\n try:\n speciesConstraints = getInput('speciesConstraints')\n except Exception:\n logging.debug('Species constraints could not be found.')\n speciesConstraints = {}\n \n if isinstance(species, Species):\n struct = species.molecule[0]\n else:\n # expects a molecule here\n struct = species\n\n explicitlyAllowedMolecules = speciesConstraints.get('explicitlyAllowedMolecules', [])\n for molecule in explicitlyAllowedMolecules:\n if struct.isIsomorphic(molecule):\n return False \n \n maxCarbonAtoms = speciesConstraints.get('maximumCarbonAtoms', -1) \n if maxCarbonAtoms != -1:\n if struct.getNumAtoms('C') > maxCarbonAtoms:\n return True\n\n maxOxygenAtoms = speciesConstraints.get('maximumOxygenAtoms', -1)\n if maxOxygenAtoms != -1:\n if struct.getNumAtoms('O') > maxOxygenAtoms:\n return True\n\n maxNitrogenAtoms = speciesConstraints.get('maximumNitrogenAtoms', -1)\n if maxNitrogenAtoms != -1:\n if struct.getNumAtoms('N') > maxNitrogenAtoms:\n return True\n\n maxSiliconAtoms = speciesConstraints.get('maximumSiliconAtoms', -1)\n if maxSiliconAtoms != -1:\n if struct.getNumAtoms('Si') > maxSiliconAtoms:\n return True\n\n maxSulfurAtoms = speciesConstraints.get('maximumSulfurAtoms', -1)\n if maxSulfurAtoms != -1:\n if struct.getNumAtoms('S') > maxSulfurAtoms:\n return True\n\n maxHeavyAtoms = speciesConstraints.get('maximumHeavyAtoms', -1)\n if maxHeavyAtoms != -1:\n if struct.getNumAtoms() - struct.getNumAtoms('H') > maxHeavyAtoms:\n return True\n\n maxRadicals = speciesConstraints.get('maximumRadicalElectrons', -1)\n if maxRadicals != -1:\n if (struct.getRadicalCount() > maxRadicals):\n return True\n\n maxCarbenes = speciesConstraints.get('maximumSingletCarbenes', 1)\n if maxRadicals != -1:\n if struct.getSingletCarbeneCount() > maxCarbenes:\n return True\n\n maxCarbeneRadicals = speciesConstraints.get('maximumCarbeneRadicals', 0)\n if maxCarbeneRadicals != -1:\n if struct.getSingletCarbeneCount() > 0 and struct.getRadicalCount() > maxCarbeneRadicals:\n return True\n\n return False","title":""},{"docid":"99a49b694643ab63474b0b2fc5beeb9c","score":"0.5315637","text":"def _valid(self) -> bool:\n put_stone_in_cauldron = self.cauldron and self.using_stone\n put_stone_in_potion = self.using_potion and self.using_stone\n return [self.end_trial, self.no_op, put_stone_in_cauldron,\n put_stone_in_potion].count(True) == 1","title":""},{"docid":"99a49b694643ab63474b0b2fc5beeb9c","score":"0.5315637","text":"def _valid(self) -> bool:\n put_stone_in_cauldron = self.cauldron and self.using_stone\n put_stone_in_potion = self.using_potion and self.using_stone\n return [self.end_trial, self.no_op, put_stone_in_cauldron,\n put_stone_in_potion].count(True) == 1","title":""},{"docid":"2f775f480525ffa3f862a3706b94d4b0","score":"0.53053993","text":"def has_pair(self):\n seen = set()\n for part in self.parts:\n if part.element in seen:\n return True\n seen.add(part.element)\n return False","title":""},{"docid":"fd2d9042ca12f4b71c3c65c9c7936856","score":"0.53012043","text":"def match_substructures(mol, substructures):\n for substructure in substructures:\n if mol.HasSubstructMatch(substructure):\n return True\n return False","title":""},{"docid":"6a1a1408106bad507ca50c9e6070b494","score":"0.5286026","text":"def test_structure_combinations(self):\n\n elem_structures = self.mysubtraction.get_all_elementary_structures(self.ggg, 3)\n combos = self.mysubtraction.get_all_combinations(elem_structures, 3)\n # The number of counterterms may turn out to be incorrect,\n # but test that the generation goes through\n self.assertEqual(1398, len(combos))","title":""},{"docid":"283bda0ee8a0eb65c5677e77e43669ce","score":"0.52839917","text":"def is_transport(reaction, compartments_list, all_compounds = False, ignore_h = False):\n # left part of the reaction\n r = list(str(x) for x in reaction.reactants)\n # right part\n p = list(str(x) for x in reaction.products)\n #if len(r) != len(p):\n # return False\n c = compartments_list\n # removing terminations\n R = []\n for i in r:\n for e in c:\n i = i.replace(e, \"\") \n R.append(i)\n P = []\n for i in p:\n for e in c:\n i = i.replace(e, \"\")\n P.append(i)\n # we sort the lists to avoid missing transport reactions where the sequence of the compounds is not maintained\n R = sorted(R)\n P = sorted(P)\n if all_compounds == False:\n if ignore_h == True:\n if \"h\" in R:\n R.remove(\"h\")\n if \"h\" in P:\n P.remove(\"h\")\n for i in R:\n if i in P:\n return True\n else:\n return False\n if R == P:\n return True\n else:\n return False","title":""},{"docid":"9195b1c34af2a2b11d0f32f9063d2cfc","score":"0.5280433","text":"def filter_mol(mol, max_heavy_atoms=100, min_heavy_atoms=10, element_list=[6,7,8,9,16,17,35]):\n if mol is not None:\n num_heavy = min_heavy_atoms<mol.GetNumHeavyAtoms()<max_heavy_atoms\n elements = all([atom.GetAtomicNum() in element_list for atom in mol.GetAtoms()])\n if num_heavy and elements:\n return True\n else:\n return False","title":""},{"docid":"57fce85412c7a52165f7d315e3a78130","score":"0.5269647","text":"def __contain__(self, item):\n if item in self.__sub:\n return True\n\n if item in self.__time:\n return True\n\n if item in self.__date:\n return True\n\n if item in self.__people:\n return True\n\n if item in self.__location:\n return True\n\n if item in self.__items:\n return True\n\n if item in self.__additional:\n return True\n\n return False","title":""},{"docid":"ab594010aa4a0561fe59a7d379631bf5","score":"0.5266355","text":"def is_matching_constraints(self, p):\n return all([c(p) for c in self.C])","title":""},{"docid":"4ce80767c2f00c0e5c2453c8293a325b","score":"0.5262244","text":"def is_valid_component(component):\n\n return component.__class__ in [Annotation, Reference, Repeat, Row]","title":""},{"docid":"c3a83b1f67718becdb46ab7a92fd6cc4","score":"0.5255005","text":"def is_composite(self):\n # walk through method resolution order and inspect methods\n # of classes and direct parents, \"adjacent\" classes in mro\n params = self.get_params(deep=False)\n composite = any(isinstance(x, BaseObject) for x in params.values())\n\n return composite","title":""},{"docid":"1acfd6005380106a7dab3b11e41b9100","score":"0.52494496","text":"def _(expr, assumptions):\n assumps = conjuncts(assumptions)\n if expr.is_commutative is not None:\n return expr.is_commutative and not ~Q.commutative(expr) in assumps\n if Q.commutative(expr) in assumps:\n return True\n elif ~Q.commutative(expr) in assumps:\n return False\n return True","title":""},{"docid":"4f7b0efdacc163d0b42c7c7e658e5a00","score":"0.52458704","text":"def isEmpty(self):\n return self.mpi.groupIsEmpty(self.capsule)","title":""},{"docid":"547277762033a896d8172cebffc75123","score":"0.523349","text":"def validate_part_b(self):\n if not self.validate_part_a():\n return False\n\n if (self.byr_is_valid()\n and self.iyr_is_valid()\n and self.eyr_is_valid()\n and self.hgt_is_valid()\n and self.hcl_is_valid()\n and self.ecl_is_valid()\n and self.pid_is_valid()):\n return True\n\n return False","title":""},{"docid":"7bfe26d418d8d678f4760219feaeac81","score":"0.5226915","text":"def requires_hubbard(self, comp):\n comp = Composition(comp)\n elements = sorted([el for el in comp.elements if comp[el] > 0],\n key=lambda el: el.X)\n most_electroneg = elements[-1].symbol\n\n usettings = self.u_settings.get(most_electroneg, {})\n\n return any([usettings.get(el.symbol, 0) for el in comp.elements])","title":""},{"docid":"8a60ac2c7788e6999a68215e55d624ac","score":"0.52262086","text":"def part_of(cls, a: str, b: str) -> bool:\n definition = cls.parser.fetch(b)\n a = cls.get_synonyms(a)\n synonyms = '|'.join(a)\n try:\n definitions = [d['definitions'] for d in definition]\n definitions = flatten(definitions)\n definitions = [d['text'] for d in definitions]\n definitions = flatten(definitions)\n except IndexError:\n return False\n for d in definitions:\n if re.search(f'(?:parts? of|elements? of|portions? of|in an?).*(?:{synonyms})', d) is not None:\n return True\n return False","title":""},{"docid":"d6be104709f649eee8f9e056d41ee5df","score":"0.5219647","text":"def isIsopropylOrGuanidinium( atmDef ):\n n = len(atmDef.real)\n if (n == 4) or (n == 6):\n return True\n return False","title":""},{"docid":"f30491ce928c18fb5874eda587c48204","score":"0.52168226","text":"def _is_cupy_compatible(arg):\n\n if isinstance(arg, ndarray):\n if not arg._supports_cupy:\n return False\n\n if isinstance(arg, (tuple, list)):\n return all(_RecursiveAttr._is_cupy_compatible(i) for i in arg)\n\n if isinstance(arg, dict):\n bools = [_RecursiveAttr._is_cupy_compatible(arg[i]) for i in arg]\n return all(bools)\n\n return True","title":""},{"docid":"617b723107d2ffc28be0d3c56425efad","score":"0.521607","text":"def can_chain(box):\n for value in values[box]:\n for chain_box in chain:\n # A box fits the chain if one of its values can be found in another box in the chain\n if value in values[chain_box] and values[box] != values[chain_box]:\n return True\n\n return False","title":""},{"docid":"8d780548b3640c12303f13a413510a75","score":"0.5208866","text":"def in_bounds(self):\n for i in range(3):\n dim_min, dim_max = 2*[self.occupied_coordinates[0][i]]\n for coord in self.occupied_coordinates:\n dim_min = min(dim_min, coord[i])\n dim_max = max(dim_max, coord[i])\n if dim_max - dim_min > 3:\n return False\n return True","title":""},{"docid":"ff308de1b942000666598378c032c240","score":"0.52005386","text":"def check_explicit_beliefs(X):\n assert type(X).__module__ == \"numpy\" # make sure it is not a matrix\n correct = set(np.hstack(X)) == set([0, 1]), \"Input X can contain only 0 or 1 (as int or float)\"\n if len(X.shape) == 1: # special case in case n=1 and no nesting: [[...]] -> [...]\n return correct and np.abs(np.sum(X)) <= 1\n else:\n return correct and (np.abs(np.sum(X,1)) <= 1).all()","title":""},{"docid":"4c1a61c7a5ae61ed70d67de8c63cdb09","score":"0.5198446","text":"def AllRequirementsMet(items: List[BuildVariant]) -> bool:\n available = set([item.name for item in items])\n\n for item in items:\n for requirement in item.requires:\n if requirement not in available:\n return False\n\n return True","title":""},{"docid":"21505ee9bda3caf3a70e705af0fb30cc","score":"0.5196396","text":"def test_Initial_conditions_contain_only_zeros_and_ones(Initial_conditions):\n # flatten up the mesh into one single list\n # and set on the list it should be [0,1] if it\n # contains only 0 and 1. Then do sum on that will \n # return 1\n \n assert (sum(set(sum(Initial_conditions,[]))) <= 1)","title":""},{"docid":"2baf40a3582f2614194a8f0b6df5f166","score":"0.5191638","text":"def contains_component(component, attr):\n if isinstance(attr, tuple):\n for ele in attr:\n if contains_component(component, ele) is True:\n return True\n else:\n if attr == component:\n return True\n \n return False","title":""},{"docid":"e2f87271c2aa3b0d700ef9eebd6b0cb7","score":"0.5189924","text":"def is_consummation(self) -> bool:\n consummation_phrases = [\n 'announcing the consummation',\n 'consummated the previously announced business combination'\n ]\n return any(phrase in self.text for phrase in consummation_phrases)","title":""},{"docid":"2c366b5a6a45515cc6a1f095ef42d9ae","score":"0.5188788","text":"def has_shell_elements(self) -> bool:\n return self._mesh._stub.List(self._mesh._message).element_shape_info.has_shell_elements","title":""},{"docid":"2297f50be25c76fd720df40cfab47c53","score":"0.5186772","text":"def is_complex_cartesian(expr):\n args=arg_add_flatten(expr)\n ni=sum([is_coeff_mul(a,sp.I) for a in args])\n nr=sum([a.is_real for a in args])\n return ni<=1 and ni+nr==len(args)","title":""},{"docid":"a2e257a5d30877289613e98ec82929f2","score":"0.51822674","text":"def _parts_interact(self):\n # Assume that multipart bijectors involve interactions unless they\n # override this method to say otherwise.\n return (tf.nest.is_nested(self.forward_min_event_ndims) or\n tf.nest.is_nested(self.inverse_min_event_ndims))","title":""},{"docid":"3a9bb98a9791d5ab6879254e65b1e7c5","score":"0.5177858","text":"def __contains__(self, item):\n\n if self.root is not None:\n\n if self.comp(item, self.root.data) == 0:\n return True\n\n if self.comp(item, self.root.data) != 0:\n return self.root.tree_contains(item, self.comp)\n\n return False","title":""},{"docid":"f9b8db5aba35f300904d77a85b45f917","score":"0.51745933","text":"def isFlexible(self):\n for key, value in self.arrayShapeRange.items():\n if key in _CONSTRAINED_KEYS:\n if value.isFlexible:\n return True\n\n return False","title":""},{"docid":"5c36df02e28ad0b93463202fd36116e0","score":"0.5173861","text":"def __contains__(self, path) -> bool:\n return any(\n str(path) in module_creations.keys()\n for module_creations\n in self.creations.values()\n )","title":""},{"docid":"2b26d4b3cd18a79a4e0de0dae8a44b9d","score":"0.5169665","text":"def has_obj(namelst):\n for name in namelst:\n if func.is_bias(name) or func.is_halogen(name) or \\\n func.is_lamp(name) or func.is_std(name):\n pass\n else:\n if func.get_grism(name).lower() == 'open' or \\\n func.get_slit(name).lower() == 'open':\n return False\n return True\n return False","title":""},{"docid":"badb3a64bf78503742472c5da5cb5c2d","score":"0.5163052","text":"def isvalid(self):\n #\n _al = self.as_adjacency_list(as_numbered=True)\n nd = {}\n for n in range(10):\n nd[n] = sum([1 for x in _al.keys() if len(_al[x]) == n])\n\n if nd[0] > 0: # has non-connected node\n print('has non-connected node')\n _ret = False\n elif nd[1] > 0: # has loose edge node\n print('has loose edge')\n _ret = False\n elif nd[2] < 3: # its not even a triangle\n print('its not even a triangle')\n _ret = False\n elif nd[3] % 2 == 1: # has odd number of branches\n print('has odd number of branches')\n _ret = False\n elif sum([1 for x in range(4, 10) if nd[x] > 0]): # has node with more than 3 edges connected\n print('has node with more than 3 edges connected')\n _ret = False\n else:\n _ret = True # but there will be other chacks\n\n if not _ret:\n return _ret\n\n # all polygons, that are truly polygonlines must be valid\n _ret = all([x.isvalid for x in self.polygons])\n\n return _ret","title":""},{"docid":"258a21f7219a114177bb4c594c79ed4d","score":"0.5161628","text":"def is_intercalated(self) -> bool:\n return 0 in self.component_dimensionalities","title":""},{"docid":"a00d5bfd423aef2e75f88bd6fd763ed0","score":"0.51614875","text":"def __contains__(self, element):\n for vector in self._columns:\n if element is vector:\n return True\n if element in vector:\n return True\n return False","title":""},{"docid":"c0d2dc7dd6dd961c65d2c8cc0beba690","score":"0.5160536","text":"def check_children(cls, module, instances): \n #{{{ \n check = []\n for m in module.modules():\n check += [any(isinstance(m,inst) for inst in instances)]\n return any(check)","title":""},{"docid":"817f9f1626473ffa1fcdebc1921cb459","score":"0.5159764","text":"def check_residue(self) -> bool:\n raise NotImplementedError","title":""},{"docid":"2dcde106e6cf1e66179ba4ac5a74afdb","score":"0.5159541","text":"def verify_if_proper_subset(self, problem_matrix):\n row_size = len(problem_matrix[0])\n for row in problem_matrix:\n if row.count(1) is row_size:\n return False\n return True","title":""},{"docid":"b37dc36ba922e222f773b8fa38a5b740","score":"0.5156301","text":"def isMasse(inzidenz):\n \n if 0 in inzidenz.shape:\n return False\n unique, count = np.unique(inzidenz, return_counts=True)\n\n #-------There are only two potencials left and one of them is the mass-potencial--------\n if(len(count) == 1):\n return True\n if(count[0] == count[-1]):\n return False\n else:\n return True","title":""}],"string":"[\n {\n \"docid\": \"23d46596c5f225108ac9b32c09840b3f\",\n \"score\": \"0.6892202\",\n \"text\": \"def _is_from_chemical_system(chemical_system, struct):\\n chemsys = list(set([sp.symbol for sp in struct.composition]))\\n if len(chemsys) != len(chemical_system):\\n return False\\n for el in chemsys:\\n if not el in chemical_system:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afa1fe54fa42619f767173bbce2f56dc\",\n \"score\": \"0.66731745\",\n \"text\": \"def __contains__(self, element):\\n if isinstance(element, BasicCompartmentCompound):\\n return element.compartment == self\\n elif isinstance(element, BasicCompound):\\n return element in self._contained\\n elif isinstance(element, BasicReaction):\\n return all(cmpd in self for cmpd in element.compounds_iter())\\n else:\\n raise PyOrganismError(\\\"unrecognised metabolic component '{0}'\\\",\\n element)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc49fb93081497391d2e72c61e872657\",\n \"score\": \"0.6550423\",\n \"text\": \"def isValid(self):\\n return self.has_input_section('rem') and \\\\\\n self.has_input_section('molecule')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3270faa06de9daff51a11231f6eb9853\",\n \"score\": \"0.6218953\",\n \"text\": \"def check_multiplicity( self, part ):\\n ok = True\\n for k,v in self.MULTIPLICITY_LIMITS.items():\\n if isinstance( part, k ):\\n ok = ok and len( [item for item in self.sub_com if isinstance( item, k )]) < v\\n return ok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"528d22cf8b4d48668056f7a352e8e749\",\n \"score\": \"0.61732703\",\n \"text\": \"def check_multiplicity( self, mod, part ):\\n ok = True\\n for k,v in self.MULTIPLICITY_LIMITS.items():\\n if isinstance( part, k ):\\n ok = ok and len( [item for item in mod.sub_com if isinstance( item, k ) ] ) < v\\n return ok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de75d99e180c982eb6c5f7012757c1a5\",\n \"score\": \"0.60713637\",\n \"text\": \"def can_install(self,part):\\n return self.is_legal_sub_com(part) and part.scale <= self.scale and part.volume <= self.free_volume and self.check_multiplicity( part )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708e7e3d05112c54838b2497f44638f4\",\n \"score\": \"0.6057568\",\n \"text\": \"def _supports(self, system: Union[ProteinLigandComplex]) -> bool:\\n super_checks = super()._supports(system)\\n proteins = [c for c in system.components if isinstance(c, self._COMPATIBLE_PROTEIN_TYPES)]\\n ligands = [c for c in system.components if isinstance(c, self._COMPATIBLE_LIGAND_TYPES)]\\n return all([super_checks, len(proteins) == 1]) and all([super_checks, len(ligands) == 1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"612e2ba637c170fb239273a092cf1b20\",\n \"score\": \"0.5942075\",\n \"text\": \"def HasCombination(self, bindings):\\n self.program.CreateSolver()\\n # Optimization: check the entire combination only if all of the bindings\\n # are possible separately.\\n return (all(self.program.solver.Solve({b}, self) for b in bindings)\\n and self.program.solver.Solve(bindings, self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c160135f8280887e8aa4bd30eaf833c\",\n \"score\": \"0.58893836\",\n \"text\": \"def contains(self, pool):\\n return (pool in self.molecule_design_pools)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0baefdec0f6c4b4c8d002ee266c0e766\",\n \"score\": \"0.5879723\",\n \"text\": \"def can_build(self, structure: common.Structure,\\n adjacentEnemies: bool) -> bool:\\n # Can't possible build if we don't have any such structures left\\n # on the board.\\n if self.structures[structure] <= 0:\\n return False\\n cost: common.Resources = self.faction.structure_cost(\\n structure, adjacentEnemies)\\n remainingResources = self.resources - cost\\n if remainingResources.is_valid():\\n return True\\n return self._can_burn_power_for_missing_resources(remainingResources)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56ebe0d450f72ce66202b53cec8e6d0f\",\n \"score\": \"0.58636296\",\n \"text\": \"def _check_structure(self):\\n if (not self._xml or self._xml.getroot().tag != self._APPLICATION\\n or self._xml.find(self._COMPONENT) is None\\n or self._xml.find(self._COMPONENT).tag != self._COMPONENT):\\n return False\\n return self._xml.find(self._COMPONENT).get(\\n self._NAME) == self._PROJECTJDKTABLE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e994788be796ccd79cfec369640dd7b\",\n \"score\": \"0.58187366\",\n \"text\": \"def check(sequence, max_homopolymer=4, max_simple_segment=6, max_content=0.6):\\r\\n if not homopolymer(sequence, max_homopolymer):\\r\\n return False\\r\\n if not repeat(sequence, max_simple_segment):\\r\\n return False\\r\\n if not dyad_repeat(sequence, max_simple_segment):\\r\\n return False\\r\\n if not inverse_repeat(sequence, max_simple_segment):\\r\\n return False\\r\\n if not cg_content(sequence, max_content):\\r\\n return False\\r\\n\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46e36f338a39d1383d960d22599ccf2d\",\n \"score\": \"0.57574743\",\n \"text\": \"def has_beam_elements(self) -> bool:\\n return self._mesh._stub.List(self._mesh._message).element_shape_info.has_beam_elements\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bd01c7f5b664152d8f751e1447dc6d4\",\n \"score\": \"0.5750804\",\n \"text\": \"def is_valid(self) -> bool:\\n gen_elements = {i.element for i in self.items if i.kind is ItemKind.GENERATOR}\\n chip_elements = {i.element for i in self.items if i.kind is ItemKind.MICROCHIP}\\n return not gen_elements or chip_elements <= gen_elements\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5eaac51f759d7d198df9ff1c69fb0c8\",\n \"score\": \"0.5745781\",\n \"text\": \"def containedStructure(containee, container):\\n\\n if isinstance(containee, dict):\\n if isinstance(container, dict):\\n # then we have shapes to check\\n return (set(containee.keys()).issubset(set(container.keys())) and\\n # so the keys are all the same\\n all(containedStructure(containee[k], container[k])\\n for k in containee.keys()))\\n # thus all values will be tested in the same way.\\n else:\\n return False # containee is a dict, but container isn't\\n elif isinstance(containee, list):\\n if isinstance(container, list):\\n return set(containee).issubset(set(container))\\n else:\\n return False # containee is a lit, but container isn't\\n elif containee is None:\\n return True\\n else:\\n # else they have to be the same type\\n return isinstance(container, type(containee))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"490662a449b4a36ad33da430a7c49a7e\",\n \"score\": \"0.5743979\",\n \"text\": \"def is_concrete(self):\\n # 1.\\n mp_concrete_ok = all(mp.is_concrete() for mp in self.monomer_patterns)\\n # 2.\\n compartment_ok = self.compartment is not None and \\\\\\n all(mp.is_site_concrete() for mp in self.monomer_patterns)\\n return mp_concrete_ok or compartment_ok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb50932784da92c7d9342ef71406fdcc\",\n \"score\": \"0.57316095\",\n \"text\": \"def is_valid_chemical_structure(cls, chemical_structures):\\n try:\\n cls._validate_chemical_structures(chemical_structures)\\n except ValueError:\\n result = False\\n else:\\n result = True\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72516625cf07fdf934147620c3a94f2f\",\n \"score\": \"0.57044786\",\n \"text\": \"def hasUnitElement(self):\\n leftUnities = [];\\n for left in range(0, self.order):\\n isLeftUnity = True;\\n for right in range(0, self.order):\\n if not self.multiply([left,right]) == right:\\n isLeftUnity = False;\\n break;\\n if isLeftUnity:\\n leftUnities.append(left);\\n\\n for right in leftUnities:\\n isRightUnity = True;\\n for left in range(0, self.order):\\n if not self.multiply([left,right]) == left: \\n isRightUnity = False;\\n break;\\n if isRightUnity:\\n return True;\\n\\n return False;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f65c5dcb120296f4f0b149034a93770\",\n \"score\": \"0.56872874\",\n \"text\": \"def tower_is_constructible(blocks):\\n for i in range(len(blocks) - 1):\\n # check that each pair of blocks is stably individually\\n top = blocks[i+1]\\n bottom = blocks[i]\\n if not pair_is_stable(bottom, top): return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87903b9a18c6d2be5d1c3e892db20559\",\n \"score\": \"0.56788063\",\n \"text\": \"def tower_is_constructable(self, blocks):\\n for i in range(1, len(blocks)+1):\\n # check that each pair of blocks is stably individually\\n subtower = blocks[:i]\\n if not self.tower_is_stable(subtower): return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42df8847f4f61a46852745b67aa89a9f\",\n \"score\": \"0.5674524\",\n \"text\": \"def f2(mol):\\n for atoms in filter(lambda x: len(x) <= 4, mol.GetRingInfo().AtomRings()):\\n atom_types = Counter((mol.GetAtomWithIdx(atom).GetSymbol() for atom in atoms))\\n if atom_types[\\\"O\\\"] + atom_types[\\\"S\\\"] + atom_types[\\\"N\\\"] > 1:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c6f18718716353849364347d69d5b39\",\n \"score\": \"0.56671256\",\n \"text\": \"def contain(self, element):\\n x, y = symbols(\\\"x, y\\\")\\n a, b = self.basis[0].coeff_e\\n c = self.basis[1].coeff_e[1]\\n a_, b_ = element.coeff_e\\n sol = solve((a * x - a_, b * x + c * y - b_), x, y)\\n a__, b__ = sol[x], sol[y]\\n\\n return a__.is_integer and b__.is_integer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"518c334071f997f897129e4926d5220d\",\n \"score\": \"0.5661782\",\n \"text\": \"def does_fit_in_room(model) -> bool:\\n\\n _, structures = walls_and_structures_in_room()\\n wall_right_x, wall_bottom_y = wall_coordinates()\\n is_valid = True\\n min_area_overlap_invalid = 1e-10 # I check are overlap to determine in a configuration is invalid, \\n # but I don't want false positives if there is effectively no overlap, but it isn't zero\\n components = storage_ring_system_components(model)\\n for component, structure in itertools.product(components, structures):\\n x, y = component.exterior.xy\\n x, y = np.array(x), np.array(y)\\n is_out_side_wall = np.any(x > wall_right_x) or np.any(y < wall_bottom_y)\\n is_overlapping = structure.intersection(component).area > min_area_overlap_invalid\\n if is_out_side_wall or is_overlapping:\\n is_valid = False\\n break\\n return is_valid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55321fe89aaf97722178227db8f188dd\",\n \"score\": \"0.56345063\",\n \"text\": \"def has_full(self):\\n if self.has_three() and self.has_pair():\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb50c75e0b19d9c33aa7f09477702164\",\n \"score\": \"0.56010145\",\n \"text\": \"def is_concrete(self):\\n # 1.\\n sites_ok = self.is_site_concrete()\\n # 2.\\n compartment_ok = not self.monomer.model().compartments or self.compartment\\n return compartment_ok and sites_ok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5f4057cf533d969a06362f420ede06d\",\n \"score\": \"0.55767643\",\n \"text\": \"def contains_named_molecule(self) -> bool:\\n return any(c[\\\"molecule_name\\\"] for c in self.components.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c817637295bf694bcc8e11ef2822d4\",\n \"score\": \"0.55751926\",\n \"text\": \"def __contains__(self, molecule_design):\\n return molecule_design in self.molecule_designs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f06be3f913e94c31e1d5fa7487659dee\",\n \"score\": \"0.5558672\",\n \"text\": \"def is_complex(self):\\n for xf in self.transforms:\\n if isinstance(xf, (ComplexTransform, ComplexSimulator)):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64bc786619dc9b9a00ec7bbdb3f6cfd0\",\n \"score\": \"0.5553932\",\n \"text\": \"def check_backbone_atoms(residue):\\n intrp = residue.residue_name_plus_atom_names_interpreter().atom_name_interpretation\\n if not intrp: return False\\n return not bool(intrp.missing_atom_names().intersection(['N','CA','C','O']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23688bd4b34e953f050c88e2bdd1a3bb\",\n \"score\": \"0.5536081\",\n \"text\": \"def _has_struct(elem):\\n return isinstance(elem, np.ndarray) and any(isinstance(\\n e, scipy.io.matlab.mio5_params.mat_struct) for e in elem)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e699b67840c99245d4bf8296713eb6ea\",\n \"score\": \"0.55340946\",\n \"text\": \"def can_equip(self,part):\\n return self.is_legal_inv_com(part) and part.scale == self.scale and not self.inv_com\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45cef083daa1baf03af46e017e392b87\",\n \"score\": \"0.5529885\",\n \"text\": \"def __contains__(self, element):\\r\\n is_parent_ok = element.parent is self\\r\\n is_child_ok = element in self.children\\r\\n if is_parent_ok:\\r\\n if is_child_ok:\\r\\n return True\\r\\n else:\\r\\n msg = \\\"Element is not in the container but parent is that \\\" \\\\\\r\\n \\\"container.\\\"\\r\\n else:\\r\\n if is_child_ok:\\r\\n msg = \\\"Element is in the container but parent is not that \\\" \\\\\\r\\n \\\"container.\\\"\\r\\n else:\\r\\n return False\\r\\n raise InconsistentParenthoodError(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a510c51a2903044be94fab64671dfa2e\",\n \"score\": \"0.55290663\",\n \"text\": \"def solved_puz(puz):\\n # All groups are solved\\n for r in puz.rows:\\n if not solved_group(r):\\n return False\\n for c in puz.cols:\\n if not solved_group(c):\\n return False\\n for b in puz.boxs:\\n if not solved_group(b):\\n return False\\n # And no nodes are unsolved\\n return len(puz.unsolved) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc7bc46bb2882afeaf2d37846a63e665\",\n \"score\": \"0.5527124\",\n \"text\": \"def check_satisfiability(self) -> bool:\\n\\t\\treturn np.all(self.get_cand_count() >= self.equiv_size_array) # returns False if any cand is 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad71063c8d01021939efcc8df7049342\",\n \"score\": \"0.5515364\",\n \"text\": \"def _satisfies_intersection_constraint(scheme, solution):\\n clusters = construct_clusters(scheme, solution)\\n clusters_instersect = False\\n for i in range(len(clusters)):\\n for j in range(i + 1, len(clusters)):\\n clusters_instersect |= bool(clusters[i].machines & clusters[j].machines)\\n clusters_instersect |= bool(clusters[i].parts & clusters[j].parts)\\n return not clusters_instersect\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99e6023f3b54bb9c5337bd987b430f76\",\n \"score\": \"0.54989064\",\n \"text\": \"def has_undesirable_elements(mol):\\n atomsHC = oechem.OEOrAtom(oechem.OEIsHydrogen(), oechem.OEIsCarbon())\\n atomsNO = oechem.OEOrAtom(oechem.OEIsNitrogen(), oechem.OEIsOxygen())\\n atomsFS = oechem.OEOrAtom(oechem.OEHasAtomicNum(9), oechem.OEIsSulfur())\\n atomsHCNO = oechem.OEOrAtom(atomsHC, atomsNO)\\n atomsHCNOFS = oechem.OEOrAtom(atomsHCNO, atomsFS)\\n atomsHCNOFSCl = oechem.OEOrAtom(atomsHCNOFS, oechem.OEHasAtomicNum(17))\\n atomsHCNOFSClP = oechem.OEOrAtom(atomsHCNOFSCl, oechem.OEIsPhosphorus())\\n\\n undesirable_atom = mol.GetAtom(oechem.OENotAtom(atomsHCNOFSClP))\\n if undesirable_atom is not None:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a524b1f006ed53c2061c69fc7516539\",\n \"score\": \"0.5472856\",\n \"text\": \"def _check_candidate(subset_types, he, cand):\\n is_candidate = False\\n if subset_types in [\\\"all\\\", \\\"empirical\\\"]:\\n if len(he) != len(cand):\\n is_candidate = True\\n elif subset_types == \\\"immediate\\\":\\n if len(he) == len(cand) - 1 or len(he) - 1 == len(cand):\\n is_candidate = True\\n return is_candidate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"001c2c67c6c6adb01140c10d2ca4e751\",\n \"score\": \"0.54618907\",\n \"text\": \"def __contains__(self, element):\\n if len(self.formula) == 0:\\n LOGGER.warn(\\\"testing element against empty formula\\\")\\n return element in self.formula\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fc5c2037ae0580f091395e8d32c8bdd\",\n \"score\": \"0.54612345\",\n \"text\": \"def atoms_in_subsys(self, alist, fsubsys):\\n return all(f in fsubsys for f in map(lambda a: self._mol.fragnumbers[a], alist))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c17526696948d8ffeba768e5d7d9b5\",\n \"score\": \"0.54607683\",\n \"text\": \"def atoms_in_active(self, alist, subsys):\\n if subsys == None: return True\\n return any(a in subsys for a in alist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda5c11b37024c3964ffadc74f8b1548\",\n \"score\": \"0.54542404\",\n \"text\": \"def CanHaveCombination(self, bindings):\\n goals = set(bindings)\\n seen = set()\\n stack = [self]\\n # TODO(kramm): Take blocked nodes into account, like in Bindings()?\\n while stack and goals:\\n node = stack.pop()\\n if node in seen:\\n continue\\n seen.add(node)\\n goals -= goals & node.bindings\\n stack.extend(node.incoming)\\n return not goals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa61cad4ffbe28e3e1698351d66be96c\",\n \"score\": \"0.5446482\",\n \"text\": \"def contains_molecule(self, molecule_name: str) -> bool:\\n return any(\\n c[\\\"molecule_name\\\"] == molecule_name for c in self.components.values()\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d816a939fa08cd743cb44021624593b8\",\n \"score\": \"0.54320884\",\n \"text\": \"def check(self):\\n flag = 0\\n if len(self.name)!=len(self.connection)+1:\\n logging.warning(\\\"The number of connection and name in Proposition might not match\\\")\\n flag=1\\n\\n if self.unit == 1 & self.out_negative==1:\\n logging.warning(\\\"There is a conflict in judging unit\\\")\\n flag =1\\n\\n sign = ['V','^','->','~','<->']\\n for i in self.connection:\\n if i not in sign:\\n logging.warning('there might be invalid sign in Proposition')\\n flag = 1\\n\\n for i in self.name:\\n if type(i)==type('a'):\\n if i < 'A' or i > 'Z':\\n logging.warning('the Proposition should be from A to Z')\\n flag=1\\n if i == 'V':\\n logging.warning('try to not use V in Proposition')\\n flag = 1\\n\\n else:\\n if False == i.check():\\n flag =1\\n\\n if self.unit == 1:\\n for i in self.name:\\n if type(i)!=type('a'):\\n logging.warning('the content of unit might not be string')\\n flag = 1\\n\\n\\n if flag==0:\\n print(show(self.result)+' has been checked without problems')\\n return True\\n else:\\n logging.warning(show(self.result)+' has been checked with problems!!!')\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd1d5727be200682530e67297b16f60f\",\n \"score\": \"0.54292655\",\n \"text\": \"def validate_sudoku(puzzle):\\n\\tif puzzle is None:\\n\\t\\treturn False\\n\\n\\tcoords, groups, all_units = sudoku_reference()\\n\\tfull = [str(x) for x in range(1, 10)] # Full set, 1-9 as strings\\n\\n\\t# Checks if all units contain a full set\\n\\treturn all([sorted([puzzle[cell] for cell in unit]) == full for unit in all_units])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"036b430d80cd0ea431e4d1765dd8e620\",\n \"score\": \"0.54260206\",\n \"text\": \"def establish_has_single_uom(self):\\n\\n self.has_single_uom_flag = True\\n common_uom = None\\n for part in self.parts():\\n part_uom = self.uom_for_part(part)\\n if common_uom is None:\\n common_uom = part_uom\\n elif part_uom != common_uom:\\n self.has_single_uom_flag = False\\n break\\n if common_uom is None: self.has_single_uom_flag = True # all uoms are None (probably discrete properties)\\n return self.has_single_uom_flag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd6a1a9e5c5c5261a3f40e3587658f9d\",\n \"score\": \"0.54250455\",\n \"text\": \"def something_is_still_afloat(fleet):\\n for x in fleet:\\n for y in x:\\n if(y):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc6cc1bd26fcf4bfcc9fad17799596c0\",\n \"score\": \"0.5420859\",\n \"text\": \"def is_enzyme_cmpd(self):\\n return any((self.is_product==True, self.is_substrate==True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaf07b4478a84666d83fec632d4c8b93\",\n \"score\": \"0.54199886\",\n \"text\": \"def can_equip(self,part):\\n return self.is_legal_inv_com(part) and part.scale <= self.scale\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaf07b4478a84666d83fec632d4c8b93\",\n \"score\": \"0.54199886\",\n \"text\": \"def can_equip(self,part):\\n return self.is_legal_inv_com(part) and part.scale <= self.scale\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca11f85244a474224030fef1d8a4af8b\",\n \"score\": \"0.5418681\",\n \"text\": \"def can_build_on_map(self, position, game_map):\\n x_init, y_init = position.value\\n length, height = self.size\\n\\n # First check if the building will be placed within the boundaries of the map:\\n if x_init - (length - 1) < 0:\\n print('Sorry, building placement too far west.')\\n return False\\n if x_init >= game_map.width:\\n print('Sorry, building placement too far east.')\\n return False\\n if y_init < 0:\\n print('Sorry, building placement too far south.')\\n return False\\n if y_init + (height - 1) >= game_map.height:\\n print('Sorry, building placement too far north.')\\n return False\\n\\n # Next, check that there are no obstructions. Buildings are allowed to be built\\n # only where there are blank spaces or wood.\\n x_final = x_init - length\\n y_final = y_init + height\\n for x in range(x_init, x_final, -1):\\n for y in range(y_init, y_final):\\n thing_on_map = game_map.bldngs_n_rsrcs[y][x]\\n if thing_on_map != ' ' and not isinstance(thing_on_map, Wood):\\n print('Sorry, there is {} occupying part of '\\n 'that space.'.format(thing_on_map))\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ec1e6a0cd4381d925f7a69295f044dd\",\n \"score\": \"0.5410975\",\n \"text\": \"def is_partkernel(\\n self, op_type: str, ifm_channels: int, ifm_dtype: str, kernel_elements: int\\n ) -> bool:\\n if op_type != \\\"ethosu_conv2d\\\":\\n return False\\n\\n depth_first_utilization = ifm_channels / _round_up(\\n ifm_channels, 32 if ifm_dtype == \\\"int8\\\" else 16\\n )\\n part_kernel_first_utilization = (ifm_channels / _round_up(ifm_channels, 8)) * (\\n kernel_elements / _round_up(kernel_elements, 4 if ifm_dtype == \\\"int8\\\" else 2)\\n )\\n\\n return part_kernel_first_utilization > depth_first_utilization or ifm_channels <= 8\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3a14f98237eda3d7a83cd756d93c64d\",\n \"score\": \"0.5409834\",\n \"text\": \"def check(self):\\n if not self.W_in.shape == (self.Nr, self.Nu + 1):\\n return False \\n elif not self.W.shape == (self.Nr, self.Nr):\\n return False \\n elif not self.W_phi.shape == (self.Nphi, self.Nr):\\n return False \\n elif not self.W_out.shape == (self.Ny, self.Nphi + 1):\\n return False \\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94f961b27e4a0671acbb4d6790672a4a\",\n \"score\": \"0.54080683\",\n \"text\": \"def constraints_supported(self):\\n for constraint in self.mol.constraints:\\n if constraint.desc not in ('position', 'distance', 'hbonds'):\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d24af7d62d29714df4a2ed9a1c74f23\",\n \"score\": \"0.5392581\",\n \"text\": \"def has_solid_elements(self) -> bool:\\n return self._mesh._stub.List(self._mesh._message).element_shape_info.has_solid_elements\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8541687f374390a1605e73ceb40afcfa\",\n \"score\": \"0.5388962\",\n \"text\": \"def is_occupied(array):\\n return all(array)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ba532aeccae07ef470a8b4769433284\",\n \"score\": \"0.53881603\",\n \"text\": \"def check_atoms(reactants, products):\\n\\n ratoms = [get_atoms(smiles) for smiles in reactants]\\n patoms = [get_atoms(smiles) for smiles in products]\\n\\n # flatten\\n ratoms = sum(ratoms, [])\\n patoms = sum(patoms, [])\\n\\n ratoms.sort()\\n patoms.sort()\\n\\n return ratoms == patoms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02e71e45e88e45d82a5860d4165d48d7\",\n \"score\": \"0.53851956\",\n \"text\": \"def check_composition(self):\\n\\n n_m = 0\\n n_f = 0\\n\\n for student in self.students:\\n if student.gender == 'm':\\n n_m += 1\\n if student.gender == 'f':\\n n_f += 1\\n\\n return not (0 < n_f < n_m)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09ca5e35d1e3b6b2b21b5e086726ee16\",\n \"score\": \"0.5381045\",\n \"text\": \"def validate(self, processes=1, fast=False, completeness_only=False):\\n\\n self._validate_structure()\\n self._validate_bagittxt()\\n\\n self.validate_fetch()\\n\\n self._validate_contents(\\n processes=processes, fast=fast, completeness_only=completeness_only\\n )\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c020d7660f28b4d750b2281caceb6f8\",\n \"score\": \"0.53562754\",\n \"text\": \"def can_compose(self, obj):\\r\\n\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbd3355ff4ad1dbbc14751557fb18218\",\n \"score\": \"0.5323333\",\n \"text\": \"def is_complex (self):\\n\\t\\treturn not (len (self.subtags) == 1\\n\\t\\t\\tor self.grandfathered\\n\\t\\t\\tand len (self.subtags[1]) != 3\\n\\t\\t\\tand ot.from_bcp_47[self.subtags[0]] == ot.from_bcp_47[self.language])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6942e76ccd2750378ee17888184b6541\",\n \"score\": \"0.53183836\",\n \"text\": \"def failsSpeciesConstraints(species):\\n \\n from rmgpy.rmg.input import getInput\\n\\n try:\\n speciesConstraints = getInput('speciesConstraints')\\n except Exception:\\n logging.debug('Species constraints could not be found.')\\n speciesConstraints = {}\\n \\n if isinstance(species, Species):\\n struct = species.molecule[0]\\n else:\\n # expects a molecule here\\n struct = species\\n\\n explicitlyAllowedMolecules = speciesConstraints.get('explicitlyAllowedMolecules', [])\\n for molecule in explicitlyAllowedMolecules:\\n if struct.isIsomorphic(molecule):\\n return False \\n \\n maxCarbonAtoms = speciesConstraints.get('maximumCarbonAtoms', -1) \\n if maxCarbonAtoms != -1:\\n if struct.getNumAtoms('C') > maxCarbonAtoms:\\n return True\\n\\n maxOxygenAtoms = speciesConstraints.get('maximumOxygenAtoms', -1)\\n if maxOxygenAtoms != -1:\\n if struct.getNumAtoms('O') > maxOxygenAtoms:\\n return True\\n\\n maxNitrogenAtoms = speciesConstraints.get('maximumNitrogenAtoms', -1)\\n if maxNitrogenAtoms != -1:\\n if struct.getNumAtoms('N') > maxNitrogenAtoms:\\n return True\\n\\n maxSiliconAtoms = speciesConstraints.get('maximumSiliconAtoms', -1)\\n if maxSiliconAtoms != -1:\\n if struct.getNumAtoms('Si') > maxSiliconAtoms:\\n return True\\n\\n maxSulfurAtoms = speciesConstraints.get('maximumSulfurAtoms', -1)\\n if maxSulfurAtoms != -1:\\n if struct.getNumAtoms('S') > maxSulfurAtoms:\\n return True\\n\\n maxHeavyAtoms = speciesConstraints.get('maximumHeavyAtoms', -1)\\n if maxHeavyAtoms != -1:\\n if struct.getNumAtoms() - struct.getNumAtoms('H') > maxHeavyAtoms:\\n return True\\n\\n maxRadicals = speciesConstraints.get('maximumRadicalElectrons', -1)\\n if maxRadicals != -1:\\n if (struct.getRadicalCount() > maxRadicals):\\n return True\\n\\n maxCarbenes = speciesConstraints.get('maximumSingletCarbenes', 1)\\n if maxRadicals != -1:\\n if struct.getSingletCarbeneCount() > maxCarbenes:\\n return True\\n\\n maxCarbeneRadicals = speciesConstraints.get('maximumCarbeneRadicals', 0)\\n if maxCarbeneRadicals != -1:\\n if struct.getSingletCarbeneCount() > 0 and struct.getRadicalCount() > maxCarbeneRadicals:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99a49b694643ab63474b0b2fc5beeb9c\",\n \"score\": \"0.5315637\",\n \"text\": \"def _valid(self) -> bool:\\n put_stone_in_cauldron = self.cauldron and self.using_stone\\n put_stone_in_potion = self.using_potion and self.using_stone\\n return [self.end_trial, self.no_op, put_stone_in_cauldron,\\n put_stone_in_potion].count(True) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99a49b694643ab63474b0b2fc5beeb9c\",\n \"score\": \"0.5315637\",\n \"text\": \"def _valid(self) -> bool:\\n put_stone_in_cauldron = self.cauldron and self.using_stone\\n put_stone_in_potion = self.using_potion and self.using_stone\\n return [self.end_trial, self.no_op, put_stone_in_cauldron,\\n put_stone_in_potion].count(True) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f775f480525ffa3f862a3706b94d4b0\",\n \"score\": \"0.53053993\",\n \"text\": \"def has_pair(self):\\n seen = set()\\n for part in self.parts:\\n if part.element in seen:\\n return True\\n seen.add(part.element)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd2d9042ca12f4b71c3c65c9c7936856\",\n \"score\": \"0.53012043\",\n \"text\": \"def match_substructures(mol, substructures):\\n for substructure in substructures:\\n if mol.HasSubstructMatch(substructure):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1a1408106bad507ca50c9e6070b494\",\n \"score\": \"0.5286026\",\n \"text\": \"def test_structure_combinations(self):\\n\\n elem_structures = self.mysubtraction.get_all_elementary_structures(self.ggg, 3)\\n combos = self.mysubtraction.get_all_combinations(elem_structures, 3)\\n # The number of counterterms may turn out to be incorrect,\\n # but test that the generation goes through\\n self.assertEqual(1398, len(combos))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"283bda0ee8a0eb65c5677e77e43669ce\",\n \"score\": \"0.52839917\",\n \"text\": \"def is_transport(reaction, compartments_list, all_compounds = False, ignore_h = False):\\n # left part of the reaction\\n r = list(str(x) for x in reaction.reactants)\\n # right part\\n p = list(str(x) for x in reaction.products)\\n #if len(r) != len(p):\\n # return False\\n c = compartments_list\\n # removing terminations\\n R = []\\n for i in r:\\n for e in c:\\n i = i.replace(e, \\\"\\\") \\n R.append(i)\\n P = []\\n for i in p:\\n for e in c:\\n i = i.replace(e, \\\"\\\")\\n P.append(i)\\n # we sort the lists to avoid missing transport reactions where the sequence of the compounds is not maintained\\n R = sorted(R)\\n P = sorted(P)\\n if all_compounds == False:\\n if ignore_h == True:\\n if \\\"h\\\" in R:\\n R.remove(\\\"h\\\")\\n if \\\"h\\\" in P:\\n P.remove(\\\"h\\\")\\n for i in R:\\n if i in P:\\n return True\\n else:\\n return False\\n if R == P:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9195b1c34af2a2b11d0f32f9063d2cfc\",\n \"score\": \"0.5280433\",\n \"text\": \"def filter_mol(mol, max_heavy_atoms=100, min_heavy_atoms=10, element_list=[6,7,8,9,16,17,35]):\\n if mol is not None:\\n num_heavy = min_heavy_atoms<mol.GetNumHeavyAtoms()<max_heavy_atoms\\n elements = all([atom.GetAtomicNum() in element_list for atom in mol.GetAtoms()])\\n if num_heavy and elements:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57fce85412c7a52165f7d315e3a78130\",\n \"score\": \"0.5269647\",\n \"text\": \"def __contain__(self, item):\\n if item in self.__sub:\\n return True\\n\\n if item in self.__time:\\n return True\\n\\n if item in self.__date:\\n return True\\n\\n if item in self.__people:\\n return True\\n\\n if item in self.__location:\\n return True\\n\\n if item in self.__items:\\n return True\\n\\n if item in self.__additional:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab594010aa4a0561fe59a7d379631bf5\",\n \"score\": \"0.5266355\",\n \"text\": \"def is_matching_constraints(self, p):\\n return all([c(p) for c in self.C])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ce80767c2f00c0e5c2453c8293a325b\",\n \"score\": \"0.5262244\",\n \"text\": \"def is_valid_component(component):\\n\\n return component.__class__ in [Annotation, Reference, Repeat, Row]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3a83b1f67718becdb46ab7a92fd6cc4\",\n \"score\": \"0.5255005\",\n \"text\": \"def is_composite(self):\\n # walk through method resolution order and inspect methods\\n # of classes and direct parents, \\\"adjacent\\\" classes in mro\\n params = self.get_params(deep=False)\\n composite = any(isinstance(x, BaseObject) for x in params.values())\\n\\n return composite\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1acfd6005380106a7dab3b11e41b9100\",\n \"score\": \"0.52494496\",\n \"text\": \"def _(expr, assumptions):\\n assumps = conjuncts(assumptions)\\n if expr.is_commutative is not None:\\n return expr.is_commutative and not ~Q.commutative(expr) in assumps\\n if Q.commutative(expr) in assumps:\\n return True\\n elif ~Q.commutative(expr) in assumps:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f7b0efdacc163d0b42c7c7e658e5a00\",\n \"score\": \"0.52458704\",\n \"text\": \"def isEmpty(self):\\n return self.mpi.groupIsEmpty(self.capsule)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"547277762033a896d8172cebffc75123\",\n \"score\": \"0.523349\",\n \"text\": \"def validate_part_b(self):\\n if not self.validate_part_a():\\n return False\\n\\n if (self.byr_is_valid()\\n and self.iyr_is_valid()\\n and self.eyr_is_valid()\\n and self.hgt_is_valid()\\n and self.hcl_is_valid()\\n and self.ecl_is_valid()\\n and self.pid_is_valid()):\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bfe26d418d8d678f4760219feaeac81\",\n \"score\": \"0.5226915\",\n \"text\": \"def requires_hubbard(self, comp):\\n comp = Composition(comp)\\n elements = sorted([el for el in comp.elements if comp[el] > 0],\\n key=lambda el: el.X)\\n most_electroneg = elements[-1].symbol\\n\\n usettings = self.u_settings.get(most_electroneg, {})\\n\\n return any([usettings.get(el.symbol, 0) for el in comp.elements])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a60ac2c7788e6999a68215e55d624ac\",\n \"score\": \"0.52262086\",\n \"text\": \"def part_of(cls, a: str, b: str) -> bool:\\n definition = cls.parser.fetch(b)\\n a = cls.get_synonyms(a)\\n synonyms = '|'.join(a)\\n try:\\n definitions = [d['definitions'] for d in definition]\\n definitions = flatten(definitions)\\n definitions = [d['text'] for d in definitions]\\n definitions = flatten(definitions)\\n except IndexError:\\n return False\\n for d in definitions:\\n if re.search(f'(?:parts? of|elements? of|portions? of|in an?).*(?:{synonyms})', d) is not None:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6be104709f649eee8f9e056d41ee5df\",\n \"score\": \"0.5219647\",\n \"text\": \"def isIsopropylOrGuanidinium( atmDef ):\\n n = len(atmDef.real)\\n if (n == 4) or (n == 6):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f30491ce928c18fb5874eda587c48204\",\n \"score\": \"0.52168226\",\n \"text\": \"def _is_cupy_compatible(arg):\\n\\n if isinstance(arg, ndarray):\\n if not arg._supports_cupy:\\n return False\\n\\n if isinstance(arg, (tuple, list)):\\n return all(_RecursiveAttr._is_cupy_compatible(i) for i in arg)\\n\\n if isinstance(arg, dict):\\n bools = [_RecursiveAttr._is_cupy_compatible(arg[i]) for i in arg]\\n return all(bools)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"617b723107d2ffc28be0d3c56425efad\",\n \"score\": \"0.521607\",\n \"text\": \"def can_chain(box):\\n for value in values[box]:\\n for chain_box in chain:\\n # A box fits the chain if one of its values can be found in another box in the chain\\n if value in values[chain_box] and values[box] != values[chain_box]:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d780548b3640c12303f13a413510a75\",\n \"score\": \"0.5208866\",\n \"text\": \"def in_bounds(self):\\n for i in range(3):\\n dim_min, dim_max = 2*[self.occupied_coordinates[0][i]]\\n for coord in self.occupied_coordinates:\\n dim_min = min(dim_min, coord[i])\\n dim_max = max(dim_max, coord[i])\\n if dim_max - dim_min > 3:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff308de1b942000666598378c032c240\",\n \"score\": \"0.52005386\",\n \"text\": \"def check_explicit_beliefs(X):\\n assert type(X).__module__ == \\\"numpy\\\" # make sure it is not a matrix\\n correct = set(np.hstack(X)) == set([0, 1]), \\\"Input X can contain only 0 or 1 (as int or float)\\\"\\n if len(X.shape) == 1: # special case in case n=1 and no nesting: [[...]] -> [...]\\n return correct and np.abs(np.sum(X)) <= 1\\n else:\\n return correct and (np.abs(np.sum(X,1)) <= 1).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1a61c7a5ae61ed70d67de8c63cdb09\",\n \"score\": \"0.5198446\",\n \"text\": \"def AllRequirementsMet(items: List[BuildVariant]) -> bool:\\n available = set([item.name for item in items])\\n\\n for item in items:\\n for requirement in item.requires:\\n if requirement not in available:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21505ee9bda3caf3a70e705af0fb30cc\",\n \"score\": \"0.5196396\",\n \"text\": \"def test_Initial_conditions_contain_only_zeros_and_ones(Initial_conditions):\\n # flatten up the mesh into one single list\\n # and set on the list it should be [0,1] if it\\n # contains only 0 and 1. Then do sum on that will \\n # return 1\\n \\n assert (sum(set(sum(Initial_conditions,[]))) <= 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2baf40a3582f2614194a8f0b6df5f166\",\n \"score\": \"0.5191638\",\n \"text\": \"def contains_component(component, attr):\\n if isinstance(attr, tuple):\\n for ele in attr:\\n if contains_component(component, ele) is True:\\n return True\\n else:\\n if attr == component:\\n return True\\n \\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2f87271c2aa3b0d700ef9eebd6b0cb7\",\n \"score\": \"0.5189924\",\n \"text\": \"def is_consummation(self) -> bool:\\n consummation_phrases = [\\n 'announcing the consummation',\\n 'consummated the previously announced business combination'\\n ]\\n return any(phrase in self.text for phrase in consummation_phrases)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c366b5a6a45515cc6a1f095ef42d9ae\",\n \"score\": \"0.5188788\",\n \"text\": \"def has_shell_elements(self) -> bool:\\n return self._mesh._stub.List(self._mesh._message).element_shape_info.has_shell_elements\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2297f50be25c76fd720df40cfab47c53\",\n \"score\": \"0.5186772\",\n \"text\": \"def is_complex_cartesian(expr):\\n args=arg_add_flatten(expr)\\n ni=sum([is_coeff_mul(a,sp.I) for a in args])\\n nr=sum([a.is_real for a in args])\\n return ni<=1 and ni+nr==len(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2e257a5d30877289613e98ec82929f2\",\n \"score\": \"0.51822674\",\n \"text\": \"def _parts_interact(self):\\n # Assume that multipart bijectors involve interactions unless they\\n # override this method to say otherwise.\\n return (tf.nest.is_nested(self.forward_min_event_ndims) or\\n tf.nest.is_nested(self.inverse_min_event_ndims))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a9bb98a9791d5ab6879254e65b1e7c5\",\n \"score\": \"0.5177858\",\n \"text\": \"def __contains__(self, item):\\n\\n if self.root is not None:\\n\\n if self.comp(item, self.root.data) == 0:\\n return True\\n\\n if self.comp(item, self.root.data) != 0:\\n return self.root.tree_contains(item, self.comp)\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9b8db5aba35f300904d77a85b45f917\",\n \"score\": \"0.51745933\",\n \"text\": \"def isFlexible(self):\\n for key, value in self.arrayShapeRange.items():\\n if key in _CONSTRAINED_KEYS:\\n if value.isFlexible:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c36df02e28ad0b93463202fd36116e0\",\n \"score\": \"0.5173861\",\n \"text\": \"def __contains__(self, path) -> bool:\\n return any(\\n str(path) in module_creations.keys()\\n for module_creations\\n in self.creations.values()\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b26d4b3cd18a79a4e0de0dae8a44b9d\",\n \"score\": \"0.5169665\",\n \"text\": \"def has_obj(namelst):\\n for name in namelst:\\n if func.is_bias(name) or func.is_halogen(name) or \\\\\\n func.is_lamp(name) or func.is_std(name):\\n pass\\n else:\\n if func.get_grism(name).lower() == 'open' or \\\\\\n func.get_slit(name).lower() == 'open':\\n return False\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"badb3a64bf78503742472c5da5cb5c2d\",\n \"score\": \"0.5163052\",\n \"text\": \"def isvalid(self):\\n #\\n _al = self.as_adjacency_list(as_numbered=True)\\n nd = {}\\n for n in range(10):\\n nd[n] = sum([1 for x in _al.keys() if len(_al[x]) == n])\\n\\n if nd[0] > 0: # has non-connected node\\n print('has non-connected node')\\n _ret = False\\n elif nd[1] > 0: # has loose edge node\\n print('has loose edge')\\n _ret = False\\n elif nd[2] < 3: # its not even a triangle\\n print('its not even a triangle')\\n _ret = False\\n elif nd[3] % 2 == 1: # has odd number of branches\\n print('has odd number of branches')\\n _ret = False\\n elif sum([1 for x in range(4, 10) if nd[x] > 0]): # has node with more than 3 edges connected\\n print('has node with more than 3 edges connected')\\n _ret = False\\n else:\\n _ret = True # but there will be other chacks\\n\\n if not _ret:\\n return _ret\\n\\n # all polygons, that are truly polygonlines must be valid\\n _ret = all([x.isvalid for x in self.polygons])\\n\\n return _ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"258a21f7219a114177bb4c594c79ed4d\",\n \"score\": \"0.5161628\",\n \"text\": \"def is_intercalated(self) -> bool:\\n return 0 in self.component_dimensionalities\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a00d5bfd423aef2e75f88bd6fd763ed0\",\n \"score\": \"0.51614875\",\n \"text\": \"def __contains__(self, element):\\n for vector in self._columns:\\n if element is vector:\\n return True\\n if element in vector:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d2dc7dd6dd961c65d2c8cc0beba690\",\n \"score\": \"0.5160536\",\n \"text\": \"def check_children(cls, module, instances): \\n #{{{ \\n check = []\\n for m in module.modules():\\n check += [any(isinstance(m,inst) for inst in instances)]\\n return any(check)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"817f9f1626473ffa1fcdebc1921cb459\",\n \"score\": \"0.5159764\",\n \"text\": \"def check_residue(self) -> bool:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dcde106e6cf1e66179ba4ac5a74afdb\",\n \"score\": \"0.5159541\",\n \"text\": \"def verify_if_proper_subset(self, problem_matrix):\\n row_size = len(problem_matrix[0])\\n for row in problem_matrix:\\n if row.count(1) is row_size:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b37dc36ba922e222f773b8fa38a5b740\",\n \"score\": \"0.5156301\",\n \"text\": \"def isMasse(inzidenz):\\n \\n if 0 in inzidenz.shape:\\n return False\\n unique, count = np.unique(inzidenz, return_counts=True)\\n\\n #-------There are only two potencials left and one of them is the mass-potencial--------\\n if(len(count) == 1):\\n return True\\n if(count[0] == count[-1]):\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993874,"cells":{"query_id":{"kind":"string","value":"4587b5b7cebb46bcd8322ff8e9547ce3"},"query":{"kind":"string","value":"Looks up entry in dictionary and populates entry's data"},"positive_passages":{"kind":"list like","value":[{"docid":"66e2be696faed2b12c692dbac7a009d7","score":"0.0","text":"def lookup(e):\n # Connect to DB\n # Get entry\n # Parse into variables\n # Check if furigana exists, or look it up if needed. Either way, compare\n if not self.furigana:\n pass","title":""}],"string":"[\n {\n \"docid\": \"66e2be696faed2b12c692dbac7a009d7\",\n \"score\": \"0.0\",\n \"text\": \"def lookup(e):\\n # Connect to DB\\n # Get entry\\n # Parse into variables\\n # Check if furigana exists, or look it up if needed. Either way, compare\\n if not self.furigana:\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"85416a4098704d90ca8311daf1beeb32","score":"0.6158545","text":"def retrieveValue(dictionary, key, entry):\n\tpass","title":""},{"docid":"465d96032b3f7cbdfd2e16f76f175212","score":"0.60910887","text":"def _update_entry_info(self, entry : dict):\n if self._node_type is not None:\n entry['node_type'] = self._node_type\n if self._router_context is not None:\n node_name = self._router_context.get_node_name(self._node_type)\n entry['node_name'] = node_name","title":""},{"docid":"ef76a946014c55a9cbd9b04c3d513139","score":"0.5952335","text":"def __convert__(self, entry):\n\n data = {}\n for key, value in entry.items():\n converter = self.convert.get(key, str)\n data[key] = converter(value)\n return data","title":""},{"docid":"e8b783de7eab9119c981df401e0ff050","score":"0.59248537","text":"def addEntryToDict(d, species, atom, nu, l1, l2, k, p, r, t, value):\n entries = [\"species\", \"atom\", \"nu\", \"l1\", \"l2\", \"k\", \"p\", \"r\", \"t\", \"value\"]\n values = [species, atom, nu, l1, l2, k, p, r, t, value]\n for key, val in zip(entries, values):\n if key not in d:\n d[key] = []\n d[key].append(val)","title":""},{"docid":"e6701b68d7b68112eff2982e8bdbde43","score":"0.5834277","text":"def populate(self, entries):\n for i, entry in enumerate(entries):\n entry[\"pos\"] = i + 1\n self.entries[entry[\"id\"]] = entry","title":""},{"docid":"5683ea45341335f21a65fc24b9c90180","score":"0.58310497","text":"def __populateData(self):\n data = self._request.getData()\n\n entrydict = self.getFromCache(self._filename)\n if not entrydict:\n fileExt = re.search(r'\\.([\\w]+)$', self._filename)\n try:\n eparser = data['extensions'][fileExt.groups()[0]]\n entrydict = eparser(self._filename, self._request)\n self.addToCache(self._filename, entrydict)\n except IOError:\n return None\n\n self.update(entrydict)\n self._populated_data = 1","title":""},{"docid":"065c93d4fc512f74be2ddd716ddeecc6","score":"0.57927346","text":"def _update(self, data):\n return {}","title":""},{"docid":"0839a0090a3bf3dabe13f28b72d57e30","score":"0.5788982","text":"def assign_entry(self, entry):\n #: todo reconfigure\n if entry is None:\n return\n\n if self.type in (enums.AdditionalInfoTypeEnum.PrimitiveTypeEnum,\n enums.AdditionalInfoTypeEnum.PrimitiveArrayTypeEnum):\n #: todo make it safe\n #: it's highly insecure as we assign byte itself not an address\n #: where it placed\n #: primitive type has byte info about primitive type\n value_ptr = cast(pointer((c_ubyte * 1)(*(entry, ))), c_void_p)\n self.value_ptr = value_ptr\n elif self.type in (enums.AdditionalInfoTypeEnum.ClassTypeInfo,\n enums.AdditionalInfoTypeEnum.LengthPrefixedString):\n self.value_ptr = cast(pointer(entry), c_void_p)\n else:\n raise TypeError(\n \"Given type `%r` of entry isn't supported\" % type(entry)\n )","title":""},{"docid":"7391f62ce1e89255790d29bda3eb28bc","score":"0.5733304","text":"def __getitem__(self, entryname):\n return self.get(entryname)","title":""},{"docid":"6c228f68a3a5f5de358f2b1a81718fcd","score":"0.57255113","text":"def update(self, key, contents):","title":""},{"docid":"265cc7598c93bb0b1fc4b2abab39ca79","score":"0.5710034","text":"def _getEntry(self, key):\n if key in self:\n return self._foundEntry.data","title":""},{"docid":"f11cba87d7557502b9af94a5e4090d27","score":"0.5691656","text":"def retrieve_person_data(self, entity):\n name = entity['text']\n # see if the entity is already known\n known_entry = self.known(name, entity['alternative'], 'persons')\n # if it is unknown, or we don't gave any additional data about it, try to find it again\n # reasoning here is that we may have encountered another way of spelling the entity's name that would be found\n if not known_entry or 'givenname' not in known_entry or known_entry['givenname'] == []:\n # if the entity is already known, but we just don't have additional data about it, try if we can find\n # information with a different way of spelling\n if known_entry:\n found, data = self.wikidata.get_person_data(name, known_entry['alternative'])\n alternative = list(set(known_entry['alternative'] + entity['alternative']))\n # if a new way of spelling is found, delete the old entry\n if found != name and self.known(name, [], 'persons'):\n self.handlers.entities.delete_with_label('persons', name)\n # if this is an entirely new entry\n else:\n found, data = self.wikidata.get_person_data(name, entity['alternative'])\n alternative = entity['alternative']\n if 'occupations' in data and 'politician' in data['occupations']:\n data = self.resolve_politicians(found, data)\n # update the list with the information that was found\n try:\n if data:\n data['key'] = name\n data['alternative'] = alternative\n entity['alternative'] = alternative\n entity['key'] = found\n for k, v in data.items():\n entity[k] = v\n self.handlers.entities.insert_one('persons', data)\n else:\n self.unknown_persons[name] += 1\n except AttributeError as e:\n print(e)\n print(data)\n except KeyError:\n print(name)\n print(known_entry)\n print(found)\n else:\n for k, v in known_entry.items():\n entity[k] = v\n return entity","title":""},{"docid":"d1cbe7e9152d1970a965db4236a73b17","score":"0.5686457","text":"def get_entry(self, name, data):\n raise NotImplementedError","title":""},{"docid":"e3296114903ef0b00aed36ee262d51e1","score":"0.5670478","text":"def _update(self, data: dict[str, Any]) -> None:","title":""},{"docid":"4044487335cde525317a1f2cc20845da","score":"0.5633371","text":"def set_entry():\n name = raw_input(\"Name: \")\n number = raw_input(\"Phone Number: \")\n entries[name] = number\n print \"Entry stored for %s.\" % name","title":""},{"docid":"c49e77bee40e85aee1aa7c490f5f0a08","score":"0.56060004","text":"def __getitem__(self, key):\n entry_dir = self.cache_key_dir(key)\n\n # Try obtain the entry from the registry first\n # It's the primary source of truth\n try:\n entry = self.registry[key]\n except KeyError:\n # Look for an entry file as the secondary source of truth\n entry_file = entry_dir / \"entry.yaml\"\n\n # Nothing, raise a KeyError\n if not entry_file.exists():\n raise KeyError(key)\n\n with open(entry_file, \"r\") as f:\n entry = yaml.safe_load(f)\n else:\n self.__setitem__(key, entry)\n\n entry['dir'] = entry_dir\n\n return entry","title":""},{"docid":"c28a57ffc7a5d5d2f55d3bacaf9c9c0f","score":"0.56036633","text":"def buildDict(self, dict) -> None:\n self.data += dict","title":""},{"docid":"fb16cb54e1de3222e453cfccc7005516","score":"0.55986845","text":"def entry(self, entry):\n\n self._entry = entry","title":""},{"docid":"27476c23472e9a0715b58edd885fa1dd","score":"0.5590556","text":"def addData(self, d):\n self.__populateDict(self._data, d)","title":""},{"docid":"6adad41a4c63626bed161949e689aed3","score":"0.55814224","text":"def new_entry():\n entry = {'lstevents': None,'artsits':None}\n entry['lstevents'] = lt.newList('ARRAY_LIST')\n entry['artists']=m.newMap(maptype='Probing',loadfactor=0.5)\n entry['tracks']=m.newMap(maptype='Probing',loadfactor=0.5)\n return entry","title":""},{"docid":"644c5bcb7872eea5c53306ae70b3a510","score":"0.55767936","text":"def data_dict_update(self, change):\n self.data_dict = change['value']","title":""},{"docid":"df7a850d457df65b54f69c1e8c081839","score":"0.55696577","text":"def make_entry(d):\n id_ = d['id']\n updated = datetime.datetime.strptime(d['updated'], '%Y-%m-%dT%H:%M:%SZ')\n title = d['title']\n summary = d.get('summary','')\n tags = ', '.join([v['term'] for v in d['tags']])\n authors = ', '.join(d['authors'])\n return id_, updated, title, summary, tags, authors","title":""},{"docid":"ac5dbbb9fe30750e0a12bb6eb4ceff49","score":"0.5561919","text":"def update(self, d):\n\t\tsuper(_dict, self).update(d)\n\t\treturn self","title":""},{"docid":"d5bdbb5c43dbf7bb09b9ade39c626952","score":"0.5535737","text":"def setup_dict(self):\n self.dict = {\n \"Phone\": self.data[0],\n \"Address\": self.data[1],\n \"Mail\": self.data[2],\n \"Web\": self.data[3],\n \"Terms\": self.data[4],\n \"Open\": self.data[5],\n \"Desc\": self.data[6]\n }","title":""},{"docid":"19e0f6921933f1e1a92265ca405f88ec","score":"0.5503605","text":"def __init__(self, dataDict):\n self._hash = None\n self._str = None\n self._entryData = dict(dataDict)","title":""},{"docid":"e1740c34bbd3c2c0bb736084434ed465","score":"0.54878193","text":"def update(self, data):\n if isinstance(data, Mapping):\n for key in data:\n self[key].update(data[key])\n else:\n for key, value in data:\n self[key].update(value)","title":""},{"docid":"d427013333fc12fb24038f7d0ad22d3a","score":"0.5478457","text":"def __init__(self):\n self.entries = {}","title":""},{"docid":"c69dd3706a06d1f15fa7ed2436067eee","score":"0.5451763","text":"def __build_entry(self, symbol, entry):\n\n self._logger.log(\n f\"Building entry for {self.id} data for the asset symbol {symbol}.\"\n )\n\n reformat_time = lambda t: datetime.fromtimestamp(t).strftime(\n \"%Y-%m-%d, %H:%M:%S\"\n )\n parse_time_series = lambda ts: [\n [\n reformat_time(ts_entry[\"time\"]),\n ts_entry[\"volume\"],\n ]\n for ts_entry in ts\n ]\n\n last_volume = entry[\"volume\"]\n parsed_time_series = parse_time_series(entry[\"timeSeries\"])\n parsed_time_series[-1][1] = last_volume\n volumes = [entry[1] for entry in parsed_time_series if entry[1] is not None]\n z_score = stats.zscore(volumes)[-1]\n\n return {\n \"time_series\": parsed_time_series,\n \"last_volume\": last_volume,\n \"last_z_score\": z_score,\n }","title":""},{"docid":"2f946ca0c6fc7caa87370d0ba3112b5e","score":"0.543964","text":"def build_new_entry(self):\n entry = self.db_schema() # as dictionary\n entry.save()\n return entry","title":""},{"docid":"b5125ff25ddd0841b5cc6c993f3afa97","score":"0.54261756","text":"def _add_entry(self, label_space, entry):\n request = collection_pb2.UpdateRequest()\n request.collection.CopyFrom(self._message)\n request.entry.dpf_type.Pack(entry._message)\n for key in label_space:\n request.label_space.label_space[key] = label_space[key]\n self._stub.UpdateEntry(request)","title":""},{"docid":"ee7ca262db069fe52bad4a0c03aa6659","score":"0.5425354","text":"def entry(self, e):\n for k in self._keys:\n self._entry[k] = None\n en = validate(e)\n if en:\n self._entry = en","title":""},{"docid":"8ddc058a23c187d3bdbf8ccb41274969","score":"0.5423164","text":"def add_entry(self, entry):\n prev = self.get_entry(entry.name)\n if prev is not None:\n # modify an existing entry\n #if prev.value is not None:\n #raise ValueError(\"changing an existing value is not allowed \"\n #\"for key '%s'\" % prev.name)\n prev.value = entry.value\n else:\n # new entry\n self._idx[entry.name] = len(self._entries)\n self._entries.append(entry)","title":""},{"docid":"760c14157e2b4ce43914ba138bfd4f27","score":"0.5422409","text":"def _formatOne(cls, entry):\n result = {}\n result['did'] = entry.did\n result['added'] = entry.added\n result['etag'] = entry.etag\n result['exported'] = entry.exported\n return result","title":""},{"docid":"1e678e18728416138304c6b44128a9c2","score":"0.541403","text":"def add_entry(prod_dict = None):\n #If no info provided then store product info from user in dictionary\n #(Else the provided dictioinary is used)\n user = False\n if prod_dict == None:\n user = True\n prod_dict = {}\n clear()\n prod_dict['product_name'] = get_input(\"\\nPlease, input the product name: \")\n prod_dict['product_quantity'] = get_input(\"Please, input the quantity: \")\n prod_dict['product_price'] = get_input(\"Please, input the price: \")\n prod_dict['date_updated'] = str(datetime.strftime(date.today(),\"%m.%d.%Y\"))\n\n #Clean up the data (Standardised data is assumed.\n #Alternatively various exceptions should be caught here.)\n for cat, val in prod_dict.items():\n if cat == 'product_price':\n #if the first character is not a number and the price is at least 2 characters long\n #(catches all kinds of currency indicators; In a production environment\n #it probably should be verified that currencies are used consistently)\n if not val[0].isnumeric() and len(val)>1:\n va = int(round(float(val[1:])*100))\n #in case there's a currency indicator only\n elif not val[0].isnumeric():\n va = 0\n else:\n #if no currency indicator\n va = int(round(float(val)*100))\n elif cat == 'date_updated':\n if '/' in val:\n va = val.replace('/','.')\n else:\n va = val\n #Parse string to datetime\n va = datetime.strptime(va,\"%m.%d.%Y\")\n else:\n va = val\n prod_dict[cat] = va\n\n pr_ex = product_exists(prod_dict['product_name'])\n #Add data from dictionary to database if:\n\n if not pr_ex:\n ##product does not exist\n Product.create(**prod_dict)\n if user:\n print(f\"\\nYou just entered a new product in the database:\\n\"\n f\"{prod_dict['product_quantity']} {prod_dict['product_name']}(s) \"\n f\"at a price of {prod_dict['product_price']} cents.\\n\")\n else:\n ##product is newer or has same date_updated\n if prod_dict['date_updated'] >= pr_ex and user or prod_dict['date_updated'] > pr_ex:\n delete_product(prod_dict['product_name'])\n Product.create(**prod_dict)\n if user:\n print(f\"\\nYou just updated a product in the database:\\n\"\n f\"{prod_dict['product_quantity']} {prod_dict['product_name']}(s) \"\n f\"at a price of {prod_dict['product_price']} cents.\\n\")","title":""},{"docid":"7ad0ca7d832497d30239df6f3671868b","score":"0.5408926","text":"def valoraDict(self, event=None):\n valor = self.getValor() # Otenemos el valor del Entry.\n llave = self.getLlave() # Y del atributo llave.\n self.master.dictopciones[llave] = valor # Y Asignamos el valor a la llave del diccionario.","title":""},{"docid":"189b80b82e0db15edad1104f621cf734","score":"0.54035896","text":"def update_entry(self, index, value):\n # TODO: Do this more efficiently.\n newvals = deepcopy(self._values)\n newvals[index] = value\n self.__init__(newvals)","title":""},{"docid":"fd57735c4ccb5eeb6ca54c9f406392b2","score":"0.53936946","text":"def addinfo(self, key, value):\r\n pass","title":""},{"docid":"e2a865eba628d32ec995099efb644c88","score":"0.53903556","text":"def _update_from_feed(self, feed_entry: GdacsFeedEntry) -> None:\n if not (event_name := feed_entry.event_name):\n # Earthquakes usually don't have an event name.\n event_name = f\"{feed_entry.country} ({feed_entry.event_id})\"\n self._attr_name = f\"{feed_entry.event_type}: {event_name}\"\n # Convert distance if not metric system.\n if self.hass.config.units is US_CUSTOMARY_SYSTEM:\n self._attr_distance = DistanceConverter.convert(\n feed_entry.distance_to_home, UnitOfLength.KILOMETERS, UnitOfLength.MILES\n )\n else:\n self._attr_distance = feed_entry.distance_to_home\n self._attr_latitude = feed_entry.coordinates[0]\n self._attr_longitude = feed_entry.coordinates[1]\n self._attr_attribution = feed_entry.attribution\n self._alert_level = feed_entry.alert_level\n self._country = feed_entry.country\n self._description = feed_entry.title\n self._duration_in_week = feed_entry.duration_in_week\n self._event_type_short = feed_entry.event_type_short\n self._event_type = feed_entry.event_type\n self._from_date = feed_entry.from_date\n self._to_date = feed_entry.to_date\n self._population = feed_entry.population\n self._severity = feed_entry.severity\n self._vulnerability = feed_entry.vulnerability\n # Round vulnerability value if presented as float.\n if isinstance(self._vulnerability, float):\n self._vulnerability = round(self._vulnerability, 1)\n self._version = feed_entry.version","title":""},{"docid":"cb40e65c5de0e1db918be37032017960","score":"0.5384876","text":"def process_entries(cls, config_dict):\n pass","title":""},{"docid":"bf9363d3403da6c7774c69d246ad7d17","score":"0.53796977","text":"def add_entry(self, entry):\r\n self.entry_keys.setdefault(entry, [])\r\n self.entries.append(entry)\r\n for dist in find_distributions(entry, True):\r\n self.add(dist, entry, False)","title":""},{"docid":"cea2830af2239baf00a28833b4a3e4e4","score":"0.5375731","text":"def update_dictionary(dictionary, base_id, new_val):\n dictionary[base_id] = new_val","title":""},{"docid":"d7d1173336db9960ee7efd732e377570","score":"0.5364577","text":"def update_submission_from_dict(self, submission, values):\n # first update any existing values, removing them from our dict as we work\n for value in submission.values.all():\n # we have a new value, update it\n if value.field.command in values:\n value.value = values[value.field.command]\n value.save()\n del values[value.field.command]\n\n # no new value, we need to remove this one\n else:\n value.delete()\n\n # now add any remaining values in our dict\n for key, value in values.items():\n # look up the field by key\n field = XFormField.objects.get(xform=self, command=key)\n sub_value = submission.values.create(field=field, value=str(value))\n\n # clear out our error flag if there were some\n if submission.has_errors:\n submission.has_errors = False\n submission.save()\n\n # trigger our signal for anybody interested in form submissions\n xform_received.send(sender=self, xform=self, submission=submission)","title":""},{"docid":"278f319879f708fbc300a18ec6aea14f","score":"0.53551435","text":"def update(self, metadata_update: Mapping):\n # Inefficient, by centralizes error handling.\n for key, value in metadata_update.items():\n self[key] = value","title":""},{"docid":"019d91c1a76a90eacb307615e711c9de","score":"0.5351616","text":"def _set_properties_from_entry(self, account):\n for field in account.entry_fields:\n self._update_property(field, account.properties[field])","title":""},{"docid":"75e81b82efa1bcd6ab8bad462aba7aa7","score":"0.5336978","text":"def add_entry(self, name, data):\n if name in self._base_names:\n return None\n\n self.names.append(name)\n self.data[name] = data\n\n self._update_gui()","title":""},{"docid":"e444e8e01528aaf273f286518f4763f8","score":"0.5335851","text":"def _add_attributes_of_entry(self, entry):\n for attribute, value in zip(entry.attributes_names, entry.attributes):\n if attribute in self.dict_of_attributes:\n if value not in self.dict_of_attributes[attribute]:\n self.dict_of_attributes[attribute].append(value)\n else:\n self.dict_of_attributes[attribute] = [value]","title":""},{"docid":"3eafd21ba266d0d68059055cda44ff36","score":"0.53316754","text":"def data(self, entry: list[str]):\n self.__parse(entry)","title":""},{"docid":"d66a88d09dfc4fce6cb958aee0e272f4","score":"0.532332","text":"def replace_entry(self, name, data):\n if name not in self.names:\n self.add_entry(name, data)\n return\n\n self.data[name] = data","title":""},{"docid":"5b2eca2e7913e6cb91dddbba1ef23c2f","score":"0.53204143","text":"def add_data_table_entry(d, table, entry):\n try:\n d['data_tables'][table].append(entry)\n except KeyError:\n raise Exception(\"add_data_table_entry: no table '%s'\" % table)","title":""},{"docid":"3e356680313b1587216f120f944c3018","score":"0.5317488","text":"def create_entry(cls, data):\n cls._db_insert(data)","title":""},{"docid":"8f8d5c3c50ba951288fef91470f00b17","score":"0.5315867","text":"def set_entry(self, table, key, data):\n key = self.serialize_key(key)\n client = self.redis_clients[self.CONFIG_DB]\n _hash = '{}{}{}'.format(table.upper(), self.TABLE_NAME_SEPARATOR, key)\n if data == None:\n client.delete(_hash)\n else:\n client.hmset(_hash, self.__typed_to_raw(data))","title":""},{"docid":"3422cfd46461f2f6193419ec02c3a893","score":"0.53151953","text":"def handle (self, key, value, new, data):\n return None","title":""},{"docid":"7a0c54a4d6c0820fc1625bb9c2b96399","score":"0.5313412","text":"def look_up_entry():\n if any(entries):\n name = raw_input(\"Name: \")\n if name in entries:\n print \"Found entry for %s: %s\" % (name, entries[name])\n else:\n print \"No entry found for %s.\" % name\n else:\n print \"The contact list is empty.\"","title":""},{"docid":"bc045eac780b794267af254f29c72cc9","score":"0.53052014","text":"def _SetDictionary(self, data):\n for key, value in data.items():\n self._Set(key, value)","title":""},{"docid":"62cdf2fd86c64251a3098a01d1c05c78","score":"0.528189","text":"def get_bibliography_entry(cache, key):\n for bibfile_cache in cache.values():\n data = bibfile_cache.data\n try:\n entry = data.entries[key]\n except KeyError:\n pass\n else:\n # entries are modified in an unpickable way\n # when formatting, so fetch a deep copy\n # and return this copy\n # we do not deep copy entry.collection because that\n # consumes enormous amounts of memory\n entry.collection = None\n entry2 = copy.deepcopy(entry)\n entry2.key = entry.key\n entry2.collection = data\n entry.collection = data\n return entry\n else:\n logger.warning(\"could not find bibtex key {0}.\".format(key))","title":""},{"docid":"aabc79b642d8ebf7c2ee8590d19f0b72","score":"0.5280413","text":"def parse_bibtex_entry(entry, dbref_key=\"bibref\"):\n field_dict = {'raw_entry':'', 'entry_type':'',\n \"bibref\":'', 'errors':\"\"} \n\n # strip all unneccesary whitespaces\n entry = r\" \".join(word for word in entry.split())\n #print entry\n \n # we store the full entry for easy retreaval later\n field_dict['bibtex'] = entry\n\n # now that we've stored the raw entry, strip bibtex markup\n entry = \" \".join(line for line in entry.split(r'\\n')).strip()\n entry = entry.strip()\n entry = entry.lstrip('@')\n entry = entry.rstrip('}')\n \n # entrytype (most often 'article')\n entrytype, entry = entry.split('{', 1) \n field_dict['category'] = entrytype\n field_list_temp = [field for field in entry.split(',')]\n field_list = []\n lcurb = rcurb = 0\n temp = \"\"\n for field in field_list_temp:\n lcurb += field.count('{')\n rcurb += field.count('}') \n temp += field\n if lcurb == rcurb:\n field_list.append(temp.strip())\n lcurb = rcurb = 0\n temp = \"\"\n if temp:\n field_list.append(temp.rstrip('}').strip())\n\n # we have a list of all fields. \n if not field_list:\n return field_dict\n\n # the first (?) field is the reference name #TODO: check bibtex standard\n\n field_dict[\"bibref\"] = field_list.pop(0)\n \n # next, we loop through all the fields and add them to dict. To do this we assume they\n # all are defined on a key = value format.\n print \"field_list: \", field_list\n for field in field_list:\n if '=' in field:\n fieldkey, fieldval = [arg.strip() for arg in field.split('=', 1)]\n field_dict[fieldkey.lower()] = fieldval.replace('{','').replace('}','') \n else:\n # malformed field\n field_dict[\"errors\"] = field\n\n print field_dict\n # store one of the entries as a special dbref field.\n if dbref_key:\n dbref = field_dict.get(dbref_key.lower(), None)\n if not dbref:\n print \"entry '%s' do not have a valid key '%s'. Ignoring this entry.\" % (field_dict[\"bibref\"], dbref_key)\n return None \n else:\n # if no dbref_key was given, use the refname\n dbref = field_dict[\"bibref\"]\n\n field_dict['dbref'] = dbref\n return field_dict","title":""},{"docid":"08f0c344f3ff78f6ed645d058a7d5e3c","score":"0.52743685","text":"def entry_from_dict(\n data: dict\n) -> typing.Union[FILE_WRITE_ENTRY, FILE_COPY_ENTRY]:\n if 'contents' in data:\n return FILE_WRITE_ENTRY(**data)\n return FILE_COPY_ENTRY(**data)","title":""},{"docid":"61d604b2583cff3ab45078372efb9b50","score":"0.5269737","text":"def buildDict(self, dict):\n self.d = dict","title":""},{"docid":"f4645e26ac2f2d105866b491e98f6e9a","score":"0.5258676","text":"def update(self, data):\n for (r,c),v in data.iteritems():\n self[(r,c)] = v","title":""},{"docid":"9101ce136368f67b8a512ccf6d6062d4","score":"0.52575743","text":"def add_entry(self, entry):\n\t\traise NotImplementedError","title":""},{"docid":"680e27e56c91509447352a5f79320c74","score":"0.5257303","text":"def insert_entry_(self, entry):\n self.updates.insert(entry)","title":""},{"docid":"d1dc3b13c75b5e72537ae3ce0b84bfcb","score":"0.52564144","text":"def create_entry(self, entity_id, attributes_map, fields):\n entry = {}\n for field in fields:\n field_translate = self.get_field_translator(field)\n translated_value = field_translate(entity_id,\n attributes_map,\n field)\n entry[field] = translated_value\n return entry","title":""},{"docid":"606adc1129ec6ddb520ee29eb39d452f","score":"0.52535856","text":"def add_item(dictionary, key, value):\n dictionary[key] = value","title":""},{"docid":"47538f3d0eed12fc3a7cf57834ef4e21","score":"0.52312565","text":"def update(data, option, value):\n for k, v in data.items():\n if k == option:\n data[k] = value\n break\n if isinstance(v, collections.abc.Mapping):\n data[k] = update(data.get(k, {}), option, value)\n return data","title":""},{"docid":"2f5b4ad0ab61a7f6a969f7feaf841b2e","score":"0.5212654","text":"def update_from_dict(self, d):\n d = d.copy()\n if \"query\" in d:\n self.query._proxied = Q(d.pop(\"query\"))\n if \"post_filter\" in d:\n self.post_filter._proxied = Q(d.pop(\"post_filter\"))\n\n aggs = d.pop(\"aggs\", d.pop(\"aggregations\", {}))\n if aggs:\n self.aggs._params = {\n \"aggs\": {name: A(value) for (name, value) in aggs.items()}\n }\n if \"sort\" in d:\n self._sort = d.pop(\"sort\")\n if \"_source\" in d:\n self._source = d.pop(\"_source\")\n if \"highlight\" in d:\n high = d.pop(\"highlight\").copy()\n self._highlight = high.pop(\"fields\")\n self._highlight_opts = high\n if \"suggest\" in d:\n self._suggest = d.pop(\"suggest\")\n if \"text\" in self._suggest:\n text = self._suggest.pop(\"text\")\n for s in self._suggest.values():\n s.setdefault(\"text\", text)\n if \"script_fields\" in d:\n self._script_fields = d.pop(\"script_fields\")\n self._extra.update(d)\n return self","title":""},{"docid":"d65430f26d3f74ac6b95b062af4db7a0","score":"0.52013826","text":"def add_entry(self, key, dict_val):\r\n hash_bucket = self.buckets[key%self.num_buckets]\r\n for i in range(len(hash_bucket)):\r\n if hash_bucket[i][0] ==key:\r\n hash_bucket[i] = (key, dict_val)\r\n return\r\n hash_bucket.append((key, dict_val))","title":""},{"docid":"db35d8b5b005f41b844296922e29d51b","score":"0.5199991","text":"def __setitem__(self, key, value):\n if not key in self:\n newEntry = Node(Entry(key, value), self._array[self._index])\n self._array[self._index] = newEntry\n self._size += 1\n else:\n self._foundEntry.data.value = value","title":""},{"docid":"082b9b0a3a8048b5e44cd5873bc9ed85","score":"0.51974165","text":"def update_data_from_lookup(\n data,\n element_to_set_in_data,\n index_element_in_data,\n lookup_data,\n element_to_find_in_lookup_data,\n index_element_in_lookup_data,\n value_in_lookup_data,\n undefined):\n\n # make a dictionary of the relevant elements from the lookup table\n root_of_lookup_data = lookup_data.getroot()\n lookup_table = dict()\n for child in root_of_lookup_data.findall(element_to_find_in_lookup_data):\n child_lookup_data = child.find(value_in_lookup_data)\n if child_lookup_data is not None:\n lookup_table[child.findtext(index_element_in_lookup_data)] = \\\n child_lookup_data.text\n # Update the field value using the lookup_table we just created\n data_root = data.getroot()\n\n count = 0\n for child in data_root:\n # get the element text, but set a default value of undefined for\n # each look up failure\n key = child.findtext(index_element_in_data)\n new_element_text = lookup_table.get(key, undefined)\n element_to_set = child.find(element_to_set_in_data)\n element_to_set.text = new_element_text","title":""},{"docid":"cd028b7fd37d23dc49bba76aa89fdab4","score":"0.5197147","text":"def parse_dict(self, data):","title":""},{"docid":"f48b8013967c814c39eb05698e8113c7","score":"0.51964474","text":"def add_new_entry_to_cache(\n self,\n location: str,\n exchange: dict,\n allocated: List[dict],\n shares: List[float],\n ) -> None:\n if \"reference product\" in exchange and \"product\" not in exchange:\n exchange[\"product\"] = exchange[\"reference product\"]\n\n exc_key = tuple(exchange[k] for k in (\"name\", \"product\", \"location\", \"unit\"))\n entry = [\n (\n e[\"name\"],\n e[\"product\"] if \"product\" in e else e[\"reference product\"],\n e[\"location\"],\n e[\"unit\"],\n s,\n )\n for e, s in zip(allocated, shares)\n ]\n\n if location not in self.cache:\n self.cache[location] = {}\n\n if self.model not in self.cache[location]:\n self.cache[location][self.model] = {}\n\n self.cache[location][self.model][exc_key] = entry","title":""},{"docid":"40a09d67b6948eaabcb3499553c9f5e2","score":"0.5191663","text":"def __setitem__(self, key, value):\n # Delete existing lines if any\n if key in self:\n del self[key]\n\n # Automatically set non-supported types to an empty EntriesList. This\n # allows for things like entries[date] = []\n if not isinstance(value, self.default_factory):\n value = self.default_factory(self, key)\n\n super(EntriesCollection, self).__setitem__(key, value)\n\n if self.synchronized:\n self.add_date(key)\n for entry in value:\n self.add_entry(entry)","title":""},{"docid":"376a73c3e87089f8ec328a88c4b0bce6","score":"0.51818347","text":"def _add_entry(self, har_entry):\n req = har_entry[\"request\"]\n resp = har_entry[\"response\"]\n content = resp.get(\"content\")\n\n # Record the usage count of the scheme+netloc, so we can pick the best\n # base_url later:\n url = req[\"url\"]\n parse_result = urlparse(url)\n prefix = \"{o.scheme}://{o.netloc}\".format(o=parse_result)\n self.prefix_counter[prefix] += 1\n\n entry_dt = har_entry[\"startedDateTime\"]\n if not self.first_entry_dt:\n self.first_entry_dt = entry_dt\n\n entry = {\n \"start\": iso_to_stamp(entry_dt),\n \"method\": req[\"method\"],\n \"url\": url,\n \"elap\": self._calc_request_time(har_entry),\n }\n if req.get(\"httpVersion\").upper() not in (\"\", \"HTTP/1.1\"):\n logger.warning(\"Unknown httpVersion: {!r}\".format(req.get(\"httpVersion\")))\n # logger.warning(\"Unknown httpVersion: {}\".format(req))\n\n if req.get(\"comment\"):\n entry[\"comment\"] = req[\"comment\"]\n\n if req.get(\"queryString\"): # List of (name, value) tuples\n entry[\"query\"] = req[\"queryString\"]\n\n if req.get(\"postData\"):\n post_data = req[\"postData\"]\n if post_data.get(\"mimeType\"):\n entry[\"mimeType\"] = post_data.get(\"mimeType\")\n\n if post_data.get(\"params\"):\n entry[\"data\"] = post_data.get(\"params\")\n elif post_data.get(\"text\"):\n # according to spec, 'text' should be mutually exclusive to 'params'\n # but Chrome produces both or sometimes text only:\n entry[\"data\"] = post_data.get(\"text\")\n\n if resp.get(\"comment\"):\n entry[\"resp_comment\"] = resp[\"comment\"]\n if content.get(\"mimeType\"):\n entry[\"resp_type\"] = content[\"mimeType\"]\n if content.get(\"size\"):\n entry[\"resp_size\"] = content[\"size\"]\n\n values = req.get(\"cookies\")\n if values:\n cookies = entry[\"cookies\"] = []\n for val in values:\n cookies.append((val[\"name\"], val[\"value\"]))\n\n values = req.get(\"headers\")\n if values:\n headers = entry[\"headers\"] = []\n for val in values:\n headers.append((val[\"name\"], val[\"value\"]))\n\n self.entries.append(entry)","title":""},{"docid":"5330a7cafd7399b00274694d54215cb6","score":"0.5181393","text":"def parse_json(self, entry):\n\n o = json.loads(entry)\n\n self.id = o[\"id\"]\n self.link = o[\"link\"]\n self.title = o[\"title\"]\n self.summary = o[\"summary\"]\n self.content = o[\"content\"]\n self.tags = o[\"tags\"]","title":""},{"docid":"34dce89ffe6443c2d08780d9ada86002","score":"0.51681423","text":"def __getitem__(self, name):\n return self.entry[name]","title":""},{"docid":"34dce89ffe6443c2d08780d9ada86002","score":"0.51681423","text":"def __getitem__(self, name):\n return self.entry[name]","title":""},{"docid":"d30bf923f716189c10dfdd1d2d6e8cea","score":"0.5165426","text":"def modify(self, entry, value, site_number=None):\n\n # Check allowed entries\n self._check_allowed_entries(entry)\n # Check that entries exists\n self._check_dict()\n if site_number is not None:\n # Check that a Site() object is supplied\n self._check_site(value)\n # Check site number\n self._check_site_number(site_number)\n # Check that position is an integer\n if not utils.is_number(site_number):\n self._logger.error(self.ERROR_MESSAGES[self.ERROR_SITE_NUMBER])\n sys.exit(self.ERROR_SITE_NUMBER)\n self.entries['sites'][site_number] = value\n else:\n if entry == 'sites':\n self._check_sites(sites=value)\n if entry == 'comment':\n self._check_comment(comment=value)\n if entry == 'unitcell':\n self._check_unitcell(unitcell=value)\n\n self.entries[entry] = value","title":""},{"docid":"d7d4968df015f3224e8fb7fce0a26875","score":"0.51603174","text":"def putter(entity_dictionary, data, defaults):\n nonlocal field_key\n data[field_key] = [\n entity.to_data(defaults = defaults, include_internals = True)\n for entity in _iter_nullable_dictionary_values(entity_dictionary)\n ]\n return data","title":""},{"docid":"210a41a9afc7d4ddf34d965b4b3a44ee","score":"0.5159938","text":"def _format_data(self, telem_field, input_dict, read_data, parsed_data):\n output_dict = {}\n if \"names\" in input_dict:\n if len(parsed_data) == 1:\n raise KeyError(\n \"Only one item parsed but subfields are listed: \" +\n telem_field)\n if len(parsed_data) > 1:\n # Multiple items parsed\n if \"names\" not in input_dict:\n raise KeyError(\n \"Must be a names field when multiple items are parsed: \" +\n telem_field)\n if len(input_dict['names']) != len(parsed_data):\n raise KeyError(\n \"Number of field names doesn't match parsing strings: \" +\n telem_field)\n for ind, field in enumerate(input_dict['names']):\n output_dict.update(\n {field: {\n 'timestamp': read_data['timestamp'],\n 'data': parsed_data[ind]}})\n\n else:\n # Single item parsed - pull in dict then update with parsed data.\n # Must be done in this order otherwise it generates a keyerror.\n output_dict[telem_field] = read_data\n output_dict[telem_field]['data'] = parsed_data[0]\n return output_dict","title":""},{"docid":"210a41a9afc7d4ddf34d965b4b3a44ee","score":"0.5159938","text":"def _format_data(self, telem_field, input_dict, read_data, parsed_data):\n output_dict = {}\n if \"names\" in input_dict:\n if len(parsed_data) == 1:\n raise KeyError(\n \"Only one item parsed but subfields are listed: \" +\n telem_field)\n if len(parsed_data) > 1:\n # Multiple items parsed\n if \"names\" not in input_dict:\n raise KeyError(\n \"Must be a names field when multiple items are parsed: \" +\n telem_field)\n if len(input_dict['names']) != len(parsed_data):\n raise KeyError(\n \"Number of field names doesn't match parsing strings: \" +\n telem_field)\n for ind, field in enumerate(input_dict['names']):\n output_dict.update(\n {field: {\n 'timestamp': read_data['timestamp'],\n 'data': parsed_data[ind]}})\n\n else:\n # Single item parsed - pull in dict then update with parsed data.\n # Must be done in this order otherwise it generates a keyerror.\n output_dict[telem_field] = read_data\n output_dict[telem_field]['data'] = parsed_data[0]\n return output_dict","title":""},{"docid":"be87460fcf383f12859cb405cea8fd5d","score":"0.51529884","text":"def register_entry(self, content):\n if type(content) is tuple and len(content) == 2:\n self.table[content[0]] = content[1]\n else:\n self.table.update(content)","title":""},{"docid":"1f4ce38df27dccc41676af7541c08c4d","score":"0.5151934","text":"def set_data(self, **kwargs):\n self.data.clear()\n for key, value in kwargs.iteritems():\n if key in self.mapping:\n d42_key = self.mapping[key]\n value = self.clean_data(d42_key, value)\n self.data.update({d42_key:value})\n elif key in self.d42_params:\n d42_key = key\n value = self.clean_data(d42_key, value)\n self.data.update({d42_key:value})","title":""},{"docid":"373333441d824b7c40e185d6f1ee1126","score":"0.51478565","text":"def handle_add_response(self, c: tuple, sentence: dict):\n _id_ = sentence['ret']\n d = {'.id': _id_,\n 'address': c[0],\n 'list': c[1]}\n super().__setitem__(d['address'], d)\n self.by_id[_id_] = d\n log.debug(\"Item added: %r\", d)","title":""},{"docid":"b275100543376260253a97ecf92d807a","score":"0.51453847","text":"def putter(entity_dictionary, data, defaults, *, include_internals = False):\n nonlocal field_key\n data[field_key] = [\n entity.to_data(defaults = defaults, include_internals = include_internals)\n for entity in _iter_nullable_dictionary_values(entity_dictionary)\n ]\n return data","title":""},{"docid":"0f0aba1358c4e693eb63f72f851a4a98","score":"0.5143458","text":"def __setitem__(self, key, value):\r\n\t\tself.map[key] = value\r\n\t\tself.save()","title":""},{"docid":"83ab81c8f81f54ef788ef0ddfaaa1936","score":"0.51388055","text":"def putter(preinstanced, data, defaults):\n nonlocal field_key\n \n data[field_key] = preinstanced.value\n \n return data","title":""},{"docid":"84e4f7839975dde8dbcaf640f2e0926b","score":"0.513607","text":"def add_entries_dict(d):\n for i in d:\n add_entries(d[i].df)","title":""},{"docid":"a3e6376a0519123eb585dd1c9d17f5ed","score":"0.5134854","text":"def __getitem__(self, key):\n if self.auto_update:\n self.update()\n \n return self.data[key]","title":""},{"docid":"45d786caf01a648b5a3443b5bec5ec96","score":"0.513459","text":"def from_dict(self, data):\n\n # return self._from_dict(self.state.update, data)\n\n list_ = self.state.get_names('update')\n\n for key in list_:\n if not key in data:\n continue\n\n from_dict_fn_name = '%s_from_dict' % key\n value = data[key]\n\n if hasattr(self, from_dict_fn_name):\n getattr(self, from_dict_fn_name)(value)\n elif hasattr(getattr(self, key), 'from_dict'):\n getattr(self, key).from_dict(value)\n else:\n setattr(self, key, value)\n\n # ===============================================================\n # try:\n # setattr(self, key, value)\n # except AttributeError, err:\n # print err.args\n # print err.message\n # print \"===========\"\n # raise AttributeError(\"Failed to set {0}\".format(key))\n # ===============================================================\n # return self # not required","title":""},{"docid":"17580dc3ab9ecdfbf34696b6f763380b","score":"0.5129681","text":"def parseEntry(self, entry):\n result = {}\n\n tokens = self.pattern.finditer(entry)\n\n # some defaults\n result['note'] = 'automatic ref import'\n for line in tokens:\n try:\n\n key=line.group(1)\n value=line.group(2)\n if key == '%A':\n\n value=value.replace('.-','-dot-')\n value=value.replace('.','. ')\n value=value.replace('-dot','.-')\n\n lastauthor=''\n if ' & ' in value:\n lastauthor = value.split(' & ',2)[-1]\n value = value.split(' & ',2)[0]\n if ' et ' in value.lower():\n lastauthor = value.split(' et ',2)[-1]\n value = value.split(' et ',2)[0]\n if ' en ' in value.lower():\n lastauthor = value.split(' en ',2)[-1]\n value = value.split(' en ',2)[0]\n authlist= value.split(',')\n if not lastauthor == '':\n authlist.append(lastauthor)\n\n for auth in authlist:\n raw = auth.strip().split(' ',3)\n if raw[0].lower() in ['van', 'von', 'du', 'de'] and len(raw)>1:\n raw[0] = raw[0]+' '+raw[1]\n raw.pop(1)\n lname = raw[0].strip()\n fname = ''\n mname = ''\n if len(raw)>1: fname = raw[1].strip()\n if len(raw)>2: mname = raw[2].strip()\n adict = {'firstname': fname\n ,'middlename': mname\n ,'lastname': lname\n }\n result.setdefault('authors',[]).append(adict)\n elif key == '%B':\n result['booktitle'] = str(value).strip()\n for i in ['acte', 'proceedings', 'akte']:\n if i in str(value).strip().lower(): result['reference_type'] = 'InproceedingsReference'\n if not result.has_key('reference_type'): result['reference_type'] = 'InbookReference'\n elif key == '%C': result['city'] = str(value).strip()\n elif key == '%D':\n date = str(value).strip().lower()\n if date == 'in press':\n if result.has_key('note'):\n result['note'] += \"\\n %s\" % str(value).strip()\n else:\n result['note'] += \"\\n %s\" % str(value).strip()\n elif not (date in ['n. d.','s. d.']):\n result['publication_year'] = str(value).strip()\n elif key == '%E':\n if not result.has_key('editor'):\n result['editor'] = str(value).strip()\n else:\n result['editor'] += ', %s' % str(value).strip()\n elif key == '%I': result['publisher'] = str(value).strip()\n elif key == '%J':\n result['journal'] = str(value).strip()\n result['reference_type'] = 'ArticleReference'\n elif key == '%K':\n if result.has_key('keywords'):\n result['keywords'] += '\\n' + '\\n'.join([key.strip() for key in str(value).split(',')])\n else:\n result['keywords'] = '\\n'.join([key.strip() for key in str(value).split(',')])\n elif key == '%N': result['number'] = str(value).strip()\n elif key == '%O':\n ap = 'CMFAuthors homepage:'\n parser = re.compile(\"A:::\"\n \"(?P<author>.*)///\"\n \"(?P<url>.*)\", reflags)\n up = 'PloneExportedFrom:'\n uparser = re.compile(\"A:::\"\n \"(?P<author>.*)///\"\n \"(?P<url>.*)\", reflags)\n if up in value:\n continue # skip, this is just a comment\n elif ap in value:\n token = [a.strip() \n for a in value.split(ap) \n if a.strip()][0]\n keys = [a \n for a in token.split(' and ')\n if a.strip()]\n for k in keys:\n m = parser.match(k)\n if m:\n infos = m.groupdict()\n for ai in result.get(\n 'authors', []):\n ak = ''\n for i in (\n 'lastname', \n 'firstname', \n 'middlename'):\n aval=ai.get(i, '').strip()\n if aval:\n ak += \" \"+aval\n ak = ak.strip()\n if ak == infos['author']:\n ai['homepage'] = infos['url']\n elif result.has_key('abstract'):\n result['abstract'] += \"\\n %s\" % str(value).strip()\n else:\n result['abstract'] = \"%s\" % str(value).strip()\n elif key == '%P': result['pages'] = str(value).strip()\n elif key == '%R':\n for i in ['acte', 'proceedings', 'akte']:\n if i in str(value).strip().lower():\n result['reference_type'] = 'ProceedingsReference'\n if result.has_key('note'):\n result['note'] += \"\\n %s\" % str(value).strip()\n else:\n result['note'] = \"%s\" % str(value).strip()\n for i in ['thesis', 'eindwerk', 'thèse']:\n if i in str(value).strip().lower():\n result['reference_type'] = 'MastersthesisReference'\n if result.has_key('note'):\n result['note'] += \"\\n %s\" % str(value).strip()\n else:\n result['note'] = \"%s\" % str(value).strip()\n for i in ['raport', 'rapport', 'report', 'verslag']:\n if i in str(value).strip().lower():\n result['reference_type'] = 'TechreportReference'\n result['type'] = str(value).strip()\n if not result.has_key('reference_type'):\n result['reference_type'] = 'BookReference'\n if result.has_key('note'):\n result['note'] += \"\\n %s\" % str(value).strip()\n else:\n result['note'] = \"%s\" % str(value).strip()\n elif key == '%T': result['title'] = str(value).strip()\n elif key == '%V': result['volume'] = str(value).strip()\n elif key == '%X':\n if result.has_key('note'):\n result['note'] += \"\\n %s\" % str(value).strip()\n else:\n result['note'] = \"%s\" % str(value).strip()\n except:\n result['upload_status'] = \"Error type: %s.\\nError value: %s\" \\\n % (sys.exc_info()[0],\n sys.exc_info()[1])\n return result\n\n if not result.has_key('reference_type'):\n result['reference_type'] = 'BookReference'\n\n return result","title":""},{"docid":"8b6ebe1ff5f904e9499dcdeb4c72ecad","score":"0.5122978","text":"def _update_from_dict(self, blog, post_dict, post=None):\n created = False\n save = True\n\n # If a post is not provided, try and fetch it\n if not post:\n if 'stardate' in post_dict:\n try:\n post = Post.objects.get(\n blog=blog,\n stardate=post_dict['stardate']\n )\n except Exception as e:\n logger.exception('something went boom')\n logger.debug(post_dict)\n if not post:\n post_dict['blog'] = blog\n\n post = Post(**post_dict)\n\n try:\n post.clean()\n post.full_clean()\n except ValidationError as e:\n # invalid posts should not be returned in this list\n logger.exception('Caught an exception processing {}'.format(post))\n save = False\n return\n\n push = False\n\n for key, value in post_dict.items():\n if key == 'body':\n post_value = getattr(post, key).raw\n else:\n post_value = getattr(post, key)\n\n if value != post_value:\n push = True\n\n # Update from dict values\n if not created:\n for att, value in post_dict.items():\n setattr(post, att, value)\n logger.debug('push is {}'.format(push))\n\n if save:\n post.save(push=push)\n logger.info('Blog: %s, Post: %s, created=%s', post.blog, post, created)\n return post","title":""},{"docid":"92b07fa00822baca7bd9e4d4cf62434e","score":"0.5120001","text":"def load_from_dict(self, instance: Any, dictionary: dict) -> None:\n if self.name in dictionary:\n self.__set__(instance, dictionary[self.name])","title":""},{"docid":"20665195d4438a1996e21bb90aa49ef1","score":"0.51175195","text":"def make_edge_data(entry):\n temp_dict = {}\n temp_dict['eid'] = int(entry[4])\n temp_dict['xcoord'] = float(entry[0])\n temp_dict['ycoord'] = float(entry[1])\n temp_dict['source'] = int(entry[2])\n temp_dict['target'] = int(entry[3])\n temp_dict['weight'] = float(entry[5])\n temp_dict['class'] = entry[6]\n temp_dict['type'] = entry[7]\n temp_dict['name'] = entry[8]\n\n return temp_dict","title":""},{"docid":"62e5e92dbe7ef646305713c88252126f","score":"0.5117514","text":"def make_entry(inscription):\n entry = {}\n if 'publication:' in inscription:\n entry['publication'] = inscription[1]\n\n if 'province:' in inscription:\n i = inscription.index('province:')\n entry['province'] = inscription[i + 1]\n\n if 'place:' in inscription:\n i = inscription.index('place:')\n entry['place'] = inscription[i + 1]\n entry['text'] = inscription[i + 2]\n\n if 'dating:' in inscription:\n i = inscription.index('dating:')\n entry['date'] = (i + 1, i + 3)\n\n return entry","title":""},{"docid":"1f679b29b29708a075fd8eb7dba5e251","score":"0.5113113","text":"def __setitem__(self, key, value):\n if not isinstance(value, dict):\n raise TypeError(\"value must be a dict\")\n\n # Is this a valid cache entry dictionary?\n validate_entry(value)\n\n entry_dir = self.cache_key_dir(key)\n\n try:\n entry_dir.mkdir(parents=True, exist_ok=True)\n except FileExistsError as e:\n raise ValueError(\"Already exists\") from e\n\n with open(entry_dir / \"entry.yaml\", \"w\") as f:\n f.write(yaml.safe_dump(value))","title":""},{"docid":"e5d55bdbb6857d26cde83351e2a4066f","score":"0.51110244","text":"def normalize_entry_dict(dico, feed_url):\n\n norm = {}\n assert isinstance(dico, dict)\n e_id = dico.get('id')\n if isinstance(e_id, basestring) and e_id[:4] == 'http':\n norm['url'] = e_id\n else:\n norm['url'] = dico.get('link')\n if not norm['url'].startswith('http'):\n norm['url'] = concat_urls(feed_url, norm['url'])\n norm['link'] = dico.get('link')\n if not norm['link'].startswith('http'):\n norm['link'] = concat_urls(feed_url, norm['link'])\n norm['title'] = dico.get('title')\n norm['content'] = dico.get('value')\n if norm['content'] is None:\n try:\n norm['content'] = dico['content'][0]['value']\n except (KeyError, IndexError) as err:\n norm['content'] = dico.get('summary')\n try:\n norm['mimetype'] = dico['content'][0]['type']\n except (KeyError, IndexError) as err:\n norm['mimetype'] = None\n structTime = dico.get('published_parsed')\n try:\n norm['created'] = datetime.datetime(*structTime[:6])\n except (TypeError, ValueError) as err:\n norm['created'] = None\n structTime = dico.get('updated_parsed')\n try:\n norm['updated'] = datetime.datetime(*structTime[:6])\n except (TypeError, ValueError) as err:\n norm['updated'] = None\n if norm['created'] is None and norm['updated'] is not None:\n norm['created'] = norm['updated']\n elif norm['updated'] is None and norm['created'] is not None:\n norm['updated'] = norm['created']\n return norm","title":""},{"docid":"72650b85f085e66c891e84b1521c8edc","score":"0.5110283","text":"def putter(entity_dictionary, data, defaults):\n nonlocal field_key\n data[field_key] = [\n entity.to_data(defaults = defaults)\n for entity in _iter_nullable_dictionary_values(entity_dictionary)\n ]\n return data","title":""},{"docid":"e21e165dcb41812d5d384da9057a45eb","score":"0.5108939","text":"def update_view(request):\n the_id = int(request.matchdict[\"id\"])\n entry = ENTRIES[the_id]\n return {\"entry\": entry}","title":""},{"docid":"272af07c546bbfdcf0b6168207b45ed3","score":"0.5107029","text":"def from_dict(self, data):\n\n def update_related(key, value, prop):\n \"\"\"Update the entity based on the type of relationship it is\"\"\"\n related_class = prop.mapper.class_\n\n with db.session.no_autoflush:\n if prop.uselist:\n new_list = list()\n for row in value:\n obj = related_class.add_or_edit(row, _commit=False)\n new_list.append(obj)\n setattr(self, key, new_list)\n else:\n setattr(self, key, related_class.add_or_edit(value, _commit=False))\n\n mapper = self.__mapper__\n entity_props = {attr.key: attr for attr in mapper.attrs}\n\n for key, value in data.items():\n prop = entity_props.get(key)\n is_column = isinstance(prop, sa.orm.properties.ColumnProperty)\n is_relationship = isinstance(prop, sa.orm.properties.RelationshipProperty)\n\n if prop is None:\n continue\n elif is_column:\n setattr(self, key, value)\n elif is_relationship:\n update_related(key, value, prop)\n else:\n raise NotImplementedError(\n _('Updating property types is not implemented.'))","title":""},{"docid":"3d199cf5669c0e4ec4eb70f3d0af243d","score":"0.5102643","text":"def fromDict(self, data):\n for key, value in data.items():\n setattr(self, key, value)","title":""},{"docid":"e2ea361aaa9555fb2e35a9965fade52b","score":"0.5101062","text":"def update(self, id, data):\n values = ''\n count = 1\n\n # lets loop over fields building up query so we only update valid fields\n for field in self.fields:\n if field not in data.keys():\n continue\n comma = ''\n if count < (len(data.keys()) - 1):\n comma = ','\n values += \"'{}' = '{}'{}\".format(field, data[field], comma)\n count += 1\n\n query = \"UPDATE {} SET {} WHERE id = {}\".format(self.table, values, id)\n\n # connect to db and make entry\n conn = self.get_connection()\n cur = conn.cursor()\n cur.execute(query)\n conn.commit()\n\n # lets get the made entry to return\n entry = self.get_by_id(id)\n return entry","title":""},{"docid":"0a2b7a379c08eef24636a0ad65cbd6a7","score":"0.51007336","text":"def process_data(data):\n # Add full name as a value\n data[\"fullname\"] = \" \".join([\n data[\"first\"],\n data[\"last\"]\n ])\n # Replace null aliases with Python None\n if data[\"alias\"] == \"null\":\n data[\"alias\"] = data[\"fullname\"]\n # Replace boolean protected values with Python bool\n data[\"protected\"] = False if data[\"protected\"] == \"FALSE\" else True\n\n data[\"handle\"] = data[\"handle\"].lstrip(\"@\")\n\n return data","title":""}],"string":"[\n {\n \"docid\": \"85416a4098704d90ca8311daf1beeb32\",\n \"score\": \"0.6158545\",\n \"text\": \"def retrieveValue(dictionary, key, entry):\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"465d96032b3f7cbdfd2e16f76f175212\",\n \"score\": \"0.60910887\",\n \"text\": \"def _update_entry_info(self, entry : dict):\\n if self._node_type is not None:\\n entry['node_type'] = self._node_type\\n if self._router_context is not None:\\n node_name = self._router_context.get_node_name(self._node_type)\\n entry['node_name'] = node_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef76a946014c55a9cbd9b04c3d513139\",\n \"score\": \"0.5952335\",\n \"text\": \"def __convert__(self, entry):\\n\\n data = {}\\n for key, value in entry.items():\\n converter = self.convert.get(key, str)\\n data[key] = converter(value)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8b783de7eab9119c981df401e0ff050\",\n \"score\": \"0.59248537\",\n \"text\": \"def addEntryToDict(d, species, atom, nu, l1, l2, k, p, r, t, value):\\n entries = [\\\"species\\\", \\\"atom\\\", \\\"nu\\\", \\\"l1\\\", \\\"l2\\\", \\\"k\\\", \\\"p\\\", \\\"r\\\", \\\"t\\\", \\\"value\\\"]\\n values = [species, atom, nu, l1, l2, k, p, r, t, value]\\n for key, val in zip(entries, values):\\n if key not in d:\\n d[key] = []\\n d[key].append(val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6701b68d7b68112eff2982e8bdbde43\",\n \"score\": \"0.5834277\",\n \"text\": \"def populate(self, entries):\\n for i, entry in enumerate(entries):\\n entry[\\\"pos\\\"] = i + 1\\n self.entries[entry[\\\"id\\\"]] = entry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5683ea45341335f21a65fc24b9c90180\",\n \"score\": \"0.58310497\",\n \"text\": \"def __populateData(self):\\n data = self._request.getData()\\n\\n entrydict = self.getFromCache(self._filename)\\n if not entrydict:\\n fileExt = re.search(r'\\\\.([\\\\w]+)$', self._filename)\\n try:\\n eparser = data['extensions'][fileExt.groups()[0]]\\n entrydict = eparser(self._filename, self._request)\\n self.addToCache(self._filename, entrydict)\\n except IOError:\\n return None\\n\\n self.update(entrydict)\\n self._populated_data = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"065c93d4fc512f74be2ddd716ddeecc6\",\n \"score\": \"0.57927346\",\n \"text\": \"def _update(self, data):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0839a0090a3bf3dabe13f28b72d57e30\",\n \"score\": \"0.5788982\",\n \"text\": \"def assign_entry(self, entry):\\n #: todo reconfigure\\n if entry is None:\\n return\\n\\n if self.type in (enums.AdditionalInfoTypeEnum.PrimitiveTypeEnum,\\n enums.AdditionalInfoTypeEnum.PrimitiveArrayTypeEnum):\\n #: todo make it safe\\n #: it's highly insecure as we assign byte itself not an address\\n #: where it placed\\n #: primitive type has byte info about primitive type\\n value_ptr = cast(pointer((c_ubyte * 1)(*(entry, ))), c_void_p)\\n self.value_ptr = value_ptr\\n elif self.type in (enums.AdditionalInfoTypeEnum.ClassTypeInfo,\\n enums.AdditionalInfoTypeEnum.LengthPrefixedString):\\n self.value_ptr = cast(pointer(entry), c_void_p)\\n else:\\n raise TypeError(\\n \\\"Given type `%r` of entry isn't supported\\\" % type(entry)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7391f62ce1e89255790d29bda3eb28bc\",\n \"score\": \"0.5733304\",\n \"text\": \"def __getitem__(self, entryname):\\n return self.get(entryname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c228f68a3a5f5de358f2b1a81718fcd\",\n \"score\": \"0.57255113\",\n \"text\": \"def update(self, key, contents):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"265cc7598c93bb0b1fc4b2abab39ca79\",\n \"score\": \"0.5710034\",\n \"text\": \"def _getEntry(self, key):\\n if key in self:\\n return self._foundEntry.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f11cba87d7557502b9af94a5e4090d27\",\n \"score\": \"0.5691656\",\n \"text\": \"def retrieve_person_data(self, entity):\\n name = entity['text']\\n # see if the entity is already known\\n known_entry = self.known(name, entity['alternative'], 'persons')\\n # if it is unknown, or we don't gave any additional data about it, try to find it again\\n # reasoning here is that we may have encountered another way of spelling the entity's name that would be found\\n if not known_entry or 'givenname' not in known_entry or known_entry['givenname'] == []:\\n # if the entity is already known, but we just don't have additional data about it, try if we can find\\n # information with a different way of spelling\\n if known_entry:\\n found, data = self.wikidata.get_person_data(name, known_entry['alternative'])\\n alternative = list(set(known_entry['alternative'] + entity['alternative']))\\n # if a new way of spelling is found, delete the old entry\\n if found != name and self.known(name, [], 'persons'):\\n self.handlers.entities.delete_with_label('persons', name)\\n # if this is an entirely new entry\\n else:\\n found, data = self.wikidata.get_person_data(name, entity['alternative'])\\n alternative = entity['alternative']\\n if 'occupations' in data and 'politician' in data['occupations']:\\n data = self.resolve_politicians(found, data)\\n # update the list with the information that was found\\n try:\\n if data:\\n data['key'] = name\\n data['alternative'] = alternative\\n entity['alternative'] = alternative\\n entity['key'] = found\\n for k, v in data.items():\\n entity[k] = v\\n self.handlers.entities.insert_one('persons', data)\\n else:\\n self.unknown_persons[name] += 1\\n except AttributeError as e:\\n print(e)\\n print(data)\\n except KeyError:\\n print(name)\\n print(known_entry)\\n print(found)\\n else:\\n for k, v in known_entry.items():\\n entity[k] = v\\n return entity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1cbe7e9152d1970a965db4236a73b17\",\n \"score\": \"0.5686457\",\n \"text\": \"def get_entry(self, name, data):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3296114903ef0b00aed36ee262d51e1\",\n \"score\": \"0.5670478\",\n \"text\": \"def _update(self, data: dict[str, Any]) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4044487335cde525317a1f2cc20845da\",\n \"score\": \"0.5633371\",\n \"text\": \"def set_entry():\\n name = raw_input(\\\"Name: \\\")\\n number = raw_input(\\\"Phone Number: \\\")\\n entries[name] = number\\n print \\\"Entry stored for %s.\\\" % name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c49e77bee40e85aee1aa7c490f5f0a08\",\n \"score\": \"0.56060004\",\n \"text\": \"def __getitem__(self, key):\\n entry_dir = self.cache_key_dir(key)\\n\\n # Try obtain the entry from the registry first\\n # It's the primary source of truth\\n try:\\n entry = self.registry[key]\\n except KeyError:\\n # Look for an entry file as the secondary source of truth\\n entry_file = entry_dir / \\\"entry.yaml\\\"\\n\\n # Nothing, raise a KeyError\\n if not entry_file.exists():\\n raise KeyError(key)\\n\\n with open(entry_file, \\\"r\\\") as f:\\n entry = yaml.safe_load(f)\\n else:\\n self.__setitem__(key, entry)\\n\\n entry['dir'] = entry_dir\\n\\n return entry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c28a57ffc7a5d5d2f55d3bacaf9c9c0f\",\n \"score\": \"0.56036633\",\n \"text\": \"def buildDict(self, dict) -> None:\\n self.data += dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb16cb54e1de3222e453cfccc7005516\",\n \"score\": \"0.55986845\",\n \"text\": \"def entry(self, entry):\\n\\n self._entry = entry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27476c23472e9a0715b58edd885fa1dd\",\n \"score\": \"0.5590556\",\n \"text\": \"def addData(self, d):\\n self.__populateDict(self._data, d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6adad41a4c63626bed161949e689aed3\",\n \"score\": \"0.55814224\",\n \"text\": \"def new_entry():\\n entry = {'lstevents': None,'artsits':None}\\n entry['lstevents'] = lt.newList('ARRAY_LIST')\\n entry['artists']=m.newMap(maptype='Probing',loadfactor=0.5)\\n entry['tracks']=m.newMap(maptype='Probing',loadfactor=0.5)\\n return entry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"644c5bcb7872eea5c53306ae70b3a510\",\n \"score\": \"0.55767936\",\n \"text\": \"def data_dict_update(self, change):\\n self.data_dict = change['value']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df7a850d457df65b54f69c1e8c081839\",\n \"score\": \"0.55696577\",\n \"text\": \"def make_entry(d):\\n id_ = d['id']\\n updated = datetime.datetime.strptime(d['updated'], '%Y-%m-%dT%H:%M:%SZ')\\n title = d['title']\\n summary = d.get('summary','')\\n tags = ', '.join([v['term'] for v in d['tags']])\\n authors = ', '.join(d['authors'])\\n return id_, updated, title, summary, tags, authors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac5dbbb9fe30750e0a12bb6eb4ceff49\",\n \"score\": \"0.5561919\",\n \"text\": \"def update(self, d):\\n\\t\\tsuper(_dict, self).update(d)\\n\\t\\treturn self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5bdbb5c43dbf7bb09b9ade39c626952\",\n \"score\": \"0.5535737\",\n \"text\": \"def setup_dict(self):\\n self.dict = {\\n \\\"Phone\\\": self.data[0],\\n \\\"Address\\\": self.data[1],\\n \\\"Mail\\\": self.data[2],\\n \\\"Web\\\": self.data[3],\\n \\\"Terms\\\": self.data[4],\\n \\\"Open\\\": self.data[5],\\n \\\"Desc\\\": self.data[6]\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e0f6921933f1e1a92265ca405f88ec\",\n \"score\": \"0.5503605\",\n \"text\": \"def __init__(self, dataDict):\\n self._hash = None\\n self._str = None\\n self._entryData = dict(dataDict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1740c34bbd3c2c0bb736084434ed465\",\n \"score\": \"0.54878193\",\n \"text\": \"def update(self, data):\\n if isinstance(data, Mapping):\\n for key in data:\\n self[key].update(data[key])\\n else:\\n for key, value in data:\\n self[key].update(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d427013333fc12fb24038f7d0ad22d3a\",\n \"score\": \"0.5478457\",\n \"text\": \"def __init__(self):\\n self.entries = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c69dd3706a06d1f15fa7ed2436067eee\",\n \"score\": \"0.5451763\",\n \"text\": \"def __build_entry(self, symbol, entry):\\n\\n self._logger.log(\\n f\\\"Building entry for {self.id} data for the asset symbol {symbol}.\\\"\\n )\\n\\n reformat_time = lambda t: datetime.fromtimestamp(t).strftime(\\n \\\"%Y-%m-%d, %H:%M:%S\\\"\\n )\\n parse_time_series = lambda ts: [\\n [\\n reformat_time(ts_entry[\\\"time\\\"]),\\n ts_entry[\\\"volume\\\"],\\n ]\\n for ts_entry in ts\\n ]\\n\\n last_volume = entry[\\\"volume\\\"]\\n parsed_time_series = parse_time_series(entry[\\\"timeSeries\\\"])\\n parsed_time_series[-1][1] = last_volume\\n volumes = [entry[1] for entry in parsed_time_series if entry[1] is not None]\\n z_score = stats.zscore(volumes)[-1]\\n\\n return {\\n \\\"time_series\\\": parsed_time_series,\\n \\\"last_volume\\\": last_volume,\\n \\\"last_z_score\\\": z_score,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f946ca0c6fc7caa87370d0ba3112b5e\",\n \"score\": \"0.543964\",\n \"text\": \"def build_new_entry(self):\\n entry = self.db_schema() # as dictionary\\n entry.save()\\n return entry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5125ff25ddd0841b5cc6c993f3afa97\",\n \"score\": \"0.54261756\",\n \"text\": \"def _add_entry(self, label_space, entry):\\n request = collection_pb2.UpdateRequest()\\n request.collection.CopyFrom(self._message)\\n request.entry.dpf_type.Pack(entry._message)\\n for key in label_space:\\n request.label_space.label_space[key] = label_space[key]\\n self._stub.UpdateEntry(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee7ca262db069fe52bad4a0c03aa6659\",\n \"score\": \"0.5425354\",\n \"text\": \"def entry(self, e):\\n for k in self._keys:\\n self._entry[k] = None\\n en = validate(e)\\n if en:\\n self._entry = en\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ddc058a23c187d3bdbf8ccb41274969\",\n \"score\": \"0.5423164\",\n \"text\": \"def add_entry(self, entry):\\n prev = self.get_entry(entry.name)\\n if prev is not None:\\n # modify an existing entry\\n #if prev.value is not None:\\n #raise ValueError(\\\"changing an existing value is not allowed \\\"\\n #\\\"for key '%s'\\\" % prev.name)\\n prev.value = entry.value\\n else:\\n # new entry\\n self._idx[entry.name] = len(self._entries)\\n self._entries.append(entry)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"760c14157e2b4ce43914ba138bfd4f27\",\n \"score\": \"0.5422409\",\n \"text\": \"def _formatOne(cls, entry):\\n result = {}\\n result['did'] = entry.did\\n result['added'] = entry.added\\n result['etag'] = entry.etag\\n result['exported'] = entry.exported\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e678e18728416138304c6b44128a9c2\",\n \"score\": \"0.541403\",\n \"text\": \"def add_entry(prod_dict = None):\\n #If no info provided then store product info from user in dictionary\\n #(Else the provided dictioinary is used)\\n user = False\\n if prod_dict == None:\\n user = True\\n prod_dict = {}\\n clear()\\n prod_dict['product_name'] = get_input(\\\"\\\\nPlease, input the product name: \\\")\\n prod_dict['product_quantity'] = get_input(\\\"Please, input the quantity: \\\")\\n prod_dict['product_price'] = get_input(\\\"Please, input the price: \\\")\\n prod_dict['date_updated'] = str(datetime.strftime(date.today(),\\\"%m.%d.%Y\\\"))\\n\\n #Clean up the data (Standardised data is assumed.\\n #Alternatively various exceptions should be caught here.)\\n for cat, val in prod_dict.items():\\n if cat == 'product_price':\\n #if the first character is not a number and the price is at least 2 characters long\\n #(catches all kinds of currency indicators; In a production environment\\n #it probably should be verified that currencies are used consistently)\\n if not val[0].isnumeric() and len(val)>1:\\n va = int(round(float(val[1:])*100))\\n #in case there's a currency indicator only\\n elif not val[0].isnumeric():\\n va = 0\\n else:\\n #if no currency indicator\\n va = int(round(float(val)*100))\\n elif cat == 'date_updated':\\n if '/' in val:\\n va = val.replace('/','.')\\n else:\\n va = val\\n #Parse string to datetime\\n va = datetime.strptime(va,\\\"%m.%d.%Y\\\")\\n else:\\n va = val\\n prod_dict[cat] = va\\n\\n pr_ex = product_exists(prod_dict['product_name'])\\n #Add data from dictionary to database if:\\n\\n if not pr_ex:\\n ##product does not exist\\n Product.create(**prod_dict)\\n if user:\\n print(f\\\"\\\\nYou just entered a new product in the database:\\\\n\\\"\\n f\\\"{prod_dict['product_quantity']} {prod_dict['product_name']}(s) \\\"\\n f\\\"at a price of {prod_dict['product_price']} cents.\\\\n\\\")\\n else:\\n ##product is newer or has same date_updated\\n if prod_dict['date_updated'] >= pr_ex and user or prod_dict['date_updated'] > pr_ex:\\n delete_product(prod_dict['product_name'])\\n Product.create(**prod_dict)\\n if user:\\n print(f\\\"\\\\nYou just updated a product in the database:\\\\n\\\"\\n f\\\"{prod_dict['product_quantity']} {prod_dict['product_name']}(s) \\\"\\n f\\\"at a price of {prod_dict['product_price']} cents.\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ad0ca7d832497d30239df6f3671868b\",\n \"score\": \"0.5408926\",\n \"text\": \"def valoraDict(self, event=None):\\n valor = self.getValor() # Otenemos el valor del Entry.\\n llave = self.getLlave() # Y del atributo llave.\\n self.master.dictopciones[llave] = valor # Y Asignamos el valor a la llave del diccionario.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"189b80b82e0db15edad1104f621cf734\",\n \"score\": \"0.54035896\",\n \"text\": \"def update_entry(self, index, value):\\n # TODO: Do this more efficiently.\\n newvals = deepcopy(self._values)\\n newvals[index] = value\\n self.__init__(newvals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd57735c4ccb5eeb6ca54c9f406392b2\",\n \"score\": \"0.53936946\",\n \"text\": \"def addinfo(self, key, value):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2a865eba628d32ec995099efb644c88\",\n \"score\": \"0.53903556\",\n \"text\": \"def _update_from_feed(self, feed_entry: GdacsFeedEntry) -> None:\\n if not (event_name := feed_entry.event_name):\\n # Earthquakes usually don't have an event name.\\n event_name = f\\\"{feed_entry.country} ({feed_entry.event_id})\\\"\\n self._attr_name = f\\\"{feed_entry.event_type}: {event_name}\\\"\\n # Convert distance if not metric system.\\n if self.hass.config.units is US_CUSTOMARY_SYSTEM:\\n self._attr_distance = DistanceConverter.convert(\\n feed_entry.distance_to_home, UnitOfLength.KILOMETERS, UnitOfLength.MILES\\n )\\n else:\\n self._attr_distance = feed_entry.distance_to_home\\n self._attr_latitude = feed_entry.coordinates[0]\\n self._attr_longitude = feed_entry.coordinates[1]\\n self._attr_attribution = feed_entry.attribution\\n self._alert_level = feed_entry.alert_level\\n self._country = feed_entry.country\\n self._description = feed_entry.title\\n self._duration_in_week = feed_entry.duration_in_week\\n self._event_type_short = feed_entry.event_type_short\\n self._event_type = feed_entry.event_type\\n self._from_date = feed_entry.from_date\\n self._to_date = feed_entry.to_date\\n self._population = feed_entry.population\\n self._severity = feed_entry.severity\\n self._vulnerability = feed_entry.vulnerability\\n # Round vulnerability value if presented as float.\\n if isinstance(self._vulnerability, float):\\n self._vulnerability = round(self._vulnerability, 1)\\n self._version = feed_entry.version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb40e65c5de0e1db918be37032017960\",\n \"score\": \"0.5384876\",\n \"text\": \"def process_entries(cls, config_dict):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf9363d3403da6c7774c69d246ad7d17\",\n \"score\": \"0.53796977\",\n \"text\": \"def add_entry(self, entry):\\r\\n self.entry_keys.setdefault(entry, [])\\r\\n self.entries.append(entry)\\r\\n for dist in find_distributions(entry, True):\\r\\n self.add(dist, entry, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cea2830af2239baf00a28833b4a3e4e4\",\n \"score\": \"0.5375731\",\n \"text\": \"def update_dictionary(dictionary, base_id, new_val):\\n dictionary[base_id] = new_val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d1173336db9960ee7efd732e377570\",\n \"score\": \"0.5364577\",\n \"text\": \"def update_submission_from_dict(self, submission, values):\\n # first update any existing values, removing them from our dict as we work\\n for value in submission.values.all():\\n # we have a new value, update it\\n if value.field.command in values:\\n value.value = values[value.field.command]\\n value.save()\\n del values[value.field.command]\\n\\n # no new value, we need to remove this one\\n else:\\n value.delete()\\n\\n # now add any remaining values in our dict\\n for key, value in values.items():\\n # look up the field by key\\n field = XFormField.objects.get(xform=self, command=key)\\n sub_value = submission.values.create(field=field, value=str(value))\\n\\n # clear out our error flag if there were some\\n if submission.has_errors:\\n submission.has_errors = False\\n submission.save()\\n\\n # trigger our signal for anybody interested in form submissions\\n xform_received.send(sender=self, xform=self, submission=submission)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"278f319879f708fbc300a18ec6aea14f\",\n \"score\": \"0.53551435\",\n \"text\": \"def update(self, metadata_update: Mapping):\\n # Inefficient, by centralizes error handling.\\n for key, value in metadata_update.items():\\n self[key] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019d91c1a76a90eacb307615e711c9de\",\n \"score\": \"0.5351616\",\n \"text\": \"def _set_properties_from_entry(self, account):\\n for field in account.entry_fields:\\n self._update_property(field, account.properties[field])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75e81b82efa1bcd6ab8bad462aba7aa7\",\n \"score\": \"0.5336978\",\n \"text\": \"def add_entry(self, name, data):\\n if name in self._base_names:\\n return None\\n\\n self.names.append(name)\\n self.data[name] = data\\n\\n self._update_gui()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e444e8e01528aaf273f286518f4763f8\",\n \"score\": \"0.5335851\",\n \"text\": \"def _add_attributes_of_entry(self, entry):\\n for attribute, value in zip(entry.attributes_names, entry.attributes):\\n if attribute in self.dict_of_attributes:\\n if value not in self.dict_of_attributes[attribute]:\\n self.dict_of_attributes[attribute].append(value)\\n else:\\n self.dict_of_attributes[attribute] = [value]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eafd21ba266d0d68059055cda44ff36\",\n \"score\": \"0.53316754\",\n \"text\": \"def data(self, entry: list[str]):\\n self.__parse(entry)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d66a88d09dfc4fce6cb958aee0e272f4\",\n \"score\": \"0.532332\",\n \"text\": \"def replace_entry(self, name, data):\\n if name not in self.names:\\n self.add_entry(name, data)\\n return\\n\\n self.data[name] = data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b2eca2e7913e6cb91dddbba1ef23c2f\",\n \"score\": \"0.53204143\",\n \"text\": \"def add_data_table_entry(d, table, entry):\\n try:\\n d['data_tables'][table].append(entry)\\n except KeyError:\\n raise Exception(\\\"add_data_table_entry: no table '%s'\\\" % table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e356680313b1587216f120f944c3018\",\n \"score\": \"0.5317488\",\n \"text\": \"def create_entry(cls, data):\\n cls._db_insert(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f8d5c3c50ba951288fef91470f00b17\",\n \"score\": \"0.5315867\",\n \"text\": \"def set_entry(self, table, key, data):\\n key = self.serialize_key(key)\\n client = self.redis_clients[self.CONFIG_DB]\\n _hash = '{}{}{}'.format(table.upper(), self.TABLE_NAME_SEPARATOR, key)\\n if data == None:\\n client.delete(_hash)\\n else:\\n client.hmset(_hash, self.__typed_to_raw(data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3422cfd46461f2f6193419ec02c3a893\",\n \"score\": \"0.53151953\",\n \"text\": \"def handle (self, key, value, new, data):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a0c54a4d6c0820fc1625bb9c2b96399\",\n \"score\": \"0.5313412\",\n \"text\": \"def look_up_entry():\\n if any(entries):\\n name = raw_input(\\\"Name: \\\")\\n if name in entries:\\n print \\\"Found entry for %s: %s\\\" % (name, entries[name])\\n else:\\n print \\\"No entry found for %s.\\\" % name\\n else:\\n print \\\"The contact list is empty.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc045eac780b794267af254f29c72cc9\",\n \"score\": \"0.53052014\",\n \"text\": \"def _SetDictionary(self, data):\\n for key, value in data.items():\\n self._Set(key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62cdf2fd86c64251a3098a01d1c05c78\",\n \"score\": \"0.528189\",\n \"text\": \"def get_bibliography_entry(cache, key):\\n for bibfile_cache in cache.values():\\n data = bibfile_cache.data\\n try:\\n entry = data.entries[key]\\n except KeyError:\\n pass\\n else:\\n # entries are modified in an unpickable way\\n # when formatting, so fetch a deep copy\\n # and return this copy\\n # we do not deep copy entry.collection because that\\n # consumes enormous amounts of memory\\n entry.collection = None\\n entry2 = copy.deepcopy(entry)\\n entry2.key = entry.key\\n entry2.collection = data\\n entry.collection = data\\n return entry\\n else:\\n logger.warning(\\\"could not find bibtex key {0}.\\\".format(key))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aabc79b642d8ebf7c2ee8590d19f0b72\",\n \"score\": \"0.5280413\",\n \"text\": \"def parse_bibtex_entry(entry, dbref_key=\\\"bibref\\\"):\\n field_dict = {'raw_entry':'', 'entry_type':'',\\n \\\"bibref\\\":'', 'errors':\\\"\\\"} \\n\\n # strip all unneccesary whitespaces\\n entry = r\\\" \\\".join(word for word in entry.split())\\n #print entry\\n \\n # we store the full entry for easy retreaval later\\n field_dict['bibtex'] = entry\\n\\n # now that we've stored the raw entry, strip bibtex markup\\n entry = \\\" \\\".join(line for line in entry.split(r'\\\\n')).strip()\\n entry = entry.strip()\\n entry = entry.lstrip('@')\\n entry = entry.rstrip('}')\\n \\n # entrytype (most often 'article')\\n entrytype, entry = entry.split('{', 1) \\n field_dict['category'] = entrytype\\n field_list_temp = [field for field in entry.split(',')]\\n field_list = []\\n lcurb = rcurb = 0\\n temp = \\\"\\\"\\n for field in field_list_temp:\\n lcurb += field.count('{')\\n rcurb += field.count('}') \\n temp += field\\n if lcurb == rcurb:\\n field_list.append(temp.strip())\\n lcurb = rcurb = 0\\n temp = \\\"\\\"\\n if temp:\\n field_list.append(temp.rstrip('}').strip())\\n\\n # we have a list of all fields. \\n if not field_list:\\n return field_dict\\n\\n # the first (?) field is the reference name #TODO: check bibtex standard\\n\\n field_dict[\\\"bibref\\\"] = field_list.pop(0)\\n \\n # next, we loop through all the fields and add them to dict. To do this we assume they\\n # all are defined on a key = value format.\\n print \\\"field_list: \\\", field_list\\n for field in field_list:\\n if '=' in field:\\n fieldkey, fieldval = [arg.strip() for arg in field.split('=', 1)]\\n field_dict[fieldkey.lower()] = fieldval.replace('{','').replace('}','') \\n else:\\n # malformed field\\n field_dict[\\\"errors\\\"] = field\\n\\n print field_dict\\n # store one of the entries as a special dbref field.\\n if dbref_key:\\n dbref = field_dict.get(dbref_key.lower(), None)\\n if not dbref:\\n print \\\"entry '%s' do not have a valid key '%s'. Ignoring this entry.\\\" % (field_dict[\\\"bibref\\\"], dbref_key)\\n return None \\n else:\\n # if no dbref_key was given, use the refname\\n dbref = field_dict[\\\"bibref\\\"]\\n\\n field_dict['dbref'] = dbref\\n return field_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08f0c344f3ff78f6ed645d058a7d5e3c\",\n \"score\": \"0.52743685\",\n \"text\": \"def entry_from_dict(\\n data: dict\\n) -> typing.Union[FILE_WRITE_ENTRY, FILE_COPY_ENTRY]:\\n if 'contents' in data:\\n return FILE_WRITE_ENTRY(**data)\\n return FILE_COPY_ENTRY(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61d604b2583cff3ab45078372efb9b50\",\n \"score\": \"0.5269737\",\n \"text\": \"def buildDict(self, dict):\\n self.d = dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4645e26ac2f2d105866b491e98f6e9a\",\n \"score\": \"0.5258676\",\n \"text\": \"def update(self, data):\\n for (r,c),v in data.iteritems():\\n self[(r,c)] = v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9101ce136368f67b8a512ccf6d6062d4\",\n \"score\": \"0.52575743\",\n \"text\": \"def add_entry(self, entry):\\n\\t\\traise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"680e27e56c91509447352a5f79320c74\",\n \"score\": \"0.5257303\",\n \"text\": \"def insert_entry_(self, entry):\\n self.updates.insert(entry)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1dc3b13c75b5e72537ae3ce0b84bfcb\",\n \"score\": \"0.52564144\",\n \"text\": \"def create_entry(self, entity_id, attributes_map, fields):\\n entry = {}\\n for field in fields:\\n field_translate = self.get_field_translator(field)\\n translated_value = field_translate(entity_id,\\n attributes_map,\\n field)\\n entry[field] = translated_value\\n return entry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"606adc1129ec6ddb520ee29eb39d452f\",\n \"score\": \"0.52535856\",\n \"text\": \"def add_item(dictionary, key, value):\\n dictionary[key] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47538f3d0eed12fc3a7cf57834ef4e21\",\n \"score\": \"0.52312565\",\n \"text\": \"def update(data, option, value):\\n for k, v in data.items():\\n if k == option:\\n data[k] = value\\n break\\n if isinstance(v, collections.abc.Mapping):\\n data[k] = update(data.get(k, {}), option, value)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f5b4ad0ab61a7f6a969f7feaf841b2e\",\n \"score\": \"0.5212654\",\n \"text\": \"def update_from_dict(self, d):\\n d = d.copy()\\n if \\\"query\\\" in d:\\n self.query._proxied = Q(d.pop(\\\"query\\\"))\\n if \\\"post_filter\\\" in d:\\n self.post_filter._proxied = Q(d.pop(\\\"post_filter\\\"))\\n\\n aggs = d.pop(\\\"aggs\\\", d.pop(\\\"aggregations\\\", {}))\\n if aggs:\\n self.aggs._params = {\\n \\\"aggs\\\": {name: A(value) for (name, value) in aggs.items()}\\n }\\n if \\\"sort\\\" in d:\\n self._sort = d.pop(\\\"sort\\\")\\n if \\\"_source\\\" in d:\\n self._source = d.pop(\\\"_source\\\")\\n if \\\"highlight\\\" in d:\\n high = d.pop(\\\"highlight\\\").copy()\\n self._highlight = high.pop(\\\"fields\\\")\\n self._highlight_opts = high\\n if \\\"suggest\\\" in d:\\n self._suggest = d.pop(\\\"suggest\\\")\\n if \\\"text\\\" in self._suggest:\\n text = self._suggest.pop(\\\"text\\\")\\n for s in self._suggest.values():\\n s.setdefault(\\\"text\\\", text)\\n if \\\"script_fields\\\" in d:\\n self._script_fields = d.pop(\\\"script_fields\\\")\\n self._extra.update(d)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d65430f26d3f74ac6b95b062af4db7a0\",\n \"score\": \"0.52013826\",\n \"text\": \"def add_entry(self, key, dict_val):\\r\\n hash_bucket = self.buckets[key%self.num_buckets]\\r\\n for i in range(len(hash_bucket)):\\r\\n if hash_bucket[i][0] ==key:\\r\\n hash_bucket[i] = (key, dict_val)\\r\\n return\\r\\n hash_bucket.append((key, dict_val))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db35d8b5b005f41b844296922e29d51b\",\n \"score\": \"0.5199991\",\n \"text\": \"def __setitem__(self, key, value):\\n if not key in self:\\n newEntry = Node(Entry(key, value), self._array[self._index])\\n self._array[self._index] = newEntry\\n self._size += 1\\n else:\\n self._foundEntry.data.value = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"082b9b0a3a8048b5e44cd5873bc9ed85\",\n \"score\": \"0.51974165\",\n \"text\": \"def update_data_from_lookup(\\n data,\\n element_to_set_in_data,\\n index_element_in_data,\\n lookup_data,\\n element_to_find_in_lookup_data,\\n index_element_in_lookup_data,\\n value_in_lookup_data,\\n undefined):\\n\\n # make a dictionary of the relevant elements from the lookup table\\n root_of_lookup_data = lookup_data.getroot()\\n lookup_table = dict()\\n for child in root_of_lookup_data.findall(element_to_find_in_lookup_data):\\n child_lookup_data = child.find(value_in_lookup_data)\\n if child_lookup_data is not None:\\n lookup_table[child.findtext(index_element_in_lookup_data)] = \\\\\\n child_lookup_data.text\\n # Update the field value using the lookup_table we just created\\n data_root = data.getroot()\\n\\n count = 0\\n for child in data_root:\\n # get the element text, but set a default value of undefined for\\n # each look up failure\\n key = child.findtext(index_element_in_data)\\n new_element_text = lookup_table.get(key, undefined)\\n element_to_set = child.find(element_to_set_in_data)\\n element_to_set.text = new_element_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd028b7fd37d23dc49bba76aa89fdab4\",\n \"score\": \"0.5197147\",\n \"text\": \"def parse_dict(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f48b8013967c814c39eb05698e8113c7\",\n \"score\": \"0.51964474\",\n \"text\": \"def add_new_entry_to_cache(\\n self,\\n location: str,\\n exchange: dict,\\n allocated: List[dict],\\n shares: List[float],\\n ) -> None:\\n if \\\"reference product\\\" in exchange and \\\"product\\\" not in exchange:\\n exchange[\\\"product\\\"] = exchange[\\\"reference product\\\"]\\n\\n exc_key = tuple(exchange[k] for k in (\\\"name\\\", \\\"product\\\", \\\"location\\\", \\\"unit\\\"))\\n entry = [\\n (\\n e[\\\"name\\\"],\\n e[\\\"product\\\"] if \\\"product\\\" in e else e[\\\"reference product\\\"],\\n e[\\\"location\\\"],\\n e[\\\"unit\\\"],\\n s,\\n )\\n for e, s in zip(allocated, shares)\\n ]\\n\\n if location not in self.cache:\\n self.cache[location] = {}\\n\\n if self.model not in self.cache[location]:\\n self.cache[location][self.model] = {}\\n\\n self.cache[location][self.model][exc_key] = entry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40a09d67b6948eaabcb3499553c9f5e2\",\n \"score\": \"0.5191663\",\n \"text\": \"def __setitem__(self, key, value):\\n # Delete existing lines if any\\n if key in self:\\n del self[key]\\n\\n # Automatically set non-supported types to an empty EntriesList. This\\n # allows for things like entries[date] = []\\n if not isinstance(value, self.default_factory):\\n value = self.default_factory(self, key)\\n\\n super(EntriesCollection, self).__setitem__(key, value)\\n\\n if self.synchronized:\\n self.add_date(key)\\n for entry in value:\\n self.add_entry(entry)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"376a73c3e87089f8ec328a88c4b0bce6\",\n \"score\": \"0.51818347\",\n \"text\": \"def _add_entry(self, har_entry):\\n req = har_entry[\\\"request\\\"]\\n resp = har_entry[\\\"response\\\"]\\n content = resp.get(\\\"content\\\")\\n\\n # Record the usage count of the scheme+netloc, so we can pick the best\\n # base_url later:\\n url = req[\\\"url\\\"]\\n parse_result = urlparse(url)\\n prefix = \\\"{o.scheme}://{o.netloc}\\\".format(o=parse_result)\\n self.prefix_counter[prefix] += 1\\n\\n entry_dt = har_entry[\\\"startedDateTime\\\"]\\n if not self.first_entry_dt:\\n self.first_entry_dt = entry_dt\\n\\n entry = {\\n \\\"start\\\": iso_to_stamp(entry_dt),\\n \\\"method\\\": req[\\\"method\\\"],\\n \\\"url\\\": url,\\n \\\"elap\\\": self._calc_request_time(har_entry),\\n }\\n if req.get(\\\"httpVersion\\\").upper() not in (\\\"\\\", \\\"HTTP/1.1\\\"):\\n logger.warning(\\\"Unknown httpVersion: {!r}\\\".format(req.get(\\\"httpVersion\\\")))\\n # logger.warning(\\\"Unknown httpVersion: {}\\\".format(req))\\n\\n if req.get(\\\"comment\\\"):\\n entry[\\\"comment\\\"] = req[\\\"comment\\\"]\\n\\n if req.get(\\\"queryString\\\"): # List of (name, value) tuples\\n entry[\\\"query\\\"] = req[\\\"queryString\\\"]\\n\\n if req.get(\\\"postData\\\"):\\n post_data = req[\\\"postData\\\"]\\n if post_data.get(\\\"mimeType\\\"):\\n entry[\\\"mimeType\\\"] = post_data.get(\\\"mimeType\\\")\\n\\n if post_data.get(\\\"params\\\"):\\n entry[\\\"data\\\"] = post_data.get(\\\"params\\\")\\n elif post_data.get(\\\"text\\\"):\\n # according to spec, 'text' should be mutually exclusive to 'params'\\n # but Chrome produces both or sometimes text only:\\n entry[\\\"data\\\"] = post_data.get(\\\"text\\\")\\n\\n if resp.get(\\\"comment\\\"):\\n entry[\\\"resp_comment\\\"] = resp[\\\"comment\\\"]\\n if content.get(\\\"mimeType\\\"):\\n entry[\\\"resp_type\\\"] = content[\\\"mimeType\\\"]\\n if content.get(\\\"size\\\"):\\n entry[\\\"resp_size\\\"] = content[\\\"size\\\"]\\n\\n values = req.get(\\\"cookies\\\")\\n if values:\\n cookies = entry[\\\"cookies\\\"] = []\\n for val in values:\\n cookies.append((val[\\\"name\\\"], val[\\\"value\\\"]))\\n\\n values = req.get(\\\"headers\\\")\\n if values:\\n headers = entry[\\\"headers\\\"] = []\\n for val in values:\\n headers.append((val[\\\"name\\\"], val[\\\"value\\\"]))\\n\\n self.entries.append(entry)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5330a7cafd7399b00274694d54215cb6\",\n \"score\": \"0.5181393\",\n \"text\": \"def parse_json(self, entry):\\n\\n o = json.loads(entry)\\n\\n self.id = o[\\\"id\\\"]\\n self.link = o[\\\"link\\\"]\\n self.title = o[\\\"title\\\"]\\n self.summary = o[\\\"summary\\\"]\\n self.content = o[\\\"content\\\"]\\n self.tags = o[\\\"tags\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34dce89ffe6443c2d08780d9ada86002\",\n \"score\": \"0.51681423\",\n \"text\": \"def __getitem__(self, name):\\n return self.entry[name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34dce89ffe6443c2d08780d9ada86002\",\n \"score\": \"0.51681423\",\n \"text\": \"def __getitem__(self, name):\\n return self.entry[name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d30bf923f716189c10dfdd1d2d6e8cea\",\n \"score\": \"0.5165426\",\n \"text\": \"def modify(self, entry, value, site_number=None):\\n\\n # Check allowed entries\\n self._check_allowed_entries(entry)\\n # Check that entries exists\\n self._check_dict()\\n if site_number is not None:\\n # Check that a Site() object is supplied\\n self._check_site(value)\\n # Check site number\\n self._check_site_number(site_number)\\n # Check that position is an integer\\n if not utils.is_number(site_number):\\n self._logger.error(self.ERROR_MESSAGES[self.ERROR_SITE_NUMBER])\\n sys.exit(self.ERROR_SITE_NUMBER)\\n self.entries['sites'][site_number] = value\\n else:\\n if entry == 'sites':\\n self._check_sites(sites=value)\\n if entry == 'comment':\\n self._check_comment(comment=value)\\n if entry == 'unitcell':\\n self._check_unitcell(unitcell=value)\\n\\n self.entries[entry] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d4968df015f3224e8fb7fce0a26875\",\n \"score\": \"0.51603174\",\n \"text\": \"def putter(entity_dictionary, data, defaults):\\n nonlocal field_key\\n data[field_key] = [\\n entity.to_data(defaults = defaults, include_internals = True)\\n for entity in _iter_nullable_dictionary_values(entity_dictionary)\\n ]\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"210a41a9afc7d4ddf34d965b4b3a44ee\",\n \"score\": \"0.5159938\",\n \"text\": \"def _format_data(self, telem_field, input_dict, read_data, parsed_data):\\n output_dict = {}\\n if \\\"names\\\" in input_dict:\\n if len(parsed_data) == 1:\\n raise KeyError(\\n \\\"Only one item parsed but subfields are listed: \\\" +\\n telem_field)\\n if len(parsed_data) > 1:\\n # Multiple items parsed\\n if \\\"names\\\" not in input_dict:\\n raise KeyError(\\n \\\"Must be a names field when multiple items are parsed: \\\" +\\n telem_field)\\n if len(input_dict['names']) != len(parsed_data):\\n raise KeyError(\\n \\\"Number of field names doesn't match parsing strings: \\\" +\\n telem_field)\\n for ind, field in enumerate(input_dict['names']):\\n output_dict.update(\\n {field: {\\n 'timestamp': read_data['timestamp'],\\n 'data': parsed_data[ind]}})\\n\\n else:\\n # Single item parsed - pull in dict then update with parsed data.\\n # Must be done in this order otherwise it generates a keyerror.\\n output_dict[telem_field] = read_data\\n output_dict[telem_field]['data'] = parsed_data[0]\\n return output_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"210a41a9afc7d4ddf34d965b4b3a44ee\",\n \"score\": \"0.5159938\",\n \"text\": \"def _format_data(self, telem_field, input_dict, read_data, parsed_data):\\n output_dict = {}\\n if \\\"names\\\" in input_dict:\\n if len(parsed_data) == 1:\\n raise KeyError(\\n \\\"Only one item parsed but subfields are listed: \\\" +\\n telem_field)\\n if len(parsed_data) > 1:\\n # Multiple items parsed\\n if \\\"names\\\" not in input_dict:\\n raise KeyError(\\n \\\"Must be a names field when multiple items are parsed: \\\" +\\n telem_field)\\n if len(input_dict['names']) != len(parsed_data):\\n raise KeyError(\\n \\\"Number of field names doesn't match parsing strings: \\\" +\\n telem_field)\\n for ind, field in enumerate(input_dict['names']):\\n output_dict.update(\\n {field: {\\n 'timestamp': read_data['timestamp'],\\n 'data': parsed_data[ind]}})\\n\\n else:\\n # Single item parsed - pull in dict then update with parsed data.\\n # Must be done in this order otherwise it generates a keyerror.\\n output_dict[telem_field] = read_data\\n output_dict[telem_field]['data'] = parsed_data[0]\\n return output_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be87460fcf383f12859cb405cea8fd5d\",\n \"score\": \"0.51529884\",\n \"text\": \"def register_entry(self, content):\\n if type(content) is tuple and len(content) == 2:\\n self.table[content[0]] = content[1]\\n else:\\n self.table.update(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f4ce38df27dccc41676af7541c08c4d\",\n \"score\": \"0.5151934\",\n \"text\": \"def set_data(self, **kwargs):\\n self.data.clear()\\n for key, value in kwargs.iteritems():\\n if key in self.mapping:\\n d42_key = self.mapping[key]\\n value = self.clean_data(d42_key, value)\\n self.data.update({d42_key:value})\\n elif key in self.d42_params:\\n d42_key = key\\n value = self.clean_data(d42_key, value)\\n self.data.update({d42_key:value})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"373333441d824b7c40e185d6f1ee1126\",\n \"score\": \"0.51478565\",\n \"text\": \"def handle_add_response(self, c: tuple, sentence: dict):\\n _id_ = sentence['ret']\\n d = {'.id': _id_,\\n 'address': c[0],\\n 'list': c[1]}\\n super().__setitem__(d['address'], d)\\n self.by_id[_id_] = d\\n log.debug(\\\"Item added: %r\\\", d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b275100543376260253a97ecf92d807a\",\n \"score\": \"0.51453847\",\n \"text\": \"def putter(entity_dictionary, data, defaults, *, include_internals = False):\\n nonlocal field_key\\n data[field_key] = [\\n entity.to_data(defaults = defaults, include_internals = include_internals)\\n for entity in _iter_nullable_dictionary_values(entity_dictionary)\\n ]\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f0aba1358c4e693eb63f72f851a4a98\",\n \"score\": \"0.5143458\",\n \"text\": \"def __setitem__(self, key, value):\\r\\n\\t\\tself.map[key] = value\\r\\n\\t\\tself.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83ab81c8f81f54ef788ef0ddfaaa1936\",\n \"score\": \"0.51388055\",\n \"text\": \"def putter(preinstanced, data, defaults):\\n nonlocal field_key\\n \\n data[field_key] = preinstanced.value\\n \\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84e4f7839975dde8dbcaf640f2e0926b\",\n \"score\": \"0.513607\",\n \"text\": \"def add_entries_dict(d):\\n for i in d:\\n add_entries(d[i].df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3e6376a0519123eb585dd1c9d17f5ed\",\n \"score\": \"0.5134854\",\n \"text\": \"def __getitem__(self, key):\\n if self.auto_update:\\n self.update()\\n \\n return self.data[key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45d786caf01a648b5a3443b5bec5ec96\",\n \"score\": \"0.513459\",\n \"text\": \"def from_dict(self, data):\\n\\n # return self._from_dict(self.state.update, data)\\n\\n list_ = self.state.get_names('update')\\n\\n for key in list_:\\n if not key in data:\\n continue\\n\\n from_dict_fn_name = '%s_from_dict' % key\\n value = data[key]\\n\\n if hasattr(self, from_dict_fn_name):\\n getattr(self, from_dict_fn_name)(value)\\n elif hasattr(getattr(self, key), 'from_dict'):\\n getattr(self, key).from_dict(value)\\n else:\\n setattr(self, key, value)\\n\\n # ===============================================================\\n # try:\\n # setattr(self, key, value)\\n # except AttributeError, err:\\n # print err.args\\n # print err.message\\n # print \\\"===========\\\"\\n # raise AttributeError(\\\"Failed to set {0}\\\".format(key))\\n # ===============================================================\\n # return self # not required\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17580dc3ab9ecdfbf34696b6f763380b\",\n \"score\": \"0.5129681\",\n \"text\": \"def parseEntry(self, entry):\\n result = {}\\n\\n tokens = self.pattern.finditer(entry)\\n\\n # some defaults\\n result['note'] = 'automatic ref import'\\n for line in tokens:\\n try:\\n\\n key=line.group(1)\\n value=line.group(2)\\n if key == '%A':\\n\\n value=value.replace('.-','-dot-')\\n value=value.replace('.','. ')\\n value=value.replace('-dot','.-')\\n\\n lastauthor=''\\n if ' & ' in value:\\n lastauthor = value.split(' & ',2)[-1]\\n value = value.split(' & ',2)[0]\\n if ' et ' in value.lower():\\n lastauthor = value.split(' et ',2)[-1]\\n value = value.split(' et ',2)[0]\\n if ' en ' in value.lower():\\n lastauthor = value.split(' en ',2)[-1]\\n value = value.split(' en ',2)[0]\\n authlist= value.split(',')\\n if not lastauthor == '':\\n authlist.append(lastauthor)\\n\\n for auth in authlist:\\n raw = auth.strip().split(' ',3)\\n if raw[0].lower() in ['van', 'von', 'du', 'de'] and len(raw)>1:\\n raw[0] = raw[0]+' '+raw[1]\\n raw.pop(1)\\n lname = raw[0].strip()\\n fname = ''\\n mname = ''\\n if len(raw)>1: fname = raw[1].strip()\\n if len(raw)>2: mname = raw[2].strip()\\n adict = {'firstname': fname\\n ,'middlename': mname\\n ,'lastname': lname\\n }\\n result.setdefault('authors',[]).append(adict)\\n elif key == '%B':\\n result['booktitle'] = str(value).strip()\\n for i in ['acte', 'proceedings', 'akte']:\\n if i in str(value).strip().lower(): result['reference_type'] = 'InproceedingsReference'\\n if not result.has_key('reference_type'): result['reference_type'] = 'InbookReference'\\n elif key == '%C': result['city'] = str(value).strip()\\n elif key == '%D':\\n date = str(value).strip().lower()\\n if date == 'in press':\\n if result.has_key('note'):\\n result['note'] += \\\"\\\\n %s\\\" % str(value).strip()\\n else:\\n result['note'] += \\\"\\\\n %s\\\" % str(value).strip()\\n elif not (date in ['n. d.','s. d.']):\\n result['publication_year'] = str(value).strip()\\n elif key == '%E':\\n if not result.has_key('editor'):\\n result['editor'] = str(value).strip()\\n else:\\n result['editor'] += ', %s' % str(value).strip()\\n elif key == '%I': result['publisher'] = str(value).strip()\\n elif key == '%J':\\n result['journal'] = str(value).strip()\\n result['reference_type'] = 'ArticleReference'\\n elif key == '%K':\\n if result.has_key('keywords'):\\n result['keywords'] += '\\\\n' + '\\\\n'.join([key.strip() for key in str(value).split(',')])\\n else:\\n result['keywords'] = '\\\\n'.join([key.strip() for key in str(value).split(',')])\\n elif key == '%N': result['number'] = str(value).strip()\\n elif key == '%O':\\n ap = 'CMFAuthors homepage:'\\n parser = re.compile(\\\"A:::\\\"\\n \\\"(?P<author>.*)///\\\"\\n \\\"(?P<url>.*)\\\", reflags)\\n up = 'PloneExportedFrom:'\\n uparser = re.compile(\\\"A:::\\\"\\n \\\"(?P<author>.*)///\\\"\\n \\\"(?P<url>.*)\\\", reflags)\\n if up in value:\\n continue # skip, this is just a comment\\n elif ap in value:\\n token = [a.strip() \\n for a in value.split(ap) \\n if a.strip()][0]\\n keys = [a \\n for a in token.split(' and ')\\n if a.strip()]\\n for k in keys:\\n m = parser.match(k)\\n if m:\\n infos = m.groupdict()\\n for ai in result.get(\\n 'authors', []):\\n ak = ''\\n for i in (\\n 'lastname', \\n 'firstname', \\n 'middlename'):\\n aval=ai.get(i, '').strip()\\n if aval:\\n ak += \\\" \\\"+aval\\n ak = ak.strip()\\n if ak == infos['author']:\\n ai['homepage'] = infos['url']\\n elif result.has_key('abstract'):\\n result['abstract'] += \\\"\\\\n %s\\\" % str(value).strip()\\n else:\\n result['abstract'] = \\\"%s\\\" % str(value).strip()\\n elif key == '%P': result['pages'] = str(value).strip()\\n elif key == '%R':\\n for i in ['acte', 'proceedings', 'akte']:\\n if i in str(value).strip().lower():\\n result['reference_type'] = 'ProceedingsReference'\\n if result.has_key('note'):\\n result['note'] += \\\"\\\\n %s\\\" % str(value).strip()\\n else:\\n result['note'] = \\\"%s\\\" % str(value).strip()\\n for i in ['thesis', 'eindwerk', 'thèse']:\\n if i in str(value).strip().lower():\\n result['reference_type'] = 'MastersthesisReference'\\n if result.has_key('note'):\\n result['note'] += \\\"\\\\n %s\\\" % str(value).strip()\\n else:\\n result['note'] = \\\"%s\\\" % str(value).strip()\\n for i in ['raport', 'rapport', 'report', 'verslag']:\\n if i in str(value).strip().lower():\\n result['reference_type'] = 'TechreportReference'\\n result['type'] = str(value).strip()\\n if not result.has_key('reference_type'):\\n result['reference_type'] = 'BookReference'\\n if result.has_key('note'):\\n result['note'] += \\\"\\\\n %s\\\" % str(value).strip()\\n else:\\n result['note'] = \\\"%s\\\" % str(value).strip()\\n elif key == '%T': result['title'] = str(value).strip()\\n elif key == '%V': result['volume'] = str(value).strip()\\n elif key == '%X':\\n if result.has_key('note'):\\n result['note'] += \\\"\\\\n %s\\\" % str(value).strip()\\n else:\\n result['note'] = \\\"%s\\\" % str(value).strip()\\n except:\\n result['upload_status'] = \\\"Error type: %s.\\\\nError value: %s\\\" \\\\\\n % (sys.exc_info()[0],\\n sys.exc_info()[1])\\n return result\\n\\n if not result.has_key('reference_type'):\\n result['reference_type'] = 'BookReference'\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b6ebe1ff5f904e9499dcdeb4c72ecad\",\n \"score\": \"0.5122978\",\n \"text\": \"def _update_from_dict(self, blog, post_dict, post=None):\\n created = False\\n save = True\\n\\n # If a post is not provided, try and fetch it\\n if not post:\\n if 'stardate' in post_dict:\\n try:\\n post = Post.objects.get(\\n blog=blog,\\n stardate=post_dict['stardate']\\n )\\n except Exception as e:\\n logger.exception('something went boom')\\n logger.debug(post_dict)\\n if not post:\\n post_dict['blog'] = blog\\n\\n post = Post(**post_dict)\\n\\n try:\\n post.clean()\\n post.full_clean()\\n except ValidationError as e:\\n # invalid posts should not be returned in this list\\n logger.exception('Caught an exception processing {}'.format(post))\\n save = False\\n return\\n\\n push = False\\n\\n for key, value in post_dict.items():\\n if key == 'body':\\n post_value = getattr(post, key).raw\\n else:\\n post_value = getattr(post, key)\\n\\n if value != post_value:\\n push = True\\n\\n # Update from dict values\\n if not created:\\n for att, value in post_dict.items():\\n setattr(post, att, value)\\n logger.debug('push is {}'.format(push))\\n\\n if save:\\n post.save(push=push)\\n logger.info('Blog: %s, Post: %s, created=%s', post.blog, post, created)\\n return post\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b07fa00822baca7bd9e4d4cf62434e\",\n \"score\": \"0.5120001\",\n \"text\": \"def load_from_dict(self, instance: Any, dictionary: dict) -> None:\\n if self.name in dictionary:\\n self.__set__(instance, dictionary[self.name])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20665195d4438a1996e21bb90aa49ef1\",\n \"score\": \"0.51175195\",\n \"text\": \"def make_edge_data(entry):\\n temp_dict = {}\\n temp_dict['eid'] = int(entry[4])\\n temp_dict['xcoord'] = float(entry[0])\\n temp_dict['ycoord'] = float(entry[1])\\n temp_dict['source'] = int(entry[2])\\n temp_dict['target'] = int(entry[3])\\n temp_dict['weight'] = float(entry[5])\\n temp_dict['class'] = entry[6]\\n temp_dict['type'] = entry[7]\\n temp_dict['name'] = entry[8]\\n\\n return temp_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62e5e92dbe7ef646305713c88252126f\",\n \"score\": \"0.5117514\",\n \"text\": \"def make_entry(inscription):\\n entry = {}\\n if 'publication:' in inscription:\\n entry['publication'] = inscription[1]\\n\\n if 'province:' in inscription:\\n i = inscription.index('province:')\\n entry['province'] = inscription[i + 1]\\n\\n if 'place:' in inscription:\\n i = inscription.index('place:')\\n entry['place'] = inscription[i + 1]\\n entry['text'] = inscription[i + 2]\\n\\n if 'dating:' in inscription:\\n i = inscription.index('dating:')\\n entry['date'] = (i + 1, i + 3)\\n\\n return entry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f679b29b29708a075fd8eb7dba5e251\",\n \"score\": \"0.5113113\",\n \"text\": \"def __setitem__(self, key, value):\\n if not isinstance(value, dict):\\n raise TypeError(\\\"value must be a dict\\\")\\n\\n # Is this a valid cache entry dictionary?\\n validate_entry(value)\\n\\n entry_dir = self.cache_key_dir(key)\\n\\n try:\\n entry_dir.mkdir(parents=True, exist_ok=True)\\n except FileExistsError as e:\\n raise ValueError(\\\"Already exists\\\") from e\\n\\n with open(entry_dir / \\\"entry.yaml\\\", \\\"w\\\") as f:\\n f.write(yaml.safe_dump(value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5d55bdbb6857d26cde83351e2a4066f\",\n \"score\": \"0.51110244\",\n \"text\": \"def normalize_entry_dict(dico, feed_url):\\n\\n norm = {}\\n assert isinstance(dico, dict)\\n e_id = dico.get('id')\\n if isinstance(e_id, basestring) and e_id[:4] == 'http':\\n norm['url'] = e_id\\n else:\\n norm['url'] = dico.get('link')\\n if not norm['url'].startswith('http'):\\n norm['url'] = concat_urls(feed_url, norm['url'])\\n norm['link'] = dico.get('link')\\n if not norm['link'].startswith('http'):\\n norm['link'] = concat_urls(feed_url, norm['link'])\\n norm['title'] = dico.get('title')\\n norm['content'] = dico.get('value')\\n if norm['content'] is None:\\n try:\\n norm['content'] = dico['content'][0]['value']\\n except (KeyError, IndexError) as err:\\n norm['content'] = dico.get('summary')\\n try:\\n norm['mimetype'] = dico['content'][0]['type']\\n except (KeyError, IndexError) as err:\\n norm['mimetype'] = None\\n structTime = dico.get('published_parsed')\\n try:\\n norm['created'] = datetime.datetime(*structTime[:6])\\n except (TypeError, ValueError) as err:\\n norm['created'] = None\\n structTime = dico.get('updated_parsed')\\n try:\\n norm['updated'] = datetime.datetime(*structTime[:6])\\n except (TypeError, ValueError) as err:\\n norm['updated'] = None\\n if norm['created'] is None and norm['updated'] is not None:\\n norm['created'] = norm['updated']\\n elif norm['updated'] is None and norm['created'] is not None:\\n norm['updated'] = norm['created']\\n return norm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72650b85f085e66c891e84b1521c8edc\",\n \"score\": \"0.5110283\",\n \"text\": \"def putter(entity_dictionary, data, defaults):\\n nonlocal field_key\\n data[field_key] = [\\n entity.to_data(defaults = defaults)\\n for entity in _iter_nullable_dictionary_values(entity_dictionary)\\n ]\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e21e165dcb41812d5d384da9057a45eb\",\n \"score\": \"0.5108939\",\n \"text\": \"def update_view(request):\\n the_id = int(request.matchdict[\\\"id\\\"])\\n entry = ENTRIES[the_id]\\n return {\\\"entry\\\": entry}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"272af07c546bbfdcf0b6168207b45ed3\",\n \"score\": \"0.5107029\",\n \"text\": \"def from_dict(self, data):\\n\\n def update_related(key, value, prop):\\n \\\"\\\"\\\"Update the entity based on the type of relationship it is\\\"\\\"\\\"\\n related_class = prop.mapper.class_\\n\\n with db.session.no_autoflush:\\n if prop.uselist:\\n new_list = list()\\n for row in value:\\n obj = related_class.add_or_edit(row, _commit=False)\\n new_list.append(obj)\\n setattr(self, key, new_list)\\n else:\\n setattr(self, key, related_class.add_or_edit(value, _commit=False))\\n\\n mapper = self.__mapper__\\n entity_props = {attr.key: attr for attr in mapper.attrs}\\n\\n for key, value in data.items():\\n prop = entity_props.get(key)\\n is_column = isinstance(prop, sa.orm.properties.ColumnProperty)\\n is_relationship = isinstance(prop, sa.orm.properties.RelationshipProperty)\\n\\n if prop is None:\\n continue\\n elif is_column:\\n setattr(self, key, value)\\n elif is_relationship:\\n update_related(key, value, prop)\\n else:\\n raise NotImplementedError(\\n _('Updating property types is not implemented.'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d199cf5669c0e4ec4eb70f3d0af243d\",\n \"score\": \"0.5102643\",\n \"text\": \"def fromDict(self, data):\\n for key, value in data.items():\\n setattr(self, key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ea361aaa9555fb2e35a9965fade52b\",\n \"score\": \"0.5101062\",\n \"text\": \"def update(self, id, data):\\n values = ''\\n count = 1\\n\\n # lets loop over fields building up query so we only update valid fields\\n for field in self.fields:\\n if field not in data.keys():\\n continue\\n comma = ''\\n if count < (len(data.keys()) - 1):\\n comma = ','\\n values += \\\"'{}' = '{}'{}\\\".format(field, data[field], comma)\\n count += 1\\n\\n query = \\\"UPDATE {} SET {} WHERE id = {}\\\".format(self.table, values, id)\\n\\n # connect to db and make entry\\n conn = self.get_connection()\\n cur = conn.cursor()\\n cur.execute(query)\\n conn.commit()\\n\\n # lets get the made entry to return\\n entry = self.get_by_id(id)\\n return entry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a2b7a379c08eef24636a0ad65cbd6a7\",\n \"score\": \"0.51007336\",\n \"text\": \"def process_data(data):\\n # Add full name as a value\\n data[\\\"fullname\\\"] = \\\" \\\".join([\\n data[\\\"first\\\"],\\n data[\\\"last\\\"]\\n ])\\n # Replace null aliases with Python None\\n if data[\\\"alias\\\"] == \\\"null\\\":\\n data[\\\"alias\\\"] = data[\\\"fullname\\\"]\\n # Replace boolean protected values with Python bool\\n data[\\\"protected\\\"] = False if data[\\\"protected\\\"] == \\\"FALSE\\\" else True\\n\\n data[\\\"handle\\\"] = data[\\\"handle\\\"].lstrip(\\\"@\\\")\\n\\n return data\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993875,"cells":{"query_id":{"kind":"string","value":"db19dd2a4ce0943483f31dd9bad55ec3"},"query":{"kind":"string","value":"Helper function to log a debug message with a descriptive heading."},"positive_passages":{"kind":"list like","value":[{"docid":"2c606d1199d941de20d57d71dd6a8322","score":"0.0","text":"def log_debug(self, line):\n logging.debug(\"Scanner #%s - %s\" % (self.device_idx,line))","title":""}],"string":"[\n {\n \"docid\": \"2c606d1199d941de20d57d71dd6a8322\",\n \"score\": \"0.0\",\n \"text\": \"def log_debug(self, line):\\n logging.debug(\\\"Scanner #%s - %s\\\" % (self.device_idx,line))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f2318cb180866a2990408274f1c09413","score":"0.6975975","text":"def debugPrint(message, debug):\n if debug:\n print(message)","title":""},{"docid":"aa5d1d36d75272e5f95b19ec31dfe6e3","score":"0.696345","text":"def debug(message):\n if options.verbose > 2:\n print(\"\\033[33m{:s}\\033[0m\".format(message))","title":""},{"docid":"075441f17b66778785bbba41dedbac84","score":"0.6925395","text":"def debug(message):\n #TODO\n print \"DEBUG: \" + message\n return","title":""},{"docid":"39b247f9d19617a6365fae96c742df78","score":"0.6891752","text":"def _debug(\n self,\n message: str,\n level: int = 1,\n prefix: str = \"INFO:\\t\",\n spacing: str = \"\",\n end_spacing: str = \"\",\n ):\n if self.verbose >= level:\n output = sys.stderr if not self.verbose > 50 else sys.stdout\n print(spacing + prefix + message + end_spacing, file=output)","title":""},{"docid":"8a198cc978b073d09a0b663cdad451d0","score":"0.6876113","text":"def log(message):\n if DEBUG:\n print (\"debug: %s\" % message)","title":""},{"docid":"8b908bd68dfd42dacd7ac7eac4fbbdea","score":"0.6827931","text":"def debug(message, **kwargs):\n log('debug', message, **kwargs)","title":""},{"docid":"daf3734eb684de0e6fa8b5f635e00bcc","score":"0.67337954","text":"def debug(msg, *args, **kwargs):\n getLogger().debug(msg, *args, **kwargs)","title":""},{"docid":"5c9f731f5e5d4d392708fdae186325ce","score":"0.6727687","text":"def debug(message: str) -> None:\n print(message, file=sys.stderr)","title":""},{"docid":"96337fc4cc05746c14fb2ee0198c9321","score":"0.6715484","text":"def print_debug(verbose_level, msg):\n if verbose_level <= VERBOSE:\n print \"# DEBUG\" + str(verbose_level) + \":\",\n print msg","title":""},{"docid":"702a43292acd2fdf03c1c615d0157633","score":"0.67076963","text":"def _log_debug(self, msg, error=False):\n if self._verbose:\n print \"(INFO) %s\" % msg","title":""},{"docid":"04ffe3a18ecad57f9dcd018ef28f8ed2","score":"0.6704115","text":"def print_debug(message):\n if is_verbose():\n print(\"[debug] \" + message)","title":""},{"docid":"5baae690fea81dd0bb265358c83805b5","score":"0.6695633","text":"def debug( self, msg ):\n self.log('debug', msg)","title":""},{"docid":"40f59af5594275ebac00448a30e3f00a","score":"0.6669513","text":"def log_debug(msg):\n logging.debug(msg)","title":""},{"docid":"8286439bc65cecf4609908e335bc1f59","score":"0.66592926","text":"def debug(msg):\n if DEBUG_ENABLED:\n print(\"Debug: {}\".format(msg))","title":""},{"docid":"de1cf2e1a48738cd9df27d92a2be157d","score":"0.6640784","text":"def debug(msg):\n if verbose > V_EXTENDED:\n print >>sys.stderr, msg","title":""},{"docid":"4236d9b39e3c1c195ae8dc76607de9fa","score":"0.66406673","text":"def debug(message):\n if not DEBUG and not DEBUG_LOG:\n pass\n else:\n message = message.__str__()\n ts = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n debug_message = ts + \":: \" + message\n if DEBUG:\n print(debug_message)\n if DEBUG_LOG:\n try:\n fh = open(DEBUG_LOG, 'a')\n fh.write(debug_message + \"\\n\")\n fh.close\n except:\n print(\"unable to write to log file {}\".format(DEBUG_LOG))","title":""},{"docid":"8a2aeeaeecd0b125998aa6d10b1a734a","score":"0.66293585","text":"def print_debug(message):\n print_verbose(message, level=DEBUG)","title":""},{"docid":"5f9741a89720945ea19374aac830a63e","score":"0.6617778","text":"def debug(message: str, /, timestamp: bool = True) -> None:\n log(LogIntensity.debug, message, timestamp)","title":""},{"docid":"5038310eb005d49c6f134370f0b70e50","score":"0.661578","text":"def dprint(msg, level, dbg=True):\n\n if dbg:\n print(\"{}: {}\".format(level, msg))","title":""},{"docid":"7a5d4f45d807915d4ebeb45d3bd231ae","score":"0.6601876","text":"def write_debug_message(message: str):\n logging.debug(\"%s\", message)","title":""},{"docid":"71c35432b840a024cb3a0dd6930cc79e","score":"0.6565339","text":"def debug(msg):\n if (CONFIG['debug']):\n logIt(msg)","title":""},{"docid":"2c240a8f0fcd9c8799166fbd189ceb23","score":"0.65640235","text":"def debug(location, msg):\n message = \"DEBUG >> \" + location + \":: \"+datetime.now().strftime(\"%H:%M:%S, %d %B %Y\") \n message += \".\\n\\t\" + msg + \"\\n ==================================== \\n\\n\"\n write_to_log(message)","title":""},{"docid":"46af25649d7b686eb5c7154d2a7c8dba","score":"0.65585256","text":"def debug(self, message=\"\", *, prefix=\"\", markup=False):\n self._log(\n preface=self.DEBUG_PREFACE,\n prefix=prefix,\n message=message,\n show=self.verbosity >= self.DEBUG,\n markup=markup,\n style=\"dim\",\n )","title":""},{"docid":"4c52d5cf7f1248fa1c3f28d70ecabd97","score":"0.6554773","text":"def debug(self, msg, *args, **kwargs):\r\n\r\n self.log(logging.DEBUG, msg, *args, **kwargs)","title":""},{"docid":"106e996167ff064fac88169a86d177bb","score":"0.6541644","text":"def debug_print(message):\n print('Debug: {!s}'.format(message)[:240]) # truncate message because damned IDLE can't handle long output!!! (I wasted several hours to figure it out!)","title":""},{"docid":"17ba8d7d71349a8af6e439df657cbbde","score":"0.6538984","text":"def debug(self, msg, *args, **kwargs):\r\n self.logger.debug( msg, *args, **kwargs )","title":""},{"docid":"8d77fffcc011f0a54844a028ea2e999d","score":"0.6536121","text":"def debug(user, message):\n message_user(user, message, constants.DEBUG)","title":""},{"docid":"528c0ac0df924670c88c9974d6efa605","score":"0.6516085","text":"def _debug_msg(self, msg):\n if self.debug:\n print(msg)","title":""},{"docid":"07c750de2a5c2fac27a7171fe64dfd95","score":"0.6511759","text":"def debug(self, message: str) -> None:\n self.log(message)","title":""},{"docid":"99c3065db76f70a6f24bd06bb6659da1","score":"0.6504899","text":"def debug(msg, html=False):\r\n write(msg, 'DEBUG', html)","title":""},{"docid":"6ec6c5fe2d271c9a6b45b1c5bc295dbf","score":"0.64759636","text":"def debug_tag(self, msg: str, tag: str):\n self.log.debug(f\"{self.host} [{tag}] {msg}\")","title":""},{"docid":"daa834065d0ddc3f8599e68f727de510","score":"0.6475924","text":"def debug(*messages):\n if DEBUG:\n log('DEBUG:', *messages)","title":""},{"docid":"fafa18c8d9d239e6dc7380791162660c","score":"0.6436231","text":"def __debug(self, message):\n if len(message) > 512:\n message = message[:512] + \"…\"\n if Layer.__last_debug_message == message:\n message = message.split(\"\\n\", 1)[0].strip()\n if len(message) > 256:\n message = message[:256] + \"…\"\n else:\n Layer.__last_debug_message = message\n assert self.debug is not None\n return commands.Log(textwrap.indent(message, self.debug), DEBUG)","title":""},{"docid":"900ef52149f6790e78a0cc5139e3e13c","score":"0.6435195","text":"def debug(self, msg, *args):\n logger.debug(self.suite_name, msg, *args)","title":""},{"docid":"508f363b09b55817346b1907ba477898","score":"0.64174473","text":"def debug(self, tag=None, msg='', *args, **kwargs):\n\n if not self.logger.isEnabledFor(logging.DEBUG):\n return\n\n t = self.get_tag(tag)\n\n if t:\n msg = '%s:' + msg\n args = (t,) + args\n\n self.logger.debug(msg, *args, **kwargs)","title":""},{"docid":"c573c5977b13eead1e7070d1e8d19302","score":"0.6404028","text":"def debug(self, msg):\n\n\t\tif( self.logger ): self.logger.debug( msg )","title":""},{"docid":"57dd17f0b259e202eb5c3a24dda9c4e0","score":"0.63937306","text":"def __debug_msg(self, msg):\n if self.__debug:\n print(msg)","title":""},{"docid":"b98355b85489821288fd409d24c07850","score":"0.6384047","text":"def debug(message):\n if bool(os.environ.get('HAWTHORNE_DEBUG', False)):\n now = datetime.datetime.now(datetime.timezone.utc)\n print(f\"{now} DEBUG: {message}\")","title":""},{"docid":"f45413cdca1ac4f17863cfefb20910e1","score":"0.63711673","text":"def debug(self, msg, *args, **kwargs):\r\n if self.isEnabledFor(logging.DEBUG):\r\n self._log(logging.DEBUG, colorama.Fore.LIGHTGREEN_EX + msg, args, **kwargs)","title":""},{"docid":"d34f745826248df2b4e1f9708484b263","score":"0.6356908","text":"def debug(self, message):\n self.log(message, logging.DEBUG)","title":""},{"docid":"4a95c81c6d501f8a2c02b1219fc33ec0","score":"0.6341688","text":"def log_message(self, format, *args):\n self.globals._logger.debug(format, *args)","title":""},{"docid":"b1d3e92694642c3edf9acdf022a55dff","score":"0.6339254","text":"def debug_print(msg):\n if DEBUG_PRINT:\n print msg","title":""},{"docid":"210c608f788cc6fb62201b59f7afa4a3","score":"0.6339113","text":"def debug(msg):\n if _debug_level > 0:\n _debugstream.write(\n \"%s: %s\\n\" % (os.path.basename(sys.argv[0]), str(msg)))","title":""},{"docid":"1bc57aa4ab58c967d88c6151520759d1","score":"0.6333502","text":"def debug(request, message, extra_tags=\"\", fail_silently=False):\n add_message(\n request,\n constants.DEBUG,\n message,\n extra_tags=extra_tags,\n fail_silently=fail_silently,\n )","title":""},{"docid":"5af46fd0c922cbef3f67c314220803ec","score":"0.6329669","text":"def debug(msg, tags=None):\n set_log_parameters(msg, log_level=8, tags=tags)","title":""},{"docid":"d76a873b959c4356894cff8d690669ed","score":"0.63225466","text":"def log(log_msg):\r\n if DEBUG:\r\n print(log_msg)","title":""},{"docid":"debd930c87ef3f4256f74c0f320d0b42","score":"0.6321037","text":"def log(message):\n print \"\"\"\n================================================================================\n%s\n================================================================================\n \"\"\" % message","title":""},{"docid":"8313b932abc078238488b9a510b0fe94","score":"0.6314219","text":"def debug(request, message, extra_tags='', fail_silently=False):\n add_message(request, constants.DEBUG, message, extra_tags=extra_tags,\n fail_silently=fail_silently)","title":""},{"docid":"4b2d2c39355b1b81a1d5ad9fece92c00","score":"0.63128656","text":"def debug(self, msg, *args):\n print(str(msg) % args, file=self.file)","title":""},{"docid":"fc197f5c28a7741911b772f54901208b","score":"0.631257","text":"def debug(self, msg, *args):\n if self.verbose:\n self.log(click.style(\"DEBUG - %s\" % msg, *args, fg='green'))","title":""},{"docid":"18255d1ecaa3f7b958ef0a756199f74c","score":"0.6301131","text":"def logDebug(self, *messageArgs):\n self._writeLog(sched_messages.DEBUG, *messageArgs)","title":""},{"docid":"76e8280166b9f31cf9870eacf648b83c","score":"0.6284885","text":"def debug(self, msg): #pragma: no cover\n if self.d:\n print(msg)","title":""},{"docid":"6362e0b818f3d411cdfb76faf2e00be0","score":"0.62659335","text":"def _debug(self, message, level=1):\n if self.debug >= level:\n print(f\"DEBUG: {message}\")","title":""},{"docid":"d2c407036f558da720b0dd2dfb57e6ac","score":"0.62652516","text":"def log(self, msg):\n if self.debug:\n print(msg)","title":""},{"docid":"21ef99ef400a6ef1ef62123b3356e373","score":"0.62520665","text":"def debug(message, end='\\n', file=stdout):\n if enableDebug:\n print(blue(message), end=end, file=file)","title":""},{"docid":"124f548cae82c68df13e93ba8477de05","score":"0.62443954","text":"def debug(request, message, extra_tags='', fail_silently=False, buttons=None):\n add_message(request, constants.DEBUG, message, extra_tags=extra_tags,\n fail_silently=fail_silently, buttons=buttons)","title":""},{"docid":"7f877a8e7c8bb34e8d21e51b6102acbb","score":"0.6229374","text":"def debug(self, msg):\n if self.ansible._debug or self.ansible._verbosity > 2:\n self.ansible.log(f'[DEBUG] {msg}')","title":""},{"docid":"52097a9c9ac46bd59e3b8ad94ad6492f","score":"0.6221631","text":"def logit(*msg):\n if USR_CONFIG.get('debug_log_enable', ''):\n print(*msg)","title":""},{"docid":"ca4301b8f6bc294adf8d3775f3214787","score":"0.6215168","text":"def debug(log_str):\n logging.debug(log_str)","title":""},{"docid":"811b6422992e45da39f856d3817782cb","score":"0.6211976","text":"def d(tag, message): # pylint: disable=invalid-name\n\n date = __get_date()\n if LOG_LEVEL_STDOUT >= 5:\n __log_to_stdout(colors.COLOR_DEB, date, tag+\"/D\", message)\n if LOG_LEVEL_FILE >= 5:\n __log_to_file(date, tag+\"/D\", message)","title":""},{"docid":"c3f29146b35bb1fd004c15c3e894ffe6","score":"0.62115455","text":"def logdebug(s):\n if not debug: return\n now = datetime.datetime.now()\n print >> logfile, now.strftime(\"%Y-%m-%d %H:%M:%S :: DEBUG : \"),\n print >> logfile, s\n logfile.flush()","title":""},{"docid":"dd726a4bfb077c621a341cbdbc482234","score":"0.6205473","text":"def print_debug(msg, log_level):\n if log_level <= LOG_LEVEL:\n print(msg)","title":""},{"docid":"0db8a98f0ed2ba99181fcc1673f494ec","score":"0.6205176","text":"def debug(self, msg: str, exc_info=None):\n if \"true\" in self.debug_mode:\n self.log.debug(f\"{self.host} [BASE] {msg}\", exc_info=exc_info)","title":""},{"docid":"2c0e6a17451ccdffeaf87f9fca71f37e","score":"0.6197399","text":"def _debug(req, msg):\n req.log_error('[scm debug] %s' % msg)","title":""},{"docid":"688a9c8ab924c8019e0e17c46155be69","score":"0.61851543","text":"def debug(string):\n click.secho('DEBUG: {}'.format(string), fg='white')","title":""},{"docid":"a3de05b1440d2683f7f47eac848ae15a","score":"0.6181768","text":"def log(msg):\n print '%s %s' % (date(), msg)","title":""},{"docid":"8aac6206c8bb989568311536fe02f392","score":"0.6175888","text":"def debug(self, format_string, *args):\n self.log(DEBUG, format_string, *args)","title":""},{"docid":"7e77f11315aeac01f37c3085eca60909","score":"0.6168947","text":"def debug_msg(name, tensor):\n return \" {3}: {0} - {1} - {2}\".format(\n tensor.shape, tensor.get_device(), tensor.dtype, name)","title":""},{"docid":"5c3766bd37147fd0ff1ed041307c94fc","score":"0.61682665","text":"def set_log_debug():\n set_log_level_format(\n logging.DEBUG,\n \"%(asctime)s [%(levelname)s %(filename)s:%(lineno)s - \"\n \"%(name)s.%(funcName)s() ] %(message)s\"\n )","title":""},{"docid":"b302b6a73f41c71d2f9166b19009b32e","score":"0.61553454","text":"def debug(text):\n \n if app.debug:\n app.logger.debug(text)","title":""},{"docid":"1cfa3dbb15aa9718fccc96b945edce49","score":"0.6154219","text":"def log(cls, txt, dt=None):\n logger.debug('%s, %s' % (dt.isoformat(), txt))","title":""},{"docid":"b472a359062e89524e23d69cb86e7c60","score":"0.61158705","text":"def warn(string):\n click.secho('DEBUG: {}'.format(string), fg='yellow')","title":""},{"docid":"3b672bc15c449117efb7945c5576154e","score":"0.6098145","text":"def debug_print( debugText , debugLevel = 1):\n\n if debugLevel > GLOBAL_DEBUG_LEVEL:\n print(\"{0}\".format(debugText) )","title":""},{"docid":"3aab2ac1f9405318822ab68925ffae81","score":"0.60907835","text":"def debug(self, msg):\n\n if (self.logger): self.logger.debug(msg)","title":""},{"docid":"0f334a9758eab615b09a27a3e3d113fe","score":"0.60905963","text":"def _debugprint(self, msg):\n \n if self.debug:\n if isinstance(self.debug, logging.Logger):\n self.debug.debug(msg)\n else:\n print(msg)","title":""},{"docid":"f491e0e28315b5bb46df3b6e2618a254","score":"0.6080457","text":"def debug(self, correlation_id, *message):\n self._write_log(LogLevel.Debug, correlation_id, message)","title":""},{"docid":"4aa9fb7bb22d05925cbf4ee906b7ceb8","score":"0.6077602","text":"def debug_log(msg):\n timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n caller = inspect.stack()[1][3]\n with io.open(\"debug.log\", \"a\", encoding='utf-8') as fp:\n fp.write(\"{} {}: {}\\n\".format(timestamp, caller, msg))","title":""},{"docid":"5d065e8ab7fba46c29b30b912f46dc16","score":"0.6072615","text":"def debug_msgprint(message):\n print(message)\n if msgprint_debug:\n msgprint(message)","title":""},{"docid":"41ef962fd220ba62298bf41c8a03803a","score":"0.60681444","text":"def debug(*args, **kwars):\n if DEBUGING:\n print(\"[DEBUG]:\", *args, **kwars)","title":""},{"docid":"4c79a25c0a05105621d047e821af7797","score":"0.6059513","text":"def formatted_print(message: str) -> None:\n\n logger.info(\"{} | {}\".format(get_timestamp(), message))","title":""},{"docid":"cdfeeaaceb037b7f2841679bf139e302","score":"0.6043487","text":"def debug(message: str):\n for line in message.splitlines():\n print(f\"::debug::{line}\")","title":""},{"docid":"83b20410301600860ed4fa606cf3dfd2","score":"0.6039536","text":"def debug(self, msg):\n\n self.reporter.debug(msg)","title":""},{"docid":"550f9bb34fb340c21743b0558e626e05","score":"0.60319847","text":"def hutil_log_info(message):\n if HUtilObject is not None:\n HUtilObject.log(message)\n else:\n print('Info: {0}'.format(message))","title":""},{"docid":"fe98a03e9d52f98919936831dc9adeaf","score":"0.60226834","text":"def debug(self,*msg):\n raise NotImplementedError","title":""},{"docid":"ebc283dbff5f2fe2cc9bb086fc3afb83","score":"0.60182935","text":"def LogNewDeprel(message):\r\n logging.info('{0}{1}{0}{2}{0}{1}{0}'.format('\\n','='*50,message))","title":""},{"docid":"89e5c9da74267ee08c82f9181e373109","score":"0.60169977","text":"def debug(self, msg, *args, color=True, **kwargs):\n\n if color:\n now = time.strftime(\"%Y%m%d-%H%M%S\")\n msg = grey(f\"[{now}] {msg}\")\n\n self.logger.debug(msg, *args, **kwargs)","title":""},{"docid":"8e6eb9a3078cdf52d2e6aa9831769bc1","score":"0.6002395","text":"def test_debug():\n\n log.LOG_LEVEL_STDOUT = 5\n log.LOG_LEVEL_FILE = 5\n\n log.d(TAG, \"This is an debug!\")","title":""},{"docid":"91e74d119e4e2fd87e45f96714157e4c","score":"0.5979257","text":"def debug(self, message):\n\n if not isinstance(message, string_types):\n message = str(message)\n\n self._message(message, 'DEBUG')","title":""},{"docid":"75dadd2c01f9130282a37ddce261d90f","score":"0.5978182","text":"def log(s):\n if DEBUG_LOG:\n print(s)","title":""},{"docid":"3dcc4fdd728d75670ede5d745baaf628","score":"0.5947899","text":"def debug_message(*args, **kwargs):\n level = kwargs.get('level', 0)\n mode = debug_mode(raw=True)\n if mode != 0 and len(args) > 0:\n stream = sys.stdout\n if mode < 0:\n stream = LogFiles.file(name=\"main\")\n text = []\n stamp = \"AsterStudy:\"\n if getattr(debug_message, 'timestamp', True):\n stamp += \" \" + timestamp()\n text.append(stamp + (\".\" * abs(level) if level else \"\"))\n for i in args:\n if not isinstance(i, (str, unicode)):\n i = str(i)\n text.append(to_unicode(i))\n line = \" \".join(text)\n stream.write(to_str(line))\n stream.write(\"\\n\")\n stream.flush()","title":""},{"docid":"d8e2b9d8cc6c2ed545081e98d68e3618","score":"0.59459656","text":"def debug(self, *args, **kwargs):\n self.logger.log(DEBUG, *((self._pfx_debug + args[0],) + args[1:]), **kwargs)","title":""},{"docid":"c4ed0195ddce1cb59969c7068ce31269","score":"0.59335953","text":"def debug(self, msg):\n self.logger.debug(f'[NODE {self.id}]' + msg)","title":""},{"docid":"6e538493cca4b872048d9dc57a4aab8d","score":"0.5927366","text":"def log(message, severity, print_debug=True):\r\n\r\n print_me = ['WARNING', 'INFO', 'DEBUG']\r\n if severity in print_me:\r\n if severity == 'DEBUG':\r\n if print_debug:\r\n print severity, message\r\n else:\r\n print severity, message\r\n else:\r\n raise Exception(\"%s: %s\" % (severity, message))","title":""},{"docid":"027bd35a6036d7a2749a1b749f9bf016","score":"0.5916674","text":"def logger(s):\n print(datetime.now(), s)","title":""},{"docid":"ca00ff25693dbb8653948738db2cd524","score":"0.5891233","text":"def _debug(self, *txt):\n\t\t\n\t\tif self.debug:\n\t\t\tprint >> sys.stderr, '\\033[31m[ePuck]:\\033[0m ', ' '.join([str(e) for e in txt])\n\t\t\n\t\treturn 0","title":""},{"docid":"2701ebbcf29489d4a52214b02cd7ecb7","score":"0.58910525","text":"def portal_debug( *args, **kw ):\n _log( logger.debug, *args, **kw )","title":""},{"docid":"e6f12672140af1f6de685b208e96b674","score":"0.5875253","text":"def log(msg, **kwargs):\n timestring = \"\\033[90m{}\\033[0m \".format(datetime.datetime.now().strftime('%H:%M:%S.%f'))\n preamble = timestring if 'end' in kwargs else ''\n print(preamble+msg, **kwargs)","title":""},{"docid":"32c30e090405e735f4dc1af314fb7e69","score":"0.587485","text":"def raiseADebug(self, *args, **kwargs):\n verbosity = kwargs.get('verbosity', 'debug')\n tag = kwargs.get('tag', 'DEBUG')\n color = kwargs.get('color', None)\n msg = ' '.join(str(a) for a in args)\n self.messageHandler.message(self, msg, str(tag), verbosity, color)","title":""},{"docid":"33ed633d6a917671c0b2575d10ed38b2","score":"0.58617127","text":"def debugPrint(str):\n if debugLevel >= 2:\n print(str)","title":""},{"docid":"6acb21c9c4766c54ed85ed7cdd2be599","score":"0.58601254","text":"def print_debug(arg):\n # print arg\n pass","title":""},{"docid":"f87c7684b0a01cbbba6554e41b732025","score":"0.58374625","text":"def _log(self, header, *messages):\n if self._logfile is not None:\n print(f\"[{header:5s}] -\", *messages, file=self._logfile, flush=True)","title":""}],"string":"[\n {\n \"docid\": \"f2318cb180866a2990408274f1c09413\",\n \"score\": \"0.6975975\",\n \"text\": \"def debugPrint(message, debug):\\n if debug:\\n print(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5d1d36d75272e5f95b19ec31dfe6e3\",\n \"score\": \"0.696345\",\n \"text\": \"def debug(message):\\n if options.verbose > 2:\\n print(\\\"\\\\033[33m{:s}\\\\033[0m\\\".format(message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"075441f17b66778785bbba41dedbac84\",\n \"score\": \"0.6925395\",\n \"text\": \"def debug(message):\\n #TODO\\n print \\\"DEBUG: \\\" + message\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39b247f9d19617a6365fae96c742df78\",\n \"score\": \"0.6891752\",\n \"text\": \"def _debug(\\n self,\\n message: str,\\n level: int = 1,\\n prefix: str = \\\"INFO:\\\\t\\\",\\n spacing: str = \\\"\\\",\\n end_spacing: str = \\\"\\\",\\n ):\\n if self.verbose >= level:\\n output = sys.stderr if not self.verbose > 50 else sys.stdout\\n print(spacing + prefix + message + end_spacing, file=output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a198cc978b073d09a0b663cdad451d0\",\n \"score\": \"0.6876113\",\n \"text\": \"def log(message):\\n if DEBUG:\\n print (\\\"debug: %s\\\" % message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b908bd68dfd42dacd7ac7eac4fbbdea\",\n \"score\": \"0.6827931\",\n \"text\": \"def debug(message, **kwargs):\\n log('debug', message, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daf3734eb684de0e6fa8b5f635e00bcc\",\n \"score\": \"0.67337954\",\n \"text\": \"def debug(msg, *args, **kwargs):\\n getLogger().debug(msg, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c9f731f5e5d4d392708fdae186325ce\",\n \"score\": \"0.6727687\",\n \"text\": \"def debug(message: str) -> None:\\n print(message, file=sys.stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96337fc4cc05746c14fb2ee0198c9321\",\n \"score\": \"0.6715484\",\n \"text\": \"def print_debug(verbose_level, msg):\\n if verbose_level <= VERBOSE:\\n print \\\"# DEBUG\\\" + str(verbose_level) + \\\":\\\",\\n print msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702a43292acd2fdf03c1c615d0157633\",\n \"score\": \"0.67076963\",\n \"text\": \"def _log_debug(self, msg, error=False):\\n if self._verbose:\\n print \\\"(INFO) %s\\\" % msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04ffe3a18ecad57f9dcd018ef28f8ed2\",\n \"score\": \"0.6704115\",\n \"text\": \"def print_debug(message):\\n if is_verbose():\\n print(\\\"[debug] \\\" + message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5baae690fea81dd0bb265358c83805b5\",\n \"score\": \"0.6695633\",\n \"text\": \"def debug( self, msg ):\\n self.log('debug', msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40f59af5594275ebac00448a30e3f00a\",\n \"score\": \"0.6669513\",\n \"text\": \"def log_debug(msg):\\n logging.debug(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8286439bc65cecf4609908e335bc1f59\",\n \"score\": \"0.66592926\",\n \"text\": \"def debug(msg):\\n if DEBUG_ENABLED:\\n print(\\\"Debug: {}\\\".format(msg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de1cf2e1a48738cd9df27d92a2be157d\",\n \"score\": \"0.6640784\",\n \"text\": \"def debug(msg):\\n if verbose > V_EXTENDED:\\n print >>sys.stderr, msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4236d9b39e3c1c195ae8dc76607de9fa\",\n \"score\": \"0.66406673\",\n \"text\": \"def debug(message):\\n if not DEBUG and not DEBUG_LOG:\\n pass\\n else:\\n message = message.__str__()\\n ts = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n debug_message = ts + \\\":: \\\" + message\\n if DEBUG:\\n print(debug_message)\\n if DEBUG_LOG:\\n try:\\n fh = open(DEBUG_LOG, 'a')\\n fh.write(debug_message + \\\"\\\\n\\\")\\n fh.close\\n except:\\n print(\\\"unable to write to log file {}\\\".format(DEBUG_LOG))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a2aeeaeecd0b125998aa6d10b1a734a\",\n \"score\": \"0.66293585\",\n \"text\": \"def print_debug(message):\\n print_verbose(message, level=DEBUG)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f9741a89720945ea19374aac830a63e\",\n \"score\": \"0.6617778\",\n \"text\": \"def debug(message: str, /, timestamp: bool = True) -> None:\\n log(LogIntensity.debug, message, timestamp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5038310eb005d49c6f134370f0b70e50\",\n \"score\": \"0.661578\",\n \"text\": \"def dprint(msg, level, dbg=True):\\n\\n if dbg:\\n print(\\\"{}: {}\\\".format(level, msg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a5d4f45d807915d4ebeb45d3bd231ae\",\n \"score\": \"0.6601876\",\n \"text\": \"def write_debug_message(message: str):\\n logging.debug(\\\"%s\\\", message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71c35432b840a024cb3a0dd6930cc79e\",\n \"score\": \"0.6565339\",\n \"text\": \"def debug(msg):\\n if (CONFIG['debug']):\\n logIt(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c240a8f0fcd9c8799166fbd189ceb23\",\n \"score\": \"0.65640235\",\n \"text\": \"def debug(location, msg):\\n message = \\\"DEBUG >> \\\" + location + \\\":: \\\"+datetime.now().strftime(\\\"%H:%M:%S, %d %B %Y\\\") \\n message += \\\".\\\\n\\\\t\\\" + msg + \\\"\\\\n ==================================== \\\\n\\\\n\\\"\\n write_to_log(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46af25649d7b686eb5c7154d2a7c8dba\",\n \"score\": \"0.65585256\",\n \"text\": \"def debug(self, message=\\\"\\\", *, prefix=\\\"\\\", markup=False):\\n self._log(\\n preface=self.DEBUG_PREFACE,\\n prefix=prefix,\\n message=message,\\n show=self.verbosity >= self.DEBUG,\\n markup=markup,\\n style=\\\"dim\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c52d5cf7f1248fa1c3f28d70ecabd97\",\n \"score\": \"0.6554773\",\n \"text\": \"def debug(self, msg, *args, **kwargs):\\r\\n\\r\\n self.log(logging.DEBUG, msg, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"106e996167ff064fac88169a86d177bb\",\n \"score\": \"0.6541644\",\n \"text\": \"def debug_print(message):\\n print('Debug: {!s}'.format(message)[:240]) # truncate message because damned IDLE can't handle long output!!! (I wasted several hours to figure it out!)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ba8d7d71349a8af6e439df657cbbde\",\n \"score\": \"0.6538984\",\n \"text\": \"def debug(self, msg, *args, **kwargs):\\r\\n self.logger.debug( msg, *args, **kwargs )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d77fffcc011f0a54844a028ea2e999d\",\n \"score\": \"0.6536121\",\n \"text\": \"def debug(user, message):\\n message_user(user, message, constants.DEBUG)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"528c0ac0df924670c88c9974d6efa605\",\n \"score\": \"0.6516085\",\n \"text\": \"def _debug_msg(self, msg):\\n if self.debug:\\n print(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07c750de2a5c2fac27a7171fe64dfd95\",\n \"score\": \"0.6511759\",\n \"text\": \"def debug(self, message: str) -> None:\\n self.log(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99c3065db76f70a6f24bd06bb6659da1\",\n \"score\": \"0.6504899\",\n \"text\": \"def debug(msg, html=False):\\r\\n write(msg, 'DEBUG', html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ec6c5fe2d271c9a6b45b1c5bc295dbf\",\n \"score\": \"0.64759636\",\n \"text\": \"def debug_tag(self, msg: str, tag: str):\\n self.log.debug(f\\\"{self.host} [{tag}] {msg}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daa834065d0ddc3f8599e68f727de510\",\n \"score\": \"0.6475924\",\n \"text\": \"def debug(*messages):\\n if DEBUG:\\n log('DEBUG:', *messages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fafa18c8d9d239e6dc7380791162660c\",\n \"score\": \"0.6436231\",\n \"text\": \"def __debug(self, message):\\n if len(message) > 512:\\n message = message[:512] + \\\"…\\\"\\n if Layer.__last_debug_message == message:\\n message = message.split(\\\"\\\\n\\\", 1)[0].strip()\\n if len(message) > 256:\\n message = message[:256] + \\\"…\\\"\\n else:\\n Layer.__last_debug_message = message\\n assert self.debug is not None\\n return commands.Log(textwrap.indent(message, self.debug), DEBUG)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"900ef52149f6790e78a0cc5139e3e13c\",\n \"score\": \"0.6435195\",\n \"text\": \"def debug(self, msg, *args):\\n logger.debug(self.suite_name, msg, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"508f363b09b55817346b1907ba477898\",\n \"score\": \"0.64174473\",\n \"text\": \"def debug(self, tag=None, msg='', *args, **kwargs):\\n\\n if not self.logger.isEnabledFor(logging.DEBUG):\\n return\\n\\n t = self.get_tag(tag)\\n\\n if t:\\n msg = '%s:' + msg\\n args = (t,) + args\\n\\n self.logger.debug(msg, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c573c5977b13eead1e7070d1e8d19302\",\n \"score\": \"0.6404028\",\n \"text\": \"def debug(self, msg):\\n\\n\\t\\tif( self.logger ): self.logger.debug( msg )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57dd17f0b259e202eb5c3a24dda9c4e0\",\n \"score\": \"0.63937306\",\n \"text\": \"def __debug_msg(self, msg):\\n if self.__debug:\\n print(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b98355b85489821288fd409d24c07850\",\n \"score\": \"0.6384047\",\n \"text\": \"def debug(message):\\n if bool(os.environ.get('HAWTHORNE_DEBUG', False)):\\n now = datetime.datetime.now(datetime.timezone.utc)\\n print(f\\\"{now} DEBUG: {message}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f45413cdca1ac4f17863cfefb20910e1\",\n \"score\": \"0.63711673\",\n \"text\": \"def debug(self, msg, *args, **kwargs):\\r\\n if self.isEnabledFor(logging.DEBUG):\\r\\n self._log(logging.DEBUG, colorama.Fore.LIGHTGREEN_EX + msg, args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d34f745826248df2b4e1f9708484b263\",\n \"score\": \"0.6356908\",\n \"text\": \"def debug(self, message):\\n self.log(message, logging.DEBUG)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a95c81c6d501f8a2c02b1219fc33ec0\",\n \"score\": \"0.6341688\",\n \"text\": \"def log_message(self, format, *args):\\n self.globals._logger.debug(format, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1d3e92694642c3edf9acdf022a55dff\",\n \"score\": \"0.6339254\",\n \"text\": \"def debug_print(msg):\\n if DEBUG_PRINT:\\n print msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"210c608f788cc6fb62201b59f7afa4a3\",\n \"score\": \"0.6339113\",\n \"text\": \"def debug(msg):\\n if _debug_level > 0:\\n _debugstream.write(\\n \\\"%s: %s\\\\n\\\" % (os.path.basename(sys.argv[0]), str(msg)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bc57aa4ab58c967d88c6151520759d1\",\n \"score\": \"0.6333502\",\n \"text\": \"def debug(request, message, extra_tags=\\\"\\\", fail_silently=False):\\n add_message(\\n request,\\n constants.DEBUG,\\n message,\\n extra_tags=extra_tags,\\n fail_silently=fail_silently,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af46fd0c922cbef3f67c314220803ec\",\n \"score\": \"0.6329669\",\n \"text\": \"def debug(msg, tags=None):\\n set_log_parameters(msg, log_level=8, tags=tags)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76a873b959c4356894cff8d690669ed\",\n \"score\": \"0.63225466\",\n \"text\": \"def log(log_msg):\\r\\n if DEBUG:\\r\\n print(log_msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"debd930c87ef3f4256f74c0f320d0b42\",\n \"score\": \"0.6321037\",\n \"text\": \"def log(message):\\n print \\\"\\\"\\\"\\n================================================================================\\n%s\\n================================================================================\\n \\\"\\\"\\\" % message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8313b932abc078238488b9a510b0fe94\",\n \"score\": \"0.6314219\",\n \"text\": \"def debug(request, message, extra_tags='', fail_silently=False):\\n add_message(request, constants.DEBUG, message, extra_tags=extra_tags,\\n fail_silently=fail_silently)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b2d2c39355b1b81a1d5ad9fece92c00\",\n \"score\": \"0.63128656\",\n \"text\": \"def debug(self, msg, *args):\\n print(str(msg) % args, file=self.file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc197f5c28a7741911b772f54901208b\",\n \"score\": \"0.631257\",\n \"text\": \"def debug(self, msg, *args):\\n if self.verbose:\\n self.log(click.style(\\\"DEBUG - %s\\\" % msg, *args, fg='green'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18255d1ecaa3f7b958ef0a756199f74c\",\n \"score\": \"0.6301131\",\n \"text\": \"def logDebug(self, *messageArgs):\\n self._writeLog(sched_messages.DEBUG, *messageArgs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76e8280166b9f31cf9870eacf648b83c\",\n \"score\": \"0.6284885\",\n \"text\": \"def debug(self, msg): #pragma: no cover\\n if self.d:\\n print(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6362e0b818f3d411cdfb76faf2e00be0\",\n \"score\": \"0.62659335\",\n \"text\": \"def _debug(self, message, level=1):\\n if self.debug >= level:\\n print(f\\\"DEBUG: {message}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2c407036f558da720b0dd2dfb57e6ac\",\n \"score\": \"0.62652516\",\n \"text\": \"def log(self, msg):\\n if self.debug:\\n print(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21ef99ef400a6ef1ef62123b3356e373\",\n \"score\": \"0.62520665\",\n \"text\": \"def debug(message, end='\\\\n', file=stdout):\\n if enableDebug:\\n print(blue(message), end=end, file=file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"124f548cae82c68df13e93ba8477de05\",\n \"score\": \"0.62443954\",\n \"text\": \"def debug(request, message, extra_tags='', fail_silently=False, buttons=None):\\n add_message(request, constants.DEBUG, message, extra_tags=extra_tags,\\n fail_silently=fail_silently, buttons=buttons)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f877a8e7c8bb34e8d21e51b6102acbb\",\n \"score\": \"0.6229374\",\n \"text\": \"def debug(self, msg):\\n if self.ansible._debug or self.ansible._verbosity > 2:\\n self.ansible.log(f'[DEBUG] {msg}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52097a9c9ac46bd59e3b8ad94ad6492f\",\n \"score\": \"0.6221631\",\n \"text\": \"def logit(*msg):\\n if USR_CONFIG.get('debug_log_enable', ''):\\n print(*msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca4301b8f6bc294adf8d3775f3214787\",\n \"score\": \"0.6215168\",\n \"text\": \"def debug(log_str):\\n logging.debug(log_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"811b6422992e45da39f856d3817782cb\",\n \"score\": \"0.6211976\",\n \"text\": \"def d(tag, message): # pylint: disable=invalid-name\\n\\n date = __get_date()\\n if LOG_LEVEL_STDOUT >= 5:\\n __log_to_stdout(colors.COLOR_DEB, date, tag+\\\"/D\\\", message)\\n if LOG_LEVEL_FILE >= 5:\\n __log_to_file(date, tag+\\\"/D\\\", message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3f29146b35bb1fd004c15c3e894ffe6\",\n \"score\": \"0.62115455\",\n \"text\": \"def logdebug(s):\\n if not debug: return\\n now = datetime.datetime.now()\\n print >> logfile, now.strftime(\\\"%Y-%m-%d %H:%M:%S :: DEBUG : \\\"),\\n print >> logfile, s\\n logfile.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd726a4bfb077c621a341cbdbc482234\",\n \"score\": \"0.6205473\",\n \"text\": \"def print_debug(msg, log_level):\\n if log_level <= LOG_LEVEL:\\n print(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0db8a98f0ed2ba99181fcc1673f494ec\",\n \"score\": \"0.6205176\",\n \"text\": \"def debug(self, msg: str, exc_info=None):\\n if \\\"true\\\" in self.debug_mode:\\n self.log.debug(f\\\"{self.host} [BASE] {msg}\\\", exc_info=exc_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c0e6a17451ccdffeaf87f9fca71f37e\",\n \"score\": \"0.6197399\",\n \"text\": \"def _debug(req, msg):\\n req.log_error('[scm debug] %s' % msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"688a9c8ab924c8019e0e17c46155be69\",\n \"score\": \"0.61851543\",\n \"text\": \"def debug(string):\\n click.secho('DEBUG: {}'.format(string), fg='white')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3de05b1440d2683f7f47eac848ae15a\",\n \"score\": \"0.6181768\",\n \"text\": \"def log(msg):\\n print '%s %s' % (date(), msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aac6206c8bb989568311536fe02f392\",\n \"score\": \"0.6175888\",\n \"text\": \"def debug(self, format_string, *args):\\n self.log(DEBUG, format_string, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e77f11315aeac01f37c3085eca60909\",\n \"score\": \"0.6168947\",\n \"text\": \"def debug_msg(name, tensor):\\n return \\\" {3}: {0} - {1} - {2}\\\".format(\\n tensor.shape, tensor.get_device(), tensor.dtype, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c3766bd37147fd0ff1ed041307c94fc\",\n \"score\": \"0.61682665\",\n \"text\": \"def set_log_debug():\\n set_log_level_format(\\n logging.DEBUG,\\n \\\"%(asctime)s [%(levelname)s %(filename)s:%(lineno)s - \\\"\\n \\\"%(name)s.%(funcName)s() ] %(message)s\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b302b6a73f41c71d2f9166b19009b32e\",\n \"score\": \"0.61553454\",\n \"text\": \"def debug(text):\\n \\n if app.debug:\\n app.logger.debug(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cfa3dbb15aa9718fccc96b945edce49\",\n \"score\": \"0.6154219\",\n \"text\": \"def log(cls, txt, dt=None):\\n logger.debug('%s, %s' % (dt.isoformat(), txt))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b472a359062e89524e23d69cb86e7c60\",\n \"score\": \"0.61158705\",\n \"text\": \"def warn(string):\\n click.secho('DEBUG: {}'.format(string), fg='yellow')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b672bc15c449117efb7945c5576154e\",\n \"score\": \"0.6098145\",\n \"text\": \"def debug_print( debugText , debugLevel = 1):\\n\\n if debugLevel > GLOBAL_DEBUG_LEVEL:\\n print(\\\"{0}\\\".format(debugText) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aab2ac1f9405318822ab68925ffae81\",\n \"score\": \"0.60907835\",\n \"text\": \"def debug(self, msg):\\n\\n if (self.logger): self.logger.debug(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f334a9758eab615b09a27a3e3d113fe\",\n \"score\": \"0.60905963\",\n \"text\": \"def _debugprint(self, msg):\\n \\n if self.debug:\\n if isinstance(self.debug, logging.Logger):\\n self.debug.debug(msg)\\n else:\\n print(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f491e0e28315b5bb46df3b6e2618a254\",\n \"score\": \"0.6080457\",\n \"text\": \"def debug(self, correlation_id, *message):\\n self._write_log(LogLevel.Debug, correlation_id, message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aa9fb7bb22d05925cbf4ee906b7ceb8\",\n \"score\": \"0.6077602\",\n \"text\": \"def debug_log(msg):\\n timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\\n caller = inspect.stack()[1][3]\\n with io.open(\\\"debug.log\\\", \\\"a\\\", encoding='utf-8') as fp:\\n fp.write(\\\"{} {}: {}\\\\n\\\".format(timestamp, caller, msg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d065e8ab7fba46c29b30b912f46dc16\",\n \"score\": \"0.6072615\",\n \"text\": \"def debug_msgprint(message):\\n print(message)\\n if msgprint_debug:\\n msgprint(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41ef962fd220ba62298bf41c8a03803a\",\n \"score\": \"0.60681444\",\n \"text\": \"def debug(*args, **kwars):\\n if DEBUGING:\\n print(\\\"[DEBUG]:\\\", *args, **kwars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c79a25c0a05105621d047e821af7797\",\n \"score\": \"0.6059513\",\n \"text\": \"def formatted_print(message: str) -> None:\\n\\n logger.info(\\\"{} | {}\\\".format(get_timestamp(), message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdfeeaaceb037b7f2841679bf139e302\",\n \"score\": \"0.6043487\",\n \"text\": \"def debug(message: str):\\n for line in message.splitlines():\\n print(f\\\"::debug::{line}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b20410301600860ed4fa606cf3dfd2\",\n \"score\": \"0.6039536\",\n \"text\": \"def debug(self, msg):\\n\\n self.reporter.debug(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"550f9bb34fb340c21743b0558e626e05\",\n \"score\": \"0.60319847\",\n \"text\": \"def hutil_log_info(message):\\n if HUtilObject is not None:\\n HUtilObject.log(message)\\n else:\\n print('Info: {0}'.format(message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe98a03e9d52f98919936831dc9adeaf\",\n \"score\": \"0.60226834\",\n \"text\": \"def debug(self,*msg):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebc283dbff5f2fe2cc9bb086fc3afb83\",\n \"score\": \"0.60182935\",\n \"text\": \"def LogNewDeprel(message):\\r\\n logging.info('{0}{1}{0}{2}{0}{1}{0}'.format('\\\\n','='*50,message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89e5c9da74267ee08c82f9181e373109\",\n \"score\": \"0.60169977\",\n \"text\": \"def debug(self, msg, *args, color=True, **kwargs):\\n\\n if color:\\n now = time.strftime(\\\"%Y%m%d-%H%M%S\\\")\\n msg = grey(f\\\"[{now}] {msg}\\\")\\n\\n self.logger.debug(msg, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e6eb9a3078cdf52d2e6aa9831769bc1\",\n \"score\": \"0.6002395\",\n \"text\": \"def test_debug():\\n\\n log.LOG_LEVEL_STDOUT = 5\\n log.LOG_LEVEL_FILE = 5\\n\\n log.d(TAG, \\\"This is an debug!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e74d119e4e2fd87e45f96714157e4c\",\n \"score\": \"0.5979257\",\n \"text\": \"def debug(self, message):\\n\\n if not isinstance(message, string_types):\\n message = str(message)\\n\\n self._message(message, 'DEBUG')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75dadd2c01f9130282a37ddce261d90f\",\n \"score\": \"0.5978182\",\n \"text\": \"def log(s):\\n if DEBUG_LOG:\\n print(s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dcc4fdd728d75670ede5d745baaf628\",\n \"score\": \"0.5947899\",\n \"text\": \"def debug_message(*args, **kwargs):\\n level = kwargs.get('level', 0)\\n mode = debug_mode(raw=True)\\n if mode != 0 and len(args) > 0:\\n stream = sys.stdout\\n if mode < 0:\\n stream = LogFiles.file(name=\\\"main\\\")\\n text = []\\n stamp = \\\"AsterStudy:\\\"\\n if getattr(debug_message, 'timestamp', True):\\n stamp += \\\" \\\" + timestamp()\\n text.append(stamp + (\\\".\\\" * abs(level) if level else \\\"\\\"))\\n for i in args:\\n if not isinstance(i, (str, unicode)):\\n i = str(i)\\n text.append(to_unicode(i))\\n line = \\\" \\\".join(text)\\n stream.write(to_str(line))\\n stream.write(\\\"\\\\n\\\")\\n stream.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8e2b9d8cc6c2ed545081e98d68e3618\",\n \"score\": \"0.59459656\",\n \"text\": \"def debug(self, *args, **kwargs):\\n self.logger.log(DEBUG, *((self._pfx_debug + args[0],) + args[1:]), **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4ed0195ddce1cb59969c7068ce31269\",\n \"score\": \"0.59335953\",\n \"text\": \"def debug(self, msg):\\n self.logger.debug(f'[NODE {self.id}]' + msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e538493cca4b872048d9dc57a4aab8d\",\n \"score\": \"0.5927366\",\n \"text\": \"def log(message, severity, print_debug=True):\\r\\n\\r\\n print_me = ['WARNING', 'INFO', 'DEBUG']\\r\\n if severity in print_me:\\r\\n if severity == 'DEBUG':\\r\\n if print_debug:\\r\\n print severity, message\\r\\n else:\\r\\n print severity, message\\r\\n else:\\r\\n raise Exception(\\\"%s: %s\\\" % (severity, message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"027bd35a6036d7a2749a1b749f9bf016\",\n \"score\": \"0.5916674\",\n \"text\": \"def logger(s):\\n print(datetime.now(), s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca00ff25693dbb8653948738db2cd524\",\n \"score\": \"0.5891233\",\n \"text\": \"def _debug(self, *txt):\\n\\t\\t\\n\\t\\tif self.debug:\\n\\t\\t\\tprint >> sys.stderr, '\\\\033[31m[ePuck]:\\\\033[0m ', ' '.join([str(e) for e in txt])\\n\\t\\t\\n\\t\\treturn 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2701ebbcf29489d4a52214b02cd7ecb7\",\n \"score\": \"0.58910525\",\n \"text\": \"def portal_debug( *args, **kw ):\\n _log( logger.debug, *args, **kw )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6f12672140af1f6de685b208e96b674\",\n \"score\": \"0.5875253\",\n \"text\": \"def log(msg, **kwargs):\\n timestring = \\\"\\\\033[90m{}\\\\033[0m \\\".format(datetime.datetime.now().strftime('%H:%M:%S.%f'))\\n preamble = timestring if 'end' in kwargs else ''\\n print(preamble+msg, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32c30e090405e735f4dc1af314fb7e69\",\n \"score\": \"0.587485\",\n \"text\": \"def raiseADebug(self, *args, **kwargs):\\n verbosity = kwargs.get('verbosity', 'debug')\\n tag = kwargs.get('tag', 'DEBUG')\\n color = kwargs.get('color', None)\\n msg = ' '.join(str(a) for a in args)\\n self.messageHandler.message(self, msg, str(tag), verbosity, color)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33ed633d6a917671c0b2575d10ed38b2\",\n \"score\": \"0.58617127\",\n \"text\": \"def debugPrint(str):\\n if debugLevel >= 2:\\n print(str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6acb21c9c4766c54ed85ed7cdd2be599\",\n \"score\": \"0.58601254\",\n \"text\": \"def print_debug(arg):\\n # print arg\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f87c7684b0a01cbbba6554e41b732025\",\n \"score\": \"0.58374625\",\n \"text\": \"def _log(self, header, *messages):\\n if self._logfile is not None:\\n print(f\\\"[{header:5s}] -\\\", *messages, file=self._logfile, flush=True)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993876,"cells":{"query_id":{"kind":"string","value":"adf6f74945f1d4f925421391e327d1ed"},"query":{"kind":"string","value":"Limpia los datos de la sesion no agregados por la aplicacion"},"positive_passages":{"kind":"list like","value":[{"docid":"760f01f72f6963af7a6e298cfd109280","score":"0.0","text":"def clear_session(session):\n for key in list(session.keys()):\n if key not in ['_auth_user_id', '_auth_user_backend', '_auth_user_hash']:\n del session[key]","title":""}],"string":"[\n {\n \"docid\": \"760f01f72f6963af7a6e298cfd109280\",\n \"score\": \"0.0\",\n \"text\": \"def clear_session(session):\\n for key in list(session.keys()):\\n if key not in ['_auth_user_id', '_auth_user_backend', '_auth_user_hash']:\\n del session[key]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ae898e68bc78666232f5c3b131cb1831","score":"0.5502195","text":"def novoAutomato(self):\n self._infoAutomatoAtual[\"EstadoInicial\"] = None\n self._infoAutomatoAtual[\"EstadosFinais\"] = set([])\n self._infoAutomatoAtual[\"Alfabeto\"] = set([])\n self._infoAutomatoAtual[\"Estados\"] = set([])\n self._infoAutomatoAtual[\"TabelaTransicao\"] = {}\n self._limparAutomatoResultante()\n self._limparEstados()\n self._limparAlfabeto()\n self._limparCaixasAutomato()","title":""},{"docid":"326b60dd697434d783d98ad188710119","score":"0.5207202","text":"def view_reset_database(self, request):\n # A pesar de la respuesta, en realidad no ha borrado nada\n # Esta respuesta es la señal para que el tween que envuelve a todas las llamadas, lo haga\n if getattr(request.app.settings, \"reset_database\", None) is None:\n allow = True\n else:\n allow = request.app.settings.reset_database.allow\n print(\"Atributo:\", allow)\n if allow:\n return \"OK. Base de datos restaurada\"\n else:\n raise HTTPForbidden(\"Opción inhabilitada\")","title":""},{"docid":"0052741111fd842738bac01165370465","score":"0.5204706","text":"def empty_session(session):\n session.pop('macaroon_root', None)\n session.pop('macaroon_discharge', None)\n session.pop('openid', None)","title":""},{"docid":"74d52da7a25099525df4bb6e17237e4f","score":"0.51951337","text":"def cleanse_user():\n session.pop(\"userid\", None)\n session.pop(\"searchdata\", None)\n session.pop(\"isbn\", None)","title":""},{"docid":"a66d5f8257a399ce849290060135bac4","score":"0.515038","text":"def limpiar_usuario(entrada, entcodigo, resultado, codigo, autor, titulo, fecha, cantidad):\r\n entrada.delete(0, 'end')\r\n entcodigo.delete(0, 'end')\r\n resultado.delete(0, 'end')\r\n codigo.delete(0, 'end')\r\n autor.delete(0, 'end')\r\n titulo.delete(0, 'end')\r\n fecha.delete(0, 'end')\r\n cantidad.delete(0, 'end')","title":""},{"docid":"5671a0838df43bbf96877718103a4967","score":"0.5149993","text":"def clear_external_first_login_anonymous_session_data(session):\n if not session:\n return\n session.pop('auth_user_external_first_login')\n session.pop('auth_user_external_id_provider')\n session.pop('auth_user_external_id')\n session.pop('auth_user_fullname')\n session.pop('service_url')\n session['post_request_removal'] = True\n session.save()","title":""},{"docid":"fe7a18931520302a1677e0f3588d9bd1","score":"0.513555","text":"def orca_session():\n orca.clear_all()","title":""},{"docid":"bda157d2d2b43ade98f6683901ff1c14","score":"0.5048495","text":"def test04_updateSession_noop(self):\r\n session = self.site.makeSession('uid', 'auth', False)\r\n self.site.updateSession(session)\r\n self.assertEquals('0', self.site.memcache.get(session.auth))","title":""},{"docid":"3b7ba7540de2fe2e2b58480c56a05d2e","score":"0.50255895","text":"def test_empty_data(self):\n self.client.force_login(self.admin)\n\n response = self.client.post(self.user_list_url)\n\n self.assertEqual(400, response.status_code)\n\n self.client.logout()","title":""},{"docid":"4baa7c9d2cfd6d4c4b17622be59f1ce8","score":"0.49812746","text":"def reset_sso(self):\n self.goto_sso(self.ssos.index(self.sso))","title":""},{"docid":"303b39d9b578f729fb270a018a01cca0","score":"0.49810764","text":"def default_data(self):\n return {\n \"username\": \"tellendil\",\n \"password\": \"admin\",\n \"email\": \"tellendil@gmail.com\"\n }","title":""},{"docid":"1a7cc64332db043045ac0fbde74a54f0","score":"0.49753088","text":"def inicio(request):\n usuario_invalido = False\n productos = Producto.objects.filter(destacado=True).order_by(\"?\")[:3]\n \n if request.method == 'POST':\n nombre = request.POST['usuario']\n password = request.POST['password']\n usuario = authenticate(username=nombre, password=password)\n if usuario is not None: \n if usuario.is_active:\n login(request, usuario)\n else:\n return direct_response(request, \"common/inicio.html\",\n {\"productos\": productos,\n \"usuario_invalido\": usuario_invalido,\n \"titulo\": \"Inicio\"})\n else:\n usuario_invalido = True\n #Prueba de error 500\n #x = Producto.objects.get(Id=8000)\n return direct_response(request, \"common/inicio.html\",\n {\"productos\": productos,\n \"usuario_invalido\": usuario_invalido,\n \"titulo\": \"Inicio\"})","title":""},{"docid":"71ba039a03a458583cb0a300dc824dea","score":"0.49317253","text":"def _fetch_registrations(self):\n data = self._load_csv_data(self.REGISTRATION_CSV)\n\n df = data.drop([\"ockovaci_misto_nazev\", \"kraj_nuts_kod\", \"kraj_nazev\"], axis=1)\n\n df['rezervace'] = df['rezervace'] == '1'\n df['datum_rezervace'] = df['datum_rezervace'].fillna('1970-01-01')\n\n df = df.groupby([\"datum\", \"ockovaci_misto_id\", \"vekova_skupina\", \"povolani\", \"stat\", \"rezervace\",\n \"datum_rezervace\"]) \\\n .size() \\\n .reset_index(name='pocet')\n\n mista_result = db.session.query(OckovaciMisto.id).all()\n mista_ids = {id for id, in mista_result}\n missing_ids = []\n missing_count = 0\n missing_sum = 0\n\n for idx, row in df.iterrows():\n misto_id = row['ockovaci_misto_id']\n pocet = row['pocet']\n\n if misto_id not in mista_ids:\n missing_count += 1\n missing_sum += pocet\n if misto_id not in missing_ids:\n missing_ids.append(misto_id)\n app.logger.warn(\"Center: '{0}' doesn't exist.\".format(misto_id))\n continue\n\n db.session.add(OckovaniRegistrace(\n datum=row['datum'],\n ockovaci_misto_id=misto_id,\n vekova_skupina=row['vekova_skupina'],\n povolani=row['povolani'],\n stat=row['stat'],\n rezervace=row['rezervace'],\n datum_rezervace=row['datum_rezervace'],\n pocet=pocet,\n import_=self._import\n ))\n\n if missing_count > 0:\n app.logger.warn(\"Some centers doesn't exist - {} rows ({} registrations) skipped.\".format(missing_count, missing_sum))\n\n app.logger.info('Fetching opendata - registrations finished.')","title":""},{"docid":"1c88d235c2de1fb6acc692f862d881bc","score":"0.49164355","text":"def log_user_out():\n session.pop(\"user\")\n session.pop(\"userid\")\n session.pop(\"userrole\")","title":""},{"docid":"ae1a25fe1a7dda05ac4b3cfd27bc70cc","score":"0.49163637","text":"def reset(self):\n self.__logged = {}","title":""},{"docid":"f24d3b5a3cc21d20867a9bd43a9da1f8","score":"0.49063522","text":"def UpdateUsers():\n url = 'http://172.16.0.8/Services/services/Srrhh/SRecursos.asmx?WSDL'\n wsclient = Client(url)\n Funcionarios = wsclient.service.DatosFuncionario('01-01-1999')\n Funcionarios = Funcionarios['Funcionarios']\n unidad = Unidad.objects.get(id=1)\n nickexistentes = []\n for i in Funcionarios:\n if i['Usuario'] is None:\n continue\n nickexistentes.append(formatutf8(i['Usuario']))\n\n for i in Funcionarios:\n if i['Usuario'] is None:\n i['Usuario'] = generatenick(formatutf8(\n formatutf8(i['Nombre'])),\n nickexistentes\n )\n nickexistentes.append(i['Usuario'])\n\n for Funcionario in Funcionarios:\n if Funcionario['Estado'] == '1':\n if not Funcionario['Usuario'] is None:\n fechaMod = datetime.datetime.strptime(\n Funcionario['FechaModificacion'],\n '%Y/%m/%d %I:%M:%S'\n )\n try:\n usuario = Usuario.objects.get(\n username=formatutf8(Funcionario['Usuario'])\n )\n except Usuario.DoesNotExist:\n usuario = None\n\n if usuario is None:\n fullname = get_names(Funcionario['Nombre'])\n usuario = Usuario.objects.create_user(\n formatutf8(Funcionario['Usuario']),\n Funcionario['Mail'],\n Funcionario['Pin']\n )\n usuario.unidad = unidad\n usuario.ip = Funcionario['Ip']\n if not fullname[1] is None:\n usuario.first_name = formatutf8(fullname[0])\n usuario.last_name = formatutf8(fullname[1])\n else:\n usuario.default_name = formatutf8(fullname[0])\n usuario.oficina = formatutf8(Funcionario['Oficina'])\n usuario.idOficina = Funcionario['IdOficina']\n usuario.cargo = formatutf8(Funcionario['Cargo'])\n usuario.fotografia = Funcionario['Foto']\n usuario.activo = True\n fechaNac = datetime.datetime.strptime(\n Funcionario['FechaModificacion'],\n '%Y/%m/%d %I:%M:%S'\n )\n usuario.fechaNacimiento = fechaNac\n usuario.fechaModificacion = fechaMod\n usuario.ci = Funcionario['Documento']\n usuario.save()\n pinUsuario = CodigoProyecto()\n pinUsuario.id = Funcionario['Pin']\n pinUsuario.activo = True\n pinUsuario.usuario = usuario\n pinUsuario.save()\n else:\n if usuario.fechaModificacion < lpz.localize(fechaMod):\n fullname = get_names(Funcionario['Nombre'])\n usuario.unidad = unidad\n usuario.ip = Funcionario['Ip']\n if not fullname[1] is None:\n usuario.first_name = formatutf8(fullname[0])\n usuario.last_name = formatutf8(fullname[1])\n else:\n usuario.default_name = formatutf8(fullname[0])\n usuario.set_password(Funcionario['Pin'])\n usuario.oficina = formatutf8(Funcionario['Oficina'])\n usuario.idOficina = Funcionario['IdOficina']\n usuario.ci = Funcionario['Documento']\n usuario.cargo = formatutf8(Funcionario['Cargo'])\n usuario.activo = True\n usuario.fechaModificacion = fechaMod\n usuario.fotografia = Funcionario['Foto']\n usuario.ci = Funcionario['Documento']\n usuario.save()\n pinUsuario = CodigoProyecto()\n pinUsuario.id = Funcionario['Pin']\n pinUsuario.activo = True\n pinUsuario.usuario = usuario\n pinUsuario.save()","title":""},{"docid":"c64f8aff37701f213420855fce7bcf19","score":"0.49037778","text":"def limpiarCampos(self):\r\n self.lineDni.clear()\r\n self.lineDni.setEnabled(True)\r\n self.lineNombre.clear()\r\n self.lineNombre.setEnabled(True)\r\n self.lineApellido.clear()\r\n self.lineApellido.setEnabled(True)\r\n self.lineDireccion.clear()\r\n self.lineTelefono.clear()\r\n self.tableClientes.setCurrentItem(None)","title":""},{"docid":"69504a31e5e708637e3fd91d381735db","score":"0.48905292","text":"def _limparAutomatoResultante(self):\n for _ in range(self.tabelaTransicoes.rowCount()):\n self.tabelaTransicoes.removeRow(0)\n \n for _ in range(self.tabelaTransicoes.columnCount()):\n self.tabelaTransicoes.removeColumn(0)","title":""},{"docid":"722d2e07619b40e4f7174401ca89820d","score":"0.48641646","text":"def login(self):\n try:\n self.o.refresh()\n self.fetch_stats()\n except:\n raise","title":""},{"docid":"708c6bc5a1d2f8c866394f8bcbd9c19a","score":"0.48580894","text":"def before_request():\n g.user = None\n g.user_location = None\n if 'sub' in session:\n g.user = db.session.query(db.User).filter_by(email=session['sub']).one_or_none()","title":""},{"docid":"7fc23bd9e881c53df7748d3daba84921","score":"0.4834209","text":"def before_request():\n if 'name' in session:\n try:\n g.user = User.get_by(name=session['name'])\n except NoResultFound:\n session['name'] = None\n g.user = None\n else:\n g.user = None","title":""},{"docid":"f1a58585f697bed92d8a131500c28abe","score":"0.48263207","text":"def reset_access(self, request, pk):\n student = self.get_object()\n student.reset_token()\n student.save()\n student.refresh_from_db()\n return Response(data=StudentSerializer(instance=student).data)","title":""},{"docid":"c44c17e9fc58d55cb072e56d9a7ad804","score":"0.48255506","text":"def clean_sessions(self):\n _WebServiceCore.session_ids = dict()\n self.query_string = 'session-id=%s'","title":""},{"docid":"fb95dd331687ff77bc778ba0401702cf","score":"0.48171723","text":"def sign_out(self):\n self.name = None\n self.email = None\n self.password = None","title":""},{"docid":"7585d7adcbd0b307a15adbf28327f090","score":"0.48120636","text":"def clean(self):\r\n self.authenticate()","title":""},{"docid":"2e60c4d0e21fe7ab446a0e3eb65f7806","score":"0.48051268","text":"def all_data(self):\n return self.session_data","title":""},{"docid":"6c292ff0068d204dc2dffa4fe5d17e08","score":"0.4803846","text":"def update_authtokens(self):\n response_data = self.rest_api.post_sessions(self.email_address, self.password)\n self.AuthToken = response_data['sessions']['token']\n self.refreshToken = response_data['sessions']['refresh_token']\n self.userID = response_data['sessions']['user_id']\n self.rest_api.headers['X-Session'] = self.AuthToken","title":""},{"docid":"77d0b248cee8c7ea9c36c5138bc22e63","score":"0.47908464","text":"def resetVisitas(self):\n\t\tself.c.execute(\" UPDATE clienteTeru SET visitas = 0\")","title":""},{"docid":"7b3ff5e16bada04170fe5afb47d7efce","score":"0.47790045","text":"def _limparEstados(self):\n for _ in range(self.listaEstados.count()): self.listaEstados.removeItem(0)","title":""},{"docid":"1680d544359c465d81f40cd415345105","score":"0.47741163","text":"def get_all_data():\n if session.get(SESSION_USER_ID) and session.get(SESSION_USER_LOGIN):\n data = get_all_public_private_data(str(session.get(SESSION_USER_ID)))\n else:\n data = get_all_public_data()\n\n response = app.response_class(\n response=data,\n status=200,\n mimetype='application/json'\n )\n return response","title":""},{"docid":"09905de1244183c30b3fc604ca324fa3","score":"0.47666332","text":"def _clear(self):\n self._data = {\n \"num_rooms\": None,\n \"num_people\": None,\n \"apt_cost\": None,\n \"rooms\": {},\n \"rooms_shared\": {}\n }","title":""},{"docid":"707c740f02b408f9c4de77cdc42d21c1","score":"0.47634852","text":"def __init__(self):\n self._session = requests.Session()\n self._session.headers.update({'Content-Type': 'application/json'})\n self._user_id = None\n self._email = None\n self._password = None","title":""},{"docid":"0be435090b07aa65d39d065e15cc2c16","score":"0.47499844","text":"def reset_user(request):\n if 'logged_in_user' in request.session:\n log.info('Resetting Persona user %s'\n % request.session['logged_in_user'])\n del request.session['logged_in_user']\n if 'mkt_permissions' in request.session:\n # This isn't strictly necessary since permissions are reset on\n # login but it's good for paranoia.\n del request.session['mkt_permissions']\n return http.HttpResponse('OK')","title":""},{"docid":"266fb14b3d65b926cb949feacc10fb82","score":"0.47441292","text":"def post_logout(self, came_from=url('/')):\n condicion=int (Variables.get_valor_by_nombre(\"rol_anterior\"))\n if condicion > 0:\n usuario=Usuario.get_user_by_alias(\n Variables.get_valor_by_nombre(\"usuario_actual\"))\n\n Variables.set_valor_by_nombre(\"fase_actual\",0)\n Variables.set_valor_by_nombre(\"proyecto_actual\",0)\n Variables.set_valor_by_nombre(\"nombre_proyecto_actual\",\"\")\n Variables.set_valor_by_nombre(\"nombre_fase_actual\",\"\")\n Variables.set_valor_by_nombre(\"usuario_actual\",\"\")\n\n rol=int (Variables.get_valor_by_nombre(\"rol_anterior\") )\n Variables.set_valor_by_nombre(\"rol_actual\",0)\n Variables.set_valor_by_nombre(\"rol_anterior\",0)\n\n rol2=Rol.get_rol_by_id(rol)\n usuario.roles=[]\n usuario.roles.append(rol2)\n DBSession.flush()\n\n flash(_('Hasta luego!') )\n redirect('/')","title":""},{"docid":"cd7065a6a982739c7512008c2a26f819","score":"0.47319514","text":"def buscar_encuesta_alumno(self, username):\n #TODO: Catchear una posible excepcion\n alumno = Usuario.query.filter_by(username=username).first()\n if alumno is None:\n raise Exception('El usuario ' + username + ' no existe')\n oferta_activa = Oferta.query.filter_by(activa=True).first()\n encuesta = Encuesta.query.filter_by(oferta_id=oferta_activa.id, alumno_id=alumno.id).first()\n if not encuesta:\n encuesta = self.crear_encuesta(oferta_activa, alumno)\n return encuesta","title":""},{"docid":"6d8ed1c2948310e4337467784551074f","score":"0.47294638","text":"def reset(self):\n self.hostname = None\n self.secret = None\n self.expiry = None\n self.login = None\n self.password = None\n self.key = None","title":""},{"docid":"21ccdc25d6266f2d684f1178d61c2106","score":"0.4728464","text":"def reset_employees_data( ):\n from setting import db\n db.session.query ( EmployeeSchedule ).delete ( )\n db.session.query ( Employee ).delete ( )","title":""},{"docid":"f45c303a5d3714729f901c51509d1618","score":"0.4724931","text":"def before_request():\n g.db = connect_db()\n g.user = None\n if 'user_id' in session:\n g.user = query_db('select * from user where user_id = ?',\n [session['user_id']], one=True)","title":""},{"docid":"487b8b9e0f31269e4f6bdc8061732aa0","score":"0.47142845","text":"def post(self, request):\n email = request.data.get('email', None)\n password = request.data.get('password', None)\n schema = request.data.get('schema', None)\n # try:\n \"\"\"si el se reciben bien los parametros busca el usuario\"\"\"\n if email and password and schema:\n user_querysets = CustomUser.objects.get(email__iexact=email)\n \"\"\"Si lo encuentra y esta activo elimna los campos sencibiles y crea el token\"\"\"\n if (user_querysets.is_active):\n user = user_querysets\n \"\"\"valida que la contraseña propocionada sea correcta\"\"\"\n if(check_password(password, user.password)):\n \"\"\"Si la contraseña es correcta trae el usuario\"\"\"\n user_token = CustomUser.objects.get(\n email__iexact=email)\n \"\"\"creamos el token de acceso ...\"\"\"\n token, created = Token.objects.get_or_create(\n user=user_token)\n \"\"\"revisa el perfil del usuario\"\"\"\n objserializer = UserSerializer1(user_token).data\n \"\"\"Identifica el plan del tenant\"\"\"\n tenant = Client.objects.get(name=schema) \n return Response({\"message\": \"Login exitoso\",\n \"data\": {\n \"token\": token.key,\n \"user_data\": objserializer,\n \"plan\": tenant.plan.name\n }})\n else:\n message = \"Contraseña incorrecta\"\n return Response({\"message\": message, 'data': {}},\n istatus=status.HTTP_204_NO_CONTENT)\n else:\n message = \"El id proporcionado no existe o el usuario no está activo\"\n return Response({\"message\": message, 'data': {}},\n status=status.HTTP_204_NO_CONTENT)\n else:\n message = \"No ha proporciando datos validos\"\n return Response({\"message\": message, 'data': {}},\n status=status.HTTP_203_NON_AUTHORITATIVE_INFORMATION)\n #except:\n # message = \"El id proporcionado no existe o el usuario no está activo\"\n # return Response({\"message\": message, 'data': {}},\n # status=status.HTTP_204_NO_CONTENT)","title":""},{"docid":"708ed7be6d143d6ebd1c0a798c3269ec","score":"0.47113943","text":"def clean(self):\n cleaned_data = super(LoginForm,self).clean()\n remember_me = cleaned_data.get('remember_me')\n if not remember_me:\n settings.SESSION_EXPIRE_AT_BROWSER_CLOSE = True\n else:\n settings.SESSION_EXPIRE_AT_BROWSER_CLOSE = False\n username = cleaned_data.get('username')\n try:\n User._default_manager.get(username=username)\n except User.DoesNotExist:\n raise forms.ValidationError('Incorrect Email or Password.')\n return self.cleaned_data","title":""},{"docid":"d5ec95db3a926b0271729350278c98a7","score":"0.47006518","text":"def reset(self):\r\n self.data=[]","title":""},{"docid":"d340981ed6b44af910aa9a7d5b33baf6","score":"0.46991765","text":"def reset_login_attemtps(self):\n\t\tself.login_attempts = 0","title":""},{"docid":"55a5e0b2b5d353ed309e07a723f23a39","score":"0.46916738","text":"def actualizar(self):\r\n self.limpiarCampos()\r\n self.limpiarTabla(self.tableClientes)\r\n self.cargarClientes()","title":""},{"docid":"7dd8febc93701d989ab38c62cbb59aed","score":"0.46741456","text":"def get_conn(self, headers):\n if self.freshsales_token:\n headers = {'Authorization': 'Token token={0}'.format(self.freshsales_token),\n 'Content-Type': 'application/json'}\n session = super().get_conn(headers)\n session.auth = None\n return session\n return super().get_conn(headers)","title":""},{"docid":"f3508e8677b3c29e85afcdce09fea98d","score":"0.46710578","text":"def iniciar(self):\n if self.iniciado == False:\n self.montar_baralho()\n self.monte = list(self.baralho.keys())\n\n\n #DELETE ESSE FOR DEPOIS DE TESTAR\n #for i in range(50):\n # self.monte = self.monte + list(self.baralho.keys())\n #self.monte = self.monte[:3]\n\n\n random.shuffle(self.monte)\n self.distribuir_cartas()\n self.nome_jogador_atual = self.jogadores.keys()[0]\n self.iniciado = True","title":""},{"docid":"9d8d0eef8aec6a58c6b8b48a348f7da4","score":"0.46647462","text":"def reset_all(self, user_id: str) -> None:\n super().reset_all(user_id) \n self.user_data.delete_many({'userId': user_id})","title":""},{"docid":"54a959290f37af2eec11a0861d632a77","score":"0.46646988","text":"def test_no_token(self):\n response = self.client.get('/public')\n data = json.loads(response.data)\n self.assertEqual(data, {}, \"No session data is set\")","title":""},{"docid":"55f373249ba302a406b27b3392f65870","score":"0.46613795","text":"def sessions():\n\n global _pma_sessions\n return _pma_sessions","title":""},{"docid":"7d63eb973eaf4aec594241af5cd2de51","score":"0.46535254","text":"async def refresh_session(self) -> dict:\n if not self.sid:\n raise exceptions.NotLoggedIn()\n results_raw = await self.post(await self.jsonrpc([[1, \"set_session\", {\"session_id\": self.sid}]]))\n return await self.pack_responses(results_raw, 0)","title":""},{"docid":"c4b7fc36bf9e1165e8533e64ca630235","score":"0.46531108","text":"def limpiar(self):\n self._palabra = \"\"\n self._letras = \"\"\n self._atril_usadas = []\n self._letra_actual = \"\"\n self._orientacion = \"\"\n self._casilleros_usados.clear()\n self._casilleros_usados = set()","title":""},{"docid":"fd2c430e9cab59344867180dcefb4ee2","score":"0.46472672","text":"def resetConsumo(self):\n\t\tself.c.execute(\" UPDATE clienteTeru SET consumo = 0\")","title":""},{"docid":"0225d4cdc265bdf84a58151024f2ac37","score":"0.46383467","text":"def __init__(self,comandos,saltos):\r\n \r\n MataCerebros.__init__(self,comandos,saltos)\r\n if len(comandos) == 0:\r\n raise IndexError\r\n self.cantidad_comandos = len(comandos)\r\n self.mensaje_actual = \"\"","title":""},{"docid":"ebed40018bc9a88d859c6fc025429f2d","score":"0.46356487","text":"def imprime_datos(solucion):\n nectar_local = funcion_nectar_online(solucion)\n piezas_local = solucion.piezas_jugadas()\n logging.info('Nectar final: ' + str(nectar_local))\n logging.info('Piezas jugadas: ' + str(piezas_local))\n logging.info('Filas eliminadas: ' + str(solucion.num_tetris()))\n solucion.imprime_tablero()","title":""},{"docid":"855bbfdecf264f065476d0c732d28eb4","score":"0.46346357","text":"def _clear(self):\n self.stdout.write(\"Clearing data\")\n\n get_user_model().objects.all().delete()\n Manager.objects.all().delete()\n Member.objects.all().delete()\n Shift.objects.all().delete()\n Priority.objects.all().delete()\n Status.objects.all().delete()\n Group.objects.all().delete()","title":""},{"docid":"6b788ec5d47f19e147550d760b07faa7","score":"0.4633331","text":"def get_session_data(self, session_id):\n\n cur = self.conn.cursor()\n cur.execute(\"SELECT * FROM sessions WHERE session_id = ?\", (session_id,))\n sess_rows = cur.fetchall()\n\n cur = self.conn.cursor()\n cur.execute(\"SELECT * FROM users WHERE in_session = ?\", (session_id,))\n user_rows = cur.fetchall()\n \n if not sess_rows or len(sess_rows) != 1:\n return None\n \n sess_row = sess_rows[0]\n sess_data = {\n \"session_id\": session_id,\n \"password\": sess_row[1],\n \"creator_id\": sess_row[2],\n \"users\": []\n }\n\n for row in user_rows:\n sess_data[\"users\"].append({\n \"user_id\": row[0],\n \"display_name\": row[2],\n \"initials\": row[3],\n \"is_examiner\": bool(row[5]),\n })\n\n return sess_data","title":""},{"docid":"f43d48b285ca3857bea404e10c2e8eed","score":"0.46306574","text":"def test_sessions_get_all_no_sessions(sessions_api_client, mock_session_manager):\n mock_session_manager.get.return_value = []\n\n response = sessions_api_client.get(\"/sessions\")\n assert response.json() == {\"data\": [], \"links\": None}\n assert response.status_code == 200","title":""},{"docid":"aabfd82b98140fb86f82e9fd7df5defd","score":"0.462928","text":"def refresh_userdata(self):\n url = \"users/me\"\n resp = self._session.get(url)\n resp_json = resp.json()\n try:\n USER_SCHEMA(resp_json)\n self._userdata = resp_json\n except MultipleInvalid as ex:\n _LOGGER.warning(\"Invalid response from %s: %s. Got: %s\", url, ex, resp_json)","title":""},{"docid":"4e38f3ff9c5fb80d6c3a6c10803eb114","score":"0.46277547","text":"def public(): # type: ignore\n data = request.environ.get('session')\n if data:\n if isinstance(data, Exception):\n raise data\n return json.dumps(domain.to_dict(data))\n return json.dumps({})","title":""},{"docid":"5c5f0d3fd84e5524e1aa62751ace22f9","score":"0.4622105","text":"def traer_datos(self):\n self.rows = self.cursor.fetchall()","title":""},{"docid":"5c5f0d3fd84e5524e1aa62751ace22f9","score":"0.4622105","text":"def traer_datos(self):\n self.rows = self.cursor.fetchall()","title":""},{"docid":"c1983beab36bd06e68e3c1b46c5b35d1","score":"0.4613787","text":"def session(self):","title":""},{"docid":"fa8066fd2191f3fb883ba176a9de0ea0","score":"0.46121228","text":"def _obtener_diccionario_de_atributos_usuario(self):\n diccionario = {\n 'id_usuario': self.id_usuario, 'calidad_imagen': self.calidad_imagen.value,\n 'es_original': self.es_original, 'formato': self.formato.value, 'ruta': self.ruta, 'hash256': self.hash256,\n 'alto': self.alto, 'ancho': self.ancho\n }\n return diccionario","title":""},{"docid":"0fc083c1e7f1ecf5252ff79db956c452","score":"0.46078888","text":"def initData(self):\n if connectionDBPostgres():\n self.chargerTable()\n self.chargerObjetK()\n if connectionGeocodeur(\"Nominatim\"):print(\"connection geolocolisation\")","title":""},{"docid":"51228da7bf8c7a604ad7849e3ee8b0c3","score":"0.4602367","text":"def test_sessions_get_all_no_sessions(\n sessions_api_client,\n mock_session_manager):\n mock_session_manager.get.return_value = []\n\n response = sessions_api_client.get(\"/sessions\")\n assert response.json() == {\n 'data': [], 'links': None\n }\n assert response.status_code == 200","title":""},{"docid":"286984c96170682d7b64b582a92a4301","score":"0.45941457","text":"def removeAll(self):\n self.__studs = {}","title":""},{"docid":"402b87560da9d831117aba8ea1fe165b","score":"0.4588744","text":"def reiniciar_partida(self):\n self.controlador.resetear_nivel(0)","title":""},{"docid":"84d4327388ad005f88d5ebde5e667295","score":"0.4587085","text":"def clearData(self):\n self.data = {}\n self.data['id'] = self.id","title":""},{"docid":"425c5f4b563c9d6b746564f2957bc027","score":"0.45798075","text":"def initialize_session_obj():\n session = {}\n session[\"startdate\"] = '00/00/00 00:00:00'\n session[\"enddate\"] = '00/00/00 00:00:00'\n session[\"account\"] = 'unknown'\n\n return session","title":""},{"docid":"5ba3ea04944c0249fb6123b841bcd7b5","score":"0.45709795","text":"def carga_datos_firma(self):\n pass","title":""},{"docid":"b14c5505d505f1ef1e102d1ea070f879","score":"0.45701495","text":"def duo_unauthenticate(request):\n try:\n del request.session['duo_authenticated']\n except KeyError:\n pass","title":""},{"docid":"c38becc69b14ca58507306275509cdfb","score":"0.45602244","text":"def reset(self):\n self.disconnect()\n self.__cached_auth = {}\n\n for key, value in self.__prepreparation_values.items():\n setattr(self, key, value)\n self.__prepreparation_values = {}\n self.__prepared = False\n\n return self","title":""},{"docid":"0580e362605341465b1bc761d22195b9","score":"0.4549385","text":"def get_all_users(self):\n pass","title":""},{"docid":"a5e08688936820df5c89d431537cdf14","score":"0.45394206","text":"def get(self):\n # Set User's status to offline\n self.user.online = False\n self.user.put()\n\n # Delete the Token for this session\n self.token.key.delete()\n\n self.return_success()","title":""},{"docid":"b867e57afc9f5835e554d1fa1c27443a","score":"0.45353192","text":"def reset_alt_auth_data(self):\n self.alt_part = None\n self.alt_auth_data = None","title":""},{"docid":"88809b19863ee2f5d6dc036baa84c06e","score":"0.45292777","text":"def logoff(self):\n return","title":""},{"docid":"949392b2673dcaa9e0fede6cdab21318","score":"0.45175472","text":"def flush(self):\n self.request.session[self.SESSION_KEY] = {}","title":""},{"docid":"7e432ca2aa598f8d7d8bcfdc27044191","score":"0.45169818","text":"def __init__(self):\n self.users = {}","title":""},{"docid":"18b6d0f2ca352e66c7ab01c6440eeff5","score":"0.451381","text":"def getUsers():\n\tif 'username' in session and session['username'] == 'admin':\n\t\t#we send out all users to the admin\n\t\tsqlquery = Users.query.all()\n\t\treturn jsonify([i.serialize for i in sqlquery])\n\treturn jsonify(error = 'not privileged'), 401","title":""},{"docid":"487eb85bd3ada981109b2f4ac969aa8d","score":"0.45058164","text":"def logout(self):\n self.api_v = 0\n self.esm_v = \"0\"\n self.request(\"logout\", http=\"delete\")\n self.logged_in = False\n self.login_info = dict()\n self.session = requests.Session()\n self.user_tz_id = None","title":""},{"docid":"fe1b22e65de27eb780a442a11ba42401","score":"0.45050862","text":"def _reset_cached_attributes(self):\n self._seas, self._veas = (None, None)","title":""},{"docid":"fe1b22e65de27eb780a442a11ba42401","score":"0.45050862","text":"def _reset_cached_attributes(self):\n self._seas, self._veas = (None, None)","title":""},{"docid":"9beec4292379b4f5e15de30ffa8fc3ae","score":"0.4504632","text":"def get_all_clients_noauthz(self, manager, context, conn=None, cur=None):\n def db_body(conn, cur):\n return {\n row.get(ID)\n for row in force_query(\n conn, cur,\n \"\"\"\nSELECT %(username)s FROM %(utable)s ;\n\"\"\" % {\n 'utable': self.provider._table(self.provider.client_storage_name),\n 'username': ID,\n}\n )\n }\n\n if conn is not None and cur is not None:\n return db_body(conn, cur)\n else:\n return self.provider._db_wrapper(db_body)","title":""},{"docid":"bfd0a0a82877d99d6fb8b11658b4f58f","score":"0.45022002","text":"def set_session(self):","title":""},{"docid":"afa81640ff9316acf4c9366524bde83a","score":"0.4502113","text":"def clear_user_id_tokens(self):\n self._msl_data.pop('user_id_tokens', None)\n self._save_msl_data()","title":""},{"docid":"6e7d0a924b3fe70a3c9a76eea3fe8a60","score":"0.4499268","text":"def reset(request):\n assert isinstance(request, HttpRequest)\n print('inside reset...............................................')\n cache.clear()\n #if request.session.has_key('module'):\n # del request.session['module']\n # del request.session['pagenum']\n # del request.session['sessionstarted']\n # del request.session['ques']\n # del request.session['candidate']\n # del request.session['questioncode']\n # del request.session['qkey']\n # del request.session['matricno']\n # del request.session['tattempt']\n # del request.session['tresult']\n # print('session deleted.........................')\n #request.session.flush()\n #logout(request)\n return HttpResponseRedirect('/logout')","title":""},{"docid":"21792e77ddb6769f11f53928adda5406","score":"0.44986385","text":"def tearDown(self):\n\n Users.user_list = []","title":""},{"docid":"f1dad839d181cca07fdaa71044ce15ca","score":"0.44961223","text":"def index(self):\n nom_proyecto=Variables.get_valor_by_nombre(\"nombre_proyecto_actual\")\n nom_fase=Variables.get_valor_by_nombre(\"nombre_fase_actual\")\n\n if not request.identity:\n redirect(url('/login', came_from=url('/')))\n return dict(pagina='index',nom_proyecto=nom_proyecto,nom_fase=nom_fase)","title":""},{"docid":"bf07ddfed1b2c21d7154adb3aba7dcfc","score":"0.44929296","text":"def tearDown(self):\n User.user_names=[]","title":""},{"docid":"3b186daa522524dba83939232560d157","score":"0.44889772","text":"def logout(username):\n #Get user\n user = session.query(Usuario).filter(Usuario.username == username).one()\n #Update status of user\n user.conectado = False\n user.disponibilidad = False\n session.add(user)\n session.commit()\n return redirect(url_for('index'))","title":""},{"docid":"6e8ff84813053d026d0b648f323ef2aa","score":"0.44868487","text":"def logout(self):","title":""},{"docid":"6e8ff84813053d026d0b648f323ef2aa","score":"0.44868487","text":"def logout(self):","title":""},{"docid":"6e8ff84813053d026d0b648f323ef2aa","score":"0.44868487","text":"def logout(self):","title":""},{"docid":"bdab412148a36f7133664caf0308ea78","score":"0.44858292","text":"def clear_user_id_tokens(self):\n self.msl_requests.crypto.clear_user_id_tokens()","title":""},{"docid":"acb17e73a71fb7656e995ed01de82901","score":"0.4482307","text":"def clear_current_user(self):\n\n\t\t# if (self.get_argument(\"logout\", None)):\n\t\tself.clear_cookie(\"user_name\")\n\t\tself.clear_cookie(\"user_email\")\n\t\tself.clear_cookie(\"user_is_connected\")","title":""},{"docid":"59642c3b2cebd0eb13ffd094c5c20316","score":"0.44794044","text":"def _refresh(self):\n self._loadjson(self._users_fname, self._users)\n self._loadjson(self._roles_fname, self._roles)","title":""},{"docid":"d3e5da62e7724c034af0b4fac347189d","score":"0.44751304","text":"def es_reset(cls):\n\n cls.es_delete()\n cls.es_create()","title":""},{"docid":"3778375b3ea8b6d70cf8ccc5271cc22c","score":"0.44703662","text":"def before_request():\r\n g.user = None\r\n if 'user' in session:\r\n g.user = session['user']","title":""},{"docid":"7b2f934317244f145f4129140d27d64a","score":"0.44680202","text":"def clear_auth(self):\n self.cache = None\n self.credentials.reset()","title":""},{"docid":"7c5c76c006c57b9ba17ba964ad7e697c","score":"0.44661942","text":"def create_session(data, config):\n data.sort_values(['user_id', 'timestamp'], ascending=True, inplace=True)\n data['timestamp'] = pd.to_datetime(data['timestamp'])\n data.reset_index(inplace = True, drop = True)\n data['Session'] = ''\n new_data = pd.DataFrame(columns = data.columns)\n users = list(data.user_id)\n users = list(dict.fromkeys(users))\n for index, user in enumerate(users):\n sess_idx = 1\n sub_data = pd.DataFrame(data[data['user_id'] == user])\n prev_time = 0\n sessions = []\n for i, time in enumerate(sub_data['timestamp']):\n if prev_time == 0:\n prev_time = time\n elif (time - prev_time).total_seconds() < config.time_interval :\n prev_time = time\n else:\n sess_idx +=1\n prev_time = time\n sessions.append('U' + str(index) + 'S' + str(sess_idx))\n # sub_data.loc[i, 'Session'] = 'U' + str(index) + 'S' + str(sess_idx)\n sub_data['Session'] = sessions\n new_data = pd.concat([new_data, sub_data])\n del sub_data\n # data.loc[data.user_id == user, 'Session'] = sub_data.Session\n new_data.dropna(inplace = True)\n new_data.reset_index(inplace = True, drop = True)\n assert len(new_data) == len(data)\n return new_data","title":""},{"docid":"260e797800ca33d8ab3e124fce928314","score":"0.44626722","text":"def getDataAlineaciones(self):\n\t\tdicAlineaciones = []\n\t\tdicTweets = {}\n\t\t\n\t\t# primer paso, se buscan los tweets que anuncian los posibles 11\n\t\tself.l.log('getAlineacion.getDataAlineaciones', ' -------------- Proceso Iniciado -------------')\n\t\ttry:\n\t\t\tdicTweets = self.getTwitterData()\n\t\texcept Exception:\n\t\t\tself.l.error('getAlineacion.getDataAlineaciones', 'Error al recuperar datos de Twitter: ')\n\t\t\treturn 1\n\t\tself.l.log('getAlineacion.getDataAlineaciones', 'Tweets recuperados:' + str(len(dicTweets)))\n\t\t\n\t\t# se realiza web scrapping sobre las paginas de los equipos con posibles 11 actualizados\n\t\tfor k, v in dicTweets.items():\n\t\t\tif len(k) > 0 and len(v) > 0:\n\t\t\t\tself.l.debug(\"getAlineacion.getDataAlineaciones: Procesando web del equipo: \" + str(k) + str(v))\n\t\t\t\ttry:\n\t\t\t\t\tdiccTemp = self.getWebData(k, v)\n\t\t\t\texcept Exception:\n\t\t\t\t\tself.l.error('getAlineacion.getDataAlineaciones', 'Error al recuperar datos de la web: ')\n\t\t\t\t\treturn 2\n\t\t\t\tdicAlineaciones.append(diccTemp)\n\t\t\t\t\t\t\n\t\t# por ultimo, se guardan los resultados a base de datos Mongo \"alineaciones\"\n\t\tfor alineacion in dicAlineaciones:\n\t\t\ttry:\n\t\t\t\tself.dbWriter.write_dictionaries_list('colAlineaciones', alineacion)\n\t\t\texcept pymongo.errors.BulkWriteError as e:\n\t\t\t\tself.l.error('getAlineacion.getDataAlineaciones', 'Error al escribir a BD: ' + e)\n\t\t\t\treturn 3\n\t\t\n\t\tself.l.debug('getAlineacion.getDataAlineaciones: Alineacion:' + str(dicAlineaciones))\n\t\tself.l.log('getAlineacion.getDataAlineaciones', ' -------------- Proceso Finalizado -------------')\n\t\t\n\t\treturn 0","title":""},{"docid":"4610c9444287925b732f3cadc33fcf19","score":"0.44597074","text":"def handle_request(request):\n session['user_id'] = None","title":""}],"string":"[\n {\n \"docid\": \"ae898e68bc78666232f5c3b131cb1831\",\n \"score\": \"0.5502195\",\n \"text\": \"def novoAutomato(self):\\n self._infoAutomatoAtual[\\\"EstadoInicial\\\"] = None\\n self._infoAutomatoAtual[\\\"EstadosFinais\\\"] = set([])\\n self._infoAutomatoAtual[\\\"Alfabeto\\\"] = set([])\\n self._infoAutomatoAtual[\\\"Estados\\\"] = set([])\\n self._infoAutomatoAtual[\\\"TabelaTransicao\\\"] = {}\\n self._limparAutomatoResultante()\\n self._limparEstados()\\n self._limparAlfabeto()\\n self._limparCaixasAutomato()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"326b60dd697434d783d98ad188710119\",\n \"score\": \"0.5207202\",\n \"text\": \"def view_reset_database(self, request):\\n # A pesar de la respuesta, en realidad no ha borrado nada\\n # Esta respuesta es la señal para que el tween que envuelve a todas las llamadas, lo haga\\n if getattr(request.app.settings, \\\"reset_database\\\", None) is None:\\n allow = True\\n else:\\n allow = request.app.settings.reset_database.allow\\n print(\\\"Atributo:\\\", allow)\\n if allow:\\n return \\\"OK. Base de datos restaurada\\\"\\n else:\\n raise HTTPForbidden(\\\"Opción inhabilitada\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0052741111fd842738bac01165370465\",\n \"score\": \"0.5204706\",\n \"text\": \"def empty_session(session):\\n session.pop('macaroon_root', None)\\n session.pop('macaroon_discharge', None)\\n session.pop('openid', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74d52da7a25099525df4bb6e17237e4f\",\n \"score\": \"0.51951337\",\n \"text\": \"def cleanse_user():\\n session.pop(\\\"userid\\\", None)\\n session.pop(\\\"searchdata\\\", None)\\n session.pop(\\\"isbn\\\", None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a66d5f8257a399ce849290060135bac4\",\n \"score\": \"0.515038\",\n \"text\": \"def limpiar_usuario(entrada, entcodigo, resultado, codigo, autor, titulo, fecha, cantidad):\\r\\n entrada.delete(0, 'end')\\r\\n entcodigo.delete(0, 'end')\\r\\n resultado.delete(0, 'end')\\r\\n codigo.delete(0, 'end')\\r\\n autor.delete(0, 'end')\\r\\n titulo.delete(0, 'end')\\r\\n fecha.delete(0, 'end')\\r\\n cantidad.delete(0, 'end')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5671a0838df43bbf96877718103a4967\",\n \"score\": \"0.5149993\",\n \"text\": \"def clear_external_first_login_anonymous_session_data(session):\\n if not session:\\n return\\n session.pop('auth_user_external_first_login')\\n session.pop('auth_user_external_id_provider')\\n session.pop('auth_user_external_id')\\n session.pop('auth_user_fullname')\\n session.pop('service_url')\\n session['post_request_removal'] = True\\n session.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe7a18931520302a1677e0f3588d9bd1\",\n \"score\": \"0.513555\",\n \"text\": \"def orca_session():\\n orca.clear_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bda157d2d2b43ade98f6683901ff1c14\",\n \"score\": \"0.5048495\",\n \"text\": \"def test04_updateSession_noop(self):\\r\\n session = self.site.makeSession('uid', 'auth', False)\\r\\n self.site.updateSession(session)\\r\\n self.assertEquals('0', self.site.memcache.get(session.auth))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b7ba7540de2fe2e2b58480c56a05d2e\",\n \"score\": \"0.50255895\",\n \"text\": \"def test_empty_data(self):\\n self.client.force_login(self.admin)\\n\\n response = self.client.post(self.user_list_url)\\n\\n self.assertEqual(400, response.status_code)\\n\\n self.client.logout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4baa7c9d2cfd6d4c4b17622be59f1ce8\",\n \"score\": \"0.49812746\",\n \"text\": \"def reset_sso(self):\\n self.goto_sso(self.ssos.index(self.sso))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"303b39d9b578f729fb270a018a01cca0\",\n \"score\": \"0.49810764\",\n \"text\": \"def default_data(self):\\n return {\\n \\\"username\\\": \\\"tellendil\\\",\\n \\\"password\\\": \\\"admin\\\",\\n \\\"email\\\": \\\"tellendil@gmail.com\\\"\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a7cc64332db043045ac0fbde74a54f0\",\n \"score\": \"0.49753088\",\n \"text\": \"def inicio(request):\\n usuario_invalido = False\\n productos = Producto.objects.filter(destacado=True).order_by(\\\"?\\\")[:3]\\n \\n if request.method == 'POST':\\n nombre = request.POST['usuario']\\n password = request.POST['password']\\n usuario = authenticate(username=nombre, password=password)\\n if usuario is not None: \\n if usuario.is_active:\\n login(request, usuario)\\n else:\\n return direct_response(request, \\\"common/inicio.html\\\",\\n {\\\"productos\\\": productos,\\n \\\"usuario_invalido\\\": usuario_invalido,\\n \\\"titulo\\\": \\\"Inicio\\\"})\\n else:\\n usuario_invalido = True\\n #Prueba de error 500\\n #x = Producto.objects.get(Id=8000)\\n return direct_response(request, \\\"common/inicio.html\\\",\\n {\\\"productos\\\": productos,\\n \\\"usuario_invalido\\\": usuario_invalido,\\n \\\"titulo\\\": \\\"Inicio\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71ba039a03a458583cb0a300dc824dea\",\n \"score\": \"0.49317253\",\n \"text\": \"def _fetch_registrations(self):\\n data = self._load_csv_data(self.REGISTRATION_CSV)\\n\\n df = data.drop([\\\"ockovaci_misto_nazev\\\", \\\"kraj_nuts_kod\\\", \\\"kraj_nazev\\\"], axis=1)\\n\\n df['rezervace'] = df['rezervace'] == '1'\\n df['datum_rezervace'] = df['datum_rezervace'].fillna('1970-01-01')\\n\\n df = df.groupby([\\\"datum\\\", \\\"ockovaci_misto_id\\\", \\\"vekova_skupina\\\", \\\"povolani\\\", \\\"stat\\\", \\\"rezervace\\\",\\n \\\"datum_rezervace\\\"]) \\\\\\n .size() \\\\\\n .reset_index(name='pocet')\\n\\n mista_result = db.session.query(OckovaciMisto.id).all()\\n mista_ids = {id for id, in mista_result}\\n missing_ids = []\\n missing_count = 0\\n missing_sum = 0\\n\\n for idx, row in df.iterrows():\\n misto_id = row['ockovaci_misto_id']\\n pocet = row['pocet']\\n\\n if misto_id not in mista_ids:\\n missing_count += 1\\n missing_sum += pocet\\n if misto_id not in missing_ids:\\n missing_ids.append(misto_id)\\n app.logger.warn(\\\"Center: '{0}' doesn't exist.\\\".format(misto_id))\\n continue\\n\\n db.session.add(OckovaniRegistrace(\\n datum=row['datum'],\\n ockovaci_misto_id=misto_id,\\n vekova_skupina=row['vekova_skupina'],\\n povolani=row['povolani'],\\n stat=row['stat'],\\n rezervace=row['rezervace'],\\n datum_rezervace=row['datum_rezervace'],\\n pocet=pocet,\\n import_=self._import\\n ))\\n\\n if missing_count > 0:\\n app.logger.warn(\\\"Some centers doesn't exist - {} rows ({} registrations) skipped.\\\".format(missing_count, missing_sum))\\n\\n app.logger.info('Fetching opendata - registrations finished.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c88d235c2de1fb6acc692f862d881bc\",\n \"score\": \"0.49164355\",\n \"text\": \"def log_user_out():\\n session.pop(\\\"user\\\")\\n session.pop(\\\"userid\\\")\\n session.pop(\\\"userrole\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae1a25fe1a7dda05ac4b3cfd27bc70cc\",\n \"score\": \"0.49163637\",\n \"text\": \"def reset(self):\\n self.__logged = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f24d3b5a3cc21d20867a9bd43a9da1f8\",\n \"score\": \"0.49063522\",\n \"text\": \"def UpdateUsers():\\n url = 'http://172.16.0.8/Services/services/Srrhh/SRecursos.asmx?WSDL'\\n wsclient = Client(url)\\n Funcionarios = wsclient.service.DatosFuncionario('01-01-1999')\\n Funcionarios = Funcionarios['Funcionarios']\\n unidad = Unidad.objects.get(id=1)\\n nickexistentes = []\\n for i in Funcionarios:\\n if i['Usuario'] is None:\\n continue\\n nickexistentes.append(formatutf8(i['Usuario']))\\n\\n for i in Funcionarios:\\n if i['Usuario'] is None:\\n i['Usuario'] = generatenick(formatutf8(\\n formatutf8(i['Nombre'])),\\n nickexistentes\\n )\\n nickexistentes.append(i['Usuario'])\\n\\n for Funcionario in Funcionarios:\\n if Funcionario['Estado'] == '1':\\n if not Funcionario['Usuario'] is None:\\n fechaMod = datetime.datetime.strptime(\\n Funcionario['FechaModificacion'],\\n '%Y/%m/%d %I:%M:%S'\\n )\\n try:\\n usuario = Usuario.objects.get(\\n username=formatutf8(Funcionario['Usuario'])\\n )\\n except Usuario.DoesNotExist:\\n usuario = None\\n\\n if usuario is None:\\n fullname = get_names(Funcionario['Nombre'])\\n usuario = Usuario.objects.create_user(\\n formatutf8(Funcionario['Usuario']),\\n Funcionario['Mail'],\\n Funcionario['Pin']\\n )\\n usuario.unidad = unidad\\n usuario.ip = Funcionario['Ip']\\n if not fullname[1] is None:\\n usuario.first_name = formatutf8(fullname[0])\\n usuario.last_name = formatutf8(fullname[1])\\n else:\\n usuario.default_name = formatutf8(fullname[0])\\n usuario.oficina = formatutf8(Funcionario['Oficina'])\\n usuario.idOficina = Funcionario['IdOficina']\\n usuario.cargo = formatutf8(Funcionario['Cargo'])\\n usuario.fotografia = Funcionario['Foto']\\n usuario.activo = True\\n fechaNac = datetime.datetime.strptime(\\n Funcionario['FechaModificacion'],\\n '%Y/%m/%d %I:%M:%S'\\n )\\n usuario.fechaNacimiento = fechaNac\\n usuario.fechaModificacion = fechaMod\\n usuario.ci = Funcionario['Documento']\\n usuario.save()\\n pinUsuario = CodigoProyecto()\\n pinUsuario.id = Funcionario['Pin']\\n pinUsuario.activo = True\\n pinUsuario.usuario = usuario\\n pinUsuario.save()\\n else:\\n if usuario.fechaModificacion < lpz.localize(fechaMod):\\n fullname = get_names(Funcionario['Nombre'])\\n usuario.unidad = unidad\\n usuario.ip = Funcionario['Ip']\\n if not fullname[1] is None:\\n usuario.first_name = formatutf8(fullname[0])\\n usuario.last_name = formatutf8(fullname[1])\\n else:\\n usuario.default_name = formatutf8(fullname[0])\\n usuario.set_password(Funcionario['Pin'])\\n usuario.oficina = formatutf8(Funcionario['Oficina'])\\n usuario.idOficina = Funcionario['IdOficina']\\n usuario.ci = Funcionario['Documento']\\n usuario.cargo = formatutf8(Funcionario['Cargo'])\\n usuario.activo = True\\n usuario.fechaModificacion = fechaMod\\n usuario.fotografia = Funcionario['Foto']\\n usuario.ci = Funcionario['Documento']\\n usuario.save()\\n pinUsuario = CodigoProyecto()\\n pinUsuario.id = Funcionario['Pin']\\n pinUsuario.activo = True\\n pinUsuario.usuario = usuario\\n pinUsuario.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c64f8aff37701f213420855fce7bcf19\",\n \"score\": \"0.49037778\",\n \"text\": \"def limpiarCampos(self):\\r\\n self.lineDni.clear()\\r\\n self.lineDni.setEnabled(True)\\r\\n self.lineNombre.clear()\\r\\n self.lineNombre.setEnabled(True)\\r\\n self.lineApellido.clear()\\r\\n self.lineApellido.setEnabled(True)\\r\\n self.lineDireccion.clear()\\r\\n self.lineTelefono.clear()\\r\\n self.tableClientes.setCurrentItem(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69504a31e5e708637e3fd91d381735db\",\n \"score\": \"0.48905292\",\n \"text\": \"def _limparAutomatoResultante(self):\\n for _ in range(self.tabelaTransicoes.rowCount()):\\n self.tabelaTransicoes.removeRow(0)\\n \\n for _ in range(self.tabelaTransicoes.columnCount()):\\n self.tabelaTransicoes.removeColumn(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"722d2e07619b40e4f7174401ca89820d\",\n \"score\": \"0.48641646\",\n \"text\": \"def login(self):\\n try:\\n self.o.refresh()\\n self.fetch_stats()\\n except:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708c6bc5a1d2f8c866394f8bcbd9c19a\",\n \"score\": \"0.48580894\",\n \"text\": \"def before_request():\\n g.user = None\\n g.user_location = None\\n if 'sub' in session:\\n g.user = db.session.query(db.User).filter_by(email=session['sub']).one_or_none()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fc23bd9e881c53df7748d3daba84921\",\n \"score\": \"0.4834209\",\n \"text\": \"def before_request():\\n if 'name' in session:\\n try:\\n g.user = User.get_by(name=session['name'])\\n except NoResultFound:\\n session['name'] = None\\n g.user = None\\n else:\\n g.user = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1a58585f697bed92d8a131500c28abe\",\n \"score\": \"0.48263207\",\n \"text\": \"def reset_access(self, request, pk):\\n student = self.get_object()\\n student.reset_token()\\n student.save()\\n student.refresh_from_db()\\n return Response(data=StudentSerializer(instance=student).data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c44c17e9fc58d55cb072e56d9a7ad804\",\n \"score\": \"0.48255506\",\n \"text\": \"def clean_sessions(self):\\n _WebServiceCore.session_ids = dict()\\n self.query_string = 'session-id=%s'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb95dd331687ff77bc778ba0401702cf\",\n \"score\": \"0.48171723\",\n \"text\": \"def sign_out(self):\\n self.name = None\\n self.email = None\\n self.password = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7585d7adcbd0b307a15adbf28327f090\",\n \"score\": \"0.48120636\",\n \"text\": \"def clean(self):\\r\\n self.authenticate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e60c4d0e21fe7ab446a0e3eb65f7806\",\n \"score\": \"0.48051268\",\n \"text\": \"def all_data(self):\\n return self.session_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c292ff0068d204dc2dffa4fe5d17e08\",\n \"score\": \"0.4803846\",\n \"text\": \"def update_authtokens(self):\\n response_data = self.rest_api.post_sessions(self.email_address, self.password)\\n self.AuthToken = response_data['sessions']['token']\\n self.refreshToken = response_data['sessions']['refresh_token']\\n self.userID = response_data['sessions']['user_id']\\n self.rest_api.headers['X-Session'] = self.AuthToken\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d0b248cee8c7ea9c36c5138bc22e63\",\n \"score\": \"0.47908464\",\n \"text\": \"def resetVisitas(self):\\n\\t\\tself.c.execute(\\\" UPDATE clienteTeru SET visitas = 0\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3ff5e16bada04170fe5afb47d7efce\",\n \"score\": \"0.47790045\",\n \"text\": \"def _limparEstados(self):\\n for _ in range(self.listaEstados.count()): self.listaEstados.removeItem(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1680d544359c465d81f40cd415345105\",\n \"score\": \"0.47741163\",\n \"text\": \"def get_all_data():\\n if session.get(SESSION_USER_ID) and session.get(SESSION_USER_LOGIN):\\n data = get_all_public_private_data(str(session.get(SESSION_USER_ID)))\\n else:\\n data = get_all_public_data()\\n\\n response = app.response_class(\\n response=data,\\n status=200,\\n mimetype='application/json'\\n )\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09905de1244183c30b3fc604ca324fa3\",\n \"score\": \"0.47666332\",\n \"text\": \"def _clear(self):\\n self._data = {\\n \\\"num_rooms\\\": None,\\n \\\"num_people\\\": None,\\n \\\"apt_cost\\\": None,\\n \\\"rooms\\\": {},\\n \\\"rooms_shared\\\": {}\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"707c740f02b408f9c4de77cdc42d21c1\",\n \"score\": \"0.47634852\",\n \"text\": \"def __init__(self):\\n self._session = requests.Session()\\n self._session.headers.update({'Content-Type': 'application/json'})\\n self._user_id = None\\n self._email = None\\n self._password = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0be435090b07aa65d39d065e15cc2c16\",\n \"score\": \"0.47499844\",\n \"text\": \"def reset_user(request):\\n if 'logged_in_user' in request.session:\\n log.info('Resetting Persona user %s'\\n % request.session['logged_in_user'])\\n del request.session['logged_in_user']\\n if 'mkt_permissions' in request.session:\\n # This isn't strictly necessary since permissions are reset on\\n # login but it's good for paranoia.\\n del request.session['mkt_permissions']\\n return http.HttpResponse('OK')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266fb14b3d65b926cb949feacc10fb82\",\n \"score\": \"0.47441292\",\n \"text\": \"def post_logout(self, came_from=url('/')):\\n condicion=int (Variables.get_valor_by_nombre(\\\"rol_anterior\\\"))\\n if condicion > 0:\\n usuario=Usuario.get_user_by_alias(\\n Variables.get_valor_by_nombre(\\\"usuario_actual\\\"))\\n\\n Variables.set_valor_by_nombre(\\\"fase_actual\\\",0)\\n Variables.set_valor_by_nombre(\\\"proyecto_actual\\\",0)\\n Variables.set_valor_by_nombre(\\\"nombre_proyecto_actual\\\",\\\"\\\")\\n Variables.set_valor_by_nombre(\\\"nombre_fase_actual\\\",\\\"\\\")\\n Variables.set_valor_by_nombre(\\\"usuario_actual\\\",\\\"\\\")\\n\\n rol=int (Variables.get_valor_by_nombre(\\\"rol_anterior\\\") )\\n Variables.set_valor_by_nombre(\\\"rol_actual\\\",0)\\n Variables.set_valor_by_nombre(\\\"rol_anterior\\\",0)\\n\\n rol2=Rol.get_rol_by_id(rol)\\n usuario.roles=[]\\n usuario.roles.append(rol2)\\n DBSession.flush()\\n\\n flash(_('Hasta luego!') )\\n redirect('/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd7065a6a982739c7512008c2a26f819\",\n \"score\": \"0.47319514\",\n \"text\": \"def buscar_encuesta_alumno(self, username):\\n #TODO: Catchear una posible excepcion\\n alumno = Usuario.query.filter_by(username=username).first()\\n if alumno is None:\\n raise Exception('El usuario ' + username + ' no existe')\\n oferta_activa = Oferta.query.filter_by(activa=True).first()\\n encuesta = Encuesta.query.filter_by(oferta_id=oferta_activa.id, alumno_id=alumno.id).first()\\n if not encuesta:\\n encuesta = self.crear_encuesta(oferta_activa, alumno)\\n return encuesta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d8ed1c2948310e4337467784551074f\",\n \"score\": \"0.47294638\",\n \"text\": \"def reset(self):\\n self.hostname = None\\n self.secret = None\\n self.expiry = None\\n self.login = None\\n self.password = None\\n self.key = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21ccdc25d6266f2d684f1178d61c2106\",\n \"score\": \"0.4728464\",\n \"text\": \"def reset_employees_data( ):\\n from setting import db\\n db.session.query ( EmployeeSchedule ).delete ( )\\n db.session.query ( Employee ).delete ( )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f45c303a5d3714729f901c51509d1618\",\n \"score\": \"0.4724931\",\n \"text\": \"def before_request():\\n g.db = connect_db()\\n g.user = None\\n if 'user_id' in session:\\n g.user = query_db('select * from user where user_id = ?',\\n [session['user_id']], one=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"487b8b9e0f31269e4f6bdc8061732aa0\",\n \"score\": \"0.47142845\",\n \"text\": \"def post(self, request):\\n email = request.data.get('email', None)\\n password = request.data.get('password', None)\\n schema = request.data.get('schema', None)\\n # try:\\n \\\"\\\"\\\"si el se reciben bien los parametros busca el usuario\\\"\\\"\\\"\\n if email and password and schema:\\n user_querysets = CustomUser.objects.get(email__iexact=email)\\n \\\"\\\"\\\"Si lo encuentra y esta activo elimna los campos sencibiles y crea el token\\\"\\\"\\\"\\n if (user_querysets.is_active):\\n user = user_querysets\\n \\\"\\\"\\\"valida que la contraseña propocionada sea correcta\\\"\\\"\\\"\\n if(check_password(password, user.password)):\\n \\\"\\\"\\\"Si la contraseña es correcta trae el usuario\\\"\\\"\\\"\\n user_token = CustomUser.objects.get(\\n email__iexact=email)\\n \\\"\\\"\\\"creamos el token de acceso ...\\\"\\\"\\\"\\n token, created = Token.objects.get_or_create(\\n user=user_token)\\n \\\"\\\"\\\"revisa el perfil del usuario\\\"\\\"\\\"\\n objserializer = UserSerializer1(user_token).data\\n \\\"\\\"\\\"Identifica el plan del tenant\\\"\\\"\\\"\\n tenant = Client.objects.get(name=schema) \\n return Response({\\\"message\\\": \\\"Login exitoso\\\",\\n \\\"data\\\": {\\n \\\"token\\\": token.key,\\n \\\"user_data\\\": objserializer,\\n \\\"plan\\\": tenant.plan.name\\n }})\\n else:\\n message = \\\"Contraseña incorrecta\\\"\\n return Response({\\\"message\\\": message, 'data': {}},\\n istatus=status.HTTP_204_NO_CONTENT)\\n else:\\n message = \\\"El id proporcionado no existe o el usuario no está activo\\\"\\n return Response({\\\"message\\\": message, 'data': {}},\\n status=status.HTTP_204_NO_CONTENT)\\n else:\\n message = \\\"No ha proporciando datos validos\\\"\\n return Response({\\\"message\\\": message, 'data': {}},\\n status=status.HTTP_203_NON_AUTHORITATIVE_INFORMATION)\\n #except:\\n # message = \\\"El id proporcionado no existe o el usuario no está activo\\\"\\n # return Response({\\\"message\\\": message, 'data': {}},\\n # status=status.HTTP_204_NO_CONTENT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708ed7be6d143d6ebd1c0a798c3269ec\",\n \"score\": \"0.47113943\",\n \"text\": \"def clean(self):\\n cleaned_data = super(LoginForm,self).clean()\\n remember_me = cleaned_data.get('remember_me')\\n if not remember_me:\\n settings.SESSION_EXPIRE_AT_BROWSER_CLOSE = True\\n else:\\n settings.SESSION_EXPIRE_AT_BROWSER_CLOSE = False\\n username = cleaned_data.get('username')\\n try:\\n User._default_manager.get(username=username)\\n except User.DoesNotExist:\\n raise forms.ValidationError('Incorrect Email or Password.')\\n return self.cleaned_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5ec95db3a926b0271729350278c98a7\",\n \"score\": \"0.47006518\",\n \"text\": \"def reset(self):\\r\\n self.data=[]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d340981ed6b44af910aa9a7d5b33baf6\",\n \"score\": \"0.46991765\",\n \"text\": \"def reset_login_attemtps(self):\\n\\t\\tself.login_attempts = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55a5e0b2b5d353ed309e07a723f23a39\",\n \"score\": \"0.46916738\",\n \"text\": \"def actualizar(self):\\r\\n self.limpiarCampos()\\r\\n self.limpiarTabla(self.tableClientes)\\r\\n self.cargarClientes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dd8febc93701d989ab38c62cbb59aed\",\n \"score\": \"0.46741456\",\n \"text\": \"def get_conn(self, headers):\\n if self.freshsales_token:\\n headers = {'Authorization': 'Token token={0}'.format(self.freshsales_token),\\n 'Content-Type': 'application/json'}\\n session = super().get_conn(headers)\\n session.auth = None\\n return session\\n return super().get_conn(headers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3508e8677b3c29e85afcdce09fea98d\",\n \"score\": \"0.46710578\",\n \"text\": \"def iniciar(self):\\n if self.iniciado == False:\\n self.montar_baralho()\\n self.monte = list(self.baralho.keys())\\n\\n\\n #DELETE ESSE FOR DEPOIS DE TESTAR\\n #for i in range(50):\\n # self.monte = self.monte + list(self.baralho.keys())\\n #self.monte = self.monte[:3]\\n\\n\\n random.shuffle(self.monte)\\n self.distribuir_cartas()\\n self.nome_jogador_atual = self.jogadores.keys()[0]\\n self.iniciado = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d8d0eef8aec6a58c6b8b48a348f7da4\",\n \"score\": \"0.46647462\",\n \"text\": \"def reset_all(self, user_id: str) -> None:\\n super().reset_all(user_id) \\n self.user_data.delete_many({'userId': user_id})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54a959290f37af2eec11a0861d632a77\",\n \"score\": \"0.46646988\",\n \"text\": \"def test_no_token(self):\\n response = self.client.get('/public')\\n data = json.loads(response.data)\\n self.assertEqual(data, {}, \\\"No session data is set\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55f373249ba302a406b27b3392f65870\",\n \"score\": \"0.46613795\",\n \"text\": \"def sessions():\\n\\n global _pma_sessions\\n return _pma_sessions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d63eb973eaf4aec594241af5cd2de51\",\n \"score\": \"0.46535254\",\n \"text\": \"async def refresh_session(self) -> dict:\\n if not self.sid:\\n raise exceptions.NotLoggedIn()\\n results_raw = await self.post(await self.jsonrpc([[1, \\\"set_session\\\", {\\\"session_id\\\": self.sid}]]))\\n return await self.pack_responses(results_raw, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4b7fc36bf9e1165e8533e64ca630235\",\n \"score\": \"0.46531108\",\n \"text\": \"def limpiar(self):\\n self._palabra = \\\"\\\"\\n self._letras = \\\"\\\"\\n self._atril_usadas = []\\n self._letra_actual = \\\"\\\"\\n self._orientacion = \\\"\\\"\\n self._casilleros_usados.clear()\\n self._casilleros_usados = set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd2c430e9cab59344867180dcefb4ee2\",\n \"score\": \"0.46472672\",\n \"text\": \"def resetConsumo(self):\\n\\t\\tself.c.execute(\\\" UPDATE clienteTeru SET consumo = 0\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0225d4cdc265bdf84a58151024f2ac37\",\n \"score\": \"0.46383467\",\n \"text\": \"def __init__(self,comandos,saltos):\\r\\n \\r\\n MataCerebros.__init__(self,comandos,saltos)\\r\\n if len(comandos) == 0:\\r\\n raise IndexError\\r\\n self.cantidad_comandos = len(comandos)\\r\\n self.mensaje_actual = \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebed40018bc9a88d859c6fc025429f2d\",\n \"score\": \"0.46356487\",\n \"text\": \"def imprime_datos(solucion):\\n nectar_local = funcion_nectar_online(solucion)\\n piezas_local = solucion.piezas_jugadas()\\n logging.info('Nectar final: ' + str(nectar_local))\\n logging.info('Piezas jugadas: ' + str(piezas_local))\\n logging.info('Filas eliminadas: ' + str(solucion.num_tetris()))\\n solucion.imprime_tablero()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"855bbfdecf264f065476d0c732d28eb4\",\n \"score\": \"0.46346357\",\n \"text\": \"def _clear(self):\\n self.stdout.write(\\\"Clearing data\\\")\\n\\n get_user_model().objects.all().delete()\\n Manager.objects.all().delete()\\n Member.objects.all().delete()\\n Shift.objects.all().delete()\\n Priority.objects.all().delete()\\n Status.objects.all().delete()\\n Group.objects.all().delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b788ec5d47f19e147550d760b07faa7\",\n \"score\": \"0.4633331\",\n \"text\": \"def get_session_data(self, session_id):\\n\\n cur = self.conn.cursor()\\n cur.execute(\\\"SELECT * FROM sessions WHERE session_id = ?\\\", (session_id,))\\n sess_rows = cur.fetchall()\\n\\n cur = self.conn.cursor()\\n cur.execute(\\\"SELECT * FROM users WHERE in_session = ?\\\", (session_id,))\\n user_rows = cur.fetchall()\\n \\n if not sess_rows or len(sess_rows) != 1:\\n return None\\n \\n sess_row = sess_rows[0]\\n sess_data = {\\n \\\"session_id\\\": session_id,\\n \\\"password\\\": sess_row[1],\\n \\\"creator_id\\\": sess_row[2],\\n \\\"users\\\": []\\n }\\n\\n for row in user_rows:\\n sess_data[\\\"users\\\"].append({\\n \\\"user_id\\\": row[0],\\n \\\"display_name\\\": row[2],\\n \\\"initials\\\": row[3],\\n \\\"is_examiner\\\": bool(row[5]),\\n })\\n\\n return sess_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f43d48b285ca3857bea404e10c2e8eed\",\n \"score\": \"0.46306574\",\n \"text\": \"def test_sessions_get_all_no_sessions(sessions_api_client, mock_session_manager):\\n mock_session_manager.get.return_value = []\\n\\n response = sessions_api_client.get(\\\"/sessions\\\")\\n assert response.json() == {\\\"data\\\": [], \\\"links\\\": None}\\n assert response.status_code == 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aabfd82b98140fb86f82e9fd7df5defd\",\n \"score\": \"0.462928\",\n \"text\": \"def refresh_userdata(self):\\n url = \\\"users/me\\\"\\n resp = self._session.get(url)\\n resp_json = resp.json()\\n try:\\n USER_SCHEMA(resp_json)\\n self._userdata = resp_json\\n except MultipleInvalid as ex:\\n _LOGGER.warning(\\\"Invalid response from %s: %s. Got: %s\\\", url, ex, resp_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e38f3ff9c5fb80d6c3a6c10803eb114\",\n \"score\": \"0.46277547\",\n \"text\": \"def public(): # type: ignore\\n data = request.environ.get('session')\\n if data:\\n if isinstance(data, Exception):\\n raise data\\n return json.dumps(domain.to_dict(data))\\n return json.dumps({})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c5f0d3fd84e5524e1aa62751ace22f9\",\n \"score\": \"0.4622105\",\n \"text\": \"def traer_datos(self):\\n self.rows = self.cursor.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c5f0d3fd84e5524e1aa62751ace22f9\",\n \"score\": \"0.4622105\",\n \"text\": \"def traer_datos(self):\\n self.rows = self.cursor.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1983beab36bd06e68e3c1b46c5b35d1\",\n \"score\": \"0.4613787\",\n \"text\": \"def session(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa8066fd2191f3fb883ba176a9de0ea0\",\n \"score\": \"0.46121228\",\n \"text\": \"def _obtener_diccionario_de_atributos_usuario(self):\\n diccionario = {\\n 'id_usuario': self.id_usuario, 'calidad_imagen': self.calidad_imagen.value,\\n 'es_original': self.es_original, 'formato': self.formato.value, 'ruta': self.ruta, 'hash256': self.hash256,\\n 'alto': self.alto, 'ancho': self.ancho\\n }\\n return diccionario\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fc083c1e7f1ecf5252ff79db956c452\",\n \"score\": \"0.46078888\",\n \"text\": \"def initData(self):\\n if connectionDBPostgres():\\n self.chargerTable()\\n self.chargerObjetK()\\n if connectionGeocodeur(\\\"Nominatim\\\"):print(\\\"connection geolocolisation\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51228da7bf8c7a604ad7849e3ee8b0c3\",\n \"score\": \"0.4602367\",\n \"text\": \"def test_sessions_get_all_no_sessions(\\n sessions_api_client,\\n mock_session_manager):\\n mock_session_manager.get.return_value = []\\n\\n response = sessions_api_client.get(\\\"/sessions\\\")\\n assert response.json() == {\\n 'data': [], 'links': None\\n }\\n assert response.status_code == 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"286984c96170682d7b64b582a92a4301\",\n \"score\": \"0.45941457\",\n \"text\": \"def removeAll(self):\\n self.__studs = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"402b87560da9d831117aba8ea1fe165b\",\n \"score\": \"0.4588744\",\n \"text\": \"def reiniciar_partida(self):\\n self.controlador.resetear_nivel(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84d4327388ad005f88d5ebde5e667295\",\n \"score\": \"0.4587085\",\n \"text\": \"def clearData(self):\\n self.data = {}\\n self.data['id'] = self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"425c5f4b563c9d6b746564f2957bc027\",\n \"score\": \"0.45798075\",\n \"text\": \"def initialize_session_obj():\\n session = {}\\n session[\\\"startdate\\\"] = '00/00/00 00:00:00'\\n session[\\\"enddate\\\"] = '00/00/00 00:00:00'\\n session[\\\"account\\\"] = 'unknown'\\n\\n return session\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ba3ea04944c0249fb6123b841bcd7b5\",\n \"score\": \"0.45709795\",\n \"text\": \"def carga_datos_firma(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b14c5505d505f1ef1e102d1ea070f879\",\n \"score\": \"0.45701495\",\n \"text\": \"def duo_unauthenticate(request):\\n try:\\n del request.session['duo_authenticated']\\n except KeyError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c38becc69b14ca58507306275509cdfb\",\n \"score\": \"0.45602244\",\n \"text\": \"def reset(self):\\n self.disconnect()\\n self.__cached_auth = {}\\n\\n for key, value in self.__prepreparation_values.items():\\n setattr(self, key, value)\\n self.__prepreparation_values = {}\\n self.__prepared = False\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0580e362605341465b1bc761d22195b9\",\n \"score\": \"0.4549385\",\n \"text\": \"def get_all_users(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e08688936820df5c89d431537cdf14\",\n \"score\": \"0.45394206\",\n \"text\": \"def get(self):\\n # Set User's status to offline\\n self.user.online = False\\n self.user.put()\\n\\n # Delete the Token for this session\\n self.token.key.delete()\\n\\n self.return_success()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b867e57afc9f5835e554d1fa1c27443a\",\n \"score\": \"0.45353192\",\n \"text\": \"def reset_alt_auth_data(self):\\n self.alt_part = None\\n self.alt_auth_data = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88809b19863ee2f5d6dc036baa84c06e\",\n \"score\": \"0.45292777\",\n \"text\": \"def logoff(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"949392b2673dcaa9e0fede6cdab21318\",\n \"score\": \"0.45175472\",\n \"text\": \"def flush(self):\\n self.request.session[self.SESSION_KEY] = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e432ca2aa598f8d7d8bcfdc27044191\",\n \"score\": \"0.45169818\",\n \"text\": \"def __init__(self):\\n self.users = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18b6d0f2ca352e66c7ab01c6440eeff5\",\n \"score\": \"0.451381\",\n \"text\": \"def getUsers():\\n\\tif 'username' in session and session['username'] == 'admin':\\n\\t\\t#we send out all users to the admin\\n\\t\\tsqlquery = Users.query.all()\\n\\t\\treturn jsonify([i.serialize for i in sqlquery])\\n\\treturn jsonify(error = 'not privileged'), 401\",\n \"title\": \"\"\n },\n {\n \"docid\": \"487eb85bd3ada981109b2f4ac969aa8d\",\n \"score\": \"0.45058164\",\n \"text\": \"def logout(self):\\n self.api_v = 0\\n self.esm_v = \\\"0\\\"\\n self.request(\\\"logout\\\", http=\\\"delete\\\")\\n self.logged_in = False\\n self.login_info = dict()\\n self.session = requests.Session()\\n self.user_tz_id = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe1b22e65de27eb780a442a11ba42401\",\n \"score\": \"0.45050862\",\n \"text\": \"def _reset_cached_attributes(self):\\n self._seas, self._veas = (None, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe1b22e65de27eb780a442a11ba42401\",\n \"score\": \"0.45050862\",\n \"text\": \"def _reset_cached_attributes(self):\\n self._seas, self._veas = (None, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9beec4292379b4f5e15de30ffa8fc3ae\",\n \"score\": \"0.4504632\",\n \"text\": \"def get_all_clients_noauthz(self, manager, context, conn=None, cur=None):\\n def db_body(conn, cur):\\n return {\\n row.get(ID)\\n for row in force_query(\\n conn, cur,\\n \\\"\\\"\\\"\\nSELECT %(username)s FROM %(utable)s ;\\n\\\"\\\"\\\" % {\\n 'utable': self.provider._table(self.provider.client_storage_name),\\n 'username': ID,\\n}\\n )\\n }\\n\\n if conn is not None and cur is not None:\\n return db_body(conn, cur)\\n else:\\n return self.provider._db_wrapper(db_body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfd0a0a82877d99d6fb8b11658b4f58f\",\n \"score\": \"0.45022002\",\n \"text\": \"def set_session(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afa81640ff9316acf4c9366524bde83a\",\n \"score\": \"0.4502113\",\n \"text\": \"def clear_user_id_tokens(self):\\n self._msl_data.pop('user_id_tokens', None)\\n self._save_msl_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e7d0a924b3fe70a3c9a76eea3fe8a60\",\n \"score\": \"0.4499268\",\n \"text\": \"def reset(request):\\n assert isinstance(request, HttpRequest)\\n print('inside reset...............................................')\\n cache.clear()\\n #if request.session.has_key('module'):\\n # del request.session['module']\\n # del request.session['pagenum']\\n # del request.session['sessionstarted']\\n # del request.session['ques']\\n # del request.session['candidate']\\n # del request.session['questioncode']\\n # del request.session['qkey']\\n # del request.session['matricno']\\n # del request.session['tattempt']\\n # del request.session['tresult']\\n # print('session deleted.........................')\\n #request.session.flush()\\n #logout(request)\\n return HttpResponseRedirect('/logout')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21792e77ddb6769f11f53928adda5406\",\n \"score\": \"0.44986385\",\n \"text\": \"def tearDown(self):\\n\\n Users.user_list = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1dad839d181cca07fdaa71044ce15ca\",\n \"score\": \"0.44961223\",\n \"text\": \"def index(self):\\n nom_proyecto=Variables.get_valor_by_nombre(\\\"nombre_proyecto_actual\\\")\\n nom_fase=Variables.get_valor_by_nombre(\\\"nombre_fase_actual\\\")\\n\\n if not request.identity:\\n redirect(url('/login', came_from=url('/')))\\n return dict(pagina='index',nom_proyecto=nom_proyecto,nom_fase=nom_fase)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf07ddfed1b2c21d7154adb3aba7dcfc\",\n \"score\": \"0.44929296\",\n \"text\": \"def tearDown(self):\\n User.user_names=[]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b186daa522524dba83939232560d157\",\n \"score\": \"0.44889772\",\n \"text\": \"def logout(username):\\n #Get user\\n user = session.query(Usuario).filter(Usuario.username == username).one()\\n #Update status of user\\n user.conectado = False\\n user.disponibilidad = False\\n session.add(user)\\n session.commit()\\n return redirect(url_for('index'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e8ff84813053d026d0b648f323ef2aa\",\n \"score\": \"0.44868487\",\n \"text\": \"def logout(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e8ff84813053d026d0b648f323ef2aa\",\n \"score\": \"0.44868487\",\n \"text\": \"def logout(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e8ff84813053d026d0b648f323ef2aa\",\n \"score\": \"0.44868487\",\n \"text\": \"def logout(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdab412148a36f7133664caf0308ea78\",\n \"score\": \"0.44858292\",\n \"text\": \"def clear_user_id_tokens(self):\\n self.msl_requests.crypto.clear_user_id_tokens()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb17e73a71fb7656e995ed01de82901\",\n \"score\": \"0.4482307\",\n \"text\": \"def clear_current_user(self):\\n\\n\\t\\t# if (self.get_argument(\\\"logout\\\", None)):\\n\\t\\tself.clear_cookie(\\\"user_name\\\")\\n\\t\\tself.clear_cookie(\\\"user_email\\\")\\n\\t\\tself.clear_cookie(\\\"user_is_connected\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59642c3b2cebd0eb13ffd094c5c20316\",\n \"score\": \"0.44794044\",\n \"text\": \"def _refresh(self):\\n self._loadjson(self._users_fname, self._users)\\n self._loadjson(self._roles_fname, self._roles)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3e5da62e7724c034af0b4fac347189d\",\n \"score\": \"0.44751304\",\n \"text\": \"def es_reset(cls):\\n\\n cls.es_delete()\\n cls.es_create()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3778375b3ea8b6d70cf8ccc5271cc22c\",\n \"score\": \"0.44703662\",\n \"text\": \"def before_request():\\r\\n g.user = None\\r\\n if 'user' in session:\\r\\n g.user = session['user']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b2f934317244f145f4129140d27d64a\",\n \"score\": \"0.44680202\",\n \"text\": \"def clear_auth(self):\\n self.cache = None\\n self.credentials.reset()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5c76c006c57b9ba17ba964ad7e697c\",\n \"score\": \"0.44661942\",\n \"text\": \"def create_session(data, config):\\n data.sort_values(['user_id', 'timestamp'], ascending=True, inplace=True)\\n data['timestamp'] = pd.to_datetime(data['timestamp'])\\n data.reset_index(inplace = True, drop = True)\\n data['Session'] = ''\\n new_data = pd.DataFrame(columns = data.columns)\\n users = list(data.user_id)\\n users = list(dict.fromkeys(users))\\n for index, user in enumerate(users):\\n sess_idx = 1\\n sub_data = pd.DataFrame(data[data['user_id'] == user])\\n prev_time = 0\\n sessions = []\\n for i, time in enumerate(sub_data['timestamp']):\\n if prev_time == 0:\\n prev_time = time\\n elif (time - prev_time).total_seconds() < config.time_interval :\\n prev_time = time\\n else:\\n sess_idx +=1\\n prev_time = time\\n sessions.append('U' + str(index) + 'S' + str(sess_idx))\\n # sub_data.loc[i, 'Session'] = 'U' + str(index) + 'S' + str(sess_idx)\\n sub_data['Session'] = sessions\\n new_data = pd.concat([new_data, sub_data])\\n del sub_data\\n # data.loc[data.user_id == user, 'Session'] = sub_data.Session\\n new_data.dropna(inplace = True)\\n new_data.reset_index(inplace = True, drop = True)\\n assert len(new_data) == len(data)\\n return new_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"260e797800ca33d8ab3e124fce928314\",\n \"score\": \"0.44626722\",\n \"text\": \"def getDataAlineaciones(self):\\n\\t\\tdicAlineaciones = []\\n\\t\\tdicTweets = {}\\n\\t\\t\\n\\t\\t# primer paso, se buscan los tweets que anuncian los posibles 11\\n\\t\\tself.l.log('getAlineacion.getDataAlineaciones', ' -------------- Proceso Iniciado -------------')\\n\\t\\ttry:\\n\\t\\t\\tdicTweets = self.getTwitterData()\\n\\t\\texcept Exception:\\n\\t\\t\\tself.l.error('getAlineacion.getDataAlineaciones', 'Error al recuperar datos de Twitter: ')\\n\\t\\t\\treturn 1\\n\\t\\tself.l.log('getAlineacion.getDataAlineaciones', 'Tweets recuperados:' + str(len(dicTweets)))\\n\\t\\t\\n\\t\\t# se realiza web scrapping sobre las paginas de los equipos con posibles 11 actualizados\\n\\t\\tfor k, v in dicTweets.items():\\n\\t\\t\\tif len(k) > 0 and len(v) > 0:\\n\\t\\t\\t\\tself.l.debug(\\\"getAlineacion.getDataAlineaciones: Procesando web del equipo: \\\" + str(k) + str(v))\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tdiccTemp = self.getWebData(k, v)\\n\\t\\t\\t\\texcept Exception:\\n\\t\\t\\t\\t\\tself.l.error('getAlineacion.getDataAlineaciones', 'Error al recuperar datos de la web: ')\\n\\t\\t\\t\\t\\treturn 2\\n\\t\\t\\t\\tdicAlineaciones.append(diccTemp)\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t# por ultimo, se guardan los resultados a base de datos Mongo \\\"alineaciones\\\"\\n\\t\\tfor alineacion in dicAlineaciones:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tself.dbWriter.write_dictionaries_list('colAlineaciones', alineacion)\\n\\t\\t\\texcept pymongo.errors.BulkWriteError as e:\\n\\t\\t\\t\\tself.l.error('getAlineacion.getDataAlineaciones', 'Error al escribir a BD: ' + e)\\n\\t\\t\\t\\treturn 3\\n\\t\\t\\n\\t\\tself.l.debug('getAlineacion.getDataAlineaciones: Alineacion:' + str(dicAlineaciones))\\n\\t\\tself.l.log('getAlineacion.getDataAlineaciones', ' -------------- Proceso Finalizado -------------')\\n\\t\\t\\n\\t\\treturn 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4610c9444287925b732f3cadc33fcf19\",\n \"score\": \"0.44597074\",\n \"text\": \"def handle_request(request):\\n session['user_id'] = None\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993877,"cells":{"query_id":{"kind":"string","value":"b2e20e834c0b1bdc1c7eec70f0d5ef3c"},"query":{"kind":"string","value":"This function uses the created my_max function and compares it to pythons max function. It creates a list of random variables and uses both functions."},"positive_passages":{"kind":"list like","value":[{"docid":"a44048cd3e2ccfbbf6aba8cea79bc4f6","score":"0.7396767","text":"def test_max():\n random_vals = []\n\n # generating 50 random values and appending them to a list\n for i in range(50):\n random_vals.append(random.randrange(0, 1000, 1))\n\n # using both max functions to find the max\n max_val = my_max(random_vals)\n py_max = max(random_vals)\n\n assert max_val == py_max, 'Failed on {} - got {}, expected {}'.format(random_vals, max_val, py_max)\n assert my_max([]) is None, 'Failed on {} - got {}, expected {}'.format([], my_max([]), None)\n assert my_max([1]) == 1, 'Failed on {} - got {}, expected {}'.format([1], my_max([1]), 1)","title":""}],"string":"[\n {\n \"docid\": \"a44048cd3e2ccfbbf6aba8cea79bc4f6\",\n \"score\": \"0.7396767\",\n \"text\": \"def test_max():\\n random_vals = []\\n\\n # generating 50 random values and appending them to a list\\n for i in range(50):\\n random_vals.append(random.randrange(0, 1000, 1))\\n\\n # using both max functions to find the max\\n max_val = my_max(random_vals)\\n py_max = max(random_vals)\\n\\n assert max_val == py_max, 'Failed on {} - got {}, expected {}'.format(random_vals, max_val, py_max)\\n assert my_max([]) is None, 'Failed on {} - got {}, expected {}'.format([], my_max([]), None)\\n assert my_max([1]) == 1, 'Failed on {} - got {}, expected {}'.format([1], my_max([1]), 1)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"39e9da22cbeb6af7dcd2479aebb8025a","score":"0.67297995","text":"def test_max_vals(self):\n self.report('Testing for Maximum value. ')\n for test_fn_name in self.synthetic_functions:\n caller = get_syn_function_caller_from_name(test_fn_name)\n self.report('Testing %s with max value %0.4f.'%(test_fn_name, caller.opt_val),\n 'test_result')\n X = np.random.random((self.num_samples, caller.domain_dim))\n eval_vals, _ = caller.eval_multiple(X)\n assert eval_vals.max() <= caller.opt_val\n if caller.opt_pt_normalised is not None:\n max_val_norm = caller.eval_single(caller.opt_pt_normalised, normalised=True)\n max_val_unnorm = caller.eval_single(caller.opt_pt_unnormalised, normalised=False)\n assert np.abs(max_val_norm[0] - caller.opt_val) < 1e-5\n assert np.abs(max_val_unnorm[0] - caller.opt_val) < 1e-5","title":""},{"docid":"84fd57a3b2bf95a51830c1728ef66579","score":"0.6709496","text":"def mymax(x,y):\n return listmax([x,y])","title":""},{"docid":"e3b8ce422e3b3587b29db9dc58aaa06e","score":"0.6458269","text":"def my_max(*args, **kwargs):\n key = kwargs.get(\"key\", lambda x: x)\n dta = args[0] if len(args) == 1 else args[:]\n temp_max = None\n for i in dta:\n if temp_max == None:\n temp_max = i\n if key(i) > key(temp_max):\n temp_max = i\n return temp_max","title":""},{"docid":"4110719b1355982f7bc2e3ca8a93d415","score":"0.63670045","text":"def strategy(hand, num_die_sides):\r\n max_hand = tuple()\r\n max_exp = 0\r\n holds_set = gen_all_holds(hand)\r\n \r\n for dummy_i in holds_set:\r\n tmp_exp = expected_value(dummy_i, num_die_sides, len(hand)-len(dummy_i))\r\n if max_exp < tmp_exp:\r\n print(\"max\")\r\n max_exp = tmp_exp\r\n max_hand = dummy_i\r\n print(max_exp, max_hand)\r\n else:\r\n print(\"tmp\")\r\n print(tmp_exp, dummy_i)\r\n return (max_exp, max_hand)","title":""},{"docid":"1e1e4eda0665695220fa2d4bc26c75f7","score":"0.62175333","text":"def argmaxe(gen):\n maxv = -math.inf # negative infinity\n maxvals = [] # list of maximal elements\n for (e,v) in gen:\n if v>maxv:\n maxvals,maxv = [e], v\n elif v==maxv:\n maxvals.append(e)\n return random.choice(maxvals)","title":""},{"docid":"b5ece9b7f5352aa0cf25d9a5c2da5dc1","score":"0.6183401","text":"def get_max():\n\n values = [12, 32, 1, 73, 25, 68, 83, 29, 55, 61, 100, 97, 2]\n\n return f.reduce(lambda x, y: x if (x > y) else y, values)","title":""},{"docid":"1b6dc0a45cbb03782ce95555ac5188bf","score":"0.6128468","text":"def maximum(self):","title":""},{"docid":"313085e9afbed55d0805edb0b87f733c","score":"0.6123956","text":"def is_max():","title":""},{"docid":"b7571415e79a22971147003e6abfa8b8","score":"0.6108703","text":"def __get_max_rand(self, da_max: np.ndarray, xy: np.ndarray):\n\n DEF_VAL = 20\n\n if self.__select_method is self._SelectMethod.DESINTY:\n # In Stamps min and max values are in separate arrays with a single element.\n patch_area = np.prod(MatlabUtils.max(xy) - MatlabUtils.min(xy)) / 1e6 # In km\n max_rand = DEF_VAL * patch_area / (len(da_max) -1)\n else:\n max_rand = DEF_VAL\n\n return max_rand","title":""},{"docid":"5c8d1d75b66b3d01881ba3d5038f6ed9","score":"0.6107316","text":"def f_max(*items):\n\n return max(*items)","title":""},{"docid":"50829834b7d27fd68eb533e9764af8f5","score":"0.6069354","text":"def calc_max_var(self, varname='depth_offset'):\r\n maxs = np.array([])\r\n for sec in self.fqpr:\r\n maxs = np.append(maxs, float(self.fqpr[sec][varname].max()))\r\n return maxs.max()","title":""},{"docid":"ebeab236d42c8d1f154967a8e7937bb6","score":"0.60662776","text":"def test_max(self):\n self.assertEqual(max_integer([4, 6, 3, 5, 78, 4, 3, 4, 1, 9]), 78)","title":""},{"docid":"2d3c676560fb94e9e5141fa7e9498497","score":"0.60483146","text":"def largest(min_factor, max_factor):\n\n return main_func(min_factor, max_factor, False)","title":""},{"docid":"39e4ad2da6c9c91c7f2a563c1db79b59","score":"0.6035652","text":"def find_max(values):\n return max(values)","title":""},{"docid":"f2cb7c94399bcf93a9c2639ce04adc27","score":"0.6024111","text":"def max_product_v2(self, numbers):\n curr_min = numbers[0]\n curr_max = numbers[0]\n global_max = numbers[0]\n\n for i in range(1, len(numbers)):\n temp = curr_max\n print(f\"curr_max: {curr_max}, curr_min: {curr_min}\")\n print(f\"cur_max * num[i] -> {curr_max*numbers[i]}\")\n print(f\"curr_min({curr_min}) * numbers[i] -> {curr_min*numbers[i]}\")\n curr_max = max(max(curr_max*numbers[i], curr_min*numbers[i]), numbers[i])\n curr_min = min(min(temp*numbers[i], curr_min*numbers[i]), numbers[i])\n print(f\"curr_max: {curr_max}, curr_min: {curr_min}\")\n global_max = max(curr_max, global_max)\n\n return global_max","title":""},{"docid":"5d728f9b0c28d988f68fbbbe3b1c08e6","score":"0.6001151","text":"def get_max(var1: int, var2: int, var3: int) -> int:\n max_var = var2\n if max_var < var1:\n max_var = var1\n\n if max_var < var3:\n max_var = var3\n\n print(\"max number is: {max}\".format(max=max_var))\n return max_var","title":""},{"docid":"f5a26c18a93f8117e46746c72d4cedbf","score":"0.5982832","text":"def maximum(a, b):\n if is_variable(a) or is_variable(b):\n return T.maximum(a, b)\n else:\n return np.maximum(a, b)","title":""},{"docid":"828b25b766ced8ad68a593acd87e3be7","score":"0.5958302","text":"def MyMax(objects, key):\n if not objects:\n raise ValueError(\"max() arg is empty\")\n maximum = objects[0]\n maximumValue = key(maximum)\n for value in objects[1:]:\n thisValue = key(value)\n if thisValue > maximumValue:\n maximumValue = thisValue\n maximum = value\n return maximum","title":""},{"docid":"fb486a67e9520d02baec70e781637a73","score":"0.59176266","text":"def test_max_2() -> None:\n assert max([1, 5, 2]) == 5","title":""},{"docid":"9a3fde3cc3db65c12d5c17c0acd91174","score":"0.5871306","text":"def arg_max(l):\n assert isinstance(l, list) or isinstance(l, np.ndarray)\n return rand.choice(np.flatnonzero(l == np.max(l)))","title":""},{"docid":"beb9ddac51f759dc38dc10d9ba47b4a1","score":"0.5868842","text":"def max(self, varName):\n v = self.values(varName)[1]\n return max(v)","title":""},{"docid":"2ecd679027ba763a84304e1553f4a60b","score":"0.58666897","text":"def test_maximum_multiple(self):\n\n allure.dynamic.title(\"Testing max_multiple function\")\n allure.dynamic.severity(allure.severity_level.NORMAL)\n allure.dynamic.description_html('<h3>Codewars badge:</h3>'\n '<img src=\"https://www.codewars.com/users/myFirstCode'\n '/badges/large\">'\n '<h3>Test Description:</h3>'\n \"<p></p>\")\n\n with allure.step(\"Enter divisor, bound and verify the output\"):\n\n data = [\n (2, 7, 6),\n (3, 10, 9),\n (7, 17, 14),\n (10, 50, 50),\n (37, 200, 185),\n (7, 100, 98),\n (37, 100, 74),\n (1, 13, 13),\n (1, 1, 1),\n (22, 9, 0),\n (43, 7, 0),\n (50, 7, 0)\n ]\n\n for divisor, bound, expected in data:\n\n print_log(divisor=divisor,\n bound=bound,\n expected=expected)\n\n self.assertEqual(expected,\n max_multiple(divisor,\n bound))","title":""},{"docid":"d8d1c68289cc77d23cab49d25598f585","score":"0.5805065","text":"def test_min():\n random_vals = []\n\n # generating 50 random values and appending them to a list\n for i in range(50):\n random_vals.append(random.randrange(0, 1000, 1))\n\n # using both max functions to find the max\n min_val = my_min(random_vals)\n py_min = min(random_vals)\n\n assert min_val == py_min, 'Failed on {} - got {}, expected {}'.format(random_vals, min_val, py_min)\n assert my_min([]) is None, 'Failed on {} - got {}, expected {}'.format([], my_min([]), None)\n assert my_min([1]) == 1, 'Failed on {} - got {}, expected {}'.format([1], my_min([1]), 1)","title":""},{"docid":"915ee525ce29cb027655c448afa72d83","score":"0.5802563","text":"def test_max_1() -> None:\n assert max([110]) == 110","title":""},{"docid":"26bd310e901725aee9066bcb8f00272e","score":"0.5797265","text":"def flawed(A):\n my_max = 0\n for v in A:\n if my_max < v:\n my_max = v\n return my_max","title":""},{"docid":"bf8564e07aa9fe2b140cc7dc51b6ff69","score":"0.5793827","text":"def _get_max_(self):\n return self.__max_","title":""},{"docid":"bf8564e07aa9fe2b140cc7dc51b6ff69","score":"0.5793827","text":"def _get_max_(self):\n return self.__max_","title":""},{"docid":"bf8564e07aa9fe2b140cc7dc51b6ff69","score":"0.5793827","text":"def _get_max_(self):\n return self.__max_","title":""},{"docid":"bf8564e07aa9fe2b140cc7dc51b6ff69","score":"0.5793827","text":"def _get_max_(self):\n return self.__max_","title":""},{"docid":"bf8564e07aa9fe2b140cc7dc51b6ff69","score":"0.5793827","text":"def _get_max_(self):\n return self.__max_","title":""},{"docid":"bf8564e07aa9fe2b140cc7dc51b6ff69","score":"0.5793827","text":"def _get_max_(self):\n return self.__max_","title":""},{"docid":"bf8564e07aa9fe2b140cc7dc51b6ff69","score":"0.5793827","text":"def _get_max_(self):\n return self.__max_","title":""},{"docid":"35f83c9e1bbccff0f628fad6f420e6f9","score":"0.57915676","text":"def personal_best(scores: List[int]) -> int:\n return max(scores)","title":""},{"docid":"0f47ac11de3293efd63dda9a2a2fb10b","score":"0.5791464","text":"def _maxscore(self):\n total = 0\n lowtot= 0\n for lli in self.ll:\n total = total + max(lli.values())\n lowtot= lowtot+ min(lli.values())\n self.maxscore = total\n self.minscore = lowtot","title":""},{"docid":"f5a92867a5318440d55cf500023a6fa2","score":"0.5783892","text":"def get_max_value(self, inputs_list):\n\n # Call the function to get a final output array of the neural network\n outputs = self.get_outputs(inputs_list)\n\n # Get the highest value from the array and return this\n return np.max(outputs)","title":""},{"docid":"730d2c1c786fbe657040f6e4d02e00c8","score":"0.5780476","text":"def max_values(self) -> int:","title":""},{"docid":"79918fbd56a8c6df08c1c58da101a65b","score":"0.5761609","text":"def my_max(the_list):\n list_length = len(the_list)\n\n if list_length <= 0:\n return None\n else:\n current_max = the_list[0]\n\n i = 0\n # loop through the entire list\n while i < list_length:\n # check if the next value if higher then the previous if so, make it the new max\n if current_max < the_list[i]:\n current_max = the_list[i]\n i = i + 1\n\n return current_max","title":""},{"docid":"936cebf81687c9e1702b5b2fbb601c7d","score":"0.5744667","text":"def randmax(action_values):\n maxes = np.flatnonzero(action_values == np.max(action_values))\n action = np.random.choice(maxes)\n\n return(action)","title":""},{"docid":"1f1356085adbafb05377a3f7ae0ffbf5","score":"0.57358843","text":"def find_schaffer_max_min():\n _max = -float(\"inf\")\n _min = +float(\"inf\")\n for i in range(10000):\n e = find_max_f1_f2(random.randint(-100000, 100000))\n _max = e if e > _max else _max\n _min = e if e < _min else _min\n return _max, _min","title":""},{"docid":"0c1072a4a29064b7a250bc5a0a3d180b","score":"0.5709178","text":"def max_value(numbers):\n pass\n max = 0\n for numner in numbers:\n if numner > 0:\n max = numner\n #meganogar sneganogar","title":""},{"docid":"b0966bcb3bc7a2b2ac96f9aef94048ea","score":"0.5709012","text":"def random_argmax(v):\n arguments = np.argwhere(v == np.amax(v)).ravel()\n return np.random.choice(arguments)","title":""},{"docid":"560d85bbe82426b98e5ebb29b3be8e0c","score":"0.57076514","text":"def maximum(inputs, **kwargs):\n return Maximum(**kwargs)(inputs)","title":""},{"docid":"95e5eebdb65732cda8d5d4ad1b549f58","score":"0.5704856","text":"def max(tab):\n global comparaisons\n # le max \"temporaire\" est initialisé à la première valeur du tableau\n max = tab[0]\n # on compare chaque élément du tableau à max, s'il est plus grand on met à jour max\n for x in tab:\n if x > max:\n max = x\n return max","title":""},{"docid":"e6e07c712a177e2de3d2dc9e4e69f2ca","score":"0.56916463","text":"def get_max(a, b):\n if a > b: return a\n else: return b","title":""},{"docid":"1f70665ece00761ed0eb88a10b6cdd17","score":"0.5674148","text":"def get_max_lift_updates(self):\n names = self.determine_muscle_group(question_text='Which max values would you like to update?')\n max_lift_names = list()\n if 'bench_press' in names:\n max_lift_names.append('bench_press_max')\n if 'squat' in names:\n max_lift_names.append('squat_max')\n if 'shoulder_press' in names:\n max_lift_names.append('shoulder_press_max')\n if 'deadlift' in names:\n max_lift_names.append('deadlift_max')\n max_lift_values = []\n for row in max_lift_names:\n while True:\n max_text = input((\"New \" + row + \"value:\\n\").replace(\"_\", \" \"))\n try:\n max_update = int(max_text)\n max_lift_values.append(max_update)\n break\n except ValueError:\n print('Invalid literal, please enter a number.')\n return max_lift_values, max_lift_names","title":""},{"docid":"f029b9017be1a7e29c2992f00b7ca488","score":"0.56691533","text":"def maximum(value1, value2, value3):\n max_value = value1\n if value2 > max_value:\n max_value = value2\n if value3 > max_value:\n max_value = value3\n return max_value","title":""},{"docid":"5c9dd5689ee569a5ded5fcf188ead345","score":"0.5663374","text":"def my_max(*args):\n iterated_list = args\n if not args:\n raise ValueError(\"Please enter some numbers\")\n if len(args) == 1:\n iterated_list = args[0]\n if not isinstance(iterated_list, (tuple, list)):\n raise ValueError(\"There is only one value {}\"\n \", and it isn't tuple or list\".format(args[0]))\n current_max = iterated_list[0]\n for num in iterated_list:\n if not isinstance(num, int):\n raise ValueError(\"Input value {}\"\n \" is not integer\".format(num))\n if num > current_max:\n current_max = num\n return current_max","title":""},{"docid":"593603fad3262865fd82bf39da353e44","score":"0.56525695","text":"def max_by(lst, fn):\n\n return max(map(fn, lst))","title":""},{"docid":"3381bafab29a5015f3b262e46eb49035","score":"0.5644715","text":"def test_dice_max_of() -> None:\n dice = Dice(\"d20\")\n assert dice.rolls == 0\n assert not dice.bonuses\n\n the_roll, rolls = dice.max_of(3)\n assert dice.rolls == 3\n assert len(rolls) == 3\n assert 1 <= rolls[0] <= 20\n assert 1 <= rolls[1] <= 20\n assert 1 <= rolls[2] <= 20\n assert the_roll == max(rolls)","title":""},{"docid":"99ee00aa6f3e9fa4c3369a40f55b62b2","score":"0.5644054","text":"def main():\n print(largest_number(items=[3, 30, 34, 5, 9, ]))\n print(largest_number(items=[54, 546, 548, 60, ]))","title":""},{"docid":"5610d6d766c22fc3a68c2ef3d0dbe3ae","score":"0.5642068","text":"def minmax ( self , nshoots = 50000 ) :\n ## try to get minmax directly from pdf/function \n if self.tricks and hasattr ( self.pdf , 'function' ) :\n if hasattr ( self.pdf , 'setPars' ) : self.pdf.setPars() \n f = self.pdf.function()\n if hasattr ( f , 'minmax' ) :\n try :\n mn , mx = f.minmax()\n if 0<= mn and mn <= mx and 0 < mx : \n return mn , mx\n except :\n pass\n if hasattr ( f , 'max' ) :\n try :\n mx = f.max()\n if 0 < mx : return 0 , mx\n except :\n pass\n if hasattr ( f , 'mode' ) :\n try :\n mode = f.mode()\n if mode in self.xvar :\n mx = f ( mode ) \n if 0 < mx : return 0 , mx\n except :\n pass \n \n\n ## check RooAbsReal functionality\n code = self.pdf.getMaxVal( ROOT.RooArgSet ( self.xvar ) )\n if 0 < code :\n mx = self.pdf.maxVal ( code )\n if 0 < mx : return 0 , mx\n \n \n mn , mx = -1 , -10\n if hasattr ( self.pdf , 'min' ) : mn = self.pdf.min()\n if hasattr ( self.pdf , 'max' ) : mx = self.pdf.max()\n if 0 <= mn and mn <= mx and 0 < mx : return mn , mx\n \n ## now try to use brute force and random shoots \n if not self.xminmax() : return ()\n \n mn , mx = -1 , -10\n xmn , xmx = self.xminmax()\n for i in range ( nshoots ) : \n xx = random.uniform ( xmn , xmx )\n with SETVAR ( self.xvar ) :\n self.xvar.setVal ( xx )\n vv = self.pdf.getVal()\n if mn < 0 or vv < mn : mn = vv\n if mx < 0 or vv > mx : mx = vv \n return mn , mx","title":""},{"docid":"b3aca393d207cf31d6337701bb590da1","score":"0.56222636","text":"def test_max(self):\n list_1 = [1, 2, 3, 4, 5]\n self.assertAlmostEqual(max_integer(list_1), 5)\n list_1.append(7)\n self.assertAlmostEqual(max_integer(list_1), 7)","title":""},{"docid":"d9d3e5fcf3a780fc1cf95ecf45ccb00d","score":"0.5621653","text":"def argmax(seq, fn):\n best = seq[0]\n best_score = fn(best)\n for x in seq:\n x_score = fn(x)\n if x_score > best_score:\n best = x\n best_score = x_score\n return best, best_score","title":""},{"docid":"ce176f1924feceed9d61c9e3d59af769","score":"0.56208324","text":"def largest(die):\n return max(die)","title":""},{"docid":"3d73f0a11f4b92529abf609252feea50","score":"0.5601255","text":"def method2(self, nums):\n cur_max = nums[0]\n global_max = nums[0]\n for i in range(1, len(nums)):\n cur_max = max(cur_max + nums[i], nums[i])\n global_max = max(global_max, cur_max)\n return global_max","title":""},{"docid":"4030a374b941863e038914455b409968","score":"0.5595445","text":"def my_max(a: int, b: int) -> int:\n if a > b:\n return a\n else:\n return b","title":""},{"docid":"1c4497238bb421050634d29c3eb89cb7","score":"0.5583275","text":"def test_softmax_basic():\n print (\"Running basic tests...\")\n test1 = softmax(np.array([1,2]))\n print (test1)\n assert np.amax(np.fabs(test1 - np.array(\n [0.26894142, 0.73105858]))) <= 1e-6\n\n test2 = softmax(np.array([[1001,1002],[3,4]]))\n print (test2)\n assert np.amax(np.fabs(test2 - np.array(\n [[0.26894142, 0.73105858], [0.26894142, 0.73105858]]))) <= 1e-6\n\n test3 = softmax(np.array([[-1001,-1002]]))\n print (test3)\n assert np.amax(np.fabs(test3 - np.array(\n [0.73105858, 0.26894142]))) <= 1e-6\n\n print (\"You should verify these results!\\n\")","title":""},{"docid":"e30999c93b205956797f59f90f1288c5","score":"0.5582823","text":"def test_softmax_basic():\n print (\"Running basic tests...\")\n test1 = softmax(np.array([1,2]))\n print (test1)\n assert np.amax(np.fabs(test1 - np.array(\n [0.26894142, 0.73105858]))) <= 1e-6\n \n test2 = softmax(np.array([[1001,1002],[3,4]]))\n print (test2)\n assert np.amax(np.fabs(test2 - np.array(\n [[0.26894142, 0.73105858], [0.26894142, 0.73105858]]))) <= 1e-6\n \n test3 = softmax(np.array([[-1001,-1002]]))\n print (test3)\n assert np.amax(np.fabs(test3 - np.array(\n [0.73105858, 0.26894142]))) <= 1e-6\n \n print (\"You should verify these results!\\n\")","title":""},{"docid":"bd6d814f11ab608a1f70063c68ca1fbe","score":"0.5572994","text":"def test_max_difference():\n try:\n import pytest\n from list_module.max_difference import max_difference\n except ImportError:\n print(\"The necessary module for this test failed to import\")\n return\n test_data1 = [1, 5, 6, 7, 12]\n test_data2 = [3, 4, 5, 56, 45]\n test_data3 = [6, 34, 23, 45, 57]\n test_data4 = []\n test_data5 = [1, 'b', 7]\n test_data6 = [-1, -7, -9]\n output1 = max_difference(test_data1)\n output2 = max_difference(test_data2)\n output3 = max_difference(test_data3)\n output4 = max_difference(test_data4)\n output5 = max_difference(test_data5)\n output6 = max_difference(test_data6)\n assert output1 == 5\n assert output2 == 51\n assert output3 == 28\n assert output4 == 0\n assert output5 is None\n assert output6 == 6\n with pytest.raises(TypeError):\n max_difference(2)\n with pytest.raises(ValueError):\n max_difference(['inf', 3, 4.0])\n with pytest.raises(ValueError):\n max_difference(['-inf', 3, 4.0])\n with pytest.raises(ValueError):\n max_difference([float('-inf'), 3, 4.0])\n with pytest.raises(ValueError):\n max_difference([float('inf'), 3, 4.0])","title":""},{"docid":"7ee721f7a153a421f31afa7cf8ae6753","score":"0.5568471","text":"def _max(self, a, b):\n return 0.5 * (a + b + abs(a - b))","title":""},{"docid":"fa443f5f13fb12d1d5b1643ac9cdc870","score":"0.5563424","text":"def max_lists(list1, list2):\n pass","title":""},{"docid":"bddaae36a163855b39133f2980186e8b","score":"0.55587953","text":"def get_random_choice(max=1):\n import random\n return random.choice(range(max))","title":""},{"docid":"d370936d371dc50109ce0b37b4d4b406","score":"0.5551241","text":"def argmax_random_tie(seq, function=identity):\n return max(shuffled(seq), key=function)","title":""},{"docid":"73cb5a8f020a57b5c0eb6db7177fc7e6","score":"0.5540149","text":"def max_util(utils):\n return max(utils, key=sum)","title":""},{"docid":"aff6a205d2c4b4c12eb11f485232a52d","score":"0.55375123","text":"def my_max(a: int, b: int) -> int:\n if a >= b:\n return a\n \n return b","title":""},{"docid":"024800fab68e469c99a66144fbed3306","score":"0.55333126","text":"def my_max(items):\n if len(items) == 0:\n return 0\n\n def nmax(nitems, cur_max):\n if len(nitems) == 0:\n return cur_max\n elif nitems[0] > cur_max:\n cur_max = nitems[0]\n return nmax(nitems[1:], cur_max)\n\n current_max = items[0]\n return nmax(items, current_max)","title":""},{"docid":"18acb1c0871eacbb34ce2597d15b6ec1","score":"0.55288893","text":"def main() -> None:\n list_1: List[int] = []\n list_2: List[int] = []\n compare_number: int = 2\n print(all(list_1, compare_number))\n print(is_equal(list_1, list_2))\n print(max(list_1))","title":""},{"docid":"bb7946ba4d53cb175bc6cd1cc7c33879","score":"0.5523523","text":"def strategy(hand, num_die_sides):\n all_holds = gen_all_holds(hand)\n score_dict = {}\n for hold in all_holds:\n temp_score = expected_value(hold, num_die_sides, len(hand) - len(hold))\n score_dict.update({hold: temp_score})\n max_score = max(score_dict.values())\n for key in score_dict:\n if score_dict[key] == max_score:\n max_hand = key\n return (max_score, max_hand)","title":""},{"docid":"e4516b6f6b23570913695604efac152b","score":"0.55216473","text":"def _get_funcs_to_max(_funcs):\n if isinstance(_funcs, tuple) and len(_funcs) == 2:\n neg_funcs = lambda *args, **kwargs: [-val for val in _funcs[0](*args, **kwargs)]\n funcs_to_max = (neg_funcs, _funcs[1])\n elif isinstance(_funcs, list):\n funcs_to_max = [_get_func_to_max(f) for f in funcs]\n else:\n raise ValueError(_FUNC_FORMAT_ERR_MSG)\n return funcs_to_max","title":""},{"docid":"95d7baeb30f7dcf26d55f7f88aa1bb44","score":"0.55071825","text":"def get_max(self):\n pass","title":""},{"docid":"178f890d67566d36391b990cfd8f14c0","score":"0.5503462","text":"def retrieve_max_fitness(populations):\n\n fittest_population = populations[0]\n\n for population in populations:\n if population == fittest_population:\n continue\n else:\n if population.fitness < fittest_population.fitness:\n fittest_population = population\n\n new_population = [pop for pop in populations if pop != fittest_population]\n\n return new_population, fittest_population","title":""},{"docid":"cf5b8404aa554e76a67b38edb90c2b64","score":"0.55021006","text":"def test_generic_2623_max_definition(self, a: ClassUnderTest) -> None:\n if len(a) > 0:\n try:\n a_max = max(a)\n except TypeError:\n return # max not accept the supplied types\n self.assertTrue(a_max in a)\n for x in a:\n self.assertLessEqual(x, a_max)\n else:\n with self.assertRaises(ValueError):\n min(a)","title":""},{"docid":"99aa508d39662540bb0b9efb8e194715","score":"0.55017835","text":"def max(*args):\n return args[0] if len(args) == 1 and isinstance(args[0], (int, str)) else Node.build(TypeNode.MAX, *args) if len(args) > 1 and any(\n isinstance(a, (Node, Variable)) for a in args) else maxPython(*args)","title":""},{"docid":"cc8c64498af58b5cdb56a04bdbdf5007","score":"0.54999495","text":"def max_compute(self, y_max, bounds, n_warmup=100000, n_iter=250):\n # Warm up with random points\n x_tries = self.random_generator.uniform(bounds[:, 0], bounds[:, 1],\n size=(n_warmup, bounds.shape[0]))\n ys = self.compute(x_tries, y_max=y_max)\n x_max = x_tries[ys.argmax()]\n max_acq = ys.max()\n\n # Explore the parameter space more throughly\n x_seeds = self.random_generator.uniform(bounds[:, 0], bounds[:, 1],\n size=(n_iter, bounds.shape[0]))\n for x_try in x_seeds:\n # Find the minimum of minus the acquisition function\n res = minimize(lambda x: -self.compute(x.reshape(1, -1), y_max=y_max),\n x_try.reshape(1, -1),\n bounds=bounds,\n method=\"L-BFGS-B\")\n\n # See if success\n if not res.success:\n continue\n\n # Store it if better than previous minimum(maximum).\n if max_acq is None or -res.fun[0] >= max_acq:\n x_max = res.x\n max_acq = -res.fun[0]\n\n # Clip output to make sure it lies within the bounds. Due to floating\n # point technicalities this is not always the case.\n return np.clip(x_max, bounds[:, 0], bounds[:, 1])","title":""},{"docid":"18c3f306dc8328f1f160f6ea67b18da9","score":"0.54975414","text":"def meu_max(elementos,funcao):\n valores = (funcao(elem) for elem in elementos)\n return max(valores)","title":""},{"docid":"eda1e76835cb3bf15ff86713229a0bfe","score":"0.5486397","text":"def _get_func_to_max(_func):\n return lambda x: - _func(x)","title":""},{"docid":"5dd269c76a40776c2d699fafee738f95","score":"0.54829097","text":"def maximum(x1: Union[ulab.array, float], x2: Union[ulab.array, float]) -> ulab.array:\n ...","title":""},{"docid":"4344a407597cb7a0d78b54e929126a84","score":"0.5476147","text":"def maxby(self, func, **kwargs):\n return max(self, key=func, **kwargs)","title":""},{"docid":"05b12b2c54981c7fc50eca7a896ccd7f","score":"0.54709977","text":"def test_max(self):\n\n self.assertEqual(max_integer([-5, 0, 30, 7, 12, -1]), 30)\n self.assertEqual(max_integer([-5, 0, -30, -7, -12, -1]), 0)\n self.assertEqual(max_integer([-5, -10, -30, -7, -12, -1]), -1)\n self.assertEqual(max_integer([8]), 8)\n self.assertEqual(max_integer([]), None)","title":""},{"docid":"c8e22ab91d6a3944523246c843e24adc","score":"0.54708046","text":"def maximum_power(data):\n\n max_powers = []\n\n for count,array_name in enumerate(ARRAYS):\n max_power = 0\n for a_tuple in data:\n powers = a_tuple[3]\n array_power = powers[count]\n if array_power > max_power:\n max_power = array_power\n max_powers.append((array_name,max_power/1000.))\n\n return max_powers","title":""},{"docid":"0978b35b240a626603683d93fab1324b","score":"0.5468988","text":"def test_nmax():\n wobj = std_wobj('wobj', indep_scale='LINEAR', interp='CONTINUOUS')\n assert peng.nmax(wobj) == 6\n assert peng.nmax(wobj, 1.5) == 5.5","title":""},{"docid":"c724b3f872fa3bdbfccc962d53c1cd0f","score":"0.5466321","text":"def my_max_nice(*args):\n if not args:\n raise ValueError(\"Please enter some numbers\")\n\n def max_finder(iterated_list):\n current_max = next(iterated_list)\n if isinstance(current_max, int):\n for num in iterated_list:\n if not isinstance(num, int):\n raise ValueError(\"Input value {}\"\n \" is not integer\".format(num))\n if num > current_max:\n current_max = num\n return current_max\n raise ValueError(\"Input value {}\"\n \" is not integer\".format(current_max))\n\n return max_finder(iter(args[0] if len(args) == 1 else args))","title":""},{"docid":"1aae79940b00df14c156b4be22f41b54","score":"0.5463787","text":"def test_max_list_iter_Functionality(self):\n #creating list and a max value that is should be equal to\n t_list = [1,2,3,4,5,6,7,8,9,10]\n self.assertEqual(max_list_iter(t_list), 10)","title":""},{"docid":"08a353f810bd56ac311cd966208487bb","score":"0.54601264","text":"def make_var_max_dict(data, var_names):\n result = {}\n for i in range(len(var_names)):\n mx = 0\n for j in range(len(data)):\n if data[j][i] > mx:\n mx = data[j][i]\n result[var_names[i]] = mx\n return result","title":""},{"docid":"1d62f387ad9236dde8f0fcfec3a22021","score":"0.54405564","text":"def _apply_max_constraint(self, f_eval, maxval):\n try:\n return (f_eval<maxval)*f_eval + (f_eval>=maxval) * maxval * np.ones(len(f_eval))\n except TypeError:\n return max(f_eval, maxval)","title":""},{"docid":"0bf40a0e4fd1a541446279a5335dcfc9","score":"0.54355866","text":"def find_max(ls: list) -> object:\n m = find_extremum_in_place(ls)\n assert m == find_extremum_not_in_place(ls)\n return m","title":""},{"docid":"02ea2f24c90de911e310d04334b88f74","score":"0.543491","text":"def _argmax(values):\n idxs = np.nonzero(values == np.max(values))[0]\n return np.random.choice(idxs)","title":""},{"docid":"a727f329b9425cc3b8e954c449f1050d","score":"0.5423817","text":"def strategy(hand, num_die_sides):\n allho=gen_all_holds(hand)\n maxv=0.0\n tup=()\n for item in allho:\n new=expected_value(item, num_die_sides, len(hand)-len(item))\n if new>maxv:\n maxv=new\n tup=item\n \n \n \n return (maxv, tup)","title":""},{"docid":"d5fafded03c4707514f6304d2d059619","score":"0.54158634","text":"def max(arg1, arg2):\n if arg1 > arg2:\n maxValue = arg1\n else:\n maxValue = arg2 \n return maxValue","title":""},{"docid":"4f5d73618511597fcc0dbb455446782e","score":"0.54139066","text":"def maxs(self):\n return np.array([self.x_max, self.y_max, self.z_max])","title":""},{"docid":"57cf9a7b13d5e17a9be2d7b6b0dab3ec","score":"0.54137444","text":"def make_max(*, low, high):\n \n def inner(first, *args):\n result = float('-inf')\n \n for arg in (first,) + args:\n if arg > result and low < arg < high:\n result = arg\n return result\n \n return inner","title":""},{"docid":"b2ea495813faad8af8117e639456a149","score":"0.5400484","text":"def get_max_with_many_arguments(*args):\n result = float('-inf')\n for arg in args:\n if arg > result:\n result = arg\n return result","title":""},{"docid":"8c9f3de3462f6ab8d0a40460ad9f9227","score":"0.5394334","text":"def max(*args) -> \"itkFixedArraySL2 const\":\n return _itkNumericTraitsPython.itkNumericTraitsFASL2_max(*args)","title":""},{"docid":"89c722185e4af3964ef1498b976587f6","score":"0.53843933","text":"def check_max_probability(self, x1, x2, x3):\n probabilities = dict()\n classes = list(set(self.y))\n classes.sort()\n for r in range(len(classes)):\n probabilities[classes[r]] = self.check_overall_probability(x1, x2, x3, classes[r], r)\n return max(probabilities, key=probabilities.get)","title":""},{"docid":"bca5fc902b8446fb3d623a7112d5ff20","score":"0.53825563","text":"def max(*args) -> \"itkFixedArraySS2 const\":\n return _itkNumericTraitsPython.itkNumericTraitsFASS2_max(*args)","title":""},{"docid":"965f1e3ddd96819ea0cadc6210a52beb","score":"0.53778625","text":"def most_similar(func):\n def sim_apply(x, y):\n max_sim = 0\n x = x.split()\n y = y.split()\n for token_x in x:\n for token_y in y:\n max_sim = max(func(token_x,token_y), max_sim)\n return max_sim\n return sim_apply","title":""},{"docid":"91b894152762942ebef0a647e9f25c4d","score":"0.5374386","text":"def dfs_maximizing(state) :\n\n \"\"\"if state.is_game_over():\n return ([state], state.get_endgame_score(is_current_player_maximizer=False),1) # maximizer = False here, interesting\n else:\n new_states=state.generate_next_states()\n new_states=sorted(new_states, key = lambda new_state : dfs_maximizing(new_state)[1],reverse = True)\n statistic=0\n for i in new_states:\n statistic+=dfs_maximizing(i)[2]\n return ([state]+dfs_maximizing(new_states[0])[0], dfs_maximizing(new_states[0])[1], statistic)\"\"\"\n if state.is_game_over():\n return ([state], state.get_endgame_score(is_current_player_maximizer=False),1)\n else:\n maxvalue=-INF\n maxstate=None\n statistic=0\n for new_state in state.generate_next_states():\n tem=dfs_maximizing(new_state)\n statistic+=tem[2]\n if maxvalue < tem[1]:\n maxstate=new_state\n maxvalue=tem[1]\n return ([state]+dfs_maximizing(maxstate)[0], dfs_maximizing(maxstate)[1], statistic)","title":""},{"docid":"90545d67a462bb169563a48b8308b7b8","score":"0.5373349","text":"def find_max_val(self):\n max_val = 0\n max_n = None\n\n for n in range(1, LIMIT):\n val = self.n_over_phi(n)\n if val > max_val:\n max_val = val\n max_n = n\n\n return (max_val, max_n)","title":""},{"docid":"6fd8092a39d933d563af94735b24c9ae","score":"0.53727996","text":"def maximum(self):\n maximum = self.arr[0]\n for elem in self.arr:\n if elem > maximum:\n maximum = elem\n return maximum","title":""},{"docid":"4f2344f590810e937b810171d34f6122","score":"0.53690714","text":"def best_predictor(user, restaurants, feature_fns):\n '''\n Q: In best_predictor, what does the variable reviewed represent?\n Choose the number of the correct choice:\n 0) a list of restaurants reviewed by the user\n 1) a list of all possible restaurants\n 2) a list of ratings for restaurants reviewed by the user\n 0\n '''\n reviewed = user_reviewed_restaurants(user, restaurants)\n # BEGIN Question 8\n \"*** REPLACE THIS LINE ***\"\n max_feature = max(feature_fns, key = lambda x: find_predictor(user, reviewed, x)[1])\n return find_predictor(user, reviewed, max_feature)[0]\n # END Question 8","title":""}],"string":"[\n {\n \"docid\": \"39e9da22cbeb6af7dcd2479aebb8025a\",\n \"score\": \"0.67297995\",\n \"text\": \"def test_max_vals(self):\\n self.report('Testing for Maximum value. ')\\n for test_fn_name in self.synthetic_functions:\\n caller = get_syn_function_caller_from_name(test_fn_name)\\n self.report('Testing %s with max value %0.4f.'%(test_fn_name, caller.opt_val),\\n 'test_result')\\n X = np.random.random((self.num_samples, caller.domain_dim))\\n eval_vals, _ = caller.eval_multiple(X)\\n assert eval_vals.max() <= caller.opt_val\\n if caller.opt_pt_normalised is not None:\\n max_val_norm = caller.eval_single(caller.opt_pt_normalised, normalised=True)\\n max_val_unnorm = caller.eval_single(caller.opt_pt_unnormalised, normalised=False)\\n assert np.abs(max_val_norm[0] - caller.opt_val) < 1e-5\\n assert np.abs(max_val_unnorm[0] - caller.opt_val) < 1e-5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84fd57a3b2bf95a51830c1728ef66579\",\n \"score\": \"0.6709496\",\n \"text\": \"def mymax(x,y):\\n return listmax([x,y])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b8ce422e3b3587b29db9dc58aaa06e\",\n \"score\": \"0.6458269\",\n \"text\": \"def my_max(*args, **kwargs):\\n key = kwargs.get(\\\"key\\\", lambda x: x)\\n dta = args[0] if len(args) == 1 else args[:]\\n temp_max = None\\n for i in dta:\\n if temp_max == None:\\n temp_max = i\\n if key(i) > key(temp_max):\\n temp_max = i\\n return temp_max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4110719b1355982f7bc2e3ca8a93d415\",\n \"score\": \"0.63670045\",\n \"text\": \"def strategy(hand, num_die_sides):\\r\\n max_hand = tuple()\\r\\n max_exp = 0\\r\\n holds_set = gen_all_holds(hand)\\r\\n \\r\\n for dummy_i in holds_set:\\r\\n tmp_exp = expected_value(dummy_i, num_die_sides, len(hand)-len(dummy_i))\\r\\n if max_exp < tmp_exp:\\r\\n print(\\\"max\\\")\\r\\n max_exp = tmp_exp\\r\\n max_hand = dummy_i\\r\\n print(max_exp, max_hand)\\r\\n else:\\r\\n print(\\\"tmp\\\")\\r\\n print(tmp_exp, dummy_i)\\r\\n return (max_exp, max_hand)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e1e4eda0665695220fa2d4bc26c75f7\",\n \"score\": \"0.62175333\",\n \"text\": \"def argmaxe(gen):\\n maxv = -math.inf # negative infinity\\n maxvals = [] # list of maximal elements\\n for (e,v) in gen:\\n if v>maxv:\\n maxvals,maxv = [e], v\\n elif v==maxv:\\n maxvals.append(e)\\n return random.choice(maxvals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5ece9b7f5352aa0cf25d9a5c2da5dc1\",\n \"score\": \"0.6183401\",\n \"text\": \"def get_max():\\n\\n values = [12, 32, 1, 73, 25, 68, 83, 29, 55, 61, 100, 97, 2]\\n\\n return f.reduce(lambda x, y: x if (x > y) else y, values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b6dc0a45cbb03782ce95555ac5188bf\",\n \"score\": \"0.6128468\",\n \"text\": \"def maximum(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"313085e9afbed55d0805edb0b87f733c\",\n \"score\": \"0.6123956\",\n \"text\": \"def is_max():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7571415e79a22971147003e6abfa8b8\",\n \"score\": \"0.6108703\",\n \"text\": \"def __get_max_rand(self, da_max: np.ndarray, xy: np.ndarray):\\n\\n DEF_VAL = 20\\n\\n if self.__select_method is self._SelectMethod.DESINTY:\\n # In Stamps min and max values are in separate arrays with a single element.\\n patch_area = np.prod(MatlabUtils.max(xy) - MatlabUtils.min(xy)) / 1e6 # In km\\n max_rand = DEF_VAL * patch_area / (len(da_max) -1)\\n else:\\n max_rand = DEF_VAL\\n\\n return max_rand\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c8d1d75b66b3d01881ba3d5038f6ed9\",\n \"score\": \"0.6107316\",\n \"text\": \"def f_max(*items):\\n\\n return max(*items)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50829834b7d27fd68eb533e9764af8f5\",\n \"score\": \"0.6069354\",\n \"text\": \"def calc_max_var(self, varname='depth_offset'):\\r\\n maxs = np.array([])\\r\\n for sec in self.fqpr:\\r\\n maxs = np.append(maxs, float(self.fqpr[sec][varname].max()))\\r\\n return maxs.max()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebeab236d42c8d1f154967a8e7937bb6\",\n \"score\": \"0.60662776\",\n \"text\": \"def test_max(self):\\n self.assertEqual(max_integer([4, 6, 3, 5, 78, 4, 3, 4, 1, 9]), 78)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d3c676560fb94e9e5141fa7e9498497\",\n \"score\": \"0.60483146\",\n \"text\": \"def largest(min_factor, max_factor):\\n\\n return main_func(min_factor, max_factor, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39e4ad2da6c9c91c7f2a563c1db79b59\",\n \"score\": \"0.6035652\",\n \"text\": \"def find_max(values):\\n return max(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2cb7c94399bcf93a9c2639ce04adc27\",\n \"score\": \"0.6024111\",\n \"text\": \"def max_product_v2(self, numbers):\\n curr_min = numbers[0]\\n curr_max = numbers[0]\\n global_max = numbers[0]\\n\\n for i in range(1, len(numbers)):\\n temp = curr_max\\n print(f\\\"curr_max: {curr_max}, curr_min: {curr_min}\\\")\\n print(f\\\"cur_max * num[i] -> {curr_max*numbers[i]}\\\")\\n print(f\\\"curr_min({curr_min}) * numbers[i] -> {curr_min*numbers[i]}\\\")\\n curr_max = max(max(curr_max*numbers[i], curr_min*numbers[i]), numbers[i])\\n curr_min = min(min(temp*numbers[i], curr_min*numbers[i]), numbers[i])\\n print(f\\\"curr_max: {curr_max}, curr_min: {curr_min}\\\")\\n global_max = max(curr_max, global_max)\\n\\n return global_max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d728f9b0c28d988f68fbbbe3b1c08e6\",\n \"score\": \"0.6001151\",\n \"text\": \"def get_max(var1: int, var2: int, var3: int) -> int:\\n max_var = var2\\n if max_var < var1:\\n max_var = var1\\n\\n if max_var < var3:\\n max_var = var3\\n\\n print(\\\"max number is: {max}\\\".format(max=max_var))\\n return max_var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5a26c18a93f8117e46746c72d4cedbf\",\n \"score\": \"0.5982832\",\n \"text\": \"def maximum(a, b):\\n if is_variable(a) or is_variable(b):\\n return T.maximum(a, b)\\n else:\\n return np.maximum(a, b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"828b25b766ced8ad68a593acd87e3be7\",\n \"score\": \"0.5958302\",\n \"text\": \"def MyMax(objects, key):\\n if not objects:\\n raise ValueError(\\\"max() arg is empty\\\")\\n maximum = objects[0]\\n maximumValue = key(maximum)\\n for value in objects[1:]:\\n thisValue = key(value)\\n if thisValue > maximumValue:\\n maximumValue = thisValue\\n maximum = value\\n return maximum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb486a67e9520d02baec70e781637a73\",\n \"score\": \"0.59176266\",\n \"text\": \"def test_max_2() -> None:\\n assert max([1, 5, 2]) == 5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a3fde3cc3db65c12d5c17c0acd91174\",\n \"score\": \"0.5871306\",\n \"text\": \"def arg_max(l):\\n assert isinstance(l, list) or isinstance(l, np.ndarray)\\n return rand.choice(np.flatnonzero(l == np.max(l)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beb9ddac51f759dc38dc10d9ba47b4a1\",\n \"score\": \"0.5868842\",\n \"text\": \"def max(self, varName):\\n v = self.values(varName)[1]\\n return max(v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ecd679027ba763a84304e1553f4a60b\",\n \"score\": \"0.58666897\",\n \"text\": \"def test_maximum_multiple(self):\\n\\n allure.dynamic.title(\\\"Testing max_multiple function\\\")\\n allure.dynamic.severity(allure.severity_level.NORMAL)\\n allure.dynamic.description_html('<h3>Codewars badge:</h3>'\\n '<img src=\\\"https://www.codewars.com/users/myFirstCode'\\n '/badges/large\\\">'\\n '<h3>Test Description:</h3>'\\n \\\"<p></p>\\\")\\n\\n with allure.step(\\\"Enter divisor, bound and verify the output\\\"):\\n\\n data = [\\n (2, 7, 6),\\n (3, 10, 9),\\n (7, 17, 14),\\n (10, 50, 50),\\n (37, 200, 185),\\n (7, 100, 98),\\n (37, 100, 74),\\n (1, 13, 13),\\n (1, 1, 1),\\n (22, 9, 0),\\n (43, 7, 0),\\n (50, 7, 0)\\n ]\\n\\n for divisor, bound, expected in data:\\n\\n print_log(divisor=divisor,\\n bound=bound,\\n expected=expected)\\n\\n self.assertEqual(expected,\\n max_multiple(divisor,\\n bound))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8d1c68289cc77d23cab49d25598f585\",\n \"score\": \"0.5805065\",\n \"text\": \"def test_min():\\n random_vals = []\\n\\n # generating 50 random values and appending them to a list\\n for i in range(50):\\n random_vals.append(random.randrange(0, 1000, 1))\\n\\n # using both max functions to find the max\\n min_val = my_min(random_vals)\\n py_min = min(random_vals)\\n\\n assert min_val == py_min, 'Failed on {} - got {}, expected {}'.format(random_vals, min_val, py_min)\\n assert my_min([]) is None, 'Failed on {} - got {}, expected {}'.format([], my_min([]), None)\\n assert my_min([1]) == 1, 'Failed on {} - got {}, expected {}'.format([1], my_min([1]), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"915ee525ce29cb027655c448afa72d83\",\n \"score\": \"0.5802563\",\n \"text\": \"def test_max_1() -> None:\\n assert max([110]) == 110\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26bd310e901725aee9066bcb8f00272e\",\n \"score\": \"0.5797265\",\n \"text\": \"def flawed(A):\\n my_max = 0\\n for v in A:\\n if my_max < v:\\n my_max = v\\n return my_max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8564e07aa9fe2b140cc7dc51b6ff69\",\n \"score\": \"0.5793827\",\n \"text\": \"def _get_max_(self):\\n return self.__max_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8564e07aa9fe2b140cc7dc51b6ff69\",\n \"score\": \"0.5793827\",\n \"text\": \"def _get_max_(self):\\n return self.__max_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8564e07aa9fe2b140cc7dc51b6ff69\",\n \"score\": \"0.5793827\",\n \"text\": \"def _get_max_(self):\\n return self.__max_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8564e07aa9fe2b140cc7dc51b6ff69\",\n \"score\": \"0.5793827\",\n \"text\": \"def _get_max_(self):\\n return self.__max_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8564e07aa9fe2b140cc7dc51b6ff69\",\n \"score\": \"0.5793827\",\n \"text\": \"def _get_max_(self):\\n return self.__max_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8564e07aa9fe2b140cc7dc51b6ff69\",\n \"score\": \"0.5793827\",\n \"text\": \"def _get_max_(self):\\n return self.__max_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8564e07aa9fe2b140cc7dc51b6ff69\",\n \"score\": \"0.5793827\",\n \"text\": \"def _get_max_(self):\\n return self.__max_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35f83c9e1bbccff0f628fad6f420e6f9\",\n \"score\": \"0.57915676\",\n \"text\": \"def personal_best(scores: List[int]) -> int:\\n return max(scores)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f47ac11de3293efd63dda9a2a2fb10b\",\n \"score\": \"0.5791464\",\n \"text\": \"def _maxscore(self):\\n total = 0\\n lowtot= 0\\n for lli in self.ll:\\n total = total + max(lli.values())\\n lowtot= lowtot+ min(lli.values())\\n self.maxscore = total\\n self.minscore = lowtot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5a92867a5318440d55cf500023a6fa2\",\n \"score\": \"0.5783892\",\n \"text\": \"def get_max_value(self, inputs_list):\\n\\n # Call the function to get a final output array of the neural network\\n outputs = self.get_outputs(inputs_list)\\n\\n # Get the highest value from the array and return this\\n return np.max(outputs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"730d2c1c786fbe657040f6e4d02e00c8\",\n \"score\": \"0.5780476\",\n \"text\": \"def max_values(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79918fbd56a8c6df08c1c58da101a65b\",\n \"score\": \"0.5761609\",\n \"text\": \"def my_max(the_list):\\n list_length = len(the_list)\\n\\n if list_length <= 0:\\n return None\\n else:\\n current_max = the_list[0]\\n\\n i = 0\\n # loop through the entire list\\n while i < list_length:\\n # check if the next value if higher then the previous if so, make it the new max\\n if current_max < the_list[i]:\\n current_max = the_list[i]\\n i = i + 1\\n\\n return current_max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"936cebf81687c9e1702b5b2fbb601c7d\",\n \"score\": \"0.5744667\",\n \"text\": \"def randmax(action_values):\\n maxes = np.flatnonzero(action_values == np.max(action_values))\\n action = np.random.choice(maxes)\\n\\n return(action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f1356085adbafb05377a3f7ae0ffbf5\",\n \"score\": \"0.57358843\",\n \"text\": \"def find_schaffer_max_min():\\n _max = -float(\\\"inf\\\")\\n _min = +float(\\\"inf\\\")\\n for i in range(10000):\\n e = find_max_f1_f2(random.randint(-100000, 100000))\\n _max = e if e > _max else _max\\n _min = e if e < _min else _min\\n return _max, _min\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c1072a4a29064b7a250bc5a0a3d180b\",\n \"score\": \"0.5709178\",\n \"text\": \"def max_value(numbers):\\n pass\\n max = 0\\n for numner in numbers:\\n if numner > 0:\\n max = numner\\n #meganogar sneganogar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0966bcb3bc7a2b2ac96f9aef94048ea\",\n \"score\": \"0.5709012\",\n \"text\": \"def random_argmax(v):\\n arguments = np.argwhere(v == np.amax(v)).ravel()\\n return np.random.choice(arguments)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"560d85bbe82426b98e5ebb29b3be8e0c\",\n \"score\": \"0.57076514\",\n \"text\": \"def maximum(inputs, **kwargs):\\n return Maximum(**kwargs)(inputs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e5eebdb65732cda8d5d4ad1b549f58\",\n \"score\": \"0.5704856\",\n \"text\": \"def max(tab):\\n global comparaisons\\n # le max \\\"temporaire\\\" est initialisé à la première valeur du tableau\\n max = tab[0]\\n # on compare chaque élément du tableau à max, s'il est plus grand on met à jour max\\n for x in tab:\\n if x > max:\\n max = x\\n return max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6e07c712a177e2de3d2dc9e4e69f2ca\",\n \"score\": \"0.56916463\",\n \"text\": \"def get_max(a, b):\\n if a > b: return a\\n else: return b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f70665ece00761ed0eb88a10b6cdd17\",\n \"score\": \"0.5674148\",\n \"text\": \"def get_max_lift_updates(self):\\n names = self.determine_muscle_group(question_text='Which max values would you like to update?')\\n max_lift_names = list()\\n if 'bench_press' in names:\\n max_lift_names.append('bench_press_max')\\n if 'squat' in names:\\n max_lift_names.append('squat_max')\\n if 'shoulder_press' in names:\\n max_lift_names.append('shoulder_press_max')\\n if 'deadlift' in names:\\n max_lift_names.append('deadlift_max')\\n max_lift_values = []\\n for row in max_lift_names:\\n while True:\\n max_text = input((\\\"New \\\" + row + \\\"value:\\\\n\\\").replace(\\\"_\\\", \\\" \\\"))\\n try:\\n max_update = int(max_text)\\n max_lift_values.append(max_update)\\n break\\n except ValueError:\\n print('Invalid literal, please enter a number.')\\n return max_lift_values, max_lift_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f029b9017be1a7e29c2992f00b7ca488\",\n \"score\": \"0.56691533\",\n \"text\": \"def maximum(value1, value2, value3):\\n max_value = value1\\n if value2 > max_value:\\n max_value = value2\\n if value3 > max_value:\\n max_value = value3\\n return max_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c9dd5689ee569a5ded5fcf188ead345\",\n \"score\": \"0.5663374\",\n \"text\": \"def my_max(*args):\\n iterated_list = args\\n if not args:\\n raise ValueError(\\\"Please enter some numbers\\\")\\n if len(args) == 1:\\n iterated_list = args[0]\\n if not isinstance(iterated_list, (tuple, list)):\\n raise ValueError(\\\"There is only one value {}\\\"\\n \\\", and it isn't tuple or list\\\".format(args[0]))\\n current_max = iterated_list[0]\\n for num in iterated_list:\\n if not isinstance(num, int):\\n raise ValueError(\\\"Input value {}\\\"\\n \\\" is not integer\\\".format(num))\\n if num > current_max:\\n current_max = num\\n return current_max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"593603fad3262865fd82bf39da353e44\",\n \"score\": \"0.56525695\",\n \"text\": \"def max_by(lst, fn):\\n\\n return max(map(fn, lst))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3381bafab29a5015f3b262e46eb49035\",\n \"score\": \"0.5644715\",\n \"text\": \"def test_dice_max_of() -> None:\\n dice = Dice(\\\"d20\\\")\\n assert dice.rolls == 0\\n assert not dice.bonuses\\n\\n the_roll, rolls = dice.max_of(3)\\n assert dice.rolls == 3\\n assert len(rolls) == 3\\n assert 1 <= rolls[0] <= 20\\n assert 1 <= rolls[1] <= 20\\n assert 1 <= rolls[2] <= 20\\n assert the_roll == max(rolls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99ee00aa6f3e9fa4c3369a40f55b62b2\",\n \"score\": \"0.5644054\",\n \"text\": \"def main():\\n print(largest_number(items=[3, 30, 34, 5, 9, ]))\\n print(largest_number(items=[54, 546, 548, 60, ]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5610d6d766c22fc3a68c2ef3d0dbe3ae\",\n \"score\": \"0.5642068\",\n \"text\": \"def minmax ( self , nshoots = 50000 ) :\\n ## try to get minmax directly from pdf/function \\n if self.tricks and hasattr ( self.pdf , 'function' ) :\\n if hasattr ( self.pdf , 'setPars' ) : self.pdf.setPars() \\n f = self.pdf.function()\\n if hasattr ( f , 'minmax' ) :\\n try :\\n mn , mx = f.minmax()\\n if 0<= mn and mn <= mx and 0 < mx : \\n return mn , mx\\n except :\\n pass\\n if hasattr ( f , 'max' ) :\\n try :\\n mx = f.max()\\n if 0 < mx : return 0 , mx\\n except :\\n pass\\n if hasattr ( f , 'mode' ) :\\n try :\\n mode = f.mode()\\n if mode in self.xvar :\\n mx = f ( mode ) \\n if 0 < mx : return 0 , mx\\n except :\\n pass \\n \\n\\n ## check RooAbsReal functionality\\n code = self.pdf.getMaxVal( ROOT.RooArgSet ( self.xvar ) )\\n if 0 < code :\\n mx = self.pdf.maxVal ( code )\\n if 0 < mx : return 0 , mx\\n \\n \\n mn , mx = -1 , -10\\n if hasattr ( self.pdf , 'min' ) : mn = self.pdf.min()\\n if hasattr ( self.pdf , 'max' ) : mx = self.pdf.max()\\n if 0 <= mn and mn <= mx and 0 < mx : return mn , mx\\n \\n ## now try to use brute force and random shoots \\n if not self.xminmax() : return ()\\n \\n mn , mx = -1 , -10\\n xmn , xmx = self.xminmax()\\n for i in range ( nshoots ) : \\n xx = random.uniform ( xmn , xmx )\\n with SETVAR ( self.xvar ) :\\n self.xvar.setVal ( xx )\\n vv = self.pdf.getVal()\\n if mn < 0 or vv < mn : mn = vv\\n if mx < 0 or vv > mx : mx = vv \\n return mn , mx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3aca393d207cf31d6337701bb590da1\",\n \"score\": \"0.56222636\",\n \"text\": \"def test_max(self):\\n list_1 = [1, 2, 3, 4, 5]\\n self.assertAlmostEqual(max_integer(list_1), 5)\\n list_1.append(7)\\n self.assertAlmostEqual(max_integer(list_1), 7)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9d3e5fcf3a780fc1cf95ecf45ccb00d\",\n \"score\": \"0.5621653\",\n \"text\": \"def argmax(seq, fn):\\n best = seq[0]\\n best_score = fn(best)\\n for x in seq:\\n x_score = fn(x)\\n if x_score > best_score:\\n best = x\\n best_score = x_score\\n return best, best_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce176f1924feceed9d61c9e3d59af769\",\n \"score\": \"0.56208324\",\n \"text\": \"def largest(die):\\n return max(die)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d73f0a11f4b92529abf609252feea50\",\n \"score\": \"0.5601255\",\n \"text\": \"def method2(self, nums):\\n cur_max = nums[0]\\n global_max = nums[0]\\n for i in range(1, len(nums)):\\n cur_max = max(cur_max + nums[i], nums[i])\\n global_max = max(global_max, cur_max)\\n return global_max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4030a374b941863e038914455b409968\",\n \"score\": \"0.5595445\",\n \"text\": \"def my_max(a: int, b: int) -> int:\\n if a > b:\\n return a\\n else:\\n return b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c4497238bb421050634d29c3eb89cb7\",\n \"score\": \"0.5583275\",\n \"text\": \"def test_softmax_basic():\\n print (\\\"Running basic tests...\\\")\\n test1 = softmax(np.array([1,2]))\\n print (test1)\\n assert np.amax(np.fabs(test1 - np.array(\\n [0.26894142, 0.73105858]))) <= 1e-6\\n\\n test2 = softmax(np.array([[1001,1002],[3,4]]))\\n print (test2)\\n assert np.amax(np.fabs(test2 - np.array(\\n [[0.26894142, 0.73105858], [0.26894142, 0.73105858]]))) <= 1e-6\\n\\n test3 = softmax(np.array([[-1001,-1002]]))\\n print (test3)\\n assert np.amax(np.fabs(test3 - np.array(\\n [0.73105858, 0.26894142]))) <= 1e-6\\n\\n print (\\\"You should verify these results!\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e30999c93b205956797f59f90f1288c5\",\n \"score\": \"0.5582823\",\n \"text\": \"def test_softmax_basic():\\n print (\\\"Running basic tests...\\\")\\n test1 = softmax(np.array([1,2]))\\n print (test1)\\n assert np.amax(np.fabs(test1 - np.array(\\n [0.26894142, 0.73105858]))) <= 1e-6\\n \\n test2 = softmax(np.array([[1001,1002],[3,4]]))\\n print (test2)\\n assert np.amax(np.fabs(test2 - np.array(\\n [[0.26894142, 0.73105858], [0.26894142, 0.73105858]]))) <= 1e-6\\n \\n test3 = softmax(np.array([[-1001,-1002]]))\\n print (test3)\\n assert np.amax(np.fabs(test3 - np.array(\\n [0.73105858, 0.26894142]))) <= 1e-6\\n \\n print (\\\"You should verify these results!\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd6d814f11ab608a1f70063c68ca1fbe\",\n \"score\": \"0.5572994\",\n \"text\": \"def test_max_difference():\\n try:\\n import pytest\\n from list_module.max_difference import max_difference\\n except ImportError:\\n print(\\\"The necessary module for this test failed to import\\\")\\n return\\n test_data1 = [1, 5, 6, 7, 12]\\n test_data2 = [3, 4, 5, 56, 45]\\n test_data3 = [6, 34, 23, 45, 57]\\n test_data4 = []\\n test_data5 = [1, 'b', 7]\\n test_data6 = [-1, -7, -9]\\n output1 = max_difference(test_data1)\\n output2 = max_difference(test_data2)\\n output3 = max_difference(test_data3)\\n output4 = max_difference(test_data4)\\n output5 = max_difference(test_data5)\\n output6 = max_difference(test_data6)\\n assert output1 == 5\\n assert output2 == 51\\n assert output3 == 28\\n assert output4 == 0\\n assert output5 is None\\n assert output6 == 6\\n with pytest.raises(TypeError):\\n max_difference(2)\\n with pytest.raises(ValueError):\\n max_difference(['inf', 3, 4.0])\\n with pytest.raises(ValueError):\\n max_difference(['-inf', 3, 4.0])\\n with pytest.raises(ValueError):\\n max_difference([float('-inf'), 3, 4.0])\\n with pytest.raises(ValueError):\\n max_difference([float('inf'), 3, 4.0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ee721f7a153a421f31afa7cf8ae6753\",\n \"score\": \"0.5568471\",\n \"text\": \"def _max(self, a, b):\\n return 0.5 * (a + b + abs(a - b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa443f5f13fb12d1d5b1643ac9cdc870\",\n \"score\": \"0.5563424\",\n \"text\": \"def max_lists(list1, list2):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bddaae36a163855b39133f2980186e8b\",\n \"score\": \"0.55587953\",\n \"text\": \"def get_random_choice(max=1):\\n import random\\n return random.choice(range(max))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d370936d371dc50109ce0b37b4d4b406\",\n \"score\": \"0.5551241\",\n \"text\": \"def argmax_random_tie(seq, function=identity):\\n return max(shuffled(seq), key=function)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73cb5a8f020a57b5c0eb6db7177fc7e6\",\n \"score\": \"0.5540149\",\n \"text\": \"def max_util(utils):\\n return max(utils, key=sum)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aff6a205d2c4b4c12eb11f485232a52d\",\n \"score\": \"0.55375123\",\n \"text\": \"def my_max(a: int, b: int) -> int:\\n if a >= b:\\n return a\\n \\n return b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"024800fab68e469c99a66144fbed3306\",\n \"score\": \"0.55333126\",\n \"text\": \"def my_max(items):\\n if len(items) == 0:\\n return 0\\n\\n def nmax(nitems, cur_max):\\n if len(nitems) == 0:\\n return cur_max\\n elif nitems[0] > cur_max:\\n cur_max = nitems[0]\\n return nmax(nitems[1:], cur_max)\\n\\n current_max = items[0]\\n return nmax(items, current_max)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18acb1c0871eacbb34ce2597d15b6ec1\",\n \"score\": \"0.55288893\",\n \"text\": \"def main() -> None:\\n list_1: List[int] = []\\n list_2: List[int] = []\\n compare_number: int = 2\\n print(all(list_1, compare_number))\\n print(is_equal(list_1, list_2))\\n print(max(list_1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb7946ba4d53cb175bc6cd1cc7c33879\",\n \"score\": \"0.5523523\",\n \"text\": \"def strategy(hand, num_die_sides):\\n all_holds = gen_all_holds(hand)\\n score_dict = {}\\n for hold in all_holds:\\n temp_score = expected_value(hold, num_die_sides, len(hand) - len(hold))\\n score_dict.update({hold: temp_score})\\n max_score = max(score_dict.values())\\n for key in score_dict:\\n if score_dict[key] == max_score:\\n max_hand = key\\n return (max_score, max_hand)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4516b6f6b23570913695604efac152b\",\n \"score\": \"0.55216473\",\n \"text\": \"def _get_funcs_to_max(_funcs):\\n if isinstance(_funcs, tuple) and len(_funcs) == 2:\\n neg_funcs = lambda *args, **kwargs: [-val for val in _funcs[0](*args, **kwargs)]\\n funcs_to_max = (neg_funcs, _funcs[1])\\n elif isinstance(_funcs, list):\\n funcs_to_max = [_get_func_to_max(f) for f in funcs]\\n else:\\n raise ValueError(_FUNC_FORMAT_ERR_MSG)\\n return funcs_to_max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95d7baeb30f7dcf26d55f7f88aa1bb44\",\n \"score\": \"0.55071825\",\n \"text\": \"def get_max(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"178f890d67566d36391b990cfd8f14c0\",\n \"score\": \"0.5503462\",\n \"text\": \"def retrieve_max_fitness(populations):\\n\\n fittest_population = populations[0]\\n\\n for population in populations:\\n if population == fittest_population:\\n continue\\n else:\\n if population.fitness < fittest_population.fitness:\\n fittest_population = population\\n\\n new_population = [pop for pop in populations if pop != fittest_population]\\n\\n return new_population, fittest_population\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf5b8404aa554e76a67b38edb90c2b64\",\n \"score\": \"0.55021006\",\n \"text\": \"def test_generic_2623_max_definition(self, a: ClassUnderTest) -> None:\\n if len(a) > 0:\\n try:\\n a_max = max(a)\\n except TypeError:\\n return # max not accept the supplied types\\n self.assertTrue(a_max in a)\\n for x in a:\\n self.assertLessEqual(x, a_max)\\n else:\\n with self.assertRaises(ValueError):\\n min(a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99aa508d39662540bb0b9efb8e194715\",\n \"score\": \"0.55017835\",\n \"text\": \"def max(*args):\\n return args[0] if len(args) == 1 and isinstance(args[0], (int, str)) else Node.build(TypeNode.MAX, *args) if len(args) > 1 and any(\\n isinstance(a, (Node, Variable)) for a in args) else maxPython(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc8c64498af58b5cdb56a04bdbdf5007\",\n \"score\": \"0.54999495\",\n \"text\": \"def max_compute(self, y_max, bounds, n_warmup=100000, n_iter=250):\\n # Warm up with random points\\n x_tries = self.random_generator.uniform(bounds[:, 0], bounds[:, 1],\\n size=(n_warmup, bounds.shape[0]))\\n ys = self.compute(x_tries, y_max=y_max)\\n x_max = x_tries[ys.argmax()]\\n max_acq = ys.max()\\n\\n # Explore the parameter space more throughly\\n x_seeds = self.random_generator.uniform(bounds[:, 0], bounds[:, 1],\\n size=(n_iter, bounds.shape[0]))\\n for x_try in x_seeds:\\n # Find the minimum of minus the acquisition function\\n res = minimize(lambda x: -self.compute(x.reshape(1, -1), y_max=y_max),\\n x_try.reshape(1, -1),\\n bounds=bounds,\\n method=\\\"L-BFGS-B\\\")\\n\\n # See if success\\n if not res.success:\\n continue\\n\\n # Store it if better than previous minimum(maximum).\\n if max_acq is None or -res.fun[0] >= max_acq:\\n x_max = res.x\\n max_acq = -res.fun[0]\\n\\n # Clip output to make sure it lies within the bounds. Due to floating\\n # point technicalities this is not always the case.\\n return np.clip(x_max, bounds[:, 0], bounds[:, 1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c3f306dc8328f1f160f6ea67b18da9\",\n \"score\": \"0.54975414\",\n \"text\": \"def meu_max(elementos,funcao):\\n valores = (funcao(elem) for elem in elementos)\\n return max(valores)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda1e76835cb3bf15ff86713229a0bfe\",\n \"score\": \"0.5486397\",\n \"text\": \"def _get_func_to_max(_func):\\n return lambda x: - _func(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd269c76a40776c2d699fafee738f95\",\n \"score\": \"0.54829097\",\n \"text\": \"def maximum(x1: Union[ulab.array, float], x2: Union[ulab.array, float]) -> ulab.array:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4344a407597cb7a0d78b54e929126a84\",\n \"score\": \"0.5476147\",\n \"text\": \"def maxby(self, func, **kwargs):\\n return max(self, key=func, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05b12b2c54981c7fc50eca7a896ccd7f\",\n \"score\": \"0.54709977\",\n \"text\": \"def test_max(self):\\n\\n self.assertEqual(max_integer([-5, 0, 30, 7, 12, -1]), 30)\\n self.assertEqual(max_integer([-5, 0, -30, -7, -12, -1]), 0)\\n self.assertEqual(max_integer([-5, -10, -30, -7, -12, -1]), -1)\\n self.assertEqual(max_integer([8]), 8)\\n self.assertEqual(max_integer([]), None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e22ab91d6a3944523246c843e24adc\",\n \"score\": \"0.54708046\",\n \"text\": \"def maximum_power(data):\\n\\n max_powers = []\\n\\n for count,array_name in enumerate(ARRAYS):\\n max_power = 0\\n for a_tuple in data:\\n powers = a_tuple[3]\\n array_power = powers[count]\\n if array_power > max_power:\\n max_power = array_power\\n max_powers.append((array_name,max_power/1000.))\\n\\n return max_powers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0978b35b240a626603683d93fab1324b\",\n \"score\": \"0.5468988\",\n \"text\": \"def test_nmax():\\n wobj = std_wobj('wobj', indep_scale='LINEAR', interp='CONTINUOUS')\\n assert peng.nmax(wobj) == 6\\n assert peng.nmax(wobj, 1.5) == 5.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c724b3f872fa3bdbfccc962d53c1cd0f\",\n \"score\": \"0.5466321\",\n \"text\": \"def my_max_nice(*args):\\n if not args:\\n raise ValueError(\\\"Please enter some numbers\\\")\\n\\n def max_finder(iterated_list):\\n current_max = next(iterated_list)\\n if isinstance(current_max, int):\\n for num in iterated_list:\\n if not isinstance(num, int):\\n raise ValueError(\\\"Input value {}\\\"\\n \\\" is not integer\\\".format(num))\\n if num > current_max:\\n current_max = num\\n return current_max\\n raise ValueError(\\\"Input value {}\\\"\\n \\\" is not integer\\\".format(current_max))\\n\\n return max_finder(iter(args[0] if len(args) == 1 else args))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aae79940b00df14c156b4be22f41b54\",\n \"score\": \"0.5463787\",\n \"text\": \"def test_max_list_iter_Functionality(self):\\n #creating list and a max value that is should be equal to\\n t_list = [1,2,3,4,5,6,7,8,9,10]\\n self.assertEqual(max_list_iter(t_list), 10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08a353f810bd56ac311cd966208487bb\",\n \"score\": \"0.54601264\",\n \"text\": \"def make_var_max_dict(data, var_names):\\n result = {}\\n for i in range(len(var_names)):\\n mx = 0\\n for j in range(len(data)):\\n if data[j][i] > mx:\\n mx = data[j][i]\\n result[var_names[i]] = mx\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d62f387ad9236dde8f0fcfec3a22021\",\n \"score\": \"0.54405564\",\n \"text\": \"def _apply_max_constraint(self, f_eval, maxval):\\n try:\\n return (f_eval<maxval)*f_eval + (f_eval>=maxval) * maxval * np.ones(len(f_eval))\\n except TypeError:\\n return max(f_eval, maxval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bf40a0e4fd1a541446279a5335dcfc9\",\n \"score\": \"0.54355866\",\n \"text\": \"def find_max(ls: list) -> object:\\n m = find_extremum_in_place(ls)\\n assert m == find_extremum_not_in_place(ls)\\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02ea2f24c90de911e310d04334b88f74\",\n \"score\": \"0.543491\",\n \"text\": \"def _argmax(values):\\n idxs = np.nonzero(values == np.max(values))[0]\\n return np.random.choice(idxs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a727f329b9425cc3b8e954c449f1050d\",\n \"score\": \"0.5423817\",\n \"text\": \"def strategy(hand, num_die_sides):\\n allho=gen_all_holds(hand)\\n maxv=0.0\\n tup=()\\n for item in allho:\\n new=expected_value(item, num_die_sides, len(hand)-len(item))\\n if new>maxv:\\n maxv=new\\n tup=item\\n \\n \\n \\n return (maxv, tup)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5fafded03c4707514f6304d2d059619\",\n \"score\": \"0.54158634\",\n \"text\": \"def max(arg1, arg2):\\n if arg1 > arg2:\\n maxValue = arg1\\n else:\\n maxValue = arg2 \\n return maxValue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f5d73618511597fcc0dbb455446782e\",\n \"score\": \"0.54139066\",\n \"text\": \"def maxs(self):\\n return np.array([self.x_max, self.y_max, self.z_max])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57cf9a7b13d5e17a9be2d7b6b0dab3ec\",\n \"score\": \"0.54137444\",\n \"text\": \"def make_max(*, low, high):\\n \\n def inner(first, *args):\\n result = float('-inf')\\n \\n for arg in (first,) + args:\\n if arg > result and low < arg < high:\\n result = arg\\n return result\\n \\n return inner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ea495813faad8af8117e639456a149\",\n \"score\": \"0.5400484\",\n \"text\": \"def get_max_with_many_arguments(*args):\\n result = float('-inf')\\n for arg in args:\\n if arg > result:\\n result = arg\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c9f3de3462f6ab8d0a40460ad9f9227\",\n \"score\": \"0.5394334\",\n \"text\": \"def max(*args) -> \\\"itkFixedArraySL2 const\\\":\\n return _itkNumericTraitsPython.itkNumericTraitsFASL2_max(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89c722185e4af3964ef1498b976587f6\",\n \"score\": \"0.53843933\",\n \"text\": \"def check_max_probability(self, x1, x2, x3):\\n probabilities = dict()\\n classes = list(set(self.y))\\n classes.sort()\\n for r in range(len(classes)):\\n probabilities[classes[r]] = self.check_overall_probability(x1, x2, x3, classes[r], r)\\n return max(probabilities, key=probabilities.get)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bca5fc902b8446fb3d623a7112d5ff20\",\n \"score\": \"0.53825563\",\n \"text\": \"def max(*args) -> \\\"itkFixedArraySS2 const\\\":\\n return _itkNumericTraitsPython.itkNumericTraitsFASS2_max(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"965f1e3ddd96819ea0cadc6210a52beb\",\n \"score\": \"0.53778625\",\n \"text\": \"def most_similar(func):\\n def sim_apply(x, y):\\n max_sim = 0\\n x = x.split()\\n y = y.split()\\n for token_x in x:\\n for token_y in y:\\n max_sim = max(func(token_x,token_y), max_sim)\\n return max_sim\\n return sim_apply\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91b894152762942ebef0a647e9f25c4d\",\n \"score\": \"0.5374386\",\n \"text\": \"def dfs_maximizing(state) :\\n\\n \\\"\\\"\\\"if state.is_game_over():\\n return ([state], state.get_endgame_score(is_current_player_maximizer=False),1) # maximizer = False here, interesting\\n else:\\n new_states=state.generate_next_states()\\n new_states=sorted(new_states, key = lambda new_state : dfs_maximizing(new_state)[1],reverse = True)\\n statistic=0\\n for i in new_states:\\n statistic+=dfs_maximizing(i)[2]\\n return ([state]+dfs_maximizing(new_states[0])[0], dfs_maximizing(new_states[0])[1], statistic)\\\"\\\"\\\"\\n if state.is_game_over():\\n return ([state], state.get_endgame_score(is_current_player_maximizer=False),1)\\n else:\\n maxvalue=-INF\\n maxstate=None\\n statistic=0\\n for new_state in state.generate_next_states():\\n tem=dfs_maximizing(new_state)\\n statistic+=tem[2]\\n if maxvalue < tem[1]:\\n maxstate=new_state\\n maxvalue=tem[1]\\n return ([state]+dfs_maximizing(maxstate)[0], dfs_maximizing(maxstate)[1], statistic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90545d67a462bb169563a48b8308b7b8\",\n \"score\": \"0.5373349\",\n \"text\": \"def find_max_val(self):\\n max_val = 0\\n max_n = None\\n\\n for n in range(1, LIMIT):\\n val = self.n_over_phi(n)\\n if val > max_val:\\n max_val = val\\n max_n = n\\n\\n return (max_val, max_n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fd8092a39d933d563af94735b24c9ae\",\n \"score\": \"0.53727996\",\n \"text\": \"def maximum(self):\\n maximum = self.arr[0]\\n for elem in self.arr:\\n if elem > maximum:\\n maximum = elem\\n return maximum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f2344f590810e937b810171d34f6122\",\n \"score\": \"0.53690714\",\n \"text\": \"def best_predictor(user, restaurants, feature_fns):\\n '''\\n Q: In best_predictor, what does the variable reviewed represent?\\n Choose the number of the correct choice:\\n 0) a list of restaurants reviewed by the user\\n 1) a list of all possible restaurants\\n 2) a list of ratings for restaurants reviewed by the user\\n 0\\n '''\\n reviewed = user_reviewed_restaurants(user, restaurants)\\n # BEGIN Question 8\\n \\\"*** REPLACE THIS LINE ***\\\"\\n max_feature = max(feature_fns, key = lambda x: find_predictor(user, reviewed, x)[1])\\n return find_predictor(user, reviewed, max_feature)[0]\\n # END Question 8\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993878,"cells":{"query_id":{"kind":"string","value":"3e47af91f1595aed11a246fcddf82f72"},"query":{"kind":"string","value":"Validate a plugin directory is valid"},"positive_passages":{"kind":"list like","value":[{"docid":"8498a80008398200bfe4ac03ccbcad22","score":"0.0","text":"def _validate(config_module: ModuleType, path: Path) -> None:\n ConfigLoader._entry_point(config_module, path)\n ConfigLoader._instances(config_module)\n ConfigLoader._args(config_module)\n ConfigLoader._environment(config_module)","title":""}],"string":"[\n {\n \"docid\": \"8498a80008398200bfe4ac03ccbcad22\",\n \"score\": \"0.0\",\n \"text\": \"def _validate(config_module: ModuleType, path: Path) -> None:\\n ConfigLoader._entry_point(config_module, path)\\n ConfigLoader._instances(config_module)\\n ConfigLoader._args(config_module)\\n ConfigLoader._environment(config_module)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8cc68037e5571eb79ecfb4d34738a791","score":"0.6602469","text":"async def validate_and_import_directory(self, **kwargs) -> str:","title":""},{"docid":"c1f03f4d1fa2e7a0349e0ef3c5ad2174","score":"0.6583628","text":"def validate_directories(featured_mod):\n # type: (FeaturedMod) -> None\n fail = False\n\n if not os.path.exists(featured_mod.git_folder()):\n logger.error(\n \"You did not configure an existing git directory: {}\".format(\n featured_mod.git_folder()\n )\n )\n fail = True\n\n if not os.path.exists(featured_mod.content_folder()):\n logger.error(\n \"You either setup the wrong content directory or did not create it yet: {}\".format(\n featured_mod.content_folder()\n )\n )\n fail = True\n\n if fail:\n exit(1)","title":""},{"docid":"17b439951e72e32710a8995613107a2d","score":"0.64337206","text":"def _is_valid_dir(dir):\n\treturn _is_valid_file(dir)","title":""},{"docid":"467987c42017488d98130c6776fd9c1b","score":"0.64184076","text":"def dirvalidate(d):\n d = str(d)\n if not os.path.isdir(d):\n raise argparse.ArgumentError(\"Cannot find {}\".format(d))\n\n return d","title":""},{"docid":"fb68de52bfab4db0b2dec9111f1d1fb4","score":"0.6353261","text":"def valid_comp_from_dir(component):\n return os.path.isdir(os.path.join('src', component)) \\\n or os.path.isdir(os.path.join('src', 'client', component)) \\\n or os.path.isdir(os.path.join('utils', component))","title":""},{"docid":"b75189a318d552fe8e361eb769fc6203","score":"0.63296074","text":"def validate_output_dir(output_dir):\n return os.path.isdir(output_dir)","title":""},{"docid":"64b1da39d2fcd4729e8a8f5fcee19eaa","score":"0.63164395","text":"def looksValid(self, aQDir):\n return True","title":""},{"docid":"976b9fd50256ea5cc034cabff54e21b6","score":"0.6185084","text":"def validate_paths(self):\n corpus = self.corpus_path.get()\n output = self.output_path.get()\n\n if not os.path.isdir(corpus) or not os.path.isdir(output):\n self.pop_up_msg(\"please choose a valid directory\")\n return False\n else:\n return True","title":""},{"docid":"dbbbf7da8aebdc5e8a4ab1a548f87eec","score":"0.6177451","text":"def validate(args):\n if not args.source_path:\n print(\"Error: You need to specify a source path.\")\n return False\n else:\n if not os.path.exists(args.source_path):\n print(\"Error: Source path is not a folder or file.\")\n return False \n\n return True","title":""},{"docid":"beb6c6db9888a2ffd07ddffb4db96bf9","score":"0.6145644","text":"def validate_args(parsed):\n src = parsed['src']\n dest = parsed['dest']\n if not os.path.isfile(src):\n raise ValueError('src=\"%s\" is not a file'%src)\n if not (src.endswith('.tex') or src.endswith('.lyx')):\n raise ValueError('src=\"%s\" is not a .lyx or .tex file'%src)\n if not os.path.isdir(dest):\n try:\n os.makedirs(dest)\n except:\n pass\n if not os.path.isdir(dest):\n raise ValueError('dest=\"%s\" is not a directory'%dest)","title":""},{"docid":"f6ed766eb8814979723888ca44b98fdb","score":"0.6119335","text":"def validated_directory(path: str) -> str:\n if not os.path.isdir(path):\n raise argparse.ArgumentTypeError(\n \"\"\"Directory '{}' doesn't exist in this directory. Check yourself!\"\"\".\n format(path))\n return path","title":""},{"docid":"9023673ef715e9c9f29727c432020b56","score":"0.6099975","text":"def validate_dir(dir_path, src=True):\n if src:\n if os.path.isdir(dir_path):\n if not os.listdir(dir_path):\n raise FileNotFoundError(\n 'No files found in source directory {}'.format(dir_path))\n else:\n raise FileNotFoundError(\n 'Source directory {} not found.'.format(dir_path))\n else:\n if os.path.isdir(dir_path):\n if os.listdir(dir_path):\n warnings.warn(\n 'Destination directory {} already has some files'.format(dir_path))\n else:\n warnings.warn(\n 'Destination {} directory does not exist so creating it now'.format(dir_path))\n os.makedirs(dir_path, exist_ok=True)","title":""},{"docid":"c8ba6a9e59968e57a9bb6d215aa70f25","score":"0.6054886","text":"def is_valid_directory(path):\n\n return os.path.exists(os.path.dirname(path))","title":""},{"docid":"91d64019087f7a5bffeebbd0e53f806e","score":"0.60383","text":"def validate_dir(ctx: Context, param: Parameter, value: Any) -> Any:\n # Do NOT check permission for 'parse' command\n # if option '--with-html' was not provided\n if \"html_yes\" in ctx.params:\n if not ctx.params[\"html_yes\"] and param.name == \"html_dir\":\n return value\n try:\n value.mkdir(parents=True, exist_ok=True)\n probe_file = value / \"tmp_dest.txt\"\n probe_file.write_text(\"Directory is writable\", encoding=\"utf-8\")\n probe_file.unlink()\n return value\n except PermissionError as ex:\n raise click.BadParameter(\"folder has no 'write' permission.\") from ex\n except OSError as ex:\n raise click.BadParameter(ex.args[1]) from ex","title":""},{"docid":"bf5f5a1837e55da99165fd7f186fadbd","score":"0.6001829","text":"def checkValidFilePath(filepath, makeValid=True):\r\n folder = os.path.split(os.path.abspath(filepath))[0]\r\n if not os.path.isdir(folder):\r\n os.makedirs(folder) #spit an error if we fail\r\n return True","title":""},{"docid":"bf249357f602e1fed009df37590d2cb3","score":"0.5966237","text":"def validate_directory(self):\n\n if not os.path.isdir(self.directory):\n raise AddressBookValueError('Provide a valid directory path')","title":""},{"docid":"83712847b0c609c9b07ae232101f2ef1","score":"0.58816403","text":"def ValidateDirPath(path):\n if not path or not isinstance(path, basestring):\n raise ValueError('Path is invalid: %r' % path)\n _ValidateCommonPath(path)","title":""},{"docid":"aa4f0e647896ef7b79c34b0d9faca11b","score":"0.5868731","text":"def is_valid_dir(ctx, param, value):\n if not os.path.isdir(value) or not os.path.exists(value):\n raise click.BadParameter(f\"Invalid directory path to {value}\")\n return value","title":""},{"docid":"1059f82ed3274aa640549ddc32a40a3a","score":"0.5866662","text":"def check_if_directory_is_valid(self):\n is_valid = path.isdir(self.directory)\n\n if is_valid:\n files = [f for f in listdir(self.directory) if\n path.isfile(path.join(self.directory, f))]\n\n # If the prefix is empty or changed (i.e not the same as currently displayed), we\n # check and edit the text of the widget\n if self.prefix == \"\" or self.prefix != self.__widget_edit_prefix.currentText():\n is_valid = False\n for f in files:\n if path.splitext(f)[1] == \".json\" and f.startswith(\"MetaData-\"):\n self.__widget_edit_prefix.addItem(\n path.splitext(f)[0].replace(\"MetaData-\", \"\"))\n is_valid = True\n # In case the prefix has not changed and is not empty, we just check\n else:\n self.check_if_prefix_is_valid()\n return\n\n if is_valid:\n self.show_valid_icon()\n else:\n self.show_invalid_icon()","title":""},{"docid":"e4bd8c577f8ecf2e3a652cae5ce60787","score":"0.585093","text":"def is_valid_dir(aparser, arg):\n\n if not os.path.isdir(arg):\n aparser.error(\"Invalid directory %s\" % arg)\n elif not os.access(arg, os.W_OK | os.X_OK):\n aparser.error(\"Can't write to %s\" % arg)\n else:\n return arg","title":""},{"docid":"e1d8c58d4a00310cac1a0ddae752ab23","score":"0.5842716","text":"def validate_dataset(path_to_validate):\n if not os.path.exists(path_to_validate):\n raise FileNotFoundError\n\n files = os.listdir(path_to_validate)\n if not files:\n raise EmptyDirectoryError\n\n if not os.path.isdir(path_to_validate):\n raise NotADirectoryError","title":""},{"docid":"ff31a7aaccad4f311f6e60156b48f0d1","score":"0.58406055","text":"def _check_path(self, _, value):\n if not (value.exists() and value.is_dir()):\n raise ValueError(\"Define an existing directory.\")","title":""},{"docid":"90f7508497409c08f1f869cf59c71b51","score":"0.583916","text":"def _DirCheck(path):\n path = _PathCheck(path)\n if not os.path.isdir(path):\n raise ValueError(_('Not a directory: %s') % path)\n return path","title":""},{"docid":"d5fe21a5d762688f319c0b1feb865ccf","score":"0.5835518","text":"def verify_path(path):\n\n if not os.path.exists(path) or not os.path.isdir(path):\n error('Path specified for project creation does not exist or is not a directory')","title":""},{"docid":"48e0b255d084bc0f2abbd293e9149cb4","score":"0.58246076","text":"def validatedirs(ctx, file, basedir, exitonfail):\n Result, Count = validate_dirs(file, basedir, exitonfail)\n if not Result:\n sys.exit(1)\n else:\n sys.exit(0)","title":""},{"docid":"8b8d7654d846db274cc2a431df33b718","score":"0.5808446","text":"def validate_cmd_param(args):\n if not os.path.isdir(args.input_dir):\n raise ValueError('[ERROR] The input folder does not exist.')\n if os.path.isdir(args.output_dir):\n raise ValueError('[ERROR] The output folder already exists.')","title":""},{"docid":"df01a6cf3813592276fb8e4f9f243c74","score":"0.57967055","text":"def validate_dataset(path_to_validate):\n if not os.path.exists(path_to_validate):\n raise FileNotFoundError\n if not os.path.isdir(path_to_validate):\n raise NotADirectoryError\n if not os.listdir(path_to_validate):\n raise EmptyDirectoryError","title":""},{"docid":"66c4696c2cd6ffea2a252d7cafded563","score":"0.57792735","text":"def __validate_folders(args):\n if not args.train and not args.export:\n if os.path.isdir(output_dir):\n raise Exception(\n output_dir +\n \" is an existing folder. Please either delete it or rename it.\")\n\n if not os.path.isdir(lasertagger_dir):\n raise Exception(\"LaserTagger not found.\")\n if not os.path.isdir(os.path.expanduser(lasertagger_dir + \"/bert\")):\n raise Exception(\"Bert not found inside the LaserTagger folder.\")\n\n if not os.path.isdir(os.path.expanduser(bert_dir)):\n raise Exception(\"Pretrained Bert model not found.\")","title":""},{"docid":"19eb1951df85eda63fa9484ce36fd284","score":"0.57376575","text":"def validate_extra_rule_directory(self, rule_directory):\n if self.debug:\n print('CustomRuleLoader - validate_extra_rule_directory'+str(lineno()))\n print('rule_directory: '+str(rule_directory)+lineno())\n\n if rule_directory == None:\n return True\n else:\n return os.path.isdir(rule_directory)","title":""},{"docid":"dc822fd7469b69c8f6ce9d2b13c5c5ea","score":"0.5687693","text":"def check_path(dir_path):\n if not os.path.exists(dir_path):\n print(\"\\n[!] ERROR -> '{}' is NOT a valid Directory ...\\n\".format(dir_path))\n print(\"\\n******* ******* *******\")\n sys.exit(1)","title":""},{"docid":"29f90324a4e9e6ed1f14a1414529f2b9","score":"0.5675421","text":"def validate_package_path(path):\n if not os.path.isdir(path):\n raise ValueError(\"Path '{0}' is not a directory or does not exist\"\n .format(path))\n if not package_exists_at(path):\n raise ValueError(\"Path '{0}' does not contain a package\".format(path))","title":""},{"docid":"2f47c9895fb166147791fcce8b7045ce","score":"0.5672126","text":"def check_input(source):\n if isdir(source):\n pass\n else:\n sys.exit(u'ERROR: The source directory does not exist, aborted.')","title":""},{"docid":"20db0dd206c5f93331d13d7948e0a1e2","score":"0.56552845","text":"def validate(parser, args, from_build_command):\n\n # Make plugins always a list. (It might be None when not specified.)\n args.plugins = args.plugins if args.plugins else []\n\n if args.help_checkers_verbose:\n print_checkers(get_checkers(args.clang, args.plugins))\n parser.exit()\n elif args.help_checkers:\n print_active_checkers(get_checkers(args.clang, args.plugins))\n parser.exit()\n\n if from_build_command and not args.build:\n parser.error('missing build command')","title":""},{"docid":"cf85131a00cc56353cd2b0224a12d298","score":"0.56540745","text":"def validate(self):\n for filename in [self.rmd_file, self.blogdown_file]:\n if not path.exists(path.join(self.source_folder, filename)):\n click.secho('Missing file: {}'.format(filename), fg='red')\n sys.exit(1)","title":""},{"docid":"1bcb098409c0e77718f06e149b11b5e5","score":"0.56466794","text":"def check_dir(self,path):\n\t\tp=path\n\t\tif not path.endswith(\"/\"):\n\t\t\traise ValueError(\"Provided Invalid Path\")\n\t\tif not os.path.exists(path):\n\t\t\traise ValueError(\"Path does not exists\")\n\t\telse:\n\t\t\tfile_l=os.listdir(path)\n\t\t\tself.formats=checkformat(file_l)\n\t\t\tclass_list=list(self.formats.keys())\n\t\t\tself.classes=class_list\n\t\tself.root_path=p","title":""},{"docid":"cc746f3d61264fc56e6710b153475b75","score":"0.56441474","text":"def get_plugin_dir(self, f): # f should be __file__\n d = f.split(os.sep)[-2]\n return os.path.abspath( os.path.join(\"plugins\", d) )","title":""},{"docid":"58970045118f87db535a77d484809d11","score":"0.5638573","text":"def validate_config(ctx, path):\n with OpsDroid() as opsdroid:\n loader = Loader(opsdroid)\n\n config = load_config_file([path] if path else DEFAULT_CONFIG_LOCATIONS)\n\n loader.load_modules_from_config(config)\n click.echo(\"Configuration validated - No errors founds!\")\n\n ctx.exit(0)","title":""},{"docid":"2d30ae4373337ee469119bffdb646df1","score":"0.5625538","text":"def test_yaml_discovery_plugin_scan_valid():\n ydp = YAMLDiscoveryPlugin()\n package = Package(\n \"valid_package\", os.path.join(os.path.dirname(__file__), \"valid_package\")\n )\n ydp.scan(package, \"level\")\n expected = [\"test.yaml\", \"test.yml\", os.path.join(\"ignore_this\", \"ignoreme.yaml\")]\n # We have to add the path to each of the above...yuck\n expected_fullpath = [os.path.join(package.path, filename) for filename in expected]\n # Neat trick to verify that two unordered lists are the same\n assert set(package[\"yaml\"]) == set(expected_fullpath)","title":""},{"docid":"3b9d8069612e473fab559109ebec6aea","score":"0.5624083","text":"def check_dirs():\n full_src = os.path.abspath(SRC)\n full_dest = os.path.abspath(DEST)\n if os.path.isdir(full_src) or os.path.isfile(full_src):\n if not os.path.exists(full_dest) and os.path.isdir(full_src):\n if not mkdir_p(full_dest):\n error_trig(\"something went wrong\")\n else:\n error_trig(\"backup.py: cannot stat '\" + SRC + \"': No such file or \\\n directory\")","title":""},{"docid":"baa9b169cc220b01d108be98ce36eb9a","score":"0.55966705","text":"def _entry_point(config_module, path: Path) -> None:\n entry_point = getattr(config_module, ConfigKeys.PLUGIN_ENTRY.name, None)\n\n if not entry_point:\n return\n\n if (path / entry_point).is_file():\n return\n\n entry_parts = entry_point.split(\" \")\n pkg = entry_parts[1] if entry_parts[0] == \"-m\" else entry_parts[0]\n pkg_path = path / pkg\n\n if (\n pkg_path.is_dir()\n and (pkg_path / \"__init__.py\").is_file()\n and (pkg_path / \"__main__.py\").is_file()\n ):\n return\n\n raise PluginValidationError(\n f\"{ConfigKeys.PLUGIN_ENTRY.name} '{entry_point}' must be a Python file or a \"\n f\"runnable package\"\n )","title":""},{"docid":"b5336409b5952866d9969e851ad9e667","score":"0.55943686","text":"def _checkDir(self, filename):\n\t\tif len(path.dirname(filename)) > 0 and not path.exists(path.dirname(filename)):\n\t\t\tmakedirs(path.dirname(filename))","title":""},{"docid":"5a9ca7fd1ecf76e97e6292f3063ee7fb","score":"0.55832845","text":"def verify_input(FLAGS):\n\n if not os.path.exists(FLAGS.directory_dir):\n raise AssertionError('Image directory not found.')","title":""},{"docid":"4e6d4b2bba492f921d75231ff25ffc24","score":"0.55688405","text":"def check_path(cls, dir):\n\t\tif not path.exists(dir):\n\t\t\tos.makedirs(dir)","title":""},{"docid":"73b3f7700e7e1dfb4098e26d36b295d2","score":"0.55619943","text":"def __validate_files(file):\n if not os.path.isfile(os.path.expanduser(file)):\n raise Exception(\"File \" + str(file) + \" not found/\")","title":""},{"docid":"06d31ccbbbd231499296c0a209916907","score":"0.55609834","text":"def validate_dir_path(path: Union[str, Path]) -> Path:\n if os.path.isdir(path):\n return path\n else:\n raise ArgumentTypeError(f\"`{path}` is not a valid path for directory\")","title":""},{"docid":"0c35295b209b1c8cc4cda8f35dfe9532","score":"0.5557613","text":"def find_plugins_dir():\n dir = os.path.join(os.path.expanduser(\"~\"), '.sasview', PLUGIN_DIR)\n\n # If the plugin directory doesn't exist, create it\n if not os.path.isdir(dir):\n os.makedirs(dir)\n\n # Find paths needed\n try:\n # For source\n if os.path.isdir(os.path.dirname(__file__)):\n p_dir = os.path.join(os.path.dirname(__file__), PLUGIN_DIR)\n else:\n raise\n except:\n # Check for data path next to exe/zip file.\n #Look for maximum n_dir up of the current dir to find plugins dir\n n_dir = 12\n p_dir = None\n f_dir = os.path.join(os.path.dirname(__file__))\n for i in range(n_dir):\n if i > 1:\n f_dir, _ = os.path.split(f_dir)\n plugin_path = os.path.join(f_dir, PLUGIN_DIR)\n if os.path.isdir(plugin_path):\n p_dir = plugin_path\n break\n if not p_dir:\n raise\n # Place example user models as needed\n if os.path.isdir(p_dir):\n for file in os.listdir(p_dir):\n file_path = os.path.join(p_dir, file)\n if os.path.isfile(file_path):\n if file.split(\".\")[-1] == 'py' and\\\n file.split(\".\")[0] != '__init__':\n if not os.path.isfile(os.path.join(dir, file)):\n shutil.copy(file_path, dir)\n\n return dir","title":""},{"docid":"9ee91618e9fa6597176dd250994e2d04","score":"0.5541999","text":"def check_dir(filepath):\n d = os.path.dirname(filepath)\n if not os.path.exists(d):\n os.makedirs(d)","title":""},{"docid":"fbc6b08fe691b4eff3d10cb8554b825b","score":"0.5524567","text":"def validateLibrary(libraryName, exclude):\n if module in exclude:\n return False;\n for folder in libpath[system()]:\n for file in listdir(folder):\n if (libraryName + \".\" in file):\n return True\n return True# False","title":""},{"docid":"e249baf002a25401879a980bc3861860","score":"0.5522382","text":"def test_directory(self):\n self.assertIn(self.audio.resources, RESOURCES)","title":""},{"docid":"ad347f78e232c49443f76f9574ce06fb","score":"0.55222845","text":"def test_is_directory(self):\n msg = '`PROJECT_ROOT` must be a directory.'\n\n self.assertTrue(os.path.isdir(lmp.path.PROJECT_ROOT), msg=msg)","title":""},{"docid":"296f882f964199ed47619be9253787f9","score":"0.55126685","text":"def scan_for_plugins(directory: str):\n base_dir = pathlib.Path(directory)\n if base_dir.exists() and base_dir.is_dir():\n sys.path.append(base_dir.as_posix())\n plugs = [p for p in base_dir.iterdir() if p.is_dir()]\n for plug in plugs:\n importlib.import_module(plug.name)\n else:\n raise ValueError('The argument passed is not a directory')","title":""},{"docid":"565cc8743f6832276222a6072d051fc8","score":"0.55081046","text":"def _is_valid(f, cap_and_dig=capital_and_digits):\n _is_valid = \\\n f.name.startswith(cap_and_dig) \\\n and f.is_dir()\n return _is_valid","title":""},{"docid":"de9e6c943abcd2bc0f129f98cb13f156","score":"0.5507245","text":"def test_register_plugin_assets_directory():\n app = create_ctfd(setup=False)\n register_plugin_assets_directory(app, base_path='/plugins/')\n app = setup_ctfd(app)\n with app.app_context():\n with app.test_client() as client:\n r = client.get('/plugins/__init__.py')\n assert len(r.get_data(as_text=True)) > 0\n assert r.status_code == 200\n r = client.get('/plugins/challenges/__init__.py')\n assert len(r.get_data(as_text=True)) > 0\n assert r.status_code == 200\n destroy_ctfd(app)","title":""},{"docid":"c6eee9211f981bb19ece9872abd4748b","score":"0.55020654","text":"def test_libdir(self):\n self.check_argument('libdir')","title":""},{"docid":"dce7d2c9a8cded7616a28a606f386d7a","score":"0.5497392","text":"def validate():\n paths = dict()\n paths['rF2root'] = os.path.isdir(rF2root)\n paths['SteamExe'] = os.path.isfile(SteamExe)\n paths['DiscordExe'] = os.path.isfile(DiscordExe)\n paths['CrewChiefExe'] = os.path.isfile(CrewChiefExe)\n paths['VolumeControlExe'] = os.path.isfile(VolumeControlExe)\n paths['TeamSpeakExe'] = os.path.isfile(TeamSpeakExe)\n\n paths['playerPath'] = os.path.isdir(playerPath)\n # Check rF2 install a little further\n _vehicles = os.path.join(rF2root, 'Installed', 'Vehicles')\n paths['vehicles'] = os.path.isdir(_vehicles)\n\n return paths","title":""},{"docid":"fac19974d418002d6d55208673441717","score":"0.54900193","text":"def check_plugins():\n _check_command('plugman')\n\n json_file = os.path.join(_get_source()[1], 'theme', 'project.json')\n if os.path.exists(json_file):\n\n def version_check(name, version, latest_version):\n if version != latest_version:\n print '*** {0}@{1} not using latest version {2} ***\\n'.format(\n name, version, latest_version)\n else:\n print '{0} up to date\\n'.format(name)\n\n plugins = json.loads(open(json_file).read())['plugins']['cordova']\n for plugin in plugins:\n if '@' in plugin:\n # plugin registry\n name, version = plugin.split('@')\n out = local('plugman info {0}'.format(name), capture=True)\n lines = out.split('\\n')\n for line in lines:\n info = line.split(':')\n if info[0] == 'version':\n latest_version = info[1].strip()\n version_check(name, version, latest_version)\n elif plugin[:14] == 'https://github':\n # github repo\n if '#' in plugin:\n url, version = plugin.split('#')\n repo = url.split('github.com/')[1].replace('.git', '')\n api_url = 'https://api.github.com/repos/{0}/tags'.format(repo)\n out = local('curl {0}'.format(api_url), capture=True)\n latest_version = json.loads(out)[0]['name']\n version_check(url, version, latest_version)\n else:\n print 'Where is the plugins file?: {0}'.format(json_file)\n exit(-1)","title":""},{"docid":"2eaabc13aa89efffb93d474806ac1737","score":"0.5485844","text":"def _check_dir_contents_single(dir: Path):\n assert dir.joinpath(\"meta.json\").is_file(), f\"meta.json is missing in {dir}\"\n assert dir.joinpath(\"part.0.parquet\"), f\"part.0.parquet is missing in {dir}\"","title":""},{"docid":"d17c60a2b31b698a81507fd196b5c549","score":"0.54841363","text":"def is_valid_project(self, path):\r\n spy_project_dir = osp.join(path, '.spyproject')\r\n if osp.isdir(path) and osp.isdir(spy_project_dir):\r\n return True\r\n else:\r\n return False","title":""},{"docid":"46b018631da363df5452c9b336a64857","score":"0.5483685","text":"def qt_plugins_dir(namespace):\n qt_info = get_qt_library_info(namespace)\n paths = [qt_info.location['PluginsPath']]\n if not paths:\n raise Exception('Cannot find {0} plugin directories'.format(namespace))\n else:\n valid_paths = []\n for path in paths:\n if os.path.isdir(path):\n valid_paths.append(str(path)) # must be 8-bit chars for one-file builds\n qt_plugin_paths = valid_paths\n if not qt_plugin_paths:\n raise Exception(\"\"\"\n Cannot find existing {0} plugin directories\n Paths checked: {1}\n \"\"\".format(namespace, \", \".join(paths)))\n return qt_plugin_paths","title":""},{"docid":"42ad234f14f7c3e87620f4be934904a9","score":"0.54812443","text":"def validate_resources(self, folder, resources):\r\n self.validate_files_exist(folder, resources)\r\n self.validate_no_duplicate_paths(resources)","title":""},{"docid":"b8a5bf1ec257cf266ab76b1df68fd0bf","score":"0.5461568","text":"def test_with_valid_sites(self):\n # This should not raise.\n validate_site_paths([os.path.dirname(__file__)])","title":""},{"docid":"d835a778cd5d849b5004468fc5c29fff","score":"0.54466915","text":"def test_validate_subdir(self):\n self._copy_file(join(\"files\", \"open_relative_file.ipynb\"), \"my_subdir/open_relative_file.ipynb\")\n self._copy_file(join(\"files\", \"data.txt\"), \"my_subdir/data.txt\")\n output = run_nbgrader([\"validate\", \"my_subdir/open_relative_file.ipynb\"], stdout=True)\n assert output.strip() == \"Success! Your notebook passes all the tests.\"","title":""},{"docid":"27e21c948f0de5db114b4678cd6a4b2e","score":"0.5446441","text":"def is_dir():\n return False","title":""},{"docid":"6283475b9c71d2d99e269a771d148d09","score":"0.54309785","text":"def validate_file_option(self, args):\n try:\n self.output_path = args.output_path\n print(\"output destination set to %s\" % self.output_path)\n except OSError:\n print(\"Creation of the directory %s failed\" % self.output_path)\n else:\n pass","title":""},{"docid":"7f845bb95ea66846f83999883e281cf2","score":"0.5430167","text":"def validate_directories(self):\n assert os.path.exists(self.results_dir)\n assert os.path.exists(self.latest_symlink)\n assert os.path.islink(self.latest_symlink)\n\n # check symlink points to correct location\n assert os.path.realpath(self.latest_symlink) == os.path.realpath(self.results_dir)","title":""},{"docid":"d882090aee449df45a03fe38cc7b950a","score":"0.5428251","text":"def check():\n assert os.name == \"posix\", \"only for linux!!!\"\n assert CFG.base_dir != None, \"Please set Config.base_dir!\"\n assert CFG.sort_dir != None, \"Please set Config.sort_dir!\"\n\n assert CFG.use_tags >= 1\n\n CFG.base_dir = os.path.expanduser(CFG.base_dir)\n CFG.sort_dir = os.path.expanduser(CFG.sort_dir)\n\n assert os.path.isdir(CFG.base_dir), \\\n \"CFG.base_dir path '%s' not exist!\" % CFG.base_dir\n assert os.path.isdir(CFG.sort_dir), \"Please create CFG.sort_dir path!\"","title":""},{"docid":"f73dc9a6cacd00da4411b89d0a4fcea0","score":"0.542701","text":"def validated_package(path: str) -> u.Package:\n p = ufs.generate_package(path)\n\n if not os.path.isdir(p.package_path):\n raise argparse.ArgumentTypeError(\n \"\"\"Directory '{}' doesn't exist in directory. Code must be\nnested in a folder that exists in the current directory.\"\"\".format(\n p.package_path))\n\n filename = p.script_path\n if not ufs.file_exists_in_cwd(filename):\n raise argparse.ArgumentTypeError(\n \"\"\"File '{}' doesn't exist locally as a script or python module; code\nmust live inside the current directory.\"\"\".format(filename))\n\n return p","title":""},{"docid":"ca6b147dac24590f942d77f04878f083","score":"0.54179597","text":"def check_directory():\n if os.path.exists(\"turbogears\") or os.path.exists(\"TurboGears\"):\n print \"\"\"There is a 'turbogears' directory in the current directory.\n EasyInstall will normally try to install from this directory rather\n than picking up the installation package from the net.\"\"\"\n if os.access(\"..\", os.W_OK) and not \\\n os.path.exists(os.path.join(\"..\", \"turbogears\")) and not \\\n os.path.exists(os.path.join(\"..\", \"TurboGears\")):\n print \"\"\"\nThis installer needs a place to store the setuptools installation\npackage to do its work. The parent directory of this one is writeable.\nShould the installation proceed from there ('n' to exit)?\"\"\"\n upone = get_yesno()\n if upone == \"n\":\n sys.exit(1)\n os.chdir(\"..\")\n else:\n print \"\"\"\nThis installer needs to run in a directory that does not contain a\n'turbogears' directory and it also needs to run in a directory that\nyou have write access to. Please change directories and rerun this\ninstaller.\n\"\"\"\n sys.exit(1)","title":""},{"docid":"ed3f9cabb04029abda48f2e6b9720eed","score":"0.5416582","text":"def test_valid_path_to_directory(self):\n path = os.path.abspath(os.curdir)\n mock_logger = MagicMock(return_value=None)\n self.actual = functions.check_path_to_directory(path, mock_logger)\n self.assertEqual(self.actual, True)","title":""},{"docid":"01d0958e7f49d1ae2af8f42fbbed01b5","score":"0.54139924","text":"def check_dir(dir_fullpath:str):\n if not os.path.isdir(dir_fullpath):\n os.mkdir(dir_fullpath)","title":""},{"docid":"88d32bf0b8aebeb03415606c4ffd4ee3","score":"0.54060996","text":"def plugin_dir(self):\n return self.tmpdir(\"plugins\")","title":""},{"docid":"05efb5a9015b4a64b8fd25547ee75e05","score":"0.54025733","text":"def _is_plugin_installed(self):","title":""},{"docid":"7390ee4397c38b6be2f6b142b4c4f9da","score":"0.54024583","text":"def check_dir(adir):\n if not os.path.exists(adir):\n return False\n if not os.path.isdir(adir):\n return False\n return True","title":""},{"docid":"f9f10d3af0914d1af223bf0c25343c6d","score":"0.54001796","text":"def valid_path(path: str):\n if _path.exists(path):\n if _path.isdir(path):\n return path\n\n return False","title":""},{"docid":"6b1beecb0a8105e8982867a54b6743ba","score":"0.5377587","text":"def check_dir(f_path=\"\"):\n # check if an extension is in the file path parameter\n if \".\" in f_path:\n f_path = path.dirname(f_path)\n return path.isdir(f_path)","title":""},{"docid":"6760f2b070fd1489055f9ec94a2c4e5d","score":"0.537233","text":"def _validate_resource_path(path):\n invalid = (\n os.path.pardir in path.split(posixpath.sep) or\n posixpath.isabs(path) or\n ntpath.isabs(path)\n )\n if not invalid:\n return\n\n msg = \"Use of .. or absolute path in a resource path is not allowed.\"\n\n # Aggressively disallow Windows absolute paths\n if ntpath.isabs(path) and not posixpath.isabs(path):\n raise ValueError(msg)\n\n # for compatibility, warn; in future\n # raise ValueError(msg)\n warnings.warn(\n msg[:-1] + \" and will raise exceptions in a future release.\",\n DeprecationWarning,\n stacklevel=4,\n )","title":""},{"docid":"127e52d57d6d5a7c4d06cee480e0fa45","score":"0.53721845","text":"def ValidateDirectoryExistsOrRaiseFunctionError(directory):\n if not os.path.exists(directory):\n raise exceptions.FunctionsError(\n 'argument --source: Provided directory does not exist. If '\n 'you intended to provide a path to Google Cloud Repository, you must '\n 'specify the --source-url argument')\n if not os.path.isdir(directory):\n raise exceptions.FunctionsError(\n 'argument --source: Provided path does not point to a directory. If '\n 'you intended to provide a path to Google Cloud Repository, you must '\n 'specify the --source-url argument')\n return directory","title":""},{"docid":"6878f58ce970eb92952ae1230b7114f5","score":"0.53618604","text":"def _check_parent_dir_is_dir(self):\n if not self.parent.is_dir():\n message = \"Parent is not a directory or not defined\"\n logging.error(message)\n print(message)\n raise Exception(message)","title":""},{"docid":"005418bcdf63236cac8e20c78c5cb318","score":"0.53478897","text":"def validate_filesystem(self):\n if not os.path.exists(self.dir_path):\n raise FileNotFoundError(\n \"project directory at {} does not exist anymore\".format(self.dir_path)\n )","title":""},{"docid":"28401f0fbf46b0bb4c330d47f09e2e94","score":"0.5334039","text":"def sanity_check_step(self):\n\n custom_paths = {\n 'files': [\"bin/xhpl\"],\n 'dirs': []\n }\n\n super(EB_HPL, self).sanity_check_step(custom_paths)","title":""},{"docid":"bc89977840ac369f6afb291eb8c7e892","score":"0.53152955","text":"def validate_settings():\n importlib.import_module(f\"{PACKAGE_NAME}.settings\")","title":""},{"docid":"e96d49012af2c1a65bfa7b801a29186a","score":"0.5310148","text":"def _verify_config_dir(self):\n if not self.config_dir:\n raise ConfigError(\"Cannot find configuration directory\")","title":""},{"docid":"2c5f74ab4c0546b0a0225a6ea2b3dca9","score":"0.5308468","text":"def is_valid_directories(paths):\n\n for path in paths:\n if not os.path.exists(os.path.dirname(path)):\n return False\n\n return True","title":""},{"docid":"0ba6c7abc2ed273ee5b43b9ee7ecb27d","score":"0.5308053","text":"def patch_is_valid_directory(self, linter_class):\n patcher = mock.patch.object(linter_class, '_is_valid_directory', return_value=True)\n patch_start = patcher.start()\n self.addCleanup(patcher.stop)\n return patch_start","title":""},{"docid":"9d9bac265287ff0ec049d09d6cccb72e","score":"0.530617","text":"def loadPlugin(self, path):\r\n logging.debug(\"looking for plugin in %s\" % path)\r\n # if path is a folder\r\n if os.path.isdir(os.path.join(self.config.plugins.root, path)):\r\n\r\n # if found plugin in folder\r\n plugin_file_path = os.path.join(\r\n self.config.plugins.root, path, \"%s.py\" % path\r\n )\r\n if os.path.isfile(plugin_file_path):\r\n try:\r\n # try to import it\r\n logging.debug(\"- importing plugin %s module\" % path)\r\n plugin_module = imp.load_source(\r\n path,\r\n plugin_file_path\r\n )\r\n logging.debug(\"Imported plugin module %s\" % path)\r\n\r\n # TODO: change to only catch import errors here\r\n except Exception as e:\r\n logging.error(\r\n \"Error importing plugin %s: %s\" % (path, repr(e))\r\n )\r\n traceback.print_exc()\r\n return False\r\n\r\n if hasattr(plugin_module, 'Plugin'):\r\n try:\r\n logging.debug(\"Creating plugin\")\r\n plugin = plugin_module.Plugin(\r\n self,\r\n self.config\r\n )\r\n logging.debug(\"Loaded plugin %s\" % plugin.name)\r\n\r\n # loading external code - catch all exceptions\r\n except Exception as e:\r\n logging.error(\"Error loading plugin %s: %s\" % (\r\n plugin_file_path, repr(e))\r\n )\r\n return False\r\n\r\n # TODO: what does the comment below mean? does this work?\r\n # this probably doesnt work singly bc it sticks the same\r\n # plugin on the end of the list\r\n for p in self.plugins:\r\n if p.name == plugin.name:\r\n self.plugins.remove(p)\r\n self.plugins.append(plugin)\r\n return True\r\n\r\n return False","title":""},{"docid":"c427eb65187dc4d519ea99ce6cae8283","score":"0.53057575","text":"def _check_path(self, path):\n if not os.path.exists(path):\n return False\n\n localpath = os.path.basename(path)\n if localpath.startswith('.'):\n return False\n\n if not localpath.endswith('.yml'):\n return False\n\n return True","title":""},{"docid":"a306d7def8caca4e1f8f571248c37616","score":"0.5302555","text":"def sanitize_directory(dir):\n # Use with os.path.join, and do not sweat separator\n import os.path\n verbose('verifying directory: {}'.format(dir))\n if not(os.path.isdir(dir)):\n raise ValueError('directory {} does not exist'.format(dir))\n return dir","title":""},{"docid":"8a3e596fc95c46d3cbc502706f5aac4d","score":"0.52988696","text":"def load_plugins(dirs):\n\n dirs = [os.path.expanduser(d) for d in dirs]\n\n for directory in dirs:\n if os.path.isdir(directory):\n livestreamer.load_plugins(directory)\n else:\n console.logger.warning(\"Plugin path {0} does not exist or is not \"\n \"a directory!\", directory)","title":""},{"docid":"23fe3bb35bcc6424788d80b13b541180","score":"0.5293855","text":"def isValid( self ):\n check = str(self._filepathEdit.text()).split(os.path.pathsep)[0]\n if ( self.filepathMode() == XFilepathEdit.Mode.SaveFile ):\n check = os.path.dirname(check)\n \n return os.path.exists(check)","title":""},{"docid":"e97441f597e44df81d5bf5f262fd1783","score":"0.528566","text":"def _is_file_valid(name: str) -> bool:\n return not name.startswith(\".\")","title":""},{"docid":"48f5a1c623a9563b892cfc3afc87aea0","score":"0.5283608","text":"def test_init_succeeds_on_existing_dir(self):\n scanner = Scanner(base=LOCATION)\n self.assertIn(__file__, scanner.last_result)","title":""},{"docid":"81349bfc0d760047078581798ac6f891","score":"0.52830756","text":"def _clean_up_folders(plugin_list, dst_path):\r\n is_cleaned_up = False\r\n\r\n if os.path.isdir(dst_path) is False:\r\n pass\r\n else:\r\n\r\n dst_subfolders = []\r\n for dst_file in os.listdir(dst_path):\r\n if os.path.isdir(dst_path + \"/\" + dst_file) is True:\r\n dst_subfolders.append(dst_file)\r\n\r\n # Remove folders which exists only in the destination\r\n for folder in dst_subfolders:\r\n if folder not in plugin_list:\r\n dst_full_path = dst_path + \"/\" + folder\r\n print(f\"\\t\\\"{dst_full_path}\\\" removed.\")\r\n shutil.rmtree(dst_full_path)\r\n is_cleaned_up = True\r\n\r\n return is_cleaned_up","title":""},{"docid":"462f69ed1531e184e493e166aa71cdc5","score":"0.52828324","text":"def validate(self, pfile):\n \n return True","title":""},{"docid":"9234d6d5a30bda6ab5065c9d8d6d2386","score":"0.52822745","text":"def check_directory(directory):\n if os.path.exists(directory):\n return os.path.abspath(directory)\n raise argparse.ArgumentTypeError('invalid directory: %s' % directory)","title":""},{"docid":"ab24f1eee53df8bbdbbe3fd9535d9c69","score":"0.5280375","text":"def path_exists_check(self, dirName):\n if not os.path.isdir(dirName):\n sys.exit(\"%s does not exist\" % dirName)","title":""},{"docid":"6377a88d732312d15402ffaa34e1bc63","score":"0.527434","text":"def testCheckRelativePathWithReferenceToEnclosingDir(self):\n p=\"../path/to/dir\"\n self.assertRaises(ValidationError, checkrelpath, p)","title":""},{"docid":"1affddab6679a89cd052611e1d4f4e10","score":"0.5272382","text":"def test_data_dir_exists_in_source_control(data_dirpath: Path) -> None:\n assert data_dirpath.exists()","title":""},{"docid":"013be8b11186ad9cb3bb6491a7176f48","score":"0.52717346","text":"def _validate_dataset_dir(self, path):\n\n # check that everything is readable by the ingestion user\n errors = False\n have_ncdf = False\n\n messages = []\n permissions = []\n checker_args = {\n 'messages': messages,\n 'permissions': permissions,\n 'continue_on_error': True \n }\n\n if not os.path.isdir(path):\n raise Exception(\"not a directory\")\n\n if not self._perms_checker.check_access(path, 'rx', **checker_args):\n errors = True\n\n for root, dirs, files in os.walk(path):\n for fn in files:\n file_path = os.path.join(root, fn)\n if fn.endswith('.nc'):\n have_ncdf = True\n if not self._perms_checker.check_access(file_path, \n 'r', **checker_args):\n errors = True\n else:\n messages.append('invalid filename (not *.nc):\\n {}'.format(file_path))\n errors = True\n \n # also check (non-recursively) that directories are readable\n # (if they contain any files then execute permission will get checked\n # as part of checking the files)\n for dn in dirs:\n dir_path = os.path.join(root, dn)\n if not self._perms_checker.check_access(dir_path, \n 'r',\n **checker_args):\n errors = True\n\n if not have_ncdf:\n messages.append(\"does not contain any valid files\")\n errors = True\n \n if errors:\n message = '\\n'.join(['dataset cannot be ingested'] + messages)\n\n for path, stat_data in permissions:\n if stat_data.st_uid == 0:\n drawline = \"=\" * 80 + \"\\n\"\n message += (\"\\n{}Please email support@jasmin.ac.uk \"\n \"to ask for user '{}' to be given access to\\n\"\n \"{}\\n{}\").format(drawline, config.ingestion_user,\n path, drawline)\n\n raise Exception(message)","title":""},{"docid":"55520d255d8264f7a544e3ab9040bb0f","score":"0.5269803","text":"def check_repo_dir():\n if input_cfg.workingdir != \"\" and os.path.isdir(input_cfg.workingdir) is not True:\n input_cfg.errmsg = \"repo directory does not exist: \" + input_cfg.workingdir","title":""},{"docid":"b64f9eaf70bd4fbf6bc3ac87713f9dc5","score":"0.52665687","text":"def check_args(parser):\n assert os.path.exists(parser.data_dir), \"Data directory does not exist\"\n assert os.path.exists(parser.save_dir), \"Save directory does not exist\"\n assert os.path.exists(parser.run_log), \"Run logging directory does not exist\"","title":""},{"docid":"7c78b06f118c6f510d8c589a3a20cc32","score":"0.5263016","text":"def validate_submission(directory):\n console.rule(\"checking hepdata submission files\",characters=\"=\")\n checks.validate_submission(directory+'/submission.yaml')\n # If no error raised all is good\n console.print(f\"The submission in '{directory}'[bold] is correct [/bold]HEPData submission!\")","title":""}],"string":"[\n {\n \"docid\": \"8cc68037e5571eb79ecfb4d34738a791\",\n \"score\": \"0.6602469\",\n \"text\": \"async def validate_and_import_directory(self, **kwargs) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1f03f4d1fa2e7a0349e0ef3c5ad2174\",\n \"score\": \"0.6583628\",\n \"text\": \"def validate_directories(featured_mod):\\n # type: (FeaturedMod) -> None\\n fail = False\\n\\n if not os.path.exists(featured_mod.git_folder()):\\n logger.error(\\n \\\"You did not configure an existing git directory: {}\\\".format(\\n featured_mod.git_folder()\\n )\\n )\\n fail = True\\n\\n if not os.path.exists(featured_mod.content_folder()):\\n logger.error(\\n \\\"You either setup the wrong content directory or did not create it yet: {}\\\".format(\\n featured_mod.content_folder()\\n )\\n )\\n fail = True\\n\\n if fail:\\n exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17b439951e72e32710a8995613107a2d\",\n \"score\": \"0.64337206\",\n \"text\": \"def _is_valid_dir(dir):\\n\\treturn _is_valid_file(dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"467987c42017488d98130c6776fd9c1b\",\n \"score\": \"0.64184076\",\n \"text\": \"def dirvalidate(d):\\n d = str(d)\\n if not os.path.isdir(d):\\n raise argparse.ArgumentError(\\\"Cannot find {}\\\".format(d))\\n\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb68de52bfab4db0b2dec9111f1d1fb4\",\n \"score\": \"0.6353261\",\n \"text\": \"def valid_comp_from_dir(component):\\n return os.path.isdir(os.path.join('src', component)) \\\\\\n or os.path.isdir(os.path.join('src', 'client', component)) \\\\\\n or os.path.isdir(os.path.join('utils', component))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b75189a318d552fe8e361eb769fc6203\",\n \"score\": \"0.63296074\",\n \"text\": \"def validate_output_dir(output_dir):\\n return os.path.isdir(output_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64b1da39d2fcd4729e8a8f5fcee19eaa\",\n \"score\": \"0.63164395\",\n \"text\": \"def looksValid(self, aQDir):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"976b9fd50256ea5cc034cabff54e21b6\",\n \"score\": \"0.6185084\",\n \"text\": \"def validate_paths(self):\\n corpus = self.corpus_path.get()\\n output = self.output_path.get()\\n\\n if not os.path.isdir(corpus) or not os.path.isdir(output):\\n self.pop_up_msg(\\\"please choose a valid directory\\\")\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbbbf7da8aebdc5e8a4ab1a548f87eec\",\n \"score\": \"0.6177451\",\n \"text\": \"def validate(args):\\n if not args.source_path:\\n print(\\\"Error: You need to specify a source path.\\\")\\n return False\\n else:\\n if not os.path.exists(args.source_path):\\n print(\\\"Error: Source path is not a folder or file.\\\")\\n return False \\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beb6c6db9888a2ffd07ddffb4db96bf9\",\n \"score\": \"0.6145644\",\n \"text\": \"def validate_args(parsed):\\n src = parsed['src']\\n dest = parsed['dest']\\n if not os.path.isfile(src):\\n raise ValueError('src=\\\"%s\\\" is not a file'%src)\\n if not (src.endswith('.tex') or src.endswith('.lyx')):\\n raise ValueError('src=\\\"%s\\\" is not a .lyx or .tex file'%src)\\n if not os.path.isdir(dest):\\n try:\\n os.makedirs(dest)\\n except:\\n pass\\n if not os.path.isdir(dest):\\n raise ValueError('dest=\\\"%s\\\" is not a directory'%dest)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ed766eb8814979723888ca44b98fdb\",\n \"score\": \"0.6119335\",\n \"text\": \"def validated_directory(path: str) -> str:\\n if not os.path.isdir(path):\\n raise argparse.ArgumentTypeError(\\n \\\"\\\"\\\"Directory '{}' doesn't exist in this directory. Check yourself!\\\"\\\"\\\".\\n format(path))\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9023673ef715e9c9f29727c432020b56\",\n \"score\": \"0.6099975\",\n \"text\": \"def validate_dir(dir_path, src=True):\\n if src:\\n if os.path.isdir(dir_path):\\n if not os.listdir(dir_path):\\n raise FileNotFoundError(\\n 'No files found in source directory {}'.format(dir_path))\\n else:\\n raise FileNotFoundError(\\n 'Source directory {} not found.'.format(dir_path))\\n else:\\n if os.path.isdir(dir_path):\\n if os.listdir(dir_path):\\n warnings.warn(\\n 'Destination directory {} already has some files'.format(dir_path))\\n else:\\n warnings.warn(\\n 'Destination {} directory does not exist so creating it now'.format(dir_path))\\n os.makedirs(dir_path, exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8ba6a9e59968e57a9bb6d215aa70f25\",\n \"score\": \"0.6054886\",\n \"text\": \"def is_valid_directory(path):\\n\\n return os.path.exists(os.path.dirname(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91d64019087f7a5bffeebbd0e53f806e\",\n \"score\": \"0.60383\",\n \"text\": \"def validate_dir(ctx: Context, param: Parameter, value: Any) -> Any:\\n # Do NOT check permission for 'parse' command\\n # if option '--with-html' was not provided\\n if \\\"html_yes\\\" in ctx.params:\\n if not ctx.params[\\\"html_yes\\\"] and param.name == \\\"html_dir\\\":\\n return value\\n try:\\n value.mkdir(parents=True, exist_ok=True)\\n probe_file = value / \\\"tmp_dest.txt\\\"\\n probe_file.write_text(\\\"Directory is writable\\\", encoding=\\\"utf-8\\\")\\n probe_file.unlink()\\n return value\\n except PermissionError as ex:\\n raise click.BadParameter(\\\"folder has no 'write' permission.\\\") from ex\\n except OSError as ex:\\n raise click.BadParameter(ex.args[1]) from ex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf5f5a1837e55da99165fd7f186fadbd\",\n \"score\": \"0.6001829\",\n \"text\": \"def checkValidFilePath(filepath, makeValid=True):\\r\\n folder = os.path.split(os.path.abspath(filepath))[0]\\r\\n if not os.path.isdir(folder):\\r\\n os.makedirs(folder) #spit an error if we fail\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf249357f602e1fed009df37590d2cb3\",\n \"score\": \"0.5966237\",\n \"text\": \"def validate_directory(self):\\n\\n if not os.path.isdir(self.directory):\\n raise AddressBookValueError('Provide a valid directory path')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83712847b0c609c9b07ae232101f2ef1\",\n \"score\": \"0.58816403\",\n \"text\": \"def ValidateDirPath(path):\\n if not path or not isinstance(path, basestring):\\n raise ValueError('Path is invalid: %r' % path)\\n _ValidateCommonPath(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa4f0e647896ef7b79c34b0d9faca11b\",\n \"score\": \"0.5868731\",\n \"text\": \"def is_valid_dir(ctx, param, value):\\n if not os.path.isdir(value) or not os.path.exists(value):\\n raise click.BadParameter(f\\\"Invalid directory path to {value}\\\")\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1059f82ed3274aa640549ddc32a40a3a\",\n \"score\": \"0.5866662\",\n \"text\": \"def check_if_directory_is_valid(self):\\n is_valid = path.isdir(self.directory)\\n\\n if is_valid:\\n files = [f for f in listdir(self.directory) if\\n path.isfile(path.join(self.directory, f))]\\n\\n # If the prefix is empty or changed (i.e not the same as currently displayed), we\\n # check and edit the text of the widget\\n if self.prefix == \\\"\\\" or self.prefix != self.__widget_edit_prefix.currentText():\\n is_valid = False\\n for f in files:\\n if path.splitext(f)[1] == \\\".json\\\" and f.startswith(\\\"MetaData-\\\"):\\n self.__widget_edit_prefix.addItem(\\n path.splitext(f)[0].replace(\\\"MetaData-\\\", \\\"\\\"))\\n is_valid = True\\n # In case the prefix has not changed and is not empty, we just check\\n else:\\n self.check_if_prefix_is_valid()\\n return\\n\\n if is_valid:\\n self.show_valid_icon()\\n else:\\n self.show_invalid_icon()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4bd8c577f8ecf2e3a652cae5ce60787\",\n \"score\": \"0.585093\",\n \"text\": \"def is_valid_dir(aparser, arg):\\n\\n if not os.path.isdir(arg):\\n aparser.error(\\\"Invalid directory %s\\\" % arg)\\n elif not os.access(arg, os.W_OK | os.X_OK):\\n aparser.error(\\\"Can't write to %s\\\" % arg)\\n else:\\n return arg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d8c58d4a00310cac1a0ddae752ab23\",\n \"score\": \"0.5842716\",\n \"text\": \"def validate_dataset(path_to_validate):\\n if not os.path.exists(path_to_validate):\\n raise FileNotFoundError\\n\\n files = os.listdir(path_to_validate)\\n if not files:\\n raise EmptyDirectoryError\\n\\n if not os.path.isdir(path_to_validate):\\n raise NotADirectoryError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff31a7aaccad4f311f6e60156b48f0d1\",\n \"score\": \"0.58406055\",\n \"text\": \"def _check_path(self, _, value):\\n if not (value.exists() and value.is_dir()):\\n raise ValueError(\\\"Define an existing directory.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90f7508497409c08f1f869cf59c71b51\",\n \"score\": \"0.583916\",\n \"text\": \"def _DirCheck(path):\\n path = _PathCheck(path)\\n if not os.path.isdir(path):\\n raise ValueError(_('Not a directory: %s') % path)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5fe21a5d762688f319c0b1feb865ccf\",\n \"score\": \"0.5835518\",\n \"text\": \"def verify_path(path):\\n\\n if not os.path.exists(path) or not os.path.isdir(path):\\n error('Path specified for project creation does not exist or is not a directory')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48e0b255d084bc0f2abbd293e9149cb4\",\n \"score\": \"0.58246076\",\n \"text\": \"def validatedirs(ctx, file, basedir, exitonfail):\\n Result, Count = validate_dirs(file, basedir, exitonfail)\\n if not Result:\\n sys.exit(1)\\n else:\\n sys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b8d7654d846db274cc2a431df33b718\",\n \"score\": \"0.5808446\",\n \"text\": \"def validate_cmd_param(args):\\n if not os.path.isdir(args.input_dir):\\n raise ValueError('[ERROR] The input folder does not exist.')\\n if os.path.isdir(args.output_dir):\\n raise ValueError('[ERROR] The output folder already exists.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df01a6cf3813592276fb8e4f9f243c74\",\n \"score\": \"0.57967055\",\n \"text\": \"def validate_dataset(path_to_validate):\\n if not os.path.exists(path_to_validate):\\n raise FileNotFoundError\\n if not os.path.isdir(path_to_validate):\\n raise NotADirectoryError\\n if not os.listdir(path_to_validate):\\n raise EmptyDirectoryError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66c4696c2cd6ffea2a252d7cafded563\",\n \"score\": \"0.57792735\",\n \"text\": \"def __validate_folders(args):\\n if not args.train and not args.export:\\n if os.path.isdir(output_dir):\\n raise Exception(\\n output_dir +\\n \\\" is an existing folder. Please either delete it or rename it.\\\")\\n\\n if not os.path.isdir(lasertagger_dir):\\n raise Exception(\\\"LaserTagger not found.\\\")\\n if not os.path.isdir(os.path.expanduser(lasertagger_dir + \\\"/bert\\\")):\\n raise Exception(\\\"Bert not found inside the LaserTagger folder.\\\")\\n\\n if not os.path.isdir(os.path.expanduser(bert_dir)):\\n raise Exception(\\\"Pretrained Bert model not found.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19eb1951df85eda63fa9484ce36fd284\",\n \"score\": \"0.57376575\",\n \"text\": \"def validate_extra_rule_directory(self, rule_directory):\\n if self.debug:\\n print('CustomRuleLoader - validate_extra_rule_directory'+str(lineno()))\\n print('rule_directory: '+str(rule_directory)+lineno())\\n\\n if rule_directory == None:\\n return True\\n else:\\n return os.path.isdir(rule_directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc822fd7469b69c8f6ce9d2b13c5c5ea\",\n \"score\": \"0.5687693\",\n \"text\": \"def check_path(dir_path):\\n if not os.path.exists(dir_path):\\n print(\\\"\\\\n[!] ERROR -> '{}' is NOT a valid Directory ...\\\\n\\\".format(dir_path))\\n print(\\\"\\\\n******* ******* *******\\\")\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29f90324a4e9e6ed1f14a1414529f2b9\",\n \"score\": \"0.5675421\",\n \"text\": \"def validate_package_path(path):\\n if not os.path.isdir(path):\\n raise ValueError(\\\"Path '{0}' is not a directory or does not exist\\\"\\n .format(path))\\n if not package_exists_at(path):\\n raise ValueError(\\\"Path '{0}' does not contain a package\\\".format(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f47c9895fb166147791fcce8b7045ce\",\n \"score\": \"0.5672126\",\n \"text\": \"def check_input(source):\\n if isdir(source):\\n pass\\n else:\\n sys.exit(u'ERROR: The source directory does not exist, aborted.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20db0dd206c5f93331d13d7948e0a1e2\",\n \"score\": \"0.56552845\",\n \"text\": \"def validate(parser, args, from_build_command):\\n\\n # Make plugins always a list. (It might be None when not specified.)\\n args.plugins = args.plugins if args.plugins else []\\n\\n if args.help_checkers_verbose:\\n print_checkers(get_checkers(args.clang, args.plugins))\\n parser.exit()\\n elif args.help_checkers:\\n print_active_checkers(get_checkers(args.clang, args.plugins))\\n parser.exit()\\n\\n if from_build_command and not args.build:\\n parser.error('missing build command')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf85131a00cc56353cd2b0224a12d298\",\n \"score\": \"0.56540745\",\n \"text\": \"def validate(self):\\n for filename in [self.rmd_file, self.blogdown_file]:\\n if not path.exists(path.join(self.source_folder, filename)):\\n click.secho('Missing file: {}'.format(filename), fg='red')\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bcb098409c0e77718f06e149b11b5e5\",\n \"score\": \"0.56466794\",\n \"text\": \"def check_dir(self,path):\\n\\t\\tp=path\\n\\t\\tif not path.endswith(\\\"/\\\"):\\n\\t\\t\\traise ValueError(\\\"Provided Invalid Path\\\")\\n\\t\\tif not os.path.exists(path):\\n\\t\\t\\traise ValueError(\\\"Path does not exists\\\")\\n\\t\\telse:\\n\\t\\t\\tfile_l=os.listdir(path)\\n\\t\\t\\tself.formats=checkformat(file_l)\\n\\t\\t\\tclass_list=list(self.formats.keys())\\n\\t\\t\\tself.classes=class_list\\n\\t\\tself.root_path=p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc746f3d61264fc56e6710b153475b75\",\n \"score\": \"0.56441474\",\n \"text\": \"def get_plugin_dir(self, f): # f should be __file__\\n d = f.split(os.sep)[-2]\\n return os.path.abspath( os.path.join(\\\"plugins\\\", d) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58970045118f87db535a77d484809d11\",\n \"score\": \"0.5638573\",\n \"text\": \"def validate_config(ctx, path):\\n with OpsDroid() as opsdroid:\\n loader = Loader(opsdroid)\\n\\n config = load_config_file([path] if path else DEFAULT_CONFIG_LOCATIONS)\\n\\n loader.load_modules_from_config(config)\\n click.echo(\\\"Configuration validated - No errors founds!\\\")\\n\\n ctx.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d30ae4373337ee469119bffdb646df1\",\n \"score\": \"0.5625538\",\n \"text\": \"def test_yaml_discovery_plugin_scan_valid():\\n ydp = YAMLDiscoveryPlugin()\\n package = Package(\\n \\\"valid_package\\\", os.path.join(os.path.dirname(__file__), \\\"valid_package\\\")\\n )\\n ydp.scan(package, \\\"level\\\")\\n expected = [\\\"test.yaml\\\", \\\"test.yml\\\", os.path.join(\\\"ignore_this\\\", \\\"ignoreme.yaml\\\")]\\n # We have to add the path to each of the above...yuck\\n expected_fullpath = [os.path.join(package.path, filename) for filename in expected]\\n # Neat trick to verify that two unordered lists are the same\\n assert set(package[\\\"yaml\\\"]) == set(expected_fullpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b9d8069612e473fab559109ebec6aea\",\n \"score\": \"0.5624083\",\n \"text\": \"def check_dirs():\\n full_src = os.path.abspath(SRC)\\n full_dest = os.path.abspath(DEST)\\n if os.path.isdir(full_src) or os.path.isfile(full_src):\\n if not os.path.exists(full_dest) and os.path.isdir(full_src):\\n if not mkdir_p(full_dest):\\n error_trig(\\\"something went wrong\\\")\\n else:\\n error_trig(\\\"backup.py: cannot stat '\\\" + SRC + \\\"': No such file or \\\\\\n directory\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baa9b169cc220b01d108be98ce36eb9a\",\n \"score\": \"0.55966705\",\n \"text\": \"def _entry_point(config_module, path: Path) -> None:\\n entry_point = getattr(config_module, ConfigKeys.PLUGIN_ENTRY.name, None)\\n\\n if not entry_point:\\n return\\n\\n if (path / entry_point).is_file():\\n return\\n\\n entry_parts = entry_point.split(\\\" \\\")\\n pkg = entry_parts[1] if entry_parts[0] == \\\"-m\\\" else entry_parts[0]\\n pkg_path = path / pkg\\n\\n if (\\n pkg_path.is_dir()\\n and (pkg_path / \\\"__init__.py\\\").is_file()\\n and (pkg_path / \\\"__main__.py\\\").is_file()\\n ):\\n return\\n\\n raise PluginValidationError(\\n f\\\"{ConfigKeys.PLUGIN_ENTRY.name} '{entry_point}' must be a Python file or a \\\"\\n f\\\"runnable package\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5336409b5952866d9969e851ad9e667\",\n \"score\": \"0.55943686\",\n \"text\": \"def _checkDir(self, filename):\\n\\t\\tif len(path.dirname(filename)) > 0 and not path.exists(path.dirname(filename)):\\n\\t\\t\\tmakedirs(path.dirname(filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a9ca7fd1ecf76e97e6292f3063ee7fb\",\n \"score\": \"0.55832845\",\n \"text\": \"def verify_input(FLAGS):\\n\\n if not os.path.exists(FLAGS.directory_dir):\\n raise AssertionError('Image directory not found.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e6d4b2bba492f921d75231ff25ffc24\",\n \"score\": \"0.55688405\",\n \"text\": \"def check_path(cls, dir):\\n\\t\\tif not path.exists(dir):\\n\\t\\t\\tos.makedirs(dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73b3f7700e7e1dfb4098e26d36b295d2\",\n \"score\": \"0.55619943\",\n \"text\": \"def __validate_files(file):\\n if not os.path.isfile(os.path.expanduser(file)):\\n raise Exception(\\\"File \\\" + str(file) + \\\" not found/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06d31ccbbbd231499296c0a209916907\",\n \"score\": \"0.55609834\",\n \"text\": \"def validate_dir_path(path: Union[str, Path]) -> Path:\\n if os.path.isdir(path):\\n return path\\n else:\\n raise ArgumentTypeError(f\\\"`{path}` is not a valid path for directory\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c35295b209b1c8cc4cda8f35dfe9532\",\n \"score\": \"0.5557613\",\n \"text\": \"def find_plugins_dir():\\n dir = os.path.join(os.path.expanduser(\\\"~\\\"), '.sasview', PLUGIN_DIR)\\n\\n # If the plugin directory doesn't exist, create it\\n if not os.path.isdir(dir):\\n os.makedirs(dir)\\n\\n # Find paths needed\\n try:\\n # For source\\n if os.path.isdir(os.path.dirname(__file__)):\\n p_dir = os.path.join(os.path.dirname(__file__), PLUGIN_DIR)\\n else:\\n raise\\n except:\\n # Check for data path next to exe/zip file.\\n #Look for maximum n_dir up of the current dir to find plugins dir\\n n_dir = 12\\n p_dir = None\\n f_dir = os.path.join(os.path.dirname(__file__))\\n for i in range(n_dir):\\n if i > 1:\\n f_dir, _ = os.path.split(f_dir)\\n plugin_path = os.path.join(f_dir, PLUGIN_DIR)\\n if os.path.isdir(plugin_path):\\n p_dir = plugin_path\\n break\\n if not p_dir:\\n raise\\n # Place example user models as needed\\n if os.path.isdir(p_dir):\\n for file in os.listdir(p_dir):\\n file_path = os.path.join(p_dir, file)\\n if os.path.isfile(file_path):\\n if file.split(\\\".\\\")[-1] == 'py' and\\\\\\n file.split(\\\".\\\")[0] != '__init__':\\n if not os.path.isfile(os.path.join(dir, file)):\\n shutil.copy(file_path, dir)\\n\\n return dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ee91618e9fa6597176dd250994e2d04\",\n \"score\": \"0.5541999\",\n \"text\": \"def check_dir(filepath):\\n d = os.path.dirname(filepath)\\n if not os.path.exists(d):\\n os.makedirs(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbc6b08fe691b4eff3d10cb8554b825b\",\n \"score\": \"0.5524567\",\n \"text\": \"def validateLibrary(libraryName, exclude):\\n if module in exclude:\\n return False;\\n for folder in libpath[system()]:\\n for file in listdir(folder):\\n if (libraryName + \\\".\\\" in file):\\n return True\\n return True# False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e249baf002a25401879a980bc3861860\",\n \"score\": \"0.5522382\",\n \"text\": \"def test_directory(self):\\n self.assertIn(self.audio.resources, RESOURCES)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad347f78e232c49443f76f9574ce06fb\",\n \"score\": \"0.55222845\",\n \"text\": \"def test_is_directory(self):\\n msg = '`PROJECT_ROOT` must be a directory.'\\n\\n self.assertTrue(os.path.isdir(lmp.path.PROJECT_ROOT), msg=msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"296f882f964199ed47619be9253787f9\",\n \"score\": \"0.55126685\",\n \"text\": \"def scan_for_plugins(directory: str):\\n base_dir = pathlib.Path(directory)\\n if base_dir.exists() and base_dir.is_dir():\\n sys.path.append(base_dir.as_posix())\\n plugs = [p for p in base_dir.iterdir() if p.is_dir()]\\n for plug in plugs:\\n importlib.import_module(plug.name)\\n else:\\n raise ValueError('The argument passed is not a directory')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"565cc8743f6832276222a6072d051fc8\",\n \"score\": \"0.55081046\",\n \"text\": \"def _is_valid(f, cap_and_dig=capital_and_digits):\\n _is_valid = \\\\\\n f.name.startswith(cap_and_dig) \\\\\\n and f.is_dir()\\n return _is_valid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de9e6c943abcd2bc0f129f98cb13f156\",\n \"score\": \"0.5507245\",\n \"text\": \"def test_register_plugin_assets_directory():\\n app = create_ctfd(setup=False)\\n register_plugin_assets_directory(app, base_path='/plugins/')\\n app = setup_ctfd(app)\\n with app.app_context():\\n with app.test_client() as client:\\n r = client.get('/plugins/__init__.py')\\n assert len(r.get_data(as_text=True)) > 0\\n assert r.status_code == 200\\n r = client.get('/plugins/challenges/__init__.py')\\n assert len(r.get_data(as_text=True)) > 0\\n assert r.status_code == 200\\n destroy_ctfd(app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6eee9211f981bb19ece9872abd4748b\",\n \"score\": \"0.55020654\",\n \"text\": \"def test_libdir(self):\\n self.check_argument('libdir')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dce7d2c9a8cded7616a28a606f386d7a\",\n \"score\": \"0.5497392\",\n \"text\": \"def validate():\\n paths = dict()\\n paths['rF2root'] = os.path.isdir(rF2root)\\n paths['SteamExe'] = os.path.isfile(SteamExe)\\n paths['DiscordExe'] = os.path.isfile(DiscordExe)\\n paths['CrewChiefExe'] = os.path.isfile(CrewChiefExe)\\n paths['VolumeControlExe'] = os.path.isfile(VolumeControlExe)\\n paths['TeamSpeakExe'] = os.path.isfile(TeamSpeakExe)\\n\\n paths['playerPath'] = os.path.isdir(playerPath)\\n # Check rF2 install a little further\\n _vehicles = os.path.join(rF2root, 'Installed', 'Vehicles')\\n paths['vehicles'] = os.path.isdir(_vehicles)\\n\\n return paths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fac19974d418002d6d55208673441717\",\n \"score\": \"0.54900193\",\n \"text\": \"def check_plugins():\\n _check_command('plugman')\\n\\n json_file = os.path.join(_get_source()[1], 'theme', 'project.json')\\n if os.path.exists(json_file):\\n\\n def version_check(name, version, latest_version):\\n if version != latest_version:\\n print '*** {0}@{1} not using latest version {2} ***\\\\n'.format(\\n name, version, latest_version)\\n else:\\n print '{0} up to date\\\\n'.format(name)\\n\\n plugins = json.loads(open(json_file).read())['plugins']['cordova']\\n for plugin in plugins:\\n if '@' in plugin:\\n # plugin registry\\n name, version = plugin.split('@')\\n out = local('plugman info {0}'.format(name), capture=True)\\n lines = out.split('\\\\n')\\n for line in lines:\\n info = line.split(':')\\n if info[0] == 'version':\\n latest_version = info[1].strip()\\n version_check(name, version, latest_version)\\n elif plugin[:14] == 'https://github':\\n # github repo\\n if '#' in plugin:\\n url, version = plugin.split('#')\\n repo = url.split('github.com/')[1].replace('.git', '')\\n api_url = 'https://api.github.com/repos/{0}/tags'.format(repo)\\n out = local('curl {0}'.format(api_url), capture=True)\\n latest_version = json.loads(out)[0]['name']\\n version_check(url, version, latest_version)\\n else:\\n print 'Where is the plugins file?: {0}'.format(json_file)\\n exit(-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eaabc13aa89efffb93d474806ac1737\",\n \"score\": \"0.5485844\",\n \"text\": \"def _check_dir_contents_single(dir: Path):\\n assert dir.joinpath(\\\"meta.json\\\").is_file(), f\\\"meta.json is missing in {dir}\\\"\\n assert dir.joinpath(\\\"part.0.parquet\\\"), f\\\"part.0.parquet is missing in {dir}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d17c60a2b31b698a81507fd196b5c549\",\n \"score\": \"0.54841363\",\n \"text\": \"def is_valid_project(self, path):\\r\\n spy_project_dir = osp.join(path, '.spyproject')\\r\\n if osp.isdir(path) and osp.isdir(spy_project_dir):\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b018631da363df5452c9b336a64857\",\n \"score\": \"0.5483685\",\n \"text\": \"def qt_plugins_dir(namespace):\\n qt_info = get_qt_library_info(namespace)\\n paths = [qt_info.location['PluginsPath']]\\n if not paths:\\n raise Exception('Cannot find {0} plugin directories'.format(namespace))\\n else:\\n valid_paths = []\\n for path in paths:\\n if os.path.isdir(path):\\n valid_paths.append(str(path)) # must be 8-bit chars for one-file builds\\n qt_plugin_paths = valid_paths\\n if not qt_plugin_paths:\\n raise Exception(\\\"\\\"\\\"\\n Cannot find existing {0} plugin directories\\n Paths checked: {1}\\n \\\"\\\"\\\".format(namespace, \\\", \\\".join(paths)))\\n return qt_plugin_paths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42ad234f14f7c3e87620f4be934904a9\",\n \"score\": \"0.54812443\",\n \"text\": \"def validate_resources(self, folder, resources):\\r\\n self.validate_files_exist(folder, resources)\\r\\n self.validate_no_duplicate_paths(resources)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8a5bf1ec257cf266ab76b1df68fd0bf\",\n \"score\": \"0.5461568\",\n \"text\": \"def test_with_valid_sites(self):\\n # This should not raise.\\n validate_site_paths([os.path.dirname(__file__)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d835a778cd5d849b5004468fc5c29fff\",\n \"score\": \"0.54466915\",\n \"text\": \"def test_validate_subdir(self):\\n self._copy_file(join(\\\"files\\\", \\\"open_relative_file.ipynb\\\"), \\\"my_subdir/open_relative_file.ipynb\\\")\\n self._copy_file(join(\\\"files\\\", \\\"data.txt\\\"), \\\"my_subdir/data.txt\\\")\\n output = run_nbgrader([\\\"validate\\\", \\\"my_subdir/open_relative_file.ipynb\\\"], stdout=True)\\n assert output.strip() == \\\"Success! Your notebook passes all the tests.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27e21c948f0de5db114b4678cd6a4b2e\",\n \"score\": \"0.5446441\",\n \"text\": \"def is_dir():\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6283475b9c71d2d99e269a771d148d09\",\n \"score\": \"0.54309785\",\n \"text\": \"def validate_file_option(self, args):\\n try:\\n self.output_path = args.output_path\\n print(\\\"output destination set to %s\\\" % self.output_path)\\n except OSError:\\n print(\\\"Creation of the directory %s failed\\\" % self.output_path)\\n else:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f845bb95ea66846f83999883e281cf2\",\n \"score\": \"0.5430167\",\n \"text\": \"def validate_directories(self):\\n assert os.path.exists(self.results_dir)\\n assert os.path.exists(self.latest_symlink)\\n assert os.path.islink(self.latest_symlink)\\n\\n # check symlink points to correct location\\n assert os.path.realpath(self.latest_symlink) == os.path.realpath(self.results_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d882090aee449df45a03fe38cc7b950a\",\n \"score\": \"0.5428251\",\n \"text\": \"def check():\\n assert os.name == \\\"posix\\\", \\\"only for linux!!!\\\"\\n assert CFG.base_dir != None, \\\"Please set Config.base_dir!\\\"\\n assert CFG.sort_dir != None, \\\"Please set Config.sort_dir!\\\"\\n\\n assert CFG.use_tags >= 1\\n\\n CFG.base_dir = os.path.expanduser(CFG.base_dir)\\n CFG.sort_dir = os.path.expanduser(CFG.sort_dir)\\n\\n assert os.path.isdir(CFG.base_dir), \\\\\\n \\\"CFG.base_dir path '%s' not exist!\\\" % CFG.base_dir\\n assert os.path.isdir(CFG.sort_dir), \\\"Please create CFG.sort_dir path!\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73dc9a6cacd00da4411b89d0a4fcea0\",\n \"score\": \"0.542701\",\n \"text\": \"def validated_package(path: str) -> u.Package:\\n p = ufs.generate_package(path)\\n\\n if not os.path.isdir(p.package_path):\\n raise argparse.ArgumentTypeError(\\n \\\"\\\"\\\"Directory '{}' doesn't exist in directory. Code must be\\nnested in a folder that exists in the current directory.\\\"\\\"\\\".format(\\n p.package_path))\\n\\n filename = p.script_path\\n if not ufs.file_exists_in_cwd(filename):\\n raise argparse.ArgumentTypeError(\\n \\\"\\\"\\\"File '{}' doesn't exist locally as a script or python module; code\\nmust live inside the current directory.\\\"\\\"\\\".format(filename))\\n\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca6b147dac24590f942d77f04878f083\",\n \"score\": \"0.54179597\",\n \"text\": \"def check_directory():\\n if os.path.exists(\\\"turbogears\\\") or os.path.exists(\\\"TurboGears\\\"):\\n print \\\"\\\"\\\"There is a 'turbogears' directory in the current directory.\\n EasyInstall will normally try to install from this directory rather\\n than picking up the installation package from the net.\\\"\\\"\\\"\\n if os.access(\\\"..\\\", os.W_OK) and not \\\\\\n os.path.exists(os.path.join(\\\"..\\\", \\\"turbogears\\\")) and not \\\\\\n os.path.exists(os.path.join(\\\"..\\\", \\\"TurboGears\\\")):\\n print \\\"\\\"\\\"\\nThis installer needs a place to store the setuptools installation\\npackage to do its work. The parent directory of this one is writeable.\\nShould the installation proceed from there ('n' to exit)?\\\"\\\"\\\"\\n upone = get_yesno()\\n if upone == \\\"n\\\":\\n sys.exit(1)\\n os.chdir(\\\"..\\\")\\n else:\\n print \\\"\\\"\\\"\\nThis installer needs to run in a directory that does not contain a\\n'turbogears' directory and it also needs to run in a directory that\\nyou have write access to. Please change directories and rerun this\\ninstaller.\\n\\\"\\\"\\\"\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed3f9cabb04029abda48f2e6b9720eed\",\n \"score\": \"0.5416582\",\n \"text\": \"def test_valid_path_to_directory(self):\\n path = os.path.abspath(os.curdir)\\n mock_logger = MagicMock(return_value=None)\\n self.actual = functions.check_path_to_directory(path, mock_logger)\\n self.assertEqual(self.actual, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01d0958e7f49d1ae2af8f42fbbed01b5\",\n \"score\": \"0.54139924\",\n \"text\": \"def check_dir(dir_fullpath:str):\\n if not os.path.isdir(dir_fullpath):\\n os.mkdir(dir_fullpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88d32bf0b8aebeb03415606c4ffd4ee3\",\n \"score\": \"0.54060996\",\n \"text\": \"def plugin_dir(self):\\n return self.tmpdir(\\\"plugins\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05efb5a9015b4a64b8fd25547ee75e05\",\n \"score\": \"0.54025733\",\n \"text\": \"def _is_plugin_installed(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7390ee4397c38b6be2f6b142b4c4f9da\",\n \"score\": \"0.54024583\",\n \"text\": \"def check_dir(adir):\\n if not os.path.exists(adir):\\n return False\\n if not os.path.isdir(adir):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9f10d3af0914d1af223bf0c25343c6d\",\n \"score\": \"0.54001796\",\n \"text\": \"def valid_path(path: str):\\n if _path.exists(path):\\n if _path.isdir(path):\\n return path\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b1beecb0a8105e8982867a54b6743ba\",\n \"score\": \"0.5377587\",\n \"text\": \"def check_dir(f_path=\\\"\\\"):\\n # check if an extension is in the file path parameter\\n if \\\".\\\" in f_path:\\n f_path = path.dirname(f_path)\\n return path.isdir(f_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6760f2b070fd1489055f9ec94a2c4e5d\",\n \"score\": \"0.537233\",\n \"text\": \"def _validate_resource_path(path):\\n invalid = (\\n os.path.pardir in path.split(posixpath.sep) or\\n posixpath.isabs(path) or\\n ntpath.isabs(path)\\n )\\n if not invalid:\\n return\\n\\n msg = \\\"Use of .. or absolute path in a resource path is not allowed.\\\"\\n\\n # Aggressively disallow Windows absolute paths\\n if ntpath.isabs(path) and not posixpath.isabs(path):\\n raise ValueError(msg)\\n\\n # for compatibility, warn; in future\\n # raise ValueError(msg)\\n warnings.warn(\\n msg[:-1] + \\\" and will raise exceptions in a future release.\\\",\\n DeprecationWarning,\\n stacklevel=4,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"127e52d57d6d5a7c4d06cee480e0fa45\",\n \"score\": \"0.53721845\",\n \"text\": \"def ValidateDirectoryExistsOrRaiseFunctionError(directory):\\n if not os.path.exists(directory):\\n raise exceptions.FunctionsError(\\n 'argument --source: Provided directory does not exist. If '\\n 'you intended to provide a path to Google Cloud Repository, you must '\\n 'specify the --source-url argument')\\n if not os.path.isdir(directory):\\n raise exceptions.FunctionsError(\\n 'argument --source: Provided path does not point to a directory. If '\\n 'you intended to provide a path to Google Cloud Repository, you must '\\n 'specify the --source-url argument')\\n return directory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6878f58ce970eb92952ae1230b7114f5\",\n \"score\": \"0.53618604\",\n \"text\": \"def _check_parent_dir_is_dir(self):\\n if not self.parent.is_dir():\\n message = \\\"Parent is not a directory or not defined\\\"\\n logging.error(message)\\n print(message)\\n raise Exception(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"005418bcdf63236cac8e20c78c5cb318\",\n \"score\": \"0.53478897\",\n \"text\": \"def validate_filesystem(self):\\n if not os.path.exists(self.dir_path):\\n raise FileNotFoundError(\\n \\\"project directory at {} does not exist anymore\\\".format(self.dir_path)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28401f0fbf46b0bb4c330d47f09e2e94\",\n \"score\": \"0.5334039\",\n \"text\": \"def sanity_check_step(self):\\n\\n custom_paths = {\\n 'files': [\\\"bin/xhpl\\\"],\\n 'dirs': []\\n }\\n\\n super(EB_HPL, self).sanity_check_step(custom_paths)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc89977840ac369f6afb291eb8c7e892\",\n \"score\": \"0.53152955\",\n \"text\": \"def validate_settings():\\n importlib.import_module(f\\\"{PACKAGE_NAME}.settings\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e96d49012af2c1a65bfa7b801a29186a\",\n \"score\": \"0.5310148\",\n \"text\": \"def _verify_config_dir(self):\\n if not self.config_dir:\\n raise ConfigError(\\\"Cannot find configuration directory\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c5f74ab4c0546b0a0225a6ea2b3dca9\",\n \"score\": \"0.5308468\",\n \"text\": \"def is_valid_directories(paths):\\n\\n for path in paths:\\n if not os.path.exists(os.path.dirname(path)):\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba6c7abc2ed273ee5b43b9ee7ecb27d\",\n \"score\": \"0.5308053\",\n \"text\": \"def patch_is_valid_directory(self, linter_class):\\n patcher = mock.patch.object(linter_class, '_is_valid_directory', return_value=True)\\n patch_start = patcher.start()\\n self.addCleanup(patcher.stop)\\n return patch_start\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d9bac265287ff0ec049d09d6cccb72e\",\n \"score\": \"0.530617\",\n \"text\": \"def loadPlugin(self, path):\\r\\n logging.debug(\\\"looking for plugin in %s\\\" % path)\\r\\n # if path is a folder\\r\\n if os.path.isdir(os.path.join(self.config.plugins.root, path)):\\r\\n\\r\\n # if found plugin in folder\\r\\n plugin_file_path = os.path.join(\\r\\n self.config.plugins.root, path, \\\"%s.py\\\" % path\\r\\n )\\r\\n if os.path.isfile(plugin_file_path):\\r\\n try:\\r\\n # try to import it\\r\\n logging.debug(\\\"- importing plugin %s module\\\" % path)\\r\\n plugin_module = imp.load_source(\\r\\n path,\\r\\n plugin_file_path\\r\\n )\\r\\n logging.debug(\\\"Imported plugin module %s\\\" % path)\\r\\n\\r\\n # TODO: change to only catch import errors here\\r\\n except Exception as e:\\r\\n logging.error(\\r\\n \\\"Error importing plugin %s: %s\\\" % (path, repr(e))\\r\\n )\\r\\n traceback.print_exc()\\r\\n return False\\r\\n\\r\\n if hasattr(plugin_module, 'Plugin'):\\r\\n try:\\r\\n logging.debug(\\\"Creating plugin\\\")\\r\\n plugin = plugin_module.Plugin(\\r\\n self,\\r\\n self.config\\r\\n )\\r\\n logging.debug(\\\"Loaded plugin %s\\\" % plugin.name)\\r\\n\\r\\n # loading external code - catch all exceptions\\r\\n except Exception as e:\\r\\n logging.error(\\\"Error loading plugin %s: %s\\\" % (\\r\\n plugin_file_path, repr(e))\\r\\n )\\r\\n return False\\r\\n\\r\\n # TODO: what does the comment below mean? does this work?\\r\\n # this probably doesnt work singly bc it sticks the same\\r\\n # plugin on the end of the list\\r\\n for p in self.plugins:\\r\\n if p.name == plugin.name:\\r\\n self.plugins.remove(p)\\r\\n self.plugins.append(plugin)\\r\\n return True\\r\\n\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c427eb65187dc4d519ea99ce6cae8283\",\n \"score\": \"0.53057575\",\n \"text\": \"def _check_path(self, path):\\n if not os.path.exists(path):\\n return False\\n\\n localpath = os.path.basename(path)\\n if localpath.startswith('.'):\\n return False\\n\\n if not localpath.endswith('.yml'):\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a306d7def8caca4e1f8f571248c37616\",\n \"score\": \"0.5302555\",\n \"text\": \"def sanitize_directory(dir):\\n # Use with os.path.join, and do not sweat separator\\n import os.path\\n verbose('verifying directory: {}'.format(dir))\\n if not(os.path.isdir(dir)):\\n raise ValueError('directory {} does not exist'.format(dir))\\n return dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a3e596fc95c46d3cbc502706f5aac4d\",\n \"score\": \"0.52988696\",\n \"text\": \"def load_plugins(dirs):\\n\\n dirs = [os.path.expanduser(d) for d in dirs]\\n\\n for directory in dirs:\\n if os.path.isdir(directory):\\n livestreamer.load_plugins(directory)\\n else:\\n console.logger.warning(\\\"Plugin path {0} does not exist or is not \\\"\\n \\\"a directory!\\\", directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23fe3bb35bcc6424788d80b13b541180\",\n \"score\": \"0.5293855\",\n \"text\": \"def isValid( self ):\\n check = str(self._filepathEdit.text()).split(os.path.pathsep)[0]\\n if ( self.filepathMode() == XFilepathEdit.Mode.SaveFile ):\\n check = os.path.dirname(check)\\n \\n return os.path.exists(check)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e97441f597e44df81d5bf5f262fd1783\",\n \"score\": \"0.528566\",\n \"text\": \"def _is_file_valid(name: str) -> bool:\\n return not name.startswith(\\\".\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48f5a1c623a9563b892cfc3afc87aea0\",\n \"score\": \"0.5283608\",\n \"text\": \"def test_init_succeeds_on_existing_dir(self):\\n scanner = Scanner(base=LOCATION)\\n self.assertIn(__file__, scanner.last_result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81349bfc0d760047078581798ac6f891\",\n \"score\": \"0.52830756\",\n \"text\": \"def _clean_up_folders(plugin_list, dst_path):\\r\\n is_cleaned_up = False\\r\\n\\r\\n if os.path.isdir(dst_path) is False:\\r\\n pass\\r\\n else:\\r\\n\\r\\n dst_subfolders = []\\r\\n for dst_file in os.listdir(dst_path):\\r\\n if os.path.isdir(dst_path + \\\"/\\\" + dst_file) is True:\\r\\n dst_subfolders.append(dst_file)\\r\\n\\r\\n # Remove folders which exists only in the destination\\r\\n for folder in dst_subfolders:\\r\\n if folder not in plugin_list:\\r\\n dst_full_path = dst_path + \\\"/\\\" + folder\\r\\n print(f\\\"\\\\t\\\\\\\"{dst_full_path}\\\\\\\" removed.\\\")\\r\\n shutil.rmtree(dst_full_path)\\r\\n is_cleaned_up = True\\r\\n\\r\\n return is_cleaned_up\",\n \"title\": \"\"\n },\n {\n \"docid\": \"462f69ed1531e184e493e166aa71cdc5\",\n \"score\": \"0.52828324\",\n \"text\": \"def validate(self, pfile):\\n \\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9234d6d5a30bda6ab5065c9d8d6d2386\",\n \"score\": \"0.52822745\",\n \"text\": \"def check_directory(directory):\\n if os.path.exists(directory):\\n return os.path.abspath(directory)\\n raise argparse.ArgumentTypeError('invalid directory: %s' % directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab24f1eee53df8bbdbbe3fd9535d9c69\",\n \"score\": \"0.5280375\",\n \"text\": \"def path_exists_check(self, dirName):\\n if not os.path.isdir(dirName):\\n sys.exit(\\\"%s does not exist\\\" % dirName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6377a88d732312d15402ffaa34e1bc63\",\n \"score\": \"0.527434\",\n \"text\": \"def testCheckRelativePathWithReferenceToEnclosingDir(self):\\n p=\\\"../path/to/dir\\\"\\n self.assertRaises(ValidationError, checkrelpath, p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1affddab6679a89cd052611e1d4f4e10\",\n \"score\": \"0.5272382\",\n \"text\": \"def test_data_dir_exists_in_source_control(data_dirpath: Path) -> None:\\n assert data_dirpath.exists()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"013be8b11186ad9cb3bb6491a7176f48\",\n \"score\": \"0.52717346\",\n \"text\": \"def _validate_dataset_dir(self, path):\\n\\n # check that everything is readable by the ingestion user\\n errors = False\\n have_ncdf = False\\n\\n messages = []\\n permissions = []\\n checker_args = {\\n 'messages': messages,\\n 'permissions': permissions,\\n 'continue_on_error': True \\n }\\n\\n if not os.path.isdir(path):\\n raise Exception(\\\"not a directory\\\")\\n\\n if not self._perms_checker.check_access(path, 'rx', **checker_args):\\n errors = True\\n\\n for root, dirs, files in os.walk(path):\\n for fn in files:\\n file_path = os.path.join(root, fn)\\n if fn.endswith('.nc'):\\n have_ncdf = True\\n if not self._perms_checker.check_access(file_path, \\n 'r', **checker_args):\\n errors = True\\n else:\\n messages.append('invalid filename (not *.nc):\\\\n {}'.format(file_path))\\n errors = True\\n \\n # also check (non-recursively) that directories are readable\\n # (if they contain any files then execute permission will get checked\\n # as part of checking the files)\\n for dn in dirs:\\n dir_path = os.path.join(root, dn)\\n if not self._perms_checker.check_access(dir_path, \\n 'r',\\n **checker_args):\\n errors = True\\n\\n if not have_ncdf:\\n messages.append(\\\"does not contain any valid files\\\")\\n errors = True\\n \\n if errors:\\n message = '\\\\n'.join(['dataset cannot be ingested'] + messages)\\n\\n for path, stat_data in permissions:\\n if stat_data.st_uid == 0:\\n drawline = \\\"=\\\" * 80 + \\\"\\\\n\\\"\\n message += (\\\"\\\\n{}Please email support@jasmin.ac.uk \\\"\\n \\\"to ask for user '{}' to be given access to\\\\n\\\"\\n \\\"{}\\\\n{}\\\").format(drawline, config.ingestion_user,\\n path, drawline)\\n\\n raise Exception(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55520d255d8264f7a544e3ab9040bb0f\",\n \"score\": \"0.5269803\",\n \"text\": \"def check_repo_dir():\\n if input_cfg.workingdir != \\\"\\\" and os.path.isdir(input_cfg.workingdir) is not True:\\n input_cfg.errmsg = \\\"repo directory does not exist: \\\" + input_cfg.workingdir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b64f9eaf70bd4fbf6bc3ac87713f9dc5\",\n \"score\": \"0.52665687\",\n \"text\": \"def check_args(parser):\\n assert os.path.exists(parser.data_dir), \\\"Data directory does not exist\\\"\\n assert os.path.exists(parser.save_dir), \\\"Save directory does not exist\\\"\\n assert os.path.exists(parser.run_log), \\\"Run logging directory does not exist\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c78b06f118c6f510d8c589a3a20cc32\",\n \"score\": \"0.5263016\",\n \"text\": \"def validate_submission(directory):\\n console.rule(\\\"checking hepdata submission files\\\",characters=\\\"=\\\")\\n checks.validate_submission(directory+'/submission.yaml')\\n # If no error raised all is good\\n console.print(f\\\"The submission in '{directory}'[bold] is correct [/bold]HEPData submission!\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993879,"cells":{"query_id":{"kind":"string","value":"79712250d44c213089530bca861aafb9"},"query":{"kind":"string","value":"gets the ctf status"},"positive_passages":{"kind":"list like","value":[{"docid":"bb44b9fc5ea77399c1c12dc12d822160","score":"0.0","text":"def is_active(self):\n\t\treturn self.__is_active","title":""}],"string":"[\n {\n \"docid\": \"bb44b9fc5ea77399c1c12dc12d822160\",\n \"score\": \"0.0\",\n \"text\": \"def is_active(self):\\n\\t\\treturn self.__is_active\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"59cd33011a252a8832a814fc751eaa2e","score":"0.71683383","text":"def get_status(self):","title":""},{"docid":"59cd33011a252a8832a814fc751eaa2e","score":"0.71683383","text":"def get_status(self):","title":""},{"docid":"c925bfd95758d8502e8ba4ed29a9b06f","score":"0.71524346","text":"def GetStatus(self):","title":""},{"docid":"2a79ef9babdd59afc1dea90b6dda0245","score":"0.70841324","text":"def getStatus():\n\tpass","title":""},{"docid":"f9b495d22056f45acc59f1e149922afe","score":"0.6942748","text":"def get_status(self):\n pass","title":""},{"docid":"f9b495d22056f45acc59f1e149922afe","score":"0.6942748","text":"def get_status(self):\n pass","title":""},{"docid":"58f3214e4a66ca5da75c01a10e7b67e3","score":"0.69176096","text":"def get_status(self) -> int:\n ...","title":""},{"docid":"e5b3559e43880f623b46ef25f689a099","score":"0.6887757","text":"def conffile_status(self):\n return self._conffile_status","title":""},{"docid":"50927751aaeb9d6f8f11aa9bec3e7072","score":"0.6879021","text":"def get_cplex_status(self):\n status = cb_getstat(self._cbstruct)\n _proc.check_status(self._cbstruct, status[0], from_cb=True)\n return status[1]","title":""},{"docid":"57a029b71f30b58a5918ddc4de7d7470","score":"0.6828316","text":"def tc_status():\n current_status = monitor_obj.get_status()\n\n success = True\n message = \"Successfully retrieved current TC status\"\n # Logging message\n logging.debug(message) if success else logging.error(message)\n return jsonify({\"success\": success, \"message\": message, \"status\": current_status})","title":""},{"docid":"aa716fc8fa1c60bd8eae7d55a653789f","score":"0.67597145","text":"def get_status(self):\n\t\treturn self.status","title":""},{"docid":"872197a8bc19d84e42aeca15fa7b19e8","score":"0.6758986","text":"def status():\n return ats_routines.Status()","title":""},{"docid":"95e0341779e3a462aa18c6892c3a3a0d","score":"0.67469645","text":"def status(self) -> str:","title":""},{"docid":"95e0341779e3a462aa18c6892c3a3a0d","score":"0.67469645","text":"def status(self) -> str:","title":""},{"docid":"ab2054c2ca51dc939dc0121e50c1f153","score":"0.6727437","text":"def __get_status(tokens: str) -> str:\n status = re.search(ffn_cst.RE_STATUS, tokens)\n return 'In Progress' if status is None else 'Complete'","title":""},{"docid":"07972bf8207a85002b6dc0d30ece933b","score":"0.6727045","text":"def get_status(self):\n with SMBus(self.i2c_channel) as bus:\n rd = bus.read_byte_data(self.i2c_address, 218)\n status = {\n 'Device calibration in progress': rd & 1,\n 'XTAL loss': (rd >> 2) & 1,\n 'CLKIN loss': (rd >> 3) & 1,\n 'PLL loss of lock': (rd >> 4) & 1\n }\n return status","title":""},{"docid":"4f417741bf8aa736b80866e3811955c3","score":"0.67155427","text":"def getStatus(self):\n\t\treturn self.status","title":""},{"docid":"a4d56631a426f7413cbad7ec761edcd5","score":"0.67056483","text":"def get_status(self):\n return self.exec_action('get_status')","title":""},{"docid":"82a794ff37b5908c2f4e9cb819fcc437","score":"0.66990626","text":"def do_status(self):\n self.log(\"Cmd : STATUS\")\n return ('OK', self.status() )","title":""},{"docid":"4dc6ca10b6e9f72ef4589ff095a8b736","score":"0.66977674","text":"def getStatus(self):","title":""},{"docid":"7774e28a41c3767733426bfcdd90d89d","score":"0.6682327","text":"def get_status(self):\n data = self._xfer(commands.GET_STATUS_REQUEST)[1:]\n return (data[0] << 8) | data[1]","title":""},{"docid":"55edcfab4d0326b1b0b2c41b3ce57aac","score":"0.66750085","text":"def get_status(self):\n msg = \"d\"\n return self.send_read(msg)","title":""},{"docid":"483a72c21651360137c4bd742c364ff5","score":"0.6674774","text":"def get_status(expnum, ccd, program, version='p', return_message=False):\n key = get_process_tag(program, ccd, version)\n status = get_tag(expnum, key)\n logger.debug('%s: %s' % (key, status))\n if return_message:\n return status\n else:\n return status == SUCCESS","title":""},{"docid":"6152f5cc800e315c2d9c7e369d87111b","score":"0.6674497","text":"def getStatus(self):\n\t\tcycletime=0\n\t\ti2cerrorcount=0\n\t\tsensor=0\n\t\tflag=0\n\t\tcurrentset=0\n\t\trdata = self._sendAndGet(self._MSPCOMMANDS.MSP_STATUS, 11)\n\t\tif rdata:\n\t\t\tcycletime = self._toUInt16(rdata[0:2])\n\t\t\ti2cerrorcount = self._toUInt16(rdata[2:4])\n\t\t\tsensor = self._toUInt16(rdata[4:6])\n\t\t\tflag = self._toUInt32(rdata[6:10])\n\t\t\tcurrentset = rdata[10]\n\t\t#end if\n\t\treturn {\"cycletime\":cycletime, \"i2cerrorcount\":i2cerrorcount, \"sensor\":sensor, \"flag\":flag, \"currentset\":currentset}","title":""},{"docid":"51f505b8fb3b32fef5d8e4d9aa848778","score":"0.6657768","text":"def getstatus(self):\n status = self._sendandreceive(\"getstatus\")\n return status","title":""},{"docid":"3b2105e1a66fc80444c7ce4b0dbeafe8","score":"0.66365767","text":"def _status(self):\n self.write(bytes([0xF3]))\n return list(self.read(1))[0]","title":""},{"docid":"42088a8b1b40bd8b2b894b37ad0e1e2c","score":"0.6607968","text":"def _get_status(self):\n return self.__status","title":""},{"docid":"f0336a5a668992bb45fcb719a816077f","score":"0.6596919","text":"def getStatus(self):\r\n pass","title":""},{"docid":"bd45ece75b4f0c67f78f961658547788","score":"0.658261","text":"def get_status(self):\n if self.get_cloud_status() != \"ACTIVE\":\n return \"stopped\"\n #wait for the vm to be ready and SSH-able\n self.wait_ready()\n status = self.run_command(\"ctool status\", indent=0, prefix='')\n return status.strip()","title":""},{"docid":"e77f7c9d343406b1b8f73af001336711","score":"0.65773284","text":"def getstatus(self):\n status, data = self.connection.command(\"getstatus\")\n if status == \"statusResponse\":\n self.parse_getstatus(data)","title":""},{"docid":"af04bd616df6993bc5df28f96cc54e1d","score":"0.6571445","text":"def getStatus(self) -> unicode:\n ...","title":""},{"docid":"af04bd616df6993bc5df28f96cc54e1d","score":"0.6571445","text":"def getStatus(self) -> unicode:\n ...","title":""},{"docid":"4ca183cce5988715db59252076e86407","score":"0.655932","text":"def _status(self):\n self.write(bytes([0x71]))\n return list(self.read(1))[0]","title":""},{"docid":"080b9761a562573d6eaa915996cedfdf","score":"0.65567267","text":"def status_conta(self):\n return self._status_conta","title":""},{"docid":"23c5df475f77e3fd8bf30dce86e61c62","score":"0.6555834","text":"def status(self):\n with self.i2c_device as i2c:\n i2c.readinto(self._buf, start=0, end=1)\n # print(\"status: \"+hex(self._buf[0]))\n return self._buf[0]","title":""},{"docid":"947aab4f2a302547c99925a93a0af9de","score":"0.6550869","text":"def ar_get_status() -> int:\n pass","title":""},{"docid":"4d00e81f4aa5468edf8eb8fc058f5bab","score":"0.65462714","text":"def get_status(self):\n return GetStatus(*self.ipcon.send_request(self, BrickletGPS.FUNCTION_GET_STATUS, (), '', 'B B B'))","title":""},{"docid":"16af8b176f5197a4568617fff19a2e13","score":"0.6532375","text":"def printStatus():","title":""},{"docid":"bbf0d5688cff2f22722ab23b838ecb57","score":"0.6532194","text":"def get_status(self):\n raise NotImplementedError","title":""},{"docid":"afc714f1d34f691153ce0cf05ef6113d","score":"0.6524577","text":"def get_status(self):\n\n return self.make_request(WATTIO_STATUS_URI)","title":""},{"docid":"aab943c6881aff606e4fded6f88d689b","score":"0.65236217","text":"def status():\n return \"OK\"","title":""},{"docid":"3e7b032bbeb691beb709fa0ea36046ab","score":"0.65131325","text":"def getStatus(self):\n pass","title":""},{"docid":"a9a56de8f3e2b5351bbfa890958e1360","score":"0.6509154","text":"def read_status(self, run=True):\n self.device_status = self.ITC.getStatus(run)\n return self.device_status","title":""},{"docid":"699c5fc03bd1851ee77d50e29d5b6e28","score":"0.6500384","text":"def get_status(self):\n command = struct.pack(\">H\", CMD_GET_STATUS)\n self._write_command(command)\n return self._read_status()","title":""},{"docid":"b64e4cc3637661dfc42cd3d93fd4e791","score":"0.64850485","text":"def get_status(self):\n return self.raw","title":""},{"docid":"a60dae99a903172dc818fb781fbe4ab6","score":"0.64728737","text":"def get_status(self):\n\n\t\treturn self.__status","title":""},{"docid":"a60dae99a903172dc818fb781fbe4ab6","score":"0.64728737","text":"def get_status(self):\n\n\t\treturn self.__status","title":""},{"docid":"a60dae99a903172dc818fb781fbe4ab6","score":"0.64728737","text":"def get_status(self):\n\n\t\treturn self.__status","title":""},{"docid":"eb96e47bc3536605361670aa05050634","score":"0.64686656","text":"def CheckStatus(self):\n return self.status_flag","title":""},{"docid":"84f7dffffe97089d56b86016441951bc","score":"0.646025","text":"def GetStatus(*args, **kwargs):\n return _stc.StyledTextCtrl_GetStatus(*args, **kwargs)","title":""},{"docid":"513d4c3044e703f84a8938cda73ed5cb","score":"0.6453283","text":"def GetStatusText(self, i=0):","title":""},{"docid":"09e1e3811babd32f65f7cff491bb0c25","score":"0.6449048","text":"def get_status(self):\n return self.status","title":""},{"docid":"09e1e3811babd32f65f7cff491bb0c25","score":"0.6449048","text":"def get_status(self):\n return self.status","title":""},{"docid":"09e1e3811babd32f65f7cff491bb0c25","score":"0.6449048","text":"def get_status(self):\n return self.status","title":""},{"docid":"09e1e3811babd32f65f7cff491bb0c25","score":"0.6449048","text":"def get_status(self):\n return self.status","title":""},{"docid":"fd7928e97017623329fdadd353fa4f0c","score":"0.64473915","text":"def fcp_service_status(self):\n return self.request( \"fcp-service-status\", {\n }, {\n 'is-available': [ bool, False ],\n } )","title":""},{"docid":"0f932163c96f953bb3e3e08ee5694b93","score":"0.6443662","text":"def status(self):\n if self.process.status == 0:\n if self.xfail:\n return \"UOK\"\n else:\n return \"OK\"\n else:\n if self.xfail:\n return \"XFAIL\"\n else:\n if self.process.status == DIFF_FAILURE:\n return \"DIFF\"\n else:\n return \"PROBLEM\"","title":""},{"docid":"63eed432504ce04b66ed4f37d030f03c","score":"0.64419204","text":"def kyc_status(self):\n return self._kyc_status","title":""},{"docid":"4e7fd031e5b74c281eb5f26d6b4d0a04","score":"0.6425467","text":"def __get_status(self):\n return self.__comm_status","title":""},{"docid":"5bb8f8b6a82d63c9c91752d27b4e8739","score":"0.6413917","text":"def getStatus(self):\n self.send_getStatus()\n return self.recv_getStatus()","title":""},{"docid":"9c5c0a7e6b1b1c5c7422309616f155c8","score":"0.64014506","text":"def status(self):\n return self.tbsresponse['cert_status'].name","title":""},{"docid":"57892d63e2c22328571b80facc49bcb3","score":"0.63979363","text":"def get_status(self):\n #status = self._device.get_calibration_status()\n #\n #return CalibrationStatus(*status)\n pass","title":""},{"docid":"89eb1b05d400eff8568804d3ce740e11","score":"0.6396726","text":"def get_status(self):\n req_url = self.compile_url([URL_STATUS])\n result = self.request(req_url)\n return result","title":""},{"docid":"069c6f5075ff6d3a6f0f30abfad5ec9c","score":"0.6396679","text":"def cifs_status(self):\n return self.request( \"cifs-status\", {\n }, {\n 'status': [ basestring, False ],\n } )","title":""},{"docid":"8fb489f686f23781ea9f08a2c7e2369a","score":"0.63915825","text":"def get_status(self):\n return self._request('GET', '/status')","title":""},{"docid":"3c7963b58e993d4007b089a6c060e398","score":"0.63874245","text":"def _getStatus(self):\n return self._status","title":""},{"docid":"6696d71a5a075955d428ad0a0d5dd89e","score":"0.6383367","text":"def getStatus(self):\n return self.status","title":""},{"docid":"6696d71a5a075955d428ad0a0d5dd89e","score":"0.6383367","text":"def getStatus(self):\n return self.status","title":""},{"docid":"a4112d945a44b0ddca3cd4e8b549dca4","score":"0.6382072","text":"def status(self):\n r = self.get(\"control/thermostat\", params={\"param\": \"state\"})\n return r.json()","title":""},{"docid":"f276dcc1fd26fc6709808934f37847f3","score":"0.637389","text":"def status(self) -> int:\n ...","title":""},{"docid":"4c36e079beb763bb50cf75e29bf33687","score":"0.63705075","text":"def cl_status(cl_moref):\n final_output = str(cl_moref.overallStatus)\n print_output_string(final_output, 'Cluster Status', 'yellow', 'red', 'gray')","title":""},{"docid":"47ed8d2b58eb52fd361ff8012815b913","score":"0.63603354","text":"def get_status(self):\n return self.service_client.GET(\"status\")","title":""},{"docid":"aa2a44725c494081af83834c63b177e9","score":"0.6354415","text":"def get_colored_status(self):\n if self.score == FileSubmission.NOT_GRADED:\n return \"info\"\n elif self.score == 0:\n return \"danger\"\n else:\n return \"success\"","title":""},{"docid":"5dc545d39fe453bde75dc64964cdf24d","score":"0.6348857","text":"def status(self):\n\n return self._request_get('')","title":""},{"docid":"af962f221109e5796ee9800821b3f74e","score":"0.6348835","text":"def get_status(self):\n return self.current_status","title":""},{"docid":"244561e043019f2d4b1a4180a582eb17","score":"0.6348286","text":"def getStatusMsg(self):\n # TODO: chech return code, generate exceptions\n return os.popen(self.binName + \" get_status_message\").read().strip()","title":""},{"docid":"790172284744d0903b6d0552c92feb6f","score":"0.6338428","text":"def cmd_status(self):\n return \"OK\"","title":""},{"docid":"f1fe4eda164d853a42933c752979b41e","score":"0.63374704","text":"def check_status():\n response = client.get_system_status()\n return response['status']","title":""},{"docid":"9907468d6fc7c94b29def84e983afa7e","score":"0.63318145","text":"def getStatus(self):\n return self.__status","title":""},{"docid":"3fc99d051f48387c16ecfe138a23e03a","score":"0.63055956","text":"def get_status(self):\n return self.service_client.GET('status')","title":""},{"docid":"57cdbda1030cc801b370132a9cdce117","score":"0.6300942","text":"def get_status():\r\n return flash.status","title":""},{"docid":"6fb62cae59b097ea1ce8ce8ddfe9fd86","score":"0.6300148","text":"def get_status(status):\n return status","title":""},{"docid":"d7743de4788e3b008efcfe294c5760c4","score":"0.629921","text":"def get_status(self):\n return self._status","title":""},{"docid":"d7743de4788e3b008efcfe294c5760c4","score":"0.629921","text":"def get_status(self):\n return self._status","title":""},{"docid":"a3689a78cfd117ee3750b62f741f6f41","score":"0.6296418","text":"def getStatus(self):\n return self._status","title":""},{"docid":"a3689a78cfd117ee3750b62f741f6f41","score":"0.6296418","text":"def getStatus(self):\n return self._status","title":""},{"docid":"a3689a78cfd117ee3750b62f741f6f41","score":"0.6296418","text":"def getStatus(self):\n return self._status","title":""},{"docid":"416e0d84270b805e9332eaf32c48f69a","score":"0.629488","text":"def _get_status(self):\n return self.session.get(self._status_url).content","title":""},{"docid":"e40869d93d1745bee594d03d741c8984","score":"0.6292104","text":"def get_status():\n return {\"status\": \"OK\"}","title":""},{"docid":"a394c4c3921690de784455a841e8d663","score":"0.6288365","text":"def get_status(self):\n self.send(com.STATUS)\n reply = self.get_reply(com.STATUS)\n return com.STATUSDICT[reply.data]","title":""},{"docid":"69b2d351cc07baf0d20cc9a7b1d404f9","score":"0.62716293","text":"def get_controller_status(self):\n\n resp = self.ctrl.http_get_request(\n self.ctrl.get_operational_url() +\n \"/lumina-controller-status:system-status\")\n\n r = {}\n if resp is not None:\n r['status_code'] = resp.status_code\n r['content'] = resp.content\n\n if resp.status_code == 200:\n return r\n\n return r","title":""},{"docid":"b2008a93c03fd5b0b9a4449cb7a34af1","score":"0.6269801","text":"def get_status(self):\n if self.simulate:\n return (*self.controller.mtr_pwrs, -1)\n if self.verblvl > 3: print(\"[CONTROLLER] status:\")\n if self.verblvl > 3: print(\" left:\", self.TB.GetMotor1())\n if self.verblvl > 3: print(\" right:\", self.TB.GetMotor2())\n if self.verblvl > 3: print(\" Volt:\", self.TB.GetBatteryReading())\n return (\n self.TB.GetMotor1(),\n self.TB.GetMotor2(),\n self.TB.GetBatteryReading()\n )","title":""},{"docid":"cd9b7f57b27c35b28065a922ca920fdb","score":"0.6257716","text":"def get(self):\n return self.status","title":""},{"docid":"1cdedcec72bf9a63de889b80fa3d75de","score":"0.6256545","text":"def get_status(self, ah):\n return ah.status","title":""},{"docid":"a19b46eff0cfaccdf51f4c1f5c1973d2","score":"0.6252884","text":"def get_status(self):\n return self.__state.get_status()","title":""},{"docid":"0f87d8668a7470ae796e7f7984a4b24d","score":"0.6250839","text":"def getStatus(self):\n #Acquire status from the Gripper\n status = self.client.getStatus(6);\n # Message to report the gripper status\n message = CModelStatus()\n #Assign the values to their respective variables\n message.gACT = (status[0] >> 0) & 0x01;\n message.gGTO = (status[0] >> 3) & 0x01;\n message.gSTA = (status[0] >> 4) & 0x03;\n message.gOBJ = (status[0] >> 6) & 0x03;\n message.gFLT = status[2]\n message.gPR = status[3]\n message.gPO = status[4]\n message.gCU = status[5]\n return message","title":""},{"docid":"4c580f757b6b1dda7a7425a74c837b62","score":"0.623713","text":"def status(self):\r\n return self._status","title":""},{"docid":"b367ac59eeb53d650b663ff67e6d0dce","score":"0.6237128","text":"def status(self):\n buf = bytearray([_REG_STATUS])\n with self.i2c_device as i2c:\n i2c.write_then_readinto(buf, buf)\n return buf[0]","title":""},{"docid":"e54edb03fd706a0e8f7d0b5c718bbba2","score":"0.62339294","text":"def status(self):\n return \"\"","title":""},{"docid":"affb3f84d3a2f358b18232d73ee5b434","score":"0.62315035","text":"def get_status(self):\n return 'Current status: {0}'.format(self._active)","title":""},{"docid":"f262c67ceac023bc0b82017a4849d264","score":"0.6231181","text":"def get_status(self):\n\n return self.status","title":""}],"string":"[\n {\n \"docid\": \"59cd33011a252a8832a814fc751eaa2e\",\n \"score\": \"0.71683383\",\n \"text\": \"def get_status(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59cd33011a252a8832a814fc751eaa2e\",\n \"score\": \"0.71683383\",\n \"text\": \"def get_status(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c925bfd95758d8502e8ba4ed29a9b06f\",\n \"score\": \"0.71524346\",\n \"text\": \"def GetStatus(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a79ef9babdd59afc1dea90b6dda0245\",\n \"score\": \"0.70841324\",\n \"text\": \"def getStatus():\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9b495d22056f45acc59f1e149922afe\",\n \"score\": \"0.6942748\",\n \"text\": \"def get_status(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9b495d22056f45acc59f1e149922afe\",\n \"score\": \"0.6942748\",\n \"text\": \"def get_status(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58f3214e4a66ca5da75c01a10e7b67e3\",\n \"score\": \"0.69176096\",\n \"text\": \"def get_status(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5b3559e43880f623b46ef25f689a099\",\n \"score\": \"0.6887757\",\n \"text\": \"def conffile_status(self):\\n return self._conffile_status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50927751aaeb9d6f8f11aa9bec3e7072\",\n \"score\": \"0.6879021\",\n \"text\": \"def get_cplex_status(self):\\n status = cb_getstat(self._cbstruct)\\n _proc.check_status(self._cbstruct, status[0], from_cb=True)\\n return status[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a029b71f30b58a5918ddc4de7d7470\",\n \"score\": \"0.6828316\",\n \"text\": \"def tc_status():\\n current_status = monitor_obj.get_status()\\n\\n success = True\\n message = \\\"Successfully retrieved current TC status\\\"\\n # Logging message\\n logging.debug(message) if success else logging.error(message)\\n return jsonify({\\\"success\\\": success, \\\"message\\\": message, \\\"status\\\": current_status})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa716fc8fa1c60bd8eae7d55a653789f\",\n \"score\": \"0.67597145\",\n \"text\": \"def get_status(self):\\n\\t\\treturn self.status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"872197a8bc19d84e42aeca15fa7b19e8\",\n \"score\": \"0.6758986\",\n \"text\": \"def status():\\n return ats_routines.Status()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e0341779e3a462aa18c6892c3a3a0d\",\n \"score\": \"0.67469645\",\n \"text\": \"def status(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e0341779e3a462aa18c6892c3a3a0d\",\n \"score\": \"0.67469645\",\n \"text\": \"def status(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab2054c2ca51dc939dc0121e50c1f153\",\n \"score\": \"0.6727437\",\n \"text\": \"def __get_status(tokens: str) -> str:\\n status = re.search(ffn_cst.RE_STATUS, tokens)\\n return 'In Progress' if status is None else 'Complete'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07972bf8207a85002b6dc0d30ece933b\",\n \"score\": \"0.6727045\",\n \"text\": \"def get_status(self):\\n with SMBus(self.i2c_channel) as bus:\\n rd = bus.read_byte_data(self.i2c_address, 218)\\n status = {\\n 'Device calibration in progress': rd & 1,\\n 'XTAL loss': (rd >> 2) & 1,\\n 'CLKIN loss': (rd >> 3) & 1,\\n 'PLL loss of lock': (rd >> 4) & 1\\n }\\n return status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f417741bf8aa736b80866e3811955c3\",\n \"score\": \"0.67155427\",\n \"text\": \"def getStatus(self):\\n\\t\\treturn self.status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d56631a426f7413cbad7ec761edcd5\",\n \"score\": \"0.67056483\",\n \"text\": \"def get_status(self):\\n return self.exec_action('get_status')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82a794ff37b5908c2f4e9cb819fcc437\",\n \"score\": \"0.66990626\",\n \"text\": \"def do_status(self):\\n self.log(\\\"Cmd : STATUS\\\")\\n return ('OK', self.status() )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dc6ca10b6e9f72ef4589ff095a8b736\",\n \"score\": \"0.66977674\",\n \"text\": \"def getStatus(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7774e28a41c3767733426bfcdd90d89d\",\n \"score\": \"0.6682327\",\n \"text\": \"def get_status(self):\\n data = self._xfer(commands.GET_STATUS_REQUEST)[1:]\\n return (data[0] << 8) | data[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55edcfab4d0326b1b0b2c41b3ce57aac\",\n \"score\": \"0.66750085\",\n \"text\": \"def get_status(self):\\n msg = \\\"d\\\"\\n return self.send_read(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"483a72c21651360137c4bd742c364ff5\",\n \"score\": \"0.6674774\",\n \"text\": \"def get_status(expnum, ccd, program, version='p', return_message=False):\\n key = get_process_tag(program, ccd, version)\\n status = get_tag(expnum, key)\\n logger.debug('%s: %s' % (key, status))\\n if return_message:\\n return status\\n else:\\n return status == SUCCESS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6152f5cc800e315c2d9c7e369d87111b\",\n \"score\": \"0.6674497\",\n \"text\": \"def getStatus(self):\\n\\t\\tcycletime=0\\n\\t\\ti2cerrorcount=0\\n\\t\\tsensor=0\\n\\t\\tflag=0\\n\\t\\tcurrentset=0\\n\\t\\trdata = self._sendAndGet(self._MSPCOMMANDS.MSP_STATUS, 11)\\n\\t\\tif rdata:\\n\\t\\t\\tcycletime = self._toUInt16(rdata[0:2])\\n\\t\\t\\ti2cerrorcount = self._toUInt16(rdata[2:4])\\n\\t\\t\\tsensor = self._toUInt16(rdata[4:6])\\n\\t\\t\\tflag = self._toUInt32(rdata[6:10])\\n\\t\\t\\tcurrentset = rdata[10]\\n\\t\\t#end if\\n\\t\\treturn {\\\"cycletime\\\":cycletime, \\\"i2cerrorcount\\\":i2cerrorcount, \\\"sensor\\\":sensor, \\\"flag\\\":flag, \\\"currentset\\\":currentset}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51f505b8fb3b32fef5d8e4d9aa848778\",\n \"score\": \"0.6657768\",\n \"text\": \"def getstatus(self):\\n status = self._sendandreceive(\\\"getstatus\\\")\\n return status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b2105e1a66fc80444c7ce4b0dbeafe8\",\n \"score\": \"0.66365767\",\n \"text\": \"def _status(self):\\n self.write(bytes([0xF3]))\\n return list(self.read(1))[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42088a8b1b40bd8b2b894b37ad0e1e2c\",\n \"score\": \"0.6607968\",\n \"text\": \"def _get_status(self):\\n return self.__status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0336a5a668992bb45fcb719a816077f\",\n \"score\": \"0.6596919\",\n \"text\": \"def getStatus(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd45ece75b4f0c67f78f961658547788\",\n \"score\": \"0.658261\",\n \"text\": \"def get_status(self):\\n if self.get_cloud_status() != \\\"ACTIVE\\\":\\n return \\\"stopped\\\"\\n #wait for the vm to be ready and SSH-able\\n self.wait_ready()\\n status = self.run_command(\\\"ctool status\\\", indent=0, prefix='')\\n return status.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e77f7c9d343406b1b8f73af001336711\",\n \"score\": \"0.65773284\",\n \"text\": \"def getstatus(self):\\n status, data = self.connection.command(\\\"getstatus\\\")\\n if status == \\\"statusResponse\\\":\\n self.parse_getstatus(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af04bd616df6993bc5df28f96cc54e1d\",\n \"score\": \"0.6571445\",\n \"text\": \"def getStatus(self) -> unicode:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af04bd616df6993bc5df28f96cc54e1d\",\n \"score\": \"0.6571445\",\n \"text\": \"def getStatus(self) -> unicode:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ca183cce5988715db59252076e86407\",\n \"score\": \"0.655932\",\n \"text\": \"def _status(self):\\n self.write(bytes([0x71]))\\n return list(self.read(1))[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"080b9761a562573d6eaa915996cedfdf\",\n \"score\": \"0.65567267\",\n \"text\": \"def status_conta(self):\\n return self._status_conta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23c5df475f77e3fd8bf30dce86e61c62\",\n \"score\": \"0.6555834\",\n \"text\": \"def status(self):\\n with self.i2c_device as i2c:\\n i2c.readinto(self._buf, start=0, end=1)\\n # print(\\\"status: \\\"+hex(self._buf[0]))\\n return self._buf[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"947aab4f2a302547c99925a93a0af9de\",\n \"score\": \"0.6550869\",\n \"text\": \"def ar_get_status() -> int:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d00e81f4aa5468edf8eb8fc058f5bab\",\n \"score\": \"0.65462714\",\n \"text\": \"def get_status(self):\\n return GetStatus(*self.ipcon.send_request(self, BrickletGPS.FUNCTION_GET_STATUS, (), '', 'B B B'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16af8b176f5197a4568617fff19a2e13\",\n \"score\": \"0.6532375\",\n \"text\": \"def printStatus():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbf0d5688cff2f22722ab23b838ecb57\",\n \"score\": \"0.6532194\",\n \"text\": \"def get_status(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afc714f1d34f691153ce0cf05ef6113d\",\n \"score\": \"0.6524577\",\n \"text\": \"def get_status(self):\\n\\n return self.make_request(WATTIO_STATUS_URI)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab943c6881aff606e4fded6f88d689b\",\n \"score\": \"0.65236217\",\n \"text\": \"def status():\\n return \\\"OK\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e7b032bbeb691beb709fa0ea36046ab\",\n \"score\": \"0.65131325\",\n \"text\": \"def getStatus(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9a56de8f3e2b5351bbfa890958e1360\",\n \"score\": \"0.6509154\",\n \"text\": \"def read_status(self, run=True):\\n self.device_status = self.ITC.getStatus(run)\\n return self.device_status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"699c5fc03bd1851ee77d50e29d5b6e28\",\n \"score\": \"0.6500384\",\n \"text\": \"def get_status(self):\\n command = struct.pack(\\\">H\\\", CMD_GET_STATUS)\\n self._write_command(command)\\n return self._read_status()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b64e4cc3637661dfc42cd3d93fd4e791\",\n \"score\": \"0.64850485\",\n \"text\": \"def get_status(self):\\n return self.raw\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a60dae99a903172dc818fb781fbe4ab6\",\n \"score\": \"0.64728737\",\n \"text\": \"def get_status(self):\\n\\n\\t\\treturn self.__status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a60dae99a903172dc818fb781fbe4ab6\",\n \"score\": \"0.64728737\",\n \"text\": \"def get_status(self):\\n\\n\\t\\treturn self.__status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a60dae99a903172dc818fb781fbe4ab6\",\n \"score\": \"0.64728737\",\n \"text\": \"def get_status(self):\\n\\n\\t\\treturn self.__status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb96e47bc3536605361670aa05050634\",\n \"score\": \"0.64686656\",\n \"text\": \"def CheckStatus(self):\\n return self.status_flag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84f7dffffe97089d56b86016441951bc\",\n \"score\": \"0.646025\",\n \"text\": \"def GetStatus(*args, **kwargs):\\n return _stc.StyledTextCtrl_GetStatus(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"513d4c3044e703f84a8938cda73ed5cb\",\n \"score\": \"0.6453283\",\n \"text\": \"def GetStatusText(self, i=0):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09e1e3811babd32f65f7cff491bb0c25\",\n \"score\": \"0.6449048\",\n \"text\": \"def get_status(self):\\n return self.status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09e1e3811babd32f65f7cff491bb0c25\",\n \"score\": \"0.6449048\",\n \"text\": \"def get_status(self):\\n return self.status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09e1e3811babd32f65f7cff491bb0c25\",\n \"score\": \"0.6449048\",\n \"text\": \"def get_status(self):\\n return self.status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09e1e3811babd32f65f7cff491bb0c25\",\n \"score\": \"0.6449048\",\n \"text\": \"def get_status(self):\\n return self.status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd7928e97017623329fdadd353fa4f0c\",\n \"score\": \"0.64473915\",\n \"text\": \"def fcp_service_status(self):\\n return self.request( \\\"fcp-service-status\\\", {\\n }, {\\n 'is-available': [ bool, False ],\\n } )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f932163c96f953bb3e3e08ee5694b93\",\n \"score\": \"0.6443662\",\n \"text\": \"def status(self):\\n if self.process.status == 0:\\n if self.xfail:\\n return \\\"UOK\\\"\\n else:\\n return \\\"OK\\\"\\n else:\\n if self.xfail:\\n return \\\"XFAIL\\\"\\n else:\\n if self.process.status == DIFF_FAILURE:\\n return \\\"DIFF\\\"\\n else:\\n return \\\"PROBLEM\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63eed432504ce04b66ed4f37d030f03c\",\n \"score\": \"0.64419204\",\n \"text\": \"def kyc_status(self):\\n return self._kyc_status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e7fd031e5b74c281eb5f26d6b4d0a04\",\n \"score\": \"0.6425467\",\n \"text\": \"def __get_status(self):\\n return self.__comm_status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb8f8b6a82d63c9c91752d27b4e8739\",\n \"score\": \"0.6413917\",\n \"text\": \"def getStatus(self):\\n self.send_getStatus()\\n return self.recv_getStatus()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c5c0a7e6b1b1c5c7422309616f155c8\",\n \"score\": \"0.64014506\",\n \"text\": \"def status(self):\\n return self.tbsresponse['cert_status'].name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57892d63e2c22328571b80facc49bcb3\",\n \"score\": \"0.63979363\",\n \"text\": \"def get_status(self):\\n #status = self._device.get_calibration_status()\\n #\\n #return CalibrationStatus(*status)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89eb1b05d400eff8568804d3ce740e11\",\n \"score\": \"0.6396726\",\n \"text\": \"def get_status(self):\\n req_url = self.compile_url([URL_STATUS])\\n result = self.request(req_url)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"069c6f5075ff6d3a6f0f30abfad5ec9c\",\n \"score\": \"0.6396679\",\n \"text\": \"def cifs_status(self):\\n return self.request( \\\"cifs-status\\\", {\\n }, {\\n 'status': [ basestring, False ],\\n } )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb489f686f23781ea9f08a2c7e2369a\",\n \"score\": \"0.63915825\",\n \"text\": \"def get_status(self):\\n return self._request('GET', '/status')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c7963b58e993d4007b089a6c060e398\",\n \"score\": \"0.63874245\",\n \"text\": \"def _getStatus(self):\\n return self._status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6696d71a5a075955d428ad0a0d5dd89e\",\n \"score\": \"0.6383367\",\n \"text\": \"def getStatus(self):\\n return self.status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6696d71a5a075955d428ad0a0d5dd89e\",\n \"score\": \"0.6383367\",\n \"text\": \"def getStatus(self):\\n return self.status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4112d945a44b0ddca3cd4e8b549dca4\",\n \"score\": \"0.6382072\",\n \"text\": \"def status(self):\\n r = self.get(\\\"control/thermostat\\\", params={\\\"param\\\": \\\"state\\\"})\\n return r.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f276dcc1fd26fc6709808934f37847f3\",\n \"score\": \"0.637389\",\n \"text\": \"def status(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c36e079beb763bb50cf75e29bf33687\",\n \"score\": \"0.63705075\",\n \"text\": \"def cl_status(cl_moref):\\n final_output = str(cl_moref.overallStatus)\\n print_output_string(final_output, 'Cluster Status', 'yellow', 'red', 'gray')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47ed8d2b58eb52fd361ff8012815b913\",\n \"score\": \"0.63603354\",\n \"text\": \"def get_status(self):\\n return self.service_client.GET(\\\"status\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa2a44725c494081af83834c63b177e9\",\n \"score\": \"0.6354415\",\n \"text\": \"def get_colored_status(self):\\n if self.score == FileSubmission.NOT_GRADED:\\n return \\\"info\\\"\\n elif self.score == 0:\\n return \\\"danger\\\"\\n else:\\n return \\\"success\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dc545d39fe453bde75dc64964cdf24d\",\n \"score\": \"0.6348857\",\n \"text\": \"def status(self):\\n\\n return self._request_get('')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af962f221109e5796ee9800821b3f74e\",\n \"score\": \"0.6348835\",\n \"text\": \"def get_status(self):\\n return self.current_status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"244561e043019f2d4b1a4180a582eb17\",\n \"score\": \"0.6348286\",\n \"text\": \"def getStatusMsg(self):\\n # TODO: chech return code, generate exceptions\\n return os.popen(self.binName + \\\" get_status_message\\\").read().strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"790172284744d0903b6d0552c92feb6f\",\n \"score\": \"0.6338428\",\n \"text\": \"def cmd_status(self):\\n return \\\"OK\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1fe4eda164d853a42933c752979b41e\",\n \"score\": \"0.63374704\",\n \"text\": \"def check_status():\\n response = client.get_system_status()\\n return response['status']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9907468d6fc7c94b29def84e983afa7e\",\n \"score\": \"0.63318145\",\n \"text\": \"def getStatus(self):\\n return self.__status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fc99d051f48387c16ecfe138a23e03a\",\n \"score\": \"0.63055956\",\n \"text\": \"def get_status(self):\\n return self.service_client.GET('status')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57cdbda1030cc801b370132a9cdce117\",\n \"score\": \"0.6300942\",\n \"text\": \"def get_status():\\r\\n return flash.status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fb62cae59b097ea1ce8ce8ddfe9fd86\",\n \"score\": \"0.6300148\",\n \"text\": \"def get_status(status):\\n return status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7743de4788e3b008efcfe294c5760c4\",\n \"score\": \"0.629921\",\n \"text\": \"def get_status(self):\\n return self._status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7743de4788e3b008efcfe294c5760c4\",\n \"score\": \"0.629921\",\n \"text\": \"def get_status(self):\\n return self._status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3689a78cfd117ee3750b62f741f6f41\",\n \"score\": \"0.6296418\",\n \"text\": \"def getStatus(self):\\n return self._status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3689a78cfd117ee3750b62f741f6f41\",\n \"score\": \"0.6296418\",\n \"text\": \"def getStatus(self):\\n return self._status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3689a78cfd117ee3750b62f741f6f41\",\n \"score\": \"0.6296418\",\n \"text\": \"def getStatus(self):\\n return self._status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"416e0d84270b805e9332eaf32c48f69a\",\n \"score\": \"0.629488\",\n \"text\": \"def _get_status(self):\\n return self.session.get(self._status_url).content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e40869d93d1745bee594d03d741c8984\",\n \"score\": \"0.6292104\",\n \"text\": \"def get_status():\\n return {\\\"status\\\": \\\"OK\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a394c4c3921690de784455a841e8d663\",\n \"score\": \"0.6288365\",\n \"text\": \"def get_status(self):\\n self.send(com.STATUS)\\n reply = self.get_reply(com.STATUS)\\n return com.STATUSDICT[reply.data]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69b2d351cc07baf0d20cc9a7b1d404f9\",\n \"score\": \"0.62716293\",\n \"text\": \"def get_controller_status(self):\\n\\n resp = self.ctrl.http_get_request(\\n self.ctrl.get_operational_url() +\\n \\\"/lumina-controller-status:system-status\\\")\\n\\n r = {}\\n if resp is not None:\\n r['status_code'] = resp.status_code\\n r['content'] = resp.content\\n\\n if resp.status_code == 200:\\n return r\\n\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2008a93c03fd5b0b9a4449cb7a34af1\",\n \"score\": \"0.6269801\",\n \"text\": \"def get_status(self):\\n if self.simulate:\\n return (*self.controller.mtr_pwrs, -1)\\n if self.verblvl > 3: print(\\\"[CONTROLLER] status:\\\")\\n if self.verblvl > 3: print(\\\" left:\\\", self.TB.GetMotor1())\\n if self.verblvl > 3: print(\\\" right:\\\", self.TB.GetMotor2())\\n if self.verblvl > 3: print(\\\" Volt:\\\", self.TB.GetBatteryReading())\\n return (\\n self.TB.GetMotor1(),\\n self.TB.GetMotor2(),\\n self.TB.GetBatteryReading()\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd9b7f57b27c35b28065a922ca920fdb\",\n \"score\": \"0.6257716\",\n \"text\": \"def get(self):\\n return self.status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cdedcec72bf9a63de889b80fa3d75de\",\n \"score\": \"0.6256545\",\n \"text\": \"def get_status(self, ah):\\n return ah.status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a19b46eff0cfaccdf51f4c1f5c1973d2\",\n \"score\": \"0.6252884\",\n \"text\": \"def get_status(self):\\n return self.__state.get_status()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f87d8668a7470ae796e7f7984a4b24d\",\n \"score\": \"0.6250839\",\n \"text\": \"def getStatus(self):\\n #Acquire status from the Gripper\\n status = self.client.getStatus(6);\\n # Message to report the gripper status\\n message = CModelStatus()\\n #Assign the values to their respective variables\\n message.gACT = (status[0] >> 0) & 0x01;\\n message.gGTO = (status[0] >> 3) & 0x01;\\n message.gSTA = (status[0] >> 4) & 0x03;\\n message.gOBJ = (status[0] >> 6) & 0x03;\\n message.gFLT = status[2]\\n message.gPR = status[3]\\n message.gPO = status[4]\\n message.gCU = status[5]\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c580f757b6b1dda7a7425a74c837b62\",\n \"score\": \"0.623713\",\n \"text\": \"def status(self):\\r\\n return self._status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b367ac59eeb53d650b663ff67e6d0dce\",\n \"score\": \"0.6237128\",\n \"text\": \"def status(self):\\n buf = bytearray([_REG_STATUS])\\n with self.i2c_device as i2c:\\n i2c.write_then_readinto(buf, buf)\\n return buf[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54edb03fd706a0e8f7d0b5c718bbba2\",\n \"score\": \"0.62339294\",\n \"text\": \"def status(self):\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"affb3f84d3a2f358b18232d73ee5b434\",\n \"score\": \"0.62315035\",\n \"text\": \"def get_status(self):\\n return 'Current status: {0}'.format(self._active)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f262c67ceac023bc0b82017a4849d264\",\n \"score\": \"0.6231181\",\n \"text\": \"def get_status(self):\\n\\n return self.status\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993880,"cells":{"query_id":{"kind":"string","value":"e32eee693062135dbbed18c72536161f"},"query":{"kind":"string","value":"Returns Modular Multiplication of a and b"},"positive_passages":{"kind":"list like","value":[{"docid":"b5b434c8d118439fec5371fcef14190e","score":"0.8792906","text":"def mod_mul(a, b):\n return (a % MOD*b % MOD) % MOD","title":""}],"string":"[\n {\n \"docid\": \"b5b434c8d118439fec5371fcef14190e\",\n \"score\": \"0.8792906\",\n \"text\": \"def mod_mul(a, b):\\n return (a % MOD*b % MOD) % MOD\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d7fff146e0e365c46ad35eb8ba20405d","score":"0.8231514","text":"def mod_mul(a, b, n):\n x = 0\n y = a\n while b > 0:\n if (b & 1) != 0:\n # odd number\n x = (x + y) % n\n y = (y + y) % n\n b = b / 2\n return x % n","title":""},{"docid":"66bd39ec8aba539c225b82d331448fc7","score":"0.8132094","text":"def mod_mul(a,b,n):\r\n return ((a%n)*(b%n))%n","title":""},{"docid":"7bfd8a99af562693cb70d48a5b122db0","score":"0.7776299","text":"def modExp2(a, b, m):\n return (a % m) ** b % m","title":""},{"docid":"2435be29dacf655e4ddad25b23b38e92","score":"0.76689214","text":"def modExp3(a, b, m):\n ret = 1\n a %= m\n while b:\n ret *= a\n ret %= m\n b -= 1\n return ret","title":""},{"docid":"9a3c7c2d86ec30e8bd26666edc19d649","score":"0.7614391","text":"def galois_multiplication(self, a, b):\r\n\t\tp = 0\r\n\t\tfor counter in range(8):\r\n\t\t\tif b & 1: p ^= a\r\n\t\t\thi_bit_set = a & 0x80\r\n\t\t\ta <<= 1\r\n\t\t\t# keep a 8 bit\r\n\t\t\ta &= 0xFF\r\n\t\t\tif hi_bit_set:\r\n\t\t\t\ta ^= 0x1b\r\n\t\t\tb >>= 1\r\n\t\treturn p","title":""},{"docid":"e371c19263020d54b05aa02596a7cc1f","score":"0.7578519","text":"def modExp1(a, b, m):\n return a ** b % m","title":""},{"docid":"5cd80b9340ecab84085f25fee6bb9abc","score":"0.75696635","text":"def multiply_numbers(a, b):\n return 0","title":""},{"docid":"c470b18af04ddf557ec83b7e8c847489","score":"0.7429252","text":"def mulinv(a, b):\n g, x, _ = egcd(a, b)\n if g == 1:\n return x % b","title":""},{"docid":"ad79b53f129e69b4c4f4b72280426947","score":"0.7417011","text":"def multiply(self, a, b):\n return a * b","title":""},{"docid":"246e49ffeecc5ebc4993491d081ca894","score":"0.7378693","text":"def divmod(a, b, m):\n return a * invmod(b, m) % m","title":""},{"docid":"be632f4844025cf547087a9d6b7f734f","score":"0.735359","text":"def __power_mod(a: int, b: int, n: int) -> int:\n v = 1\n p = a\n while b > 0:\n if __is_odd(b):\n v = (v * p) % n\n p = (p ** 2) % n\n b //= 2\n return v","title":""},{"docid":"755179291e421306ad3dbab1d7fc5576","score":"0.73522556","text":"def mod_solve(a, b, mod):\n a %= mod\n rem = mod - b % mod\n return extended_gcd(a, mod)[0] * rem","title":""},{"docid":"d49eecbd6732cf94eb5788ed07608513","score":"0.73302317","text":"def mod_add(a, b):\n return (a+b) % MOD","title":""},{"docid":"d49eecbd6732cf94eb5788ed07608513","score":"0.73302317","text":"def mod_add(a, b):\n return (a+b) % MOD","title":""},{"docid":"e1527d1ab594f26a339d246fa5291ad4","score":"0.7326518","text":"def mod(a: int, b: int) -> int:\n return (a % b + b) % b","title":""},{"docid":"6af49d7b3d311bb4f4479fc8fd0a957f","score":"0.7318195","text":"def arearectangle(a,b):\n return a*b","title":""},{"docid":"954b99ff7202739bcf77e20d9d9923ee","score":"0.72949994","text":"def mod_expo(a, b):\n result = 0\n while b > 0:\n if b % 2 == 1:\n result = UtilityHelper.mod_add(result, a)\n a = UtilityHelper.mod_mul(a, a)\n b //= 2\n return result","title":""},{"docid":"52e1c78ba0d61d8eac5f8ec7e11dc66b","score":"0.72948164","text":"def mulinv(a, b):\n g, x, _ = xgcd(a, b)\n if g == 1:\n return x % b","title":""},{"docid":"bf3c369ff30ad6f8c4e7e2041cba1c7a","score":"0.72894526","text":"def gf_mult(a, b):\n # Initialise\n product = 0\n\n # Mask the unwanted bits\n a = a & 0x0F\n b = b & 0x0F\n\n # While both multiplicands are non-zero\n while a and b:\n\n # If LSB of b is 1\n if b & 1:\n\n # Add current a to product\n product = product ^ a\n\n # Update a to a * 2\n a = a << 1\n\n # If a overflows beyond 4th bit\n \n if a & (1 << 4):\n\n # XOR with irreducible polynomial with high term eliminated\n a = a ^ 0b10011\n\n # Update b to b // 2\n b = b >> 1\n\n return product","title":""},{"docid":"ce6b3f943731f91c150dcd61b459cbf3","score":"0.7278229","text":"def mult(a,b):\n p = 0\n for i in range(8):\n if b % 2 == 1:\n p = p ^ a\n a = a << 1\n if a > 255 and a < 2 * 256:\n a = a ^ 0x11b\n b = b >> 1\n return p","title":""},{"docid":"5e1f2d26c0577fe7dd6fcbd205c24b95","score":"0.72633296","text":"def mul(a, b):\n return P.Mul()(a, b)","title":""},{"docid":"7a136f6373bccc8f8e02d5c2bc2ac3ac","score":"0.72508013","text":"def _mod_(a, b):\n\treturn a & b","title":""},{"docid":"259203d1e1ddba460759ec316b1beed5","score":"0.72477454","text":"def multiply(a, b):\r\n return a*b + 1","title":""},{"docid":"28f78a217aa69de0436530905db1921d","score":"0.72427016","text":"def mod(a, b):\n return (a % b)","title":""},{"docid":"398efc0199979cc5a1c64d43bdc4f69d","score":"0.72326","text":"def MOD(self, register_a, register_b):\n if self.register[register_b] == 0:\n print(\"Unable to divide by 0\")\n sys.exit()\n\n self.register[register_a] = self.register[register_a] % self.register[register_b]","title":""},{"docid":"2271e83ab217a922d50c615b849b94e3","score":"0.72189885","text":"def modular_inverse(a, b):\n\n if not _valid_input(0, a, b):\n return 0\n \n # receive the euclidean sequence and remove the last value as it is not needed for this algorithm.\n euclidean_seq = get_euclidean_sequence(b, a)\n euclidean_seq.pop()\n\n # Initialize p values of the algorithm\n p1 = 0\n p2 = 1\n\n for q in euclidean_seq:\n\n this_p = (p1 - (p2 * q)) % b\n\n # shift p values of the algorithm over one spot, updating p2 with the newest value\n p1 = p2\n p2 = this_p\n \n return p2","title":""},{"docid":"bfdce1264a9a925f527a5f276058ffef","score":"0.71850014","text":"def __rmul__(a, b):\n return a * b","title":""},{"docid":"8767a50647bfc6774f9655d4fa697274","score":"0.71623313","text":"def mul(a, b):\n return a * b","title":""},{"docid":"70d01657874b45d56fbe9d14659c717a","score":"0.7156563","text":"def _mul_(a, b):\n\treturn a * b","title":""},{"docid":"6e45982f1ec222ab9dde17a3e2416f4f","score":"0.7145545","text":"def mod_res(a,b,n):\r\n return ((a%n)-(b%n))%n","title":""},{"docid":"b19b98fd7b8da32c679cb18ba3131c49","score":"0.7145155","text":"def mult(a, b):\n return a * b","title":""},{"docid":"b19b98fd7b8da32c679cb18ba3131c49","score":"0.7145155","text":"def mult(a, b):\n return a * b","title":""},{"docid":"b19b98fd7b8da32c679cb18ba3131c49","score":"0.7145155","text":"def mult(a, b):\n return a * b","title":""},{"docid":"cfcb2b15a2bb2787fe78601d812e3ac0","score":"0.70972025","text":"def multiple(num_a, num_b):\n return num_a * num_b","title":""},{"docid":"8fcdc1f2aff63bb2a68cd815afd77225","score":"0.7095458","text":"def mod_exp(a, b, q):\r\n val = 1\r\n mult = a\r\n while b > 0:\r\n odd = b & 1 # bitwise and\r\n if odd == 1:\r\n val = (val * mult) % q\r\n b -= 1\r\n if b == 0:\r\n break\r\n mult = (mult * mult) % q\r\n b = b >> 1 # bitwise divide by 2\r\n return val","title":""},{"docid":"70161be9042c95633a715140a467e19d","score":"0.70921725","text":"def mod_exp(a, b, n):\n x = 1\n y = a\n while b > 0:\n if (b & 1) != 0:\n # odd number\n x = CryptoMath.mod_mul(x, y, n)\n y = CryptoMath.mod_mul(y, y, n)\n b = b / 2\n return x % n","title":""},{"docid":"223b133f02bba7a02194243fb8e8299f","score":"0.70855635","text":"def multiply(a, b):\r\n return a*b","title":""},{"docid":"ef2d5327491b482a765d4a12d56807d4","score":"0.7064435","text":"def mul(a, b):\n b = np.copy(b)\n c = 0\n i = 0\n while np.any(b):\n c ^= (a * np.array(b&1, bool)) << i\n b >>= 1\n i += 1\n return c","title":""},{"docid":"be55c8c3ff74956b7d49a0d73dbb1235","score":"0.70584255","text":"def mul(a, b):\n return a*b","title":""},{"docid":"c3acae3b3000d05ffd2b749e56476f00","score":"0.7057469","text":"def MUL(self, register_a, register_b):\n self.register[register_a] *= self.register[register_b]","title":""},{"docid":"ba4a28f8df9cf456aea288bb669ec1f8","score":"0.7045925","text":"def mod_exp(a, b, q):\n val = 1\n mult = a\n while b > 0:\n odd = b & 1 # bitwise and\n if odd == 1:\n val = (val * mult) % q\n b -= 1\n if b == 0:\n break\n mult = (mult * mult) % q\n b = b >> 1 # bitwise divide by 2\n return val","title":""},{"docid":"f510db1d87e31985ba5f5aff046e5d68","score":"0.7023329","text":"def iterative_multiplication(a,b):\n result = 0\n while b>0:\n result = result+a\n b=b-1\n return result","title":""},{"docid":"041514adf0a7ce1bffa1c491e9294892","score":"0.7015748","text":"def modular_exp(a, b, n):\n res = 1\n q = a\n while b > 0:\n if b % 2 == 1:\n res = q*res % n\n q = q*q % n\n b //= 2\n\n return res","title":""},{"docid":"9d63becc69fecc4f749e86914cf286e8","score":"0.7014976","text":"def mul(a,b):\n return a*b","title":""},{"docid":"d43132c02eb9e51a47718e62a27e1c15","score":"0.6989532","text":"def mod(x, y):","title":""},{"docid":"6eaacf136f264dd4ed333375d7d5ee6a","score":"0.6970625","text":"def modpower(a, b, c):\r\n\tresult = 1\r\n\twhile b > 0: # while b is nonzero\r\n\t\tif b % 2 == 1: # b is odd\r\n\t\t\tresult = (result * a) % c\r\n\t\ta = (a * a) % c\r\n\t\tb = b // 2\r\n\treturn result","title":""},{"docid":"8cdcffe71babc47b12bde838eac5929b","score":"0.69607913","text":"def multiply(a, b):\n return a * b","title":""},{"docid":"8cdcffe71babc47b12bde838eac5929b","score":"0.69607913","text":"def multiply(a, b):\n return a * b","title":""},{"docid":"8cdcffe71babc47b12bde838eac5929b","score":"0.69607913","text":"def multiply(a, b):\n return a * b","title":""},{"docid":"90d6135a07918fd6c7e98326ee007586","score":"0.6953207","text":"def multiply(a, b):\n res = 16*[0]\n for i in xrange(16):\n for j in xrange(i+1):\n res[i] += a[j] * b[i-j]\n return res","title":""},{"docid":"55fac8664ec1de4fea9273a8a34a7a5a","score":"0.6935819","text":"def multiply(a=0,b=0):\n\treturn str(int(a)*int(b))","title":""},{"docid":"1e5c5a98f941890bd53dfa2667c3178e","score":"0.69345623","text":"def product_on_basis(self, a, b):\n return self(a+b)","title":""},{"docid":"3d438fc4de2cfc452e88030c5dd51c62","score":"0.6931937","text":"def producto(a,b): \n \n return a*b","title":""},{"docid":"241016821ad113af1df7f351b7d0621a","score":"0.687885","text":"def multiply(a, b):\n return a*b","title":""},{"docid":"5fe7ac53286548b0491637dd5fe1397e","score":"0.6878289","text":"def mul(A, b):\n return A.from_rep(A.rep.mul(b))","title":""},{"docid":"aa81a4f526a57aa89c73abc369d760a1","score":"0.68779373","text":"def print_mult_inverses(a: int, b: int):\n am, bm, g = extended_gcd(a, b)\n if g == 0:\n raise ValueError(f\"{a} and {b} are not relatively prime.\")\n if am < 0:\n am = b + am\n if bm < 0:\n bm = a + bm\n print(f\"{a} * {am} % {b} == {a * am % b}\"\\\n f\"\\n{b} * {bm} % {a} == {b * bm % a}\")","title":""},{"docid":"7a4be89cbbf78e15ffeca546ba57a331","score":"0.68637675","text":"def multiplication_1(a, b):\n\n if b == 1:\n return a\n\n return a + multiplication_1(a, b - 1)","title":""},{"docid":"11dc8a00923d6610e582cf94089e1a64","score":"0.6862879","text":"def gmul(a, b):\n\tc = 0\n\tif (a & 1) == 1:\n\t\tc = b\n\n\tfor _ in range(1, 8):\n\t\thi_bit = (b & 0x80)\n\t\tb <<= 1\n\t\tb &= 0xff # Get rid of the most significant bit outside 2 bytes.\n\t\tif hi_bit == 0x80:\n\t\t\tb ^= 0x1b\n\t\ta >>= 1\n\t\tif (a & 1) == 1:\n\t\t\tc ^= b\n\treturn c","title":""},{"docid":"476f98cae064b91017d2cee29edea9a0","score":"0.6859","text":"def mod_sum(a,b,n):\r\n return ((a%n)+(b%n))%n","title":""},{"docid":"7373515d8c69a99a13f4a55e66e535bf","score":"0.6842136","text":"def mul_rat(a, b):\n return make_rat(num(a) * num(b), den(a) * den(b))","title":""},{"docid":"e4a650dc0309ef6b980783b34f41058c","score":"0.6840613","text":"def multiplication(a,b,N):\n la = table_ln(a,N)\n lb = table_ln(b,N)\n lc = la + lb\n c = table_exp(lc,N)\n return c","title":""},{"docid":"2ac4e57a5805039fa844d0caf00a3eb8","score":"0.6828702","text":"def divmod(self, a, b):\n a = a @ self\n b = b @ self\n\n if a.var != b.var:\n raise ValueError(\"variables must be the same\")\n\n quotient, remainder = self.zero, a\n\n while remainder.degree >= b.degree:\n monomial_exponent = remainder.degree - b.degree\n monomial_zeros = [self.zero for _ in range(monomial_exponent)]\n monomial_divisor = Polynomial(monomial_zeros +\n [self._base_ring.div(remainder.coefficients[-1], b.coefficients[-1])],\n a.var)\n\n quotient = self.add(quotient, monomial_divisor)\n remainder = self.sub(remainder, self.mul(monomial_divisor, b))\n\n return quotient, remainder","title":""},{"docid":"58fe1178cbb5af434180ace86bf5ecbb","score":"0.6824406","text":"def galoisMult(a, b):\n p = 0\n hiBitSet = 0\n for i in range(8):\n if b & 1 == 1:\n p ^= a\n hiBitSet = a & 0x80\n a <<= 1\n if hiBitSet == 0x80:\n a ^= 0x1b\n b >>= 1\n return p % 256","title":""},{"docid":"cd6b38f1f267ea838109e6c4df012b36","score":"0.6823742","text":"def m_div(a, b):\n return a+ b","title":""},{"docid":"10020706eba7f46b18bbf4ee41274055","score":"0.68231744","text":"def product(a,b):\n return a*b","title":""},{"docid":"4c56947507d27b676de8f9e8e21c7b24","score":"0.6815682","text":"def fastPower(a, b, m):\r\n number = 1\r\n while b:\r\n if b & 1:\r\n number = number * a % m\r\n b >>= 1\r\n a = a * a % m\r\n return number","title":""},{"docid":"850a348b38bf486207cb6e3c477056a7","score":"0.680911","text":"def multElements(self, a, b):\n exp_a = self.elementToExp(a)\n exp_b = self.elementToExp(b)\n exp_product = exp_a + exp_b # multiplication = adding the exponents\n product = self.elementFromExp(exp_product)\n return int(product)","title":""},{"docid":"1a79cc6f10fb5639f6443705e89c3385","score":"0.6801696","text":"def mult_inverse(a: int, b: int):\n am, bm, g = extended_gcd(a, b)\n if g == 0:\n raise ValueError(f\"{a} and {b} are not relatively prime.\")\n return am if am > 0 else b + am","title":""},{"docid":"dfeb177f05d3ea370576e9bd7704686e","score":"0.6795403","text":"def pow(a, b):\n if b == 0:\n return 1\n rem = pow(a**2 % length, b // 2) % length\n if b % 2 == 1:\n return a * rem\n return rem","title":""},{"docid":"da1108a390c51ef46409b2f81da00fbd","score":"0.6773538","text":"def _powmod(self, a, n, m):\n res = 1\n while n != 0:\n if n % 2 != 0:\n res *= a\n res %= m\n n -= 1\n else:\n a *= a\n a %= m\n n //= 2\n return res","title":""},{"docid":"23790e067fcad1c66e287e4e783a79f2","score":"0.67587584","text":"def mod_large(a,b,c):\n ans = 1\n while b>0:\n ans *= a\n ans %= c\n b -= 1\n return ans","title":""},{"docid":"ce3e4660d88b34d13644a80e45e1d077","score":"0.6752904","text":"def vector_mul(a, b):\n assert len(a) == len(b), 'a and b must have equal length'\n res = GF(0)\n\n for i in range(len(a)):\n res += GF(a[i]) * GF(b[i])\n\n return res.int_value","title":""},{"docid":"ff39472f6027c409f1122ba213bbc497","score":"0.6744542","text":"def raw_multiply(self, a, b, randomization=None):\n n2 = self.nsquare\n\n # explicitely reduces to avoid large exponents\n b %= self.n\n if b > self.n // 2:\n b -= self.n\n\n # if a is of the form (1+n)^q, then we can avoid the exponentiation\n q, r = divmod(a-1, self.n)\n if r == 0:\n raw_value = (1 + self.n * q * b) % n2\n else:\n raw_value = util.powmod(a, b, n2)\n\n # apply randomization\n if randomization is None:\n randomization = random.SystemRandom().randrange(self.n)\n raw_value = raw_value * util.powmod(randomization, self.n, n2) % n2\n\n return raw_value, randomization","title":""},{"docid":"58d51134fa40d91b50fb9fc5f9d315a8","score":"0.67423606","text":"def bit_math(x: int, y: int, b: int) -> int:\n return (x * b) + (y * (b ^ 1))","title":""},{"docid":"9ebd63c3e773560217e803d1a5483b26","score":"0.67411655","text":"def bin_exp_mod(a, n, b):\n # mod b\n assert b != 0, \"This cannot accept modulo that is == 0\"\n if n == 0:\n return 1\n\n if n % 2 == 1:\n return (bin_exp_mod(a, n - 1, b) * a) % b\n\n r = bin_exp_mod(a, n / 2, b)\n return (r * r) % b","title":""},{"docid":"253964279dd72a290cecab3039f1a552","score":"0.6740051","text":"def _m(self, a, b, amount=1):\n new = []\n for i in range(len(a)):\n new.append(a[i]+(b[i]*amount))\n return new","title":""},{"docid":"20b09012400dd5d66e1881b21a1336af","score":"0.6738363","text":"def mod(int_a, int_b):\n if isinstance(int_a, int) and isinstance(int_b, int):\n return int_a % int_b","title":""},{"docid":"6c8c1d07083de55a2329d828129e2b92","score":"0.67336816","text":"def opp_mult():\n a = int(request.args[\"a\"])\n b = int(request.args[\"b\"])\n result = mult(a,b)\n return str(result)","title":""},{"docid":"3af8e5b7ddfe47f94b47b5014c950d1f","score":"0.6715987","text":"def power(a, b):\n\n return a ** b","title":""},{"docid":"372d4cec3f05caedd3b3d6373abf4a3e","score":"0.6697999","text":"def matmul(a, b):\n pass","title":""},{"docid":"7a88f3c26a5a8c919f93850e8bd62666","score":"0.6692014","text":"def mult(A, B):\n return A*B","title":""},{"docid":"6c97dd37bab9479071fd60c54ab280cc","score":"0.66853356","text":"def mult(a, b):\n a = int(request.args.get('a'))\n b = int(request.args.get('b'))\n total = mult(a, b)\n\n return str(total)","title":""},{"docid":"53c820661b58980336e150e3d316caaf","score":"0.6675671","text":"def mod_sub(a, b):\n return (a-b+MOD) % MOD","title":""},{"docid":"53c820661b58980336e150e3d316caaf","score":"0.6675671","text":"def mod_sub(a, b):\n return (a-b+MOD) % MOD","title":""},{"docid":"237ed696ac51d5755e7e8e9e6effaccf","score":"0.66743296","text":"def mcd(a, b):\n if (b == 0):\n return a\n else:\n return mcd(b, a % b)","title":""},{"docid":"57399d4114f4e042d3c4039e1b616cae","score":"0.66633725","text":"def _c_mul(a, b):\n return eval(hex((int(a) * b) & 0xFFFFFFFF)[:-1])","title":""},{"docid":"d1596c672a07a2b1f6f1ff3dfc686ca8","score":"0.6662958","text":"def arithemtic(a, b):\n return a - b, a * b","title":""},{"docid":"a177452bdc71a9b1d5dd0b7186e488e8","score":"0.6652532","text":"def _b_divmod_ ( b , p ) :\n rr = b.divmod(p)\n return rr.first, rr.second","title":""},{"docid":"f4a79619ad1f736717f8375b858d031f","score":"0.6640183","text":"def mod_exp(a, b, n):\n bb = \"{0:b}\".format(b)\n bc = [int(bb[i]) for i in range(len(bb))]\n bc.reverse()\n k = {0: a % n}\n\n for i in range(1, len(bc)):\n k[i] = (k[i-1] ** 2) % n \n\n r = 1\n for m in range(len(bc)):\n if bc[m] == 1:\n r = r * k[m]\n return r % n","title":""},{"docid":"5b0556ecdcf64e320a605751c5c2c3bf","score":"0.6631444","text":"def mod(num1, num2):\n m = num1 % num2\n return m","title":""},{"docid":"f40bfc71de3dc3a6a03563c64df95821","score":"0.6630117","text":"def modinv(a,b):\n g, x, y = egcd(a,b)\n if y !=1:\n return None\n else:\n return x%b","title":""},{"docid":"1a22f7f0d5ce9f8e1a57fa7d6062e81e","score":"0.66218835","text":"def mul_function(a, b):\n return float(a) * float(b)","title":""},{"docid":"c1c8739042ae4c757bfe87bae0c10b9e","score":"0.6621445","text":"def mul_rat(a, b):\n \"*** YOUR CODE HERE ***\"","title":""},{"docid":"49ed1d7568e265f2d7dde20818b22510","score":"0.6607737","text":"def get_matrix_mul(a, b):\n if len(a) == 0 or len(b) == 0:\n raise Exception(ERRORS.INVALID_MATRIX)\n if len(a[0]) != len(b):\n raise Exception(ERRORS.INVALID_MATRIX_MUL_COMBO)\n result = [[0 for _ in range(len(b[0]))] for _ in range(len(a))]\n for i in range(len(a)):\n for j in range(len(b[0])):\n for k in range(len(b)):\n result[i][j] = UtilityHelper.mod_add(\n result[i][j], UtilityHelper.mod_mul(a[i][k], b[k][j]))\n return result","title":""},{"docid":"55993c13ea6daeee4b447071d9c6c5e7","score":"0.6607196","text":"def modinverse(a, b):\n g, x, _ = extended_gcd(a, b)\n if g != 1:\n raise ValueError('gcd(a, b) != 1')\n return x % b","title":""},{"docid":"04b298d0cdf51cef5d388969ef5f4cc0","score":"0.6606618","text":"def _mul_(self, right):","title":""},{"docid":"aeab25634e5b8706c3ac1d4891dd3229","score":"0.6603027","text":"def rem(a, b):\n q = abs(a) % b\n s = np.sign(a)\n return q*s","title":""},{"docid":"b0e36f785cf99b28ffe2fdbd966cb828","score":"0.6587561","text":"def i_matmul(a, b):\n try:\n return a @ b\n except TypeError:\n return a * b","title":""},{"docid":"006ca660040685aacacdb9734cd70bba","score":"0.6587509","text":"def numbers_multiplication(number_one, number_two):\r\n return number_one*number_two","title":""}],"string":"[\n {\n \"docid\": \"d7fff146e0e365c46ad35eb8ba20405d\",\n \"score\": \"0.8231514\",\n \"text\": \"def mod_mul(a, b, n):\\n x = 0\\n y = a\\n while b > 0:\\n if (b & 1) != 0:\\n # odd number\\n x = (x + y) % n\\n y = (y + y) % n\\n b = b / 2\\n return x % n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66bd39ec8aba539c225b82d331448fc7\",\n \"score\": \"0.8132094\",\n \"text\": \"def mod_mul(a,b,n):\\r\\n return ((a%n)*(b%n))%n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bfd8a99af562693cb70d48a5b122db0\",\n \"score\": \"0.7776299\",\n \"text\": \"def modExp2(a, b, m):\\n return (a % m) ** b % m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2435be29dacf655e4ddad25b23b38e92\",\n \"score\": \"0.76689214\",\n \"text\": \"def modExp3(a, b, m):\\n ret = 1\\n a %= m\\n while b:\\n ret *= a\\n ret %= m\\n b -= 1\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a3c7c2d86ec30e8bd26666edc19d649\",\n \"score\": \"0.7614391\",\n \"text\": \"def galois_multiplication(self, a, b):\\r\\n\\t\\tp = 0\\r\\n\\t\\tfor counter in range(8):\\r\\n\\t\\t\\tif b & 1: p ^= a\\r\\n\\t\\t\\thi_bit_set = a & 0x80\\r\\n\\t\\t\\ta <<= 1\\r\\n\\t\\t\\t# keep a 8 bit\\r\\n\\t\\t\\ta &= 0xFF\\r\\n\\t\\t\\tif hi_bit_set:\\r\\n\\t\\t\\t\\ta ^= 0x1b\\r\\n\\t\\t\\tb >>= 1\\r\\n\\t\\treturn p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e371c19263020d54b05aa02596a7cc1f\",\n \"score\": \"0.7578519\",\n \"text\": \"def modExp1(a, b, m):\\n return a ** b % m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cd80b9340ecab84085f25fee6bb9abc\",\n \"score\": \"0.75696635\",\n \"text\": \"def multiply_numbers(a, b):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c470b18af04ddf557ec83b7e8c847489\",\n \"score\": \"0.7429252\",\n \"text\": \"def mulinv(a, b):\\n g, x, _ = egcd(a, b)\\n if g == 1:\\n return x % b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad79b53f129e69b4c4f4b72280426947\",\n \"score\": \"0.7417011\",\n \"text\": \"def multiply(self, a, b):\\n return a * b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"246e49ffeecc5ebc4993491d081ca894\",\n \"score\": \"0.7378693\",\n \"text\": \"def divmod(a, b, m):\\n return a * invmod(b, m) % m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be632f4844025cf547087a9d6b7f734f\",\n \"score\": \"0.735359\",\n \"text\": \"def __power_mod(a: int, b: int, n: int) -> int:\\n v = 1\\n p = a\\n while b > 0:\\n if __is_odd(b):\\n v = (v * p) % n\\n p = (p ** 2) % n\\n b //= 2\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"755179291e421306ad3dbab1d7fc5576\",\n \"score\": \"0.73522556\",\n \"text\": \"def mod_solve(a, b, mod):\\n a %= mod\\n rem = mod - b % mod\\n return extended_gcd(a, mod)[0] * rem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d49eecbd6732cf94eb5788ed07608513\",\n \"score\": \"0.73302317\",\n \"text\": \"def mod_add(a, b):\\n return (a+b) % MOD\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d49eecbd6732cf94eb5788ed07608513\",\n \"score\": \"0.73302317\",\n \"text\": \"def mod_add(a, b):\\n return (a+b) % MOD\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1527d1ab594f26a339d246fa5291ad4\",\n \"score\": \"0.7326518\",\n \"text\": \"def mod(a: int, b: int) -> int:\\n return (a % b + b) % b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6af49d7b3d311bb4f4479fc8fd0a957f\",\n \"score\": \"0.7318195\",\n \"text\": \"def arearectangle(a,b):\\n return a*b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"954b99ff7202739bcf77e20d9d9923ee\",\n \"score\": \"0.72949994\",\n \"text\": \"def mod_expo(a, b):\\n result = 0\\n while b > 0:\\n if b % 2 == 1:\\n result = UtilityHelper.mod_add(result, a)\\n a = UtilityHelper.mod_mul(a, a)\\n b //= 2\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52e1c78ba0d61d8eac5f8ec7e11dc66b\",\n \"score\": \"0.72948164\",\n \"text\": \"def mulinv(a, b):\\n g, x, _ = xgcd(a, b)\\n if g == 1:\\n return x % b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf3c369ff30ad6f8c4e7e2041cba1c7a\",\n \"score\": \"0.72894526\",\n \"text\": \"def gf_mult(a, b):\\n # Initialise\\n product = 0\\n\\n # Mask the unwanted bits\\n a = a & 0x0F\\n b = b & 0x0F\\n\\n # While both multiplicands are non-zero\\n while a and b:\\n\\n # If LSB of b is 1\\n if b & 1:\\n\\n # Add current a to product\\n product = product ^ a\\n\\n # Update a to a * 2\\n a = a << 1\\n\\n # If a overflows beyond 4th bit\\n \\n if a & (1 << 4):\\n\\n # XOR with irreducible polynomial with high term eliminated\\n a = a ^ 0b10011\\n\\n # Update b to b // 2\\n b = b >> 1\\n\\n return product\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce6b3f943731f91c150dcd61b459cbf3\",\n \"score\": \"0.7278229\",\n \"text\": \"def mult(a,b):\\n p = 0\\n for i in range(8):\\n if b % 2 == 1:\\n p = p ^ a\\n a = a << 1\\n if a > 255 and a < 2 * 256:\\n a = a ^ 0x11b\\n b = b >> 1\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e1f2d26c0577fe7dd6fcbd205c24b95\",\n \"score\": \"0.72633296\",\n \"text\": \"def mul(a, b):\\n return P.Mul()(a, b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a136f6373bccc8f8e02d5c2bc2ac3ac\",\n \"score\": \"0.72508013\",\n \"text\": \"def _mod_(a, b):\\n\\treturn a & b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"259203d1e1ddba460759ec316b1beed5\",\n \"score\": \"0.72477454\",\n \"text\": \"def multiply(a, b):\\r\\n return a*b + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28f78a217aa69de0436530905db1921d\",\n \"score\": \"0.72427016\",\n \"text\": \"def mod(a, b):\\n return (a % b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"398efc0199979cc5a1c64d43bdc4f69d\",\n \"score\": \"0.72326\",\n \"text\": \"def MOD(self, register_a, register_b):\\n if self.register[register_b] == 0:\\n print(\\\"Unable to divide by 0\\\")\\n sys.exit()\\n\\n self.register[register_a] = self.register[register_a] % self.register[register_b]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2271e83ab217a922d50c615b849b94e3\",\n \"score\": \"0.72189885\",\n \"text\": \"def modular_inverse(a, b):\\n\\n if not _valid_input(0, a, b):\\n return 0\\n \\n # receive the euclidean sequence and remove the last value as it is not needed for this algorithm.\\n euclidean_seq = get_euclidean_sequence(b, a)\\n euclidean_seq.pop()\\n\\n # Initialize p values of the algorithm\\n p1 = 0\\n p2 = 1\\n\\n for q in euclidean_seq:\\n\\n this_p = (p1 - (p2 * q)) % b\\n\\n # shift p values of the algorithm over one spot, updating p2 with the newest value\\n p1 = p2\\n p2 = this_p\\n \\n return p2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfdce1264a9a925f527a5f276058ffef\",\n \"score\": \"0.71850014\",\n \"text\": \"def __rmul__(a, b):\\n return a * b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8767a50647bfc6774f9655d4fa697274\",\n \"score\": \"0.71623313\",\n \"text\": \"def mul(a, b):\\n return a * b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d01657874b45d56fbe9d14659c717a\",\n \"score\": \"0.7156563\",\n \"text\": \"def _mul_(a, b):\\n\\treturn a * b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e45982f1ec222ab9dde17a3e2416f4f\",\n \"score\": \"0.7145545\",\n \"text\": \"def mod_res(a,b,n):\\r\\n return ((a%n)-(b%n))%n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19b98fd7b8da32c679cb18ba3131c49\",\n \"score\": \"0.7145155\",\n \"text\": \"def mult(a, b):\\n return a * b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19b98fd7b8da32c679cb18ba3131c49\",\n \"score\": \"0.7145155\",\n \"text\": \"def mult(a, b):\\n return a * b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19b98fd7b8da32c679cb18ba3131c49\",\n \"score\": \"0.7145155\",\n \"text\": \"def mult(a, b):\\n return a * b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfcb2b15a2bb2787fe78601d812e3ac0\",\n \"score\": \"0.70972025\",\n \"text\": \"def multiple(num_a, num_b):\\n return num_a * num_b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fcdc1f2aff63bb2a68cd815afd77225\",\n \"score\": \"0.7095458\",\n \"text\": \"def mod_exp(a, b, q):\\r\\n val = 1\\r\\n mult = a\\r\\n while b > 0:\\r\\n odd = b & 1 # bitwise and\\r\\n if odd == 1:\\r\\n val = (val * mult) % q\\r\\n b -= 1\\r\\n if b == 0:\\r\\n break\\r\\n mult = (mult * mult) % q\\r\\n b = b >> 1 # bitwise divide by 2\\r\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70161be9042c95633a715140a467e19d\",\n \"score\": \"0.70921725\",\n \"text\": \"def mod_exp(a, b, n):\\n x = 1\\n y = a\\n while b > 0:\\n if (b & 1) != 0:\\n # odd number\\n x = CryptoMath.mod_mul(x, y, n)\\n y = CryptoMath.mod_mul(y, y, n)\\n b = b / 2\\n return x % n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"223b133f02bba7a02194243fb8e8299f\",\n \"score\": \"0.70855635\",\n \"text\": \"def multiply(a, b):\\r\\n return a*b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef2d5327491b482a765d4a12d56807d4\",\n \"score\": \"0.7064435\",\n \"text\": \"def mul(a, b):\\n b = np.copy(b)\\n c = 0\\n i = 0\\n while np.any(b):\\n c ^= (a * np.array(b&1, bool)) << i\\n b >>= 1\\n i += 1\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be55c8c3ff74956b7d49a0d73dbb1235\",\n \"score\": \"0.70584255\",\n \"text\": \"def mul(a, b):\\n return a*b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3acae3b3000d05ffd2b749e56476f00\",\n \"score\": \"0.7057469\",\n \"text\": \"def MUL(self, register_a, register_b):\\n self.register[register_a] *= self.register[register_b]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba4a28f8df9cf456aea288bb669ec1f8\",\n \"score\": \"0.7045925\",\n \"text\": \"def mod_exp(a, b, q):\\n val = 1\\n mult = a\\n while b > 0:\\n odd = b & 1 # bitwise and\\n if odd == 1:\\n val = (val * mult) % q\\n b -= 1\\n if b == 0:\\n break\\n mult = (mult * mult) % q\\n b = b >> 1 # bitwise divide by 2\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f510db1d87e31985ba5f5aff046e5d68\",\n \"score\": \"0.7023329\",\n \"text\": \"def iterative_multiplication(a,b):\\n result = 0\\n while b>0:\\n result = result+a\\n b=b-1\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"041514adf0a7ce1bffa1c491e9294892\",\n \"score\": \"0.7015748\",\n \"text\": \"def modular_exp(a, b, n):\\n res = 1\\n q = a\\n while b > 0:\\n if b % 2 == 1:\\n res = q*res % n\\n q = q*q % n\\n b //= 2\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d63becc69fecc4f749e86914cf286e8\",\n \"score\": \"0.7014976\",\n \"text\": \"def mul(a,b):\\n return a*b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d43132c02eb9e51a47718e62a27e1c15\",\n \"score\": \"0.6989532\",\n \"text\": \"def mod(x, y):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eaacf136f264dd4ed333375d7d5ee6a\",\n \"score\": \"0.6970625\",\n \"text\": \"def modpower(a, b, c):\\r\\n\\tresult = 1\\r\\n\\twhile b > 0: # while b is nonzero\\r\\n\\t\\tif b % 2 == 1: # b is odd\\r\\n\\t\\t\\tresult = (result * a) % c\\r\\n\\t\\ta = (a * a) % c\\r\\n\\t\\tb = b // 2\\r\\n\\treturn result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cdcffe71babc47b12bde838eac5929b\",\n \"score\": \"0.69607913\",\n \"text\": \"def multiply(a, b):\\n return a * b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cdcffe71babc47b12bde838eac5929b\",\n \"score\": \"0.69607913\",\n \"text\": \"def multiply(a, b):\\n return a * b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cdcffe71babc47b12bde838eac5929b\",\n \"score\": \"0.69607913\",\n \"text\": \"def multiply(a, b):\\n return a * b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90d6135a07918fd6c7e98326ee007586\",\n \"score\": \"0.6953207\",\n \"text\": \"def multiply(a, b):\\n res = 16*[0]\\n for i in xrange(16):\\n for j in xrange(i+1):\\n res[i] += a[j] * b[i-j]\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55fac8664ec1de4fea9273a8a34a7a5a\",\n \"score\": \"0.6935819\",\n \"text\": \"def multiply(a=0,b=0):\\n\\treturn str(int(a)*int(b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e5c5a98f941890bd53dfa2667c3178e\",\n \"score\": \"0.69345623\",\n \"text\": \"def product_on_basis(self, a, b):\\n return self(a+b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d438fc4de2cfc452e88030c5dd51c62\",\n \"score\": \"0.6931937\",\n \"text\": \"def producto(a,b): \\n \\n return a*b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"241016821ad113af1df7f351b7d0621a\",\n \"score\": \"0.687885\",\n \"text\": \"def multiply(a, b):\\n return a*b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fe7ac53286548b0491637dd5fe1397e\",\n \"score\": \"0.6878289\",\n \"text\": \"def mul(A, b):\\n return A.from_rep(A.rep.mul(b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa81a4f526a57aa89c73abc369d760a1\",\n \"score\": \"0.68779373\",\n \"text\": \"def print_mult_inverses(a: int, b: int):\\n am, bm, g = extended_gcd(a, b)\\n if g == 0:\\n raise ValueError(f\\\"{a} and {b} are not relatively prime.\\\")\\n if am < 0:\\n am = b + am\\n if bm < 0:\\n bm = a + bm\\n print(f\\\"{a} * {am} % {b} == {a * am % b}\\\"\\\\\\n f\\\"\\\\n{b} * {bm} % {a} == {b * bm % a}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a4be89cbbf78e15ffeca546ba57a331\",\n \"score\": \"0.68637675\",\n \"text\": \"def multiplication_1(a, b):\\n\\n if b == 1:\\n return a\\n\\n return a + multiplication_1(a, b - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11dc8a00923d6610e582cf94089e1a64\",\n \"score\": \"0.6862879\",\n \"text\": \"def gmul(a, b):\\n\\tc = 0\\n\\tif (a & 1) == 1:\\n\\t\\tc = b\\n\\n\\tfor _ in range(1, 8):\\n\\t\\thi_bit = (b & 0x80)\\n\\t\\tb <<= 1\\n\\t\\tb &= 0xff # Get rid of the most significant bit outside 2 bytes.\\n\\t\\tif hi_bit == 0x80:\\n\\t\\t\\tb ^= 0x1b\\n\\t\\ta >>= 1\\n\\t\\tif (a & 1) == 1:\\n\\t\\t\\tc ^= b\\n\\treturn c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"476f98cae064b91017d2cee29edea9a0\",\n \"score\": \"0.6859\",\n \"text\": \"def mod_sum(a,b,n):\\r\\n return ((a%n)+(b%n))%n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7373515d8c69a99a13f4a55e66e535bf\",\n \"score\": \"0.6842136\",\n \"text\": \"def mul_rat(a, b):\\n return make_rat(num(a) * num(b), den(a) * den(b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a650dc0309ef6b980783b34f41058c\",\n \"score\": \"0.6840613\",\n \"text\": \"def multiplication(a,b,N):\\n la = table_ln(a,N)\\n lb = table_ln(b,N)\\n lc = la + lb\\n c = table_exp(lc,N)\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ac4e57a5805039fa844d0caf00a3eb8\",\n \"score\": \"0.6828702\",\n \"text\": \"def divmod(self, a, b):\\n a = a @ self\\n b = b @ self\\n\\n if a.var != b.var:\\n raise ValueError(\\\"variables must be the same\\\")\\n\\n quotient, remainder = self.zero, a\\n\\n while remainder.degree >= b.degree:\\n monomial_exponent = remainder.degree - b.degree\\n monomial_zeros = [self.zero for _ in range(monomial_exponent)]\\n monomial_divisor = Polynomial(monomial_zeros +\\n [self._base_ring.div(remainder.coefficients[-1], b.coefficients[-1])],\\n a.var)\\n\\n quotient = self.add(quotient, monomial_divisor)\\n remainder = self.sub(remainder, self.mul(monomial_divisor, b))\\n\\n return quotient, remainder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58fe1178cbb5af434180ace86bf5ecbb\",\n \"score\": \"0.6824406\",\n \"text\": \"def galoisMult(a, b):\\n p = 0\\n hiBitSet = 0\\n for i in range(8):\\n if b & 1 == 1:\\n p ^= a\\n hiBitSet = a & 0x80\\n a <<= 1\\n if hiBitSet == 0x80:\\n a ^= 0x1b\\n b >>= 1\\n return p % 256\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd6b38f1f267ea838109e6c4df012b36\",\n \"score\": \"0.6823742\",\n \"text\": \"def m_div(a, b):\\n return a+ b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10020706eba7f46b18bbf4ee41274055\",\n \"score\": \"0.68231744\",\n \"text\": \"def product(a,b):\\n return a*b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c56947507d27b676de8f9e8e21c7b24\",\n \"score\": \"0.6815682\",\n \"text\": \"def fastPower(a, b, m):\\r\\n number = 1\\r\\n while b:\\r\\n if b & 1:\\r\\n number = number * a % m\\r\\n b >>= 1\\r\\n a = a * a % m\\r\\n return number\",\n \"title\": \"\"\n },\n {\n \"docid\": \"850a348b38bf486207cb6e3c477056a7\",\n \"score\": \"0.680911\",\n \"text\": \"def multElements(self, a, b):\\n exp_a = self.elementToExp(a)\\n exp_b = self.elementToExp(b)\\n exp_product = exp_a + exp_b # multiplication = adding the exponents\\n product = self.elementFromExp(exp_product)\\n return int(product)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a79cc6f10fb5639f6443705e89c3385\",\n \"score\": \"0.6801696\",\n \"text\": \"def mult_inverse(a: int, b: int):\\n am, bm, g = extended_gcd(a, b)\\n if g == 0:\\n raise ValueError(f\\\"{a} and {b} are not relatively prime.\\\")\\n return am if am > 0 else b + am\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfeb177f05d3ea370576e9bd7704686e\",\n \"score\": \"0.6795403\",\n \"text\": \"def pow(a, b):\\n if b == 0:\\n return 1\\n rem = pow(a**2 % length, b // 2) % length\\n if b % 2 == 1:\\n return a * rem\\n return rem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da1108a390c51ef46409b2f81da00fbd\",\n \"score\": \"0.6773538\",\n \"text\": \"def _powmod(self, a, n, m):\\n res = 1\\n while n != 0:\\n if n % 2 != 0:\\n res *= a\\n res %= m\\n n -= 1\\n else:\\n a *= a\\n a %= m\\n n //= 2\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23790e067fcad1c66e287e4e783a79f2\",\n \"score\": \"0.67587584\",\n \"text\": \"def mod_large(a,b,c):\\n ans = 1\\n while b>0:\\n ans *= a\\n ans %= c\\n b -= 1\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce3e4660d88b34d13644a80e45e1d077\",\n \"score\": \"0.6752904\",\n \"text\": \"def vector_mul(a, b):\\n assert len(a) == len(b), 'a and b must have equal length'\\n res = GF(0)\\n\\n for i in range(len(a)):\\n res += GF(a[i]) * GF(b[i])\\n\\n return res.int_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff39472f6027c409f1122ba213bbc497\",\n \"score\": \"0.6744542\",\n \"text\": \"def raw_multiply(self, a, b, randomization=None):\\n n2 = self.nsquare\\n\\n # explicitely reduces to avoid large exponents\\n b %= self.n\\n if b > self.n // 2:\\n b -= self.n\\n\\n # if a is of the form (1+n)^q, then we can avoid the exponentiation\\n q, r = divmod(a-1, self.n)\\n if r == 0:\\n raw_value = (1 + self.n * q * b) % n2\\n else:\\n raw_value = util.powmod(a, b, n2)\\n\\n # apply randomization\\n if randomization is None:\\n randomization = random.SystemRandom().randrange(self.n)\\n raw_value = raw_value * util.powmod(randomization, self.n, n2) % n2\\n\\n return raw_value, randomization\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58d51134fa40d91b50fb9fc5f9d315a8\",\n \"score\": \"0.67423606\",\n \"text\": \"def bit_math(x: int, y: int, b: int) -> int:\\n return (x * b) + (y * (b ^ 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ebd63c3e773560217e803d1a5483b26\",\n \"score\": \"0.67411655\",\n \"text\": \"def bin_exp_mod(a, n, b):\\n # mod b\\n assert b != 0, \\\"This cannot accept modulo that is == 0\\\"\\n if n == 0:\\n return 1\\n\\n if n % 2 == 1:\\n return (bin_exp_mod(a, n - 1, b) * a) % b\\n\\n r = bin_exp_mod(a, n / 2, b)\\n return (r * r) % b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"253964279dd72a290cecab3039f1a552\",\n \"score\": \"0.6740051\",\n \"text\": \"def _m(self, a, b, amount=1):\\n new = []\\n for i in range(len(a)):\\n new.append(a[i]+(b[i]*amount))\\n return new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20b09012400dd5d66e1881b21a1336af\",\n \"score\": \"0.6738363\",\n \"text\": \"def mod(int_a, int_b):\\n if isinstance(int_a, int) and isinstance(int_b, int):\\n return int_a % int_b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c8c1d07083de55a2329d828129e2b92\",\n \"score\": \"0.67336816\",\n \"text\": \"def opp_mult():\\n a = int(request.args[\\\"a\\\"])\\n b = int(request.args[\\\"b\\\"])\\n result = mult(a,b)\\n return str(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3af8e5b7ddfe47f94b47b5014c950d1f\",\n \"score\": \"0.6715987\",\n \"text\": \"def power(a, b):\\n\\n return a ** b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"372d4cec3f05caedd3b3d6373abf4a3e\",\n \"score\": \"0.6697999\",\n \"text\": \"def matmul(a, b):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a88f3c26a5a8c919f93850e8bd62666\",\n \"score\": \"0.6692014\",\n \"text\": \"def mult(A, B):\\n return A*B\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c97dd37bab9479071fd60c54ab280cc\",\n \"score\": \"0.66853356\",\n \"text\": \"def mult(a, b):\\n a = int(request.args.get('a'))\\n b = int(request.args.get('b'))\\n total = mult(a, b)\\n\\n return str(total)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53c820661b58980336e150e3d316caaf\",\n \"score\": \"0.6675671\",\n \"text\": \"def mod_sub(a, b):\\n return (a-b+MOD) % MOD\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53c820661b58980336e150e3d316caaf\",\n \"score\": \"0.6675671\",\n \"text\": \"def mod_sub(a, b):\\n return (a-b+MOD) % MOD\",\n \"title\": \"\"\n },\n {\n \"docid\": \"237ed696ac51d5755e7e8e9e6effaccf\",\n \"score\": \"0.66743296\",\n \"text\": \"def mcd(a, b):\\n if (b == 0):\\n return a\\n else:\\n return mcd(b, a % b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57399d4114f4e042d3c4039e1b616cae\",\n \"score\": \"0.66633725\",\n \"text\": \"def _c_mul(a, b):\\n return eval(hex((int(a) * b) & 0xFFFFFFFF)[:-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1596c672a07a2b1f6f1ff3dfc686ca8\",\n \"score\": \"0.6662958\",\n \"text\": \"def arithemtic(a, b):\\n return a - b, a * b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a177452bdc71a9b1d5dd0b7186e488e8\",\n \"score\": \"0.6652532\",\n \"text\": \"def _b_divmod_ ( b , p ) :\\n rr = b.divmod(p)\\n return rr.first, rr.second\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4a79619ad1f736717f8375b858d031f\",\n \"score\": \"0.6640183\",\n \"text\": \"def mod_exp(a, b, n):\\n bb = \\\"{0:b}\\\".format(b)\\n bc = [int(bb[i]) for i in range(len(bb))]\\n bc.reverse()\\n k = {0: a % n}\\n\\n for i in range(1, len(bc)):\\n k[i] = (k[i-1] ** 2) % n \\n\\n r = 1\\n for m in range(len(bc)):\\n if bc[m] == 1:\\n r = r * k[m]\\n return r % n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b0556ecdcf64e320a605751c5c2c3bf\",\n \"score\": \"0.6631444\",\n \"text\": \"def mod(num1, num2):\\n m = num1 % num2\\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f40bfc71de3dc3a6a03563c64df95821\",\n \"score\": \"0.6630117\",\n \"text\": \"def modinv(a,b):\\n g, x, y = egcd(a,b)\\n if y !=1:\\n return None\\n else:\\n return x%b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a22f7f0d5ce9f8e1a57fa7d6062e81e\",\n \"score\": \"0.66218835\",\n \"text\": \"def mul_function(a, b):\\n return float(a) * float(b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1c8739042ae4c757bfe87bae0c10b9e\",\n \"score\": \"0.6621445\",\n \"text\": \"def mul_rat(a, b):\\n \\\"*** YOUR CODE HERE ***\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49ed1d7568e265f2d7dde20818b22510\",\n \"score\": \"0.6607737\",\n \"text\": \"def get_matrix_mul(a, b):\\n if len(a) == 0 or len(b) == 0:\\n raise Exception(ERRORS.INVALID_MATRIX)\\n if len(a[0]) != len(b):\\n raise Exception(ERRORS.INVALID_MATRIX_MUL_COMBO)\\n result = [[0 for _ in range(len(b[0]))] for _ in range(len(a))]\\n for i in range(len(a)):\\n for j in range(len(b[0])):\\n for k in range(len(b)):\\n result[i][j] = UtilityHelper.mod_add(\\n result[i][j], UtilityHelper.mod_mul(a[i][k], b[k][j]))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55993c13ea6daeee4b447071d9c6c5e7\",\n \"score\": \"0.6607196\",\n \"text\": \"def modinverse(a, b):\\n g, x, _ = extended_gcd(a, b)\\n if g != 1:\\n raise ValueError('gcd(a, b) != 1')\\n return x % b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04b298d0cdf51cef5d388969ef5f4cc0\",\n \"score\": \"0.6606618\",\n \"text\": \"def _mul_(self, right):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeab25634e5b8706c3ac1d4891dd3229\",\n \"score\": \"0.6603027\",\n \"text\": \"def rem(a, b):\\n q = abs(a) % b\\n s = np.sign(a)\\n return q*s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0e36f785cf99b28ffe2fdbd966cb828\",\n \"score\": \"0.6587561\",\n \"text\": \"def i_matmul(a, b):\\n try:\\n return a @ b\\n except TypeError:\\n return a * b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"006ca660040685aacacdb9734cd70bba\",\n \"score\": \"0.6587509\",\n \"text\": \"def numbers_multiplication(number_one, number_two):\\r\\n return number_one*number_two\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993881,"cells":{"query_id":{"kind":"string","value":"c0eef44b6e327688044e5a4469ca8db7"},"query":{"kind":"string","value":"Gets the descendant bin ``Id`` query terms."},"positive_passages":{"kind":"list like","value":[{"docid":"a8c894c30b6990b7165b13f238f150f7","score":"0.8539272","text":"def get_descendant_bin_id_terms(self):\n return # osid.search.terms.IdTerm","title":""}],"string":"[\n {\n \"docid\": \"a8c894c30b6990b7165b13f238f150f7\",\n \"score\": \"0.8539272\",\n \"text\": \"def get_descendant_bin_id_terms(self):\\n return # osid.search.terms.IdTerm\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"324e7100bb46e6702f52c2db2b502450","score":"0.7650223","text":"def get_ancestor_bin_id_terms(self):\n return # osid.search.terms.IdTerm","title":""},{"docid":"df255db9024986f93117154bbc607910","score":"0.74250394","text":"def get_bin_id_terms(self):\n return # osid.search.terms.IdTerm","title":""},{"docid":"df255db9024986f93117154bbc607910","score":"0.74250394","text":"def get_bin_id_terms(self):\n return # osid.search.terms.IdTerm","title":""},{"docid":"44c07bcf4354623d994c07e36e75fc3c","score":"0.72232616","text":"def get_descendant_bin_terms(self):\n return # osid.resource.BinQueryInspector","title":""},{"docid":"e3074c2ac52be0076136d4b742e2f001","score":"0.65526414","text":"def get_ancestor_bin_terms(self):\n return # osid.resource.BinQueryInspector","title":""},{"docid":"e7716c15742da96c6c89406c831f6296","score":"0.5946379","text":"def get_bin_terms(self):\n return # osid.resource.BinQueryInspector","title":""},{"docid":"e7716c15742da96c6c89406c831f6296","score":"0.5946379","text":"def get_bin_terms(self):\n return # osid.resource.BinQueryInspector","title":""},{"docid":"6af521b7589d4ffc3ab4f4fbccf9dadb","score":"0.58041036","text":"def get_resource_relationship_id_terms(self):\n return # osid.search.terms.IdTerm","title":""},{"docid":"4628c65593bc9d7d57c4a7dc0f44e3be","score":"0.57497823","text":"def _get_bin_enthalpy(self, bin_id):\n return bin_id * self._window[2] + self._window[0]","title":""},{"docid":"ec3f3f23985704939a0a5dee55587b4f","score":"0.55940044","text":"def get_resource_id_terms(self):\n return # osid.search.terms.IdTerm","title":""},{"docid":"5ef60c9d252e0cf63b2eaffd9de5da11","score":"0.55061215","text":"def find_descendant_ids(self):\n stack = [self.id]\n venture_ids = []\n visited = {self.id}\n while stack:\n venture_id = stack.pop()\n venture_ids.append(venture_id)\n for v_id, in Venture.objects.filter(\n parent_id=venture_id\n ).values_list('id'):\n if v_id in visited:\n # Make sure we don't do the same device twice.\n continue\n visited.add(v_id)\n stack.append(v_id)\n return venture_ids","title":""},{"docid":"1dea17da9e10c0b3e7a0060c1dfa2def","score":"0.5443015","text":"def get_agent_id_terms(self):\n return # osid.search.terms.IdTerm","title":""},{"docid":"950587c7c73b1f346f299ec69c0f5c8f","score":"0.5418906","text":"def get_containing_group_id_terms(self):\n return # osid.search.terms.IdTerm","title":""},{"docid":"34e7ec79ea9f077d61c2d9434b59b737","score":"0.5257579","text":"def get_bin_node_ids(self, bin_id, ancestor_levels, descendant_levels, include_siblings):\n # Implemented from template for\n # osid.resource.BinHierarchySession.get_bin_node_ids\n if self._catalog_session is not None:\n return self._catalog_session.get_catalog_node_ids(\n catalog_id=bin_id,\n ancestor_levels=ancestor_levels,\n descendant_levels=descendant_levels,\n include_siblings=include_siblings)\n return self._hierarchy_session.get_nodes(\n id_=bin_id,\n ancestor_levels=ancestor_levels,\n descendant_levels=descendant_levels,\n include_siblings=include_siblings)","title":""},{"docid":"cd874fe5b2986a29c71dc229820d8f00","score":"0.5199123","text":"def get_child_bin_ids(self, bin_id):\n # Implemented from template for\n # osid.resource.BinHierarchySession.get_child_bin_ids\n if self._catalog_session is not None:\n return self._catalog_session.get_child_catalog_ids(catalog_id=bin_id)\n return self._hierarchy_session.get_children(id_=bin_id)","title":""},{"docid":"8972a54172b3c3edfb721bd43d38ba8f","score":"0.517019","text":"def get_destination_resource_id_terms(self):\n return # osid.search.terms.IdTerm","title":""},{"docid":"42925279499d0a30d76046a30c42b0b5","score":"0.5150369","text":"def get_avatar_id_terms(self):\n return # osid.search.terms.IdTerm","title":""},{"docid":"797cccc34e44c9d89f56c902c9369e6a","score":"0.5115213","text":"def ancestor_ids(self) -> list[int]:\n with connection.cursor() as cursor:\n cursor.execute(\n ANCESTOR_QUERY.format(relationship_table=self.edge_model_table),\n {'id': self.id},\n )\n return [row[0] for row in cursor.fetchall()]","title":""},{"docid":"6273f93b0538ff510d2819c10b19d618","score":"0.5112473","text":"def clear_descendant_gradebook_id_terms(self):\n pass","title":""},{"docid":"0e9edffbd40a63f8a08c440810c8f46d","score":"0.5055445","text":"async def get_go_hierarchy_go_id(id: str = Path(..., description=\"A GO-Term ID(e.g. GO:0005885, GO:0097136 ...)\")):\n cmaps = get_prefixes(\"go\")\n ont_r = OntologyResource(url=get_sparql_endpoint())\n si = SparqlImplementation(ont_r)\n converter = Converter.from_prefix_map(cmaps, strict=False)\n id = converter.expand(id)\n\n query = (\n \"\"\"\n PREFIX definition: <http://purl.obolibrary.org/obo/IAO_0000115>\n SELECT ?hierarchy ?GO ?label WHERE {\n BIND(<%s> as ?goquery)\n {\n {\n ?goquery rdfs:subClassOf+ ?GO .\n ?GO rdfs:label ?label .\n FILTER (LANG(?label) != \"en\")\n BIND(\"parent\" as ?hierarchy)\n }\n UNION\n {\n ?GO rdfs:subClassOf* ?goquery .\n ?GO rdfs:label ?label .\n FILTER (LANG(?label) != \"en\")\n BIND(IF(?goquery = ?GO, \"query\", \"child\") as ?hierarchy) .\n }\n }\n }\n \"\"\"\n % id\n )\n results = si._sparql_query(query)\n collated_results = []\n collated = {}\n for result in results:\n collated[\"GO\"] = result[\"GO\"].get(\"value\")\n collated[\"label\"] = result[\"label\"].get(\"value\")\n collated[\"hierarchy\"] = result[\"hierarchy\"].get(\"value\")\n collated_results.append(collated)\n return collated_results","title":""},{"docid":"9b7293d3ccd7afa7c1ab668bd039d871","score":"0.5054175","text":"def get_child_bins(self, bin_id):\n # Implemented from template for\n # osid.resource.BinHierarchySession.get_child_bins\n if self._catalog_session is not None:\n return self._catalog_session.get_child_catalogs(catalog_id=bin_id)\n return BinLookupSession(\n self._proxy,\n self._runtime).get_bins_by_ids(\n list(self.get_child_bin_ids(bin_id)))","title":""},{"docid":"d45fd8ef5921c8cd3110ae419217cff6","score":"0.5009101","text":"def get_bin_query(self):\n # Implemented from template for\n # osid.resource.BinQuerySession.get_bin_query_template\n return queries.BinQuery(runtime=self._runtime)","title":""},{"docid":"ddf8a2ee2ee7c394f163ae81ef2f216a","score":"0.49984586","text":"def childIds(self, spec=None):\n unused(spec)\n return [k.id for k in self.children()]","title":""},{"docid":"52de014f357707f770c04c005c2271e5","score":"0.49717396","text":"def get_bin_edges( self, xbin ):\n return [edge[xbin] for edge in self.edges],[edge[xbin+1] for edge in self.edges]","title":""},{"docid":"6c426edbc61ee01b8fb5614873f84062","score":"0.49638805","text":"def bodybuilders_id_get(id): # noqa: E501\n\n\n return query_manager.get_resource(id=id,\n rdf_type_uri=BODYBUILDER_TYPE_URI,\n rdf_type_name=BODYBUILDER_TYPE_NAME, \n kls=Bodybuilder)","title":""},{"docid":"83df03ec25948f5f0462adf1219f2db4","score":"0.49471837","text":"def get_edges(self):\n return self.bin_edges","title":""},{"docid":"bc913af07c63726070e0f6b3a651722b","score":"0.49366572","text":"def btrIds(self) -> ObjectIdCollection:","title":""},{"docid":"bd550a441e0189d6251f32773f84c77e","score":"0.49218273","text":"def get_bin_hierarchy_id(self):\n # Implemented from template for\n # osid.resource.BinHierarchySession.get_bin_hierarchy_id\n if self._catalog_session is not None:\n return self._catalog_session.get_catalog_hierarchy_id()\n return self._hierarchy_session.get_hierarchy_id()","title":""},{"docid":"bd550a441e0189d6251f32773f84c77e","score":"0.49218273","text":"def get_bin_hierarchy_id(self):\n # Implemented from template for\n # osid.resource.BinHierarchySession.get_bin_hierarchy_id\n if self._catalog_session is not None:\n return self._catalog_session.get_catalog_hierarchy_id()\n return self._hierarchy_session.get_hierarchy_id()","title":""},{"docid":"fee831fa4d6d424d7e585a66c8c4ec21","score":"0.48975003","text":"def is_descendant_of_bin(self, id_, bin_id):\n # Implemented from template for\n # osid.resource.BinHierarchySession.is_descendant_of_bin\n if self._catalog_session is not None:\n return self._catalog_session.is_descendant_of_catalog(id_=id_, catalog_id=bin_id)\n return self._hierarchy_session.is_descendant(id_=id_, descendant_id=bin_id)","title":""},{"docid":"fb6f1731871adae71621e4a8adfbfeb5","score":"0.48912504","text":"def binddn(self) :\n\t\ttry :\n\t\t\treturn self._binddn\n\t\texcept Exception as e:\n\t\t\traise e","title":""},{"docid":"c520961e15b65a104e4d439a263600ea","score":"0.4882913","text":"def bins(self):\n return self._bins","title":""},{"docid":"c520961e15b65a104e4d439a263600ea","score":"0.4882913","text":"def bins(self):\n return self._bins","title":""},{"docid":"e455480726777dbeb38c5f4dcad29c55","score":"0.4875605","text":"def go_terms(pdb_id):\n request = f'https://www.rcsb.org/pdb/rest/goTerms?structureId={pdb_id}'\n response = fetch_xml(request)\n data = response['goTerms']['term']\n return data","title":""},{"docid":"9d0c143210c8f03fc5a204ce21daf9c3","score":"0.48273125","text":"def subgraph_ids(self) -> List[str]:\n return self._subgraph_ids","title":""},{"docid":"6bbee18623ca0d7eaf6af0a3decca931","score":"0.48225656","text":"def get_child_objective_bank_ids(self, objective_bank_id=None):\n pass","title":""},{"docid":"1a202a8232cef64bc54f86daf6a8cdf0","score":"0.48164767","text":"def bond_term(self):","title":""},{"docid":"0527803bf90dfe464bfff305d638f5af","score":"0.48082575","text":"def histogram_baths(self, block_data):\n return self.fetch_identifier_component(\"block/histogram_baths\", block_data)","title":""},{"docid":"4ab71e8388a6dc73ef6dbf23a6fbc587","score":"0.48056874","text":"def get_resource_ids_by_bin(self, bin_id):\n # Implemented from template for\n # osid.resource.ResourceBinSession.get_resource_ids_by_bin\n id_list = []\n for resource in self.get_resources_by_bin(bin_id):\n id_list.append(resource.get_id())\n return IdList(id_list)","title":""},{"docid":"8191add6ac6de4c48ddd050d0200a9a8","score":"0.47966605","text":"def get_objective_bank_node_ids(self,\n objective_bank_id=None,\n ancestor_levels=None,\n descendant_levels=None,\n include_siblings=None):\n pass","title":""},{"docid":"09f293ae3b35e54054d8330fc48616e5","score":"0.47890717","text":"def get(self, id):\n args = parser.parse_args()\n return lego_query(\"\"\"\n CONSTRUCT { ?i ?p ?v } WHERE \n {?i rdfs:isDefinedBy <http://model.geneontology.org/%s>; \n ?p ?v\n }\"\"\" % id, limit=1000)\n\n return []","title":""},{"docid":"55daf9f8b62ebcc7287c1451bd0ba219","score":"0.47707036","text":"def get_descendant_nodes(request, node_id):\n if request.method == 'GET':\n try:\n if node_id == '':\n error = dict(success=\"false\", data={}, error_message=\"No ID was given when getting descendant nodes of\"\n \"a node by its ID.\")\n return JSONResponse(error)\n\n descendant_nodes_list = Nodes().retrieveDescendent(node_id)\n get_descendant_nodes_response = {\n \"success\": \"true\",\n \"data\": descendant_nodes_list\n }\n return JSONResponse(get_descendant_nodes_response)\n except Exception, e:\n error = dict(success=\"false\", data={}, error_message=str(e))\n return JSONResponse(error)\n else: # only allow GET\n error = dict(success=\"false\", data={}, error_message=\"Only GET allowed for getting descendant nodes of\"\n \"a node by its ID.\")\n return JSONResponse(error)","title":""},{"docid":"eeb3b856febd53f6c9d5d149fa97c0d0","score":"0.47585076","text":"def get_descendant_gradebook_query(self):\n return # osid.grading.GradebookQuery","title":""},{"docid":"ed5ef0721b110c91ecee3f5be23f8958","score":"0.474145","text":"def getBroaderTerms(self):\n return self.getXPath(\n \"/find-doc/record/metadata/oai_marc/varfield[@id='550'][subfield[@label='w']='g']/subfield[@label='a']\"\n )","title":""},{"docid":"e483a0f108833aa60092a798ace0d4b6","score":"0.47377455","text":"def getId(self):\n return self.base.get(\"id\", [])","title":""},{"docid":"e483a0f108833aa60092a798ace0d4b6","score":"0.47377455","text":"def getId(self):\n return self.base.get(\"id\", [])","title":""},{"docid":"081b8937ce66290573906dad4e911918","score":"0.47259697","text":"def get_bin_nodes(self, bin_id, ancestor_levels, descendant_levels, include_siblings):\n # Implemented from template for\n # osid.resource.BinHierarchySession.get_bin_nodes\n return objects.BinNode(self.get_bin_node_ids(\n bin_id=bin_id,\n ancestor_levels=ancestor_levels,\n descendant_levels=descendant_levels,\n include_siblings=include_siblings)._my_map, runtime=self._runtime, proxy=self._proxy)","title":""},{"docid":"0841bc54ca05e64f92623146d9d41fef","score":"0.4723538","text":"def id(self) -> int:\n return self._ids","title":""},{"docid":"15f8c5de35419bb603cf9b7259ec3df5","score":"0.47232932","text":"def get(self, entry_id: str, params: dict = None) -> dict:\n entry_id = f\"terms?short_form=NCBITaxon_{entry_id}\"\n return super().get(entry_id, params)","title":""},{"docid":"d45681c1a2a0fdae2a45223dbed36ff1","score":"0.4720204","text":"def get_objective_bank_hierarchy_id(self):\n pass","title":""},{"docid":"d45681c1a2a0fdae2a45223dbed36ff1","score":"0.4720204","text":"def get_objective_bank_hierarchy_id(self):\n pass","title":""},{"docid":"53bd172957577dbf06dcb86de8f4498f","score":"0.47144505","text":"def btrId(self) -> ObjectId:","title":""},{"docid":"a2fd758e69a250b449a743680b0a75d1","score":"0.47113705","text":"def block_histogram_baths(self, data):\n return self.fetch_identifier_component(\"property/block_histogram_baths\", data)","title":""},{"docid":"7312af59565f488f2b0698b12f249f0f","score":"0.4708659","text":"def get_term(self, entry_id: str):\n return self.get(\n f\"http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252F{entry_id}\"\n )","title":""},{"docid":"a6f92670610b6bc224f29eeacada03fa","score":"0.4704278","text":"def get_bin_ids_by_resource(self, resource_id):\n # Implemented from template for\n # osid.resource.ResourceBinSession.get_bin_ids_by_resource\n mgr = self._get_provider_manager('RESOURCE', local=True)\n lookup_session = mgr.get_resource_lookup_session(proxy=self._proxy)\n lookup_session.use_federated_bin_view()\n resource = lookup_session.get_resource(resource_id)\n id_list = []\n for idstr in resource._my_map['assignedBinIds']:\n id_list.append(Id(idstr))\n return IdList(id_list)","title":""},{"docid":"8b9b66f45a47ccd16e042eb2f589eee5","score":"0.4703034","text":"def getNormalIds(*args, **kwargs):\n \n pass","title":""},{"docid":"609ffbe438bc901e36ad93bc706a6ece","score":"0.4701109","text":"def get_objective_bank_hierarchy_id(self):\n return # osid.id.Id","title":""},{"docid":"609ffbe438bc901e36ad93bc706a6ece","score":"0.4701109","text":"def get_objective_bank_hierarchy_id(self):\n return # osid.id.Id","title":""},{"docid":"2adef24754c1ae23f187102e14a3c256","score":"0.4696119","text":"def get_descendents(self, node_id):\n nodes = []\n for child in self.get_children(node_id):\n nodes.append(child)\n nodes += self.get_descendents(child[\"id\"])\n return nodes","title":""},{"docid":"223239f7a2db3a9f7014dcab8bf2caad","score":"0.46765596","text":"def descendant(h, d):\n h = get(h, d)\n return d[d[\"nodeIndex\"] == h[\"descendantIndex\"]][0]","title":""},{"docid":"ce928bd9e5a554f2b9d52d3e514f950d","score":"0.46595198","text":"def get_bins(self):\n # Implemented from template for\n # osid.resource.BinLookupSession.get_bins_template\n # NOTE: This implementation currently ignores plenary view\n if self._catalog_session is not None:\n return self._catalog_session.get_catalogs()\n collection = JSONClientValidated('resource',\n collection='Bin',\n runtime=self._runtime)\n result = collection.find().sort('_id', DESCENDING)\n\n return objects.BinList(result, runtime=self._runtime, proxy=self._proxy)","title":""},{"docid":"a97a01da8e54f3464559bb945e9c256f","score":"0.46349737","text":"def GetIDs(self):\n return [self.ID] + [\"{}.{}\".format(self.ID, x) for x in self.Appendix]","title":""},{"docid":"a3290815d68e66e938d0483902f311ea","score":"0.4631585","text":"def get_ids(self):\n\t\treturn self._id_dict.keys()","title":""},{"docid":"abe45ecbb5062079961d64c431741869","score":"0.46197516","text":"def get_bin_id(self):\n # Implemented from template for osid.resource.ResourceLookupSession.get_bin_id\n return self._catalog_id","title":""},{"docid":"abe45ecbb5062079961d64c431741869","score":"0.46197516","text":"def get_bin_id(self):\n # Implemented from template for osid.resource.ResourceLookupSession.get_bin_id\n return self._catalog_id","title":""},{"docid":"abe45ecbb5062079961d64c431741869","score":"0.46197516","text":"def get_bin_id(self):\n # Implemented from template for osid.resource.ResourceLookupSession.get_bin_id\n return self._catalog_id","title":""},{"docid":"abe45ecbb5062079961d64c431741869","score":"0.46197516","text":"def get_bin_id(self):\n # Implemented from template for osid.resource.ResourceLookupSession.get_bin_id\n return self._catalog_id","title":""},{"docid":"abe45ecbb5062079961d64c431741869","score":"0.46197516","text":"def get_bin_id(self):\n # Implemented from template for osid.resource.ResourceLookupSession.get_bin_id\n return self._catalog_id","title":""},{"docid":"abe45ecbb5062079961d64c431741869","score":"0.46197516","text":"def get_bin_id(self):\n # Implemented from template for osid.resource.ResourceLookupSession.get_bin_id\n return self._catalog_id","title":""},{"docid":"32791b0f7aa1d084e2915b34540a944c","score":"0.46191418","text":"def get_resource_ids_by_bins(self, bin_ids):\n # Implemented from template for\n # osid.resource.ResourceBinSession.get_resource_ids_by_bins\n id_list = []\n for resource in self.get_resources_by_bins(bin_ids):\n id_list.append(resource.get_id())\n return IdList(id_list)","title":""},{"docid":"372b9306d2a485bcdbe95c603d800a50","score":"0.46036774","text":"def binset(self):\n return self.obsmode.bandwave","title":""},{"docid":"7486132c3a330a2bf41857a4265a3946","score":"0.4602269","text":"def get_assignable_bin_ids(self, bin_id):\n # Implemented from template for\n # osid.resource.ResourceBinAssignmentSession.get_assignable_bin_ids\n # This will likely be overridden by an authorization adapter\n mgr = self._get_provider_manager('RESOURCE', local=True)\n lookup_session = mgr.get_bin_lookup_session(proxy=self._proxy)\n bins = lookup_session.get_bins()\n id_list = []\n for bin in bins:\n id_list.append(bin.get_id())\n return IdList(id_list)","title":""},{"docid":"93afbbf7bfd54e6179224822420b05d4","score":"0.46007088","text":"def get_chembl_id(chebi_id):\n return chebi_chembl.get(_add_prefix(chebi_id))","title":""},{"docid":"3687f2e3d12998c78c0457726b145caf","score":"0.4600179","text":"def get_child_objective_bank_ids(self, objective_bank_id):\n return # osid.id.IdList","title":""},{"docid":"4cc34d340145403f5c4f6ef196e4a5c9","score":"0.4600061","text":"def get_objective_bank_node_ids(self, objective_bank_id, ancestor_levels, descendant_levels, include_siblings):\n return # osid.hierarchy.Node","title":""},{"docid":"b697349ec0a8f7b8a016a59e86e9d4be","score":"0.45960596","text":"def get_bins_by_ids(self, bin_ids):\n # Implemented from template for\n # osid.resource.BinLookupSession.get_bins_by_ids_template\n # NOTE: This implementation currently ignores plenary view\n # Also, this should be implemented to use get_Bin() instead of direct to database\n if self._catalog_session is not None:\n return self._catalog_session.get_catalogs_by_ids(catalog_ids=bin_ids)\n catalog_id_list = []\n for i in bin_ids:\n catalog_id_list.append(ObjectId(i.get_identifier()))\n collection = JSONClientValidated('resource',\n collection='Bin',\n runtime=self._runtime)\n result = collection.find({'_id': {'$in': catalog_id_list}}).sort('_id', DESCENDING)\n\n return objects.BinList(result, runtime=self._runtime, proxy=self._proxy)","title":""},{"docid":"a0cf0d34fb0b4c139ba915e6ab7e216b","score":"0.45877272","text":"def getancestors(self, termids):\n t2a = {}\n for t in termids:\n tas = self.getancetorsbyid(t)\n if len(tas) > 0:\n t2a[t] = tas\n return t2a","title":""},{"docid":"9fc7eb853cbc7574a7a2f51eaad5d2d1","score":"0.45871562","text":"def getRelatedTerms(self):\n return self.getXPath(\n \"/find-doc/record/metadata/oai_marc/varfield[@id='550'][not(subfield[@label='w'])]/subfield[@label='a']\"\n )","title":""},{"docid":"f5916d3af2b2665824923f4da377da96","score":"0.45847875","text":"def iterids(self):\r\n if self._index is None:\r\n raise _errors.SearchError(\"SearchConnection has been closed\")\r\n return _indexerconnection.PrefixedTermIter('Q', self._index.allterms())","title":""},{"docid":"48299aab7a9ea53e7458468013e4a6fa","score":"0.45796618","text":"def ids(self):\n return self['ids']","title":""},{"docid":"4f4591658429df7831c527dcd0de3e40","score":"0.45764086","text":"def bin_edges(self, bins, field_key):\n\n all_values = np.concatenate([fields[field_key]\n for fields\n in self.contigtree.wepy_h5.iter_trajs_fields([field_key])])\n\n bin_edges = np.histogram_bin_edges(all_values, bins=bins)\n\n return bin_edges","title":""},{"docid":"768bddf6c3050090c1ba49a9d484fa77","score":"0.45679593","text":"def get_distances(self, id):\n return [x[1] for x in self.neighbors[id]]","title":""},{"docid":"89a3c8bc7881de3b02263e84a7ae444c","score":"0.45675638","text":"def GetRootId(self):","title":""},{"docid":"f915e8e2d44d627505358d6b9097e7d2","score":"0.45667973","text":"def descendant_substrings(self):\n substrings = []\n for child in self.children:\n grandchild_substrings = child.descendant_substrings()\n if grandchild_substrings:\n child_substrings = [child.value + s for s in grandchild_substrings]\n else:\n child_substrings = [child.value]\n substrings.extend(child_substrings)\n return substrings","title":""},{"docid":"50384c3d6fee196b26e2a71827d9b353","score":"0.456445","text":"def enumbins(self):\n return self._enumbins","title":""},{"docid":"7e02ec3f8988012e2bc39224bbe44c26","score":"0.45565787","text":"def get_ids(self):\n # Complete this function\n ids = []\n\n return ids","title":""},{"docid":"d2465d4ae061153bcd35220fe939c6b2","score":"0.45533267","text":"def ids(self):\n return self[\"ids\"]","title":""},{"docid":"1641f9aafa2a4830e9ae3134f4d651f8","score":"0.45528013","text":"def get_root_bin_ids(self):\n # Implemented from template for\n # osid.resource.BinHierarchySession.get_root_bin_ids\n if self._catalog_session is not None:\n return self._catalog_session.get_root_catalog_ids()\n return self._hierarchy_session.get_roots()","title":""},{"docid":"717cc414dac5cda9030dcfaf8cf5e66f","score":"0.45500702","text":"def termToWordIds(text):","title":""},{"docid":"717cc414dac5cda9030dcfaf8cf5e66f","score":"0.45500702","text":"def termToWordIds(text):","title":""},{"docid":"e8163e88a28ef2ec5c39373d307a9b72","score":"0.45479932","text":"def get_bins_by_resource(self, resource_id):\n # Implemented from template for\n # osid.resource.ResourceBinSession.get_bins_by_resource\n mgr = self._get_provider_manager('RESOURCE', local=True)\n lookup_session = mgr.get_bin_lookup_session(proxy=self._proxy)\n return lookup_session.get_bins_by_ids(\n self.get_bin_ids_by_resource(resource_id))","title":""},{"docid":"b91f724084b24bb800fb160953e53979","score":"0.45442188","text":"def get(self, id):\n return list_id(id)","title":""},{"docid":"d209baaaffa2a9153a11dbf740b4bc08","score":"0.4540475","text":"def get_edge_idx( self ):\n\n self.edges = []\n # You've provided a number of bins. Get the weights and define bin edges such that there exists equal weight in each bin.\n if not self.params['split_by_w']:\n\n for x_ in self.x:\n xw = np.ones(len(x_))\n normcumsum = xw.cumsum() / xw.sum()\n self.edges.append( np.searchsorted(normcumsum, np.linspace(0, 1, self.bins+1, endpoint=True)) )\n\n else:\n\n w,R = self.calibrator.calibrate(self.xcol,return_full_w=True,weight_only=True,include_Rg=True)\n for x_,w_ in tuple(zip(self.x,w)):\n xw = w_*R\n normcumsum = xw.cumsum() / xw.sum()\n self.edges.append( np.searchsorted(normcumsum, np.linspace(0, 1, self.bins+1, endpoint=True)) )\n\n return","title":""},{"docid":"c6d25fe801ec0a5b42e76b517601c65e","score":"0.45391673","text":"def get_source_resource_id_terms(self):\n return # osid.search.terms.IdTerm","title":""},{"docid":"aa07546d6d19c910ebb05a2f29f1850a","score":"0.4534293","text":"def find_bin(self):\n return self.binstr","title":""},{"docid":"7707fd11f185dfff4a5412b5dd3e2679","score":"0.45315948","text":"def getNarrowerTerms(self):\n return self.getXPath(\n \"/find-doc/record/metadata/oai_marc/varfield[@id='550'][subfield[@label='w']='h']/subfield[@label='a']\"\n )","title":""},{"docid":"12afc0cd77495a3e14b3063ba2f5670f","score":"0.45283815","text":"def relation_ids(self):\n if not self._is_extracted:\n return\n return self.to_id(self.relations)","title":""},{"docid":"82261298403f8b302c30b0c61a323f10","score":"0.45282623","text":"def iterids(self):\r\n if self._index is None:\r\n raise errors.IndexerError(\"IndexerConnection has been closed\")\r\n return PrefixedTermIter('Q', self._index.allterms())","title":""},{"docid":"56fc4f7d56f1be21dc408b4b2eebead0","score":"0.4525562","text":"def bend_list(self) -> List[int]:\n return list(self.bends())","title":""},{"docid":"72ccffe8cd1053b05ebc0aeba2d531c6","score":"0.45250118","text":"def get_residue(self, res_id):\n sel = []\n for p,s in zip(self.prefix, res_id.split(',')):\n if s:\n sel .append(p + ' ' + s)\n sel = ' and '.join(sel)\n return self.glycoprotein.select(sel)","title":""}],"string":"[\n {\n \"docid\": \"324e7100bb46e6702f52c2db2b502450\",\n \"score\": \"0.7650223\",\n \"text\": \"def get_ancestor_bin_id_terms(self):\\n return # osid.search.terms.IdTerm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df255db9024986f93117154bbc607910\",\n \"score\": \"0.74250394\",\n \"text\": \"def get_bin_id_terms(self):\\n return # osid.search.terms.IdTerm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df255db9024986f93117154bbc607910\",\n \"score\": \"0.74250394\",\n \"text\": \"def get_bin_id_terms(self):\\n return # osid.search.terms.IdTerm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c07bcf4354623d994c07e36e75fc3c\",\n \"score\": \"0.72232616\",\n \"text\": \"def get_descendant_bin_terms(self):\\n return # osid.resource.BinQueryInspector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3074c2ac52be0076136d4b742e2f001\",\n \"score\": \"0.65526414\",\n \"text\": \"def get_ancestor_bin_terms(self):\\n return # osid.resource.BinQueryInspector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7716c15742da96c6c89406c831f6296\",\n \"score\": \"0.5946379\",\n \"text\": \"def get_bin_terms(self):\\n return # osid.resource.BinQueryInspector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7716c15742da96c6c89406c831f6296\",\n \"score\": \"0.5946379\",\n \"text\": \"def get_bin_terms(self):\\n return # osid.resource.BinQueryInspector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6af521b7589d4ffc3ab4f4fbccf9dadb\",\n \"score\": \"0.58041036\",\n \"text\": \"def get_resource_relationship_id_terms(self):\\n return # osid.search.terms.IdTerm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4628c65593bc9d7d57c4a7dc0f44e3be\",\n \"score\": \"0.57497823\",\n \"text\": \"def _get_bin_enthalpy(self, bin_id):\\n return bin_id * self._window[2] + self._window[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec3f3f23985704939a0a5dee55587b4f\",\n \"score\": \"0.55940044\",\n \"text\": \"def get_resource_id_terms(self):\\n return # osid.search.terms.IdTerm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ef60c9d252e0cf63b2eaffd9de5da11\",\n \"score\": \"0.55061215\",\n \"text\": \"def find_descendant_ids(self):\\n stack = [self.id]\\n venture_ids = []\\n visited = {self.id}\\n while stack:\\n venture_id = stack.pop()\\n venture_ids.append(venture_id)\\n for v_id, in Venture.objects.filter(\\n parent_id=venture_id\\n ).values_list('id'):\\n if v_id in visited:\\n # Make sure we don't do the same device twice.\\n continue\\n visited.add(v_id)\\n stack.append(v_id)\\n return venture_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dea17da9e10c0b3e7a0060c1dfa2def\",\n \"score\": \"0.5443015\",\n \"text\": \"def get_agent_id_terms(self):\\n return # osid.search.terms.IdTerm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"950587c7c73b1f346f299ec69c0f5c8f\",\n \"score\": \"0.5418906\",\n \"text\": \"def get_containing_group_id_terms(self):\\n return # osid.search.terms.IdTerm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34e7ec79ea9f077d61c2d9434b59b737\",\n \"score\": \"0.5257579\",\n \"text\": \"def get_bin_node_ids(self, bin_id, ancestor_levels, descendant_levels, include_siblings):\\n # Implemented from template for\\n # osid.resource.BinHierarchySession.get_bin_node_ids\\n if self._catalog_session is not None:\\n return self._catalog_session.get_catalog_node_ids(\\n catalog_id=bin_id,\\n ancestor_levels=ancestor_levels,\\n descendant_levels=descendant_levels,\\n include_siblings=include_siblings)\\n return self._hierarchy_session.get_nodes(\\n id_=bin_id,\\n ancestor_levels=ancestor_levels,\\n descendant_levels=descendant_levels,\\n include_siblings=include_siblings)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd874fe5b2986a29c71dc229820d8f00\",\n \"score\": \"0.5199123\",\n \"text\": \"def get_child_bin_ids(self, bin_id):\\n # Implemented from template for\\n # osid.resource.BinHierarchySession.get_child_bin_ids\\n if self._catalog_session is not None:\\n return self._catalog_session.get_child_catalog_ids(catalog_id=bin_id)\\n return self._hierarchy_session.get_children(id_=bin_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8972a54172b3c3edfb721bd43d38ba8f\",\n \"score\": \"0.517019\",\n \"text\": \"def get_destination_resource_id_terms(self):\\n return # osid.search.terms.IdTerm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42925279499d0a30d76046a30c42b0b5\",\n \"score\": \"0.5150369\",\n \"text\": \"def get_avatar_id_terms(self):\\n return # osid.search.terms.IdTerm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"797cccc34e44c9d89f56c902c9369e6a\",\n \"score\": \"0.5115213\",\n \"text\": \"def ancestor_ids(self) -> list[int]:\\n with connection.cursor() as cursor:\\n cursor.execute(\\n ANCESTOR_QUERY.format(relationship_table=self.edge_model_table),\\n {'id': self.id},\\n )\\n return [row[0] for row in cursor.fetchall()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6273f93b0538ff510d2819c10b19d618\",\n \"score\": \"0.5112473\",\n \"text\": \"def clear_descendant_gradebook_id_terms(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e9edffbd40a63f8a08c440810c8f46d\",\n \"score\": \"0.5055445\",\n \"text\": \"async def get_go_hierarchy_go_id(id: str = Path(..., description=\\\"A GO-Term ID(e.g. GO:0005885, GO:0097136 ...)\\\")):\\n cmaps = get_prefixes(\\\"go\\\")\\n ont_r = OntologyResource(url=get_sparql_endpoint())\\n si = SparqlImplementation(ont_r)\\n converter = Converter.from_prefix_map(cmaps, strict=False)\\n id = converter.expand(id)\\n\\n query = (\\n \\\"\\\"\\\"\\n PREFIX definition: <http://purl.obolibrary.org/obo/IAO_0000115>\\n SELECT ?hierarchy ?GO ?label WHERE {\\n BIND(<%s> as ?goquery)\\n {\\n {\\n ?goquery rdfs:subClassOf+ ?GO .\\n ?GO rdfs:label ?label .\\n FILTER (LANG(?label) != \\\"en\\\")\\n BIND(\\\"parent\\\" as ?hierarchy)\\n }\\n UNION\\n {\\n ?GO rdfs:subClassOf* ?goquery .\\n ?GO rdfs:label ?label .\\n FILTER (LANG(?label) != \\\"en\\\")\\n BIND(IF(?goquery = ?GO, \\\"query\\\", \\\"child\\\") as ?hierarchy) .\\n }\\n }\\n }\\n \\\"\\\"\\\"\\n % id\\n )\\n results = si._sparql_query(query)\\n collated_results = []\\n collated = {}\\n for result in results:\\n collated[\\\"GO\\\"] = result[\\\"GO\\\"].get(\\\"value\\\")\\n collated[\\\"label\\\"] = result[\\\"label\\\"].get(\\\"value\\\")\\n collated[\\\"hierarchy\\\"] = result[\\\"hierarchy\\\"].get(\\\"value\\\")\\n collated_results.append(collated)\\n return collated_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b7293d3ccd7afa7c1ab668bd039d871\",\n \"score\": \"0.5054175\",\n \"text\": \"def get_child_bins(self, bin_id):\\n # Implemented from template for\\n # osid.resource.BinHierarchySession.get_child_bins\\n if self._catalog_session is not None:\\n return self._catalog_session.get_child_catalogs(catalog_id=bin_id)\\n return BinLookupSession(\\n self._proxy,\\n self._runtime).get_bins_by_ids(\\n list(self.get_child_bin_ids(bin_id)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d45fd8ef5921c8cd3110ae419217cff6\",\n \"score\": \"0.5009101\",\n \"text\": \"def get_bin_query(self):\\n # Implemented from template for\\n # osid.resource.BinQuerySession.get_bin_query_template\\n return queries.BinQuery(runtime=self._runtime)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddf8a2ee2ee7c394f163ae81ef2f216a\",\n \"score\": \"0.49984586\",\n \"text\": \"def childIds(self, spec=None):\\n unused(spec)\\n return [k.id for k in self.children()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52de014f357707f770c04c005c2271e5\",\n \"score\": \"0.49717396\",\n \"text\": \"def get_bin_edges( self, xbin ):\\n return [edge[xbin] for edge in self.edges],[edge[xbin+1] for edge in self.edges]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c426edbc61ee01b8fb5614873f84062\",\n \"score\": \"0.49638805\",\n \"text\": \"def bodybuilders_id_get(id): # noqa: E501\\n\\n\\n return query_manager.get_resource(id=id,\\n rdf_type_uri=BODYBUILDER_TYPE_URI,\\n rdf_type_name=BODYBUILDER_TYPE_NAME, \\n kls=Bodybuilder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83df03ec25948f5f0462adf1219f2db4\",\n \"score\": \"0.49471837\",\n \"text\": \"def get_edges(self):\\n return self.bin_edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc913af07c63726070e0f6b3a651722b\",\n \"score\": \"0.49366572\",\n \"text\": \"def btrIds(self) -> ObjectIdCollection:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd550a441e0189d6251f32773f84c77e\",\n \"score\": \"0.49218273\",\n \"text\": \"def get_bin_hierarchy_id(self):\\n # Implemented from template for\\n # osid.resource.BinHierarchySession.get_bin_hierarchy_id\\n if self._catalog_session is not None:\\n return self._catalog_session.get_catalog_hierarchy_id()\\n return self._hierarchy_session.get_hierarchy_id()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd550a441e0189d6251f32773f84c77e\",\n \"score\": \"0.49218273\",\n \"text\": \"def get_bin_hierarchy_id(self):\\n # Implemented from template for\\n # osid.resource.BinHierarchySession.get_bin_hierarchy_id\\n if self._catalog_session is not None:\\n return self._catalog_session.get_catalog_hierarchy_id()\\n return self._hierarchy_session.get_hierarchy_id()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fee831fa4d6d424d7e585a66c8c4ec21\",\n \"score\": \"0.48975003\",\n \"text\": \"def is_descendant_of_bin(self, id_, bin_id):\\n # Implemented from template for\\n # osid.resource.BinHierarchySession.is_descendant_of_bin\\n if self._catalog_session is not None:\\n return self._catalog_session.is_descendant_of_catalog(id_=id_, catalog_id=bin_id)\\n return self._hierarchy_session.is_descendant(id_=id_, descendant_id=bin_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb6f1731871adae71621e4a8adfbfeb5\",\n \"score\": \"0.48912504\",\n \"text\": \"def binddn(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._binddn\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c520961e15b65a104e4d439a263600ea\",\n \"score\": \"0.4882913\",\n \"text\": \"def bins(self):\\n return self._bins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c520961e15b65a104e4d439a263600ea\",\n \"score\": \"0.4882913\",\n \"text\": \"def bins(self):\\n return self._bins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e455480726777dbeb38c5f4dcad29c55\",\n \"score\": \"0.4875605\",\n \"text\": \"def go_terms(pdb_id):\\n request = f'https://www.rcsb.org/pdb/rest/goTerms?structureId={pdb_id}'\\n response = fetch_xml(request)\\n data = response['goTerms']['term']\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d0c143210c8f03fc5a204ce21daf9c3\",\n \"score\": \"0.48273125\",\n \"text\": \"def subgraph_ids(self) -> List[str]:\\n return self._subgraph_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bbee18623ca0d7eaf6af0a3decca931\",\n \"score\": \"0.48225656\",\n \"text\": \"def get_child_objective_bank_ids(self, objective_bank_id=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a202a8232cef64bc54f86daf6a8cdf0\",\n \"score\": \"0.48164767\",\n \"text\": \"def bond_term(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0527803bf90dfe464bfff305d638f5af\",\n \"score\": \"0.48082575\",\n \"text\": \"def histogram_baths(self, block_data):\\n return self.fetch_identifier_component(\\\"block/histogram_baths\\\", block_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab71e8388a6dc73ef6dbf23a6fbc587\",\n \"score\": \"0.48056874\",\n \"text\": \"def get_resource_ids_by_bin(self, bin_id):\\n # Implemented from template for\\n # osid.resource.ResourceBinSession.get_resource_ids_by_bin\\n id_list = []\\n for resource in self.get_resources_by_bin(bin_id):\\n id_list.append(resource.get_id())\\n return IdList(id_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8191add6ac6de4c48ddd050d0200a9a8\",\n \"score\": \"0.47966605\",\n \"text\": \"def get_objective_bank_node_ids(self,\\n objective_bank_id=None,\\n ancestor_levels=None,\\n descendant_levels=None,\\n include_siblings=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09f293ae3b35e54054d8330fc48616e5\",\n \"score\": \"0.47890717\",\n \"text\": \"def get(self, id):\\n args = parser.parse_args()\\n return lego_query(\\\"\\\"\\\"\\n CONSTRUCT { ?i ?p ?v } WHERE \\n {?i rdfs:isDefinedBy <http://model.geneontology.org/%s>; \\n ?p ?v\\n }\\\"\\\"\\\" % id, limit=1000)\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55daf9f8b62ebcc7287c1451bd0ba219\",\n \"score\": \"0.47707036\",\n \"text\": \"def get_descendant_nodes(request, node_id):\\n if request.method == 'GET':\\n try:\\n if node_id == '':\\n error = dict(success=\\\"false\\\", data={}, error_message=\\\"No ID was given when getting descendant nodes of\\\"\\n \\\"a node by its ID.\\\")\\n return JSONResponse(error)\\n\\n descendant_nodes_list = Nodes().retrieveDescendent(node_id)\\n get_descendant_nodes_response = {\\n \\\"success\\\": \\\"true\\\",\\n \\\"data\\\": descendant_nodes_list\\n }\\n return JSONResponse(get_descendant_nodes_response)\\n except Exception, e:\\n error = dict(success=\\\"false\\\", data={}, error_message=str(e))\\n return JSONResponse(error)\\n else: # only allow GET\\n error = dict(success=\\\"false\\\", data={}, error_message=\\\"Only GET allowed for getting descendant nodes of\\\"\\n \\\"a node by its ID.\\\")\\n return JSONResponse(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeb3b856febd53f6c9d5d149fa97c0d0\",\n \"score\": \"0.47585076\",\n \"text\": \"def get_descendant_gradebook_query(self):\\n return # osid.grading.GradebookQuery\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed5ef0721b110c91ecee3f5be23f8958\",\n \"score\": \"0.474145\",\n \"text\": \"def getBroaderTerms(self):\\n return self.getXPath(\\n \\\"/find-doc/record/metadata/oai_marc/varfield[@id='550'][subfield[@label='w']='g']/subfield[@label='a']\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e483a0f108833aa60092a798ace0d4b6\",\n \"score\": \"0.47377455\",\n \"text\": \"def getId(self):\\n return self.base.get(\\\"id\\\", [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e483a0f108833aa60092a798ace0d4b6\",\n \"score\": \"0.47377455\",\n \"text\": \"def getId(self):\\n return self.base.get(\\\"id\\\", [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"081b8937ce66290573906dad4e911918\",\n \"score\": \"0.47259697\",\n \"text\": \"def get_bin_nodes(self, bin_id, ancestor_levels, descendant_levels, include_siblings):\\n # Implemented from template for\\n # osid.resource.BinHierarchySession.get_bin_nodes\\n return objects.BinNode(self.get_bin_node_ids(\\n bin_id=bin_id,\\n ancestor_levels=ancestor_levels,\\n descendant_levels=descendant_levels,\\n include_siblings=include_siblings)._my_map, runtime=self._runtime, proxy=self._proxy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0841bc54ca05e64f92623146d9d41fef\",\n \"score\": \"0.4723538\",\n \"text\": \"def id(self) -> int:\\n return self._ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15f8c5de35419bb603cf9b7259ec3df5\",\n \"score\": \"0.47232932\",\n \"text\": \"def get(self, entry_id: str, params: dict = None) -> dict:\\n entry_id = f\\\"terms?short_form=NCBITaxon_{entry_id}\\\"\\n return super().get(entry_id, params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d45681c1a2a0fdae2a45223dbed36ff1\",\n \"score\": \"0.4720204\",\n \"text\": \"def get_objective_bank_hierarchy_id(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d45681c1a2a0fdae2a45223dbed36ff1\",\n \"score\": \"0.4720204\",\n \"text\": \"def get_objective_bank_hierarchy_id(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53bd172957577dbf06dcb86de8f4498f\",\n \"score\": \"0.47144505\",\n \"text\": \"def btrId(self) -> ObjectId:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2fd758e69a250b449a743680b0a75d1\",\n \"score\": \"0.47113705\",\n \"text\": \"def block_histogram_baths(self, data):\\n return self.fetch_identifier_component(\\\"property/block_histogram_baths\\\", data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7312af59565f488f2b0698b12f249f0f\",\n \"score\": \"0.4708659\",\n \"text\": \"def get_term(self, entry_id: str):\\n return self.get(\\n f\\\"http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252F{entry_id}\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6f92670610b6bc224f29eeacada03fa\",\n \"score\": \"0.4704278\",\n \"text\": \"def get_bin_ids_by_resource(self, resource_id):\\n # Implemented from template for\\n # osid.resource.ResourceBinSession.get_bin_ids_by_resource\\n mgr = self._get_provider_manager('RESOURCE', local=True)\\n lookup_session = mgr.get_resource_lookup_session(proxy=self._proxy)\\n lookup_session.use_federated_bin_view()\\n resource = lookup_session.get_resource(resource_id)\\n id_list = []\\n for idstr in resource._my_map['assignedBinIds']:\\n id_list.append(Id(idstr))\\n return IdList(id_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b9b66f45a47ccd16e042eb2f589eee5\",\n \"score\": \"0.4703034\",\n \"text\": \"def getNormalIds(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"609ffbe438bc901e36ad93bc706a6ece\",\n \"score\": \"0.4701109\",\n \"text\": \"def get_objective_bank_hierarchy_id(self):\\n return # osid.id.Id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"609ffbe438bc901e36ad93bc706a6ece\",\n \"score\": \"0.4701109\",\n \"text\": \"def get_objective_bank_hierarchy_id(self):\\n return # osid.id.Id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2adef24754c1ae23f187102e14a3c256\",\n \"score\": \"0.4696119\",\n \"text\": \"def get_descendents(self, node_id):\\n nodes = []\\n for child in self.get_children(node_id):\\n nodes.append(child)\\n nodes += self.get_descendents(child[\\\"id\\\"])\\n return nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"223239f7a2db3a9f7014dcab8bf2caad\",\n \"score\": \"0.46765596\",\n \"text\": \"def descendant(h, d):\\n h = get(h, d)\\n return d[d[\\\"nodeIndex\\\"] == h[\\\"descendantIndex\\\"]][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce928bd9e5a554f2b9d52d3e514f950d\",\n \"score\": \"0.46595198\",\n \"text\": \"def get_bins(self):\\n # Implemented from template for\\n # osid.resource.BinLookupSession.get_bins_template\\n # NOTE: This implementation currently ignores plenary view\\n if self._catalog_session is not None:\\n return self._catalog_session.get_catalogs()\\n collection = JSONClientValidated('resource',\\n collection='Bin',\\n runtime=self._runtime)\\n result = collection.find().sort('_id', DESCENDING)\\n\\n return objects.BinList(result, runtime=self._runtime, proxy=self._proxy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a97a01da8e54f3464559bb945e9c256f\",\n \"score\": \"0.46349737\",\n \"text\": \"def GetIDs(self):\\n return [self.ID] + [\\\"{}.{}\\\".format(self.ID, x) for x in self.Appendix]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3290815d68e66e938d0483902f311ea\",\n \"score\": \"0.4631585\",\n \"text\": \"def get_ids(self):\\n\\t\\treturn self._id_dict.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe45ecbb5062079961d64c431741869\",\n \"score\": \"0.46197516\",\n \"text\": \"def get_bin_id(self):\\n # Implemented from template for osid.resource.ResourceLookupSession.get_bin_id\\n return self._catalog_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe45ecbb5062079961d64c431741869\",\n \"score\": \"0.46197516\",\n \"text\": \"def get_bin_id(self):\\n # Implemented from template for osid.resource.ResourceLookupSession.get_bin_id\\n return self._catalog_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe45ecbb5062079961d64c431741869\",\n \"score\": \"0.46197516\",\n \"text\": \"def get_bin_id(self):\\n # Implemented from template for osid.resource.ResourceLookupSession.get_bin_id\\n return self._catalog_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe45ecbb5062079961d64c431741869\",\n \"score\": \"0.46197516\",\n \"text\": \"def get_bin_id(self):\\n # Implemented from template for osid.resource.ResourceLookupSession.get_bin_id\\n return self._catalog_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe45ecbb5062079961d64c431741869\",\n \"score\": \"0.46197516\",\n \"text\": \"def get_bin_id(self):\\n # Implemented from template for osid.resource.ResourceLookupSession.get_bin_id\\n return self._catalog_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe45ecbb5062079961d64c431741869\",\n \"score\": \"0.46197516\",\n \"text\": \"def get_bin_id(self):\\n # Implemented from template for osid.resource.ResourceLookupSession.get_bin_id\\n return self._catalog_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32791b0f7aa1d084e2915b34540a944c\",\n \"score\": \"0.46191418\",\n \"text\": \"def get_resource_ids_by_bins(self, bin_ids):\\n # Implemented from template for\\n # osid.resource.ResourceBinSession.get_resource_ids_by_bins\\n id_list = []\\n for resource in self.get_resources_by_bins(bin_ids):\\n id_list.append(resource.get_id())\\n return IdList(id_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"372b9306d2a485bcdbe95c603d800a50\",\n \"score\": \"0.46036774\",\n \"text\": \"def binset(self):\\n return self.obsmode.bandwave\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7486132c3a330a2bf41857a4265a3946\",\n \"score\": \"0.4602269\",\n \"text\": \"def get_assignable_bin_ids(self, bin_id):\\n # Implemented from template for\\n # osid.resource.ResourceBinAssignmentSession.get_assignable_bin_ids\\n # This will likely be overridden by an authorization adapter\\n mgr = self._get_provider_manager('RESOURCE', local=True)\\n lookup_session = mgr.get_bin_lookup_session(proxy=self._proxy)\\n bins = lookup_session.get_bins()\\n id_list = []\\n for bin in bins:\\n id_list.append(bin.get_id())\\n return IdList(id_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93afbbf7bfd54e6179224822420b05d4\",\n \"score\": \"0.46007088\",\n \"text\": \"def get_chembl_id(chebi_id):\\n return chebi_chembl.get(_add_prefix(chebi_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3687f2e3d12998c78c0457726b145caf\",\n \"score\": \"0.4600179\",\n \"text\": \"def get_child_objective_bank_ids(self, objective_bank_id):\\n return # osid.id.IdList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cc34d340145403f5c4f6ef196e4a5c9\",\n \"score\": \"0.4600061\",\n \"text\": \"def get_objective_bank_node_ids(self, objective_bank_id, ancestor_levels, descendant_levels, include_siblings):\\n return # osid.hierarchy.Node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b697349ec0a8f7b8a016a59e86e9d4be\",\n \"score\": \"0.45960596\",\n \"text\": \"def get_bins_by_ids(self, bin_ids):\\n # Implemented from template for\\n # osid.resource.BinLookupSession.get_bins_by_ids_template\\n # NOTE: This implementation currently ignores plenary view\\n # Also, this should be implemented to use get_Bin() instead of direct to database\\n if self._catalog_session is not None:\\n return self._catalog_session.get_catalogs_by_ids(catalog_ids=bin_ids)\\n catalog_id_list = []\\n for i in bin_ids:\\n catalog_id_list.append(ObjectId(i.get_identifier()))\\n collection = JSONClientValidated('resource',\\n collection='Bin',\\n runtime=self._runtime)\\n result = collection.find({'_id': {'$in': catalog_id_list}}).sort('_id', DESCENDING)\\n\\n return objects.BinList(result, runtime=self._runtime, proxy=self._proxy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0cf0d34fb0b4c139ba915e6ab7e216b\",\n \"score\": \"0.45877272\",\n \"text\": \"def getancestors(self, termids):\\n t2a = {}\\n for t in termids:\\n tas = self.getancetorsbyid(t)\\n if len(tas) > 0:\\n t2a[t] = tas\\n return t2a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fc7eb853cbc7574a7a2f51eaad5d2d1\",\n \"score\": \"0.45871562\",\n \"text\": \"def getRelatedTerms(self):\\n return self.getXPath(\\n \\\"/find-doc/record/metadata/oai_marc/varfield[@id='550'][not(subfield[@label='w'])]/subfield[@label='a']\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5916d3af2b2665824923f4da377da96\",\n \"score\": \"0.45847875\",\n \"text\": \"def iterids(self):\\r\\n if self._index is None:\\r\\n raise _errors.SearchError(\\\"SearchConnection has been closed\\\")\\r\\n return _indexerconnection.PrefixedTermIter('Q', self._index.allterms())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48299aab7a9ea53e7458468013e4a6fa\",\n \"score\": \"0.45796618\",\n \"text\": \"def ids(self):\\n return self['ids']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f4591658429df7831c527dcd0de3e40\",\n \"score\": \"0.45764086\",\n \"text\": \"def bin_edges(self, bins, field_key):\\n\\n all_values = np.concatenate([fields[field_key]\\n for fields\\n in self.contigtree.wepy_h5.iter_trajs_fields([field_key])])\\n\\n bin_edges = np.histogram_bin_edges(all_values, bins=bins)\\n\\n return bin_edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"768bddf6c3050090c1ba49a9d484fa77\",\n \"score\": \"0.45679593\",\n \"text\": \"def get_distances(self, id):\\n return [x[1] for x in self.neighbors[id]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89a3c8bc7881de3b02263e84a7ae444c\",\n \"score\": \"0.45675638\",\n \"text\": \"def GetRootId(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f915e8e2d44d627505358d6b9097e7d2\",\n \"score\": \"0.45667973\",\n \"text\": \"def descendant_substrings(self):\\n substrings = []\\n for child in self.children:\\n grandchild_substrings = child.descendant_substrings()\\n if grandchild_substrings:\\n child_substrings = [child.value + s for s in grandchild_substrings]\\n else:\\n child_substrings = [child.value]\\n substrings.extend(child_substrings)\\n return substrings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50384c3d6fee196b26e2a71827d9b353\",\n \"score\": \"0.456445\",\n \"text\": \"def enumbins(self):\\n return self._enumbins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e02ec3f8988012e2bc39224bbe44c26\",\n \"score\": \"0.45565787\",\n \"text\": \"def get_ids(self):\\n # Complete this function\\n ids = []\\n\\n return ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2465d4ae061153bcd35220fe939c6b2\",\n \"score\": \"0.45533267\",\n \"text\": \"def ids(self):\\n return self[\\\"ids\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1641f9aafa2a4830e9ae3134f4d651f8\",\n \"score\": \"0.45528013\",\n \"text\": \"def get_root_bin_ids(self):\\n # Implemented from template for\\n # osid.resource.BinHierarchySession.get_root_bin_ids\\n if self._catalog_session is not None:\\n return self._catalog_session.get_root_catalog_ids()\\n return self._hierarchy_session.get_roots()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"717cc414dac5cda9030dcfaf8cf5e66f\",\n \"score\": \"0.45500702\",\n \"text\": \"def termToWordIds(text):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"717cc414dac5cda9030dcfaf8cf5e66f\",\n \"score\": \"0.45500702\",\n \"text\": \"def termToWordIds(text):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8163e88a28ef2ec5c39373d307a9b72\",\n \"score\": \"0.45479932\",\n \"text\": \"def get_bins_by_resource(self, resource_id):\\n # Implemented from template for\\n # osid.resource.ResourceBinSession.get_bins_by_resource\\n mgr = self._get_provider_manager('RESOURCE', local=True)\\n lookup_session = mgr.get_bin_lookup_session(proxy=self._proxy)\\n return lookup_session.get_bins_by_ids(\\n self.get_bin_ids_by_resource(resource_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b91f724084b24bb800fb160953e53979\",\n \"score\": \"0.45442188\",\n \"text\": \"def get(self, id):\\n return list_id(id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d209baaaffa2a9153a11dbf740b4bc08\",\n \"score\": \"0.4540475\",\n \"text\": \"def get_edge_idx( self ):\\n\\n self.edges = []\\n # You've provided a number of bins. Get the weights and define bin edges such that there exists equal weight in each bin.\\n if not self.params['split_by_w']:\\n\\n for x_ in self.x:\\n xw = np.ones(len(x_))\\n normcumsum = xw.cumsum() / xw.sum()\\n self.edges.append( np.searchsorted(normcumsum, np.linspace(0, 1, self.bins+1, endpoint=True)) )\\n\\n else:\\n\\n w,R = self.calibrator.calibrate(self.xcol,return_full_w=True,weight_only=True,include_Rg=True)\\n for x_,w_ in tuple(zip(self.x,w)):\\n xw = w_*R\\n normcumsum = xw.cumsum() / xw.sum()\\n self.edges.append( np.searchsorted(normcumsum, np.linspace(0, 1, self.bins+1, endpoint=True)) )\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6d25fe801ec0a5b42e76b517601c65e\",\n \"score\": \"0.45391673\",\n \"text\": \"def get_source_resource_id_terms(self):\\n return # osid.search.terms.IdTerm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa07546d6d19c910ebb05a2f29f1850a\",\n \"score\": \"0.4534293\",\n \"text\": \"def find_bin(self):\\n return self.binstr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7707fd11f185dfff4a5412b5dd3e2679\",\n \"score\": \"0.45315948\",\n \"text\": \"def getNarrowerTerms(self):\\n return self.getXPath(\\n \\\"/find-doc/record/metadata/oai_marc/varfield[@id='550'][subfield[@label='w']='h']/subfield[@label='a']\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12afc0cd77495a3e14b3063ba2f5670f\",\n \"score\": \"0.45283815\",\n \"text\": \"def relation_ids(self):\\n if not self._is_extracted:\\n return\\n return self.to_id(self.relations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82261298403f8b302c30b0c61a323f10\",\n \"score\": \"0.45282623\",\n \"text\": \"def iterids(self):\\r\\n if self._index is None:\\r\\n raise errors.IndexerError(\\\"IndexerConnection has been closed\\\")\\r\\n return PrefixedTermIter('Q', self._index.allterms())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56fc4f7d56f1be21dc408b4b2eebead0\",\n \"score\": \"0.4525562\",\n \"text\": \"def bend_list(self) -> List[int]:\\n return list(self.bends())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72ccffe8cd1053b05ebc0aeba2d531c6\",\n \"score\": \"0.45250118\",\n \"text\": \"def get_residue(self, res_id):\\n sel = []\\n for p,s in zip(self.prefix, res_id.split(',')):\\n if s:\\n sel .append(p + ' ' + s)\\n sel = ' and '.join(sel)\\n return self.glycoprotein.select(sel)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993882,"cells":{"query_id":{"kind":"string","value":"cdfe2130854a137c1d6753d10488d77b"},"query":{"kind":"string","value":"Abstract Function Implementation Task Reads the input dataset and stores it in self.sentences as a list of tokens."},"positive_passages":{"kind":"list like","value":[{"docid":"da4b2f6df8ade16e947826f6375f0122","score":"0.0","text":"def read_Dataset(self, name=\"text8\", path=\"data/text8\"):\n\t\tprint(\"Reading dataset ....\")\n\t\tif name == \"text8\":\n\t\t\tself.sentences = Text8Corpus(path)\n\t\telif name == \"OneBilCorpus\":\n\t\t\tself.sentences = OneBilCorpus(path)\n\t\telif name in ['cornellMD', 'conll2003', 'semeval', 'categorical']:\n\t\t\tself.is_benchmark = True\n\t\t\tif name == 'conll2003':\n\t\t\t\tself.sentences = CoNLL2003(to_lower=\"False\", sources=path)\n\t\telse:\n\t\t\twith open(path) as fname:\n\t\t\t\tself.sentences = fname.read().lower().split()\n\t\tself.dataset = name\n\t\tself.dataset_path = path\n\t\tprint(\"Dataset loaded into conec object ....\")","title":""}],"string":"[\n {\n \"docid\": \"da4b2f6df8ade16e947826f6375f0122\",\n \"score\": \"0.0\",\n \"text\": \"def read_Dataset(self, name=\\\"text8\\\", path=\\\"data/text8\\\"):\\n\\t\\tprint(\\\"Reading dataset ....\\\")\\n\\t\\tif name == \\\"text8\\\":\\n\\t\\t\\tself.sentences = Text8Corpus(path)\\n\\t\\telif name == \\\"OneBilCorpus\\\":\\n\\t\\t\\tself.sentences = OneBilCorpus(path)\\n\\t\\telif name in ['cornellMD', 'conll2003', 'semeval', 'categorical']:\\n\\t\\t\\tself.is_benchmark = True\\n\\t\\t\\tif name == 'conll2003':\\n\\t\\t\\t\\tself.sentences = CoNLL2003(to_lower=\\\"False\\\", sources=path)\\n\\t\\telse:\\n\\t\\t\\twith open(path) as fname:\\n\\t\\t\\t\\tself.sentences = fname.read().lower().split()\\n\\t\\tself.dataset = name\\n\\t\\tself.dataset_path = path\\n\\t\\tprint(\\\"Dataset loaded into conec object ....\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"588216166c5e9382d18abc8829484081","score":"0.6711342","text":"def tokenize_sentences(self):\n data = []\n labels = []\n\n for sentence in self.sentences:\n data_tmp, label_tmp = self.get_data_from_sentence(sentence)\n\n if data_tmp != None and label_tmp != None:\n data.extend(data_tmp)\n labels.extend(label_tmp)\n\n return data, labels","title":""},{"docid":"84780e1862d555c40f4e34abc15ff97e","score":"0.65370023","text":"def __read_sentences_data(self):\n self.__sentence_files = os.listdir(self.__sentence_dir)\n\n data_lists = []\n for file in self.__sentence_files:\n with open(os.path.join(self.__sentence_dir, file), mode='r', encoding='utf8') as f:\n sentence = f.read().replace('\\n', '')\n data_lists.append(sentence)\n self.__sentence_datas = data_lists","title":""},{"docid":"3f1eac925bf3cc6da095377eed1a155a","score":"0.6405178","text":"def get_dataset(self, dataset):\n tokenizer = get_tokenizer('basic_english')\n words_in_dataset = []\n\n for idx, (label, line) in enumerate(dataset):\n tokens = tokenizer(line)\n words_in_dataset.append(tokens)\n\n return words_in_dataset","title":""},{"docid":"5e6ca43685d048962e23e28de4bbc108","score":"0.6393619","text":"def get_tokenized_input(self):\n return [\n line.strip().split() for line in open(self.data_files[\n self.current_split][self.input_varname()])\n ]","title":""},{"docid":"2bd72bbae34f8ab84e0a7443de37d568","score":"0.6262996","text":"def process_file(self) -> List:\n\n samples = []\n\n question_id = 0\n with open(self.input_path, encoding=\"latin-1\") as input_file:\n lines = input_file.readlines()\n\n current_context = {} # filled with all sentences belonging to one question\n\n for line in lines:\n\n if line.startswith(\"1 \"):\n current_context = {} # if sentence counter is reset to 1, a new context begins\n\n key_content_split = line.split(\" \", 1)\n sentence_key = key_content_split[0]\n content = key_content_split[1]\n\n tab_split = content.split(\"\\t\")\n\n text = tab_split[0]\n tokenized_text = self.word_tokenizer.tokenize(text)\n\n if len(tab_split) == 1: # lines with context sentences do not contain tabs\n\n # add sentence to sample context\n current_context[int(sentence_key)] = tokenized_text\n\n else:\n # Lines containing a question have an additional tab for supporting fact ids. A question line\n # always denotes the end of one sample.\n\n question_length = len(tokenized_text) # count question tokens\n question = \" \".join(tokenized_text)\n\n sup_facts = tab_split[2].split(\" \")\n sup_facts = [int(s) for s in sup_facts] # store supporting fact ids\n\n targets = []\n context = \"\"\n current_token_pos = question_length\n\n sentence_keys = sorted(current_context) # sort sentences in current context per key\n\n # go through all sentences in current context\n for key in sentence_keys:\n sentence_tokens = current_context[key]\n sentence_length = len(sentence_tokens)\n\n context += \" \".join(sentence_tokens) + \" \"\n\n sentence_span = [current_token_pos, current_token_pos + sentence_length]\n\n # if sentence belongs to supporting facts, set label to \"1\"\n if key in sup_facts:\n label = \"1\"\n else:\n label = \"0\"\n\n targets.append(self.create_target(question_length, sentence_span, label))\n\n current_token_pos = current_token_pos + sentence_length # increment token position\n\n entry = {\"info\": {\"doc_id\": self.DOC_ID,\n \"q_id\": str(question_id)},\n \"text\": question + \" \" + context.strip(),\n \"targets\": targets}\n\n samples.append(entry)\n question_id += 1\n\n return samples","title":""},{"docid":"293d523da476939c0477b67faf62b0e7","score":"0.6237957","text":"def parse(self):\n self.__read_sentences_data()\n self.__parse_features()","title":""},{"docid":"250a7184971ac7570921fc6d224ddba0","score":"0.6231222","text":"def __call__(self, input_data):\n # ---- INPUT CHECKS ---- #\n is_corpus = isinstance(input_data, Corpus)\n if isinstance(input_data, str):\n data = [input_data]\n elif is_corpus:\n data = input_data.documents\n elif isinstance(input_data, list):\n data = input_data\n else:\n raise ValueError(\n 'Type \"{}\" not supported.'.format(type(input_data))\n )\n\n # ---- TOKENIZE ---- #\n tokens = []\n for document in data:\n tokens.append(self.tokenizer(document))\n if self.callback:\n self.callback()\n\n # ---- DETERMINE DF STOP WORDS ---- #\n if self.use_df_sw: # DF parameters were specified.\n self._get_corpus_specific_stopwords(tokens, self.lowercase)\n\n # ---- PRE-PROCESS DOCUMENTS ---- #\n pp_tokens = []\n stop_words = self.stop_words | self.df_stop_words\n assert isinstance(stop_words, set)\n for doc in tokens:\n if self.lowercase: # Lowercase.\n doc = [token.lower() for token in doc]\n\n if stop_words: # Remove stop words.\n doc = [word for word in doc if word not in stop_words]\n\n if self.transformation is not None: # Transform.\n doc = self.transformation(doc)\n\n pp_tokens.append(doc)\n if self.callback:\n self.callback()\n\n if is_corpus:\n input_data.store_tokens(pp_tokens)\n return input_data\n return pp_tokens","title":""},{"docid":"07c36f805c59d353028f3eb2149ad6a6","score":"0.6069735","text":"def _tokenize(self, sentence: str) -> (torch.Tensor, list):\n inputs = self.tokenizer(sentence, return_tensors=\"pt\")\n return inputs.to(self.device)","title":""},{"docid":"ddae11b5f05370ba750231a5030922ae","score":"0.6056712","text":"def sentences( filestream , label_filter = None):\n i = 0\n sentence = []\n for line in filestream:\n line = line.rstrip()\n if line:\n sentence.append(Token(line))\n elif sentence:\n postprocess_tokens(sentence, label_filter)\n yield sentence\n sentence = []\n if sentence:\n postprocess_tokens(sentence, label_filter)\n yield sentence","title":""},{"docid":"15308a0ce6ad498225da78f8999461b7","score":"0.5999839","text":"def tokens(self, string) -> list:\n\n from hazm import SentenceTokenizer, WordTokenizer\n sent_tokenizer = SentenceTokenizer().tokenize\n word_tokenizer = WordTokenizer().tokenize\n string = self.normal_string(string)\n sentences_list = sent_tokenizer(string)\n _sents = []\n for sent in sentences_list:\n words = word_tokenizer(sent)\n _sents.append(words)\n sentences_list = _sents\n return sentences_list","title":""},{"docid":"0b7415f4f69be448b2306475db6442f0","score":"0.5960486","text":"def get_tokenized_data(data):\n\n # Get the sentences by splitting up the data\n sentences = split_to_sentences(data)\n logger.info(len(sentences))\n # Get the list of lists of tokens by tokenizing the sentences\n tokenized_sentences = tokenize_sentences(sentences)\n\n return tokenized_sentences","title":""},{"docid":"6c2433f2cdaab19969564094591a44cc","score":"0.5942005","text":"def tokenize_sentences(sentences):\n \n # Initialize the list of lists of tokenized sentences\n tokenized_sentences = []\n ### START CODE HERE (Replace instances of 'None' with your code) ###\n \n # Go through each sentence\n for sentence in sentences:\n \n # Convert to lowercase letters\n sentence = sentence.lower()\n \n # Convert into a list of words\n tokenized = nltk.word_tokenize(sentence)\n \n # append the list of words to the list of lists\n tokenized_sentences.append(tokenized)\n \n ### END CODE HERE ###\n \n return tokenized_sentences","title":""},{"docid":"f2519b77695e405ccf19a88913c8d44f","score":"0.5925383","text":"def _get_values(self, inputs):\n tokenized = []\n \n for tweet in inputs[0]:\n sentences = nltk.sent_tokenize(tweet)\n tokenized_tweet = []\n \n for sentence in sentences:\n words = nltk.word_tokenize(sentence)\n tokenized_tweet += words\n \n tokenized.append(str(tokenized_tweet))\n \n return tokenized","title":""},{"docid":"554140e42fbeb46902e8263af6c41feb","score":"0.59251755","text":"def read(self):\n\n if self.path.endswith('.json'):\n sentences = json.load(open(self.path, 'r'))\n else:\n sentences = []\n with open(self.path, 'r', encoding='latin-1') as f:\n for line in f:\n print(line[:20])\n # first strip away newline symbols and the like, then replace ' and \" with the empty\n # string and get rid of possible remaining trailing spaces\n line = line.strip().translate({ord(i): None for i in '\"\\'\\\\'}).strip(' ')\n # lowercase and split at the white space (the corpus has ben previously tokenized)\n sentences.append(line.lower().split(' '))\n\n return sentences","title":""},{"docid":"9090f357af581398bd0bab41530f45f8","score":"0.5923357","text":"def _read_data(cls, input_file, quotechar=None):\n with tf.gfile.Open(input_file, \"r\") as f:\n reader = csv.reader(f, delimiter=\"\\t\", quotechar=quotechar)\n lines = []\n words = []\n labels = []\n for line in reader:\n if len(line) == 0:\n l = ' '.join([label for label in labels if len(label) > 0])\n w = ' '.join([word for word in words if len(word) > 0])\n lines.append((w, l))\n words = []\n labels = []\n else:\n word = line[0].strip()\n label = line[1].strip()\n words.append(word)\n labels.append(label)\n\n return lines","title":""},{"docid":"20cab7a056e7f7268f4728056d8af9bc","score":"0.5910446","text":"def list_dataset():\n # Accumlator lists\n subtopics = []\n source_sentences = []\n target_sentences = []\n golds = []\n for topic in os.listdir(STE_PATH):\n # Dont process non directories\n if not os.path.isdir(os.path.join(STE_PATH, topic)):\n continue\n # Keep track of the current subtopic\n # being processed within the csv file\n current_subtopic = None\n source_sentence = None\n with open(os.path.join(STE_PATH, topic, 'results.csv'), 'r') as fp:\n LOG.debug('Processing topic: %s', topic)\n reader = csv.reader(fp)\n # ignore header\n next(reader)\n for row in reader:\n # ignore empty row\n if not row:\n continue\n sentence, _, _, gold, order, subtopic = row\n subtopic = os.path.join(topic, subtopic)\n if order == '0':\n current_subtopic = subtopic\n source_sentence = sentence\n continue\n if order == '1' and subtopic == current_subtopic:\n # Filter out sentences which have >100 words\n source_sentence = source_sentence.decode('utf-8').encode('ascii', 'ignore')\n sentence = sentence.decode('utf-8').encode('ascii', 'ignore')\n source_sents = [sent\n for sent in sent_tokenize(source_sentence)\n if len(word_tokenize(sent)) <= 100]\n target_sents = [sent\n for sent in sent_tokenize(sentence)\n if len(word_tokenize(sent)) <= 100]\n if source_sents and target_sents:\n subtopics.append(subtopic)\n source_sentences.append(source_sents)\n target_sentences.append(target_sents)\n golds.append(1 if gold == 'True' else 0)\n current_subtopic = None\n source_sentence = None\n LOG.debug('Completed processing topic: %s', topic)\n return subtopics, source_sentences, target_sentences, golds","title":""},{"docid":"5c222f8a3abc2c45ca3457692969d1eb","score":"0.58925796","text":"def process_sentences(data, model, is_training, learningrate, config, name,\n epoch=0, write_out=\"\"):\n evaluators = {task: MLTEvaluator(config) for task in data.keys()}\n task_sent_id_batches = create_batches_of_sentence_ids(\n data, config, is_training, epoch)\n if is_training:\n for task, sentence_ids_in_batch in task_sent_id_batches:\n random.shuffle(sentence_ids_in_batch)\n\n for task, sentence_ids_in_batch in tqdm(task_sent_id_batches):\n batch = [data[task][i] for i in sentence_ids_in_batch]\n cost, sentence_scores, token_scores_list, attention_scores_list = \\\n model.process_batch(task, batch, is_training, learningrate)\n\n evaluators[task].append_data(cost, batch, sentence_scores, token_scores_list, attention_scores_list)\n\n while config[\"garbage_collection\"] and gc.collect() > 0:\n pass\n\n results = {}\n for task in data.keys():\n print(\"\\n=== TASK: {} ===\".format(task))\n results[task] = evaluators[task].get_results(name)\n for key, res in results[task].items():\n print(task + \" \" + key + \": \" + str(res))\n\n if write_out:\n write_out_task = write_out+\"_\"+task\n if not os.path.exists(write_out_task):\n os.mkdir(write_out_task)\n evaluators[task].write_predictions(write_out_task)\n\n return results","title":""},{"docid":"d7fd417bdd0c049d26c9aab381458d07","score":"0.58777773","text":"def load_data(self):\n if self.input_format == 'txt':\n doc_tokens, doc_id = read_txt(self.path, language=self.language)\n elif self.input_format == 'ltf':\n doc_tokens, doc_id = read_ltf(self.path)\n else:\n raise ValueError('Unknown input format: {}'.format(self.input_format))\n self.doc_id = doc_id\n self.data = doc_tokens\n self.ori_sent_num = len(doc_tokens)","title":""},{"docid":"a47e380c2f06bb6fc64394e904c658df","score":"0.58737177","text":"def load_tokenize_and_batchify(data_dir = '../SemSynLSTM/word_language_model/data/wikitext-2/', input_len = 128):\n tensor_datasets = []\n\n corpus = TaggedCorpus(data_dir)\n train_data = corpus.train\n val_data = corpus.valid\n test_data = corpus.test\n\n for dataset in [train_data, val_data, test_data]:\n ##divide data by batch, truncate to fit into batches\n n_batch = len(dataset[0]) // input_len\n input_ids = dataset[0][: n_batch * input_len].reshape(n_batch, input_len)\n pos_ids = dataset[1][: n_batch * input_len].reshape(n_batch, input_len)\n all_inputs = (input_ids, pos_ids)\n tensor_datasets.append(tuple(t for t in all_inputs))\n\n return tensor_datasets[0], tensor_datasets[1],tensor_datasets[2], corpus.dictionary, corpus.pos_dictionary","title":""},{"docid":"5aae2083e57b14909160ab89585d3abd","score":"0.58451325","text":"def get_sentences(self):\n corpus = self.get_corpus()\n\n sentences = []\n for sentence in corpus:\n #print sentence\n sentences.append(sentence.split())\n\n #print sentences\n return sentences","title":""},{"docid":"d87d91fb0f1659d1d61011addaab0e2e","score":"0.5829288","text":"def __init__(self, md=None):\n self.task = 'ner'\n self._set_metadata(md)\n # loads word_dict and tags_dict\n self.load_dictionary()\n self.load_tag_dict()\n\n self.sentences = []\n sent = []\n for line in sys.stdin:\n line = line.decode('utf-8').strip()\n if line:\n (form, pos, tag) = line.split(None, 2)\n sent.append([form, pos])\n else:\n self.sentences.append(sent)\n sent = []","title":""},{"docid":"3b69d72115ef80456a7a4f6af57c5a80","score":"0.582484","text":"def get_tokens(self) -> List[List[str]]:\n if self.total_sentence_count == 0:\n return []\n\n def _get_tokens_from_sentence(sentence: List[Tuple[str]]) -> List[str]:\n return [token[0] for token in sentence]\n\n return [\n _get_tokens_from_sentence(sentence)\n for sentence in self.sentences\n ]","title":""},{"docid":"a600dfc9fd9fafdb54ca5d706be73277","score":"0.5818996","text":"def __init__(self, sentences: list) -> None:\n ### self.sentence_generator = get_tokenized_sentences(file_name)\n self.sentences = sentences\n self.count()","title":""},{"docid":"1cb2303ef022f844f1d5a4751fe63d4e","score":"0.57839704","text":"def build_sentences():\n pass","title":""},{"docid":"5d7ffede626db2bda7e7444831f8e52d","score":"0.5764835","text":"def stream_tokens(self):\n for document in open(self.fname):\n post = []\n for sentences in self.tokenize(document):\n post.extend(self.ngrams[list(sentences)])\n yield post","title":""},{"docid":"bfb92adb19b1b278b06c7415c0c54519","score":"0.5734833","text":"def getData(self,n):\n\t\twords = []\n\t\tsentences = []\n\t\tcount = 0\n\t\tfor i in range(1,n):\n\t\t\tfilename = self.train_name.format(i)\n\t\t\tprint \"Loading sentences from {0}\".format(filename)\n\t\t\twith codecs.open(filename, \"r\", \"utf-8\") as file:\n\t\t\t\tfor line in file:\n\t\t\t\t\tline = line.split()\n\t\t\t\t\t# If the line has words then we keep the relevant word\n\t\t\t\t\tif len(line)>2:\n\t\t\t\t\t\twords.append(line[1])\n\t\t\t\t\t\tcontinue\n\t\t\t\t\t# If the sentence has no words we ignore it\n\t\t\t\t\tif not len(words):\n\t\t\t\t\t\tcontinue\n\t\t\t\t\t# We are finished with the current word\n\t\t\t\t\tsentences.append([words, -1])\n\t\t\t\t\t# Reset the state\n\t\t\t\t\twords = []\n\t\t\t\t\tcount += 1\n\t\t# Check that the sentences look good\n\t\treturn sentences","title":""},{"docid":"8581b37f1a2faecd0ac2e55ae823f7b7","score":"0.5730079","text":"def getSentence():\n data_str = input(\"Enter a sentence to scan for sentiment : \")\n print(\" Your input is: \",data_str, type(data_str))\n\n return data_str.split() #return a list","title":""},{"docid":"37d28222678d95431ae3b896b9f7d3a2","score":"0.5722489","text":"def _process_input_file(filename, stats):\n tf.logging.info(\"Processing input file: %s\", filename)\n processed = []\n\n predecessor = None # Predecessor sentence (list of words).\n current = None # Current sentence (list of words).\n successor = None # Successor sentence (list of words).\n\n for successor_str in tf.gfile.FastGFile(filename):\n stats.update([\"sentences_seen\"])\n successor = successor_str#.split()\n # The first 2 sentences per file will be skipped.\n if predecessor and current and successor:\n stats.update([\"sentences_considered\"])\n\n # Note that we are going to insert <EOS> later, so we only allow\n # sentences with strictly less than max_sentence_length to pass.\n if FLAGS.max_sentence_length and (\n len(predecessor) >= FLAGS.max_sentence_length or len(current) >=\n FLAGS.max_sentence_length or len(successor) >=\n FLAGS.max_sentence_length):\n stats.update([\"sentences_too_long\"])\n else:\n serialized = _create_serialized_example(predecessor, current, successor)\n processed.append(serialized)\n stats.update([\"sentences_output\"])\n\n predecessor = current\n current = successor\n\n sentences_seen = stats[\"sentences_seen\"]\n sentences_output = stats[\"sentences_output\"]\n if sentences_seen and sentences_seen % 100000 == 0:\n tf.logging.info(\"Processed %d sentences (%d output)\", sentences_seen,\n sentences_output)\n if FLAGS.max_sentences and sentences_output >= FLAGS.max_sentences:\n break\n\n tf.logging.info(\"Completed processing file %s\", filename)\n return processed","title":""},{"docid":"3634228b44f9cc1307ab486d6366c4e7","score":"0.5711307","text":"def _sents(self):\n # Define a tokenizer pattern that is more precise than the default nltk\n # pattern\n\n pattern = r'''(?ix) # set flag to allow verbose regexps and ignore case\n (?:mr\\.|mrs\\.) # abreviation for mister and missus\n | (?:[A-Z]+\\'[A-Z]{1,2}) # neg or to be abreviations, e.g can't\n | (?:[A-Z]\\.)+ # abbreviations, e.g. U.S.A.\n | \\w+(?:-\\w+)* # words with optional internal hyphens\n | \\$?\\d+(?:\\.\\d+)?%? # currency and percentages e.g. $12.40, 82%\n | \\.\\.\\. # ellipsis\n | [][.,;\"'?():-_`] # these are separate tokens; includes ], [\n '''\n\n # Instantiate the tokenizer\n tokenizer = RegexpTokenizer(pattern)\n\n # Create a new corpus by specifying the parameters\n # (1) directory of the new corpus\n # (2) the fileids of the corpus\n # The fileids are simply the filenames.\n\n my_corpus = PlaintextCorpusReader(self.corpusdir,\n self.fileId,\n word_tokenizer=tokenizer)\n\n # Access sentences in the corpus (list of list of strings)\n self.sents = my_corpus.sents()","title":""},{"docid":"e22a9f2856c52c28967fb893e75903c2","score":"0.5711085","text":"def read_dataset(dataset, tier, vocab):\n\n context_data = []\n query_data = []\n question_uuid_data = []\n\n for articles_id in tqdm(range(len(dataset['data'])), desc=\"Preprocessing {}\".format(tier)):\n article_paragraphs = dataset['data'][articles_id]['paragraphs']\n for pid in range(len(article_paragraphs)):\n context = article_paragraphs[pid]['context']\n # The following replacements are suggested in the paper\n # BidAF (Seo et al., 2016)\n context = context.replace(\"''\", '\" ')\n context = context.replace(\"``\", '\" ')\n\n context_tokens = tokenize(context)\n\n qas = article_paragraphs[pid]['qas']\n for qid in range(len(qas)):\n question = qas[qid]['question']\n question_tokens = tokenize(question)\n question_uuid = qas[qid]['id']\n\n context_ids = [str(vocab.get(w, qa_data.UNK_ID)) for w in context_tokens]\n qustion_ids = [str(vocab.get(w, qa_data.UNK_ID)) for w in question_tokens]\n\n context_data.append(' '.join(context_ids))\n query_data.append(' '.join(qustion_ids))\n question_uuid_data.append(question_uuid)\n\n return context_data, query_data, question_uuid_data","title":""},{"docid":"e75c0256b70981e36acb8178864c0511","score":"0.57083285","text":"def read_dataset(dataset, tier, vocab):\n\n context_data = []\n query_data = []\n question_uuid_data = []\n\n for articles_id in tqdm(range(len(dataset['data'])), desc=\"Preprocessing {}\".format(tier)):\n article_paragraphs = dataset['data'][articles_id]['paragraphs']\n for pid in range(len(article_paragraphs)):\n context = article_paragraphs[pid]['context']\n # The following replacements are suggested in the paper\n # BidAF (Seo et al., 2016)\n context = context.replace(\"''\", '\" ')\n context = context.replace(\"``\", '\" ')\n\n context_tokens = tokenize(context)\n\n qas = article_paragraphs[pid]['qas']\n for qid in range(len(qas)):\n question = qas[qid]['question']\n question_tokens = tokenize(question)\n question_uuid = qas[qid]['id']\n\n context_ids = [vocab.get(w, qa_data.UNK_ID) for w in context_tokens]\n qustion_ids = [vocab.get(w, qa_data.UNK_ID) for w in question_tokens]\n\n context_data.append(context_ids)\n query_data.append(qustion_ids)\n question_uuid_data.append(question_uuid)\n\n return context_data, query_data, question_uuid_data","title":""},{"docid":"9a41fed53e9e8fd6fc9e809d5b1df8ec","score":"0.57013583","text":"def transform(self, input_: str) -> list:\n return nltk.word_tokenize(input_)","title":""},{"docid":"4fa20d544c61ebb969653fe5306dc0e2","score":"0.5690682","text":"def read_simple_ner_dataset(file_paths: Union[list, str], token_idx: int = 0,\n entity_idx: int = 1):\n if isinstance(file_paths, str):\n file_paths = [file_paths]\n\n sentences = []\n entities = []\n\n for path in file_paths:\n with open(path, 'r') as f:\n sentence = []\n ent = []\n for line in f:\n if line == \"\\n\":\n assert len(sentence) == len(ent)\n sentences.append(sentence)\n entities.append(ent)\n\n sentence = []\n ent = []\n elif not line.startswith(\"#\"):\n sentence.append(line.split()[token_idx])\n ent.append(line.split()[entity_idx].strip())\n\n return sentences, entities","title":""},{"docid":"d39bad2dad35979454f5e74f3739b685","score":"0.56748545","text":"def input_batch_generator(self, tokenized_sentences, batch_size):\n while True:\n for chunk in chunks(batch_size, range(len(tokenized_sentences))):\n selected_sentences = [tokenized_sentences[index] for index in chunk]\n model_inputs = self.prepare_inputs_from_pretokenized(selected_sentences)\n yield model_inputs","title":""},{"docid":"37fc1c4052fc2bdbaaab5685be793486","score":"0.5669986","text":"def prepare_sentences(input_file):\n\n with open(input_file) as i:\n data = i.readlines()\n sentences = [line.strip() for line in data]\n return sentences","title":""},{"docid":"ba8ebcbc8b18dd9028f0cb2ea569f172","score":"0.56618977","text":"def load_data(input_file, label_file, en_vocab_file, de_vocab_file, sentence_fn):\n en_sentences = sentence_fn(input_file)\n de_sentences = sentence_fn(label_file)\n return create_data(en_sentences, de_sentences, en_vocab_file, de_vocab_file)","title":""},{"docid":"d7ca90173fa4e1d353bc4152a3df6d2a","score":"0.5651888","text":"def prepare_data(self,data):\n\t\tinp = []\n\t\tout = []\n\n\t\tfor line in data:\n\t\t\ttokenized_inp = self.bert_tokenizer(line.split(\"\\t\")[0])\n\t\t\tif len(tokenized_inp) > self.max_len_in:\n\t\t\t\ttokenized_inp = tokenized_inp[:self.max_len_in]\n\t\t\telse:\n\t\t\t\ttokenized_inp.extend([self.bert_tokenizer.encode(\"[PAD]\")]*(self.max_len_in-len(tokenized_inp)))\n\n\t\t\ttokenized_out = self.bert_tokenizer(line.split(\"\\t\")[1])\n\t\t\tif len(tokenized_out) > self.max_len_out:\n\t\t\t\ttokenized_out = tokenized_out[:self.max_len_out]\n\t\t\telse:\n\t\t\t\ttokenized_out.extend([self.bert_tokenizer.encode(\"[PAD]\")]*(self.max_len_out-len(tokenized_out)))\n\n\n\t\t\tinp.append(tokenized_inp)\n\t\t\tout.append(tokenized_out)\n\n\t\treturn inp, out","title":""},{"docid":"b79b6e6539506a74c930eb7be5a13412","score":"0.5647186","text":"def categorize_sentences(self, sentences):\n # encode sentences\n sentences = [s.encode('utf-8') for s in sentences]\n\n q = queue.Queue()\n reader = TextReader(file=None, max_word_length=self.max_word_length)\n n_batch = len(sentences) // self.BATCH_SIZE\n pred = self.prediction\n saver = tf.train.Saver()\n results = []\n\n def fill_list(list, length):\n while len(list) != length:\n list.append('empty sentence.')\n return list\n\n # Fill queue with minibatches\n for i in range(n_batch + 1):\n if i == n_batch:\n q.put(fill_list(sentences, self.BATCH_SIZE))\n else:\n q.put(sentences[i * self.BATCH_SIZE: (i + 1) * self.BATCH_SIZE])\n\n # Predict\n with tf.Session() as sess:\n print('Loading model %s...' % SAVE_PATH)\n saver.restore(sess, SAVE_PATH)\n print('Model loaded')\n\n while not q.empty():\n batch = q.get()\n batch = ['0, ' + s for s in batch]\n batch_x, batch_y = reader.make_minibatch(batch)\n p = sess.run([pred], feed_dict={self.X: batch_x, self.Y: batch_y})\n results.append(p)\n\n return results","title":""},{"docid":"6fe49eff21bfe4455a8000d1eaadb0e8","score":"0.56468076","text":"def tokenize(self, sentence):\n ...","title":""},{"docid":"a12dcfc7b973a38a2010653b54c1e11e","score":"0.5625808","text":"def _read_data(cls, input_file):\n from improv.read_ner import get_ner_lines\n return get_ner_lines(Path(input_file))","title":""},{"docid":"ecbf55765955496729400b628d6ea652","score":"0.5615864","text":"def _read_data(cls, input_file):\n with open(input_file) as f:\n # out_lines = []\n out_lists = []\n entries = f.read().strip().split(\"\\n\\n\")\n for entry in entries:\n words = []\n ner_labels = []\n pos_tags = []\n bio_pos_tags = []\n for line in entry.splitlines():\n pieces = line.strip().split()\n if len(pieces) < 1:\n continue\n word = pieces[0]\n # if word == \"-DOCSTART-\" or word == '':\n # continue\n words.append(word)\n pos_tags.append(pieces[1])\n bio_pos_tags.append(pieces[2])\n ner_labels.append(pieces[-1])\n # sentence = ' '.join(words)\n # ner_seq = ' '.join(ner_labels)\n # pos_tag_seq = ' '.join(pos_tags)\n # bio_pos_tag_seq = ' '.join(bio_pos_tags)\n # out_lines.append([sentence, pos_tag_seq, bio_pos_tag_seq, ner_seq])\n # out_lines.append([sentence, ner_seq])\n out_lists.append([words,pos_tags,bio_pos_tags,ner_labels])\n return out_lists","title":""},{"docid":"cd94ce3e72078adc41aad34bd9e61732","score":"0.5608255","text":"def read(self):\n f = codecs.open(self.__filePath, \"r\", \"utf-8\")\n nmTokens = 0\n\n # Tokens and their labels for a whole sentence.\n tkns = []\n labels = []\n\n sep = self.__labelTknSep\n\n for line in f:\n line = line.strip()\n\n if len(line) == 0:\n # Blank line separates sentences.\n yield (tkns, labels)\n tkns = []\n labels = []\n continue\n\n tknLabel = line.split(sep)\n \n if(len(tknLabel) < 2):\n continue\n \n tkns.append(tknLabel[0])\n labels.append(tknLabel[1])\n\n nmTokens += 1\n\n assert len(tkns) == len(labels)\n\n if len(tkns) > 0:\n # Last sentence.\n yield (tkns, labels)\n\n if not self.__printedNumberTokensRead:\n self.__log.info(\"Number of tokens read: %d\" % nmTokens)","title":""},{"docid":"53f4be2998ad2766303012fd966667c1","score":"0.5600364","text":"def reader():\n ids = line.strip().split()\n conv_ids = [int(i) for i in ids]\n boundary = len(ids) - 1\n src = conv_ids[:boundary]\n pos_tgt = [conv_ids[boundary]]\n feature_name = [\"user\", \"all_item\", \"p_item\"]\n yield list(\n zip(feature_name, [src] + [\n np.arange(self.vocab_size).astype(\"int64\").tolist()\n ] + [pos_tgt]))","title":""},{"docid":"bd6d7dec7eeb512c0aacc3dea576dec9","score":"0.559833","text":"def get_sentence(self, dataset, batch_size=None):\n\n dataset = dataset.map(\n lambda seq_len, word_ids: (seq_len, word_ids, tf.gather(self.vocabulary.get_lookup_tensor(), word_ids))\n )\n if batch_size is None:\n batch_size = self.config.batch_size\n dataset = dataset.padded_batch(\n batch_size,\n padded_shapes=((), (-1,), (-1, self.vocabulary.vector_length()))\n )\n\n iterator = dataset.make_initializable_iterator()\n sentence = iterator.get_next()\n return sentence, iterator","title":""},{"docid":"077afca2802843ae49a21a30de81ee65","score":"0.559238","text":"def build_vocab(self, data_set):\n self.dataset = data_set\n\n self.data = data_set.text # data is a list of text in text_dataset\n types = ['lower', 'remove_punctuations', 'snowball', 'remove_emojis'\n 'lemmatize', 'remove_tags', 'replace_consecutive', 'n_grams']\n # print('Data: ', self.data)\n print('Pending ......... ')\n for sentence in tqdm(self.data):\n # print('Sentence: ', sentence)\n for token in self.tokenizer.preprocess(sentence, types):\n # print('Token after preprocess: ', token)\n if token not in self.stoi:\n if self.max_length is not None:\n if self.max_length <= self.vocab_size:\n continue\n\n self.stoi[token] = self.vocab_size # index increase from 4\n self.itos[self.vocab_size] = token\n self.vocab_size += 1\n self.freqs[token] = 1\n\n else:\n self.freqs[token] += 1\n\n self.freqs = {tok: freq for tok, freq in sorted(\n self.freqs.items(), key=lambda item: item[1], reverse=True)}\n\n # Vocab dataset only has min_freqs token\n if self.min_freqs is not None and self.min_freqs > 1:\n self.reset()\n new_freq = {}\n freq_list = list(self.freqs.items())\n for tok, freq in freq_list:\n if freq >= self.min_freqs:\n new_freq[tok] = freq\n self.stoi[tok] = self.vocab_size # index increase from 4\n self.itos[self.vocab_size] = tok\n self.vocab_size += 1\n\n self.freqs = new_freq\n\n if self.max_length is not None and self.max_length < self.vocab_size:\n self.reset()\n new_freq = {}\n freq_list = list(self.freqs.items())\n for tok, freq in freq_list:\n if self.vocab_size >= self.max_length:\n break\n new_freq[tok] = freq\n self.stoi[tok] = self.vocab_size # index increase from 4\n self.itos[self.vocab_size] = tok\n self.vocab_size += 1\n\n self.freqs = new_freq\n\n print('Done Bulding!')","title":""},{"docid":"0f0c62b32a38d0146ea2e13932cf996f","score":"0.55737823","text":"def process_sentences(self, sentences):\n id_sequences = list()\n sequence_lengths = list()\n\n # Reset word statistics.\n self._statistics['n_words_total'] = 0\n self._statistics['n_words_unique'] = 0\n self._statistics['n_words_clip_avg'] = 0\n\n # Reset character statistics.\n self._statistics['n_chars_total'] = 0\n self._statistics['n_chars_unique'] = 0\n self._statistics['n_chars_clip_avg'] = 0\n\n # Unique word lookup set.\n word_set = set()\n\n # Unique character lookup set.\n character_set = set()\n\n # TODO: Refactor.\n def __update_word_statistics(sentence):\n words = sentence.split(' ')\n self._statistics['n_words_total'] += len(words)\n word_set.update(words)\n\n # TODO: Refactor.\n def __update_character_statistics(sentence):\n self._statistics['n_chars_total'] += len(sentence)\n character_set.update(list(sentence))\n\n for sentence in sentences:\n # Make sentence lowercase.\n sentence = sentence.lower()\n\n # Collect word statistics.\n __update_word_statistics(sentence)\n\n # Collect character statistics.\n __update_character_statistics(sentence)\n\n self._statistics['n_words_unique'] = len(word_set)\n self._statistics['n_chars_unique'] = len(character_set)\n self._statistics['n_words_clip_avg'] = self._statistics['n_words_total'] / len(sentences)\n self._statistics['n_chars_clip_avg'] = self._statistics['n_chars_total'] / len(sentences)\n\n eos_token = self._char2idx_dict['eos']\n\n for sentence in sentences:\n # Make sentence lowercase.\n sentence = sentence.lower()\n\n # Replace abbreviations.\n sentence = self.replace_abbreviations(sentence)\n\n # Update character dictionary with the chars contained in the sentence.\n if self._fill_dict:\n self.update_char_dict(sentence)\n\n # Convert each character into its dictionary index.\n idx = self.sent2idx(sentence)\n\n # Append the EOS token to the sentence.\n idx.append(eos_token)\n\n # Append str. representation so that tf.Tensor handles this as an collection of objects.\n # This allows us to store sequences of different length in a single tensor.\n id_sequences.append(np.array(idx, dtype=np.int32).tostring())\n sequence_lengths.append(len(idx))\n\n return id_sequences, sequence_lengths","title":""},{"docid":"0aab3259f14ec7f23cb62757a1d64e4d","score":"0.5572674","text":"def tokenize(self):\n pass","title":""},{"docid":"0433eff744d254b284c2675eb1ffdad7","score":"0.55627257","text":"def train(self):\n if not self.inited:\n self.setup()\n\n # train\n for line in self.data:\n # preprocessing\n line = line.strip()\n if not line:\n continue\n\n # get observes\n observes = []\n for i in range(len(line)):\n if line[i] == \" \":\n continue\n observes.append(line[i])\n\n # get states\n # split line by space\n words = line.split(\" \")\n states = []\n for word in words:\n if word in seg_stop_words:\n continue\n states.extend(getTags(word))\n # resume train\n self.do_train(observes, states)","title":""},{"docid":"adbb47c59a657a9206407f59db7fd226","score":"0.5560937","text":"def get_tokenized_sentences(file_name: str) -> Iterator[str]:\n # with open(file_name) as file_handle:\n # for sentence in file_handle.read().splitlines():\n # tokenized_sentence = sentence.split(',')\n # yield tokenized_sentence\n\n for sent in file_name:\n tokenized_sentence = sent.split(',')\n yield tokenized_sentence","title":""},{"docid":"5d8920b34aec2ba22126d11e190fc99e","score":"0.5560473","text":"def preprocess(sentences):\n \n cleaned_sentences = []\n sent_length = 0\n print('Preprocessing...')\n for sentence in sentences:\n sentence = word_tokenize(sentence) # Preprocessing step: Tokenize\n \n cleaned_sentences.append(sentence)\n print('Done')\n return cleaned_sentences","title":""},{"docid":"12637c3c219b6ab8fe7daf59b5addcfb","score":"0.5559983","text":"def sentences(self):\n ...","title":""},{"docid":"d9ecae7fcdffa8feca0f70de05ade26c","score":"0.55547184","text":"def sents(self):\n sents = []\n for i in range(len(self.parser_output.sentence)): \n sent_tokens = [elem.word for elem in self.parser_output.sentence[i].token]\n sents.append(sent_tokens)\n return sents","title":""},{"docid":"119452ae5e2df458aab47373b381257a","score":"0.55545","text":"def train(self, corpus): \n\n for sentence in corpus.corpus: # iterate over sentences in the corpus\n for datum_data in sentence.data: # iterate over datums in the sentence\n token = datum_data.word # get the word\n # for each time increase a token by one\n # every time you see a token increase number of total tokens\n self.unigram_language_model[token] = self.unigram_language_model[token]+1 \n self.total_tokens +=1\n # print(\"===========================================\")\n # print(self.unigram_language_model[token])\n # print(self.unigram_language_model[token])\n # print(self.total_tokens[token])","title":""},{"docid":"07b2cf6308ea681bfb29f6c9f0a77d1d","score":"0.5550696","text":"def tokenize(self):","title":""},{"docid":"c71242efe01a01f135591ff8df533df0","score":"0.5545742","text":"def tokenize(self, text):\n if isinstance(text[0], str):\n return [self.tokenizer.tokenize(x) for x in tqdm(text)]\n else:\n return [\n [self.tokenizer.tokenize(x) for x in sentences]\n for sentences in tqdm(text)\n ]","title":""},{"docid":"99ff9e4ffc2ed58710e99d0baec73e0e","score":"0.5538299","text":"def load_sentences(f_path):\n for line in open(f_path, 'rb'):\n l = read_ptsv(line)\n yield SentenceInput._make(l + [range(len(l[3]))])","title":""},{"docid":"f7312a636cc2078d04cc5c5f35e16a91","score":"0.5532633","text":"def load_input() -> t.List[t.List[str]]:\n with open('input.txt') as f:\n return [[c for c in line.strip(\"\\n\").split(\"\\n\")] for line in f.read().split(\"\\n\\n\")]","title":""},{"docid":"db4b9ac10acd8c4401888b8b12de6c3f","score":"0.5524474","text":"def get_data_from_sentence(self, sentence):\n if len(sentence) < self.length_train + 1:\n return None, None\n\n data = []\n target = []\n\n index = 0\n while self.length_train + index < len(sentence):\n data_tmp = sentence[index:index + self.length_train]\n target_tmp = sentence[index + self.length_train]\n\n data.append(data_tmp)\n target.append(target_tmp)\n\n index += 1\n\n return data, target","title":""},{"docid":"c93452acd898501b43d8aa0464464d09","score":"0.55200404","text":"def lstm_tokenize(sentences: list) -> list:\n # Attention! `[PAD]` tokens are being considered out of vocabulary words here\n return AMI.pad_sentences([sent.split(' ') for sent in sentences], 'oov')","title":""},{"docid":"ce262173f7248cda059789f205968cec","score":"0.5519581","text":"def read_dataset(dataset, tier, vocab):\n\n # depends on qa_data.py constants\n context_data = []\n query_data = []\n question_uuid_data = []\n\n for articles_id in tqdm(range(len(dataset['data'])), desc=\"Preprocessing {}\".format(tier)):\n article_paragraphs = dataset['data'][articles_id]['paragraphs']\n for pid in range(len(article_paragraphs)):\n context = article_paragraphs[pid]['context']\n # The following replacements are suggested in the paper\n # BidAF (Seo et al., 2016)\n context = context.replace(\"''\", '\" ')\n context = context.replace(\"``\", '\" ')\n\n context_tokens = tokenize(context)\n\n qas = article_paragraphs[pid]['qas']\n for qid in range(len(qas)):\n question = qas[qid]['question']\n question_tokens = tokenize(question)\n question_uuid = qas[qid]['id']\n\n context_ids = [str(vocab.get(w, qa_data.UNK_ID)) for w in context_tokens]\n qustion_ids = [str(vocab.get(w, qa_data.UNK_ID)) for w in question_tokens]\n\n context_data.append(' '.join(context_ids))\n query_data.append(' '.join(qustion_ids))\n question_uuid_data.append(question_uuid)\n\n return context_data, query_data, question_uuid_data","title":""},{"docid":"e9fe834a137fa7dc5a097aa3bf882f52","score":"0.55101264","text":"def load_sentences(self):\n if self.print_only:\n infile = 'data/sentences_clean.txt'\n with open(infile) as infile:\n lines = infile.readlines()\n sentences = [l.lower().strip() for l in lines]\n else:\n infile = resource_filename('typer_game', 'data/audio_lookup_subset.txt')\n sentences = pickle.load(open(infile, 'rb'))\n return sentences","title":""},{"docid":"deb6c34a1494116308e8f3573675a173","score":"0.5509206","text":"def input_fn(mode, sentences, labels, params):\n # Load all the dataset in memory for shuffling is training\n is_training = (mode == 'train')\n buffer_size = params.buffer_size if is_training else 1\n\n # Zip the sentence and the labels together\n dataset = tf.data.Dataset.zip((sentences, labels))\n\n # Create batches and pad the sentences of different length\n padded_shapes = ((tf.TensorShape([None]), # sentence of unknown size\n tf.TensorShape([])), # size(words)\n (tf.TensorShape([None]), # labels of unknown size\n tf.TensorShape([]))) # size(tags)\n\n padding_values = ((params.id_pad_word, # sentence padded on the right with id_pad_word\n 0), # size(words) -- unused\n (params.id_pad_tag, # labels padded on the right with id_pad_tag\n 0)) # size(tags) -- unused\n\n\n dataset = (dataset\n .shuffle(buffer_size=buffer_size)\n .padded_batch(params.batch_size, padded_shapes=padded_shapes, padding_values=padding_values)\n .prefetch(1) # make sure you always have one batch ready to serve\n )\n\n # Create initializable iterator from this dataset so that we can reset at each epoch\n iterator = dataset.make_initializable_iterator()\n\n # Query the output of the iterator for input to the model\n ((sentence, sentence_lengths), (labels, _)) = iterator.get_next()\n init_op = iterator.initializer\n\n # Build and return a dictionnary containing the nodes / ops\n inputs = {\n 'sentence': sentence,\n 'labels': labels,\n 'sentence_lengths': sentence_lengths,\n 'iterator_init_op': init_op\n }\n\n return inputs","title":""},{"docid":"8fe75a47ee7d3dbfacb1a98e79ac6073","score":"0.55052745","text":"def __init__(self, allsentences, orders, doc_len, ewords, elocs, slocs,blocs=None, dataset=None,\n device=None):\n if data is not None:\n setattr(self, 'doc_len', doc_len)\n setattr(self, 'elocs', elocs)\n setattr(self, 'slocs', slocs)\n setattr(self, 'blocs', blocs)\n\n self.batch_size = len(doc_len)\n self.dataset = dataset\n self.fields = dataset.fields.keys() # copy field names\n self.input_fields = [k for k, v in dataset.fields.items() if\n v is not None and not v.is_target]\n self.target_fields = [k for k, v in dataset.fields.items() if\n v is not None and v.is_target]\n\n '''\n maxdoclen=max(doc_len)\n for i in range(self.batch_size):\n batch_sent = []\n for j in range(maxdoclen):\n batch_sent.append(' '.join(allsentences[i*maxdoclen+j]))\n bert_tok.append(tokenizer(batch_sent,return_tensors='pt',padding=True).to('cuda'))\n '''\n\n setattr(self, 'order', dataset.fields['order'].process(orders, device=device))\n setattr(self, 'doc', dataset.fields['doc'].process(allsentences, device=device))\n\n allsentences = [' '.join(sent) for sent in allsentences]\n #bert_tok = tokenizer(allsentences, return_tensors='pt', padding=True).to('cuda')\n bert_tok = []\n setattr(self, 'e_words', dataset.fields['doc'].process(ewords, device=device))\n setattr(self, 'token', bert_tok)\n # setattr(self, 'docwords', dataset.fields['doc'].process(doc_words, device=device))\n # setattr(self, 'graph', dataset.fields['e2e'].process_graph(e2ebatch, e2sbatch, orders,\n # doc_sent_word_len, device=device))\n # setattr(self, 'alllen', doc_sent_word_len)\n\n # for (name, field) in dataset.fields.items():\n # if field is not None:\n # batch = [getattr(x, name) for x in data]\n # setattr(self, name, field.process(batch, device=device))","title":""},{"docid":"e0805a4cda7d7c0d50cf93234f312af5","score":"0.5500408","text":"def _add_sentence_tokens(self, data: pd.DataFrame) -> pd.DataFrame:\n tokenizer = nltk.data.load(\"tokenizers/punkt/english.pickle\")\n data[_Column.TOKENS.name] = data[_Column.MESSAGE.name].apply(tokenizer.tokenize)\n return data","title":""},{"docid":"2df389f01aa0aac876ddf79f6095355a","score":"0.54996413","text":"def extract_questions():\n df1 = pd.read_csv(\"./data/{}.csv\".format(params.train), keep_default_na=False)\n df2 = pd.read_csv(\"./data/{}.csv\".format(params.test), keep_default_na=False)\n\n for dataset in [df1, df2]:\n for i, row in dataset.iterrows():\n if i != 0 and i % 100000 == 0:\n print(\"example tokenization\", row['question1'], tokenize_sentence(row['question1']))\n if i != 0 and i % 10000 == 0:\n logging.info(\"read {0} sentences\".format(i))\n\n if row['question1']:\n yield tokenize_sentence(row['question1'])\n if row['question2']:\n yield tokenize_sentence(row['question2'])","title":""},{"docid":"4924d5ba91fd2086c179d2560ee9703a","score":"0.54834133","text":"def _tokenize(self, sentence: str) -> (torch.Tensor, list):\n indexed_tokens = self.tokenizer.encode(sentence)\n tokens_tensor = torch.tensor([indexed_tokens])\n tokens_tensor = tokens_tensor.to(self.device)\n return tokens_tensor, indexed_tokens","title":""},{"docid":"8f5448d4e48f10c32ae126ddc873e449","score":"0.54780805","text":"def processData(self):\n sentences = []\n # filter item behave less than self.min_count\n support = self.data.groupby('item_id').size()\n self.data = self.data[np.in1d(self.data['item_id'], support[support >= self.min_count].index)]\n vocab = list(map(str, self.data['item_id'].unique().tolist()))\n self.data = self.data.sort_values(by=['user_id', 'dates'])\n groups = self.data.groupby('user_id')\n for name, group in groups:\n seq = groups.get_group(name)['item_id'].tolist()\n seq = list(map(str, seq))\n sentences.append(seq)\n return sentences, vocab","title":""},{"docid":"f672cfb43887b8fb98a8fcd1869f3984","score":"0.5476506","text":"def __init__(self, data_type=ONE_HOT_AVERAGE, use_sub_phrases=True,\n\t dataset_path=\"stanfordSentimentTreebank\", batch_size=50,\n\t embedding_dim=None):\n\n\t\t# load the dataset\n\t\tself.sentiment_dataset = data_loader. \\\n\t\t\tSentimentTreeBank(dataset_path, split_words=True)\n\t\t# map data splits to sentences lists\n\t\tself.sentences = {}\n\t\tif use_sub_phrases:\n\t\t\tself.sentences[\n\t\t\t\tTRAIN] = self.sentiment_dataset.get_train_set_phrases()\n\t\telse:\n\t\t\tself.sentences[TRAIN] = self.sentiment_dataset.get_train_set()\n\n\t\tself.sentences[VAL] = self.sentiment_dataset.get_validation_set()\n\t\tself.sentences[TEST] = self.sentiment_dataset.get_test_set()\n\n\t\t# map data splits to sentence input preperation functions\n\t\twords_list = list(self.sentiment_dataset.get_word_counts().keys())\n\t\tif data_type == ONE_HOT_AVERAGE:\n\t\t\tself.sent_func = average_one_hots\n\t\t\tself.sent_func_kwargs = {\n\t\t\t\t\"word_to_ind\": get_word_to_ind(words_list)}\n\t\telif data_type == W2V_SEQUENCE:\n\t\t\tself.sent_func = sentence_to_embedding\n\n\t\t\tself.sent_func_kwargs = {\"seq_len\": SEQ_LEN,\n\t\t\t \"word_to_vec\": create_or_load_slim_w2v(\n\t\t\t\t words_list),\n\t\t\t \"embedding_dim\": embedding_dim\n\t\t\t }\n\t\telif data_type == W2V_AVERAGE:\n\t\t\tself.sent_func = get_w2v_average\n\t\t\twords_list = list(self.sentiment_dataset.get_word_counts().keys())\n\t\t\tself.sent_func_kwargs = {\n\t\t\t\t\"word_to_vec\": create_or_load_slim_w2v(words_list),\n\t\t\t\t\"embedding_dim\": embedding_dim\n\t\t\t}\n\t\telse:\n\t\t\traise ValueError(\"invalid data_type: {}\".format(data_type))\n\t\t# map data splits to torch datasets and iterators\n\t\tself.torch_datasets = {\n\t\t\tk: OnlineDataset(sentences, self.sent_func, self.sent_func_kwargs)\n\t\t\tfor k, sentences in tqdm(self.sentences.items())}\n\t\tself.torch_iterators = {k: DataLoader(dataset, batch_size=batch_size, shuffle=k == TRAIN)\n\t\t for k, dataset in self.torch_datasets.items()\n\t\t }","title":""},{"docid":"a1302cf955b533825aae0beb4bb954ba","score":"0.5469566","text":"def tokenize_sentences(sentences):\n\n # Initialize the list of lists of tokenized sentences\n tokenized_sentences = []\n\n # Go through each sentence\n for sentence in sentences:\n # Convert to lowercase letters\n sentence = sentence.lower()\n\n # Convert into a list of words\n tokenized = nltk.word_tokenize(sentence)\n\n # append the list of words to the list of lists\n tokenized_sentences.append(tokenized)\n\n return tokenized_sentences","title":""},{"docid":"638829de3b414b7d107d8cec9ea30bd2","score":"0.5465221","text":"def split(self):\n documents = []\n for sentence in self.sentences:\n document = Document()\n document.add_sentence(sentence)\n documents.append(document)\n\n return documents","title":""},{"docid":"8fb62ad5d015c35ee1829c6d683602f1","score":"0.5464618","text":"def __call__(self, text):\n\n doc = self.model(text)\n\n tokens = []\n for token in doc:\n if token.is_stop:\n continue\n tokens.append(token.lemma_)\n\n return tokens","title":""},{"docid":"3a2b1a89ae5507a23c86330350bd1c9a","score":"0.5464104","text":"def make_data(sentences):\n data = []\n for sentence in sentences:\n words = [tup[0] for tup in sentence]\n labels = [tup[1] for tup in sentence]\n for idx, word in enumerate(words):\n nerfeat = NerFeatures()\n features = nerfeat.word_features(idx,words)\n data.append((features, labels[idx]))\n return data","title":""},{"docid":"dbcad2bd981137a2101bdb38bc22c597","score":"0.5454016","text":"def process_thread(self, sentence_readings):\n ...","title":""},{"docid":"3eb1842b5e5864bfc7454412d9bf7508","score":"0.54515433","text":"def read_corpus(file_path, source):\n data = []\n for line in open(file_path):\n sent = nltk.word_tokenize(line)\n # only append <s> and </s> to the target sentence\n if source == 'tgt':\n sent = ['<s>'] + sent + ['</s>']\n data.append(sent)\n\n return data","title":""},{"docid":"6ec555c859975d90a1b112f58aa09743","score":"0.5449063","text":"def read_all(self):\n self.train_txt = self.read_abstracts(self._train_text_path)\n self.dev_txt = self.read_abstracts(self._dev_text_path)\n self.eval_txt = self.read_abstracts(self._eval_text_path)\n print(f\"Finished reading abstracts.\\n# of sentences read: Train: {len(self.train_txt)}, Dev: {len(self.dev_txt)}, Eval: {len(self.eval_txt)}\")\n \n self.train_anno = self.read_annotations(self._train_label_path)\n self.dev_anno = self.read_annotations(self._dev_label_path)\n self.eval_anno = self.read_annotations(self._eval_label_path)\n print(f\"Finished reading annotations\")\n\n self.punkt_tokenizer = punkt.PunktSentenceTokenizer(self.all_texts)","title":""},{"docid":"8b90f266d7d9b4b17d4c7b519c015756","score":"0.544312","text":"def _read_tsv(self, input_file, batch_size=16, quotechar=None):\n with open(input_file, 'r', encoding='utf8') as f:\n reader = csv_reader(f)\n #headers = next(reader)\n headers = 'query\\ttitle\\tpara\\tlabel'.split('\\t')\n text_indices = [\n index for index, h in enumerate(headers) if h != \"label\"\n ]\n Example = namedtuple('Example', headers)\n\n examples = []\n for line in reader:\n for index, text in enumerate(line):\n if index in text_indices:\n if self.for_cn:\n line[index] = text.replace(' ', '')\n else:\n line[index] = text\n example = Example(*line)\n examples.append(example)\n while len(examples) % batch_size != 0:\n examples.append(example)\n return examples","title":""},{"docid":"2797a5a4912cf909c0194808d3cef500","score":"0.5432627","text":"def corpusReader(corpus):\n\n # Read the corpus and create two lists with all the data\n with open(corpus, 'r') as corp:\n sentences = [] # All the sentences\n correctTags = [] # Correct tags\n tagData = [] # Sentences with word and tags\n\n # Initiate temporary data holders\n words, tags, oneTag = [], [], []\n\n # Read corpus\n for line in corp:\n # Sentences are marked by newlines\n if line == '\\n':\n # Add correct tags to list\n correctTags.append(tags)\n # Add words to list of sentence\n sentences.append(words)\n # Add tagdata\n tagData.append(oneTag)\n\n # Reset words and tags\n words, tags, oneTag = [], [], []\n else:\n # Add correct tags and words\n words.append(line.split()[0])\n tags.append(line.split()[1])\n bothTags = (line.split()[0], line.split()[1])\n oneTag.append(bothTags)\n print(len(sentences))\n return sentences, correctTags, tagData","title":""},{"docid":"b6f4f39cca75da3767b18f317304e9f5","score":"0.5430335","text":"def tokenize(self, sequence: str) -> list:\n raise NotImplementedError","title":""},{"docid":"e487a88c97a413c21a7b05c10b9ff688","score":"0.5429843","text":"def parse_input(self, input):\n nlp = self.load_model()\n doc = nlp(input.title())\n nlp_output = defaultdict(list)\n for token in doc:\n nlp_output[token.i] = [token.text, token.pos_, token.tag_, token.dep_, token.head.text]\n return nlp_output","title":""},{"docid":"66049748d71788ac72cfc19b29caed54","score":"0.54291993","text":"def _read_data(cls, input_file):\n with codecs.open(input_file, 'r', encoding='utf-8') as f:\n lines = []\n words = []\n labels = []\n for line in f:\n contends = line.strip()\n tokens = contends.split('\\t')\n if len(tokens) == 2:\n word = line.strip().split('\\t')[0]\n label = line.strip().split('\\t')[-1]\n else:\n if len(contends) == 0:\n l = ' '.join([label for label in labels if len(label) > 0])\n w = ' '.join([word for word in words if len(word) > 0])\n lines.append([l, w])\n words = []\n labels = []\n continue\n if contends.startswith(\"-DOCSTART-\"):\n words.append('')\n continue\n words.append(word)\n labels.append(label)\n return lines","title":""},{"docid":"2e213135c1247f661b1f88723f6fba7d","score":"0.54265004","text":"def tokenize_batch(self, seqs: List[str]) -> List[List[str]]:\n return [self.tokenize(s) for s in seqs]","title":""},{"docid":"998d8927b40ec4af07f2c0ddb6e4b9a4","score":"0.54194725","text":"def tokenize( self, path ):\r\n assert os.path.exists( path )\r\n # Add words to the dictionary\r\n max_len = 0\r\n with open( path, 'r' ) as f:\r\n Reader = csv.reader( f, delimiter=',', quoting=csv.QUOTE_MINIMAL )\r\n count = 0\r\n for record in Reader:\r\n count += 1\r\n review = record[ 0 ]#.decode( 'utf-8' )\r\n # words = [ word for sent in sent_tokenize( review ) for word in word_tokenize( sent ) ]\r\n words = review.split()\r\n if len( words ) > max_len:\r\n max_len = len( words )\r\n for word in words:\r\n self.dictionary.add_word( word )\r\n\r\n # Tokenize file content\r\n with open( path, 'r' ) as f:\r\n Reader = csv.reader( f, delimiter=',', quoting=csv.QUOTE_MINIMAL )\r\n data = torch.LongTensor( count, max_len ).fill_( 0 )\r\n lengths = []\r\n labels = torch.LongTensor( count ).fill_( 0 )\r\n\r\n for idx, record in enumerate( Reader ):\r\n labels[ idx ] = int( record[ 1 ] )\r\n review = record[ 0 ]#.decode( 'utf-8' )\r\n # words = [ word for sent in sent_tokenize( review ) for word in word_tokenize( sent ) ]\r\n words = review.split()\r\n lengths.append( len( words ) )\r\n \r\n for i, word in enumerate( words ):\r\n data[ idx, i ] = self.dictionary.word2idx[word]\r\n \r\n return data, lengths, labels","title":""},{"docid":"8e8f913dfd8895f6d55f5c6ef64e82a1","score":"0.54156566","text":"def tokenize_dataset(X_train_path=X_train_path):\n\tword2index()\n\twith open(X_train_path, 'r', encoding='utf8') as f:\n\t original_lines = f.readlines()\n\t original_lines = [line.replace(\"\\u3000\",\"\")for line in original_lines]\n\t for line in original_lines:\n\t \twords = line.split()\n\t \tfor word in words:\n\t \t\tchar = []\n\t \t\tfor c in word:\n\t \t\t\ttry:\n\t \t\t\t\tchar.append(unigram_word_to_id[c])\n\t \t\t\texcept KeyError:\n\t \t\t\t\tchar.append(unigram_word_to_id[\"<UNK>\"])\n\t \tX_train_uni.append(char)\n\treturn X_train_uni","title":""},{"docid":"a2ec2b13d0b799a0592c0a934efd3b6f","score":"0.541384","text":"def parse(self, sent):\r\n self.toks = self.nlp(unicode(sent, errors = 'ignore'))\r\n self.idx_to_word_index = self.get_idx_to_word_index()","title":""},{"docid":"1babe2081aab7f53bf968a24d6a24be1","score":"0.5413271","text":"def tokenize_data(self, data, lower=True):\n data = copy.deepcopy(data)\n for sample in tqdm(data['data']):\n for paragraph in sample['paragraphs']:\n paragraph['context_raw'] = paragraph['context']\n if lower:\n paragraph['context'] = paragraph['context'].lower()\n\n # tokenize context\n paragraph['context'] = self.tokenize(paragraph['context'])\n paragraph['context_tokenized'] = [tok.text for tok in paragraph['context']]\n\n # tokenize question\n for qa in paragraph['qas']:\n qa['orig_answer']['raw'] = qa['orig_answer']['text']\n if lower:\n qa['question'] = qa['question'].lower()\n qa['orig_answer']['text'] = \\\n qa['orig_answer']['text'].lower()\n\n qa['question'] = self.tokenize(qa['question'])\n qa['question_tokenized'] = [tok.text for tok in qa['question']]\n qa['orig_answer']['text'] = \\\n self.tokenize(qa['orig_answer']['text'])\n\n # tokenize other answers\n if 'answers' in qa:\n for answer in qa['answers']:\n answer['raw'] = answer['text']\n if lower:\n answer['text'] = answer['text'].lower()\n\n answer['text'] = self.tokenize(answer['text'])\n\n return data","title":""},{"docid":"6e2e62aa5a568ce21344804d0e6c0983","score":"0.5412524","text":"def create_words_list_from_file(spark_context, input_file_endpoint):\n return spark_context.textFile(input_file_endpoint) \\\n .flatMap(lambda line: preprocess(line))","title":""},{"docid":"8b0b3066c99e950897af39e31948d0fb","score":"0.5411329","text":"def read_data(self):\n print('Reading file ...')\n self.lines = [line.strip().lower().split() for line in open(self.file_path, 'r', encoding='utf-8')]\n self.lines = [line for line in self.lines if len(line) < self.max_length]\n print('Building vocabulary ...')\n self.word2id, self.id2word = self.construct_vocab(self.lines, self.vocab_size)","title":""},{"docid":"17083bc4992097bcbe07583522809a70","score":"0.54109085","text":"def _read_data(self, input_file):\n with codecs.open(input_file, 'r', encoding='utf-8') as f:\n lines = []\n words = []\n labels = []\n for line in f:\n contends = line.strip()\n tokens = contends.split('\\t')\n if len(tokens) == 2:\n words.append(tokens[0])\n labels.append(tokens[1])\n else:\n if len(contends) == 0:\n l = ' '.join([label for label in labels if len(label) > 0])\n w = ' '.join([word for word in words if len(word) > 0])\n lines.append([l, w])\n words = []\n labels = []\n continue\n if contends.startswith(\"-DOCSTART-\"):\n words.append('')\n continue\n return lines","title":""},{"docid":"a47992c7b003e9c82d5077ff4531ec12","score":"0.54061186","text":"def lemmatized_sentence_corpus(self, contents):\n sentents = []\n \n for content in self.nlp.pipe(contents,batch_size=500, n_threads=8):\n \n for sent in content.sents:\n #sentents.append(u' '.join([token.lemma_ for token in sent\n # if not punct_space(token)]))\n #sentents.append([token.lemma_ for token in sent\n # if not punct_space(token)])\n tokens = []\n for token in sent:\n if self.punct_space(token):\n continue\n \n #if token.lemma_ == '-PRON-':\n # token.lemma_ = token.lower_\n tokens.append(token.lemma_)\n \n sentents.append(tokens)\n \n return sentents","title":""},{"docid":"c2939a4137b3b9b01f57dc74a1c00218","score":"0.54060394","text":"def PreprocessSentenceLevel(self,document):\n sentences=document.splitlines()\n \n #lower case transformation \n sentences = [sent.lower() for sent in sentences]\n \n #tokenize\n toks = [self.wpt.tokenize(sent) for sent in sentences]\n return toks","title":""},{"docid":"e1c3f73cfa252a10278717fbcc7c0149","score":"0.54059327","text":"def read_dataset(self, input_file, *args, **kwargs):\n\n print(\"Reading input file\")\n\n dataType = kwargs.get(\"dataType\",\"common\");\n\n data = []\n if(dataType == \"common\"):\n\n lines = [line.strip() for line in open(input_file)]\n for line in lines:\n data.append(line.split('\\t'))\n\n\n\n return data","title":""},{"docid":"30f2a6829180110cdc25fc6e96823ef1","score":"0.54042745","text":"def _FillInputQueue(self):\n start_id = self._vocab.WordToId(data.SENTENCE_START)\n end_id = self._vocab.WordToId(data.SENTENCE_END)\n pad_id = self._vocab.WordToId(data.PAD_TOKEN)\n input_gen = self._TextGenerator(data.ExampleGen(self._data_path))\n while True:\n (article, abstract) = input_gen.next()\n article_sentences = [sent.strip() for sent in\n data.ToSentences(article, include_token=False)]\n abstract_sentences = [sent.strip() for sent in\n data.ToSentences(abstract, include_token=False)]\n\n enc_inputs = []\n # Use the <s> as the <GO> symbol for decoder inputs.\n dec_inputs = [start_id]\n\n # Convert first N sentences to word IDs, stripping existing <s> and </s>.\n for i in xrange(min(self._max_article_sentences,\n len(article_sentences))):\n enc_inputs += data.GetWordIds(article_sentences[i], self._vocab)\n for i in xrange(min(self._max_abstract_sentences,\n len(abstract_sentences))):\n dec_inputs += data.GetWordIds(abstract_sentences[i], self._vocab)\n\n # Filter out too-short input\n if (len(enc_inputs) < self._hps.min_input_len or\n len(dec_inputs) < self._hps.min_input_len):\n tf.logging.warning('Drop an example - too short.\\nenc:%d\\ndec:%d',\n len(enc_inputs), len(dec_inputs))\n continue\n\n # If we're not truncating input, throw out too-long input\n if not self._truncate_input:\n if (len(enc_inputs) > self._hps.enc_timesteps or\n len(dec_inputs) > self._hps.dec_timesteps):\n tf.logging.warning('Drop an example - too long.\\nenc:%d\\ndec:%d',\n len(enc_inputs), len(dec_inputs))\n continue\n # If we are truncating input, do so if necessary\n else:\n if len(enc_inputs) > self._hps.enc_timesteps:\n enc_inputs = enc_inputs[:self._hps.enc_timesteps]\n if len(dec_inputs) > self._hps.dec_timesteps:\n dec_inputs = dec_inputs[:self._hps.dec_timesteps]\n\n # targets is dec_inputs without <s> at beginning, plus </s> at end\n targets = dec_inputs[1:]\n targets.append(end_id)\n\n # Now len(enc_inputs) should be <= enc_timesteps, and\n # len(targets) = len(dec_inputs) should be <= dec_timesteps\n\n enc_input_len = len(enc_inputs)\n dec_output_len = len(targets)\n\n # Pad if necessary\n while len(enc_inputs) < self._hps.enc_timesteps:\n enc_inputs.append(pad_id)\n while len(dec_inputs) < self._hps.dec_timesteps:\n dec_inputs.append(end_id)\n while len(targets) < self._hps.dec_timesteps:\n targets.append(end_id)\n\n element = ModelInput(enc_inputs, dec_inputs, targets, enc_input_len,\n dec_output_len, ' '.join(article_sentences),\n ' '.join(abstract_sentences))\n self._input_queue.put(element)","title":""},{"docid":"7fe36f824177faf317529fa21b89d146","score":"0.5403482","text":"def __init__(self, data_type=ONEHOT_AVERAGE, use_sub_phrases=True, dataset_path=\"stanfordSentimentTreebank\", batch_size=50,\n embedding_dim=None):\n\n # load the dataset\n self.sentiment_dataset = data_loader.SentimentTreeBank(dataset_path, split_words=True)\n # map data splits to sentences lists\n self.sentences = {}\n if use_sub_phrases:\n self.sentences[TRAIN] = self.sentiment_dataset.get_train_set_phrases()\n else:\n self.sentences[TRAIN] = self.sentiment_dataset.get_train_set()\n\n self.sentences[VAL] = self.sentiment_dataset.get_validation_set()\n self.sentences[TEST] = self.sentiment_dataset.get_test_set()\n\n # map data splits to sentence input preperation functions\n words_list = list(self.sentiment_dataset.get_word_counts().keys())\n if data_type == ONEHOT_AVERAGE:\n self.sent_func = average_one_hots\n self.sent_func_kwargs = {\"word_to_ind\": get_word_to_ind(words_list)}\n elif data_type == W2V_SEQUENCE:\n self.sent_func = sentence_to_embedding\n\n self.sent_func_kwargs = {\"seq_len\": SEQ_LEN,\n \"word_to_vec\": create_or_load_slim_w2v(words_list),\n \"embedding_dim\": embedding_dim\n }\n elif data_type == W2V_AVERAGE:\n self.sent_func = get_w2v_average\n words_list = list(self.sentiment_dataset.get_word_counts().keys())\n self.sent_func_kwargs = {\"word_to_vec\": create_or_load_slim_w2v(words_list),\n \"embedding_dim\": embedding_dim\n }\n else:\n raise ValueError(\"invalid data_type: {}\".format(data_type))\n # map data splits to torch datasets and iterators\n self.torch_datasets = {k: OnlineDataset(sentences, self.sent_func, self.sent_func_kwargs) for\n k, sentences in self.sentences.items()}\n self.torch_iterators = {k: DataLoader(dataset, batch_size=batch_size, shuffle=k == TRAIN)\n for k, dataset in self.torch_datasets.items()}","title":""},{"docid":"ee0f33c150c61829c7cb8c594be9d3d5","score":"0.5400625","text":"def _get_token_set(self, datasets):\n tokens = []\n for ds in (datasets):\n for doc in ds.documents:\n for sentence in doc.utterances:\n for token in sentence:\n tokens.append(token)\n return set(tokens)","title":""},{"docid":"0c26f8c97540a0f8bbe7bb251fc2bbc9","score":"0.53943","text":"def tokenize_with_sentencepiece(in_file, out_file, sp_model_filename):\n sp = sentencepiece.SentencePieceProcessor()\n sp.load(sp_model_filename)\n packer = msgpack.Packer()\n for line in in_file:\n ids = sp.encode_as_ids(line.rstrip())\n out_file.write(packer.pack(ids))","title":""},{"docid":"2f52266536e63aeb3370331d83451a5b","score":"0.5393468","text":"def get_sentences(input_file):\n \n with open(input_file) as f:\n \treturn [line.rstrip('\\n') for line in f if line.rstrip('\\n')]","title":""},{"docid":"0bf5f78cee41792f6a15f23fc3f9f674","score":"0.5392137","text":"def tokenize_dataset(self, data):\n Portuguese = []\n English = []\n for pt, en in data:\n Portuguese.append(pt.numpy())\n English.append(en.numpy())\n tok_pt = tfds.deprecated.text.SubwordTextEncoder.build_from_corpus(\n Portuguese, target_vocab_size=2**15)\n tok_en = tfds.deprecated.text.SubwordTextEncoder.build_from_corpus(\n English, target_vocab_size=2**15)\n return tok_pt, tok_en","title":""},{"docid":"1a79822a6e1ae19f0720721d0636cd73","score":"0.53901577","text":"def read_data(self):\n print('Reading file ...')\n self.lines = [line.strip().lower().split() for line in open(self.file_path, 'r', encoding='utf-8')]\n print('Building vocabulary ...')\n self.word2id, self.id2word = self.construct_vocab(self.lines, self.vocab_size)","title":""},{"docid":"50ee13ab48526a661f5eef41cd45b5c5","score":"0.53842103","text":"def read_data(data_path):\n sentences = []\n with open(data_path, \"r\") as f:\n for line in f:\n sentences.append(line.strip(\"\\n\").split(\" \"))\n\n return sentences","title":""},{"docid":"f15f1d259b752fdde55cae61212c6fed","score":"0.53826135","text":"def get_data(train_file, test_file):\n # Reading and organizing the training data\n train_reversed_sentences = []\n train_sentences = read_data(train_file)\n for x in train_sentences:\n x2 = x.copy()\n\n # Reverse the output sentences so a rhyme scheme could be added if desired\n x2.reverse()\n train_reversed_sentences.append(x2)\n\n train_inputs = train_sentences[:-1]\n train_labels = train_reversed_sentences[1:]\n\n train_inputs_padded, train_labels_padded = pad_corpus(train_inputs, train_labels)\n dict, pad = build_vocab(train_inputs_padded)\n dict[START_TOKEN] = len(dict)\n\n train_inputs = convert_to_id(dict, train_inputs_padded)\n train_labels = convert_to_id(dict, train_labels_padded)\n\n\n # Reading and organizing the testing data\n test_reversed_sentences = []\n test_sentences = read_data(test_file)\n for x in test_sentences:\n x2 = x.copy()\n x2.reverse()\n test_reversed_sentences.append(x2)\n\n test_inputs = test_sentences[:-1]\n test_labels = test_reversed_sentences[1:]\n\n test_inputs_padded, test_labels_padded = pad_corpus(test_inputs, test_labels)\n\n test_inputs = convert_to_id(dict, test_inputs_padded)\n test_labels = convert_to_id(dict, test_labels_padded)\n\n return train_inputs, train_labels, test_inputs, test_labels, pad, dict","title":""},{"docid":"2ad9cb5eb9a6d08fb5c9bc110deca07a","score":"0.53796786","text":"def tokenize_sents(self, sentences, keep_token_positions: bool = ...):\n ...","title":""}],"string":"[\n {\n \"docid\": \"588216166c5e9382d18abc8829484081\",\n \"score\": \"0.6711342\",\n \"text\": \"def tokenize_sentences(self):\\n data = []\\n labels = []\\n\\n for sentence in self.sentences:\\n data_tmp, label_tmp = self.get_data_from_sentence(sentence)\\n\\n if data_tmp != None and label_tmp != None:\\n data.extend(data_tmp)\\n labels.extend(label_tmp)\\n\\n return data, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84780e1862d555c40f4e34abc15ff97e\",\n \"score\": \"0.65370023\",\n \"text\": \"def __read_sentences_data(self):\\n self.__sentence_files = os.listdir(self.__sentence_dir)\\n\\n data_lists = []\\n for file in self.__sentence_files:\\n with open(os.path.join(self.__sentence_dir, file), mode='r', encoding='utf8') as f:\\n sentence = f.read().replace('\\\\n', '')\\n data_lists.append(sentence)\\n self.__sentence_datas = data_lists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f1eac925bf3cc6da095377eed1a155a\",\n \"score\": \"0.6405178\",\n \"text\": \"def get_dataset(self, dataset):\\n tokenizer = get_tokenizer('basic_english')\\n words_in_dataset = []\\n\\n for idx, (label, line) in enumerate(dataset):\\n tokens = tokenizer(line)\\n words_in_dataset.append(tokens)\\n\\n return words_in_dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e6ca43685d048962e23e28de4bbc108\",\n \"score\": \"0.6393619\",\n \"text\": \"def get_tokenized_input(self):\\n return [\\n line.strip().split() for line in open(self.data_files[\\n self.current_split][self.input_varname()])\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bd72bbae34f8ab84e0a7443de37d568\",\n \"score\": \"0.6262996\",\n \"text\": \"def process_file(self) -> List:\\n\\n samples = []\\n\\n question_id = 0\\n with open(self.input_path, encoding=\\\"latin-1\\\") as input_file:\\n lines = input_file.readlines()\\n\\n current_context = {} # filled with all sentences belonging to one question\\n\\n for line in lines:\\n\\n if line.startswith(\\\"1 \\\"):\\n current_context = {} # if sentence counter is reset to 1, a new context begins\\n\\n key_content_split = line.split(\\\" \\\", 1)\\n sentence_key = key_content_split[0]\\n content = key_content_split[1]\\n\\n tab_split = content.split(\\\"\\\\t\\\")\\n\\n text = tab_split[0]\\n tokenized_text = self.word_tokenizer.tokenize(text)\\n\\n if len(tab_split) == 1: # lines with context sentences do not contain tabs\\n\\n # add sentence to sample context\\n current_context[int(sentence_key)] = tokenized_text\\n\\n else:\\n # Lines containing a question have an additional tab for supporting fact ids. A question line\\n # always denotes the end of one sample.\\n\\n question_length = len(tokenized_text) # count question tokens\\n question = \\\" \\\".join(tokenized_text)\\n\\n sup_facts = tab_split[2].split(\\\" \\\")\\n sup_facts = [int(s) for s in sup_facts] # store supporting fact ids\\n\\n targets = []\\n context = \\\"\\\"\\n current_token_pos = question_length\\n\\n sentence_keys = sorted(current_context) # sort sentences in current context per key\\n\\n # go through all sentences in current context\\n for key in sentence_keys:\\n sentence_tokens = current_context[key]\\n sentence_length = len(sentence_tokens)\\n\\n context += \\\" \\\".join(sentence_tokens) + \\\" \\\"\\n\\n sentence_span = [current_token_pos, current_token_pos + sentence_length]\\n\\n # if sentence belongs to supporting facts, set label to \\\"1\\\"\\n if key in sup_facts:\\n label = \\\"1\\\"\\n else:\\n label = \\\"0\\\"\\n\\n targets.append(self.create_target(question_length, sentence_span, label))\\n\\n current_token_pos = current_token_pos + sentence_length # increment token position\\n\\n entry = {\\\"info\\\": {\\\"doc_id\\\": self.DOC_ID,\\n \\\"q_id\\\": str(question_id)},\\n \\\"text\\\": question + \\\" \\\" + context.strip(),\\n \\\"targets\\\": targets}\\n\\n samples.append(entry)\\n question_id += 1\\n\\n return samples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"293d523da476939c0477b67faf62b0e7\",\n \"score\": \"0.6237957\",\n \"text\": \"def parse(self):\\n self.__read_sentences_data()\\n self.__parse_features()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"250a7184971ac7570921fc6d224ddba0\",\n \"score\": \"0.6231222\",\n \"text\": \"def __call__(self, input_data):\\n # ---- INPUT CHECKS ---- #\\n is_corpus = isinstance(input_data, Corpus)\\n if isinstance(input_data, str):\\n data = [input_data]\\n elif is_corpus:\\n data = input_data.documents\\n elif isinstance(input_data, list):\\n data = input_data\\n else:\\n raise ValueError(\\n 'Type \\\"{}\\\" not supported.'.format(type(input_data))\\n )\\n\\n # ---- TOKENIZE ---- #\\n tokens = []\\n for document in data:\\n tokens.append(self.tokenizer(document))\\n if self.callback:\\n self.callback()\\n\\n # ---- DETERMINE DF STOP WORDS ---- #\\n if self.use_df_sw: # DF parameters were specified.\\n self._get_corpus_specific_stopwords(tokens, self.lowercase)\\n\\n # ---- PRE-PROCESS DOCUMENTS ---- #\\n pp_tokens = []\\n stop_words = self.stop_words | self.df_stop_words\\n assert isinstance(stop_words, set)\\n for doc in tokens:\\n if self.lowercase: # Lowercase.\\n doc = [token.lower() for token in doc]\\n\\n if stop_words: # Remove stop words.\\n doc = [word for word in doc if word not in stop_words]\\n\\n if self.transformation is not None: # Transform.\\n doc = self.transformation(doc)\\n\\n pp_tokens.append(doc)\\n if self.callback:\\n self.callback()\\n\\n if is_corpus:\\n input_data.store_tokens(pp_tokens)\\n return input_data\\n return pp_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07c36f805c59d353028f3eb2149ad6a6\",\n \"score\": \"0.6069735\",\n \"text\": \"def _tokenize(self, sentence: str) -> (torch.Tensor, list):\\n inputs = self.tokenizer(sentence, return_tensors=\\\"pt\\\")\\n return inputs.to(self.device)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddae11b5f05370ba750231a5030922ae\",\n \"score\": \"0.6056712\",\n \"text\": \"def sentences( filestream , label_filter = None):\\n i = 0\\n sentence = []\\n for line in filestream:\\n line = line.rstrip()\\n if line:\\n sentence.append(Token(line))\\n elif sentence:\\n postprocess_tokens(sentence, label_filter)\\n yield sentence\\n sentence = []\\n if sentence:\\n postprocess_tokens(sentence, label_filter)\\n yield sentence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15308a0ce6ad498225da78f8999461b7\",\n \"score\": \"0.5999839\",\n \"text\": \"def tokens(self, string) -> list:\\n\\n from hazm import SentenceTokenizer, WordTokenizer\\n sent_tokenizer = SentenceTokenizer().tokenize\\n word_tokenizer = WordTokenizer().tokenize\\n string = self.normal_string(string)\\n sentences_list = sent_tokenizer(string)\\n _sents = []\\n for sent in sentences_list:\\n words = word_tokenizer(sent)\\n _sents.append(words)\\n sentences_list = _sents\\n return sentences_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7415f4f69be448b2306475db6442f0\",\n \"score\": \"0.5960486\",\n \"text\": \"def get_tokenized_data(data):\\n\\n # Get the sentences by splitting up the data\\n sentences = split_to_sentences(data)\\n logger.info(len(sentences))\\n # Get the list of lists of tokens by tokenizing the sentences\\n tokenized_sentences = tokenize_sentences(sentences)\\n\\n return tokenized_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c2433f2cdaab19969564094591a44cc\",\n \"score\": \"0.5942005\",\n \"text\": \"def tokenize_sentences(sentences):\\n \\n # Initialize the list of lists of tokenized sentences\\n tokenized_sentences = []\\n ### START CODE HERE (Replace instances of 'None' with your code) ###\\n \\n # Go through each sentence\\n for sentence in sentences:\\n \\n # Convert to lowercase letters\\n sentence = sentence.lower()\\n \\n # Convert into a list of words\\n tokenized = nltk.word_tokenize(sentence)\\n \\n # append the list of words to the list of lists\\n tokenized_sentences.append(tokenized)\\n \\n ### END CODE HERE ###\\n \\n return tokenized_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2519b77695e405ccf19a88913c8d44f\",\n \"score\": \"0.5925383\",\n \"text\": \"def _get_values(self, inputs):\\n tokenized = []\\n \\n for tweet in inputs[0]:\\n sentences = nltk.sent_tokenize(tweet)\\n tokenized_tweet = []\\n \\n for sentence in sentences:\\n words = nltk.word_tokenize(sentence)\\n tokenized_tweet += words\\n \\n tokenized.append(str(tokenized_tweet))\\n \\n return tokenized\",\n \"title\": \"\"\n },\n {\n \"docid\": \"554140e42fbeb46902e8263af6c41feb\",\n \"score\": \"0.59251755\",\n \"text\": \"def read(self):\\n\\n if self.path.endswith('.json'):\\n sentences = json.load(open(self.path, 'r'))\\n else:\\n sentences = []\\n with open(self.path, 'r', encoding='latin-1') as f:\\n for line in f:\\n print(line[:20])\\n # first strip away newline symbols and the like, then replace ' and \\\" with the empty\\n # string and get rid of possible remaining trailing spaces\\n line = line.strip().translate({ord(i): None for i in '\\\"\\\\'\\\\\\\\'}).strip(' ')\\n # lowercase and split at the white space (the corpus has ben previously tokenized)\\n sentences.append(line.lower().split(' '))\\n\\n return sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9090f357af581398bd0bab41530f45f8\",\n \"score\": \"0.5923357\",\n \"text\": \"def _read_data(cls, input_file, quotechar=None):\\n with tf.gfile.Open(input_file, \\\"r\\\") as f:\\n reader = csv.reader(f, delimiter=\\\"\\\\t\\\", quotechar=quotechar)\\n lines = []\\n words = []\\n labels = []\\n for line in reader:\\n if len(line) == 0:\\n l = ' '.join([label for label in labels if len(label) > 0])\\n w = ' '.join([word for word in words if len(word) > 0])\\n lines.append((w, l))\\n words = []\\n labels = []\\n else:\\n word = line[0].strip()\\n label = line[1].strip()\\n words.append(word)\\n labels.append(label)\\n\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20cab7a056e7f7268f4728056d8af9bc\",\n \"score\": \"0.5910446\",\n \"text\": \"def list_dataset():\\n # Accumlator lists\\n subtopics = []\\n source_sentences = []\\n target_sentences = []\\n golds = []\\n for topic in os.listdir(STE_PATH):\\n # Dont process non directories\\n if not os.path.isdir(os.path.join(STE_PATH, topic)):\\n continue\\n # Keep track of the current subtopic\\n # being processed within the csv file\\n current_subtopic = None\\n source_sentence = None\\n with open(os.path.join(STE_PATH, topic, 'results.csv'), 'r') as fp:\\n LOG.debug('Processing topic: %s', topic)\\n reader = csv.reader(fp)\\n # ignore header\\n next(reader)\\n for row in reader:\\n # ignore empty row\\n if not row:\\n continue\\n sentence, _, _, gold, order, subtopic = row\\n subtopic = os.path.join(topic, subtopic)\\n if order == '0':\\n current_subtopic = subtopic\\n source_sentence = sentence\\n continue\\n if order == '1' and subtopic == current_subtopic:\\n # Filter out sentences which have >100 words\\n source_sentence = source_sentence.decode('utf-8').encode('ascii', 'ignore')\\n sentence = sentence.decode('utf-8').encode('ascii', 'ignore')\\n source_sents = [sent\\n for sent in sent_tokenize(source_sentence)\\n if len(word_tokenize(sent)) <= 100]\\n target_sents = [sent\\n for sent in sent_tokenize(sentence)\\n if len(word_tokenize(sent)) <= 100]\\n if source_sents and target_sents:\\n subtopics.append(subtopic)\\n source_sentences.append(source_sents)\\n target_sentences.append(target_sents)\\n golds.append(1 if gold == 'True' else 0)\\n current_subtopic = None\\n source_sentence = None\\n LOG.debug('Completed processing topic: %s', topic)\\n return subtopics, source_sentences, target_sentences, golds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c222f8a3abc2c45ca3457692969d1eb\",\n \"score\": \"0.58925796\",\n \"text\": \"def process_sentences(data, model, is_training, learningrate, config, name,\\n epoch=0, write_out=\\\"\\\"):\\n evaluators = {task: MLTEvaluator(config) for task in data.keys()}\\n task_sent_id_batches = create_batches_of_sentence_ids(\\n data, config, is_training, epoch)\\n if is_training:\\n for task, sentence_ids_in_batch in task_sent_id_batches:\\n random.shuffle(sentence_ids_in_batch)\\n\\n for task, sentence_ids_in_batch in tqdm(task_sent_id_batches):\\n batch = [data[task][i] for i in sentence_ids_in_batch]\\n cost, sentence_scores, token_scores_list, attention_scores_list = \\\\\\n model.process_batch(task, batch, is_training, learningrate)\\n\\n evaluators[task].append_data(cost, batch, sentence_scores, token_scores_list, attention_scores_list)\\n\\n while config[\\\"garbage_collection\\\"] and gc.collect() > 0:\\n pass\\n\\n results = {}\\n for task in data.keys():\\n print(\\\"\\\\n=== TASK: {} ===\\\".format(task))\\n results[task] = evaluators[task].get_results(name)\\n for key, res in results[task].items():\\n print(task + \\\" \\\" + key + \\\": \\\" + str(res))\\n\\n if write_out:\\n write_out_task = write_out+\\\"_\\\"+task\\n if not os.path.exists(write_out_task):\\n os.mkdir(write_out_task)\\n evaluators[task].write_predictions(write_out_task)\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7fd417bdd0c049d26c9aab381458d07\",\n \"score\": \"0.58777773\",\n \"text\": \"def load_data(self):\\n if self.input_format == 'txt':\\n doc_tokens, doc_id = read_txt(self.path, language=self.language)\\n elif self.input_format == 'ltf':\\n doc_tokens, doc_id = read_ltf(self.path)\\n else:\\n raise ValueError('Unknown input format: {}'.format(self.input_format))\\n self.doc_id = doc_id\\n self.data = doc_tokens\\n self.ori_sent_num = len(doc_tokens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a47e380c2f06bb6fc64394e904c658df\",\n \"score\": \"0.58737177\",\n \"text\": \"def load_tokenize_and_batchify(data_dir = '../SemSynLSTM/word_language_model/data/wikitext-2/', input_len = 128):\\n tensor_datasets = []\\n\\n corpus = TaggedCorpus(data_dir)\\n train_data = corpus.train\\n val_data = corpus.valid\\n test_data = corpus.test\\n\\n for dataset in [train_data, val_data, test_data]:\\n ##divide data by batch, truncate to fit into batches\\n n_batch = len(dataset[0]) // input_len\\n input_ids = dataset[0][: n_batch * input_len].reshape(n_batch, input_len)\\n pos_ids = dataset[1][: n_batch * input_len].reshape(n_batch, input_len)\\n all_inputs = (input_ids, pos_ids)\\n tensor_datasets.append(tuple(t for t in all_inputs))\\n\\n return tensor_datasets[0], tensor_datasets[1],tensor_datasets[2], corpus.dictionary, corpus.pos_dictionary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aae2083e57b14909160ab89585d3abd\",\n \"score\": \"0.58451325\",\n \"text\": \"def get_sentences(self):\\n corpus = self.get_corpus()\\n\\n sentences = []\\n for sentence in corpus:\\n #print sentence\\n sentences.append(sentence.split())\\n\\n #print sentences\\n return sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d87d91fb0f1659d1d61011addaab0e2e\",\n \"score\": \"0.5829288\",\n \"text\": \"def __init__(self, md=None):\\n self.task = 'ner'\\n self._set_metadata(md)\\n # loads word_dict and tags_dict\\n self.load_dictionary()\\n self.load_tag_dict()\\n\\n self.sentences = []\\n sent = []\\n for line in sys.stdin:\\n line = line.decode('utf-8').strip()\\n if line:\\n (form, pos, tag) = line.split(None, 2)\\n sent.append([form, pos])\\n else:\\n self.sentences.append(sent)\\n sent = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b69d72115ef80456a7a4f6af57c5a80\",\n \"score\": \"0.582484\",\n \"text\": \"def get_tokens(self) -> List[List[str]]:\\n if self.total_sentence_count == 0:\\n return []\\n\\n def _get_tokens_from_sentence(sentence: List[Tuple[str]]) -> List[str]:\\n return [token[0] for token in sentence]\\n\\n return [\\n _get_tokens_from_sentence(sentence)\\n for sentence in self.sentences\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a600dfc9fd9fafdb54ca5d706be73277\",\n \"score\": \"0.5818996\",\n \"text\": \"def __init__(self, sentences: list) -> None:\\n ### self.sentence_generator = get_tokenized_sentences(file_name)\\n self.sentences = sentences\\n self.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cb2303ef022f844f1d5a4751fe63d4e\",\n \"score\": \"0.57839704\",\n \"text\": \"def build_sentences():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d7ffede626db2bda7e7444831f8e52d\",\n \"score\": \"0.5764835\",\n \"text\": \"def stream_tokens(self):\\n for document in open(self.fname):\\n post = []\\n for sentences in self.tokenize(document):\\n post.extend(self.ngrams[list(sentences)])\\n yield post\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfb92adb19b1b278b06c7415c0c54519\",\n \"score\": \"0.5734833\",\n \"text\": \"def getData(self,n):\\n\\t\\twords = []\\n\\t\\tsentences = []\\n\\t\\tcount = 0\\n\\t\\tfor i in range(1,n):\\n\\t\\t\\tfilename = self.train_name.format(i)\\n\\t\\t\\tprint \\\"Loading sentences from {0}\\\".format(filename)\\n\\t\\t\\twith codecs.open(filename, \\\"r\\\", \\\"utf-8\\\") as file:\\n\\t\\t\\t\\tfor line in file:\\n\\t\\t\\t\\t\\tline = line.split()\\n\\t\\t\\t\\t\\t# If the line has words then we keep the relevant word\\n\\t\\t\\t\\t\\tif len(line)>2:\\n\\t\\t\\t\\t\\t\\twords.append(line[1])\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\t# If the sentence has no words we ignore it\\n\\t\\t\\t\\t\\tif not len(words):\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\t# We are finished with the current word\\n\\t\\t\\t\\t\\tsentences.append([words, -1])\\n\\t\\t\\t\\t\\t# Reset the state\\n\\t\\t\\t\\t\\twords = []\\n\\t\\t\\t\\t\\tcount += 1\\n\\t\\t# Check that the sentences look good\\n\\t\\treturn sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8581b37f1a2faecd0ac2e55ae823f7b7\",\n \"score\": \"0.5730079\",\n \"text\": \"def getSentence():\\n data_str = input(\\\"Enter a sentence to scan for sentiment : \\\")\\n print(\\\" Your input is: \\\",data_str, type(data_str))\\n\\n return data_str.split() #return a list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37d28222678d95431ae3b896b9f7d3a2\",\n \"score\": \"0.5722489\",\n \"text\": \"def _process_input_file(filename, stats):\\n tf.logging.info(\\\"Processing input file: %s\\\", filename)\\n processed = []\\n\\n predecessor = None # Predecessor sentence (list of words).\\n current = None # Current sentence (list of words).\\n successor = None # Successor sentence (list of words).\\n\\n for successor_str in tf.gfile.FastGFile(filename):\\n stats.update([\\\"sentences_seen\\\"])\\n successor = successor_str#.split()\\n # The first 2 sentences per file will be skipped.\\n if predecessor and current and successor:\\n stats.update([\\\"sentences_considered\\\"])\\n\\n # Note that we are going to insert <EOS> later, so we only allow\\n # sentences with strictly less than max_sentence_length to pass.\\n if FLAGS.max_sentence_length and (\\n len(predecessor) >= FLAGS.max_sentence_length or len(current) >=\\n FLAGS.max_sentence_length or len(successor) >=\\n FLAGS.max_sentence_length):\\n stats.update([\\\"sentences_too_long\\\"])\\n else:\\n serialized = _create_serialized_example(predecessor, current, successor)\\n processed.append(serialized)\\n stats.update([\\\"sentences_output\\\"])\\n\\n predecessor = current\\n current = successor\\n\\n sentences_seen = stats[\\\"sentences_seen\\\"]\\n sentences_output = stats[\\\"sentences_output\\\"]\\n if sentences_seen and sentences_seen % 100000 == 0:\\n tf.logging.info(\\\"Processed %d sentences (%d output)\\\", sentences_seen,\\n sentences_output)\\n if FLAGS.max_sentences and sentences_output >= FLAGS.max_sentences:\\n break\\n\\n tf.logging.info(\\\"Completed processing file %s\\\", filename)\\n return processed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3634228b44f9cc1307ab486d6366c4e7\",\n \"score\": \"0.5711307\",\n \"text\": \"def _sents(self):\\n # Define a tokenizer pattern that is more precise than the default nltk\\n # pattern\\n\\n pattern = r'''(?ix) # set flag to allow verbose regexps and ignore case\\n (?:mr\\\\.|mrs\\\\.) # abreviation for mister and missus\\n | (?:[A-Z]+\\\\'[A-Z]{1,2}) # neg or to be abreviations, e.g can't\\n | (?:[A-Z]\\\\.)+ # abbreviations, e.g. U.S.A.\\n | \\\\w+(?:-\\\\w+)* # words with optional internal hyphens\\n | \\\\$?\\\\d+(?:\\\\.\\\\d+)?%? # currency and percentages e.g. $12.40, 82%\\n | \\\\.\\\\.\\\\. # ellipsis\\n | [][.,;\\\"'?():-_`] # these are separate tokens; includes ], [\\n '''\\n\\n # Instantiate the tokenizer\\n tokenizer = RegexpTokenizer(pattern)\\n\\n # Create a new corpus by specifying the parameters\\n # (1) directory of the new corpus\\n # (2) the fileids of the corpus\\n # The fileids are simply the filenames.\\n\\n my_corpus = PlaintextCorpusReader(self.corpusdir,\\n self.fileId,\\n word_tokenizer=tokenizer)\\n\\n # Access sentences in the corpus (list of list of strings)\\n self.sents = my_corpus.sents()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e22a9f2856c52c28967fb893e75903c2\",\n \"score\": \"0.5711085\",\n \"text\": \"def read_dataset(dataset, tier, vocab):\\n\\n context_data = []\\n query_data = []\\n question_uuid_data = []\\n\\n for articles_id in tqdm(range(len(dataset['data'])), desc=\\\"Preprocessing {}\\\".format(tier)):\\n article_paragraphs = dataset['data'][articles_id]['paragraphs']\\n for pid in range(len(article_paragraphs)):\\n context = article_paragraphs[pid]['context']\\n # The following replacements are suggested in the paper\\n # BidAF (Seo et al., 2016)\\n context = context.replace(\\\"''\\\", '\\\" ')\\n context = context.replace(\\\"``\\\", '\\\" ')\\n\\n context_tokens = tokenize(context)\\n\\n qas = article_paragraphs[pid]['qas']\\n for qid in range(len(qas)):\\n question = qas[qid]['question']\\n question_tokens = tokenize(question)\\n question_uuid = qas[qid]['id']\\n\\n context_ids = [str(vocab.get(w, qa_data.UNK_ID)) for w in context_tokens]\\n qustion_ids = [str(vocab.get(w, qa_data.UNK_ID)) for w in question_tokens]\\n\\n context_data.append(' '.join(context_ids))\\n query_data.append(' '.join(qustion_ids))\\n question_uuid_data.append(question_uuid)\\n\\n return context_data, query_data, question_uuid_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e75c0256b70981e36acb8178864c0511\",\n \"score\": \"0.57083285\",\n \"text\": \"def read_dataset(dataset, tier, vocab):\\n\\n context_data = []\\n query_data = []\\n question_uuid_data = []\\n\\n for articles_id in tqdm(range(len(dataset['data'])), desc=\\\"Preprocessing {}\\\".format(tier)):\\n article_paragraphs = dataset['data'][articles_id]['paragraphs']\\n for pid in range(len(article_paragraphs)):\\n context = article_paragraphs[pid]['context']\\n # The following replacements are suggested in the paper\\n # BidAF (Seo et al., 2016)\\n context = context.replace(\\\"''\\\", '\\\" ')\\n context = context.replace(\\\"``\\\", '\\\" ')\\n\\n context_tokens = tokenize(context)\\n\\n qas = article_paragraphs[pid]['qas']\\n for qid in range(len(qas)):\\n question = qas[qid]['question']\\n question_tokens = tokenize(question)\\n question_uuid = qas[qid]['id']\\n\\n context_ids = [vocab.get(w, qa_data.UNK_ID) for w in context_tokens]\\n qustion_ids = [vocab.get(w, qa_data.UNK_ID) for w in question_tokens]\\n\\n context_data.append(context_ids)\\n query_data.append(qustion_ids)\\n question_uuid_data.append(question_uuid)\\n\\n return context_data, query_data, question_uuid_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a41fed53e9e8fd6fc9e809d5b1df8ec\",\n \"score\": \"0.57013583\",\n \"text\": \"def transform(self, input_: str) -> list:\\n return nltk.word_tokenize(input_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fa20d544c61ebb969653fe5306dc0e2\",\n \"score\": \"0.5690682\",\n \"text\": \"def read_simple_ner_dataset(file_paths: Union[list, str], token_idx: int = 0,\\n entity_idx: int = 1):\\n if isinstance(file_paths, str):\\n file_paths = [file_paths]\\n\\n sentences = []\\n entities = []\\n\\n for path in file_paths:\\n with open(path, 'r') as f:\\n sentence = []\\n ent = []\\n for line in f:\\n if line == \\\"\\\\n\\\":\\n assert len(sentence) == len(ent)\\n sentences.append(sentence)\\n entities.append(ent)\\n\\n sentence = []\\n ent = []\\n elif not line.startswith(\\\"#\\\"):\\n sentence.append(line.split()[token_idx])\\n ent.append(line.split()[entity_idx].strip())\\n\\n return sentences, entities\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d39bad2dad35979454f5e74f3739b685\",\n \"score\": \"0.56748545\",\n \"text\": \"def input_batch_generator(self, tokenized_sentences, batch_size):\\n while True:\\n for chunk in chunks(batch_size, range(len(tokenized_sentences))):\\n selected_sentences = [tokenized_sentences[index] for index in chunk]\\n model_inputs = self.prepare_inputs_from_pretokenized(selected_sentences)\\n yield model_inputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37fc1c4052fc2bdbaaab5685be793486\",\n \"score\": \"0.5669986\",\n \"text\": \"def prepare_sentences(input_file):\\n\\n with open(input_file) as i:\\n data = i.readlines()\\n sentences = [line.strip() for line in data]\\n return sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba8ebcbc8b18dd9028f0cb2ea569f172\",\n \"score\": \"0.56618977\",\n \"text\": \"def load_data(input_file, label_file, en_vocab_file, de_vocab_file, sentence_fn):\\n en_sentences = sentence_fn(input_file)\\n de_sentences = sentence_fn(label_file)\\n return create_data(en_sentences, de_sentences, en_vocab_file, de_vocab_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7ca90173fa4e1d353bc4152a3df6d2a\",\n \"score\": \"0.5651888\",\n \"text\": \"def prepare_data(self,data):\\n\\t\\tinp = []\\n\\t\\tout = []\\n\\n\\t\\tfor line in data:\\n\\t\\t\\ttokenized_inp = self.bert_tokenizer(line.split(\\\"\\\\t\\\")[0])\\n\\t\\t\\tif len(tokenized_inp) > self.max_len_in:\\n\\t\\t\\t\\ttokenized_inp = tokenized_inp[:self.max_len_in]\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttokenized_inp.extend([self.bert_tokenizer.encode(\\\"[PAD]\\\")]*(self.max_len_in-len(tokenized_inp)))\\n\\n\\t\\t\\ttokenized_out = self.bert_tokenizer(line.split(\\\"\\\\t\\\")[1])\\n\\t\\t\\tif len(tokenized_out) > self.max_len_out:\\n\\t\\t\\t\\ttokenized_out = tokenized_out[:self.max_len_out]\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttokenized_out.extend([self.bert_tokenizer.encode(\\\"[PAD]\\\")]*(self.max_len_out-len(tokenized_out)))\\n\\n\\n\\t\\t\\tinp.append(tokenized_inp)\\n\\t\\t\\tout.append(tokenized_out)\\n\\n\\t\\treturn inp, out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b79b6e6539506a74c930eb7be5a13412\",\n \"score\": \"0.5647186\",\n \"text\": \"def categorize_sentences(self, sentences):\\n # encode sentences\\n sentences = [s.encode('utf-8') for s in sentences]\\n\\n q = queue.Queue()\\n reader = TextReader(file=None, max_word_length=self.max_word_length)\\n n_batch = len(sentences) // self.BATCH_SIZE\\n pred = self.prediction\\n saver = tf.train.Saver()\\n results = []\\n\\n def fill_list(list, length):\\n while len(list) != length:\\n list.append('empty sentence.')\\n return list\\n\\n # Fill queue with minibatches\\n for i in range(n_batch + 1):\\n if i == n_batch:\\n q.put(fill_list(sentences, self.BATCH_SIZE))\\n else:\\n q.put(sentences[i * self.BATCH_SIZE: (i + 1) * self.BATCH_SIZE])\\n\\n # Predict\\n with tf.Session() as sess:\\n print('Loading model %s...' % SAVE_PATH)\\n saver.restore(sess, SAVE_PATH)\\n print('Model loaded')\\n\\n while not q.empty():\\n batch = q.get()\\n batch = ['0, ' + s for s in batch]\\n batch_x, batch_y = reader.make_minibatch(batch)\\n p = sess.run([pred], feed_dict={self.X: batch_x, self.Y: batch_y})\\n results.append(p)\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fe49eff21bfe4455a8000d1eaadb0e8\",\n \"score\": \"0.56468076\",\n \"text\": \"def tokenize(self, sentence):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a12dcfc7b973a38a2010653b54c1e11e\",\n \"score\": \"0.5625808\",\n \"text\": \"def _read_data(cls, input_file):\\n from improv.read_ner import get_ner_lines\\n return get_ner_lines(Path(input_file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecbf55765955496729400b628d6ea652\",\n \"score\": \"0.5615864\",\n \"text\": \"def _read_data(cls, input_file):\\n with open(input_file) as f:\\n # out_lines = []\\n out_lists = []\\n entries = f.read().strip().split(\\\"\\\\n\\\\n\\\")\\n for entry in entries:\\n words = []\\n ner_labels = []\\n pos_tags = []\\n bio_pos_tags = []\\n for line in entry.splitlines():\\n pieces = line.strip().split()\\n if len(pieces) < 1:\\n continue\\n word = pieces[0]\\n # if word == \\\"-DOCSTART-\\\" or word == '':\\n # continue\\n words.append(word)\\n pos_tags.append(pieces[1])\\n bio_pos_tags.append(pieces[2])\\n ner_labels.append(pieces[-1])\\n # sentence = ' '.join(words)\\n # ner_seq = ' '.join(ner_labels)\\n # pos_tag_seq = ' '.join(pos_tags)\\n # bio_pos_tag_seq = ' '.join(bio_pos_tags)\\n # out_lines.append([sentence, pos_tag_seq, bio_pos_tag_seq, ner_seq])\\n # out_lines.append([sentence, ner_seq])\\n out_lists.append([words,pos_tags,bio_pos_tags,ner_labels])\\n return out_lists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd94ce3e72078adc41aad34bd9e61732\",\n \"score\": \"0.5608255\",\n \"text\": \"def read(self):\\n f = codecs.open(self.__filePath, \\\"r\\\", \\\"utf-8\\\")\\n nmTokens = 0\\n\\n # Tokens and their labels for a whole sentence.\\n tkns = []\\n labels = []\\n\\n sep = self.__labelTknSep\\n\\n for line in f:\\n line = line.strip()\\n\\n if len(line) == 0:\\n # Blank line separates sentences.\\n yield (tkns, labels)\\n tkns = []\\n labels = []\\n continue\\n\\n tknLabel = line.split(sep)\\n \\n if(len(tknLabel) < 2):\\n continue\\n \\n tkns.append(tknLabel[0])\\n labels.append(tknLabel[1])\\n\\n nmTokens += 1\\n\\n assert len(tkns) == len(labels)\\n\\n if len(tkns) > 0:\\n # Last sentence.\\n yield (tkns, labels)\\n\\n if not self.__printedNumberTokensRead:\\n self.__log.info(\\\"Number of tokens read: %d\\\" % nmTokens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53f4be2998ad2766303012fd966667c1\",\n \"score\": \"0.5600364\",\n \"text\": \"def reader():\\n ids = line.strip().split()\\n conv_ids = [int(i) for i in ids]\\n boundary = len(ids) - 1\\n src = conv_ids[:boundary]\\n pos_tgt = [conv_ids[boundary]]\\n feature_name = [\\\"user\\\", \\\"all_item\\\", \\\"p_item\\\"]\\n yield list(\\n zip(feature_name, [src] + [\\n np.arange(self.vocab_size).astype(\\\"int64\\\").tolist()\\n ] + [pos_tgt]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd6d7dec7eeb512c0aacc3dea576dec9\",\n \"score\": \"0.559833\",\n \"text\": \"def get_sentence(self, dataset, batch_size=None):\\n\\n dataset = dataset.map(\\n lambda seq_len, word_ids: (seq_len, word_ids, tf.gather(self.vocabulary.get_lookup_tensor(), word_ids))\\n )\\n if batch_size is None:\\n batch_size = self.config.batch_size\\n dataset = dataset.padded_batch(\\n batch_size,\\n padded_shapes=((), (-1,), (-1, self.vocabulary.vector_length()))\\n )\\n\\n iterator = dataset.make_initializable_iterator()\\n sentence = iterator.get_next()\\n return sentence, iterator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"077afca2802843ae49a21a30de81ee65\",\n \"score\": \"0.559238\",\n \"text\": \"def build_vocab(self, data_set):\\n self.dataset = data_set\\n\\n self.data = data_set.text # data is a list of text in text_dataset\\n types = ['lower', 'remove_punctuations', 'snowball', 'remove_emojis'\\n 'lemmatize', 'remove_tags', 'replace_consecutive', 'n_grams']\\n # print('Data: ', self.data)\\n print('Pending ......... ')\\n for sentence in tqdm(self.data):\\n # print('Sentence: ', sentence)\\n for token in self.tokenizer.preprocess(sentence, types):\\n # print('Token after preprocess: ', token)\\n if token not in self.stoi:\\n if self.max_length is not None:\\n if self.max_length <= self.vocab_size:\\n continue\\n\\n self.stoi[token] = self.vocab_size # index increase from 4\\n self.itos[self.vocab_size] = token\\n self.vocab_size += 1\\n self.freqs[token] = 1\\n\\n else:\\n self.freqs[token] += 1\\n\\n self.freqs = {tok: freq for tok, freq in sorted(\\n self.freqs.items(), key=lambda item: item[1], reverse=True)}\\n\\n # Vocab dataset only has min_freqs token\\n if self.min_freqs is not None and self.min_freqs > 1:\\n self.reset()\\n new_freq = {}\\n freq_list = list(self.freqs.items())\\n for tok, freq in freq_list:\\n if freq >= self.min_freqs:\\n new_freq[tok] = freq\\n self.stoi[tok] = self.vocab_size # index increase from 4\\n self.itos[self.vocab_size] = tok\\n self.vocab_size += 1\\n\\n self.freqs = new_freq\\n\\n if self.max_length is not None and self.max_length < self.vocab_size:\\n self.reset()\\n new_freq = {}\\n freq_list = list(self.freqs.items())\\n for tok, freq in freq_list:\\n if self.vocab_size >= self.max_length:\\n break\\n new_freq[tok] = freq\\n self.stoi[tok] = self.vocab_size # index increase from 4\\n self.itos[self.vocab_size] = tok\\n self.vocab_size += 1\\n\\n self.freqs = new_freq\\n\\n print('Done Bulding!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f0c62b32a38d0146ea2e13932cf996f\",\n \"score\": \"0.55737823\",\n \"text\": \"def process_sentences(self, sentences):\\n id_sequences = list()\\n sequence_lengths = list()\\n\\n # Reset word statistics.\\n self._statistics['n_words_total'] = 0\\n self._statistics['n_words_unique'] = 0\\n self._statistics['n_words_clip_avg'] = 0\\n\\n # Reset character statistics.\\n self._statistics['n_chars_total'] = 0\\n self._statistics['n_chars_unique'] = 0\\n self._statistics['n_chars_clip_avg'] = 0\\n\\n # Unique word lookup set.\\n word_set = set()\\n\\n # Unique character lookup set.\\n character_set = set()\\n\\n # TODO: Refactor.\\n def __update_word_statistics(sentence):\\n words = sentence.split(' ')\\n self._statistics['n_words_total'] += len(words)\\n word_set.update(words)\\n\\n # TODO: Refactor.\\n def __update_character_statistics(sentence):\\n self._statistics['n_chars_total'] += len(sentence)\\n character_set.update(list(sentence))\\n\\n for sentence in sentences:\\n # Make sentence lowercase.\\n sentence = sentence.lower()\\n\\n # Collect word statistics.\\n __update_word_statistics(sentence)\\n\\n # Collect character statistics.\\n __update_character_statistics(sentence)\\n\\n self._statistics['n_words_unique'] = len(word_set)\\n self._statistics['n_chars_unique'] = len(character_set)\\n self._statistics['n_words_clip_avg'] = self._statistics['n_words_total'] / len(sentences)\\n self._statistics['n_chars_clip_avg'] = self._statistics['n_chars_total'] / len(sentences)\\n\\n eos_token = self._char2idx_dict['eos']\\n\\n for sentence in sentences:\\n # Make sentence lowercase.\\n sentence = sentence.lower()\\n\\n # Replace abbreviations.\\n sentence = self.replace_abbreviations(sentence)\\n\\n # Update character dictionary with the chars contained in the sentence.\\n if self._fill_dict:\\n self.update_char_dict(sentence)\\n\\n # Convert each character into its dictionary index.\\n idx = self.sent2idx(sentence)\\n\\n # Append the EOS token to the sentence.\\n idx.append(eos_token)\\n\\n # Append str. representation so that tf.Tensor handles this as an collection of objects.\\n # This allows us to store sequences of different length in a single tensor.\\n id_sequences.append(np.array(idx, dtype=np.int32).tostring())\\n sequence_lengths.append(len(idx))\\n\\n return id_sequences, sequence_lengths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aab3259f14ec7f23cb62757a1d64e4d\",\n \"score\": \"0.5572674\",\n \"text\": \"def tokenize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0433eff744d254b284c2675eb1ffdad7\",\n \"score\": \"0.55627257\",\n \"text\": \"def train(self):\\n if not self.inited:\\n self.setup()\\n\\n # train\\n for line in self.data:\\n # preprocessing\\n line = line.strip()\\n if not line:\\n continue\\n\\n # get observes\\n observes = []\\n for i in range(len(line)):\\n if line[i] == \\\" \\\":\\n continue\\n observes.append(line[i])\\n\\n # get states\\n # split line by space\\n words = line.split(\\\" \\\")\\n states = []\\n for word in words:\\n if word in seg_stop_words:\\n continue\\n states.extend(getTags(word))\\n # resume train\\n self.do_train(observes, states)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adbb47c59a657a9206407f59db7fd226\",\n \"score\": \"0.5560937\",\n \"text\": \"def get_tokenized_sentences(file_name: str) -> Iterator[str]:\\n # with open(file_name) as file_handle:\\n # for sentence in file_handle.read().splitlines():\\n # tokenized_sentence = sentence.split(',')\\n # yield tokenized_sentence\\n\\n for sent in file_name:\\n tokenized_sentence = sent.split(',')\\n yield tokenized_sentence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d8920b34aec2ba22126d11e190fc99e\",\n \"score\": \"0.5560473\",\n \"text\": \"def preprocess(sentences):\\n \\n cleaned_sentences = []\\n sent_length = 0\\n print('Preprocessing...')\\n for sentence in sentences:\\n sentence = word_tokenize(sentence) # Preprocessing step: Tokenize\\n \\n cleaned_sentences.append(sentence)\\n print('Done')\\n return cleaned_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12637c3c219b6ab8fe7daf59b5addcfb\",\n \"score\": \"0.5559983\",\n \"text\": \"def sentences(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9ecae7fcdffa8feca0f70de05ade26c\",\n \"score\": \"0.55547184\",\n \"text\": \"def sents(self):\\n sents = []\\n for i in range(len(self.parser_output.sentence)): \\n sent_tokens = [elem.word for elem in self.parser_output.sentence[i].token]\\n sents.append(sent_tokens)\\n return sents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"119452ae5e2df458aab47373b381257a\",\n \"score\": \"0.55545\",\n \"text\": \"def train(self, corpus): \\n\\n for sentence in corpus.corpus: # iterate over sentences in the corpus\\n for datum_data in sentence.data: # iterate over datums in the sentence\\n token = datum_data.word # get the word\\n # for each time increase a token by one\\n # every time you see a token increase number of total tokens\\n self.unigram_language_model[token] = self.unigram_language_model[token]+1 \\n self.total_tokens +=1\\n # print(\\\"===========================================\\\")\\n # print(self.unigram_language_model[token])\\n # print(self.unigram_language_model[token])\\n # print(self.total_tokens[token])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07b2cf6308ea681bfb29f6c9f0a77d1d\",\n \"score\": \"0.5550696\",\n \"text\": \"def tokenize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c71242efe01a01f135591ff8df533df0\",\n \"score\": \"0.5545742\",\n \"text\": \"def tokenize(self, text):\\n if isinstance(text[0], str):\\n return [self.tokenizer.tokenize(x) for x in tqdm(text)]\\n else:\\n return [\\n [self.tokenizer.tokenize(x) for x in sentences]\\n for sentences in tqdm(text)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99ff9e4ffc2ed58710e99d0baec73e0e\",\n \"score\": \"0.5538299\",\n \"text\": \"def load_sentences(f_path):\\n for line in open(f_path, 'rb'):\\n l = read_ptsv(line)\\n yield SentenceInput._make(l + [range(len(l[3]))])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7312a636cc2078d04cc5c5f35e16a91\",\n \"score\": \"0.5532633\",\n \"text\": \"def load_input() -> t.List[t.List[str]]:\\n with open('input.txt') as f:\\n return [[c for c in line.strip(\\\"\\\\n\\\").split(\\\"\\\\n\\\")] for line in f.read().split(\\\"\\\\n\\\\n\\\")]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db4b9ac10acd8c4401888b8b12de6c3f\",\n \"score\": \"0.5524474\",\n \"text\": \"def get_data_from_sentence(self, sentence):\\n if len(sentence) < self.length_train + 1:\\n return None, None\\n\\n data = []\\n target = []\\n\\n index = 0\\n while self.length_train + index < len(sentence):\\n data_tmp = sentence[index:index + self.length_train]\\n target_tmp = sentence[index + self.length_train]\\n\\n data.append(data_tmp)\\n target.append(target_tmp)\\n\\n index += 1\\n\\n return data, target\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c93452acd898501b43d8aa0464464d09\",\n \"score\": \"0.55200404\",\n \"text\": \"def lstm_tokenize(sentences: list) -> list:\\n # Attention! `[PAD]` tokens are being considered out of vocabulary words here\\n return AMI.pad_sentences([sent.split(' ') for sent in sentences], 'oov')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce262173f7248cda059789f205968cec\",\n \"score\": \"0.5519581\",\n \"text\": \"def read_dataset(dataset, tier, vocab):\\n\\n # depends on qa_data.py constants\\n context_data = []\\n query_data = []\\n question_uuid_data = []\\n\\n for articles_id in tqdm(range(len(dataset['data'])), desc=\\\"Preprocessing {}\\\".format(tier)):\\n article_paragraphs = dataset['data'][articles_id]['paragraphs']\\n for pid in range(len(article_paragraphs)):\\n context = article_paragraphs[pid]['context']\\n # The following replacements are suggested in the paper\\n # BidAF (Seo et al., 2016)\\n context = context.replace(\\\"''\\\", '\\\" ')\\n context = context.replace(\\\"``\\\", '\\\" ')\\n\\n context_tokens = tokenize(context)\\n\\n qas = article_paragraphs[pid]['qas']\\n for qid in range(len(qas)):\\n question = qas[qid]['question']\\n question_tokens = tokenize(question)\\n question_uuid = qas[qid]['id']\\n\\n context_ids = [str(vocab.get(w, qa_data.UNK_ID)) for w in context_tokens]\\n qustion_ids = [str(vocab.get(w, qa_data.UNK_ID)) for w in question_tokens]\\n\\n context_data.append(' '.join(context_ids))\\n query_data.append(' '.join(qustion_ids))\\n question_uuid_data.append(question_uuid)\\n\\n return context_data, query_data, question_uuid_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9fe834a137fa7dc5a097aa3bf882f52\",\n \"score\": \"0.55101264\",\n \"text\": \"def load_sentences(self):\\n if self.print_only:\\n infile = 'data/sentences_clean.txt'\\n with open(infile) as infile:\\n lines = infile.readlines()\\n sentences = [l.lower().strip() for l in lines]\\n else:\\n infile = resource_filename('typer_game', 'data/audio_lookup_subset.txt')\\n sentences = pickle.load(open(infile, 'rb'))\\n return sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb6c34a1494116308e8f3573675a173\",\n \"score\": \"0.5509206\",\n \"text\": \"def input_fn(mode, sentences, labels, params):\\n # Load all the dataset in memory for shuffling is training\\n is_training = (mode == 'train')\\n buffer_size = params.buffer_size if is_training else 1\\n\\n # Zip the sentence and the labels together\\n dataset = tf.data.Dataset.zip((sentences, labels))\\n\\n # Create batches and pad the sentences of different length\\n padded_shapes = ((tf.TensorShape([None]), # sentence of unknown size\\n tf.TensorShape([])), # size(words)\\n (tf.TensorShape([None]), # labels of unknown size\\n tf.TensorShape([]))) # size(tags)\\n\\n padding_values = ((params.id_pad_word, # sentence padded on the right with id_pad_word\\n 0), # size(words) -- unused\\n (params.id_pad_tag, # labels padded on the right with id_pad_tag\\n 0)) # size(tags) -- unused\\n\\n\\n dataset = (dataset\\n .shuffle(buffer_size=buffer_size)\\n .padded_batch(params.batch_size, padded_shapes=padded_shapes, padding_values=padding_values)\\n .prefetch(1) # make sure you always have one batch ready to serve\\n )\\n\\n # Create initializable iterator from this dataset so that we can reset at each epoch\\n iterator = dataset.make_initializable_iterator()\\n\\n # Query the output of the iterator for input to the model\\n ((sentence, sentence_lengths), (labels, _)) = iterator.get_next()\\n init_op = iterator.initializer\\n\\n # Build and return a dictionnary containing the nodes / ops\\n inputs = {\\n 'sentence': sentence,\\n 'labels': labels,\\n 'sentence_lengths': sentence_lengths,\\n 'iterator_init_op': init_op\\n }\\n\\n return inputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fe75a47ee7d3dbfacb1a98e79ac6073\",\n \"score\": \"0.55052745\",\n \"text\": \"def __init__(self, allsentences, orders, doc_len, ewords, elocs, slocs,blocs=None, dataset=None,\\n device=None):\\n if data is not None:\\n setattr(self, 'doc_len', doc_len)\\n setattr(self, 'elocs', elocs)\\n setattr(self, 'slocs', slocs)\\n setattr(self, 'blocs', blocs)\\n\\n self.batch_size = len(doc_len)\\n self.dataset = dataset\\n self.fields = dataset.fields.keys() # copy field names\\n self.input_fields = [k for k, v in dataset.fields.items() if\\n v is not None and not v.is_target]\\n self.target_fields = [k for k, v in dataset.fields.items() if\\n v is not None and v.is_target]\\n\\n '''\\n maxdoclen=max(doc_len)\\n for i in range(self.batch_size):\\n batch_sent = []\\n for j in range(maxdoclen):\\n batch_sent.append(' '.join(allsentences[i*maxdoclen+j]))\\n bert_tok.append(tokenizer(batch_sent,return_tensors='pt',padding=True).to('cuda'))\\n '''\\n\\n setattr(self, 'order', dataset.fields['order'].process(orders, device=device))\\n setattr(self, 'doc', dataset.fields['doc'].process(allsentences, device=device))\\n\\n allsentences = [' '.join(sent) for sent in allsentences]\\n #bert_tok = tokenizer(allsentences, return_tensors='pt', padding=True).to('cuda')\\n bert_tok = []\\n setattr(self, 'e_words', dataset.fields['doc'].process(ewords, device=device))\\n setattr(self, 'token', bert_tok)\\n # setattr(self, 'docwords', dataset.fields['doc'].process(doc_words, device=device))\\n # setattr(self, 'graph', dataset.fields['e2e'].process_graph(e2ebatch, e2sbatch, orders,\\n # doc_sent_word_len, device=device))\\n # setattr(self, 'alllen', doc_sent_word_len)\\n\\n # for (name, field) in dataset.fields.items():\\n # if field is not None:\\n # batch = [getattr(x, name) for x in data]\\n # setattr(self, name, field.process(batch, device=device))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0805a4cda7d7c0d50cf93234f312af5\",\n \"score\": \"0.5500408\",\n \"text\": \"def _add_sentence_tokens(self, data: pd.DataFrame) -> pd.DataFrame:\\n tokenizer = nltk.data.load(\\\"tokenizers/punkt/english.pickle\\\")\\n data[_Column.TOKENS.name] = data[_Column.MESSAGE.name].apply(tokenizer.tokenize)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2df389f01aa0aac876ddf79f6095355a\",\n \"score\": \"0.54996413\",\n \"text\": \"def extract_questions():\\n df1 = pd.read_csv(\\\"./data/{}.csv\\\".format(params.train), keep_default_na=False)\\n df2 = pd.read_csv(\\\"./data/{}.csv\\\".format(params.test), keep_default_na=False)\\n\\n for dataset in [df1, df2]:\\n for i, row in dataset.iterrows():\\n if i != 0 and i % 100000 == 0:\\n print(\\\"example tokenization\\\", row['question1'], tokenize_sentence(row['question1']))\\n if i != 0 and i % 10000 == 0:\\n logging.info(\\\"read {0} sentences\\\".format(i))\\n\\n if row['question1']:\\n yield tokenize_sentence(row['question1'])\\n if row['question2']:\\n yield tokenize_sentence(row['question2'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4924d5ba91fd2086c179d2560ee9703a\",\n \"score\": \"0.54834133\",\n \"text\": \"def _tokenize(self, sentence: str) -> (torch.Tensor, list):\\n indexed_tokens = self.tokenizer.encode(sentence)\\n tokens_tensor = torch.tensor([indexed_tokens])\\n tokens_tensor = tokens_tensor.to(self.device)\\n return tokens_tensor, indexed_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f5448d4e48f10c32ae126ddc873e449\",\n \"score\": \"0.54780805\",\n \"text\": \"def processData(self):\\n sentences = []\\n # filter item behave less than self.min_count\\n support = self.data.groupby('item_id').size()\\n self.data = self.data[np.in1d(self.data['item_id'], support[support >= self.min_count].index)]\\n vocab = list(map(str, self.data['item_id'].unique().tolist()))\\n self.data = self.data.sort_values(by=['user_id', 'dates'])\\n groups = self.data.groupby('user_id')\\n for name, group in groups:\\n seq = groups.get_group(name)['item_id'].tolist()\\n seq = list(map(str, seq))\\n sentences.append(seq)\\n return sentences, vocab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f672cfb43887b8fb98a8fcd1869f3984\",\n \"score\": \"0.5476506\",\n \"text\": \"def __init__(self, data_type=ONE_HOT_AVERAGE, use_sub_phrases=True,\\n\\t dataset_path=\\\"stanfordSentimentTreebank\\\", batch_size=50,\\n\\t embedding_dim=None):\\n\\n\\t\\t# load the dataset\\n\\t\\tself.sentiment_dataset = data_loader. \\\\\\n\\t\\t\\tSentimentTreeBank(dataset_path, split_words=True)\\n\\t\\t# map data splits to sentences lists\\n\\t\\tself.sentences = {}\\n\\t\\tif use_sub_phrases:\\n\\t\\t\\tself.sentences[\\n\\t\\t\\t\\tTRAIN] = self.sentiment_dataset.get_train_set_phrases()\\n\\t\\telse:\\n\\t\\t\\tself.sentences[TRAIN] = self.sentiment_dataset.get_train_set()\\n\\n\\t\\tself.sentences[VAL] = self.sentiment_dataset.get_validation_set()\\n\\t\\tself.sentences[TEST] = self.sentiment_dataset.get_test_set()\\n\\n\\t\\t# map data splits to sentence input preperation functions\\n\\t\\twords_list = list(self.sentiment_dataset.get_word_counts().keys())\\n\\t\\tif data_type == ONE_HOT_AVERAGE:\\n\\t\\t\\tself.sent_func = average_one_hots\\n\\t\\t\\tself.sent_func_kwargs = {\\n\\t\\t\\t\\t\\\"word_to_ind\\\": get_word_to_ind(words_list)}\\n\\t\\telif data_type == W2V_SEQUENCE:\\n\\t\\t\\tself.sent_func = sentence_to_embedding\\n\\n\\t\\t\\tself.sent_func_kwargs = {\\\"seq_len\\\": SEQ_LEN,\\n\\t\\t\\t \\\"word_to_vec\\\": create_or_load_slim_w2v(\\n\\t\\t\\t\\t words_list),\\n\\t\\t\\t \\\"embedding_dim\\\": embedding_dim\\n\\t\\t\\t }\\n\\t\\telif data_type == W2V_AVERAGE:\\n\\t\\t\\tself.sent_func = get_w2v_average\\n\\t\\t\\twords_list = list(self.sentiment_dataset.get_word_counts().keys())\\n\\t\\t\\tself.sent_func_kwargs = {\\n\\t\\t\\t\\t\\\"word_to_vec\\\": create_or_load_slim_w2v(words_list),\\n\\t\\t\\t\\t\\\"embedding_dim\\\": embedding_dim\\n\\t\\t\\t}\\n\\t\\telse:\\n\\t\\t\\traise ValueError(\\\"invalid data_type: {}\\\".format(data_type))\\n\\t\\t# map data splits to torch datasets and iterators\\n\\t\\tself.torch_datasets = {\\n\\t\\t\\tk: OnlineDataset(sentences, self.sent_func, self.sent_func_kwargs)\\n\\t\\t\\tfor k, sentences in tqdm(self.sentences.items())}\\n\\t\\tself.torch_iterators = {k: DataLoader(dataset, batch_size=batch_size, shuffle=k == TRAIN)\\n\\t\\t for k, dataset in self.torch_datasets.items()\\n\\t\\t }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1302cf955b533825aae0beb4bb954ba\",\n \"score\": \"0.5469566\",\n \"text\": \"def tokenize_sentences(sentences):\\n\\n # Initialize the list of lists of tokenized sentences\\n tokenized_sentences = []\\n\\n # Go through each sentence\\n for sentence in sentences:\\n # Convert to lowercase letters\\n sentence = sentence.lower()\\n\\n # Convert into a list of words\\n tokenized = nltk.word_tokenize(sentence)\\n\\n # append the list of words to the list of lists\\n tokenized_sentences.append(tokenized)\\n\\n return tokenized_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"638829de3b414b7d107d8cec9ea30bd2\",\n \"score\": \"0.5465221\",\n \"text\": \"def split(self):\\n documents = []\\n for sentence in self.sentences:\\n document = Document()\\n document.add_sentence(sentence)\\n documents.append(document)\\n\\n return documents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb62ad5d015c35ee1829c6d683602f1\",\n \"score\": \"0.5464618\",\n \"text\": \"def __call__(self, text):\\n\\n doc = self.model(text)\\n\\n tokens = []\\n for token in doc:\\n if token.is_stop:\\n continue\\n tokens.append(token.lemma_)\\n\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2b1a89ae5507a23c86330350bd1c9a\",\n \"score\": \"0.5464104\",\n \"text\": \"def make_data(sentences):\\n data = []\\n for sentence in sentences:\\n words = [tup[0] for tup in sentence]\\n labels = [tup[1] for tup in sentence]\\n for idx, word in enumerate(words):\\n nerfeat = NerFeatures()\\n features = nerfeat.word_features(idx,words)\\n data.append((features, labels[idx]))\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbcad2bd981137a2101bdb38bc22c597\",\n \"score\": \"0.5454016\",\n \"text\": \"def process_thread(self, sentence_readings):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eb1842b5e5864bfc7454412d9bf7508\",\n \"score\": \"0.54515433\",\n \"text\": \"def read_corpus(file_path, source):\\n data = []\\n for line in open(file_path):\\n sent = nltk.word_tokenize(line)\\n # only append <s> and </s> to the target sentence\\n if source == 'tgt':\\n sent = ['<s>'] + sent + ['</s>']\\n data.append(sent)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ec555c859975d90a1b112f58aa09743\",\n \"score\": \"0.5449063\",\n \"text\": \"def read_all(self):\\n self.train_txt = self.read_abstracts(self._train_text_path)\\n self.dev_txt = self.read_abstracts(self._dev_text_path)\\n self.eval_txt = self.read_abstracts(self._eval_text_path)\\n print(f\\\"Finished reading abstracts.\\\\n# of sentences read: Train: {len(self.train_txt)}, Dev: {len(self.dev_txt)}, Eval: {len(self.eval_txt)}\\\")\\n \\n self.train_anno = self.read_annotations(self._train_label_path)\\n self.dev_anno = self.read_annotations(self._dev_label_path)\\n self.eval_anno = self.read_annotations(self._eval_label_path)\\n print(f\\\"Finished reading annotations\\\")\\n\\n self.punkt_tokenizer = punkt.PunktSentenceTokenizer(self.all_texts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b90f266d7d9b4b17d4c7b519c015756\",\n \"score\": \"0.544312\",\n \"text\": \"def _read_tsv(self, input_file, batch_size=16, quotechar=None):\\n with open(input_file, 'r', encoding='utf8') as f:\\n reader = csv_reader(f)\\n #headers = next(reader)\\n headers = 'query\\\\ttitle\\\\tpara\\\\tlabel'.split('\\\\t')\\n text_indices = [\\n index for index, h in enumerate(headers) if h != \\\"label\\\"\\n ]\\n Example = namedtuple('Example', headers)\\n\\n examples = []\\n for line in reader:\\n for index, text in enumerate(line):\\n if index in text_indices:\\n if self.for_cn:\\n line[index] = text.replace(' ', '')\\n else:\\n line[index] = text\\n example = Example(*line)\\n examples.append(example)\\n while len(examples) % batch_size != 0:\\n examples.append(example)\\n return examples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2797a5a4912cf909c0194808d3cef500\",\n \"score\": \"0.5432627\",\n \"text\": \"def corpusReader(corpus):\\n\\n # Read the corpus and create two lists with all the data\\n with open(corpus, 'r') as corp:\\n sentences = [] # All the sentences\\n correctTags = [] # Correct tags\\n tagData = [] # Sentences with word and tags\\n\\n # Initiate temporary data holders\\n words, tags, oneTag = [], [], []\\n\\n # Read corpus\\n for line in corp:\\n # Sentences are marked by newlines\\n if line == '\\\\n':\\n # Add correct tags to list\\n correctTags.append(tags)\\n # Add words to list of sentence\\n sentences.append(words)\\n # Add tagdata\\n tagData.append(oneTag)\\n\\n # Reset words and tags\\n words, tags, oneTag = [], [], []\\n else:\\n # Add correct tags and words\\n words.append(line.split()[0])\\n tags.append(line.split()[1])\\n bothTags = (line.split()[0], line.split()[1])\\n oneTag.append(bothTags)\\n print(len(sentences))\\n return sentences, correctTags, tagData\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6f4f39cca75da3767b18f317304e9f5\",\n \"score\": \"0.5430335\",\n \"text\": \"def tokenize(self, sequence: str) -> list:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e487a88c97a413c21a7b05c10b9ff688\",\n \"score\": \"0.5429843\",\n \"text\": \"def parse_input(self, input):\\n nlp = self.load_model()\\n doc = nlp(input.title())\\n nlp_output = defaultdict(list)\\n for token in doc:\\n nlp_output[token.i] = [token.text, token.pos_, token.tag_, token.dep_, token.head.text]\\n return nlp_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66049748d71788ac72cfc19b29caed54\",\n \"score\": \"0.54291993\",\n \"text\": \"def _read_data(cls, input_file):\\n with codecs.open(input_file, 'r', encoding='utf-8') as f:\\n lines = []\\n words = []\\n labels = []\\n for line in f:\\n contends = line.strip()\\n tokens = contends.split('\\\\t')\\n if len(tokens) == 2:\\n word = line.strip().split('\\\\t')[0]\\n label = line.strip().split('\\\\t')[-1]\\n else:\\n if len(contends) == 0:\\n l = ' '.join([label for label in labels if len(label) > 0])\\n w = ' '.join([word for word in words if len(word) > 0])\\n lines.append([l, w])\\n words = []\\n labels = []\\n continue\\n if contends.startswith(\\\"-DOCSTART-\\\"):\\n words.append('')\\n continue\\n words.append(word)\\n labels.append(label)\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e213135c1247f661b1f88723f6fba7d\",\n \"score\": \"0.54265004\",\n \"text\": \"def tokenize_batch(self, seqs: List[str]) -> List[List[str]]:\\n return [self.tokenize(s) for s in seqs]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"998d8927b40ec4af07f2c0ddb6e4b9a4\",\n \"score\": \"0.54194725\",\n \"text\": \"def tokenize( self, path ):\\r\\n assert os.path.exists( path )\\r\\n # Add words to the dictionary\\r\\n max_len = 0\\r\\n with open( path, 'r' ) as f:\\r\\n Reader = csv.reader( f, delimiter=',', quoting=csv.QUOTE_MINIMAL )\\r\\n count = 0\\r\\n for record in Reader:\\r\\n count += 1\\r\\n review = record[ 0 ]#.decode( 'utf-8' )\\r\\n # words = [ word for sent in sent_tokenize( review ) for word in word_tokenize( sent ) ]\\r\\n words = review.split()\\r\\n if len( words ) > max_len:\\r\\n max_len = len( words )\\r\\n for word in words:\\r\\n self.dictionary.add_word( word )\\r\\n\\r\\n # Tokenize file content\\r\\n with open( path, 'r' ) as f:\\r\\n Reader = csv.reader( f, delimiter=',', quoting=csv.QUOTE_MINIMAL )\\r\\n data = torch.LongTensor( count, max_len ).fill_( 0 )\\r\\n lengths = []\\r\\n labels = torch.LongTensor( count ).fill_( 0 )\\r\\n\\r\\n for idx, record in enumerate( Reader ):\\r\\n labels[ idx ] = int( record[ 1 ] )\\r\\n review = record[ 0 ]#.decode( 'utf-8' )\\r\\n # words = [ word for sent in sent_tokenize( review ) for word in word_tokenize( sent ) ]\\r\\n words = review.split()\\r\\n lengths.append( len( words ) )\\r\\n \\r\\n for i, word in enumerate( words ):\\r\\n data[ idx, i ] = self.dictionary.word2idx[word]\\r\\n \\r\\n return data, lengths, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e8f913dfd8895f6d55f5c6ef64e82a1\",\n \"score\": \"0.54156566\",\n \"text\": \"def tokenize_dataset(X_train_path=X_train_path):\\n\\tword2index()\\n\\twith open(X_train_path, 'r', encoding='utf8') as f:\\n\\t original_lines = f.readlines()\\n\\t original_lines = [line.replace(\\\"\\\\u3000\\\",\\\"\\\")for line in original_lines]\\n\\t for line in original_lines:\\n\\t \\twords = line.split()\\n\\t \\tfor word in words:\\n\\t \\t\\tchar = []\\n\\t \\t\\tfor c in word:\\n\\t \\t\\t\\ttry:\\n\\t \\t\\t\\t\\tchar.append(unigram_word_to_id[c])\\n\\t \\t\\t\\texcept KeyError:\\n\\t \\t\\t\\t\\tchar.append(unigram_word_to_id[\\\"<UNK>\\\"])\\n\\t \\tX_train_uni.append(char)\\n\\treturn X_train_uni\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ec2b13d0b799a0592c0a934efd3b6f\",\n \"score\": \"0.541384\",\n \"text\": \"def parse(self, sent):\\r\\n self.toks = self.nlp(unicode(sent, errors = 'ignore'))\\r\\n self.idx_to_word_index = self.get_idx_to_word_index()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1babe2081aab7f53bf968a24d6a24be1\",\n \"score\": \"0.5413271\",\n \"text\": \"def tokenize_data(self, data, lower=True):\\n data = copy.deepcopy(data)\\n for sample in tqdm(data['data']):\\n for paragraph in sample['paragraphs']:\\n paragraph['context_raw'] = paragraph['context']\\n if lower:\\n paragraph['context'] = paragraph['context'].lower()\\n\\n # tokenize context\\n paragraph['context'] = self.tokenize(paragraph['context'])\\n paragraph['context_tokenized'] = [tok.text for tok in paragraph['context']]\\n\\n # tokenize question\\n for qa in paragraph['qas']:\\n qa['orig_answer']['raw'] = qa['orig_answer']['text']\\n if lower:\\n qa['question'] = qa['question'].lower()\\n qa['orig_answer']['text'] = \\\\\\n qa['orig_answer']['text'].lower()\\n\\n qa['question'] = self.tokenize(qa['question'])\\n qa['question_tokenized'] = [tok.text for tok in qa['question']]\\n qa['orig_answer']['text'] = \\\\\\n self.tokenize(qa['orig_answer']['text'])\\n\\n # tokenize other answers\\n if 'answers' in qa:\\n for answer in qa['answers']:\\n answer['raw'] = answer['text']\\n if lower:\\n answer['text'] = answer['text'].lower()\\n\\n answer['text'] = self.tokenize(answer['text'])\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e2e62aa5a568ce21344804d0e6c0983\",\n \"score\": \"0.5412524\",\n \"text\": \"def create_words_list_from_file(spark_context, input_file_endpoint):\\n return spark_context.textFile(input_file_endpoint) \\\\\\n .flatMap(lambda line: preprocess(line))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b0b3066c99e950897af39e31948d0fb\",\n \"score\": \"0.5411329\",\n \"text\": \"def read_data(self):\\n print('Reading file ...')\\n self.lines = [line.strip().lower().split() for line in open(self.file_path, 'r', encoding='utf-8')]\\n self.lines = [line for line in self.lines if len(line) < self.max_length]\\n print('Building vocabulary ...')\\n self.word2id, self.id2word = self.construct_vocab(self.lines, self.vocab_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17083bc4992097bcbe07583522809a70\",\n \"score\": \"0.54109085\",\n \"text\": \"def _read_data(self, input_file):\\n with codecs.open(input_file, 'r', encoding='utf-8') as f:\\n lines = []\\n words = []\\n labels = []\\n for line in f:\\n contends = line.strip()\\n tokens = contends.split('\\\\t')\\n if len(tokens) == 2:\\n words.append(tokens[0])\\n labels.append(tokens[1])\\n else:\\n if len(contends) == 0:\\n l = ' '.join([label for label in labels if len(label) > 0])\\n w = ' '.join([word for word in words if len(word) > 0])\\n lines.append([l, w])\\n words = []\\n labels = []\\n continue\\n if contends.startswith(\\\"-DOCSTART-\\\"):\\n words.append('')\\n continue\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a47992c7b003e9c82d5077ff4531ec12\",\n \"score\": \"0.54061186\",\n \"text\": \"def lemmatized_sentence_corpus(self, contents):\\n sentents = []\\n \\n for content in self.nlp.pipe(contents,batch_size=500, n_threads=8):\\n \\n for sent in content.sents:\\n #sentents.append(u' '.join([token.lemma_ for token in sent\\n # if not punct_space(token)]))\\n #sentents.append([token.lemma_ for token in sent\\n # if not punct_space(token)])\\n tokens = []\\n for token in sent:\\n if self.punct_space(token):\\n continue\\n \\n #if token.lemma_ == '-PRON-':\\n # token.lemma_ = token.lower_\\n tokens.append(token.lemma_)\\n \\n sentents.append(tokens)\\n \\n return sentents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2939a4137b3b9b01f57dc74a1c00218\",\n \"score\": \"0.54060394\",\n \"text\": \"def PreprocessSentenceLevel(self,document):\\n sentences=document.splitlines()\\n \\n #lower case transformation \\n sentences = [sent.lower() for sent in sentences]\\n \\n #tokenize\\n toks = [self.wpt.tokenize(sent) for sent in sentences]\\n return toks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1c3f73cfa252a10278717fbcc7c0149\",\n \"score\": \"0.54059327\",\n \"text\": \"def read_dataset(self, input_file, *args, **kwargs):\\n\\n print(\\\"Reading input file\\\")\\n\\n dataType = kwargs.get(\\\"dataType\\\",\\\"common\\\");\\n\\n data = []\\n if(dataType == \\\"common\\\"):\\n\\n lines = [line.strip() for line in open(input_file)]\\n for line in lines:\\n data.append(line.split('\\\\t'))\\n\\n\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30f2a6829180110cdc25fc6e96823ef1\",\n \"score\": \"0.54042745\",\n \"text\": \"def _FillInputQueue(self):\\n start_id = self._vocab.WordToId(data.SENTENCE_START)\\n end_id = self._vocab.WordToId(data.SENTENCE_END)\\n pad_id = self._vocab.WordToId(data.PAD_TOKEN)\\n input_gen = self._TextGenerator(data.ExampleGen(self._data_path))\\n while True:\\n (article, abstract) = input_gen.next()\\n article_sentences = [sent.strip() for sent in\\n data.ToSentences(article, include_token=False)]\\n abstract_sentences = [sent.strip() for sent in\\n data.ToSentences(abstract, include_token=False)]\\n\\n enc_inputs = []\\n # Use the <s> as the <GO> symbol for decoder inputs.\\n dec_inputs = [start_id]\\n\\n # Convert first N sentences to word IDs, stripping existing <s> and </s>.\\n for i in xrange(min(self._max_article_sentences,\\n len(article_sentences))):\\n enc_inputs += data.GetWordIds(article_sentences[i], self._vocab)\\n for i in xrange(min(self._max_abstract_sentences,\\n len(abstract_sentences))):\\n dec_inputs += data.GetWordIds(abstract_sentences[i], self._vocab)\\n\\n # Filter out too-short input\\n if (len(enc_inputs) < self._hps.min_input_len or\\n len(dec_inputs) < self._hps.min_input_len):\\n tf.logging.warning('Drop an example - too short.\\\\nenc:%d\\\\ndec:%d',\\n len(enc_inputs), len(dec_inputs))\\n continue\\n\\n # If we're not truncating input, throw out too-long input\\n if not self._truncate_input:\\n if (len(enc_inputs) > self._hps.enc_timesteps or\\n len(dec_inputs) > self._hps.dec_timesteps):\\n tf.logging.warning('Drop an example - too long.\\\\nenc:%d\\\\ndec:%d',\\n len(enc_inputs), len(dec_inputs))\\n continue\\n # If we are truncating input, do so if necessary\\n else:\\n if len(enc_inputs) > self._hps.enc_timesteps:\\n enc_inputs = enc_inputs[:self._hps.enc_timesteps]\\n if len(dec_inputs) > self._hps.dec_timesteps:\\n dec_inputs = dec_inputs[:self._hps.dec_timesteps]\\n\\n # targets is dec_inputs without <s> at beginning, plus </s> at end\\n targets = dec_inputs[1:]\\n targets.append(end_id)\\n\\n # Now len(enc_inputs) should be <= enc_timesteps, and\\n # len(targets) = len(dec_inputs) should be <= dec_timesteps\\n\\n enc_input_len = len(enc_inputs)\\n dec_output_len = len(targets)\\n\\n # Pad if necessary\\n while len(enc_inputs) < self._hps.enc_timesteps:\\n enc_inputs.append(pad_id)\\n while len(dec_inputs) < self._hps.dec_timesteps:\\n dec_inputs.append(end_id)\\n while len(targets) < self._hps.dec_timesteps:\\n targets.append(end_id)\\n\\n element = ModelInput(enc_inputs, dec_inputs, targets, enc_input_len,\\n dec_output_len, ' '.join(article_sentences),\\n ' '.join(abstract_sentences))\\n self._input_queue.put(element)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fe36f824177faf317529fa21b89d146\",\n \"score\": \"0.5403482\",\n \"text\": \"def __init__(self, data_type=ONEHOT_AVERAGE, use_sub_phrases=True, dataset_path=\\\"stanfordSentimentTreebank\\\", batch_size=50,\\n embedding_dim=None):\\n\\n # load the dataset\\n self.sentiment_dataset = data_loader.SentimentTreeBank(dataset_path, split_words=True)\\n # map data splits to sentences lists\\n self.sentences = {}\\n if use_sub_phrases:\\n self.sentences[TRAIN] = self.sentiment_dataset.get_train_set_phrases()\\n else:\\n self.sentences[TRAIN] = self.sentiment_dataset.get_train_set()\\n\\n self.sentences[VAL] = self.sentiment_dataset.get_validation_set()\\n self.sentences[TEST] = self.sentiment_dataset.get_test_set()\\n\\n # map data splits to sentence input preperation functions\\n words_list = list(self.sentiment_dataset.get_word_counts().keys())\\n if data_type == ONEHOT_AVERAGE:\\n self.sent_func = average_one_hots\\n self.sent_func_kwargs = {\\\"word_to_ind\\\": get_word_to_ind(words_list)}\\n elif data_type == W2V_SEQUENCE:\\n self.sent_func = sentence_to_embedding\\n\\n self.sent_func_kwargs = {\\\"seq_len\\\": SEQ_LEN,\\n \\\"word_to_vec\\\": create_or_load_slim_w2v(words_list),\\n \\\"embedding_dim\\\": embedding_dim\\n }\\n elif data_type == W2V_AVERAGE:\\n self.sent_func = get_w2v_average\\n words_list = list(self.sentiment_dataset.get_word_counts().keys())\\n self.sent_func_kwargs = {\\\"word_to_vec\\\": create_or_load_slim_w2v(words_list),\\n \\\"embedding_dim\\\": embedding_dim\\n }\\n else:\\n raise ValueError(\\\"invalid data_type: {}\\\".format(data_type))\\n # map data splits to torch datasets and iterators\\n self.torch_datasets = {k: OnlineDataset(sentences, self.sent_func, self.sent_func_kwargs) for\\n k, sentences in self.sentences.items()}\\n self.torch_iterators = {k: DataLoader(dataset, batch_size=batch_size, shuffle=k == TRAIN)\\n for k, dataset in self.torch_datasets.items()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee0f33c150c61829c7cb8c594be9d3d5\",\n \"score\": \"0.5400625\",\n \"text\": \"def _get_token_set(self, datasets):\\n tokens = []\\n for ds in (datasets):\\n for doc in ds.documents:\\n for sentence in doc.utterances:\\n for token in sentence:\\n tokens.append(token)\\n return set(tokens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c26f8c97540a0f8bbe7bb251fc2bbc9\",\n \"score\": \"0.53943\",\n \"text\": \"def tokenize_with_sentencepiece(in_file, out_file, sp_model_filename):\\n sp = sentencepiece.SentencePieceProcessor()\\n sp.load(sp_model_filename)\\n packer = msgpack.Packer()\\n for line in in_file:\\n ids = sp.encode_as_ids(line.rstrip())\\n out_file.write(packer.pack(ids))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f52266536e63aeb3370331d83451a5b\",\n \"score\": \"0.5393468\",\n \"text\": \"def get_sentences(input_file):\\n \\n with open(input_file) as f:\\n \\treturn [line.rstrip('\\\\n') for line in f if line.rstrip('\\\\n')]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bf5f78cee41792f6a15f23fc3f9f674\",\n \"score\": \"0.5392137\",\n \"text\": \"def tokenize_dataset(self, data):\\n Portuguese = []\\n English = []\\n for pt, en in data:\\n Portuguese.append(pt.numpy())\\n English.append(en.numpy())\\n tok_pt = tfds.deprecated.text.SubwordTextEncoder.build_from_corpus(\\n Portuguese, target_vocab_size=2**15)\\n tok_en = tfds.deprecated.text.SubwordTextEncoder.build_from_corpus(\\n English, target_vocab_size=2**15)\\n return tok_pt, tok_en\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a79822a6e1ae19f0720721d0636cd73\",\n \"score\": \"0.53901577\",\n \"text\": \"def read_data(self):\\n print('Reading file ...')\\n self.lines = [line.strip().lower().split() for line in open(self.file_path, 'r', encoding='utf-8')]\\n print('Building vocabulary ...')\\n self.word2id, self.id2word = self.construct_vocab(self.lines, self.vocab_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50ee13ab48526a661f5eef41cd45b5c5\",\n \"score\": \"0.53842103\",\n \"text\": \"def read_data(data_path):\\n sentences = []\\n with open(data_path, \\\"r\\\") as f:\\n for line in f:\\n sentences.append(line.strip(\\\"\\\\n\\\").split(\\\" \\\"))\\n\\n return sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15f1d259b752fdde55cae61212c6fed\",\n \"score\": \"0.53826135\",\n \"text\": \"def get_data(train_file, test_file):\\n # Reading and organizing the training data\\n train_reversed_sentences = []\\n train_sentences = read_data(train_file)\\n for x in train_sentences:\\n x2 = x.copy()\\n\\n # Reverse the output sentences so a rhyme scheme could be added if desired\\n x2.reverse()\\n train_reversed_sentences.append(x2)\\n\\n train_inputs = train_sentences[:-1]\\n train_labels = train_reversed_sentences[1:]\\n\\n train_inputs_padded, train_labels_padded = pad_corpus(train_inputs, train_labels)\\n dict, pad = build_vocab(train_inputs_padded)\\n dict[START_TOKEN] = len(dict)\\n\\n train_inputs = convert_to_id(dict, train_inputs_padded)\\n train_labels = convert_to_id(dict, train_labels_padded)\\n\\n\\n # Reading and organizing the testing data\\n test_reversed_sentences = []\\n test_sentences = read_data(test_file)\\n for x in test_sentences:\\n x2 = x.copy()\\n x2.reverse()\\n test_reversed_sentences.append(x2)\\n\\n test_inputs = test_sentences[:-1]\\n test_labels = test_reversed_sentences[1:]\\n\\n test_inputs_padded, test_labels_padded = pad_corpus(test_inputs, test_labels)\\n\\n test_inputs = convert_to_id(dict, test_inputs_padded)\\n test_labels = convert_to_id(dict, test_labels_padded)\\n\\n return train_inputs, train_labels, test_inputs, test_labels, pad, dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ad9cb5eb9a6d08fb5c9bc110deca07a\",\n \"score\": \"0.53796786\",\n \"text\": \"def tokenize_sents(self, sentences, keep_token_positions: bool = ...):\\n ...\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993883,"cells":{"query_id":{"kind":"string","value":"12ae536e1be853f36fd4bbaf1591d538"},"query":{"kind":"string","value":"Activate or deactivate the auto pop feature."},"positive_passages":{"kind":"list like","value":[{"docid":"3e636a39b7a2583660972c17883921ef","score":"0.76103395","text":"def toggle_auto_pop(self):\n self.__auto_pop = not self.__auto_pop","title":""}],"string":"[\n {\n \"docid\": \"3e636a39b7a2583660972c17883921ef\",\n \"score\": \"0.76103395\",\n \"text\": \"def toggle_auto_pop(self):\\n self.__auto_pop = not self.__auto_pop\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"14e749386ee2783666321d92f9bc8f6d","score":"0.6201403","text":"def activate(self):\n self.is_activated = True","title":""},{"docid":"14e749386ee2783666321d92f9bc8f6d","score":"0.6201403","text":"def activate(self):\n self.is_activated = True","title":""},{"docid":"1a6041f926c7353b3dd107d561792b3b","score":"0.61582303","text":"def activate(self):\n self._is_active = True","title":""},{"docid":"1ea57f1e2d562d962f3ce9c3f9120c6a","score":"0.6116671","text":"def activate(self):\n self.active = True","title":""},{"docid":"1ea57f1e2d562d962f3ce9c3f9120c6a","score":"0.6116671","text":"def activate(self):\n self.active = True","title":""},{"docid":"b7a9a33141c2036485dd11e81a62ae50","score":"0.60883534","text":"def turn_auto_on(self) -> None:\n self.set_hvac_mode(\"AutoChangeOver\")","title":""},{"docid":"969df80ad15025d60d428eb5146bbf0d","score":"0.6004758","text":"def activate(self):\n self._active = True","title":""},{"docid":"920b8bdc91eb447dd28d02eb2a06ed3a","score":"0.5908829","text":"def activate(self):\n api_args = {'activate': True}\n self._update(api_args)","title":""},{"docid":"6c4f0f060a08c15adbf4d984a6ca4397","score":"0.5901495","text":"def activate(self) -> None:\n self._active = True","title":""},{"docid":"3503a7b6042be2a531dc8ab5320aaa0d","score":"0.5895016","text":"def activate(self, yesno):\n self.is_active = yesno","title":""},{"docid":"4604c42537c1ade3d7d7debb093794ce","score":"0.5890384","text":"def togglePump():\r\n\t\tADRY.togglePump()","title":""},{"docid":"e44a6bc9454baa0a56a04b234174e875","score":"0.588306","text":"def toggle(self):\n self.active = not self.active","title":""},{"docid":"17309343017530cab7789aea115c1c2c","score":"0.57908684","text":"def _activate(self):\n self.enable()","title":""},{"docid":"2ccd89e0ea6a140cf2ca02a230c75b0a","score":"0.5785833","text":"def makeActive(self):\r\n self.active = True","title":""},{"docid":"2ed607b549d9a07cc43cbca0b5d60ffd","score":"0.5744871","text":"def activate(self):\n self.active = True\n self.show()","title":""},{"docid":"37e832294453d37bfa3eaaed8c344967","score":"0.56774795","text":"def activate(self):\n self.is_active = True\n self.activation = None\n self.save()","title":""},{"docid":"8183efab83f6388b9d724c9b0e5e1b0f","score":"0.5676363","text":"def Activate(self) -> bool:\n ...","title":""},{"docid":"9584780e75965a65754ec51ecf4e81db","score":"0.5661749","text":"def set_active(self):\n self.active = True","title":""},{"docid":"44fd2d8363a6d07ae26d75a1e8ffc576","score":"0.5614073","text":"def activate(self):\r\n #TODO\r","title":""},{"docid":"b92c73eb19b8a554840ec07347dcf3cb","score":"0.5613045","text":"def deactivate(self):\n api_args = {'deactivate': True}\n self._update(api_args)","title":""},{"docid":"7e444791c40fa10e57be9965721c7527","score":"0.56092453","text":"def handle_activate(self, msg):\n self.set_active_behavior(msg.data)","title":""},{"docid":"d4c3ef07078d0f08c3ceea36ad2ab9e4","score":"0.5585258","text":"def deactivate(self):\n self.is_activated = False","title":""},{"docid":"d4c3ef07078d0f08c3ceea36ad2ab9e4","score":"0.5585258","text":"def deactivate(self):\n self.is_activated = False","title":""},{"docid":"ae9ff6b067a97c1b4014d1077e0a5094","score":"0.5560292","text":"def setManipulationActive(state):\n\n pass","title":""},{"docid":"6a4eb3c837f6af96dfe5055445f786d6","score":"0.5548107","text":"def deactivate(self):\n pass","title":""},{"docid":"81bbbee3b43905848f61cdfef0c26bf6","score":"0.5540306","text":"def force_on(self):\n self.action(1)","title":""},{"docid":"3c86ebbf201eb6cd0ca43fd45f89b357","score":"0.5534762","text":"def BCPopupMenuSetActivatedFunction(pm, funct, data):","title":""},{"docid":"75871323e2eebc526654daf94bb3a1d1","score":"0.5531838","text":"def setPopedOut(self):\n if not self.isPopedOut :\n self.setWindowFlags(Qt.Window)\n self.show()\n self.isPopedOut = True\n else :\n self.isPopedOut = False","title":""},{"docid":"09a2ed166e1231d5ecb4965fb1c7a76c","score":"0.5522945","text":"def UsePopUp(self, allowPopUp):","title":""},{"docid":"7657e1ce1b61d78a40c35a678aeb5101","score":"0.5504402","text":"def toggle(self):\n self.active = not self.active\n self.set_state()","title":""},{"docid":"ff8858ea3be8464d05f36f8d2770f160","score":"0.5502029","text":"def __toggleAutoCompletionEnable(self):\n if self.menuActs[\"AutoCompletionEnable\"].isChecked():\n self.setAutoCompletionEnabled(True)\n else:\n self.setAutoCompletionEnabled(False)","title":""},{"docid":"ad5897dee3e58375958d5a4d3b448c86","score":"0.5496792","text":"def togglePumpValve():\r\n\t\tADRY.togglePumpValve()","title":""},{"docid":"164e2607de30a0f7a9c911d465e3f192","score":"0.5491453","text":"def turn_on(self, **kwargs):\n self._automation.set_active(True)","title":""},{"docid":"d7d05ffda078e753874cf0c57663e522","score":"0.54780716","text":"def enable_auto_mode(self) -> None:\n self._set_configuration(auto=\"ON\")","title":""},{"docid":"bdc4a479a6091ed04e3012f89997f2e8","score":"0.5465736","text":"def toolbar_toggle(self, obj, data=None):\n if obj.get_active():\n self.toolbar.show()\n config.set('interface.toolbar-on', True)\n else:\n self.toolbar.hide()\n config.set('interface.toolbar-on', False)\n config.save()","title":""},{"docid":"c0ecf4c264718d764175037bee9b7b04","score":"0.54591775","text":"def Activate(self):","title":""},{"docid":"10545de926bb28efd883d49d314011fd","score":"0.5453108","text":"async def _global_auto(self, ctx: commands.Context, true_or_false: bool):\n await self.config.auto.set(true_or_false)\n return await ctx.tick()","title":""},{"docid":"4701c1cf9fb51ea1ad6fb94a2624b0f4","score":"0.5440088","text":"def activate(self):\n pass","title":""},{"docid":"4701c1cf9fb51ea1ad6fb94a2624b0f4","score":"0.5440088","text":"def activate(self):\n pass","title":""},{"docid":"ff4dd148b79175333e525efbbfdc9f61","score":"0.54327685","text":"def last_action(self, event):\n\n # We launch the automatic by unstopping it then activate it\n self.is_stopped = False\n self.is_auto = True","title":""},{"docid":"0067e51a8855bd9b7ff6043839ac0913","score":"0.54321873","text":"def open_gripper(self) -> None:\n self.set_gripper_target_state(0)","title":""},{"docid":"0e9614e30da51a1b01b7ff2abfd6e4fc","score":"0.541344","text":"def deactivate(self):\n self.active = False","title":""},{"docid":"6d192ac1e58b9595404c59df62902e74","score":"0.5406592","text":"def turn_on_or_off(activate):\n return True if activate == 'on' else False","title":""},{"docid":"854f3fbc023c530a5501dcbbcb08072a","score":"0.53927314","text":"def toggle(self):\n\t\treturn","title":""},{"docid":"40210c6844947c346f7ba9e9257a88bf","score":"0.5388568","text":"def on_activate(self, data=None):\n self.toggle_wicd_gui()","title":""},{"docid":"6821ad0a0368bd8d30b751a8263ce88a","score":"0.5385757","text":"def deactivate(self):\n self.__is_active = False","title":""},{"docid":"22a63a3d482de1d0c1ca1675f5e8fd4a","score":"0.5385492","text":"def __autosaveEnable(self):\n if self.menuActs[\"AutosaveEnable\"].isChecked():\n self.autosaveManuallyDisabled = False\n else:\n self.autosaveManuallyDisabled = True","title":""},{"docid":"0f2ccf9bea9873054af1de7f672d562d","score":"0.5384494","text":"def activated(self):\n pass","title":""},{"docid":"90a5460c3113479824685261b90d6bf3","score":"0.5376509","text":"def active(self, active):\n\n\n self._active = active","title":""},{"docid":"e669cf97cbb46d04d711abecb9437db1","score":"0.5374556","text":"def setPopupMode( self ):\n self.setCurrentMode(XPopupWidget.Mode.Popup)","title":""},{"docid":"89c1b0a9c8be2049b9a4dbb7bedcdf6f","score":"0.5366417","text":"def do_activate(self, mode=-1):\n if mode == -1:\n self._active = not self._active\n elif mode == 0:\n self._active = False\n else:\n self._active = True\n return 'Active status: {0}'.format(self._active)","title":""},{"docid":"efad5a89ace95b11d490bcdcf88add7b","score":"0.5355317","text":"def activate(self):\n if self.get_descriptor().activate:\n self.get_descriptor().activate(self.get_handle())","title":""},{"docid":"e28c5773bb0d02eec9e6e2d4aec17fe9","score":"0.5351237","text":"def activate(self):\n self.addBut.config(state='normal')\n self.delBut.config(state='normal')\n self.darkscreen.grid_forget()\n self.darkscreen.destroy()","title":""},{"docid":"019988ab46009f659d5a7cfb17058ff9","score":"0.53431916","text":"def toggle(self, env, pos):\n return False","title":""},{"docid":"019988ab46009f659d5a7cfb17058ff9","score":"0.53431916","text":"def toggle(self, env, pos):\n return False","title":""},{"docid":"7669859ba4f9e72c908050dc6108b2f4","score":"0.5339921","text":"async def async_turn_on(self, **kwargs):\n await self._smartplug.set_away_mode_action(\"Off\")\n await self.async_force_update()\n return True","title":""},{"docid":"5143eb2e224e4649f8183d5760110e50","score":"0.533768","text":"def setup (self):\n self.active = True\n return True","title":""},{"docid":"bad42c4ec041db41f07bef8badd380c0","score":"0.5313663","text":"def trigger_single(self):\n self.clear_sweeps()\n self.trigger_mode = \"Single\"","title":""},{"docid":"bbc82ae97e4e5dea224b805eda05a6bc","score":"0.5313484","text":"def enableAutoExit(self):\n ret = self.uEyeDll.is_EnableAutoExit (self.cam, c_uint(1))\n if ret == IS_SUCCESS:\n self.logger.info(\"Successfully enabled auto exit\")\n else:\n self.logger.error(\"Failed to enable auto exit; error code: \" + str(ret))\n return","title":""},{"docid":"db47960773b0f39b48e81c651dcf11ba","score":"0.5307835","text":"def activate_behavior(self, behavior):\n self.active_behaviors.append(behavior)","title":""},{"docid":"45f6702d5dab307a8184900612ccf413","score":"0.5305725","text":"def activate(self):\r\n self._active = True\r\n self.process()","title":""},{"docid":"62016cf8f03920a6a0d27449a1d15d91","score":"0.53047377","text":"def on_deactivate(self):\n return True","title":""},{"docid":"4bce9dc9ea561abfc88b23d55a5d9a70","score":"0.53002375","text":"def activate(self):\n pass","title":""},{"docid":"ffe447722cbf5fb9e622701bbaebac38","score":"0.5299131","text":"def auto(self):\n\n super().auto()\n\n # We used to use restore=True instead of restore = \"always\"\n if self.restore in [\"True\", \"true\", True]:\n self.restore = \"always\"","title":""},{"docid":"2944ffa8408fa22751c4790859ea9ccb","score":"0.5298243","text":"def deactivate(self):\n self._is_active = False","title":""},{"docid":"5954110d5fdd3f875ed62a07ab74a90b","score":"0.5294311","text":"def set_pop_scroll_active(active: bool):\n global pop_scroll_active\n global pop_scroll_currently_scrolling\n global pop_scrolling_direction\n\n pop_scroll_active = active\n if pop_scroll_active:\n gui_pop_scroll_active.show()\n else:\n pop_scroll_active = False\n pop_scroll_currently_scrolling = False\n pop_scrolling_direction = \"down\"\n actions.user.mouse_scroll_stop()\n gui_pop_scroll_active.hide()","title":""},{"docid":"cfc540d065854e162e6a0da3a5a5dfac","score":"0.5294188","text":"def Activate(self):\n self._inspector_backend.Activate()","title":""},{"docid":"818111bc3f95e801c64c533d0d72d424","score":"0.52923334","text":"def on_activate(self):\n pass","title":""},{"docid":"818111bc3f95e801c64c533d0d72d424","score":"0.52923334","text":"def on_activate(self):\n pass","title":""},{"docid":"4751d825a6ada30987fbe7fd5c369217","score":"0.5278565","text":"def toggle_activate(self, vobj):\n if self.is_active:\n self.deactivate(vobj)\n else:\n self.activate(vobj)","title":""},{"docid":"b2990157c65c6c127ea4621e7e5e5933","score":"0.52649194","text":"def SetHotspotActiveForeground(self, useSetting, fore):","title":""},{"docid":"51b1da5d975c5fdebf65c890a7785772","score":"0.5260555","text":"def deactivate(self):\n pass","title":""},{"docid":"51b1da5d975c5fdebf65c890a7785772","score":"0.5260555","text":"def deactivate(self):\n pass","title":""},{"docid":"a91deac4d876aacea9b787622c0400d0","score":"0.5257478","text":"def deactivate(self):\n self.is_active = False\n self.activation = None\n self.save()","title":""},{"docid":"05cd810f0b4941730d11ada66ebc09b2","score":"0.5252188","text":"def set_autoeval(self, args):\n try:\n self.autoeval = self.get_onoff(args[1])\n except ValueError:\n pass\n return","title":""},{"docid":"87647aace08f3ce59a416229964071a6","score":"0.5247696","text":"def open_gripper(self):\n self.robot.send_program(\"set_tool_digital_out(%s, %s)\" % (0, False))\n time.sleep(0.5)","title":""},{"docid":"91508a6274b1336de5b180bcb345d06a","score":"0.524723","text":"def _enable_autopx(self):\n if self.active_view is None:\n raise UsageError(NO_ACTIVE_VIEW)\n\n # override run_cell\n self._original_run_cell = self.shell.run_cell\n self.shell.run_cell = self.pxrun_cell\n\n self._autopx = True\n print \"%autopx enabled\"","title":""},{"docid":"6884734668c97997379a9b1cbc22d8fe","score":"0.5246529","text":"def SetActive(loop):","title":""},{"docid":"34231f5a81e339cfdfe5f17e79e737bb","score":"0.5243358","text":"def activate (self):\n self.lock ()\n if not self.sensitive:\n return\n self.focus = True\n self.set_active (not self.active)\n self.unlock ()\n self.run_signal_handlers (SIG_TOGGLED)","title":""},{"docid":"e8ca32f80e336dbc2493bfe822c1a318","score":"0.5241219","text":"def activate_ble_pairing_mode(self):\n self.pri_ad.activate_ble_pairing_mode()","title":""},{"docid":"4b810950928b9cab7b1ef5664b71e1dd","score":"0.52383345","text":"def setActive(self) -> None:\n ...","title":""},{"docid":"67d2eb927c1e9a159f0304ce5064f2f3","score":"0.52343154","text":"def AutoCompActive(self):","title":""},{"docid":"01ef5fc2631b1a5f55310733ee6ed9a2","score":"0.5231285","text":"def _disable_autopx(self):\n if self._autopx:\n self.shell.run_cell = self._original_run_cell\n self._autopx = False\n print \"%autopx disabled\"","title":""},{"docid":"225029a88bd6077dd5251b62f378a9bf","score":"0.5225826","text":"def toggle_active(self):\n delivery_carrier_obj = self.env['delivery.carrier']\n delivery_methods = delivery_carrier_obj.search([('shipping_instance_id','=',self.id)])\n if delivery_methods:\n delivery_methods.write({'active':False}) \n for record in self:\n record.active = not record.active","title":""},{"docid":"c5e129b02461901540589af63fea0193","score":"0.52230155","text":"def toggle_pop_up(box: MouseBox, *_: Any, **__: Any) -> None:\n nonlocal toggled\n if toggled:\n log.debug(f\"Toggled remove pop up {pop_up} from {box}.\")\n box.remove(pop_up)\n else:\n log.debug(f\"Toggled create pop up {pop_up} from {box}.\")\n box.add(pop_up)\n toggled = not toggled","title":""},{"docid":"f4e83098b62eedffdd2193f2c0fa6489","score":"0.5218558","text":"def do_toggle(self):\n args = self.args\n wf = self.wf\n\n key = args.get('<key>')\n value = wf.settings.get(key)\n if value:\n wf.settings[key] = False\n status = 'off'\n else:\n wf.settings[key] = True\n status = 'on'\n print(\"Option '{0}' turned {1}\".format(key, status))\n\n # Clear cached scripts\n wf.clear_cache(lambda filename: filename.startswith('appscripts-'))","title":""},{"docid":"ba20933c20d8d49b88eb4ccfc0369a65","score":"0.52046543","text":"def deactivate(self):\n self._active = False","title":""},{"docid":"6b2eca4b67a8962afe93e27462ba7ed4","score":"0.5196033","text":"def _activate(self):\n self._toggle_until = time.monotonic() + self._toggle_for.total_seconds()\n track_point_in_time(\n self.hass,\n self.async_update_ha_state,\n dt_util.utcnow() + self._toggle_for,\n )\n momentary_remote_rpi_gpio.write_output(self._switch, 1)\n self._state = True\n _LOGGER.debug(\"turned on\")\n self.schedule_update_ha_state()","title":""},{"docid":"99c1119d19b493fd599bcd2ea98e143c","score":"0.51938653","text":"def DoSetPopupControl(self, popup):","title":""},{"docid":"df9f2502467f0e86a92ef931a2fa7083","score":"0.5187798","text":"def toggle(self) -> None:\n self.enabled = not self.enabled","title":""},{"docid":"a73493a08ebf71b2e98127638d2013af","score":"0.5182848","text":"def turn_on(self):\n hub.my_pages.smartplug.set(self._id, 'on')\n hub.my_pages.smartplug.wait_while_updating(self._id, 'on')\n self.update()","title":""},{"docid":"2f6d852713e8165801166a05e7b6bd4b","score":"0.51809615","text":"def mouse_on_pop():\n global last_pop_at\n global pop_scroll_currently_scrolling\n global pop_scrolling_direction\n\n is_double = False\n if last_pop_at is not None and time.perf_counter() - last_pop_at < 0.25:\n is_double = True\n\n last_pop_at = time.perf_counter()\n\n if pop_scroll_active:\n if is_double:\n # Set currently_scrolling to False, so that it will always be started again in the next if statement\n pop_scroll_currently_scrolling = False\n pop_scrolling_direction = \"up\" if pop_scrolling_direction == \"down\" else \"down\"\n\n if pop_scroll_currently_scrolling == False:\n if pop_scrolling_direction == \"down\":\n actions.user.mouse_scroll_down_continuous()\n else:\n actions.user.mouse_scroll_up_continuous()\n pop_scroll_currently_scrolling = True\n else:\n actions.user.mouse_scroll_stop()\n pop_scroll_currently_scrolling = False","title":""},{"docid":"f69aca03d521597fecf904619a8176b1","score":"0.51800144","text":"def do_auto(self, line):\t\n\t\tself.auto = not self.auto\t\n\t\tif(self.auto):\n\t\t\tprint 'Enabled'\n\t\telse:\n\t\t\tprint 'Disabled'","title":""},{"docid":"62ca91d044630e07b11a8b3ace7ec5d3","score":"0.51761997","text":"def test_activate(self):\n g = self.goal1(self.world)\n \n #active by default\n self.assertTrue(g.active)\n self.assertTrue(g.name in self.world.active_goals)\n \n #deactivate works\n g.deactivate()\n self.assertFalse(g.active)\n self.assertFalse(g.name in self.world.active_goals)\n \n #activate works\n g.activate()\n self.assertTrue(g.active)\n self.assertTrue(g.name in self.world.active_goals)\n \n #set_activation works\n active = g.set_activation(False)\n self.assertTrue(active)\n self.assertFalse(g.active)","title":""},{"docid":"344bd58619d2061c992d25a74815539a","score":"0.5175975","text":"def active(self, active):\n self._active = active","title":""},{"docid":"dce013547d007860fce14af2150b38fc","score":"0.5158029","text":"def activate():\n game.current_player.confirm()\n\n # TODO: All AbstractActions return True. Remove this if possible\n return True","title":""},{"docid":"8708b65e6191ea32117606f0877262b1","score":"0.5152921","text":"def set_active(self, active):\n\t\tself.active = active","title":""},{"docid":"6e3b7903170c1f09bdce70a822c850d8","score":"0.51504624","text":"def ActivatePrevious(self):","title":""},{"docid":"d07edeccb4acb751cf9f633d43664956","score":"0.51430744","text":"def deactivate(self) -> None:\n self._active = False","title":""},{"docid":"8ee83a4616b1094516be2f582036de5a","score":"0.5139641","text":"def man_active(self, state):\n if state == QtCore.Qt.Checked:\n self.man_active_flag = 1\n self.manual_cut_btn.setEnabled(True)\n else:\n self.man_active_flag = 0\n self.manual_cut_btn.setEnabled(False)","title":""}],"string":"[\n {\n \"docid\": \"14e749386ee2783666321d92f9bc8f6d\",\n \"score\": \"0.6201403\",\n \"text\": \"def activate(self):\\n self.is_activated = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e749386ee2783666321d92f9bc8f6d\",\n \"score\": \"0.6201403\",\n \"text\": \"def activate(self):\\n self.is_activated = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a6041f926c7353b3dd107d561792b3b\",\n \"score\": \"0.61582303\",\n \"text\": \"def activate(self):\\n self._is_active = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ea57f1e2d562d962f3ce9c3f9120c6a\",\n \"score\": \"0.6116671\",\n \"text\": \"def activate(self):\\n self.active = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ea57f1e2d562d962f3ce9c3f9120c6a\",\n \"score\": \"0.6116671\",\n \"text\": \"def activate(self):\\n self.active = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7a9a33141c2036485dd11e81a62ae50\",\n \"score\": \"0.60883534\",\n \"text\": \"def turn_auto_on(self) -> None:\\n self.set_hvac_mode(\\\"AutoChangeOver\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"969df80ad15025d60d428eb5146bbf0d\",\n \"score\": \"0.6004758\",\n \"text\": \"def activate(self):\\n self._active = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"920b8bdc91eb447dd28d02eb2a06ed3a\",\n \"score\": \"0.5908829\",\n \"text\": \"def activate(self):\\n api_args = {'activate': True}\\n self._update(api_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c4f0f060a08c15adbf4d984a6ca4397\",\n \"score\": \"0.5901495\",\n \"text\": \"def activate(self) -> None:\\n self._active = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3503a7b6042be2a531dc8ab5320aaa0d\",\n \"score\": \"0.5895016\",\n \"text\": \"def activate(self, yesno):\\n self.is_active = yesno\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4604c42537c1ade3d7d7debb093794ce\",\n \"score\": \"0.5890384\",\n \"text\": \"def togglePump():\\r\\n\\t\\tADRY.togglePump()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e44a6bc9454baa0a56a04b234174e875\",\n \"score\": \"0.588306\",\n \"text\": \"def toggle(self):\\n self.active = not self.active\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17309343017530cab7789aea115c1c2c\",\n \"score\": \"0.57908684\",\n \"text\": \"def _activate(self):\\n self.enable()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ccd89e0ea6a140cf2ca02a230c75b0a\",\n \"score\": \"0.5785833\",\n \"text\": \"def makeActive(self):\\r\\n self.active = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ed607b549d9a07cc43cbca0b5d60ffd\",\n \"score\": \"0.5744871\",\n \"text\": \"def activate(self):\\n self.active = True\\n self.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37e832294453d37bfa3eaaed8c344967\",\n \"score\": \"0.56774795\",\n \"text\": \"def activate(self):\\n self.is_active = True\\n self.activation = None\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8183efab83f6388b9d724c9b0e5e1b0f\",\n \"score\": \"0.5676363\",\n \"text\": \"def Activate(self) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9584780e75965a65754ec51ecf4e81db\",\n \"score\": \"0.5661749\",\n \"text\": \"def set_active(self):\\n self.active = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44fd2d8363a6d07ae26d75a1e8ffc576\",\n \"score\": \"0.5614073\",\n \"text\": \"def activate(self):\\r\\n #TODO\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b92c73eb19b8a554840ec07347dcf3cb\",\n \"score\": \"0.5613045\",\n \"text\": \"def deactivate(self):\\n api_args = {'deactivate': True}\\n self._update(api_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e444791c40fa10e57be9965721c7527\",\n \"score\": \"0.56092453\",\n \"text\": \"def handle_activate(self, msg):\\n self.set_active_behavior(msg.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4c3ef07078d0f08c3ceea36ad2ab9e4\",\n \"score\": \"0.5585258\",\n \"text\": \"def deactivate(self):\\n self.is_activated = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4c3ef07078d0f08c3ceea36ad2ab9e4\",\n \"score\": \"0.5585258\",\n \"text\": \"def deactivate(self):\\n self.is_activated = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae9ff6b067a97c1b4014d1077e0a5094\",\n \"score\": \"0.5560292\",\n \"text\": \"def setManipulationActive(state):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a4eb3c837f6af96dfe5055445f786d6\",\n \"score\": \"0.5548107\",\n \"text\": \"def deactivate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81bbbee3b43905848f61cdfef0c26bf6\",\n \"score\": \"0.5540306\",\n \"text\": \"def force_on(self):\\n self.action(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c86ebbf201eb6cd0ca43fd45f89b357\",\n \"score\": \"0.5534762\",\n \"text\": \"def BCPopupMenuSetActivatedFunction(pm, funct, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75871323e2eebc526654daf94bb3a1d1\",\n \"score\": \"0.5531838\",\n \"text\": \"def setPopedOut(self):\\n if not self.isPopedOut :\\n self.setWindowFlags(Qt.Window)\\n self.show()\\n self.isPopedOut = True\\n else :\\n self.isPopedOut = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09a2ed166e1231d5ecb4965fb1c7a76c\",\n \"score\": \"0.5522945\",\n \"text\": \"def UsePopUp(self, allowPopUp):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7657e1ce1b61d78a40c35a678aeb5101\",\n \"score\": \"0.5504402\",\n \"text\": \"def toggle(self):\\n self.active = not self.active\\n self.set_state()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff8858ea3be8464d05f36f8d2770f160\",\n \"score\": \"0.5502029\",\n \"text\": \"def __toggleAutoCompletionEnable(self):\\n if self.menuActs[\\\"AutoCompletionEnable\\\"].isChecked():\\n self.setAutoCompletionEnabled(True)\\n else:\\n self.setAutoCompletionEnabled(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad5897dee3e58375958d5a4d3b448c86\",\n \"score\": \"0.5496792\",\n \"text\": \"def togglePumpValve():\\r\\n\\t\\tADRY.togglePumpValve()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"164e2607de30a0f7a9c911d465e3f192\",\n \"score\": \"0.5491453\",\n \"text\": \"def turn_on(self, **kwargs):\\n self._automation.set_active(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d05ffda078e753874cf0c57663e522\",\n \"score\": \"0.54780716\",\n \"text\": \"def enable_auto_mode(self) -> None:\\n self._set_configuration(auto=\\\"ON\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdc4a479a6091ed04e3012f89997f2e8\",\n \"score\": \"0.5465736\",\n \"text\": \"def toolbar_toggle(self, obj, data=None):\\n if obj.get_active():\\n self.toolbar.show()\\n config.set('interface.toolbar-on', True)\\n else:\\n self.toolbar.hide()\\n config.set('interface.toolbar-on', False)\\n config.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0ecf4c264718d764175037bee9b7b04\",\n \"score\": \"0.54591775\",\n \"text\": \"def Activate(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10545de926bb28efd883d49d314011fd\",\n \"score\": \"0.5453108\",\n \"text\": \"async def _global_auto(self, ctx: commands.Context, true_or_false: bool):\\n await self.config.auto.set(true_or_false)\\n return await ctx.tick()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4701c1cf9fb51ea1ad6fb94a2624b0f4\",\n \"score\": \"0.5440088\",\n \"text\": \"def activate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4701c1cf9fb51ea1ad6fb94a2624b0f4\",\n \"score\": \"0.5440088\",\n \"text\": \"def activate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff4dd148b79175333e525efbbfdc9f61\",\n \"score\": \"0.54327685\",\n \"text\": \"def last_action(self, event):\\n\\n # We launch the automatic by unstopping it then activate it\\n self.is_stopped = False\\n self.is_auto = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0067e51a8855bd9b7ff6043839ac0913\",\n \"score\": \"0.54321873\",\n \"text\": \"def open_gripper(self) -> None:\\n self.set_gripper_target_state(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e9614e30da51a1b01b7ff2abfd6e4fc\",\n \"score\": \"0.541344\",\n \"text\": \"def deactivate(self):\\n self.active = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d192ac1e58b9595404c59df62902e74\",\n \"score\": \"0.5406592\",\n \"text\": \"def turn_on_or_off(activate):\\n return True if activate == 'on' else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"854f3fbc023c530a5501dcbbcb08072a\",\n \"score\": \"0.53927314\",\n \"text\": \"def toggle(self):\\n\\t\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40210c6844947c346f7ba9e9257a88bf\",\n \"score\": \"0.5388568\",\n \"text\": \"def on_activate(self, data=None):\\n self.toggle_wicd_gui()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6821ad0a0368bd8d30b751a8263ce88a\",\n \"score\": \"0.5385757\",\n \"text\": \"def deactivate(self):\\n self.__is_active = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22a63a3d482de1d0c1ca1675f5e8fd4a\",\n \"score\": \"0.5385492\",\n \"text\": \"def __autosaveEnable(self):\\n if self.menuActs[\\\"AutosaveEnable\\\"].isChecked():\\n self.autosaveManuallyDisabled = False\\n else:\\n self.autosaveManuallyDisabled = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f2ccf9bea9873054af1de7f672d562d\",\n \"score\": \"0.5384494\",\n \"text\": \"def activated(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90a5460c3113479824685261b90d6bf3\",\n \"score\": \"0.5376509\",\n \"text\": \"def active(self, active):\\n\\n\\n self._active = active\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e669cf97cbb46d04d711abecb9437db1\",\n \"score\": \"0.5374556\",\n \"text\": \"def setPopupMode( self ):\\n self.setCurrentMode(XPopupWidget.Mode.Popup)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89c1b0a9c8be2049b9a4dbb7bedcdf6f\",\n \"score\": \"0.5366417\",\n \"text\": \"def do_activate(self, mode=-1):\\n if mode == -1:\\n self._active = not self._active\\n elif mode == 0:\\n self._active = False\\n else:\\n self._active = True\\n return 'Active status: {0}'.format(self._active)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efad5a89ace95b11d490bcdcf88add7b\",\n \"score\": \"0.5355317\",\n \"text\": \"def activate(self):\\n if self.get_descriptor().activate:\\n self.get_descriptor().activate(self.get_handle())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e28c5773bb0d02eec9e6e2d4aec17fe9\",\n \"score\": \"0.5351237\",\n \"text\": \"def activate(self):\\n self.addBut.config(state='normal')\\n self.delBut.config(state='normal')\\n self.darkscreen.grid_forget()\\n self.darkscreen.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019988ab46009f659d5a7cfb17058ff9\",\n \"score\": \"0.53431916\",\n \"text\": \"def toggle(self, env, pos):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019988ab46009f659d5a7cfb17058ff9\",\n \"score\": \"0.53431916\",\n \"text\": \"def toggle(self, env, pos):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7669859ba4f9e72c908050dc6108b2f4\",\n \"score\": \"0.5339921\",\n \"text\": \"async def async_turn_on(self, **kwargs):\\n await self._smartplug.set_away_mode_action(\\\"Off\\\")\\n await self.async_force_update()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5143eb2e224e4649f8183d5760110e50\",\n \"score\": \"0.533768\",\n \"text\": \"def setup (self):\\n self.active = True\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bad42c4ec041db41f07bef8badd380c0\",\n \"score\": \"0.5313663\",\n \"text\": \"def trigger_single(self):\\n self.clear_sweeps()\\n self.trigger_mode = \\\"Single\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbc82ae97e4e5dea224b805eda05a6bc\",\n \"score\": \"0.5313484\",\n \"text\": \"def enableAutoExit(self):\\n ret = self.uEyeDll.is_EnableAutoExit (self.cam, c_uint(1))\\n if ret == IS_SUCCESS:\\n self.logger.info(\\\"Successfully enabled auto exit\\\")\\n else:\\n self.logger.error(\\\"Failed to enable auto exit; error code: \\\" + str(ret))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db47960773b0f39b48e81c651dcf11ba\",\n \"score\": \"0.5307835\",\n \"text\": \"def activate_behavior(self, behavior):\\n self.active_behaviors.append(behavior)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45f6702d5dab307a8184900612ccf413\",\n \"score\": \"0.5305725\",\n \"text\": \"def activate(self):\\r\\n self._active = True\\r\\n self.process()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62016cf8f03920a6a0d27449a1d15d91\",\n \"score\": \"0.53047377\",\n \"text\": \"def on_deactivate(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bce9dc9ea561abfc88b23d55a5d9a70\",\n \"score\": \"0.53002375\",\n \"text\": \"def activate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffe447722cbf5fb9e622701bbaebac38\",\n \"score\": \"0.5299131\",\n \"text\": \"def auto(self):\\n\\n super().auto()\\n\\n # We used to use restore=True instead of restore = \\\"always\\\"\\n if self.restore in [\\\"True\\\", \\\"true\\\", True]:\\n self.restore = \\\"always\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2944ffa8408fa22751c4790859ea9ccb\",\n \"score\": \"0.5298243\",\n \"text\": \"def deactivate(self):\\n self._is_active = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5954110d5fdd3f875ed62a07ab74a90b\",\n \"score\": \"0.5294311\",\n \"text\": \"def set_pop_scroll_active(active: bool):\\n global pop_scroll_active\\n global pop_scroll_currently_scrolling\\n global pop_scrolling_direction\\n\\n pop_scroll_active = active\\n if pop_scroll_active:\\n gui_pop_scroll_active.show()\\n else:\\n pop_scroll_active = False\\n pop_scroll_currently_scrolling = False\\n pop_scrolling_direction = \\\"down\\\"\\n actions.user.mouse_scroll_stop()\\n gui_pop_scroll_active.hide()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfc540d065854e162e6a0da3a5a5dfac\",\n \"score\": \"0.5294188\",\n \"text\": \"def Activate(self):\\n self._inspector_backend.Activate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"818111bc3f95e801c64c533d0d72d424\",\n \"score\": \"0.52923334\",\n \"text\": \"def on_activate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"818111bc3f95e801c64c533d0d72d424\",\n \"score\": \"0.52923334\",\n \"text\": \"def on_activate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4751d825a6ada30987fbe7fd5c369217\",\n \"score\": \"0.5278565\",\n \"text\": \"def toggle_activate(self, vobj):\\n if self.is_active:\\n self.deactivate(vobj)\\n else:\\n self.activate(vobj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2990157c65c6c127ea4621e7e5e5933\",\n \"score\": \"0.52649194\",\n \"text\": \"def SetHotspotActiveForeground(self, useSetting, fore):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51b1da5d975c5fdebf65c890a7785772\",\n \"score\": \"0.5260555\",\n \"text\": \"def deactivate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51b1da5d975c5fdebf65c890a7785772\",\n \"score\": \"0.5260555\",\n \"text\": \"def deactivate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a91deac4d876aacea9b787622c0400d0\",\n \"score\": \"0.5257478\",\n \"text\": \"def deactivate(self):\\n self.is_active = False\\n self.activation = None\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05cd810f0b4941730d11ada66ebc09b2\",\n \"score\": \"0.5252188\",\n \"text\": \"def set_autoeval(self, args):\\n try:\\n self.autoeval = self.get_onoff(args[1])\\n except ValueError:\\n pass\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87647aace08f3ce59a416229964071a6\",\n \"score\": \"0.5247696\",\n \"text\": \"def open_gripper(self):\\n self.robot.send_program(\\\"set_tool_digital_out(%s, %s)\\\" % (0, False))\\n time.sleep(0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91508a6274b1336de5b180bcb345d06a\",\n \"score\": \"0.524723\",\n \"text\": \"def _enable_autopx(self):\\n if self.active_view is None:\\n raise UsageError(NO_ACTIVE_VIEW)\\n\\n # override run_cell\\n self._original_run_cell = self.shell.run_cell\\n self.shell.run_cell = self.pxrun_cell\\n\\n self._autopx = True\\n print \\\"%autopx enabled\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6884734668c97997379a9b1cbc22d8fe\",\n \"score\": \"0.5246529\",\n \"text\": \"def SetActive(loop):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34231f5a81e339cfdfe5f17e79e737bb\",\n \"score\": \"0.5243358\",\n \"text\": \"def activate (self):\\n self.lock ()\\n if not self.sensitive:\\n return\\n self.focus = True\\n self.set_active (not self.active)\\n self.unlock ()\\n self.run_signal_handlers (SIG_TOGGLED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8ca32f80e336dbc2493bfe822c1a318\",\n \"score\": \"0.5241219\",\n \"text\": \"def activate_ble_pairing_mode(self):\\n self.pri_ad.activate_ble_pairing_mode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b810950928b9cab7b1ef5664b71e1dd\",\n \"score\": \"0.52383345\",\n \"text\": \"def setActive(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67d2eb927c1e9a159f0304ce5064f2f3\",\n \"score\": \"0.52343154\",\n \"text\": \"def AutoCompActive(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01ef5fc2631b1a5f55310733ee6ed9a2\",\n \"score\": \"0.5231285\",\n \"text\": \"def _disable_autopx(self):\\n if self._autopx:\\n self.shell.run_cell = self._original_run_cell\\n self._autopx = False\\n print \\\"%autopx disabled\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"225029a88bd6077dd5251b62f378a9bf\",\n \"score\": \"0.5225826\",\n \"text\": \"def toggle_active(self):\\n delivery_carrier_obj = self.env['delivery.carrier']\\n delivery_methods = delivery_carrier_obj.search([('shipping_instance_id','=',self.id)])\\n if delivery_methods:\\n delivery_methods.write({'active':False}) \\n for record in self:\\n record.active = not record.active\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5e129b02461901540589af63fea0193\",\n \"score\": \"0.52230155\",\n \"text\": \"def toggle_pop_up(box: MouseBox, *_: Any, **__: Any) -> None:\\n nonlocal toggled\\n if toggled:\\n log.debug(f\\\"Toggled remove pop up {pop_up} from {box}.\\\")\\n box.remove(pop_up)\\n else:\\n log.debug(f\\\"Toggled create pop up {pop_up} from {box}.\\\")\\n box.add(pop_up)\\n toggled = not toggled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4e83098b62eedffdd2193f2c0fa6489\",\n \"score\": \"0.5218558\",\n \"text\": \"def do_toggle(self):\\n args = self.args\\n wf = self.wf\\n\\n key = args.get('<key>')\\n value = wf.settings.get(key)\\n if value:\\n wf.settings[key] = False\\n status = 'off'\\n else:\\n wf.settings[key] = True\\n status = 'on'\\n print(\\\"Option '{0}' turned {1}\\\".format(key, status))\\n\\n # Clear cached scripts\\n wf.clear_cache(lambda filename: filename.startswith('appscripts-'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba20933c20d8d49b88eb4ccfc0369a65\",\n \"score\": \"0.52046543\",\n \"text\": \"def deactivate(self):\\n self._active = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b2eca4b67a8962afe93e27462ba7ed4\",\n \"score\": \"0.5196033\",\n \"text\": \"def _activate(self):\\n self._toggle_until = time.monotonic() + self._toggle_for.total_seconds()\\n track_point_in_time(\\n self.hass,\\n self.async_update_ha_state,\\n dt_util.utcnow() + self._toggle_for,\\n )\\n momentary_remote_rpi_gpio.write_output(self._switch, 1)\\n self._state = True\\n _LOGGER.debug(\\\"turned on\\\")\\n self.schedule_update_ha_state()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99c1119d19b493fd599bcd2ea98e143c\",\n \"score\": \"0.51938653\",\n \"text\": \"def DoSetPopupControl(self, popup):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df9f2502467f0e86a92ef931a2fa7083\",\n \"score\": \"0.5187798\",\n \"text\": \"def toggle(self) -> None:\\n self.enabled = not self.enabled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a73493a08ebf71b2e98127638d2013af\",\n \"score\": \"0.5182848\",\n \"text\": \"def turn_on(self):\\n hub.my_pages.smartplug.set(self._id, 'on')\\n hub.my_pages.smartplug.wait_while_updating(self._id, 'on')\\n self.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f6d852713e8165801166a05e7b6bd4b\",\n \"score\": \"0.51809615\",\n \"text\": \"def mouse_on_pop():\\n global last_pop_at\\n global pop_scroll_currently_scrolling\\n global pop_scrolling_direction\\n\\n is_double = False\\n if last_pop_at is not None and time.perf_counter() - last_pop_at < 0.25:\\n is_double = True\\n\\n last_pop_at = time.perf_counter()\\n\\n if pop_scroll_active:\\n if is_double:\\n # Set currently_scrolling to False, so that it will always be started again in the next if statement\\n pop_scroll_currently_scrolling = False\\n pop_scrolling_direction = \\\"up\\\" if pop_scrolling_direction == \\\"down\\\" else \\\"down\\\"\\n\\n if pop_scroll_currently_scrolling == False:\\n if pop_scrolling_direction == \\\"down\\\":\\n actions.user.mouse_scroll_down_continuous()\\n else:\\n actions.user.mouse_scroll_up_continuous()\\n pop_scroll_currently_scrolling = True\\n else:\\n actions.user.mouse_scroll_stop()\\n pop_scroll_currently_scrolling = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f69aca03d521597fecf904619a8176b1\",\n \"score\": \"0.51800144\",\n \"text\": \"def do_auto(self, line):\\t\\n\\t\\tself.auto = not self.auto\\t\\n\\t\\tif(self.auto):\\n\\t\\t\\tprint 'Enabled'\\n\\t\\telse:\\n\\t\\t\\tprint 'Disabled'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62ca91d044630e07b11a8b3ace7ec5d3\",\n \"score\": \"0.51761997\",\n \"text\": \"def test_activate(self):\\n g = self.goal1(self.world)\\n \\n #active by default\\n self.assertTrue(g.active)\\n self.assertTrue(g.name in self.world.active_goals)\\n \\n #deactivate works\\n g.deactivate()\\n self.assertFalse(g.active)\\n self.assertFalse(g.name in self.world.active_goals)\\n \\n #activate works\\n g.activate()\\n self.assertTrue(g.active)\\n self.assertTrue(g.name in self.world.active_goals)\\n \\n #set_activation works\\n active = g.set_activation(False)\\n self.assertTrue(active)\\n self.assertFalse(g.active)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"344bd58619d2061c992d25a74815539a\",\n \"score\": \"0.5175975\",\n \"text\": \"def active(self, active):\\n self._active = active\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dce013547d007860fce14af2150b38fc\",\n \"score\": \"0.5158029\",\n \"text\": \"def activate():\\n game.current_player.confirm()\\n\\n # TODO: All AbstractActions return True. Remove this if possible\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8708b65e6191ea32117606f0877262b1\",\n \"score\": \"0.5152921\",\n \"text\": \"def set_active(self, active):\\n\\t\\tself.active = active\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e3b7903170c1f09bdce70a822c850d8\",\n \"score\": \"0.51504624\",\n \"text\": \"def ActivatePrevious(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d07edeccb4acb751cf9f633d43664956\",\n \"score\": \"0.51430744\",\n \"text\": \"def deactivate(self) -> None:\\n self._active = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ee83a4616b1094516be2f582036de5a\",\n \"score\": \"0.5139641\",\n \"text\": \"def man_active(self, state):\\n if state == QtCore.Qt.Checked:\\n self.man_active_flag = 1\\n self.manual_cut_btn.setEnabled(True)\\n else:\\n self.man_active_flag = 0\\n self.manual_cut_btn.setEnabled(False)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993884,"cells":{"query_id":{"kind":"string","value":"f3e40a2aca9f7f2de33ba0c5cf4d77a1"},"query":{"kind":"string","value":"test standard bindit mapping bind referenced dir, no other manual binds."},"positive_passages":{"kind":"list like","value":[{"docid":"56b3dc3796c0b29a77d78999e62a3ce7","score":"0.69535625","text":"def test_bind():\n with tempfile.TemporaryDirectory(prefix=TEMPFILE_PREFIX) as sourcedir:\n sourcedir_resolved = pathlib.Path(sourcedir).resolve()\n sourcefile = pathlib.Path(\n tempfile.mkstemp(dir=sourcedir, prefix=TEMPFILE_PREFIX)[1]\n )\n with DockerRun(\"ls\", sourcefile) as container:\n assert container.is_running()\n # check that sourcedir has been mounted\n mounts = container.get_mounts()\n assert sourcedir_resolved in mounts\n # check that the destination has the correct root directory\n assert pathlib.Path(\"/bindit\") in mounts[sourcedir_resolved].parents","title":""}],"string":"[\n {\n \"docid\": \"56b3dc3796c0b29a77d78999e62a3ce7\",\n \"score\": \"0.69535625\",\n \"text\": \"def test_bind():\\n with tempfile.TemporaryDirectory(prefix=TEMPFILE_PREFIX) as sourcedir:\\n sourcedir_resolved = pathlib.Path(sourcedir).resolve()\\n sourcefile = pathlib.Path(\\n tempfile.mkstemp(dir=sourcedir, prefix=TEMPFILE_PREFIX)[1]\\n )\\n with DockerRun(\\\"ls\\\", sourcefile) as container:\\n assert container.is_running()\\n # check that sourcedir has been mounted\\n mounts = container.get_mounts()\\n assert sourcedir_resolved in mounts\\n # check that the destination has the correct root directory\\n assert pathlib.Path(\\\"/bindit\\\") in mounts[sourcedir_resolved].parents\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"685b1d1fbedb236e7d0adef4cef659fa","score":"0.6799697","text":"def test_manual_bind_mount():\n manual_bind_runner(bindit.docker.mount_bind_args)","title":""},{"docid":"45be098ba0da542a8d83bf55eafcb293","score":"0.67655706","text":"def test_bind_folder():\n with tempfile.TemporaryDirectory(prefix=TEMPFILE_PREFIX) as sourcedir:\n sourcedir_resolved = pathlib.Path(sourcedir).resolve()\n destdir_expected = pathlib.PosixPath(\n \"/bindit\"\n ) / sourcedir_resolved.relative_to(sourcedir_resolved.anchor)\n with DockerRun(\"ls\", sourcedir) as container:\n assert container.is_running()\n # check that sourcedir has been mounted\n mounts = container.get_mounts()\n assert sourcedir_resolved in mounts\n assert pathlib.PosixPath(mounts[sourcedir_resolved]) == destdir_expected","title":""},{"docid":"085ca8e7b94168e922833b26003c584f","score":"0.64074254","text":"def test_manual_bind_volume():\n manual_bind_runner(bindit.docker.volume_bind_args)","title":""},{"docid":"41f12d0f1ee856d58b0fdf9bf75219e3","score":"0.6204146","text":"def _bind(self):\n return runner.call(\"mount -o bind,%s %s %s\" % (self._option, \n self._olddir, \n self._newdir))[0] == 0","title":""},{"docid":"1f1edd0b8c0ae3b87cfb81d79eb07f8d","score":"0.61872214","text":"def test_parse_bind_mount():\n for sourcekey in [\"source\", \"src\"]:\n for destkey in [\"destination\", \"dst\", \"target\"]:\n val = f\"type=bind,{sourcekey}={SOURCE_ABSTRACT},{destkey}={DEST_ABSTRACT}\"\n mapping = bindit.docker.parse_bind_mount(val)\n assert list(mapping.keys())[0] == SOURCE_ABSTRACT\n assert list(mapping.values())[0] == DEST_ABSTRACT","title":""},{"docid":"ac29eadcadc31d25adba54ab50fbc398","score":"0.6181631","text":"def test_no_image_args():\n with tempfile.TemporaryDirectory(prefix=TEMPFILE_PREFIX) as sourcedir:\n sourcefile = pathlib.Path(\n tempfile.mkstemp(dir=sourcedir, prefix=TEMPFILE_PREFIX)[1]\n )\n sourcedir_resolved = pathlib.Path(sourcedir).resolve()\n destdir = pathlib.PosixPath(\"/manualbind\")\n destfile = destdir / sourcefile.name\n with DockerRun(\n runner_arg=list(bindit.docker.volume_bind_args(sourcedir, destdir))\n ) as container:\n assert container.is_running()\n mounts = container.get_mounts()\n # verify that the source is there\n assert sourcedir_resolved in mounts\n # and that the destination is the manual destination and not some /bindit\n # stuff\n assert mounts[sourcedir_resolved] == destdir\n # and that we have no other mounts\n assert list(mounts.keys()) == [sourcedir_resolved]\n assert container.has_file(destfile)","title":""},{"docid":"5500764e29a921bfd4fec56e891ce47b","score":"0.6117683","text":"def test__bind(self):\n self.assertEqual(self.test_profile._bind(), self.ref_bound)","title":""},{"docid":"01728758408330488543babe7c45d377","score":"0.5769343","text":"def testBuildBinds(self):\n\n #This class may become obselete soon. There is a TODO stuck onto DBCore.buildbinds()\n #This just checks to see that the sequence properly packages the first value is set\n #So that it sets the key seqname to the proper name in the files list\n #Also right now tests that it sets the keys right in each dict, but this seems redundant\n # -mnorman\n\n seqname = 'file'\n dictbinds = {'lumi':123, 'test':100}\n files = ['testA', 'testB', 'testC']\n\n myThread = threading.currentThread()\n\n testInterface = myThread.dbi\n\n binds = testInterface.buildbinds(files, seqname, dictbinds)\n\n #This should return a dict for every value in files with additional elements\n #from dictbinds. We then loop over every dict (which should be equal to the\n #number of elements in files), and look to see that the filename matches and that\n #At least one of the dictbinds keys matches to its proper element\n\n for i in range(len(files)):\n self.assertEqual(binds[i][seqname], files[i])\n self.assertEqual(binds[i][next(iter(dictbinds))], dictbinds[next(iter(dictbinds))])\n\n\n return","title":""},{"docid":"bbcbf669debf30a8eb90d408b141513f","score":"0.56490296","text":"def test_external_building_with_port_bind_to_environ_wrong_servername():\n map = Map([\n Rule('/', endpoint='index'),\n ])\n environ = create_environ('/', 'http://example.org:5000/')\n assert_raises(ValueError, lambda: map.bind_to_environ(environ, server_name=\"example.org\"))","title":""},{"docid":"68517124bd0da94e8c9c1423b4431839","score":"0.55983245","text":"def bind(self):\n if self.binded(): \n return\n\n if not os.path.exists(self._newdir):\n self._mkstat = aux.make_dirs(self._newdir)\n\n self._bind() or self._mkstat and aux.remove_dirs(self._newdir)","title":""},{"docid":"9f17b1578a8b207aea745c9e3798e3f7","score":"0.5478761","text":"def test_cli_inference():\n assert bindit.docker.ARGS\n assert bindit.docker.LETTERS\n for bind in bindit.docker.BIND_PARSER:\n assert bind in bindit.docker.ARGS","title":""},{"docid":"19b3d550443daaeda5b578ec7d350fa1","score":"0.54256535","text":"def test_bind():\n global testcount\n testcount += 1\n\n port = unique_port( mincemeat.DEFAULT_PORT )\n cred = credentials.copy()\n cred.update({\"port\": port})\n \n s1 = mincemeat.Server_daemon(credentials=cred, timeout=5.)\n state = s1.state()\n assert state == \"idle\"\n s1.start()\n time.sleep(1)\n state = s1.state()\n assert state == \"authenticated\"\n\n try:\n s2 = mincemeat.Server()\n s2.conn(**cred)\n assert False == \"Should have thrown Exception in bind()!\"\n except Exception, e:\n assert \"Only one usage of each socket address\" in str(e) \\\n or \"Address already in use\" in str(e)\n\n # Server wasn't provided with a datasource; it will remain idle, and report\n # failed: incomplete.\n s1.stop()\n state = s1.state()\n assert state == \"failed: incomplete\"","title":""},{"docid":"7693614c9f9a18e6b2ab9718a4523a28","score":"0.54163766","text":"def test_python_bind(self):\n\n server_port = 12345\n script_content = \"\"\"\nimport socket\nserver_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver_socket.bind((socket.gethostname(), %d))\nserver_socket.listen(5)\nserver_socket.close()\n\"\"\" % server_port\n\n win32_api_definitions.Win32Hook_bind._debug_counter_before = 0\n win32_api_definitions.Win32Hook_bind._debug_counter_after = 0\n\n dwProcessId = self._debug_python_script(script_content)\n\n print(\"Win32Hook_bind BEFORE:\", win32_api_definitions.Win32Hook_bind._debug_counter_before)\n print(\"Win32Hook_bind AFTER :\", win32_api_definitions.Win32Hook_bind._debug_counter_after)\n\n print(\"debug_counter_WaitForDebugEvent:\", self.hooks_manager.debug_counter_WaitForDebugEvent)\n print(\"debug_counter_exception_breakpoint:\", self.hooks_manager.debug_counter_exception_breakpoint)\n\n self.hooks_manager.debug_print_hooks_counter()\n\n calls_counter_process = win32_api_definitions.tracer_object.calls_counter[dwProcessId]\n\n self.assertTrue(calls_counter_process[b'CreateFileW'] > 0)\n self.assertEqual(calls_counter_process[b'bind'], 1)\n\n # 'addr': [{'Id': '192.168.1.10:12345'}\n server_address = socket.gethostbyname(socket.gethostname())\n expected_addr = \"%s:%d\" % (server_address, server_port)\n print(\"expected_addr=\", expected_addr)\n self.assertTrue('CIM_DataFile' in win32_api_definitions.tracer_object.created_objects)\n self.assertTrue({'Id': expected_addr} in win32_api_definitions.tracer_object.created_objects['addr'])","title":""},{"docid":"e41f8b53668dc946569e97dcfc7c756a","score":"0.53630775","text":"def bind_mount(source_path, mountpoint):","title":""},{"docid":"1132046ba0fb1878dbb85b2cc2a20b07","score":"0.53486","text":"def get_bind_name(self, component_name, test_config='default_binding'):\n sid = self.get_bind_id(component_name, test_config)\n if not sid:\n return False\n sut = self.get_sut(sid)\n if not sut:\n sut = {}\n return sut.get('path', False)","title":""},{"docid":"a1404fe2beb095cb30f2caaaa3bb928c","score":"0.53414744","text":"def boundIt(*args, **kwargs):\n \n pass","title":""},{"docid":"405832d7db955a2b1d231c70e0f674e2","score":"0.5331331","text":"def test_directory(self):\n module = reflect.filenameToModuleName(os.path.join('twisted', 'test'))\n self.assertEquals(module, 'test')\n module = reflect.filenameToModuleName(os.path.join('twisted', 'test')\n + os.path.sep)\n self.assertEquals(module, 'test')","title":""},{"docid":"36a2027119d19a46343f71457a27f8c0","score":"0.5321691","text":"def lsp_bind(self, port, chassis, may_exist=False):","title":""},{"docid":"4730035dc5701f31e4f7ecac8e5df872","score":"0.5301599","text":"def test_server_name_interpolation():\n server_name = 'example.invalid'\n map = Map([Rule('/', endpoint='index'),\n Rule('/', endpoint='alt', subdomain='alt')])\n\n env = create_environ('/', 'http://%s/' % server_name)\n adapter = map.bind_to_environ(env, server_name=server_name)\n assert adapter.match() == ('index', {})\n\n env = create_environ('/', 'http://alt.%s/' % server_name)\n adapter = map.bind_to_environ(env, server_name=server_name)\n assert adapter.match() == ('alt', {})\n\n try:\n env = create_environ('/', 'http://%s/' % server_name)\n adapter = map.bind_to_environ(env, server_name='foo')\n except ValueError, e:\n msg = str(e)\n assert 'provided (%r)' % 'foo' in msg\n assert 'environment (%r)' % server_name in msg\n else:\n assert False, 'expected exception'","title":""},{"docid":"09afd8954367b5023240e509ce3f2339","score":"0.52959895","text":"def test_find_gateways(self):\n pass","title":""},{"docid":"dd7df1f3b41e0eba6a9add393dc5cb8a","score":"0.52836597","text":"def do_bind(self,stanza):\n fr=stanza.get_from()\n if fr and fr!=self.peer:\n r=stanza.make_error_response(\"forbidden\")\n self.send(r)\n r.free()\n return\n resource_n=stanza.xpath_eval(\"bind:bind/bind:resource\",{\"bind\":BIND_NS})\n if resource_n:\n resource=resource_n[0].getContent()\n else:\n resource=\"auto\"\n if not resource:\n r=stanza.make_error_response(\"bad-request\")\n else:\n self.unset_iq_set_handler(\"bind\",BIND_NS)\n r=stanza.make_result_response()\n self.peer.set_resource(resource)\n q=r.new_query(BIND_NS,\"bind\")\n q.newTextChild(None,\"jid\",to_utf8(self.peer.as_unicode()))\n self.state_change(\"authorized\",self.peer)\n r.set_to(None)\n self.send(r)\n r.free()","title":""},{"docid":"086aa629462549fbce56ada19b51eb81","score":"0.5282458","text":"def test_missing_filepath(self):\n BindingsManager.load_from_filepath(\"/tmp/foo.yaml\")","title":""},{"docid":"38882ee1be8b5d1a05ca597a8f60b60f","score":"0.52007556","text":"def setup_basics() -> None:\r\n if not os.path.exists('bindsets/'):\r\n os.mkdir('bindsets')","title":""},{"docid":"6a3d107d052e925cbffaaff73b5729a2","score":"0.5193542","text":"def test_resolve_uri_nonexistant_dir():\n from server import resolve_uri\n path = resolve_uri('/WTF')\n result = False\n assert path == result","title":""},{"docid":"3dbad7f982680048aed3ee3d68134fb0","score":"0.51632357","text":"def test_bind_addr_unix(http_server, unix_sock_file):\n httpserver = http_server.send(unix_sock_file)\n\n assert httpserver.bind_addr == unix_sock_file","title":""},{"docid":"71e2cf04a75b9b5e97ed0c86b03a6a82","score":"0.5163094","text":"def test_on_config_changed_with_bind_exporter_relation(self):\n relation_id = self._add_bind_exporter_relation()\n self.harness.update_config({\"exporter-listen-port\": \"9120\"})\n\n relation_data = self.harness.get_relation_data(relation_id, self.harness.charm.unit.name)\n self.assertDictEqual(relation_data, {\"hostname\": \"127.0.0.1\", \"port\": \"9120\"})\n self.assert_active_unit(self.harness.charm.unit)","title":""},{"docid":"7c4df7a65c2b1a3f4d3fcbe3b7a1e9c3","score":"0.5123959","text":"def test_emptyMapper(self):\n verifyObject(IMapper, emptyMapper)\n for name in [\"sys\", \"email\", \"exocet\", \"exocet.test\"]:\n self.assertRaises(ImportError, emptyMapper.lookup, name)\n self.assertFalse(emptyMapper.contains(name))","title":""},{"docid":"0a52d38ff721d29f3e0a77ca52314aa2","score":"0.5123125","text":"def test_external_building_with_port_bind_to_environ():\n map = Map([\n Rule('/', endpoint='index'),\n ])\n adapter = map.bind_to_environ(\n create_environ('/', 'http://example.org:5000/'),\n server_name=\"example.org:5000\"\n )\n built_url = adapter.build('index', {}, force_external=True)\n assert built_url == 'http://example.org:5000/', built_url","title":""},{"docid":"66cf1532f8ae8055590a7ea4198638b7","score":"0.51190525","text":"def test_bind_atp_feed(self):\n pass","title":""},{"docid":"0e45a1caadf30c650534c90bbf3ac30d","score":"0.50976026","text":"def binded(self):\n # Sometimes os.path.ismount performs incorrectly.\n # Check mount table is a better way.\n with open(\"/proc/mounts\", 'r') as mnts:\n for mnt in mnts.readlines():\n newdir = aux.cano_path(mnt.split()[1])\n if self._newdir == newdir: \n return True\n else:\n return False","title":""},{"docid":"b28836e12a24cd1bc5e0683aeb06ad0d","score":"0.50929266","text":"def test_dictMapper(self):\n d = {\"sys\": object(), \"exocet.test\": object()}\n dm = DictMapper(d)\n verifyObject(IMapper, dm)\n for name in [\"sys\", \"exocet.test\"]:\n self.assertEqual(d[name], dm.lookup(name))\n self.assertTrue(dm.contains(name))\n\n for name in [\"email\", \"foobaz\"]:\n self.assertRaises(ImportError, dm.lookup, name)\n self.assertFalse(dm.contains(name))","title":""},{"docid":"6113dd887a506ecafba0bf073199597e","score":"0.50790834","text":"def bind(self) -> Optional[pulumi.Input['CacheDirectoryLdapBindArgs']]:\n return pulumi.get(self, \"bind\")","title":""},{"docid":"570003b2ab6aaa87a63dd6e2609217e1","score":"0.5071621","text":"def test_temp_connection_custom_bind_iface(self):\n bind_iface = \"0.0.0.0\"\n with patch('py2neo.neo4j.GraphDatabaseService', lambda uri: uri):\n conn = get_connection('temp://{}'.format(bind_iface))\n\n assert conn == \"http://localhost:7475/db/data/\"\n self.write_config.assert_called_once_with(\n ANY, 7475, ANY, bind_iface\n )","title":""},{"docid":"e0b335a4f6d4e4e559f9eae4e1349d94","score":"0.50385594","text":"def test_pep302Mapper(self):\n import exocet, exocet.test, compiler.visitor\n pep302Mapper._oldSysModules = sys.modules.copy()\n verifyObject(IMapper, pep302Mapper)\n for (name, mod) in {\"sys\": sys,\n \"exocet\": exocet,\n \"exocet.test\": exocet.test,\n \"compiler.visitor\": compiler.visitor}.iteritems():\n assertIdentical(self, pep302Mapper.lookup(name), mod)\n self.assertTrue(pep302Mapper.contains(name))\n\n self.assertRaises(ImportError, pep302Mapper.lookup,\n \"exocet._nonexistentModule\")","title":""},{"docid":"c7630098a6b7c3d7dea301f3754e19ce","score":"0.50343823","text":"def bind(self, bindings):\n raise NotImplementedError()","title":""},{"docid":"c7630098a6b7c3d7dea301f3754e19ce","score":"0.50343823","text":"def bind(self, bindings):\n raise NotImplementedError()","title":""},{"docid":"c7630098a6b7c3d7dea301f3754e19ce","score":"0.50343823","text":"def bind(self, bindings):\n raise NotImplementedError()","title":""},{"docid":"bd656b239ca3f1f8b8673d6dd66929da","score":"0.50154316","text":"def simple_module(binder):\n from unuo.config import config\n from unuo.filebackend import FileBackend\n from unuo.filelogging import FileLoggerManager\n from unuo.docker import Docker_1_1_x\n binder.bind(\n buildsfolder_key,\n to=config.builds_folder)\n binder.bind(\n logsfolder_key,\n to=config.logs_folder)\n binder.bind(\n backend_key,\n to=FileBackend,\n scope=singleton)\n binder.bind(\n logmanager_key,\n to=FileLoggerManager,\n scope=singleton)\n binder.bind(\n docker_key,\n to=Docker_1_1_x,\n scope=singleton)","title":""},{"docid":"a71f6e817540941a250090e9dc86397e","score":"0.5013581","text":"def test_resolve_uri_dir():\n from server import resolve_uri\n our_tuple = resolve_uri('/images')\n assert our_tuple[1] == 'text/html'","title":""},{"docid":"9ae9269a32cd9ead201a1fe5fbc1c22e","score":"0.49917224","text":"def test_perl_socket_bind(self):\n\n server_port = 12345\n\n script_content = \"\"\"\\\nuse Socket;\nsocket(SERVER, PF_INET, SOCK_STREAM, getprotobyname('tcp'));\nsetsockopt(SERVER, SOL_SOCKET, SO_REUSEADDR, 1);\n$my_addr = sockaddr_in(%s, INADDR_ANY);\nbind(SERVER, $my_addr) or die \"Couldn't bind to port $server_port : $!\\n\";\n\"\"\" % server_port\n\n dwProcessId = self._debug_perl_script(script_content)\n\n print(\"Win32Hook_bind BEFORE:\", win32_api_definitions.Win32Hook_bind._debug_counter_before)\n print(\"Win32Hook_bind AFTER :\", win32_api_definitions.Win32Hook_bind._debug_counter_after)\n\n self.hooks_manager.debug_print_hooks_counter()\n\n print(\"debug_counter_WaitForDebugEvent:\", self.hooks_manager.debug_counter_WaitForDebugEvent)\n print(\"debug_counter_exception_breakpoint:\", self.hooks_manager.debug_counter_exception_breakpoint)\n\n # No subprocess creation\n self.assertEqual(len(win32_api_definitions.tracer_object.calls_counter), 1)\n\n root_process_calls = win32_api_definitions.tracer_object.calls_counter[dwProcessId]\n self.assertTrue(root_process_calls[b'CreateFileA'] > 0)\n self.assertEqual(root_process_calls[b'bind'], 1)\n if not is_windows10:\n self.assertTrue(root_process_calls[b'ReadFile'] > 0)\n self.assertEqual(root_process_calls[b'fopen'], 1)\n self.assertEqual(win32_api_definitions.Win32Hook_bind._debug_counter_before, 1)\n self.assertEqual(win32_api_definitions.Win32Hook_bind._debug_counter_after, 1)\n\n # b'CreateFileA' 1 1\n # b'CreateFileW' 1 1\n # b'CreateProcessA' 0 0\n # b'CreateProcessAsUserA' 0 0\n # b'CreateProcessAsUserW' 0 0\n # b'CreateProcessW' 0 0\n # b'CreateRemoteThread' 0 0\n # b'CreateRemoteThreadEx' 0 0\n # b'CreateThread' 1 1\n # b'DeleteFileA' 0 0\n # b'DeleteFileW' 0 0\n # b'ReadFile' 1 0\n # b'ReadFileEx' 0 0\n # b'ReadFileScatter' 0 0\n # b'RemoveDirectoryA' 0 0\n # b'RemoveDirectoryW' 0 0\n # b'TerminateProcess' 1 0\n\n\n # TODO: Results with fsopen are not stable. Maybe a thread ?\n\n print(\"created_objects=\", win32_api_definitions.tracer_object.created_objects)\n server_address = \"0.0.0.0\"\n expected_addr = \"%s:%d\" % (server_address, server_port)\n print(\"expected_addr=\", expected_addr)\n self.assertTrue({'Id': expected_addr} in win32_api_definitions.tracer_object.created_objects['addr'])","title":""},{"docid":"7f004639ea5b988ede9de8cc2fc2acca","score":"0.49800903","text":"def test_parse_bind_volume():\n # two-field version\n mapping = bindit.docker.parse_bind_volume(f\"{SOURCE_ABSTRACT}:{DEST_ABSTRACT}\")\n assert list(mapping.keys())[0] == SOURCE_ABSTRACT\n assert list(mapping.values())[0] == DEST_ABSTRACT\n # three-field\n mapping = bindit.docker.parse_bind_volume(\n f\"{SOURCE_ABSTRACT}:{DEST_ABSTRACT}:ro,consistent\"\n )\n assert list(mapping.keys())[0] == SOURCE_ABSTRACT\n assert list(mapping.values())[0] == DEST_ABSTRACT","title":""},{"docid":"39d1b15445cbe31422a695fdc93607d0","score":"0.4939582","text":"def testMap(self, thing):\n pass","title":""},{"docid":"d79650936ca420258d8693a8a0599265","score":"0.49355534","text":"def test_specified_port_bind(self):\n TestCase = type(\"TestCase\", (LiveServerBase,), {})\n # Find an open port and tell TestCase to use it.\n s = socket.socket()\n s.bind((\"\", 0))\n TestCase.port = s.getsockname()[1]\n s.close()\n TestCase._start_server_thread()\n try:\n self.assertEqual(\n TestCase.port,\n TestCase.server_thread.port,\n f\"Did not use specified port for LiveServerTestCase thread: \"\n f\"{TestCase.port}\",\n )\n finally:\n TestCase.doClassCleanups()","title":""},{"docid":"5c23fe6a29a952c0217e4c2e7b4d9338","score":"0.49352533","text":"def rdt_bind(sockd, port):\r\n\t######## Your implementation #######\r\n\t#Reusing part 1 code\r\n\ttry:\r\n\t\tsockd.bind((\"\",port))\r\n\texcept socket.error as emsg:\r\n\t\tprint(\"Socket bind error: \", emsg)\r\n\t\treturn -1\r\n\treturn 0","title":""},{"docid":"30a96842da00518e8d868018b03ecf7a","score":"0.49324587","text":"def test_bind_addr_unix_abstract(http_server, unix_abstract_sock):\n httpserver = http_server.send(unix_abstract_sock)\n\n assert httpserver.bind_addr == unix_abstract_sock","title":""},{"docid":"c3c74e4dbaa8e57e4e149bd8a99a9c57","score":"0.49312508","text":"def test_bindingAccess(self):\n gg = self.classTested(\"stuff = '1':a ('2':b | '3':c)\")\n t = gg.parseGrammar('TestGrammar', TreeBuilder)\n G = moduleFromGrammar(t, 'TestGrammar', OMetaBase, {})\n g = G(\"12\")\n self.assertEqual(g.apply(\"stuff\")[0], '2')\n self.assertEqual(g.locals['stuff']['a'], '1')\n self.assertEqual(g.locals['stuff']['b'], '2')\n g = G(\"13\")\n self.assertEqual(g.apply(\"stuff\")[0], '3')\n self.assertEqual(g.locals['stuff']['a'], '1')\n self.assertEqual(g.locals['stuff']['c'], '3')","title":""},{"docid":"e8083c085f97ff5f793aa83dbc7e70d9","score":"0.49138045","text":"def test_http_host_before_server_name():\n env = {\n 'HTTP_HOST': 'wiki.example.com',\n 'SERVER_NAME': 'web0.example.com',\n 'SERVER_PORT': '80',\n 'SCRIPT_NAME': '',\n 'PATH_INFO': '',\n 'REQUEST_METHOD': 'GET',\n 'wsgi.url_scheme': 'http'\n }\n map = Map([Rule('/', endpoint='index', subdomain='wiki')])\n adapter = map.bind_to_environ(env, server_name='example.com')\n assert adapter.match('/') == ('index', {})\n assert adapter.build('index', force_external=True) == 'http://wiki.example.com/'\n assert adapter.build('index') == '/'\n\n env['HTTP_HOST'] = 'admin.example.com'\n adapter = map.bind_to_environ(env, server_name='example.com')\n assert adapter.build('index') == 'http://wiki.example.com/'","title":""},{"docid":"777a322ca8930b4fac7150db3e9928ec","score":"0.49028188","text":"def test_exclusiveMapper(self):\n\n l = [\"sys\", \"exocet.test\"]\n em = ExclusiveMapper(pep302Mapper, l)\n\n verifyObject(IMapper, em)\n\n for name in l:\n self.assertRaises(ImportError, em.lookup, name)\n self.assertFalse(em.contains(name))","title":""},{"docid":"965ac7152711968857111ff244ee8dc9","score":"0.48984364","text":"def test_search_relative(self):\n pass","title":""},{"docid":"88777c813343f6483d4a2fa8048d3ad3","score":"0.48930928","text":"def server_bind(self):\n pass","title":""},{"docid":"5aed56d6883f369add6f59b7da7c6bf8","score":"0.48892355","text":"def test_bindNameOnly(self):\n g = self.compile(\"foo = '1' :x '2' -> x\")\n self.assertEqual(g.foo(\"132\"), \"3\")","title":""},{"docid":"7e7bf65ffa4ab7696f1207de27b873fe","score":"0.48838958","text":"def test_read_sample_dir(self):\n # TODO\n pass","title":""},{"docid":"1c1481af811ae9409310b361f3af33bc","score":"0.48753136","text":"def test_main__setup_paths_absolute_path(self, chmod_mock, mkdir_mock):\n sys.argv = [\"\"]\n callpath = sys.argv[0]\n if not os.path.isabs(callpath):\n callpath = os.path.abspath(callpath)\n sys.argv = [callpath]\n self.ec2rl._write_initialized = False # Force re-initialization of writing for test\n self.assertTrue(self.ec2rl._setup_write_paths())\n\n self.assertTrue(chmod_mock.called)\n self.assertTrue(mkdir_mock.called)","title":""},{"docid":"87f77178bf5cc2a847b3fd529b206dec","score":"0.48651186","text":"def test_nexusvmbinding_get(self):\r\n npb11 = self._npb_test_obj(10, 100)\r\n npb21 = self._npb_test_obj(20, 100)\r\n npb22 = self._npb_test_obj(20, 200)\r\n self._add_bindings_to_db([npb11, npb21, npb22])\r\n\r\n npb = self._get_nexusvm_binding(npb21)\r\n self._assert_bindings_match(npb, npb21)\r\n npb = self._get_nexusvm_binding(npb22)\r\n self._assert_bindings_match(npb, npb22)\r\n\r\n with testtools.ExpectedException(exceptions.NexusPortBindingNotFound):\r\n nexus_db_v2.get_nexusvm_bindings(npb21.vlan, \"dummyInstance\")[0]","title":""},{"docid":"78880fb5638390dcdd19e557fa9d6e54","score":"0.48579472","text":"def transfer_bind(self):\n\n pass","title":""},{"docid":"a3d2c9bb1dd809a1dbc418f1629a0a94","score":"0.48514378","text":"def test_path():\n map = Map([\n Rule('/', defaults={'name': 'FrontPage'}, endpoint='page'),\n Rule('/Special', endpoint='special'),\n Rule('/<int:year>', endpoint='year'),\n Rule('/<path:name>', endpoint='page'),\n Rule('/<path:name>/edit', endpoint='editpage'),\n Rule('/<path:name>/silly/<path:name2>', endpoint='sillypage'),\n Rule('/<path:name>/silly/<path:name2>/edit', endpoint='editsillypage'),\n Rule('/Talk:<path:name>', endpoint='talk'),\n Rule('/User:<username>', endpoint='user'),\n Rule('/User:<username>/<path:name>', endpoint='userpage'),\n Rule('/Files/<path:file>', endpoint='files'),\n ])\n adapter = map.bind('example.org', '/')\n\n assert adapter.match('/') == ('page', {'name':'FrontPage'})\n assert_raises(RequestRedirect, lambda: adapter.match('/FrontPage'))\n assert adapter.match('/Special') == ('special', {})\n assert adapter.match('/2007') == ('year', {'year':2007})\n assert adapter.match('/Some/Page') == ('page', {'name':'Some/Page'})\n assert adapter.match('/Some/Page/edit') == ('editpage', {'name':'Some/Page'})\n assert adapter.match('/Foo/silly/bar') == ('sillypage', {'name':'Foo', 'name2':'bar'})\n assert adapter.match('/Foo/silly/bar/edit') == ('editsillypage', {'name':'Foo', 'name2':'bar'})\n assert adapter.match('/Talk:Foo/Bar') == ('talk', {'name':'Foo/Bar'})\n assert adapter.match('/User:thomas') == ('user', {'username':'thomas'})\n assert adapter.match('/User:thomas/projects/werkzeug') == ('userpage', {'username':'thomas', 'name':'projects/werkzeug'})\n assert adapter.match('/Files/downloads/werkzeug/0.2.zip') == ('files', {'file':'downloads/werkzeug/0.2.zip'})","title":""},{"docid":"d5905079d4f0c4027fc6808d2f5d8b5c","score":"0.48480138","text":"def test_named_conf(self):\n filename = \"/etc/bind/named.conf.local\"\n expected = (self.orgConf.controller_ip, )\n self.correctly_written(filename, expected)","title":""},{"docid":"cdabece9e5f881f59feec8d2e22a9093","score":"0.48326653","text":"def test_bind(value):\n var = TestVariable()\n callback = Mock()\n binding = VariableBinding(callback, var)\n\n binding.bind()\n var.set(value)\n\n assert callback.call_args == call(value)","title":""},{"docid":"49e7fc950d7245d661c24ea3df03d903","score":"0.48314098","text":"def test_is_valid_correct_invalid_location(self):\n directives = [\n { \"signature\" : \"container1:0.0.0.0:80:a.b.c:/location1/\"},\n { \"signature\" : \"container2:0.0.0.0:80:a.b.c:/location1/\"}\n ]\n handle_listen = Listen(**{})\n for directive in directives:\n handle_listen.directives = directive \n self.assertFalse(handle_listen.is_valid)\n del handle_listen","title":""},{"docid":"996f4d6c3237fb52d34d899020f0796d","score":"0.48239592","text":"def bin_test(ctx):\n\tfrom wafhelpers.bin_test import cmd_bin_test\n\tcmd_bin_test(ctx, config)","title":""},{"docid":"161375cd1e18c870943efb34dfc590a9","score":"0.4820161","text":"def bound(*args, **kwargs):\n \n pass","title":""},{"docid":"8e4a4c32cf4aab49e630ec9358a4e9bb","score":"0.48165792","text":"def test_namedModuleLookup(self):\n self.assertIdentical(\n reflect.namedModule(\"twisted.python.reflect\"), reflect)","title":""},{"docid":"01bb4df6723aea468d1d961a3ee520d1","score":"0.48088744","text":"def test_graph_store_bind(\n graph_factory: GraphFactory,\n ops: GraphOperations,\n expected_bindings: NamespaceBindings,\n) -> None:\n graph = graph_factory()\n for op in ops:\n op(graph)\n check_ns(graph, expected_bindings)","title":""},{"docid":"cc8bc246c87b54774375cb1215806fc3","score":"0.47882962","text":"def test_configure_fn() -> None:\n\n class MyModule(Module):\n def configure(self, binder: Binder) -> None:\n binder.bind(IFileSystem, to=InMemoryFileSystem)\n\n kernel = Kernel()\n kernel.install(MyModule())\n\n inst = kernel.get(IFileSystem) # type: ignore\n assert isinstance(inst, InMemoryFileSystem)","title":""},{"docid":"c5e478f3c4d8522a606abd53621d282e","score":"0.4785687","text":"def bind(app):\n instance.init_app(app)\n\n return True","title":""},{"docid":"039e1da398447a086c25b64a8fa79f44","score":"0.47691762","text":"def testMapMap(self, hello):\n pass","title":""},{"docid":"d7c63fe84dfa92db5afe1c6ab3ab11bd","score":"0.47631738","text":"def test_gds_map_file(self) -> None:\n import hammer_config\n\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\"dummy28\")\n tech_json_filename = os.path.join(tech_dir, \"dummy28.tech.json\")\n\n def add_gds_map(d: Dict[str, Any]) -> Dict[str, Any]:\n r = deepdict(d)\n r.update({\"gds map file\": \"test/gds_map_file\"})\n return r\n\n HammerToolTestHelpers.write_tech_json(tech_json_filename, add_gds_map)\n tech_opt = hammer_tech.HammerTechnology.load_from_dir(\"dummy28\", tech_dir)\n if tech_opt is None:\n self.assertTrue(False, \"Unable to load technology\")\n else:\n tech = tech_opt # type: hammer_tech.HammerTechnology\n tech.cache_dir = tech_dir\n\n tool = DummyTool()\n tool.technology = tech\n database = hammer_config.HammerDatabase()\n tool.set_database(database)\n\n # Test that empty for gds_map_mode results in no map file.\n database.update_project([{\n 'par.inputs.gds_map_mode': 'empty',\n 'par.inputs.gds_map_file': None\n }])\n self.assertEqual(tool.get_gds_map_file(), None)\n\n # Test that manual mode for gds_map_mode works.\n database.update_project([{\n 'par.inputs.gds_map_mode': 'manual',\n 'par.inputs.gds_map_file': '/tmp/foo/bar'\n }])\n self.assertEqual(tool.get_gds_map_file(), '/tmp/foo/bar')\n\n # Test that auto mode for gds_map_mode works if the technology has a map file.\n database.update_project([{\n 'par.inputs.gds_map_mode': 'auto',\n 'par.inputs.gds_map_file': None\n }])\n self.assertEqual(tool.get_gds_map_file(), '{tech}/gds_map_file'.format(tech=tech_dir))\n\n # Cleanup\n shutil.rmtree(tech_dir_base)\n\n # Create a new technology with no GDS map file.\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\"dummy28\")\n\n tech_json_filename = os.path.join(tech_dir, \"dummy28.tech.json\")\n HammerToolTestHelpers.write_tech_json(tech_json_filename)\n tech_opt = hammer_tech.HammerTechnology.load_from_dir(\"dummy28\", tech_dir)\n if tech_opt is None:\n self.assertTrue(False, \"Unable to load technology\")\n else:\n tech = tech_opt\n tech.cache_dir = tech_dir\n\n tool.technology = tech\n\n # Test that auto mode for gds_map_mode works if the technology has no map file.\n database.update_project([{\n 'par.inputs.gds_map_mode': 'auto',\n 'par.inputs.gds_map_file': None\n }])\n self.assertEqual(tool.get_gds_map_file(), None)\n\n # Cleanup\n shutil.rmtree(tech_dir_base)","title":""},{"docid":"36004881847b1507a3257b654da30fde","score":"0.47588235","text":"def test_simple_lookup():\r\n\r\n env = Environment({\"var\": 42})\r\n assert_equals(42, env.lookup(\"var\"))","title":""},{"docid":"3054e4a23d7e47b0bdcb947a47e551c6","score":"0.47522664","text":"def test_windows_missing_prepdir():\n system_params = {}\n salt_config = {}\n\n system_params['logdir'] = \"logdir\"\n system_params['workingdir'] = \"workingdir\"\n\n with pytest.raises(KeyError, match='prepdir'):\n SaltWindows(system_params, **salt_config)","title":""},{"docid":"0f62b1b377857620d668268f33c0ed0d","score":"0.47377548","text":"def test_namedAnyModuleLookup(self):\n self.assertIdentical(\n reflect.namedAny(\"twisted.python.reflect\"), reflect)","title":""},{"docid":"3039e76ae70dc717f231f3592c3c4212","score":"0.47310522","text":"def test_config_directories(self):\n # Initializing key variables\n # Initialize key variables\n save_directory = None\n\n if 'SWITCHMAP_CONFIGDIR' in os.environ:\n save_directory = os.environ['SWITCHMAP_CONFIGDIR']\n\n # Try with no SWITCHMAP_CONFIGDIR\n os.environ.pop('SWITCHMAP_CONFIGDIR', None)\n directory = '{}/etc'.format(general.root_directory())\n result = general.config_directories()\n self.assertEqual(result, [directory])\n\n # Test with SWITCHMAP_CONFIGDIR set\n directory = tempfile.mkdtemp()\n os.environ['SWITCHMAP_CONFIGDIR'] = directory\n result = general.config_directories()\n self.assertEqual(result, [directory])\n\n # Restore state\n if save_directory is not None:\n os.environ['SWITCHMAP_CONFIGDIR'] = save_directory","title":""},{"docid":"4b61f90a2135a0cbc25bf9922732f00b","score":"0.47309","text":"def test_external_building_with_port():\n map = Map([\n Rule('/', endpoint='index'),\n ])\n adapter = map.bind('example.org:5000', '/')\n built_url = adapter.build('index', {}, force_external=True)\n assert built_url == 'http://example.org:5000/', built_url","title":""},{"docid":"6b997ae8c7d241e616c2369f9ed46e38","score":"0.4722874","text":"def test_greedy():\n map = Map([\n Rule('/foo', endpoint='foo'),\n Rule('/<path:bar>', endpoint='bar'),\n Rule('/<path:bar>/<path:blub>', endpoint='bar')\n ])\n adapter = map.bind('example.org', '/')\n\n assert adapter.match('/foo') == ('foo', {})\n assert adapter.match('/blub') == ('bar', {'bar': 'blub'})\n assert adapter.match('/he/he') == ('bar', {'bar': 'he', 'blub': 'he'})\n\n assert adapter.build('foo', {}) == '/foo'\n assert adapter.build('bar', {'bar': 'blub'}) == '/blub'\n assert adapter.build('bar', {'bar': 'blub', 'blub': 'bar'}) == '/blub/bar'","title":""},{"docid":"3440560acf4aece1aef7a69b93915d39","score":"0.4716483","text":"def test_register_lookup_handler_str(mocker: MockerFixture) -> None:\n mocker.patch.dict(RUNWAY_LOOKUP_HANDLERS, {})\n register_lookup_handler(\"test\", \"runway.lookups.handlers.env.EnvLookup\")\n assert \"test\" in RUNWAY_LOOKUP_HANDLERS\n assert RUNWAY_LOOKUP_HANDLERS[\"test\"] == EnvLookup","title":""},{"docid":"55469deef9684c9486134d77a523ede3","score":"0.47106376","text":"def bindmount(self, mountpoint):\n\n if not self.mountpoint:\n raise NotMountedError(self)\n try:\n _util.check_call_(['mount', '--bind', self.mountpoint, mountpoint], stdout=subprocess.PIPE)\n if 'bindmounts' in self._paths:\n self._paths['bindmounts'].append(mountpoint)\n else:\n self._paths['bindmounts'] = [mountpoint]\n return True\n except Exception as e:\n logger.exception(\"Error bind mounting {0}.\".format(self))\n raise SubsystemError(e)","title":""},{"docid":"e942a5300964b09b099b460d07220118","score":"0.47079128","text":"def setup_port_binding(self):\n success = False\n while not success:\n try:\n self.info(f'Attempting bind to port {self.bind_port}')\n self.pub_socket.bind(f'tcp://*:{self.bind_port}')\n success = True\n self.info(f'Successful bind to port {self.bind_port}')\n except:\n try:\n self.error(f'Port {self.bind_port} already in use, attempting next port')\n success = False\n self.bind_port += 1\n except Exception as e:\n self.debug(e)\n self.debug(\"Finished loop\")","title":""},{"docid":"0d10417627cc6af2819bbdcda6bea02a","score":"0.4706957","text":"def bind_url(url):\n if url.find('?') == -1: # like http://host/wms\n binder = '?'\n\n # if like http://host/wms?foo=bar& or http://host/wms?foo=bar\n if url.find('=') != -1:\n if url.find('&', -1) != -1: # like http://host/wms?foo=bar&\n binder = ''\n else: # like http://host/wms?foo=bar\n binder = '&'\n\n # if like http://host/wms?foo\n if url.find('?') != -1:\n if url.find('?', -1) != -1: # like http://host/wms?\n binder = ''\n elif url.find('&', -1) == -1: # like http://host/wms?foo=bar\n binder = '&'\n return '%s%s' % (url, binder)","title":""},{"docid":"7217e4f21170d35db9df6fda1687d2c5","score":"0.47022092","text":"def bindir(self, *args, **kwargs):\n return fbuild.path.Path(self(('--bindir',), *args, **kwargs))","title":""},{"docid":"a45082ed2e633b3e6320ac97269a889a","score":"0.47016984","text":"def test_bind_addr_inet(http_server, ip_addr):\n httpserver = http_server.send((ip_addr, EPHEMERAL_PORT))\n\n assert httpserver.bind_addr[0] == ip_addr\n assert httpserver.bind_addr[1] != EPHEMERAL_PORT","title":""},{"docid":"817ff11af272054e213193761dc588e7","score":"0.4696746","text":"def bind_check(self, bind, interval=1, constant=1000):","title":""},{"docid":"3e89440fba9770523da123ba4447b806","score":"0.46961316","text":"def test_inside_non_bindable(self):\n\n server = LocalAngularServer()\n server.start(LocalAngularServer.HANDLER_SCOPE_TEST, {\"position\": \"inside_non_bindable\"})\n\n try:\n shell_command = [\"python\", \"acstis.py\", \"--verify-payload\", \"--domain\", \"http://\" + server.url + \"?vulnerable=payload\"]\n\n process = subprocess.Popen(\n shell_command\n )\n\n exitcode = process.wait()\n except Exception as e:\n print(\"Exception: \" + str(e))\n exitcode = 1\n\n server.stop()\n\n self.assertNotEqual(exitcode, 0)","title":""},{"docid":"f0be53eace7f8ed931bf2e70a0ff321b","score":"0.46952796","text":"def test_bin_dir(self):\n\n # pylint: disable=line-too-long\n with tests.MockLdd(\n out=\"\\n\".join([\n \"\\tlinux-vdso.so.1 (0x00007ffd66ce2000)\",\n \"\\tlibselinux.so.1 => /lib/x86_64-linux-gnu/libselinux.so.1 (0x00007f72b88fc000)\",\n \"\\tlibc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007f72b850b000)\",\n \"\\tlibpcre.so.3 => /lib/x86_64-linux-gnu/libpcre.so.3 (0x00007f72b8299000)\",\n \"\\tlibdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007f72b8095000)\",\n \"\\t/lib64/ld-linux-x86-64.so.2 (0x00007f72b8d46000)\",\n \"\\tlibpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007f72b7e76000)\",\n \"\",\n ]),\n out_unused=''):\n # pylint: enable=line-too-long\n deps = lddwrap.list_dependencies(\n path=pathlib.Path(DIR), unused=False)\n\n expected_deps = [\n lddwrap.Dependency(\n soname=\"linux-vdso.so.1\",\n path=None,\n found=True,\n mem_address=\"0x00007ffd66ce2000\",\n unused=None),\n lddwrap.Dependency(\n soname=\"libselinux.so.1\",\n path=pathlib.Path(\"/lib/x86_64-linux-gnu/libselinux.so.1\"),\n found=True,\n mem_address=\"0x00007f72b88fc000\",\n unused=None),\n lddwrap.Dependency(\n soname=\"libc.so.6\",\n path=pathlib.Path(\"/lib/x86_64-linux-gnu/libc.so.6\"),\n found=True,\n mem_address=\"0x00007f72b850b000\",\n unused=None),\n lddwrap.Dependency(\n soname=\"libpcre.so.3\",\n path=pathlib.Path(\"/lib/x86_64-linux-gnu/libpcre.so.3\"),\n found=True,\n mem_address=\"0x00007f72b8299000\",\n unused=None),\n lddwrap.Dependency(\n soname=\"libdl.so.2\",\n path=pathlib.Path(\"/lib/x86_64-linux-gnu/libdl.so.2\"),\n found=True,\n mem_address=\"0x00007f72b8095000\",\n unused=None),\n lddwrap.Dependency(\n soname=None,\n path=pathlib.Path(\"/lib64/ld-linux-x86-64.so.2\"),\n found=True,\n mem_address=\"0x00007f72b8d46000\",\n unused=None),\n lddwrap.Dependency(\n soname=\"libpthread.so.0\",\n path=pathlib.Path(\"/lib/x86_64-linux-gnu/libpthread.so.0\"),\n found=True,\n mem_address=\"0x00007f72b7e76000\",\n unused=None),\n ]\n\n self.assertEqual(len(expected_deps), len(deps))\n\n for i, (dep, expected_dep) in enumerate(zip(deps, expected_deps)):\n self.assertListEqual([],\n diff_dependencies(\n ours=dep, theirs=expected_dep),\n \"Mismatch at the dependency {}\".format(i))","title":""},{"docid":"52b30ead4e6c0bd1469a7e820f3c4e9d","score":"0.46897256","text":"def bound_driver(self):\r\n pass","title":""},{"docid":"c60c028fdaf86e2fc8780a311ab5538c","score":"0.46872205","text":"def test_smbpath1():\n p = SMBPath(\n '\\\\\\\\filex.com\\\\it\\\\stg\\\\meh',\n find_dfs_share=mock_find_dfs_share\n )\n assert p.domain == 'filex.com'\n assert p.server_name == 'fxb04fs0301'\n assert p.share == 'filerouter_stage'\n assert p.relpath == 'meh', \"{}\".format(p.relpath)","title":""},{"docid":"87ce15a470d241d26f4fbcb30aa7145b","score":"0.4686332","text":"def test_absolute_path(self):\n test_path = \"/a/b/Packages\"\n when(sublime).packages_path().thenReturn(test_path)\n self.assertEqual(test_path, path.packages_path(path.ABSOLUTE))\n unstub(sublime)","title":""},{"docid":"a9cc120bc4d8961d68b5bf2d999d8b6c","score":"0.46797383","text":"def bound_source():","title":""},{"docid":"4029447d226eb705ad80898244c0aa33","score":"0.46691942","text":"def test_set_no_bound(self):\n to_bound = self.prob.to_bound\n test_func = self.prob.test_func\n self.prob.bound('to_bound')\n self.assertIs(to_bound, self.prob.to_bound)\n self.assertIs(test_func, self.prob.test_func)","title":""},{"docid":"a106b6ad0a0a51bc5729023c6a23df11","score":"0.46615082","text":"def test_main__setup_paths_gather(self, chmod_mock, mkdir_mock):\n self.ec2rl._write_initialized = False # Force re-initialization of writing for test\n with self.assertRaises(ec2rlcore.main.MainDirectoryError):\n self.ec2rl._setup_write_paths()\n\n self.assertTrue(chmod_mock.called)\n self.assertTrue(mkdir_mock.called)","title":""},{"docid":"2408517c29c1c172d21cd5f1a5dffb0c","score":"0.46579495","text":"def _bind_fabric(self, context, segment):\n port_id = context.current['id']\n physnet = self._get_physnet(context)\n if not physnet:\n LOG.debug(\"bind_port for port %(port)s: no physical_network \"\n \"found\", {'port': port_id})\n return False\n\n next_segment = context.allocate_dynamic_segment(\n {'network_id': context.network.current['id'],\n 'network_type': n_const.TYPE_VLAN,\n 'physical_network': physnet})\n LOG.debug(\"bind_port for port %(port)s: \"\n \"current_segment=%(current_seg)s, \"\n \"next_segment=%(next_seg)s\",\n {'port': port_id, 'current_seg': segment,\n 'next_seg': next_segment})\n context.continue_binding(segment['id'], [next_segment])\n return True","title":""},{"docid":"4848655ef8c8cad9f0596e7c12846c21","score":"0.4656527","text":"def test_resolve_uri_files():\n from server import resolve_uri\n our_tuple = resolve_uri('/sample.txt')\n assert our_tuple[1] == 'txt'","title":""},{"docid":"c5042990c46e87c1b91889a8a2049d81","score":"0.46457344","text":"def main():\n import sys\n if len(sys.argv) < 3:\n print 'usage: python base.py [server] [port]'\n else:\n server, port = sys.argv[1:]\n print testLDAPConnectivity(server, int(port))","title":""},{"docid":"eccfcc8bc3d0237bd073c37d64055590","score":"0.46453148","text":"def test_resolve_keys(self):\n pass","title":""},{"docid":"a1052d83b474f0b2cbfe40802f1c01f0","score":"0.46435797","text":"def test_on_bind_exporter_relation_changed(self):\n relation_id = self._add_bind_exporter_relation()\n # update relation -> trigger bind_exporter_relation_changed hook\n self.harness.update_relation_data(relation_id, \"prometheus2/0\", {})\n\n relation_data = self.harness.get_relation_data(relation_id, self.harness.charm.unit.name)\n self.assertDictEqual(relation_data, {\"hostname\": \"127.0.0.1\", \"port\": \"9119\"})\n self.assert_active_unit(self.harness.charm.unit)","title":""},{"docid":"d7660e445b521e847fa5795aa2bcd007","score":"0.46425244","text":"def test_resolve_locations_special_no_locations(minimal_location_chooser, caplog):\n caplog.set_level(logging.INFO)\n partial_event = make_partial_event()\n special_location = make_special_location(location = \"\")\n location = minimal_location_chooser.resolve_locations(partial_event, \"\", special_location)\n assert location == SAMPLE_DEFAULT_EVENT_LOCATION\n assert \"Unknown location\" not in caplog.text","title":""},{"docid":"a4576a527faf202204256e6c223ddbd1","score":"0.46392307","text":"def test_handle_transform(self, runway_context: MockRunwayContext) -> None:\n lookup_handlers = RUNWAY_LOOKUP_HANDLERS.copy()\n runway_context.env.vars.update(VALUES)\n\n for name, lookup in lookup_handlers.items():\n if name not in SUPPORTS_DEFAULT:\n continue\n result = lookup.handle(\n \"NOT_VALID::default=false, transform=bool\",\n context=runway_context,\n variables=VARIABLES,\n )\n\n assert not result","title":""},{"docid":"5fb8e83c28a43c04219923b796d596f2","score":"0.4631673","text":"def test_finds_fuzzer(self):\n self.assertEqual(self._setup_fuzzer(), self._find_fuzzer_path())","title":""},{"docid":"45e6f6d31060c3e2f5773f0a977078e0","score":"0.4631054","text":"def test_main__setup_paths_workdir(self, mkdir_mock):\n self.ec2rl._write_initialized = False # Force re-initialization of writing for test\n with self.assertRaises(ec2rlcore.main.MainDirectoryError):\n self.ec2rl._setup_write_paths()\n\n self.assertTrue(mkdir_mock.called)","title":""},{"docid":"be4725f6bcf0690f3b184f2993595fd2","score":"0.46260536","text":"def testSock(t, env):\n c1 = env.c1.new_client(env.testname(t))\n sess1 = c1.create_session()\n ops = use_obj(env.opts.usesocket) + [op.lookupp()]\n res = sess1.compound(ops)\n check(res, NFS4ERR_NOTDIR, \"LOOKUPP with non-dir <cfh>\")","title":""},{"docid":"63a147dc1a3b0e11e3afd8608401e429","score":"0.46235862","text":"def test_nexusbinding_update(self):\r\n npb11 = self._npb_test_obj(10, 100, switch='1.1.1.1', instance='test')\r\n npb21 = self._npb_test_obj(20, 100, switch='1.1.1.1', instance='test')\r\n self._add_bindings_to_db([npb11, npb21])\r\n\r\n npb_all_v100 = nexus_db_v2.get_nexusvlan_binding(100, '1.1.1.1')\r\n self.assertEqual(len(npb_all_v100), 2)\r\n\r\n npb22 = self._npb_test_obj(20, 200, switch='1.1.1.1', instance='test')\r\n npb = nexus_db_v2.update_nexusport_binding(npb21.port, 200)\r\n self._assert_bindings_match(npb, npb22)\r\n\r\n npb_all_v100 = nexus_db_v2.get_nexusvlan_binding(100, '1.1.1.1')\r\n self.assertEqual(len(npb_all_v100), 1)\r\n self._assert_bindings_match(npb_all_v100[0], npb11)\r\n\r\n npb = nexus_db_v2.update_nexusport_binding(npb21.port, 0)\r\n self.assertIsNone(npb)\r\n\r\n npb33 = self._npb_test_obj(30, 300, switch='1.1.1.1', instance='test')\r\n with testtools.ExpectedException(exceptions.NexusPortBindingNotFound):\r\n nexus_db_v2.update_nexusport_binding(npb33.port, 200)","title":""},{"docid":"4b3fcd67ede4f569ffc28c06058245dc","score":"0.46215114","text":"def testLink(t, env):\n c1 = env.c1.new_client(env.testname(t))\n sess1 = c1.create_session()\n ops = use_obj(env.opts.uselink) + [op.lookupp()]\n res = sess1.compound(ops)\n check(res, NFS4ERR_SYMLINK, \"LOOKUPP with non-dir <cfh>\")","title":""}],"string":"[\n {\n \"docid\": \"685b1d1fbedb236e7d0adef4cef659fa\",\n \"score\": \"0.6799697\",\n \"text\": \"def test_manual_bind_mount():\\n manual_bind_runner(bindit.docker.mount_bind_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45be098ba0da542a8d83bf55eafcb293\",\n \"score\": \"0.67655706\",\n \"text\": \"def test_bind_folder():\\n with tempfile.TemporaryDirectory(prefix=TEMPFILE_PREFIX) as sourcedir:\\n sourcedir_resolved = pathlib.Path(sourcedir).resolve()\\n destdir_expected = pathlib.PosixPath(\\n \\\"/bindit\\\"\\n ) / sourcedir_resolved.relative_to(sourcedir_resolved.anchor)\\n with DockerRun(\\\"ls\\\", sourcedir) as container:\\n assert container.is_running()\\n # check that sourcedir has been mounted\\n mounts = container.get_mounts()\\n assert sourcedir_resolved in mounts\\n assert pathlib.PosixPath(mounts[sourcedir_resolved]) == destdir_expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"085ca8e7b94168e922833b26003c584f\",\n \"score\": \"0.64074254\",\n \"text\": \"def test_manual_bind_volume():\\n manual_bind_runner(bindit.docker.volume_bind_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41f12d0f1ee856d58b0fdf9bf75219e3\",\n \"score\": \"0.6204146\",\n \"text\": \"def _bind(self):\\n return runner.call(\\\"mount -o bind,%s %s %s\\\" % (self._option, \\n self._olddir, \\n self._newdir))[0] == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f1edd0b8c0ae3b87cfb81d79eb07f8d\",\n \"score\": \"0.61872214\",\n \"text\": \"def test_parse_bind_mount():\\n for sourcekey in [\\\"source\\\", \\\"src\\\"]:\\n for destkey in [\\\"destination\\\", \\\"dst\\\", \\\"target\\\"]:\\n val = f\\\"type=bind,{sourcekey}={SOURCE_ABSTRACT},{destkey}={DEST_ABSTRACT}\\\"\\n mapping = bindit.docker.parse_bind_mount(val)\\n assert list(mapping.keys())[0] == SOURCE_ABSTRACT\\n assert list(mapping.values())[0] == DEST_ABSTRACT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac29eadcadc31d25adba54ab50fbc398\",\n \"score\": \"0.6181631\",\n \"text\": \"def test_no_image_args():\\n with tempfile.TemporaryDirectory(prefix=TEMPFILE_PREFIX) as sourcedir:\\n sourcefile = pathlib.Path(\\n tempfile.mkstemp(dir=sourcedir, prefix=TEMPFILE_PREFIX)[1]\\n )\\n sourcedir_resolved = pathlib.Path(sourcedir).resolve()\\n destdir = pathlib.PosixPath(\\\"/manualbind\\\")\\n destfile = destdir / sourcefile.name\\n with DockerRun(\\n runner_arg=list(bindit.docker.volume_bind_args(sourcedir, destdir))\\n ) as container:\\n assert container.is_running()\\n mounts = container.get_mounts()\\n # verify that the source is there\\n assert sourcedir_resolved in mounts\\n # and that the destination is the manual destination and not some /bindit\\n # stuff\\n assert mounts[sourcedir_resolved] == destdir\\n # and that we have no other mounts\\n assert list(mounts.keys()) == [sourcedir_resolved]\\n assert container.has_file(destfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5500764e29a921bfd4fec56e891ce47b\",\n \"score\": \"0.6117683\",\n \"text\": \"def test__bind(self):\\n self.assertEqual(self.test_profile._bind(), self.ref_bound)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01728758408330488543babe7c45d377\",\n \"score\": \"0.5769343\",\n \"text\": \"def testBuildBinds(self):\\n\\n #This class may become obselete soon. There is a TODO stuck onto DBCore.buildbinds()\\n #This just checks to see that the sequence properly packages the first value is set\\n #So that it sets the key seqname to the proper name in the files list\\n #Also right now tests that it sets the keys right in each dict, but this seems redundant\\n # -mnorman\\n\\n seqname = 'file'\\n dictbinds = {'lumi':123, 'test':100}\\n files = ['testA', 'testB', 'testC']\\n\\n myThread = threading.currentThread()\\n\\n testInterface = myThread.dbi\\n\\n binds = testInterface.buildbinds(files, seqname, dictbinds)\\n\\n #This should return a dict for every value in files with additional elements\\n #from dictbinds. We then loop over every dict (which should be equal to the\\n #number of elements in files), and look to see that the filename matches and that\\n #At least one of the dictbinds keys matches to its proper element\\n\\n for i in range(len(files)):\\n self.assertEqual(binds[i][seqname], files[i])\\n self.assertEqual(binds[i][next(iter(dictbinds))], dictbinds[next(iter(dictbinds))])\\n\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbcbf669debf30a8eb90d408b141513f\",\n \"score\": \"0.56490296\",\n \"text\": \"def test_external_building_with_port_bind_to_environ_wrong_servername():\\n map = Map([\\n Rule('/', endpoint='index'),\\n ])\\n environ = create_environ('/', 'http://example.org:5000/')\\n assert_raises(ValueError, lambda: map.bind_to_environ(environ, server_name=\\\"example.org\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68517124bd0da94e8c9c1423b4431839\",\n \"score\": \"0.55983245\",\n \"text\": \"def bind(self):\\n if self.binded(): \\n return\\n\\n if not os.path.exists(self._newdir):\\n self._mkstat = aux.make_dirs(self._newdir)\\n\\n self._bind() or self._mkstat and aux.remove_dirs(self._newdir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f17b1578a8b207aea745c9e3798e3f7\",\n \"score\": \"0.5478761\",\n \"text\": \"def test_cli_inference():\\n assert bindit.docker.ARGS\\n assert bindit.docker.LETTERS\\n for bind in bindit.docker.BIND_PARSER:\\n assert bind in bindit.docker.ARGS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19b3d550443daaeda5b578ec7d350fa1\",\n \"score\": \"0.54256535\",\n \"text\": \"def test_bind():\\n global testcount\\n testcount += 1\\n\\n port = unique_port( mincemeat.DEFAULT_PORT )\\n cred = credentials.copy()\\n cred.update({\\\"port\\\": port})\\n \\n s1 = mincemeat.Server_daemon(credentials=cred, timeout=5.)\\n state = s1.state()\\n assert state == \\\"idle\\\"\\n s1.start()\\n time.sleep(1)\\n state = s1.state()\\n assert state == \\\"authenticated\\\"\\n\\n try:\\n s2 = mincemeat.Server()\\n s2.conn(**cred)\\n assert False == \\\"Should have thrown Exception in bind()!\\\"\\n except Exception, e:\\n assert \\\"Only one usage of each socket address\\\" in str(e) \\\\\\n or \\\"Address already in use\\\" in str(e)\\n\\n # Server wasn't provided with a datasource; it will remain idle, and report\\n # failed: incomplete.\\n s1.stop()\\n state = s1.state()\\n assert state == \\\"failed: incomplete\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7693614c9f9a18e6b2ab9718a4523a28\",\n \"score\": \"0.54163766\",\n \"text\": \"def test_python_bind(self):\\n\\n server_port = 12345\\n script_content = \\\"\\\"\\\"\\nimport socket\\nserver_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\nserver_socket.bind((socket.gethostname(), %d))\\nserver_socket.listen(5)\\nserver_socket.close()\\n\\\"\\\"\\\" % server_port\\n\\n win32_api_definitions.Win32Hook_bind._debug_counter_before = 0\\n win32_api_definitions.Win32Hook_bind._debug_counter_after = 0\\n\\n dwProcessId = self._debug_python_script(script_content)\\n\\n print(\\\"Win32Hook_bind BEFORE:\\\", win32_api_definitions.Win32Hook_bind._debug_counter_before)\\n print(\\\"Win32Hook_bind AFTER :\\\", win32_api_definitions.Win32Hook_bind._debug_counter_after)\\n\\n print(\\\"debug_counter_WaitForDebugEvent:\\\", self.hooks_manager.debug_counter_WaitForDebugEvent)\\n print(\\\"debug_counter_exception_breakpoint:\\\", self.hooks_manager.debug_counter_exception_breakpoint)\\n\\n self.hooks_manager.debug_print_hooks_counter()\\n\\n calls_counter_process = win32_api_definitions.tracer_object.calls_counter[dwProcessId]\\n\\n self.assertTrue(calls_counter_process[b'CreateFileW'] > 0)\\n self.assertEqual(calls_counter_process[b'bind'], 1)\\n\\n # 'addr': [{'Id': '192.168.1.10:12345'}\\n server_address = socket.gethostbyname(socket.gethostname())\\n expected_addr = \\\"%s:%d\\\" % (server_address, server_port)\\n print(\\\"expected_addr=\\\", expected_addr)\\n self.assertTrue('CIM_DataFile' in win32_api_definitions.tracer_object.created_objects)\\n self.assertTrue({'Id': expected_addr} in win32_api_definitions.tracer_object.created_objects['addr'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e41f8b53668dc946569e97dcfc7c756a\",\n \"score\": \"0.53630775\",\n \"text\": \"def bind_mount(source_path, mountpoint):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1132046ba0fb1878dbb85b2cc2a20b07\",\n \"score\": \"0.53486\",\n \"text\": \"def get_bind_name(self, component_name, test_config='default_binding'):\\n sid = self.get_bind_id(component_name, test_config)\\n if not sid:\\n return False\\n sut = self.get_sut(sid)\\n if not sut:\\n sut = {}\\n return sut.get('path', False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1404fe2beb095cb30f2caaaa3bb928c\",\n \"score\": \"0.53414744\",\n \"text\": \"def boundIt(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"405832d7db955a2b1d231c70e0f674e2\",\n \"score\": \"0.5331331\",\n \"text\": \"def test_directory(self):\\n module = reflect.filenameToModuleName(os.path.join('twisted', 'test'))\\n self.assertEquals(module, 'test')\\n module = reflect.filenameToModuleName(os.path.join('twisted', 'test')\\n + os.path.sep)\\n self.assertEquals(module, 'test')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36a2027119d19a46343f71457a27f8c0\",\n \"score\": \"0.5321691\",\n \"text\": \"def lsp_bind(self, port, chassis, may_exist=False):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4730035dc5701f31e4f7ecac8e5df872\",\n \"score\": \"0.5301599\",\n \"text\": \"def test_server_name_interpolation():\\n server_name = 'example.invalid'\\n map = Map([Rule('/', endpoint='index'),\\n Rule('/', endpoint='alt', subdomain='alt')])\\n\\n env = create_environ('/', 'http://%s/' % server_name)\\n adapter = map.bind_to_environ(env, server_name=server_name)\\n assert adapter.match() == ('index', {})\\n\\n env = create_environ('/', 'http://alt.%s/' % server_name)\\n adapter = map.bind_to_environ(env, server_name=server_name)\\n assert adapter.match() == ('alt', {})\\n\\n try:\\n env = create_environ('/', 'http://%s/' % server_name)\\n adapter = map.bind_to_environ(env, server_name='foo')\\n except ValueError, e:\\n msg = str(e)\\n assert 'provided (%r)' % 'foo' in msg\\n assert 'environment (%r)' % server_name in msg\\n else:\\n assert False, 'expected exception'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09afd8954367b5023240e509ce3f2339\",\n \"score\": \"0.52959895\",\n \"text\": \"def test_find_gateways(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd7df1f3b41e0eba6a9add393dc5cb8a\",\n \"score\": \"0.52836597\",\n \"text\": \"def do_bind(self,stanza):\\n fr=stanza.get_from()\\n if fr and fr!=self.peer:\\n r=stanza.make_error_response(\\\"forbidden\\\")\\n self.send(r)\\n r.free()\\n return\\n resource_n=stanza.xpath_eval(\\\"bind:bind/bind:resource\\\",{\\\"bind\\\":BIND_NS})\\n if resource_n:\\n resource=resource_n[0].getContent()\\n else:\\n resource=\\\"auto\\\"\\n if not resource:\\n r=stanza.make_error_response(\\\"bad-request\\\")\\n else:\\n self.unset_iq_set_handler(\\\"bind\\\",BIND_NS)\\n r=stanza.make_result_response()\\n self.peer.set_resource(resource)\\n q=r.new_query(BIND_NS,\\\"bind\\\")\\n q.newTextChild(None,\\\"jid\\\",to_utf8(self.peer.as_unicode()))\\n self.state_change(\\\"authorized\\\",self.peer)\\n r.set_to(None)\\n self.send(r)\\n r.free()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"086aa629462549fbce56ada19b51eb81\",\n \"score\": \"0.5282458\",\n \"text\": \"def test_missing_filepath(self):\\n BindingsManager.load_from_filepath(\\\"/tmp/foo.yaml\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38882ee1be8b5d1a05ca597a8f60b60f\",\n \"score\": \"0.52007556\",\n \"text\": \"def setup_basics() -> None:\\r\\n if not os.path.exists('bindsets/'):\\r\\n os.mkdir('bindsets')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a3d107d052e925cbffaaff73b5729a2\",\n \"score\": \"0.5193542\",\n \"text\": \"def test_resolve_uri_nonexistant_dir():\\n from server import resolve_uri\\n path = resolve_uri('/WTF')\\n result = False\\n assert path == result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dbad7f982680048aed3ee3d68134fb0\",\n \"score\": \"0.51632357\",\n \"text\": \"def test_bind_addr_unix(http_server, unix_sock_file):\\n httpserver = http_server.send(unix_sock_file)\\n\\n assert httpserver.bind_addr == unix_sock_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71e2cf04a75b9b5e97ed0c86b03a6a82\",\n \"score\": \"0.5163094\",\n \"text\": \"def test_on_config_changed_with_bind_exporter_relation(self):\\n relation_id = self._add_bind_exporter_relation()\\n self.harness.update_config({\\\"exporter-listen-port\\\": \\\"9120\\\"})\\n\\n relation_data = self.harness.get_relation_data(relation_id, self.harness.charm.unit.name)\\n self.assertDictEqual(relation_data, {\\\"hostname\\\": \\\"127.0.0.1\\\", \\\"port\\\": \\\"9120\\\"})\\n self.assert_active_unit(self.harness.charm.unit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c4df7a65c2b1a3f4d3fcbe3b7a1e9c3\",\n \"score\": \"0.5123959\",\n \"text\": \"def test_emptyMapper(self):\\n verifyObject(IMapper, emptyMapper)\\n for name in [\\\"sys\\\", \\\"email\\\", \\\"exocet\\\", \\\"exocet.test\\\"]:\\n self.assertRaises(ImportError, emptyMapper.lookup, name)\\n self.assertFalse(emptyMapper.contains(name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a52d38ff721d29f3e0a77ca52314aa2\",\n \"score\": \"0.5123125\",\n \"text\": \"def test_external_building_with_port_bind_to_environ():\\n map = Map([\\n Rule('/', endpoint='index'),\\n ])\\n adapter = map.bind_to_environ(\\n create_environ('/', 'http://example.org:5000/'),\\n server_name=\\\"example.org:5000\\\"\\n )\\n built_url = adapter.build('index', {}, force_external=True)\\n assert built_url == 'http://example.org:5000/', built_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66cf1532f8ae8055590a7ea4198638b7\",\n \"score\": \"0.51190525\",\n \"text\": \"def test_bind_atp_feed(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e45a1caadf30c650534c90bbf3ac30d\",\n \"score\": \"0.50976026\",\n \"text\": \"def binded(self):\\n # Sometimes os.path.ismount performs incorrectly.\\n # Check mount table is a better way.\\n with open(\\\"/proc/mounts\\\", 'r') as mnts:\\n for mnt in mnts.readlines():\\n newdir = aux.cano_path(mnt.split()[1])\\n if self._newdir == newdir: \\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b28836e12a24cd1bc5e0683aeb06ad0d\",\n \"score\": \"0.50929266\",\n \"text\": \"def test_dictMapper(self):\\n d = {\\\"sys\\\": object(), \\\"exocet.test\\\": object()}\\n dm = DictMapper(d)\\n verifyObject(IMapper, dm)\\n for name in [\\\"sys\\\", \\\"exocet.test\\\"]:\\n self.assertEqual(d[name], dm.lookup(name))\\n self.assertTrue(dm.contains(name))\\n\\n for name in [\\\"email\\\", \\\"foobaz\\\"]:\\n self.assertRaises(ImportError, dm.lookup, name)\\n self.assertFalse(dm.contains(name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6113dd887a506ecafba0bf073199597e\",\n \"score\": \"0.50790834\",\n \"text\": \"def bind(self) -> Optional[pulumi.Input['CacheDirectoryLdapBindArgs']]:\\n return pulumi.get(self, \\\"bind\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"570003b2ab6aaa87a63dd6e2609217e1\",\n \"score\": \"0.5071621\",\n \"text\": \"def test_temp_connection_custom_bind_iface(self):\\n bind_iface = \\\"0.0.0.0\\\"\\n with patch('py2neo.neo4j.GraphDatabaseService', lambda uri: uri):\\n conn = get_connection('temp://{}'.format(bind_iface))\\n\\n assert conn == \\\"http://localhost:7475/db/data/\\\"\\n self.write_config.assert_called_once_with(\\n ANY, 7475, ANY, bind_iface\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0b335a4f6d4e4e559f9eae4e1349d94\",\n \"score\": \"0.50385594\",\n \"text\": \"def test_pep302Mapper(self):\\n import exocet, exocet.test, compiler.visitor\\n pep302Mapper._oldSysModules = sys.modules.copy()\\n verifyObject(IMapper, pep302Mapper)\\n for (name, mod) in {\\\"sys\\\": sys,\\n \\\"exocet\\\": exocet,\\n \\\"exocet.test\\\": exocet.test,\\n \\\"compiler.visitor\\\": compiler.visitor}.iteritems():\\n assertIdentical(self, pep302Mapper.lookup(name), mod)\\n self.assertTrue(pep302Mapper.contains(name))\\n\\n self.assertRaises(ImportError, pep302Mapper.lookup,\\n \\\"exocet._nonexistentModule\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7630098a6b7c3d7dea301f3754e19ce\",\n \"score\": \"0.50343823\",\n \"text\": \"def bind(self, bindings):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7630098a6b7c3d7dea301f3754e19ce\",\n \"score\": \"0.50343823\",\n \"text\": \"def bind(self, bindings):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7630098a6b7c3d7dea301f3754e19ce\",\n \"score\": \"0.50343823\",\n \"text\": \"def bind(self, bindings):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd656b239ca3f1f8b8673d6dd66929da\",\n \"score\": \"0.50154316\",\n \"text\": \"def simple_module(binder):\\n from unuo.config import config\\n from unuo.filebackend import FileBackend\\n from unuo.filelogging import FileLoggerManager\\n from unuo.docker import Docker_1_1_x\\n binder.bind(\\n buildsfolder_key,\\n to=config.builds_folder)\\n binder.bind(\\n logsfolder_key,\\n to=config.logs_folder)\\n binder.bind(\\n backend_key,\\n to=FileBackend,\\n scope=singleton)\\n binder.bind(\\n logmanager_key,\\n to=FileLoggerManager,\\n scope=singleton)\\n binder.bind(\\n docker_key,\\n to=Docker_1_1_x,\\n scope=singleton)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a71f6e817540941a250090e9dc86397e\",\n \"score\": \"0.5013581\",\n \"text\": \"def test_resolve_uri_dir():\\n from server import resolve_uri\\n our_tuple = resolve_uri('/images')\\n assert our_tuple[1] == 'text/html'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae9269a32cd9ead201a1fe5fbc1c22e\",\n \"score\": \"0.49917224\",\n \"text\": \"def test_perl_socket_bind(self):\\n\\n server_port = 12345\\n\\n script_content = \\\"\\\"\\\"\\\\\\nuse Socket;\\nsocket(SERVER, PF_INET, SOCK_STREAM, getprotobyname('tcp'));\\nsetsockopt(SERVER, SOL_SOCKET, SO_REUSEADDR, 1);\\n$my_addr = sockaddr_in(%s, INADDR_ANY);\\nbind(SERVER, $my_addr) or die \\\"Couldn't bind to port $server_port : $!\\\\n\\\";\\n\\\"\\\"\\\" % server_port\\n\\n dwProcessId = self._debug_perl_script(script_content)\\n\\n print(\\\"Win32Hook_bind BEFORE:\\\", win32_api_definitions.Win32Hook_bind._debug_counter_before)\\n print(\\\"Win32Hook_bind AFTER :\\\", win32_api_definitions.Win32Hook_bind._debug_counter_after)\\n\\n self.hooks_manager.debug_print_hooks_counter()\\n\\n print(\\\"debug_counter_WaitForDebugEvent:\\\", self.hooks_manager.debug_counter_WaitForDebugEvent)\\n print(\\\"debug_counter_exception_breakpoint:\\\", self.hooks_manager.debug_counter_exception_breakpoint)\\n\\n # No subprocess creation\\n self.assertEqual(len(win32_api_definitions.tracer_object.calls_counter), 1)\\n\\n root_process_calls = win32_api_definitions.tracer_object.calls_counter[dwProcessId]\\n self.assertTrue(root_process_calls[b'CreateFileA'] > 0)\\n self.assertEqual(root_process_calls[b'bind'], 1)\\n if not is_windows10:\\n self.assertTrue(root_process_calls[b'ReadFile'] > 0)\\n self.assertEqual(root_process_calls[b'fopen'], 1)\\n self.assertEqual(win32_api_definitions.Win32Hook_bind._debug_counter_before, 1)\\n self.assertEqual(win32_api_definitions.Win32Hook_bind._debug_counter_after, 1)\\n\\n # b'CreateFileA' 1 1\\n # b'CreateFileW' 1 1\\n # b'CreateProcessA' 0 0\\n # b'CreateProcessAsUserA' 0 0\\n # b'CreateProcessAsUserW' 0 0\\n # b'CreateProcessW' 0 0\\n # b'CreateRemoteThread' 0 0\\n # b'CreateRemoteThreadEx' 0 0\\n # b'CreateThread' 1 1\\n # b'DeleteFileA' 0 0\\n # b'DeleteFileW' 0 0\\n # b'ReadFile' 1 0\\n # b'ReadFileEx' 0 0\\n # b'ReadFileScatter' 0 0\\n # b'RemoveDirectoryA' 0 0\\n # b'RemoveDirectoryW' 0 0\\n # b'TerminateProcess' 1 0\\n\\n\\n # TODO: Results with fsopen are not stable. Maybe a thread ?\\n\\n print(\\\"created_objects=\\\", win32_api_definitions.tracer_object.created_objects)\\n server_address = \\\"0.0.0.0\\\"\\n expected_addr = \\\"%s:%d\\\" % (server_address, server_port)\\n print(\\\"expected_addr=\\\", expected_addr)\\n self.assertTrue({'Id': expected_addr} in win32_api_definitions.tracer_object.created_objects['addr'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f004639ea5b988ede9de8cc2fc2acca\",\n \"score\": \"0.49800903\",\n \"text\": \"def test_parse_bind_volume():\\n # two-field version\\n mapping = bindit.docker.parse_bind_volume(f\\\"{SOURCE_ABSTRACT}:{DEST_ABSTRACT}\\\")\\n assert list(mapping.keys())[0] == SOURCE_ABSTRACT\\n assert list(mapping.values())[0] == DEST_ABSTRACT\\n # three-field\\n mapping = bindit.docker.parse_bind_volume(\\n f\\\"{SOURCE_ABSTRACT}:{DEST_ABSTRACT}:ro,consistent\\\"\\n )\\n assert list(mapping.keys())[0] == SOURCE_ABSTRACT\\n assert list(mapping.values())[0] == DEST_ABSTRACT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39d1b15445cbe31422a695fdc93607d0\",\n \"score\": \"0.4939582\",\n \"text\": \"def testMap(self, thing):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d79650936ca420258d8693a8a0599265\",\n \"score\": \"0.49355534\",\n \"text\": \"def test_specified_port_bind(self):\\n TestCase = type(\\\"TestCase\\\", (LiveServerBase,), {})\\n # Find an open port and tell TestCase to use it.\\n s = socket.socket()\\n s.bind((\\\"\\\", 0))\\n TestCase.port = s.getsockname()[1]\\n s.close()\\n TestCase._start_server_thread()\\n try:\\n self.assertEqual(\\n TestCase.port,\\n TestCase.server_thread.port,\\n f\\\"Did not use specified port for LiveServerTestCase thread: \\\"\\n f\\\"{TestCase.port}\\\",\\n )\\n finally:\\n TestCase.doClassCleanups()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c23fe6a29a952c0217e4c2e7b4d9338\",\n \"score\": \"0.49352533\",\n \"text\": \"def rdt_bind(sockd, port):\\r\\n\\t######## Your implementation #######\\r\\n\\t#Reusing part 1 code\\r\\n\\ttry:\\r\\n\\t\\tsockd.bind((\\\"\\\",port))\\r\\n\\texcept socket.error as emsg:\\r\\n\\t\\tprint(\\\"Socket bind error: \\\", emsg)\\r\\n\\t\\treturn -1\\r\\n\\treturn 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30a96842da00518e8d868018b03ecf7a\",\n \"score\": \"0.49324587\",\n \"text\": \"def test_bind_addr_unix_abstract(http_server, unix_abstract_sock):\\n httpserver = http_server.send(unix_abstract_sock)\\n\\n assert httpserver.bind_addr == unix_abstract_sock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3c74e4dbaa8e57e4e149bd8a99a9c57\",\n \"score\": \"0.49312508\",\n \"text\": \"def test_bindingAccess(self):\\n gg = self.classTested(\\\"stuff = '1':a ('2':b | '3':c)\\\")\\n t = gg.parseGrammar('TestGrammar', TreeBuilder)\\n G = moduleFromGrammar(t, 'TestGrammar', OMetaBase, {})\\n g = G(\\\"12\\\")\\n self.assertEqual(g.apply(\\\"stuff\\\")[0], '2')\\n self.assertEqual(g.locals['stuff']['a'], '1')\\n self.assertEqual(g.locals['stuff']['b'], '2')\\n g = G(\\\"13\\\")\\n self.assertEqual(g.apply(\\\"stuff\\\")[0], '3')\\n self.assertEqual(g.locals['stuff']['a'], '1')\\n self.assertEqual(g.locals['stuff']['c'], '3')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8083c085f97ff5f793aa83dbc7e70d9\",\n \"score\": \"0.49138045\",\n \"text\": \"def test_http_host_before_server_name():\\n env = {\\n 'HTTP_HOST': 'wiki.example.com',\\n 'SERVER_NAME': 'web0.example.com',\\n 'SERVER_PORT': '80',\\n 'SCRIPT_NAME': '',\\n 'PATH_INFO': '',\\n 'REQUEST_METHOD': 'GET',\\n 'wsgi.url_scheme': 'http'\\n }\\n map = Map([Rule('/', endpoint='index', subdomain='wiki')])\\n adapter = map.bind_to_environ(env, server_name='example.com')\\n assert adapter.match('/') == ('index', {})\\n assert adapter.build('index', force_external=True) == 'http://wiki.example.com/'\\n assert adapter.build('index') == '/'\\n\\n env['HTTP_HOST'] = 'admin.example.com'\\n adapter = map.bind_to_environ(env, server_name='example.com')\\n assert adapter.build('index') == 'http://wiki.example.com/'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"777a322ca8930b4fac7150db3e9928ec\",\n \"score\": \"0.49028188\",\n \"text\": \"def test_exclusiveMapper(self):\\n\\n l = [\\\"sys\\\", \\\"exocet.test\\\"]\\n em = ExclusiveMapper(pep302Mapper, l)\\n\\n verifyObject(IMapper, em)\\n\\n for name in l:\\n self.assertRaises(ImportError, em.lookup, name)\\n self.assertFalse(em.contains(name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"965ac7152711968857111ff244ee8dc9\",\n \"score\": \"0.48984364\",\n \"text\": \"def test_search_relative(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88777c813343f6483d4a2fa8048d3ad3\",\n \"score\": \"0.48930928\",\n \"text\": \"def server_bind(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aed56d6883f369add6f59b7da7c6bf8\",\n \"score\": \"0.48892355\",\n \"text\": \"def test_bindNameOnly(self):\\n g = self.compile(\\\"foo = '1' :x '2' -> x\\\")\\n self.assertEqual(g.foo(\\\"132\\\"), \\\"3\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e7bf65ffa4ab7696f1207de27b873fe\",\n \"score\": \"0.48838958\",\n \"text\": \"def test_read_sample_dir(self):\\n # TODO\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c1481af811ae9409310b361f3af33bc\",\n \"score\": \"0.48753136\",\n \"text\": \"def test_main__setup_paths_absolute_path(self, chmod_mock, mkdir_mock):\\n sys.argv = [\\\"\\\"]\\n callpath = sys.argv[0]\\n if not os.path.isabs(callpath):\\n callpath = os.path.abspath(callpath)\\n sys.argv = [callpath]\\n self.ec2rl._write_initialized = False # Force re-initialization of writing for test\\n self.assertTrue(self.ec2rl._setup_write_paths())\\n\\n self.assertTrue(chmod_mock.called)\\n self.assertTrue(mkdir_mock.called)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87f77178bf5cc2a847b3fd529b206dec\",\n \"score\": \"0.48651186\",\n \"text\": \"def test_nexusvmbinding_get(self):\\r\\n npb11 = self._npb_test_obj(10, 100)\\r\\n npb21 = self._npb_test_obj(20, 100)\\r\\n npb22 = self._npb_test_obj(20, 200)\\r\\n self._add_bindings_to_db([npb11, npb21, npb22])\\r\\n\\r\\n npb = self._get_nexusvm_binding(npb21)\\r\\n self._assert_bindings_match(npb, npb21)\\r\\n npb = self._get_nexusvm_binding(npb22)\\r\\n self._assert_bindings_match(npb, npb22)\\r\\n\\r\\n with testtools.ExpectedException(exceptions.NexusPortBindingNotFound):\\r\\n nexus_db_v2.get_nexusvm_bindings(npb21.vlan, \\\"dummyInstance\\\")[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78880fb5638390dcdd19e557fa9d6e54\",\n \"score\": \"0.48579472\",\n \"text\": \"def transfer_bind(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3d2c9bb1dd809a1dbc418f1629a0a94\",\n \"score\": \"0.48514378\",\n \"text\": \"def test_path():\\n map = Map([\\n Rule('/', defaults={'name': 'FrontPage'}, endpoint='page'),\\n Rule('/Special', endpoint='special'),\\n Rule('/<int:year>', endpoint='year'),\\n Rule('/<path:name>', endpoint='page'),\\n Rule('/<path:name>/edit', endpoint='editpage'),\\n Rule('/<path:name>/silly/<path:name2>', endpoint='sillypage'),\\n Rule('/<path:name>/silly/<path:name2>/edit', endpoint='editsillypage'),\\n Rule('/Talk:<path:name>', endpoint='talk'),\\n Rule('/User:<username>', endpoint='user'),\\n Rule('/User:<username>/<path:name>', endpoint='userpage'),\\n Rule('/Files/<path:file>', endpoint='files'),\\n ])\\n adapter = map.bind('example.org', '/')\\n\\n assert adapter.match('/') == ('page', {'name':'FrontPage'})\\n assert_raises(RequestRedirect, lambda: adapter.match('/FrontPage'))\\n assert adapter.match('/Special') == ('special', {})\\n assert adapter.match('/2007') == ('year', {'year':2007})\\n assert adapter.match('/Some/Page') == ('page', {'name':'Some/Page'})\\n assert adapter.match('/Some/Page/edit') == ('editpage', {'name':'Some/Page'})\\n assert adapter.match('/Foo/silly/bar') == ('sillypage', {'name':'Foo', 'name2':'bar'})\\n assert adapter.match('/Foo/silly/bar/edit') == ('editsillypage', {'name':'Foo', 'name2':'bar'})\\n assert adapter.match('/Talk:Foo/Bar') == ('talk', {'name':'Foo/Bar'})\\n assert adapter.match('/User:thomas') == ('user', {'username':'thomas'})\\n assert adapter.match('/User:thomas/projects/werkzeug') == ('userpage', {'username':'thomas', 'name':'projects/werkzeug'})\\n assert adapter.match('/Files/downloads/werkzeug/0.2.zip') == ('files', {'file':'downloads/werkzeug/0.2.zip'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5905079d4f0c4027fc6808d2f5d8b5c\",\n \"score\": \"0.48480138\",\n \"text\": \"def test_named_conf(self):\\n filename = \\\"/etc/bind/named.conf.local\\\"\\n expected = (self.orgConf.controller_ip, )\\n self.correctly_written(filename, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdabece9e5f881f59feec8d2e22a9093\",\n \"score\": \"0.48326653\",\n \"text\": \"def test_bind(value):\\n var = TestVariable()\\n callback = Mock()\\n binding = VariableBinding(callback, var)\\n\\n binding.bind()\\n var.set(value)\\n\\n assert callback.call_args == call(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e7fc950d7245d661c24ea3df03d903\",\n \"score\": \"0.48314098\",\n \"text\": \"def test_is_valid_correct_invalid_location(self):\\n directives = [\\n { \\\"signature\\\" : \\\"container1:0.0.0.0:80:a.b.c:/location1/\\\"},\\n { \\\"signature\\\" : \\\"container2:0.0.0.0:80:a.b.c:/location1/\\\"}\\n ]\\n handle_listen = Listen(**{})\\n for directive in directives:\\n handle_listen.directives = directive \\n self.assertFalse(handle_listen.is_valid)\\n del handle_listen\",\n \"title\": \"\"\n },\n {\n \"docid\": \"996f4d6c3237fb52d34d899020f0796d\",\n \"score\": \"0.48239592\",\n \"text\": \"def bin_test(ctx):\\n\\tfrom wafhelpers.bin_test import cmd_bin_test\\n\\tcmd_bin_test(ctx, config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"161375cd1e18c870943efb34dfc590a9\",\n \"score\": \"0.4820161\",\n \"text\": \"def bound(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e4a4c32cf4aab49e630ec9358a4e9bb\",\n \"score\": \"0.48165792\",\n \"text\": \"def test_namedModuleLookup(self):\\n self.assertIdentical(\\n reflect.namedModule(\\\"twisted.python.reflect\\\"), reflect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01bb4df6723aea468d1d961a3ee520d1\",\n \"score\": \"0.48088744\",\n \"text\": \"def test_graph_store_bind(\\n graph_factory: GraphFactory,\\n ops: GraphOperations,\\n expected_bindings: NamespaceBindings,\\n) -> None:\\n graph = graph_factory()\\n for op in ops:\\n op(graph)\\n check_ns(graph, expected_bindings)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc8bc246c87b54774375cb1215806fc3\",\n \"score\": \"0.47882962\",\n \"text\": \"def test_configure_fn() -> None:\\n\\n class MyModule(Module):\\n def configure(self, binder: Binder) -> None:\\n binder.bind(IFileSystem, to=InMemoryFileSystem)\\n\\n kernel = Kernel()\\n kernel.install(MyModule())\\n\\n inst = kernel.get(IFileSystem) # type: ignore\\n assert isinstance(inst, InMemoryFileSystem)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5e478f3c4d8522a606abd53621d282e\",\n \"score\": \"0.4785687\",\n \"text\": \"def bind(app):\\n instance.init_app(app)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039e1da398447a086c25b64a8fa79f44\",\n \"score\": \"0.47691762\",\n \"text\": \"def testMapMap(self, hello):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7c63fe84dfa92db5afe1c6ab3ab11bd\",\n \"score\": \"0.47631738\",\n \"text\": \"def test_gds_map_file(self) -> None:\\n import hammer_config\\n\\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\\\"dummy28\\\")\\n tech_json_filename = os.path.join(tech_dir, \\\"dummy28.tech.json\\\")\\n\\n def add_gds_map(d: Dict[str, Any]) -> Dict[str, Any]:\\n r = deepdict(d)\\n r.update({\\\"gds map file\\\": \\\"test/gds_map_file\\\"})\\n return r\\n\\n HammerToolTestHelpers.write_tech_json(tech_json_filename, add_gds_map)\\n tech_opt = hammer_tech.HammerTechnology.load_from_dir(\\\"dummy28\\\", tech_dir)\\n if tech_opt is None:\\n self.assertTrue(False, \\\"Unable to load technology\\\")\\n else:\\n tech = tech_opt # type: hammer_tech.HammerTechnology\\n tech.cache_dir = tech_dir\\n\\n tool = DummyTool()\\n tool.technology = tech\\n database = hammer_config.HammerDatabase()\\n tool.set_database(database)\\n\\n # Test that empty for gds_map_mode results in no map file.\\n database.update_project([{\\n 'par.inputs.gds_map_mode': 'empty',\\n 'par.inputs.gds_map_file': None\\n }])\\n self.assertEqual(tool.get_gds_map_file(), None)\\n\\n # Test that manual mode for gds_map_mode works.\\n database.update_project([{\\n 'par.inputs.gds_map_mode': 'manual',\\n 'par.inputs.gds_map_file': '/tmp/foo/bar'\\n }])\\n self.assertEqual(tool.get_gds_map_file(), '/tmp/foo/bar')\\n\\n # Test that auto mode for gds_map_mode works if the technology has a map file.\\n database.update_project([{\\n 'par.inputs.gds_map_mode': 'auto',\\n 'par.inputs.gds_map_file': None\\n }])\\n self.assertEqual(tool.get_gds_map_file(), '{tech}/gds_map_file'.format(tech=tech_dir))\\n\\n # Cleanup\\n shutil.rmtree(tech_dir_base)\\n\\n # Create a new technology with no GDS map file.\\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\\\"dummy28\\\")\\n\\n tech_json_filename = os.path.join(tech_dir, \\\"dummy28.tech.json\\\")\\n HammerToolTestHelpers.write_tech_json(tech_json_filename)\\n tech_opt = hammer_tech.HammerTechnology.load_from_dir(\\\"dummy28\\\", tech_dir)\\n if tech_opt is None:\\n self.assertTrue(False, \\\"Unable to load technology\\\")\\n else:\\n tech = tech_opt\\n tech.cache_dir = tech_dir\\n\\n tool.technology = tech\\n\\n # Test that auto mode for gds_map_mode works if the technology has no map file.\\n database.update_project([{\\n 'par.inputs.gds_map_mode': 'auto',\\n 'par.inputs.gds_map_file': None\\n }])\\n self.assertEqual(tool.get_gds_map_file(), None)\\n\\n # Cleanup\\n shutil.rmtree(tech_dir_base)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36004881847b1507a3257b654da30fde\",\n \"score\": \"0.47588235\",\n \"text\": \"def test_simple_lookup():\\r\\n\\r\\n env = Environment({\\\"var\\\": 42})\\r\\n assert_equals(42, env.lookup(\\\"var\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3054e4a23d7e47b0bdcb947a47e551c6\",\n \"score\": \"0.47522664\",\n \"text\": \"def test_windows_missing_prepdir():\\n system_params = {}\\n salt_config = {}\\n\\n system_params['logdir'] = \\\"logdir\\\"\\n system_params['workingdir'] = \\\"workingdir\\\"\\n\\n with pytest.raises(KeyError, match='prepdir'):\\n SaltWindows(system_params, **salt_config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f62b1b377857620d668268f33c0ed0d\",\n \"score\": \"0.47377548\",\n \"text\": \"def test_namedAnyModuleLookup(self):\\n self.assertIdentical(\\n reflect.namedAny(\\\"twisted.python.reflect\\\"), reflect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3039e76ae70dc717f231f3592c3c4212\",\n \"score\": \"0.47310522\",\n \"text\": \"def test_config_directories(self):\\n # Initializing key variables\\n # Initialize key variables\\n save_directory = None\\n\\n if 'SWITCHMAP_CONFIGDIR' in os.environ:\\n save_directory = os.environ['SWITCHMAP_CONFIGDIR']\\n\\n # Try with no SWITCHMAP_CONFIGDIR\\n os.environ.pop('SWITCHMAP_CONFIGDIR', None)\\n directory = '{}/etc'.format(general.root_directory())\\n result = general.config_directories()\\n self.assertEqual(result, [directory])\\n\\n # Test with SWITCHMAP_CONFIGDIR set\\n directory = tempfile.mkdtemp()\\n os.environ['SWITCHMAP_CONFIGDIR'] = directory\\n result = general.config_directories()\\n self.assertEqual(result, [directory])\\n\\n # Restore state\\n if save_directory is not None:\\n os.environ['SWITCHMAP_CONFIGDIR'] = save_directory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b61f90a2135a0cbc25bf9922732f00b\",\n \"score\": \"0.47309\",\n \"text\": \"def test_external_building_with_port():\\n map = Map([\\n Rule('/', endpoint='index'),\\n ])\\n adapter = map.bind('example.org:5000', '/')\\n built_url = adapter.build('index', {}, force_external=True)\\n assert built_url == 'http://example.org:5000/', built_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b997ae8c7d241e616c2369f9ed46e38\",\n \"score\": \"0.4722874\",\n \"text\": \"def test_greedy():\\n map = Map([\\n Rule('/foo', endpoint='foo'),\\n Rule('/<path:bar>', endpoint='bar'),\\n Rule('/<path:bar>/<path:blub>', endpoint='bar')\\n ])\\n adapter = map.bind('example.org', '/')\\n\\n assert adapter.match('/foo') == ('foo', {})\\n assert adapter.match('/blub') == ('bar', {'bar': 'blub'})\\n assert adapter.match('/he/he') == ('bar', {'bar': 'he', 'blub': 'he'})\\n\\n assert adapter.build('foo', {}) == '/foo'\\n assert adapter.build('bar', {'bar': 'blub'}) == '/blub'\\n assert adapter.build('bar', {'bar': 'blub', 'blub': 'bar'}) == '/blub/bar'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3440560acf4aece1aef7a69b93915d39\",\n \"score\": \"0.4716483\",\n \"text\": \"def test_register_lookup_handler_str(mocker: MockerFixture) -> None:\\n mocker.patch.dict(RUNWAY_LOOKUP_HANDLERS, {})\\n register_lookup_handler(\\\"test\\\", \\\"runway.lookups.handlers.env.EnvLookup\\\")\\n assert \\\"test\\\" in RUNWAY_LOOKUP_HANDLERS\\n assert RUNWAY_LOOKUP_HANDLERS[\\\"test\\\"] == EnvLookup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55469deef9684c9486134d77a523ede3\",\n \"score\": \"0.47106376\",\n \"text\": \"def bindmount(self, mountpoint):\\n\\n if not self.mountpoint:\\n raise NotMountedError(self)\\n try:\\n _util.check_call_(['mount', '--bind', self.mountpoint, mountpoint], stdout=subprocess.PIPE)\\n if 'bindmounts' in self._paths:\\n self._paths['bindmounts'].append(mountpoint)\\n else:\\n self._paths['bindmounts'] = [mountpoint]\\n return True\\n except Exception as e:\\n logger.exception(\\\"Error bind mounting {0}.\\\".format(self))\\n raise SubsystemError(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e942a5300964b09b099b460d07220118\",\n \"score\": \"0.47079128\",\n \"text\": \"def setup_port_binding(self):\\n success = False\\n while not success:\\n try:\\n self.info(f'Attempting bind to port {self.bind_port}')\\n self.pub_socket.bind(f'tcp://*:{self.bind_port}')\\n success = True\\n self.info(f'Successful bind to port {self.bind_port}')\\n except:\\n try:\\n self.error(f'Port {self.bind_port} already in use, attempting next port')\\n success = False\\n self.bind_port += 1\\n except Exception as e:\\n self.debug(e)\\n self.debug(\\\"Finished loop\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d10417627cc6af2819bbdcda6bea02a\",\n \"score\": \"0.4706957\",\n \"text\": \"def bind_url(url):\\n if url.find('?') == -1: # like http://host/wms\\n binder = '?'\\n\\n # if like http://host/wms?foo=bar& or http://host/wms?foo=bar\\n if url.find('=') != -1:\\n if url.find('&', -1) != -1: # like http://host/wms?foo=bar&\\n binder = ''\\n else: # like http://host/wms?foo=bar\\n binder = '&'\\n\\n # if like http://host/wms?foo\\n if url.find('?') != -1:\\n if url.find('?', -1) != -1: # like http://host/wms?\\n binder = ''\\n elif url.find('&', -1) == -1: # like http://host/wms?foo=bar\\n binder = '&'\\n return '%s%s' % (url, binder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7217e4f21170d35db9df6fda1687d2c5\",\n \"score\": \"0.47022092\",\n \"text\": \"def bindir(self, *args, **kwargs):\\n return fbuild.path.Path(self(('--bindir',), *args, **kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a45082ed2e633b3e6320ac97269a889a\",\n \"score\": \"0.47016984\",\n \"text\": \"def test_bind_addr_inet(http_server, ip_addr):\\n httpserver = http_server.send((ip_addr, EPHEMERAL_PORT))\\n\\n assert httpserver.bind_addr[0] == ip_addr\\n assert httpserver.bind_addr[1] != EPHEMERAL_PORT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"817ff11af272054e213193761dc588e7\",\n \"score\": \"0.4696746\",\n \"text\": \"def bind_check(self, bind, interval=1, constant=1000):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e89440fba9770523da123ba4447b806\",\n \"score\": \"0.46961316\",\n \"text\": \"def test_inside_non_bindable(self):\\n\\n server = LocalAngularServer()\\n server.start(LocalAngularServer.HANDLER_SCOPE_TEST, {\\\"position\\\": \\\"inside_non_bindable\\\"})\\n\\n try:\\n shell_command = [\\\"python\\\", \\\"acstis.py\\\", \\\"--verify-payload\\\", \\\"--domain\\\", \\\"http://\\\" + server.url + \\\"?vulnerable=payload\\\"]\\n\\n process = subprocess.Popen(\\n shell_command\\n )\\n\\n exitcode = process.wait()\\n except Exception as e:\\n print(\\\"Exception: \\\" + str(e))\\n exitcode = 1\\n\\n server.stop()\\n\\n self.assertNotEqual(exitcode, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0be53eace7f8ed931bf2e70a0ff321b\",\n \"score\": \"0.46952796\",\n \"text\": \"def test_bin_dir(self):\\n\\n # pylint: disable=line-too-long\\n with tests.MockLdd(\\n out=\\\"\\\\n\\\".join([\\n \\\"\\\\tlinux-vdso.so.1 (0x00007ffd66ce2000)\\\",\\n \\\"\\\\tlibselinux.so.1 => /lib/x86_64-linux-gnu/libselinux.so.1 (0x00007f72b88fc000)\\\",\\n \\\"\\\\tlibc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007f72b850b000)\\\",\\n \\\"\\\\tlibpcre.so.3 => /lib/x86_64-linux-gnu/libpcre.so.3 (0x00007f72b8299000)\\\",\\n \\\"\\\\tlibdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007f72b8095000)\\\",\\n \\\"\\\\t/lib64/ld-linux-x86-64.so.2 (0x00007f72b8d46000)\\\",\\n \\\"\\\\tlibpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007f72b7e76000)\\\",\\n \\\"\\\",\\n ]),\\n out_unused=''):\\n # pylint: enable=line-too-long\\n deps = lddwrap.list_dependencies(\\n path=pathlib.Path(DIR), unused=False)\\n\\n expected_deps = [\\n lddwrap.Dependency(\\n soname=\\\"linux-vdso.so.1\\\",\\n path=None,\\n found=True,\\n mem_address=\\\"0x00007ffd66ce2000\\\",\\n unused=None),\\n lddwrap.Dependency(\\n soname=\\\"libselinux.so.1\\\",\\n path=pathlib.Path(\\\"/lib/x86_64-linux-gnu/libselinux.so.1\\\"),\\n found=True,\\n mem_address=\\\"0x00007f72b88fc000\\\",\\n unused=None),\\n lddwrap.Dependency(\\n soname=\\\"libc.so.6\\\",\\n path=pathlib.Path(\\\"/lib/x86_64-linux-gnu/libc.so.6\\\"),\\n found=True,\\n mem_address=\\\"0x00007f72b850b000\\\",\\n unused=None),\\n lddwrap.Dependency(\\n soname=\\\"libpcre.so.3\\\",\\n path=pathlib.Path(\\\"/lib/x86_64-linux-gnu/libpcre.so.3\\\"),\\n found=True,\\n mem_address=\\\"0x00007f72b8299000\\\",\\n unused=None),\\n lddwrap.Dependency(\\n soname=\\\"libdl.so.2\\\",\\n path=pathlib.Path(\\\"/lib/x86_64-linux-gnu/libdl.so.2\\\"),\\n found=True,\\n mem_address=\\\"0x00007f72b8095000\\\",\\n unused=None),\\n lddwrap.Dependency(\\n soname=None,\\n path=pathlib.Path(\\\"/lib64/ld-linux-x86-64.so.2\\\"),\\n found=True,\\n mem_address=\\\"0x00007f72b8d46000\\\",\\n unused=None),\\n lddwrap.Dependency(\\n soname=\\\"libpthread.so.0\\\",\\n path=pathlib.Path(\\\"/lib/x86_64-linux-gnu/libpthread.so.0\\\"),\\n found=True,\\n mem_address=\\\"0x00007f72b7e76000\\\",\\n unused=None),\\n ]\\n\\n self.assertEqual(len(expected_deps), len(deps))\\n\\n for i, (dep, expected_dep) in enumerate(zip(deps, expected_deps)):\\n self.assertListEqual([],\\n diff_dependencies(\\n ours=dep, theirs=expected_dep),\\n \\\"Mismatch at the dependency {}\\\".format(i))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b30ead4e6c0bd1469a7e820f3c4e9d\",\n \"score\": \"0.46897256\",\n \"text\": \"def bound_driver(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c60c028fdaf86e2fc8780a311ab5538c\",\n \"score\": \"0.46872205\",\n \"text\": \"def test_smbpath1():\\n p = SMBPath(\\n '\\\\\\\\\\\\\\\\filex.com\\\\\\\\it\\\\\\\\stg\\\\\\\\meh',\\n find_dfs_share=mock_find_dfs_share\\n )\\n assert p.domain == 'filex.com'\\n assert p.server_name == 'fxb04fs0301'\\n assert p.share == 'filerouter_stage'\\n assert p.relpath == 'meh', \\\"{}\\\".format(p.relpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87ce15a470d241d26f4fbcb30aa7145b\",\n \"score\": \"0.4686332\",\n \"text\": \"def test_absolute_path(self):\\n test_path = \\\"/a/b/Packages\\\"\\n when(sublime).packages_path().thenReturn(test_path)\\n self.assertEqual(test_path, path.packages_path(path.ABSOLUTE))\\n unstub(sublime)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9cc120bc4d8961d68b5bf2d999d8b6c\",\n \"score\": \"0.46797383\",\n \"text\": \"def bound_source():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4029447d226eb705ad80898244c0aa33\",\n \"score\": \"0.46691942\",\n \"text\": \"def test_set_no_bound(self):\\n to_bound = self.prob.to_bound\\n test_func = self.prob.test_func\\n self.prob.bound('to_bound')\\n self.assertIs(to_bound, self.prob.to_bound)\\n self.assertIs(test_func, self.prob.test_func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a106b6ad0a0a51bc5729023c6a23df11\",\n \"score\": \"0.46615082\",\n \"text\": \"def test_main__setup_paths_gather(self, chmod_mock, mkdir_mock):\\n self.ec2rl._write_initialized = False # Force re-initialization of writing for test\\n with self.assertRaises(ec2rlcore.main.MainDirectoryError):\\n self.ec2rl._setup_write_paths()\\n\\n self.assertTrue(chmod_mock.called)\\n self.assertTrue(mkdir_mock.called)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2408517c29c1c172d21cd5f1a5dffb0c\",\n \"score\": \"0.46579495\",\n \"text\": \"def _bind_fabric(self, context, segment):\\n port_id = context.current['id']\\n physnet = self._get_physnet(context)\\n if not physnet:\\n LOG.debug(\\\"bind_port for port %(port)s: no physical_network \\\"\\n \\\"found\\\", {'port': port_id})\\n return False\\n\\n next_segment = context.allocate_dynamic_segment(\\n {'network_id': context.network.current['id'],\\n 'network_type': n_const.TYPE_VLAN,\\n 'physical_network': physnet})\\n LOG.debug(\\\"bind_port for port %(port)s: \\\"\\n \\\"current_segment=%(current_seg)s, \\\"\\n \\\"next_segment=%(next_seg)s\\\",\\n {'port': port_id, 'current_seg': segment,\\n 'next_seg': next_segment})\\n context.continue_binding(segment['id'], [next_segment])\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4848655ef8c8cad9f0596e7c12846c21\",\n \"score\": \"0.4656527\",\n \"text\": \"def test_resolve_uri_files():\\n from server import resolve_uri\\n our_tuple = resolve_uri('/sample.txt')\\n assert our_tuple[1] == 'txt'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5042990c46e87c1b91889a8a2049d81\",\n \"score\": \"0.46457344\",\n \"text\": \"def main():\\n import sys\\n if len(sys.argv) < 3:\\n print 'usage: python base.py [server] [port]'\\n else:\\n server, port = sys.argv[1:]\\n print testLDAPConnectivity(server, int(port))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eccfcc8bc3d0237bd073c37d64055590\",\n \"score\": \"0.46453148\",\n \"text\": \"def test_resolve_keys(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1052d83b474f0b2cbfe40802f1c01f0\",\n \"score\": \"0.46435797\",\n \"text\": \"def test_on_bind_exporter_relation_changed(self):\\n relation_id = self._add_bind_exporter_relation()\\n # update relation -> trigger bind_exporter_relation_changed hook\\n self.harness.update_relation_data(relation_id, \\\"prometheus2/0\\\", {})\\n\\n relation_data = self.harness.get_relation_data(relation_id, self.harness.charm.unit.name)\\n self.assertDictEqual(relation_data, {\\\"hostname\\\": \\\"127.0.0.1\\\", \\\"port\\\": \\\"9119\\\"})\\n self.assert_active_unit(self.harness.charm.unit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7660e445b521e847fa5795aa2bcd007\",\n \"score\": \"0.46425244\",\n \"text\": \"def test_resolve_locations_special_no_locations(minimal_location_chooser, caplog):\\n caplog.set_level(logging.INFO)\\n partial_event = make_partial_event()\\n special_location = make_special_location(location = \\\"\\\")\\n location = minimal_location_chooser.resolve_locations(partial_event, \\\"\\\", special_location)\\n assert location == SAMPLE_DEFAULT_EVENT_LOCATION\\n assert \\\"Unknown location\\\" not in caplog.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4576a527faf202204256e6c223ddbd1\",\n \"score\": \"0.46392307\",\n \"text\": \"def test_handle_transform(self, runway_context: MockRunwayContext) -> None:\\n lookup_handlers = RUNWAY_LOOKUP_HANDLERS.copy()\\n runway_context.env.vars.update(VALUES)\\n\\n for name, lookup in lookup_handlers.items():\\n if name not in SUPPORTS_DEFAULT:\\n continue\\n result = lookup.handle(\\n \\\"NOT_VALID::default=false, transform=bool\\\",\\n context=runway_context,\\n variables=VARIABLES,\\n )\\n\\n assert not result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fb8e83c28a43c04219923b796d596f2\",\n \"score\": \"0.4631673\",\n \"text\": \"def test_finds_fuzzer(self):\\n self.assertEqual(self._setup_fuzzer(), self._find_fuzzer_path())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e6f6d31060c3e2f5773f0a977078e0\",\n \"score\": \"0.4631054\",\n \"text\": \"def test_main__setup_paths_workdir(self, mkdir_mock):\\n self.ec2rl._write_initialized = False # Force re-initialization of writing for test\\n with self.assertRaises(ec2rlcore.main.MainDirectoryError):\\n self.ec2rl._setup_write_paths()\\n\\n self.assertTrue(mkdir_mock.called)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be4725f6bcf0690f3b184f2993595fd2\",\n \"score\": \"0.46260536\",\n \"text\": \"def testSock(t, env):\\n c1 = env.c1.new_client(env.testname(t))\\n sess1 = c1.create_session()\\n ops = use_obj(env.opts.usesocket) + [op.lookupp()]\\n res = sess1.compound(ops)\\n check(res, NFS4ERR_NOTDIR, \\\"LOOKUPP with non-dir <cfh>\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63a147dc1a3b0e11e3afd8608401e429\",\n \"score\": \"0.46235862\",\n \"text\": \"def test_nexusbinding_update(self):\\r\\n npb11 = self._npb_test_obj(10, 100, switch='1.1.1.1', instance='test')\\r\\n npb21 = self._npb_test_obj(20, 100, switch='1.1.1.1', instance='test')\\r\\n self._add_bindings_to_db([npb11, npb21])\\r\\n\\r\\n npb_all_v100 = nexus_db_v2.get_nexusvlan_binding(100, '1.1.1.1')\\r\\n self.assertEqual(len(npb_all_v100), 2)\\r\\n\\r\\n npb22 = self._npb_test_obj(20, 200, switch='1.1.1.1', instance='test')\\r\\n npb = nexus_db_v2.update_nexusport_binding(npb21.port, 200)\\r\\n self._assert_bindings_match(npb, npb22)\\r\\n\\r\\n npb_all_v100 = nexus_db_v2.get_nexusvlan_binding(100, '1.1.1.1')\\r\\n self.assertEqual(len(npb_all_v100), 1)\\r\\n self._assert_bindings_match(npb_all_v100[0], npb11)\\r\\n\\r\\n npb = nexus_db_v2.update_nexusport_binding(npb21.port, 0)\\r\\n self.assertIsNone(npb)\\r\\n\\r\\n npb33 = self._npb_test_obj(30, 300, switch='1.1.1.1', instance='test')\\r\\n with testtools.ExpectedException(exceptions.NexusPortBindingNotFound):\\r\\n nexus_db_v2.update_nexusport_binding(npb33.port, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b3fcd67ede4f569ffc28c06058245dc\",\n \"score\": \"0.46215114\",\n \"text\": \"def testLink(t, env):\\n c1 = env.c1.new_client(env.testname(t))\\n sess1 = c1.create_session()\\n ops = use_obj(env.opts.uselink) + [op.lookupp()]\\n res = sess1.compound(ops)\\n check(res, NFS4ERR_SYMLINK, \\\"LOOKUPP with non-dir <cfh>\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993885,"cells":{"query_id":{"kind":"string","value":"097d889d8c010a3f964bb2318d6fdc7d"},"query":{"kind":"string","value":"Returns xTr,yTr,xTe,yTe xTr, xTe are in the form nxd yTr, yTe are in the form nx1"},"positive_passages":{"kind":"list like","value":[{"docid":"cd1a66d6dcdf54f7e6246d0554f449d8","score":"0.0","text":"def loaddata(filename):\n data = loadmat(filename)\n xTr = data[\"xTr\"] # load in Training data\n yTr = np.round(data[\"yTr\"]) # load in Training labels\n xTe = data[\"xTe\"] # load in Testing data\n yTe = np.round(data[\"yTe\"]) # load in Testing labels\n return xTr.T, yTr.T, xTe.T, yTe.T","title":""}],"string":"[\n {\n \"docid\": \"cd1a66d6dcdf54f7e6246d0554f449d8\",\n \"score\": \"0.0\",\n \"text\": \"def loaddata(filename):\\n data = loadmat(filename)\\n xTr = data[\\\"xTr\\\"] # load in Training data\\n yTr = np.round(data[\\\"yTr\\\"]) # load in Training labels\\n xTe = data[\\\"xTe\\\"] # load in Testing data\\n yTe = np.round(data[\\\"yTe\\\"]) # load in Testing labels\\n return xTr.T, yTr.T, xTe.T, yTe.T\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c368212fe8899d1fda158fbf08a31d89","score":"0.60926294","text":"def teax():","title":""},{"docid":"04917ac1922191ebcdd6f268d82c706f","score":"0.5846229","text":"def get_x_tuple(self):\n tup = (self.n_x,)\n if self.has_y:\n tup += (self.n_y,)\n if self.has_z:\n tup += (self.n_z,)\n return tup","title":""},{"docid":"73669075ca121bc78e86b5c5489d9887","score":"0.5774198","text":"def tuple(self):\n return self.x, self.y","title":""},{"docid":"f09b021c09cb23d6b8846b15fe69d8de","score":"0.5739852","text":"def getElemXY(self) -> tuple:\n return (self.nelx, self.nely)","title":""},{"docid":"e3edd15365a89c0bdd3e757c8fe73e9d","score":"0.5733312","text":"def tree_to_xy(self, tree):\n # code for when t is a list of trees\n if isinstance(tree, list):\n x = []\n y = []\n for t in tree:\n xx, yy = self.tree_to_xy(t)\n x.extend(xx)\n y.extend(yy)\n x.append(None)\n y.append(None)\n return (x, y)\n\n # code for when tree is a scalar\n x = [self[tree].x]\n y = [self[tree].y]\n for c in tree.children:\n xx, yy = self.tree_to_xy(c)\n x.extend([self[tree].x, xx[0]])\n y.extend([self[tree].y, self[tree].y])\n x += xx\n y += yy\n x.append(None)\n y.append(None)\n return (x, y)","title":""},{"docid":"62bdc4e674deb5d1c7a1a49029b29371","score":"0.57092226","text":"def _tx(self, x):\n return np.array([\n [1, 0, 0, x],\n [0, 1, 0, 0],\n [0, 0, 1, 0],\n [0, 0, 0, 1]\n ])","title":""},{"docid":"69f7e202b9ecca80dea2d18ad98c9eeb","score":"0.570546","text":"def calc_tangent(self):\r\n if not self.__dict__.has_key('_tx'):\r\n dx = np.zeros(self.cells.shape) \r\n dy = np.zeros(self.cells.shape) \r\n \r\n dx[:,0:-1] = self.xp[self.cells[:,1::]] - self.xp[self.cells[:,0:-1]] \r\n dy[:,0:-1] = self.yp[self.cells[:,1::]] - self.yp[self.cells[:,0:-1]] \r\n \r\n for ii in range(self.Nc):\r\n dx[ii,self.nfaces[ii]-1] = self.xp[self.cells[ii,0]] - self.xp[self.cells[ii,self.nfaces[ii]-1]] \r\n dy[ii,self.nfaces[ii]-1] = self.yp[self.cells[ii,0]] - self.yp[self.cells[ii,self.nfaces[ii]-1]] \r\n \r\n \r\n mag = np.sqrt(dx*dx + dy*dy)\r\n \r\n self._tx = dx/mag\r\n self._ty = dy/mag\r\n self._mag = mag\r\n \r\n return self._tx, self._ty, self._mag","title":""},{"docid":"93a484d6c86b5487afd461608d51a9f7","score":"0.56945467","text":"def coordinate_system(self):\r\n return {'x': self.meta.get('ctype1', 'HPLN-TAN'),\r\n 'y': self.meta.get('ctype2', 'HPLT-TAN'),}","title":""},{"docid":"bf96daa6a93078ad2b4bce7f144e245c","score":"0.56160235","text":"def transform(self, x: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:\r\n x_new = np.hstack([x[i:-self.order + i].reshape(-1, 1) for i in range(self.order)])\r\n y = x[self.order:]\r\n x_new = np.array([self.sep.join(i) for i in x_new]).reshape(-1, 1)\r\n return x_new, y","title":""},{"docid":"6f5c856195fb0e490f13460705711c44","score":"0.56147754","text":"def t_e(self):\n return self.t_x[0]","title":""},{"docid":"0b202688676144b3e556a990d0d4de8e","score":"0.5613338","text":"def getIt(self):\n\t\treturn (x,y)","title":""},{"docid":"43a5dd968ce08e0070b621995402556f","score":"0.5608797","text":"def get_x_y(self):\n return np.array(self.x), np.array(self.y)","title":""},{"docid":"44866f8da0b5ec0d002067c145989503","score":"0.56001073","text":"def exportDT(self):\n # Filter out coordinates in the extended BBox\n coord = self.coords[4:]\n\n # Filter out triangles with any vertex in the extended BBox\n tris = [(a-4, b-4, c-4)\n for (a, b, c) in self.triangles if a > 3 and b > 3 and c > 3]\n return coord, tris","title":""},{"docid":"82c36cf40695b7ab36cd907c161270de","score":"0.5577664","text":"def T(self):\n return self.transpose","title":""},{"docid":"b03a09b874ab00fe32fa9711286ab97c","score":"0.55731666","text":"def getElemXYZ(self) -> tuple:\n return (self.nelx, self.nely, self.nelz)","title":""},{"docid":"b3e8f2db1ed3de05ba43cfd9b92bcb31","score":"0.5542293","text":"def x_y(self):\n return (self.x, self.y)","title":""},{"docid":"69f59026f071e098c4054f38f58e1a69","score":"0.55169296","text":"def vecS_TE(self):\n from numpy import sqrt\n derS=self.yssp.derivatives(1)\n derP=self.ypsp.derivatives(1)\n vec_SuctSide_TEtoWkx= 1/(sqrt(1+derS[1]**2)) # above TE\n vec_SuctSide_TEtoWky= derS[1]/(sqrt(1+derS[1]**2)) # above TE\n vec_PresSide_TEtoLEx= -1/(sqrt(1+derP[1]**2)) # below TE\n vec_PresSide_TEtoLEy= -derP[1]/(sqrt(1+derP[1]**2)) # below TE\n return [vec_PresSide_TEtoLEx, vec_PresSide_TEtoLEy, vec_SuctSide_TEtoWkx, vec_SuctSide_TEtoWky]","title":""},{"docid":"842f79196043a8b72dd29493d933cdbd","score":"0.5497672","text":"def T(self):\n # TODO - your code here\n # initialize matrix to hold the results\n # For the transpose each row becomes the new column and vice versa [𝐀𝐓]𝑖𝑗=[𝐀]𝑗𝑖\n matrixTrans = zeroes(self.w, self.h)\n # Loop through columns on outside loop\n for c in range(self.w):\n # Loop through columns on inner loop\n for r in range(self.h):\n # Column values will be filled by what were each row before\n matrixTrans[r][c] = self.g[c][r]\n return matrixTrans","title":""},{"docid":"8be7c822bc7060dfaa09c0e73742dd30","score":"0.5478184","text":"def getxy(self):\n return (self.xvalue, self.yvalue)","title":""},{"docid":"d25c6fc63be3cc292d949b8aebb01293","score":"0.54779994","text":"def t(self):\n return self.tvec.aslin","title":""},{"docid":"3a15476e8c37167c2295437223855355","score":"0.5477176","text":"def T(self): \n return self.copy().transpose()","title":""},{"docid":"63ef1148c116e7b8d3040ee0a8c3cd20","score":"0.545771","text":"def get_temp(self):\n [x, y, z, t] = self.get_data()\n return t","title":""},{"docid":"24239bfeebc1f82da4219f8b404adf01","score":"0.5444867","text":"def dndt2(i,j,h, n, u, v, dx, dy) : # for individual vars\n dn_dt = dndt2_x(i,j,h, n, u, dx )+dndt2_y(i,j,h, n, v, dy )\n \n return dn_dt # minus or plus??","title":""},{"docid":"6f185d3d2af4a6f4e5aab2c9e50b3162","score":"0.54344153","text":"def get_xy():\n return (self.x, self.y)","title":""},{"docid":"a93d3a3768465e865b9a820bbc895900","score":"0.54179126","text":"def __format(self):\n return ([], format_elemwise([self.t] + self.x_elems))","title":""},{"docid":"d35562eca0a4c7f8517d57931169df29","score":"0.53984094","text":"def tr2rt(t):\n if t.shape == (4, 4):\n assert t.shape[0] == t.shape[1]\n R = t2r(t)\n t = np.matrix([[t[0, 3]], [t[1, 3]], [t[2, 3]]])\n return [R, t]\n else:\n assert t.shape[0] == t.shape[1]\n R = t2r(t)\n t = np.matrix([[t[0, 2]], [t[1, 2]]])\n return [R, t]","title":""},{"docid":"a00f177c419c7d12c62518daf3a9d584","score":"0.5390611","text":"def derivative_operators(self):\n gradx = self.element_x.derivative_operator()\n grady = self.element_y.derivative_operator()\n gradx = gradx.reshape(self.Nx, 1, 1, self.Nx)\n grady = grady.reshape(self.Ny, 1, self.Ny)\n return gradx, grady","title":""},{"docid":"a00f177c419c7d12c62518daf3a9d584","score":"0.5390611","text":"def derivative_operators(self):\n gradx = self.element_x.derivative_operator()\n grady = self.element_y.derivative_operator()\n gradx = gradx.reshape(self.Nx, 1, 1, self.Nx)\n grady = grady.reshape(self.Ny, 1, self.Ny)\n return gradx, grady","title":""},{"docid":"17fe003203b61a1c5868bc8096dff1c7","score":"0.5389917","text":"def getTelescopeCoords(self):\n return self.header['ANT_X'],self.header['ANT_Y'],self.header['ANT_Z']","title":""},{"docid":"e86f5ffc59e46eee480111dd2c2494dc","score":"0.5389698","text":"def get_coordinates(self):\n ra,dec,ast,I,Q,U,V = np.loadtxt(self.chan_file,unpack=True)\n return ra, dec, ast","title":""},{"docid":"bd4236915a0586c9eb7876a2a526389a","score":"0.538705","text":"def yt(self,x):\n pass","title":""},{"docid":"db6d9e61d591dc9879b67caef1a83933","score":"0.5370639","text":"def get_xy(self) -> (int, int):\n return self.x, self.y","title":""},{"docid":"b1ada8bb4a22a7b58095be0b9abe83b3","score":"0.53692776","text":"def node_coords(self, n):\n x = self.node[n][\"x\"]\n y = self.node[n][\"y\"]\n z = self.node[n][\"z\"]\n\n return x, y, z","title":""},{"docid":"6a696ad4189006d5244b1555c6a5c6f3","score":"0.53642637","text":"def coordinates(self):\n return self.x, self.y","title":""},{"docid":"48fe2c498c3aba88e7b9f7af0fa08735","score":"0.535934","text":"def get(self):\n return (int(self.x), int(self.y))","title":""},{"docid":"e83373eb3452196f20888d985756a978","score":"0.535267","text":"def dirt_coords(self):\n try:\n return self._dirt_coords\n except AttributeError:\n self._dirt_coords = []\n for row_ind, row in enumerate(self.matrix):\n for col_ind, cell in enumerate(row):\n if cell == \"d\":\n self._dirt_coords.append((row_ind, col_ind,))\n self._dirt_coords = tuple(self._dirt_coords)\n return self._dirt_coords","title":""},{"docid":"dacaa977f744ce9710666ecaf2f30a13","score":"0.5349185","text":"def TP(self) -> Tuple[float, float]:","title":""},{"docid":"fb1a803791adb06398cd77ff6dece994","score":"0.5344267","text":"def getx(self):\n return self.trajectory[:,1]","title":""},{"docid":"4bff0ff1053a1810252c69363dd989d6","score":"0.5323084","text":"def transform(self):\n cx = self.dcx\n cy = self.dcy\n self.Tm = np.mat([[ cx, cy, 0, 0, 0, 0],\n [-cy, cx, 0, 0, 0, 0],\n [ 0, 0, 1, 0, 0, 0],\n [ 0, 0, 0, cx, cy, 0],\n [ 0, 0, 0, -cy, cx, 0],\n [ 0, 0, 0, 0, 0, 1]])\n return self.Tm","title":""},{"docid":"3b01ba8c332d94261b5be080c0ad7a44","score":"0.5317813","text":"def process(self, X, Xd, U, Upert, t):\n Unom = U-Upert\n return X, Xd, U, Unom, t","title":""},{"docid":"fa1b51187b212b74bb0632eeb9d47435","score":"0.530214","text":"def coordinates(self):\n return (self.x, self.y)","title":""},{"docid":"48561d0aaea447559926ddcaf6d345cb","score":"0.5298321","text":"def homogeneous_coordinates(self):\n return self.__coordinates.T","title":""},{"docid":"c0e27e0105415e7ff9a2768b36e820e8","score":"0.52941144","text":"def get_x(self):\n pass","title":""},{"docid":"538f1199ffcb4a42d3bf88f419b0dcfd","score":"0.52924055","text":"def xy(self):\n self._update()\n m = self.__len__()\n x = array('d')\n y = array('d')\n temp = c_double()\n for i in range(m):\n lgeos.GEOSCoordSeq_getX(self._cseq, i, byref(temp))\n x.append(temp.value)\n lgeos.GEOSCoordSeq_getY(self._cseq, i, byref(temp))\n y.append(temp.value)\n return x, y","title":""},{"docid":"438f99c8dcdc81224e0f135a53946eed","score":"0.52877724","text":"def __repr__(self):\n s = '('\n s += str(self.xpart)\n s += ', '\n s += str(self.ypart)\n s += ' + O(%s^%s))'%(self.t,self.order)\n return s","title":""},{"docid":"da6f1e63c020da2d312389ea91bca664","score":"0.5275663","text":"def cartesian(self, t):\n # self.update_x(t)\n u = self.get_u(self.sigma_c, self.sigma_c)\n z = self.x_pos + u\n # test prints\n # print(\"product u; sigma * normrnd(0,1):\\n\", u)\n # print(\"sum: H*x + sigma*normrnd(0,1):\\n\", z)\n return z","title":""},{"docid":"53ecd181aed528b166ed069e00614299","score":"0.5260949","text":"def get_transform_values(self):\n a = b = c = d = e = f = 0\n if 'transform' in self._node.attrib:\n (a,c,e),(b,d,f) = st.parseTransform(self._node.attrib['transform'])\n return a, b, c, d, e, f","title":""},{"docid":"45e31547f40b6161b15afac53933547b","score":"0.525373","text":"def _extract_T(self, info_db):\n\n T = info_db.find('transform').text\n T = T.replace('\\n', ' ')\n T = T.replace('\\t', ' ')\n T = T.split(' ')\n T[:] = [x for x in T if x != '']\n m = []\n mi = []\n for t in T:\n try:\n mi.append(float(t))\n except:\n None\n if len(mi) == 4:\n m.append(mi)\n mi = []\n m = array(m)\n if m.shape == (4, 4):\n return m\n else:\n raise SyntaxError(\"Invalid transform shape in info.xml\")","title":""},{"docid":"8c08e98b305c3a5a9b22d1ad11ffa2a3","score":"0.52381516","text":"def get_tr_te_set():\n # extracting dlib features and gender labels from training set and test set\n print(\"Extraction begin\")\n features, labels = ex_a2.extract_features_labels()\n features_te, labels_te = ex_te_a2.extract_features_labels()\n print(\"Extraction end\")\n\n # transfer to numpy array to do matrix operation\n features = np.array(features)\n features_te = np.array(features_te)\n\n # flatten data for training\n features = features.reshape(features.shape[0], 68 * 2)\n features_te = features_te.reshape(features_te.shape[0], 68 * 2)\n\n # return features and labels for training and testing\n return features, features_te, labels, labels_te","title":""},{"docid":"f5b76df3e7e60c6c16cc8f0b716efaad","score":"0.52357584","text":"def stddim_yx(self) -> Tuple[NcPlane, int, int]:\n raise NotImplementedError('Stub')","title":""},{"docid":"c5f84445b3f341fe363d2b05384fe16f","score":"0.52267975","text":"def as_p_wh(self) -> tuple:\n return (self.x0.x, self.x0.y, self.x2.x - self.x0.x, self.x2.y - self.x0.y)","title":""},{"docid":"157c4ac3ad449c9996845235189a417a","score":"0.5226771","text":"def triangulate(geom):\n from shapely import ops\n triangles = ops.triangulate(geom)\n return np.stack([np.asarray(t.exterior.coords.xy).T[:-1] for t in triangles])","title":""},{"docid":"7983a36e0cf66718ab6c9f2c4591ee84","score":"0.5220874","text":"def get4by4coords():\n x1 = [0.04,0.28,0.52,0.76,0.04,0.28,0.52,0.76,0.04,0.28,0.52,0.76,0.04,0.28,0.52,0.76]\n x2 = [0.22,0.46,0.7,0.94,0.22,0.46,0.7,0.94,0.22,0.46,0.7,0.94,0.22,0.46,0.7,0.94]\n y1 = [0.8,0.8,0.8,0.8,0.59,0.59,0.59,0.59,0.38,0.38,0.38,0.38,0.17,0.17,0.17,0.17]\n y2 = [0.95,0.95,0.95,0.95,0.74,0.74,0.74,0.74,0.53,0.53,0.53,0.53,0.32,0.32,0.32,0.32]\n\n return x1, x2, y1, y2","title":""},{"docid":"2133facd31d3318d0c777c38b6d7641b","score":"0.5216206","text":"def points(self):\n if self == Tetrimino.I:\n return np.array([[-2, 0], [-1, 0], [0, 0], [1, 0]])\n if self == Tetrimino.O:\n return np.array([[-1, 0], [0, 0], [0, -1], [-1, -1]])\n if self == Tetrimino.T:\n return np.array([[-1, 0], [0, 0], [1, 0], [0, -1]])\n if self == Tetrimino.J:\n return np.array([[0, 1], [0, 0], [0, -1], [-1, -1]])\n if self == Tetrimino.L:\n return np.array([[-1, 1], [-1, 0], [-1, -1], [0, -1]])\n if self == Tetrimino.S:\n return np.array([[0, 0], [-1, 0], [-1, -1], [-2, -1]])\n if self == Tetrimino.Z:\n return np.array([[-1, 0], [0, 0], [0, -1], [1, -1]])","title":""},{"docid":"2e8928acb4f9663d56c7b79c2318c743","score":"0.52111375","text":"def tex_coord(x, y, n=4):\n m = 1.0 / n\n dx = x * m\n dy = y * m\n return dx, dy, dx + m, dy, dx + m, dy + m, dx, dy + m","title":""},{"docid":"c4ff5fd5bd6d5662c1efc450798be390","score":"0.52083147","text":"def __str__(self):\n return \"x\".join((str(self._x), str(self._y)))","title":""},{"docid":"2269ac6b7dec0bc32004e05497c18dee","score":"0.5204414","text":"def form1(x, y, n, ind, ax, line):\n return f\"x: {x:.5g}\\ny: {y:.5g}\"","title":""},{"docid":"af7d00d0079ba1acbfada39a6906b51a","score":"0.52039427","text":"def get_x1_y1(self):\n self.x1, self.y1 = self.x + self.w - 1, self.y + self.h - 1","title":""},{"docid":"4eacdbdd0944958d75c683165c3d531a","score":"0.51934606","text":"def Xs(self,x):\n return x","title":""},{"docid":"31308b101d6740018f96862e1f1a6801","score":"0.519013","text":"def get_xy(dls):\n x, y = [], []\n for batch in dls.train:\n x.append(torch.cat(list(batch)[:-1], dim=-1) if len(batch) > 2 else batch[0])\n y.append(batch[-1])\n return torch.cat(x, dim=0), torch.cat(y, dim=0)","title":""},{"docid":"18794e9d1d5225cc53079142b2aa7c60","score":"0.51897675","text":"def ti_s():\n titgx.put(tgx())\n tihx.put(hexx.get())\n tihy.put(hexy.get())\n tihz.put(hexz.get())","title":""},{"docid":"8fa77593e740838c526b01ea84d6a348","score":"0.51887876","text":"def get_data(self):\n return self._x, self._y","title":""},{"docid":"caf54832249879d11a57ad9e7a9f5b55","score":"0.5174342","text":"def _uvw_to_xyz(cls,rst,abc) :\n return np.transpose(rst)","title":""},{"docid":"a9da1478f8eedd0bf13778955df00e77","score":"0.5172873","text":"def get_u_tuple(self):\n tup = self.get_x_tuple()\n if self.has_t:\n tup += (self.n_t,)\n return (self.n_v,) + tup","title":""},{"docid":"d08dfe4fe8bad1b2d34251b5092abc3e","score":"0.5169985","text":"def DerivTX(self, *args):\n return _GeomFill.GeomFill_FunctionDraft_DerivTX(self, *args)","title":""},{"docid":"1b3e717f501a7eeb6efe2133fe6688d2","score":"0.51670647","text":"def y(self):\r\n\t\treturn self[1]","title":""},{"docid":"5f355e1a9d1e53011a4fd42ce31b4684","score":"0.51388997","text":"def flatt_values(self) -> np.ndarray:\n # return [x0, y0, x1, y1, ...]\n return self._values.reshape((self._n * 2,)).ravel()","title":""},{"docid":"339d434ab6fecb59191a045dfe10a8ef","score":"0.5136351","text":"def getpx(self):\n return self.trajectory[:,4]","title":""},{"docid":"f2d00db7dff53ea6d293075c80b9cbdf","score":"0.51353353","text":"def branch_to_xy(self, branch):\n # code for when branch is a list of branches\n if isinstance(branch, list):\n x = []\n y = []\n for b in branch:\n xx, yy = self.branch_to_xy(b)\n x.extend(xx)\n y.extend(yy)\n x.append(None)\n y.append(None)\n return (x, y)\n\n # code for when branch is a scalar\n node = self[branch].node\n parent = node.parent\n if parent:\n x = [self[branch].x, self[branch].x, self[parent].x]\n y = [self[branch].y, self[parent].y, self[parent].y]\n return (x, y)\n else:\n return ([self[branch].x], [self[branch].y])","title":""},{"docid":"ebd98ec9a16429e56ab1f9fd075a8a70","score":"0.51337814","text":"def coordinates(self):\n return (self._x, self._y)","title":""},{"docid":"8db97aa75b326a0a628e71c5356301bd","score":"0.5131568","text":"def pos(self) -> tuple:\n return round(self.x, 10), round(self.y, 10)","title":""},{"docid":"1e7737419843c9255f30426531b423c3","score":"0.5121365","text":"def x_y_from_line(line):\n return (line[:, 0],line[:, 1])","title":""},{"docid":"b28a7cdf41547211242eeb8f0e95eda4","score":"0.510751","text":"def nx( self ):\n return cfunc.get_nx( self )","title":""},{"docid":"9e1b71107150eb736d2c7de05d934f3a","score":"0.5103917","text":"def shape_str(self):\n return np.repeat('trapezoid', self.nx)","title":""},{"docid":"ae6b4af37c6837d721e32d01aaec09c2","score":"0.5101589","text":"def coordinate_values(self):\n string = [\"First cell: \" + str(self.first_values)]\n string.append(\"Last cell: \" + str(self.last_values))\n string.append(\"First bounds: \" + str(self.first_bounds))\n string.append(\"Last bounds: \" + str(self.last_bounds))\n\n return \"\\n\".join(string)","title":""},{"docid":"6e7a76bc0781dd52a0a01b7bced659bc","score":"0.50988454","text":"def y(self):\r\n return self[1]","title":""},{"docid":"6e7a76bc0781dd52a0a01b7bced659bc","score":"0.50988454","text":"def y(self):\r\n return self[1]","title":""},{"docid":"ddf24e1a403fff1ccfa124a759f8e245","score":"0.5096956","text":"def as_tuple(self) -> Tuple[float, float]:\r\n return self.x, self.y","title":""},{"docid":"ca830d4d1c54f9fd90ef5baf49efc885","score":"0.5096532","text":"def as_tuple(self) -> Tuple[float, float, float, float]:\r\n return self.x_start, self.y_start, self.x_end, self.y_end","title":""},{"docid":"6ed272d0108106bc91590a137725015d","score":"0.509473","text":"def origin(self):\n return self[0], self[3]","title":""},{"docid":"eec92bf14e1cc0a1a25357feeb2f84bd","score":"0.5087419","text":"def __str__(self):\n out = str(self.weight_0)\n for i in range(self.n_vars):\n value = self.get_term([i])\n if value != 0:\n out += \" + \"\n if value != 1:\n out += (str(value) + \"*\")\n out += (\"x_\" + str(i))\n for i in range(self.n_vars-1):\n for j in range(i+1, self.n_vars):\n value = self.get_term([i, j])\n if value != 0:\n out += \" + \"\n if value != 1:\n out += (str(value) + \"*\")\n out += (\"x_\" + str(i) + \"*x_\" + str(j))\n for i in range(self.n_vars-2):\n for j in range(i+1, self.n_vars-1):\n for k in range(j+1, self.n_vars):\n value = self.get_term([i, j, k])\n if value != 0:\n out += \" + \"\n if value != 1:\n out += (str(value) + \"*\")\n out += (\"x_\" + str(i) + \"*x_\" + str(j) +\n \"*x_\" + str(k))\n return out","title":""},{"docid":"983350b3177b74091f64af943a4fbdcc","score":"0.508034","text":"def __repr__( self ):\n winding = \"X\"\n if self.winding_order is not None: winding=\"%d\"%self.winding_order\n return \"tri(%d:%s,%d,%d,%d,%6.2f)\"%(self.triangle_num,winding,self.pts[0].entry_number,self.pts[1].entry_number,self.pts[2].entry_number,self.get_area())","title":""},{"docid":"c1ca7efa7e4d5f9691e19ae2b37fce15","score":"0.5079989","text":"def gdu(self):\n\t\treturn (self.__x, self.__y, self.__e, self.__f)","title":""},{"docid":"f54fd2d0a557e32152de299e17bf9d0e","score":"0.5074132","text":"def as_tuple(self):\n if self.dimensions == 1:\n return (self.x)\n elif self.dimensions == 2:\n return (self.x, self.y)\n elif self.dimensions == 3:\n return (self.x, self.y, self.z)\n else:\n #This should never happen, but just in case...\n return tuple()","title":""},{"docid":"e2ee69f015e1880f384e637779bdd036","score":"0.5071353","text":"def getXCoord(self) :\r\n coord_x = np.zeros((self.nx, self.ny))\r\n for i in range(self.nx) : # verticle direction up to down\r\n for j in range(self.ny): # lateral direction left to right\t\r\n idx = i*self.ny + j\r\n coord_x[i,j] = self.meshes[idx].get_x_coord()\r\n \r\n return coord_x","title":""},{"docid":"3f2ff9ab76acff9ce20e966f81ec4487","score":"0.5067448","text":"def get_rt_to_xy_plane(self):\n try:\n return self._rt_to_xy\n except AttributeError as no_rad:\n self.compute_rt_to_xy()\n return self._rt_to_xy","title":""},{"docid":"b2bbe71453053769377c766ee0fcc1de","score":"0.5064559","text":"def get_coord(self):\n return self._x, self._y","title":""},{"docid":"29b8ef8b11d56b263321d2f5e13766cf","score":"0.506395","text":"def cartesian_coordinates(self):\n return self.__coordinates[0:3, ::].T","title":""},{"docid":"69df906f59c688209ce3c67581ff01cf","score":"0.50610733","text":"def ntuple(self):\n return self.nt","title":""},{"docid":"b3274711f225d6f808100f1f0de5b558","score":"0.5060391","text":"def getLocation(self):\n\t\treturn self.x, self.y, self.val, self.n","title":""},{"docid":"e0cfcbe1c90ba8486b71e5e6fb077b0e","score":"0.5057198","text":"def Nt(self):\n return sp.shape(self.get('throat.all'))[0]","title":""},{"docid":"3eea463b97fe987f3b9e8387fb2f838e","score":"0.5054072","text":"def get_tensors(self, num_tensors):\n x_raw = [self.sample_str() for _ in xrange(num_tensors)]\n y_raw = [[self.eval_func(s[:j + 1]) for j in xrange(len(s))]\n for s in x_raw]\n\n x_sent = [[unicode(w) for w in s] for s in x_raw]\n y_sent = [[unicode(w) for w in s] for s in y_raw]\n\n x_var = self.sentences_to_one_hot(self.max_length, *x_sent)\n y_var = self.sentences_to_codes(self.max_length, *y_sent)\n\n return x_var, y_var","title":""},{"docid":"3170f704e32195471f1551bc2a619dc1","score":"0.505012","text":"def _ty(self, y):\n return np.array([\n [1, 0, 0, 0],\n [0, 1, 0, y],\n [0, 0, 1, 0],\n [0, 0, 0, 1]\n ])","title":""},{"docid":"e5cc7d0d268ea4b099c960775df9c0b0","score":"0.50457597","text":"def second_derivative_operators(self):\n grad2x = self.element_x.second_derivative_operator()\n grad2y = self.element_y.second_derivative_operator()\n grad2x = grad2x.reshape(self.Nx, 1, 1, self.Nx)\n grad2y = grad2y.reshape(self.Ny, 1, self.Ny)\n return grad2x, grad2y","title":""},{"docid":"e5cc7d0d268ea4b099c960775df9c0b0","score":"0.50457597","text":"def second_derivative_operators(self):\n grad2x = self.element_x.second_derivative_operator()\n grad2y = self.element_y.second_derivative_operator()\n grad2x = grad2x.reshape(self.Nx, 1, 1, self.Nx)\n grad2y = grad2y.reshape(self.Ny, 1, self.Ny)\n return grad2x, grad2y","title":""},{"docid":"a4948a6d9e38f5a67148d8a774b6ec46","score":"0.50435233","text":"def get_tensors(self, num_tensors):\n x_raw = [self.sample_str() for _ in xrange(num_tensors)]\n y_raw = [[self.eval_func(s[:j]) for j in xrange(len(s))]\n for s in x_raw]\n\n x_sent = [[unicode(w) for w in s] for s in x_raw]\n y_sent = [[unicode(w) for w in s] for s in y_raw]\n\n x_var = self.sentences_to_one_hot(self.max_length, *x_sent)\n y_var = self.sentences_to_codes(self.max_length, *y_sent)\n\n return x_var, y_var","title":""},{"docid":"93616381d29e57ab3064c0d0eaa0f524","score":"0.50415766","text":"def get_direction(interpret, x, y):\n if interpret == \"N\":\n y += 1\n return x, y\n if interpret == \"E\":\n x += 1\n return x, y\n if interpret == \"S\":\n y -= 1\n return x, y\n if interpret == \"W\":\n x -= 1\n return x, y\n else:\n return x, y","title":""},{"docid":"c58c23e51ef8ed1620035243b8c83263","score":"0.5037428","text":"def case_xdata_X_and_Y(self):\n return (\n rf'5B090059\"SIA-DCS\"0004L0#{ACCOUNT}[#{ACCOUNT}|Nri1/PA501][X006E07.7777777][Y49N06.6666666]_20:45:46,04-22-2021',\n ACCOUNT,\n \"Panic Alarm\",\n \"PA\",\n None,\n True,\n )","title":""},{"docid":"64762d6ebd216a98fb34f3a89caff92e","score":"0.5034314","text":"def position(self):\n return self.xcor(), self.ycor()","title":""},{"docid":"065852f9c57170baaaca32f200771aab","score":"0.5033514","text":"def GetSRowX(self):\n ...","title":""},{"docid":"7ff40a551eb879b5529fa995abde045f","score":"0.5030483","text":"def hadridx(self):\n return (self.mix_idx, self.d)","title":""}],"string":"[\n {\n \"docid\": \"c368212fe8899d1fda158fbf08a31d89\",\n \"score\": \"0.60926294\",\n \"text\": \"def teax():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04917ac1922191ebcdd6f268d82c706f\",\n \"score\": \"0.5846229\",\n \"text\": \"def get_x_tuple(self):\\n tup = (self.n_x,)\\n if self.has_y:\\n tup += (self.n_y,)\\n if self.has_z:\\n tup += (self.n_z,)\\n return tup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73669075ca121bc78e86b5c5489d9887\",\n \"score\": \"0.5774198\",\n \"text\": \"def tuple(self):\\n return self.x, self.y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f09b021c09cb23d6b8846b15fe69d8de\",\n \"score\": \"0.5739852\",\n \"text\": \"def getElemXY(self) -> tuple:\\n return (self.nelx, self.nely)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3edd15365a89c0bdd3e757c8fe73e9d\",\n \"score\": \"0.5733312\",\n \"text\": \"def tree_to_xy(self, tree):\\n # code for when t is a list of trees\\n if isinstance(tree, list):\\n x = []\\n y = []\\n for t in tree:\\n xx, yy = self.tree_to_xy(t)\\n x.extend(xx)\\n y.extend(yy)\\n x.append(None)\\n y.append(None)\\n return (x, y)\\n\\n # code for when tree is a scalar\\n x = [self[tree].x]\\n y = [self[tree].y]\\n for c in tree.children:\\n xx, yy = self.tree_to_xy(c)\\n x.extend([self[tree].x, xx[0]])\\n y.extend([self[tree].y, self[tree].y])\\n x += xx\\n y += yy\\n x.append(None)\\n y.append(None)\\n return (x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62bdc4e674deb5d1c7a1a49029b29371\",\n \"score\": \"0.57092226\",\n \"text\": \"def _tx(self, x):\\n return np.array([\\n [1, 0, 0, x],\\n [0, 1, 0, 0],\\n [0, 0, 1, 0],\\n [0, 0, 0, 1]\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69f7e202b9ecca80dea2d18ad98c9eeb\",\n \"score\": \"0.570546\",\n \"text\": \"def calc_tangent(self):\\r\\n if not self.__dict__.has_key('_tx'):\\r\\n dx = np.zeros(self.cells.shape) \\r\\n dy = np.zeros(self.cells.shape) \\r\\n \\r\\n dx[:,0:-1] = self.xp[self.cells[:,1::]] - self.xp[self.cells[:,0:-1]] \\r\\n dy[:,0:-1] = self.yp[self.cells[:,1::]] - self.yp[self.cells[:,0:-1]] \\r\\n \\r\\n for ii in range(self.Nc):\\r\\n dx[ii,self.nfaces[ii]-1] = self.xp[self.cells[ii,0]] - self.xp[self.cells[ii,self.nfaces[ii]-1]] \\r\\n dy[ii,self.nfaces[ii]-1] = self.yp[self.cells[ii,0]] - self.yp[self.cells[ii,self.nfaces[ii]-1]] \\r\\n \\r\\n \\r\\n mag = np.sqrt(dx*dx + dy*dy)\\r\\n \\r\\n self._tx = dx/mag\\r\\n self._ty = dy/mag\\r\\n self._mag = mag\\r\\n \\r\\n return self._tx, self._ty, self._mag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93a484d6c86b5487afd461608d51a9f7\",\n \"score\": \"0.56945467\",\n \"text\": \"def coordinate_system(self):\\r\\n return {'x': self.meta.get('ctype1', 'HPLN-TAN'),\\r\\n 'y': self.meta.get('ctype2', 'HPLT-TAN'),}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf96daa6a93078ad2b4bce7f144e245c\",\n \"score\": \"0.56160235\",\n \"text\": \"def transform(self, x: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:\\r\\n x_new = np.hstack([x[i:-self.order + i].reshape(-1, 1) for i in range(self.order)])\\r\\n y = x[self.order:]\\r\\n x_new = np.array([self.sep.join(i) for i in x_new]).reshape(-1, 1)\\r\\n return x_new, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f5c856195fb0e490f13460705711c44\",\n \"score\": \"0.56147754\",\n \"text\": \"def t_e(self):\\n return self.t_x[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b202688676144b3e556a990d0d4de8e\",\n \"score\": \"0.5613338\",\n \"text\": \"def getIt(self):\\n\\t\\treturn (x,y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43a5dd968ce08e0070b621995402556f\",\n \"score\": \"0.5608797\",\n \"text\": \"def get_x_y(self):\\n return np.array(self.x), np.array(self.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44866f8da0b5ec0d002067c145989503\",\n \"score\": \"0.56001073\",\n \"text\": \"def exportDT(self):\\n # Filter out coordinates in the extended BBox\\n coord = self.coords[4:]\\n\\n # Filter out triangles with any vertex in the extended BBox\\n tris = [(a-4, b-4, c-4)\\n for (a, b, c) in self.triangles if a > 3 and b > 3 and c > 3]\\n return coord, tris\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82c36cf40695b7ab36cd907c161270de\",\n \"score\": \"0.5577664\",\n \"text\": \"def T(self):\\n return self.transpose\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b03a09b874ab00fe32fa9711286ab97c\",\n \"score\": \"0.55731666\",\n \"text\": \"def getElemXYZ(self) -> tuple:\\n return (self.nelx, self.nely, self.nelz)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3e8f2db1ed3de05ba43cfd9b92bcb31\",\n \"score\": \"0.5542293\",\n \"text\": \"def x_y(self):\\n return (self.x, self.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69f59026f071e098c4054f38f58e1a69\",\n \"score\": \"0.55169296\",\n \"text\": \"def vecS_TE(self):\\n from numpy import sqrt\\n derS=self.yssp.derivatives(1)\\n derP=self.ypsp.derivatives(1)\\n vec_SuctSide_TEtoWkx= 1/(sqrt(1+derS[1]**2)) # above TE\\n vec_SuctSide_TEtoWky= derS[1]/(sqrt(1+derS[1]**2)) # above TE\\n vec_PresSide_TEtoLEx= -1/(sqrt(1+derP[1]**2)) # below TE\\n vec_PresSide_TEtoLEy= -derP[1]/(sqrt(1+derP[1]**2)) # below TE\\n return [vec_PresSide_TEtoLEx, vec_PresSide_TEtoLEy, vec_SuctSide_TEtoWkx, vec_SuctSide_TEtoWky]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"842f79196043a8b72dd29493d933cdbd\",\n \"score\": \"0.5497672\",\n \"text\": \"def T(self):\\n # TODO - your code here\\n # initialize matrix to hold the results\\n # For the transpose each row becomes the new column and vice versa [𝐀𝐓]𝑖𝑗=[𝐀]𝑗𝑖\\n matrixTrans = zeroes(self.w, self.h)\\n # Loop through columns on outside loop\\n for c in range(self.w):\\n # Loop through columns on inner loop\\n for r in range(self.h):\\n # Column values will be filled by what were each row before\\n matrixTrans[r][c] = self.g[c][r]\\n return matrixTrans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8be7c822bc7060dfaa09c0e73742dd30\",\n \"score\": \"0.5478184\",\n \"text\": \"def getxy(self):\\n return (self.xvalue, self.yvalue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d25c6fc63be3cc292d949b8aebb01293\",\n \"score\": \"0.54779994\",\n \"text\": \"def t(self):\\n return self.tvec.aslin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a15476e8c37167c2295437223855355\",\n \"score\": \"0.5477176\",\n \"text\": \"def T(self): \\n return self.copy().transpose()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63ef1148c116e7b8d3040ee0a8c3cd20\",\n \"score\": \"0.545771\",\n \"text\": \"def get_temp(self):\\n [x, y, z, t] = self.get_data()\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24239bfeebc1f82da4219f8b404adf01\",\n \"score\": \"0.5444867\",\n \"text\": \"def dndt2(i,j,h, n, u, v, dx, dy) : # for individual vars\\n dn_dt = dndt2_x(i,j,h, n, u, dx )+dndt2_y(i,j,h, n, v, dy )\\n \\n return dn_dt # minus or plus??\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f185d3d2af4a6f4e5aab2c9e50b3162\",\n \"score\": \"0.54344153\",\n \"text\": \"def get_xy():\\n return (self.x, self.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a93d3a3768465e865b9a820bbc895900\",\n \"score\": \"0.54179126\",\n \"text\": \"def __format(self):\\n return ([], format_elemwise([self.t] + self.x_elems))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d35562eca0a4c7f8517d57931169df29\",\n \"score\": \"0.53984094\",\n \"text\": \"def tr2rt(t):\\n if t.shape == (4, 4):\\n assert t.shape[0] == t.shape[1]\\n R = t2r(t)\\n t = np.matrix([[t[0, 3]], [t[1, 3]], [t[2, 3]]])\\n return [R, t]\\n else:\\n assert t.shape[0] == t.shape[1]\\n R = t2r(t)\\n t = np.matrix([[t[0, 2]], [t[1, 2]]])\\n return [R, t]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a00f177c419c7d12c62518daf3a9d584\",\n \"score\": \"0.5390611\",\n \"text\": \"def derivative_operators(self):\\n gradx = self.element_x.derivative_operator()\\n grady = self.element_y.derivative_operator()\\n gradx = gradx.reshape(self.Nx, 1, 1, self.Nx)\\n grady = grady.reshape(self.Ny, 1, self.Ny)\\n return gradx, grady\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a00f177c419c7d12c62518daf3a9d584\",\n \"score\": \"0.5390611\",\n \"text\": \"def derivative_operators(self):\\n gradx = self.element_x.derivative_operator()\\n grady = self.element_y.derivative_operator()\\n gradx = gradx.reshape(self.Nx, 1, 1, self.Nx)\\n grady = grady.reshape(self.Ny, 1, self.Ny)\\n return gradx, grady\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17fe003203b61a1c5868bc8096dff1c7\",\n \"score\": \"0.5389917\",\n \"text\": \"def getTelescopeCoords(self):\\n return self.header['ANT_X'],self.header['ANT_Y'],self.header['ANT_Z']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e86f5ffc59e46eee480111dd2c2494dc\",\n \"score\": \"0.5389698\",\n \"text\": \"def get_coordinates(self):\\n ra,dec,ast,I,Q,U,V = np.loadtxt(self.chan_file,unpack=True)\\n return ra, dec, ast\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd4236915a0586c9eb7876a2a526389a\",\n \"score\": \"0.538705\",\n \"text\": \"def yt(self,x):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db6d9e61d591dc9879b67caef1a83933\",\n \"score\": \"0.5370639\",\n \"text\": \"def get_xy(self) -> (int, int):\\n return self.x, self.y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1ada8bb4a22a7b58095be0b9abe83b3\",\n \"score\": \"0.53692776\",\n \"text\": \"def node_coords(self, n):\\n x = self.node[n][\\\"x\\\"]\\n y = self.node[n][\\\"y\\\"]\\n z = self.node[n][\\\"z\\\"]\\n\\n return x, y, z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a696ad4189006d5244b1555c6a5c6f3\",\n \"score\": \"0.53642637\",\n \"text\": \"def coordinates(self):\\n return self.x, self.y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48fe2c498c3aba88e7b9f7af0fa08735\",\n \"score\": \"0.535934\",\n \"text\": \"def get(self):\\n return (int(self.x), int(self.y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e83373eb3452196f20888d985756a978\",\n \"score\": \"0.535267\",\n \"text\": \"def dirt_coords(self):\\n try:\\n return self._dirt_coords\\n except AttributeError:\\n self._dirt_coords = []\\n for row_ind, row in enumerate(self.matrix):\\n for col_ind, cell in enumerate(row):\\n if cell == \\\"d\\\":\\n self._dirt_coords.append((row_ind, col_ind,))\\n self._dirt_coords = tuple(self._dirt_coords)\\n return self._dirt_coords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dacaa977f744ce9710666ecaf2f30a13\",\n \"score\": \"0.5349185\",\n \"text\": \"def TP(self) -> Tuple[float, float]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb1a803791adb06398cd77ff6dece994\",\n \"score\": \"0.5344267\",\n \"text\": \"def getx(self):\\n return self.trajectory[:,1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bff0ff1053a1810252c69363dd989d6\",\n \"score\": \"0.5323084\",\n \"text\": \"def transform(self):\\n cx = self.dcx\\n cy = self.dcy\\n self.Tm = np.mat([[ cx, cy, 0, 0, 0, 0],\\n [-cy, cx, 0, 0, 0, 0],\\n [ 0, 0, 1, 0, 0, 0],\\n [ 0, 0, 0, cx, cy, 0],\\n [ 0, 0, 0, -cy, cx, 0],\\n [ 0, 0, 0, 0, 0, 1]])\\n return self.Tm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b01ba8c332d94261b5be080c0ad7a44\",\n \"score\": \"0.5317813\",\n \"text\": \"def process(self, X, Xd, U, Upert, t):\\n Unom = U-Upert\\n return X, Xd, U, Unom, t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa1b51187b212b74bb0632eeb9d47435\",\n \"score\": \"0.530214\",\n \"text\": \"def coordinates(self):\\n return (self.x, self.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48561d0aaea447559926ddcaf6d345cb\",\n \"score\": \"0.5298321\",\n \"text\": \"def homogeneous_coordinates(self):\\n return self.__coordinates.T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0e27e0105415e7ff9a2768b36e820e8\",\n \"score\": \"0.52941144\",\n \"text\": \"def get_x(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"538f1199ffcb4a42d3bf88f419b0dcfd\",\n \"score\": \"0.52924055\",\n \"text\": \"def xy(self):\\n self._update()\\n m = self.__len__()\\n x = array('d')\\n y = array('d')\\n temp = c_double()\\n for i in range(m):\\n lgeos.GEOSCoordSeq_getX(self._cseq, i, byref(temp))\\n x.append(temp.value)\\n lgeos.GEOSCoordSeq_getY(self._cseq, i, byref(temp))\\n y.append(temp.value)\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"438f99c8dcdc81224e0f135a53946eed\",\n \"score\": \"0.52877724\",\n \"text\": \"def __repr__(self):\\n s = '('\\n s += str(self.xpart)\\n s += ', '\\n s += str(self.ypart)\\n s += ' + O(%s^%s))'%(self.t,self.order)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da6f1e63c020da2d312389ea91bca664\",\n \"score\": \"0.5275663\",\n \"text\": \"def cartesian(self, t):\\n # self.update_x(t)\\n u = self.get_u(self.sigma_c, self.sigma_c)\\n z = self.x_pos + u\\n # test prints\\n # print(\\\"product u; sigma * normrnd(0,1):\\\\n\\\", u)\\n # print(\\\"sum: H*x + sigma*normrnd(0,1):\\\\n\\\", z)\\n return z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53ecd181aed528b166ed069e00614299\",\n \"score\": \"0.5260949\",\n \"text\": \"def get_transform_values(self):\\n a = b = c = d = e = f = 0\\n if 'transform' in self._node.attrib:\\n (a,c,e),(b,d,f) = st.parseTransform(self._node.attrib['transform'])\\n return a, b, c, d, e, f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e31547f40b6161b15afac53933547b\",\n \"score\": \"0.525373\",\n \"text\": \"def _extract_T(self, info_db):\\n\\n T = info_db.find('transform').text\\n T = T.replace('\\\\n', ' ')\\n T = T.replace('\\\\t', ' ')\\n T = T.split(' ')\\n T[:] = [x for x in T if x != '']\\n m = []\\n mi = []\\n for t in T:\\n try:\\n mi.append(float(t))\\n except:\\n None\\n if len(mi) == 4:\\n m.append(mi)\\n mi = []\\n m = array(m)\\n if m.shape == (4, 4):\\n return m\\n else:\\n raise SyntaxError(\\\"Invalid transform shape in info.xml\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c08e98b305c3a5a9b22d1ad11ffa2a3\",\n \"score\": \"0.52381516\",\n \"text\": \"def get_tr_te_set():\\n # extracting dlib features and gender labels from training set and test set\\n print(\\\"Extraction begin\\\")\\n features, labels = ex_a2.extract_features_labels()\\n features_te, labels_te = ex_te_a2.extract_features_labels()\\n print(\\\"Extraction end\\\")\\n\\n # transfer to numpy array to do matrix operation\\n features = np.array(features)\\n features_te = np.array(features_te)\\n\\n # flatten data for training\\n features = features.reshape(features.shape[0], 68 * 2)\\n features_te = features_te.reshape(features_te.shape[0], 68 * 2)\\n\\n # return features and labels for training and testing\\n return features, features_te, labels, labels_te\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5b76df3e7e60c6c16cc8f0b716efaad\",\n \"score\": \"0.52357584\",\n \"text\": \"def stddim_yx(self) -> Tuple[NcPlane, int, int]:\\n raise NotImplementedError('Stub')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f84445b3f341fe363d2b05384fe16f\",\n \"score\": \"0.52267975\",\n \"text\": \"def as_p_wh(self) -> tuple:\\n return (self.x0.x, self.x0.y, self.x2.x - self.x0.x, self.x2.y - self.x0.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"157c4ac3ad449c9996845235189a417a\",\n \"score\": \"0.5226771\",\n \"text\": \"def triangulate(geom):\\n from shapely import ops\\n triangles = ops.triangulate(geom)\\n return np.stack([np.asarray(t.exterior.coords.xy).T[:-1] for t in triangles])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7983a36e0cf66718ab6c9f2c4591ee84\",\n \"score\": \"0.5220874\",\n \"text\": \"def get4by4coords():\\n x1 = [0.04,0.28,0.52,0.76,0.04,0.28,0.52,0.76,0.04,0.28,0.52,0.76,0.04,0.28,0.52,0.76]\\n x2 = [0.22,0.46,0.7,0.94,0.22,0.46,0.7,0.94,0.22,0.46,0.7,0.94,0.22,0.46,0.7,0.94]\\n y1 = [0.8,0.8,0.8,0.8,0.59,0.59,0.59,0.59,0.38,0.38,0.38,0.38,0.17,0.17,0.17,0.17]\\n y2 = [0.95,0.95,0.95,0.95,0.74,0.74,0.74,0.74,0.53,0.53,0.53,0.53,0.32,0.32,0.32,0.32]\\n\\n return x1, x2, y1, y2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2133facd31d3318d0c777c38b6d7641b\",\n \"score\": \"0.5216206\",\n \"text\": \"def points(self):\\n if self == Tetrimino.I:\\n return np.array([[-2, 0], [-1, 0], [0, 0], [1, 0]])\\n if self == Tetrimino.O:\\n return np.array([[-1, 0], [0, 0], [0, -1], [-1, -1]])\\n if self == Tetrimino.T:\\n return np.array([[-1, 0], [0, 0], [1, 0], [0, -1]])\\n if self == Tetrimino.J:\\n return np.array([[0, 1], [0, 0], [0, -1], [-1, -1]])\\n if self == Tetrimino.L:\\n return np.array([[-1, 1], [-1, 0], [-1, -1], [0, -1]])\\n if self == Tetrimino.S:\\n return np.array([[0, 0], [-1, 0], [-1, -1], [-2, -1]])\\n if self == Tetrimino.Z:\\n return np.array([[-1, 0], [0, 0], [0, -1], [1, -1]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e8928acb4f9663d56c7b79c2318c743\",\n \"score\": \"0.52111375\",\n \"text\": \"def tex_coord(x, y, n=4):\\n m = 1.0 / n\\n dx = x * m\\n dy = y * m\\n return dx, dy, dx + m, dy, dx + m, dy + m, dx, dy + m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4ff5fd5bd6d5662c1efc450798be390\",\n \"score\": \"0.52083147\",\n \"text\": \"def __str__(self):\\n return \\\"x\\\".join((str(self._x), str(self._y)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2269ac6b7dec0bc32004e05497c18dee\",\n \"score\": \"0.5204414\",\n \"text\": \"def form1(x, y, n, ind, ax, line):\\n return f\\\"x: {x:.5g}\\\\ny: {y:.5g}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af7d00d0079ba1acbfada39a6906b51a\",\n \"score\": \"0.52039427\",\n \"text\": \"def get_x1_y1(self):\\n self.x1, self.y1 = self.x + self.w - 1, self.y + self.h - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eacdbdd0944958d75c683165c3d531a\",\n \"score\": \"0.51934606\",\n \"text\": \"def Xs(self,x):\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31308b101d6740018f96862e1f1a6801\",\n \"score\": \"0.519013\",\n \"text\": \"def get_xy(dls):\\n x, y = [], []\\n for batch in dls.train:\\n x.append(torch.cat(list(batch)[:-1], dim=-1) if len(batch) > 2 else batch[0])\\n y.append(batch[-1])\\n return torch.cat(x, dim=0), torch.cat(y, dim=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18794e9d1d5225cc53079142b2aa7c60\",\n \"score\": \"0.51897675\",\n \"text\": \"def ti_s():\\n titgx.put(tgx())\\n tihx.put(hexx.get())\\n tihy.put(hexy.get())\\n tihz.put(hexz.get())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fa77593e740838c526b01ea84d6a348\",\n \"score\": \"0.51887876\",\n \"text\": \"def get_data(self):\\n return self._x, self._y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caf54832249879d11a57ad9e7a9f5b55\",\n \"score\": \"0.5174342\",\n \"text\": \"def _uvw_to_xyz(cls,rst,abc) :\\n return np.transpose(rst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9da1478f8eedd0bf13778955df00e77\",\n \"score\": \"0.5172873\",\n \"text\": \"def get_u_tuple(self):\\n tup = self.get_x_tuple()\\n if self.has_t:\\n tup += (self.n_t,)\\n return (self.n_v,) + tup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d08dfe4fe8bad1b2d34251b5092abc3e\",\n \"score\": \"0.5169985\",\n \"text\": \"def DerivTX(self, *args):\\n return _GeomFill.GeomFill_FunctionDraft_DerivTX(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b3e717f501a7eeb6efe2133fe6688d2\",\n \"score\": \"0.51670647\",\n \"text\": \"def y(self):\\r\\n\\t\\treturn self[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f355e1a9d1e53011a4fd42ce31b4684\",\n \"score\": \"0.51388997\",\n \"text\": \"def flatt_values(self) -> np.ndarray:\\n # return [x0, y0, x1, y1, ...]\\n return self._values.reshape((self._n * 2,)).ravel()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"339d434ab6fecb59191a045dfe10a8ef\",\n \"score\": \"0.5136351\",\n \"text\": \"def getpx(self):\\n return self.trajectory[:,4]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d00db7dff53ea6d293075c80b9cbdf\",\n \"score\": \"0.51353353\",\n \"text\": \"def branch_to_xy(self, branch):\\n # code for when branch is a list of branches\\n if isinstance(branch, list):\\n x = []\\n y = []\\n for b in branch:\\n xx, yy = self.branch_to_xy(b)\\n x.extend(xx)\\n y.extend(yy)\\n x.append(None)\\n y.append(None)\\n return (x, y)\\n\\n # code for when branch is a scalar\\n node = self[branch].node\\n parent = node.parent\\n if parent:\\n x = [self[branch].x, self[branch].x, self[parent].x]\\n y = [self[branch].y, self[parent].y, self[parent].y]\\n return (x, y)\\n else:\\n return ([self[branch].x], [self[branch].y])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd98ec9a16429e56ab1f9fd075a8a70\",\n \"score\": \"0.51337814\",\n \"text\": \"def coordinates(self):\\n return (self._x, self._y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8db97aa75b326a0a628e71c5356301bd\",\n \"score\": \"0.5131568\",\n \"text\": \"def pos(self) -> tuple:\\n return round(self.x, 10), round(self.y, 10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e7737419843c9255f30426531b423c3\",\n \"score\": \"0.5121365\",\n \"text\": \"def x_y_from_line(line):\\n return (line[:, 0],line[:, 1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b28a7cdf41547211242eeb8f0e95eda4\",\n \"score\": \"0.510751\",\n \"text\": \"def nx( self ):\\n return cfunc.get_nx( self )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e1b71107150eb736d2c7de05d934f3a\",\n \"score\": \"0.5103917\",\n \"text\": \"def shape_str(self):\\n return np.repeat('trapezoid', self.nx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae6b4af37c6837d721e32d01aaec09c2\",\n \"score\": \"0.5101589\",\n \"text\": \"def coordinate_values(self):\\n string = [\\\"First cell: \\\" + str(self.first_values)]\\n string.append(\\\"Last cell: \\\" + str(self.last_values))\\n string.append(\\\"First bounds: \\\" + str(self.first_bounds))\\n string.append(\\\"Last bounds: \\\" + str(self.last_bounds))\\n\\n return \\\"\\\\n\\\".join(string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e7a76bc0781dd52a0a01b7bced659bc\",\n \"score\": \"0.50988454\",\n \"text\": \"def y(self):\\r\\n return self[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e7a76bc0781dd52a0a01b7bced659bc\",\n \"score\": \"0.50988454\",\n \"text\": \"def y(self):\\r\\n return self[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddf24e1a403fff1ccfa124a759f8e245\",\n \"score\": \"0.5096956\",\n \"text\": \"def as_tuple(self) -> Tuple[float, float]:\\r\\n return self.x, self.y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca830d4d1c54f9fd90ef5baf49efc885\",\n \"score\": \"0.5096532\",\n \"text\": \"def as_tuple(self) -> Tuple[float, float, float, float]:\\r\\n return self.x_start, self.y_start, self.x_end, self.y_end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ed272d0108106bc91590a137725015d\",\n \"score\": \"0.509473\",\n \"text\": \"def origin(self):\\n return self[0], self[3]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eec92bf14e1cc0a1a25357feeb2f84bd\",\n \"score\": \"0.5087419\",\n \"text\": \"def __str__(self):\\n out = str(self.weight_0)\\n for i in range(self.n_vars):\\n value = self.get_term([i])\\n if value != 0:\\n out += \\\" + \\\"\\n if value != 1:\\n out += (str(value) + \\\"*\\\")\\n out += (\\\"x_\\\" + str(i))\\n for i in range(self.n_vars-1):\\n for j in range(i+1, self.n_vars):\\n value = self.get_term([i, j])\\n if value != 0:\\n out += \\\" + \\\"\\n if value != 1:\\n out += (str(value) + \\\"*\\\")\\n out += (\\\"x_\\\" + str(i) + \\\"*x_\\\" + str(j))\\n for i in range(self.n_vars-2):\\n for j in range(i+1, self.n_vars-1):\\n for k in range(j+1, self.n_vars):\\n value = self.get_term([i, j, k])\\n if value != 0:\\n out += \\\" + \\\"\\n if value != 1:\\n out += (str(value) + \\\"*\\\")\\n out += (\\\"x_\\\" + str(i) + \\\"*x_\\\" + str(j) +\\n \\\"*x_\\\" + str(k))\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"983350b3177b74091f64af943a4fbdcc\",\n \"score\": \"0.508034\",\n \"text\": \"def __repr__( self ):\\n winding = \\\"X\\\"\\n if self.winding_order is not None: winding=\\\"%d\\\"%self.winding_order\\n return \\\"tri(%d:%s,%d,%d,%d,%6.2f)\\\"%(self.triangle_num,winding,self.pts[0].entry_number,self.pts[1].entry_number,self.pts[2].entry_number,self.get_area())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1ca7efa7e4d5f9691e19ae2b37fce15\",\n \"score\": \"0.5079989\",\n \"text\": \"def gdu(self):\\n\\t\\treturn (self.__x, self.__y, self.__e, self.__f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f54fd2d0a557e32152de299e17bf9d0e\",\n \"score\": \"0.5074132\",\n \"text\": \"def as_tuple(self):\\n if self.dimensions == 1:\\n return (self.x)\\n elif self.dimensions == 2:\\n return (self.x, self.y)\\n elif self.dimensions == 3:\\n return (self.x, self.y, self.z)\\n else:\\n #This should never happen, but just in case...\\n return tuple()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ee69f015e1880f384e637779bdd036\",\n \"score\": \"0.5071353\",\n \"text\": \"def getXCoord(self) :\\r\\n coord_x = np.zeros((self.nx, self.ny))\\r\\n for i in range(self.nx) : # verticle direction up to down\\r\\n for j in range(self.ny): # lateral direction left to right\\t\\r\\n idx = i*self.ny + j\\r\\n coord_x[i,j] = self.meshes[idx].get_x_coord()\\r\\n \\r\\n return coord_x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f2ff9ab76acff9ce20e966f81ec4487\",\n \"score\": \"0.5067448\",\n \"text\": \"def get_rt_to_xy_plane(self):\\n try:\\n return self._rt_to_xy\\n except AttributeError as no_rad:\\n self.compute_rt_to_xy()\\n return self._rt_to_xy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2bbe71453053769377c766ee0fcc1de\",\n \"score\": \"0.5064559\",\n \"text\": \"def get_coord(self):\\n return self._x, self._y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29b8ef8b11d56b263321d2f5e13766cf\",\n \"score\": \"0.506395\",\n \"text\": \"def cartesian_coordinates(self):\\n return self.__coordinates[0:3, ::].T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69df906f59c688209ce3c67581ff01cf\",\n \"score\": \"0.50610733\",\n \"text\": \"def ntuple(self):\\n return self.nt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3274711f225d6f808100f1f0de5b558\",\n \"score\": \"0.5060391\",\n \"text\": \"def getLocation(self):\\n\\t\\treturn self.x, self.y, self.val, self.n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0cfcbe1c90ba8486b71e5e6fb077b0e\",\n \"score\": \"0.5057198\",\n \"text\": \"def Nt(self):\\n return sp.shape(self.get('throat.all'))[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eea463b97fe987f3b9e8387fb2f838e\",\n \"score\": \"0.5054072\",\n \"text\": \"def get_tensors(self, num_tensors):\\n x_raw = [self.sample_str() for _ in xrange(num_tensors)]\\n y_raw = [[self.eval_func(s[:j + 1]) for j in xrange(len(s))]\\n for s in x_raw]\\n\\n x_sent = [[unicode(w) for w in s] for s in x_raw]\\n y_sent = [[unicode(w) for w in s] for s in y_raw]\\n\\n x_var = self.sentences_to_one_hot(self.max_length, *x_sent)\\n y_var = self.sentences_to_codes(self.max_length, *y_sent)\\n\\n return x_var, y_var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3170f704e32195471f1551bc2a619dc1\",\n \"score\": \"0.505012\",\n \"text\": \"def _ty(self, y):\\n return np.array([\\n [1, 0, 0, 0],\\n [0, 1, 0, y],\\n [0, 0, 1, 0],\\n [0, 0, 0, 1]\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5cc7d0d268ea4b099c960775df9c0b0\",\n \"score\": \"0.50457597\",\n \"text\": \"def second_derivative_operators(self):\\n grad2x = self.element_x.second_derivative_operator()\\n grad2y = self.element_y.second_derivative_operator()\\n grad2x = grad2x.reshape(self.Nx, 1, 1, self.Nx)\\n grad2y = grad2y.reshape(self.Ny, 1, self.Ny)\\n return grad2x, grad2y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5cc7d0d268ea4b099c960775df9c0b0\",\n \"score\": \"0.50457597\",\n \"text\": \"def second_derivative_operators(self):\\n grad2x = self.element_x.second_derivative_operator()\\n grad2y = self.element_y.second_derivative_operator()\\n grad2x = grad2x.reshape(self.Nx, 1, 1, self.Nx)\\n grad2y = grad2y.reshape(self.Ny, 1, self.Ny)\\n return grad2x, grad2y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4948a6d9e38f5a67148d8a774b6ec46\",\n \"score\": \"0.50435233\",\n \"text\": \"def get_tensors(self, num_tensors):\\n x_raw = [self.sample_str() for _ in xrange(num_tensors)]\\n y_raw = [[self.eval_func(s[:j]) for j in xrange(len(s))]\\n for s in x_raw]\\n\\n x_sent = [[unicode(w) for w in s] for s in x_raw]\\n y_sent = [[unicode(w) for w in s] for s in y_raw]\\n\\n x_var = self.sentences_to_one_hot(self.max_length, *x_sent)\\n y_var = self.sentences_to_codes(self.max_length, *y_sent)\\n\\n return x_var, y_var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93616381d29e57ab3064c0d0eaa0f524\",\n \"score\": \"0.50415766\",\n \"text\": \"def get_direction(interpret, x, y):\\n if interpret == \\\"N\\\":\\n y += 1\\n return x, y\\n if interpret == \\\"E\\\":\\n x += 1\\n return x, y\\n if interpret == \\\"S\\\":\\n y -= 1\\n return x, y\\n if interpret == \\\"W\\\":\\n x -= 1\\n return x, y\\n else:\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c58c23e51ef8ed1620035243b8c83263\",\n \"score\": \"0.5037428\",\n \"text\": \"def case_xdata_X_and_Y(self):\\n return (\\n rf'5B090059\\\"SIA-DCS\\\"0004L0#{ACCOUNT}[#{ACCOUNT}|Nri1/PA501][X006E07.7777777][Y49N06.6666666]_20:45:46,04-22-2021',\\n ACCOUNT,\\n \\\"Panic Alarm\\\",\\n \\\"PA\\\",\\n None,\\n True,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64762d6ebd216a98fb34f3a89caff92e\",\n \"score\": \"0.5034314\",\n \"text\": \"def position(self):\\n return self.xcor(), self.ycor()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"065852f9c57170baaaca32f200771aab\",\n \"score\": \"0.5033514\",\n \"text\": \"def GetSRowX(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ff40a551eb879b5529fa995abde045f\",\n \"score\": \"0.5030483\",\n \"text\": \"def hadridx(self):\\n return (self.mix_idx, self.d)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993886,"cells":{"query_id":{"kind":"string","value":"4f938c74238954c3edbb785491861f2e"},"query":{"kind":"string","value":"test_get_cinderclient_unknown_module check that we could not retrieve a Session client to work with cinder if there is no modules defined"},"positive_passages":{"kind":"list like","value":[{"docid":"ab92b3bde7e9989429107596424a5dc4","score":"0.8516438","text":"def test_get_cinderclient_unknown_module(self):\n try:\n osclients = OpenStackClients(modules=\"\")\n osclients.get_cinderclient()\n except Exception as ex:\n self.assertRaises(ex)","title":""}],"string":"[\n {\n \"docid\": \"ab92b3bde7e9989429107596424a5dc4\",\n \"score\": \"0.8516438\",\n \"text\": \"def test_get_cinderclient_unknown_module(self):\\n try:\\n osclients = OpenStackClients(modules=\\\"\\\")\\n osclients.get_cinderclient()\\n except Exception as ex:\\n self.assertRaises(ex)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f7327525d1d6f0075e2207867225b3e4","score":"0.74871695","text":"def test_implement_client_with_unknown_module(self):\n\n try:\n OpenStackClients(modules=\"fakeOpenstackModule\")\n except Exception as ex:\n self.assertRaises(ex)","title":""},{"docid":"ffaede393529b7fb3ea943907fb573e7","score":"0.72420913","text":"def test_get_neutronclient_with_all_modules(self):\n\n osclients = OpenStackClients(modules=\"auto\")\n neutronClient = osclients.get_neutronclient()\n\n self.assertIsInstance(neutronClient, neutronclient.v2_0.client.Client)","title":""},{"docid":"8e07eb5732319248643a8d6ed6b5eb7d","score":"0.7129611","text":"def test_implement_client_with_selected_module(self):\n selected_modules = \"keystone,cinder,\"\n\n osclients = OpenStackClients(modules=selected_modules)\n\n self.assertIsNotNone(osclients)","title":""},{"docid":"369cb6822fcb004c25d3d44ec46b936e","score":"0.7060576","text":"def test_get_cinderclient(self):\n osclients = OpenStackClients(modules=\"cinder\")\n cinderClient = osclients.get_cinderclient()\n\n # api_version = cinderClient.get_volume_api_version_from_endpoint() --> This should return \"2\" against a server\n self.assertIsInstance(cinderClient, cinderclient.v2.client.Client)","title":""},{"docid":"3bc27505d8a997c5190231a6d8975edc","score":"0.67548","text":"def test_implement_client_with_all_modules(self):\n\n OS_AUTH_URL = 'http://host.com:4731/v3'\n\n osclients = OpenStackClients(modules=\"all\", auth_url=OS_AUTH_URL)\n self.assertIsNotNone(osclients)","title":""},{"docid":"e508a748ba933cf590b11300d825d4d6","score":"0.6729623","text":"def test_get_cinderclient_v1(self):\n osclients = OpenStackClients(modules=\"cinder\")\n cinderClient = osclients.get_cinderclientv1()\n # api_version = cinderClient.get_volume_api_version_from_endpoint() --> This should return \"1\" against a server\n\n self.assertIsInstance(cinderClient, cinderclient.v2.client.Client)","title":""},{"docid":"43a7f98a790bc5d1b39806fd0f595759","score":"0.6524887","text":"def test_implement_client(self):\n\n osclients = OpenStackClients()\n self.assertIsNotNone(osclients)","title":""},{"docid":"639dffa1b489f9850992503c376890e1","score":"0.6469845","text":"def test_implement_client_with_env(self):\n\n osclients = OpenStackClients()\n\n self.assertIsNotNone(osclients)","title":""},{"docid":"459d213b0e15523ec57a684d50257097","score":"0.6261802","text":"def test_get_session_without_username_nor_token(self):\n\n osclients = OpenStackClients()\n\n osclients.set_credential(\"\", self.OS_PASSWORD, tenant_id=self.OS_TENANT_ID)\n\n # Checking v3\n try:\n osclients.get_session()\n except Exception as ex:\n self.assertRaises(ex)\n\n # Checking v2\n osclients.use_v3 = False\n try:\n osclients.get_session()\n except Exception as ex:\n self.assertRaises(ex)","title":""},{"docid":"51de2cb8b33f1e621299cc420fe84a8d","score":"0.6192851","text":"def test_get_session_without_auth_url(self):\n\n osclients = OpenStackClients()\n osclients.auth_url = None\n\n # Checking v3\n try:\n osclients.get_session()\n except Exception as ex:\n self.assertRaises(ex)\n\n # Checking v2\n osclients.use_v3 = False\n try:\n osclients.get_session()\n except Exception as ex:\n self.assertRaises(ex)","title":""},{"docid":"819e57c0234a8b18acf2f15c26198617","score":"0.616063","text":"def test_get_glanceclient(self):\n\n osclients = OpenStackClients(modules=\"glance\")\n osclients.set_keystone_version(use_v3=False)\n glanceClient = osclients.get_glanceclient()\n\n self.assertIsInstance(glanceClient, glanceclient.v1.client.Client)","title":""},{"docid":"876724adcc22dfa524dfd6b1376421c2","score":"0.61028904","text":"def test_get_swiftclient(self):\n\n osclients = OpenStackClients(modules=\"swift\")\n swiftClient = osclients.get_swiftclient()\n\n self.assertIsInstance(swiftClient, Connection)","title":""},{"docid":"dc40b031386a799e991f88ae33176cf4","score":"0.60571325","text":"def test_get_swiftclient_with_keystone_v2(self):\n\n osclients = OpenStackClients(modules=\"swift\")\n osclients.use_v3 = False\n swiftClient = osclients.get_swiftclient()\n\n self.assertIsInstance(swiftClient, Connection)","title":""},{"docid":"1e26330d302f82a6951a2ebfaa10adce","score":"0.60244447","text":"def test_get_keystoneclient_v2(self):\n osclients = OpenStackClients()\n osclients.use_v3 = False\n keystoneClient = osclients.get_keystoneclient()\n\n self.assertIsInstance(keystoneClient, keystoneclient.v2_0.client.Client)","title":""},{"docid":"c15055b7d92786540559b372a80f0294","score":"0.59922767","text":"def test_get_novaclient(self):\n osclients = OpenStackClients(modules=\"nova\")\n novaClient = osclients.get_novaclient()\n\n self.assertIsInstance(novaClient, novaclient.v2.client.Client)","title":""},{"docid":"4c585438060b018792d3da8dabeba1cd","score":"0.5864168","text":"def test_get_keystoneclient_v3(self):\n osclients = OpenStackClients()\n keystoneClient = osclients.get_keystoneclient()\n\n self.assertIsInstance(keystoneClient, keystoneclient.v3.client.Client)","title":""},{"docid":"cdca71a29f7d9b0da270d2ba0e920ae3","score":"0.58547527","text":"def validate_client():\n\tassert \"client\" in GLOBALS, \"No client\"","title":""},{"docid":"1aefe0f89c3d10b6e1752b52f81c00bf","score":"0.5827553","text":"def testAccesInexistantModule(self):\n self.assertRaises(mib.SMIException, mib.getNodes, \"idontexist.kjgf\");\n self.assertRaises(mib.SMIException, mib.getScalars, \"idontexist.kjgf\");\n self.assertRaises(mib.SMIException, mib.getTables, \"idontexist.kjgf\");\n self.assertRaises(mib.SMIException, mib.getColumns, \"idontexist.kjgf\");","title":""},{"docid":"24c74bc875e5d479641b10aee54c7531","score":"0.57956004","text":"def test_get_missing_hardware() -> None:\n subject = make_module_view(slot_by_module_id={\"module-id\": DeckSlotName.SLOT_1})\n\n with pytest.raises(errors.ModuleNotLoadedError):\n subject.get(\"module-id\")","title":""},{"docid":"f34b4209b4d5e564f8df4eac4b493d33","score":"0.57769907","text":"def test_module(client: Client) -> str:\n # INTEGRATION FEED DEVELOPER TIP\n # Client class should raise the exceptions, but if the test fails\n # the exception text is printed to the Cortex XSOAR UI.\n # If you have some specific errors you want to capture (i.e. auth failure)\n # you should catch the exception here and return a string with a more\n # readable output (for example return 'Authentication Error, API Key\n # invalid').\n # Cortex XSOAR will print everything you return different than 'ok' as\n # an error\n\n fetch_indicators(client, limit=1)\n return 'ok'","title":""},{"docid":"a658751ea33e46ef05889423cd6311b8","score":"0.57401943","text":"def test_module(client: Client) -> str:\n return client.authenticate()","title":""},{"docid":"1d41a8cb7b25d701f71ff18752aed284","score":"0.5658385","text":"def test_module(client: Client, demisto_last_run: Dict[str, Any], demisto_params: Dict[str, Any]) -> str:\n\n try:\n if demisto_params.get('isFetch'):\n fetch_incidents(client=client,\n last_run=demisto_last_run,\n demisto_params=demisto_params)\n else:\n client.get_list(\"/incidents/call_types\")\n except DemistoException as e:\n if 'Error 401' in str(e):\n return 'Authorization Error: make sure username and password are correctly set'\n if '[404] - Not Found' in str(e):\n return 'Page Not Found: make sure the url is correctly set'\n else:\n raise e\n return 'ok'","title":""},{"docid":"4de0e67c23b40015341b0720401cb543","score":"0.5654354","text":"def test_ec_client_loaded():\n assert 'eccode' == ec_client._client.prefix\n assert ec_client._client.entries\n assert ec_client._client.name_to_id","title":""},{"docid":"2cb0cfbc84ae5c8f8c01374592168791","score":"0.56363523","text":"def no_such_client(self):\n return self._hasflag(_lib.JackNoSuchClient)","title":""},{"docid":"cf0af0ed02cffdeb78044d1b111e8cf5","score":"0.5627076","text":"def test_missing_modules(self, cmd):\n var = {\"host\": redhat_host, \"config\": deepcopy(rhel_conf['config'][0])}\n r = RedHatNetConfig().configure_network(var)\n self.assertListEqual(r, list(), \"Excepted emtpy results\")","title":""},{"docid":"3c641ca097afbbe1dcdf475b6d58c38f","score":"0.5624228","text":"def test_module_command(client: Client, *_) -> Tuple[None, None, str]:\n results = client.test_module()\n if 'Templates' in results or 'CertificateInformation' in results:\n return None, None, 'ok'\n raise DemistoException(f'Test module failed, {results}')","title":""},{"docid":"442ce3d3503cd5814ef79c75ceb018b1","score":"0.55861014","text":"def test_instantiate_without_args(self):\n with self.assertRaises(ValueError):\n api.EdxModulestoreImportClient()","title":""},{"docid":"56fdc5c356690f78cc3e61c04c617122","score":"0.55702","text":"def test_import():\n with patch(\"salt.config.check_driver_dependencies\", return_value=True) as p:\n get_deps = gce.get_dependencies()\n assert get_deps is True\n if LooseVersion(mock_version) >= LooseVersion(\"2.0.0\"):\n p.assert_called_once()","title":""},{"docid":"98ea9aaabb6347d6a775668eaf2ba5a8","score":"0.5567115","text":"def test_080(self):\n # client =gmn.tests.gmn_test_client.GMNTestClient(GMN_URL)\n # self.assertRaises(\n # d1_common.types.exceptions.NotAuthorized, client.get, pid,\n # vendorSpecific=self.session(d1_common.const.SUBJECT_PUBLIC)\n # )","title":""},{"docid":"517a3e6840c8012d6f0586ae65b544aa","score":"0.5565295","text":"def test_get_keystoneclient_v2_with_tenant_name(self):\n\n osclients = OpenStackClients()\n osclients.use_v3 = False\n osclients.set_credential(self.OS_USERNAME, self.OS_PASSWORD, tenant_name=self.OS_TENANT_NAME)\n\n keystoneClient = osclients.get_keystoneclient()\n\n self.assertIsInstance(keystoneClient, keystoneclient.v2_0.client.Client)","title":""},{"docid":"5e82387e84e20caa4eebe5595ce5b1f4","score":"0.5562546","text":"async def test_import_fail(\n hass: HomeAssistant, component_factory: ComponentFactory\n) -> None:\n api = MagicMock(spec=GogoGate2Api)\n api.info.side_effect = ApiError(22, \"Error\")\n\n component_factory.api_class_mock.return_value = api\n\n await component_factory.configure_component(\n cover_config=[\n {\n CONF_PLATFORM: \"gogogate2\",\n CONF_NAME: \"cover0\",\n CONF_IP_ADDRESS: \"127.0.1.0\",\n CONF_USERNAME: \"user0\",\n CONF_PASSWORD: \"password0\",\n }\n ]\n )\n\n entity_ids = hass.states.async_entity_ids(COVER_DOMAIN)\n assert not entity_ids","title":""},{"docid":"1b3bb24f804f2dd6f14822e0053c9971","score":"0.555835","text":"def test_no_credentials(self):\n\n self.assertRaises(Exception, client.get_client, 'foo', 'bar')","title":""},{"docid":"bae66de0abbcc1a007fde06ff950d05e","score":"0.552381","text":"def test_get_keystoneclient_v2_with_tenant_id(self):\n\n osclients = OpenStackClients()\n osclients.use_v3 = False\n osclients.set_credential(self.OS_USERNAME, self.OS_PASSWORD, tenant_id=self.OS_TENANT_ID)\n\n keystoneClient = osclients.get_keystoneclient()\n\n self.assertIsInstance(keystoneClient, keystoneclient.v2_0.client.Client)","title":""},{"docid":"f0c1431bbb10f95f30179d3859c229f1","score":"0.55220324","text":"def commandTestModule(sensorClient: SensorClient):\n try:\n commandGetSensors(sensorClient, {})\n return 'ok'\n except Exception as e:\n demisto.error(f'Module test failed: {e}')\n raise e","title":""},{"docid":"c17fff62681af4214a2c580fd38c431d","score":"0.5500935","text":"def test_090(self):\n # client =gmn.tests.gmn_test_client.GMNTestClient(GMN_URL)\n # self.assertRaises(\n # d1_common.types.exceptions.NotAuthorized, client.get, pid,\n # vendorSpecific=self.session('other_subject')\n # )","title":""},{"docid":"6da39aa5143584726b098aec49b6fb67","score":"0.5492073","text":"def test_module(client: Client, *_) -> Tuple[str, Dict, Dict]:\n client.test_module_request()\n demisto.results('ok')\n return '', {}, {}","title":""},{"docid":"2541c29999c313bceb8369cd114c5d5e","score":"0.5448104","text":"def test_cobbler_alive(self):\n if OPENSTACK_RELEASE_CENTOS not in OPENSTACK_RELEASE:\n raise SkipTest()\n self.env.revert_snapshot(\"empty\")\n wait(\n lambda: http(host=self.env.get_admin_node_ip(), url='/cobbler_api',\n waited_code=501),\n timeout=60\n )\n server = xmlrpclib.Server(\n 'http://%s/cobbler_api' % self.env.get_admin_node_ip())\n\n config = self.env.get_fuel_settings()\n username = config['cobbler']['user']\n password = config['cobbler']['password']\n\n # raises an error if something isn't right\n server.login(username, password)","title":""},{"docid":"0184830b96061ea7a5e7a7097889e6b3","score":"0.54460514","text":"def test_applications_id_extclients_get(self):\n pass","title":""},{"docid":"4a9cb6402ad6edf89ef1ab9ca2167d16","score":"0.54020774","text":"def test_200_api_connection(self):\n logging.info('Instantiating gnocchi client...')\n overcloud_auth = openstack_utils.get_overcloud_auth()\n keystone = openstack_utils.get_keystone_client(overcloud_auth)\n gnocchi_ep = keystone.service_catalog.url_for(\n service_type='metric',\n interface='publicURL'\n )\n gnocchi = gnocchi_client.Client(\n session=openstack_utils.get_overcloud_keystone_session(),\n adapter_options={\n 'endpoint_override': gnocchi_ep,\n }\n )\n\n logging.info('Checking api functionality...')\n assert(gnocchi.status.get() != [])","title":""},{"docid":"00dd6393e363ba2d0ee0c621df518efa","score":"0.5385197","text":"def test_import():\n import gdc_client","title":""},{"docid":"8ebc9d85b7c0e36de519abc859b4543c","score":"0.53806794","text":"def test_get_wrong_network_driver(self, driver):\n self.assertRaises(ModuleImportError, get_network_driver, driver)","title":""},{"docid":"623706965bc2ba39701e20e426813cc3","score":"0.5374103","text":"def _cb_client(self):\n\n if not self._codebuild_is_connected:\n try:\n self._codebuild_client = self._sts_session.client('codebuild')\n except Exception as e:\n log(str(e))\n raise\n else:\n self._codebuild_is_connected = True\n return self._codebuild_is_connected\n else:\n return self._codebuild_is_connected","title":""},{"docid":"50e32de75eb94f2400e758e57e87b4f5","score":"0.53634304","text":"def setUp(self):\n super(TestCongress, self).setUp()\n # create neutron mock and tell cage to use that mock\n # https://code.google.com/p/pymox/wiki/MoxDocumentation\n mock_factory = mox.Mox()\n neutron_mock = mock_factory.CreateMock(\n neutronclient.v2_0.client.Client)\n neutron_mock2 = mock_factory.CreateMock(\n neutronclient.v2_0.client.Client)\n override = {}\n override['neutron'] = {'client': neutron_mock, 'poll_time': 0}\n override['neutron2'] = {'client': neutron_mock2, 'poll_time': 0}\n override['nova'] = {'poll_time': 0}\n\n cage = harness.create(helper.root_path(), helper.state_path(),\n helper.datasource_config_path(), override)\n engine = cage.service_object('engine')\n api = {'policy': cage.service_object('api-policy'),\n 'rule': cage.service_object('api-rule'),\n 'table': cage.service_object('api-table'),\n 'row': cage.service_object('api-row'),\n 'datasource': cage.service_object('api-datasource'),\n 'status': cage.service_object('api-status'),\n 'schema': cage.service_object('api-schema')}\n\n # Turn off schema checking\n engine.module_schema = None\n\n # initialize neutron_mocks\n network1 = test_neutron.network_response\n port_response = test_neutron.port_response\n router_response = test_neutron.router_response\n sg_group_response = test_neutron.security_group_response\n neutron_mock.list_networks().InAnyOrder().AndReturn(network1)\n neutron_mock.list_ports().InAnyOrder().AndReturn(port_response)\n neutron_mock.list_routers().InAnyOrder().AndReturn(router_response)\n neutron_mock.list_security_groups().InAnyOrder().AndReturn(\n sg_group_response)\n neutron_mock2.list_networks().InAnyOrder().AndReturn(network1)\n neutron_mock2.list_ports().InAnyOrder().AndReturn(port_response)\n neutron_mock2.list_routers().InAnyOrder().AndReturn(router_response)\n neutron_mock2.list_security_groups().InAnyOrder().AndReturn(\n sg_group_response)\n mock_factory.ReplayAll()\n\n self.cage = cage\n self.engine = engine\n self.api = api","title":""},{"docid":"ad91b618ef39494f521e3e137e498cc3","score":"0.5348685","text":"def test_provider_matches():\n p = gce.get_configured_provider()\n assert p is not None","title":""},{"docid":"3a5af31b022cdc40dda2073fab434a00","score":"0.5340612","text":"def test_find(self):\n self.clist = bundy.datasrc.ConfigurableClientList(bundy.dns.RRClass.IN)\n self.configure_helper()\n self.find_helper()","title":""},{"docid":"913eeabec1d858997f175b06301c6b8e","score":"0.53296083","text":"def IsSDKTrybot():\n return '_nacl_sdk' in os.getenv('BUILDBOT_BUILDERNAME', '')","title":""},{"docid":"cb6272cd6443ed3e25c31f7ff77b8659","score":"0.5327222","text":"def _get_client(self):\n # Regular test client\n return get_client()","title":""},{"docid":"4d561f5f502c480daef84c8437062f4d","score":"0.532091","text":"def test_api_v1_cloud_compliance_get(self):\n pass","title":""},{"docid":"93ed0d99bb387d89bf5fa3cde1319428","score":"0.5317385","text":"def _get_default_glance_client():\n keystone_session = openstack_utils.get_overcloud_keystone_session()\n glance_client = openstack_utils.get_glance_session_client(keystone_session)\n return glance_client","title":""},{"docid":"23671c8bece4f8fd388ead27320b64d2","score":"0.5300621","text":"def test_030(self):\n # client =gmn.tests.gmn_test_client.GMNTestClient(GMN_URL)\n # obj = client.get(pid, vendorSpecific=self.session(test_owner_1))\n # self.assertEqual(obj_str, obj.read())","title":""},{"docid":"46635fdc74e2b76522880b721f2e0ef6","score":"0.53006107","text":"def check_client_and_app_authentication(self):\n try:\n if self.crowdClient is None:\n self.crowdClient = CrowdRestClient()\n except:\n crowd_logger.exception(\"Create Crowd client failed\")","title":""},{"docid":"41361a5cdbc075b2ebb921d5d5863d74","score":"0.52977353","text":"def test_oidc_config_unavailable(self, monkeypatch):\n app = Flask(__name__)\n app.config['FOCA'] = Config()\n monkeypatch.setattr(\n 'requests.get',\n lambda **kwargs: _raise(ConnectionError)\n )\n with app.app_context():\n with pytest.raises(Unauthorized):\n validate_token(token=MOCK_TOKEN_HEADER_KID)","title":""},{"docid":"38c1559cd1d8a5d023abe281b9c1f906","score":"0.52824575","text":"def test_module(client):\n result = client.get_status_http_request()\n if result[\"status\"] == \"Active\" or result[\"status\"] == \"Standby\":\n return 'ok'\n else:\n return 'Something went wrong with the risk api checking'","title":""},{"docid":"38d0ae5c69a81ae8178cd8f220ec08b4","score":"0.5272958","text":"async def test_client_error():\n async with ClientSession() as session:\n dtv = DIRECTV(\"#\", session=session)\n with pytest.raises(DIRECTVConnectionError):\n assert await dtv._request(\"/info/getVersion\")","title":""},{"docid":"d284b9d297c955d5c397aecbf475b743","score":"0.5271243","text":"def test_swift_connection(auth_version, auth_url, access_key, secret_key, tenant_name,\n user_domain_name, project_domain_name, container):\n if not (auth_version and auth_url and access_key and secret_key and tenant_name and container):\n return ({\n 'message': 'All the fields above are required.'\n }, http_status.HTTP_400_BAD_REQUEST)\n if auth_version == '3' and not user_domain_name:\n return ({\n 'message': 'The field `user_domain_name` is required when you choose identity V3.'\n }, http_status.HTTP_400_BAD_REQUEST)\n if auth_version == '3' and not project_domain_name:\n return ({\n 'message': 'The field `project_domain_name` is required when you choose identity V3.'\n }, http_status.HTTP_400_BAD_REQUEST)\n\n user_info = swift_utils.get_user_info(auth_version, auth_url, access_key,\n user_domain_name, secret_key, tenant_name,\n project_domain_name)\n\n if not user_info:\n return ({\n 'message': 'Unable to access account.\\n'\n 'Check to make sure that the above credentials are valid, '\n 'and that they have permission to list containers.'\n }, http_status.HTTP_400_BAD_REQUEST)\n\n try:\n _, containers = swift_utils.connect_swift(\n auth_version, auth_url, access_key, user_domain_name, secret_key, tenant_name,\n timeout=swift_settings.TEST_TIMEOUT\n ).get_account()\n except swift_exceptions.ClientException:\n return ({\n 'message': 'Unable to list containers.\\n'\n 'Listing containers is required permission.'\n }, http_status.HTTP_400_BAD_REQUEST)\n\n if container not in map(lambda c: c['name'], containers):\n return ({\n 'message': 'Invalid container name.'\n }, http_status.HTTP_400_BAD_REQUEST)\n\n provider = SwiftProvider(account=None, auth_version=auth_version,\n auth_url=auth_url, tenant_name=tenant_name,\n project_domain_name=project_domain_name,\n username=access_key,\n user_domain_name=user_domain_name,\n password=secret_key)\n swift_response = {\n 'id': provider.account.id,\n 'display_name': provider.account.display_name,\n }\n return ({\n 'message': 'Credentials are valid',\n 'data': swift_response\n }, http_status.HTTP_200_OK)","title":""},{"docid":"df725a00dc13ffaa72db2a2d7f200157","score":"0.5264315","text":"def test_get_tf_modules_early_return(self):\n config = Mock(return_value={}, build_directory=self._build_directory)\n assert_false(get_tf_modules(config, generate=True))","title":""},{"docid":"b8ba1477295384e16172345f26e9079b","score":"0.5263503","text":"def test_api_v1_cloud_discovery_get(self):\n pass","title":""},{"docid":"7f4a4b6d453d5ea694d8111964ce8e36","score":"0.52454287","text":"def test_get_keystoneclient_v2_with_trust_id(self):\n\n osclients = OpenStackClients()\n osclients.use_v3 = False\n trust_id = \"randomid0000000000000000000000001\"\n osclients.set_credential(self.OS_USERNAME, self.OS_PASSWORD, trust_id=trust_id)\n\n keystoneClient = osclients.get_keystoneclient()\n\n self.assertIsInstance(keystoneClient, keystoneclient.v2_0.client.Client)","title":""},{"docid":"62cd2539f627ae81a6c18c8350ede94b","score":"0.52439654","text":"def test_with_libraries_disabled(self):\n response = self.client.get_json(LIBRARY_REST_URL)\n self.assertEqual(response.status_code, 404)","title":""},{"docid":"ed266c928d00652322e263373241fd11","score":"0.5239862","text":"def test_need_username(self):\n self.assertRaises(KeyError, Client, login_uri=self.cfixt.login_uri, password=self.cfixt.password)","title":""},{"docid":"fb61e9122a6de1c4597392f49be07069","score":"0.52305186","text":"def check_client_pv(client_name, py_version, skip_fails, build_path):\n py_env = 'py'\n if '.' in py_version:\n py_env += py_version.replace('.', '')[0:2]\n else:\n py_env += py_version[0:2]\n\n config = tox.session.prepare(\n [\n \"-c{}/{}-tox.ini\".format(build_path, client_name),\n \"-l\"\n ]\n )\n found = py_env in config.envlist\n\n if not found:\n msg = \"Not found venv {} for client {}\".format(py_env, client_name)\n if not skip_fails:\n raise SystemExit(msg)\n else:\n LOG.warning(msg)\n\n return found","title":""},{"docid":"c5b6f04c52dcf065f9f5950694620573","score":"0.5227111","text":"def check_for_setup_error(self):\n with self._client.open_connection() as api:\n api.find_sc()\n self.is_direct_connect = api.is_direct_connect\n if self.is_direct_connect and self.replication_enabled:\n msg = _('Dell Cinder driver configuration error replication '\n 'not supported with direct connect.')\n raise exception.InvalidHost(reason=msg)\n\n # If we are a healthy replicated system make sure our backend\n # is alive.\n if self.replication_enabled and not self.failed_over:\n # Check that our replication destinations are available.\n for backend in self.backends:\n replssn = backend['target_device_id']\n try:\n # Just do a find_sc on it. If it raises we catch\n # that and raise with a correct exception.\n api.find_sc(int(replssn))\n except exception.VolumeBackendAPIException:\n msg = _('Dell Cinder driver configuration error '\n 'replication_device %s not found') % replssn\n raise exception.InvalidHost(reason=msg)","title":""},{"docid":"37647d52f60434a5cb3ede4185f85162","score":"0.5221668","text":"def test_get_keystone_session_v2(self, mock_auth_v2, mock_ks_session):\n\n os_auth_version = \"2.0\"\n os_insecure = True\n self.CONF.config(os_auth_version=os_auth_version,\n os_insecure=os_insecure,\n group=\"openstack\")\n client.get_keystone_session()\n mock_auth_v2.assert_called_once_with()\n mock_ks_session.assert_called_once_with(auth=mock_auth_v2(),\n verify=False)","title":""},{"docid":"a34dbb16c2720e6cced500d330bccf56","score":"0.5221368","text":"def test_client_capabilities():\n\n transport = Mock(send=Mock(side_effect=Exception(\"this test mocks MSAL, so no request should be sent\")))\n\n credential = UsernamePasswordCredential(\"client-id\", \"username\", \"password\", transport=transport)\n with patch(\"msal.PublicClientApplication\") as PublicClientApplication:\n credential._get_app()\n\n assert PublicClientApplication.call_count == 1\n _, kwargs = PublicClientApplication.call_args\n assert kwargs[\"client_capabilities\"] == None\n\n credential._get_app(enable_cae=True)\n\n assert PublicClientApplication.call_count == 2\n _, kwargs = PublicClientApplication.call_args\n assert kwargs[\"client_capabilities\"] == [\"CP1\"]","title":""},{"docid":"11ba0d6fbcda016d5b8d09b91410cabb","score":"0.5219593","text":"def test_verify_ceph_deploy(self, host):","title":""},{"docid":"a984d51f8966f9a2ad089fe112697ad8","score":"0.5216299","text":"def test_openstack_ids(self):\n self.createSwiftService(False)\n url = (self.json_body['access']['serviceCatalog'][0]\n ['endpoints'][0]['publicURL'])\n self.assertIn(\"/fun_tenant\", url)\n self.assertNotIn(\"/MossoCloudFS_\", url)","title":""},{"docid":"7131468769b13f0dbb58aae0be561829","score":"0.5210334","text":"def test_need_login_uri(self):\n self.assertRaises(KeyError, Client, username=self.cfixt.username, password=self.cfixt.password)","title":""},{"docid":"5dea7ceab317a4a63a4f24199ee97208","score":"0.5205165","text":"def test_check_enclave_object(self):\r\n enclave = self._get_test_enclave()\r\n self.assertIsInstance(enclave, legion.k8s.Enclave)\r\n\r\n self.assertIsNotNone(enclave.edi_service, 'cannot find EDI service')\r\n self.assertIsNotNone(enclave.api_service, 'cannot find API (EDGE) service')\r\n self.assertIsNotNone(enclave.grafana_service, 'cannot find Grafana service')\r\n self.assertIsNotNone(enclave.graphite_service, 'cannot find Graphite service')","title":""},{"docid":"20f7a1911f29326ac61297940b963123","score":"0.52003706","text":"def no_creds_needed(self):\n # mock this request so we don't depend on the existence of a client\n @httmock.all_requests\n def auth_response(url, request):\n self.assertEqual(urllib.parse.urlunsplit(url),\n 'https://auth.taskcluster.net/v1/clients/abc')\n self.failIf('Authorization' in request.headers)\n headers = {'content-type': 'application/json'}\n content = {\"clientId\": \"abc\"}\n return httmock.response(200, content, headers, None, 5, request)\n\n with httmock.HTTMock(auth_response):\n client = self.testClass({\"credentials\": {}})\n result = client.client('abc')\n self.assertEqual(result, {\"clientId\": \"abc\"})","title":""},{"docid":"7bdafa5aadf7fe0a09dfdc7640afc378","score":"0.5194178","text":"def _import_ncclient(self):\n return importutils.import_module('ncclient.manager')","title":""},{"docid":"d217eef1e078b2bcffe182c53395f12c","score":"0.5189456","text":"def test_module() -> str:\n try:\n message = 'ok'\n except DemistoException as e:\n if 'Forbidden' in str(e) or 'Authorization' in str(e):\n message = 'Authorization Error: make sure API Key is correctly set.'\n else:\n raise\n return message","title":""},{"docid":"3aad990a8693dc969d00c85377291267","score":"0.51833785","text":"def test_context_aware_command_source_not_found(_, capsys):\n with pytest.raises(SystemExit):\n ContextAwareCommand(name='test-command').invoke(Mock())\n\n assert capsys.readouterr().out == 'Error: Data connection test not found. Has it been connected?\\n'","title":""},{"docid":"9d198d348c5f2fc1435d92c4617883bb","score":"0.5182276","text":"def test_bad_eth_client(log, default_cli_args):\n runner = CliRunner()\n with patch(\"pathfinding_service.cli.PathfindingService\"):\n result = runner.invoke(\n main,\n default_cli_args + [\"--eth-rpc\", \"http://localhost:12345\"],\n catch_exceptions=False,\n )\n assert result.exit_code == 1\n assert log.has(\n \"Can not connect to the Ethereum client. Please check that it is running \"\n \"and that your settings are correct.\"\n )","title":""},{"docid":"681015b754e396141d64a8be6eed9314","score":"0.51769406","text":"def test_get_box_device(self):\n pass","title":""},{"docid":"bca496676170572364af4c0cc4a5d1fc","score":"0.517241","text":"def test_module_command(client: Client, *_) -> Tuple[None, None, str]:\n results = client.test_module()\n if 'links' in results:\n return None, None, 'ok'\n raise DemistoException(f'Test module failed, {results}')","title":""},{"docid":"0e64c105c27f3fd5e73ae14c91b85e83","score":"0.51710063","text":"def test_get_service(self):\n pass","title":""},{"docid":"be405f69e64d562447fcfd72634478dd","score":"0.5159146","text":"def test_get_docker_client(self, mck_isfile):\n client = watcher._get_docker_client()\n self.assertEquals(client.base_url, 'https://docker.example.com:4242')\n self.assertEquals(client.verify, '/var/stolos/certs/ca.pem')\n self.assertEquals(client.cert, ('/var/stolos/certs/cert.pem',\n '/var/stolos/certs/key.pem'))","title":""},{"docid":"f440ff807af929fad27641fe413eec69","score":"0.51584613","text":"def test_import_outside_repo(self):\n eq_(mock_ch([]), 1)\n self._check_log(0, 'gbp:error: No Git repository at ')","title":""},{"docid":"1334e7000d3373bf22031f833c1e46d7","score":"0.5157747","text":"def testLoadInexistantModule(self):\n self.assertRaises(mib.SMIException, mib.load, \"idontexist.gfdgfdg\")","title":""},{"docid":"d47fddd5d2fb413c84b061b5ceaa90fb","score":"0.5153655","text":"def get_module_list(self, c):\r\n if self.device_detected == True:\r\n resp = yield subprocess.check_output(\"cacli modlist\")\r\n else:\r\n resp = \"Device not connected.\"\r\n print \"Device not connected. \"\r\n #Eventually make this actually throw an error instead of printing something\r\n returnValue(resp)","title":""},{"docid":"e48fc1dae9706ed8d87971f54fb15822","score":"0.5153549","text":"def test_nonexisting_file(self):\n with self.assertRaises(ConfigFileReadError):\n with Client.from_config(config_file='nonexisting', legacy_config_fallback=False) as client:\n pass","title":""},{"docid":"2870c0bf648d9adfa71721353ec3bf6e","score":"0.5145843","text":"def test_provider_project_development_service_get(self):\n pass","title":""},{"docid":"3543b911235f15e5bbc4a23008b66f22","score":"0.5143378","text":"def test_discover_no_cli_creds(self):\n entry = mock.MagicMock(user=None, password=None)\n vendor = mock.MagicMock()\n vendor_settings = mock.MagicMock()\n self.pdu_handler._get_cli_credentials = mock.MagicMock(return_value=None)\n\n # act\n result = self.pdu_handler.discover(entry=entry,\n vendor=vendor,\n vendor_settings=vendor_settings)\n\n # verify\n self.assertEqual(result, entry)\n self.assertEqual(entry.comment, \"Unable to discover device user/password\")\n self.assertIsNone(entry.user)\n self.assertIsNone(entry.password)","title":""},{"docid":"03fa7ed3f5d9bcd66bb44969b9828e40","score":"0.5142187","text":"def test_get_platform_access(self):\n pass","title":""},{"docid":"67f4081e5c40925ba88c6272bf1010d3","score":"0.5137773","text":"def test_load_partner_not_existent(self):\n response = CLIENT.get(self.partners_endpoint + \"999\")\n assert response.status_code == status.HTTP_404_NOT_FOUND","title":""},{"docid":"3f33ac893223141260270f38885a8320","score":"0.5137659","text":"def test_test_client_model(self):\n pass","title":""},{"docid":"80d3e1dd4874fb44a6177a24ee2a1ec6","score":"0.5135152","text":"def test_asterisk_modules_get(self):\n pass","title":""},{"docid":"3b0eaf2e8ec33f388f4c2d65b2671701","score":"0.5135067","text":"def check_for_setup_error(self) -> None:\n if rados is None:\n msg = _('rados and rbd python libraries not found')\n raise exception.VolumeBackendAPIException(data=msg)\n\n for attr in ['rbd_cluster_name', 'rbd_pool']:\n val = getattr(self.configuration, attr)\n if not val:\n raise exception.InvalidConfigurationValue(option=attr,\n value=val)\n # NOTE: Checking connection to ceph\n # RADOSClient __init__ method invokes _connect_to_rados\n # so no need to check for self.rados.Error here.\n with RADOSClient(self):\n pass\n\n # NOTE(arne_wiebalck): If deferred deletion is enabled, check if the\n # local Ceph client has support for the trash API.\n if self.configuration.enable_deferred_deletion:\n if not hasattr(self.RBDProxy(), 'trash_list'):\n msg = _(\"Deferred deletion is enabled, but the local Ceph \"\n \"client has no support for the trash API. Support \"\n \"for this feature started with v12.2.0 Luminous.\")\n LOG.error(msg)\n raise exception.VolumeBackendAPIException(data=msg)\n\n # If the keyring is defined (cinderlib usage), then the contents are\n # necessary.\n if self.keyring_file and not self.keyring_data:\n msg = _('No keyring data found')\n LOG.error(msg)\n raise exception.InvalidConfigurationValue(\n option='rbd_keyring_conf', value=self.keyring_file)\n\n self._start_periodic_tasks()","title":""},{"docid":"1da5f67d4e4fdd88bbab03754c6d0250","score":"0.5130586","text":"def test_backend_discover() -> None:\n with pytest.raises(NotImplementedError):\n SRV4PowerBoardConsoleBackend.discover()","title":""},{"docid":"38224dd15cc7fe1082ea3042f9e09f55","score":"0.512834","text":"def test_list_modules_err(self, requests_mock):\n\n url = endpoint[\"list_modules\"].format(host=self.host, port=self.port)\n requests_mock.get(\n url,\n text=\"error\",\n status_code=500,\n )\n response = self.tfd_cursor.list_modules(self.team, self.project, self.name, 1)\n expected = \"list_modules error: error\"\n self.assertEqual(\n response,\n expected,\n msg=\"Got '{r}', expected: '{e}'\".format(r=response, e=expected),\n )","title":""},{"docid":"c3c768301a2493e93ab95443120a55a6","score":"0.5117118","text":"def setUp(self):\n super().setUp()\n self.mock_library = mock.MagicMock()\n self.modulestore_mock = mock.MagicMock()\n self.client = api.EdxModulestoreImportClient(\n modulestore_instance=self.modulestore_mock,\n library=self.mock_library\n )","title":""},{"docid":"8a4d5ecfa60ed75343782373f42da0fd","score":"0.5115248","text":"def test_need_key(self):\n self.assertRaises(KeyError, Client, base_url=self.cfixt.base_url, login_uri=self.cfixt.login_uri,\n username=self.cfixt.username, cert_auth=True, user_crt_file=self.cfixt.user_crt_file)","title":""},{"docid":"c4e25b12474ae41374aa2465b9c61739","score":"0.5103864","text":"def test_kernel_modules():\n _os_query_results = {\n \"data\": [\n {\n \"address\": \"0xffffffffc14f2000\",\n \"name\": \"nls_utf8\",\n \"size\": \"16384\",\n \"status\": \"Live\",\n \"used_by\": \"-\",\n },\n {\n \"address\": \"0xffffffffc1599000\",\n \"name\": \"udf\",\n \"size\": \"90112\",\n \"status\": \"Live\",\n \"used_by\": \"-\",\n },\n {\n \"address\": \"0xffffffffc14b5000\",\n \"name\": \"crc_itu_t\",\n \"size\": \"16384\",\n \"status\": \"Live\",\n \"used_by\": \"udf\",\n },\n ],\n \"result\": True,\n }\n with patch.object(osquery, \"_osquery\", MagicMock(return_value=_os_query_results)):\n with patch.dict(osquery.__grains__, {\"os_family\": \"Debian\"}):\n assert osquery.kernel_modules() == _os_query_results","title":""},{"docid":"bade68ce8c091c1ae3f1086d8efb15b1","score":"0.5101338","text":"def test_unknown_user(self):\n user = 'unknown_test'\n response = self.client.get_api_data(user)\n self.assertEqual(response, 'Client doesnot exist')","title":""},{"docid":"1ff49f833a7500e487bd91bebe45314b","score":"0.5097878","text":"def test_applications_oauthclient_client_id_get(self):\n pass","title":""},{"docid":"140a1b4772c3f67f33ef21d054165dcd","score":"0.5094973","text":"def test_config_not_initialized(self):\n with self.assertRaises(Exception) as e:\n Config.get(\"database\")\n self.assertEqual(e.exception.message, \"[XOS-Config] Module has not been initialized\")","title":""},{"docid":"842096b33d77234d01e2a3fe4185fcbe","score":"0.509477","text":"def test_test_module_cloud(mocker):\n from ServiceDeskPlus import test_module as module\n mocker.patch('ServiceDeskPlus.Client.get_access_token')\n client = Client('server_url', 'use_ssl', 'use_proxy', 'client_id', 'client_secret', refresh_token='refresh_token')\n\n mocker.patch('ServiceDeskPlus.parse_date_range', return_value=('2020-06-23 04:18:00', 'never mind'))\n mocker.patch.object(client, 'http_request', return_value=RESPONSE_FETCH_INCIDENTS)\n result = module(client)\n assert result == 'ok'","title":""},{"docid":"8ecdc4a5f844d8ad7ee281b72bca4a8c","score":"0.509432","text":"def test_connect_volume_nocli(self):\n self.assertRaises(exception.BrickException,\n self.connector_nocli.connect_volume,\n self.connection_properties)","title":""},{"docid":"fab44836240993aa88e9b32427a86a61","score":"0.5091817","text":"def test_get_keystoneclient_v3_with_trust_id(self):\n osclients = OpenStackClients()\n trust_id = \"randomid0000000000000000000000001\"\n osclients.set_credential(self.OS_USERNAME, self.OS_PASSWORD, trust_id=trust_id)\n\n keystoneClient = osclients.get_keystoneclient()\n\n self.assertIsInstance(keystoneClient, keystoneclient.v3.client.Client)","title":""},{"docid":"95b6b38c9e8a37db16c6138af047a2b3","score":"0.5087712","text":"def test_get_device_unknown():\n device = get_device(SERIAL, CREDENTIAL, \"unknown\")\n assert device is None","title":""}],"string":"[\n {\n \"docid\": \"f7327525d1d6f0075e2207867225b3e4\",\n \"score\": \"0.74871695\",\n \"text\": \"def test_implement_client_with_unknown_module(self):\\n\\n try:\\n OpenStackClients(modules=\\\"fakeOpenstackModule\\\")\\n except Exception as ex:\\n self.assertRaises(ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffaede393529b7fb3ea943907fb573e7\",\n \"score\": \"0.72420913\",\n \"text\": \"def test_get_neutronclient_with_all_modules(self):\\n\\n osclients = OpenStackClients(modules=\\\"auto\\\")\\n neutronClient = osclients.get_neutronclient()\\n\\n self.assertIsInstance(neutronClient, neutronclient.v2_0.client.Client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e07eb5732319248643a8d6ed6b5eb7d\",\n \"score\": \"0.7129611\",\n \"text\": \"def test_implement_client_with_selected_module(self):\\n selected_modules = \\\"keystone,cinder,\\\"\\n\\n osclients = OpenStackClients(modules=selected_modules)\\n\\n self.assertIsNotNone(osclients)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"369cb6822fcb004c25d3d44ec46b936e\",\n \"score\": \"0.7060576\",\n \"text\": \"def test_get_cinderclient(self):\\n osclients = OpenStackClients(modules=\\\"cinder\\\")\\n cinderClient = osclients.get_cinderclient()\\n\\n # api_version = cinderClient.get_volume_api_version_from_endpoint() --> This should return \\\"2\\\" against a server\\n self.assertIsInstance(cinderClient, cinderclient.v2.client.Client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bc27505d8a997c5190231a6d8975edc\",\n \"score\": \"0.67548\",\n \"text\": \"def test_implement_client_with_all_modules(self):\\n\\n OS_AUTH_URL = 'http://host.com:4731/v3'\\n\\n osclients = OpenStackClients(modules=\\\"all\\\", auth_url=OS_AUTH_URL)\\n self.assertIsNotNone(osclients)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e508a748ba933cf590b11300d825d4d6\",\n \"score\": \"0.6729623\",\n \"text\": \"def test_get_cinderclient_v1(self):\\n osclients = OpenStackClients(modules=\\\"cinder\\\")\\n cinderClient = osclients.get_cinderclientv1()\\n # api_version = cinderClient.get_volume_api_version_from_endpoint() --> This should return \\\"1\\\" against a server\\n\\n self.assertIsInstance(cinderClient, cinderclient.v2.client.Client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43a7f98a790bc5d1b39806fd0f595759\",\n \"score\": \"0.6524887\",\n \"text\": \"def test_implement_client(self):\\n\\n osclients = OpenStackClients()\\n self.assertIsNotNone(osclients)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"639dffa1b489f9850992503c376890e1\",\n \"score\": \"0.6469845\",\n \"text\": \"def test_implement_client_with_env(self):\\n\\n osclients = OpenStackClients()\\n\\n self.assertIsNotNone(osclients)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"459d213b0e15523ec57a684d50257097\",\n \"score\": \"0.6261802\",\n \"text\": \"def test_get_session_without_username_nor_token(self):\\n\\n osclients = OpenStackClients()\\n\\n osclients.set_credential(\\\"\\\", self.OS_PASSWORD, tenant_id=self.OS_TENANT_ID)\\n\\n # Checking v3\\n try:\\n osclients.get_session()\\n except Exception as ex:\\n self.assertRaises(ex)\\n\\n # Checking v2\\n osclients.use_v3 = False\\n try:\\n osclients.get_session()\\n except Exception as ex:\\n self.assertRaises(ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51de2cb8b33f1e621299cc420fe84a8d\",\n \"score\": \"0.6192851\",\n \"text\": \"def test_get_session_without_auth_url(self):\\n\\n osclients = OpenStackClients()\\n osclients.auth_url = None\\n\\n # Checking v3\\n try:\\n osclients.get_session()\\n except Exception as ex:\\n self.assertRaises(ex)\\n\\n # Checking v2\\n osclients.use_v3 = False\\n try:\\n osclients.get_session()\\n except Exception as ex:\\n self.assertRaises(ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"819e57c0234a8b18acf2f15c26198617\",\n \"score\": \"0.616063\",\n \"text\": \"def test_get_glanceclient(self):\\n\\n osclients = OpenStackClients(modules=\\\"glance\\\")\\n osclients.set_keystone_version(use_v3=False)\\n glanceClient = osclients.get_glanceclient()\\n\\n self.assertIsInstance(glanceClient, glanceclient.v1.client.Client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"876724adcc22dfa524dfd6b1376421c2\",\n \"score\": \"0.61028904\",\n \"text\": \"def test_get_swiftclient(self):\\n\\n osclients = OpenStackClients(modules=\\\"swift\\\")\\n swiftClient = osclients.get_swiftclient()\\n\\n self.assertIsInstance(swiftClient, Connection)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc40b031386a799e991f88ae33176cf4\",\n \"score\": \"0.60571325\",\n \"text\": \"def test_get_swiftclient_with_keystone_v2(self):\\n\\n osclients = OpenStackClients(modules=\\\"swift\\\")\\n osclients.use_v3 = False\\n swiftClient = osclients.get_swiftclient()\\n\\n self.assertIsInstance(swiftClient, Connection)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e26330d302f82a6951a2ebfaa10adce\",\n \"score\": \"0.60244447\",\n \"text\": \"def test_get_keystoneclient_v2(self):\\n osclients = OpenStackClients()\\n osclients.use_v3 = False\\n keystoneClient = osclients.get_keystoneclient()\\n\\n self.assertIsInstance(keystoneClient, keystoneclient.v2_0.client.Client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c15055b7d92786540559b372a80f0294\",\n \"score\": \"0.59922767\",\n \"text\": \"def test_get_novaclient(self):\\n osclients = OpenStackClients(modules=\\\"nova\\\")\\n novaClient = osclients.get_novaclient()\\n\\n self.assertIsInstance(novaClient, novaclient.v2.client.Client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c585438060b018792d3da8dabeba1cd\",\n \"score\": \"0.5864168\",\n \"text\": \"def test_get_keystoneclient_v3(self):\\n osclients = OpenStackClients()\\n keystoneClient = osclients.get_keystoneclient()\\n\\n self.assertIsInstance(keystoneClient, keystoneclient.v3.client.Client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdca71a29f7d9b0da270d2ba0e920ae3\",\n \"score\": \"0.58547527\",\n \"text\": \"def validate_client():\\n\\tassert \\\"client\\\" in GLOBALS, \\\"No client\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aefe0f89c3d10b6e1752b52f81c00bf\",\n \"score\": \"0.5827553\",\n \"text\": \"def testAccesInexistantModule(self):\\n self.assertRaises(mib.SMIException, mib.getNodes, \\\"idontexist.kjgf\\\");\\n self.assertRaises(mib.SMIException, mib.getScalars, \\\"idontexist.kjgf\\\");\\n self.assertRaises(mib.SMIException, mib.getTables, \\\"idontexist.kjgf\\\");\\n self.assertRaises(mib.SMIException, mib.getColumns, \\\"idontexist.kjgf\\\");\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24c74bc875e5d479641b10aee54c7531\",\n \"score\": \"0.57956004\",\n \"text\": \"def test_get_missing_hardware() -> None:\\n subject = make_module_view(slot_by_module_id={\\\"module-id\\\": DeckSlotName.SLOT_1})\\n\\n with pytest.raises(errors.ModuleNotLoadedError):\\n subject.get(\\\"module-id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f34b4209b4d5e564f8df4eac4b493d33\",\n \"score\": \"0.57769907\",\n \"text\": \"def test_module(client: Client) -> str:\\n # INTEGRATION FEED DEVELOPER TIP\\n # Client class should raise the exceptions, but if the test fails\\n # the exception text is printed to the Cortex XSOAR UI.\\n # If you have some specific errors you want to capture (i.e. auth failure)\\n # you should catch the exception here and return a string with a more\\n # readable output (for example return 'Authentication Error, API Key\\n # invalid').\\n # Cortex XSOAR will print everything you return different than 'ok' as\\n # an error\\n\\n fetch_indicators(client, limit=1)\\n return 'ok'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a658751ea33e46ef05889423cd6311b8\",\n \"score\": \"0.57401943\",\n \"text\": \"def test_module(client: Client) -> str:\\n return client.authenticate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d41a8cb7b25d701f71ff18752aed284\",\n \"score\": \"0.5658385\",\n \"text\": \"def test_module(client: Client, demisto_last_run: Dict[str, Any], demisto_params: Dict[str, Any]) -> str:\\n\\n try:\\n if demisto_params.get('isFetch'):\\n fetch_incidents(client=client,\\n last_run=demisto_last_run,\\n demisto_params=demisto_params)\\n else:\\n client.get_list(\\\"/incidents/call_types\\\")\\n except DemistoException as e:\\n if 'Error 401' in str(e):\\n return 'Authorization Error: make sure username and password are correctly set'\\n if '[404] - Not Found' in str(e):\\n return 'Page Not Found: make sure the url is correctly set'\\n else:\\n raise e\\n return 'ok'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4de0e67c23b40015341b0720401cb543\",\n \"score\": \"0.5654354\",\n \"text\": \"def test_ec_client_loaded():\\n assert 'eccode' == ec_client._client.prefix\\n assert ec_client._client.entries\\n assert ec_client._client.name_to_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cb0cfbc84ae5c8f8c01374592168791\",\n \"score\": \"0.56363523\",\n \"text\": \"def no_such_client(self):\\n return self._hasflag(_lib.JackNoSuchClient)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf0af0ed02cffdeb78044d1b111e8cf5\",\n \"score\": \"0.5627076\",\n \"text\": \"def test_missing_modules(self, cmd):\\n var = {\\\"host\\\": redhat_host, \\\"config\\\": deepcopy(rhel_conf['config'][0])}\\n r = RedHatNetConfig().configure_network(var)\\n self.assertListEqual(r, list(), \\\"Excepted emtpy results\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c641ca097afbbe1dcdf475b6d58c38f\",\n \"score\": \"0.5624228\",\n \"text\": \"def test_module_command(client: Client, *_) -> Tuple[None, None, str]:\\n results = client.test_module()\\n if 'Templates' in results or 'CertificateInformation' in results:\\n return None, None, 'ok'\\n raise DemistoException(f'Test module failed, {results}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"442ce3d3503cd5814ef79c75ceb018b1\",\n \"score\": \"0.55861014\",\n \"text\": \"def test_instantiate_without_args(self):\\n with self.assertRaises(ValueError):\\n api.EdxModulestoreImportClient()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56fdc5c356690f78cc3e61c04c617122\",\n \"score\": \"0.55702\",\n \"text\": \"def test_import():\\n with patch(\\\"salt.config.check_driver_dependencies\\\", return_value=True) as p:\\n get_deps = gce.get_dependencies()\\n assert get_deps is True\\n if LooseVersion(mock_version) >= LooseVersion(\\\"2.0.0\\\"):\\n p.assert_called_once()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98ea9aaabb6347d6a775668eaf2ba5a8\",\n \"score\": \"0.5567115\",\n \"text\": \"def test_080(self):\\n # client =gmn.tests.gmn_test_client.GMNTestClient(GMN_URL)\\n # self.assertRaises(\\n # d1_common.types.exceptions.NotAuthorized, client.get, pid,\\n # vendorSpecific=self.session(d1_common.const.SUBJECT_PUBLIC)\\n # )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"517a3e6840c8012d6f0586ae65b544aa\",\n \"score\": \"0.5565295\",\n \"text\": \"def test_get_keystoneclient_v2_with_tenant_name(self):\\n\\n osclients = OpenStackClients()\\n osclients.use_v3 = False\\n osclients.set_credential(self.OS_USERNAME, self.OS_PASSWORD, tenant_name=self.OS_TENANT_NAME)\\n\\n keystoneClient = osclients.get_keystoneclient()\\n\\n self.assertIsInstance(keystoneClient, keystoneclient.v2_0.client.Client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e82387e84e20caa4eebe5595ce5b1f4\",\n \"score\": \"0.5562546\",\n \"text\": \"async def test_import_fail(\\n hass: HomeAssistant, component_factory: ComponentFactory\\n) -> None:\\n api = MagicMock(spec=GogoGate2Api)\\n api.info.side_effect = ApiError(22, \\\"Error\\\")\\n\\n component_factory.api_class_mock.return_value = api\\n\\n await component_factory.configure_component(\\n cover_config=[\\n {\\n CONF_PLATFORM: \\\"gogogate2\\\",\\n CONF_NAME: \\\"cover0\\\",\\n CONF_IP_ADDRESS: \\\"127.0.1.0\\\",\\n CONF_USERNAME: \\\"user0\\\",\\n CONF_PASSWORD: \\\"password0\\\",\\n }\\n ]\\n )\\n\\n entity_ids = hass.states.async_entity_ids(COVER_DOMAIN)\\n assert not entity_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b3bb24f804f2dd6f14822e0053c9971\",\n \"score\": \"0.555835\",\n \"text\": \"def test_no_credentials(self):\\n\\n self.assertRaises(Exception, client.get_client, 'foo', 'bar')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bae66de0abbcc1a007fde06ff950d05e\",\n \"score\": \"0.552381\",\n \"text\": \"def test_get_keystoneclient_v2_with_tenant_id(self):\\n\\n osclients = OpenStackClients()\\n osclients.use_v3 = False\\n osclients.set_credential(self.OS_USERNAME, self.OS_PASSWORD, tenant_id=self.OS_TENANT_ID)\\n\\n keystoneClient = osclients.get_keystoneclient()\\n\\n self.assertIsInstance(keystoneClient, keystoneclient.v2_0.client.Client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0c1431bbb10f95f30179d3859c229f1\",\n \"score\": \"0.55220324\",\n \"text\": \"def commandTestModule(sensorClient: SensorClient):\\n try:\\n commandGetSensors(sensorClient, {})\\n return 'ok'\\n except Exception as e:\\n demisto.error(f'Module test failed: {e}')\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17fff62681af4214a2c580fd38c431d\",\n \"score\": \"0.5500935\",\n \"text\": \"def test_090(self):\\n # client =gmn.tests.gmn_test_client.GMNTestClient(GMN_URL)\\n # self.assertRaises(\\n # d1_common.types.exceptions.NotAuthorized, client.get, pid,\\n # vendorSpecific=self.session('other_subject')\\n # )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6da39aa5143584726b098aec49b6fb67\",\n \"score\": \"0.5492073\",\n \"text\": \"def test_module(client: Client, *_) -> Tuple[str, Dict, Dict]:\\n client.test_module_request()\\n demisto.results('ok')\\n return '', {}, {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2541c29999c313bceb8369cd114c5d5e\",\n \"score\": \"0.5448104\",\n \"text\": \"def test_cobbler_alive(self):\\n if OPENSTACK_RELEASE_CENTOS not in OPENSTACK_RELEASE:\\n raise SkipTest()\\n self.env.revert_snapshot(\\\"empty\\\")\\n wait(\\n lambda: http(host=self.env.get_admin_node_ip(), url='/cobbler_api',\\n waited_code=501),\\n timeout=60\\n )\\n server = xmlrpclib.Server(\\n 'http://%s/cobbler_api' % self.env.get_admin_node_ip())\\n\\n config = self.env.get_fuel_settings()\\n username = config['cobbler']['user']\\n password = config['cobbler']['password']\\n\\n # raises an error if something isn't right\\n server.login(username, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0184830b96061ea7a5e7a7097889e6b3\",\n \"score\": \"0.54460514\",\n \"text\": \"def test_applications_id_extclients_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a9cb6402ad6edf89ef1ab9ca2167d16\",\n \"score\": \"0.54020774\",\n \"text\": \"def test_200_api_connection(self):\\n logging.info('Instantiating gnocchi client...')\\n overcloud_auth = openstack_utils.get_overcloud_auth()\\n keystone = openstack_utils.get_keystone_client(overcloud_auth)\\n gnocchi_ep = keystone.service_catalog.url_for(\\n service_type='metric',\\n interface='publicURL'\\n )\\n gnocchi = gnocchi_client.Client(\\n session=openstack_utils.get_overcloud_keystone_session(),\\n adapter_options={\\n 'endpoint_override': gnocchi_ep,\\n }\\n )\\n\\n logging.info('Checking api functionality...')\\n assert(gnocchi.status.get() != [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00dd6393e363ba2d0ee0c621df518efa\",\n \"score\": \"0.5385197\",\n \"text\": \"def test_import():\\n import gdc_client\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ebc9d85b7c0e36de519abc859b4543c\",\n \"score\": \"0.53806794\",\n \"text\": \"def test_get_wrong_network_driver(self, driver):\\n self.assertRaises(ModuleImportError, get_network_driver, driver)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"623706965bc2ba39701e20e426813cc3\",\n \"score\": \"0.5374103\",\n \"text\": \"def _cb_client(self):\\n\\n if not self._codebuild_is_connected:\\n try:\\n self._codebuild_client = self._sts_session.client('codebuild')\\n except Exception as e:\\n log(str(e))\\n raise\\n else:\\n self._codebuild_is_connected = True\\n return self._codebuild_is_connected\\n else:\\n return self._codebuild_is_connected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50e32de75eb94f2400e758e57e87b4f5\",\n \"score\": \"0.53634304\",\n \"text\": \"def setUp(self):\\n super(TestCongress, self).setUp()\\n # create neutron mock and tell cage to use that mock\\n # https://code.google.com/p/pymox/wiki/MoxDocumentation\\n mock_factory = mox.Mox()\\n neutron_mock = mock_factory.CreateMock(\\n neutronclient.v2_0.client.Client)\\n neutron_mock2 = mock_factory.CreateMock(\\n neutronclient.v2_0.client.Client)\\n override = {}\\n override['neutron'] = {'client': neutron_mock, 'poll_time': 0}\\n override['neutron2'] = {'client': neutron_mock2, 'poll_time': 0}\\n override['nova'] = {'poll_time': 0}\\n\\n cage = harness.create(helper.root_path(), helper.state_path(),\\n helper.datasource_config_path(), override)\\n engine = cage.service_object('engine')\\n api = {'policy': cage.service_object('api-policy'),\\n 'rule': cage.service_object('api-rule'),\\n 'table': cage.service_object('api-table'),\\n 'row': cage.service_object('api-row'),\\n 'datasource': cage.service_object('api-datasource'),\\n 'status': cage.service_object('api-status'),\\n 'schema': cage.service_object('api-schema')}\\n\\n # Turn off schema checking\\n engine.module_schema = None\\n\\n # initialize neutron_mocks\\n network1 = test_neutron.network_response\\n port_response = test_neutron.port_response\\n router_response = test_neutron.router_response\\n sg_group_response = test_neutron.security_group_response\\n neutron_mock.list_networks().InAnyOrder().AndReturn(network1)\\n neutron_mock.list_ports().InAnyOrder().AndReturn(port_response)\\n neutron_mock.list_routers().InAnyOrder().AndReturn(router_response)\\n neutron_mock.list_security_groups().InAnyOrder().AndReturn(\\n sg_group_response)\\n neutron_mock2.list_networks().InAnyOrder().AndReturn(network1)\\n neutron_mock2.list_ports().InAnyOrder().AndReturn(port_response)\\n neutron_mock2.list_routers().InAnyOrder().AndReturn(router_response)\\n neutron_mock2.list_security_groups().InAnyOrder().AndReturn(\\n sg_group_response)\\n mock_factory.ReplayAll()\\n\\n self.cage = cage\\n self.engine = engine\\n self.api = api\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad91b618ef39494f521e3e137e498cc3\",\n \"score\": \"0.5348685\",\n \"text\": \"def test_provider_matches():\\n p = gce.get_configured_provider()\\n assert p is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a5af31b022cdc40dda2073fab434a00\",\n \"score\": \"0.5340612\",\n \"text\": \"def test_find(self):\\n self.clist = bundy.datasrc.ConfigurableClientList(bundy.dns.RRClass.IN)\\n self.configure_helper()\\n self.find_helper()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"913eeabec1d858997f175b06301c6b8e\",\n \"score\": \"0.53296083\",\n \"text\": \"def IsSDKTrybot():\\n return '_nacl_sdk' in os.getenv('BUILDBOT_BUILDERNAME', '')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb6272cd6443ed3e25c31f7ff77b8659\",\n \"score\": \"0.5327222\",\n \"text\": \"def _get_client(self):\\n # Regular test client\\n return get_client()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d561f5f502c480daef84c8437062f4d\",\n \"score\": \"0.532091\",\n \"text\": \"def test_api_v1_cloud_compliance_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93ed0d99bb387d89bf5fa3cde1319428\",\n \"score\": \"0.5317385\",\n \"text\": \"def _get_default_glance_client():\\n keystone_session = openstack_utils.get_overcloud_keystone_session()\\n glance_client = openstack_utils.get_glance_session_client(keystone_session)\\n return glance_client\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23671c8bece4f8fd388ead27320b64d2\",\n \"score\": \"0.5300621\",\n \"text\": \"def test_030(self):\\n # client =gmn.tests.gmn_test_client.GMNTestClient(GMN_URL)\\n # obj = client.get(pid, vendorSpecific=self.session(test_owner_1))\\n # self.assertEqual(obj_str, obj.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46635fdc74e2b76522880b721f2e0ef6\",\n \"score\": \"0.53006107\",\n \"text\": \"def check_client_and_app_authentication(self):\\n try:\\n if self.crowdClient is None:\\n self.crowdClient = CrowdRestClient()\\n except:\\n crowd_logger.exception(\\\"Create Crowd client failed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41361a5cdbc075b2ebb921d5d5863d74\",\n \"score\": \"0.52977353\",\n \"text\": \"def test_oidc_config_unavailable(self, monkeypatch):\\n app = Flask(__name__)\\n app.config['FOCA'] = Config()\\n monkeypatch.setattr(\\n 'requests.get',\\n lambda **kwargs: _raise(ConnectionError)\\n )\\n with app.app_context():\\n with pytest.raises(Unauthorized):\\n validate_token(token=MOCK_TOKEN_HEADER_KID)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c1559cd1d8a5d023abe281b9c1f906\",\n \"score\": \"0.52824575\",\n \"text\": \"def test_module(client):\\n result = client.get_status_http_request()\\n if result[\\\"status\\\"] == \\\"Active\\\" or result[\\\"status\\\"] == \\\"Standby\\\":\\n return 'ok'\\n else:\\n return 'Something went wrong with the risk api checking'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38d0ae5c69a81ae8178cd8f220ec08b4\",\n \"score\": \"0.5272958\",\n \"text\": \"async def test_client_error():\\n async with ClientSession() as session:\\n dtv = DIRECTV(\\\"#\\\", session=session)\\n with pytest.raises(DIRECTVConnectionError):\\n assert await dtv._request(\\\"/info/getVersion\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d284b9d297c955d5c397aecbf475b743\",\n \"score\": \"0.5271243\",\n \"text\": \"def test_swift_connection(auth_version, auth_url, access_key, secret_key, tenant_name,\\n user_domain_name, project_domain_name, container):\\n if not (auth_version and auth_url and access_key and secret_key and tenant_name and container):\\n return ({\\n 'message': 'All the fields above are required.'\\n }, http_status.HTTP_400_BAD_REQUEST)\\n if auth_version == '3' and not user_domain_name:\\n return ({\\n 'message': 'The field `user_domain_name` is required when you choose identity V3.'\\n }, http_status.HTTP_400_BAD_REQUEST)\\n if auth_version == '3' and not project_domain_name:\\n return ({\\n 'message': 'The field `project_domain_name` is required when you choose identity V3.'\\n }, http_status.HTTP_400_BAD_REQUEST)\\n\\n user_info = swift_utils.get_user_info(auth_version, auth_url, access_key,\\n user_domain_name, secret_key, tenant_name,\\n project_domain_name)\\n\\n if not user_info:\\n return ({\\n 'message': 'Unable to access account.\\\\n'\\n 'Check to make sure that the above credentials are valid, '\\n 'and that they have permission to list containers.'\\n }, http_status.HTTP_400_BAD_REQUEST)\\n\\n try:\\n _, containers = swift_utils.connect_swift(\\n auth_version, auth_url, access_key, user_domain_name, secret_key, tenant_name,\\n timeout=swift_settings.TEST_TIMEOUT\\n ).get_account()\\n except swift_exceptions.ClientException:\\n return ({\\n 'message': 'Unable to list containers.\\\\n'\\n 'Listing containers is required permission.'\\n }, http_status.HTTP_400_BAD_REQUEST)\\n\\n if container not in map(lambda c: c['name'], containers):\\n return ({\\n 'message': 'Invalid container name.'\\n }, http_status.HTTP_400_BAD_REQUEST)\\n\\n provider = SwiftProvider(account=None, auth_version=auth_version,\\n auth_url=auth_url, tenant_name=tenant_name,\\n project_domain_name=project_domain_name,\\n username=access_key,\\n user_domain_name=user_domain_name,\\n password=secret_key)\\n swift_response = {\\n 'id': provider.account.id,\\n 'display_name': provider.account.display_name,\\n }\\n return ({\\n 'message': 'Credentials are valid',\\n 'data': swift_response\\n }, http_status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df725a00dc13ffaa72db2a2d7f200157\",\n \"score\": \"0.5264315\",\n \"text\": \"def test_get_tf_modules_early_return(self):\\n config = Mock(return_value={}, build_directory=self._build_directory)\\n assert_false(get_tf_modules(config, generate=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8ba1477295384e16172345f26e9079b\",\n \"score\": \"0.5263503\",\n \"text\": \"def test_api_v1_cloud_discovery_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f4a4b6d453d5ea694d8111964ce8e36\",\n \"score\": \"0.52454287\",\n \"text\": \"def test_get_keystoneclient_v2_with_trust_id(self):\\n\\n osclients = OpenStackClients()\\n osclients.use_v3 = False\\n trust_id = \\\"randomid0000000000000000000000001\\\"\\n osclients.set_credential(self.OS_USERNAME, self.OS_PASSWORD, trust_id=trust_id)\\n\\n keystoneClient = osclients.get_keystoneclient()\\n\\n self.assertIsInstance(keystoneClient, keystoneclient.v2_0.client.Client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62cd2539f627ae81a6c18c8350ede94b\",\n \"score\": \"0.52439654\",\n \"text\": \"def test_with_libraries_disabled(self):\\n response = self.client.get_json(LIBRARY_REST_URL)\\n self.assertEqual(response.status_code, 404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed266c928d00652322e263373241fd11\",\n \"score\": \"0.5239862\",\n \"text\": \"def test_need_username(self):\\n self.assertRaises(KeyError, Client, login_uri=self.cfixt.login_uri, password=self.cfixt.password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb61e9122a6de1c4597392f49be07069\",\n \"score\": \"0.52305186\",\n \"text\": \"def check_client_pv(client_name, py_version, skip_fails, build_path):\\n py_env = 'py'\\n if '.' in py_version:\\n py_env += py_version.replace('.', '')[0:2]\\n else:\\n py_env += py_version[0:2]\\n\\n config = tox.session.prepare(\\n [\\n \\\"-c{}/{}-tox.ini\\\".format(build_path, client_name),\\n \\\"-l\\\"\\n ]\\n )\\n found = py_env in config.envlist\\n\\n if not found:\\n msg = \\\"Not found venv {} for client {}\\\".format(py_env, client_name)\\n if not skip_fails:\\n raise SystemExit(msg)\\n else:\\n LOG.warning(msg)\\n\\n return found\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5b6f04c52dcf065f9f5950694620573\",\n \"score\": \"0.5227111\",\n \"text\": \"def check_for_setup_error(self):\\n with self._client.open_connection() as api:\\n api.find_sc()\\n self.is_direct_connect = api.is_direct_connect\\n if self.is_direct_connect and self.replication_enabled:\\n msg = _('Dell Cinder driver configuration error replication '\\n 'not supported with direct connect.')\\n raise exception.InvalidHost(reason=msg)\\n\\n # If we are a healthy replicated system make sure our backend\\n # is alive.\\n if self.replication_enabled and not self.failed_over:\\n # Check that our replication destinations are available.\\n for backend in self.backends:\\n replssn = backend['target_device_id']\\n try:\\n # Just do a find_sc on it. If it raises we catch\\n # that and raise with a correct exception.\\n api.find_sc(int(replssn))\\n except exception.VolumeBackendAPIException:\\n msg = _('Dell Cinder driver configuration error '\\n 'replication_device %s not found') % replssn\\n raise exception.InvalidHost(reason=msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37647d52f60434a5cb3ede4185f85162\",\n \"score\": \"0.5221668\",\n \"text\": \"def test_get_keystone_session_v2(self, mock_auth_v2, mock_ks_session):\\n\\n os_auth_version = \\\"2.0\\\"\\n os_insecure = True\\n self.CONF.config(os_auth_version=os_auth_version,\\n os_insecure=os_insecure,\\n group=\\\"openstack\\\")\\n client.get_keystone_session()\\n mock_auth_v2.assert_called_once_with()\\n mock_ks_session.assert_called_once_with(auth=mock_auth_v2(),\\n verify=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a34dbb16c2720e6cced500d330bccf56\",\n \"score\": \"0.5221368\",\n \"text\": \"def test_client_capabilities():\\n\\n transport = Mock(send=Mock(side_effect=Exception(\\\"this test mocks MSAL, so no request should be sent\\\")))\\n\\n credential = UsernamePasswordCredential(\\\"client-id\\\", \\\"username\\\", \\\"password\\\", transport=transport)\\n with patch(\\\"msal.PublicClientApplication\\\") as PublicClientApplication:\\n credential._get_app()\\n\\n assert PublicClientApplication.call_count == 1\\n _, kwargs = PublicClientApplication.call_args\\n assert kwargs[\\\"client_capabilities\\\"] == None\\n\\n credential._get_app(enable_cae=True)\\n\\n assert PublicClientApplication.call_count == 2\\n _, kwargs = PublicClientApplication.call_args\\n assert kwargs[\\\"client_capabilities\\\"] == [\\\"CP1\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11ba0d6fbcda016d5b8d09b91410cabb\",\n \"score\": \"0.5219593\",\n \"text\": \"def test_verify_ceph_deploy(self, host):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a984d51f8966f9a2ad089fe112697ad8\",\n \"score\": \"0.5216299\",\n \"text\": \"def test_openstack_ids(self):\\n self.createSwiftService(False)\\n url = (self.json_body['access']['serviceCatalog'][0]\\n ['endpoints'][0]['publicURL'])\\n self.assertIn(\\\"/fun_tenant\\\", url)\\n self.assertNotIn(\\\"/MossoCloudFS_\\\", url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7131468769b13f0dbb58aae0be561829\",\n \"score\": \"0.5210334\",\n \"text\": \"def test_need_login_uri(self):\\n self.assertRaises(KeyError, Client, username=self.cfixt.username, password=self.cfixt.password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dea7ceab317a4a63a4f24199ee97208\",\n \"score\": \"0.5205165\",\n \"text\": \"def test_check_enclave_object(self):\\r\\n enclave = self._get_test_enclave()\\r\\n self.assertIsInstance(enclave, legion.k8s.Enclave)\\r\\n\\r\\n self.assertIsNotNone(enclave.edi_service, 'cannot find EDI service')\\r\\n self.assertIsNotNone(enclave.api_service, 'cannot find API (EDGE) service')\\r\\n self.assertIsNotNone(enclave.grafana_service, 'cannot find Grafana service')\\r\\n self.assertIsNotNone(enclave.graphite_service, 'cannot find Graphite service')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f7a1911f29326ac61297940b963123\",\n \"score\": \"0.52003706\",\n \"text\": \"def no_creds_needed(self):\\n # mock this request so we don't depend on the existence of a client\\n @httmock.all_requests\\n def auth_response(url, request):\\n self.assertEqual(urllib.parse.urlunsplit(url),\\n 'https://auth.taskcluster.net/v1/clients/abc')\\n self.failIf('Authorization' in request.headers)\\n headers = {'content-type': 'application/json'}\\n content = {\\\"clientId\\\": \\\"abc\\\"}\\n return httmock.response(200, content, headers, None, 5, request)\\n\\n with httmock.HTTMock(auth_response):\\n client = self.testClass({\\\"credentials\\\": {}})\\n result = client.client('abc')\\n self.assertEqual(result, {\\\"clientId\\\": \\\"abc\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bdafa5aadf7fe0a09dfdc7640afc378\",\n \"score\": \"0.5194178\",\n \"text\": \"def _import_ncclient(self):\\n return importutils.import_module('ncclient.manager')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d217eef1e078b2bcffe182c53395f12c\",\n \"score\": \"0.5189456\",\n \"text\": \"def test_module() -> str:\\n try:\\n message = 'ok'\\n except DemistoException as e:\\n if 'Forbidden' in str(e) or 'Authorization' in str(e):\\n message = 'Authorization Error: make sure API Key is correctly set.'\\n else:\\n raise\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aad990a8693dc969d00c85377291267\",\n \"score\": \"0.51833785\",\n \"text\": \"def test_context_aware_command_source_not_found(_, capsys):\\n with pytest.raises(SystemExit):\\n ContextAwareCommand(name='test-command').invoke(Mock())\\n\\n assert capsys.readouterr().out == 'Error: Data connection test not found. Has it been connected?\\\\n'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d198d348c5f2fc1435d92c4617883bb\",\n \"score\": \"0.5182276\",\n \"text\": \"def test_bad_eth_client(log, default_cli_args):\\n runner = CliRunner()\\n with patch(\\\"pathfinding_service.cli.PathfindingService\\\"):\\n result = runner.invoke(\\n main,\\n default_cli_args + [\\\"--eth-rpc\\\", \\\"http://localhost:12345\\\"],\\n catch_exceptions=False,\\n )\\n assert result.exit_code == 1\\n assert log.has(\\n \\\"Can not connect to the Ethereum client. Please check that it is running \\\"\\n \\\"and that your settings are correct.\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"681015b754e396141d64a8be6eed9314\",\n \"score\": \"0.51769406\",\n \"text\": \"def test_get_box_device(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bca496676170572364af4c0cc4a5d1fc\",\n \"score\": \"0.517241\",\n \"text\": \"def test_module_command(client: Client, *_) -> Tuple[None, None, str]:\\n results = client.test_module()\\n if 'links' in results:\\n return None, None, 'ok'\\n raise DemistoException(f'Test module failed, {results}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e64c105c27f3fd5e73ae14c91b85e83\",\n \"score\": \"0.51710063\",\n \"text\": \"def test_get_service(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be405f69e64d562447fcfd72634478dd\",\n \"score\": \"0.5159146\",\n \"text\": \"def test_get_docker_client(self, mck_isfile):\\n client = watcher._get_docker_client()\\n self.assertEquals(client.base_url, 'https://docker.example.com:4242')\\n self.assertEquals(client.verify, '/var/stolos/certs/ca.pem')\\n self.assertEquals(client.cert, ('/var/stolos/certs/cert.pem',\\n '/var/stolos/certs/key.pem'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f440ff807af929fad27641fe413eec69\",\n \"score\": \"0.51584613\",\n \"text\": \"def test_import_outside_repo(self):\\n eq_(mock_ch([]), 1)\\n self._check_log(0, 'gbp:error: No Git repository at ')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1334e7000d3373bf22031f833c1e46d7\",\n \"score\": \"0.5157747\",\n \"text\": \"def testLoadInexistantModule(self):\\n self.assertRaises(mib.SMIException, mib.load, \\\"idontexist.gfdgfdg\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d47fddd5d2fb413c84b061b5ceaa90fb\",\n \"score\": \"0.5153655\",\n \"text\": \"def get_module_list(self, c):\\r\\n if self.device_detected == True:\\r\\n resp = yield subprocess.check_output(\\\"cacli modlist\\\")\\r\\n else:\\r\\n resp = \\\"Device not connected.\\\"\\r\\n print \\\"Device not connected. \\\"\\r\\n #Eventually make this actually throw an error instead of printing something\\r\\n returnValue(resp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e48fc1dae9706ed8d87971f54fb15822\",\n \"score\": \"0.5153549\",\n \"text\": \"def test_nonexisting_file(self):\\n with self.assertRaises(ConfigFileReadError):\\n with Client.from_config(config_file='nonexisting', legacy_config_fallback=False) as client:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2870c0bf648d9adfa71721353ec3bf6e\",\n \"score\": \"0.5145843\",\n \"text\": \"def test_provider_project_development_service_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3543b911235f15e5bbc4a23008b66f22\",\n \"score\": \"0.5143378\",\n \"text\": \"def test_discover_no_cli_creds(self):\\n entry = mock.MagicMock(user=None, password=None)\\n vendor = mock.MagicMock()\\n vendor_settings = mock.MagicMock()\\n self.pdu_handler._get_cli_credentials = mock.MagicMock(return_value=None)\\n\\n # act\\n result = self.pdu_handler.discover(entry=entry,\\n vendor=vendor,\\n vendor_settings=vendor_settings)\\n\\n # verify\\n self.assertEqual(result, entry)\\n self.assertEqual(entry.comment, \\\"Unable to discover device user/password\\\")\\n self.assertIsNone(entry.user)\\n self.assertIsNone(entry.password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03fa7ed3f5d9bcd66bb44969b9828e40\",\n \"score\": \"0.5142187\",\n \"text\": \"def test_get_platform_access(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67f4081e5c40925ba88c6272bf1010d3\",\n \"score\": \"0.5137773\",\n \"text\": \"def test_load_partner_not_existent(self):\\n response = CLIENT.get(self.partners_endpoint + \\\"999\\\")\\n assert response.status_code == status.HTTP_404_NOT_FOUND\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f33ac893223141260270f38885a8320\",\n \"score\": \"0.5137659\",\n \"text\": \"def test_test_client_model(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80d3e1dd4874fb44a6177a24ee2a1ec6\",\n \"score\": \"0.5135152\",\n \"text\": \"def test_asterisk_modules_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b0eaf2e8ec33f388f4c2d65b2671701\",\n \"score\": \"0.5135067\",\n \"text\": \"def check_for_setup_error(self) -> None:\\n if rados is None:\\n msg = _('rados and rbd python libraries not found')\\n raise exception.VolumeBackendAPIException(data=msg)\\n\\n for attr in ['rbd_cluster_name', 'rbd_pool']:\\n val = getattr(self.configuration, attr)\\n if not val:\\n raise exception.InvalidConfigurationValue(option=attr,\\n value=val)\\n # NOTE: Checking connection to ceph\\n # RADOSClient __init__ method invokes _connect_to_rados\\n # so no need to check for self.rados.Error here.\\n with RADOSClient(self):\\n pass\\n\\n # NOTE(arne_wiebalck): If deferred deletion is enabled, check if the\\n # local Ceph client has support for the trash API.\\n if self.configuration.enable_deferred_deletion:\\n if not hasattr(self.RBDProxy(), 'trash_list'):\\n msg = _(\\\"Deferred deletion is enabled, but the local Ceph \\\"\\n \\\"client has no support for the trash API. Support \\\"\\n \\\"for this feature started with v12.2.0 Luminous.\\\")\\n LOG.error(msg)\\n raise exception.VolumeBackendAPIException(data=msg)\\n\\n # If the keyring is defined (cinderlib usage), then the contents are\\n # necessary.\\n if self.keyring_file and not self.keyring_data:\\n msg = _('No keyring data found')\\n LOG.error(msg)\\n raise exception.InvalidConfigurationValue(\\n option='rbd_keyring_conf', value=self.keyring_file)\\n\\n self._start_periodic_tasks()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1da5f67d4e4fdd88bbab03754c6d0250\",\n \"score\": \"0.5130586\",\n \"text\": \"def test_backend_discover() -> None:\\n with pytest.raises(NotImplementedError):\\n SRV4PowerBoardConsoleBackend.discover()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38224dd15cc7fe1082ea3042f9e09f55\",\n \"score\": \"0.512834\",\n \"text\": \"def test_list_modules_err(self, requests_mock):\\n\\n url = endpoint[\\\"list_modules\\\"].format(host=self.host, port=self.port)\\n requests_mock.get(\\n url,\\n text=\\\"error\\\",\\n status_code=500,\\n )\\n response = self.tfd_cursor.list_modules(self.team, self.project, self.name, 1)\\n expected = \\\"list_modules error: error\\\"\\n self.assertEqual(\\n response,\\n expected,\\n msg=\\\"Got '{r}', expected: '{e}'\\\".format(r=response, e=expected),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3c768301a2493e93ab95443120a55a6\",\n \"score\": \"0.5117118\",\n \"text\": \"def setUp(self):\\n super().setUp()\\n self.mock_library = mock.MagicMock()\\n self.modulestore_mock = mock.MagicMock()\\n self.client = api.EdxModulestoreImportClient(\\n modulestore_instance=self.modulestore_mock,\\n library=self.mock_library\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a4d5ecfa60ed75343782373f42da0fd\",\n \"score\": \"0.5115248\",\n \"text\": \"def test_need_key(self):\\n self.assertRaises(KeyError, Client, base_url=self.cfixt.base_url, login_uri=self.cfixt.login_uri,\\n username=self.cfixt.username, cert_auth=True, user_crt_file=self.cfixt.user_crt_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e25b12474ae41374aa2465b9c61739\",\n \"score\": \"0.5103864\",\n \"text\": \"def test_kernel_modules():\\n _os_query_results = {\\n \\\"data\\\": [\\n {\\n \\\"address\\\": \\\"0xffffffffc14f2000\\\",\\n \\\"name\\\": \\\"nls_utf8\\\",\\n \\\"size\\\": \\\"16384\\\",\\n \\\"status\\\": \\\"Live\\\",\\n \\\"used_by\\\": \\\"-\\\",\\n },\\n {\\n \\\"address\\\": \\\"0xffffffffc1599000\\\",\\n \\\"name\\\": \\\"udf\\\",\\n \\\"size\\\": \\\"90112\\\",\\n \\\"status\\\": \\\"Live\\\",\\n \\\"used_by\\\": \\\"-\\\",\\n },\\n {\\n \\\"address\\\": \\\"0xffffffffc14b5000\\\",\\n \\\"name\\\": \\\"crc_itu_t\\\",\\n \\\"size\\\": \\\"16384\\\",\\n \\\"status\\\": \\\"Live\\\",\\n \\\"used_by\\\": \\\"udf\\\",\\n },\\n ],\\n \\\"result\\\": True,\\n }\\n with patch.object(osquery, \\\"_osquery\\\", MagicMock(return_value=_os_query_results)):\\n with patch.dict(osquery.__grains__, {\\\"os_family\\\": \\\"Debian\\\"}):\\n assert osquery.kernel_modules() == _os_query_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bade68ce8c091c1ae3f1086d8efb15b1\",\n \"score\": \"0.5101338\",\n \"text\": \"def test_unknown_user(self):\\n user = 'unknown_test'\\n response = self.client.get_api_data(user)\\n self.assertEqual(response, 'Client doesnot exist')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ff49f833a7500e487bd91bebe45314b\",\n \"score\": \"0.5097878\",\n \"text\": \"def test_applications_oauthclient_client_id_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"140a1b4772c3f67f33ef21d054165dcd\",\n \"score\": \"0.5094973\",\n \"text\": \"def test_config_not_initialized(self):\\n with self.assertRaises(Exception) as e:\\n Config.get(\\\"database\\\")\\n self.assertEqual(e.exception.message, \\\"[XOS-Config] Module has not been initialized\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"842096b33d77234d01e2a3fe4185fcbe\",\n \"score\": \"0.509477\",\n \"text\": \"def test_test_module_cloud(mocker):\\n from ServiceDeskPlus import test_module as module\\n mocker.patch('ServiceDeskPlus.Client.get_access_token')\\n client = Client('server_url', 'use_ssl', 'use_proxy', 'client_id', 'client_secret', refresh_token='refresh_token')\\n\\n mocker.patch('ServiceDeskPlus.parse_date_range', return_value=('2020-06-23 04:18:00', 'never mind'))\\n mocker.patch.object(client, 'http_request', return_value=RESPONSE_FETCH_INCIDENTS)\\n result = module(client)\\n assert result == 'ok'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ecdc4a5f844d8ad7ee281b72bca4a8c\",\n \"score\": \"0.509432\",\n \"text\": \"def test_connect_volume_nocli(self):\\n self.assertRaises(exception.BrickException,\\n self.connector_nocli.connect_volume,\\n self.connection_properties)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fab44836240993aa88e9b32427a86a61\",\n \"score\": \"0.5091817\",\n \"text\": \"def test_get_keystoneclient_v3_with_trust_id(self):\\n osclients = OpenStackClients()\\n trust_id = \\\"randomid0000000000000000000000001\\\"\\n osclients.set_credential(self.OS_USERNAME, self.OS_PASSWORD, trust_id=trust_id)\\n\\n keystoneClient = osclients.get_keystoneclient()\\n\\n self.assertIsInstance(keystoneClient, keystoneclient.v3.client.Client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95b6b38c9e8a37db16c6138af047a2b3\",\n \"score\": \"0.5087712\",\n \"text\": \"def test_get_device_unknown():\\n device = get_device(SERIAL, CREDENTIAL, \\\"unknown\\\")\\n assert device is None\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993887,"cells":{"query_id":{"kind":"string","value":"b2ac1bae8c15b61f857fd75e574ad723"},"query":{"kind":"string","value":"Monta a resposta com os bytes do arquivo"},"positive_passages":{"kind":"list like","value":[{"docid":"9dcf6692d41eb6d24114a8c8facca69e","score":"0.0","text":"def read_file(file):\n\n\t\text = file.split('.')[1]\n\t\tcontent_type = 'text/html'\n\t\tif ext == 'jpg' or ext == 'jpeg':\n\t\t\tcontent_type = 'image/jpeg'\n\t\telif ext == 'ico':\n\t\t\tcontent_type = 'image/x-icon'\n\t\treturn 'HTTP/1.1 200 OK\\r\\nContent-Type: {}\\r\\n\\r\\n'.format(content_type).encode('utf-8') + open(file, 'rb').read()","title":""}],"string":"[\n {\n \"docid\": \"9dcf6692d41eb6d24114a8c8facca69e\",\n \"score\": \"0.0\",\n \"text\": \"def read_file(file):\\n\\n\\t\\text = file.split('.')[1]\\n\\t\\tcontent_type = 'text/html'\\n\\t\\tif ext == 'jpg' or ext == 'jpeg':\\n\\t\\t\\tcontent_type = 'image/jpeg'\\n\\t\\telif ext == 'ico':\\n\\t\\t\\tcontent_type = 'image/x-icon'\\n\\t\\treturn 'HTTP/1.1 200 OK\\\\r\\\\nContent-Type: {}\\\\r\\\\n\\\\r\\\\n'.format(content_type).encode('utf-8') + open(file, 'rb').read()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4785d1e6c36afd80214ff7cc0687736b","score":"0.6302854","text":"def make_res_file(data):\r\n res_bytes = bytearray()\r\n magic_num = int.to_bytes(0x497E, 2, \"big\")\r\n mode_type = int.to_bytes(2, 1, \"big\")\r\n res_bytes.extend(magic_num)\r\n res_bytes.extend(mode_type)\r\n # Data will equal -1 if there is no file with the requested name,\r\n # or it can't be opened.\r\n if data == -1:\r\n status_code = int.to_bytes(0, 1, \"big\")\r\n data_length = int.to_bytes(0, 4, \"big\")\r\n\r\n else:\r\n status_code = int.to_bytes(1, 1, \"big\")\r\n data_length = int.to_bytes(len(data), 4, \"big\")\r\n\r\n res_bytes.extend(status_code)\r\n res_bytes.extend(data_length)\r\n if data != -1:\r\n res_bytes.extend(data)\r\n return res_bytes","title":""},{"docid":"afd509133851c2c92de91eccef05e9ab","score":"0.62716156","text":"def download_binary(self):\n\n return self._content_resp().content","title":""},{"docid":"3391e1caa4e5e08b3fc8e8e0fe7ced2f","score":"0.6187527","text":"def get_file_binary(self, path):\n output = bytearray()\n self.src_server.retrbinary('RETR {}'.format(path), output.extend)\n return output","title":""},{"docid":"64720b6242c06fed989209728af11795","score":"0.6115677","text":"def getBytes(self) -> List[int]:\n ...","title":""},{"docid":"64720b6242c06fed989209728af11795","score":"0.6115677","text":"def getBytes(self) -> List[int]:\n ...","title":""},{"docid":"1c7e63a8cff74ce05846c967bb9850f8","score":"0.6091245","text":"def get_file_data(self):\n data = ByteArray()\n for s in self.strings:\n data.extend(ByteArray(s))\n return data","title":""},{"docid":"86a149da10a7bbdad3fde09abde603c2","score":"0.60671455","text":"def read_file_bytes(self):\n with open(self.file_name, \"rb\") as data_file:\n data = data_file.read()\n return data","title":""},{"docid":"1df31748bfee626404b918124295f260","score":"0.6033039","text":"def read_bytes(self):\n with self.open(\"rb\", None) as f:\n return f.read()","title":""},{"docid":"74b361ed132ed53f14ea5ec9294dc5bc","score":"0.5962343","text":"def bytes(self):\r\n f = self.open('rb')\r\n try:\r\n return f.read()\r\n finally:\r\n f.close()","title":""},{"docid":"cadcf49510339c5c86f445207688ac4a","score":"0.5907723","text":"def stream_it(file_path):\n return Response(\n get_binary_content(file_path), \n headers=get_headers(file_path),\n mimetype=get_mime_type(file_path), \n content_type='application/octet-stream')","title":""},{"docid":"7483ba3c4d54df539d331c93bd11733c","score":"0.5906088","text":"def stream_bytes(data, chunk_size=default_chunk_size):\n\tstream = BytesFileStream(data, chunk_size=chunk_size)\n\treturn stream.body(), stream.headers()","title":""},{"docid":"3f3baf5a450632bffb126934ee35ac13","score":"0.5871599","text":"def readbytes(self, n):\n chunks = []\n while n > 0:\n try:\n chunk = self.rfile.read(n)\n except:\n chunk = None\n if not chunk:\n raise ClientError(\"Failed to read command from client\")\n chunks.append(chunk)\n n -= len(chunk)\n return \"\".join(chunks)","title":""},{"docid":"fc2f75d351c486c7c9f0364d76ee8111","score":"0.58495915","text":"def get_response():\n result = ''\n line = ''\n while True:\n result += line\n line = FROMFILE.readline()\n if line == '\\n' and len(result) > 0:\n break\n return result","title":""},{"docid":"f6b39d89eb48ae593beb3b492ffb0ddd","score":"0.5839738","text":"def filebytes(self):\n f = _File()\n # TODO: Fix this once new HDU writing API is settled on\n return self._writeheader(f)[1] + self._writedata(f)[1]","title":""},{"docid":"c01f662ad08e9425f1540c48759e94c4","score":"0.5831824","text":"def save_file(self, resp, full_path):\n total = int(resp.headers.get('content-length', 0))\n with open(str(full_path.absolute()), 'wb') as file:\n for data in resp.iter_content(chunk_size=1024):\n size = file.write(data)","title":""},{"docid":"384c8d174bf9b0d60d908a83f3abd332","score":"0.5829522","text":"def get_file(self, path: str) -> BytesIO:","title":""},{"docid":"068a4f824945b50e9dccfdd07ee35dcf","score":"0.57933664","text":"def read(self, _bytes):","title":""},{"docid":"bde008ddffa49d6ea0fad962cd94b554","score":"0.5789279","text":"def buffered_data(self) -> bytes:","title":""},{"docid":"fd9c1a7c9b53f223db5114e58ea9c63b","score":"0.57748395","text":"def get_file_data(self):\n if self.get_type() == SHT_NOBITS:\n return ByteArray()\n else:\n return self._data","title":""},{"docid":"55ae738f10c9377e7a25a57a212666aa","score":"0.5767827","text":"def get_response():\n result = ''\n line = ''\n while line != '\\n':\n result += line\n line = FROMFILE.readline()\n #print(\" I read line:[\"+line+\"]\")\n return result","title":""},{"docid":"e8a82b7e5f7ddeafb523d35bbb131db3","score":"0.57251275","text":"def test_get_raw_file_content(self):\n pass","title":""},{"docid":"53544897b94ed07d5fca379630106ca5","score":"0.56894314","text":"def get_bytes(self, entities_to_update) -> bytes:\n chunk_data = BytesIO()\n\n # Get the chunk data as NBT data and write it to 'chunk_data'\n nbt_data = self.get_nbt(entities_to_update)\n nbt_data.write_file(buffer=chunk_data)\n\n # Compress the data\n chunk_data.seek(0)\n chunk_data = zlib.compress(chunk_data.read())\n\n # Create the byte block\n bytes_data = (len(chunk_data) + 1).to_bytes(4, \"big\") + b\"\\x02\" + chunk_data\n\n # Padding to be a multiple of 4KiB long\n bytes_data += bytes(4096 - (len(bytes_data) % 4096))\n assert len(bytes_data) % 4096 == 0\n\n return bytes_data","title":""},{"docid":"7dd15a2bf4480e06c0852d51f00c8f1e","score":"0.5687858","text":"def download_file(self, uri: ParseResult) -> IO[bytes]:\n pass","title":""},{"docid":"0cf17c85c289bed6ae77ac5750eda093","score":"0.5686154","text":"def get_file(self) -> bytes:\n assert isinstance(self._segments[\"preceding\"], bytes)\n img_bytes = self._segments[\"preceding\"]\n\n if self._has_exif:\n assert isinstance(self._segments[\"APP1\"], App1MetaData)\n img_bytes += self._segments[\"APP1\"].get_segment_bytes()\n\n assert isinstance(self._segments[\"succeeding\"], bytes)\n img_bytes += self._segments[\"succeeding\"]\n\n return img_bytes","title":""},{"docid":"a287a22e9fe0afb623c2f5997b74b52d","score":"0.5622689","text":"def process_body_data(content_bytes: bytes) -> list[str]:\n entries: list[str] = []\n\n header_bytes = content_bytes[:96]\n content_bytes = content_bytes[96:]\n header_values = [\n to_int(header_bytes[idx - INT_SIZE : idx])\n for idx in range(len(header_bytes), 1, -INT_SIZE)\n if header_bytes[idx - INT_SIZE : idx] != HEADER_FIELD_SEP\n ]\n num_chunks = header_values[0]\n header_size = header_values[2]\n header_content_size = header_values[4]\n\n # Check that the remainder of the header is all \\x00's\n zero_pad = Counter(header_bytes[:-header_size])[0]\n assert zero_pad == len(header_bytes) - header_size\n\n entries, content_size = extract_chunks(content_bytes, num_chunks)\n # Check that we got the expected amount of content\n # and that that end of the file is \\x00 padded.\n content_bytes = content_bytes[content_size:]\n content_size += header_size\n assert Counter(content_bytes)[0] == len(content_bytes)\n assert content_size == header_content_size\n\n return entries","title":""},{"docid":"f626a050cd116d0e6577edeb628ec75e","score":"0.5618997","text":"async def content_as_bytes(self, max_concurrency=1):\n stream = BytesIO()\n await self.download_to_stream(stream, max_concurrency=max_concurrency)\n return stream.getvalue()","title":""},{"docid":"2540abe184c2af4449699d63494432aa","score":"0.5618683","text":"def read_chunked_response_data(http_client_socket):\n\n data = b''\n\n while (chunkSize := http_get_word(http_client_socket)[0]) != b'0':\n if chunkSize == b'':\n continue\n print(\"Chunk of\", chunkSize, \"bytes\")\n for i in range(0, int(chunkSize.decode('ASCII'), 16)):\n data += http_client_socket.recv(1)\n\n return data","title":""},{"docid":"d60eda899b916ebbcd3b22108a50c968","score":"0.5588068","text":"def readbytes(path):\n with open(path, \"rb\") as f:\n return f.read()","title":""},{"docid":"072bcb1174542c37110b8d00ebe42de2","score":"0.5576383","text":"def get_chunk(self) -> bytes:\n if self._read_length < 0:\n raise StopIteration()\n rtn = self._rest_part + self._client.get(path=self._path, offset=self._offset, length=self._length)\n self._iter += 1\n self._offset = self._iter * self._length\n self._read_length = min(self._length, self._size - self._offset)\n return rtn","title":""},{"docid":"012954cd8d602a18f98abd93b03aea71","score":"0.5564775","text":"def getDataResponse(self):\n\n data = b\"\"\n response = self._openURL()\n\n if not response:\n return data\n\n if response.headers['Content-Encoding'] == 'gzip':\n data = gzip.GzipFile(fileobj=response).read()\n else:\n data = response.read()\n\n return data","title":""},{"docid":"de5d2df195f0a9fd654413f773829bf7","score":"0.55638117","text":"def get_file(name):\n key = get_key(name, 'count')\n expected_count = int(client.get(key))\n log.info(\"Get expected count: %s\", expected_count)\n\n data = b''\n index = 0\n while True:\n key = get_key(name, index)\n chunk = client.get(key)\n if not chunk:\n break\n\n log.info(\"Get %s = %s\", key, len(chunk))\n data += chunk\n index += 1\n\n log.info(\"Get count: %s\", index)\n assert index == expected_count\n\n return data","title":""},{"docid":"c230c4e1c32045891acb171ee2d0b379","score":"0.555587","text":"def _read_body(response) -> Tuple[int, Optional[str]]:\n content = StringIO()\n size = 0\n start_time = time.time()\n for chunk in response.iter_content(102400):\n if size < 5 * 1048576:\n content.write(chunk.decode(\"utf-8\", \"ignore\"))\n elif size > 30 * 1048576:\n response.close()\n return -1, None\n if time.time() - start_time > 10:\n response.close()\n return -1, None\n size += len(chunk)\n\n return size, content.getvalue()","title":""},{"docid":"a20366912f989e1e316cbeff5792f1d7","score":"0.55534464","text":"def get(self,caminho):\n try:\n arquivo=open(caminho,\"rb\")\n file=arquivo.read()\n #print(\"F\",file)\n arquivo.close()\n return True,file\n except Exception as e:\n return False,None","title":""},{"docid":"bdd0ecae922b706a2dee64a21833ab76","score":"0.55341244","text":"def _download_file(self, _content_type, filename, response):\n\n chunk_size = 1024 * 1000\n\n try:\n with open(filename, 'wb') as f:\n for chunk in response.iter_content(chunk_size=chunk_size):\n f.write(chunk)\n except TypeError:\n with open(filename, 'w') as f:\n for chunk in response.iter_content(chunk_size=chunk_size):\n f.write(chunk)\n except (IOError, Exception):\n return {'Status': 'FAIL', 'Filename': filename}\n\n return {'Status': 'OK', 'Filename': filename}","title":""},{"docid":"02fa6b921fe209f9f4ebc32c3a005b41","score":"0.5530555","text":"def get_media_data_binary(self, id):\n return self._get(JSONRequest('/media/{id}/file'.format(id=id)))","title":""},{"docid":"6dfa03c3d4dd3aede2f7009bf244da6b","score":"0.5508311","text":"def _getData(self):\n content = bytearray()\n content += self.MAGIC_NUMBER\n content += self._version\n content += self.HEADER['dimension'] + self._width + self._height\n if(self._comment is not None):\n content += self.HEADER['comment'] + self._comment\n\n content += self.HEADER['author'] + self._author\n content += self.HEADER['numberColor'] + self._numberColor\n content += self.HEADER['color'] + self._listColor\n if(self._extra is not None):\n content += self._extra\n content += self.HEADER['end']\n content += self._pixels\n return bytes(content)","title":""},{"docid":"11225205b426de672183714b02ac2224","score":"0.5498546","text":"def Data(self) -> ResultBuffer:","title":""},{"docid":"df2bc8bcd32ed8c8401aa4cdb1688533","score":"0.54977036","text":"def get_chunk(self) -> bytes:\n try:\n d = self.body.get_nowait()\n if d == REQUEST_FINISHED:\n return b\"\"\n except asyncio.QueueEmpty:\n return b\"\"\n\n return d","title":""},{"docid":"26f74177055831b14b40280cad800994","score":"0.5486102","text":"def read(self) -> bytes:\n pass","title":""},{"docid":"b82b64bd275916a1623555ae373adafc","score":"0.5485689","text":"def get_all_file_by_user_toEncode(id):\n\t#1 generation jeton\n\tjeton = generate_jeton()\n\tprint(\">jeton\"+str(jeton))\n\tbytes_jeton = (jeton).to_bytes(size_bytes_jeton, byteorder='big')\n\n\t#convertion de la data a en voyer en bytes\n\tbytes_size = id.bit_length()\n\n\teffective_data = (id).to_bytes(bytes_size, byteorder='big')\n\tif nbr_trame_needed( len(effective_data)) != 1:\n\t\traise ValueError(\"Error Test nbr_trame_needed by \"+ \\\n\t\t\t\t\"get_all_file_by_user_toBytes cas n'est pas supposé arriver\")\n\ttrame = struct_protocole(5,bytes_jeton+effective_data)\n\tprint(\">{}\".format(trame))\n\tconnexion_avec_serveur = connect_to_serveur()\n\t#msg_recu = connexion_avec_serveur.recv(1024)\n\t#print(msg_recu)\n\tconnexion_avec_serveur.send(trame)\n\tmsg_recu = connexion_avec_serveur.recv(1024)\n\tprint(msg_recu)\n\treturn trame","title":""},{"docid":"1d65c82ff907009ff3909c2bd6a3da76","score":"0.54820144","text":"def get_contents(self, chunk_size: int = bioblend.CHUNK_SIZE) -> bytes:\n return b\"\".join(self.get_stream(chunk_size=chunk_size))","title":""},{"docid":"db4c5c5633ac36780728292ec803a5c2","score":"0.54671687","text":"def InputStream(self) -> _n_5_t_0:","title":""},{"docid":"0d12f9fc13c6ae6940cfaf02e167efd8","score":"0.54563665","text":"def readall(self):\n res = bytearray()\n while True:\n data = self.read(DEFAULT_BUFFER_SIZE)\n if not data:\n break\n res += data\n if res:\n return bytes(res)\n else:\n # b'' or None\n return data","title":""},{"docid":"ae3ba3c550e2651221d20df967065258","score":"0.5450503","text":"def open_bytes(self):\n raise NotImplementedError()","title":""},{"docid":"a460a6b6e13c9eb12f7cb22569a472f4","score":"0.5425246","text":"def handle_response(self, headers: List[Tuple], content: bytes) -> str:\n for header in headers:\n if header[0] == \"Content-Encoding\":\n encoding = header[1]\n if encoding is not None:\n content = self.decompress_content(content, encoding)\n return content.decode(\"utf-8\", \"ignore\")","title":""},{"docid":"c29d0493cdb82d1ee156e93a2fa6f1ed","score":"0.54233134","text":"async def read(self) -> bytes:\r\n async with self.session.get(self.url) as resp:\r\n img = await resp.read()\r\n return img","title":""},{"docid":"d42fc5319e8bddd115a30f77129b6ffb","score":"0.5421874","text":"def getFile(self, filename):\n\n logger.debug(\"filename {}\".format(filename))\n\n from urllib.parse import quote, unquote\n\n # check if string is already urlencoded\n # if unquote is equal to string, then it is not urlencoded (unquote respects plus sign)\n if unquote(filename) != filename:\n filename = unquote(filename)\n\n from io import BytesIO\n\n buffer = BytesIO(b\"\")\n\n res1 = self.client.resource(filename)\n res1.write_to(buffer)\n buffer.seek(0)\n\n logger.debug(\"file content: {}\".format(buffer.getvalue()))\n\n return buffer","title":""},{"docid":"a0bdadd5af3ac3d6aa7da0b5db606794","score":"0.54150933","text":"def _get_image_bytes(image_path):\n\n with open(image_path, 'rb') as f:\n image_content = f.read()\n\n return image_content","title":""},{"docid":"a9ba7dbc9b985b7c644af8ecdd5a40a2","score":"0.5415058","text":"def _read_null_bytes(self):\n nullbuffer = 0\n while 1:\n if self.f.read(1) == '\\x00':\n nullbuffer += 1\n else:\n break\n return nullbuffer","title":""},{"docid":"ddbeb09841508e697b0ea1af2426bcc9","score":"0.54026437","text":"def filestream_view(request, path):\n f = open(path, 'rb')\n basename = os.path.basename(path)\n size = os.path.getsize(path)\n ct, encoding = guess_type(path)\n content_range = request.META.get('HTTP_RANGE')\n if ct is None: ct = 'application/octet-stream'\n\n if content_range:\n # \"Range\" header parsing\n # Valid examples:\n # > Range: bytes=a-b // return bytes from a to b\n # > Range: bytes=a- // return bytes from a to end of file\n # > Range: bytes=-b // return last b bytes\n # > Range: bytes=a1-b1,a2-b2,... // return bytes from a1 to b1, from a2 to b2 etc\n\n ranges = [] # list of non-empty ranges\n raw_ranges = re.match(r'^bytes=(?P<ranges>(\\d*-\\d*,)*(\\d*-\\d*))$', content_range).group('ranges')\n\n for r in raw_ranges.split(','):\n a, b = r.split('-')\n if (a != '') and (b != ''):\n a, b = int(a), int(b)\n elif b == '':\n a, b = int(a), size - 1\n elif a == '':\n a, b = size-int(b), size-1\n if b >= size: b = size - 1\n\n if b-a >= 0:\n a_real, b_real = a, b\n ranges.append((a, b))\n\n if len(ranges) == 0:\n return HttpResponse(status=416)\n elif len(ranges) == 1:\n ### Only one range.\n # HTTP 206 Partial Content\n # Content-Type = (content type of file)\n # Content-Length = (range size)\n # Content-Range = (range range)\n #\n # (range data)\n a, b = a_real, b_real\n response = StreamingHttpResponse(read_in_chunks(f, a, b), status=206)\n response['Content-Length'] = b - a + 1\n response['Content-Range'] = 'bytes {}-{}/{}'.format(a, b, size)\n else:\n ### Several ranges.\n # > HTTP 206 Partial Content\n # > Content-Type = multipart/byteranges; boundary=BOUNDARY\n # >\n # > --BOUNDARY\n # > Content-Type: (content type of file)\n # > Content-Range: (range_1 range)\n # >\n # > (range_1 data)\n # > --BOUNDARY\n # > Content-Type: (content type of file)\n # > Content-Range: (range_2 range)\n # >\n # > (range_2 data)\n # > (et cetera)\n # > --BOUNDARY--\n\n # There is one pretty unlikely, but possible situation,\n # when file (one of range to be specific) contains boundary.\n boundary = ''.join(choice(ascii_letters+'_') for x in range(50))\n response = StreamingHttpResponse(read_in_ranges(f, ranges, size, boundary=boundary, content_type=ct), status=206)\n ct = 'multipart/byteranges; boundary={}'\n else:\n response = StreamingHttpResponse(read_in_chunks(f, 0, size), status=200)\n response['Content-Length'] = size\n\n if encoding: response['Content-Encoding'] = encoding\n response['Content-Type'] = ct\n\n return response","title":""},{"docid":"e2f9c973c79b12571d4956910d123458","score":"0.5402352","text":"def read_response(response):\n\tif response.info().get('Content-Encoding') == 'gzip':\n\t\tbuf = StringIO(response.read())\n\t\treturn gzip.GzipFile(fileobj=buf).read()\n\n\telif response.info().get('Content-Encoding') == 'deflate':\n\t\tdecompress = zlib.decompressobj(-zlib.MAX_WBITS)\n\t\tinflated = decompress.decompress(response.read())\n\t\tinflated += decompress.flush()\n\t\treturn inflated\n\n\treturn response.read()","title":""},{"docid":"e112bb6db967578adc7c5bdddf61dcd8","score":"0.5398823","text":"def readResponse(self, fileObj):\n raise NotImplementedError","title":""},{"docid":"68690fbb1ab9577f684ff06dd72bde99","score":"0.53986675","text":"async def download(self, name) -> bytes:\n return b''","title":""},{"docid":"d40cee1aa08757b1d2fd1ff3a701567b","score":"0.53976977","text":"def get_bytes(self, key):\n reply = self.call(\n Methods.Get, api_pb2.GetRequest(header=api_pb2.RequestHeader(key=key)))\n return reply.HasField('value'), reply.value.timestamp, reply.value.bytes","title":""},{"docid":"a41e3b646e60fe3269702c0a87eaabff","score":"0.5378542","text":"def _get_content ( self ):\r\n return read_file( self.path )","title":""},{"docid":"2bb06ee29cb10db1bef2a51606a61a0f","score":"0.53782004","text":"def BytesToWrite(self) -> int:","title":""},{"docid":"bdcfe2a0f117b7142e860d5f7528f996","score":"0.53679824","text":"def GetFile(self):","title":""},{"docid":"0800fcc4edf15f0f6772329826240f1b","score":"0.5362829","text":"def _download_file(cls, response, filepath):\n\n with open(filepath, 'wb') as outfile:\n for chunk in response.iter_content(chunk_size=1024):\n outfile.write(chunk)","title":""},{"docid":"59cf4a54bcb6063d17bfaff641d5ac4f","score":"0.5359217","text":"def read_request(self):\n buffer = bytes()\n max_counter = 100\n current_counter = 0\n while True:\n _, data = win32file.ReadFile(self.http_usb_handle, READ_BUFFER_SIZE)\n if len(data) == 0:\n current_counter += 1\n if current_counter >= max_counter:\n break\n buffer += data\n return buffer","title":""},{"docid":"a44223c8675987c78b15bafb28e6fe58","score":"0.5357636","text":"def read_image_bytes_on_server(chunks):\n img_data = {}\n for chunk in chunks:\n stream = img_data.get(chunk.fileindex, BytesIO())\n stream.write(chunk.buffer)\n img_data[chunk.fileindex] = stream\n return img_data.values()","title":""},{"docid":"3a45f9ff4e937950e96b5f9c824562c7","score":"0.5351856","text":"def read(self, size):","title":""},{"docid":"85d5fbb1e08c232d544f95d115ae470b","score":"0.5351496","text":"def get_uploaded_bytes(self, file_id):\n raise NotImplementedError()","title":""},{"docid":"eeeb1c35d0a105755f0cde2ce7672b6b","score":"0.5343499","text":"def read_data(self, num_of_bytes):\n log.debug(\"num_of_bytes = {}\".format(num_of_bytes))\n buffer = bytes()\n max_counter = 20\n current_counter = 0\n num_of_data_read = 0\n while True:\n data_to_read = num_of_bytes-num_of_data_read\n log.debug(\"data_to_read = {}\".format(data_to_read))\n rc, data = win32file.ReadFile(self.http_usb_handle, data_to_read)\n log.debug(\"read_data rc = {} len(data) = {}\".format(rc, len(data)))\n if len(data) == 0:\n current_counter += 1\n if current_counter >= max_counter:\n break\n buffer += data\n num_of_data_read += len(data)\n if num_of_data_read == num_of_bytes:\n break\n return buffer","title":""},{"docid":"62635b95b4b7d38983de880ef0a84cbe","score":"0.53240645","text":"def chunk_read(response, chunk_size=8192):\n total_size = int(response.info()['Content-Length'].strip())\n n_iter = int(total_size / chunk_size)\n data = []\n\n for datum in tqdm(iter_content(response, chunk_size),\n total=n_iter, unit='B', unit_scale=True):\n data += datum\n\n return b\"\".join(data)","title":""},{"docid":"478a0f883a667b3e03ff3989e6c91618","score":"0.5314397","text":"def download_data(self):\n try:\n response = requests.get(self.url, stream=True)\n if response.status_code == 200:\n with open(self.src_file , 'wb+') as f:\n f.write(response.raw.read())\n except Exception as e:\n print(e)","title":""},{"docid":"bf96412fa0f89461393b5c8ae1e41602","score":"0.5312065","text":"def get_file_content(self) -> str:\n raise NotImplementedError('TBI')","title":""},{"docid":"03be7c87322f8344c2a9badcd0b037e0","score":"0.53117394","text":"def read_bytes(self, path: str) -> Try[OSError, bytes]:\n @catch_io_bound(OSError)\n def f() -> bytes:\n with open(path, 'rb') as f:\n return f.read()\n\n return f()","title":""},{"docid":"49750dd93b523ed5a49e7b7bd5a7576f","score":"0.53042614","text":"def bytes(self):","title":""},{"docid":"a85a89a3e68e03c5903b74bd5c36c5cf","score":"0.5289722","text":"def read_response(response):\n\tif response.info().get('Content-Encoding') == 'gzip':\n\t\tbuf = BytesIO(response.read())\n\t\treturn gzip.GzipFile(fileobj=buf).read().decode('utf8')\n\n\telif response.info().get('Content-Encoding') == 'deflate':\n\t\tdecompress = zlib.decompressobj(-zlib.MAX_WBITS)\n\t\tinflated = decompress.decompress(response.read())\n\t\tinflated += decompress.flush()\n\t\treturn inflated.decode('utf8')\n\n\treturn response.read().decode('utf8')","title":""},{"docid":"58289c883dc0cd35c145dde34b82a3b5","score":"0.52872074","text":"def get_bytes(self) -> bytes:\n\n questions = b''\n for question in self.Questions:\n questions += question.get_bytes()\n answers = b''\n for answer in self.Answers:\n answers += answer.get_bytes()\n authorities = b''\n for authority in self.Authorities:\n authorities += authority.get_bytes()\n additionals = b''\n for additional in self.Additionals:\n additionals += additional.get_bytes()\n \n return self.Header.get_bytes() + questions + answers + authorities + additionals","title":""},{"docid":"88a295d4844cee6d666b8f5d25621c48","score":"0.5283079","text":"def _save_body_to_file(self, download_path, http_resp, chunksize=2**16):\n with open(download_path, 'wb') as f:\n with contextlib.closing(http_resp):\n for c in http_resp.iter_content(chunksize):\n f.write(c)","title":""},{"docid":"0f78c5fabbdbba01bc69e434b86056fe","score":"0.52814597","text":"def get_content(self, object_id) -> Union[str, bytes]:\n data_object = self.nuvla.get(object_id)\n response = self.nuvla.operation(data_object, 'download')\n download_url = response.data['uri']\n content_type = data_object.data['content-type']\n\n headers = {'content-type': content_type}\n response = requests.get(download_url, headers=headers)\n return response.content","title":""},{"docid":"e49b0eeefa94c78ab109476f6e9e5619","score":"0.52652365","text":"def read_bytes(path: str) -> Effect[HasFiles, OSError, bytes]:\n return depend(HasFiles).and_then(lambda env: env.files.read_bytes(path))","title":""},{"docid":"69a7b7cc1fe4d5feac9c455adaa693fa","score":"0.5258088","text":"def get_data(file_name):\r\n data = bytearray()\r\n # Gets the current path of this file and the python,\r\n # scripts name and replaces itself with the given file name.\r\n script_name = os.path.basename(__file__)\r\n file_path = __file__[:-len(script_name)] + file_name\r\n\r\n try:\r\n with open(file_path, \"rb\") as file:\r\n # Converts each file to a set of bytes.\r\n data.extend(file.read())\r\n except FileNotFoundError: # Checks that the file exists.\r\n data = -1\r\n except PermissionError: # Checks that the file can be read.\r\n data = -1\r\n except OSError:\r\n data = -1\r\n return data","title":""},{"docid":"2d9ae2f7d74d1f61dc87bdc824497f9c","score":"0.52580684","text":"def byte() -> Path:\n with NamedTemporaryFile(suffix=\".tif\") as file:\n file = Path(file.name)\n link = \"https://raw.githubusercontent.com/rasterio/rasterio/master/tests/data/byte.tif\"\n urlretrieve(link, file)\n\n yield file\n\n return","title":""},{"docid":"c771c8842a7d6ca5ce88f1efc0e400a2","score":"0.5245632","text":"def read(self) -> bytes:\r\n resp = self.session.get(self.url)\r\n return resp.content","title":""},{"docid":"76bdff0df3ac8ac769f67804e8cc6529","score":"0.52425796","text":"def download(decoder):","title":""},{"docid":"399f1218073cd2a7f67ca5ffd17e1275","score":"0.52395624","text":"def download_file(self, uri: ParseResult) -> IO[bytes]:\n url = uri.path\n file_obj = open(url, \"rb\")\n return file_obj","title":""},{"docid":"7be75d818328e1b76a329487358bc126","score":"0.5237463","text":"def get_data(self, encoding = \"utf-8\"):\n val = b\"\"\n if isinstance(self.client_wfile, io.BytesIO):\n val = self.client_wfile.getvalue()\n if encoding is not None:\n val = val.decode(encoding)\n return val","title":""},{"docid":"f0798ea2dd23d90783f8783fced1cf1b","score":"0.5231819","text":"def _response(self):\n result = []\n logger.debug('res:')\n done = False\n while not done:\n raw_result = self._serial.readline()\n logger.debug(repr(raw_result))\n if raw_result == '':\n # TODO: Might be better to raise GrblTimeoutException here,\n # something like:\n # raise GrblTimeoutException(\n # 'Timeout encountered on read.')\n logger.warning('Timeout encountered on read.')\n done = True\n if raw_result == 'ok\\r\\n':\n done = True\n result.append(raw_result.strip())\n return result","title":""},{"docid":"3e59583530c97394fd0d1658d9b1e02a","score":"0.52219945","text":"def to_bytes(self):\n result = io.BytesIO()\n result.write(ServerIO.HEADER)\n\n ServerIO.write_int(result, len(self.request_times_ds))\n for request_time_ds in self.request_times_ds:\n ServerIO.write_int(result, request_time_ds)\n\n result.write(self.screensaver_id)\n ServerIO.write_int(result, self.screensaver_time_ds)\n\n ServerIO.write_int(result, len(self.image_data))\n result.write(self.image_data)\n return result.getvalue()","title":""},{"docid":"9a5a8401e1e5a6bebe45dd96084d5dbe","score":"0.5217589","text":"def read_from_handler(handler):\n max_chunk_size = 10 * 1024 * 1024\n size_remaining = int(handler.headers[\"content-length\"])\n chunks_list = []\n while size_remaining:\n chunk_size = min(size_remaining, max_chunk_size)\n chunks_list.append(handler.rfile.read(chunk_size).decode())\n size_remaining -= len(chunks_list[-1])\n\n return ''.join(chunks_list)","title":""},{"docid":"5f8fd19a5c5fc3f4c7e194a6a6ac7d3b","score":"0.5211043","text":"def recv_data(file_name,size):\n fiber_fp = open(file_name,\"rb\",0)\n\n try:\n #size = 0xff7\n #size = 0xff\n b = fiber_fp.read(size)\n if b:\n print(\"has read %d num from %s\" % (len(b),file_name))\n xxd_bin(b)\n else:\n print(\"none data read from %s\" % file_name)\n print()\n\n finally:\n fiber_fp.close()\n return","title":""},{"docid":"984fe7a6edd4d0caeaa287685333d919","score":"0.52055705","text":"def test_chunk_download(self):\n pass","title":""},{"docid":"798a56ee7d0d0d1d38a7f68c3b0e7c3d","score":"0.5205474","text":"def process(self) -> bytes:\n super().process(self.__raw_audio)\n self.finish()\n with open(self.__output_file, 'rb') as f:\n return f.read()","title":""},{"docid":"f508faacd7e61ef382f5485a8bf846f9","score":"0.52040225","text":"def response(self):\n return self._send(bytes([0xef,0xfe,0x02]+([0x0]*57)))","title":""},{"docid":"044fb0a35906fef03f2ee8197339d4a1","score":"0.5203674","text":"def file_to_bytes(inp, bs=4096):\n buf = array(\"B\")\n done = False\n while not done:\n temp = inp.read(bs)\n buf.fromstring(temp)\n done = len(temp) < bs\n return buf","title":""},{"docid":"a5db1726ad85f05656aa12e5b1f5b2aa","score":"0.52010775","text":"def chunked_transmission():\n ajax = request.form\n data = ajax.get(\"data\")\n\n ext = ajax.get(\"extension\")\n if ext in ALLOWED_VIDEO:\n root = VIDEO_ROOT\n elif ext in ALLOWED_DOCUMENTS:\n root = DOCUMENT_ROOT\n else:\n return jsonify({'ERROR': '405: filetype not allowed'})\n\n path = os.path.join(root, request.form.get('company'),\\\n request.form.get('project'), request.form.get('subproject'),\\\n request.form.get('file')+'.part')\n\n chunk_count = int(ajax.get(\"intent\"))\n\n if chunk_count > 1:\n with open(path, 'a') as f:\n f.write(data)\n else:\n with open(path, 'w') as f:\n f.write(data)\n\n if os.path.isfile(path):\n fileSize = os.stat(path).st_size\n else:\n fileSize = None\n\n return jsonify({\"serverSize\": fileSize, \"intent\": ajax.get('intent')})","title":""},{"docid":"fb540c780949dae6d8bae57b77204ef0","score":"0.51998407","text":"def finish_response(self):\n # try:\n for data in self.result:\n self.write(data)\n # finally:\n # self.close()","title":""},{"docid":"d36e5c9d1228efa5b67e6301efc4f432","score":"0.5186607","text":"def replay_bytes(filename):\n for packet in replay_packets(filename):\n yield struct.pack('BBBBBBBBBBBBBBBBB', *packet)","title":""},{"docid":"70084ed0785b6d7906a9ed18212c41a3","score":"0.51826406","text":"def receive_data(self):\n blocks = int_response.unpack(self.conn.recv(4))[0]\n\n for block in range(0, blocks):\n # Receive block header\n block_header_size = int_response.unpack(self.conn.recv(4))[0]\n (file_list, total_size, root_path) = pickle.loads(self.conn.recv(block_header_size))\n\n for file in file_list:\n # Create file previous chunk is empty\n # Create directory if not exists\n dir = os.path.abspath(\"../download/\" + os.path.dirname(file[0]))\n if not os.path.exists(dir):\n os.makedirs(dir)\n\n # Close previous file\n with open(dir + \"/\" + os.path.basename(file[0]), \"wb+\") as f:\n total_size = file[1]\n for i in range(0, math.ceil(file[1] / settings.block_size)):\n total_size -= settings.block_size\n f.write(self.conn.recv((settings.block_size + total_size) if total_size < 0 else settings.block_size))","title":""},{"docid":"97928615b87c2c768fa99802c5e10ae4","score":"0.51811254","text":"def downloadFile_v2(self, element, out_path):\n if element['media_files'] is not None:\n archival = element['media_files'].get('archival',[])\n streaming = element['media_files'].get('streaming',[])\n split=urlsplit(self.url)\n if len(archival) > 0:\n url = urljoin(\"https://\"+split.netloc, archival[0]['path'])\n elif len(streaming) > 0:\n url = urljoin(\"https://\"+split.netloc, streaming[0]['path'])\n else:\n # Legacy way of using streaming prior to streaming\n url=os.path.join(\"media\", element['file'])\n if 'original' in element:\n if element['original']:\n url=os.path.join(\"data/raw\", element['original'])\n split=urlsplit(self.url)\n url = urljoin(\"https://\"+split.netloc, url)\n\n # Supply token here for eventual media authorization\n with requests.get(url, stream=True, headers=self.headers) as r:\n r.raise_for_status()\n total_size = r.headers['Content-Length']\n download_chunk = 2 * 1024 * 1024\n total_chunks = math.ceil(int(total_size) / download_chunk)\n chunk_count = 0\n last_progress = 0\n yield last_progress\n with open(out_path, 'wb') as f:\n for chunk in r.iter_content(chunk_size=download_chunk):\n if chunk:\n chunk_count += 1\n f.write(chunk)\n this_progress = round((chunk_count / total_chunks) *100,1)\n if this_progress != last_progress:\n yield this_progress\n last_progress = this_progress\n yield 100","title":""},{"docid":"3b9cd61d8ff36d98d1c9a6b843bbdc7f","score":"0.51809394","text":"def _read(self, sz):\n return self._reader.read_response(sz)","title":""},{"docid":"43157e6fb207876e88d500bfed95b9a8","score":"0.5177744","text":"def get_file_content(self, file_hash):\n data = self._client.cat(file_hash).decode('utf-8')\n return data","title":""},{"docid":"d0474dc660f5cbde1406a5a35c5a26e8","score":"0.5173269","text":"def _stream_content(self):\n if self.response is None:\n self._file_path = storage.load_file(self._content_hash)\n else:\n fd, self._file_path = tempfile.mkstemp()\n os.close(fd)\n content_hash = sha1()\n with open(self._file_path, 'wb') as fh:\n for chunk in self.response.iter_content(chunk_size=8192):\n content_hash.update(chunk)\n fh.write(chunk)\n self._remove_file = True\n chash = content_hash.hexdigest()\n self._content_hash = storage.archive_file(self._file_path,\n content_hash=chash)\n\n if self.http.cache and self.ok:\n self.context.set_tag(self.request_id, self.serialize())\n return self._file_path","title":""},{"docid":"92159be848c8cbbff4fa5a0b7dab9956","score":"0.5170318","text":"def _get_fixed_data(self, length, output_file=None):\n\n # check that client is connected\n self._check_connection()\n\n length = int(length)\n\n if output_file: # stream data to a file\n bytes_recv = 0\n chunk = b\"\"\n with open(output_file, \"wb\") as fd:\n while bytes_recv < length:\n chunk = self.socket.recv(self.__class__.CHIRP_LINE_MAX)\n fd.write(chunk)\n bytes_recv += len(chunk)\n return bytes_recv\n\n else: # return data to method call\n data = b\"\"\n chunk = b\"\"\n while len(data) < length:\n chunk = self.socket.recv(self.__class__.CHIRP_LINE_MAX)\n data += chunk\n return data","title":""},{"docid":"1b6e29f6e1e8712394109431a82290a9","score":"0.5158794","text":"def readall(self):\n self._checkClosed()\n self._checkReadable()\n bufsize = DEFAULT_BUFFER_SIZE\n try:\n pos = os.lseek(self._fd, 0, SEEK_CUR)\n end = os.fstat(self._fd).st_size\n if end >= pos:\n bufsize = end - pos + 1\n except OSError:\n pass\n\n result = bytearray()\n while True:\n if len(result) >= bufsize:\n bufsize = len(result)\n bufsize += max(bufsize, DEFAULT_BUFFER_SIZE)\n n = bufsize - len(result)\n try:\n chunk = os.read(self._fd, n)\n except BlockingIOError:\n if result:\n break\n return None\n if not chunk: # reached the end of the file\n break\n result += chunk\n\n return bytes(result)","title":""},{"docid":"c06c0e1ba146e73e03b19067d377cb31","score":"0.51585686","text":"async def read_bytes(path: Path) -> bytes:\n return await _run(path.read_bytes)","title":""},{"docid":"c97b2e12d11bfcfd37cb498802d62d58","score":"0.5158237","text":"def recv(sock):\n data = \"\"\n file_size = 0\t\n file_size_buff = \"\"\n # header size buffer\n file_size_buff = recvAll(sock, 10)\n try:\n file_size = int(file_size_buff)\n # recieve a file given a file size\n data = recvAll(sock, file_size)\n except:\n pass\n return data","title":""},{"docid":"89a122cd6846ec5e7200c6d13af6b529","score":"0.5153641","text":"def getFile(self, ipaddr, title = \"\", author = \"\"):\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.sock.connect((ipaddr,self.port))\n print \"getFile Method: Socket connection established\"\n #Sending request for a file\n self.sock.send(\"GetFile\")\n resp = self.sock.recv(self.size)\n if resp != \"Okay\":\n return\n data = \"Title:\"+title+\":Author:\"+author\n self.sock.send(data)\n print \"getFile Method: Socket request sent\"\n run = 1\n strn = \"\"\n while(run):\n temp = self.sock.recv(self.size)\n self.sock.send(\"Okay\")\n if temp != \"EOF\":\n strn = strn + temp\n else:\n break\n print \"getFile Method: Response received\"\n print strn\n self.sock.send(\"Stop\")\n self.sock.close()\n return strn","title":""},{"docid":"aff250f4d9f45769be8d475df1f4674d","score":"0.51526767","text":"def server_recv_data(self) -> bytes:\n\t\tfragments = []\n\t\twhile True:\n\t\t\tchunk = self.conn.recv(1024)\n\t\t\tif not chunk:\n\t\t\t\tbreak\n\t\t\tfragments.append(chunk)\n\t\tarr = b''.join(fragments)\n\t\treturn arr","title":""}],"string":"[\n {\n \"docid\": \"4785d1e6c36afd80214ff7cc0687736b\",\n \"score\": \"0.6302854\",\n \"text\": \"def make_res_file(data):\\r\\n res_bytes = bytearray()\\r\\n magic_num = int.to_bytes(0x497E, 2, \\\"big\\\")\\r\\n mode_type = int.to_bytes(2, 1, \\\"big\\\")\\r\\n res_bytes.extend(magic_num)\\r\\n res_bytes.extend(mode_type)\\r\\n # Data will equal -1 if there is no file with the requested name,\\r\\n # or it can't be opened.\\r\\n if data == -1:\\r\\n status_code = int.to_bytes(0, 1, \\\"big\\\")\\r\\n data_length = int.to_bytes(0, 4, \\\"big\\\")\\r\\n\\r\\n else:\\r\\n status_code = int.to_bytes(1, 1, \\\"big\\\")\\r\\n data_length = int.to_bytes(len(data), 4, \\\"big\\\")\\r\\n\\r\\n res_bytes.extend(status_code)\\r\\n res_bytes.extend(data_length)\\r\\n if data != -1:\\r\\n res_bytes.extend(data)\\r\\n return res_bytes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afd509133851c2c92de91eccef05e9ab\",\n \"score\": \"0.62716156\",\n \"text\": \"def download_binary(self):\\n\\n return self._content_resp().content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3391e1caa4e5e08b3fc8e8e0fe7ced2f\",\n \"score\": \"0.6187527\",\n \"text\": \"def get_file_binary(self, path):\\n output = bytearray()\\n self.src_server.retrbinary('RETR {}'.format(path), output.extend)\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64720b6242c06fed989209728af11795\",\n \"score\": \"0.6115677\",\n \"text\": \"def getBytes(self) -> List[int]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64720b6242c06fed989209728af11795\",\n \"score\": \"0.6115677\",\n \"text\": \"def getBytes(self) -> List[int]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7e63a8cff74ce05846c967bb9850f8\",\n \"score\": \"0.6091245\",\n \"text\": \"def get_file_data(self):\\n data = ByteArray()\\n for s in self.strings:\\n data.extend(ByteArray(s))\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86a149da10a7bbdad3fde09abde603c2\",\n \"score\": \"0.60671455\",\n \"text\": \"def read_file_bytes(self):\\n with open(self.file_name, \\\"rb\\\") as data_file:\\n data = data_file.read()\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1df31748bfee626404b918124295f260\",\n \"score\": \"0.6033039\",\n \"text\": \"def read_bytes(self):\\n with self.open(\\\"rb\\\", None) as f:\\n return f.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74b361ed132ed53f14ea5ec9294dc5bc\",\n \"score\": \"0.5962343\",\n \"text\": \"def bytes(self):\\r\\n f = self.open('rb')\\r\\n try:\\r\\n return f.read()\\r\\n finally:\\r\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cadcf49510339c5c86f445207688ac4a\",\n \"score\": \"0.5907723\",\n \"text\": \"def stream_it(file_path):\\n return Response(\\n get_binary_content(file_path), \\n headers=get_headers(file_path),\\n mimetype=get_mime_type(file_path), \\n content_type='application/octet-stream')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7483ba3c4d54df539d331c93bd11733c\",\n \"score\": \"0.5906088\",\n \"text\": \"def stream_bytes(data, chunk_size=default_chunk_size):\\n\\tstream = BytesFileStream(data, chunk_size=chunk_size)\\n\\treturn stream.body(), stream.headers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f3baf5a450632bffb126934ee35ac13\",\n \"score\": \"0.5871599\",\n \"text\": \"def readbytes(self, n):\\n chunks = []\\n while n > 0:\\n try:\\n chunk = self.rfile.read(n)\\n except:\\n chunk = None\\n if not chunk:\\n raise ClientError(\\\"Failed to read command from client\\\")\\n chunks.append(chunk)\\n n -= len(chunk)\\n return \\\"\\\".join(chunks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc2f75d351c486c7c9f0364d76ee8111\",\n \"score\": \"0.58495915\",\n \"text\": \"def get_response():\\n result = ''\\n line = ''\\n while True:\\n result += line\\n line = FROMFILE.readline()\\n if line == '\\\\n' and len(result) > 0:\\n break\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6b39d89eb48ae593beb3b492ffb0ddd\",\n \"score\": \"0.5839738\",\n \"text\": \"def filebytes(self):\\n f = _File()\\n # TODO: Fix this once new HDU writing API is settled on\\n return self._writeheader(f)[1] + self._writedata(f)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c01f662ad08e9425f1540c48759e94c4\",\n \"score\": \"0.5831824\",\n \"text\": \"def save_file(self, resp, full_path):\\n total = int(resp.headers.get('content-length', 0))\\n with open(str(full_path.absolute()), 'wb') as file:\\n for data in resp.iter_content(chunk_size=1024):\\n size = file.write(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"384c8d174bf9b0d60d908a83f3abd332\",\n \"score\": \"0.5829522\",\n \"text\": \"def get_file(self, path: str) -> BytesIO:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"068a4f824945b50e9dccfdd07ee35dcf\",\n \"score\": \"0.57933664\",\n \"text\": \"def read(self, _bytes):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde008ddffa49d6ea0fad962cd94b554\",\n \"score\": \"0.5789279\",\n \"text\": \"def buffered_data(self) -> bytes:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd9c1a7c9b53f223db5114e58ea9c63b\",\n \"score\": \"0.57748395\",\n \"text\": \"def get_file_data(self):\\n if self.get_type() == SHT_NOBITS:\\n return ByteArray()\\n else:\\n return self._data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55ae738f10c9377e7a25a57a212666aa\",\n \"score\": \"0.5767827\",\n \"text\": \"def get_response():\\n result = ''\\n line = ''\\n while line != '\\\\n':\\n result += line\\n line = FROMFILE.readline()\\n #print(\\\" I read line:[\\\"+line+\\\"]\\\")\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8a82b7e5f7ddeafb523d35bbb131db3\",\n \"score\": \"0.57251275\",\n \"text\": \"def test_get_raw_file_content(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53544897b94ed07d5fca379630106ca5\",\n \"score\": \"0.56894314\",\n \"text\": \"def get_bytes(self, entities_to_update) -> bytes:\\n chunk_data = BytesIO()\\n\\n # Get the chunk data as NBT data and write it to 'chunk_data'\\n nbt_data = self.get_nbt(entities_to_update)\\n nbt_data.write_file(buffer=chunk_data)\\n\\n # Compress the data\\n chunk_data.seek(0)\\n chunk_data = zlib.compress(chunk_data.read())\\n\\n # Create the byte block\\n bytes_data = (len(chunk_data) + 1).to_bytes(4, \\\"big\\\") + b\\\"\\\\x02\\\" + chunk_data\\n\\n # Padding to be a multiple of 4KiB long\\n bytes_data += bytes(4096 - (len(bytes_data) % 4096))\\n assert len(bytes_data) % 4096 == 0\\n\\n return bytes_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dd15a2bf4480e06c0852d51f00c8f1e\",\n \"score\": \"0.5687858\",\n \"text\": \"def download_file(self, uri: ParseResult) -> IO[bytes]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cf17c85c289bed6ae77ac5750eda093\",\n \"score\": \"0.5686154\",\n \"text\": \"def get_file(self) -> bytes:\\n assert isinstance(self._segments[\\\"preceding\\\"], bytes)\\n img_bytes = self._segments[\\\"preceding\\\"]\\n\\n if self._has_exif:\\n assert isinstance(self._segments[\\\"APP1\\\"], App1MetaData)\\n img_bytes += self._segments[\\\"APP1\\\"].get_segment_bytes()\\n\\n assert isinstance(self._segments[\\\"succeeding\\\"], bytes)\\n img_bytes += self._segments[\\\"succeeding\\\"]\\n\\n return img_bytes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a287a22e9fe0afb623c2f5997b74b52d\",\n \"score\": \"0.5622689\",\n \"text\": \"def process_body_data(content_bytes: bytes) -> list[str]:\\n entries: list[str] = []\\n\\n header_bytes = content_bytes[:96]\\n content_bytes = content_bytes[96:]\\n header_values = [\\n to_int(header_bytes[idx - INT_SIZE : idx])\\n for idx in range(len(header_bytes), 1, -INT_SIZE)\\n if header_bytes[idx - INT_SIZE : idx] != HEADER_FIELD_SEP\\n ]\\n num_chunks = header_values[0]\\n header_size = header_values[2]\\n header_content_size = header_values[4]\\n\\n # Check that the remainder of the header is all \\\\x00's\\n zero_pad = Counter(header_bytes[:-header_size])[0]\\n assert zero_pad == len(header_bytes) - header_size\\n\\n entries, content_size = extract_chunks(content_bytes, num_chunks)\\n # Check that we got the expected amount of content\\n # and that that end of the file is \\\\x00 padded.\\n content_bytes = content_bytes[content_size:]\\n content_size += header_size\\n assert Counter(content_bytes)[0] == len(content_bytes)\\n assert content_size == header_content_size\\n\\n return entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f626a050cd116d0e6577edeb628ec75e\",\n \"score\": \"0.5618997\",\n \"text\": \"async def content_as_bytes(self, max_concurrency=1):\\n stream = BytesIO()\\n await self.download_to_stream(stream, max_concurrency=max_concurrency)\\n return stream.getvalue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2540abe184c2af4449699d63494432aa\",\n \"score\": \"0.5618683\",\n \"text\": \"def read_chunked_response_data(http_client_socket):\\n\\n data = b''\\n\\n while (chunkSize := http_get_word(http_client_socket)[0]) != b'0':\\n if chunkSize == b'':\\n continue\\n print(\\\"Chunk of\\\", chunkSize, \\\"bytes\\\")\\n for i in range(0, int(chunkSize.decode('ASCII'), 16)):\\n data += http_client_socket.recv(1)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d60eda899b916ebbcd3b22108a50c968\",\n \"score\": \"0.5588068\",\n \"text\": \"def readbytes(path):\\n with open(path, \\\"rb\\\") as f:\\n return f.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"072bcb1174542c37110b8d00ebe42de2\",\n \"score\": \"0.5576383\",\n \"text\": \"def get_chunk(self) -> bytes:\\n if self._read_length < 0:\\n raise StopIteration()\\n rtn = self._rest_part + self._client.get(path=self._path, offset=self._offset, length=self._length)\\n self._iter += 1\\n self._offset = self._iter * self._length\\n self._read_length = min(self._length, self._size - self._offset)\\n return rtn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"012954cd8d602a18f98abd93b03aea71\",\n \"score\": \"0.5564775\",\n \"text\": \"def getDataResponse(self):\\n\\n data = b\\\"\\\"\\n response = self._openURL()\\n\\n if not response:\\n return data\\n\\n if response.headers['Content-Encoding'] == 'gzip':\\n data = gzip.GzipFile(fileobj=response).read()\\n else:\\n data = response.read()\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de5d2df195f0a9fd654413f773829bf7\",\n \"score\": \"0.55638117\",\n \"text\": \"def get_file(name):\\n key = get_key(name, 'count')\\n expected_count = int(client.get(key))\\n log.info(\\\"Get expected count: %s\\\", expected_count)\\n\\n data = b''\\n index = 0\\n while True:\\n key = get_key(name, index)\\n chunk = client.get(key)\\n if not chunk:\\n break\\n\\n log.info(\\\"Get %s = %s\\\", key, len(chunk))\\n data += chunk\\n index += 1\\n\\n log.info(\\\"Get count: %s\\\", index)\\n assert index == expected_count\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c230c4e1c32045891acb171ee2d0b379\",\n \"score\": \"0.555587\",\n \"text\": \"def _read_body(response) -> Tuple[int, Optional[str]]:\\n content = StringIO()\\n size = 0\\n start_time = time.time()\\n for chunk in response.iter_content(102400):\\n if size < 5 * 1048576:\\n content.write(chunk.decode(\\\"utf-8\\\", \\\"ignore\\\"))\\n elif size > 30 * 1048576:\\n response.close()\\n return -1, None\\n if time.time() - start_time > 10:\\n response.close()\\n return -1, None\\n size += len(chunk)\\n\\n return size, content.getvalue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a20366912f989e1e316cbeff5792f1d7\",\n \"score\": \"0.55534464\",\n \"text\": \"def get(self,caminho):\\n try:\\n arquivo=open(caminho,\\\"rb\\\")\\n file=arquivo.read()\\n #print(\\\"F\\\",file)\\n arquivo.close()\\n return True,file\\n except Exception as e:\\n return False,None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdd0ecae922b706a2dee64a21833ab76\",\n \"score\": \"0.55341244\",\n \"text\": \"def _download_file(self, _content_type, filename, response):\\n\\n chunk_size = 1024 * 1000\\n\\n try:\\n with open(filename, 'wb') as f:\\n for chunk in response.iter_content(chunk_size=chunk_size):\\n f.write(chunk)\\n except TypeError:\\n with open(filename, 'w') as f:\\n for chunk in response.iter_content(chunk_size=chunk_size):\\n f.write(chunk)\\n except (IOError, Exception):\\n return {'Status': 'FAIL', 'Filename': filename}\\n\\n return {'Status': 'OK', 'Filename': filename}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02fa6b921fe209f9f4ebc32c3a005b41\",\n \"score\": \"0.5530555\",\n \"text\": \"def get_media_data_binary(self, id):\\n return self._get(JSONRequest('/media/{id}/file'.format(id=id)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dfa03c3d4dd3aede2f7009bf244da6b\",\n \"score\": \"0.5508311\",\n \"text\": \"def _getData(self):\\n content = bytearray()\\n content += self.MAGIC_NUMBER\\n content += self._version\\n content += self.HEADER['dimension'] + self._width + self._height\\n if(self._comment is not None):\\n content += self.HEADER['comment'] + self._comment\\n\\n content += self.HEADER['author'] + self._author\\n content += self.HEADER['numberColor'] + self._numberColor\\n content += self.HEADER['color'] + self._listColor\\n if(self._extra is not None):\\n content += self._extra\\n content += self.HEADER['end']\\n content += self._pixels\\n return bytes(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11225205b426de672183714b02ac2224\",\n \"score\": \"0.5498546\",\n \"text\": \"def Data(self) -> ResultBuffer:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df2bc8bcd32ed8c8401aa4cdb1688533\",\n \"score\": \"0.54977036\",\n \"text\": \"def get_chunk(self) -> bytes:\\n try:\\n d = self.body.get_nowait()\\n if d == REQUEST_FINISHED:\\n return b\\\"\\\"\\n except asyncio.QueueEmpty:\\n return b\\\"\\\"\\n\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26f74177055831b14b40280cad800994\",\n \"score\": \"0.5486102\",\n \"text\": \"def read(self) -> bytes:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b82b64bd275916a1623555ae373adafc\",\n \"score\": \"0.5485689\",\n \"text\": \"def get_all_file_by_user_toEncode(id):\\n\\t#1 generation jeton\\n\\tjeton = generate_jeton()\\n\\tprint(\\\">jeton\\\"+str(jeton))\\n\\tbytes_jeton = (jeton).to_bytes(size_bytes_jeton, byteorder='big')\\n\\n\\t#convertion de la data a en voyer en bytes\\n\\tbytes_size = id.bit_length()\\n\\n\\teffective_data = (id).to_bytes(bytes_size, byteorder='big')\\n\\tif nbr_trame_needed( len(effective_data)) != 1:\\n\\t\\traise ValueError(\\\"Error Test nbr_trame_needed by \\\"+ \\\\\\n\\t\\t\\t\\t\\\"get_all_file_by_user_toBytes cas n'est pas supposé arriver\\\")\\n\\ttrame = struct_protocole(5,bytes_jeton+effective_data)\\n\\tprint(\\\">{}\\\".format(trame))\\n\\tconnexion_avec_serveur = connect_to_serveur()\\n\\t#msg_recu = connexion_avec_serveur.recv(1024)\\n\\t#print(msg_recu)\\n\\tconnexion_avec_serveur.send(trame)\\n\\tmsg_recu = connexion_avec_serveur.recv(1024)\\n\\tprint(msg_recu)\\n\\treturn trame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d65c82ff907009ff3909c2bd6a3da76\",\n \"score\": \"0.54820144\",\n \"text\": \"def get_contents(self, chunk_size: int = bioblend.CHUNK_SIZE) -> bytes:\\n return b\\\"\\\".join(self.get_stream(chunk_size=chunk_size))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db4c5c5633ac36780728292ec803a5c2\",\n \"score\": \"0.54671687\",\n \"text\": \"def InputStream(self) -> _n_5_t_0:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d12f9fc13c6ae6940cfaf02e167efd8\",\n \"score\": \"0.54563665\",\n \"text\": \"def readall(self):\\n res = bytearray()\\n while True:\\n data = self.read(DEFAULT_BUFFER_SIZE)\\n if not data:\\n break\\n res += data\\n if res:\\n return bytes(res)\\n else:\\n # b'' or None\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae3ba3c550e2651221d20df967065258\",\n \"score\": \"0.5450503\",\n \"text\": \"def open_bytes(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a460a6b6e13c9eb12f7cb22569a472f4\",\n \"score\": \"0.5425246\",\n \"text\": \"def handle_response(self, headers: List[Tuple], content: bytes) -> str:\\n for header in headers:\\n if header[0] == \\\"Content-Encoding\\\":\\n encoding = header[1]\\n if encoding is not None:\\n content = self.decompress_content(content, encoding)\\n return content.decode(\\\"utf-8\\\", \\\"ignore\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29d0493cdb82d1ee156e93a2fa6f1ed\",\n \"score\": \"0.54233134\",\n \"text\": \"async def read(self) -> bytes:\\r\\n async with self.session.get(self.url) as resp:\\r\\n img = await resp.read()\\r\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d42fc5319e8bddd115a30f77129b6ffb\",\n \"score\": \"0.5421874\",\n \"text\": \"def getFile(self, filename):\\n\\n logger.debug(\\\"filename {}\\\".format(filename))\\n\\n from urllib.parse import quote, unquote\\n\\n # check if string is already urlencoded\\n # if unquote is equal to string, then it is not urlencoded (unquote respects plus sign)\\n if unquote(filename) != filename:\\n filename = unquote(filename)\\n\\n from io import BytesIO\\n\\n buffer = BytesIO(b\\\"\\\")\\n\\n res1 = self.client.resource(filename)\\n res1.write_to(buffer)\\n buffer.seek(0)\\n\\n logger.debug(\\\"file content: {}\\\".format(buffer.getvalue()))\\n\\n return buffer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0bdadd5af3ac3d6aa7da0b5db606794\",\n \"score\": \"0.54150933\",\n \"text\": \"def _get_image_bytes(image_path):\\n\\n with open(image_path, 'rb') as f:\\n image_content = f.read()\\n\\n return image_content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9ba7dbc9b985b7c644af8ecdd5a40a2\",\n \"score\": \"0.5415058\",\n \"text\": \"def _read_null_bytes(self):\\n nullbuffer = 0\\n while 1:\\n if self.f.read(1) == '\\\\x00':\\n nullbuffer += 1\\n else:\\n break\\n return nullbuffer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddbeb09841508e697b0ea1af2426bcc9\",\n \"score\": \"0.54026437\",\n \"text\": \"def filestream_view(request, path):\\n f = open(path, 'rb')\\n basename = os.path.basename(path)\\n size = os.path.getsize(path)\\n ct, encoding = guess_type(path)\\n content_range = request.META.get('HTTP_RANGE')\\n if ct is None: ct = 'application/octet-stream'\\n\\n if content_range:\\n # \\\"Range\\\" header parsing\\n # Valid examples:\\n # > Range: bytes=a-b // return bytes from a to b\\n # > Range: bytes=a- // return bytes from a to end of file\\n # > Range: bytes=-b // return last b bytes\\n # > Range: bytes=a1-b1,a2-b2,... // return bytes from a1 to b1, from a2 to b2 etc\\n\\n ranges = [] # list of non-empty ranges\\n raw_ranges = re.match(r'^bytes=(?P<ranges>(\\\\d*-\\\\d*,)*(\\\\d*-\\\\d*))$', content_range).group('ranges')\\n\\n for r in raw_ranges.split(','):\\n a, b = r.split('-')\\n if (a != '') and (b != ''):\\n a, b = int(a), int(b)\\n elif b == '':\\n a, b = int(a), size - 1\\n elif a == '':\\n a, b = size-int(b), size-1\\n if b >= size: b = size - 1\\n\\n if b-a >= 0:\\n a_real, b_real = a, b\\n ranges.append((a, b))\\n\\n if len(ranges) == 0:\\n return HttpResponse(status=416)\\n elif len(ranges) == 1:\\n ### Only one range.\\n # HTTP 206 Partial Content\\n # Content-Type = (content type of file)\\n # Content-Length = (range size)\\n # Content-Range = (range range)\\n #\\n # (range data)\\n a, b = a_real, b_real\\n response = StreamingHttpResponse(read_in_chunks(f, a, b), status=206)\\n response['Content-Length'] = b - a + 1\\n response['Content-Range'] = 'bytes {}-{}/{}'.format(a, b, size)\\n else:\\n ### Several ranges.\\n # > HTTP 206 Partial Content\\n # > Content-Type = multipart/byteranges; boundary=BOUNDARY\\n # >\\n # > --BOUNDARY\\n # > Content-Type: (content type of file)\\n # > Content-Range: (range_1 range)\\n # >\\n # > (range_1 data)\\n # > --BOUNDARY\\n # > Content-Type: (content type of file)\\n # > Content-Range: (range_2 range)\\n # >\\n # > (range_2 data)\\n # > (et cetera)\\n # > --BOUNDARY--\\n\\n # There is one pretty unlikely, but possible situation,\\n # when file (one of range to be specific) contains boundary.\\n boundary = ''.join(choice(ascii_letters+'_') for x in range(50))\\n response = StreamingHttpResponse(read_in_ranges(f, ranges, size, boundary=boundary, content_type=ct), status=206)\\n ct = 'multipart/byteranges; boundary={}'\\n else:\\n response = StreamingHttpResponse(read_in_chunks(f, 0, size), status=200)\\n response['Content-Length'] = size\\n\\n if encoding: response['Content-Encoding'] = encoding\\n response['Content-Type'] = ct\\n\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2f9c973c79b12571d4956910d123458\",\n \"score\": \"0.5402352\",\n \"text\": \"def read_response(response):\\n\\tif response.info().get('Content-Encoding') == 'gzip':\\n\\t\\tbuf = StringIO(response.read())\\n\\t\\treturn gzip.GzipFile(fileobj=buf).read()\\n\\n\\telif response.info().get('Content-Encoding') == 'deflate':\\n\\t\\tdecompress = zlib.decompressobj(-zlib.MAX_WBITS)\\n\\t\\tinflated = decompress.decompress(response.read())\\n\\t\\tinflated += decompress.flush()\\n\\t\\treturn inflated\\n\\n\\treturn response.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e112bb6db967578adc7c5bdddf61dcd8\",\n \"score\": \"0.5398823\",\n \"text\": \"def readResponse(self, fileObj):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68690fbb1ab9577f684ff06dd72bde99\",\n \"score\": \"0.53986675\",\n \"text\": \"async def download(self, name) -> bytes:\\n return b''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d40cee1aa08757b1d2fd1ff3a701567b\",\n \"score\": \"0.53976977\",\n \"text\": \"def get_bytes(self, key):\\n reply = self.call(\\n Methods.Get, api_pb2.GetRequest(header=api_pb2.RequestHeader(key=key)))\\n return reply.HasField('value'), reply.value.timestamp, reply.value.bytes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a41e3b646e60fe3269702c0a87eaabff\",\n \"score\": \"0.5378542\",\n \"text\": \"def _get_content ( self ):\\r\\n return read_file( self.path )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bb06ee29cb10db1bef2a51606a61a0f\",\n \"score\": \"0.53782004\",\n \"text\": \"def BytesToWrite(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdcfe2a0f117b7142e860d5f7528f996\",\n \"score\": \"0.53679824\",\n \"text\": \"def GetFile(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0800fcc4edf15f0f6772329826240f1b\",\n \"score\": \"0.5362829\",\n \"text\": \"def _download_file(cls, response, filepath):\\n\\n with open(filepath, 'wb') as outfile:\\n for chunk in response.iter_content(chunk_size=1024):\\n outfile.write(chunk)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59cf4a54bcb6063d17bfaff641d5ac4f\",\n \"score\": \"0.5359217\",\n \"text\": \"def read_request(self):\\n buffer = bytes()\\n max_counter = 100\\n current_counter = 0\\n while True:\\n _, data = win32file.ReadFile(self.http_usb_handle, READ_BUFFER_SIZE)\\n if len(data) == 0:\\n current_counter += 1\\n if current_counter >= max_counter:\\n break\\n buffer += data\\n return buffer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a44223c8675987c78b15bafb28e6fe58\",\n \"score\": \"0.5357636\",\n \"text\": \"def read_image_bytes_on_server(chunks):\\n img_data = {}\\n for chunk in chunks:\\n stream = img_data.get(chunk.fileindex, BytesIO())\\n stream.write(chunk.buffer)\\n img_data[chunk.fileindex] = stream\\n return img_data.values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a45f9ff4e937950e96b5f9c824562c7\",\n \"score\": \"0.5351856\",\n \"text\": \"def read(self, size):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85d5fbb1e08c232d544f95d115ae470b\",\n \"score\": \"0.5351496\",\n \"text\": \"def get_uploaded_bytes(self, file_id):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeeb1c35d0a105755f0cde2ce7672b6b\",\n \"score\": \"0.5343499\",\n \"text\": \"def read_data(self, num_of_bytes):\\n log.debug(\\\"num_of_bytes = {}\\\".format(num_of_bytes))\\n buffer = bytes()\\n max_counter = 20\\n current_counter = 0\\n num_of_data_read = 0\\n while True:\\n data_to_read = num_of_bytes-num_of_data_read\\n log.debug(\\\"data_to_read = {}\\\".format(data_to_read))\\n rc, data = win32file.ReadFile(self.http_usb_handle, data_to_read)\\n log.debug(\\\"read_data rc = {} len(data) = {}\\\".format(rc, len(data)))\\n if len(data) == 0:\\n current_counter += 1\\n if current_counter >= max_counter:\\n break\\n buffer += data\\n num_of_data_read += len(data)\\n if num_of_data_read == num_of_bytes:\\n break\\n return buffer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62635b95b4b7d38983de880ef0a84cbe\",\n \"score\": \"0.53240645\",\n \"text\": \"def chunk_read(response, chunk_size=8192):\\n total_size = int(response.info()['Content-Length'].strip())\\n n_iter = int(total_size / chunk_size)\\n data = []\\n\\n for datum in tqdm(iter_content(response, chunk_size),\\n total=n_iter, unit='B', unit_scale=True):\\n data += datum\\n\\n return b\\\"\\\".join(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"478a0f883a667b3e03ff3989e6c91618\",\n \"score\": \"0.5314397\",\n \"text\": \"def download_data(self):\\n try:\\n response = requests.get(self.url, stream=True)\\n if response.status_code == 200:\\n with open(self.src_file , 'wb+') as f:\\n f.write(response.raw.read())\\n except Exception as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf96412fa0f89461393b5c8ae1e41602\",\n \"score\": \"0.5312065\",\n \"text\": \"def get_file_content(self) -> str:\\n raise NotImplementedError('TBI')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03be7c87322f8344c2a9badcd0b037e0\",\n \"score\": \"0.53117394\",\n \"text\": \"def read_bytes(self, path: str) -> Try[OSError, bytes]:\\n @catch_io_bound(OSError)\\n def f() -> bytes:\\n with open(path, 'rb') as f:\\n return f.read()\\n\\n return f()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49750dd93b523ed5a49e7b7bd5a7576f\",\n \"score\": \"0.53042614\",\n \"text\": \"def bytes(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a85a89a3e68e03c5903b74bd5c36c5cf\",\n \"score\": \"0.5289722\",\n \"text\": \"def read_response(response):\\n\\tif response.info().get('Content-Encoding') == 'gzip':\\n\\t\\tbuf = BytesIO(response.read())\\n\\t\\treturn gzip.GzipFile(fileobj=buf).read().decode('utf8')\\n\\n\\telif response.info().get('Content-Encoding') == 'deflate':\\n\\t\\tdecompress = zlib.decompressobj(-zlib.MAX_WBITS)\\n\\t\\tinflated = decompress.decompress(response.read())\\n\\t\\tinflated += decompress.flush()\\n\\t\\treturn inflated.decode('utf8')\\n\\n\\treturn response.read().decode('utf8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58289c883dc0cd35c145dde34b82a3b5\",\n \"score\": \"0.52872074\",\n \"text\": \"def get_bytes(self) -> bytes:\\n\\n questions = b''\\n for question in self.Questions:\\n questions += question.get_bytes()\\n answers = b''\\n for answer in self.Answers:\\n answers += answer.get_bytes()\\n authorities = b''\\n for authority in self.Authorities:\\n authorities += authority.get_bytes()\\n additionals = b''\\n for additional in self.Additionals:\\n additionals += additional.get_bytes()\\n \\n return self.Header.get_bytes() + questions + answers + authorities + additionals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88a295d4844cee6d666b8f5d25621c48\",\n \"score\": \"0.5283079\",\n \"text\": \"def _save_body_to_file(self, download_path, http_resp, chunksize=2**16):\\n with open(download_path, 'wb') as f:\\n with contextlib.closing(http_resp):\\n for c in http_resp.iter_content(chunksize):\\n f.write(c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f78c5fabbdbba01bc69e434b86056fe\",\n \"score\": \"0.52814597\",\n \"text\": \"def get_content(self, object_id) -> Union[str, bytes]:\\n data_object = self.nuvla.get(object_id)\\n response = self.nuvla.operation(data_object, 'download')\\n download_url = response.data['uri']\\n content_type = data_object.data['content-type']\\n\\n headers = {'content-type': content_type}\\n response = requests.get(download_url, headers=headers)\\n return response.content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e49b0eeefa94c78ab109476f6e9e5619\",\n \"score\": \"0.52652365\",\n \"text\": \"def read_bytes(path: str) -> Effect[HasFiles, OSError, bytes]:\\n return depend(HasFiles).and_then(lambda env: env.files.read_bytes(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a7b7cc1fe4d5feac9c455adaa693fa\",\n \"score\": \"0.5258088\",\n \"text\": \"def get_data(file_name):\\r\\n data = bytearray()\\r\\n # Gets the current path of this file and the python,\\r\\n # scripts name and replaces itself with the given file name.\\r\\n script_name = os.path.basename(__file__)\\r\\n file_path = __file__[:-len(script_name)] + file_name\\r\\n\\r\\n try:\\r\\n with open(file_path, \\\"rb\\\") as file:\\r\\n # Converts each file to a set of bytes.\\r\\n data.extend(file.read())\\r\\n except FileNotFoundError: # Checks that the file exists.\\r\\n data = -1\\r\\n except PermissionError: # Checks that the file can be read.\\r\\n data = -1\\r\\n except OSError:\\r\\n data = -1\\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9ae2f7d74d1f61dc87bdc824497f9c\",\n \"score\": \"0.52580684\",\n \"text\": \"def byte() -> Path:\\n with NamedTemporaryFile(suffix=\\\".tif\\\") as file:\\n file = Path(file.name)\\n link = \\\"https://raw.githubusercontent.com/rasterio/rasterio/master/tests/data/byte.tif\\\"\\n urlretrieve(link, file)\\n\\n yield file\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c771c8842a7d6ca5ce88f1efc0e400a2\",\n \"score\": \"0.5245632\",\n \"text\": \"def read(self) -> bytes:\\r\\n resp = self.session.get(self.url)\\r\\n return resp.content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76bdff0df3ac8ac769f67804e8cc6529\",\n \"score\": \"0.52425796\",\n \"text\": \"def download(decoder):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"399f1218073cd2a7f67ca5ffd17e1275\",\n \"score\": \"0.52395624\",\n \"text\": \"def download_file(self, uri: ParseResult) -> IO[bytes]:\\n url = uri.path\\n file_obj = open(url, \\\"rb\\\")\\n return file_obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be75d818328e1b76a329487358bc126\",\n \"score\": \"0.5237463\",\n \"text\": \"def get_data(self, encoding = \\\"utf-8\\\"):\\n val = b\\\"\\\"\\n if isinstance(self.client_wfile, io.BytesIO):\\n val = self.client_wfile.getvalue()\\n if encoding is not None:\\n val = val.decode(encoding)\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0798ea2dd23d90783f8783fced1cf1b\",\n \"score\": \"0.5231819\",\n \"text\": \"def _response(self):\\n result = []\\n logger.debug('res:')\\n done = False\\n while not done:\\n raw_result = self._serial.readline()\\n logger.debug(repr(raw_result))\\n if raw_result == '':\\n # TODO: Might be better to raise GrblTimeoutException here,\\n # something like:\\n # raise GrblTimeoutException(\\n # 'Timeout encountered on read.')\\n logger.warning('Timeout encountered on read.')\\n done = True\\n if raw_result == 'ok\\\\r\\\\n':\\n done = True\\n result.append(raw_result.strip())\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e59583530c97394fd0d1658d9b1e02a\",\n \"score\": \"0.52219945\",\n \"text\": \"def to_bytes(self):\\n result = io.BytesIO()\\n result.write(ServerIO.HEADER)\\n\\n ServerIO.write_int(result, len(self.request_times_ds))\\n for request_time_ds in self.request_times_ds:\\n ServerIO.write_int(result, request_time_ds)\\n\\n result.write(self.screensaver_id)\\n ServerIO.write_int(result, self.screensaver_time_ds)\\n\\n ServerIO.write_int(result, len(self.image_data))\\n result.write(self.image_data)\\n return result.getvalue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a5a8401e1e5a6bebe45dd96084d5dbe\",\n \"score\": \"0.5217589\",\n \"text\": \"def read_from_handler(handler):\\n max_chunk_size = 10 * 1024 * 1024\\n size_remaining = int(handler.headers[\\\"content-length\\\"])\\n chunks_list = []\\n while size_remaining:\\n chunk_size = min(size_remaining, max_chunk_size)\\n chunks_list.append(handler.rfile.read(chunk_size).decode())\\n size_remaining -= len(chunks_list[-1])\\n\\n return ''.join(chunks_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f8fd19a5c5fc3f4c7e194a6a6ac7d3b\",\n \"score\": \"0.5211043\",\n \"text\": \"def recv_data(file_name,size):\\n fiber_fp = open(file_name,\\\"rb\\\",0)\\n\\n try:\\n #size = 0xff7\\n #size = 0xff\\n b = fiber_fp.read(size)\\n if b:\\n print(\\\"has read %d num from %s\\\" % (len(b),file_name))\\n xxd_bin(b)\\n else:\\n print(\\\"none data read from %s\\\" % file_name)\\n print()\\n\\n finally:\\n fiber_fp.close()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"984fe7a6edd4d0caeaa287685333d919\",\n \"score\": \"0.52055705\",\n \"text\": \"def test_chunk_download(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"798a56ee7d0d0d1d38a7f68c3b0e7c3d\",\n \"score\": \"0.5205474\",\n \"text\": \"def process(self) -> bytes:\\n super().process(self.__raw_audio)\\n self.finish()\\n with open(self.__output_file, 'rb') as f:\\n return f.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f508faacd7e61ef382f5485a8bf846f9\",\n \"score\": \"0.52040225\",\n \"text\": \"def response(self):\\n return self._send(bytes([0xef,0xfe,0x02]+([0x0]*57)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"044fb0a35906fef03f2ee8197339d4a1\",\n \"score\": \"0.5203674\",\n \"text\": \"def file_to_bytes(inp, bs=4096):\\n buf = array(\\\"B\\\")\\n done = False\\n while not done:\\n temp = inp.read(bs)\\n buf.fromstring(temp)\\n done = len(temp) < bs\\n return buf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5db1726ad85f05656aa12e5b1f5b2aa\",\n \"score\": \"0.52010775\",\n \"text\": \"def chunked_transmission():\\n ajax = request.form\\n data = ajax.get(\\\"data\\\")\\n\\n ext = ajax.get(\\\"extension\\\")\\n if ext in ALLOWED_VIDEO:\\n root = VIDEO_ROOT\\n elif ext in ALLOWED_DOCUMENTS:\\n root = DOCUMENT_ROOT\\n else:\\n return jsonify({'ERROR': '405: filetype not allowed'})\\n\\n path = os.path.join(root, request.form.get('company'),\\\\\\n request.form.get('project'), request.form.get('subproject'),\\\\\\n request.form.get('file')+'.part')\\n\\n chunk_count = int(ajax.get(\\\"intent\\\"))\\n\\n if chunk_count > 1:\\n with open(path, 'a') as f:\\n f.write(data)\\n else:\\n with open(path, 'w') as f:\\n f.write(data)\\n\\n if os.path.isfile(path):\\n fileSize = os.stat(path).st_size\\n else:\\n fileSize = None\\n\\n return jsonify({\\\"serverSize\\\": fileSize, \\\"intent\\\": ajax.get('intent')})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb540c780949dae6d8bae57b77204ef0\",\n \"score\": \"0.51998407\",\n \"text\": \"def finish_response(self):\\n # try:\\n for data in self.result:\\n self.write(data)\\n # finally:\\n # self.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d36e5c9d1228efa5b67e6301efc4f432\",\n \"score\": \"0.5186607\",\n \"text\": \"def replay_bytes(filename):\\n for packet in replay_packets(filename):\\n yield struct.pack('BBBBBBBBBBBBBBBBB', *packet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70084ed0785b6d7906a9ed18212c41a3\",\n \"score\": \"0.51826406\",\n \"text\": \"def receive_data(self):\\n blocks = int_response.unpack(self.conn.recv(4))[0]\\n\\n for block in range(0, blocks):\\n # Receive block header\\n block_header_size = int_response.unpack(self.conn.recv(4))[0]\\n (file_list, total_size, root_path) = pickle.loads(self.conn.recv(block_header_size))\\n\\n for file in file_list:\\n # Create file previous chunk is empty\\n # Create directory if not exists\\n dir = os.path.abspath(\\\"../download/\\\" + os.path.dirname(file[0]))\\n if not os.path.exists(dir):\\n os.makedirs(dir)\\n\\n # Close previous file\\n with open(dir + \\\"/\\\" + os.path.basename(file[0]), \\\"wb+\\\") as f:\\n total_size = file[1]\\n for i in range(0, math.ceil(file[1] / settings.block_size)):\\n total_size -= settings.block_size\\n f.write(self.conn.recv((settings.block_size + total_size) if total_size < 0 else settings.block_size))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97928615b87c2c768fa99802c5e10ae4\",\n \"score\": \"0.51811254\",\n \"text\": \"def downloadFile_v2(self, element, out_path):\\n if element['media_files'] is not None:\\n archival = element['media_files'].get('archival',[])\\n streaming = element['media_files'].get('streaming',[])\\n split=urlsplit(self.url)\\n if len(archival) > 0:\\n url = urljoin(\\\"https://\\\"+split.netloc, archival[0]['path'])\\n elif len(streaming) > 0:\\n url = urljoin(\\\"https://\\\"+split.netloc, streaming[0]['path'])\\n else:\\n # Legacy way of using streaming prior to streaming\\n url=os.path.join(\\\"media\\\", element['file'])\\n if 'original' in element:\\n if element['original']:\\n url=os.path.join(\\\"data/raw\\\", element['original'])\\n split=urlsplit(self.url)\\n url = urljoin(\\\"https://\\\"+split.netloc, url)\\n\\n # Supply token here for eventual media authorization\\n with requests.get(url, stream=True, headers=self.headers) as r:\\n r.raise_for_status()\\n total_size = r.headers['Content-Length']\\n download_chunk = 2 * 1024 * 1024\\n total_chunks = math.ceil(int(total_size) / download_chunk)\\n chunk_count = 0\\n last_progress = 0\\n yield last_progress\\n with open(out_path, 'wb') as f:\\n for chunk in r.iter_content(chunk_size=download_chunk):\\n if chunk:\\n chunk_count += 1\\n f.write(chunk)\\n this_progress = round((chunk_count / total_chunks) *100,1)\\n if this_progress != last_progress:\\n yield this_progress\\n last_progress = this_progress\\n yield 100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b9cd61d8ff36d98d1c9a6b843bbdc7f\",\n \"score\": \"0.51809394\",\n \"text\": \"def _read(self, sz):\\n return self._reader.read_response(sz)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43157e6fb207876e88d500bfed95b9a8\",\n \"score\": \"0.5177744\",\n \"text\": \"def get_file_content(self, file_hash):\\n data = self._client.cat(file_hash).decode('utf-8')\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0474dc660f5cbde1406a5a35c5a26e8\",\n \"score\": \"0.5173269\",\n \"text\": \"def _stream_content(self):\\n if self.response is None:\\n self._file_path = storage.load_file(self._content_hash)\\n else:\\n fd, self._file_path = tempfile.mkstemp()\\n os.close(fd)\\n content_hash = sha1()\\n with open(self._file_path, 'wb') as fh:\\n for chunk in self.response.iter_content(chunk_size=8192):\\n content_hash.update(chunk)\\n fh.write(chunk)\\n self._remove_file = True\\n chash = content_hash.hexdigest()\\n self._content_hash = storage.archive_file(self._file_path,\\n content_hash=chash)\\n\\n if self.http.cache and self.ok:\\n self.context.set_tag(self.request_id, self.serialize())\\n return self._file_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92159be848c8cbbff4fa5a0b7dab9956\",\n \"score\": \"0.5170318\",\n \"text\": \"def _get_fixed_data(self, length, output_file=None):\\n\\n # check that client is connected\\n self._check_connection()\\n\\n length = int(length)\\n\\n if output_file: # stream data to a file\\n bytes_recv = 0\\n chunk = b\\\"\\\"\\n with open(output_file, \\\"wb\\\") as fd:\\n while bytes_recv < length:\\n chunk = self.socket.recv(self.__class__.CHIRP_LINE_MAX)\\n fd.write(chunk)\\n bytes_recv += len(chunk)\\n return bytes_recv\\n\\n else: # return data to method call\\n data = b\\\"\\\"\\n chunk = b\\\"\\\"\\n while len(data) < length:\\n chunk = self.socket.recv(self.__class__.CHIRP_LINE_MAX)\\n data += chunk\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b6e29f6e1e8712394109431a82290a9\",\n \"score\": \"0.5158794\",\n \"text\": \"def readall(self):\\n self._checkClosed()\\n self._checkReadable()\\n bufsize = DEFAULT_BUFFER_SIZE\\n try:\\n pos = os.lseek(self._fd, 0, SEEK_CUR)\\n end = os.fstat(self._fd).st_size\\n if end >= pos:\\n bufsize = end - pos + 1\\n except OSError:\\n pass\\n\\n result = bytearray()\\n while True:\\n if len(result) >= bufsize:\\n bufsize = len(result)\\n bufsize += max(bufsize, DEFAULT_BUFFER_SIZE)\\n n = bufsize - len(result)\\n try:\\n chunk = os.read(self._fd, n)\\n except BlockingIOError:\\n if result:\\n break\\n return None\\n if not chunk: # reached the end of the file\\n break\\n result += chunk\\n\\n return bytes(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c06c0e1ba146e73e03b19067d377cb31\",\n \"score\": \"0.51585686\",\n \"text\": \"async def read_bytes(path: Path) -> bytes:\\n return await _run(path.read_bytes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c97b2e12d11bfcfd37cb498802d62d58\",\n \"score\": \"0.5158237\",\n \"text\": \"def recv(sock):\\n data = \\\"\\\"\\n file_size = 0\\t\\n file_size_buff = \\\"\\\"\\n # header size buffer\\n file_size_buff = recvAll(sock, 10)\\n try:\\n file_size = int(file_size_buff)\\n # recieve a file given a file size\\n data = recvAll(sock, file_size)\\n except:\\n pass\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89a122cd6846ec5e7200c6d13af6b529\",\n \"score\": \"0.5153641\",\n \"text\": \"def getFile(self, ipaddr, title = \\\"\\\", author = \\\"\\\"):\\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n self.sock.connect((ipaddr,self.port))\\n print \\\"getFile Method: Socket connection established\\\"\\n #Sending request for a file\\n self.sock.send(\\\"GetFile\\\")\\n resp = self.sock.recv(self.size)\\n if resp != \\\"Okay\\\":\\n return\\n data = \\\"Title:\\\"+title+\\\":Author:\\\"+author\\n self.sock.send(data)\\n print \\\"getFile Method: Socket request sent\\\"\\n run = 1\\n strn = \\\"\\\"\\n while(run):\\n temp = self.sock.recv(self.size)\\n self.sock.send(\\\"Okay\\\")\\n if temp != \\\"EOF\\\":\\n strn = strn + temp\\n else:\\n break\\n print \\\"getFile Method: Response received\\\"\\n print strn\\n self.sock.send(\\\"Stop\\\")\\n self.sock.close()\\n return strn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aff250f4d9f45769be8d475df1f4674d\",\n \"score\": \"0.51526767\",\n \"text\": \"def server_recv_data(self) -> bytes:\\n\\t\\tfragments = []\\n\\t\\twhile True:\\n\\t\\t\\tchunk = self.conn.recv(1024)\\n\\t\\t\\tif not chunk:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\tfragments.append(chunk)\\n\\t\\tarr = b''.join(fragments)\\n\\t\\treturn arr\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993888,"cells":{"query_id":{"kind":"string","value":"be3d4e90ce876ad3c24fd666a0ec6399"},"query":{"kind":"string","value":"Takes file_id string. Initializes temp files. Returns tuple of vars. Called by grab_latest_file()"},"positive_passages":{"kind":"list like","value":[{"docid":"5553c248c1e9126d396d91c0abc1ea0c","score":"0.68019724","text":"def _setup_grab_files( self, file_id ):\n ( temp_stdout_filepath, temp_stderr_filepath ) = ( self._make_temp_filepath(u'stdout_vcs'), self._make_temp_filepath(u'stderr_vcs') )\n f_stdout = open( temp_stdout_filepath, u'w' )\n f_stderr = open( temp_stderr_filepath, u'w' )\n f_stdout.write( u'' )\n f_stderr.write( u'' )\n file_url = u'%s/%s.xml' % ( self.VC_XML_URL, file_id )\n xml_destination_path = u'%s/%s.xml' % ( self.XML_DIR_PATH, file_id )\n return ( f_stdout, f_stderr, file_url, xml_destination_path, temp_stdout_filepath, temp_stderr_filepath )","title":""}],"string":"[\n {\n \"docid\": \"5553c248c1e9126d396d91c0abc1ea0c\",\n \"score\": \"0.68019724\",\n \"text\": \"def _setup_grab_files( self, file_id ):\\n ( temp_stdout_filepath, temp_stderr_filepath ) = ( self._make_temp_filepath(u'stdout_vcs'), self._make_temp_filepath(u'stderr_vcs') )\\n f_stdout = open( temp_stdout_filepath, u'w' )\\n f_stderr = open( temp_stderr_filepath, u'w' )\\n f_stdout.write( u'' )\\n f_stderr.write( u'' )\\n file_url = u'%s/%s.xml' % ( self.VC_XML_URL, file_id )\\n xml_destination_path = u'%s/%s.xml' % ( self.XML_DIR_PATH, file_id )\\n return ( f_stdout, f_stderr, file_url, xml_destination_path, temp_stdout_filepath, temp_stderr_filepath )\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3d6872523ef015b79e75b7b6e6599e0d","score":"0.60206676","text":"def initialize_tmpfile(data, directory=None):","title":""},{"docid":"d2e9181334398851a16b4029dd7632b8","score":"0.5896506","text":"def _prepare_temp_file(self):\n if not self._details.__contains__(N.TEMP_FILE) or \\\n self._details[N.TEMP_FILE] is None:\n _temp_file = tempfile.NamedTemporaryFile(delete=False)\n self._details[N.TEMP_FILE] = _temp_file.name\n _temp_file.close()\n return\n if not os.path.exists(self._details[N.TEMP_FILE]):\n _temp_file = tempfile.NamedTemporaryFile(delete=False)\n self._details[N.TEMP_FILE] = _temp_file.name\n _temp_file.close()","title":""},{"docid":"d1bf597ff25050766a674cd129c8e881","score":"0.5788638","text":"def mktemp(root):\n global _tmpfileCounter\n basename = root + `_os.getpid()`\n while 1:\n _tmpfileCounter = _tmpfileCounter + 1\n if _tmpfileCounter <= 26:\n # use letters to start\n suffix = chr(ord(\"a\")+_tmpfileCounter-1)\n else:\n # use numbers once we've used up letters\n suffix = \"_\" + `_tmpfileCounter-26`\n file = basename + suffix\n if not _os.path.exists(Expand(file)):\n return file","title":""},{"docid":"7cb86694d235618e7477aaabdb426f16","score":"0.5782142","text":"def my_init(self):\n self.temp_files = {}\n\n from datetime import timedelta\n self.max_allowed_age = timedelta(hours=6)","title":""},{"docid":"dea19afbc8d6e05911da7228f6527357","score":"0.5739387","text":"def init(cls, files=[], keep_temps=False, force=False):\n cls.counter = defaultdict(int)\n cls.files = files\n cls.temp_files = set()\n cls.keep_temps = keep_temps\n cls.force = force","title":""},{"docid":"a10844eb3259df6a783504f950a6b7ac","score":"0.5734672","text":"def get_temp_file_path_once():\n global_tmp_dir = get_global_temp_dir()\n tmp_file_path = os.path.join(global_tmp_dir, uuid.uuid4().hex)\n GLOBAL_TMP_FILE_PATHS.append(tmp_file_path)\n return tmp_file_path","title":""},{"docid":"39aff160e54dea631b08acbf267cec83","score":"0.57326645","text":"def get_temp_file_path_once():\n global_tmp_dir = get_global_temp_dir()\n tmp_file_path = os.path.join(global_tmp_dir, uuid.uuid4().hex)\n return tmp_file_path","title":""},{"docid":"185b70c6e5d2ce8d90d5455bca2558d5","score":"0.5672035","text":"def _create_temp_file_path(self, file_path):\n self.temp_dir = self.temp_dir or tempfile.mkdtemp(prefix='paralyze')\n # TODO: Create unique file names!\n return os.path.join(self.temp_dir, os.path.basename(file_path))","title":""},{"docid":"95616599688c1b7fcee0294fe9ef3fc1","score":"0.56601363","text":"def __init__(self, id=0, files=None):","title":""},{"docid":"8c2ff40064fb3fac671f6e2df02bc2bc","score":"0.5651953","text":"def _init_temp_dir():\n global _TEMP_DIR\n _TEMP_DIR = tempfile.TemporaryDirectory() # noqa\n atexit.register(_TEMP_DIR.cleanup)","title":""},{"docid":"f9ff9beae678fc07a0454aa801e6558c","score":"0.5621467","text":"def temp_file(self):\n\n temp_file_name = f\"/tmp/test_token_{random.randint(1,1000)}.tmp\"\n open(temp_file_name, 'a').close()\n\n self.temp_files.append(temp_file_name)\n return temp_file_name","title":""},{"docid":"f832548a7113febd0190b6411dd226e0","score":"0.5618245","text":"def _tempfile(self):\n self._debug(\"_tempfile\", \"started\")\n temp_file = grass.tempfile()\n if temp_file is None:\n grass.fatal(_(\"Unable to create temporary files\"))\n\n # list of created tempfiles for destructor\n self.temp_files_to_cleanup.append(temp_file)\n self._debug(\"_tempfile\", \"finished\")\n\n return temp_file","title":""},{"docid":"f8deb47ce2994b7e75f331023e282c76","score":"0.5599414","text":"def initialize_tmpdir(self, data, **kwargs):\n tmpdir = tempfile.mktemp()\n os.mkdir(tmpdir)\n filename = kwargs.get(\"filename\", '')\n fullname = join(tmpdir, basename(filename))\n filedest = open(fullname , \"wb\").write(data)\n return tmpdir, fullname","title":""},{"docid":"a178d4bbb0bf48f76f487bb7054fe5c4","score":"0.55952233","text":"def retr_init_file(self, filename):\n # Check if the temp folder exists\n if not os.path.exists(os.path.dirname('temp_fromfs/' + filename)):\n try:\n os.makedirs(os.path.dirname('temp_fromfs/' + filename))\n except OSError as exc:\n if exc.errno != errno.EEXIST:\n raise\n # Find the object and fetch it\n with open('temp_fromfs/' + filename, 'wb') as fileObject:\n try:\n fileObject.write(self.fs.find_one({'filename': filename}).read())\n except AttributeError:\n return None\n return filename","title":""},{"docid":"e219c94dd71b552890732fcb21f50179","score":"0.5591149","text":"def temp_dir():\n return write_files({})","title":""},{"docid":"8417edb4b182d552782a929307a78333","score":"0.55820805","text":"def svn_io_open_unique_file3(apr_file_t_file, char_temp_path, char_dirpath, svn_io_file_del_t_delete_when, apr_pool_t_result_pool, apr_pool_t_scratch_pool): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"95d6b4b2f7335ae558f4dc4996cc3346","score":"0.5580749","text":"def create_temp_files():\n f = NamedTemporaryFile(mode=\"wb\", delete=False)\n tempFileContents = (\n b\"I'm a temporary file for testing\\nAnd this is the second line for testing\\n\"\n b\"This is the third line for testing.\"\n )\n tempFilePath = f.name\n f.write(tempFileContents)\n f.close()\n return tempFilePath, tempFileContents","title":""},{"docid":"1d3a168aad3d6432bdc7b023d98c113a","score":"0.55535036","text":"def create_files():\n temp_folder = tempfile.mkdtemp()\n os.mkdir(os.path.join(temp_folder, \"log\"))\n executor_outbound = tempfile.NamedTemporaryFile(delete=False).name\n executor_inbound = tempfile.NamedTemporaryFile(delete=False).name\n control_worker_outbound = tempfile.NamedTemporaryFile(delete=False).name\n control_worker_inbound = tempfile.NamedTemporaryFile(delete=False).name\n return (\n temp_folder,\n executor_outbound,\n executor_inbound,\n control_worker_outbound,\n control_worker_inbound,\n )","title":""},{"docid":"ac7d396ab111ae557a2187efabc9bf37","score":"0.551139","text":"def _mktemp(self):\n fd, path = tempfile.mkstemp()\n fh = os.fdopen(fd, 'wt')\n self.addCleanup(os.remove, path)\n return fh, path","title":""},{"docid":"4ace8020ea80c3a69e86fd087942fc53","score":"0.55035037","text":"def grab_latest_file( self, file_id ):\n ## setup\n ( f_stdout, f_stderr, file_url, xml_destination_path, temp_stdout_filepath, temp_stderr_filepath ) = self._setup_grab_files( file_id )\n ## work\n subprocess.call( [u'svn', u'export', u'--force', file_url, xml_destination_path], stdout=f_stdout, stderr=f_stderr )\n ## populate output\n ( f_stdout, f_stderr, var_stdout, var_stderr ) = self._prep_grab_output( f_stdout, f_stderr, temp_stdout_filepath, temp_stderr_filepath )\n ## return\n return_dict = {\n u'stderr': var_stderr, u'stdout': var_stdout,\n u'submitted_file_id': file_id, u'submitted_vc_url': file_url, u'submitted_destination_path': xml_destination_path }\n log.debug( u'in models.Processor.grab_latest_file(); return_dict, ```%s```' % unicode(pprint.pformat(return_dict)) )\n return return_dict","title":""},{"docid":"ea2cafe80dd56f7014e061c1c574673d","score":"0.5489287","text":"def __init__(self, maxFiles=9, idBase=ID_FILE1):","title":""},{"docid":"fe248b4c9b06fff9688988da4854852a","score":"0.54862046","text":"def new_file(id):","title":""},{"docid":"937e713c6cf9798e96cc8d19e93eefed","score":"0.54741615","text":"def _get_temp_filename():\n return os.path.join(tempfile.gettempdir(), f'{str(uuid.uuid4())}.py')","title":""},{"docid":"f06c534b87ef0aeb64992e1eaff12a49","score":"0.5459639","text":"def __init__(self, delete_files=True):\n self._temp_dir = None\n self._delete_files = delete_files","title":""},{"docid":"46d345ff270ae16b8b4b2744fce1419b","score":"0.54021895","text":"def get_temp_file():\n if is_p3k():\n fn = \"/tmp/{0}-{1}\".format(time.time(), random.random())\n return (fn, open(fn, \"w+\"))\n else:\n tf = tempfile.NamedTemporaryFile()\n return (tf.name, tf)","title":""},{"docid":"88fec5f1a2c8f81cb773968c3109b28c","score":"0.5384337","text":"def _temp_analyze_files():\r\n fd, orig_img = mkstemp(suffix='.img', dir=mkdtemp())\r\n orig_hdr = orig_img[:-4] + '.hdr'\r\n fp = file(orig_hdr, 'w+')\r\n fp.close()\r\n return orig_img, orig_hdr","title":""},{"docid":"520dd892ffe4cf823b604566347364ee","score":"0.5377258","text":"def get_new_file_id():\n return str(uuid.uuid1())","title":""},{"docid":"e66a57365c2043a5e86ca54adde5c8bf","score":"0.53549737","text":"def _make_tmp_files():\n fh, name = tempfile.mkstemp()\n os.close(fh)\n os.remove(name)\n\n train_filename = name + '_train.txt'\n model_filename = name + '_model'\n\n fh, name = tempfile.mkstemp()\n os.close(fh)\n os.remove(name)\n\n predict_filename = name + '_pred_.txt'\n return train_filename, model_filename, predict_filename","title":""},{"docid":"f95fab28b452ed412c16f31debe5e146","score":"0.5333638","text":"def setup_temp():\n clean_temp()\n os.mkdir(FILES_DIR)\n os.mkdir(os.path.join(tempfile.gettempdir(), REPO_DIR))","title":""},{"docid":"750b1c3a6d55dc4b31697322cafd71e9","score":"0.53288525","text":"def __init__(self):\n self.file_dict = {}","title":""},{"docid":"3ce76feddd11412a1037b721d71afeba","score":"0.53281385","text":"def make_product_fileinfo(self, file_id):\n file_path = os.path.abspath(file_id)\n file_dir = os.path.dirname(file_path)\n file_basename = os.path.basename(file_path)\n file_basename_noext = os.path.splitext(file_basename)[0]\n hawkeye_dir = os.path.join(file_dir, '__Hawkeye__')\n scanvid_basename = file_basename_noext + '_640x480.mp4'\n scanvid_path = os.path.join(hawkeye_dir, scanvid_basename)\n notes_basename = file_basename_noext + '_notes.pkl'\n notes_path = os.path.join(hawkeye_dir, notes_basename)\n csvfile_path = os.path.join(file_dir, file_basename + '.csv')\n\n result = dict()\n result['file_id'] = file_id\n result['file_path'] = file_path\n result['file_dir'] = file_dir\n result['file_basename'] = file_basename\n result['file_basename_noext'] = file_basename_noext\n result['hawkeye_dir'] = hawkeye_dir\n result['scanvid_basename'] = scanvid_basename\n result['scanvid_path'] = scanvid_path\n result['notes_basename'] = notes_basename\n result['notes_path'] = notes_path\n result['csvfile_path'] = csvfile_path\n return result","title":""},{"docid":"01048a77d64612d5e440096c9d1301a3","score":"0.53219527","text":"def get_temp_file():\n return tempfile.mktemp('.h5')","title":""},{"docid":"572710d6a732a722af94ae63fb880057","score":"0.53143126","text":"def create_temp_files(nb_files):\n return [\n NamedTemporaryFile(prefix=PREFIX_INPUT, suffix=SUFFIX, dir=MEMORY_FS_ROOT)\n for _ in range(nb_files)\n ]","title":""},{"docid":"f4201c18ee5f9f87fb98ac8f9a142c98","score":"0.530706","text":"def get_file_info(file: object) -> tuple:\n fp = getattr(file, \"filename\", None)\n if not fp: # if a file object was provided\n home = os.path.expanduser(\"~\")\n created_dt = datetime.now().replace(microsecond=0).isoformat().replace(\":\", \"\")\n filename = created_dt\n fp = os.path.join(home, filename)\n return fp","title":""},{"docid":"bf8ea7195fe4ba6f091ad91debecd02d","score":"0.5289873","text":"def _initialise_dict(number_train_files, number_test_files):\n file_id_train = {}\n file_id_test = {}\n for i in np.arange(number_train_files):\n file_id_train[i] = []\n file_id_train[f'meta_{i}'] = []\n for i in np.arange(number_test_files):\n file_id_test[i] = []\n file_id_test[f'meta_{i}'] = []\n return file_id_train, file_id_test","title":""},{"docid":"48d8710ee07205e7b96ce6cb08bf3e70","score":"0.52744484","text":"def tempFile(self, tempfile):\n temp_file = ''\n #print tempfile, self.temp_path\n for path in self.temp_path:\n if os.path.isdir(path) and tempfile in os.listdir(path):\n temp_file = os.path.join(path, tempfile)\n break\n return temp_file","title":""},{"docid":"32713155f9cc7b984697a025d976da12","score":"0.5273322","text":"def subHashAndFileSize(file):\n return tuple","title":""},{"docid":"74d13b165de2518b62a48fdf8f59c831","score":"0.5265498","text":"def file_dir(self, file_id: str) -> Path:\n return self.temp_dir / file_id","title":""},{"docid":"d2e377863fd940612161bb64923b2baa","score":"0.5259816","text":"def __copy_to_temp(self):\n randnum = str(random.getrandbits(128))\n tempname = \"cache/temp\" + randnum\n with open(self.filename, 'r') as f:\n with open(tempname, 'w') as temp:\n for line in f:\n temp.write(line)\n return tempname","title":""},{"docid":"66e09148c48ebe9cc9c2904aac5057ae","score":"0.52513206","text":"def __init__(self, *, zip: str = None, id: str = None):\n\n assert zip or id\n self._tmp_dir = tempfile.TemporaryDirectory()\n if zip:\n self._source = zip\n zipfile.ZipFile(zip, mode='r').extractall(self._tmp_dir.name)\n dirs = os.listdir(self._tmp_dir.name)\n assert len(dirs) == 1, 'One package directory expected'\n self._id = dirs[0]\n assert not id or self.id == id, 'Wrong task ID'\n elif id:\n self._id = id\n os.mkdir(os.path.join(self._tmp_dir.name, id), mode=0o755)","title":""},{"docid":"c6342dc4ec9732f652dbe5928ec90f26","score":"0.524917","text":"def get_variables(files):\n var_0 = files[0](u'clt')\n var_0.id = 'clt'\n var_1 = files[0](u'u')\n var_1.id = 'u'\n var_2 = files[0](u'v')\n var_2.id = 'v'\n return var_0, var_1, var_2","title":""},{"docid":"d05f5db7f2f7b61cb019f639c6009d51","score":"0.5232884","text":"def _get_result_files_base(self, temp_dir):\n return temp_dir","title":""},{"docid":"087a6ba7d91dd56ef38a29abf23cbf9c","score":"0.52311677","text":"def get_temp_filename(filename):\n global config_dict\n tmpdir=config_dict['tmpdir'][0]\n return tmpdir+\"/\"+filename","title":""},{"docid":"aefcaa92bf03e1f3096f80b3869636f6","score":"0.52288705","text":"def create_zip_id(zip_id: int, filename: str) -> None:\n pass","title":""},{"docid":"2f476edb3534fd7f36ec9b5d32259fbe","score":"0.51963574","text":"def _get_file_info(self, file_id: str):\n file_info = (\n self.get_conn()\n .files()\n .get(\n fileId=file_id,\n fields=\"id,name,parents\",\n supportsAllDrives=True,\n )\n .execute(num_retries=2)\n )\n return file_info","title":""},{"docid":"b9b0b962bfb2dd0b314cf1d3518947fa","score":"0.5169887","text":"def __init__(self, files: List[str]) -> None:\n self.info: List[Tuple[str, int]] = []\n for file in sorted(files):\n st = os.stat(file)\n self.info.append((file, st.st_mtime_ns))","title":""},{"docid":"68d57574192640b26e045fad24746051","score":"0.5163068","text":"def fill_directory_with_temp_files(directory, num_files, delete=False):\n return [tempfile.NamedTemporaryFile(dir=directory, delete=delete).name\n for _ in range(num_files)]","title":""},{"docid":"73f56990d11ac0230c43a527e9e115d4","score":"0.51582277","text":"def _temp_analyze_files():\r\n fd, orig_img = mkstemp(suffix = '.img')\r\n orig_hdr = orig_img[:-4] + '.hdr'\r\n fp = file(orig_hdr, 'w+')\r\n fp.close()\r\n return orig_img, orig_hdr","title":""},{"docid":"4a4eca3ef856ee517d275446d6f51b6e","score":"0.51562726","text":"def tmp_file():\n return tempfile.mkstemp()[1]","title":""},{"docid":"fe5b2a819ccb3ecf4f8436bc6d6b2b9c","score":"0.51442605","text":"def prepareTestFiles(self):\n\n\t\tmFname = os.path.basename(self.mctalObject.mctalFileName)\n\t\tself.mctalOutFile = tempfile.NamedTemporaryFile(prefix=mFname,delete=False)\n\n\t\trFname = os.path.basename(self.rootFileName)\n\t\tself.rootOutFile = tempfile.NamedTemporaryFile(prefix=rFname, delete=False)","title":""},{"docid":"6c5a31b68d87dccb19202ba33d5c6e33","score":"0.5144006","text":"def __init__(self, file_path, temp_file, file_name, do_rm=True):\n\n self.__file_path = str(file_path)\n self.__temp_file = str(temp_file)\n self.__file_name = str(file_name)\n self.__do_rm = bool(do_rm)","title":""},{"docid":"587ad767cc0079488195af25a065a0e7","score":"0.5143944","text":"def tmp_file(fname):\n import uuid\n\n return fname + \".\" + str(uuid.uuid4())","title":""},{"docid":"67f7ceccfeab284a84bd0ea4a726601c","score":"0.5130646","text":"def __load_temp_file(self):\n if not osp.isfile(self.TEMPFILE_PATH):\n # Creating temporary file\n default = ['# -*- coding: utf-8 -*-', '\"\"\"', \n 'Tellurium oscillation', '\"\"\"',\n '', 'import tellurium as te', 'import roadrunner',\n 'import antimony', '',\n \"r = te.loada ('''\", 'model feedback()', ' // Reactions:',\n ' J0: $X0 -> S1; (VM1 * (X0 - S1/Keq1))/(1 + X0 + S1 + S4^h);',\n ' J1: S1 -> S2; (10 * S1 - 2 * S2) / (1 + S1 + S2);',\n ' J2: S2 -> S3; (10 * S2 - 2 * S3) / (1 + S2 + S3);',\n ' J3: S3 -> S4; (10 * S3 - 2 * S4) / (1 + S3 + S4);',\n ' J4: S4 -> $X1; (V4 * S4) / (KS4 + S4);','',\n ' // Species initializations:', ' S1 = 0; S2 = 0; S3 = 0;',\n ' S4 = 0; X0 = 10; X1 = 0;','', ' // Variable initialization:',\n ' VM1 = 10; Keq1 = 10; h = 10; V4 = 2.5; KS4 = 0.5;',\n\"end''')\", '', 'result = r.simulate(0, 40, 500)', 'r.plot(result)', '']\n text = os.linesep.join([encoding.to_unicode(qstr)\n for qstr in default])\n try:\n encoding.write(to_text_string(text), self.TEMPFILE_PATH,\n 'utf-8')\n except EnvironmentError:\n self.new()\n return\n\n self.load(self.TEMPFILE_PATH)","title":""},{"docid":"6736097adef4f58e243c7f626001264b","score":"0.511258","text":"def get_temp_path(self, *args):\n if self._id is None:\n self._id = str(uuid.uuid4())\n return os.path.join(\"/var/tmp\", \"xii-{}-start\".format(self._id), *args)","title":""},{"docid":"62bb02a26ca1dd9e6fa82ab642883b41","score":"0.51123244","text":"def list_tuples(self):\n for temp_file in self.__registered_file:\n entry = self.__registered_file[temp_file]\n yield (entry.temp_file, entry.file_path)","title":""},{"docid":"8a6df917244c734589b147b22adb404e","score":"0.511074","text":"def __init__(self, root_directory=None, file_id=''):\n self.raw_files = []\n self.dataset_variables = []\n self.dataset_levels = []\n self.dataset_dates = None\n if root_directory is None:\n self._root_directory = '%s/.era5' % os.path.expanduser('~')\n else:\n self._root_directory = root_directory\n self._file_id = file_id\n self._delete_temp = False\n self.level_coord = [1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450,\n 500, 550, 600, 650, 700, 750] + list(range(775, 1001, 25))\n self.inverse_lat = True\n # Data\n self.Dataset = None\n self.basemap = None\n self._lat_array = None\n self._lon_array = None","title":""},{"docid":"8f3d40fc9ea132595b78b6a5ed9eb468","score":"0.5103816","text":"def tempdir():\n return mkdtemp()","title":""},{"docid":"3ce65a2663e06d5a664bd86322858486","score":"0.50825053","text":"def tempdir():\n return tempfile.TemporaryDirectory()","title":""},{"docid":"c812bc26b2535c5f36aec17a784703fc","score":"0.50797063","text":"def setup_temp_data():\n temp_folder = os.getcwd()+'/temp'\n if not os.path.isdir(temp_folder):\n os.mkdir(temp_folder)\n print(\"\\nsetting up temp folder\")\n\n # setup files, provide memmap type\n img = np.random.random((2048, 2048))\n np.save(temp_folder+'/img.npy', img)\n print(\"setting up temp file\")\n yield np.memmap(temp_folder+'/img.npy', shape=(2048, 2048), dtype=np.uint8)\n\n # breakdown files\n if os.path.isfile(temp_folder+'/img.npy'):\n os.remove(temp_folder+'/img.npy')\n print(\"\\nbreaking down temp file\")\n if os.path.isdir(temp_folder):\n os.rmdir(temp_folder)\n print(\"breaking down temp folder\")","title":""},{"docid":"534a0d57971c4f67328eda6153ff63a4","score":"0.50745153","text":"def _prepare_filenames(self):\n self.timestamp = time.strftime('%Y-%m-%d_%H-%M-%S')\n self._logfile_prefix = '%s.%s' % (self.base_path, self.timestamp)\n self.latest_logfile = '%s.latest.log' % self.base_path\n self.default_latest_logfile = ('%s/%s.log' %\n (self.base_dir, self.DEFAULT_LINK_FILE))\n # For string representation\n self.__logfile_blob = '%s.*' % self._logfile_prefix\n self.debug_logfile = '%s.DEBUG.log' % self._logfile_prefix\n # Designates the file to link 'recent' to\n self.linked_logfile = self.debug_logfile","title":""},{"docid":"35f5c0755b2ec09b4bbcc6a6673b6ff8","score":"0.507082","text":"def getTempFileName(self):\n return 'hellanzb-tmp-' + self.nzb.archiveName + '.file' + str(self.number).zfill(4)","title":""},{"docid":"3f36b916c3b5ee7841b29c2f9b83dcef","score":"0.50700736","text":"def get_file(self, *, file_id):\n pass","title":""},{"docid":"3bddae26e505f078721d4cdfa7997b17","score":"0.5065458","text":"def __init__(self):\n fd, self.filename = tempfile.mkstemp()\n self.file = os.fdopen(fd, 'w')","title":""},{"docid":"a5482ea2bfdea419425db49fd67b63e4","score":"0.50612444","text":"def get_file(run_id, file_name):\n target_file = TEMP_DIR + run_id + '/' + file_name","title":""},{"docid":"725fb2918146af5b8878338a1f1072f2","score":"0.50565153","text":"def temporary_file(file_name: str = None) -> Iterator[Path]:\n with temporary_directory() as tempdir:\n file_name = file_name or str(uuid.uuid4())\n file_path = tempdir / file_name\n file_path.touch()\n yield file_path","title":""},{"docid":"5579f337cb31aeb1c42e048fe7f88b54","score":"0.5054828","text":"def _mktmpfile(data):\n fn = tmp_path_factory.mktemp(\"mktmpfile\")\n fn_file = str(fn) + \"/file\"\n with open(fn_file, \"w\") as fn_handle:\n fn_handle.write(data)\n return fn_file","title":""},{"docid":"7fb040770df3c0c4d3599201656ca06b","score":"0.50515854","text":"def copy_temp(self):\n # Create temporary file\n paths = {}\n for k, v in self._packets.items():\n paths[k] = IP.InformationPacket(_path.Path(_temp.NamedTemporaryFile(delete=True).name))\n self._temp_packets = PacketRegister(paths)\n return self._temp_packets","title":""},{"docid":"a922830388b3aa20be0c7fd0fa2c9c4d","score":"0.50493103","text":"def generateFileIdMap():\n\tpass","title":""},{"docid":"0f62267f7888f31a70eaabc59bb0c155","score":"0.50465375","text":"def _push_temp_file(self, prefix=None, suffix=None):\n # we want to make sure our temp naming convention is consistent\n # this is important for hashing zip files and gz files, because the name of the internal\n # file becomes part of the content and then hash changes\n\n # temp filename is based on the assumption\n\n # put a consistent the file into a temp folder because the filename is part of\n # the compressed artifact and we want consistency. After that we rename compressed file to temp file and\n # delete temp folder\n temp_folder = mkdtemp(prefix='artifacts_')\n local_filename = Path(temp_folder) / (str(prefix).rstrip(\".\") + \".\" + str(suffix).lstrip(\".\"))\n local_filename = local_filename.as_posix()\n self._temp_files_lookup[local_filename] = (temp_folder, deepcopy(prefix), deepcopy(suffix))\n return local_filename","title":""},{"docid":"cfb1c8951d0abf63d25d4711168ac697","score":"0.5045576","text":"def __init__(self, file_id: int):\n super().__init__(file_id)","title":""},{"docid":"4c93664652413d69fefb899ec8c52250","score":"0.50392187","text":"def pre_queue(self, filename, file_handle):\n artifact = utils.single(\n sorted([shared_file for shared_file in self.artifact_service.shared_files()\n if shared_file.name == file_handle], key=lambda x: x.id))\n full_path = os.path.join(self.temp_path, filename)\n return full_path, artifact","title":""},{"docid":"182121653f8387f3eb756067c1af182d","score":"0.50379026","text":"def _temp_dict_file_name():\n return '__shared_dictionary__'","title":""},{"docid":"10c4136ba63ad5fdcd3ab73c18286da8","score":"0.50337845","text":"def __call__(self, file_id: int, forge_file_name: str = None, datafile_id: int = None) -> Union[None, TempFile]:\n\n\t\tif file_id == 0:\n\t\t\treturn\n\n\t\tif forge_file_name is not None and datafile_id is None:\n\t\t\tif file_id in self._temp_files and forge_file_name == self._temp_files[file_id][0]:\n\t\t\t\tdatafile_id = self._temp_files[file_id][1]\n\t\t\telse:\n\t\t\t\t# preferentially use one found in the forgeFile asked but look in others if needed\n\t\t\t\tif forge_file_name in self.pyUbiForge.forge_files and file_id in self.pyUbiForge.forge_files[forge_file_name].datafiles:\n\t\t\t\t\tdatafile_id = file_id\n\t\t\t\telse:\n\t\t\t\t\tforge_file_name, datafile_id = self._light_dictionary.get(file_id, forge_file_name)\n\n\t\tif forge_file_name is None:\n\t\t\tforge_file_name = next((fF for fF in self.pyUbiForge.forge_files.keys() if file_id in self.pyUbiForge.forge_files[fF].datafiles), None)\n\t\t\tif forge_file_name is None:\n\t\t\t\tforge_file_name, datafile_id = self._light_dictionary.get(file_id)\n\t\t\t\tif datafile_id is None:\n\t\t\t\t\treturn\n\t\t\telse:\n\t\t\t\tdatafile_id = file_id\n\n\t\tif not (file_id in self._temp_files and forge_file_name == self._temp_files[file_id][0] and datafile_id == self._temp_files[file_id][1]):\n\t\t\tself.pyUbiForge.forge_files[forge_file_name].decompress_datafile(datafile_id)\n\t\tself.refresh_usage(file_id)\n\t\tif file_id in self._temp_files and forge_file_name == self._temp_files[file_id][0] and datafile_id == self._temp_files[file_id][1]:\n\t\t\treturn TempFile(\n\t\t\t\tself.pyUbiForge,\n\t\t\t\tforge_file_name,\n\t\t\t\tdatafile_id,\n\t\t\t\tfile_id,\n\t\t\t\tf'{self._temp_files[file_id][2]:08X}',\n\t\t\t\tself._temp_files[file_id][3],\n\t\t\t\tself._temp_files[file_id][4]\n\t\t\t)\n\t\telse:\n\t\t\treturn","title":""},{"docid":"a07fb29170c06fd7b8b3f5448e65490f","score":"0.503274","text":"def tempdir():\n return tempfile.gettempdir()","title":""},{"docid":"c5ebfeff05ad19abedc7eeb36b7af335","score":"0.50224966","text":"def file_vars():\n\n server_flag = True\n if server_flag:\n n_threads = 11\n loadpath = './loadpath'\n savepath = './savepath'\n else:\n # e.g. LBL desktop\n n_threads = 4\n loadpath = './loadpath'\n savepath = './savepath'\n loadglob = 'load_*.h5'\n saveglob = 'save_*.h5'\n\n v = 1 # verbosity\n\n return server_flag, loadpath, savepath, loadglob, saveglob, v, n_threads","title":""},{"docid":"2698f2922626ace8d44b8f26490afede","score":"0.50174403","text":"def _file_setup(self, incoming_dictionary, create_dict):\n\n if \"file_collection_id\" in incoming_dictionary:\n\n create_dict['file_collection_id'] = incoming_dictionary[\n 'file_collection_id']\n elif \"paths\" in incoming_dictionary:\n # transform file paths to file_collection_id\n create_dict['file_collection_id'] = self.file_collection_controller.\\\n create(incoming_dictionary['paths']).id\n else:\n # create some default file collection\n create_dict['file_collection_id'] = self.file_collection_controller.\\\n create([]).id","title":""},{"docid":"960e391564d7361ba9a3ad5dab4af3e1","score":"0.5017395","text":"def tempdir():\n return tempfile.mkdtemp()","title":""},{"docid":"d12d62c3199d091ee4bef611ee8dfb7a","score":"0.501325","text":"def read_file(file_id: str) -> Tuple[bytes, int, str]:\n try:\n file_info = demisto.getFilePath(file_id)\n with open(file_info['path'], 'rb') as file_data:\n data = file_data.read()\n file_size = os.path.getsize(file_info['path'])\n return data, file_size, file_info['name']\n except Exception as e:\n raise Exception(f'Unable to read file with id {file_id}', e)","title":""},{"docid":"93169e2bcbd249d2a66406ad44cd955f","score":"0.5012287","text":"def _tmp(self):\n tmp = tempfile.NamedTemporaryFile(prefix='tmp', suffix='.json',\n delete=False)\n return tmp.name","title":""},{"docid":"0bb75c7e5b49d4cc084af00b229ac4f7","score":"0.50119764","text":"def _tmp(self):\n tmpfn = tempfile.NamedTemporaryFile(prefix='tmp',\n suffix='.json',\n delete=False)\n return tmpfn.name","title":""},{"docid":"ee7b4d3b52f70d9e1cb7b7b72af83e48","score":"0.5011796","text":"def test_file_file_uuid_get(self):\n pass","title":""},{"docid":"ff3488b72bbfa6852ab078e2aabb3fdc","score":"0.5009506","text":"def tempfile ( suffix = '' , prefix = 'ostap-tmp-' , dir = None , date = True , keep = False ) :\n fname = CleanUp.get_temp_file ( suffix = suffix , prefix = prefix ,\n dir = dir , date = date )\n assert not os.path.exists ( fname )\n CleanUp._tmpfiles.add ( fname )\n \n if keep : CleanUp._protected.add ( fname )\n return fname","title":""},{"docid":"0ef22aa82b04228eb4c0fd44ceeb0e31","score":"0.50044686","text":"def add_tempfile(self, filename):","title":""},{"docid":"00e93ea11b1e33f8330734408e6d06bd","score":"0.5000769","text":"def GetTempDir(self):","title":""},{"docid":"47002122e947c489256c92715b13badb","score":"0.49906933","text":"def _create_file(self):\n f = NamedTemporaryFile(delete=False)\n f.write('\\n'.join([\n '%s\\t%s' % (self._key1, self._value1),\n '%s\\t%s' % (self._key2, self._value2)\n ]).encode('ascii'))\n temp_path = f.name\n f.close()\n return temp_path","title":""},{"docid":"e8a5191ab49931c6e04f53395d2f61d3","score":"0.49861383","text":"def process_file( self, file_id, grab_latest_file, display_status ):\n process_dict = {\n u'a__grab_latest_file': None,\n u'b__grab_source_xml': None,\n u'c__run_munger': None,\n u'd__make_initial_solr_doc': None,\n u'e__update_display_facet': None,\n u'f__post_to_solr': None\n }\n process_dict[u'a__grab_latest_file'] = self.grab_latest_file(\n file_id )\n process_dict[u'b__grab_source_xml'] = self.grab_original_xml(\n file_id )\n process_dict[u'c__run_munger'] = self.run_munger(\n source_xml=process_dict[u'b__grab_source_xml'][u'xml'] )\n process_dict[u'd__make_initial_solr_doc'] = self.make_initial_solr_doc(\n munged_xml=process_dict[u'c__run_munger'][u'munged_xml'] )\n process_dict[u'e__update_display_facet'] = self.update_display_facet(\n initial_solr_xml=process_dict[u'd__make_initial_solr_doc'][u'transformed_xml'], display_status=display_status )\n process_dict[u'f__post_to_solr'] = self.update_solr(\n updated_solr_xml=process_dict[u'e__update_display_facet'][u'updated_xml'] )\n return process_dict","title":""},{"docid":"0b67bd414839dbafcd75a1a9520d2b1c","score":"0.49815804","text":"def get_temp_file ( suffix = '' , prefix = 'ostap-tmp-' , dir = None , date = True ) :\n with UseTmpDir ( tmp_dir () ) :\n if date :\n now = datetime.datetime.now()\n if prefix and prefix.endswith('-') :\n prefix = \"%s%s-\" % ( prefix , now.strftime ( date_format ) )\n else :\n prefix = \"%s-%s-\" % ( prefix , now.strftime ( date_format ) )\n\n td = tempfile.gettempdir()\n if user and not user in td : prefix = '%s%s-' % ( prefix , user )\n\n with tempfile.NamedTemporaryFile ( suffix = suffix ,\n prefix = prefix ,\n dir = dir , \n delete = False ) as tfile :\n fname = tfile.name\n \n os.unlink ( fname ) \n assert not os.path.exists ( fname )\n logger.verbose ( 'temporary file requested %s' % fname )\n return fname","title":""},{"docid":"c6a04b204b9c8a141240ddf17e2f366f","score":"0.49793208","text":"def _create_tmp(self): #pragma NO COVERAGE\n # Skipping coverage because the mailbox module doesn't let us\n # hook the file creation.\n klass = self.__class__\n now = time.time()\n now_i, now_f = math.modf(now)\n hostname = socket.gethostname()\n if '/' in hostname:\n hostname = hostname.replace('/', r'\\057')\n if ':' in hostname:\n hostname = hostname.replace(':', r'\\072')\n uniq = \"%010d.M%06dP%06dQ%06d.%s\" % (now,\n now_f % 1e6,\n os.getpid(),\n klass._count,\n hostname)\n path = os.path.join(self._path, 'tmp', uniq)\n try:\n os.stat(path)\n except OSError, e:\n if e.errno == errno.ENOENT:\n klass._count += 1\n try:\n return mailbox._create_carefully(path)\n except OSError, e:\n if e.errno != errno.EEXIST:\n raise\n else:\n raise\n\n # Fall through to here if stat succeeded or open raised EEXIST.\n raise mailbox.ExternalClashError(\n 'Name clash prevented file creation: %s' % path)","title":""},{"docid":"8f7bf65e216a45523cb089ce542de6b6","score":"0.4978764","text":"def generate_fileInfo_objects(self):\n if not self.skip_files_list:\n self.skip_files_list = []\n for file_in_dir in self.files_in_dir:\n if file_in_dir not in self.skip_files_list:\n self.file_infos[file_in_dir] = fileInfo(os.path.join(self.dirpath, file_in_dir), self.blocksize)\n return self.file_infos","title":""},{"docid":"2214279b9618f989b927cabd548d34a2","score":"0.49775958","text":"def load_init_files(self):\n listOfFiles = []\n for filetype in ['*.h5', '*.json', '*.hdf5', '*.pkl']:\n listOfFiles.extend(g.glob('binFiles/' + filetype))\n for filename in listOfFiles:\n temp_name = filename.split('/')[1]\n if not self.fs.exists(filename=temp_name):\n with io.FileIO(filename, 'r') as fileObject:\n objectId = self.fs.put(fileObject, filename=temp_name)","title":""},{"docid":"31fc27143beb705c36323997648477e8","score":"0.4977542","text":"def create_temp_file():\n temp_file = tempfile.NamedTemporaryFile(delete=False)\n temp_file.close()\n return temp_file.name","title":""},{"docid":"6d33a92d894f22fb1cca564800f6906a","score":"0.497457","text":"def process_IN_CREATE(self, event):\n self.sleepshop()\n\n if event.pathname.endswith('.part'):\n import datetime\n self.temp_files[event.pathname] = datetime.datetime.now()","title":""},{"docid":"3f9a6a2094149035c120629d95f086a8","score":"0.4974205","text":"def _tempFile(self, head=\"tmp\", ext=\".tif\"):\n if (not hasattr(self, \"_TMPDIR\")):\n # Create a temporary directory to use with this shape (and associated processes)\n self._TMPDIR = TemporaryDirectory()\n return NamedTemporaryFile(suffix=ext, prefix=head, dir=self._TMPDIR.name, delete=True).name","title":""},{"docid":"7557a84822f9c4449c19fba85b51a37b","score":"0.4967648","text":"def get_file_data(self, vault_id, file_id):\n raise NotImplementedError","title":""},{"docid":"b36bb9c38b1f2874ce394c136f9acddd","score":"0.49650565","text":"def retrieve_from(self, file_id, **kwargs):\n\t\treturn None","title":""},{"docid":"8de4505c9849ed4bb73f822349694c4d","score":"0.4963452","text":"def split_file_dummy(changed_file, project_paths):\n return (None, changed_file)","title":""},{"docid":"75c7f86e1a5fff0f0a93b5e39d0d71f4","score":"0.4952642","text":"def get_file_by_id(self, id):\n connection = None\n cur = None\n try:\n connection = self.pool.get_connection_from_pool()\n while connection is None:\n connection = self.pool.get_connection_from_pool()\n cur = connection.cursor()\n cur.execute('SELECT id, id_user, hash, name, file_system_path FROM files WHERE id = %s;', (id))\n connection.commit()\n file = File()\n i = 0\n for row in cur:\n file.id = row[0]\n file.user_id = row[1]\n file.hash = row[2]\n file.name = row[3]\n file.file_system_path = row[4]\n i += 1\n if i == 1:\n return file\n return None\n except pymysql.MySQLError as ex:\n self.logger.log_error('FileManager.get_file_by_id', ex)\n return None\n finally:\n if cur:\n cur.close()\n self.pool.release_connection(connection)","title":""},{"docid":"2852432b3d48df5dca90b06765aa5052","score":"0.49515566","text":"def get_temp_furlfile(filename):\n return tempfile.mktemp(dir=os.path.dirname(filename),\n prefix=os.path.basename(filename))","title":""},{"docid":"e6bd3f544a3be2f7bbd3feb95036e679","score":"0.49499393","text":"def get_file_data():\n\n # Make the subdirectory specified by DATA_PATH within the\n # directory DATA_PARENT, if it does not exist already. Generated\n # test data files will go here. \n if os.listdir(settings.DATA_PARENT).count(settings.DATA_FOLDER) == 0:\n os.mkdir(settings.DATA_PATH)\n if os.listdir(settings.DATA_PATH).count('templates') == 0:\n os.mkdir( ''.join([settings.DATA_PATH, 'templates/']) )\n if os.listdir(settings.DATA_PATH).count('bal_count_tot') == 0:\n os.mkdir( ''.join([settings.DATA_PATH, 'bal_count_tot/']) )\n if os.listdir(settings.DATA_PATH).count('tab_aggr') == 0:\n os.mkdir( ''.join([settings.DATA_PATH, 'tab_aggr/']) )\n if os.listdir(settings.DATA_PATH).count('reports') == 0:\n os.mkdir( ''.join([settings.DATA_PATH, 'reports/']) )\n\n # Get a list of files so far generated, by type. Leave off the .yml\n # and .xml file extensions, as well as redundancies.\n templates = os.listdir( ''.join([settings.DATA_PATH, 'templates/']) )\n juris_files = []\n prec_files = []\n for i in range(0, len(templates)):\n if templates[i][len(templates[i]) - len('.yml'):] == '.yml':\n a = audit_header.AuditHeader()\n with open( '%stemplates/%s' % (settings.DATA_PATH,\n templates[i]),'r' ) as s:\n a.load_from_file(s)\n if a.type == 'jurisdiction_slate':\n juris_files.append \\\n (templates[i][:len(templates[i]) - len('.yml')])\n else:\n prec_files.append \\\n (templates[i][:len(templates[i]) - len('.yml')])\n templates[i] = templates[i][:templates[i].rfind('.')]\n template_files = set(templates)\n bal_files = os.listdir( ''.join([settings.DATA_PATH, 'bal_count_tot/']) )\n for i in range(0, len(bal_files)):\n bal_files[i] = bal_files[i][:bal_files[i].rfind('.')]\n bal_files = set(bal_files)\n tab_files = os.listdir( ''.join([settings.DATA_PATH + 'tab_aggr/']) )\n for i in range(0, len(tab_files)):\n tab_files[i] = tab_files[i][:tab_files[i].rfind('.')]\n no_log_files = []\n for i in tab_files:\n if tab_files.count(i) != 1:\n no_log_files.append(i)\n no_log_files = set(no_log_files)\n tab_files = set(tab_files)\n report_files = os.listdir( ''.join([settings.DATA_PATH, 'reports/']) )\n for i in range(0, len(report_files)):\n report_files[i] = report_files[i][:report_files[i].rfind('_report')]\n report_files = set(report_files)\n\n tdg_files = set(prec_files).union(set(juris_files)).union(bal_files)\n merge_files = bal_files.union(no_log_files) \n\n # Get version / last revision info from file\n with open('VERSION', 'r') as stream:\n version = stream.readlines()\n\n return Context({'prec_files':prec_files, 'juris_files':juris_files,\n 'bal_files':bal_files, 'tdg_files':tdg_files,\n 'tab_files':tab_files, 'merge_files':merge_files,\n 'report_files':report_files, 'no_log_files': no_log_files,\n 'template_files':template_files, 'version':version})","title":""},{"docid":"29d6b0a759ff9df5b9d480e85450d4d1","score":"0.4939915","text":"def pull_templates(self) -> Tuple[List[str], List[str]]:\n self.logger.info(\n f'Initialising templates from bucket \"{self.remote_template_bucket}\"'\n )\n\n filenames: Generator[str, None, None] = (\n obj.object_name for obj in self.minio_instance.list_objects(self.remote_template_bucket)\n )\n successes: List[str] = []\n fails: List[str] = []\n for filename in filenames:\n try:\n self.pull_template(filename)\n successes.append(filename)\n except:\n fails.append(filename)\n\n self.logger.info(\n f'Initialisation finished for bucket \"{self.remote_template_bucket}\"'\n )\n return successes, fails","title":""}],"string":"[\n {\n \"docid\": \"3d6872523ef015b79e75b7b6e6599e0d\",\n \"score\": \"0.60206676\",\n \"text\": \"def initialize_tmpfile(data, directory=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2e9181334398851a16b4029dd7632b8\",\n \"score\": \"0.5896506\",\n \"text\": \"def _prepare_temp_file(self):\\n if not self._details.__contains__(N.TEMP_FILE) or \\\\\\n self._details[N.TEMP_FILE] is None:\\n _temp_file = tempfile.NamedTemporaryFile(delete=False)\\n self._details[N.TEMP_FILE] = _temp_file.name\\n _temp_file.close()\\n return\\n if not os.path.exists(self._details[N.TEMP_FILE]):\\n _temp_file = tempfile.NamedTemporaryFile(delete=False)\\n self._details[N.TEMP_FILE] = _temp_file.name\\n _temp_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1bf597ff25050766a674cd129c8e881\",\n \"score\": \"0.5788638\",\n \"text\": \"def mktemp(root):\\n global _tmpfileCounter\\n basename = root + `_os.getpid()`\\n while 1:\\n _tmpfileCounter = _tmpfileCounter + 1\\n if _tmpfileCounter <= 26:\\n # use letters to start\\n suffix = chr(ord(\\\"a\\\")+_tmpfileCounter-1)\\n else:\\n # use numbers once we've used up letters\\n suffix = \\\"_\\\" + `_tmpfileCounter-26`\\n file = basename + suffix\\n if not _os.path.exists(Expand(file)):\\n return file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cb86694d235618e7477aaabdb426f16\",\n \"score\": \"0.5782142\",\n \"text\": \"def my_init(self):\\n self.temp_files = {}\\n\\n from datetime import timedelta\\n self.max_allowed_age = timedelta(hours=6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dea19afbc8d6e05911da7228f6527357\",\n \"score\": \"0.5739387\",\n \"text\": \"def init(cls, files=[], keep_temps=False, force=False):\\n cls.counter = defaultdict(int)\\n cls.files = files\\n cls.temp_files = set()\\n cls.keep_temps = keep_temps\\n cls.force = force\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a10844eb3259df6a783504f950a6b7ac\",\n \"score\": \"0.5734672\",\n \"text\": \"def get_temp_file_path_once():\\n global_tmp_dir = get_global_temp_dir()\\n tmp_file_path = os.path.join(global_tmp_dir, uuid.uuid4().hex)\\n GLOBAL_TMP_FILE_PATHS.append(tmp_file_path)\\n return tmp_file_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39aff160e54dea631b08acbf267cec83\",\n \"score\": \"0.57326645\",\n \"text\": \"def get_temp_file_path_once():\\n global_tmp_dir = get_global_temp_dir()\\n tmp_file_path = os.path.join(global_tmp_dir, uuid.uuid4().hex)\\n return tmp_file_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"185b70c6e5d2ce8d90d5455bca2558d5\",\n \"score\": \"0.5672035\",\n \"text\": \"def _create_temp_file_path(self, file_path):\\n self.temp_dir = self.temp_dir or tempfile.mkdtemp(prefix='paralyze')\\n # TODO: Create unique file names!\\n return os.path.join(self.temp_dir, os.path.basename(file_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95616599688c1b7fcee0294fe9ef3fc1\",\n \"score\": \"0.56601363\",\n \"text\": \"def __init__(self, id=0, files=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c2ff40064fb3fac671f6e2df02bc2bc\",\n \"score\": \"0.5651953\",\n \"text\": \"def _init_temp_dir():\\n global _TEMP_DIR\\n _TEMP_DIR = tempfile.TemporaryDirectory() # noqa\\n atexit.register(_TEMP_DIR.cleanup)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9ff9beae678fc07a0454aa801e6558c\",\n \"score\": \"0.5621467\",\n \"text\": \"def temp_file(self):\\n\\n temp_file_name = f\\\"/tmp/test_token_{random.randint(1,1000)}.tmp\\\"\\n open(temp_file_name, 'a').close()\\n\\n self.temp_files.append(temp_file_name)\\n return temp_file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f832548a7113febd0190b6411dd226e0\",\n \"score\": \"0.5618245\",\n \"text\": \"def _tempfile(self):\\n self._debug(\\\"_tempfile\\\", \\\"started\\\")\\n temp_file = grass.tempfile()\\n if temp_file is None:\\n grass.fatal(_(\\\"Unable to create temporary files\\\"))\\n\\n # list of created tempfiles for destructor\\n self.temp_files_to_cleanup.append(temp_file)\\n self._debug(\\\"_tempfile\\\", \\\"finished\\\")\\n\\n return temp_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8deb47ce2994b7e75f331023e282c76\",\n \"score\": \"0.5599414\",\n \"text\": \"def initialize_tmpdir(self, data, **kwargs):\\n tmpdir = tempfile.mktemp()\\n os.mkdir(tmpdir)\\n filename = kwargs.get(\\\"filename\\\", '')\\n fullname = join(tmpdir, basename(filename))\\n filedest = open(fullname , \\\"wb\\\").write(data)\\n return tmpdir, fullname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a178d4bbb0bf48f76f487bb7054fe5c4\",\n \"score\": \"0.55952233\",\n \"text\": \"def retr_init_file(self, filename):\\n # Check if the temp folder exists\\n if not os.path.exists(os.path.dirname('temp_fromfs/' + filename)):\\n try:\\n os.makedirs(os.path.dirname('temp_fromfs/' + filename))\\n except OSError as exc:\\n if exc.errno != errno.EEXIST:\\n raise\\n # Find the object and fetch it\\n with open('temp_fromfs/' + filename, 'wb') as fileObject:\\n try:\\n fileObject.write(self.fs.find_one({'filename': filename}).read())\\n except AttributeError:\\n return None\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e219c94dd71b552890732fcb21f50179\",\n \"score\": \"0.5591149\",\n \"text\": \"def temp_dir():\\n return write_files({})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8417edb4b182d552782a929307a78333\",\n \"score\": \"0.55820805\",\n \"text\": \"def svn_io_open_unique_file3(apr_file_t_file, char_temp_path, char_dirpath, svn_io_file_del_t_delete_when, apr_pool_t_result_pool, apr_pool_t_scratch_pool): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95d6b4b2f7335ae558f4dc4996cc3346\",\n \"score\": \"0.5580749\",\n \"text\": \"def create_temp_files():\\n f = NamedTemporaryFile(mode=\\\"wb\\\", delete=False)\\n tempFileContents = (\\n b\\\"I'm a temporary file for testing\\\\nAnd this is the second line for testing\\\\n\\\"\\n b\\\"This is the third line for testing.\\\"\\n )\\n tempFilePath = f.name\\n f.write(tempFileContents)\\n f.close()\\n return tempFilePath, tempFileContents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d3a168aad3d6432bdc7b023d98c113a\",\n \"score\": \"0.55535036\",\n \"text\": \"def create_files():\\n temp_folder = tempfile.mkdtemp()\\n os.mkdir(os.path.join(temp_folder, \\\"log\\\"))\\n executor_outbound = tempfile.NamedTemporaryFile(delete=False).name\\n executor_inbound = tempfile.NamedTemporaryFile(delete=False).name\\n control_worker_outbound = tempfile.NamedTemporaryFile(delete=False).name\\n control_worker_inbound = tempfile.NamedTemporaryFile(delete=False).name\\n return (\\n temp_folder,\\n executor_outbound,\\n executor_inbound,\\n control_worker_outbound,\\n control_worker_inbound,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac7d396ab111ae557a2187efabc9bf37\",\n \"score\": \"0.551139\",\n \"text\": \"def _mktemp(self):\\n fd, path = tempfile.mkstemp()\\n fh = os.fdopen(fd, 'wt')\\n self.addCleanup(os.remove, path)\\n return fh, path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ace8020ea80c3a69e86fd087942fc53\",\n \"score\": \"0.55035037\",\n \"text\": \"def grab_latest_file( self, file_id ):\\n ## setup\\n ( f_stdout, f_stderr, file_url, xml_destination_path, temp_stdout_filepath, temp_stderr_filepath ) = self._setup_grab_files( file_id )\\n ## work\\n subprocess.call( [u'svn', u'export', u'--force', file_url, xml_destination_path], stdout=f_stdout, stderr=f_stderr )\\n ## populate output\\n ( f_stdout, f_stderr, var_stdout, var_stderr ) = self._prep_grab_output( f_stdout, f_stderr, temp_stdout_filepath, temp_stderr_filepath )\\n ## return\\n return_dict = {\\n u'stderr': var_stderr, u'stdout': var_stdout,\\n u'submitted_file_id': file_id, u'submitted_vc_url': file_url, u'submitted_destination_path': xml_destination_path }\\n log.debug( u'in models.Processor.grab_latest_file(); return_dict, ```%s```' % unicode(pprint.pformat(return_dict)) )\\n return return_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea2cafe80dd56f7014e061c1c574673d\",\n \"score\": \"0.5489287\",\n \"text\": \"def __init__(self, maxFiles=9, idBase=ID_FILE1):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe248b4c9b06fff9688988da4854852a\",\n \"score\": \"0.54862046\",\n \"text\": \"def new_file(id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"937e713c6cf9798e96cc8d19e93eefed\",\n \"score\": \"0.54741615\",\n \"text\": \"def _get_temp_filename():\\n return os.path.join(tempfile.gettempdir(), f'{str(uuid.uuid4())}.py')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f06c534b87ef0aeb64992e1eaff12a49\",\n \"score\": \"0.5459639\",\n \"text\": \"def __init__(self, delete_files=True):\\n self._temp_dir = None\\n self._delete_files = delete_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46d345ff270ae16b8b4b2744fce1419b\",\n \"score\": \"0.54021895\",\n \"text\": \"def get_temp_file():\\n if is_p3k():\\n fn = \\\"/tmp/{0}-{1}\\\".format(time.time(), random.random())\\n return (fn, open(fn, \\\"w+\\\"))\\n else:\\n tf = tempfile.NamedTemporaryFile()\\n return (tf.name, tf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88fec5f1a2c8f81cb773968c3109b28c\",\n \"score\": \"0.5384337\",\n \"text\": \"def _temp_analyze_files():\\r\\n fd, orig_img = mkstemp(suffix='.img', dir=mkdtemp())\\r\\n orig_hdr = orig_img[:-4] + '.hdr'\\r\\n fp = file(orig_hdr, 'w+')\\r\\n fp.close()\\r\\n return orig_img, orig_hdr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"520dd892ffe4cf823b604566347364ee\",\n \"score\": \"0.5377258\",\n \"text\": \"def get_new_file_id():\\n return str(uuid.uuid1())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e66a57365c2043a5e86ca54adde5c8bf\",\n \"score\": \"0.53549737\",\n \"text\": \"def _make_tmp_files():\\n fh, name = tempfile.mkstemp()\\n os.close(fh)\\n os.remove(name)\\n\\n train_filename = name + '_train.txt'\\n model_filename = name + '_model'\\n\\n fh, name = tempfile.mkstemp()\\n os.close(fh)\\n os.remove(name)\\n\\n predict_filename = name + '_pred_.txt'\\n return train_filename, model_filename, predict_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f95fab28b452ed412c16f31debe5e146\",\n \"score\": \"0.5333638\",\n \"text\": \"def setup_temp():\\n clean_temp()\\n os.mkdir(FILES_DIR)\\n os.mkdir(os.path.join(tempfile.gettempdir(), REPO_DIR))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"750b1c3a6d55dc4b31697322cafd71e9\",\n \"score\": \"0.53288525\",\n \"text\": \"def __init__(self):\\n self.file_dict = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ce76feddd11412a1037b721d71afeba\",\n \"score\": \"0.53281385\",\n \"text\": \"def make_product_fileinfo(self, file_id):\\n file_path = os.path.abspath(file_id)\\n file_dir = os.path.dirname(file_path)\\n file_basename = os.path.basename(file_path)\\n file_basename_noext = os.path.splitext(file_basename)[0]\\n hawkeye_dir = os.path.join(file_dir, '__Hawkeye__')\\n scanvid_basename = file_basename_noext + '_640x480.mp4'\\n scanvid_path = os.path.join(hawkeye_dir, scanvid_basename)\\n notes_basename = file_basename_noext + '_notes.pkl'\\n notes_path = os.path.join(hawkeye_dir, notes_basename)\\n csvfile_path = os.path.join(file_dir, file_basename + '.csv')\\n\\n result = dict()\\n result['file_id'] = file_id\\n result['file_path'] = file_path\\n result['file_dir'] = file_dir\\n result['file_basename'] = file_basename\\n result['file_basename_noext'] = file_basename_noext\\n result['hawkeye_dir'] = hawkeye_dir\\n result['scanvid_basename'] = scanvid_basename\\n result['scanvid_path'] = scanvid_path\\n result['notes_basename'] = notes_basename\\n result['notes_path'] = notes_path\\n result['csvfile_path'] = csvfile_path\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01048a77d64612d5e440096c9d1301a3\",\n \"score\": \"0.53219527\",\n \"text\": \"def get_temp_file():\\n return tempfile.mktemp('.h5')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"572710d6a732a722af94ae63fb880057\",\n \"score\": \"0.53143126\",\n \"text\": \"def create_temp_files(nb_files):\\n return [\\n NamedTemporaryFile(prefix=PREFIX_INPUT, suffix=SUFFIX, dir=MEMORY_FS_ROOT)\\n for _ in range(nb_files)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4201c18ee5f9f87fb98ac8f9a142c98\",\n \"score\": \"0.530706\",\n \"text\": \"def get_file_info(file: object) -> tuple:\\n fp = getattr(file, \\\"filename\\\", None)\\n if not fp: # if a file object was provided\\n home = os.path.expanduser(\\\"~\\\")\\n created_dt = datetime.now().replace(microsecond=0).isoformat().replace(\\\":\\\", \\\"\\\")\\n filename = created_dt\\n fp = os.path.join(home, filename)\\n return fp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8ea7195fe4ba6f091ad91debecd02d\",\n \"score\": \"0.5289873\",\n \"text\": \"def _initialise_dict(number_train_files, number_test_files):\\n file_id_train = {}\\n file_id_test = {}\\n for i in np.arange(number_train_files):\\n file_id_train[i] = []\\n file_id_train[f'meta_{i}'] = []\\n for i in np.arange(number_test_files):\\n file_id_test[i] = []\\n file_id_test[f'meta_{i}'] = []\\n return file_id_train, file_id_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48d8710ee07205e7b96ce6cb08bf3e70\",\n \"score\": \"0.52744484\",\n \"text\": \"def tempFile(self, tempfile):\\n temp_file = ''\\n #print tempfile, self.temp_path\\n for path in self.temp_path:\\n if os.path.isdir(path) and tempfile in os.listdir(path):\\n temp_file = os.path.join(path, tempfile)\\n break\\n return temp_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32713155f9cc7b984697a025d976da12\",\n \"score\": \"0.5273322\",\n \"text\": \"def subHashAndFileSize(file):\\n return tuple\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74d13b165de2518b62a48fdf8f59c831\",\n \"score\": \"0.5265498\",\n \"text\": \"def file_dir(self, file_id: str) -> Path:\\n return self.temp_dir / file_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2e377863fd940612161bb64923b2baa\",\n \"score\": \"0.5259816\",\n \"text\": \"def __copy_to_temp(self):\\n randnum = str(random.getrandbits(128))\\n tempname = \\\"cache/temp\\\" + randnum\\n with open(self.filename, 'r') as f:\\n with open(tempname, 'w') as temp:\\n for line in f:\\n temp.write(line)\\n return tempname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66e09148c48ebe9cc9c2904aac5057ae\",\n \"score\": \"0.52513206\",\n \"text\": \"def __init__(self, *, zip: str = None, id: str = None):\\n\\n assert zip or id\\n self._tmp_dir = tempfile.TemporaryDirectory()\\n if zip:\\n self._source = zip\\n zipfile.ZipFile(zip, mode='r').extractall(self._tmp_dir.name)\\n dirs = os.listdir(self._tmp_dir.name)\\n assert len(dirs) == 1, 'One package directory expected'\\n self._id = dirs[0]\\n assert not id or self.id == id, 'Wrong task ID'\\n elif id:\\n self._id = id\\n os.mkdir(os.path.join(self._tmp_dir.name, id), mode=0o755)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6342dc4ec9732f652dbe5928ec90f26\",\n \"score\": \"0.524917\",\n \"text\": \"def get_variables(files):\\n var_0 = files[0](u'clt')\\n var_0.id = 'clt'\\n var_1 = files[0](u'u')\\n var_1.id = 'u'\\n var_2 = files[0](u'v')\\n var_2.id = 'v'\\n return var_0, var_1, var_2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d05f5db7f2f7b61cb019f639c6009d51\",\n \"score\": \"0.5232884\",\n \"text\": \"def _get_result_files_base(self, temp_dir):\\n return temp_dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"087a6ba7d91dd56ef38a29abf23cbf9c\",\n \"score\": \"0.52311677\",\n \"text\": \"def get_temp_filename(filename):\\n global config_dict\\n tmpdir=config_dict['tmpdir'][0]\\n return tmpdir+\\\"/\\\"+filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aefcaa92bf03e1f3096f80b3869636f6\",\n \"score\": \"0.52288705\",\n \"text\": \"def create_zip_id(zip_id: int, filename: str) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f476edb3534fd7f36ec9b5d32259fbe\",\n \"score\": \"0.51963574\",\n \"text\": \"def _get_file_info(self, file_id: str):\\n file_info = (\\n self.get_conn()\\n .files()\\n .get(\\n fileId=file_id,\\n fields=\\\"id,name,parents\\\",\\n supportsAllDrives=True,\\n )\\n .execute(num_retries=2)\\n )\\n return file_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9b0b962bfb2dd0b314cf1d3518947fa\",\n \"score\": \"0.5169887\",\n \"text\": \"def __init__(self, files: List[str]) -> None:\\n self.info: List[Tuple[str, int]] = []\\n for file in sorted(files):\\n st = os.stat(file)\\n self.info.append((file, st.st_mtime_ns))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68d57574192640b26e045fad24746051\",\n \"score\": \"0.5163068\",\n \"text\": \"def fill_directory_with_temp_files(directory, num_files, delete=False):\\n return [tempfile.NamedTemporaryFile(dir=directory, delete=delete).name\\n for _ in range(num_files)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73f56990d11ac0230c43a527e9e115d4\",\n \"score\": \"0.51582277\",\n \"text\": \"def _temp_analyze_files():\\r\\n fd, orig_img = mkstemp(suffix = '.img')\\r\\n orig_hdr = orig_img[:-4] + '.hdr'\\r\\n fp = file(orig_hdr, 'w+')\\r\\n fp.close()\\r\\n return orig_img, orig_hdr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a4eca3ef856ee517d275446d6f51b6e\",\n \"score\": \"0.51562726\",\n \"text\": \"def tmp_file():\\n return tempfile.mkstemp()[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe5b2a819ccb3ecf4f8436bc6d6b2b9c\",\n \"score\": \"0.51442605\",\n \"text\": \"def prepareTestFiles(self):\\n\\n\\t\\tmFname = os.path.basename(self.mctalObject.mctalFileName)\\n\\t\\tself.mctalOutFile = tempfile.NamedTemporaryFile(prefix=mFname,delete=False)\\n\\n\\t\\trFname = os.path.basename(self.rootFileName)\\n\\t\\tself.rootOutFile = tempfile.NamedTemporaryFile(prefix=rFname, delete=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c5a31b68d87dccb19202ba33d5c6e33\",\n \"score\": \"0.5144006\",\n \"text\": \"def __init__(self, file_path, temp_file, file_name, do_rm=True):\\n\\n self.__file_path = str(file_path)\\n self.__temp_file = str(temp_file)\\n self.__file_name = str(file_name)\\n self.__do_rm = bool(do_rm)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"587ad767cc0079488195af25a065a0e7\",\n \"score\": \"0.5143944\",\n \"text\": \"def tmp_file(fname):\\n import uuid\\n\\n return fname + \\\".\\\" + str(uuid.uuid4())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67f7ceccfeab284a84bd0ea4a726601c\",\n \"score\": \"0.5130646\",\n \"text\": \"def __load_temp_file(self):\\n if not osp.isfile(self.TEMPFILE_PATH):\\n # Creating temporary file\\n default = ['# -*- coding: utf-8 -*-', '\\\"\\\"\\\"', \\n 'Tellurium oscillation', '\\\"\\\"\\\"',\\n '', 'import tellurium as te', 'import roadrunner',\\n 'import antimony', '',\\n \\\"r = te.loada ('''\\\", 'model feedback()', ' // Reactions:',\\n ' J0: $X0 -> S1; (VM1 * (X0 - S1/Keq1))/(1 + X0 + S1 + S4^h);',\\n ' J1: S1 -> S2; (10 * S1 - 2 * S2) / (1 + S1 + S2);',\\n ' J2: S2 -> S3; (10 * S2 - 2 * S3) / (1 + S2 + S3);',\\n ' J3: S3 -> S4; (10 * S3 - 2 * S4) / (1 + S3 + S4);',\\n ' J4: S4 -> $X1; (V4 * S4) / (KS4 + S4);','',\\n ' // Species initializations:', ' S1 = 0; S2 = 0; S3 = 0;',\\n ' S4 = 0; X0 = 10; X1 = 0;','', ' // Variable initialization:',\\n ' VM1 = 10; Keq1 = 10; h = 10; V4 = 2.5; KS4 = 0.5;',\\n\\\"end''')\\\", '', 'result = r.simulate(0, 40, 500)', 'r.plot(result)', '']\\n text = os.linesep.join([encoding.to_unicode(qstr)\\n for qstr in default])\\n try:\\n encoding.write(to_text_string(text), self.TEMPFILE_PATH,\\n 'utf-8')\\n except EnvironmentError:\\n self.new()\\n return\\n\\n self.load(self.TEMPFILE_PATH)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6736097adef4f58e243c7f626001264b\",\n \"score\": \"0.511258\",\n \"text\": \"def get_temp_path(self, *args):\\n if self._id is None:\\n self._id = str(uuid.uuid4())\\n return os.path.join(\\\"/var/tmp\\\", \\\"xii-{}-start\\\".format(self._id), *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62bb02a26ca1dd9e6fa82ab642883b41\",\n \"score\": \"0.51123244\",\n \"text\": \"def list_tuples(self):\\n for temp_file in self.__registered_file:\\n entry = self.__registered_file[temp_file]\\n yield (entry.temp_file, entry.file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a6df917244c734589b147b22adb404e\",\n \"score\": \"0.511074\",\n \"text\": \"def __init__(self, root_directory=None, file_id=''):\\n self.raw_files = []\\n self.dataset_variables = []\\n self.dataset_levels = []\\n self.dataset_dates = None\\n if root_directory is None:\\n self._root_directory = '%s/.era5' % os.path.expanduser('~')\\n else:\\n self._root_directory = root_directory\\n self._file_id = file_id\\n self._delete_temp = False\\n self.level_coord = [1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450,\\n 500, 550, 600, 650, 700, 750] + list(range(775, 1001, 25))\\n self.inverse_lat = True\\n # Data\\n self.Dataset = None\\n self.basemap = None\\n self._lat_array = None\\n self._lon_array = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f3d40fc9ea132595b78b6a5ed9eb468\",\n \"score\": \"0.5103816\",\n \"text\": \"def tempdir():\\n return mkdtemp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ce65a2663e06d5a664bd86322858486\",\n \"score\": \"0.50825053\",\n \"text\": \"def tempdir():\\n return tempfile.TemporaryDirectory()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c812bc26b2535c5f36aec17a784703fc\",\n \"score\": \"0.50797063\",\n \"text\": \"def setup_temp_data():\\n temp_folder = os.getcwd()+'/temp'\\n if not os.path.isdir(temp_folder):\\n os.mkdir(temp_folder)\\n print(\\\"\\\\nsetting up temp folder\\\")\\n\\n # setup files, provide memmap type\\n img = np.random.random((2048, 2048))\\n np.save(temp_folder+'/img.npy', img)\\n print(\\\"setting up temp file\\\")\\n yield np.memmap(temp_folder+'/img.npy', shape=(2048, 2048), dtype=np.uint8)\\n\\n # breakdown files\\n if os.path.isfile(temp_folder+'/img.npy'):\\n os.remove(temp_folder+'/img.npy')\\n print(\\\"\\\\nbreaking down temp file\\\")\\n if os.path.isdir(temp_folder):\\n os.rmdir(temp_folder)\\n print(\\\"breaking down temp folder\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"534a0d57971c4f67328eda6153ff63a4\",\n \"score\": \"0.50745153\",\n \"text\": \"def _prepare_filenames(self):\\n self.timestamp = time.strftime('%Y-%m-%d_%H-%M-%S')\\n self._logfile_prefix = '%s.%s' % (self.base_path, self.timestamp)\\n self.latest_logfile = '%s.latest.log' % self.base_path\\n self.default_latest_logfile = ('%s/%s.log' %\\n (self.base_dir, self.DEFAULT_LINK_FILE))\\n # For string representation\\n self.__logfile_blob = '%s.*' % self._logfile_prefix\\n self.debug_logfile = '%s.DEBUG.log' % self._logfile_prefix\\n # Designates the file to link 'recent' to\\n self.linked_logfile = self.debug_logfile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35f5c0755b2ec09b4bbcc6a6673b6ff8\",\n \"score\": \"0.507082\",\n \"text\": \"def getTempFileName(self):\\n return 'hellanzb-tmp-' + self.nzb.archiveName + '.file' + str(self.number).zfill(4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f36b916c3b5ee7841b29c2f9b83dcef\",\n \"score\": \"0.50700736\",\n \"text\": \"def get_file(self, *, file_id):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bddae26e505f078721d4cdfa7997b17\",\n \"score\": \"0.5065458\",\n \"text\": \"def __init__(self):\\n fd, self.filename = tempfile.mkstemp()\\n self.file = os.fdopen(fd, 'w')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5482ea2bfdea419425db49fd67b63e4\",\n \"score\": \"0.50612444\",\n \"text\": \"def get_file(run_id, file_name):\\n target_file = TEMP_DIR + run_id + '/' + file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"725fb2918146af5b8878338a1f1072f2\",\n \"score\": \"0.50565153\",\n \"text\": \"def temporary_file(file_name: str = None) -> Iterator[Path]:\\n with temporary_directory() as tempdir:\\n file_name = file_name or str(uuid.uuid4())\\n file_path = tempdir / file_name\\n file_path.touch()\\n yield file_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5579f337cb31aeb1c42e048fe7f88b54\",\n \"score\": \"0.5054828\",\n \"text\": \"def _mktmpfile(data):\\n fn = tmp_path_factory.mktemp(\\\"mktmpfile\\\")\\n fn_file = str(fn) + \\\"/file\\\"\\n with open(fn_file, \\\"w\\\") as fn_handle:\\n fn_handle.write(data)\\n return fn_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb040770df3c0c4d3599201656ca06b\",\n \"score\": \"0.50515854\",\n \"text\": \"def copy_temp(self):\\n # Create temporary file\\n paths = {}\\n for k, v in self._packets.items():\\n paths[k] = IP.InformationPacket(_path.Path(_temp.NamedTemporaryFile(delete=True).name))\\n self._temp_packets = PacketRegister(paths)\\n return self._temp_packets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a922830388b3aa20be0c7fd0fa2c9c4d\",\n \"score\": \"0.50493103\",\n \"text\": \"def generateFileIdMap():\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f62267f7888f31a70eaabc59bb0c155\",\n \"score\": \"0.50465375\",\n \"text\": \"def _push_temp_file(self, prefix=None, suffix=None):\\n # we want to make sure our temp naming convention is consistent\\n # this is important for hashing zip files and gz files, because the name of the internal\\n # file becomes part of the content and then hash changes\\n\\n # temp filename is based on the assumption\\n\\n # put a consistent the file into a temp folder because the filename is part of\\n # the compressed artifact and we want consistency. After that we rename compressed file to temp file and\\n # delete temp folder\\n temp_folder = mkdtemp(prefix='artifacts_')\\n local_filename = Path(temp_folder) / (str(prefix).rstrip(\\\".\\\") + \\\".\\\" + str(suffix).lstrip(\\\".\\\"))\\n local_filename = local_filename.as_posix()\\n self._temp_files_lookup[local_filename] = (temp_folder, deepcopy(prefix), deepcopy(suffix))\\n return local_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfb1c8951d0abf63d25d4711168ac697\",\n \"score\": \"0.5045576\",\n \"text\": \"def __init__(self, file_id: int):\\n super().__init__(file_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c93664652413d69fefb899ec8c52250\",\n \"score\": \"0.50392187\",\n \"text\": \"def pre_queue(self, filename, file_handle):\\n artifact = utils.single(\\n sorted([shared_file for shared_file in self.artifact_service.shared_files()\\n if shared_file.name == file_handle], key=lambda x: x.id))\\n full_path = os.path.join(self.temp_path, filename)\\n return full_path, artifact\",\n \"title\": \"\"\n },\n {\n \"docid\": \"182121653f8387f3eb756067c1af182d\",\n \"score\": \"0.50379026\",\n \"text\": \"def _temp_dict_file_name():\\n return '__shared_dictionary__'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10c4136ba63ad5fdcd3ab73c18286da8\",\n \"score\": \"0.50337845\",\n \"text\": \"def __call__(self, file_id: int, forge_file_name: str = None, datafile_id: int = None) -> Union[None, TempFile]:\\n\\n\\t\\tif file_id == 0:\\n\\t\\t\\treturn\\n\\n\\t\\tif forge_file_name is not None and datafile_id is None:\\n\\t\\t\\tif file_id in self._temp_files and forge_file_name == self._temp_files[file_id][0]:\\n\\t\\t\\t\\tdatafile_id = self._temp_files[file_id][1]\\n\\t\\t\\telse:\\n\\t\\t\\t\\t# preferentially use one found in the forgeFile asked but look in others if needed\\n\\t\\t\\t\\tif forge_file_name in self.pyUbiForge.forge_files and file_id in self.pyUbiForge.forge_files[forge_file_name].datafiles:\\n\\t\\t\\t\\t\\tdatafile_id = file_id\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tforge_file_name, datafile_id = self._light_dictionary.get(file_id, forge_file_name)\\n\\n\\t\\tif forge_file_name is None:\\n\\t\\t\\tforge_file_name = next((fF for fF in self.pyUbiForge.forge_files.keys() if file_id in self.pyUbiForge.forge_files[fF].datafiles), None)\\n\\t\\t\\tif forge_file_name is None:\\n\\t\\t\\t\\tforge_file_name, datafile_id = self._light_dictionary.get(file_id)\\n\\t\\t\\t\\tif datafile_id is None:\\n\\t\\t\\t\\t\\treturn\\n\\t\\t\\telse:\\n\\t\\t\\t\\tdatafile_id = file_id\\n\\n\\t\\tif not (file_id in self._temp_files and forge_file_name == self._temp_files[file_id][0] and datafile_id == self._temp_files[file_id][1]):\\n\\t\\t\\tself.pyUbiForge.forge_files[forge_file_name].decompress_datafile(datafile_id)\\n\\t\\tself.refresh_usage(file_id)\\n\\t\\tif file_id in self._temp_files and forge_file_name == self._temp_files[file_id][0] and datafile_id == self._temp_files[file_id][1]:\\n\\t\\t\\treturn TempFile(\\n\\t\\t\\t\\tself.pyUbiForge,\\n\\t\\t\\t\\tforge_file_name,\\n\\t\\t\\t\\tdatafile_id,\\n\\t\\t\\t\\tfile_id,\\n\\t\\t\\t\\tf'{self._temp_files[file_id][2]:08X}',\\n\\t\\t\\t\\tself._temp_files[file_id][3],\\n\\t\\t\\t\\tself._temp_files[file_id][4]\\n\\t\\t\\t)\\n\\t\\telse:\\n\\t\\t\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a07fb29170c06fd7b8b3f5448e65490f\",\n \"score\": \"0.503274\",\n \"text\": \"def tempdir():\\n return tempfile.gettempdir()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5ebfeff05ad19abedc7eeb36b7af335\",\n \"score\": \"0.50224966\",\n \"text\": \"def file_vars():\\n\\n server_flag = True\\n if server_flag:\\n n_threads = 11\\n loadpath = './loadpath'\\n savepath = './savepath'\\n else:\\n # e.g. LBL desktop\\n n_threads = 4\\n loadpath = './loadpath'\\n savepath = './savepath'\\n loadglob = 'load_*.h5'\\n saveglob = 'save_*.h5'\\n\\n v = 1 # verbosity\\n\\n return server_flag, loadpath, savepath, loadglob, saveglob, v, n_threads\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2698f2922626ace8d44b8f26490afede\",\n \"score\": \"0.50174403\",\n \"text\": \"def _file_setup(self, incoming_dictionary, create_dict):\\n\\n if \\\"file_collection_id\\\" in incoming_dictionary:\\n\\n create_dict['file_collection_id'] = incoming_dictionary[\\n 'file_collection_id']\\n elif \\\"paths\\\" in incoming_dictionary:\\n # transform file paths to file_collection_id\\n create_dict['file_collection_id'] = self.file_collection_controller.\\\\\\n create(incoming_dictionary['paths']).id\\n else:\\n # create some default file collection\\n create_dict['file_collection_id'] = self.file_collection_controller.\\\\\\n create([]).id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"960e391564d7361ba9a3ad5dab4af3e1\",\n \"score\": \"0.5017395\",\n \"text\": \"def tempdir():\\n return tempfile.mkdtemp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d12d62c3199d091ee4bef611ee8dfb7a\",\n \"score\": \"0.501325\",\n \"text\": \"def read_file(file_id: str) -> Tuple[bytes, int, str]:\\n try:\\n file_info = demisto.getFilePath(file_id)\\n with open(file_info['path'], 'rb') as file_data:\\n data = file_data.read()\\n file_size = os.path.getsize(file_info['path'])\\n return data, file_size, file_info['name']\\n except Exception as e:\\n raise Exception(f'Unable to read file with id {file_id}', e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93169e2bcbd249d2a66406ad44cd955f\",\n \"score\": \"0.5012287\",\n \"text\": \"def _tmp(self):\\n tmp = tempfile.NamedTemporaryFile(prefix='tmp', suffix='.json',\\n delete=False)\\n return tmp.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bb75c7e5b49d4cc084af00b229ac4f7\",\n \"score\": \"0.50119764\",\n \"text\": \"def _tmp(self):\\n tmpfn = tempfile.NamedTemporaryFile(prefix='tmp',\\n suffix='.json',\\n delete=False)\\n return tmpfn.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee7b4d3b52f70d9e1cb7b7b72af83e48\",\n \"score\": \"0.5011796\",\n \"text\": \"def test_file_file_uuid_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff3488b72bbfa6852ab078e2aabb3fdc\",\n \"score\": \"0.5009506\",\n \"text\": \"def tempfile ( suffix = '' , prefix = 'ostap-tmp-' , dir = None , date = True , keep = False ) :\\n fname = CleanUp.get_temp_file ( suffix = suffix , prefix = prefix ,\\n dir = dir , date = date )\\n assert not os.path.exists ( fname )\\n CleanUp._tmpfiles.add ( fname )\\n \\n if keep : CleanUp._protected.add ( fname )\\n return fname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef22aa82b04228eb4c0fd44ceeb0e31\",\n \"score\": \"0.50044686\",\n \"text\": \"def add_tempfile(self, filename):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e93ea11b1e33f8330734408e6d06bd\",\n \"score\": \"0.5000769\",\n \"text\": \"def GetTempDir(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47002122e947c489256c92715b13badb\",\n \"score\": \"0.49906933\",\n \"text\": \"def _create_file(self):\\n f = NamedTemporaryFile(delete=False)\\n f.write('\\\\n'.join([\\n '%s\\\\t%s' % (self._key1, self._value1),\\n '%s\\\\t%s' % (self._key2, self._value2)\\n ]).encode('ascii'))\\n temp_path = f.name\\n f.close()\\n return temp_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8a5191ab49931c6e04f53395d2f61d3\",\n \"score\": \"0.49861383\",\n \"text\": \"def process_file( self, file_id, grab_latest_file, display_status ):\\n process_dict = {\\n u'a__grab_latest_file': None,\\n u'b__grab_source_xml': None,\\n u'c__run_munger': None,\\n u'd__make_initial_solr_doc': None,\\n u'e__update_display_facet': None,\\n u'f__post_to_solr': None\\n }\\n process_dict[u'a__grab_latest_file'] = self.grab_latest_file(\\n file_id )\\n process_dict[u'b__grab_source_xml'] = self.grab_original_xml(\\n file_id )\\n process_dict[u'c__run_munger'] = self.run_munger(\\n source_xml=process_dict[u'b__grab_source_xml'][u'xml'] )\\n process_dict[u'd__make_initial_solr_doc'] = self.make_initial_solr_doc(\\n munged_xml=process_dict[u'c__run_munger'][u'munged_xml'] )\\n process_dict[u'e__update_display_facet'] = self.update_display_facet(\\n initial_solr_xml=process_dict[u'd__make_initial_solr_doc'][u'transformed_xml'], display_status=display_status )\\n process_dict[u'f__post_to_solr'] = self.update_solr(\\n updated_solr_xml=process_dict[u'e__update_display_facet'][u'updated_xml'] )\\n return process_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b67bd414839dbafcd75a1a9520d2b1c\",\n \"score\": \"0.49815804\",\n \"text\": \"def get_temp_file ( suffix = '' , prefix = 'ostap-tmp-' , dir = None , date = True ) :\\n with UseTmpDir ( tmp_dir () ) :\\n if date :\\n now = datetime.datetime.now()\\n if prefix and prefix.endswith('-') :\\n prefix = \\\"%s%s-\\\" % ( prefix , now.strftime ( date_format ) )\\n else :\\n prefix = \\\"%s-%s-\\\" % ( prefix , now.strftime ( date_format ) )\\n\\n td = tempfile.gettempdir()\\n if user and not user in td : prefix = '%s%s-' % ( prefix , user )\\n\\n with tempfile.NamedTemporaryFile ( suffix = suffix ,\\n prefix = prefix ,\\n dir = dir , \\n delete = False ) as tfile :\\n fname = tfile.name\\n \\n os.unlink ( fname ) \\n assert not os.path.exists ( fname )\\n logger.verbose ( 'temporary file requested %s' % fname )\\n return fname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6a04b204b9c8a141240ddf17e2f366f\",\n \"score\": \"0.49793208\",\n \"text\": \"def _create_tmp(self): #pragma NO COVERAGE\\n # Skipping coverage because the mailbox module doesn't let us\\n # hook the file creation.\\n klass = self.__class__\\n now = time.time()\\n now_i, now_f = math.modf(now)\\n hostname = socket.gethostname()\\n if '/' in hostname:\\n hostname = hostname.replace('/', r'\\\\057')\\n if ':' in hostname:\\n hostname = hostname.replace(':', r'\\\\072')\\n uniq = \\\"%010d.M%06dP%06dQ%06d.%s\\\" % (now,\\n now_f % 1e6,\\n os.getpid(),\\n klass._count,\\n hostname)\\n path = os.path.join(self._path, 'tmp', uniq)\\n try:\\n os.stat(path)\\n except OSError, e:\\n if e.errno == errno.ENOENT:\\n klass._count += 1\\n try:\\n return mailbox._create_carefully(path)\\n except OSError, e:\\n if e.errno != errno.EEXIST:\\n raise\\n else:\\n raise\\n\\n # Fall through to here if stat succeeded or open raised EEXIST.\\n raise mailbox.ExternalClashError(\\n 'Name clash prevented file creation: %s' % path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f7bf65e216a45523cb089ce542de6b6\",\n \"score\": \"0.4978764\",\n \"text\": \"def generate_fileInfo_objects(self):\\n if not self.skip_files_list:\\n self.skip_files_list = []\\n for file_in_dir in self.files_in_dir:\\n if file_in_dir not in self.skip_files_list:\\n self.file_infos[file_in_dir] = fileInfo(os.path.join(self.dirpath, file_in_dir), self.blocksize)\\n return self.file_infos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2214279b9618f989b927cabd548d34a2\",\n \"score\": \"0.49775958\",\n \"text\": \"def load_init_files(self):\\n listOfFiles = []\\n for filetype in ['*.h5', '*.json', '*.hdf5', '*.pkl']:\\n listOfFiles.extend(g.glob('binFiles/' + filetype))\\n for filename in listOfFiles:\\n temp_name = filename.split('/')[1]\\n if not self.fs.exists(filename=temp_name):\\n with io.FileIO(filename, 'r') as fileObject:\\n objectId = self.fs.put(fileObject, filename=temp_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31fc27143beb705c36323997648477e8\",\n \"score\": \"0.4977542\",\n \"text\": \"def create_temp_file():\\n temp_file = tempfile.NamedTemporaryFile(delete=False)\\n temp_file.close()\\n return temp_file.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d33a92d894f22fb1cca564800f6906a\",\n \"score\": \"0.497457\",\n \"text\": \"def process_IN_CREATE(self, event):\\n self.sleepshop()\\n\\n if event.pathname.endswith('.part'):\\n import datetime\\n self.temp_files[event.pathname] = datetime.datetime.now()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f9a6a2094149035c120629d95f086a8\",\n \"score\": \"0.4974205\",\n \"text\": \"def _tempFile(self, head=\\\"tmp\\\", ext=\\\".tif\\\"):\\n if (not hasattr(self, \\\"_TMPDIR\\\")):\\n # Create a temporary directory to use with this shape (and associated processes)\\n self._TMPDIR = TemporaryDirectory()\\n return NamedTemporaryFile(suffix=ext, prefix=head, dir=self._TMPDIR.name, delete=True).name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7557a84822f9c4449c19fba85b51a37b\",\n \"score\": \"0.4967648\",\n \"text\": \"def get_file_data(self, vault_id, file_id):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b36bb9c38b1f2874ce394c136f9acddd\",\n \"score\": \"0.49650565\",\n \"text\": \"def retrieve_from(self, file_id, **kwargs):\\n\\t\\treturn None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8de4505c9849ed4bb73f822349694c4d\",\n \"score\": \"0.4963452\",\n \"text\": \"def split_file_dummy(changed_file, project_paths):\\n return (None, changed_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75c7f86e1a5fff0f0a93b5e39d0d71f4\",\n \"score\": \"0.4952642\",\n \"text\": \"def get_file_by_id(self, id):\\n connection = None\\n cur = None\\n try:\\n connection = self.pool.get_connection_from_pool()\\n while connection is None:\\n connection = self.pool.get_connection_from_pool()\\n cur = connection.cursor()\\n cur.execute('SELECT id, id_user, hash, name, file_system_path FROM files WHERE id = %s;', (id))\\n connection.commit()\\n file = File()\\n i = 0\\n for row in cur:\\n file.id = row[0]\\n file.user_id = row[1]\\n file.hash = row[2]\\n file.name = row[3]\\n file.file_system_path = row[4]\\n i += 1\\n if i == 1:\\n return file\\n return None\\n except pymysql.MySQLError as ex:\\n self.logger.log_error('FileManager.get_file_by_id', ex)\\n return None\\n finally:\\n if cur:\\n cur.close()\\n self.pool.release_connection(connection)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2852432b3d48df5dca90b06765aa5052\",\n \"score\": \"0.49515566\",\n \"text\": \"def get_temp_furlfile(filename):\\n return tempfile.mktemp(dir=os.path.dirname(filename),\\n prefix=os.path.basename(filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6bd3f544a3be2f7bbd3feb95036e679\",\n \"score\": \"0.49499393\",\n \"text\": \"def get_file_data():\\n\\n # Make the subdirectory specified by DATA_PATH within the\\n # directory DATA_PARENT, if it does not exist already. Generated\\n # test data files will go here. \\n if os.listdir(settings.DATA_PARENT).count(settings.DATA_FOLDER) == 0:\\n os.mkdir(settings.DATA_PATH)\\n if os.listdir(settings.DATA_PATH).count('templates') == 0:\\n os.mkdir( ''.join([settings.DATA_PATH, 'templates/']) )\\n if os.listdir(settings.DATA_PATH).count('bal_count_tot') == 0:\\n os.mkdir( ''.join([settings.DATA_PATH, 'bal_count_tot/']) )\\n if os.listdir(settings.DATA_PATH).count('tab_aggr') == 0:\\n os.mkdir( ''.join([settings.DATA_PATH, 'tab_aggr/']) )\\n if os.listdir(settings.DATA_PATH).count('reports') == 0:\\n os.mkdir( ''.join([settings.DATA_PATH, 'reports/']) )\\n\\n # Get a list of files so far generated, by type. Leave off the .yml\\n # and .xml file extensions, as well as redundancies.\\n templates = os.listdir( ''.join([settings.DATA_PATH, 'templates/']) )\\n juris_files = []\\n prec_files = []\\n for i in range(0, len(templates)):\\n if templates[i][len(templates[i]) - len('.yml'):] == '.yml':\\n a = audit_header.AuditHeader()\\n with open( '%stemplates/%s' % (settings.DATA_PATH,\\n templates[i]),'r' ) as s:\\n a.load_from_file(s)\\n if a.type == 'jurisdiction_slate':\\n juris_files.append \\\\\\n (templates[i][:len(templates[i]) - len('.yml')])\\n else:\\n prec_files.append \\\\\\n (templates[i][:len(templates[i]) - len('.yml')])\\n templates[i] = templates[i][:templates[i].rfind('.')]\\n template_files = set(templates)\\n bal_files = os.listdir( ''.join([settings.DATA_PATH, 'bal_count_tot/']) )\\n for i in range(0, len(bal_files)):\\n bal_files[i] = bal_files[i][:bal_files[i].rfind('.')]\\n bal_files = set(bal_files)\\n tab_files = os.listdir( ''.join([settings.DATA_PATH + 'tab_aggr/']) )\\n for i in range(0, len(tab_files)):\\n tab_files[i] = tab_files[i][:tab_files[i].rfind('.')]\\n no_log_files = []\\n for i in tab_files:\\n if tab_files.count(i) != 1:\\n no_log_files.append(i)\\n no_log_files = set(no_log_files)\\n tab_files = set(tab_files)\\n report_files = os.listdir( ''.join([settings.DATA_PATH, 'reports/']) )\\n for i in range(0, len(report_files)):\\n report_files[i] = report_files[i][:report_files[i].rfind('_report')]\\n report_files = set(report_files)\\n\\n tdg_files = set(prec_files).union(set(juris_files)).union(bal_files)\\n merge_files = bal_files.union(no_log_files) \\n\\n # Get version / last revision info from file\\n with open('VERSION', 'r') as stream:\\n version = stream.readlines()\\n\\n return Context({'prec_files':prec_files, 'juris_files':juris_files,\\n 'bal_files':bal_files, 'tdg_files':tdg_files,\\n 'tab_files':tab_files, 'merge_files':merge_files,\\n 'report_files':report_files, 'no_log_files': no_log_files,\\n 'template_files':template_files, 'version':version})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29d6b0a759ff9df5b9d480e85450d4d1\",\n \"score\": \"0.4939915\",\n \"text\": \"def pull_templates(self) -> Tuple[List[str], List[str]]:\\n self.logger.info(\\n f'Initialising templates from bucket \\\"{self.remote_template_bucket}\\\"'\\n )\\n\\n filenames: Generator[str, None, None] = (\\n obj.object_name for obj in self.minio_instance.list_objects(self.remote_template_bucket)\\n )\\n successes: List[str] = []\\n fails: List[str] = []\\n for filename in filenames:\\n try:\\n self.pull_template(filename)\\n successes.append(filename)\\n except:\\n fails.append(filename)\\n\\n self.logger.info(\\n f'Initialisation finished for bucket \\\"{self.remote_template_bucket}\\\"'\\n )\\n return successes, fails\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993889,"cells":{"query_id":{"kind":"string","value":"834422ee3835d1f972850906bdfdc885"},"query":{"kind":"string","value":"Save frame data as an HDF5 file."},"positive_passages":{"kind":"list like","value":[{"docid":"1b00b556c8358c176a6a9ae32ecffd8a","score":"0.74281675","text":"def save_h5(self, filename, max_load=1):\n self.save_hdf5(filename, max_load=max_load)","title":""}],"string":"[\n {\n \"docid\": \"1b00b556c8358c176a6a9ae32ecffd8a\",\n \"score\": \"0.74281675\",\n \"text\": \"def save_h5(self, filename, max_load=1):\\n self.save_hdf5(filename, max_load=max_load)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2240ae2fa11185698b2a1d51bc73e042","score":"0.77490896","text":"def save_H5(self, avg=False):\n if not os.path.isfile(self.get_H5_path()):\n self.df.to_hdf(self.get_H5_path(), key=\"df\", mode=\"w\")\n if avg:\n self.df_avg.to_hdf(self.get_H5_avg_path(), key=\"df\", mode=\"w\")","title":""},{"docid":"7b4abd696f93215781adbc6cc43b61a9","score":"0.73503816","text":"def save_hd5(self, filename, create_path=False):\n self.__assert_ready()\n import h5py\n\n directory = os.path.dirname(filename)\n basename = os.path.basename(filename)\n\n if basename == \"\":\n raise RuntimeError(\"A non-empty filename must be specified for `save_as`.\")\n if not os.path.isdir(directory) and not create_path:\n raise RuntimeError(\n 'Directory path \"{}\" does not exist; use `create_path` option to automatically create it.'.format(\n directory\n )\n )\n if create_path and not os.path.isdir(directory):\n os.makedirs(directory)\n\n if not os.path.splitext(basename)[1].lower() in [\n \".hd5\",\n \".hdf5\",\n \".hdf\",\n \".h5\",\n ]:\n basename = \"{}.hd5\".format(basename)\n\n filename = os.path.join(directory, basename)\n\n fp = h5py.File(filename, \"w\")\n fp[\"img\"] = self.__img\n fp[\"mm_per_voxel\"] = self.__mm_per_voxel\n fp[\"original_shape\"] = self.__orig_shape\n fp[\"dicom_orientation\"] = 0 if self.__orientation == \"view\" else 1\n fp.close()","title":""},{"docid":"51d6f392716fdefa8f09792679b02a57","score":"0.73190427","text":"def save(self, hdf5, save_function = bob.bio.base.save):\n if not len(self):\n logger.warn(\"Saving empty FrameContainer '%s'\", hdf5.filename)\n for frame_id, data, quality in self:\n hdf5.create_group(\"Frame_%s\" % frame_id)\n hdf5.cd(\"Frame_%s\" % frame_id)\n save_function(data, hdf5)\n if quality is not None:\n hdf5.set(\"FrameQuality\", quality)\n hdf5.cd(\"..\")","title":""},{"docid":"65b4085b30a7ff6540195bce4b86ec78","score":"0.7308886","text":"def save(self, path, as_hd5=False ):\n if as_hd5:\n self.data.to_hdf(path+\".hd5\", key=\"Dataset\", mode='w')\n else:\n self.data.to_csv(path+\".csv\" )","title":""},{"docid":"22364dfb657b6a7360d6e5cb2043b240","score":"0.72904813","text":"def saveHDF(self, filename, dataset, data, metadata_func=None):\n with h5py.File(filename, 'r+') as f:\n f.create_dataset(dataset, data=data)\n f[dataset].attrs['mode'] = self._name\n f[dataset].attrs['exposure'] = self._cam.exposure\n f[dataset].attrs['em_gain'] = self._cam.EM._read_gain_from_camera()\n f[dataset].attrs['created'] = time.strftime(\"%d/%m/%Y %H:%M:%S\")\n if metadata_func is not None:\n metadata_func(f[dataset])","title":""},{"docid":"60f4d7732c7fc6e71cdae122c55a3e9d","score":"0.72688127","text":"def test_write_dataframe(self):\n df = pandas.DataFrame(self.table_data)\n fname = \"test_write_dataframe.h5\"\n with h5py.File(fname, \"w\", **self.memory_kwargs) as fid:\n self.assertIsNone(hdf5.write_dataframe(df, \"dataframe\", fid))","title":""},{"docid":"4ab81b4cd28f34d41ecf99278463eb68","score":"0.7246936","text":"def _save_hdf5(self, output_file_path=None):\n #save sync data\n if output_file_path:\n filename = output_file_path\n else:\n filename = self.output_path+\".h5\"\n data = np.fromfile(self.output_path, dtype=np.uint32)\n total_samples = len(data)\n\n events = self._get_events(data)\n\n h5_output = h5.File(filename, 'w')\n h5_output.create_dataset(\"data\", data=events)\n #save meta data\n meta_data = self._get_meta_data()\n meta_data['total_samples'] = total_samples\n\n meta_data_np = np.string_(str(meta_data))\n h5_output.create_dataset(\"meta\", data=meta_data_np)\n h5_output.close()\n\n #remove raw file\n if not self.save_raw:\n os.remove(self.output_path)\n\n if self.verbose:\n logging.info(\"Recorded %i events.\" % (len(events)-1))\n logging.info(\"Metadata: %s\" % meta_data)\n logging.info(\"Saving to %s\" % filename)\n try:\n ds = Dataset(filename)\n ds.stats()\n ds.close()\n except Exception as e:\n logging.warning(\"Failed to print quick stats: %s\" % e)","title":""},{"docid":"d0951b75bb111c69b64c01b231297257","score":"0.71313345","text":"def write(self, frames):\n with pd.HDFStore(self._path, 'w',\n complevel=self._complevel, complib=self._complib) \\\n as store:\n panel = pd.Panel.from_dict(dict(frames))\n panel.to_hdf(store, 'updates')\n with tables.open_file(self._path, mode='r+') as h5file:\n h5file.set_node_attr('/', 'version', 0)","title":""},{"docid":"9214b328d1ae290f5067cbab8e51b9be","score":"0.7128886","text":"def saveh5(outfile, data):\n with h5py.File(outfile, \"w\") as f:\n [f.create_dataset(key, data=val) for key, val in list(data.items())]\n f.close()","title":""},{"docid":"23417bd27c50abc9e96408998601ff84","score":"0.71100056","text":"def save_hdf5(self, filename, t):\n # ask if file has already been created. If not, create it.\n if not hasattr(self, 'h5fileWrite'):\n self.h5fileWrite = dolfinh5tools.Create(filename, self.functionspace)\n self.h5fileWrite.save_mesh()\n self.h5fileWrite.write(self.f, self.name, t)","title":""},{"docid":"39e2b07fe23eaf8d4fbb5ce44ab00bef","score":"0.7078768","text":"def save_to_hdf5(h5group, obj, path='/'):\n return Hdf5Saver(h5group).save(obj, path)","title":""},{"docid":"622aea71a63e78a232ede13055dc97e3","score":"0.70593697","text":"def write_hdf5(data, filename):\n import h5py as hp\n import numpy as np\n hfile = hp.File(filename, 'w')\n typ = type(data)\n if typ == dict:\n for k in data.iterkeys():\n # The straight code gives ustrings, which I don't like.\n# hfile[k] = data[k]\n exec(\"hfile['\" + k + \"'] = data['\" + k + \"']\")\n elif typ == np.ndarray:\n hfile['data'] = data\n hfile.close()","title":""},{"docid":"f145c58f1d516c4dc386c6dd8ef6cbe5","score":"0.70541835","text":"def save_hdf5(self, hdf5_saver, h5gr, subpath):\n # for new implementations, use:\n # hdf5_saver.save(data, subpath + \"key\") # for big content/data\n # h5gr.attrs[\"name\"] = info # for metadata\n\n # here: assume all the data is given in self.__dict__\n type_repr = hdf5_saver.save_dict_content(self.__dict__, h5gr, subpath)\n h5gr.attrs[ATTR_FORMAT] = type_repr","title":""},{"docid":"119208943c253c0933bc666d316ab012","score":"0.7027556","text":"def save_h5(self, file_path):\n\n with h5py.File(file_path, \"w\") as f:\n model_set = self.model_set.astype(\"S\")\n seg_set = self.seg_set.astype(\"S\")\n f.create_dataset(\"ID/row_ids\", data=model_set)\n f.create_dataset(\"ID/column_ids\", data=seg_set)\n trial_mask = self.tar.astype(\"int8\") - self.non.astype(\"int8\")\n f.create_dataset(\"trial_mask\", data=trial_mask)\n if self.model_cond is not None:\n f.create_dataset(\"model_cond\", data=self.model_cond.astype(\"uint8\"))\n if self.seg_cond is not None:\n f.create_dataset(\"seg_cond\", data=self.seg_cond.astype(\"uint8\"))\n if self.trial_cond is not None:\n f.create_dataset(\"trial_cond\", data=self.trial_cond.astype(\"uint8\"))\n if self.model_cond_name is not None:\n model_cond_name = self.model_cond_name.astype(\"S\")\n f.create_dataset(\"model_cond_name\", data=model_cond_name)\n if self.seg_cond_name is not None:\n seg_cond_name = self.seg_cond_name.astype(\"S\")\n f.create_dataset(\"seg_cond_name\", data=seg_cond_name)\n if self.trial_cond_name is not None:\n trial_cond_name = self.trial_cond_name.astype(\"S\")\n f.create_dataset(\"trial_cond_name\", data=trial_cond_name)","title":""},{"docid":"9fb57aa6d36fa74e284ca813989cd677","score":"0.69632363","text":"def save_hdf5(self, filename, max_load=1):\n self._update_waterfall(filename=filename, max_load=max_load)\n self._encode_bytestrings()\n self.waterfall.write_to_hdf5(filename)\n self._decode_bytestrings()","title":""},{"docid":"203204e6cdc6244e3dfd35a77cf0ae9d","score":"0.6924733","text":"def Save(self):\n self._h5_data.flush()\n self._h5_data.close()\n # # Make pickle filename\n # filename = pth.join(self._data_dir, \"{}.pickle\".format(self._name))\n # # Dump data into pickle file\n # with open(filename, 'wb') as f:\n # pickle.dump(self._data_dict, f)\n # print(\" - {} saved - \".format(self._path))","title":""},{"docid":"9551b671c1367774a30f924931542277","score":"0.69075036","text":"def saveToFile(self,filename,mode='w',fmt='HDF5'):\n if fmt=='HDF5':\n f = h5py.File(filename,mode)\n self.saveToHDF5(f['/'])\n else:\n sys.stderr.write('ERROR: format '+fmt+' not yet implemented\\n')","title":""},{"docid":"9551b671c1367774a30f924931542277","score":"0.69075036","text":"def saveToFile(self,filename,mode='w',fmt='HDF5'):\n if fmt=='HDF5':\n f = h5py.File(filename,mode)\n self.saveToHDF5(f['/'])\n else:\n sys.stderr.write('ERROR: format '+fmt+' not yet implemented\\n')","title":""},{"docid":"a1a3e88175826ef1a97c6ed345ddec11","score":"0.68897575","text":"def save_to_hdf5(dic, filename):\n with h5py.File(filename, 'w') as h5file:\n save_dict_contents(h5file, '/', dic)","title":""},{"docid":"a6331a54887dd8ac23943a7cdb841196","score":"0.6843921","text":"def save(self, filename):\n if (filename[-5:] != '.hmat'):\n filename += '.hmat'\n h5f = h5py.File(filename, 'w')\n h5f.create_dataset('matrix', data=self.matrix, compression = 'gzip', compression_opts=9)\n h5f.create_dataset('idx', data=self.idx, compression = 'gzip', compression_opts=9)\n h5f.create_dataset('applyedMethods', data=cPickle.dumps(self._applyedMethods))\n if hasattr(self,\"genome\") and hasattr(self,\"resolution\"):\n h5f.create_dataset('genome',data = cPickle.dumps(self.genome))\n h5f.create_dataset('resolution',data = cPickle.dumps(self.resolution))\n else:\n warnings.warn(\"No genome and resolution is specified, attributes are recommended for matrix.\")\n \n h5f.close()","title":""},{"docid":"736ff8472a09387115710c1463ab9916","score":"0.679858","text":"def hdf5_to_csv(filename):","title":""},{"docid":"22523dd112857d4b67a96b1b13ab6bb4","score":"0.679558","text":"def saveToHDF5(self,Hdf5Group):\n Hdf5Group.attrs['n']=self.n\n Hdf5Group.attrs['dt']=self.dt\n for idx in range(self.n):\n if self.data[idx]: #we cannot save empty lists\n Hdf5Group.create_dataset('%05d'%idx,data=self.data[idx])","title":""},{"docid":"048c443caed5aba2c1e91e605f9c7c03","score":"0.6779579","text":"def save_df(df, path_str, index=True, header=True):\n\n path1 = os.path.splitext(path_str)\n\n if path1[1] in '.h5':\n df.to_hdf(path_str, 'df', mode='w')\n if path1[1] in '.csv':\n df.to_csv(path_str, index=index, header=header)","title":""},{"docid":"97ddd9af07b5c00607d47d3a290adb8c","score":"0.6752614","text":"def save_df(df, path_str, index=True, header=True):\n\n path1 = os.path.splitext(os.path_str)\n\n if path1[1] in '.h5':\n df.to_hdf(path_str, 'df', mode='w')\n if path1[1] in '.csv':\n df.to_csv(path_str, index=index, header=header)","title":""},{"docid":"3948e3fab6d982441346b29eac125887","score":"0.66994435","text":"def write_to_hdf5(self, filename_out, *args, **kwargs):\n\n print(\"[Filterbank] Warning: Non-standard function to write in HDF5 (.h5) format. Please use Waterfall.\")\n\n if not HAS_HDF5:\n raise RuntimeError(\"h5py package required for HDF5 output.\")\n\n with h5py.File(filename_out, 'w') as h5:\n\n dset = h5.create_dataset(b'data',\n data=self.data,\n compression='lzf')\n\n dset_mask = h5.create_dataset(b'mask',\n shape=self.data.shape,\n compression='lzf',\n dtype='uint8')\n\n dset.dims[0].label = b\"frequency\"\n dset.dims[1].label = b\"feed_id\"\n dset.dims[2].label = b\"time\"\n\n dset_mask.dims[0].label = b\"frequency\"\n dset_mask.dims[1].label = b\"feed_id\"\n dset_mask.dims[2].label = b\"time\"\n\n # Copy over header information as attributes\n for key, value in self.header.items():\n dset.attrs[key] = value","title":""},{"docid":"cbe16abce3375134628a16a7368e64f8","score":"0.6684277","text":"def writeHD5():\n global Data1\n\n store = HDFStore('.\\store.h5')\n store['listCrisis'] = Data1\n store.close()","title":""},{"docid":"68111fba2fda84ec3d1f4f36238dc07b","score":"0.66615635","text":"def write_to_h5py(history, instruments, filepath):\n \n with h5py.File(filepath, 'w') as f:\n f.create_dataset('history', data=history)\n instruments_array = np.array(instruments, dtype=object)\n # string_dt = h5py.special_dtype(vlen=str)\n string_dt = h5py.special_dtype(vlen=bytes)\n f.create_dataset(\"instruments\", data=instruments_array, dtype=string_dt)","title":""},{"docid":"f9187f847885be6fc6694d74eee61735","score":"0.66236395","text":"def save(self, fname, overwrite=False, verbose=None):\n ending = '-%s.h5' % (self['kind'].lower(),)\n check_fname(fname, self['kind'], (ending,))\n csd_orig = None\n try:\n if 'csd' in self:\n csd_orig = self['csd']\n self['csd'] = self['csd'].__getstate__()\n write_hdf5(fname, self, overwrite=overwrite, title='mnepython')\n finally:\n if csd_orig is not None:\n self['csd'] = csd_orig","title":""},{"docid":"fca5124c8c3e938f2ebe4e519063cc59","score":"0.6615021","text":"def savehdf5(self, filename, compression='gzip'):\n if HDF5ERROR:\n raise ImportError(HDF5ERROR)\n\n raise NotImplementedError","title":""},{"docid":"a277b737894fe70b638b88efca3f3da9","score":"0.66114277","text":"def save_hdf(self, filename, path='', overwrite=False, append=False):\n if os.path.exists(filename):\n store = pd.HDFStore(filename)\n if path in store:\n store.close()\n if overwrite:\n os.remove(filename)\n elif not append:\n raise IOError('{} in {} exists. Set either overwrite or append option.'.format(path,filename))\n else:\n store.close()\n\n if self.samples is not None:\n self.samples.to_hdf(filename, path+'/samples')\n else:\n pd.DataFrame().to_hdf(filename, path+'/samples')\n\n self.obs.save_hdf(filename, path+'/obs', append=True)\n\n with pd.HDFStore(filename) as store:\n # store = pd.HDFStore(filename)\n attrs = store.get_storer('{}/samples'.format(path)).attrs\n\n attrs.ic_type = type(self.ic)\n attrs.ic_bands = list(self.ic.bands)\n attrs.use_emcee = self.use_emcee\n attrs._mnest_basename = self._mnest_basename\n\n attrs._bounds = self._bounds\n attrs._priors = self._priors\n\n attrs.name = self.name\n store.close()","title":""},{"docid":"bda9fe6e4ee0e75a1b5018ff2a1c2c32","score":"0.6602205","text":"def dendro_export_hdf5(d, filename):\n import h5py\n f = h5py.File(filename, 'w')\n\n f.attrs['n_dim'] = d.n_dim\n\n f.create_dataset('newick', data=d.to_newick())\n\n ds = f.create_dataset('index_map', data=d.index_map, compression=True)\n ds.attrs['CLASS'] = 'IMAGE'\n ds.attrs['IMAGE_VERSION'] = '1.2'\n ds.attrs['IMAGE_MINMAXRANGE'] = [d.index_map.min(), d.index_map.max()]\n\n ds = f.create_dataset('data', data=d.data, compression=True)\n ds.attrs['CLASS'] = 'IMAGE'\n ds.attrs['IMAGE_VERSION'] = '1.2'\n ds.attrs['IMAGE_MINMAXRANGE'] = [d.data.min(), d.data.max()]\n\n f.close()","title":""},{"docid":"97f3f9bac822fbb504d74265fc091b2b","score":"0.65799123","text":"def saveToHDF5(self,Hdf5Group):\n Hdf5Group.attrs['cutOff']=self.cutOff\n Hdf5Group.attrs['shape']=self.shape\n A=self.tocoo() #cast to coo_matrix to retrieve row and col attributes\n #unfortunately, HDF5 doesn't allow saving empty map\n if A.data.any(): #map is not empty\n Hdf5Group.create_dataset('row',data=A.row)\n Hdf5Group.create_dataset('col',data=A.col)\n Hdf5Group.create_dataset('data',data=A.data)","title":""},{"docid":"ec5c0348db747263abbf3efe1e340b9a","score":"0.65718824","text":"def save(self, h5f):\n\n # Use bob.io.base.HDF5File to save: 'theta', norm[0] and norm[1] into a file\n h5f.set('theta', self.theta)\n h5f.set('subtract', self.norm[0])\n h5f.set('divide', self.norm[1])","title":""},{"docid":"02d3c63b001e67a2cd9ec3085f7f3ab4","score":"0.65649176","text":"def save_df(df, path):\n\n pickle.dump(df, open(path, \"wb\"))","title":""},{"docid":"02d3c63b001e67a2cd9ec3085f7f3ab4","score":"0.65649176","text":"def save_df(df, path):\n\n pickle.dump(df, open(path, \"wb\"))","title":""},{"docid":"eec4b10b7934dac2165f0738e3ed6f27","score":"0.6560859","text":"def save_dataframe(dataframe, filepath):\r\n dataframe.to_pickle(filepath)","title":""},{"docid":"47edd72af084178f4426463fdd3a89ff","score":"0.6559613","text":"def test_write_h5_table(self):\n data = self.table_data\n fname = \"test_write_h5_table.h5\"\n with h5py.File(fname, \"w\", **self.memory_kwargs) as fid:\n self.assertIsNone(hdf5.write_h5_table(data, \"table\", fid))","title":""},{"docid":"e55476f5d8e4b4cfac9739ab4cfbe3fa","score":"0.653944","text":"def save_h5(self, f, path):\n f.write_attr(path, 'sparse_type', 'csr')\n f.write_attr(path, 'shape', self._shape)\n f.write(path + '/data', self._data)\n f.write(path + '/channels', self._channels)\n f.write(path + '/spikes_ptr', self._spikes_ptr)","title":""},{"docid":"131a3649741fb7d3e0d7353563d686d8","score":"0.653774","text":"def write_fx_to_h5py(history, bid, ask, instruments, filepath, fill_missing_values):\n \n if fill_missing_values:\n history = pandas_fill(history) \n \n with h5py.File(filepath, 'w') as f:\n f.create_dataset('history', data=history)\n f.create_dataset('bid', data=bid)\n f.create_dataset('ask', data=ask)\n instruments_array = np.array(instruments, dtype=object)\n # string_dt = h5py.special_dtype(vlen=str)\n string_dt = h5py.special_dtype(vlen=bytes)\n f.create_dataset(\"instruments\", data=instruments_array, dtype=string_dt)","title":""},{"docid":"a672f5cc1c9ecef34178eb3d3bcc7b6d","score":"0.6524428","text":"def save_df(df, path):\n outfile = open(path, 'wb')\n pickle.dump(df, outfile)\n outfile.close()","title":""},{"docid":"235f7f6116b55e41d6687ecbe31dc170","score":"0.65164244","text":"def saveToFile(fname, data, column_names, column_dtype):\n log.info(\"Saving to {}\".format(args.outdir + fname))\n with h5py.File(args.outdir + fname, 'w') as hf:\n for var in column_names:\n hf.create_dataset( f'{var}',\n data=data[:,column_names.index(f'{var}')],\n dtype=column_dtype[f'{var}'],\n chunks=True,\n maxshape= (None,),\n compression='lzf')","title":""},{"docid":"e4122f0190c08da2fa3176edd057e7b9","score":"0.6513976","text":"def save_dataframe(self):\n if not self.dataset_path.parent.exists():\n self.dataset_path.parent.mkdir(parents=True)\n logger.info(f\"Saving dataframe to {self.dataset_path}\")\n self.dataframe.to_csv(self.dataset_path, index=False)","title":""},{"docid":"350c7ce3de2183baf269a3b5d71bb7e1","score":"0.64929986","text":"def write_h5(fname: str, data: dict, mode: str='a') -> None:\n with h5py.File(fname, mode) as f:\n recursive_save_dict_to_h5group(f,'/',data)","title":""},{"docid":"b63ad7b8c414ffa282a79111ed5e7261","score":"0.6450487","text":"def save_dataframe(df, filename):\n \n df.to_pickle(\"./datasets/processed_pickle_files/\" + filename)","title":""},{"docid":"fcc0c151822938633b57bb3a74dba3d8","score":"0.6418552","text":"def store(self, station_id: int, df: pd.DataFrame) -> None:\n\n # Make sure that there is data that can be stored\n if df.empty:\n return\n\n # Replace IntegerArrays by float64\n for column in df:\n if column in QUALITY_FIELDS or column in INTEGER_FIELDS:\n df[column] = df[column].astype(\"float64\")\n\n log.info(f\"Storing HDF5 data to {self.filepath}\")\n self.filepath.parent.mkdir(parents=True, exist_ok=True)\n df.to_hdf(path_or_buf=self.filepath, key=self.hdf5_key(station_id))","title":""},{"docid":"ab853717245fa438d4c669cd405da0ba","score":"0.64169145","text":"def close_hdf5(self):\n\n if hasattr(self, 'h5fileWrite'):\n self.h5fileWrite.close()\n del self.h5fileWrite","title":""},{"docid":"310697bc68c29ded5a3ce4c656d39ea6","score":"0.6408751","text":"def save_data(df, database_filename):","title":""},{"docid":"c32b3e8c3c427ac23cc327732ebb6411","score":"0.6367277","text":"def write_hdf5(hfile, run_params, model, obs, sampler=None,\n optimize_result_list=None, tsample=0.0, toptimize=0.0,\n sampling_initial_center=[], sps=None, write_model_params=True,\n **extras):\n\n if not _has_h5py_:\n warnings.warn(\"HDF5 file could not be opened, as h5py could not be imported.\")\n return\n\n # If ``hfile`` is not a file object, assume it is a filename and open\n if type(hfile) is str:\n # Check for existence of file, modify name if it already exists\n if os.path.exists(hfile):\n import time\n time_string = time.strftime(\"%y%b%d-%H.%M\", time.localtime())\n print(\"Appending current time ({0}) to output file \".format(time_string) + \\\n \"in order to guarantee a unique name.\")\n name, ext = os.path.splitext(hfile)\n hfile = name+'_{0}'.format(time_string)+ext\n print(\"New output filename: {0}\".format(hfile))\n\n hf = h5py.File(hfile, \"a\")\n else:\n hf = hfile\n\n # ----------------------\n # Sampling info\n try:\n # emcee\n a = sampler.acceptance_fraction\n write_emcee_h5(hf, sampler, model, sampling_initial_center, tsample)\n except(AttributeError):\n # dynesty or nestle\n if sampler is None:\n sdat = hf.create_group('sampling')\n elif 'eff' in sampler:\n write_dynesty_h5(hf, sampler, model, tsample)\n else:\n write_nestle_h5(hf, sampler, model, tsample)\n\n # -----------------\n # Optimizer info\n if optimize_result_list is not None:\n out = optresultlist_to_ndarray(optimize_result_list)\n mgroup = hf.create_group('optimization')\n mdat = mgroup.create_dataset('optimizer_results', data=out)\n\n # ----------------------\n # High level parameter and version info\n write_h5_header(hf, run_params, model, write_model_params=write_model_params)\n hf.attrs['optimizer_duration'] = json.dumps(toptimize)\n hf.flush()\n\n # ----------------------\n # Observational data\n write_obs_to_h5(hf, obs)\n hf.flush()\n\n # ---------------\n # Best fitting model in space of data\n if sps is not None:\n if \"sampling/chain\" in hf:\n from ..plotting.utils import best_sample\n pbest = best_sample(hf[\"sampling\"])\n spec, phot, mfrac = model.predict(pbest, obs=obs, sps=sps)\n best = hf.create_group(\"bestfit\")\n best.create_dataset(\"spectrum\", data=spec)\n best.create_dataset(\"photometry\", data=phot)\n best.create_dataset(\"parameter\", data=pbest)\n best.attrs[\"mfrac\"] = mfrac\n if obs[\"wavelength\"] is None:\n best.create_dataset(\"restframe_wavelengths\", data=sps.wavelengths)\n\n # Store the githash last after flushing since getting it might cause an\n # uncatchable crash\n bgh = githash(**run_params)\n hf.attrs['prospector_version'] = json.dumps(bgh)\n hf.close()","title":""},{"docid":"ee0481fc50ba1dbfe2f8fd71bd91ba49","score":"0.6365389","text":"def save_model_to_h5(model_obj, model_file_path):\n if not model_file_path.endswith('.h5'):\n model_file_path = f'{model_file_path}.h5'\n model_obj.save(model_file_path)\n logging.info(f'Model saved to {model_file_path}')","title":""},{"docid":"a33a8779e35b819b5caab26d952df523","score":"0.63634515","text":"def to_hdf5(self, h):\n h.create_dataset('triangles', data=np.array(self.triangles_))\n h.create_dataset('vertices', data=np.array(self.vertices_))\n if self.normals_ is not None:\n h.create_dataset('normals', data=np.array(self.normals_))","title":""},{"docid":"54878fc7432287dff14097deaf9fdfa2","score":"0.63594466","text":"def save(self):\n\t\th5FilePath = self._getSavePath() + '.h5f'\n\t\tprint('=== bVascularTracing.save() h5FilePath:', h5FilePath)\n\n\t\tsaveDict = OrderedDict()\n\t\tsaveDict['nodeDictList'] = self.nodeDictList\n\t\tsaveDict['edgeDictList'] = self.edgeDictList\n\n\t\t# todo: should use try:except ValueError and delete file if we get an error\n\t\twith h5py.File(h5FilePath, \"w\") as f:\n\t\t\tfor idx, node in enumerate(self.nodeDictList):\n\t\t\t\tnode = self.getNode(idx)\n\t\t\t\t#print(' idx:', idx, 'shape:', shape)\n\t\t\t\t# each node will have a group\n\t\t\t\tnodeGroup = f.create_group('node' + str(idx))\n\t\t\t\t# each node group will have a node dict with all parameters\n\t\t\t\tnodeDict_json = json.dumps(node)\n\t\t\t\tnodeGroup.attrs['nodeDict'] = nodeDict_json\n\n\t\t\tfor idx, edge in enumerate(self.edgeDictList):\n\t\t\t\tedge = self.getEdge(idx)\n\t\t\t\t#print('idx:', idx, 'edge:', edge)\n\t\t\t\t# debug mostly to check for numpy types that are not able to be converted to json\n\t\t\t\t'''\n\t\t\t\tif idx==0:\n\t\t\t\t\tfor k,v in edge.items():\n\t\t\t\t\t\tprint(k, v, type(v))\n\t\t\t\t'''\n\n\t\t\t\t# convert numpy int64 to int\n\t\t\t\ttmpSLabList = [int(tmpSlab) for tmpSlab in edge['slabList']]\n\t\t\t\tedge['slabList'] = tmpSLabList # careful, we are changing backend data !!!\n\n\t\t\t\t# each edge will have a group\n\t\t\t\tedgeGroup = f.create_group('edge' + str(idx))\n\t\t\t\t# each edge group will have a dict with all parameters\n\t\t\t\tedgeDict_json = json.dumps(edge)\n\t\t\t\tedgeGroup.attrs['edgeDict'] = edgeDict_json\n\n\t\t\t# slabs are in a dataset\n\t\t\tslabData = np.column_stack((self.x, self.y, self.z,\n\t\t\t\t\t\t\tself.d, self.d2, self.int,\n\t\t\t\t\t\t\tself.edgeIdx, self.nodeIdx,\n\t\t\t\t\t\t\tself.isBad,\n\t\t\t\t\t\t\tself.lpMin, self.lpMax, self.lpSNR,))\n\t\t\t#print('slabData:', slabData.shape)\n\t\t\tf.create_dataset('slabs', data=slabData)\n\n\t\t\t#\n\t\t\t# maskedEdgesDict\n\t\t\t# oct 2020, we are loading this but we are always calling _preComputeAllMasks !!!!\n\t\t\tfor k,v in self.maskedEdgesDict.items():\n\t\t\t\tf.create_dataset(k, data=v)\n\n\t\treturn h5FilePath","title":""},{"docid":"6df542a383c4e283a78d91fa95e42424","score":"0.6354747","text":"def save(self, filename):\n\n self._filename = filename\n\n with h5py.File(filename, \"w\") as f:\n\n f.create_dataset(\"true_energy\", data=self.true_energy)\n\n f.create_dataset(\"arrival_energy\", data=self.arrival_energy)\n\n f.create_dataset(\"reco_energy\", data=self.reco_energy)\n\n f.create_dataset(\"ra\", data=self.ra)\n\n f.create_dataset(\"dec\", data=self.dec)\n\n f.create_dataset(\"ang_err\", data=self.ang_err)\n\n f.create_dataset(\"source_label\", data=self.source_label)\n\n for i, source in enumerate(self.sources):\n\n s = f.create_group(\"source_\" + str(i))\n\n if isinstance(source.flux_model, PowerLawFlux):\n\n s.create_dataset(\"index\", data=source.flux_model._index)\n\n s.create_dataset(\n \"normalisation_energy\",\n data=source.flux_model._normalisation_energy,\n )\n\n elif isinstance(source.flux_model, BrokenPowerLawFlux):\n\n s.create_dataset(\"index1\", data=source.flux_model._index1)\n\n s.create_dataset(\"index2\", data=source.flux_model._index2)\n\n s.create_dataset(\n \"break_energy\", data=source.flux_model._break_energy\n )\n\n s.create_dataset(\"source_type\", data=source.source_type)\n\n s.create_dataset(\"normalisation\", data=source.flux_model._normalisation)","title":""},{"docid":"1124fdbc29ff09d05d25ce784fcd3a58","score":"0.6337732","text":"def save_frame(frame, appname):\n frame.to_pickle(\"{}.pkl\".format(appname))","title":""},{"docid":"e87918dada7c1367be20247d644965bf","score":"0.632097","text":"def save_to_hdf5_file(self):\n fan_array = np.zeros((n_gain, n_pixels, self.n_harmonics))\n fbn_array = np.zeros((n_gain, n_pixels, self.n_harmonics))\n\n for pix_id in range(n_pixels):\n self.fit(pix_id, gain=high_gain)\n fan_array[high_gain, pix_id, :] = self.fan\n fbn_array[high_gain, pix_id, :] = self.fbn\n\n self.fit(pix_id, gain=low_gain)\n fan_array[low_gain, pix_id, :] = self.fan\n fbn_array[low_gain, pix_id, :] = self.fbn\n\n try:\n with h5py.File(self.calib_file_path, 'w') as hf:\n hf.create_dataset('fan', data=fan_array)\n hf.create_dataset('fbn', data=fbn_array)\n hf.attrs['n_events'] = self.sum_events\n hf.attrs['n_harm'] = self.n_harmonics\n # need pytables and time calib container\n # to use lstchain.io.add_config_metadata\n hf.attrs['config'] = str(self.config)\n\n metadata = global_metadata()\n write_metadata(metadata, self.calib_file_path)\n\n except Exception:\n raise IOError(f\"Failed to create the file {self.calib_file_path}\")","title":""},{"docid":"052496e4e27a06d8661bf7fc5646ca59","score":"0.6320223","text":"def write_VCF_to_hdf5(VCF_dat, out_file):\n import h5py\n \n f = h5py.File(out_file, 'w')\n f.create_dataset(\"contigs\", data=np.string_(VCF_dat['contigs']), \n compression=\"gzip\", compression_opts=9)\n f.create_dataset(\"samples\", data=np.string_(VCF_dat['samples']), \n compression=\"gzip\", compression_opts=9)\n f.create_dataset(\"variants\", data=np.string_(VCF_dat['variants']), \n compression=\"gzip\", compression_opts=9)\n f.create_dataset(\"comments\", data=np.string_(VCF_dat['comments']), \n compression=\"gzip\", compression_opts=9)\n \n ## variant fixed information\n fixed = f.create_group(\"FixedINFO\")\n for _key in VCF_dat['FixedINFO']:\n fixed.create_dataset(_key, data=np.string_(VCF_dat['FixedINFO'][_key]), \n compression=\"gzip\", compression_opts=9)\n \n ## genotype information for each sample\n geno = f.create_group(\"GenoINFO\")\n for _key in VCF_dat['GenoINFO']:\n geno.create_dataset(_key, data=np.string_(VCF_dat['GenoINFO'][_key]), \n compression=\"gzip\", compression_opts=9)\n \n f.close()","title":""},{"docid":"2c2b1c05132f09975ed980c7b0a807cd","score":"0.63196135","text":"def dump(self):\n f = h5py.File(self.h5file, 'w')\n for i, k in enumerate(self.kwslist):\n v = self.h5data[:, i]\n dset = f.create_dataset(k, shape=v.shape, dtype=v.dtype)\n dset[...] = v\n f.close()","title":""},{"docid":"69f3a8b24bef78292372ebe82f1bba38","score":"0.63174295","text":"def to_file(self, dir_out: str, extention: str = \"h5\") -> None:\n df = pd.DataFrame(\n data=self.B,\n index=self.l,\n columns=[\"B\"],\n )\n filename = self._create_filename(dir_out)\n IO._remove_existing_file(filename)\n print(f\"Saving results to -> {filename}\")\n df.to_hdf(filename, key=\"df\", mode=\"w\")","title":""},{"docid":"30d50f56a080d85fb3eecb00f999b113","score":"0.63112646","text":"def close_hdf_file(self):\r\n self.h5f.close()","title":""},{"docid":"da013b5e3622954ce1394a32f6ac5507","score":"0.63004047","text":"def to_file(self, path: str, info: Optional[InfoDict] = None) -> None:\n import h5py\n\n with h5py.File(path, \"w\") as fp:\n # write the actual emulsion data\n for i, droplet_track in enumerate(self):\n droplet_track._write_hdf_dataset(fp, f\"track_{i:06d}\")\n\n # write additional information\n if info:\n for k, v in info.items():\n fp.attrs[k] = json.dumps(v)","title":""},{"docid":"ce81bd2a5b55871674cf052f510ee978","score":"0.6298771","text":"def write(self, asset_type, frames, scaling_factors=None):\n # Add id to the index, so the frame is indexed by (date, id).\n # ohlcv_frame.set_index(sid_ix, append=True, inplace=True)\n if scaling_factors is None:\n scaling_factors = DEFAULT_SCALING_FACTORS\n\n with self.h5_file(mode='a') as h5_file:\n # ensure that the file version has been written\n # h5_file.attrs['version'] = VERSION\n #多维度\n category_group = h5_file.create_group(asset_type)\n # 创建group\n data_group = category_group.create_group('data')\n data_group.attrs['scaling'] = scaling_factors\n index_group = category_group.create_group('index')\n # Note that this functions validates that all of the frames\n # share the same days and sids.\n days, sids,fields = days_and_sids_for_frames(frames)\n # Write sid and date indices.\n index_group.create_dataset('sid', data=sids)\n # h5py does not support datetimes, so they need to be stored\n # as integers.\n index_group.create_dataset('day', data=days.astype(np.int64))\n # Write fields of data\n index_group.create_group('field', data=fields)\n #\n for sid, frame in frames.items():\n frame = frame * scaling_factors\n frame.sort_index(inplace=True)\n data_group.create_dataset(sid,\n compression='lzf',\n shuffle=True,\n data=frame,\n # chunks=chunks,\n )","title":""},{"docid":"8d1931ae330c185bbe18a72eee92fd77","score":"0.6297747","text":"def export_to_hdf5(cls, h5_file, model, pids):\n #comments = []\n mids = []\n A = []\n J = []\n I = []\n\n c = []\n d = []\n e = []\n f = []\n k = []\n nsm = []\n for pid in pids:\n prop = model.properties[pid]\n #comments.append(prop.comment)\n mids.append(prop.mid)\n A.append(prop.A)\n I.append([prop.i1, prop.i2, prop.i12])\n J.append(prop.j)\n\n c.append([prop.c1, prop.c2])\n d.append([prop.d1, prop.d2])\n e.append([prop.e1, prop.e2])\n f.append([prop.f1, prop.f2])\n\n ki = []\n if prop.k1 is None:\n ki.append(np.nan)\n else:\n ki.append(prop.k1)\n if prop.k2 is None:\n ki.append(np.nan)\n else:\n ki.append(prop.k2)\n\n k.append(ki)\n nsm.append(prop.nsm)\n #h5_file.create_dataset('_comment', data=comments)\n h5_file.create_dataset('pid', data=pids)\n h5_file.create_dataset('mid', data=mids)\n h5_file.create_dataset('A', data=A)\n h5_file.create_dataset('J', data=J)\n h5_file.create_dataset('I', data=I)\n h5_file.create_dataset('c', data=c)\n h5_file.create_dataset('d', data=d)\n h5_file.create_dataset('e', data=e)\n h5_file.create_dataset('f', data=f)\n h5_file.create_dataset('k', data=k)\n h5_file.create_dataset('nsm', data=nsm)\n #h5_file.create_dataset('_comment', data=comments)","title":""},{"docid":"55f0dd890f986fb38c76a51ec91e07a0","score":"0.62494373","text":"def save_frames(self):\n np_frames = np.array(self.frames)\n df_frames = pd.DataFrame()\n df_frames['frames'] = np_frames[:, 0]\n df_frames['label'] = np_frames[:, 1]\n df_frames['is_feas'] = np_frames[:, 2]\n\n filename = os.path.split(self.filepaths[0])[-1]\n filename = filename.split('.')[0]\n filename = filename.split('_')\n folder_path = os.path.join(self.output_folder, filename[0])\n if not os.path.exists(folder_path):\n os.makedirs(folder_path)\n file_path = os.path.join(folder_path, '{}_{}.pkl'.format(filename[0], filename[1]))\n df_frames.to_pickle(file_path)","title":""},{"docid":"82aadb289a582196376c225477f148d1","score":"0.624555","text":"def save(self, h5group, manager):","title":""},{"docid":"72d35cd829f6b90a9431d051342d51ed","score":"0.6235141","text":"def to_hdf(self, hdf, group_name=\"outcar\"):\n with hdf.open(group_name) as hdf5_output:\n for key in self.parse_dict.keys():\n hdf5_output[key] = self.parse_dict[key]","title":""},{"docid":"8f02b1c34a71d5906d7378f5aced56e1","score":"0.623229","text":"def mat2h5(config):\n dataset_name = config.dataset_name\n base_path = config.data_path\n mat_dir = os.path.join(base_path, 'data_mat')\n h5_dir = os.path.join(base_path, 'data_h5')\n if dataset_name == 'Salinas':\n dataset_mat_dir = os.path.join(mat_dir, '{name}/{name}_corrected.mat'.format(name=dataset_name))\n dataset_gt_dir = os.path.join(mat_dir, '{name}/{name}_gt.mat'.format(name=dataset_name))\n dataset_h5_save_dir = os.path.join(h5_dir, '{}.h5'.format(dataset_name))\n elif dataset_name == 'Indian':\n dataset_mat_dir = os.path.join(mat_dir, '{name}/{name}_pines_corrected.mat'.format(name=dataset_name))\n dataset_gt_dir = os.path.join(mat_dir, '{name}/{name}_pines_gt.mat'.format(name=dataset_name))\n dataset_h5_save_dir = os.path.join(h5_dir, '{}.h5'.format(dataset_name))\n elif dataset_name == 'WHU_Hi_HongHu':\n dataset_mat_dir = os.path.join(mat_dir, '{name}/{name}.mat'.format(name=dataset_name))\n dataset_gt_dir = os.path.join(mat_dir, '{name}/{name}_gt.mat'.format(name=dataset_name))\n dataset_h5_save_dir = os.path.join(h5_dir, '{}.h5'.format(dataset_name))\n hsi_data = sio.loadmat(dataset_mat_dir)[config.dataset_HSI]\n hsi_gt = sio.loadmat(dataset_gt_dir)[config.dataset_gt]\n with h5py.File(dataset_h5_save_dir, 'w') as f:\n f['data'] = hsi_data\n f['label'] = hsi_gt","title":""},{"docid":"17175b1a9e57bf3d066c5543ead2a635","score":"0.62224144","text":"def to_file(self, path: str, info: Optional[InfoDict] = None) -> None:\n import h5py\n\n with h5py.File(path, \"w\") as fp:\n self._write_hdf_dataset(fp)\n\n # write additional information\n if info:\n for k, v in info.items():\n fp.attrs[k] = json.dumps(v)","title":""},{"docid":"48757a673890b20816c0f42ff481d191","score":"0.62183267","text":"def write_metadata_to_hdf5(hdf5_file: str, trace_metadata: trace.TraceMetaData) -> str:\n try:\n opened_hdf5_file = h5py.File(hdf5_file, 'a')\n except FileNotFoundError:\n print(f'==ERROR== Could not open the HDF5 file {hdf5_file}')\n return ''\n\n metadata = opened_hdf5_file[trace.HDF5_ROOT]\n metadata.attrs[trace.HDF5_METADATA_NAME] = trace_metadata.name\n metadata.attrs[trace.HDF5_METADATA_PATH] = trace_metadata.path\n metadata.attrs[trace.HDF5_METADATA_TYPE] = trace_metadata.type\n metadata.attrs[trace.HDF5_METADATA_EXEC_TIME] = trace_metadata.exec_time\n metadata.attrs[trace.HDF5_METADATA_DATE] = trace_metadata.date_time.isoformat()\n metadata.attrs[trace.HDF5_METADATA_NODES] = trace_metadata.nodes\n metadata.attrs[trace.HDF5_METADATA_APPS] = json.dumps(trace_metadata.apps)\n metadata.attrs[trace.HDF5_METADATA_NUM_PROCESSES] = trace_metadata.num_processes\n metadata.attrs[trace.HDF5_METADATA_TASKS_PER_NODE] = trace_metadata.tasks_per_node\n metadata.create_dataset(trace.HDF5_METADATA_HWCPU, (len(trace_metadata.cpu_list), 1), 'S25', trace_metadata.cpu_list)\n metadata.create_dataset(trace.HDF5_METADATA_HWNODES, (len(trace_metadata.node_list), 1), 'S25', trace_metadata.node_list)\n metadata.create_dataset(trace.HDF5_METADATA_THREADS, (len(trace_metadata.thread_list), 1), 'S25', trace_metadata.thread_list)\n opened_hdf5_file.close()\n return hdf5_file","title":""},{"docid":"0619b39bde13bc3e63099fbfcc41bc12","score":"0.6176862","text":"def saveToFile(self,filename,mode='a',fmt='HDF5',index=0):\n if fmt=='HDF5':\n f = h5py.File(filename,mode)\n Hdf5Group=f.create_group('%05d'%index)\n Hdf5Group.attrs['index']=index\n self.saveToHDF5(Hdf5Group)\n else:\n sys.stderr.write('ERROR: format '+fmt+' not yet implemented\\n')","title":""},{"docid":"e302ef0c4f8fa24c2c287c9f694bb357","score":"0.6159076","text":"def save_df_to_hdf5(var_results, var_meta, path, Days, Hours, Periods):\n # Format results into pandas series and dataframe objects\n columns = ['Var_name', 'Value']\n var_results_df = {}\n \n vi = var_index[0]\n var_results_df[vi] = pd.DataFrame(var_results[vi].items(), columns=columns)\n \n vi = var_index[1]\n index_names = ['Var_name', 'Days', 'Hours']\n K = list(var_results[vi].keys())\n index = pd.MultiIndex.from_product([K, Days, Hours], names=index_names)\n var_results_df[vi] = make_df(index, var_results[vi])\n \n vi = var_index[2]\n index_names = ['Var_name', 'Periods']\n K = list(var_results[vi].keys())\n index = pd.MultiIndex.from_product([K, Periods], names=index_names)\n var_results_df[vi] = make_df(index, var_results[vi])\n \n vi = var_index[3]\n index_names = ['Var_index', 'Var_name', 'Bounds']\n K, meta = [], []\n for i in var_index[:-1]:\n K.extend(list(var_meta[i].keys()))\n meta.extend(np.array(list(var_meta[i].values())))\n meta = np.array(meta)\n lb, ub, vtype = meta[:,0], meta[:,1], meta[:,2]\n dic = dict(zip(K, np.zeros(len(K))))\n var_results_df[vi] = pd.DataFrame.from_dict(dic.items())\n var_results_df[vi][1] = lb\n var_results_df[vi]['Upper_bound'] = ub\n var_results_df[vi]['Var_type'] = vtype\n var_results_df[vi].rename(columns={0:'Var_name', 1: 'Lower_bound'}, \n inplace=True)\n \n result_dir, file_name = get_directory(path)\n for vi in var_index: \n with pd.HDFStore(path) as hdf:\n hdf.put(vi, var_results_df[vi], format='table', data_columns=True)\n print(f'All results where saved to {file_name} in {result_dir}')","title":""},{"docid":"ee6042cde9127f1b331c59559a90cc72","score":"0.61537665","text":"def save_h5(f, path, arr, overwrite=False):\n if isinstance(arr, SparseCSR):\n arr.save_h5(f, path)\n elif isinstance(arr, np.ndarray):\n f.write(path, arr, overwrite=overwrite)\n else:\n raise ValueError(\"The array should be a SparseCSR or \"\n \"dense NumPy array.\")","title":""},{"docid":"156cf0e68a5f707a46442f503960de03","score":"0.61459076","text":"def save_photon_hdf5(data_dict,\n h5_fname = None,\n user_descr = None,\n overwrite = False,\n compression = dict(complevel=6, complib='zlib'),\n close = True,\n validate = True,\n warnings = True,\n skip_measurement_specs = False,\n require_setup = True,\n debug = False):\n comp_filter = tables.Filters(**compression)\n\n ## Compute file names\n if h5_fname is None:\n basename, extension = os.path.splitext(data_dict['_filename'])\n if compression['complib'] == 'blosc':\n basename += '_blosc'\n h5_fname = basename + '.hdf5'\n\n if os.path.isfile(h5_fname) and not overwrite:\n basename, extension = os.path.splitext(h5_fname)\n h5_fname = basename + '_new_copy.hdf5'\n\n ## Prefill and fix user-provided data_dict\n _populate_provenance(data_dict)\n _sanitize_data(data_dict, require_setup)\n _compute_acquisition_duration(data_dict)\n\n ## Create the HDF5 file\n print('Saving: %s' % h5_fname)\n title = official_fields_specs['/'][0].encode()\n h5file = tables.open_file(h5_fname, mode=\"w\", title=title,\n filters=comp_filter)\n # Saving a file reference is useful in case of error\n data_dict.update(_data_file=h5file)\n\n ## Identity info needs to be added after the file is created\n _populate_identity(data_dict, h5file)\n\n ## Save root attributes\n for name, value in root_attributes.items():\n h5file.root._f_setattr(name, value)\n\n ## Save everything else to disk\n fields_descr = {k: v[0] for k, v in official_fields_specs.items()}\n if user_descr is not None:\n fields_descr.update(user_descr)\n _save_photon_hdf5_dict(h5file.root, data_dict,\n fields_descr=fields_descr, debug=debug)\n h5file.flush()\n\n ## Validation\n if validate:\n kwargs = dict(skip_measurement_specs=skip_measurement_specs,\n warnings=warnings, require_setup=require_setup)\n assert_valid_photon_hdf5(h5file, **kwargs)\n if close:\n h5file.close()","title":""},{"docid":"f06626a23f9019b8d7d2db3dc51fdfc1","score":"0.61375695","text":"def saveToHDF5(self,Hdf5Group):\n idx=0\n for cm in self:\n subgroup = Hdf5Group.create_group('%05d'%idx)\n subgroup.attrs['index']=idx\n cm.saveToHDF5(subgroup)\n idx+=1","title":""},{"docid":"eaf977522ff2343f488820eb1fe0b5d3","score":"0.61346644","text":"def to_HDF5(self, filename, date=True, **kwargs):\n self.filename = os.path.expanduser(filename)\n if 'TIME_UNITS' not in kwargs.keys():\n kwargs['TIME_UNITS'] = 'years'\n if 'TIME_LONGNAME' not in kwargs.keys():\n kwargs['TIME_LONGNAME'] = 'Date_in_Decimal_Years'\n hdf5_stokes(self.clm, self.slm, self.l, self.m, self.time, self.month,\n FILENAME=self.filename, DATE=date, **kwargs)","title":""},{"docid":"c72c3a1bbe362c6e427081ae363bce29","score":"0.6121616","text":"def write_obs_to_h5(hf, obs):\n try:\n odat = hf.create_group('obs')\n except(ValueError):\n # We already have an 'obs' group\n return\n for k, v in list(obs.items()):\n if k == 'filters':\n try:\n v = [f.name for f in v]\n except:\n pass\n if isinstance(v, np.ndarray):\n odat.create_dataset(k, data=v)\n else:\n try:\n odat.attrs[k] = json.dumps(v) #, cls=NumpyEncoder)\n except(TypeError):\n # Should this fall back to pickle.dumps?\n odat.attrs[k] = pick(v)\n warnings.warn(\"Could not JSON serialize {}, pickled instead\".format(k))\n except:\n odat.attrs[k] = unserial\n warnings.warn(\"Could not serialize {}\".format(k))\n\n hf.flush()","title":""},{"docid":"560deadc1e1bfd6754ae6606cbfca814","score":"0.6107587","text":"def save(self, output_filename):\n # raw_data = [[X, m, desc] for X, m, desc in zip(\n # self.landmarks, self.miss_counts, self.descriptors)]\n self.dataframe.to_pickle(output_filename)\n # pd.DataFrame(data=raw_data,\n # columns=['Position',\n # 'Miss count',\n # 'Descriptor']).to_pickle(output_filename)","title":""},{"docid":"9e371daaa71d9c50643079c47dba8e09","score":"0.6106506","text":"def _export_dataframe_report(self, df, output_dir, basename):\n fmt = self._report_global_settings.format\n filename = os.path.join(output_dir, basename + \".\" + fmt.value)\n compress = True if fmt == \"h5\" else False\n write_dataframe(df, filename, compress=compress)\n logger.info(\"Generated %s\", filename)\n return filename","title":""},{"docid":"fcd3bef29520c855e7219f41e7166cc8","score":"0.60862666","text":"def touch_result_hdf5_file(target_dir, poe, ds_names, n_realizations,\n n_periods):\n file_name = _HDF5_FILE_NAME_FMT % poe\n full_path = os.path.join(target_dir, file_name)\n\n ds_shape = (n_realizations, n_periods)\n\n with h5py.File(full_path, 'w') as h5_file:\n for name in ds_names:\n h5_file.create_dataset(name, dtype=numpy.float64, shape=ds_shape)\n\n return full_path","title":""},{"docid":"18a9e5fb4f378148929aeed46d74aa0e","score":"0.60608864","text":"def write_to_file(self, struct, fName):\n\n f = h5py.File(fName, \"w\")\n self._recursive_write(f, struct)\n f.close()","title":""},{"docid":"7bf49dc4baed73e0b11de98452467c37","score":"0.6052166","text":"def save(self, obj, path='/'):\n obj_id = id(obj)\n in_memo = self.memo_save.get(obj_id) # default=None\n if in_memo is not None: # saved the object before\n h5gr, _ = in_memo\n self.h5group[path] = h5gr # create hdf5 hard link\n # hard linked objects share an hdf5 id,\n # which we use in the loader to distinguish them\n return h5gr\n\n disp = self.dispatch_save.get(type(obj))\n if disp is not None:\n f, type_repr = disp\n # `f` is a dispatcher function, which should\n # - save the `obj` in self.h5group['path'],\n # - call :meth:`memorize_save`, and\n # - set ``h5gr.attr[ATTR_TYPE] = type_repr`` to a string `type_repr`\n # to allow loading with the dispatcher ``Hdf5Loader.dispatch_load[type_repr]``\n # call unbound method `f` with explicit self\n h5gr = f(self, obj, path, type_repr)\n return h5gr\n\n # handle classes with `save_hdf5` method\n obj_save_hdf5 = getattr(obj, 'save_hdf5', None)\n if obj_save_hdf5 is not None: # of Hdf5Exportable type\n # `obj_save_hdf5` should be the bound method `obj.save_hdf5`,\n # so it does not need an explicit reference of `obj`\n h5gr, subpath = self.create_group_for_obj(path, obj)\n h5gr.attrs[ATTR_TYPE] = REPR_HDF5EXPORTABLE\n h5gr.attrs[ATTR_CLASS] = obj.__class__.__qualname__\n h5gr.attrs[ATTR_MODULE] = obj.__class__.__module__\n obj_save_hdf5(self, h5gr, subpath) # should save the actual data\n return h5gr\n\n warnings.warn(\n \"Hdf5Saver: object of type {t!r} without explicit HDF5 format; \"\n \"fall back to pickle protocol\".format(t=type(obj)), UserWarning)\n\n obj_reduce = getattr(obj, \"__reduce__\", None)\n if obj_reduce is not None:\n\n rv = obj_reduce()\n if isinstance(rv, str):\n h5gr = self.save_global(obj, REPR_GLOBAL)\n return h5gr\n if not isinstance(rv, tuple) or not 2 <= len(rv) < 7:\n raise Hdf5ExportError(\"Wrong return value of {0!r}\".format(obj_reduce))\n\n h5gr = self.save_reduce(*rv, obj=obj, path=path)\n return h5gr\n\n # unknown case\n msg = \"Don't know how to save object of type {0!r}:\\n{1!r}\".format(type(obj), obj)\n raise Hdf5ExportError(msg)","title":""},{"docid":"a460fe1bf2a6f5ac25e22df5ec75494a","score":"0.6049246","text":"def dict_to_h5(dict_save, filename, compression=None, compression_opts=None):\n fid = h5py.File(filename, 'w', driver='mpio', comm=MPI.COMM_WORLD)\n for kk in list(dict_save.keys()): \n fid.create_dataset(kk, data=dict_save[kk], compression=compression, compression_opts=compression_opts)\n fid.close()","title":""},{"docid":"54195964ce7a69f087778c38fe5207d0","score":"0.6028586","text":"def _save_df(self, df:pd.DataFrame, summary_file:pd.DataFrame, \n name:str, sub_folder:str):\n if not os.path.exists(self.directory + sub_folder):\n os.makedirs(self.directory + sub_folder)\n pd.to_pickle(df, self.directory + sub_folder + '/' + name)\n #filepath = sub_folder + '/' + name\n filepath = name\n row = pd.DataFrame.from_dict(data={'filename':[filepath]})\n return summary_file.append(row, ignore_index=True)","title":""},{"docid":"ac661d4d8adedb027328c5ca2f3330b7","score":"0.602839","text":"def saveh5(self, h5file, qpi_slice=None, series_slice=None,\n time_interval=None, count=None, max_count=None):\n # set up slice to export\n if series_slice is None:\n sl = range(len(self))\n else:\n sl = range(series_slice.start, series_slice.stop)\n # set up time interval\n if time_interval is None:\n ta = -np.inf\n tb = np.inf\n else:\n ta, tb = time_interval\n # set max_count according to slice\n if max_count is not None:\n max_count.value += len(sl)\n\n qpskw = {\"h5file\": h5file,\n \"h5mode\": \"w\",\n }\n\n if (qpi_slice is None and\n series_slice is None and\n time_interval is None):\n # Only add series identifier if series complete.\n # (We assume that if any of the above kwargs is set,\n # the series data is somehow modified)\n qpskw[\"identifier\"] = self.identifier\n\n with qpimage.QPSeries(**qpskw) as qps:\n increment = 0\n for ii in sl:\n ti = self.get_metadata(ii).get(\"time\", np.nan)\n if ti < ta or ti > tb:\n # Not part of the series\n pass\n else:\n increment += 1\n if increment == 1 or len(self._bgdata) != 1:\n # initial image or series data where each image\n # has a unique background image\n qpi = self.get_qpimage(ii)\n if qpi_slice is not None:\n qpi = qpi[qpi_slice]\n qps.add_qpimage(qpi)\n else:\n # hard-link the background data\n qpiraw = self.get_qpimage_raw(ii)\n if qpi_slice is not None:\n qpiraw = qpiraw[qpi_slice]\n qps.add_qpimage(qpiraw, bg_from_idx=0)\n if count is not None:\n count.value += 1","title":""},{"docid":"2367a134586d71eab1959d3eaf53f4f6","score":"0.60246533","text":"def to_hdf(self, outfile):\n print(\"Saving SpecMatch object to HDF file\")\n # Allow either a string or h5 file object ot be passed.\n is_path = False\n if isinstance(outfile, string_types):\n outfile = h5py.File(outfile, 'w')\n is_path = True\n\n # Save target spectrum\n if self.target is not None:\n outfile.create_group('target')\n self.target.to_hdf(outfile['target'])\n\n # ------------------------ Shift results ------------------------\n # Save unshifted spectrum\n if self._shifted is True:\n # (Check if unshifted and shifted targets are different)\n outfile.create_group('unshifted')\n self.target_unshifted.to_hdf(outfile['unshifted'])\n\n # Save shift reference\n if self.shift_ref is not None:\n outfile.create_group('shift_ref')\n self.shift_ref.to_hdf(outfile['shift_ref'])\n\n # Save shift data\n if len(self.shift_data) > 0:\n grp = outfile.create_group('shift_data')\n for k, v in self.shift_data.items():\n grp[k] = v\n\n # ------------------------ Match results ------------------------\n # Wavelength regions\n if self.regions is not None:\n outfile['regions'] = self.regions\n\n # Save match results by converting to record array\n if not self.match_results.empty:\n if 'lib_index' in self.match_results:\n self.match_results.drop('lib_index', inplace=True, axis=1)\n match_rec = self.match_results.to_records()\n dt = match_rec.dtype.descr\n\n for i in range(len(dt)):\n if dt[i][1] == \"|O\":\n # max string length = 5000\n dt[i] = (str(dt[i][0]), 'S5000')\n else:\n dt[i] = (str(dt[i][0]), dt[i][1])\n\n match_rec = np.array(match_rec, dtype=dt)\n\n outfile['match_results'] = match_rec\n\n # restore lib_index column\n self.match_results['lib_index'] = self.match_results.index\n\n # ----------------------- Lincomb results -----------------------\n if self.num_best is not None:\n outfile['num_best'] = self.num_best\n if self.coeffs is not None:\n outfile['coeffs'] = self.coeffs\n if self.lincomb_regions is not None:\n outfile['lincomb_regions'] = self.lincomb_regions\n if len(self.lincomb_matches) > 0:\n grp = outfile.create_group('lincomb')\n for i in range(len(self.lincomb_regions)):\n reg = self.lincomb_regions[i]\n subgrp = grp.create_group('{0:.0f}'.format(reg[0]))\n subgrp['ref_idxs'] = self.ref_idxs[i]\n self.lincomb_matches[i].to_hdf(subgrp)\n res_grp = subgrp.create_group('results')\n for k, v in self.lincomb_results[i].items():\n res_grp[k] = v\n\n # Save averaged results\n if len(self.results_nodetrend) > 0:\n res_grp = outfile.create_group('results_nodetrend')\n for k, v in self.results_nodetrend.items():\n res_grp[k] = v\n\n if len(self.results) > 0:\n res_grp = outfile.create_group('results')\n for k, v in self.results.items():\n res_grp[k] = v\n\n if is_path:\n outfile.close()","title":""},{"docid":"a7a38c013cc8ac6e1292e0aa6c53afc7","score":"0.59947145","text":"def save(self):\n if self.debug or self.rank > 0: return\n\n # save images and replace the image array with the\n # file name\n self.__save_images(self.metrics)\n metrics_file_path = self.get_data_path(self.name, self.version) + '/metrics.csv'\n meta_tags_path = self.get_data_path(self.name, self.version) + '/meta_tags.csv'\n\n obj = {\n 'name': self.name,\n 'version': self.version,\n 'tags_path': meta_tags_path,\n 'metrics_path': metrics_file_path,\n 'autosave': self.autosave,\n 'description': self.description,\n 'created_at': self.created_at,\n 'exp_hash': self.exp_hash\n }\n\n # save the experiment meta file\n with atomic_write(self.__get_log_name()) as tmp_path:\n with open(tmp_path, 'w') as file:\n json.dump(obj, file, ensure_ascii=False)\n\n # save the metatags file\n df = pd.DataFrame({'key': list(self.tags.keys()), 'value': list(self.tags.values())})\n with atomic_write(meta_tags_path) as tmp_path:\n df.to_csv(tmp_path, index=False)\n\n # save the metrics data\n df = pd.DataFrame(self.metrics)\n with atomic_write(metrics_file_path) as tmp_path:\n df.to_csv(tmp_path, index=False)\n\n # write new vals to disk\n self.flush()\n\n # until hparam plugin is fixed, generate hparams as text\n if not self.tag_markdown_saved and len(self.tags) > 0:\n self.tag_markdown_saved = True\n self.add_text('hparams', self.__generate_tfx_meta_log())","title":""},{"docid":"1fb3a6fd81a40639b5f6fc6e32064294","score":"0.5983791","text":"def save(self, filename):\n self.get_model().save(\"models/\" + filename + \".h5\")","title":""},{"docid":"d4dbbc4e1a0b18c01501e56661107cef","score":"0.59792453","text":"def test_write_h5_image(self):\n data = self.image_data\n fname = \"test_write_h5_image.h5\"\n with h5py.File(fname, \"w\", **self.memory_kwargs) as fid:\n self.assertIsNone(hdf5.write_h5_image(data, \"image\", fid))","title":""},{"docid":"1edd673406ca2acee7f4ac7899621266","score":"0.5970013","text":"def xr_DataArray_to_hdf5(\tda, hdf5_file_name, group_name, compression_level=5):\n\t\n\tif da.name is None:\n\t\tda.name = 'data'\n\t\n\txr_Dataset_to_hdf5(\t\tds=da.to_dataset(),\n\t\t\t\t\t\t\thdf5_file_name=hdf5_file_name,\n\t\t\t\t\t\t\tgroup_name=group_name,\n\t\t\t\t\t\t\tcompression_level=compression_level)","title":""},{"docid":"3710644b133a2cf7b7e484fd4bd19a1e","score":"0.5969826","text":"def write(data, filename, db_name, write_mode=\"w\"):\n def _check_directory(filename):\n directory = os.path.dirname(filename)\n if not os.path.exists(directory):\n os.mkdir(directory)\n\n _check_directory(filename) \n # todo : overwrite check\n db = h5py.File(filename, write_mode)\n dataset = db.create_dataset(db_name, data.shape, dtype=\"float\")\n dataset[:] = data[:]\n db.close()","title":""},{"docid":"9fe75468a171f9a27e2e4fbac129ac46","score":"0.59678084","text":"def write_summary_file(self):\n self._summaryfile = Trace.get_summary_filename(self._tracefile)\n\n packed_metadata = self.metadata.pack_dataframe()\n\n packed_metadata[Trace._formatversionkey] = pandas.Series(\n data=Trace._formatversion, dtype=numpy.int32\n )\n\n with HDFStoreContext(self._summaryfile, mode=\"w\") as hdfstore:\n hdfstore.put(Trace._metadatakey, packed_metadata, format=\"t\")\n hdfstore.put(Trace._statisticskey, self.statistics, format=\"t\")","title":""},{"docid":"b1e7d32799a9800e9aaf374be7b5bbf7","score":"0.5962721","text":"def to_h5(self):\n import time\n from pymicro import __version__ as pymicro_version\n\n print('opening file %s.h5 for writing' % self.name)\n f = h5py.File('%s.h5' % self.name, 'w')\n f.attrs['Pymicro_Version'] = np.string_(pymicro_version)\n f.attrs['HDF5_Version'] = h5py.version.hdf5_version\n f.attrs['h5py_version'] = h5py.version.version\n f.attrs['file_time'] = time.time()\n f.attrs['microstructure_name'] = self.name\n if hasattr(self, 'data_dir'):\n f.attrs['data_dir'] = self.data_dir\n # ensemble data\n ed = f.create_group('EnsembleData')\n cs = ed.create_group('CrystalStructure')\n sym = self.get_lattice().get_symmetry()\n cs.attrs['symmetry'] = sym.to_string()\n lp = cs.create_dataset('LatticeParameters',\n data=np.array(self.get_lattice().get_lattice_parameters(), dtype=np.float32))\n # feature data\n fd = f.create_group('FeatureData')\n grain_ids = fd.create_dataset('grain_ids',\n data=np.array([g.id for g in self.grains], dtype=np.int))\n avg_rods = fd.create_dataset('R_vectors',\n data=np.array([g.orientation.rod for g in self.grains], dtype=np.float32))\n centers = fd.create_dataset('centers',\n data=np.array([g.center for g in self.grains], dtype=np.float32))\n # cell data\n cd = f.create_group('CellData')\n if hasattr(self, 'grain_map') and self.grain_map is not None:\n gm = cd.create_dataset('grain_ids', data=self.grain_map, compression='gzip', compression_opts=9)\n gm.attrs['voxel_size'] = self.voxel_size\n if hasattr(self, 'mask') and self.mask is not None:\n ma = cd.create_dataset('mask', data=self.mask, compression='gzip', compression_opts=9)\n ma.attrs['voxel_size'] = self.voxel_size\n print('done writing')\n f.close()","title":""},{"docid":"58c65385478add68584e6099bfd0c0d9","score":"0.5957029","text":"def saveModel(self, fileName):\n self.model.save('{}.h5'.format(fileName)) # creates a HDF5 file 'my_model.h5'","title":""},{"docid":"84782e332be73445003b4cbfba971d44","score":"0.5946952","text":"def save_model(self,file):\n name = file+'.h5'\n self.nn_model.save(name)","title":""},{"docid":"355b25611b9ff57a4215e18bbb691c7a","score":"0.59389853","text":"def save_frame(self, save_path_filename):\n raise NotImplementedError","title":""},{"docid":"9bc7b00cf588e27d57a091b25989e316","score":"0.5936832","text":"def save_h8_l1_hdf5(h08_data, h08_time, h08_coords, path_out, \\\n hrit_listb = hrit_listb):\n title = \"HS_H08_\" + h08_time +\"_PH_R20_S030405.hdf5\"\n savefilename = os.path.join(path_out, title)\n\n metadata = {}\n metadata['date_time'] = h08_time\n\n f = h5py.File(savefilename,'w')\n grp = f.create_group('HIMAWARI')\n \n f.create_dataset('HIMAWARI/COORDINATES/longitude/',\\\n data = h08_coords[\"Longitude\"], compression = 'gzip',compression_opts=9)\n f.create_dataset('HIMAWARI/COORDINATES/latitude/',\\\n data = h08_coords[\"Latitude\"], compression = 'gzip', compression_opts=9)\n \n for k in hrit_listb:\n f.create_dataset('HIMAWARI/DATA/'+k,\\\n data = h08_data[k], compression = 'gzip', compression_opts=9)\n \n f.create_dataset('HIMAWARI/ANGLE/SOZ/',\\\n data = h08_data[\"SOZ\"], compression = 'gzip',compression_opts=9)\n f.create_dataset('HIMAWARI/ANGLE/SOA/',\\\n data = h08_data[\"SOA\"], compression = 'gzip', compression_opts=9) \n \n for key in metadata.keys():\n grp.attrs[key] = metadata[key]\n print \"\\n\"+savefilename +\" SAVED\"\n f.close()\n return title","title":""},{"docid":"8758a00cb10b52dbff0ccd43e5a748cc","score":"0.59354293","text":"def generate_file(self, hdf5_file_name: str) -> None:\n self.f = h5py.File(hdf5_file_name, \"w\")\n\n self._save_metadata()\n self._collect_simulation_conditions()\n self._process_condition_table()\n\n logger.info(Fore.GREEN + \"Generating simulation condition list...\")\n self._generate_simulation_condition_map()\n\n logger.info(Fore.GREEN + \"Generating parameter list...\")\n self._generate_parameter_list()\n self._generate_simulation_to_optimization_parameter_mapping()\n\n logger.info(Fore.GREEN + \"Generating measurement matrix...\")\n self._generate_measurement_matrices()\n\n logger.info(Fore.GREEN + \"Handling scaling parameters...\")\n self._generate_hierarchical_optimization_data()\n\n logger.info(Fore.GREEN + \"Copying default AMICI options...\")\n self._write_amici_options()\n\n logger.info(Fore.GREEN + \"Writing default optimization options...\")\n write_optimization_options(self.f)\n self._write_bounds()\n self._write_starting_points()","title":""},{"docid":"d5c528f6dbe1846a2598cde8c15c2841","score":"0.5935056","text":"def tohdf5(matrices: Iter[np.ndarray], labels: Iter[int], outfile: str) -> None:\n try:\n import h5py\n except ImportError:\n raise ImportError(\"can't use h5 backend, h5py not available\")\n\n f = h5py.File(outfile, \"w\", libver='latest')\n columns = \"time freq amp phase bw\"\n labellist = aslist(labels) # type: List[int]\n metadata = {\n 'version': 1.1,\n 'numpartials': len(labellist),\n 'columns': columns.split()\n }\n for key, value in metadata.items():\n f.attrs.create(key, data=value)\n f.create_dataset(\"labels\", data=labellist)\n partialroot = f.create_group(\"partials\")\n numchars = len(\"999999\")\n keyformat = \"%.{numchars}d\".format(numchars=numchars)\n for i, matrix in enumerate(matrices):\n key = keyformat % i\n partialroot.create_dataset(key, data=matrix)\n f.flush()\n f.close()","title":""},{"docid":"9549ba3ba73243f7c8ee4a7bba6afbf8","score":"0.59183824","text":"def write_h5mu(self, filename: Optional[str] = None, **kwargs):\n from .io import write_h5mu, _write_h5mu\n\n if self.isbacked and (filename is None or filename == self.filename):\n import h5py\n\n self.file.close()\n with h5py.File(self.filename, \"a\") as f:\n _write_h5mu(f, self, write_data=False, **kwargs)\n elif filename is None:\n raise ValueError(\"Provide a filename!\")\n else:\n write_h5mu(filename, self, **kwargs)\n if self.isbacked:\n self.file.filename = filename","title":""},{"docid":"c3c745ad999e831e2fdadcc938ed2fc3","score":"0.5912255","text":"def exportHDF5(self, fileName, groupName=None, writeSamples=False):\n # Check if already in hdf5 file first\n if os.path.exists(fileName):\n mode = \"a\"\n else:\n mode = \"w\"\n\n # Specify the root address for writing\n ROOT = \"/\"\n if groupName is None:\n baseAddress = ROOT\n else:\n baseAddress = os.path.join(ROOT, groupName)\n\n # And the data is stored in\n bootAddress = os.path.join(baseAddress, \"bootstrap\")\n\n # Open the HDF5 file\n with h5py.File(fileName, mode) as f:\n # Check whether group already exists\n baseGroup = f.get(baseAddress)\n if baseGroup is None:\n baseGroup = f.create_group(baseAddress)\n # Check wether bootstrap data exists\n if \"bootstrap\" in baseGroup.keys():\n raise KeyError(\n \"Group >bootstrap< already exist for base group >{}<. Stop writing\".format(\n groupName\n ),\n groupName\n )\n bootGroup = f.create_group(bootAddress)\n\n # Now write parameters\n for key, val in self.parameters.items():\n bootGroup.create_dataset(key, data=val)\n # Write indices\n bootGroup.create_dataset(\"indices\", data=self.indices)\n # Write samples if requested\n if writeSamples:\n bootGroup.create_dataset(\"samples\", data=self.samples)","title":""},{"docid":"3ec1406405ddd157981f84deacdc6d93","score":"0.591161","text":"def save_to_hdf(key, item, path):\n df = pd.DataFrame(item)\n with pd.HDFStore(path) as hdf:\n hdf.put(key, df)\n print(f'Item {key} saved to {path}')","title":""},{"docid":"305237bd8c32fe3b33e5d655d103823b","score":"0.591117","text":"def save_plot(dataframe, filename):\n plt.clf()\n dataframe.plot()\n plt.savefig(filename)","title":""}],"string":"[\n {\n \"docid\": \"2240ae2fa11185698b2a1d51bc73e042\",\n \"score\": \"0.77490896\",\n \"text\": \"def save_H5(self, avg=False):\\n if not os.path.isfile(self.get_H5_path()):\\n self.df.to_hdf(self.get_H5_path(), key=\\\"df\\\", mode=\\\"w\\\")\\n if avg:\\n self.df_avg.to_hdf(self.get_H5_avg_path(), key=\\\"df\\\", mode=\\\"w\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b4abd696f93215781adbc6cc43b61a9\",\n \"score\": \"0.73503816\",\n \"text\": \"def save_hd5(self, filename, create_path=False):\\n self.__assert_ready()\\n import h5py\\n\\n directory = os.path.dirname(filename)\\n basename = os.path.basename(filename)\\n\\n if basename == \\\"\\\":\\n raise RuntimeError(\\\"A non-empty filename must be specified for `save_as`.\\\")\\n if not os.path.isdir(directory) and not create_path:\\n raise RuntimeError(\\n 'Directory path \\\"{}\\\" does not exist; use `create_path` option to automatically create it.'.format(\\n directory\\n )\\n )\\n if create_path and not os.path.isdir(directory):\\n os.makedirs(directory)\\n\\n if not os.path.splitext(basename)[1].lower() in [\\n \\\".hd5\\\",\\n \\\".hdf5\\\",\\n \\\".hdf\\\",\\n \\\".h5\\\",\\n ]:\\n basename = \\\"{}.hd5\\\".format(basename)\\n\\n filename = os.path.join(directory, basename)\\n\\n fp = h5py.File(filename, \\\"w\\\")\\n fp[\\\"img\\\"] = self.__img\\n fp[\\\"mm_per_voxel\\\"] = self.__mm_per_voxel\\n fp[\\\"original_shape\\\"] = self.__orig_shape\\n fp[\\\"dicom_orientation\\\"] = 0 if self.__orientation == \\\"view\\\" else 1\\n fp.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51d6f392716fdefa8f09792679b02a57\",\n \"score\": \"0.73190427\",\n \"text\": \"def save(self, hdf5, save_function = bob.bio.base.save):\\n if not len(self):\\n logger.warn(\\\"Saving empty FrameContainer '%s'\\\", hdf5.filename)\\n for frame_id, data, quality in self:\\n hdf5.create_group(\\\"Frame_%s\\\" % frame_id)\\n hdf5.cd(\\\"Frame_%s\\\" % frame_id)\\n save_function(data, hdf5)\\n if quality is not None:\\n hdf5.set(\\\"FrameQuality\\\", quality)\\n hdf5.cd(\\\"..\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65b4085b30a7ff6540195bce4b86ec78\",\n \"score\": \"0.7308886\",\n \"text\": \"def save(self, path, as_hd5=False ):\\n if as_hd5:\\n self.data.to_hdf(path+\\\".hd5\\\", key=\\\"Dataset\\\", mode='w')\\n else:\\n self.data.to_csv(path+\\\".csv\\\" )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22364dfb657b6a7360d6e5cb2043b240\",\n \"score\": \"0.72904813\",\n \"text\": \"def saveHDF(self, filename, dataset, data, metadata_func=None):\\n with h5py.File(filename, 'r+') as f:\\n f.create_dataset(dataset, data=data)\\n f[dataset].attrs['mode'] = self._name\\n f[dataset].attrs['exposure'] = self._cam.exposure\\n f[dataset].attrs['em_gain'] = self._cam.EM._read_gain_from_camera()\\n f[dataset].attrs['created'] = time.strftime(\\\"%d/%m/%Y %H:%M:%S\\\")\\n if metadata_func is not None:\\n metadata_func(f[dataset])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60f4d7732c7fc6e71cdae122c55a3e9d\",\n \"score\": \"0.72688127\",\n \"text\": \"def test_write_dataframe(self):\\n df = pandas.DataFrame(self.table_data)\\n fname = \\\"test_write_dataframe.h5\\\"\\n with h5py.File(fname, \\\"w\\\", **self.memory_kwargs) as fid:\\n self.assertIsNone(hdf5.write_dataframe(df, \\\"dataframe\\\", fid))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab81b4cd28f34d41ecf99278463eb68\",\n \"score\": \"0.7246936\",\n \"text\": \"def _save_hdf5(self, output_file_path=None):\\n #save sync data\\n if output_file_path:\\n filename = output_file_path\\n else:\\n filename = self.output_path+\\\".h5\\\"\\n data = np.fromfile(self.output_path, dtype=np.uint32)\\n total_samples = len(data)\\n\\n events = self._get_events(data)\\n\\n h5_output = h5.File(filename, 'w')\\n h5_output.create_dataset(\\\"data\\\", data=events)\\n #save meta data\\n meta_data = self._get_meta_data()\\n meta_data['total_samples'] = total_samples\\n\\n meta_data_np = np.string_(str(meta_data))\\n h5_output.create_dataset(\\\"meta\\\", data=meta_data_np)\\n h5_output.close()\\n\\n #remove raw file\\n if not self.save_raw:\\n os.remove(self.output_path)\\n\\n if self.verbose:\\n logging.info(\\\"Recorded %i events.\\\" % (len(events)-1))\\n logging.info(\\\"Metadata: %s\\\" % meta_data)\\n logging.info(\\\"Saving to %s\\\" % filename)\\n try:\\n ds = Dataset(filename)\\n ds.stats()\\n ds.close()\\n except Exception as e:\\n logging.warning(\\\"Failed to print quick stats: %s\\\" % e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0951b75bb111c69b64c01b231297257\",\n \"score\": \"0.71313345\",\n \"text\": \"def write(self, frames):\\n with pd.HDFStore(self._path, 'w',\\n complevel=self._complevel, complib=self._complib) \\\\\\n as store:\\n panel = pd.Panel.from_dict(dict(frames))\\n panel.to_hdf(store, 'updates')\\n with tables.open_file(self._path, mode='r+') as h5file:\\n h5file.set_node_attr('/', 'version', 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9214b328d1ae290f5067cbab8e51b9be\",\n \"score\": \"0.7128886\",\n \"text\": \"def saveh5(outfile, data):\\n with h5py.File(outfile, \\\"w\\\") as f:\\n [f.create_dataset(key, data=val) for key, val in list(data.items())]\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23417bd27c50abc9e96408998601ff84\",\n \"score\": \"0.71100056\",\n \"text\": \"def save_hdf5(self, filename, t):\\n # ask if file has already been created. If not, create it.\\n if not hasattr(self, 'h5fileWrite'):\\n self.h5fileWrite = dolfinh5tools.Create(filename, self.functionspace)\\n self.h5fileWrite.save_mesh()\\n self.h5fileWrite.write(self.f, self.name, t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39e2b07fe23eaf8d4fbb5ce44ab00bef\",\n \"score\": \"0.7078768\",\n \"text\": \"def save_to_hdf5(h5group, obj, path='/'):\\n return Hdf5Saver(h5group).save(obj, path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"622aea71a63e78a232ede13055dc97e3\",\n \"score\": \"0.70593697\",\n \"text\": \"def write_hdf5(data, filename):\\n import h5py as hp\\n import numpy as np\\n hfile = hp.File(filename, 'w')\\n typ = type(data)\\n if typ == dict:\\n for k in data.iterkeys():\\n # The straight code gives ustrings, which I don't like.\\n# hfile[k] = data[k]\\n exec(\\\"hfile['\\\" + k + \\\"'] = data['\\\" + k + \\\"']\\\")\\n elif typ == np.ndarray:\\n hfile['data'] = data\\n hfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f145c58f1d516c4dc386c6dd8ef6cbe5\",\n \"score\": \"0.70541835\",\n \"text\": \"def save_hdf5(self, hdf5_saver, h5gr, subpath):\\n # for new implementations, use:\\n # hdf5_saver.save(data, subpath + \\\"key\\\") # for big content/data\\n # h5gr.attrs[\\\"name\\\"] = info # for metadata\\n\\n # here: assume all the data is given in self.__dict__\\n type_repr = hdf5_saver.save_dict_content(self.__dict__, h5gr, subpath)\\n h5gr.attrs[ATTR_FORMAT] = type_repr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"119208943c253c0933bc666d316ab012\",\n \"score\": \"0.7027556\",\n \"text\": \"def save_h5(self, file_path):\\n\\n with h5py.File(file_path, \\\"w\\\") as f:\\n model_set = self.model_set.astype(\\\"S\\\")\\n seg_set = self.seg_set.astype(\\\"S\\\")\\n f.create_dataset(\\\"ID/row_ids\\\", data=model_set)\\n f.create_dataset(\\\"ID/column_ids\\\", data=seg_set)\\n trial_mask = self.tar.astype(\\\"int8\\\") - self.non.astype(\\\"int8\\\")\\n f.create_dataset(\\\"trial_mask\\\", data=trial_mask)\\n if self.model_cond is not None:\\n f.create_dataset(\\\"model_cond\\\", data=self.model_cond.astype(\\\"uint8\\\"))\\n if self.seg_cond is not None:\\n f.create_dataset(\\\"seg_cond\\\", data=self.seg_cond.astype(\\\"uint8\\\"))\\n if self.trial_cond is not None:\\n f.create_dataset(\\\"trial_cond\\\", data=self.trial_cond.astype(\\\"uint8\\\"))\\n if self.model_cond_name is not None:\\n model_cond_name = self.model_cond_name.astype(\\\"S\\\")\\n f.create_dataset(\\\"model_cond_name\\\", data=model_cond_name)\\n if self.seg_cond_name is not None:\\n seg_cond_name = self.seg_cond_name.astype(\\\"S\\\")\\n f.create_dataset(\\\"seg_cond_name\\\", data=seg_cond_name)\\n if self.trial_cond_name is not None:\\n trial_cond_name = self.trial_cond_name.astype(\\\"S\\\")\\n f.create_dataset(\\\"trial_cond_name\\\", data=trial_cond_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fb57aa6d36fa74e284ca813989cd677\",\n \"score\": \"0.69632363\",\n \"text\": \"def save_hdf5(self, filename, max_load=1):\\n self._update_waterfall(filename=filename, max_load=max_load)\\n self._encode_bytestrings()\\n self.waterfall.write_to_hdf5(filename)\\n self._decode_bytestrings()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"203204e6cdc6244e3dfd35a77cf0ae9d\",\n \"score\": \"0.6924733\",\n \"text\": \"def Save(self):\\n self._h5_data.flush()\\n self._h5_data.close()\\n # # Make pickle filename\\n # filename = pth.join(self._data_dir, \\\"{}.pickle\\\".format(self._name))\\n # # Dump data into pickle file\\n # with open(filename, 'wb') as f:\\n # pickle.dump(self._data_dict, f)\\n # print(\\\" - {} saved - \\\".format(self._path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9551b671c1367774a30f924931542277\",\n \"score\": \"0.69075036\",\n \"text\": \"def saveToFile(self,filename,mode='w',fmt='HDF5'):\\n if fmt=='HDF5':\\n f = h5py.File(filename,mode)\\n self.saveToHDF5(f['/'])\\n else:\\n sys.stderr.write('ERROR: format '+fmt+' not yet implemented\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9551b671c1367774a30f924931542277\",\n \"score\": \"0.69075036\",\n \"text\": \"def saveToFile(self,filename,mode='w',fmt='HDF5'):\\n if fmt=='HDF5':\\n f = h5py.File(filename,mode)\\n self.saveToHDF5(f['/'])\\n else:\\n sys.stderr.write('ERROR: format '+fmt+' not yet implemented\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1a3e88175826ef1a97c6ed345ddec11\",\n \"score\": \"0.68897575\",\n \"text\": \"def save_to_hdf5(dic, filename):\\n with h5py.File(filename, 'w') as h5file:\\n save_dict_contents(h5file, '/', dic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6331a54887dd8ac23943a7cdb841196\",\n \"score\": \"0.6843921\",\n \"text\": \"def save(self, filename):\\n if (filename[-5:] != '.hmat'):\\n filename += '.hmat'\\n h5f = h5py.File(filename, 'w')\\n h5f.create_dataset('matrix', data=self.matrix, compression = 'gzip', compression_opts=9)\\n h5f.create_dataset('idx', data=self.idx, compression = 'gzip', compression_opts=9)\\n h5f.create_dataset('applyedMethods', data=cPickle.dumps(self._applyedMethods))\\n if hasattr(self,\\\"genome\\\") and hasattr(self,\\\"resolution\\\"):\\n h5f.create_dataset('genome',data = cPickle.dumps(self.genome))\\n h5f.create_dataset('resolution',data = cPickle.dumps(self.resolution))\\n else:\\n warnings.warn(\\\"No genome and resolution is specified, attributes are recommended for matrix.\\\")\\n \\n h5f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"736ff8472a09387115710c1463ab9916\",\n \"score\": \"0.679858\",\n \"text\": \"def hdf5_to_csv(filename):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22523dd112857d4b67a96b1b13ab6bb4\",\n \"score\": \"0.679558\",\n \"text\": \"def saveToHDF5(self,Hdf5Group):\\n Hdf5Group.attrs['n']=self.n\\n Hdf5Group.attrs['dt']=self.dt\\n for idx in range(self.n):\\n if self.data[idx]: #we cannot save empty lists\\n Hdf5Group.create_dataset('%05d'%idx,data=self.data[idx])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"048c443caed5aba2c1e91e605f9c7c03\",\n \"score\": \"0.6779579\",\n \"text\": \"def save_df(df, path_str, index=True, header=True):\\n\\n path1 = os.path.splitext(path_str)\\n\\n if path1[1] in '.h5':\\n df.to_hdf(path_str, 'df', mode='w')\\n if path1[1] in '.csv':\\n df.to_csv(path_str, index=index, header=header)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97ddd9af07b5c00607d47d3a290adb8c\",\n \"score\": \"0.6752614\",\n \"text\": \"def save_df(df, path_str, index=True, header=True):\\n\\n path1 = os.path.splitext(os.path_str)\\n\\n if path1[1] in '.h5':\\n df.to_hdf(path_str, 'df', mode='w')\\n if path1[1] in '.csv':\\n df.to_csv(path_str, index=index, header=header)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3948e3fab6d982441346b29eac125887\",\n \"score\": \"0.66994435\",\n \"text\": \"def write_to_hdf5(self, filename_out, *args, **kwargs):\\n\\n print(\\\"[Filterbank] Warning: Non-standard function to write in HDF5 (.h5) format. Please use Waterfall.\\\")\\n\\n if not HAS_HDF5:\\n raise RuntimeError(\\\"h5py package required for HDF5 output.\\\")\\n\\n with h5py.File(filename_out, 'w') as h5:\\n\\n dset = h5.create_dataset(b'data',\\n data=self.data,\\n compression='lzf')\\n\\n dset_mask = h5.create_dataset(b'mask',\\n shape=self.data.shape,\\n compression='lzf',\\n dtype='uint8')\\n\\n dset.dims[0].label = b\\\"frequency\\\"\\n dset.dims[1].label = b\\\"feed_id\\\"\\n dset.dims[2].label = b\\\"time\\\"\\n\\n dset_mask.dims[0].label = b\\\"frequency\\\"\\n dset_mask.dims[1].label = b\\\"feed_id\\\"\\n dset_mask.dims[2].label = b\\\"time\\\"\\n\\n # Copy over header information as attributes\\n for key, value in self.header.items():\\n dset.attrs[key] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbe16abce3375134628a16a7368e64f8\",\n \"score\": \"0.6684277\",\n \"text\": \"def writeHD5():\\n global Data1\\n\\n store = HDFStore('.\\\\store.h5')\\n store['listCrisis'] = Data1\\n store.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68111fba2fda84ec3d1f4f36238dc07b\",\n \"score\": \"0.66615635\",\n \"text\": \"def write_to_h5py(history, instruments, filepath):\\n \\n with h5py.File(filepath, 'w') as f:\\n f.create_dataset('history', data=history)\\n instruments_array = np.array(instruments, dtype=object)\\n # string_dt = h5py.special_dtype(vlen=str)\\n string_dt = h5py.special_dtype(vlen=bytes)\\n f.create_dataset(\\\"instruments\\\", data=instruments_array, dtype=string_dt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9187f847885be6fc6694d74eee61735\",\n \"score\": \"0.66236395\",\n \"text\": \"def save(self, fname, overwrite=False, verbose=None):\\n ending = '-%s.h5' % (self['kind'].lower(),)\\n check_fname(fname, self['kind'], (ending,))\\n csd_orig = None\\n try:\\n if 'csd' in self:\\n csd_orig = self['csd']\\n self['csd'] = self['csd'].__getstate__()\\n write_hdf5(fname, self, overwrite=overwrite, title='mnepython')\\n finally:\\n if csd_orig is not None:\\n self['csd'] = csd_orig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fca5124c8c3e938f2ebe4e519063cc59\",\n \"score\": \"0.6615021\",\n \"text\": \"def savehdf5(self, filename, compression='gzip'):\\n if HDF5ERROR:\\n raise ImportError(HDF5ERROR)\\n\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a277b737894fe70b638b88efca3f3da9\",\n \"score\": \"0.66114277\",\n \"text\": \"def save_hdf(self, filename, path='', overwrite=False, append=False):\\n if os.path.exists(filename):\\n store = pd.HDFStore(filename)\\n if path in store:\\n store.close()\\n if overwrite:\\n os.remove(filename)\\n elif not append:\\n raise IOError('{} in {} exists. Set either overwrite or append option.'.format(path,filename))\\n else:\\n store.close()\\n\\n if self.samples is not None:\\n self.samples.to_hdf(filename, path+'/samples')\\n else:\\n pd.DataFrame().to_hdf(filename, path+'/samples')\\n\\n self.obs.save_hdf(filename, path+'/obs', append=True)\\n\\n with pd.HDFStore(filename) as store:\\n # store = pd.HDFStore(filename)\\n attrs = store.get_storer('{}/samples'.format(path)).attrs\\n\\n attrs.ic_type = type(self.ic)\\n attrs.ic_bands = list(self.ic.bands)\\n attrs.use_emcee = self.use_emcee\\n attrs._mnest_basename = self._mnest_basename\\n\\n attrs._bounds = self._bounds\\n attrs._priors = self._priors\\n\\n attrs.name = self.name\\n store.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bda9fe6e4ee0e75a1b5018ff2a1c2c32\",\n \"score\": \"0.6602205\",\n \"text\": \"def dendro_export_hdf5(d, filename):\\n import h5py\\n f = h5py.File(filename, 'w')\\n\\n f.attrs['n_dim'] = d.n_dim\\n\\n f.create_dataset('newick', data=d.to_newick())\\n\\n ds = f.create_dataset('index_map', data=d.index_map, compression=True)\\n ds.attrs['CLASS'] = 'IMAGE'\\n ds.attrs['IMAGE_VERSION'] = '1.2'\\n ds.attrs['IMAGE_MINMAXRANGE'] = [d.index_map.min(), d.index_map.max()]\\n\\n ds = f.create_dataset('data', data=d.data, compression=True)\\n ds.attrs['CLASS'] = 'IMAGE'\\n ds.attrs['IMAGE_VERSION'] = '1.2'\\n ds.attrs['IMAGE_MINMAXRANGE'] = [d.data.min(), d.data.max()]\\n\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97f3f9bac822fbb504d74265fc091b2b\",\n \"score\": \"0.65799123\",\n \"text\": \"def saveToHDF5(self,Hdf5Group):\\n Hdf5Group.attrs['cutOff']=self.cutOff\\n Hdf5Group.attrs['shape']=self.shape\\n A=self.tocoo() #cast to coo_matrix to retrieve row and col attributes\\n #unfortunately, HDF5 doesn't allow saving empty map\\n if A.data.any(): #map is not empty\\n Hdf5Group.create_dataset('row',data=A.row)\\n Hdf5Group.create_dataset('col',data=A.col)\\n Hdf5Group.create_dataset('data',data=A.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec5c0348db747263abbf3efe1e340b9a\",\n \"score\": \"0.65718824\",\n \"text\": \"def save(self, h5f):\\n\\n # Use bob.io.base.HDF5File to save: 'theta', norm[0] and norm[1] into a file\\n h5f.set('theta', self.theta)\\n h5f.set('subtract', self.norm[0])\\n h5f.set('divide', self.norm[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02d3c63b001e67a2cd9ec3085f7f3ab4\",\n \"score\": \"0.65649176\",\n \"text\": \"def save_df(df, path):\\n\\n pickle.dump(df, open(path, \\\"wb\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02d3c63b001e67a2cd9ec3085f7f3ab4\",\n \"score\": \"0.65649176\",\n \"text\": \"def save_df(df, path):\\n\\n pickle.dump(df, open(path, \\\"wb\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eec4b10b7934dac2165f0738e3ed6f27\",\n \"score\": \"0.6560859\",\n \"text\": \"def save_dataframe(dataframe, filepath):\\r\\n dataframe.to_pickle(filepath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47edd72af084178f4426463fdd3a89ff\",\n \"score\": \"0.6559613\",\n \"text\": \"def test_write_h5_table(self):\\n data = self.table_data\\n fname = \\\"test_write_h5_table.h5\\\"\\n with h5py.File(fname, \\\"w\\\", **self.memory_kwargs) as fid:\\n self.assertIsNone(hdf5.write_h5_table(data, \\\"table\\\", fid))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e55476f5d8e4b4cfac9739ab4cfbe3fa\",\n \"score\": \"0.653944\",\n \"text\": \"def save_h5(self, f, path):\\n f.write_attr(path, 'sparse_type', 'csr')\\n f.write_attr(path, 'shape', self._shape)\\n f.write(path + '/data', self._data)\\n f.write(path + '/channels', self._channels)\\n f.write(path + '/spikes_ptr', self._spikes_ptr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"131a3649741fb7d3e0d7353563d686d8\",\n \"score\": \"0.653774\",\n \"text\": \"def write_fx_to_h5py(history, bid, ask, instruments, filepath, fill_missing_values):\\n \\n if fill_missing_values:\\n history = pandas_fill(history) \\n \\n with h5py.File(filepath, 'w') as f:\\n f.create_dataset('history', data=history)\\n f.create_dataset('bid', data=bid)\\n f.create_dataset('ask', data=ask)\\n instruments_array = np.array(instruments, dtype=object)\\n # string_dt = h5py.special_dtype(vlen=str)\\n string_dt = h5py.special_dtype(vlen=bytes)\\n f.create_dataset(\\\"instruments\\\", data=instruments_array, dtype=string_dt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a672f5cc1c9ecef34178eb3d3bcc7b6d\",\n \"score\": \"0.6524428\",\n \"text\": \"def save_df(df, path):\\n outfile = open(path, 'wb')\\n pickle.dump(df, outfile)\\n outfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"235f7f6116b55e41d6687ecbe31dc170\",\n \"score\": \"0.65164244\",\n \"text\": \"def saveToFile(fname, data, column_names, column_dtype):\\n log.info(\\\"Saving to {}\\\".format(args.outdir + fname))\\n with h5py.File(args.outdir + fname, 'w') as hf:\\n for var in column_names:\\n hf.create_dataset( f'{var}',\\n data=data[:,column_names.index(f'{var}')],\\n dtype=column_dtype[f'{var}'],\\n chunks=True,\\n maxshape= (None,),\\n compression='lzf')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4122f0190c08da2fa3176edd057e7b9\",\n \"score\": \"0.6513976\",\n \"text\": \"def save_dataframe(self):\\n if not self.dataset_path.parent.exists():\\n self.dataset_path.parent.mkdir(parents=True)\\n logger.info(f\\\"Saving dataframe to {self.dataset_path}\\\")\\n self.dataframe.to_csv(self.dataset_path, index=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"350c7ce3de2183baf269a3b5d71bb7e1\",\n \"score\": \"0.64929986\",\n \"text\": \"def write_h5(fname: str, data: dict, mode: str='a') -> None:\\n with h5py.File(fname, mode) as f:\\n recursive_save_dict_to_h5group(f,'/',data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b63ad7b8c414ffa282a79111ed5e7261\",\n \"score\": \"0.6450487\",\n \"text\": \"def save_dataframe(df, filename):\\n \\n df.to_pickle(\\\"./datasets/processed_pickle_files/\\\" + filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcc0c151822938633b57bb3a74dba3d8\",\n \"score\": \"0.6418552\",\n \"text\": \"def store(self, station_id: int, df: pd.DataFrame) -> None:\\n\\n # Make sure that there is data that can be stored\\n if df.empty:\\n return\\n\\n # Replace IntegerArrays by float64\\n for column in df:\\n if column in QUALITY_FIELDS or column in INTEGER_FIELDS:\\n df[column] = df[column].astype(\\\"float64\\\")\\n\\n log.info(f\\\"Storing HDF5 data to {self.filepath}\\\")\\n self.filepath.parent.mkdir(parents=True, exist_ok=True)\\n df.to_hdf(path_or_buf=self.filepath, key=self.hdf5_key(station_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab853717245fa438d4c669cd405da0ba\",\n \"score\": \"0.64169145\",\n \"text\": \"def close_hdf5(self):\\n\\n if hasattr(self, 'h5fileWrite'):\\n self.h5fileWrite.close()\\n del self.h5fileWrite\",\n \"title\": \"\"\n },\n {\n \"docid\": \"310697bc68c29ded5a3ce4c656d39ea6\",\n \"score\": \"0.6408751\",\n \"text\": \"def save_data(df, database_filename):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c32b3e8c3c427ac23cc327732ebb6411\",\n \"score\": \"0.6367277\",\n \"text\": \"def write_hdf5(hfile, run_params, model, obs, sampler=None,\\n optimize_result_list=None, tsample=0.0, toptimize=0.0,\\n sampling_initial_center=[], sps=None, write_model_params=True,\\n **extras):\\n\\n if not _has_h5py_:\\n warnings.warn(\\\"HDF5 file could not be opened, as h5py could not be imported.\\\")\\n return\\n\\n # If ``hfile`` is not a file object, assume it is a filename and open\\n if type(hfile) is str:\\n # Check for existence of file, modify name if it already exists\\n if os.path.exists(hfile):\\n import time\\n time_string = time.strftime(\\\"%y%b%d-%H.%M\\\", time.localtime())\\n print(\\\"Appending current time ({0}) to output file \\\".format(time_string) + \\\\\\n \\\"in order to guarantee a unique name.\\\")\\n name, ext = os.path.splitext(hfile)\\n hfile = name+'_{0}'.format(time_string)+ext\\n print(\\\"New output filename: {0}\\\".format(hfile))\\n\\n hf = h5py.File(hfile, \\\"a\\\")\\n else:\\n hf = hfile\\n\\n # ----------------------\\n # Sampling info\\n try:\\n # emcee\\n a = sampler.acceptance_fraction\\n write_emcee_h5(hf, sampler, model, sampling_initial_center, tsample)\\n except(AttributeError):\\n # dynesty or nestle\\n if sampler is None:\\n sdat = hf.create_group('sampling')\\n elif 'eff' in sampler:\\n write_dynesty_h5(hf, sampler, model, tsample)\\n else:\\n write_nestle_h5(hf, sampler, model, tsample)\\n\\n # -----------------\\n # Optimizer info\\n if optimize_result_list is not None:\\n out = optresultlist_to_ndarray(optimize_result_list)\\n mgroup = hf.create_group('optimization')\\n mdat = mgroup.create_dataset('optimizer_results', data=out)\\n\\n # ----------------------\\n # High level parameter and version info\\n write_h5_header(hf, run_params, model, write_model_params=write_model_params)\\n hf.attrs['optimizer_duration'] = json.dumps(toptimize)\\n hf.flush()\\n\\n # ----------------------\\n # Observational data\\n write_obs_to_h5(hf, obs)\\n hf.flush()\\n\\n # ---------------\\n # Best fitting model in space of data\\n if sps is not None:\\n if \\\"sampling/chain\\\" in hf:\\n from ..plotting.utils import best_sample\\n pbest = best_sample(hf[\\\"sampling\\\"])\\n spec, phot, mfrac = model.predict(pbest, obs=obs, sps=sps)\\n best = hf.create_group(\\\"bestfit\\\")\\n best.create_dataset(\\\"spectrum\\\", data=spec)\\n best.create_dataset(\\\"photometry\\\", data=phot)\\n best.create_dataset(\\\"parameter\\\", data=pbest)\\n best.attrs[\\\"mfrac\\\"] = mfrac\\n if obs[\\\"wavelength\\\"] is None:\\n best.create_dataset(\\\"restframe_wavelengths\\\", data=sps.wavelengths)\\n\\n # Store the githash last after flushing since getting it might cause an\\n # uncatchable crash\\n bgh = githash(**run_params)\\n hf.attrs['prospector_version'] = json.dumps(bgh)\\n hf.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee0481fc50ba1dbfe2f8fd71bd91ba49\",\n \"score\": \"0.6365389\",\n \"text\": \"def save_model_to_h5(model_obj, model_file_path):\\n if not model_file_path.endswith('.h5'):\\n model_file_path = f'{model_file_path}.h5'\\n model_obj.save(model_file_path)\\n logging.info(f'Model saved to {model_file_path}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a33a8779e35b819b5caab26d952df523\",\n \"score\": \"0.63634515\",\n \"text\": \"def to_hdf5(self, h):\\n h.create_dataset('triangles', data=np.array(self.triangles_))\\n h.create_dataset('vertices', data=np.array(self.vertices_))\\n if self.normals_ is not None:\\n h.create_dataset('normals', data=np.array(self.normals_))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54878fc7432287dff14097deaf9fdfa2\",\n \"score\": \"0.63594466\",\n \"text\": \"def save(self):\\n\\t\\th5FilePath = self._getSavePath() + '.h5f'\\n\\t\\tprint('=== bVascularTracing.save() h5FilePath:', h5FilePath)\\n\\n\\t\\tsaveDict = OrderedDict()\\n\\t\\tsaveDict['nodeDictList'] = self.nodeDictList\\n\\t\\tsaveDict['edgeDictList'] = self.edgeDictList\\n\\n\\t\\t# todo: should use try:except ValueError and delete file if we get an error\\n\\t\\twith h5py.File(h5FilePath, \\\"w\\\") as f:\\n\\t\\t\\tfor idx, node in enumerate(self.nodeDictList):\\n\\t\\t\\t\\tnode = self.getNode(idx)\\n\\t\\t\\t\\t#print(' idx:', idx, 'shape:', shape)\\n\\t\\t\\t\\t# each node will have a group\\n\\t\\t\\t\\tnodeGroup = f.create_group('node' + str(idx))\\n\\t\\t\\t\\t# each node group will have a node dict with all parameters\\n\\t\\t\\t\\tnodeDict_json = json.dumps(node)\\n\\t\\t\\t\\tnodeGroup.attrs['nodeDict'] = nodeDict_json\\n\\n\\t\\t\\tfor idx, edge in enumerate(self.edgeDictList):\\n\\t\\t\\t\\tedge = self.getEdge(idx)\\n\\t\\t\\t\\t#print('idx:', idx, 'edge:', edge)\\n\\t\\t\\t\\t# debug mostly to check for numpy types that are not able to be converted to json\\n\\t\\t\\t\\t'''\\n\\t\\t\\t\\tif idx==0:\\n\\t\\t\\t\\t\\tfor k,v in edge.items():\\n\\t\\t\\t\\t\\t\\tprint(k, v, type(v))\\n\\t\\t\\t\\t'''\\n\\n\\t\\t\\t\\t# convert numpy int64 to int\\n\\t\\t\\t\\ttmpSLabList = [int(tmpSlab) for tmpSlab in edge['slabList']]\\n\\t\\t\\t\\tedge['slabList'] = tmpSLabList # careful, we are changing backend data !!!\\n\\n\\t\\t\\t\\t# each edge will have a group\\n\\t\\t\\t\\tedgeGroup = f.create_group('edge' + str(idx))\\n\\t\\t\\t\\t# each edge group will have a dict with all parameters\\n\\t\\t\\t\\tedgeDict_json = json.dumps(edge)\\n\\t\\t\\t\\tedgeGroup.attrs['edgeDict'] = edgeDict_json\\n\\n\\t\\t\\t# slabs are in a dataset\\n\\t\\t\\tslabData = np.column_stack((self.x, self.y, self.z,\\n\\t\\t\\t\\t\\t\\t\\tself.d, self.d2, self.int,\\n\\t\\t\\t\\t\\t\\t\\tself.edgeIdx, self.nodeIdx,\\n\\t\\t\\t\\t\\t\\t\\tself.isBad,\\n\\t\\t\\t\\t\\t\\t\\tself.lpMin, self.lpMax, self.lpSNR,))\\n\\t\\t\\t#print('slabData:', slabData.shape)\\n\\t\\t\\tf.create_dataset('slabs', data=slabData)\\n\\n\\t\\t\\t#\\n\\t\\t\\t# maskedEdgesDict\\n\\t\\t\\t# oct 2020, we are loading this but we are always calling _preComputeAllMasks !!!!\\n\\t\\t\\tfor k,v in self.maskedEdgesDict.items():\\n\\t\\t\\t\\tf.create_dataset(k, data=v)\\n\\n\\t\\treturn h5FilePath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6df542a383c4e283a78d91fa95e42424\",\n \"score\": \"0.6354747\",\n \"text\": \"def save(self, filename):\\n\\n self._filename = filename\\n\\n with h5py.File(filename, \\\"w\\\") as f:\\n\\n f.create_dataset(\\\"true_energy\\\", data=self.true_energy)\\n\\n f.create_dataset(\\\"arrival_energy\\\", data=self.arrival_energy)\\n\\n f.create_dataset(\\\"reco_energy\\\", data=self.reco_energy)\\n\\n f.create_dataset(\\\"ra\\\", data=self.ra)\\n\\n f.create_dataset(\\\"dec\\\", data=self.dec)\\n\\n f.create_dataset(\\\"ang_err\\\", data=self.ang_err)\\n\\n f.create_dataset(\\\"source_label\\\", data=self.source_label)\\n\\n for i, source in enumerate(self.sources):\\n\\n s = f.create_group(\\\"source_\\\" + str(i))\\n\\n if isinstance(source.flux_model, PowerLawFlux):\\n\\n s.create_dataset(\\\"index\\\", data=source.flux_model._index)\\n\\n s.create_dataset(\\n \\\"normalisation_energy\\\",\\n data=source.flux_model._normalisation_energy,\\n )\\n\\n elif isinstance(source.flux_model, BrokenPowerLawFlux):\\n\\n s.create_dataset(\\\"index1\\\", data=source.flux_model._index1)\\n\\n s.create_dataset(\\\"index2\\\", data=source.flux_model._index2)\\n\\n s.create_dataset(\\n \\\"break_energy\\\", data=source.flux_model._break_energy\\n )\\n\\n s.create_dataset(\\\"source_type\\\", data=source.source_type)\\n\\n s.create_dataset(\\\"normalisation\\\", data=source.flux_model._normalisation)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1124fdbc29ff09d05d25ce784fcd3a58\",\n \"score\": \"0.6337732\",\n \"text\": \"def save_frame(frame, appname):\\n frame.to_pickle(\\\"{}.pkl\\\".format(appname))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e87918dada7c1367be20247d644965bf\",\n \"score\": \"0.632097\",\n \"text\": \"def save_to_hdf5_file(self):\\n fan_array = np.zeros((n_gain, n_pixels, self.n_harmonics))\\n fbn_array = np.zeros((n_gain, n_pixels, self.n_harmonics))\\n\\n for pix_id in range(n_pixels):\\n self.fit(pix_id, gain=high_gain)\\n fan_array[high_gain, pix_id, :] = self.fan\\n fbn_array[high_gain, pix_id, :] = self.fbn\\n\\n self.fit(pix_id, gain=low_gain)\\n fan_array[low_gain, pix_id, :] = self.fan\\n fbn_array[low_gain, pix_id, :] = self.fbn\\n\\n try:\\n with h5py.File(self.calib_file_path, 'w') as hf:\\n hf.create_dataset('fan', data=fan_array)\\n hf.create_dataset('fbn', data=fbn_array)\\n hf.attrs['n_events'] = self.sum_events\\n hf.attrs['n_harm'] = self.n_harmonics\\n # need pytables and time calib container\\n # to use lstchain.io.add_config_metadata\\n hf.attrs['config'] = str(self.config)\\n\\n metadata = global_metadata()\\n write_metadata(metadata, self.calib_file_path)\\n\\n except Exception:\\n raise IOError(f\\\"Failed to create the file {self.calib_file_path}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"052496e4e27a06d8661bf7fc5646ca59\",\n \"score\": \"0.6320223\",\n \"text\": \"def write_VCF_to_hdf5(VCF_dat, out_file):\\n import h5py\\n \\n f = h5py.File(out_file, 'w')\\n f.create_dataset(\\\"contigs\\\", data=np.string_(VCF_dat['contigs']), \\n compression=\\\"gzip\\\", compression_opts=9)\\n f.create_dataset(\\\"samples\\\", data=np.string_(VCF_dat['samples']), \\n compression=\\\"gzip\\\", compression_opts=9)\\n f.create_dataset(\\\"variants\\\", data=np.string_(VCF_dat['variants']), \\n compression=\\\"gzip\\\", compression_opts=9)\\n f.create_dataset(\\\"comments\\\", data=np.string_(VCF_dat['comments']), \\n compression=\\\"gzip\\\", compression_opts=9)\\n \\n ## variant fixed information\\n fixed = f.create_group(\\\"FixedINFO\\\")\\n for _key in VCF_dat['FixedINFO']:\\n fixed.create_dataset(_key, data=np.string_(VCF_dat['FixedINFO'][_key]), \\n compression=\\\"gzip\\\", compression_opts=9)\\n \\n ## genotype information for each sample\\n geno = f.create_group(\\\"GenoINFO\\\")\\n for _key in VCF_dat['GenoINFO']:\\n geno.create_dataset(_key, data=np.string_(VCF_dat['GenoINFO'][_key]), \\n compression=\\\"gzip\\\", compression_opts=9)\\n \\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c2b1c05132f09975ed980c7b0a807cd\",\n \"score\": \"0.63196135\",\n \"text\": \"def dump(self):\\n f = h5py.File(self.h5file, 'w')\\n for i, k in enumerate(self.kwslist):\\n v = self.h5data[:, i]\\n dset = f.create_dataset(k, shape=v.shape, dtype=v.dtype)\\n dset[...] = v\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69f3a8b24bef78292372ebe82f1bba38\",\n \"score\": \"0.63174295\",\n \"text\": \"def to_file(self, dir_out: str, extention: str = \\\"h5\\\") -> None:\\n df = pd.DataFrame(\\n data=self.B,\\n index=self.l,\\n columns=[\\\"B\\\"],\\n )\\n filename = self._create_filename(dir_out)\\n IO._remove_existing_file(filename)\\n print(f\\\"Saving results to -> {filename}\\\")\\n df.to_hdf(filename, key=\\\"df\\\", mode=\\\"w\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30d50f56a080d85fb3eecb00f999b113\",\n \"score\": \"0.63112646\",\n \"text\": \"def close_hdf_file(self):\\r\\n self.h5f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da013b5e3622954ce1394a32f6ac5507\",\n \"score\": \"0.63004047\",\n \"text\": \"def to_file(self, path: str, info: Optional[InfoDict] = None) -> None:\\n import h5py\\n\\n with h5py.File(path, \\\"w\\\") as fp:\\n # write the actual emulsion data\\n for i, droplet_track in enumerate(self):\\n droplet_track._write_hdf_dataset(fp, f\\\"track_{i:06d}\\\")\\n\\n # write additional information\\n if info:\\n for k, v in info.items():\\n fp.attrs[k] = json.dumps(v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce81bd2a5b55871674cf052f510ee978\",\n \"score\": \"0.6298771\",\n \"text\": \"def write(self, asset_type, frames, scaling_factors=None):\\n # Add id to the index, so the frame is indexed by (date, id).\\n # ohlcv_frame.set_index(sid_ix, append=True, inplace=True)\\n if scaling_factors is None:\\n scaling_factors = DEFAULT_SCALING_FACTORS\\n\\n with self.h5_file(mode='a') as h5_file:\\n # ensure that the file version has been written\\n # h5_file.attrs['version'] = VERSION\\n #多维度\\n category_group = h5_file.create_group(asset_type)\\n # 创建group\\n data_group = category_group.create_group('data')\\n data_group.attrs['scaling'] = scaling_factors\\n index_group = category_group.create_group('index')\\n # Note that this functions validates that all of the frames\\n # share the same days and sids.\\n days, sids,fields = days_and_sids_for_frames(frames)\\n # Write sid and date indices.\\n index_group.create_dataset('sid', data=sids)\\n # h5py does not support datetimes, so they need to be stored\\n # as integers.\\n index_group.create_dataset('day', data=days.astype(np.int64))\\n # Write fields of data\\n index_group.create_group('field', data=fields)\\n #\\n for sid, frame in frames.items():\\n frame = frame * scaling_factors\\n frame.sort_index(inplace=True)\\n data_group.create_dataset(sid,\\n compression='lzf',\\n shuffle=True,\\n data=frame,\\n # chunks=chunks,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d1931ae330c185bbe18a72eee92fd77\",\n \"score\": \"0.6297747\",\n \"text\": \"def export_to_hdf5(cls, h5_file, model, pids):\\n #comments = []\\n mids = []\\n A = []\\n J = []\\n I = []\\n\\n c = []\\n d = []\\n e = []\\n f = []\\n k = []\\n nsm = []\\n for pid in pids:\\n prop = model.properties[pid]\\n #comments.append(prop.comment)\\n mids.append(prop.mid)\\n A.append(prop.A)\\n I.append([prop.i1, prop.i2, prop.i12])\\n J.append(prop.j)\\n\\n c.append([prop.c1, prop.c2])\\n d.append([prop.d1, prop.d2])\\n e.append([prop.e1, prop.e2])\\n f.append([prop.f1, prop.f2])\\n\\n ki = []\\n if prop.k1 is None:\\n ki.append(np.nan)\\n else:\\n ki.append(prop.k1)\\n if prop.k2 is None:\\n ki.append(np.nan)\\n else:\\n ki.append(prop.k2)\\n\\n k.append(ki)\\n nsm.append(prop.nsm)\\n #h5_file.create_dataset('_comment', data=comments)\\n h5_file.create_dataset('pid', data=pids)\\n h5_file.create_dataset('mid', data=mids)\\n h5_file.create_dataset('A', data=A)\\n h5_file.create_dataset('J', data=J)\\n h5_file.create_dataset('I', data=I)\\n h5_file.create_dataset('c', data=c)\\n h5_file.create_dataset('d', data=d)\\n h5_file.create_dataset('e', data=e)\\n h5_file.create_dataset('f', data=f)\\n h5_file.create_dataset('k', data=k)\\n h5_file.create_dataset('nsm', data=nsm)\\n #h5_file.create_dataset('_comment', data=comments)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55f0dd890f986fb38c76a51ec91e07a0\",\n \"score\": \"0.62494373\",\n \"text\": \"def save_frames(self):\\n np_frames = np.array(self.frames)\\n df_frames = pd.DataFrame()\\n df_frames['frames'] = np_frames[:, 0]\\n df_frames['label'] = np_frames[:, 1]\\n df_frames['is_feas'] = np_frames[:, 2]\\n\\n filename = os.path.split(self.filepaths[0])[-1]\\n filename = filename.split('.')[0]\\n filename = filename.split('_')\\n folder_path = os.path.join(self.output_folder, filename[0])\\n if not os.path.exists(folder_path):\\n os.makedirs(folder_path)\\n file_path = os.path.join(folder_path, '{}_{}.pkl'.format(filename[0], filename[1]))\\n df_frames.to_pickle(file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82aadb289a582196376c225477f148d1\",\n \"score\": \"0.624555\",\n \"text\": \"def save(self, h5group, manager):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d35cd829f6b90a9431d051342d51ed\",\n \"score\": \"0.6235141\",\n \"text\": \"def to_hdf(self, hdf, group_name=\\\"outcar\\\"):\\n with hdf.open(group_name) as hdf5_output:\\n for key in self.parse_dict.keys():\\n hdf5_output[key] = self.parse_dict[key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f02b1c34a71d5906d7378f5aced56e1\",\n \"score\": \"0.623229\",\n \"text\": \"def mat2h5(config):\\n dataset_name = config.dataset_name\\n base_path = config.data_path\\n mat_dir = os.path.join(base_path, 'data_mat')\\n h5_dir = os.path.join(base_path, 'data_h5')\\n if dataset_name == 'Salinas':\\n dataset_mat_dir = os.path.join(mat_dir, '{name}/{name}_corrected.mat'.format(name=dataset_name))\\n dataset_gt_dir = os.path.join(mat_dir, '{name}/{name}_gt.mat'.format(name=dataset_name))\\n dataset_h5_save_dir = os.path.join(h5_dir, '{}.h5'.format(dataset_name))\\n elif dataset_name == 'Indian':\\n dataset_mat_dir = os.path.join(mat_dir, '{name}/{name}_pines_corrected.mat'.format(name=dataset_name))\\n dataset_gt_dir = os.path.join(mat_dir, '{name}/{name}_pines_gt.mat'.format(name=dataset_name))\\n dataset_h5_save_dir = os.path.join(h5_dir, '{}.h5'.format(dataset_name))\\n elif dataset_name == 'WHU_Hi_HongHu':\\n dataset_mat_dir = os.path.join(mat_dir, '{name}/{name}.mat'.format(name=dataset_name))\\n dataset_gt_dir = os.path.join(mat_dir, '{name}/{name}_gt.mat'.format(name=dataset_name))\\n dataset_h5_save_dir = os.path.join(h5_dir, '{}.h5'.format(dataset_name))\\n hsi_data = sio.loadmat(dataset_mat_dir)[config.dataset_HSI]\\n hsi_gt = sio.loadmat(dataset_gt_dir)[config.dataset_gt]\\n with h5py.File(dataset_h5_save_dir, 'w') as f:\\n f['data'] = hsi_data\\n f['label'] = hsi_gt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17175b1a9e57bf3d066c5543ead2a635\",\n \"score\": \"0.62224144\",\n \"text\": \"def to_file(self, path: str, info: Optional[InfoDict] = None) -> None:\\n import h5py\\n\\n with h5py.File(path, \\\"w\\\") as fp:\\n self._write_hdf_dataset(fp)\\n\\n # write additional information\\n if info:\\n for k, v in info.items():\\n fp.attrs[k] = json.dumps(v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48757a673890b20816c0f42ff481d191\",\n \"score\": \"0.62183267\",\n \"text\": \"def write_metadata_to_hdf5(hdf5_file: str, trace_metadata: trace.TraceMetaData) -> str:\\n try:\\n opened_hdf5_file = h5py.File(hdf5_file, 'a')\\n except FileNotFoundError:\\n print(f'==ERROR== Could not open the HDF5 file {hdf5_file}')\\n return ''\\n\\n metadata = opened_hdf5_file[trace.HDF5_ROOT]\\n metadata.attrs[trace.HDF5_METADATA_NAME] = trace_metadata.name\\n metadata.attrs[trace.HDF5_METADATA_PATH] = trace_metadata.path\\n metadata.attrs[trace.HDF5_METADATA_TYPE] = trace_metadata.type\\n metadata.attrs[trace.HDF5_METADATA_EXEC_TIME] = trace_metadata.exec_time\\n metadata.attrs[trace.HDF5_METADATA_DATE] = trace_metadata.date_time.isoformat()\\n metadata.attrs[trace.HDF5_METADATA_NODES] = trace_metadata.nodes\\n metadata.attrs[trace.HDF5_METADATA_APPS] = json.dumps(trace_metadata.apps)\\n metadata.attrs[trace.HDF5_METADATA_NUM_PROCESSES] = trace_metadata.num_processes\\n metadata.attrs[trace.HDF5_METADATA_TASKS_PER_NODE] = trace_metadata.tasks_per_node\\n metadata.create_dataset(trace.HDF5_METADATA_HWCPU, (len(trace_metadata.cpu_list), 1), 'S25', trace_metadata.cpu_list)\\n metadata.create_dataset(trace.HDF5_METADATA_HWNODES, (len(trace_metadata.node_list), 1), 'S25', trace_metadata.node_list)\\n metadata.create_dataset(trace.HDF5_METADATA_THREADS, (len(trace_metadata.thread_list), 1), 'S25', trace_metadata.thread_list)\\n opened_hdf5_file.close()\\n return hdf5_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0619b39bde13bc3e63099fbfcc41bc12\",\n \"score\": \"0.6176862\",\n \"text\": \"def saveToFile(self,filename,mode='a',fmt='HDF5',index=0):\\n if fmt=='HDF5':\\n f = h5py.File(filename,mode)\\n Hdf5Group=f.create_group('%05d'%index)\\n Hdf5Group.attrs['index']=index\\n self.saveToHDF5(Hdf5Group)\\n else:\\n sys.stderr.write('ERROR: format '+fmt+' not yet implemented\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e302ef0c4f8fa24c2c287c9f694bb357\",\n \"score\": \"0.6159076\",\n \"text\": \"def save_df_to_hdf5(var_results, var_meta, path, Days, Hours, Periods):\\n # Format results into pandas series and dataframe objects\\n columns = ['Var_name', 'Value']\\n var_results_df = {}\\n \\n vi = var_index[0]\\n var_results_df[vi] = pd.DataFrame(var_results[vi].items(), columns=columns)\\n \\n vi = var_index[1]\\n index_names = ['Var_name', 'Days', 'Hours']\\n K = list(var_results[vi].keys())\\n index = pd.MultiIndex.from_product([K, Days, Hours], names=index_names)\\n var_results_df[vi] = make_df(index, var_results[vi])\\n \\n vi = var_index[2]\\n index_names = ['Var_name', 'Periods']\\n K = list(var_results[vi].keys())\\n index = pd.MultiIndex.from_product([K, Periods], names=index_names)\\n var_results_df[vi] = make_df(index, var_results[vi])\\n \\n vi = var_index[3]\\n index_names = ['Var_index', 'Var_name', 'Bounds']\\n K, meta = [], []\\n for i in var_index[:-1]:\\n K.extend(list(var_meta[i].keys()))\\n meta.extend(np.array(list(var_meta[i].values())))\\n meta = np.array(meta)\\n lb, ub, vtype = meta[:,0], meta[:,1], meta[:,2]\\n dic = dict(zip(K, np.zeros(len(K))))\\n var_results_df[vi] = pd.DataFrame.from_dict(dic.items())\\n var_results_df[vi][1] = lb\\n var_results_df[vi]['Upper_bound'] = ub\\n var_results_df[vi]['Var_type'] = vtype\\n var_results_df[vi].rename(columns={0:'Var_name', 1: 'Lower_bound'}, \\n inplace=True)\\n \\n result_dir, file_name = get_directory(path)\\n for vi in var_index: \\n with pd.HDFStore(path) as hdf:\\n hdf.put(vi, var_results_df[vi], format='table', data_columns=True)\\n print(f'All results where saved to {file_name} in {result_dir}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee6042cde9127f1b331c59559a90cc72\",\n \"score\": \"0.61537665\",\n \"text\": \"def save_h5(f, path, arr, overwrite=False):\\n if isinstance(arr, SparseCSR):\\n arr.save_h5(f, path)\\n elif isinstance(arr, np.ndarray):\\n f.write(path, arr, overwrite=overwrite)\\n else:\\n raise ValueError(\\\"The array should be a SparseCSR or \\\"\\n \\\"dense NumPy array.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"156cf0e68a5f707a46442f503960de03\",\n \"score\": \"0.61459076\",\n \"text\": \"def save_photon_hdf5(data_dict,\\n h5_fname = None,\\n user_descr = None,\\n overwrite = False,\\n compression = dict(complevel=6, complib='zlib'),\\n close = True,\\n validate = True,\\n warnings = True,\\n skip_measurement_specs = False,\\n require_setup = True,\\n debug = False):\\n comp_filter = tables.Filters(**compression)\\n\\n ## Compute file names\\n if h5_fname is None:\\n basename, extension = os.path.splitext(data_dict['_filename'])\\n if compression['complib'] == 'blosc':\\n basename += '_blosc'\\n h5_fname = basename + '.hdf5'\\n\\n if os.path.isfile(h5_fname) and not overwrite:\\n basename, extension = os.path.splitext(h5_fname)\\n h5_fname = basename + '_new_copy.hdf5'\\n\\n ## Prefill and fix user-provided data_dict\\n _populate_provenance(data_dict)\\n _sanitize_data(data_dict, require_setup)\\n _compute_acquisition_duration(data_dict)\\n\\n ## Create the HDF5 file\\n print('Saving: %s' % h5_fname)\\n title = official_fields_specs['/'][0].encode()\\n h5file = tables.open_file(h5_fname, mode=\\\"w\\\", title=title,\\n filters=comp_filter)\\n # Saving a file reference is useful in case of error\\n data_dict.update(_data_file=h5file)\\n\\n ## Identity info needs to be added after the file is created\\n _populate_identity(data_dict, h5file)\\n\\n ## Save root attributes\\n for name, value in root_attributes.items():\\n h5file.root._f_setattr(name, value)\\n\\n ## Save everything else to disk\\n fields_descr = {k: v[0] for k, v in official_fields_specs.items()}\\n if user_descr is not None:\\n fields_descr.update(user_descr)\\n _save_photon_hdf5_dict(h5file.root, data_dict,\\n fields_descr=fields_descr, debug=debug)\\n h5file.flush()\\n\\n ## Validation\\n if validate:\\n kwargs = dict(skip_measurement_specs=skip_measurement_specs,\\n warnings=warnings, require_setup=require_setup)\\n assert_valid_photon_hdf5(h5file, **kwargs)\\n if close:\\n h5file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f06626a23f9019b8d7d2db3dc51fdfc1\",\n \"score\": \"0.61375695\",\n \"text\": \"def saveToHDF5(self,Hdf5Group):\\n idx=0\\n for cm in self:\\n subgroup = Hdf5Group.create_group('%05d'%idx)\\n subgroup.attrs['index']=idx\\n cm.saveToHDF5(subgroup)\\n idx+=1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaf977522ff2343f488820eb1fe0b5d3\",\n \"score\": \"0.61346644\",\n \"text\": \"def to_HDF5(self, filename, date=True, **kwargs):\\n self.filename = os.path.expanduser(filename)\\n if 'TIME_UNITS' not in kwargs.keys():\\n kwargs['TIME_UNITS'] = 'years'\\n if 'TIME_LONGNAME' not in kwargs.keys():\\n kwargs['TIME_LONGNAME'] = 'Date_in_Decimal_Years'\\n hdf5_stokes(self.clm, self.slm, self.l, self.m, self.time, self.month,\\n FILENAME=self.filename, DATE=date, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c72c3a1bbe362c6e427081ae363bce29\",\n \"score\": \"0.6121616\",\n \"text\": \"def write_obs_to_h5(hf, obs):\\n try:\\n odat = hf.create_group('obs')\\n except(ValueError):\\n # We already have an 'obs' group\\n return\\n for k, v in list(obs.items()):\\n if k == 'filters':\\n try:\\n v = [f.name for f in v]\\n except:\\n pass\\n if isinstance(v, np.ndarray):\\n odat.create_dataset(k, data=v)\\n else:\\n try:\\n odat.attrs[k] = json.dumps(v) #, cls=NumpyEncoder)\\n except(TypeError):\\n # Should this fall back to pickle.dumps?\\n odat.attrs[k] = pick(v)\\n warnings.warn(\\\"Could not JSON serialize {}, pickled instead\\\".format(k))\\n except:\\n odat.attrs[k] = unserial\\n warnings.warn(\\\"Could not serialize {}\\\".format(k))\\n\\n hf.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"560deadc1e1bfd6754ae6606cbfca814\",\n \"score\": \"0.6107587\",\n \"text\": \"def save(self, output_filename):\\n # raw_data = [[X, m, desc] for X, m, desc in zip(\\n # self.landmarks, self.miss_counts, self.descriptors)]\\n self.dataframe.to_pickle(output_filename)\\n # pd.DataFrame(data=raw_data,\\n # columns=['Position',\\n # 'Miss count',\\n # 'Descriptor']).to_pickle(output_filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e371daaa71d9c50643079c47dba8e09\",\n \"score\": \"0.6106506\",\n \"text\": \"def _export_dataframe_report(self, df, output_dir, basename):\\n fmt = self._report_global_settings.format\\n filename = os.path.join(output_dir, basename + \\\".\\\" + fmt.value)\\n compress = True if fmt == \\\"h5\\\" else False\\n write_dataframe(df, filename, compress=compress)\\n logger.info(\\\"Generated %s\\\", filename)\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcd3bef29520c855e7219f41e7166cc8\",\n \"score\": \"0.60862666\",\n \"text\": \"def touch_result_hdf5_file(target_dir, poe, ds_names, n_realizations,\\n n_periods):\\n file_name = _HDF5_FILE_NAME_FMT % poe\\n full_path = os.path.join(target_dir, file_name)\\n\\n ds_shape = (n_realizations, n_periods)\\n\\n with h5py.File(full_path, 'w') as h5_file:\\n for name in ds_names:\\n h5_file.create_dataset(name, dtype=numpy.float64, shape=ds_shape)\\n\\n return full_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18a9e5fb4f378148929aeed46d74aa0e\",\n \"score\": \"0.60608864\",\n \"text\": \"def write_to_file(self, struct, fName):\\n\\n f = h5py.File(fName, \\\"w\\\")\\n self._recursive_write(f, struct)\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bf49dc4baed73e0b11de98452467c37\",\n \"score\": \"0.6052166\",\n \"text\": \"def save(self, obj, path='/'):\\n obj_id = id(obj)\\n in_memo = self.memo_save.get(obj_id) # default=None\\n if in_memo is not None: # saved the object before\\n h5gr, _ = in_memo\\n self.h5group[path] = h5gr # create hdf5 hard link\\n # hard linked objects share an hdf5 id,\\n # which we use in the loader to distinguish them\\n return h5gr\\n\\n disp = self.dispatch_save.get(type(obj))\\n if disp is not None:\\n f, type_repr = disp\\n # `f` is a dispatcher function, which should\\n # - save the `obj` in self.h5group['path'],\\n # - call :meth:`memorize_save`, and\\n # - set ``h5gr.attr[ATTR_TYPE] = type_repr`` to a string `type_repr`\\n # to allow loading with the dispatcher ``Hdf5Loader.dispatch_load[type_repr]``\\n # call unbound method `f` with explicit self\\n h5gr = f(self, obj, path, type_repr)\\n return h5gr\\n\\n # handle classes with `save_hdf5` method\\n obj_save_hdf5 = getattr(obj, 'save_hdf5', None)\\n if obj_save_hdf5 is not None: # of Hdf5Exportable type\\n # `obj_save_hdf5` should be the bound method `obj.save_hdf5`,\\n # so it does not need an explicit reference of `obj`\\n h5gr, subpath = self.create_group_for_obj(path, obj)\\n h5gr.attrs[ATTR_TYPE] = REPR_HDF5EXPORTABLE\\n h5gr.attrs[ATTR_CLASS] = obj.__class__.__qualname__\\n h5gr.attrs[ATTR_MODULE] = obj.__class__.__module__\\n obj_save_hdf5(self, h5gr, subpath) # should save the actual data\\n return h5gr\\n\\n warnings.warn(\\n \\\"Hdf5Saver: object of type {t!r} without explicit HDF5 format; \\\"\\n \\\"fall back to pickle protocol\\\".format(t=type(obj)), UserWarning)\\n\\n obj_reduce = getattr(obj, \\\"__reduce__\\\", None)\\n if obj_reduce is not None:\\n\\n rv = obj_reduce()\\n if isinstance(rv, str):\\n h5gr = self.save_global(obj, REPR_GLOBAL)\\n return h5gr\\n if not isinstance(rv, tuple) or not 2 <= len(rv) < 7:\\n raise Hdf5ExportError(\\\"Wrong return value of {0!r}\\\".format(obj_reduce))\\n\\n h5gr = self.save_reduce(*rv, obj=obj, path=path)\\n return h5gr\\n\\n # unknown case\\n msg = \\\"Don't know how to save object of type {0!r}:\\\\n{1!r}\\\".format(type(obj), obj)\\n raise Hdf5ExportError(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a460fe1bf2a6f5ac25e22df5ec75494a\",\n \"score\": \"0.6049246\",\n \"text\": \"def dict_to_h5(dict_save, filename, compression=None, compression_opts=None):\\n fid = h5py.File(filename, 'w', driver='mpio', comm=MPI.COMM_WORLD)\\n for kk in list(dict_save.keys()): \\n fid.create_dataset(kk, data=dict_save[kk], compression=compression, compression_opts=compression_opts)\\n fid.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54195964ce7a69f087778c38fe5207d0\",\n \"score\": \"0.6028586\",\n \"text\": \"def _save_df(self, df:pd.DataFrame, summary_file:pd.DataFrame, \\n name:str, sub_folder:str):\\n if not os.path.exists(self.directory + sub_folder):\\n os.makedirs(self.directory + sub_folder)\\n pd.to_pickle(df, self.directory + sub_folder + '/' + name)\\n #filepath = sub_folder + '/' + name\\n filepath = name\\n row = pd.DataFrame.from_dict(data={'filename':[filepath]})\\n return summary_file.append(row, ignore_index=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac661d4d8adedb027328c5ca2f3330b7\",\n \"score\": \"0.602839\",\n \"text\": \"def saveh5(self, h5file, qpi_slice=None, series_slice=None,\\n time_interval=None, count=None, max_count=None):\\n # set up slice to export\\n if series_slice is None:\\n sl = range(len(self))\\n else:\\n sl = range(series_slice.start, series_slice.stop)\\n # set up time interval\\n if time_interval is None:\\n ta = -np.inf\\n tb = np.inf\\n else:\\n ta, tb = time_interval\\n # set max_count according to slice\\n if max_count is not None:\\n max_count.value += len(sl)\\n\\n qpskw = {\\\"h5file\\\": h5file,\\n \\\"h5mode\\\": \\\"w\\\",\\n }\\n\\n if (qpi_slice is None and\\n series_slice is None and\\n time_interval is None):\\n # Only add series identifier if series complete.\\n # (We assume that if any of the above kwargs is set,\\n # the series data is somehow modified)\\n qpskw[\\\"identifier\\\"] = self.identifier\\n\\n with qpimage.QPSeries(**qpskw) as qps:\\n increment = 0\\n for ii in sl:\\n ti = self.get_metadata(ii).get(\\\"time\\\", np.nan)\\n if ti < ta or ti > tb:\\n # Not part of the series\\n pass\\n else:\\n increment += 1\\n if increment == 1 or len(self._bgdata) != 1:\\n # initial image or series data where each image\\n # has a unique background image\\n qpi = self.get_qpimage(ii)\\n if qpi_slice is not None:\\n qpi = qpi[qpi_slice]\\n qps.add_qpimage(qpi)\\n else:\\n # hard-link the background data\\n qpiraw = self.get_qpimage_raw(ii)\\n if qpi_slice is not None:\\n qpiraw = qpiraw[qpi_slice]\\n qps.add_qpimage(qpiraw, bg_from_idx=0)\\n if count is not None:\\n count.value += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2367a134586d71eab1959d3eaf53f4f6\",\n \"score\": \"0.60246533\",\n \"text\": \"def to_hdf(self, outfile):\\n print(\\\"Saving SpecMatch object to HDF file\\\")\\n # Allow either a string or h5 file object ot be passed.\\n is_path = False\\n if isinstance(outfile, string_types):\\n outfile = h5py.File(outfile, 'w')\\n is_path = True\\n\\n # Save target spectrum\\n if self.target is not None:\\n outfile.create_group('target')\\n self.target.to_hdf(outfile['target'])\\n\\n # ------------------------ Shift results ------------------------\\n # Save unshifted spectrum\\n if self._shifted is True:\\n # (Check if unshifted and shifted targets are different)\\n outfile.create_group('unshifted')\\n self.target_unshifted.to_hdf(outfile['unshifted'])\\n\\n # Save shift reference\\n if self.shift_ref is not None:\\n outfile.create_group('shift_ref')\\n self.shift_ref.to_hdf(outfile['shift_ref'])\\n\\n # Save shift data\\n if len(self.shift_data) > 0:\\n grp = outfile.create_group('shift_data')\\n for k, v in self.shift_data.items():\\n grp[k] = v\\n\\n # ------------------------ Match results ------------------------\\n # Wavelength regions\\n if self.regions is not None:\\n outfile['regions'] = self.regions\\n\\n # Save match results by converting to record array\\n if not self.match_results.empty:\\n if 'lib_index' in self.match_results:\\n self.match_results.drop('lib_index', inplace=True, axis=1)\\n match_rec = self.match_results.to_records()\\n dt = match_rec.dtype.descr\\n\\n for i in range(len(dt)):\\n if dt[i][1] == \\\"|O\\\":\\n # max string length = 5000\\n dt[i] = (str(dt[i][0]), 'S5000')\\n else:\\n dt[i] = (str(dt[i][0]), dt[i][1])\\n\\n match_rec = np.array(match_rec, dtype=dt)\\n\\n outfile['match_results'] = match_rec\\n\\n # restore lib_index column\\n self.match_results['lib_index'] = self.match_results.index\\n\\n # ----------------------- Lincomb results -----------------------\\n if self.num_best is not None:\\n outfile['num_best'] = self.num_best\\n if self.coeffs is not None:\\n outfile['coeffs'] = self.coeffs\\n if self.lincomb_regions is not None:\\n outfile['lincomb_regions'] = self.lincomb_regions\\n if len(self.lincomb_matches) > 0:\\n grp = outfile.create_group('lincomb')\\n for i in range(len(self.lincomb_regions)):\\n reg = self.lincomb_regions[i]\\n subgrp = grp.create_group('{0:.0f}'.format(reg[0]))\\n subgrp['ref_idxs'] = self.ref_idxs[i]\\n self.lincomb_matches[i].to_hdf(subgrp)\\n res_grp = subgrp.create_group('results')\\n for k, v in self.lincomb_results[i].items():\\n res_grp[k] = v\\n\\n # Save averaged results\\n if len(self.results_nodetrend) > 0:\\n res_grp = outfile.create_group('results_nodetrend')\\n for k, v in self.results_nodetrend.items():\\n res_grp[k] = v\\n\\n if len(self.results) > 0:\\n res_grp = outfile.create_group('results')\\n for k, v in self.results.items():\\n res_grp[k] = v\\n\\n if is_path:\\n outfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a38c013cc8ac6e1292e0aa6c53afc7\",\n \"score\": \"0.59947145\",\n \"text\": \"def save(self):\\n if self.debug or self.rank > 0: return\\n\\n # save images and replace the image array with the\\n # file name\\n self.__save_images(self.metrics)\\n metrics_file_path = self.get_data_path(self.name, self.version) + '/metrics.csv'\\n meta_tags_path = self.get_data_path(self.name, self.version) + '/meta_tags.csv'\\n\\n obj = {\\n 'name': self.name,\\n 'version': self.version,\\n 'tags_path': meta_tags_path,\\n 'metrics_path': metrics_file_path,\\n 'autosave': self.autosave,\\n 'description': self.description,\\n 'created_at': self.created_at,\\n 'exp_hash': self.exp_hash\\n }\\n\\n # save the experiment meta file\\n with atomic_write(self.__get_log_name()) as tmp_path:\\n with open(tmp_path, 'w') as file:\\n json.dump(obj, file, ensure_ascii=False)\\n\\n # save the metatags file\\n df = pd.DataFrame({'key': list(self.tags.keys()), 'value': list(self.tags.values())})\\n with atomic_write(meta_tags_path) as tmp_path:\\n df.to_csv(tmp_path, index=False)\\n\\n # save the metrics data\\n df = pd.DataFrame(self.metrics)\\n with atomic_write(metrics_file_path) as tmp_path:\\n df.to_csv(tmp_path, index=False)\\n\\n # write new vals to disk\\n self.flush()\\n\\n # until hparam plugin is fixed, generate hparams as text\\n if not self.tag_markdown_saved and len(self.tags) > 0:\\n self.tag_markdown_saved = True\\n self.add_text('hparams', self.__generate_tfx_meta_log())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb3a6fd81a40639b5f6fc6e32064294\",\n \"score\": \"0.5983791\",\n \"text\": \"def save(self, filename):\\n self.get_model().save(\\\"models/\\\" + filename + \\\".h5\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4dbbc4e1a0b18c01501e56661107cef\",\n \"score\": \"0.59792453\",\n \"text\": \"def test_write_h5_image(self):\\n data = self.image_data\\n fname = \\\"test_write_h5_image.h5\\\"\\n with h5py.File(fname, \\\"w\\\", **self.memory_kwargs) as fid:\\n self.assertIsNone(hdf5.write_h5_image(data, \\\"image\\\", fid))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1edd673406ca2acee7f4ac7899621266\",\n \"score\": \"0.5970013\",\n \"text\": \"def xr_DataArray_to_hdf5(\\tda, hdf5_file_name, group_name, compression_level=5):\\n\\t\\n\\tif da.name is None:\\n\\t\\tda.name = 'data'\\n\\t\\n\\txr_Dataset_to_hdf5(\\t\\tds=da.to_dataset(),\\n\\t\\t\\t\\t\\t\\t\\thdf5_file_name=hdf5_file_name,\\n\\t\\t\\t\\t\\t\\t\\tgroup_name=group_name,\\n\\t\\t\\t\\t\\t\\t\\tcompression_level=compression_level)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3710644b133a2cf7b7e484fd4bd19a1e\",\n \"score\": \"0.5969826\",\n \"text\": \"def write(data, filename, db_name, write_mode=\\\"w\\\"):\\n def _check_directory(filename):\\n directory = os.path.dirname(filename)\\n if not os.path.exists(directory):\\n os.mkdir(directory)\\n\\n _check_directory(filename) \\n # todo : overwrite check\\n db = h5py.File(filename, write_mode)\\n dataset = db.create_dataset(db_name, data.shape, dtype=\\\"float\\\")\\n dataset[:] = data[:]\\n db.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe75468a171f9a27e2e4fbac129ac46\",\n \"score\": \"0.59678084\",\n \"text\": \"def write_summary_file(self):\\n self._summaryfile = Trace.get_summary_filename(self._tracefile)\\n\\n packed_metadata = self.metadata.pack_dataframe()\\n\\n packed_metadata[Trace._formatversionkey] = pandas.Series(\\n data=Trace._formatversion, dtype=numpy.int32\\n )\\n\\n with HDFStoreContext(self._summaryfile, mode=\\\"w\\\") as hdfstore:\\n hdfstore.put(Trace._metadatakey, packed_metadata, format=\\\"t\\\")\\n hdfstore.put(Trace._statisticskey, self.statistics, format=\\\"t\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1e7d32799a9800e9aaf374be7b5bbf7\",\n \"score\": \"0.5962721\",\n \"text\": \"def to_h5(self):\\n import time\\n from pymicro import __version__ as pymicro_version\\n\\n print('opening file %s.h5 for writing' % self.name)\\n f = h5py.File('%s.h5' % self.name, 'w')\\n f.attrs['Pymicro_Version'] = np.string_(pymicro_version)\\n f.attrs['HDF5_Version'] = h5py.version.hdf5_version\\n f.attrs['h5py_version'] = h5py.version.version\\n f.attrs['file_time'] = time.time()\\n f.attrs['microstructure_name'] = self.name\\n if hasattr(self, 'data_dir'):\\n f.attrs['data_dir'] = self.data_dir\\n # ensemble data\\n ed = f.create_group('EnsembleData')\\n cs = ed.create_group('CrystalStructure')\\n sym = self.get_lattice().get_symmetry()\\n cs.attrs['symmetry'] = sym.to_string()\\n lp = cs.create_dataset('LatticeParameters',\\n data=np.array(self.get_lattice().get_lattice_parameters(), dtype=np.float32))\\n # feature data\\n fd = f.create_group('FeatureData')\\n grain_ids = fd.create_dataset('grain_ids',\\n data=np.array([g.id for g in self.grains], dtype=np.int))\\n avg_rods = fd.create_dataset('R_vectors',\\n data=np.array([g.orientation.rod for g in self.grains], dtype=np.float32))\\n centers = fd.create_dataset('centers',\\n data=np.array([g.center for g in self.grains], dtype=np.float32))\\n # cell data\\n cd = f.create_group('CellData')\\n if hasattr(self, 'grain_map') and self.grain_map is not None:\\n gm = cd.create_dataset('grain_ids', data=self.grain_map, compression='gzip', compression_opts=9)\\n gm.attrs['voxel_size'] = self.voxel_size\\n if hasattr(self, 'mask') and self.mask is not None:\\n ma = cd.create_dataset('mask', data=self.mask, compression='gzip', compression_opts=9)\\n ma.attrs['voxel_size'] = self.voxel_size\\n print('done writing')\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c65385478add68584e6099bfd0c0d9\",\n \"score\": \"0.5957029\",\n \"text\": \"def saveModel(self, fileName):\\n self.model.save('{}.h5'.format(fileName)) # creates a HDF5 file 'my_model.h5'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84782e332be73445003b4cbfba971d44\",\n \"score\": \"0.5946952\",\n \"text\": \"def save_model(self,file):\\n name = file+'.h5'\\n self.nn_model.save(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"355b25611b9ff57a4215e18bbb691c7a\",\n \"score\": \"0.59389853\",\n \"text\": \"def save_frame(self, save_path_filename):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc7b00cf588e27d57a091b25989e316\",\n \"score\": \"0.5936832\",\n \"text\": \"def save_h8_l1_hdf5(h08_data, h08_time, h08_coords, path_out, \\\\\\n hrit_listb = hrit_listb):\\n title = \\\"HS_H08_\\\" + h08_time +\\\"_PH_R20_S030405.hdf5\\\"\\n savefilename = os.path.join(path_out, title)\\n\\n metadata = {}\\n metadata['date_time'] = h08_time\\n\\n f = h5py.File(savefilename,'w')\\n grp = f.create_group('HIMAWARI')\\n \\n f.create_dataset('HIMAWARI/COORDINATES/longitude/',\\\\\\n data = h08_coords[\\\"Longitude\\\"], compression = 'gzip',compression_opts=9)\\n f.create_dataset('HIMAWARI/COORDINATES/latitude/',\\\\\\n data = h08_coords[\\\"Latitude\\\"], compression = 'gzip', compression_opts=9)\\n \\n for k in hrit_listb:\\n f.create_dataset('HIMAWARI/DATA/'+k,\\\\\\n data = h08_data[k], compression = 'gzip', compression_opts=9)\\n \\n f.create_dataset('HIMAWARI/ANGLE/SOZ/',\\\\\\n data = h08_data[\\\"SOZ\\\"], compression = 'gzip',compression_opts=9)\\n f.create_dataset('HIMAWARI/ANGLE/SOA/',\\\\\\n data = h08_data[\\\"SOA\\\"], compression = 'gzip', compression_opts=9) \\n \\n for key in metadata.keys():\\n grp.attrs[key] = metadata[key]\\n print \\\"\\\\n\\\"+savefilename +\\\" SAVED\\\"\\n f.close()\\n return title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8758a00cb10b52dbff0ccd43e5a748cc\",\n \"score\": \"0.59354293\",\n \"text\": \"def generate_file(self, hdf5_file_name: str) -> None:\\n self.f = h5py.File(hdf5_file_name, \\\"w\\\")\\n\\n self._save_metadata()\\n self._collect_simulation_conditions()\\n self._process_condition_table()\\n\\n logger.info(Fore.GREEN + \\\"Generating simulation condition list...\\\")\\n self._generate_simulation_condition_map()\\n\\n logger.info(Fore.GREEN + \\\"Generating parameter list...\\\")\\n self._generate_parameter_list()\\n self._generate_simulation_to_optimization_parameter_mapping()\\n\\n logger.info(Fore.GREEN + \\\"Generating measurement matrix...\\\")\\n self._generate_measurement_matrices()\\n\\n logger.info(Fore.GREEN + \\\"Handling scaling parameters...\\\")\\n self._generate_hierarchical_optimization_data()\\n\\n logger.info(Fore.GREEN + \\\"Copying default AMICI options...\\\")\\n self._write_amici_options()\\n\\n logger.info(Fore.GREEN + \\\"Writing default optimization options...\\\")\\n write_optimization_options(self.f)\\n self._write_bounds()\\n self._write_starting_points()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5c528f6dbe1846a2598cde8c15c2841\",\n \"score\": \"0.5935056\",\n \"text\": \"def tohdf5(matrices: Iter[np.ndarray], labels: Iter[int], outfile: str) -> None:\\n try:\\n import h5py\\n except ImportError:\\n raise ImportError(\\\"can't use h5 backend, h5py not available\\\")\\n\\n f = h5py.File(outfile, \\\"w\\\", libver='latest')\\n columns = \\\"time freq amp phase bw\\\"\\n labellist = aslist(labels) # type: List[int]\\n metadata = {\\n 'version': 1.1,\\n 'numpartials': len(labellist),\\n 'columns': columns.split()\\n }\\n for key, value in metadata.items():\\n f.attrs.create(key, data=value)\\n f.create_dataset(\\\"labels\\\", data=labellist)\\n partialroot = f.create_group(\\\"partials\\\")\\n numchars = len(\\\"999999\\\")\\n keyformat = \\\"%.{numchars}d\\\".format(numchars=numchars)\\n for i, matrix in enumerate(matrices):\\n key = keyformat % i\\n partialroot.create_dataset(key, data=matrix)\\n f.flush()\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9549ba3ba73243f7c8ee4a7bba6afbf8\",\n \"score\": \"0.59183824\",\n \"text\": \"def write_h5mu(self, filename: Optional[str] = None, **kwargs):\\n from .io import write_h5mu, _write_h5mu\\n\\n if self.isbacked and (filename is None or filename == self.filename):\\n import h5py\\n\\n self.file.close()\\n with h5py.File(self.filename, \\\"a\\\") as f:\\n _write_h5mu(f, self, write_data=False, **kwargs)\\n elif filename is None:\\n raise ValueError(\\\"Provide a filename!\\\")\\n else:\\n write_h5mu(filename, self, **kwargs)\\n if self.isbacked:\\n self.file.filename = filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3c745ad999e831e2fdadcc938ed2fc3\",\n \"score\": \"0.5912255\",\n \"text\": \"def exportHDF5(self, fileName, groupName=None, writeSamples=False):\\n # Check if already in hdf5 file first\\n if os.path.exists(fileName):\\n mode = \\\"a\\\"\\n else:\\n mode = \\\"w\\\"\\n\\n # Specify the root address for writing\\n ROOT = \\\"/\\\"\\n if groupName is None:\\n baseAddress = ROOT\\n else:\\n baseAddress = os.path.join(ROOT, groupName)\\n\\n # And the data is stored in\\n bootAddress = os.path.join(baseAddress, \\\"bootstrap\\\")\\n\\n # Open the HDF5 file\\n with h5py.File(fileName, mode) as f:\\n # Check whether group already exists\\n baseGroup = f.get(baseAddress)\\n if baseGroup is None:\\n baseGroup = f.create_group(baseAddress)\\n # Check wether bootstrap data exists\\n if \\\"bootstrap\\\" in baseGroup.keys():\\n raise KeyError(\\n \\\"Group >bootstrap< already exist for base group >{}<. Stop writing\\\".format(\\n groupName\\n ),\\n groupName\\n )\\n bootGroup = f.create_group(bootAddress)\\n\\n # Now write parameters\\n for key, val in self.parameters.items():\\n bootGroup.create_dataset(key, data=val)\\n # Write indices\\n bootGroup.create_dataset(\\\"indices\\\", data=self.indices)\\n # Write samples if requested\\n if writeSamples:\\n bootGroup.create_dataset(\\\"samples\\\", data=self.samples)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ec1406405ddd157981f84deacdc6d93\",\n \"score\": \"0.591161\",\n \"text\": \"def save_to_hdf(key, item, path):\\n df = pd.DataFrame(item)\\n with pd.HDFStore(path) as hdf:\\n hdf.put(key, df)\\n print(f'Item {key} saved to {path}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"305237bd8c32fe3b33e5d655d103823b\",\n \"score\": \"0.591117\",\n \"text\": \"def save_plot(dataframe, filename):\\n plt.clf()\\n dataframe.plot()\\n plt.savefig(filename)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993890,"cells":{"query_id":{"kind":"string","value":"49158efcf16c9ac85c88c1f9d2ba429c"},"query":{"kind":"string","value":"Get names of attributes (without protected or private sign) and theirs values."},"positive_passages":{"kind":"list like","value":[{"docid":"a72c105d48731a8c6de6203d97053c34","score":"0.6518105","text":"def get_attributes(self):\n\n attributes_info = basic_backend.get_attributes(self)\n return attributes_info","title":""}],"string":"[\n {\n \"docid\": \"a72c105d48731a8c6de6203d97053c34\",\n \"score\": \"0.6518105\",\n \"text\": \"def get_attributes(self):\\n\\n attributes_info = basic_backend.get_attributes(self)\\n return attributes_info\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4fa03f4879e3a499790c4f0f0ae91d6a","score":"0.7723248","text":"def get_attribute_names():\n\n _round = Round()\n return _round.get_attributes().keys()","title":""},{"docid":"84e9ad410474b850e8ecf40557e65ca6","score":"0.7629509","text":"def get_attribute_names(self):\r\n\r\n return self.data[0].keys()","title":""},{"docid":"0885adb37ffa4d273a52a2a9f0483804","score":"0.7604202","text":"def get_attr_name_attrs(self):\n\n last_idx = len(self.attr) - 1\n return self.attr[last_idx][2]","title":""},{"docid":"06189345a92ce938109d47d96164a210","score":"0.74878496","text":"def attr_names(cls) -> List[str]:\n return [k for k, v in cls.attr_types().items()]","title":""},{"docid":"a3606433f357765b3ed9050da7c4782a","score":"0.72844046","text":"def attribute_names(self) -> List[str]:\n return [attribute.name for attribute in self._attributes]","title":""},{"docid":"6ce2cc45e566d817e236144efb2ee356","score":"0.72407264","text":"def getAttributes(self):\n \n pass","title":""},{"docid":"ca94c6bea821e563c3126a3e2bf5f81c","score":"0.71758413","text":"def getAttrNames(cls):\n for attr in cls.config['attrs']:\n yield attr['name']","title":""},{"docid":"4a8812474ec4cae18cdeded1274974d2","score":"0.7085207","text":"def show_attributes(self):\r\n\t\tself.attributes = '\\n'.join(\"%s: %s\" % item for item in vars(self).items())\r\n\t\tprint('\\nHere are the attributes you can access using .:')\r\n\t\tprint(self.attributes)","title":""},{"docid":"2672c957680b78ceb645b10f219a637e","score":"0.7079462","text":"def attributes(self):\n result = []\n for p in self.parts:\n if not isinstance(p, AttrSep):\n result.append(p)\n return tuple(result)","title":""},{"docid":"727be8ec16ec8cd27808e9def99dc054","score":"0.7067999","text":"def _attrs(self):\n return {k: v for k, v in self.__dict__.items() if not k.startswith(\"_\")}","title":""},{"docid":"7570de2fef89e08a4821304d2efa0216","score":"0.7064929","text":"def attributes(self):\n _attrs = []\n if self.filename:\n _attrs.append(\"filename\")\n if self.metadata:\n _attrs.append(\"metadata\")\n if self.has_schema:\n _attrs.append(\"schema\")\n if self.is_support_frozen:\n _attrs.append(\"support\")\n if self.has_mask_index:\n _attrs.append(\"mask_index\")\n if self.attrs:\n _attrs.append(\"attrs\")\n if self.frames:\n _attrs.append(\"frames\")\n if self.objects:\n _attrs.append(\"objects\")\n if self.events:\n _attrs.append(\"events\")\n return _attrs","title":""},{"docid":"d4d97ec7cb7cf7c03cfaecc845f99241","score":"0.70552677","text":"def getFieldsAttrs (self):\n\t\tres = []\n\t\tfields = self.getFields ()\n\t\tfor elem in fields:\n\t\t\ttry:\n\t\t\t\tres.append (elem.getAttributes())\n\t\t\texcept:\n\t\t\t\tpass\n\t\treturn res","title":""},{"docid":"a96a86cdf925acd9548f2a7e499fec2d","score":"0.70341015","text":"def get_attributes(self): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"888ec8597d679f2c2c246557a2a1d7a5","score":"0.6985435","text":"def attributes(self):\n return list(self._data.keys())","title":""},{"docid":"87627828e5d689fc4436201987ba51c0","score":"0.69672084","text":"def gen_attr_list(self):\n attrlist = []\n for k in self.gen_header_list():\n attrlist.append(str(getattr(self, k)))\n return attrlist","title":""},{"docid":"8bec6c7ecbed78c3825899a6ddf2a7ca","score":"0.6962971","text":"def __tostring_attrs(self):\n \n attrs = []\n for attr_name in self.__get_attrs_name():\n val = self.__get_attr_value(attr_name)\n attr = \"%s%s\" % (self.__get_attr_type(attr_name), attr_name)\n \n if val != None:\n attr = \"%s%s%s\" % (attr, ATTR_ASIGN, val)\n attrs.append(attr)\n \n if attrs: \n return \"%s%s%s\" % (S_ATTRS, ATTR_SEP.join(attrs), E_ATTRS)\n else:\n return \"\"","title":""},{"docid":"36d90e61bc4c924ecd0d37dfe743ef7f","score":"0.6923293","text":"def attributes(self, full=0):\n\n # Get number of variable attributes.\n\tnatts = self.inq_natts()\n\n\t# Inquire each attribute\n\tres = {}\n\tfor n in range(natts):\n a = self.attr(n)\n\t name = a.inq_name()\n\t if full:\n\t res[name] = (a.get(), a.inq_id(), a.inq_type(), \n\t a.inq_len())\n\t else:\n\t res[name] = a.get()\n\n return res","title":""},{"docid":"d572cd60be9c3f424c0104ca786f863b","score":"0.6916542","text":"def state_attributes(self):\n attr = {ATTR_FRIENDLY_NAME: self.name}\n for name, prop in self._attrs.items():\n attr[name] = getattr(self, prop)\n attr = self._attr_filter(attr)\n return attr","title":""},{"docid":"03bdeba05094389fe4e8acac21030156","score":"0.69072807","text":"def list_attrs():\n return []","title":""},{"docid":"3bca89e7d824543a66f4f17f2d7e5d40","score":"0.68845755","text":"def getAttributeNames(self):\n return FlagCoding_getAttributeNames(self._obj)","title":""},{"docid":"fa3f157f10f9210d13ee743d7c362978","score":"0.68708575","text":"def getAttributes(self):\n return self.attributes","title":""},{"docid":"fa3f157f10f9210d13ee743d7c362978","score":"0.68708575","text":"def getAttributes(self):\n return self.attributes","title":""},{"docid":"fa3f157f10f9210d13ee743d7c362978","score":"0.68708575","text":"def getAttributes(self):\n return self.attributes","title":""},{"docid":"3918f317ad32ac372ee681673b44677f","score":"0.6825388","text":"def attributes():\n attribute_dict = NameOrderedDict()\n attribute_dict.append(AttributeInfo(\"gender\", enum=_ENUMS))\n return attribute_dict","title":""},{"docid":"1d4816830e397ffdc33428f9a84bc93d","score":"0.6824421","text":"def attributes(self, full=0):\n\n # Get the number of global attributes.\n\tnatts = self.inq_natts()\n\n\t# Inquire each attribute\n\tres = {}\n\tfor n in range(natts):\n a = self.attr(n)\n\t name = a.inq_name()\n\t if full:\n\t res[name] = (a.get(), a.inq_id(), a.inq_type(), \n\t a.inq_len())\n\t else:\n\t res[name] = a.get()\n\n return res","title":""},{"docid":"4e3bb21a438dd049b2a49108960b215e","score":"0.6819931","text":"def getAttributeNames(self):\n return MetadataElement_getAttributeNames(self._obj)","title":""},{"docid":"8a028f2065c72056341985d0dab42af1","score":"0.68141055","text":"def attributes(self):\n return \"%s%s%s%s%s\" % (\"-R\"[1&self.attr], \"-H\"[1&self.attr>>1], \"-S\"[1&self.attr>>2], \"-A\"[1&self.attr>>5], \"L\" if 1&self.attr>>3 else \"D\" if 1&self.attr>>4 else \"F\")","title":""},{"docid":"18c870c70fd78d8bc509464f8a0b0682","score":"0.6802686","text":"def _serialized_attrs(self):\r\n serialized = []\r\n for attr, value in self.attributes.items():\r\n serialized.append(' %s=%s' % (\r\n self._nameprep(attr), saxutils.quoteattr(value)\r\n ))\r\n return ''.join(serialized)","title":""},{"docid":"aba035cbaebf4fc4ae40e45bcc821fcf","score":"0.6771937","text":"def attrs(self):\n for var in vars(self):\n print(var, ' ; type: ', type(vars(self)[var]), ' : ', vars(self)[var])","title":""},{"docid":"d072652ffc59a40a510beb66c73d0781","score":"0.6768322","text":"def get_attributes(self):\n return [AMOUNT, TOPPINGS, SIZE_UPGRADE]","title":""},{"docid":"70e7e39bd9ba56ac733ec17b8d76a81b","score":"0.6744674","text":"def _getAttributeNames(self, kind):\n for interface in self.__implemented__.flattened():\n for name, description in interface.namesAndDescriptions():\n if not isinstance(description, kind):\n continue\n yield name","title":""},{"docid":"9c84fe3ba9907b3228e1b79879067b30","score":"0.67369187","text":"def attributes(self):\n return self.__attributes","title":""},{"docid":"9c84fe3ba9907b3228e1b79879067b30","score":"0.67369187","text":"def attributes(self):\n return self.__attributes","title":""},{"docid":"288f49fdc53262dfc10b9f4b3812c4f7","score":"0.6731184","text":"def attrs(self):\n return self._attrs","title":""},{"docid":"063148e203e6e04e3f623265e5778bcd","score":"0.671603","text":"def attributes(self):\n attr = self.attribute\n if isinstance(attr, Attribute):\n return (attr,)\n else:\n return self.attribute.attributes","title":""},{"docid":"58f0b8558966da1fa8c15820a087e8dc","score":"0.67025083","text":"def get_all_attributes(self):\n dict = self.__dict__.keys()\n return list(dict)","title":""},{"docid":"b4c241c65dd2ec7353e966c9702985e7","score":"0.66936296","text":"def get_attributes(self):\r\n\r\n _values = Resistor.get_attributes(self)\r\n\r\n _values = _values + (self.type, )\r\n\r\n return _values","title":""},{"docid":"81e7749b83cbbff91269aaaafeac6bf1","score":"0.6687561","text":"def attributes(self):\n return self._attributes","title":""},{"docid":"81e7749b83cbbff91269aaaafeac6bf1","score":"0.6687561","text":"def attributes(self):\n return self._attributes","title":""},{"docid":"d600e470456d311dadd8a5a012e4f8ee","score":"0.6674977","text":"def attributes(self):\n return self.object.attribute.attributes","title":""},{"docid":"d50a4b413335aad19eb1d63a7d5098cc","score":"0.6647579","text":"def getAttributes(self):\n\n return self.attributes","title":""},{"docid":"8fe234e137149d1bd940086947439a4a","score":"0.6645032","text":"def get_attr_list(self):\n return self.attr","title":""},{"docid":"a8292b618ddccd9705cd5bc272ca9d25","score":"0.6642347","text":"def attributes(self):\n return DbAttribute.get_all_values_for_node(self)","title":""},{"docid":"ff7159171ad63cdfb377f294c85491e4","score":"0.6632937","text":"def getAttributeNames(self):\n return IndexCoding_getAttributeNames(self._obj)","title":""},{"docid":"7643169d5c56e333a6f6d0c80f8b143e","score":"0.66314805","text":"def attributes():\n this_module_name = __name__\n return [obj for name, obj in inspect.getmembers(sys.modules[this_module_name])\n if (inspect.isfunction(obj) and\n name != 'attributes')]","title":""},{"docid":"bbc1b82f1b510cf6fe22fb84217f8112","score":"0.66220224","text":"def get_needed_attribute_names(self):\n\n return []","title":""},{"docid":"7da3801bb901d025ef442899f80cfe7d","score":"0.6621419","text":"def iterAttrs(self):\r\n return self.requested_attributes.itervalues()","title":""},{"docid":"30880dc5150359f9cae50e1e82198a24","score":"0.66199","text":"def getAttributes(cls):\n return 0","title":""},{"docid":"dcdec58f3a5a912fa0317a425e364aa2","score":"0.6611092","text":"def all_attributes(cls):\n\n return cls.all_readable_attributes + cls.all_not_readable_attributes","title":""},{"docid":"5484f277ba44b9e67b610ec76c387097","score":"0.6607221","text":"def attributes(cls):\n return [attr for attr in cls.__slots__]","title":""},{"docid":"37fa1cd607f006a59df0e7c9f54346d8","score":"0.659966","text":"def get_Attributes(self):\n return self.list_attributes","title":""},{"docid":"9c2f02987066f3ac9ed3a9245244922a","score":"0.656546","text":"def getPublicAttributeNames(self):\n return self._getAttributeNames(PublicAttribute)","title":""},{"docid":"b2f10cb66e176d7a0abad378777ac550","score":"0.65603393","text":"def get_serializable_attributes(self) -> List[str]:\n return list(set(self.get_member_names()) - self._serialize_ignore)","title":""},{"docid":"233b1ccedf79f65d71f92ad7af32e95f","score":"0.6559506","text":"def getAttributeNames(object, includeMagic=1, includeSingle=1, includeDouble=1):\n attributes = []\n dict = {}\n if not hasattrAlwaysReturnsTrue(object):\n # Add some attributes that don't always get picked up.\n # If they don't apply, they'll get filtered out at the end.\n attributes += ['__bases__', '__class__', '__dict__', '__name__', \\\n 'func_closure', 'func_code', 'func_defaults', \\\n 'func_dict', 'func_doc', 'func_globals', 'func_name']\n if includeMagic:\n try: attributes += object._getAttributeNames()\n except: pass\n # Get all attribute names.\n attrdict = getAllAttributeNames(object)\n for attrlist in attrdict.values():\n attributes += attrlist\n # Remove duplicates from the attribute list.\n for item in attributes:\n dict[item] = None\n attributes = dict.keys()\n attributes.sort(lambda x, y: cmp(x.upper(), y.upper()))\n if not includeSingle:\n attributes = filter(lambda item: item[0]!='_' \\\n or item[1]=='_', attributes)\n if not includeDouble:\n attributes = filter(lambda item: item[:2]!='__', attributes)\n # Make sure we haven't picked up any bogus attributes somehow.\n attributes = [attribute for attribute in attributes \\\n if hasattr(object, attribute)]\n return attributes","title":""},{"docid":"fe4aca1e31a13d5ca640e9a22d3e2236","score":"0.6550913","text":"def get_all_attribute_names(self):\n client = self.redis_client\n keys = client.keys('{0}{1}at{1}*'.format(self.prefix, self.divider))\n attr_names = set([])\n thing = re.compile(r'{0}at{0}(.*)'.format(self.divider))\n for key in keys:\n match = thing.search(key)\n if match:\n attr_names.add(match.groups()[0])\n return attr_names","title":""},{"docid":"ecc0cd1100a5bfb2272e34ab032eb3a9","score":"0.6536799","text":"def attributes(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:\n return pulumi.get(self, \"attributes\")","title":""},{"docid":"a27af25de5e6ef5193ec78cc2ba89ce5","score":"0.653585","text":"def get_reserved_attrs(self):\n return self._reserved_attrs","title":""},{"docid":"b3c34f97ce44ca69d94aa93771c5afe3","score":"0.6511225","text":"def getAttributes(semObj):\n return list( semObj.generatedAttributes.keys() )","title":""},{"docid":"052ad4fc168ae810f44156828c4b2971","score":"0.6477591","text":"def _return_parsed_attrs(self):\n attrs = {}\n for key, item in self.ATTRS.items():\n if item:\n attrs[key] = item\n return attrs","title":""},{"docid":"1defc4aac572247f7ac5567ab79a4c84","score":"0.6476502","text":"def attributes(self):","title":""},{"docid":"1defc4aac572247f7ac5567ab79a4c84","score":"0.6476502","text":"def attributes(self):","title":""},{"docid":"1defc4aac572247f7ac5567ab79a4c84","score":"0.6476502","text":"def attributes(self):","title":""},{"docid":"67e527d5d6dc95f96982a099a1e73d4c","score":"0.6450401","text":"def _html_attributes(self) -> list:\n # This is basically a list of all the FieldProperty names except for\n # the `css` property, which is not an HTML attribute.\n return [\n \"id\",\n \"klass\", # will be changed to `class`\n \"style\",\n \"title\",\n \"lang\",\n \"onclick\",\n \"ondblclick\",\n \"onmousedown\",\n \"onmouseup\",\n \"onmouseover\",\n \"onmousemove\",\n \"onmouseout\",\n \"onkeypress\",\n \"onkeydown\",\n \"onkeyup\",\n \"disabled\",\n \"tabindex\",\n \"onfocus\",\n \"onblur\",\n \"onchange\",\n ]","title":""},{"docid":"b48a2fb0268526bb21802cd43bc1a4c2","score":"0.6445547","text":"def public_attribs():\n return []","title":""},{"docid":"04714e246f26499a7492ce55e41f6eeb","score":"0.6430177","text":"def get_attributes(self):\n attributes = {}\n soup = BeautifulSoup(self.file, 'html.parser')\n for elm in soup():\n if elm.name in attributes.keys():\n attributes[elm.name] += list(elm.attrs)\n else:\n attributes[elm.name] = list(elm.attrs)\n\n for tag in attributes:\n attributes[tag] = list(set(attributes[tag]))\n\n return attributes","title":""},{"docid":"a5e0f309f2327bb73beceec452a1a8e7","score":"0.64144707","text":"def attributes(self):\n return ['visible', 'bounding_box','dead']","title":""},{"docid":"b2f740b1ccf1f17cc8a998cfd3e9dfa3","score":"0.6412963","text":"def attributes(self):\n return [key for key in dir(self) if \n not key.startswith('__')\n and not key in ['schema']\n and not callable(getattr(self, key))\n ]","title":""},{"docid":"9b2a3c5f9c434c59ef4cf9d242e861eb","score":"0.63530403","text":"def attributes(self):\n _attrs = [\n \"start_time\",\n \"frame_size\",\n \"frame_rate\",\n \"total_frame_count\",\n \"duration\",\n \"size_bytes\",\n \"mime_type\",\n \"encoding_str\",\n \"gps_waypoints\",\n ]\n return [a for a in _attrs if getattr(self, a) is not None]","title":""},{"docid":"a2b8b06049939240d2ac33b8462c266b","score":"0.63320404","text":"def properties(self):\n return self._attrs","title":""},{"docid":"50d0288cb34bd0f2837eb316934c876c","score":"0.63302845","text":"def get_attribs(self):\n return self._attribs","title":""},{"docid":"189f5b6e55ef4bb3fd18c664db6dccb1","score":"0.6299015","text":"def all_instrumented_attributes(cls):\n\n all_columns = cls.primary_key_columns + cls.foreign_key_columns + cls.all_columns\n result = []\n for name in all_columns:\n attribute = getattr(cls, name)\n result.append(attribute)\n\n return tuple(result)","title":""},{"docid":"643a86ba1928fd040a086cdd0ec0dc9b","score":"0.629017","text":"def all_attributes(self):\n return self.backend.all_attributes()","title":""},{"docid":"9ec695ac1c4fe56e5c81b2630443df48","score":"0.62871236","text":"def list_attrs(self, path):\n raise NotImplementedError(\"Attribute listing not implemented!\")","title":""},{"docid":"33f703411bfa25f4c4fbcfedf2ab4bb2","score":"0.6278681","text":"def items(self):\n return self.__attributes.items()","title":""},{"docid":"01e7017e44d12b96e6ee4aa8f91f9e49","score":"0.6269026","text":"def propnames(self):\n idlessprops = self._klass.getprops(protected=0).keys()\n idlessprops.sort()\n return ['id'] + idlessprops","title":""},{"docid":"e063983cb18df52c71f52776a1cb8535","score":"0.62538296","text":"def readmeta(self):\n return self.read('eai:acl', 'eai:attributes')","title":""},{"docid":"ab9e9c301dd2e659b67b605d1be44dd6","score":"0.6251663","text":"def get_attr_info(node):\n # These are the attrs gathered when maya saves a preset\n # \"listAttr -multi -read -write -visible -hasData\";\n str_attrs = cmds.listAttr(node,\n multi=True,\n write=True,\n visible=True,\n hasData=True)\n\n # \"listAttr -multi -write -scalar -visible -hasData\";\n scalar_attrs = cmds.listAttr(node,\n multi=True,\n write=True,\n visible=True,\n hasData=True,\n scalar=True)\n\n attr_info = get_str_attrs(node, str_attrs)\n attr_info.update(get_attrs(node, scalar_attrs))\n\n return attr_info","title":""},{"docid":"3d432bd18e76e3812230bf06423ee555","score":"0.6239502","text":"def get_attributes(self):\n\n _attributes = (self.revision_id, self.software_id, self.a, self.aloc,\n self.am, self.application_id, self.ax, self.budget_test,\n self.budget_dev, self.bx, self.category_id, self.cb,\n self.cx, self.d, self.dc, self.dd, self.description,\n self.development_id, self.dev_assess_type_id, self.df,\n self.do, self.dr, self.dr_eot, self.dr_test, self.e,\n self.ec, self.et, self.ev, self.ew, self.f, self.ft1,\n self.ft2, self.hloc, self.labor_hours_dev,\n self.labor_hours_test, self.level, self.loc,\n self.n_branches, self.n_branches_test, self.n_inputs,\n self.n_inputs_test, self.n_interfaces,\n self.n_interfaces_test, self.ncb, self.nm, self.nm_test,\n self.os, self.parent_id, self.phase_id, self.ren_avg,\n self.ren_eot, self.rpfom, self.s1, self.s2, self.sa,\n self.schedule_dev, self.schedule_test, self.sl, self.sm,\n self.sq, self.sr, self.st, self.sx, self.t, self.tc,\n self.tcl, self.te, self.test_approach, self.test_effort,\n self.test_path, self.test_time, self.test_time_eot,\n self.tm, self.um, self.wm, self.xm)\n\n return _attributes","title":""},{"docid":"e83aab869d9976a94791d783403d0586","score":"0.62277716","text":"def cls_attr_val(cls) -> list:\r\n attr_dict = {k: v for k, v in cls.__dict__.items() if not k.startswith('_')}\r\n return list(attr_dict.values())","title":""},{"docid":"9f15260a1784694944ce24d751045bad","score":"0.62257725","text":"def get_protected_properties():\n return [\"RequestName\", \"_id\"]","title":""},{"docid":"a427e6737dd45323c2d6b08c10982e62","score":"0.62197495","text":"def getIDAttribs(self):\n a = [x for x in self.attributes if x.isIdentifier]\n return a","title":""},{"docid":"5008c0e865d34bf2374b3185e5f515e7","score":"0.6218383","text":"def get_attrs(service):\n pricing = boto3.client('pricing', region_name=region_name)\n response = pricing.describe_services(ServiceCode=service)\n return response['Services'][0]['AttributeNames']","title":""},{"docid":"87fc50142b790c229b81d3e02a3fe8ee","score":"0.6205407","text":"def describe_account_attributes():\n pass","title":""},{"docid":"929cc99cff15de54e1d689b6aca1636d","score":"0.6198002","text":"def ncattrs(self):\n return list(self.attrs)","title":""},{"docid":"f6a41c17d49cd354ad65fa3b1aeeda4b","score":"0.61898","text":"def get_attributes(self):\r\n\r\n _values = (self.revision_id, self.assembly_id, self.test_id,\r\n self.name, self.description, self.test_type,\r\n self.attachment, self.cum_time, self.cum_failures,\r\n self.confidence, self.consumer_risk, self.producer_risk)\r\n\r\n return _values","title":""},{"docid":"d56e3d39ad05c6041e6ae814ef9e19cb","score":"0.6188066","text":"def attrs(service):\n for attr in get_attrs(service):\n print(attr)","title":""},{"docid":"1984635d1289b7188e92a30500970393","score":"0.6173762","text":"def attribute(self):\r\n type = self.consume(r'[^\\s]+')\r\n name = self.consume(r'[^\\s]+')\r\n\r\n values = []\r\n while not self.peek(';'):\r\n value = self.consume(\r\n r'''\r\n \"\" # empty attribute\r\n | # or\r\n \".*?[^\\\\]\" # from quote up to an unquoted quote\r\n | # or\r\n [^;,]+ # up to semicolon or comma\r\n '''\r\n )\r\n\r\n if type.lower() in ['string', 'url']:\r\n value = str(value).strip('\"')\r\n elif value.lower() in ['nan', 'nan.', '-nan']:\r\n value = float('nan')\r\n elif value.lower() in ['inf', 'inf.']:\r\n value = float('inf')\r\n elif value.lower() in ['-inf', '-inf.']:\r\n value = float('-inf')\r\n else:\r\n value = ast.literal_eval(value)\r\n\r\n values.append(value)\r\n if self.peek(','):\r\n self.consume(',')\r\n\r\n self.consume(';')\r\n\r\n if len(values) == 1:\r\n values = values[0]\r\n\r\n return name, values","title":""},{"docid":"8a90e6b31edd7bf9f361540a135b3798","score":"0.6161665","text":"def attributes(self):\n attributes = {\n \"BaseModel\":\n {\"id\": str,\n \"created_at\": datetime.datetime,\n \"updated_at\": datetime.datetime},\n \"User\":\n {\"email\": str,\n \"password\": str,\n \"first_name\": str,\n \"last_name\": str},\n \"State\":\n {\"name\": str},\n \"City\":\n {\"state_id\": str,\n \"name\": str},\n \"Amenity\":\n {\"name\": str},\n \"Place\":\n {\"city_id\": str,\n \"user_id\": str,\n \"name\": str,\n \"description\": str,\n \"number_rooms\": int,\n \"number_bathrooms\": int,\n \"max_guest\": int,\n \"price_by_night\": int,\n \"latitude\": float,\n \"longitude\": float,\n \"amenity_ids\": list},\n \"Review\":\n {\"place_id\": str,\n \"user_id\": str,\n \"text\": str}\n }\n return attributes","title":""},{"docid":"7d2b10003dd7561d30f9b749ed49759f","score":"0.61583185","text":"def extra_state_attributes(self):\n attributes = []\n for attribute in self._data.items():\n attributes.append(attribute)\n return attributes","title":""},{"docid":"aa4e622b1c37ff5bf4d5bf4a253cb3e2","score":"0.61555785","text":"def attributes(self) -> dict:\n return self._attributes","title":""},{"docid":"7229ec0a1e98f2e80b6d6706939d5691","score":"0.6153483","text":"def getPublicWritableAttributeNames(self):\n return self._getAttributeNames(PublicWritableAttribute)","title":""},{"docid":"dce0e0212a7af493f7a38474cbe70ddc","score":"0.6133799","text":"def _attributes(self, person):\n\n # filter out the nicknames\n attr_list = [ attr for attr in person.get_attribute_list()\n if attr.get_type() != AttributeType.NICKNAME ]\n\n for attr in attr_list:\n\n attr_type = int(attr.get_type())\n name = libgedcom.PERSONALCONSTANTATTRIBUTES.get(attr_type)\n key = str(attr.get_type())\n value = attr.get_value().strip().replace('\\r', ' ')\n\n if key in (\"AFN\", \"RFN\", \"REFN\", \"_UID\", \"_FSFTID\"):\n self._writeln(1, key, value)\n continue\n\n if key == \"RESN\":\n self._writeln(1, 'RESN')\n continue\n\n if key.startswith('Ancestry APID H:'):\n continue\n\n if name and name.strip():\n self._writeln(1, name, value)\n elif value:\n self._writeln(1, 'FACT', value)\n self._writeln(2, 'TYPE', key)\n else:\n continue\n self._note_references(attr.get_note_list(), 2)\n self._source_references(attr.get_citation_list(), 2, person)","title":""},{"docid":"24811a18530b6cdd226113c9d9f74c03","score":"0.6116169","text":"def get_attribute_names(self, attr_type=None):\n return [\n name for name in sorted(self.__dict__.keys())\n if attr_type is None or isinstance(getattr(self, name), attr_type)]","title":""},{"docid":"d37378770c1edfee00af965dcc8ec97a","score":"0.6102916","text":"def attributes():\n attribute_classes = get_attribute_classes()\n for name, class_ in attribute_classes.items():\n click.echo(\n u'{name} - Added in: {ai} ({cv})'.format(\n name=click.style(name, fg='green'),\n ai=click.style(class_.ADDED_IN, fg='yellow'),\n cv=click.style(\n ClassVersion(*class_.MINIMUM_CLASS_VERSION).human,\n fg='yellow'\n )\n )\n )","title":""},{"docid":"d5dbc7cb695cd01c7e51330dec167602","score":"0.6100152","text":"def get_attrs(self, attrs, undefs=None, stringify=True):\n values = []\n for name in attrs:\n nodes = [self]\n if name.startswith('p_'):\n nodes, name = [self.parent], name[2:]\n elif name.startswith('c_'):\n nodes, name = self.children, name[2:]\n elif name.startswith('l_'):\n nodes, name = [self.prev_node], name[2:]\n elif name.startswith('r_'):\n nodes, name = [self.next_node], name[2:]\n for node in (n for n in nodes if n is not None):\n if name == 'feats_split':\n values.extend(node._get_attr(name))\n else:\n values.append(node._get_attr(name))\n\n if undefs is not None:\n values = [x if x is not None else undefs for x in values]\n if stringify:\n values = [str(x) if x is not None else None for x in values]\n return values","title":""},{"docid":"1625dbb13158e5e3567f751b1148695a","score":"0.6092317","text":"def parameterNames(self):\n result = set()\n for pyattrlist in self._pycard.getParameters().values():\n for pyattr in pyattrlist:\n result.add(pyattr.getName())\n return result","title":""},{"docid":"b9507a9299101475cf60430a60bf7659","score":"0.6091171","text":"def enum_attrs(self) -> list[str]:\n return list(self._enum_attrs)","title":""},{"docid":"c185b262ab9b1c66f2a97a16dc3fb972","score":"0.60888326","text":"def print_attributes(self):\r\n\r\n variables = vars(self)\r\n for var in variables:\r\n if var[0] != '_':\r\n if var == 'arrays':\r\n print(var, ':', len(variables[var]), ' numpy array(s) ')\r\n else:\r\n print(var, ': ', variables[var])","title":""},{"docid":"d59dfe3e154983ccc4db41a442f44249","score":"0.6081949","text":"def attributes(self):\n attrs = {}\n for name, value in self.__dict__.items():\n if name in self._int_values:\n attrs[name] = int(value)\n elif name in self._bool_values:\n attrs[name] = bool(int(value))\n else:\n attrs[name] = value\n return attrs","title":""},{"docid":"5fd9dbb0d006215a4d5f377edca95164","score":"0.6074661","text":"def attributes(self):\n return self.custom_attributes(dynamic=True)","title":""}],"string":"[\n {\n \"docid\": \"4fa03f4879e3a499790c4f0f0ae91d6a\",\n \"score\": \"0.7723248\",\n \"text\": \"def get_attribute_names():\\n\\n _round = Round()\\n return _round.get_attributes().keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84e9ad410474b850e8ecf40557e65ca6\",\n \"score\": \"0.7629509\",\n \"text\": \"def get_attribute_names(self):\\r\\n\\r\\n return self.data[0].keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0885adb37ffa4d273a52a2a9f0483804\",\n \"score\": \"0.7604202\",\n \"text\": \"def get_attr_name_attrs(self):\\n\\n last_idx = len(self.attr) - 1\\n return self.attr[last_idx][2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06189345a92ce938109d47d96164a210\",\n \"score\": \"0.74878496\",\n \"text\": \"def attr_names(cls) -> List[str]:\\n return [k for k, v in cls.attr_types().items()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3606433f357765b3ed9050da7c4782a\",\n \"score\": \"0.72844046\",\n \"text\": \"def attribute_names(self) -> List[str]:\\n return [attribute.name for attribute in self._attributes]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ce2cc45e566d817e236144efb2ee356\",\n \"score\": \"0.72407264\",\n \"text\": \"def getAttributes(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca94c6bea821e563c3126a3e2bf5f81c\",\n \"score\": \"0.71758413\",\n \"text\": \"def getAttrNames(cls):\\n for attr in cls.config['attrs']:\\n yield attr['name']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a8812474ec4cae18cdeded1274974d2\",\n \"score\": \"0.7085207\",\n \"text\": \"def show_attributes(self):\\r\\n\\t\\tself.attributes = '\\\\n'.join(\\\"%s: %s\\\" % item for item in vars(self).items())\\r\\n\\t\\tprint('\\\\nHere are the attributes you can access using .:')\\r\\n\\t\\tprint(self.attributes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2672c957680b78ceb645b10f219a637e\",\n \"score\": \"0.7079462\",\n \"text\": \"def attributes(self):\\n result = []\\n for p in self.parts:\\n if not isinstance(p, AttrSep):\\n result.append(p)\\n return tuple(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"727be8ec16ec8cd27808e9def99dc054\",\n \"score\": \"0.7067999\",\n \"text\": \"def _attrs(self):\\n return {k: v for k, v in self.__dict__.items() if not k.startswith(\\\"_\\\")}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7570de2fef89e08a4821304d2efa0216\",\n \"score\": \"0.7064929\",\n \"text\": \"def attributes(self):\\n _attrs = []\\n if self.filename:\\n _attrs.append(\\\"filename\\\")\\n if self.metadata:\\n _attrs.append(\\\"metadata\\\")\\n if self.has_schema:\\n _attrs.append(\\\"schema\\\")\\n if self.is_support_frozen:\\n _attrs.append(\\\"support\\\")\\n if self.has_mask_index:\\n _attrs.append(\\\"mask_index\\\")\\n if self.attrs:\\n _attrs.append(\\\"attrs\\\")\\n if self.frames:\\n _attrs.append(\\\"frames\\\")\\n if self.objects:\\n _attrs.append(\\\"objects\\\")\\n if self.events:\\n _attrs.append(\\\"events\\\")\\n return _attrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4d97ec7cb7cf7c03cfaecc845f99241\",\n \"score\": \"0.70552677\",\n \"text\": \"def getFieldsAttrs (self):\\n\\t\\tres = []\\n\\t\\tfields = self.getFields ()\\n\\t\\tfor elem in fields:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tres.append (elem.getAttributes())\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tpass\\n\\t\\treturn res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a96a86cdf925acd9548f2a7e499fec2d\",\n \"score\": \"0.70341015\",\n \"text\": \"def get_attributes(self): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"888ec8597d679f2c2c246557a2a1d7a5\",\n \"score\": \"0.6985435\",\n \"text\": \"def attributes(self):\\n return list(self._data.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87627828e5d689fc4436201987ba51c0\",\n \"score\": \"0.69672084\",\n \"text\": \"def gen_attr_list(self):\\n attrlist = []\\n for k in self.gen_header_list():\\n attrlist.append(str(getattr(self, k)))\\n return attrlist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bec6c7ecbed78c3825899a6ddf2a7ca\",\n \"score\": \"0.6962971\",\n \"text\": \"def __tostring_attrs(self):\\n \\n attrs = []\\n for attr_name in self.__get_attrs_name():\\n val = self.__get_attr_value(attr_name)\\n attr = \\\"%s%s\\\" % (self.__get_attr_type(attr_name), attr_name)\\n \\n if val != None:\\n attr = \\\"%s%s%s\\\" % (attr, ATTR_ASIGN, val)\\n attrs.append(attr)\\n \\n if attrs: \\n return \\\"%s%s%s\\\" % (S_ATTRS, ATTR_SEP.join(attrs), E_ATTRS)\\n else:\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d90e61bc4c924ecd0d37dfe743ef7f\",\n \"score\": \"0.6923293\",\n \"text\": \"def attributes(self, full=0):\\n\\n # Get number of variable attributes.\\n\\tnatts = self.inq_natts()\\n\\n\\t# Inquire each attribute\\n\\tres = {}\\n\\tfor n in range(natts):\\n a = self.attr(n)\\n\\t name = a.inq_name()\\n\\t if full:\\n\\t res[name] = (a.get(), a.inq_id(), a.inq_type(), \\n\\t a.inq_len())\\n\\t else:\\n\\t res[name] = a.get()\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d572cd60be9c3f424c0104ca786f863b\",\n \"score\": \"0.6916542\",\n \"text\": \"def state_attributes(self):\\n attr = {ATTR_FRIENDLY_NAME: self.name}\\n for name, prop in self._attrs.items():\\n attr[name] = getattr(self, prop)\\n attr = self._attr_filter(attr)\\n return attr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03bdeba05094389fe4e8acac21030156\",\n \"score\": \"0.69072807\",\n \"text\": \"def list_attrs():\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bca89e7d824543a66f4f17f2d7e5d40\",\n \"score\": \"0.68845755\",\n \"text\": \"def getAttributeNames(self):\\n return FlagCoding_getAttributeNames(self._obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3f157f10f9210d13ee743d7c362978\",\n \"score\": \"0.68708575\",\n \"text\": \"def getAttributes(self):\\n return self.attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3f157f10f9210d13ee743d7c362978\",\n \"score\": \"0.68708575\",\n \"text\": \"def getAttributes(self):\\n return self.attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3f157f10f9210d13ee743d7c362978\",\n \"score\": \"0.68708575\",\n \"text\": \"def getAttributes(self):\\n return self.attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3918f317ad32ac372ee681673b44677f\",\n \"score\": \"0.6825388\",\n \"text\": \"def attributes():\\n attribute_dict = NameOrderedDict()\\n attribute_dict.append(AttributeInfo(\\\"gender\\\", enum=_ENUMS))\\n return attribute_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d4816830e397ffdc33428f9a84bc93d\",\n \"score\": \"0.6824421\",\n \"text\": \"def attributes(self, full=0):\\n\\n # Get the number of global attributes.\\n\\tnatts = self.inq_natts()\\n\\n\\t# Inquire each attribute\\n\\tres = {}\\n\\tfor n in range(natts):\\n a = self.attr(n)\\n\\t name = a.inq_name()\\n\\t if full:\\n\\t res[name] = (a.get(), a.inq_id(), a.inq_type(), \\n\\t a.inq_len())\\n\\t else:\\n\\t res[name] = a.get()\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e3bb21a438dd049b2a49108960b215e\",\n \"score\": \"0.6819931\",\n \"text\": \"def getAttributeNames(self):\\n return MetadataElement_getAttributeNames(self._obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a028f2065c72056341985d0dab42af1\",\n \"score\": \"0.68141055\",\n \"text\": \"def attributes(self):\\n return \\\"%s%s%s%s%s\\\" % (\\\"-R\\\"[1&self.attr], \\\"-H\\\"[1&self.attr>>1], \\\"-S\\\"[1&self.attr>>2], \\\"-A\\\"[1&self.attr>>5], \\\"L\\\" if 1&self.attr>>3 else \\\"D\\\" if 1&self.attr>>4 else \\\"F\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c870c70fd78d8bc509464f8a0b0682\",\n \"score\": \"0.6802686\",\n \"text\": \"def _serialized_attrs(self):\\r\\n serialized = []\\r\\n for attr, value in self.attributes.items():\\r\\n serialized.append(' %s=%s' % (\\r\\n self._nameprep(attr), saxutils.quoteattr(value)\\r\\n ))\\r\\n return ''.join(serialized)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba035cbaebf4fc4ae40e45bcc821fcf\",\n \"score\": \"0.6771937\",\n \"text\": \"def attrs(self):\\n for var in vars(self):\\n print(var, ' ; type: ', type(vars(self)[var]), ' : ', vars(self)[var])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d072652ffc59a40a510beb66c73d0781\",\n \"score\": \"0.6768322\",\n \"text\": \"def get_attributes(self):\\n return [AMOUNT, TOPPINGS, SIZE_UPGRADE]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70e7e39bd9ba56ac733ec17b8d76a81b\",\n \"score\": \"0.6744674\",\n \"text\": \"def _getAttributeNames(self, kind):\\n for interface in self.__implemented__.flattened():\\n for name, description in interface.namesAndDescriptions():\\n if not isinstance(description, kind):\\n continue\\n yield name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c84fe3ba9907b3228e1b79879067b30\",\n \"score\": \"0.67369187\",\n \"text\": \"def attributes(self):\\n return self.__attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c84fe3ba9907b3228e1b79879067b30\",\n \"score\": \"0.67369187\",\n \"text\": \"def attributes(self):\\n return self.__attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"288f49fdc53262dfc10b9f4b3812c4f7\",\n \"score\": \"0.6731184\",\n \"text\": \"def attrs(self):\\n return self._attrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"063148e203e6e04e3f623265e5778bcd\",\n \"score\": \"0.671603\",\n \"text\": \"def attributes(self):\\n attr = self.attribute\\n if isinstance(attr, Attribute):\\n return (attr,)\\n else:\\n return self.attribute.attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58f0b8558966da1fa8c15820a087e8dc\",\n \"score\": \"0.67025083\",\n \"text\": \"def get_all_attributes(self):\\n dict = self.__dict__.keys()\\n return list(dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4c241c65dd2ec7353e966c9702985e7\",\n \"score\": \"0.66936296\",\n \"text\": \"def get_attributes(self):\\r\\n\\r\\n _values = Resistor.get_attributes(self)\\r\\n\\r\\n _values = _values + (self.type, )\\r\\n\\r\\n return _values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e7749b83cbbff91269aaaafeac6bf1\",\n \"score\": \"0.6687561\",\n \"text\": \"def attributes(self):\\n return self._attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e7749b83cbbff91269aaaafeac6bf1\",\n \"score\": \"0.6687561\",\n \"text\": \"def attributes(self):\\n return self._attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d600e470456d311dadd8a5a012e4f8ee\",\n \"score\": \"0.6674977\",\n \"text\": \"def attributes(self):\\n return self.object.attribute.attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d50a4b413335aad19eb1d63a7d5098cc\",\n \"score\": \"0.6647579\",\n \"text\": \"def getAttributes(self):\\n\\n return self.attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fe234e137149d1bd940086947439a4a\",\n \"score\": \"0.6645032\",\n \"text\": \"def get_attr_list(self):\\n return self.attr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8292b618ddccd9705cd5bc272ca9d25\",\n \"score\": \"0.6642347\",\n \"text\": \"def attributes(self):\\n return DbAttribute.get_all_values_for_node(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff7159171ad63cdfb377f294c85491e4\",\n \"score\": \"0.6632937\",\n \"text\": \"def getAttributeNames(self):\\n return IndexCoding_getAttributeNames(self._obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7643169d5c56e333a6f6d0c80f8b143e\",\n \"score\": \"0.66314805\",\n \"text\": \"def attributes():\\n this_module_name = __name__\\n return [obj for name, obj in inspect.getmembers(sys.modules[this_module_name])\\n if (inspect.isfunction(obj) and\\n name != 'attributes')]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbc1b82f1b510cf6fe22fb84217f8112\",\n \"score\": \"0.66220224\",\n \"text\": \"def get_needed_attribute_names(self):\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7da3801bb901d025ef442899f80cfe7d\",\n \"score\": \"0.6621419\",\n \"text\": \"def iterAttrs(self):\\r\\n return self.requested_attributes.itervalues()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30880dc5150359f9cae50e1e82198a24\",\n \"score\": \"0.66199\",\n \"text\": \"def getAttributes(cls):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcdec58f3a5a912fa0317a425e364aa2\",\n \"score\": \"0.6611092\",\n \"text\": \"def all_attributes(cls):\\n\\n return cls.all_readable_attributes + cls.all_not_readable_attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5484f277ba44b9e67b610ec76c387097\",\n \"score\": \"0.6607221\",\n \"text\": \"def attributes(cls):\\n return [attr for attr in cls.__slots__]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37fa1cd607f006a59df0e7c9f54346d8\",\n \"score\": \"0.659966\",\n \"text\": \"def get_Attributes(self):\\n return self.list_attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c2f02987066f3ac9ed3a9245244922a\",\n \"score\": \"0.656546\",\n \"text\": \"def getPublicAttributeNames(self):\\n return self._getAttributeNames(PublicAttribute)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2f10cb66e176d7a0abad378777ac550\",\n \"score\": \"0.65603393\",\n \"text\": \"def get_serializable_attributes(self) -> List[str]:\\n return list(set(self.get_member_names()) - self._serialize_ignore)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"233b1ccedf79f65d71f92ad7af32e95f\",\n \"score\": \"0.6559506\",\n \"text\": \"def getAttributeNames(object, includeMagic=1, includeSingle=1, includeDouble=1):\\n attributes = []\\n dict = {}\\n if not hasattrAlwaysReturnsTrue(object):\\n # Add some attributes that don't always get picked up.\\n # If they don't apply, they'll get filtered out at the end.\\n attributes += ['__bases__', '__class__', '__dict__', '__name__', \\\\\\n 'func_closure', 'func_code', 'func_defaults', \\\\\\n 'func_dict', 'func_doc', 'func_globals', 'func_name']\\n if includeMagic:\\n try: attributes += object._getAttributeNames()\\n except: pass\\n # Get all attribute names.\\n attrdict = getAllAttributeNames(object)\\n for attrlist in attrdict.values():\\n attributes += attrlist\\n # Remove duplicates from the attribute list.\\n for item in attributes:\\n dict[item] = None\\n attributes = dict.keys()\\n attributes.sort(lambda x, y: cmp(x.upper(), y.upper()))\\n if not includeSingle:\\n attributes = filter(lambda item: item[0]!='_' \\\\\\n or item[1]=='_', attributes)\\n if not includeDouble:\\n attributes = filter(lambda item: item[:2]!='__', attributes)\\n # Make sure we haven't picked up any bogus attributes somehow.\\n attributes = [attribute for attribute in attributes \\\\\\n if hasattr(object, attribute)]\\n return attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe4aca1e31a13d5ca640e9a22d3e2236\",\n \"score\": \"0.6550913\",\n \"text\": \"def get_all_attribute_names(self):\\n client = self.redis_client\\n keys = client.keys('{0}{1}at{1}*'.format(self.prefix, self.divider))\\n attr_names = set([])\\n thing = re.compile(r'{0}at{0}(.*)'.format(self.divider))\\n for key in keys:\\n match = thing.search(key)\\n if match:\\n attr_names.add(match.groups()[0])\\n return attr_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecc0cd1100a5bfb2272e34ab032eb3a9\",\n \"score\": \"0.6536799\",\n \"text\": \"def attributes(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"attributes\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a27af25de5e6ef5193ec78cc2ba89ce5\",\n \"score\": \"0.653585\",\n \"text\": \"def get_reserved_attrs(self):\\n return self._reserved_attrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3c34f97ce44ca69d94aa93771c5afe3\",\n \"score\": \"0.6511225\",\n \"text\": \"def getAttributes(semObj):\\n return list( semObj.generatedAttributes.keys() )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"052ad4fc168ae810f44156828c4b2971\",\n \"score\": \"0.6477591\",\n \"text\": \"def _return_parsed_attrs(self):\\n attrs = {}\\n for key, item in self.ATTRS.items():\\n if item:\\n attrs[key] = item\\n return attrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1defc4aac572247f7ac5567ab79a4c84\",\n \"score\": \"0.6476502\",\n \"text\": \"def attributes(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1defc4aac572247f7ac5567ab79a4c84\",\n \"score\": \"0.6476502\",\n \"text\": \"def attributes(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1defc4aac572247f7ac5567ab79a4c84\",\n \"score\": \"0.6476502\",\n \"text\": \"def attributes(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67e527d5d6dc95f96982a099a1e73d4c\",\n \"score\": \"0.6450401\",\n \"text\": \"def _html_attributes(self) -> list:\\n # This is basically a list of all the FieldProperty names except for\\n # the `css` property, which is not an HTML attribute.\\n return [\\n \\\"id\\\",\\n \\\"klass\\\", # will be changed to `class`\\n \\\"style\\\",\\n \\\"title\\\",\\n \\\"lang\\\",\\n \\\"onclick\\\",\\n \\\"ondblclick\\\",\\n \\\"onmousedown\\\",\\n \\\"onmouseup\\\",\\n \\\"onmouseover\\\",\\n \\\"onmousemove\\\",\\n \\\"onmouseout\\\",\\n \\\"onkeypress\\\",\\n \\\"onkeydown\\\",\\n \\\"onkeyup\\\",\\n \\\"disabled\\\",\\n \\\"tabindex\\\",\\n \\\"onfocus\\\",\\n \\\"onblur\\\",\\n \\\"onchange\\\",\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b48a2fb0268526bb21802cd43bc1a4c2\",\n \"score\": \"0.6445547\",\n \"text\": \"def public_attribs():\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04714e246f26499a7492ce55e41f6eeb\",\n \"score\": \"0.6430177\",\n \"text\": \"def get_attributes(self):\\n attributes = {}\\n soup = BeautifulSoup(self.file, 'html.parser')\\n for elm in soup():\\n if elm.name in attributes.keys():\\n attributes[elm.name] += list(elm.attrs)\\n else:\\n attributes[elm.name] = list(elm.attrs)\\n\\n for tag in attributes:\\n attributes[tag] = list(set(attributes[tag]))\\n\\n return attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e0f309f2327bb73beceec452a1a8e7\",\n \"score\": \"0.64144707\",\n \"text\": \"def attributes(self):\\n return ['visible', 'bounding_box','dead']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2f740b1ccf1f17cc8a998cfd3e9dfa3\",\n \"score\": \"0.6412963\",\n \"text\": \"def attributes(self):\\n return [key for key in dir(self) if \\n not key.startswith('__')\\n and not key in ['schema']\\n and not callable(getattr(self, key))\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b2a3c5f9c434c59ef4cf9d242e861eb\",\n \"score\": \"0.63530403\",\n \"text\": \"def attributes(self):\\n _attrs = [\\n \\\"start_time\\\",\\n \\\"frame_size\\\",\\n \\\"frame_rate\\\",\\n \\\"total_frame_count\\\",\\n \\\"duration\\\",\\n \\\"size_bytes\\\",\\n \\\"mime_type\\\",\\n \\\"encoding_str\\\",\\n \\\"gps_waypoints\\\",\\n ]\\n return [a for a in _attrs if getattr(self, a) is not None]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2b8b06049939240d2ac33b8462c266b\",\n \"score\": \"0.63320404\",\n \"text\": \"def properties(self):\\n return self._attrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50d0288cb34bd0f2837eb316934c876c\",\n \"score\": \"0.63302845\",\n \"text\": \"def get_attribs(self):\\n return self._attribs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"189f5b6e55ef4bb3fd18c664db6dccb1\",\n \"score\": \"0.6299015\",\n \"text\": \"def all_instrumented_attributes(cls):\\n\\n all_columns = cls.primary_key_columns + cls.foreign_key_columns + cls.all_columns\\n result = []\\n for name in all_columns:\\n attribute = getattr(cls, name)\\n result.append(attribute)\\n\\n return tuple(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"643a86ba1928fd040a086cdd0ec0dc9b\",\n \"score\": \"0.629017\",\n \"text\": \"def all_attributes(self):\\n return self.backend.all_attributes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ec695ac1c4fe56e5c81b2630443df48\",\n \"score\": \"0.62871236\",\n \"text\": \"def list_attrs(self, path):\\n raise NotImplementedError(\\\"Attribute listing not implemented!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f703411bfa25f4c4fbcfedf2ab4bb2\",\n \"score\": \"0.6278681\",\n \"text\": \"def items(self):\\n return self.__attributes.items()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01e7017e44d12b96e6ee4aa8f91f9e49\",\n \"score\": \"0.6269026\",\n \"text\": \"def propnames(self):\\n idlessprops = self._klass.getprops(protected=0).keys()\\n idlessprops.sort()\\n return ['id'] + idlessprops\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e063983cb18df52c71f52776a1cb8535\",\n \"score\": \"0.62538296\",\n \"text\": \"def readmeta(self):\\n return self.read('eai:acl', 'eai:attributes')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab9e9c301dd2e659b67b605d1be44dd6\",\n \"score\": \"0.6251663\",\n \"text\": \"def get_attr_info(node):\\n # These are the attrs gathered when maya saves a preset\\n # \\\"listAttr -multi -read -write -visible -hasData\\\";\\n str_attrs = cmds.listAttr(node,\\n multi=True,\\n write=True,\\n visible=True,\\n hasData=True)\\n\\n # \\\"listAttr -multi -write -scalar -visible -hasData\\\";\\n scalar_attrs = cmds.listAttr(node,\\n multi=True,\\n write=True,\\n visible=True,\\n hasData=True,\\n scalar=True)\\n\\n attr_info = get_str_attrs(node, str_attrs)\\n attr_info.update(get_attrs(node, scalar_attrs))\\n\\n return attr_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d432bd18e76e3812230bf06423ee555\",\n \"score\": \"0.6239502\",\n \"text\": \"def get_attributes(self):\\n\\n _attributes = (self.revision_id, self.software_id, self.a, self.aloc,\\n self.am, self.application_id, self.ax, self.budget_test,\\n self.budget_dev, self.bx, self.category_id, self.cb,\\n self.cx, self.d, self.dc, self.dd, self.description,\\n self.development_id, self.dev_assess_type_id, self.df,\\n self.do, self.dr, self.dr_eot, self.dr_test, self.e,\\n self.ec, self.et, self.ev, self.ew, self.f, self.ft1,\\n self.ft2, self.hloc, self.labor_hours_dev,\\n self.labor_hours_test, self.level, self.loc,\\n self.n_branches, self.n_branches_test, self.n_inputs,\\n self.n_inputs_test, self.n_interfaces,\\n self.n_interfaces_test, self.ncb, self.nm, self.nm_test,\\n self.os, self.parent_id, self.phase_id, self.ren_avg,\\n self.ren_eot, self.rpfom, self.s1, self.s2, self.sa,\\n self.schedule_dev, self.schedule_test, self.sl, self.sm,\\n self.sq, self.sr, self.st, self.sx, self.t, self.tc,\\n self.tcl, self.te, self.test_approach, self.test_effort,\\n self.test_path, self.test_time, self.test_time_eot,\\n self.tm, self.um, self.wm, self.xm)\\n\\n return _attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e83aab869d9976a94791d783403d0586\",\n \"score\": \"0.62277716\",\n \"text\": \"def cls_attr_val(cls) -> list:\\r\\n attr_dict = {k: v for k, v in cls.__dict__.items() if not k.startswith('_')}\\r\\n return list(attr_dict.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f15260a1784694944ce24d751045bad\",\n \"score\": \"0.62257725\",\n \"text\": \"def get_protected_properties():\\n return [\\\"RequestName\\\", \\\"_id\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a427e6737dd45323c2d6b08c10982e62\",\n \"score\": \"0.62197495\",\n \"text\": \"def getIDAttribs(self):\\n a = [x for x in self.attributes if x.isIdentifier]\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5008c0e865d34bf2374b3185e5f515e7\",\n \"score\": \"0.6218383\",\n \"text\": \"def get_attrs(service):\\n pricing = boto3.client('pricing', region_name=region_name)\\n response = pricing.describe_services(ServiceCode=service)\\n return response['Services'][0]['AttributeNames']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87fc50142b790c229b81d3e02a3fe8ee\",\n \"score\": \"0.6205407\",\n \"text\": \"def describe_account_attributes():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"929cc99cff15de54e1d689b6aca1636d\",\n \"score\": \"0.6198002\",\n \"text\": \"def ncattrs(self):\\n return list(self.attrs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6a41c17d49cd354ad65fa3b1aeeda4b\",\n \"score\": \"0.61898\",\n \"text\": \"def get_attributes(self):\\r\\n\\r\\n _values = (self.revision_id, self.assembly_id, self.test_id,\\r\\n self.name, self.description, self.test_type,\\r\\n self.attachment, self.cum_time, self.cum_failures,\\r\\n self.confidence, self.consumer_risk, self.producer_risk)\\r\\n\\r\\n return _values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d56e3d39ad05c6041e6ae814ef9e19cb\",\n \"score\": \"0.6188066\",\n \"text\": \"def attrs(service):\\n for attr in get_attrs(service):\\n print(attr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1984635d1289b7188e92a30500970393\",\n \"score\": \"0.6173762\",\n \"text\": \"def attribute(self):\\r\\n type = self.consume(r'[^\\\\s]+')\\r\\n name = self.consume(r'[^\\\\s]+')\\r\\n\\r\\n values = []\\r\\n while not self.peek(';'):\\r\\n value = self.consume(\\r\\n r'''\\r\\n \\\"\\\" # empty attribute\\r\\n | # or\\r\\n \\\".*?[^\\\\\\\\]\\\" # from quote up to an unquoted quote\\r\\n | # or\\r\\n [^;,]+ # up to semicolon or comma\\r\\n '''\\r\\n )\\r\\n\\r\\n if type.lower() in ['string', 'url']:\\r\\n value = str(value).strip('\\\"')\\r\\n elif value.lower() in ['nan', 'nan.', '-nan']:\\r\\n value = float('nan')\\r\\n elif value.lower() in ['inf', 'inf.']:\\r\\n value = float('inf')\\r\\n elif value.lower() in ['-inf', '-inf.']:\\r\\n value = float('-inf')\\r\\n else:\\r\\n value = ast.literal_eval(value)\\r\\n\\r\\n values.append(value)\\r\\n if self.peek(','):\\r\\n self.consume(',')\\r\\n\\r\\n self.consume(';')\\r\\n\\r\\n if len(values) == 1:\\r\\n values = values[0]\\r\\n\\r\\n return name, values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a90e6b31edd7bf9f361540a135b3798\",\n \"score\": \"0.6161665\",\n \"text\": \"def attributes(self):\\n attributes = {\\n \\\"BaseModel\\\":\\n {\\\"id\\\": str,\\n \\\"created_at\\\": datetime.datetime,\\n \\\"updated_at\\\": datetime.datetime},\\n \\\"User\\\":\\n {\\\"email\\\": str,\\n \\\"password\\\": str,\\n \\\"first_name\\\": str,\\n \\\"last_name\\\": str},\\n \\\"State\\\":\\n {\\\"name\\\": str},\\n \\\"City\\\":\\n {\\\"state_id\\\": str,\\n \\\"name\\\": str},\\n \\\"Amenity\\\":\\n {\\\"name\\\": str},\\n \\\"Place\\\":\\n {\\\"city_id\\\": str,\\n \\\"user_id\\\": str,\\n \\\"name\\\": str,\\n \\\"description\\\": str,\\n \\\"number_rooms\\\": int,\\n \\\"number_bathrooms\\\": int,\\n \\\"max_guest\\\": int,\\n \\\"price_by_night\\\": int,\\n \\\"latitude\\\": float,\\n \\\"longitude\\\": float,\\n \\\"amenity_ids\\\": list},\\n \\\"Review\\\":\\n {\\\"place_id\\\": str,\\n \\\"user_id\\\": str,\\n \\\"text\\\": str}\\n }\\n return attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d2b10003dd7561d30f9b749ed49759f\",\n \"score\": \"0.61583185\",\n \"text\": \"def extra_state_attributes(self):\\n attributes = []\\n for attribute in self._data.items():\\n attributes.append(attribute)\\n return attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa4e622b1c37ff5bf4d5bf4a253cb3e2\",\n \"score\": \"0.61555785\",\n \"text\": \"def attributes(self) -> dict:\\n return self._attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7229ec0a1e98f2e80b6d6706939d5691\",\n \"score\": \"0.6153483\",\n \"text\": \"def getPublicWritableAttributeNames(self):\\n return self._getAttributeNames(PublicWritableAttribute)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dce0e0212a7af493f7a38474cbe70ddc\",\n \"score\": \"0.6133799\",\n \"text\": \"def _attributes(self, person):\\n\\n # filter out the nicknames\\n attr_list = [ attr for attr in person.get_attribute_list()\\n if attr.get_type() != AttributeType.NICKNAME ]\\n\\n for attr in attr_list:\\n\\n attr_type = int(attr.get_type())\\n name = libgedcom.PERSONALCONSTANTATTRIBUTES.get(attr_type)\\n key = str(attr.get_type())\\n value = attr.get_value().strip().replace('\\\\r', ' ')\\n\\n if key in (\\\"AFN\\\", \\\"RFN\\\", \\\"REFN\\\", \\\"_UID\\\", \\\"_FSFTID\\\"):\\n self._writeln(1, key, value)\\n continue\\n\\n if key == \\\"RESN\\\":\\n self._writeln(1, 'RESN')\\n continue\\n\\n if key.startswith('Ancestry APID H:'):\\n continue\\n\\n if name and name.strip():\\n self._writeln(1, name, value)\\n elif value:\\n self._writeln(1, 'FACT', value)\\n self._writeln(2, 'TYPE', key)\\n else:\\n continue\\n self._note_references(attr.get_note_list(), 2)\\n self._source_references(attr.get_citation_list(), 2, person)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24811a18530b6cdd226113c9d9f74c03\",\n \"score\": \"0.6116169\",\n \"text\": \"def get_attribute_names(self, attr_type=None):\\n return [\\n name for name in sorted(self.__dict__.keys())\\n if attr_type is None or isinstance(getattr(self, name), attr_type)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d37378770c1edfee00af965dcc8ec97a\",\n \"score\": \"0.6102916\",\n \"text\": \"def attributes():\\n attribute_classes = get_attribute_classes()\\n for name, class_ in attribute_classes.items():\\n click.echo(\\n u'{name} - Added in: {ai} ({cv})'.format(\\n name=click.style(name, fg='green'),\\n ai=click.style(class_.ADDED_IN, fg='yellow'),\\n cv=click.style(\\n ClassVersion(*class_.MINIMUM_CLASS_VERSION).human,\\n fg='yellow'\\n )\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5dbc7cb695cd01c7e51330dec167602\",\n \"score\": \"0.6100152\",\n \"text\": \"def get_attrs(self, attrs, undefs=None, stringify=True):\\n values = []\\n for name in attrs:\\n nodes = [self]\\n if name.startswith('p_'):\\n nodes, name = [self.parent], name[2:]\\n elif name.startswith('c_'):\\n nodes, name = self.children, name[2:]\\n elif name.startswith('l_'):\\n nodes, name = [self.prev_node], name[2:]\\n elif name.startswith('r_'):\\n nodes, name = [self.next_node], name[2:]\\n for node in (n for n in nodes if n is not None):\\n if name == 'feats_split':\\n values.extend(node._get_attr(name))\\n else:\\n values.append(node._get_attr(name))\\n\\n if undefs is not None:\\n values = [x if x is not None else undefs for x in values]\\n if stringify:\\n values = [str(x) if x is not None else None for x in values]\\n return values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1625dbb13158e5e3567f751b1148695a\",\n \"score\": \"0.6092317\",\n \"text\": \"def parameterNames(self):\\n result = set()\\n for pyattrlist in self._pycard.getParameters().values():\\n for pyattr in pyattrlist:\\n result.add(pyattr.getName())\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9507a9299101475cf60430a60bf7659\",\n \"score\": \"0.6091171\",\n \"text\": \"def enum_attrs(self) -> list[str]:\\n return list(self._enum_attrs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c185b262ab9b1c66f2a97a16dc3fb972\",\n \"score\": \"0.60888326\",\n \"text\": \"def print_attributes(self):\\r\\n\\r\\n variables = vars(self)\\r\\n for var in variables:\\r\\n if var[0] != '_':\\r\\n if var == 'arrays':\\r\\n print(var, ':', len(variables[var]), ' numpy array(s) ')\\r\\n else:\\r\\n print(var, ': ', variables[var])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d59dfe3e154983ccc4db41a442f44249\",\n \"score\": \"0.6081949\",\n \"text\": \"def attributes(self):\\n attrs = {}\\n for name, value in self.__dict__.items():\\n if name in self._int_values:\\n attrs[name] = int(value)\\n elif name in self._bool_values:\\n attrs[name] = bool(int(value))\\n else:\\n attrs[name] = value\\n return attrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fd9dbb0d006215a4d5f377edca95164\",\n \"score\": \"0.6074661\",\n \"text\": \"def attributes(self):\\n return self.custom_attributes(dynamic=True)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993891,"cells":{"query_id":{"kind":"string","value":"d141728462746520515df7079f0b42f9"},"query":{"kind":"string","value":"Return a list of documented arguments with arguments metadata for specific method."},"positive_passages":{"kind":"list like","value":[{"docid":"c613af79aedd77a70e66d5666c23d4d0","score":"0.6292819","text":"def get_arguments_documentation(method_ref, as_params_collection=False, spilt_or=False, ignore_non_args=False):\n\n PARAM_ANNOTATION = '@param'\n IVAR_ANNOTATION = '@ivar'\n params_list = []\n ivars_list = []\n PERIOD_SYMBOL = '-'\n\n if method_ref and method_ref.__doc__:\n doc = method_ref.__doc__\n params_arr = doc.split('\\n')\n\n for var in params_arr:\n if not ignore_non_args and var.find('Overload') != -1:\n params_list.append('\\n' + var.strip() + MethodHelper.NON_ARG_TEMPLATE + '\\n')\n elif '' != var and var.find(PARAM_ANNOTATION) != -1:\n splitted_line = var.strip().split(' ')\n if len(splitted_line) >= 2:\n prefix = splitted_line[0].replace((PARAM_ANNOTATION + ' '),\n '--').replace(PARAM_ANNOTATION, '--')\n param = splitted_line[1].replace('**', '')\n\n if len(splitted_line) > 3 and splitted_line[3].startswith('('):\n typ = ' '.join(splitted_line[2:3])\n if prefix.startswith('['):\n typ = typ + ']'\n else:\n typ = ' '.join(splitted_line[2:])\n\n if param.find('.') != -1:\n splitted_param = param.split('.')\n new_param = PERIOD_SYMBOL.join(splitted_param[1:])\n param = new_param\n\n# params_hash[param.replace(':', '')] = splitted_line[1].replace(':', '') \\\n# .replace('id|name', 'name')\n if as_params_collection:\n if spilt_or and param.find('|') != -1:\n spl = param.replace(':', '').split('|')\n if len(spl) == 2:\n p1 = spl[0]\n if spl[0].find(PERIOD_SYMBOL) != -1 :\n spl_ = spl[0].split('-')\n p2 = '-'.join(spl_[:len(spl_) - 1]) + '-' + spl[1]\n else:\n p2 = spl[1]\n\n params_list.append(p1)\n params_list.append(p2)\n else:\n params_list.append(param.replace(':', ''))\n else:\n params_list.append(param.replace(':', ''))\n else:\n if spilt_or and param.find('|') != -1:\n spl = param.split('|')\n if len(spl) == 2:\n p1 = spl[0]\n if spl[0].find(PERIOD_SYMBOL) != -1 :\n spl_ = spl[0].split('-')\n p2 = '-'.join(spl_[:len(spl_) - 1]) + '-' + spl[1]\n else:\n p2 = spl[1]\n\n params_list.append(prefix + p1 + ' ' + typ)\n params_list.append(prefix + p2 + ' ' + typ)\n else:\n params_list.append(prefix + param + ' ' + typ)\n else:\n params_list.append(prefix + param + ' ' + typ)\n if params_list and params_list[-1].find('collection') != -1:\n params_list.append(' {' + MethodHelper.NON_ARG_TEMPLATE)\n elif not ignore_non_args and '' != var and var.find(IVAR_ANNOTATION) != -1:\n params_list.append(' ' + var.lstrip().replace(IVAR_ANNOTATION + ' ', '') + MethodHelper.NON_ARG_TEMPLATE)\n elif not ignore_non_args and '' != var and var.find('}') != -1:\n params_list.append(' }' + MethodHelper.NON_ARG_TEMPLATE)\n\n return params_list","title":""}],"string":"[\n {\n \"docid\": \"c613af79aedd77a70e66d5666c23d4d0\",\n \"score\": \"0.6292819\",\n \"text\": \"def get_arguments_documentation(method_ref, as_params_collection=False, spilt_or=False, ignore_non_args=False):\\n\\n PARAM_ANNOTATION = '@param'\\n IVAR_ANNOTATION = '@ivar'\\n params_list = []\\n ivars_list = []\\n PERIOD_SYMBOL = '-'\\n\\n if method_ref and method_ref.__doc__:\\n doc = method_ref.__doc__\\n params_arr = doc.split('\\\\n')\\n\\n for var in params_arr:\\n if not ignore_non_args and var.find('Overload') != -1:\\n params_list.append('\\\\n' + var.strip() + MethodHelper.NON_ARG_TEMPLATE + '\\\\n')\\n elif '' != var and var.find(PARAM_ANNOTATION) != -1:\\n splitted_line = var.strip().split(' ')\\n if len(splitted_line) >= 2:\\n prefix = splitted_line[0].replace((PARAM_ANNOTATION + ' '),\\n '--').replace(PARAM_ANNOTATION, '--')\\n param = splitted_line[1].replace('**', '')\\n\\n if len(splitted_line) > 3 and splitted_line[3].startswith('('):\\n typ = ' '.join(splitted_line[2:3])\\n if prefix.startswith('['):\\n typ = typ + ']'\\n else:\\n typ = ' '.join(splitted_line[2:])\\n\\n if param.find('.') != -1:\\n splitted_param = param.split('.')\\n new_param = PERIOD_SYMBOL.join(splitted_param[1:])\\n param = new_param\\n\\n# params_hash[param.replace(':', '')] = splitted_line[1].replace(':', '') \\\\\\n# .replace('id|name', 'name')\\n if as_params_collection:\\n if spilt_or and param.find('|') != -1:\\n spl = param.replace(':', '').split('|')\\n if len(spl) == 2:\\n p1 = spl[0]\\n if spl[0].find(PERIOD_SYMBOL) != -1 :\\n spl_ = spl[0].split('-')\\n p2 = '-'.join(spl_[:len(spl_) - 1]) + '-' + spl[1]\\n else:\\n p2 = spl[1]\\n\\n params_list.append(p1)\\n params_list.append(p2)\\n else:\\n params_list.append(param.replace(':', ''))\\n else:\\n params_list.append(param.replace(':', ''))\\n else:\\n if spilt_or and param.find('|') != -1:\\n spl = param.split('|')\\n if len(spl) == 2:\\n p1 = spl[0]\\n if spl[0].find(PERIOD_SYMBOL) != -1 :\\n spl_ = spl[0].split('-')\\n p2 = '-'.join(spl_[:len(spl_) - 1]) + '-' + spl[1]\\n else:\\n p2 = spl[1]\\n\\n params_list.append(prefix + p1 + ' ' + typ)\\n params_list.append(prefix + p2 + ' ' + typ)\\n else:\\n params_list.append(prefix + param + ' ' + typ)\\n else:\\n params_list.append(prefix + param + ' ' + typ)\\n if params_list and params_list[-1].find('collection') != -1:\\n params_list.append(' {' + MethodHelper.NON_ARG_TEMPLATE)\\n elif not ignore_non_args and '' != var and var.find(IVAR_ANNOTATION) != -1:\\n params_list.append(' ' + var.lstrip().replace(IVAR_ANNOTATION + ' ', '') + MethodHelper.NON_ARG_TEMPLATE)\\n elif not ignore_non_args and '' != var and var.find('}') != -1:\\n params_list.append(' }' + MethodHelper.NON_ARG_TEMPLATE)\\n\\n return params_list\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"7cb01420f7c7a316d7caf8d70c0e9a07","score":"0.7419464","text":"def get_documented_arguments(method_ref, as_params_collection=False, spilt_or=False):\n return MethodHelper.get_arguments_documentation(method_ref, as_params_collection, spilt_or, ignore_non_args=True)","title":""},{"docid":"f4be9f68738702f4a59bfbe7e1a9c8e6","score":"0.6316271","text":"def build_arguments(self):\n if not self.arguments:\n self.method_format[\"argument_format\"] = \"\"\n self.method_format[\"break_before_close\"] = \"\"\n self.method_format[\"empty_line\"] = \"\"\n return\n\n config = self.config.get(\"arguments\")\n formatted_args = \"{prefix} {type} {name}: {doc}\"\n\n title = self.argument_format.get(\"title\")\n if title:\n underline = \"-\" * len(title)\n self.argument_format[\"title\"] = (\n \"{}\\n{}\\n\".format(title, underline)\n if config.get(\"underline\")\n else \"{}\\n\".format(title)\n )\n\n result = []\n\n if self.arguments: # if len(self.arguments) > 0\n for argument_details in self.arg_docstring:\n argument_details[\"prefix\"] = self.argument_format.get(\"prefix\")\n result.append(\n self.fmt.format(formatted_args, **argument_details).strip()\n )\n\n self.argument_format[\"body\"] = \"\\n\".join(result)\n self.method_format[\"argument_format\"] = self.fmt.format(\n \"{title}{body}\", **self.argument_format\n )","title":""},{"docid":"566cfb80a9e35d19c874ed1687e115a0","score":"0.6307494","text":"def _package_arguments(valid_method_spec, fn_args):\n arguments = []\n for arg_type, arg_val in zip(valid_method_spec['arguments'], fn_args):\n if isinstance(arg_val, JavaObjectShadow):\n arguments.append(_serialize_arg(arg_val))\n else:\n arguments.append(_serialize_arg(_JAVA_TYPE_NAME_TO_PYTHON_TYPE[arg_type](arg_val)))\n return arguments","title":""},{"docid":"25d4a29521169eb96107ca791b20be87","score":"0.6212363","text":"def get_args_list(self, **kwargs): # real signature unknown; restored from __doc__\n return []","title":""},{"docid":"e3c64e2b02ec120f22bf6a09487e8202","score":"0.6070744","text":"def list_methods(self):\r\n return [(method, doc) for method, (function, returns, args, doc) in self.methods.items()]","title":""},{"docid":"7f3e79ab7ad1758b2480d61e0e03a666","score":"0.6029665","text":"def methodLinkingTexts(self, method: widlparser.OperationRest) -> list[str]:\n for i, arg in enumerate(method.arguments or []):\n assert isinstance(arg, widlparser.constructs.Argument)\n if arg.optional or arg.variadic:\n optStart = i\n break\n else:\n optStart = None\n\n texts = []\n\n if optStart is not None:\n prefixes = [t.cast(str, method.name)]\n if method.name == \"constructor\":\n prefixes.append(t.cast(str, method.parent.name))\n for i in range(optStart, len(method.arguments)):\n argText = \", \".join(t.cast(str, arg.name) for arg in list(method.arguments)[:i])\n for prefix in prefixes:\n texts.append(prefix + \"(\" + argText + \")\")\n\n texts.append(t.cast(str, method.normal_name))\n if method.name == \"constructor\":\n texts.append(t.cast(str, method.parent.name) + t.cast(str, method.normal_name)[11:])\n return list(reversed(texts))","title":""},{"docid":"a395007b99ec43aeb7632478046c7206","score":"0.599155","text":"def args(self):\n return [self.as_basic()]","title":""},{"docid":"f8e86f573be958390d79677279423841","score":"0.5954009","text":"def positional_extra(self):\n try:\n return inspect.getfullargspec(self.callableObject).args # Cannot handle @deco_cached\n except TypeError:\n return self.positionalArgNames","title":""},{"docid":"e4e83ebdf0effa0e6ff2fe0b14f169a4","score":"0.5908483","text":"def get_function_args(fn, *args, **kwargs):\n # Convert tuple of positional parameters to dictionary\n arg_names = list(signature(fn).parameters.keys())\n args_ = dict(zip(arg_names[:len(args)], args))\n\n # Create metadata dict with default values\n metadata = {\n arg: None if param.default is _empty else param.default\n for arg, param in signature(fn).parameters.items()}\n\n # Update metadata dict with positional and keyword parameters\n metadata.update(args_)\n metadata.update(kwargs)\n return metadata","title":""},{"docid":"2b9f96bc9ed4d15027251595e6805cba","score":"0.59037286","text":"def known_arguments(self):\n return self._arguments","title":""},{"docid":"5c20b6b00fcb46b0938e8bcad1047053","score":"0.58861834","text":"def getargspec_init(method):\n try:\n return compat.inspect_getfullargspec(method)\n except TypeError:\n if method is object.__init__:\n return ([\"self\"], None, None, None)\n else:\n return ([\"self\"], \"args\", \"kwargs\", None)","title":""},{"docid":"ec9f7576643d9d0dd220a72cc8e97a51","score":"0.5839742","text":"def find_args(self):\n raise NotImplementedError('find_args must be implemented in a subclass')","title":""},{"docid":"8c7999f66f1835463c6ac3c7ac093b4a","score":"0.5767862","text":"def _getArguments(self, func):\n # this returns (args, varargs, keywords, defaults)\n argspec = list(inspect.getargspec(func))\n if argspec[0] and argspec[0][0] == 'self':\n argspec[0] = argspec[0][1:]\n args, defaults = argspec[0], argspec[3]\n defaults = [] if defaults is None else defaults\n argspec[0] = args[0:len(args) - len(defaults)]\n argspec[3] = args[len(args) - len(defaults):]\n return argspec","title":""},{"docid":"8ec6f530e32d4b7111f550b538b9d7a2","score":"0.573449","text":"def args(self):\n return self.find_args()","title":""},{"docid":"1a8d1aad45909d1ccecb3320436f39f5","score":"0.571819","text":"def getArgs(self):\n raise Exception(\"Not yet implemented\")","title":""},{"docid":"4ba9f8779882609c274de78f5a2f121d","score":"0.57062393","text":"def add_arguments(self):\n raise NotImplementedError","title":""},{"docid":"facdd061edcaeb5c0adac0d109770fe1","score":"0.5649677","text":"def arguments(self):\n\t\treturn self._arguments","title":""},{"docid":"a9773424278eb85685ab1061bc1cedcd","score":"0.563587","text":"def parse_user_args(method, *args, **kwargs):\n sig = inspect.signature(method)\n if 'self' in sig.parameters or 'cls' in sig.parameters:\n ba = sig.bind(method, *args, **kwargs)\n ba.apply_defaults()\n params = list(sig.parameters.keys())[1:]\n else:\n ba = sig.bind(*args, **kwargs)\n ba.apply_defaults()\n params = list(sig.parameters.keys())\n\n user_filled_args = [ba.arguments.get(arg_value) for arg_value in params]\n return user_filled_args, ba.arguments","title":""},{"docid":"b7ea3aed9a2ef067b40141b3743bdd3a","score":"0.5631804","text":"def class_or_method_args():\n return [x for x in COMMON_DATA_ARGS]","title":""},{"docid":"c0abfe9ea5daa8af5b9ef0e575525d8d","score":"0.56316894","text":"def methodSignature(self, req, method):\n p = self.get_method(method)\n req.perm.assert_permission(p.permission)\n return [','.join([RPC_TYPES[x] for x in sig]) for sig in p.xmlrpc_signatures()]","title":""},{"docid":"12b7aa352e64bc161f3260bb1b545b8e","score":"0.56273514","text":"def _get_argnames(f) -> List[str]:\n try:\n return list(inspect.signature(f).parameters.keys())\n except AttributeError:\n return inspect.getargspec(f).args","title":""},{"docid":"ace6cf79f3f9fc40d3fcf2f76fad867e","score":"0.56271076","text":"def arguments():\n from inspect import getargvalues, stack\n posname, kwname, args = getargvalues(stack()[1][0])[-3:]\n posargs = args.pop(posname, [])\n args.update(args.pop(kwname, []))\n return args, posargs","title":""},{"docid":"9fa986fcd7a60a8444975a9c103e51ac","score":"0.5612015","text":"def arguments_from_docstring(doc):\n\n if doc is None:\n raise RuntimeError('__doc__ is None')\n\n doc = doc.lstrip()\n\n # care only the firstline\n # docstring can be long\n line = doc.split('\\n', 1)[0] # get the firstline\n if line.startswith(\"('...',)\"):\n line = doc.split('\\n', 2)[1] # get the second line\n p = re.compile(r'^[\\w|\\s.]+\\(([^)]*)\\).*')\n # 'min(iterable[, key=func])\\n' -> 'iterable[, key=func]'\n sig = p.search(line)\n if sig is None:\n return []\n # iterable[, key=func]' -> ['iterable[' ,' key=func]']\n sig = sig.groups()[0].split(',')\n ret = []\n for s in sig:\n # get the last one after all space after =\n # ex: int x= True\n tmp = s.split('=')[0].split()[-1]\n # clean up non _+alphanum character\n tmp = ''.join([x for x in tmp if x.isalnum() or x == '_'])\n ret.append(tmp)\n # re.compile(r'[\\s|\\[]*(\\w+)(?:\\s*=\\s*.*)')\n # ret += self.docstring_kwd_re.findall(s)\n ret = list(filter(lambda x: x != '', ret))\n\n if len(ret) == 0:\n raise RuntimeError('Your doc is unparsable\\n' + doc)\n\n return ret","title":""},{"docid":"8afb182e6ca988b90b31a466648f107d","score":"0.5591545","text":"def arguments(self):\n if self._format in (self._FORM_ARG_LIST, self._FORM_NAMED_ARG_LIST):\n return self._arguments\n raise ValueError('\"{}\" does not have arguments.'.format(str(self)))","title":""},{"docid":"6af37e85b0779c20b42871eac5bfecec","score":"0.5578555","text":"def arguments(self):\n self._arguments, self._unknown_arguments = self.parser.parse_known_args()\n return self._arguments","title":""},{"docid":"be70eb5053160547b41df4f7355b70f9","score":"0.5557773","text":"def get_docs(module, method):\n response = {'status':'success','data':{}}\n response['data']['docstring'] = []\n\n #Extract the method from the method dict\n method = funcs[module][method]\n\n #Introspect the docs\n docs = inspect.getdoc(method)\n for l in docs.split('\\n'):\n response['data']['docstring'].append(l)\n return jsonify(response)","title":""},{"docid":"a625e4829978fcec0596e5e9a0fa1bf0","score":"0.5545234","text":"def parameters(self):\n return list(inspect.signature(self.callableObject).parameters.values())","title":""},{"docid":"378b35ecbd9f4e599b1f396155eb05a5","score":"0.55394685","text":"def extract_docs():\n\n methods = []\n\n def _key(entry):\n return\n\n sorted_entries = sorted(Client.__dict__.items(), key=lambda x: x[0])\n\n tree = {}\n meta_key = '__doc_meta__'\n\n for attr_name, attr_value in sorted_entries:\n\n if not hasattr(attr_value, meta_key):\n continue\n\n func = attr_value\n meta = getattr(func, meta_key)\n\n arg_spec = inspect.getargspec(func)\n if arg_spec[0] and arg_spec[0][0] in ('cls', 'self'):\n del arg_spec[0][0]\n\n func_name = func.__name__\n func_spec = func_name + inspect.formatargspec(*arg_spec)\n\n doc = textwrap.dedent((func.__doc__ or ''))\n doc = indent(doc, n=3)\n\n func_desc_lines = []\n for i, line in enumerate(func.__doc__.splitlines()):\n if i == 0:\n continue\n func_desc_lines.append(line.strip())\n if not line:\n break\n func_desc = ' '.join(func_desc_lines)\n\n doc = textwrap.dedent(\n \"\"\"\\\n {func_name}\n {func_name_line}\n\n {func_desc}\n :py:func:`nano.rpc.Client.{func_spec} <nano.rpc.Client.{func_name}>`\n\n .. .. py:function:: nano.rpc.Client.{func_spec}\n\n .. {doc}\n \"\"\"\n ).format(\n func_spec=func_spec,\n func_name_line='-' * len(func_name),\n func_name=func_name,\n func_desc=func_desc,\n doc=doc,\n )\n\n categories = meta['categories']\n for category in categories:\n tree.setdefault(category, []).append(doc)\n\n directory = 'rpc/methods'\n for file in os.listdir(directory):\n if file.endswith('.rst'):\n os.unlink(os.path.join(directory, file))\n\n for category, func_docs in sorted(tree.items(), key=lambda x: x[0]):\n category = category or 'other'\n file_path = os.path.join(directory, category) + '.rst'\n with open(file_path, 'w') as docfile:\n docfile.write('.. _%s-ref:\\n' % category + '\\n')\n title = '{category}'.format(category=category.capitalize())\n docfile.write('%s\\n' % title)\n docfile.write('%s\\n' % (len(title) * '='))\n docfile.write('\\n')\n\n for func_doc in func_docs:\n docfile.write(func_doc + '\\n')","title":""},{"docid":"d966e6b76156f0506d0e15b3b9808a5b","score":"0.5517862","text":"def arguments(self):\n return self._arguments","title":""},{"docid":"d966e6b76156f0506d0e15b3b9808a5b","score":"0.5517862","text":"def arguments(self):\n return self._arguments","title":""},{"docid":"d966e6b76156f0506d0e15b3b9808a5b","score":"0.5517862","text":"def arguments(self):\n return self._arguments","title":""},{"docid":"d966e6b76156f0506d0e15b3b9808a5b","score":"0.5517862","text":"def arguments(self):\n return self._arguments","title":""},{"docid":"14c157167849a4d0168a0c1d4a87c6a5","score":"0.5479797","text":"def all_args(self) -> Iterator[str]:\n yield from _arg_names(self._path)","title":""},{"docid":"93380744d90ef76b072748a3761e231d","score":"0.54776716","text":"def get_method_arguments(mappings: dict, **kwargs):\n \n optional_args = {}\n for key, value in mappings.items(): \n try:\n if isinstance(value, ArgumentValueKind):\n optional_args[key] = create_value(kwargs[key], value)\n elif isinstance(value, tuple):\n data = [create_value(item, value[1]).format() for item in kwargs[key]]\n optional_args[key] = create_value(data, value[0])\n elif isinstance(value, dict):\n optional_args[key] = get_method_arguments(value, **(kwargs[key]))\n except KeyError:\n # Ignore if no kwargs found\n continue\n for arg in optional_args:\n kwargs[arg] = optional_args[arg]\n return kwargs","title":""},{"docid":"0e557de72abfc7012fcd93c104b2a1c2","score":"0.5468677","text":"def get_documentation_extras(self):\n return []","title":""},{"docid":"0ec2452d75a4808204eec89816adef94","score":"0.5457662","text":"def methodHelp(self, req, method):\n p = self.get_method(method)\n req.perm.assert_permission(p.permission)\n return '\\n'.join((p.signature, '', p.description))","title":""},{"docid":"707ad1f3bbf19756460e313937956f36","score":"0.5453258","text":"def invocation_metadata(self):\n raise NotImplementedError()","title":""},{"docid":"8f1d33474874e4fafa1aef492f2d2008","score":"0.544153","text":"def extract_arguments(self):\n raise Exception('Not implemented exception')","title":""},{"docid":"e3ecbde6be8400055455d81e0148237b","score":"0.54389393","text":"def get_docstring_param_info_list(docstring: str) -> List[Dict[str, str]]:\n if docstring == '':\n return []\n if not _parameters_exists_in_docstring(docstring=docstring):\n return []\n splitted_param_doc_list: List[str] = get_splitted_param_doc_list(\n docstring=docstring\n )\n param_info_list: List[Dict[str, str]] = []\n for splitted_param_doc in splitted_param_doc_list:\n arg_name: str = _get_docstring_var_name(var_doc=splitted_param_doc)\n type_name: str = _get_docstring_type_name(var_doc=splitted_param_doc)\n default_val: str = _get_docstring_default_value(\n var_doc=splitted_param_doc)\n description: str = _get_docstring_var_description(\n var_doc=splitted_param_doc)\n param_info_list = _append_param_info_to_list(\n param_info_list=param_info_list,\n arg_name=arg_name,\n type_name=type_name,\n default_val=default_val,\n description=description)\n return param_info_list","title":""},{"docid":"4d95020dcbe1b4647270b1d6b4b22325","score":"0.5430044","text":"def get_arguments():\n opts = dict()\n parser = Parser()\n for arg in vars(parser.args):\n opts[arg] = getattr(parser.args, arg)\n return opts","title":""},{"docid":"d0e4bf0b8d23932b8795e58f44a00f56","score":"0.54173857","text":"def op_args(self) -> List[OpArg]:\n return self._op_args","title":""},{"docid":"adab8916abc9acc709d22225d85bbf1a","score":"0.54167056","text":"def get_method_params(self, method, **kwargs):\n return getattr(self.adapter, \"params_\" + method)(**kwargs)","title":""},{"docid":"f36a25f42c1fceef46c25a1ab594345d","score":"0.5411934","text":"def getdoc(method):\n help = getattr(method, 'help', None)\n if help is None:\n help = method.__doc__\n doc = inspect.getdoc(MethodHelp(help))\n if doc is None:\n return ''\n return doc","title":""},{"docid":"6fcaaae107341a3f9167ffb3ac1bc265","score":"0.5388873","text":"def args(self) -> List[str]: # noqa: D401\n return self._args","title":""},{"docid":"2b48178472eb3ff08bbd3200e8292f04","score":"0.53886926","text":"def reqArguments(self):\n pass","title":""},{"docid":"42e54c5cc4057d8a64f90bd3818ecfaa","score":"0.53829575","text":"def get_function_args(self):\n return self._args","title":""},{"docid":"09621a5ceca71a220addac031afecaeb","score":"0.5371842","text":"def get_arguments(self) -> Iterable[WorkflowArgument]:\n return map(\n lambda argNode: WorkflowArgument(argNode, self.document.getroot()),\n self.document.iterfind(\n \"./x:Members/x:Property\",\n namespaces=self.default_namespaces))","title":""},{"docid":"a5dd3771e4e39026d07513ef2a8d82be","score":"0.53714263","text":"def flatten_signature_kwargs(func, loc):\n kwargs_name = get_signature_kwargs_key(func)\n # remove unwanted metadata for class methods\n attributes = get_signature_locals(func, loc)\n if kwargs_name:\n attributes.pop(kwargs_name)\n attributes.update(loc[kwargs_name])\n return attributes","title":""},{"docid":"93dceca3fd880d7f5ceafc381922da9c","score":"0.5369019","text":"def load_all_method_args(language, API=False):\n module_names = []\n modules_args = {}\n # get module names\n for _lib in glob(os.path.dirname(inspect.getfile(lib)) + '/*/*/engine.py'):\n _lib = _lib.replace('/', '.').replace('\\\\', '.')\n if '.lib.brute.' in _lib or '.lib.scan.' in _lib or '.lib.vuln.' in _lib:\n _lib = 'lib.' + _lib.rsplit('.lib.')[-1].rsplit('.py')[0]\n if _lib not in module_names:\n module_names.append(_lib)\n # get args\n res = \"\"\n for imodule in module_names:\n try:\n extra_requirements_dict = getattr(__import__(imodule, fromlist=['extra_requirements_dict']),\n 'extra_requirements_dict')\n except:\n __die_failure(messages(language, 112).format(imodule))\n imodule_args = extra_requirements_dict()\n modules_args[imodule] = []\n for imodule_arg in imodule_args:\n if API:\n res += imodule_arg + \"=\" + \",\".join(map(str, imodule_args[imodule_arg])) + \"\\n\"\n modules_args[imodule].append(imodule_arg)\n if API:\n return res\n for imodule in modules_args:\n info(imodule.rsplit('.')[2] + '_' + imodule.rsplit('.')[1] + ' --> '\n + \", \".join(modules_args[imodule]))\n return module_names","title":""},{"docid":"4ab308975d84d683b42993a8ae5f843e","score":"0.5341901","text":"def api_arg_spec(fn):\n return signature(getattr(API, fn)).parameters","title":""},{"docid":"deaa82be8d820cf641887107fe2954a1","score":"0.5330546","text":"def args(self):\r\n return []","title":""},{"docid":"fd4fa0abcf40ce3c423e2c8d16cf9ab6","score":"0.5330223","text":"def extract_permission_by_apicall(self, method):\n perm_methods = []\n def match_api_call(line):\n for api in self.api_mapping:\n if api.lower() in line.lower():\n return self.api_mapping[api]\n return None\n for line in method[1:-1]:\n requested_perm = match_api_call(line) \n if requested_perm:\n perm_methods.extend(requested_perm)\n return perm_methods","title":""},{"docid":"0f78b58e36321904014b88656affc7f1","score":"0.5315243","text":"def add_arguments(self):\n pass","title":""},{"docid":"d7b3fd24cf265d652698e834ad0c8eec","score":"0.5299918","text":"def apply_to_each_input(method):\n @wraps(method)\n def decorator(self, *args, attr=None, **kwargs):\n \"\"\"Returned decorator.\"\"\"\n is_list = True\n if isinstance(attr, str):\n attr = (attr, )\n is_list = False\n elif attr is None:\n attr = self.attributes\n if not self.attributes:\n return None\n\n res = []\n for att in attr:\n res.append(method(self, *args, attr=att.upper(), **kwargs))\n if isinstance(res[0], self.__class__):\n return self\n return res if is_list else res[0]\n return decorator","title":""},{"docid":"e0432697be323b9ac94dce4a8bdc501f","score":"0.52952546","text":"def cmd_args(*arguments: _Argument) -> Any:\n def decorate(obj):\n if hasattr(obj, ATTR_CMD_ARGS):\n raise RuntimeError(f'{ATTR_CMD_ARGS} has been set on {obj} already')\n _add_arguments = _add_arguments_factory(arguments)\n _verify_arguments(_add_arguments, obj)\n if isinstance(obj, type):\n fn = lambda self, parser: _add_arguments(parser)\n else:\n fn = _add_arguments\n setattr(obj, ATTR_CMD_ARGS, fn)\n return obj\n return decorate","title":""},{"docid":"382788cada031f24037e19616a660577","score":"0.5294809","text":"def args(self):\n return self._args","title":""},{"docid":"bce124f1e27f8716edffecf6f1392492","score":"0.5278355","text":"def args(self):\n return self._args_","title":""},{"docid":"20d1af34f1e12cae838c86eacb4bc98f","score":"0.5274308","text":"def getargs(self, *args):\n return args","title":""},{"docid":"c706bb7d4fcc8cd3279a6e1897ae2ddc","score":"0.5273934","text":"def docstring(self):\n docs = []\n for func in set(self.values()):\n try:\n sig = inspect.signature(func)\n except ValueError:\n sig = ''\n doc = func.__doc__ or ''\n docs.append(f'{func.__name__}{sig}\\n {doc}')\n return '\\n\\n'.join(docs)","title":""},{"docid":"1cba3db7002e8bf956a95dce100624f1","score":"0.52726704","text":"def args(self) -> Sequence[str]:\n return self._args","title":""},{"docid":"af06529e1ec3a131752635e0b8319010","score":"0.52671695","text":"def read_arguments(parser):","title":""},{"docid":"16505dae9029c8a5349d86f1a0567615","score":"0.5254659","text":"def get_function_args(cls, func):\n return list(cls._get_function_args(func))","title":""},{"docid":"d085e507f31458d2e6fc45c95e3b371b","score":"0.5250189","text":"def iter_args(self, args):\n for i, posarg in enumerate(args.posargs):\n if i < len(self.param_names):\n name = self.param_names[i]\n yield (name, posarg, self.annotations.get(name))\n elif self.varargs_name and self.varargs_name in self.annotations:\n varargs_type = self.annotations[self.varargs_name]\n formal = varargs_type.ctx.convert.get_element_type(varargs_type)\n yield (argname(i), posarg, formal)\n else:\n yield (argname(i), posarg, None)\n for name in sorted(args.namedargs):\n namedarg = args.namedargs[name]\n if name in self.param_names[:self.posonly_count]:\n formal = None\n else:\n formal = self.annotations.get(name)\n if formal is None and self.kwargs_name:\n kwargs_type = self.annotations.get(self.kwargs_name)\n if kwargs_type:\n formal = kwargs_type.ctx.convert.get_element_type(kwargs_type)\n yield (name, namedarg, formal)\n if self.varargs_name is not None and args.starargs is not None:\n yield (self.varargs_name, args.starargs,\n self.annotations.get(self.varargs_name))\n if self.kwargs_name is not None and args.starstarargs is not None:\n yield (self.kwargs_name, args.starstarargs,\n self.annotations.get(self.kwargs_name))","title":""},{"docid":"af7ede988863a68de97304e6bf995beb","score":"0.52406657","text":"def request_args(self):\n return [getattr(self, k) for k in self.request_keys]","title":""},{"docid":"1d0054c0d73a51ae2506dce2f3fb467b","score":"0.52337515","text":"def build_docstrings(self):\n text = self.method_docstring or \"Missing Docstring!\"\n self.method_format[\"method_docstring\"] = self.wrap_strings(text.split(\" \"))","title":""},{"docid":"d70cf487dc2f43c6c3b2c36f43f2f051","score":"0.52275515","text":"def documentation(cls):\r\n for name, meth in sorted(cls.__dict__.items()):\r\n if name.startswith('_'):\r\n return\r\n yield name, meth.doc()","title":""},{"docid":"e43eb5480672f2bea7023118c2725d00","score":"0.5227063","text":"def leadingArgNames(self):\n leadingArgNames = []\n for param in self.parameters:\n if param.name == \"self\":\n continue\n\n noDefault = param.default is inspect.Parameter.empty\n includedKind = param.kind.name in (\"POSITIONAL_OR_KEYWORD\", \"POSITIONAL_ONLY\")\n if not (noDefault and includedKind):\n break\n\n leadingArgNames.append(param.name)\n return leadingArgNames","title":""},{"docid":"4f81ae369c17ee9a9b33b81ce518c3e1","score":"0.5223203","text":"def MacroAssemblerMethodArgs(self):\n return \", \".join([\n operand.GetArgumentType() + \" \" + operand.name\n for operand in self.operands\n ])","title":""},{"docid":"67c5e8c1c11e442ba6d66dcb5b910944","score":"0.5205255","text":"def help(method):\n info = request('JSONRPC.Introspect', {'filter': {'id': method, 'type': 'method'}})\n return info","title":""},{"docid":"77d94319f2de0873f80a7b27c2443d57","score":"0.5196424","text":"def named_method_params(method, args, kwargs):\n args_names = method.__code__.co_varnames[:method.__code__.co_argcount]\n params = get_default_args(method)\n args_dict = {**dict(zip(args_names, args)), **kwargs}\n params.update(args_dict)\n params.pop('self', None)\n return params","title":""},{"docid":"a80d9bbb8af1159f5580c987f9860ee1","score":"0.51957786","text":"def class_or_method_args():\n return [Flag(\"criterions\", dtype=Flag.TYPE.STRING,\n default=None, help=\"A list of multiple criterions. Each element should be a dict like \"\n \"\\\"{'criterion.class': '...', 'criterion.params': '...', 'alpha': 1.0}\\\",\"\n \"where alpha denotes the weight of the corresponding criterion \"\n \"and will be set to 1.0 by default.\")]","title":""},{"docid":"38bf48dc7ddceb02348aac63dc6f480e","score":"0.51933914","text":"def visit_arguments(self, node, parent):\n vararg, kwarg = node.vararg, node.kwarg\n newnode = nodes.Arguments(\n vararg.arg if vararg else None, kwarg.arg if kwarg else None, parent\n )\n args = [self.visit(child, newnode) for child in node.args]\n defaults = [self.visit(child, newnode) for child in node.defaults]\n varargannotation = None\n kwargannotation = None\n posonlyargs = []\n # change added in 82732 (7c5c678e4164), vararg and kwarg\n # are instances of `_ast.arg`, not strings\n if vararg:\n if node.vararg.annotation:\n varargannotation = self.visit(node.vararg.annotation, newnode)\n vararg = vararg.arg\n if kwarg:\n if node.kwarg.annotation:\n kwargannotation = self.visit(node.kwarg.annotation, newnode)\n kwarg = kwarg.arg\n kwonlyargs = [self.visit(child, newnode) for child in node.kwonlyargs]\n kw_defaults = [\n self.visit(child, newnode) if child else None for child in node.kw_defaults\n ]\n annotations = [\n self.visit(arg.annotation, newnode) if arg.annotation else None\n for arg in node.args\n ]\n kwonlyargs_annotations = [\n self.visit(arg.annotation, newnode) if arg.annotation else None\n for arg in node.kwonlyargs\n ]\n\n posonlyargs_annotations = []\n if PY38:\n posonlyargs = [self.visit(child, newnode) for child in node.posonlyargs]\n posonlyargs_annotations = [\n self.visit(arg.annotation, newnode) if arg.annotation else None\n for arg in node.posonlyargs\n ]\n type_comment_args = [\n self.check_type_comment(child, parent=newnode) for child in node.args\n ]\n type_comment_kwonlyargs = [\n self.check_type_comment(child, parent=newnode) for child in node.kwonlyargs\n ]\n type_comment_posonlyargs = []\n if PY38:\n type_comment_posonlyargs = [\n self.check_type_comment(child, parent=newnode)\n for child in node.posonlyargs\n ]\n\n newnode.postinit(\n args=args,\n defaults=defaults,\n kwonlyargs=kwonlyargs,\n posonlyargs=posonlyargs,\n kw_defaults=kw_defaults,\n annotations=annotations,\n kwonlyargs_annotations=kwonlyargs_annotations,\n posonlyargs_annotations=posonlyargs_annotations,\n varargannotation=varargannotation,\n kwargannotation=kwargannotation,\n type_comment_args=type_comment_args,\n type_comment_kwonlyargs=type_comment_kwonlyargs,\n type_comment_posonlyargs=type_comment_posonlyargs,\n )\n # save argument names in locals:\n if vararg:\n newnode.parent.set_local(vararg, newnode)\n if kwarg:\n newnode.parent.set_local(kwarg, newnode)\n return newnode","title":""},{"docid":"ea01ab37672e15f4727d24d718c2f6a0","score":"0.51924455","text":"def test_method_docs(self):\n for func in dir(Base):\n self.assertTrue(len(func.__doc__) > 0)","title":""},{"docid":"5981b488b6193bbbac3a75cee623a578","score":"0.51831853","text":"def _get_method_context(self, method, interface):\n def construct_arg_context(arg_number, arg_type_full):\n arg_type = self.last(arg_type_full).strip('[]')\n arg_context = {\n arg_number + '_type': arg_type,\n arg_number + '_type_under': camel_to_under(arg_type),\n arg_number + '_type_mixed': camel_to_mixed(arg_type),\n arg_number + '_abcapp_name': self._app_name(package_name=get_pkg_name(arg_type_full.strip('[]')),\n abstract=True),\n arg_number + '_abcpkg_name': self._abc_pkg_name(package_name=get_pkg_name(arg_type_full.strip('[]')),\n abc=False),\n arg_number + '_module': self.get_interface_module(\n self._abc_pkg_name(package_name=get_pkg_name(arg_type_full),\n abc=False),\n arg_type)\n }\n return arg_context\n\n context = {}\n\n if interface['shortname'] + '.' + method['name'] in self.patterns:\n context = self.patterns[interface['shortname'] + '.' + method['name']]['kwargs']\n\n arg_list = []\n for arg in method['args']:\n arg_list.append(arg['var_name'])\n context['arg_list'] = ', '.join(arg_list)\n\n if 'package_name' in context:\n # Add keyword arguments to template kwargs that are particular\n # to the json implementation\n fixed_package_name = fix_reserved_word(self.package['name'], is_module=True)\n context['app_name'] = self._app_name()\n context['implpkg_name'] = self._abc_pkg_name(abc=False, reserved_word=False)\n context['abcapp_name'] = self._app_name()\n context['abcpkg_name'] = self._abc_pkg_name(abc=False)\n context['interface_name_under'] = camel_to_under(context['interface_name'])\n context['interface_name_dot'] = '.'.join(context['interface_name_under'].split('_')[:-1])\n context['package_name_caps'] = self.replace(self.package['name'].title(), desired='')\n context['package_name_upper'] = self.package['name'].upper()\n context['package_name_replace'] = self.replace(self.package['name'])\n context['package_name_replace_reserved'] = self.replace(fixed_package_name)\n context['package_name_replace_upper'] = self.replace(self.package['name']).upper()\n\n if method['args']:\n context['args_kwargs_or_nothing'] = '*args, **kwargs'\n else:\n context['args_kwargs_or_nothing'] = ''\n\n if context['interface_name_under'].endswith('_session'):\n context['session_shortname_dot'] = '.'.join(context['interface_name_under'].split('_')[:-1])\n\n if 'arg0_type_full' in context:\n context.update(construct_arg_context('arg0',\n context['arg0_type_full']))\n if 'arg1_type_full' in context:\n context.update(construct_arg_context('arg1',\n context['arg1_type_full']))\n if 'arg2_type_full' in context:\n context.update(construct_arg_context('arg2',\n context['arg2_type_full']))\n if 'arg3_type_full' in context:\n context.update(construct_arg_context('arg3',\n context['arg3_type_full']))\n if 'arg0_object' in context:\n context['arg0_object_under'] = camel_to_under(context['arg0_object'])\n context['arg0_object_mixed'] = camel_to_mixed(context['arg0_object'])\n if 'return_type_full' in context:\n context['return_type'] = context['return_type_full'].split('.')[-1]\n context['return_pkg'] = get_pkg_name(context['return_type_full'])\n context['return_module'] = self.get_interface_module(\n self._abc_pkg_name(package_name=get_pkg_name(context['return_type_full']),\n abc=False),\n context['return_type_full'].split('.')[-1])\n if 'return_pkg' in context:\n context['return_app_name'] = self._app_name(package_name=context['return_pkg'])\n context['return_implpkg_name'] = self._abc_pkg_name(package_name=context['return_pkg'],\n abc=False)\n context['return_pkg_title'] = context['return_pkg'].title()\n context['return_pkg_caps'] = context['return_pkg'].upper()\n context['return_pkg_replace_caps'] = self.replace(context['return_pkg'].upper())\n context['return_pkg_replace_title'] = self.replace(context['return_pkg'].title())\n if 'return_cat_name' in context:\n context['return_cat_name_under'] = camel_to_under(context['return_cat_name'])\n if 'object_name_under' in context:\n context['object_name_upper'] = context['object_name_under'].upper()\n context['object_name_mixed'] = under_to_mixed(context['object_name_under'])\n # Might want to add creating kwargs['object_name' from this as well]\n if 'object_name' in context and 'package_name' in context:\n context['object_app_name'] = self._app_name()\n context['object_implpkg_name'] = self._abc_pkg_name(abc=False)\n context['object_module'] = self.get_interface_module('package_name', 'object_name')\n if 'var_name' in context:\n context['var_name_upper'] = context['var_name'].upper()\n context['var_name_mixed'] = under_to_mixed(context['var_name'])\n context['var_name_plural'] = make_plural(context['var_name'])\n context['var_name_plural_mixed'] = under_to_mixed(context['var_name_plural'])\n context['var_name_singular'] = remove_plural(context['var_name'])\n context['var_name_singular_mixed'] = under_to_mixed(context['var_name_singular'])\n if 'return_type' in context:\n context['return_type_under'] = camel_to_under(context['return_type'])\n if 'return_type' in context and context['return_type'].endswith('List'):\n context['return_type_list_object'] = context['return_type'][:-4]\n context['return_type_list_object_under'] = camel_to_under(context['return_type_list_object'])\n context['return_type_list_object_plural_under'] = make_plural(context['return_type_list_object_under'])\n if ('containable_object_name' in context and\n 'containable_object_name_under' not in context and\n 'containable_object_name_upper' not in context):\n context['containable_object_name_under'] = camel_to_under(context['containable_object_name'])\n context['containable_object_name_plural'] = make_plural(context['containable_object_name'])\n context['containable_object_name_plural_under'] = camel_to_under(context['containable_object_name_plural'])\n context['containable_object_name_mixed'] = camel_to_mixed(context['containable_object_name'])\n context['containable_object_name_upper'] = camel_to_under(context['containable_object_name']).upper()\n if 'object_namespace' in context:\n context['object_package_name'] = '.'.join(context['object_namespace'].split('.')[:-1])\n context['object_package_name_replace'] = '_'.join(context['object_package_name'].split('.'))\n context['object_package_name_replace_upper'] = context['object_package_name_replace'].upper()\n context['object_namespace_replace'] = '_'.join(context['object_namespace'].split('.'))\n if ('object_name' in context and\n 'object_name_under' not in context and\n 'object_name_upper' not in context):\n context['object_name_plural'] = make_plural(context['object_name'])\n context['object_name_under'] = camel_to_under(context['object_name'])\n context['object_name_mixed'] = camel_to_mixed(context['object_name'])\n context['object_name_upper'] = camel_to_under(context['object_name']).upper()\n if 'object_name_under' in context:\n context['object_name_plural_under'] = make_plural(context['object_name_under'])\n if 'aggregated_object_name' in context:\n context['aggregated_object_name_under'] = camel_to_under(context['aggregated_object_name'])\n context['aggregated_object_name_mixed'] = camel_to_mixed(context['aggregated_object_name'])\n context['aggregated_objects_name_under'] = camel_to_under(make_plural(context['aggregated_object_name']))\n context['aggregated_objects_name_mixed'] = camel_to_mixed(make_plural(context['aggregated_object_name']))\n if 'source_name' in context:\n context['source_name_mixed'] = under_to_mixed(context['source_name'])\n if 'destination_name' in context:\n context['destination_name_mixed'] = under_to_mixed(context['destination_name'])\n if 'cat_name' in context:\n context['cat_name_under'] = camel_to_under(context['cat_name'])\n context['cat_name_lower'] = context['cat_name'].lower()\n context['cat_name_mixed'] = camel_to_mixed(context['cat_name'])\n context['cat_name_plural'] = make_plural(context['cat_name'])\n context['cat_name_plural_under'] = camel_to_under(context['cat_name_plural'])\n context['cat_name_plural_lower'] = context['cat_name_plural'].lower()\n context['cat_name_plural_mixed'] = camel_to_mixed(context['cat_name_plural'])\n if 'return_cat_name' in context:\n context['return_cat_name_under'] = camel_to_under(context['return_cat_name'])\n context['return_cat_name_lower'] = context['return_cat_name'].lower()\n context['return_cat_name_mixed'] = camel_to_mixed(context['return_cat_name'])\n if 'Proxy' in context['interface_name']:\n context['non_proxy_interface_name'] = ''.join(context['interface_name'].split('Proxy'))\n if ('return_pkg' in context and 'return_module' in context and\n context['package_name'] == context['return_pkg'] and\n context['module_name'] == context['return_module']):\n context['import_str'] = ''\n elif ('package_name' in context and 'return_pkg' in context and\n 'return_type' in context and 'return_module' in context):\n context['import_str'] = '{0}from ..{1}.{2} import {3}\\n'.format(self._dind,\n context['return_implpkg_name'],\n context['return_module'],\n context['return_type'])\n\n if 'method_name' in context and context['method_name'].startswith('can_'):\n context['func_name'] = context['method_name'].split('_')[1]\n if 'method_name' in context:\n context['method_session_name'] = context['method_name'].replace('get_', '')\n if 'syntax' in context:\n context['syntax_under'] = syntax_to_under(context['syntax'])\n\n # Special one for services test builder:\n if self._is_manager_session(interface, self.package['name']):\n context['svc_mgr_or_catalog'] = 'svc_mgr'\n else:\n context['svc_mgr_or_catalog'] = 'catalog'\n return context","title":""},{"docid":"561dcd63b2767d1a15cbf024f86b00d6","score":"0.5176718","text":"def generate_docstring(args_dict: Dict[str, str], signature: inspect.Signature,\n method_docstring: str) -> str:\n try:\n parsed_docstring = docstring_parser.parse(method_docstring)\n except ValueError:\n # If failed to parse docstring use the origional instead\n # TODO Log Warning that parsing docstring failed.\n return method_docstring\n\n doc = f'{parsed_docstring.short_description}\\n'\n if parsed_docstring.long_description:\n doc += f'{parsed_docstring.long_description}\\n'\n if args_dict:\n doc += '\\nArgs:\\n'\n for key, val in args_dict.items():\n formated_description = val.replace('\\n', '\\n ')\n doc = doc + f' {key}:\\n {formated_description}\\n'\n\n if parsed_docstring.returns:\n formated_return = parsed_docstring.returns.description.replace(\n '\\n', '\\n ')\n doc += 'Returns:\\n'\n doc += f' {formated_return}\\n'\n\n if parsed_docstring.raises:\n doc += 'Raises:\\n'\n raises_dict = {p.type_name: p.description for p in parsed_docstring.raises}\n for key, val in raises_dict.items():\n formated_description = val.replace('\\n', '\\n ')\n doc = doc + f' {key}:\\n {formated_description}\\n'\n return doc","title":""},{"docid":"f11b8261fd73bccb842821857a746afa","score":"0.5170921","text":"def get_ctor_arguments(clazz):\n args = ['self']\n for C in clazz.__mro__:\n if '__init__' in C.__dict__:\n args += inspect.getfullargspec(C).args[1:]\n args += inspect.getfullargspec(C).kwonlyargs\n return args","title":""},{"docid":"9510056b121c8cb53f0582bb461c8b77","score":"0.51686615","text":"def args(self):","title":""},{"docid":"a312b26963f59c4c73994700dfa2e256","score":"0.5168527","text":"def GetAttributeFlags(arg_data, arg_name, method, shared_resource_args):\n name = GetFlagName(arg_name)\n resource_arg = arg_data.GenerateResourceArg(\n method, name, shared_resource_args).GetInfo(name)\n return resource_arg.GetAttributeArgs()[:-1]","title":""},{"docid":"efc0c367486b91740950a549dcd09a9a","score":"0.5161594","text":"def find_args(func_list, **kwargs):\n if not isinstance(func_list, list):\n func_list = [func_list]\n enabled_nest = [\n list(signature(func).parameters.keys()) for func in func_list\n ]\n enabled_set = set(sum(enabled_nest, list()))\n enabled_set = enabled_set - set([\"self\", \"cls\"])\n return {k: v for (k, v) in kwargs.items() if k in enabled_set}","title":""},{"docid":"c575c1ab780711f644724e7e95ddb240","score":"0.51575077","text":"def get_method_list(self):\n return self.method_list","title":""},{"docid":"e9ce3247b3f8c6d513ceae43b43a847f","score":"0.5155015","text":"def args(self) -> Sequence[str]:\n return pulumi.get(self, \"args\")","title":""},{"docid":"e9ce3247b3f8c6d513ceae43b43a847f","score":"0.5155015","text":"def args(self) -> Sequence[str]:\n return pulumi.get(self, \"args\")","title":""},{"docid":"e9ce3247b3f8c6d513ceae43b43a847f","score":"0.5155015","text":"def args(self) -> Sequence[str]:\n return pulumi.get(self, \"args\")","title":""},{"docid":"e9ce3247b3f8c6d513ceae43b43a847f","score":"0.5155015","text":"def args(self) -> Sequence[str]:\n return pulumi.get(self, \"args\")","title":""},{"docid":"3f36a5b3545b4f186ade5a77937a5e3c","score":"0.5148317","text":"def call_args(self):\n return self._call_args","title":""},{"docid":"0a0c35c6830b131a77df69e1364cf708","score":"0.5131882","text":"def get_args(typ):\n try:\n return typing.get_args(typ) # python>=3.8 typing module has get_args.\n except AttributeError:\n return typing_inspect.get_args(typ)","title":""},{"docid":"f9869ced41645fb8c7fa933dc08cefc7","score":"0.51318437","text":"def get_args():\n usage = inspect.getdoc(handle_command_line)\n parser = argparse.ArgumentParser(usage=usage)\n jaraco.logging.add_arguments(parser)\n Command.add_subparsers(parser)\n return parser.parse_args()","title":""},{"docid":"7051201d318248ed529798fb9e1a6025","score":"0.5129526","text":"def _extract_parameters_names(handler, parameters_count, method):\n if parameters_count == 0:\n return []\n\n parameters = [\"{?}\" for _ in range(parameters_count)]\n\n method_handler = getattr(handler, method.lower())\n args = _try_extract_args(method_handler)\n\n for i, arg in enumerate(args):\n if set(arg) != {\"_\"} and i < len(parameters):\n parameters[i] = arg\n\n return parameters","title":""},{"docid":"e6ea77c33f1ccaddb0df4413f53f9031","score":"0.51292604","text":"def _arguments(self, attribute: Any) -> AnyStr:\n\n try:\n sig = signature(attribute)\n attrs = dict(sig.parameters)\n\n except ValueError:\n attrs = None\n except TypeError:\n attrs = None\n\n attrs = [str(attr) for attr in dict(attrs or []).values()]\n return self._delimit_argument_list(attrs)","title":""},{"docid":"79bacc3f2766a5e4be3aeb5abc86601a","score":"0.5119526","text":"def add_arguments(self, parser):","title":""},{"docid":"d5734b4697babef023ee72180964b0ab","score":"0.5118895","text":"def args(self):\n return self._args_[: self.nargs()]","title":""},{"docid":"35906cc25249d14186093cc26fdf16cc","score":"0.5114649","text":"def add_arguments(parser):\n return","title":""},{"docid":"e7899cca0429de554d7ccbd00eb4b263","score":"0.5110981","text":"def positionalArgNames(self):\n return [param.name for param in self.parameters if param.kind.name in (\"POSITIONAL_ONLY\", \"POSITIONAL_OR_KEYWORD\", \"VAR_POSITIONAL\")]","title":""},{"docid":"53dbb3aecf48749596eb54505e1c9fe7","score":"0.5110965","text":"async def _parse_arguments(self, ctx: ApplicationContext) -> None:\n raise NotImplementedError","title":""},{"docid":"7f7d5a339372a9ceebd32b4aeeac2099","score":"0.5107883","text":"def get_args(self):\n return self.args","title":""},{"docid":"7f7d5a339372a9ceebd32b4aeeac2099","score":"0.5107883","text":"def get_args(self):\n return self.args","title":""},{"docid":"7f7d5a339372a9ceebd32b4aeeac2099","score":"0.5107883","text":"def get_args(self):\n return self.args","title":""},{"docid":"6752c194a9407cafeb10467f8dc645c5","score":"0.5102942","text":"def foo(x: int, y: str, z: object = None) -> list:\n # sphinx style docstring","title":""},{"docid":"6c6066aaf43babd7a05b5b1c71f3e4c9","score":"0.5098302","text":"def cmd_args():\n if argparse_error:\n raise argparse_error\n parser = argparse.ArgumentParser()\n # named arguments\n parser.add_argument('--ikey', required=True)\n parser.add_argument('--skey', required=True)\n parser.add_argument('--host', required=True)\n parser.add_argument('--method', required=True)\n parser.add_argument('--path', required=True)\n parser.add_argument('--ca')\n parser.add_argument('--sig-version', type=int, default=2)\n parser.add_argument('--sig-timezone', default='UTC')\n parser.add_argument('--show-header', action='append', default=[])\n # optional positional arguments are used for GET/POST params, name=val\n parser.add_argument('param', nargs='*')\n args = parser.parse_args()\n param = collections.defaultdict(list)\n for p in args.param:\n try:\n (k, v) = p.split('=', 1)\n except ValueError:\n sys.exit('Error: Positional argument %s is not '\n 'in key=value format.' % (p,))\n param[k].append(v)\n for (k, v) in param.items():\n if len(v) != 1:\n # Each parameter must have a single value. No Duo API\n # endpoints have arguments that accept multiple values for\n # the same parameter. Thus, the call() and sign() provided\n # here can't handle a list of values for the same\n # parameter.\n raise NotImplementedError\n (v,) = v\n param[k] = v\n del args.param\n for (k, v) in param.items():\n setattr(args, k, v)\n return args","title":""},{"docid":"473b66cd176e200e465b48963c59621a","score":"0.5085734","text":"def Args(cls, parser):\n flags.ListIntegrationsOfService(parser)\n flags.ListIntegrationsOfType(parser)","title":""}],"string":"[\n {\n \"docid\": \"7cb01420f7c7a316d7caf8d70c0e9a07\",\n \"score\": \"0.7419464\",\n \"text\": \"def get_documented_arguments(method_ref, as_params_collection=False, spilt_or=False):\\n return MethodHelper.get_arguments_documentation(method_ref, as_params_collection, spilt_or, ignore_non_args=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4be9f68738702f4a59bfbe7e1a9c8e6\",\n \"score\": \"0.6316271\",\n \"text\": \"def build_arguments(self):\\n if not self.arguments:\\n self.method_format[\\\"argument_format\\\"] = \\\"\\\"\\n self.method_format[\\\"break_before_close\\\"] = \\\"\\\"\\n self.method_format[\\\"empty_line\\\"] = \\\"\\\"\\n return\\n\\n config = self.config.get(\\\"arguments\\\")\\n formatted_args = \\\"{prefix} {type} {name}: {doc}\\\"\\n\\n title = self.argument_format.get(\\\"title\\\")\\n if title:\\n underline = \\\"-\\\" * len(title)\\n self.argument_format[\\\"title\\\"] = (\\n \\\"{}\\\\n{}\\\\n\\\".format(title, underline)\\n if config.get(\\\"underline\\\")\\n else \\\"{}\\\\n\\\".format(title)\\n )\\n\\n result = []\\n\\n if self.arguments: # if len(self.arguments) > 0\\n for argument_details in self.arg_docstring:\\n argument_details[\\\"prefix\\\"] = self.argument_format.get(\\\"prefix\\\")\\n result.append(\\n self.fmt.format(formatted_args, **argument_details).strip()\\n )\\n\\n self.argument_format[\\\"body\\\"] = \\\"\\\\n\\\".join(result)\\n self.method_format[\\\"argument_format\\\"] = self.fmt.format(\\n \\\"{title}{body}\\\", **self.argument_format\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"566cfb80a9e35d19c874ed1687e115a0\",\n \"score\": \"0.6307494\",\n \"text\": \"def _package_arguments(valid_method_spec, fn_args):\\n arguments = []\\n for arg_type, arg_val in zip(valid_method_spec['arguments'], fn_args):\\n if isinstance(arg_val, JavaObjectShadow):\\n arguments.append(_serialize_arg(arg_val))\\n else:\\n arguments.append(_serialize_arg(_JAVA_TYPE_NAME_TO_PYTHON_TYPE[arg_type](arg_val)))\\n return arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25d4a29521169eb96107ca791b20be87\",\n \"score\": \"0.6212363\",\n \"text\": \"def get_args_list(self, **kwargs): # real signature unknown; restored from __doc__\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3c64e2b02ec120f22bf6a09487e8202\",\n \"score\": \"0.6070744\",\n \"text\": \"def list_methods(self):\\r\\n return [(method, doc) for method, (function, returns, args, doc) in self.methods.items()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f3e79ab7ad1758b2480d61e0e03a666\",\n \"score\": \"0.6029665\",\n \"text\": \"def methodLinkingTexts(self, method: widlparser.OperationRest) -> list[str]:\\n for i, arg in enumerate(method.arguments or []):\\n assert isinstance(arg, widlparser.constructs.Argument)\\n if arg.optional or arg.variadic:\\n optStart = i\\n break\\n else:\\n optStart = None\\n\\n texts = []\\n\\n if optStart is not None:\\n prefixes = [t.cast(str, method.name)]\\n if method.name == \\\"constructor\\\":\\n prefixes.append(t.cast(str, method.parent.name))\\n for i in range(optStart, len(method.arguments)):\\n argText = \\\", \\\".join(t.cast(str, arg.name) for arg in list(method.arguments)[:i])\\n for prefix in prefixes:\\n texts.append(prefix + \\\"(\\\" + argText + \\\")\\\")\\n\\n texts.append(t.cast(str, method.normal_name))\\n if method.name == \\\"constructor\\\":\\n texts.append(t.cast(str, method.parent.name) + t.cast(str, method.normal_name)[11:])\\n return list(reversed(texts))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a395007b99ec43aeb7632478046c7206\",\n \"score\": \"0.599155\",\n \"text\": \"def args(self):\\n return [self.as_basic()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8e86f573be958390d79677279423841\",\n \"score\": \"0.5954009\",\n \"text\": \"def positional_extra(self):\\n try:\\n return inspect.getfullargspec(self.callableObject).args # Cannot handle @deco_cached\\n except TypeError:\\n return self.positionalArgNames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4e83ebdf0effa0e6ff2fe0b14f169a4\",\n \"score\": \"0.5908483\",\n \"text\": \"def get_function_args(fn, *args, **kwargs):\\n # Convert tuple of positional parameters to dictionary\\n arg_names = list(signature(fn).parameters.keys())\\n args_ = dict(zip(arg_names[:len(args)], args))\\n\\n # Create metadata dict with default values\\n metadata = {\\n arg: None if param.default is _empty else param.default\\n for arg, param in signature(fn).parameters.items()}\\n\\n # Update metadata dict with positional and keyword parameters\\n metadata.update(args_)\\n metadata.update(kwargs)\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b9f96bc9ed4d15027251595e6805cba\",\n \"score\": \"0.59037286\",\n \"text\": \"def known_arguments(self):\\n return self._arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c20b6b00fcb46b0938e8bcad1047053\",\n \"score\": \"0.58861834\",\n \"text\": \"def getargspec_init(method):\\n try:\\n return compat.inspect_getfullargspec(method)\\n except TypeError:\\n if method is object.__init__:\\n return ([\\\"self\\\"], None, None, None)\\n else:\\n return ([\\\"self\\\"], \\\"args\\\", \\\"kwargs\\\", None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec9f7576643d9d0dd220a72cc8e97a51\",\n \"score\": \"0.5839742\",\n \"text\": \"def find_args(self):\\n raise NotImplementedError('find_args must be implemented in a subclass')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c7999f66f1835463c6ac3c7ac093b4a\",\n \"score\": \"0.5767862\",\n \"text\": \"def _getArguments(self, func):\\n # this returns (args, varargs, keywords, defaults)\\n argspec = list(inspect.getargspec(func))\\n if argspec[0] and argspec[0][0] == 'self':\\n argspec[0] = argspec[0][1:]\\n args, defaults = argspec[0], argspec[3]\\n defaults = [] if defaults is None else defaults\\n argspec[0] = args[0:len(args) - len(defaults)]\\n argspec[3] = args[len(args) - len(defaults):]\\n return argspec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ec6f530e32d4b7111f550b538b9d7a2\",\n \"score\": \"0.573449\",\n \"text\": \"def args(self):\\n return self.find_args()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a8d1aad45909d1ccecb3320436f39f5\",\n \"score\": \"0.571819\",\n \"text\": \"def getArgs(self):\\n raise Exception(\\\"Not yet implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ba9f8779882609c274de78f5a2f121d\",\n \"score\": \"0.57062393\",\n \"text\": \"def add_arguments(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"facdd061edcaeb5c0adac0d109770fe1\",\n \"score\": \"0.5649677\",\n \"text\": \"def arguments(self):\\n\\t\\treturn self._arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9773424278eb85685ab1061bc1cedcd\",\n \"score\": \"0.563587\",\n \"text\": \"def parse_user_args(method, *args, **kwargs):\\n sig = inspect.signature(method)\\n if 'self' in sig.parameters or 'cls' in sig.parameters:\\n ba = sig.bind(method, *args, **kwargs)\\n ba.apply_defaults()\\n params = list(sig.parameters.keys())[1:]\\n else:\\n ba = sig.bind(*args, **kwargs)\\n ba.apply_defaults()\\n params = list(sig.parameters.keys())\\n\\n user_filled_args = [ba.arguments.get(arg_value) for arg_value in params]\\n return user_filled_args, ba.arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7ea3aed9a2ef067b40141b3743bdd3a\",\n \"score\": \"0.5631804\",\n \"text\": \"def class_or_method_args():\\n return [x for x in COMMON_DATA_ARGS]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0abfe9ea5daa8af5b9ef0e575525d8d\",\n \"score\": \"0.56316894\",\n \"text\": \"def methodSignature(self, req, method):\\n p = self.get_method(method)\\n req.perm.assert_permission(p.permission)\\n return [','.join([RPC_TYPES[x] for x in sig]) for sig in p.xmlrpc_signatures()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12b7aa352e64bc161f3260bb1b545b8e\",\n \"score\": \"0.56273514\",\n \"text\": \"def _get_argnames(f) -> List[str]:\\n try:\\n return list(inspect.signature(f).parameters.keys())\\n except AttributeError:\\n return inspect.getargspec(f).args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ace6cf79f3f9fc40d3fcf2f76fad867e\",\n \"score\": \"0.56271076\",\n \"text\": \"def arguments():\\n from inspect import getargvalues, stack\\n posname, kwname, args = getargvalues(stack()[1][0])[-3:]\\n posargs = args.pop(posname, [])\\n args.update(args.pop(kwname, []))\\n return args, posargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa986fcd7a60a8444975a9c103e51ac\",\n \"score\": \"0.5612015\",\n \"text\": \"def arguments_from_docstring(doc):\\n\\n if doc is None:\\n raise RuntimeError('__doc__ is None')\\n\\n doc = doc.lstrip()\\n\\n # care only the firstline\\n # docstring can be long\\n line = doc.split('\\\\n', 1)[0] # get the firstline\\n if line.startswith(\\\"('...',)\\\"):\\n line = doc.split('\\\\n', 2)[1] # get the second line\\n p = re.compile(r'^[\\\\w|\\\\s.]+\\\\(([^)]*)\\\\).*')\\n # 'min(iterable[, key=func])\\\\n' -> 'iterable[, key=func]'\\n sig = p.search(line)\\n if sig is None:\\n return []\\n # iterable[, key=func]' -> ['iterable[' ,' key=func]']\\n sig = sig.groups()[0].split(',')\\n ret = []\\n for s in sig:\\n # get the last one after all space after =\\n # ex: int x= True\\n tmp = s.split('=')[0].split()[-1]\\n # clean up non _+alphanum character\\n tmp = ''.join([x for x in tmp if x.isalnum() or x == '_'])\\n ret.append(tmp)\\n # re.compile(r'[\\\\s|\\\\[]*(\\\\w+)(?:\\\\s*=\\\\s*.*)')\\n # ret += self.docstring_kwd_re.findall(s)\\n ret = list(filter(lambda x: x != '', ret))\\n\\n if len(ret) == 0:\\n raise RuntimeError('Your doc is unparsable\\\\n' + doc)\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8afb182e6ca988b90b31a466648f107d\",\n \"score\": \"0.5591545\",\n \"text\": \"def arguments(self):\\n if self._format in (self._FORM_ARG_LIST, self._FORM_NAMED_ARG_LIST):\\n return self._arguments\\n raise ValueError('\\\"{}\\\" does not have arguments.'.format(str(self)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6af37e85b0779c20b42871eac5bfecec\",\n \"score\": \"0.5578555\",\n \"text\": \"def arguments(self):\\n self._arguments, self._unknown_arguments = self.parser.parse_known_args()\\n return self._arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be70eb5053160547b41df4f7355b70f9\",\n \"score\": \"0.5557773\",\n \"text\": \"def get_docs(module, method):\\n response = {'status':'success','data':{}}\\n response['data']['docstring'] = []\\n\\n #Extract the method from the method dict\\n method = funcs[module][method]\\n\\n #Introspect the docs\\n docs = inspect.getdoc(method)\\n for l in docs.split('\\\\n'):\\n response['data']['docstring'].append(l)\\n return jsonify(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a625e4829978fcec0596e5e9a0fa1bf0\",\n \"score\": \"0.5545234\",\n \"text\": \"def parameters(self):\\n return list(inspect.signature(self.callableObject).parameters.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"378b35ecbd9f4e599b1f396155eb05a5\",\n \"score\": \"0.55394685\",\n \"text\": \"def extract_docs():\\n\\n methods = []\\n\\n def _key(entry):\\n return\\n\\n sorted_entries = sorted(Client.__dict__.items(), key=lambda x: x[0])\\n\\n tree = {}\\n meta_key = '__doc_meta__'\\n\\n for attr_name, attr_value in sorted_entries:\\n\\n if not hasattr(attr_value, meta_key):\\n continue\\n\\n func = attr_value\\n meta = getattr(func, meta_key)\\n\\n arg_spec = inspect.getargspec(func)\\n if arg_spec[0] and arg_spec[0][0] in ('cls', 'self'):\\n del arg_spec[0][0]\\n\\n func_name = func.__name__\\n func_spec = func_name + inspect.formatargspec(*arg_spec)\\n\\n doc = textwrap.dedent((func.__doc__ or ''))\\n doc = indent(doc, n=3)\\n\\n func_desc_lines = []\\n for i, line in enumerate(func.__doc__.splitlines()):\\n if i == 0:\\n continue\\n func_desc_lines.append(line.strip())\\n if not line:\\n break\\n func_desc = ' '.join(func_desc_lines)\\n\\n doc = textwrap.dedent(\\n \\\"\\\"\\\"\\\\\\n {func_name}\\n {func_name_line}\\n\\n {func_desc}\\n :py:func:`nano.rpc.Client.{func_spec} <nano.rpc.Client.{func_name}>`\\n\\n .. .. py:function:: nano.rpc.Client.{func_spec}\\n\\n .. {doc}\\n \\\"\\\"\\\"\\n ).format(\\n func_spec=func_spec,\\n func_name_line='-' * len(func_name),\\n func_name=func_name,\\n func_desc=func_desc,\\n doc=doc,\\n )\\n\\n categories = meta['categories']\\n for category in categories:\\n tree.setdefault(category, []).append(doc)\\n\\n directory = 'rpc/methods'\\n for file in os.listdir(directory):\\n if file.endswith('.rst'):\\n os.unlink(os.path.join(directory, file))\\n\\n for category, func_docs in sorted(tree.items(), key=lambda x: x[0]):\\n category = category or 'other'\\n file_path = os.path.join(directory, category) + '.rst'\\n with open(file_path, 'w') as docfile:\\n docfile.write('.. _%s-ref:\\\\n' % category + '\\\\n')\\n title = '{category}'.format(category=category.capitalize())\\n docfile.write('%s\\\\n' % title)\\n docfile.write('%s\\\\n' % (len(title) * '='))\\n docfile.write('\\\\n')\\n\\n for func_doc in func_docs:\\n docfile.write(func_doc + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d966e6b76156f0506d0e15b3b9808a5b\",\n \"score\": \"0.5517862\",\n \"text\": \"def arguments(self):\\n return self._arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d966e6b76156f0506d0e15b3b9808a5b\",\n \"score\": \"0.5517862\",\n \"text\": \"def arguments(self):\\n return self._arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d966e6b76156f0506d0e15b3b9808a5b\",\n \"score\": \"0.5517862\",\n \"text\": \"def arguments(self):\\n return self._arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d966e6b76156f0506d0e15b3b9808a5b\",\n \"score\": \"0.5517862\",\n \"text\": \"def arguments(self):\\n return self._arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14c157167849a4d0168a0c1d4a87c6a5\",\n \"score\": \"0.5479797\",\n \"text\": \"def all_args(self) -> Iterator[str]:\\n yield from _arg_names(self._path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93380744d90ef76b072748a3761e231d\",\n \"score\": \"0.54776716\",\n \"text\": \"def get_method_arguments(mappings: dict, **kwargs):\\n \\n optional_args = {}\\n for key, value in mappings.items(): \\n try:\\n if isinstance(value, ArgumentValueKind):\\n optional_args[key] = create_value(kwargs[key], value)\\n elif isinstance(value, tuple):\\n data = [create_value(item, value[1]).format() for item in kwargs[key]]\\n optional_args[key] = create_value(data, value[0])\\n elif isinstance(value, dict):\\n optional_args[key] = get_method_arguments(value, **(kwargs[key]))\\n except KeyError:\\n # Ignore if no kwargs found\\n continue\\n for arg in optional_args:\\n kwargs[arg] = optional_args[arg]\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e557de72abfc7012fcd93c104b2a1c2\",\n \"score\": \"0.5468677\",\n \"text\": \"def get_documentation_extras(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ec2452d75a4808204eec89816adef94\",\n \"score\": \"0.5457662\",\n \"text\": \"def methodHelp(self, req, method):\\n p = self.get_method(method)\\n req.perm.assert_permission(p.permission)\\n return '\\\\n'.join((p.signature, '', p.description))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"707ad1f3bbf19756460e313937956f36\",\n \"score\": \"0.5453258\",\n \"text\": \"def invocation_metadata(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f1d33474874e4fafa1aef492f2d2008\",\n \"score\": \"0.544153\",\n \"text\": \"def extract_arguments(self):\\n raise Exception('Not implemented exception')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3ecbde6be8400055455d81e0148237b\",\n \"score\": \"0.54389393\",\n \"text\": \"def get_docstring_param_info_list(docstring: str) -> List[Dict[str, str]]:\\n if docstring == '':\\n return []\\n if not _parameters_exists_in_docstring(docstring=docstring):\\n return []\\n splitted_param_doc_list: List[str] = get_splitted_param_doc_list(\\n docstring=docstring\\n )\\n param_info_list: List[Dict[str, str]] = []\\n for splitted_param_doc in splitted_param_doc_list:\\n arg_name: str = _get_docstring_var_name(var_doc=splitted_param_doc)\\n type_name: str = _get_docstring_type_name(var_doc=splitted_param_doc)\\n default_val: str = _get_docstring_default_value(\\n var_doc=splitted_param_doc)\\n description: str = _get_docstring_var_description(\\n var_doc=splitted_param_doc)\\n param_info_list = _append_param_info_to_list(\\n param_info_list=param_info_list,\\n arg_name=arg_name,\\n type_name=type_name,\\n default_val=default_val,\\n description=description)\\n return param_info_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d95020dcbe1b4647270b1d6b4b22325\",\n \"score\": \"0.5430044\",\n \"text\": \"def get_arguments():\\n opts = dict()\\n parser = Parser()\\n for arg in vars(parser.args):\\n opts[arg] = getattr(parser.args, arg)\\n return opts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0e4bf0b8d23932b8795e58f44a00f56\",\n \"score\": \"0.54173857\",\n \"text\": \"def op_args(self) -> List[OpArg]:\\n return self._op_args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adab8916abc9acc709d22225d85bbf1a\",\n \"score\": \"0.54167056\",\n \"text\": \"def get_method_params(self, method, **kwargs):\\n return getattr(self.adapter, \\\"params_\\\" + method)(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f36a25f42c1fceef46c25a1ab594345d\",\n \"score\": \"0.5411934\",\n \"text\": \"def getdoc(method):\\n help = getattr(method, 'help', None)\\n if help is None:\\n help = method.__doc__\\n doc = inspect.getdoc(MethodHelp(help))\\n if doc is None:\\n return ''\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fcaaae107341a3f9167ffb3ac1bc265\",\n \"score\": \"0.5388873\",\n \"text\": \"def args(self) -> List[str]: # noqa: D401\\n return self._args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b48178472eb3ff08bbd3200e8292f04\",\n \"score\": \"0.53886926\",\n \"text\": \"def reqArguments(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42e54c5cc4057d8a64f90bd3818ecfaa\",\n \"score\": \"0.53829575\",\n \"text\": \"def get_function_args(self):\\n return self._args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09621a5ceca71a220addac031afecaeb\",\n \"score\": \"0.5371842\",\n \"text\": \"def get_arguments(self) -> Iterable[WorkflowArgument]:\\n return map(\\n lambda argNode: WorkflowArgument(argNode, self.document.getroot()),\\n self.document.iterfind(\\n \\\"./x:Members/x:Property\\\",\\n namespaces=self.default_namespaces))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5dd3771e4e39026d07513ef2a8d82be\",\n \"score\": \"0.53714263\",\n \"text\": \"def flatten_signature_kwargs(func, loc):\\n kwargs_name = get_signature_kwargs_key(func)\\n # remove unwanted metadata for class methods\\n attributes = get_signature_locals(func, loc)\\n if kwargs_name:\\n attributes.pop(kwargs_name)\\n attributes.update(loc[kwargs_name])\\n return attributes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93dceca3fd880d7f5ceafc381922da9c\",\n \"score\": \"0.5369019\",\n \"text\": \"def load_all_method_args(language, API=False):\\n module_names = []\\n modules_args = {}\\n # get module names\\n for _lib in glob(os.path.dirname(inspect.getfile(lib)) + '/*/*/engine.py'):\\n _lib = _lib.replace('/', '.').replace('\\\\\\\\', '.')\\n if '.lib.brute.' in _lib or '.lib.scan.' in _lib or '.lib.vuln.' in _lib:\\n _lib = 'lib.' + _lib.rsplit('.lib.')[-1].rsplit('.py')[0]\\n if _lib not in module_names:\\n module_names.append(_lib)\\n # get args\\n res = \\\"\\\"\\n for imodule in module_names:\\n try:\\n extra_requirements_dict = getattr(__import__(imodule, fromlist=['extra_requirements_dict']),\\n 'extra_requirements_dict')\\n except:\\n __die_failure(messages(language, 112).format(imodule))\\n imodule_args = extra_requirements_dict()\\n modules_args[imodule] = []\\n for imodule_arg in imodule_args:\\n if API:\\n res += imodule_arg + \\\"=\\\" + \\\",\\\".join(map(str, imodule_args[imodule_arg])) + \\\"\\\\n\\\"\\n modules_args[imodule].append(imodule_arg)\\n if API:\\n return res\\n for imodule in modules_args:\\n info(imodule.rsplit('.')[2] + '_' + imodule.rsplit('.')[1] + ' --> '\\n + \\\", \\\".join(modules_args[imodule]))\\n return module_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab308975d84d683b42993a8ae5f843e\",\n \"score\": \"0.5341901\",\n \"text\": \"def api_arg_spec(fn):\\n return signature(getattr(API, fn)).parameters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deaa82be8d820cf641887107fe2954a1\",\n \"score\": \"0.5330546\",\n \"text\": \"def args(self):\\r\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd4fa0abcf40ce3c423e2c8d16cf9ab6\",\n \"score\": \"0.5330223\",\n \"text\": \"def extract_permission_by_apicall(self, method):\\n perm_methods = []\\n def match_api_call(line):\\n for api in self.api_mapping:\\n if api.lower() in line.lower():\\n return self.api_mapping[api]\\n return None\\n for line in method[1:-1]:\\n requested_perm = match_api_call(line) \\n if requested_perm:\\n perm_methods.extend(requested_perm)\\n return perm_methods\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f78b58e36321904014b88656affc7f1\",\n \"score\": \"0.5315243\",\n \"text\": \"def add_arguments(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7b3fd24cf265d652698e834ad0c8eec\",\n \"score\": \"0.5299918\",\n \"text\": \"def apply_to_each_input(method):\\n @wraps(method)\\n def decorator(self, *args, attr=None, **kwargs):\\n \\\"\\\"\\\"Returned decorator.\\\"\\\"\\\"\\n is_list = True\\n if isinstance(attr, str):\\n attr = (attr, )\\n is_list = False\\n elif attr is None:\\n attr = self.attributes\\n if not self.attributes:\\n return None\\n\\n res = []\\n for att in attr:\\n res.append(method(self, *args, attr=att.upper(), **kwargs))\\n if isinstance(res[0], self.__class__):\\n return self\\n return res if is_list else res[0]\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0432697be323b9ac94dce4a8bdc501f\",\n \"score\": \"0.52952546\",\n \"text\": \"def cmd_args(*arguments: _Argument) -> Any:\\n def decorate(obj):\\n if hasattr(obj, ATTR_CMD_ARGS):\\n raise RuntimeError(f'{ATTR_CMD_ARGS} has been set on {obj} already')\\n _add_arguments = _add_arguments_factory(arguments)\\n _verify_arguments(_add_arguments, obj)\\n if isinstance(obj, type):\\n fn = lambda self, parser: _add_arguments(parser)\\n else:\\n fn = _add_arguments\\n setattr(obj, ATTR_CMD_ARGS, fn)\\n return obj\\n return decorate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"382788cada031f24037e19616a660577\",\n \"score\": \"0.5294809\",\n \"text\": \"def args(self):\\n return self._args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bce124f1e27f8716edffecf6f1392492\",\n \"score\": \"0.5278355\",\n \"text\": \"def args(self):\\n return self._args_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20d1af34f1e12cae838c86eacb4bc98f\",\n \"score\": \"0.5274308\",\n \"text\": \"def getargs(self, *args):\\n return args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c706bb7d4fcc8cd3279a6e1897ae2ddc\",\n \"score\": \"0.5273934\",\n \"text\": \"def docstring(self):\\n docs = []\\n for func in set(self.values()):\\n try:\\n sig = inspect.signature(func)\\n except ValueError:\\n sig = ''\\n doc = func.__doc__ or ''\\n docs.append(f'{func.__name__}{sig}\\\\n {doc}')\\n return '\\\\n\\\\n'.join(docs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cba3db7002e8bf956a95dce100624f1\",\n \"score\": \"0.52726704\",\n \"text\": \"def args(self) -> Sequence[str]:\\n return self._args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af06529e1ec3a131752635e0b8319010\",\n \"score\": \"0.52671695\",\n \"text\": \"def read_arguments(parser):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16505dae9029c8a5349d86f1a0567615\",\n \"score\": \"0.5254659\",\n \"text\": \"def get_function_args(cls, func):\\n return list(cls._get_function_args(func))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d085e507f31458d2e6fc45c95e3b371b\",\n \"score\": \"0.5250189\",\n \"text\": \"def iter_args(self, args):\\n for i, posarg in enumerate(args.posargs):\\n if i < len(self.param_names):\\n name = self.param_names[i]\\n yield (name, posarg, self.annotations.get(name))\\n elif self.varargs_name and self.varargs_name in self.annotations:\\n varargs_type = self.annotations[self.varargs_name]\\n formal = varargs_type.ctx.convert.get_element_type(varargs_type)\\n yield (argname(i), posarg, formal)\\n else:\\n yield (argname(i), posarg, None)\\n for name in sorted(args.namedargs):\\n namedarg = args.namedargs[name]\\n if name in self.param_names[:self.posonly_count]:\\n formal = None\\n else:\\n formal = self.annotations.get(name)\\n if formal is None and self.kwargs_name:\\n kwargs_type = self.annotations.get(self.kwargs_name)\\n if kwargs_type:\\n formal = kwargs_type.ctx.convert.get_element_type(kwargs_type)\\n yield (name, namedarg, formal)\\n if self.varargs_name is not None and args.starargs is not None:\\n yield (self.varargs_name, args.starargs,\\n self.annotations.get(self.varargs_name))\\n if self.kwargs_name is not None and args.starstarargs is not None:\\n yield (self.kwargs_name, args.starstarargs,\\n self.annotations.get(self.kwargs_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af7ede988863a68de97304e6bf995beb\",\n \"score\": \"0.52406657\",\n \"text\": \"def request_args(self):\\n return [getattr(self, k) for k in self.request_keys]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d0054c0d73a51ae2506dce2f3fb467b\",\n \"score\": \"0.52337515\",\n \"text\": \"def build_docstrings(self):\\n text = self.method_docstring or \\\"Missing Docstring!\\\"\\n self.method_format[\\\"method_docstring\\\"] = self.wrap_strings(text.split(\\\" \\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d70cf487dc2f43c6c3b2c36f43f2f051\",\n \"score\": \"0.52275515\",\n \"text\": \"def documentation(cls):\\r\\n for name, meth in sorted(cls.__dict__.items()):\\r\\n if name.startswith('_'):\\r\\n return\\r\\n yield name, meth.doc()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43eb5480672f2bea7023118c2725d00\",\n \"score\": \"0.5227063\",\n \"text\": \"def leadingArgNames(self):\\n leadingArgNames = []\\n for param in self.parameters:\\n if param.name == \\\"self\\\":\\n continue\\n\\n noDefault = param.default is inspect.Parameter.empty\\n includedKind = param.kind.name in (\\\"POSITIONAL_OR_KEYWORD\\\", \\\"POSITIONAL_ONLY\\\")\\n if not (noDefault and includedKind):\\n break\\n\\n leadingArgNames.append(param.name)\\n return leadingArgNames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f81ae369c17ee9a9b33b81ce518c3e1\",\n \"score\": \"0.5223203\",\n \"text\": \"def MacroAssemblerMethodArgs(self):\\n return \\\", \\\".join([\\n operand.GetArgumentType() + \\\" \\\" + operand.name\\n for operand in self.operands\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67c5e8c1c11e442ba6d66dcb5b910944\",\n \"score\": \"0.5205255\",\n \"text\": \"def help(method):\\n info = request('JSONRPC.Introspect', {'filter': {'id': method, 'type': 'method'}})\\n return info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d94319f2de0873f80a7b27c2443d57\",\n \"score\": \"0.5196424\",\n \"text\": \"def named_method_params(method, args, kwargs):\\n args_names = method.__code__.co_varnames[:method.__code__.co_argcount]\\n params = get_default_args(method)\\n args_dict = {**dict(zip(args_names, args)), **kwargs}\\n params.update(args_dict)\\n params.pop('self', None)\\n return params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a80d9bbb8af1159f5580c987f9860ee1\",\n \"score\": \"0.51957786\",\n \"text\": \"def class_or_method_args():\\n return [Flag(\\\"criterions\\\", dtype=Flag.TYPE.STRING,\\n default=None, help=\\\"A list of multiple criterions. Each element should be a dict like \\\"\\n \\\"\\\\\\\"{'criterion.class': '...', 'criterion.params': '...', 'alpha': 1.0}\\\\\\\",\\\"\\n \\\"where alpha denotes the weight of the corresponding criterion \\\"\\n \\\"and will be set to 1.0 by default.\\\")]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38bf48dc7ddceb02348aac63dc6f480e\",\n \"score\": \"0.51933914\",\n \"text\": \"def visit_arguments(self, node, parent):\\n vararg, kwarg = node.vararg, node.kwarg\\n newnode = nodes.Arguments(\\n vararg.arg if vararg else None, kwarg.arg if kwarg else None, parent\\n )\\n args = [self.visit(child, newnode) for child in node.args]\\n defaults = [self.visit(child, newnode) for child in node.defaults]\\n varargannotation = None\\n kwargannotation = None\\n posonlyargs = []\\n # change added in 82732 (7c5c678e4164), vararg and kwarg\\n # are instances of `_ast.arg`, not strings\\n if vararg:\\n if node.vararg.annotation:\\n varargannotation = self.visit(node.vararg.annotation, newnode)\\n vararg = vararg.arg\\n if kwarg:\\n if node.kwarg.annotation:\\n kwargannotation = self.visit(node.kwarg.annotation, newnode)\\n kwarg = kwarg.arg\\n kwonlyargs = [self.visit(child, newnode) for child in node.kwonlyargs]\\n kw_defaults = [\\n self.visit(child, newnode) if child else None for child in node.kw_defaults\\n ]\\n annotations = [\\n self.visit(arg.annotation, newnode) if arg.annotation else None\\n for arg in node.args\\n ]\\n kwonlyargs_annotations = [\\n self.visit(arg.annotation, newnode) if arg.annotation else None\\n for arg in node.kwonlyargs\\n ]\\n\\n posonlyargs_annotations = []\\n if PY38:\\n posonlyargs = [self.visit(child, newnode) for child in node.posonlyargs]\\n posonlyargs_annotations = [\\n self.visit(arg.annotation, newnode) if arg.annotation else None\\n for arg in node.posonlyargs\\n ]\\n type_comment_args = [\\n self.check_type_comment(child, parent=newnode) for child in node.args\\n ]\\n type_comment_kwonlyargs = [\\n self.check_type_comment(child, parent=newnode) for child in node.kwonlyargs\\n ]\\n type_comment_posonlyargs = []\\n if PY38:\\n type_comment_posonlyargs = [\\n self.check_type_comment(child, parent=newnode)\\n for child in node.posonlyargs\\n ]\\n\\n newnode.postinit(\\n args=args,\\n defaults=defaults,\\n kwonlyargs=kwonlyargs,\\n posonlyargs=posonlyargs,\\n kw_defaults=kw_defaults,\\n annotations=annotations,\\n kwonlyargs_annotations=kwonlyargs_annotations,\\n posonlyargs_annotations=posonlyargs_annotations,\\n varargannotation=varargannotation,\\n kwargannotation=kwargannotation,\\n type_comment_args=type_comment_args,\\n type_comment_kwonlyargs=type_comment_kwonlyargs,\\n type_comment_posonlyargs=type_comment_posonlyargs,\\n )\\n # save argument names in locals:\\n if vararg:\\n newnode.parent.set_local(vararg, newnode)\\n if kwarg:\\n newnode.parent.set_local(kwarg, newnode)\\n return newnode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea01ab37672e15f4727d24d718c2f6a0\",\n \"score\": \"0.51924455\",\n \"text\": \"def test_method_docs(self):\\n for func in dir(Base):\\n self.assertTrue(len(func.__doc__) > 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5981b488b6193bbbac3a75cee623a578\",\n \"score\": \"0.51831853\",\n \"text\": \"def _get_method_context(self, method, interface):\\n def construct_arg_context(arg_number, arg_type_full):\\n arg_type = self.last(arg_type_full).strip('[]')\\n arg_context = {\\n arg_number + '_type': arg_type,\\n arg_number + '_type_under': camel_to_under(arg_type),\\n arg_number + '_type_mixed': camel_to_mixed(arg_type),\\n arg_number + '_abcapp_name': self._app_name(package_name=get_pkg_name(arg_type_full.strip('[]')),\\n abstract=True),\\n arg_number + '_abcpkg_name': self._abc_pkg_name(package_name=get_pkg_name(arg_type_full.strip('[]')),\\n abc=False),\\n arg_number + '_module': self.get_interface_module(\\n self._abc_pkg_name(package_name=get_pkg_name(arg_type_full),\\n abc=False),\\n arg_type)\\n }\\n return arg_context\\n\\n context = {}\\n\\n if interface['shortname'] + '.' + method['name'] in self.patterns:\\n context = self.patterns[interface['shortname'] + '.' + method['name']]['kwargs']\\n\\n arg_list = []\\n for arg in method['args']:\\n arg_list.append(arg['var_name'])\\n context['arg_list'] = ', '.join(arg_list)\\n\\n if 'package_name' in context:\\n # Add keyword arguments to template kwargs that are particular\\n # to the json implementation\\n fixed_package_name = fix_reserved_word(self.package['name'], is_module=True)\\n context['app_name'] = self._app_name()\\n context['implpkg_name'] = self._abc_pkg_name(abc=False, reserved_word=False)\\n context['abcapp_name'] = self._app_name()\\n context['abcpkg_name'] = self._abc_pkg_name(abc=False)\\n context['interface_name_under'] = camel_to_under(context['interface_name'])\\n context['interface_name_dot'] = '.'.join(context['interface_name_under'].split('_')[:-1])\\n context['package_name_caps'] = self.replace(self.package['name'].title(), desired='')\\n context['package_name_upper'] = self.package['name'].upper()\\n context['package_name_replace'] = self.replace(self.package['name'])\\n context['package_name_replace_reserved'] = self.replace(fixed_package_name)\\n context['package_name_replace_upper'] = self.replace(self.package['name']).upper()\\n\\n if method['args']:\\n context['args_kwargs_or_nothing'] = '*args, **kwargs'\\n else:\\n context['args_kwargs_or_nothing'] = ''\\n\\n if context['interface_name_under'].endswith('_session'):\\n context['session_shortname_dot'] = '.'.join(context['interface_name_under'].split('_')[:-1])\\n\\n if 'arg0_type_full' in context:\\n context.update(construct_arg_context('arg0',\\n context['arg0_type_full']))\\n if 'arg1_type_full' in context:\\n context.update(construct_arg_context('arg1',\\n context['arg1_type_full']))\\n if 'arg2_type_full' in context:\\n context.update(construct_arg_context('arg2',\\n context['arg2_type_full']))\\n if 'arg3_type_full' in context:\\n context.update(construct_arg_context('arg3',\\n context['arg3_type_full']))\\n if 'arg0_object' in context:\\n context['arg0_object_under'] = camel_to_under(context['arg0_object'])\\n context['arg0_object_mixed'] = camel_to_mixed(context['arg0_object'])\\n if 'return_type_full' in context:\\n context['return_type'] = context['return_type_full'].split('.')[-1]\\n context['return_pkg'] = get_pkg_name(context['return_type_full'])\\n context['return_module'] = self.get_interface_module(\\n self._abc_pkg_name(package_name=get_pkg_name(context['return_type_full']),\\n abc=False),\\n context['return_type_full'].split('.')[-1])\\n if 'return_pkg' in context:\\n context['return_app_name'] = self._app_name(package_name=context['return_pkg'])\\n context['return_implpkg_name'] = self._abc_pkg_name(package_name=context['return_pkg'],\\n abc=False)\\n context['return_pkg_title'] = context['return_pkg'].title()\\n context['return_pkg_caps'] = context['return_pkg'].upper()\\n context['return_pkg_replace_caps'] = self.replace(context['return_pkg'].upper())\\n context['return_pkg_replace_title'] = self.replace(context['return_pkg'].title())\\n if 'return_cat_name' in context:\\n context['return_cat_name_under'] = camel_to_under(context['return_cat_name'])\\n if 'object_name_under' in context:\\n context['object_name_upper'] = context['object_name_under'].upper()\\n context['object_name_mixed'] = under_to_mixed(context['object_name_under'])\\n # Might want to add creating kwargs['object_name' from this as well]\\n if 'object_name' in context and 'package_name' in context:\\n context['object_app_name'] = self._app_name()\\n context['object_implpkg_name'] = self._abc_pkg_name(abc=False)\\n context['object_module'] = self.get_interface_module('package_name', 'object_name')\\n if 'var_name' in context:\\n context['var_name_upper'] = context['var_name'].upper()\\n context['var_name_mixed'] = under_to_mixed(context['var_name'])\\n context['var_name_plural'] = make_plural(context['var_name'])\\n context['var_name_plural_mixed'] = under_to_mixed(context['var_name_plural'])\\n context['var_name_singular'] = remove_plural(context['var_name'])\\n context['var_name_singular_mixed'] = under_to_mixed(context['var_name_singular'])\\n if 'return_type' in context:\\n context['return_type_under'] = camel_to_under(context['return_type'])\\n if 'return_type' in context and context['return_type'].endswith('List'):\\n context['return_type_list_object'] = context['return_type'][:-4]\\n context['return_type_list_object_under'] = camel_to_under(context['return_type_list_object'])\\n context['return_type_list_object_plural_under'] = make_plural(context['return_type_list_object_under'])\\n if ('containable_object_name' in context and\\n 'containable_object_name_under' not in context and\\n 'containable_object_name_upper' not in context):\\n context['containable_object_name_under'] = camel_to_under(context['containable_object_name'])\\n context['containable_object_name_plural'] = make_plural(context['containable_object_name'])\\n context['containable_object_name_plural_under'] = camel_to_under(context['containable_object_name_plural'])\\n context['containable_object_name_mixed'] = camel_to_mixed(context['containable_object_name'])\\n context['containable_object_name_upper'] = camel_to_under(context['containable_object_name']).upper()\\n if 'object_namespace' in context:\\n context['object_package_name'] = '.'.join(context['object_namespace'].split('.')[:-1])\\n context['object_package_name_replace'] = '_'.join(context['object_package_name'].split('.'))\\n context['object_package_name_replace_upper'] = context['object_package_name_replace'].upper()\\n context['object_namespace_replace'] = '_'.join(context['object_namespace'].split('.'))\\n if ('object_name' in context and\\n 'object_name_under' not in context and\\n 'object_name_upper' not in context):\\n context['object_name_plural'] = make_plural(context['object_name'])\\n context['object_name_under'] = camel_to_under(context['object_name'])\\n context['object_name_mixed'] = camel_to_mixed(context['object_name'])\\n context['object_name_upper'] = camel_to_under(context['object_name']).upper()\\n if 'object_name_under' in context:\\n context['object_name_plural_under'] = make_plural(context['object_name_under'])\\n if 'aggregated_object_name' in context:\\n context['aggregated_object_name_under'] = camel_to_under(context['aggregated_object_name'])\\n context['aggregated_object_name_mixed'] = camel_to_mixed(context['aggregated_object_name'])\\n context['aggregated_objects_name_under'] = camel_to_under(make_plural(context['aggregated_object_name']))\\n context['aggregated_objects_name_mixed'] = camel_to_mixed(make_plural(context['aggregated_object_name']))\\n if 'source_name' in context:\\n context['source_name_mixed'] = under_to_mixed(context['source_name'])\\n if 'destination_name' in context:\\n context['destination_name_mixed'] = under_to_mixed(context['destination_name'])\\n if 'cat_name' in context:\\n context['cat_name_under'] = camel_to_under(context['cat_name'])\\n context['cat_name_lower'] = context['cat_name'].lower()\\n context['cat_name_mixed'] = camel_to_mixed(context['cat_name'])\\n context['cat_name_plural'] = make_plural(context['cat_name'])\\n context['cat_name_plural_under'] = camel_to_under(context['cat_name_plural'])\\n context['cat_name_plural_lower'] = context['cat_name_plural'].lower()\\n context['cat_name_plural_mixed'] = camel_to_mixed(context['cat_name_plural'])\\n if 'return_cat_name' in context:\\n context['return_cat_name_under'] = camel_to_under(context['return_cat_name'])\\n context['return_cat_name_lower'] = context['return_cat_name'].lower()\\n context['return_cat_name_mixed'] = camel_to_mixed(context['return_cat_name'])\\n if 'Proxy' in context['interface_name']:\\n context['non_proxy_interface_name'] = ''.join(context['interface_name'].split('Proxy'))\\n if ('return_pkg' in context and 'return_module' in context and\\n context['package_name'] == context['return_pkg'] and\\n context['module_name'] == context['return_module']):\\n context['import_str'] = ''\\n elif ('package_name' in context and 'return_pkg' in context and\\n 'return_type' in context and 'return_module' in context):\\n context['import_str'] = '{0}from ..{1}.{2} import {3}\\\\n'.format(self._dind,\\n context['return_implpkg_name'],\\n context['return_module'],\\n context['return_type'])\\n\\n if 'method_name' in context and context['method_name'].startswith('can_'):\\n context['func_name'] = context['method_name'].split('_')[1]\\n if 'method_name' in context:\\n context['method_session_name'] = context['method_name'].replace('get_', '')\\n if 'syntax' in context:\\n context['syntax_under'] = syntax_to_under(context['syntax'])\\n\\n # Special one for services test builder:\\n if self._is_manager_session(interface, self.package['name']):\\n context['svc_mgr_or_catalog'] = 'svc_mgr'\\n else:\\n context['svc_mgr_or_catalog'] = 'catalog'\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"561dcd63b2767d1a15cbf024f86b00d6\",\n \"score\": \"0.5176718\",\n \"text\": \"def generate_docstring(args_dict: Dict[str, str], signature: inspect.Signature,\\n method_docstring: str) -> str:\\n try:\\n parsed_docstring = docstring_parser.parse(method_docstring)\\n except ValueError:\\n # If failed to parse docstring use the origional instead\\n # TODO Log Warning that parsing docstring failed.\\n return method_docstring\\n\\n doc = f'{parsed_docstring.short_description}\\\\n'\\n if parsed_docstring.long_description:\\n doc += f'{parsed_docstring.long_description}\\\\n'\\n if args_dict:\\n doc += '\\\\nArgs:\\\\n'\\n for key, val in args_dict.items():\\n formated_description = val.replace('\\\\n', '\\\\n ')\\n doc = doc + f' {key}:\\\\n {formated_description}\\\\n'\\n\\n if parsed_docstring.returns:\\n formated_return = parsed_docstring.returns.description.replace(\\n '\\\\n', '\\\\n ')\\n doc += 'Returns:\\\\n'\\n doc += f' {formated_return}\\\\n'\\n\\n if parsed_docstring.raises:\\n doc += 'Raises:\\\\n'\\n raises_dict = {p.type_name: p.description for p in parsed_docstring.raises}\\n for key, val in raises_dict.items():\\n formated_description = val.replace('\\\\n', '\\\\n ')\\n doc = doc + f' {key}:\\\\n {formated_description}\\\\n'\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f11b8261fd73bccb842821857a746afa\",\n \"score\": \"0.5170921\",\n \"text\": \"def get_ctor_arguments(clazz):\\n args = ['self']\\n for C in clazz.__mro__:\\n if '__init__' in C.__dict__:\\n args += inspect.getfullargspec(C).args[1:]\\n args += inspect.getfullargspec(C).kwonlyargs\\n return args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9510056b121c8cb53f0582bb461c8b77\",\n \"score\": \"0.51686615\",\n \"text\": \"def args(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a312b26963f59c4c73994700dfa2e256\",\n \"score\": \"0.5168527\",\n \"text\": \"def GetAttributeFlags(arg_data, arg_name, method, shared_resource_args):\\n name = GetFlagName(arg_name)\\n resource_arg = arg_data.GenerateResourceArg(\\n method, name, shared_resource_args).GetInfo(name)\\n return resource_arg.GetAttributeArgs()[:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efc0c367486b91740950a549dcd09a9a\",\n \"score\": \"0.5161594\",\n \"text\": \"def find_args(func_list, **kwargs):\\n if not isinstance(func_list, list):\\n func_list = [func_list]\\n enabled_nest = [\\n list(signature(func).parameters.keys()) for func in func_list\\n ]\\n enabled_set = set(sum(enabled_nest, list()))\\n enabled_set = enabled_set - set([\\\"self\\\", \\\"cls\\\"])\\n return {k: v for (k, v) in kwargs.items() if k in enabled_set}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c575c1ab780711f644724e7e95ddb240\",\n \"score\": \"0.51575077\",\n \"text\": \"def get_method_list(self):\\n return self.method_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ce3247b3f8c6d513ceae43b43a847f\",\n \"score\": \"0.5155015\",\n \"text\": \"def args(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"args\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ce3247b3f8c6d513ceae43b43a847f\",\n \"score\": \"0.5155015\",\n \"text\": \"def args(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"args\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ce3247b3f8c6d513ceae43b43a847f\",\n \"score\": \"0.5155015\",\n \"text\": \"def args(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"args\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ce3247b3f8c6d513ceae43b43a847f\",\n \"score\": \"0.5155015\",\n \"text\": \"def args(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"args\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f36a5b3545b4f186ade5a77937a5e3c\",\n \"score\": \"0.5148317\",\n \"text\": \"def call_args(self):\\n return self._call_args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a0c35c6830b131a77df69e1364cf708\",\n \"score\": \"0.5131882\",\n \"text\": \"def get_args(typ):\\n try:\\n return typing.get_args(typ) # python>=3.8 typing module has get_args.\\n except AttributeError:\\n return typing_inspect.get_args(typ)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9869ced41645fb8c7fa933dc08cefc7\",\n \"score\": \"0.51318437\",\n \"text\": \"def get_args():\\n usage = inspect.getdoc(handle_command_line)\\n parser = argparse.ArgumentParser(usage=usage)\\n jaraco.logging.add_arguments(parser)\\n Command.add_subparsers(parser)\\n return parser.parse_args()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7051201d318248ed529798fb9e1a6025\",\n \"score\": \"0.5129526\",\n \"text\": \"def _extract_parameters_names(handler, parameters_count, method):\\n if parameters_count == 0:\\n return []\\n\\n parameters = [\\\"{?}\\\" for _ in range(parameters_count)]\\n\\n method_handler = getattr(handler, method.lower())\\n args = _try_extract_args(method_handler)\\n\\n for i, arg in enumerate(args):\\n if set(arg) != {\\\"_\\\"} and i < len(parameters):\\n parameters[i] = arg\\n\\n return parameters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6ea77c33f1ccaddb0df4413f53f9031\",\n \"score\": \"0.51292604\",\n \"text\": \"def _arguments(self, attribute: Any) -> AnyStr:\\n\\n try:\\n sig = signature(attribute)\\n attrs = dict(sig.parameters)\\n\\n except ValueError:\\n attrs = None\\n except TypeError:\\n attrs = None\\n\\n attrs = [str(attr) for attr in dict(attrs or []).values()]\\n return self._delimit_argument_list(attrs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79bacc3f2766a5e4be3aeb5abc86601a\",\n \"score\": \"0.5119526\",\n \"text\": \"def add_arguments(self, parser):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5734b4697babef023ee72180964b0ab\",\n \"score\": \"0.5118895\",\n \"text\": \"def args(self):\\n return self._args_[: self.nargs()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35906cc25249d14186093cc26fdf16cc\",\n \"score\": \"0.5114649\",\n \"text\": \"def add_arguments(parser):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7899cca0429de554d7ccbd00eb4b263\",\n \"score\": \"0.5110981\",\n \"text\": \"def positionalArgNames(self):\\n return [param.name for param in self.parameters if param.kind.name in (\\\"POSITIONAL_ONLY\\\", \\\"POSITIONAL_OR_KEYWORD\\\", \\\"VAR_POSITIONAL\\\")]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53dbb3aecf48749596eb54505e1c9fe7\",\n \"score\": \"0.5110965\",\n \"text\": \"async def _parse_arguments(self, ctx: ApplicationContext) -> None:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f7d5a339372a9ceebd32b4aeeac2099\",\n \"score\": \"0.5107883\",\n \"text\": \"def get_args(self):\\n return self.args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f7d5a339372a9ceebd32b4aeeac2099\",\n \"score\": \"0.5107883\",\n \"text\": \"def get_args(self):\\n return self.args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f7d5a339372a9ceebd32b4aeeac2099\",\n \"score\": \"0.5107883\",\n \"text\": \"def get_args(self):\\n return self.args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6752c194a9407cafeb10467f8dc645c5\",\n \"score\": \"0.5102942\",\n \"text\": \"def foo(x: int, y: str, z: object = None) -> list:\\n # sphinx style docstring\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c6066aaf43babd7a05b5b1c71f3e4c9\",\n \"score\": \"0.5098302\",\n \"text\": \"def cmd_args():\\n if argparse_error:\\n raise argparse_error\\n parser = argparse.ArgumentParser()\\n # named arguments\\n parser.add_argument('--ikey', required=True)\\n parser.add_argument('--skey', required=True)\\n parser.add_argument('--host', required=True)\\n parser.add_argument('--method', required=True)\\n parser.add_argument('--path', required=True)\\n parser.add_argument('--ca')\\n parser.add_argument('--sig-version', type=int, default=2)\\n parser.add_argument('--sig-timezone', default='UTC')\\n parser.add_argument('--show-header', action='append', default=[])\\n # optional positional arguments are used for GET/POST params, name=val\\n parser.add_argument('param', nargs='*')\\n args = parser.parse_args()\\n param = collections.defaultdict(list)\\n for p in args.param:\\n try:\\n (k, v) = p.split('=', 1)\\n except ValueError:\\n sys.exit('Error: Positional argument %s is not '\\n 'in key=value format.' % (p,))\\n param[k].append(v)\\n for (k, v) in param.items():\\n if len(v) != 1:\\n # Each parameter must have a single value. No Duo API\\n # endpoints have arguments that accept multiple values for\\n # the same parameter. Thus, the call() and sign() provided\\n # here can't handle a list of values for the same\\n # parameter.\\n raise NotImplementedError\\n (v,) = v\\n param[k] = v\\n del args.param\\n for (k, v) in param.items():\\n setattr(args, k, v)\\n return args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"473b66cd176e200e465b48963c59621a\",\n \"score\": \"0.5085734\",\n \"text\": \"def Args(cls, parser):\\n flags.ListIntegrationsOfService(parser)\\n flags.ListIntegrationsOfType(parser)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993892,"cells":{"query_id":{"kind":"string","value":"5e214a171ac431e3c9503bf27268fa04"},"query":{"kind":"string","value":"limit_labmda Cheesy as fuck hook to pass limit to a list newer_than Stream items greater than this BACKSTORY You probably want to deliver all unacknowledged but you might not want the entire history of everything they've already seen."},"positive_passages":{"kind":"list like","value":[{"docid":"8a477347b9b0440842084615bfcf4918","score":"0.0","text":"def user_stream(user, limit=None, newer_than=None):\n conn = get_cache_client()\n return get_stream(\n user.pk, conn,\n limit=limit, newer_than=newer_than, renderer=compile_stream)","title":""}],"string":"[\n {\n \"docid\": \"8a477347b9b0440842084615bfcf4918\",\n \"score\": \"0.0\",\n \"text\": \"def user_stream(user, limit=None, newer_than=None):\\n conn = get_cache_client()\\n return get_stream(\\n user.pk, conn,\\n limit=limit, newer_than=newer_than, renderer=compile_stream)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ddf21283e58c1f92eb4c910709521cc2","score":"0.6336143","text":"def get_latest_spam(self, limit: int) -> list:","title":""},{"docid":"d193da18897c79e4b6729ddc3832bcc3","score":"0.6221573","text":"def on_limit(self, track):\r\n return","title":""},{"docid":"3d45ee732d53871ed0aceb3dd80fbaab","score":"0.5893698","text":"def on_limit(self, track):\n print \"!!! Limitation notice received: %s\" % str(track)\n return","title":""},{"docid":"0431841e57695194f93851b5658af648","score":"0.58796567","text":"def on_limit(self, data):\n return","title":""},{"docid":"4ceb9eaba0dbd62ffbfe276338e634d2","score":"0.5756522","text":"def test_it_ignores_limits_greater_than_500(self):\n request = mock.MagicMock()\n request.params = {\"limit\": 501}\n\n views.stream_atom(request)\n\n params = request.api_client.get.call_args[1][\"params\"]\n assert params[\"limit\"] == 500","title":""},{"docid":"460b84c09f592eaffc8c9e85c70177c1","score":"0.5752935","text":"def test_it_falls_back_to_100_if_limit_is_negative(self):\n request = mock.MagicMock()\n request.params = {\"limit\": -50}\n\n views.stream_atom(request)\n\n params = request.api_client.get.call_args[1][\"params\"]\n assert params[\"limit\"] == 100","title":""},{"docid":"7374309aa339d3d9e6befadf921774fd","score":"0.57340425","text":"def test_it_forwards_user_supplied_limits(self):\n for limit in (0, 250, 500):\n request = mock.MagicMock()\n request.params = {\"limit\": limit}\n\n views.stream_atom(request)\n\n params = request.api_client.get.call_args[1][\"params\"]\n assert params[\"limit\"] == limit","title":""},{"docid":"91bc77db77da7742a97d28cdd8c9e811","score":"0.57219565","text":"def test_limit(self):\n logs = retrieve_logs(self.rater, limit=5)\n self.assertEqual(len(logs), 5)","title":""},{"docid":"89bfd634e7392adaa3d829340a133c61","score":"0.56735986","text":"def get_last_fifteen_messages():\n return Message.objects.all().order_by('created')[:50]","title":""},{"docid":"9fb8cb6f5975cba2f6e8f98bb1184bdb","score":"0.56570816","text":"def _limit_call(self, amount_per_10: int) -> None:\r\n the_time = time.time()\r\n\r\n try:\r\n prev_call = self._prev_call[amount_per_10]\r\n\r\n except KeyError:\r\n self._prev_call[amount_per_10] = [the_time]\r\n\r\n else:\r\n # Remove calls that have expired\r\n self._prev_call[amount_per_10] = [i for i in self._prev_call[amount_per_10] if the_time - i < 10]\r\n the_list = self._prev_call[amount_per_10]\r\n\r\n if len(the_list) < amount_per_10:\r\n the_list.append(the_time)\r\n self._prev_call[amount_per_10] = the_list\r\n\r\n else:\r\n to_wait = 10 - (the_time - self._prev_call[amount_per_10][0])\r\n\r\n logger.warning(\"Throttle limit exceeded. Waiting for {} seconds.\".format(to_wait))\r\n time.sleep(to_wait)","title":""},{"docid":"4603de45aba9b270bbc207fe45d46dff","score":"0.56050915","text":"def _get_receive_as_over_limit_count(self):\n return self.__receive_as_over_limit_count","title":""},{"docid":"4603de45aba9b270bbc207fe45d46dff","score":"0.56050915","text":"def _get_receive_as_over_limit_count(self):\n return self.__receive_as_over_limit_count","title":""},{"docid":"4b322881bd757b070954421833f0c28e","score":"0.55920833","text":"def at_limit(self, dynamic_event):\r\n #print(f\"{self.rule_name} has reached its limit.\")\r\n pass","title":""},{"docid":"9e193703bc3c7942e4c39aa703d9cb04","score":"0.55866224","text":"def __call__(self, args):\n # First check if the deque is full; that indicates that we'd better check whether\n # we need to pause.\n if len(self.tab) == self.requests:\n # Grab the oldest (leftmost) timestamp and check to see if it is greater than 1 second\n delta = datetime.now() - self.tab[0]\n if delta < self.timeframe: # Has it been less than configured timeframe since oldest request?\n if self.raise_exc:\n raise exc.RateLimitExceeded(\"Rate limit exceeded (can try again in {0})\".format(self.timeframe - delta))\n else:\n # Wait the difference between timeframe and the oldest request.\n td = self.timeframe - delta\n sleeptime = hasattr(td, 'total_seconds') and td.total_seconds() or total_seconds(td)\n self.log.debug(\"Rate limit triggered; sleeping for {0}\".format(sleeptime))\n time.sleep(sleeptime)\n self.tab.append(datetime.now())","title":""},{"docid":"edcf16c1901495da7b82b9c3f14de03e","score":"0.5536365","text":"def test_sms_status_list_since(self):\n january_2009 = datetime(2009,01,01,0,0,0)\n new_smss = mock_sent_messages(self.user, count=50)\n resp = self.client.get(reverse('api:opera:sms-status-list'), {\n 'since': january_2009\n })\n from django.utils import simplejson\n data = simplejson.loads(resp.content)\n self.assertEquals(len(data), 51) # respects the `since` parameter\n # overriding the `limit` parameter.\n # On top of the 50 newly created\n # entries it should also return the \n # 51st entry which is one from 2009\n # in the fixtures file.\n self.assertEquals(resp.status_code, 200)","title":""},{"docid":"579c68bc82a75e3a4d77ab51f9173796","score":"0.55335224","text":"def limit(cls, refs, hints):\r\n NOT_LIMITED = False\r\n LIMITED = True\r\n\r\n if hints is None or hints.get_limit() is None:\r\n # No truncation was requested\r\n return NOT_LIMITED, refs\r\n\r\n list_limit = hints.get_limit()\r\n if list_limit.get('truncated', False):\r\n # The driver did truncate the list\r\n return LIMITED, refs\r\n\r\n if len(refs) > list_limit['limit']:\r\n # The driver layer wasn't able to truncate it for us, so we must\r\n # do it here\r\n return LIMITED, refs[:list_limit['limit']]\r\n\r\n return NOT_LIMITED, refs","title":""},{"docid":"f70f6b4b2585ef2375756e852b43b186","score":"0.551103","text":"def get_past_messages(group_id, limit=5):\n\n query = \"\"\"\n SELECT newsgroup_post_id, subject, message, time_sent, user_id, post_as\n FROM newsgroup_posts WHERE group_id=%s ORDER BY time_sent DESC LIMIT %s\n \"\"\"\n with flask.g.pymysql_db.cursor() as cursor:\n cursor.execute(query, (group_id, limit))\n return cursor.fetchall()","title":""},{"docid":"dde6311e649b3188203b5bf103645786","score":"0.5500251","text":"def test_it_falls_back_to_100_if_limit_is_invalid(self):\n for limit in (\"not a valid integer\", None, [1, 2, 3]):\n request = mock.MagicMock()\n request.params = {\"limit\": limit}\n\n views.stream_atom(request)\n\n params = request.api_client.get.call_args[1][\"params\"]\n assert params[\"limit\"] == 100","title":""},{"docid":"2781afaa387caa6b747dc6f5a29fa4f2","score":"0.5492259","text":"def over_usage_limit():\n #TODO redo. this call is not performant. Remove for scalability\n if LIFETIME_MSG_USAGE_LIMIT:\n if Messages.count() >= LIFETIME_MSG_USAGE_LIMIT:\n CACHE.set(\"over_usage_limit\", True)\n return True\n if MONTHLY_MSG_USAGE_LIMIT:\n cached_value = CACHE.get(\"over_usage_limit\")\n if cached_value is not None:\n return cached_value\n # get current month\n curr_month = now().month\n # get latest MONTHLY_MSG_USAGE_LIMIT messages\n msgs = Messages(ascending=False, page_size=MONTHLY_MSG_USAGE_LIMIT)\n # get latest MONTHLY_MSG_USAGE_LIMIT control messages\n control_msgs = ControlMessages(ascending=False, page_size=MONTHLY_MSG_USAGE_LIMIT)\n # put together in one list\n all_messages = msgs + control_msgs\n # sort list in-place by time from latest message to earliest message\n all_messages.sort(key=lambda m: m['time'], reverse=True)\n # truncate list to latest MONTHLY_MSG_USAGE_LIMIT messages\n all_messages = all_messages[:MONTHLY_MSG_USAGE_LIMIT]\n # if there exists MONTHLY_MSG_USAGE_LIMIT messages and the earliest message in the latest MONTHLY_MSG_USAGE_LIMIT messages\n # has the same month date as current month, then admin is over the message limit\n if len(all_messages) == MONTHLY_MSG_USAGE_LIMIT and all_messages[-1][\"time\"].month == curr_month:\n CACHE.set(\"over_usage_limit\", True, timeout=100)\n return True\n CACHE.set(\"over_usage_limit\", False, timeout=1)\n return False","title":""},{"docid":"4698e27623d8fe80ebbe150ce30215a6","score":"0.54812706","text":"def limit (self, size) :\n\treturn self.limit(0, size)","title":""},{"docid":"81f5bcadd25f2c8f333f2023e43f1f55","score":"0.547557","text":"def _trim_tracker_by_max_history(\n tracker: DialogueStateTracker, max_history: Optional[int]\n) -> DialogueStateTracker:\n max_applied_events = _get_max_applied_events_for_max_history(tracker, max_history)\n if not max_applied_events:\n return tracker\n\n applied_events = tracker.applied_events()[-max_applied_events:]\n new_tracker = tracker.init_copy()\n for event in applied_events:\n new_tracker.update(event)\n return new_tracker","title":""},{"docid":"faa1cbcd0f098134a2a454adfe0b4cad","score":"0.54590654","text":"def message_list(max = 5):\n query = Message.objects.order_by('-date')\n query = apply_database_settings(query)\n\n return list(query[:max])","title":""},{"docid":"78ea990b66cf5d62fdc3bb8ff2a02c86","score":"0.53783965","text":"def remove_older_notifications():\n\n # cur_date = date.today()\n # deleted_day_notifications = cur_date - timedelta(days=settings.NOTIFICATION_EXPIRATION_DAYS)\n # Notification.objects.filter(is_read=True, created_date__lte=deleted_day_notifications).delete()\n # Friend.objects.filter(is_read=True, created_date__lte=deleted_day_notifications).delete()\n\n # limit max number of notifications to 30 for each user\n notification_users = User.objects.filter(id__in=Notification.objects.values_list('user_id', flat=True))\n for user in notification_users:\n Notification.objects.filter(\n id__in=Notification.objects.filter(user_id=user).order_by('-updated_date').values_list('id')[30:]).delete()","title":""},{"docid":"46f50baf8642cc041ccc33fc5643ea2f","score":"0.5377397","text":"def limit(self, _amount: int) -> None:\n pass","title":""},{"docid":"4e3faf89a941f79811c7de9652aa84de","score":"0.53551257","text":"def overdraft_limit(self, force_update=False):\n self._refresh(force_update)\n return self._overdraft_limit","title":""},{"docid":"260c3766c23c0d04eeedb5cbee6147aa","score":"0.5349556","text":"def limit(cls, refs, hints):\n NOT_LIMITED = False\n LIMITED = True\n\n if hints is None or hints.limit is None:\n # No truncation was requested\n return NOT_LIMITED, refs\n\n if hints.limit.get('truncated', False):\n # The driver did truncate the list\n return LIMITED, refs\n\n if len(refs) > hints.limit['limit']:\n # The driver layer wasn't able to truncate it for us, so we must\n # do it here\n return LIMITED, refs[:hints.limit['limit']]\n\n return NOT_LIMITED, refs","title":""},{"docid":"de9c51c40959e495d1c6fec6327ff571","score":"0.53300023","text":"def test_list_batch_change_summaries_with_max_items(list_fixture):\n client = list_fixture.client\n batch_change_summaries_result = client.list_batch_change_summaries(status=200, max_items=1)\n\n list_fixture.check_batch_change_summaries_page_accuracy(batch_change_summaries_result, size=1, max_items=1, next_id=1)","title":""},{"docid":"af1d78477cb13ae1516fd89f83393346","score":"0.53048354","text":"def test_queue_max_receive_failure_standard_rollback_previous():","title":""},{"docid":"f41acf1518421e043571a8631a29b64f","score":"0.5301671","text":"def test_queue_max_receive_failure_standard():","title":""},{"docid":"0e1d62a218527b5eb335506db1e5b49d","score":"0.5276541","text":"def fairshare(msgs, limit, levels):\n count = 0\n last_priority = None\n postponed = []\n while msgs or postponed:\n if not msgs:\n msgs = postponed\n count = 0\n last_priority = None\n postponed = [ ]\n msg = msgs.pop(0)\n if last_priority and priority_level(msg.priority, levels) == last_priority:\n count += 1\n else:\n last_priority = priority_level(msg.priority, levels)\n count = 1\n l = limit(last_priority)\n if (l and count > l):\n postponed.append(msg)\n else:\n yield msg\n return","title":""},{"docid":"039074a52f795f22788727341587dac8","score":"0.52725273","text":"def shift_rec_list_cutoff(rec_list, datareader):\n\n if datareader.online():\n print('ATTENTION: this method is intended to be used only for offline evaluation!!!')\n\n # Get array of holdouts\n holdouts = datareader.get_df_test_playlists()['num_holdouts'].as_matrix()\n\n # Initialize shifted rec_list\n rec_list_shifted = []\n\n # Set as bad track the last track of the dataset which is not present in our test set\n bad_track = 2262291\n\n for i in tqdm(range(len(rec_list)), desc='Shifting rec_list'):\n # Initialization\n row = rec_list[i]\n holdout = holdouts[i]\n\n # Create new row\n new_row = [bad_track for x in range(holdout)]\n new_row = new_row + row\n new_row = new_row[:500]\n\n rec_list_shifted.append(new_row)\n\n return rec_list_shifted","title":""},{"docid":"ad032e16ed7e400e308e999f370e6cea","score":"0.5234815","text":"def sticker_limit(self):\n limit = self.premium_perks.sticker_limit\n if limit < 30 and self.has_feature(GuildFeature.more_sticker):\n limit = 30\n \n return limit","title":""},{"docid":"cfc2e22c97affd5b5441ec55bc9406f7","score":"0.52307516","text":"def queue_listing(self, limit=None, **kwargs):\n\n uplimit = self._limits_conf.queue_paging_uplimit\n if limit is not None and not (0 < limit <= uplimit):\n raise ValidationFailed(\n 'Limit must be at least 1 and no greater than %d.' %\n self._limits_conf.queue_paging_uplimit)","title":""},{"docid":"8cd8ad64ba2c60f701546f312e63b880","score":"0.52157575","text":"def limit(self, n):\n return self.__append({\"limit\": n})","title":""},{"docid":"cc8df0d808933113ae889fbd982673c7","score":"0.5206451","text":"def requeue_failed_raw_logs_uploaded_after(cutoff):\n\tprefix = \"failed\"\n\tfor raw_upload in _list_raw_uploads_by_prefix(prefix):\n\t\tif raw_upload.timestamp >= cutoff:\n\t\t\t_publish_requeue_message_for_failed_raw_log(raw_upload)","title":""},{"docid":"4d3b37a83bd95882989447c0a5699ad3","score":"0.519614","text":"def _limit(query, hints):\r\n # NOTE(henry-nash): If we were to implement pagination, then we\r\n # we would expand this method to support pagination and limiting.\r\n\r\n # If we satisfied all the filters, set an upper limit if supplied\r\n list_limit = hints.get_limit()\r\n if list_limit:\r\n query = query.limit(list_limit['limit'])\r\n return query","title":""},{"docid":"00e44efc7b97c45c3385da8c440d7ddb","score":"0.5186664","text":"def _limit_handler(self, args):\n hosts = self._get_hosts_by_ids(args.id)\n if hosts is None or len(hosts) == 0:\n return\n\n try:\n rate = BitRate.from_rate_string(args.rate)\n except Exception:\n IO.error('limit rate is invalid.')\n return\n\n direction = self._parse_direction_args(args)\n discordText = \"```\"\n\n for host in hosts:\n self.arp_spoofer.add(host)\n self.limiter.limit(host, direction, rate)\n self.bandwidth_monitor.add(host)\n\n IO.ok('{}{}{r} {} {}limited{r} to {}.'.format(IO.Fore.LIGHTYELLOW_EX, host.ip, Direction.pretty_direction(direction), IO.Fore.LIGHTRED_EX, rate, r=IO.Style.RESET_ALL))\n discordText += '{} pada IP {} dilimit menjadi {}\\n'.format(Direction.pretty_direction(direction), host.ip, rate)\n \n discordText += '```'\n IO.discord(discordText)","title":""},{"docid":"d670c3178043d24875b0968a37f4e5c3","score":"0.51838946","text":"def _updateMaxLens(self) :\n self.faliureHistoryOfRecentHosts_maxLen=max([self.faliureHistoryOfRecentHosts_maxLen,len(self.faliureHistoryOfRecentHosts)])\n self.recentFailedLoginAttempts_maxLen=max([self.recentFailedLoginAttempts_maxLen,len(self.recentFailedLoginAttempts)])\n self.blackList_maxLen=max([self.blackList_maxLen, len(self.blackList)])","title":""},{"docid":"921cc2c93258843fb987a330a6bc6cf8","score":"0.51814836","text":"def test_recent_trackback_pings(self) -> None:\n tbs: List = TestBrowseDatabaseService.database_service.get_recent_trackback_pings(\n max_trackbacks=-1\n )\n self.assertEqual(len(tbs), 0, \"List should be empty\")\n tbs: List = TestBrowseDatabaseService.database_service.get_recent_trackback_pings(\n max_trackbacks=25\n )\n self.assertGreater(len(tbs), 0, \"List should be nonempty\")\n for tb in tbs:\n self.assertIsInstance(tb[0], TrackbackPing)\n self.assertIsInstance(tb[1], str)\n self.assertIsInstance(\n Identifier(arxiv_id=tb[1]), Identifier, \"Value looks like an Identifier\"\n )\n self.assertIsInstance(tb[2], str)","title":""},{"docid":"ef94cff8cdec591f6af6e5ca81790312","score":"0.5179508","text":"def _limit(self, _from: str, _amount: int, _price: int) -> None:\n pass","title":""},{"docid":"bb59ecf98884ec866e179deae569d984","score":"0.5157193","text":"def get_latest_blog_entries(limit=5):\n return BlogEntry.objects.order_by(\"-pub_date\")[:limit]","title":""},{"docid":"aeb8144264978e95b3217de966d8d64d","score":"0.5145257","text":"def test_limit(self) -> None:\n self._create_users_with_media(10, 2)\n\n channel = self.make_request(\n \"GET\",\n self.url + \"?limit=5\",\n access_token=self.admin_user_tok,\n )\n\n self.assertEqual(200, channel.code, msg=channel.json_body)\n self.assertEqual(channel.json_body[\"total\"], 10)\n self.assertEqual(len(channel.json_body[\"users\"]), 5)\n self.assertEqual(channel.json_body[\"next_token\"], 5)\n self._check_fields(channel.json_body[\"users\"])","title":""},{"docid":"12d561ae3fe85c272886446ebfd4bdad","score":"0.513415","text":"def recent(self, limit=5):\n search = Search(using=self.es, index='tweets', extra={\"size\": limit})\n search = search.sort('-timestamp_ms')\n result = search.execute()\n return map(lambda item: item.to_dict(), result)","title":""},{"docid":"8ad20ca74094a00809dd498e15ddf71d","score":"0.5132022","text":"def test_at_limit(self):\r\n\r\n # Test this by overriding the general limit with a higher\r\n # driver-specific limit (allowing all entities to be returned\r\n # in the collection), which should result in a non truncated list\r\n self.config_fixture.config(list_limit=5)\r\n self.config_fixture.config(group='catalog', list_limit=10)\r\n r = self.get('/services', auth=self.auth)\r\n self.assertEqual(len(r.result.get('services')), 10)\r\n self.assertIsNone(r.result.get('truncated'))","title":""},{"docid":"bcb877e3032de9b3c6edf98fb066e5ec","score":"0.51316416","text":"def queue_last_items(self, queue, count=10):\r\n queue = self.normalize(queue)\r\n lkey = QUEUE_NAME % self.normalize(queue)\r\n multivalue = yield self.redis.lrange(lkey, 0, count-1)\r\n\r\n defer.returnValue( multivalue)","title":""},{"docid":"118f2994da8f3ad8e4325bf3a5181608","score":"0.5131255","text":"def _get_advertise_as_over_limit_count(self):\n return self.__advertise_as_over_limit_count","title":""},{"docid":"118f2994da8f3ad8e4325bf3a5181608","score":"0.5131255","text":"def _get_advertise_as_over_limit_count(self):\n return self.__advertise_as_over_limit_count","title":""},{"docid":"e50c2a5deb7c67bcc7c3b3f60a2fa281","score":"0.51310325","text":"def get_recent_tracks(self, limit):\n return []","title":""},{"docid":"6970049827363de8e3083d47bc94fa01","score":"0.51289076","text":"def limit(self, limit):\n self._limit = limit","title":""},{"docid":"6970049827363de8e3083d47bc94fa01","score":"0.51289076","text":"def limit(self, limit):\n self._limit = limit","title":""},{"docid":"a7be0306b2be32cafe044d6acdc484fc","score":"0.51284564","text":"def remove_rec_list_after_cutoff(rec_list, datareader):\n\n # Get array of holdouts\n holdouts = datareader.get_df_test_playlists()['num_holdouts'].as_matrix()\n\n # Initialize shrinked rec_list\n rec_list_shrinked = []\n\n for i in range(len(rec_list)):\n row = rec_list[i]\n holdout = holdouts[i]\n\n rec_list_shrinked.append(row[:holdout])\n\n return rec_list_shrinked","title":""},{"docid":"0385dd5dbd0d6d38fa25a5450c9c2f38","score":"0.51264185","text":"def following(self, offset=0, limit=25, feeds=None):\n pass","title":""},{"docid":"3e93dcd2a3458b86be269208db24d1a3","score":"0.5116962","text":"def prune_streams():\n while len(g.pafs) > g.max_cached_streams:\n g.pafs.popitem(last=False)\n\n while len(g.streams) > g.max_cached_streams:\n g.streams.popitem(last=False)\n\n # prune time expired items\n\n now = time.time()\n oldpafs = [k for k in g.pafs if g.pafs[k].expiry < now]\n\n if len(oldpafs):\n dbg(c.r + \"%s old pafy items pruned%s\", len(oldpafs), c.w)\n\n for oldpaf in oldpafs:\n g.pafs.pop(oldpaf, 0)\n\n oldstreams = [k for k in g.streams if g.streams[k]['expiry'] < now]\n\n if len(oldstreams):\n dbg(c.r + \"%s old stream items pruned%s\", len(oldstreams), c.w)\n\n for oldstream in oldstreams:\n g.streams.pop(oldstream, 0)\n\n dbg(c.b + \"paf: %s, streams: %s%s\", len(g.pafs), len(g.streams), c.w)","title":""},{"docid":"b175e389c18301e8eabc7dc75cba1019","score":"0.5100534","text":"def limits(self):","title":""},{"docid":"d6bf0906cf165841530b4985c597b3ad","score":"0.5082931","text":"def limit(c, runner, rule):\n cmd = \"ufw limit {}\".format(rule)\n return runner(cmd, hide=True, warn=True).ok","title":""},{"docid":"5855270cea2e02653c1b2526320313a8","score":"0.50800633","text":"def limit(self, l):\n self._limit = l\n return self","title":""},{"docid":"e283b5c65ae473aa552a0e4ba3617fb6","score":"0.5080025","text":"def message_listing(self, limit=None, **kwargs):\n\n uplimit = self._limits_conf.message_paging_uplimit\n if limit is not None and not (0 < limit <= uplimit):\n raise ValidationFailed(\n 'Limit must be at least 1 and may not be greater than %d. ' %\n self._limits_conf.message_paging_uplimit)","title":""},{"docid":"0cc0127b699b35bcb9fca2860d0a5856","score":"0.50780094","text":"def _exceeds_threshold(self, selection, command_str, open_args,\n item_type='track'):\n warning_threshold = config['play']['warning_threshold'].get(int)\n\n # Warn user before playing any huge playlists.\n if warning_threshold and len(selection) > warning_threshold:\n if len(selection) > 1:\n item_type += 's'\n\n ui.print_(ui.colorize(\n 'text_warning',\n 'You are about to queue {} {}.'.format(\n len(selection), item_type)))\n\n if ui.input_options(('Continue', 'Abort')) == 'a':\n return True\n\n return False","title":""},{"docid":"f785f4a8cb9edc2a6782f93eb45bc5a2","score":"0.50635666","text":"def messaging_history(self, limit=10):\n return self.__API('messaging/history', True, 200, limit=limit)","title":""},{"docid":"55774094e316f4ddee0ff1347583e1d8","score":"0.5053848","text":"def queue_five_items(queue):\n queue.enqueue('potato')\n queue.enqueue(5)\n queue.enqueue(10)\n queue.enqueue('car')\n queue.enqueue('apple')\n return queue","title":""},{"docid":"9f4f91f9d54b1aadb45a22666b55dc96","score":"0.5053274","text":"async def history(self, ctx, target_channel: TextChannel, history_limit=10):\n async with ctx.channel.typing():\n\n messages = await target_channel.history(reverse=True, limit=history_limit).filter(lambda x: x.author == ctx.me).flatten()\n\n output = \"\\n\".join([f\"{x.id}: {x.clean_content}\" for x in messages])\n\n await ctx.send(f\"```{output}```\", delete_after=30)","title":""},{"docid":"33e25361baf2983cafaea6222a3dcbc3","score":"0.5052636","text":"def test_limit_and_from(self) -> None:\n self._create_users_with_media(20, 2)\n\n channel = self.make_request(\n \"GET\",\n self.url + \"?from=5&limit=10\",\n access_token=self.admin_user_tok,\n )\n\n self.assertEqual(200, channel.code, msg=channel.json_body)\n self.assertEqual(channel.json_body[\"total\"], 20)\n self.assertEqual(channel.json_body[\"next_token\"], 15)\n self.assertEqual(len(channel.json_body[\"users\"]), 10)\n self._check_fields(channel.json_body[\"users\"])","title":""},{"docid":"9d3e888efa3002bc6832dd02b6584c65","score":"0.5051231","text":"def test_snapshot_list_param_limit(self):\n self._list_snapshots_by_param_limit(limit=1, expected_elements=1)","title":""},{"docid":"87767fb721b442cad6b028e956b7f5a4","score":"0.5050978","text":"def recent_actions(self, max_count=100):\n if max_count > 100:\n raise OverflowError(\"Max_count should be less or equal to 1000\")\n request_url = self.generate_url(\"recentActions\", **{\"maxCount\": str(max_count)})\n request = requests.get(request_url)\n return self.get_response(request)","title":""},{"docid":"6e17d8a7ed564f1320a8d4155daaebe0","score":"0.5048258","text":"def limit(self, limit):\n\n return self[:limit]","title":""},{"docid":"f4fb705d53c2310d8deb7264adbce02c","score":"0.5040705","text":"def top_rated():\n\n subject = \"Untappd Gems\"\n body = \"Here are the new beers your friends are excited about:\\n\"\n send_ready = False\n\n cursor = conn.cursor()\n\n old_recent = cursor.execute(\"SELECT value FROM BREW_app_data WHERE parameter = 'recent_checkin'\").fetchone()[0]\n\n # get new checkins\n endpoint = f'https://api.untappd.com/v4/checkin/recent?limit=50&min_id={old_recent}&access_token={secrets.untappd_token}'\n res = httpx.get(endpoint)\n block = res.json()\n\n # if new checkins, process\n if block['response']['checkins']['count'] > 0:\n\n new_recent = str(block['response']['checkins']['items'][0]['checkin_id'])\n checkins = block['response']['checkins']['items']\n new_beer_list = []\n\n current_beer_list = cursor.execute(\"SELECT * FROM BREW_top_rated\")\n current_bids = [row['beer_id'] for row in current_beer_list]\n\n for i in checkins:\n name = f\"{i['user']['first_name']} {i['user']['last_name']}\"\n beer = i['beer']['beer_name']\n bid = i['beer']['bid']\n brewery = i['brewery']['brewery_name']\n rating = i['rating_score']\n if name not in secrets.bad_taste:\n if rating >= 4.5:\n new_beer = {\"bid\":bid, \"name\":beer, \"brewery\":brewery}\n if new_beer['bid'] not in current_bids:\n add = f\"\\n - {name} gave {str(rating)} stars to {beer} by {brewery}\"\n body = body + add\n new_beer_list.append((new_beer['bid'], new_beer['name'], new_beer['brewery']))\n current_bids.append(new_beer['bid'])\n send_ready = True\n \n if send_ready == True:\n send_email(subject, body) \n logging.info(\"Beers email sent. New recent: \" + new_recent)\n else:\n logging.info(\"New checkins. Nothing premium.\")\n \n cursor.executemany(\"INSERT INTO BREW_top_rated (beer_id, beer_name, brewery) VALUES (?,?,?)\", new_beer_list)\n cursor.execute(\"UPDATE BREW_app_data SET value=? WHERE parameter == 'recent_checkin'\", (new_recent,))\n else:\n logging.info(\"No new checkins\")\n\n conn.commit()\n conn.close()","title":""},{"docid":"6e2205b48173a2a59891c7ec7e59404d","score":"0.50264627","text":"def setLimit(self, limit):\n if self.limit > limit:\n self.limit = limit\n else:\n self.limit = self.limit","title":""},{"docid":"d8c7899ab0a0723ec70fd67ef28e4c01","score":"0.50185245","text":"def limit (self, lfrom, lto) :\n\tr = self.clone(cloneData = false)\n\tr.data = self.data[lfrom:lto]\n return r","title":""},{"docid":"dfff909e370df9c97ae1e5d6a6528cdd","score":"0.5001268","text":"def get_limit(self): \n return self._limit","title":""},{"docid":"76ed2390059a57675b4ef0cbf4657049","score":"0.49979234","text":"def get_items_since(self, start, count):\n self._commit_puts_stale_ok()\n with self.get() as conn:\n curs = conn.execute('''\n SELECT * FROM %s WHERE ROWID > ? ORDER BY ROWID ASC LIMIT ?\n ''' % self.db_contains_type, (start, count))\n curs.row_factory = dict_factory\n return [r for r in curs]","title":""},{"docid":"0a966190cac6f39d87a3e80da6485453","score":"0.49850872","text":"def private_unread(limit=10, offset=0, asc=False, before=None):\n if before and isinstance(before, (int, long)):\n offset = before - 157321\n\n redis = RedisPool(settings.storage_socket)\n ids = redis.get('private_unread:%s' % env.user.id)\n if not ids:\n return []\n\n ids = map(lambda id: int(id), ids.split(','))[offset:offset+limit]\n\n res = db.fetchall(\n \"SELECT \"\n \"p.id, p.author, u.login, i.name, i.avatar, p.private, \"\n \"p.created at time zone %(tz)s AS created,\"\n \"p.resource, \"\n \"p.type, p.title, p.link, p.tags, p.text, \"\n \"p.edited,\"\n \"(p.edited=false AND p.created+interval '%(edit_expire)s second' >= now())\"\n \"AS editable, \"\n \"p.archive, p.files, \"\n \"sp.post_id AS subscribed, \"\n \"rp.post_id AS recommended, \"\n \"rb.post_id AS bookmarked \"\n \"FROM unnest(%(ids)s) \"\n \"JOIN posts.posts p ON p.id=unnest \"\n \"JOIN users.logins u ON u.id=p.author \"\n \"LEFT OUTER JOIN users.info i ON i.id=p.author \"\n \"LEFT OUTER JOIN posts.recipients rcp ON rcp.post_id=p.id \"\n \"LEFT OUTER JOIN subs.posts sp ON sp.post_id=p.id \"\n \"AND sp.user_id=%(user_id)s \"\n \"LEFT OUTER JOIN posts.recommendations rp \"\n \"ON %(user_id)s=rp.user_id AND \"\n \"p.id=rp.post_id AND rp.comment_id=0 \"\n \"LEFT OUTER JOIN posts.bookmarks rb \"\n \"ON p.id=rb.post_id AND %(user_id)s=rb.user_id AND \"\n \"rb.comment_id=0 \"\n \"WHERE p.private=true;\",\n {'ids': ids, 'user_id': env.user.id, 'tz': env.user.get_profile('tz'),\n 'limit': limit, 'edit_expire': settings.edit_expire})\n\n plist = []\n for i, r in enumerate(res):\n r = dict(r)\n r['uid'] = 157321 + offset + i + 1\n plist.append(r)\n return _plist(plist)","title":""},{"docid":"4722ccf814a4d3d6578fd3cdc3c6fa8e","score":"0.4981565","text":"def recently_changed_packages_activity_list(client=client.Client(), offset='', limit=''):\n args = client.sanitize_params(locals())\n\n resp = client.request(action='recently_changed_packages_activity_list', data=args)\n if not resp['success']:\n raise exceptions.CKANError(resp.error)\n return resp","title":""},{"docid":"0f10c54adf90adbec6e6116eb912cb65","score":"0.49766478","text":"def recent_changes(self, count=10):\n \n recent_blog = qon.blog.recent_items(self.get_all_blogs(), count=count)\n recent_wiki = qon.wiki.recent_items(self.get_all_wikis(), count=count)\n bydate = []\n for i in recent_blog + recent_wiki:\n bydate.append((i.watchable_last_change(), i))\n bydate.sort()\n bydate.reverse()\n \n return [i for date, i in bydate]","title":""},{"docid":"1c4b8c6f211e2af54e43c25bbda99e25","score":"0.49759784","text":"def test_get_filtered_list_limit(self):\n flexmock(errata).should_receive(\"Erratum\").and_return(None)\n\n response = flexmock(status_code=200)\n response.should_receive(\"json\").and_return(test_structures.example_erratum_filtered_list)\n\n flexmock(errata.requests).should_receive(\"get\").and_return(response)\n\n res = errata.get_filtered_list(limit=1)\n self.assertEqual(1, len(res))","title":""},{"docid":"634e34527eb6060323c78016569e9aae","score":"0.49677092","text":"def test_snapshot_list_param_limit_equals_zero(self):\n self._list_snapshots_by_param_limit(limit=0, expected_elements=0)","title":""},{"docid":"62ea8890ff4d0fdcecf67f0dbe56d756","score":"0.49645263","text":"def limit(self, limit):\n\n self._limit = limit","title":""},{"docid":"62ea8890ff4d0fdcecf67f0dbe56d756","score":"0.49645263","text":"def limit(self, limit):\n\n self._limit = limit","title":""},{"docid":"f6a785549d22bad05f4b91dc0c894793","score":"0.49529153","text":"def get_new_messages(self, partner, last_message):\n\n all_messages = self.get_messages()\n messages_with_partner = all_messages[partner]\n new_messages = [message for message in messages_with_partner if int(message[0]) > int(last_message)]\n\n return new_messages","title":""},{"docid":"48b2789445e83a76bc87217438481088","score":"0.49394128","text":"def check_limit(self):\n notifications_limit = self.request.query_params.get(\"limit\", None)\n limit = None\n if notifications_limit:\n try:\n limit = int(notifications_limit)\n except ValueError:\n pass # if limit has not a valid format, let's not use it\n return limit","title":""},{"docid":"61a81155a4f7d2fd4f8516d112173410","score":"0.4934796","text":"def recent(cls,site_id=0,ct=15,filter='recent'):\n return meta.DBSession.query(Help).filter_by(site_id=site_id\n ).order_by(help_table.c.created.desc()).limit(ct)","title":""},{"docid":"99911eb1d7887869761d1a76fcaf77a2","score":"0.49343956","text":"def recent(self, bot, args):\n plists = bot.gmusic.get_newest_playlists()\n return self.format_playlists(bot, plists)","title":""},{"docid":"cca2cfd4f317f0dfc7b0bd2aef4a8adc","score":"0.4931534","text":"def limit(self, limit):\n\t\tobj = copy.deepcopy(self)\n\t\tobj.__limit = limit\n\t\treturn obj","title":""},{"docid":"5800eb3cb0ccafc9c9d5828b29e230ec","score":"0.4931328","text":"def get_queryset(self, request):\n qs = super(MaximumStayReason, self).get_queryset(request)\n if request.user.is_superuser:\n return qs\n group = models.MooringAreaGroup.objects.filter(members__in=[request.user,])\n return qs.filter(mooring_group__in=group)","title":""},{"docid":"77a5f27538caa1c2126dcf8bb7a88dfe","score":"0.49210572","text":"def backlog():\n current_date = datetime.now().strftime('%Y-%m-%d')\n results = get_all_tasks()\n todo_items = []\n # filter out results,we want only tasks that are not scheduled for today\n for r in results:\n if r[\"due_date\"] != current_date:\n todo_items.append(r)\n return render_template(\n 'backlog.html',\n title='Backlog',\n todo_items=todo_items\n )","title":""},{"docid":"3b018df829436c2777e6e91dec9339f4","score":"0.4908892","text":"def purgelist(artifactory):\n purgable = artifactory.count_based_retention(retention_count=5)\n return purgable","title":""},{"docid":"59608e03ad61b1f9ea6ee7cbec837575","score":"0.49079606","text":"def get_purchases_limit(conn, limit):\n return execute_sql(conn, sqlite_list_purchase_limit, values=(limit,))","title":""},{"docid":"712d061af9e6ea5649e57fd12f6ce542","score":"0.49070376","text":"def get_brief_watched(self):\n watched = self.ownedstock_set.filter(watch=True, quantity=0)\n fin = []\n for watched in watched:\n stock = Stock.objects.get(ticker=watched.ticker)\n fin.append((watched, stock))\n return fin","title":""},{"docid":"612b89cb3a7e25169a30adc08514d066","score":"0.48989064","text":"def setOverdraftLimit(self, newLimit):\r\n\r\n # Ensures overdraftLimit isn't set to less than it currently is if the account has a negative balance\r\n if newLimit > -(self.balance):\r\n self.overdraftLimit = float(newLimit)","title":""},{"docid":"d09b5528afbc69ddcb7246ba7cb7cf87","score":"0.48987466","text":"def truncated(f):\r\n @functools.wraps(f)\r\n def wrapper(self, hints, *args, **kwargs):\r\n if not hasattr(hints, 'get_limit'):\r\n raise exception.UnexpectedError(\r\n _('Cannot truncate a driver call without hints list as '\r\n 'first parameter after self '))\r\n\r\n limit_dict = hints.get_limit()\r\n if limit_dict is None:\r\n return f(self, hints, *args, **kwargs)\r\n\r\n # A limit is set, so ask for one more entry than we need\r\n list_limit = limit_dict['limit']\r\n hints.set_limit(list_limit + 1)\r\n ref_list = f(self, hints, *args, **kwargs)\r\n\r\n # If we got more than the original limit then trim back the list and\r\n # mark it truncated. In both cases, make sure we set the limit back\r\n # to its original value.\r\n if len(ref_list) > list_limit:\r\n hints.set_limit(list_limit, truncated=True)\r\n return ref_list[:list_limit]\r\n else:\r\n hints.set_limit(list_limit)\r\n return ref_list\r\n return wrapper","title":""},{"docid":"83b1d079e638bc6ced99dcb49db4a4fa","score":"0.48958537","text":"def revdatelimit(ui, revset):\n days = ui.configint(b'remotefilelog', b'prefetchdays')\n if days > 0:\n revset = b'(%s) & date(-%s)' % (revset, days)\n return revset","title":""},{"docid":"6163bd1758054659e049f869d8774ee5","score":"0.4894288","text":"def limit(self, value):\n self._limit = value","title":""},{"docid":"967d8d3285f66a68ed2808bc7df6cef3","score":"0.4893634","text":"def get_last_logs(self, limit: int=10):\n count = logs_db.count({'session_id': self.session_id})\n return logs_db.find({'session_id': self.session_id}, skip=count-limit)","title":""},{"docid":"968a1357c8c8368a160a4e2d0253d924","score":"0.48934558","text":"def popular_last_week(self):\n return self.extra(where={\"created > NOW() - INTERVAL '7 days'\"}).order_by('-score', '-ups', 'created')[:10]","title":""},{"docid":"8bb291fb294e70d614749e6d99112ea6","score":"0.48912013","text":"def cmd_recent(self, source, nickname=None):\n lm = self.getLinkManager(source)\n entries = list(lm.recent(3, nickname))\n if entries:\n for e in entries:\n source.notice(e.displayCompleteHumanReadable(modified=True))\n else:\n if nickname is None:\n msg = u'No recent entries.'\n else:\n msg = u'No recent entries for \\002%s\\002.' % (nickname,)\n source.reply(msg)","title":""},{"docid":"47da360553fb2a17b23620d225ab41c0","score":"0.48804876","text":"def __init__(self):\n self.items = []\n\tself.max = -sys.maxsize\n\tself.popped_after_max = False","title":""},{"docid":"8450a5bc3a4345f9f125b2865542e605","score":"0.48786524","text":"def consume(self, limit=None):\n for total_message_count in count():\n if limit and total_message_count >= limit:\n raise StopIteration\n self.drain_events()\n yield True","title":""},{"docid":"1bb72f267d828a4205f7c9379e69643f","score":"0.486774","text":"def modify_ack_deadline(self, items):\n # We must potentially split the request into multiple smaller requests\n # to avoid the server-side max request size limit.\n ack_ids = (item.ack_id for item in items)\n seconds = (item.seconds for item in items)\n total_chunks = int(math.ceil(len(items) / _ACK_IDS_BATCH_SIZE))\n\n for _ in range(total_chunks):\n request = types.StreamingPullRequest(\n modify_deadline_ack_ids=itertools.islice(ack_ids, _ACK_IDS_BATCH_SIZE),\n modify_deadline_seconds=itertools.islice(seconds, _ACK_IDS_BATCH_SIZE),\n )\n self._manager.send(request)","title":""},{"docid":"28ca60747faf33419a2eaa60868cea89","score":"0.48618472","text":"def heartbeat(self):\n\n now = time.time()\n\n # purge messages that have exceed their rentention time\n purge = [r['id'] for r in self.db.execute(\n 'select id from messages where purge>0 and purge<?',\n (now,))]\n for msg_id in purge:\n logging.debug('Exceeded retention on msg {}'.format(msg_id))\n self.stats['msg-retention'] = (\n self.stats.get('msg-retention', 0) + 1)\n self.count_deleted(msg_id)\n\n # try to deliver messages that exceeds the visibility timeout\n visible = (dict((k, r[k]) for k in r.keys()) for r in self.db.execute(\n 'select * from messages where show>0 and show<=? and not '\n '(purge>0 and purge<?) order by priority', (now, now)))\n undelivered = []\n for msg in visible:\n logging.debug('Exceeded timeout on msg {}'.format(msg['id']))\n self.stats['msg-timeouts'] = self.stats.get('msg-timeouts', 0) + 1\n if msg['deliver'] == 0 or msg['dcount'] < msg['deliver']:\n # try to deliver the message\n if not self.deliver_message(msg):\n # count it as shown\n self.stats['msg-show'] = self.stats.get('msg-show', 0) + 1\n undelivered.append(msg['id'])\n else:\n # message delivered to many times, purge it\n purge.append(msg['id'])\n self.count_deleted(msg['id'])\n\n if undelivered:\n # set messages to be visible (out of timeout)\n self.db.executemany('update messages set show=0.0 where id=?',\n ((i,) for i in undelivered))\n if purge:\n self.db.executemany('delete from messages where id=?',\n ((i,) for i in purge))\n if purge or undelivered:\n self.db.commit()\n\n # check if channel has listeners, otherwise remove it when timed out\n unused = [(ch, lst) for ch, lst in self.channels.items()\n if not lst and lst.ts + self.channel_ttl < now]\n for channel, listeners in unused:\n # channel is no longer in use\n logging.debug('Removing empty channel {}'.format(channel))\n listeners.stop()\n del self.channels[channel]\n self.stats['channel-remove'] = (\n self.stats.get('channel-remove', 0) + 1)","title":""},{"docid":"0bcca7a0305f1da0eaba3a7e4d524f0a","score":"0.48614404","text":"def fetch_actions(self, action_filter, action_limit=50, before=None, since=None):\n raise NotImplementedError()","title":""},{"docid":"9e945321068c9bc5edb2134347e6921f","score":"0.48601106","text":"async def last(self, ctx):\n if not_botlogs(ctx.message.channel): # prevent from deleting #botlogs\n return\n\n async for message in ctx.message.channel.history(limit=3, oldest_first=False):\n await message.delete()","title":""},{"docid":"9c07ab40d3c74382fe98fac8e9c680a4","score":"0.48584053","text":"def find_bad_channels_maxwell(\n raw,\n limit=7.0,\n duration=5.0,\n min_count=5,\n return_scores=False,\n origin=\"auto\",\n int_order=8,\n ext_order=3,\n calibration=None,\n cross_talk=None,\n coord_frame=\"head\",\n regularize=\"in\",\n ignore_ref=False,\n bad_condition=\"error\",\n head_pos=None,\n mag_scale=100.0,\n skip_by_annotation=(\"edge\", \"bad_acq_skip\"),\n h_freq=40.0,\n extended_proj=(),\n verbose=None,\n):\n if h_freq is not None:\n if raw.info.get(\"lowpass\") and raw.info[\"lowpass\"] <= h_freq:\n freq_loc = \"below\" if raw.info[\"lowpass\"] < h_freq else \"equal to\"\n msg = (\n f\"The input data has already been low-pass filtered with a \"\n f'{raw.info[\"lowpass\"]} Hz cutoff frequency, which is '\n f\"{freq_loc} the requested cutoff of {h_freq} Hz. Not \"\n f\"applying low-pass filter.\"\n )\n logger.info(msg)\n else:\n logger.info(\n f\"Applying low-pass filter with {h_freq} Hz cutoff \" f\"frequency ...\"\n )\n raw = raw.copy().load_data().filter(l_freq=None, h_freq=h_freq)\n\n limit = float(limit)\n onsets, ends = _annotations_starts_stops(raw, skip_by_annotation, invert=True)\n del skip_by_annotation\n # operate on chunks\n starts = list()\n stops = list()\n step = int(round(raw.info[\"sfreq\"] * duration))\n for onset, end in zip(onsets, ends):\n if end - onset >= step:\n ss = np.arange(onset, end - step + 1, step)\n starts.extend(ss)\n ss = ss + step\n ss[-1] = end\n stops.extend(ss)\n min_count = min(_ensure_int(min_count, \"min_count\"), len(starts))\n logger.info(\n \"Scanning for bad channels in %d interval%s (%0.1f s) ...\"\n % (len(starts), _pl(starts), step / raw.info[\"sfreq\"])\n )\n params = _prep_maxwell_filter(\n raw,\n skip_by_annotation=[], # already accounted for\n origin=origin,\n int_order=int_order,\n ext_order=ext_order,\n calibration=calibration,\n cross_talk=cross_talk,\n coord_frame=coord_frame,\n regularize=regularize,\n ignore_ref=ignore_ref,\n bad_condition=bad_condition,\n head_pos=head_pos,\n mag_scale=mag_scale,\n extended_proj=extended_proj,\n )\n del origin, int_order, ext_order, calibration, cross_talk, coord_frame\n del regularize, ignore_ref, bad_condition, head_pos, mag_scale\n good_meg_picks = params[\"meg_picks\"][params[\"good_mask\"]]\n assert len(params[\"meg_picks\"]) == len(params[\"coil_scale\"])\n assert len(params[\"good_mask\"]) == len(params[\"meg_picks\"])\n noisy_chs = Counter()\n flat_chs = Counter()\n flat_limits = dict(grad=0.01e-13, mag=0.01e-15)\n these_limits = np.array(\n [\n flat_limits[\"grad\"] if pick in params[\"grad_picks\"] else flat_limits[\"mag\"]\n for pick in good_meg_picks\n ]\n )\n\n flat_step = max(20, int(30 * raw.info[\"sfreq\"] / 1000.0))\n all_flats = set()\n\n # Prepare variables to return if `return_scores=True`.\n bins = np.empty((len(starts), 2)) # To store start, stop of each segment\n # We create ndarrays with one row per channel, regardless of channel type\n # and whether the channel has been marked as \"bad\" in info or not. This\n # makes indexing in the loop easier. We only filter this down to the subset\n # of MEG channels after all processing is done.\n ch_names = np.array(raw.ch_names)\n ch_types = np.array(raw.get_channel_types())\n\n scores_flat = np.full((len(ch_names), len(starts)), np.nan)\n scores_noisy = np.full_like(scores_flat, fill_value=np.nan)\n\n thresh_flat = np.full((len(ch_names), 1), np.nan)\n thresh_noisy = np.full_like(thresh_flat, fill_value=np.nan)\n\n for si, (start, stop) in enumerate(zip(starts, stops)):\n n_iter = 0\n orig_data = raw.get_data(None, start, stop, verbose=False)\n chunk_raw = RawArray(\n orig_data,\n params[\"info\"],\n first_samp=raw.first_samp + start,\n copy=\"data\",\n verbose=False,\n )\n\n t = chunk_raw.times[[0, -1]] + start / raw.info[\"sfreq\"]\n logger.info(\n \" Interval %3d: %8.3f - %8.3f\" % ((si + 1,) + tuple(t[[0, -1]]))\n )\n\n # Flat pass: SD < 0.01 fT/cm or 0.01 fT for at 30 ms (or 20 samples)\n n = stop - start\n flat_stop = n - (n % flat_step)\n data = chunk_raw.get_data(good_meg_picks, 0, flat_stop)\n data.shape = (data.shape[0], -1, flat_step)\n delta = np.std(data, axis=-1).min(-1) # min std across segments\n\n # We may want to return this later if `return_scores=True`.\n bins[si, :] = t[0], t[-1]\n scores_flat[good_meg_picks, si] = delta\n thresh_flat[good_meg_picks] = these_limits.reshape(-1, 1)\n\n chunk_flats = delta < these_limits\n chunk_flats = np.where(chunk_flats)[0]\n chunk_flats = [\n raw.ch_names[good_meg_picks[chunk_flat]] for chunk_flat in chunk_flats\n ]\n flat_chs.update(chunk_flats)\n all_flats |= set(chunk_flats)\n chunk_flats = sorted(all_flats)\n these_picks = [\n pick for pick in good_meg_picks if raw.ch_names[pick] not in chunk_flats\n ]\n if len(these_picks) == 0:\n logger.info(f\" Flat ({len(chunk_flats):2d}): <all>\")\n warn(\n \"All-flat segment detected, all channels will be marked as \"\n f\"flat and processing will stop (t={t[0]:0.3f}). \"\n \"Consider using annotate_amplitude before calling this \"\n 'function with skip_by_annotation=\"bad_flat\" (or similar) to '\n \"properly process all segments.\"\n )\n break # no reason to continue\n # Bad pass\n chunk_noisy = list()\n params[\"st_duration\"] = int(round(chunk_raw.times[-1] * raw.info[\"sfreq\"]))\n for n_iter in range(1, 101): # iteratively exclude the worst ones\n assert set(raw.info[\"bads\"]) & set(chunk_noisy) == set()\n params[\"good_mask\"][:] = [\n chunk_raw.ch_names[pick]\n not in raw.info[\"bads\"] + chunk_noisy + chunk_flats\n for pick in params[\"meg_picks\"]\n ]\n chunk_raw._data[:] = orig_data\n delta = chunk_raw.get_data(these_picks)\n with use_log_level(False):\n _run_maxwell_filter(chunk_raw, reconstruct=\"orig\", copy=False, **params)\n\n if n_iter == 1 and len(chunk_flats):\n logger.info(\n \" Flat (%2d): %s\"\n % (len(chunk_flats), \" \".join(chunk_flats))\n )\n delta -= chunk_raw.get_data(these_picks)\n # p2p\n range_ = np.ptp(delta, axis=-1)\n cs_picks = np.searchsorted(params[\"meg_picks\"], these_picks)\n range_ *= params[\"coil_scale\"][cs_picks, 0]\n mean, std = np.mean(range_), np.std(range_)\n # z score\n z = (range_ - mean) / std\n idx = np.argmax(z)\n max_ = z[idx]\n\n # We may want to return this later if `return_scores=True`.\n scores_noisy[these_picks, si] = z\n thresh_noisy[these_picks] = limit\n\n if max_ < limit:\n break\n\n name = raw.ch_names[these_picks[idx]]\n logger.debug(\" Bad: %s %0.1f\" % (name, max_))\n these_picks.pop(idx)\n chunk_noisy.append(name)\n noisy_chs.update(chunk_noisy)\n noisy_chs = sorted(\n (b for b, c in noisy_chs.items() if c >= min_count),\n key=lambda x: raw.ch_names.index(x),\n )\n flat_chs = sorted(\n (f for f, c in flat_chs.items() if c >= min_count),\n key=lambda x: raw.ch_names.index(x),\n )\n\n # Only include MEG channels.\n ch_names = ch_names[params[\"meg_picks\"]]\n ch_types = ch_types[params[\"meg_picks\"]]\n scores_flat = scores_flat[params[\"meg_picks\"]]\n thresh_flat = thresh_flat[params[\"meg_picks\"]]\n scores_noisy = scores_noisy[params[\"meg_picks\"]]\n thresh_noisy = thresh_noisy[params[\"meg_picks\"]]\n\n logger.info(\" Static bad channels: %s\" % (noisy_chs,))\n logger.info(\" Static flat channels: %s\" % (flat_chs,))\n logger.info(\"[done]\")\n\n if return_scores:\n scores = dict(\n ch_names=ch_names,\n ch_types=ch_types,\n bins=bins,\n scores_flat=scores_flat,\n limits_flat=thresh_flat,\n scores_noisy=scores_noisy,\n limits_noisy=thresh_noisy,\n )\n return noisy_chs, flat_chs, scores\n else:\n return noisy_chs, flat_chs","title":""}],"string":"[\n {\n \"docid\": \"ddf21283e58c1f92eb4c910709521cc2\",\n \"score\": \"0.6336143\",\n \"text\": \"def get_latest_spam(self, limit: int) -> list:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d193da18897c79e4b6729ddc3832bcc3\",\n \"score\": \"0.6221573\",\n \"text\": \"def on_limit(self, track):\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d45ee732d53871ed0aceb3dd80fbaab\",\n \"score\": \"0.5893698\",\n \"text\": \"def on_limit(self, track):\\n print \\\"!!! Limitation notice received: %s\\\" % str(track)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0431841e57695194f93851b5658af648\",\n \"score\": \"0.58796567\",\n \"text\": \"def on_limit(self, data):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ceb9eaba0dbd62ffbfe276338e634d2\",\n \"score\": \"0.5756522\",\n \"text\": \"def test_it_ignores_limits_greater_than_500(self):\\n request = mock.MagicMock()\\n request.params = {\\\"limit\\\": 501}\\n\\n views.stream_atom(request)\\n\\n params = request.api_client.get.call_args[1][\\\"params\\\"]\\n assert params[\\\"limit\\\"] == 500\",\n \"title\": \"\"\n },\n {\n \"docid\": \"460b84c09f592eaffc8c9e85c70177c1\",\n \"score\": \"0.5752935\",\n \"text\": \"def test_it_falls_back_to_100_if_limit_is_negative(self):\\n request = mock.MagicMock()\\n request.params = {\\\"limit\\\": -50}\\n\\n views.stream_atom(request)\\n\\n params = request.api_client.get.call_args[1][\\\"params\\\"]\\n assert params[\\\"limit\\\"] == 100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7374309aa339d3d9e6befadf921774fd\",\n \"score\": \"0.57340425\",\n \"text\": \"def test_it_forwards_user_supplied_limits(self):\\n for limit in (0, 250, 500):\\n request = mock.MagicMock()\\n request.params = {\\\"limit\\\": limit}\\n\\n views.stream_atom(request)\\n\\n params = request.api_client.get.call_args[1][\\\"params\\\"]\\n assert params[\\\"limit\\\"] == limit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91bc77db77da7742a97d28cdd8c9e811\",\n \"score\": \"0.57219565\",\n \"text\": \"def test_limit(self):\\n logs = retrieve_logs(self.rater, limit=5)\\n self.assertEqual(len(logs), 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89bfd634e7392adaa3d829340a133c61\",\n \"score\": \"0.56735986\",\n \"text\": \"def get_last_fifteen_messages():\\n return Message.objects.all().order_by('created')[:50]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fb8cb6f5975cba2f6e8f98bb1184bdb\",\n \"score\": \"0.56570816\",\n \"text\": \"def _limit_call(self, amount_per_10: int) -> None:\\r\\n the_time = time.time()\\r\\n\\r\\n try:\\r\\n prev_call = self._prev_call[amount_per_10]\\r\\n\\r\\n except KeyError:\\r\\n self._prev_call[amount_per_10] = [the_time]\\r\\n\\r\\n else:\\r\\n # Remove calls that have expired\\r\\n self._prev_call[amount_per_10] = [i for i in self._prev_call[amount_per_10] if the_time - i < 10]\\r\\n the_list = self._prev_call[amount_per_10]\\r\\n\\r\\n if len(the_list) < amount_per_10:\\r\\n the_list.append(the_time)\\r\\n self._prev_call[amount_per_10] = the_list\\r\\n\\r\\n else:\\r\\n to_wait = 10 - (the_time - self._prev_call[amount_per_10][0])\\r\\n\\r\\n logger.warning(\\\"Throttle limit exceeded. Waiting for {} seconds.\\\".format(to_wait))\\r\\n time.sleep(to_wait)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4603de45aba9b270bbc207fe45d46dff\",\n \"score\": \"0.56050915\",\n \"text\": \"def _get_receive_as_over_limit_count(self):\\n return self.__receive_as_over_limit_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4603de45aba9b270bbc207fe45d46dff\",\n \"score\": \"0.56050915\",\n \"text\": \"def _get_receive_as_over_limit_count(self):\\n return self.__receive_as_over_limit_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b322881bd757b070954421833f0c28e\",\n \"score\": \"0.55920833\",\n \"text\": \"def at_limit(self, dynamic_event):\\r\\n #print(f\\\"{self.rule_name} has reached its limit.\\\")\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e193703bc3c7942e4c39aa703d9cb04\",\n \"score\": \"0.55866224\",\n \"text\": \"def __call__(self, args):\\n # First check if the deque is full; that indicates that we'd better check whether\\n # we need to pause.\\n if len(self.tab) == self.requests:\\n # Grab the oldest (leftmost) timestamp and check to see if it is greater than 1 second\\n delta = datetime.now() - self.tab[0]\\n if delta < self.timeframe: # Has it been less than configured timeframe since oldest request?\\n if self.raise_exc:\\n raise exc.RateLimitExceeded(\\\"Rate limit exceeded (can try again in {0})\\\".format(self.timeframe - delta))\\n else:\\n # Wait the difference between timeframe and the oldest request.\\n td = self.timeframe - delta\\n sleeptime = hasattr(td, 'total_seconds') and td.total_seconds() or total_seconds(td)\\n self.log.debug(\\\"Rate limit triggered; sleeping for {0}\\\".format(sleeptime))\\n time.sleep(sleeptime)\\n self.tab.append(datetime.now())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edcf16c1901495da7b82b9c3f14de03e\",\n \"score\": \"0.5536365\",\n \"text\": \"def test_sms_status_list_since(self):\\n january_2009 = datetime(2009,01,01,0,0,0)\\n new_smss = mock_sent_messages(self.user, count=50)\\n resp = self.client.get(reverse('api:opera:sms-status-list'), {\\n 'since': january_2009\\n })\\n from django.utils import simplejson\\n data = simplejson.loads(resp.content)\\n self.assertEquals(len(data), 51) # respects the `since` parameter\\n # overriding the `limit` parameter.\\n # On top of the 50 newly created\\n # entries it should also return the \\n # 51st entry which is one from 2009\\n # in the fixtures file.\\n self.assertEquals(resp.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"579c68bc82a75e3a4d77ab51f9173796\",\n \"score\": \"0.55335224\",\n \"text\": \"def limit(cls, refs, hints):\\r\\n NOT_LIMITED = False\\r\\n LIMITED = True\\r\\n\\r\\n if hints is None or hints.get_limit() is None:\\r\\n # No truncation was requested\\r\\n return NOT_LIMITED, refs\\r\\n\\r\\n list_limit = hints.get_limit()\\r\\n if list_limit.get('truncated', False):\\r\\n # The driver did truncate the list\\r\\n return LIMITED, refs\\r\\n\\r\\n if len(refs) > list_limit['limit']:\\r\\n # The driver layer wasn't able to truncate it for us, so we must\\r\\n # do it here\\r\\n return LIMITED, refs[:list_limit['limit']]\\r\\n\\r\\n return NOT_LIMITED, refs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f70f6b4b2585ef2375756e852b43b186\",\n \"score\": \"0.551103\",\n \"text\": \"def get_past_messages(group_id, limit=5):\\n\\n query = \\\"\\\"\\\"\\n SELECT newsgroup_post_id, subject, message, time_sent, user_id, post_as\\n FROM newsgroup_posts WHERE group_id=%s ORDER BY time_sent DESC LIMIT %s\\n \\\"\\\"\\\"\\n with flask.g.pymysql_db.cursor() as cursor:\\n cursor.execute(query, (group_id, limit))\\n return cursor.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dde6311e649b3188203b5bf103645786\",\n \"score\": \"0.5500251\",\n \"text\": \"def test_it_falls_back_to_100_if_limit_is_invalid(self):\\n for limit in (\\\"not a valid integer\\\", None, [1, 2, 3]):\\n request = mock.MagicMock()\\n request.params = {\\\"limit\\\": limit}\\n\\n views.stream_atom(request)\\n\\n params = request.api_client.get.call_args[1][\\\"params\\\"]\\n assert params[\\\"limit\\\"] == 100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2781afaa387caa6b747dc6f5a29fa4f2\",\n \"score\": \"0.5492259\",\n \"text\": \"def over_usage_limit():\\n #TODO redo. this call is not performant. Remove for scalability\\n if LIFETIME_MSG_USAGE_LIMIT:\\n if Messages.count() >= LIFETIME_MSG_USAGE_LIMIT:\\n CACHE.set(\\\"over_usage_limit\\\", True)\\n return True\\n if MONTHLY_MSG_USAGE_LIMIT:\\n cached_value = CACHE.get(\\\"over_usage_limit\\\")\\n if cached_value is not None:\\n return cached_value\\n # get current month\\n curr_month = now().month\\n # get latest MONTHLY_MSG_USAGE_LIMIT messages\\n msgs = Messages(ascending=False, page_size=MONTHLY_MSG_USAGE_LIMIT)\\n # get latest MONTHLY_MSG_USAGE_LIMIT control messages\\n control_msgs = ControlMessages(ascending=False, page_size=MONTHLY_MSG_USAGE_LIMIT)\\n # put together in one list\\n all_messages = msgs + control_msgs\\n # sort list in-place by time from latest message to earliest message\\n all_messages.sort(key=lambda m: m['time'], reverse=True)\\n # truncate list to latest MONTHLY_MSG_USAGE_LIMIT messages\\n all_messages = all_messages[:MONTHLY_MSG_USAGE_LIMIT]\\n # if there exists MONTHLY_MSG_USAGE_LIMIT messages and the earliest message in the latest MONTHLY_MSG_USAGE_LIMIT messages\\n # has the same month date as current month, then admin is over the message limit\\n if len(all_messages) == MONTHLY_MSG_USAGE_LIMIT and all_messages[-1][\\\"time\\\"].month == curr_month:\\n CACHE.set(\\\"over_usage_limit\\\", True, timeout=100)\\n return True\\n CACHE.set(\\\"over_usage_limit\\\", False, timeout=1)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4698e27623d8fe80ebbe150ce30215a6\",\n \"score\": \"0.54812706\",\n \"text\": \"def limit (self, size) :\\n\\treturn self.limit(0, size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f5bcadd25f2c8f333f2023e43f1f55\",\n \"score\": \"0.547557\",\n \"text\": \"def _trim_tracker_by_max_history(\\n tracker: DialogueStateTracker, max_history: Optional[int]\\n) -> DialogueStateTracker:\\n max_applied_events = _get_max_applied_events_for_max_history(tracker, max_history)\\n if not max_applied_events:\\n return tracker\\n\\n applied_events = tracker.applied_events()[-max_applied_events:]\\n new_tracker = tracker.init_copy()\\n for event in applied_events:\\n new_tracker.update(event)\\n return new_tracker\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faa1cbcd0f098134a2a454adfe0b4cad\",\n \"score\": \"0.54590654\",\n \"text\": \"def message_list(max = 5):\\n query = Message.objects.order_by('-date')\\n query = apply_database_settings(query)\\n\\n return list(query[:max])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78ea990b66cf5d62fdc3bb8ff2a02c86\",\n \"score\": \"0.53783965\",\n \"text\": \"def remove_older_notifications():\\n\\n # cur_date = date.today()\\n # deleted_day_notifications = cur_date - timedelta(days=settings.NOTIFICATION_EXPIRATION_DAYS)\\n # Notification.objects.filter(is_read=True, created_date__lte=deleted_day_notifications).delete()\\n # Friend.objects.filter(is_read=True, created_date__lte=deleted_day_notifications).delete()\\n\\n # limit max number of notifications to 30 for each user\\n notification_users = User.objects.filter(id__in=Notification.objects.values_list('user_id', flat=True))\\n for user in notification_users:\\n Notification.objects.filter(\\n id__in=Notification.objects.filter(user_id=user).order_by('-updated_date').values_list('id')[30:]).delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46f50baf8642cc041ccc33fc5643ea2f\",\n \"score\": \"0.5377397\",\n \"text\": \"def limit(self, _amount: int) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e3faf89a941f79811c7de9652aa84de\",\n \"score\": \"0.53551257\",\n \"text\": \"def overdraft_limit(self, force_update=False):\\n self._refresh(force_update)\\n return self._overdraft_limit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"260c3766c23c0d04eeedb5cbee6147aa\",\n \"score\": \"0.5349556\",\n \"text\": \"def limit(cls, refs, hints):\\n NOT_LIMITED = False\\n LIMITED = True\\n\\n if hints is None or hints.limit is None:\\n # No truncation was requested\\n return NOT_LIMITED, refs\\n\\n if hints.limit.get('truncated', False):\\n # The driver did truncate the list\\n return LIMITED, refs\\n\\n if len(refs) > hints.limit['limit']:\\n # The driver layer wasn't able to truncate it for us, so we must\\n # do it here\\n return LIMITED, refs[:hints.limit['limit']]\\n\\n return NOT_LIMITED, refs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de9c51c40959e495d1c6fec6327ff571\",\n \"score\": \"0.53300023\",\n \"text\": \"def test_list_batch_change_summaries_with_max_items(list_fixture):\\n client = list_fixture.client\\n batch_change_summaries_result = client.list_batch_change_summaries(status=200, max_items=1)\\n\\n list_fixture.check_batch_change_summaries_page_accuracy(batch_change_summaries_result, size=1, max_items=1, next_id=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af1d78477cb13ae1516fd89f83393346\",\n \"score\": \"0.53048354\",\n \"text\": \"def test_queue_max_receive_failure_standard_rollback_previous():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f41acf1518421e043571a8631a29b64f\",\n \"score\": \"0.5301671\",\n \"text\": \"def test_queue_max_receive_failure_standard():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e1d62a218527b5eb335506db1e5b49d\",\n \"score\": \"0.5276541\",\n \"text\": \"def fairshare(msgs, limit, levels):\\n count = 0\\n last_priority = None\\n postponed = []\\n while msgs or postponed:\\n if not msgs:\\n msgs = postponed\\n count = 0\\n last_priority = None\\n postponed = [ ]\\n msg = msgs.pop(0)\\n if last_priority and priority_level(msg.priority, levels) == last_priority:\\n count += 1\\n else:\\n last_priority = priority_level(msg.priority, levels)\\n count = 1\\n l = limit(last_priority)\\n if (l and count > l):\\n postponed.append(msg)\\n else:\\n yield msg\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039074a52f795f22788727341587dac8\",\n \"score\": \"0.52725273\",\n \"text\": \"def shift_rec_list_cutoff(rec_list, datareader):\\n\\n if datareader.online():\\n print('ATTENTION: this method is intended to be used only for offline evaluation!!!')\\n\\n # Get array of holdouts\\n holdouts = datareader.get_df_test_playlists()['num_holdouts'].as_matrix()\\n\\n # Initialize shifted rec_list\\n rec_list_shifted = []\\n\\n # Set as bad track the last track of the dataset which is not present in our test set\\n bad_track = 2262291\\n\\n for i in tqdm(range(len(rec_list)), desc='Shifting rec_list'):\\n # Initialization\\n row = rec_list[i]\\n holdout = holdouts[i]\\n\\n # Create new row\\n new_row = [bad_track for x in range(holdout)]\\n new_row = new_row + row\\n new_row = new_row[:500]\\n\\n rec_list_shifted.append(new_row)\\n\\n return rec_list_shifted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad032e16ed7e400e308e999f370e6cea\",\n \"score\": \"0.5234815\",\n \"text\": \"def sticker_limit(self):\\n limit = self.premium_perks.sticker_limit\\n if limit < 30 and self.has_feature(GuildFeature.more_sticker):\\n limit = 30\\n \\n return limit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfc2e22c97affd5b5441ec55bc9406f7\",\n \"score\": \"0.52307516\",\n \"text\": \"def queue_listing(self, limit=None, **kwargs):\\n\\n uplimit = self._limits_conf.queue_paging_uplimit\\n if limit is not None and not (0 < limit <= uplimit):\\n raise ValidationFailed(\\n 'Limit must be at least 1 and no greater than %d.' %\\n self._limits_conf.queue_paging_uplimit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd8ad64ba2c60f701546f312e63b880\",\n \"score\": \"0.52157575\",\n \"text\": \"def limit(self, n):\\n return self.__append({\\\"limit\\\": n})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc8df0d808933113ae889fbd982673c7\",\n \"score\": \"0.5206451\",\n \"text\": \"def requeue_failed_raw_logs_uploaded_after(cutoff):\\n\\tprefix = \\\"failed\\\"\\n\\tfor raw_upload in _list_raw_uploads_by_prefix(prefix):\\n\\t\\tif raw_upload.timestamp >= cutoff:\\n\\t\\t\\t_publish_requeue_message_for_failed_raw_log(raw_upload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d3b37a83bd95882989447c0a5699ad3\",\n \"score\": \"0.519614\",\n \"text\": \"def _limit(query, hints):\\r\\n # NOTE(henry-nash): If we were to implement pagination, then we\\r\\n # we would expand this method to support pagination and limiting.\\r\\n\\r\\n # If we satisfied all the filters, set an upper limit if supplied\\r\\n list_limit = hints.get_limit()\\r\\n if list_limit:\\r\\n query = query.limit(list_limit['limit'])\\r\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e44efc7b97c45c3385da8c440d7ddb\",\n \"score\": \"0.5186664\",\n \"text\": \"def _limit_handler(self, args):\\n hosts = self._get_hosts_by_ids(args.id)\\n if hosts is None or len(hosts) == 0:\\n return\\n\\n try:\\n rate = BitRate.from_rate_string(args.rate)\\n except Exception:\\n IO.error('limit rate is invalid.')\\n return\\n\\n direction = self._parse_direction_args(args)\\n discordText = \\\"```\\\"\\n\\n for host in hosts:\\n self.arp_spoofer.add(host)\\n self.limiter.limit(host, direction, rate)\\n self.bandwidth_monitor.add(host)\\n\\n IO.ok('{}{}{r} {} {}limited{r} to {}.'.format(IO.Fore.LIGHTYELLOW_EX, host.ip, Direction.pretty_direction(direction), IO.Fore.LIGHTRED_EX, rate, r=IO.Style.RESET_ALL))\\n discordText += '{} pada IP {} dilimit menjadi {}\\\\n'.format(Direction.pretty_direction(direction), host.ip, rate)\\n \\n discordText += '```'\\n IO.discord(discordText)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d670c3178043d24875b0968a37f4e5c3\",\n \"score\": \"0.51838946\",\n \"text\": \"def _updateMaxLens(self) :\\n self.faliureHistoryOfRecentHosts_maxLen=max([self.faliureHistoryOfRecentHosts_maxLen,len(self.faliureHistoryOfRecentHosts)])\\n self.recentFailedLoginAttempts_maxLen=max([self.recentFailedLoginAttempts_maxLen,len(self.recentFailedLoginAttempts)])\\n self.blackList_maxLen=max([self.blackList_maxLen, len(self.blackList)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"921cc2c93258843fb987a330a6bc6cf8\",\n \"score\": \"0.51814836\",\n \"text\": \"def test_recent_trackback_pings(self) -> None:\\n tbs: List = TestBrowseDatabaseService.database_service.get_recent_trackback_pings(\\n max_trackbacks=-1\\n )\\n self.assertEqual(len(tbs), 0, \\\"List should be empty\\\")\\n tbs: List = TestBrowseDatabaseService.database_service.get_recent_trackback_pings(\\n max_trackbacks=25\\n )\\n self.assertGreater(len(tbs), 0, \\\"List should be nonempty\\\")\\n for tb in tbs:\\n self.assertIsInstance(tb[0], TrackbackPing)\\n self.assertIsInstance(tb[1], str)\\n self.assertIsInstance(\\n Identifier(arxiv_id=tb[1]), Identifier, \\\"Value looks like an Identifier\\\"\\n )\\n self.assertIsInstance(tb[2], str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef94cff8cdec591f6af6e5ca81790312\",\n \"score\": \"0.5179508\",\n \"text\": \"def _limit(self, _from: str, _amount: int, _price: int) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb59ecf98884ec866e179deae569d984\",\n \"score\": \"0.5157193\",\n \"text\": \"def get_latest_blog_entries(limit=5):\\n return BlogEntry.objects.order_by(\\\"-pub_date\\\")[:limit]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeb8144264978e95b3217de966d8d64d\",\n \"score\": \"0.5145257\",\n \"text\": \"def test_limit(self) -> None:\\n self._create_users_with_media(10, 2)\\n\\n channel = self.make_request(\\n \\\"GET\\\",\\n self.url + \\\"?limit=5\\\",\\n access_token=self.admin_user_tok,\\n )\\n\\n self.assertEqual(200, channel.code, msg=channel.json_body)\\n self.assertEqual(channel.json_body[\\\"total\\\"], 10)\\n self.assertEqual(len(channel.json_body[\\\"users\\\"]), 5)\\n self.assertEqual(channel.json_body[\\\"next_token\\\"], 5)\\n self._check_fields(channel.json_body[\\\"users\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12d561ae3fe85c272886446ebfd4bdad\",\n \"score\": \"0.513415\",\n \"text\": \"def recent(self, limit=5):\\n search = Search(using=self.es, index='tweets', extra={\\\"size\\\": limit})\\n search = search.sort('-timestamp_ms')\\n result = search.execute()\\n return map(lambda item: item.to_dict(), result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ad20ca74094a00809dd498e15ddf71d\",\n \"score\": \"0.5132022\",\n \"text\": \"def test_at_limit(self):\\r\\n\\r\\n # Test this by overriding the general limit with a higher\\r\\n # driver-specific limit (allowing all entities to be returned\\r\\n # in the collection), which should result in a non truncated list\\r\\n self.config_fixture.config(list_limit=5)\\r\\n self.config_fixture.config(group='catalog', list_limit=10)\\r\\n r = self.get('/services', auth=self.auth)\\r\\n self.assertEqual(len(r.result.get('services')), 10)\\r\\n self.assertIsNone(r.result.get('truncated'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcb877e3032de9b3c6edf98fb066e5ec\",\n \"score\": \"0.51316416\",\n \"text\": \"def queue_last_items(self, queue, count=10):\\r\\n queue = self.normalize(queue)\\r\\n lkey = QUEUE_NAME % self.normalize(queue)\\r\\n multivalue = yield self.redis.lrange(lkey, 0, count-1)\\r\\n\\r\\n defer.returnValue( multivalue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"118f2994da8f3ad8e4325bf3a5181608\",\n \"score\": \"0.5131255\",\n \"text\": \"def _get_advertise_as_over_limit_count(self):\\n return self.__advertise_as_over_limit_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"118f2994da8f3ad8e4325bf3a5181608\",\n \"score\": \"0.5131255\",\n \"text\": \"def _get_advertise_as_over_limit_count(self):\\n return self.__advertise_as_over_limit_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e50c2a5deb7c67bcc7c3b3f60a2fa281\",\n \"score\": \"0.51310325\",\n \"text\": \"def get_recent_tracks(self, limit):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6970049827363de8e3083d47bc94fa01\",\n \"score\": \"0.51289076\",\n \"text\": \"def limit(self, limit):\\n self._limit = limit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6970049827363de8e3083d47bc94fa01\",\n \"score\": \"0.51289076\",\n \"text\": \"def limit(self, limit):\\n self._limit = limit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7be0306b2be32cafe044d6acdc484fc\",\n \"score\": \"0.51284564\",\n \"text\": \"def remove_rec_list_after_cutoff(rec_list, datareader):\\n\\n # Get array of holdouts\\n holdouts = datareader.get_df_test_playlists()['num_holdouts'].as_matrix()\\n\\n # Initialize shrinked rec_list\\n rec_list_shrinked = []\\n\\n for i in range(len(rec_list)):\\n row = rec_list[i]\\n holdout = holdouts[i]\\n\\n rec_list_shrinked.append(row[:holdout])\\n\\n return rec_list_shrinked\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0385dd5dbd0d6d38fa25a5450c9c2f38\",\n \"score\": \"0.51264185\",\n \"text\": \"def following(self, offset=0, limit=25, feeds=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e93dcd2a3458b86be269208db24d1a3\",\n \"score\": \"0.5116962\",\n \"text\": \"def prune_streams():\\n while len(g.pafs) > g.max_cached_streams:\\n g.pafs.popitem(last=False)\\n\\n while len(g.streams) > g.max_cached_streams:\\n g.streams.popitem(last=False)\\n\\n # prune time expired items\\n\\n now = time.time()\\n oldpafs = [k for k in g.pafs if g.pafs[k].expiry < now]\\n\\n if len(oldpafs):\\n dbg(c.r + \\\"%s old pafy items pruned%s\\\", len(oldpafs), c.w)\\n\\n for oldpaf in oldpafs:\\n g.pafs.pop(oldpaf, 0)\\n\\n oldstreams = [k for k in g.streams if g.streams[k]['expiry'] < now]\\n\\n if len(oldstreams):\\n dbg(c.r + \\\"%s old stream items pruned%s\\\", len(oldstreams), c.w)\\n\\n for oldstream in oldstreams:\\n g.streams.pop(oldstream, 0)\\n\\n dbg(c.b + \\\"paf: %s, streams: %s%s\\\", len(g.pafs), len(g.streams), c.w)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b175e389c18301e8eabc7dc75cba1019\",\n \"score\": \"0.5100534\",\n \"text\": \"def limits(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6bf0906cf165841530b4985c597b3ad\",\n \"score\": \"0.5082931\",\n \"text\": \"def limit(c, runner, rule):\\n cmd = \\\"ufw limit {}\\\".format(rule)\\n return runner(cmd, hide=True, warn=True).ok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5855270cea2e02653c1b2526320313a8\",\n \"score\": \"0.50800633\",\n \"text\": \"def limit(self, l):\\n self._limit = l\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e283b5c65ae473aa552a0e4ba3617fb6\",\n \"score\": \"0.5080025\",\n \"text\": \"def message_listing(self, limit=None, **kwargs):\\n\\n uplimit = self._limits_conf.message_paging_uplimit\\n if limit is not None and not (0 < limit <= uplimit):\\n raise ValidationFailed(\\n 'Limit must be at least 1 and may not be greater than %d. ' %\\n self._limits_conf.message_paging_uplimit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cc0127b699b35bcb9fca2860d0a5856\",\n \"score\": \"0.50780094\",\n \"text\": \"def _exceeds_threshold(self, selection, command_str, open_args,\\n item_type='track'):\\n warning_threshold = config['play']['warning_threshold'].get(int)\\n\\n # Warn user before playing any huge playlists.\\n if warning_threshold and len(selection) > warning_threshold:\\n if len(selection) > 1:\\n item_type += 's'\\n\\n ui.print_(ui.colorize(\\n 'text_warning',\\n 'You are about to queue {} {}.'.format(\\n len(selection), item_type)))\\n\\n if ui.input_options(('Continue', 'Abort')) == 'a':\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f785f4a8cb9edc2a6782f93eb45bc5a2\",\n \"score\": \"0.50635666\",\n \"text\": \"def messaging_history(self, limit=10):\\n return self.__API('messaging/history', True, 200, limit=limit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55774094e316f4ddee0ff1347583e1d8\",\n \"score\": \"0.5053848\",\n \"text\": \"def queue_five_items(queue):\\n queue.enqueue('potato')\\n queue.enqueue(5)\\n queue.enqueue(10)\\n queue.enqueue('car')\\n queue.enqueue('apple')\\n return queue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f4f91f9d54b1aadb45a22666b55dc96\",\n \"score\": \"0.5053274\",\n \"text\": \"async def history(self, ctx, target_channel: TextChannel, history_limit=10):\\n async with ctx.channel.typing():\\n\\n messages = await target_channel.history(reverse=True, limit=history_limit).filter(lambda x: x.author == ctx.me).flatten()\\n\\n output = \\\"\\\\n\\\".join([f\\\"{x.id}: {x.clean_content}\\\" for x in messages])\\n\\n await ctx.send(f\\\"```{output}```\\\", delete_after=30)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33e25361baf2983cafaea6222a3dcbc3\",\n \"score\": \"0.5052636\",\n \"text\": \"def test_limit_and_from(self) -> None:\\n self._create_users_with_media(20, 2)\\n\\n channel = self.make_request(\\n \\\"GET\\\",\\n self.url + \\\"?from=5&limit=10\\\",\\n access_token=self.admin_user_tok,\\n )\\n\\n self.assertEqual(200, channel.code, msg=channel.json_body)\\n self.assertEqual(channel.json_body[\\\"total\\\"], 20)\\n self.assertEqual(channel.json_body[\\\"next_token\\\"], 15)\\n self.assertEqual(len(channel.json_body[\\\"users\\\"]), 10)\\n self._check_fields(channel.json_body[\\\"users\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d3e888efa3002bc6832dd02b6584c65\",\n \"score\": \"0.5051231\",\n \"text\": \"def test_snapshot_list_param_limit(self):\\n self._list_snapshots_by_param_limit(limit=1, expected_elements=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87767fb721b442cad6b028e956b7f5a4\",\n \"score\": \"0.5050978\",\n \"text\": \"def recent_actions(self, max_count=100):\\n if max_count > 100:\\n raise OverflowError(\\\"Max_count should be less or equal to 1000\\\")\\n request_url = self.generate_url(\\\"recentActions\\\", **{\\\"maxCount\\\": str(max_count)})\\n request = requests.get(request_url)\\n return self.get_response(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e17d8a7ed564f1320a8d4155daaebe0\",\n \"score\": \"0.5048258\",\n \"text\": \"def limit(self, limit):\\n\\n return self[:limit]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4fb705d53c2310d8deb7264adbce02c\",\n \"score\": \"0.5040705\",\n \"text\": \"def top_rated():\\n\\n subject = \\\"Untappd Gems\\\"\\n body = \\\"Here are the new beers your friends are excited about:\\\\n\\\"\\n send_ready = False\\n\\n cursor = conn.cursor()\\n\\n old_recent = cursor.execute(\\\"SELECT value FROM BREW_app_data WHERE parameter = 'recent_checkin'\\\").fetchone()[0]\\n\\n # get new checkins\\n endpoint = f'https://api.untappd.com/v4/checkin/recent?limit=50&min_id={old_recent}&access_token={secrets.untappd_token}'\\n res = httpx.get(endpoint)\\n block = res.json()\\n\\n # if new checkins, process\\n if block['response']['checkins']['count'] > 0:\\n\\n new_recent = str(block['response']['checkins']['items'][0]['checkin_id'])\\n checkins = block['response']['checkins']['items']\\n new_beer_list = []\\n\\n current_beer_list = cursor.execute(\\\"SELECT * FROM BREW_top_rated\\\")\\n current_bids = [row['beer_id'] for row in current_beer_list]\\n\\n for i in checkins:\\n name = f\\\"{i['user']['first_name']} {i['user']['last_name']}\\\"\\n beer = i['beer']['beer_name']\\n bid = i['beer']['bid']\\n brewery = i['brewery']['brewery_name']\\n rating = i['rating_score']\\n if name not in secrets.bad_taste:\\n if rating >= 4.5:\\n new_beer = {\\\"bid\\\":bid, \\\"name\\\":beer, \\\"brewery\\\":brewery}\\n if new_beer['bid'] not in current_bids:\\n add = f\\\"\\\\n - {name} gave {str(rating)} stars to {beer} by {brewery}\\\"\\n body = body + add\\n new_beer_list.append((new_beer['bid'], new_beer['name'], new_beer['brewery']))\\n current_bids.append(new_beer['bid'])\\n send_ready = True\\n \\n if send_ready == True:\\n send_email(subject, body) \\n logging.info(\\\"Beers email sent. New recent: \\\" + new_recent)\\n else:\\n logging.info(\\\"New checkins. Nothing premium.\\\")\\n \\n cursor.executemany(\\\"INSERT INTO BREW_top_rated (beer_id, beer_name, brewery) VALUES (?,?,?)\\\", new_beer_list)\\n cursor.execute(\\\"UPDATE BREW_app_data SET value=? WHERE parameter == 'recent_checkin'\\\", (new_recent,))\\n else:\\n logging.info(\\\"No new checkins\\\")\\n\\n conn.commit()\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e2205b48173a2a59891c7ec7e59404d\",\n \"score\": \"0.50264627\",\n \"text\": \"def setLimit(self, limit):\\n if self.limit > limit:\\n self.limit = limit\\n else:\\n self.limit = self.limit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8c7899ab0a0723ec70fd67ef28e4c01\",\n \"score\": \"0.50185245\",\n \"text\": \"def limit (self, lfrom, lto) :\\n\\tr = self.clone(cloneData = false)\\n\\tr.data = self.data[lfrom:lto]\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfff909e370df9c97ae1e5d6a6528cdd\",\n \"score\": \"0.5001268\",\n \"text\": \"def get_limit(self): \\n return self._limit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ed2390059a57675b4ef0cbf4657049\",\n \"score\": \"0.49979234\",\n \"text\": \"def get_items_since(self, start, count):\\n self._commit_puts_stale_ok()\\n with self.get() as conn:\\n curs = conn.execute('''\\n SELECT * FROM %s WHERE ROWID > ? ORDER BY ROWID ASC LIMIT ?\\n ''' % self.db_contains_type, (start, count))\\n curs.row_factory = dict_factory\\n return [r for r in curs]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a966190cac6f39d87a3e80da6485453\",\n \"score\": \"0.49850872\",\n \"text\": \"def private_unread(limit=10, offset=0, asc=False, before=None):\\n if before and isinstance(before, (int, long)):\\n offset = before - 157321\\n\\n redis = RedisPool(settings.storage_socket)\\n ids = redis.get('private_unread:%s' % env.user.id)\\n if not ids:\\n return []\\n\\n ids = map(lambda id: int(id), ids.split(','))[offset:offset+limit]\\n\\n res = db.fetchall(\\n \\\"SELECT \\\"\\n \\\"p.id, p.author, u.login, i.name, i.avatar, p.private, \\\"\\n \\\"p.created at time zone %(tz)s AS created,\\\"\\n \\\"p.resource, \\\"\\n \\\"p.type, p.title, p.link, p.tags, p.text, \\\"\\n \\\"p.edited,\\\"\\n \\\"(p.edited=false AND p.created+interval '%(edit_expire)s second' >= now())\\\"\\n \\\"AS editable, \\\"\\n \\\"p.archive, p.files, \\\"\\n \\\"sp.post_id AS subscribed, \\\"\\n \\\"rp.post_id AS recommended, \\\"\\n \\\"rb.post_id AS bookmarked \\\"\\n \\\"FROM unnest(%(ids)s) \\\"\\n \\\"JOIN posts.posts p ON p.id=unnest \\\"\\n \\\"JOIN users.logins u ON u.id=p.author \\\"\\n \\\"LEFT OUTER JOIN users.info i ON i.id=p.author \\\"\\n \\\"LEFT OUTER JOIN posts.recipients rcp ON rcp.post_id=p.id \\\"\\n \\\"LEFT OUTER JOIN subs.posts sp ON sp.post_id=p.id \\\"\\n \\\"AND sp.user_id=%(user_id)s \\\"\\n \\\"LEFT OUTER JOIN posts.recommendations rp \\\"\\n \\\"ON %(user_id)s=rp.user_id AND \\\"\\n \\\"p.id=rp.post_id AND rp.comment_id=0 \\\"\\n \\\"LEFT OUTER JOIN posts.bookmarks rb \\\"\\n \\\"ON p.id=rb.post_id AND %(user_id)s=rb.user_id AND \\\"\\n \\\"rb.comment_id=0 \\\"\\n \\\"WHERE p.private=true;\\\",\\n {'ids': ids, 'user_id': env.user.id, 'tz': env.user.get_profile('tz'),\\n 'limit': limit, 'edit_expire': settings.edit_expire})\\n\\n plist = []\\n for i, r in enumerate(res):\\n r = dict(r)\\n r['uid'] = 157321 + offset + i + 1\\n plist.append(r)\\n return _plist(plist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4722ccf814a4d3d6578fd3cdc3c6fa8e\",\n \"score\": \"0.4981565\",\n \"text\": \"def recently_changed_packages_activity_list(client=client.Client(), offset='', limit=''):\\n args = client.sanitize_params(locals())\\n\\n resp = client.request(action='recently_changed_packages_activity_list', data=args)\\n if not resp['success']:\\n raise exceptions.CKANError(resp.error)\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f10c54adf90adbec6e6116eb912cb65\",\n \"score\": \"0.49766478\",\n \"text\": \"def recent_changes(self, count=10):\\n \\n recent_blog = qon.blog.recent_items(self.get_all_blogs(), count=count)\\n recent_wiki = qon.wiki.recent_items(self.get_all_wikis(), count=count)\\n bydate = []\\n for i in recent_blog + recent_wiki:\\n bydate.append((i.watchable_last_change(), i))\\n bydate.sort()\\n bydate.reverse()\\n \\n return [i for date, i in bydate]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c4b8c6f211e2af54e43c25bbda99e25\",\n \"score\": \"0.49759784\",\n \"text\": \"def test_get_filtered_list_limit(self):\\n flexmock(errata).should_receive(\\\"Erratum\\\").and_return(None)\\n\\n response = flexmock(status_code=200)\\n response.should_receive(\\\"json\\\").and_return(test_structures.example_erratum_filtered_list)\\n\\n flexmock(errata.requests).should_receive(\\\"get\\\").and_return(response)\\n\\n res = errata.get_filtered_list(limit=1)\\n self.assertEqual(1, len(res))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"634e34527eb6060323c78016569e9aae\",\n \"score\": \"0.49677092\",\n \"text\": \"def test_snapshot_list_param_limit_equals_zero(self):\\n self._list_snapshots_by_param_limit(limit=0, expected_elements=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62ea8890ff4d0fdcecf67f0dbe56d756\",\n \"score\": \"0.49645263\",\n \"text\": \"def limit(self, limit):\\n\\n self._limit = limit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62ea8890ff4d0fdcecf67f0dbe56d756\",\n \"score\": \"0.49645263\",\n \"text\": \"def limit(self, limit):\\n\\n self._limit = limit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6a785549d22bad05f4b91dc0c894793\",\n \"score\": \"0.49529153\",\n \"text\": \"def get_new_messages(self, partner, last_message):\\n\\n all_messages = self.get_messages()\\n messages_with_partner = all_messages[partner]\\n new_messages = [message for message in messages_with_partner if int(message[0]) > int(last_message)]\\n\\n return new_messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48b2789445e83a76bc87217438481088\",\n \"score\": \"0.49394128\",\n \"text\": \"def check_limit(self):\\n notifications_limit = self.request.query_params.get(\\\"limit\\\", None)\\n limit = None\\n if notifications_limit:\\n try:\\n limit = int(notifications_limit)\\n except ValueError:\\n pass # if limit has not a valid format, let's not use it\\n return limit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61a81155a4f7d2fd4f8516d112173410\",\n \"score\": \"0.4934796\",\n \"text\": \"def recent(cls,site_id=0,ct=15,filter='recent'):\\n return meta.DBSession.query(Help).filter_by(site_id=site_id\\n ).order_by(help_table.c.created.desc()).limit(ct)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99911eb1d7887869761d1a76fcaf77a2\",\n \"score\": \"0.49343956\",\n \"text\": \"def recent(self, bot, args):\\n plists = bot.gmusic.get_newest_playlists()\\n return self.format_playlists(bot, plists)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cca2cfd4f317f0dfc7b0bd2aef4a8adc\",\n \"score\": \"0.4931534\",\n \"text\": \"def limit(self, limit):\\n\\t\\tobj = copy.deepcopy(self)\\n\\t\\tobj.__limit = limit\\n\\t\\treturn obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5800eb3cb0ccafc9c9d5828b29e230ec\",\n \"score\": \"0.4931328\",\n \"text\": \"def get_queryset(self, request):\\n qs = super(MaximumStayReason, self).get_queryset(request)\\n if request.user.is_superuser:\\n return qs\\n group = models.MooringAreaGroup.objects.filter(members__in=[request.user,])\\n return qs.filter(mooring_group__in=group)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77a5f27538caa1c2126dcf8bb7a88dfe\",\n \"score\": \"0.49210572\",\n \"text\": \"def backlog():\\n current_date = datetime.now().strftime('%Y-%m-%d')\\n results = get_all_tasks()\\n todo_items = []\\n # filter out results,we want only tasks that are not scheduled for today\\n for r in results:\\n if r[\\\"due_date\\\"] != current_date:\\n todo_items.append(r)\\n return render_template(\\n 'backlog.html',\\n title='Backlog',\\n todo_items=todo_items\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b018df829436c2777e6e91dec9339f4\",\n \"score\": \"0.4908892\",\n \"text\": \"def purgelist(artifactory):\\n purgable = artifactory.count_based_retention(retention_count=5)\\n return purgable\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59608e03ad61b1f9ea6ee7cbec837575\",\n \"score\": \"0.49079606\",\n \"text\": \"def get_purchases_limit(conn, limit):\\n return execute_sql(conn, sqlite_list_purchase_limit, values=(limit,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"712d061af9e6ea5649e57fd12f6ce542\",\n \"score\": \"0.49070376\",\n \"text\": \"def get_brief_watched(self):\\n watched = self.ownedstock_set.filter(watch=True, quantity=0)\\n fin = []\\n for watched in watched:\\n stock = Stock.objects.get(ticker=watched.ticker)\\n fin.append((watched, stock))\\n return fin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"612b89cb3a7e25169a30adc08514d066\",\n \"score\": \"0.48989064\",\n \"text\": \"def setOverdraftLimit(self, newLimit):\\r\\n\\r\\n # Ensures overdraftLimit isn't set to less than it currently is if the account has a negative balance\\r\\n if newLimit > -(self.balance):\\r\\n self.overdraftLimit = float(newLimit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d09b5528afbc69ddcb7246ba7cb7cf87\",\n \"score\": \"0.48987466\",\n \"text\": \"def truncated(f):\\r\\n @functools.wraps(f)\\r\\n def wrapper(self, hints, *args, **kwargs):\\r\\n if not hasattr(hints, 'get_limit'):\\r\\n raise exception.UnexpectedError(\\r\\n _('Cannot truncate a driver call without hints list as '\\r\\n 'first parameter after self '))\\r\\n\\r\\n limit_dict = hints.get_limit()\\r\\n if limit_dict is None:\\r\\n return f(self, hints, *args, **kwargs)\\r\\n\\r\\n # A limit is set, so ask for one more entry than we need\\r\\n list_limit = limit_dict['limit']\\r\\n hints.set_limit(list_limit + 1)\\r\\n ref_list = f(self, hints, *args, **kwargs)\\r\\n\\r\\n # If we got more than the original limit then trim back the list and\\r\\n # mark it truncated. In both cases, make sure we set the limit back\\r\\n # to its original value.\\r\\n if len(ref_list) > list_limit:\\r\\n hints.set_limit(list_limit, truncated=True)\\r\\n return ref_list[:list_limit]\\r\\n else:\\r\\n hints.set_limit(list_limit)\\r\\n return ref_list\\r\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b1d079e638bc6ced99dcb49db4a4fa\",\n \"score\": \"0.48958537\",\n \"text\": \"def revdatelimit(ui, revset):\\n days = ui.configint(b'remotefilelog', b'prefetchdays')\\n if days > 0:\\n revset = b'(%s) & date(-%s)' % (revset, days)\\n return revset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6163bd1758054659e049f869d8774ee5\",\n \"score\": \"0.4894288\",\n \"text\": \"def limit(self, value):\\n self._limit = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"967d8d3285f66a68ed2808bc7df6cef3\",\n \"score\": \"0.4893634\",\n \"text\": \"def get_last_logs(self, limit: int=10):\\n count = logs_db.count({'session_id': self.session_id})\\n return logs_db.find({'session_id': self.session_id}, skip=count-limit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"968a1357c8c8368a160a4e2d0253d924\",\n \"score\": \"0.48934558\",\n \"text\": \"def popular_last_week(self):\\n return self.extra(where={\\\"created > NOW() - INTERVAL '7 days'\\\"}).order_by('-score', '-ups', 'created')[:10]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bb291fb294e70d614749e6d99112ea6\",\n \"score\": \"0.48912013\",\n \"text\": \"def cmd_recent(self, source, nickname=None):\\n lm = self.getLinkManager(source)\\n entries = list(lm.recent(3, nickname))\\n if entries:\\n for e in entries:\\n source.notice(e.displayCompleteHumanReadable(modified=True))\\n else:\\n if nickname is None:\\n msg = u'No recent entries.'\\n else:\\n msg = u'No recent entries for \\\\002%s\\\\002.' % (nickname,)\\n source.reply(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47da360553fb2a17b23620d225ab41c0\",\n \"score\": \"0.48804876\",\n \"text\": \"def __init__(self):\\n self.items = []\\n\\tself.max = -sys.maxsize\\n\\tself.popped_after_max = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8450a5bc3a4345f9f125b2865542e605\",\n \"score\": \"0.48786524\",\n \"text\": \"def consume(self, limit=None):\\n for total_message_count in count():\\n if limit and total_message_count >= limit:\\n raise StopIteration\\n self.drain_events()\\n yield True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bb72f267d828a4205f7c9379e69643f\",\n \"score\": \"0.486774\",\n \"text\": \"def modify_ack_deadline(self, items):\\n # We must potentially split the request into multiple smaller requests\\n # to avoid the server-side max request size limit.\\n ack_ids = (item.ack_id for item in items)\\n seconds = (item.seconds for item in items)\\n total_chunks = int(math.ceil(len(items) / _ACK_IDS_BATCH_SIZE))\\n\\n for _ in range(total_chunks):\\n request = types.StreamingPullRequest(\\n modify_deadline_ack_ids=itertools.islice(ack_ids, _ACK_IDS_BATCH_SIZE),\\n modify_deadline_seconds=itertools.islice(seconds, _ACK_IDS_BATCH_SIZE),\\n )\\n self._manager.send(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28ca60747faf33419a2eaa60868cea89\",\n \"score\": \"0.48618472\",\n \"text\": \"def heartbeat(self):\\n\\n now = time.time()\\n\\n # purge messages that have exceed their rentention time\\n purge = [r['id'] for r in self.db.execute(\\n 'select id from messages where purge>0 and purge<?',\\n (now,))]\\n for msg_id in purge:\\n logging.debug('Exceeded retention on msg {}'.format(msg_id))\\n self.stats['msg-retention'] = (\\n self.stats.get('msg-retention', 0) + 1)\\n self.count_deleted(msg_id)\\n\\n # try to deliver messages that exceeds the visibility timeout\\n visible = (dict((k, r[k]) for k in r.keys()) for r in self.db.execute(\\n 'select * from messages where show>0 and show<=? and not '\\n '(purge>0 and purge<?) order by priority', (now, now)))\\n undelivered = []\\n for msg in visible:\\n logging.debug('Exceeded timeout on msg {}'.format(msg['id']))\\n self.stats['msg-timeouts'] = self.stats.get('msg-timeouts', 0) + 1\\n if msg['deliver'] == 0 or msg['dcount'] < msg['deliver']:\\n # try to deliver the message\\n if not self.deliver_message(msg):\\n # count it as shown\\n self.stats['msg-show'] = self.stats.get('msg-show', 0) + 1\\n undelivered.append(msg['id'])\\n else:\\n # message delivered to many times, purge it\\n purge.append(msg['id'])\\n self.count_deleted(msg['id'])\\n\\n if undelivered:\\n # set messages to be visible (out of timeout)\\n self.db.executemany('update messages set show=0.0 where id=?',\\n ((i,) for i in undelivered))\\n if purge:\\n self.db.executemany('delete from messages where id=?',\\n ((i,) for i in purge))\\n if purge or undelivered:\\n self.db.commit()\\n\\n # check if channel has listeners, otherwise remove it when timed out\\n unused = [(ch, lst) for ch, lst in self.channels.items()\\n if not lst and lst.ts + self.channel_ttl < now]\\n for channel, listeners in unused:\\n # channel is no longer in use\\n logging.debug('Removing empty channel {}'.format(channel))\\n listeners.stop()\\n del self.channels[channel]\\n self.stats['channel-remove'] = (\\n self.stats.get('channel-remove', 0) + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bcca7a0305f1da0eaba3a7e4d524f0a\",\n \"score\": \"0.48614404\",\n \"text\": \"def fetch_actions(self, action_filter, action_limit=50, before=None, since=None):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e945321068c9bc5edb2134347e6921f\",\n \"score\": \"0.48601106\",\n \"text\": \"async def last(self, ctx):\\n if not_botlogs(ctx.message.channel): # prevent from deleting #botlogs\\n return\\n\\n async for message in ctx.message.channel.history(limit=3, oldest_first=False):\\n await message.delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c07ab40d3c74382fe98fac8e9c680a4\",\n \"score\": \"0.48584053\",\n \"text\": \"def find_bad_channels_maxwell(\\n raw,\\n limit=7.0,\\n duration=5.0,\\n min_count=5,\\n return_scores=False,\\n origin=\\\"auto\\\",\\n int_order=8,\\n ext_order=3,\\n calibration=None,\\n cross_talk=None,\\n coord_frame=\\\"head\\\",\\n regularize=\\\"in\\\",\\n ignore_ref=False,\\n bad_condition=\\\"error\\\",\\n head_pos=None,\\n mag_scale=100.0,\\n skip_by_annotation=(\\\"edge\\\", \\\"bad_acq_skip\\\"),\\n h_freq=40.0,\\n extended_proj=(),\\n verbose=None,\\n):\\n if h_freq is not None:\\n if raw.info.get(\\\"lowpass\\\") and raw.info[\\\"lowpass\\\"] <= h_freq:\\n freq_loc = \\\"below\\\" if raw.info[\\\"lowpass\\\"] < h_freq else \\\"equal to\\\"\\n msg = (\\n f\\\"The input data has already been low-pass filtered with a \\\"\\n f'{raw.info[\\\"lowpass\\\"]} Hz cutoff frequency, which is '\\n f\\\"{freq_loc} the requested cutoff of {h_freq} Hz. Not \\\"\\n f\\\"applying low-pass filter.\\\"\\n )\\n logger.info(msg)\\n else:\\n logger.info(\\n f\\\"Applying low-pass filter with {h_freq} Hz cutoff \\\" f\\\"frequency ...\\\"\\n )\\n raw = raw.copy().load_data().filter(l_freq=None, h_freq=h_freq)\\n\\n limit = float(limit)\\n onsets, ends = _annotations_starts_stops(raw, skip_by_annotation, invert=True)\\n del skip_by_annotation\\n # operate on chunks\\n starts = list()\\n stops = list()\\n step = int(round(raw.info[\\\"sfreq\\\"] * duration))\\n for onset, end in zip(onsets, ends):\\n if end - onset >= step:\\n ss = np.arange(onset, end - step + 1, step)\\n starts.extend(ss)\\n ss = ss + step\\n ss[-1] = end\\n stops.extend(ss)\\n min_count = min(_ensure_int(min_count, \\\"min_count\\\"), len(starts))\\n logger.info(\\n \\\"Scanning for bad channels in %d interval%s (%0.1f s) ...\\\"\\n % (len(starts), _pl(starts), step / raw.info[\\\"sfreq\\\"])\\n )\\n params = _prep_maxwell_filter(\\n raw,\\n skip_by_annotation=[], # already accounted for\\n origin=origin,\\n int_order=int_order,\\n ext_order=ext_order,\\n calibration=calibration,\\n cross_talk=cross_talk,\\n coord_frame=coord_frame,\\n regularize=regularize,\\n ignore_ref=ignore_ref,\\n bad_condition=bad_condition,\\n head_pos=head_pos,\\n mag_scale=mag_scale,\\n extended_proj=extended_proj,\\n )\\n del origin, int_order, ext_order, calibration, cross_talk, coord_frame\\n del regularize, ignore_ref, bad_condition, head_pos, mag_scale\\n good_meg_picks = params[\\\"meg_picks\\\"][params[\\\"good_mask\\\"]]\\n assert len(params[\\\"meg_picks\\\"]) == len(params[\\\"coil_scale\\\"])\\n assert len(params[\\\"good_mask\\\"]) == len(params[\\\"meg_picks\\\"])\\n noisy_chs = Counter()\\n flat_chs = Counter()\\n flat_limits = dict(grad=0.01e-13, mag=0.01e-15)\\n these_limits = np.array(\\n [\\n flat_limits[\\\"grad\\\"] if pick in params[\\\"grad_picks\\\"] else flat_limits[\\\"mag\\\"]\\n for pick in good_meg_picks\\n ]\\n )\\n\\n flat_step = max(20, int(30 * raw.info[\\\"sfreq\\\"] / 1000.0))\\n all_flats = set()\\n\\n # Prepare variables to return if `return_scores=True`.\\n bins = np.empty((len(starts), 2)) # To store start, stop of each segment\\n # We create ndarrays with one row per channel, regardless of channel type\\n # and whether the channel has been marked as \\\"bad\\\" in info or not. This\\n # makes indexing in the loop easier. We only filter this down to the subset\\n # of MEG channels after all processing is done.\\n ch_names = np.array(raw.ch_names)\\n ch_types = np.array(raw.get_channel_types())\\n\\n scores_flat = np.full((len(ch_names), len(starts)), np.nan)\\n scores_noisy = np.full_like(scores_flat, fill_value=np.nan)\\n\\n thresh_flat = np.full((len(ch_names), 1), np.nan)\\n thresh_noisy = np.full_like(thresh_flat, fill_value=np.nan)\\n\\n for si, (start, stop) in enumerate(zip(starts, stops)):\\n n_iter = 0\\n orig_data = raw.get_data(None, start, stop, verbose=False)\\n chunk_raw = RawArray(\\n orig_data,\\n params[\\\"info\\\"],\\n first_samp=raw.first_samp + start,\\n copy=\\\"data\\\",\\n verbose=False,\\n )\\n\\n t = chunk_raw.times[[0, -1]] + start / raw.info[\\\"sfreq\\\"]\\n logger.info(\\n \\\" Interval %3d: %8.3f - %8.3f\\\" % ((si + 1,) + tuple(t[[0, -1]]))\\n )\\n\\n # Flat pass: SD < 0.01 fT/cm or 0.01 fT for at 30 ms (or 20 samples)\\n n = stop - start\\n flat_stop = n - (n % flat_step)\\n data = chunk_raw.get_data(good_meg_picks, 0, flat_stop)\\n data.shape = (data.shape[0], -1, flat_step)\\n delta = np.std(data, axis=-1).min(-1) # min std across segments\\n\\n # We may want to return this later if `return_scores=True`.\\n bins[si, :] = t[0], t[-1]\\n scores_flat[good_meg_picks, si] = delta\\n thresh_flat[good_meg_picks] = these_limits.reshape(-1, 1)\\n\\n chunk_flats = delta < these_limits\\n chunk_flats = np.where(chunk_flats)[0]\\n chunk_flats = [\\n raw.ch_names[good_meg_picks[chunk_flat]] for chunk_flat in chunk_flats\\n ]\\n flat_chs.update(chunk_flats)\\n all_flats |= set(chunk_flats)\\n chunk_flats = sorted(all_flats)\\n these_picks = [\\n pick for pick in good_meg_picks if raw.ch_names[pick] not in chunk_flats\\n ]\\n if len(these_picks) == 0:\\n logger.info(f\\\" Flat ({len(chunk_flats):2d}): <all>\\\")\\n warn(\\n \\\"All-flat segment detected, all channels will be marked as \\\"\\n f\\\"flat and processing will stop (t={t[0]:0.3f}). \\\"\\n \\\"Consider using annotate_amplitude before calling this \\\"\\n 'function with skip_by_annotation=\\\"bad_flat\\\" (or similar) to '\\n \\\"properly process all segments.\\\"\\n )\\n break # no reason to continue\\n # Bad pass\\n chunk_noisy = list()\\n params[\\\"st_duration\\\"] = int(round(chunk_raw.times[-1] * raw.info[\\\"sfreq\\\"]))\\n for n_iter in range(1, 101): # iteratively exclude the worst ones\\n assert set(raw.info[\\\"bads\\\"]) & set(chunk_noisy) == set()\\n params[\\\"good_mask\\\"][:] = [\\n chunk_raw.ch_names[pick]\\n not in raw.info[\\\"bads\\\"] + chunk_noisy + chunk_flats\\n for pick in params[\\\"meg_picks\\\"]\\n ]\\n chunk_raw._data[:] = orig_data\\n delta = chunk_raw.get_data(these_picks)\\n with use_log_level(False):\\n _run_maxwell_filter(chunk_raw, reconstruct=\\\"orig\\\", copy=False, **params)\\n\\n if n_iter == 1 and len(chunk_flats):\\n logger.info(\\n \\\" Flat (%2d): %s\\\"\\n % (len(chunk_flats), \\\" \\\".join(chunk_flats))\\n )\\n delta -= chunk_raw.get_data(these_picks)\\n # p2p\\n range_ = np.ptp(delta, axis=-1)\\n cs_picks = np.searchsorted(params[\\\"meg_picks\\\"], these_picks)\\n range_ *= params[\\\"coil_scale\\\"][cs_picks, 0]\\n mean, std = np.mean(range_), np.std(range_)\\n # z score\\n z = (range_ - mean) / std\\n idx = np.argmax(z)\\n max_ = z[idx]\\n\\n # We may want to return this later if `return_scores=True`.\\n scores_noisy[these_picks, si] = z\\n thresh_noisy[these_picks] = limit\\n\\n if max_ < limit:\\n break\\n\\n name = raw.ch_names[these_picks[idx]]\\n logger.debug(\\\" Bad: %s %0.1f\\\" % (name, max_))\\n these_picks.pop(idx)\\n chunk_noisy.append(name)\\n noisy_chs.update(chunk_noisy)\\n noisy_chs = sorted(\\n (b for b, c in noisy_chs.items() if c >= min_count),\\n key=lambda x: raw.ch_names.index(x),\\n )\\n flat_chs = sorted(\\n (f for f, c in flat_chs.items() if c >= min_count),\\n key=lambda x: raw.ch_names.index(x),\\n )\\n\\n # Only include MEG channels.\\n ch_names = ch_names[params[\\\"meg_picks\\\"]]\\n ch_types = ch_types[params[\\\"meg_picks\\\"]]\\n scores_flat = scores_flat[params[\\\"meg_picks\\\"]]\\n thresh_flat = thresh_flat[params[\\\"meg_picks\\\"]]\\n scores_noisy = scores_noisy[params[\\\"meg_picks\\\"]]\\n thresh_noisy = thresh_noisy[params[\\\"meg_picks\\\"]]\\n\\n logger.info(\\\" Static bad channels: %s\\\" % (noisy_chs,))\\n logger.info(\\\" Static flat channels: %s\\\" % (flat_chs,))\\n logger.info(\\\"[done]\\\")\\n\\n if return_scores:\\n scores = dict(\\n ch_names=ch_names,\\n ch_types=ch_types,\\n bins=bins,\\n scores_flat=scores_flat,\\n limits_flat=thresh_flat,\\n scores_noisy=scores_noisy,\\n limits_noisy=thresh_noisy,\\n )\\n return noisy_chs, flat_chs, scores\\n else:\\n return noisy_chs, flat_chs\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993893,"cells":{"query_id":{"kind":"string","value":"f6e3df0a207abdfe02e591d71511ee1a"},"query":{"kind":"string","value":"Get function that returns distances between examples in learned space."},"positive_passages":{"kind":"list like","value":[{"docid":"81e5a0405eb8930cf0ef7fefed8254f7","score":"0.66239303","text":"def get_dist_func(data : Array[np.float64], target : Array[np.float64], n : np.int) -> Callable[[Callable[[np.float64, np.float64], np.float64], np.int, np.int], np.float64]:\n\n # Get transformed data.\n data_trans : Array[np.float64] = LinearDiscriminantAnalysis(n_components=n).fit_transform(StandardScaler().fit_transform(data), target)\n\n # Computing distance:\n def dist_func_res(metric : Callable[[np.float64, np.float64], np.float64], i1 : np.int, i2 : np.int) -> np.float64:\n \"\"\" \n distance function that takes metric function and indices of examples in training set and returns distance\n in learned space using specified distance metric.\n\n Args:\n metric: Callable[[np.flaot64, np.float64], np.float64] - metric to use in learned metric space.\n i1 : int - index of first training example\n i2 : int - index of second training example\n Returns:\n np.float64 - distance in learned metric space using specified metric\n between specified training examples.\n \"\"\"\n\n # Compute distance in learned metric space using specified metric.\n return metric(data_trans[i1, :], data_trans[i2, :])\n\n return dist_func_res # Return distance function.","title":""}],"string":"[\n {\n \"docid\": \"81e5a0405eb8930cf0ef7fefed8254f7\",\n \"score\": \"0.66239303\",\n \"text\": \"def get_dist_func(data : Array[np.float64], target : Array[np.float64], n : np.int) -> Callable[[Callable[[np.float64, np.float64], np.float64], np.int, np.int], np.float64]:\\n\\n # Get transformed data.\\n data_trans : Array[np.float64] = LinearDiscriminantAnalysis(n_components=n).fit_transform(StandardScaler().fit_transform(data), target)\\n\\n # Computing distance:\\n def dist_func_res(metric : Callable[[np.float64, np.float64], np.float64], i1 : np.int, i2 : np.int) -> np.float64:\\n \\\"\\\"\\\" \\n distance function that takes metric function and indices of examples in training set and returns distance\\n in learned space using specified distance metric.\\n\\n Args:\\n metric: Callable[[np.flaot64, np.float64], np.float64] - metric to use in learned metric space.\\n i1 : int - index of first training example\\n i2 : int - index of second training example\\n Returns:\\n np.float64 - distance in learned metric space using specified metric\\n between specified training examples.\\n \\\"\\\"\\\"\\n\\n # Compute distance in learned metric space using specified metric.\\n return metric(data_trans[i1, :], data_trans[i2, :])\\n\\n return dist_func_res # Return distance function.\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5b90c6db1c4c7a51d2b69c6d61c11091","score":"0.7252572","text":"def get_distances():","title":""},{"docid":"784bc0b08043ecf1f5b80dbff811c330","score":"0.6990197","text":"def dist(self) -> Distance:","title":""},{"docid":"8269db68ee68dc5cddccb3020612f651","score":"0.6680347","text":"def distance(self):\n raise NotImplementedError","title":""},{"docid":"bd29e35832939e8e44de064bf43d11dd","score":"0.6593241","text":"def distances_(self):\n return self.session.run(self._scores)","title":""},{"docid":"53f766a8c6e676f0d8819670a570ff45","score":"0.6525181","text":"def distance(self):\n pass","title":""},{"docid":"1c7444051fe1f1915d271a0a56983fbf","score":"0.6355777","text":"def distances(self) -> List[List]:\n raise NotImplementedError","title":""},{"docid":"aae727eff6a66b6f9a750ccf6f6ae0e6","score":"0.6314876","text":"def _get_distance(self):\n return self.__distance","title":""},{"docid":"aae727eff6a66b6f9a750ccf6f6ae0e6","score":"0.6314876","text":"def _get_distance(self):\n return self.__distance","title":""},{"docid":"e1fbe6d0e0a00ca3b34cb68fb5ec0701","score":"0.6259694","text":"def L2_distance(self, X):\n\t\tnum_test = X.shape[0]\n\t\tnum_train = self.Xtr.shape[0]\n\t\tdists = np.zeros((num_test, num_train)) \n\t\tprint(\"Running ...\")\n\t\tdists = np.reshape(np.sum(np.power(X,2), axis=1), [num_test,1]) + np.sum(np.power(self.Xtr,2), axis=1) - 2 * np.matmul(X, self.Xtr.T)\n\t\tdists = np.sqrt(dists)\n\t\treturn dists","title":""},{"docid":"9a9995c372ead52a5236088efae03682","score":"0.62333035","text":"def distance(self, X):\n n = X.shape[0]\n Y = self.predict(X)\n X = X.numpy()\n\n distances = []\n for i in range(n):\n label_options = list(range(self.weights.shape[0])) # create list of length num_classes\n del label_options[Y[i]]\n dists = []\n for j in label_options:\n v = try_region_multi([self], [j], X[i])\n dists.append(np.linalg.norm(v))\n distances.append(min(dists))\n return torch.tensor(distances)","title":""},{"docid":"081b5fef80add0612f0c7b461ceaf124","score":"0.62328655","text":"def distances(self):\n return self._distances","title":""},{"docid":"081b5fef80add0612f0c7b461ceaf124","score":"0.62328655","text":"def distances(self):\n return self._distances","title":""},{"docid":"fedf8383a9a6f4635380892d998781ac","score":"0.6212885","text":"def get_distance(self, a, b):\n return np.sum((a - b)**2)","title":""},{"docid":"cf1e559b8d2877fa90e740de8059ecb6","score":"0.62094885","text":"def get_distance(self, pred, target):\n logits_pred = self.i3d.extract_features(pred).squeeze() # [b, n]\n logits_target = self.i3d.extract_features(target).squeeze()\n\n if pred.shape[0] == 1: # if batch size is 1, the prev. squeeze also removed the batch dim\n logits_pred = logits_pred.unsqueeze(dim=0)\n logits_target = logits_target.unsqueeze(dim=0)\n\n return calculate_2_wasserstein_dist(logits_pred, logits_target)","title":""},{"docid":"e5f3cf4bc4f6f6429dd02974786cfdfe","score":"0.6201153","text":"def calc_distance(self, layer, idxs=None):\n if not idxs:\n return torch.dist(self.theta_f[layer], self.theta_i[layer])\n else:\n return torch.dist(self.theta_f[layer][idxs], self.theta_i[layer][idxs])","title":""},{"docid":"eca62223ff8d88d2e13860de1dcb1eab","score":"0.61937904","text":"def _calc_distances(sample, features) -> ndarray:\n distances = np.empty(0)\n powers = np.power(np.subtract(sample, features), 2)\n\n for i in range(0, powers.shape[0]):\n d = np.sqrt(np.sum(powers[i]))\n distances = np.append(distances, d)\n\n return distances","title":""},{"docid":"46c6f84b20532823fb0faa8064a5c6e3","score":"0.60940427","text":"def _abs_dist_func(x, y):\n n = x.size(1)\n m = y.size(1)\n d = x.size(2)\n x = x.unsqueeze(2).expand(-1, n, m, d)\n y = y.unsqueeze(1).expand(-1, n, m, d)\n return torch.abs(x - y).sum(3)","title":""},{"docid":"52dab359559f25bbceff85c1f8979332","score":"0.60817724","text":"def edist(self, inputs, weights):\n \n euclideanDistance = 0\n \n if len(inputs) != len(weights):\n print len(inputs), \"is different length than\", len(weights)\n return\n \n for i in range(len(inputs)):\n nth = inputs[i] - weights[i]\n nth = nth ** 2\n euclideanDistance += nth\n \n return euclideanDistance ** (0.5)","title":""},{"docid":"358af3716ba6d8b1580ef48c49ee257c","score":"0.6075651","text":"def dist_vec(self) -> np.ndarray:","title":""},{"docid":"5e7e63bf4189f462f830cd49c93fba71","score":"0.6059089","text":"def _get_distance(self, targets, y_hats):\n total_dist = 0\n total_length = 0\n\n for (target, y_hat) in zip(targets, y_hats):\n s1 = self.vocab.label_to_string(target)\n s2 = self.vocab.label_to_string(y_hat)\n\n dist, length = self._metric(s1, s2)\n\n total_dist += dist\n total_length += length\n\n return total_dist, total_length","title":""},{"docid":"72b2e46ed50eb128fd4476b1cba2ee06","score":"0.60589665","text":"def distance(self, policy_logits: Tensor, y: Tensor) -> Tensor:\n raise NotImplementedError('Implement me')","title":""},{"docid":"5377be6369ca3ad90fffe686654675e8","score":"0.6017524","text":"def dist(self, X):\n\n if self.X.shape != X.shape:\n raise ValueError(\n \"The two samples must have the same number of features\"\n )\n\n # Remarks: as we only use this distance to sort the learning samples\n # by their distance to a sample to predict, we could get\n # rid of the 'sqrt()' call.\n\n return math.sqrt(\n sum(\n (feature1 - feature2)**2\n for feature1, feature2 in zip(self.X, X)\n )\n )","title":""},{"docid":"87a4526b50d9abeb498fe55a8275d50e","score":"0.60036236","text":"def get_distance():\n return ultraSensor.distance_centimeters","title":""},{"docid":"752480bb88dd62d51fdccee9cfda7484","score":"0.59986347","text":"def getDistance(self):\n return super().distance_cm()","title":""},{"docid":"7a05a5697e01a308bd415678990e51b1","score":"0.5998059","text":"def calc_distance(self):\n return rssi_to_feet(self.distance)","title":""},{"docid":"8d58a87e2de5493a8e9c2b5d9c3ee9c0","score":"0.5995062","text":"def _calculate_distance(self) -> float:\n ds = 100 * (\n np.linalg.norm(self.zs[self.h + 1] - self.problem.nadir)\n / (np.linalg.norm(self.fs[self.h + 1] - self.problem.nadir))\n )\n return ds","title":""},{"docid":"1413c23aff7b426e0b979c69cb08fa6a","score":"0.59868205","text":"def euclidean_distance_measure_getter(X):\n return {\"distance_measure\": [numba_wrapper(dtw_distance)], \"w\": [0]}","title":""},{"docid":"3bac5ad2ead4cbf384d50058bc1eefda","score":"0.5985756","text":"def calculate_distance(tfidf_scores, vocab_size):\n cosine_sim = SparseMatrixSimilarity(tfidf_scores, num_features=vocab_size)\n\n distance = []\n i=0\n for row in tfidf_scores:\n print i\n i += 1\n distance.append(1 - cosine_sim[row])\n\n return distance","title":""},{"docid":"fc60c2b0ff53427512cac8395e0c4257","score":"0.5976525","text":"def fitted_dist_func(self, x, y):\n if self._transform_matrix is not None:\n w = self._transform_matrix\n g = lambda x, y: weighted_euclidean(x, y, w)\n return g(x, y)\n else:\n None","title":""},{"docid":"462be3ac0abaa7f06f94daa1af7a5370","score":"0.5973414","text":"def getDistance(*args):\n return _coin.SbDPPlane_getDistance(*args)","title":""},{"docid":"ef68402a2811dc7c20847e95298bd39c","score":"0.59718275","text":"def get_distance(self, n):\n return self.metadata[n][\"distance\"]","title":""},{"docid":"75190bf0581efdc26920b26abecac9b2","score":"0.5970038","text":"def lcss_distance_measure_getter(X):\n stdp = _stdp(X)\n instance_length = _max_instance_length(X) # todo should this use the max instance\n # length for unequal length dataset instances?\n max_raw_warping_window = np.floor((instance_length + 1) / 4)\n n_dimensions = 1 # todo use other dimensions\n return {\n \"distance_measure\": [numba_wrapper(lcss_distance)],\n \"dim_to_use\": stats.randint(low=0, high=n_dimensions),\n \"epsilon\": stats.uniform(0.2 * stdp, stdp - 0.2 * stdp),\n # scipy stats randint is exclusive on the max value, hence + 1\n \"delta\": stats.randint(low=0, high=max_raw_warping_window + 1),\n }","title":""},{"docid":"168eea0aa1c8b36476d340a78051a0e8","score":"0.5968558","text":"def ComputeDistance(self, a, b):\n embeddings = self.BatchEmbed([a, b])\n return np.linalg.norm(embeddings[0] - embeddings[1])","title":""},{"docid":"1ecb5fdd948bdd33ee1413c33d6970ad","score":"0.5965392","text":"def test_distances():\n p1 = randint(100, size=(100,))\n p2 = randint(100, size=(100,))\n print(\"Simple calculation\")\n print(d1(p1, p2))\n print(\"Dot product\")\n print(d2(p1, p2))\n print(\"Linear algebra 2-norm\")\n print(d3(p1, p2))\n print(\"Scipy Euclidean distance\")\n print(d4(p1, p2))","title":""},{"docid":"0d98e762c4c8cb4247ab3beed521d64c","score":"0.5961792","text":"def distance_as_regression():\n pts = np.array([randint(100, size=(5,)) for i in range(10)])\n z = np.random.rand(10)\n ref_pt = randint(100, size=(5,))\n a = pts - ref_pt\n b = a ** 2\n c = b.sum(axis=1)\n d = np.sqrt(c)\n sorted_indices = d.argsort()\n sorted_z = z[sorted_indices]\n weights = 1 / d[sorted_indices] ** 2\n zw = (sorted_z* weights).sum()\n sw = weights.sum()\n print(\"Sorted points:\", pts[sorted_indices])\n print(\"Sorted values:\", sorted_z)\n print(\"Reference point:\", ref_pt)\n print(\"Estimated value:\", zw/sw)","title":""},{"docid":"30d2943921c7228cb7aef560093a7ec7","score":"0.59554857","text":"def distance(self, lla0, lla1):\n\t\treturn distance(self, lla0, lla1)","title":""},{"docid":"cc57bda4401ed880556950cfbaa76c9c","score":"0.5945074","text":"def distance_function(self, summary_stats, summary_stats_synth):\n # print('summary_stats:',summary_stats)\n # print('summary_stats_synth:',summary_stats_synth)\n assert summary_stats.shape == summary_stats_synth.shape, \"summary_stats dimensions mismatch\"\n number = summary_stats.shape[0]\n distances = np.zeros(number)\n for num in range(number):\n distances[num] = 0.2 * sum(abs(summary_stats[num] - summary_stats_synth[num]))\n # print('distances:',distances)\n return distances","title":""},{"docid":"3d92a89cde39d38bf513a1a59a6da349","score":"0.5939494","text":"def distance_metric(self) -> metric.Metric:\n return self.__metric_space.distance_metric","title":""},{"docid":"274a605492853c7a90c7a44cf2ef3274","score":"0.5933374","text":"def _sample_distances_sum(self, x: float, y: float):\n return sum([math.hypot(target.x - x, target.y - y) for target in self.sample])","title":""},{"docid":"6b3c56ac6c831826cfcf5534331a2604","score":"0.593324","text":"def _calculate_l2_distance(preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:\n return torch.norm(target_distribution - preds_distribution, p=2, dim=-1)","title":""},{"docid":"243e2bff29febd778138c2dcfb037e5c","score":"0.59320694","text":"def calculate_distances(self):\n self.cosine_sim = cosine_similarity(self.X, self.X).astype(\"float32\")\n self.sigmoid_sim = sigmoid_kernel(self.X, self.X).astype(\"float32\")\n self.linear_sim = linear_kernel(self.X, self.X).astype(\"float32\")\n print(\"Datafile with similarity distances saved\")\n return self","title":""},{"docid":"441cceabb4a237a6ad65382d96dd9dd4","score":"0.59281677","text":"def get_distance(a, b):\r\n if a.n_dim != b.n_dim:\r\n raise Exception(\"Error: the dim of samples different, distance can not be calculated\")\r\n\r\n ac_diff = 0.0\r\n for i in range(a.n_dim):\r\n square_diff = pow((a.coords[i] - b.coords[i]), 2)\r\n ac_diff += square_diff\r\n distance = math.sqrt(ac_diff)\r\n\r\n return distance","title":""},{"docid":"4a17a5884c9cbaf103994ea895b79c97","score":"0.5925591","text":"def _distance(self, x, y):\n\n return np.sqrt(np.sum((x - y) ** 2))","title":""},{"docid":"e58e39b3c7cc5f29ae7bff2e419b50f3","score":"0.5919492","text":"def get_travelled_distance(self):\n dists = []\n overall_d = 0\n dists.append(0)\n for i in range(self.L-1):\n d = np.linalg.norm(self.path_coords[i+1] - self.path_coords[i])\n overall_d += d# / np.log(10)\n dists.append(overall_d)\n self.dists = dists","title":""},{"docid":"c27ab5714da276f499bfce45adc6b51e","score":"0.59116614","text":"def get_distance(self, net):\n\n squares = 0\n for l_index, layer in enumerate(self.layers):\n for n_index, neuron in enumerate(layer):\n for w_index, weight in enumerate(neuron.weights):\n squares += (weight[0] - net.layers[l_index][n_index].weights[w_index][0]) ** 2.0\n squares += (weight[1] - net.layers[l_index][n_index].weights[w_index][1]) ** 2.0\n return sqrt(squares)","title":""},{"docid":"4b8773fddcb9cef59fc3e9c524286094","score":"0.590488","text":"def get_dist_between_points(self):\n return self.point_dist","title":""},{"docid":"e6ca8dd4afdf2d1c1ff3c52ed5e0de8e","score":"0.5903778","text":"def getDistance(*args):\n return _coin.SbPlane_getDistance(*args)","title":""},{"docid":"23845c40fa18b86a4ac7bb3d0de10ff6","score":"0.58970076","text":"def distance(self, feature):\n return self.distanceClass.distance(self.dataset.feature_data,feature)","title":""},{"docid":"ae9cf2362be329cc1c3474bcd97d057c","score":"0.58950526","text":"def _count_inter_class_distances(mean_vectors: Tensor) -> Tensor:\n distance = torch.cdist(x1=mean_vectors, x2=mean_vectors, p=2)\n return distance","title":""},{"docid":"d91d0c4c2cb3c2483a990b4c7ca3f0bf","score":"0.5889592","text":"def _calculate_distance_difference(self, ref_coords):\n\n traj = self.traj\n\n metrics = np.zeros((traj.n_frames, traj.n_atoms))\n\n for frame, coords in enumerate(traj.xyz):\n\n metrics[frame, :] = np.apply_along_axis(distance.euclidean, 1,\n coords, ref_coords[frame])\n\n return metrics","title":""},{"docid":"e40b96fc491627b458967df7d63a74e3","score":"0.5888571","text":"def build_distances(x, y):\n xn = tf.tile(x[:, :, tf.newaxis], (1, 1, tf.shape(y)[1]))\n ym = tf.transpose(\n tf.tile(y[:, :, tf.newaxis], (1, 1, tf.shape(x)[1])), (0, 2, 1))\n return tf.math.abs(xn - ym)","title":""},{"docid":"5b55c9a5b942bd8074b02106c447df6f","score":"0.58877057","text":"def distance(self, *args):\n return _openshot.SwigPyIterator_distance(self, *args)","title":""},{"docid":"8298ac89303174e65909403a873d4262","score":"0.5876775","text":"def distance(arr):\n return np.linalg.norm(arr)","title":""},{"docid":"8e4de456e64a0d3d096e21173ecef630","score":"0.58547044","text":"def dist(self):\n return 0","title":""},{"docid":"bc129b739694e1043dddcadc19a8dabd","score":"0.5853273","text":"def get_distances(a, b):\r\n # Create tiles of shape [a_count, b_count, hidden_size]\r\n a = tf.expand_dims(a, 1)\r\n b = tf.expand_dims(b, 0)\r\n a = tf.tile(a, [1, b.shape[1], 1])\r\n b = tf.tile(b, [a.shape[0], 1, 1])\r\n\r\n sub = a - b # d(a,b) = |a-b|\r\n return tf.math.reduce_euclidean_norm(sub, axis=-1)","title":""},{"docid":"63ac2230b8f87d89159d227bf5e30b6c","score":"0.585052","text":"def distance(self, *args):\n return _stomp.SwigPyIterator_distance(self, *args)","title":""},{"docid":"7c325e70e2ba1d6bc14770e83b553329","score":"0.58484983","text":"def distance(self) -> util.Distance:\n return self._distance","title":""},{"docid":"8ef198c5a360ccf4565a236bb30528ea","score":"0.5848167","text":"def distance(a, b): # MADE\n return sim.euclidean_dist(a, b)","title":""},{"docid":"0c6acbc640df68c7ade4346f197874bb","score":"0.5842918","text":"def _compute_distance(self, new_data, norm=\"euclidian\"):\n nb_classes = self.class_means.shape[0]\n distances = np.zeros((new_data.shape[0], nb_classes))\n\n for c in range(nb_classes): # per class\n if self.isotropic:\n # Add the prior later, ln(p(Ck))\n distances[:, c] = np.apply_along_axis(self._weighted_norm, 1, new_data - self.class_means[c, :], cov=self.cov, norm=norm)\n else:\n distances[:, c] = -np.apply_along_axis(self._weighted_norm, 1, new_data - self.class_means[c, :], cov=self.cov[c, :, :], norm=norm)\n distances[:, c] -= (1 / 2) * np.log(np.linalg.det(self.cov[c, :, :]))\n return distances","title":""},{"docid":"4d30e76526c927ce033b1e510dde6277","score":"0.5840813","text":"def setup_ddtw_distance_measure_getter(transformer):\n\n def getter(X):\n return {\n \"distance_measure\": [\n _derivative_distance(numba_wrapper(dtw_distance), transformer)\n ],\n \"w\": stats.uniform(0, 0.25),\n }\n\n return getter","title":""},{"docid":"ae5fc6795cd62524aa187e39fa678345","score":"0.5830782","text":"def distance(self, p):\n dist = np.dot(self.normal, p) + self.offset\n return dist","title":""},{"docid":"36c0de63b872cd679c8f82989ed98ba3","score":"0.5830572","text":"def _compute_distance(self, x1: np.ndarray, x2: np.ndarray) -> float:\n return euclidean_distance(x1=x1, x2=x2)","title":""},{"docid":"65f70a07c0534767bb4be2b32446806d","score":"0.582804","text":"def distto(self, *others):\r\n return self.metric.dist(self, *others)","title":""},{"docid":"bb17fe9c638c773fee036200dd0a1541","score":"0.5815014","text":"def calculate_distances(self, words):\n indexes = np.array([self.dictionary[w] for w in words])\n if len(indexes) == 0:\n return np.array([]), {}\n embeds = self.embeddings[indexes]\n distance_matrix = euclidean_distances(embeds,embeds)\n submatrix_indexes = dict(zip(indexes, np.arange(indexes.shape[0])))\n return distance_matrix, submatrix_indexes","title":""},{"docid":"b6cb3f7f13a57d3e760616dd12e9ff9b","score":"0.58058715","text":"def ddpg_distance_metric(actions1, actions2):\n diff = actions1-actions2\n mean_diff = np.mean(np.square(diff), axis=0)\n dist = sqrt(np.mean(mean_diff))\n return dist","title":""},{"docid":"8b12812034bd0858025870322a5dce3a","score":"0.5803674","text":"def _derivative_distance(distance_measure, transformer):\n\n def distance(instance_a, instance_b, **params):\n df = pd.DataFrame([instance_a, instance_b])\n df = transformer.transform(X=df)\n instance_a = df.iloc[0, :]\n instance_b = df.iloc[1, :]\n return distance_measure(instance_a, instance_b, **params)\n\n return distance","title":""},{"docid":"40596732edcfc69108be3b5794c12d09","score":"0.58016336","text":"def calculateDistance(lab1, lab2):\n labDiff = np.array(lab2) - np.array(lab1)\n return np.sqrt(np.sum(labDiff * labDiff))","title":""},{"docid":"74b2f2cb123914e29fddc025e7760e42","score":"0.5797932","text":"def distance(self, idx1, idx2):\n # current using euclidean\n node_1 = np.array((self.xs[idx1], self.ys[idx1]))\n node_2 = np.array((self.xs[idx2], self.ys[idx2]))\n return np.linalg.norm(node_1 - node_2)","title":""},{"docid":"9c1bac9b46e70f84015fd5feb6454a8f","score":"0.57924265","text":"def ddpg_distance_metric(actions1, actions2):\n diff = actions1-actions2\n mean_diff = np.mean(np.square(diff), axis=0)\n dist = np.sqrt(np.mean(mean_diff))\n return dist","title":""},{"docid":"1cf5b2623b49671e1c37518324bb222e","score":"0.57908434","text":"def _distance_heuristic(self, n1, n2):\n l1 = np.array(self._graph.nodes[n1]['vertex'])\n l2 = np.array(self._graph.nodes[n2]['vertex'])\n return np.linalg.norm(l1-l2)","title":""},{"docid":"6be8f83140f8bc66cc2d081332f45e42","score":"0.57799363","text":"def compute_distance(transform):\n # return numpy.linalg.norm(transform[0:3,3])\n # t = tf.reshape(tf.slice(transform,[0,0,3],[-1,3,1]),[-1,3])\n t = tf.reshape(tf.slice(transform,[0,0,3],[-1,3,1]),[-1,3])\n # t should now be bs x 3 \n return tf.sqrt(tf.reduce_sum(tf.square(t),axis=1))","title":""},{"docid":"8085f212f4f7ff977158397c5a484d3b","score":"0.57794636","text":"def distance_callback(from_node, to_node):\n return distances[from_node][to_node]","title":""},{"docid":"1935c7550cd78849f2363b125bd0e495","score":"0.57781154","text":"def distance(self, x, y, x_, y_):\n return (x - x_)**2 + (y - y_)**2","title":""},{"docid":"7f6c49793be8e9694ef3462270d52daa","score":"0.5774951","text":"def compute_dist_matrix(self):\n # Use broadcasting to get an array of shape (N,N,n) with the differences of every\n # possible pair of points\n differences = self.points[np.newaxis,:] - self.points[:, np.newaxis]\n\n # Calculate the squares of the distances by summing along the last axis\n return (differences**2).sum(axis = -1)","title":""},{"docid":"a16d23ad3c1a64aad5ac6a6c46931d19","score":"0.5758453","text":"def _pairwise_distances(embeddings):\n # Get the dot product between all embeddings\n # shape (batch_size, batch_size)\n dot_product = tf.matmul(embeddings, tf.transpose(embeddings))\n\n # Get squared L2 norm for each embedding. We can just take the diagonal of `dot_product`.\n # This also provides more numerical stability (the diagonal of the result will be exactly 0).\n # shape (batch_size,)\n square_norm = tf.diag_part(dot_product)\n\n # Compute the pairwise distance matrix as we have:\n # ||a - b||^2 = ||a||^2 - 2 <a, b> + ||b||^2\n # shape (batch_size, batch_size)\n distances = tf.expand_dims(square_norm, 1) - 2.0 * dot_product + tf.expand_dims(square_norm, 0)\n\n # Because of computation errors, some distances might be negative so we put everything >= 0.0\n distances = tf.maximum(distances, 0.0)\n\n return distances","title":""},{"docid":"1d31b5d58bd269c71aa59ed4a3216664","score":"0.5754819","text":"def compute(self):\n return self.be.pairwise_euclidean_distances(self.X, self.Y) ** 2","title":""},{"docid":"0eaa7ca5d94cf2ee091557d2e44501b8","score":"0.57528883","text":"def _calculate_distances(self, new_point):\r\n\r\n # Expand by repeating to increase speed (avoid looping) \r\n new_point = np.resize(new_point, (self.n, new_point.shape[0]))\r\n euclidean_distance = np.sum((self.data[:,0:-1] - new_point)**2, axis=1)\r\n return euclidean_distance","title":""},{"docid":"d36acb642f8a5cff559d51e842f5fabe","score":"0.5749607","text":"def dist(self):\n return self._dist_mat","title":""},{"docid":"f7fe1c006213bde2a2f4617a93f01004","score":"0.5746641","text":"def compute_distances_no_loops(self, X):\n num_test = X.shape[0]\n num_train = self.X_train.shape[0]\n dists = np.zeros((num_test, num_train)) \n #########################################################################\n # TODO: #\n # Compute the l2 distance between all test points and all training #\n # points without using any explicit loops, and store the result in #\n # dists. #\n # #\n # You should implement this function using only basic array operations; #\n # in particular you should not use functions from scipy. #\n # #\n # HINT: Try to formulate the l2 distance using matrix multiplication #\n # and two broadcast sums. #\n #########################################################################\n A = X\n B = self.X_train\n \n A_square = np.sum(np.square(A), axis = 1)\n B_square = np.sum(np.square(B), axis = 1)\n A_square_plus_B_square = A_square.reshape(A_square.shape[0],1) + B_square\n AB = np.dot(A, np.transpose(B))\n squaress = A_square_plus_B_square - 2*AB\n \n dists = np.sqrt(squaress)\n \n #########################################################################\n # END OF YOUR CODE #\n #########################################################################\n return dists","title":""},{"docid":"7dd3eb08860c8bbbed584f8ca0c5aae4","score":"0.57395846","text":"def get_path_distances(self):\n\n return self.path_distances","title":""},{"docid":"48b11fdaa0bfb33aaeff66584ff02a60","score":"0.5736189","text":"def _pairwise_dist(self, theta):\n\t\treturn torch.norm(theta[:, None] - theta, dim = 2, p = 2)**2","title":""},{"docid":"2ed824ea4157bd57564ec4f0321d4210","score":"0.5735477","text":"def distance(self, source, target, attrs):\n\n # Distance is 1 - score. Skip minimal distances as they are near duplicates.\n distance = max(1.0 - attrs[\"weight\"], 0.0)\n return distance if distance >= 0.15 else 1.00","title":""},{"docid":"fafcb72587661c8336e43bbc5238b821","score":"0.5733769","text":"def feedDistance(self):\n return float()","title":""},{"docid":"bfef3c05fcac6bbc8e6c59b5c1d36cd3","score":"0.5721309","text":"def _get_distances(self, town_i: int, town_j: int):\n\n t1 = self.map[town_i]\n t2 = self.map[town_j]\n\n return np.sqrt((t1[0] - t2[0]) ** 2 + (t1[1] - t2[1]) ** 2)","title":""},{"docid":"5bd0bc5b7ee4b164befa4a46b33320a2","score":"0.5717693","text":"def m(self) -> float:\n return self._distance","title":""},{"docid":"839633f24b153e473042333e6ab16abc","score":"0.5715332","text":"def test_distance(self):\n m = Metric()\n self.assertEqual(m.distance(\"abc\", \"abc\"), 0)\n self.assertEqual(m.distance(\"abc\", \"def\"), 1)\n self.assertEqual(m.distance(\"abc\", \"\"), 1)\n self.assertEqual(m.distance(\"\", \"abc\"), 1)\n self.assertEqual(m.distance(\"\", \"\"), 0)","title":""},{"docid":"ac45e12e537b51931cdc7c223c5cab2c","score":"0.5711865","text":"def _cal_distance(self, p1: np.ndarray, p2: np.ndarray) -> np.float64:\n return np.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)","title":""},{"docid":"f7e60d794b367da3a7369814e87e3c62","score":"0.57105076","text":"def compute_distances_one_loop(self, X):\n num_test = X.shape[0]\n num_train = self.X_train.shape[0]\n dists = np.zeros((num_test, num_train))\n for i in xrange(num_test):\n #######################################################################\n # TODO: #\n # Compute the l2 distance between the ith test point and all training #\n # points, and store the result in dists[i, :]. #\n #######################################################################\n #pass\n dists[i, :] = np.linalg.norm(self.X_train - X[i, :], axis=1)\n #######################################################################\n # END OF YOUR CODE #\n #######################################################################\n return dists","title":""},{"docid":"d15cde4c067c795fd709da4bb476c88f","score":"0.570547","text":"def dist(self) -> Distance:\n return Distance(np.linalg.norm(self.dist_vec), units=\"ang\")","title":""},{"docid":"4b6ea82462f68629636a9338e86d27a6","score":"0.57048017","text":"def test_compute_dists():\n feats1 = np.array(\n [\n [.707, .707],\n [-.707, .707], \n [-.707, -.707]\n ])\n feats2 = np.array(\n [\n [-.5, -.866],\n [.866, -.5],\n [.5, .866],\n [-.866, .5]\n ])\n dists = np.array(\n [\n [1.98271985, 1.21742762, 0.26101724, 1.58656169],\n [1.58656169, 1.98271985, 1.21742762, 0.26101724],\n [0.26101724, 1.58656169, 1.98271985, 1.21742762]\n ])\n inter_distances = compute_feature_distances(feats1, feats2)\n assert inter_distances.shape[0] == 3\n assert inter_distances.shape[1] == 4\n assert np.allclose(dists, inter_distances, atol = 1e-03)","title":""},{"docid":"70500a7c89c5ec3356e8157290f89281","score":"0.5702044","text":"def internal_distance(self):\n return min([\n self.nw.distance_to(self.se),\n self.ne.distance_to(self.sw),\n ])","title":""},{"docid":"1d52914a2368f2b06c148b5767f2b266","score":"0.569691","text":"def get_distance(a, b):\n return Vector(a).distance(Vector(b))","title":""},{"docid":"dd620bf2e11c439484663af1e581b20a","score":"0.5695866","text":"def distance(p1,p2):\n \n return np.mean((p1 - p2) ** 2)","title":""},{"docid":"c03f1d11697ba543bb6b8145129e5de8","score":"0.56954277","text":"def calc_distance(a,b):\n return np.sqrt(np.sum((a-b)**2))","title":""},{"docid":"da713fffef169e1731301480577b92b5","score":"0.56913614","text":"def get_ds(self):\n if self.local:\n x1 = self.x[:self.nat1]; x2 = self.x[-self.nat2:]\n else:\n x1 = self.x[:self.n1]; x2 = self.x[-self.n2:]\n ds1 = qd.l2_distance(x1, x1)\n ds2 = qd.l2_distance(x2, x1)\n return ds1, ds2","title":""},{"docid":"f277d2f14d84b9e823c0172822958001","score":"0.56894326","text":"def compute_distances_two_loops(self, X):\n num_test = X.shape[0]\n num_train = self.X_train.shape[0]\n dists = np.zeros((num_test, num_train))\n for i in xrange(num_test):\n for j in xrange(num_train):\n #####################################################################\n # TODO: #\n # Compute the l2 distance between the ith test point and the jth #\n # training point, and store the result in dists[i, j]. You should #\n # not use a loop over dimension. #\n #####################################################################\n # pass\n # the other option would be to use linalg\n dists[i, j] = np.linalg.norm(self.X_train[j] - X[i])\n # dists[i,j] = np.sqrt(np.sum(np.square(self.X_train[i] - X[j])))\n #####################################################################\n # END OF YOUR CODE #\n #####################################################################\n return dists","title":""},{"docid":"b02dd3a7e9cc802c6c13b8332c5c5848","score":"0.56762385","text":"def test_dist(self):\n print dist(1,1)\n print dist(1,4)\n\n # ... and then evaluate the output","title":""},{"docid":"89f2e8a985c34d19b99b16fb8d2f77c1","score":"0.567621","text":"def dist(self, ind1, ind2):\n\n centers_distance = (np.array(self.positions[ind1]) - np.array(self.positions[ind2]))\n r1 = self.length(ind1)\n r2 = self.length(ind2)\n d = np.abs(centers_distance) - (r1 + r2) / 2\n print(d)\n return d","title":""},{"docid":"d5a53f1b9633b8f7386d692a0a2f8345","score":"0.5675909","text":"def _calculate_l1_distance(preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:\n return torch.norm(target_distribution - preds_distribution, p=1, dim=-1)","title":""},{"docid":"fec9e85bd390ff7a1089977abd8c6fd7","score":"0.566709","text":"def _threshold_function(self):\n if self.dist_metric == \"euclidean\":\n dist = cdist(self.data, self.data, metric=self.dist_metric)\n else:\n dist = udm.categorical_distance(self.data, self.indexes_ordinal, self.indexes_nominal)\n return dist","title":""}],"string":"[\n {\n \"docid\": \"5b90c6db1c4c7a51d2b69c6d61c11091\",\n \"score\": \"0.7252572\",\n \"text\": \"def get_distances():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"784bc0b08043ecf1f5b80dbff811c330\",\n \"score\": \"0.6990197\",\n \"text\": \"def dist(self) -> Distance:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8269db68ee68dc5cddccb3020612f651\",\n \"score\": \"0.6680347\",\n \"text\": \"def distance(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd29e35832939e8e44de064bf43d11dd\",\n \"score\": \"0.6593241\",\n \"text\": \"def distances_(self):\\n return self.session.run(self._scores)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53f766a8c6e676f0d8819670a570ff45\",\n \"score\": \"0.6525181\",\n \"text\": \"def distance(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7444051fe1f1915d271a0a56983fbf\",\n \"score\": \"0.6355777\",\n \"text\": \"def distances(self) -> List[List]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aae727eff6a66b6f9a750ccf6f6ae0e6\",\n \"score\": \"0.6314876\",\n \"text\": \"def _get_distance(self):\\n return self.__distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aae727eff6a66b6f9a750ccf6f6ae0e6\",\n \"score\": \"0.6314876\",\n \"text\": \"def _get_distance(self):\\n return self.__distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1fbe6d0e0a00ca3b34cb68fb5ec0701\",\n \"score\": \"0.6259694\",\n \"text\": \"def L2_distance(self, X):\\n\\t\\tnum_test = X.shape[0]\\n\\t\\tnum_train = self.Xtr.shape[0]\\n\\t\\tdists = np.zeros((num_test, num_train)) \\n\\t\\tprint(\\\"Running ...\\\")\\n\\t\\tdists = np.reshape(np.sum(np.power(X,2), axis=1), [num_test,1]) + np.sum(np.power(self.Xtr,2), axis=1) - 2 * np.matmul(X, self.Xtr.T)\\n\\t\\tdists = np.sqrt(dists)\\n\\t\\treturn dists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a9995c372ead52a5236088efae03682\",\n \"score\": \"0.62333035\",\n \"text\": \"def distance(self, X):\\n n = X.shape[0]\\n Y = self.predict(X)\\n X = X.numpy()\\n\\n distances = []\\n for i in range(n):\\n label_options = list(range(self.weights.shape[0])) # create list of length num_classes\\n del label_options[Y[i]]\\n dists = []\\n for j in label_options:\\n v = try_region_multi([self], [j], X[i])\\n dists.append(np.linalg.norm(v))\\n distances.append(min(dists))\\n return torch.tensor(distances)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"081b5fef80add0612f0c7b461ceaf124\",\n \"score\": \"0.62328655\",\n \"text\": \"def distances(self):\\n return self._distances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"081b5fef80add0612f0c7b461ceaf124\",\n \"score\": \"0.62328655\",\n \"text\": \"def distances(self):\\n return self._distances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fedf8383a9a6f4635380892d998781ac\",\n \"score\": \"0.6212885\",\n \"text\": \"def get_distance(self, a, b):\\n return np.sum((a - b)**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf1e559b8d2877fa90e740de8059ecb6\",\n \"score\": \"0.62094885\",\n \"text\": \"def get_distance(self, pred, target):\\n logits_pred = self.i3d.extract_features(pred).squeeze() # [b, n]\\n logits_target = self.i3d.extract_features(target).squeeze()\\n\\n if pred.shape[0] == 1: # if batch size is 1, the prev. squeeze also removed the batch dim\\n logits_pred = logits_pred.unsqueeze(dim=0)\\n logits_target = logits_target.unsqueeze(dim=0)\\n\\n return calculate_2_wasserstein_dist(logits_pred, logits_target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5f3cf4bc4f6f6429dd02974786cfdfe\",\n \"score\": \"0.6201153\",\n \"text\": \"def calc_distance(self, layer, idxs=None):\\n if not idxs:\\n return torch.dist(self.theta_f[layer], self.theta_i[layer])\\n else:\\n return torch.dist(self.theta_f[layer][idxs], self.theta_i[layer][idxs])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eca62223ff8d88d2e13860de1dcb1eab\",\n \"score\": \"0.61937904\",\n \"text\": \"def _calc_distances(sample, features) -> ndarray:\\n distances = np.empty(0)\\n powers = np.power(np.subtract(sample, features), 2)\\n\\n for i in range(0, powers.shape[0]):\\n d = np.sqrt(np.sum(powers[i]))\\n distances = np.append(distances, d)\\n\\n return distances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46c6f84b20532823fb0faa8064a5c6e3\",\n \"score\": \"0.60940427\",\n \"text\": \"def _abs_dist_func(x, y):\\n n = x.size(1)\\n m = y.size(1)\\n d = x.size(2)\\n x = x.unsqueeze(2).expand(-1, n, m, d)\\n y = y.unsqueeze(1).expand(-1, n, m, d)\\n return torch.abs(x - y).sum(3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52dab359559f25bbceff85c1f8979332\",\n \"score\": \"0.60817724\",\n \"text\": \"def edist(self, inputs, weights):\\n \\n euclideanDistance = 0\\n \\n if len(inputs) != len(weights):\\n print len(inputs), \\\"is different length than\\\", len(weights)\\n return\\n \\n for i in range(len(inputs)):\\n nth = inputs[i] - weights[i]\\n nth = nth ** 2\\n euclideanDistance += nth\\n \\n return euclideanDistance ** (0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"358af3716ba6d8b1580ef48c49ee257c\",\n \"score\": \"0.6075651\",\n \"text\": \"def dist_vec(self) -> np.ndarray:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e7e63bf4189f462f830cd49c93fba71\",\n \"score\": \"0.6059089\",\n \"text\": \"def _get_distance(self, targets, y_hats):\\n total_dist = 0\\n total_length = 0\\n\\n for (target, y_hat) in zip(targets, y_hats):\\n s1 = self.vocab.label_to_string(target)\\n s2 = self.vocab.label_to_string(y_hat)\\n\\n dist, length = self._metric(s1, s2)\\n\\n total_dist += dist\\n total_length += length\\n\\n return total_dist, total_length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72b2e46ed50eb128fd4476b1cba2ee06\",\n \"score\": \"0.60589665\",\n \"text\": \"def distance(self, policy_logits: Tensor, y: Tensor) -> Tensor:\\n raise NotImplementedError('Implement me')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5377be6369ca3ad90fffe686654675e8\",\n \"score\": \"0.6017524\",\n \"text\": \"def dist(self, X):\\n\\n if self.X.shape != X.shape:\\n raise ValueError(\\n \\\"The two samples must have the same number of features\\\"\\n )\\n\\n # Remarks: as we only use this distance to sort the learning samples\\n # by their distance to a sample to predict, we could get\\n # rid of the 'sqrt()' call.\\n\\n return math.sqrt(\\n sum(\\n (feature1 - feature2)**2\\n for feature1, feature2 in zip(self.X, X)\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87a4526b50d9abeb498fe55a8275d50e\",\n \"score\": \"0.60036236\",\n \"text\": \"def get_distance():\\n return ultraSensor.distance_centimeters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"752480bb88dd62d51fdccee9cfda7484\",\n \"score\": \"0.59986347\",\n \"text\": \"def getDistance(self):\\n return super().distance_cm()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a05a5697e01a308bd415678990e51b1\",\n \"score\": \"0.5998059\",\n \"text\": \"def calc_distance(self):\\n return rssi_to_feet(self.distance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d58a87e2de5493a8e9c2b5d9c3ee9c0\",\n \"score\": \"0.5995062\",\n \"text\": \"def _calculate_distance(self) -> float:\\n ds = 100 * (\\n np.linalg.norm(self.zs[self.h + 1] - self.problem.nadir)\\n / (np.linalg.norm(self.fs[self.h + 1] - self.problem.nadir))\\n )\\n return ds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1413c23aff7b426e0b979c69cb08fa6a\",\n \"score\": \"0.59868205\",\n \"text\": \"def euclidean_distance_measure_getter(X):\\n return {\\\"distance_measure\\\": [numba_wrapper(dtw_distance)], \\\"w\\\": [0]}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bac5ad2ead4cbf384d50058bc1eefda\",\n \"score\": \"0.5985756\",\n \"text\": \"def calculate_distance(tfidf_scores, vocab_size):\\n cosine_sim = SparseMatrixSimilarity(tfidf_scores, num_features=vocab_size)\\n\\n distance = []\\n i=0\\n for row in tfidf_scores:\\n print i\\n i += 1\\n distance.append(1 - cosine_sim[row])\\n\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc60c2b0ff53427512cac8395e0c4257\",\n \"score\": \"0.5976525\",\n \"text\": \"def fitted_dist_func(self, x, y):\\n if self._transform_matrix is not None:\\n w = self._transform_matrix\\n g = lambda x, y: weighted_euclidean(x, y, w)\\n return g(x, y)\\n else:\\n None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"462be3ac0abaa7f06f94daa1af7a5370\",\n \"score\": \"0.5973414\",\n \"text\": \"def getDistance(*args):\\n return _coin.SbDPPlane_getDistance(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef68402a2811dc7c20847e95298bd39c\",\n \"score\": \"0.59718275\",\n \"text\": \"def get_distance(self, n):\\n return self.metadata[n][\\\"distance\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75190bf0581efdc26920b26abecac9b2\",\n \"score\": \"0.5970038\",\n \"text\": \"def lcss_distance_measure_getter(X):\\n stdp = _stdp(X)\\n instance_length = _max_instance_length(X) # todo should this use the max instance\\n # length for unequal length dataset instances?\\n max_raw_warping_window = np.floor((instance_length + 1) / 4)\\n n_dimensions = 1 # todo use other dimensions\\n return {\\n \\\"distance_measure\\\": [numba_wrapper(lcss_distance)],\\n \\\"dim_to_use\\\": stats.randint(low=0, high=n_dimensions),\\n \\\"epsilon\\\": stats.uniform(0.2 * stdp, stdp - 0.2 * stdp),\\n # scipy stats randint is exclusive on the max value, hence + 1\\n \\\"delta\\\": stats.randint(low=0, high=max_raw_warping_window + 1),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"168eea0aa1c8b36476d340a78051a0e8\",\n \"score\": \"0.5968558\",\n \"text\": \"def ComputeDistance(self, a, b):\\n embeddings = self.BatchEmbed([a, b])\\n return np.linalg.norm(embeddings[0] - embeddings[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ecb5fdd948bdd33ee1413c33d6970ad\",\n \"score\": \"0.5965392\",\n \"text\": \"def test_distances():\\n p1 = randint(100, size=(100,))\\n p2 = randint(100, size=(100,))\\n print(\\\"Simple calculation\\\")\\n print(d1(p1, p2))\\n print(\\\"Dot product\\\")\\n print(d2(p1, p2))\\n print(\\\"Linear algebra 2-norm\\\")\\n print(d3(p1, p2))\\n print(\\\"Scipy Euclidean distance\\\")\\n print(d4(p1, p2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d98e762c4c8cb4247ab3beed521d64c\",\n \"score\": \"0.5961792\",\n \"text\": \"def distance_as_regression():\\n pts = np.array([randint(100, size=(5,)) for i in range(10)])\\n z = np.random.rand(10)\\n ref_pt = randint(100, size=(5,))\\n a = pts - ref_pt\\n b = a ** 2\\n c = b.sum(axis=1)\\n d = np.sqrt(c)\\n sorted_indices = d.argsort()\\n sorted_z = z[sorted_indices]\\n weights = 1 / d[sorted_indices] ** 2\\n zw = (sorted_z* weights).sum()\\n sw = weights.sum()\\n print(\\\"Sorted points:\\\", pts[sorted_indices])\\n print(\\\"Sorted values:\\\", sorted_z)\\n print(\\\"Reference point:\\\", ref_pt)\\n print(\\\"Estimated value:\\\", zw/sw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30d2943921c7228cb7aef560093a7ec7\",\n \"score\": \"0.59554857\",\n \"text\": \"def distance(self, lla0, lla1):\\n\\t\\treturn distance(self, lla0, lla1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc57bda4401ed880556950cfbaa76c9c\",\n \"score\": \"0.5945074\",\n \"text\": \"def distance_function(self, summary_stats, summary_stats_synth):\\n # print('summary_stats:',summary_stats)\\n # print('summary_stats_synth:',summary_stats_synth)\\n assert summary_stats.shape == summary_stats_synth.shape, \\\"summary_stats dimensions mismatch\\\"\\n number = summary_stats.shape[0]\\n distances = np.zeros(number)\\n for num in range(number):\\n distances[num] = 0.2 * sum(abs(summary_stats[num] - summary_stats_synth[num]))\\n # print('distances:',distances)\\n return distances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d92a89cde39d38bf513a1a59a6da349\",\n \"score\": \"0.5939494\",\n \"text\": \"def distance_metric(self) -> metric.Metric:\\n return self.__metric_space.distance_metric\",\n \"title\": \"\"\n },\n {\n \"docid\": \"274a605492853c7a90c7a44cf2ef3274\",\n \"score\": \"0.5933374\",\n \"text\": \"def _sample_distances_sum(self, x: float, y: float):\\n return sum([math.hypot(target.x - x, target.y - y) for target in self.sample])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b3c56ac6c831826cfcf5534331a2604\",\n \"score\": \"0.593324\",\n \"text\": \"def _calculate_l2_distance(preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:\\n return torch.norm(target_distribution - preds_distribution, p=2, dim=-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"243e2bff29febd778138c2dcfb037e5c\",\n \"score\": \"0.59320694\",\n \"text\": \"def calculate_distances(self):\\n self.cosine_sim = cosine_similarity(self.X, self.X).astype(\\\"float32\\\")\\n self.sigmoid_sim = sigmoid_kernel(self.X, self.X).astype(\\\"float32\\\")\\n self.linear_sim = linear_kernel(self.X, self.X).astype(\\\"float32\\\")\\n print(\\\"Datafile with similarity distances saved\\\")\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"441cceabb4a237a6ad65382d96dd9dd4\",\n \"score\": \"0.59281677\",\n \"text\": \"def get_distance(a, b):\\r\\n if a.n_dim != b.n_dim:\\r\\n raise Exception(\\\"Error: the dim of samples different, distance can not be calculated\\\")\\r\\n\\r\\n ac_diff = 0.0\\r\\n for i in range(a.n_dim):\\r\\n square_diff = pow((a.coords[i] - b.coords[i]), 2)\\r\\n ac_diff += square_diff\\r\\n distance = math.sqrt(ac_diff)\\r\\n\\r\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a17a5884c9cbaf103994ea895b79c97\",\n \"score\": \"0.5925591\",\n \"text\": \"def _distance(self, x, y):\\n\\n return np.sqrt(np.sum((x - y) ** 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e58e39b3c7cc5f29ae7bff2e419b50f3\",\n \"score\": \"0.5919492\",\n \"text\": \"def get_travelled_distance(self):\\n dists = []\\n overall_d = 0\\n dists.append(0)\\n for i in range(self.L-1):\\n d = np.linalg.norm(self.path_coords[i+1] - self.path_coords[i])\\n overall_d += d# / np.log(10)\\n dists.append(overall_d)\\n self.dists = dists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c27ab5714da276f499bfce45adc6b51e\",\n \"score\": \"0.59116614\",\n \"text\": \"def get_distance(self, net):\\n\\n squares = 0\\n for l_index, layer in enumerate(self.layers):\\n for n_index, neuron in enumerate(layer):\\n for w_index, weight in enumerate(neuron.weights):\\n squares += (weight[0] - net.layers[l_index][n_index].weights[w_index][0]) ** 2.0\\n squares += (weight[1] - net.layers[l_index][n_index].weights[w_index][1]) ** 2.0\\n return sqrt(squares)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b8773fddcb9cef59fc3e9c524286094\",\n \"score\": \"0.590488\",\n \"text\": \"def get_dist_between_points(self):\\n return self.point_dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6ca8dd4afdf2d1c1ff3c52ed5e0de8e\",\n \"score\": \"0.5903778\",\n \"text\": \"def getDistance(*args):\\n return _coin.SbPlane_getDistance(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23845c40fa18b86a4ac7bb3d0de10ff6\",\n \"score\": \"0.58970076\",\n \"text\": \"def distance(self, feature):\\n return self.distanceClass.distance(self.dataset.feature_data,feature)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae9cf2362be329cc1c3474bcd97d057c\",\n \"score\": \"0.58950526\",\n \"text\": \"def _count_inter_class_distances(mean_vectors: Tensor) -> Tensor:\\n distance = torch.cdist(x1=mean_vectors, x2=mean_vectors, p=2)\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d91d0c4c2cb3c2483a990b4c7ca3f0bf\",\n \"score\": \"0.5889592\",\n \"text\": \"def _calculate_distance_difference(self, ref_coords):\\n\\n traj = self.traj\\n\\n metrics = np.zeros((traj.n_frames, traj.n_atoms))\\n\\n for frame, coords in enumerate(traj.xyz):\\n\\n metrics[frame, :] = np.apply_along_axis(distance.euclidean, 1,\\n coords, ref_coords[frame])\\n\\n return metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e40b96fc491627b458967df7d63a74e3\",\n \"score\": \"0.5888571\",\n \"text\": \"def build_distances(x, y):\\n xn = tf.tile(x[:, :, tf.newaxis], (1, 1, tf.shape(y)[1]))\\n ym = tf.transpose(\\n tf.tile(y[:, :, tf.newaxis], (1, 1, tf.shape(x)[1])), (0, 2, 1))\\n return tf.math.abs(xn - ym)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b55c9a5b942bd8074b02106c447df6f\",\n \"score\": \"0.58877057\",\n \"text\": \"def distance(self, *args):\\n return _openshot.SwigPyIterator_distance(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8298ac89303174e65909403a873d4262\",\n \"score\": \"0.5876775\",\n \"text\": \"def distance(arr):\\n return np.linalg.norm(arr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e4de456e64a0d3d096e21173ecef630\",\n \"score\": \"0.58547044\",\n \"text\": \"def dist(self):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc129b739694e1043dddcadc19a8dabd\",\n \"score\": \"0.5853273\",\n \"text\": \"def get_distances(a, b):\\r\\n # Create tiles of shape [a_count, b_count, hidden_size]\\r\\n a = tf.expand_dims(a, 1)\\r\\n b = tf.expand_dims(b, 0)\\r\\n a = tf.tile(a, [1, b.shape[1], 1])\\r\\n b = tf.tile(b, [a.shape[0], 1, 1])\\r\\n\\r\\n sub = a - b # d(a,b) = |a-b|\\r\\n return tf.math.reduce_euclidean_norm(sub, axis=-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63ac2230b8f87d89159d227bf5e30b6c\",\n \"score\": \"0.585052\",\n \"text\": \"def distance(self, *args):\\n return _stomp.SwigPyIterator_distance(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c325e70e2ba1d6bc14770e83b553329\",\n \"score\": \"0.58484983\",\n \"text\": \"def distance(self) -> util.Distance:\\n return self._distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ef198c5a360ccf4565a236bb30528ea\",\n \"score\": \"0.5848167\",\n \"text\": \"def distance(a, b): # MADE\\n return sim.euclidean_dist(a, b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c6acbc640df68c7ade4346f197874bb\",\n \"score\": \"0.5842918\",\n \"text\": \"def _compute_distance(self, new_data, norm=\\\"euclidian\\\"):\\n nb_classes = self.class_means.shape[0]\\n distances = np.zeros((new_data.shape[0], nb_classes))\\n\\n for c in range(nb_classes): # per class\\n if self.isotropic:\\n # Add the prior later, ln(p(Ck))\\n distances[:, c] = np.apply_along_axis(self._weighted_norm, 1, new_data - self.class_means[c, :], cov=self.cov, norm=norm)\\n else:\\n distances[:, c] = -np.apply_along_axis(self._weighted_norm, 1, new_data - self.class_means[c, :], cov=self.cov[c, :, :], norm=norm)\\n distances[:, c] -= (1 / 2) * np.log(np.linalg.det(self.cov[c, :, :]))\\n return distances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d30e76526c927ce033b1e510dde6277\",\n \"score\": \"0.5840813\",\n \"text\": \"def setup_ddtw_distance_measure_getter(transformer):\\n\\n def getter(X):\\n return {\\n \\\"distance_measure\\\": [\\n _derivative_distance(numba_wrapper(dtw_distance), transformer)\\n ],\\n \\\"w\\\": stats.uniform(0, 0.25),\\n }\\n\\n return getter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae5fc6795cd62524aa187e39fa678345\",\n \"score\": \"0.5830782\",\n \"text\": \"def distance(self, p):\\n dist = np.dot(self.normal, p) + self.offset\\n return dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36c0de63b872cd679c8f82989ed98ba3\",\n \"score\": \"0.5830572\",\n \"text\": \"def _compute_distance(self, x1: np.ndarray, x2: np.ndarray) -> float:\\n return euclidean_distance(x1=x1, x2=x2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65f70a07c0534767bb4be2b32446806d\",\n \"score\": \"0.582804\",\n \"text\": \"def distto(self, *others):\\r\\n return self.metric.dist(self, *others)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb17fe9c638c773fee036200dd0a1541\",\n \"score\": \"0.5815014\",\n \"text\": \"def calculate_distances(self, words):\\n indexes = np.array([self.dictionary[w] for w in words])\\n if len(indexes) == 0:\\n return np.array([]), {}\\n embeds = self.embeddings[indexes]\\n distance_matrix = euclidean_distances(embeds,embeds)\\n submatrix_indexes = dict(zip(indexes, np.arange(indexes.shape[0])))\\n return distance_matrix, submatrix_indexes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6cb3f7f13a57d3e760616dd12e9ff9b\",\n \"score\": \"0.58058715\",\n \"text\": \"def ddpg_distance_metric(actions1, actions2):\\n diff = actions1-actions2\\n mean_diff = np.mean(np.square(diff), axis=0)\\n dist = sqrt(np.mean(mean_diff))\\n return dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b12812034bd0858025870322a5dce3a\",\n \"score\": \"0.5803674\",\n \"text\": \"def _derivative_distance(distance_measure, transformer):\\n\\n def distance(instance_a, instance_b, **params):\\n df = pd.DataFrame([instance_a, instance_b])\\n df = transformer.transform(X=df)\\n instance_a = df.iloc[0, :]\\n instance_b = df.iloc[1, :]\\n return distance_measure(instance_a, instance_b, **params)\\n\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40596732edcfc69108be3b5794c12d09\",\n \"score\": \"0.58016336\",\n \"text\": \"def calculateDistance(lab1, lab2):\\n labDiff = np.array(lab2) - np.array(lab1)\\n return np.sqrt(np.sum(labDiff * labDiff))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74b2f2cb123914e29fddc025e7760e42\",\n \"score\": \"0.5797932\",\n \"text\": \"def distance(self, idx1, idx2):\\n # current using euclidean\\n node_1 = np.array((self.xs[idx1], self.ys[idx1]))\\n node_2 = np.array((self.xs[idx2], self.ys[idx2]))\\n return np.linalg.norm(node_1 - node_2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c1bac9b46e70f84015fd5feb6454a8f\",\n \"score\": \"0.57924265\",\n \"text\": \"def ddpg_distance_metric(actions1, actions2):\\n diff = actions1-actions2\\n mean_diff = np.mean(np.square(diff), axis=0)\\n dist = np.sqrt(np.mean(mean_diff))\\n return dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cf5b2623b49671e1c37518324bb222e\",\n \"score\": \"0.57908434\",\n \"text\": \"def _distance_heuristic(self, n1, n2):\\n l1 = np.array(self._graph.nodes[n1]['vertex'])\\n l2 = np.array(self._graph.nodes[n2]['vertex'])\\n return np.linalg.norm(l1-l2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6be8f83140f8bc66cc2d081332f45e42\",\n \"score\": \"0.57799363\",\n \"text\": \"def compute_distance(transform):\\n # return numpy.linalg.norm(transform[0:3,3])\\n # t = tf.reshape(tf.slice(transform,[0,0,3],[-1,3,1]),[-1,3])\\n t = tf.reshape(tf.slice(transform,[0,0,3],[-1,3,1]),[-1,3])\\n # t should now be bs x 3 \\n return tf.sqrt(tf.reduce_sum(tf.square(t),axis=1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8085f212f4f7ff977158397c5a484d3b\",\n \"score\": \"0.57794636\",\n \"text\": \"def distance_callback(from_node, to_node):\\n return distances[from_node][to_node]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1935c7550cd78849f2363b125bd0e495\",\n \"score\": \"0.57781154\",\n \"text\": \"def distance(self, x, y, x_, y_):\\n return (x - x_)**2 + (y - y_)**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f6c49793be8e9694ef3462270d52daa\",\n \"score\": \"0.5774951\",\n \"text\": \"def compute_dist_matrix(self):\\n # Use broadcasting to get an array of shape (N,N,n) with the differences of every\\n # possible pair of points\\n differences = self.points[np.newaxis,:] - self.points[:, np.newaxis]\\n\\n # Calculate the squares of the distances by summing along the last axis\\n return (differences**2).sum(axis = -1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a16d23ad3c1a64aad5ac6a6c46931d19\",\n \"score\": \"0.5758453\",\n \"text\": \"def _pairwise_distances(embeddings):\\n # Get the dot product between all embeddings\\n # shape (batch_size, batch_size)\\n dot_product = tf.matmul(embeddings, tf.transpose(embeddings))\\n\\n # Get squared L2 norm for each embedding. We can just take the diagonal of `dot_product`.\\n # This also provides more numerical stability (the diagonal of the result will be exactly 0).\\n # shape (batch_size,)\\n square_norm = tf.diag_part(dot_product)\\n\\n # Compute the pairwise distance matrix as we have:\\n # ||a - b||^2 = ||a||^2 - 2 <a, b> + ||b||^2\\n # shape (batch_size, batch_size)\\n distances = tf.expand_dims(square_norm, 1) - 2.0 * dot_product + tf.expand_dims(square_norm, 0)\\n\\n # Because of computation errors, some distances might be negative so we put everything >= 0.0\\n distances = tf.maximum(distances, 0.0)\\n\\n return distances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d31b5d58bd269c71aa59ed4a3216664\",\n \"score\": \"0.5754819\",\n \"text\": \"def compute(self):\\n return self.be.pairwise_euclidean_distances(self.X, self.Y) ** 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eaa7ca5d94cf2ee091557d2e44501b8\",\n \"score\": \"0.57528883\",\n \"text\": \"def _calculate_distances(self, new_point):\\r\\n\\r\\n # Expand by repeating to increase speed (avoid looping) \\r\\n new_point = np.resize(new_point, (self.n, new_point.shape[0]))\\r\\n euclidean_distance = np.sum((self.data[:,0:-1] - new_point)**2, axis=1)\\r\\n return euclidean_distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d36acb642f8a5cff559d51e842f5fabe\",\n \"score\": \"0.5749607\",\n \"text\": \"def dist(self):\\n return self._dist_mat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7fe1c006213bde2a2f4617a93f01004\",\n \"score\": \"0.5746641\",\n \"text\": \"def compute_distances_no_loops(self, X):\\n num_test = X.shape[0]\\n num_train = self.X_train.shape[0]\\n dists = np.zeros((num_test, num_train)) \\n #########################################################################\\n # TODO: #\\n # Compute the l2 distance between all test points and all training #\\n # points without using any explicit loops, and store the result in #\\n # dists. #\\n # #\\n # You should implement this function using only basic array operations; #\\n # in particular you should not use functions from scipy. #\\n # #\\n # HINT: Try to formulate the l2 distance using matrix multiplication #\\n # and two broadcast sums. #\\n #########################################################################\\n A = X\\n B = self.X_train\\n \\n A_square = np.sum(np.square(A), axis = 1)\\n B_square = np.sum(np.square(B), axis = 1)\\n A_square_plus_B_square = A_square.reshape(A_square.shape[0],1) + B_square\\n AB = np.dot(A, np.transpose(B))\\n squaress = A_square_plus_B_square - 2*AB\\n \\n dists = np.sqrt(squaress)\\n \\n #########################################################################\\n # END OF YOUR CODE #\\n #########################################################################\\n return dists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dd3eb08860c8bbbed584f8ca0c5aae4\",\n \"score\": \"0.57395846\",\n \"text\": \"def get_path_distances(self):\\n\\n return self.path_distances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48b11fdaa0bfb33aaeff66584ff02a60\",\n \"score\": \"0.5736189\",\n \"text\": \"def _pairwise_dist(self, theta):\\n\\t\\treturn torch.norm(theta[:, None] - theta, dim = 2, p = 2)**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ed824ea4157bd57564ec4f0321d4210\",\n \"score\": \"0.5735477\",\n \"text\": \"def distance(self, source, target, attrs):\\n\\n # Distance is 1 - score. Skip minimal distances as they are near duplicates.\\n distance = max(1.0 - attrs[\\\"weight\\\"], 0.0)\\n return distance if distance >= 0.15 else 1.00\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fafcb72587661c8336e43bbc5238b821\",\n \"score\": \"0.5733769\",\n \"text\": \"def feedDistance(self):\\n return float()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfef3c05fcac6bbc8e6c59b5c1d36cd3\",\n \"score\": \"0.5721309\",\n \"text\": \"def _get_distances(self, town_i: int, town_j: int):\\n\\n t1 = self.map[town_i]\\n t2 = self.map[town_j]\\n\\n return np.sqrt((t1[0] - t2[0]) ** 2 + (t1[1] - t2[1]) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bd0bc5b7ee4b164befa4a46b33320a2\",\n \"score\": \"0.5717693\",\n \"text\": \"def m(self) -> float:\\n return self._distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"839633f24b153e473042333e6ab16abc\",\n \"score\": \"0.5715332\",\n \"text\": \"def test_distance(self):\\n m = Metric()\\n self.assertEqual(m.distance(\\\"abc\\\", \\\"abc\\\"), 0)\\n self.assertEqual(m.distance(\\\"abc\\\", \\\"def\\\"), 1)\\n self.assertEqual(m.distance(\\\"abc\\\", \\\"\\\"), 1)\\n self.assertEqual(m.distance(\\\"\\\", \\\"abc\\\"), 1)\\n self.assertEqual(m.distance(\\\"\\\", \\\"\\\"), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac45e12e537b51931cdc7c223c5cab2c\",\n \"score\": \"0.5711865\",\n \"text\": \"def _cal_distance(self, p1: np.ndarray, p2: np.ndarray) -> np.float64:\\n return np.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7e60d794b367da3a7369814e87e3c62\",\n \"score\": \"0.57105076\",\n \"text\": \"def compute_distances_one_loop(self, X):\\n num_test = X.shape[0]\\n num_train = self.X_train.shape[0]\\n dists = np.zeros((num_test, num_train))\\n for i in xrange(num_test):\\n #######################################################################\\n # TODO: #\\n # Compute the l2 distance between the ith test point and all training #\\n # points, and store the result in dists[i, :]. #\\n #######################################################################\\n #pass\\n dists[i, :] = np.linalg.norm(self.X_train - X[i, :], axis=1)\\n #######################################################################\\n # END OF YOUR CODE #\\n #######################################################################\\n return dists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d15cde4c067c795fd709da4bb476c88f\",\n \"score\": \"0.570547\",\n \"text\": \"def dist(self) -> Distance:\\n return Distance(np.linalg.norm(self.dist_vec), units=\\\"ang\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b6ea82462f68629636a9338e86d27a6\",\n \"score\": \"0.57048017\",\n \"text\": \"def test_compute_dists():\\n feats1 = np.array(\\n [\\n [.707, .707],\\n [-.707, .707], \\n [-.707, -.707]\\n ])\\n feats2 = np.array(\\n [\\n [-.5, -.866],\\n [.866, -.5],\\n [.5, .866],\\n [-.866, .5]\\n ])\\n dists = np.array(\\n [\\n [1.98271985, 1.21742762, 0.26101724, 1.58656169],\\n [1.58656169, 1.98271985, 1.21742762, 0.26101724],\\n [0.26101724, 1.58656169, 1.98271985, 1.21742762]\\n ])\\n inter_distances = compute_feature_distances(feats1, feats2)\\n assert inter_distances.shape[0] == 3\\n assert inter_distances.shape[1] == 4\\n assert np.allclose(dists, inter_distances, atol = 1e-03)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70500a7c89c5ec3356e8157290f89281\",\n \"score\": \"0.5702044\",\n \"text\": \"def internal_distance(self):\\n return min([\\n self.nw.distance_to(self.se),\\n self.ne.distance_to(self.sw),\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d52914a2368f2b06c148b5767f2b266\",\n \"score\": \"0.569691\",\n \"text\": \"def get_distance(a, b):\\n return Vector(a).distance(Vector(b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd620bf2e11c439484663af1e581b20a\",\n \"score\": \"0.5695866\",\n \"text\": \"def distance(p1,p2):\\n \\n return np.mean((p1 - p2) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c03f1d11697ba543bb6b8145129e5de8\",\n \"score\": \"0.56954277\",\n \"text\": \"def calc_distance(a,b):\\n return np.sqrt(np.sum((a-b)**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da713fffef169e1731301480577b92b5\",\n \"score\": \"0.56913614\",\n \"text\": \"def get_ds(self):\\n if self.local:\\n x1 = self.x[:self.nat1]; x2 = self.x[-self.nat2:]\\n else:\\n x1 = self.x[:self.n1]; x2 = self.x[-self.n2:]\\n ds1 = qd.l2_distance(x1, x1)\\n ds2 = qd.l2_distance(x2, x1)\\n return ds1, ds2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f277d2f14d84b9e823c0172822958001\",\n \"score\": \"0.56894326\",\n \"text\": \"def compute_distances_two_loops(self, X):\\n num_test = X.shape[0]\\n num_train = self.X_train.shape[0]\\n dists = np.zeros((num_test, num_train))\\n for i in xrange(num_test):\\n for j in xrange(num_train):\\n #####################################################################\\n # TODO: #\\n # Compute the l2 distance between the ith test point and the jth #\\n # training point, and store the result in dists[i, j]. You should #\\n # not use a loop over dimension. #\\n #####################################################################\\n # pass\\n # the other option would be to use linalg\\n dists[i, j] = np.linalg.norm(self.X_train[j] - X[i])\\n # dists[i,j] = np.sqrt(np.sum(np.square(self.X_train[i] - X[j])))\\n #####################################################################\\n # END OF YOUR CODE #\\n #####################################################################\\n return dists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b02dd3a7e9cc802c6c13b8332c5c5848\",\n \"score\": \"0.56762385\",\n \"text\": \"def test_dist(self):\\n print dist(1,1)\\n print dist(1,4)\\n\\n # ... and then evaluate the output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89f2e8a985c34d19b99b16fb8d2f77c1\",\n \"score\": \"0.567621\",\n \"text\": \"def dist(self, ind1, ind2):\\n\\n centers_distance = (np.array(self.positions[ind1]) - np.array(self.positions[ind2]))\\n r1 = self.length(ind1)\\n r2 = self.length(ind2)\\n d = np.abs(centers_distance) - (r1 + r2) / 2\\n print(d)\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5a53f1b9633b8f7386d692a0a2f8345\",\n \"score\": \"0.5675909\",\n \"text\": \"def _calculate_l1_distance(preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:\\n return torch.norm(target_distribution - preds_distribution, p=1, dim=-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fec9e85bd390ff7a1089977abd8c6fd7\",\n \"score\": \"0.566709\",\n \"text\": \"def _threshold_function(self):\\n if self.dist_metric == \\\"euclidean\\\":\\n dist = cdist(self.data, self.data, metric=self.dist_metric)\\n else:\\n dist = udm.categorical_distance(self.data, self.indexes_ordinal, self.indexes_nominal)\\n return dist\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993894,"cells":{"query_id":{"kind":"string","value":"a9bfebd5f0f713c108a4f8f2102afde3"},"query":{"kind":"string","value":"Get path to write and attach corresponding file extension."},"positive_passages":{"kind":"list like","value":[{"docid":"bf1f77f0ec7a3a829b392792fd70b7e0","score":"0.71833307","text":"def _get_write_path(self, file_extension: str):\n if file_extension[0] != \".\":\n file_extension += \".\"\n return os.path.join(self._result_dir, self._filename) + file_extension","title":""}],"string":"[\n {\n \"docid\": \"bf1f77f0ec7a3a829b392792fd70b7e0\",\n \"score\": \"0.71833307\",\n \"text\": \"def _get_write_path(self, file_extension: str):\\n if file_extension[0] != \\\".\\\":\\n file_extension += \\\".\\\"\\n return os.path.join(self._result_dir, self._filename) + file_extension\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ec57b0d5838d03e7f48a93fb1846bc4c","score":"0.6766839","text":"def append_file_extension(path: str, ext: Optional[str]) -> str:\n\n if ext is not None:\n return '%s.%s' % (path, ext)\n return path","title":""},{"docid":"fae1c0631c9baf21a2c2eaa6294f514c","score":"0.6531698","text":"def append_to_filename(filepath:str, name_suffix:str, new_ext:Optional[str]=None)->str:\n ext = new_ext or filepath_ext(filepath)\n name = filepath_name_only(filepath)\n return str(pathlib.Path(filepath).with_name(name+name_suffix).with_suffix(ext))","title":""},{"docid":"8d59bbec6674e63d0168aafb784453aa","score":"0.63846576","text":"def getOutputFile(self, ext):\n f = self.getOutputDir() + \"/\" + self.getId() + ext\n ensureFileDir(f)\n return f","title":""},{"docid":"d996160a2408d1f22b6d7af0e7aaed63","score":"0.6266995","text":"def set_extension(self, ext):\n path = self._filename.split(\"/\")\n parts = path[-1].split(\".\")\n if len(parts) == 1:\n parts.append(ext)\n else:\n parts[-1] = ext\n\n path[-1] = \".\".join(parts)\n return File(\"/\".join(path))","title":""},{"docid":"a050870fc0be9291fca68c58f7bc2bd5","score":"0.62595","text":"def get_write_to_file_fn(file_ext):\n if file_ext == '.json':\n writefn = json_dump\n elif file_ext == '.yml' or file_ext == '.yaml':\n writefn = yaml_safe_dump\n else:\n writefn = None\n return writefn","title":""},{"docid":"1d7f211bc8d79b11cc6453bf4d50b531","score":"0.6199132","text":"def append_file_extension_for_format(path: str, format: Formats) -> str:\n\n ext = file_extension_for_format(format)\n return append_file_extension(path, ext)","title":""},{"docid":"4040f383e188341a2bf32bc6a47ce457","score":"0.6107944","text":"def join_outdir_filename_extension(\n path: PathOrStr, outdir: PathOrStr, extension: str\n) -> Path:\n filename = Path(Path(path).name)\n extension = extension if extension.startswith('.') else '.' + extension\n filename_with_ext = filename.stem + extension\n outpath = Path(outdir) / filename_with_ext\n return outpath","title":""},{"docid":"4cb6be2fc9e82a40faeb3c8c05b20652","score":"0.6071368","text":"def swapext(pth, ext):\n ext = ext.lstrip(\".\")\n dirname = os.path.dirname(pth)\n basename = os.path.basename(pth)\n newname = f\"{os.path.splitext(basename)[0]}.{ext}\"\n\n return os.path.join(dirname, newname)","title":""},{"docid":"3a2e8b1c7350d0135e1b3625bf636860","score":"0.6039224","text":"def get_path(self, name, ext):\n return os.path.join('yell', '{0}{1}'.format(name, ext))","title":""},{"docid":"fdd86738da3b1af5cd1e241fc8c186f3","score":"0.60057396","text":"def set_file_path_name(self, file_name_ext: str) -> str:\n return f\"{get_iso_date()}/{self.content_type.name}/{file_name_ext}\"","title":""},{"docid":"fdd86738da3b1af5cd1e241fc8c186f3","score":"0.60057396","text":"def set_file_path_name(self, file_name_ext: str) -> str:\n return f\"{get_iso_date()}/{self.content_type.name}/{file_name_ext}\"","title":""},{"docid":"fb180dffc0cdd6cbadebb4b1e47c053f","score":"0.5974307","text":"def filenameVerbose(self, kind, descr, extension = '.txt'):\n fn = self.filename(kind, extension = extension)\n logging.info(\"Writing {0} to:\".format(descr))\n fnUrl = fn\n if fnUrl.startswith('\\\\\\\\?'):\n fnUrl = fnUrl[4:]\n logging.info(\"{0}'{1}'\".format(self._indent,\n QUrl.fromLocalFile(fnUrl).toEncoded()))\n return fn","title":""},{"docid":"43d9df6f95e5cd6ad52fd5a5555cf11d","score":"0.5966643","text":"def file_extension(self):","title":""},{"docid":"a0d650f42c2c1a85e67df6147a95249d","score":"0.5958428","text":"def make_path(self, directory=None, extension=None):\n\n if not directory: directory = ''\n if not extension: extension = ''\n return os.path.join(directory, self.filename + extension)","title":""},{"docid":"6d93836f6cd1aaca111c5c50edf8a81a","score":"0.58954835","text":"def add_extension(file_name: str, extension: str) -> str:\n if file_name.endswith(\".\" + extension):\n return file_name\n else:\n return file_name + \".\" + extension","title":""},{"docid":"1ab0f584aebeb5894a0e6edf74a266d2","score":"0.589129","text":"def makeext(name, new_extension):\n base = os.path.splitext(name)[0]\n return base + \".\" + new_extension","title":""},{"docid":"24e30acd3d8d073558e09f0e4d8c3261","score":"0.58477324","text":"def add_extension(cls, file_name_base: str) -> str:\n pass","title":""},{"docid":"e2c94b5991d0e832ca262aa4bcaee27c","score":"0.58353704","text":"def with_extension(filename: str, extension: str = 'json') -> str:\n dir_name = os.path.dirname(filename)\n fname = '.'.join([os.path.basename(filename).split('.')[0], extension])\n return dir_name + '/' + fname","title":""},{"docid":"b9b378827490c61cc4ee2693683623fc","score":"0.58350617","text":"def file_extension(self) -> str:\r\n raise NotImplementedError","title":""},{"docid":"a83b4fa5c006d66fc2f6c9078cdc02ba","score":"0.5818211","text":"def ensure_extension(path, ext):\n if path.endswith(ext):\n return ext\n else:\n return path + ext","title":""},{"docid":"a835f9545f8df4c1bc8903bad28b7fc2","score":"0.5783803","text":"def join_filename(file, ext):\n return os.path.join('image_test', file + ext)","title":""},{"docid":"40cb9a701f2373356f0cfbadbf9ba301","score":"0.57721484","text":"def extention(self, x: str):\n if self.path is None:\n raise ValueError(f\"Can't add the extention to the path: {self.path}\")\n if x in SUPPORTED_VIDEO_EXTENTIONS:\n root, _ = os.path.splitext(self.path)\n self.path = root + x\n else:\n ValueError(f\"The extention '{x}' is not supported.\")","title":""},{"docid":"7007d2c2313dd757d2799b8cd8a04f0b","score":"0.5717794","text":"def _construct_out_filename(self, in_file, ext, work_dir):\n dir, base = os.path.split(in_file)\n if os.path.isdir(in_file):\n dir = in_file\n base = ''\n if base == '':\n base = os.path.basename(dir)\n out_base = os.path.splitext(base)[0] + ext\n out_file = os.path.join(dir, 'target', out_base)\n return os.path.abspath(out_file)","title":""},{"docid":"f39c843cd9183f1647c7fe336cec8591","score":"0.571676","text":"def format_filepath(self, name, label, suffix=''):\n suffix = '_' + suffix if suffix else ''\n filename = '%s_%s%s.pth' % (name, label, suffix)\n filepath = os.path.join(self.save_dir, filename)\n return filepath","title":""},{"docid":"6ac51fd1fc6165246f721e5386ab4ff2","score":"0.5707478","text":"def write_filename(self, filename):\n if self.parent.check_file(filename):\n return self.write_filename('1' + filename)\n else:\n return filename","title":""},{"docid":"593e52472454bc7694f6b2ef9f8e08bc","score":"0.57056004","text":"def change_extension(path: str, ext: str) -> str:\n\n return path.split('.')[0] + '.' + ext","title":""},{"docid":"c5295e3fabc755b959fcc7316981cf66","score":"0.5680847","text":"def joinext(prefix, ext):\n if ext[0] != \".\":\n ext = \".{0}\".format(ext)\n # require leading separator to match os.path.splitext\n return prefix + EXTSEP + ext[1:]","title":""},{"docid":"b0545f5f634678126e3ac06038d200c8","score":"0.5678778","text":"def createOutfileName(extension):\n \n global outfile\n\n if gbl.playFile and gbl.outfile:\n error(\"You cannot use the -f option with -P\")\n\n if gbl.outfile:\n outfile = gbl.outfile\n\n elif gbl.playFile:\n outfile = \"MMAtmp%s.mid\" % os.getpid()\n MMA.exits.files.append(outfile)\n\n else:\n outfile, ext = os.path.splitext(gbl.infile)\n if ext != gbl.ext:\n outfile=gbl.infile\n outfile += extension\n\n outfile=MMA.file.fixfname(outfile)","title":""},{"docid":"3e73fcc2c2a13dfb60eda652e1c81b81","score":"0.5659241","text":"def storeFile(self, episode):\n source, name = os.path.split(episode.path)\n return os.path.join(source, self.getFormattedName(episode))","title":""},{"docid":"aa533f611d9ddca1189ca3cbc8b764cb","score":"0.56484157","text":"def save_to_file(file_name: str, content: str, extension: str = \"ispl\") -> str:\n file_name = FileTools.add_extension(file_name, extension)\n f = open(f\"{file_name}\", \"w\")\n f.write(content)\n f.close()\n return file_name","title":""},{"docid":"12b84e6d23b77d2162d2622a4f4af13a","score":"0.5636684","text":"def asfilename(self, text, extension = 'png', prefix= '', dirname = None):\n if 'file' not in self.mapping[text]:\n self.mapping[text]['file'] = text.encode('ascii', 'ignore')\n fname = \"%s%s.%s\" % (prefix, self.mapping[text]['file'], extension)\n #logging.debug(\"self.mapping=\" + str(self.mapping))\n if dirname:\n return os.path.join(dirname, fname)\n return fname","title":""},{"docid":"25a9c63ede8d8ef217d1255250a8bc29","score":"0.56222856","text":"def _get_extension_string(self, obj):\n return \".\" + obj.extension if obj.extension else \"\"","title":""},{"docid":"44e11409bf86e6e73b407f7297138148","score":"0.5615433","text":"def asfilename(self, text, extension = 'png', prefix= '', dirname = None):\n if 'file' not in self.mapping[text]:\n self.mapping[text]['file'] = text.encode('ascii', 'ignore')\n fname = \"%s%s.%s\" % (prefix, self.mapping[text]['file'], extension)\n logging.debug(\"self.mapping=\" + str(self.mapping))\n if dirname:\n return os.path.join(dirname, fname)\n return fname","title":""},{"docid":"c51648799914e05dd52c4e5562681f57","score":"0.5600323","text":"def _getPath(self, suffix, count=''):\n path = '{}{}{}.txt'.format(self.fileName, suffix, str(count))\n if self.saveto is not None:\n path = os.path.join(self.saveto, path)\n return path","title":""},{"docid":"d6824640db66862a321bbd79a1fe3348","score":"0.55963844","text":"def _create_attachment_filepath(self, directory_path, filename):\n filename = secure_filename(filename)\n ext = self._get_file_extension(filename)\n new_filename = \"{}{}\".format(self.attachment_file_name, ext)\n return self._create_filepath(directory_path, new_filename)","title":""},{"docid":"c260cff83ba3cbedbfb53021f3c40010","score":"0.5594497","text":"def get_attachment_path(instance, filename: str) -> str:\n path = settings.EMAIL_LOG_ATTACHMENTS_PATH\n if callable(path):\n return path(instance, filename)\n return str(pathlib.Path(str(path)) / filename)","title":""},{"docid":"4c83193f18ee8276a95173fa2852f44a","score":"0.55894244","text":"def append_before_ext(path, addition):\n dot_index = path.rfind('.')\n if dot_index == -1: # . Not found\n return path + addition\n return path[:dot_index] + addition + path[dot_index:]","title":""},{"docid":"bed7fd10000e1e88b347c5677904a943","score":"0.55868995","text":"def filepath_ext(filepath:str)->str:\n return pathlib.Path(filepath).suffix","title":""},{"docid":"5be158729bf4527383080304c05a751a","score":"0.55858254","text":"def gen_path_file(spt_folder='', snm_file='', st_file_type=''):\n st_suffix = ''\n if st_file_type == 'log':\n st_suffix = '.log.py'\n\n return os.path.join(spt_folder, snm_file + st_suffix)","title":""},{"docid":"bcae8e762d78858d56d8004cdbe0746d","score":"0.557719","text":"def save_as_file(self, *args, **kwargs):\n filename = self.get_filename(*args, **kwargs)\n extension = self.extension\n full_filename = f'{filename}.{extension}'\n with open(f'tmp_storage/{full_filename}', 'wb') as outfile:\n outfile.write(self.output.get_buffer())","title":""},{"docid":"16f9dee9caa632dedf0b5702f6a5ca12","score":"0.55733275","text":"def write(self, name, *_args, **_kwargs):\n name_wo_ext = os.path.splitext(name)[0]\n if name == name_wo_ext:\n name += self.default_suffix\n self.write_wav(name)","title":""},{"docid":"a7bb4522a58c77c90a8de7b934faea0f","score":"0.5552688","text":"def get_output_filepath(base_dir, tstep, extension):\n return base_dir + get_base_output_name() + str(tstep) + extension","title":""},{"docid":"95a5c5ff96762a707e92f6aaa8ea98a4","score":"0.554867","text":"def get_output_path(vocabs_path, suffix='_interersing'):\n # Split the path by the extension -> returns part\n # before extension, extension with dot\n path, extension = splitext(vocabs_path)\n # Join all parts back together.\n return ''.join([path, suffix, extension])\n # return '{}{}{}'.format(path, suffix, extension)","title":""},{"docid":"bdbe89e1c44932fec57396dd8db1e8e9","score":"0.5546051","text":"def attach_file(src,resource):\n original_name = os.path.basename(src)\n new_name = resource.id + '_' + original_name\n dir = os.getcwd()\n location = os.path.join(dir,new_name)\n copy(src,location)\n return location","title":""},{"docid":"8bd5c5d2b0c4cf9714f64ce94c63299a","score":"0.5545908","text":"def append_default_extension(filename, default_extension='.png'):\n\n if '.' in filename:\n return filename\n else:\n return filename + default_extension","title":""},{"docid":"50b81f10746c15e4a19d237dbcf8e73d","score":"0.55415285","text":"def filepath(self):\n _filename = self.filename\n if not _filename:\n _filename = self.name + '.txt'\n return join(self.store, _filename)","title":""},{"docid":"80e1407b3088b1b48ec60d2923ba2101","score":"0.5538885","text":"def results_path(content, extension):\n try:\n path = \"{0}/graphmanager/{1}\".format(data[\"directory\"], uuid.uuid4().hex)\n if not os.path.exists(path):\n os.makedirs(path)\n full_path = \"{0}/{1}.{2}\".format(path, uuid.uuid1().hex, extension)\n f = open(full_path, \"w+\")\n f.write(content)\n app_logger.info('Content available in path: {0}'.format(full_path))\n return \"file://{0}\".format(full_path)\n except Exception as error:\n app_logger.error('Something is wrong: {0}'.format(error))\n raise\n finally:\n f.close()","title":""},{"docid":"688df123711e534db77064290f419607","score":"0.5528349","text":"def _writePath(self):\n pass\n # do nothing, we don't want to rename the script","title":""},{"docid":"5fe0f36909ac3f3f88e1014d45b11ef6","score":"0.5518041","text":"def save_file(self, data, extension):\n raise NotImplementedError(\n \"Inheriting collector class needs to define this function\"\n )","title":""},{"docid":"f990afb8a2336f16c30f124720a4ee70","score":"0.55126524","text":"def generate_output_filepath(input_filepath: str, output_dir: str,\n file_ext: str) -> str:\n pass","title":""},{"docid":"eb30c3831ba6e61126e02a2101b04c98","score":"0.5510599","text":"def filehandle(pth, ext=\"JSON\"):\n return os.path.join(pth, tstamp(\"fh\") + os.path.extsep + ext)","title":""},{"docid":"0fdc774732a54db6ecd7d9c137ae273e","score":"0.55085015","text":"def extension():\n return ''","title":""},{"docid":"8a20529ac50c9bd49b339a302d909eb9","score":"0.55079734","text":"def path(filename):","title":""},{"docid":"2f2f981da38f19b0ec98e246d9425ccb","score":"0.5494166","text":"def extension(self):\n return os.path.splitext(self.path)[1] if self.isfile else None","title":""},{"docid":"594d52741e55a6d7f1281576b1369999","score":"0.5490611","text":"def write_to_file(crypt, feed, chnk, path, ext):\n if crypt == 'decrypt':\n newpath = path # .bam\n if path.endswith(tuple(ext)):\n newpath, extension = os.path.splitext(path)\n with open(newpath, 'wb+') as f:\n for chunk in feed.iter_content(chunk_size=chnk):\n if chunk:\n f.write(chunk)\n elif crypt == 'encrypt':\n newpath = path + '.cip.csc'\n with open(newpath, 'wb+') as f:\n for chunk in feed.iter_content(chunk_size=chnk):\n if chunk:\n f.write(chunk)\n else:\n raise Exception('invalid var(crypt) for write_to_file()')\n return","title":""},{"docid":"90b37bd771f7883fbd102b01b2dbeeed","score":"0.5485882","text":"def extend_path(path,name):\n\tpass","title":""},{"docid":"c7517d37db54c8f7ddaac72a6770c2c6","score":"0.5483617","text":"def out_filename(self, filetype, dir, format='old'):\n filename = self.filename(filetype=filetype, format=format)\n return Path(dir) / filename","title":""},{"docid":"c8b31c6d84dbb4357f69548d460c68da","score":"0.5483192","text":"def get_file_path(file_dir: str):\n # extension = filename.split('.')[-1]\n # filename = \"%s.%s\" % (uuid.uuid4(), ext)\n # return join(file_dir, filename)\n global give_path\n\n def give_path(instance, filename):\n return join(file_dir, \"%s.%s\" % (uuid.uuid4().int, filename.split('.')[-1]))\n\n return give_path","title":""},{"docid":"4015eaab3977e01dadc8cf2e9b52b223","score":"0.54715854","text":"def extant_file(x):\n\t\n\tif not os.path.exists(x):\n\t\tprint \"Error: {0} does not exist\".format(x)\n\t\texit()\n\tx = str(x)\n\treturn x","title":""},{"docid":"4015eaab3977e01dadc8cf2e9b52b223","score":"0.54715854","text":"def extant_file(x):\n\t\n\tif not os.path.exists(x):\n\t\tprint \"Error: {0} does not exist\".format(x)\n\t\texit()\n\tx = str(x)\n\treturn x","title":""},{"docid":"d441f30303c269dc09da9b67509da316","score":"0.54548985","text":"def add_to_filename(self,\n string=None,\n new_extension=None,\n timestamp=False):\n\n if timestamp:\n timestamp = datetime.datetime.now().isoformat().replace('-', '').replace(':', '').split('.')[0]\n else:\n timestamp = ''\n\n if string is not None:\n string += timestamp\n else:\n string = timestamp\n\n file_name, file_ext = os.path.splitext(self.basename)\n\n file_name += string\n\n if new_extension is not None:\n self.basename = file_name + new_extension\n else:\n self.basename = file_name + file_ext\n\n if self.dirname is not None:\n return self.dirname + self.sep + self.basename\n else:\n return self.basename","title":""},{"docid":"854a3c3ccc5fa13be08aa0cc1ccb0368","score":"0.54539806","text":"def add_suffix(filename, suffix):\n path = filename.split(\"/\")\n parts = path[-1].split(\".\")\n i = max(len(parts) - 2, 0)\n parts[i] = parts[i] + suffix\n path[-1] = \".\".join(parts)\n return \"/\".join(path)","title":""},{"docid":"fe4f71832b314bcbc99ada6b7a5feceb","score":"0.54533166","text":"def _get_path(self, app):\r\n if self.fdist_dir == '':\r\n return app + self.ext\r\n else:\r\n return os.sep.join([self.fdist_dir, app]) + self.ext","title":""},{"docid":"121ac7125fe9edaa1d8068db8b5a4ece","score":"0.544883","text":"def get_extension(extension: str) -> str:\n return (\".\" + extension) if len(extension) > 0 else \"\"","title":""},{"docid":"c5abe044421deb9cfc09428aaecb2d50","score":"0.54363364","text":"def get_file_path(fileName):\n path = session['pkg']\n (_, ext) = os.path.splitext(fileName)\n if ext == \".pl\":\n path = os.path.join(path, \"prolog\")\n elif ext == \".owl\":\n path = os.path.join(path, \"owl\")\n return os.path.join(path, fileName)","title":""},{"docid":"555633dc5bb3312a0f8f54c4e601e2db","score":"0.54159546","text":"def FileExtension(self):\n return None","title":""},{"docid":"b48c5384811eca1ab98212a9b1fc3903","score":"0.5412737","text":"def check_for_extension(path_name_ext, extension=\"\"):\n # check if there is an extension, but only if the extension is given.\n if extension:\n if path_name_ext[-len(extension):] != extension:\n path_name_ext += extension\n# print(\"NOTE (imports, check_for_extension): the extension\", extension, \"was added to the filename.\")\n \n return path_name_ext","title":""},{"docid":"5bd7f837c907e49b169145dcbb21f06f","score":"0.54112744","text":"def extant_file(x):\n\n\t\n\n\tif not os.path.exists(x):\n\n\t\tprint \"Error: {0} does not exist\".format(x)\n\n\t\texit()\n\n\tx = str(x)\n\n\treturn x","title":""},{"docid":"3d30994194eb89f3dd258d266097b6e8","score":"0.54089755","text":"def change_file_extension(filename: str, extension: str) -> str:\n return filename.split('.')[0] + extension","title":""},{"docid":"acf01d78414556a7420432d53a97ade3","score":"0.54077286","text":"def filename(self, kind = None, extension = '.txt'):\n fn = [self._basename]\n if isString(kind) and len(kind):\n fn += [\"_\", kind]\n if isString(extension):\n fn += extension\n fn = os.path.join(self._outDir, \"\".join(fn))\n return fixFilename(fn)","title":""},{"docid":"0df344144f452c53934c356f15e7aa12","score":"0.5401847","text":"def getFilePath(self, f=None):\n return os.path.join(self.bot.getDataPath(self.moduleName), (f if f else ''))","title":""},{"docid":"da542ea057051539482d74a8f22d0744","score":"0.53763664","text":"def createOutputFile(self, ext, contents=\"\"):\n fpath = self.getOutputFile(ext)\n ensureFileDir(fpath)\n fh = open(fpath, \"w\")\n try:\n fh.write(contents)\n finally:\n fh.close()","title":""},{"docid":"030380ef49e6166e4cd1f0beaa698237","score":"0.53565586","text":"def addext(filename, extension):\n\n return filename + ('' if splitext(filename)[1] else extension)","title":""},{"docid":"102fd09a54f0cce8832b6a09494f3a49","score":"0.5354405","text":"def path_of_part(self, ext):\n path_to_part = self._get_folder(self.extract_path(), ext)\n if ext in path_to_part:\n return path_to_part\n else:\n new_pieces = os.listdir(path_to_part)\n for piece in new_pieces:\n if ext in piece:\n return os.path.join(path_to_part, piece)","title":""},{"docid":"033f8cadcd70d202ebbf70e85591888a","score":"0.5342961","text":"def file (self, name ):\n\t\tif self.isDir():\n\t\t\treturn self.join( name )\n\t\telse:\n\t\t\traise ValueError( \"\"\"Can't currently create a file in a file\"\"\" )","title":""},{"docid":"11a2c069908c66bf36206388094cc0a6","score":"0.53319633","text":"def changeExtension(filename, ext):\n aux = filename.split('.')\n return aux[0]+'.'+ext","title":""},{"docid":"4f9a7b85b58c334da75ef6a82430c812","score":"0.5322633","text":"def attach_file_num(filepath, file_num):\n\n path, basename = os.path.split(os.path.abspath(filepath))\n new_basename, ext = basename.split('.')\n new_basename += '_' + str(file_num) + '.' + ext\n return os.path.join(path, new_basename)","title":""},{"docid":"06a2c131bb35f4948c550e5e881c5768","score":"0.53143764","text":"def extension():\n return \".\"+param.extension.strip(\".\")","title":""},{"docid":"9fa458736ff3476a5c9ff2559dc3b50e","score":"0.5308334","text":"def get_file_path(path, get_path=False):\n current_path = path\n ext = os.path.splitext(current_path)[1]\n while (\".\" in current_path):\n current_path = os.path.splitext(current_path)[0]\n ext = (os.path.splitext(current_path)[1] + ext)\n if get_path:\n return current_path\n else:\n return ext","title":""},{"docid":"9208b591a4608dd6ea1a941112f21f70","score":"0.530598","text":"def add_extension(self, extension: str) -> None:\n if extension:\n extension_to_add = os.path.abspath(os.path.expanduser(extension))\n if os.path.exists(extension_to_add):\n self._extension_files.append(extension_to_add)\n else:\n raise OSError(\"Path to the extension doesn't exist\")\n else:\n raise ValueError(\"argument can not be null\")","title":""},{"docid":"3ffe6d30e868557b8e92edd153bc6277","score":"0.52989304","text":"def replace_ext(filename, ext=''):\n return '.'.join(filename.split('.')[:-1]) + ext","title":""},{"docid":"8154d999fae5cee8c0e83acd076709bf","score":"0.5283287","text":"def make_filename(title, ext):\n return os.path.join(prefix, title.replace(\" \", \"_\").replace(\":\", \"_\").replace(\"/\", \"_\") + os.path.extsep + ext)","title":""},{"docid":"dd24a7c431ce1353011935f5fcf060ee","score":"0.52795875","text":"def path(self, f):\n\t\treturn os.path.join(self.directory, f)","title":""},{"docid":"8b7aa232e2f5a646307295cef041b052","score":"0.5260127","text":"def get_file(filename: str) -> str:\n return os.path.join('files', filename)","title":""},{"docid":"d4aaba246ee31728267682b083fbc8f8","score":"0.5256028","text":"def combine_path(base_path, relative_ref):\n #if (base_path != \"\"):\n #print \"os.getcwd()\", os.getcwd()\n #os.chdir(base_path)\n filePath = os.path.abspath(relative_ref)\n filePath = filePath + \".tex\"\n return filePath","title":""},{"docid":"e5e4711f4c6dcce7b34cb0a297de980e","score":"0.52504486","text":"def __get_paths_to(self, ext, paths):\n for file in os.listdir(self.path_dir):\n if file.endswith(ext):\n paths.append(os.path.join(self.path_dir, file))","title":""},{"docid":"b2993852a03dd1e71b6af551c5109af2","score":"0.52446425","text":"def filepath(self) -> Path:\n if self._use_tar:\n return self.dest_dir/'{obj.name}-{obj.version}{ext}'.format(\n obj=self, ext='.tar'\n )\n # Just to keep track of something on disk.\n return self.dirname/'manifest.json'","title":""},{"docid":"b39d41c4d95e40b6660c6b7342d41f92","score":"0.52344954","text":"def write_file(self, path, data):\n self._path_to_proto[posix_path(path)] = data","title":""},{"docid":"d747e47adc0a7e4a0c93cc2a0112d6ee","score":"0.5231172","text":"def append_new_file(self, msg):\n file_data = {'path': msg.get('path'),\n 'fileExtension': msg.get('format'),\n 'resolution': msg.get('resolution')}\n print(f'new file: {file_data}')\n self.conversion_files.put(file_data)","title":""},{"docid":"12d1034821b3bbb96407a5a0101c8e22","score":"0.5230035","text":"def get_aeroo_filename(self, record, output_format):\n if self.attachment:\n filename = self._eval_aeroo_attachment_filename(self.attachment, record)\n return '.'.join((filename, output_format))\n else:\n return '.'.join((self.name, output_format))","title":""},{"docid":"2564837c9e46527b7b5ffcece1a1d7ce","score":"0.5229681","text":"def get_actual_filename_to_use(cls, args):\n filename, extmode = args.filename, args.extension_mode\n assert extmode in ['auto', 'never', 'always']\n\n # if extension mode is auto, we check if the extension is required\n # (if it is not in the list of extensions) and fallback to always/never\n if extmode == 'auto':\n extmode = 'never' if (os.path.splitext(filename)[1].lower() in\n (\".\" + e.lower() for e in cls.extensions)\n ) else 'always'\n assert extmode in ['never', 'always']\n\n # if extension is required, add the first one\n if extmode == 'always':\n filename += \".\" + cls.extensions[0]\n\n return filename","title":""},{"docid":"336cd3d9b1c5ce7f6c2e1ba359dc0f2c","score":"0.5223622","text":"def __joinpath(path,file):\n return os.path.join(path + os.sep + file.lower())","title":""},{"docid":"bd5a0a30c5a84991ba21331b2244950f","score":"0.52227396","text":"def _new_filename(filepath, new_root='{}_funpdbe', new_ext=None):\n # Break filepath into components\n path, basename = os.path.split(filepath)\n root, ext = os.path.splitext(basename)\n\n # Construct new filename\n if new_ext:\n ext = new_ext\n if new_root:\n root = new_root.format(root) # new_root can include original root\n basename = root + ext\n\n # Construct derived filepath\n return os.path.join(path, basename)","title":""},{"docid":"7b9e13149387cfec65ffe4cdab7a4d15","score":"0.5221859","text":"def add_triggerer_suffix(full_path, job_id=None):\n full_path = Path(full_path).as_posix()\n full_path += f\".{LogType.TRIGGER.value}\"\n if job_id:\n full_path += f\".{job_id}.log\"\n return full_path","title":""},{"docid":"9f473ba31c82cf7b681a1f82bd86f0c6","score":"0.5221026","text":"def extension (self):\n\t\treturn os.path.splitext(self)[1]","title":""},{"docid":"5ab6e5429f9f1c12e04d1c25845c0de1","score":"0.5214518","text":"def get_file_path(dotted_path, extension='json'):\n # If the operating system's file path seperator character is in the string\n if os.sep in dotted_path or '/' in dotted_path:\n # Assume the path is a valid file path\n return dotted_path\n\n parts = dotted_path.split('.')\n corpus_path = os.path.join(*parts)\n\n if os.path.exists(corpus_path + '.{}'.format(extension)):\n corpus_path += '.{}'.format(extension)\n\n return corpus_path","title":""},{"docid":"090361d99395e95ed7c3f6c8664f7d6c","score":"0.5209273","text":"def _get_final_filename(self):\n if output_dir is None:\n raise TypeError(\"Output directory is not a string\")\n os.makedirs(output_dir, exist_ok = True)\n\n pref_name_array = self.preferred_filename.split('.')\n filename, extension = os.path.join(output_dir, '.'.join(pref_name_array[:-1])), pref_name_array[-1]\n output_filename = \"{}.{}\".format(filename, extension)\n\n counter = 1\n while os.path.exists(output_filename):\n output_filename = \"{}-{}.{}\".format(filename, counter, extension)\n counter += 1\n return output_filename","title":""},{"docid":"3d0a9c185a3ef49b4698413759e6a1a9","score":"0.52020603","text":"def get_hips_extra_file(filename):\n path = Path(os.environ['HIPS_EXTRA'])\n return path / filename","title":""},{"docid":"95ab90dddc92a213ec624fde91dbe825","score":"0.51956844","text":"def write_to_file(self, filepath, text=\"\"):\n if \"/tmp/tmp\" not in filepath:\n raise SecurityFaultInvalidFileAccessAttempt(filepath)\n with open(filepath, \"w\") as f:\n f.write(\"{text}\".format(\n text=text))\n return f.name","title":""},{"docid":"01dabf554b5677855a5114f8ff2ed25e","score":"0.5192978","text":"def get_file_upload_to(self, filename):\n return os.path.join(machina_settings.ATTACHMENT_FILE_UPLOAD_TO, filename)","title":""}],"string":"[\n {\n \"docid\": \"ec57b0d5838d03e7f48a93fb1846bc4c\",\n \"score\": \"0.6766839\",\n \"text\": \"def append_file_extension(path: str, ext: Optional[str]) -> str:\\n\\n if ext is not None:\\n return '%s.%s' % (path, ext)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fae1c0631c9baf21a2c2eaa6294f514c\",\n \"score\": \"0.6531698\",\n \"text\": \"def append_to_filename(filepath:str, name_suffix:str, new_ext:Optional[str]=None)->str:\\n ext = new_ext or filepath_ext(filepath)\\n name = filepath_name_only(filepath)\\n return str(pathlib.Path(filepath).with_name(name+name_suffix).with_suffix(ext))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d59bbec6674e63d0168aafb784453aa\",\n \"score\": \"0.63846576\",\n \"text\": \"def getOutputFile(self, ext):\\n f = self.getOutputDir() + \\\"/\\\" + self.getId() + ext\\n ensureFileDir(f)\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d996160a2408d1f22b6d7af0e7aaed63\",\n \"score\": \"0.6266995\",\n \"text\": \"def set_extension(self, ext):\\n path = self._filename.split(\\\"/\\\")\\n parts = path[-1].split(\\\".\\\")\\n if len(parts) == 1:\\n parts.append(ext)\\n else:\\n parts[-1] = ext\\n\\n path[-1] = \\\".\\\".join(parts)\\n return File(\\\"/\\\".join(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a050870fc0be9291fca68c58f7bc2bd5\",\n \"score\": \"0.62595\",\n \"text\": \"def get_write_to_file_fn(file_ext):\\n if file_ext == '.json':\\n writefn = json_dump\\n elif file_ext == '.yml' or file_ext == '.yaml':\\n writefn = yaml_safe_dump\\n else:\\n writefn = None\\n return writefn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d7f211bc8d79b11cc6453bf4d50b531\",\n \"score\": \"0.6199132\",\n \"text\": \"def append_file_extension_for_format(path: str, format: Formats) -> str:\\n\\n ext = file_extension_for_format(format)\\n return append_file_extension(path, ext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4040f383e188341a2bf32bc6a47ce457\",\n \"score\": \"0.6107944\",\n \"text\": \"def join_outdir_filename_extension(\\n path: PathOrStr, outdir: PathOrStr, extension: str\\n) -> Path:\\n filename = Path(Path(path).name)\\n extension = extension if extension.startswith('.') else '.' + extension\\n filename_with_ext = filename.stem + extension\\n outpath = Path(outdir) / filename_with_ext\\n return outpath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cb6be2fc9e82a40faeb3c8c05b20652\",\n \"score\": \"0.6071368\",\n \"text\": \"def swapext(pth, ext):\\n ext = ext.lstrip(\\\".\\\")\\n dirname = os.path.dirname(pth)\\n basename = os.path.basename(pth)\\n newname = f\\\"{os.path.splitext(basename)[0]}.{ext}\\\"\\n\\n return os.path.join(dirname, newname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2e8b1c7350d0135e1b3625bf636860\",\n \"score\": \"0.6039224\",\n \"text\": \"def get_path(self, name, ext):\\n return os.path.join('yell', '{0}{1}'.format(name, ext))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdd86738da3b1af5cd1e241fc8c186f3\",\n \"score\": \"0.60057396\",\n \"text\": \"def set_file_path_name(self, file_name_ext: str) -> str:\\n return f\\\"{get_iso_date()}/{self.content_type.name}/{file_name_ext}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdd86738da3b1af5cd1e241fc8c186f3\",\n \"score\": \"0.60057396\",\n \"text\": \"def set_file_path_name(self, file_name_ext: str) -> str:\\n return f\\\"{get_iso_date()}/{self.content_type.name}/{file_name_ext}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb180dffc0cdd6cbadebb4b1e47c053f\",\n \"score\": \"0.5974307\",\n \"text\": \"def filenameVerbose(self, kind, descr, extension = '.txt'):\\n fn = self.filename(kind, extension = extension)\\n logging.info(\\\"Writing {0} to:\\\".format(descr))\\n fnUrl = fn\\n if fnUrl.startswith('\\\\\\\\\\\\\\\\?'):\\n fnUrl = fnUrl[4:]\\n logging.info(\\\"{0}'{1}'\\\".format(self._indent,\\n QUrl.fromLocalFile(fnUrl).toEncoded()))\\n return fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d9df6f95e5cd6ad52fd5a5555cf11d\",\n \"score\": \"0.5966643\",\n \"text\": \"def file_extension(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0d650f42c2c1a85e67df6147a95249d\",\n \"score\": \"0.5958428\",\n \"text\": \"def make_path(self, directory=None, extension=None):\\n\\n if not directory: directory = ''\\n if not extension: extension = ''\\n return os.path.join(directory, self.filename + extension)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d93836f6cd1aaca111c5c50edf8a81a\",\n \"score\": \"0.58954835\",\n \"text\": \"def add_extension(file_name: str, extension: str) -> str:\\n if file_name.endswith(\\\".\\\" + extension):\\n return file_name\\n else:\\n return file_name + \\\".\\\" + extension\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ab0f584aebeb5894a0e6edf74a266d2\",\n \"score\": \"0.589129\",\n \"text\": \"def makeext(name, new_extension):\\n base = os.path.splitext(name)[0]\\n return base + \\\".\\\" + new_extension\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24e30acd3d8d073558e09f0e4d8c3261\",\n \"score\": \"0.58477324\",\n \"text\": \"def add_extension(cls, file_name_base: str) -> str:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c94b5991d0e832ca262aa4bcaee27c\",\n \"score\": \"0.58353704\",\n \"text\": \"def with_extension(filename: str, extension: str = 'json') -> str:\\n dir_name = os.path.dirname(filename)\\n fname = '.'.join([os.path.basename(filename).split('.')[0], extension])\\n return dir_name + '/' + fname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9b378827490c61cc4ee2693683623fc\",\n \"score\": \"0.58350617\",\n \"text\": \"def file_extension(self) -> str:\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a83b4fa5c006d66fc2f6c9078cdc02ba\",\n \"score\": \"0.5818211\",\n \"text\": \"def ensure_extension(path, ext):\\n if path.endswith(ext):\\n return ext\\n else:\\n return path + ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a835f9545f8df4c1bc8903bad28b7fc2\",\n \"score\": \"0.5783803\",\n \"text\": \"def join_filename(file, ext):\\n return os.path.join('image_test', file + ext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40cb9a701f2373356f0cfbadbf9ba301\",\n \"score\": \"0.57721484\",\n \"text\": \"def extention(self, x: str):\\n if self.path is None:\\n raise ValueError(f\\\"Can't add the extention to the path: {self.path}\\\")\\n if x in SUPPORTED_VIDEO_EXTENTIONS:\\n root, _ = os.path.splitext(self.path)\\n self.path = root + x\\n else:\\n ValueError(f\\\"The extention '{x}' is not supported.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7007d2c2313dd757d2799b8cd8a04f0b\",\n \"score\": \"0.5717794\",\n \"text\": \"def _construct_out_filename(self, in_file, ext, work_dir):\\n dir, base = os.path.split(in_file)\\n if os.path.isdir(in_file):\\n dir = in_file\\n base = ''\\n if base == '':\\n base = os.path.basename(dir)\\n out_base = os.path.splitext(base)[0] + ext\\n out_file = os.path.join(dir, 'target', out_base)\\n return os.path.abspath(out_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f39c843cd9183f1647c7fe336cec8591\",\n \"score\": \"0.571676\",\n \"text\": \"def format_filepath(self, name, label, suffix=''):\\n suffix = '_' + suffix if suffix else ''\\n filename = '%s_%s%s.pth' % (name, label, suffix)\\n filepath = os.path.join(self.save_dir, filename)\\n return filepath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ac51fd1fc6165246f721e5386ab4ff2\",\n \"score\": \"0.5707478\",\n \"text\": \"def write_filename(self, filename):\\n if self.parent.check_file(filename):\\n return self.write_filename('1' + filename)\\n else:\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"593e52472454bc7694f6b2ef9f8e08bc\",\n \"score\": \"0.57056004\",\n \"text\": \"def change_extension(path: str, ext: str) -> str:\\n\\n return path.split('.')[0] + '.' + ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5295e3fabc755b959fcc7316981cf66\",\n \"score\": \"0.5680847\",\n \"text\": \"def joinext(prefix, ext):\\n if ext[0] != \\\".\\\":\\n ext = \\\".{0}\\\".format(ext)\\n # require leading separator to match os.path.splitext\\n return prefix + EXTSEP + ext[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0545f5f634678126e3ac06038d200c8\",\n \"score\": \"0.5678778\",\n \"text\": \"def createOutfileName(extension):\\n \\n global outfile\\n\\n if gbl.playFile and gbl.outfile:\\n error(\\\"You cannot use the -f option with -P\\\")\\n\\n if gbl.outfile:\\n outfile = gbl.outfile\\n\\n elif gbl.playFile:\\n outfile = \\\"MMAtmp%s.mid\\\" % os.getpid()\\n MMA.exits.files.append(outfile)\\n\\n else:\\n outfile, ext = os.path.splitext(gbl.infile)\\n if ext != gbl.ext:\\n outfile=gbl.infile\\n outfile += extension\\n\\n outfile=MMA.file.fixfname(outfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e73fcc2c2a13dfb60eda652e1c81b81\",\n \"score\": \"0.5659241\",\n \"text\": \"def storeFile(self, episode):\\n source, name = os.path.split(episode.path)\\n return os.path.join(source, self.getFormattedName(episode))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa533f611d9ddca1189ca3cbc8b764cb\",\n \"score\": \"0.56484157\",\n \"text\": \"def save_to_file(file_name: str, content: str, extension: str = \\\"ispl\\\") -> str:\\n file_name = FileTools.add_extension(file_name, extension)\\n f = open(f\\\"{file_name}\\\", \\\"w\\\")\\n f.write(content)\\n f.close()\\n return file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12b84e6d23b77d2162d2622a4f4af13a\",\n \"score\": \"0.5636684\",\n \"text\": \"def asfilename(self, text, extension = 'png', prefix= '', dirname = None):\\n if 'file' not in self.mapping[text]:\\n self.mapping[text]['file'] = text.encode('ascii', 'ignore')\\n fname = \\\"%s%s.%s\\\" % (prefix, self.mapping[text]['file'], extension)\\n #logging.debug(\\\"self.mapping=\\\" + str(self.mapping))\\n if dirname:\\n return os.path.join(dirname, fname)\\n return fname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25a9c63ede8d8ef217d1255250a8bc29\",\n \"score\": \"0.56222856\",\n \"text\": \"def _get_extension_string(self, obj):\\n return \\\".\\\" + obj.extension if obj.extension else \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44e11409bf86e6e73b407f7297138148\",\n \"score\": \"0.5615433\",\n \"text\": \"def asfilename(self, text, extension = 'png', prefix= '', dirname = None):\\n if 'file' not in self.mapping[text]:\\n self.mapping[text]['file'] = text.encode('ascii', 'ignore')\\n fname = \\\"%s%s.%s\\\" % (prefix, self.mapping[text]['file'], extension)\\n logging.debug(\\\"self.mapping=\\\" + str(self.mapping))\\n if dirname:\\n return os.path.join(dirname, fname)\\n return fname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c51648799914e05dd52c4e5562681f57\",\n \"score\": \"0.5600323\",\n \"text\": \"def _getPath(self, suffix, count=''):\\n path = '{}{}{}.txt'.format(self.fileName, suffix, str(count))\\n if self.saveto is not None:\\n path = os.path.join(self.saveto, path)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6824640db66862a321bbd79a1fe3348\",\n \"score\": \"0.55963844\",\n \"text\": \"def _create_attachment_filepath(self, directory_path, filename):\\n filename = secure_filename(filename)\\n ext = self._get_file_extension(filename)\\n new_filename = \\\"{}{}\\\".format(self.attachment_file_name, ext)\\n return self._create_filepath(directory_path, new_filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c260cff83ba3cbedbfb53021f3c40010\",\n \"score\": \"0.5594497\",\n \"text\": \"def get_attachment_path(instance, filename: str) -> str:\\n path = settings.EMAIL_LOG_ATTACHMENTS_PATH\\n if callable(path):\\n return path(instance, filename)\\n return str(pathlib.Path(str(path)) / filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c83193f18ee8276a95173fa2852f44a\",\n \"score\": \"0.55894244\",\n \"text\": \"def append_before_ext(path, addition):\\n dot_index = path.rfind('.')\\n if dot_index == -1: # . Not found\\n return path + addition\\n return path[:dot_index] + addition + path[dot_index:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bed7fd10000e1e88b347c5677904a943\",\n \"score\": \"0.55868995\",\n \"text\": \"def filepath_ext(filepath:str)->str:\\n return pathlib.Path(filepath).suffix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5be158729bf4527383080304c05a751a\",\n \"score\": \"0.55858254\",\n \"text\": \"def gen_path_file(spt_folder='', snm_file='', st_file_type=''):\\n st_suffix = ''\\n if st_file_type == 'log':\\n st_suffix = '.log.py'\\n\\n return os.path.join(spt_folder, snm_file + st_suffix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcae8e762d78858d56d8004cdbe0746d\",\n \"score\": \"0.557719\",\n \"text\": \"def save_as_file(self, *args, **kwargs):\\n filename = self.get_filename(*args, **kwargs)\\n extension = self.extension\\n full_filename = f'{filename}.{extension}'\\n with open(f'tmp_storage/{full_filename}', 'wb') as outfile:\\n outfile.write(self.output.get_buffer())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f9dee9caa632dedf0b5702f6a5ca12\",\n \"score\": \"0.55733275\",\n \"text\": \"def write(self, name, *_args, **_kwargs):\\n name_wo_ext = os.path.splitext(name)[0]\\n if name == name_wo_ext:\\n name += self.default_suffix\\n self.write_wav(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7bb4522a58c77c90a8de7b934faea0f\",\n \"score\": \"0.5552688\",\n \"text\": \"def get_output_filepath(base_dir, tstep, extension):\\n return base_dir + get_base_output_name() + str(tstep) + extension\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95a5c5ff96762a707e92f6aaa8ea98a4\",\n \"score\": \"0.554867\",\n \"text\": \"def get_output_path(vocabs_path, suffix='_interersing'):\\n # Split the path by the extension -> returns part\\n # before extension, extension with dot\\n path, extension = splitext(vocabs_path)\\n # Join all parts back together.\\n return ''.join([path, suffix, extension])\\n # return '{}{}{}'.format(path, suffix, extension)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdbe89e1c44932fec57396dd8db1e8e9\",\n \"score\": \"0.5546051\",\n \"text\": \"def attach_file(src,resource):\\n original_name = os.path.basename(src)\\n new_name = resource.id + '_' + original_name\\n dir = os.getcwd()\\n location = os.path.join(dir,new_name)\\n copy(src,location)\\n return location\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bd5c5d2b0c4cf9714f64ce94c63299a\",\n \"score\": \"0.5545908\",\n \"text\": \"def append_default_extension(filename, default_extension='.png'):\\n\\n if '.' in filename:\\n return filename\\n else:\\n return filename + default_extension\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50b81f10746c15e4a19d237dbcf8e73d\",\n \"score\": \"0.55415285\",\n \"text\": \"def filepath(self):\\n _filename = self.filename\\n if not _filename:\\n _filename = self.name + '.txt'\\n return join(self.store, _filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80e1407b3088b1b48ec60d2923ba2101\",\n \"score\": \"0.5538885\",\n \"text\": \"def results_path(content, extension):\\n try:\\n path = \\\"{0}/graphmanager/{1}\\\".format(data[\\\"directory\\\"], uuid.uuid4().hex)\\n if not os.path.exists(path):\\n os.makedirs(path)\\n full_path = \\\"{0}/{1}.{2}\\\".format(path, uuid.uuid1().hex, extension)\\n f = open(full_path, \\\"w+\\\")\\n f.write(content)\\n app_logger.info('Content available in path: {0}'.format(full_path))\\n return \\\"file://{0}\\\".format(full_path)\\n except Exception as error:\\n app_logger.error('Something is wrong: {0}'.format(error))\\n raise\\n finally:\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"688df123711e534db77064290f419607\",\n \"score\": \"0.5528349\",\n \"text\": \"def _writePath(self):\\n pass\\n # do nothing, we don't want to rename the script\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fe0f36909ac3f3f88e1014d45b11ef6\",\n \"score\": \"0.5518041\",\n \"text\": \"def save_file(self, data, extension):\\n raise NotImplementedError(\\n \\\"Inheriting collector class needs to define this function\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f990afb8a2336f16c30f124720a4ee70\",\n \"score\": \"0.55126524\",\n \"text\": \"def generate_output_filepath(input_filepath: str, output_dir: str,\\n file_ext: str) -> str:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb30c3831ba6e61126e02a2101b04c98\",\n \"score\": \"0.5510599\",\n \"text\": \"def filehandle(pth, ext=\\\"JSON\\\"):\\n return os.path.join(pth, tstamp(\\\"fh\\\") + os.path.extsep + ext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fdc774732a54db6ecd7d9c137ae273e\",\n \"score\": \"0.55085015\",\n \"text\": \"def extension():\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a20529ac50c9bd49b339a302d909eb9\",\n \"score\": \"0.55079734\",\n \"text\": \"def path(filename):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f2f981da38f19b0ec98e246d9425ccb\",\n \"score\": \"0.5494166\",\n \"text\": \"def extension(self):\\n return os.path.splitext(self.path)[1] if self.isfile else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"594d52741e55a6d7f1281576b1369999\",\n \"score\": \"0.5490611\",\n \"text\": \"def write_to_file(crypt, feed, chnk, path, ext):\\n if crypt == 'decrypt':\\n newpath = path # .bam\\n if path.endswith(tuple(ext)):\\n newpath, extension = os.path.splitext(path)\\n with open(newpath, 'wb+') as f:\\n for chunk in feed.iter_content(chunk_size=chnk):\\n if chunk:\\n f.write(chunk)\\n elif crypt == 'encrypt':\\n newpath = path + '.cip.csc'\\n with open(newpath, 'wb+') as f:\\n for chunk in feed.iter_content(chunk_size=chnk):\\n if chunk:\\n f.write(chunk)\\n else:\\n raise Exception('invalid var(crypt) for write_to_file()')\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90b37bd771f7883fbd102b01b2dbeeed\",\n \"score\": \"0.5485882\",\n \"text\": \"def extend_path(path,name):\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7517d37db54c8f7ddaac72a6770c2c6\",\n \"score\": \"0.5483617\",\n \"text\": \"def out_filename(self, filetype, dir, format='old'):\\n filename = self.filename(filetype=filetype, format=format)\\n return Path(dir) / filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8b31c6d84dbb4357f69548d460c68da\",\n \"score\": \"0.5483192\",\n \"text\": \"def get_file_path(file_dir: str):\\n # extension = filename.split('.')[-1]\\n # filename = \\\"%s.%s\\\" % (uuid.uuid4(), ext)\\n # return join(file_dir, filename)\\n global give_path\\n\\n def give_path(instance, filename):\\n return join(file_dir, \\\"%s.%s\\\" % (uuid.uuid4().int, filename.split('.')[-1]))\\n\\n return give_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4015eaab3977e01dadc8cf2e9b52b223\",\n \"score\": \"0.54715854\",\n \"text\": \"def extant_file(x):\\n\\t\\n\\tif not os.path.exists(x):\\n\\t\\tprint \\\"Error: {0} does not exist\\\".format(x)\\n\\t\\texit()\\n\\tx = str(x)\\n\\treturn x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4015eaab3977e01dadc8cf2e9b52b223\",\n \"score\": \"0.54715854\",\n \"text\": \"def extant_file(x):\\n\\t\\n\\tif not os.path.exists(x):\\n\\t\\tprint \\\"Error: {0} does not exist\\\".format(x)\\n\\t\\texit()\\n\\tx = str(x)\\n\\treturn x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d441f30303c269dc09da9b67509da316\",\n \"score\": \"0.54548985\",\n \"text\": \"def add_to_filename(self,\\n string=None,\\n new_extension=None,\\n timestamp=False):\\n\\n if timestamp:\\n timestamp = datetime.datetime.now().isoformat().replace('-', '').replace(':', '').split('.')[0]\\n else:\\n timestamp = ''\\n\\n if string is not None:\\n string += timestamp\\n else:\\n string = timestamp\\n\\n file_name, file_ext = os.path.splitext(self.basename)\\n\\n file_name += string\\n\\n if new_extension is not None:\\n self.basename = file_name + new_extension\\n else:\\n self.basename = file_name + file_ext\\n\\n if self.dirname is not None:\\n return self.dirname + self.sep + self.basename\\n else:\\n return self.basename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"854a3c3ccc5fa13be08aa0cc1ccb0368\",\n \"score\": \"0.54539806\",\n \"text\": \"def add_suffix(filename, suffix):\\n path = filename.split(\\\"/\\\")\\n parts = path[-1].split(\\\".\\\")\\n i = max(len(parts) - 2, 0)\\n parts[i] = parts[i] + suffix\\n path[-1] = \\\".\\\".join(parts)\\n return \\\"/\\\".join(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe4f71832b314bcbc99ada6b7a5feceb\",\n \"score\": \"0.54533166\",\n \"text\": \"def _get_path(self, app):\\r\\n if self.fdist_dir == '':\\r\\n return app + self.ext\\r\\n else:\\r\\n return os.sep.join([self.fdist_dir, app]) + self.ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"121ac7125fe9edaa1d8068db8b5a4ece\",\n \"score\": \"0.544883\",\n \"text\": \"def get_extension(extension: str) -> str:\\n return (\\\".\\\" + extension) if len(extension) > 0 else \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5abe044421deb9cfc09428aaecb2d50\",\n \"score\": \"0.54363364\",\n \"text\": \"def get_file_path(fileName):\\n path = session['pkg']\\n (_, ext) = os.path.splitext(fileName)\\n if ext == \\\".pl\\\":\\n path = os.path.join(path, \\\"prolog\\\")\\n elif ext == \\\".owl\\\":\\n path = os.path.join(path, \\\"owl\\\")\\n return os.path.join(path, fileName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"555633dc5bb3312a0f8f54c4e601e2db\",\n \"score\": \"0.54159546\",\n \"text\": \"def FileExtension(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b48c5384811eca1ab98212a9b1fc3903\",\n \"score\": \"0.5412737\",\n \"text\": \"def check_for_extension(path_name_ext, extension=\\\"\\\"):\\n # check if there is an extension, but only if the extension is given.\\n if extension:\\n if path_name_ext[-len(extension):] != extension:\\n path_name_ext += extension\\n# print(\\\"NOTE (imports, check_for_extension): the extension\\\", extension, \\\"was added to the filename.\\\")\\n \\n return path_name_ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bd7f837c907e49b169145dcbb21f06f\",\n \"score\": \"0.54112744\",\n \"text\": \"def extant_file(x):\\n\\n\\t\\n\\n\\tif not os.path.exists(x):\\n\\n\\t\\tprint \\\"Error: {0} does not exist\\\".format(x)\\n\\n\\t\\texit()\\n\\n\\tx = str(x)\\n\\n\\treturn x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d30994194eb89f3dd258d266097b6e8\",\n \"score\": \"0.54089755\",\n \"text\": \"def change_file_extension(filename: str, extension: str) -> str:\\n return filename.split('.')[0] + extension\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acf01d78414556a7420432d53a97ade3\",\n \"score\": \"0.54077286\",\n \"text\": \"def filename(self, kind = None, extension = '.txt'):\\n fn = [self._basename]\\n if isString(kind) and len(kind):\\n fn += [\\\"_\\\", kind]\\n if isString(extension):\\n fn += extension\\n fn = os.path.join(self._outDir, \\\"\\\".join(fn))\\n return fixFilename(fn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0df344144f452c53934c356f15e7aa12\",\n \"score\": \"0.5401847\",\n \"text\": \"def getFilePath(self, f=None):\\n return os.path.join(self.bot.getDataPath(self.moduleName), (f if f else ''))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da542ea057051539482d74a8f22d0744\",\n \"score\": \"0.53763664\",\n \"text\": \"def createOutputFile(self, ext, contents=\\\"\\\"):\\n fpath = self.getOutputFile(ext)\\n ensureFileDir(fpath)\\n fh = open(fpath, \\\"w\\\")\\n try:\\n fh.write(contents)\\n finally:\\n fh.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"030380ef49e6166e4cd1f0beaa698237\",\n \"score\": \"0.53565586\",\n \"text\": \"def addext(filename, extension):\\n\\n return filename + ('' if splitext(filename)[1] else extension)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"102fd09a54f0cce8832b6a09494f3a49\",\n \"score\": \"0.5354405\",\n \"text\": \"def path_of_part(self, ext):\\n path_to_part = self._get_folder(self.extract_path(), ext)\\n if ext in path_to_part:\\n return path_to_part\\n else:\\n new_pieces = os.listdir(path_to_part)\\n for piece in new_pieces:\\n if ext in piece:\\n return os.path.join(path_to_part, piece)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"033f8cadcd70d202ebbf70e85591888a\",\n \"score\": \"0.5342961\",\n \"text\": \"def file (self, name ):\\n\\t\\tif self.isDir():\\n\\t\\t\\treturn self.join( name )\\n\\t\\telse:\\n\\t\\t\\traise ValueError( \\\"\\\"\\\"Can't currently create a file in a file\\\"\\\"\\\" )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11a2c069908c66bf36206388094cc0a6\",\n \"score\": \"0.53319633\",\n \"text\": \"def changeExtension(filename, ext):\\n aux = filename.split('.')\\n return aux[0]+'.'+ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9a7b85b58c334da75ef6a82430c812\",\n \"score\": \"0.5322633\",\n \"text\": \"def attach_file_num(filepath, file_num):\\n\\n path, basename = os.path.split(os.path.abspath(filepath))\\n new_basename, ext = basename.split('.')\\n new_basename += '_' + str(file_num) + '.' + ext\\n return os.path.join(path, new_basename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a2c131bb35f4948c550e5e881c5768\",\n \"score\": \"0.53143764\",\n \"text\": \"def extension():\\n return \\\".\\\"+param.extension.strip(\\\".\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa458736ff3476a5c9ff2559dc3b50e\",\n \"score\": \"0.5308334\",\n \"text\": \"def get_file_path(path, get_path=False):\\n current_path = path\\n ext = os.path.splitext(current_path)[1]\\n while (\\\".\\\" in current_path):\\n current_path = os.path.splitext(current_path)[0]\\n ext = (os.path.splitext(current_path)[1] + ext)\\n if get_path:\\n return current_path\\n else:\\n return ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9208b591a4608dd6ea1a941112f21f70\",\n \"score\": \"0.530598\",\n \"text\": \"def add_extension(self, extension: str) -> None:\\n if extension:\\n extension_to_add = os.path.abspath(os.path.expanduser(extension))\\n if os.path.exists(extension_to_add):\\n self._extension_files.append(extension_to_add)\\n else:\\n raise OSError(\\\"Path to the extension doesn't exist\\\")\\n else:\\n raise ValueError(\\\"argument can not be null\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ffe6d30e868557b8e92edd153bc6277\",\n \"score\": \"0.52989304\",\n \"text\": \"def replace_ext(filename, ext=''):\\n return '.'.join(filename.split('.')[:-1]) + ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8154d999fae5cee8c0e83acd076709bf\",\n \"score\": \"0.5283287\",\n \"text\": \"def make_filename(title, ext):\\n return os.path.join(prefix, title.replace(\\\" \\\", \\\"_\\\").replace(\\\":\\\", \\\"_\\\").replace(\\\"/\\\", \\\"_\\\") + os.path.extsep + ext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd24a7c431ce1353011935f5fcf060ee\",\n \"score\": \"0.52795875\",\n \"text\": \"def path(self, f):\\n\\t\\treturn os.path.join(self.directory, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b7aa232e2f5a646307295cef041b052\",\n \"score\": \"0.5260127\",\n \"text\": \"def get_file(filename: str) -> str:\\n return os.path.join('files', filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4aaba246ee31728267682b083fbc8f8\",\n \"score\": \"0.5256028\",\n \"text\": \"def combine_path(base_path, relative_ref):\\n #if (base_path != \\\"\\\"):\\n #print \\\"os.getcwd()\\\", os.getcwd()\\n #os.chdir(base_path)\\n filePath = os.path.abspath(relative_ref)\\n filePath = filePath + \\\".tex\\\"\\n return filePath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e4711f4c6dcce7b34cb0a297de980e\",\n \"score\": \"0.52504486\",\n \"text\": \"def __get_paths_to(self, ext, paths):\\n for file in os.listdir(self.path_dir):\\n if file.endswith(ext):\\n paths.append(os.path.join(self.path_dir, file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2993852a03dd1e71b6af551c5109af2\",\n \"score\": \"0.52446425\",\n \"text\": \"def filepath(self) -> Path:\\n if self._use_tar:\\n return self.dest_dir/'{obj.name}-{obj.version}{ext}'.format(\\n obj=self, ext='.tar'\\n )\\n # Just to keep track of something on disk.\\n return self.dirname/'manifest.json'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b39d41c4d95e40b6660c6b7342d41f92\",\n \"score\": \"0.52344954\",\n \"text\": \"def write_file(self, path, data):\\n self._path_to_proto[posix_path(path)] = data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d747e47adc0a7e4a0c93cc2a0112d6ee\",\n \"score\": \"0.5231172\",\n \"text\": \"def append_new_file(self, msg):\\n file_data = {'path': msg.get('path'),\\n 'fileExtension': msg.get('format'),\\n 'resolution': msg.get('resolution')}\\n print(f'new file: {file_data}')\\n self.conversion_files.put(file_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12d1034821b3bbb96407a5a0101c8e22\",\n \"score\": \"0.5230035\",\n \"text\": \"def get_aeroo_filename(self, record, output_format):\\n if self.attachment:\\n filename = self._eval_aeroo_attachment_filename(self.attachment, record)\\n return '.'.join((filename, output_format))\\n else:\\n return '.'.join((self.name, output_format))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2564837c9e46527b7b5ffcece1a1d7ce\",\n \"score\": \"0.5229681\",\n \"text\": \"def get_actual_filename_to_use(cls, args):\\n filename, extmode = args.filename, args.extension_mode\\n assert extmode in ['auto', 'never', 'always']\\n\\n # if extension mode is auto, we check if the extension is required\\n # (if it is not in the list of extensions) and fallback to always/never\\n if extmode == 'auto':\\n extmode = 'never' if (os.path.splitext(filename)[1].lower() in\\n (\\\".\\\" + e.lower() for e in cls.extensions)\\n ) else 'always'\\n assert extmode in ['never', 'always']\\n\\n # if extension is required, add the first one\\n if extmode == 'always':\\n filename += \\\".\\\" + cls.extensions[0]\\n\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"336cd3d9b1c5ce7f6c2e1ba359dc0f2c\",\n \"score\": \"0.5223622\",\n \"text\": \"def __joinpath(path,file):\\n return os.path.join(path + os.sep + file.lower())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd5a0a30c5a84991ba21331b2244950f\",\n \"score\": \"0.52227396\",\n \"text\": \"def _new_filename(filepath, new_root='{}_funpdbe', new_ext=None):\\n # Break filepath into components\\n path, basename = os.path.split(filepath)\\n root, ext = os.path.splitext(basename)\\n\\n # Construct new filename\\n if new_ext:\\n ext = new_ext\\n if new_root:\\n root = new_root.format(root) # new_root can include original root\\n basename = root + ext\\n\\n # Construct derived filepath\\n return os.path.join(path, basename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b9e13149387cfec65ffe4cdab7a4d15\",\n \"score\": \"0.5221859\",\n \"text\": \"def add_triggerer_suffix(full_path, job_id=None):\\n full_path = Path(full_path).as_posix()\\n full_path += f\\\".{LogType.TRIGGER.value}\\\"\\n if job_id:\\n full_path += f\\\".{job_id}.log\\\"\\n return full_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f473ba31c82cf7b681a1f82bd86f0c6\",\n \"score\": \"0.5221026\",\n \"text\": \"def extension (self):\\n\\t\\treturn os.path.splitext(self)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ab6e5429f9f1c12e04d1c25845c0de1\",\n \"score\": \"0.5214518\",\n \"text\": \"def get_file_path(dotted_path, extension='json'):\\n # If the operating system's file path seperator character is in the string\\n if os.sep in dotted_path or '/' in dotted_path:\\n # Assume the path is a valid file path\\n return dotted_path\\n\\n parts = dotted_path.split('.')\\n corpus_path = os.path.join(*parts)\\n\\n if os.path.exists(corpus_path + '.{}'.format(extension)):\\n corpus_path += '.{}'.format(extension)\\n\\n return corpus_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"090361d99395e95ed7c3f6c8664f7d6c\",\n \"score\": \"0.5209273\",\n \"text\": \"def _get_final_filename(self):\\n if output_dir is None:\\n raise TypeError(\\\"Output directory is not a string\\\")\\n os.makedirs(output_dir, exist_ok = True)\\n\\n pref_name_array = self.preferred_filename.split('.')\\n filename, extension = os.path.join(output_dir, '.'.join(pref_name_array[:-1])), pref_name_array[-1]\\n output_filename = \\\"{}.{}\\\".format(filename, extension)\\n\\n counter = 1\\n while os.path.exists(output_filename):\\n output_filename = \\\"{}-{}.{}\\\".format(filename, counter, extension)\\n counter += 1\\n return output_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d0a9c185a3ef49b4698413759e6a1a9\",\n \"score\": \"0.52020603\",\n \"text\": \"def get_hips_extra_file(filename):\\n path = Path(os.environ['HIPS_EXTRA'])\\n return path / filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95ab90dddc92a213ec624fde91dbe825\",\n \"score\": \"0.51956844\",\n \"text\": \"def write_to_file(self, filepath, text=\\\"\\\"):\\n if \\\"/tmp/tmp\\\" not in filepath:\\n raise SecurityFaultInvalidFileAccessAttempt(filepath)\\n with open(filepath, \\\"w\\\") as f:\\n f.write(\\\"{text}\\\".format(\\n text=text))\\n return f.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01dabf554b5677855a5114f8ff2ed25e\",\n \"score\": \"0.5192978\",\n \"text\": \"def get_file_upload_to(self, filename):\\n return os.path.join(machina_settings.ATTACHMENT_FILE_UPLOAD_TO, filename)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993895,"cells":{"query_id":{"kind":"string","value":"8c1c0015e095064e7a47460cfae784b1"},"query":{"kind":"string","value":"Decrement the integer value of a key by the given number"},"positive_passages":{"kind":"list like","value":[{"docid":"f508a6af265ff36873fd63de38632270","score":"0.74317914","text":"def decrby(self, key:NativeType, increment:int) -> int:\n return self._query(b'decrby', self.encode_from_native(key), self._encode_int(increment))","title":""}],"string":"[\n {\n \"docid\": \"f508a6af265ff36873fd63de38632270\",\n \"score\": \"0.74317914\",\n \"text\": \"def decrby(self, key:NativeType, increment:int) -> int:\\n return self._query(b'decrby', self.encode_from_native(key), self._encode_int(increment))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"16da0a9d2e42a401a0be02abacf3724c","score":"0.82384324","text":"def decrement_int_value(self, key):\n return self._redis.decr(name=key)","title":""},{"docid":"f1cd9fbc3a0a8519344f377e45eb3e85","score":"0.7788498","text":"def dec(self, key):\r\n database = self._database\r\n new_count = database[key] - 1\r\n if new_count == 0:\r\n del database[key]\r\n else:\r\n database[key] = new_count\r\n return new_count","title":""},{"docid":"a2b2d30cb4451e6af143f67ffc61b365","score":"0.7668736","text":"def dec(self, key):\n if key in self.d_value:\n self.d_key[self.d_value[key]].remove(key)\n if not self.d_key[self.d_value[key]]:\n self.d_key.pop(self.d_value[key])\n self.d_value[key] -= 1\n if self.d_value[key] > 0:\n self.d_key[self.d_value[key]] = self.d_key.get(self.d_value[key], [])\n self.d_key[self.d_value[key]].append(key)\n else:\n self.d_value.pop(key)","title":""},{"docid":"3f9d920f634d3602f8b673be41cf6de4","score":"0.7286453","text":"def dec(self, key):","title":""},{"docid":"f695244d5291fd5ffe5e5aa9db0fcfd5","score":"0.7267902","text":"def dec(self, key: str) -> None:\n v = self.key_to_val.get(key, -1)\n if v < 0:\n return \n # 删除这个键\n self.key_to_val.pop(key)\n self.val_to_key[v].remove(key)\n \n if v == 1:\n # 需要更新最大值和最小值\n \n\n\n\n else:","title":""},{"docid":"420021b4a9a3e2a911c017bbd4d65561","score":"0.7185764","text":"def testDecrementANegativeInteger(self):\n self.assertTrue(memcache.set(self.key1, -5))\n self.assertEqual(None, memcache.decr(self.key1))\n self.assertEqual(-5, memcache.get(self.key1))","title":""},{"docid":"108e0bb5282b24a140339eb9c4eb466f","score":"0.71577436","text":"def decr(self, key:NativeType) -> int:\n return self._query(b'decr', self.encode_from_native(key))","title":""},{"docid":"da317c753b8bbdeb28a85776bd8b40cd","score":"0.6979131","text":"def decr(self, key, delta=1):\n return self.stash.incr(key, 0 - delta)","title":""},{"docid":"94d1f23638b086cfaeee10781c8a054a","score":"0.69340396","text":"def revert(key, num):\n return obscure(key, num)","title":""},{"docid":"db828325a2c475f29e69cba04713476b","score":"0.6903613","text":"def decrby(self, key, decrement, callback=None):\n args = [\"DECRBY\"]\n args.append(key)\n args.append(decrement)\n self.send_message(args, callback)","title":""},{"docid":"95bfdf0149ee7c76526c29de40bba3f2","score":"0.6848593","text":"def dec(self, key, delta=1):\r\n value = (self.get(key) or 0) - delta\r\n return value if self.set(key, value) else None","title":""},{"docid":"40cc96badf68287fad1b5cefd5338a2b","score":"0.68267363","text":"def dec(self, key: str) -> None:","title":""},{"docid":"3af2647a26f42c430d0e41e80df54377","score":"0.67994255","text":"def decrement(self):\r\n \r\n self.number = self.number - 1","title":""},{"docid":"2915b05b8e6ed95d4e3790ed2ca07295","score":"0.6798188","text":"def testDecrementByOne(self):\n self.assertTrue(memcache.set(self.key1, '10'))\n self.assertEqual(9, memcache.decr(self.key1))\n self.assertEqual('9', memcache.get(self.key1))","title":""},{"docid":"e6922db0e86f4f607a60ac9cabd9d781","score":"0.67827314","text":"def decr(self, key, delta=1, version=None):\n return self.incr(key, -delta, version=version)","title":""},{"docid":"5b027039cd872f672132a8ab445a6cf8","score":"0.677203","text":"def dec(self, key):\n if key in self.d:\n curr_node = self.d[key]\n prev_val = curr_node.val - 1\n if curr_node.prev and curr_node.prev.val == prev_val:\n prev_node = curr_node.prev\n elif prev_val > 0:\n prev_node = curr_node.appendleft(prev_val)\n curr_node.remove(key)\n\n if prev_val == 0:\n del self.d[key]\n else:\n prev_node.keys.add(key)\n self.d[key] = prev_node","title":""},{"docid":"d2da7c8acec62327c043403346bea147","score":"0.67442715","text":"def testDecrementByDelta(self):\n self.assertTrue(memcache.set(self.key1, '11'))\n self.assertEqual(1, memcache.decr(self.key1, delta=10))\n self.assertEqual('1', memcache.get(self.key1))","title":""},{"docid":"c0ab281d115422dd127122dabf6b8505","score":"0.6730673","text":"def dec(self, key):\r\n return DatabaseReferenceCounter.dec(self, key + self._suffix)","title":""},{"docid":"d02cc420f27e50a068dff3c9465998f2","score":"0.6675536","text":"def dec(self, key):\n if key not in self.keys:\n return\n curNode = self.keys[key]\n if curNode.val == 1:\n # 在双向链表中删除该节点\n prevNode = curNode.prev\n nextNode = curNode.next\n prevNode.next = nextNode\n nextNode.prev = prevNode\n else:\n # 该key对应的value减1 并通过交换节点的方式保持双向链表有序\n curNode.val -= 1\n while curNode.prev != self.head and curNode.val < curNode.prev.val:\n prepreNode = curNode.prev.prev\n nextNode = curNode.next\n nextNode.prev = curNode.prev\n curNode.prev.prev = curNode\n curNode.prev = prepreNode\n curNode.next = nextNode.prev\n nextNode.prev.next = nextNode\n prepreNode.next = curNode","title":""},{"docid":"78c2d89a4b3ac6c95d7e289978255dea","score":"0.6516995","text":"def testDecrementCap(self):\n self.assertTrue(memcache.set(self.key1, '5'))\n self.assertEqual(0, memcache.decr(self.key1, delta=10))\n self.assertEqual('0', memcache.get(self.key1))","title":""},{"docid":"67fef31f70cbc09ca57cff7998615e8c","score":"0.650766","text":"def dec(self, key):\n if key not in self.bucket_of_key:\n return\n\n bucket, prev_bucket = self.bucket_of_key[key], self.bucket_of_key[key].prev\n self.bucket_of_key.pop(key, None)\n if bucket.value > 1:\n if bucket is self.buckets.begin() or prev_bucket.value < bucket.value-1:\n prev_bucket = self.buckets.insert(bucket, Node(bucket.value-1, set()))\n prev_bucket.keys.add(key)\n self.bucket_of_key[key] = prev_bucket\n\n bucket.keys.remove(key)\n if not bucket.keys:\n self.buckets.erase(bucket)","title":""},{"docid":"494d83ac5e02065ded781c18d7f3ea00","score":"0.6440148","text":"def dec(self, key: str) -> None:\n if key in self.dic:\n node = self.dic[key]\n node.keys.remove(key)\n self.dic.pop(key)\n if node.value != 1:\n prevnode = node.prev\n if prevnode.value == (node.value - 1):\n prevnode.keys.add(key)\n self.dic[key] = prevnode\n else:\n newnode = Node(node.value - 1)\n newnode.keys.add(key)\n prevnode.insert(newnode)\n self.dic[key] = newnode\n if len(node.keys) == 0:\n node.remove()","title":""},{"docid":"2468a33ed4d4bdf79d68fa4b092f87da","score":"0.64269656","text":"def dec(self, key: str) -> None:\n \n if key in self.d:\n \n node = self.d[key]\n node.remove(key)\n \n if node.value == 1:\n self.d.remove(key)\n else:\n if node.prev.value == node.value - 1:\n node.prev.add(key)\n else:\n new_node = Node(node.value - 1)\n node.insert_left(new_node)\n \n if len(node) == 0:\n node.delete()","title":""},{"docid":"ece3b2bc04df801546d194bff83aa87f","score":"0.64168453","text":"def __delitem__(self,key):\n if 0 <= key <= 3:\n self[key] = 0\n return self\n else:\n raise RuntimeError('list index out of range')","title":""},{"docid":"7cdb848aee6a81fd83af38f75b484a4e","score":"0.6379582","text":"def dec(self, key: str) -> None:\n if key not in self.dic_k_c:\n return\n \n cnt = self.dic_k_c[key]\n \n if cnt != 1:\n self.dic_c_k[cnt-1] = self.dic_c_k.get(cnt-1, set())\n self.dic_c_k[cnt-1].add(key)\n self.dic_k_c[key] = cnt-1\n else:\n del self.dic_k_c[key]\n \n \n if len(self.dic_c_k[cnt]) == 1:\n del self.dic_c_k[cnt]\n else:\n self.dic_c_k[cnt].remove(key)\n \n \n if self.min_key == 1:\n if self.min_key not in self.dic_c_k:\n if not self.dic_c_k:\n self.min_key = float('inf')\n else:\n self.min_key = sorted(self.dic_c_k.keys())[0]\n else:\n self.min_key = min(self.min_key, cnt-1)\n \n if self.max_key == 1:\n if self.max_key not in self.dic_c_k:\n self.max_key = -float('inf')\n else:\n if self.max_key not in self.dic_c_k:\n self.max_key = cnt - 1","title":""},{"docid":"6a2418d2078fe71e64e960a3cdec3eaa","score":"0.63749975","text":"def decr(self, key, callback=None):\n args = [\"DECR\"]\n args.append(key)\n self.send_message(args, callback)","title":""},{"docid":"c95107a95c5ec9b7967507e26279b551","score":"0.6282394","text":"def testDecrementUnknownKey(self):\n self.assertEqual(None, memcache.decr(self.key1))\n self.assertEqual(None, memcache.get(self.key1))","title":""},{"docid":"14413a4965a3c049531f77a915fbaf1f","score":"0.62719816","text":"def remove(self, key: int) -> None:\n return self.myDict[key % self.space].remove(key)","title":""},{"docid":"c4f2dcee26603a68dfc9153d14547fbd","score":"0.6254852","text":"def lrem(self, key:NativeType, count:int=0, value='') -> int:\n return self._query(b'lrem', self.encode_from_native(key), self._encode_int(count), self.encode_from_native(value))","title":""},{"docid":"95d043e1a4af0b5666d086f96adf59dd","score":"0.6235124","text":"def decr_version(self, key, delta=1, version=None):\n return self.incr_version(key, -delta, version)","title":""},{"docid":"041572ce22e67af95180ee13ede663cf","score":"0.61714554","text":"def decrease_key(self, i, k):\n # NOTE: assumes a valid index i\n # NOTE: assumes heap[i] >= k\n\n self.heap[i].key = k\n item = self.heap[i]\n p = self.parent(i)\n\n while i > 0 and k < self.heap[p].key:\n self.heap[i] = self.heap[p]\n i = p\n p = self.parent(i)\n\n self.heap[i] = item","title":""},{"docid":"28302658ae4285301e885690d7b55fed","score":"0.6159204","text":"def lrem(self, key, count, value, callback=None):\n args = [\"LREM\"]\n args.append(key)\n args.append(count)\n args.append(value)\n self.send_message(args, callback)","title":""},{"docid":"b9ce28263e0008a376c967ed5d67750d","score":"0.61189187","text":"def del_key(n, key):\n del n[key]\n return n","title":""},{"docid":"bc64c4f804726e2139a8ee8ca6fd913d","score":"0.6112761","text":"def remove(self, key: int) -> None:\n index = key % self.map_size\n self.bucket_array[index].remove(key)","title":""},{"docid":"d324d79bdb7cacf5428a9315256a22b9","score":"0.6076193","text":"def testDecrementBadValue(self):\n self.assertTrue(memcache.set(self.key1, 'some data'))\n self.assertEqual(None, memcache.decr(self.key1))\n self.assertEqual('some data', memcache.get(self.key1))","title":""},{"docid":"f6d0347133b022ccdee3bdb34aacc264","score":"0.6071566","text":"def init_dict_or_substract(dict_, key, value):\n if key in dict_.keys():\n dict_[key] -= value\n\n else:\n dict_[key] = 0\n dict_[key] -= value\n \n return dict_","title":""},{"docid":"7d13f17c63c702b45fba8d0a73148db5","score":"0.6047952","text":"def decreasekey(self, node: HeapNode, key: int) -> HeapNode:\n\n # if node.key < key:\n # raise ValueError(\"Cannot increase key\")\n node.key = key\n if not node.parent or node.parent.key <= key:\n return node\n node.cut()\n self.root = self.meld(self.root, node)\n return node","title":""},{"docid":"a5c49bcc82691963799808a8cf9c59d7","score":"0.60228205","text":"def test_decrement_by(self):\n self._test_decrement(expected_value=2, expected_expr=F('counter') - 3,\n decrement_by=3)","title":""},{"docid":"04851a6399ff97f03da550f7ff02e239","score":"0.6007732","text":"def decr(self, key, amount=1):\n # An alias for ``decr()``, because it is already implemented\n # as DECRBY redis command.\n return self._redis_client.decr(key, amount)","title":""},{"docid":"5ef60f92ffe482d767169a14957d7347","score":"0.6007321","text":"def remove(self, key: int) -> None:\n if key in self.keys:\n index = self.keys.index(key)\n self.keys.pop(index)\n self.values.pop(index)\n return","title":""},{"docid":"ab244d2b077c04bfd194231f7ce0914f","score":"0.5976782","text":"def _b_delitem_ ( self , i ) :\n if isinstance ( i , int ) :\n if 0 <= i < self.n () : return self.remove ( i )\n raise IndexError ('Invalid key %s' % i )\n \n raise TypeError ('Invalid key/value %s' % i )","title":""},{"docid":"12750b4ed7244f3ac8e6a91c5f610275","score":"0.59685963","text":"def decreasekey(self, node: HeapNode, key: int) -> HeapNode:\n\n # if node.key < key:\n # raise ValueError(\"Cannot increase key\")\n node.key = key\n parent = node.parent\n if parent and node.key < parent.key:\n self.cut(node)\n self.cascading_cut(parent)\n if key < self.minroot.key:\n self.minroot = node\n return node","title":""},{"docid":"21a2d803c7b23f3425eb09f88a9ce320","score":"0.5948123","text":"def decreaseCount(self):\n self.__count -= 1","title":""},{"docid":"50a8d690a98c99cc36d24d6969d13d75","score":"0.5928681","text":"def remove_item(self, key):\n for i, k in enumerate(self.keys):\n if k >= key:\n self.children[i].remove_item(key)\n return\n return self.children[-1].remove_item(key)","title":""},{"docid":"a0901390777dccde4c741ea15fbae3b9","score":"0.59180003","text":"def remove(self, key: int) -> None:\n hashval = self.hashfunc(key)\n self.hashmap[hashval].remove(key)","title":""},{"docid":"0e7a75828626d62829a33fc1be9eda1a","score":"0.58987945","text":"def _a_delitem_ ( self , i ) :\n if isinstance ( i , int ) :\n if 0 <= i < self.n () : return self.remove ( i )\n raise IndexError ('Invalid key %s' % i )\n \n raise TypeError ('Invalid key/value %s' % i )","title":""},{"docid":"47081a23ddf99d2814cc91097f198630","score":"0.5884938","text":"def decrementCount(self, hash):\n if len(hash) != 40:\n hash = sha1helper.sha1ToString(hash)\n cu = self.db.cursor()\n cu.execute(\"SELECT count FROM DataStore WHERE hash=?\", hash)\n count = cu.next()[0]\n if count == 1:\n cu.execute(\"DELETE FROM DataStore WHERE hash=?\", hash)\n else:\n count -= 1\n cu.execute(\"UPDATE DataStore SET count=? WHERE hash=?\",\n count, hash)","title":""},{"docid":"5f8b6b6f78a280348a99a07503eac354","score":"0.58795524","text":"def DecrementIndex(self):\n if self.index is None:\n self.index = 0\n elif self.index > 0:\n if self.index <= self._window_low:\n self._window_low -= 1\n self._window_high -= 1\n self.index -= 1","title":""},{"docid":"478203282975027162a3b70b01198116","score":"0.5868972","text":"def _w_delitem_ ( self , i ) :\n if isinstance ( i , int ) :\n if 0 <= i < self.n () : return self.remove ( i )\n raise IndexError ('Invalid key %s' % i )\n \n raise TypeError ('Invalid key/value %s' % i )","title":""},{"docid":"629d77eda2c59716cb34f24578a43622","score":"0.5865965","text":"def _p_delitem_ ( self , i ) :\n if isinstance ( i , int ) :\n if 0 <= i < self.n () : return self.remove ( i )\n raise IndexError ('Invalid key %s' % i )\n \n raise TypeError ('Invalid key/value %s' % i )","title":""},{"docid":"4a7f8ec0bdf143ba10a5cc6c33563a0a","score":"0.58259237","text":"def dec(self, key: str) -> None:\n if key not in self.dic_k_n:\n return\n \n node = self.dic_k_n[key]\n if node.cnt == 1:\n if len(node.keys) == 1:\n self.delNode(node)\n else:\n node.keys.remove(key)\n del self.dic_k_n[key]\n \n else:\n prv = node.prev\n node.keys.remove(key)\n if prv == self.head or prv.cnt < node.cnt-1:\n new_node = Node()\n new_node.keys.add(key)\n new_node.cnt = node.cnt-1\n self.addNode(node.prev, new_node)\n self.dic_k_n[key] = new_node\n \n else:\n prv.keys.add(key)\n self.dic_k_n[key] = prv\n \n if len(node.keys) == 0:\n self.delNode(node)","title":""},{"docid":"5bf29802b77fc4162f3d791274ca05f1","score":"0.5817354","text":"def testIncrementANegativeInteger(self):\n self.assertTrue(memcache.set(self.key1, -5))\n self.assertEqual(None, memcache.incr(self.key1))\n self.assertEqual(-5, memcache.get(self.key1))","title":""},{"docid":"dc38b2893674fd49441cc78513040f3c","score":"0.58112115","text":"def remove(self, key: int) -> None:\n # compute hash\n h = key % self.CAPACITY\n # check the self.keySet\n for i in range(len(self.keySet[h])):\n if self.keySet[h][i][0] == key:\n self.keySet[h].pop(i)\n return","title":""},{"docid":"26750c0c392782ab671fbfeb1bc414e1","score":"0.5802215","text":"def decrease_key(self, entry, new_priority):\n self._check_priority(new_priority)\n if new_priority > entry.m_priority:\n raise ValueError(\"New priority exceeds old.\")\n\n # Forward this to a helper function.\n self.decrease_key_unchecked(entry, new_priority)","title":""},{"docid":"88a0fdcf244079941ba75a58f2af4c4d","score":"0.5764471","text":"def remove(self, e):\n \n if e in self.vals:\n self.vals[e] -= 1","title":""},{"docid":"9acff1665f69870e046de0d86bc5bb5a","score":"0.5762802","text":"def remove(self,key):\n for i in range(self.reps)\n self.db.remove((key,i))\n return self.db.remove(key)","title":""},{"docid":"808d879026524be7747ae33c0e5dd034","score":"0.57463104","text":"def _get_num(self, key):\n # This might use implementation defined behaviur in pyglet...\n if keysym._0 <= key <= keysym._9:\n return key - keysym._0\n if keysym.NUM_0 <= key <= keysym.NUM_9:\n return key - keysym.NUM_0\n return None","title":""},{"docid":"c1b9f83956961580653c123ddfbd4941","score":"0.57356465","text":"def decrement_items(inventory, items):\n\n for item in items:\n value = inventory.get(item, 0)\n if value > 0:\n value -= 1\n\n inventory[item] = value\n\n return inventory","title":""},{"docid":"bcbbd859a21b9dbfd5b5ef60988e4a46","score":"0.57332355","text":"def test_decrement_no_reload(self):\n self._test_decrement(expected_value=5, expected_expr=F('counter') - 1,\n reload_object=False)","title":""},{"docid":"05f547895bf2a1b90841eecb6dc1d46f","score":"0.5718732","text":"def dec( self ):\n if self.count > 0: self.count -= 1","title":""},{"docid":"1c86b30a930386d39c4f563a15399894","score":"0.5708756","text":"def remove(self, item, value=1):\n if item in self.numbers:\n self.numbers[item] -= value\n elif item in self.inventory:\n self.inventory.remove(item)","title":""},{"docid":"7b5a004dd3e7bef5e73db68b3ada905a","score":"0.5701371","text":"def __sub__(self, x):\n old = self.cnt.get(x, 0)\n if old > 0:\n self.cnt[x] = old - 1","title":""},{"docid":"59e866e386ac4aabd90553da3a43f427","score":"0.5682671","text":"def get(self, key: int) -> int:\n if key not in self.cache:\n return -1\n else:\n self.cache.move_to_end(key)\n return self.cache[key]","title":""},{"docid":"c7de11e93610154dc4331f8274c1979a","score":"0.56692225","text":"def remove(key):","title":""},{"docid":"8f736e442de395d5efbdfcdbae8e574a","score":"0.56688666","text":"def decrease(self):\n\n\t\tk_to_remove = []\n\t\tfor k in self._pheromone.iterkeys():\n\t\t\tif self._pheromone[k] > 0:\n\t\t\t\tself._pheromone[k] -= 0.1\n\t\t\telse:\n\t\t\t\tk_to_remove.append(k)\n\t\tfor k in k_to_remove:\n\t\t\tdel self._pheromone[k]","title":""},{"docid":"c98ab94eca952ad019e87ec7cc7a49e0","score":"0.56494266","text":"def del_value(self, key):\n raise NotImplemented","title":""},{"docid":"92c63b812b0e620fad7f0a732b8f7c62","score":"0.56343114","text":"def __renumber(dictionary) :\r\n count = 0\r\n ret = dictionary.copy()\r\n new_values = dict([])\r\n\r\n for key in dictionary.keys() :\r\n value = dictionary[key]\r\n new_value = new_values.get(value, -1)\r\n if new_value == -1 :\r\n new_values[value] = count\r\n new_value = count\r\n count = count + 1\r\n ret[key] = new_value\r\n\r\n return ret","title":""},{"docid":"29cc1919f253b68007ffc8e131664e77","score":"0.56239474","text":"def test_priority_queue_decrease_key(self):\n pq = PriorityQueue(data=[(5, 'E'), (2, 'B'), (4, 'D')])\n pq.decrease_key('D', 1)\n\n self.assertEqual(pq.pop_min(), (1, 'D'))\n with self.assertRaises(KeyError):\n pq.decrease_key('A', 3)\n with self.assertRaises(ValueError):\n pq.decrease_key('E', 7)","title":""},{"docid":"614a1b1ec657af8568a8f75ef047eab0","score":"0.56187767","text":"def remove(self, key: int) -> None:\n hashkey = self.hash(key)\n if self.table[hashkey]:\n self.table[hashkey][self.pos(key)] = None","title":""},{"docid":"994c43df4245f46c0b564693ea2b078b","score":"0.5600307","text":"def remove(self, key: int) -> None:\n hashkey = key % self.numBuckets\n for idx, elem in enumerate(self.hashmap[hashkey]):\n if elem[0] == key:\n del self.hashmap[hashkey][idx]","title":""},{"docid":"f1528c8d872f6df83bc130ff68726787","score":"0.56002945","text":"def remove(self, n):\n self.set_value(n, 0)\n return","title":""},{"docid":"d7a6fcd6e4966fd491400b33bab64929","score":"0.5596613","text":"def dec(self, by=1):\n self._ensure_has_value()\n self.value -= by","title":""},{"docid":"b87500832f678b2f3b6febdb297339cb","score":"0.55902624","text":"def get(self, key: int) -> int:\n return self.myDict[key % self.space].get(key)","title":""},{"docid":"450551d6f5c2087e022bb75012a5cf6f","score":"0.5590019","text":"def decr(self, *args):\n if self._cluster:\n return self.execute(b'DECR', *args, shard_key=args[0])\n return self.execute(b'DECR', *args)","title":""},{"docid":"97303ffba5bef2146b4f3e3f6995b582","score":"0.5576438","text":"def pop_key(self, k):\n lst = self.get(k)\n val = lst.pop()\n return val","title":""},{"docid":"bbfe7e2ea401c039510acd0202d986c5","score":"0.5566095","text":"def remove(self, key: int) -> None:\n hash_key = self.hash_function(key)\n bucket = self.internal_map[hash_key]\n\n for i in range(len(bucket)):\n if bucket[i][0] == key:\n temp = bucket[i]\n bucket[i] = bucket[0]\n bucket[0] = temp\n bucket.pop(0)\n break","title":""},{"docid":"2f5dd7395cb26aadab970827ea206ba0","score":"0.5566003","text":"def _DecrementScoreIndex(self, _unused_event):\n self._scoreboard.DecrementIndex()","title":""},{"docid":"8d1e0f74e3e48a6c839f04da68c767c1","score":"0.556364","text":"def remove(self, val: int) -> bool:\n #删除时将index_key中键为len的覆盖到键为key_index[val]，然后删除键为len的；key_index操作类似。最后len-1\n if val in self.key_index:\n self.index_key[self.key_index[val]]=self.index_key[self.len]\n self.key_index[self.index_key[self.len]]=self.key_index[val]\n self.key_index.pop(val)\n self.index_key.pop(self.len)\n self.len-=1\n return True\n else:\n return False","title":""},{"docid":"cc426e8bde7e65e62b7780c8d4e0623e","score":"0.55618495","text":"def __delitem__(self, key):\n\t\tself.be_table()\n\t\tdel self.points[float(key)]","title":""},{"docid":"b65afe78fc5e81c3200943a2bbf97e5f","score":"0.5557206","text":"def decrease_key(self, id_, newkey):\n node = self.id2node[id_]\n node.key = newkey\n parent = node.parent\n while parent and node < parent:\n self._exchange_nodes(node, parent)\n node = parent\n parent = node.parent","title":""},{"docid":"a8f14fd96f7eaf9b6c19a7c9257c7491","score":"0.55555725","text":"def __delitem__(self, k):\n i = self._hash(k)\n d = self.lst[i]\n if k in d:\n self.n -= 1\n del d[k]","title":""},{"docid":"a7a5e99570964180cb54376a7bdf5599","score":"0.5548644","text":"def unlinkKey(key):","title":""},{"docid":"c7ab05df4f2c17c002ab40d70492a996","score":"0.5541342","text":"def decrement(self):\n with self._lock:\n self.count -= 1\n if self._canceled:\n return\n elif not self.count:\n self._on_zero(None)","title":""},{"docid":"3fba06a19c9fb0992c2549bd4bfc1608","score":"0.55381745","text":"def decrease_key(self, element, new_prior):\n i = self.elements.index(element)\n self.elements[i] = tuple([element[0], new_prior])\n temp = self.elements[i]\n while i > 0:\n parent_idx = math.floor((i - 1) / 2)\n if not self.elements[parent_idx][1] > temp[1]:\n break\n self.elements[i] = self.elements[parent_idx]\n i = parent_idx\n self.elements[i] = temp","title":""},{"docid":"b735bb6336f9d0a9e8f30557dc0b4c4a","score":"0.5533121","text":"def delete(self, key):","title":""},{"docid":"84770c8359ac67c8f97aef36a2259811","score":"0.55198216","text":"def __delitem__(self, key):\n indices = []\n for i, (k, v) in enumerate(self.list):\n if k == key: indices.insert(0, i)\n if indices:\n for i in indices: self.list[:] = self.list[:i] + self.list[i+1:]\n else: raise KeyError(key)","title":""},{"docid":"cb8ae4a5b37dcac0f0b1c7ed25fb8010","score":"0.5512258","text":"def mifare_decrement(self, blk_no, value) -> STATUS:\n \n b = blk_no.to_bytes(1, 'little')\n smp = Message('cmd', 'nfc', 'mfc_dec', b, b'\\x00', value.to_bytes(4, 'little', signed=True))\n smp = self._send_receive(smp)\n r = smp.decode()\n return r['status']","title":""},{"docid":"41951aaf1b8467d4a1f5a3ee3de34299","score":"0.548394","text":"def decrementBKcoin(self,username):\n result1=self.db.userCollection.update_one({\"username\":username}, {\"$inc\": {\"bkcoin\": -1}})\n print(\"Numero bkcoin decrementati {}\".format(result1.modified_count))","title":""},{"docid":"e2dab27d0850322019d298b62b452508","score":"0.5472129","text":"def get(self, key: int) -> int:\n hashkey = key % self.chabi\n temp = self.Map[hashkey]\n while temp.next is not None and temp.next.tup[0] != key:\n temp = temp.next\n if temp.next is None:\n return -1\n else:\n return temp.next.tup[1]","title":""},{"docid":"f2490285f8d31a3cded9bf9a16154446","score":"0.546145","text":"def count_down(self, num=1):\n if num <= 0:\n raise ValueError('cannot decrease \\'{}\\' counts'.format(num))\n self._value -= num\n\n released = False\n if self._value <= 0:\n for waiter in self._waiters:\n if not waiter.done():\n waiter.set_result(True)\n released = True\n return released","title":""},{"docid":"ec8654da82456958eec6fc36646bc147","score":"0.5461231","text":"def delete(self, key):\n # Your code here\n self.count -= 1\n # index = self.hash_index(key)\n # if self.list[index]:\n # current = self.list[index]\n # while current.next is not None:\n # if current.next.key == key:\n # # remove the node\n # break\n # current = current.next\n # return current.value\n # else:\n # return None\n bucket = self.list[self.hash_index(key)]\n bucket.delete(key)","title":""},{"docid":"cfd8ecae19b94109f4c61c148b203579","score":"0.5460662","text":"def decr(self, n=1):\r\n return _osgGA.SwigPyIterator_decr(self, n)","title":""},{"docid":"da22c79b664d7427d739e06f2d2ccfac","score":"0.54567677","text":"def remove(self, num=None):\n self.count -= 1\n\n return self","title":""},{"docid":"df1cca9fc693734da2eb214d6eba96fb","score":"0.54514325","text":"def remove(self, key: int) -> None:\n hashkey = key % self.chabi\n temp = self.Map[hashkey]\n while temp.next is not None and temp.next.tup[0] != key:\n temp = temp.next\n if temp.next is None:\n return None\n else:\n temp.next = temp.next.next\n return None","title":""},{"docid":"e151f5cb1844654843cf3a90e23206e1","score":"0.5430203","text":"def remove(self, *key: int) -> None:\n self.set_partial_width(*key, 0)\n del self._data[key]","title":""},{"docid":"296103c4c38737fcf87ea64e2de9bb92","score":"0.5416194","text":"def down(self, item, reason=None):\n global db\n if not db: logging.error(\"plugin isnt initialised yet\") ; return 0\n item = item.lower()\n value = db.execute(\"\"\" SELECT value FROM karma WHERE item = %s \"\"\", item)\n try: val = value[0][0]\n except (TypeError, IndexError):\n result = db.execute(\"\"\" INSERT INTO karma(item, value) VALUES (%s, -1) \"\"\", item)\n if reason: self.addwhy(item, 'down', reason.strip())\n return result\n val -= 1\n result = db.execute(\"\"\" UPDATE karma SET value = %s WHERE item = %s \"\"\", (val, item))\n if reason: self.addwhy(item, 'down', reason.strip())\n return result","title":""},{"docid":"252413fbf326b74e2e62d418b3e89628","score":"0.54118854","text":"def remove(self, key: str) -> None:\n hash_val = self.hash_function(key)\n da_index = hash_val % self.capacity\n\n if self.buckets.get_at_index(da_index).remove(key):\n self.size -= 1","title":""},{"docid":"f97799d6be04ef753a3e5a55a5fdc2bc","score":"0.54067034","text":"def ceiling_key(self, key):\r\n return self.ceiling_item(key)[0]","title":""},{"docid":"f5006eac82d25521147c32b47c5fb5d4","score":"0.540583","text":"def delete_key(self, k):\n self._mapz.pop(k, None)","title":""},{"docid":"e46eaec3b8a7d7fdc3925dfed4fc8b7d","score":"0.53967565","text":"def __delitem__(self, key):","title":""}],"string":"[\n {\n \"docid\": \"16da0a9d2e42a401a0be02abacf3724c\",\n \"score\": \"0.82384324\",\n \"text\": \"def decrement_int_value(self, key):\\n return self._redis.decr(name=key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1cd9fbc3a0a8519344f377e45eb3e85\",\n \"score\": \"0.7788498\",\n \"text\": \"def dec(self, key):\\r\\n database = self._database\\r\\n new_count = database[key] - 1\\r\\n if new_count == 0:\\r\\n del database[key]\\r\\n else:\\r\\n database[key] = new_count\\r\\n return new_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2b2d30cb4451e6af143f67ffc61b365\",\n \"score\": \"0.7668736\",\n \"text\": \"def dec(self, key):\\n if key in self.d_value:\\n self.d_key[self.d_value[key]].remove(key)\\n if not self.d_key[self.d_value[key]]:\\n self.d_key.pop(self.d_value[key])\\n self.d_value[key] -= 1\\n if self.d_value[key] > 0:\\n self.d_key[self.d_value[key]] = self.d_key.get(self.d_value[key], [])\\n self.d_key[self.d_value[key]].append(key)\\n else:\\n self.d_value.pop(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f9d920f634d3602f8b673be41cf6de4\",\n \"score\": \"0.7286453\",\n \"text\": \"def dec(self, key):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f695244d5291fd5ffe5e5aa9db0fcfd5\",\n \"score\": \"0.7267902\",\n \"text\": \"def dec(self, key: str) -> None:\\n v = self.key_to_val.get(key, -1)\\n if v < 0:\\n return \\n # 删除这个键\\n self.key_to_val.pop(key)\\n self.val_to_key[v].remove(key)\\n \\n if v == 1:\\n # 需要更新最大值和最小值\\n \\n\\n\\n\\n else:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"420021b4a9a3e2a911c017bbd4d65561\",\n \"score\": \"0.7185764\",\n \"text\": \"def testDecrementANegativeInteger(self):\\n self.assertTrue(memcache.set(self.key1, -5))\\n self.assertEqual(None, memcache.decr(self.key1))\\n self.assertEqual(-5, memcache.get(self.key1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"108e0bb5282b24a140339eb9c4eb466f\",\n \"score\": \"0.71577436\",\n \"text\": \"def decr(self, key:NativeType) -> int:\\n return self._query(b'decr', self.encode_from_native(key))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da317c753b8bbdeb28a85776bd8b40cd\",\n \"score\": \"0.6979131\",\n \"text\": \"def decr(self, key, delta=1):\\n return self.stash.incr(key, 0 - delta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94d1f23638b086cfaeee10781c8a054a\",\n \"score\": \"0.69340396\",\n \"text\": \"def revert(key, num):\\n return obscure(key, num)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db828325a2c475f29e69cba04713476b\",\n \"score\": \"0.6903613\",\n \"text\": \"def decrby(self, key, decrement, callback=None):\\n args = [\\\"DECRBY\\\"]\\n args.append(key)\\n args.append(decrement)\\n self.send_message(args, callback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95bfdf0149ee7c76526c29de40bba3f2\",\n \"score\": \"0.6848593\",\n \"text\": \"def dec(self, key, delta=1):\\r\\n value = (self.get(key) or 0) - delta\\r\\n return value if self.set(key, value) else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40cc96badf68287fad1b5cefd5338a2b\",\n \"score\": \"0.68267363\",\n \"text\": \"def dec(self, key: str) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3af2647a26f42c430d0e41e80df54377\",\n \"score\": \"0.67994255\",\n \"text\": \"def decrement(self):\\r\\n \\r\\n self.number = self.number - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2915b05b8e6ed95d4e3790ed2ca07295\",\n \"score\": \"0.6798188\",\n \"text\": \"def testDecrementByOne(self):\\n self.assertTrue(memcache.set(self.key1, '10'))\\n self.assertEqual(9, memcache.decr(self.key1))\\n self.assertEqual('9', memcache.get(self.key1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6922db0e86f4f607a60ac9cabd9d781\",\n \"score\": \"0.67827314\",\n \"text\": \"def decr(self, key, delta=1, version=None):\\n return self.incr(key, -delta, version=version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b027039cd872f672132a8ab445a6cf8\",\n \"score\": \"0.677203\",\n \"text\": \"def dec(self, key):\\n if key in self.d:\\n curr_node = self.d[key]\\n prev_val = curr_node.val - 1\\n if curr_node.prev and curr_node.prev.val == prev_val:\\n prev_node = curr_node.prev\\n elif prev_val > 0:\\n prev_node = curr_node.appendleft(prev_val)\\n curr_node.remove(key)\\n\\n if prev_val == 0:\\n del self.d[key]\\n else:\\n prev_node.keys.add(key)\\n self.d[key] = prev_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2da7c8acec62327c043403346bea147\",\n \"score\": \"0.67442715\",\n \"text\": \"def testDecrementByDelta(self):\\n self.assertTrue(memcache.set(self.key1, '11'))\\n self.assertEqual(1, memcache.decr(self.key1, delta=10))\\n self.assertEqual('1', memcache.get(self.key1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0ab281d115422dd127122dabf6b8505\",\n \"score\": \"0.6730673\",\n \"text\": \"def dec(self, key):\\r\\n return DatabaseReferenceCounter.dec(self, key + self._suffix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d02cc420f27e50a068dff3c9465998f2\",\n \"score\": \"0.6675536\",\n \"text\": \"def dec(self, key):\\n if key not in self.keys:\\n return\\n curNode = self.keys[key]\\n if curNode.val == 1:\\n # 在双向链表中删除该节点\\n prevNode = curNode.prev\\n nextNode = curNode.next\\n prevNode.next = nextNode\\n nextNode.prev = prevNode\\n else:\\n # 该key对应的value减1 并通过交换节点的方式保持双向链表有序\\n curNode.val -= 1\\n while curNode.prev != self.head and curNode.val < curNode.prev.val:\\n prepreNode = curNode.prev.prev\\n nextNode = curNode.next\\n nextNode.prev = curNode.prev\\n curNode.prev.prev = curNode\\n curNode.prev = prepreNode\\n curNode.next = nextNode.prev\\n nextNode.prev.next = nextNode\\n prepreNode.next = curNode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78c2d89a4b3ac6c95d7e289978255dea\",\n \"score\": \"0.6516995\",\n \"text\": \"def testDecrementCap(self):\\n self.assertTrue(memcache.set(self.key1, '5'))\\n self.assertEqual(0, memcache.decr(self.key1, delta=10))\\n self.assertEqual('0', memcache.get(self.key1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67fef31f70cbc09ca57cff7998615e8c\",\n \"score\": \"0.650766\",\n \"text\": \"def dec(self, key):\\n if key not in self.bucket_of_key:\\n return\\n\\n bucket, prev_bucket = self.bucket_of_key[key], self.bucket_of_key[key].prev\\n self.bucket_of_key.pop(key, None)\\n if bucket.value > 1:\\n if bucket is self.buckets.begin() or prev_bucket.value < bucket.value-1:\\n prev_bucket = self.buckets.insert(bucket, Node(bucket.value-1, set()))\\n prev_bucket.keys.add(key)\\n self.bucket_of_key[key] = prev_bucket\\n\\n bucket.keys.remove(key)\\n if not bucket.keys:\\n self.buckets.erase(bucket)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"494d83ac5e02065ded781c18d7f3ea00\",\n \"score\": \"0.6440148\",\n \"text\": \"def dec(self, key: str) -> None:\\n if key in self.dic:\\n node = self.dic[key]\\n node.keys.remove(key)\\n self.dic.pop(key)\\n if node.value != 1:\\n prevnode = node.prev\\n if prevnode.value == (node.value - 1):\\n prevnode.keys.add(key)\\n self.dic[key] = prevnode\\n else:\\n newnode = Node(node.value - 1)\\n newnode.keys.add(key)\\n prevnode.insert(newnode)\\n self.dic[key] = newnode\\n if len(node.keys) == 0:\\n node.remove()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2468a33ed4d4bdf79d68fa4b092f87da\",\n \"score\": \"0.64269656\",\n \"text\": \"def dec(self, key: str) -> None:\\n \\n if key in self.d:\\n \\n node = self.d[key]\\n node.remove(key)\\n \\n if node.value == 1:\\n self.d.remove(key)\\n else:\\n if node.prev.value == node.value - 1:\\n node.prev.add(key)\\n else:\\n new_node = Node(node.value - 1)\\n node.insert_left(new_node)\\n \\n if len(node) == 0:\\n node.delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ece3b2bc04df801546d194bff83aa87f\",\n \"score\": \"0.64168453\",\n \"text\": \"def __delitem__(self,key):\\n if 0 <= key <= 3:\\n self[key] = 0\\n return self\\n else:\\n raise RuntimeError('list index out of range')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cdb848aee6a81fd83af38f75b484a4e\",\n \"score\": \"0.6379582\",\n \"text\": \"def dec(self, key: str) -> None:\\n if key not in self.dic_k_c:\\n return\\n \\n cnt = self.dic_k_c[key]\\n \\n if cnt != 1:\\n self.dic_c_k[cnt-1] = self.dic_c_k.get(cnt-1, set())\\n self.dic_c_k[cnt-1].add(key)\\n self.dic_k_c[key] = cnt-1\\n else:\\n del self.dic_k_c[key]\\n \\n \\n if len(self.dic_c_k[cnt]) == 1:\\n del self.dic_c_k[cnt]\\n else:\\n self.dic_c_k[cnt].remove(key)\\n \\n \\n if self.min_key == 1:\\n if self.min_key not in self.dic_c_k:\\n if not self.dic_c_k:\\n self.min_key = float('inf')\\n else:\\n self.min_key = sorted(self.dic_c_k.keys())[0]\\n else:\\n self.min_key = min(self.min_key, cnt-1)\\n \\n if self.max_key == 1:\\n if self.max_key not in self.dic_c_k:\\n self.max_key = -float('inf')\\n else:\\n if self.max_key not in self.dic_c_k:\\n self.max_key = cnt - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a2418d2078fe71e64e960a3cdec3eaa\",\n \"score\": \"0.63749975\",\n \"text\": \"def decr(self, key, callback=None):\\n args = [\\\"DECR\\\"]\\n args.append(key)\\n self.send_message(args, callback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c95107a95c5ec9b7967507e26279b551\",\n \"score\": \"0.6282394\",\n \"text\": \"def testDecrementUnknownKey(self):\\n self.assertEqual(None, memcache.decr(self.key1))\\n self.assertEqual(None, memcache.get(self.key1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14413a4965a3c049531f77a915fbaf1f\",\n \"score\": \"0.62719816\",\n \"text\": \"def remove(self, key: int) -> None:\\n return self.myDict[key % self.space].remove(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4f2dcee26603a68dfc9153d14547fbd\",\n \"score\": \"0.6254852\",\n \"text\": \"def lrem(self, key:NativeType, count:int=0, value='') -> int:\\n return self._query(b'lrem', self.encode_from_native(key), self._encode_int(count), self.encode_from_native(value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95d043e1a4af0b5666d086f96adf59dd\",\n \"score\": \"0.6235124\",\n \"text\": \"def decr_version(self, key, delta=1, version=None):\\n return self.incr_version(key, -delta, version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"041572ce22e67af95180ee13ede663cf\",\n \"score\": \"0.61714554\",\n \"text\": \"def decrease_key(self, i, k):\\n # NOTE: assumes a valid index i\\n # NOTE: assumes heap[i] >= k\\n\\n self.heap[i].key = k\\n item = self.heap[i]\\n p = self.parent(i)\\n\\n while i > 0 and k < self.heap[p].key:\\n self.heap[i] = self.heap[p]\\n i = p\\n p = self.parent(i)\\n\\n self.heap[i] = item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28302658ae4285301e885690d7b55fed\",\n \"score\": \"0.6159204\",\n \"text\": \"def lrem(self, key, count, value, callback=None):\\n args = [\\\"LREM\\\"]\\n args.append(key)\\n args.append(count)\\n args.append(value)\\n self.send_message(args, callback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9ce28263e0008a376c967ed5d67750d\",\n \"score\": \"0.61189187\",\n \"text\": \"def del_key(n, key):\\n del n[key]\\n return n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc64c4f804726e2139a8ee8ca6fd913d\",\n \"score\": \"0.6112761\",\n \"text\": \"def remove(self, key: int) -> None:\\n index = key % self.map_size\\n self.bucket_array[index].remove(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d324d79bdb7cacf5428a9315256a22b9\",\n \"score\": \"0.6076193\",\n \"text\": \"def testDecrementBadValue(self):\\n self.assertTrue(memcache.set(self.key1, 'some data'))\\n self.assertEqual(None, memcache.decr(self.key1))\\n self.assertEqual('some data', memcache.get(self.key1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6d0347133b022ccdee3bdb34aacc264\",\n \"score\": \"0.6071566\",\n \"text\": \"def init_dict_or_substract(dict_, key, value):\\n if key in dict_.keys():\\n dict_[key] -= value\\n\\n else:\\n dict_[key] = 0\\n dict_[key] -= value\\n \\n return dict_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d13f17c63c702b45fba8d0a73148db5\",\n \"score\": \"0.6047952\",\n \"text\": \"def decreasekey(self, node: HeapNode, key: int) -> HeapNode:\\n\\n # if node.key < key:\\n # raise ValueError(\\\"Cannot increase key\\\")\\n node.key = key\\n if not node.parent or node.parent.key <= key:\\n return node\\n node.cut()\\n self.root = self.meld(self.root, node)\\n return node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5c49bcc82691963799808a8cf9c59d7\",\n \"score\": \"0.60228205\",\n \"text\": \"def test_decrement_by(self):\\n self._test_decrement(expected_value=2, expected_expr=F('counter') - 3,\\n decrement_by=3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04851a6399ff97f03da550f7ff02e239\",\n \"score\": \"0.6007732\",\n \"text\": \"def decr(self, key, amount=1):\\n # An alias for ``decr()``, because it is already implemented\\n # as DECRBY redis command.\\n return self._redis_client.decr(key, amount)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ef60f92ffe482d767169a14957d7347\",\n \"score\": \"0.6007321\",\n \"text\": \"def remove(self, key: int) -> None:\\n if key in self.keys:\\n index = self.keys.index(key)\\n self.keys.pop(index)\\n self.values.pop(index)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab244d2b077c04bfd194231f7ce0914f\",\n \"score\": \"0.5976782\",\n \"text\": \"def _b_delitem_ ( self , i ) :\\n if isinstance ( i , int ) :\\n if 0 <= i < self.n () : return self.remove ( i )\\n raise IndexError ('Invalid key %s' % i )\\n \\n raise TypeError ('Invalid key/value %s' % i )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12750b4ed7244f3ac8e6a91c5f610275\",\n \"score\": \"0.59685963\",\n \"text\": \"def decreasekey(self, node: HeapNode, key: int) -> HeapNode:\\n\\n # if node.key < key:\\n # raise ValueError(\\\"Cannot increase key\\\")\\n node.key = key\\n parent = node.parent\\n if parent and node.key < parent.key:\\n self.cut(node)\\n self.cascading_cut(parent)\\n if key < self.minroot.key:\\n self.minroot = node\\n return node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21a2d803c7b23f3425eb09f88a9ce320\",\n \"score\": \"0.5948123\",\n \"text\": \"def decreaseCount(self):\\n self.__count -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50a8d690a98c99cc36d24d6969d13d75\",\n \"score\": \"0.5928681\",\n \"text\": \"def remove_item(self, key):\\n for i, k in enumerate(self.keys):\\n if k >= key:\\n self.children[i].remove_item(key)\\n return\\n return self.children[-1].remove_item(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0901390777dccde4c741ea15fbae3b9\",\n \"score\": \"0.59180003\",\n \"text\": \"def remove(self, key: int) -> None:\\n hashval = self.hashfunc(key)\\n self.hashmap[hashval].remove(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e7a75828626d62829a33fc1be9eda1a\",\n \"score\": \"0.58987945\",\n \"text\": \"def _a_delitem_ ( self , i ) :\\n if isinstance ( i , int ) :\\n if 0 <= i < self.n () : return self.remove ( i )\\n raise IndexError ('Invalid key %s' % i )\\n \\n raise TypeError ('Invalid key/value %s' % i )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47081a23ddf99d2814cc91097f198630\",\n \"score\": \"0.5884938\",\n \"text\": \"def decrementCount(self, hash):\\n if len(hash) != 40:\\n hash = sha1helper.sha1ToString(hash)\\n cu = self.db.cursor()\\n cu.execute(\\\"SELECT count FROM DataStore WHERE hash=?\\\", hash)\\n count = cu.next()[0]\\n if count == 1:\\n cu.execute(\\\"DELETE FROM DataStore WHERE hash=?\\\", hash)\\n else:\\n count -= 1\\n cu.execute(\\\"UPDATE DataStore SET count=? WHERE hash=?\\\",\\n count, hash)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f8b6b6f78a280348a99a07503eac354\",\n \"score\": \"0.58795524\",\n \"text\": \"def DecrementIndex(self):\\n if self.index is None:\\n self.index = 0\\n elif self.index > 0:\\n if self.index <= self._window_low:\\n self._window_low -= 1\\n self._window_high -= 1\\n self.index -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"478203282975027162a3b70b01198116\",\n \"score\": \"0.5868972\",\n \"text\": \"def _w_delitem_ ( self , i ) :\\n if isinstance ( i , int ) :\\n if 0 <= i < self.n () : return self.remove ( i )\\n raise IndexError ('Invalid key %s' % i )\\n \\n raise TypeError ('Invalid key/value %s' % i )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"629d77eda2c59716cb34f24578a43622\",\n \"score\": \"0.5865965\",\n \"text\": \"def _p_delitem_ ( self , i ) :\\n if isinstance ( i , int ) :\\n if 0 <= i < self.n () : return self.remove ( i )\\n raise IndexError ('Invalid key %s' % i )\\n \\n raise TypeError ('Invalid key/value %s' % i )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a7f8ec0bdf143ba10a5cc6c33563a0a\",\n \"score\": \"0.58259237\",\n \"text\": \"def dec(self, key: str) -> None:\\n if key not in self.dic_k_n:\\n return\\n \\n node = self.dic_k_n[key]\\n if node.cnt == 1:\\n if len(node.keys) == 1:\\n self.delNode(node)\\n else:\\n node.keys.remove(key)\\n del self.dic_k_n[key]\\n \\n else:\\n prv = node.prev\\n node.keys.remove(key)\\n if prv == self.head or prv.cnt < node.cnt-1:\\n new_node = Node()\\n new_node.keys.add(key)\\n new_node.cnt = node.cnt-1\\n self.addNode(node.prev, new_node)\\n self.dic_k_n[key] = new_node\\n \\n else:\\n prv.keys.add(key)\\n self.dic_k_n[key] = prv\\n \\n if len(node.keys) == 0:\\n self.delNode(node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf29802b77fc4162f3d791274ca05f1\",\n \"score\": \"0.5817354\",\n \"text\": \"def testIncrementANegativeInteger(self):\\n self.assertTrue(memcache.set(self.key1, -5))\\n self.assertEqual(None, memcache.incr(self.key1))\\n self.assertEqual(-5, memcache.get(self.key1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc38b2893674fd49441cc78513040f3c\",\n \"score\": \"0.58112115\",\n \"text\": \"def remove(self, key: int) -> None:\\n # compute hash\\n h = key % self.CAPACITY\\n # check the self.keySet\\n for i in range(len(self.keySet[h])):\\n if self.keySet[h][i][0] == key:\\n self.keySet[h].pop(i)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26750c0c392782ab671fbfeb1bc414e1\",\n \"score\": \"0.5802215\",\n \"text\": \"def decrease_key(self, entry, new_priority):\\n self._check_priority(new_priority)\\n if new_priority > entry.m_priority:\\n raise ValueError(\\\"New priority exceeds old.\\\")\\n\\n # Forward this to a helper function.\\n self.decrease_key_unchecked(entry, new_priority)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88a0fdcf244079941ba75a58f2af4c4d\",\n \"score\": \"0.5764471\",\n \"text\": \"def remove(self, e):\\n \\n if e in self.vals:\\n self.vals[e] -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9acff1665f69870e046de0d86bc5bb5a\",\n \"score\": \"0.5762802\",\n \"text\": \"def remove(self,key):\\n for i in range(self.reps)\\n self.db.remove((key,i))\\n return self.db.remove(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"808d879026524be7747ae33c0e5dd034\",\n \"score\": \"0.57463104\",\n \"text\": \"def _get_num(self, key):\\n # This might use implementation defined behaviur in pyglet...\\n if keysym._0 <= key <= keysym._9:\\n return key - keysym._0\\n if keysym.NUM_0 <= key <= keysym.NUM_9:\\n return key - keysym.NUM_0\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1b9f83956961580653c123ddfbd4941\",\n \"score\": \"0.57356465\",\n \"text\": \"def decrement_items(inventory, items):\\n\\n for item in items:\\n value = inventory.get(item, 0)\\n if value > 0:\\n value -= 1\\n\\n inventory[item] = value\\n\\n return inventory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcbbd859a21b9dbfd5b5ef60988e4a46\",\n \"score\": \"0.57332355\",\n \"text\": \"def test_decrement_no_reload(self):\\n self._test_decrement(expected_value=5, expected_expr=F('counter') - 1,\\n reload_object=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05f547895bf2a1b90841eecb6dc1d46f\",\n \"score\": \"0.5718732\",\n \"text\": \"def dec( self ):\\n if self.count > 0: self.count -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c86b30a930386d39c4f563a15399894\",\n \"score\": \"0.5708756\",\n \"text\": \"def remove(self, item, value=1):\\n if item in self.numbers:\\n self.numbers[item] -= value\\n elif item in self.inventory:\\n self.inventory.remove(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b5a004dd3e7bef5e73db68b3ada905a\",\n \"score\": \"0.5701371\",\n \"text\": \"def __sub__(self, x):\\n old = self.cnt.get(x, 0)\\n if old > 0:\\n self.cnt[x] = old - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59e866e386ac4aabd90553da3a43f427\",\n \"score\": \"0.5682671\",\n \"text\": \"def get(self, key: int) -> int:\\n if key not in self.cache:\\n return -1\\n else:\\n self.cache.move_to_end(key)\\n return self.cache[key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7de11e93610154dc4331f8274c1979a\",\n \"score\": \"0.56692225\",\n \"text\": \"def remove(key):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f736e442de395d5efbdfcdbae8e574a\",\n \"score\": \"0.56688666\",\n \"text\": \"def decrease(self):\\n\\n\\t\\tk_to_remove = []\\n\\t\\tfor k in self._pheromone.iterkeys():\\n\\t\\t\\tif self._pheromone[k] > 0:\\n\\t\\t\\t\\tself._pheromone[k] -= 0.1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tk_to_remove.append(k)\\n\\t\\tfor k in k_to_remove:\\n\\t\\t\\tdel self._pheromone[k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98ab94eca952ad019e87ec7cc7a49e0\",\n \"score\": \"0.56494266\",\n \"text\": \"def del_value(self, key):\\n raise NotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92c63b812b0e620fad7f0a732b8f7c62\",\n \"score\": \"0.56343114\",\n \"text\": \"def __renumber(dictionary) :\\r\\n count = 0\\r\\n ret = dictionary.copy()\\r\\n new_values = dict([])\\r\\n\\r\\n for key in dictionary.keys() :\\r\\n value = dictionary[key]\\r\\n new_value = new_values.get(value, -1)\\r\\n if new_value == -1 :\\r\\n new_values[value] = count\\r\\n new_value = count\\r\\n count = count + 1\\r\\n ret[key] = new_value\\r\\n\\r\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29cc1919f253b68007ffc8e131664e77\",\n \"score\": \"0.56239474\",\n \"text\": \"def test_priority_queue_decrease_key(self):\\n pq = PriorityQueue(data=[(5, 'E'), (2, 'B'), (4, 'D')])\\n pq.decrease_key('D', 1)\\n\\n self.assertEqual(pq.pop_min(), (1, 'D'))\\n with self.assertRaises(KeyError):\\n pq.decrease_key('A', 3)\\n with self.assertRaises(ValueError):\\n pq.decrease_key('E', 7)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614a1b1ec657af8568a8f75ef047eab0\",\n \"score\": \"0.56187767\",\n \"text\": \"def remove(self, key: int) -> None:\\n hashkey = self.hash(key)\\n if self.table[hashkey]:\\n self.table[hashkey][self.pos(key)] = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"994c43df4245f46c0b564693ea2b078b\",\n \"score\": \"0.5600307\",\n \"text\": \"def remove(self, key: int) -> None:\\n hashkey = key % self.numBuckets\\n for idx, elem in enumerate(self.hashmap[hashkey]):\\n if elem[0] == key:\\n del self.hashmap[hashkey][idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1528c8d872f6df83bc130ff68726787\",\n \"score\": \"0.56002945\",\n \"text\": \"def remove(self, n):\\n self.set_value(n, 0)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7a6fcd6e4966fd491400b33bab64929\",\n \"score\": \"0.5596613\",\n \"text\": \"def dec(self, by=1):\\n self._ensure_has_value()\\n self.value -= by\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b87500832f678b2f3b6febdb297339cb\",\n \"score\": \"0.55902624\",\n \"text\": \"def get(self, key: int) -> int:\\n return self.myDict[key % self.space].get(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"450551d6f5c2087e022bb75012a5cf6f\",\n \"score\": \"0.5590019\",\n \"text\": \"def decr(self, *args):\\n if self._cluster:\\n return self.execute(b'DECR', *args, shard_key=args[0])\\n return self.execute(b'DECR', *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97303ffba5bef2146b4f3e3f6995b582\",\n \"score\": \"0.5576438\",\n \"text\": \"def pop_key(self, k):\\n lst = self.get(k)\\n val = lst.pop()\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbfe7e2ea401c039510acd0202d986c5\",\n \"score\": \"0.5566095\",\n \"text\": \"def remove(self, key: int) -> None:\\n hash_key = self.hash_function(key)\\n bucket = self.internal_map[hash_key]\\n\\n for i in range(len(bucket)):\\n if bucket[i][0] == key:\\n temp = bucket[i]\\n bucket[i] = bucket[0]\\n bucket[0] = temp\\n bucket.pop(0)\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f5dd7395cb26aadab970827ea206ba0\",\n \"score\": \"0.5566003\",\n \"text\": \"def _DecrementScoreIndex(self, _unused_event):\\n self._scoreboard.DecrementIndex()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d1e0f74e3e48a6c839f04da68c767c1\",\n \"score\": \"0.556364\",\n \"text\": \"def remove(self, val: int) -> bool:\\n #删除时将index_key中键为len的覆盖到键为key_index[val]，然后删除键为len的；key_index操作类似。最后len-1\\n if val in self.key_index:\\n self.index_key[self.key_index[val]]=self.index_key[self.len]\\n self.key_index[self.index_key[self.len]]=self.key_index[val]\\n self.key_index.pop(val)\\n self.index_key.pop(self.len)\\n self.len-=1\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc426e8bde7e65e62b7780c8d4e0623e\",\n \"score\": \"0.55618495\",\n \"text\": \"def __delitem__(self, key):\\n\\t\\tself.be_table()\\n\\t\\tdel self.points[float(key)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b65afe78fc5e81c3200943a2bbf97e5f\",\n \"score\": \"0.5557206\",\n \"text\": \"def decrease_key(self, id_, newkey):\\n node = self.id2node[id_]\\n node.key = newkey\\n parent = node.parent\\n while parent and node < parent:\\n self._exchange_nodes(node, parent)\\n node = parent\\n parent = node.parent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8f14fd96f7eaf9b6c19a7c9257c7491\",\n \"score\": \"0.55555725\",\n \"text\": \"def __delitem__(self, k):\\n i = self._hash(k)\\n d = self.lst[i]\\n if k in d:\\n self.n -= 1\\n del d[k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a5e99570964180cb54376a7bdf5599\",\n \"score\": \"0.5548644\",\n \"text\": \"def unlinkKey(key):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ab05df4f2c17c002ab40d70492a996\",\n \"score\": \"0.5541342\",\n \"text\": \"def decrement(self):\\n with self._lock:\\n self.count -= 1\\n if self._canceled:\\n return\\n elif not self.count:\\n self._on_zero(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fba06a19c9fb0992c2549bd4bfc1608\",\n \"score\": \"0.55381745\",\n \"text\": \"def decrease_key(self, element, new_prior):\\n i = self.elements.index(element)\\n self.elements[i] = tuple([element[0], new_prior])\\n temp = self.elements[i]\\n while i > 0:\\n parent_idx = math.floor((i - 1) / 2)\\n if not self.elements[parent_idx][1] > temp[1]:\\n break\\n self.elements[i] = self.elements[parent_idx]\\n i = parent_idx\\n self.elements[i] = temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b735bb6336f9d0a9e8f30557dc0b4c4a\",\n \"score\": \"0.5533121\",\n \"text\": \"def delete(self, key):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84770c8359ac67c8f97aef36a2259811\",\n \"score\": \"0.55198216\",\n \"text\": \"def __delitem__(self, key):\\n indices = []\\n for i, (k, v) in enumerate(self.list):\\n if k == key: indices.insert(0, i)\\n if indices:\\n for i in indices: self.list[:] = self.list[:i] + self.list[i+1:]\\n else: raise KeyError(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb8ae4a5b37dcac0f0b1c7ed25fb8010\",\n \"score\": \"0.5512258\",\n \"text\": \"def mifare_decrement(self, blk_no, value) -> STATUS:\\n \\n b = blk_no.to_bytes(1, 'little')\\n smp = Message('cmd', 'nfc', 'mfc_dec', b, b'\\\\x00', value.to_bytes(4, 'little', signed=True))\\n smp = self._send_receive(smp)\\n r = smp.decode()\\n return r['status']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41951aaf1b8467d4a1f5a3ee3de34299\",\n \"score\": \"0.548394\",\n \"text\": \"def decrementBKcoin(self,username):\\n result1=self.db.userCollection.update_one({\\\"username\\\":username}, {\\\"$inc\\\": {\\\"bkcoin\\\": -1}})\\n print(\\\"Numero bkcoin decrementati {}\\\".format(result1.modified_count))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2dab27d0850322019d298b62b452508\",\n \"score\": \"0.5472129\",\n \"text\": \"def get(self, key: int) -> int:\\n hashkey = key % self.chabi\\n temp = self.Map[hashkey]\\n while temp.next is not None and temp.next.tup[0] != key:\\n temp = temp.next\\n if temp.next is None:\\n return -1\\n else:\\n return temp.next.tup[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2490285f8d31a3cded9bf9a16154446\",\n \"score\": \"0.546145\",\n \"text\": \"def count_down(self, num=1):\\n if num <= 0:\\n raise ValueError('cannot decrease \\\\'{}\\\\' counts'.format(num))\\n self._value -= num\\n\\n released = False\\n if self._value <= 0:\\n for waiter in self._waiters:\\n if not waiter.done():\\n waiter.set_result(True)\\n released = True\\n return released\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec8654da82456958eec6fc36646bc147\",\n \"score\": \"0.5461231\",\n \"text\": \"def delete(self, key):\\n # Your code here\\n self.count -= 1\\n # index = self.hash_index(key)\\n # if self.list[index]:\\n # current = self.list[index]\\n # while current.next is not None:\\n # if current.next.key == key:\\n # # remove the node\\n # break\\n # current = current.next\\n # return current.value\\n # else:\\n # return None\\n bucket = self.list[self.hash_index(key)]\\n bucket.delete(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfd8ecae19b94109f4c61c148b203579\",\n \"score\": \"0.5460662\",\n \"text\": \"def decr(self, n=1):\\r\\n return _osgGA.SwigPyIterator_decr(self, n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da22c79b664d7427d739e06f2d2ccfac\",\n \"score\": \"0.54567677\",\n \"text\": \"def remove(self, num=None):\\n self.count -= 1\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df1cca9fc693734da2eb214d6eba96fb\",\n \"score\": \"0.54514325\",\n \"text\": \"def remove(self, key: int) -> None:\\n hashkey = key % self.chabi\\n temp = self.Map[hashkey]\\n while temp.next is not None and temp.next.tup[0] != key:\\n temp = temp.next\\n if temp.next is None:\\n return None\\n else:\\n temp.next = temp.next.next\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e151f5cb1844654843cf3a90e23206e1\",\n \"score\": \"0.5430203\",\n \"text\": \"def remove(self, *key: int) -> None:\\n self.set_partial_width(*key, 0)\\n del self._data[key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"296103c4c38737fcf87ea64e2de9bb92\",\n \"score\": \"0.5416194\",\n \"text\": \"def down(self, item, reason=None):\\n global db\\n if not db: logging.error(\\\"plugin isnt initialised yet\\\") ; return 0\\n item = item.lower()\\n value = db.execute(\\\"\\\"\\\" SELECT value FROM karma WHERE item = %s \\\"\\\"\\\", item)\\n try: val = value[0][0]\\n except (TypeError, IndexError):\\n result = db.execute(\\\"\\\"\\\" INSERT INTO karma(item, value) VALUES (%s, -1) \\\"\\\"\\\", item)\\n if reason: self.addwhy(item, 'down', reason.strip())\\n return result\\n val -= 1\\n result = db.execute(\\\"\\\"\\\" UPDATE karma SET value = %s WHERE item = %s \\\"\\\"\\\", (val, item))\\n if reason: self.addwhy(item, 'down', reason.strip())\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"252413fbf326b74e2e62d418b3e89628\",\n \"score\": \"0.54118854\",\n \"text\": \"def remove(self, key: str) -> None:\\n hash_val = self.hash_function(key)\\n da_index = hash_val % self.capacity\\n\\n if self.buckets.get_at_index(da_index).remove(key):\\n self.size -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f97799d6be04ef753a3e5a55a5fdc2bc\",\n \"score\": \"0.54067034\",\n \"text\": \"def ceiling_key(self, key):\\r\\n return self.ceiling_item(key)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5006eac82d25521147c32b47c5fb5d4\",\n \"score\": \"0.540583\",\n \"text\": \"def delete_key(self, k):\\n self._mapz.pop(k, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e46eaec3b8a7d7fdc3925dfed4fc8b7d\",\n \"score\": \"0.53967565\",\n \"text\": \"def __delitem__(self, key):\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993896,"cells":{"query_id":{"kind":"string","value":"1d8935b2d9ad8819e0c4381d50f0d81e"},"query":{"kind":"string","value":"Initializes hidden layers, based on defined number of layers."},"positive_passages":{"kind":"list like","value":[{"docid":"cc63545b27bc51814908aea07aabddd9","score":"0.7333883","text":"def _initialize_hidden_layers(self, neural_network):\n \n number_of_layers = randint(self.init_minimum_layers, self.layers)\n neurons_per_layer = [randint(1, self.init_maximum_neurons_per_layer) for i in range(number_of_layers - 1)]\n hidden_layers = [[create_neuron(None, neural_network.bias, maximum_bias_connection_weight=self.maximum_bias_connection_weight) for neuron in range(neurons_per_layer[layer])] for layer in range(number_of_layers - 1)]\n \n # From Jan: Create hidden layers with one neuron with random activation function each.\n # hidden_layers = [[create_neuron(None, neural_network.bias)] for i in range(self.layers - 1)]\n \n # Add final hidden layer with one neuron\n activation_function = choice(list(_NON_LINEAR_ACTIVATION_FUNCTIONS.keys()))\n hidden_layers.append([create_neuron(activation_function, neural_network.bias, maximum_bias_connection_weight=self.maximum_bias_connection_weight)])\n # Returns hidden layers.\n return hidden_layers","title":""}],"string":"[\n {\n \"docid\": \"cc63545b27bc51814908aea07aabddd9\",\n \"score\": \"0.7333883\",\n \"text\": \"def _initialize_hidden_layers(self, neural_network):\\n \\n number_of_layers = randint(self.init_minimum_layers, self.layers)\\n neurons_per_layer = [randint(1, self.init_maximum_neurons_per_layer) for i in range(number_of_layers - 1)]\\n hidden_layers = [[create_neuron(None, neural_network.bias, maximum_bias_connection_weight=self.maximum_bias_connection_weight) for neuron in range(neurons_per_layer[layer])] for layer in range(number_of_layers - 1)]\\n \\n # From Jan: Create hidden layers with one neuron with random activation function each.\\n # hidden_layers = [[create_neuron(None, neural_network.bias)] for i in range(self.layers - 1)]\\n \\n # Add final hidden layer with one neuron\\n activation_function = choice(list(_NON_LINEAR_ACTIVATION_FUNCTIONS.keys()))\\n hidden_layers.append([create_neuron(activation_function, neural_network.bias, maximum_bias_connection_weight=self.maximum_bias_connection_weight)])\\n # Returns hidden layers.\\n return hidden_layers\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4fa5825f794dd872fe20c41cb694ed3f","score":"0.7226314","text":"def init_hidden(self):\n return torch.zeros(self.num_layers, self.batch_size, self.hidden_size)","title":""},{"docid":"68b498dd3486faea72dc3a9779706daf","score":"0.7152341","text":"def init_hidden(self):\r\n #print('layer size =\\t', self.layer_amnt)\r\n #print('bat_size =\\t', self.batch_sz)\r\n #print('hidden size =\\t',self.h_size)\r\n return (torch.zeros(self.layer_amnt,self.batch_sz,self.h_size),\r\n torch.zeros(self.layer_amnt,self.batch_sz,self.h_size))","title":""},{"docid":"91a111127e22b2df8b213c0b2217069e","score":"0.70427036","text":"def __init__(self, hidden_layer_size, i, o, reg):\n self.reg = reg\n self.layers = [] \n for num_layer in range(1):\n self.layers.append(FullyConnectedLayer(i,hidden_layer_size))\n self.layers.append(ReLULayer())\n \n self.layers.append(FullyConnectedLayer(hidden_layer_size,o))","title":""},{"docid":"02d43cf7890717be5bd92838fa1c0295","score":"0.7031576","text":"def hidden_layers(cls):\n pass","title":""},{"docid":"e2df617f0d3ff7a73e5272c8e9f256aa","score":"0.7031383","text":"def create_hidden_layers(self, input_layer=None):\r\n pass","title":""},{"docid":"549239f862386503cfced0f36aff9ab1","score":"0.69802195","text":"def _init_layers(self) -> None:\n self.mask_convs = nn.ModuleList()\n self.cls_convs = nn.ModuleList()\n for i in range(self.stacked_convs):\n chn = self.in_channels + 2 if i == 0 else self.feat_channels\n self.mask_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n norm_cfg=self.norm_cfg))\n chn = self.in_channels if i == 0 else self.feat_channels\n self.cls_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n norm_cfg=self.norm_cfg))\n self.conv_mask_list = nn.ModuleList()\n for num_grid in self.num_grids:\n self.conv_mask_list.append(\n nn.Conv2d(self.feat_channels, num_grid**2, 1))\n\n self.conv_cls = nn.Conv2d(\n self.feat_channels, self.cls_out_channels, 3, padding=1)","title":""},{"docid":"1e42efd604d74fa7683640f57278e6a6","score":"0.694852","text":"def init_hidden(self, batch_size):\n if torch.cuda.is_available():\n self.hidden = torch.zeros(\n self.num_layers * 2, batch_size, self.hidden_size).cuda()\n else:\n self.hidden = torch.zeros(\n self.num_layers * 2, batch_size, self.hidden_size)","title":""},{"docid":"fa5bf31fe3cecc60d58ed7ea86e60adf","score":"0.6827073","text":"def __init__(self, sizes):\n super(CGNN_block, self).__init__()\n layers = []\n\n for i, j in zip(sizes[:-2], sizes[1:-1]):\n layers.append(th.nn.Linear(i, j))\n layers.append(th.nn.ReLU())\n\n layers.append(th.nn.Linear(sizes[-2], sizes[-1]))\n self.layers = th.nn.Sequential(*layers)","title":""},{"docid":"5563fc98704299a20e9ce9c396b54d08","score":"0.6818739","text":"def _init_layers(self):\n self.relu = nn.ReLU(inplace=True)\n self.cls_convs = nn.ModuleList()\n self.reg_convs = nn.ModuleList()\n for i in range(self.stacked_convs):\n chn = self.in_channels if i == 0 else self.feat_channels\n self.cls_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg))\n self.reg_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg))\n self.retina_cls = nn.Conv2d(\n self.feat_channels,\n self.num_anchors * self.cls_out_channels,\n 3,\n padding=1)\n self.retina_reg = nn.Conv2d(\n self.feat_channels, self.num_anchors * 5, 3, padding=1)","title":""},{"docid":"14a78e266857e29a06a948b621a58eb2","score":"0.68021","text":"def _init_layers(self):\n self._init_cls_convs()\n self._init_reg_convs()\n self._init_predictor()","title":""},{"docid":"b246671cbf38b739800b4117028c010b","score":"0.67531097","text":"def hide_all_layers(self):\n self.shown_layers_index = []","title":""},{"docid":"5a241f139219d666935d0c10e904f591","score":"0.67359835","text":"def _build_hidden_layers_var(self) -> Tuple[nn.ModuleList, int]:\n raise NotImplementedError","title":""},{"docid":"2e09cfa6ae54cc3fa2029907403ea470","score":"0.6673928","text":"def init_memory(self, batch_size):\n init_states = []\n # Only iterate even indexes\n for i in range(0, 2 * self.num_layers, 2):\n init_states.append(self.cell_list[i].init_hidden(batch_size=batch_size))\n\n return init_states","title":""},{"docid":"2c3644b3ceafac16e16609d3ccdc6fda","score":"0.66443205","text":"def __init__(self):\n self._n_layers = 0\n self._dim_layers = []\n self._layers = {}\n self._layers_input = {}\n return","title":""},{"docid":"a3b48ac0b58218cc4f7485672698be16","score":"0.6638811","text":"def set_layers(self, sizes, init='random'):\n self.init_weights(sizes, init, None, None)","title":""},{"docid":"a3b48ac0b58218cc4f7485672698be16","score":"0.6638811","text":"def set_layers(self, sizes, init='random'):\n self.init_weights(sizes, init, None, None)","title":""},{"docid":"82ad13e028f361081963df90b32db4a7","score":"0.6618463","text":"def init_hidden(self, batch_size: int, stddev=1):\n h = (\n torch.randn(\n self.n_layers * (1 + self.bidirectional),\n batch_size,\n self.hidden_dim,\n ).to(self.device)\n * stddev\n )\n c = (\n torch.randn(\n self.n_layers * (1 + self.bidirectional),\n batch_size,\n self.hidden_dim,\n ).to(self.device)\n * stddev\n )\n self.hidden = (h, c)","title":""},{"docid":"0fdbfa97cc09c975428cc2b65c2b1758","score":"0.66135967","text":"def init_layers(self):\n self.level_embeds = nn.Parameter(\n torch.Tensor(self.num_feature_levels, self.embed_dims))\n\n if self.as_two_stage:\n self.enc_output = nn.Linear(self.embed_dims, self.embed_dims)\n self.enc_output_norm = nn.LayerNorm(self.embed_dims)\n self.pos_trans = nn.Linear(self.embed_dims * 2,\n self.embed_dims * 2)\n self.pos_trans_norm = nn.LayerNorm(self.embed_dims * 2)\n else:\n self.reference_points = nn.Linear(self.embed_dims, 2)","title":""},{"docid":"c5d19eebccfcabaf34640f233631da15","score":"0.6566221","text":"def _instantiate_layers(self):\n\n with self._enter_variable_scope():\n self._layers = [basic.Linear(self._output_sizes[i],\n name=\"linear_{}\".format(i),\n initializers=self._initializers,\n partitioners=self._partitioners,\n regularizers=self._regularizers,\n use_bias=self.use_bias)\n for i in xrange(self._num_layers)]","title":""},{"docid":"ec91fb4bdb8c1ed18be81896a076a05b","score":"0.65616316","text":"def populate_hidden_layers(network, hidden_layers_parameters):\n number_of_layers = len(hidden_layers_parameters)\n\n for i in range(number_of_layers):\n number_of_connections = network[0+i][0]\n network.append(populate_values(number_of_connections, hidden_layers_parameters[i]))","title":""},{"docid":"c12416592d2eb74bb81b07dd06130752","score":"0.6551426","text":"def init_hidden(self, batch_size):\n return (torch.zeros((1, batch_size, self.hidden_size), device=device), \\\n torch.zeros((1, batch_size, self.hidden_size), device=device))","title":""},{"docid":"fc66054f533f0e3052534a5fd42c2398","score":"0.6538289","text":"def __init__(self, n_in=62500, hidden=4, n_outs=121):\n if n_in is None:\n # allow us to create an empty network we can fill\n # with layers ourselves\n self.layers = []\n return\n\n if isinstance(hidden, int):\n args = hidden * [500] + [n_outs]\n else:\n args = hidden + [n_outs]\n\n args = iter(args)\n prev = next(args)\n self.layers = [Layer(n_in, prev, 'tanh')]\n for i in args:\n self.layers.append(Layer(prev, i, 'tanh'))\n prev = i\n\n # add a softmax layer at the end\n self.layers.append(Layer(i, i, 'softmax'))","title":""},{"docid":"ffbeda11c565a303fb6e15a3cd26221b","score":"0.6536499","text":"def __init__(self, layers):\n self.layers = layers","title":""},{"docid":"59fb8796cc31c5b67c14a1bd4bac8000","score":"0.6526113","text":"def init_hidden(self, bs):\n return torch.zeros(bs, self.hidden_size)","title":""},{"docid":"59fb8796cc31c5b67c14a1bd4bac8000","score":"0.6526113","text":"def init_hidden(self, bs):\n return torch.zeros(bs, self.hidden_size)","title":""},{"docid":"bbbd4aefef43d6dcd40e4c7c0cb1fb7d","score":"0.65155184","text":"def initialize(self):\n # self.gc1.reset_parameters()\n # self.gc2.reset_parameters()\n for layer in self.layers:\n layer.reset_parameters()","title":""},{"docid":"1f623334f20f381ce1a27255e851a86a","score":"0.6416998","text":"def init_layers(self):\n self.in_x = nn.ModuleList()\n self.in_y = nn.ModuleList()\n self.in_z = nn.ModuleList()\n self.in_t = nn.ModuleList()\n lenInput = 1\n noInLayers = 3\n\n self.in_x.append(nn.Linear(lenInput,self.numFeatures))\n for _ in range(noInLayers):\n self.in_x.append(nn.Linear(self.numFeatures, self.numFeatures)) \n\n self.in_y.append(nn.Linear(lenInput,self.numFeatures))\n for _ in range(noInLayers):\n self.in_y.append(nn.Linear(self.numFeatures, self.numFeatures)) \n\n self.in_z.append(nn.Linear(lenInput,self.numFeatures))\n for _ in range(noInLayers):\n self.in_z.append(nn.Linear(self.numFeatures, self.numFeatures)) \n\n self.in_t.append(nn.Linear(1,self.numFeatures))\n for _ in range(noInLayers):\n self.in_t.append(nn.Linear(self.numFeatures, self.numFeatures)) \n\n for m in [self.in_x,self.in_y,self.in_z,self.in_t]:\n if isinstance(m, nn.Linear):\n nn.init.xavier_uniform_(m.weight)\n #nn.init.constant_(m.bias, 0)\n \n self.lin_layers.append(nn.Linear(4 * self.numFeatures, self.numFeatures))\n for i in range(self.numLayers):\n inFeatures = self.numFeatures\n self.lin_layers.append(nn.Linear(inFeatures,self.numFeatures))\n inFeatures = self.numFeatures\n self.lin_layers.append(nn.Linear(inFeatures,1))\n for m in self.lin_layers:\n if isinstance(m, nn.Linear):\n nn.init.xavier_uniform_(m.weight)\n nn.init.constant_(m.bias, 0)","title":""},{"docid":"379edec6afd135c0e5df06877725e795","score":"0.64118886","text":"def init_hidden(self, batch_size, device):\n # only the first block hidden is needed\n hidden_list = self.encoder.init_memory(batch_size=batch_size, device=device)\n\n return hidden_list","title":""},{"docid":"e441c9c78ff7f6ae547bf786cf561101","score":"0.63546467","text":"def _init_layers(self) -> None:\n if self.num_convs > 1:\n rpn_convs = []\n for i in range(self.num_convs):\n if i == 0:\n in_channels = self.in_channels\n else:\n in_channels = self.feat_channels\n # use ``inplace=False`` to avoid error: one of the variables\n # needed for gradient computation has been modified by an\n # inplace operation.\n rpn_convs.append(\n ConvModule(\n in_channels,\n self.feat_channels,\n 3,\n padding=1,\n inplace=False))\n self.rpn_conv = nn.Sequential(*rpn_convs)\n else:\n self.rpn_conv = nn.Conv2d(\n self.in_channels, self.feat_channels, 3, padding=1)\n self.rpn_cls = nn.Conv2d(self.feat_channels,\n self.num_base_priors * self.cls_out_channels,\n 1)\n reg_dim = self.bbox_coder.encode_size\n self.rpn_reg = nn.Conv2d(self.feat_channels,\n self.num_base_priors * reg_dim, 1)","title":""},{"docid":"de6ea4b9e2778a56e915e68656ee42cc","score":"0.6338492","text":"def _init_layers(self):\n self.rpn_conv = nn.Conv2d(\n self.in_channels, self.feat_channels, 3, padding=1)\n self.rpn_cls = nn.Conv2d(self.feat_channels,\n self.num_anchors * self.cls_out_channels, 1)\n self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1)","title":""},{"docid":"de6ea4b9e2778a56e915e68656ee42cc","score":"0.6338492","text":"def _init_layers(self):\n self.rpn_conv = nn.Conv2d(\n self.in_channels, self.feat_channels, 3, padding=1)\n self.rpn_cls = nn.Conv2d(self.feat_channels,\n self.num_anchors * self.cls_out_channels, 1)\n self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1)","title":""},{"docid":"5c3877658fea239f80c4ed10276a3627","score":"0.6314476","text":"def layer_configuration(n_x,number_h,hidden_dimension,n_y):\n layer_dim=[]\n layer_dim.append(n_x)\n for i in range(number_h):\n layer_dim.append(hidden_dimension[i])\n layer_dim.append(n_y)\n return layer_dim","title":""},{"docid":"3b69a47a557e6f58633c965c65f7ced2","score":"0.63052344","text":"def init_memory(self, batch_size, device):\n init_states = []\n # Only iterate odd indexes\n for i in range(2, 3 * self.num_layers, 3):\n init_states.append(self.cell_list[i].init_hidden(batch_size, device))\n\n return init_states","title":""},{"docid":"5047694914a4bffae87716646ef59321","score":"0.6296289","text":"def _init_layers(self):\n self.cls_convs = nn.ModuleList()\n self.energy_convs = None if not self.split_convs else nn.ModuleList()\n self.reg_convs = nn.ModuleList()\n\n # Create the stacked convolutions\n for i in range(self.stacked_convs):\n chn = self.in_channels if i == 0 else self.feat_channels\n\n # Make the different convolution stacks\n self.cls_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n bias=self.norm_cfg is None))\n if self.split_convs:\n self.energy_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n bias=self.norm_cfg is None\n )\n )\n self.reg_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n bias=self.norm_cfg is None))\n\n # Classifier convolution\n self.wfcos_cls = nn.Conv2d(\n self.feat_channels, self.cls_out_channels, 3, padding=1)\n # Bounding box regression convolution\n self.wfcos_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)\n # Energy map convolution\n self.wfcos_energy = nn.Conv2d(self.feat_channels, self.max_energy, 1, padding=0)\n\n # Scaling factor for the different heads\n self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])","title":""},{"docid":"79e5be79eb5d16461af0ecfd8a660f9d","score":"0.62848467","text":"def __init__(self,layers,num_nets,batch_size):\n self.networks = []\n for i in range (num_nets):\n net = Network(layers)\n self.networks.append(net)\n\n self.output = np.zeros((batch_size,num_nets))","title":""},{"docid":"58cfb572f5c11b3424f5b1576a3a57d8","score":"0.62056947","text":"def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3):\n super().__init__()\n in_dims = [input_dim] + [hidden_dim] * (num_layers - 1)\n out_dims = [hidden_dim] * (num_layers - 1) + [output_dim]\n\n layers = []\n for i, (in_dim, out_dim) in enumerate(zip(in_dims, out_dims)):\n layers.append(\n PredictionBlock(in_dim, out_dim, activation=nn.ReLU() if i < num_layers - 1 else nn.Identity())\n )\n\n self.layers = nn.Sequential(*layers)","title":""},{"docid":"127464ade351c7323b866545969316ef","score":"0.61966705","text":"def __init__(\n self,\n parameters: dict,\n num_layers=4,\n ):\n super(MSDNet, self).__init__(parameters)\n\n self.layer_list = add_conv_block(\n self.Conv, self.BatchNorm, in_channels=self.n_channels\n )\n\n current_in_channels = 1\n # Add N layers\n for i in range(num_layers):\n s = i % 10 + 1\n self.layer_list += add_conv_block(\n self.Conv, self.BatchNorm, in_channels=current_in_channels, dilation=s\n )\n current_in_channels += 1\n\n # Add final output block\n self.layer_list += add_conv_block(\n self.Conv,\n self.BatchNorm,\n in_channels=current_in_channels + self.n_channels,\n out_channels=self.n_classes,\n kernel_size=1,\n last=True,\n )\n\n # Add to Module List\n self.layers = nn.ModuleList(self.layer_list)\n\n self.apply(self.weight_init)","title":""},{"docid":"75da62efa578756da2f8fdb9109abf13","score":"0.6193985","text":"def __init_hidden(self):\n return (torch.zeros(1, 1, self.hidden_dim).to(device),\n torch.zeros(1, 1, self.hidden_dim).to(device))","title":""},{"docid":"cc31c93aa181e2266a7dedcee5b4230c","score":"0.6191021","text":"def __init__(self, n_layers: int, in_channels: int, out_channels: int, use_checkpoint: bool = False):\n super().__init__()\n self.layers = nn.ModuleList()\n\n # Build the dense layers\n for i in range(n_layers):\n self.layers.append(DenseLayer(\n in_channels + i * out_channels, out_channels, use_checkpoint=use_checkpoint\n ))","title":""},{"docid":"f4428d7eee740b37fe083d8fc67074e0","score":"0.61865777","text":"def init_layer(layer):\n\n if layer.weight.ndimension() == 4:\n (n_out, n_in, height, width) = layer.weight.size()\n n = n_in * height * width\n elif layer.weight.ndimension() == 3:\n (n_out, n_in, height) = layer.weight.size()\n n = n_in * height\n elif layer.weight.ndimension() == 2:\n (n_out, n) = layer.weight.size()\n\n std = math.sqrt(2. / n)\n scale = std * math.sqrt(3.)\n layer.weight.data.uniform_(-scale, scale)\n\n if layer.bias is not None:\n layer.bias.data.fill_(0.)","title":""},{"docid":"d9a1c536781b1043e43d1c74635e514f","score":"0.61841536","text":"def __init__(self, hidden_size, eps=1e-12):\n super(SketchLayerNorm, self).__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.bias = nn.Parameter(torch.zeros(hidden_size))\n self.variance_epsilon = eps","title":""},{"docid":"548387f4b008d053c3b62dc5ed68a243","score":"0.616783","text":"def _init_layers(self) -> None:\n dconv3x3_config = dict(\n type='DCNv2',\n kernel_size=3,\n use_bias=True,\n deform_groups=2,\n padding=1)\n conv3x3_config = dict(type='Conv', kernel_size=3, padding=1)\n conv1x1_config = dict(type='Conv', kernel_size=1)\n\n self.arch_config = [\n dconv3x3_config, conv3x3_config, dconv3x3_config, conv1x1_config\n ]\n self.cls_convs = nn.ModuleList()\n self.reg_convs = nn.ModuleList()\n for i, op_ in enumerate(self.arch_config):\n op = copy.deepcopy(op_)\n chn = self.in_channels if i == 0 else self.feat_channels\n assert isinstance(op, dict)\n use_bias = op.pop('use_bias', False)\n padding = op.pop('padding', 0)\n kernel_size = op.pop('kernel_size')\n module = ConvModule(\n chn,\n self.feat_channels,\n kernel_size,\n stride=1,\n padding=padding,\n norm_cfg=self.norm_cfg,\n bias=use_bias,\n conv_cfg=op)\n\n self.cls_convs.append(copy.deepcopy(module))\n self.reg_convs.append(copy.deepcopy(module))\n\n self.conv_cls = nn.Conv2d(\n self.feat_channels, self.cls_out_channels, 3, padding=1)\n self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)\n self.conv_centerness = nn.Conv2d(self.feat_channels, 1, 3, padding=1)\n\n self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])","title":""},{"docid":"1e0da101fb33cd0cae91dc2c4859d094","score":"0.61636263","text":"def __init__(self, num_inputs=3, hidden_layers=[3,3], num_outputs=2):\n \n self.num_inputs = num_inputs\n self.hidden_layers = hidden_layers\n self.num_outputs = num_outputs\n \n layers = [num_inputs] + hidden_layers + [num_outputs]\n \n #initiate random weights\n \n weights = []\n for i in range(len(layers)-1):\n w = np.random.rand(layers[i], layers[i+1])\n weights.append(w)\n \n self.weights = weights\n \n \n activations = []\n for i in range(len(layers)):\n a = np.zeros(layers[i])\n activations.append(a)\n self.activations = activations\n \n \n derivatives = []\n for i in range(len(layers)-1):\n d = np.zeros((layers[i], layers[i+1]))\n derivatives.append(d)\n self.derivatives = derivatives","title":""},{"docid":"3e2d72e44201ae8cfafad3a710505b34","score":"0.6161767","text":"def _init_layers(self) -> None:\n self.relu = nn.ReLU(inplace=True)\n self.cls_convs = nn.ModuleList()\n self.reg_convs = nn.ModuleList()\n for i in range(self.stacked_convs):\n chn = self.in_channels if i == 0 else self.feat_channels\n self.cls_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=dict(type='DCN', deform_groups=1)\n if i == 0 and self.use_dcn else self.conv_cfg,\n norm_cfg=self.norm_cfg))\n self.reg_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=dict(type='DCN', deform_groups=1)\n if i == 0 and self.use_dcn else self.conv_cfg,\n norm_cfg=self.norm_cfg))\n self.atss_cls = nn.Conv2d(\n self.feat_channels,\n self.num_base_priors * self.cls_out_channels,\n 3,\n padding=1)\n self.atss_reg = nn.Conv2d(\n self.feat_channels, self.num_base_priors * 4, 3, padding=1)\n self.atss_iou = nn.Conv2d(\n self.feat_channels, self.num_base_priors * 1, 3, padding=1)\n self.scales = nn.ModuleList(\n [Scale(1.0) for _ in self.prior_generator.strides])\n\n # we use the global list in loss\n self.cls_num_pos_samples_per_level = [\n 0. for _ in range(len(self.prior_generator.strides))\n ]\n self.reg_num_pos_samples_per_level = [\n 0. for _ in range(len(self.prior_generator.strides))\n ]","title":""},{"docid":"4c6918cfd76da7dddc1e6bace5ccb6d2","score":"0.61613536","text":"def _init_layers(self, layer_sizes, output_dim, drop_pct):\n n_layers = len(layer_sizes)\n seq_model = nn.Sequential()\n # Create all the blocks for the neural network\n # the sub-unit is:\n for i in range(n_layers - 1):\n # gets the current layer size from\n current_layer_size = layer_sizes[i]\n next_layer_size = layer_sizes[i + 1]\n\n # initialize the linear layer\n linlayer = nn.Linear(current_layer_size, next_layer_size)\n kaiming_normal(linlayer.weight.data)\n\n # One Subunit:\n # -- Linear\n # -- Relu\n # -- BatchNorm\n # -- Drop_out\n seq_model.add_module(\"lin_%d\" % i, linlayer)\n seq_model.add_module(\"relu_%d\" % i, nn.ReLU())\n seq_model.add_module(\"batch_norm_%d\" % i,\n nn.BatchNorm1d(next_layer_size))\n seq_model.add_module(\"drop_out_%d\" % i, nn.Dropout(drop_pct))\n\n out_lin = nn.Linear(layer_sizes[-1], output_dim)\n kaiming_normal(out_lin.weight.data)\n\n seq_model.add_module(\"output\", out_lin)\n\n # initialize the weights of linear layers\n for ly in seq_model:\n if type(ly) == nn.Linear:\n kaiming_normal(ly.weight.data)\n\n return seq_model","title":""},{"docid":"6bf292f10998328057e05d853e0fc226","score":"0.61368626","text":"def _init_layers(self):\n\n self._init_cls_convs()\n self._init_reg_convs()\n self._init_predictor()\n\n self.conv_centerness_prev = self._init_branch(\n conv_channels=self.centerness_branch,\n conv_strides=(1,) * len(self.centerness_branch),\n )\n self.conv_centerness = nn.Conv2d(self.centerness_branch[-1], 1, 1)\n\n \"\"\"\n separate scale for every specific task in each feature-level\n default scales for offset, depth, size, (bbox_2d), (keypoints)\n \"\"\"\n\n scale_related_attributes = [\n \"offset\",\n \"depth\",\n \"bbox2d\",\n \"corners\",\n ]\n\n self.scales = nn.ModuleList()\n for _ in self.strides:\n level_scales = nn.ModuleDict()\n for attr in scale_related_attributes:\n if attr in self.reg_keys:\n level_scales[attr] = Scale(1.0)\n\n self.scales.append(level_scales)","title":""},{"docid":"a48a401190d0ea7e6720ea2b059c1ea5","score":"0.61345786","text":"def __init__(self, hidden_dim, **kwargs):\n self.supports_masking = True\n self.hidden_dim = hidden_dim\n super().__init__(**kwargs)","title":""},{"docid":"5f67029c884d749fc490dff26b45ff55","score":"0.6131776","text":"def initialise(self, weight_init=glorot_normal):\n self.add(NadeInputLayer(self.hidden_dim, self.input_dim, weight_init=weight_init))\n self.add(Activation(self.nonlinearity))\n self.add(NadeOutputBinaryLayer(self.input_dim, weight_init=weight_init))\n self.add(Activation('sigmoid'))","title":""},{"docid":"8832694896fc1770232c0ae8d669314a","score":"0.6123773","text":"def _init_layers(self):\n super(DeiTClsHead, self)._init_layers()\n if self.hidden_dim is None:\n head_dist = nn.Linear(self.in_channels, self.num_classes)\n else:\n head_dist = nn.Linear(self.hidden_dim, self.num_classes)\n self.layers.add_module('head_dist', head_dist)","title":""},{"docid":"a9219f1aade8f60920a20f30e71787fd","score":"0.61223406","text":"def __init__(self, num_outputs):\r\n super(MyDenseLayer, self).__init__()\r\n self.num_outputs = num_outputs","title":""},{"docid":"bdbe16c9d5915dc796df9022f9d769fc","score":"0.6121616","text":"def __init__(self, n_input, n_output, hidden_layer_size, reg):\n self.reg = reg\n self.fcl1 = FullyConnectedLayer(n_input, hidden_layer_size)\n self.act = ReLULayer()\n self.fcl2 = FullyConnectedLayer(hidden_layer_size, n_output)","title":""},{"docid":"32a1d531acc5773899aeec09bf24648b","score":"0.6116278","text":"def num_layers(self):\n pass","title":""},{"docid":"9eab81657b27497b367d2022fcbdc3e8","score":"0.611398","text":"def _init_none(self):\n for lname in self.layer_manager.background_layer_names:\n self.remove_layer(lname)","title":""},{"docid":"70d2ab596c8a307e60d2dba95c2384a4","score":"0.61096615","text":"def __init__(self, *layers):\n super().__init__()\n self.layers = layers","title":""},{"docid":"be6fa8e80abf1b0f170843abdc6b220e","score":"0.6099555","text":"def init_hidden(self, input_shape):\n return torch.zeros(1,input_shape[0],\n self.hidden_size,\n device=self.device)","title":""},{"docid":"b5e934c5e772b60e8385ee66269082c4","score":"0.6098212","text":"def initialize_hidden_state(self) -> tf.Tensor:\n return tf.zeros(shape=(self.batch, self.units))","title":""},{"docid":"2097dc08478c4c781fc74a3495608f60","score":"0.6085364","text":"def __init__(self):\n self.output_perceptron_list = []\n self.hidden_perceptron_list = []\n # self.hidden_layer_output = []\n # self.output_layer_output = []\n\n for i in range(26):\n self.output_perceptron_list.append(OutputPerceptron(i))\n\n for j in range(NUM_HIDDEN_UNITS):\n self.hidden_perceptron_list.append(HiddenPerceptron())","title":""},{"docid":"86a89f8d3ce8496d63764397152f01ad","score":"0.60804224","text":"def initialize_layers(X, y, n_nodes):\n N = X.shape[0] # number of examples\n output_shape = np.unique(y).shape[0]\n layers = dict()\n layers['input'] = X.reshape(N, -1) \n for i in range( len(n_nodes) ):\n layers['h{}'.format(i+1)] = np.zeros( (N, n_nodes[i]) )\n layers['output'] = np.zeros( (N, output_shape) )\n return layers","title":""},{"docid":"7b4c10ec6ce59e2ee4fad457812e5b16","score":"0.60781825","text":"def __init__(self, n_inputs=784, hidden_layers=[64], n_outputs=10):\n self.n_inputs = n_inputs\n self.hidden_layers = hidden_layers\n self.n_outputs = n_outputs\n\n # Represent the layers\n layers = [n_inputs] + hidden_layers + [n_outputs]\n\n # Assign random weights, multiplying by 0.1 to keep them reasonably small\n weights = []\n for i in range(len(layers) - 1):\n weight = np.random.rand(layers[i], layers[i + 1]) * 0.1\n weights.append(weight)\n self.weights = weights\n\n # Saving the derivatives for each layer\n derivs = []\n for i in range(len(layers) - 1):\n deriv = np.zeros((layers[i], layers[i + 1]))\n derivs.append(deriv)\n self.derivatives = derivs\n\n # Saving the activations for each layer\n activations = []\n for i in range(len(layers)):\n activation = np.zeros(layers[i])\n activations.append(activation)\n self.activations = activations","title":""},{"docid":"558de1daf9f0462c98eb181d0d882d6a","score":"0.6064517","text":"def init_hidden(self, batch_size, device):\n hidden = Variable(torch.zeros(batch_size, self.hidden_dim, self.height, self.width).to(device))\n\n return hidden","title":""},{"docid":"07c5dd6d9f643f1b8d9b40b4401f7567","score":"0.60543394","text":"def init_weights(self):\n for child in self.children():\n if child.bias is None:\n init_layer(child)\n else:\n init_layer(child, True)","title":""},{"docid":"53e4101d32d1c0f9a4c3cf34b18d7a5a","score":"0.60477746","text":"def _init_layers(self):\n self.relu = nn.ReLU(inplace=True)\n self.cls_convs = nn.ModuleList()\n self.reg_convs = nn.ModuleList()\n for i in range(self.stacked_convs):\n chn = self.in_channels if i == 0 else self.feat_channels\n self.cls_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n bias=self.conv_bias))\n self.reg_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n bias=self.conv_bias))\n pts_out_dim = 2 * self.num_points\n self.reppoints_cls_conv = DeformConv2d(self.feat_channels,\n self.point_feat_channels,\n self.dcn_kernel, 1,\n self.dcn_pad)\n self.reppoints_cls_out = nn.Conv2d(self.point_feat_channels,\n self.cls_out_channels, 1, 1, 0)\n self.reppoints_pts_init_conv = nn.Conv2d(self.feat_channels,\n self.point_feat_channels, 3,\n 1, 1)\n self.reppoints_pts_init_out = nn.Conv2d(self.point_feat_channels,\n pts_out_dim, 1, 1, 0)\n self.reppoints_pts_refine_conv = DeformConv2d(self.feat_channels,\n self.point_feat_channels,\n self.dcn_kernel, 1,\n self.dcn_pad)\n self.reppoints_pts_refine_out = nn.Conv2d(self.point_feat_channels,\n pts_out_dim, 1, 1, 0)","title":""},{"docid":"91a13c0fd7d4275e26caa22e88c41cb3","score":"0.60308754","text":"def __init__(self, gamma, layer_sizes, num_hidden, epsilon=None, max_iters=None):\n self.gamma = gamma\n self.layer_sizes = layer_sizes\n self.num_hidden = num_hidden\n self.epsilon = epsilon\n self.max_iters = max_iters","title":""},{"docid":"64f3a82a9a0ed460328048625a7c51b4","score":"0.6019582","text":"def __init__(self, hidden_layers, alpha=0.001, epoch=8000, activation=sigmoid, optimiser='gradient', error='SSE', random_state=0):\r\n\t\tself._hidden_layers = hidden_layers\r\n\t\tself._alpha = alpha\r\n\t\tself._epoch = epoch\r\n\t\tself._activation = activation\r\n\t\tself._seed = random_state","title":""},{"docid":"49e91e016580fb5473146839d7332abc","score":"0.6010819","text":"def initialization(self):\n for layer in self.conv:\n if not isinstance(layer, nn.Conv2d):\n continue\n try:\n nn.init.xavier_uniform_(layer.weight.data)\n nn.init.constant_(layer.bias.data, 0.0)\n except:\n pass\n\n for layer in self.conv:\n if not isinstance(layer, nn.Linear):\n continue\n try:\n nn.init.xavier_uniform_(layer.weight.data)\n nn.init.constant_(layer.bias.data, 0.0)\n except:\n pass","title":""},{"docid":"a753c2e7c6edbc0f38409d790335396b","score":"0.60003537","text":"def _init_layers(self):\n assert isinstance(self.feat_channels, list) and len(\n self.feat_channels) == self.head_num\n assert isinstance(self.in_channels, list) and len(\n self.in_channels) == self.head_num\n assert isinstance(self.head_type, list)\n\n if len(self.head_type) == 1:\n self.head_type = self.head_type*self.head_num\n assert len(self.head_type) == self.head_num\n branches = 'cls_convs, reg_convs, shared_convs, hem_tl, hem_br, reppoints_cls_conv, reppoints_cls_out, reppoints_pts_init_conv, reppoints_pts_init_out,reppoints_pts_refine_conv, reppoints_pts_refine_out, reppoints_hem_tl_score_out, reppoints_hem_br_score_out, reppoints_hem_tl_offset_out, reppoints_hem_br_offset_out, reppoints_sem_out, reppoints_sem_embedding'.split(\n ',')\n for branch in branches:\n setattr(self, branch.strip(), nn.ModuleList())\n\n for i in range(self.head_num):\n subnets = self._init_head(\n self.in_channels[i], self.feat_channels[i], self.head_type[i])\n assert len(subnets) == len(branches)\n for branch, subnet in zip(branches, subnets):\n getattr(self, branch.strip()).append(subnet)\n # fuse feature from second branch to the first branch\n if self.head_fuse:\n in_channels = [self.feat_channels[idx] for idx in self.head_pairs]\n out_channels = in_channels\n self.head_fuse_config.update(dict(in_channels=in_channels,out_channels=out_channels))\n self.fuse_loc = build_fuse_layer(self.head_fuse_config)\n self.fuse_cls = build_fuse_layer(self.head_fuse_config)","title":""},{"docid":"6b4cd2cb163430b1c39cce0481b7b77e","score":"0.5996683","text":"def __init__(self, hidden_size, eps=1e-12):\n super(LayerNorm, self).__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.bias = nn.Parameter(torch.zeros(hidden_size))\n self.variance_epsilon = eps","title":""},{"docid":"6b4cd2cb163430b1c39cce0481b7b77e","score":"0.5996683","text":"def __init__(self, hidden_size, eps=1e-12):\n super(LayerNorm, self).__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.bias = nn.Parameter(torch.zeros(hidden_size))\n self.variance_epsilon = eps","title":""},{"docid":"6b4cd2cb163430b1c39cce0481b7b77e","score":"0.5996683","text":"def __init__(self, hidden_size, eps=1e-12):\n super(LayerNorm, self).__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.bias = nn.Parameter(torch.zeros(hidden_size))\n self.variance_epsilon = eps","title":""},{"docid":"6b4cd2cb163430b1c39cce0481b7b77e","score":"0.5996683","text":"def __init__(self, hidden_size, eps=1e-12):\n super(LayerNorm, self).__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.bias = nn.Parameter(torch.zeros(hidden_size))\n self.variance_epsilon = eps","title":""},{"docid":"4f4cb76ae945bf7a334ac071ee7d2de6","score":"0.5996277","text":"def __init__(self,):\n self.layer_list = []\n self.weights = []","title":""},{"docid":"0bcdf153b5c0c5d2c2efe9eb5b9bdf8f","score":"0.5968303","text":"def __init__(self, dims, dropout=False):\n super().__init__()\n input_size = dims[0]\n h_sizes = dims[1:]\n\n mods = [nn.Linear(input_size, h_sizes[0])]\n for i in range(len(h_sizes) - 1):\n mods.append(nn.ReLU())\n if dropout:\n mods.append(nn.Dropout(p=dropout))\n mods.append(nn.Linear(h_sizes[i], h_sizes[i + 1]))\n\n self.layers = nn.Sequential(*mods)","title":""},{"docid":"6cfac6662587b9c35834fc21b401a5da","score":"0.5963964","text":"def _manager_show_all_layers(self):\n self.layer_manager.show_all_layers()\n self.canvas.setLayers(self.layer_manager.shown_layers)","title":""},{"docid":"1268c7a3e4fbfb635d5b87add8c80a6e","score":"0.59609795","text":"def show_all_layers(self):\n self.shown_layers_index = np.arange(len(self.names_in_order)).tolist()","title":""},{"docid":"6f9d5de223ee84d8e8ab242cc6f27f3c","score":"0.5950844","text":"def test_setget_hlayer(self):\n for x in range(_NUM):\n M = TestMutableNeuralNetwork.__inititem()\n #to ensure that the count of inputs does not changes:\n ninps = M.ninps #initial count of inputs\n H = M.hidden_layers\n shape = M.shape\n start = randrange(len(M) - 1)\n inset = [Primitives.initlayer(\n randrange(1, _SIZE), shape[start])]\n inset.append(Primitives.initlayer(randrange(1, _SIZE), \n len(inset[0])))\n H[start:] = inset\n for o in M.output_layer:\n self.assertEqual(len(o), len(inset[1]))\n #deletion test:\n del H[:]\n for o in M.output_layer:\n self.assertEqual(len(o), ninps)","title":""},{"docid":"05325f44528860e87f02faea3e72d06e","score":"0.59479916","text":"def reset_n_prop_layers(self, n_prop_layers):\n self._n_prop_layers = n_prop_layers","title":""},{"docid":"c05bcdb140ce7398843c976015e001f3","score":"0.59377265","text":"def __init__(self, num_hidden_layers=2, hidden_layer_size=[10, 10], activation=\"relu\",\n input_dim=100, output_dim=10):\n\n super(MLP, self).__init__()\n self._num_hidden_layers = num_hidden_layers\n self._hidden_layer_size = hidden_layer_size\n self._activation = get_activation(activation)\n self._input_dim = input_dim\n self._output_dim = output_dim\n\n self._fc_layers = []\n self._fc_layers.append(nn.Linear(input_dim, hidden_layer_size[0]))\n nn.init.normal_(self._fc_layers[-1].weight, mean=0, std=0.01)\n nn.init.constant_(self._fc_layers[-1].bias, 0)\n for i in range(1, num_hidden_layers):\n self._fc_layers.append(nn.Linear(hidden_layer_size[i-1], hidden_layer_size[i]))\n nn.init.normal_(self._fc_layers[-1].weight, mean=0, std=0.01)\n nn.init.constant_(self._fc_layers[-1].bias, 0)\n self._fc_layers.append(nn.Linear(hidden_layer_size[-1], output_dim))\n nn.init.normal_(self._fc_layers[-1].weight, mean=0, std=0.01)\n nn.init.constant_(self._fc_layers[-1].bias, 0)\n\n self._module_list = nn.ModuleList(self._fc_layers)","title":""},{"docid":"425950230462580bdabd3c72bc2de9c9","score":"0.5936416","text":"def __init__(self, num_visible, num_hidden, learning_rate=0.05):\n self.num_visible = num_visible\n self.num_hidden = num_hidden\n self.learning_rate = learning_rate\n\n # Set the network's weight matrix to all zeros, initially\n self.weights = np.zeros((self.num_hidden, self.num_visible)) \n \n # Initialize the network's visible (binary) states\n self.visible = np.zeros((self.num_visible, 1))\n\n # Initialize the network's hidden (binary) states\n self.hidden = np.zeros((self.num_hidden, 1))\n\n # Bias weights for the visible layer\n self.visible_bias = np.random.randn(self.num_visible)\n\n # Bias weights for the hidden layer\n self.hidden_bias = np.random.randn(self.num_hidden, 1)\n print(self.hidden_bias)\n # Vectorize the network's activation function\n self.vectorized_activation = np.vectorize(self.activation_fn)\n\n # Vectorize the boolean activation function\n self.vec_bool_activation = np.vectorize(self.bool_activation)","title":""},{"docid":"996bd0659a1379155c2afafa26048a0b","score":"0.5932562","text":"def __init__(self, sizeOfLayers, activationFunctions):\n\n\n\t\t# L\n\t\tself.nnDepth = len(sizeOfLayers)\n\t\tself.outputIndex = self.nnDepth - 1\n\n\t\tself.sizeOfLayers = sizeOfLayers","title":""},{"docid":"56257d4a3990109e9ef11b5f258a1b23","score":"0.592432","text":"def __init__(self, Neurons_layer=[512,256,128]):\n nn.Module.__init__(self)\n self.fc_layers = nn.ModuleList(nn.Linear(in_features=n_in, out_features=n_out) for n_in, n_out in zip(Neurons_layer[:-1], Neurons_layer[1:]))\n self.relu = nn.ReLU()","title":""},{"docid":"467e645c0572d3f1557970d110594a76","score":"0.59138006","text":"def __init__(self, layers):\n self.layers = layers\n \n # When we build layer, we need to know input and output dimensions.\n self.m = self.layers[0].m\n self.n = self.layers[0].n\n for layer in self.layers[1:]:\n self.n = getattr(layer, 'n', self.n)\n \n # This is the static graph representing our model\n self.graph = tf.Graph()\n \n # This is the runtime instance of our model\n self.sess = tf.Session(graph=self.graph)","title":""},{"docid":"5406fe8fcb768430e8f4b3060a8ce6ab","score":"0.5908669","text":"def initialize_parameters(self):\n\n\t\tnum_layer = len(self.layers)\n\t\tfor i in range(1, num_layer):\n\t\t\tn_c = 1./np.sqrt(self.layers[i])\n\t\t\tself.parameters[\"W\" + str(i)] = np.ones((self.layers[i],\n\t\t\t\tself.layers[i-1])) * np.random.uniform(-n_c, n_c,\n\t\t\t\t\t(self.layers[i],self.layers[i-1]))\n\t\t\tself.parameters[\"b\" + str(i)] = np.zeros((self.layers[i],1))","title":""},{"docid":"0073609fabb1d3c27fd83d5723c372f4","score":"0.58704156","text":"def _build_hidden_layers_mean(self) -> Tuple[nn.ModuleList, int]:\n raise NotImplementedError","title":""},{"docid":"0073609fabb1d3c27fd83d5723c372f4","score":"0.58704156","text":"def _build_hidden_layers_mean(self) -> Tuple[nn.ModuleList, int]:\n raise NotImplementedError","title":""},{"docid":"fe622e8059fbb421a66bfcf340c95fba","score":"0.5867704","text":"def __init__(self, sizes, activation = Sigmoid(), normal_initialization = False):\n\n BaseLayer.__init__(self, sizes, activation)\n\n if normal_initialization:\n self.weights = [np.random.randn(j, i)\n for i, j in zip(sizes[:-1], sizes[1:])]\n else:\n self.weights = [np.random.randn(j, i) / np.sqrt(i)\n for i, j in zip(sizes[:-1], sizes[1:])]\n self.biases = [np.random.randn(j, 1) for j in sizes[1:]]\n\n self.grad_w = [np.zeros(w.shape) for w in self.weights]\n self.grad_b = [np.zeros(b.shape) for b in self.biases]","title":""},{"docid":"7331403d3fcd69ed8de9ec28de77bcb6","score":"0.5863243","text":"def detach_hidden(self, batch_size):\n _, hidden_batch_size, _ = self.hidden.size()\n if hidden_batch_size != batch_size:\n self.init_hidden(batch_size)\n else:\n detached_hidden = self.hidden.detach()\n detached_hidden.zero_()\n self.hidden = detached_hidden","title":""},{"docid":"551cbbaf7a16ff10a5c469f784044f65","score":"0.5862765","text":"def n_layers(self):\n return self._n_layers","title":""},{"docid":"a01ebf3e15b351d542fa1ad111b476b9","score":"0.5862482","text":"def __init__(self, n_input, n_hidden, n_output, name=\"test\"):\n self.nam = name\n\n super(MLPNet, self).__init__()\n self.n_layers = copy.copy(n_hidden)\n self.n_layers.insert(0, n_input)\n self.n_layers.append(n_output)\n\n self.layers = nn.ModuleList()\n\n for i in range(len(self.n_layers) - 1):\n self.layers.append(nn.Linear(self.n_layers[i], self.n_layers[i+1]))","title":""},{"docid":"0326351db0abace5c481110596770a74","score":"0.5845479","text":"def init_submodules(self):\n \n if self.cell_type == \"VanillaRNN\":\n net = [nn.RNNCell(input_size=self.input_size, hidden_size=self.hidden_state_size)]\n net.extend([nn.RNNCell(input_size=self.hidden_state_size, hidden_size=self.hidden_state_size)\n for _ in range(self.num_hidden_layers - 1)])\n elif self.cell_type == \"LSTM\":\n net = [nn.LSTMCell(input_size=self.input_size, hidden_size=self.hidden_state_size)]\n net.extend([nn.LSTMCell(input_size=self.hidden_state_size, hidden_size=self.hidden_state_size)\n for _ in range(self.num_hidden_layers - 1)])\n \n else:\n net = [nn.GRUCell(input_size=self.input_size, hidden_size=self.hidden_state_size)]\n net.extend([nn.GRUCell(input_size=self.hidden_state_size, hidden_size=self.hidden_state_size)\n for _ in range(self.num_hidden_layers - 1)])\n \n self.rnn_forward = nn.Sequential(*net)\n self.rnn_backward = nn.Sequential(*net.copy())\n\n self.project_to_input_dim_forward = nn.Linear(self.hidden_state_size, self.input_size)\n self.project_to_input_dim_backward = nn.Linear(self.hidden_state_size, self.input_size)\n\n if self.attn_type == \"Additive\":\n attn = [AdditiveAttention(hidden_size=self.seq_len, encoder=True) for _ in range(self.num_hidden_layers)]\n elif self.attn_type == \"Scaled-Dot\":\n attn = [ScaledDotAttention(hidden_size=self.seq_len, hidden_size2=self.input_size, encoder=True)]\n attn.extend([ScaledDotAttention(hidden_size=self.seq_len, hidden_size2 = self.hidden_state_size, encoder=True) for _ in range(self.num_hidden_layers - 1)])\n elif self.attn_type == \"Linear\":\n attn = [LinearAttention(hidden_size=self.seq_len, encoder=True) for _ in range(self.num_hidden_layers)]\n else:\n attn = [DaRnnAttention(hidden_size=self.seq_len) for _ in range(self.num_hidden_layers)]\n\n self.attn_forward = nn.Sequential(*attn)\n self.attn_backward = nn.Sequential(*attn.copy()) \n \n self.forward_attn_weights = None\n self.backward_attn_weights = None","title":""},{"docid":"3137d27b6f58aa19b805532a796dbc2c","score":"0.5842405","text":"def __init__(self, *args, **kw):\n self.layers = [dict(), dict()]","title":""},{"docid":"c207f07aa22de8f7c7a3c1dd16bb20a7","score":"0.58417034","text":"def __init__(self, **kwargs):\n super().__init__()\n self.hidden_size = kwargs[\"hidden_size\"]\n self.num_layers = kwargs[\"num_layers\"]\n\n self.gru = nn.GRU(input_size=256, hidden_size=self.hidden_size, num_layers=self.num_layers)\n self.output_layer = nn.Linear(self.hidden_size, 4)\n self.h0 = nn.Parameter(torch.randn(self.num_layers, 1, self.hidden_size))","title":""},{"docid":"a905c92a5e209439724882d0d9d4d523","score":"0.5838338","text":"def __init__(self, hidden_size, hidden_size2=0, encoder=False):\n super().__init__()\n\n self.encoder = encoder\n self.hidden_size = hidden_size\n if self.encoder:\n self.hidden_size2 = hidden_size2\n else:\n self.hidden_size2 = hidden_size\n self.init_submodules()","title":""},{"docid":"7dcf9bddbdebd765558bdca16a170a31","score":"0.5836774","text":"def __init__(self, n_inputs, n_hidden, n_classes):\n super(MLP, self).__init__() \n self.layer=nn.Sequential(nn.Linear(n_inputs,n_hidden[0]))\n for i in range(1,len(n_hidden)):\n self.layer.add_module('relu',nn.ReLU(True))\n self.layer.add_module('linear',nn.Linear(n_hidden[i-1],n_hidden[i]))\n self.layer.add_module('softmax',nn.Softmax())","title":""},{"docid":"c3ad1448551a5d3470d16cd2b2a087c6","score":"0.58345795","text":"def layers(self):\n\n if self.numlayerdims == 0:\n yield 0\n else:\n for ld in range(self.numlayerdims):\n cc = self.layercurrcat[ld]\n for c in range(self.layercats[ld]):\n yield cc\n cc = (cc+1) % self.layercats[ld]\n self.ptmgr.LayerDimension(ld).SetCurrentCategory(cc)","title":""},{"docid":"dc22fa857ea7e0df330a30e4e9eaa15e","score":"0.5828875","text":"def init_weights(self):\n init_layer(self.conv1)\n init_bn(self.bn1)","title":""},{"docid":"0a046dfee2a4a8a9b460e2cfdc9fe4d9","score":"0.5826577","text":"def __init__(self, nx, layers):\n\n if not isinstance(nx, int):\n raise TypeError('nx must be an integer')\n\n if nx < 1:\n raise ValueError('nx must be a positive integer')\n\n if not isinstance(layers, list) or len(layers) == 0:\n raise TypeError('layers must be a list of positive integers')\n\n self.__L = len(layers)\n self.__cache = {}\n self.__weights = {}\n\n for layer in range(len(layers)):\n if not isinstance(layers[layer], int) or layers[layer] <= 0:\n raise TypeError('layers must be a list of positive integers')\n\n if layer == 0:\n w = np.random.randn(layers[layer], nx) * np.sqrt(2 / nx)\n\n else:\n w = np.random.randn(layers[layer], layers[layer-1])\n w = w * np.sqrt(2 / layers[layer-1])\n\n self.__weights[\"b\"+str(layer+1)] = np.zeros((layers[layer], 1))\n self.__weights[\"W\"+str(layer+1)] = w","title":""},{"docid":"52e437d2609639b1c0f87ee06b10cf04","score":"0.5826106","text":"def __init__(self, layerSize):#default 3 layer, 1 percep per layer\r\n self.layerSize = layerSize #Holds number of inputs and percepetrons in each layer\r\n self.outputLayer = []\r\n self.numHiddenLayers = len(layerSize)-2\r\n self.hiddenLayers = [[] for x in range(self.numHiddenLayers)]\r\n self.numLayers = self.numHiddenLayers+1\r\n \r\n #build hidden layer(s) \r\n for h in range(self.numHiddenLayers):\r\n for p in range(layerSize[h+1]):\r\n percep = Perceptron(layerSize[h]) # num of perceps feeding into this one\r\n self.hiddenLayers[h].append(percep)\r\n \r\n #build output layer\r\n for i in range(layerSize[-1]):\r\n percep = Perceptron(layerSize[-2]) # num of perceps feeding into this one\r\n self.outputLayer.append(percep)\r\n \r\n #build layers list that holds all layers in order - use this structure\r\n # to implement back propagation\r\n self.layers = [self.hiddenLayers[h] for h in xrange(self.numHiddenLayers)] + [self.outputLayer]","title":""},{"docid":"52e437d2609639b1c0f87ee06b10cf04","score":"0.5826106","text":"def __init__(self, layerSize):#default 3 layer, 1 percep per layer\r\n self.layerSize = layerSize #Holds number of inputs and percepetrons in each layer\r\n self.outputLayer = []\r\n self.numHiddenLayers = len(layerSize)-2\r\n self.hiddenLayers = [[] for x in range(self.numHiddenLayers)]\r\n self.numLayers = self.numHiddenLayers+1\r\n \r\n #build hidden layer(s) \r\n for h in range(self.numHiddenLayers):\r\n for p in range(layerSize[h+1]):\r\n percep = Perceptron(layerSize[h]) # num of perceps feeding into this one\r\n self.hiddenLayers[h].append(percep)\r\n \r\n #build output layer\r\n for i in range(layerSize[-1]):\r\n percep = Perceptron(layerSize[-2]) # num of perceps feeding into this one\r\n self.outputLayer.append(percep)\r\n \r\n #build layers list that holds all layers in order - use this structure\r\n # to implement back propagation\r\n self.layers = [self.hiddenLayers[h] for h in xrange(self.numHiddenLayers)] + [self.outputLayer]","title":""},{"docid":"8d3e8c44b973319aa762d952bcd85a95","score":"0.5823384","text":"def __init__(self, init_hiddens = None, weights=None, bias=None, weight_type=None):\n self.valid_layer_types = dict()\n self.valid_layer_types['visible_hidden'] = ['rbm_gaussian_bernoulli', 'rbm_bernoulli_bernoulli']\n self.valid_layer_types['hidden_hidden'] = ['rbm_bernoulli_bernoulli']\n self.valid_layer_types['hidden_output'] = ['logistic']\n self.bias_keys = ['visible', 'hidden_forward', 'hidden_backward', 'output']\n self.weights_keys = ['visible_hidden', 'hidden_hidden_forward', 'hidden_hidden_backward', 'hidden_output_forward', 'hidden_output_backward']\n self.init_hiddens_keys = ['forward', 'backward']\n \n if init_hiddens is None:\n self.init_hiddens = dict()\n else:\n self.init_hiddens = copy.deepcopy(init_hiddens)\n if weights is None:\n self.weights = dict()\n else:\n self.weights = copy.deepcopy(weights)\n if bias is None:\n self.bias = dict()\n else:\n self.bias = copy.deepcopy(bias)\n if weight_type is None:\n self.weight_type = dict()\n else:\n self.weight_type = copy.deepcopy(weight_type)","title":""},{"docid":"9f4c656cd319f47a7b24a1635a11285d","score":"0.58225554","text":"def _setup_layers(self):\n num_layers = 5\n\n # Input grayscale images with height 32px and width 128px\n self.input_layer = Input(shape=(self.image_height, self.image_width, 1))\n\n model = self.input_layer\n # Keras functional API\n model = self._create_conv_net()(model)\n\n model = Dropout(rate=0.5)(model)\n\n for idx in range(num_layers):\n model = Bidirectional(LSTM(self.lstm_units, return_sequences=True, dropout=0.5))(model)\n\n model = Dropout(rate=0.5)(model)\n\n self.output_layer = TimeDistributed(Dense(units=self._label_count, activation='softmax'))(model)\n\n self.weights = Model(self.input_layer, self.output_layer)","title":""}],"string":"[\n {\n \"docid\": \"4fa5825f794dd872fe20c41cb694ed3f\",\n \"score\": \"0.7226314\",\n \"text\": \"def init_hidden(self):\\n return torch.zeros(self.num_layers, self.batch_size, self.hidden_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68b498dd3486faea72dc3a9779706daf\",\n \"score\": \"0.7152341\",\n \"text\": \"def init_hidden(self):\\r\\n #print('layer size =\\\\t', self.layer_amnt)\\r\\n #print('bat_size =\\\\t', self.batch_sz)\\r\\n #print('hidden size =\\\\t',self.h_size)\\r\\n return (torch.zeros(self.layer_amnt,self.batch_sz,self.h_size),\\r\\n torch.zeros(self.layer_amnt,self.batch_sz,self.h_size))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91a111127e22b2df8b213c0b2217069e\",\n \"score\": \"0.70427036\",\n \"text\": \"def __init__(self, hidden_layer_size, i, o, reg):\\n self.reg = reg\\n self.layers = [] \\n for num_layer in range(1):\\n self.layers.append(FullyConnectedLayer(i,hidden_layer_size))\\n self.layers.append(ReLULayer())\\n \\n self.layers.append(FullyConnectedLayer(hidden_layer_size,o))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02d43cf7890717be5bd92838fa1c0295\",\n \"score\": \"0.7031576\",\n \"text\": \"def hidden_layers(cls):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2df617f0d3ff7a73e5272c8e9f256aa\",\n \"score\": \"0.7031383\",\n \"text\": \"def create_hidden_layers(self, input_layer=None):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"549239f862386503cfced0f36aff9ab1\",\n \"score\": \"0.69802195\",\n \"text\": \"def _init_layers(self) -> None:\\n self.mask_convs = nn.ModuleList()\\n self.cls_convs = nn.ModuleList()\\n for i in range(self.stacked_convs):\\n chn = self.in_channels + 2 if i == 0 else self.feat_channels\\n self.mask_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n norm_cfg=self.norm_cfg))\\n chn = self.in_channels if i == 0 else self.feat_channels\\n self.cls_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n norm_cfg=self.norm_cfg))\\n self.conv_mask_list = nn.ModuleList()\\n for num_grid in self.num_grids:\\n self.conv_mask_list.append(\\n nn.Conv2d(self.feat_channels, num_grid**2, 1))\\n\\n self.conv_cls = nn.Conv2d(\\n self.feat_channels, self.cls_out_channels, 3, padding=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e42efd604d74fa7683640f57278e6a6\",\n \"score\": \"0.694852\",\n \"text\": \"def init_hidden(self, batch_size):\\n if torch.cuda.is_available():\\n self.hidden = torch.zeros(\\n self.num_layers * 2, batch_size, self.hidden_size).cuda()\\n else:\\n self.hidden = torch.zeros(\\n self.num_layers * 2, batch_size, self.hidden_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa5bf31fe3cecc60d58ed7ea86e60adf\",\n \"score\": \"0.6827073\",\n \"text\": \"def __init__(self, sizes):\\n super(CGNN_block, self).__init__()\\n layers = []\\n\\n for i, j in zip(sizes[:-2], sizes[1:-1]):\\n layers.append(th.nn.Linear(i, j))\\n layers.append(th.nn.ReLU())\\n\\n layers.append(th.nn.Linear(sizes[-2], sizes[-1]))\\n self.layers = th.nn.Sequential(*layers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5563fc98704299a20e9ce9c396b54d08\",\n \"score\": \"0.6818739\",\n \"text\": \"def _init_layers(self):\\n self.relu = nn.ReLU(inplace=True)\\n self.cls_convs = nn.ModuleList()\\n self.reg_convs = nn.ModuleList()\\n for i in range(self.stacked_convs):\\n chn = self.in_channels if i == 0 else self.feat_channels\\n self.cls_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg))\\n self.reg_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg))\\n self.retina_cls = nn.Conv2d(\\n self.feat_channels,\\n self.num_anchors * self.cls_out_channels,\\n 3,\\n padding=1)\\n self.retina_reg = nn.Conv2d(\\n self.feat_channels, self.num_anchors * 5, 3, padding=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14a78e266857e29a06a948b621a58eb2\",\n \"score\": \"0.68021\",\n \"text\": \"def _init_layers(self):\\n self._init_cls_convs()\\n self._init_reg_convs()\\n self._init_predictor()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b246671cbf38b739800b4117028c010b\",\n \"score\": \"0.67531097\",\n \"text\": \"def hide_all_layers(self):\\n self.shown_layers_index = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a241f139219d666935d0c10e904f591\",\n \"score\": \"0.67359835\",\n \"text\": \"def _build_hidden_layers_var(self) -> Tuple[nn.ModuleList, int]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e09cfa6ae54cc3fa2029907403ea470\",\n \"score\": \"0.6673928\",\n \"text\": \"def init_memory(self, batch_size):\\n init_states = []\\n # Only iterate even indexes\\n for i in range(0, 2 * self.num_layers, 2):\\n init_states.append(self.cell_list[i].init_hidden(batch_size=batch_size))\\n\\n return init_states\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c3644b3ceafac16e16609d3ccdc6fda\",\n \"score\": \"0.66443205\",\n \"text\": \"def __init__(self):\\n self._n_layers = 0\\n self._dim_layers = []\\n self._layers = {}\\n self._layers_input = {}\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3b48ac0b58218cc4f7485672698be16\",\n \"score\": \"0.6638811\",\n \"text\": \"def set_layers(self, sizes, init='random'):\\n self.init_weights(sizes, init, None, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3b48ac0b58218cc4f7485672698be16\",\n \"score\": \"0.6638811\",\n \"text\": \"def set_layers(self, sizes, init='random'):\\n self.init_weights(sizes, init, None, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82ad13e028f361081963df90b32db4a7\",\n \"score\": \"0.6618463\",\n \"text\": \"def init_hidden(self, batch_size: int, stddev=1):\\n h = (\\n torch.randn(\\n self.n_layers * (1 + self.bidirectional),\\n batch_size,\\n self.hidden_dim,\\n ).to(self.device)\\n * stddev\\n )\\n c = (\\n torch.randn(\\n self.n_layers * (1 + self.bidirectional),\\n batch_size,\\n self.hidden_dim,\\n ).to(self.device)\\n * stddev\\n )\\n self.hidden = (h, c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fdbfa97cc09c975428cc2b65c2b1758\",\n \"score\": \"0.66135967\",\n \"text\": \"def init_layers(self):\\n self.level_embeds = nn.Parameter(\\n torch.Tensor(self.num_feature_levels, self.embed_dims))\\n\\n if self.as_two_stage:\\n self.enc_output = nn.Linear(self.embed_dims, self.embed_dims)\\n self.enc_output_norm = nn.LayerNorm(self.embed_dims)\\n self.pos_trans = nn.Linear(self.embed_dims * 2,\\n self.embed_dims * 2)\\n self.pos_trans_norm = nn.LayerNorm(self.embed_dims * 2)\\n else:\\n self.reference_points = nn.Linear(self.embed_dims, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5d19eebccfcabaf34640f233631da15\",\n \"score\": \"0.6566221\",\n \"text\": \"def _instantiate_layers(self):\\n\\n with self._enter_variable_scope():\\n self._layers = [basic.Linear(self._output_sizes[i],\\n name=\\\"linear_{}\\\".format(i),\\n initializers=self._initializers,\\n partitioners=self._partitioners,\\n regularizers=self._regularizers,\\n use_bias=self.use_bias)\\n for i in xrange(self._num_layers)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec91fb4bdb8c1ed18be81896a076a05b\",\n \"score\": \"0.65616316\",\n \"text\": \"def populate_hidden_layers(network, hidden_layers_parameters):\\n number_of_layers = len(hidden_layers_parameters)\\n\\n for i in range(number_of_layers):\\n number_of_connections = network[0+i][0]\\n network.append(populate_values(number_of_connections, hidden_layers_parameters[i]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c12416592d2eb74bb81b07dd06130752\",\n \"score\": \"0.6551426\",\n \"text\": \"def init_hidden(self, batch_size):\\n return (torch.zeros((1, batch_size, self.hidden_size), device=device), \\\\\\n torch.zeros((1, batch_size, self.hidden_size), device=device))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc66054f533f0e3052534a5fd42c2398\",\n \"score\": \"0.6538289\",\n \"text\": \"def __init__(self, n_in=62500, hidden=4, n_outs=121):\\n if n_in is None:\\n # allow us to create an empty network we can fill\\n # with layers ourselves\\n self.layers = []\\n return\\n\\n if isinstance(hidden, int):\\n args = hidden * [500] + [n_outs]\\n else:\\n args = hidden + [n_outs]\\n\\n args = iter(args)\\n prev = next(args)\\n self.layers = [Layer(n_in, prev, 'tanh')]\\n for i in args:\\n self.layers.append(Layer(prev, i, 'tanh'))\\n prev = i\\n\\n # add a softmax layer at the end\\n self.layers.append(Layer(i, i, 'softmax'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffbeda11c565a303fb6e15a3cd26221b\",\n \"score\": \"0.6536499\",\n \"text\": \"def __init__(self, layers):\\n self.layers = layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59fb8796cc31c5b67c14a1bd4bac8000\",\n \"score\": \"0.6526113\",\n \"text\": \"def init_hidden(self, bs):\\n return torch.zeros(bs, self.hidden_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59fb8796cc31c5b67c14a1bd4bac8000\",\n \"score\": \"0.6526113\",\n \"text\": \"def init_hidden(self, bs):\\n return torch.zeros(bs, self.hidden_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbbd4aefef43d6dcd40e4c7c0cb1fb7d\",\n \"score\": \"0.65155184\",\n \"text\": \"def initialize(self):\\n # self.gc1.reset_parameters()\\n # self.gc2.reset_parameters()\\n for layer in self.layers:\\n layer.reset_parameters()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f623334f20f381ce1a27255e851a86a\",\n \"score\": \"0.6416998\",\n \"text\": \"def init_layers(self):\\n self.in_x = nn.ModuleList()\\n self.in_y = nn.ModuleList()\\n self.in_z = nn.ModuleList()\\n self.in_t = nn.ModuleList()\\n lenInput = 1\\n noInLayers = 3\\n\\n self.in_x.append(nn.Linear(lenInput,self.numFeatures))\\n for _ in range(noInLayers):\\n self.in_x.append(nn.Linear(self.numFeatures, self.numFeatures)) \\n\\n self.in_y.append(nn.Linear(lenInput,self.numFeatures))\\n for _ in range(noInLayers):\\n self.in_y.append(nn.Linear(self.numFeatures, self.numFeatures)) \\n\\n self.in_z.append(nn.Linear(lenInput,self.numFeatures))\\n for _ in range(noInLayers):\\n self.in_z.append(nn.Linear(self.numFeatures, self.numFeatures)) \\n\\n self.in_t.append(nn.Linear(1,self.numFeatures))\\n for _ in range(noInLayers):\\n self.in_t.append(nn.Linear(self.numFeatures, self.numFeatures)) \\n\\n for m in [self.in_x,self.in_y,self.in_z,self.in_t]:\\n if isinstance(m, nn.Linear):\\n nn.init.xavier_uniform_(m.weight)\\n #nn.init.constant_(m.bias, 0)\\n \\n self.lin_layers.append(nn.Linear(4 * self.numFeatures, self.numFeatures))\\n for i in range(self.numLayers):\\n inFeatures = self.numFeatures\\n self.lin_layers.append(nn.Linear(inFeatures,self.numFeatures))\\n inFeatures = self.numFeatures\\n self.lin_layers.append(nn.Linear(inFeatures,1))\\n for m in self.lin_layers:\\n if isinstance(m, nn.Linear):\\n nn.init.xavier_uniform_(m.weight)\\n nn.init.constant_(m.bias, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"379edec6afd135c0e5df06877725e795\",\n \"score\": \"0.64118886\",\n \"text\": \"def init_hidden(self, batch_size, device):\\n # only the first block hidden is needed\\n hidden_list = self.encoder.init_memory(batch_size=batch_size, device=device)\\n\\n return hidden_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e441c9c78ff7f6ae547bf786cf561101\",\n \"score\": \"0.63546467\",\n \"text\": \"def _init_layers(self) -> None:\\n if self.num_convs > 1:\\n rpn_convs = []\\n for i in range(self.num_convs):\\n if i == 0:\\n in_channels = self.in_channels\\n else:\\n in_channels = self.feat_channels\\n # use ``inplace=False`` to avoid error: one of the variables\\n # needed for gradient computation has been modified by an\\n # inplace operation.\\n rpn_convs.append(\\n ConvModule(\\n in_channels,\\n self.feat_channels,\\n 3,\\n padding=1,\\n inplace=False))\\n self.rpn_conv = nn.Sequential(*rpn_convs)\\n else:\\n self.rpn_conv = nn.Conv2d(\\n self.in_channels, self.feat_channels, 3, padding=1)\\n self.rpn_cls = nn.Conv2d(self.feat_channels,\\n self.num_base_priors * self.cls_out_channels,\\n 1)\\n reg_dim = self.bbox_coder.encode_size\\n self.rpn_reg = nn.Conv2d(self.feat_channels,\\n self.num_base_priors * reg_dim, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6ea4b9e2778a56e915e68656ee42cc\",\n \"score\": \"0.6338492\",\n \"text\": \"def _init_layers(self):\\n self.rpn_conv = nn.Conv2d(\\n self.in_channels, self.feat_channels, 3, padding=1)\\n self.rpn_cls = nn.Conv2d(self.feat_channels,\\n self.num_anchors * self.cls_out_channels, 1)\\n self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6ea4b9e2778a56e915e68656ee42cc\",\n \"score\": \"0.6338492\",\n \"text\": \"def _init_layers(self):\\n self.rpn_conv = nn.Conv2d(\\n self.in_channels, self.feat_channels, 3, padding=1)\\n self.rpn_cls = nn.Conv2d(self.feat_channels,\\n self.num_anchors * self.cls_out_channels, 1)\\n self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c3877658fea239f80c4ed10276a3627\",\n \"score\": \"0.6314476\",\n \"text\": \"def layer_configuration(n_x,number_h,hidden_dimension,n_y):\\n layer_dim=[]\\n layer_dim.append(n_x)\\n for i in range(number_h):\\n layer_dim.append(hidden_dimension[i])\\n layer_dim.append(n_y)\\n return layer_dim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b69a47a557e6f58633c965c65f7ced2\",\n \"score\": \"0.63052344\",\n \"text\": \"def init_memory(self, batch_size, device):\\n init_states = []\\n # Only iterate odd indexes\\n for i in range(2, 3 * self.num_layers, 3):\\n init_states.append(self.cell_list[i].init_hidden(batch_size, device))\\n\\n return init_states\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5047694914a4bffae87716646ef59321\",\n \"score\": \"0.6296289\",\n \"text\": \"def _init_layers(self):\\n self.cls_convs = nn.ModuleList()\\n self.energy_convs = None if not self.split_convs else nn.ModuleList()\\n self.reg_convs = nn.ModuleList()\\n\\n # Create the stacked convolutions\\n for i in range(self.stacked_convs):\\n chn = self.in_channels if i == 0 else self.feat_channels\\n\\n # Make the different convolution stacks\\n self.cls_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg,\\n bias=self.norm_cfg is None))\\n if self.split_convs:\\n self.energy_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg,\\n bias=self.norm_cfg is None\\n )\\n )\\n self.reg_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg,\\n bias=self.norm_cfg is None))\\n\\n # Classifier convolution\\n self.wfcos_cls = nn.Conv2d(\\n self.feat_channels, self.cls_out_channels, 3, padding=1)\\n # Bounding box regression convolution\\n self.wfcos_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)\\n # Energy map convolution\\n self.wfcos_energy = nn.Conv2d(self.feat_channels, self.max_energy, 1, padding=0)\\n\\n # Scaling factor for the different heads\\n self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79e5be79eb5d16461af0ecfd8a660f9d\",\n \"score\": \"0.62848467\",\n \"text\": \"def __init__(self,layers,num_nets,batch_size):\\n self.networks = []\\n for i in range (num_nets):\\n net = Network(layers)\\n self.networks.append(net)\\n\\n self.output = np.zeros((batch_size,num_nets))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58cfb572f5c11b3424f5b1576a3a57d8\",\n \"score\": \"0.62056947\",\n \"text\": \"def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3):\\n super().__init__()\\n in_dims = [input_dim] + [hidden_dim] * (num_layers - 1)\\n out_dims = [hidden_dim] * (num_layers - 1) + [output_dim]\\n\\n layers = []\\n for i, (in_dim, out_dim) in enumerate(zip(in_dims, out_dims)):\\n layers.append(\\n PredictionBlock(in_dim, out_dim, activation=nn.ReLU() if i < num_layers - 1 else nn.Identity())\\n )\\n\\n self.layers = nn.Sequential(*layers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"127464ade351c7323b866545969316ef\",\n \"score\": \"0.61966705\",\n \"text\": \"def __init__(\\n self,\\n parameters: dict,\\n num_layers=4,\\n ):\\n super(MSDNet, self).__init__(parameters)\\n\\n self.layer_list = add_conv_block(\\n self.Conv, self.BatchNorm, in_channels=self.n_channels\\n )\\n\\n current_in_channels = 1\\n # Add N layers\\n for i in range(num_layers):\\n s = i % 10 + 1\\n self.layer_list += add_conv_block(\\n self.Conv, self.BatchNorm, in_channels=current_in_channels, dilation=s\\n )\\n current_in_channels += 1\\n\\n # Add final output block\\n self.layer_list += add_conv_block(\\n self.Conv,\\n self.BatchNorm,\\n in_channels=current_in_channels + self.n_channels,\\n out_channels=self.n_classes,\\n kernel_size=1,\\n last=True,\\n )\\n\\n # Add to Module List\\n self.layers = nn.ModuleList(self.layer_list)\\n\\n self.apply(self.weight_init)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75da62efa578756da2f8fdb9109abf13\",\n \"score\": \"0.6193985\",\n \"text\": \"def __init_hidden(self):\\n return (torch.zeros(1, 1, self.hidden_dim).to(device),\\n torch.zeros(1, 1, self.hidden_dim).to(device))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc31c93aa181e2266a7dedcee5b4230c\",\n \"score\": \"0.6191021\",\n \"text\": \"def __init__(self, n_layers: int, in_channels: int, out_channels: int, use_checkpoint: bool = False):\\n super().__init__()\\n self.layers = nn.ModuleList()\\n\\n # Build the dense layers\\n for i in range(n_layers):\\n self.layers.append(DenseLayer(\\n in_channels + i * out_channels, out_channels, use_checkpoint=use_checkpoint\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4428d7eee740b37fe083d8fc67074e0\",\n \"score\": \"0.61865777\",\n \"text\": \"def init_layer(layer):\\n\\n if layer.weight.ndimension() == 4:\\n (n_out, n_in, height, width) = layer.weight.size()\\n n = n_in * height * width\\n elif layer.weight.ndimension() == 3:\\n (n_out, n_in, height) = layer.weight.size()\\n n = n_in * height\\n elif layer.weight.ndimension() == 2:\\n (n_out, n) = layer.weight.size()\\n\\n std = math.sqrt(2. / n)\\n scale = std * math.sqrt(3.)\\n layer.weight.data.uniform_(-scale, scale)\\n\\n if layer.bias is not None:\\n layer.bias.data.fill_(0.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9a1c536781b1043e43d1c74635e514f\",\n \"score\": \"0.61841536\",\n \"text\": \"def __init__(self, hidden_size, eps=1e-12):\\n super(SketchLayerNorm, self).__init__()\\n self.weight = nn.Parameter(torch.ones(hidden_size))\\n self.bias = nn.Parameter(torch.zeros(hidden_size))\\n self.variance_epsilon = eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"548387f4b008d053c3b62dc5ed68a243\",\n \"score\": \"0.616783\",\n \"text\": \"def _init_layers(self) -> None:\\n dconv3x3_config = dict(\\n type='DCNv2',\\n kernel_size=3,\\n use_bias=True,\\n deform_groups=2,\\n padding=1)\\n conv3x3_config = dict(type='Conv', kernel_size=3, padding=1)\\n conv1x1_config = dict(type='Conv', kernel_size=1)\\n\\n self.arch_config = [\\n dconv3x3_config, conv3x3_config, dconv3x3_config, conv1x1_config\\n ]\\n self.cls_convs = nn.ModuleList()\\n self.reg_convs = nn.ModuleList()\\n for i, op_ in enumerate(self.arch_config):\\n op = copy.deepcopy(op_)\\n chn = self.in_channels if i == 0 else self.feat_channels\\n assert isinstance(op, dict)\\n use_bias = op.pop('use_bias', False)\\n padding = op.pop('padding', 0)\\n kernel_size = op.pop('kernel_size')\\n module = ConvModule(\\n chn,\\n self.feat_channels,\\n kernel_size,\\n stride=1,\\n padding=padding,\\n norm_cfg=self.norm_cfg,\\n bias=use_bias,\\n conv_cfg=op)\\n\\n self.cls_convs.append(copy.deepcopy(module))\\n self.reg_convs.append(copy.deepcopy(module))\\n\\n self.conv_cls = nn.Conv2d(\\n self.feat_channels, self.cls_out_channels, 3, padding=1)\\n self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)\\n self.conv_centerness = nn.Conv2d(self.feat_channels, 1, 3, padding=1)\\n\\n self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e0da101fb33cd0cae91dc2c4859d094\",\n \"score\": \"0.61636263\",\n \"text\": \"def __init__(self, num_inputs=3, hidden_layers=[3,3], num_outputs=2):\\n \\n self.num_inputs = num_inputs\\n self.hidden_layers = hidden_layers\\n self.num_outputs = num_outputs\\n \\n layers = [num_inputs] + hidden_layers + [num_outputs]\\n \\n #initiate random weights\\n \\n weights = []\\n for i in range(len(layers)-1):\\n w = np.random.rand(layers[i], layers[i+1])\\n weights.append(w)\\n \\n self.weights = weights\\n \\n \\n activations = []\\n for i in range(len(layers)):\\n a = np.zeros(layers[i])\\n activations.append(a)\\n self.activations = activations\\n \\n \\n derivatives = []\\n for i in range(len(layers)-1):\\n d = np.zeros((layers[i], layers[i+1]))\\n derivatives.append(d)\\n self.derivatives = derivatives\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e2d72e44201ae8cfafad3a710505b34\",\n \"score\": \"0.6161767\",\n \"text\": \"def _init_layers(self) -> None:\\n self.relu = nn.ReLU(inplace=True)\\n self.cls_convs = nn.ModuleList()\\n self.reg_convs = nn.ModuleList()\\n for i in range(self.stacked_convs):\\n chn = self.in_channels if i == 0 else self.feat_channels\\n self.cls_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=dict(type='DCN', deform_groups=1)\\n if i == 0 and self.use_dcn else self.conv_cfg,\\n norm_cfg=self.norm_cfg))\\n self.reg_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=dict(type='DCN', deform_groups=1)\\n if i == 0 and self.use_dcn else self.conv_cfg,\\n norm_cfg=self.norm_cfg))\\n self.atss_cls = nn.Conv2d(\\n self.feat_channels,\\n self.num_base_priors * self.cls_out_channels,\\n 3,\\n padding=1)\\n self.atss_reg = nn.Conv2d(\\n self.feat_channels, self.num_base_priors * 4, 3, padding=1)\\n self.atss_iou = nn.Conv2d(\\n self.feat_channels, self.num_base_priors * 1, 3, padding=1)\\n self.scales = nn.ModuleList(\\n [Scale(1.0) for _ in self.prior_generator.strides])\\n\\n # we use the global list in loss\\n self.cls_num_pos_samples_per_level = [\\n 0. for _ in range(len(self.prior_generator.strides))\\n ]\\n self.reg_num_pos_samples_per_level = [\\n 0. for _ in range(len(self.prior_generator.strides))\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c6918cfd76da7dddc1e6bace5ccb6d2\",\n \"score\": \"0.61613536\",\n \"text\": \"def _init_layers(self, layer_sizes, output_dim, drop_pct):\\n n_layers = len(layer_sizes)\\n seq_model = nn.Sequential()\\n # Create all the blocks for the neural network\\n # the sub-unit is:\\n for i in range(n_layers - 1):\\n # gets the current layer size from\\n current_layer_size = layer_sizes[i]\\n next_layer_size = layer_sizes[i + 1]\\n\\n # initialize the linear layer\\n linlayer = nn.Linear(current_layer_size, next_layer_size)\\n kaiming_normal(linlayer.weight.data)\\n\\n # One Subunit:\\n # -- Linear\\n # -- Relu\\n # -- BatchNorm\\n # -- Drop_out\\n seq_model.add_module(\\\"lin_%d\\\" % i, linlayer)\\n seq_model.add_module(\\\"relu_%d\\\" % i, nn.ReLU())\\n seq_model.add_module(\\\"batch_norm_%d\\\" % i,\\n nn.BatchNorm1d(next_layer_size))\\n seq_model.add_module(\\\"drop_out_%d\\\" % i, nn.Dropout(drop_pct))\\n\\n out_lin = nn.Linear(layer_sizes[-1], output_dim)\\n kaiming_normal(out_lin.weight.data)\\n\\n seq_model.add_module(\\\"output\\\", out_lin)\\n\\n # initialize the weights of linear layers\\n for ly in seq_model:\\n if type(ly) == nn.Linear:\\n kaiming_normal(ly.weight.data)\\n\\n return seq_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bf292f10998328057e05d853e0fc226\",\n \"score\": \"0.61368626\",\n \"text\": \"def _init_layers(self):\\n\\n self._init_cls_convs()\\n self._init_reg_convs()\\n self._init_predictor()\\n\\n self.conv_centerness_prev = self._init_branch(\\n conv_channels=self.centerness_branch,\\n conv_strides=(1,) * len(self.centerness_branch),\\n )\\n self.conv_centerness = nn.Conv2d(self.centerness_branch[-1], 1, 1)\\n\\n \\\"\\\"\\\"\\n separate scale for every specific task in each feature-level\\n default scales for offset, depth, size, (bbox_2d), (keypoints)\\n \\\"\\\"\\\"\\n\\n scale_related_attributes = [\\n \\\"offset\\\",\\n \\\"depth\\\",\\n \\\"bbox2d\\\",\\n \\\"corners\\\",\\n ]\\n\\n self.scales = nn.ModuleList()\\n for _ in self.strides:\\n level_scales = nn.ModuleDict()\\n for attr in scale_related_attributes:\\n if attr in self.reg_keys:\\n level_scales[attr] = Scale(1.0)\\n\\n self.scales.append(level_scales)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a48a401190d0ea7e6720ea2b059c1ea5\",\n \"score\": \"0.61345786\",\n \"text\": \"def __init__(self, hidden_dim, **kwargs):\\n self.supports_masking = True\\n self.hidden_dim = hidden_dim\\n super().__init__(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f67029c884d749fc490dff26b45ff55\",\n \"score\": \"0.6131776\",\n \"text\": \"def initialise(self, weight_init=glorot_normal):\\n self.add(NadeInputLayer(self.hidden_dim, self.input_dim, weight_init=weight_init))\\n self.add(Activation(self.nonlinearity))\\n self.add(NadeOutputBinaryLayer(self.input_dim, weight_init=weight_init))\\n self.add(Activation('sigmoid'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8832694896fc1770232c0ae8d669314a\",\n \"score\": \"0.6123773\",\n \"text\": \"def _init_layers(self):\\n super(DeiTClsHead, self)._init_layers()\\n if self.hidden_dim is None:\\n head_dist = nn.Linear(self.in_channels, self.num_classes)\\n else:\\n head_dist = nn.Linear(self.hidden_dim, self.num_classes)\\n self.layers.add_module('head_dist', head_dist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9219f1aade8f60920a20f30e71787fd\",\n \"score\": \"0.61223406\",\n \"text\": \"def __init__(self, num_outputs):\\r\\n super(MyDenseLayer, self).__init__()\\r\\n self.num_outputs = num_outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdbe16c9d5915dc796df9022f9d769fc\",\n \"score\": \"0.6121616\",\n \"text\": \"def __init__(self, n_input, n_output, hidden_layer_size, reg):\\n self.reg = reg\\n self.fcl1 = FullyConnectedLayer(n_input, hidden_layer_size)\\n self.act = ReLULayer()\\n self.fcl2 = FullyConnectedLayer(hidden_layer_size, n_output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32a1d531acc5773899aeec09bf24648b\",\n \"score\": \"0.6116278\",\n \"text\": \"def num_layers(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eab81657b27497b367d2022fcbdc3e8\",\n \"score\": \"0.611398\",\n \"text\": \"def _init_none(self):\\n for lname in self.layer_manager.background_layer_names:\\n self.remove_layer(lname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d2ab596c8a307e60d2dba95c2384a4\",\n \"score\": \"0.61096615\",\n \"text\": \"def __init__(self, *layers):\\n super().__init__()\\n self.layers = layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be6fa8e80abf1b0f170843abdc6b220e\",\n \"score\": \"0.6099555\",\n \"text\": \"def init_hidden(self, input_shape):\\n return torch.zeros(1,input_shape[0],\\n self.hidden_size,\\n device=self.device)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5e934c5e772b60e8385ee66269082c4\",\n \"score\": \"0.6098212\",\n \"text\": \"def initialize_hidden_state(self) -> tf.Tensor:\\n return tf.zeros(shape=(self.batch, self.units))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2097dc08478c4c781fc74a3495608f60\",\n \"score\": \"0.6085364\",\n \"text\": \"def __init__(self):\\n self.output_perceptron_list = []\\n self.hidden_perceptron_list = []\\n # self.hidden_layer_output = []\\n # self.output_layer_output = []\\n\\n for i in range(26):\\n self.output_perceptron_list.append(OutputPerceptron(i))\\n\\n for j in range(NUM_HIDDEN_UNITS):\\n self.hidden_perceptron_list.append(HiddenPerceptron())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86a89f8d3ce8496d63764397152f01ad\",\n \"score\": \"0.60804224\",\n \"text\": \"def initialize_layers(X, y, n_nodes):\\n N = X.shape[0] # number of examples\\n output_shape = np.unique(y).shape[0]\\n layers = dict()\\n layers['input'] = X.reshape(N, -1) \\n for i in range( len(n_nodes) ):\\n layers['h{}'.format(i+1)] = np.zeros( (N, n_nodes[i]) )\\n layers['output'] = np.zeros( (N, output_shape) )\\n return layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b4c10ec6ce59e2ee4fad457812e5b16\",\n \"score\": \"0.60781825\",\n \"text\": \"def __init__(self, n_inputs=784, hidden_layers=[64], n_outputs=10):\\n self.n_inputs = n_inputs\\n self.hidden_layers = hidden_layers\\n self.n_outputs = n_outputs\\n\\n # Represent the layers\\n layers = [n_inputs] + hidden_layers + [n_outputs]\\n\\n # Assign random weights, multiplying by 0.1 to keep them reasonably small\\n weights = []\\n for i in range(len(layers) - 1):\\n weight = np.random.rand(layers[i], layers[i + 1]) * 0.1\\n weights.append(weight)\\n self.weights = weights\\n\\n # Saving the derivatives for each layer\\n derivs = []\\n for i in range(len(layers) - 1):\\n deriv = np.zeros((layers[i], layers[i + 1]))\\n derivs.append(deriv)\\n self.derivatives = derivs\\n\\n # Saving the activations for each layer\\n activations = []\\n for i in range(len(layers)):\\n activation = np.zeros(layers[i])\\n activations.append(activation)\\n self.activations = activations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"558de1daf9f0462c98eb181d0d882d6a\",\n \"score\": \"0.6064517\",\n \"text\": \"def init_hidden(self, batch_size, device):\\n hidden = Variable(torch.zeros(batch_size, self.hidden_dim, self.height, self.width).to(device))\\n\\n return hidden\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07c5dd6d9f643f1b8d9b40b4401f7567\",\n \"score\": \"0.60543394\",\n \"text\": \"def init_weights(self):\\n for child in self.children():\\n if child.bias is None:\\n init_layer(child)\\n else:\\n init_layer(child, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53e4101d32d1c0f9a4c3cf34b18d7a5a\",\n \"score\": \"0.60477746\",\n \"text\": \"def _init_layers(self):\\n self.relu = nn.ReLU(inplace=True)\\n self.cls_convs = nn.ModuleList()\\n self.reg_convs = nn.ModuleList()\\n for i in range(self.stacked_convs):\\n chn = self.in_channels if i == 0 else self.feat_channels\\n self.cls_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg,\\n bias=self.conv_bias))\\n self.reg_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg,\\n bias=self.conv_bias))\\n pts_out_dim = 2 * self.num_points\\n self.reppoints_cls_conv = DeformConv2d(self.feat_channels,\\n self.point_feat_channels,\\n self.dcn_kernel, 1,\\n self.dcn_pad)\\n self.reppoints_cls_out = nn.Conv2d(self.point_feat_channels,\\n self.cls_out_channels, 1, 1, 0)\\n self.reppoints_pts_init_conv = nn.Conv2d(self.feat_channels,\\n self.point_feat_channels, 3,\\n 1, 1)\\n self.reppoints_pts_init_out = nn.Conv2d(self.point_feat_channels,\\n pts_out_dim, 1, 1, 0)\\n self.reppoints_pts_refine_conv = DeformConv2d(self.feat_channels,\\n self.point_feat_channels,\\n self.dcn_kernel, 1,\\n self.dcn_pad)\\n self.reppoints_pts_refine_out = nn.Conv2d(self.point_feat_channels,\\n pts_out_dim, 1, 1, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91a13c0fd7d4275e26caa22e88c41cb3\",\n \"score\": \"0.60308754\",\n \"text\": \"def __init__(self, gamma, layer_sizes, num_hidden, epsilon=None, max_iters=None):\\n self.gamma = gamma\\n self.layer_sizes = layer_sizes\\n self.num_hidden = num_hidden\\n self.epsilon = epsilon\\n self.max_iters = max_iters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64f3a82a9a0ed460328048625a7c51b4\",\n \"score\": \"0.6019582\",\n \"text\": \"def __init__(self, hidden_layers, alpha=0.001, epoch=8000, activation=sigmoid, optimiser='gradient', error='SSE', random_state=0):\\r\\n\\t\\tself._hidden_layers = hidden_layers\\r\\n\\t\\tself._alpha = alpha\\r\\n\\t\\tself._epoch = epoch\\r\\n\\t\\tself._activation = activation\\r\\n\\t\\tself._seed = random_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e91e016580fb5473146839d7332abc\",\n \"score\": \"0.6010819\",\n \"text\": \"def initialization(self):\\n for layer in self.conv:\\n if not isinstance(layer, nn.Conv2d):\\n continue\\n try:\\n nn.init.xavier_uniform_(layer.weight.data)\\n nn.init.constant_(layer.bias.data, 0.0)\\n except:\\n pass\\n\\n for layer in self.conv:\\n if not isinstance(layer, nn.Linear):\\n continue\\n try:\\n nn.init.xavier_uniform_(layer.weight.data)\\n nn.init.constant_(layer.bias.data, 0.0)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a753c2e7c6edbc0f38409d790335396b\",\n \"score\": \"0.60003537\",\n \"text\": \"def _init_layers(self):\\n assert isinstance(self.feat_channels, list) and len(\\n self.feat_channels) == self.head_num\\n assert isinstance(self.in_channels, list) and len(\\n self.in_channels) == self.head_num\\n assert isinstance(self.head_type, list)\\n\\n if len(self.head_type) == 1:\\n self.head_type = self.head_type*self.head_num\\n assert len(self.head_type) == self.head_num\\n branches = 'cls_convs, reg_convs, shared_convs, hem_tl, hem_br, reppoints_cls_conv, reppoints_cls_out, reppoints_pts_init_conv, reppoints_pts_init_out,reppoints_pts_refine_conv, reppoints_pts_refine_out, reppoints_hem_tl_score_out, reppoints_hem_br_score_out, reppoints_hem_tl_offset_out, reppoints_hem_br_offset_out, reppoints_sem_out, reppoints_sem_embedding'.split(\\n ',')\\n for branch in branches:\\n setattr(self, branch.strip(), nn.ModuleList())\\n\\n for i in range(self.head_num):\\n subnets = self._init_head(\\n self.in_channels[i], self.feat_channels[i], self.head_type[i])\\n assert len(subnets) == len(branches)\\n for branch, subnet in zip(branches, subnets):\\n getattr(self, branch.strip()).append(subnet)\\n # fuse feature from second branch to the first branch\\n if self.head_fuse:\\n in_channels = [self.feat_channels[idx] for idx in self.head_pairs]\\n out_channels = in_channels\\n self.head_fuse_config.update(dict(in_channels=in_channels,out_channels=out_channels))\\n self.fuse_loc = build_fuse_layer(self.head_fuse_config)\\n self.fuse_cls = build_fuse_layer(self.head_fuse_config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b4cd2cb163430b1c39cce0481b7b77e\",\n \"score\": \"0.5996683\",\n \"text\": \"def __init__(self, hidden_size, eps=1e-12):\\n super(LayerNorm, self).__init__()\\n self.weight = nn.Parameter(torch.ones(hidden_size))\\n self.bias = nn.Parameter(torch.zeros(hidden_size))\\n self.variance_epsilon = eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b4cd2cb163430b1c39cce0481b7b77e\",\n \"score\": \"0.5996683\",\n \"text\": \"def __init__(self, hidden_size, eps=1e-12):\\n super(LayerNorm, self).__init__()\\n self.weight = nn.Parameter(torch.ones(hidden_size))\\n self.bias = nn.Parameter(torch.zeros(hidden_size))\\n self.variance_epsilon = eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b4cd2cb163430b1c39cce0481b7b77e\",\n \"score\": \"0.5996683\",\n \"text\": \"def __init__(self, hidden_size, eps=1e-12):\\n super(LayerNorm, self).__init__()\\n self.weight = nn.Parameter(torch.ones(hidden_size))\\n self.bias = nn.Parameter(torch.zeros(hidden_size))\\n self.variance_epsilon = eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b4cd2cb163430b1c39cce0481b7b77e\",\n \"score\": \"0.5996683\",\n \"text\": \"def __init__(self, hidden_size, eps=1e-12):\\n super(LayerNorm, self).__init__()\\n self.weight = nn.Parameter(torch.ones(hidden_size))\\n self.bias = nn.Parameter(torch.zeros(hidden_size))\\n self.variance_epsilon = eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f4cb76ae945bf7a334ac071ee7d2de6\",\n \"score\": \"0.5996277\",\n \"text\": \"def __init__(self,):\\n self.layer_list = []\\n self.weights = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bcdf153b5c0c5d2c2efe9eb5b9bdf8f\",\n \"score\": \"0.5968303\",\n \"text\": \"def __init__(self, dims, dropout=False):\\n super().__init__()\\n input_size = dims[0]\\n h_sizes = dims[1:]\\n\\n mods = [nn.Linear(input_size, h_sizes[0])]\\n for i in range(len(h_sizes) - 1):\\n mods.append(nn.ReLU())\\n if dropout:\\n mods.append(nn.Dropout(p=dropout))\\n mods.append(nn.Linear(h_sizes[i], h_sizes[i + 1]))\\n\\n self.layers = nn.Sequential(*mods)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cfac6662587b9c35834fc21b401a5da\",\n \"score\": \"0.5963964\",\n \"text\": \"def _manager_show_all_layers(self):\\n self.layer_manager.show_all_layers()\\n self.canvas.setLayers(self.layer_manager.shown_layers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1268c7a3e4fbfb635d5b87add8c80a6e\",\n \"score\": \"0.59609795\",\n \"text\": \"def show_all_layers(self):\\n self.shown_layers_index = np.arange(len(self.names_in_order)).tolist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f9d5de223ee84d8e8ab242cc6f27f3c\",\n \"score\": \"0.5950844\",\n \"text\": \"def test_setget_hlayer(self):\\n for x in range(_NUM):\\n M = TestMutableNeuralNetwork.__inititem()\\n #to ensure that the count of inputs does not changes:\\n ninps = M.ninps #initial count of inputs\\n H = M.hidden_layers\\n shape = M.shape\\n start = randrange(len(M) - 1)\\n inset = [Primitives.initlayer(\\n randrange(1, _SIZE), shape[start])]\\n inset.append(Primitives.initlayer(randrange(1, _SIZE), \\n len(inset[0])))\\n H[start:] = inset\\n for o in M.output_layer:\\n self.assertEqual(len(o), len(inset[1]))\\n #deletion test:\\n del H[:]\\n for o in M.output_layer:\\n self.assertEqual(len(o), ninps)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05325f44528860e87f02faea3e72d06e\",\n \"score\": \"0.59479916\",\n \"text\": \"def reset_n_prop_layers(self, n_prop_layers):\\n self._n_prop_layers = n_prop_layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c05bcdb140ce7398843c976015e001f3\",\n \"score\": \"0.59377265\",\n \"text\": \"def __init__(self, num_hidden_layers=2, hidden_layer_size=[10, 10], activation=\\\"relu\\\",\\n input_dim=100, output_dim=10):\\n\\n super(MLP, self).__init__()\\n self._num_hidden_layers = num_hidden_layers\\n self._hidden_layer_size = hidden_layer_size\\n self._activation = get_activation(activation)\\n self._input_dim = input_dim\\n self._output_dim = output_dim\\n\\n self._fc_layers = []\\n self._fc_layers.append(nn.Linear(input_dim, hidden_layer_size[0]))\\n nn.init.normal_(self._fc_layers[-1].weight, mean=0, std=0.01)\\n nn.init.constant_(self._fc_layers[-1].bias, 0)\\n for i in range(1, num_hidden_layers):\\n self._fc_layers.append(nn.Linear(hidden_layer_size[i-1], hidden_layer_size[i]))\\n nn.init.normal_(self._fc_layers[-1].weight, mean=0, std=0.01)\\n nn.init.constant_(self._fc_layers[-1].bias, 0)\\n self._fc_layers.append(nn.Linear(hidden_layer_size[-1], output_dim))\\n nn.init.normal_(self._fc_layers[-1].weight, mean=0, std=0.01)\\n nn.init.constant_(self._fc_layers[-1].bias, 0)\\n\\n self._module_list = nn.ModuleList(self._fc_layers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"425950230462580bdabd3c72bc2de9c9\",\n \"score\": \"0.5936416\",\n \"text\": \"def __init__(self, num_visible, num_hidden, learning_rate=0.05):\\n self.num_visible = num_visible\\n self.num_hidden = num_hidden\\n self.learning_rate = learning_rate\\n\\n # Set the network's weight matrix to all zeros, initially\\n self.weights = np.zeros((self.num_hidden, self.num_visible)) \\n \\n # Initialize the network's visible (binary) states\\n self.visible = np.zeros((self.num_visible, 1))\\n\\n # Initialize the network's hidden (binary) states\\n self.hidden = np.zeros((self.num_hidden, 1))\\n\\n # Bias weights for the visible layer\\n self.visible_bias = np.random.randn(self.num_visible)\\n\\n # Bias weights for the hidden layer\\n self.hidden_bias = np.random.randn(self.num_hidden, 1)\\n print(self.hidden_bias)\\n # Vectorize the network's activation function\\n self.vectorized_activation = np.vectorize(self.activation_fn)\\n\\n # Vectorize the boolean activation function\\n self.vec_bool_activation = np.vectorize(self.bool_activation)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"996bd0659a1379155c2afafa26048a0b\",\n \"score\": \"0.5932562\",\n \"text\": \"def __init__(self, sizeOfLayers, activationFunctions):\\n\\n\\n\\t\\t# L\\n\\t\\tself.nnDepth = len(sizeOfLayers)\\n\\t\\tself.outputIndex = self.nnDepth - 1\\n\\n\\t\\tself.sizeOfLayers = sizeOfLayers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56257d4a3990109e9ef11b5f258a1b23\",\n \"score\": \"0.592432\",\n \"text\": \"def __init__(self, Neurons_layer=[512,256,128]):\\n nn.Module.__init__(self)\\n self.fc_layers = nn.ModuleList(nn.Linear(in_features=n_in, out_features=n_out) for n_in, n_out in zip(Neurons_layer[:-1], Neurons_layer[1:]))\\n self.relu = nn.ReLU()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"467e645c0572d3f1557970d110594a76\",\n \"score\": \"0.59138006\",\n \"text\": \"def __init__(self, layers):\\n self.layers = layers\\n \\n # When we build layer, we need to know input and output dimensions.\\n self.m = self.layers[0].m\\n self.n = self.layers[0].n\\n for layer in self.layers[1:]:\\n self.n = getattr(layer, 'n', self.n)\\n \\n # This is the static graph representing our model\\n self.graph = tf.Graph()\\n \\n # This is the runtime instance of our model\\n self.sess = tf.Session(graph=self.graph)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5406fe8fcb768430e8f4b3060a8ce6ab\",\n \"score\": \"0.5908669\",\n \"text\": \"def initialize_parameters(self):\\n\\n\\t\\tnum_layer = len(self.layers)\\n\\t\\tfor i in range(1, num_layer):\\n\\t\\t\\tn_c = 1./np.sqrt(self.layers[i])\\n\\t\\t\\tself.parameters[\\\"W\\\" + str(i)] = np.ones((self.layers[i],\\n\\t\\t\\t\\tself.layers[i-1])) * np.random.uniform(-n_c, n_c,\\n\\t\\t\\t\\t\\t(self.layers[i],self.layers[i-1]))\\n\\t\\t\\tself.parameters[\\\"b\\\" + str(i)] = np.zeros((self.layers[i],1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0073609fabb1d3c27fd83d5723c372f4\",\n \"score\": \"0.58704156\",\n \"text\": \"def _build_hidden_layers_mean(self) -> Tuple[nn.ModuleList, int]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0073609fabb1d3c27fd83d5723c372f4\",\n \"score\": \"0.58704156\",\n \"text\": \"def _build_hidden_layers_mean(self) -> Tuple[nn.ModuleList, int]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe622e8059fbb421a66bfcf340c95fba\",\n \"score\": \"0.5867704\",\n \"text\": \"def __init__(self, sizes, activation = Sigmoid(), normal_initialization = False):\\n\\n BaseLayer.__init__(self, sizes, activation)\\n\\n if normal_initialization:\\n self.weights = [np.random.randn(j, i)\\n for i, j in zip(sizes[:-1], sizes[1:])]\\n else:\\n self.weights = [np.random.randn(j, i) / np.sqrt(i)\\n for i, j in zip(sizes[:-1], sizes[1:])]\\n self.biases = [np.random.randn(j, 1) for j in sizes[1:]]\\n\\n self.grad_w = [np.zeros(w.shape) for w in self.weights]\\n self.grad_b = [np.zeros(b.shape) for b in self.biases]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7331403d3fcd69ed8de9ec28de77bcb6\",\n \"score\": \"0.5863243\",\n \"text\": \"def detach_hidden(self, batch_size):\\n _, hidden_batch_size, _ = self.hidden.size()\\n if hidden_batch_size != batch_size:\\n self.init_hidden(batch_size)\\n else:\\n detached_hidden = self.hidden.detach()\\n detached_hidden.zero_()\\n self.hidden = detached_hidden\",\n \"title\": \"\"\n },\n {\n \"docid\": \"551cbbaf7a16ff10a5c469f784044f65\",\n \"score\": \"0.5862765\",\n \"text\": \"def n_layers(self):\\n return self._n_layers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a01ebf3e15b351d542fa1ad111b476b9\",\n \"score\": \"0.5862482\",\n \"text\": \"def __init__(self, n_input, n_hidden, n_output, name=\\\"test\\\"):\\n self.nam = name\\n\\n super(MLPNet, self).__init__()\\n self.n_layers = copy.copy(n_hidden)\\n self.n_layers.insert(0, n_input)\\n self.n_layers.append(n_output)\\n\\n self.layers = nn.ModuleList()\\n\\n for i in range(len(self.n_layers) - 1):\\n self.layers.append(nn.Linear(self.n_layers[i], self.n_layers[i+1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0326351db0abace5c481110596770a74\",\n \"score\": \"0.5845479\",\n \"text\": \"def init_submodules(self):\\n \\n if self.cell_type == \\\"VanillaRNN\\\":\\n net = [nn.RNNCell(input_size=self.input_size, hidden_size=self.hidden_state_size)]\\n net.extend([nn.RNNCell(input_size=self.hidden_state_size, hidden_size=self.hidden_state_size)\\n for _ in range(self.num_hidden_layers - 1)])\\n elif self.cell_type == \\\"LSTM\\\":\\n net = [nn.LSTMCell(input_size=self.input_size, hidden_size=self.hidden_state_size)]\\n net.extend([nn.LSTMCell(input_size=self.hidden_state_size, hidden_size=self.hidden_state_size)\\n for _ in range(self.num_hidden_layers - 1)])\\n \\n else:\\n net = [nn.GRUCell(input_size=self.input_size, hidden_size=self.hidden_state_size)]\\n net.extend([nn.GRUCell(input_size=self.hidden_state_size, hidden_size=self.hidden_state_size)\\n for _ in range(self.num_hidden_layers - 1)])\\n \\n self.rnn_forward = nn.Sequential(*net)\\n self.rnn_backward = nn.Sequential(*net.copy())\\n\\n self.project_to_input_dim_forward = nn.Linear(self.hidden_state_size, self.input_size)\\n self.project_to_input_dim_backward = nn.Linear(self.hidden_state_size, self.input_size)\\n\\n if self.attn_type == \\\"Additive\\\":\\n attn = [AdditiveAttention(hidden_size=self.seq_len, encoder=True) for _ in range(self.num_hidden_layers)]\\n elif self.attn_type == \\\"Scaled-Dot\\\":\\n attn = [ScaledDotAttention(hidden_size=self.seq_len, hidden_size2=self.input_size, encoder=True)]\\n attn.extend([ScaledDotAttention(hidden_size=self.seq_len, hidden_size2 = self.hidden_state_size, encoder=True) for _ in range(self.num_hidden_layers - 1)])\\n elif self.attn_type == \\\"Linear\\\":\\n attn = [LinearAttention(hidden_size=self.seq_len, encoder=True) for _ in range(self.num_hidden_layers)]\\n else:\\n attn = [DaRnnAttention(hidden_size=self.seq_len) for _ in range(self.num_hidden_layers)]\\n\\n self.attn_forward = nn.Sequential(*attn)\\n self.attn_backward = nn.Sequential(*attn.copy()) \\n \\n self.forward_attn_weights = None\\n self.backward_attn_weights = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3137d27b6f58aa19b805532a796dbc2c\",\n \"score\": \"0.5842405\",\n \"text\": \"def __init__(self, *args, **kw):\\n self.layers = [dict(), dict()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c207f07aa22de8f7c7a3c1dd16bb20a7\",\n \"score\": \"0.58417034\",\n \"text\": \"def __init__(self, **kwargs):\\n super().__init__()\\n self.hidden_size = kwargs[\\\"hidden_size\\\"]\\n self.num_layers = kwargs[\\\"num_layers\\\"]\\n\\n self.gru = nn.GRU(input_size=256, hidden_size=self.hidden_size, num_layers=self.num_layers)\\n self.output_layer = nn.Linear(self.hidden_size, 4)\\n self.h0 = nn.Parameter(torch.randn(self.num_layers, 1, self.hidden_size))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a905c92a5e209439724882d0d9d4d523\",\n \"score\": \"0.5838338\",\n \"text\": \"def __init__(self, hidden_size, hidden_size2=0, encoder=False):\\n super().__init__()\\n\\n self.encoder = encoder\\n self.hidden_size = hidden_size\\n if self.encoder:\\n self.hidden_size2 = hidden_size2\\n else:\\n self.hidden_size2 = hidden_size\\n self.init_submodules()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dcf9bddbdebd765558bdca16a170a31\",\n \"score\": \"0.5836774\",\n \"text\": \"def __init__(self, n_inputs, n_hidden, n_classes):\\n super(MLP, self).__init__() \\n self.layer=nn.Sequential(nn.Linear(n_inputs,n_hidden[0]))\\n for i in range(1,len(n_hidden)):\\n self.layer.add_module('relu',nn.ReLU(True))\\n self.layer.add_module('linear',nn.Linear(n_hidden[i-1],n_hidden[i]))\\n self.layer.add_module('softmax',nn.Softmax())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3ad1448551a5d3470d16cd2b2a087c6\",\n \"score\": \"0.58345795\",\n \"text\": \"def layers(self):\\n\\n if self.numlayerdims == 0:\\n yield 0\\n else:\\n for ld in range(self.numlayerdims):\\n cc = self.layercurrcat[ld]\\n for c in range(self.layercats[ld]):\\n yield cc\\n cc = (cc+1) % self.layercats[ld]\\n self.ptmgr.LayerDimension(ld).SetCurrentCategory(cc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc22fa857ea7e0df330a30e4e9eaa15e\",\n \"score\": \"0.5828875\",\n \"text\": \"def init_weights(self):\\n init_layer(self.conv1)\\n init_bn(self.bn1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a046dfee2a4a8a9b460e2cfdc9fe4d9\",\n \"score\": \"0.5826577\",\n \"text\": \"def __init__(self, nx, layers):\\n\\n if not isinstance(nx, int):\\n raise TypeError('nx must be an integer')\\n\\n if nx < 1:\\n raise ValueError('nx must be a positive integer')\\n\\n if not isinstance(layers, list) or len(layers) == 0:\\n raise TypeError('layers must be a list of positive integers')\\n\\n self.__L = len(layers)\\n self.__cache = {}\\n self.__weights = {}\\n\\n for layer in range(len(layers)):\\n if not isinstance(layers[layer], int) or layers[layer] <= 0:\\n raise TypeError('layers must be a list of positive integers')\\n\\n if layer == 0:\\n w = np.random.randn(layers[layer], nx) * np.sqrt(2 / nx)\\n\\n else:\\n w = np.random.randn(layers[layer], layers[layer-1])\\n w = w * np.sqrt(2 / layers[layer-1])\\n\\n self.__weights[\\\"b\\\"+str(layer+1)] = np.zeros((layers[layer], 1))\\n self.__weights[\\\"W\\\"+str(layer+1)] = w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52e437d2609639b1c0f87ee06b10cf04\",\n \"score\": \"0.5826106\",\n \"text\": \"def __init__(self, layerSize):#default 3 layer, 1 percep per layer\\r\\n self.layerSize = layerSize #Holds number of inputs and percepetrons in each layer\\r\\n self.outputLayer = []\\r\\n self.numHiddenLayers = len(layerSize)-2\\r\\n self.hiddenLayers = [[] for x in range(self.numHiddenLayers)]\\r\\n self.numLayers = self.numHiddenLayers+1\\r\\n \\r\\n #build hidden layer(s) \\r\\n for h in range(self.numHiddenLayers):\\r\\n for p in range(layerSize[h+1]):\\r\\n percep = Perceptron(layerSize[h]) # num of perceps feeding into this one\\r\\n self.hiddenLayers[h].append(percep)\\r\\n \\r\\n #build output layer\\r\\n for i in range(layerSize[-1]):\\r\\n percep = Perceptron(layerSize[-2]) # num of perceps feeding into this one\\r\\n self.outputLayer.append(percep)\\r\\n \\r\\n #build layers list that holds all layers in order - use this structure\\r\\n # to implement back propagation\\r\\n self.layers = [self.hiddenLayers[h] for h in xrange(self.numHiddenLayers)] + [self.outputLayer]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52e437d2609639b1c0f87ee06b10cf04\",\n \"score\": \"0.5826106\",\n \"text\": \"def __init__(self, layerSize):#default 3 layer, 1 percep per layer\\r\\n self.layerSize = layerSize #Holds number of inputs and percepetrons in each layer\\r\\n self.outputLayer = []\\r\\n self.numHiddenLayers = len(layerSize)-2\\r\\n self.hiddenLayers = [[] for x in range(self.numHiddenLayers)]\\r\\n self.numLayers = self.numHiddenLayers+1\\r\\n \\r\\n #build hidden layer(s) \\r\\n for h in range(self.numHiddenLayers):\\r\\n for p in range(layerSize[h+1]):\\r\\n percep = Perceptron(layerSize[h]) # num of perceps feeding into this one\\r\\n self.hiddenLayers[h].append(percep)\\r\\n \\r\\n #build output layer\\r\\n for i in range(layerSize[-1]):\\r\\n percep = Perceptron(layerSize[-2]) # num of perceps feeding into this one\\r\\n self.outputLayer.append(percep)\\r\\n \\r\\n #build layers list that holds all layers in order - use this structure\\r\\n # to implement back propagation\\r\\n self.layers = [self.hiddenLayers[h] for h in xrange(self.numHiddenLayers)] + [self.outputLayer]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d3e8c44b973319aa762d952bcd85a95\",\n \"score\": \"0.5823384\",\n \"text\": \"def __init__(self, init_hiddens = None, weights=None, bias=None, weight_type=None):\\n self.valid_layer_types = dict()\\n self.valid_layer_types['visible_hidden'] = ['rbm_gaussian_bernoulli', 'rbm_bernoulli_bernoulli']\\n self.valid_layer_types['hidden_hidden'] = ['rbm_bernoulli_bernoulli']\\n self.valid_layer_types['hidden_output'] = ['logistic']\\n self.bias_keys = ['visible', 'hidden_forward', 'hidden_backward', 'output']\\n self.weights_keys = ['visible_hidden', 'hidden_hidden_forward', 'hidden_hidden_backward', 'hidden_output_forward', 'hidden_output_backward']\\n self.init_hiddens_keys = ['forward', 'backward']\\n \\n if init_hiddens is None:\\n self.init_hiddens = dict()\\n else:\\n self.init_hiddens = copy.deepcopy(init_hiddens)\\n if weights is None:\\n self.weights = dict()\\n else:\\n self.weights = copy.deepcopy(weights)\\n if bias is None:\\n self.bias = dict()\\n else:\\n self.bias = copy.deepcopy(bias)\\n if weight_type is None:\\n self.weight_type = dict()\\n else:\\n self.weight_type = copy.deepcopy(weight_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f4c656cd319f47a7b24a1635a11285d\",\n \"score\": \"0.58225554\",\n \"text\": \"def _setup_layers(self):\\n num_layers = 5\\n\\n # Input grayscale images with height 32px and width 128px\\n self.input_layer = Input(shape=(self.image_height, self.image_width, 1))\\n\\n model = self.input_layer\\n # Keras functional API\\n model = self._create_conv_net()(model)\\n\\n model = Dropout(rate=0.5)(model)\\n\\n for idx in range(num_layers):\\n model = Bidirectional(LSTM(self.lstm_units, return_sequences=True, dropout=0.5))(model)\\n\\n model = Dropout(rate=0.5)(model)\\n\\n self.output_layer = TimeDistributed(Dense(units=self._label_count, activation='softmax'))(model)\\n\\n self.weights = Model(self.input_layer, self.output_layer)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993897,"cells":{"query_id":{"kind":"string","value":"41f1216a5d7b8ef4758b1bc9d568f4f2"},"query":{"kind":"string","value":"Mix species of different families. All possible combinations are extracted."},"positive_passages":{"kind":"list like","value":[{"docid":"04d8761f1f3aa61edccb2ca7ddeca5a0","score":"0.72845006","text":"def mix_species(self, species, optional=False):\n family_numbers = np.unique([s.family_number for s in species])\n if len(family_numbers) == 0:\n return []\n families = [ [s for s in species if s.family_number == i] for i in family_numbers]\n if optional:\n families = [ l+[None] for l in families]\n mix = np.stack(np.meshgrid(*families)).T.reshape(-1, len(families))\n #Remove None elements if they exist\n b = []\n for l in mix:\n cleaned = [elem for elem in l if elem is not None]\n if len(cleaned):\n b.append(np.array(cleaned))\n mix = np.array(b, dtype=object)\n return mix","title":""}],"string":"[\n {\n \"docid\": \"04d8761f1f3aa61edccb2ca7ddeca5a0\",\n \"score\": \"0.72845006\",\n \"text\": \"def mix_species(self, species, optional=False):\\n family_numbers = np.unique([s.family_number for s in species])\\n if len(family_numbers) == 0:\\n return []\\n families = [ [s for s in species if s.family_number == i] for i in family_numbers]\\n if optional:\\n families = [ l+[None] for l in families]\\n mix = np.stack(np.meshgrid(*families)).T.reshape(-1, len(families))\\n #Remove None elements if they exist\\n b = []\\n for l in mix:\\n cleaned = [elem for elem in l if elem is not None]\\n if len(cleaned):\\n b.append(np.array(cleaned))\\n mix = np.array(b, dtype=object)\\n return mix\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f4baea9f29fddc8c0948f2b7ca1e9cfc","score":"0.59810746","text":"def expand_mix(self, mix):\n new_dict = mix[0].species()\n for i in range(1, len(mix)):\n new_dict = self.merge_dicts_mz(new_dict, mix[i].species())\n return new_dict","title":""},{"docid":"7e2c4795633eac66c5cbb6c201f03f47","score":"0.55427945","text":"def infer_families(\n relationship_ht: hl.Table,\n sex: Union[hl.Table, Dict[str, bool]],\n duplicate_samples_ht: hl.Table,\n i_col: str = \"i\",\n j_col: str = \"j\",\n relationship_col: str = \"relationship\",\n) -> hl.Pedigree:\n\n def group_parent_child_pairs_by_fam(\n parent_child_pairs: Iterable[Tuple[str, str]]\n ) -> List[List[Tuple[str, str]]]:\n \"\"\"\n Group parent-child pairs into a list of families.\n\n A family here is defined as a list of sample-pairs which all share at least one sample with at least one other\n sample-pair in the list.\n\n :param parent_child_pairs: All the parent-children pairs\n :return: A list of families, where each element of the list is a list of the parent-children pairs\n \"\"\"\n fam_id = 1 # stores the current family id\n s_fam = dict() # stores the family id for each sample\n fams = defaultdict(list) # stores fam_id -> sample-pairs\n for pair in parent_child_pairs:\n if pair[0] in s_fam:\n if pair[1] in s_fam:\n if (\n s_fam[pair[0]] != s_fam[pair[1]]\n ): # If both samples are in different families, merge the families\n new_fam_id = s_fam[pair[0]]\n fam_id_to_merge = s_fam[pair[1]]\n for s in s_fam:\n if s_fam[s] == fam_id_to_merge:\n s_fam[s] = new_fam_id\n fams[new_fam_id].extend(fams.pop(fam_id_to_merge))\n else: # If only the 1st sample in the pair is already in a family, assign the 2nd sample in the pair to the same family\n s_fam[pair[1]] = s_fam[pair[0]]\n fams[s_fam[pair[0]]].append(pair)\n elif (\n pair[1] in s_fam\n ): # If only the 2nd sample in the pair is already in a family, assign the 1st sample in the pair to the same family\n s_fam[pair[0]] = s_fam[pair[1]]\n fams[s_fam[pair[1]]].append(pair)\n else: # If none of the samples in the pair is already in a family, create a new family\n s_fam[pair[0]] = fam_id\n s_fam[pair[1]] = fam_id\n fams[fam_id].append(pair)\n fam_id += 1\n\n return list(fams.values())\n\n def get_trios(\n fam_id: str,\n parent_child_pairs: List[Tuple[str, str]],\n related_pairs: Dict[Tuple[str, str], str],\n ) -> List[hl.Trio]:\n \"\"\"\n Generate trios based on the list of parent-child pairs in the family and all related pairs in the data.\n\n Only complete parent/offspring trios are included in the results.\n\n The trios are assembled as follows:\n 1. All pairs of unrelated samples with different sexes within the family are extracted as possible parent pairs\n 2. For each possible parent pair, a list of all children is constructed (each child in the list has a parent-offspring pair with each parent)\n 3. If there are multiple children for a given parent pair, all children should be siblings with each other\n 4. Check that each child was only assigned a single pair of parents. If a child is found to have multiple parent pairs, they are ALL discarded.\n\n :param fam_id: The family ID\n :param parent_child_pairs: The parent-child pairs for this family\n :param related_pairs: All related sample pairs in the data\n :return: List of trios in the family\n \"\"\"\n\n def get_possible_parents(samples: List[str]) -> List[Tuple[str, str]]:\n \"\"\"\n Return all pairs of unrelated samples with different sexes within the family are extracted as possible parent pairs.\n\n :param samples: All samples in the family\n :return: Possible parent pairs\n \"\"\"\n possible_parents = []\n for i in range(len(samples)):\n for j in range(i + 1, len(samples)):\n if (\n related_pairs.get(tuple(sorted([samples[i], samples[j]])))\n is None\n ):\n if sex.get(samples[i]) is False and sex.get(samples[j]) is True:\n possible_parents.append((samples[i], samples[j]))\n elif (\n sex.get(samples[i]) is True and sex.get(samples[j]) is False\n ):\n possible_parents.append((samples[j], samples[i]))\n return possible_parents\n\n def get_children(possible_parents: Tuple[str, str]) -> List[str]:\n \"\"\"\n Construct a list of all children for a given possible parent pair.\n\n Each child in the list has a parent-offspring pair with each parent.\n\n :param possible_parents: A pair of possible parents\n :return: The list of all children (if any) corresponding to the possible parents\n \"\"\"\n possible_offsprings = defaultdict(\n set\n ) # stores sample -> set of parents in the possible_parents where (sample, parent) is found in possible_child_pairs\n for pair in parent_child_pairs:\n if possible_parents[0] == pair[0]:\n possible_offsprings[pair[1]].add(possible_parents[0])\n elif possible_parents[0] == pair[1]:\n possible_offsprings[pair[0]].add(possible_parents[0])\n elif possible_parents[1] == pair[0]:\n possible_offsprings[pair[1]].add(possible_parents[1])\n elif possible_parents[1] == pair[1]:\n possible_offsprings[pair[0]].add(possible_parents[1])\n\n return [\n s for s, parents in possible_offsprings.items() if len(parents) == 2\n ]\n\n def check_sibs(children: List[str]) -> bool:\n \"\"\"\n Confirm that all children of a parent pair are siblings with each other.\n\n If there are multiple children for a given parent pair, all children should be siblings with each other.\n\n :param children: List of all children for a given parent pair\n :return: Whether all children in the list are siblings\n \"\"\"\n for i in range(len(children)):\n for j in range(i + 1, len(children)):\n if (\n related_pairs[tuple(sorted([children[i], children[j]]))]\n != SIBLINGS\n ):\n return False\n return True\n\n def discard_multi_parents_children(trios: List[hl.Trio]):\n \"\"\"\n Check that each child was only assigned a single pair of parents.\n\n If a child is found to have multiple parent pairs, they are ALL discarded.\n\n :param trios: All trios formed for this family\n :return: The list of trios for which each child has a single parents pair.\n \"\"\"\n children_trios = defaultdict(list)\n for trio in trios:\n children_trios[trio.s].append(trio)\n\n for s, s_trios in children_trios.items():\n if len(s_trios) > 1:\n logger.warning(\n \"Discarded duplicated child %s found multiple in trios: %s\",\n s,\n \", \".join([str(trio) for trio in s_trios]),\n )\n\n return [trios[0] for trios in children_trios.values() if len(trios) == 1]\n\n # Get all possible pairs of parents in (father, mother) order\n all_possible_parents = get_possible_parents(\n list({s for pair in parent_child_pairs for s in pair})\n )\n\n trios = []\n for possible_parents in all_possible_parents:\n children = get_children(possible_parents)\n if check_sibs(children):\n trios.extend(\n [\n hl.Trio(\n s=s,\n fam_id=fam_id,\n pat_id=possible_parents[0],\n mat_id=possible_parents[1],\n is_female=sex.get(s),\n )\n for s in children\n ]\n )\n else:\n logger.warning(\n \"Discarded family with same parents, and multiple offspring that weren't siblings:\"\n \"\\nMother: %s\\nFather:%s\\nChildren:%s\",\n possible_parents[0],\n possible_parents[1],\n \", \".join(children),\n )\n\n return discard_multi_parents_children(trios)\n\n # Get all the relations we care about:\n # => Remove unrelateds and duplicates\n dups = duplicate_samples_ht.aggregate(\n hl.agg.explode(\n lambda dup: hl.agg.collect_as_set(dup), duplicate_samples_ht.filtered\n ),\n _localize=False,\n )\n relationship_ht = relationship_ht.filter(\n ~dups.contains(relationship_ht[i_col])\n & ~dups.contains(relationship_ht[j_col])\n & (relationship_ht[relationship_col] != UNRELATED)\n )\n\n # Check relatedness table format\n if not relationship_ht[i_col].dtype == relationship_ht[j_col].dtype:\n logger.error(\n \"i_col and j_col of the relatedness table need to be of the same type.\"\n )\n\n # If i_col and j_col aren't str, then convert them\n if not isinstance(relationship_ht[i_col], hl.expr.StringExpression):\n logger.warning(\n \"Pedigrees can only be constructed from string IDs, but your relatedness_ht ID column is of type: %s. \"\n \"Expression will be converted to string in Pedigrees.\",\n relationship_ht[i_col].dtype,\n )\n if isinstance(relationship_ht[i_col], hl.expr.StructExpression):\n logger.warning(\n \"Struct fields %s will be joined by underscores to use as sample names in Pedigree.\",\n list(relationship_ht[i_col]),\n )\n relationship_ht = relationship_ht.key_by(\n **{\n i_col: hl.delimit(\n hl.array(\n [\n hl.str(relationship_ht[i_col][x])\n for x in relationship_ht[i_col]\n ]\n ),\n \"_\",\n ),\n j_col: hl.delimit(\n hl.array(\n [\n hl.str(relationship_ht[j_col][x])\n for x in relationship_ht[j_col]\n ]\n ),\n \"_\",\n ),\n }\n )\n else:\n raise NotImplementedError(\n \"The `i_col` and `j_col` columns of the `relationship_ht` argument passed to infer_families are not of type StringExpression or Struct.\"\n )\n\n # If sex is a Table, extract sex information as a Dict\n if isinstance(sex, hl.Table):\n sex = dict(hl.tuple([sex.s, sex.is_female]).collect())\n\n # Collect all related sample pairs and\n # create a dictionnary with pairs as keys and relationships as values\n # Sample-pairs are tuples ordered by sample name\n related_pairs = {\n tuple(sorted([i, j])): rel\n for i, j, rel in hl.tuple(\n [relationship_ht.i, relationship_ht.j, relationship_ht.relationship]\n ).collect()\n }\n\n parent_child_pairs_by_fam = group_parent_child_pairs_by_fam(\n [pair for pair, rel in related_pairs.items() if rel == PARENT_CHILD]\n )\n return hl.Pedigree(\n [\n trio\n for fam_index, parent_child_pairs in enumerate(parent_child_pairs_by_fam)\n for trio in get_trios(str(fam_index), parent_child_pairs, related_pairs)\n ]\n )","title":""},{"docid":"352eca632ae0bc5006907e56936c1a17","score":"0.5232809","text":"def _process_species(self) -> None:\n species = self.sbml.getListOfSpecies()\n\n self.species_index = {\n species_element.getId(): species_index\n for species_index, species_element in enumerate(species)\n }\n\n self.symbols['species']['identifier'] = sp.Matrix(\n [sp.Symbol(spec.getId(), real=True) for spec in species]\n )\n self.symbols['species']['name'] = [spec.getName() for spec in species]\n\n self.species_compartment = sp.Matrix(\n [sp.Symbol(spec.getCompartment(), real=True) for spec in species]\n )\n\n self.constant_species = [species_element.getId()\n for species_element in species\n if species_element.getConstant()]\n\n self.boundary_condition_species = [\n species_element.getId()\n for species_element in species\n if species_element.getBoundaryCondition()\n ]\n self.species_has_only_substance_units = [\n specie.getHasOnlySubstanceUnits() for specie in species\n ]\n\n concentrations = [spec.getInitialConcentration() for spec in species]\n amounts = [spec.getInitialAmount() for spec in species]\n\n def get_species_initial(index, conc):\n # We always simulate concentrations!\n if self.species_has_only_substance_units[index]:\n if species[index].isSetInitialAmount() \\\n and not math.isnan(amounts[index]):\n return sp.sympify(amounts[index]) \\\n / self.species_compartment[index]\n if species[index].isSetInitialConcentration():\n return sp.sympify(conc)\n else:\n if species[index].isSetInitialConcentration():\n return sp.sympify(conc)\n\n if species[index].isSetInitialAmount() \\\n and not math.isnan(amounts[index]):\n return sp.sympify(amounts[index]) \\\n / self.species_compartment[index]\n\n return self.symbols['species']['identifier'][index]\n\n species_initial = sp.Matrix(\n [get_species_initial(index, conc)\n for index, conc in enumerate(concentrations)]\n )\n\n species_ids = [spec.getId() for spec in self.sbml.getListOfSpecies()]\n for initial_assignment in self.sbml.getListOfInitialAssignments():\n if initial_assignment.getId() in species_ids:\n index = species_ids.index(\n initial_assignment.getId()\n )\n sym_math = sp.sympify(_parse_logical_operators(\n sbml.formulaToL3String(initial_assignment.getMath())),\n locals=self.local_symbols\n )\n if sym_math is not None:\n sym_math = _parse_special_functions(sym_math)\n _check_unsupported_functions(sym_math, 'InitialAssignment')\n species_initial[index] = sym_math\n\n for ix, (symbol, init) in enumerate(zip(\n self.symbols['species']['identifier'], species_initial\n )):\n if symbol == init:\n species_initial[ix] = sp.sympify(0.0)\n\n # flatten initSpecies\n while any([species in species_initial.free_symbols\n for species in self.symbols['species']['identifier']]):\n species_initial = species_initial.subs([\n (symbol, init)\n for symbol, init in zip(\n self.symbols['species']['identifier'], species_initial\n )\n ])\n\n self.symbols['species']['value'] = species_initial\n\n if self.sbml.isSetConversionFactor():\n conversion_factor = sp.Symbol(self.sbml.getConversionFactor(),\n real=True)\n else:\n conversion_factor = 1.0\n\n self.species_conversion_factor = sp.Matrix([\n sp.sympify(specie.getConversionFactor())\n if specie.isSetConversionFactor()\n else conversion_factor\n for specie in species\n ])\n\n # Process assignment and rate rules for species and compartments.\n # Compartments with rate rules are implemented as species. Species and\n # compartments with assignments are implemented as observables (and\n # replaced with their assignment in all expressions). Note that, in the\n # case of species, rate rules may describe the change in amount, not\n # concentration, of a species.\n rules = self.sbml.getListOfRules()\n compartmentvars = self.compartment_symbols.free_symbols\n # compartments with rules are replaced with constants in the relevant\n # equations during the _replace_in_all_expressions call inside\n # _process_rules\n for rule in rules:\n ### Taken from _process_rules\n if rule.getFormula() == '':\n continue\n variable = sp.sympify(rule.getVariable(),\n locals=self.local_symbols)\n formula = sp.sympify(_parse_logical_operators(\n sbml.formulaToL3String(rule.getMath())),\n locals=self.local_symbols)\n formula = _parse_special_functions(formula)\n _check_unsupported_functions(formula, 'Rule')\n ###\n\n # Species rules are processed first, to avoid processing\n # compartments twice (as compartments with rate rules are\n # implemented as species). Could also be avoided with a\n # `not in self.compartment_rate_rules` condition.\n if variable in self.symbols['species']['identifier']:\n if rule.getTypeCode() == sbml.SBML_ASSIGNMENT_RULE:\n # Handled in _process_rules and _process_observables.\n pass\n elif rule.getTypeCode() == sbml.SBML_RATE_RULE:\n self.add_d_dt(\n formula,\n variable,\n self.symbols['species']['value'],\n sbml.SBML_SPECIES)\n else:\n raise SBMLException('The only rules currently supported '\n 'for species are assignment and rate '\n 'rules!')\n\n if variable in compartmentvars:\n if rule.getTypeCode() == sbml.SBML_ASSIGNMENT_RULE:\n # Handled in _process_rules and _process_observables\n # SBML Assignment Rules can be used to specify initial\n # values (see SBML L3V2 manual, Section 3.4.8).\n # Priority appears to be above InitialAssignment.\n self.compartment_volume[list(\n self.compartment_symbols\n ).index(variable)] = formula\n elif rule.getTypeCode() == sbml.SBML_RATE_RULE:\n self.add_d_dt(\n formula,\n variable,\n self.compartment_volume[list(\n self.compartment_symbols\n ).index(variable)],\n sbml.SBML_COMPARTMENT)\n else:\n raise SBMLException('The only rules currently supported '\n 'for compartments are assignment and '\n 'rate rules!')","title":""},{"docid":"65395feb9ba2d85229f67770d1d3d4ee","score":"0.5211056","text":"def species(self):\n pass","title":""},{"docid":"db03b4da78f79b9f1d2a045b72773438","score":"0.51774824","text":"def project_family(sets, X):\n proj_family = set({})\n for A in sets:\n proj_family.update(set({frozenset(A.difference(X))}))\n return proj_family","title":""},{"docid":"d2d0354a965108c9a727cdb4ecb5591a","score":"0.5156593","text":"def iter_families(self):\n raise NotImplementedError","title":""},{"docid":"0ced6999b2afb21feb92a401cb5f6d66","score":"0.5075314","text":"def populate(self):\n for organism in assemblies_by_org:\n # for organism in [\"Homo sapiens\"]:\n self.populate_by_org(organism)","title":""},{"docid":"f91221730a44a57fd5d02c7df14ad5c1","score":"0.50619406","text":"def iter_families(self):\n return map(self.get_family_from_handle, self.flist)","title":""},{"docid":"b654353aed5ea5362d61deef62dc285b","score":"0.50481504","text":"def get_species(self) -> Set[Specie]:\n return set(self.species)","title":""},{"docid":"eb80b2fe1eb05c3a70308fee901a5513","score":"0.50437903","text":"def get_genus_family(soup, id_name):\n genus_list = []\n family_list = []\n\n for plant in soup.find_all(id=id_name):\n for genus in plant.find_all(class_=re.compile(\"genus\")):\n name = genus.get_text()\n genus_list.append(name)\n for family in plant.find_all(class_=\"family\"):\n name2 = family.get_text()\n family_list.append(name2)\n\n # join family_list and genus_list into a dataframe.\n result = pd.concat([pd.DataFrame(genus_list, columns=['genus']),\n pd.DataFrame(family_list, columns=['family'])], axis=1)\n return result","title":""},{"docid":"af0667e29dad878e203ed55b706fd0f4","score":"0.49826998","text":"def mixer(self, F):\n ## get number of streams\n n_streams = np.size(F)\n ## verify if there are more than one stream\n if n_streams > 1: \n ## get number of species of key stream\n n_species = self[0].n_species\n ## preallocate variable\n P = np.zeros(n_streams)\n h = 0.0\n ## set initial vector of mass (kg/s)\n mass = F[0]*self[0].Y # [kg/s][kg/kg]=[kg/s] \n for j in range(n_streams):\n ## get pressure of streams\n P[j] = self[j].P\n ## get total energy of streams\n h += F[j]*self[j].enthalpy_mass # [kg/s][J/kg]=[J/s]\n if j > 0:\n for k in range(self[j].n_species):\n for i in range(n_species):\n if self[0].species_name(i) == self[j].species_name(k):\n ## get total mass of each species\n mass[i] += F[j]*self[j].Y[k] # [kg/s] \n ## mix stream\n ## mass fraction\n mass = mass/sum(mass) # [kg/kg]\n ## specific enthalpy\n h = h/np.sum(F) # [J/s][s/kg]=[J/kg]\n ## pressure\n P = np.amin(P) # [Pa]\n ## mass flow\n F = np.sum(F) \n ## redefine stream #1 as mix stream\n self = self[0] \n ## re-calculate mix stream state\n self.Y = mass\n self.HP = h, P\n return self, F","title":""},{"docid":"94ff0e418995e409192e7ee82ad9ad77","score":"0.49815553","text":"def species(self):\n return list(species_from_basis(self._basis))","title":""},{"docid":"8360c53d8a9d2075d7ce443d62de0a29","score":"0.49427173","text":"def family(self):\n return choice([\n 'Alkali Metals',\n 'Alkaline Earth Metals',\n 'Transition Metals',\n 'Earth Metals',\n 'Tetrels',\n 'Pnictogens',\n 'Chalcogens',\n 'Halogens',\n 'Noble Gases',\n ])","title":""},{"docid":"72a131c93af6cbf2dd9b2509190da531","score":"0.4933283","text":"def filterBySpecies(self, species_list):\n mapSpecies = {'hs' : 'Homo sapiens', 'mm' : 'Mus musculus',\n 'dm' : 'Drosophila melanogaster',\n 'ce' : 'Caenorhabditis elegans'}\n #MAP short names to long names\n sl = map(lambda s: mapSpecies[s], species_list)\n motifs = []\n for m in self:\n #take the motif IFF species = [], OR an element in species is\n #in species_list\n if m.species:\n #print m.species\n found = False\n i = 0\n while (i < len(m.species) and not found):\n if (m.species[i] in sl): found = True\n i += 1\n if found: motifs.append(m)\n else:\n motifs.append(m)\n return MotifList(motifs)","title":""},{"docid":"0aa88ba80cae9ab00665991f2ed09e29","score":"0.49098432","text":"def GetUniqueSpeciesGroups(self):\n sources_set = set()\n for sg in self.all_species_groups:\n source = sg.formation_energy_source\n if source not in sources_set:\n sources_set.add(source)\n yield sg","title":""},{"docid":"41cf28eff184cc297a6a2cf573d26da7","score":"0.49084294","text":"def get_families_by_genus_type(self, *args, **kwargs):\n # Implemented from kitosid template for -\n # osid.resource.BinLookupSession.get_bins_by_genus_type\n catalogs = self._get_provider_session('family_lookup_session').get_families_by_genus_type(*args, **kwargs)\n cat_list = []\n for cat in catalogs:\n cat_list.append(Family(self._provider_manager, cat, self._runtime, self._proxy))\n return FamilyList(cat_list)","title":""},{"docid":"6283cd8c6833f8ed5cc5ec033f93e73d","score":"0.48841792","text":"def update_species(self):\n species = []\n warn(\"Unsubclassed update_species called for \" + repr(self))\n return species","title":""},{"docid":"3235323a30d0d194da9b16e82154c144","score":"0.4878384","text":"def evolve(self):\n self.update_fitness()\n global_fitness_sum = 0\n for s in self._species:\n global_fitness_sum += s._fitness_sum\n\n if global_fitness_sum == 0:\n # No progress, mutate everybody\n for s in self._species:\n for g in s._members:\n g.mutate()\n else:\n # 1. Eliminate lowest performing genomes per specie\n # 2. Repopulate\n for s in self._species:\n s.cull_genomes(False)\n\n children = []\n for i, s in enumerate(self._species):\n ratio = s._fitness_sum/global_fitness_sum\n offspring = math.floor(ratio * (self._population-self.get_population()))\n for j in range(int(offspring)):\n children.append(s.breed())\n\n for g in children:\n self.classify_genome(g)\n\n self._generation += 1","title":""},{"docid":"9e33e5faf465f437a0ccd1aa07ac1ced","score":"0.48525405","text":"def GetSpecies(self):\n if self._all_species is None:\n self._all_species = self.species.all()\n return self._all_species","title":""},{"docid":"f46757afcf728cf2f9e720ae9c13b976","score":"0.48503226","text":"def extractFeatures(self, num_samples_to_keep=3):\n res = []\n for i, row in self.variants.iterrows():\n try:\n ind_id = row['ind_id']\n fname = self.ped.getIndivVCF(ind_id)\n if fname is None:\n continue\n fam_id = self.ped.getFamily(ind_id)\n chrom = row['chr'] \n pos_vcf = row['pos']\n print ind_id, fam_id, chrom, pos_vcf - 1, fname\n myvars = variants.Variants(fname, fam_id, chrom, pos_vcf - 1, pos_vcf)\n myvars.readFromVcf()\n df = myvars.variants\n if df is None:\n continue\n if df.empty:\n continue\n if num_samples_to_keep != 1 and num_samples_to_keep != 3:\n res.append(df)\n continue\n samples_to_keep = {ind_id: 'offspring'}\n if num_samples_to_keep == 3:\n samples_to_keep[self.ped.getFather(fam_id)] = 'father'\n samples_to_keep[self.ped.getMother(fam_id)] = 'mother'\n a = set(samples_to_keep.keys())\n b = set(myvars.samples)\n if a.intersection(b) != a:\n print 'some amples from ', a, ' are not found in ', b \n raise\n samples_to_drop = b.difference(a)\n clmns_to_drop = []\n for smpl in samples_to_drop:\n for c in df.columns:\n if smpl in c:\n clmns_to_drop.append(c)\n else:\n continue\n if clmns_to_drop:\n df.drop(clmns_to_drop, axis=1, inplace=True)\n #now rename remaining format columns\n new_col_names = list(df.columns)\n for i, c in enumerate(df.columns):\n for key, item in samples_to_keep.iteritems():\n if key in c:\n new_col_names[i] = c.replace(key, item)\n else:\n continue\n df.columns = new_col_names\n df['pheno'] = self.ped.isAffected(ind_id)\n df['ind_id'] = ind_id\n res.append(df)\n except:\n continue\n\n # x = [i for i in res if not i is None]\n # y = [i for i in x if not i.empty]\n# df = self.variants.apply(lambda row: self._fileHasVariant(\\\n# self.ped.getIndivVCF(row['ind_id']),\\\n# self.ped.getFamily(row['ind_id']),\\\n# row['chr'], row['pos'] - 1, row['pos']), axis=1)\n# df = df[df.notnull()]\n# c1 =df.apply(lambda x: x.tolist() != [])\n# df = df[c1]\n# res = [x for y in df for x in y.tolist()]\n return res","title":""},{"docid":"903906683164346e53c20665f60043c8","score":"0.48450297","text":"def populate_catalog_families():\n for family in FAMILIES:\n sql = insert_family.substitute(family)\n request_to_postgres(sql)","title":""},{"docid":"0b818328174dd4af8bff0b064f9cc307","score":"0.48357406","text":"def test_species_from_species_at_sites():\n species_at_sites = [\"Si\"]\n assert [d.dict() for d in species_from_species_at_sites(species_at_sites)] == [\n {\n \"name\": \"Si\",\n \"concentration\": [1.0],\n \"chemical_symbols\": [\"Si\"],\n \"attached\": None,\n \"mass\": None,\n \"original_name\": None,\n \"nattached\": None,\n },\n ]\n\n species_at_sites = [\"Si\", \"Si\", \"O\", \"O\", \"O\", \"O\"]\n assert sorted(\n [d.dict() for d in species_from_species_at_sites(species_at_sites)],\n key=lambda _: _[\"name\"],\n ) == sorted(\n [\n {\n \"name\": \"O\",\n \"concentration\": [1.0],\n \"chemical_symbols\": [\"O\"],\n \"attached\": None,\n \"mass\": None,\n \"original_name\": None,\n \"nattached\": None,\n },\n {\n \"name\": \"Si\",\n \"concentration\": [1.0],\n \"chemical_symbols\": [\"Si\"],\n \"attached\": None,\n \"mass\": None,\n \"original_name\": None,\n \"nattached\": None,\n },\n ],\n key=lambda _: _[\"name\"],\n )","title":""},{"docid":"6b98886bb95d8405f27f0a6f135850be","score":"0.48295453","text":"def iterclust(self, species=[]):\n for cf in self._basis[\"cluster_functions\"]:\n #TODO: Not too flexible. Also should this iterator stuff just be it's own class?\n good_species=True\n for sp in species:\n formula=cf[\"prototype_function\"]\n if not self._formula_has_specie(formula, sp):\n good_species=False\n break\n\n if not good_species:\n continue\n\n yield cf\n \n return","title":""},{"docid":"bdea447bf7069831ab605fb4b713bb4b","score":"0.48099178","text":"def testFamily(self):\n\t\t\n\t\tG = self.G\n\t\tc,s,r,w = G.v['c'],G.v['s'], \\\n\t\t\t\t G.v['r'],G.v['w']\n\t\t\n\t\tassert(c.distribution.parents == [] and \\\n\t\t\t set(w.distribution.parents) == set([r,s]) and \\\n\t\t\t r.distribution.parents == [c] and \\\n\t\t\t s.distribution.parents == [c]), \\\n\t\t\t \"Error with parents\"\n\n\t\tassert(c.distribution.family == [c] and \\\n\t\t\t set(s.distribution.family) == set([c,s]) and \\\n\t\t\t set(r.distribution.family) == set([r,c]) and \\\n\t\t\t set(w.distribution.family) == set([w,r,s])), \\\n\t\t\t \"Error with family\"\n\n\t\tassert(c.distribution.nvalues == c.nvalues), \\\n\t\t\t\t\"nvalues not set properly\"","title":""},{"docid":"60c427e44edf3f59c6a5e3f887835020","score":"0.4809334","text":"def fill_ingredient_families():\n families_choices = [('', '-----------')]\n\n for family in IngredientFamily.objects.all():\n families_choices.append((family.id, family.name))\n\n return list(families_choices)","title":""},{"docid":"cfa8207217672c3b88aaad3bb8ea1a73","score":"0.47952598","text":"def gas_species(self):\n return NotImplementedError","title":""},{"docid":"b8c5d3422d99cdb58ff409b92501c6f0","score":"0.47798732","text":"def basis_species(self):\n return [k for k in self._species_to_basis]","title":""},{"docid":"30a91b9e187f627da2ed208389cc4640","score":"0.47707072","text":"def get_families(connected_families: List[CompoundFamily], bgc_names: Dict[str, str]) -> Dict[str, Set[str]]:\n families_names = {}\n for index, components in enumerate(sorted(connected_families,\n key=len,\n reverse=True),\n start=1):\n family_name = str(index) + \"_\" + components.compound_class\n for component in components.clusters:\n # don't add the same name over and over\n if component.startswith(\"BGC\") \\\n and not bgc_names[component] in family_name:\n family_name += \"_\" + bgc_names[component]\n families_names[family_name] = components.clusters\n return families_names","title":""},{"docid":"5a9ef95c7d1d667256ee4a66221ee8be","score":"0.47702327","text":"def mix_molecules_regexp(self, mix, molecules):\n molecules_accepted = []\n for mol in mix:\n molecules = self.molecules_regexp(mol, molecules)\n molecules_accepted.append(molecules)\n molecules_matching_all = molecules_accepted[0]\n for i in range(1, len(molecules_accepted)):\n d1 = molecules_accepted[i]\n molecules_matching_all = {k:v for k, v in molecules_matching_all.items() if k in d1}\n return molecules_matching_all","title":""},{"docid":"e716d88a0e57f9640898771e9c66c132","score":"0.4769611","text":"def _get_families(self):\n families = {}\n for directory in (self.path_ofl, self.path_ufl, self.path_apache):\n for family_dir in os.listdir(directory):\n if not '.' in family_dir:\n family_path = os.path.join(self.path, directory, family_dir)\n families[family_dir] = Family(family_path)\n return families","title":""},{"docid":"64268a755928c21df2477530f04ff2f4","score":"0.47690684","text":"def makeFromNetLists(self, speciesList, varietyList):\n self.flowerlist = []\n # Fill in the lists by running through the parallel lists of properties\n for species, variety in zip(speciesList, varietyList):\n self.flowerlist.append( FlowerBase.FlowerBase(species, variety) )","title":""},{"docid":"bf8b2617e4b2c61f9b78a601f15a2567","score":"0.4759988","text":"def remove_families_from(filtering, filtered_list):\n if 'family' in filtering and filtering.get('family') != 'none':\n to_be_excluded_list = []\n families_attr = ['famille_{0}'.format(i) for i in range(1, 6)]\n for he_nom in filtered_list:\n he = HuileEssentielle.objects.get(pk=he_nom)\n families = []\n for attr in families_attr:\n he_famille = getattr(he, attr)\n if he_famille is not None:\n families.append(he_famille.nom)\n if filtering.get('family') not in families:\n to_be_excluded_list.append(he_nom)\n for excluded in to_be_excluded_list:\n filtered_list.remove(excluded)\n return filtered_list","title":""},{"docid":"6fdc1b698092f29d269ac9305ce08920","score":"0.47598493","text":"def get_families_by_ids(self, *args, **kwargs):\n # Implemented from kitosid template for -\n # osid.resource.BinLookupSession.get_bins_by_ids\n catalogs = self._get_provider_session('family_lookup_session').get_families_by_ids(*args, **kwargs)\n cat_list = []\n for cat in catalogs:\n cat_list.append(Family(self._provider_manager, cat, self._runtime, self._proxy))\n return FamilyList(cat_list)","title":""},{"docid":"78bcd06cddfde7ffa81f68518fdd2d9d","score":"0.47579056","text":"def calculate(self, Species='all', method='default'):\n\n\n if (Species == 'all') or (Species == 'default'):\n species = ('CO' , 'CO2' , 'SO2' , 'OC' , 'BC' , 'NH3' , \n 'PM25', 'NO' , 'MEK' , 'C3H6', 'C2H6', 'C3H8', \n 'ALK4', 'ALD2', 'CH2O', 'ACET', 'CH4')\n else:\n species = Species[:]\n\n\n # factor needed to convert B_f from [g/kg] to [kg/kg]\n units_factor = 1e-3\n\n n_biomes = len(self.biomes)\n\n A_l = zeros((self.im, self.jm))\n A_w = zeros((self.im, self.jm))\n A_c = zeros((self.im, self.jm))\n\n for sat in self.Sat:\n A_l += self.Land[sat]\n A_w += self.Water[sat]\n A_c += self.Cloud[sat]\n\n A_o = A_l + A_w\n\n i = (A_l > 0)\n j = ((A_l + A_c) > 0)\n\n E = {}\n E_= {}\n for s in species:\n E[s] = zeros((n_biomes, self.im, self.jm))\n E_[s] = zeros((n_biomes, self.im, self.jm))\n\n for sat in self.Sat:\n FRP = self.FRP[sat]\n F = self.F[sat]\n A_ = self.Cloud[sat]\n \n for b in range(n_biomes):\n E[s][b,:,:] += units_factor * A_f[s][b] * S_f[sat] * B_f[s][b] * FRP[b]\n E_[s][b,:,:] += units_factor * A_f[s][b] * S_f[sat] * B_f[s][b] * F[b] * A_\n \n for b in range(n_biomes):\n E_b = E[s][b,:,:]\n E_b_ = E_[s][b,:,:]\n\n if (method == 'default') or (method == 'sequential'):\n E_b[j] = ( (E_b[j] / (A_o[j] + A_c[j])) * (1 + A_c[j] / (A_l[j] + A_c[j])) +\n (E_b_[j] / (A_o[j] + A_c[j])) * ( A_c[j] / (A_l[j] + A_c[j])) )\n\n if method == 'sequential-zero':\n E_b[i] = E_b[i] / (A_o[i] + A_c[i]) * (1.0 + A_c[i] / (A_l[i] + A_c[i]))\n\n if method == 'nofires':\n E_b[i] = E_b[i] / (A_o[i] + A_c[i])\n\n if method == 'similarity':\n E_b[i] = E_b[i] / A_l[i] * ((A_l[i] + A_c[i]) / (A_o[i] + A_c[i]))\n\n if method == 'similarity_qfed-2.2':\n E_b[i] = E_b[i] / A_o[i]\n \n\n# Save Forecast dictionary\n# ------------------------\n dt = 1.0 # days\n tau = 3.0 # days\n\n for sat in self.Sat:\n for b in range(n_biomes):\n s = species[0]\n\n self.F[sat][b][:,:] = (E[s][b,:,:] / (units_factor * A_f[s][b] * S_f[sat] * B_f[s][b])) * exp(-dt/tau)\n self.F[sat][b][j] = self.F[sat][b][j] * ((A_o[j] + A_c[j]) / (A_l[j] + A_c[j]))\n\n# Save Emission dictionary\n# ------------------------\n self.Species = species\n self.Emissions = E","title":""},{"docid":"e871fa81235e14ff172d7acc7ea87883","score":"0.47530967","text":"def group_parent_child_pairs_by_fam(\n parent_child_pairs: Iterable[Tuple[str, str]]\n ) -> List[List[Tuple[str, str]]]:\n fam_id = 1 # stores the current family id\n s_fam = dict() # stores the family id for each sample\n fams = defaultdict(list) # stores fam_id -> sample-pairs\n for pair in parent_child_pairs:\n if pair[0] in s_fam:\n if pair[1] in s_fam:\n if (\n s_fam[pair[0]] != s_fam[pair[1]]\n ): # If both samples are in different families, merge the families\n new_fam_id = s_fam[pair[0]]\n fam_id_to_merge = s_fam[pair[1]]\n for s in s_fam:\n if s_fam[s] == fam_id_to_merge:\n s_fam[s] = new_fam_id\n fams[new_fam_id].extend(fams.pop(fam_id_to_merge))\n else: # If only the 1st sample in the pair is already in a family, assign the 2nd sample in the pair to the same family\n s_fam[pair[1]] = s_fam[pair[0]]\n fams[s_fam[pair[0]]].append(pair)\n elif (\n pair[1] in s_fam\n ): # If only the 2nd sample in the pair is already in a family, assign the 1st sample in the pair to the same family\n s_fam[pair[0]] = s_fam[pair[1]]\n fams[s_fam[pair[1]]].append(pair)\n else: # If none of the samples in the pair is already in a family, create a new family\n s_fam[pair[0]] = fam_id\n s_fam[pair[1]] = fam_id\n fams[fam_id].append(pair)\n fam_id += 1\n\n return list(fams.values())","title":""},{"docid":"a0b385dd3647cef150ae9c345e7d8086","score":"0.474709","text":"def get_families(self):\n # Implemented from kitosid template for -\n # osid.resource.BinLookupSession.get_bins_template\n catalogs = self._get_provider_session('family_lookup_session').get_families()\n cat_list = []\n for cat in catalogs:\n cat_list.append(Family(self._provider_manager, cat, self._runtime, self._proxy))\n return FamilyList(cat_list)","title":""},{"docid":"1a4bd2191c7d280e8ed3e1725aca3547","score":"0.47307527","text":"def species(self) -> ISpecies:\n return assert_set(self._species)","title":""},{"docid":"ddd0a060562b1a824166bc104d3fa758","score":"0.4725433","text":"def test_helix_groups(self) -> None:\n helices = [sc.Helix(max_offset=7, group='a'), sc.Helix(max_offset=7, group='a'),\n sc.Helix(max_offset=7, group='b'), sc.Helix(max_offset=7, group='b')]\n groups = {\n 'a': sc.HelixGroup(position=sc.Position3D(0, 0, 0), grid=sc.honeycomb),\n 'b': sc.HelixGroup(position=sc.Position3D(100, 0, 0), grid=sc.square),\n }\n design = sc.Design(helices=helices, groups=groups)\n design.draw_strand(0, 0).move(7).cross(1).move(-7)\n design.draw_strand(2, 7).move(-7).cross(3).move(7) # unlike basic design, put strand on helices 2,3\n\n # expected values for verification\n expected_num_nucleotides = 7 * 4\n expected_strand_length = 7 * 2\n\n dat, top = design.to_oxdna_format()\n dat_lines = dat.strip().split('\\n')\n top_lines = top.strip().split('\\n')\n\n # check length of output files are as expected (matches # of nucleotides plus header size)\n self.assertEqual(expected_num_nucleotides + 3, len(dat_lines))\n self.assertEqual(expected_num_nucleotides + 1, len(top_lines))\n\n # find relevant values for nucleotides\n cm_poss = [] # center of mass position\n nbrs_3p = []\n nbrs_5p = []\n\n for line in dat_lines[3:]:\n data = line.strip().split()\n # make sure there are 15 values per line (3 values per vector * 5 vectors per line)\n # order of vectors: center of mass position, backbone base versor, normal versor, velocity, angular velocity (more info on versors: https://eater.net/quaternions)\n self.assertEqual(15, len(data))\n\n cm_poss.append(tuple([float(x) for x in data[0:3]]))\n bb_vec = tuple([float(x) for x in data[3:6]]) # backbone base vector\n nm_vec = tuple([float(x) for x in data[6:9]]) # normal vector\n\n # make sure normal vectors and backbone vectors are unit length\n sqr_bb_vec = sum([x ** 2 for x in bb_vec])\n sqr_nm_vec = sum([x ** 2 for x in nm_vec])\n self.assertAlmostEqual(1.0, sqr_bb_vec)\n self.assertAlmostEqual(1.0, sqr_nm_vec)\n\n for value in data[9:]: # values for velocity and angular velocity vectors are 0\n self.assertAlmostEqual(0, float(value))\n\n strand1_idxs = []\n strand2_idxs = []\n for nuc_idx, line in enumerate(top_lines[1:]):\n data = line.strip().split()\n # make sure there are 4 values per line: strand, base, 3' neighbor, 5' neighbor\n self.assertEqual(4, len(data))\n\n # make sure there are only 2 strands\n strand_num = int(data[0])\n self.assertIn(strand_num, [1, 2])\n # make sure base is valid\n base = data[1]\n self.assertIn(base, ['A', 'C', 'G', 'T'])\n\n nbrs_3p.append(int(data[2]))\n nbrs_5p.append(int(data[3]))\n\n # append start of strand (no 5' neighbor) to list of indexes for strand\n neighbor_5 = int(data[3])\n if neighbor_5 == -1:\n if strand_num == 1:\n strand1_start = nuc_idx\n strand1_idxs.append(strand1_start)\n else:\n strand2_start = nuc_idx\n strand2_idxs.append(strand2_start)\n\n # reconstruct strands using indices from oxDNA files\n next_idx = nbrs_3p[strand1_start]\n while next_idx >= 0:\n strand1_idxs.append(next_idx)\n next_idx = nbrs_3p[strand1_idxs[-1]]\n\n next_idx = nbrs_3p[strand2_start]\n while next_idx >= 0:\n strand2_idxs.append(next_idx)\n next_idx = nbrs_3p[strand2_idxs[-1]]\n\n # assert that strands are the correct length\n self.assertEqual(expected_strand_length, len(strand1_idxs))\n self.assertEqual(expected_strand_length, len(strand2_idxs))\n\n for i in range(expected_strand_length - 1):\n # ignore nucleotide distance between domains (on crossover)\n if i == 6:\n continue\n\n strand1_nuc_idx1 = strand1_idxs[i]\n strand1_nuc_idx2 = strand1_idxs[i + 1]\n strand2_nuc_idx1 = strand2_idxs[i]\n strand2_nuc_idx2 = strand2_idxs[i + 1]\n\n # find the center of mass for adjacent nucleotides\n s1_cmp1 = cm_poss[strand1_nuc_idx1]\n s1_cmp2 = cm_poss[strand1_nuc_idx2]\n s2_cmp1 = cm_poss[strand2_nuc_idx1]\n s2_cmp2 = cm_poss[strand2_nuc_idx2]\n\n # calculate and verify squared distance between adjacent nucleotides in a domain\n diff1 = tuple([s1_cmp1[j] - s1_cmp2[j] for j in range(3)])\n diff2 = tuple([s2_cmp1[j] - s2_cmp2[j] for j in range(3)])\n sqr_dist1 = sum([x ** 2 for x in diff1])\n sqr_dist2 = sum([x ** 2 for x in diff2])\n\n self.assertAlmostEqual(self.EXPECTED_ADJ_NUC_CM_DIST2, sqr_dist1)\n self.assertAlmostEqual(self.EXPECTED_ADJ_NUC_CM_DIST2, sqr_dist2)","title":""},{"docid":"f11192e48d1873c444fce2670835e857","score":"0.47059622","text":"def cbh_n(reactants, products, scheme, do_canonical=True):\n\n if do_canonical:\n reactants = [canonical(smiles) for smiles in reactants]\n products = [canonical(smiles) for smiles in products]\n\n left, right = resultant(reactants, products, scheme=scheme)\n\n return left, right","title":""},{"docid":"08ed140cb850a833719d77c33ae11945","score":"0.4705379","text":"def __call__(self, results):\n\n results = self._mixup_or_mosaic_transform(results)\n return results","title":""},{"docid":"dbf6ddee66483b4280034265cff3b4d3","score":"0.47023052","text":"def createDicoFamily():\n\tdicoFam = {'Coding' : ['IG_C_gene', 'IG_D_gene', 'IG_J_gene',\n\t\t\t\t\t\t\t'IG_LV_gene', 'IG_M_gene', 'IG_V_gene',\n\t\t\t\t\t\t\t'IG_Z_gene', 'nonsense_mediated_decay',\n\t\t\t\t\t\t\t'nontranslating_CDS', 'non_stop_decay',\n\t\t\t\t\t\t\t'protein_coding', 'TR_C_gene', 'TR_D_gene',\n\t\t\t\t\t\t\t'TR_gene', 'TR_J_gene', 'TR_V_gene'],\n\t\t\t\t'Pseudogene' : ['transcribed_unitary_pseudogene',\n\t\t\t\t\t\t\t\t'disrupted_domain', 'IG_C_pseudogene',\n\t\t\t\t\t\t\t\t'IG_J_pseudogene', 'IG_pseudogene',\n\t\t\t\t\t\t\t\t'IG_V_pseudogene', 'processed_pseudogene',\n\t\t\t\t\t\t\t\t'pseudogene',\n\t\t\t\t\t\t\t\t'transcribed_processed_pseudogene',\n\t\t\t\t\t\t\t\t'transcribed_unprocessed_pseudogene',\n\t\t\t\t\t\t\t\t'translated_processed_pseudogene',\n\t\t\t\t\t\t\t\t'translated_unprocessed_pseudogene',\n\t\t\t\t\t\t\t\t'TR_J_pseudogene', 'TR_V_pseudogene',\n\t\t\t\t\t\t\t\t'unitary_pseudogene', 'unprocessed_pseudogene',\n\t\t\t\t\t\t\t\t'polymorphic_pseudogene'],\n\t\t\t\t'LongNC' : ['macro_lncRNA', 'bidirectional_promoter_lncRNA',\n\t\t\t\t\t\t\t'sense_intronic', '3prime_overlapping_ncRNA',\n\t\t\t\t\t\t\t'ambiguous_orf', 'antisense',\n\t\t\t\t\t\t\t'lincRNA', 'ncrna_host','non_coding',\n\t\t\t\t\t\t\t'processed_transcript', 'retained_intron',\n\t\t\t\t\t\t\t'sense_overlapping'],\n\t\t\t\t'ShortNC' : ['vaultRNA', 'scaRNA', 'miRNA',\n\t\t\t\t\t\t\t'miRNA_pseudogene', 'misc_RNA',\n\t\t\t\t\t\t\t'misc_RNA_pseudogene', 'Mt_rRNA',\n\t\t\t\t\t\t\t'Mt_tRNA', 'Mt_tRNA_pseudogene',\n\t\t\t\t\t\t\t'ncRNA', 'pre_miRNA', 'RNase_MRP_RNA',\n\t\t\t\t\t\t\t'RNase_P_RNA', 'rRNA', 'rRNA_pseudogene',\n\t\t\t\t\t\t\t'scRNA', 'scRNA_pseudogene', 'snlRNA',\n\t\t\t\t\t\t\t'snoRNA', 'snoRNA_pseudogene', 'snRNA',\n\t\t\t\t\t\t\t'snRNA_pseudogene', 'SRP_RNA', 'tmRNA',\n\t\t\t\t\t\t\t'tRNA', 'tRNA_pseudogene','ribozyme'],\n\t\t\t\t'Predictif' : ['TEC']}\n\treturn dicoFam","title":""},{"docid":"c13d4d857a8e1473d3f195406df933d4","score":"0.4699714","text":"def select_chunks(self, samples, battleships, *args, **kwargs):\n chunks = [\n (key, chunk)\n for key in samples\n for chunk in self._generate_bship_chunks(battleships[key])\n ]\n random.shuffle(chunks)\n return chunks","title":""},{"docid":"b7f87d665b07c2b8f94dcb5320917966","score":"0.46781754","text":"def get_families_by_provider(self, *args, **kwargs):\n # Implemented from kitosid template for -\n # osid.resource.BinLookupSession.get_bins_by_provider\n catalogs = self._get_provider_session('family_lookup_session').get_families_by_provider(*args, **kwargs)\n cat_list = []\n for cat in catalogs:\n cat_list.append(Family(self._provider_manager, cat, self._runtime, self._proxy))\n return FamilyList(cat_list)","title":""},{"docid":"99462b0a555f94943322fd070a36b618","score":"0.46641037","text":"def get_variants_shared_by_father_and_son(self):\n vcfSon = vcf.Reader(open(\"AmpliseqExome.20141120.NA24385.vcf\", \"r\"))\n vcfFather = vcf.Reader(open(\"AmpliseqExome.20141120.NA24149.vcf\", \"r\"))\n sharedVariantsFatherSon = 0\n shared_variants = vcf.utils.walk_together(vcfFather, vcfSon)\n for record in shared_variants:\n if not record[0] is None and not record[1] is None:\n sharedVariantsFatherSon += 1\n print(\"\\nnumber of variants shared by father and son:\")\n print(sharedVariantsFatherSon)","title":""},{"docid":"a777fc26818d59c22d30cdb04ccf8b67","score":"0.46492454","text":"def __forAll__( c, prop_value_dict, types=['FilterDesktop','CombineParticles',\"DVAlgorithm\", \"DaVinciAlgorithm\", \"DaVinciHistoAlgorithm\", \"DaVinciTupleAlgorithm\", \"*\" ] ) :\n if type(prop_value_dict) is not dict:\n raise TypeError(\"Hey, you need to give me a dictionary, you passed me a, \"+str(type(prop_value_dict)))\n\n if \"*\" in types or \".*\" in types or c.getType() in types:\n for prop in prop_value_dict:\n if hasattr(c,prop) or (hasattr(c,\"properties\") and prop in c.properties()):\n c.setProp(prop,prop_value_dict[prop])\n\n #iterate/recurse over all subparts\n for p in [ 'Members','Filter0','Filter1' ] :\n if not hasattr(c,p) : continue\n seq = getattr(c,p)\n if type(seq) is not list: seq = [ seq ]\n for i in seq : __forAll__(i,prop_value_dict,types)","title":""},{"docid":"51c6f280b3c7201bc9eb8d6565a0dd6b","score":"0.46478474","text":"def get_families_by_parent_genus_type(self, *args, **kwargs):\n # Implemented from kitosid template for -\n # osid.resource.BinLookupSession.get_bins_by_parent_genus_type\n catalogs = self._get_provider_session('family_lookup_session').get_families_by_parent_genus_type(*args, **kwargs)\n cat_list = []\n for cat in catalogs:\n cat_list.append(Family(self._provider_manager, cat, self._runtime, self._proxy))\n return FamilyList(cat_list)","title":""},{"docid":"8eec83ee6f46f23a6d174d09f478de55","score":"0.46375394","text":"def _partition_species(composition, max_components=2):\n\n def _partition(collection):\n # thanks https://stackoverflow.com/a/30134039\n\n if len(collection) == 1:\n yield [collection]\n return\n\n first = collection[0]\n for smaller in _partition(collection[1:]):\n # insert `first` in each of the subpartition's subsets\n for n, subset in enumerate(smaller):\n yield smaller[:n] + [[first] + subset] + smaller[n + 1:]\n # put `first` in its own subset\n yield [[first]] + smaller\n\n def _sort_partitions(partitions_to_sort):\n \"\"\"\n Sort partitions by those we want to check first\n (typically, merging two sites into one is the\n one to try first).\n \"\"\"\n\n partition_indices = [(idx, [len(p) for p in partition])\n for idx, partition in enumerate(partitions_to_sort)]\n\n # sort by maximum length of partition first (try smallest maximums first)\n # and secondarily by number of partitions (most partitions first, i.e.\n # create the 'least disordered' structures first)\n partition_indices = sorted(partition_indices, key=lambda x: (max(x[1]), -len(x[1])))\n\n # merge at most max_component sites,\n # e.g. merge at most 2 species into 1 disordered site\n partition_indices = [x for x in partition_indices if max(x[1]) <= max_components]\n\n partition_indices.pop(0) # this is just the input structure\n\n sorted_partitions = [partitions_to_sort[x[0]] for x in partition_indices]\n\n return sorted_partitions\n\n collection = list(composition.keys())\n partitions = list(_partition(collection))\n partitions = _sort_partitions(partitions)\n\n return partitions","title":""},{"docid":"7dd31d1b5b908d37fe2ed9102fef8ab3","score":"0.46372262","text":"def GetSpecies(self):\n if self._species_group_to_use:\n return self._species_group_to_use.all_species\n\n # TODO(flamholz): Should we return something here?\n return None","title":""},{"docid":"8596de61a3ede4054f6159fc7646763c","score":"0.46275145","text":"def react_species(species_tuple, only_families=None):\n\n species_tuple = tuple([spc.copy(deep=True) for spc in species_tuple])\n\n reactions = getDB('kinetics').generate_reactions_from_families(species_tuple, only_families=only_families)\n\n return reactions","title":""},{"docid":"444ea5d0c93dcfe0368ed4c8340e26ae","score":"0.46246204","text":"def reproduce(self, other):\n child = Individual(None, self.fit_func)\n for i in range(16):\n child.inherit_gene(i, self, other)\n return child","title":""},{"docid":"33f16ce52a28e5a415774be6a8f66d05","score":"0.46166497","text":"def test_GeneFusion(self):\n dsl = GeneFusion(\n Gene(\"HGNC\", \"TMPRSS2\"),\n Gene(\"HGNC\", \"ERG\"),\n EnumeratedFusionRange(\"c\", 1, 79),\n EnumeratedFusionRange(\"c\", 312, 5034),\n )\n self.assertEqual('g(fus(HGNC:TMPRSS2, \"c.1_79\", HGNC:ERG, \"c.312_5034\"))', dsl.as_bel())","title":""},{"docid":"ddcbf6bfe83f9849a81984831b511e96","score":"0.46014696","text":"def _extractfaces(label_pairs):\n \n key, (subvolume, gtmap, segmap, gtvol, segvol) = label_pairs\n\n # extract unique bodies not remapped\n allgt = set(numpy.unique(gtvol))\n allseg = set(numpy.unique(segvol))\n\n gtmapbodies = set()\n for key2, body in gtmap.items():\n gtmapbodies.add(key2)\n segmapbodies = set()\n for key2, body in segmap.items():\n segmapbodies.add(key2)\n allgt = allgt.difference(gtmapbodies)\n if 0 in allgt:\n allgt.remove(0)\n allseg = allseg.difference(segmapbodies)\n if 0 in allseg:\n allseg.remove(0)\n \"\"\"\n if 0 in allseg:\n allseg.remove(0)\n if 0 in allgt:\n allgt.remove(0)\n \"\"\"\n\n zmax,ymax,xmax = gtvol.shape\n start = (subvolume.box.z1, subvolume.box.y1, subvolume.box.x1)\n\n mappedfaces = []\n\n # grab 6 faces for gt\n slicex0 = gtvol[:,:,0]\n slicexmax = gtvol[:,:,xmax-1]\n\n slicey0 = gtvol[:,0,:]\n sliceymax = gtvol[:,ymax-1,:]\n \n slicez0 = gtvol[0,:,:]\n slicezmax = gtvol[zmax-1,:,:]\n\n mappedfaces.append(( (start, (start[0]+zmax, start[1]+ymax, start[2]+1), True), \n [(slicex0, gtmap, key, True, allgt)] ))\n mappedfaces.append(( ((start[0], start[1], start[2]+xmax),\n (start[0]+zmax, start[1]+ymax, start[2]+xmax+1), True), \n [(slicexmax, gtmap, key, False, set())] ))\n \n mappedfaces.append(( (start, (start[0]+zmax, start[1]+1, start[2]+xmax), True), \n [(slicey0, gtmap, key, False, set())] ))\n mappedfaces.append(( ((start[0], start[1]+ymax, start[2]),\n (start[0]+zmax, start[1]+ymax+1, start[2]+xmax), True), \n [(sliceymax, gtmap, key, False, set())] ))\n \n mappedfaces.append(( (start, (start[0]+1, start[1]+ymax, start[2]+xmax), True), \n [(slicez0, gtmap, key, False, set())] ))\n mappedfaces.append(( ((start[0]+zmax, start[1], start[2]),\n (start[0]+zmax+1, start[1]+ymax, start[2]+xmax), True), \n [(slicezmax, gtmap, key, False, set())] ))\n\n # grab 6 faces for seg\n segslicex0 = segvol[:,:,0]\n segslicexmax = segvol[:,:,xmax-1]\n\n segslicey0 = segvol[:,0,:]\n segsliceymax = segvol[:,ymax-1,:]\n \n segslicez0 = segvol[0,:,:]\n segslicezmax = segvol[zmax-1,:,:]\n\n mappedfaces.append(( (start, (start[0]+zmax, start[1]+ymax, start[2]+1), False), \n [(segslicex0, segmap, key, True, allseg)] ))\n mappedfaces.append(( ((start[0], start[1], start[2]+xmax),\n (start[0]+zmax, start[1]+ymax, start[2]+xmax+1), False), \n [(segslicexmax, segmap, key, False, set())] ))\n \n mappedfaces.append(( (start, (start[0]+zmax, start[1]+1, start[2]+xmax), False), \n [(segslicey0, segmap, key, False, set())] ))\n mappedfaces.append(( ((start[0], start[1]+ymax, start[2]),\n (start[0]+zmax, start[1]+ymax+1, start[2]+xmax), False), \n [(segsliceymax, segmap, key, False, set())] ))\n \n mappedfaces.append(( (start, (start[0]+1, start[1]+ymax, start[2]+xmax), False), \n [(segslicez0, segmap, key, False, set())] ))\n mappedfaces.append(( ((start[0]+zmax, start[1], start[2]),\n (start[0]+zmax+1, start[1]+ymax, start[2]+xmax), False), \n [(segslicezmax, segmap, key, False, set())] ))\n \n return mappedfaces","title":""},{"docid":"b4a4daec0d394136d7881fe3e927da35","score":"0.46013784","text":"def get_possible_family_edges():\n possible_edges = []\n for family1 in POSSIBLE_FAMILIES:\n for family2 in POSSIBLE_FAMILIES:\n possible_edges.append((family1, family2))\n \n return possible_edges","title":""},{"docid":"b588e8bbd998dea711df747f6d330074","score":"0.4601263","text":"def reproduce(population):\n new_population = []\n\n for a in population:\n for b in population:\n child = []\n for n in range(0, len(a)):\n child.append(random.choice([a, b])[n])\n\n new_population.append(child)\n\n return random.sample(new_population, 5)","title":""},{"docid":"a8fec70b45b6cc6e7f299a7914fd2d88","score":"0.4593503","text":"def assign_species(df_bacteroides_data, clusters, gene_oi=\"I_6\"):\n pseudo = 1e-15\n color_dict = {\n \"thetaiotaomicron\": \"#a6d854ff\",\n \"ovatus\": \"#8da0cbff\",\n \"fragilis\": \"#ffd92fff\",\n \"oleiciplenus\": \"blue\",\n \"propionicifaciens\": \"red\",\n \"faecis\": \"yellow\",\n \"xylanisolvens\": \"#a6d854ff\",\n \"coprophilus\": \"red\"}\n plt.rcParams[\"font.family\"] = \"Arial\"\n group = 1\n # For each of the clusters on the tree\n for cluster in clusters:\n srs_use = [x for x in cluster if x in df_bacteroides_data.index]\n # Minimum 3 sequences in the cluster to consider\n if len(srs_use) >= 3:\n # Calculate the MSE\n df_error = single_species_model(df_bacteroides_data.loc[srs_use, :], transform=\"\")\n df_error.reset_index(inplace=True)\n\n plt.rc('font', size=8)\n plt.rc('xtick', labelsize=8)\n\n if (df_error.ix[0, \"Species\"] == \"xylanisolvens\") & (group == 3):\n color_use = \"#ffd92fff\"\n else:\n color_use = color_dict[df_error.ix[0, \"Species\"]]\n\n # Plot a scatter plot for the abundance of the best fitting species against the orphan immunity abundance\n # Figure 2B, D\n f, ax = plt.subplots(figsize=(1.6, 1.6))\n plt.xlim([-9.0, -7.0])\n plt.ylim([-9.0, -7.0])\n ax.plot(ax.get_xlim(), ax.get_ylim(), ls=\"--\", c=\".3\")\n ax.scatter(\n np.log10(df_bacteroides_data.loc[srs_use, \"markers_s__Bacteroides_\" + DfError.ix[0, \"Species\"] + \".fasta\"] + pseudo),\n np.log10(df_bacteroides_data.loc[srs_use, \"OrphanI\"] + pseudo), color=color_use, edgecolors=\"black\")\n plt.xlabel(\"Bacteroides %s\" % (DfError.ix[0, \"Species\"]), style=\"italic\")\n plt.ylabel(\"Orphan I\")\n plt.xticks([-9.0, -8.5, -8.0, -7.5, -7.0])\n plt.savefig(\"Figure2_Species_%s_Group_%i_%s_Scatter.pdf\" % (DfError.ix[0, \"Species\"], group, gene_oi))\n plt.savefig(\n \"Figure2_Species_%s_Group_%i_%s_Scatter_SecondHit.pdf\" % (DfError.ix[1, \"Species\"], group, gene_oi))\n plt.clf()\n f.clf()\n\n # Plot the MSE of the top 5 species as a barplot\n # Figure 2C, E\n df_error_sub = df_error.ix[:5, :]\n df_error_sub[\"Species\"] = [\"B.\" + x[:2] for x in df_error_sub[\"Species\"]]\n plt.rc('font', size=8)\n plt.rc('xtick', labelsize=6)\n f, ax = plt.subplots(figsize=(0.8, 1.6))\n ind = np.arange(df_error_sub.shape[0])\n ax.bar(ind, df_error_sub[\"MSE\"], edgecolor=\"black\", color=\"#d3d3d3\", linewidth=0.25)\n plt.xticks(ind, df_error_sub[\"Species\"], rotation=90, style=\"italic\")\n plt.title(\"MSE\")\n plt.savefig(\"Figure2_Species_%s_Group_%i_%s_MSE.pdf\" % (DfError.ix[0, \"Species\"], group, gene_oi))\n plt.clf()\n f.clf()\n\n plt.rc('font', size=8)\n plt.rc('xtick', labelsize=8)\n group += 1","title":""},{"docid":"370c59aea4293211875fe1cb6973fddc","score":"0.45925134","text":"def __species_annotation__(self,aggregation_so_far,annotation):\n for species_ann in annotation:\n species = species_ann[\"choice\"]\n\n # is the first classification we've encountered for this species?\n if species not in aggregation_so_far:\n aggregation_so_far[species] = {\"num votes\": 1,\"followup\":{}}\n\n else:\n aggregation_so_far[species][\"num votes\"] += 1\n\n # go through each of the follow up questions\n for question,answer in species_ann[\"answers\"].items():\n # is this the first time we've seen this particular followup question\n # should probably always happen the first time we see the species (since most, if not\n # all of the time, all of the follow up questions should be required)\n if question not in aggregation_so_far[species][\"followup\"]:\n aggregation_so_far[species][\"followup\"][question] = {}\n\n # if list - multiple answers are allowed\n if isinstance(answer,list):\n for ans in answer:\n # first time we've seen this particular answer?\n if ans not in aggregation_so_far[species][\"followup\"][question]:\n aggregation_so_far[species][\"followup\"][question][ans] = 1\n else:\n aggregation_so_far[species][\"followup\"][question][ans] += 1\n else:\n # first time we've seen this particular answer\n if answer not in aggregation_so_far[species][\"followup\"][question]:\n aggregation_so_far[species][\"followup\"][question][answer] = 1\n else:\n aggregation_so_far[species][\"followup\"][question][answer] += 1\n\n return aggregation_so_far","title":""},{"docid":"1fd3c413745adf8441ebe6fe1ef6f8cf","score":"0.45756882","text":"def train_family(class1path, class2path, multi_data, multi_labels, skf, shapes, name=\"image+word\", seqnum1=1, seqnum2=2):\n for i, (train_set,val_set) in enumerate(skf):\n classifier_multi = None#ensure empty\n img_encoder = nn_lib.reuse_model(class1path, 'sequential_' + str(2*i + seqnum1))\n wrd_encoder = nn_lib.reuse_model(class2path, 'sequential_' + str(2*i + seqnum2))\n classifier_multi = nn_lib.encoders2classifier(img_encoder, wrd_encoder, shapes)\n if i == 0: classifier_multi.summary()\n\n hist_it = nn_lib.train_net(classifier_multi, [medium[train_set] for medium in multi_data], multi_y[train_set], \n [medium[val_set] for medium in multi_data], multi_y[val_set], name + str(i), epochs=150, path=path_neuralmodels,\n earlystop=[keras.callbacks.EarlyStopping(monitor='val_acc', min_delta=0.002, patience=2, mode='auto')])\n\n score = classifier_multi.evaluate( [medium[val_set] for medium in multi_data], multi_y[val_set], batch_size=25)\n output.append({'type':name,'i':i, 'score':score[1]})\n history.append({'type':name, 'i':i, 'history':hist_it.history[\"val_acc\"]})","title":""},{"docid":"db76ef20977de84bba5c42aace1a6c23","score":"0.45728546","text":"def GetSpeciesGroups(self):\n if self._all_species_groups is None:\n self._all_species_groups = \\\n self.species_groups.all().prefetch_related('species')\n\n return self._all_species_groups","title":""},{"docid":"fc639a2d963234e6d666c2bb5c65e3d8","score":"0.45718077","text":"def vcf_row_to_variants(fields, het_or_hom_or_hemi=None):\n\n chrom, pos, ref, alt_alleles, info_field = fields[0], fields[1], fields[3], fields[4].split(\",\"), fields[7]\n\n if het_or_hom_or_hemi is not None:\n possible_genotypes = (het_or_hom_or_hemi, )\n else:\n possible_genotypes = (\"het\", \"hom\", \"hemi\") if chrom in ('X', 'Y') else (\"het\", \"hom\")\n\n # parse info field\n n_het_list, n_hom_list, n_hemi_list = parse_info_field(fields, alt_alleles, info_field, chrom=chrom, pos=pos)\n\n # handle genotypes if valid_sample_ids arg is not None\n sample_id_to_genotype = None\n if valid_sample_ids is not None:\n sample_id_to_genotype = parse_genotypes(fields, alt_alleles, vcf_sample_ids, chrom=chrom, pos=pos)\n\n\n for alt_allele_index, (alt, n_het, n_hom, n_hemi) in enumerate(zip(alt_alleles, n_het_list, n_hom_list, n_hemi_list)):\n minrep_pos, minrep_ref, minrep_alt = get_minimal_representation(pos, ref, alt)\n\n for het_or_hom_or_hemi in possible_genotypes:\n if het_or_hom_or_hemi == \"het\":\n n_expected_samples = n_het\n elif het_or_hom_or_hemi == \"hom\":\n n_expected_samples = n_hom\n elif het_or_hom_or_hemi == \"hemi\":\n n_expected_samples = n_hemi\n else:\n raise ValueError(\"Unexpected value for het_or_hom_or_hemi: %s\" % str(het_or_hom_or_hemi))\n\n yield VariantGT(chrom=chrom,\n pos=minrep_pos,\n ref=minrep_ref,\n alt=minrep_alt,\n alt_allele_index=alt_allele_index,\n het_or_hom_or_hemi=het_or_hom_or_hemi,\n n_expected_samples=n_expected_samples,\n all_genotypes_in_row=sample_id_to_genotype)","title":""},{"docid":"9c634f7e00d8855a082cad1ca43b3900","score":"0.45634657","text":"def getTF_Families_exp(barcode, batch, db):\n cmdStr = \"SELECT vi.TF_Family from Vaquerizas_InterproAnnot vi inner join Vaquerizas_AllPossible_DBD_InterproIDs va on vi.InterproID = va.InterproID inner join Vaquerizas_TF_DBDs vd on va.InterproID = vd.InterproID inner join Vaquerizas_TFs vt on vd.EnsembleID = vt.EnsembleID inner join TranscriptionFactors tf on vt.EnsembleID = tf.EnsembleID inner join CloneDetail cd on tf.EnsembleID = cd.EnsembleID inner join ProteinProduction pp on cd.CloneID = pp.CloneID inner join SELEX_experiments se on pp.ProductionID = se.ProductionID where se.Barcode = \\\"\" + barcode + \"\\\" and se.BarcodeBatch = \\\"\" + batch +\"\\\";\"\n cursor=db.cursor()\n cursor.execute(cmdStr)\n rows = cursor.fetchall()\n if (len(rows) > 0):\n return map(lambda row: row[0], rows)\n else:\n return None","title":""},{"docid":"32924b38d3ef0198e66ed68e1e784586","score":"0.45491555","text":"def _scattered_crossover(self, mother, father):\n\n vector = self._create_vector()\n child1 = Chromosome(config[\"num_categories\"])\n child1.set_fitness(0)\n child2 = Chromosome(config[\"num_categories\"])\n child2.set_fitness(0)\n gene_list = self.phones\n\n # exchanging the phones into their new, mated categories\n for index in range(len(gene_list)):\n # father\n if vector[index] == 0:\n category = father.get_category(gene_list[index])\n child1.insert_into_category(category,gene_list[index])\n category = mother.get_category(gene_list[index])\n child2.insert_into_category(category,gene_list[index])\n # mother\n elif vector[index] == 1:\n category = father.get_category(gene_list[index])\n child2.insert_into_category(category,gene_list[index])\n category = mother.get_category(gene_list[index])\n child1.insert_into_category(category,gene_list[index])\n return child1,child2","title":""},{"docid":"349f6010a66b67ad13b9ab5f51136989","score":"0.4547002","text":"def plot_mixtures():\n\tunity = UnimodalPrior(\"unity_with_sf\")\n\tsmal_to_seg(title=\"unimodal\", betas=unity.mean)\n\n\tshapeA = unity.sample_from()\n\tshapeB = unity.sample_from()\n\tshapeA[20] -= .4 # Short legs\n\tshapeA[22] -= .4 # Short tail\n\n\tshapeB[22] = -.3 # reasonable tail\n\tshapeB[25] = .8 # Tall ears\n\tshapeB[20] = .3 # Short legs\n\n\tsmal_to_seg(title=\"shapeA\", betas=shapeA)\n\tsmal_to_seg(title=\"shapeB\", betas=shapeB)\n\n\tshapeC = .3 * shapeA + .7 * shapeB\n\tsmal_to_seg(title=\"shapeC\", betas=shapeC)","title":""},{"docid":"08f33961fb0c86ec7fa35d35f4047c7d","score":"0.4545083","text":"def _family_ids(self):\n ancestor = self._head_of_family\n family = ancestor._descendants_ids\n return family","title":""},{"docid":"361e1c144b06c351e8799b2b9ca42215","score":"0.4539374","text":"def create_family_from_base(base):\n uc_fam = copy.deepcopy(base)\n curr_size = len(uc_fam)\n prev_size = 0\n while curr_size > prev_size:\n prev_fam = copy.deepcopy(uc_fam)\n for a in prev_fam:\n for b in base:\n uc_fam.update(set({frozenset(a.union(b))}))\n prev_size = curr_size\n curr_size = len(uc_fam)\n return uc_fam","title":""},{"docid":"b6da08df264c9f9705a61d6dc0f3e975","score":"0.4529036","text":"def _set_up_mutate(self):\n self.genes = [\n ['b', 'h', 'z', 'm', '0', 'x', 'Z'],\n ['p', 'C', 'T', '_', 'v'],\n ['k'],\n ['d', 'n'],\n ['$', 'I', 'P', '2', '5', '~'],\n ['@', 'E', 'i', '1', 'u', '7', '8', 'U'],\n ['t', 'N'],\n ['H', '#', 'r', 'c', 'F'],\n ['D', 'j', 'Q', '3', '4', 'V', '9', '{'],\n ['S', 'R', 'l', '6'],\n ['q', 's'],\n ['J', 'w', 'g', 'f']\n ]\n self.population = []\n for j in range(config[\"num_chroms_in_deviation\"]):\n new_chromosome = Chromosome(config[\"num_categories\"])\n new_chromosome.set_fitness(88.9)\n for i in range(len(self.genes) - 1):\n for gene in self.genes[i]:\n new_chromosome.insert_into_category(i, gene)\n self.population.append(new_chromosome)","title":""},{"docid":"13a9ec3c56419135ebadf7738950f442","score":"0.45252407","text":"def do_mixup(inputs, targets, alpha=1., random_seed=1233, mode=\"multilabel\"):\n random_state = np.random.RandomState(random_seed)\n bsize = len(inputs)\n lam = torch.from_numpy(random_state.beta(alpha, alpha, bsize)).to(inputs.device).float()\n perms = torch.randperm(bsize).to(inputs.device)\n if mode == \"multilabel\":\n mixed_x = inputs * lam.view(bsize, 1, 1) + inputs[perms] * (1 - lam.view(bsize, 1, 1))\n mixed_y = targets * lam.view(bsize, 1) + targets[perms] * (1 - lam.view(bsize, 1))\n return mixed_x, mixed_y, None, None\n else:\n mixed_x = inputs * lam.view(bsize, 1, 1) + inputs[perms] * (1 - lam.view(bsize, 1, 1))\n y_a, y_b = targets, targets[perms]\n return mixed_x, y_a, y_b, lam","title":""},{"docid":"17cd8bac1aa08c06df022d1b53d0a8dd","score":"0.45237303","text":"def GetSpeciesWithoutMg(self):\n for s in self.all_species:\n if s.number_of_mgs == 0:\n yield s","title":""},{"docid":"2c51858021c5f7892e6668dbe3241c0a","score":"0.45226184","text":"def test_convert_mag_to_jhk():\n\n full_list = ['JpgMag', 'FpgMag', 'NpgMag', 'tmassJMag', 'tmassHMag',\n 'tmassKsMag', 'SDSSuMag', 'SDSSgMag', 'SDSSrMag',\n 'SDSSiMag', 'SDSSzMag']\n\n for L in range(0, len(full_list) + 1):\n for subset in itertools.combinations(full_list, L):\n if subset == ():\n continue\n\n # Recreate data\n fgs = FGSCountrate(guide_star_id=\"N13I000018\", guider=1)\n fgs.gsc_series = BASE_DATA\n\n # Compute conversion\n delete_list = list(set(full_list) - set(subset))\n data2 = copy.copy(fgs.gsc_series)\n for i in delete_list:\n data2[i] = -999\n fgs.calc_jhk_mag(data=data2)\n\n # Compare output to here\n method_names = []\n for i in ['tmassJMag', 'tmassHMag', 'tmassKsMag']:\n if i in list(subset):\n method_name_test = \"convert_tmass_to_jhk\"\n\n elif {'SDSSgMag', 'SDSSzMag'}.issubset(subset):\n method_name_test = \"convert_sdssgz_to_jhk\"\n elif {'SDSSrMag', 'SDSSzMag'}.issubset(subset):\n method_name_test = \"convert_sdssrz_to_jhk\"\n elif {'SDSSgMag', 'SDSSiMag'}.issubset(subset):\n method_name_test = \"convert_sdssgi_to_jhk\"\n # elif {'SDSSiMag', 'SDSSzMag'}.issubset(subset): # Add back in once GSSS uses this pair\n # method_name_test = \"convert_sdssiz_to_jhk\"\n\n elif {'JpgMag', 'NpgMag'}.issubset(subset):\n method_name_test = \"convert_gsc2bjin_to_jhk\"\n elif {'FpgMag', 'NpgMag'}.issubset(subset):\n method_name_test = \"convert_gsc2rfin_to_jhk\"\n elif {'JpgMag', 'FpgMag'}.issubset(subset):\n method_name_test = \"convert_gsc2bjrf_to_jhk\"\n\n else:\n method_name_test = 'cannot_calculate'\n\n method_names.append(method_name_test)\n\n failure_message = f'For input {list(subset)} and band {i[5]}: the test called {method_name_test} ' \\\n f'while the calc_jhk_mag method called ' \\\n f'{getattr(fgs, f\"{i[5].lower()}_convert_method\")}'\n\n assert method_name_test == getattr(fgs, f'{i[5].lower()}_convert_method'), failure_message","title":""},{"docid":"56bd231d1a0165bfd00cae4f8021fdf8","score":"0.4521947","text":"def get_compound_hets(c, args):\n # build a mapping of the numpy array index to the appropriate sample name\n # e.g. 0 == 109400005\n # 37 == 147800025\n idx_to_sample = util.map_indicies_to_samples(c)\n\n comp_hets = collections.defaultdict(lambda: collections.defaultdict(list))\n\n query = \"SELECT * FROM variants \\\n WHERE impact_severity != 'LOW'\" # is_exonic - what about splice?\n c.execute(query)\n\n # step 1. collect all candidate heterozygptes for all\n # genes and samples. the list will be refined in step 2.\n for row in c:\n gt_types = compression.unpack_genotype_blob(row['gt_types'])\n gt_phases = compression.unpack_genotype_blob(row['gt_phases'])\n gt_bases = compression.unpack_genotype_blob(row['gts'])\n\n site = Site(row)\n\n # filter putative sites that the user doesn't care about\n if site.num_hets > 1 and not args.allow_other_hets:\n continue\n if not site.is_lof and args.only_lof:\n continue\n\n # track each sample that is heteroyzgous at this site.\n for idx, gt_type in enumerate(gt_types):\n if gt_type == HET:\n sample = idx_to_sample[idx]\n # (testing)\n # sample = \"NA19002\"\n sample_site = copy(site)\n sample_site.phased = gt_phases[idx]\n\n # require phased genotypes\n if not sample_site.phased and not args.ignore_phasing:\n continue\n\n sample_site.gt = gt_bases[idx]\n # add the site to the list of candidates\n # for this sample/gene\n comp_hets[sample][site.gene].append(sample_site)\n\n # header\n print \"sample\\tgene\\thet1\\thet2\"\n # step 2. now, cull the list of candidate heterozygotes for each\n # gene/sample to those het pairs where the alternate alleles\n # were inherited on opposite haplotypes.\n for sample in comp_hets:\n for gene in comp_hets[sample]:\n for site1 in comp_hets[sample][gene]:\n for site2 in comp_hets[sample][gene]:\n if site1 == site2:\n continue\n\n # expand the genotypes for this sample\n # at each site into it's composite\n # alleles. e.g. A|G -> ['A', 'G']\n alleles_site1 = []\n alleles_site2 = []\n if not args.ignore_phasing:\n alleles_site1 = site1.gt.split('|')\n alleles_site2 = site2.gt.split('|')\n else:\n # split on phased (|) or unphased (/) genotypes\n alleles_site1 = re.split('\\||/', site1.gt)\n alleles_site2 = re.split('\\||/', site2.gt)\n\n # it is only a true compound heterozygote iff\n # the alternates are on opposite haplotypes.\n if not args.ignore_phasing:\n # return the haplotype on which the alternate\n # allele was observed for this sample at each\n # candidate het. site.\n # e.g., if ALT=G and alleles_site1=['A', 'G']\n # then alt_hap_1 = 1. if ALT=A, then alt_hap_1 = 0\n alt_hap_1 = alleles_site1.index(site1.alt)\n alt_hap_2 = alleles_site2.index(site2.alt)\n\n if alt_hap_1 != alt_hap_2:\n print \"\\t\".join([sample,\n gene,\n str(site1),\n str(site2)])\n else:\n # user has asked us to not care about phasing\n print \"\\t\".join([sample,\n gene,\n str(site1),\n str(site2)])","title":""},{"docid":"fe22d80fd5557c102b2c75f9813ce057","score":"0.4520698","text":"def get_families_orthofinder(orthofinder_df, species):\n df = pd.read_csv(orthofinder_df, sep=\"\\t\", index_col=0)\n # let's be forgiving when it comes to the species names, allow prefixes\n unique_prefix(df.columns, species)\n f = lambda x, y: x.startswith(y)\n cols = [x for x in df.columns if any([f(x, y) for y in species])]\n df = df[cols]\n df.fillna(\"\", inplace=True)\n return df","title":""},{"docid":"e0be24bc7bb061ed98a7d7dbcd8e27c9","score":"0.4516397","text":"def _specialize_clusters(cls, clusters, **kwargs):\n # To create temporaries\n counter = generator()\n template = lambda: \"r%d\" % counter()\n\n # Toposort+Fusion (the former to expose more fusion opportunities)\n clusters = Toposort().process(clusters)\n clusters = fuse(clusters)\n\n # Flop reduction via the DSE\n clusters = rewrite(clusters, template, **kwargs)\n\n # Lifting\n clusters = Lift().process(clusters)\n\n # Lifting may create fusion opportunities, which in turn may enable\n # further optimizations\n clusters = fuse(clusters)\n clusters = eliminate_arrays(clusters, template)\n clusters = scalarize(clusters, template)\n\n return clusters","title":""},{"docid":"6c21f8ac7e72317338081a2aa84a1bba","score":"0.45106366","text":"def get_all_extras_to_submit(self) -> ty.Set[ty.Tuple[str]]:\n extras_projections = self.get_process_extra_projections()\n extras_projections += ('attributes.kinds', )\n\n qbuild = orm.QueryBuilder()\n qbuild.append(orm.Group,\n filters={'id': self.parent_group.pk},\n tag='group')\n qbuild.append(orm.Node,\n filters=self._parent_group_filters,\n project=extras_projections,\n tag='process',\n with_group='group')\n results = qbuild.all()\n\n pseudo_family_elements = set(self._pseudo_family.elements)\n extras = []\n for (mpid, kinds) in results:\n kind_names = set(kind['name'] for kind in kinds)\n if kind_names.issubset(pseudo_family_elements):\n extras.append((mpid, ))\n\n # I return sorted results for reproducibility of the order of execution\n extras = sorted(extras)\n\n assert len(set(extras)) == len(\n extras), 'There are duplicate extras in the parent group'\n return set(extras)","title":""},{"docid":"6074796bffb6ef61dd617fbfc071e2ca","score":"0.450857","text":"def infer_pose_each_part_mix_gaussian(factor_graph_list):\n num_body_parts = len(factor_graph_list)\n # Generate the intial sample to start Gibbs Sampling\n inferred_pose = generate_initial_pose_matrix(num_body_parts)\n\n # Generate nodes which will be selected for Gibbs Sampling\n node_indices_array = np.ndarray.tolist(np.random.randint(0, 24, size=num_samples))\n \n # Start Gibbs Sampling over Factor Graph\n for node_index in node_indices_array:\n node_to_update = factor_graph_list[node_index]\n\n # Getting GaussianMixture object for unary pot\n unary_pot = node_to_update.unary_pot\n\n # Prepare object to represent mixture of Gaussians\n mix_gaussians_obj = GaussianDistributionsWithWeights()\n mix_gaussians_obj.set_weights_means_covs(unary_pot.weights_, unary_pot.means_, unary_pot.covariances_)\n\n # Conditioning on all the neighbors to get resulting distributions\n for neighbor_index in node_to_update.neighbors:\n # Finding value of conditioned variable (neighbor)\n pose = get_node_val_pose_only(neighbor_index, inferred_pose)\n \n # Gaussian Mixture instance for pairwise potential\n pairwise_pots_gauss_mixture_obj = node_to_update.pairwise_pots[neighbor_index]\n\n # Condition on all mixtures in Pairwise potentials\n weights = pairwise_pots_gauss_mixture_obj.weights_\n\n means_conditioned = []\n covs_conditioned = []\n for index in range(len(weights)):\n pairwise_mean = pairwise_pots_gauss_mixture_obj.means_[index]\n pairwise_cov = pairwise_pots_gauss_mixture_obj.covariances_[index]\n mean_cond, cov_cond = condition_on_pairwise_potential(\n node_index, neighbor_index, pairwise_mean, pairwise_cov, pose)\n \n means_conditioned.append(mean_cond)\n covs_conditioned.append(cov_cond)\n\n # Add these mixtures to the object\n mix_gaussians_obj.add_gaussian_distributions(weights, means_conditioned, covs_conditioned)\n\n # Selecting the Gaussian distribution to sample from in the Gaussian Mixture\n mean_gaussian, cov_gaussian = mix_gaussians_obj.get_gaussian_to_sample_from()\n\n # Perform Sampling \n pose_curr_node = np.random.multivariate_normal(mean=mean_gaussian, cov=cov_gaussian)\n\n # Updating the value in the overall pose matrix\n inferred_pose[node_to_update.node_index] = pose_curr_node\n\n return inferred_pose","title":""},{"docid":"541038450fb6f1cf283ded6c164f394f","score":"0.449938","text":"def pack_features(args):\n workspace = args.workspace\n data_type = args.data_type\n n_concat = args.n_concat\n n_hop = args.n_hop\n TF = args.TF\n \n x_all = [] # (n_segs, n_concat, n_freq)\n\n y_all = [] # (n_segs, n_freq)\n\n \n cnt = 0\n t1 = time.time()\n \n # Load all features. data = [mixed_complx_x, speech_x, noise_x, alpha, out_bare_na]\n\n feat_dir = os.path.join(workspace, \"features\", data_type)\n \n #feat_dir = os.path.join(workspace, \"features\", data_type)\n names = os.listdir(feat_dir)\n for na in names:\n # Load feature. \n feat_path = os.path.join(feat_dir, na)\n data = cPickle.load(open(feat_path, 'rb'))\n [mixed_complx_x, speech_complx_x] = data\n \n n_pad = (n_concat - 1) / 2\n speech_x = pad_with_border(np.abs(speech_complx_x), n_pad)\n # speech_phase = pad_with_border(np.angle(speech_complx_x), n_pad) #phase = np.exp(1j * np.angle(D))\n '''\n mixed_x = np.abs(mixed_complx_x)\n\n # Pad start and finish of the spectrogram with boarder values. \n # mixed_x.shape=frames x framesize\n n_pad = (n_concat - 1) / 2\n mixed_x = pad_with_border(mixed_x, n_pad)\n speech_x = pad_with_border(speech_x, n_pad)\n \n # Cut input spectrogram to 3D segments with n_concat. \n mixed_x_3d = mat_2d_to_3d(mixed_x, agg_num=n_concat, hop=n_hop)\n #x_all.append(mixed_x_3d)\n '''\n \n if TF == \"spectrogram\":\n mixed_x = np.abs(mixed_complx_x)\n # mixed_phase = np.angle(mixed_complx_x)\n\n # Pad start and finish of the spectrogram with boarder values. \n # mixed_x.shape=frames x framesize\n #n_pad = (n_concat - 1) / 2\n mixed_x = pad_with_border(mixed_x, n_pad)\n # mixed_phase = pad_with_border(mixed_phase, n_pad)\n #speech_x = pad_with_border(speech_x, n_pad)\n \n # Cut input spectrogram to 3D segments with n_concat. \n mixed_x_3d = mat_2d_to_3d(mixed_x, agg_num=n_concat, hop=n_hop)\n # mixed_phase_3d = mat_2d_to_3d(mixed_phase, agg_num=n_concat, hop=n_hop)\n log_x = log_sp(mixed_x_3d.copy())\n x_all.append(log_x)\n # x_phase.append(mixed_phase_3d)\n\n else:\n raise Exception(\"TF must be spectrogram, timedomain or fftmagnitude!\")\n # Cut target spectrogram and take the center frame of each 3D segment. \n speech_x_3d = mat_2d_to_3d(speech_x, agg_num=n_concat, hop=n_hop)\n # speech_phase_3d = mat_2d_to_3d(speech_phase, agg_num=n_concat, hop=n_hop)\n y = speech_x_3d[:, (n_concat - 1) / 2, :]\n # y_pha = speech_phase_3d[:, (n_concat - 1) / 2, :]\n if TF == \"spectrogram\":\n print(\"convert to log-spectra features\")\n log_y = log_sp(y.copy())\n y_all.append(log_y)\n # y_phase.append(y_pha)\n\n \n else:\n raise Exception(\"TF must be spectrogram, timedomain or fftmagnitude!\")\n \n # Print. \n if cnt % 1000 == 0:\n print(\"Packing all the {} utterances to 3D tensor, remaining {} utterances\".format(cnt, len(names)-cnt))\n \n # if cnt == 3: break\n cnt += 1\n \n nwav_all = np.concatenate(x_all, axis=0) # transfer to array(n_segs, n_concat, n_freq)\n cwav_all = np.concatenate(y_all, axis=0) # (n_segs, n_freq)\n \n # x_phase = np.concatenate(x_phase, axis=0) # transfer to array(n_segs, n_concat, n_freq)\n # y_phase = np.concatenate(y_phase, axis=0)\n \n nwav_all = nwav_all.astype(np.float32)\n cwav_all = cwav_all.astype(np.float32)\n # x_phase = x_phase.astype(np.float32)\n # y_phase = y_phase.astype(np.float32)\n '''\n if TF == \"spectrogram\":\n x_all = log_sp(x_all).astype(np.float32)\n y_all = log_sp(y_all).astype(np.float32)\n elif TF == \"timedomain\":\n x_all = x_all.astype(np.float32)\n y_all = y_all.astype(np.float32)\n else:\n raise Exception(\"TF must be spectrogram or timedomain!\")\n '''\n # Write out data to .h5 file. \n\n mag_out_path = os.path.join(workspace, \"packed_features\", data_type, \"mag.h5\")\n # phase_out_path = os.path.join(workspace, \"packed_features\", data_type, \"phase.h5\")\n \n #out_path = os.path.join(workspace, \"packed_features\", data_type, \"data.h5\")\n create_folder(os.path.dirname(mag_out_path))\n with h5py.File(mag_out_path, 'w') as hf:\n hf.create_dataset('x', data=nwav_all)\n hf.create_dataset('y', data=cwav_all)\n \n print(\"Write out to %s\" % mag_out_path)\n\n print(\"Pack features finished! %f s\" % (time.time() - t1))","title":""},{"docid":"14d019342e5a644d6067c500c363229f","score":"0.44981048","text":"def breed(mother, father, num_children=2):\r\n if mother.population is not father.population:\r\n raise ValueError(\"cannot breed members of different \" +\r\n \"populations\")\r\n\r\n children = []\r\n for __ in range(num_children):\r\n genome = {}\r\n\r\n for gene in mother.population.gene_pool.keys():\r\n genome[gene] = random.choice((\r\n mother.genome[gene], father.genome[gene]\r\n ))\r\n\r\n child = Organism(mother.population, genome)\r\n child.mutate()\r\n\r\n children.append(child)\r\n\r\n return children","title":""},{"docid":"5a23acae6cdccf52c749aee41ce3d485","score":"0.4497921","text":"def all_puzzles(center):\r\n\r\n # Find possible sequences of matching marks around the ring.\r\n for matching in product(range(6), repeat=6):\r\n halves = tuple(zip(matching, center, matching[-1:] + matching[:-1]))\r\n\r\n # If the result is valid, we can freely permute the remaining 3\r\n # outer marks on each nut around the ring.\r\n if all(len(set(inner)) == 3 for inner in halves):\r\n for ring in product(*([inner + outer\r\n for outer in permutations(\r\n set(range(6)) - set(inner))]\r\n for inner in halves)):\r\n yield pack(ring + (center,))","title":""},{"docid":"bfcbc3b1439ce814c4c784d3f0251636","score":"0.44963923","text":"def tester_fermat_test():\r\n c_list = carmichael_1(100000)\r\n r_list = [random.randrange(2, 1.e8) for i in range(1000)]\r\n com_list = [n for n in r_list if first_test(n) == 'composite']\r\n for c in c_list:\r\n print(fermat_test(c, 100))\r\n for r in r_list:\r\n print(fermat_test(r, 100))\r\n for com in com_list:\r\n print(fermat_test(com, 100))","title":""},{"docid":"70e75f9d240ca271f9d3f092d6ae7069","score":"0.44849333","text":"def get_Reduced_SpeciesID(pro_seq, clade_df, num_random_species=10):\n\n # pro_input = \"/home/luhongzhong/ortholog_343/protein_align_s2_R/OG1587_aa_aligned.fasta\"\n # OG_test = list(SeqIO.parse(pro_dir, \"fasta\"))\n OG_test = pro_seq\n all_OG_ID = []\n for record in OG_test:\n # print(record.id)\n all_OG_ID.append(record.id)\n OG_df = pd.DataFrame({\"OG\": all_OG_ID})\n OG_df[[\"OG\", \"id\"]] = OG_df.OG.str.split(\"@\", expand=True, )\n # get clade information for each species\n OG_df[\"clade\"] = singleMapping(clade_df[\"clade\"], clade_df[\"speciesID\"], OG_df[\"OG\"])\n # establish six list\n list1 = [] # Phaffomycetaceae\n list2 = [] # Dipodascaceae-Trichomonascaceae\n list3 = [] # Pichiaceae\n list4 = [] # Saccharomycetaceae\n list5 = [] # CUG-Ser1\n list6 = [] # Saccharomycodaceae\n list7 = [] # Lipomycetaceae\n list_o = [] # others\n for i, x in OG_df.iterrows():\n # print(i, x[\"clade\"])\n s0 = x[\"clade\"]\n if s0 == \"Phaffomycetaceae\":\n list1.append(x[\"OG\"])\n elif s0 == \"Dipodascaceae-Trichomonascaceae\":\n list2.append(x[\"OG\"])\n elif s0 == \"Pichiaceae\":\n list3.append(x[\"OG\"])\n elif s0 == \"Saccharomycetaceae\":\n list4.append(x[\"OG\"])\n elif s0 == \"CUG-Ser1\":\n list5.append(x[\"OG\"])\n\n elif s0 == \"Saccharomycodaceae\": # newly added\n list6.append(x[\"OG\"])\n elif s0 == \"Lipomycetaceae\": # newly added\n list7.append(x[\"OG\"])\n\n else:\n list_o.append(x[\"OG\"])\n # randomely choose then species from each dict\n list_1r = randome_choose_n_species(species_list=list1, num_species=num_random_species)\n list_2r = randome_choose_n_species(species_list=list2, num_species=num_random_species)\n list_3r = randome_choose_n_species(species_list=list3, num_species=num_random_species)\n list_4r = randome_choose_n_species(species_list=list4, num_species=num_random_species)\n list_5r = randome_choose_n_species(species_list=list5, num_species=num_random_species)\n\n list_6r = randome_choose_n_species(species_list=list6, num_species=num_random_species)\n list_7r = randome_choose_n_species(species_list=list7, num_species=num_random_species)\n # next choose seq id for each species\n # firstly combine the choosed species id\n all_species_id = list(list_1r) + list(list_2r) + list(list_3r) + list(list_4r) + list(list_5r) + list(\n list_6r) + list(list_7r) + list_o\n # filter OG id based on the choosed species id\n OG_ID_filter1 = [x for x in all_OG_ID if x.split(\"@\")[0] in all_species_id]\n # check the paralog number\n ratio_seq_per_species = len(set(OG_ID_filter1)) / len(set(all_species_id))\n # print(pro_dir, \"===>\", ratio_seq_per_species)\n return OG_ID_filter1, ratio_seq_per_species","title":""},{"docid":"25e3db2710c6219109184db6783a8ae5","score":"0.44849","text":"def get_variants_shared_by_mother_and_son(self):\n vcfSon = vcf.Reader(open(\"AmpliseqExome.20141120.NA24385.vcf\", \"r\"))\n vcfMother = vcf.Reader(open(\"AmpliseqExome.20141120.NA24143.vcf\", \"r\"))\n sharedVariantsMotherSon = 0\n shared_variants = vcf.utils.walk_together(vcfMother, vcfSon)\n for record in shared_variants:\n if not record[0] is None and not record[1] is None:\n sharedVariantsMotherSon += 1\n print(\"\\nnumber of variants shared by mother and son:\")\n print(sharedVariantsMotherSon)","title":""},{"docid":"abad4045066144416dc28e6ee17315d5","score":"0.44816265","text":"def random_functionalization(self, maxgroups=2):\n h_atoms, functional_hist = {}, {}\n # determine base building units from structure\n base_building_units = self.get_base_units()\n # generate dictionary of hydrogen atoms for each building unit\n for building_unit in base_building_units:\n id = (building_unit.index, building_unit.metal,\n building_unit.parent)\n hydrogen_sites = self.get_hydrogens(building_unit)\n h_atoms[id] = hydrogen_sites\n done = False\n while not done:\n # copy of structure to functionalize\n structure = deepcopy(self.structure)\n sites = {}\n # randomly select hydrogens on each unit\n for building_unit in base_building_units:\n id = (building_unit.index, building_unit.metal,\n building_unit.parent)\n if h_atoms[id]:\n sites[id] = self.select(h_atoms[id])\n else:\n sites[id] = []\n # randomly choose functional groups\n assigned_fnlgrps = self.choose_fnl_group(maxgroups)\n # assign fnl groups to each hydrogen\n new_func = False\n while not new_func:\n record = []\n for key, value in sites.items():\n # reset the site to a dictionary\n # site[(building_unit)] = {hydrogen:functional_group}\n sites[key] = {}\n # descend into each hydrogen atom and assign a\n # random choice of functional group.\n h_record = []\n for site in value:\n group = choice(assigned_fnlgrps)\n sites[key][site] = group\n rec = tuple([site, group.index])\n h_record.append(rec)\n h_record.sort()\n rec = tuple([key, tuple(h_record)])\n record.append(rec)\n record.sort()\n # fastest check if the functional has been tried: dictionary\n # lookup?\n try:\n functional_hist[tuple(record)]\n except KeyError:\n # if the key is not in functional_hist, then exit this loop\n new_func = True\n # keep record to prevent repeats.\n functional_hist[tuple(record)] = 1\n \n # iterate through each building unit in the structure, and\n # append functional groups\n for building_unit in structure.building_units:\n site_id = (building_unit.index, building_unit.metal,\n building_unit.parent)\n replace_dic = sites[site_id]\n for hydrogen in replace_dic.keys():\n fnl_grp = deepcopy(replace_dic[hydrogen])\n H_atom = building_unit.atoms[hydrogen]\n # find the associated bond\n bond = building_unit.find_bonds(H_atom)\n if len(bond) != 1:\n raise Error(\"Hydrogen reports as having no bonds..\")\n bond = bond[0]\n # nasty one-liner to assign the Joined atom\n J_atom = bond.frm if bond.to == H_atom else bond.to\n axis = bond.vector\n # rotate and translate\n fnl_grp.orient(bond.vector, J_atom.coordinates)\n # check for overlap\n overlap_atoms = self.overlap(fnl_grp, J_atom, structure)\n if overlap_atoms:\n # check if one of the overlap atoms is the\n # connecting atom, in this case we just\n # scrap the structure because no amount\n # of rotation will help.\n for overlap_atom in overlap_atoms:\n if fnl_grp.atoms[overlap_atom].connectivity:\n # need to break and try a different randomization.\n # no amount of rotation will help here.\n pass\n # generate angles of 360 degrees split into 10 intervals.\n rot_angle = 2 * pi / 10\n tot_angle = 0.\n while overlap_atoms and tot_angle < (2*pi):\n fnl_grp.rotate(rot_angle)\n tot_angle += rot_angle\n overlap_atoms = self.overlap(fnl_grp, J_atom)\n \n # append the functional group to the Structure.\n fnl_grp.MOF_atom = J_atom\n building_unit.functionalize(fnl_grp)\n # update connectivities\n for building_unit in structure.building_units:\n pass\n # write cif file\n done = True","title":""},{"docid":"4e0bf8912cffefc0f39fde7836605931","score":"0.44632077","text":"def many_body_mc_jit(\n q_array_1,\n q_array_2,\n q_neigh_array_1,\n q_neigh_array_2,\n q_neigh_grads_1,\n q_neigh_grads_2,\n c1,\n c2,\n etypes1,\n etypes2,\n species1,\n species2,\n d1,\n d2,\n sig,\n ls,\n):\n\n kern = 0\n\n useful_species = np.array(\n list(set(species1).intersection(set(species2))), dtype=np.int8\n )\n\n # loop over all possible species\n for s in useful_species:\n\n # Calculate many-body descriptor values for central atoms 1 and 2\n s1 = np.where(species1 == s)[0][0]\n s2 = np.where(species2 == s)[0][0]\n q1 = q_array_1[s1]\n q2 = q_array_2[s2]\n\n # compute kernel between central atoms only if central atoms are of\n # the same species\n if c1 == c2:\n k12 = k_sq_exp_double_dev(q1, q2, sig, ls)\n else:\n k12 = 0\n\n # initialize arrays of many body descriptors and gradients for the\n # neighbour atoms in the two configurations\n # Loop over neighbours i of 1st configuration\n for i in range(q_neigh_array_1.shape[0]):\n qis = q1i_grads = qi1_grads = ki2s = 0\n\n if etypes1[i] == s:\n # derivative of pairwise component of many body descriptor q1i\n q1i_grads = q_neigh_grads_1[i, d1 - 1]\n\n if c1 == s:\n # derivative of pairwise component of many body descriptor qi1\n qi1_grads = q_neigh_grads_1[i, d1 - 1]\n\n # Calculate many-body descriptor value for i\n qis = q_neigh_array_1[i, s1]\n\n if c2 == etypes1[i]:\n ki2s = k_sq_exp_double_dev(qis, q2, sig, ls)\n\n # Loop over neighbours j of 2\n for j in range(q_neigh_array_2.shape[0]):\n qjs = qj2_grads = q2j_grads = k1js = 0\n\n if etypes2[j] == s:\n q2j_grads = q_neigh_grads_2[j, d2 - 1]\n\n if c2 == s:\n qj2_grads = q_neigh_grads_2[j, d2 - 1]\n\n # Calculate many-body descriptor value for j\n qjs = q_neigh_array_2[j, s2]\n\n if c1 == etypes2[j]:\n k1js = k_sq_exp_double_dev(q1, qjs, sig, ls)\n\n if etypes1[i] == etypes2[j]:\n kij = k_sq_exp_double_dev(qis, qjs, sig, ls)\n else:\n kij = 0\n\n kern += q1i_grads * q2j_grads * k12\n kern += qi1_grads * q2j_grads * ki2s\n kern += q1i_grads * qj2_grads * k1js\n kern += qi1_grads * qj2_grads * kij\n return kern","title":""},{"docid":"6024b96ed2f58a329e9d248edd049274","score":"0.44614676","text":"def react_all(core_spc_list, numOldCoreSpecies, unimolecularReact, bimolecularReact, trimolecularReact=None, procnum=1):\n # Select reactive species that can undergo unimolecular reactions:\n spc_tuples = [(core_spc_list[i],)\n for i in xrange(numOldCoreSpecies) if (unimolecularReact[i] and core_spc_list[i].reactive)]\n\n for i in xrange(numOldCoreSpecies):\n for j in xrange(i, numOldCoreSpecies):\n # Find reactions involving the species that are bimolecular.\n # This includes a species reacting with itself (if its own concentration is high enough).\n if bimolecularReact[i, j]:\n if core_spc_list[i].reactive and core_spc_list[j].reactive:\n spc_tuples.append((core_spc_list[i], core_spc_list[j]))\n\n if trimolecularReact is not None:\n for i in xrange(numOldCoreSpecies):\n for j in xrange(i, numOldCoreSpecies):\n for k in xrange(j, numOldCoreSpecies):\n # Find reactions involving the species that are trimolecular.\n if trimolecularReact[i, j, k]:\n if core_spc_list[i].reactive and core_spc_list[j].reactive and core_spc_list[k].reactive:\n spc_tuples.append((core_spc_list[i], core_spc_list[j], core_spc_list[k]))\n\n if procnum == 1:\n # React all families like normal (provide empty argument for only_families)\n spc_fam_tuples = zip(spc_tuples)\n else:\n # Identify and split families that are prone to generate many reactions into sublists.\n family_list = getDB('kinetics').families.keys()\n major_families = [\n 'H_Abstraction', 'R_Recombination', 'Intra_Disproportionation', 'Intra_RH_Add_Endocyclic',\n 'Singlet_Carbene_Intra_Disproportionation', 'Intra_ene_reaction', 'Disproportionation',\n '1,4_Linear_birad_scission', 'R_Addition_MultipleBond', '2+2_cycloaddition_Cd', 'Diels_alder_addition',\n 'Intra_RH_Add_Exocyclic', 'Intra_Retro_Diels_alder_bicyclic', 'Intra_2+2_cycloaddition_Cd',\n 'Birad_recombination', 'Intra_Diels_alder_monocyclic', '1,4_Cyclic_birad_scission', '1,2_Insertion_carbene',\n ]\n\n split_list = []\n leftovers = []\n for fam in family_list:\n if fam in major_families:\n split_list.append([fam])\n else:\n leftovers.append(fam)\n split_list.append(leftovers)\n\n # Only employ family splitting for reactants that have a larger number than min_atoms\n min_atoms = 10\n spc_fam_tuples = []\n for i, spc_tuple in enumerate(spc_tuples):\n if any([len(spc.molecule[0].atoms) > min_atoms for spc in spc_tuple]):\n for item in split_list:\n spc_fam_tuples.append((spc_tuple, item))\n else:\n spc_fam_tuples.append((spc_tuple, ))\n\n return list(react(spc_fam_tuples, procnum))","title":""},{"docid":"1f952d4581c678ea0f7a821747a8b9e9","score":"0.44589335","text":"def mix_atoms(s1, s2, roll=True):\n from random import choice\n from itertools import chain\n from numpy.linalg import det\n from numpy import dot, abs\n from ..crystal import Structure\n\n # swap structures randomly.\n if choice([True, False]): s1, s2 = s2, s1\n\n # chem. symbols and scaled positions of the two parents\n sc_pos1 = zip([atom.type for atom in s1], fractional_pos(s1, roll))\n sc_pos2 = zip([atom.type for atom in s2], fractional_pos(s2, roll))\n\n result = Structure(s1.cell)\n\n for pos, type in chain(sc_pos1, sc_pos2):\n if choice([True, False]):\n result.add_atom(*dot(result.cell, pot), type=type)\n\n result.scale = s1.scale * (float(len(result)) / float(len(s1)))**(1./3.)\n\n return result","title":""},{"docid":"06ef7ce14637e294684793ef67909d80","score":"0.44579798","text":"def apply(self, population):\n parent_pairs = []\n for pair_idx in range(int(len(population)*self.params['part_with_offsprings']/2.)):\n pair = []\n for parent_idx in np.arange(2):\n selection_indexes = np.random.choice(len(population), self.params['tournament_groups'], replace = False)\n candidates = [population[idx] for idx in selection_indexes]\n pair.append([idx for _, idx in sorted(zip(candidates, selection_indexes), key=lambda pair: pair[0].fitness_value)][-1])\n parent_pairs.append(pair)\n return parent_pairs","title":""},{"docid":"75acaef00f7f6d1892ea43a6f0b137a5","score":"0.4456152","text":"def generate_pokemon_names_and_images_by_colour(self, colour):\n \n # list species matching colour\n pokemon_species_list = self.get_species_by_colour(colour)\n \n for pokemon_species_link in pokemon_species_list:\n print(pokemon_species_link['name'])\n\n # GET information about a species\n pokemon_species_json = self.get(pokemon_species_link['url'])\n\n\n # since a species may have multiple varieties\n # GET each variety\n varieties = pokemon_species_json['varieties']\n for variety in varieties:\n # (a variety is a pokemon)\n # GET the pokemon information\n pokemon_json = self.get(variety['pokemon']['url'])\n\n pokemon_name = pokemon_json['name']\n image_url = pokemon_json['sprites']['front_default']\n \n yield (pokemon_name, image_url)","title":""},{"docid":"760c91e62f025b391a7134ac031bb4f4","score":"0.4454435","text":"def __get_datasets_per_family_with(db,include_dataset='Fossil'):\n datasets_per_family = __get_datasets_per_family\n datasets_per_family_with={}\n for family in datasets_per_family:\n if include_dataset in datasets_per_family[family]:\n datasets_per_family_with[family]=datasets_per_family[family]\n return datasets_per_family_with","title":""},{"docid":"af30ed98bea73f8f9e4448375d4d6015","score":"0.4454323","text":"def get_species(self):\n speciesnodes = [[] for _ in range(0, self.speciesnum)]\n for j in range(0, len(self.V)):\n speciesnodes[self.species[j]].append(self.V[j])\n return speciesnodes","title":""},{"docid":"8a99a980b56e5376fba5cecb6b6991d1","score":"0.44517902","text":"def combine_populations(self):\n\n self.combined_pop['binary'] = [[[]] * self.num_var for i in range(self.num_pop * 2)]\n self.combined_pop['decoded'] = [[[]] * self.num_var for i in range(self.num_pop * 2)]\n self.combined_pop['scaled'] = [[[]] * self.num_var for i in range(self.num_pop * 2)]\n self.combined_pop['fit_values'] = [[[]] * self.num_fit_func for i in range(self.num_pop * 2)]\n\n for i in range(self.num_pop):\n self.combined_pop['binary'][i] = self.parent_pop['binary'][i]\n self.combined_pop['binary'][i + self.num_pop] = self.current_pop['binary'][i]\n\n self.combined_pop['decoded'][i] = self.parent_pop['decoded'][i]\n self.combined_pop['decoded'][i + self.num_pop] = self.current_pop['decoded'][i]\n\n self.combined_pop['scaled'][i] = self.parent_pop['scaled'][i]\n self.combined_pop['scaled'][i + self.num_pop] = self.current_pop['scaled'][i]\n\n self.combined_pop['fit_values'][i] = self.parent_pop['fit_values'][i]\n self.combined_pop['fit_values'][i + self.num_pop] = self.current_pop['fit_values'][i]","title":""},{"docid":"c5d28341cfe0373f3b648ebe6c44655e","score":"0.4451452","text":"def flavors(self, details=True, get_extra_specs=False, **query):\n base_path = '/flavors/detail' if details else '/flavors'\n for flv in self._list(_flavor.Flavor, base_path=base_path, **query):\n if get_extra_specs and not flv.extra_specs:\n flv = flv.fetch_extra_specs(self)\n yield flv","title":""},{"docid":"e08e0d652e0313fc7bec7126e3070e84","score":"0.44403917","text":"def gather_mol(mols):\n atom_feat_ls = []\n pair_feat_ls = []\n atom_split_ls = []\n atom2pair_ls = []\n pair_split_ls = []\n start = 0\n for im, mol in enumerate(mols):\n atom_feat, pair_feat, atom_in_pair = mol\n # n_atoms = mol.get_num_atoms()\n # number of atoms in each molecule\n # atom_split.extend([im] * n_atoms)\n # index of pair features\n # C0, C1 = np.meshgrid(np.arange(n_atoms), np.arange(n_atoms))\n # atom_to_pair.append(\n # np.transpose(\n # np.array([C1.flatten() + start,\n # C0.flatten() + start])))\n # number of pairs for each atom\n # pair_split.extend(C1.flatten() + start)\n # start = start + n_atoms\n #\n # atom features\n # atom_feat.append(mol.get_atom_features())\n # pair features\n # pair_feat.append(\n # np.reshape(mol.get_pair_features(),\n # (n_atoms * n_atoms, -1)))\n n_atom = atom_feat.shape[0]\n atom_feat_ls.append(atom_feat)\n pair_feat_ls.append(pair_feat)\n\n atom2pair = atom_in_pair + start\n pair_head = atom2pair[:, 0]\n pair_split_ls.append(pair_head)\n atom2pair_ls.append(atom2pair)\n atom_split_ls.extend([im] * n_atom)\n\n start += n_atom\n\n atom_feat = np.concatenate(atom_feat_ls, axis=0)\n bond_feat = np.concatenate(pair_feat_ls, axis=0)\n\n atom_feat_ls = [atom_feat[:, i] for i in range(atom_feat.shape[1])]\n bond_feat_ls = [bond_feat[:, i] for i in range(bond_feat.shape[1])]\n mol_merged = [\n atom_feat_ls,\n bond_feat_ls,\n np.concatenate(pair_split_ls, axis= 0),\n np.array(atom_split_ls),\n np.concatenate(atom2pair_ls, axis=0),\n start # num of atoms\n ]\n return mol_merged","title":""},{"docid":"bd9bcaa23fadef8020c56f38f38e22c8","score":"0.44341436","text":"def __call__(self, *feature: 'dsl.Feature') -> frozenset['dsl.Feature']:\n for col in feature:\n col.accept(self)\n return frozenset(self._match)","title":""},{"docid":"546de05aa159258575fb1a35304e9bcd","score":"0.4431054","text":"def get_sub_species(self, reference_type: str, species: str) -> list:\n return list(self.yaml.get(reference_type).get(species).keys())","title":""},{"docid":"26f0d1dd7b184468165f2e40f71c8f9b","score":"0.44306764","text":"def test_supergroup(self):\n data = [\n (\"NbO2\", 141, 5),\n ]\n for d in data:\n (cif, g, N) = d\n s = pyxtal()\n s.from_seed(cif_path + cif + '.cif')\n strucs, sols = s.supergroup(g, 0.5)\n self.assertTrue(len(strucs) > 0)","title":""},{"docid":"e8df9b7c1e8bd35bda42ea6450cee7cf","score":"0.44299644","text":"def get_reactants_and_products4tagged_fam(fam='LOx', KPP_output_mech=None,\n folder=None):\n import pandas as pd\n # Get the outputted KPP mechanism if not provided.\n if isinstance(KPP_output_mech, type(None)):\n KPP_output_mech = get_dict_of_KPP_mech(wd=folder)\n # Make a DataFrame from the dictionary\n df = pd.DataFrame(KPP_output_mech.values(), index=KPP_output_mech.keys())\n df.columns = ['rxn str']\n# df.index = df.index - 1 # Adjust Fortran numbering in output to Pythonic #\n # Split apart reaction str to give products and reactants\n\n def extract_products(input):\n return input.split(' --> ')[-1].strip()\n\n def extract_reactants(input):\n return input.split(' --> ')[0].strip()\n df['prod'] = df['rxn str'].map(extract_products)\n df['react'] = df['rxn str'].map(extract_reactants)\n # Make the formating the same as in input files\n # ---- Check if the file mapping can work between KPP input & output\n df['KPP input react'] = df['react'].map(update_KPP_rxn_str)\n# KPP_Input_df['KPP input react'] = KPP_Input_df['react'].map(update_rxn_str)\n find_breakpoint = False\n if find_breakpoint: # Seems to be C3H8 + OH = A3O2 :\n # for s,e in [ [0, 50], [] ]:\n for s, e in zip(range(0, 700, 50), range(50, 750, 50)):\n bool = df['react'][s:e] == KPP_Input_df['KPP input react'][s:e]\n a_ = df['react'][s:e][~bool]\n b_ = KPP_Input_df['KPP input react'][s:e][~bool]\n# print dict( zip( a_, b_ ) )\n # turn into sorted lists\n a_ = [list(sorted([ii.strip()for ii in i.split('+')])) for i in a_]\n b_ = [list(sorted([ii.strip()for ii in i.split('+')])) for i in b_]\n # compare these and only print if they differ\n for n, a__ in enumerate(a_):\n if a__ != b_[n]:\n print(a__, b_[n])\n # Only consider reaction that include family in the products\n def fam_in_rxn(input):\n return (fam in input)\n rtn_vars = ['react', 'prod', 'KPP input react']\n df = df[df['prod'].map(fam_in_rxn)][rtn_vars]\n return df","title":""},{"docid":"f1fd22e25b2185028305088342812533","score":"0.44281232","text":"def test_cat_feats(zipper, feat_subset=None, method=None, print_data=False):\n\n p_values = dict()\n\n # consider all features if no feature subset was specified\n if feat_subset is None:\n feat_subset = zipper.keys()\n\n # set default method to chi-square test\n if method is None:\n method = \"chi\"\n\n for feat in feat_subset:\n # initiate dict in dict for dataset1 vs dataset2, d1 vs d3 etc. per feature\n p_values[feat] = dict()\n\n for i in range(len(zipper[feat]) - 1): # select dataset1\n for j in range(i + 1, len(zipper[feat])): # select dataset2\n # count occurences of categorical features like in a confusion matrix for Chi2 tests\n test_data = [_categorical_table(zipper[feat][i]), _categorical_table(zipper[feat][j])]\n\n # fill missing keys in test data:\n test_data = _non_present_values_to_zero(test_data)\n\n # sort testing data by index(categories) to align the counts for the categories\n test_data = [data.sort_index() for data in test_data]\n\n # print testing data if specified\n if print_data:\n print(feat)\n print(pd.DataFrame(test_data))\n print()\n\n if method == \"chi\":\n # skip feature if number of events per group is smaller than 5\n if (test_data[0] < 5).any() or (test_data[1] < 5).any():\n warnings.warn(feat + \" has under 5 observations in one or more groups.\", UserWarning)\n # calculate u statistic and return p-value\n p_val = chisquare(*test_data).pvalue\n\n elif method == \"fisher\":\n p_val = fisher_exact(test_data)[1]\n\n p_values[feat][i + 1, j + 1] = p_val\n\n return p_values","title":""},{"docid":"d8b851f74b85bec44187b7556656aa25","score":"0.44253698","text":"def detect_species_bboxes(src_gpath_list, species, quick=True, use_chunks=False, **detectkw):\n nImgs = len(src_gpath_list)\n print('[detect.rf] Begining %s detection' % (species,))\n detect_lbl = 'detect %s ' % species\n mark_prog, end_prog = utool.progress_func(nImgs, detect_lbl, flush_after=1)\n\n detect_config = _get_detect_config(**detectkw)\n detector, forest = _get_detector(species, quick=quick)\n detector.set_detect_params(**detect_config)\n\n dst_gpath_list = [splitext(gpath)[0] for gpath in src_gpath_list]\n # FIXME: Doing this in a generator may cause unnecessary page-faults\n # Maybe there is a better way of doing this, or generating results\n # in batch. It could be a utool batch serial process\n\n chunksize = 8\n use_chunks_ = use_chunks and nImgs >= chunksize\n\n if use_chunks_:\n print('[rf] detect in chunks')\n pathtup_iter = zip(src_gpath_list, dst_gpath_list)\n for ic, chunk in enumerate(utool.ichunks(pathtup_iter, chunksize)):\n src_gpath_list = [tup[0] for tup in chunk]\n dst_gpath_list = [tup[1] for tup in chunk]\n mark_prog(ic * chunksize)\n results_list = detector.detect_many(forest, src_gpath_list, dst_gpath_list)\n\n for results in results_list:\n bboxes = [(minx, miny, (maxx - minx), (maxy - miny))\n for (centx, centy, minx, miny, maxx, maxy, confidence, supressed)\n in results if supressed == 0]\n\n #x_arr = results[:, 2]\n #y_arr = results[:, 3]\n #w_arr = results[:, 4] - results[:, 2]\n #h_arr = results[:, 5] - results[:, 3]\n #bboxes = np.hstack((x_arr, y_arr, w_arr, h_arr))\n # Unpack unsupressed bounding boxes\n\n confidences = [confidence\n for (centx, centy, minx, miny, maxx, maxy, confidence, supressed)\n in results if supressed == 0]\n\n if len(results) > 0:\n image_confidence = max([float(result[6]) for result in results])\n else:\n image_confidence = 0.0\n\n yield bboxes, confidences, image_confidence\n else:\n print('[rf] detect one image at a time')\n pathtup_iter = izip(src_gpath_list, dst_gpath_list)\n for ix, (src_gpath, dst_gpath) in enumerate(pathtup_iter):\n mark_prog(ix)\n results = detector.detect(forest, src_gpath, dst_gpath)\n bboxes = [(minx, miny, (maxx - minx), (maxy - miny))\n for (centx, centy, minx, miny, maxx, maxy, confidence, supressed)\n in results if supressed == 0]\n\n confidences = [confidence\n for (centx, centy, minx, miny, maxx, maxy, confidence, supressed)\n in results if supressed == 0]\n\n if len(results) > 0:\n image_confidence = max([float(result[6]) for result in results])\n else:\n image_confidence = 0.0\n\n yield bboxes, confidences, image_confidence\n end_prog()","title":""},{"docid":"c802c027159addf54304f55b259d248e","score":"0.4424991","text":"def missing_weighed_baskets_check(self):\n result = {\"status\": \"Passed\", \"errors\": \"Success\", \"errorCount\": 0}\n errors = []\n\n try:\n catch = Catch.select().where(Catch.operation == self._app.state_machine.haul[\"haul_id\"],\n Catch.parent_catch.is_null(True))\n for species in catch:\n child_baskets = Catch.select().where(Catch.parent_catch == species.catch)\n if child_baskets.count() == 0:\n errors.append(species.display_name)\n\n # Mixes\n if \"mix\" in species.display_name.lower():\n for mix_species in child_baskets:\n mix_child_baskets = Catch.select().where(Catch.parent_catch == mix_species.catch)\n if \"mix #\" not in mix_species.display_name.lower() and mix_child_baskets.count() == 0:\n errors.append(species.display_name + \" > \" + mix_species.display_name)\n\n # Submixes\n if \"submix\" in mix_species.display_name.lower():\n submix_catch = Catch.select(Catch, CatchContentLu, TypesLu) \\\n .join(CatchContentLu,\n on=(CatchContentLu.catch_content == Catch.catch_content).alias(\"content\")) \\\n .join(TypesLu, on=(CatchContentLu.content_type == TypesLu.type_id).alias(\"types\")) \\\n .where(Catch.parent_catch == mix_species.catch, \\\n TypesLu.category == \"Content\", \\\n TypesLu.type == \"Taxon\")\n for submix_species in submix_catch:\n submix_child_baskets = Catch.select().where(Catch.parent_catch == submix_species.catch)\n if submix_child_baskets.count() == 0:\n errors.append(species.display_name + \" > \" + mix_species.display_name + \" > \" +\n submix_species.display_name)\n\n except Exception as ex:\n logging.info(\"error in query: \" + str(ex))\n\n if len(errors) > 0:\n result[\"status\"] = \"Failed\"\n result[\"errorCount\"] = len(errors)\n errors = \"\\n\".join(sorted(errors))\n result[\"errors\"] = errors\n\n return result","title":""},{"docid":"5775cebea189417b1cac13fb2c340b57","score":"0.44232428","text":"def recombine_population(self, population):\n offspring = []\n\n # Create mating pool\n mating_pool = self.select_parents(population)\n\n # Shuffle mating pool\n mating_pool = self.random_selection_without_replacement(mating_pool,\n len(mating_pool))\n\n index = 0\n # Recombine parents of different encodings pairwise\n while (len(offspring) < self.ea_lambda):\n parent1 = mating_pool[index]\n index += 1\n parent2 = mating_pool[index]\n index += 1\n\n # Each pair has a p_c chance of mating. p_c defaults to 1.0.\n if ((random.random() < self.p_c)\n and (parent1.encoding != parent2.encoding)):\n new_offspring = self.crossover(parent1, parent2)\n offspring.append(new_offspring[0])\n if (len(offspring) < self.ea_lambda):\n offspring.append(new_offspring[1])\n # Sometimes we end up with a population of all\n # the same encoding. I can either carry them\n # on to the next generation, or just sit in this loop forever.\n else:\n offspring.append(parent1)\n if (len(offspring) < self.ea_lambda):\n offspring.append(parent2)\n\n return offspring","title":""},{"docid":"5775cebea189417b1cac13fb2c340b57","score":"0.44232428","text":"def recombine_population(self, population):\n offspring = []\n\n # Create mating pool\n mating_pool = self.select_parents(population)\n\n # Shuffle mating pool\n mating_pool = self.random_selection_without_replacement(mating_pool,\n len(mating_pool))\n\n index = 0\n # Recombine parents of different encodings pairwise\n while (len(offspring) < self.ea_lambda):\n parent1 = mating_pool[index]\n index += 1\n parent2 = mating_pool[index]\n index += 1\n\n # Each pair has a p_c chance of mating. p_c defaults to 1.0.\n if ((random.random() < self.p_c)\n and (parent1.encoding != parent2.encoding)):\n new_offspring = self.crossover(parent1, parent2)\n offspring.append(new_offspring[0])\n if (len(offspring) < self.ea_lambda):\n offspring.append(new_offspring[1])\n # Sometimes we end up with a population of all\n # the same encoding. I can either carry them\n # on to the next generation, or just sit in this loop forever.\n else:\n offspring.append(parent1)\n if (len(offspring) < self.ea_lambda):\n offspring.append(parent2)\n\n return offspring","title":""},{"docid":"acaa6d04cdb191645122f4a997d44b36","score":"0.4421154","text":"def get_family(person):\n spouse = get_spouse(person)\n if spouse == None:\n #If no spouse, then no children, so return self\n return [person]\n else:\n #If spouse, check for children\n children = get_children(person)\n return [person,spouse] + children","title":""}],"string":"[\n {\n \"docid\": \"f4baea9f29fddc8c0948f2b7ca1e9cfc\",\n \"score\": \"0.59810746\",\n \"text\": \"def expand_mix(self, mix):\\n new_dict = mix[0].species()\\n for i in range(1, len(mix)):\\n new_dict = self.merge_dicts_mz(new_dict, mix[i].species())\\n return new_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e2c4795633eac66c5cbb6c201f03f47\",\n \"score\": \"0.55427945\",\n \"text\": \"def infer_families(\\n relationship_ht: hl.Table,\\n sex: Union[hl.Table, Dict[str, bool]],\\n duplicate_samples_ht: hl.Table,\\n i_col: str = \\\"i\\\",\\n j_col: str = \\\"j\\\",\\n relationship_col: str = \\\"relationship\\\",\\n) -> hl.Pedigree:\\n\\n def group_parent_child_pairs_by_fam(\\n parent_child_pairs: Iterable[Tuple[str, str]]\\n ) -> List[List[Tuple[str, str]]]:\\n \\\"\\\"\\\"\\n Group parent-child pairs into a list of families.\\n\\n A family here is defined as a list of sample-pairs which all share at least one sample with at least one other\\n sample-pair in the list.\\n\\n :param parent_child_pairs: All the parent-children pairs\\n :return: A list of families, where each element of the list is a list of the parent-children pairs\\n \\\"\\\"\\\"\\n fam_id = 1 # stores the current family id\\n s_fam = dict() # stores the family id for each sample\\n fams = defaultdict(list) # stores fam_id -> sample-pairs\\n for pair in parent_child_pairs:\\n if pair[0] in s_fam:\\n if pair[1] in s_fam:\\n if (\\n s_fam[pair[0]] != s_fam[pair[1]]\\n ): # If both samples are in different families, merge the families\\n new_fam_id = s_fam[pair[0]]\\n fam_id_to_merge = s_fam[pair[1]]\\n for s in s_fam:\\n if s_fam[s] == fam_id_to_merge:\\n s_fam[s] = new_fam_id\\n fams[new_fam_id].extend(fams.pop(fam_id_to_merge))\\n else: # If only the 1st sample in the pair is already in a family, assign the 2nd sample in the pair to the same family\\n s_fam[pair[1]] = s_fam[pair[0]]\\n fams[s_fam[pair[0]]].append(pair)\\n elif (\\n pair[1] in s_fam\\n ): # If only the 2nd sample in the pair is already in a family, assign the 1st sample in the pair to the same family\\n s_fam[pair[0]] = s_fam[pair[1]]\\n fams[s_fam[pair[1]]].append(pair)\\n else: # If none of the samples in the pair is already in a family, create a new family\\n s_fam[pair[0]] = fam_id\\n s_fam[pair[1]] = fam_id\\n fams[fam_id].append(pair)\\n fam_id += 1\\n\\n return list(fams.values())\\n\\n def get_trios(\\n fam_id: str,\\n parent_child_pairs: List[Tuple[str, str]],\\n related_pairs: Dict[Tuple[str, str], str],\\n ) -> List[hl.Trio]:\\n \\\"\\\"\\\"\\n Generate trios based on the list of parent-child pairs in the family and all related pairs in the data.\\n\\n Only complete parent/offspring trios are included in the results.\\n\\n The trios are assembled as follows:\\n 1. All pairs of unrelated samples with different sexes within the family are extracted as possible parent pairs\\n 2. For each possible parent pair, a list of all children is constructed (each child in the list has a parent-offspring pair with each parent)\\n 3. If there are multiple children for a given parent pair, all children should be siblings with each other\\n 4. Check that each child was only assigned a single pair of parents. If a child is found to have multiple parent pairs, they are ALL discarded.\\n\\n :param fam_id: The family ID\\n :param parent_child_pairs: The parent-child pairs for this family\\n :param related_pairs: All related sample pairs in the data\\n :return: List of trios in the family\\n \\\"\\\"\\\"\\n\\n def get_possible_parents(samples: List[str]) -> List[Tuple[str, str]]:\\n \\\"\\\"\\\"\\n Return all pairs of unrelated samples with different sexes within the family are extracted as possible parent pairs.\\n\\n :param samples: All samples in the family\\n :return: Possible parent pairs\\n \\\"\\\"\\\"\\n possible_parents = []\\n for i in range(len(samples)):\\n for j in range(i + 1, len(samples)):\\n if (\\n related_pairs.get(tuple(sorted([samples[i], samples[j]])))\\n is None\\n ):\\n if sex.get(samples[i]) is False and sex.get(samples[j]) is True:\\n possible_parents.append((samples[i], samples[j]))\\n elif (\\n sex.get(samples[i]) is True and sex.get(samples[j]) is False\\n ):\\n possible_parents.append((samples[j], samples[i]))\\n return possible_parents\\n\\n def get_children(possible_parents: Tuple[str, str]) -> List[str]:\\n \\\"\\\"\\\"\\n Construct a list of all children for a given possible parent pair.\\n\\n Each child in the list has a parent-offspring pair with each parent.\\n\\n :param possible_parents: A pair of possible parents\\n :return: The list of all children (if any) corresponding to the possible parents\\n \\\"\\\"\\\"\\n possible_offsprings = defaultdict(\\n set\\n ) # stores sample -> set of parents in the possible_parents where (sample, parent) is found in possible_child_pairs\\n for pair in parent_child_pairs:\\n if possible_parents[0] == pair[0]:\\n possible_offsprings[pair[1]].add(possible_parents[0])\\n elif possible_parents[0] == pair[1]:\\n possible_offsprings[pair[0]].add(possible_parents[0])\\n elif possible_parents[1] == pair[0]:\\n possible_offsprings[pair[1]].add(possible_parents[1])\\n elif possible_parents[1] == pair[1]:\\n possible_offsprings[pair[0]].add(possible_parents[1])\\n\\n return [\\n s for s, parents in possible_offsprings.items() if len(parents) == 2\\n ]\\n\\n def check_sibs(children: List[str]) -> bool:\\n \\\"\\\"\\\"\\n Confirm that all children of a parent pair are siblings with each other.\\n\\n If there are multiple children for a given parent pair, all children should be siblings with each other.\\n\\n :param children: List of all children for a given parent pair\\n :return: Whether all children in the list are siblings\\n \\\"\\\"\\\"\\n for i in range(len(children)):\\n for j in range(i + 1, len(children)):\\n if (\\n related_pairs[tuple(sorted([children[i], children[j]]))]\\n != SIBLINGS\\n ):\\n return False\\n return True\\n\\n def discard_multi_parents_children(trios: List[hl.Trio]):\\n \\\"\\\"\\\"\\n Check that each child was only assigned a single pair of parents.\\n\\n If a child is found to have multiple parent pairs, they are ALL discarded.\\n\\n :param trios: All trios formed for this family\\n :return: The list of trios for which each child has a single parents pair.\\n \\\"\\\"\\\"\\n children_trios = defaultdict(list)\\n for trio in trios:\\n children_trios[trio.s].append(trio)\\n\\n for s, s_trios in children_trios.items():\\n if len(s_trios) > 1:\\n logger.warning(\\n \\\"Discarded duplicated child %s found multiple in trios: %s\\\",\\n s,\\n \\\", \\\".join([str(trio) for trio in s_trios]),\\n )\\n\\n return [trios[0] for trios in children_trios.values() if len(trios) == 1]\\n\\n # Get all possible pairs of parents in (father, mother) order\\n all_possible_parents = get_possible_parents(\\n list({s for pair in parent_child_pairs for s in pair})\\n )\\n\\n trios = []\\n for possible_parents in all_possible_parents:\\n children = get_children(possible_parents)\\n if check_sibs(children):\\n trios.extend(\\n [\\n hl.Trio(\\n s=s,\\n fam_id=fam_id,\\n pat_id=possible_parents[0],\\n mat_id=possible_parents[1],\\n is_female=sex.get(s),\\n )\\n for s in children\\n ]\\n )\\n else:\\n logger.warning(\\n \\\"Discarded family with same parents, and multiple offspring that weren't siblings:\\\"\\n \\\"\\\\nMother: %s\\\\nFather:%s\\\\nChildren:%s\\\",\\n possible_parents[0],\\n possible_parents[1],\\n \\\", \\\".join(children),\\n )\\n\\n return discard_multi_parents_children(trios)\\n\\n # Get all the relations we care about:\\n # => Remove unrelateds and duplicates\\n dups = duplicate_samples_ht.aggregate(\\n hl.agg.explode(\\n lambda dup: hl.agg.collect_as_set(dup), duplicate_samples_ht.filtered\\n ),\\n _localize=False,\\n )\\n relationship_ht = relationship_ht.filter(\\n ~dups.contains(relationship_ht[i_col])\\n & ~dups.contains(relationship_ht[j_col])\\n & (relationship_ht[relationship_col] != UNRELATED)\\n )\\n\\n # Check relatedness table format\\n if not relationship_ht[i_col].dtype == relationship_ht[j_col].dtype:\\n logger.error(\\n \\\"i_col and j_col of the relatedness table need to be of the same type.\\\"\\n )\\n\\n # If i_col and j_col aren't str, then convert them\\n if not isinstance(relationship_ht[i_col], hl.expr.StringExpression):\\n logger.warning(\\n \\\"Pedigrees can only be constructed from string IDs, but your relatedness_ht ID column is of type: %s. \\\"\\n \\\"Expression will be converted to string in Pedigrees.\\\",\\n relationship_ht[i_col].dtype,\\n )\\n if isinstance(relationship_ht[i_col], hl.expr.StructExpression):\\n logger.warning(\\n \\\"Struct fields %s will be joined by underscores to use as sample names in Pedigree.\\\",\\n list(relationship_ht[i_col]),\\n )\\n relationship_ht = relationship_ht.key_by(\\n **{\\n i_col: hl.delimit(\\n hl.array(\\n [\\n hl.str(relationship_ht[i_col][x])\\n for x in relationship_ht[i_col]\\n ]\\n ),\\n \\\"_\\\",\\n ),\\n j_col: hl.delimit(\\n hl.array(\\n [\\n hl.str(relationship_ht[j_col][x])\\n for x in relationship_ht[j_col]\\n ]\\n ),\\n \\\"_\\\",\\n ),\\n }\\n )\\n else:\\n raise NotImplementedError(\\n \\\"The `i_col` and `j_col` columns of the `relationship_ht` argument passed to infer_families are not of type StringExpression or Struct.\\\"\\n )\\n\\n # If sex is a Table, extract sex information as a Dict\\n if isinstance(sex, hl.Table):\\n sex = dict(hl.tuple([sex.s, sex.is_female]).collect())\\n\\n # Collect all related sample pairs and\\n # create a dictionnary with pairs as keys and relationships as values\\n # Sample-pairs are tuples ordered by sample name\\n related_pairs = {\\n tuple(sorted([i, j])): rel\\n for i, j, rel in hl.tuple(\\n [relationship_ht.i, relationship_ht.j, relationship_ht.relationship]\\n ).collect()\\n }\\n\\n parent_child_pairs_by_fam = group_parent_child_pairs_by_fam(\\n [pair for pair, rel in related_pairs.items() if rel == PARENT_CHILD]\\n )\\n return hl.Pedigree(\\n [\\n trio\\n for fam_index, parent_child_pairs in enumerate(parent_child_pairs_by_fam)\\n for trio in get_trios(str(fam_index), parent_child_pairs, related_pairs)\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"352eca632ae0bc5006907e56936c1a17\",\n \"score\": \"0.5232809\",\n \"text\": \"def _process_species(self) -> None:\\n species = self.sbml.getListOfSpecies()\\n\\n self.species_index = {\\n species_element.getId(): species_index\\n for species_index, species_element in enumerate(species)\\n }\\n\\n self.symbols['species']['identifier'] = sp.Matrix(\\n [sp.Symbol(spec.getId(), real=True) for spec in species]\\n )\\n self.symbols['species']['name'] = [spec.getName() for spec in species]\\n\\n self.species_compartment = sp.Matrix(\\n [sp.Symbol(spec.getCompartment(), real=True) for spec in species]\\n )\\n\\n self.constant_species = [species_element.getId()\\n for species_element in species\\n if species_element.getConstant()]\\n\\n self.boundary_condition_species = [\\n species_element.getId()\\n for species_element in species\\n if species_element.getBoundaryCondition()\\n ]\\n self.species_has_only_substance_units = [\\n specie.getHasOnlySubstanceUnits() for specie in species\\n ]\\n\\n concentrations = [spec.getInitialConcentration() for spec in species]\\n amounts = [spec.getInitialAmount() for spec in species]\\n\\n def get_species_initial(index, conc):\\n # We always simulate concentrations!\\n if self.species_has_only_substance_units[index]:\\n if species[index].isSetInitialAmount() \\\\\\n and not math.isnan(amounts[index]):\\n return sp.sympify(amounts[index]) \\\\\\n / self.species_compartment[index]\\n if species[index].isSetInitialConcentration():\\n return sp.sympify(conc)\\n else:\\n if species[index].isSetInitialConcentration():\\n return sp.sympify(conc)\\n\\n if species[index].isSetInitialAmount() \\\\\\n and not math.isnan(amounts[index]):\\n return sp.sympify(amounts[index]) \\\\\\n / self.species_compartment[index]\\n\\n return self.symbols['species']['identifier'][index]\\n\\n species_initial = sp.Matrix(\\n [get_species_initial(index, conc)\\n for index, conc in enumerate(concentrations)]\\n )\\n\\n species_ids = [spec.getId() for spec in self.sbml.getListOfSpecies()]\\n for initial_assignment in self.sbml.getListOfInitialAssignments():\\n if initial_assignment.getId() in species_ids:\\n index = species_ids.index(\\n initial_assignment.getId()\\n )\\n sym_math = sp.sympify(_parse_logical_operators(\\n sbml.formulaToL3String(initial_assignment.getMath())),\\n locals=self.local_symbols\\n )\\n if sym_math is not None:\\n sym_math = _parse_special_functions(sym_math)\\n _check_unsupported_functions(sym_math, 'InitialAssignment')\\n species_initial[index] = sym_math\\n\\n for ix, (symbol, init) in enumerate(zip(\\n self.symbols['species']['identifier'], species_initial\\n )):\\n if symbol == init:\\n species_initial[ix] = sp.sympify(0.0)\\n\\n # flatten initSpecies\\n while any([species in species_initial.free_symbols\\n for species in self.symbols['species']['identifier']]):\\n species_initial = species_initial.subs([\\n (symbol, init)\\n for symbol, init in zip(\\n self.symbols['species']['identifier'], species_initial\\n )\\n ])\\n\\n self.symbols['species']['value'] = species_initial\\n\\n if self.sbml.isSetConversionFactor():\\n conversion_factor = sp.Symbol(self.sbml.getConversionFactor(),\\n real=True)\\n else:\\n conversion_factor = 1.0\\n\\n self.species_conversion_factor = sp.Matrix([\\n sp.sympify(specie.getConversionFactor())\\n if specie.isSetConversionFactor()\\n else conversion_factor\\n for specie in species\\n ])\\n\\n # Process assignment and rate rules for species and compartments.\\n # Compartments with rate rules are implemented as species. Species and\\n # compartments with assignments are implemented as observables (and\\n # replaced with their assignment in all expressions). Note that, in the\\n # case of species, rate rules may describe the change in amount, not\\n # concentration, of a species.\\n rules = self.sbml.getListOfRules()\\n compartmentvars = self.compartment_symbols.free_symbols\\n # compartments with rules are replaced with constants in the relevant\\n # equations during the _replace_in_all_expressions call inside\\n # _process_rules\\n for rule in rules:\\n ### Taken from _process_rules\\n if rule.getFormula() == '':\\n continue\\n variable = sp.sympify(rule.getVariable(),\\n locals=self.local_symbols)\\n formula = sp.sympify(_parse_logical_operators(\\n sbml.formulaToL3String(rule.getMath())),\\n locals=self.local_symbols)\\n formula = _parse_special_functions(formula)\\n _check_unsupported_functions(formula, 'Rule')\\n ###\\n\\n # Species rules are processed first, to avoid processing\\n # compartments twice (as compartments with rate rules are\\n # implemented as species). Could also be avoided with a\\n # `not in self.compartment_rate_rules` condition.\\n if variable in self.symbols['species']['identifier']:\\n if rule.getTypeCode() == sbml.SBML_ASSIGNMENT_RULE:\\n # Handled in _process_rules and _process_observables.\\n pass\\n elif rule.getTypeCode() == sbml.SBML_RATE_RULE:\\n self.add_d_dt(\\n formula,\\n variable,\\n self.symbols['species']['value'],\\n sbml.SBML_SPECIES)\\n else:\\n raise SBMLException('The only rules currently supported '\\n 'for species are assignment and rate '\\n 'rules!')\\n\\n if variable in compartmentvars:\\n if rule.getTypeCode() == sbml.SBML_ASSIGNMENT_RULE:\\n # Handled in _process_rules and _process_observables\\n # SBML Assignment Rules can be used to specify initial\\n # values (see SBML L3V2 manual, Section 3.4.8).\\n # Priority appears to be above InitialAssignment.\\n self.compartment_volume[list(\\n self.compartment_symbols\\n ).index(variable)] = formula\\n elif rule.getTypeCode() == sbml.SBML_RATE_RULE:\\n self.add_d_dt(\\n formula,\\n variable,\\n self.compartment_volume[list(\\n self.compartment_symbols\\n ).index(variable)],\\n sbml.SBML_COMPARTMENT)\\n else:\\n raise SBMLException('The only rules currently supported '\\n 'for compartments are assignment and '\\n 'rate rules!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65395feb9ba2d85229f67770d1d3d4ee\",\n \"score\": \"0.5211056\",\n \"text\": \"def species(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db03b4da78f79b9f1d2a045b72773438\",\n \"score\": \"0.51774824\",\n \"text\": \"def project_family(sets, X):\\n proj_family = set({})\\n for A in sets:\\n proj_family.update(set({frozenset(A.difference(X))}))\\n return proj_family\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2d0354a965108c9a727cdb4ecb5591a\",\n \"score\": \"0.5156593\",\n \"text\": \"def iter_families(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ced6999b2afb21feb92a401cb5f6d66\",\n \"score\": \"0.5075314\",\n \"text\": \"def populate(self):\\n for organism in assemblies_by_org:\\n # for organism in [\\\"Homo sapiens\\\"]:\\n self.populate_by_org(organism)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f91221730a44a57fd5d02c7df14ad5c1\",\n \"score\": \"0.50619406\",\n \"text\": \"def iter_families(self):\\n return map(self.get_family_from_handle, self.flist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b654353aed5ea5362d61deef62dc285b\",\n \"score\": \"0.50481504\",\n \"text\": \"def get_species(self) -> Set[Specie]:\\n return set(self.species)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb80b2fe1eb05c3a70308fee901a5513\",\n \"score\": \"0.50437903\",\n \"text\": \"def get_genus_family(soup, id_name):\\n genus_list = []\\n family_list = []\\n\\n for plant in soup.find_all(id=id_name):\\n for genus in plant.find_all(class_=re.compile(\\\"genus\\\")):\\n name = genus.get_text()\\n genus_list.append(name)\\n for family in plant.find_all(class_=\\\"family\\\"):\\n name2 = family.get_text()\\n family_list.append(name2)\\n\\n # join family_list and genus_list into a dataframe.\\n result = pd.concat([pd.DataFrame(genus_list, columns=['genus']),\\n pd.DataFrame(family_list, columns=['family'])], axis=1)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af0667e29dad878e203ed55b706fd0f4\",\n \"score\": \"0.49826998\",\n \"text\": \"def mixer(self, F):\\n ## get number of streams\\n n_streams = np.size(F)\\n ## verify if there are more than one stream\\n if n_streams > 1: \\n ## get number of species of key stream\\n n_species = self[0].n_species\\n ## preallocate variable\\n P = np.zeros(n_streams)\\n h = 0.0\\n ## set initial vector of mass (kg/s)\\n mass = F[0]*self[0].Y # [kg/s][kg/kg]=[kg/s] \\n for j in range(n_streams):\\n ## get pressure of streams\\n P[j] = self[j].P\\n ## get total energy of streams\\n h += F[j]*self[j].enthalpy_mass # [kg/s][J/kg]=[J/s]\\n if j > 0:\\n for k in range(self[j].n_species):\\n for i in range(n_species):\\n if self[0].species_name(i) == self[j].species_name(k):\\n ## get total mass of each species\\n mass[i] += F[j]*self[j].Y[k] # [kg/s] \\n ## mix stream\\n ## mass fraction\\n mass = mass/sum(mass) # [kg/kg]\\n ## specific enthalpy\\n h = h/np.sum(F) # [J/s][s/kg]=[J/kg]\\n ## pressure\\n P = np.amin(P) # [Pa]\\n ## mass flow\\n F = np.sum(F) \\n ## redefine stream #1 as mix stream\\n self = self[0] \\n ## re-calculate mix stream state\\n self.Y = mass\\n self.HP = h, P\\n return self, F\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94ff0e418995e409192e7ee82ad9ad77\",\n \"score\": \"0.49815553\",\n \"text\": \"def species(self):\\n return list(species_from_basis(self._basis))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8360c53d8a9d2075d7ce443d62de0a29\",\n \"score\": \"0.49427173\",\n \"text\": \"def family(self):\\n return choice([\\n 'Alkali Metals',\\n 'Alkaline Earth Metals',\\n 'Transition Metals',\\n 'Earth Metals',\\n 'Tetrels',\\n 'Pnictogens',\\n 'Chalcogens',\\n 'Halogens',\\n 'Noble Gases',\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72a131c93af6cbf2dd9b2509190da531\",\n \"score\": \"0.4933283\",\n \"text\": \"def filterBySpecies(self, species_list):\\n mapSpecies = {'hs' : 'Homo sapiens', 'mm' : 'Mus musculus',\\n 'dm' : 'Drosophila melanogaster',\\n 'ce' : 'Caenorhabditis elegans'}\\n #MAP short names to long names\\n sl = map(lambda s: mapSpecies[s], species_list)\\n motifs = []\\n for m in self:\\n #take the motif IFF species = [], OR an element in species is\\n #in species_list\\n if m.species:\\n #print m.species\\n found = False\\n i = 0\\n while (i < len(m.species) and not found):\\n if (m.species[i] in sl): found = True\\n i += 1\\n if found: motifs.append(m)\\n else:\\n motifs.append(m)\\n return MotifList(motifs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aa88ba80cae9ab00665991f2ed09e29\",\n \"score\": \"0.49098432\",\n \"text\": \"def GetUniqueSpeciesGroups(self):\\n sources_set = set()\\n for sg in self.all_species_groups:\\n source = sg.formation_energy_source\\n if source not in sources_set:\\n sources_set.add(source)\\n yield sg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41cf28eff184cc297a6a2cf573d26da7\",\n \"score\": \"0.49084294\",\n \"text\": \"def get_families_by_genus_type(self, *args, **kwargs):\\n # Implemented from kitosid template for -\\n # osid.resource.BinLookupSession.get_bins_by_genus_type\\n catalogs = self._get_provider_session('family_lookup_session').get_families_by_genus_type(*args, **kwargs)\\n cat_list = []\\n for cat in catalogs:\\n cat_list.append(Family(self._provider_manager, cat, self._runtime, self._proxy))\\n return FamilyList(cat_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6283cd8c6833f8ed5cc5ec033f93e73d\",\n \"score\": \"0.48841792\",\n \"text\": \"def update_species(self):\\n species = []\\n warn(\\\"Unsubclassed update_species called for \\\" + repr(self))\\n return species\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3235323a30d0d194da9b16e82154c144\",\n \"score\": \"0.4878384\",\n \"text\": \"def evolve(self):\\n self.update_fitness()\\n global_fitness_sum = 0\\n for s in self._species:\\n global_fitness_sum += s._fitness_sum\\n\\n if global_fitness_sum == 0:\\n # No progress, mutate everybody\\n for s in self._species:\\n for g in s._members:\\n g.mutate()\\n else:\\n # 1. Eliminate lowest performing genomes per specie\\n # 2. Repopulate\\n for s in self._species:\\n s.cull_genomes(False)\\n\\n children = []\\n for i, s in enumerate(self._species):\\n ratio = s._fitness_sum/global_fitness_sum\\n offspring = math.floor(ratio * (self._population-self.get_population()))\\n for j in range(int(offspring)):\\n children.append(s.breed())\\n\\n for g in children:\\n self.classify_genome(g)\\n\\n self._generation += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e33e5faf465f437a0ccd1aa07ac1ced\",\n \"score\": \"0.48525405\",\n \"text\": \"def GetSpecies(self):\\n if self._all_species is None:\\n self._all_species = self.species.all()\\n return self._all_species\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f46757afcf728cf2f9e720ae9c13b976\",\n \"score\": \"0.48503226\",\n \"text\": \"def extractFeatures(self, num_samples_to_keep=3):\\n res = []\\n for i, row in self.variants.iterrows():\\n try:\\n ind_id = row['ind_id']\\n fname = self.ped.getIndivVCF(ind_id)\\n if fname is None:\\n continue\\n fam_id = self.ped.getFamily(ind_id)\\n chrom = row['chr'] \\n pos_vcf = row['pos']\\n print ind_id, fam_id, chrom, pos_vcf - 1, fname\\n myvars = variants.Variants(fname, fam_id, chrom, pos_vcf - 1, pos_vcf)\\n myvars.readFromVcf()\\n df = myvars.variants\\n if df is None:\\n continue\\n if df.empty:\\n continue\\n if num_samples_to_keep != 1 and num_samples_to_keep != 3:\\n res.append(df)\\n continue\\n samples_to_keep = {ind_id: 'offspring'}\\n if num_samples_to_keep == 3:\\n samples_to_keep[self.ped.getFather(fam_id)] = 'father'\\n samples_to_keep[self.ped.getMother(fam_id)] = 'mother'\\n a = set(samples_to_keep.keys())\\n b = set(myvars.samples)\\n if a.intersection(b) != a:\\n print 'some amples from ', a, ' are not found in ', b \\n raise\\n samples_to_drop = b.difference(a)\\n clmns_to_drop = []\\n for smpl in samples_to_drop:\\n for c in df.columns:\\n if smpl in c:\\n clmns_to_drop.append(c)\\n else:\\n continue\\n if clmns_to_drop:\\n df.drop(clmns_to_drop, axis=1, inplace=True)\\n #now rename remaining format columns\\n new_col_names = list(df.columns)\\n for i, c in enumerate(df.columns):\\n for key, item in samples_to_keep.iteritems():\\n if key in c:\\n new_col_names[i] = c.replace(key, item)\\n else:\\n continue\\n df.columns = new_col_names\\n df['pheno'] = self.ped.isAffected(ind_id)\\n df['ind_id'] = ind_id\\n res.append(df)\\n except:\\n continue\\n\\n # x = [i for i in res if not i is None]\\n # y = [i for i in x if not i.empty]\\n# df = self.variants.apply(lambda row: self._fileHasVariant(\\\\\\n# self.ped.getIndivVCF(row['ind_id']),\\\\\\n# self.ped.getFamily(row['ind_id']),\\\\\\n# row['chr'], row['pos'] - 1, row['pos']), axis=1)\\n# df = df[df.notnull()]\\n# c1 =df.apply(lambda x: x.tolist() != [])\\n# df = df[c1]\\n# res = [x for y in df for x in y.tolist()]\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"903906683164346e53c20665f60043c8\",\n \"score\": \"0.48450297\",\n \"text\": \"def populate_catalog_families():\\n for family in FAMILIES:\\n sql = insert_family.substitute(family)\\n request_to_postgres(sql)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b818328174dd4af8bff0b064f9cc307\",\n \"score\": \"0.48357406\",\n \"text\": \"def test_species_from_species_at_sites():\\n species_at_sites = [\\\"Si\\\"]\\n assert [d.dict() for d in species_from_species_at_sites(species_at_sites)] == [\\n {\\n \\\"name\\\": \\\"Si\\\",\\n \\\"concentration\\\": [1.0],\\n \\\"chemical_symbols\\\": [\\\"Si\\\"],\\n \\\"attached\\\": None,\\n \\\"mass\\\": None,\\n \\\"original_name\\\": None,\\n \\\"nattached\\\": None,\\n },\\n ]\\n\\n species_at_sites = [\\\"Si\\\", \\\"Si\\\", \\\"O\\\", \\\"O\\\", \\\"O\\\", \\\"O\\\"]\\n assert sorted(\\n [d.dict() for d in species_from_species_at_sites(species_at_sites)],\\n key=lambda _: _[\\\"name\\\"],\\n ) == sorted(\\n [\\n {\\n \\\"name\\\": \\\"O\\\",\\n \\\"concentration\\\": [1.0],\\n \\\"chemical_symbols\\\": [\\\"O\\\"],\\n \\\"attached\\\": None,\\n \\\"mass\\\": None,\\n \\\"original_name\\\": None,\\n \\\"nattached\\\": None,\\n },\\n {\\n \\\"name\\\": \\\"Si\\\",\\n \\\"concentration\\\": [1.0],\\n \\\"chemical_symbols\\\": [\\\"Si\\\"],\\n \\\"attached\\\": None,\\n \\\"mass\\\": None,\\n \\\"original_name\\\": None,\\n \\\"nattached\\\": None,\\n },\\n ],\\n key=lambda _: _[\\\"name\\\"],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b98886bb95d8405f27f0a6f135850be\",\n \"score\": \"0.48295453\",\n \"text\": \"def iterclust(self, species=[]):\\n for cf in self._basis[\\\"cluster_functions\\\"]:\\n #TODO: Not too flexible. Also should this iterator stuff just be it's own class?\\n good_species=True\\n for sp in species:\\n formula=cf[\\\"prototype_function\\\"]\\n if not self._formula_has_specie(formula, sp):\\n good_species=False\\n break\\n\\n if not good_species:\\n continue\\n\\n yield cf\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdea447bf7069831ab605fb4b713bb4b\",\n \"score\": \"0.48099178\",\n \"text\": \"def testFamily(self):\\n\\t\\t\\n\\t\\tG = self.G\\n\\t\\tc,s,r,w = G.v['c'],G.v['s'], \\\\\\n\\t\\t\\t\\t G.v['r'],G.v['w']\\n\\t\\t\\n\\t\\tassert(c.distribution.parents == [] and \\\\\\n\\t\\t\\t set(w.distribution.parents) == set([r,s]) and \\\\\\n\\t\\t\\t r.distribution.parents == [c] and \\\\\\n\\t\\t\\t s.distribution.parents == [c]), \\\\\\n\\t\\t\\t \\\"Error with parents\\\"\\n\\n\\t\\tassert(c.distribution.family == [c] and \\\\\\n\\t\\t\\t set(s.distribution.family) == set([c,s]) and \\\\\\n\\t\\t\\t set(r.distribution.family) == set([r,c]) and \\\\\\n\\t\\t\\t set(w.distribution.family) == set([w,r,s])), \\\\\\n\\t\\t\\t \\\"Error with family\\\"\\n\\n\\t\\tassert(c.distribution.nvalues == c.nvalues), \\\\\\n\\t\\t\\t\\t\\\"nvalues not set properly\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60c427e44edf3f59c6a5e3f887835020\",\n \"score\": \"0.4809334\",\n \"text\": \"def fill_ingredient_families():\\n families_choices = [('', '-----------')]\\n\\n for family in IngredientFamily.objects.all():\\n families_choices.append((family.id, family.name))\\n\\n return list(families_choices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfa8207217672c3b88aaad3bb8ea1a73\",\n \"score\": \"0.47952598\",\n \"text\": \"def gas_species(self):\\n return NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c5d3422d99cdb58ff409b92501c6f0\",\n \"score\": \"0.47798732\",\n \"text\": \"def basis_species(self):\\n return [k for k in self._species_to_basis]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30a91b9e187f627da2ed208389cc4640\",\n \"score\": \"0.47707072\",\n \"text\": \"def get_families(connected_families: List[CompoundFamily], bgc_names: Dict[str, str]) -> Dict[str, Set[str]]:\\n families_names = {}\\n for index, components in enumerate(sorted(connected_families,\\n key=len,\\n reverse=True),\\n start=1):\\n family_name = str(index) + \\\"_\\\" + components.compound_class\\n for component in components.clusters:\\n # don't add the same name over and over\\n if component.startswith(\\\"BGC\\\") \\\\\\n and not bgc_names[component] in family_name:\\n family_name += \\\"_\\\" + bgc_names[component]\\n families_names[family_name] = components.clusters\\n return families_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a9ef95c7d1d667256ee4a66221ee8be\",\n \"score\": \"0.47702327\",\n \"text\": \"def mix_molecules_regexp(self, mix, molecules):\\n molecules_accepted = []\\n for mol in mix:\\n molecules = self.molecules_regexp(mol, molecules)\\n molecules_accepted.append(molecules)\\n molecules_matching_all = molecules_accepted[0]\\n for i in range(1, len(molecules_accepted)):\\n d1 = molecules_accepted[i]\\n molecules_matching_all = {k:v for k, v in molecules_matching_all.items() if k in d1}\\n return molecules_matching_all\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e716d88a0e57f9640898771e9c66c132\",\n \"score\": \"0.4769611\",\n \"text\": \"def _get_families(self):\\n families = {}\\n for directory in (self.path_ofl, self.path_ufl, self.path_apache):\\n for family_dir in os.listdir(directory):\\n if not '.' in family_dir:\\n family_path = os.path.join(self.path, directory, family_dir)\\n families[family_dir] = Family(family_path)\\n return families\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64268a755928c21df2477530f04ff2f4\",\n \"score\": \"0.47690684\",\n \"text\": \"def makeFromNetLists(self, speciesList, varietyList):\\n self.flowerlist = []\\n # Fill in the lists by running through the parallel lists of properties\\n for species, variety in zip(speciesList, varietyList):\\n self.flowerlist.append( FlowerBase.FlowerBase(species, variety) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8b2617e4b2c61f9b78a601f15a2567\",\n \"score\": \"0.4759988\",\n \"text\": \"def remove_families_from(filtering, filtered_list):\\n if 'family' in filtering and filtering.get('family') != 'none':\\n to_be_excluded_list = []\\n families_attr = ['famille_{0}'.format(i) for i in range(1, 6)]\\n for he_nom in filtered_list:\\n he = HuileEssentielle.objects.get(pk=he_nom)\\n families = []\\n for attr in families_attr:\\n he_famille = getattr(he, attr)\\n if he_famille is not None:\\n families.append(he_famille.nom)\\n if filtering.get('family') not in families:\\n to_be_excluded_list.append(he_nom)\\n for excluded in to_be_excluded_list:\\n filtered_list.remove(excluded)\\n return filtered_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fdc1b698092f29d269ac9305ce08920\",\n \"score\": \"0.47598493\",\n \"text\": \"def get_families_by_ids(self, *args, **kwargs):\\n # Implemented from kitosid template for -\\n # osid.resource.BinLookupSession.get_bins_by_ids\\n catalogs = self._get_provider_session('family_lookup_session').get_families_by_ids(*args, **kwargs)\\n cat_list = []\\n for cat in catalogs:\\n cat_list.append(Family(self._provider_manager, cat, self._runtime, self._proxy))\\n return FamilyList(cat_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78bcd06cddfde7ffa81f68518fdd2d9d\",\n \"score\": \"0.47579056\",\n \"text\": \"def calculate(self, Species='all', method='default'):\\n\\n\\n if (Species == 'all') or (Species == 'default'):\\n species = ('CO' , 'CO2' , 'SO2' , 'OC' , 'BC' , 'NH3' , \\n 'PM25', 'NO' , 'MEK' , 'C3H6', 'C2H6', 'C3H8', \\n 'ALK4', 'ALD2', 'CH2O', 'ACET', 'CH4')\\n else:\\n species = Species[:]\\n\\n\\n # factor needed to convert B_f from [g/kg] to [kg/kg]\\n units_factor = 1e-3\\n\\n n_biomes = len(self.biomes)\\n\\n A_l = zeros((self.im, self.jm))\\n A_w = zeros((self.im, self.jm))\\n A_c = zeros((self.im, self.jm))\\n\\n for sat in self.Sat:\\n A_l += self.Land[sat]\\n A_w += self.Water[sat]\\n A_c += self.Cloud[sat]\\n\\n A_o = A_l + A_w\\n\\n i = (A_l > 0)\\n j = ((A_l + A_c) > 0)\\n\\n E = {}\\n E_= {}\\n for s in species:\\n E[s] = zeros((n_biomes, self.im, self.jm))\\n E_[s] = zeros((n_biomes, self.im, self.jm))\\n\\n for sat in self.Sat:\\n FRP = self.FRP[sat]\\n F = self.F[sat]\\n A_ = self.Cloud[sat]\\n \\n for b in range(n_biomes):\\n E[s][b,:,:] += units_factor * A_f[s][b] * S_f[sat] * B_f[s][b] * FRP[b]\\n E_[s][b,:,:] += units_factor * A_f[s][b] * S_f[sat] * B_f[s][b] * F[b] * A_\\n \\n for b in range(n_biomes):\\n E_b = E[s][b,:,:]\\n E_b_ = E_[s][b,:,:]\\n\\n if (method == 'default') or (method == 'sequential'):\\n E_b[j] = ( (E_b[j] / (A_o[j] + A_c[j])) * (1 + A_c[j] / (A_l[j] + A_c[j])) +\\n (E_b_[j] / (A_o[j] + A_c[j])) * ( A_c[j] / (A_l[j] + A_c[j])) )\\n\\n if method == 'sequential-zero':\\n E_b[i] = E_b[i] / (A_o[i] + A_c[i]) * (1.0 + A_c[i] / (A_l[i] + A_c[i]))\\n\\n if method == 'nofires':\\n E_b[i] = E_b[i] / (A_o[i] + A_c[i])\\n\\n if method == 'similarity':\\n E_b[i] = E_b[i] / A_l[i] * ((A_l[i] + A_c[i]) / (A_o[i] + A_c[i]))\\n\\n if method == 'similarity_qfed-2.2':\\n E_b[i] = E_b[i] / A_o[i]\\n \\n\\n# Save Forecast dictionary\\n# ------------------------\\n dt = 1.0 # days\\n tau = 3.0 # days\\n\\n for sat in self.Sat:\\n for b in range(n_biomes):\\n s = species[0]\\n\\n self.F[sat][b][:,:] = (E[s][b,:,:] / (units_factor * A_f[s][b] * S_f[sat] * B_f[s][b])) * exp(-dt/tau)\\n self.F[sat][b][j] = self.F[sat][b][j] * ((A_o[j] + A_c[j]) / (A_l[j] + A_c[j]))\\n\\n# Save Emission dictionary\\n# ------------------------\\n self.Species = species\\n self.Emissions = E\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e871fa81235e14ff172d7acc7ea87883\",\n \"score\": \"0.47530967\",\n \"text\": \"def group_parent_child_pairs_by_fam(\\n parent_child_pairs: Iterable[Tuple[str, str]]\\n ) -> List[List[Tuple[str, str]]]:\\n fam_id = 1 # stores the current family id\\n s_fam = dict() # stores the family id for each sample\\n fams = defaultdict(list) # stores fam_id -> sample-pairs\\n for pair in parent_child_pairs:\\n if pair[0] in s_fam:\\n if pair[1] in s_fam:\\n if (\\n s_fam[pair[0]] != s_fam[pair[1]]\\n ): # If both samples are in different families, merge the families\\n new_fam_id = s_fam[pair[0]]\\n fam_id_to_merge = s_fam[pair[1]]\\n for s in s_fam:\\n if s_fam[s] == fam_id_to_merge:\\n s_fam[s] = new_fam_id\\n fams[new_fam_id].extend(fams.pop(fam_id_to_merge))\\n else: # If only the 1st sample in the pair is already in a family, assign the 2nd sample in the pair to the same family\\n s_fam[pair[1]] = s_fam[pair[0]]\\n fams[s_fam[pair[0]]].append(pair)\\n elif (\\n pair[1] in s_fam\\n ): # If only the 2nd sample in the pair is already in a family, assign the 1st sample in the pair to the same family\\n s_fam[pair[0]] = s_fam[pair[1]]\\n fams[s_fam[pair[1]]].append(pair)\\n else: # If none of the samples in the pair is already in a family, create a new family\\n s_fam[pair[0]] = fam_id\\n s_fam[pair[1]] = fam_id\\n fams[fam_id].append(pair)\\n fam_id += 1\\n\\n return list(fams.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0b385dd3647cef150ae9c345e7d8086\",\n \"score\": \"0.474709\",\n \"text\": \"def get_families(self):\\n # Implemented from kitosid template for -\\n # osid.resource.BinLookupSession.get_bins_template\\n catalogs = self._get_provider_session('family_lookup_session').get_families()\\n cat_list = []\\n for cat in catalogs:\\n cat_list.append(Family(self._provider_manager, cat, self._runtime, self._proxy))\\n return FamilyList(cat_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a4bd2191c7d280e8ed3e1725aca3547\",\n \"score\": \"0.47307527\",\n \"text\": \"def species(self) -> ISpecies:\\n return assert_set(self._species)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddd0a060562b1a824166bc104d3fa758\",\n \"score\": \"0.4725433\",\n \"text\": \"def test_helix_groups(self) -> None:\\n helices = [sc.Helix(max_offset=7, group='a'), sc.Helix(max_offset=7, group='a'),\\n sc.Helix(max_offset=7, group='b'), sc.Helix(max_offset=7, group='b')]\\n groups = {\\n 'a': sc.HelixGroup(position=sc.Position3D(0, 0, 0), grid=sc.honeycomb),\\n 'b': sc.HelixGroup(position=sc.Position3D(100, 0, 0), grid=sc.square),\\n }\\n design = sc.Design(helices=helices, groups=groups)\\n design.draw_strand(0, 0).move(7).cross(1).move(-7)\\n design.draw_strand(2, 7).move(-7).cross(3).move(7) # unlike basic design, put strand on helices 2,3\\n\\n # expected values for verification\\n expected_num_nucleotides = 7 * 4\\n expected_strand_length = 7 * 2\\n\\n dat, top = design.to_oxdna_format()\\n dat_lines = dat.strip().split('\\\\n')\\n top_lines = top.strip().split('\\\\n')\\n\\n # check length of output files are as expected (matches # of nucleotides plus header size)\\n self.assertEqual(expected_num_nucleotides + 3, len(dat_lines))\\n self.assertEqual(expected_num_nucleotides + 1, len(top_lines))\\n\\n # find relevant values for nucleotides\\n cm_poss = [] # center of mass position\\n nbrs_3p = []\\n nbrs_5p = []\\n\\n for line in dat_lines[3:]:\\n data = line.strip().split()\\n # make sure there are 15 values per line (3 values per vector * 5 vectors per line)\\n # order of vectors: center of mass position, backbone base versor, normal versor, velocity, angular velocity (more info on versors: https://eater.net/quaternions)\\n self.assertEqual(15, len(data))\\n\\n cm_poss.append(tuple([float(x) for x in data[0:3]]))\\n bb_vec = tuple([float(x) for x in data[3:6]]) # backbone base vector\\n nm_vec = tuple([float(x) for x in data[6:9]]) # normal vector\\n\\n # make sure normal vectors and backbone vectors are unit length\\n sqr_bb_vec = sum([x ** 2 for x in bb_vec])\\n sqr_nm_vec = sum([x ** 2 for x in nm_vec])\\n self.assertAlmostEqual(1.0, sqr_bb_vec)\\n self.assertAlmostEqual(1.0, sqr_nm_vec)\\n\\n for value in data[9:]: # values for velocity and angular velocity vectors are 0\\n self.assertAlmostEqual(0, float(value))\\n\\n strand1_idxs = []\\n strand2_idxs = []\\n for nuc_idx, line in enumerate(top_lines[1:]):\\n data = line.strip().split()\\n # make sure there are 4 values per line: strand, base, 3' neighbor, 5' neighbor\\n self.assertEqual(4, len(data))\\n\\n # make sure there are only 2 strands\\n strand_num = int(data[0])\\n self.assertIn(strand_num, [1, 2])\\n # make sure base is valid\\n base = data[1]\\n self.assertIn(base, ['A', 'C', 'G', 'T'])\\n\\n nbrs_3p.append(int(data[2]))\\n nbrs_5p.append(int(data[3]))\\n\\n # append start of strand (no 5' neighbor) to list of indexes for strand\\n neighbor_5 = int(data[3])\\n if neighbor_5 == -1:\\n if strand_num == 1:\\n strand1_start = nuc_idx\\n strand1_idxs.append(strand1_start)\\n else:\\n strand2_start = nuc_idx\\n strand2_idxs.append(strand2_start)\\n\\n # reconstruct strands using indices from oxDNA files\\n next_idx = nbrs_3p[strand1_start]\\n while next_idx >= 0:\\n strand1_idxs.append(next_idx)\\n next_idx = nbrs_3p[strand1_idxs[-1]]\\n\\n next_idx = nbrs_3p[strand2_start]\\n while next_idx >= 0:\\n strand2_idxs.append(next_idx)\\n next_idx = nbrs_3p[strand2_idxs[-1]]\\n\\n # assert that strands are the correct length\\n self.assertEqual(expected_strand_length, len(strand1_idxs))\\n self.assertEqual(expected_strand_length, len(strand2_idxs))\\n\\n for i in range(expected_strand_length - 1):\\n # ignore nucleotide distance between domains (on crossover)\\n if i == 6:\\n continue\\n\\n strand1_nuc_idx1 = strand1_idxs[i]\\n strand1_nuc_idx2 = strand1_idxs[i + 1]\\n strand2_nuc_idx1 = strand2_idxs[i]\\n strand2_nuc_idx2 = strand2_idxs[i + 1]\\n\\n # find the center of mass for adjacent nucleotides\\n s1_cmp1 = cm_poss[strand1_nuc_idx1]\\n s1_cmp2 = cm_poss[strand1_nuc_idx2]\\n s2_cmp1 = cm_poss[strand2_nuc_idx1]\\n s2_cmp2 = cm_poss[strand2_nuc_idx2]\\n\\n # calculate and verify squared distance between adjacent nucleotides in a domain\\n diff1 = tuple([s1_cmp1[j] - s1_cmp2[j] for j in range(3)])\\n diff2 = tuple([s2_cmp1[j] - s2_cmp2[j] for j in range(3)])\\n sqr_dist1 = sum([x ** 2 for x in diff1])\\n sqr_dist2 = sum([x ** 2 for x in diff2])\\n\\n self.assertAlmostEqual(self.EXPECTED_ADJ_NUC_CM_DIST2, sqr_dist1)\\n self.assertAlmostEqual(self.EXPECTED_ADJ_NUC_CM_DIST2, sqr_dist2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f11192e48d1873c444fce2670835e857\",\n \"score\": \"0.47059622\",\n \"text\": \"def cbh_n(reactants, products, scheme, do_canonical=True):\\n\\n if do_canonical:\\n reactants = [canonical(smiles) for smiles in reactants]\\n products = [canonical(smiles) for smiles in products]\\n\\n left, right = resultant(reactants, products, scheme=scheme)\\n\\n return left, right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08ed140cb850a833719d77c33ae11945\",\n \"score\": \"0.4705379\",\n \"text\": \"def __call__(self, results):\\n\\n results = self._mixup_or_mosaic_transform(results)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbf6ddee66483b4280034265cff3b4d3\",\n \"score\": \"0.47023052\",\n \"text\": \"def createDicoFamily():\\n\\tdicoFam = {'Coding' : ['IG_C_gene', 'IG_D_gene', 'IG_J_gene',\\n\\t\\t\\t\\t\\t\\t\\t'IG_LV_gene', 'IG_M_gene', 'IG_V_gene',\\n\\t\\t\\t\\t\\t\\t\\t'IG_Z_gene', 'nonsense_mediated_decay',\\n\\t\\t\\t\\t\\t\\t\\t'nontranslating_CDS', 'non_stop_decay',\\n\\t\\t\\t\\t\\t\\t\\t'protein_coding', 'TR_C_gene', 'TR_D_gene',\\n\\t\\t\\t\\t\\t\\t\\t'TR_gene', 'TR_J_gene', 'TR_V_gene'],\\n\\t\\t\\t\\t'Pseudogene' : ['transcribed_unitary_pseudogene',\\n\\t\\t\\t\\t\\t\\t\\t\\t'disrupted_domain', 'IG_C_pseudogene',\\n\\t\\t\\t\\t\\t\\t\\t\\t'IG_J_pseudogene', 'IG_pseudogene',\\n\\t\\t\\t\\t\\t\\t\\t\\t'IG_V_pseudogene', 'processed_pseudogene',\\n\\t\\t\\t\\t\\t\\t\\t\\t'pseudogene',\\n\\t\\t\\t\\t\\t\\t\\t\\t'transcribed_processed_pseudogene',\\n\\t\\t\\t\\t\\t\\t\\t\\t'transcribed_unprocessed_pseudogene',\\n\\t\\t\\t\\t\\t\\t\\t\\t'translated_processed_pseudogene',\\n\\t\\t\\t\\t\\t\\t\\t\\t'translated_unprocessed_pseudogene',\\n\\t\\t\\t\\t\\t\\t\\t\\t'TR_J_pseudogene', 'TR_V_pseudogene',\\n\\t\\t\\t\\t\\t\\t\\t\\t'unitary_pseudogene', 'unprocessed_pseudogene',\\n\\t\\t\\t\\t\\t\\t\\t\\t'polymorphic_pseudogene'],\\n\\t\\t\\t\\t'LongNC' : ['macro_lncRNA', 'bidirectional_promoter_lncRNA',\\n\\t\\t\\t\\t\\t\\t\\t'sense_intronic', '3prime_overlapping_ncRNA',\\n\\t\\t\\t\\t\\t\\t\\t'ambiguous_orf', 'antisense',\\n\\t\\t\\t\\t\\t\\t\\t'lincRNA', 'ncrna_host','non_coding',\\n\\t\\t\\t\\t\\t\\t\\t'processed_transcript', 'retained_intron',\\n\\t\\t\\t\\t\\t\\t\\t'sense_overlapping'],\\n\\t\\t\\t\\t'ShortNC' : ['vaultRNA', 'scaRNA', 'miRNA',\\n\\t\\t\\t\\t\\t\\t\\t'miRNA_pseudogene', 'misc_RNA',\\n\\t\\t\\t\\t\\t\\t\\t'misc_RNA_pseudogene', 'Mt_rRNA',\\n\\t\\t\\t\\t\\t\\t\\t'Mt_tRNA', 'Mt_tRNA_pseudogene',\\n\\t\\t\\t\\t\\t\\t\\t'ncRNA', 'pre_miRNA', 'RNase_MRP_RNA',\\n\\t\\t\\t\\t\\t\\t\\t'RNase_P_RNA', 'rRNA', 'rRNA_pseudogene',\\n\\t\\t\\t\\t\\t\\t\\t'scRNA', 'scRNA_pseudogene', 'snlRNA',\\n\\t\\t\\t\\t\\t\\t\\t'snoRNA', 'snoRNA_pseudogene', 'snRNA',\\n\\t\\t\\t\\t\\t\\t\\t'snRNA_pseudogene', 'SRP_RNA', 'tmRNA',\\n\\t\\t\\t\\t\\t\\t\\t'tRNA', 'tRNA_pseudogene','ribozyme'],\\n\\t\\t\\t\\t'Predictif' : ['TEC']}\\n\\treturn dicoFam\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c13d4d857a8e1473d3f195406df933d4\",\n \"score\": \"0.4699714\",\n \"text\": \"def select_chunks(self, samples, battleships, *args, **kwargs):\\n chunks = [\\n (key, chunk)\\n for key in samples\\n for chunk in self._generate_bship_chunks(battleships[key])\\n ]\\n random.shuffle(chunks)\\n return chunks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7f87d665b07c2b8f94dcb5320917966\",\n \"score\": \"0.46781754\",\n \"text\": \"def get_families_by_provider(self, *args, **kwargs):\\n # Implemented from kitosid template for -\\n # osid.resource.BinLookupSession.get_bins_by_provider\\n catalogs = self._get_provider_session('family_lookup_session').get_families_by_provider(*args, **kwargs)\\n cat_list = []\\n for cat in catalogs:\\n cat_list.append(Family(self._provider_manager, cat, self._runtime, self._proxy))\\n return FamilyList(cat_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99462b0a555f94943322fd070a36b618\",\n \"score\": \"0.46641037\",\n \"text\": \"def get_variants_shared_by_father_and_son(self):\\n vcfSon = vcf.Reader(open(\\\"AmpliseqExome.20141120.NA24385.vcf\\\", \\\"r\\\"))\\n vcfFather = vcf.Reader(open(\\\"AmpliseqExome.20141120.NA24149.vcf\\\", \\\"r\\\"))\\n sharedVariantsFatherSon = 0\\n shared_variants = vcf.utils.walk_together(vcfFather, vcfSon)\\n for record in shared_variants:\\n if not record[0] is None and not record[1] is None:\\n sharedVariantsFatherSon += 1\\n print(\\\"\\\\nnumber of variants shared by father and son:\\\")\\n print(sharedVariantsFatherSon)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a777fc26818d59c22d30cdb04ccf8b67\",\n \"score\": \"0.46492454\",\n \"text\": \"def __forAll__( c, prop_value_dict, types=['FilterDesktop','CombineParticles',\\\"DVAlgorithm\\\", \\\"DaVinciAlgorithm\\\", \\\"DaVinciHistoAlgorithm\\\", \\\"DaVinciTupleAlgorithm\\\", \\\"*\\\" ] ) :\\n if type(prop_value_dict) is not dict:\\n raise TypeError(\\\"Hey, you need to give me a dictionary, you passed me a, \\\"+str(type(prop_value_dict)))\\n\\n if \\\"*\\\" in types or \\\".*\\\" in types or c.getType() in types:\\n for prop in prop_value_dict:\\n if hasattr(c,prop) or (hasattr(c,\\\"properties\\\") and prop in c.properties()):\\n c.setProp(prop,prop_value_dict[prop])\\n\\n #iterate/recurse over all subparts\\n for p in [ 'Members','Filter0','Filter1' ] :\\n if not hasattr(c,p) : continue\\n seq = getattr(c,p)\\n if type(seq) is not list: seq = [ seq ]\\n for i in seq : __forAll__(i,prop_value_dict,types)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c6f280b3c7201bc9eb8d6565a0dd6b\",\n \"score\": \"0.46478474\",\n \"text\": \"def get_families_by_parent_genus_type(self, *args, **kwargs):\\n # Implemented from kitosid template for -\\n # osid.resource.BinLookupSession.get_bins_by_parent_genus_type\\n catalogs = self._get_provider_session('family_lookup_session').get_families_by_parent_genus_type(*args, **kwargs)\\n cat_list = []\\n for cat in catalogs:\\n cat_list.append(Family(self._provider_manager, cat, self._runtime, self._proxy))\\n return FamilyList(cat_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eec83ee6f46f23a6d174d09f478de55\",\n \"score\": \"0.46375394\",\n \"text\": \"def _partition_species(composition, max_components=2):\\n\\n def _partition(collection):\\n # thanks https://stackoverflow.com/a/30134039\\n\\n if len(collection) == 1:\\n yield [collection]\\n return\\n\\n first = collection[0]\\n for smaller in _partition(collection[1:]):\\n # insert `first` in each of the subpartition's subsets\\n for n, subset in enumerate(smaller):\\n yield smaller[:n] + [[first] + subset] + smaller[n + 1:]\\n # put `first` in its own subset\\n yield [[first]] + smaller\\n\\n def _sort_partitions(partitions_to_sort):\\n \\\"\\\"\\\"\\n Sort partitions by those we want to check first\\n (typically, merging two sites into one is the\\n one to try first).\\n \\\"\\\"\\\"\\n\\n partition_indices = [(idx, [len(p) for p in partition])\\n for idx, partition in enumerate(partitions_to_sort)]\\n\\n # sort by maximum length of partition first (try smallest maximums first)\\n # and secondarily by number of partitions (most partitions first, i.e.\\n # create the 'least disordered' structures first)\\n partition_indices = sorted(partition_indices, key=lambda x: (max(x[1]), -len(x[1])))\\n\\n # merge at most max_component sites,\\n # e.g. merge at most 2 species into 1 disordered site\\n partition_indices = [x for x in partition_indices if max(x[1]) <= max_components]\\n\\n partition_indices.pop(0) # this is just the input structure\\n\\n sorted_partitions = [partitions_to_sort[x[0]] for x in partition_indices]\\n\\n return sorted_partitions\\n\\n collection = list(composition.keys())\\n partitions = list(_partition(collection))\\n partitions = _sort_partitions(partitions)\\n\\n return partitions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dd31d1b5b908d37fe2ed9102fef8ab3\",\n \"score\": \"0.46372262\",\n \"text\": \"def GetSpecies(self):\\n if self._species_group_to_use:\\n return self._species_group_to_use.all_species\\n\\n # TODO(flamholz): Should we return something here?\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8596de61a3ede4054f6159fc7646763c\",\n \"score\": \"0.46275145\",\n \"text\": \"def react_species(species_tuple, only_families=None):\\n\\n species_tuple = tuple([spc.copy(deep=True) for spc in species_tuple])\\n\\n reactions = getDB('kinetics').generate_reactions_from_families(species_tuple, only_families=only_families)\\n\\n return reactions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"444ea5d0c93dcfe0368ed4c8340e26ae\",\n \"score\": \"0.46246204\",\n \"text\": \"def reproduce(self, other):\\n child = Individual(None, self.fit_func)\\n for i in range(16):\\n child.inherit_gene(i, self, other)\\n return child\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f16ce52a28e5a415774be6a8f66d05\",\n \"score\": \"0.46166497\",\n \"text\": \"def test_GeneFusion(self):\\n dsl = GeneFusion(\\n Gene(\\\"HGNC\\\", \\\"TMPRSS2\\\"),\\n Gene(\\\"HGNC\\\", \\\"ERG\\\"),\\n EnumeratedFusionRange(\\\"c\\\", 1, 79),\\n EnumeratedFusionRange(\\\"c\\\", 312, 5034),\\n )\\n self.assertEqual('g(fus(HGNC:TMPRSS2, \\\"c.1_79\\\", HGNC:ERG, \\\"c.312_5034\\\"))', dsl.as_bel())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddcbf6bfe83f9849a81984831b511e96\",\n \"score\": \"0.46014696\",\n \"text\": \"def _extractfaces(label_pairs):\\n \\n key, (subvolume, gtmap, segmap, gtvol, segvol) = label_pairs\\n\\n # extract unique bodies not remapped\\n allgt = set(numpy.unique(gtvol))\\n allseg = set(numpy.unique(segvol))\\n\\n gtmapbodies = set()\\n for key2, body in gtmap.items():\\n gtmapbodies.add(key2)\\n segmapbodies = set()\\n for key2, body in segmap.items():\\n segmapbodies.add(key2)\\n allgt = allgt.difference(gtmapbodies)\\n if 0 in allgt:\\n allgt.remove(0)\\n allseg = allseg.difference(segmapbodies)\\n if 0 in allseg:\\n allseg.remove(0)\\n \\\"\\\"\\\"\\n if 0 in allseg:\\n allseg.remove(0)\\n if 0 in allgt:\\n allgt.remove(0)\\n \\\"\\\"\\\"\\n\\n zmax,ymax,xmax = gtvol.shape\\n start = (subvolume.box.z1, subvolume.box.y1, subvolume.box.x1)\\n\\n mappedfaces = []\\n\\n # grab 6 faces for gt\\n slicex0 = gtvol[:,:,0]\\n slicexmax = gtvol[:,:,xmax-1]\\n\\n slicey0 = gtvol[:,0,:]\\n sliceymax = gtvol[:,ymax-1,:]\\n \\n slicez0 = gtvol[0,:,:]\\n slicezmax = gtvol[zmax-1,:,:]\\n\\n mappedfaces.append(( (start, (start[0]+zmax, start[1]+ymax, start[2]+1), True), \\n [(slicex0, gtmap, key, True, allgt)] ))\\n mappedfaces.append(( ((start[0], start[1], start[2]+xmax),\\n (start[0]+zmax, start[1]+ymax, start[2]+xmax+1), True), \\n [(slicexmax, gtmap, key, False, set())] ))\\n \\n mappedfaces.append(( (start, (start[0]+zmax, start[1]+1, start[2]+xmax), True), \\n [(slicey0, gtmap, key, False, set())] ))\\n mappedfaces.append(( ((start[0], start[1]+ymax, start[2]),\\n (start[0]+zmax, start[1]+ymax+1, start[2]+xmax), True), \\n [(sliceymax, gtmap, key, False, set())] ))\\n \\n mappedfaces.append(( (start, (start[0]+1, start[1]+ymax, start[2]+xmax), True), \\n [(slicez0, gtmap, key, False, set())] ))\\n mappedfaces.append(( ((start[0]+zmax, start[1], start[2]),\\n (start[0]+zmax+1, start[1]+ymax, start[2]+xmax), True), \\n [(slicezmax, gtmap, key, False, set())] ))\\n\\n # grab 6 faces for seg\\n segslicex0 = segvol[:,:,0]\\n segslicexmax = segvol[:,:,xmax-1]\\n\\n segslicey0 = segvol[:,0,:]\\n segsliceymax = segvol[:,ymax-1,:]\\n \\n segslicez0 = segvol[0,:,:]\\n segslicezmax = segvol[zmax-1,:,:]\\n\\n mappedfaces.append(( (start, (start[0]+zmax, start[1]+ymax, start[2]+1), False), \\n [(segslicex0, segmap, key, True, allseg)] ))\\n mappedfaces.append(( ((start[0], start[1], start[2]+xmax),\\n (start[0]+zmax, start[1]+ymax, start[2]+xmax+1), False), \\n [(segslicexmax, segmap, key, False, set())] ))\\n \\n mappedfaces.append(( (start, (start[0]+zmax, start[1]+1, start[2]+xmax), False), \\n [(segslicey0, segmap, key, False, set())] ))\\n mappedfaces.append(( ((start[0], start[1]+ymax, start[2]),\\n (start[0]+zmax, start[1]+ymax+1, start[2]+xmax), False), \\n [(segsliceymax, segmap, key, False, set())] ))\\n \\n mappedfaces.append(( (start, (start[0]+1, start[1]+ymax, start[2]+xmax), False), \\n [(segslicez0, segmap, key, False, set())] ))\\n mappedfaces.append(( ((start[0]+zmax, start[1], start[2]),\\n (start[0]+zmax+1, start[1]+ymax, start[2]+xmax), False), \\n [(segslicezmax, segmap, key, False, set())] ))\\n \\n return mappedfaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4a4daec0d394136d7881fe3e927da35\",\n \"score\": \"0.46013784\",\n \"text\": \"def get_possible_family_edges():\\n possible_edges = []\\n for family1 in POSSIBLE_FAMILIES:\\n for family2 in POSSIBLE_FAMILIES:\\n possible_edges.append((family1, family2))\\n \\n return possible_edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b588e8bbd998dea711df747f6d330074\",\n \"score\": \"0.4601263\",\n \"text\": \"def reproduce(population):\\n new_population = []\\n\\n for a in population:\\n for b in population:\\n child = []\\n for n in range(0, len(a)):\\n child.append(random.choice([a, b])[n])\\n\\n new_population.append(child)\\n\\n return random.sample(new_population, 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8fec70b45b6cc6e7f299a7914fd2d88\",\n \"score\": \"0.4593503\",\n \"text\": \"def assign_species(df_bacteroides_data, clusters, gene_oi=\\\"I_6\\\"):\\n pseudo = 1e-15\\n color_dict = {\\n \\\"thetaiotaomicron\\\": \\\"#a6d854ff\\\",\\n \\\"ovatus\\\": \\\"#8da0cbff\\\",\\n \\\"fragilis\\\": \\\"#ffd92fff\\\",\\n \\\"oleiciplenus\\\": \\\"blue\\\",\\n \\\"propionicifaciens\\\": \\\"red\\\",\\n \\\"faecis\\\": \\\"yellow\\\",\\n \\\"xylanisolvens\\\": \\\"#a6d854ff\\\",\\n \\\"coprophilus\\\": \\\"red\\\"}\\n plt.rcParams[\\\"font.family\\\"] = \\\"Arial\\\"\\n group = 1\\n # For each of the clusters on the tree\\n for cluster in clusters:\\n srs_use = [x for x in cluster if x in df_bacteroides_data.index]\\n # Minimum 3 sequences in the cluster to consider\\n if len(srs_use) >= 3:\\n # Calculate the MSE\\n df_error = single_species_model(df_bacteroides_data.loc[srs_use, :], transform=\\\"\\\")\\n df_error.reset_index(inplace=True)\\n\\n plt.rc('font', size=8)\\n plt.rc('xtick', labelsize=8)\\n\\n if (df_error.ix[0, \\\"Species\\\"] == \\\"xylanisolvens\\\") & (group == 3):\\n color_use = \\\"#ffd92fff\\\"\\n else:\\n color_use = color_dict[df_error.ix[0, \\\"Species\\\"]]\\n\\n # Plot a scatter plot for the abundance of the best fitting species against the orphan immunity abundance\\n # Figure 2B, D\\n f, ax = plt.subplots(figsize=(1.6, 1.6))\\n plt.xlim([-9.0, -7.0])\\n plt.ylim([-9.0, -7.0])\\n ax.plot(ax.get_xlim(), ax.get_ylim(), ls=\\\"--\\\", c=\\\".3\\\")\\n ax.scatter(\\n np.log10(df_bacteroides_data.loc[srs_use, \\\"markers_s__Bacteroides_\\\" + DfError.ix[0, \\\"Species\\\"] + \\\".fasta\\\"] + pseudo),\\n np.log10(df_bacteroides_data.loc[srs_use, \\\"OrphanI\\\"] + pseudo), color=color_use, edgecolors=\\\"black\\\")\\n plt.xlabel(\\\"Bacteroides %s\\\" % (DfError.ix[0, \\\"Species\\\"]), style=\\\"italic\\\")\\n plt.ylabel(\\\"Orphan I\\\")\\n plt.xticks([-9.0, -8.5, -8.0, -7.5, -7.0])\\n plt.savefig(\\\"Figure2_Species_%s_Group_%i_%s_Scatter.pdf\\\" % (DfError.ix[0, \\\"Species\\\"], group, gene_oi))\\n plt.savefig(\\n \\\"Figure2_Species_%s_Group_%i_%s_Scatter_SecondHit.pdf\\\" % (DfError.ix[1, \\\"Species\\\"], group, gene_oi))\\n plt.clf()\\n f.clf()\\n\\n # Plot the MSE of the top 5 species as a barplot\\n # Figure 2C, E\\n df_error_sub = df_error.ix[:5, :]\\n df_error_sub[\\\"Species\\\"] = [\\\"B.\\\" + x[:2] for x in df_error_sub[\\\"Species\\\"]]\\n plt.rc('font', size=8)\\n plt.rc('xtick', labelsize=6)\\n f, ax = plt.subplots(figsize=(0.8, 1.6))\\n ind = np.arange(df_error_sub.shape[0])\\n ax.bar(ind, df_error_sub[\\\"MSE\\\"], edgecolor=\\\"black\\\", color=\\\"#d3d3d3\\\", linewidth=0.25)\\n plt.xticks(ind, df_error_sub[\\\"Species\\\"], rotation=90, style=\\\"italic\\\")\\n plt.title(\\\"MSE\\\")\\n plt.savefig(\\\"Figure2_Species_%s_Group_%i_%s_MSE.pdf\\\" % (DfError.ix[0, \\\"Species\\\"], group, gene_oi))\\n plt.clf()\\n f.clf()\\n\\n plt.rc('font', size=8)\\n plt.rc('xtick', labelsize=8)\\n group += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"370c59aea4293211875fe1cb6973fddc\",\n \"score\": \"0.45925134\",\n \"text\": \"def __species_annotation__(self,aggregation_so_far,annotation):\\n for species_ann in annotation:\\n species = species_ann[\\\"choice\\\"]\\n\\n # is the first classification we've encountered for this species?\\n if species not in aggregation_so_far:\\n aggregation_so_far[species] = {\\\"num votes\\\": 1,\\\"followup\\\":{}}\\n\\n else:\\n aggregation_so_far[species][\\\"num votes\\\"] += 1\\n\\n # go through each of the follow up questions\\n for question,answer in species_ann[\\\"answers\\\"].items():\\n # is this the first time we've seen this particular followup question\\n # should probably always happen the first time we see the species (since most, if not\\n # all of the time, all of the follow up questions should be required)\\n if question not in aggregation_so_far[species][\\\"followup\\\"]:\\n aggregation_so_far[species][\\\"followup\\\"][question] = {}\\n\\n # if list - multiple answers are allowed\\n if isinstance(answer,list):\\n for ans in answer:\\n # first time we've seen this particular answer?\\n if ans not in aggregation_so_far[species][\\\"followup\\\"][question]:\\n aggregation_so_far[species][\\\"followup\\\"][question][ans] = 1\\n else:\\n aggregation_so_far[species][\\\"followup\\\"][question][ans] += 1\\n else:\\n # first time we've seen this particular answer\\n if answer not in aggregation_so_far[species][\\\"followup\\\"][question]:\\n aggregation_so_far[species][\\\"followup\\\"][question][answer] = 1\\n else:\\n aggregation_so_far[species][\\\"followup\\\"][question][answer] += 1\\n\\n return aggregation_so_far\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fd3c413745adf8441ebe6fe1ef6f8cf\",\n \"score\": \"0.45756882\",\n \"text\": \"def train_family(class1path, class2path, multi_data, multi_labels, skf, shapes, name=\\\"image+word\\\", seqnum1=1, seqnum2=2):\\n for i, (train_set,val_set) in enumerate(skf):\\n classifier_multi = None#ensure empty\\n img_encoder = nn_lib.reuse_model(class1path, 'sequential_' + str(2*i + seqnum1))\\n wrd_encoder = nn_lib.reuse_model(class2path, 'sequential_' + str(2*i + seqnum2))\\n classifier_multi = nn_lib.encoders2classifier(img_encoder, wrd_encoder, shapes)\\n if i == 0: classifier_multi.summary()\\n\\n hist_it = nn_lib.train_net(classifier_multi, [medium[train_set] for medium in multi_data], multi_y[train_set], \\n [medium[val_set] for medium in multi_data], multi_y[val_set], name + str(i), epochs=150, path=path_neuralmodels,\\n earlystop=[keras.callbacks.EarlyStopping(monitor='val_acc', min_delta=0.002, patience=2, mode='auto')])\\n\\n score = classifier_multi.evaluate( [medium[val_set] for medium in multi_data], multi_y[val_set], batch_size=25)\\n output.append({'type':name,'i':i, 'score':score[1]})\\n history.append({'type':name, 'i':i, 'history':hist_it.history[\\\"val_acc\\\"]})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db76ef20977de84bba5c42aace1a6c23\",\n \"score\": \"0.45728546\",\n \"text\": \"def GetSpeciesGroups(self):\\n if self._all_species_groups is None:\\n self._all_species_groups = \\\\\\n self.species_groups.all().prefetch_related('species')\\n\\n return self._all_species_groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc639a2d963234e6d666c2bb5c65e3d8\",\n \"score\": \"0.45718077\",\n \"text\": \"def vcf_row_to_variants(fields, het_or_hom_or_hemi=None):\\n\\n chrom, pos, ref, alt_alleles, info_field = fields[0], fields[1], fields[3], fields[4].split(\\\",\\\"), fields[7]\\n\\n if het_or_hom_or_hemi is not None:\\n possible_genotypes = (het_or_hom_or_hemi, )\\n else:\\n possible_genotypes = (\\\"het\\\", \\\"hom\\\", \\\"hemi\\\") if chrom in ('X', 'Y') else (\\\"het\\\", \\\"hom\\\")\\n\\n # parse info field\\n n_het_list, n_hom_list, n_hemi_list = parse_info_field(fields, alt_alleles, info_field, chrom=chrom, pos=pos)\\n\\n # handle genotypes if valid_sample_ids arg is not None\\n sample_id_to_genotype = None\\n if valid_sample_ids is not None:\\n sample_id_to_genotype = parse_genotypes(fields, alt_alleles, vcf_sample_ids, chrom=chrom, pos=pos)\\n\\n\\n for alt_allele_index, (alt, n_het, n_hom, n_hemi) in enumerate(zip(alt_alleles, n_het_list, n_hom_list, n_hemi_list)):\\n minrep_pos, minrep_ref, minrep_alt = get_minimal_representation(pos, ref, alt)\\n\\n for het_or_hom_or_hemi in possible_genotypes:\\n if het_or_hom_or_hemi == \\\"het\\\":\\n n_expected_samples = n_het\\n elif het_or_hom_or_hemi == \\\"hom\\\":\\n n_expected_samples = n_hom\\n elif het_or_hom_or_hemi == \\\"hemi\\\":\\n n_expected_samples = n_hemi\\n else:\\n raise ValueError(\\\"Unexpected value for het_or_hom_or_hemi: %s\\\" % str(het_or_hom_or_hemi))\\n\\n yield VariantGT(chrom=chrom,\\n pos=minrep_pos,\\n ref=minrep_ref,\\n alt=minrep_alt,\\n alt_allele_index=alt_allele_index,\\n het_or_hom_or_hemi=het_or_hom_or_hemi,\\n n_expected_samples=n_expected_samples,\\n all_genotypes_in_row=sample_id_to_genotype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c634f7e00d8855a082cad1ca43b3900\",\n \"score\": \"0.45634657\",\n \"text\": \"def getTF_Families_exp(barcode, batch, db):\\n cmdStr = \\\"SELECT vi.TF_Family from Vaquerizas_InterproAnnot vi inner join Vaquerizas_AllPossible_DBD_InterproIDs va on vi.InterproID = va.InterproID inner join Vaquerizas_TF_DBDs vd on va.InterproID = vd.InterproID inner join Vaquerizas_TFs vt on vd.EnsembleID = vt.EnsembleID inner join TranscriptionFactors tf on vt.EnsembleID = tf.EnsembleID inner join CloneDetail cd on tf.EnsembleID = cd.EnsembleID inner join ProteinProduction pp on cd.CloneID = pp.CloneID inner join SELEX_experiments se on pp.ProductionID = se.ProductionID where se.Barcode = \\\\\\\"\\\" + barcode + \\\"\\\\\\\" and se.BarcodeBatch = \\\\\\\"\\\" + batch +\\\"\\\\\\\";\\\"\\n cursor=db.cursor()\\n cursor.execute(cmdStr)\\n rows = cursor.fetchall()\\n if (len(rows) > 0):\\n return map(lambda row: row[0], rows)\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32924b38d3ef0198e66ed68e1e784586\",\n \"score\": \"0.45491555\",\n \"text\": \"def _scattered_crossover(self, mother, father):\\n\\n vector = self._create_vector()\\n child1 = Chromosome(config[\\\"num_categories\\\"])\\n child1.set_fitness(0)\\n child2 = Chromosome(config[\\\"num_categories\\\"])\\n child2.set_fitness(0)\\n gene_list = self.phones\\n\\n # exchanging the phones into their new, mated categories\\n for index in range(len(gene_list)):\\n # father\\n if vector[index] == 0:\\n category = father.get_category(gene_list[index])\\n child1.insert_into_category(category,gene_list[index])\\n category = mother.get_category(gene_list[index])\\n child2.insert_into_category(category,gene_list[index])\\n # mother\\n elif vector[index] == 1:\\n category = father.get_category(gene_list[index])\\n child2.insert_into_category(category,gene_list[index])\\n category = mother.get_category(gene_list[index])\\n child1.insert_into_category(category,gene_list[index])\\n return child1,child2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"349f6010a66b67ad13b9ab5f51136989\",\n \"score\": \"0.4547002\",\n \"text\": \"def plot_mixtures():\\n\\tunity = UnimodalPrior(\\\"unity_with_sf\\\")\\n\\tsmal_to_seg(title=\\\"unimodal\\\", betas=unity.mean)\\n\\n\\tshapeA = unity.sample_from()\\n\\tshapeB = unity.sample_from()\\n\\tshapeA[20] -= .4 # Short legs\\n\\tshapeA[22] -= .4 # Short tail\\n\\n\\tshapeB[22] = -.3 # reasonable tail\\n\\tshapeB[25] = .8 # Tall ears\\n\\tshapeB[20] = .3 # Short legs\\n\\n\\tsmal_to_seg(title=\\\"shapeA\\\", betas=shapeA)\\n\\tsmal_to_seg(title=\\\"shapeB\\\", betas=shapeB)\\n\\n\\tshapeC = .3 * shapeA + .7 * shapeB\\n\\tsmal_to_seg(title=\\\"shapeC\\\", betas=shapeC)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08f33961fb0c86ec7fa35d35f4047c7d\",\n \"score\": \"0.4545083\",\n \"text\": \"def _family_ids(self):\\n ancestor = self._head_of_family\\n family = ancestor._descendants_ids\\n return family\",\n \"title\": \"\"\n },\n {\n \"docid\": \"361e1c144b06c351e8799b2b9ca42215\",\n \"score\": \"0.4539374\",\n \"text\": \"def create_family_from_base(base):\\n uc_fam = copy.deepcopy(base)\\n curr_size = len(uc_fam)\\n prev_size = 0\\n while curr_size > prev_size:\\n prev_fam = copy.deepcopy(uc_fam)\\n for a in prev_fam:\\n for b in base:\\n uc_fam.update(set({frozenset(a.union(b))}))\\n prev_size = curr_size\\n curr_size = len(uc_fam)\\n return uc_fam\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6da08df264c9f9705a61d6dc0f3e975\",\n \"score\": \"0.4529036\",\n \"text\": \"def _set_up_mutate(self):\\n self.genes = [\\n ['b', 'h', 'z', 'm', '0', 'x', 'Z'],\\n ['p', 'C', 'T', '_', 'v'],\\n ['k'],\\n ['d', 'n'],\\n ['$', 'I', 'P', '2', '5', '~'],\\n ['@', 'E', 'i', '1', 'u', '7', '8', 'U'],\\n ['t', 'N'],\\n ['H', '#', 'r', 'c', 'F'],\\n ['D', 'j', 'Q', '3', '4', 'V', '9', '{'],\\n ['S', 'R', 'l', '6'],\\n ['q', 's'],\\n ['J', 'w', 'g', 'f']\\n ]\\n self.population = []\\n for j in range(config[\\\"num_chroms_in_deviation\\\"]):\\n new_chromosome = Chromosome(config[\\\"num_categories\\\"])\\n new_chromosome.set_fitness(88.9)\\n for i in range(len(self.genes) - 1):\\n for gene in self.genes[i]:\\n new_chromosome.insert_into_category(i, gene)\\n self.population.append(new_chromosome)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13a9ec3c56419135ebadf7738950f442\",\n \"score\": \"0.45252407\",\n \"text\": \"def do_mixup(inputs, targets, alpha=1., random_seed=1233, mode=\\\"multilabel\\\"):\\n random_state = np.random.RandomState(random_seed)\\n bsize = len(inputs)\\n lam = torch.from_numpy(random_state.beta(alpha, alpha, bsize)).to(inputs.device).float()\\n perms = torch.randperm(bsize).to(inputs.device)\\n if mode == \\\"multilabel\\\":\\n mixed_x = inputs * lam.view(bsize, 1, 1) + inputs[perms] * (1 - lam.view(bsize, 1, 1))\\n mixed_y = targets * lam.view(bsize, 1) + targets[perms] * (1 - lam.view(bsize, 1))\\n return mixed_x, mixed_y, None, None\\n else:\\n mixed_x = inputs * lam.view(bsize, 1, 1) + inputs[perms] * (1 - lam.view(bsize, 1, 1))\\n y_a, y_b = targets, targets[perms]\\n return mixed_x, y_a, y_b, lam\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17cd8bac1aa08c06df022d1b53d0a8dd\",\n \"score\": \"0.45237303\",\n \"text\": \"def GetSpeciesWithoutMg(self):\\n for s in self.all_species:\\n if s.number_of_mgs == 0:\\n yield s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c51858021c5f7892e6668dbe3241c0a\",\n \"score\": \"0.45226184\",\n \"text\": \"def test_convert_mag_to_jhk():\\n\\n full_list = ['JpgMag', 'FpgMag', 'NpgMag', 'tmassJMag', 'tmassHMag',\\n 'tmassKsMag', 'SDSSuMag', 'SDSSgMag', 'SDSSrMag',\\n 'SDSSiMag', 'SDSSzMag']\\n\\n for L in range(0, len(full_list) + 1):\\n for subset in itertools.combinations(full_list, L):\\n if subset == ():\\n continue\\n\\n # Recreate data\\n fgs = FGSCountrate(guide_star_id=\\\"N13I000018\\\", guider=1)\\n fgs.gsc_series = BASE_DATA\\n\\n # Compute conversion\\n delete_list = list(set(full_list) - set(subset))\\n data2 = copy.copy(fgs.gsc_series)\\n for i in delete_list:\\n data2[i] = -999\\n fgs.calc_jhk_mag(data=data2)\\n\\n # Compare output to here\\n method_names = []\\n for i in ['tmassJMag', 'tmassHMag', 'tmassKsMag']:\\n if i in list(subset):\\n method_name_test = \\\"convert_tmass_to_jhk\\\"\\n\\n elif {'SDSSgMag', 'SDSSzMag'}.issubset(subset):\\n method_name_test = \\\"convert_sdssgz_to_jhk\\\"\\n elif {'SDSSrMag', 'SDSSzMag'}.issubset(subset):\\n method_name_test = \\\"convert_sdssrz_to_jhk\\\"\\n elif {'SDSSgMag', 'SDSSiMag'}.issubset(subset):\\n method_name_test = \\\"convert_sdssgi_to_jhk\\\"\\n # elif {'SDSSiMag', 'SDSSzMag'}.issubset(subset): # Add back in once GSSS uses this pair\\n # method_name_test = \\\"convert_sdssiz_to_jhk\\\"\\n\\n elif {'JpgMag', 'NpgMag'}.issubset(subset):\\n method_name_test = \\\"convert_gsc2bjin_to_jhk\\\"\\n elif {'FpgMag', 'NpgMag'}.issubset(subset):\\n method_name_test = \\\"convert_gsc2rfin_to_jhk\\\"\\n elif {'JpgMag', 'FpgMag'}.issubset(subset):\\n method_name_test = \\\"convert_gsc2bjrf_to_jhk\\\"\\n\\n else:\\n method_name_test = 'cannot_calculate'\\n\\n method_names.append(method_name_test)\\n\\n failure_message = f'For input {list(subset)} and band {i[5]}: the test called {method_name_test} ' \\\\\\n f'while the calc_jhk_mag method called ' \\\\\\n f'{getattr(fgs, f\\\"{i[5].lower()}_convert_method\\\")}'\\n\\n assert method_name_test == getattr(fgs, f'{i[5].lower()}_convert_method'), failure_message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56bd231d1a0165bfd00cae4f8021fdf8\",\n \"score\": \"0.4521947\",\n \"text\": \"def get_compound_hets(c, args):\\n # build a mapping of the numpy array index to the appropriate sample name\\n # e.g. 0 == 109400005\\n # 37 == 147800025\\n idx_to_sample = util.map_indicies_to_samples(c)\\n\\n comp_hets = collections.defaultdict(lambda: collections.defaultdict(list))\\n\\n query = \\\"SELECT * FROM variants \\\\\\n WHERE impact_severity != 'LOW'\\\" # is_exonic - what about splice?\\n c.execute(query)\\n\\n # step 1. collect all candidate heterozygptes for all\\n # genes and samples. the list will be refined in step 2.\\n for row in c:\\n gt_types = compression.unpack_genotype_blob(row['gt_types'])\\n gt_phases = compression.unpack_genotype_blob(row['gt_phases'])\\n gt_bases = compression.unpack_genotype_blob(row['gts'])\\n\\n site = Site(row)\\n\\n # filter putative sites that the user doesn't care about\\n if site.num_hets > 1 and not args.allow_other_hets:\\n continue\\n if not site.is_lof and args.only_lof:\\n continue\\n\\n # track each sample that is heteroyzgous at this site.\\n for idx, gt_type in enumerate(gt_types):\\n if gt_type == HET:\\n sample = idx_to_sample[idx]\\n # (testing)\\n # sample = \\\"NA19002\\\"\\n sample_site = copy(site)\\n sample_site.phased = gt_phases[idx]\\n\\n # require phased genotypes\\n if not sample_site.phased and not args.ignore_phasing:\\n continue\\n\\n sample_site.gt = gt_bases[idx]\\n # add the site to the list of candidates\\n # for this sample/gene\\n comp_hets[sample][site.gene].append(sample_site)\\n\\n # header\\n print \\\"sample\\\\tgene\\\\thet1\\\\thet2\\\"\\n # step 2. now, cull the list of candidate heterozygotes for each\\n # gene/sample to those het pairs where the alternate alleles\\n # were inherited on opposite haplotypes.\\n for sample in comp_hets:\\n for gene in comp_hets[sample]:\\n for site1 in comp_hets[sample][gene]:\\n for site2 in comp_hets[sample][gene]:\\n if site1 == site2:\\n continue\\n\\n # expand the genotypes for this sample\\n # at each site into it's composite\\n # alleles. e.g. A|G -> ['A', 'G']\\n alleles_site1 = []\\n alleles_site2 = []\\n if not args.ignore_phasing:\\n alleles_site1 = site1.gt.split('|')\\n alleles_site2 = site2.gt.split('|')\\n else:\\n # split on phased (|) or unphased (/) genotypes\\n alleles_site1 = re.split('\\\\||/', site1.gt)\\n alleles_site2 = re.split('\\\\||/', site2.gt)\\n\\n # it is only a true compound heterozygote iff\\n # the alternates are on opposite haplotypes.\\n if not args.ignore_phasing:\\n # return the haplotype on which the alternate\\n # allele was observed for this sample at each\\n # candidate het. site.\\n # e.g., if ALT=G and alleles_site1=['A', 'G']\\n # then alt_hap_1 = 1. if ALT=A, then alt_hap_1 = 0\\n alt_hap_1 = alleles_site1.index(site1.alt)\\n alt_hap_2 = alleles_site2.index(site2.alt)\\n\\n if alt_hap_1 != alt_hap_2:\\n print \\\"\\\\t\\\".join([sample,\\n gene,\\n str(site1),\\n str(site2)])\\n else:\\n # user has asked us to not care about phasing\\n print \\\"\\\\t\\\".join([sample,\\n gene,\\n str(site1),\\n str(site2)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe22d80fd5557c102b2c75f9813ce057\",\n \"score\": \"0.4520698\",\n \"text\": \"def get_families_orthofinder(orthofinder_df, species):\\n df = pd.read_csv(orthofinder_df, sep=\\\"\\\\t\\\", index_col=0)\\n # let's be forgiving when it comes to the species names, allow prefixes\\n unique_prefix(df.columns, species)\\n f = lambda x, y: x.startswith(y)\\n cols = [x for x in df.columns if any([f(x, y) for y in species])]\\n df = df[cols]\\n df.fillna(\\\"\\\", inplace=True)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0be24bc7bb061ed98a7d7dbcd8e27c9\",\n \"score\": \"0.4516397\",\n \"text\": \"def _specialize_clusters(cls, clusters, **kwargs):\\n # To create temporaries\\n counter = generator()\\n template = lambda: \\\"r%d\\\" % counter()\\n\\n # Toposort+Fusion (the former to expose more fusion opportunities)\\n clusters = Toposort().process(clusters)\\n clusters = fuse(clusters)\\n\\n # Flop reduction via the DSE\\n clusters = rewrite(clusters, template, **kwargs)\\n\\n # Lifting\\n clusters = Lift().process(clusters)\\n\\n # Lifting may create fusion opportunities, which in turn may enable\\n # further optimizations\\n clusters = fuse(clusters)\\n clusters = eliminate_arrays(clusters, template)\\n clusters = scalarize(clusters, template)\\n\\n return clusters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c21f8ac7e72317338081a2aa84a1bba\",\n \"score\": \"0.45106366\",\n \"text\": \"def get_all_extras_to_submit(self) -> ty.Set[ty.Tuple[str]]:\\n extras_projections = self.get_process_extra_projections()\\n extras_projections += ('attributes.kinds', )\\n\\n qbuild = orm.QueryBuilder()\\n qbuild.append(orm.Group,\\n filters={'id': self.parent_group.pk},\\n tag='group')\\n qbuild.append(orm.Node,\\n filters=self._parent_group_filters,\\n project=extras_projections,\\n tag='process',\\n with_group='group')\\n results = qbuild.all()\\n\\n pseudo_family_elements = set(self._pseudo_family.elements)\\n extras = []\\n for (mpid, kinds) in results:\\n kind_names = set(kind['name'] for kind in kinds)\\n if kind_names.issubset(pseudo_family_elements):\\n extras.append((mpid, ))\\n\\n # I return sorted results for reproducibility of the order of execution\\n extras = sorted(extras)\\n\\n assert len(set(extras)) == len(\\n extras), 'There are duplicate extras in the parent group'\\n return set(extras)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6074796bffb6ef61dd617fbfc071e2ca\",\n \"score\": \"0.450857\",\n \"text\": \"def infer_pose_each_part_mix_gaussian(factor_graph_list):\\n num_body_parts = len(factor_graph_list)\\n # Generate the intial sample to start Gibbs Sampling\\n inferred_pose = generate_initial_pose_matrix(num_body_parts)\\n\\n # Generate nodes which will be selected for Gibbs Sampling\\n node_indices_array = np.ndarray.tolist(np.random.randint(0, 24, size=num_samples))\\n \\n # Start Gibbs Sampling over Factor Graph\\n for node_index in node_indices_array:\\n node_to_update = factor_graph_list[node_index]\\n\\n # Getting GaussianMixture object for unary pot\\n unary_pot = node_to_update.unary_pot\\n\\n # Prepare object to represent mixture of Gaussians\\n mix_gaussians_obj = GaussianDistributionsWithWeights()\\n mix_gaussians_obj.set_weights_means_covs(unary_pot.weights_, unary_pot.means_, unary_pot.covariances_)\\n\\n # Conditioning on all the neighbors to get resulting distributions\\n for neighbor_index in node_to_update.neighbors:\\n # Finding value of conditioned variable (neighbor)\\n pose = get_node_val_pose_only(neighbor_index, inferred_pose)\\n \\n # Gaussian Mixture instance for pairwise potential\\n pairwise_pots_gauss_mixture_obj = node_to_update.pairwise_pots[neighbor_index]\\n\\n # Condition on all mixtures in Pairwise potentials\\n weights = pairwise_pots_gauss_mixture_obj.weights_\\n\\n means_conditioned = []\\n covs_conditioned = []\\n for index in range(len(weights)):\\n pairwise_mean = pairwise_pots_gauss_mixture_obj.means_[index]\\n pairwise_cov = pairwise_pots_gauss_mixture_obj.covariances_[index]\\n mean_cond, cov_cond = condition_on_pairwise_potential(\\n node_index, neighbor_index, pairwise_mean, pairwise_cov, pose)\\n \\n means_conditioned.append(mean_cond)\\n covs_conditioned.append(cov_cond)\\n\\n # Add these mixtures to the object\\n mix_gaussians_obj.add_gaussian_distributions(weights, means_conditioned, covs_conditioned)\\n\\n # Selecting the Gaussian distribution to sample from in the Gaussian Mixture\\n mean_gaussian, cov_gaussian = mix_gaussians_obj.get_gaussian_to_sample_from()\\n\\n # Perform Sampling \\n pose_curr_node = np.random.multivariate_normal(mean=mean_gaussian, cov=cov_gaussian)\\n\\n # Updating the value in the overall pose matrix\\n inferred_pose[node_to_update.node_index] = pose_curr_node\\n\\n return inferred_pose\",\n \"title\": \"\"\n },\n {\n \"docid\": \"541038450fb6f1cf283ded6c164f394f\",\n \"score\": \"0.449938\",\n \"text\": \"def pack_features(args):\\n workspace = args.workspace\\n data_type = args.data_type\\n n_concat = args.n_concat\\n n_hop = args.n_hop\\n TF = args.TF\\n \\n x_all = [] # (n_segs, n_concat, n_freq)\\n\\n y_all = [] # (n_segs, n_freq)\\n\\n \\n cnt = 0\\n t1 = time.time()\\n \\n # Load all features. data = [mixed_complx_x, speech_x, noise_x, alpha, out_bare_na]\\n\\n feat_dir = os.path.join(workspace, \\\"features\\\", data_type)\\n \\n #feat_dir = os.path.join(workspace, \\\"features\\\", data_type)\\n names = os.listdir(feat_dir)\\n for na in names:\\n # Load feature. \\n feat_path = os.path.join(feat_dir, na)\\n data = cPickle.load(open(feat_path, 'rb'))\\n [mixed_complx_x, speech_complx_x] = data\\n \\n n_pad = (n_concat - 1) / 2\\n speech_x = pad_with_border(np.abs(speech_complx_x), n_pad)\\n # speech_phase = pad_with_border(np.angle(speech_complx_x), n_pad) #phase = np.exp(1j * np.angle(D))\\n '''\\n mixed_x = np.abs(mixed_complx_x)\\n\\n # Pad start and finish of the spectrogram with boarder values. \\n # mixed_x.shape=frames x framesize\\n n_pad = (n_concat - 1) / 2\\n mixed_x = pad_with_border(mixed_x, n_pad)\\n speech_x = pad_with_border(speech_x, n_pad)\\n \\n # Cut input spectrogram to 3D segments with n_concat. \\n mixed_x_3d = mat_2d_to_3d(mixed_x, agg_num=n_concat, hop=n_hop)\\n #x_all.append(mixed_x_3d)\\n '''\\n \\n if TF == \\\"spectrogram\\\":\\n mixed_x = np.abs(mixed_complx_x)\\n # mixed_phase = np.angle(mixed_complx_x)\\n\\n # Pad start and finish of the spectrogram with boarder values. \\n # mixed_x.shape=frames x framesize\\n #n_pad = (n_concat - 1) / 2\\n mixed_x = pad_with_border(mixed_x, n_pad)\\n # mixed_phase = pad_with_border(mixed_phase, n_pad)\\n #speech_x = pad_with_border(speech_x, n_pad)\\n \\n # Cut input spectrogram to 3D segments with n_concat. \\n mixed_x_3d = mat_2d_to_3d(mixed_x, agg_num=n_concat, hop=n_hop)\\n # mixed_phase_3d = mat_2d_to_3d(mixed_phase, agg_num=n_concat, hop=n_hop)\\n log_x = log_sp(mixed_x_3d.copy())\\n x_all.append(log_x)\\n # x_phase.append(mixed_phase_3d)\\n\\n else:\\n raise Exception(\\\"TF must be spectrogram, timedomain or fftmagnitude!\\\")\\n # Cut target spectrogram and take the center frame of each 3D segment. \\n speech_x_3d = mat_2d_to_3d(speech_x, agg_num=n_concat, hop=n_hop)\\n # speech_phase_3d = mat_2d_to_3d(speech_phase, agg_num=n_concat, hop=n_hop)\\n y = speech_x_3d[:, (n_concat - 1) / 2, :]\\n # y_pha = speech_phase_3d[:, (n_concat - 1) / 2, :]\\n if TF == \\\"spectrogram\\\":\\n print(\\\"convert to log-spectra features\\\")\\n log_y = log_sp(y.copy())\\n y_all.append(log_y)\\n # y_phase.append(y_pha)\\n\\n \\n else:\\n raise Exception(\\\"TF must be spectrogram, timedomain or fftmagnitude!\\\")\\n \\n # Print. \\n if cnt % 1000 == 0:\\n print(\\\"Packing all the {} utterances to 3D tensor, remaining {} utterances\\\".format(cnt, len(names)-cnt))\\n \\n # if cnt == 3: break\\n cnt += 1\\n \\n nwav_all = np.concatenate(x_all, axis=0) # transfer to array(n_segs, n_concat, n_freq)\\n cwav_all = np.concatenate(y_all, axis=0) # (n_segs, n_freq)\\n \\n # x_phase = np.concatenate(x_phase, axis=0) # transfer to array(n_segs, n_concat, n_freq)\\n # y_phase = np.concatenate(y_phase, axis=0)\\n \\n nwav_all = nwav_all.astype(np.float32)\\n cwav_all = cwav_all.astype(np.float32)\\n # x_phase = x_phase.astype(np.float32)\\n # y_phase = y_phase.astype(np.float32)\\n '''\\n if TF == \\\"spectrogram\\\":\\n x_all = log_sp(x_all).astype(np.float32)\\n y_all = log_sp(y_all).astype(np.float32)\\n elif TF == \\\"timedomain\\\":\\n x_all = x_all.astype(np.float32)\\n y_all = y_all.astype(np.float32)\\n else:\\n raise Exception(\\\"TF must be spectrogram or timedomain!\\\")\\n '''\\n # Write out data to .h5 file. \\n\\n mag_out_path = os.path.join(workspace, \\\"packed_features\\\", data_type, \\\"mag.h5\\\")\\n # phase_out_path = os.path.join(workspace, \\\"packed_features\\\", data_type, \\\"phase.h5\\\")\\n \\n #out_path = os.path.join(workspace, \\\"packed_features\\\", data_type, \\\"data.h5\\\")\\n create_folder(os.path.dirname(mag_out_path))\\n with h5py.File(mag_out_path, 'w') as hf:\\n hf.create_dataset('x', data=nwav_all)\\n hf.create_dataset('y', data=cwav_all)\\n \\n print(\\\"Write out to %s\\\" % mag_out_path)\\n\\n print(\\\"Pack features finished! %f s\\\" % (time.time() - t1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14d019342e5a644d6067c500c363229f\",\n \"score\": \"0.44981048\",\n \"text\": \"def breed(mother, father, num_children=2):\\r\\n if mother.population is not father.population:\\r\\n raise ValueError(\\\"cannot breed members of different \\\" +\\r\\n \\\"populations\\\")\\r\\n\\r\\n children = []\\r\\n for __ in range(num_children):\\r\\n genome = {}\\r\\n\\r\\n for gene in mother.population.gene_pool.keys():\\r\\n genome[gene] = random.choice((\\r\\n mother.genome[gene], father.genome[gene]\\r\\n ))\\r\\n\\r\\n child = Organism(mother.population, genome)\\r\\n child.mutate()\\r\\n\\r\\n children.append(child)\\r\\n\\r\\n return children\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a23acae6cdccf52c749aee41ce3d485\",\n \"score\": \"0.4497921\",\n \"text\": \"def all_puzzles(center):\\r\\n\\r\\n # Find possible sequences of matching marks around the ring.\\r\\n for matching in product(range(6), repeat=6):\\r\\n halves = tuple(zip(matching, center, matching[-1:] + matching[:-1]))\\r\\n\\r\\n # If the result is valid, we can freely permute the remaining 3\\r\\n # outer marks on each nut around the ring.\\r\\n if all(len(set(inner)) == 3 for inner in halves):\\r\\n for ring in product(*([inner + outer\\r\\n for outer in permutations(\\r\\n set(range(6)) - set(inner))]\\r\\n for inner in halves)):\\r\\n yield pack(ring + (center,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfcbc3b1439ce814c4c784d3f0251636\",\n \"score\": \"0.44963923\",\n \"text\": \"def tester_fermat_test():\\r\\n c_list = carmichael_1(100000)\\r\\n r_list = [random.randrange(2, 1.e8) for i in range(1000)]\\r\\n com_list = [n for n in r_list if first_test(n) == 'composite']\\r\\n for c in c_list:\\r\\n print(fermat_test(c, 100))\\r\\n for r in r_list:\\r\\n print(fermat_test(r, 100))\\r\\n for com in com_list:\\r\\n print(fermat_test(com, 100))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70e75f9d240ca271f9d3f092d6ae7069\",\n \"score\": \"0.44849333\",\n \"text\": \"def get_Reduced_SpeciesID(pro_seq, clade_df, num_random_species=10):\\n\\n # pro_input = \\\"/home/luhongzhong/ortholog_343/protein_align_s2_R/OG1587_aa_aligned.fasta\\\"\\n # OG_test = list(SeqIO.parse(pro_dir, \\\"fasta\\\"))\\n OG_test = pro_seq\\n all_OG_ID = []\\n for record in OG_test:\\n # print(record.id)\\n all_OG_ID.append(record.id)\\n OG_df = pd.DataFrame({\\\"OG\\\": all_OG_ID})\\n OG_df[[\\\"OG\\\", \\\"id\\\"]] = OG_df.OG.str.split(\\\"@\\\", expand=True, )\\n # get clade information for each species\\n OG_df[\\\"clade\\\"] = singleMapping(clade_df[\\\"clade\\\"], clade_df[\\\"speciesID\\\"], OG_df[\\\"OG\\\"])\\n # establish six list\\n list1 = [] # Phaffomycetaceae\\n list2 = [] # Dipodascaceae-Trichomonascaceae\\n list3 = [] # Pichiaceae\\n list4 = [] # Saccharomycetaceae\\n list5 = [] # CUG-Ser1\\n list6 = [] # Saccharomycodaceae\\n list7 = [] # Lipomycetaceae\\n list_o = [] # others\\n for i, x in OG_df.iterrows():\\n # print(i, x[\\\"clade\\\"])\\n s0 = x[\\\"clade\\\"]\\n if s0 == \\\"Phaffomycetaceae\\\":\\n list1.append(x[\\\"OG\\\"])\\n elif s0 == \\\"Dipodascaceae-Trichomonascaceae\\\":\\n list2.append(x[\\\"OG\\\"])\\n elif s0 == \\\"Pichiaceae\\\":\\n list3.append(x[\\\"OG\\\"])\\n elif s0 == \\\"Saccharomycetaceae\\\":\\n list4.append(x[\\\"OG\\\"])\\n elif s0 == \\\"CUG-Ser1\\\":\\n list5.append(x[\\\"OG\\\"])\\n\\n elif s0 == \\\"Saccharomycodaceae\\\": # newly added\\n list6.append(x[\\\"OG\\\"])\\n elif s0 == \\\"Lipomycetaceae\\\": # newly added\\n list7.append(x[\\\"OG\\\"])\\n\\n else:\\n list_o.append(x[\\\"OG\\\"])\\n # randomely choose then species from each dict\\n list_1r = randome_choose_n_species(species_list=list1, num_species=num_random_species)\\n list_2r = randome_choose_n_species(species_list=list2, num_species=num_random_species)\\n list_3r = randome_choose_n_species(species_list=list3, num_species=num_random_species)\\n list_4r = randome_choose_n_species(species_list=list4, num_species=num_random_species)\\n list_5r = randome_choose_n_species(species_list=list5, num_species=num_random_species)\\n\\n list_6r = randome_choose_n_species(species_list=list6, num_species=num_random_species)\\n list_7r = randome_choose_n_species(species_list=list7, num_species=num_random_species)\\n # next choose seq id for each species\\n # firstly combine the choosed species id\\n all_species_id = list(list_1r) + list(list_2r) + list(list_3r) + list(list_4r) + list(list_5r) + list(\\n list_6r) + list(list_7r) + list_o\\n # filter OG id based on the choosed species id\\n OG_ID_filter1 = [x for x in all_OG_ID if x.split(\\\"@\\\")[0] in all_species_id]\\n # check the paralog number\\n ratio_seq_per_species = len(set(OG_ID_filter1)) / len(set(all_species_id))\\n # print(pro_dir, \\\"===>\\\", ratio_seq_per_species)\\n return OG_ID_filter1, ratio_seq_per_species\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25e3db2710c6219109184db6783a8ae5\",\n \"score\": \"0.44849\",\n \"text\": \"def get_variants_shared_by_mother_and_son(self):\\n vcfSon = vcf.Reader(open(\\\"AmpliseqExome.20141120.NA24385.vcf\\\", \\\"r\\\"))\\n vcfMother = vcf.Reader(open(\\\"AmpliseqExome.20141120.NA24143.vcf\\\", \\\"r\\\"))\\n sharedVariantsMotherSon = 0\\n shared_variants = vcf.utils.walk_together(vcfMother, vcfSon)\\n for record in shared_variants:\\n if not record[0] is None and not record[1] is None:\\n sharedVariantsMotherSon += 1\\n print(\\\"\\\\nnumber of variants shared by mother and son:\\\")\\n print(sharedVariantsMotherSon)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abad4045066144416dc28e6ee17315d5\",\n \"score\": \"0.44816265\",\n \"text\": \"def random_functionalization(self, maxgroups=2):\\n h_atoms, functional_hist = {}, {}\\n # determine base building units from structure\\n base_building_units = self.get_base_units()\\n # generate dictionary of hydrogen atoms for each building unit\\n for building_unit in base_building_units:\\n id = (building_unit.index, building_unit.metal,\\n building_unit.parent)\\n hydrogen_sites = self.get_hydrogens(building_unit)\\n h_atoms[id] = hydrogen_sites\\n done = False\\n while not done:\\n # copy of structure to functionalize\\n structure = deepcopy(self.structure)\\n sites = {}\\n # randomly select hydrogens on each unit\\n for building_unit in base_building_units:\\n id = (building_unit.index, building_unit.metal,\\n building_unit.parent)\\n if h_atoms[id]:\\n sites[id] = self.select(h_atoms[id])\\n else:\\n sites[id] = []\\n # randomly choose functional groups\\n assigned_fnlgrps = self.choose_fnl_group(maxgroups)\\n # assign fnl groups to each hydrogen\\n new_func = False\\n while not new_func:\\n record = []\\n for key, value in sites.items():\\n # reset the site to a dictionary\\n # site[(building_unit)] = {hydrogen:functional_group}\\n sites[key] = {}\\n # descend into each hydrogen atom and assign a\\n # random choice of functional group.\\n h_record = []\\n for site in value:\\n group = choice(assigned_fnlgrps)\\n sites[key][site] = group\\n rec = tuple([site, group.index])\\n h_record.append(rec)\\n h_record.sort()\\n rec = tuple([key, tuple(h_record)])\\n record.append(rec)\\n record.sort()\\n # fastest check if the functional has been tried: dictionary\\n # lookup?\\n try:\\n functional_hist[tuple(record)]\\n except KeyError:\\n # if the key is not in functional_hist, then exit this loop\\n new_func = True\\n # keep record to prevent repeats.\\n functional_hist[tuple(record)] = 1\\n \\n # iterate through each building unit in the structure, and\\n # append functional groups\\n for building_unit in structure.building_units:\\n site_id = (building_unit.index, building_unit.metal,\\n building_unit.parent)\\n replace_dic = sites[site_id]\\n for hydrogen in replace_dic.keys():\\n fnl_grp = deepcopy(replace_dic[hydrogen])\\n H_atom = building_unit.atoms[hydrogen]\\n # find the associated bond\\n bond = building_unit.find_bonds(H_atom)\\n if len(bond) != 1:\\n raise Error(\\\"Hydrogen reports as having no bonds..\\\")\\n bond = bond[0]\\n # nasty one-liner to assign the Joined atom\\n J_atom = bond.frm if bond.to == H_atom else bond.to\\n axis = bond.vector\\n # rotate and translate\\n fnl_grp.orient(bond.vector, J_atom.coordinates)\\n # check for overlap\\n overlap_atoms = self.overlap(fnl_grp, J_atom, structure)\\n if overlap_atoms:\\n # check if one of the overlap atoms is the\\n # connecting atom, in this case we just\\n # scrap the structure because no amount\\n # of rotation will help.\\n for overlap_atom in overlap_atoms:\\n if fnl_grp.atoms[overlap_atom].connectivity:\\n # need to break and try a different randomization.\\n # no amount of rotation will help here.\\n pass\\n # generate angles of 360 degrees split into 10 intervals.\\n rot_angle = 2 * pi / 10\\n tot_angle = 0.\\n while overlap_atoms and tot_angle < (2*pi):\\n fnl_grp.rotate(rot_angle)\\n tot_angle += rot_angle\\n overlap_atoms = self.overlap(fnl_grp, J_atom)\\n \\n # append the functional group to the Structure.\\n fnl_grp.MOF_atom = J_atom\\n building_unit.functionalize(fnl_grp)\\n # update connectivities\\n for building_unit in structure.building_units:\\n pass\\n # write cif file\\n done = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e0bf8912cffefc0f39fde7836605931\",\n \"score\": \"0.44632077\",\n \"text\": \"def many_body_mc_jit(\\n q_array_1,\\n q_array_2,\\n q_neigh_array_1,\\n q_neigh_array_2,\\n q_neigh_grads_1,\\n q_neigh_grads_2,\\n c1,\\n c2,\\n etypes1,\\n etypes2,\\n species1,\\n species2,\\n d1,\\n d2,\\n sig,\\n ls,\\n):\\n\\n kern = 0\\n\\n useful_species = np.array(\\n list(set(species1).intersection(set(species2))), dtype=np.int8\\n )\\n\\n # loop over all possible species\\n for s in useful_species:\\n\\n # Calculate many-body descriptor values for central atoms 1 and 2\\n s1 = np.where(species1 == s)[0][0]\\n s2 = np.where(species2 == s)[0][0]\\n q1 = q_array_1[s1]\\n q2 = q_array_2[s2]\\n\\n # compute kernel between central atoms only if central atoms are of\\n # the same species\\n if c1 == c2:\\n k12 = k_sq_exp_double_dev(q1, q2, sig, ls)\\n else:\\n k12 = 0\\n\\n # initialize arrays of many body descriptors and gradients for the\\n # neighbour atoms in the two configurations\\n # Loop over neighbours i of 1st configuration\\n for i in range(q_neigh_array_1.shape[0]):\\n qis = q1i_grads = qi1_grads = ki2s = 0\\n\\n if etypes1[i] == s:\\n # derivative of pairwise component of many body descriptor q1i\\n q1i_grads = q_neigh_grads_1[i, d1 - 1]\\n\\n if c1 == s:\\n # derivative of pairwise component of many body descriptor qi1\\n qi1_grads = q_neigh_grads_1[i, d1 - 1]\\n\\n # Calculate many-body descriptor value for i\\n qis = q_neigh_array_1[i, s1]\\n\\n if c2 == etypes1[i]:\\n ki2s = k_sq_exp_double_dev(qis, q2, sig, ls)\\n\\n # Loop over neighbours j of 2\\n for j in range(q_neigh_array_2.shape[0]):\\n qjs = qj2_grads = q2j_grads = k1js = 0\\n\\n if etypes2[j] == s:\\n q2j_grads = q_neigh_grads_2[j, d2 - 1]\\n\\n if c2 == s:\\n qj2_grads = q_neigh_grads_2[j, d2 - 1]\\n\\n # Calculate many-body descriptor value for j\\n qjs = q_neigh_array_2[j, s2]\\n\\n if c1 == etypes2[j]:\\n k1js = k_sq_exp_double_dev(q1, qjs, sig, ls)\\n\\n if etypes1[i] == etypes2[j]:\\n kij = k_sq_exp_double_dev(qis, qjs, sig, ls)\\n else:\\n kij = 0\\n\\n kern += q1i_grads * q2j_grads * k12\\n kern += qi1_grads * q2j_grads * ki2s\\n kern += q1i_grads * qj2_grads * k1js\\n kern += qi1_grads * qj2_grads * kij\\n return kern\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6024b96ed2f58a329e9d248edd049274\",\n \"score\": \"0.44614676\",\n \"text\": \"def react_all(core_spc_list, numOldCoreSpecies, unimolecularReact, bimolecularReact, trimolecularReact=None, procnum=1):\\n # Select reactive species that can undergo unimolecular reactions:\\n spc_tuples = [(core_spc_list[i],)\\n for i in xrange(numOldCoreSpecies) if (unimolecularReact[i] and core_spc_list[i].reactive)]\\n\\n for i in xrange(numOldCoreSpecies):\\n for j in xrange(i, numOldCoreSpecies):\\n # Find reactions involving the species that are bimolecular.\\n # This includes a species reacting with itself (if its own concentration is high enough).\\n if bimolecularReact[i, j]:\\n if core_spc_list[i].reactive and core_spc_list[j].reactive:\\n spc_tuples.append((core_spc_list[i], core_spc_list[j]))\\n\\n if trimolecularReact is not None:\\n for i in xrange(numOldCoreSpecies):\\n for j in xrange(i, numOldCoreSpecies):\\n for k in xrange(j, numOldCoreSpecies):\\n # Find reactions involving the species that are trimolecular.\\n if trimolecularReact[i, j, k]:\\n if core_spc_list[i].reactive and core_spc_list[j].reactive and core_spc_list[k].reactive:\\n spc_tuples.append((core_spc_list[i], core_spc_list[j], core_spc_list[k]))\\n\\n if procnum == 1:\\n # React all families like normal (provide empty argument for only_families)\\n spc_fam_tuples = zip(spc_tuples)\\n else:\\n # Identify and split families that are prone to generate many reactions into sublists.\\n family_list = getDB('kinetics').families.keys()\\n major_families = [\\n 'H_Abstraction', 'R_Recombination', 'Intra_Disproportionation', 'Intra_RH_Add_Endocyclic',\\n 'Singlet_Carbene_Intra_Disproportionation', 'Intra_ene_reaction', 'Disproportionation',\\n '1,4_Linear_birad_scission', 'R_Addition_MultipleBond', '2+2_cycloaddition_Cd', 'Diels_alder_addition',\\n 'Intra_RH_Add_Exocyclic', 'Intra_Retro_Diels_alder_bicyclic', 'Intra_2+2_cycloaddition_Cd',\\n 'Birad_recombination', 'Intra_Diels_alder_monocyclic', '1,4_Cyclic_birad_scission', '1,2_Insertion_carbene',\\n ]\\n\\n split_list = []\\n leftovers = []\\n for fam in family_list:\\n if fam in major_families:\\n split_list.append([fam])\\n else:\\n leftovers.append(fam)\\n split_list.append(leftovers)\\n\\n # Only employ family splitting for reactants that have a larger number than min_atoms\\n min_atoms = 10\\n spc_fam_tuples = []\\n for i, spc_tuple in enumerate(spc_tuples):\\n if any([len(spc.molecule[0].atoms) > min_atoms for spc in spc_tuple]):\\n for item in split_list:\\n spc_fam_tuples.append((spc_tuple, item))\\n else:\\n spc_fam_tuples.append((spc_tuple, ))\\n\\n return list(react(spc_fam_tuples, procnum))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f952d4581c678ea0f7a821747a8b9e9\",\n \"score\": \"0.44589335\",\n \"text\": \"def mix_atoms(s1, s2, roll=True):\\n from random import choice\\n from itertools import chain\\n from numpy.linalg import det\\n from numpy import dot, abs\\n from ..crystal import Structure\\n\\n # swap structures randomly.\\n if choice([True, False]): s1, s2 = s2, s1\\n\\n # chem. symbols and scaled positions of the two parents\\n sc_pos1 = zip([atom.type for atom in s1], fractional_pos(s1, roll))\\n sc_pos2 = zip([atom.type for atom in s2], fractional_pos(s2, roll))\\n\\n result = Structure(s1.cell)\\n\\n for pos, type in chain(sc_pos1, sc_pos2):\\n if choice([True, False]):\\n result.add_atom(*dot(result.cell, pot), type=type)\\n\\n result.scale = s1.scale * (float(len(result)) / float(len(s1)))**(1./3.)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ef7ce14637e294684793ef67909d80\",\n \"score\": \"0.44579798\",\n \"text\": \"def apply(self, population):\\n parent_pairs = []\\n for pair_idx in range(int(len(population)*self.params['part_with_offsprings']/2.)):\\n pair = []\\n for parent_idx in np.arange(2):\\n selection_indexes = np.random.choice(len(population), self.params['tournament_groups'], replace = False)\\n candidates = [population[idx] for idx in selection_indexes]\\n pair.append([idx for _, idx in sorted(zip(candidates, selection_indexes), key=lambda pair: pair[0].fitness_value)][-1])\\n parent_pairs.append(pair)\\n return parent_pairs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75acaef00f7f6d1892ea43a6f0b137a5\",\n \"score\": \"0.4456152\",\n \"text\": \"def generate_pokemon_names_and_images_by_colour(self, colour):\\n \\n # list species matching colour\\n pokemon_species_list = self.get_species_by_colour(colour)\\n \\n for pokemon_species_link in pokemon_species_list:\\n print(pokemon_species_link['name'])\\n\\n # GET information about a species\\n pokemon_species_json = self.get(pokemon_species_link['url'])\\n\\n\\n # since a species may have multiple varieties\\n # GET each variety\\n varieties = pokemon_species_json['varieties']\\n for variety in varieties:\\n # (a variety is a pokemon)\\n # GET the pokemon information\\n pokemon_json = self.get(variety['pokemon']['url'])\\n\\n pokemon_name = pokemon_json['name']\\n image_url = pokemon_json['sprites']['front_default']\\n \\n yield (pokemon_name, image_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"760c91e62f025b391a7134ac031bb4f4\",\n \"score\": \"0.4454435\",\n \"text\": \"def __get_datasets_per_family_with(db,include_dataset='Fossil'):\\n datasets_per_family = __get_datasets_per_family\\n datasets_per_family_with={}\\n for family in datasets_per_family:\\n if include_dataset in datasets_per_family[family]:\\n datasets_per_family_with[family]=datasets_per_family[family]\\n return datasets_per_family_with\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af30ed98bea73f8f9e4448375d4d6015\",\n \"score\": \"0.4454323\",\n \"text\": \"def get_species(self):\\n speciesnodes = [[] for _ in range(0, self.speciesnum)]\\n for j in range(0, len(self.V)):\\n speciesnodes[self.species[j]].append(self.V[j])\\n return speciesnodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a99a980b56e5376fba5cecb6b6991d1\",\n \"score\": \"0.44517902\",\n \"text\": \"def combine_populations(self):\\n\\n self.combined_pop['binary'] = [[[]] * self.num_var for i in range(self.num_pop * 2)]\\n self.combined_pop['decoded'] = [[[]] * self.num_var for i in range(self.num_pop * 2)]\\n self.combined_pop['scaled'] = [[[]] * self.num_var for i in range(self.num_pop * 2)]\\n self.combined_pop['fit_values'] = [[[]] * self.num_fit_func for i in range(self.num_pop * 2)]\\n\\n for i in range(self.num_pop):\\n self.combined_pop['binary'][i] = self.parent_pop['binary'][i]\\n self.combined_pop['binary'][i + self.num_pop] = self.current_pop['binary'][i]\\n\\n self.combined_pop['decoded'][i] = self.parent_pop['decoded'][i]\\n self.combined_pop['decoded'][i + self.num_pop] = self.current_pop['decoded'][i]\\n\\n self.combined_pop['scaled'][i] = self.parent_pop['scaled'][i]\\n self.combined_pop['scaled'][i + self.num_pop] = self.current_pop['scaled'][i]\\n\\n self.combined_pop['fit_values'][i] = self.parent_pop['fit_values'][i]\\n self.combined_pop['fit_values'][i + self.num_pop] = self.current_pop['fit_values'][i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5d28341cfe0373f3b648ebe6c44655e\",\n \"score\": \"0.4451452\",\n \"text\": \"def flavors(self, details=True, get_extra_specs=False, **query):\\n base_path = '/flavors/detail' if details else '/flavors'\\n for flv in self._list(_flavor.Flavor, base_path=base_path, **query):\\n if get_extra_specs and not flv.extra_specs:\\n flv = flv.fetch_extra_specs(self)\\n yield flv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e08e0d652e0313fc7bec7126e3070e84\",\n \"score\": \"0.44403917\",\n \"text\": \"def gather_mol(mols):\\n atom_feat_ls = []\\n pair_feat_ls = []\\n atom_split_ls = []\\n atom2pair_ls = []\\n pair_split_ls = []\\n start = 0\\n for im, mol in enumerate(mols):\\n atom_feat, pair_feat, atom_in_pair = mol\\n # n_atoms = mol.get_num_atoms()\\n # number of atoms in each molecule\\n # atom_split.extend([im] * n_atoms)\\n # index of pair features\\n # C0, C1 = np.meshgrid(np.arange(n_atoms), np.arange(n_atoms))\\n # atom_to_pair.append(\\n # np.transpose(\\n # np.array([C1.flatten() + start,\\n # C0.flatten() + start])))\\n # number of pairs for each atom\\n # pair_split.extend(C1.flatten() + start)\\n # start = start + n_atoms\\n #\\n # atom features\\n # atom_feat.append(mol.get_atom_features())\\n # pair features\\n # pair_feat.append(\\n # np.reshape(mol.get_pair_features(),\\n # (n_atoms * n_atoms, -1)))\\n n_atom = atom_feat.shape[0]\\n atom_feat_ls.append(atom_feat)\\n pair_feat_ls.append(pair_feat)\\n\\n atom2pair = atom_in_pair + start\\n pair_head = atom2pair[:, 0]\\n pair_split_ls.append(pair_head)\\n atom2pair_ls.append(atom2pair)\\n atom_split_ls.extend([im] * n_atom)\\n\\n start += n_atom\\n\\n atom_feat = np.concatenate(atom_feat_ls, axis=0)\\n bond_feat = np.concatenate(pair_feat_ls, axis=0)\\n\\n atom_feat_ls = [atom_feat[:, i] for i in range(atom_feat.shape[1])]\\n bond_feat_ls = [bond_feat[:, i] for i in range(bond_feat.shape[1])]\\n mol_merged = [\\n atom_feat_ls,\\n bond_feat_ls,\\n np.concatenate(pair_split_ls, axis= 0),\\n np.array(atom_split_ls),\\n np.concatenate(atom2pair_ls, axis=0),\\n start # num of atoms\\n ]\\n return mol_merged\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd9bcaa23fadef8020c56f38f38e22c8\",\n \"score\": \"0.44341436\",\n \"text\": \"def __call__(self, *feature: 'dsl.Feature') -> frozenset['dsl.Feature']:\\n for col in feature:\\n col.accept(self)\\n return frozenset(self._match)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"546de05aa159258575fb1a35304e9bcd\",\n \"score\": \"0.4431054\",\n \"text\": \"def get_sub_species(self, reference_type: str, species: str) -> list:\\n return list(self.yaml.get(reference_type).get(species).keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26f0d1dd7b184468165f2e40f71c8f9b\",\n \"score\": \"0.44306764\",\n \"text\": \"def test_supergroup(self):\\n data = [\\n (\\\"NbO2\\\", 141, 5),\\n ]\\n for d in data:\\n (cif, g, N) = d\\n s = pyxtal()\\n s.from_seed(cif_path + cif + '.cif')\\n strucs, sols = s.supergroup(g, 0.5)\\n self.assertTrue(len(strucs) > 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8df9b7c1e8bd35bda42ea6450cee7cf\",\n \"score\": \"0.44299644\",\n \"text\": \"def get_reactants_and_products4tagged_fam(fam='LOx', KPP_output_mech=None,\\n folder=None):\\n import pandas as pd\\n # Get the outputted KPP mechanism if not provided.\\n if isinstance(KPP_output_mech, type(None)):\\n KPP_output_mech = get_dict_of_KPP_mech(wd=folder)\\n # Make a DataFrame from the dictionary\\n df = pd.DataFrame(KPP_output_mech.values(), index=KPP_output_mech.keys())\\n df.columns = ['rxn str']\\n# df.index = df.index - 1 # Adjust Fortran numbering in output to Pythonic #\\n # Split apart reaction str to give products and reactants\\n\\n def extract_products(input):\\n return input.split(' --> ')[-1].strip()\\n\\n def extract_reactants(input):\\n return input.split(' --> ')[0].strip()\\n df['prod'] = df['rxn str'].map(extract_products)\\n df['react'] = df['rxn str'].map(extract_reactants)\\n # Make the formating the same as in input files\\n # ---- Check if the file mapping can work between KPP input & output\\n df['KPP input react'] = df['react'].map(update_KPP_rxn_str)\\n# KPP_Input_df['KPP input react'] = KPP_Input_df['react'].map(update_rxn_str)\\n find_breakpoint = False\\n if find_breakpoint: # Seems to be C3H8 + OH = A3O2 :\\n # for s,e in [ [0, 50], [] ]:\\n for s, e in zip(range(0, 700, 50), range(50, 750, 50)):\\n bool = df['react'][s:e] == KPP_Input_df['KPP input react'][s:e]\\n a_ = df['react'][s:e][~bool]\\n b_ = KPP_Input_df['KPP input react'][s:e][~bool]\\n# print dict( zip( a_, b_ ) )\\n # turn into sorted lists\\n a_ = [list(sorted([ii.strip()for ii in i.split('+')])) for i in a_]\\n b_ = [list(sorted([ii.strip()for ii in i.split('+')])) for i in b_]\\n # compare these and only print if they differ\\n for n, a__ in enumerate(a_):\\n if a__ != b_[n]:\\n print(a__, b_[n])\\n # Only consider reaction that include family in the products\\n def fam_in_rxn(input):\\n return (fam in input)\\n rtn_vars = ['react', 'prod', 'KPP input react']\\n df = df[df['prod'].map(fam_in_rxn)][rtn_vars]\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1fd22e25b2185028305088342812533\",\n \"score\": \"0.44281232\",\n \"text\": \"def test_cat_feats(zipper, feat_subset=None, method=None, print_data=False):\\n\\n p_values = dict()\\n\\n # consider all features if no feature subset was specified\\n if feat_subset is None:\\n feat_subset = zipper.keys()\\n\\n # set default method to chi-square test\\n if method is None:\\n method = \\\"chi\\\"\\n\\n for feat in feat_subset:\\n # initiate dict in dict for dataset1 vs dataset2, d1 vs d3 etc. per feature\\n p_values[feat] = dict()\\n\\n for i in range(len(zipper[feat]) - 1): # select dataset1\\n for j in range(i + 1, len(zipper[feat])): # select dataset2\\n # count occurences of categorical features like in a confusion matrix for Chi2 tests\\n test_data = [_categorical_table(zipper[feat][i]), _categorical_table(zipper[feat][j])]\\n\\n # fill missing keys in test data:\\n test_data = _non_present_values_to_zero(test_data)\\n\\n # sort testing data by index(categories) to align the counts for the categories\\n test_data = [data.sort_index() for data in test_data]\\n\\n # print testing data if specified\\n if print_data:\\n print(feat)\\n print(pd.DataFrame(test_data))\\n print()\\n\\n if method == \\\"chi\\\":\\n # skip feature if number of events per group is smaller than 5\\n if (test_data[0] < 5).any() or (test_data[1] < 5).any():\\n warnings.warn(feat + \\\" has under 5 observations in one or more groups.\\\", UserWarning)\\n # calculate u statistic and return p-value\\n p_val = chisquare(*test_data).pvalue\\n\\n elif method == \\\"fisher\\\":\\n p_val = fisher_exact(test_data)[1]\\n\\n p_values[feat][i + 1, j + 1] = p_val\\n\\n return p_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8b851f74b85bec44187b7556656aa25\",\n \"score\": \"0.44253698\",\n \"text\": \"def detect_species_bboxes(src_gpath_list, species, quick=True, use_chunks=False, **detectkw):\\n nImgs = len(src_gpath_list)\\n print('[detect.rf] Begining %s detection' % (species,))\\n detect_lbl = 'detect %s ' % species\\n mark_prog, end_prog = utool.progress_func(nImgs, detect_lbl, flush_after=1)\\n\\n detect_config = _get_detect_config(**detectkw)\\n detector, forest = _get_detector(species, quick=quick)\\n detector.set_detect_params(**detect_config)\\n\\n dst_gpath_list = [splitext(gpath)[0] for gpath in src_gpath_list]\\n # FIXME: Doing this in a generator may cause unnecessary page-faults\\n # Maybe there is a better way of doing this, or generating results\\n # in batch. It could be a utool batch serial process\\n\\n chunksize = 8\\n use_chunks_ = use_chunks and nImgs >= chunksize\\n\\n if use_chunks_:\\n print('[rf] detect in chunks')\\n pathtup_iter = zip(src_gpath_list, dst_gpath_list)\\n for ic, chunk in enumerate(utool.ichunks(pathtup_iter, chunksize)):\\n src_gpath_list = [tup[0] for tup in chunk]\\n dst_gpath_list = [tup[1] for tup in chunk]\\n mark_prog(ic * chunksize)\\n results_list = detector.detect_many(forest, src_gpath_list, dst_gpath_list)\\n\\n for results in results_list:\\n bboxes = [(minx, miny, (maxx - minx), (maxy - miny))\\n for (centx, centy, minx, miny, maxx, maxy, confidence, supressed)\\n in results if supressed == 0]\\n\\n #x_arr = results[:, 2]\\n #y_arr = results[:, 3]\\n #w_arr = results[:, 4] - results[:, 2]\\n #h_arr = results[:, 5] - results[:, 3]\\n #bboxes = np.hstack((x_arr, y_arr, w_arr, h_arr))\\n # Unpack unsupressed bounding boxes\\n\\n confidences = [confidence\\n for (centx, centy, minx, miny, maxx, maxy, confidence, supressed)\\n in results if supressed == 0]\\n\\n if len(results) > 0:\\n image_confidence = max([float(result[6]) for result in results])\\n else:\\n image_confidence = 0.0\\n\\n yield bboxes, confidences, image_confidence\\n else:\\n print('[rf] detect one image at a time')\\n pathtup_iter = izip(src_gpath_list, dst_gpath_list)\\n for ix, (src_gpath, dst_gpath) in enumerate(pathtup_iter):\\n mark_prog(ix)\\n results = detector.detect(forest, src_gpath, dst_gpath)\\n bboxes = [(minx, miny, (maxx - minx), (maxy - miny))\\n for (centx, centy, minx, miny, maxx, maxy, confidence, supressed)\\n in results if supressed == 0]\\n\\n confidences = [confidence\\n for (centx, centy, minx, miny, maxx, maxy, confidence, supressed)\\n in results if supressed == 0]\\n\\n if len(results) > 0:\\n image_confidence = max([float(result[6]) for result in results])\\n else:\\n image_confidence = 0.0\\n\\n yield bboxes, confidences, image_confidence\\n end_prog()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c802c027159addf54304f55b259d248e\",\n \"score\": \"0.4424991\",\n \"text\": \"def missing_weighed_baskets_check(self):\\n result = {\\\"status\\\": \\\"Passed\\\", \\\"errors\\\": \\\"Success\\\", \\\"errorCount\\\": 0}\\n errors = []\\n\\n try:\\n catch = Catch.select().where(Catch.operation == self._app.state_machine.haul[\\\"haul_id\\\"],\\n Catch.parent_catch.is_null(True))\\n for species in catch:\\n child_baskets = Catch.select().where(Catch.parent_catch == species.catch)\\n if child_baskets.count() == 0:\\n errors.append(species.display_name)\\n\\n # Mixes\\n if \\\"mix\\\" in species.display_name.lower():\\n for mix_species in child_baskets:\\n mix_child_baskets = Catch.select().where(Catch.parent_catch == mix_species.catch)\\n if \\\"mix #\\\" not in mix_species.display_name.lower() and mix_child_baskets.count() == 0:\\n errors.append(species.display_name + \\\" > \\\" + mix_species.display_name)\\n\\n # Submixes\\n if \\\"submix\\\" in mix_species.display_name.lower():\\n submix_catch = Catch.select(Catch, CatchContentLu, TypesLu) \\\\\\n .join(CatchContentLu,\\n on=(CatchContentLu.catch_content == Catch.catch_content).alias(\\\"content\\\")) \\\\\\n .join(TypesLu, on=(CatchContentLu.content_type == TypesLu.type_id).alias(\\\"types\\\")) \\\\\\n .where(Catch.parent_catch == mix_species.catch, \\\\\\n TypesLu.category == \\\"Content\\\", \\\\\\n TypesLu.type == \\\"Taxon\\\")\\n for submix_species in submix_catch:\\n submix_child_baskets = Catch.select().where(Catch.parent_catch == submix_species.catch)\\n if submix_child_baskets.count() == 0:\\n errors.append(species.display_name + \\\" > \\\" + mix_species.display_name + \\\" > \\\" +\\n submix_species.display_name)\\n\\n except Exception as ex:\\n logging.info(\\\"error in query: \\\" + str(ex))\\n\\n if len(errors) > 0:\\n result[\\\"status\\\"] = \\\"Failed\\\"\\n result[\\\"errorCount\\\"] = len(errors)\\n errors = \\\"\\\\n\\\".join(sorted(errors))\\n result[\\\"errors\\\"] = errors\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5775cebea189417b1cac13fb2c340b57\",\n \"score\": \"0.44232428\",\n \"text\": \"def recombine_population(self, population):\\n offspring = []\\n\\n # Create mating pool\\n mating_pool = self.select_parents(population)\\n\\n # Shuffle mating pool\\n mating_pool = self.random_selection_without_replacement(mating_pool,\\n len(mating_pool))\\n\\n index = 0\\n # Recombine parents of different encodings pairwise\\n while (len(offspring) < self.ea_lambda):\\n parent1 = mating_pool[index]\\n index += 1\\n parent2 = mating_pool[index]\\n index += 1\\n\\n # Each pair has a p_c chance of mating. p_c defaults to 1.0.\\n if ((random.random() < self.p_c)\\n and (parent1.encoding != parent2.encoding)):\\n new_offspring = self.crossover(parent1, parent2)\\n offspring.append(new_offspring[0])\\n if (len(offspring) < self.ea_lambda):\\n offspring.append(new_offspring[1])\\n # Sometimes we end up with a population of all\\n # the same encoding. I can either carry them\\n # on to the next generation, or just sit in this loop forever.\\n else:\\n offspring.append(parent1)\\n if (len(offspring) < self.ea_lambda):\\n offspring.append(parent2)\\n\\n return offspring\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5775cebea189417b1cac13fb2c340b57\",\n \"score\": \"0.44232428\",\n \"text\": \"def recombine_population(self, population):\\n offspring = []\\n\\n # Create mating pool\\n mating_pool = self.select_parents(population)\\n\\n # Shuffle mating pool\\n mating_pool = self.random_selection_without_replacement(mating_pool,\\n len(mating_pool))\\n\\n index = 0\\n # Recombine parents of different encodings pairwise\\n while (len(offspring) < self.ea_lambda):\\n parent1 = mating_pool[index]\\n index += 1\\n parent2 = mating_pool[index]\\n index += 1\\n\\n # Each pair has a p_c chance of mating. p_c defaults to 1.0.\\n if ((random.random() < self.p_c)\\n and (parent1.encoding != parent2.encoding)):\\n new_offspring = self.crossover(parent1, parent2)\\n offspring.append(new_offspring[0])\\n if (len(offspring) < self.ea_lambda):\\n offspring.append(new_offspring[1])\\n # Sometimes we end up with a population of all\\n # the same encoding. I can either carry them\\n # on to the next generation, or just sit in this loop forever.\\n else:\\n offspring.append(parent1)\\n if (len(offspring) < self.ea_lambda):\\n offspring.append(parent2)\\n\\n return offspring\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acaa6d04cdb191645122f4a997d44b36\",\n \"score\": \"0.4421154\",\n \"text\": \"def get_family(person):\\n spouse = get_spouse(person)\\n if spouse == None:\\n #If no spouse, then no children, so return self\\n return [person]\\n else:\\n #If spouse, check for children\\n children = get_children(person)\\n return [person,spouse] + children\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993898,"cells":{"query_id":{"kind":"string","value":"1212537f420017b9738fde435fa396fa"},"query":{"kind":"string","value":"r\"\"\"Return a list of sequences to run to perform gate set tomography according to the given spec, with standard poweroftwo sequence lengths up to (approximately)"},"positive_passages":{"kind":"list like","value":[{"docid":"f64510c4db40c41c7c3c5ba2f27316cf","score":"0.57117736","text":"def generate_std_gst_sequences(spec: GSTSpec,\n max_len_exponent: int) -> List[GateSequence]:\n return generate_gst_sequences(spec, (2**i for i in range(max_len_exponent + 1)))","title":""}],"string":"[\n {\n \"docid\": \"f64510c4db40c41c7c3c5ba2f27316cf\",\n \"score\": \"0.57117736\",\n \"text\": \"def generate_std_gst_sequences(spec: GSTSpec,\\n max_len_exponent: int) -> List[GateSequence]:\\n return generate_gst_sequences(spec, (2**i for i in range(max_len_exponent + 1)))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2a8461217b4174229a6b47de662dc87f","score":"0.62755734","text":"def generate_gst_sequences(spec: GSTSpec,\n target_lens: Sequence[int]) -> List[GateSequence]:\n # Collect sequences in set for deduplication.\n seqs = set()\n for target_len in target_lens:\n for prep in spec.prep_fiducials:\n for meas in spec.meas_fiducials:\n for germ in spec.germs:\n num_repeats = target_len // len(germ)\n seqs.add(prep + germ * num_repeats + meas)\n return list(seqs)","title":""},{"docid":"e30174a8af91333ed99f26b33f3a0625","score":"0.5990837","text":"def generation(generator,predictor,configReinforce): \r\n generated = []\r\n pbar = tqdm(range(400))\r\n for i in pbar:\r\n pbar.set_description(\"Generating molecules...\")\r\n predictSMILES = PredictSMILES(generator,None,False,0.1,configReinforce)\r\n generated.append(predictSMILES.sample())\r\n\r\n sanitized,valid = canonical_smiles(generated,sanitize=True, throw_warning=False)# validar \r\n unique_smiles = list(np.unique(sanitized))[1:]\r\n \r\n #prediction kor\r\n prediction_kor = predictor.predict(unique_smiles)\r\n #prediction sas\r\n mol_list = smiles2mol(unique_smiles) \r\n prediction_qed = qed_calculator(mol_list)\r\n \r\n new_pred_qed = [np.exp(element/4) for element in prediction_qed]\r\n new_pred_kor = [np.exp(element/4 - 1) for element in prediction_kor]\r\n \r\n ranges = []\r\n ranges.append(np.max(new_pred_kor))\r\n ranges.append(np.min(new_pred_kor))\r\n ranges.append(np.max(new_pred_qed))\r\n ranges.append(np.min(new_pred_qed))\r\n return ranges","title":""},{"docid":"a2555825df7d8613501306d922f061b3","score":"0.58358234","text":"def getsequences(self, ):\n\t\tpass","title":""},{"docid":"cd47b9c8ea9f28fab3ab4ab11f3fd955","score":"0.55552644","text":"def get_sequences(count=0, length=0, different=False, file=None):\n if file is not None:\n return read_fasta_data(file)\n\n # only select as many sequences as possible\n count = min(count, 25)\n output = []\n i = 0\n\n # loop over all generated sequences and return as many sequences as specified or available within the specification\n while i < len(sequences) and len(output) < count:\n if not different or not sequences[i][:length] in output:\n output.append(sequences[i][:length])\n i += 1\n return output","title":""},{"docid":"9d33c590801cdecba4550b2bd2636a05","score":"0.55266863","text":"def sequences(self):\n pass","title":""},{"docid":"9b645f9ded8ca0aceef6efb3d41f7c1f","score":"0.55019766","text":"def paulis_gen_func(nr_seqs, cycles):\n def gen_random(cycles):\n s_gates = [\"X90 \", \"Y90 \", \"Z45 \"]\n lis = []\n for length in cycles:\n i = 0\n gates = []\n gates.append(s_gates[1] + \"qb_1\")\n sim_str = ' ' if 'Z' in s_gates[1][0:3] else 's '\n gates.append(s_gates[1][0:3] + sim_str + \"qb_2\")\n gates.append(s_gates[2] + \"qb_1\")\n sim_str = ' ' if 'Z' in s_gates[2][0:3] else 's '\n gates.append(s_gates[2][0:3] + sim_str + \"qb_2\")\n gates.append(\"CZ \" + \"qb_1 qb_2\")\n if length > 0:\n while i < (length - 1):\n last_1_gate1 = gates[-3][0:4]\n\n choice1 = []\n for gate in s_gates:\n choice1.append(gate)\n choice1.remove(last_1_gate1)\n gate1 = random.choice(choice1)\n gates.append(gate1 + 'qb_1')\n\n last_1_gate2 = gates[-3][0:3] + ' '\n choice2 = []\n for gate in s_gates:\n choice2.append(gate)\n choice2.remove(last_1_gate2)\n gate2 = random.choice(choice2)\n sim_str = ' ' if 'Z' in gate2[:3] else 's '\n gates.append(gate2[:3] + sim_str + 'qb_2')\n gates.append(\"CZ \" + 'qb_1 qb_2')\n i += 1\n lis.append(gates)\n return lis\n return [gen_random(cycles) for _ in range(nr_seqs)]","title":""},{"docid":"927d0d9a9b79b055f8f1dbd631eada5b","score":"0.541498","text":"def generate_sequence(self, config):\r\n\r\n frequency = config.get('Parameter #1')\r\n amplitude = config.get('Parameter #2')\r\n width = config.get('Parameter #3')\r\n plateau = config.get('Parameter #4')\r\n shapeID=config.get('Parameter #5') # (0) gaussian, (1) cosine\r\n waitTime=config.get('Parameter #10')\r\n self.add_gate_to_all(Gate.Xp)\r\n\r\n pulse12 = Pulse()\r\n pulse12.width = width\r\n pulse12.plateau = plateau\r\n pulse12.amplitude = amplitude\r\n pulse12.frequency = frequency\r\n if shapeID==0:\r\n pulse12.shape=PulseShape.GAUSSIAN\r\n if shapeID==1:\r\n pulse12.shape=PulseShape.COSINE\r\n pulse12.pulse_type=PulseType.XY\r\n pulse12.phase = 0\r\n gateP = CustomGate(pulse12)\r\n wait=Pulse()\r\n wait.shape=PulseShape.COSINE\r\n wait.width = 0\r\n wait.plateau = waitTime\r\n wait.amplitude = 0\r\n wait.frequency = 0\r\n gateWait=CustomGate(wait)\r\n self.add_gate_to_all(gateP)\r\n self.add_gate_to_all(gateWait)\r\n self.add_gate_to_all(gateP)\r\n\r\n\r\n # self.add_gate_to_all(Gate.Xp)\r","title":""},{"docid":"a59e9735c60720397a6070368c311f9e","score":"0.5407481","text":"def run(simulation,N_step,skip):\n return [tissue.copy() for tissue in itertools.islice(simulation,0,N_step,skip)]","title":""},{"docid":"e61e73b58566ea8cc94e81a5d33c951e","score":"0.54031354","text":"def t_sequencing_specialized(**kargs):\n\n n_parts = 10\n basedir = sys_config.read('DataExpRoot')\n basedir_sequencing = os.path.join(basedir, 'sequencing') #\n tRunRQ = False # run the query that attempts to find out the range of an attribute in the DB (e.g. person_id)\n\n # template files (condition and drug)\n # condition_occurrence-0.csv, drug_exposure-0.csv\n if tRunRQ: \n rangeQuery(**kargs) # use this to prepare intermediate dataframes\n\n # diag code only\n n_persons = n_persons_eff = 0\n for tstamp in (True, ): \n # for i in range(n_parts): \n for i in range(0, 10): # condition_occurrence-0.csv, drug_exposure-0.csv \n cohort_name = 'group-%s' % (i+1) # starting from 1\n ret = make_seq(include_timestamps=tstamp, # save_intermediate=True\n include_diag=True, include_med=False, \n meta='diag', \n inputdir=basedir_sequencing, \n condition_table='condition_occurrence-%s.csv' % i, # zero-based\n drug_table='drug_exposure-%s.csv' % i, \n cohort=cohort_name, \n save_csv=False, save_id=True, \n test_mode=False) # save .csv file and .id file\n min_id, max_id = ret['min_id'], ret['max_id'] \n n_persons += len(ret['person_id'])\n n_persons_eff += len(ret['person_id_eff'])\n div('status> finished %d sets of documements (n_persons=%d, n_persons_eff=%d, min_id=%s, max_id=%s)' % \\\n ((i+1), n_persons, n_persons_eff, min_id, max_id))\n\n print('info> n_persons=%d, n_persons_eff=%d' % (n_persons, n_persons_eff))\n return","title":""},{"docid":"31b86430b45dc2717c5a3df0b45c840f","score":"0.5401859","text":"def attractor_space_candidates(maxts,trmaxts):\n\n L = []\n for t in maxts+trmaxts: L.append([t])\n return L","title":""},{"docid":"f33e559e05f9eedadcfe72efe9c8a1e6","score":"0.5383528","text":"def __call__(self):\n ts_beg_i = 0\n for ii in range(len(self.periods)):\n periods = self.periods[ii : ii + self.tr_n + self.ts_n]\n\n # slide testing window forward until it would drop off end;\n # do NOT reset <ts_beg_i>; once it's at the rightmost part of\n # <periods>, it stays at the rightmost of all subsequent <periods>\n for jj in range(ts_beg_i, len(periods) - self.ts_n + 1):\n ts_periods = periods[jj:jj+self.ts_n]\n tr_periods = np.setdiff1d(periods, ts_periods)\n yield tr_periods, ts_periods\n ts_beg_i = jj","title":""},{"docid":"21a52d05f6c3161f86031cfb1734e286","score":"0.5368995","text":"def digest(self,seq):\n result = []\n seq = self.seq_formation(seq)\n for enzyme in self.selectedenzyme:\n pos = []\n if enzyme[1] == 1:\n pattern = re.compile(enzyme[2])\n m = pattern.search(seq) \n while(m):\n start = m.start()\n pos.append(str(start+int(enzyme[3])))\n m = pattern.search(seq,m.start()+1)\n else:\n re_l = enzyme[2].split(',')\n pos_l_t = enzyme[3].split(',')\n pos_l = []\n for i in pos_l_t:\n pos_l.append( int(i))\n for i in range(len(re_l)):\n pattern = re.compile(re_l[i])\n m = pattern.search(seq)\n while(m):\n start = m.start()\n pos.append(str(start+pos_l[i]))\n m = pattern.search(seq,m.start()+1)\n pos = list(set(pos)).sort()\n\n result.append(pos)\n\n return result","title":""},{"docid":"bf7de6923952f10b88f0cd6197526b37","score":"0.53623706","text":"def simulate(self):\n ch=[] #create localized chain and wait time to allow for multiple sims\n wT=[]\n qi=self.discSamp(self.stationaryFreq()) #pull a starting state from the stationary frequencies\n self.startStates.append(qi) \n #print qi\n ch.extend(qi) #add state to the markov chain\n while sum(wT) < self.chainLen:\n qiRow=self.q[self.stateSpace.index(qi)]\n l=self.qii(qi) #pull qii from row of state qi\n #print l\n wait=random.expovariate(l) #pull waiting time from exponendial distribution, lambda=l=|qii|\n #print wait\n if wait+sum(wT) < self.chainLen: #only add another time if it doesn't go over the total length\n wT.append(wait) #add to list\n #print wT\n conditionalProbs=[qij/l for qij in qiRow] #conditional probabilities\n for i in conditionalProbs:\n if i == -1:\n conditionalProbs[conditionalProbs.index(i)]=0\n qi=self.discSamp(conditionalProbs) #sample new state from qi row, based on condProbs,\n ch.extend(qi) # create new qii and add state to list \n #print ch\n elif wait+sum(wT) >= self.chainLen: #if adding another time will extend it past the branch length, just add a length that covers the rest of the time. \n last_time=self.chainLen-sum(wT)\n #print \n wT.append(last_time)\n #print \n conditionalProbs=[qij/l for qij in qiRow] #conditional probabilities\n for i in conditionalProbs:\n if i == -1:\n conditionalProbs[conditionalProbs.index(i)]=0\n last_qi=self.discSamp(conditionalProbs) \n ch.extend(last_qi)\n self.endStates.append(last_qi)\n #print ch \n self.waitTimes.append(wT)\n self.chains.append(ch)\n pair=[ch,wT]\n return ch,wT,pair","title":""},{"docid":"cc8159f612d028555960a6ee31425622","score":"0.53215766","text":"def get_TR(detector,sequences,number_sequences,show_time=False):\n list_TR = []\n start = time.time()\n \n for i in range(number_sequences):\n \n TR = sequences[i].detect(denovo = True, detection = {\"detectors\": [detector]})\n list_TR.append(TR)\n \n end = time.time()\n \n if show_time:\n \n print(end - start) \n \n return list_TR","title":""},{"docid":"500e8342d38a48b8a0ca196684bba464","score":"0.53051776","text":"def simulationDelayedTreatment(numTrials):\n\n numViruses = [100, 100, 100, 100]\n # numViruses = [100, 200, 300, 400]\n\n maxPop = [1000, 1000, 1000, 1000]\n # maxPop = [1000, 1500, 2000, 2500]\n\n maxBirthProb = [0.1, 0.1, 0.1, 0.1]\n # maxBirthProb = [0.1, 0.2, 0.3, 0.4]\n\n clearProb = [0.05, 0.05, 0.05, 0.05]\n # clearProb = [0.05, 0.10, 0.15, 0.20]\n\n time_before = [300, 150, 75, 0]\n # time_before = [150, 150, 150, 150]\n\n time_after = 150\n\n time_steps = [v + time_after for v in time_before]\n\n for condition in range(4):\n popul = [0 for i in range(time_steps[condition])]\n f_pop = []\n for trial in range(numTrials):\n viruses = [ResistantVirus(maxBirthProb = maxBirthProb[condition],\n clearProb = clearProb[condition],\n resistances = {'guttagonol': False},\n mutProb = 0.005)\n for i in range(numViruses[condition])]\n pat = TreatedPatient(viruses=viruses, maxPop=maxPop[condition])\n\n for t in range(time_before[condition]):\n popul[t] += pat.update()\n pat.addPrescription('guttagonol')\n\n t_after = [t + time_before[condition] for t in range(time_after)]\n for t in t_after:\n final_pop = pat.update()\n popul[t] += final_pop\n if t == time_steps[condition] - 1:\n f_pop.append(final_pop)\n\n pylab.figure('Histograms')\n pylab.subplot(2, 2, condition+1)\n pylab.title('Delayed Treatment: {}'.format(time_before[condition]))\n pylab.hist(f_pop, bins=50, label='{}'.format(time_before[condition]))\n pylab.xlabel('total virus population')\n pylab.ylabel('trials'.format(numTrials))\n pylab.legend(loc='best')\n\n pylab.figure('Graphs')\n means = [p/numTrials for p in popul]\n pylab.subplot(2, 2, condition+1)\n pylab.title('Delayed Treatment: {}'.format(time_before[condition]))\n pylab.plot(means, 'r-', label = 'Virus Pop')\n pylab.xlabel('Time Steps')\n pylab.ylabel('Virus Population')\n pylab.legend(loc='best')\n pylab.show()","title":""},{"docid":"c75b4a32dfea06545c2e330ef1474175","score":"0.5289533","text":"def generate_input_and_target(times_notes_dict, seq_len=50):\n \n # Get the start time and end time\n start_time, end_time = list(times_notes_dict.keys())[0], list(times_notes_dict.keys())[-1]\n \n # make the times_notes_dict an array for sliding window purpose\n notes_tuple_array = []\n for i in range(start_time, end_time+1):\n if i in times_notes_dict:\n notes_tuple_array.append(times_notes_dict[i])\n \n list_training, list_target = [], []\n \n for window_index in range(len(notes_tuple_array) - seq_len - 1):\n \n # initialize current traing list and target list\n list_append_training, list_append_target = [], []\n start_iterate = 0\n flag_target_append = False # flag to append the test list\n \n # pad 'e' in the front for the first \"seq_len\" sequences\n if window_index < seq_len - 1:\n start_iterate = seq_len - window_index - 1\n for i in range(start_iterate): \n list_append_training.append(('e',1))\n flag_target_append = True\n\n # append the following tuples to the current training list\n remain_tuples_num = seq_len - start_iterate\n for j in range(remain_tuples_num):\n next_tuple = notes_tuple_array[j]\n next_tuple_str = (','.join(str(x) for x in next_tuple[0]), next_tuple[1])\n list_append_training.append(next_tuple_str)\n target_tuple = notes_tuple_array[remain_tuples_num]\n \n elif window_index >= seq_len - 1:\n for j in range(window_index - seq_len + 1, window_index - seq_len + 11):\n next_tuple = notes_tuple_array[j]\n next_tuple_str = (','.join(str(x) for x in next_tuple[0]), next_tuple[1])\n list_append_training.append(next_tuple_str)\n target_tuple = notes_tuple_array[window_index - seq_len + 11]\n\n # add the next target tuple to the list_append_target\n target_tuple_str = (','.join(str(x) for x in target_tuple[0]), target_tuple[1])\n list_append_target.append(target_tuple_str)\n \n list_training.append(list_append_training)\n list_target.append(list_append_target)\n \n return list_training, list_target","title":""},{"docid":"463236a2d61bb401046134be0e703a92","score":"0.5265391","text":"def _generate_combinations(tId, vs, seq, usedVs, offset, isReverse):\n transOff = generate_peptides_from_variants.transOff\n #print \"TransOffset \", transOff, tId,usedVs\n if vs:\n v = vs.pop()\n if v.isHomozygous:\n pos = v.coding[tId].tranPos + offset\n usedVs[pos] = v\n offset = _incorp.get(v.type, lambda a, b, c, d, e, f: e)(seq, v, tId, pos, offset, isReverse)\n for s in _generate_combinations(tId, vs, seq, usedVs, offset,isReverse):\n yield s\n else:\n vs_tmp = vs[:]\n tmp_seq = seq[:]\n tmp_usedVs = usedVs.copy()\n\n #generate transcript without the current variant\n for s in _generate_combinations(tId, vs_tmp, tmp_seq, tmp_usedVs, offset, isReverse):\n yield s\n\n #and one transcript with current variant as we can't resolve haplotypes\n # update the transcript variant id\n generate_peptides_from_variants.transOff += 1\n pos = v.coding[tId].tranPos + offset\n usedVs[pos] = v\n offset = _incorp.get(v.type, lambda a, b, c, d, e, f: e)(seq, v, tId, pos, offset, isReverse)\n\n for s in _generate_combinations(tId, vs, seq, usedVs, offset, isReverse):\n yield s\n else:\n yield tId + \":FRED2_%i\"%transOff, seq, usedVs","title":""},{"docid":"b637de6617290a7d631ba528a0ee8744","score":"0.52637666","text":"def gen_delay_trial(lengthStimulus, delay, lengthSignal, pattern='random', exp_speed=40):\n\n stimulus = [[1, 0], [0, 1]]\n st1 = random.choice(stimulus)\n st2 = random.choice(stimulus)\n\n\n stim_lib = {'random': [st1, st2], 'AA': [stimulus[0], stimulus[0]], 'AB': [stimulus[0], stimulus[1]],\n 'BB': [stimulus[1], stimulus[1]], 'BA': [stimulus[1], stimulus[0]]}\n\n st1 = stim_lib[pattern][0]\n st2 = stim_lib[pattern][1]\n\n u1 = [st1[0] for k in range(lengthStimulus)] + [0 for k in range(delay)] + [st2[0] for k in range(lengthStimulus)] +\\\n [0 for k in range(lengthSignal - 2 * lengthStimulus - delay)]\n u2 = [st1[1] for k in range(lengthStimulus)] + [0 for k in range(delay)] + [st2[1] for k in range(lengthStimulus)] + \\\n [0 for k in range(lengthSignal - 2 * lengthStimulus - delay)]\n start_target = delay + lengthStimulus * 2\n if st1 == st2:\n target = [0 for k in range(lengthStimulus * 2 + delay)] + [-np.exp(-(n - start_target) / exp_speed) + 1 for n in\n range(start_target, lengthSignal)]\n\n else:\n target = [0 for k in range(lengthStimulus * 2 + delay)] + [np.exp(-(n - start_target) / exp_speed) - 1 for n in\n range(start_target, lengthSignal)]\n\n return u1, u2, target","title":""},{"docid":"e077496abe9980e4152c352494104fd5","score":"0.52543825","text":"def make_timeseries(self, change_times):\n self.timeseries = []\n i = 0\n time_len = len(self.times)\n for change_time in change_times:\n while i < time_len and change_time >= self.times[i]:\n i += 1\n self.timeseries.append(self.instructions[self.times[i-1]])","title":""},{"docid":"dfc78ede9406bc1a9c3410cceb7bd320","score":"0.52477765","text":"def getspectra(self, choices, spec_old):\r\n if self.v1.stream[0].stats.station == 'No data available':\r\n npts = self.v1.stream[0].stats.npts\r\n data = np.ones(npts)\r\n fspec1 = data[:]\r\n fspec2 = data[:]\r\n fspec3 = data[:]\r\n freqs = np.linspace(0.01, 20, npts) \r\n return fspec1, fspec2, fspec3, freqs\r\n \r\n idx = choices.index(spec_old)\r\n if idx == 0:\r\n if self.specs[idx] is None:\r\n tr1 = self.v2.stream[2]\r\n tr2 = self.v2.stream[5]\r\n tr3 = self.v2.stream[8]\r\n self.specs[idx] = self.ftransform(tr1, tr2, tr3) \r\n if idx == 1:\r\n if self.specs[idx] is None:\r\n tr1 = self.v2.stream[0]\r\n tr2 = self.v2.stream[3]\r\n tr3 = self.v2.stream[6]\r\n self.specs[idx] = self.ftransform(tr1, tr2, tr3) \r\n if idx == 2:\r\n if self.specs[idx] is None:\r\n tr1 = self.v1.stream[0]\r\n tr2 = self.v1.stream[1]\r\n tr3 = self.v1.stream[2]\r\n self.specs[idx] = self.ftransform(tr1, tr2, tr3) \r\n return self.specs[idx]","title":""},{"docid":"a8f2c8d5dcd22be617794b0e1b0b1b91","score":"0.52378535","text":"def generate_candidates(S, M, max_t=10, enforce=False):\n candslist=[]\n for m in M:\n wid = title2id(m[1])\n if wid is None:\n raise Exception(m[1].encode('utf-8') + ' not found')\n \n clist = anchor2concept(S[m[0]])\n clist = sorted(clist, key=lambda x: -x[1])\n\n smooth=0 \n trg = [(i,(c,f)) for i,(c,f) in enumerate(clist) if c==wid]\n if enforce and (not trg):\n trg=[(len(clist), (wid,0))]\n smooth=1\n\n \n clist = clist[:max_t]\n if enforce and (smooth==1 or trg[0][0]>=max_t): \n if clist:\n clist.pop()\n clist.append(trg[0][1])\n s = sum(c[1]+smooth for c in clist ) \n clist = [(c,float(f+smooth)/s) for c,f in clist ]\n \n candslist.append(clist)\n return candslist","title":""},{"docid":"58169fb6f7998dafaed7f0bf18a52410","score":"0.52106977","text":"def generate_ut_comb(A,B,H,Q,R,x0,timesteps,actions):\n act_num = len(actions)\n gxzts = []\n gx,zt = generate_fake(A,B,H,Q,R,x0,timesteps,actions[0])\n gxzts.append(gx)\n gxzts.append(zt)\n for i in range(1,act_num): \n gx_act,zt_act = generate_ut_not0(A,B,H,Q,R,timesteps,actions[i],gx,zt)\n gxzts.append(gx_act)\n gxzts.append(zt_act)\n return gxzts","title":""},{"docid":"70b33a79eaa8ca48ff5965a83da6a5ac","score":"0.52025497","text":"def simulationDelayedTreatment(numTrials, timeRange):\n\n allViruses = []\n for i in range(numTrials):\n viruses = []\n for i in range(100):\n viruses.append(ResistantVirus(0.1, 0.05, {'guttagonol': False, 'grimpex': False}, 0.005))\n patient1 = TreatedPatient(viruses, 1000)\n \n # run trial, administering drug at point t=timeRange\n for t in range(150 + timeRange + 150):\n if t == 150:\n patient1.addPrescription('guttagonol')\n if t == 150 + timeRange:\n patient1.addPrescription('grimpex')\n try:\n count = patient1.update()\n except AssertionError:\n pass\n allViruses.append(count)\n return allViruses","title":""},{"docid":"2e85da97b6e460b8061520c72c9e1a46","score":"0.5198447","text":"def next_takes(self):\n if not self.current_generator:\n return self.takes\n else:\n return []","title":""},{"docid":"2e85da97b6e460b8061520c72c9e1a46","score":"0.5198447","text":"def next_takes(self):\n if not self.current_generator:\n return self.takes\n else:\n return []","title":""},{"docid":"1baa03e5afee81ce66c7349765accec6","score":"0.51981646","text":"def test_pomodori_segment_generator(self):\n self.assertListEqual(\n list(islice(self.calculator.pomodori_segments(), 16)),\n [\n 'pomodoro',\n 'short-break',\n 'pomodoro',\n 'short-break',\n 'pomodoro',\n 'short-break',\n 'pomodoro',\n 'long-break',\n 'pomodoro',\n 'short-break',\n 'pomodoro',\n 'short-break',\n 'pomodoro',\n 'short-break',\n 'pomodoro',\n 'long-break',\n ]\n )","title":""},{"docid":"2e66e062414968060ce6fd7e420dbff1","score":"0.51796937","text":"def test_13(self):\n main = Main()\n for i in range(0, 30):\n main.generate_systems(n=10,\n min_m=10,\n max_n=100,\n max_m=300,\n step=10,\n save_results=True,\n save_systems=True,\n upper_bound=3,\n lower_bound=1,\n max_tries=30,\n update_strongly_k=True,\n gi=Gi())","title":""},{"docid":"dde0edcc9d043bc81d6e4be90d5bd825","score":"0.5179239","text":"def output_seq(self,seq):\n result_seq = []\n result_num = []\n output_seq = []\n output_num = []\n for i in range(0,len(seq)-10,10):\n seq_temp = ' '\n seq_temp += seq[i:i+10]\n result_seq.append(seq_temp)\n result_num.append(i+10)\n if len(seq) % 10 != 0:\n seq_temp = ' '\n seq_temp += seq[len(seq)/10 *10:len(seq)]\n result_seq.append(seq_temp)\n result_num.append(len(seq))\n \n for i in range(0,len(result_seq)-6,6):\n temp = []\n for j in range(i,i+6):\n temp.append(result_num[j])\n output_num.append(temp)\n temp = []\n for j in range(i,i+6):\n temp.append(result_seq[j])\n output_seq.append(temp)\n \n temp = []\n for j in range((len(result_seq)-1)/6 *6,len(result_seq)):\n temp.append(result_num[j])\n output_num.append(temp)\n temp = []\n for j in range((len(result_seq)-1)/6 *6,len(result_seq)):\n temp.append(result_seq[j])\n output_seq.append(temp)\n output = []\n for i in range(len(output_num)):\n output.append(output_num[i])\n output.append(output_seq[i])\n return output","title":""},{"docid":"77522fa6f45dfb9664fb6c40b7b5f205","score":"0.51779944","text":"def indist_configs(G,q):\n return [chi_from_tau(tau_config,G) for qp in range(q+1)\n for tau_config in itertools.combinations(range(G),qp)]\n #for degeneracy in range(int(fac(sum(tau_config))*choose(q,sum(tau_config))))]","title":""},{"docid":"c507583fccd004555c29b49deaca3f30","score":"0.5177002","text":"def _generate_combinations(tId, vs, seq, usedVs, offset, isReverse=False):\n transOff = generate_transcripts_from_variants.transOff\n #print \"TransOffset \", transOff, tId,usedVs\n if vs:\n v = vs.pop()\n if v.isHomozygous:\n pos = v.coding[tId].tranPos + offset\n usedVs[pos] = v\n offset = _incorp.get(v.type, lambda a, b, c, d, e, f: e)(seq, v, tId, pos, offset, isReverse)\n for s in _generate_combinations(tId, vs, seq, usedVs, offset, isReverse):\n yield s\n else:\n vs_tmp = vs[:]\n tmp_seq = seq[:]\n tmp_usedVs = usedVs.copy()\n\n for s in _generate_combinations(tId, vs_tmp, tmp_seq, tmp_usedVs, offset, isReverse):\n yield s\n\n # update the transcript variant id\n generate_transcripts_from_variants.transOff += 1\n pos = v.coding[tId].tranPos + offset\n usedVs[pos] = v\n\n offset = _incorp.get(v.type, lambda a, b, c, d, e, f: e)(seq, v, tId, pos, offset, isReverse)\n\n for s in _generate_combinations(tId, vs, seq, usedVs, offset, isReverse):\n yield s\n else:\n yield tId+\":FRED2_%i\"%transOff, seq, usedVs","title":""},{"docid":"26d4f1325147a4485c1e853f8ec150d9","score":"0.5172283","text":"def recur(seq, t1_cvrd, t2_cvrd, t1_evts, t2_evts, t1_name, t2_name):\n global seq_cnt\n is_t1_first = True\n\n if t1_cvrd == len(t1_evts):\n seq_cnt += 1\n final_seq = seq + t2_evts[t2_cvrd:]\n print str(seq_cnt) + \". \",\n print \"\".join([str(elem) for elem in final_seq])\n gen_plantuml_msc(seq_cnt, final_seq, t1_evts, t2_evts, t1_name, t2_name)\n elif t2_cvrd == len(t2_evts):\n seq_cnt += 1\n final_seq = seq + t1_evts[t1_cvrd:]\n print str(seq_cnt) + \". \",\n print \"\".join([str(elem) for elem in final_seq])\n gen_plantuml_msc(seq_cnt, final_seq, t1_evts, t2_evts, t1_name, t2_name)\n else:\n # print seq\n is_t1_first = (t1_cvrd <= t2_cvrd)\n\n if is_t1_first:\n recur(seq + [t1_evts[t1_cvrd]], t1_cvrd+1, t2_cvrd, t1_evts, t2_evts, t1_name, t2_name)\n recur(seq + [t2_evts[t2_cvrd]], t1_cvrd, t2_cvrd+1, t1_evts, t2_evts, t1_name, t2_name)\n else:\n recur(seq + [t2_evts[t2_cvrd]], t1_cvrd, t2_cvrd+1, t1_evts, t2_evts, t1_name, t2_name)\n recur(seq + [t1_evts[t1_cvrd]], t1_cvrd+1, t2_cvrd, t1_evts, t2_evts, t1_name, t2_name)","title":""},{"docid":"f65668daa670de3488546ae40b0d8f92","score":"0.5156483","text":"def make_sequence(config, ps_config, network=None, rng=None, test=False,\n enforce_aht_0=False, max_difference=96, refocus_every=60):\n cache_size = 1000\n \n @lru_cache(maxsize=cache_size)\n def get_frame(sequence):\n if len(sequence) == 0:\n return np.eye(3)\n else:\n return ps.rotations[sequence[-1]] @ get_frame(sequence[:-1])\n \n @lru_cache(maxsize=cache_size)\n def get_axis_counts(sequence):\n if len(sequence) == 0:\n return np.zeros((6,))\n else:\n counts = get_axis_counts(sequence[:-1]).copy()\n frame = get_frame(sequence)\n axis = np.where(frame[-1, :])[0][0]\n is_negative = np.sum(frame[-1, :]) < 0\n counts[axis + 3 * is_negative] += 1\n return counts\n \n @lru_cache(maxsize=cache_size)\n def get_propagators(sequence):\n if len(sequence) == 0:\n return ([qt.identity(ps_config.Utarget.dims[0])]\n * ps_config.ensemble_size)\n else:\n propagators = get_propagators(sequence[:-1])\n propagators = [prop.copy() for prop in propagators]\n for s in range(ps_config.ensemble_size):\n propagators[s] = (\n ps_config.pulses_ensemble[s][sequence[-1]]\n * propagators[s]\n )\n return propagators\n \n @lru_cache(maxsize=cache_size)\n def get_reward(sequence):\n propagators = get_propagators(sequence)\n fidelity = 0\n for s in range(ps_config.ensemble_size):\n fidelity += np.clip(\n qt.metrics.average_gate_fidelity(\n propagators[s],\n ps_config.Utarget\n ), 0, 1\n )\n fidelity *= 1 / ps_config.ensemble_size\n reward = -1 * np.log10(1 - fidelity + 1e-200)\n return reward\n \n @lru_cache(maxsize=cache_size)\n def get_valid_pulses(sequence):\n \"\"\"\n Args:\n sequence (tuple): Pulse sequence\n \"\"\"\n valid_pulses = []\n for pulse_index in range(len(ps_config.pulses_ensemble[0])):\n # count whether there are equal numbers of each pulse\n # (not including delays)\n # equivalent to chirality condition, for pulse rotation errors\n if len(sequence) > 0:\n delays_applied = (np.array(sequence) == 0).sum()\n pulse_total = (np.array(sequence) == pulse_index).sum()\n if pulse_total >= (ps_config.max_sequence_length\n - delays_applied) / 4:\n continue\n # AHT constraints\n # commented out the .copy() below, I don't think this should break\n # anything...\n counts = get_axis_counts(sequence + (pulse_index,)) # .copy()\n if enforce_aht_0:\n if not (counts <= ps_config.max_sequence_length / 6).all():\n continue\n # axis counts on ±x, ±y, ±z axes\n pm_counts = np.array([counts[0] + counts[3],\n counts[1] + counts[4],\n counts[2] + counts[5]])\n diff = np.max(pm_counts) - np.min(pm_counts)\n if diff > max_difference:\n continue\n max_count = (np.ceil((len(sequence) + 1) / refocus_every)\n * refocus_every)\n if (counts <= max_count / 6).all():\n valid_pulses.append(pulse_index)\n return valid_pulses\n \n @lru_cache(maxsize=cache_size)\n def get_inference(sequence):\n network.eval() # switch network to evaluation mode\n if len(sequence) == 0:\n state = one_hot_encode(sequence, start=True).unsqueeze(0)\n with torch.no_grad():\n (policy, val, h) = network(state)\n else:\n (_, _, h) = get_inference(sequence[:-1])\n state = one_hot_encode(sequence[-1:], start=False).unsqueeze(0)\n with torch.no_grad():\n (policy, val, h) = network(state, h_0=h)\n policy = policy.squeeze().numpy()\n val = val.squeeze().numpy()\n return policy, val, h\n \n sequence_funcs = (get_frame, get_reward, get_valid_pulses, get_inference)\n \n # create random number generator (ensure randomness with multiprocessing)\n if rng is None:\n rng = np.random.default_rng()\n search_statistics = []\n while not ps_config.is_done():\n pulse, root = run_mcts(config, ps_config, network=network, rng=rng,\n sequence_funcs=sequence_funcs, test=test)\n probabilities = np.zeros((5,))\n for p in root.children:\n probabilities[p] = root.children[p].visit_count / root.visit_count\n search_statistics.append(\n (ps_config.sequence.copy(),\n probabilities)\n )\n if pulse is not None:\n ps_config.apply(pulse)\n else:\n break\n if pulse is None:\n value = -1\n else:\n value = get_reward(tuple(ps_config.sequence))\n search_statistics = [\n stat + (value, ) for stat in search_statistics\n ]\n return search_statistics","title":""},{"docid":"a47715e26dd7d27d516a5b7fde492c00","score":"0.51522934","text":"def simulationTwoDrugsDelayedTreatment(numTrials):\n numViruses = [100, 100, 100, 100]\n maxPop = [1000, 1000, 1000, 1000]\n maxBirthProb = [0.1, 0.1, 0.1, 0.1]\n clearProb = [0.05, 0.05, 0.05, 0.05]\n time_inter = [300, 150, 75, 0]\n time_bafter = 150\n\n time_steps = [v + 2 * time_bafter for v in time_inter]\n\n for condition in range(4):\n popul = [0 for i in range(time_steps[condition])]\n f_pop = []\n for trial in range(numTrials):\n viruses = [ResistantVirus(maxBirthProb = maxBirthProb[condition],\n clearProb = clearProb[condition],\n resistances = {'guttagonol': False,\n 'grimpex': False},\n mutProb = 0.005)\n for i in range(numViruses[condition])]\n pat = TreatedPatient(viruses=viruses, maxPop=maxPop[condition])\n\n for t in range(time_bafter):\n popul[t] += pat.update()\n pat.addPrescription('guttagonol')\n\n t_after_1 = [t + time_bafter for t in range(time_inter[condition])]\n for t in t_after_1:\n popul[t] += pat.update()\n pat.addPrescription('grimpex')\n\n t_after_2 = [t + time_bafter + time_inter[condition] for t in range(time_bafter)]\n for t in t_after_2:\n final_pop = pat.update()\n popul[t] += final_pop\n if t == time_steps[condition] - 1:\n f_pop.append(final_pop)\n\n pylab.figure('Histograms')\n pylab.subplot(2, 2, condition+1)\n pylab.title('Delayed Treatment: {}'.format(time_inter[condition]))\n pylab.hist(f_pop, bins=50, label='{}'.format(time_inter[condition]))\n pylab.xlabel('total virus population')\n pylab.ylabel('trials'.format(numTrials))\n pylab.legend(loc='best')\n\n pylab.figure('Graphs')\n means = [p/numTrials for p in popul]\n pylab.subplot(2, 2, condition+1)\n pylab.title('Delayed Treatment: {}'.format(time_inter[condition]))\n pylab.plot(means, 'r-', label = 'Virus Pop')\n pylab.xlabel('Time Steps')\n pylab.ylabel('Virus Population')\n pylab.legend(loc='best')\n pylab.show()","title":""},{"docid":"25344870376423e91421b4808cc49798","score":"0.5151055","text":"def test_make_sequences(self):\n self.assertEqual(self.sequence.sequences[:3],['SSSSSSSSSSS', 'SSSSSSSSSD', 'SSSSSSSSDS'])","title":""},{"docid":"bdeb3f6595cda9f98bb8cc02a9c9c8da","score":"0.5137381","text":"def solve_part_2(pots, patterns, generations):\n offset = 0\n score = 0\n prev_score = 0\n prev_diff = 0\n consecutive = 0 #number of same diff occuring consecutively\n\n print(\"Gen\\tScore\\tDiff\")\n for gen in range(1, generations + 1):\n pots, adjust_offset = pass_generation(pots, patterns)\n offset += adjust_offset\n score = score_pots(pots, offset)\n diff = score - prev_score\n print(\"{}\\t{}\\t{}\".format(gen, score, diff))\n if diff == prev_diff:\n consecutive += 1\n else:\n consecutive = 0\n prev_score = score\n prev_diff = diff\n if consecutive > 5:\n break\n \n return calculate_score_at(gen, generations, score, diff)","title":""},{"docid":"6666d6cea7d526097b02b57d05722638","score":"0.51351064","text":"def times(self):\n ts = sorted(iter(self.spec_map))\n return ts","title":""},{"docid":"763196d71218feab3b62492dac2ad73d","score":"0.51313096","text":"def random_parameter_generation(ranges, is_exp, num_combinations):\r\n lists = list()\r\n for (i, (min_b, max_b)) in enumerate(ranges):\r\n sublist = list()\r\n for _ in range(num_combinations):\r\n rnd = np.random.uniform() * (max_b - min_b) + min_b\r\n if is_exp[i] is True:\r\n rnd = np.power(10., rnd)\r\n sublist.append(rnd)\r\n lists.append(sublist)\r\n return lists","title":""},{"docid":"b631ebf3674ccae5f3a69016649b3d06","score":"0.5127005","text":"def run_it(seq):\n\n measurements = []\n for measurement in seq.splitlines():\n measurements.append(sorted(map(int, measurement.split('x'))))\n\n paper = 0\n ribbon = 0\n for l, w, h in measurements:\n area = [l * w, w * h, h * l]\n paper += 2 * sum(area) + min(area)\n ribbon += 2 * (l + w) + (l * w * h)\n\n print('Part 1: ', paper)\n print('Part 2: ', ribbon)","title":""},{"docid":"5c12039cf13c0f1fcd57a59316b94bd9","score":"0.5117616","text":"def simulationTwoDrugsDelayedTreatment(numTrials):\n # TODO","title":""},{"docid":"ca1d1067b1ba2747558abc921ab5288c","score":"0.51163834","text":"def sample_sequences(CAG: DiGraph, s0: Series, steps: int, samples: int,\n Δt: float = 1.0) -> List[Series]:\n\n s0 = s0.values[np.newaxis].T\n return take(samples, repeatfunc(sample_sequence, CAG, s0, steps, Δt))","title":""},{"docid":"eb0ecf31eac8e9aa4de411fcb4850b3a","score":"0.5110198","text":"def generateSequence(self, T, numberSamples=1):\n (objectives,measurements,_)=samplePFSequence(self._pf,T,numberSamples=numberSamples)\n \n return (objectives,measurements)","title":""},{"docid":"3e7ae9d65d82fb9560205f6699b579cc","score":"0.5108246","text":"def _generate_Sequence(self, min, max, step):\n i = min\n I = []\n while i <= max:\n I.append(i)\n i = i + step\n return I","title":""},{"docid":"0448b9d148eeb1c1fc721c942002a938","score":"0.5106794","text":"def test_seq_permuts():\n existing_handles = \"\"\"\n GGGCTCATGCGAGGCTGTATGT\n TTCCTCTACCACCTACATCAC\n GGTCGTGTTCGATCAGAGCGC\n GTTGAGTCCTGTCAC\n GTTGCATCCTCCAAC\n GTGCAGACAAC\n CGGGAACCGCGTATCTTGCCA\n CGGTTCATAACGGAACCGCCG\n GATTCGGGAAGCAAACCCGCG\n CCAGTACGCGGCGTAGTACCC\n CGGAATACTTGAATCGGGTTC\n GCCAGCTCAGCC\n \"\"\"\n # existing_handles=existing_handles.split()\n # permuts = seq_permuts(existing_handles[0])\n permuts = seq_permuts(existing_handles, permlen=5, copytoclipboard=True, includerevcompl=True)\n print(existing_handles)\n print(\",\".join(permuts))\n\n permuts4 = seq_permuts(existing_handles, permlen=4, includerevcompl=True)\n\n permuts4 = [seq for seq in permuts4 if sum([x.upper() in \"GC\" for x in seq]) > 2]\n\n print(\",\".join(permuts4))","title":""},{"docid":"a717c2a67e254a731ba794f0e7356cb7","score":"0.510483","text":"def genConceptChain(concept_desc, sequential):\n concept_chain = []\n num_samples = 0\n more_appearences = True\n appearence = 0\n while more_appearences:\n concepts_still_to_appear = []\n for cID in concept_desc:\n concept = concept_desc[cID]\n if concept.appearences > appearence:\n concepts_still_to_appear.append(concept)\n more_appearences = len(concepts_still_to_appear) > 0\n for concept in concepts_still_to_appear:\n concept_chain.append(concept.id)\n num_samples += concept.examples_per_appearence\n appearence += 1\n # for cID in concept_desc:\n # concept = concept_desc[cID]\n # concept_chain += [concept.id] * (concept.appearences)\n # num_samples += (concept.appearences * concept.examples_per_appearence)\n if not sequential:\n random.shuffle(concept_chain)\n return concept_chain, num_samples","title":""},{"docid":"b46bee8bb9d409084b01d98824f855a6","score":"0.5098435","text":"def repeat_sequence_input(num_trials):\n\n half = [np.sin(2 * np.pi * t / 200) for t in range(100)]\n whole = [np.sin(2 * np.pi * t / 400) for t in range(200)]\n pause = [0 for k in range(100)]\n response_time = np.zeros([4, 1400])\n signal = np.empty([4, 2800 * num_trials])\n target = np.empty([4, 2800 * num_trials])\n for tr in range(num_trials):\n signal_tr = np.empty([4, 1400])\n E = np.concatenate((half, half, whole, pause, half, half, whole, pause, half, pause, pause, pause))\n G = np.concatenate(\n (pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, half, pause, pause))\n D = np.concatenate(\n (pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, half))\n C = np.concatenate(\n (pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, half, pause))\n signal_tr[0, :] = G\n signal_tr[1, :] = E\n signal_tr[2, :] = D\n signal_tr[3, :] = C\n target_tr = np.concatenate((response_time, signal_tr), axis=1)\n signal_tr = np.concatenate((signal_tr, response_time), axis=1)\n signal[:, tr * 2800:(tr + 1) * 2800] = signal_tr\n target[:, tr * 2800:(tr + 1) * 2800] = target_tr\n\n return np.array(signal), np.array(target)","title":""},{"docid":"8b095ef625c291f4c9044e8e628582f4","score":"0.5094453","text":"def generate_candidates(S, M, max_t=20, enforce=False):\n candslist=[]\n for m in M:\n \n clist = anchor2concept(S[m[0]])\n if not clist:\n clist=((0L,1L),)\n \n clist = sorted(clist, key=lambda x: -x[1])\n clist = clist[:max_t]\n \n smooth=0 \n if enforce: \n wid = title2id(m[1]) \n # if wid is None:\n # raise Exception(m[1].encode('utf-8') + ' not found')\n \n \n trg = [(i,(c,f)) for i,(c,f) in enumerate(clist) if c==wid]\n if not trg:\n trg=[(len(clist), (wid,0))]\n smooth=1\n\n \n if smooth==1 or trg[0][0]>=max_t: \n if clist:\n clist.pop()\n clist.append(trg[0][1])\n \n s = sum(c[1]+smooth for c in clist ) \n clist = [(c,float(f+smooth)/s) for c,f in clist ]\n \n candslist.append(clist)\n return candslist","title":""},{"docid":"924cdb54cd9aada313e4f2dafe759608","score":"0.50824124","text":"def generate_system_from_periods(rng: np.random.Generator, n: int,\n min_period: int, max_period: int, sum_util,\n deadline_type: str, sum_dens, max_jitter: int,\n abs_tol: float):\n ones = [1.0] * n\n while True:\n periods = np.exp(\n rng.uniform(low=np.log(min_period),\n high=np.log(max_period),\n size=n)).tolist()\n periods = [math.ceil(p) for p in periods]\n us = drs(rng, n, sum_util)\n wcets = [max(1, math.floor(p * u)) for (p, u) in zip(periods, us)]\n us = [w / p for (w, p) in zip(wcets, periods)]\n if not math.isclose(sum_util, sum(us), abs_tol=abs_tol):\n continue\n if deadline_type == 'implicit':\n deadlines = periods\n elif deadline_type == 'constrained':\n if sum(us) < sum_dens:\n # assume implicit deadlines because total utilization must be\n # less than total density for a constrained-deadline system.\n deadlines = periods\n else:\n ds = drs(rng, n, sum_dens, lower_bounds=us, upper_bounds=ones)\n deadlines = [\n math.floor(wcet / d) for d, wcet in zip(ds, wcets)\n ]\n assert all(d <= p for d, p in zip(deadlines, periods))\n else:\n ds = drs(rng, n, sum_dens, upper_bounds=ones)\n deadlines = [math.floor(wcet / d) for d, wcet in zip(ds, wcets)]\n jitters = rng.integers(low=0,\n high=[\n min(max_jitter, d - w) + 1\n for w, d in zip(wcets, deadlines)\n ],\n size=n)\n return [\n Task(wcet=w, period=p, deadline=d, jitter=j.item())\n for w, p, d, j in zip(wcets, periods, deadlines, jitters)\n ]","title":""},{"docid":"05d03bdd3f3b03f9bfe440a7f3ea7e0b","score":"0.50739104","text":"def genmotionsequencewaistarm(robot, robotplot, handpkg, start, goal, obstacleslist = []):\n\n # the waist range is always\n jointlimits = [[-90.0, 90.0]]\n for i in robot.targetjoints:\n jointlimits.append([robot.rgtarm[i]['rngmin'], robot.rgtarm[i]['rngmax']])\n planner = rrt.RRT(start=start, goal=goal, iscollidedfunc = iscollidedfunc,\n jointlimits = jointlimits, expanddis = 20, robot = robot,\n robotplot = robotplot, handpkg = handpkg)\n import time\n tic = time.clock()\n [path, sampledpoints] = planner.planning()\n toc = time.clock()\n print toc-tic\n return [path, sampledpoints]","title":""},{"docid":"02f72c18d370c3f486a9412f79b64880","score":"0.50656134","text":"def generate_examples(params) -> Tuple[list, list, dict]:\n\n interrupted = False\n def signal_handler(signal, frame):\n nonlocal interrupted\n print(f\"Received signal {signal:d}, interrupting generation\")\n interrupted = True\n signal.signal(signal.SIGINT, signal_handler)\n\n if params.num_variables > 26:\n raise ValueError(\"Cannot generate more than 26 APs\")\n variables = list(map(chr, range(97, 97 + params.num_variables)))\n if not isinstance(params.tree_size, tuple):\n params.tree_size = (1, params.tree_size)\n\n class SpecPatternGenWrapper:\n def __next__(self):\n d = SpecPatternGen(variables, params.tree_size)\n return d.run()\n formula_generator = SpecPatternGenWrapper()\n\n dist_gate = DistributionGate('formula size', params.formula_dist, params.tree_size, params.num_examples, start_calc_from=10, alpha=params.alpha)\n\n # generate samples\n print('Generating examples...')\n examples = []\n timeout_formulas = []\n sat_examples = 0\n unsat_examples = 0\n total_examples = 0\n dropped_sat_examples = 0\n dropped_unsat_examples = 0\n dropped_dist_examples = 0\n dropped_timeout_examples = 0\n time_started = datetime.datetime.now()\n last_msg_time = time_started\n last_msg_percent = 0\n accu = { k : 0 for k in {'in_length', 'out_length', 'solve_time'}}\n info = {'max_in_length' : 0, 'max_out_length' : 0}\n while True:\n current_percent = total_examples / params.num_examples * 100\n now = datetime.datetime.now()\n if current_percent - last_msg_percent >= params.log_each_x_percent or now - last_msg_time > datetime.timedelta(hours=1):\n last_msg_percent = current_percent\n last_msg_time = now\n print(\"Generated {:,d} of {:,d} examples ({:4.1f}%); dropped {:,d} sat, {:,d} unsat, {:,d} dist, {:,d} timeout; at {:s} runtime\".format(total_examples, \n params.num_examples, current_percent, dropped_sat_examples, dropped_unsat_examples, dropped_dist_examples, dropped_timeout_examples, utils.strfdelta_hms(now - time_started)))\n sys.stdout.flush()\n if total_examples >= params.num_examples or dist_gate.full() or interrupted:\n break\n if params.max_runtime != 0.0 and (now - time_started).total_seconds() > params.max_runtime:\n print('Exiting due to exceeded runtime')\n break\n\n formula = next(formula_generator)\n if not isinstance(formula, ltl_parser.LTLFormula):\n if not isinstance(formula, str):\n if formula is None:\n continue\n formula = formula.to_str()\n formula_obj = ltl_parser.ltl_formula(formula, 'spot')\n else:\n formula_obj = formula\n\n if not dist_gate.gate(formula_obj): # formula doesn't fit distribution\n dropped_dist_examples += 1\n continue\n if formula == '1':\n continue\n\n is_sat, witness, d = solve_ltl(formula_obj, timeout=params.timeout, binary_path=params.binary_dir)\n\n if is_sat is None: # due to timeout\n if params.save_timeouts:\n timeout_formulas.append(formula_obj.to_str('spot', spacing='all ops', full_parens=True))\n dropped_timeout_examples += 1\n continue\n elif not is_sat: # unsat\n if (params.frac_unsat is not None) and unsat_examples >= params.frac_unsat * params.num_examples:\n dropped_unsat_examples += 1\n continue\n else: # more unsat samples needed\n witness_str = '0'\n dist_gate.update(formula_obj)\n unsat_examples += 1\n else: # is_sat\n if (params.frac_unsat is not None) and sat_examples >= (1 - params.frac_unsat) * params.num_examples:\n dropped_sat_examples += 1\n continue\n elif random.random() < params.drop_sat_prob:\n # don't log\n continue\n else: # more sat samples needed\n witness_str = '1'\n out_length = len(witness_str)\n info['max_out_length'] = max(info['max_out_length'], out_length)\n accu['out_length'] += out_length\n dist_gate.update(formula_obj)\n sat_examples += 1\n\n for k, v in d.items(): # update info accumulator with this samples' info\n if k in accu:\n accu[k] += v\n in_length = formula_obj.size()\n info['max_in_length'] = max(info['max_in_length'], in_length)\n accu['in_length'] += in_length\n formula_str = formula_obj.to_str('network-polish')\n examples.append((formula_str, witness_str, d))\n total_examples += 1\n\n if params.include_solve_time:\n info['avg_solve_time'] = accu['solve_time'] / total_examples\n print('Average solve time: {avg_solve_time:.2f} ms'.format(**info))\n info['avg_in_length'] = accu['in_length'] / total_examples\n info['avg_out_length'] = accu['out_length'] / sat_examples\n info['runtime'] = utils.strfdelta_hms(datetime.datetime.now() - time_started)\n info['_dist_gate'] = dist_gate\n info['examples_generated'] = total_examples\n info['examples_generated_sat'] = sat_examples\n info['examples_generated_unsat'] = unsat_examples\n print('Average formula length {avg_in_length:.1f} and witness length {avg_out_length:.1f}'.format(**info))\n print('Generated {:d} examples ({:d} sat, {:d} unsat). {:d} requested.'.format(total_examples, sat_examples, unsat_examples, params.num_examples))\n return examples, timeout_formulas, info","title":""},{"docid":"9ae60e4847e1310309ac846fdf499903","score":"0.5060221","text":"def simule_sequence(length, props):\n liste = [np.full((int(round(length * p))), k) for k, p in enumerate(props)]\n flattened = np.hstack(liste)\n return np.random.permutation(flattened)","title":""},{"docid":"06065833693d318e1709d3d833f67036","score":"0.5058347","text":"def generate_haps(self, pheno_seq):\n \n allele_list = self.generate_allele_list(pheno_seq)\n hap_list = combinations(allele_list)\n return hap_list","title":""},{"docid":"340697720c0220e2d1fe4fc49bf89269","score":"0.5055516","text":"def gen_tus(self):\n\n # validate these options in the options method\n options = self.options\n # 7 bp default (E. coli, wikipedia)\n five_prime_len = options.get('five_prime_len')\n three_prime_len = options.get('three_prime_len') # 5 bp default guess\n operon_prop = options.get('operon_prop') # 0.2 default guess\n # 3 genes default (https://academic.oup.com/gbe/article/5/11/2242/653613)\n operon_gen_num = options.get('operon_gen_num')\n\n for i_chr, chromosome in enumerate(self.knowledge_base.cell.species_types.get(__type=wc_kb.core.DnaSpeciesType)):\n seq = chromosome.get_seq()\n i_gene = 0\n transcription_loci = []\n # print(chromosome.loci)\n # print(len(chromosome.loci))\n # Todo make this into a proper for loop that deals with repeats/additional loci\n while i_gene < len(chromosome.loci):\n\n gene = chromosome.loci[i_gene]\n\n if gene.type == wc_kb.core.GeneType.mRna:\n # polycistronic mRNA (multiple GeneLocus objects per TranscriptionUnitLocus)\n # print(\"mrna\")\n\n five_prime = self.rand(five_prime_len)[0]\n three_prime = self.rand(three_prime_len)[0]\n operon_prob = random.random()\n\n # make an operon (polycistronic mRNA, put multiple genes in one TransUnitLocus)\n if operon_prob <= operon_prop:\n # print(\"ploycistronic\")\n operon_genes = self.rand(operon_gen_num)[0]\n while operon_genes <= 1:\n operon_genes = self.rand(operon_gen_num)[0]\n\n # add 3', 5' UTRs to the ends of the transcription unit (upstream of first gene, downstream of last gene)\n tu = self.knowledge_base.cell.loci.get_or_create(\n id='tu_{}_{}'.format(i_chr + 1, i_gene + 1), __type=wc_kb.prokaryote.TranscriptionUnitLocus)\n tu.name = 'tu {} {}'.format(i_chr+1, i_gene+1)\n\n five_prime_start = gene.start - five_prime\n if five_prime_start < 0:\n five_prime_start = 0\n tu.genes.append(gene)\n tu.start = five_prime_start\n\n # print(chromosome.loci)\n # print(operon_genes)\n for k in range(operon_genes-1):\n\n i_gene += 1\n\n if i_gene >= len(chromosome.loci):\n break\n\n if (chromosome.loci[i_gene]).type == wc_kb.core.GeneType.mRna:\n gene = chromosome.loci[i_gene]\n tu.genes.append(gene)\n\n else:\n break\n\n three_prime_end = gene.end + three_prime\n if three_prime_end >= len(seq):\n three_prime_end = len(seq) - 1\n tu.end = three_prime_end\n transcription_loci.append(tu)\n\n else: # make an individual transcription unit for the gene\n # print(\"single\")\n five_prime_start = gene.start - five_prime\n three_prime_end = gene.end + three_prime\n if five_prime_start < 0:\n five_prime_start = 0\n if three_prime_end >= len(seq):\n three_prime_end = len(seq) - 1\n tu = self.knowledge_base.cell.loci.get_or_create(\n id='tu_{}_{}'.format(i_chr + 1, i_gene + 1), __type=wc_kb.prokaryote.TranscriptionUnitLocus)\n tu.start = five_prime_start\n tu.end = three_prime_end\n tu.name = 'tu {} {}'.format(i_chr+1, i_gene+1)\n tu.genes.append(gene)\n transcription_loci.append(tu)\n\n # make a transcription unit that transcribes other types of RNA (tRNA, rRNA, sRNA)\n else:\n # print(\"not mrna\")\n tu = self.knowledge_base.cell.loci.get_or_create(\n id='tu_{}_{}'.format(i_chr + 1, i_gene + 1), __type=wc_kb.prokaryote.TranscriptionUnitLocus)\n tu.name = 'tu {} {}'.format(i_chr+1, i_gene+1)\n tu.start = gene.start\n tu.end = gene.end\n tu.genes.append(gene)\n transcription_loci.append(tu)\n\n i_gene += 1\n for locus in transcription_loci:\n locus.polymer = chromosome","title":""},{"docid":"a8531f85c247f64c62df3b567ae7ac86","score":"0.50372565","text":"def get_all_TR(detector,sequences,show_time=False):\n \n list_TR = []\n start = time.time()\n \n for i in range(len(sequences)):\n \n TR = sequences[i].detect(denovo = True, detection = {\"detectors\": [detector]})\n list_TR.append(TR)\n \n end = time.time()\n \n if show_time:\n \n print(end - start) \n \n return list_TR","title":""},{"docid":"31acdfe950912194abc4d4f40ebf0110","score":"0.50330013","text":"def print_actual_sequence_info(participant_config):\n num_stimuli = participant_config['num_stimuli']\n\n # Hold the counts of the number of trials\n target_trials = np.zeros(num_stimuli)\n nontarget_trials = np.zeros(num_stimuli)\n\n for sequence in participant_config['sequences']:\n for block_index in range(participant_config['blocks_per_sequence']):\n # What stimulus is the current target\n target_index = sequence['targets'][block_index]\n for trial in sequence['blocks'][block_index]:\n if trial == target_index:\n target_trials[trial] += 1\n else:\n nontarget_trials[trial] += 1\n\n print('non-target/left: {}'.format(nontarget_trials[0]))\n print('non-target/middle: {}'.format(nontarget_trials[1]))\n print('non-target/right: {}'.format(nontarget_trials[2]))\n print('target/left: {}'.format(target_trials[0]))\n print('target/middle: {}'.format(target_trials[1]))\n print('target/right: {}'.format(target_trials[2]))","title":""},{"docid":"1f596faceb0f9e3e868624af0fe22655","score":"0.5031925","text":"def generate_chunking_trial(self, num_pulses, analysis, num_fixed):\n\n # rule signal can appear at the end of delay1_time\n trial_length = par['num_time_steps']\n\n # end of trial epochs\n eodead = par['dead_time']//par['dt']\n eof = (par['dead_time']+par['fix_time'])//par['dt'] \n eos = [(par['dead_time']+par['fix_time']+ n*par['delay_time'] + (n+1)*par['sample_time'])//par['dt'] for n in range(num_pulses)]\n eods = [(par['dead_time']+par['fix_time']+(n+1)*(par['sample_time']+par['delay_time']))//par['dt'] for n in range(num_pulses-1)]\n eods.append(eos[-1])\n eolongd = (par['dead_time']+par['fix_time'] + num_pulses * par['sample_time'] + (num_pulses-1)*par['delay_time'] + par['long_delay_time'])//par['dt']\n eor = [(par['dead_time']+par['fix_time'] + num_pulses * par['sample_time'] + (num_pulses-1)*par['delay_time'] + par['long_delay_time'] + \\\n n*par['delay_time'] + (n+1)*par['resp_cue_time'])//par['dt'] for n in range(num_pulses)]\n eodr = [(par['dead_time']+par['fix_time'] + num_pulses * par['sample_time'] + (num_pulses-1)*par['delay_time'] + par['long_delay_time'] + \\\n (n+1)*(par['resp_cue_time']+par['delay_time']))//par['dt'] for n in range(num_pulses-1)]\n eodr.append(eor[-1])\n # end of neuron indices\n emt = par['num_motion_tuned']\n eft = par['num_fix_tuned']+par['num_motion_tuned']\n ert = par['num_fix_tuned']+par['num_motion_tuned'] + par['num_resp_cue_tuned']\n if par['order_cue']:\n eot = par['num_fix_tuned']+par['num_motion_tuned'] + par['num_resp_cue_tuned'] + par['num_order_cue_tuned']\n\n trial_info = {'desired_output' : np.zeros((par['n_output'], trial_length, par['batch_train_size']),dtype=np.float32),\n 'train_mask' : np.ones((trial_length, par['batch_train_size']),dtype=np.float32),\n 'rule' : np.zeros((par['batch_train_size']),dtype=np.int8),\n 'sample' : np.zeros((par['batch_train_size'], par['num_pulses']),dtype=np.int8),\n 'neural_input' : np.random.normal(par['input_mean'], par['noise_in'], size=(par['n_input'], trial_length, par['batch_train_size'])),\n 'timeline' : [eodead, eof]}\n\n trial_info['timeline'].append(eos[0])\n for i in range(1,num_pulses):\n trial_info['timeline'].append(eods[i-1])\n trial_info['timeline'].append(eos[i])\n trial_info['timeline'].append(eolongd)\n trial_info['timeline'].append(eor[0])\n for i in range(1,num_pulses):\n trial_info['timeline'].append(eodr[i-1])\n trial_info['timeline'].append(eor[i])\n\n # set to mask equal to zero during the dead time\n trial_info['train_mask'][:eodead, :] = 0\n trial_info['train_mask'][eolongd:eolongd+par['mask_duration']//par['dt'], :] = 0\n for i in range(1, par['num_pulses']):\n trial_info['train_mask'][eodr[i-1]:eodr[i-1]+par['mask_duration']//par['dt'], :] = 0\n # If the DMS and DMS rotate are being performed together,\n # or if I need to make the test more challenging, this will eliminate easry test directions\n # If so, reduce set of test stimuli so that a single strategy can't be used\n #limit_test_directions = par['trial_type']=='DMS+DMRS'\n\n for t in range(par['batch_train_size']):\n\n \"\"\"\n Generate trial paramaters\n \"\"\"\n if not analysis:\n sample_dirs = [np.random.randint(par['num_motion_dirs']) for i in range(num_pulses)]\n else:\n sample_dirs = [0]*num_fixed + [np.random.randint(par['num_motion_dirs']) for i in range(num_pulses-num_fixed)]\n\n rule = np.random.randint(par['num_rules'])\n\n \"\"\"\n Calculate neural input based on sample, tests, fixation, rule, and probe\n \"\"\"\n # SAMPLE stimulus\n trial_info['neural_input'][:emt, eof:eos[0], t] += np.reshape(self.motion_tuning[:,sample_dirs[0]],(-1,1))\n for i in range(1,num_pulses):\n trial_info['neural_input'][:emt, eods[i-1]:eos[i], t] += np.reshape(self.motion_tuning[:,sample_dirs[i]],(-1,1))\n\n # FIXATION cue\n if par['num_fix_tuned'] > 0:\n trial_info['neural_input'][emt:eft, eodead:eolongd, t] += np.reshape(self.fix_tuning[:,0],(-1,1))\n for i in range(num_pulses):\n trial_info['neural_input'][emt:eft, eor[i]:eodr[i], t] += np.reshape(self.fix_tuning[:,0],(-1,1))\n\n # RESPONSE CUE\n trial_info['neural_input'][eft:ert, eolongd:eor[0], t] += np.reshape(self.response_tuning[:,0],(-1,1))\n for i in range(1, num_pulses):\n trial_info['neural_input'][eft:ert, eodr[i-1]:eor[i], t] += np.reshape(self.response_tuning[:,0],(-1,1))\n\n # ORDER CUE\n if par['order_cue']:\n trial_info['neural_input'][ert, eolongd:eor[0], t] += par['tuning_height']\n trial_info['neural_input'][ert, eof:eos[0], t] += par['tuning_height']\n for i in range(1,par['num_pulses']):\n trial_info['neural_input'][ert+i, eodr[i-1]:eor[i], t] += par['tuning_height']\n trial_info['neural_input'][ert+i, eods[i-1]:eos[i], t] += par['tuning_height']\n\n \"\"\"\n Determine the desired network output response\n \"\"\"\n trial_info['desired_output'][0, eodead:eolongd, t] = 1\n for i in range(num_pulses):\n trial_info['desired_output'][0, eor[i]:eodr[i], t] = 1\n\n trial_info['desired_output'][sample_dirs[0]+1, eolongd:eor[0], t] = 1\n for i in range(1, num_pulses):\n trial_info['desired_output'][sample_dirs[i]+1, eodr[i-1]:eor[i], t] = 1\n\n \"\"\"\n Append trial info\n \"\"\"\n trial_info['sample'][t,:] = sample_dirs\n trial_info['rule'][t] = rule\n\n return trial_info","title":""},{"docid":"723bc585f611f44c87816378c199bb62","score":"0.502416","text":"def _get_stim_times(self):\n raise NotImplementedError","title":""},{"docid":"20ba50ff67af534364f4044beedb54db","score":"0.50218195","text":"def genmotionsequencearm(robot, robotplot, handpkg, start, goal, obstacleslist = []):\n\n jointlimits = []\n for i in robot.targetjoints:\n jointlimits.append([robot.rgtarm[i]['rngmin'], robot.rgtarm[i]['rngmax']])\n planner = rrt.RRT(start=start, goal=goal, iscollidedfunc = iscollidedfunc,\n jointlimits = jointlimits, expanddis = 20, robot = robot,\n robotplot = robotplot, handpkg = handpkg)\n import time\n tic = time.clock()\n [path, sampledpoints] = planner.planning()\n toc = time.clock()\n print toc-tic\n return [path, sampledpoints]","title":""},{"docid":"8907c109c5b1ef04e5c652f62bb6f84a","score":"0.5020906","text":"def simule_sequence_markov(length, prop, sequence):\n tmatrix = transition_matrix(sequence)\n seq = []\n proba = prop\n for _ in range(length):\n nb = np.random.choice(nucleobases, p=proba)\n proba = tmatrix[nb]\n seq.append(nb)\n return seq","title":""},{"docid":"bd48b9abfc82fad455fe7984c3af8568","score":"0.5018895","text":"def day21(inp,part1=True):\n s2s = [] # size 2s\n s3s = [] # size 3s\n for line in inp.rstrip().split('\\n'):\n patfrom,patto = line.split(' => ')\n s = np.sqrt(len(patfrom.replace('/',''))).round().astype(int)\n stmp = s2s if s == 2 else s3s\n stmp.append((patfrom,patto))\n \n n2,n3 = len(s2s),len(s3s)\n # construct transformed pattern arrays: (n2,8,2,2) and (n3,8,3,3) complete with rotations\n # construct single output arrays: (n2,3,3) and (n3,4,4)\n for i,inlst in enumerate([s2s,s3s]):\n templst = []\n outlst = []\n for patfrom,patto in inlst:\n templst.append(genpatt(parseit(patfrom)))\n outlst.append(parseit(patto))\n temparr = np.array(templst)\n outarr = np.array(outlst)\n if i == 0:\n templates2 = temparr\n outs2 = outarr\n else:\n templates3 = temparr\n outs3 = outarr\n \n state = parseit('.#./..#/###')\n numsteps = 5 if part1 else 18\n for _ in range(numsteps):\n if state.shape[0]%2 == 0:\n n = 2 # size of chunks\n templates = templates2\n outs = outs2\n else:\n n = 3\n templates = templates3\n outs = outs3\n m = state.shape[0] // n # the number of chunks along a dimension\n state = state.reshape(m,n,m,n).transpose(0,2,1,3).reshape(-1,n,n) # (m*m,n,n)-shaped board\n\n # find a match for each chunk: broadcast (m*m,1,1,n,n) vs (l,8,n,n)\n inds = (state[:,None,None,...] == templates).reshape(m**2,-1,8,n**2).all(-1).any(-1).nonzero() # nonzeros of shape (m*m,l), and for each m1m2 there should be exactly one l\n \n # construct output state, shape (m*(n+1),m*(n+1))\n state = outs[inds[-1],...].reshape(m,m,n+1,n+1).transpose(0,2,1,3).reshape(m*(n+1),m*(n+1))\n\n return state.sum()","title":""},{"docid":"e1554645727a6deec863c88964466e95","score":"0.5011649","text":"def metropolis_par(ediff_fun, tn, nsample, ss0, is_traj=False):\n\n if np.array(ss0).ndim == 1:\n ss0 = np.array([ss0])\n N = ss0.shape[1]\n L = int(np.sqrt(N))\n nchains = ss0.shape[0]\n\n ss_traj = []\n if is_traj:\n ss_traj = np.zeros((int(np.ceil(tn / nsample)) * nchains, N))\n\n accept_count = 0\n sample_step = 0\n ss = np.copy(ss0)\n\n random_spin_flip = np.random.randint(N, size=(tn, nchains))\n\n # progress = pb.ProgressBar(max_value=tn, widgets=wgt).start()\n for tstep, ssidx in enumerate(random_spin_flip):\n prop_ss = np.copy(ss)\n prop_ss[range(nchains), ssidx] *= -1\n\n energy_diff = ediff_fun(\n [ss.reshape((-1, L, L)), prop_ss.reshape((-1, L, L))]\n ).reshape((nchains))\n flipidx = np.random.random(nchains) < energy_diff\n\n accept_count += np.count_nonzero(flipidx)\n ss[np.arange(nchains)[flipidx], ssidx[flipidx]] *= -1\n\n if tstep % nsample == 0:\n if is_traj:\n ss_traj[sample_step :: int(np.ceil(tn / nsample)), :] = ss\n sample_step += 1\n\n # progress.update(tstep + 1)\n\n return (ss, ss_traj, accept_count / (tn * nchains))","title":""},{"docid":"0e993115c40cc10e10dfdc78277ed0d5","score":"0.4997244","text":"def simulationTwoDrugsDelayedTreatment(numTrials):\n delay0 = []\n delay75 = []\n delay150 = []\n delay300 = []\n for i in xrange(numTrials):\n delay0.append(calcFinalPop(100, 1000, 0.1, 0.05, {'guttagonol': False, 'grimpex': False},\n .005, 0))\n delay75.append(calcFinalPop(100, 1000, 0.1, 0.05, {'guttagonol': False, 'grimpex': False},\n .005, 75))\n delay150.append(calcFinalPop(100, 1000, 0.1, 0.05, {'guttagonol': False, 'grimpex': False},\n .005, 150))\n delay300.append(calcFinalPop(100, 1000, 0.1, 0.05, {'guttagonol': False, 'grimpex': False},\n .005, 300))\n pylab.figure('Delay 0')\n pylab.hist(delay0, bins = [0,50,1000], label = \"0 step delay\", histtype='bar', alpha=1.0)\n pylab.figure('Delay 75')\n pylab.hist(delay75, bins = [0,50,1000], label = \"75 step delay\", histtype='bar', alpha=0.85)\n pylab.figure('Delay 150')\n pylab.hist(delay150, bins = [0,50,1000], label = \"150 step delay\", histtype='bar', alpha=0.6)\n pylab.figure('Delay 300')\n pylab.hist(delay300, bins = [0,50,1000], label = \"300 step delay\", histtype='bar', alpha=.45)\n pylab.show()","title":""},{"docid":"14e1205eb2495e78d9fd873f0eee4487","score":"0.49942884","text":"def print_solution(data, manager, routing, solution, total_stops):\n calculated_sequence = [0]*(total_stops)\n time_dimension = routing.GetDimensionOrDie('Time')\n for vehicle_id in range(data['num_vehicles']):\n i = 0\n index = routing.Start(vehicle_id)\n while not routing.IsEnd(index):\n calculated_sequence[index] = i\n index = solution.Value(routing.NextVar(index))\n i += 1 \n return (calculated_sequence)","title":""},{"docid":"800c2d296dea5dba93ae208ead025c3e","score":"0.499225","text":"def times():\n return []","title":""},{"docid":"16ac0f64a2057f933da601e3a6b6742f","score":"0.49863502","text":"def test_15(self):\n main = Main()\n main.generate_systems(n=100,\n min_m=100,\n max_n=200,\n max_m=200,\n step=1,\n save_results=True,\n save_systems=True,\n upper_bound=1,\n lower_bound=1,\n max_tries=10000,\n update_strongly_k=True,\n gi=Gi())","title":""},{"docid":"d619f2370d896b80b37d753cf2b358cb","score":"0.49734825","text":"def __generations(self):\n return max(0,len(self.energy_history)-1) #XXX: slower for k=-1 ?","title":""},{"docid":"4563fce2c1f3e3e02a081ecc860241cf","score":"0.49734706","text":"def generate_list(self):\n lista_tiempos = []\n i = 0\n ntime = self.gen_rep_time()\n while i < self.reps:\n lista_tiempos.append(ntime)\n i += 1\n return lista_tiempos","title":""},{"docid":"fb4b4b45a336c0de64d0255768fea96c","score":"0.4958152","text":"def generate_rb_sequence(\n self,\n depth: int,\n gateset: Sequence[Gate],\n seed: Optional[int] = None,\n interleaver: Optional[Program] = None,\n ) -> List[Program]:","title":""},{"docid":"13dea0cc367d11f0a48aa3b580f172d7","score":"0.49543616","text":"def test_11(self):\n main = Main()\n main.generate_systems(n=1000,\n min_m=1000,\n max_n=10000,\n max_m=20000,\n step=1000,\n save_results=True,\n save_systems=True,\n upper_bound=2,\n lower_bound=1,\n max_tries=30,\n gi=Gi())","title":""},{"docid":"1e4e322df665f6a0f7bc4ec3e589a5c1","score":"0.49514282","text":"def compute_accepted_sequences(peptide):\n sequences_accepted = []\n for i in range(len(peptide.sims)):\n temp = peptide.sims[i]['Results']\n for j in range(len(temp)):\n if temp[j]['Status'] == 'Accepted':\n sequences_accepted.append(temp[j]['Sequence'])\n return sequences_accepted","title":""},{"docid":"8ddf91bf40db8d8a3053c23927a8faaf","score":"0.49513906","text":"def seq_partitioning(data_arr,ind,tm_cutoff=21,marker_set='ROX'):\n\n \n #vectors dictationg the marker sizes and differences between markers in the tape measure\n if marker_set.upper()=='ROX':\n \n marker_sizes = np.array([50, 60, 90, 100, 120, 150, 160, 180, 190, 200, 220, 240, 260, 280, 290, 300, 320, 340, 360, 380,400])\n \n elif marker_set.upper()=='LIZ':\n \n marker_sizes = np.array([20, 40, 60, 80, 100, 114, 120, 140, 160, 180, 200, 214, 220, 240, 250, 260, 280, 300, 314, 320, 340, 360, 380, 400, 414, 420, 440, 460, 480, 500, 514, 520, 540, 560, 580, 600])\n else:\n print 'unknown marker set!'\n quit()\n \n marker_diffs=np.diff(marker_sizes)\n \n #find peaks in tape measure\n peaksTM = peak_finder(data_arr,4,.25)\n #sm_plotter(data_arr,peaksTM,fl) \n #array of nucleotide position\n nuc_pos = np.arange(351)\n \n #initialise peak binning array\n reduced_peaks = []\n \n #iterate through data \n for i in range(len(data_arr)):\n nuc_pos = range(350)\n \n #extract data index\n data = data_arr[i]\n \n #extract peaks\n peaks = peaksTM[i]\n \n if tm_cutoff<21:\n peaks = peaksTM[i][:tm_cutoff]\n else:\n peaks = peaksTM[i]\n \n \n \n \n \n #initials peak lists\n peak_list = []\n pos_list = []\n bin_list = []\n \n if isinstance(data,pd.DataFrame):\n \n #extract data values\n data1 = data.values\n \n \n else:\n\n #transpose data - fudge factor can be improved\n data1=np.transpose(data)\n \n \n\n peaks_trace1 = funcPeakAlign.fPeakList(data1[:,ind],repType = 'Cubic')\n\n p_ind = peaks_trace1['pos']\n \n peak_ind=np.unique(p_ind)\n \n p_pos = data1[peak_ind,0]\n \n \n p_amp =data1[peak_ind,ind]\n p_width = peaks_trace1['averW']\n p_std=peaks_trace1['stdW']\n \n peak_list=[p_pos,p_amp]\n peak_list=np.transpose(peak_list)\n \n shoulder_data = shoulder_finder(p_pos,data1,ind,i)\n #if shoulders found add them to peak list\n if len(shoulder_data)>0:\n peak_list1 = np.vstack((peak_list,shoulder_data))\n ind1=np.argsort(peak_list1[:,0])\n peak_list1=peak_list1[ind1]\n else:\n peak_list1 = peak_list\n \n \n #delete peaks if they are too close to each other\n x = 0\n while x<len(peak_list1)-1:\n \n #find the position and amplitudes of two neighbouring peaks\n p_pos1 = peak_list1[x,0]\n p_pos2 = peak_list1[x+1,0]\n \n p_amp1 = peak_list1[x,1]\n p_amp2 = peak_list1[x+1,1]\n \n \n #calculate difference in position between the two peaks\n diff1 = p_pos2-p_pos1\n \n #if the difference is less than 4\n if diff1<4:\n \n #if peak1 is in original peak list but not peak2 delete peak2 \n if (p_pos1 in peak_list[:,0]) and (p_pos2 not in peak_list[:,0]):\n del_x = x+1\n peak_list1 = np.delete(peak_list1,del_x,0)\n x+=1\n \n #if peak2 is in original peak list but not peak1 delete peak1 \n elif(p_pos2 in peak_list[:,0]) and (p_pos1 not in peak_list[:,0]):\n del_x = x\n peak_list1 = np.delete(peak_list1,del_x,0)\n else:\n x+=1\n \n \n \n else:\n x+=1\n \n x=0\n \n \n miss_pos = []\n miss_amp = []\n \n #fill in any positions that have missing peaks due to poor data quality\n \n #read through peak list\n for x in range(len(peak_list1)-1):\n \n #look at the difference in position between neighbouring peaks \n diff1=peak_list1[x+1,0]-peak_list1[x,0]\n\n #determine spacing\n spacing_we=2*p_width-4*p_std\n \n #calculate number of missing peaks expected in a space\n miss_peaks=np.floor(diff1/(p_width-2*p_std))\n \n \n #if more that one missing peak space suspected\n if miss_peaks>1:\n \n\n #fill the missing space with peak data\n for z in range(int(miss_peaks)-1):\n \n \n #determine position of new peak\n av_pos=peak_list1[x,0]+(z+1)*int(p_width)\n \n #add new position to missing position array\n miss_pos=np.append(miss_pos,av_pos)\n\n #find amplitude of new peak\n av_amp=data1[data1[:,0]==av_pos,ind]\n \n #add to missing amplitude data\n miss_amp = np.append(miss_amp,av_amp)\n\n #create array for missing peak information\n miss_arr = np.transpose([miss_pos,miss_amp])\n \n \n #add the missing peaks to the new peak array\n if len(miss_arr)>0:\n peak_list2=np.vstack((peak_list1,miss_arr))\n ind2=np.argsort(peak_list2[:,0])\n peak_list2=peak_list2[ind2]\n else:\n peak_list2=peak_list1\n\n \n \n peak_list5=np.zeros((350,2))\n \n pos_ind=0\n #iterate through all peaks\n for j in range(len(peaks)-1):\n #extract TM peaks that define the trace region of interest\n start = peaks[j]\n end = peaks[j+1]\n \n #calculate difference between peak postions\n diff = end-start \n \n \n #extract number of bins for specific region\n bins = marker_diffs[j]\n \n #set up the bin width\n nuc_sep = diff/bins\n \n #extract all of those trace values above TM start position\n peak_list3=peak_list2[peak_list2[:,0]>int(start),:]\n \n peak_list4=peak_list3[peak_list3[:,0]<int(end),:]\n \n #iterate through bins\n for t in range(bins):\n \n #extract all trace values within bin\n S_bin = peak_list4[(peak_list4[:,0]>(start+(t)*nuc_sep)),:]\n \n \n \n S_bin=S_bin[(S_bin[:,0]<(start+(t+1)*nuc_sep)),:]\n \n peak_list5[pos_ind,0]=start+(t+0.5)*nuc_sep\n #initialise binning array\n if len(S_bin)==0:\n pos_ind+=1 \n continue\n elif len(S_bin)==1:\n peak_list5[pos_ind,1]=S_bin[:,1]\n \n elif len(S_bin)>1:\n \n peak_list5[pos_ind,1]=np.max(S_bin[:,1])\n \n \n pos_ind+=1 \n \n \n \n \n reduced_peaks.append(peak_list5)\n \n \n return reduced_peaks","title":""},{"docid":"2e3af0429f60a8d9d0a7cc319d5505bf","score":"0.49466643","text":"def make_trial_set(self):\n\n self.trialSet = []\n all_pairs = list(itertools.product(self.sRightOrientations, self.sLeftOrientations))\n for numSLeft in self.sLeftPresentations:\n for p in all_pairs:\n (sRightOrientation, sLeftOrientation) = p\n if abs(sRightOrientation - sLeftOrientation) < 0.001:\n # make sure SPLUS and SMINUS are different\n continue\n\n t = Trial_2AFC()\n t.numSLeft = numSLeft\n t.sRightOrientation = sRightOrientation\n t.sLeftOrientation = sLeftOrientation\n\n self.trialSet.append(t)\n\n print str(len(self.trialSet)) + \" different trial conditions.\"\n\n self.sequencer = Sequencer(self.trialSet, self.sequenceType)\n if (self.sequenceType == Sequences.INTERVAL or\n self.sequenceType == Sequences.INTERVAL_RETRY):\n self.sequencer.sequencer.easyOris = self.easyOris\n self.sequencer.sequencer.hardOris = self.hardOris\n self.sequencer.sequencer.numEasy = self.numEasy\n self.sequencer.sequencer.numHard = self.numHard","title":""},{"docid":"fff1f6259d3fe41880af10294dcaa472","score":"0.4945969","text":"def powerset(seq):\n import itertools as it\n s = list(seq)\n return list(it.chain.from_iterable(it.combinations(s, r) for r in range(len(s) + 1)))[1:]","title":""},{"docid":"c6024b6c374c96654d91f478d880e714","score":"0.49425095","text":"def _make_io_examples(self, n, min_len, max_len):\r\n rand = random.Random(6849275409234) # Test cases are fixed, but varied.\r\n io_examples = []\r\n for _ in xrange(n):\r\n length = rand.randrange(min_len, max_len + 1)\r\n input_seq = [rand.randrange(1, self.base) for _ in xrange(length)]\r\n output_seq = list(input_seq)\r\n output_seq.reverse()\r\n output_seq.append(0)\r\n io_examples.append((input_seq, output_seq))\r\n return io_examples","title":""},{"docid":"4d802ab90cb1006e157167aa44e73f3e","score":"0.49334067","text":"def wRMS_sed_time_sugar_salt(rp, training=True, validation=False, sn_list=[]):\n \n residual_sel = []\n residual_sucre = []\n residual_sucre_1 = []\n residual_sucre_2 = [] \n residual_error = []\n phase = [] \n nspectra = 0\n\n SN = N.array(rp.dicS.keys())\n try:\n SAMPLE = N.array([rp.dic[sn]['0']['sample'] for sn in SN])\n except:\n print('continue')\n\n if training and validation:\n FILTER = N.array([True]*len(SN))\n else:\n try:\n if training:\n FILTER = (SAMPLE == 'training')\n if validation:\n FILTER = (SAMPLE == 'validation')\n except:\n print('continue')\n\n for sn in range(len(SN)):\n if SN[sn] in sn_list:\n FILTER[sn] = False\n print(len(SN))\n SN = SN[FILTER]\n print(len(SN))\n\n i=0\n for sn in SN:\n sucre, sucre_1, sucre_2, sel, res_error, phas = rp.plot_spectra_reconstruct_residuals(sn,T_min=-12,T_max=48)\n residual_sel.append(sel)\n residual_sucre.append(sucre)\n residual_sucre_1.append(sucre_1)\n residual_sucre_2.append(sucre_2)\n residual_error.append(res_error)\n phase.append(phas)\n nspectra += len(N.array(residual_sucre[i])[:,0])\n i+=1\n P.close('all')\n \n binning_phase = N.linspace(-12,48,21)\n\n res_sel = []\n res_sucre = []\n res_sucre_1 = []\n res_sucre_2 = []\n res_err = []\n\n wave = rp.dic['PTF09dnl']['0']['X']\n \n for Bin in range(21):\n print(Bin)\n res_sel.append([])\n res_sucre.append([])\n res_sucre_1.append([])\n res_sucre_2.append([])\n res_err.append([])\n for sn in range(len(phase)):\n for ph in range(len(phase[sn])):\n if abs(phase[sn][ph]-binning_phase[Bin])<1.5:\n [res_sel[Bin].append(residual_sel[sn][ph][wav]) for wav in range(len(wave))]\n [res_sucre[Bin].append(residual_sucre[sn][ph][wav]) for wav in range(len(wave))]\n [res_sucre_1[Bin].append(residual_sucre_1[sn][ph][wav]) for wav in range(len(wave))]\n [res_sucre_2[Bin].append(residual_sucre_2[sn][ph][wav]) for wav in range(len(wave))]\n [res_err[Bin].append(residual_error[sn][ph][wav]) for wav in range(len(wave))]\n\n wrms_sel = N.zeros(21)\n wrms_sucre = N.zeros(21)\n wrms_sucre_1 = N.zeros(21)\n wrms_sucre_2 = N.zeros(21)\n\n for Bin in range(21):\n \n res_clipped, err_clipped = sigma_clip(N.array(res_sel[Bin]), N.array(res_err[Bin]))\n wrms_sel[Bin] = N.sqrt(N.sum((res_clipped**2)/(err_clipped**2)) / N.sum(1./(err_clipped**2)))\n\n res_clipped, err_clipped = sigma_clip(N.array(res_sucre[Bin]), N.array(res_err[Bin]))\n wrms_sucre[Bin] = N.sqrt(N.sum((res_clipped**2)/(err_clipped**2)) / N.sum(1./(err_clipped**2)))\n\n res_clipped, err_clipped = sigma_clip(N.array(res_sucre_1[Bin]), N.array(res_err[Bin]))\n wrms_sucre_1[Bin] = N.sqrt(N.sum((res_clipped**2)/(err_clipped**2)) / N.sum(1./(err_clipped**2)))\n\n res_clipped, err_clipped = sigma_clip(N.array(res_sucre_2[Bin]), N.array(res_err[Bin]))\n wrms_sucre_2[Bin] = N.sqrt(N.sum((res_clipped**2)/(err_clipped**2)) / N.sum(1./(err_clipped**2)))\n \n P.figure(figsize=(16,6))\n P.subplots_adjust(top=0.9,right=0.65,left=0.05,bottom=0.15)\n P.plot(binning_phase,wrms_sel,'r',linewidth=5,label='SALT2.4 ($X_0$ + $X_1$ + $C$)')\n P.plot(binning_phase,wrms_sucre_1,'b-.',linewidth=3,label='SUGAR ($\\Delta M_{grey}$ + $q_1$ + $A_V$)')\n P.plot(binning_phase,wrms_sucre_2,'b--',linewidth=3,label='SUGAR ($\\Delta M_{grey}$ +$q_1$ + $q_2$ + $A_V$)')\n P.plot(binning_phase,wrms_sucre,'b',linewidth=5,label='SUGAR ($\\Delta M_{grey}$ +$q_1$ + $q_2$ + $q_3$ + $A_V$)')\n P.xlabel('Phase (day)',fontsize=20)\n P.ylabel('wRMS (mag)',fontsize=20)\n P.ylim(0,0.43)\n P.xlim(-13,50)\n P.legend(bbox_to_anchor=(1.01, 0.7), loc=2, borderaxespad=0.,fontsize=16)\n return res_sel\n #P.show()","title":""},{"docid":"59cc5c2bb1e18bf347d2c4113106369a","score":"0.4918269","text":"def create_list_random_AAIMON_ratios(n_mutations_array, aa_prop_TM, aa_prop_nonTM, seq_len):\n AAIMON_list = []\n # iterate through each number of mutations (1/1000, 2/1000 ..... 700/1000)\n for n, number_mutations in enumerate(n_mutations_array):\n # get a random pairwise alignment for the TM region\n perc_ident_TM = korbinian.cons_ratio.norm.get_perc_ident_random_pairwise_alignment(aa_prop_TM, seq_len, number_mutations)\n # get a random pairwise alignment for the nonTM region\n perc_ident_nonTM = korbinian.cons_ratio.norm.get_perc_ident_random_pairwise_alignment(aa_prop_nonTM, seq_len, number_mutations)\n # calculate artificial AAIMON\n AAIMON = perc_ident_TM / perc_ident_nonTM\n # append to AAIMON_list\n AAIMON_list.append(AAIMON)\n # calculate the percentage aa substitutions for this position\n perc_aa_subst = number_mutations / seq_len\n # if n % 100 == 0:\n # sys.stdout.write(\"\\n{}, {:.03f}, {:.03f}, {:.03f}, {:.03f}\\n\".format(number_mutations, perc_aa_subst, perc_ident_TM, perc_ident_nonTM, AAIMON))\n sys.stdout.flush()\n\n return AAIMON_list","title":""},{"docid":"28c3b3be9c12949417e3781935b23f30","score":"0.4916316","text":"async def getSpecs():\n return specString","title":""},{"docid":"796b4785031219d25dc867f588379045","score":"0.49081177","text":"def ltsm_gen(net, seq_len, file_name, n_steps=100, hidden_size=89):\n\n notes = [] # Will contain a sequence of the predicted notes\n x = torch.zeros((seq_len, 1, 89)).float().cuda() # Input notes. All 0s minus the last one\n x[-1] = notes_encoded[0, :, :] # which is the first note of the piece\n notes.append(x[-1].cpu().numpy()) # Saves the first note\n h_state = torch.zeros(1, 1, hidden_size).float().cuda()\n c_state = torch.zeros(1, 1, hidden_size).float().cuda()\n print_first = True\n for _ in range(n_steps):\n chosen = False # To account for when no dimension's probability is bigger than 0.9\n y_pred, h_c_state = net(x, (h_state, c_state))\n h_state, c_state = h_c_state[0].data, h_c_state[1].data\n y_pred = y_pred.data\n y_pred = y_pred[-1] # We only care about the last predicted note (next note after last note of input sequence)\n choose = torch.zeros((1, 1, 89)) # Coverts the probabilities to the actual note vector\n for idx in range(89):\n if y_pred[:, idx] > 0.9:\n choose[:, :, idx] = 1\n chosen = True\n if not chosen:\n if print_first:\n print(\"\\nPrinting out the maximum prob of all notes for a time step\",\n \"when this maximum prob is less than 0.9\")\n print_first = False\n choose[:, :, np.argmax(y_pred.cpu())] = 1\n print(_, y_pred[:, np.argmax(y_pred.cpu())]) # Maximum probability out of all components\n x_new = torch.empty(x.shape) # Uses the output of the last time_step\n for idx, nt in enumerate(x[1:]): # As the input for the next time_step\n x_new[idx] = nt # So the new sequence will be the same past sequence minus the first note\n x_new[-1] = choose # of such past sequence, and plus the predicted note from this iteration\n x = x_new.cuda() # We will use this new sequence to predict in the next iteration the next note\n notes.append(choose) # Saves the predicted note\n\n # Gets the notes into the correct NumPy array shape\n gen_notes = np.empty((len(notes)-seq_len+1, 89)) # Doesn't use the first predicted notes\n for idx, nt in enumerate(notes[seq_len-1:]): # Because of the 0s init problem # TODO: Figure this out\n gen_notes[idx] = nt[0]\n\n # Decodes the generated music and saves it as a MIDI file\n gen_midi = decode(gen_notes, time_step=0.25)\n gen_midi.write(\"midi\", file_name + \".mid\")","title":""},{"docid":"a8dab091084702b746bdc1797fdec904","score":"0.4905279","text":"def specwindow(freq,time):\n\t\tn = len(time)\n\t\tW = [sum([np.exp(-2.j*pi*f*t) for t in time])/float(n) for f in freq]\n\t\tamp = [np.sqrt(t.real*t.real + t.imag*t.imag) for t in W]\n\t\tphase = [np.arctan2(t.imag,t.real) for t in W]\n\t\treturn amp,phase","title":""},{"docid":"a3b18b6b20abb5f8ed1a74e325f6a6eb","score":"0.490144","text":"def generate_rep(seq):\n\tseq_rep = np.array([])\n\n\tfor base in seq:\n\t\tbase_rep = [1 if i in rep_dir[base] else 0 for i in range(6)]\n\t\tseq_rep = np.concatenate((seq_rep, base_rep))\n\treturn np.atleast_2d(seq_rep)","title":""},{"docid":"83e509c139709d473ea3699f2eb6045e","score":"0.4899097","text":"def run_til_fix(simulation,N_step,skip,include_fixed=True):\n return [tissue.copy() for tissue in generate_til_fix(simulation,N_step,skip,include_fixed=include_fixed)]","title":""},{"docid":"8bb98e3457958d34b0d9538904af51a1","score":"0.48978472","text":"def simulation2(start, end, init_time, results, timing, time_stamps, feedback=True, learning=False):\n\n attempts = collections.Counter()\n\n if learning:\n\n addition.productionstring(name=\"retrieve_addition\", string=\"\"\"\n =g>\n isa add\n state reading\n arg1 =num1\n arg2 =num2\n sum None\n ?retrieval>\n state free\n buffer empty\n ==>\n =g>\n isa add\n state None\n +retrieval>\n isa add\n arg1 =num1\n arg2 =num2\"\"\", utility=1)\n\n addition.productionstring(name=\"terminate_addition\", string=\"\"\"\n =g>\n isa add\n state None\n =retrieval>\n isa add\n arg1 ~None\n sum =v7\n ==>\n ~retrieval>\n =g>\n isa add\n sum =v7\"\"\", utility=1)\n\n addition.productionstring(name=\"done\", string=\"\"\"\n =g>\n isa add\n state None\n sum =v7\n sum ~None\n ==>\n =g>\n isa add\n state None\"\"\", reward=15)\n\n addition.model_parameters[\"production_compilation\"] = True\n addition.model_parameters[\"utility_learning\"] = True\n addition.model_parameters[\"utility_noise\"] = 0.8\n\n for loop in range(start+1,end+1):\n x = random.uniform(0, 1) #what problem should be used \n for i in range(len(PROB2)):\n if x < PROB2[i]:\n break\n else:\n i = UPTO\n x = random.uniform(0, 1) #what problem should be used \n for j in range(len(PROB2)):\n if x < PROB2[j]:\n break\n else:\n j = UPTO\n #print(i, j)\n correct = i + j\n attempts.update([(i, j)])\n addition.goal.add(actr.makechunk(\"\", \"add\", state=\"reading\", arg1=numbers[i], arg2=numbers[j]))\n\n sim = addition.simulation(initial_time=init_time, trace=False, gui=False)\n addition.used_productions.rules.used_rulenames = {}\n while True:\n try:\n sim.step()\n except simpy.core.EmptySchedule:\n break\n if re.search(\"RETRIEVED: None\", sim.current_event.action):\n results.setdefault((str(i), str(j)), collections.Counter()).update('X')\n if feedback and random.randint(0,1) == 1:\n addition.retrieval.add(actr.makechunk(\"\", \"add\", arg1=numbers[i], arg2=numbers[j], sum=numbers[correct]))\n addition.retrieval.clear(sim.show_time()) #advise -- teacher tells the correct answer\n break\n if re.search(\"RULE FIRED: done\", sim.current_event.action):\n x = addition.goal.copy().pop()\n addition.goal.clear(sim.show_time())\n x = x._asdict()\n results.setdefault((str(i), str(j)), collections.Counter()).update([str(x[\"sum\"])])\n if str(x[\"sum\"]) != str(numbers[correct]):\n if feedback and random.randint(0,1) == 1:\n addition.retrieval.add(actr.makechunk(\"\", \"add\", arg1=numbers[i], arg2=numbers[j], sum=numbers[correct]))\n addition.retrieval.clear(sim.show_time()) #advise -- teacher tells the correct answer in roughly 50% of cases if incorrect\n if learning:\n utilities.modify_utilities(sim.show_time(), -15, addition.used_productions.rules.used_rulenames, addition.used_productions.rules, addition.used_productions.model_parameters)\n elif timing:\n time_stamps.setdefault((str(i), str(j)), []).append(sim.show_time() - init_time)\n break\n addition.retrieval.state = addition.retrieval._FREE\n addition.retrieval.clear()\n init_time = loop*DELAY\n return results, time_stamps, init_time","title":""},{"docid":"45e44f9bd89cfcdebb87beb80802504f","score":"0.48944435","text":"def test_9(self):\n main = Main()\n main.generate_systems(n=4,\n min_m=4,\n max_n=100,\n max_m=100,\n step=10,\n save_results=True,\n save_systems=True,\n gi=Gi())","title":""},{"docid":"712f72c867014c21088ec4db9f53cbb3","score":"0.48924717","text":"def get_sample_G7_solution1(self):\n\t\treturn util.make_notes([86, 84, 81, 82, 83, 81, 79, 78])","title":""},{"docid":"b023fad3a6540e074499ae758d400a04","score":"0.4891167","text":"def generatetimes(ndots, starttime):\n cumsum, sample = starttime, finalsample(INTERVAL, ndots, THRESHOLD)\n timesid = []\n for i in range(ndots):\n timesid.append(cumsum)\n cumsum += sample[i]\n return timesid","title":""},{"docid":"f62381525b1caeca076734e858f824d3","score":"0.4889272","text":"def gen_control(proband_id, stimulation):\n pj, pd, cwd = os.path.join, os.path.dirname, os.getcwd()\n sentence_path = pj(pd(cwd), 'stimuli', 'audio', 'test')\n\n speaker_trials = list(TRIALS_THIS_SESSION)\n np.random.shuffle(speaker_trials)\n speech_trials = list(TRIALS_THIS_SESSION)\n np.random.shuffle(speech_trials)\n\n def get_f(speaker, intonation, word):\n \"\"\"Determines the filename given the speaker, intonation, or word.\"\"\"\n a = dict(zip(NAMES, CODES))[speaker]\n b = intonation\n c = ''\n i = WORDS.index(word)\n if i < 100:\n c += '0'\n if i < 10:\n c += '0'\n c += str(i)\n return pj(sentence_path, '_'.join([a, b, c]) + '.wav')\n\n def test_trial(blockn, trialn):\n \"\"\"Determines the trial info for a particular trial.\"\"\"\n if blockn % 2 == 0:\n task = TASKS[0]\n speaker, intonation, word = speaker_trials.pop(0)\n ans = speaker\n alt1, alt2, alt3, alt4 = NAMES\n else:\n task = TASKS[1]\n speaker, intonation, word = speech_trials.pop(0)\n ans = word\n alt1, alt2, alt3, alt4 = sorted(WORDS_DICT[word])\n f = get_f(speaker, intonation, word)\n phase = 'test'\n return (proband_id, TEST_NAME, stimulation, phase, blockn, task,\n trialn, speaker, intonation, word, f, ans, alt1, alt2, alt3,\n alt4)\n\n def prac_trial(blockn, trialn):\n \"\"\"Determines the trial info for a particular trial.\"\"\"\n speaker, intonation, word = PRACTICE_TRIALS.pop(0)\n if blockn % 2 == 0:\n task = TASKS[0]\n ans = speaker\n alt1, alt2, alt3, alt4 = NAMES\n else:\n task = TASKS[1]\n ans = word\n alt1, alt2, alt3, alt4 = sorted(WORDS_DICT[word])\n f = get_f(speaker, intonation, word)\n phase = 'practice'\n return (proband_id, TEST_NAME, stimulation, phase, blockn, task,\n trialn, speaker, intonation, word, f, ans, alt1, alt2, alt3,\n alt4)\n\n control = []\n for blockn in [-2, -1]:\n t = [prac_trial(blockn, trialn) for trialn in xrange(PRACTICE_TRIALSN)]\n control += t\n for blockn in xrange(len(TASKS) * BLOCKS_PER_TASK):\n t = [test_trial(blockn, trialn) for trialn in xrange(TRIALS_PER_BLOCK)]\n control += t\n\n return control","title":""},{"docid":"bd9cc0c499a4d2732e70cf59189e79a3","score":"0.48884326","text":"def sweep(self,startFrequetimes1to2ncyMHz, endFrequenciesHz, times, triggerMask): \n \n fs, ts = [],[]\n start = startFrequencyMHz\n c=0\n for endFreq, t in zip(endFrequenciesHz,times):\n newFs, newts = self.freqsAndTimes(start,endFreq,t )\n fs+=newFs\n ts+=newts\n start = endFrequenciesHz[c]\n c+=1\n \n return fs,ts","title":""},{"docid":"fb6b230ab9762e8b16d5e040315f766c","score":"0.48856592","text":"def clifford_gates( target:int )->List:\n## Basic\n## Pi\n rg_i = Gate(\"RX\", target, arg_value= 0)\n rg_x = Gate(\"RX\", target, arg_value= np.pi)\n rg_y = Gate(\"RY\", target, arg_value= np.pi)\n ## Pi/2\n rg_px2 = Gate(\"RX\", target, arg_value= +np.pi/2)\n rg_py2 = Gate(\"RY\", target, arg_value= +np.pi/2)\n rg_nx2 = Gate(\"RX\", target, arg_value= -np.pi/2)\n rg_ny2 = Gate(\"RY\", target, arg_value= -np.pi/2)\n\n ## Decompose\n ## Pi\n g_z = [rg_y,rg_x]\n ## Pi/2\n g_phz = [rg_nx2,rg_py2,rg_px2]\n g_nhz = [rg_nx2,rg_ny2,rg_px2]\n ## Had\n g_hpxz = [rg_x,rg_ny2]\n g_hnxz = [rg_x,rg_py2]\n g_hpyz = [rg_y,rg_px2]\n g_hnyz = [rg_y,rg_nx2]\n g_hpxy = [rg_px2,rg_py2,rg_px2]\n g_hnxy = [rg_nx2,rg_py2,rg_nx2]\n ## 2pi/3 \n g_pc1 = [rg_py2,rg_px2]\n g_pc2 = [rg_py2,rg_nx2]\n g_pc4 = [rg_ny2,rg_px2]\n g_pc3 = [rg_ny2,rg_nx2]\n\n g_nc1 = [rg_nx2,rg_py2]\n g_nc2 = [rg_px2,rg_py2]\n g_nc4 = [rg_nx2,rg_ny2]\n g_nc3 = [rg_px2,rg_ny2]\n\n gates_set = [\n [rg_i],[rg_x],[rg_y],[rg_px2],[rg_nx2],[rg_py2],[rg_ny2],\n ## Pi\n g_z,\n ## Pi/2\n g_phz,g_nhz,\n ## Had\n g_hpxz,g_hnxz,g_hpyz,g_hnyz,g_hpxy,g_hnxy,\n ## 2pi/3 \n g_pc1,g_pc2,g_pc4,g_pc3,\n g_nc1,g_nc2,g_nc4,g_nc3\n ]\n return gates_set","title":""},{"docid":"a844a8069a13d5760bea2737a36a3e8c","score":"0.48853913","text":"def generate_steps(stepNumber, tread, riser, stepWidth):\n\tsteps = []\n\tstep2d = MKPOL([[[tread, 0],[tread, riser*2], [tread*2, riser*2], [tread*2, riser]], [[1,2,3,4]], None])\n\tfirstStep = CUBOID([tread, riser, stepWidth])\n\tsteps.append(firstStep)\n\tfor i in range(int(stepNumber-1)):\n\t\tsteps.append(T([1,2])([(tread*i), riser*i])(PROD([step2d, Q(stepWidth)])))\n\treturn steps","title":""},{"docid":"188e0a2ed53433e160ad978c6632baa9","score":"0.4880787","text":"def simulate(self, tmax, verbose=False):\n res = []\n tmin = 0\n while tmin < tmax:\n tmin = tmax\n for s in self.students:\n statement = s.study()\n t = statement['timestamp']\n tmin = min(t, tmin)\n if t < tmax:\n res.append(statement)\n if verbose:\n print(\"statement: {}\".format(statement))\n return res","title":""},{"docid":"da98868aa70c8f633c21c925f3cf0271","score":"0.48727334","text":"def beginSequenceandProbe(win, mouse, n):\n global iPattern\n squares = squarePattern[iPattern]\n for square in squares:\n square.setAutoDraw(True)\n iPattern += 1\n #done creating irregular square pattern\n sequence = range(NUM_SQUARES)\n random.shuffle(sequence)\n for i in sequence[:n]:\n squares[i].setFillColor(SQUARE_HIGHLIGHT_COLOR)\n win.flip()\n core.wait(HIGHLIGHT_TIME)\n squares[i].setFillColor(SQUARE_COLOR)\n #erase screen\n #for square in squares:\n # square.setAutoDraw(False)\n #win.flip()\n #core.wait(ISI_TIME)\n #begin asking for the subject to recall pattern\n for square in squares:\n square.setAutoDraw(True)\n instructions = visual.TextStim(win,text=\"Select the squares in the order they appeared. If you accidently click a square, click Back.\", pos=(0,10),wrapWidth=30)\n submitText = visual.TextStim(win,text=\"Submit\",pos=(5,-8))\n submitButton = visual.Rect(win,width=4, height=1.2, lineWidth=2)\n backText = visual.TextStim(win,text=\"Undo last click\",pos=(-5,-8))\n backButton = visual.Rect(win,width=7.3, height=1.2, lineWidth=2)\n backButton.setPos((-5,-8))\n submitButton.setPos((5,-8))\n submitButton.setAutoDraw(True)\n submitText.setAutoDraw(True)\n backButton.setAutoDraw(True)\n backText.setAutoDraw(True)\n instructions.setAutoDraw(True)\n instructions.draw()\n submitButton.draw()\n submitText.draw()\n backButton.draw()\n backText.draw()\n win.flip()\n #done rendering, time to start timer for subject response\n timer = core.Clock()\n clicked = []\n while(True):\n for i in range(len(squares)):\n if mouse.isPressedIn(squares[i]):\n clicked.append(i)\n squares[i].setFillColor(SQUARE_HIGHLIGHT_COLOR)\n win.flip()\n core.wait(0.200)\n while mouse.isPressedIn(squares[i]):\n pass\n squares[i].setFillColor(SQUARE_COLOR)\n win.flip()\n if(mouse.isPressedIn(backButton) and len(clicked) > 0):\n clicked.remove(clicked[len(clicked)-1])\n backText.setColor(SQUARE_HIGHLIGHT_COLOR)\n win.flip()\n core.wait(0.200)\n while mouse.isPressedIn(backButton):\n pass\n backText.setColor(\"White\")\n win.flip()\n if(mouse.isPressedIn(submitButton)):\n #erase display\n submitButton.setAutoDraw(False)\n submitText.setAutoDraw(False)\n backButton.setAutoDraw(False)\n backText.setAutoDraw(False)\n instructions.setAutoDraw(False)\n for i in range(len(squares)):\n squares[i].setAutoDraw(False)\n return (sequence[:n]==clicked,sequence[:n],clicked,correctness(clicked,sequence[:n]), n, timer.getTime())\n if(event.getKeys(keyList=['q','escape'])):\n quit()","title":""},{"docid":"08748c5e89e1e9f736a5266c4931d4d3","score":"0.48711482","text":"def _terminalSymbolsGenerator(self):\n UPPAs = [i for i in range(1, 1000000)]\n for i in UPPAs:\n yield ([-i])\n raise ValueError(\"Too many input sequences.\")","title":""},{"docid":"dbde053ed7ac3b67939ab6dc16fdae7f","score":"0.48691732","text":"def dist_configs(G,q):\n return [chi_from_tau(tau_config,G) for qp in range(q+1)\n for tau_config in itertools.combinations(range(G),qp)\n for degeneracy in range(int(fac(q)/fac(show(q-qp))))]","title":""},{"docid":"d04914b691d62c3e25ae75bc93d7c259","score":"0.48672348","text":"def trialbhv(mouse, start_time=-1, end_time=6, verbose=True):\n\n # build your runs object\n runs = flow.RunSorter.frommeta(mice=[mouse], exclude_tags=['bad'])\n\n trial_list = []\n for r in range(len(runs)):\n\n run = runs[r]\n\n # get your t2p object\n t2p = run.trace2p()\n\n # trigger all running speed around stim onsets\n speed_traces = behaviortraces(t2p, '', start_s=start_time, end_s=end_time,\n trace_type='speed', cutoff_before_lick_ms=-1)\n print(np.shape(speed_trace))\n # trigger all running speed around stim onsets\n lick_traces = behaviortraces(t2p, '', start_s=start_time, end_s=end_time,\n trace_type='lick', cutoff_before_lick_ms=-1)\n print(np.shape(lick_trace))\n # make timestamps\n timestep = 1 / np.round(t2p.d['framerate'])\n timestamps = np.concatenate((np.arange(start_time, 0, timestep),\n np.arange(0, end_time, timestep)))\n\n # loop through and append each trial (slice of cstraces)\n for trial in range(np.shape(run_traces)[2]):\n index = pd.MultiIndex.from_arrays([\n [run.mouse] * np.shape(run_traces)[1],\n [run.date] * np.shape(run_traces)[1],\n [run.run] * np.shape(run_traces)[1],\n [int(trial)] * np.shape(run_traces)[1],\n timestamps\n ],\n names=['mouse', 'date', 'run', 'trial_idx', 'timestamp'])\n\n data = {'speed': np.squeeze(speed_traces[0, :, trial]),\n 'licking': np.squeeze(lick_traces[0, :, trial])}\n # append all trials across all runs together in a list\n trial_list.append(pd.DataFrame(data, index=index))\n\n # clear your t2p to save RAM\n run._t2p = None\n\n trial_df = pd.concat(trial_list, axis=0)\n save_path = os.path.join(flow.paths.outd, str(run.mouse) + '_df_bhv.pkl')\n trial_df.to_pickle(save_path)\n if verbose:\n print('Done.')","title":""},{"docid":"273fbabebce5f5f983ee3d398b550220","score":"0.48647377","text":"def extract_chain_sequences(lines):\n seq = []\n for line in lines:\n if line.startswith('SEQRES'):\n seq.append(line[0:])\n return seq","title":""},{"docid":"df9cfedbee6238dc6dc39b39b2acb869","score":"0.48646474","text":"def adjust_intervals(self) -> list:\n if self.begin == 0 and self.end == 86400:\n return self.intervals\n if self.begin >= 86399 or self.end <= 1 or self.begin >= self.end:\n print(\"inadequate parameters, try again.\")\n return []\n new_intervals = []\n\n # determine first to last relevant interval\n # interval: [begin, end, percentage]\n for interval in self.intervals:\n # print(new_intervals)\n if interval[0] <= self.begin < interval[1]:\n temp = [self.begin, interval[1], interval[2]]\n new_intervals.append(temp)\n elif self.begin < interval[0] and interval[1] < self.end:\n new_intervals.append(interval)\n elif interval[0] < self.end <= interval[1]:\n temp = [interval[0], self.end, interval[2]]\n new_intervals.append(temp)\n # print(\"breaking\")\n break\n\n # adjusting the demand percentages of the new intervals to interval length and demand of the entire simulation\n entire_demand = 0\n for interval in new_intervals:\n entire_demand = entire_demand + ((interval[1] - interval[0]) * interval[2])\n for interval in new_intervals:\n interval[2] = ((interval[1] - interval[0]) * interval[2]) / entire_demand\n\n # print(entire_demand)","title":""},{"docid":"9eabfac6f19461bb21e361120c960533","score":"0.48527104","text":"def makePrimers(self, sequence, interval, fuzziness=500, cb=None):\n included_region = [[x[0], x[1] - x[0]] for x in interval]\n self.seq_args['SEQUENCE_TEMPLATE'] = str(sequence.seq)\n qualList = sequence.letter_annotations[\"phred_quality\"]\n qual = ' '.join([str(x) for x in qualList])\n self.seq_args['SEQUENCE_QUALITY'] = qual\n output = []\n \n for target in included_region:\n self.seq_args['PRIMER_PRODUCT_SIZE_RANGE'] = str(target[1]) + '-' + str(target[1] + fuzziness)\n self.seq_args['SEQUENCE_TARGET'] = \"%d,%d\" % (target[0], target[1])\n #pdb.set_trace() # Debug\n primers = primer3.wrappers.designPrimers(self.seq_args)\n yield primers #self.seq_args\n # cb and cb(target)\n # pdb.set_trace() # Debug","title":""},{"docid":"097e15d292a173861cd2fbe5abcb07ae","score":"0.48510003","text":"def sequence_list():\n zeros = [0] * 5\n ones = [1] * 5\n return zeros + ones","title":""},{"docid":"a9b3bf7ec8ea6f15615bbb7b98e44fe2","score":"0.4842891","text":"def get_all_possible_single_nucleotide_mutations(gene_sequence, gene_kmers, sort_results=True):\n length = len(gene_sequence)\n assert length % 3 == 0, 'Transcript nucleotide sequence not multiple of 3'\n assert gene_kmers is not None\n bases = 'ACGT'\n results = []\n\n aa_length = int(length / 3)\n\n # Make a long sequence with all changes\n # For each base positions, change to each base.\n # Can join them all then translate at once.\n\n # There are 13 repeats of the gene sequence.\n # The first is the WT\n # 2-5 is first base position in codon, 6-9 is 2nd, 10-13 is 3rd\n # 2, 6, 10 are changing to A\n # 3, 7, 11 are changing to C\n # 4, 8, 12 are changing to G\n # 5, 9, 13 are changing to T\n #\n # So the ath codon represents:\n # n = a//aa_length is the sequence number\n # r = a%aa_length is the residue number (add 1 to get 1-based coordinates)\n # m = (n-1)//4 gives the base position\n # b = (n-1)%4 gives the base change (index of A, C, G, T)\n extended_seq = gene_sequence\n for j in range(3): # base position\n for b in bases: # Base change\n new_seq = \"\".join([s if ii % 3 != j else b for (ii, s) in enumerate(gene_sequence)])\n extended_seq += new_seq\n\n extended_seq_translated = Seq(extended_seq).translate()\n\n for i in range(aa_length, aa_length * 13): # 13 is not a typo, see comments above\n residue = i % aa_length + 1\n n = i // aa_length\n base_position = (n - 1) // 4\n mut = bases[(n - 1) % 4]\n cds_pos = (residue - 1) * 3 + base_position + 1\n aa = extended_seq_translated[residue - 1]\n aa2 = extended_seq_translated[i]\n seq = extended_seq[(residue - 1) * 3:residue * 3]\n seq2 = extended_seq[i * 3:(i + 1) * 3]\n if seq != seq2:\n aachange = '{}{}{}'.format(aa, residue, aa2)\n codon_change = '{}>{}'.format(seq, seq2)\n if aa == aa2:\n effect = 'synonymous'\n elif aa2 == '*':\n effect = 'nonsense'\n elif aa == '*':\n effect = 'stop_lost'\n elif residue == 1:\n effect = 'start_lost'\n else:\n effect = 'missense'\n d = {'residue': residue, 'base': base_position,\n 'ref': seq[base_position], 'mut': seq2[base_position], 'cdspos': cds_pos,\n 'aachange': aachange, 'aaref': str(aa), 'aamut': str(aa2), 'codon_change': codon_change,\n 'effect': effect}\n for k, kmers in gene_kmers.items():\n kmer = kmers[(residue - 1) * 3 + base_position]\n d['{}mer_ref'.format(k)] = kmer\n results.append(d)\n\n all_possible_mutations = pd.DataFrame(results)\n if sort_results:\n return all_possible_mutations.sort_values(['residue', 'base', 'mut']).reset_index(drop=True)\n else:\n return all_possible_mutations","title":""},{"docid":"5bd5a363976b955e62508d7ee289adaf","score":"0.48426214","text":"def calculate_makespan(sol):\n\n sequence_np = np.array(sol.sequence)\n sequence_length = len(sequence_np)\n num_machines=sol.num_machines\n processing_times=sol.processing_times\n # for i in range(sol.num_jobs()): \n # processing_times.append(sol.get_job_costs(i))\n e = np.zeros((sequence_length+1,num_machines+1), dtype='int64')\n\n for i in range(1,sequence_length+1 ):\n\t e[i,0]=0\n\t for j in range(1,num_machines+1):\n\t\t if i == 0:\n\t\t\t e[0,j]=0\n\n\t\t if e[i - 1, j] > e[i, j - 1]:\n\t\t\t e[i, j] = e[i - 1][j] + processing_times[sequence_np[i - 1] - 1][j-1]\n\t\t else:\n\t\t\t e[i, j] = e[i][j - 1] + processing_times[sequence_np[i - 1]- 1][j-1]\n\n\n\t# Return makespan (integer)\n #print(\"e\",e)\n sol.makespan=e[sequence_length, num_machines]\n return e[sequence_length, num_machines]","title":""},{"docid":"3e30438f8ef1f1dfaf4486ff9aaa82e4","score":"0.4842601","text":"def algebra_generators(self):\n\t\t\t\treturn [self.x(i) for i in range(1, self._n+1)]+[self.t(i) for i in range(1, self._n)]+[self.e(p) for p in self._P]","title":""}],"string":"[\n {\n \"docid\": \"2a8461217b4174229a6b47de662dc87f\",\n \"score\": \"0.62755734\",\n \"text\": \"def generate_gst_sequences(spec: GSTSpec,\\n target_lens: Sequence[int]) -> List[GateSequence]:\\n # Collect sequences in set for deduplication.\\n seqs = set()\\n for target_len in target_lens:\\n for prep in spec.prep_fiducials:\\n for meas in spec.meas_fiducials:\\n for germ in spec.germs:\\n num_repeats = target_len // len(germ)\\n seqs.add(prep + germ * num_repeats + meas)\\n return list(seqs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e30174a8af91333ed99f26b33f3a0625\",\n \"score\": \"0.5990837\",\n \"text\": \"def generation(generator,predictor,configReinforce): \\r\\n generated = []\\r\\n pbar = tqdm(range(400))\\r\\n for i in pbar:\\r\\n pbar.set_description(\\\"Generating molecules...\\\")\\r\\n predictSMILES = PredictSMILES(generator,None,False,0.1,configReinforce)\\r\\n generated.append(predictSMILES.sample())\\r\\n\\r\\n sanitized,valid = canonical_smiles(generated,sanitize=True, throw_warning=False)# validar \\r\\n unique_smiles = list(np.unique(sanitized))[1:]\\r\\n \\r\\n #prediction kor\\r\\n prediction_kor = predictor.predict(unique_smiles)\\r\\n #prediction sas\\r\\n mol_list = smiles2mol(unique_smiles) \\r\\n prediction_qed = qed_calculator(mol_list)\\r\\n \\r\\n new_pred_qed = [np.exp(element/4) for element in prediction_qed]\\r\\n new_pred_kor = [np.exp(element/4 - 1) for element in prediction_kor]\\r\\n \\r\\n ranges = []\\r\\n ranges.append(np.max(new_pred_kor))\\r\\n ranges.append(np.min(new_pred_kor))\\r\\n ranges.append(np.max(new_pred_qed))\\r\\n ranges.append(np.min(new_pred_qed))\\r\\n return ranges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2555825df7d8613501306d922f061b3\",\n \"score\": \"0.58358234\",\n \"text\": \"def getsequences(self, ):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd47b9c8ea9f28fab3ab4ab11f3fd955\",\n \"score\": \"0.55552644\",\n \"text\": \"def get_sequences(count=0, length=0, different=False, file=None):\\n if file is not None:\\n return read_fasta_data(file)\\n\\n # only select as many sequences as possible\\n count = min(count, 25)\\n output = []\\n i = 0\\n\\n # loop over all generated sequences and return as many sequences as specified or available within the specification\\n while i < len(sequences) and len(output) < count:\\n if not different or not sequences[i][:length] in output:\\n output.append(sequences[i][:length])\\n i += 1\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d33c590801cdecba4550b2bd2636a05\",\n \"score\": \"0.55266863\",\n \"text\": \"def sequences(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b645f9ded8ca0aceef6efb3d41f7c1f\",\n \"score\": \"0.55019766\",\n \"text\": \"def paulis_gen_func(nr_seqs, cycles):\\n def gen_random(cycles):\\n s_gates = [\\\"X90 \\\", \\\"Y90 \\\", \\\"Z45 \\\"]\\n lis = []\\n for length in cycles:\\n i = 0\\n gates = []\\n gates.append(s_gates[1] + \\\"qb_1\\\")\\n sim_str = ' ' if 'Z' in s_gates[1][0:3] else 's '\\n gates.append(s_gates[1][0:3] + sim_str + \\\"qb_2\\\")\\n gates.append(s_gates[2] + \\\"qb_1\\\")\\n sim_str = ' ' if 'Z' in s_gates[2][0:3] else 's '\\n gates.append(s_gates[2][0:3] + sim_str + \\\"qb_2\\\")\\n gates.append(\\\"CZ \\\" + \\\"qb_1 qb_2\\\")\\n if length > 0:\\n while i < (length - 1):\\n last_1_gate1 = gates[-3][0:4]\\n\\n choice1 = []\\n for gate in s_gates:\\n choice1.append(gate)\\n choice1.remove(last_1_gate1)\\n gate1 = random.choice(choice1)\\n gates.append(gate1 + 'qb_1')\\n\\n last_1_gate2 = gates[-3][0:3] + ' '\\n choice2 = []\\n for gate in s_gates:\\n choice2.append(gate)\\n choice2.remove(last_1_gate2)\\n gate2 = random.choice(choice2)\\n sim_str = ' ' if 'Z' in gate2[:3] else 's '\\n gates.append(gate2[:3] + sim_str + 'qb_2')\\n gates.append(\\\"CZ \\\" + 'qb_1 qb_2')\\n i += 1\\n lis.append(gates)\\n return lis\\n return [gen_random(cycles) for _ in range(nr_seqs)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"927d0d9a9b79b055f8f1dbd631eada5b\",\n \"score\": \"0.541498\",\n \"text\": \"def generate_sequence(self, config):\\r\\n\\r\\n frequency = config.get('Parameter #1')\\r\\n amplitude = config.get('Parameter #2')\\r\\n width = config.get('Parameter #3')\\r\\n plateau = config.get('Parameter #4')\\r\\n shapeID=config.get('Parameter #5') # (0) gaussian, (1) cosine\\r\\n waitTime=config.get('Parameter #10')\\r\\n self.add_gate_to_all(Gate.Xp)\\r\\n\\r\\n pulse12 = Pulse()\\r\\n pulse12.width = width\\r\\n pulse12.plateau = plateau\\r\\n pulse12.amplitude = amplitude\\r\\n pulse12.frequency = frequency\\r\\n if shapeID==0:\\r\\n pulse12.shape=PulseShape.GAUSSIAN\\r\\n if shapeID==1:\\r\\n pulse12.shape=PulseShape.COSINE\\r\\n pulse12.pulse_type=PulseType.XY\\r\\n pulse12.phase = 0\\r\\n gateP = CustomGate(pulse12)\\r\\n wait=Pulse()\\r\\n wait.shape=PulseShape.COSINE\\r\\n wait.width = 0\\r\\n wait.plateau = waitTime\\r\\n wait.amplitude = 0\\r\\n wait.frequency = 0\\r\\n gateWait=CustomGate(wait)\\r\\n self.add_gate_to_all(gateP)\\r\\n self.add_gate_to_all(gateWait)\\r\\n self.add_gate_to_all(gateP)\\r\\n\\r\\n\\r\\n # self.add_gate_to_all(Gate.Xp)\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a59e9735c60720397a6070368c311f9e\",\n \"score\": \"0.5407481\",\n \"text\": \"def run(simulation,N_step,skip):\\n return [tissue.copy() for tissue in itertools.islice(simulation,0,N_step,skip)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e61e73b58566ea8cc94e81a5d33c951e\",\n \"score\": \"0.54031354\",\n \"text\": \"def t_sequencing_specialized(**kargs):\\n\\n n_parts = 10\\n basedir = sys_config.read('DataExpRoot')\\n basedir_sequencing = os.path.join(basedir, 'sequencing') #\\n tRunRQ = False # run the query that attempts to find out the range of an attribute in the DB (e.g. person_id)\\n\\n # template files (condition and drug)\\n # condition_occurrence-0.csv, drug_exposure-0.csv\\n if tRunRQ: \\n rangeQuery(**kargs) # use this to prepare intermediate dataframes\\n\\n # diag code only\\n n_persons = n_persons_eff = 0\\n for tstamp in (True, ): \\n # for i in range(n_parts): \\n for i in range(0, 10): # condition_occurrence-0.csv, drug_exposure-0.csv \\n cohort_name = 'group-%s' % (i+1) # starting from 1\\n ret = make_seq(include_timestamps=tstamp, # save_intermediate=True\\n include_diag=True, include_med=False, \\n meta='diag', \\n inputdir=basedir_sequencing, \\n condition_table='condition_occurrence-%s.csv' % i, # zero-based\\n drug_table='drug_exposure-%s.csv' % i, \\n cohort=cohort_name, \\n save_csv=False, save_id=True, \\n test_mode=False) # save .csv file and .id file\\n min_id, max_id = ret['min_id'], ret['max_id'] \\n n_persons += len(ret['person_id'])\\n n_persons_eff += len(ret['person_id_eff'])\\n div('status> finished %d sets of documements (n_persons=%d, n_persons_eff=%d, min_id=%s, max_id=%s)' % \\\\\\n ((i+1), n_persons, n_persons_eff, min_id, max_id))\\n\\n print('info> n_persons=%d, n_persons_eff=%d' % (n_persons, n_persons_eff))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b86430b45dc2717c5a3df0b45c840f\",\n \"score\": \"0.5401859\",\n \"text\": \"def attractor_space_candidates(maxts,trmaxts):\\n\\n L = []\\n for t in maxts+trmaxts: L.append([t])\\n return L\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f33e559e05f9eedadcfe72efe9c8a1e6\",\n \"score\": \"0.5383528\",\n \"text\": \"def __call__(self):\\n ts_beg_i = 0\\n for ii in range(len(self.periods)):\\n periods = self.periods[ii : ii + self.tr_n + self.ts_n]\\n\\n # slide testing window forward until it would drop off end;\\n # do NOT reset <ts_beg_i>; once it's at the rightmost part of\\n # <periods>, it stays at the rightmost of all subsequent <periods>\\n for jj in range(ts_beg_i, len(periods) - self.ts_n + 1):\\n ts_periods = periods[jj:jj+self.ts_n]\\n tr_periods = np.setdiff1d(periods, ts_periods)\\n yield tr_periods, ts_periods\\n ts_beg_i = jj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21a52d05f6c3161f86031cfb1734e286\",\n \"score\": \"0.5368995\",\n \"text\": \"def digest(self,seq):\\n result = []\\n seq = self.seq_formation(seq)\\n for enzyme in self.selectedenzyme:\\n pos = []\\n if enzyme[1] == 1:\\n pattern = re.compile(enzyme[2])\\n m = pattern.search(seq) \\n while(m):\\n start = m.start()\\n pos.append(str(start+int(enzyme[3])))\\n m = pattern.search(seq,m.start()+1)\\n else:\\n re_l = enzyme[2].split(',')\\n pos_l_t = enzyme[3].split(',')\\n pos_l = []\\n for i in pos_l_t:\\n pos_l.append( int(i))\\n for i in range(len(re_l)):\\n pattern = re.compile(re_l[i])\\n m = pattern.search(seq)\\n while(m):\\n start = m.start()\\n pos.append(str(start+pos_l[i]))\\n m = pattern.search(seq,m.start()+1)\\n pos = list(set(pos)).sort()\\n\\n result.append(pos)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf7de6923952f10b88f0cd6197526b37\",\n \"score\": \"0.53623706\",\n \"text\": \"def simulate(self):\\n ch=[] #create localized chain and wait time to allow for multiple sims\\n wT=[]\\n qi=self.discSamp(self.stationaryFreq()) #pull a starting state from the stationary frequencies\\n self.startStates.append(qi) \\n #print qi\\n ch.extend(qi) #add state to the markov chain\\n while sum(wT) < self.chainLen:\\n qiRow=self.q[self.stateSpace.index(qi)]\\n l=self.qii(qi) #pull qii from row of state qi\\n #print l\\n wait=random.expovariate(l) #pull waiting time from exponendial distribution, lambda=l=|qii|\\n #print wait\\n if wait+sum(wT) < self.chainLen: #only add another time if it doesn't go over the total length\\n wT.append(wait) #add to list\\n #print wT\\n conditionalProbs=[qij/l for qij in qiRow] #conditional probabilities\\n for i in conditionalProbs:\\n if i == -1:\\n conditionalProbs[conditionalProbs.index(i)]=0\\n qi=self.discSamp(conditionalProbs) #sample new state from qi row, based on condProbs,\\n ch.extend(qi) # create new qii and add state to list \\n #print ch\\n elif wait+sum(wT) >= self.chainLen: #if adding another time will extend it past the branch length, just add a length that covers the rest of the time. \\n last_time=self.chainLen-sum(wT)\\n #print \\n wT.append(last_time)\\n #print \\n conditionalProbs=[qij/l for qij in qiRow] #conditional probabilities\\n for i in conditionalProbs:\\n if i == -1:\\n conditionalProbs[conditionalProbs.index(i)]=0\\n last_qi=self.discSamp(conditionalProbs) \\n ch.extend(last_qi)\\n self.endStates.append(last_qi)\\n #print ch \\n self.waitTimes.append(wT)\\n self.chains.append(ch)\\n pair=[ch,wT]\\n return ch,wT,pair\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc8159f612d028555960a6ee31425622\",\n \"score\": \"0.53215766\",\n \"text\": \"def get_TR(detector,sequences,number_sequences,show_time=False):\\n list_TR = []\\n start = time.time()\\n \\n for i in range(number_sequences):\\n \\n TR = sequences[i].detect(denovo = True, detection = {\\\"detectors\\\": [detector]})\\n list_TR.append(TR)\\n \\n end = time.time()\\n \\n if show_time:\\n \\n print(end - start) \\n \\n return list_TR\",\n \"title\": \"\"\n },\n {\n \"docid\": \"500e8342d38a48b8a0ca196684bba464\",\n \"score\": \"0.53051776\",\n \"text\": \"def simulationDelayedTreatment(numTrials):\\n\\n numViruses = [100, 100, 100, 100]\\n # numViruses = [100, 200, 300, 400]\\n\\n maxPop = [1000, 1000, 1000, 1000]\\n # maxPop = [1000, 1500, 2000, 2500]\\n\\n maxBirthProb = [0.1, 0.1, 0.1, 0.1]\\n # maxBirthProb = [0.1, 0.2, 0.3, 0.4]\\n\\n clearProb = [0.05, 0.05, 0.05, 0.05]\\n # clearProb = [0.05, 0.10, 0.15, 0.20]\\n\\n time_before = [300, 150, 75, 0]\\n # time_before = [150, 150, 150, 150]\\n\\n time_after = 150\\n\\n time_steps = [v + time_after for v in time_before]\\n\\n for condition in range(4):\\n popul = [0 for i in range(time_steps[condition])]\\n f_pop = []\\n for trial in range(numTrials):\\n viruses = [ResistantVirus(maxBirthProb = maxBirthProb[condition],\\n clearProb = clearProb[condition],\\n resistances = {'guttagonol': False},\\n mutProb = 0.005)\\n for i in range(numViruses[condition])]\\n pat = TreatedPatient(viruses=viruses, maxPop=maxPop[condition])\\n\\n for t in range(time_before[condition]):\\n popul[t] += pat.update()\\n pat.addPrescription('guttagonol')\\n\\n t_after = [t + time_before[condition] for t in range(time_after)]\\n for t in t_after:\\n final_pop = pat.update()\\n popul[t] += final_pop\\n if t == time_steps[condition] - 1:\\n f_pop.append(final_pop)\\n\\n pylab.figure('Histograms')\\n pylab.subplot(2, 2, condition+1)\\n pylab.title('Delayed Treatment: {}'.format(time_before[condition]))\\n pylab.hist(f_pop, bins=50, label='{}'.format(time_before[condition]))\\n pylab.xlabel('total virus population')\\n pylab.ylabel('trials'.format(numTrials))\\n pylab.legend(loc='best')\\n\\n pylab.figure('Graphs')\\n means = [p/numTrials for p in popul]\\n pylab.subplot(2, 2, condition+1)\\n pylab.title('Delayed Treatment: {}'.format(time_before[condition]))\\n pylab.plot(means, 'r-', label = 'Virus Pop')\\n pylab.xlabel('Time Steps')\\n pylab.ylabel('Virus Population')\\n pylab.legend(loc='best')\\n pylab.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c75b4a32dfea06545c2e330ef1474175\",\n \"score\": \"0.5289533\",\n \"text\": \"def generate_input_and_target(times_notes_dict, seq_len=50):\\n \\n # Get the start time and end time\\n start_time, end_time = list(times_notes_dict.keys())[0], list(times_notes_dict.keys())[-1]\\n \\n # make the times_notes_dict an array for sliding window purpose\\n notes_tuple_array = []\\n for i in range(start_time, end_time+1):\\n if i in times_notes_dict:\\n notes_tuple_array.append(times_notes_dict[i])\\n \\n list_training, list_target = [], []\\n \\n for window_index in range(len(notes_tuple_array) - seq_len - 1):\\n \\n # initialize current traing list and target list\\n list_append_training, list_append_target = [], []\\n start_iterate = 0\\n flag_target_append = False # flag to append the test list\\n \\n # pad 'e' in the front for the first \\\"seq_len\\\" sequences\\n if window_index < seq_len - 1:\\n start_iterate = seq_len - window_index - 1\\n for i in range(start_iterate): \\n list_append_training.append(('e',1))\\n flag_target_append = True\\n\\n # append the following tuples to the current training list\\n remain_tuples_num = seq_len - start_iterate\\n for j in range(remain_tuples_num):\\n next_tuple = notes_tuple_array[j]\\n next_tuple_str = (','.join(str(x) for x in next_tuple[0]), next_tuple[1])\\n list_append_training.append(next_tuple_str)\\n target_tuple = notes_tuple_array[remain_tuples_num]\\n \\n elif window_index >= seq_len - 1:\\n for j in range(window_index - seq_len + 1, window_index - seq_len + 11):\\n next_tuple = notes_tuple_array[j]\\n next_tuple_str = (','.join(str(x) for x in next_tuple[0]), next_tuple[1])\\n list_append_training.append(next_tuple_str)\\n target_tuple = notes_tuple_array[window_index - seq_len + 11]\\n\\n # add the next target tuple to the list_append_target\\n target_tuple_str = (','.join(str(x) for x in target_tuple[0]), target_tuple[1])\\n list_append_target.append(target_tuple_str)\\n \\n list_training.append(list_append_training)\\n list_target.append(list_append_target)\\n \\n return list_training, list_target\",\n \"title\": \"\"\n },\n {\n \"docid\": \"463236a2d61bb401046134be0e703a92\",\n \"score\": \"0.5265391\",\n \"text\": \"def _generate_combinations(tId, vs, seq, usedVs, offset, isReverse):\\n transOff = generate_peptides_from_variants.transOff\\n #print \\\"TransOffset \\\", transOff, tId,usedVs\\n if vs:\\n v = vs.pop()\\n if v.isHomozygous:\\n pos = v.coding[tId].tranPos + offset\\n usedVs[pos] = v\\n offset = _incorp.get(v.type, lambda a, b, c, d, e, f: e)(seq, v, tId, pos, offset, isReverse)\\n for s in _generate_combinations(tId, vs, seq, usedVs, offset,isReverse):\\n yield s\\n else:\\n vs_tmp = vs[:]\\n tmp_seq = seq[:]\\n tmp_usedVs = usedVs.copy()\\n\\n #generate transcript without the current variant\\n for s in _generate_combinations(tId, vs_tmp, tmp_seq, tmp_usedVs, offset, isReverse):\\n yield s\\n\\n #and one transcript with current variant as we can't resolve haplotypes\\n # update the transcript variant id\\n generate_peptides_from_variants.transOff += 1\\n pos = v.coding[tId].tranPos + offset\\n usedVs[pos] = v\\n offset = _incorp.get(v.type, lambda a, b, c, d, e, f: e)(seq, v, tId, pos, offset, isReverse)\\n\\n for s in _generate_combinations(tId, vs, seq, usedVs, offset, isReverse):\\n yield s\\n else:\\n yield tId + \\\":FRED2_%i\\\"%transOff, seq, usedVs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b637de6617290a7d631ba528a0ee8744\",\n \"score\": \"0.52637666\",\n \"text\": \"def gen_delay_trial(lengthStimulus, delay, lengthSignal, pattern='random', exp_speed=40):\\n\\n stimulus = [[1, 0], [0, 1]]\\n st1 = random.choice(stimulus)\\n st2 = random.choice(stimulus)\\n\\n\\n stim_lib = {'random': [st1, st2], 'AA': [stimulus[0], stimulus[0]], 'AB': [stimulus[0], stimulus[1]],\\n 'BB': [stimulus[1], stimulus[1]], 'BA': [stimulus[1], stimulus[0]]}\\n\\n st1 = stim_lib[pattern][0]\\n st2 = stim_lib[pattern][1]\\n\\n u1 = [st1[0] for k in range(lengthStimulus)] + [0 for k in range(delay)] + [st2[0] for k in range(lengthStimulus)] +\\\\\\n [0 for k in range(lengthSignal - 2 * lengthStimulus - delay)]\\n u2 = [st1[1] for k in range(lengthStimulus)] + [0 for k in range(delay)] + [st2[1] for k in range(lengthStimulus)] + \\\\\\n [0 for k in range(lengthSignal - 2 * lengthStimulus - delay)]\\n start_target = delay + lengthStimulus * 2\\n if st1 == st2:\\n target = [0 for k in range(lengthStimulus * 2 + delay)] + [-np.exp(-(n - start_target) / exp_speed) + 1 for n in\\n range(start_target, lengthSignal)]\\n\\n else:\\n target = [0 for k in range(lengthStimulus * 2 + delay)] + [np.exp(-(n - start_target) / exp_speed) - 1 for n in\\n range(start_target, lengthSignal)]\\n\\n return u1, u2, target\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e077496abe9980e4152c352494104fd5\",\n \"score\": \"0.52543825\",\n \"text\": \"def make_timeseries(self, change_times):\\n self.timeseries = []\\n i = 0\\n time_len = len(self.times)\\n for change_time in change_times:\\n while i < time_len and change_time >= self.times[i]:\\n i += 1\\n self.timeseries.append(self.instructions[self.times[i-1]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfc78ede9406bc1a9c3410cceb7bd320\",\n \"score\": \"0.52477765\",\n \"text\": \"def getspectra(self, choices, spec_old):\\r\\n if self.v1.stream[0].stats.station == 'No data available':\\r\\n npts = self.v1.stream[0].stats.npts\\r\\n data = np.ones(npts)\\r\\n fspec1 = data[:]\\r\\n fspec2 = data[:]\\r\\n fspec3 = data[:]\\r\\n freqs = np.linspace(0.01, 20, npts) \\r\\n return fspec1, fspec2, fspec3, freqs\\r\\n \\r\\n idx = choices.index(spec_old)\\r\\n if idx == 0:\\r\\n if self.specs[idx] is None:\\r\\n tr1 = self.v2.stream[2]\\r\\n tr2 = self.v2.stream[5]\\r\\n tr3 = self.v2.stream[8]\\r\\n self.specs[idx] = self.ftransform(tr1, tr2, tr3) \\r\\n if idx == 1:\\r\\n if self.specs[idx] is None:\\r\\n tr1 = self.v2.stream[0]\\r\\n tr2 = self.v2.stream[3]\\r\\n tr3 = self.v2.stream[6]\\r\\n self.specs[idx] = self.ftransform(tr1, tr2, tr3) \\r\\n if idx == 2:\\r\\n if self.specs[idx] is None:\\r\\n tr1 = self.v1.stream[0]\\r\\n tr2 = self.v1.stream[1]\\r\\n tr3 = self.v1.stream[2]\\r\\n self.specs[idx] = self.ftransform(tr1, tr2, tr3) \\r\\n return self.specs[idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8f2c8d5dcd22be617794b0e1b0b1b91\",\n \"score\": \"0.52378535\",\n \"text\": \"def generate_candidates(S, M, max_t=10, enforce=False):\\n candslist=[]\\n for m in M:\\n wid = title2id(m[1])\\n if wid is None:\\n raise Exception(m[1].encode('utf-8') + ' not found')\\n \\n clist = anchor2concept(S[m[0]])\\n clist = sorted(clist, key=lambda x: -x[1])\\n\\n smooth=0 \\n trg = [(i,(c,f)) for i,(c,f) in enumerate(clist) if c==wid]\\n if enforce and (not trg):\\n trg=[(len(clist), (wid,0))]\\n smooth=1\\n\\n \\n clist = clist[:max_t]\\n if enforce and (smooth==1 or trg[0][0]>=max_t): \\n if clist:\\n clist.pop()\\n clist.append(trg[0][1])\\n s = sum(c[1]+smooth for c in clist ) \\n clist = [(c,float(f+smooth)/s) for c,f in clist ]\\n \\n candslist.append(clist)\\n return candslist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58169fb6f7998dafaed7f0bf18a52410\",\n \"score\": \"0.52106977\",\n \"text\": \"def generate_ut_comb(A,B,H,Q,R,x0,timesteps,actions):\\n act_num = len(actions)\\n gxzts = []\\n gx,zt = generate_fake(A,B,H,Q,R,x0,timesteps,actions[0])\\n gxzts.append(gx)\\n gxzts.append(zt)\\n for i in range(1,act_num): \\n gx_act,zt_act = generate_ut_not0(A,B,H,Q,R,timesteps,actions[i],gx,zt)\\n gxzts.append(gx_act)\\n gxzts.append(zt_act)\\n return gxzts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70b33a79eaa8ca48ff5965a83da6a5ac\",\n \"score\": \"0.52025497\",\n \"text\": \"def simulationDelayedTreatment(numTrials, timeRange):\\n\\n allViruses = []\\n for i in range(numTrials):\\n viruses = []\\n for i in range(100):\\n viruses.append(ResistantVirus(0.1, 0.05, {'guttagonol': False, 'grimpex': False}, 0.005))\\n patient1 = TreatedPatient(viruses, 1000)\\n \\n # run trial, administering drug at point t=timeRange\\n for t in range(150 + timeRange + 150):\\n if t == 150:\\n patient1.addPrescription('guttagonol')\\n if t == 150 + timeRange:\\n patient1.addPrescription('grimpex')\\n try:\\n count = patient1.update()\\n except AssertionError:\\n pass\\n allViruses.append(count)\\n return allViruses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e85da97b6e460b8061520c72c9e1a46\",\n \"score\": \"0.5198447\",\n \"text\": \"def next_takes(self):\\n if not self.current_generator:\\n return self.takes\\n else:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e85da97b6e460b8061520c72c9e1a46\",\n \"score\": \"0.5198447\",\n \"text\": \"def next_takes(self):\\n if not self.current_generator:\\n return self.takes\\n else:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1baa03e5afee81ce66c7349765accec6\",\n \"score\": \"0.51981646\",\n \"text\": \"def test_pomodori_segment_generator(self):\\n self.assertListEqual(\\n list(islice(self.calculator.pomodori_segments(), 16)),\\n [\\n 'pomodoro',\\n 'short-break',\\n 'pomodoro',\\n 'short-break',\\n 'pomodoro',\\n 'short-break',\\n 'pomodoro',\\n 'long-break',\\n 'pomodoro',\\n 'short-break',\\n 'pomodoro',\\n 'short-break',\\n 'pomodoro',\\n 'short-break',\\n 'pomodoro',\\n 'long-break',\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e66e062414968060ce6fd7e420dbff1\",\n \"score\": \"0.51796937\",\n \"text\": \"def test_13(self):\\n main = Main()\\n for i in range(0, 30):\\n main.generate_systems(n=10,\\n min_m=10,\\n max_n=100,\\n max_m=300,\\n step=10,\\n save_results=True,\\n save_systems=True,\\n upper_bound=3,\\n lower_bound=1,\\n max_tries=30,\\n update_strongly_k=True,\\n gi=Gi())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dde0edcc9d043bc81d6e4be90d5bd825\",\n \"score\": \"0.5179239\",\n \"text\": \"def output_seq(self,seq):\\n result_seq = []\\n result_num = []\\n output_seq = []\\n output_num = []\\n for i in range(0,len(seq)-10,10):\\n seq_temp = ' '\\n seq_temp += seq[i:i+10]\\n result_seq.append(seq_temp)\\n result_num.append(i+10)\\n if len(seq) % 10 != 0:\\n seq_temp = ' '\\n seq_temp += seq[len(seq)/10 *10:len(seq)]\\n result_seq.append(seq_temp)\\n result_num.append(len(seq))\\n \\n for i in range(0,len(result_seq)-6,6):\\n temp = []\\n for j in range(i,i+6):\\n temp.append(result_num[j])\\n output_num.append(temp)\\n temp = []\\n for j in range(i,i+6):\\n temp.append(result_seq[j])\\n output_seq.append(temp)\\n \\n temp = []\\n for j in range((len(result_seq)-1)/6 *6,len(result_seq)):\\n temp.append(result_num[j])\\n output_num.append(temp)\\n temp = []\\n for j in range((len(result_seq)-1)/6 *6,len(result_seq)):\\n temp.append(result_seq[j])\\n output_seq.append(temp)\\n output = []\\n for i in range(len(output_num)):\\n output.append(output_num[i])\\n output.append(output_seq[i])\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77522fa6f45dfb9664fb6c40b7b5f205\",\n \"score\": \"0.51779944\",\n \"text\": \"def indist_configs(G,q):\\n return [chi_from_tau(tau_config,G) for qp in range(q+1)\\n for tau_config in itertools.combinations(range(G),qp)]\\n #for degeneracy in range(int(fac(sum(tau_config))*choose(q,sum(tau_config))))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c507583fccd004555c29b49deaca3f30\",\n \"score\": \"0.5177002\",\n \"text\": \"def _generate_combinations(tId, vs, seq, usedVs, offset, isReverse=False):\\n transOff = generate_transcripts_from_variants.transOff\\n #print \\\"TransOffset \\\", transOff, tId,usedVs\\n if vs:\\n v = vs.pop()\\n if v.isHomozygous:\\n pos = v.coding[tId].tranPos + offset\\n usedVs[pos] = v\\n offset = _incorp.get(v.type, lambda a, b, c, d, e, f: e)(seq, v, tId, pos, offset, isReverse)\\n for s in _generate_combinations(tId, vs, seq, usedVs, offset, isReverse):\\n yield s\\n else:\\n vs_tmp = vs[:]\\n tmp_seq = seq[:]\\n tmp_usedVs = usedVs.copy()\\n\\n for s in _generate_combinations(tId, vs_tmp, tmp_seq, tmp_usedVs, offset, isReverse):\\n yield s\\n\\n # update the transcript variant id\\n generate_transcripts_from_variants.transOff += 1\\n pos = v.coding[tId].tranPos + offset\\n usedVs[pos] = v\\n\\n offset = _incorp.get(v.type, lambda a, b, c, d, e, f: e)(seq, v, tId, pos, offset, isReverse)\\n\\n for s in _generate_combinations(tId, vs, seq, usedVs, offset, isReverse):\\n yield s\\n else:\\n yield tId+\\\":FRED2_%i\\\"%transOff, seq, usedVs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26d4f1325147a4485c1e853f8ec150d9\",\n \"score\": \"0.5172283\",\n \"text\": \"def recur(seq, t1_cvrd, t2_cvrd, t1_evts, t2_evts, t1_name, t2_name):\\n global seq_cnt\\n is_t1_first = True\\n\\n if t1_cvrd == len(t1_evts):\\n seq_cnt += 1\\n final_seq = seq + t2_evts[t2_cvrd:]\\n print str(seq_cnt) + \\\". \\\",\\n print \\\"\\\".join([str(elem) for elem in final_seq])\\n gen_plantuml_msc(seq_cnt, final_seq, t1_evts, t2_evts, t1_name, t2_name)\\n elif t2_cvrd == len(t2_evts):\\n seq_cnt += 1\\n final_seq = seq + t1_evts[t1_cvrd:]\\n print str(seq_cnt) + \\\". \\\",\\n print \\\"\\\".join([str(elem) for elem in final_seq])\\n gen_plantuml_msc(seq_cnt, final_seq, t1_evts, t2_evts, t1_name, t2_name)\\n else:\\n # print seq\\n is_t1_first = (t1_cvrd <= t2_cvrd)\\n\\n if is_t1_first:\\n recur(seq + [t1_evts[t1_cvrd]], t1_cvrd+1, t2_cvrd, t1_evts, t2_evts, t1_name, t2_name)\\n recur(seq + [t2_evts[t2_cvrd]], t1_cvrd, t2_cvrd+1, t1_evts, t2_evts, t1_name, t2_name)\\n else:\\n recur(seq + [t2_evts[t2_cvrd]], t1_cvrd, t2_cvrd+1, t1_evts, t2_evts, t1_name, t2_name)\\n recur(seq + [t1_evts[t1_cvrd]], t1_cvrd+1, t2_cvrd, t1_evts, t2_evts, t1_name, t2_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f65668daa670de3488546ae40b0d8f92\",\n \"score\": \"0.5156483\",\n \"text\": \"def make_sequence(config, ps_config, network=None, rng=None, test=False,\\n enforce_aht_0=False, max_difference=96, refocus_every=60):\\n cache_size = 1000\\n \\n @lru_cache(maxsize=cache_size)\\n def get_frame(sequence):\\n if len(sequence) == 0:\\n return np.eye(3)\\n else:\\n return ps.rotations[sequence[-1]] @ get_frame(sequence[:-1])\\n \\n @lru_cache(maxsize=cache_size)\\n def get_axis_counts(sequence):\\n if len(sequence) == 0:\\n return np.zeros((6,))\\n else:\\n counts = get_axis_counts(sequence[:-1]).copy()\\n frame = get_frame(sequence)\\n axis = np.where(frame[-1, :])[0][0]\\n is_negative = np.sum(frame[-1, :]) < 0\\n counts[axis + 3 * is_negative] += 1\\n return counts\\n \\n @lru_cache(maxsize=cache_size)\\n def get_propagators(sequence):\\n if len(sequence) == 0:\\n return ([qt.identity(ps_config.Utarget.dims[0])]\\n * ps_config.ensemble_size)\\n else:\\n propagators = get_propagators(sequence[:-1])\\n propagators = [prop.copy() for prop in propagators]\\n for s in range(ps_config.ensemble_size):\\n propagators[s] = (\\n ps_config.pulses_ensemble[s][sequence[-1]]\\n * propagators[s]\\n )\\n return propagators\\n \\n @lru_cache(maxsize=cache_size)\\n def get_reward(sequence):\\n propagators = get_propagators(sequence)\\n fidelity = 0\\n for s in range(ps_config.ensemble_size):\\n fidelity += np.clip(\\n qt.metrics.average_gate_fidelity(\\n propagators[s],\\n ps_config.Utarget\\n ), 0, 1\\n )\\n fidelity *= 1 / ps_config.ensemble_size\\n reward = -1 * np.log10(1 - fidelity + 1e-200)\\n return reward\\n \\n @lru_cache(maxsize=cache_size)\\n def get_valid_pulses(sequence):\\n \\\"\\\"\\\"\\n Args:\\n sequence (tuple): Pulse sequence\\n \\\"\\\"\\\"\\n valid_pulses = []\\n for pulse_index in range(len(ps_config.pulses_ensemble[0])):\\n # count whether there are equal numbers of each pulse\\n # (not including delays)\\n # equivalent to chirality condition, for pulse rotation errors\\n if len(sequence) > 0:\\n delays_applied = (np.array(sequence) == 0).sum()\\n pulse_total = (np.array(sequence) == pulse_index).sum()\\n if pulse_total >= (ps_config.max_sequence_length\\n - delays_applied) / 4:\\n continue\\n # AHT constraints\\n # commented out the .copy() below, I don't think this should break\\n # anything...\\n counts = get_axis_counts(sequence + (pulse_index,)) # .copy()\\n if enforce_aht_0:\\n if not (counts <= ps_config.max_sequence_length / 6).all():\\n continue\\n # axis counts on ±x, ±y, ±z axes\\n pm_counts = np.array([counts[0] + counts[3],\\n counts[1] + counts[4],\\n counts[2] + counts[5]])\\n diff = np.max(pm_counts) - np.min(pm_counts)\\n if diff > max_difference:\\n continue\\n max_count = (np.ceil((len(sequence) + 1) / refocus_every)\\n * refocus_every)\\n if (counts <= max_count / 6).all():\\n valid_pulses.append(pulse_index)\\n return valid_pulses\\n \\n @lru_cache(maxsize=cache_size)\\n def get_inference(sequence):\\n network.eval() # switch network to evaluation mode\\n if len(sequence) == 0:\\n state = one_hot_encode(sequence, start=True).unsqueeze(0)\\n with torch.no_grad():\\n (policy, val, h) = network(state)\\n else:\\n (_, _, h) = get_inference(sequence[:-1])\\n state = one_hot_encode(sequence[-1:], start=False).unsqueeze(0)\\n with torch.no_grad():\\n (policy, val, h) = network(state, h_0=h)\\n policy = policy.squeeze().numpy()\\n val = val.squeeze().numpy()\\n return policy, val, h\\n \\n sequence_funcs = (get_frame, get_reward, get_valid_pulses, get_inference)\\n \\n # create random number generator (ensure randomness with multiprocessing)\\n if rng is None:\\n rng = np.random.default_rng()\\n search_statistics = []\\n while not ps_config.is_done():\\n pulse, root = run_mcts(config, ps_config, network=network, rng=rng,\\n sequence_funcs=sequence_funcs, test=test)\\n probabilities = np.zeros((5,))\\n for p in root.children:\\n probabilities[p] = root.children[p].visit_count / root.visit_count\\n search_statistics.append(\\n (ps_config.sequence.copy(),\\n probabilities)\\n )\\n if pulse is not None:\\n ps_config.apply(pulse)\\n else:\\n break\\n if pulse is None:\\n value = -1\\n else:\\n value = get_reward(tuple(ps_config.sequence))\\n search_statistics = [\\n stat + (value, ) for stat in search_statistics\\n ]\\n return search_statistics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a47715e26dd7d27d516a5b7fde492c00\",\n \"score\": \"0.51522934\",\n \"text\": \"def simulationTwoDrugsDelayedTreatment(numTrials):\\n numViruses = [100, 100, 100, 100]\\n maxPop = [1000, 1000, 1000, 1000]\\n maxBirthProb = [0.1, 0.1, 0.1, 0.1]\\n clearProb = [0.05, 0.05, 0.05, 0.05]\\n time_inter = [300, 150, 75, 0]\\n time_bafter = 150\\n\\n time_steps = [v + 2 * time_bafter for v in time_inter]\\n\\n for condition in range(4):\\n popul = [0 for i in range(time_steps[condition])]\\n f_pop = []\\n for trial in range(numTrials):\\n viruses = [ResistantVirus(maxBirthProb = maxBirthProb[condition],\\n clearProb = clearProb[condition],\\n resistances = {'guttagonol': False,\\n 'grimpex': False},\\n mutProb = 0.005)\\n for i in range(numViruses[condition])]\\n pat = TreatedPatient(viruses=viruses, maxPop=maxPop[condition])\\n\\n for t in range(time_bafter):\\n popul[t] += pat.update()\\n pat.addPrescription('guttagonol')\\n\\n t_after_1 = [t + time_bafter for t in range(time_inter[condition])]\\n for t in t_after_1:\\n popul[t] += pat.update()\\n pat.addPrescription('grimpex')\\n\\n t_after_2 = [t + time_bafter + time_inter[condition] for t in range(time_bafter)]\\n for t in t_after_2:\\n final_pop = pat.update()\\n popul[t] += final_pop\\n if t == time_steps[condition] - 1:\\n f_pop.append(final_pop)\\n\\n pylab.figure('Histograms')\\n pylab.subplot(2, 2, condition+1)\\n pylab.title('Delayed Treatment: {}'.format(time_inter[condition]))\\n pylab.hist(f_pop, bins=50, label='{}'.format(time_inter[condition]))\\n pylab.xlabel('total virus population')\\n pylab.ylabel('trials'.format(numTrials))\\n pylab.legend(loc='best')\\n\\n pylab.figure('Graphs')\\n means = [p/numTrials for p in popul]\\n pylab.subplot(2, 2, condition+1)\\n pylab.title('Delayed Treatment: {}'.format(time_inter[condition]))\\n pylab.plot(means, 'r-', label = 'Virus Pop')\\n pylab.xlabel('Time Steps')\\n pylab.ylabel('Virus Population')\\n pylab.legend(loc='best')\\n pylab.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25344870376423e91421b4808cc49798\",\n \"score\": \"0.5151055\",\n \"text\": \"def test_make_sequences(self):\\n self.assertEqual(self.sequence.sequences[:3],['SSSSSSSSSSS', 'SSSSSSSSSD', 'SSSSSSSSDS'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdeb3f6595cda9f98bb8cc02a9c9c8da\",\n \"score\": \"0.5137381\",\n \"text\": \"def solve_part_2(pots, patterns, generations):\\n offset = 0\\n score = 0\\n prev_score = 0\\n prev_diff = 0\\n consecutive = 0 #number of same diff occuring consecutively\\n\\n print(\\\"Gen\\\\tScore\\\\tDiff\\\")\\n for gen in range(1, generations + 1):\\n pots, adjust_offset = pass_generation(pots, patterns)\\n offset += adjust_offset\\n score = score_pots(pots, offset)\\n diff = score - prev_score\\n print(\\\"{}\\\\t{}\\\\t{}\\\".format(gen, score, diff))\\n if diff == prev_diff:\\n consecutive += 1\\n else:\\n consecutive = 0\\n prev_score = score\\n prev_diff = diff\\n if consecutive > 5:\\n break\\n \\n return calculate_score_at(gen, generations, score, diff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6666d6cea7d526097b02b57d05722638\",\n \"score\": \"0.51351064\",\n \"text\": \"def times(self):\\n ts = sorted(iter(self.spec_map))\\n return ts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"763196d71218feab3b62492dac2ad73d\",\n \"score\": \"0.51313096\",\n \"text\": \"def random_parameter_generation(ranges, is_exp, num_combinations):\\r\\n lists = list()\\r\\n for (i, (min_b, max_b)) in enumerate(ranges):\\r\\n sublist = list()\\r\\n for _ in range(num_combinations):\\r\\n rnd = np.random.uniform() * (max_b - min_b) + min_b\\r\\n if is_exp[i] is True:\\r\\n rnd = np.power(10., rnd)\\r\\n sublist.append(rnd)\\r\\n lists.append(sublist)\\r\\n return lists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b631ebf3674ccae5f3a69016649b3d06\",\n \"score\": \"0.5127005\",\n \"text\": \"def run_it(seq):\\n\\n measurements = []\\n for measurement in seq.splitlines():\\n measurements.append(sorted(map(int, measurement.split('x'))))\\n\\n paper = 0\\n ribbon = 0\\n for l, w, h in measurements:\\n area = [l * w, w * h, h * l]\\n paper += 2 * sum(area) + min(area)\\n ribbon += 2 * (l + w) + (l * w * h)\\n\\n print('Part 1: ', paper)\\n print('Part 2: ', ribbon)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c12039cf13c0f1fcd57a59316b94bd9\",\n \"score\": \"0.5117616\",\n \"text\": \"def simulationTwoDrugsDelayedTreatment(numTrials):\\n # TODO\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca1d1067b1ba2747558abc921ab5288c\",\n \"score\": \"0.51163834\",\n \"text\": \"def sample_sequences(CAG: DiGraph, s0: Series, steps: int, samples: int,\\n Δt: float = 1.0) -> List[Series]:\\n\\n s0 = s0.values[np.newaxis].T\\n return take(samples, repeatfunc(sample_sequence, CAG, s0, steps, Δt))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb0ecf31eac8e9aa4de411fcb4850b3a\",\n \"score\": \"0.5110198\",\n \"text\": \"def generateSequence(self, T, numberSamples=1):\\n (objectives,measurements,_)=samplePFSequence(self._pf,T,numberSamples=numberSamples)\\n \\n return (objectives,measurements)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e7ae9d65d82fb9560205f6699b579cc\",\n \"score\": \"0.5108246\",\n \"text\": \"def _generate_Sequence(self, min, max, step):\\n i = min\\n I = []\\n while i <= max:\\n I.append(i)\\n i = i + step\\n return I\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0448b9d148eeb1c1fc721c942002a938\",\n \"score\": \"0.5106794\",\n \"text\": \"def test_seq_permuts():\\n existing_handles = \\\"\\\"\\\"\\n GGGCTCATGCGAGGCTGTATGT\\n TTCCTCTACCACCTACATCAC\\n GGTCGTGTTCGATCAGAGCGC\\n GTTGAGTCCTGTCAC\\n GTTGCATCCTCCAAC\\n GTGCAGACAAC\\n CGGGAACCGCGTATCTTGCCA\\n CGGTTCATAACGGAACCGCCG\\n GATTCGGGAAGCAAACCCGCG\\n CCAGTACGCGGCGTAGTACCC\\n CGGAATACTTGAATCGGGTTC\\n GCCAGCTCAGCC\\n \\\"\\\"\\\"\\n # existing_handles=existing_handles.split()\\n # permuts = seq_permuts(existing_handles[0])\\n permuts = seq_permuts(existing_handles, permlen=5, copytoclipboard=True, includerevcompl=True)\\n print(existing_handles)\\n print(\\\",\\\".join(permuts))\\n\\n permuts4 = seq_permuts(existing_handles, permlen=4, includerevcompl=True)\\n\\n permuts4 = [seq for seq in permuts4 if sum([x.upper() in \\\"GC\\\" for x in seq]) > 2]\\n\\n print(\\\",\\\".join(permuts4))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a717c2a67e254a731ba794f0e7356cb7\",\n \"score\": \"0.510483\",\n \"text\": \"def genConceptChain(concept_desc, sequential):\\n concept_chain = []\\n num_samples = 0\\n more_appearences = True\\n appearence = 0\\n while more_appearences:\\n concepts_still_to_appear = []\\n for cID in concept_desc:\\n concept = concept_desc[cID]\\n if concept.appearences > appearence:\\n concepts_still_to_appear.append(concept)\\n more_appearences = len(concepts_still_to_appear) > 0\\n for concept in concepts_still_to_appear:\\n concept_chain.append(concept.id)\\n num_samples += concept.examples_per_appearence\\n appearence += 1\\n # for cID in concept_desc:\\n # concept = concept_desc[cID]\\n # concept_chain += [concept.id] * (concept.appearences)\\n # num_samples += (concept.appearences * concept.examples_per_appearence)\\n if not sequential:\\n random.shuffle(concept_chain)\\n return concept_chain, num_samples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b46bee8bb9d409084b01d98824f855a6\",\n \"score\": \"0.5098435\",\n \"text\": \"def repeat_sequence_input(num_trials):\\n\\n half = [np.sin(2 * np.pi * t / 200) for t in range(100)]\\n whole = [np.sin(2 * np.pi * t / 400) for t in range(200)]\\n pause = [0 for k in range(100)]\\n response_time = np.zeros([4, 1400])\\n signal = np.empty([4, 2800 * num_trials])\\n target = np.empty([4, 2800 * num_trials])\\n for tr in range(num_trials):\\n signal_tr = np.empty([4, 1400])\\n E = np.concatenate((half, half, whole, pause, half, half, whole, pause, half, pause, pause, pause))\\n G = np.concatenate(\\n (pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, half, pause, pause))\\n D = np.concatenate(\\n (pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, half))\\n C = np.concatenate(\\n (pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, pause, half, pause))\\n signal_tr[0, :] = G\\n signal_tr[1, :] = E\\n signal_tr[2, :] = D\\n signal_tr[3, :] = C\\n target_tr = np.concatenate((response_time, signal_tr), axis=1)\\n signal_tr = np.concatenate((signal_tr, response_time), axis=1)\\n signal[:, tr * 2800:(tr + 1) * 2800] = signal_tr\\n target[:, tr * 2800:(tr + 1) * 2800] = target_tr\\n\\n return np.array(signal), np.array(target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b095ef625c291f4c9044e8e628582f4\",\n \"score\": \"0.5094453\",\n \"text\": \"def generate_candidates(S, M, max_t=20, enforce=False):\\n candslist=[]\\n for m in M:\\n \\n clist = anchor2concept(S[m[0]])\\n if not clist:\\n clist=((0L,1L),)\\n \\n clist = sorted(clist, key=lambda x: -x[1])\\n clist = clist[:max_t]\\n \\n smooth=0 \\n if enforce: \\n wid = title2id(m[1]) \\n # if wid is None:\\n # raise Exception(m[1].encode('utf-8') + ' not found')\\n \\n \\n trg = [(i,(c,f)) for i,(c,f) in enumerate(clist) if c==wid]\\n if not trg:\\n trg=[(len(clist), (wid,0))]\\n smooth=1\\n\\n \\n if smooth==1 or trg[0][0]>=max_t: \\n if clist:\\n clist.pop()\\n clist.append(trg[0][1])\\n \\n s = sum(c[1]+smooth for c in clist ) \\n clist = [(c,float(f+smooth)/s) for c,f in clist ]\\n \\n candslist.append(clist)\\n return candslist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"924cdb54cd9aada313e4f2dafe759608\",\n \"score\": \"0.50824124\",\n \"text\": \"def generate_system_from_periods(rng: np.random.Generator, n: int,\\n min_period: int, max_period: int, sum_util,\\n deadline_type: str, sum_dens, max_jitter: int,\\n abs_tol: float):\\n ones = [1.0] * n\\n while True:\\n periods = np.exp(\\n rng.uniform(low=np.log(min_period),\\n high=np.log(max_period),\\n size=n)).tolist()\\n periods = [math.ceil(p) for p in periods]\\n us = drs(rng, n, sum_util)\\n wcets = [max(1, math.floor(p * u)) for (p, u) in zip(periods, us)]\\n us = [w / p for (w, p) in zip(wcets, periods)]\\n if not math.isclose(sum_util, sum(us), abs_tol=abs_tol):\\n continue\\n if deadline_type == 'implicit':\\n deadlines = periods\\n elif deadline_type == 'constrained':\\n if sum(us) < sum_dens:\\n # assume implicit deadlines because total utilization must be\\n # less than total density for a constrained-deadline system.\\n deadlines = periods\\n else:\\n ds = drs(rng, n, sum_dens, lower_bounds=us, upper_bounds=ones)\\n deadlines = [\\n math.floor(wcet / d) for d, wcet in zip(ds, wcets)\\n ]\\n assert all(d <= p for d, p in zip(deadlines, periods))\\n else:\\n ds = drs(rng, n, sum_dens, upper_bounds=ones)\\n deadlines = [math.floor(wcet / d) for d, wcet in zip(ds, wcets)]\\n jitters = rng.integers(low=0,\\n high=[\\n min(max_jitter, d - w) + 1\\n for w, d in zip(wcets, deadlines)\\n ],\\n size=n)\\n return [\\n Task(wcet=w, period=p, deadline=d, jitter=j.item())\\n for w, p, d, j in zip(wcets, periods, deadlines, jitters)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05d03bdd3f3b03f9bfe440a7f3ea7e0b\",\n \"score\": \"0.50739104\",\n \"text\": \"def genmotionsequencewaistarm(robot, robotplot, handpkg, start, goal, obstacleslist = []):\\n\\n # the waist range is always\\n jointlimits = [[-90.0, 90.0]]\\n for i in robot.targetjoints:\\n jointlimits.append([robot.rgtarm[i]['rngmin'], robot.rgtarm[i]['rngmax']])\\n planner = rrt.RRT(start=start, goal=goal, iscollidedfunc = iscollidedfunc,\\n jointlimits = jointlimits, expanddis = 20, robot = robot,\\n robotplot = robotplot, handpkg = handpkg)\\n import time\\n tic = time.clock()\\n [path, sampledpoints] = planner.planning()\\n toc = time.clock()\\n print toc-tic\\n return [path, sampledpoints]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02f72c18d370c3f486a9412f79b64880\",\n \"score\": \"0.50656134\",\n \"text\": \"def generate_examples(params) -> Tuple[list, list, dict]:\\n\\n interrupted = False\\n def signal_handler(signal, frame):\\n nonlocal interrupted\\n print(f\\\"Received signal {signal:d}, interrupting generation\\\")\\n interrupted = True\\n signal.signal(signal.SIGINT, signal_handler)\\n\\n if params.num_variables > 26:\\n raise ValueError(\\\"Cannot generate more than 26 APs\\\")\\n variables = list(map(chr, range(97, 97 + params.num_variables)))\\n if not isinstance(params.tree_size, tuple):\\n params.tree_size = (1, params.tree_size)\\n\\n class SpecPatternGenWrapper:\\n def __next__(self):\\n d = SpecPatternGen(variables, params.tree_size)\\n return d.run()\\n formula_generator = SpecPatternGenWrapper()\\n\\n dist_gate = DistributionGate('formula size', params.formula_dist, params.tree_size, params.num_examples, start_calc_from=10, alpha=params.alpha)\\n\\n # generate samples\\n print('Generating examples...')\\n examples = []\\n timeout_formulas = []\\n sat_examples = 0\\n unsat_examples = 0\\n total_examples = 0\\n dropped_sat_examples = 0\\n dropped_unsat_examples = 0\\n dropped_dist_examples = 0\\n dropped_timeout_examples = 0\\n time_started = datetime.datetime.now()\\n last_msg_time = time_started\\n last_msg_percent = 0\\n accu = { k : 0 for k in {'in_length', 'out_length', 'solve_time'}}\\n info = {'max_in_length' : 0, 'max_out_length' : 0}\\n while True:\\n current_percent = total_examples / params.num_examples * 100\\n now = datetime.datetime.now()\\n if current_percent - last_msg_percent >= params.log_each_x_percent or now - last_msg_time > datetime.timedelta(hours=1):\\n last_msg_percent = current_percent\\n last_msg_time = now\\n print(\\\"Generated {:,d} of {:,d} examples ({:4.1f}%); dropped {:,d} sat, {:,d} unsat, {:,d} dist, {:,d} timeout; at {:s} runtime\\\".format(total_examples, \\n params.num_examples, current_percent, dropped_sat_examples, dropped_unsat_examples, dropped_dist_examples, dropped_timeout_examples, utils.strfdelta_hms(now - time_started)))\\n sys.stdout.flush()\\n if total_examples >= params.num_examples or dist_gate.full() or interrupted:\\n break\\n if params.max_runtime != 0.0 and (now - time_started).total_seconds() > params.max_runtime:\\n print('Exiting due to exceeded runtime')\\n break\\n\\n formula = next(formula_generator)\\n if not isinstance(formula, ltl_parser.LTLFormula):\\n if not isinstance(formula, str):\\n if formula is None:\\n continue\\n formula = formula.to_str()\\n formula_obj = ltl_parser.ltl_formula(formula, 'spot')\\n else:\\n formula_obj = formula\\n\\n if not dist_gate.gate(formula_obj): # formula doesn't fit distribution\\n dropped_dist_examples += 1\\n continue\\n if formula == '1':\\n continue\\n\\n is_sat, witness, d = solve_ltl(formula_obj, timeout=params.timeout, binary_path=params.binary_dir)\\n\\n if is_sat is None: # due to timeout\\n if params.save_timeouts:\\n timeout_formulas.append(formula_obj.to_str('spot', spacing='all ops', full_parens=True))\\n dropped_timeout_examples += 1\\n continue\\n elif not is_sat: # unsat\\n if (params.frac_unsat is not None) and unsat_examples >= params.frac_unsat * params.num_examples:\\n dropped_unsat_examples += 1\\n continue\\n else: # more unsat samples needed\\n witness_str = '0'\\n dist_gate.update(formula_obj)\\n unsat_examples += 1\\n else: # is_sat\\n if (params.frac_unsat is not None) and sat_examples >= (1 - params.frac_unsat) * params.num_examples:\\n dropped_sat_examples += 1\\n continue\\n elif random.random() < params.drop_sat_prob:\\n # don't log\\n continue\\n else: # more sat samples needed\\n witness_str = '1'\\n out_length = len(witness_str)\\n info['max_out_length'] = max(info['max_out_length'], out_length)\\n accu['out_length'] += out_length\\n dist_gate.update(formula_obj)\\n sat_examples += 1\\n\\n for k, v in d.items(): # update info accumulator with this samples' info\\n if k in accu:\\n accu[k] += v\\n in_length = formula_obj.size()\\n info['max_in_length'] = max(info['max_in_length'], in_length)\\n accu['in_length'] += in_length\\n formula_str = formula_obj.to_str('network-polish')\\n examples.append((formula_str, witness_str, d))\\n total_examples += 1\\n\\n if params.include_solve_time:\\n info['avg_solve_time'] = accu['solve_time'] / total_examples\\n print('Average solve time: {avg_solve_time:.2f} ms'.format(**info))\\n info['avg_in_length'] = accu['in_length'] / total_examples\\n info['avg_out_length'] = accu['out_length'] / sat_examples\\n info['runtime'] = utils.strfdelta_hms(datetime.datetime.now() - time_started)\\n info['_dist_gate'] = dist_gate\\n info['examples_generated'] = total_examples\\n info['examples_generated_sat'] = sat_examples\\n info['examples_generated_unsat'] = unsat_examples\\n print('Average formula length {avg_in_length:.1f} and witness length {avg_out_length:.1f}'.format(**info))\\n print('Generated {:d} examples ({:d} sat, {:d} unsat). {:d} requested.'.format(total_examples, sat_examples, unsat_examples, params.num_examples))\\n return examples, timeout_formulas, info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae60e4847e1310309ac846fdf499903\",\n \"score\": \"0.5060221\",\n \"text\": \"def simule_sequence(length, props):\\n liste = [np.full((int(round(length * p))), k) for k, p in enumerate(props)]\\n flattened = np.hstack(liste)\\n return np.random.permutation(flattened)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06065833693d318e1709d3d833f67036\",\n \"score\": \"0.5058347\",\n \"text\": \"def generate_haps(self, pheno_seq):\\n \\n allele_list = self.generate_allele_list(pheno_seq)\\n hap_list = combinations(allele_list)\\n return hap_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"340697720c0220e2d1fe4fc49bf89269\",\n \"score\": \"0.5055516\",\n \"text\": \"def gen_tus(self):\\n\\n # validate these options in the options method\\n options = self.options\\n # 7 bp default (E. coli, wikipedia)\\n five_prime_len = options.get('five_prime_len')\\n three_prime_len = options.get('three_prime_len') # 5 bp default guess\\n operon_prop = options.get('operon_prop') # 0.2 default guess\\n # 3 genes default (https://academic.oup.com/gbe/article/5/11/2242/653613)\\n operon_gen_num = options.get('operon_gen_num')\\n\\n for i_chr, chromosome in enumerate(self.knowledge_base.cell.species_types.get(__type=wc_kb.core.DnaSpeciesType)):\\n seq = chromosome.get_seq()\\n i_gene = 0\\n transcription_loci = []\\n # print(chromosome.loci)\\n # print(len(chromosome.loci))\\n # Todo make this into a proper for loop that deals with repeats/additional loci\\n while i_gene < len(chromosome.loci):\\n\\n gene = chromosome.loci[i_gene]\\n\\n if gene.type == wc_kb.core.GeneType.mRna:\\n # polycistronic mRNA (multiple GeneLocus objects per TranscriptionUnitLocus)\\n # print(\\\"mrna\\\")\\n\\n five_prime = self.rand(five_prime_len)[0]\\n three_prime = self.rand(three_prime_len)[0]\\n operon_prob = random.random()\\n\\n # make an operon (polycistronic mRNA, put multiple genes in one TransUnitLocus)\\n if operon_prob <= operon_prop:\\n # print(\\\"ploycistronic\\\")\\n operon_genes = self.rand(operon_gen_num)[0]\\n while operon_genes <= 1:\\n operon_genes = self.rand(operon_gen_num)[0]\\n\\n # add 3', 5' UTRs to the ends of the transcription unit (upstream of first gene, downstream of last gene)\\n tu = self.knowledge_base.cell.loci.get_or_create(\\n id='tu_{}_{}'.format(i_chr + 1, i_gene + 1), __type=wc_kb.prokaryote.TranscriptionUnitLocus)\\n tu.name = 'tu {} {}'.format(i_chr+1, i_gene+1)\\n\\n five_prime_start = gene.start - five_prime\\n if five_prime_start < 0:\\n five_prime_start = 0\\n tu.genes.append(gene)\\n tu.start = five_prime_start\\n\\n # print(chromosome.loci)\\n # print(operon_genes)\\n for k in range(operon_genes-1):\\n\\n i_gene += 1\\n\\n if i_gene >= len(chromosome.loci):\\n break\\n\\n if (chromosome.loci[i_gene]).type == wc_kb.core.GeneType.mRna:\\n gene = chromosome.loci[i_gene]\\n tu.genes.append(gene)\\n\\n else:\\n break\\n\\n three_prime_end = gene.end + three_prime\\n if three_prime_end >= len(seq):\\n three_prime_end = len(seq) - 1\\n tu.end = three_prime_end\\n transcription_loci.append(tu)\\n\\n else: # make an individual transcription unit for the gene\\n # print(\\\"single\\\")\\n five_prime_start = gene.start - five_prime\\n three_prime_end = gene.end + three_prime\\n if five_prime_start < 0:\\n five_prime_start = 0\\n if three_prime_end >= len(seq):\\n three_prime_end = len(seq) - 1\\n tu = self.knowledge_base.cell.loci.get_or_create(\\n id='tu_{}_{}'.format(i_chr + 1, i_gene + 1), __type=wc_kb.prokaryote.TranscriptionUnitLocus)\\n tu.start = five_prime_start\\n tu.end = three_prime_end\\n tu.name = 'tu {} {}'.format(i_chr+1, i_gene+1)\\n tu.genes.append(gene)\\n transcription_loci.append(tu)\\n\\n # make a transcription unit that transcribes other types of RNA (tRNA, rRNA, sRNA)\\n else:\\n # print(\\\"not mrna\\\")\\n tu = self.knowledge_base.cell.loci.get_or_create(\\n id='tu_{}_{}'.format(i_chr + 1, i_gene + 1), __type=wc_kb.prokaryote.TranscriptionUnitLocus)\\n tu.name = 'tu {} {}'.format(i_chr+1, i_gene+1)\\n tu.start = gene.start\\n tu.end = gene.end\\n tu.genes.append(gene)\\n transcription_loci.append(tu)\\n\\n i_gene += 1\\n for locus in transcription_loci:\\n locus.polymer = chromosome\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8531f85c247f64c62df3b567ae7ac86\",\n \"score\": \"0.50372565\",\n \"text\": \"def get_all_TR(detector,sequences,show_time=False):\\n \\n list_TR = []\\n start = time.time()\\n \\n for i in range(len(sequences)):\\n \\n TR = sequences[i].detect(denovo = True, detection = {\\\"detectors\\\": [detector]})\\n list_TR.append(TR)\\n \\n end = time.time()\\n \\n if show_time:\\n \\n print(end - start) \\n \\n return list_TR\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31acdfe950912194abc4d4f40ebf0110\",\n \"score\": \"0.50330013\",\n \"text\": \"def print_actual_sequence_info(participant_config):\\n num_stimuli = participant_config['num_stimuli']\\n\\n # Hold the counts of the number of trials\\n target_trials = np.zeros(num_stimuli)\\n nontarget_trials = np.zeros(num_stimuli)\\n\\n for sequence in participant_config['sequences']:\\n for block_index in range(participant_config['blocks_per_sequence']):\\n # What stimulus is the current target\\n target_index = sequence['targets'][block_index]\\n for trial in sequence['blocks'][block_index]:\\n if trial == target_index:\\n target_trials[trial] += 1\\n else:\\n nontarget_trials[trial] += 1\\n\\n print('non-target/left: {}'.format(nontarget_trials[0]))\\n print('non-target/middle: {}'.format(nontarget_trials[1]))\\n print('non-target/right: {}'.format(nontarget_trials[2]))\\n print('target/left: {}'.format(target_trials[0]))\\n print('target/middle: {}'.format(target_trials[1]))\\n print('target/right: {}'.format(target_trials[2]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f596faceb0f9e3e868624af0fe22655\",\n \"score\": \"0.5031925\",\n \"text\": \"def generate_chunking_trial(self, num_pulses, analysis, num_fixed):\\n\\n # rule signal can appear at the end of delay1_time\\n trial_length = par['num_time_steps']\\n\\n # end of trial epochs\\n eodead = par['dead_time']//par['dt']\\n eof = (par['dead_time']+par['fix_time'])//par['dt'] \\n eos = [(par['dead_time']+par['fix_time']+ n*par['delay_time'] + (n+1)*par['sample_time'])//par['dt'] for n in range(num_pulses)]\\n eods = [(par['dead_time']+par['fix_time']+(n+1)*(par['sample_time']+par['delay_time']))//par['dt'] for n in range(num_pulses-1)]\\n eods.append(eos[-1])\\n eolongd = (par['dead_time']+par['fix_time'] + num_pulses * par['sample_time'] + (num_pulses-1)*par['delay_time'] + par['long_delay_time'])//par['dt']\\n eor = [(par['dead_time']+par['fix_time'] + num_pulses * par['sample_time'] + (num_pulses-1)*par['delay_time'] + par['long_delay_time'] + \\\\\\n n*par['delay_time'] + (n+1)*par['resp_cue_time'])//par['dt'] for n in range(num_pulses)]\\n eodr = [(par['dead_time']+par['fix_time'] + num_pulses * par['sample_time'] + (num_pulses-1)*par['delay_time'] + par['long_delay_time'] + \\\\\\n (n+1)*(par['resp_cue_time']+par['delay_time']))//par['dt'] for n in range(num_pulses-1)]\\n eodr.append(eor[-1])\\n # end of neuron indices\\n emt = par['num_motion_tuned']\\n eft = par['num_fix_tuned']+par['num_motion_tuned']\\n ert = par['num_fix_tuned']+par['num_motion_tuned'] + par['num_resp_cue_tuned']\\n if par['order_cue']:\\n eot = par['num_fix_tuned']+par['num_motion_tuned'] + par['num_resp_cue_tuned'] + par['num_order_cue_tuned']\\n\\n trial_info = {'desired_output' : np.zeros((par['n_output'], trial_length, par['batch_train_size']),dtype=np.float32),\\n 'train_mask' : np.ones((trial_length, par['batch_train_size']),dtype=np.float32),\\n 'rule' : np.zeros((par['batch_train_size']),dtype=np.int8),\\n 'sample' : np.zeros((par['batch_train_size'], par['num_pulses']),dtype=np.int8),\\n 'neural_input' : np.random.normal(par['input_mean'], par['noise_in'], size=(par['n_input'], trial_length, par['batch_train_size'])),\\n 'timeline' : [eodead, eof]}\\n\\n trial_info['timeline'].append(eos[0])\\n for i in range(1,num_pulses):\\n trial_info['timeline'].append(eods[i-1])\\n trial_info['timeline'].append(eos[i])\\n trial_info['timeline'].append(eolongd)\\n trial_info['timeline'].append(eor[0])\\n for i in range(1,num_pulses):\\n trial_info['timeline'].append(eodr[i-1])\\n trial_info['timeline'].append(eor[i])\\n\\n # set to mask equal to zero during the dead time\\n trial_info['train_mask'][:eodead, :] = 0\\n trial_info['train_mask'][eolongd:eolongd+par['mask_duration']//par['dt'], :] = 0\\n for i in range(1, par['num_pulses']):\\n trial_info['train_mask'][eodr[i-1]:eodr[i-1]+par['mask_duration']//par['dt'], :] = 0\\n # If the DMS and DMS rotate are being performed together,\\n # or if I need to make the test more challenging, this will eliminate easry test directions\\n # If so, reduce set of test stimuli so that a single strategy can't be used\\n #limit_test_directions = par['trial_type']=='DMS+DMRS'\\n\\n for t in range(par['batch_train_size']):\\n\\n \\\"\\\"\\\"\\n Generate trial paramaters\\n \\\"\\\"\\\"\\n if not analysis:\\n sample_dirs = [np.random.randint(par['num_motion_dirs']) for i in range(num_pulses)]\\n else:\\n sample_dirs = [0]*num_fixed + [np.random.randint(par['num_motion_dirs']) for i in range(num_pulses-num_fixed)]\\n\\n rule = np.random.randint(par['num_rules'])\\n\\n \\\"\\\"\\\"\\n Calculate neural input based on sample, tests, fixation, rule, and probe\\n \\\"\\\"\\\"\\n # SAMPLE stimulus\\n trial_info['neural_input'][:emt, eof:eos[0], t] += np.reshape(self.motion_tuning[:,sample_dirs[0]],(-1,1))\\n for i in range(1,num_pulses):\\n trial_info['neural_input'][:emt, eods[i-1]:eos[i], t] += np.reshape(self.motion_tuning[:,sample_dirs[i]],(-1,1))\\n\\n # FIXATION cue\\n if par['num_fix_tuned'] > 0:\\n trial_info['neural_input'][emt:eft, eodead:eolongd, t] += np.reshape(self.fix_tuning[:,0],(-1,1))\\n for i in range(num_pulses):\\n trial_info['neural_input'][emt:eft, eor[i]:eodr[i], t] += np.reshape(self.fix_tuning[:,0],(-1,1))\\n\\n # RESPONSE CUE\\n trial_info['neural_input'][eft:ert, eolongd:eor[0], t] += np.reshape(self.response_tuning[:,0],(-1,1))\\n for i in range(1, num_pulses):\\n trial_info['neural_input'][eft:ert, eodr[i-1]:eor[i], t] += np.reshape(self.response_tuning[:,0],(-1,1))\\n\\n # ORDER CUE\\n if par['order_cue']:\\n trial_info['neural_input'][ert, eolongd:eor[0], t] += par['tuning_height']\\n trial_info['neural_input'][ert, eof:eos[0], t] += par['tuning_height']\\n for i in range(1,par['num_pulses']):\\n trial_info['neural_input'][ert+i, eodr[i-1]:eor[i], t] += par['tuning_height']\\n trial_info['neural_input'][ert+i, eods[i-1]:eos[i], t] += par['tuning_height']\\n\\n \\\"\\\"\\\"\\n Determine the desired network output response\\n \\\"\\\"\\\"\\n trial_info['desired_output'][0, eodead:eolongd, t] = 1\\n for i in range(num_pulses):\\n trial_info['desired_output'][0, eor[i]:eodr[i], t] = 1\\n\\n trial_info['desired_output'][sample_dirs[0]+1, eolongd:eor[0], t] = 1\\n for i in range(1, num_pulses):\\n trial_info['desired_output'][sample_dirs[i]+1, eodr[i-1]:eor[i], t] = 1\\n\\n \\\"\\\"\\\"\\n Append trial info\\n \\\"\\\"\\\"\\n trial_info['sample'][t,:] = sample_dirs\\n trial_info['rule'][t] = rule\\n\\n return trial_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"723bc585f611f44c87816378c199bb62\",\n \"score\": \"0.502416\",\n \"text\": \"def _get_stim_times(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20ba50ff67af534364f4044beedb54db\",\n \"score\": \"0.50218195\",\n \"text\": \"def genmotionsequencearm(robot, robotplot, handpkg, start, goal, obstacleslist = []):\\n\\n jointlimits = []\\n for i in robot.targetjoints:\\n jointlimits.append([robot.rgtarm[i]['rngmin'], robot.rgtarm[i]['rngmax']])\\n planner = rrt.RRT(start=start, goal=goal, iscollidedfunc = iscollidedfunc,\\n jointlimits = jointlimits, expanddis = 20, robot = robot,\\n robotplot = robotplot, handpkg = handpkg)\\n import time\\n tic = time.clock()\\n [path, sampledpoints] = planner.planning()\\n toc = time.clock()\\n print toc-tic\\n return [path, sampledpoints]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8907c109c5b1ef04e5c652f62bb6f84a\",\n \"score\": \"0.5020906\",\n \"text\": \"def simule_sequence_markov(length, prop, sequence):\\n tmatrix = transition_matrix(sequence)\\n seq = []\\n proba = prop\\n for _ in range(length):\\n nb = np.random.choice(nucleobases, p=proba)\\n proba = tmatrix[nb]\\n seq.append(nb)\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd48b9abfc82fad455fe7984c3af8568\",\n \"score\": \"0.5018895\",\n \"text\": \"def day21(inp,part1=True):\\n s2s = [] # size 2s\\n s3s = [] # size 3s\\n for line in inp.rstrip().split('\\\\n'):\\n patfrom,patto = line.split(' => ')\\n s = np.sqrt(len(patfrom.replace('/',''))).round().astype(int)\\n stmp = s2s if s == 2 else s3s\\n stmp.append((patfrom,patto))\\n \\n n2,n3 = len(s2s),len(s3s)\\n # construct transformed pattern arrays: (n2,8,2,2) and (n3,8,3,3) complete with rotations\\n # construct single output arrays: (n2,3,3) and (n3,4,4)\\n for i,inlst in enumerate([s2s,s3s]):\\n templst = []\\n outlst = []\\n for patfrom,patto in inlst:\\n templst.append(genpatt(parseit(patfrom)))\\n outlst.append(parseit(patto))\\n temparr = np.array(templst)\\n outarr = np.array(outlst)\\n if i == 0:\\n templates2 = temparr\\n outs2 = outarr\\n else:\\n templates3 = temparr\\n outs3 = outarr\\n \\n state = parseit('.#./..#/###')\\n numsteps = 5 if part1 else 18\\n for _ in range(numsteps):\\n if state.shape[0]%2 == 0:\\n n = 2 # size of chunks\\n templates = templates2\\n outs = outs2\\n else:\\n n = 3\\n templates = templates3\\n outs = outs3\\n m = state.shape[0] // n # the number of chunks along a dimension\\n state = state.reshape(m,n,m,n).transpose(0,2,1,3).reshape(-1,n,n) # (m*m,n,n)-shaped board\\n\\n # find a match for each chunk: broadcast (m*m,1,1,n,n) vs (l,8,n,n)\\n inds = (state[:,None,None,...] == templates).reshape(m**2,-1,8,n**2).all(-1).any(-1).nonzero() # nonzeros of shape (m*m,l), and for each m1m2 there should be exactly one l\\n \\n # construct output state, shape (m*(n+1),m*(n+1))\\n state = outs[inds[-1],...].reshape(m,m,n+1,n+1).transpose(0,2,1,3).reshape(m*(n+1),m*(n+1))\\n\\n return state.sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1554645727a6deec863c88964466e95\",\n \"score\": \"0.5011649\",\n \"text\": \"def metropolis_par(ediff_fun, tn, nsample, ss0, is_traj=False):\\n\\n if np.array(ss0).ndim == 1:\\n ss0 = np.array([ss0])\\n N = ss0.shape[1]\\n L = int(np.sqrt(N))\\n nchains = ss0.shape[0]\\n\\n ss_traj = []\\n if is_traj:\\n ss_traj = np.zeros((int(np.ceil(tn / nsample)) * nchains, N))\\n\\n accept_count = 0\\n sample_step = 0\\n ss = np.copy(ss0)\\n\\n random_spin_flip = np.random.randint(N, size=(tn, nchains))\\n\\n # progress = pb.ProgressBar(max_value=tn, widgets=wgt).start()\\n for tstep, ssidx in enumerate(random_spin_flip):\\n prop_ss = np.copy(ss)\\n prop_ss[range(nchains), ssidx] *= -1\\n\\n energy_diff = ediff_fun(\\n [ss.reshape((-1, L, L)), prop_ss.reshape((-1, L, L))]\\n ).reshape((nchains))\\n flipidx = np.random.random(nchains) < energy_diff\\n\\n accept_count += np.count_nonzero(flipidx)\\n ss[np.arange(nchains)[flipidx], ssidx[flipidx]] *= -1\\n\\n if tstep % nsample == 0:\\n if is_traj:\\n ss_traj[sample_step :: int(np.ceil(tn / nsample)), :] = ss\\n sample_step += 1\\n\\n # progress.update(tstep + 1)\\n\\n return (ss, ss_traj, accept_count / (tn * nchains))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e993115c40cc10e10dfdc78277ed0d5\",\n \"score\": \"0.4997244\",\n \"text\": \"def simulationTwoDrugsDelayedTreatment(numTrials):\\n delay0 = []\\n delay75 = []\\n delay150 = []\\n delay300 = []\\n for i in xrange(numTrials):\\n delay0.append(calcFinalPop(100, 1000, 0.1, 0.05, {'guttagonol': False, 'grimpex': False},\\n .005, 0))\\n delay75.append(calcFinalPop(100, 1000, 0.1, 0.05, {'guttagonol': False, 'grimpex': False},\\n .005, 75))\\n delay150.append(calcFinalPop(100, 1000, 0.1, 0.05, {'guttagonol': False, 'grimpex': False},\\n .005, 150))\\n delay300.append(calcFinalPop(100, 1000, 0.1, 0.05, {'guttagonol': False, 'grimpex': False},\\n .005, 300))\\n pylab.figure('Delay 0')\\n pylab.hist(delay0, bins = [0,50,1000], label = \\\"0 step delay\\\", histtype='bar', alpha=1.0)\\n pylab.figure('Delay 75')\\n pylab.hist(delay75, bins = [0,50,1000], label = \\\"75 step delay\\\", histtype='bar', alpha=0.85)\\n pylab.figure('Delay 150')\\n pylab.hist(delay150, bins = [0,50,1000], label = \\\"150 step delay\\\", histtype='bar', alpha=0.6)\\n pylab.figure('Delay 300')\\n pylab.hist(delay300, bins = [0,50,1000], label = \\\"300 step delay\\\", histtype='bar', alpha=.45)\\n pylab.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1205eb2495e78d9fd873f0eee4487\",\n \"score\": \"0.49942884\",\n \"text\": \"def print_solution(data, manager, routing, solution, total_stops):\\n calculated_sequence = [0]*(total_stops)\\n time_dimension = routing.GetDimensionOrDie('Time')\\n for vehicle_id in range(data['num_vehicles']):\\n i = 0\\n index = routing.Start(vehicle_id)\\n while not routing.IsEnd(index):\\n calculated_sequence[index] = i\\n index = solution.Value(routing.NextVar(index))\\n i += 1 \\n return (calculated_sequence)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800c2d296dea5dba93ae208ead025c3e\",\n \"score\": \"0.499225\",\n \"text\": \"def times():\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ac0f64a2057f933da601e3a6b6742f\",\n \"score\": \"0.49863502\",\n \"text\": \"def test_15(self):\\n main = Main()\\n main.generate_systems(n=100,\\n min_m=100,\\n max_n=200,\\n max_m=200,\\n step=1,\\n save_results=True,\\n save_systems=True,\\n upper_bound=1,\\n lower_bound=1,\\n max_tries=10000,\\n update_strongly_k=True,\\n gi=Gi())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d619f2370d896b80b37d753cf2b358cb\",\n \"score\": \"0.49734825\",\n \"text\": \"def __generations(self):\\n return max(0,len(self.energy_history)-1) #XXX: slower for k=-1 ?\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4563fce2c1f3e3e02a081ecc860241cf\",\n \"score\": \"0.49734706\",\n \"text\": \"def generate_list(self):\\n lista_tiempos = []\\n i = 0\\n ntime = self.gen_rep_time()\\n while i < self.reps:\\n lista_tiempos.append(ntime)\\n i += 1\\n return lista_tiempos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb4b4b45a336c0de64d0255768fea96c\",\n \"score\": \"0.4958152\",\n \"text\": \"def generate_rb_sequence(\\n self,\\n depth: int,\\n gateset: Sequence[Gate],\\n seed: Optional[int] = None,\\n interleaver: Optional[Program] = None,\\n ) -> List[Program]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13dea0cc367d11f0a48aa3b580f172d7\",\n \"score\": \"0.49543616\",\n \"text\": \"def test_11(self):\\n main = Main()\\n main.generate_systems(n=1000,\\n min_m=1000,\\n max_n=10000,\\n max_m=20000,\\n step=1000,\\n save_results=True,\\n save_systems=True,\\n upper_bound=2,\\n lower_bound=1,\\n max_tries=30,\\n gi=Gi())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4e322df665f6a0f7bc4ec3e589a5c1\",\n \"score\": \"0.49514282\",\n \"text\": \"def compute_accepted_sequences(peptide):\\n sequences_accepted = []\\n for i in range(len(peptide.sims)):\\n temp = peptide.sims[i]['Results']\\n for j in range(len(temp)):\\n if temp[j]['Status'] == 'Accepted':\\n sequences_accepted.append(temp[j]['Sequence'])\\n return sequences_accepted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ddf91bf40db8d8a3053c23927a8faaf\",\n \"score\": \"0.49513906\",\n \"text\": \"def seq_partitioning(data_arr,ind,tm_cutoff=21,marker_set='ROX'):\\n\\n \\n #vectors dictationg the marker sizes and differences between markers in the tape measure\\n if marker_set.upper()=='ROX':\\n \\n marker_sizes = np.array([50, 60, 90, 100, 120, 150, 160, 180, 190, 200, 220, 240, 260, 280, 290, 300, 320, 340, 360, 380,400])\\n \\n elif marker_set.upper()=='LIZ':\\n \\n marker_sizes = np.array([20, 40, 60, 80, 100, 114, 120, 140, 160, 180, 200, 214, 220, 240, 250, 260, 280, 300, 314, 320, 340, 360, 380, 400, 414, 420, 440, 460, 480, 500, 514, 520, 540, 560, 580, 600])\\n else:\\n print 'unknown marker set!'\\n quit()\\n \\n marker_diffs=np.diff(marker_sizes)\\n \\n #find peaks in tape measure\\n peaksTM = peak_finder(data_arr,4,.25)\\n #sm_plotter(data_arr,peaksTM,fl) \\n #array of nucleotide position\\n nuc_pos = np.arange(351)\\n \\n #initialise peak binning array\\n reduced_peaks = []\\n \\n #iterate through data \\n for i in range(len(data_arr)):\\n nuc_pos = range(350)\\n \\n #extract data index\\n data = data_arr[i]\\n \\n #extract peaks\\n peaks = peaksTM[i]\\n \\n if tm_cutoff<21:\\n peaks = peaksTM[i][:tm_cutoff]\\n else:\\n peaks = peaksTM[i]\\n \\n \\n \\n \\n \\n #initials peak lists\\n peak_list = []\\n pos_list = []\\n bin_list = []\\n \\n if isinstance(data,pd.DataFrame):\\n \\n #extract data values\\n data1 = data.values\\n \\n \\n else:\\n\\n #transpose data - fudge factor can be improved\\n data1=np.transpose(data)\\n \\n \\n\\n peaks_trace1 = funcPeakAlign.fPeakList(data1[:,ind],repType = 'Cubic')\\n\\n p_ind = peaks_trace1['pos']\\n \\n peak_ind=np.unique(p_ind)\\n \\n p_pos = data1[peak_ind,0]\\n \\n \\n p_amp =data1[peak_ind,ind]\\n p_width = peaks_trace1['averW']\\n p_std=peaks_trace1['stdW']\\n \\n peak_list=[p_pos,p_amp]\\n peak_list=np.transpose(peak_list)\\n \\n shoulder_data = shoulder_finder(p_pos,data1,ind,i)\\n #if shoulders found add them to peak list\\n if len(shoulder_data)>0:\\n peak_list1 = np.vstack((peak_list,shoulder_data))\\n ind1=np.argsort(peak_list1[:,0])\\n peak_list1=peak_list1[ind1]\\n else:\\n peak_list1 = peak_list\\n \\n \\n #delete peaks if they are too close to each other\\n x = 0\\n while x<len(peak_list1)-1:\\n \\n #find the position and amplitudes of two neighbouring peaks\\n p_pos1 = peak_list1[x,0]\\n p_pos2 = peak_list1[x+1,0]\\n \\n p_amp1 = peak_list1[x,1]\\n p_amp2 = peak_list1[x+1,1]\\n \\n \\n #calculate difference in position between the two peaks\\n diff1 = p_pos2-p_pos1\\n \\n #if the difference is less than 4\\n if diff1<4:\\n \\n #if peak1 is in original peak list but not peak2 delete peak2 \\n if (p_pos1 in peak_list[:,0]) and (p_pos2 not in peak_list[:,0]):\\n del_x = x+1\\n peak_list1 = np.delete(peak_list1,del_x,0)\\n x+=1\\n \\n #if peak2 is in original peak list but not peak1 delete peak1 \\n elif(p_pos2 in peak_list[:,0]) and (p_pos1 not in peak_list[:,0]):\\n del_x = x\\n peak_list1 = np.delete(peak_list1,del_x,0)\\n else:\\n x+=1\\n \\n \\n \\n else:\\n x+=1\\n \\n x=0\\n \\n \\n miss_pos = []\\n miss_amp = []\\n \\n #fill in any positions that have missing peaks due to poor data quality\\n \\n #read through peak list\\n for x in range(len(peak_list1)-1):\\n \\n #look at the difference in position between neighbouring peaks \\n diff1=peak_list1[x+1,0]-peak_list1[x,0]\\n\\n #determine spacing\\n spacing_we=2*p_width-4*p_std\\n \\n #calculate number of missing peaks expected in a space\\n miss_peaks=np.floor(diff1/(p_width-2*p_std))\\n \\n \\n #if more that one missing peak space suspected\\n if miss_peaks>1:\\n \\n\\n #fill the missing space with peak data\\n for z in range(int(miss_peaks)-1):\\n \\n \\n #determine position of new peak\\n av_pos=peak_list1[x,0]+(z+1)*int(p_width)\\n \\n #add new position to missing position array\\n miss_pos=np.append(miss_pos,av_pos)\\n\\n #find amplitude of new peak\\n av_amp=data1[data1[:,0]==av_pos,ind]\\n \\n #add to missing amplitude data\\n miss_amp = np.append(miss_amp,av_amp)\\n\\n #create array for missing peak information\\n miss_arr = np.transpose([miss_pos,miss_amp])\\n \\n \\n #add the missing peaks to the new peak array\\n if len(miss_arr)>0:\\n peak_list2=np.vstack((peak_list1,miss_arr))\\n ind2=np.argsort(peak_list2[:,0])\\n peak_list2=peak_list2[ind2]\\n else:\\n peak_list2=peak_list1\\n\\n \\n \\n peak_list5=np.zeros((350,2))\\n \\n pos_ind=0\\n #iterate through all peaks\\n for j in range(len(peaks)-1):\\n #extract TM peaks that define the trace region of interest\\n start = peaks[j]\\n end = peaks[j+1]\\n \\n #calculate difference between peak postions\\n diff = end-start \\n \\n \\n #extract number of bins for specific region\\n bins = marker_diffs[j]\\n \\n #set up the bin width\\n nuc_sep = diff/bins\\n \\n #extract all of those trace values above TM start position\\n peak_list3=peak_list2[peak_list2[:,0]>int(start),:]\\n \\n peak_list4=peak_list3[peak_list3[:,0]<int(end),:]\\n \\n #iterate through bins\\n for t in range(bins):\\n \\n #extract all trace values within bin\\n S_bin = peak_list4[(peak_list4[:,0]>(start+(t)*nuc_sep)),:]\\n \\n \\n \\n S_bin=S_bin[(S_bin[:,0]<(start+(t+1)*nuc_sep)),:]\\n \\n peak_list5[pos_ind,0]=start+(t+0.5)*nuc_sep\\n #initialise binning array\\n if len(S_bin)==0:\\n pos_ind+=1 \\n continue\\n elif len(S_bin)==1:\\n peak_list5[pos_ind,1]=S_bin[:,1]\\n \\n elif len(S_bin)>1:\\n \\n peak_list5[pos_ind,1]=np.max(S_bin[:,1])\\n \\n \\n pos_ind+=1 \\n \\n \\n \\n \\n reduced_peaks.append(peak_list5)\\n \\n \\n return reduced_peaks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e3af0429f60a8d9d0a7cc319d5505bf\",\n \"score\": \"0.49466643\",\n \"text\": \"def make_trial_set(self):\\n\\n self.trialSet = []\\n all_pairs = list(itertools.product(self.sRightOrientations, self.sLeftOrientations))\\n for numSLeft in self.sLeftPresentations:\\n for p in all_pairs:\\n (sRightOrientation, sLeftOrientation) = p\\n if abs(sRightOrientation - sLeftOrientation) < 0.001:\\n # make sure SPLUS and SMINUS are different\\n continue\\n\\n t = Trial_2AFC()\\n t.numSLeft = numSLeft\\n t.sRightOrientation = sRightOrientation\\n t.sLeftOrientation = sLeftOrientation\\n\\n self.trialSet.append(t)\\n\\n print str(len(self.trialSet)) + \\\" different trial conditions.\\\"\\n\\n self.sequencer = Sequencer(self.trialSet, self.sequenceType)\\n if (self.sequenceType == Sequences.INTERVAL or\\n self.sequenceType == Sequences.INTERVAL_RETRY):\\n self.sequencer.sequencer.easyOris = self.easyOris\\n self.sequencer.sequencer.hardOris = self.hardOris\\n self.sequencer.sequencer.numEasy = self.numEasy\\n self.sequencer.sequencer.numHard = self.numHard\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fff1f6259d3fe41880af10294dcaa472\",\n \"score\": \"0.4945969\",\n \"text\": \"def powerset(seq):\\n import itertools as it\\n s = list(seq)\\n return list(it.chain.from_iterable(it.combinations(s, r) for r in range(len(s) + 1)))[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6024b6c374c96654d91f478d880e714\",\n \"score\": \"0.49425095\",\n \"text\": \"def _make_io_examples(self, n, min_len, max_len):\\r\\n rand = random.Random(6849275409234) # Test cases are fixed, but varied.\\r\\n io_examples = []\\r\\n for _ in xrange(n):\\r\\n length = rand.randrange(min_len, max_len + 1)\\r\\n input_seq = [rand.randrange(1, self.base) for _ in xrange(length)]\\r\\n output_seq = list(input_seq)\\r\\n output_seq.reverse()\\r\\n output_seq.append(0)\\r\\n io_examples.append((input_seq, output_seq))\\r\\n return io_examples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d802ab90cb1006e157167aa44e73f3e\",\n \"score\": \"0.49334067\",\n \"text\": \"def wRMS_sed_time_sugar_salt(rp, training=True, validation=False, sn_list=[]):\\n \\n residual_sel = []\\n residual_sucre = []\\n residual_sucre_1 = []\\n residual_sucre_2 = [] \\n residual_error = []\\n phase = [] \\n nspectra = 0\\n\\n SN = N.array(rp.dicS.keys())\\n try:\\n SAMPLE = N.array([rp.dic[sn]['0']['sample'] for sn in SN])\\n except:\\n print('continue')\\n\\n if training and validation:\\n FILTER = N.array([True]*len(SN))\\n else:\\n try:\\n if training:\\n FILTER = (SAMPLE == 'training')\\n if validation:\\n FILTER = (SAMPLE == 'validation')\\n except:\\n print('continue')\\n\\n for sn in range(len(SN)):\\n if SN[sn] in sn_list:\\n FILTER[sn] = False\\n print(len(SN))\\n SN = SN[FILTER]\\n print(len(SN))\\n\\n i=0\\n for sn in SN:\\n sucre, sucre_1, sucre_2, sel, res_error, phas = rp.plot_spectra_reconstruct_residuals(sn,T_min=-12,T_max=48)\\n residual_sel.append(sel)\\n residual_sucre.append(sucre)\\n residual_sucre_1.append(sucre_1)\\n residual_sucre_2.append(sucre_2)\\n residual_error.append(res_error)\\n phase.append(phas)\\n nspectra += len(N.array(residual_sucre[i])[:,0])\\n i+=1\\n P.close('all')\\n \\n binning_phase = N.linspace(-12,48,21)\\n\\n res_sel = []\\n res_sucre = []\\n res_sucre_1 = []\\n res_sucre_2 = []\\n res_err = []\\n\\n wave = rp.dic['PTF09dnl']['0']['X']\\n \\n for Bin in range(21):\\n print(Bin)\\n res_sel.append([])\\n res_sucre.append([])\\n res_sucre_1.append([])\\n res_sucre_2.append([])\\n res_err.append([])\\n for sn in range(len(phase)):\\n for ph in range(len(phase[sn])):\\n if abs(phase[sn][ph]-binning_phase[Bin])<1.5:\\n [res_sel[Bin].append(residual_sel[sn][ph][wav]) for wav in range(len(wave))]\\n [res_sucre[Bin].append(residual_sucre[sn][ph][wav]) for wav in range(len(wave))]\\n [res_sucre_1[Bin].append(residual_sucre_1[sn][ph][wav]) for wav in range(len(wave))]\\n [res_sucre_2[Bin].append(residual_sucre_2[sn][ph][wav]) for wav in range(len(wave))]\\n [res_err[Bin].append(residual_error[sn][ph][wav]) for wav in range(len(wave))]\\n\\n wrms_sel = N.zeros(21)\\n wrms_sucre = N.zeros(21)\\n wrms_sucre_1 = N.zeros(21)\\n wrms_sucre_2 = N.zeros(21)\\n\\n for Bin in range(21):\\n \\n res_clipped, err_clipped = sigma_clip(N.array(res_sel[Bin]), N.array(res_err[Bin]))\\n wrms_sel[Bin] = N.sqrt(N.sum((res_clipped**2)/(err_clipped**2)) / N.sum(1./(err_clipped**2)))\\n\\n res_clipped, err_clipped = sigma_clip(N.array(res_sucre[Bin]), N.array(res_err[Bin]))\\n wrms_sucre[Bin] = N.sqrt(N.sum((res_clipped**2)/(err_clipped**2)) / N.sum(1./(err_clipped**2)))\\n\\n res_clipped, err_clipped = sigma_clip(N.array(res_sucre_1[Bin]), N.array(res_err[Bin]))\\n wrms_sucre_1[Bin] = N.sqrt(N.sum((res_clipped**2)/(err_clipped**2)) / N.sum(1./(err_clipped**2)))\\n\\n res_clipped, err_clipped = sigma_clip(N.array(res_sucre_2[Bin]), N.array(res_err[Bin]))\\n wrms_sucre_2[Bin] = N.sqrt(N.sum((res_clipped**2)/(err_clipped**2)) / N.sum(1./(err_clipped**2)))\\n \\n P.figure(figsize=(16,6))\\n P.subplots_adjust(top=0.9,right=0.65,left=0.05,bottom=0.15)\\n P.plot(binning_phase,wrms_sel,'r',linewidth=5,label='SALT2.4 ($X_0$ + $X_1$ + $C$)')\\n P.plot(binning_phase,wrms_sucre_1,'b-.',linewidth=3,label='SUGAR ($\\\\Delta M_{grey}$ + $q_1$ + $A_V$)')\\n P.plot(binning_phase,wrms_sucre_2,'b--',linewidth=3,label='SUGAR ($\\\\Delta M_{grey}$ +$q_1$ + $q_2$ + $A_V$)')\\n P.plot(binning_phase,wrms_sucre,'b',linewidth=5,label='SUGAR ($\\\\Delta M_{grey}$ +$q_1$ + $q_2$ + $q_3$ + $A_V$)')\\n P.xlabel('Phase (day)',fontsize=20)\\n P.ylabel('wRMS (mag)',fontsize=20)\\n P.ylim(0,0.43)\\n P.xlim(-13,50)\\n P.legend(bbox_to_anchor=(1.01, 0.7), loc=2, borderaxespad=0.,fontsize=16)\\n return res_sel\\n #P.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59cc5c2bb1e18bf347d2c4113106369a\",\n \"score\": \"0.4918269\",\n \"text\": \"def create_list_random_AAIMON_ratios(n_mutations_array, aa_prop_TM, aa_prop_nonTM, seq_len):\\n AAIMON_list = []\\n # iterate through each number of mutations (1/1000, 2/1000 ..... 700/1000)\\n for n, number_mutations in enumerate(n_mutations_array):\\n # get a random pairwise alignment for the TM region\\n perc_ident_TM = korbinian.cons_ratio.norm.get_perc_ident_random_pairwise_alignment(aa_prop_TM, seq_len, number_mutations)\\n # get a random pairwise alignment for the nonTM region\\n perc_ident_nonTM = korbinian.cons_ratio.norm.get_perc_ident_random_pairwise_alignment(aa_prop_nonTM, seq_len, number_mutations)\\n # calculate artificial AAIMON\\n AAIMON = perc_ident_TM / perc_ident_nonTM\\n # append to AAIMON_list\\n AAIMON_list.append(AAIMON)\\n # calculate the percentage aa substitutions for this position\\n perc_aa_subst = number_mutations / seq_len\\n # if n % 100 == 0:\\n # sys.stdout.write(\\\"\\\\n{}, {:.03f}, {:.03f}, {:.03f}, {:.03f}\\\\n\\\".format(number_mutations, perc_aa_subst, perc_ident_TM, perc_ident_nonTM, AAIMON))\\n sys.stdout.flush()\\n\\n return AAIMON_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28c3b3be9c12949417e3781935b23f30\",\n \"score\": \"0.4916316\",\n \"text\": \"async def getSpecs():\\n return specString\",\n \"title\": \"\"\n },\n {\n \"docid\": \"796b4785031219d25dc867f588379045\",\n \"score\": \"0.49081177\",\n \"text\": \"def ltsm_gen(net, seq_len, file_name, n_steps=100, hidden_size=89):\\n\\n notes = [] # Will contain a sequence of the predicted notes\\n x = torch.zeros((seq_len, 1, 89)).float().cuda() # Input notes. All 0s minus the last one\\n x[-1] = notes_encoded[0, :, :] # which is the first note of the piece\\n notes.append(x[-1].cpu().numpy()) # Saves the first note\\n h_state = torch.zeros(1, 1, hidden_size).float().cuda()\\n c_state = torch.zeros(1, 1, hidden_size).float().cuda()\\n print_first = True\\n for _ in range(n_steps):\\n chosen = False # To account for when no dimension's probability is bigger than 0.9\\n y_pred, h_c_state = net(x, (h_state, c_state))\\n h_state, c_state = h_c_state[0].data, h_c_state[1].data\\n y_pred = y_pred.data\\n y_pred = y_pred[-1] # We only care about the last predicted note (next note after last note of input sequence)\\n choose = torch.zeros((1, 1, 89)) # Coverts the probabilities to the actual note vector\\n for idx in range(89):\\n if y_pred[:, idx] > 0.9:\\n choose[:, :, idx] = 1\\n chosen = True\\n if not chosen:\\n if print_first:\\n print(\\\"\\\\nPrinting out the maximum prob of all notes for a time step\\\",\\n \\\"when this maximum prob is less than 0.9\\\")\\n print_first = False\\n choose[:, :, np.argmax(y_pred.cpu())] = 1\\n print(_, y_pred[:, np.argmax(y_pred.cpu())]) # Maximum probability out of all components\\n x_new = torch.empty(x.shape) # Uses the output of the last time_step\\n for idx, nt in enumerate(x[1:]): # As the input for the next time_step\\n x_new[idx] = nt # So the new sequence will be the same past sequence minus the first note\\n x_new[-1] = choose # of such past sequence, and plus the predicted note from this iteration\\n x = x_new.cuda() # We will use this new sequence to predict in the next iteration the next note\\n notes.append(choose) # Saves the predicted note\\n\\n # Gets the notes into the correct NumPy array shape\\n gen_notes = np.empty((len(notes)-seq_len+1, 89)) # Doesn't use the first predicted notes\\n for idx, nt in enumerate(notes[seq_len-1:]): # Because of the 0s init problem # TODO: Figure this out\\n gen_notes[idx] = nt[0]\\n\\n # Decodes the generated music and saves it as a MIDI file\\n gen_midi = decode(gen_notes, time_step=0.25)\\n gen_midi.write(\\\"midi\\\", file_name + \\\".mid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8dab091084702b746bdc1797fdec904\",\n \"score\": \"0.4905279\",\n \"text\": \"def specwindow(freq,time):\\n\\t\\tn = len(time)\\n\\t\\tW = [sum([np.exp(-2.j*pi*f*t) for t in time])/float(n) for f in freq]\\n\\t\\tamp = [np.sqrt(t.real*t.real + t.imag*t.imag) for t in W]\\n\\t\\tphase = [np.arctan2(t.imag,t.real) for t in W]\\n\\t\\treturn amp,phase\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3b18b6b20abb5f8ed1a74e325f6a6eb\",\n \"score\": \"0.490144\",\n \"text\": \"def generate_rep(seq):\\n\\tseq_rep = np.array([])\\n\\n\\tfor base in seq:\\n\\t\\tbase_rep = [1 if i in rep_dir[base] else 0 for i in range(6)]\\n\\t\\tseq_rep = np.concatenate((seq_rep, base_rep))\\n\\treturn np.atleast_2d(seq_rep)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83e509c139709d473ea3699f2eb6045e\",\n \"score\": \"0.4899097\",\n \"text\": \"def run_til_fix(simulation,N_step,skip,include_fixed=True):\\n return [tissue.copy() for tissue in generate_til_fix(simulation,N_step,skip,include_fixed=include_fixed)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bb98e3457958d34b0d9538904af51a1\",\n \"score\": \"0.48978472\",\n \"text\": \"def simulation2(start, end, init_time, results, timing, time_stamps, feedback=True, learning=False):\\n\\n attempts = collections.Counter()\\n\\n if learning:\\n\\n addition.productionstring(name=\\\"retrieve_addition\\\", string=\\\"\\\"\\\"\\n =g>\\n isa add\\n state reading\\n arg1 =num1\\n arg2 =num2\\n sum None\\n ?retrieval>\\n state free\\n buffer empty\\n ==>\\n =g>\\n isa add\\n state None\\n +retrieval>\\n isa add\\n arg1 =num1\\n arg2 =num2\\\"\\\"\\\", utility=1)\\n\\n addition.productionstring(name=\\\"terminate_addition\\\", string=\\\"\\\"\\\"\\n =g>\\n isa add\\n state None\\n =retrieval>\\n isa add\\n arg1 ~None\\n sum =v7\\n ==>\\n ~retrieval>\\n =g>\\n isa add\\n sum =v7\\\"\\\"\\\", utility=1)\\n\\n addition.productionstring(name=\\\"done\\\", string=\\\"\\\"\\\"\\n =g>\\n isa add\\n state None\\n sum =v7\\n sum ~None\\n ==>\\n =g>\\n isa add\\n state None\\\"\\\"\\\", reward=15)\\n\\n addition.model_parameters[\\\"production_compilation\\\"] = True\\n addition.model_parameters[\\\"utility_learning\\\"] = True\\n addition.model_parameters[\\\"utility_noise\\\"] = 0.8\\n\\n for loop in range(start+1,end+1):\\n x = random.uniform(0, 1) #what problem should be used \\n for i in range(len(PROB2)):\\n if x < PROB2[i]:\\n break\\n else:\\n i = UPTO\\n x = random.uniform(0, 1) #what problem should be used \\n for j in range(len(PROB2)):\\n if x < PROB2[j]:\\n break\\n else:\\n j = UPTO\\n #print(i, j)\\n correct = i + j\\n attempts.update([(i, j)])\\n addition.goal.add(actr.makechunk(\\\"\\\", \\\"add\\\", state=\\\"reading\\\", arg1=numbers[i], arg2=numbers[j]))\\n\\n sim = addition.simulation(initial_time=init_time, trace=False, gui=False)\\n addition.used_productions.rules.used_rulenames = {}\\n while True:\\n try:\\n sim.step()\\n except simpy.core.EmptySchedule:\\n break\\n if re.search(\\\"RETRIEVED: None\\\", sim.current_event.action):\\n results.setdefault((str(i), str(j)), collections.Counter()).update('X')\\n if feedback and random.randint(0,1) == 1:\\n addition.retrieval.add(actr.makechunk(\\\"\\\", \\\"add\\\", arg1=numbers[i], arg2=numbers[j], sum=numbers[correct]))\\n addition.retrieval.clear(sim.show_time()) #advise -- teacher tells the correct answer\\n break\\n if re.search(\\\"RULE FIRED: done\\\", sim.current_event.action):\\n x = addition.goal.copy().pop()\\n addition.goal.clear(sim.show_time())\\n x = x._asdict()\\n results.setdefault((str(i), str(j)), collections.Counter()).update([str(x[\\\"sum\\\"])])\\n if str(x[\\\"sum\\\"]) != str(numbers[correct]):\\n if feedback and random.randint(0,1) == 1:\\n addition.retrieval.add(actr.makechunk(\\\"\\\", \\\"add\\\", arg1=numbers[i], arg2=numbers[j], sum=numbers[correct]))\\n addition.retrieval.clear(sim.show_time()) #advise -- teacher tells the correct answer in roughly 50% of cases if incorrect\\n if learning:\\n utilities.modify_utilities(sim.show_time(), -15, addition.used_productions.rules.used_rulenames, addition.used_productions.rules, addition.used_productions.model_parameters)\\n elif timing:\\n time_stamps.setdefault((str(i), str(j)), []).append(sim.show_time() - init_time)\\n break\\n addition.retrieval.state = addition.retrieval._FREE\\n addition.retrieval.clear()\\n init_time = loop*DELAY\\n return results, time_stamps, init_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e44f9bd89cfcdebb87beb80802504f\",\n \"score\": \"0.48944435\",\n \"text\": \"def test_9(self):\\n main = Main()\\n main.generate_systems(n=4,\\n min_m=4,\\n max_n=100,\\n max_m=100,\\n step=10,\\n save_results=True,\\n save_systems=True,\\n gi=Gi())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"712f72c867014c21088ec4db9f53cbb3\",\n \"score\": \"0.48924717\",\n \"text\": \"def get_sample_G7_solution1(self):\\n\\t\\treturn util.make_notes([86, 84, 81, 82, 83, 81, 79, 78])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b023fad3a6540e074499ae758d400a04\",\n \"score\": \"0.4891167\",\n \"text\": \"def generatetimes(ndots, starttime):\\n cumsum, sample = starttime, finalsample(INTERVAL, ndots, THRESHOLD)\\n timesid = []\\n for i in range(ndots):\\n timesid.append(cumsum)\\n cumsum += sample[i]\\n return timesid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f62381525b1caeca076734e858f824d3\",\n \"score\": \"0.4889272\",\n \"text\": \"def gen_control(proband_id, stimulation):\\n pj, pd, cwd = os.path.join, os.path.dirname, os.getcwd()\\n sentence_path = pj(pd(cwd), 'stimuli', 'audio', 'test')\\n\\n speaker_trials = list(TRIALS_THIS_SESSION)\\n np.random.shuffle(speaker_trials)\\n speech_trials = list(TRIALS_THIS_SESSION)\\n np.random.shuffle(speech_trials)\\n\\n def get_f(speaker, intonation, word):\\n \\\"\\\"\\\"Determines the filename given the speaker, intonation, or word.\\\"\\\"\\\"\\n a = dict(zip(NAMES, CODES))[speaker]\\n b = intonation\\n c = ''\\n i = WORDS.index(word)\\n if i < 100:\\n c += '0'\\n if i < 10:\\n c += '0'\\n c += str(i)\\n return pj(sentence_path, '_'.join([a, b, c]) + '.wav')\\n\\n def test_trial(blockn, trialn):\\n \\\"\\\"\\\"Determines the trial info for a particular trial.\\\"\\\"\\\"\\n if blockn % 2 == 0:\\n task = TASKS[0]\\n speaker, intonation, word = speaker_trials.pop(0)\\n ans = speaker\\n alt1, alt2, alt3, alt4 = NAMES\\n else:\\n task = TASKS[1]\\n speaker, intonation, word = speech_trials.pop(0)\\n ans = word\\n alt1, alt2, alt3, alt4 = sorted(WORDS_DICT[word])\\n f = get_f(speaker, intonation, word)\\n phase = 'test'\\n return (proband_id, TEST_NAME, stimulation, phase, blockn, task,\\n trialn, speaker, intonation, word, f, ans, alt1, alt2, alt3,\\n alt4)\\n\\n def prac_trial(blockn, trialn):\\n \\\"\\\"\\\"Determines the trial info for a particular trial.\\\"\\\"\\\"\\n speaker, intonation, word = PRACTICE_TRIALS.pop(0)\\n if blockn % 2 == 0:\\n task = TASKS[0]\\n ans = speaker\\n alt1, alt2, alt3, alt4 = NAMES\\n else:\\n task = TASKS[1]\\n ans = word\\n alt1, alt2, alt3, alt4 = sorted(WORDS_DICT[word])\\n f = get_f(speaker, intonation, word)\\n phase = 'practice'\\n return (proband_id, TEST_NAME, stimulation, phase, blockn, task,\\n trialn, speaker, intonation, word, f, ans, alt1, alt2, alt3,\\n alt4)\\n\\n control = []\\n for blockn in [-2, -1]:\\n t = [prac_trial(blockn, trialn) for trialn in xrange(PRACTICE_TRIALSN)]\\n control += t\\n for blockn in xrange(len(TASKS) * BLOCKS_PER_TASK):\\n t = [test_trial(blockn, trialn) for trialn in xrange(TRIALS_PER_BLOCK)]\\n control += t\\n\\n return control\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd9cc0c499a4d2732e70cf59189e79a3\",\n \"score\": \"0.48884326\",\n \"text\": \"def sweep(self,startFrequetimes1to2ncyMHz, endFrequenciesHz, times, triggerMask): \\n \\n fs, ts = [],[]\\n start = startFrequencyMHz\\n c=0\\n for endFreq, t in zip(endFrequenciesHz,times):\\n newFs, newts = self.freqsAndTimes(start,endFreq,t )\\n fs+=newFs\\n ts+=newts\\n start = endFrequenciesHz[c]\\n c+=1\\n \\n return fs,ts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb6b230ab9762e8b16d5e040315f766c\",\n \"score\": \"0.48856592\",\n \"text\": \"def clifford_gates( target:int )->List:\\n## Basic\\n## Pi\\n rg_i = Gate(\\\"RX\\\", target, arg_value= 0)\\n rg_x = Gate(\\\"RX\\\", target, arg_value= np.pi)\\n rg_y = Gate(\\\"RY\\\", target, arg_value= np.pi)\\n ## Pi/2\\n rg_px2 = Gate(\\\"RX\\\", target, arg_value= +np.pi/2)\\n rg_py2 = Gate(\\\"RY\\\", target, arg_value= +np.pi/2)\\n rg_nx2 = Gate(\\\"RX\\\", target, arg_value= -np.pi/2)\\n rg_ny2 = Gate(\\\"RY\\\", target, arg_value= -np.pi/2)\\n\\n ## Decompose\\n ## Pi\\n g_z = [rg_y,rg_x]\\n ## Pi/2\\n g_phz = [rg_nx2,rg_py2,rg_px2]\\n g_nhz = [rg_nx2,rg_ny2,rg_px2]\\n ## Had\\n g_hpxz = [rg_x,rg_ny2]\\n g_hnxz = [rg_x,rg_py2]\\n g_hpyz = [rg_y,rg_px2]\\n g_hnyz = [rg_y,rg_nx2]\\n g_hpxy = [rg_px2,rg_py2,rg_px2]\\n g_hnxy = [rg_nx2,rg_py2,rg_nx2]\\n ## 2pi/3 \\n g_pc1 = [rg_py2,rg_px2]\\n g_pc2 = [rg_py2,rg_nx2]\\n g_pc4 = [rg_ny2,rg_px2]\\n g_pc3 = [rg_ny2,rg_nx2]\\n\\n g_nc1 = [rg_nx2,rg_py2]\\n g_nc2 = [rg_px2,rg_py2]\\n g_nc4 = [rg_nx2,rg_ny2]\\n g_nc3 = [rg_px2,rg_ny2]\\n\\n gates_set = [\\n [rg_i],[rg_x],[rg_y],[rg_px2],[rg_nx2],[rg_py2],[rg_ny2],\\n ## Pi\\n g_z,\\n ## Pi/2\\n g_phz,g_nhz,\\n ## Had\\n g_hpxz,g_hnxz,g_hpyz,g_hnyz,g_hpxy,g_hnxy,\\n ## 2pi/3 \\n g_pc1,g_pc2,g_pc4,g_pc3,\\n g_nc1,g_nc2,g_nc4,g_nc3\\n ]\\n return gates_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a844a8069a13d5760bea2737a36a3e8c\",\n \"score\": \"0.48853913\",\n \"text\": \"def generate_steps(stepNumber, tread, riser, stepWidth):\\n\\tsteps = []\\n\\tstep2d = MKPOL([[[tread, 0],[tread, riser*2], [tread*2, riser*2], [tread*2, riser]], [[1,2,3,4]], None])\\n\\tfirstStep = CUBOID([tread, riser, stepWidth])\\n\\tsteps.append(firstStep)\\n\\tfor i in range(int(stepNumber-1)):\\n\\t\\tsteps.append(T([1,2])([(tread*i), riser*i])(PROD([step2d, Q(stepWidth)])))\\n\\treturn steps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"188e0a2ed53433e160ad978c6632baa9\",\n \"score\": \"0.4880787\",\n \"text\": \"def simulate(self, tmax, verbose=False):\\n res = []\\n tmin = 0\\n while tmin < tmax:\\n tmin = tmax\\n for s in self.students:\\n statement = s.study()\\n t = statement['timestamp']\\n tmin = min(t, tmin)\\n if t < tmax:\\n res.append(statement)\\n if verbose:\\n print(\\\"statement: {}\\\".format(statement))\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da98868aa70c8f633c21c925f3cf0271\",\n \"score\": \"0.48727334\",\n \"text\": \"def beginSequenceandProbe(win, mouse, n):\\n global iPattern\\n squares = squarePattern[iPattern]\\n for square in squares:\\n square.setAutoDraw(True)\\n iPattern += 1\\n #done creating irregular square pattern\\n sequence = range(NUM_SQUARES)\\n random.shuffle(sequence)\\n for i in sequence[:n]:\\n squares[i].setFillColor(SQUARE_HIGHLIGHT_COLOR)\\n win.flip()\\n core.wait(HIGHLIGHT_TIME)\\n squares[i].setFillColor(SQUARE_COLOR)\\n #erase screen\\n #for square in squares:\\n # square.setAutoDraw(False)\\n #win.flip()\\n #core.wait(ISI_TIME)\\n #begin asking for the subject to recall pattern\\n for square in squares:\\n square.setAutoDraw(True)\\n instructions = visual.TextStim(win,text=\\\"Select the squares in the order they appeared. If you accidently click a square, click Back.\\\", pos=(0,10),wrapWidth=30)\\n submitText = visual.TextStim(win,text=\\\"Submit\\\",pos=(5,-8))\\n submitButton = visual.Rect(win,width=4, height=1.2, lineWidth=2)\\n backText = visual.TextStim(win,text=\\\"Undo last click\\\",pos=(-5,-8))\\n backButton = visual.Rect(win,width=7.3, height=1.2, lineWidth=2)\\n backButton.setPos((-5,-8))\\n submitButton.setPos((5,-8))\\n submitButton.setAutoDraw(True)\\n submitText.setAutoDraw(True)\\n backButton.setAutoDraw(True)\\n backText.setAutoDraw(True)\\n instructions.setAutoDraw(True)\\n instructions.draw()\\n submitButton.draw()\\n submitText.draw()\\n backButton.draw()\\n backText.draw()\\n win.flip()\\n #done rendering, time to start timer for subject response\\n timer = core.Clock()\\n clicked = []\\n while(True):\\n for i in range(len(squares)):\\n if mouse.isPressedIn(squares[i]):\\n clicked.append(i)\\n squares[i].setFillColor(SQUARE_HIGHLIGHT_COLOR)\\n win.flip()\\n core.wait(0.200)\\n while mouse.isPressedIn(squares[i]):\\n pass\\n squares[i].setFillColor(SQUARE_COLOR)\\n win.flip()\\n if(mouse.isPressedIn(backButton) and len(clicked) > 0):\\n clicked.remove(clicked[len(clicked)-1])\\n backText.setColor(SQUARE_HIGHLIGHT_COLOR)\\n win.flip()\\n core.wait(0.200)\\n while mouse.isPressedIn(backButton):\\n pass\\n backText.setColor(\\\"White\\\")\\n win.flip()\\n if(mouse.isPressedIn(submitButton)):\\n #erase display\\n submitButton.setAutoDraw(False)\\n submitText.setAutoDraw(False)\\n backButton.setAutoDraw(False)\\n backText.setAutoDraw(False)\\n instructions.setAutoDraw(False)\\n for i in range(len(squares)):\\n squares[i].setAutoDraw(False)\\n return (sequence[:n]==clicked,sequence[:n],clicked,correctness(clicked,sequence[:n]), n, timer.getTime())\\n if(event.getKeys(keyList=['q','escape'])):\\n quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08748c5e89e1e9f736a5266c4931d4d3\",\n \"score\": \"0.48711482\",\n \"text\": \"def _terminalSymbolsGenerator(self):\\n UPPAs = [i for i in range(1, 1000000)]\\n for i in UPPAs:\\n yield ([-i])\\n raise ValueError(\\\"Too many input sequences.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbde053ed7ac3b67939ab6dc16fdae7f\",\n \"score\": \"0.48691732\",\n \"text\": \"def dist_configs(G,q):\\n return [chi_from_tau(tau_config,G) for qp in range(q+1)\\n for tau_config in itertools.combinations(range(G),qp)\\n for degeneracy in range(int(fac(q)/fac(show(q-qp))))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d04914b691d62c3e25ae75bc93d7c259\",\n \"score\": \"0.48672348\",\n \"text\": \"def trialbhv(mouse, start_time=-1, end_time=6, verbose=True):\\n\\n # build your runs object\\n runs = flow.RunSorter.frommeta(mice=[mouse], exclude_tags=['bad'])\\n\\n trial_list = []\\n for r in range(len(runs)):\\n\\n run = runs[r]\\n\\n # get your t2p object\\n t2p = run.trace2p()\\n\\n # trigger all running speed around stim onsets\\n speed_traces = behaviortraces(t2p, '', start_s=start_time, end_s=end_time,\\n trace_type='speed', cutoff_before_lick_ms=-1)\\n print(np.shape(speed_trace))\\n # trigger all running speed around stim onsets\\n lick_traces = behaviortraces(t2p, '', start_s=start_time, end_s=end_time,\\n trace_type='lick', cutoff_before_lick_ms=-1)\\n print(np.shape(lick_trace))\\n # make timestamps\\n timestep = 1 / np.round(t2p.d['framerate'])\\n timestamps = np.concatenate((np.arange(start_time, 0, timestep),\\n np.arange(0, end_time, timestep)))\\n\\n # loop through and append each trial (slice of cstraces)\\n for trial in range(np.shape(run_traces)[2]):\\n index = pd.MultiIndex.from_arrays([\\n [run.mouse] * np.shape(run_traces)[1],\\n [run.date] * np.shape(run_traces)[1],\\n [run.run] * np.shape(run_traces)[1],\\n [int(trial)] * np.shape(run_traces)[1],\\n timestamps\\n ],\\n names=['mouse', 'date', 'run', 'trial_idx', 'timestamp'])\\n\\n data = {'speed': np.squeeze(speed_traces[0, :, trial]),\\n 'licking': np.squeeze(lick_traces[0, :, trial])}\\n # append all trials across all runs together in a list\\n trial_list.append(pd.DataFrame(data, index=index))\\n\\n # clear your t2p to save RAM\\n run._t2p = None\\n\\n trial_df = pd.concat(trial_list, axis=0)\\n save_path = os.path.join(flow.paths.outd, str(run.mouse) + '_df_bhv.pkl')\\n trial_df.to_pickle(save_path)\\n if verbose:\\n print('Done.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"273fbabebce5f5f983ee3d398b550220\",\n \"score\": \"0.48647377\",\n \"text\": \"def extract_chain_sequences(lines):\\n seq = []\\n for line in lines:\\n if line.startswith('SEQRES'):\\n seq.append(line[0:])\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df9cfedbee6238dc6dc39b39b2acb869\",\n \"score\": \"0.48646474\",\n \"text\": \"def adjust_intervals(self) -> list:\\n if self.begin == 0 and self.end == 86400:\\n return self.intervals\\n if self.begin >= 86399 or self.end <= 1 or self.begin >= self.end:\\n print(\\\"inadequate parameters, try again.\\\")\\n return []\\n new_intervals = []\\n\\n # determine first to last relevant interval\\n # interval: [begin, end, percentage]\\n for interval in self.intervals:\\n # print(new_intervals)\\n if interval[0] <= self.begin < interval[1]:\\n temp = [self.begin, interval[1], interval[2]]\\n new_intervals.append(temp)\\n elif self.begin < interval[0] and interval[1] < self.end:\\n new_intervals.append(interval)\\n elif interval[0] < self.end <= interval[1]:\\n temp = [interval[0], self.end, interval[2]]\\n new_intervals.append(temp)\\n # print(\\\"breaking\\\")\\n break\\n\\n # adjusting the demand percentages of the new intervals to interval length and demand of the entire simulation\\n entire_demand = 0\\n for interval in new_intervals:\\n entire_demand = entire_demand + ((interval[1] - interval[0]) * interval[2])\\n for interval in new_intervals:\\n interval[2] = ((interval[1] - interval[0]) * interval[2]) / entire_demand\\n\\n # print(entire_demand)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eabfac6f19461bb21e361120c960533\",\n \"score\": \"0.48527104\",\n \"text\": \"def makePrimers(self, sequence, interval, fuzziness=500, cb=None):\\n included_region = [[x[0], x[1] - x[0]] for x in interval]\\n self.seq_args['SEQUENCE_TEMPLATE'] = str(sequence.seq)\\n qualList = sequence.letter_annotations[\\\"phred_quality\\\"]\\n qual = ' '.join([str(x) for x in qualList])\\n self.seq_args['SEQUENCE_QUALITY'] = qual\\n output = []\\n \\n for target in included_region:\\n self.seq_args['PRIMER_PRODUCT_SIZE_RANGE'] = str(target[1]) + '-' + str(target[1] + fuzziness)\\n self.seq_args['SEQUENCE_TARGET'] = \\\"%d,%d\\\" % (target[0], target[1])\\n #pdb.set_trace() # Debug\\n primers = primer3.wrappers.designPrimers(self.seq_args)\\n yield primers #self.seq_args\\n # cb and cb(target)\\n # pdb.set_trace() # Debug\",\n \"title\": \"\"\n },\n {\n \"docid\": \"097e15d292a173861cd2fbe5abcb07ae\",\n \"score\": \"0.48510003\",\n \"text\": \"def sequence_list():\\n zeros = [0] * 5\\n ones = [1] * 5\\n return zeros + ones\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9b3bf7ec8ea6f15615bbb7b98e44fe2\",\n \"score\": \"0.4842891\",\n \"text\": \"def get_all_possible_single_nucleotide_mutations(gene_sequence, gene_kmers, sort_results=True):\\n length = len(gene_sequence)\\n assert length % 3 == 0, 'Transcript nucleotide sequence not multiple of 3'\\n assert gene_kmers is not None\\n bases = 'ACGT'\\n results = []\\n\\n aa_length = int(length / 3)\\n\\n # Make a long sequence with all changes\\n # For each base positions, change to each base.\\n # Can join them all then translate at once.\\n\\n # There are 13 repeats of the gene sequence.\\n # The first is the WT\\n # 2-5 is first base position in codon, 6-9 is 2nd, 10-13 is 3rd\\n # 2, 6, 10 are changing to A\\n # 3, 7, 11 are changing to C\\n # 4, 8, 12 are changing to G\\n # 5, 9, 13 are changing to T\\n #\\n # So the ath codon represents:\\n # n = a//aa_length is the sequence number\\n # r = a%aa_length is the residue number (add 1 to get 1-based coordinates)\\n # m = (n-1)//4 gives the base position\\n # b = (n-1)%4 gives the base change (index of A, C, G, T)\\n extended_seq = gene_sequence\\n for j in range(3): # base position\\n for b in bases: # Base change\\n new_seq = \\\"\\\".join([s if ii % 3 != j else b for (ii, s) in enumerate(gene_sequence)])\\n extended_seq += new_seq\\n\\n extended_seq_translated = Seq(extended_seq).translate()\\n\\n for i in range(aa_length, aa_length * 13): # 13 is not a typo, see comments above\\n residue = i % aa_length + 1\\n n = i // aa_length\\n base_position = (n - 1) // 4\\n mut = bases[(n - 1) % 4]\\n cds_pos = (residue - 1) * 3 + base_position + 1\\n aa = extended_seq_translated[residue - 1]\\n aa2 = extended_seq_translated[i]\\n seq = extended_seq[(residue - 1) * 3:residue * 3]\\n seq2 = extended_seq[i * 3:(i + 1) * 3]\\n if seq != seq2:\\n aachange = '{}{}{}'.format(aa, residue, aa2)\\n codon_change = '{}>{}'.format(seq, seq2)\\n if aa == aa2:\\n effect = 'synonymous'\\n elif aa2 == '*':\\n effect = 'nonsense'\\n elif aa == '*':\\n effect = 'stop_lost'\\n elif residue == 1:\\n effect = 'start_lost'\\n else:\\n effect = 'missense'\\n d = {'residue': residue, 'base': base_position,\\n 'ref': seq[base_position], 'mut': seq2[base_position], 'cdspos': cds_pos,\\n 'aachange': aachange, 'aaref': str(aa), 'aamut': str(aa2), 'codon_change': codon_change,\\n 'effect': effect}\\n for k, kmers in gene_kmers.items():\\n kmer = kmers[(residue - 1) * 3 + base_position]\\n d['{}mer_ref'.format(k)] = kmer\\n results.append(d)\\n\\n all_possible_mutations = pd.DataFrame(results)\\n if sort_results:\\n return all_possible_mutations.sort_values(['residue', 'base', 'mut']).reset_index(drop=True)\\n else:\\n return all_possible_mutations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bd5a363976b955e62508d7ee289adaf\",\n \"score\": \"0.48426214\",\n \"text\": \"def calculate_makespan(sol):\\n\\n sequence_np = np.array(sol.sequence)\\n sequence_length = len(sequence_np)\\n num_machines=sol.num_machines\\n processing_times=sol.processing_times\\n # for i in range(sol.num_jobs()): \\n # processing_times.append(sol.get_job_costs(i))\\n e = np.zeros((sequence_length+1,num_machines+1), dtype='int64')\\n\\n for i in range(1,sequence_length+1 ):\\n\\t e[i,0]=0\\n\\t for j in range(1,num_machines+1):\\n\\t\\t if i == 0:\\n\\t\\t\\t e[0,j]=0\\n\\n\\t\\t if e[i - 1, j] > e[i, j - 1]:\\n\\t\\t\\t e[i, j] = e[i - 1][j] + processing_times[sequence_np[i - 1] - 1][j-1]\\n\\t\\t else:\\n\\t\\t\\t e[i, j] = e[i][j - 1] + processing_times[sequence_np[i - 1]- 1][j-1]\\n\\n\\n\\t# Return makespan (integer)\\n #print(\\\"e\\\",e)\\n sol.makespan=e[sequence_length, num_machines]\\n return e[sequence_length, num_machines]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e30438f8ef1f1dfaf4486ff9aaa82e4\",\n \"score\": \"0.4842601\",\n \"text\": \"def algebra_generators(self):\\n\\t\\t\\t\\treturn [self.x(i) for i in range(1, self._n+1)]+[self.t(i) for i in range(1, self._n)]+[self.e(p) for p in self._P]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993899,"cells":{"query_id":{"kind":"string","value":"2376dd75aefe8d1ed378c729882d320a"},"query":{"kind":"string","value":"Create a list of windows to be slid over / searched on, for a given image Takes an image, start and stop positions in both x and y, window size (x and y dimensions), and overlap fraction (for both x and y)"},"positive_passages":{"kind":"list like","value":[{"docid":"0bcaaf2bb4003482140a814aa8362ab7","score":"0.7977463","text":"def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None],\n xy_window=(64, 64), xy_overlap=(0.5, 0.5)):\n # If x and/or y start/stop positions not defined, set to image size\n if x_start_stop[0] == None:\n x_start_stop[0] = 0\n if x_start_stop[1] == None:\n x_start_stop[1] = img.shape[1]\n if y_start_stop[0] == None:\n y_start_stop[0] = 0\n if y_start_stop[1] == None:\n y_start_stop[1] = img.shape[0]\n # Compute the span of the region to be searched\n xspan = x_start_stop[1] - x_start_stop[0]\n yspan = y_start_stop[1] - y_start_stop[0]\n # Compute the number of pixels per step in x/y\n nx_pix_per_step = np.int(xy_window[0] * (1 - xy_overlap[0]))\n ny_pix_per_step = np.int(xy_window[1] * (1 - xy_overlap[1]))\n # Compute the number of windows in x/y\n nx_windows = np.int(xspan / nx_pix_per_step) - 1\n ny_windows = np.int(yspan / ny_pix_per_step) - 1\n # Initialize a list to append window positions to\n window_list = []\n # Loop through finding x and y window positions\n # Note: you could vectorize this step, but in practice\n # you'll be considering windows one by one with your\n # classifier, so looping makes sense\n for ys in range(ny_windows):\n for xs in range(nx_windows):\n # Calculate window position\n startx = xs * nx_pix_per_step + x_start_stop[0]\n endx = startx + xy_window[0]\n starty = ys * ny_pix_per_step + y_start_stop[0]\n endy = starty + xy_window[1]\n # Append window position to list\n window_list.append(((startx, starty), (endx, endy)))\n # Return the list of windows\n return window_list","title":""}],"string":"[\n {\n \"docid\": \"0bcaaf2bb4003482140a814aa8362ab7\",\n \"score\": \"0.7977463\",\n \"text\": \"def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None],\\n xy_window=(64, 64), xy_overlap=(0.5, 0.5)):\\n # If x and/or y start/stop positions not defined, set to image size\\n if x_start_stop[0] == None:\\n x_start_stop[0] = 0\\n if x_start_stop[1] == None:\\n x_start_stop[1] = img.shape[1]\\n if y_start_stop[0] == None:\\n y_start_stop[0] = 0\\n if y_start_stop[1] == None:\\n y_start_stop[1] = img.shape[0]\\n # Compute the span of the region to be searched\\n xspan = x_start_stop[1] - x_start_stop[0]\\n yspan = y_start_stop[1] - y_start_stop[0]\\n # Compute the number of pixels per step in x/y\\n nx_pix_per_step = np.int(xy_window[0] * (1 - xy_overlap[0]))\\n ny_pix_per_step = np.int(xy_window[1] * (1 - xy_overlap[1]))\\n # Compute the number of windows in x/y\\n nx_windows = np.int(xspan / nx_pix_per_step) - 1\\n ny_windows = np.int(yspan / ny_pix_per_step) - 1\\n # Initialize a list to append window positions to\\n window_list = []\\n # Loop through finding x and y window positions\\n # Note: you could vectorize this step, but in practice\\n # you'll be considering windows one by one with your\\n # classifier, so looping makes sense\\n for ys in range(ny_windows):\\n for xs in range(nx_windows):\\n # Calculate window position\\n startx = xs * nx_pix_per_step + x_start_stop[0]\\n endx = startx + xy_window[0]\\n starty = ys * ny_pix_per_step + y_start_stop[0]\\n endy = starty + xy_window[1]\\n # Append window position to list\\n window_list.append(((startx, starty), (endx, endy)))\\n # Return the list of windows\\n return window_list\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"cf9ffb3c2b3f1cbe7a7e2ac9920c7980","score":"0.8306893","text":"def generate_windows_list(img,\n x_start_stop,\n y_start_stop,\n xy_window,\n xy_overlap):\n if x_start_stop[0] is None:\n x_start_stop[0] = 0\n if x_start_stop[1] is None:\n x_start_stop[1] = img.shape[1]\n if y_start_stop[0] is None:\n y_start_stop[0] = 0\n if y_start_stop[1] is None:\n y_start_stop[1] = img.shape[0]\n\n # Compute the span of the region to be searched\n xspan = x_start_stop[1] - x_start_stop[0]\n yspan = y_start_stop[1] - y_start_stop[0]\n\n # Compute the number of pixels per step in x/y\n nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))\n ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))\n\n # Compute the number of windows in x/y\n nx_windows = np.int(xspan/nx_pix_per_step) - 1\n ny_windows = np.int(yspan/ny_pix_per_step) - 1\n\n window_list = []\n\n # Loop through finding x and y window positions\n for ys in range(ny_windows):\n for xs in range(nx_windows):\n\n startx = xs*nx_pix_per_step + x_start_stop[0]\n endx = startx + xy_window[0]\n starty = ys*ny_pix_per_step + y_start_stop[0]\n endy = starty + xy_window[1]\n\n window_list.append(((startx, starty), (endx, endy)))\n\n return window_list","title":""},{"docid":"3761212d4864356bd18305158d56bc48","score":"0.81766343","text":"def slide_window(image, x_start=0, x_stop=None, y_start=0, y_stop=None, xy_window=(64, 64), xy_overlap=(0.5, 0.5)):\n # If x and/or y start/stop positions not defined, set to image size\n if x_stop == None:\n x_stop = image.shape[1]\n if y_stop == None:\n y_stop = image.shape[0]\n # Compute the span of the region to be searched\n xspan = x_stop - x_start\n yspan = y_stop - y_start\n # Compute the number of pixels per step in x/y\n nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))\n ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))\n # Compute the number of windows in x/y\n nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))\n ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))\n nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step)\n ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step)\n # Initialize a list to append window positions to\n window_list = []\n # Loop through finding x and y window positions\n # Note: you could vectorize this step, but in practice you'll be considering windows one by one with your\n # classifier, so looping makes sense\n for ys in range(ny_windows):\n for xs in range(nx_windows):\n # Calculate window position\n startx = xs*nx_pix_per_step + x_start\n endx = startx + xy_window[0]\n starty = ys*ny_pix_per_step + y_start\n endy = starty + xy_window[1]\n # Append window position to list\n window_list.append(((startx, starty), (endx, endy)))\n # Return the list of windows\n return window_list","title":""},{"docid":"3d7d71ca2a819488aee14113ac8db52d","score":"0.80311435","text":"def slide_window(\n img,\n x_start_stop=[None, None], y_start_stop=[None, None], \n xy_window=(64, 64), xy_overlap=(0.5, 0.5)):\n # If x and/or y start/stop positions not defined, set to image size\n if not x_start_stop[0]:\n x_start_stop[0] = 0\n if not x_start_stop[1]:\n x_start_stop[1] = img.shape[1]\n if not y_start_stop[0]:\n y_start_stop[0] = 0\n if not y_start_stop[1]:\n y_start_stop[1] = img.shape[0]\n # Compute the span of the region to be searched\n w = x_start_stop[1] - x_start_stop[0]\n h = y_start_stop[1] - y_start_stop[0]\n # Compute the number of pixels per step in x/y\n pps_x = int((1.0 - xy_overlap[0]) * xy_window[0])\n pps_y = int((1.0 - xy_overlap[1]) * xy_window[1])\n # Compute the number of windows in x/y\n n_x = int((w - xy_window[0])/pps_x + 1)\n n_y = int((h - xy_window[1])/pps_y + 1)\n # Initialize a list to append window positions to\n window_list = []\n for i in range(n_y):\n y_pos = i * pps_y + y_start_stop[0]\n for j in range(n_x):\n x_pos = j * pps_x + x_start_stop[0]\n bbox = ((x_pos,y_pos), (x_pos+xy_window[0],y_pos+xy_window[1]))\n window_list.append(bbox)\n \n return window_list","title":""},{"docid":"ff9aa0b2717797aec7f90fbaa1bbb602","score":"0.7582017","text":"def get_image_patches_by_sliding_window(img, stepSize, window_size, overlapping):\n # read the image and define the stepSize and window size\n # (width,height)\n if overlapping == 100:\n return None\n # generation step size for overlapping\n overlapping = 100 - overlapping\n stepSize = int(stepSize * (overlapping / 100))\n\n patches = []\n image = img # your image path\n tmp = image # for drawing a rectangle\n (w_width, w_height) = (window_size, window_size) # window size\n for x in range(0, image.shape[1] - w_width, stepSize):\n for y in range(0, image.shape[0] - w_height, stepSize):\n window = image[x:x + w_width, y:y + w_height, :]\n # add the window into your patches array.\n patches.append(window)\n\n return patches","title":""},{"docid":"ae3e267148a2d61226c88ab9c172c864","score":"0.74507797","text":"def get_windows(img):\n w_far = slide_window(img, x_start_stop=[0, 1280], y_start_stop=[350, 550],\n xy_window=(96,64), xy_overlap=(0.5, 0.5))\n w_medium_s = slide_window(img, x_start_stop=[0, 1280], y_start_stop=[400, 650],\n xy_window=(128,96), xy_overlap=(0.7, 0.7))\n w_medium_l = slide_window(img, x_start_stop=[0, 1280], y_start_stop=[400, 660],\n xy_window=(128,128), xy_overlap=(0.5, 0.5))\n far = slide_window(img, x_start_stop=[600, 1100], y_start_stop=[390, 530],\n xy_window=(48,48), xy_overlap=(0.8, 0.5))\n\n windows = w_far + w_medium_s + w_medium_l + far\n return windows","title":""},{"docid":"556424d8ac66b58260d76d199f42dc7d","score":"0.71355724","text":"def slidingWindow(self, img):\n # 720 x 1280\n # y --> 720 (0)\n # x --> 1280 (1)\n\n sizeY, sizeX = img.shape\n\n outputImg = np.dstack((img, img, img)) * 255\n\n # Compute histogram for the bottom half of the image along the x-axis\n hist = np.sum(img[sizeY//2:,:], axis=0)\n\n # Height of each window\n window_height = np.int(sizeY // self.nwindows)\n\n # Check indexes != 0\n nonzero = np.nonzero(img)\n nonzeroInY = np.array(nonzero[0])\n nonzeroInX = np.array(nonzero[1])\n\n # Split the image in two and set the centers\n leftXCenter = np.argmax(hist[:sizeX // 2])\n rightXCenter = np.argmax(hist[sizeX // 2:]) + sizeX // 2\n\n # Set the x-center of the boxes, which will be corrected over time\n leftXCurrent = leftXCenter\n rightXCurrent = rightXCenter\n \n # Lists to save indexes of pixel inside the rectangle\n leftSidePixels = []\n rightSidePixels = []\n\n for window in range(self.nwindows):\n # Make the boxes\n # Calculate the Y coords\n yLow = sizeY - (1 + window) * window_height\n yHigh = sizeY - window * window_height\n \n # Calculate the X coords for the left and right side\n xLowLeft = leftXCurrent - self.margin\n xHighLeft = leftXCurrent + self.margin\n xLowRight = rightXCurrent - self.margin\n xHighRight = rightXCurrent + self.margin\n\n # Draw rectangle for the left lane\n cv2.rectangle(outputImg, (xLowLeft, yLow), (xHighLeft, yHigh), (0, 255, 0), 3)\n \n # Draw rectangle for the right lane\n cv2.rectangle(outputImg, (xLowRight, yLow), (xHighRight, yHigh), (0, 255, 0), 3)\n\n # Check if pixels's values != 0 are inside the window (rectanle)\n\n # Check if the indexes are in the boxes and their values != 0\n leftSidePixelsInsideBox = ((nonzeroInX >= xLowLeft) & (nonzeroInX <= xHighLeft) & (nonzeroInY >= yLow) & (nonzeroInY <= yHigh)).nonzero()[0]\n rightSidePixelsInsideBox = ((nonzeroInX >= xLowRight) & (nonzeroInX <=xHighRight) & (nonzeroInY >= yLow) & (nonzeroInY <= yHigh)).nonzero()[0]\n\n leftSidePixels.append(leftSidePixelsInsideBox)\n rightSidePixels.append(rightSidePixelsInsideBox)\n\n if len(leftSidePixelsInsideBox) > self.minpixels:\n leftXCurrent = np.int(np.mean(nonzeroInX[leftSidePixelsInsideBox]))\n\n if len(rightSidePixelsInsideBox) > self.minpixels:\n rightXCurrent = np.int(np.mean(nonzeroInX[rightSidePixelsInsideBox]))\n\n try:\n leftSidePixels = np.concatenate(leftSidePixels)\n rightSidePixels = np.concatenate(rightSidePixels)\n except ValueError:\n # Avoids an error if the above is not implemented fully\n pass\n\n leftLaneY = nonzeroInY[leftSidePixels]\n leftLaneX = nonzeroInX[leftSidePixels]\n rightLaneY = nonzeroInY[rightSidePixels]\n rightLaneX = nonzeroInX[rightSidePixels]\n\n # Get the coefficients (A, B, C)\n leftFit = np.polyfit(leftLaneX, leftLaneY, 2)\n rightFit = np.polyfit(rightLaneX, rightLaneY, 2)\n \n # Generate x values. These will be the y for plotting\n ploty = np.linspace(0, outputImg.shape[0]-1, outputImg.shape[0])\n \n try:\n leftFitX = ploty*leftFit[0]**2 + ploty*leftFit[1] + leftFit[2]\n rightFitX = ploty*rightFit[0]**2 + ploty*rightFit[1] + leftFit[2]\n \n except TypeError:\n # In case there is no C\n leftFitX = ploty*leftFit[0]**2 + ploty*leftFit[1]\n rightFitX = ploty*rightFit[0]**2 + ploty*rightFit[1]\n\n windowImg = np.zeros_like(outputImg)\n\n outputImg[leftLaneY, leftLaneX] = [255, 0, 0]\n outputImg[rightLaneY, rightLaneX] = [0, 0, 255]\n\n leftLineWindow1 = np.array([np.transpose(np.vstack([leftFitX - self.margin, ploty]))])\n leftLineWindow2 = np.array([np.flipud(np.transpose(np.vstack([leftFitX + self.margin, ploty])))])\n leftLinePts = np.hstack((leftLineWindow1, leftLineWindow2))\n \n rightLineWindow1 = np.array([np.transpose(np.vstack([rightFitX - self.margin, ploty]))])\n rightLineWindow2 = np.array([np.flipud(np.transpose(np.vstack([rightFitX + self.margin, ploty])))])\n rightLinePts = np.hstack((rightLineWindow1, rightLineWindow2))\n\n cv2.fillPoly(windowImg, np.int_([leftLinePts]), (0, 255, 0))\n cv2.fillPoly(windowImg, np.int_([rightLinePts]), (0, 255, 0))\n result = cv2.addWeighted(outputImg, 1, windowImg, 0.3, 0)\n\n plt.plot(leftFitX, ploty, color = 'yellow')\n plt.plot(rightFitX, ploty, color = 'yellow')\n\n # leftFitX -> Formula for the left lane\n # rightFitX -> Formula for the right lane\n # leftLaneX -> X - index inside the left window and their values != 0\n # rightLaneX -> X - index inside the right window and their values != 0\n return leftFitX, leftLaneX, rightFitX, rightLaneX, result","title":""},{"docid":"f67415d9def9f912cf12dbc529bec832","score":"0.71134883","text":"def get_multiscale_windows(img):\n window_list = list()\n method = \"right\"\n if method == \"right\":\n # for the included video this is fine to improve speed\n # but for other videos, method == \"full\" should be used\n window_list += slide_window(img,\n xy_overlap = (0.75, 0.75),\n x_start_stop = [620, 620 + 6*96],\n y_start_stop = [385, 385 + 2*96],\n xy_window = (96, 96))\n window_list += slide_window(img,\n xy_overlap = (0.75, 0.75),\n x_start_stop = [620, None],\n y_start_stop = [385, 385 + 2*128],\n xy_window = (128, 128))\n elif method == \"full\":\n window_list += slide_window(img,\n xy_overlap = (0.75, 0.75),\n x_start_stop = [620 - 6*96, 620 + 6*96],\n y_start_stop = [385, 385 + 2*96],\n xy_window = (96, 96))\n window_list += slide_window(img,\n xy_overlap = (0.75, 0.75),\n y_start_stop = [385, 385 + 2*128],\n xy_window = (128, 128))\n else:\n raise ValueError(method)\n return window_list","title":""},{"docid":"06833c53e9f880942e6be8819145c199","score":"0.6974579","text":"def calculateWindow(image, coord, window_size):\r\n image_shape = np.shape(image)\r\n sz = (window_size-1)//2\r\n x_list = np.arange(coord[0]-sz, coord[0]+sz+1, 1)\r\n y_list = np.arange(coord[1]-sz, coord[1]+sz+1, 1)\r\n xs = [x for x in x_list if (x >= 0) and (x < image_shape[0])]\r\n ys = [y for y in y_list if (y >= 0) and (y < image_shape[1])]\r\n x1 = np.min(xs)\r\n x2 = np.max(xs)\r\n y1 = np.min(ys)\r\n y2 = np.max(ys)\r\n return image[x1:x2+1, y1:y2+1]","title":""},{"docid":"b06a5928375e74bb02f7e78a8952ece8","score":"0.6835137","text":"def sliding_hog_windows(self, image):\n # initialization\n image_height, image_width = 48, 48\n window_size = 24\n window_step = 6\n hog_windows = []\n for y in range(0, image_height, window_step):\n for x in range(0, image_width, window_step):\n window = image[y:y+window_size, x:x+window_size]\n hog_windows.extend(hog(window, orientations=8, pixels_per_cell=(8, 8),\n cells_per_block=(1, 1)))\n return hog_windows","title":""},{"docid":"a2f567ed90f43b37d511ae194327f64a","score":"0.6657261","text":"def find_lane_sliding_window(binary_warped):\n # Take a histogram of the bottom half of the image\n histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)\n # Create an output image to draw on and visualize the result\n out_img = np.dstack((binary_warped, binary_warped, binary_warped))\n # Find the peak of the left and right halves of the histogram\n # These will be the starting point for the left and right lines\n midpoint = np.int(histogram.shape[0]//2)\n leftx_base = np.argmax(histogram[:midpoint])\n rightx_base = np.argmax(histogram[midpoint:]) + midpoint\n\n # HYPERPARAMETERS\n # Choose the number of sliding windows\n nwindows = 10\n # Set the width of the windows +/- margin\n margin = 50\n # Set minimum number of pixels found to recenter window\n minpix = 100\n\n # Set height of windows - based on nwindows above and image shape\n window_height = np.int(binary_warped.shape[0]//nwindows)\n # Identify the x and y positions of all nonzero pixels in the image\n nonzero = binary_warped.nonzero()\n nonzeroy = np.array(nonzero[0])\n nonzerox = np.array(nonzero[1])\n # Current positions to be updated later for each window in nwindows\n leftx_current = leftx_base\n rightx_current = rightx_base\n\n # Create empty lists to receive left and right lane pixel indices\n left_lane_inds = []\n right_lane_inds = []\n\n # Step through the windows one by one\n for window in range(nwindows):\n # Identify window boundaries in x and y (and right and left)\n win_y_low = binary_warped.shape[0] - (window+1)*window_height\n win_y_high = binary_warped.shape[0] - window*window_height\n win_xleft_low = leftx_current - margin\n win_xleft_high = leftx_current + margin\n win_xright_low = rightx_current - margin\n win_xright_high = rightx_current + margin\n \n # Draw the windows on the visualization image\n cv2.rectangle(out_img,(win_xleft_low,win_y_low),\n (win_xleft_high,win_y_high),(0,255,0), 2) \n cv2.rectangle(out_img,(win_xright_low,win_y_low),\n (win_xright_high,win_y_high),(0,255,0), 2) \n \n # Identify the nonzero pixels in x and y within the window #\n good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & \n (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]\n good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & \n (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]\n \n # Append these indices to the lists\n left_lane_inds.append(good_left_inds)\n right_lane_inds.append(good_right_inds)\n \n # If you found > minpix pixels, recenter next window on their mean position\n if len(good_left_inds) > minpix:\n leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\n if len(good_right_inds) > minpix: \n rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\n\n # Concatenate the arrays of indices (previously was a list of lists of pixels)\n try:\n left_lane_inds = np.concatenate(left_lane_inds)\n right_lane_inds = np.concatenate(right_lane_inds)\n except ValueError:\n # Avoids an error if the above is not implemented fully\n pass\n\n # Extract left and right line pixel positions\n leftx = nonzerox[left_lane_inds]\n lefty = nonzeroy[left_lane_inds] \n rightx = nonzerox[right_lane_inds]\n righty = nonzeroy[right_lane_inds]\n\n return leftx, lefty, rightx, righty, out_img","title":""},{"docid":"5f35a6e4f769bade7f932388ca82aa3e","score":"0.64788586","text":"def window_bounds(i, n, wn):\n\n at_end = False\n hw = wn // 2\n\n start = i - hw\n stop = i + hw + 1\n\n if start < 0:\n at_end = True\n start = 0\n elif stop > n:\n at_end = True\n stop = n\n\n return start, stop, at_end","title":""},{"docid":"53cefcce900c926c25c7f6ffbd5e1445","score":"0.64113986","text":"def search_windows(img,\n windows_list,\n clf,\n scaler,\n color_space='RGB',\n spatial_size=(32, 32),\n hist_bins=32,\n orient=9,\n pix_per_cell=8,\n cell_per_block=2,\n hog_channel=0,\n use_spatial_feat=True,\n use_hist_feat=True,\n use_hog_feat=True):\n windows_positive = []\n\n for window in windows_list:\n\n test_img_part = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))\n\n features = extract_features(test_img_part,\n color_space=color_space,\n spatial_size=spatial_size,\n hist_bins=hist_bins,\n orient=orient,\n pix_per_cell=pix_per_cell,\n cell_per_block=cell_per_block,\n hog_channel=hog_channel,\n use_spatial_feat=use_spatial_feat,\n use_hist_feat=use_hist_feat,\n use_hog_feat=use_hog_feat)\n\n test_features = scaler.transform(np.array(features).reshape(1, -1))\n\n prediction = clf.predict(test_features)\n\n if prediction == 1:\n windows_positive.append(window)\n\n return windows_positive","title":""},{"docid":"d95a678c71c7a8ca08fc750df42c9407","score":"0.6403148","text":"def sliding_window(image, patch_size: tuple, step: int, show_debug: bool = False) -> list:\n if isinstance(image, Image.Image):\n image = np.array(image)\n\n if step == 0:\n h, w = image.shape[0], image.shape[1] # 720, 1280\n w_iter, h_iter = w // patch_size[0], h // patch_size[1]\n crop_image_list = []\n for i in range(h_iter):\n for j in range(w_iter):\n bbox = (i*patch_size[0], j*patch_size[0],\n (i+1)*patch_size[0], (j+1)*patch_size[0])\n crop_image = image[bbox[0]:bbox[2], bbox[1]: bbox[3]]\n if show_debug:\n crop_image = Image.fromarray(crop_image)\n crop_image.save(f\"/data/jiangmingchao/patches/{i}.png\")\n cv2.rectangle(image,\n (i*patch_size[0], j*patch_size[0]),\n ((i+1)*patch_size[0], (j+1)*patch_size[0]),\n (255, 255, 0),\n 2,\n )\n\n crop_image_list.append(Image.fromarray(crop_image))\n\n if show_debug:\n cv2.imwrite(\"1.jpg\", image)\n\n else:\n h, w = image.shape[0], image.shape[1]\n step_w_iter, step_h_iter = (w - patch_size[0]) // step, (h - patch_size[0]) // step\n crop_image_list = []\n for i in range(step_h_iter):\n for j in range(step_w_iter):\n bbox = (i * step, j * step, patch_size[0] + i * step, patch_size[1] + j * step)\n crop_image = image[bbox[0]: bbox[2], bbox[1]: bbox[3]]\n print(crop_image.shape)\n crop_image_list.append(Image.fromarray(crop_image))\n\n return crop_image_list","title":""},{"docid":"48020790766bfd0bf962258e28d37b58","score":"0.6313725","text":"def windows(self, size, overlap=0, rm_offset=False):\r\n rows = self.data.shape[0]\r\n if (0 < size <= rows) and (0 <= overlap < size):\r\n n = (rows - size) // (size - overlap) + 1\r\n\r\n for i in range(n):\r\n start = (size - overlap) * i\r\n end = start + size\r\n win = self.data.iloc[start:end, :]\r\n if rm_offset:\r\n win_offset = win - win.mean()\r\n win_offset['t'] = win['t']\r\n yield win_offset\r\n\r\n yield win\r\n\r\n else:\r\n raise IndexError(f\"{self} no possible window of size '{size}'.\")","title":""},{"docid":"2f747666284cacf54c84d4fc0ecaeb17","score":"0.6293102","text":"def sliding_window_init(lane, binary_warped, draw=False):\n\n # Assuming you have created a warped binary image called \"binary_warped\"\n # Take a histogram of the bottom half of the image\n histogram1 = np.sum(binary_warped[binary_warped.shape[0] / 2:, :], axis=0)\n # Create an output image to draw on and visualize the result\n out_img = np.dstack((binary_warped, binary_warped, binary_warped)) * 255\n\n # Find the peak of the left and right halves of the histogram\n # These will be the starting point for the left and right lines\n midpoint = np.int(histogram1.shape[0] / 2)\n leftx_base = np.argmax(histogram1[:midpoint])\n rightx_base = np.argmax(histogram1[midpoint:]) + midpoint\n\n # Choose the number of sliding windows\n nwindows = 9\n # Set height of windows\n window_height = np.int(binary_warped.shape[0] / nwindows)\n # Identify the x and y positions of all nonzero pixels in the image\n nonzero = binary_warped.nonzero()\n nonzeroy = np.array(nonzero[0])\n nonzerox = np.array(nonzero[1])\n # Current positions to be updated for each window\n leftx_current = leftx_base\n rightx_current = rightx_base\n # Set the width of the windows +/- margin\n margin = 100\n # Set minimum number of pixels found to recenter window\n minpix = 50\n # Create empty lists to receive left and right lane pixel indices\n left_lane_inds = []\n right_lane_inds = []\n\n # Step through the windows one by one\n for window in range(nwindows):\n # Identify window boundaries in x and y (and right and left)\n win_y_low = binary_warped.shape[0] - (window + 1) * window_height\n win_y_high = binary_warped.shape[0] - window * window_height\n win_xleft_low = leftx_current - margin\n win_xleft_high = leftx_current + margin\n win_xright_low = rightx_current - margin\n win_xright_high = rightx_current + margin\n # Draw the windows on the visualization image\n cv2.rectangle(out_img, (win_xleft_low, win_y_low), (win_xleft_high, win_y_high), (0, 255, 0), 2)\n cv2.rectangle(out_img, (win_xright_low, win_y_low), (win_xright_high, win_y_high), (0, 255, 0), 2)\n # Identify the nonzero pixels in x and y within the window\n good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (\n nonzerox < win_xleft_high)).nonzero()[0]\n good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (\n nonzerox < win_xright_high)).nonzero()[0]\n # Append these indices to the lists\n left_lane_inds.append(good_left_inds)\n right_lane_inds.append(good_right_inds)\n # If you found > minpix pixels, recenter next window on their mean position\n if len(good_left_inds) > minpix:\n leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\n if len(good_right_inds) > minpix:\n rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\n\n # Concatenate the arrays of indices\n left_lane_inds = np.concatenate(left_lane_inds)\n right_lane_inds = np.concatenate(right_lane_inds)\n\n # Extract left and right line pixel positions\n leftx = nonzerox[left_lane_inds]\n lefty = nonzeroy[left_lane_inds]\n rightx = nonzerox[right_lane_inds]\n righty = nonzeroy[right_lane_inds]\n\n # Fit a second order polynomial to each\n if len(leftx) == 0:\n left_fit = lane.recent_left_fit\n else:\n left_fit = np.polyfit(lefty, leftx, 2)\n\n if len(rightx) == 0:\n right_fit = lane.recent_right_fit\n else:\n right_fit = np.polyfit(righty, rightx, 2)\n\n if draw:\n # Generate x and y values for plotting\n ploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])\n left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2]\n right_fitx = right_fit[0] * ploty ** 2 + right_fit[1] * ploty + right_fit[2]\n\n out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]\n out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]\n\n plt.imshow(out_img)\n plt.plot(left_fitx, ploty, color='yellow')\n plt.plot(right_fitx, ploty, color='yellow')\n plt.xlim(0, 1280)\n plt.ylim(720, 0)\n plt.show()\n plt.close()\n\n lane.recent_left_fit = left_fit\n lane.recent_right_fit = right_fit\n lane.detected = True\n\n return left_fit, right_fit","title":""},{"docid":"a9cb7b5076abc527ff630702b52557bd","score":"0.6273185","text":"def search_windows(img, windows, clf, scaler, color_space='RGB',\n spatial_size=(32, 32), hist_bins=32,\n orient=9,\n pix_per_cell=8, cell_per_block=2,\n hog_channel=0, spatial_feat=True,\n hist_feat=True, hog_feat=True):\n # 1) Create an empty list to receive positive detection windows\n on_windows = []\n # 2) Iterate over all windows in the list\n for window in windows:\n # 3) Extract the test window from original image and resize to the same size as used in Training cutouts\n test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))\n # 4) Extract features for that window using single_img_features()\n features = single_img_features(test_img, color_space=color_space,\n spatial_size=spatial_size, hist_bins=hist_bins,\n orient=orient, pix_per_cell=pix_per_cell,\n cell_per_block=cell_per_block,\n hog_channel=hog_channel, spatial_feat=spatial_feat,\n hist_feat=hist_feat, hog_feat=hog_feat)\n # 5) Scale extracted features to be fed to classifier using scaler that we fit using the Training data\n test_features = scaler.transform(np.array(features).reshape(1, -1))\n # 6) Predict using your classifier\n prediction = clf.predict(test_features)\n # 7) If positive (prediction == 1) then save the window\n if prediction == 1:\n on_windows.append(window)\n # 8) Return windows for positive detections\n return on_windows","title":""},{"docid":"fd25f3f0f4dc8515d93a7b5d4a069460","score":"0.6266863","text":"def search_windows(image, windows, clf, hyperparams):\n # Create an empty list to receive positive detection windows\n on_windows = []\n confidences = []\n # Iterate over all windows in the list\n for window in windows:\n # Extract the test window from original image\n test_img = cv2.resize(image[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))\n # Extract features for that window using single_img_features()\n features = get_features_image(test_img, hyperparams)\n # Scale extracted features to be fed to classifier\n test_features = np.array(features).reshape(1, -1)\n # Predict using your classifier\n prediction = clf.predict(test_features)\n # If positive (prediction == 1) then save the window\n if prediction == 1:\n on_windows.append(window)\n confidences.append(clf.decision_function(test_features))\n # Return windows for positive detections\n return on_windows, confidences","title":""},{"docid":"fb80bcf8ae7d19b7272679809335eec1","score":"0.62667805","text":"def apply_sliding_windows(self, binary_warped, leftx_base, rightx_base):\r\n # Choose the number of sliding windows\r\n nwindows = 9\r\n # Set height of windows\r\n window_height = np.int(binary_warped.shape[0] / nwindows)\r\n # Identify the x and y positions of all nonzero pixels in the image\r\n nonzero = binary_warped.nonzero()\r\n nonzeroy, nonzerox = np.array(nonzero[0]), np.array(nonzero[1])\r\n # Current positions to be updated for each window\r\n leftx_current, rightx_current = leftx_base, rightx_base\r\n # Set the width of the windows +/- margin\r\n margin = 100\r\n # Set minimum number of pixels found to recenter window\r\n minpix = 50\r\n # Create empty lists to receive left and right lane pixel indices\r\n left_lane_inds, right_lane_inds = [], []\r\n\r\n # Step through the windows one by one\r\n # Create an output image to draw on and visualize the result\r\n out_img = np.dstack((binary_warped, binary_warped, binary_warped)) * 255\r\n for window in range(nwindows):\r\n # Identify window boundaries in x and y (and right and left)\r\n win_y_low = binary_warped.shape[0] - (window + 1) * window_height\r\n win_y_high = binary_warped.shape[0] - window * window_height\r\n win_xleft_low, win_xleft_high = leftx_current - margin, leftx_current + margin\r\n win_xright_low, win_xright_high = rightx_current - margin, rightx_current + margin\r\n # Draw the windows on the visualization image\r\n if self.debug:\r\n cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)\r\n cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)\r\n # Identify the nonzero pixels in x and y within the window\r\n good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (\r\n nonzerox < win_xleft_high)).nonzero()[0]\r\n good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (\r\n nonzerox < win_xright_high)).nonzero()[0]\r\n # Append these indices to the lists\r\n left_lane_inds.append(good_left_inds)\r\n right_lane_inds.append(good_right_inds)\r\n # If you found > minpix pixels, recenter next window on their mean position\r\n if len(good_left_inds) > minpix:\r\n leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\r\n if len(good_right_inds) > minpix:\r\n rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\r\n if self.debug:\r\n cv_rgb = cv2.cvtColor(out_img.astype(np.uint8), cv2.COLOR_BGR2RGB)\r\n plt.imshow(cv_rgb)\r\n #cv2.imshow('Sliding window computation',out_img)\r\n # Concatenate the arrays of indices\r\n left_lane_inds = np.concatenate(left_lane_inds)\r\n right_lane_inds = np.concatenate(right_lane_inds)\r\n if self.debug:\r\n self.fit_dict['left_lane_inds'] = left_lane_inds\r\n self.fit_dict['right_lane_inds'] = right_lane_inds\r\n\r\n # Extract left and right line pixel positions\r\n leftx, lefty = nonzerox[left_lane_inds], nonzeroy[left_lane_inds]\r\n rightx, righty = nonzerox[right_lane_inds], nonzeroy[right_lane_inds]\r\n return leftx, lefty, rightx, righty","title":""},{"docid":"46539bb4a828191168316b97260a908c","score":"0.62284154","text":"def sliding_window(image, stepSize, windowSize):\n # slide a window across the image\n for y in xrange(0, image.shape[0], stepSize):\n for x in xrange(0, image.shape[1], stepSize):\n # yield the current window\n yield (x, y, image[y:y + windowSize[1], x:x + windowSize[0]])","title":""},{"docid":"52badf4d6015aeb8d703218b8d271b4b","score":"0.61782295","text":"def sliding_window(top, step=10, window_size=(20,20)):\n\tfor x in range(0, top.shape[0], step):\n\t\tif x + window_size[0] > top.shape[0]:\n\t\t\tx = top.shape[0] - window_size[0]\n\t\tfor y in range(0, top.shape[1], step):\n\t\t\tif y + window_size[1] > top.shape[1]:\n\t\t\t\ty = top.shape[1] - window_size[1]\n\t\t\tyield x, y, window_size[0], window_size[1]","title":""},{"docid":"b5d3103bb90ca4c71c2f85523fd2b4ad","score":"0.6137019","text":"def find_lanes_from_scratch(binary_warped, nwindows=9, margin=100, minpix=50, draw_lanes=True):\n # Find histogram peaks\n histogram = hist(binary_warped)\n out_img = np.dstack((binary_warped, binary_warped, binary_warped)) # create output image to draw on (not necessary)\n midpoint = np.int(histogram.shape[0]//2) # 640\n leftx_base = np.argmax(histogram[:midpoint]) # find index of left peak (indicates ll)\n rightx_base = np.argmax(histogram[midpoint:]) + midpoint # find index of right peak (indicates rl)\n \n # Sliding windows\n window_height = np.int(binary_warped.shape[0]//nwindows) # 80\n nonzero = binary_warped.nonzero() # a tuple for x and y\n nonzeroy = np.array(nonzero[0])\n nonzerox = np.array(nonzero[1])\n leftx_current = leftx_base\n rightx_current = rightx_base\n left_lane_inds = []\n right_lane_inds = []\n for window in range(nwindows): # index 0 to 8\n win_y_low = binary_warped.shape[0] - (window+1)*window_height \n win_y_high = binary_warped.shape[0] - window*window_height\n win_xleft_low = leftx_current - margin\n win_xleft_high = leftx_current + margin\n win_xright_low = rightx_current - margin\n win_xright_high = rightx_current + margin\n if draw_lanes:\n cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2) # bottom left to top right, in green, with thickness 2\n cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)\n good_left_inds = ((nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high) & \n (nonzeroy >= win_y_low ) & (nonzeroy < win_y_high)).nonzero()[0]\n good_right_inds = ((nonzerox >= win_xright_low) & (nonzerox < win_xright_high) & \n (nonzeroy >= win_y_low ) & (nonzeroy < win_y_high)).nonzero()[0]\n left_lane_inds.append(good_left_inds) # indices\n right_lane_inds.append(good_right_inds)\n if len(good_left_inds) > minpix:\n leftx_current = int(np.mean(nonzerox[good_left_inds]))\n # print(leftx_current)\n if len(good_right_inds) > minpix:\n rightx_current = int(np.mean(nonzerox[good_right_inds]))\n \n # Find indices of left and right lane lines\n left_lane_inds = np.concatenate(left_lane_inds)\n right_lane_inds = np.concatenate(right_lane_inds)\n leftx = nonzerox[left_lane_inds]\n lefty = nonzeroy[left_lane_inds] \n rightx = nonzerox[right_lane_inds]\n righty = nonzeroy[right_lane_inds]\n\n # Fit polynomial of second degree\n left_fit = np.polyfit(lefty, leftx, deg=2)\n right_fit = np.polyfit(righty, rightx, deg=2)\n ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0])\n\n left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]\n right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]\n \n detected_flag = True\n \n # Visualize\n if draw_lanes:\n out_img[lefty, leftx] = [255, 0, 0]\n out_img[righty, rightx] = [0, 0, 255]\n for index in range(out_img.shape[0]-1):\n cv2.line(out_img, (int(left_fitx[index]), int(ploty[index])), (int(left_fitx[index+1]), int(ploty[index+1])), (255,255,0), 3)\n cv2.line(out_img, (int(right_fitx[index]), int(ploty[index])), (int(right_fitx[index+1]), int(ploty[index+1])), (255,255,0), 3)\n return left_fitx, right_fitx, left_fit, right_fit, ploty, out_img, detected_flag","title":""},{"docid":"243a6aa28eae496654e33dc6c81479b9","score":"0.61151534","text":"def win_slide(stream, start_time, win_size, step_size, max_windows):\n stream_list=[]\n for i in range(max_windows):\n ts = start_time + (i*step_size)\n st = stream.slice(ts, ts+win_size)\n # skip missing data\n if len(st)!=3: continue\n if not st[0].stats.starttime == st[1].stats.starttime == st[2].stats.starttime: continue\n if not st[0].stats.endtime == st[1].stats.endtime == st[2].stats.endtime: continue\n if len(st[0])!=int(win_size*100+1): continue\n if st.max()[0]==0.0 or st.max()[1]==0.0 or st.max()[2]==0.0: continue\n # add preprocessed time window\n st = preprocess_stream(st)\n stream_list.append(st)\n return stream_list","title":""},{"docid":"aed16aa7d01e1a71efd38f77e8ca4511","score":"0.61132944","text":"def window_index(serieslength,windowsize,overlap):\r\n\r\n p1=0\r\n p2=p1 + windowsize\r\n pt1=[p1]\r\n pt2=[p2]\r\n while p2 < serieslength:\r\n p1 = p2 - overlap\r\n p2 = min((p1 + windowsize, serieslength))\r\n pt1.append(p1)\r\n pt2.append(p2)\r\n \r\n return pt1, pt2","title":""},{"docid":"1085eef77fcbf55851a0cccab327d48a","score":"0.60835075","text":"def window_inds(dataset, window_sz, overlap):\r\n\tdata_len = len(dataset[0])\r\n\tassert window_sz < data_len\r\n\tind1 = 0\r\n\tind2 = window_sz-1\r\n\tind_list = []\r\n\tov_ind_diff = int(np.ceil(np.abs(overlap*window_sz)))\r\n\tif ov_ind_diff == window_sz:\r\n\t\tov_ind_diff += -1\r\n\twhile ind2 < data_len:\r\n\t\tind_list.append((ind1,ind2))\r\n\t\tind1 += window_sz-ov_ind_diff\r\n\t\tind2 += window_sz-ov_ind_diff\r\n\treturn ind_list","title":""},{"docid":"436e972e7f70b57189edfcfa7b159bec","score":"0.60333884","text":"def find_lane_slow(img):\n histogram = np.sum(img[img.shape[0] // 2:, :], axis=0)\n\n # Find the peak of the left and right halves of the histogram\n # These will be the starting point for the left and right lines\n midpoint = np.int(histogram.shape[0] / 2)\n leftx_base = np.argmax(histogram[:midpoint])\n rightx_base = np.argmax(histogram[midpoint:]) + midpoint\n\n # Choose the number of sliding windows\n nwindows = 9\n # Set height of windows\n window_height = np.int(img.shape[0] / nwindows)\n # Identify the x and y positions of all nonzero pixels in the image\n nonzero = img.nonzero()\n nonzeroy = np.array(nonzero[0])\n nonzerox = np.array(nonzero[1])\n # Current positions to be updated for each window\n leftx_current = leftx_base\n rightx_current = rightx_base\n # Set the width of the windows +/- margin\n margin = 150\n # Set minimum number of pixels found to recenter window\n minpix = 50\n # Create empty lists to receive left and right lane pixel indices\n left_lane_inds = []\n right_lane_inds = []\n\n # Step through the windows one by one\n for window in range(nwindows):\n # Identify window boundaries in x and y (and right and left)\n win_y_low = img.shape[0] - (window + 1) * window_height\n win_y_high = img.shape[0] - window * window_height\n win_xleft_low = leftx_current - margin\n win_xleft_high = leftx_current + margin\n win_xright_low = rightx_current - margin\n win_xright_high = rightx_current + margin\n\n # Identify the nonzero pixels in x and y within the window\n good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (\n nonzerox < win_xleft_high)).nonzero()[0]\n good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (\n nonzerox < win_xright_high)).nonzero()[0]\n # Append these indices to the lists\n left_lane_inds.append(good_left_inds)\n right_lane_inds.append(good_right_inds)\n # If you found > minpix pixels, recenter next window on their mean position\n if len(good_left_inds) > minpix:\n leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\n if len(good_right_inds) > minpix:\n rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\n\n # Concatenate the arrays of indices\n left_lane_inds = np.concatenate(left_lane_inds)\n right_lane_inds = np.concatenate(right_lane_inds)\n\n # Extract left and right line pixel positions\n leftx = nonzerox[left_lane_inds]\n lefty = nonzeroy[left_lane_inds]\n rightx = nonzerox[right_lane_inds]\n righty = nonzeroy[right_lane_inds]\n\n # Fit a second order polynomial to each\n left_fit = np.polyfit(lefty, leftx, 2)\n right_fit = np.polyfit(righty, rightx, 2)\n\n return left_fit, right_fit","title":""},{"docid":"797ff69edbe37accf9d29b0fbd2a611f","score":"0.5950051","text":"def select_windows(start, stop, num_windows,\n window_width=1, window_units=\"D\",\n sampling=1, sampling_units=\"T\",\n no_overlaps=True, verbose=True):\n\n # Create all sample candidates\n dt_range = pd.date_range(start, stop-pd.Timedelta(window_width),\n freq=\"%i%s\" % (sampling, sampling_units))\n\n # Sample candidate windows\n selected_windows = np.random.choice(dt_range, num_windows, replace=False)\n selected_windows = pd.DataFrame(selected_windows, columns=[\"start\"])\n\n # Calculate window end\n end_delta = (pd.Timedelta(window_width, unit=window_units)\n - pd.Timedelta(sampling,\n unit=\"m\" if sampling_units==\"T\" else sampling_units))\n selected_windows[\"end\"] = (selected_windows[\"start\"] + end_delta)\n\n # Filter overlaps\n if not no_overlaps:\n return selected_windows\n else:\n selected_windows = filter_overlaps(selected_windows,\n pd.Timedelta(window_width,\n unit=window_units))\n\n while selected_windows.shape[0]<num_windows:\n if verbose:\n print(\"Got %i windows...\" % selected_windows.shape[0])\n\n selected_windows = pd.concat([selected_windows,\n select_windows(start, stop, num_windows,\n window_width, window_units,\n sampling, sampling_units,\n no_overlaps=False)],\n ignore_index=True)\n selected_windows = filter_overlaps(selected_windows,\n pd.Timedelta(window_width,\n unit=window_units))\n return selected_windows.iloc[:num_windows]","title":""},{"docid":"bde3c286ec275538f4db047b0c076868","score":"0.5935757","text":"def _sliding_window(self, image, mask, window_radius=3):\n height, width = image.shape[:2]\n features = []\n for yy in range(window_radius, height - window_radius):\n for xx in range(window_radius, width - window_radius):\n features.append(image[yy - window_radius: yy + window_radius + 1, xx - window_radius: xx + window_radius + 1].ravel())\n labels = mask[window_radius: -1 * window_radius, window_radius: -1 * window_radius].ravel()\n return np.array(features), labels","title":""},{"docid":"e9b3945d5398e886d2b03a03c91a48dc","score":"0.5928829","text":"def sliding_windows(size, whole=False, step_size=None, *, width, height):\n if not step_size:\n step_size = size\n w, h = (size, size)\n sw, sh = (step_size, step_size)\n end_i = height - h if whole else height\n end_j = width - w if whole else width\n for pos_i, i in enumerate(range(0, end_i, sh)):\n for pos_j, j in enumerate(range(0, end_j, sw)):\n real_w = w if whole else min(w, abs(width - j))\n real_h = h if whole else min(h, abs(height - i))\n yield rasterio.windows.Window(j, i, real_w, real_h), (pos_i, pos_j)","title":""},{"docid":"03ce5ccce5505f88a44801d3d4c7e060","score":"0.5920694","text":"def find_instances(self, image, region, overlap):\n self.image = np.copy(image)\n\n self.eff_step_size = int((1.0-overlap)*self.eff_box_size)\n\n y_steps = (region[3]-region[1])//self.eff_step_size\n x_steps = (region[2]-region[0])//self.eff_step_size\n\n if region[0]+(x_steps-1)*self.eff_step_size+self.eff_box_size>region[2]:\n x_steps -= 1\n if region[1]+(y_steps-1)*self.eff_step_size+self.eff_box_size>region[3]:\n y_steps -= 1\n\n if self.single_hog:\n self.resized_image = image[region[1]:region[3],region[0]:region[2],:]\n self.resized_image = cv2.resize(self.resized_image, (int(self.resized_image.shape[1]/self.scaling), int(self.resized_image.shape[0]/self.scaling)))\n features, img = self.hogger.hog_image(self.resized_image, visualize=False, feature_vector=False)\n features = np.array(features)\n self.find_instances_in_features(features, region)\n return self.image, self.resized_image\n else:\n for row in range(y_steps):\n off_y = region[1] + row * self.eff_step_size\n for col in range(x_steps):\n off_x = region[0]+col * self.eff_step_size\n sub_sample = self.get_resized_sub_sample(off_x, off_y)\n pred = self.classifier.classify(sub_sample)\n if(pred==1.0):\n cv2.rectangle(self.image, (off_x, off_y), (off_x+self.eff_box_size, off_y+self.eff_box_size), color=(255,255,255), thickness=2)\n self.boundings.append(((off_x, off_y), (off_x+self.eff_box_size, off_y+self.eff_box_size)))\n\n return self.image, None","title":""},{"docid":"03ab53484ed7467ff634626fc74f1c01","score":"0.5849172","text":"def extract_window(img, bbox):\n row_begin = bbox[0][1]\n row_end = bbox[1][1]\n col_begin = bbox[0][0]\n col_end = bbox[1][0]\n patch = img[row_begin:row_end, col_begin:col_end, :]\n window = cv2.resize(patch, (64,64))\n return window","title":""},{"docid":"ca0627a8f5a0f7005ebbebffea23f7f1","score":"0.5833552","text":"def build_bounds_index(image_files):\n idx = index.Index()\n xs = []\n ys = []\n for i, img_path in tqdm(list(enumerate(image_files))):\n with rasterio.open(img_path) as src:\n left, bottom, right, top = src.bounds\n xs.extend([left, right])\n ys.extend([bottom, top])\n idx.insert(i, (left, bottom, right, top))\n dst_w, dst_s, dst_e, dst_n = min(xs), min(ys), max(xs), max(ys)\n return idx, (dst_w, dst_s, dst_e, dst_n)","title":""},{"docid":"79f547ca4bc04cc7a0ad3892082e6df9","score":"0.58190084","text":"def windows(self,windowSize):\n for i in range(0,len(self)-windowSize):\n yield (i,i+windowSize)","title":""},{"docid":"8ac5b37ccad61308c2a58ff9869f781d","score":"0.581421","text":"def search_windows(img, windows, clf, scaler, color_space='RGB',\n spatial_size=(32, 32), hist_bins=32,\n hist_range=(0, 256), orient=9,\n pix_per_cell=8, cell_per_block=2,\n hog_channel=0, hist_feat=True):\n # use caching for the hog features to speed things up\n h = hashlib.sha1(img).hexdigest()\n hn = os.path.join(cache_dir, h)\n if os.path.exists(hn):\n with open(hn, 'rb') as f:\n cached = pickle.load(f)\n return cached\n\n # if there is no cached result, compute it...\n\n #1) Create an empty list to receive positive detection windows\n on_windows = []\n #2) Iterate over all windows in the list\n for window in windows:\n #3) Extract the test window from original image\n test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))\n\n #4) Extract features for that window using single_img_features()\n features = single_img_features(test_img, color_space=color_space,\n spatial_size=spatial_size, hist_bins=hist_bins,\n orient=orient, pix_per_cell=pix_per_cell,\n cell_per_block=cell_per_block,\n hog_channel=hog_channel, hist_feat=hist_feat)\n #5) Scale extracted features to be fed to classifier\n test_features = scaler.transform(np.array(features).reshape(1, -1))\n #6) Predict using your classifier\n prediction = clf.predict(test_features)\n #7) If positive (prediction == 1) then save the window\n if prediction == 1:\n on_windows.append(window)\n #8) Return windows for positive detections\n\n # but save the result for next time\n with open(hn, 'wb') as f:\n pickle.dump(on_windows, f)\n print(\"wrote to cache\", hn)\n\n return on_windows","title":""},{"docid":"151cf6cce39a02a51ebad6c76f8911c3","score":"0.58108246","text":"def get_windows(self, x_train, y_train):\n\n def roundMultiple(x, base=4):\n \"\"\"Round n up to nearest multiple of base.\"\"\"\n return int(base * round(float(x)/base))\n\n def auto_set_stride():\n self.stride = roundMultiple(\n int(self.window_size / 10), base=2)\n debug(\"Stride auto set to \", self.stride)\n\n def auto_set_window_size(sequence):\n threshold = (self.left_epsilon + self.right_epsilon) * 2\n time_arr = sequence[:, self.X_TIME_COLUMN]\n self.window_size = roundMultiple(\n np.argmax(time_arr > threshold), base=4)\n debug(\"Window size auto set to \", self.window_size)\n\n windows_x = []\n windows_y = []\n debug(\"Making windows...\")\n if self.window_size is None:\n auto_set_window_size(x_train[0])\n if self.stride is None:\n auto_set_stride()\n\n for index in tqdm(range(len(x_train))):\n sequence_extractions, sequence_extraction_labels = \\\n self.get_windows_for_sequence(\n x_train[index], y_train[index])\n windows_x.append(sequence_extractions)\n windows_y.append(sequence_extraction_labels)\n return np.array(windows_x), np.array(windows_y)","title":""},{"docid":"8b9e3a89d8aeafb905b3a50a41842c03","score":"0.579171","text":"def filter_windows(sliding_windows_file, genes_file, output_file):\n\n\t# Read sliding windows file and create a list in the form\n\t# genes = [('gene1', 1000, 2000), ('gene2', 4000, 45000)]\n\tgenes = []\t\t# this could be a dictionary but I prefer not\n\tfor line in genes_file:\n\t\tline = line.strip()\n\n\t\tif line and not line.startswith('#'):\t\t# if line is not empty and not a comment\n#\t\tif line and re.match('\\d+', line):\n\t\t\tlogging.debug((\"line: %s\" %line))\n\t\t\tfields = line.split()\t\t# it is better to use the default splitting algorithm here.\n\t\t\t\t\t\t\t\t\t\t# read help(''.split)\t\n\n\t\t\tgene_name = fields[0]\n\t\t\tlogging.debug((\"fields: %s\" %fields))\n\t\t\tstart = int(fields[2])\n\t\t\tend = int(fields[3].strip())\t\t# remove \\n\\r, like chomp\n\t\t\tgenes.append((gene_name, start, end))\n\t\t\t\n#\tlogging.debug((\"genes :\", genes))\t\t# print the contents of genes, if level=loggin.DEBUG\n\n\t# read sliding windows file, and select windows that fall in genes\n\toutput = '#gene_name, gene_start, gene_end, window_start, window_middle, window_end, population, number, score\\n'\n\toutputlineskeleton = \"%s\\t%d\\t%d\\t%d\\t%d\\t%d\\t%s\\t%s\\t%s\\n\"\t# %(gene_name, gene_start, gene_end, window_start, window_middle, window_end, population, number, score)\n\n\tfor line in sliding_windows_file:\n\t\tline = line.strip()\t\t# remove trailing characters (like chomp)\n\t\tif line and not line.startswith('#'):\n\t\t\twindow_fields = line.split()\n\n#\t\t\tlogging.debug(window_fields)\n\t\t\twindow_start = int(window_fields[0])\n\t\t\twindow_middle = int(window_fields[2])\n\t\t\twindow_end = int(window_fields[1])\n#\t\t\tgene = window_fields[3]\n\t\t\tpopulation = window_fields[4]\n\t\t\tnumber = window_fields[5]\n\t\t\tscore = window_fields[6]\n\n\t\t\tfor gene in genes:\n\t\t\t\tgene_start = int(gene[1])\n\t\t\t\tgene_end = int(gene[2])\n\t\t\t\tgene_name = gene[0]\n\t\t\t\t# if window_start is comprised between gene_end and gene_start\n\t\t\t\tif gene_end > window_start >= gene_start:\n\t\t\t\t\tlogging.debug(\"This window starts inside gene %s (%s, %s)\" %(gene[0], gene_start, gene_end))\n\t\t\t\t\tlogging.debug(line)\n\t\t\t\t\toutput += outputlineskeleton % (gene_name, gene_start, gene_end, window_start, window_middle, window_end, population, number, score)\n\t\t\t\telif gene_end >= window_end > gene_start:\n\t\t\t\t\tlogging.debug(\"This window ends inside gene %s (%s, %s)\" %(gene[0], gene_start, gene_end))\n\t\t\t\t\tlogging.debug(line)\n\t\t\t\t\toutput += outputlineskeleton % (gene_name, gene_start, gene_end, window_start, window_middle, window_end, population, number, score)\n\t\n\tlogging.debug(output)\n\toutput_file.write(output)\n\toutput_file.seek(0)\n\treturn output_file","title":""},{"docid":"d0665489e5a95d4d07a04087f55a1fa1","score":"0.5787658","text":"def get_subwindow_tracking_(im, pos, model_sz, original_sz, avg_chans):\n if isinstance(pos, float):\n pos = [pos, pos]\n sz = original_sz\n N, C, H, W = im.shape\n c = (original_sz + 1) / 2\n context_xmin = round(pos[0] - c)\n context_xmax = context_xmin + sz - 1\n context_ymin = round(pos[1] - c)\n context_ymax = context_ymin + sz - 1\n left_pad = int(max(0., -context_xmin))\n top_pad = int(max(0., -context_ymin))\n right_pad = int(max(0., context_xmax - W + 1))\n bottom_pad = int(max(0., context_ymax - H + 1))\n\n context_xmin = max(int(context_xmin), 0)\n context_xmax = min(int(context_xmax), W-1)\n context_ymin = max(int(context_ymin), 0)\n context_ymax = min(int(context_ymax), H-1)\n\n im = torch.narrow(im, 2, context_ymin, context_ymax-context_ymin+1)\n im = torch.narrow(im, 3, context_xmin, context_xmax-context_xmin+1)\n \n im_sub = F.pad(im, pad=(left_pad, right_pad, top_pad, bottom_pad), mode='constant', value=avg_chans) \n im_sub = F.interpolate(im_sub, size=(model_sz, model_sz), mode='bilinear')\n \n return im_sub","title":""},{"docid":"359de69d360671748b0aab7abaf5338a","score":"0.5781956","text":"def sliding_window(frame_length, step, Xsampleslist, ysampleslist):\n Xsamples = []\n ysamples = []\n for j in range(len(Xsampleslist)):\n X = Xsampleslist[j]\n ybinary = ysampleslist[j]\n for i in range(0, X.shape[0] - frame_length, step):\n xsub = X[i:i + frame_length, :]\n ysub = ybinary\n Xsamples.append(xsub)\n ysamples.append(ysub)\n return Xsamples, ysamples","title":""},{"docid":"70c53a0b18216491fa83cb15e16d4b62","score":"0.5769098","text":"def find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size,\n hist_bins):\n draw_img = np.copy(img)\n img_tosearch = img[ystart:ystop, :, :]\n ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')\n if scale != 1:\n imshape = ctrans_tosearch.shape\n ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1] / scale), np.int(imshape[0] / scale)))\n\n # Channel extraction\n ch1 = ctrans_tosearch[:, :, 0]\n ch2 = ctrans_tosearch[:, :, 1]\n ch3 = ctrans_tosearch[:, :, 2]\n\n # Define blocks and steps as above\n nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1\n nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1\n\n # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell\n window = 64\n nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1\n cells_per_step = 2 # Instead of overlap, define how many cells to step\n nxsteps = (nxblocks - nblocks_per_window) // cells_per_step\n nysteps = (nyblocks - nblocks_per_window) // cells_per_step\n\n # Compute individual channel HOG features for the entire image\n hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\n hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)\n hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)\n detections = []\n\n for xb in range(nxsteps):\n for yb in range(nysteps):\n ypos = yb * cells_per_step\n xpos = xb * cells_per_step\n # Extract HOG for this patch\n\n # Flatten the HOG features for each channel position\n hog_feat1 = hog1[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\n hog_feat2 = hog2[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\n hog_feat3 = hog3[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\n # Build hog_features array by stacking each channel\n hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))\n\n xleft = xpos * pix_per_cell\n ytop = ypos * pix_per_cell\n\n # Extract the image patch\n subimg = cv2.resize(ctrans_tosearch[ytop:ytop + window, xleft:xleft + window], (64, 64))\n\n # Get color features\n spatial_features = bin_spatial(subimg, size=spatial_size)\n hist_features = color_hist(subimg, nbins=hist_bins)\n\n # Scale features and make a prediction\n test_features = X_scaler.transform(\n np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))\n test_prediction = svc.predict(test_features)\n confidence = svc.decision_function(test_features)\n\n if test_prediction == 1 and confidence > 0.6:\n xbox_left = np.int(xleft * scale)\n ytop_draw = np.int(ytop * scale)\n win_draw = np.int(window * scale)\n x1 = xbox_left\n y1 = ytop_draw + ystart\n x2 = xbox_left + win_draw\n y2 = ytop_draw + win_draw + ystart\n detections.append((x1, y1, x2, y2))\n return detections","title":""},{"docid":"580caec624c34660f2def761164a8dad","score":"0.5762163","text":"def subwindow_search(numpoints, width, height, xpos, ypos, clstid, weights):\n return subwindow_search_pyramid(numpoints, width, height, xpos, ypos, \\\n clstid, max(clstid)+1, 1, weights)","title":""},{"docid":"57e7441b90bb1580313fcd5e8b122306","score":"0.5747657","text":"def detect_lanes(binary_warped):\n histogram = np.sum(binary_warped[(binary_warped.shape[0] // 2):, :], axis=0)\n out_img = np.dstack((binary_warped, binary_warped, binary_warped)) * 255\n\n midpoint = np.int(histogram.shape[0] / 2)\n leftx_base = np.argmax(histogram[:midpoint])\n rightx_base = np.argmax(histogram[midpoint:]) + midpoint\n\n nwindows = 9\n\n window_height = np.int(binary_warped.shape[0] / nwindows)\n\n nonzero = binary_warped.nonzero()\n nonzeroy = np.array(nonzero[0])\n nonzerox = np.array(nonzero[1])\n\n # Current positions to be updated for each window\n leftx_current = leftx_base\n rightx_current = rightx_base\n\n # Set the width of the windows +/- margin\n margin = 100\n\n # Set minimum number of pixels found to recenter window\n minpix = 50\n\n # Create empty lists to receive left and right lane pixel indices\n left_lane_inds = []\n right_lane_inds = []\n\n # Step through the windows one by one\n for window in range(nwindows):\n # Identify window boundaries in x and y (and right and left)\n win_y_low = binary_warped.shape[0] - (window + 1) * window_height\n win_y_high = binary_warped.shape[0] - window * window_height\n win_xleft_low = leftx_current - margin\n win_xleft_high = leftx_current + margin\n win_xright_low = rightx_current - margin\n win_xright_high = rightx_current + margin\n\n # Draw the windows on the visualization image\n cv2.rectangle(out_img, (win_xleft_low, win_y_low), (win_xleft_high, win_y_high), (0, 255, 0), 2)\n cv2.rectangle(out_img, (win_xright_low, win_y_low), (win_xright_high, win_y_high), (0, 255, 0), 2)\n\n # Identify the nonzero pixels in x and y within the window\n good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) &\n (nonzerox < win_xleft_high)).nonzero()[0]\n good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) &\n (nonzerox < win_xright_high)).nonzero()[0]\n\n # Append these indices to the lists\n left_lane_inds.append(good_left_inds)\n right_lane_inds.append(good_right_inds)\n\n # If you found > minpix pixels, recenter next window on their mean position\n if len(good_left_inds) > minpix:\n leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\n if len(good_right_inds) > minpix:\n rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\n\n # Concatenate the arrays of indices\n left_lane_inds = np.concatenate(left_lane_inds)\n right_lane_inds = np.concatenate(right_lane_inds)\n\n # Extract left and right line pixel positions\n leftx = nonzerox[left_lane_inds]\n lefty = nonzeroy[left_lane_inds]\n rightx = nonzerox[right_lane_inds]\n righty = nonzeroy[right_lane_inds]\n\n # Fit a second order polynomial to each\n left_fit = np.polyfit(lefty, leftx, 2)\n right_fit = np.polyfit(righty, rightx, 2)\n\n # Visualize\n ploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])\n left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2]\n right_fitx = right_fit[0] * ploty ** 2 + right_fit[1] * ploty + right_fit[2]\n\n out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]\n out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]\n\n return Lane(left_fit, right_fit, left_fitx, right_fitx, leftx, rightx, lefty, righty), out_img","title":""},{"docid":"b0782a82c4b8d863ab74debc02abc11a","score":"0.5724442","text":"def split_image_into_number_of_tiles(\n arr: Image, x_ntiles: int, y_ntiles: int, overlap: int\n):\n img_width, img_height = arr.shape[-1], arr.shape[-2]\n tile_w = img_width // x_ntiles\n tile_h = img_height // y_ntiles\n return split_image_into_tiles_of_size(arr, tile_w, tile_h, overlap)","title":""},{"docid":"6c3bb8665285bc07906fb5584c9a35ba","score":"0.5699675","text":"def cut_window(image, window):\n \n return image[window[0][0]: window[0][1] + 1, window[1][0]: window[1][1] + 1].copy()","title":""},{"docid":"9665ea215f97b46c9f7fe40b25e48dac","score":"0.5686169","text":"def _window(self, x, y, dims, random_state):\n height, width = dims\n\n # Compute image and mask dimensions\n if x.ndim == 3:\n x_height, x_width, x_channels = x.shape\n # discard alpha\n if x_channels == 4:\n x = x[:, :, :-1]\n else:\n x_height, x_width = x.shape\n\n if y.ndim == 3:\n y_height, y_width, _ = y.shape\n else:\n y_height, y_width = y.shape\n\n if y_height != x_height or y_width != x_width:\n raise ValueError(\n \"Invalid size between y ({}) and x ({})\".format(y.shape, x.shape))\n\n if x_height < height or x_width < width:\n raise ValueError(\"Windows size {} inconsistent with image dimensions {}\".format(dims, x.shape))\n\n # extract window\n height_offset = random_state.randint(x_height - height)\n width_offset = random_state.randint(x_width - width)\n height_end_idx = height_offset + height\n width_end_idx = width_offset + width\n\n x_window = x[height_offset:height_end_idx, width_offset:width_end_idx]\n y_window = y[height_offset:height_end_idx, width_offset:width_end_idx]\n return x_window, y_window","title":""},{"docid":"5f89e087820f5fab051c1c1b932ab002","score":"0.5660113","text":"def make_slices(data, win_size):\n rows = data.shape[0] - win_size[0] + 1\n cols = data.shape[1] - win_size[1] + 1\n slices = []\n for i in range(win_size[0]):\n for j in range(win_size[1]):\n slices.append(data[i:rows+i, j:cols+j])\n return slices","title":""},{"docid":"d50e0707ed618c15668f365b633f78fb","score":"0.56400967","text":"def _construct_window_around_peak(fs, irs, tleft, tright, alpha=0.5):\n orig_shape = irs.shape\n flat_irs = irs.reshape(-1, irs.shape[-1])\n\n sleft = int(fs * tleft)\n sright = int(fs * tright)\n\n windows = np.ones(flat_irs.shape)\n for i in range(flat_irs.shape[0]):\n ipeak = np.argmax(np.abs(flat_irs[i]))\n iwstart = max(ipeak - sleft, 0)\n iwend = min(ipeak + sright, flat_irs.shape[-1])\n\n window = tukey(iwend - iwstart, alpha=alpha)\n\n windows[i, iwstart:iwend] *= window\n windows[i, :iwstart] = 0\n windows[i, iwend:] = 0\n\n return windows.reshape(orig_shape)","title":""},{"docid":"70d8bbbfecdd596d454390597823f02b","score":"0.5624814","text":"def _window(self, get_lims=False):\n\t\timg_h, img_w = self.od_model.img_shape\n\t\th_llim = 0\n\t\tw_llim = img_w // 3\n\t\th_ulim = img_h - (img_h // 4)\n\t\tw_ulim = 1- wllim\n\n\t\tif get_lims:\n\t\t\treturn (h_llim, h_ulim), (w_llim, w_ulim)\n\n\t\twindow = slice(h_llim, h_ulim), slice(w_llim, w_ulim)\n\t\treturn window","title":""},{"docid":"6e08603ceb21aa8a41c89e2979c763e5","score":"0.56205153","text":"def make_tiles_limits(im, n_splits, margin=0):\n \n if n_splits == 1:\n return [0, im.shape[1], 0, im.shape[0]]\n # number of splits per axis\n ax_splits = int(np.log2(n_splits))\n x_segments = split_range(im.shape[1], ax_splits)\n y_segments = split_range(im.shape[0], ax_splits)\n \n if margin > 0:\n x_segments = extend_indices(x_segments, margin=margin)\n y_segments = extend_indices(y_segments, margin=margin)\n \n # make combinations of [xmin, xmax, ymin, ymax] indices of tiles\n tiles_indices = []\n for xlim in x_segments:\n for ylim in y_segments:\n tiles_indices.append(xlim + ylim)\n return tiles_indices","title":""},{"docid":"e98cb15564504122ebddaf7c0a7d61c3","score":"0.56179106","text":"def split_window(num_x, num_y):\n\n viewPorts = np.zeros((num_x*num_y, 4))\n counter = 0\n for i in range(num_x):\n for j in range(num_y):\n viewPorts[num_x*j + i, :] = [i*(1.0/float(num_x)),\n 1.0 - (j+1.0)*(1.0/float(num_y)),\n (i+1.0)*1.0/float(num_x),\n 1.0 - j*(1.0/float(num_y))\n ] # [num_y*i + j,:] for vertical fill first\n\n return viewPorts","title":""},{"docid":"de4daac8e34f335868cee29f550808a3","score":"0.5555511","text":"def subwindow_search_pyramid(numpoints, width, height, xpos, ypos, clstid, numbins, numlevels, weights):\n pyramidlib = load_library(\"libess.so\",\".\")\n\n pyramidlib.pyramid_search.restype = Box_struct\n pyramidlib.pyramid_search.argtypes = [c_int,c_int,c_int, \n ndpointer(dtype=c_double, ndim=1, flags='C_CONTIGUOUS'),\n ndpointer(dtype=c_double, ndim=1, flags='C_CONTIGUOUS'),\n ndpointer(dtype=c_double, ndim=1, flags='C_CONTIGUOUS'),\n c_int, c_int, \n ndpointer(dtype=c_double, ndim=1, flags='C_CONTIGUOUS')]\n\n box = pyramidlib.pyramid_search(numpoints, width, height, \n xpos, ypos, clstid, numbins, numlevels, weights)\n return box","title":""},{"docid":"5aa3247841be78c0e5ab71f9f1da00a2","score":"0.5545451","text":"def _subset_to_windows(\n insertions, # type: List[Insertion]\n gene_windows # type: Dict[str, Tuple[str, int, int]]\n): # type: (...) -> List[Insertion]\n\n # Create lookup trees.\n trees = {\n chrom: IntervalTree.from_tuples((i[1:]) for i in chrom_int)\n for chrom, chrom_int in itertools.groupby(\n sorted(gene_windows.values()), operator.itemgetter(0))\n }\n\n # Determine which insertions overlap tree intervals and\n # correspond to genes with known gene window.\n def _in_windows(ins, trees):\n try:\n return trees[ins.seqname].overlaps(ins.position)\n except KeyError:\n return False\n\n return [\n ins for ins in insertions\n if ins.metadata['gene_id'] in gene_windows and _in_windows(ins, trees)\n ]","title":""},{"docid":"4b8911f656fe7a0ecbb895d2e633df06","score":"0.5541185","text":"def windows(items, size, jump):\n\n for start in range(0, len(items), jump):\n yield next(zip(*([islice(items, start, None)] * size)))","title":""},{"docid":"7da2bbdf1d437e9f18c0c12ecb4fd99f","score":"0.5538898","text":"def extract_patches(image, patchshape, overlap_allowed=0.1, cropvalue=None, crop_fraction_allowed=0.1):\r\n jump_cols = int(patchshape[1] * overlap_allowed)\r\n jump_rows = int(patchshape[0] * overlap_allowed)\r\n\r\n # Restrict ourselves to the rectangle containing non-cropped pixels\r\n if cropvalue is not None:\r\n rows, cols = np.where(image != cropvalue)\r\n rows.sort()\r\n cols.sort()\r\n active = image[rows[0]:rows[-1], cols[0]:cols[-1]]\r\n else:\r\n active = image\r\n\r\n rowstart = 0\r\n colstart = 0\r\n\r\n # Array tracking where we've already taken patches.\r\n covered = np.zeros(active.shape, dtype=bool)\r\n patches = []\r\n regions = []\r\n while rowstart <= active.shape[0] - patchshape[0]:\r\n # Record whether or not e've found a patch in this row,\r\n # so we know whether to skip ahead.\r\n got_a_patch_this_row = False\r\n colstart = 0\r\n while colstart <= active.shape[1] - patchshape[1]:\r\n # Slice tuple indexing the region of our proposed patch\r\n region = (slice(rowstart, rowstart + patchshape[0]),\r\n slice(colstart, colstart + patchshape[1]))\r\n\r\n # The actual pixels in that region.\r\n patch = active[region]\r\n\r\n # The current mask value for that region.\r\n cover_p = covered[region]\r\n if cropvalue is None or \\\r\n frac_eq_to(patch, cropvalue) <= crop_fraction_allowed and \\\r\n frac_eq_to(cover_p, True) <= overlap_allowed:\r\n # Accept the patch.\r\n patches.append(patch)\r\n regions.append(region)\r\n # Mask the area.\r\n covered[region] = True\r\n\r\n # Jump ahead in the x direction.\r\n colstart += jump_cols\r\n got_a_patch_this_row = True\r\n # print \"Got a patch at %d, %d\" % (rowstart, colstart)\r\n else:\r\n # Otherwise, shift window across by one pixel.\r\n colstart += 1\r\n\r\n if got_a_patch_this_row:\r\n # Jump ahead in the y direction.\r\n rowstart += jump_rows\r\n else:\r\n # Otherwise, shift the window down by one pixel.\r\n rowstart += 1\r\n\r\n # Return a 3D array of the patches with the patch index as the first\r\n # dimension (so that patch pixels stay contiguous in memory, in a\r\n # C-ordered array).\r\n return np.concatenate([pat[np.newaxis, ...] for pat in patches], axis=0),regions","title":""},{"docid":"c3777ad9f6172b4d32e497f3796a0900","score":"0.5538282","text":"def scan_multiple_win_sizes(image, clf, hyperparams, box_colors=None):\n rescales = hyperparams[\"RESCALES\"]\n draw_image = np.zeros_like(image)\n heatmap = np.zeros(image.shape[0:2])\n for i, rescale in enumerate(rescales):\n box_color = None\n if box_colors is not None:\n box_color = box_colors[i%len(box_colors)]\n d, heatmap = scan_single_win_size(image, clf, rescale, hyperparams, box_color, heatmap)\n draw_image[(draw_image == 0).all(2)] = d[(draw_image == 0).all(2)]\n return draw_image, heatmap","title":""},{"docid":"f34e0146666535ffa8bf8e570286f048","score":"0.55361724","text":"def get_window_img(img: np.ndarray, window_range: tuple) -> np.ndarray:\n img_: np.ndarray = img.copy()\n start, end = window_range\n img_[img_ < start] = start\n img_[img_ > end] = end\n img_ -= img_.min()\n max_ = img_.max()\n if max_ <= 0:\n return img_.astype(np.uint8)\n img_ /= img_.max()\n img_ *= 255\n return img_.astype(np.uint8)","title":""},{"docid":"afb96ceaa06b60c8818417f4ba7ea769","score":"0.55344933","text":"def find_cars(img, scale):\n img_boxes = [] # Clears img_boxes so we don't keep unwanted heatmap history\n count = 0\n draw_img = np.copy(img)\n\n # Make a heatmap of zeros\n heatmap = np.zeros_like(img[:, :, 0])\n\n # IMPORTANT : reading *.jpeg's (scaled 0-255, aka scaling needed), but\n # # trained on *.png's (scaled 0-1, aka scaling not needed)\n if img.dtype == 'uint8':\n img = img.astype(np.float32) / 255 # aka scaling needed\n\n img_tosearch = img[ystart:ystop, :, :]\n ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')\n\n if scale != 1: # resize whole image instead of separate windows\n imshape = ctrans_tosearch.shape\n ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1] / scale), np.int(imshape[0] / scale)))\n\n ch1 = ctrans_tosearch[:, :, 0]\n ch2 = ctrans_tosearch[:, :, 1]\n ch3 = ctrans_tosearch[:, :, 2]\n\n # Define blocks and steps as above\n # These hold the number of HOG cells\n nxblocks = (ch1.shape[1] // pix_per_cell) - 1 # Note : '//' causes integers to be result, instead of floats\n nyblocks = (ch1.shape[0] // pix_per_cell) - 1\n # How many features per block are we going to be extracting\n nfeat_per_block = orient * cell_per_block ** 2\n window = 64\n nblocks_per_window = (window // pix_per_cell) - 1\n # aka 75% overlap between cells\n cells_per_step = 2 # Instead of overlap, define how many cells to step\n nxsteps = (nxblocks - nblocks_per_window) // cells_per_step\n nysteps = (nyblocks - nblocks_per_window) // cells_per_step\n\n # Compute individual channel HOG features for the entire image\n hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\n hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)\n hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)\n\n for xb in range(nxsteps):\n for yb in range(nysteps):\n count += 1\n ypos = yb * cells_per_step\n xpos = xb * cells_per_step\n # Extract HOG for this patch\n hog_feat1 = hog1[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\n hog_feat2 = hog2[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\n hog_feat3 = hog3[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\n hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))\n\n xleft = xpos * pix_per_cell\n ytop = ypos * pix_per_cell\n\n # Extract the image patch\n subimg = cv2.resize(ctrans_tosearch[ytop:ytop + window, xleft:xleft + window], (64, 64))\n\n # Get colour features\n spatial_features = bin_spatial(subimg, size=spatial_size)\n hist_features = color_hist(subimg, nbins=hist_bins)\n\n # Scale features and make a prediction\n test_features = X_scaler.transform(\n np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))\n test_prediction = svc.predict((test_features))\n\n if test_prediction == 1:\n xbox_left = np.int(xleft * scale)\n ytop_draw = np.int(ytop * scale)\n win_draw = np.int(window * scale)\n cv2.rectangle(draw_img, (xbox_left, ytop_draw + ystart),\n (xbox_left + win_draw, ytop_draw + win_draw + ystart), (0, 0, 255))\n img_boxes.append(\n ((xbox_left, ytop_draw + ystart), (xbox_left + win_draw, ytop_draw + win_draw + ystart)))\n heatmap[ytop_draw + ystart:ytop_draw + win_draw + ystart, xbox_left:xbox_left + win_draw] += 1\n\n return draw_img, img_boxes, heatmap","title":""},{"docid":"5fde1094430ca15028bbfb7e7b595bd6","score":"0.5523551","text":"def detect(img, window_list, pipeline):\n #t = Timer()\n windows = []\n for bbox in window_list:\n window = extract_window(img, bbox)\n windows.append(window)\n windows = np.stack(windows)\n detections = pipeline.predict(windows)\n #print(\"Time to detect: {:.2f}\".format(t.tock()))\n return detections","title":""},{"docid":"57ca0b100ebf641821926ce2aa29efa0","score":"0.55216813","text":"def test_sliding_window(data, x):\n\n win_dim = data.draw(st.integers(1, x.ndim), label=\"win_dim\")\n win_shape = data.draw(\n st.tuples(*(st.integers(1, s) for s in x.shape[-win_dim:])), label=\"win_shape\"\n )\n step = data.draw(\n st.tuples(*(st.integers(1, s) for s in x.shape[-win_dim:])), label=\"step\"\n )\n\n max_dilation = np.array(x.shape[-win_dim:]) // win_shape\n dilation = data.draw(\n st.one_of(st.none(), st.tuples(*(st.integers(1, s) for s in max_dilation))),\n label=\"dilation\",\n )\n y = sliding_window_view(x, window_shape=win_shape, step=step, dilation=dilation)\n\n if dilation is None:\n dilation = np.ones((len(win_shape),), dtype=int)\n\n for ind in np.ndindex(*y.shape[:win_dim]):\n slices = tuple(\n slice(i * s, i * s + w * d, d)\n for i, w, s, d in zip(ind, win_shape, step, dilation)\n )\n assert_allclose(actual=y[tuple([*ind])], desired=x[(..., *slices)])","title":""},{"docid":"e1e8c9465054fa63d1d5f13cc799079f","score":"0.5516395","text":"def new_image_sample_grid(dset, window_dims, window_overlap=0.0,\n classes_of_interest=None, ignore_coverage_thresh=0.6,\n negative_classes={'ignore', 'background'},\n use_annots=True, legacy=True, verbose=1):\n # import netharn as nh\n import kwarray\n from ndsampler import isect_indexer\n keepbound = True\n\n # Create a sliding window object for each specific image (because they may\n # have different sizes, technically we could memoize this)\n gid_to_slider = {}\n for img in dset.imgs.values():\n full_dims = [img['height'], img['width']]\n window_dims_ = full_dims if window_dims == 'full' else window_dims\n slider = kwarray.SlidingWindow(full_dims, window_dims_,\n overlap=window_overlap, keepbound=keepbound,\n allow_overshoot=True)\n gid_to_slider[img['id']] = slider\n\n if use_annots:\n _isect_index = isect_indexer.FrameIntersectionIndex.from_coco(dset)\n\n positives = []\n negatives = []\n targets = []\n positive_idxs = []\n negative_idxs = []\n for gid, slider in gid_to_slider.items():\n\n # For each image, create a box for each spatial region in the slider\n boxes = []\n regions = list(slider)\n for region in regions:\n y_sl, x_sl = region\n boxes.append([x_sl.start, y_sl.start, x_sl.stop, y_sl.stop])\n boxes = kwimage.Boxes(np.array(boxes), 'ltrb')\n\n for region, box in zip(regions, boxes):\n\n if use_annots:\n # Check to see what annotations this window-box overlaps with\n aids = _isect_index.overlapping_aids(gid, box)\n\n # Look at the categories within this region\n catnames = [\n dset.cats[dset.anns[aid]['category_id']]['name'].lower()\n for aid in aids\n ]\n\n if ignore_coverage_thresh:\n ignore_flags = [catname == 'ignore' for catname in catnames]\n if any(ignore_flags):\n # If the almost the entire window is marked as ignored then\n # just skip this window.\n ignore_aids = list(ub.compress(aids, ignore_flags))\n ignore_boxes = dset.annots(ignore_aids).boxes\n\n # Get an upper bound on coverage to short circuit extra\n # computation in simple cases.\n box_area = box.area.sum()\n coverage_ub = ignore_boxes.area.sum() / box_area\n if coverage_ub > ignore_coverage_thresh:\n max_coverage = ignore_boxes.iooas(box).max()\n if max_coverage > ignore_coverage_thresh:\n continue\n elif len(ignore_boxes) > 1:\n # We have to test the complex case\n try:\n from shapely.ops import cascaded_union\n ignore_shape = cascaded_union(ignore_boxes.to_shapley())\n region_shape = box[None, :].to_shapley()[0]\n coverage_shape = ignore_shape.intersection(region_shape)\n real_coverage = coverage_shape.area / box_area\n if real_coverage > ignore_coverage_thresh:\n continue\n except Exception as ex:\n import warnings\n warnings.warn(\n 'ignore region select had non-critical '\n 'issue ex = {!r}'.format(ex))\n\n if classes_of_interest:\n # If there are CoIs then only count a region as positive if one\n # of those is in this region\n interest_flags = np.array([\n catname in classes_of_interest for catname in catnames])\n pos_aids = list(ub.compress(aids, interest_flags))\n elif negative_classes:\n # Don't count negative classes as positives\n nonnegative_flags = np.array([\n catname not in negative_classes for catname in catnames])\n pos_aids = list(ub.compress(aids, nonnegative_flags))\n else:\n pos_aids = aids\n else:\n aids = None\n pos_aids = None\n\n # aids = sampler.regions.overlapping_aids(gid, box, visible_thresh=0.001)\n tr = {\n 'gid': gid,\n 'slices': region,\n 'aids': aids,\n }\n targets.append(tr)\n if pos_aids:\n positive_idxs.append(len(targets))\n if legacy:\n positives.append(tr)\n else:\n negative_idxs.append(len(targets))\n if legacy:\n negatives.append(tr)\n\n if verbose:\n print('Found {} targets'.format(len(targets)))\n if use_annots:\n print('Found {} positives'.format(len(positive_idxs)))\n print('Found {} negatives'.format(len(negative_idxs)))\n\n sample_grid = {\n 'targets': targets,\n 'positives_indexes': positive_idxs,\n 'negatives_indexes': negative_idxs,\n }\n if legacy:\n sample_grid.update({\n # Deprecated:\n 'positives': positives,\n 'negatives': negatives,\n })\n return sample_grid","title":""},{"docid":"5ed907076369c7bd0e5711ef71837dac","score":"0.55058306","text":"def iter_windows(hits, radius):\n\n hits = util.PushIter(hits)\n cache = LinkedList()\n\n upstream = set()\n downstream = set()\n\n try:\n center = hits.next()\n except StopIteration:\n return\n\n\n while True:\n # discard anyone in the upstream that is not within radius distance\n for hit in list(upstream):\n if hit[0].end + radius < center[0].start:\n upstream.remove(hit)\n\n # populate downstream with all regions within in radius\n for hit in hits:\n if hit[0].start - radius > center[0].end:\n hits.push(hit)\n break\n downstream.add(hit)\n cache.append(hit)\n\n yield (center, upstream, downstream)\n\n # populate upstream\n upstream.add(center)\n \n # move center to next hit\n try:\n center = cache.pop_front()\n except IndexError:\n break\n\n # remove new center from downstream\n downstream.remove(center)","title":""},{"docid":"027896f225fcb2365292c4a83d4c3750","score":"0.55004793","text":"def _get_intersections_in_frame_parameter_space(lines,img_width,img_height):\n pts = []\n for i in range(len(lines)):\n for j in range(i+1,len(lines)): # Compare each point\n x,y = _get_intersection_parameter_space(lines[i],lines[j]) # Get intercetions\n if x < img_width and x >= 0 and y < img_height and y >= 0: # Intercetion is within frame bounds\n pts.append((x,y))\n return pts","title":""},{"docid":"90209085cada59130d8f739b0a7664fa","score":"0.54784346","text":"def tile_window(shape, px):\n nr, nc = shape\n lu = product(range(0, nc, px), range(0, nr, px))\n\n ## create the window\n for l, u in lu:\n h = min(px, nr - u)\n w = min(px, nc - l)\n yield (l, u, w, h)","title":""},{"docid":"02a37b8a7d36b2eb7bba304710d205bb","score":"0.54510176","text":"def extract_windows(\n spectrograms: numba.typed.List[np.ndarray], wlength: int\n) -> Tuple[numba.typed.List[np.ndarray], List[int]]:\n windows = numba.typed.List()\n n_windows = []\n for spec in spectrograms:\n for i, w in enumerate(window(spec, wlength)):\n windows.append(w)\n n_windows.append(i + 1)\n return windows, n_windows","title":""},{"docid":"eab3b3e1d2e3aa39e7c8c2898b920fb5","score":"0.54456574","text":"def divide_genome(genome, L):\n\n m = L\n L_windows = []\n\n while m <= len(genome):\n new_window = genome[m-L:m]\n L_windows.append(new_window)\n\n m += 1\n\n return L_windows","title":""},{"docid":"b86b4ff63772e4b052d1182f3d6ae2cd","score":"0.5436157","text":"def _boxes_in_window(self, od_bbox):\n\t\t(h_llim, h_ulim), (w_llim, w_ulim) = self._window(get_lims=True)\n\t\txmin = od_bbox[:, 0]\n\t\t# ymin = od_bbox[:, 1]\n\t\txmax = od_bbox[:, 2]\n\t\t# ymax = od_bbox[:, 3]\n\n\t\tin_window = (xmax > w_llim & xmin < w_ulim)\n\t\tprint(in_window.shape) # (n)\n\t\treturn in_window","title":""},{"docid":"fc7fecb90094d2b4d9c0b7a677c177e2","score":"0.5432923","text":"def detect_cars(img, clf, xy_window, stride, cur_sizes_factors, cur_y_start_stop, cur_x_padding):\n\n image_tar_size = (int(img.shape[0] * cur_sizes_factors),\n int(img.shape[1] * cur_sizes_factors))\n\n # open cv needs the shape in reversed order (width, height)\n img_scaled = cv2.resize(img, image_tar_size[::-1])\n # check if search area is smaller than window.\n cur_y_start_stop = (cur_y_start_stop * cur_sizes_factors).astype(np.uint32)\n\n # if the window size is bigger than the search area return an empty array\n search_area_height = cur_y_start_stop[1] - cur_y_start_stop[0]\n if search_area_height < xy_window[1] or img_scaled.shape[1] < xy_window[0]:\n return np.ndarray((0, 4))\n\n # Add padding (zeros) on the x axis\n img_scaled = add_padding(img_scaled, cur_x_padding)\n windows = slide_window(img_scaled, y_start_stop=cur_y_start_stop, xy_window=xy_window,\n stride=stride)\n\n features = extract_features(img_scaled, clf, windows, cur_y_start_stop, xy_window, stride)\n des_func = clf.named_steps['clf'].decision_function(features)\n\n windows = remove_padding_from_bb(windows, cur_x_padding)\n # windows have to be rescaled to account for the resized image\n windows = (windows / cur_sizes_factors).astype(np.uint32)\n windows = windows[des_func > 0]\n\n des_func = des_func[des_func > 0]\n\n return windows, des_func","title":""},{"docid":"8f505d555f82f16231df018f7a595583","score":"0.54229987","text":"def scan_single_win_size(image, clf, rescale, hyperparams, box_color=(0,0,1.0), heatmap=None):\n # get important hyperparams\n y_start = hyperparams['Y_START']\n y_stop = hyperparams['Y_STOP']\n color_space = hyperparams['COLOR_SPACE']\n spatial_feat = hyperparams['SPATIAL_FEAT']\n spatial_size = hyperparams['SPATIAL_SIZE']\n hist_feat = hyperparams['HIST_FEAT']\n hist_bin = hyperparams['HIST_BIN']\n hist_range = hyperparams['HIST_RANGE']\n hog_orient = hyperparams['HOG_ORIENT']\n hog_cell_per_block = hyperparams['HOG_CELL_PER_BLOCK']\n hog_pix_per_cell = hyperparams['HOG_PIX_PER_CELL']\n hog_feat = hyperparams['HOG_FEAT']\n hog_channel = hyperparams['HOG_CHANNEL']\n # if heatmap was not provided, initialize it\n if heatmap is None:\n heatmap = np.zeros(image.shape[0:2])\n # copy image for visualization purposes\n draw_image = np.zeros_like(image)\n # image to be analyzed is color-converted to target color space and resized to match\n # appropriately rescaled window size\n rescaled_image = convert_color(image[y_start:y_stop, :, :], color_space)\n if rescale != 1:\n imshape = image.shape\n rescaled_image = cv2.resize(rescaled_image, (np.int(imshape[1] / rescale), np.int(imshape[0] / rescale)))\n # Define number of blocks\n n_x_blocks = (rescaled_image.shape[1] // hog_pix_per_cell) - hog_cell_per_block + 1\n n_y_blocks = (rescaled_image.shape[0] // hog_pix_per_cell) - hog_cell_per_block + 1\n window = 64 # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell\n blocks_per_window = (window // hog_pix_per_cell) - hog_cell_per_block + 1\n cells_per_step = 2 # Instead of overlap, define how many cells to step\n n_x_steps = (n_x_blocks - blocks_per_window) // cells_per_step\n n_y_steps = (n_y_blocks - blocks_per_window) // cells_per_step\n # Prepare the hog features\n hogs = []\n if hog_feat:\n for channel in range(0, rescaled_image.shape[2]):\n hogs.append(get_hog_features(rescaled_image[:, :, channel],\n hog_orient, hog_pix_per_cell, hog_cell_per_block, feature_vec=False))\n # Go through blocks of image step by step\n for x_window in range(n_x_steps):\n for y_window in range(n_y_steps):\n y_pos = y_window * cells_per_step\n x_pos = x_window * cells_per_step\n x_left = x_pos * hog_pix_per_cell\n y_top = y_pos * hog_pix_per_cell\n sub_image = rescaled_image[y_top:y_top + window, x_left:x_left + window]\n # Extract HOG for this patch\n features = []\n if spatial_feat:\n features.append(get_spatial_features(sub_image, size=spatial_size))\n if hist_feat:\n features.append(get_hist_features(sub_image, nbins=hist_bin, bins_range=hist_range)[4])\n if hog_feat:\n if hog_channel == \"ALL\":\n for h in hogs:\n features.append(h[y_pos:y_pos+blocks_per_window, x_pos:x_pos+blocks_per_window].ravel())\n else:\n features.append(hogs[hog_channel][y_pos:y_pos+blocks_per_window, x_pos:x_pos+blocks_per_window].ravel())\n # Scale features and make a prediction\n test_features = np.hstack(features).reshape(1, -1)\n test_prediction = clf.predict(test_features)\n if test_prediction == 1:\n # If prediction is true, re-calculate sliding window position\n x_box_left = np.int(x_left * rescale)\n y_box_top = np.int(y_top * rescale)\n box_size = np.int(window * rescale)\n # if box color was set, draw box on image\n if box_color is not None:\n cv2.rectangle(draw_image, (x_box_left, y_box_top + y_start),\n (x_box_left + box_size, y_box_top + box_size + y_start), box_color, 6)\n # add heat to heatmap\n heatmap[y_box_top+y_start:y_box_top+box_size+y_start, x_box_left:x_box_left+box_size] += \\\n clf.decision_function(test_features)\n return draw_image, heatmap","title":""},{"docid":"852e87ccaea0503a459b3f38639877da","score":"0.54190135","text":"def horizontal_scroll(self, image, padding=True):\n\n image_list = list()\n height = image.size[1]\n\n # Scroll into the blank image.\n if padding:\n for y in range(1,17):\n section = image.crop((0, 0, 8, y))\n print section.width,section.height\n display_section = self.create_blank_image()\n display_section.paste(section, (0, 16 - y, 8, 16))\n image_list.append(display_section)\n return image_list\n\n #Scroll across the input image.\n for y in range(16, height + 1):\n section = image.crop((0, y - 16, 8, y))\n display_section = self.create_blank_image()\n display_section.paste(section, (0, 0, 8, 16))\n image_list.append(display_section)\n\n #Scroll out, leaving the blank image.\n if padding:\n for y in range(height - 15, height + 1):\n section = image.crop((0, y, 8, height))\n display_section = self.create_blank_image()\n display_section.paste(section, (0, 0, 8, 15 - (y - (height - 15))))\n image_list.append(display_section)\n\n #Return the list of images created\n return image_list","title":""},{"docid":"8accef89eb01fb3de0b74edf59856154","score":"0.5409532","text":"def get_hist_slice(img, slices=10, margin=100):\n h_img = img.shape[0]\n w_img = img.shape[1]\n location_l = []\n location_r = []\n location_ry = []\n location_ly = []\n\n \"\"\"\n Createa a mask to ignore the center and the extremes of the image.\n ****-----***-----****\n ****-----***-----****\n ****-----***-----****\n ****-----***-----****\n ****-----***-----****\n ****-----***-----****\n ****-----***-----****\n \"\"\"\n zero_patch = np.zeros((h_img, margin))\n one_patch = np.ones((h_img, (w_img//2)-(1.5*margin)))\n\n mask = np.c_[zero_patch, one_patch]\n mask = np.c_[mask, zero_patch]\n mask = np.c_[mask, one_patch]\n mask = np.c_[mask, zero_patch]\n img = np.uint8(img)\n mask = np.uint8(mask)\n print(\"img:\"+str(img.shape))\n print(\"mask:\"+str(mask.shape))\n img = cv2.bitwise_and(img,img,mask = mask)\n\n for window in reversed(range(0,h_img, int(h_img/ slices))):\n sli = img[window:int(window+(h_img/slices)), :]\n sli_sum = np.sum(sli, axis=0) # get the sum from all the columns\n \"\"\"\n Add a margin to the histogram to not take pixels at the far left or right\n \"\"\"\n sli_l, sli_r = (sli_sum[:w_img//2], sli_sum[w_img//2:])\n\n # get the location of 5 top max elements\n l_arg = np.argpartition(sli_l, -5)[-5:]\n r_arg = np.argpartition(sli_r, -5)[-5:]\n # Get the value of the max values and decide of this portion\n # of frame contains something interesiting\n mag_r = sum(sli_r[r_arg])\n mag_l = sum(sli_l[l_arg])\n if mag_l > 100:\n l_indx = np.mean(l_arg)\n location_l.append(l_indx)\n location_ly.append(window)\n\n if mag_r > 100:\n r_indx = np.mean(r_arg) + w_img//2\n location_ry.append(window)\n location_r.append(r_indx)\n #print(\"r_indx: \" + str(r_indx) + \" sli_r: \" + str(sli_r))\n # add condtion for the case when the index is 0\n # if a point is 0 make its value the median btw the point before and the point after\n location = {'l':location_l, 'r':location_r, 'ly':location_ly, 'ry':location_ry}\n print(\"l : \" + str(len(location_l)))\n print(location_l)\n print(\"ly : \" + str(len(location_ly)))\n print(location_ly)\n\n print(\"r : \" + str(len(location_r)))\n print(location_r)\n print(\"ry : \" + str(len(location_ry)))\n print(location_ry)\n\n\n return location","title":""},{"docid":"5707636a9a7ad2c9d9d3aad10ec5e871","score":"0.54067117","text":"def paintings_detection(query_image, mask):\n\n image = cv2.imread(query_image)\n\n image_width = mask.shape[0]\n image_height = mask.shape[1]\n x_box_1, y_box_1, w_box_1, h_box_1, x_box_2, y_box_2, w_box_2, h_box_2 = 0, 0, 0, 0, 0, 0, 0, 0, \n\n contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:]\n\n for cnt in contours:\n x, y, w, h = cv2.boundingRect(cnt)\n \n if (w > 0.15 * image_width) & (h > 0.15 * image_height) & (w < 0.98 * image_width) & (x_box_1 == 0):\n x_box_1, y_box_1, w_box_1, h_box_1 = x, y, w, h\n elif (w > 0.15 * image_width) & (h > 0.15 * image_height) & (w < 0.98 * image_width) & (x_box_1 != 0):\n x_box_2, y_box_2, w_box_2, h_box_2 = x, y, w, h\n\n if x_box_2 == 0:\n x_value_to_split = 0\n else:\n x_value_to_split = (x_box_1 + w_box_1/2 + x_box_2 + w_box_2/2) / 2\n\n\n return(x_value_to_split)","title":""},{"docid":"9c3b50773b43ee5f30cec6bdfaebca66","score":"0.53983605","text":"def define_windows(w, data):\n data_w1 = data[0:w, :]\n data_w2 = data[w:w * 2, :]\n data_w3 = data[w * 2:w * 3, :]\n data_w4 = data[w * 3:w * 4, :]\n data_w5 = data[w * 4:w * 5, :]\n data_w6 = data[w * 5:, :]\n\n return data_w1, data_w2, data_w3, data_w4, data_w5, data_w6","title":""},{"docid":"824fad31db5b1b10f97cacd2560694e8","score":"0.5366726","text":"def car_zones(self, image, Hp):\n # 1) Load data from the object\n windows_feat = self.sampling_data\n clf = self.clf\n scaler = self.scaler\n overlap = self.overlap\n color_space = self.color_space\n spatial_size = self.spatial_size\n hist_bins = self.hist_bins\n orient = self.orient\n pix_per_cell = self.pix_per_cell\n cell_per_block = self.cell_per_block\n hog_channel = self.hog_channel \n spatial_feat = self.spatial_feat\n hist_feat = self.hist_feat \n hog_feat = self.hog_feat\n \n # 2) Create a copy of the image\n cimage = np.copy (image)\n # 3) Create a empty list where to store the detected windows\n detected_windows_list = []\n # 4) Loop into the windows \n for window_feat in windows_feat:\n # 4.1) Get the window using sliding_window\n windows = slide_window(image, x_start_stop=window_feat[0],\n y_start_stop=window_feat[1], \n xy_window=window_feat[2], \n xy_overlap=overlap)\n # 4.2) Get the local probabilities\n hp, hot_windows = search_windows(cimage, windows, clf, scaler, color_space=color_space, \n spatial_size=spatial_size, hist_bins=hist_bins, \n orient=orient, pix_per_cell=pix_per_cell, \n cell_per_block=cell_per_block, \n hog_channel=hog_channel, spatial_feat=spatial_feat, \n hist_feat=hist_feat, hog_feat=hog_feat) \n # 4.3) Store the probabilities into the Hp matrix\n Hp = add_probabilities(Hp, hp, windows)\n # 4.4) Store the detected boxes into the list\n detected_windows_list.extend(hot_windows)\n \n # 5) Return the detected windows and the probability matrix (Hp)\n return detected_windows_list, Hp","title":""},{"docid":"7eebf6352f7d3dd1c427aff985e32be7","score":"0.5354472","text":"def moving_windows(self,channels = None, size = None, overlap = None, threads=False):\n\n class ThreadedWindowReader(threading.Thread):\n\n def __init__(self,eeg,ch_bs,size,overlap,qsize=10):\n threading.Thread.__init__(self)\n self.eeg = eeg\n self.ch_bs = ch_bs\n self.size=size\n self.overlap=overlap\n self.q = Queue.Queue(qsize)\n self.has_more = True\n self.is_running = False\n \n def run(self):\n start=0\n self.is_running = True\n while start+self.size<self.eeg.num_datapoints:\n data = self.eeg[start:start+self.size,self.ch_bs]\n self.q.put((data, start))\n start = start+self.size-self.overlap\n self.is_running=False\n\n def get(self):\n if self.is_running:\n data,start = self.q.get()\n else:\n data,start = self.q.get_nowait()\n return data, start\n\n if channels==None:\n channels=range(self.num_channels)\n if size==None:\n size=4096\n if overlap==None:\n overlap=0\n start = 0\n ch_bs = n.zeros((self.num_channels),n.bool)\n for ch in channels:\n ch_bs[ch] = True\n if not threads:\n while start+size<self.num_datapoints:\n data = self[start:start+size,ch_bs]\n yield data, start\n start = start+size-overlap\n else:\n twr = ThreadedWindowReader(self,ch_bs,size,overlap)\n twr.start()\n try:\n while True:\n yield twr.get()\n except Queue.Empty:\n print \"Queue now empty\"","title":""},{"docid":"0afad9ee0a429f9071f8afc92c3059cc","score":"0.53522754","text":"def __init__(self, master, row, column, ystart, xleft, xright, nx, ny, checker):\n\n # A pair of windows needs 5 parameters to specify it:\n #\n # ystart -- first y value to be read, same for each window\n # xleft -- leftmost x pixel of lefthand window\n # xright -- leftmost x pixel of righthand window\n # nx -- X dimension, unbinned pixels\n # ny -- Y dimension, unbinned pixels\n\n self.ystart = RangedPosInt(master, ystart, 1, 1024, checker, width=4)\n self.ystart.grid(row=row,column=column)\n\n self.xleft = RangedPosInt(master, xleft, 1, 512, checker, width=4)\n self.xleft.grid(row=row,column=column+1)\n\n self.xright = RangedPosInt(master, xright, 513, 1024, checker, width=4)\n self.xright.grid(row=row,column=column+2)\n\n self.nx = RangedPosInt(master, nx, 1, 512, checker, width=4)\n self.nx.grid(row=row,column=column+3)\n\n self.ny = RangedPosInt(master, ny, 1, 1024, checker, width=4)\n self.ny.grid(row=row,column=column+4)","title":""},{"docid":"5caf6b3417a35e5d8d786740da4d08aa","score":"0.5343791","text":"def get_active_sources(source, source_list, image):\n def intersect(sa, sb, image):\n xlima, ylima = sa.bounding_boxes[image]\n xlimb, ylimb = sb.bounding_boxes[image]\n widtha, heighta = xlima[1] - xlima[0], ylima[1] - ylima[0]\n widthb, heightb = xlimb[1] - xlimb[0], ylimb[1] - ylimb[0]\n return (np.abs(xlima[0] - xlimb[0])*2 < (widtha + widthb)) and \\\n (np.abs(ylima[0] - ylimb[0])*2 < (heighta + heightb))\n return [s for s in source_list if intersect(s, source, image) and s is not source]","title":""},{"docid":"dfcc3d91e6339665b4d87ec645b6e673","score":"0.5336894","text":"def window_index_time(t,windowsize,overlap):\r\n \r\n try:\r\n t=t.tolist()\r\n except:\r\n t=t\r\n \r\n t1=t[0]\r\n t2=t1 + timedelta(seconds=windowsize)\r\n pt1=[0]\r\n pt2=[othertime.findNearest(t2,t)]\r\n while t2 < t[-1]:\r\n t1 = t2 - timedelta(seconds=overlap)\r\n t2 = t1 + timedelta(seconds=windowsize)\r\n\r\n pt1.append(othertime.findNearest(t1,t))\r\n pt2.append(othertime.findNearest(t2,t))\r\n \r\n return pt1, pt2","title":""},{"docid":"ee0fb54fc294738096866db948cf9574","score":"0.53322244","text":"def stackingWindows():\n space = 50\n offset = 70\n cv2.moveWindow(\"Original image\", space, space)\n cv2.moveWindow(\"Keypoints original\", space, hsize + space + offset)\n cv2.moveWindow(\"Color matched\", wsize + space, space)\n cv2.moveWindow(\"Keypoints Dark\", wsize + space, hsize + space + offset)","title":""},{"docid":"a2dbe93c405931c89b45b85775eb374d","score":"0.5329009","text":"def window_data(data: np.ndarray):\n\n w_len = 128\n stride = w_len // 2\n\n no_offset_windows = np.split(data, 10)\n offset_windows = np.split(data[stride:-stride], 9)\n windows = [0] * 19\n windows[::2] = no_offset_windows\n windows[1::2] = offset_windows\n windows = np.array(windows, dtype=np.float32)\n\n return windows","title":""},{"docid":"a72cd3206e200d74355e33797ccd1663","score":"0.5326207","text":"def find_components(image,deltaPix,lens_rad_arcsec = 6.0,lens_rad_ratio = None,\n center_x = None,center_y = None, gal_rad_ratio = 0.1,\n min_size_arcsec=0.7,thresh=0.5, many_sources = True,\n show_locations=False, title = None):\n\n # convert minimum component size in pixel units\n min_size = int(min_size_arcsec / deltaPix)\n \n #Convert lens radius and central galaxy radius to pixels\n if lens_rad_ratio == None:\n lens_rad = int(lens_rad_arcsec / deltaPix)\n else: lens_rad = int(len(image) * lens_rad_ratio)\n gal_rad = int(len(image) * gal_rad_ratio)\n \n \n# im2[im2 < im2.min() + 10.*thresh] = 0.\n \n # downscale source image to data resolution (for speed + easier for converting to data units)\n #down = image_util.re_size(image, factor=supersampling_factor_source)\n \n # apply laplacian of gaussian (LoG) filter to enhance maxima\n LoG = - gaussian_laplace(deepcopy(image), sigma = min_size, mode='constant', cval=0.) \n \n# LoG = - gaussian_laplace(deepcopy(im2), sigma = 2., mode='constant', cval=0.)\n \n filtered = deepcopy(LoG)\n \n# print(LoG.min(),LoG.max(),np.abs(LoG.min()) + thresh )\n \n# print(type(filtered))\n \n #background mean and std of filtered image \n corners = np.zeros([4,5,5])\n corners[0] = LoG[0:5,0:5]\n corners[1] = LoG[-5:,0:5]\n corners[2] = LoG[0:5,-5:]\n corners[3] = LoG[-5:,-5:]\n means = []\n stds = []\n for c in corners:\n mn,med,s = sigma_clipped_stats(c,sigma=3.0)\n means.append(mn)\n stds.append(s)\n \n stds=np.array(stds)\n means = np.array(means)\n means_std = np.std(means)\n# means_good = means[(means >= means.mean() - 1.0 * means_std) & (means <= means.mean() + 1.0 * means_std)]\n means_good = means[(np.abs(means) <= np.abs(means).min() + 1.0 * means_std)]\n mean_bg = np.mean(means_good)\n std_bg = np.mean(stds[(np.abs(means) <= np.abs(means).min() + 1.0 * means_std)])\n# print('LoG means: {}, Log means std: {}, Log means good: {}, LoG avg mean: {}'.format(means,means_std,means_good,mean_bg))\n# print('min: {}, max: {}, cut: {}'.format(LoG.min(),LoG.max(),mean_bg + thresh))\n# print(LoG.min(),LoG.max(),filtered.min() + thresh)\n \n \n # assume all value below max*threshold can not be maxima, so put all to zero\n# filtered[filtered < thresh*filtered.max()] = 0.\n \n# assume all value below min*threshold can not be maxima, so put all to zero\n# filtered[filtered < filtered.min() + thresh * np.abs(filtered.min())] = 0.\n# filtered[filtered < mean_bg + thresh] = 0.\n filtered[filtered < mean_bg + 6.*std_bg] = 0. #set pixels below the mean + 6x threshold to 0\n \n # find coordinates of local maxima\n #print(int(0.5 * min_size))\n max_idx_2d_small = peak_local_max(filtered, min_distance=0) #All bright pixels\n max_idx_2d_large = peak_local_max(filtered, min_distance=1) #peaks with min size of 1 pixel\n \n x_list_small, y_list_small = max_idx_2d_small[:, 1], max_idx_2d_small[:, 0]\n x_list_large, y_list_large = max_idx_2d_large[:, 1], max_idx_2d_large[:, 0]\n \n im_center_x, im_center_y = len(image) / 2., len(image) / 2. #center of image\n \n if (center_x == None) & (center_y == None):\n new_center_x, new_center_y = im_center_x,im_center_y\n else:\n new_center_x, new_center_y = center_x,center_y #new \"center\" = location of lens galaxy\n \n \n #distance of each detected peak from center\n R_small = np.sqrt((x_list_small - new_center_x)**2 + (y_list_small - new_center_y)**2) \n R_large = np.sqrt((x_list_large - new_center_x)**2 + (y_list_large - new_center_y)**2)\n \n #Contaminant light is only bright pixels further from center than lens_rad\n x_sats, y_sats = x_list_small[R_small > lens_rad], y_list_small[R_small > lens_rad]\n \n if many_sources:\n x_lens, y_lens = deepcopy(x_list_small), deepcopy(y_list_small)\n else:\n x_lens, y_lens = deepcopy(x_list_large), deepcopy(y_list_large)\n \n# x_lens, y_lens = x_list_small[R_small <= lens_rad], y_list_small[R_small <= lens_rad]\n \n if (len(x_lens) == 0) & (len(y_lens) == 0):\n x_lens = [0,15]\n y_lens = [0,15]\n \n sources = QTable([x_lens, y_lens],names={'x_local_peak','y_local_peak'}) #make table of all detected objects\n# print(x_list_large)\n# print(y_list_large)\n# print(sources)\n \n # show maxima on image for debug\n \n if show_locations:\n# fig = plt.figure(figsize=(4, 4))\n #plt.imshow(image, origin='lower', cmap=cmap_flux, norm=LogNorm(1e-2))\n \n f, axes = plt.subplots(1, 5, figsize=(20,5), sharex=False, sharey=False)\n# plt.figure(figsize = (8,8))\n# plt.subplot(1,2,1)\n \n axes[0].imshow(image, origin='lower', norm=SymLogNorm(5))\n axes[0].set_title('Image')\n axes[0].set_axis_off()\n \n \n axes[1].imshow(LoG, origin='lower', norm=SymLogNorm(5))\n axes[1].set_title('LoG Filtered Image')\n axes[1].set_axis_off()\n\n# plt.subplot(1,2,2)\n axes[2].imshow(filtered, origin='lower', norm=SymLogNorm(5))\n axes[2].set_title('Final Filtered Image')\n axes[2].set_axis_off()\n \n axes[3].imshow(image, origin='lower', norm=SymLogNorm(5))\n for i in range(len(x_lens)):\n axes[3].scatter([x_lens[i]], [y_lens[i]], c='red', s=60, marker='+')\n \n for i in range(len(x_list_large)):\n axes[3].scatter([x_list_large[i]], [y_list_large[i]], c='black', s=100, marker='x')\n axes[3].set_title('Detected Objects')\n axes[3].set_axis_off()\n \n axes[4].imshow(image, origin='lower', norm=SymLogNorm(5))\n \n for i in range(len(x_sats)):\n axes[4].scatter([x_sats[i]], [y_sats[i]], c='red', s=60, marker='+')\n \n# plt.annotate(i+1, (x_list[i], y_list[i]), color='black')\n \n# for i in range(len(x_mask)):\n# plt.scatter([x_mask[i]], [y_mask[i]], c='red', s=100, marker='*')\n# plt.annotate(i+1, (x_mask[i], y_mask[i]), color='red')\n axes[4].scatter(new_center_x, new_center_y,c='red', s=100, marker='*')\n \n draw_lens_circle = Circle((new_center_x, new_center_y),lens_rad ,fill=False)\n draw_gal_circle = Circle((new_center_x, new_center_y),gal_rad, fill = False)\n# plt.gcf().gca().add_artist(draw_lens_circle)\n# plt.gcf().gca().add_artist(draw_gal_circle)\n axes[4].add_patch(draw_lens_circle)\n# axes[4].add_patch(draw_gal_circle)\n \n axes[4].set_title('Pixels to Mask: \\n r = {:.3f}'.format(lens_rad_arcsec))\n axes[4].text(1, 1, \"detected components\", color='red')\n axes[4].set_axis_off()\n \n if title != None:\n f.suptitle(title, fontsize = 15)\n# plt.show()\n \n \n return (x_sats, y_sats), (new_center_x, new_center_y), sources","title":""},{"docid":"60ed9ffa87ce8d1c7e0bca3debca77a0","score":"0.53208643","text":"def slide_window(gray_image, neighbor=2, current_row=0, current_col=0, fill='constant'):\n max_row, max_col = gray_image.shape\n win_shape = (2 * neighbor + 1, 2 * neighbor + 1) # window shape - 5 x 5\n win_img = np.zeros(win_shape)\n\n for row_offset in range(-1 * neighbor, neighbor + 1):\n for col_offset in range(-1 * neighbor, neighbor + 1):\n\n cp_i = current_row + row_offset # pixel row index on the image with offsets\n cp_j = current_col + col_offset # pixel col index on the image with offsets\n\n if 0 <= cp_i < max_row and 0 <= cp_j < max_col:\n win_img[neighbor + row_offset, neighbor + col_offset] = gray_image[cp_i, cp_j]\n else:\n if fill is 'constant':\n win_img[neighbor + row_offset, neighbor + col_offset] = 0\n elif fill is 'mirror':\n if cp_i >= max_row:\n cp_i = max_row - row_offset\n if cp_j >= max_col:\n cp_j = max_col - col_offset\n if cp_i < 0:\n cp_i = -1 - cp_i\n if cp_j < 0:\n cp_j = -1 - cp_j\n\n win_img[neighbor + row_offset, neighbor + col_offset] = gray_image[cp_i, cp_j]\n\n return win_img","title":""},{"docid":"99bf6a0f83afb7987a1af25179ec6a53","score":"0.5320519","text":"def _check_windows(\n self,\n window_midpoints: Iterable[float],\n obscode: str,\n orbit: Orbit,\n tolerance: float,\n start_mjd: Optional[float] = None,\n end_mjd: Optional[float] = None,\n window_size: int = 7,\n datasets: Optional[set[str]] = None,\n ) -> Iterable[Union[PrecoveryCandidate, FrameCandidate]]:\n # Propagate the orbit with n-body to every window center\n # Since the window midpoints are calculated from the observations\n # in the database then they are in the UTC timescale so let's use that\n orbit_propagated = orbit.propagate(\n window_midpoints,\n PropagationIntegrator.N_BODY,\n time_scale=EpochTimescale.UTC,\n )\n\n # Calculate the location of the orbit on the sky with n-body propagation\n # Again, we do this in the UTC timescale to match the observations in the database\n window_ephems = orbit.compute_ephemeris(\n obscode,\n window_midpoints,\n PropagationIntegrator.N_BODY,\n time_scale=EpochTimescale.UTC,\n )\n window_healpixels = radec_to_healpixel(\n np.array([w.ra for w in window_ephems]),\n np.array([w.dec for w in window_ephems]),\n self.frames.healpix_nside,\n ).astype(int)\n\n # Using the propagated orbits, check each window. Propagate the orbit from the center of\n # window using 2-body to find any HealpixFrames where a detection could have occured\n for window_midpoint, window_ephem, window_healpixel, orbit_window in zip(\n window_midpoints, window_ephems, window_healpixels, orbit_propagated\n ):\n start_mjd_window = window_midpoint - (window_size / 2)\n end_mjd_window = window_midpoint + (window_size / 2)\n\n # Check if start_mjd_window is not earlier than start_mjd (if defined)\n # If start_mjd_window is earlier, then set start_mjd_window to start_mjd\n if (start_mjd is not None) and (start_mjd_window < start_mjd):\n logger.debug(\n f\"Window start MJD [UTC] ({start_mjd_window}) is earlier than\"\n f\" desired start MJD [UTC] ({start_mjd}).\"\n )\n start_mjd_window = start_mjd\n\n # Check if end_mjd_window is not later than end_mjd (if defined)\n # If end_mjd_window is later, then set end_mjd_window to end_mjd\n if (end_mjd is not None) and (end_mjd_window > end_mjd):\n logger.debug(\n f\"Window end MJD [UTC] ({end_mjd_window}) is later than desired end\"\n f\" MJD [UTC] ({end_mjd}).\"\n )\n end_mjd_window = end_mjd\n\n yield from self._check_window(\n window_start=start_mjd_window,\n window_end=end_mjd_window,\n orbit=orbit_window,\n obscode=obscode,\n tolerance=tolerance,\n datasets=datasets,\n )","title":""},{"docid":"9b546b2fb710590edfdefc0a7247c076","score":"0.53176826","text":"def viz_windows(self, score_img, mode):\n if mode == 'filtered':\n lw_img = window_image(self.windows_left, 'x_filtered', color=(0, 255, 0))\n rw_img = window_image(self.windows_right, 'x_filtered', color=(0, 255, 0))\n elif mode == 'raw':\n color = (255, 0, 0)\n win_left_detected, arg = filter_window_list(self.windows_left, False, False, remove_undetected=True)\n win_right_detected, arg = filter_window_list(self.windows_right, False, False, remove_undetected=True)\n lw_img = window_image(win_left_detected, 'x_measured', color, color, color)\n rw_img = window_image(win_right_detected, 'x_measured', color, color, color)\n else:\n raise Exception('mode is not valid')\n combined = lw_img + rw_img\n return cv2.addWeighted(score_img, 1, combined, 0.5, 0)","title":""},{"docid":"2856d02b53742c6158ba9555be6c6677","score":"0.53174835","text":"def detect_cars_multi_area(img,\n clf,\n xy_window=(64, 64),\n stride=None,\n y_start_stops=None,\n image_size_factors=[1],\n x_padding=None,\n heatmap=False,\n n_jobs=1):\n\n if n_jobs < 0:\n n_jobs = multiprocessing.cpu_count()\n\n if stride is None:\n stride = np.repeat([[xy_window[0], xy_window[1]]], len(image_size_factors), axis=0)\n\n if y_start_stops is None:\n y_start_stops = np.repeat([[0, img.shape[0] - 1]], len(image_size_factors), axis=0)\n\n if x_padding is None:\n x_padding = np.repeat([0], len(image_size_factors), axis=0)\n\n # use joblib to run processing in parallel\n result = Parallel(n_jobs=n_jobs)(\n delayed(detect_cars)(\n img,\n clf,\n xy_window,\n cur_stride,\n cur_sizes_factors,\n cur_y_start_stop,\n cur_x_padding)\n for cur_stride, cur_sizes_factors, cur_y_start_stop, cur_x_padding in\n zip(stride, image_size_factors, y_start_stops, x_padding))\n\n bounding_boxes, des_func = zip(*result)\n bounding_boxes = np.vstack(bounding_boxes)\n\n des_func = np.concatenate(des_func)\n\n if heatmap:\n heat = np.zeros(img.shape[:2], dtype=np.float32)\n for bb, df in zip(bounding_boxes, des_func):\n heat[bb[1]:bb[3], bb[0]:bb[2]] += df\n\n return heat\n\n return bounding_boxes","title":""},{"docid":"51656b55e9b521df4304960c7878d632","score":"0.5315555","text":"def construct_sliding_windows(sequence_length: int, sliding_window_size: int):\n sliding_windows = []\n stride = int(sliding_window_size / 2)\n start_index = 0\n end_index = 0\n while end_index < sequence_length:\n end_index = min(start_index + sliding_window_size, sequence_length)\n left_value = 1 if start_index == 0 else 0\n right_value = 1 if end_index == sequence_length else 0\n mask = [left_value] * int(sliding_window_size / 4) + [1] * int(sliding_window_size / 2) \\\n + [right_value] * (sliding_window_size - int(sliding_window_size / 2) - int(sliding_window_size / 4))\n mask = mask[: end_index - start_index]\n sliding_windows.append((start_index, end_index, mask))\n start_index += stride\n assert sum([sum(window[2]) for window in sliding_windows]) == sequence_length\n return sliding_windows","title":""},{"docid":"6ca3413298edcef1ccf39896c0f7524d","score":"0.5311973","text":"def SH_FindOverlap(xcenter, ycenter, xlength, ylength, xp_corner, yp_corner):\n\n areaClipped = 0.0\n top = ycenter + 0.5 * ylength\n bottom = ycenter - 0.5 * ylength\n\n left = xcenter - 0.5 * xlength\n right = xcenter + 0.5 * xlength\n\n nVertices = 4 # input detector pixel vertices\n MaxVertices = 9\n # initialize xPixel, yPixel to the detector pixel corners.\n # xPixel,yPixel will become the clipped polygon vertices inside the cube pixel\n # xnew,ynew xpixel and ypixel of size MaxVertices\n\n xPixel = []\n yPixel = []\n\n xnew = []\n ynew = []\n\n for j in range(0, 9):\n xnew.append(0.0)\n ynew.append(0.0)\n xPixel.append(0.0)\n yPixel.append(0.0)\n\n\n # Xpixel, YPixel closed (5 corners)\n for i in range(0, 4):\n xPixel[i] = xp_corner[i]\n yPixel[i] = yp_corner[i]\n xPixel[4] = xp_corner[0]\n yPixel[4] = yp_corner[0]\n\n\n for i in range(0, 4): # 0:left, 1: right, 2: bottom, 3: top\n nVertices2 = 0\n for j in range(0, nVertices):\n x1 = xPixel[j]\n y1 = yPixel[j]\n x2 = xPixel[j + 1]\n y2 = yPixel[j + 1]\n condition = calcCondition(i, x1, y1, x2, y2, left, right, top, bottom)\n x = 0\n y = 0\n\n if condition == 1:\n x, y = solveIntersection(i, x1, y1, x2, y2,\n left, right, top, bottom)\n nVertices2 = addpoint(x, y, xnew, ynew, nVertices2);\n nVertices2 = addpoint(x2, y2, xnew, ynew, nVertices2)\n\n elif condition == 2:\n nVertices2 = addpoint(x2, y2, xnew, ynew, nVertices2)\n elif condition == 3:\n x, y = solveIntersection(i, x1, y1, x2, y2,\n left, right, top, bottom)\n nVertices2 = addpoint(x, y, xnew, ynew, nVertices2)\n\n#\tcondition == 4: points outside\n# Done looping over J corners\n nVertices2 = addpoint(xnew[0], ynew[0], xnew, ynew, nVertices2) # close polygon\n\n if nVertices2 > MaxVertices:\n raise Error2DPolygon(\" Failure in finding the clipped polygon, nVertices2 > 9 \")\n\n\n nVertices = nVertices2 - 1;\n\n for k in range(0, nVertices2):\n xPixel[k] = xnew[k]\n yPixel[k] = ynew[k]\n\n# done loop over top,bottom,left,right\n nVertices = nVertices + 1\n\n\n if nVertices > 0:\n areaClipped = FindAreaPoly(nVertices, xPixel, yPixel);\n\n\n return areaClipped;","title":""},{"docid":"9abeec8f8b04f1c6be3f43d68ee829c0","score":"0.5310946","text":"def neighborhood((y, x), (height, width)):\n return [(yt, xt) for xt in [x + 1, x, x - 1]\n for yt in [y + 1, y, y - 1]\n if 0 <= xt < width and 0 <= yt < height\n and (xt, yt) != (x, y)]","title":""},{"docid":"d41a170efe92eb3cb5758107557e4d05","score":"0.53097194","text":"def find_window_centroids(self, warped):\n window_width = self.window_width\n window_height = self.window_height\n margin = self.margin\n \n window_centroids = [] # store the (left,right) window centroids positions per level\n window = np.ones(window_width) # Create window templates for doing convolutions\n \n # First find the two starting positions right and left lines by using np.sum to get the vertical image slice\n # and then np.convolve the vertical image slice with the window template\n \n # sum the bottom quarter of image to get slice\n l_sum = np.sum(warped[int(3*warped.shape[0]/4):,:int(3*warped.shape[1]/2)], axis=0)\n l_center = np.argmax(np.convolve(window,l_sum)) - window_width/2\n r_sum = np.sum(warped[int(3*warped.shape[0]/4):int(3*warped.shape[1]/2),:], axis=0)\n r_center = np.argmax(np.convolve(window,r_sum)) - window_width/2 + int(warped.shape[1]/2)\n \n # add what we found for the first layer\n window_centroids.append((l_center,r_center))\n \n # Go through each layer looking for max pixel location\n for level in range(1,(int)(warped.shape[0]/window_height)):\n # convolve the window into the vertical slice of the image\n image_layer = np.sum(warped[int(warped.shape[0]-(level+1)*window_height):int(warped.shape[0]-level*window_height),:], axis=0)\n conv_signal = np.convolve(window,image_layer)\n # Find the best left centroid by using past left center as a reference \n # Use window_width/2 as offset because convolution signal reference is at right side of window, not center of window\n offset = window_width/2\n l_min_index = int(max(l_center+offset-margin,0))\n l_max_index = int(max(l_center+offset+margin,warped.shape[1]))\n l_center = np.argmax(conv_signal[l_min_index:l_max_index]) + l_min_index - offset\n \n # Find the best right centroid by using past right center as a reference\n r_min_index = int(max(r_center+offset-margin,0))\n r_max_index = int(max(r_center+offset+margin,warped.shape[1]))\n r_center = np.argmax(conv_signal[r_min_index:r_max_index]) + r_min_index - offset\n # Add what we found for the layer\n window_centroids.append((l_center,r_center))\n \n self.recent_centers.append(window_centroids)\n # return averaged value of of the line centers, helps to keep the markers from jumping around too much\n average_line_centers = np.average(self.recent_centers[-self.smooth_factor:], axis=0)\n return average_line_centers","title":""},{"docid":"ad6e66b49222543fa70ecf05631af562","score":"0.53083515","text":"def locate_slice_chunk(slice_start, slice_stop, height, overlap_metadata):\n if slice_stop < slice_start:\n raise ValueError(\n \"Stopping index must be larger than the starting index!!!\")\n g_nrow = overlap_metadata.shape[0] + 1\n side = overlap_metadata[0, 0, 1]\n overlap_list = overlap_metadata[:, 0, 0]\n if side == 1:\n list_slices = [(np.arange(i * height, i * height + height) -\n np.sum(overlap_list[0: i])) for i in range(g_nrow)]\n else:\n list_slices = [\n (np.arange(i * height + height - 1, i * height - 1, -1) -\n np.sum(overlap_list[0: i])) for i in range(g_nrow)]\n list_slices = np.asarray(list_slices)\n results = []\n for i, list1 in enumerate(list_slices):\n result1 = []\n if side == 1:\n for slice_idx in range(slice_start, slice_stop):\n pos = np.squeeze(np.where(list1 == slice_idx)[0])\n if pos.size == 1:\n fact = 1.0\n if i == 0:\n ver_overlap = overlap_list[i]\n dis1 = len(list1) - pos - 1\n if dis1 < ver_overlap:\n fact = dis1 / (ver_overlap - 1)\n elif i == (g_nrow - 1):\n ver_overlap = overlap_list[i - 1]\n if pos < ver_overlap:\n fact = pos / (ver_overlap - 1)\n else:\n ver_overlap1 = overlap_list[i]\n dis1 = len(list1) - pos - 1\n if dis1 < ver_overlap1:\n fact = dis1 / (ver_overlap1 - 1)\n if pos < ver_overlap1:\n fact = pos / (ver_overlap1 - 1)\n ver_overlap2 = overlap_list[i - 1]\n dis1 = len(list1) - pos - 1\n if dis1 < ver_overlap2:\n fact = dis1 / (ver_overlap2 - 1)\n if pos < ver_overlap2:\n fact = pos / (ver_overlap2 - 1)\n result1.append([i, pos, fact])\n else:\n for slice_idx in range(slice_start, slice_stop):\n pos = np.squeeze(np.where(list1 == slice_idx)[0])\n if pos.size == 1:\n fact = 1.0\n if i == 0:\n ver_overlap = overlap_list[i]\n if pos < ver_overlap:\n fact = 1.0 * pos / (ver_overlap - 1)\n elif i == (g_nrow - 1):\n ver_overlap = overlap_list[i - 1]\n dis1 = len(list1) - pos - 1\n if dis1 < ver_overlap:\n fact = 1.0 * dis1 / (ver_overlap - 1)\n else:\n ver_overlap1 = overlap_list[i]\n dis1 = len(list1) - pos - 1\n if dis1 < ver_overlap1:\n fact = 1.0 * dis1 / (ver_overlap1 - 1)\n if pos < ver_overlap1:\n fact = 1.0 * pos / (ver_overlap1 - 1)\n ver_overlap2 = overlap_list[i - 1]\n dis1 = len(list1) - pos - 1\n if dis1 < ver_overlap2:\n fact = 1.0 * dis1 / (ver_overlap2 - 1)\n if pos < ver_overlap2:\n fact = 1.0 * pos / (ver_overlap2 - 1)\n result1.append([i, pos, fact])\n if len(result1) > 0:\n results.append(result1)\n return results","title":""},{"docid":"f22069458f02f2cb41745af34accc5c1","score":"0.5306441","text":"def identify_dbs(image):\n locations = {\"red\": Point(), \"green\": Point(), \"blue\": Point()}\n masks = {\"red\": [], \"green\": [], \"blue\": []}\n\n bridge = cv_bridge.CvBridge()\n image = bridge.imgmsg_to_cv2(image, \"bgr8\")\n hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n\n # upper and lower bounds for red\n # using python 3 bgr [0,0,188] = hsv [0, 255, 188]\n lower_red = numpy.array([0, 100, 100]) \n upper_red = numpy.array([10, 255, 255])\n masks[\"red\"] = cv2.inRange(hsv, lower_red, upper_red)\n\n # upper and lower bounds for green\n # using python 3 bgr [0,175,0] = hsv [60, 255, 175]\n lower_green = numpy.array([50, 100, 100]) \n upper_green = numpy.array([70, 255, 255])\n masks[\"green\"] = cv2.inRange(hsv, lower_green, upper_green)\n\n # upper and lower bounds for blue\n # using python 3 bgr [176, 0, 17] = hsv [123, 255, 176]\n lower_blue = numpy.array([113, 100, 100])\n upper_blue = numpy.array([133, 255, 255])\n masks[\"blue\"] = cv2.inRange(hsv, lower_blue, upper_blue)\n\n x, y, w, h = 0, 0, image.shape[1]//3, image.shape[0]\n\n for color, mask in masks.items():\n pixels = {\"left\": 0, \"middle\": 0, \"right\": 0}\n \n # define section of image to use for left, middle and right\n left = mask[y:y+h, x:x+w]\n middle = mask[y:y+h, x+w:x+w+w]\n right = mask[y:y+h, x+w+w:x+3*w]\n\n # count the number of pixels in each section\n pixels[\"left\"] = cv2.countNonZero(left)\n pixels[\"middle\"] = cv2.countNonZero(middle)\n pixels[\"right\"] = cv2.countNonZero(right)\n location = max(pixels, key=pixels.get)\n\n # map the relative position of the db (left, middle, right) to the correct Point()\n locations[color] = db_locations[location]\n \n return locations","title":""},{"docid":"41d6de92bdbd04ed9490e59ca49a4072","score":"0.53025955","text":"def apply_windows(slided_signals, window_type=\"hann\"):\n # length of each slided signal\n n = slided_signals.shape[-1]\n window = signal.get_window(window_type, n)\n windowed_signals = numpy.multiply(slided_signals, window)\n return windowed_signals","title":""},{"docid":"393196ceb7c954688e854c3991b678b7","score":"0.52932894","text":"def fit_ms2(stack, min_distances=(70,50,50), sigma_small=1, \n sigma_big=4, bg_radius=4, fitwindow_rad_xy=10, \n fitwindow_rad_z=2):\n\n # Task: change size to minimum distance\n def get_fitwindow(data, peak, xy_rad, z_rad):\n \"\"\"Retrieve section of image stack corresponding to given\n window around a point and the coordinate adjustments necessary\n to convert window coordinates to coordinates in the original image\"\"\"\n \n # Set the start points for windows and \"adjust\" them if they get \n # to negative numbers.\n zmin = peak[0] - z_rad\n xmin = peak[1] - xy_rad\n ymin = peak[2] - xy_rad\n # Initialize adjustments to values that are correct if no edge problems \n # are encountered.\n z_adj = -z_rad\n x_adj = -xy_rad\n y_adj = -xy_rad\n # Update mins and adjustments if windows start at negative coordinates.\n if (zmin < 0):\n zmin = 0\n z_adj = -peak[0]\n if (xmin < 0):\n xmin = 0\n x_adj = -peak[1]\n if (ymin < 0):\n ymin = 0\n y_adj = -peak[2]\n\n # Get end points, constained by max coordinate in data.\n zmax = min(data.shape[0] - 1, peak[0] + z_rad)\n xmax = min(data.shape[1] - 1, peak[1] + xy_rad)\n ymax = min(data.shape[2] - 1, peak[2] + xy_rad)\n\n return (data[zmin:(zmax+1), xmin:(xmax+1), ymin:(ymax+1)], z_adj, x_adj, y_adj)\n \n def relabel(peak_ids, oldparams, mask):\n \"\"\"Renumber labelmask and corresponding fit parameters\n Set background as 0, objects in order 1...end.\n \"\"\"\n spot_data = {}\n peak_num = 1\n for peak in peak_ids:\n #coords = np.where(mask == peak)\n paramsnew = oldparams[peak-1,:] # object 1 will be fitparams row 0\n # Rearrange params from fit function so coordinates lead.\n spot_data[peak_num] = paramsnew[[1,2,3,0,4,5,6]]\n peak_num = peak_num + 1\n return spot_data\n\n def clamp(n, minn, maxn):\n \"\"\"Bound a number between two constants\"\"\"\n return max(min(maxn, n), minn)\n \n def fit_frame(substack, min_distances, sigma_small, \n sigma_big, bg_radius, fitwindow_rad_xy, \n fitwindow_rad_z):\n \"\"\"Perform 3D gaussian fitting on a 3D image stack.\"\"\"\n\n # Filter and background subtract image.\n dog = dog_filter(substack, sigma_small, sigma_big)\n bg = ndi.filters.minimum_filter(dog, bg_radius)\n dog_bs = dog - bg\n\n # Make a labelmask corresponding to local maxima peaks.\n peak_window_size = (min_distances[0] * 2 + 1, min_distances[1] * 2 + 1, min_distances[2] * 2 + 1)\n mask, peaks = peak_local_max_nD(dog_bs, peak_window_size)\n\n # Fit 3D gaussian in window surrounding each local maximum.\n fitparams = np.ndarray((0,7))\n for peak in peaks:\n fitwindow, z_adj, x_adj, y_adj = get_fitwindow(substack, peak, fitwindow_rad_xy, \n fitwindow_rad_z)\n opt = fitgaussian3d(fitwindow)\n if opt.success:\n peak_fitparams = opt.x\n # Move center coordinates to match center of gaussian fit, ensure they're within image. \n # If they're outside the image, coordinate is assigned as the edge of the image.\n peak_fitparams[0] = int(round(clamp((peak[0] + peak_fitparams[0] + z_adj), 0, substack.shape[-3]-1)))\n peak_fitparams[1] = int(round(clamp((peak[1] + peak_fitparams[1] + x_adj), 0, substack.shape[-2]-1)))\n peak_fitparams[2] = int(round(clamp((peak[2] + peak_fitparams[2] + y_adj), 0, substack.shape[-1]-1)))\n fitparams = np.vstack((fitparams, peak_fitparams))\n # If fit fails, add dummy entry for spot.\n else:\n fitparams = np.vstack((fitparams, np.array([z_adj,x_adj,y_adj,0,np.inf,np.inf,np.inf])))\n return fitparams\n \n #### Main ####\n # Do fitting on first frame.\n fit_data_frame0 = fit_frame(stack[0], min_distances, sigma_small, \n sigma_big, bg_radius, fitwindow_rad_xy, \n fitwindow_rad_z)\n # Make fit_data a list of ndarrays.\n fit_data = [fit_data_frame0]\n \n # Fit the rest of the frames, add their data to fit_data.\n for i in range(1, stack.shape[0]):\n print(i, end=' ')\n fit_data_thisframe = fit_frame(stack[i], min_distances, sigma_small, \n sigma_big, bg_radius, fitwindow_rad_xy, \n fitwindow_rad_z)\n fit_data.append(fit_data_thisframe)\n \n return fit_data","title":""},{"docid":"76779ed5aa0fe270c3be6772117e8adb","score":"0.5292007","text":"def find_objects_in_image(self, image, visualize):\n self.select(image)\n\n for scan_region in self.scan_regions:\n self.set_scaling(scan_region[0])\n full, small = self.find_instances(image, scan_region[1], 0.5)\n if (visualize):\n fig = plt.figure(figsize=(30, 20))\n plt.imshow(small)\n plt.title(\"Scan region {} using scaling {}\".format(scan_region[1], scan_region[0]))\n plt.show()","title":""},{"docid":"133d29c53c08a20a55c8bd001c6f0865","score":"0.5288611","text":"def basic_lane_find(self, binary_warped):\n\t\t# Take a histogram of the bottom half of the image\n\t\thistogram = np.sum(binary_warped[binary_warped.shape[0] // 2:, :, 0], axis=0)\n\t\t# Find the peak of the left and right halves of the histogram\n\t\tmidpoint = np.int(histogram.shape[0] / 2)\n\t\tleftx_base = np.argmax(histogram[:midpoint])\n\t\trightx_base = np.argmax(histogram[midpoint:]) + midpoint\n\t\t# Choose the number of sliding windows\n\t\tnwindows = 9\n\t\t# Set height of windows\n\t\twindow_height = np.int(binary_warped.shape[0] / nwindows)\n\t\t# Identify the x and y positions of all nonzero pixels in the image\n\t\tnonzero = binary_warped.nonzero()\n\t\tnonzeroy = np.array(nonzero[0])\n\t\tnonzerox = np.array(nonzero[1])\n\t\t# Current positions to be updated for each window\n\t\tleftx_current = leftx_base\n\t\trightx_current = rightx_base\n\t\t# Set the width of the windows +/- margin\n\t\tmargin = 100\n\t\t# Set the minimum number of pixels found to recenter window\n\t\tminpix = 50\n\t\t# Create empty lists to receive left and right lane pixel indices\n\t\tleft_lane_inds = []\n\t\tright_lane_inds = []\n\t\t# Step through the windows one by one\n\t\tfor window in range(nwindows):\n\t\t\t# Identify window boundaries in x and y (and right and left)\n\t\t\twin_y_low = binary_warped.shape[0] - (window + 1) * window_height\n\t\t\twin_y_high = binary_warped.shape[0] - window * window_height\n\t\t\twin_xleft_low = leftx_current - margin\n\t\t\twin_xleft_high = leftx_current + margin\n\t\t\twin_xright_low = rightx_current - margin\n\t\t\twin_xright_high = rightx_current + margin\n\t\t\t# Identify the nonzero pixels in x and y within the window\n\t\t\tgood_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (\n\t\t\t\tnonzerox < win_xleft_high)).nonzero()[0]\n\t\t\tgood_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (\n\t\t\t\tnonzerox < win_xright_high)).nonzero()[0]\n\t\t\t# Append these indices to the lists\n\t\t\tleft_lane_inds.append(good_left_inds)\n\t\t\tright_lane_inds.append(good_right_inds)\n\t\t\t# If you found > minpix pixels, recenter next window on their mean position\n\t\t\tif len(good_left_inds) > minpix:\n\t\t\t\tleftx_current = np.int(np.mean(nonzerox[good_left_inds]))\n\t\t\tif len(good_right_inds) > minpix:\n\t\t\t\trightx_current = np.int(np.mean(nonzerox[good_right_inds]))\n\t\t# Concatenate the arrays of indices\n\t\tleft_lane_inds = np.concatenate(left_lane_inds)\n\t\tright_lane_inds = np.concatenate(right_lane_inds)\n\t\t# Extract left and right line pixel positions\n\t\tleftx = nonzerox[left_lane_inds]\n\t\tlefty = nonzeroy[left_lane_inds]\n\t\trightx = nonzerox[right_lane_inds]\n\t\trighty = nonzeroy[right_lane_inds]\n\t\t# Fit a second order polynomial to each\n\t\tself.left_fit = np.polyfit(lefty, leftx, 2)\n\t\tself.right_fit = np.polyfit(righty, rightx, 2)\n\t\t#Generate x and y values for plotting\n\t\tploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])\n\t\tleft_fitx = self.left_fit[0] * ploty ** 2 + self.left_fit[1] * ploty + self.left_fit[2]\n\t\tright_fitx = self.right_fit[0] * ploty ** 2 + self.right_fit[1] * ploty + self.right_fit[2]\n\t\tself.detected = True\n\n\t\treturn left_fitx, right_fitx","title":""},{"docid":"0f9e4956db8a8b85763e63f1592d1d43","score":"0.5281523","text":"def new_video_sample_grid(dset, window_dims=None, window_overlap=0.0,\n space_dims=None, time_dim=None, # TODO\n classes_of_interest=None, ignore_coverage_thresh=0.6,\n negative_classes={'ignore', 'background'},\n use_annots=True, legacy=True, verbose=1):\n import kwarray\n from ndsampler import isect_indexer\n keepbound = True\n\n if classes_of_interest:\n raise NotImplementedError\n\n # Create a sliding window object for each specific image (because they may\n # have different sizes, technically we could memoize this)\n vidid_to_slider = {}\n for vidid, video in dset.index.videos.items():\n gids = dset.index.vidid_to_gids[vidid]\n num_frames = len(gids)\n full_dims = [num_frames, video['height'], video['width']]\n window_dims_ = full_dims if window_dims == 'full' else window_dims\n slider = kwarray.SlidingWindow(full_dims, window_dims_,\n overlap=window_overlap,\n keepbound=keepbound,\n allow_overshoot=True)\n\n vidid_to_slider[vidid] = slider\n\n @ub.memoize\n def _lut_warp(gid):\n warp_img_to_vid = dset.index.imgs[gid].get('warp_img_to_vid', None)\n return kwimage.Affine.coerce(warp_img_to_vid)\n\n _isect_index = isect_indexer.FrameIntersectionIndex.from_coco(dset)\n\n positives = []\n negatives = []\n targets = []\n positive_idxs = []\n negative_idxs = []\n for vidid, slider in vidid_to_slider.items():\n video_regions = list(slider)\n gids = dset.index.vidid_to_gids[vidid]\n for vid_region in video_regions:\n t_sl, y_sl, x_sl = vid_region\n vid_box = kwimage.Boxes.from_slice((y_sl, x_sl))\n region_gids = gids[t_sl]\n\n if use_annots:\n region_aids = []\n for gid in region_gids:\n warp_img_to_vid = _lut_warp(gid)\n # Check to see what annotations this window-box overlaps with\n # (in image space!)\n img_box = vid_box.warp(warp_img_to_vid.inv())\n aids = _isect_index.overlapping_aids(gid, img_box)\n region_aids.extend(aids)\n\n pos_aids = sorted(region_aids)\n else:\n pos_aids = None\n\n time_slice = vid_region[0]\n space_slice = vid_region[1:3]\n tr = {\n 'vidid': vidid,\n 'time_slice': time_slice,\n 'space_slice': space_slice,\n # 'slices': region,\n 'gids': region_gids,\n 'aids': pos_aids,\n }\n targets.append(tr)\n if pos_aids:\n positive_idxs.append(len(targets))\n if legacy:\n positives.append(tr)\n else:\n negative_idxs.append(len(targets))\n if legacy:\n negatives.append(tr)\n\n if verbose:\n print('Found {} targets'.format(len(targets)))\n if use_annots:\n print('Found {} positives'.format(len(positive_idxs)))\n print('Found {} negatives'.format(len(negative_idxs)))\n\n sample_grid = {\n 'targets': targets,\n 'positives_indexes': positive_idxs,\n 'negatives_indexes': negative_idxs,\n }\n if legacy:\n sample_grid.update({\n # Deprecated:\n 'positives': positives,\n 'negatives': negatives,\n })\n return sample_grid","title":""},{"docid":"e02e5203cedd5eed4ea61b521ff23ebb","score":"0.52775663","text":"def extract_walls(img_array,x_scale,y_scale,wall_height):\n\n wall_th = 2\n length = 0\n wall_list = []\n\n #check for horizontal walls first\n for row in range(img_array.shape[0]):\n for col in range(img_array.shape[1]):\n \n sec = img_array.astype(int)[row:row+2,col:col+2]\n \n if left_edge(sec):\n #check two steps to the right\n next_sec = img_array.astype(int)[row:row+2, col+1:col+3]\n next_next_sec = img_array.astype(int)[row:row+2, col+2:col+4]\n\n #if horizontal wall, get coordinates and count length\n if is_wall(next_sec) and not right_edge(next_next_sec): \n #record corner coordinates\n x = col +1\n y = row\n while is_wall(next_sec):\n #start counting length across, until right edge found\n length +=1\n col +=1\n next_sec = img_array.astype(int)[row:row+2, col:col+2]\n #create wall object and store in list \n new_wall = Wall(x*x_scale,y*y_scale,length*x_scale,wall_th*y_scale,wall_height)\n wall_list.append(new_wall)\n length = 0\n\n #check for vertical walls\n for col in range(img_array.shape[1]):\n for row in range(img_array.shape[0]):\n\n sec = img_array.astype(int)[row:row+2,col:col+2]\n \n if top_edge(sec): \n #check two steps below\n next_sec = img_array.astype(int)[row+1:row+3, col:col+2]\n next_next_sec = img_array.astype(int)[row+2:row+4, col:col+2]\n\n #if vertical wall, get coordinates and count length\n if is_wall(next_sec) and is_wall(next_next_sec):\n x = col\n y = row\n while is_wall(next_sec):\n #start counting length downwards, until bottom edge found\n length += 1\n row += 1\n next_sec = img_array.astype(int)[row:row+2, col:col+2]\n #create wall object and store in list\n new_wall = Wall(x*x_scale,y*y_scale,wall_th*x_scale,length*y_scale, wall_height)\n wall_list.append(new_wall)\n length = 0\n\n return wall_list","title":""},{"docid":"bb0f0f43c5edbdaba02c45f2dae541aa","score":"0.5274863","text":"def get_train_windows(self, scene: Scene) -> List[Box]:\n\n def filter_windows(windows):\n if scene.aoi_polygons:\n windows = Box.filter_by_aoi(windows, scene.aoi_polygons)\n return windows\n\n raster_source = scene.raster_source\n extent = raster_source.get_extent()\n label_store = scene.ground_truth_label_source\n chip_size = self.config.chip_size\n\n chip_options = self.config.chip_options\n\n if chip_options.window_method == 'random_sample':\n return get_random_sample_train_windows(\n label_store, chip_size, self.config.class_map, extent,\n chip_options, filter_windows)\n elif chip_options.window_method == 'sliding':\n stride = chip_options.stride\n if stride is None:\n stride = chip_size / 2\n\n return list(\n filter_windows((extent.get_windows(chip_size, stride))))","title":""},{"docid":"7e4ba92fdd4431ba52cdcc344fed42c4","score":"0.52736163","text":"def find_shapes(im_bw, kernel, th):\n im_convolved = scipy.ndimage.filters.convolve(im_bw, kernel[::-1, ::-1])\n im_hotspots = im_convolved >= th\n bin_dy, bin_dx = kernel.shape\n radius_x = bin_dx // 2\n radius_y = bin_dy // 2\n positions = []\n open_for_interpretation = np.ones_like(im_convolved, dtype=bool)\n xmax = im_hotspots.shape[1] - 1\n ymax = im_hotspots.shape[0] - 1\n for x in range(xmax + 1):\n for y in range(ymax + 1):\n if open_for_interpretation[y, x] and im_hotspots[y, x]:\n x0 = max(0, x - radius_x)\n x1 = min(xmax, x + radius_x)\n y0 = max(0, y - radius_y)\n y1 = min(ymax, y + radius_y)\n window = im_hotspots[y0:y1, x0:x1]\n cx, cy = center_of_mass(window, x_offset=x - radius_x, y_offset=y - radius_y)\n positions.append([cx, cy])\n open_for_interpretation[y0:y1, x0:x1] = False\n return positions","title":""},{"docid":"c0700dff11be5a8aa3432e3de5a89c87","score":"0.527155","text":"def sliding_window_analysis(sequence, function,\n window_size=100000, step_size=50000):\n for start in range(0, len(sequence), step_size):\n end = start + window_size\n if end > len(sequence):\n break\n yield start, end, function(sequence[start:end])","title":""},{"docid":"893199d45136ec9e51f4a1638f081f26","score":"0.52654827","text":"def dwindow(window):\r\n \r\n h=window\r\n nh=len(h)\r\n lh=(nh-1)/2\r\n stepheight=(h[0]+h[-1])/2.\r\n ramp=float((h[-1]-h[0]))/nh\r\n h2=np.zeros(nh+2)\r\n h2[1:nh+1]=h-stepheight-ramp*np.arange(start=-lh,stop=lh+1,step=1)\r\n \r\n dwin=(h2[2:nh+2]-h2[0:nh])/2.+ramp\r\n dwin[0]=dwin[0]+stepheight\r\n dwin[-1]=dwin[-1]-stepheight\r\n \r\n return dwin","title":""},{"docid":"401c9f59c3345203414b663b523f9b9b","score":"0.52646315","text":"def nine_regions(self):\n\n coordinateList = []\n\n # Top left.\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] )\n coordinateList.append( [x, y] )\n\n # Top center.\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] ) \n coordinateList.append( [x, y] )\n\n # Top right.\n x = (int)( self.oriImgSize[IDX_WIDTH] * ( 1.0 - self.ratioTopLeft[IDX_X] ) - self.regionSize[IDX_WIDTH] )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] )\n coordinateList.append( [x, y] )\n\n # Center left.\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\n coordinateList.append( [x, y] )\n\n # Center.\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\n coordinateList.append( [x, y] )\n\n # Center right.\n x = (int)( self.oriImgSize[IDX_WIDTH] * (1.0 - self.ratioTopLeft[IDX_X]) - self.regionSize[IDX_WIDTH] )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\n coordinateList.append( [x, y] )\n\n # Bottom left.\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\n coordinateList.append( [x, y] )\n\n # Bottom center.\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\n coordinateList.append( [x, y] )\n\n # Bottom right.\n x = (int)( self.oriImgSize[IDX_WIDTH] * (1.0 - self.ratioTopLeft[IDX_X]) - self.regionSize[IDX_WIDTH] )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\n coordinateList.append( [x, y] )\n\n return coordinateList","title":""},{"docid":"267a1ca629396d87234907aae8b53149","score":"0.52632356","text":"def find_rects(image: np.ndarray) -> List[np.ndarray]:\n\n gray = cv.cvtColor(image.copy(), cv.COLOR_RGB2GRAY)\n gray = cv.GaussianBlur(gray, (5, 5), 0)\n #edged = cv.Canny(gray, 70, 180) # this numbers is hand picked guess from a few photos\n edged = auto_canny(gray)\n\n contours = cv.findContours(edged.copy(), cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)\n contours = contours[0] if len(contours) == 2 else contours[1]\n contours.sort(key=cv.contourArea, reverse=True)\n contours = [cnt for cnt in contours if cv.contourArea(cnt) > 15] # 15px contour area, basically cnt>=4x4\n\n rects = list(map(cv.minAreaRect, contours))\n boxes = list(map(lambda r: Rect.from_cvrect(*r[0], *r[1]), rects))\n\n boxes = Rect.nms_merge(boxes)\n\n return boxes or list()","title":""}],"string":"[\n {\n \"docid\": \"cf9ffb3c2b3f1cbe7a7e2ac9920c7980\",\n \"score\": \"0.8306893\",\n \"text\": \"def generate_windows_list(img,\\n x_start_stop,\\n y_start_stop,\\n xy_window,\\n xy_overlap):\\n if x_start_stop[0] is None:\\n x_start_stop[0] = 0\\n if x_start_stop[1] is None:\\n x_start_stop[1] = img.shape[1]\\n if y_start_stop[0] is None:\\n y_start_stop[0] = 0\\n if y_start_stop[1] is None:\\n y_start_stop[1] = img.shape[0]\\n\\n # Compute the span of the region to be searched\\n xspan = x_start_stop[1] - x_start_stop[0]\\n yspan = y_start_stop[1] - y_start_stop[0]\\n\\n # Compute the number of pixels per step in x/y\\n nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))\\n ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))\\n\\n # Compute the number of windows in x/y\\n nx_windows = np.int(xspan/nx_pix_per_step) - 1\\n ny_windows = np.int(yspan/ny_pix_per_step) - 1\\n\\n window_list = []\\n\\n # Loop through finding x and y window positions\\n for ys in range(ny_windows):\\n for xs in range(nx_windows):\\n\\n startx = xs*nx_pix_per_step + x_start_stop[0]\\n endx = startx + xy_window[0]\\n starty = ys*ny_pix_per_step + y_start_stop[0]\\n endy = starty + xy_window[1]\\n\\n window_list.append(((startx, starty), (endx, endy)))\\n\\n return window_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3761212d4864356bd18305158d56bc48\",\n \"score\": \"0.81766343\",\n \"text\": \"def slide_window(image, x_start=0, x_stop=None, y_start=0, y_stop=None, xy_window=(64, 64), xy_overlap=(0.5, 0.5)):\\n # If x and/or y start/stop positions not defined, set to image size\\n if x_stop == None:\\n x_stop = image.shape[1]\\n if y_stop == None:\\n y_stop = image.shape[0]\\n # Compute the span of the region to be searched\\n xspan = x_stop - x_start\\n yspan = y_stop - y_start\\n # Compute the number of pixels per step in x/y\\n nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))\\n ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))\\n # Compute the number of windows in x/y\\n nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))\\n ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))\\n nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step)\\n ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step)\\n # Initialize a list to append window positions to\\n window_list = []\\n # Loop through finding x and y window positions\\n # Note: you could vectorize this step, but in practice you'll be considering windows one by one with your\\n # classifier, so looping makes sense\\n for ys in range(ny_windows):\\n for xs in range(nx_windows):\\n # Calculate window position\\n startx = xs*nx_pix_per_step + x_start\\n endx = startx + xy_window[0]\\n starty = ys*ny_pix_per_step + y_start\\n endy = starty + xy_window[1]\\n # Append window position to list\\n window_list.append(((startx, starty), (endx, endy)))\\n # Return the list of windows\\n return window_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d7d71ca2a819488aee14113ac8db52d\",\n \"score\": \"0.80311435\",\n \"text\": \"def slide_window(\\n img,\\n x_start_stop=[None, None], y_start_stop=[None, None], \\n xy_window=(64, 64), xy_overlap=(0.5, 0.5)):\\n # If x and/or y start/stop positions not defined, set to image size\\n if not x_start_stop[0]:\\n x_start_stop[0] = 0\\n if not x_start_stop[1]:\\n x_start_stop[1] = img.shape[1]\\n if not y_start_stop[0]:\\n y_start_stop[0] = 0\\n if not y_start_stop[1]:\\n y_start_stop[1] = img.shape[0]\\n # Compute the span of the region to be searched\\n w = x_start_stop[1] - x_start_stop[0]\\n h = y_start_stop[1] - y_start_stop[0]\\n # Compute the number of pixels per step in x/y\\n pps_x = int((1.0 - xy_overlap[0]) * xy_window[0])\\n pps_y = int((1.0 - xy_overlap[1]) * xy_window[1])\\n # Compute the number of windows in x/y\\n n_x = int((w - xy_window[0])/pps_x + 1)\\n n_y = int((h - xy_window[1])/pps_y + 1)\\n # Initialize a list to append window positions to\\n window_list = []\\n for i in range(n_y):\\n y_pos = i * pps_y + y_start_stop[0]\\n for j in range(n_x):\\n x_pos = j * pps_x + x_start_stop[0]\\n bbox = ((x_pos,y_pos), (x_pos+xy_window[0],y_pos+xy_window[1]))\\n window_list.append(bbox)\\n \\n return window_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff9aa0b2717797aec7f90fbaa1bbb602\",\n \"score\": \"0.7582017\",\n \"text\": \"def get_image_patches_by_sliding_window(img, stepSize, window_size, overlapping):\\n # read the image and define the stepSize and window size\\n # (width,height)\\n if overlapping == 100:\\n return None\\n # generation step size for overlapping\\n overlapping = 100 - overlapping\\n stepSize = int(stepSize * (overlapping / 100))\\n\\n patches = []\\n image = img # your image path\\n tmp = image # for drawing a rectangle\\n (w_width, w_height) = (window_size, window_size) # window size\\n for x in range(0, image.shape[1] - w_width, stepSize):\\n for y in range(0, image.shape[0] - w_height, stepSize):\\n window = image[x:x + w_width, y:y + w_height, :]\\n # add the window into your patches array.\\n patches.append(window)\\n\\n return patches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae3e267148a2d61226c88ab9c172c864\",\n \"score\": \"0.74507797\",\n \"text\": \"def get_windows(img):\\n w_far = slide_window(img, x_start_stop=[0, 1280], y_start_stop=[350, 550],\\n xy_window=(96,64), xy_overlap=(0.5, 0.5))\\n w_medium_s = slide_window(img, x_start_stop=[0, 1280], y_start_stop=[400, 650],\\n xy_window=(128,96), xy_overlap=(0.7, 0.7))\\n w_medium_l = slide_window(img, x_start_stop=[0, 1280], y_start_stop=[400, 660],\\n xy_window=(128,128), xy_overlap=(0.5, 0.5))\\n far = slide_window(img, x_start_stop=[600, 1100], y_start_stop=[390, 530],\\n xy_window=(48,48), xy_overlap=(0.8, 0.5))\\n\\n windows = w_far + w_medium_s + w_medium_l + far\\n return windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"556424d8ac66b58260d76d199f42dc7d\",\n \"score\": \"0.71355724\",\n \"text\": \"def slidingWindow(self, img):\\n # 720 x 1280\\n # y --> 720 (0)\\n # x --> 1280 (1)\\n\\n sizeY, sizeX = img.shape\\n\\n outputImg = np.dstack((img, img, img)) * 255\\n\\n # Compute histogram for the bottom half of the image along the x-axis\\n hist = np.sum(img[sizeY//2:,:], axis=0)\\n\\n # Height of each window\\n window_height = np.int(sizeY // self.nwindows)\\n\\n # Check indexes != 0\\n nonzero = np.nonzero(img)\\n nonzeroInY = np.array(nonzero[0])\\n nonzeroInX = np.array(nonzero[1])\\n\\n # Split the image in two and set the centers\\n leftXCenter = np.argmax(hist[:sizeX // 2])\\n rightXCenter = np.argmax(hist[sizeX // 2:]) + sizeX // 2\\n\\n # Set the x-center of the boxes, which will be corrected over time\\n leftXCurrent = leftXCenter\\n rightXCurrent = rightXCenter\\n \\n # Lists to save indexes of pixel inside the rectangle\\n leftSidePixels = []\\n rightSidePixels = []\\n\\n for window in range(self.nwindows):\\n # Make the boxes\\n # Calculate the Y coords\\n yLow = sizeY - (1 + window) * window_height\\n yHigh = sizeY - window * window_height\\n \\n # Calculate the X coords for the left and right side\\n xLowLeft = leftXCurrent - self.margin\\n xHighLeft = leftXCurrent + self.margin\\n xLowRight = rightXCurrent - self.margin\\n xHighRight = rightXCurrent + self.margin\\n\\n # Draw rectangle for the left lane\\n cv2.rectangle(outputImg, (xLowLeft, yLow), (xHighLeft, yHigh), (0, 255, 0), 3)\\n \\n # Draw rectangle for the right lane\\n cv2.rectangle(outputImg, (xLowRight, yLow), (xHighRight, yHigh), (0, 255, 0), 3)\\n\\n # Check if pixels's values != 0 are inside the window (rectanle)\\n\\n # Check if the indexes are in the boxes and their values != 0\\n leftSidePixelsInsideBox = ((nonzeroInX >= xLowLeft) & (nonzeroInX <= xHighLeft) & (nonzeroInY >= yLow) & (nonzeroInY <= yHigh)).nonzero()[0]\\n rightSidePixelsInsideBox = ((nonzeroInX >= xLowRight) & (nonzeroInX <=xHighRight) & (nonzeroInY >= yLow) & (nonzeroInY <= yHigh)).nonzero()[0]\\n\\n leftSidePixels.append(leftSidePixelsInsideBox)\\n rightSidePixels.append(rightSidePixelsInsideBox)\\n\\n if len(leftSidePixelsInsideBox) > self.minpixels:\\n leftXCurrent = np.int(np.mean(nonzeroInX[leftSidePixelsInsideBox]))\\n\\n if len(rightSidePixelsInsideBox) > self.minpixels:\\n rightXCurrent = np.int(np.mean(nonzeroInX[rightSidePixelsInsideBox]))\\n\\n try:\\n leftSidePixels = np.concatenate(leftSidePixels)\\n rightSidePixels = np.concatenate(rightSidePixels)\\n except ValueError:\\n # Avoids an error if the above is not implemented fully\\n pass\\n\\n leftLaneY = nonzeroInY[leftSidePixels]\\n leftLaneX = nonzeroInX[leftSidePixels]\\n rightLaneY = nonzeroInY[rightSidePixels]\\n rightLaneX = nonzeroInX[rightSidePixels]\\n\\n # Get the coefficients (A, B, C)\\n leftFit = np.polyfit(leftLaneX, leftLaneY, 2)\\n rightFit = np.polyfit(rightLaneX, rightLaneY, 2)\\n \\n # Generate x values. These will be the y for plotting\\n ploty = np.linspace(0, outputImg.shape[0]-1, outputImg.shape[0])\\n \\n try:\\n leftFitX = ploty*leftFit[0]**2 + ploty*leftFit[1] + leftFit[2]\\n rightFitX = ploty*rightFit[0]**2 + ploty*rightFit[1] + leftFit[2]\\n \\n except TypeError:\\n # In case there is no C\\n leftFitX = ploty*leftFit[0]**2 + ploty*leftFit[1]\\n rightFitX = ploty*rightFit[0]**2 + ploty*rightFit[1]\\n\\n windowImg = np.zeros_like(outputImg)\\n\\n outputImg[leftLaneY, leftLaneX] = [255, 0, 0]\\n outputImg[rightLaneY, rightLaneX] = [0, 0, 255]\\n\\n leftLineWindow1 = np.array([np.transpose(np.vstack([leftFitX - self.margin, ploty]))])\\n leftLineWindow2 = np.array([np.flipud(np.transpose(np.vstack([leftFitX + self.margin, ploty])))])\\n leftLinePts = np.hstack((leftLineWindow1, leftLineWindow2))\\n \\n rightLineWindow1 = np.array([np.transpose(np.vstack([rightFitX - self.margin, ploty]))])\\n rightLineWindow2 = np.array([np.flipud(np.transpose(np.vstack([rightFitX + self.margin, ploty])))])\\n rightLinePts = np.hstack((rightLineWindow1, rightLineWindow2))\\n\\n cv2.fillPoly(windowImg, np.int_([leftLinePts]), (0, 255, 0))\\n cv2.fillPoly(windowImg, np.int_([rightLinePts]), (0, 255, 0))\\n result = cv2.addWeighted(outputImg, 1, windowImg, 0.3, 0)\\n\\n plt.plot(leftFitX, ploty, color = 'yellow')\\n plt.plot(rightFitX, ploty, color = 'yellow')\\n\\n # leftFitX -> Formula for the left lane\\n # rightFitX -> Formula for the right lane\\n # leftLaneX -> X - index inside the left window and their values != 0\\n # rightLaneX -> X - index inside the right window and their values != 0\\n return leftFitX, leftLaneX, rightFitX, rightLaneX, result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f67415d9def9f912cf12dbc529bec832\",\n \"score\": \"0.71134883\",\n \"text\": \"def get_multiscale_windows(img):\\n window_list = list()\\n method = \\\"right\\\"\\n if method == \\\"right\\\":\\n # for the included video this is fine to improve speed\\n # but for other videos, method == \\\"full\\\" should be used\\n window_list += slide_window(img,\\n xy_overlap = (0.75, 0.75),\\n x_start_stop = [620, 620 + 6*96],\\n y_start_stop = [385, 385 + 2*96],\\n xy_window = (96, 96))\\n window_list += slide_window(img,\\n xy_overlap = (0.75, 0.75),\\n x_start_stop = [620, None],\\n y_start_stop = [385, 385 + 2*128],\\n xy_window = (128, 128))\\n elif method == \\\"full\\\":\\n window_list += slide_window(img,\\n xy_overlap = (0.75, 0.75),\\n x_start_stop = [620 - 6*96, 620 + 6*96],\\n y_start_stop = [385, 385 + 2*96],\\n xy_window = (96, 96))\\n window_list += slide_window(img,\\n xy_overlap = (0.75, 0.75),\\n y_start_stop = [385, 385 + 2*128],\\n xy_window = (128, 128))\\n else:\\n raise ValueError(method)\\n return window_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06833c53e9f880942e6be8819145c199\",\n \"score\": \"0.6974579\",\n \"text\": \"def calculateWindow(image, coord, window_size):\\r\\n image_shape = np.shape(image)\\r\\n sz = (window_size-1)//2\\r\\n x_list = np.arange(coord[0]-sz, coord[0]+sz+1, 1)\\r\\n y_list = np.arange(coord[1]-sz, coord[1]+sz+1, 1)\\r\\n xs = [x for x in x_list if (x >= 0) and (x < image_shape[0])]\\r\\n ys = [y for y in y_list if (y >= 0) and (y < image_shape[1])]\\r\\n x1 = np.min(xs)\\r\\n x2 = np.max(xs)\\r\\n y1 = np.min(ys)\\r\\n y2 = np.max(ys)\\r\\n return image[x1:x2+1, y1:y2+1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b06a5928375e74bb02f7e78a8952ece8\",\n \"score\": \"0.6835137\",\n \"text\": \"def sliding_hog_windows(self, image):\\n # initialization\\n image_height, image_width = 48, 48\\n window_size = 24\\n window_step = 6\\n hog_windows = []\\n for y in range(0, image_height, window_step):\\n for x in range(0, image_width, window_step):\\n window = image[y:y+window_size, x:x+window_size]\\n hog_windows.extend(hog(window, orientations=8, pixels_per_cell=(8, 8),\\n cells_per_block=(1, 1)))\\n return hog_windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2f567ed90f43b37d511ae194327f64a\",\n \"score\": \"0.6657261\",\n \"text\": \"def find_lane_sliding_window(binary_warped):\\n # Take a histogram of the bottom half of the image\\n histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)\\n # Create an output image to draw on and visualize the result\\n out_img = np.dstack((binary_warped, binary_warped, binary_warped))\\n # Find the peak of the left and right halves of the histogram\\n # These will be the starting point for the left and right lines\\n midpoint = np.int(histogram.shape[0]//2)\\n leftx_base = np.argmax(histogram[:midpoint])\\n rightx_base = np.argmax(histogram[midpoint:]) + midpoint\\n\\n # HYPERPARAMETERS\\n # Choose the number of sliding windows\\n nwindows = 10\\n # Set the width of the windows +/- margin\\n margin = 50\\n # Set minimum number of pixels found to recenter window\\n minpix = 100\\n\\n # Set height of windows - based on nwindows above and image shape\\n window_height = np.int(binary_warped.shape[0]//nwindows)\\n # Identify the x and y positions of all nonzero pixels in the image\\n nonzero = binary_warped.nonzero()\\n nonzeroy = np.array(nonzero[0])\\n nonzerox = np.array(nonzero[1])\\n # Current positions to be updated later for each window in nwindows\\n leftx_current = leftx_base\\n rightx_current = rightx_base\\n\\n # Create empty lists to receive left and right lane pixel indices\\n left_lane_inds = []\\n right_lane_inds = []\\n\\n # Step through the windows one by one\\n for window in range(nwindows):\\n # Identify window boundaries in x and y (and right and left)\\n win_y_low = binary_warped.shape[0] - (window+1)*window_height\\n win_y_high = binary_warped.shape[0] - window*window_height\\n win_xleft_low = leftx_current - margin\\n win_xleft_high = leftx_current + margin\\n win_xright_low = rightx_current - margin\\n win_xright_high = rightx_current + margin\\n \\n # Draw the windows on the visualization image\\n cv2.rectangle(out_img,(win_xleft_low,win_y_low),\\n (win_xleft_high,win_y_high),(0,255,0), 2) \\n cv2.rectangle(out_img,(win_xright_low,win_y_low),\\n (win_xright_high,win_y_high),(0,255,0), 2) \\n \\n # Identify the nonzero pixels in x and y within the window #\\n good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & \\n (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]\\n good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & \\n (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]\\n \\n # Append these indices to the lists\\n left_lane_inds.append(good_left_inds)\\n right_lane_inds.append(good_right_inds)\\n \\n # If you found > minpix pixels, recenter next window on their mean position\\n if len(good_left_inds) > minpix:\\n leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\\n if len(good_right_inds) > minpix: \\n rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\\n\\n # Concatenate the arrays of indices (previously was a list of lists of pixels)\\n try:\\n left_lane_inds = np.concatenate(left_lane_inds)\\n right_lane_inds = np.concatenate(right_lane_inds)\\n except ValueError:\\n # Avoids an error if the above is not implemented fully\\n pass\\n\\n # Extract left and right line pixel positions\\n leftx = nonzerox[left_lane_inds]\\n lefty = nonzeroy[left_lane_inds] \\n rightx = nonzerox[right_lane_inds]\\n righty = nonzeroy[right_lane_inds]\\n\\n return leftx, lefty, rightx, righty, out_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f35a6e4f769bade7f932388ca82aa3e\",\n \"score\": \"0.64788586\",\n \"text\": \"def window_bounds(i, n, wn):\\n\\n at_end = False\\n hw = wn // 2\\n\\n start = i - hw\\n stop = i + hw + 1\\n\\n if start < 0:\\n at_end = True\\n start = 0\\n elif stop > n:\\n at_end = True\\n stop = n\\n\\n return start, stop, at_end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53cefcce900c926c25c7f6ffbd5e1445\",\n \"score\": \"0.64113986\",\n \"text\": \"def search_windows(img,\\n windows_list,\\n clf,\\n scaler,\\n color_space='RGB',\\n spatial_size=(32, 32),\\n hist_bins=32,\\n orient=9,\\n pix_per_cell=8,\\n cell_per_block=2,\\n hog_channel=0,\\n use_spatial_feat=True,\\n use_hist_feat=True,\\n use_hog_feat=True):\\n windows_positive = []\\n\\n for window in windows_list:\\n\\n test_img_part = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))\\n\\n features = extract_features(test_img_part,\\n color_space=color_space,\\n spatial_size=spatial_size,\\n hist_bins=hist_bins,\\n orient=orient,\\n pix_per_cell=pix_per_cell,\\n cell_per_block=cell_per_block,\\n hog_channel=hog_channel,\\n use_spatial_feat=use_spatial_feat,\\n use_hist_feat=use_hist_feat,\\n use_hog_feat=use_hog_feat)\\n\\n test_features = scaler.transform(np.array(features).reshape(1, -1))\\n\\n prediction = clf.predict(test_features)\\n\\n if prediction == 1:\\n windows_positive.append(window)\\n\\n return windows_positive\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d95a678c71c7a8ca08fc750df42c9407\",\n \"score\": \"0.6403148\",\n \"text\": \"def sliding_window(image, patch_size: tuple, step: int, show_debug: bool = False) -> list:\\n if isinstance(image, Image.Image):\\n image = np.array(image)\\n\\n if step == 0:\\n h, w = image.shape[0], image.shape[1] # 720, 1280\\n w_iter, h_iter = w // patch_size[0], h // patch_size[1]\\n crop_image_list = []\\n for i in range(h_iter):\\n for j in range(w_iter):\\n bbox = (i*patch_size[0], j*patch_size[0],\\n (i+1)*patch_size[0], (j+1)*patch_size[0])\\n crop_image = image[bbox[0]:bbox[2], bbox[1]: bbox[3]]\\n if show_debug:\\n crop_image = Image.fromarray(crop_image)\\n crop_image.save(f\\\"/data/jiangmingchao/patches/{i}.png\\\")\\n cv2.rectangle(image,\\n (i*patch_size[0], j*patch_size[0]),\\n ((i+1)*patch_size[0], (j+1)*patch_size[0]),\\n (255, 255, 0),\\n 2,\\n )\\n\\n crop_image_list.append(Image.fromarray(crop_image))\\n\\n if show_debug:\\n cv2.imwrite(\\\"1.jpg\\\", image)\\n\\n else:\\n h, w = image.shape[0], image.shape[1]\\n step_w_iter, step_h_iter = (w - patch_size[0]) // step, (h - patch_size[0]) // step\\n crop_image_list = []\\n for i in range(step_h_iter):\\n for j in range(step_w_iter):\\n bbox = (i * step, j * step, patch_size[0] + i * step, patch_size[1] + j * step)\\n crop_image = image[bbox[0]: bbox[2], bbox[1]: bbox[3]]\\n print(crop_image.shape)\\n crop_image_list.append(Image.fromarray(crop_image))\\n\\n return crop_image_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48020790766bfd0bf962258e28d37b58\",\n \"score\": \"0.6313725\",\n \"text\": \"def windows(self, size, overlap=0, rm_offset=False):\\r\\n rows = self.data.shape[0]\\r\\n if (0 < size <= rows) and (0 <= overlap < size):\\r\\n n = (rows - size) // (size - overlap) + 1\\r\\n\\r\\n for i in range(n):\\r\\n start = (size - overlap) * i\\r\\n end = start + size\\r\\n win = self.data.iloc[start:end, :]\\r\\n if rm_offset:\\r\\n win_offset = win - win.mean()\\r\\n win_offset['t'] = win['t']\\r\\n yield win_offset\\r\\n\\r\\n yield win\\r\\n\\r\\n else:\\r\\n raise IndexError(f\\\"{self} no possible window of size '{size}'.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f747666284cacf54c84d4fc0ecaeb17\",\n \"score\": \"0.6293102\",\n \"text\": \"def sliding_window_init(lane, binary_warped, draw=False):\\n\\n # Assuming you have created a warped binary image called \\\"binary_warped\\\"\\n # Take a histogram of the bottom half of the image\\n histogram1 = np.sum(binary_warped[binary_warped.shape[0] / 2:, :], axis=0)\\n # Create an output image to draw on and visualize the result\\n out_img = np.dstack((binary_warped, binary_warped, binary_warped)) * 255\\n\\n # Find the peak of the left and right halves of the histogram\\n # These will be the starting point for the left and right lines\\n midpoint = np.int(histogram1.shape[0] / 2)\\n leftx_base = np.argmax(histogram1[:midpoint])\\n rightx_base = np.argmax(histogram1[midpoint:]) + midpoint\\n\\n # Choose the number of sliding windows\\n nwindows = 9\\n # Set height of windows\\n window_height = np.int(binary_warped.shape[0] / nwindows)\\n # Identify the x and y positions of all nonzero pixels in the image\\n nonzero = binary_warped.nonzero()\\n nonzeroy = np.array(nonzero[0])\\n nonzerox = np.array(nonzero[1])\\n # Current positions to be updated for each window\\n leftx_current = leftx_base\\n rightx_current = rightx_base\\n # Set the width of the windows +/- margin\\n margin = 100\\n # Set minimum number of pixels found to recenter window\\n minpix = 50\\n # Create empty lists to receive left and right lane pixel indices\\n left_lane_inds = []\\n right_lane_inds = []\\n\\n # Step through the windows one by one\\n for window in range(nwindows):\\n # Identify window boundaries in x and y (and right and left)\\n win_y_low = binary_warped.shape[0] - (window + 1) * window_height\\n win_y_high = binary_warped.shape[0] - window * window_height\\n win_xleft_low = leftx_current - margin\\n win_xleft_high = leftx_current + margin\\n win_xright_low = rightx_current - margin\\n win_xright_high = rightx_current + margin\\n # Draw the windows on the visualization image\\n cv2.rectangle(out_img, (win_xleft_low, win_y_low), (win_xleft_high, win_y_high), (0, 255, 0), 2)\\n cv2.rectangle(out_img, (win_xright_low, win_y_low), (win_xright_high, win_y_high), (0, 255, 0), 2)\\n # Identify the nonzero pixels in x and y within the window\\n good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (\\n nonzerox < win_xleft_high)).nonzero()[0]\\n good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (\\n nonzerox < win_xright_high)).nonzero()[0]\\n # Append these indices to the lists\\n left_lane_inds.append(good_left_inds)\\n right_lane_inds.append(good_right_inds)\\n # If you found > minpix pixels, recenter next window on their mean position\\n if len(good_left_inds) > minpix:\\n leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\\n if len(good_right_inds) > minpix:\\n rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\\n\\n # Concatenate the arrays of indices\\n left_lane_inds = np.concatenate(left_lane_inds)\\n right_lane_inds = np.concatenate(right_lane_inds)\\n\\n # Extract left and right line pixel positions\\n leftx = nonzerox[left_lane_inds]\\n lefty = nonzeroy[left_lane_inds]\\n rightx = nonzerox[right_lane_inds]\\n righty = nonzeroy[right_lane_inds]\\n\\n # Fit a second order polynomial to each\\n if len(leftx) == 0:\\n left_fit = lane.recent_left_fit\\n else:\\n left_fit = np.polyfit(lefty, leftx, 2)\\n\\n if len(rightx) == 0:\\n right_fit = lane.recent_right_fit\\n else:\\n right_fit = np.polyfit(righty, rightx, 2)\\n\\n if draw:\\n # Generate x and y values for plotting\\n ploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])\\n left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2]\\n right_fitx = right_fit[0] * ploty ** 2 + right_fit[1] * ploty + right_fit[2]\\n\\n out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]\\n out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]\\n\\n plt.imshow(out_img)\\n plt.plot(left_fitx, ploty, color='yellow')\\n plt.plot(right_fitx, ploty, color='yellow')\\n plt.xlim(0, 1280)\\n plt.ylim(720, 0)\\n plt.show()\\n plt.close()\\n\\n lane.recent_left_fit = left_fit\\n lane.recent_right_fit = right_fit\\n lane.detected = True\\n\\n return left_fit, right_fit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9cb7b5076abc527ff630702b52557bd\",\n \"score\": \"0.6273185\",\n \"text\": \"def search_windows(img, windows, clf, scaler, color_space='RGB',\\n spatial_size=(32, 32), hist_bins=32,\\n orient=9,\\n pix_per_cell=8, cell_per_block=2,\\n hog_channel=0, spatial_feat=True,\\n hist_feat=True, hog_feat=True):\\n # 1) Create an empty list to receive positive detection windows\\n on_windows = []\\n # 2) Iterate over all windows in the list\\n for window in windows:\\n # 3) Extract the test window from original image and resize to the same size as used in Training cutouts\\n test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))\\n # 4) Extract features for that window using single_img_features()\\n features = single_img_features(test_img, color_space=color_space,\\n spatial_size=spatial_size, hist_bins=hist_bins,\\n orient=orient, pix_per_cell=pix_per_cell,\\n cell_per_block=cell_per_block,\\n hog_channel=hog_channel, spatial_feat=spatial_feat,\\n hist_feat=hist_feat, hog_feat=hog_feat)\\n # 5) Scale extracted features to be fed to classifier using scaler that we fit using the Training data\\n test_features = scaler.transform(np.array(features).reshape(1, -1))\\n # 6) Predict using your classifier\\n prediction = clf.predict(test_features)\\n # 7) If positive (prediction == 1) then save the window\\n if prediction == 1:\\n on_windows.append(window)\\n # 8) Return windows for positive detections\\n return on_windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd25f3f0f4dc8515d93a7b5d4a069460\",\n \"score\": \"0.6266863\",\n \"text\": \"def search_windows(image, windows, clf, hyperparams):\\n # Create an empty list to receive positive detection windows\\n on_windows = []\\n confidences = []\\n # Iterate over all windows in the list\\n for window in windows:\\n # Extract the test window from original image\\n test_img = cv2.resize(image[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))\\n # Extract features for that window using single_img_features()\\n features = get_features_image(test_img, hyperparams)\\n # Scale extracted features to be fed to classifier\\n test_features = np.array(features).reshape(1, -1)\\n # Predict using your classifier\\n prediction = clf.predict(test_features)\\n # If positive (prediction == 1) then save the window\\n if prediction == 1:\\n on_windows.append(window)\\n confidences.append(clf.decision_function(test_features))\\n # Return windows for positive detections\\n return on_windows, confidences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb80bcf8ae7d19b7272679809335eec1\",\n \"score\": \"0.62667805\",\n \"text\": \"def apply_sliding_windows(self, binary_warped, leftx_base, rightx_base):\\r\\n # Choose the number of sliding windows\\r\\n nwindows = 9\\r\\n # Set height of windows\\r\\n window_height = np.int(binary_warped.shape[0] / nwindows)\\r\\n # Identify the x and y positions of all nonzero pixels in the image\\r\\n nonzero = binary_warped.nonzero()\\r\\n nonzeroy, nonzerox = np.array(nonzero[0]), np.array(nonzero[1])\\r\\n # Current positions to be updated for each window\\r\\n leftx_current, rightx_current = leftx_base, rightx_base\\r\\n # Set the width of the windows +/- margin\\r\\n margin = 100\\r\\n # Set minimum number of pixels found to recenter window\\r\\n minpix = 50\\r\\n # Create empty lists to receive left and right lane pixel indices\\r\\n left_lane_inds, right_lane_inds = [], []\\r\\n\\r\\n # Step through the windows one by one\\r\\n # Create an output image to draw on and visualize the result\\r\\n out_img = np.dstack((binary_warped, binary_warped, binary_warped)) * 255\\r\\n for window in range(nwindows):\\r\\n # Identify window boundaries in x and y (and right and left)\\r\\n win_y_low = binary_warped.shape[0] - (window + 1) * window_height\\r\\n win_y_high = binary_warped.shape[0] - window * window_height\\r\\n win_xleft_low, win_xleft_high = leftx_current - margin, leftx_current + margin\\r\\n win_xright_low, win_xright_high = rightx_current - margin, rightx_current + margin\\r\\n # Draw the windows on the visualization image\\r\\n if self.debug:\\r\\n cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)\\r\\n cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)\\r\\n # Identify the nonzero pixels in x and y within the window\\r\\n good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (\\r\\n nonzerox < win_xleft_high)).nonzero()[0]\\r\\n good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (\\r\\n nonzerox < win_xright_high)).nonzero()[0]\\r\\n # Append these indices to the lists\\r\\n left_lane_inds.append(good_left_inds)\\r\\n right_lane_inds.append(good_right_inds)\\r\\n # If you found > minpix pixels, recenter next window on their mean position\\r\\n if len(good_left_inds) > minpix:\\r\\n leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\\r\\n if len(good_right_inds) > minpix:\\r\\n rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\\r\\n if self.debug:\\r\\n cv_rgb = cv2.cvtColor(out_img.astype(np.uint8), cv2.COLOR_BGR2RGB)\\r\\n plt.imshow(cv_rgb)\\r\\n #cv2.imshow('Sliding window computation',out_img)\\r\\n # Concatenate the arrays of indices\\r\\n left_lane_inds = np.concatenate(left_lane_inds)\\r\\n right_lane_inds = np.concatenate(right_lane_inds)\\r\\n if self.debug:\\r\\n self.fit_dict['left_lane_inds'] = left_lane_inds\\r\\n self.fit_dict['right_lane_inds'] = right_lane_inds\\r\\n\\r\\n # Extract left and right line pixel positions\\r\\n leftx, lefty = nonzerox[left_lane_inds], nonzeroy[left_lane_inds]\\r\\n rightx, righty = nonzerox[right_lane_inds], nonzeroy[right_lane_inds]\\r\\n return leftx, lefty, rightx, righty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46539bb4a828191168316b97260a908c\",\n \"score\": \"0.62284154\",\n \"text\": \"def sliding_window(image, stepSize, windowSize):\\n # slide a window across the image\\n for y in xrange(0, image.shape[0], stepSize):\\n for x in xrange(0, image.shape[1], stepSize):\\n # yield the current window\\n yield (x, y, image[y:y + windowSize[1], x:x + windowSize[0]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52badf4d6015aeb8d703218b8d271b4b\",\n \"score\": \"0.61782295\",\n \"text\": \"def sliding_window(top, step=10, window_size=(20,20)):\\n\\tfor x in range(0, top.shape[0], step):\\n\\t\\tif x + window_size[0] > top.shape[0]:\\n\\t\\t\\tx = top.shape[0] - window_size[0]\\n\\t\\tfor y in range(0, top.shape[1], step):\\n\\t\\t\\tif y + window_size[1] > top.shape[1]:\\n\\t\\t\\t\\ty = top.shape[1] - window_size[1]\\n\\t\\t\\tyield x, y, window_size[0], window_size[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5d3103bb90ca4c71c2f85523fd2b4ad\",\n \"score\": \"0.6137019\",\n \"text\": \"def find_lanes_from_scratch(binary_warped, nwindows=9, margin=100, minpix=50, draw_lanes=True):\\n # Find histogram peaks\\n histogram = hist(binary_warped)\\n out_img = np.dstack((binary_warped, binary_warped, binary_warped)) # create output image to draw on (not necessary)\\n midpoint = np.int(histogram.shape[0]//2) # 640\\n leftx_base = np.argmax(histogram[:midpoint]) # find index of left peak (indicates ll)\\n rightx_base = np.argmax(histogram[midpoint:]) + midpoint # find index of right peak (indicates rl)\\n \\n # Sliding windows\\n window_height = np.int(binary_warped.shape[0]//nwindows) # 80\\n nonzero = binary_warped.nonzero() # a tuple for x and y\\n nonzeroy = np.array(nonzero[0])\\n nonzerox = np.array(nonzero[1])\\n leftx_current = leftx_base\\n rightx_current = rightx_base\\n left_lane_inds = []\\n right_lane_inds = []\\n for window in range(nwindows): # index 0 to 8\\n win_y_low = binary_warped.shape[0] - (window+1)*window_height \\n win_y_high = binary_warped.shape[0] - window*window_height\\n win_xleft_low = leftx_current - margin\\n win_xleft_high = leftx_current + margin\\n win_xright_low = rightx_current - margin\\n win_xright_high = rightx_current + margin\\n if draw_lanes:\\n cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2) # bottom left to top right, in green, with thickness 2\\n cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)\\n good_left_inds = ((nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high) & \\n (nonzeroy >= win_y_low ) & (nonzeroy < win_y_high)).nonzero()[0]\\n good_right_inds = ((nonzerox >= win_xright_low) & (nonzerox < win_xright_high) & \\n (nonzeroy >= win_y_low ) & (nonzeroy < win_y_high)).nonzero()[0]\\n left_lane_inds.append(good_left_inds) # indices\\n right_lane_inds.append(good_right_inds)\\n if len(good_left_inds) > minpix:\\n leftx_current = int(np.mean(nonzerox[good_left_inds]))\\n # print(leftx_current)\\n if len(good_right_inds) > minpix:\\n rightx_current = int(np.mean(nonzerox[good_right_inds]))\\n \\n # Find indices of left and right lane lines\\n left_lane_inds = np.concatenate(left_lane_inds)\\n right_lane_inds = np.concatenate(right_lane_inds)\\n leftx = nonzerox[left_lane_inds]\\n lefty = nonzeroy[left_lane_inds] \\n rightx = nonzerox[right_lane_inds]\\n righty = nonzeroy[right_lane_inds]\\n\\n # Fit polynomial of second degree\\n left_fit = np.polyfit(lefty, leftx, deg=2)\\n right_fit = np.polyfit(righty, rightx, deg=2)\\n ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0])\\n\\n left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]\\n right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]\\n \\n detected_flag = True\\n \\n # Visualize\\n if draw_lanes:\\n out_img[lefty, leftx] = [255, 0, 0]\\n out_img[righty, rightx] = [0, 0, 255]\\n for index in range(out_img.shape[0]-1):\\n cv2.line(out_img, (int(left_fitx[index]), int(ploty[index])), (int(left_fitx[index+1]), int(ploty[index+1])), (255,255,0), 3)\\n cv2.line(out_img, (int(right_fitx[index]), int(ploty[index])), (int(right_fitx[index+1]), int(ploty[index+1])), (255,255,0), 3)\\n return left_fitx, right_fitx, left_fit, right_fit, ploty, out_img, detected_flag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"243a6aa28eae496654e33dc6c81479b9\",\n \"score\": \"0.61151534\",\n \"text\": \"def win_slide(stream, start_time, win_size, step_size, max_windows):\\n stream_list=[]\\n for i in range(max_windows):\\n ts = start_time + (i*step_size)\\n st = stream.slice(ts, ts+win_size)\\n # skip missing data\\n if len(st)!=3: continue\\n if not st[0].stats.starttime == st[1].stats.starttime == st[2].stats.starttime: continue\\n if not st[0].stats.endtime == st[1].stats.endtime == st[2].stats.endtime: continue\\n if len(st[0])!=int(win_size*100+1): continue\\n if st.max()[0]==0.0 or st.max()[1]==0.0 or st.max()[2]==0.0: continue\\n # add preprocessed time window\\n st = preprocess_stream(st)\\n stream_list.append(st)\\n return stream_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aed16aa7d01e1a71efd38f77e8ca4511\",\n \"score\": \"0.61132944\",\n \"text\": \"def window_index(serieslength,windowsize,overlap):\\r\\n\\r\\n p1=0\\r\\n p2=p1 + windowsize\\r\\n pt1=[p1]\\r\\n pt2=[p2]\\r\\n while p2 < serieslength:\\r\\n p1 = p2 - overlap\\r\\n p2 = min((p1 + windowsize, serieslength))\\r\\n pt1.append(p1)\\r\\n pt2.append(p2)\\r\\n \\r\\n return pt1, pt2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1085eef77fcbf55851a0cccab327d48a\",\n \"score\": \"0.60835075\",\n \"text\": \"def window_inds(dataset, window_sz, overlap):\\r\\n\\tdata_len = len(dataset[0])\\r\\n\\tassert window_sz < data_len\\r\\n\\tind1 = 0\\r\\n\\tind2 = window_sz-1\\r\\n\\tind_list = []\\r\\n\\tov_ind_diff = int(np.ceil(np.abs(overlap*window_sz)))\\r\\n\\tif ov_ind_diff == window_sz:\\r\\n\\t\\tov_ind_diff += -1\\r\\n\\twhile ind2 < data_len:\\r\\n\\t\\tind_list.append((ind1,ind2))\\r\\n\\t\\tind1 += window_sz-ov_ind_diff\\r\\n\\t\\tind2 += window_sz-ov_ind_diff\\r\\n\\treturn ind_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"436e972e7f70b57189edfcfa7b159bec\",\n \"score\": \"0.60333884\",\n \"text\": \"def find_lane_slow(img):\\n histogram = np.sum(img[img.shape[0] // 2:, :], axis=0)\\n\\n # Find the peak of the left and right halves of the histogram\\n # These will be the starting point for the left and right lines\\n midpoint = np.int(histogram.shape[0] / 2)\\n leftx_base = np.argmax(histogram[:midpoint])\\n rightx_base = np.argmax(histogram[midpoint:]) + midpoint\\n\\n # Choose the number of sliding windows\\n nwindows = 9\\n # Set height of windows\\n window_height = np.int(img.shape[0] / nwindows)\\n # Identify the x and y positions of all nonzero pixels in the image\\n nonzero = img.nonzero()\\n nonzeroy = np.array(nonzero[0])\\n nonzerox = np.array(nonzero[1])\\n # Current positions to be updated for each window\\n leftx_current = leftx_base\\n rightx_current = rightx_base\\n # Set the width of the windows +/- margin\\n margin = 150\\n # Set minimum number of pixels found to recenter window\\n minpix = 50\\n # Create empty lists to receive left and right lane pixel indices\\n left_lane_inds = []\\n right_lane_inds = []\\n\\n # Step through the windows one by one\\n for window in range(nwindows):\\n # Identify window boundaries in x and y (and right and left)\\n win_y_low = img.shape[0] - (window + 1) * window_height\\n win_y_high = img.shape[0] - window * window_height\\n win_xleft_low = leftx_current - margin\\n win_xleft_high = leftx_current + margin\\n win_xright_low = rightx_current - margin\\n win_xright_high = rightx_current + margin\\n\\n # Identify the nonzero pixels in x and y within the window\\n good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (\\n nonzerox < win_xleft_high)).nonzero()[0]\\n good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (\\n nonzerox < win_xright_high)).nonzero()[0]\\n # Append these indices to the lists\\n left_lane_inds.append(good_left_inds)\\n right_lane_inds.append(good_right_inds)\\n # If you found > minpix pixels, recenter next window on their mean position\\n if len(good_left_inds) > minpix:\\n leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\\n if len(good_right_inds) > minpix:\\n rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\\n\\n # Concatenate the arrays of indices\\n left_lane_inds = np.concatenate(left_lane_inds)\\n right_lane_inds = np.concatenate(right_lane_inds)\\n\\n # Extract left and right line pixel positions\\n leftx = nonzerox[left_lane_inds]\\n lefty = nonzeroy[left_lane_inds]\\n rightx = nonzerox[right_lane_inds]\\n righty = nonzeroy[right_lane_inds]\\n\\n # Fit a second order polynomial to each\\n left_fit = np.polyfit(lefty, leftx, 2)\\n right_fit = np.polyfit(righty, rightx, 2)\\n\\n return left_fit, right_fit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"797ff69edbe37accf9d29b0fbd2a611f\",\n \"score\": \"0.5950051\",\n \"text\": \"def select_windows(start, stop, num_windows,\\n window_width=1, window_units=\\\"D\\\",\\n sampling=1, sampling_units=\\\"T\\\",\\n no_overlaps=True, verbose=True):\\n\\n # Create all sample candidates\\n dt_range = pd.date_range(start, stop-pd.Timedelta(window_width),\\n freq=\\\"%i%s\\\" % (sampling, sampling_units))\\n\\n # Sample candidate windows\\n selected_windows = np.random.choice(dt_range, num_windows, replace=False)\\n selected_windows = pd.DataFrame(selected_windows, columns=[\\\"start\\\"])\\n\\n # Calculate window end\\n end_delta = (pd.Timedelta(window_width, unit=window_units)\\n - pd.Timedelta(sampling,\\n unit=\\\"m\\\" if sampling_units==\\\"T\\\" else sampling_units))\\n selected_windows[\\\"end\\\"] = (selected_windows[\\\"start\\\"] + end_delta)\\n\\n # Filter overlaps\\n if not no_overlaps:\\n return selected_windows\\n else:\\n selected_windows = filter_overlaps(selected_windows,\\n pd.Timedelta(window_width,\\n unit=window_units))\\n\\n while selected_windows.shape[0]<num_windows:\\n if verbose:\\n print(\\\"Got %i windows...\\\" % selected_windows.shape[0])\\n\\n selected_windows = pd.concat([selected_windows,\\n select_windows(start, stop, num_windows,\\n window_width, window_units,\\n sampling, sampling_units,\\n no_overlaps=False)],\\n ignore_index=True)\\n selected_windows = filter_overlaps(selected_windows,\\n pd.Timedelta(window_width,\\n unit=window_units))\\n return selected_windows.iloc[:num_windows]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde3c286ec275538f4db047b0c076868\",\n \"score\": \"0.5935757\",\n \"text\": \"def _sliding_window(self, image, mask, window_radius=3):\\n height, width = image.shape[:2]\\n features = []\\n for yy in range(window_radius, height - window_radius):\\n for xx in range(window_radius, width - window_radius):\\n features.append(image[yy - window_radius: yy + window_radius + 1, xx - window_radius: xx + window_radius + 1].ravel())\\n labels = mask[window_radius: -1 * window_radius, window_radius: -1 * window_radius].ravel()\\n return np.array(features), labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b3945d5398e886d2b03a03c91a48dc\",\n \"score\": \"0.5928829\",\n \"text\": \"def sliding_windows(size, whole=False, step_size=None, *, width, height):\\n if not step_size:\\n step_size = size\\n w, h = (size, size)\\n sw, sh = (step_size, step_size)\\n end_i = height - h if whole else height\\n end_j = width - w if whole else width\\n for pos_i, i in enumerate(range(0, end_i, sh)):\\n for pos_j, j in enumerate(range(0, end_j, sw)):\\n real_w = w if whole else min(w, abs(width - j))\\n real_h = h if whole else min(h, abs(height - i))\\n yield rasterio.windows.Window(j, i, real_w, real_h), (pos_i, pos_j)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03ce5ccce5505f88a44801d3d4c7e060\",\n \"score\": \"0.5920694\",\n \"text\": \"def find_instances(self, image, region, overlap):\\n self.image = np.copy(image)\\n\\n self.eff_step_size = int((1.0-overlap)*self.eff_box_size)\\n\\n y_steps = (region[3]-region[1])//self.eff_step_size\\n x_steps = (region[2]-region[0])//self.eff_step_size\\n\\n if region[0]+(x_steps-1)*self.eff_step_size+self.eff_box_size>region[2]:\\n x_steps -= 1\\n if region[1]+(y_steps-1)*self.eff_step_size+self.eff_box_size>region[3]:\\n y_steps -= 1\\n\\n if self.single_hog:\\n self.resized_image = image[region[1]:region[3],region[0]:region[2],:]\\n self.resized_image = cv2.resize(self.resized_image, (int(self.resized_image.shape[1]/self.scaling), int(self.resized_image.shape[0]/self.scaling)))\\n features, img = self.hogger.hog_image(self.resized_image, visualize=False, feature_vector=False)\\n features = np.array(features)\\n self.find_instances_in_features(features, region)\\n return self.image, self.resized_image\\n else:\\n for row in range(y_steps):\\n off_y = region[1] + row * self.eff_step_size\\n for col in range(x_steps):\\n off_x = region[0]+col * self.eff_step_size\\n sub_sample = self.get_resized_sub_sample(off_x, off_y)\\n pred = self.classifier.classify(sub_sample)\\n if(pred==1.0):\\n cv2.rectangle(self.image, (off_x, off_y), (off_x+self.eff_box_size, off_y+self.eff_box_size), color=(255,255,255), thickness=2)\\n self.boundings.append(((off_x, off_y), (off_x+self.eff_box_size, off_y+self.eff_box_size)))\\n\\n return self.image, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03ab53484ed7467ff634626fc74f1c01\",\n \"score\": \"0.5849172\",\n \"text\": \"def extract_window(img, bbox):\\n row_begin = bbox[0][1]\\n row_end = bbox[1][1]\\n col_begin = bbox[0][0]\\n col_end = bbox[1][0]\\n patch = img[row_begin:row_end, col_begin:col_end, :]\\n window = cv2.resize(patch, (64,64))\\n return window\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca0627a8f5a0f7005ebbebffea23f7f1\",\n \"score\": \"0.5833552\",\n \"text\": \"def build_bounds_index(image_files):\\n idx = index.Index()\\n xs = []\\n ys = []\\n for i, img_path in tqdm(list(enumerate(image_files))):\\n with rasterio.open(img_path) as src:\\n left, bottom, right, top = src.bounds\\n xs.extend([left, right])\\n ys.extend([bottom, top])\\n idx.insert(i, (left, bottom, right, top))\\n dst_w, dst_s, dst_e, dst_n = min(xs), min(ys), max(xs), max(ys)\\n return idx, (dst_w, dst_s, dst_e, dst_n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f547ca4bc04cc7a0ad3892082e6df9\",\n \"score\": \"0.58190084\",\n \"text\": \"def windows(self,windowSize):\\n for i in range(0,len(self)-windowSize):\\n yield (i,i+windowSize)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac5b37ccad61308c2a58ff9869f781d\",\n \"score\": \"0.581421\",\n \"text\": \"def search_windows(img, windows, clf, scaler, color_space='RGB',\\n spatial_size=(32, 32), hist_bins=32,\\n hist_range=(0, 256), orient=9,\\n pix_per_cell=8, cell_per_block=2,\\n hog_channel=0, hist_feat=True):\\n # use caching for the hog features to speed things up\\n h = hashlib.sha1(img).hexdigest()\\n hn = os.path.join(cache_dir, h)\\n if os.path.exists(hn):\\n with open(hn, 'rb') as f:\\n cached = pickle.load(f)\\n return cached\\n\\n # if there is no cached result, compute it...\\n\\n #1) Create an empty list to receive positive detection windows\\n on_windows = []\\n #2) Iterate over all windows in the list\\n for window in windows:\\n #3) Extract the test window from original image\\n test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))\\n\\n #4) Extract features for that window using single_img_features()\\n features = single_img_features(test_img, color_space=color_space,\\n spatial_size=spatial_size, hist_bins=hist_bins,\\n orient=orient, pix_per_cell=pix_per_cell,\\n cell_per_block=cell_per_block,\\n hog_channel=hog_channel, hist_feat=hist_feat)\\n #5) Scale extracted features to be fed to classifier\\n test_features = scaler.transform(np.array(features).reshape(1, -1))\\n #6) Predict using your classifier\\n prediction = clf.predict(test_features)\\n #7) If positive (prediction == 1) then save the window\\n if prediction == 1:\\n on_windows.append(window)\\n #8) Return windows for positive detections\\n\\n # but save the result for next time\\n with open(hn, 'wb') as f:\\n pickle.dump(on_windows, f)\\n print(\\\"wrote to cache\\\", hn)\\n\\n return on_windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"151cf6cce39a02a51ebad6c76f8911c3\",\n \"score\": \"0.58108246\",\n \"text\": \"def get_windows(self, x_train, y_train):\\n\\n def roundMultiple(x, base=4):\\n \\\"\\\"\\\"Round n up to nearest multiple of base.\\\"\\\"\\\"\\n return int(base * round(float(x)/base))\\n\\n def auto_set_stride():\\n self.stride = roundMultiple(\\n int(self.window_size / 10), base=2)\\n debug(\\\"Stride auto set to \\\", self.stride)\\n\\n def auto_set_window_size(sequence):\\n threshold = (self.left_epsilon + self.right_epsilon) * 2\\n time_arr = sequence[:, self.X_TIME_COLUMN]\\n self.window_size = roundMultiple(\\n np.argmax(time_arr > threshold), base=4)\\n debug(\\\"Window size auto set to \\\", self.window_size)\\n\\n windows_x = []\\n windows_y = []\\n debug(\\\"Making windows...\\\")\\n if self.window_size is None:\\n auto_set_window_size(x_train[0])\\n if self.stride is None:\\n auto_set_stride()\\n\\n for index in tqdm(range(len(x_train))):\\n sequence_extractions, sequence_extraction_labels = \\\\\\n self.get_windows_for_sequence(\\n x_train[index], y_train[index])\\n windows_x.append(sequence_extractions)\\n windows_y.append(sequence_extraction_labels)\\n return np.array(windows_x), np.array(windows_y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b9e3a89d8aeafb905b3a50a41842c03\",\n \"score\": \"0.579171\",\n \"text\": \"def filter_windows(sliding_windows_file, genes_file, output_file):\\n\\n\\t# Read sliding windows file and create a list in the form\\n\\t# genes = [('gene1', 1000, 2000), ('gene2', 4000, 45000)]\\n\\tgenes = []\\t\\t# this could be a dictionary but I prefer not\\n\\tfor line in genes_file:\\n\\t\\tline = line.strip()\\n\\n\\t\\tif line and not line.startswith('#'):\\t\\t# if line is not empty and not a comment\\n#\\t\\tif line and re.match('\\\\d+', line):\\n\\t\\t\\tlogging.debug((\\\"line: %s\\\" %line))\\n\\t\\t\\tfields = line.split()\\t\\t# it is better to use the default splitting algorithm here.\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# read help(''.split)\\t\\n\\n\\t\\t\\tgene_name = fields[0]\\n\\t\\t\\tlogging.debug((\\\"fields: %s\\\" %fields))\\n\\t\\t\\tstart = int(fields[2])\\n\\t\\t\\tend = int(fields[3].strip())\\t\\t# remove \\\\n\\\\r, like chomp\\n\\t\\t\\tgenes.append((gene_name, start, end))\\n\\t\\t\\t\\n#\\tlogging.debug((\\\"genes :\\\", genes))\\t\\t# print the contents of genes, if level=loggin.DEBUG\\n\\n\\t# read sliding windows file, and select windows that fall in genes\\n\\toutput = '#gene_name, gene_start, gene_end, window_start, window_middle, window_end, population, number, score\\\\n'\\n\\toutputlineskeleton = \\\"%s\\\\t%d\\\\t%d\\\\t%d\\\\t%d\\\\t%d\\\\t%s\\\\t%s\\\\t%s\\\\n\\\"\\t# %(gene_name, gene_start, gene_end, window_start, window_middle, window_end, population, number, score)\\n\\n\\tfor line in sliding_windows_file:\\n\\t\\tline = line.strip()\\t\\t# remove trailing characters (like chomp)\\n\\t\\tif line and not line.startswith('#'):\\n\\t\\t\\twindow_fields = line.split()\\n\\n#\\t\\t\\tlogging.debug(window_fields)\\n\\t\\t\\twindow_start = int(window_fields[0])\\n\\t\\t\\twindow_middle = int(window_fields[2])\\n\\t\\t\\twindow_end = int(window_fields[1])\\n#\\t\\t\\tgene = window_fields[3]\\n\\t\\t\\tpopulation = window_fields[4]\\n\\t\\t\\tnumber = window_fields[5]\\n\\t\\t\\tscore = window_fields[6]\\n\\n\\t\\t\\tfor gene in genes:\\n\\t\\t\\t\\tgene_start = int(gene[1])\\n\\t\\t\\t\\tgene_end = int(gene[2])\\n\\t\\t\\t\\tgene_name = gene[0]\\n\\t\\t\\t\\t# if window_start is comprised between gene_end and gene_start\\n\\t\\t\\t\\tif gene_end > window_start >= gene_start:\\n\\t\\t\\t\\t\\tlogging.debug(\\\"This window starts inside gene %s (%s, %s)\\\" %(gene[0], gene_start, gene_end))\\n\\t\\t\\t\\t\\tlogging.debug(line)\\n\\t\\t\\t\\t\\toutput += outputlineskeleton % (gene_name, gene_start, gene_end, window_start, window_middle, window_end, population, number, score)\\n\\t\\t\\t\\telif gene_end >= window_end > gene_start:\\n\\t\\t\\t\\t\\tlogging.debug(\\\"This window ends inside gene %s (%s, %s)\\\" %(gene[0], gene_start, gene_end))\\n\\t\\t\\t\\t\\tlogging.debug(line)\\n\\t\\t\\t\\t\\toutput += outputlineskeleton % (gene_name, gene_start, gene_end, window_start, window_middle, window_end, population, number, score)\\n\\t\\n\\tlogging.debug(output)\\n\\toutput_file.write(output)\\n\\toutput_file.seek(0)\\n\\treturn output_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0665489e5a95d4d07a04087f55a1fa1\",\n \"score\": \"0.5787658\",\n \"text\": \"def get_subwindow_tracking_(im, pos, model_sz, original_sz, avg_chans):\\n if isinstance(pos, float):\\n pos = [pos, pos]\\n sz = original_sz\\n N, C, H, W = im.shape\\n c = (original_sz + 1) / 2\\n context_xmin = round(pos[0] - c)\\n context_xmax = context_xmin + sz - 1\\n context_ymin = round(pos[1] - c)\\n context_ymax = context_ymin + sz - 1\\n left_pad = int(max(0., -context_xmin))\\n top_pad = int(max(0., -context_ymin))\\n right_pad = int(max(0., context_xmax - W + 1))\\n bottom_pad = int(max(0., context_ymax - H + 1))\\n\\n context_xmin = max(int(context_xmin), 0)\\n context_xmax = min(int(context_xmax), W-1)\\n context_ymin = max(int(context_ymin), 0)\\n context_ymax = min(int(context_ymax), H-1)\\n\\n im = torch.narrow(im, 2, context_ymin, context_ymax-context_ymin+1)\\n im = torch.narrow(im, 3, context_xmin, context_xmax-context_xmin+1)\\n \\n im_sub = F.pad(im, pad=(left_pad, right_pad, top_pad, bottom_pad), mode='constant', value=avg_chans) \\n im_sub = F.interpolate(im_sub, size=(model_sz, model_sz), mode='bilinear')\\n \\n return im_sub\",\n \"title\": \"\"\n },\n {\n \"docid\": \"359de69d360671748b0aab7abaf5338a\",\n \"score\": \"0.5781956\",\n \"text\": \"def sliding_window(frame_length, step, Xsampleslist, ysampleslist):\\n Xsamples = []\\n ysamples = []\\n for j in range(len(Xsampleslist)):\\n X = Xsampleslist[j]\\n ybinary = ysampleslist[j]\\n for i in range(0, X.shape[0] - frame_length, step):\\n xsub = X[i:i + frame_length, :]\\n ysub = ybinary\\n Xsamples.append(xsub)\\n ysamples.append(ysub)\\n return Xsamples, ysamples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70c53a0b18216491fa83cb15e16d4b62\",\n \"score\": \"0.5769098\",\n \"text\": \"def find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size,\\n hist_bins):\\n draw_img = np.copy(img)\\n img_tosearch = img[ystart:ystop, :, :]\\n ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')\\n if scale != 1:\\n imshape = ctrans_tosearch.shape\\n ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1] / scale), np.int(imshape[0] / scale)))\\n\\n # Channel extraction\\n ch1 = ctrans_tosearch[:, :, 0]\\n ch2 = ctrans_tosearch[:, :, 1]\\n ch3 = ctrans_tosearch[:, :, 2]\\n\\n # Define blocks and steps as above\\n nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1\\n nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1\\n\\n # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell\\n window = 64\\n nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1\\n cells_per_step = 2 # Instead of overlap, define how many cells to step\\n nxsteps = (nxblocks - nblocks_per_window) // cells_per_step\\n nysteps = (nyblocks - nblocks_per_window) // cells_per_step\\n\\n # Compute individual channel HOG features for the entire image\\n hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\\n hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)\\n hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)\\n detections = []\\n\\n for xb in range(nxsteps):\\n for yb in range(nysteps):\\n ypos = yb * cells_per_step\\n xpos = xb * cells_per_step\\n # Extract HOG for this patch\\n\\n # Flatten the HOG features for each channel position\\n hog_feat1 = hog1[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\\n hog_feat2 = hog2[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\\n hog_feat3 = hog3[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\\n # Build hog_features array by stacking each channel\\n hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))\\n\\n xleft = xpos * pix_per_cell\\n ytop = ypos * pix_per_cell\\n\\n # Extract the image patch\\n subimg = cv2.resize(ctrans_tosearch[ytop:ytop + window, xleft:xleft + window], (64, 64))\\n\\n # Get color features\\n spatial_features = bin_spatial(subimg, size=spatial_size)\\n hist_features = color_hist(subimg, nbins=hist_bins)\\n\\n # Scale features and make a prediction\\n test_features = X_scaler.transform(\\n np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))\\n test_prediction = svc.predict(test_features)\\n confidence = svc.decision_function(test_features)\\n\\n if test_prediction == 1 and confidence > 0.6:\\n xbox_left = np.int(xleft * scale)\\n ytop_draw = np.int(ytop * scale)\\n win_draw = np.int(window * scale)\\n x1 = xbox_left\\n y1 = ytop_draw + ystart\\n x2 = xbox_left + win_draw\\n y2 = ytop_draw + win_draw + ystart\\n detections.append((x1, y1, x2, y2))\\n return detections\",\n \"title\": \"\"\n },\n {\n \"docid\": \"580caec624c34660f2def761164a8dad\",\n \"score\": \"0.5762163\",\n \"text\": \"def subwindow_search(numpoints, width, height, xpos, ypos, clstid, weights):\\n return subwindow_search_pyramid(numpoints, width, height, xpos, ypos, \\\\\\n clstid, max(clstid)+1, 1, weights)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57e7441b90bb1580313fcd5e8b122306\",\n \"score\": \"0.5747657\",\n \"text\": \"def detect_lanes(binary_warped):\\n histogram = np.sum(binary_warped[(binary_warped.shape[0] // 2):, :], axis=0)\\n out_img = np.dstack((binary_warped, binary_warped, binary_warped)) * 255\\n\\n midpoint = np.int(histogram.shape[0] / 2)\\n leftx_base = np.argmax(histogram[:midpoint])\\n rightx_base = np.argmax(histogram[midpoint:]) + midpoint\\n\\n nwindows = 9\\n\\n window_height = np.int(binary_warped.shape[0] / nwindows)\\n\\n nonzero = binary_warped.nonzero()\\n nonzeroy = np.array(nonzero[0])\\n nonzerox = np.array(nonzero[1])\\n\\n # Current positions to be updated for each window\\n leftx_current = leftx_base\\n rightx_current = rightx_base\\n\\n # Set the width of the windows +/- margin\\n margin = 100\\n\\n # Set minimum number of pixels found to recenter window\\n minpix = 50\\n\\n # Create empty lists to receive left and right lane pixel indices\\n left_lane_inds = []\\n right_lane_inds = []\\n\\n # Step through the windows one by one\\n for window in range(nwindows):\\n # Identify window boundaries in x and y (and right and left)\\n win_y_low = binary_warped.shape[0] - (window + 1) * window_height\\n win_y_high = binary_warped.shape[0] - window * window_height\\n win_xleft_low = leftx_current - margin\\n win_xleft_high = leftx_current + margin\\n win_xright_low = rightx_current - margin\\n win_xright_high = rightx_current + margin\\n\\n # Draw the windows on the visualization image\\n cv2.rectangle(out_img, (win_xleft_low, win_y_low), (win_xleft_high, win_y_high), (0, 255, 0), 2)\\n cv2.rectangle(out_img, (win_xright_low, win_y_low), (win_xright_high, win_y_high), (0, 255, 0), 2)\\n\\n # Identify the nonzero pixels in x and y within the window\\n good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) &\\n (nonzerox < win_xleft_high)).nonzero()[0]\\n good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) &\\n (nonzerox < win_xright_high)).nonzero()[0]\\n\\n # Append these indices to the lists\\n left_lane_inds.append(good_left_inds)\\n right_lane_inds.append(good_right_inds)\\n\\n # If you found > minpix pixels, recenter next window on their mean position\\n if len(good_left_inds) > minpix:\\n leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\\n if len(good_right_inds) > minpix:\\n rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\\n\\n # Concatenate the arrays of indices\\n left_lane_inds = np.concatenate(left_lane_inds)\\n right_lane_inds = np.concatenate(right_lane_inds)\\n\\n # Extract left and right line pixel positions\\n leftx = nonzerox[left_lane_inds]\\n lefty = nonzeroy[left_lane_inds]\\n rightx = nonzerox[right_lane_inds]\\n righty = nonzeroy[right_lane_inds]\\n\\n # Fit a second order polynomial to each\\n left_fit = np.polyfit(lefty, leftx, 2)\\n right_fit = np.polyfit(righty, rightx, 2)\\n\\n # Visualize\\n ploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])\\n left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2]\\n right_fitx = right_fit[0] * ploty ** 2 + right_fit[1] * ploty + right_fit[2]\\n\\n out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]\\n out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]\\n\\n return Lane(left_fit, right_fit, left_fitx, right_fitx, leftx, rightx, lefty, righty), out_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0782a82c4b8d863ab74debc02abc11a\",\n \"score\": \"0.5724442\",\n \"text\": \"def split_image_into_number_of_tiles(\\n arr: Image, x_ntiles: int, y_ntiles: int, overlap: int\\n):\\n img_width, img_height = arr.shape[-1], arr.shape[-2]\\n tile_w = img_width // x_ntiles\\n tile_h = img_height // y_ntiles\\n return split_image_into_tiles_of_size(arr, tile_w, tile_h, overlap)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c3bb8665285bc07906fb5584c9a35ba\",\n \"score\": \"0.5699675\",\n \"text\": \"def cut_window(image, window):\\n \\n return image[window[0][0]: window[0][1] + 1, window[1][0]: window[1][1] + 1].copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9665ea215f97b46c9f7fe40b25e48dac\",\n \"score\": \"0.5686169\",\n \"text\": \"def _window(self, x, y, dims, random_state):\\n height, width = dims\\n\\n # Compute image and mask dimensions\\n if x.ndim == 3:\\n x_height, x_width, x_channels = x.shape\\n # discard alpha\\n if x_channels == 4:\\n x = x[:, :, :-1]\\n else:\\n x_height, x_width = x.shape\\n\\n if y.ndim == 3:\\n y_height, y_width, _ = y.shape\\n else:\\n y_height, y_width = y.shape\\n\\n if y_height != x_height or y_width != x_width:\\n raise ValueError(\\n \\\"Invalid size between y ({}) and x ({})\\\".format(y.shape, x.shape))\\n\\n if x_height < height or x_width < width:\\n raise ValueError(\\\"Windows size {} inconsistent with image dimensions {}\\\".format(dims, x.shape))\\n\\n # extract window\\n height_offset = random_state.randint(x_height - height)\\n width_offset = random_state.randint(x_width - width)\\n height_end_idx = height_offset + height\\n width_end_idx = width_offset + width\\n\\n x_window = x[height_offset:height_end_idx, width_offset:width_end_idx]\\n y_window = y[height_offset:height_end_idx, width_offset:width_end_idx]\\n return x_window, y_window\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f89e087820f5fab051c1c1b932ab002\",\n \"score\": \"0.5660113\",\n \"text\": \"def make_slices(data, win_size):\\n rows = data.shape[0] - win_size[0] + 1\\n cols = data.shape[1] - win_size[1] + 1\\n slices = []\\n for i in range(win_size[0]):\\n for j in range(win_size[1]):\\n slices.append(data[i:rows+i, j:cols+j])\\n return slices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d50e0707ed618c15668f365b633f78fb\",\n \"score\": \"0.56400967\",\n \"text\": \"def _construct_window_around_peak(fs, irs, tleft, tright, alpha=0.5):\\n orig_shape = irs.shape\\n flat_irs = irs.reshape(-1, irs.shape[-1])\\n\\n sleft = int(fs * tleft)\\n sright = int(fs * tright)\\n\\n windows = np.ones(flat_irs.shape)\\n for i in range(flat_irs.shape[0]):\\n ipeak = np.argmax(np.abs(flat_irs[i]))\\n iwstart = max(ipeak - sleft, 0)\\n iwend = min(ipeak + sright, flat_irs.shape[-1])\\n\\n window = tukey(iwend - iwstart, alpha=alpha)\\n\\n windows[i, iwstart:iwend] *= window\\n windows[i, :iwstart] = 0\\n windows[i, iwend:] = 0\\n\\n return windows.reshape(orig_shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d8bbbfecdd596d454390597823f02b\",\n \"score\": \"0.5624814\",\n \"text\": \"def _window(self, get_lims=False):\\n\\t\\timg_h, img_w = self.od_model.img_shape\\n\\t\\th_llim = 0\\n\\t\\tw_llim = img_w // 3\\n\\t\\th_ulim = img_h - (img_h // 4)\\n\\t\\tw_ulim = 1- wllim\\n\\n\\t\\tif get_lims:\\n\\t\\t\\treturn (h_llim, h_ulim), (w_llim, w_ulim)\\n\\n\\t\\twindow = slice(h_llim, h_ulim), slice(w_llim, w_ulim)\\n\\t\\treturn window\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e08603ceb21aa8a41c89e2979c763e5\",\n \"score\": \"0.56205153\",\n \"text\": \"def make_tiles_limits(im, n_splits, margin=0):\\n \\n if n_splits == 1:\\n return [0, im.shape[1], 0, im.shape[0]]\\n # number of splits per axis\\n ax_splits = int(np.log2(n_splits))\\n x_segments = split_range(im.shape[1], ax_splits)\\n y_segments = split_range(im.shape[0], ax_splits)\\n \\n if margin > 0:\\n x_segments = extend_indices(x_segments, margin=margin)\\n y_segments = extend_indices(y_segments, margin=margin)\\n \\n # make combinations of [xmin, xmax, ymin, ymax] indices of tiles\\n tiles_indices = []\\n for xlim in x_segments:\\n for ylim in y_segments:\\n tiles_indices.append(xlim + ylim)\\n return tiles_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e98cb15564504122ebddaf7c0a7d61c3\",\n \"score\": \"0.56179106\",\n \"text\": \"def split_window(num_x, num_y):\\n\\n viewPorts = np.zeros((num_x*num_y, 4))\\n counter = 0\\n for i in range(num_x):\\n for j in range(num_y):\\n viewPorts[num_x*j + i, :] = [i*(1.0/float(num_x)),\\n 1.0 - (j+1.0)*(1.0/float(num_y)),\\n (i+1.0)*1.0/float(num_x),\\n 1.0 - j*(1.0/float(num_y))\\n ] # [num_y*i + j,:] for vertical fill first\\n\\n return viewPorts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de4daac8e34f335868cee29f550808a3\",\n \"score\": \"0.5555511\",\n \"text\": \"def subwindow_search_pyramid(numpoints, width, height, xpos, ypos, clstid, numbins, numlevels, weights):\\n pyramidlib = load_library(\\\"libess.so\\\",\\\".\\\")\\n\\n pyramidlib.pyramid_search.restype = Box_struct\\n pyramidlib.pyramid_search.argtypes = [c_int,c_int,c_int, \\n ndpointer(dtype=c_double, ndim=1, flags='C_CONTIGUOUS'),\\n ndpointer(dtype=c_double, ndim=1, flags='C_CONTIGUOUS'),\\n ndpointer(dtype=c_double, ndim=1, flags='C_CONTIGUOUS'),\\n c_int, c_int, \\n ndpointer(dtype=c_double, ndim=1, flags='C_CONTIGUOUS')]\\n\\n box = pyramidlib.pyramid_search(numpoints, width, height, \\n xpos, ypos, clstid, numbins, numlevels, weights)\\n return box\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aa3247841be78c0e5ab71f9f1da00a2\",\n \"score\": \"0.5545451\",\n \"text\": \"def _subset_to_windows(\\n insertions, # type: List[Insertion]\\n gene_windows # type: Dict[str, Tuple[str, int, int]]\\n): # type: (...) -> List[Insertion]\\n\\n # Create lookup trees.\\n trees = {\\n chrom: IntervalTree.from_tuples((i[1:]) for i in chrom_int)\\n for chrom, chrom_int in itertools.groupby(\\n sorted(gene_windows.values()), operator.itemgetter(0))\\n }\\n\\n # Determine which insertions overlap tree intervals and\\n # correspond to genes with known gene window.\\n def _in_windows(ins, trees):\\n try:\\n return trees[ins.seqname].overlaps(ins.position)\\n except KeyError:\\n return False\\n\\n return [\\n ins for ins in insertions\\n if ins.metadata['gene_id'] in gene_windows and _in_windows(ins, trees)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b8911f656fe7a0ecbb895d2e633df06\",\n \"score\": \"0.5541185\",\n \"text\": \"def windows(items, size, jump):\\n\\n for start in range(0, len(items), jump):\\n yield next(zip(*([islice(items, start, None)] * size)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7da2bbdf1d437e9f18c0c12ecb4fd99f\",\n \"score\": \"0.5538898\",\n \"text\": \"def extract_patches(image, patchshape, overlap_allowed=0.1, cropvalue=None, crop_fraction_allowed=0.1):\\r\\n jump_cols = int(patchshape[1] * overlap_allowed)\\r\\n jump_rows = int(patchshape[0] * overlap_allowed)\\r\\n\\r\\n # Restrict ourselves to the rectangle containing non-cropped pixels\\r\\n if cropvalue is not None:\\r\\n rows, cols = np.where(image != cropvalue)\\r\\n rows.sort()\\r\\n cols.sort()\\r\\n active = image[rows[0]:rows[-1], cols[0]:cols[-1]]\\r\\n else:\\r\\n active = image\\r\\n\\r\\n rowstart = 0\\r\\n colstart = 0\\r\\n\\r\\n # Array tracking where we've already taken patches.\\r\\n covered = np.zeros(active.shape, dtype=bool)\\r\\n patches = []\\r\\n regions = []\\r\\n while rowstart <= active.shape[0] - patchshape[0]:\\r\\n # Record whether or not e've found a patch in this row,\\r\\n # so we know whether to skip ahead.\\r\\n got_a_patch_this_row = False\\r\\n colstart = 0\\r\\n while colstart <= active.shape[1] - patchshape[1]:\\r\\n # Slice tuple indexing the region of our proposed patch\\r\\n region = (slice(rowstart, rowstart + patchshape[0]),\\r\\n slice(colstart, colstart + patchshape[1]))\\r\\n\\r\\n # The actual pixels in that region.\\r\\n patch = active[region]\\r\\n\\r\\n # The current mask value for that region.\\r\\n cover_p = covered[region]\\r\\n if cropvalue is None or \\\\\\r\\n frac_eq_to(patch, cropvalue) <= crop_fraction_allowed and \\\\\\r\\n frac_eq_to(cover_p, True) <= overlap_allowed:\\r\\n # Accept the patch.\\r\\n patches.append(patch)\\r\\n regions.append(region)\\r\\n # Mask the area.\\r\\n covered[region] = True\\r\\n\\r\\n # Jump ahead in the x direction.\\r\\n colstart += jump_cols\\r\\n got_a_patch_this_row = True\\r\\n # print \\\"Got a patch at %d, %d\\\" % (rowstart, colstart)\\r\\n else:\\r\\n # Otherwise, shift window across by one pixel.\\r\\n colstart += 1\\r\\n\\r\\n if got_a_patch_this_row:\\r\\n # Jump ahead in the y direction.\\r\\n rowstart += jump_rows\\r\\n else:\\r\\n # Otherwise, shift the window down by one pixel.\\r\\n rowstart += 1\\r\\n\\r\\n # Return a 3D array of the patches with the patch index as the first\\r\\n # dimension (so that patch pixels stay contiguous in memory, in a\\r\\n # C-ordered array).\\r\\n return np.concatenate([pat[np.newaxis, ...] for pat in patches], axis=0),regions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3777ad9f6172b4d32e497f3796a0900\",\n \"score\": \"0.5538282\",\n \"text\": \"def scan_multiple_win_sizes(image, clf, hyperparams, box_colors=None):\\n rescales = hyperparams[\\\"RESCALES\\\"]\\n draw_image = np.zeros_like(image)\\n heatmap = np.zeros(image.shape[0:2])\\n for i, rescale in enumerate(rescales):\\n box_color = None\\n if box_colors is not None:\\n box_color = box_colors[i%len(box_colors)]\\n d, heatmap = scan_single_win_size(image, clf, rescale, hyperparams, box_color, heatmap)\\n draw_image[(draw_image == 0).all(2)] = d[(draw_image == 0).all(2)]\\n return draw_image, heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f34e0146666535ffa8bf8e570286f048\",\n \"score\": \"0.55361724\",\n \"text\": \"def get_window_img(img: np.ndarray, window_range: tuple) -> np.ndarray:\\n img_: np.ndarray = img.copy()\\n start, end = window_range\\n img_[img_ < start] = start\\n img_[img_ > end] = end\\n img_ -= img_.min()\\n max_ = img_.max()\\n if max_ <= 0:\\n return img_.astype(np.uint8)\\n img_ /= img_.max()\\n img_ *= 255\\n return img_.astype(np.uint8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afb96ceaa06b60c8818417f4ba7ea769\",\n \"score\": \"0.55344933\",\n \"text\": \"def find_cars(img, scale):\\n img_boxes = [] # Clears img_boxes so we don't keep unwanted heatmap history\\n count = 0\\n draw_img = np.copy(img)\\n\\n # Make a heatmap of zeros\\n heatmap = np.zeros_like(img[:, :, 0])\\n\\n # IMPORTANT : reading *.jpeg's (scaled 0-255, aka scaling needed), but\\n # # trained on *.png's (scaled 0-1, aka scaling not needed)\\n if img.dtype == 'uint8':\\n img = img.astype(np.float32) / 255 # aka scaling needed\\n\\n img_tosearch = img[ystart:ystop, :, :]\\n ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')\\n\\n if scale != 1: # resize whole image instead of separate windows\\n imshape = ctrans_tosearch.shape\\n ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1] / scale), np.int(imshape[0] / scale)))\\n\\n ch1 = ctrans_tosearch[:, :, 0]\\n ch2 = ctrans_tosearch[:, :, 1]\\n ch3 = ctrans_tosearch[:, :, 2]\\n\\n # Define blocks and steps as above\\n # These hold the number of HOG cells\\n nxblocks = (ch1.shape[1] // pix_per_cell) - 1 # Note : '//' causes integers to be result, instead of floats\\n nyblocks = (ch1.shape[0] // pix_per_cell) - 1\\n # How many features per block are we going to be extracting\\n nfeat_per_block = orient * cell_per_block ** 2\\n window = 64\\n nblocks_per_window = (window // pix_per_cell) - 1\\n # aka 75% overlap between cells\\n cells_per_step = 2 # Instead of overlap, define how many cells to step\\n nxsteps = (nxblocks - nblocks_per_window) // cells_per_step\\n nysteps = (nyblocks - nblocks_per_window) // cells_per_step\\n\\n # Compute individual channel HOG features for the entire image\\n hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\\n hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)\\n hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)\\n\\n for xb in range(nxsteps):\\n for yb in range(nysteps):\\n count += 1\\n ypos = yb * cells_per_step\\n xpos = xb * cells_per_step\\n # Extract HOG for this patch\\n hog_feat1 = hog1[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\\n hog_feat2 = hog2[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\\n hog_feat3 = hog3[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\\n hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))\\n\\n xleft = xpos * pix_per_cell\\n ytop = ypos * pix_per_cell\\n\\n # Extract the image patch\\n subimg = cv2.resize(ctrans_tosearch[ytop:ytop + window, xleft:xleft + window], (64, 64))\\n\\n # Get colour features\\n spatial_features = bin_spatial(subimg, size=spatial_size)\\n hist_features = color_hist(subimg, nbins=hist_bins)\\n\\n # Scale features and make a prediction\\n test_features = X_scaler.transform(\\n np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))\\n test_prediction = svc.predict((test_features))\\n\\n if test_prediction == 1:\\n xbox_left = np.int(xleft * scale)\\n ytop_draw = np.int(ytop * scale)\\n win_draw = np.int(window * scale)\\n cv2.rectangle(draw_img, (xbox_left, ytop_draw + ystart),\\n (xbox_left + win_draw, ytop_draw + win_draw + ystart), (0, 0, 255))\\n img_boxes.append(\\n ((xbox_left, ytop_draw + ystart), (xbox_left + win_draw, ytop_draw + win_draw + ystart)))\\n heatmap[ytop_draw + ystart:ytop_draw + win_draw + ystart, xbox_left:xbox_left + win_draw] += 1\\n\\n return draw_img, img_boxes, heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fde1094430ca15028bbfb7e7b595bd6\",\n \"score\": \"0.5523551\",\n \"text\": \"def detect(img, window_list, pipeline):\\n #t = Timer()\\n windows = []\\n for bbox in window_list:\\n window = extract_window(img, bbox)\\n windows.append(window)\\n windows = np.stack(windows)\\n detections = pipeline.predict(windows)\\n #print(\\\"Time to detect: {:.2f}\\\".format(t.tock()))\\n return detections\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57ca0b100ebf641821926ce2aa29efa0\",\n \"score\": \"0.55216813\",\n \"text\": \"def test_sliding_window(data, x):\\n\\n win_dim = data.draw(st.integers(1, x.ndim), label=\\\"win_dim\\\")\\n win_shape = data.draw(\\n st.tuples(*(st.integers(1, s) for s in x.shape[-win_dim:])), label=\\\"win_shape\\\"\\n )\\n step = data.draw(\\n st.tuples(*(st.integers(1, s) for s in x.shape[-win_dim:])), label=\\\"step\\\"\\n )\\n\\n max_dilation = np.array(x.shape[-win_dim:]) // win_shape\\n dilation = data.draw(\\n st.one_of(st.none(), st.tuples(*(st.integers(1, s) for s in max_dilation))),\\n label=\\\"dilation\\\",\\n )\\n y = sliding_window_view(x, window_shape=win_shape, step=step, dilation=dilation)\\n\\n if dilation is None:\\n dilation = np.ones((len(win_shape),), dtype=int)\\n\\n for ind in np.ndindex(*y.shape[:win_dim]):\\n slices = tuple(\\n slice(i * s, i * s + w * d, d)\\n for i, w, s, d in zip(ind, win_shape, step, dilation)\\n )\\n assert_allclose(actual=y[tuple([*ind])], desired=x[(..., *slices)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1e8c9465054fa63d1d5f13cc799079f\",\n \"score\": \"0.5516395\",\n \"text\": \"def new_image_sample_grid(dset, window_dims, window_overlap=0.0,\\n classes_of_interest=None, ignore_coverage_thresh=0.6,\\n negative_classes={'ignore', 'background'},\\n use_annots=True, legacy=True, verbose=1):\\n # import netharn as nh\\n import kwarray\\n from ndsampler import isect_indexer\\n keepbound = True\\n\\n # Create a sliding window object for each specific image (because they may\\n # have different sizes, technically we could memoize this)\\n gid_to_slider = {}\\n for img in dset.imgs.values():\\n full_dims = [img['height'], img['width']]\\n window_dims_ = full_dims if window_dims == 'full' else window_dims\\n slider = kwarray.SlidingWindow(full_dims, window_dims_,\\n overlap=window_overlap, keepbound=keepbound,\\n allow_overshoot=True)\\n gid_to_slider[img['id']] = slider\\n\\n if use_annots:\\n _isect_index = isect_indexer.FrameIntersectionIndex.from_coco(dset)\\n\\n positives = []\\n negatives = []\\n targets = []\\n positive_idxs = []\\n negative_idxs = []\\n for gid, slider in gid_to_slider.items():\\n\\n # For each image, create a box for each spatial region in the slider\\n boxes = []\\n regions = list(slider)\\n for region in regions:\\n y_sl, x_sl = region\\n boxes.append([x_sl.start, y_sl.start, x_sl.stop, y_sl.stop])\\n boxes = kwimage.Boxes(np.array(boxes), 'ltrb')\\n\\n for region, box in zip(regions, boxes):\\n\\n if use_annots:\\n # Check to see what annotations this window-box overlaps with\\n aids = _isect_index.overlapping_aids(gid, box)\\n\\n # Look at the categories within this region\\n catnames = [\\n dset.cats[dset.anns[aid]['category_id']]['name'].lower()\\n for aid in aids\\n ]\\n\\n if ignore_coverage_thresh:\\n ignore_flags = [catname == 'ignore' for catname in catnames]\\n if any(ignore_flags):\\n # If the almost the entire window is marked as ignored then\\n # just skip this window.\\n ignore_aids = list(ub.compress(aids, ignore_flags))\\n ignore_boxes = dset.annots(ignore_aids).boxes\\n\\n # Get an upper bound on coverage to short circuit extra\\n # computation in simple cases.\\n box_area = box.area.sum()\\n coverage_ub = ignore_boxes.area.sum() / box_area\\n if coverage_ub > ignore_coverage_thresh:\\n max_coverage = ignore_boxes.iooas(box).max()\\n if max_coverage > ignore_coverage_thresh:\\n continue\\n elif len(ignore_boxes) > 1:\\n # We have to test the complex case\\n try:\\n from shapely.ops import cascaded_union\\n ignore_shape = cascaded_union(ignore_boxes.to_shapley())\\n region_shape = box[None, :].to_shapley()[0]\\n coverage_shape = ignore_shape.intersection(region_shape)\\n real_coverage = coverage_shape.area / box_area\\n if real_coverage > ignore_coverage_thresh:\\n continue\\n except Exception as ex:\\n import warnings\\n warnings.warn(\\n 'ignore region select had non-critical '\\n 'issue ex = {!r}'.format(ex))\\n\\n if classes_of_interest:\\n # If there are CoIs then only count a region as positive if one\\n # of those is in this region\\n interest_flags = np.array([\\n catname in classes_of_interest for catname in catnames])\\n pos_aids = list(ub.compress(aids, interest_flags))\\n elif negative_classes:\\n # Don't count negative classes as positives\\n nonnegative_flags = np.array([\\n catname not in negative_classes for catname in catnames])\\n pos_aids = list(ub.compress(aids, nonnegative_flags))\\n else:\\n pos_aids = aids\\n else:\\n aids = None\\n pos_aids = None\\n\\n # aids = sampler.regions.overlapping_aids(gid, box, visible_thresh=0.001)\\n tr = {\\n 'gid': gid,\\n 'slices': region,\\n 'aids': aids,\\n }\\n targets.append(tr)\\n if pos_aids:\\n positive_idxs.append(len(targets))\\n if legacy:\\n positives.append(tr)\\n else:\\n negative_idxs.append(len(targets))\\n if legacy:\\n negatives.append(tr)\\n\\n if verbose:\\n print('Found {} targets'.format(len(targets)))\\n if use_annots:\\n print('Found {} positives'.format(len(positive_idxs)))\\n print('Found {} negatives'.format(len(negative_idxs)))\\n\\n sample_grid = {\\n 'targets': targets,\\n 'positives_indexes': positive_idxs,\\n 'negatives_indexes': negative_idxs,\\n }\\n if legacy:\\n sample_grid.update({\\n # Deprecated:\\n 'positives': positives,\\n 'negatives': negatives,\\n })\\n return sample_grid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed907076369c7bd0e5711ef71837dac\",\n \"score\": \"0.55058306\",\n \"text\": \"def iter_windows(hits, radius):\\n\\n hits = util.PushIter(hits)\\n cache = LinkedList()\\n\\n upstream = set()\\n downstream = set()\\n\\n try:\\n center = hits.next()\\n except StopIteration:\\n return\\n\\n\\n while True:\\n # discard anyone in the upstream that is not within radius distance\\n for hit in list(upstream):\\n if hit[0].end + radius < center[0].start:\\n upstream.remove(hit)\\n\\n # populate downstream with all regions within in radius\\n for hit in hits:\\n if hit[0].start - radius > center[0].end:\\n hits.push(hit)\\n break\\n downstream.add(hit)\\n cache.append(hit)\\n\\n yield (center, upstream, downstream)\\n\\n # populate upstream\\n upstream.add(center)\\n \\n # move center to next hit\\n try:\\n center = cache.pop_front()\\n except IndexError:\\n break\\n\\n # remove new center from downstream\\n downstream.remove(center)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"027896f225fcb2365292c4a83d4c3750\",\n \"score\": \"0.55004793\",\n \"text\": \"def _get_intersections_in_frame_parameter_space(lines,img_width,img_height):\\n pts = []\\n for i in range(len(lines)):\\n for j in range(i+1,len(lines)): # Compare each point\\n x,y = _get_intersection_parameter_space(lines[i],lines[j]) # Get intercetions\\n if x < img_width and x >= 0 and y < img_height and y >= 0: # Intercetion is within frame bounds\\n pts.append((x,y))\\n return pts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90209085cada59130d8f739b0a7664fa\",\n \"score\": \"0.54784346\",\n \"text\": \"def tile_window(shape, px):\\n nr, nc = shape\\n lu = product(range(0, nc, px), range(0, nr, px))\\n\\n ## create the window\\n for l, u in lu:\\n h = min(px, nr - u)\\n w = min(px, nc - l)\\n yield (l, u, w, h)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02a37b8a7d36b2eb7bba304710d205bb\",\n \"score\": \"0.54510176\",\n \"text\": \"def extract_windows(\\n spectrograms: numba.typed.List[np.ndarray], wlength: int\\n) -> Tuple[numba.typed.List[np.ndarray], List[int]]:\\n windows = numba.typed.List()\\n n_windows = []\\n for spec in spectrograms:\\n for i, w in enumerate(window(spec, wlength)):\\n windows.append(w)\\n n_windows.append(i + 1)\\n return windows, n_windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eab3b3e1d2e3aa39e7c8c2898b920fb5\",\n \"score\": \"0.54456574\",\n \"text\": \"def divide_genome(genome, L):\\n\\n m = L\\n L_windows = []\\n\\n while m <= len(genome):\\n new_window = genome[m-L:m]\\n L_windows.append(new_window)\\n\\n m += 1\\n\\n return L_windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b86b4ff63772e4b052d1182f3d6ae2cd\",\n \"score\": \"0.5436157\",\n \"text\": \"def _boxes_in_window(self, od_bbox):\\n\\t\\t(h_llim, h_ulim), (w_llim, w_ulim) = self._window(get_lims=True)\\n\\t\\txmin = od_bbox[:, 0]\\n\\t\\t# ymin = od_bbox[:, 1]\\n\\t\\txmax = od_bbox[:, 2]\\n\\t\\t# ymax = od_bbox[:, 3]\\n\\n\\t\\tin_window = (xmax > w_llim & xmin < w_ulim)\\n\\t\\tprint(in_window.shape) # (n)\\n\\t\\treturn in_window\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc7fecb90094d2b4d9c0b7a677c177e2\",\n \"score\": \"0.5432923\",\n \"text\": \"def detect_cars(img, clf, xy_window, stride, cur_sizes_factors, cur_y_start_stop, cur_x_padding):\\n\\n image_tar_size = (int(img.shape[0] * cur_sizes_factors),\\n int(img.shape[1] * cur_sizes_factors))\\n\\n # open cv needs the shape in reversed order (width, height)\\n img_scaled = cv2.resize(img, image_tar_size[::-1])\\n # check if search area is smaller than window.\\n cur_y_start_stop = (cur_y_start_stop * cur_sizes_factors).astype(np.uint32)\\n\\n # if the window size is bigger than the search area return an empty array\\n search_area_height = cur_y_start_stop[1] - cur_y_start_stop[0]\\n if search_area_height < xy_window[1] or img_scaled.shape[1] < xy_window[0]:\\n return np.ndarray((0, 4))\\n\\n # Add padding (zeros) on the x axis\\n img_scaled = add_padding(img_scaled, cur_x_padding)\\n windows = slide_window(img_scaled, y_start_stop=cur_y_start_stop, xy_window=xy_window,\\n stride=stride)\\n\\n features = extract_features(img_scaled, clf, windows, cur_y_start_stop, xy_window, stride)\\n des_func = clf.named_steps['clf'].decision_function(features)\\n\\n windows = remove_padding_from_bb(windows, cur_x_padding)\\n # windows have to be rescaled to account for the resized image\\n windows = (windows / cur_sizes_factors).astype(np.uint32)\\n windows = windows[des_func > 0]\\n\\n des_func = des_func[des_func > 0]\\n\\n return windows, des_func\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f505d555f82f16231df018f7a595583\",\n \"score\": \"0.54229987\",\n \"text\": \"def scan_single_win_size(image, clf, rescale, hyperparams, box_color=(0,0,1.0), heatmap=None):\\n # get important hyperparams\\n y_start = hyperparams['Y_START']\\n y_stop = hyperparams['Y_STOP']\\n color_space = hyperparams['COLOR_SPACE']\\n spatial_feat = hyperparams['SPATIAL_FEAT']\\n spatial_size = hyperparams['SPATIAL_SIZE']\\n hist_feat = hyperparams['HIST_FEAT']\\n hist_bin = hyperparams['HIST_BIN']\\n hist_range = hyperparams['HIST_RANGE']\\n hog_orient = hyperparams['HOG_ORIENT']\\n hog_cell_per_block = hyperparams['HOG_CELL_PER_BLOCK']\\n hog_pix_per_cell = hyperparams['HOG_PIX_PER_CELL']\\n hog_feat = hyperparams['HOG_FEAT']\\n hog_channel = hyperparams['HOG_CHANNEL']\\n # if heatmap was not provided, initialize it\\n if heatmap is None:\\n heatmap = np.zeros(image.shape[0:2])\\n # copy image for visualization purposes\\n draw_image = np.zeros_like(image)\\n # image to be analyzed is color-converted to target color space and resized to match\\n # appropriately rescaled window size\\n rescaled_image = convert_color(image[y_start:y_stop, :, :], color_space)\\n if rescale != 1:\\n imshape = image.shape\\n rescaled_image = cv2.resize(rescaled_image, (np.int(imshape[1] / rescale), np.int(imshape[0] / rescale)))\\n # Define number of blocks\\n n_x_blocks = (rescaled_image.shape[1] // hog_pix_per_cell) - hog_cell_per_block + 1\\n n_y_blocks = (rescaled_image.shape[0] // hog_pix_per_cell) - hog_cell_per_block + 1\\n window = 64 # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell\\n blocks_per_window = (window // hog_pix_per_cell) - hog_cell_per_block + 1\\n cells_per_step = 2 # Instead of overlap, define how many cells to step\\n n_x_steps = (n_x_blocks - blocks_per_window) // cells_per_step\\n n_y_steps = (n_y_blocks - blocks_per_window) // cells_per_step\\n # Prepare the hog features\\n hogs = []\\n if hog_feat:\\n for channel in range(0, rescaled_image.shape[2]):\\n hogs.append(get_hog_features(rescaled_image[:, :, channel],\\n hog_orient, hog_pix_per_cell, hog_cell_per_block, feature_vec=False))\\n # Go through blocks of image step by step\\n for x_window in range(n_x_steps):\\n for y_window in range(n_y_steps):\\n y_pos = y_window * cells_per_step\\n x_pos = x_window * cells_per_step\\n x_left = x_pos * hog_pix_per_cell\\n y_top = y_pos * hog_pix_per_cell\\n sub_image = rescaled_image[y_top:y_top + window, x_left:x_left + window]\\n # Extract HOG for this patch\\n features = []\\n if spatial_feat:\\n features.append(get_spatial_features(sub_image, size=spatial_size))\\n if hist_feat:\\n features.append(get_hist_features(sub_image, nbins=hist_bin, bins_range=hist_range)[4])\\n if hog_feat:\\n if hog_channel == \\\"ALL\\\":\\n for h in hogs:\\n features.append(h[y_pos:y_pos+blocks_per_window, x_pos:x_pos+blocks_per_window].ravel())\\n else:\\n features.append(hogs[hog_channel][y_pos:y_pos+blocks_per_window, x_pos:x_pos+blocks_per_window].ravel())\\n # Scale features and make a prediction\\n test_features = np.hstack(features).reshape(1, -1)\\n test_prediction = clf.predict(test_features)\\n if test_prediction == 1:\\n # If prediction is true, re-calculate sliding window position\\n x_box_left = np.int(x_left * rescale)\\n y_box_top = np.int(y_top * rescale)\\n box_size = np.int(window * rescale)\\n # if box color was set, draw box on image\\n if box_color is not None:\\n cv2.rectangle(draw_image, (x_box_left, y_box_top + y_start),\\n (x_box_left + box_size, y_box_top + box_size + y_start), box_color, 6)\\n # add heat to heatmap\\n heatmap[y_box_top+y_start:y_box_top+box_size+y_start, x_box_left:x_box_left+box_size] += \\\\\\n clf.decision_function(test_features)\\n return draw_image, heatmap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"852e87ccaea0503a459b3f38639877da\",\n \"score\": \"0.54190135\",\n \"text\": \"def horizontal_scroll(self, image, padding=True):\\n\\n image_list = list()\\n height = image.size[1]\\n\\n # Scroll into the blank image.\\n if padding:\\n for y in range(1,17):\\n section = image.crop((0, 0, 8, y))\\n print section.width,section.height\\n display_section = self.create_blank_image()\\n display_section.paste(section, (0, 16 - y, 8, 16))\\n image_list.append(display_section)\\n return image_list\\n\\n #Scroll across the input image.\\n for y in range(16, height + 1):\\n section = image.crop((0, y - 16, 8, y))\\n display_section = self.create_blank_image()\\n display_section.paste(section, (0, 0, 8, 16))\\n image_list.append(display_section)\\n\\n #Scroll out, leaving the blank image.\\n if padding:\\n for y in range(height - 15, height + 1):\\n section = image.crop((0, y, 8, height))\\n display_section = self.create_blank_image()\\n display_section.paste(section, (0, 0, 8, 15 - (y - (height - 15))))\\n image_list.append(display_section)\\n\\n #Return the list of images created\\n return image_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8accef89eb01fb3de0b74edf59856154\",\n \"score\": \"0.5409532\",\n \"text\": \"def get_hist_slice(img, slices=10, margin=100):\\n h_img = img.shape[0]\\n w_img = img.shape[1]\\n location_l = []\\n location_r = []\\n location_ry = []\\n location_ly = []\\n\\n \\\"\\\"\\\"\\n Createa a mask to ignore the center and the extremes of the image.\\n ****-----***-----****\\n ****-----***-----****\\n ****-----***-----****\\n ****-----***-----****\\n ****-----***-----****\\n ****-----***-----****\\n ****-----***-----****\\n \\\"\\\"\\\"\\n zero_patch = np.zeros((h_img, margin))\\n one_patch = np.ones((h_img, (w_img//2)-(1.5*margin)))\\n\\n mask = np.c_[zero_patch, one_patch]\\n mask = np.c_[mask, zero_patch]\\n mask = np.c_[mask, one_patch]\\n mask = np.c_[mask, zero_patch]\\n img = np.uint8(img)\\n mask = np.uint8(mask)\\n print(\\\"img:\\\"+str(img.shape))\\n print(\\\"mask:\\\"+str(mask.shape))\\n img = cv2.bitwise_and(img,img,mask = mask)\\n\\n for window in reversed(range(0,h_img, int(h_img/ slices))):\\n sli = img[window:int(window+(h_img/slices)), :]\\n sli_sum = np.sum(sli, axis=0) # get the sum from all the columns\\n \\\"\\\"\\\"\\n Add a margin to the histogram to not take pixels at the far left or right\\n \\\"\\\"\\\"\\n sli_l, sli_r = (sli_sum[:w_img//2], sli_sum[w_img//2:])\\n\\n # get the location of 5 top max elements\\n l_arg = np.argpartition(sli_l, -5)[-5:]\\n r_arg = np.argpartition(sli_r, -5)[-5:]\\n # Get the value of the max values and decide of this portion\\n # of frame contains something interesiting\\n mag_r = sum(sli_r[r_arg])\\n mag_l = sum(sli_l[l_arg])\\n if mag_l > 100:\\n l_indx = np.mean(l_arg)\\n location_l.append(l_indx)\\n location_ly.append(window)\\n\\n if mag_r > 100:\\n r_indx = np.mean(r_arg) + w_img//2\\n location_ry.append(window)\\n location_r.append(r_indx)\\n #print(\\\"r_indx: \\\" + str(r_indx) + \\\" sli_r: \\\" + str(sli_r))\\n # add condtion for the case when the index is 0\\n # if a point is 0 make its value the median btw the point before and the point after\\n location = {'l':location_l, 'r':location_r, 'ly':location_ly, 'ry':location_ry}\\n print(\\\"l : \\\" + str(len(location_l)))\\n print(location_l)\\n print(\\\"ly : \\\" + str(len(location_ly)))\\n print(location_ly)\\n\\n print(\\\"r : \\\" + str(len(location_r)))\\n print(location_r)\\n print(\\\"ry : \\\" + str(len(location_ry)))\\n print(location_ry)\\n\\n\\n return location\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5707636a9a7ad2c9d9d3aad10ec5e871\",\n \"score\": \"0.54067117\",\n \"text\": \"def paintings_detection(query_image, mask):\\n\\n image = cv2.imread(query_image)\\n\\n image_width = mask.shape[0]\\n image_height = mask.shape[1]\\n x_box_1, y_box_1, w_box_1, h_box_1, x_box_2, y_box_2, w_box_2, h_box_2 = 0, 0, 0, 0, 0, 0, 0, 0, \\n\\n contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:]\\n\\n for cnt in contours:\\n x, y, w, h = cv2.boundingRect(cnt)\\n \\n if (w > 0.15 * image_width) & (h > 0.15 * image_height) & (w < 0.98 * image_width) & (x_box_1 == 0):\\n x_box_1, y_box_1, w_box_1, h_box_1 = x, y, w, h\\n elif (w > 0.15 * image_width) & (h > 0.15 * image_height) & (w < 0.98 * image_width) & (x_box_1 != 0):\\n x_box_2, y_box_2, w_box_2, h_box_2 = x, y, w, h\\n\\n if x_box_2 == 0:\\n x_value_to_split = 0\\n else:\\n x_value_to_split = (x_box_1 + w_box_1/2 + x_box_2 + w_box_2/2) / 2\\n\\n\\n return(x_value_to_split)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c3b50773b43ee5f30cec6bdfaebca66\",\n \"score\": \"0.53983605\",\n \"text\": \"def define_windows(w, data):\\n data_w1 = data[0:w, :]\\n data_w2 = data[w:w * 2, :]\\n data_w3 = data[w * 2:w * 3, :]\\n data_w4 = data[w * 3:w * 4, :]\\n data_w5 = data[w * 4:w * 5, :]\\n data_w6 = data[w * 5:, :]\\n\\n return data_w1, data_w2, data_w3, data_w4, data_w5, data_w6\",\n \"title\": \"\"\n },\n {\n \"docid\": \"824fad31db5b1b10f97cacd2560694e8\",\n \"score\": \"0.5366726\",\n \"text\": \"def car_zones(self, image, Hp):\\n # 1) Load data from the object\\n windows_feat = self.sampling_data\\n clf = self.clf\\n scaler = self.scaler\\n overlap = self.overlap\\n color_space = self.color_space\\n spatial_size = self.spatial_size\\n hist_bins = self.hist_bins\\n orient = self.orient\\n pix_per_cell = self.pix_per_cell\\n cell_per_block = self.cell_per_block\\n hog_channel = self.hog_channel \\n spatial_feat = self.spatial_feat\\n hist_feat = self.hist_feat \\n hog_feat = self.hog_feat\\n \\n # 2) Create a copy of the image\\n cimage = np.copy (image)\\n # 3) Create a empty list where to store the detected windows\\n detected_windows_list = []\\n # 4) Loop into the windows \\n for window_feat in windows_feat:\\n # 4.1) Get the window using sliding_window\\n windows = slide_window(image, x_start_stop=window_feat[0],\\n y_start_stop=window_feat[1], \\n xy_window=window_feat[2], \\n xy_overlap=overlap)\\n # 4.2) Get the local probabilities\\n hp, hot_windows = search_windows(cimage, windows, clf, scaler, color_space=color_space, \\n spatial_size=spatial_size, hist_bins=hist_bins, \\n orient=orient, pix_per_cell=pix_per_cell, \\n cell_per_block=cell_per_block, \\n hog_channel=hog_channel, spatial_feat=spatial_feat, \\n hist_feat=hist_feat, hog_feat=hog_feat) \\n # 4.3) Store the probabilities into the Hp matrix\\n Hp = add_probabilities(Hp, hp, windows)\\n # 4.4) Store the detected boxes into the list\\n detected_windows_list.extend(hot_windows)\\n \\n # 5) Return the detected windows and the probability matrix (Hp)\\n return detected_windows_list, Hp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eebf6352f7d3dd1c427aff985e32be7\",\n \"score\": \"0.5354472\",\n \"text\": \"def moving_windows(self,channels = None, size = None, overlap = None, threads=False):\\n\\n class ThreadedWindowReader(threading.Thread):\\n\\n def __init__(self,eeg,ch_bs,size,overlap,qsize=10):\\n threading.Thread.__init__(self)\\n self.eeg = eeg\\n self.ch_bs = ch_bs\\n self.size=size\\n self.overlap=overlap\\n self.q = Queue.Queue(qsize)\\n self.has_more = True\\n self.is_running = False\\n \\n def run(self):\\n start=0\\n self.is_running = True\\n while start+self.size<self.eeg.num_datapoints:\\n data = self.eeg[start:start+self.size,self.ch_bs]\\n self.q.put((data, start))\\n start = start+self.size-self.overlap\\n self.is_running=False\\n\\n def get(self):\\n if self.is_running:\\n data,start = self.q.get()\\n else:\\n data,start = self.q.get_nowait()\\n return data, start\\n\\n if channels==None:\\n channels=range(self.num_channels)\\n if size==None:\\n size=4096\\n if overlap==None:\\n overlap=0\\n start = 0\\n ch_bs = n.zeros((self.num_channels),n.bool)\\n for ch in channels:\\n ch_bs[ch] = True\\n if not threads:\\n while start+size<self.num_datapoints:\\n data = self[start:start+size,ch_bs]\\n yield data, start\\n start = start+size-overlap\\n else:\\n twr = ThreadedWindowReader(self,ch_bs,size,overlap)\\n twr.start()\\n try:\\n while True:\\n yield twr.get()\\n except Queue.Empty:\\n print \\\"Queue now empty\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0afad9ee0a429f9071f8afc92c3059cc\",\n \"score\": \"0.53522754\",\n \"text\": \"def __init__(self, master, row, column, ystart, xleft, xright, nx, ny, checker):\\n\\n # A pair of windows needs 5 parameters to specify it:\\n #\\n # ystart -- first y value to be read, same for each window\\n # xleft -- leftmost x pixel of lefthand window\\n # xright -- leftmost x pixel of righthand window\\n # nx -- X dimension, unbinned pixels\\n # ny -- Y dimension, unbinned pixels\\n\\n self.ystart = RangedPosInt(master, ystart, 1, 1024, checker, width=4)\\n self.ystart.grid(row=row,column=column)\\n\\n self.xleft = RangedPosInt(master, xleft, 1, 512, checker, width=4)\\n self.xleft.grid(row=row,column=column+1)\\n\\n self.xright = RangedPosInt(master, xright, 513, 1024, checker, width=4)\\n self.xright.grid(row=row,column=column+2)\\n\\n self.nx = RangedPosInt(master, nx, 1, 512, checker, width=4)\\n self.nx.grid(row=row,column=column+3)\\n\\n self.ny = RangedPosInt(master, ny, 1, 1024, checker, width=4)\\n self.ny.grid(row=row,column=column+4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5caf6b3417a35e5d8d786740da4d08aa\",\n \"score\": \"0.5343791\",\n \"text\": \"def get_active_sources(source, source_list, image):\\n def intersect(sa, sb, image):\\n xlima, ylima = sa.bounding_boxes[image]\\n xlimb, ylimb = sb.bounding_boxes[image]\\n widtha, heighta = xlima[1] - xlima[0], ylima[1] - ylima[0]\\n widthb, heightb = xlimb[1] - xlimb[0], ylimb[1] - ylimb[0]\\n return (np.abs(xlima[0] - xlimb[0])*2 < (widtha + widthb)) and \\\\\\n (np.abs(ylima[0] - ylimb[0])*2 < (heighta + heightb))\\n return [s for s in source_list if intersect(s, source, image) and s is not source]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfcc3d91e6339665b4d87ec645b6e673\",\n \"score\": \"0.5336894\",\n \"text\": \"def window_index_time(t,windowsize,overlap):\\r\\n \\r\\n try:\\r\\n t=t.tolist()\\r\\n except:\\r\\n t=t\\r\\n \\r\\n t1=t[0]\\r\\n t2=t1 + timedelta(seconds=windowsize)\\r\\n pt1=[0]\\r\\n pt2=[othertime.findNearest(t2,t)]\\r\\n while t2 < t[-1]:\\r\\n t1 = t2 - timedelta(seconds=overlap)\\r\\n t2 = t1 + timedelta(seconds=windowsize)\\r\\n\\r\\n pt1.append(othertime.findNearest(t1,t))\\r\\n pt2.append(othertime.findNearest(t2,t))\\r\\n \\r\\n return pt1, pt2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee0fb54fc294738096866db948cf9574\",\n \"score\": \"0.53322244\",\n \"text\": \"def stackingWindows():\\n space = 50\\n offset = 70\\n cv2.moveWindow(\\\"Original image\\\", space, space)\\n cv2.moveWindow(\\\"Keypoints original\\\", space, hsize + space + offset)\\n cv2.moveWindow(\\\"Color matched\\\", wsize + space, space)\\n cv2.moveWindow(\\\"Keypoints Dark\\\", wsize + space, hsize + space + offset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2dbe93c405931c89b45b85775eb374d\",\n \"score\": \"0.5329009\",\n \"text\": \"def window_data(data: np.ndarray):\\n\\n w_len = 128\\n stride = w_len // 2\\n\\n no_offset_windows = np.split(data, 10)\\n offset_windows = np.split(data[stride:-stride], 9)\\n windows = [0] * 19\\n windows[::2] = no_offset_windows\\n windows[1::2] = offset_windows\\n windows = np.array(windows, dtype=np.float32)\\n\\n return windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a72cd3206e200d74355e33797ccd1663\",\n \"score\": \"0.5326207\",\n \"text\": \"def find_components(image,deltaPix,lens_rad_arcsec = 6.0,lens_rad_ratio = None,\\n center_x = None,center_y = None, gal_rad_ratio = 0.1,\\n min_size_arcsec=0.7,thresh=0.5, many_sources = True,\\n show_locations=False, title = None):\\n\\n # convert minimum component size in pixel units\\n min_size = int(min_size_arcsec / deltaPix)\\n \\n #Convert lens radius and central galaxy radius to pixels\\n if lens_rad_ratio == None:\\n lens_rad = int(lens_rad_arcsec / deltaPix)\\n else: lens_rad = int(len(image) * lens_rad_ratio)\\n gal_rad = int(len(image) * gal_rad_ratio)\\n \\n \\n# im2[im2 < im2.min() + 10.*thresh] = 0.\\n \\n # downscale source image to data resolution (for speed + easier for converting to data units)\\n #down = image_util.re_size(image, factor=supersampling_factor_source)\\n \\n # apply laplacian of gaussian (LoG) filter to enhance maxima\\n LoG = - gaussian_laplace(deepcopy(image), sigma = min_size, mode='constant', cval=0.) \\n \\n# LoG = - gaussian_laplace(deepcopy(im2), sigma = 2., mode='constant', cval=0.)\\n \\n filtered = deepcopy(LoG)\\n \\n# print(LoG.min(),LoG.max(),np.abs(LoG.min()) + thresh )\\n \\n# print(type(filtered))\\n \\n #background mean and std of filtered image \\n corners = np.zeros([4,5,5])\\n corners[0] = LoG[0:5,0:5]\\n corners[1] = LoG[-5:,0:5]\\n corners[2] = LoG[0:5,-5:]\\n corners[3] = LoG[-5:,-5:]\\n means = []\\n stds = []\\n for c in corners:\\n mn,med,s = sigma_clipped_stats(c,sigma=3.0)\\n means.append(mn)\\n stds.append(s)\\n \\n stds=np.array(stds)\\n means = np.array(means)\\n means_std = np.std(means)\\n# means_good = means[(means >= means.mean() - 1.0 * means_std) & (means <= means.mean() + 1.0 * means_std)]\\n means_good = means[(np.abs(means) <= np.abs(means).min() + 1.0 * means_std)]\\n mean_bg = np.mean(means_good)\\n std_bg = np.mean(stds[(np.abs(means) <= np.abs(means).min() + 1.0 * means_std)])\\n# print('LoG means: {}, Log means std: {}, Log means good: {}, LoG avg mean: {}'.format(means,means_std,means_good,mean_bg))\\n# print('min: {}, max: {}, cut: {}'.format(LoG.min(),LoG.max(),mean_bg + thresh))\\n# print(LoG.min(),LoG.max(),filtered.min() + thresh)\\n \\n \\n # assume all value below max*threshold can not be maxima, so put all to zero\\n# filtered[filtered < thresh*filtered.max()] = 0.\\n \\n# assume all value below min*threshold can not be maxima, so put all to zero\\n# filtered[filtered < filtered.min() + thresh * np.abs(filtered.min())] = 0.\\n# filtered[filtered < mean_bg + thresh] = 0.\\n filtered[filtered < mean_bg + 6.*std_bg] = 0. #set pixels below the mean + 6x threshold to 0\\n \\n # find coordinates of local maxima\\n #print(int(0.5 * min_size))\\n max_idx_2d_small = peak_local_max(filtered, min_distance=0) #All bright pixels\\n max_idx_2d_large = peak_local_max(filtered, min_distance=1) #peaks with min size of 1 pixel\\n \\n x_list_small, y_list_small = max_idx_2d_small[:, 1], max_idx_2d_small[:, 0]\\n x_list_large, y_list_large = max_idx_2d_large[:, 1], max_idx_2d_large[:, 0]\\n \\n im_center_x, im_center_y = len(image) / 2., len(image) / 2. #center of image\\n \\n if (center_x == None) & (center_y == None):\\n new_center_x, new_center_y = im_center_x,im_center_y\\n else:\\n new_center_x, new_center_y = center_x,center_y #new \\\"center\\\" = location of lens galaxy\\n \\n \\n #distance of each detected peak from center\\n R_small = np.sqrt((x_list_small - new_center_x)**2 + (y_list_small - new_center_y)**2) \\n R_large = np.sqrt((x_list_large - new_center_x)**2 + (y_list_large - new_center_y)**2)\\n \\n #Contaminant light is only bright pixels further from center than lens_rad\\n x_sats, y_sats = x_list_small[R_small > lens_rad], y_list_small[R_small > lens_rad]\\n \\n if many_sources:\\n x_lens, y_lens = deepcopy(x_list_small), deepcopy(y_list_small)\\n else:\\n x_lens, y_lens = deepcopy(x_list_large), deepcopy(y_list_large)\\n \\n# x_lens, y_lens = x_list_small[R_small <= lens_rad], y_list_small[R_small <= lens_rad]\\n \\n if (len(x_lens) == 0) & (len(y_lens) == 0):\\n x_lens = [0,15]\\n y_lens = [0,15]\\n \\n sources = QTable([x_lens, y_lens],names={'x_local_peak','y_local_peak'}) #make table of all detected objects\\n# print(x_list_large)\\n# print(y_list_large)\\n# print(sources)\\n \\n # show maxima on image for debug\\n \\n if show_locations:\\n# fig = plt.figure(figsize=(4, 4))\\n #plt.imshow(image, origin='lower', cmap=cmap_flux, norm=LogNorm(1e-2))\\n \\n f, axes = plt.subplots(1, 5, figsize=(20,5), sharex=False, sharey=False)\\n# plt.figure(figsize = (8,8))\\n# plt.subplot(1,2,1)\\n \\n axes[0].imshow(image, origin='lower', norm=SymLogNorm(5))\\n axes[0].set_title('Image')\\n axes[0].set_axis_off()\\n \\n \\n axes[1].imshow(LoG, origin='lower', norm=SymLogNorm(5))\\n axes[1].set_title('LoG Filtered Image')\\n axes[1].set_axis_off()\\n\\n# plt.subplot(1,2,2)\\n axes[2].imshow(filtered, origin='lower', norm=SymLogNorm(5))\\n axes[2].set_title('Final Filtered Image')\\n axes[2].set_axis_off()\\n \\n axes[3].imshow(image, origin='lower', norm=SymLogNorm(5))\\n for i in range(len(x_lens)):\\n axes[3].scatter([x_lens[i]], [y_lens[i]], c='red', s=60, marker='+')\\n \\n for i in range(len(x_list_large)):\\n axes[3].scatter([x_list_large[i]], [y_list_large[i]], c='black', s=100, marker='x')\\n axes[3].set_title('Detected Objects')\\n axes[3].set_axis_off()\\n \\n axes[4].imshow(image, origin='lower', norm=SymLogNorm(5))\\n \\n for i in range(len(x_sats)):\\n axes[4].scatter([x_sats[i]], [y_sats[i]], c='red', s=60, marker='+')\\n \\n# plt.annotate(i+1, (x_list[i], y_list[i]), color='black')\\n \\n# for i in range(len(x_mask)):\\n# plt.scatter([x_mask[i]], [y_mask[i]], c='red', s=100, marker='*')\\n# plt.annotate(i+1, (x_mask[i], y_mask[i]), color='red')\\n axes[4].scatter(new_center_x, new_center_y,c='red', s=100, marker='*')\\n \\n draw_lens_circle = Circle((new_center_x, new_center_y),lens_rad ,fill=False)\\n draw_gal_circle = Circle((new_center_x, new_center_y),gal_rad, fill = False)\\n# plt.gcf().gca().add_artist(draw_lens_circle)\\n# plt.gcf().gca().add_artist(draw_gal_circle)\\n axes[4].add_patch(draw_lens_circle)\\n# axes[4].add_patch(draw_gal_circle)\\n \\n axes[4].set_title('Pixels to Mask: \\\\n r = {:.3f}'.format(lens_rad_arcsec))\\n axes[4].text(1, 1, \\\"detected components\\\", color='red')\\n axes[4].set_axis_off()\\n \\n if title != None:\\n f.suptitle(title, fontsize = 15)\\n# plt.show()\\n \\n \\n return (x_sats, y_sats), (new_center_x, new_center_y), sources\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60ed9ffa87ce8d1c7e0bca3debca77a0\",\n \"score\": \"0.53208643\",\n \"text\": \"def slide_window(gray_image, neighbor=2, current_row=0, current_col=0, fill='constant'):\\n max_row, max_col = gray_image.shape\\n win_shape = (2 * neighbor + 1, 2 * neighbor + 1) # window shape - 5 x 5\\n win_img = np.zeros(win_shape)\\n\\n for row_offset in range(-1 * neighbor, neighbor + 1):\\n for col_offset in range(-1 * neighbor, neighbor + 1):\\n\\n cp_i = current_row + row_offset # pixel row index on the image with offsets\\n cp_j = current_col + col_offset # pixel col index on the image with offsets\\n\\n if 0 <= cp_i < max_row and 0 <= cp_j < max_col:\\n win_img[neighbor + row_offset, neighbor + col_offset] = gray_image[cp_i, cp_j]\\n else:\\n if fill is 'constant':\\n win_img[neighbor + row_offset, neighbor + col_offset] = 0\\n elif fill is 'mirror':\\n if cp_i >= max_row:\\n cp_i = max_row - row_offset\\n if cp_j >= max_col:\\n cp_j = max_col - col_offset\\n if cp_i < 0:\\n cp_i = -1 - cp_i\\n if cp_j < 0:\\n cp_j = -1 - cp_j\\n\\n win_img[neighbor + row_offset, neighbor + col_offset] = gray_image[cp_i, cp_j]\\n\\n return win_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99bf6a0f83afb7987a1af25179ec6a53\",\n \"score\": \"0.5320519\",\n \"text\": \"def _check_windows(\\n self,\\n window_midpoints: Iterable[float],\\n obscode: str,\\n orbit: Orbit,\\n tolerance: float,\\n start_mjd: Optional[float] = None,\\n end_mjd: Optional[float] = None,\\n window_size: int = 7,\\n datasets: Optional[set[str]] = None,\\n ) -> Iterable[Union[PrecoveryCandidate, FrameCandidate]]:\\n # Propagate the orbit with n-body to every window center\\n # Since the window midpoints are calculated from the observations\\n # in the database then they are in the UTC timescale so let's use that\\n orbit_propagated = orbit.propagate(\\n window_midpoints,\\n PropagationIntegrator.N_BODY,\\n time_scale=EpochTimescale.UTC,\\n )\\n\\n # Calculate the location of the orbit on the sky with n-body propagation\\n # Again, we do this in the UTC timescale to match the observations in the database\\n window_ephems = orbit.compute_ephemeris(\\n obscode,\\n window_midpoints,\\n PropagationIntegrator.N_BODY,\\n time_scale=EpochTimescale.UTC,\\n )\\n window_healpixels = radec_to_healpixel(\\n np.array([w.ra for w in window_ephems]),\\n np.array([w.dec for w in window_ephems]),\\n self.frames.healpix_nside,\\n ).astype(int)\\n\\n # Using the propagated orbits, check each window. Propagate the orbit from the center of\\n # window using 2-body to find any HealpixFrames where a detection could have occured\\n for window_midpoint, window_ephem, window_healpixel, orbit_window in zip(\\n window_midpoints, window_ephems, window_healpixels, orbit_propagated\\n ):\\n start_mjd_window = window_midpoint - (window_size / 2)\\n end_mjd_window = window_midpoint + (window_size / 2)\\n\\n # Check if start_mjd_window is not earlier than start_mjd (if defined)\\n # If start_mjd_window is earlier, then set start_mjd_window to start_mjd\\n if (start_mjd is not None) and (start_mjd_window < start_mjd):\\n logger.debug(\\n f\\\"Window start MJD [UTC] ({start_mjd_window}) is earlier than\\\"\\n f\\\" desired start MJD [UTC] ({start_mjd}).\\\"\\n )\\n start_mjd_window = start_mjd\\n\\n # Check if end_mjd_window is not later than end_mjd (if defined)\\n # If end_mjd_window is later, then set end_mjd_window to end_mjd\\n if (end_mjd is not None) and (end_mjd_window > end_mjd):\\n logger.debug(\\n f\\\"Window end MJD [UTC] ({end_mjd_window}) is later than desired end\\\"\\n f\\\" MJD [UTC] ({end_mjd}).\\\"\\n )\\n end_mjd_window = end_mjd\\n\\n yield from self._check_window(\\n window_start=start_mjd_window,\\n window_end=end_mjd_window,\\n orbit=orbit_window,\\n obscode=obscode,\\n tolerance=tolerance,\\n datasets=datasets,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b546b2fb710590edfdefc0a7247c076\",\n \"score\": \"0.53176826\",\n \"text\": \"def viz_windows(self, score_img, mode):\\n if mode == 'filtered':\\n lw_img = window_image(self.windows_left, 'x_filtered', color=(0, 255, 0))\\n rw_img = window_image(self.windows_right, 'x_filtered', color=(0, 255, 0))\\n elif mode == 'raw':\\n color = (255, 0, 0)\\n win_left_detected, arg = filter_window_list(self.windows_left, False, False, remove_undetected=True)\\n win_right_detected, arg = filter_window_list(self.windows_right, False, False, remove_undetected=True)\\n lw_img = window_image(win_left_detected, 'x_measured', color, color, color)\\n rw_img = window_image(win_right_detected, 'x_measured', color, color, color)\\n else:\\n raise Exception('mode is not valid')\\n combined = lw_img + rw_img\\n return cv2.addWeighted(score_img, 1, combined, 0.5, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2856d02b53742c6158ba9555be6c6677\",\n \"score\": \"0.53174835\",\n \"text\": \"def detect_cars_multi_area(img,\\n clf,\\n xy_window=(64, 64),\\n stride=None,\\n y_start_stops=None,\\n image_size_factors=[1],\\n x_padding=None,\\n heatmap=False,\\n n_jobs=1):\\n\\n if n_jobs < 0:\\n n_jobs = multiprocessing.cpu_count()\\n\\n if stride is None:\\n stride = np.repeat([[xy_window[0], xy_window[1]]], len(image_size_factors), axis=0)\\n\\n if y_start_stops is None:\\n y_start_stops = np.repeat([[0, img.shape[0] - 1]], len(image_size_factors), axis=0)\\n\\n if x_padding is None:\\n x_padding = np.repeat([0], len(image_size_factors), axis=0)\\n\\n # use joblib to run processing in parallel\\n result = Parallel(n_jobs=n_jobs)(\\n delayed(detect_cars)(\\n img,\\n clf,\\n xy_window,\\n cur_stride,\\n cur_sizes_factors,\\n cur_y_start_stop,\\n cur_x_padding)\\n for cur_stride, cur_sizes_factors, cur_y_start_stop, cur_x_padding in\\n zip(stride, image_size_factors, y_start_stops, x_padding))\\n\\n bounding_boxes, des_func = zip(*result)\\n bounding_boxes = np.vstack(bounding_boxes)\\n\\n des_func = np.concatenate(des_func)\\n\\n if heatmap:\\n heat = np.zeros(img.shape[:2], dtype=np.float32)\\n for bb, df in zip(bounding_boxes, des_func):\\n heat[bb[1]:bb[3], bb[0]:bb[2]] += df\\n\\n return heat\\n\\n return bounding_boxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51656b55e9b521df4304960c7878d632\",\n \"score\": \"0.5315555\",\n \"text\": \"def construct_sliding_windows(sequence_length: int, sliding_window_size: int):\\n sliding_windows = []\\n stride = int(sliding_window_size / 2)\\n start_index = 0\\n end_index = 0\\n while end_index < sequence_length:\\n end_index = min(start_index + sliding_window_size, sequence_length)\\n left_value = 1 if start_index == 0 else 0\\n right_value = 1 if end_index == sequence_length else 0\\n mask = [left_value] * int(sliding_window_size / 4) + [1] * int(sliding_window_size / 2) \\\\\\n + [right_value] * (sliding_window_size - int(sliding_window_size / 2) - int(sliding_window_size / 4))\\n mask = mask[: end_index - start_index]\\n sliding_windows.append((start_index, end_index, mask))\\n start_index += stride\\n assert sum([sum(window[2]) for window in sliding_windows]) == sequence_length\\n return sliding_windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ca3413298edcef1ccf39896c0f7524d\",\n \"score\": \"0.5311973\",\n \"text\": \"def SH_FindOverlap(xcenter, ycenter, xlength, ylength, xp_corner, yp_corner):\\n\\n areaClipped = 0.0\\n top = ycenter + 0.5 * ylength\\n bottom = ycenter - 0.5 * ylength\\n\\n left = xcenter - 0.5 * xlength\\n right = xcenter + 0.5 * xlength\\n\\n nVertices = 4 # input detector pixel vertices\\n MaxVertices = 9\\n # initialize xPixel, yPixel to the detector pixel corners.\\n # xPixel,yPixel will become the clipped polygon vertices inside the cube pixel\\n # xnew,ynew xpixel and ypixel of size MaxVertices\\n\\n xPixel = []\\n yPixel = []\\n\\n xnew = []\\n ynew = []\\n\\n for j in range(0, 9):\\n xnew.append(0.0)\\n ynew.append(0.0)\\n xPixel.append(0.0)\\n yPixel.append(0.0)\\n\\n\\n # Xpixel, YPixel closed (5 corners)\\n for i in range(0, 4):\\n xPixel[i] = xp_corner[i]\\n yPixel[i] = yp_corner[i]\\n xPixel[4] = xp_corner[0]\\n yPixel[4] = yp_corner[0]\\n\\n\\n for i in range(0, 4): # 0:left, 1: right, 2: bottom, 3: top\\n nVertices2 = 0\\n for j in range(0, nVertices):\\n x1 = xPixel[j]\\n y1 = yPixel[j]\\n x2 = xPixel[j + 1]\\n y2 = yPixel[j + 1]\\n condition = calcCondition(i, x1, y1, x2, y2, left, right, top, bottom)\\n x = 0\\n y = 0\\n\\n if condition == 1:\\n x, y = solveIntersection(i, x1, y1, x2, y2,\\n left, right, top, bottom)\\n nVertices2 = addpoint(x, y, xnew, ynew, nVertices2);\\n nVertices2 = addpoint(x2, y2, xnew, ynew, nVertices2)\\n\\n elif condition == 2:\\n nVertices2 = addpoint(x2, y2, xnew, ynew, nVertices2)\\n elif condition == 3:\\n x, y = solveIntersection(i, x1, y1, x2, y2,\\n left, right, top, bottom)\\n nVertices2 = addpoint(x, y, xnew, ynew, nVertices2)\\n\\n#\\tcondition == 4: points outside\\n# Done looping over J corners\\n nVertices2 = addpoint(xnew[0], ynew[0], xnew, ynew, nVertices2) # close polygon\\n\\n if nVertices2 > MaxVertices:\\n raise Error2DPolygon(\\\" Failure in finding the clipped polygon, nVertices2 > 9 \\\")\\n\\n\\n nVertices = nVertices2 - 1;\\n\\n for k in range(0, nVertices2):\\n xPixel[k] = xnew[k]\\n yPixel[k] = ynew[k]\\n\\n# done loop over top,bottom,left,right\\n nVertices = nVertices + 1\\n\\n\\n if nVertices > 0:\\n areaClipped = FindAreaPoly(nVertices, xPixel, yPixel);\\n\\n\\n return areaClipped;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9abeec8f8b04f1c6be3f43d68ee829c0\",\n \"score\": \"0.5310946\",\n \"text\": \"def neighborhood((y, x), (height, width)):\\n return [(yt, xt) for xt in [x + 1, x, x - 1]\\n for yt in [y + 1, y, y - 1]\\n if 0 <= xt < width and 0 <= yt < height\\n and (xt, yt) != (x, y)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d41a170efe92eb3cb5758107557e4d05\",\n \"score\": \"0.53097194\",\n \"text\": \"def find_window_centroids(self, warped):\\n window_width = self.window_width\\n window_height = self.window_height\\n margin = self.margin\\n \\n window_centroids = [] # store the (left,right) window centroids positions per level\\n window = np.ones(window_width) # Create window templates for doing convolutions\\n \\n # First find the two starting positions right and left lines by using np.sum to get the vertical image slice\\n # and then np.convolve the vertical image slice with the window template\\n \\n # sum the bottom quarter of image to get slice\\n l_sum = np.sum(warped[int(3*warped.shape[0]/4):,:int(3*warped.shape[1]/2)], axis=0)\\n l_center = np.argmax(np.convolve(window,l_sum)) - window_width/2\\n r_sum = np.sum(warped[int(3*warped.shape[0]/4):int(3*warped.shape[1]/2),:], axis=0)\\n r_center = np.argmax(np.convolve(window,r_sum)) - window_width/2 + int(warped.shape[1]/2)\\n \\n # add what we found for the first layer\\n window_centroids.append((l_center,r_center))\\n \\n # Go through each layer looking for max pixel location\\n for level in range(1,(int)(warped.shape[0]/window_height)):\\n # convolve the window into the vertical slice of the image\\n image_layer = np.sum(warped[int(warped.shape[0]-(level+1)*window_height):int(warped.shape[0]-level*window_height),:], axis=0)\\n conv_signal = np.convolve(window,image_layer)\\n # Find the best left centroid by using past left center as a reference \\n # Use window_width/2 as offset because convolution signal reference is at right side of window, not center of window\\n offset = window_width/2\\n l_min_index = int(max(l_center+offset-margin,0))\\n l_max_index = int(max(l_center+offset+margin,warped.shape[1]))\\n l_center = np.argmax(conv_signal[l_min_index:l_max_index]) + l_min_index - offset\\n \\n # Find the best right centroid by using past right center as a reference\\n r_min_index = int(max(r_center+offset-margin,0))\\n r_max_index = int(max(r_center+offset+margin,warped.shape[1]))\\n r_center = np.argmax(conv_signal[r_min_index:r_max_index]) + r_min_index - offset\\n # Add what we found for the layer\\n window_centroids.append((l_center,r_center))\\n \\n self.recent_centers.append(window_centroids)\\n # return averaged value of of the line centers, helps to keep the markers from jumping around too much\\n average_line_centers = np.average(self.recent_centers[-self.smooth_factor:], axis=0)\\n return average_line_centers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad6e66b49222543fa70ecf05631af562\",\n \"score\": \"0.53083515\",\n \"text\": \"def locate_slice_chunk(slice_start, slice_stop, height, overlap_metadata):\\n if slice_stop < slice_start:\\n raise ValueError(\\n \\\"Stopping index must be larger than the starting index!!!\\\")\\n g_nrow = overlap_metadata.shape[0] + 1\\n side = overlap_metadata[0, 0, 1]\\n overlap_list = overlap_metadata[:, 0, 0]\\n if side == 1:\\n list_slices = [(np.arange(i * height, i * height + height) -\\n np.sum(overlap_list[0: i])) for i in range(g_nrow)]\\n else:\\n list_slices = [\\n (np.arange(i * height + height - 1, i * height - 1, -1) -\\n np.sum(overlap_list[0: i])) for i in range(g_nrow)]\\n list_slices = np.asarray(list_slices)\\n results = []\\n for i, list1 in enumerate(list_slices):\\n result1 = []\\n if side == 1:\\n for slice_idx in range(slice_start, slice_stop):\\n pos = np.squeeze(np.where(list1 == slice_idx)[0])\\n if pos.size == 1:\\n fact = 1.0\\n if i == 0:\\n ver_overlap = overlap_list[i]\\n dis1 = len(list1) - pos - 1\\n if dis1 < ver_overlap:\\n fact = dis1 / (ver_overlap - 1)\\n elif i == (g_nrow - 1):\\n ver_overlap = overlap_list[i - 1]\\n if pos < ver_overlap:\\n fact = pos / (ver_overlap - 1)\\n else:\\n ver_overlap1 = overlap_list[i]\\n dis1 = len(list1) - pos - 1\\n if dis1 < ver_overlap1:\\n fact = dis1 / (ver_overlap1 - 1)\\n if pos < ver_overlap1:\\n fact = pos / (ver_overlap1 - 1)\\n ver_overlap2 = overlap_list[i - 1]\\n dis1 = len(list1) - pos - 1\\n if dis1 < ver_overlap2:\\n fact = dis1 / (ver_overlap2 - 1)\\n if pos < ver_overlap2:\\n fact = pos / (ver_overlap2 - 1)\\n result1.append([i, pos, fact])\\n else:\\n for slice_idx in range(slice_start, slice_stop):\\n pos = np.squeeze(np.where(list1 == slice_idx)[0])\\n if pos.size == 1:\\n fact = 1.0\\n if i == 0:\\n ver_overlap = overlap_list[i]\\n if pos < ver_overlap:\\n fact = 1.0 * pos / (ver_overlap - 1)\\n elif i == (g_nrow - 1):\\n ver_overlap = overlap_list[i - 1]\\n dis1 = len(list1) - pos - 1\\n if dis1 < ver_overlap:\\n fact = 1.0 * dis1 / (ver_overlap - 1)\\n else:\\n ver_overlap1 = overlap_list[i]\\n dis1 = len(list1) - pos - 1\\n if dis1 < ver_overlap1:\\n fact = 1.0 * dis1 / (ver_overlap1 - 1)\\n if pos < ver_overlap1:\\n fact = 1.0 * pos / (ver_overlap1 - 1)\\n ver_overlap2 = overlap_list[i - 1]\\n dis1 = len(list1) - pos - 1\\n if dis1 < ver_overlap2:\\n fact = 1.0 * dis1 / (ver_overlap2 - 1)\\n if pos < ver_overlap2:\\n fact = 1.0 * pos / (ver_overlap2 - 1)\\n result1.append([i, pos, fact])\\n if len(result1) > 0:\\n results.append(result1)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f22069458f02f2cb41745af34accc5c1\",\n \"score\": \"0.5306441\",\n \"text\": \"def identify_dbs(image):\\n locations = {\\\"red\\\": Point(), \\\"green\\\": Point(), \\\"blue\\\": Point()}\\n masks = {\\\"red\\\": [], \\\"green\\\": [], \\\"blue\\\": []}\\n\\n bridge = cv_bridge.CvBridge()\\n image = bridge.imgmsg_to_cv2(image, \\\"bgr8\\\")\\n hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\\n\\n # upper and lower bounds for red\\n # using python 3 bgr [0,0,188] = hsv [0, 255, 188]\\n lower_red = numpy.array([0, 100, 100]) \\n upper_red = numpy.array([10, 255, 255])\\n masks[\\\"red\\\"] = cv2.inRange(hsv, lower_red, upper_red)\\n\\n # upper and lower bounds for green\\n # using python 3 bgr [0,175,0] = hsv [60, 255, 175]\\n lower_green = numpy.array([50, 100, 100]) \\n upper_green = numpy.array([70, 255, 255])\\n masks[\\\"green\\\"] = cv2.inRange(hsv, lower_green, upper_green)\\n\\n # upper and lower bounds for blue\\n # using python 3 bgr [176, 0, 17] = hsv [123, 255, 176]\\n lower_blue = numpy.array([113, 100, 100])\\n upper_blue = numpy.array([133, 255, 255])\\n masks[\\\"blue\\\"] = cv2.inRange(hsv, lower_blue, upper_blue)\\n\\n x, y, w, h = 0, 0, image.shape[1]//3, image.shape[0]\\n\\n for color, mask in masks.items():\\n pixels = {\\\"left\\\": 0, \\\"middle\\\": 0, \\\"right\\\": 0}\\n \\n # define section of image to use for left, middle and right\\n left = mask[y:y+h, x:x+w]\\n middle = mask[y:y+h, x+w:x+w+w]\\n right = mask[y:y+h, x+w+w:x+3*w]\\n\\n # count the number of pixels in each section\\n pixels[\\\"left\\\"] = cv2.countNonZero(left)\\n pixels[\\\"middle\\\"] = cv2.countNonZero(middle)\\n pixels[\\\"right\\\"] = cv2.countNonZero(right)\\n location = max(pixels, key=pixels.get)\\n\\n # map the relative position of the db (left, middle, right) to the correct Point()\\n locations[color] = db_locations[location]\\n \\n return locations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41d6de92bdbd04ed9490e59ca49a4072\",\n \"score\": \"0.53025955\",\n \"text\": \"def apply_windows(slided_signals, window_type=\\\"hann\\\"):\\n # length of each slided signal\\n n = slided_signals.shape[-1]\\n window = signal.get_window(window_type, n)\\n windowed_signals = numpy.multiply(slided_signals, window)\\n return windowed_signals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"393196ceb7c954688e854c3991b678b7\",\n \"score\": \"0.52932894\",\n \"text\": \"def fit_ms2(stack, min_distances=(70,50,50), sigma_small=1, \\n sigma_big=4, bg_radius=4, fitwindow_rad_xy=10, \\n fitwindow_rad_z=2):\\n\\n # Task: change size to minimum distance\\n def get_fitwindow(data, peak, xy_rad, z_rad):\\n \\\"\\\"\\\"Retrieve section of image stack corresponding to given\\n window around a point and the coordinate adjustments necessary\\n to convert window coordinates to coordinates in the original image\\\"\\\"\\\"\\n \\n # Set the start points for windows and \\\"adjust\\\" them if they get \\n # to negative numbers.\\n zmin = peak[0] - z_rad\\n xmin = peak[1] - xy_rad\\n ymin = peak[2] - xy_rad\\n # Initialize adjustments to values that are correct if no edge problems \\n # are encountered.\\n z_adj = -z_rad\\n x_adj = -xy_rad\\n y_adj = -xy_rad\\n # Update mins and adjustments if windows start at negative coordinates.\\n if (zmin < 0):\\n zmin = 0\\n z_adj = -peak[0]\\n if (xmin < 0):\\n xmin = 0\\n x_adj = -peak[1]\\n if (ymin < 0):\\n ymin = 0\\n y_adj = -peak[2]\\n\\n # Get end points, constained by max coordinate in data.\\n zmax = min(data.shape[0] - 1, peak[0] + z_rad)\\n xmax = min(data.shape[1] - 1, peak[1] + xy_rad)\\n ymax = min(data.shape[2] - 1, peak[2] + xy_rad)\\n\\n return (data[zmin:(zmax+1), xmin:(xmax+1), ymin:(ymax+1)], z_adj, x_adj, y_adj)\\n \\n def relabel(peak_ids, oldparams, mask):\\n \\\"\\\"\\\"Renumber labelmask and corresponding fit parameters\\n Set background as 0, objects in order 1...end.\\n \\\"\\\"\\\"\\n spot_data = {}\\n peak_num = 1\\n for peak in peak_ids:\\n #coords = np.where(mask == peak)\\n paramsnew = oldparams[peak-1,:] # object 1 will be fitparams row 0\\n # Rearrange params from fit function so coordinates lead.\\n spot_data[peak_num] = paramsnew[[1,2,3,0,4,5,6]]\\n peak_num = peak_num + 1\\n return spot_data\\n\\n def clamp(n, minn, maxn):\\n \\\"\\\"\\\"Bound a number between two constants\\\"\\\"\\\"\\n return max(min(maxn, n), minn)\\n \\n def fit_frame(substack, min_distances, sigma_small, \\n sigma_big, bg_radius, fitwindow_rad_xy, \\n fitwindow_rad_z):\\n \\\"\\\"\\\"Perform 3D gaussian fitting on a 3D image stack.\\\"\\\"\\\"\\n\\n # Filter and background subtract image.\\n dog = dog_filter(substack, sigma_small, sigma_big)\\n bg = ndi.filters.minimum_filter(dog, bg_radius)\\n dog_bs = dog - bg\\n\\n # Make a labelmask corresponding to local maxima peaks.\\n peak_window_size = (min_distances[0] * 2 + 1, min_distances[1] * 2 + 1, min_distances[2] * 2 + 1)\\n mask, peaks = peak_local_max_nD(dog_bs, peak_window_size)\\n\\n # Fit 3D gaussian in window surrounding each local maximum.\\n fitparams = np.ndarray((0,7))\\n for peak in peaks:\\n fitwindow, z_adj, x_adj, y_adj = get_fitwindow(substack, peak, fitwindow_rad_xy, \\n fitwindow_rad_z)\\n opt = fitgaussian3d(fitwindow)\\n if opt.success:\\n peak_fitparams = opt.x\\n # Move center coordinates to match center of gaussian fit, ensure they're within image. \\n # If they're outside the image, coordinate is assigned as the edge of the image.\\n peak_fitparams[0] = int(round(clamp((peak[0] + peak_fitparams[0] + z_adj), 0, substack.shape[-3]-1)))\\n peak_fitparams[1] = int(round(clamp((peak[1] + peak_fitparams[1] + x_adj), 0, substack.shape[-2]-1)))\\n peak_fitparams[2] = int(round(clamp((peak[2] + peak_fitparams[2] + y_adj), 0, substack.shape[-1]-1)))\\n fitparams = np.vstack((fitparams, peak_fitparams))\\n # If fit fails, add dummy entry for spot.\\n else:\\n fitparams = np.vstack((fitparams, np.array([z_adj,x_adj,y_adj,0,np.inf,np.inf,np.inf])))\\n return fitparams\\n \\n #### Main ####\\n # Do fitting on first frame.\\n fit_data_frame0 = fit_frame(stack[0], min_distances, sigma_small, \\n sigma_big, bg_radius, fitwindow_rad_xy, \\n fitwindow_rad_z)\\n # Make fit_data a list of ndarrays.\\n fit_data = [fit_data_frame0]\\n \\n # Fit the rest of the frames, add their data to fit_data.\\n for i in range(1, stack.shape[0]):\\n print(i, end=' ')\\n fit_data_thisframe = fit_frame(stack[i], min_distances, sigma_small, \\n sigma_big, bg_radius, fitwindow_rad_xy, \\n fitwindow_rad_z)\\n fit_data.append(fit_data_thisframe)\\n \\n return fit_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76779ed5aa0fe270c3be6772117e8adb\",\n \"score\": \"0.5292007\",\n \"text\": \"def find_objects_in_image(self, image, visualize):\\n self.select(image)\\n\\n for scan_region in self.scan_regions:\\n self.set_scaling(scan_region[0])\\n full, small = self.find_instances(image, scan_region[1], 0.5)\\n if (visualize):\\n fig = plt.figure(figsize=(30, 20))\\n plt.imshow(small)\\n plt.title(\\\"Scan region {} using scaling {}\\\".format(scan_region[1], scan_region[0]))\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"133d29c53c08a20a55c8bd001c6f0865\",\n \"score\": \"0.5288611\",\n \"text\": \"def basic_lane_find(self, binary_warped):\\n\\t\\t# Take a histogram of the bottom half of the image\\n\\t\\thistogram = np.sum(binary_warped[binary_warped.shape[0] // 2:, :, 0], axis=0)\\n\\t\\t# Find the peak of the left and right halves of the histogram\\n\\t\\tmidpoint = np.int(histogram.shape[0] / 2)\\n\\t\\tleftx_base = np.argmax(histogram[:midpoint])\\n\\t\\trightx_base = np.argmax(histogram[midpoint:]) + midpoint\\n\\t\\t# Choose the number of sliding windows\\n\\t\\tnwindows = 9\\n\\t\\t# Set height of windows\\n\\t\\twindow_height = np.int(binary_warped.shape[0] / nwindows)\\n\\t\\t# Identify the x and y positions of all nonzero pixels in the image\\n\\t\\tnonzero = binary_warped.nonzero()\\n\\t\\tnonzeroy = np.array(nonzero[0])\\n\\t\\tnonzerox = np.array(nonzero[1])\\n\\t\\t# Current positions to be updated for each window\\n\\t\\tleftx_current = leftx_base\\n\\t\\trightx_current = rightx_base\\n\\t\\t# Set the width of the windows +/- margin\\n\\t\\tmargin = 100\\n\\t\\t# Set the minimum number of pixels found to recenter window\\n\\t\\tminpix = 50\\n\\t\\t# Create empty lists to receive left and right lane pixel indices\\n\\t\\tleft_lane_inds = []\\n\\t\\tright_lane_inds = []\\n\\t\\t# Step through the windows one by one\\n\\t\\tfor window in range(nwindows):\\n\\t\\t\\t# Identify window boundaries in x and y (and right and left)\\n\\t\\t\\twin_y_low = binary_warped.shape[0] - (window + 1) * window_height\\n\\t\\t\\twin_y_high = binary_warped.shape[0] - window * window_height\\n\\t\\t\\twin_xleft_low = leftx_current - margin\\n\\t\\t\\twin_xleft_high = leftx_current + margin\\n\\t\\t\\twin_xright_low = rightx_current - margin\\n\\t\\t\\twin_xright_high = rightx_current + margin\\n\\t\\t\\t# Identify the nonzero pixels in x and y within the window\\n\\t\\t\\tgood_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (\\n\\t\\t\\t\\tnonzerox < win_xleft_high)).nonzero()[0]\\n\\t\\t\\tgood_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (\\n\\t\\t\\t\\tnonzerox < win_xright_high)).nonzero()[0]\\n\\t\\t\\t# Append these indices to the lists\\n\\t\\t\\tleft_lane_inds.append(good_left_inds)\\n\\t\\t\\tright_lane_inds.append(good_right_inds)\\n\\t\\t\\t# If you found > minpix pixels, recenter next window on their mean position\\n\\t\\t\\tif len(good_left_inds) > minpix:\\n\\t\\t\\t\\tleftx_current = np.int(np.mean(nonzerox[good_left_inds]))\\n\\t\\t\\tif len(good_right_inds) > minpix:\\n\\t\\t\\t\\trightx_current = np.int(np.mean(nonzerox[good_right_inds]))\\n\\t\\t# Concatenate the arrays of indices\\n\\t\\tleft_lane_inds = np.concatenate(left_lane_inds)\\n\\t\\tright_lane_inds = np.concatenate(right_lane_inds)\\n\\t\\t# Extract left and right line pixel positions\\n\\t\\tleftx = nonzerox[left_lane_inds]\\n\\t\\tlefty = nonzeroy[left_lane_inds]\\n\\t\\trightx = nonzerox[right_lane_inds]\\n\\t\\trighty = nonzeroy[right_lane_inds]\\n\\t\\t# Fit a second order polynomial to each\\n\\t\\tself.left_fit = np.polyfit(lefty, leftx, 2)\\n\\t\\tself.right_fit = np.polyfit(righty, rightx, 2)\\n\\t\\t#Generate x and y values for plotting\\n\\t\\tploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])\\n\\t\\tleft_fitx = self.left_fit[0] * ploty ** 2 + self.left_fit[1] * ploty + self.left_fit[2]\\n\\t\\tright_fitx = self.right_fit[0] * ploty ** 2 + self.right_fit[1] * ploty + self.right_fit[2]\\n\\t\\tself.detected = True\\n\\n\\t\\treturn left_fitx, right_fitx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f9e4956db8a8b85763e63f1592d1d43\",\n \"score\": \"0.5281523\",\n \"text\": \"def new_video_sample_grid(dset, window_dims=None, window_overlap=0.0,\\n space_dims=None, time_dim=None, # TODO\\n classes_of_interest=None, ignore_coverage_thresh=0.6,\\n negative_classes={'ignore', 'background'},\\n use_annots=True, legacy=True, verbose=1):\\n import kwarray\\n from ndsampler import isect_indexer\\n keepbound = True\\n\\n if classes_of_interest:\\n raise NotImplementedError\\n\\n # Create a sliding window object for each specific image (because they may\\n # have different sizes, technically we could memoize this)\\n vidid_to_slider = {}\\n for vidid, video in dset.index.videos.items():\\n gids = dset.index.vidid_to_gids[vidid]\\n num_frames = len(gids)\\n full_dims = [num_frames, video['height'], video['width']]\\n window_dims_ = full_dims if window_dims == 'full' else window_dims\\n slider = kwarray.SlidingWindow(full_dims, window_dims_,\\n overlap=window_overlap,\\n keepbound=keepbound,\\n allow_overshoot=True)\\n\\n vidid_to_slider[vidid] = slider\\n\\n @ub.memoize\\n def _lut_warp(gid):\\n warp_img_to_vid = dset.index.imgs[gid].get('warp_img_to_vid', None)\\n return kwimage.Affine.coerce(warp_img_to_vid)\\n\\n _isect_index = isect_indexer.FrameIntersectionIndex.from_coco(dset)\\n\\n positives = []\\n negatives = []\\n targets = []\\n positive_idxs = []\\n negative_idxs = []\\n for vidid, slider in vidid_to_slider.items():\\n video_regions = list(slider)\\n gids = dset.index.vidid_to_gids[vidid]\\n for vid_region in video_regions:\\n t_sl, y_sl, x_sl = vid_region\\n vid_box = kwimage.Boxes.from_slice((y_sl, x_sl))\\n region_gids = gids[t_sl]\\n\\n if use_annots:\\n region_aids = []\\n for gid in region_gids:\\n warp_img_to_vid = _lut_warp(gid)\\n # Check to see what annotations this window-box overlaps with\\n # (in image space!)\\n img_box = vid_box.warp(warp_img_to_vid.inv())\\n aids = _isect_index.overlapping_aids(gid, img_box)\\n region_aids.extend(aids)\\n\\n pos_aids = sorted(region_aids)\\n else:\\n pos_aids = None\\n\\n time_slice = vid_region[0]\\n space_slice = vid_region[1:3]\\n tr = {\\n 'vidid': vidid,\\n 'time_slice': time_slice,\\n 'space_slice': space_slice,\\n # 'slices': region,\\n 'gids': region_gids,\\n 'aids': pos_aids,\\n }\\n targets.append(tr)\\n if pos_aids:\\n positive_idxs.append(len(targets))\\n if legacy:\\n positives.append(tr)\\n else:\\n negative_idxs.append(len(targets))\\n if legacy:\\n negatives.append(tr)\\n\\n if verbose:\\n print('Found {} targets'.format(len(targets)))\\n if use_annots:\\n print('Found {} positives'.format(len(positive_idxs)))\\n print('Found {} negatives'.format(len(negative_idxs)))\\n\\n sample_grid = {\\n 'targets': targets,\\n 'positives_indexes': positive_idxs,\\n 'negatives_indexes': negative_idxs,\\n }\\n if legacy:\\n sample_grid.update({\\n # Deprecated:\\n 'positives': positives,\\n 'negatives': negatives,\\n })\\n return sample_grid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e02e5203cedd5eed4ea61b521ff23ebb\",\n \"score\": \"0.52775663\",\n \"text\": \"def extract_walls(img_array,x_scale,y_scale,wall_height):\\n\\n wall_th = 2\\n length = 0\\n wall_list = []\\n\\n #check for horizontal walls first\\n for row in range(img_array.shape[0]):\\n for col in range(img_array.shape[1]):\\n \\n sec = img_array.astype(int)[row:row+2,col:col+2]\\n \\n if left_edge(sec):\\n #check two steps to the right\\n next_sec = img_array.astype(int)[row:row+2, col+1:col+3]\\n next_next_sec = img_array.astype(int)[row:row+2, col+2:col+4]\\n\\n #if horizontal wall, get coordinates and count length\\n if is_wall(next_sec) and not right_edge(next_next_sec): \\n #record corner coordinates\\n x = col +1\\n y = row\\n while is_wall(next_sec):\\n #start counting length across, until right edge found\\n length +=1\\n col +=1\\n next_sec = img_array.astype(int)[row:row+2, col:col+2]\\n #create wall object and store in list \\n new_wall = Wall(x*x_scale,y*y_scale,length*x_scale,wall_th*y_scale,wall_height)\\n wall_list.append(new_wall)\\n length = 0\\n\\n #check for vertical walls\\n for col in range(img_array.shape[1]):\\n for row in range(img_array.shape[0]):\\n\\n sec = img_array.astype(int)[row:row+2,col:col+2]\\n \\n if top_edge(sec): \\n #check two steps below\\n next_sec = img_array.astype(int)[row+1:row+3, col:col+2]\\n next_next_sec = img_array.astype(int)[row+2:row+4, col:col+2]\\n\\n #if vertical wall, get coordinates and count length\\n if is_wall(next_sec) and is_wall(next_next_sec):\\n x = col\\n y = row\\n while is_wall(next_sec):\\n #start counting length downwards, until bottom edge found\\n length += 1\\n row += 1\\n next_sec = img_array.astype(int)[row:row+2, col:col+2]\\n #create wall object and store in list\\n new_wall = Wall(x*x_scale,y*y_scale,wall_th*x_scale,length*y_scale, wall_height)\\n wall_list.append(new_wall)\\n length = 0\\n\\n return wall_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb0f0f43c5edbdaba02c45f2dae541aa\",\n \"score\": \"0.5274863\",\n \"text\": \"def get_train_windows(self, scene: Scene) -> List[Box]:\\n\\n def filter_windows(windows):\\n if scene.aoi_polygons:\\n windows = Box.filter_by_aoi(windows, scene.aoi_polygons)\\n return windows\\n\\n raster_source = scene.raster_source\\n extent = raster_source.get_extent()\\n label_store = scene.ground_truth_label_source\\n chip_size = self.config.chip_size\\n\\n chip_options = self.config.chip_options\\n\\n if chip_options.window_method == 'random_sample':\\n return get_random_sample_train_windows(\\n label_store, chip_size, self.config.class_map, extent,\\n chip_options, filter_windows)\\n elif chip_options.window_method == 'sliding':\\n stride = chip_options.stride\\n if stride is None:\\n stride = chip_size / 2\\n\\n return list(\\n filter_windows((extent.get_windows(chip_size, stride))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e4ba92fdd4431ba52cdcc344fed42c4\",\n \"score\": \"0.52736163\",\n \"text\": \"def find_shapes(im_bw, kernel, th):\\n im_convolved = scipy.ndimage.filters.convolve(im_bw, kernel[::-1, ::-1])\\n im_hotspots = im_convolved >= th\\n bin_dy, bin_dx = kernel.shape\\n radius_x = bin_dx // 2\\n radius_y = bin_dy // 2\\n positions = []\\n open_for_interpretation = np.ones_like(im_convolved, dtype=bool)\\n xmax = im_hotspots.shape[1] - 1\\n ymax = im_hotspots.shape[0] - 1\\n for x in range(xmax + 1):\\n for y in range(ymax + 1):\\n if open_for_interpretation[y, x] and im_hotspots[y, x]:\\n x0 = max(0, x - radius_x)\\n x1 = min(xmax, x + radius_x)\\n y0 = max(0, y - radius_y)\\n y1 = min(ymax, y + radius_y)\\n window = im_hotspots[y0:y1, x0:x1]\\n cx, cy = center_of_mass(window, x_offset=x - radius_x, y_offset=y - radius_y)\\n positions.append([cx, cy])\\n open_for_interpretation[y0:y1, x0:x1] = False\\n return positions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0700dff11be5a8aa3432e3de5a89c87\",\n \"score\": \"0.527155\",\n \"text\": \"def sliding_window_analysis(sequence, function,\\n window_size=100000, step_size=50000):\\n for start in range(0, len(sequence), step_size):\\n end = start + window_size\\n if end > len(sequence):\\n break\\n yield start, end, function(sequence[start:end])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"893199d45136ec9e51f4a1638f081f26\",\n \"score\": \"0.52654827\",\n \"text\": \"def dwindow(window):\\r\\n \\r\\n h=window\\r\\n nh=len(h)\\r\\n lh=(nh-1)/2\\r\\n stepheight=(h[0]+h[-1])/2.\\r\\n ramp=float((h[-1]-h[0]))/nh\\r\\n h2=np.zeros(nh+2)\\r\\n h2[1:nh+1]=h-stepheight-ramp*np.arange(start=-lh,stop=lh+1,step=1)\\r\\n \\r\\n dwin=(h2[2:nh+2]-h2[0:nh])/2.+ramp\\r\\n dwin[0]=dwin[0]+stepheight\\r\\n dwin[-1]=dwin[-1]-stepheight\\r\\n \\r\\n return dwin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"401c9f59c3345203414b663b523f9b9b\",\n \"score\": \"0.52646315\",\n \"text\": \"def nine_regions(self):\\n\\n coordinateList = []\\n\\n # Top left.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] )\\n coordinateList.append( [x, y] )\\n\\n # Top center.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] ) \\n coordinateList.append( [x, y] )\\n\\n # Top right.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * ( 1.0 - self.ratioTopLeft[IDX_X] ) - self.regionSize[IDX_WIDTH] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] )\\n coordinateList.append( [x, y] )\\n\\n # Center left.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\\n coordinateList.append( [x, y] )\\n\\n # Center.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\\n coordinateList.append( [x, y] )\\n\\n # Center right.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * (1.0 - self.ratioTopLeft[IDX_X]) - self.regionSize[IDX_WIDTH] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\\n coordinateList.append( [x, y] )\\n\\n # Bottom left.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\\n coordinateList.append( [x, y] )\\n\\n # Bottom center.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\\n coordinateList.append( [x, y] )\\n\\n # Bottom right.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * (1.0 - self.ratioTopLeft[IDX_X]) - self.regionSize[IDX_WIDTH] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\\n coordinateList.append( [x, y] )\\n\\n return coordinateList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"267a1ca629396d87234907aae8b53149\",\n \"score\": \"0.52632356\",\n \"text\": \"def find_rects(image: np.ndarray) -> List[np.ndarray]:\\n\\n gray = cv.cvtColor(image.copy(), cv.COLOR_RGB2GRAY)\\n gray = cv.GaussianBlur(gray, (5, 5), 0)\\n #edged = cv.Canny(gray, 70, 180) # this numbers is hand picked guess from a few photos\\n edged = auto_canny(gray)\\n\\n contours = cv.findContours(edged.copy(), cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)\\n contours = contours[0] if len(contours) == 2 else contours[1]\\n contours.sort(key=cv.contourArea, reverse=True)\\n contours = [cnt for cnt in contours if cv.contourArea(cnt) > 15] # 15px contour area, basically cnt>=4x4\\n\\n rects = list(map(cv.minAreaRect, contours))\\n boxes = list(map(lambda r: Rect.from_cvrect(*r[0], *r[1]), rects))\\n\\n boxes = Rect.nms_merge(boxes)\\n\\n return boxes or list()\",\n \"title\": \"\"\n 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query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
1bdc0b737b9b955f8ec29a4a1e1d7312 | Parses the different request parameters of an endpoint found within the swagger JSON file and returns them as a list. | [
{
"docid": "7f3e47b93cce6ba54973e0076cd87a14",
"score": "0.6717176",
"text": "def parse_rest_endpoint_request_params(request_properties, definitions):\n\n request_params = request_properties.get('parameters')\n if not request_params:\n return []\n\n if len(request_params) == 1 and reques... | [
{
"docid": "2fca9c8192490e9927b2216ad2475ad6",
"score": "0.64737785",
"text": "def get_endpoint_data(app: APPApi, endpoint: Endpoints) -> list:\n if Methods.LIST not in endpoint.methods:\n raise AttributeError(\n f\"Method {Methods.LIST.name} is not available for endpoint {endpoint.... |
04307c3d78636f5b992d2fafd977bc3b | Initialize Rsa object by a private key or creating a new one by its key_size or importing an existing one from a key_file | [
{
"docid": "608644edfac881b3e462a562b80e35bd",
"score": "0.75208986",
"text": "def __init__(self, private_key=None, key_size=0, key_file=None):\n\n if private_key is not None:\n self.import_private_key(private_key)\n if key_file is not None:\n self.import_from_file(ke... | [
{
"docid": "089fa36a69d444de8083206742d89de6",
"score": "0.6528461",
"text": "def get_new_key(self, key_size):\n\n random_generator = Random.new().read\n self.private_key = RSA.generate(key_size, random_generator)",
"title": ""
},
{
"docid": "386e3ab9f9215187b10239e4bae7c46e",
... |
0a05f823b84bf6261bbfade0e2a6796e | Test remove specific row from a table | [
{
"docid": "e0ffdfb25317c3a1153dc3019132a6c3",
"score": "0.7255831",
"text": "def test_remove_row(self, server):\n server.remove.when.called_with(\"posts\", 3).should.throw(RowNotFound, \"Row with id '3' in table 'posts' not found\")\n\n server.get_table(\"posts\").should.be.equal({\"posts... | [
{
"docid": "113816543ec31ca162f867ac3265f983",
"score": "0.73844546",
"text": "def removeRow(self,row):\n if row < len(self)-1:\n self._table[row:-1,:] = self._table[row+1:,:]\n self._meta[\"length\"] -= 1\n if self._meta[\"index\"] >= row:\n self._meta[\"index\"]-=1\n self.notify(... |
e6db919f61f5b8ef17b4050c15accaf4 | This function will open the file and then it will insert the data (lines from that file) into the list. | [
{
"docid": "cb08ac96f4b9e12cda594ddc3969eaa5",
"score": "0.6923297",
"text": "def read_filename(self, file_name):\n self.text_lines = task2.ListADT(40)\n # This is to open the file\n f = open(file_name,\"r\")\n # This will read the files\n f1 = f.readlines()\n #... | [
{
"docid": "3c95d3b5dea17035faedcbfbbc569474",
"score": "0.68577355",
"text": "def load(self, f):\n\t\tfor line in f:\n\t\t\tself.add(*line.split(\"\\t\"))",
"title": ""
},
{
"docid": "fa8944139268786722a6e5d4698b415e",
"score": "0.67339957",
"text": "def add_items_from_file(self, fi... |
0aab8c9ac9d58d754dc30b188ada4bb7 | Calculates the cardinality (number of Paretooptimal estimates) for each action in the given position, and returns the int representing an action with maximum cardinality. Ties are broken randomly among max cardinality actions. (EvaluationMechanism.C) | [
{
"docid": "8aebfd68c50567768847726a5ece7e52",
"score": "0.7154346",
"text": "def cardinality_evaluation(self, state: object) -> int:\n\n # List of all Qs\n all_q = list()\n\n # Getting action_space\n action_space = self.environment.action_space\n\n # for each a in act... | [
{
"docid": "9d26eb5ff38fbeaef57b16492ac16419",
"score": "0.63622975",
"text": "def find_max_action(self):\n\n\t\tbest_actions = []\n\t\taction_values = []\n\n\t\tfor action in self.actionset:\n\t\t\taction_values.append(action.value)\n\n\t\tfor action in self.actionset:\n\t\t\tif (action.value == max(ac... |
b63f69ba794c8f8efa5bf79f991d9384 | Add a context item for a target. Only adds context if either vocab is not fixed, or if limitingVocab contains the relevant word. | [
{
"docid": "2c6bb3dd5fe288a12c35e264a05b7aae",
"score": "0.8017833",
"text": "def add_context(self, target, contextItem, count=1):\n\n # if the ctx item has already been observed for this target, increment it\n if contextItem in self.contextCount[target]:\n self.contextCount[tar... | [
{
"docid": "2b81fbfd5b119bbd4ed3a5845cedf3d0",
"score": "0.59948903",
"text": "def add(self, context: torch.Tensor, action: int, reward: int):\n self.db[\"contexts\"].append(context)\n self.db[\"actions\"].append(action)\n self.db[\"rewards\"].append(reward)\n self.db_size +=... |
c531f1b72956807270a86ae30196fb2a | Test utilization calculation is fair between suballocs. | [
{
"docid": "cd61dc75978e742a4885d01f1f7e7533",
"score": "0.7152675",
"text": "def test_sub_alloc_reservation(self):\n alloc = scheduler.Allocation()\n\n sub_alloc_poor = scheduler.Allocation()\n alloc.add_sub_alloc('poor', sub_alloc_poor)\n sub_alloc_poor.add(scheduler.Applic... | [
{
"docid": "b8df5ab8b22ac757ee9498654ee2c12f",
"score": "0.74618477",
"text": "def test_sub_allocs(self):\n alloc = scheduler.Allocation([3, 3])\n self.assertEqual(3, alloc.total_reserved()[0])\n\n queue = list(alloc.utilization_queue([20., 20.]))\n\n sub_alloc_a = scheduler.... |
ed2ea21b0090aefc46271db70220ca75 | Clones a workshop unit from the corresponding template. Each cloned workshop unit will be placed into a group with the following naming scheme Units. | [
{
"docid": "776574e7e8969078d9f0b9ec3d5c6b45",
"score": "0.7478491",
"text": "def clone_unit(self, workshop, session_id):\n group_list = list(self.vbox.machine_groups)\n template = \"/\" + workshop + \"-Template\"\n l.info(\"Cloning template: %s\", template)\n\n if template n... | [
{
"docid": "6230a6a42b855bc89b6d9d158d33d0b6",
"score": "0.64617246",
"text": "def Clone(self, standard=0):\n clone = UnitItem(self.unitSystem)\n clone.id = 0 # dummy until added to list\n clone.typeID = self.typeID\n clone.name = '%s-clone' % self.name\n clone.sca... |
445b26ed5c153918c12ea421277bb979 | Predicts data inputs. data should be the remaining part of dataset without the label column. | [
{
"docid": "921c67cc27b57f143fc1329675a765e7",
"score": "0.7459156",
"text": "def predict(self, data) -> Iterable:\n raise NotImplementedError",
"title": ""
}
] | [
{
"docid": "50fea84c43bcfcf8806b1e5928b963fd",
"score": "0.8207221",
"text": "def predict(self, data):\n pass",
"title": ""
},
{
"docid": "ee204aa532cbed03d2f369b7858014b7",
"score": "0.81683457",
"text": "def predict(self, data):\n raise NotImplementedError",
"titl... |
cebb7140719d5ef055620b77831b3884 | Calculates the global transformation matrix for rotating the local panel coordinates with thetaLocal around cmGlobal of each body. Usage | [
{
"docid": "d63d818f7d1411fa5c9a8c26653e152a",
"score": "0.63766783",
"text": "def __updateRotMat(self):\n\n # The global rotational matrix\n self.__rotMat = [_numpy.array([[_numpy.cos(geometryData['thetaLocal']), -_numpy.sin(geometryData['thetaLocal'])],\n ... | [
{
"docid": "6d33b8d779a76ac2364815be4a563711",
"score": "0.67033803",
"text": "def _convert_local_to_global(position, local_coords):\n local_x = normalize(local_coords[0])\n global_x = (1, 0, 0)\n # Find angle between x-axes and rotation axis perpendicular to both x-axes\n angle = np.arccos(... |
e7bc52a4486763d1221f66c79aef62ef | Test that the cache is cleared for each owner removing | [
{
"docid": "e32227af3cf5185723559b577ceba8c7",
"score": "0.78745973",
"text": "def test_removing_clears_cache(self):\n group = self.create_group()\n user1 = self.create_user()\n user2 = self.create_user()\n\n # Because the cache is cleared when we add the user to the group us... | [
{
"docid": "8e724f2b9ed121392d066df28145c545",
"score": "0.7653182",
"text": "def test_adding_clears_cache(self, mock):\n group = self.create_group()\n user1 = self.create_user()\n user2 = self.create_user()\n\n group.owners.add(user1)\n group.owners.add(user2)\n\n ... |
8d026cbd0a27b13599b6abc6de286584 | Overwrite sensor frame id to return fake frame ID for NAC representing a mounting point with a 180 degree rotation. ID was taken from ISIS's IAK kernel for Cassini. This is because NAC requires an extra rotation not in NAIF's Cassini kernels. Wac does not require an extra rotation so we simply return original sensor fr... | [
{
"docid": "a206870ceb2aa4b3944c9785bc9688b4",
"score": "0.66167575",
"text": "def sensor_frame_id(self):\n if self.instrument_id == \"CASSINI_ISS_NAC\":\n return 14082360\n elif self.instrument_id == \"CASSINI_ISS_WAC\":\n return 14082361",
"title": ""
}
] | [
{
"docid": "d052c3135a9fd5ed52f16d7bf40b4172",
"score": "0.67812043",
"text": "def _original_naif_sensor_frame_id(self):\n return self.ikid",
"title": ""
},
{
"docid": "d052c3135a9fd5ed52f16d7bf40b4172",
"score": "0.67812043",
"text": "def _original_naif_sensor_frame_id(self):... |
d60463d062e7ad6a3a0dabcc927400d0 | Waits for an element to be present, visible and enabled such that you can click it. | [
{
"docid": "1f59004ac5149b59e59cf958fa803b64",
"score": "0.60819745",
"text": "def wait_for_clickable_by_xpath(self, xpath):\n print \"Executing wait_for_clickable_by_xpath('{0}')\".format(xpath)\n\n try:\n WebDriverWait(self.driver, self.timeout_to_locate_element_in_seconds).un... | [
{
"docid": "71acfed97aab7add24a1a64e99d6e75b",
"score": "0.7337957",
"text": "def wait_until_visible(self,\n element: typing.Union[model.AnyConcretePageElement, str],\n timeout=None, ) -> None:\n if isinstance(element, str):\n element... |
a7f7ecb3cdcd13b01703aeba174442d2 | The integration runtime reference. | [
{
"docid": "7908c1f7098349a33eca5896c442ac49",
"score": "0.0",
"text": "def connect_via(self) -> Optional[pulumi.Input['IntegrationRuntimeReferenceArgs']]:\n return pulumi.get(self, \"connect_via\")",
"title": ""
}
] | [
{
"docid": "0ddf151c5c093edd0ba08adffc9c832d",
"score": "0.6418033",
"text": "def get_legate_runtime() -> Runtime:\n return runtime",
"title": ""
},
{
"docid": "203d6fdad3c2c2263b7d8868d7e8212c",
"score": "0.5748983",
"text": "def _get_reference(self):\n pass",
"title":... |
8393a93f5012ff474df530bd5136ba62 | Get download requests Create a list of DownloadRequests for all Sentinel2 acquisitions within request's time interval and acceptable cloud coverage. | [
{
"docid": "7e34b1ec14e21d060531fcf4ea96d81e",
"score": "0.57102734",
"text": "def get_request(self, request):\n return [DownloadRequest(url=self.get_url(request=request, geometry=geometry),\n filename=self.get_filename(request, geometry),\n ... | [
{
"docid": "7542edfefbd9405c164624932397269a",
"score": "0.64738226",
"text": "def create_request(self):\n fis_service = FisService(config=self.config)\n self.download_list = fis_service.get_request(self)",
"title": ""
},
{
"docid": "1d51d4f408534a42d85ffed5b12f8cc0",
"scor... |
f775a8a4bd8be8c19d5b660aafa9531c | Reads EAM parameters from a LAMMPS file and returns relevant spline fits. This function reads singleelement EAM potential fit parameters from a file | [
{
"docid": "acafef618142324256b4f1670b062c57",
"score": "0.7398796",
"text": "def load_lammps_eam_parameters(f):\n raw_text = f.read().split('\\n')\n if 'setfl' not in raw_text[0]:\n raise ValueError('File format is incorrect, expected LAMMPS setfl format.')\n temp_params = raw_text[4].split()\n ... | [
{
"docid": "06ce7f29990872f4a8ba8e039b3a7550",
"score": "0.599541",
"text": "def read_params(run_name, exact=False):\n if \".tersoff\" in run_name:\n run_name = run_name.split(\".tersoff\")[0]\n if not exact:\n filename = \"input_%s.tersoff\" % run_name\n else:\n filename =... |
25fb8eeebc3c0b032b2830a046459ba9 | Finds all config.yml files in subdirectories | [
{
"docid": "4162f13f6c6261322069fed68b31198e",
"score": "0.8162081",
"text": "def _find_config_files(self):\n config_file_list = []\n for directory in os.listdir('src'):\n for config_file in glob.iglob('src/' + directory + '**/*.yml', recursive=True):\n config_fil... | [
{
"docid": "7ba99741fc5a53e8fa448fa86ad5d30f",
"score": "0.70910406",
"text": "def list_configs():\n _, _, filenames = next(os.walk(CONF_DIR))\n return filenames",
"title": ""
},
{
"docid": "10a24ca7dd85bc19783e5e195c4ebc3a",
"score": "0.7014341",
"text": "def _read_configurati... |
deeaddcd63975a789275f8768301cd9c | Use pretrained TRPO and reusume experiment. | [
{
"docid": "c05eaadc0b61d87bbd74bbb890780f3d",
"score": "0.57973146",
"text": "def pre_trained_trpo_cartpole(\n ctxt=None,\n snapshot_dir='data/local/experiment/trpo_gym_tf_cartpole',\n seed=1):\n set_seed(seed)\n with TFTrainer(snapshot_config=ctxt) as trainer:\n train... | [
{
"docid": "365083a3d920b15814c94222f0d58ad2",
"score": "0.70100135",
"text": "def auto_trpo_benchmarks():\r\n iterate_experiments(trpo_garage_pytorch, MuJoCo1M_ENV_SET)\r\n iterate_experiments(trpo_garage_tf, MuJoCo1M_ENV_SET)",
"title": ""
},
{
"docid": "853c0fff618b416a2026d49d0fdf1... |
020bcbea04fd71797b7ad19c80fe5c8b | r""" The gradings of the generators of the polynomial ring. | [
{
"docid": "e9c44b520e88585ad7bbba19c423ba7a",
"score": "0.0",
"text": "def gens(self) :\n return tuple(self.__ngens*[self.__index])",
"title": ""
}
] | [
{
"docid": "e0fbaa8d113ffb05037096609bd6d674",
"score": "0.6354953",
"text": "def grad_recipe(self):\n x = self.data[0]\n c = 0.5 / np.sin(x)\n return ([[c, 0.0, 2 * x], [-c, 0.0, 0.0]],)",
"title": ""
},
{
"docid": "1f9bff64bd11ca6757776bf3ad32da68",
"score": "0.628... |
53e405bd4cb35f5c754ead041c2f11e7 | Test case for submissions_list | [
{
"docid": "1f21d1d64d50968d027c40ba1b2bbe85",
"score": "0.90333706",
"text": "def test_submissions_list(self):\n pass",
"title": ""
}
] | [
{
"docid": "2e57b88f0ef9f8840641f835daa80685",
"score": "0.8283224",
"text": "def test_lists_submissions(self):\r\n course_id = None # Change me!!\r\n date = None # Change me!!\r\n grader_id = None # Change me!!\r\n assignment_id = None # Change me!!\r\n\r\n r = se... |
b193acdd42f66aed0dc6c5daef0dd653 | Return pretrained word vectors. | [
{
"docid": "053d19975554f116caf46d717a1dc662",
"score": "0.0",
"text": "def create_vocab_embeddings(vocab2index):\n # embedding_dim = 300\n # word_embedding_lookup = embeddings.GloveEmbedding('common_crawl_840', d_emb=embedding_dim, show_progress=True)\n word_embedding_lookup, embedding_dim = word_em... | [
{
"docid": "fc1d07dac3a52689b5dd04a5483a761e",
"score": "0.77720606",
"text": "def __read_pretrained_word_vecs(self):\n num = 0\n word2idx_glove = {'<unk>': num} # initial unk\n with open(self.file_path, 'r', encoding='utf-8') as file:\n file = file.readlines()\n ... |
5980e48718ab996f4a94b725c413d6a6 | Adds tests to _tests via list extension | [
{
"docid": "21203eb69531b17a94292d1a0fee2cff",
"score": "0.7253698",
"text": "def add_tests(self, tests):\n\t\tif not isinstance(tests, list):\n\t\t\traise(TypeError(f'add_tests expects list'))\n\t\telif len(tests) == 0:\n\t\t\traise(ValueError(f'0 tests passed, expects a minimum of 1'))\n\t\telif not a... | [
{
"docid": "742e912e336b2acdd4458b69d7aefc1a",
"score": "0.7248542",
"text": "def _build_tests_list_helper(self, suite):\n tests = list(iterate_tests(suite))\n return tests",
"title": ""
},
{
"docid": "d52a46a0849d1a8afd20c60ab6fed1c6",
"score": "0.72028774",
"text": "d... |
f1851900537bbba86ee1d4f6443488ef | Logs the useranswer into CSV File | [
{
"docid": "08d0021c1e7a0804ba8d2e5fe99453d9",
"score": "0.6550432",
"text": "def log_answer(self, answer: dict):\n folder_path = self.result_path.parents[0]\n if not os.path.exists(folder_path):\n os.mkdir(folder_path)\n\n resultexist = os.path.exists(self.result_path)\n... | [
{
"docid": "a86b683c1051603dd44e484630768b02",
"score": "0.62461",
"text": "def save_file_as_csv(self):\r\n timestamp = datetime.datetime.now().strftime(\"%Y%m%d\")\r\n filepointer = asksaveasfile(defaultextension=\".csv\", initialfile=\"output_%s.csv\" % (timestamp),\r\n ... |
e673c1606d22d1b23e316e5036365108 | SearchCh(TStr self, char const & Ch, int const & BChN=0) > int | [
{
"docid": "5e064a161caf456ca6a361d9538b06e6",
"score": "0.8020564",
"text": "def SearchCh(self, *args):\n return _snap.TStr_SearchCh(self, *args)",
"title": ""
}
] | [
{
"docid": "aef2247c84f5825c7a121040502c63d1",
"score": "0.72751737",
"text": "def SearchCh(self, *args):\n return _snap.TChA_SearchCh(self, *args)",
"title": ""
},
{
"docid": "3903c5e7bf6d655744e84ad534c43482",
"score": "0.6394132",
"text": "def GetCh(self, *args):\n r... |
d0525c28892b5fa620141c44ca68dd3f | Return y value float Get Value from Chart , Value of x x_limit limit of x axis | [
{
"docid": "e45b82c6e9db6b5c8fc85f928446a457",
"score": "0.62933195",
"text": "def get_y(x, ser=None):\n df = pd.read_csv(data[ser])\n xvalues = list(df['x'].values)\n yvalues = list(df['y'].values)\n x_limit = xvalues[-1]\n xmin = xvalues[0]\n y_limit = yvalues[-1]\n ymin = yvalues... | [
{
"docid": "af4726fcf373e970fb90b87522f4801e",
"score": "0.6479902",
"text": "def __call__(self):\n vmin, vmax = self.axis.get_view_interval()\n return self.tick_values(vmin, vmax)",
"title": ""
},
{
"docid": "5324865d1132bec759fd55a820e97d3e",
"score": "0.62105405",
"t... |
7f7cdba96793c49aa3b3065d0984c5d4 | Reader for SSEC DMV binary files using "pure Python". This function is currently capable of reading RNC, RFC, RLC, and CXS files, but not SUM files. The function returns an xarray Dataset that contains various data variables and metadata for those variables. | [
{
"docid": "8cbcbe59537efe74447138f682a0c937",
"score": "0.5972535",
"text": "def readDMV(filename):\n import numpy as np\n import pandas as pd\n import xarray as xr\n from collections import OrderedDict\n from ohwhio import getDMVformat\n\n def readTOC(sizeTOC):\n dependentVari... | [
{
"docid": "5abf62bf338b1451b3b3dc451e09f339",
"score": "0.6292946",
"text": "def read(cls, file_str, drop_variables_str=None, decode_cf=True, decode_times=True, engine_str=None):\n ds = xr.open_dataset(file_str, drop_variables=drop_variables_str, decode_cf=decode_cf, decode_times=decode_times, e... |
26409943bae31857aaaa9854506b8c66 | Converts the input JSON data into list of Object/BucketAccessControls. | [
{
"docid": "474672c7fbda5caf930210225a610c64",
"score": "0.56806",
"text": "def JsonToMessage(cls, json_data, message_type):\n try:\n deserialized_acl = json.loads(json_data)\n\n acl = []\n for acl_entry in deserialized_acl:\n acl.append(encoding.DictToMessage(acl_entry, message... | [
{
"docid": "dce820e8956476dbf2e702d4a71f6cca",
"score": "0.5801024",
"text": "def getAccessControlList(resource):\n users = _getResourceUsers(resource)\n jsonarr = []\n resource_text=None\n reeource_url=None\n resource_rec = Resource.query.filter(Resource.name==resource).first()\n if r... |
c574954914086f79c6870ff1c737846c | Set black_white to true if vessels are darker than the background and to false if vessels are brighter than the background. | [
{
"docid": "7f2e26591b466d1ac5c41e9f522d05a2",
"score": "0.59593964",
"text": "def filter_out_background(voxel_data, black_white, eigen2, eigen3):\n if black_white:\n voxel_data[eigen2 < 0] = 0\n voxel_data[eigen3 < 0] = 0\n else:\n voxel_data[eigen2 > 0] = 0\n voxel_da... | [
{
"docid": "98e0db3707a31c52183f59b7f8b1ae57",
"score": "0.68847424",
"text": "def set_white(self):\n value = self.sensor.reflection()\n if value > self.white:\n self.white = value",
"title": ""
},
{
"docid": "39d952d2cf002342f37ffc0e6449c85f",
"score": "0.664935... |
704338d432a3a5db746ae4a599d1f944 | Test the group_kwargs decorator | [
{
"docid": "8e5c638eb711f21e3ab23d90b40f79d6",
"score": "0.71135956",
"text": "def testDecorator(self):\n\n selftop = self\n\n class C1(object):\n\n @group_kwargs(prefixes=['slave_'], assign=True)\n def __init__(self, **kwargs):\n selftop.failUnless(has... | [
{
"docid": "c6b5308c0715c069070cc8701b4e218c",
"score": "0.657985",
"text": "def keyingGroup(*args, **kwargs):\n pass",
"title": ""
},
{
"docid": "ab9190b3a955bd685b5c20b8fc82d10f",
"score": "0.6334385",
"text": "def setgroups(*args, **kwargs): # real signature unknown\n p... |
3980c501ae555356c2379ee7ca6b01e7 | Devices that are owned by the user. Readonly. Nullable. Supports $expand and $filter (/$count eq 0, /$count ne 0, /$count eq 1, /$count ne 1). | [
{
"docid": "0b2d98ccef8133f0ede417492496e571",
"score": "0.7089999",
"text": "def owned_devices(self):\n return self.properties.get('ownedDevices',\n DirectoryObjectCollection(self.context,\n ResourcePat... | [
{
"docid": "b5606b192cfe7a3a34bdbd518d4d22eb",
"score": "0.55271053",
"text": "def device_owner(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"device_owner\")",
"title": ""
},
{
"docid": "b5606b192cfe7a3a34bdbd518d4d22eb",
"score": "0.55271053",
"text": "def... |
b0db6ef783b093b98fd570d24b24e72d | Loads the map file from the specified location. Returns a numpy array | [
{
"docid": "ebef75f3abde74c41f76e323b81f76ef",
"score": "0.7459294",
"text": "def load_map(file_location):\n\n def rgb2gray(rgb):\n gray = np.dot(rgb[..., :3], [0.2989, 0.587, 0.114])\n gray[gray < 0.1] = 0.\n gray[gray > 0.1] = 1.\n return gray\n\n img = mpimg.imread(f... | [
{
"docid": "7720f4628c0af7f17f8ce9d32a206bdc",
"score": "0.67278665",
"text": "def load_map(cls, filename):\n return None",
"title": ""
},
{
"docid": "c34e8e1175840b4628fcbf28009ac314",
"score": "0.66093856",
"text": "def readMap(fileName, fileFormat,logger):\n \n #Open fi... |
b4ea335edd14eef3e844af60bd22c232 | Converts input data (1 txt file) to SearchStruct | [
{
"docid": "057b6c219b666894d8f4494e475cae94",
"score": "0.55288476",
"text": "def to_SearchStruct(art_dir, max_count=1000) -> SearchStruct:\n start = perf_counter()\n articles = files_to_articles(art_dir, max_count)\n print(f\"{max_count} articles processed\")\n end1 = perf_counter()\n S... | [
{
"docid": "10f77ef9c1bc4d6bac546cfb0422bf5e",
"score": "0.5858171",
"text": "def _read_data(cls,input_file):\n rf = open(input_file,'r')\n lines = [];words = [];labels = []\n for line in rf:\n word = line.strip().split(' ')[0]\n label = line.strip().split(' ')... |
73fd191c91c9040e9e507cf649c072af | initialize a UDPv6 connector | [
{
"docid": "d9ce8707f99676403d30945a245111cf",
"score": "0.6049554",
"text": "def __init__(self, name = None):\n try:\n self.file = socket(AF_INET6, SOCK_DGRAM, IPPROTO_UDP)\n except:\n raise\n\n if (address != None and port != None):\n try:\n ... | [
{
"docid": "b9aef9a3031c8c2c4ae717c16d33e69c",
"score": "0.62338203",
"text": "def connect(self):\n self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)",
"title": ""
},
{
"docid": "fdbfb040ad4ae87b1003806c62d2d996",
"score": "0.6182419",
"text": "def __init__(self, ... |
9f36c55a713d140d44b91d416c9f5249 | Return the path to the map directory associated with the given ``tag`` file. | [
{
"docid": "a830857fdc18d7c2ffd2a8d322fffeaa",
"score": "0.8115694",
"text": "def tagfile_to_map_dir(tagfile: Path) -> Path:\n if not tagfile.exists():\n raise exceptions.TagNotFound(f'The tag {tagfile.stem} was not found')\n uid = uuid.UUID(tagfile.read_text())\n return map_dir_path(uid... | [
{
"docid": "0b9fa97568a2c08edf9a5e56c69a2704",
"score": "0.86819744",
"text": "def tag_to_map_dir(tag: str) -> Path:\n return tagfile_to_map_dir(tags.tag_file_path(tag))",
"title": ""
},
{
"docid": "355d8406d853f3743d2a2845982bf21a",
"score": "0.7170766",
"text": "def pathname(sel... |
33adde44787b437f1851077575ac55c6 | Given electoral college rows and state edges, returns the outcome of the Electoral College, as a map from "Dem" or "Rep" to a number of electoral votes won. If a state has an edge of exactly 0.0, its votes are evenly divided between both parties. | [
{
"docid": "68bffec74ecb34ac97f1b2e1d7447b8b",
"score": "0.852372",
"text": "def electoral_college_outcome(ec_rows, state_edges):\n ec_votes = {} # maps from state to number of electoral votes\n for row in ec_rows:\n ec_votes[row[\"State\"]] = float(row[\"Electors\"])\n\n o... | [
{
"docid": "1a4f01102b4d02792ccaaddeea254fee",
"score": "0.69664186",
"text": "def state_edges(election_result_rows):\n #TODO: Implement this function\n state_edges = {}#create a dictionary \n for row in election_result_rows: #implement each row in election_result_rows\n state_name = row... |
a0b4083514f10b8a7b23591bf09c6412 | Sets the group1 of this DesignComparison. | [
{
"docid": "72a73ff8b7ae11ea4fddd5a666b60aae",
"score": "0.8002115",
"text": "def group1(self, group1: str):\n if group1 is None:\n raise ValueError(\"Invalid value for `group1`, must not be `None`\") # noqa: E501\n\n self._group1 = group1",
"title": ""
}
] | [
{
"docid": "b85afa594d235ab1c97b3e978011362c",
"score": "0.6944517",
"text": "def group1(self) -> str:\n return self._group1",
"title": ""
},
{
"docid": "c9095bbe0a140abdc41f54d19cab9bb3",
"score": "0.65480804",
"text": "def set_group(self, group_no):\n self.__group = g... |
5df63f13bdd0a54f3e8d1615db2a5eda | Internal utility method; retrieve the base JSON object for this element of the response data. | [
{
"docid": "da22c4fa28a6b84e05319c34e8f0366a",
"score": "0.5827524",
"text": "def getBase(self):\n return self.base",
"title": ""
}
] | [
{
"docid": "25cd3f881482d7710d497f48f761ccf1",
"score": "0.6537689",
"text": "def json(self):\n return self.json_data",
"title": ""
},
{
"docid": "53d99cb95e8b517d419e6b4ba6b3abc3",
"score": "0.6452126",
"text": "def json(self):\n return self.json_data",
"title"... |
a60067009e4c5c0fbdc3267233c3fb87 | Convert shot gather to phase velocty vs. frequency panel | [
{
"docid": "a15028dcecb6d9a9ec0706db194537a6",
"score": "0.55621666",
"text": "def d_to_fv(d,dt,side,f_abs=0):\n # Create phase velocity vs. frequency spectrum\n fv,v,freq = phase_vel_spectrum(d,DX,dt,FMAX,VMIN,VMAX,side=1,f_abs=f_abs)\n # The rest of the function is about editing the fv panel\... | [
{
"docid": "8fd1810fe69ea42801b322080701a919",
"score": "0.58816457",
"text": "def phase_vocoder(spect, rate, phi_advance):\n\n time_steps = torch.arange(0, spect.size(\n 3), rate, device=spect.device) # (new_bins,)\n\n alphas = (time_steps % 1) # (new_bins,)\n\n phase_0 = angle(spect[... |
e3826ddac93602023a18a0e222aa7d70 | get current session id | [
{
"docid": "e5101736f0919e593f750b92bd8874e6",
"score": "0.6773285",
"text": "def session(self):\n return self.current_status[\"$SESSION\"]",
"title": ""
}
] | [
{
"docid": "8773b20ac4d63fd6515df513127f4821",
"score": "0.8484392",
"text": "def session_id(self) -> str:\n return pulumi.get(self, \"session_id\")",
"title": ""
},
{
"docid": "7a4ca68d98358859f196bf53c0b98fe6",
"score": "0.8388298",
"text": "def session_id(self):\n ..... |
2dc80f694836a6f5591d0cb816072a54 | Select a question based on what will narrow the animal list the most. | [
{
"docid": "8aefa086385f7e3a096b1ca35767f03b",
"score": "0.64690036",
"text": "def get_question(questions, animals):\n true_counts = [len(animals) * -1 for i in animals[random.choice(animals.keys())]]\n false_counts = [len(animals) for i in animals[random.choice(animals.keys())]]\n for name, an... | [
{
"docid": "07f4e19a8aa570777d8afab10e929945",
"score": "0.5841787",
"text": "def _select_theory(self, theories):\r\n if theories:\r\n values = tuple(theories.values())\r\n best = max(values)\r\n confidence = float(best) / sum(values)\r\n if confidence ... |
1647bd3633e1b487174da7c52b81a25a | Get 'n' top ranked candidates according to maximin sampling to add to current samples x | [
{
"docid": "1cbd3b684f76d980fb22fd6c40497bbd",
"score": "0.0",
"text": "def select_points(self, x, n):\n x = da.from_array(x, chunks='auto')\n c = []\n for idx in range(0, n):\n c_new = self.select_point(x)\n x = da.vstack((x, c_new))\n c.append(c_ne... | [
{
"docid": "cdd381228f5ca71e8cd33213a18329b3",
"score": "0.6600673",
"text": "def top_n(distributions, n=5):\n if not n:\n n = len(distributions)\n return sorted(distributions, key=lambda y: y[1], reverse=True)[:n]",
"title": ""
},
{
"docid": "9ffaeacd0b71c2e442896dd2b536cc43",
... |
4f62f5ab655ac62180b2332ab40492b7 | This function will fetch the User role by id and return the dictionary as response | [
{
"docid": "9f8f6d751154be55075f511f5fafd3b1",
"score": "0.71706206",
"text": "def get_user_role_id(self, role_id):\n try:\n url = KEY_CLOAK_API_URI + KEY_CLOAK_USER_ROLES_ENDPOINT\n url += '/' + role_id\n headers = {'Content-Type': 'application/json',\n ... | [
{
"docid": "ce738c1c5f7a3f6281bba2dc8f5aa14c",
"score": "0.7288991",
"text": "def get(self, id):\n role = Role.query.filter_by(id=id).first()\n if role is None:\n return { 'message': 'Role does not exist'}, 404\n\n return role_schema.dump(role)",
"title": ""
},
{
... |
9f52165a8cf9b36369a2e30886ef75aa | How many elements were retrieved from the iterator. | [
{
"docid": "942d2f5c624334adb89e2511e1577412",
"score": "0.72104114",
"text": "def retrieved_elements(self) -> int:",
"title": ""
}
] | [
{
"docid": "5f9155403c07aa051433d9fcfc223f2d",
"score": "0.8210471",
"text": "def num_entries(self):\n assert self.iterator is not None, 'iterator is not set'\n return self.iterator.num_entries()",
"title": ""
},
{
"docid": "d2b5f7cbdc8e791084fba76463cf2a05",
"score": "0.77... |
3808ec16ed340183ec2f098067ec552d | Return a list of links from html | [
{
"docid": "271ee6dad8597fb43d92a7cf8838fbc1",
"score": "0.7916816",
"text": "def get_links(html):\n # a regular expression to extract all links from the webpage/通过正则表达式取出href=所有网络连接地址，re.IGNORECASE-忽略大小写\n webpage_regex = re.compile('<a[^>]+href=[\"\\'](.*?)[\"\\']', re.IGNORECASE)\n # list of... | [
{
"docid": "9c58f98f6cc9899d5f5304afe74a8798",
"score": "0.8474521",
"text": "def get_links(html):\n #print(\"Getting links from: \", html)\n webpage_regex = re.compile('<a[^>]+href=[\"\\'](.*?)[\"\\']', re.IGNORECASE)\n links = webpage_regex.findall(html)\n print(\"Got links are: \", links)... |
b8cfc188a3866e276c5b31380962f0eb | Update state of the current player | [
{
"docid": "c6310cd2d75b2ca7827fd1f4adb54525",
"score": "0.6370497",
"text": "def update_state(self, board):\n\n me = board.current_player\n\n # fail the game or not\n alive = [is_alive(board.step, me)]\n\n # the status of opponents\n terminals = [\n is_aliv... | [
{
"docid": "a0a56d382f4cdaf364117fa5d4eb9c3d",
"score": "0.7834334",
"text": "def update(self):\n self.current_player = self.players[self.turn_number%len(self.players)]",
"title": ""
},
{
"docid": "1a4b6732d1dfaea3d541ca86ce904080",
"score": "0.73502195",
"text": "def update_c... |
bc043ba7491b4eb2c5c542502bf37aa4 | Displays a list of cities in their states in a website | [
{
"docid": "6fa3ce413e0657d1a383c0e7c041e1d1",
"score": "0.59353393",
"text": "def list_state():\n state_list = storage.all(State).values()\n return render_template('7-states_list.html', state_list=state_list)",
"title": ""
}
] | [
{
"docid": "f3948016f555b0629e7a5e86ea7536c4",
"score": "0.8651738",
"text": "def display_cities():\n states = storage.all(\"State\").values()\n return render_template('8-cities_by_states.html', states=states)",
"title": ""
},
{
"docid": "5f4bc67fcf99e85011fe951511b490e2",
"score":... |
5e19045594f6cfd897219518e8ddd7e1 | Calculate the location of a given word. | [
{
"docid": "7fef96c7c08e997a46dae825f9933a5d",
"score": "0.617547",
"text": "def calculate_word_index(\n current_line_tabified_text: str,\n source_text_word: str,\n find_word_count: int,\n source_text_spaces: Optional[str],\n ) -> int:\n POGGER.debug(\"source_text_w... | [
{
"docid": "9571c5b9cec0c7d2fe93a056f207ecf2",
"score": "0.73364264",
"text": "def find_word(self, word):\n coordinates = self.__add_coordinates([], word, self.find_word_horizontal)\n coordinates = self.__add_coordinates(coordinates, word, self.find_word_vertical)\n coordinates = se... |
05d2d951f991764175e2c83171880cf0 | Getting count articles of users. | [
{
"docid": "f0461f0cbfbb9da622ed2c25aeb6a05c",
"score": "0.66980654",
"text": "def objects_with_count_articles(self, queryset=None):\n\n return self.annotate(count_articles=models.Count('articles', distinct=True))",
"title": ""
}
] | [
{
"docid": "ab25738f524b3c8db2bdc4936b6cb0ea",
"score": "0.7179851",
"text": "def get_users_count():\n count = mongo.db.users.find().count()\n return count",
"title": ""
},
{
"docid": "aca28459712ca605d5ddffd8743caa5d",
"score": "0.7110084",
"text": "def get_users_count():\n ... |
8e34bb7ab5b42efa1bc8f5e4ac2195c8 | Set the raw payload of this recipe | [
{
"docid": "dcd9f2ce8915cf710735243367a68071",
"score": "0.61381423",
"text": "def set_json_payload(self, payload):\n self.data['payload'] = json.dumps(payload)",
"title": ""
}
] | [
{
"docid": "c0023d54ed246cfe2d9d111c5e10a0ed",
"score": "0.7026609",
"text": "def payload(self, payload: bytes):\n self._payload = payload\n return",
"title": ""
},
{
"docid": "e4caea2967ebfad1f8a6300c478406a7",
"score": "0.69749093",
"text": "def set_payload(self, payl... |
512646c750b13176a046c87998c7290e | Empty input will return None | [
{
"docid": "f6d3eede99db0e2f1e7fc282bab3178c",
"score": "0.0",
"text": "def test_ngrams_from_empty(self):\n no_ngrams = sujmarkov.get_ngrams([])\n for x in no_ngrams:\n self.fail(\"We should not have any ngrams\")",
"title": ""
}
] | [
{
"docid": "b6cfdba532b8b2f26045b26940b4c8b2",
"score": "0.69700664",
"text": "def optionalInput(self):\n raise NotImplementedError(\"This function is not written yet. Please put in an issue on the github page.\")",
"title": ""
},
{
"docid": "2eb006acd0bef8bd341bc39e47eb28ee",
"sc... |
efe0ffe0f5a2def9d4781cc7e10c82a5 | Perform Kfold cross validation on an automatically generated tree at temperature T after finding an appropriate node for kinetics estimation it will move up the tree iters times. | [
{
"docid": "6b8f2a8f498bb4d829b1d7496c48c9d3",
"score": "0.5725403",
"text": "def cross_validate(self, folds=5, template_rxn_map=None, test_rxn_inds=None, T=1000.0, iters=0, random_state=1):\n\n if template_rxn_map is None:\n template_rxn_map = self.get_reaction_matches(remove_degenera... | [
{
"docid": "7a53e699035fb6408ddf09c72ce046b0",
"score": "0.6641648",
"text": "def CrossValidation(data, labels, test, start, finish, optimizer=None,\r\n layers=7, nodes=4):\r\n filefmt = 'weights.Epoch-{epoch:03d};Loss-{val_loss:.6f}.hdf5'\r\n #listOfErrors = list()\r\n kf = ... |
ab586ca229703afa22fcb260a888b7f0 | Set up the Tesla sensor platform. | [
{
"docid": "ae4db8111c384fea15bf56955cd5d40c",
"score": "0.5802595",
"text": "async def async_setup_platform(hass, config, add_entities, discovery_info=None):\n\n # We only want this platform to be set up via discovery.\n if discovery_info is None:\n return\n\n controller = hass.data[DOM... | [
{
"docid": "986be4df08781a7181605e690d4a32c3",
"score": "0.6661704",
"text": "def setup(self):\n print('Initializing...')\n self.init_sensors()\n self.left_vel = 3\n self.right_vel = 3\n self.write_motors()",
"title": ""
},
{
"docid": "b8d98366fe6f33aeab17e... |
de01bf8b7ae61e8260755da403dd1e02 | Write a frame via the handle. | [
{
"docid": "9b6613eea6bb27d27b611b4869206242",
"score": "0.5374797",
"text": "def write_frame(self, frame: Frame) -> int:\n\n result = bytearray()\n length = len(frame.data or [])\n\n # Check length of message.\n if length > self.max_length:\n raise ValueError(\n ... | [
{
"docid": "419e0561d146a2f2eb520537811fd521",
"score": "0.7532052",
"text": "def write(self, frame):\n assert hasattr(self, 'writer')\n self.writer.write(frame)",
"title": ""
},
{
"docid": "770a5c777fcc204030e6f5572c5db899",
"score": "0.71338356",
"text": "def write(se... |
814a369b5ee8bd3a28feaccefca97922 | Collects the remote environment static assets. | [
{
"docid": "9d4ada4c4cebd60bbccf9cc996675f71",
"score": "0.6859368",
"text": "def collectstatic():\n print yellow('Collecting static assets.')\n dj_heroku('collectstatic --noinput '\n '--ignore=*.scss --ignore=admin --ignore=tiny_mce',\n env.app, env.slug)",
"title": ... | [
{
"docid": "cfe6e206be014f6fc4acd4cf9a6e83eb",
"score": "0.72331625",
"text": "def collectstatic():\n public_dir = os.path.join(os.getcwd(), 'public')\n\n if os.path.isdir(public_dir):\n print('directory exists')\n else:\n os.mkdir(public_dir)\n\n local_static = os.path.join(os... |
bf81bb4328293bb0f4448d427846f942 | Parses a typedef statement | [
{
"docid": "f25bed49215e7f2fdc99b7bb9fe1fd66",
"score": "0.73862123",
"text": "def parse_typedef(typedef):\n typedef_name = typedef.spelling\n underlying_typename = typedef.underlying_typedef_type.spelling\n\n if '(anonymous struct' in underlying_typename or underlying_typename.star... | [
{
"docid": "78abf412ddfeb92c11d6c97f07383de8",
"score": "0.73537874",
"text": "def typedef_handle(self, tokens):\n if len(tokens) == 1: # return typedef\n if self.target.startswith(\"3\"):\n return \" -> \" + self.wrap_typedef(tokens[0]) + \":\"\n else:\n ... |
3049e74141282bb91364f0d82a65523e | Load csv format data Assuming no header | [
{
"docid": "b8fcab84bf19bbf8c611abf49b085c05",
"score": "0.0",
"text": "def load_csv(path: Path, delimiter: str = ','):\n return pd.read_csv(str(path), header=None, delimiter=delimiter, dtype=np.float_).dropna(axis=1).astype('int8')",
"title": ""
}
] | [
{
"docid": "961f13da9b871f70e7c2b0f1f05aaf3a",
"score": "0.7722899",
"text": "def load_csv():\n\tpath = ''\n\tdata = []\n\tif len(sys.argv)<2:\n\t\tpath = input('Please enter path of raw data:')\n\telse:\n\t\tpath = sys.argv[-1]\n\tif path == '':\n\t\traise Exception(\"Empty input \")\n\tlogger.info(pat... |
dd7c99571c0384fe02f0b694bf5ce4a0 | counter_opponent_win() > None Check if the opponent is about to win, and if so, counter that move which they will make. | [
{
"docid": "198700be1e9ab4fe868c811d06007227",
"score": "0.8313787",
"text": "def counter_opponent_win(self):\n\n # get essential values\n board = self.get_game_space()\n affinity = self.get_opponent().get_affinity()\n \n # pick the right check for the game we are play... | [
{
"docid": "bc21249d7575e9f67e65aa0f57da9ccf",
"score": "0.681446",
"text": "def counter_opponent_adv(self):\n\n # get essential values\n board = self.get_game_space()\n affinity = self.get_affinity()\n opaffinity = self.get_opponent().get_affinity()\n\n # pick the rig... |
3da2794ba262075984f8f0a06938a76e | INCORRECT DO NOT USE todo maybe repurpose | [
{
"docid": "345df7e04397ee47c92e84b6e6acaeee",
"score": "0.0",
"text": "def __init__(self, *args, **kwargs):\n Formulation.__init__(self, *args, **kwargs)",
"title": ""
}
] | [
{
"docid": "8b16d489ebb537b1411490eee4770a91",
"score": "0.66647446",
"text": "def helper(self):",
"title": ""
},
{
"docid": "0bcfc9f4e8ba5d6d08a5a57f2e4506ab",
"score": "0.64894384",
"text": "def malicious():",
"title": ""
},
{
"docid": "f2b8dbc0734266e239bff49f54de121a"... |
3a1bfeb16f00458bf9d1d64b9ceb9fe1 | Test composed_query_string() method using all parameters. | [
{
"docid": "1b179e83f032a4f58ef5f5c51333221f",
"score": "0.65375584",
"text": "def test_composed_string_all(self):\n table = self.fake.word()\n field = self.fake.word()\n operation = self.fake.word()\n parameter = self.fake.word()\n filt = QueryFilter(table, field, ope... | [
{
"docid": "c3a038cb14e2e593ef242a40b7499e24",
"score": "0.6757804",
"text": "def test_run_url_encoded_query(self):\n pass",
"title": ""
},
{
"docid": "abfcb5c2b9e0ec4e2795773cc9a66b9a",
"score": "0.6543141",
"text": "def test_query_parsing(self):\n params = QueryParams... |
c23927ae476b2ef599450aa5587b04db | Given the training set and the indices of the labeled examples, return the indices of the unlabeled examples. | [
{
"docid": "0a582999b8153109974e7c30b393dd94",
"score": "0.79658794",
"text": "def get_unlabeled_idx(X_train, labeled_idx):\n return np.arange(X_train.shape[0])[np.logical_not(np.in1d(np.arange(X_train.shape[0]), labeled_idx))]",
"title": ""
}
] | [
{
"docid": "e99c296c16753106b5c2a77452b3b6c2",
"score": "0.62091047",
"text": "def find_wrong_indices(self) -> None:\n self.wrong_idx = [\n index\n for index, elem in enumerate(self.max_preds)\n if elem != self.labels[index]\n ]",
"title": ""
},
{
... |
1de210ee273934ff29a850c04b83dbd5 | Add painting of the error flag to the paint event. | [
{
"docid": "735577ff37b1b3b4cc825e215bf583b3",
"score": "0.78163135",
"text": "def paintEvent(self, event):\n super().paintEvent(event)\n if self.errorFlag:\n painter = QPainter(self)\n path = QPainterPath(QPointF(0, 0))\n path.lineTo(0, 10)\n pa... | [
{
"docid": "f932088550171254c1bd49a4ec2963d0",
"score": "0.6624911",
"text": "def setErrorFlag(self):\n self.lineEdit().errorFlag = True\n self.update()",
"title": ""
},
{
"docid": "f1debdc35ed31e6d7d42eba09b04f670",
"score": "0.6377457",
"text": "def setErrorFlag(self)... |
7cf20c45b1193d6cc4de3423bbc9922c | main function that initializes the data structures and functions necessary to communicate with GDAX, handle orders, and log messages. | [
{
"docid": "474a333352db7956a0bec24c60e14cd1",
"score": "0.7030655",
"text": "def main():\n\n\t# Load account information\n\tapi_key, api_secret_key, api_passphrase = load_api_keys()\n\n\t# General setup\n\tlogger = Log()\n\tob_updated_cond = threading.Condition()\n\n\t# Setup trading objects\n\taccount... | [
{
"docid": "7c0dd0af72a23fb19e0e30f462533437",
"score": "0.6468741",
"text": "def main():\n initialise_servos()\n initialise_motor_controllers()\n initialise_ultrasonics()",
"title": ""
},
{
"docid": "228e0d19dc0a089ee6e9827da5e6ed56",
"score": "0.6319809",
"text": "def main... |
a1f043844ca7b091598d0af5c47293eb | create a dictionary > {antenna_id > (lat,lon)} | [
{
"docid": "f1d3370755a9a27d097f01733c087005",
"score": "0.7094621",
"text": "def antenna_loc_todict():\n\n r = {}\n f = open(\"data/location_withgps.txt\",\"rb\")\n for line in f:\n a = line.split(\"\\t\")\n r[a[0]] = (a[3],a[4])\n dill.dump(r,open(os.path.join(output_path_fi... | [
{
"docid": "f3fa4e9f4f25b615df1207c15852e457",
"score": "0.5810438",
"text": "def _prepare_distances_dict(self, unknown_area):\n\n new_d = self.joined_datasets.copy()\n new_d = new_d.append(unknown_area, ignore_index=True)\n\n try:\n new_d['px'] = new_d['geometry'].apply(... |
d8da593e72b39cdc9787ff794e07c9a7 | Generator to handle geoingeo queries without the user having to worry about wrangling the counties. | [
{
"docid": "12a1a61664e13d5944e16bc9922ad1df",
"score": "0.0",
"text": "def genstate_to_tract(stfips, cxn, *columns):\n counties = cxn.query([\"NAME\"], geo_unit=\"county\", geo_filter={\"state\": stfips})\n counties = counties.county.tolist()\n for county in tqdm(counties):\n tract = cx... | [
{
"docid": "44a7f12de11e3a7f9fcaa4d891900d1e",
"score": "0.5714338",
"text": "def geo_coverage():",
"title": ""
},
{
"docid": "10fb8fe1b8eaa61cd1179efc86d243a1",
"score": "0.5556623",
"text": "def main(countries=[], geotagged=False):\n\n all_locations = utils.craigslist_regions()\... |
aaca0b030b7545120b39cf7b2d783a13 | Cuts current tile to clipboard | [
{
"docid": "6e3218f1311d8d4c5ec2fde4710776dd",
"score": "0.7260197",
"text": "def tile_cut(self):\n self.tile_copy()\n self._tile_set.modified=True\n self._tile_set[self.current_tile_num].frombytes(b\"\\0\" * BYTES_PER_TILE)\n self._ui.update_tile(self._tlayer, self._tile_set... | [
{
"docid": "44e67c533a04fa90b3b73c84157c6573",
"score": "0.79440224",
"text": "def tile_copy(self):\n self._ui.clipboard_set( self._tile_set[self.current_tile_num] )",
"title": ""
},
{
"docid": "c994d70862c4aadc8d46555c225ea124",
"score": "0.74110913",
"text": "def tile_paste(... |
31aa651ea068d8b0ab4252f4bd2e205b | Returns the density of the bivariate normal distribution at point (x,y). | [
{
"docid": "df30f05f9923c0eb8af9b361ad72e1c3",
"score": "0.5889413",
"text": "def pdf_gauss_2d(x, y):\n return pdf_gauss_1d(x)*pdf_gauss_1d(y)",
"title": ""
}
] | [
{
"docid": "f6c81f88f244aa9007f7a230f50c06d0",
"score": "0.7614624",
"text": "def density(self, y, x):\n\n if type(y) == np.ndarray:\n y = y.reshape((len(y), 1))\n elif type(y) == list:\n y = np.array(y)\n y = y.reshape((len(y), 1))\n else:\n ... |
8524aeccfd9867a7f73e27f830e71232 | Make endpoint watcher function. | [
{
"docid": "3355a693b07436a87ec1de3ef6db04cf",
"score": "0.61150706",
"text": "def make_endpoint_watcher(zkclient, state, proid):\n proid_instances = z.join_zookeeper_path(z.ENDPOINTS, proid)\n\n @exc.exit_on_unhandled\n @zkclient.ChildrenWatch(proid_instances)\n def _watch_instances(childre... | [
{
"docid": "3d7d9a0b104dce10ef5f9d019c687228",
"score": "0.6148808",
"text": "def watch(self):\n uvicorn.run(\n \"pet_store.entrypoints.api:app\",\n port=8000,\n host=\"0.0.0.0\", #nosec\n log_level=\"debug\",\n reload=True,\n )",
... |
801a8436e823c26cff706e034f86c849 | Gets a bytes array, which corresponds to the xml string in utf8 encoding, identified as using the namespace given by `urn` (if given). | [
{
"docid": "ec847d2fde482ddd6c9e4724b3227335",
"score": "0.5629308",
"text": "def to_xml_bytes(self, urn=None, tag=None, check_validity=False, strict=DEFAULT_STRICT):\n if tag is None:\n tag = self.__class__.__name__\n the_etree = ElementTree.ElementTree()\n node = self.t... | [
{
"docid": "141224eafe0c00a9605645a4075c4182",
"score": "0.5546509",
"text": "def toutf8(ustr):\n if isinstance(ustr, str):\n return ustr\n return ustr.encode(\"UTF-8\")",
"title": ""
},
{
"docid": "21ed2e3a0f5d4be4ea6b2fb00212502e",
"score": "0.5204712",
"text": "def st... |
e9d2b7d30b9fe089d8447f09e407e195 | Check if spectra is good to use. If fits file was not downloaded properly spectra will be all nans too many bad pixels, or too little coverage on common grip will also be removed | [
{
"docid": "9d3ec3aab58fd85a8b8e9b673b59b4e3",
"score": "0.73258793",
"text": "def check_spectra(spec):\n\n nof_pixels = len(spec)\n nof_good_pixels = numpy.sum(numpy.isnan(spec))\n\n is_good = float(nof_good_pixels)/float(nof_pixels) > 0.75\n\n return is_good",
"title": ""
}
] | [
{
"docid": "8d7270edfbadfb2f062cc9bf4a43e3c5",
"score": "0.6475039",
"text": "def test_scan_loading_badchans(self):\n fname = \"srt_data_roach_polar.fits0\"\n with pytest.warns(UserWarning) as record:\n Scan(os.path.join(self.datadir, \"spectrum\", fname))\n assert np.any... |
376b58c25717b050758be0d64eaa2eb2 | Remove the job from our list | [
{
"docid": "a75a17ca49bddd56e38136f9b675e206",
"score": "0.73029447",
"text": "def remove_job(self, job_uuid: uuid.UUID):\n #logging.info('Removing job %s'%str(job_uuid))\n del self.active_jobs[job_uuid]",
"title": ""
}
] | [
{
"docid": "2af9d7c2cc3de22b7f745e4c3fc8eaeb",
"score": "0.8540207",
"text": "def remove_job(self, job_id):",
"title": ""
},
{
"docid": "906851a4b61b8f9b49ea728f2408e8d3",
"score": "0.82992834",
"text": "def remove_job(self, job):\r\n raise NotImplementedError",
"title": "... |
c111c0c52a7284f5cf748bfcd13b6ffc | Sets the input files | [
{
"docid": "8670a3b37e541f0e0344ab8b4f36666e",
"score": "0.84427613",
"text": "def set_input_files(self,path, input_files):\n self.path = path\n self.input_files = input_files\n self.setFiles = True",
"title": ""
}
] | [
{
"docid": "bacaa22b6baf2b11fd0fff66e7cf5a15",
"score": "0.7570599",
"text": "def setInputFiles(ekppath=None, nafpath=None):\n\treturn InputFiles(ekppath=ekppath, nafpath=nafpath)",
"title": ""
},
{
"docid": "aaab3fe02e3a31d189dfc300a4d07aba",
"score": "0.75171465",
"text": "def SetF... |
69fb0bf1875df0c52a4c17ee2057f45e | Func applies to the list consisting of all elements of d, and return a list. | [
{
"docid": "5eecaa8e589fc0c932187ad51f244c81",
"score": "0.50239545",
"text": "def apply_function_to_dict(func, d):\n from itertools import chain\n result = func(list(chain(*d.values())))\n csum = np.cumsum(map(len, d.values()))\n new_d = {k: result[(0 if i == 0 else csum[i-1]):csum[i]] for ... | [
{
"docid": "285b5e74e38092d4ec48121d9b3016ae",
"score": "0.64130175",
"text": "def apply_func_to_list(func):\n return lambda l,*args: [func(x,*args) for x in l]",
"title": ""
},
{
"docid": "fe91e1c4d9ce48383724c42391102536",
"score": "0.63807935",
"text": "def do_list_elems(func):... |
ec13e714ad6f0f7d2ae0bb539c8ae730 | Check if new unit successes to create if form is valid. | [
{
"docid": "838eb328099637e1749bdda100618682",
"score": "0.6981745",
"text": "def test_new_unit_post_success(self):\n\n response = self.client.post(\n reverse('reports:new_unit'),\n {u'name': u'sztuki'},\n follow=True\n )\n\n self.assertRedirects(res... | [
{
"docid": "b158a666ce01f55cd671b2063193f078",
"score": "0.6659576",
"text": "def test_new_unit_post_fail(self):\n\n response = self.client.post(\n reverse('reports:new_unit'),\n {u'name': u''},\n follow=True\n )\n\n self.assertEqual(response.status_... |
fcac08a3fde20e7c1b9f5eec5b4927ac | Checking if a molecular system is composed of specific elements. The function returns True or False depending on whether or not the system is entirely composed of the elements required. | [
{
"docid": "9367000d4507d11fa32011a52b5c36ee",
"score": "0.7346343",
"text": "def is_composed_of(molecular_system, selection='all', syntax='MolSysMT', **kwargs):\n\n\n\n\n from . import get\n\n if len(kwargs):\n\n # molecules in kwargs\n set_molecules = {'n_ions', 'n_waters', 'n_smal... | [
{
"docid": "23d46596c5f225108ac9b32c09840b3f",
"score": "0.6892202",
"text": "def _is_from_chemical_system(chemical_system, struct):\n chemsys = list(set([sp.symbol for sp in struct.composition]))\n if len(chemsys) != len(chemical_system):\n return False\n for el in chems... |
4587b5b7cebb46bcd8322ff8e9547ce3 | Looks up entry in dictionary and populates entry's data | [
{
"docid": "66e2be696faed2b12c692dbac7a009d7",
"score": "0.0",
"text": "def lookup(e):\n # Connect to DB\n # Get entry\n # Parse into variables\n # Check if furigana exists, or look it up if needed. Either way, compare\n if not self.furigana:\n pass",
"title": ""
}
] | [
{
"docid": "85416a4098704d90ca8311daf1beeb32",
"score": "0.6158545",
"text": "def retrieveValue(dictionary, key, entry):\n\tpass",
"title": ""
},
{
"docid": "465d96032b3f7cbdfd2e16f76f175212",
"score": "0.60910887",
"text": "def _update_entry_info(self, entry : dict):\n if self.... |
db19dd2a4ce0943483f31dd9bad55ec3 | Helper function to log a debug message with a descriptive heading. | [
{
"docid": "2c606d1199d941de20d57d71dd6a8322",
"score": "0.0",
"text": "def log_debug(self, line):\n logging.debug(\"Scanner #%s - %s\" % (self.device_idx,line))",
"title": ""
}
] | [
{
"docid": "f2318cb180866a2990408274f1c09413",
"score": "0.6975975",
"text": "def debugPrint(message, debug):\n if debug:\n print(message)",
"title": ""
},
{
"docid": "aa5d1d36d75272e5f95b19ec31dfe6e3",
"score": "0.696345",
"text": "def debug(message):\n if options.verbose > 2... |
adf6f74945f1d4f925421391e327d1ed | Limpia los datos de la sesion no agregados por la aplicacion | [
{
"docid": "760f01f72f6963af7a6e298cfd109280",
"score": "0.0",
"text": "def clear_session(session):\n for key in list(session.keys()):\n if key not in ['_auth_user_id', '_auth_user_backend', '_auth_user_hash']:\n del session[key]",
"title": ""
}
] | [
{
"docid": "ae898e68bc78666232f5c3b131cb1831",
"score": "0.5502195",
"text": "def novoAutomato(self):\n self._infoAutomatoAtual[\"EstadoInicial\"] = None\n self._infoAutomatoAtual[\"EstadosFinais\"] = set([])\n self._infoAutomatoAtual[\"Alfabeto\"] = set([])\n self._infoAutom... |
b2e20e834c0b1bdc1c7eec70f0d5ef3c | This function uses the created my_max function and compares it to pythons max function. It creates a list of random variables and uses both functions. | [
{
"docid": "a44048cd3e2ccfbbf6aba8cea79bc4f6",
"score": "0.7396767",
"text": "def test_max():\n random_vals = []\n\n # generating 50 random values and appending them to a list\n for i in range(50):\n random_vals.append(random.randrange(0, 1000, 1))\n\n # using both max functions to fi... | [
{
"docid": "39e9da22cbeb6af7dcd2479aebb8025a",
"score": "0.67297995",
"text": "def test_max_vals(self):\n self.report('Testing for Maximum value. ')\n for test_fn_name in self.synthetic_functions:\n caller = get_syn_function_caller_from_name(test_fn_name)\n self.report('Testing %s with m... |
3e47af91f1595aed11a246fcddf82f72 | Validate a plugin directory is valid | [
{
"docid": "8498a80008398200bfe4ac03ccbcad22",
"score": "0.0",
"text": "def _validate(config_module: ModuleType, path: Path) -> None:\n ConfigLoader._entry_point(config_module, path)\n ConfigLoader._instances(config_module)\n ConfigLoader._args(config_module)\n ConfigLoader._... | [
{
"docid": "8cc68037e5571eb79ecfb4d34738a791",
"score": "0.6602469",
"text": "async def validate_and_import_directory(self, **kwargs) -> str:",
"title": ""
},
{
"docid": "c1f03f4d1fa2e7a0349e0ef3c5ad2174",
"score": "0.6583628",
"text": "def validate_directories(featured_mod):\n # ... |
79712250d44c213089530bca861aafb9 | gets the ctf status | [
{
"docid": "bb44b9fc5ea77399c1c12dc12d822160",
"score": "0.0",
"text": "def is_active(self):\n\t\treturn self.__is_active",
"title": ""
}
] | [
{
"docid": "59cd33011a252a8832a814fc751eaa2e",
"score": "0.71683383",
"text": "def get_status(self):",
"title": ""
},
{
"docid": "59cd33011a252a8832a814fc751eaa2e",
"score": "0.71683383",
"text": "def get_status(self):",
"title": ""
},
{
"docid": "c925bfd95758d8502e8ba4ed... |
e32eee693062135dbbed18c72536161f | Returns Modular Multiplication of a and b | [
{
"docid": "b5b434c8d118439fec5371fcef14190e",
"score": "0.8792906",
"text": "def mod_mul(a, b):\n return (a % MOD*b % MOD) % MOD",
"title": ""
}
] | [
{
"docid": "d7fff146e0e365c46ad35eb8ba20405d",
"score": "0.8231514",
"text": "def mod_mul(a, b, n):\n x = 0\n y = a\n while b > 0:\n if (b & 1) != 0:\n # odd number\n x = (x + y) % n\n y = (y + y) % n\n b = b / 2\n ... |
c0eef44b6e327688044e5a4469ca8db7 | Gets the descendant bin ``Id`` query terms. | [
{
"docid": "a8c894c30b6990b7165b13f238f150f7",
"score": "0.8539272",
"text": "def get_descendant_bin_id_terms(self):\n return # osid.search.terms.IdTerm",
"title": ""
}
] | [
{
"docid": "324e7100bb46e6702f52c2db2b502450",
"score": "0.7650223",
"text": "def get_ancestor_bin_id_terms(self):\n return # osid.search.terms.IdTerm",
"title": ""
},
{
"docid": "df255db9024986f93117154bbc607910",
"score": "0.74250394",
"text": "def get_bin_id_terms(self):\n... |
cdfe2130854a137c1d6753d10488d77b | Abstract Function Implementation Task Reads the input dataset and stores it in self.sentences as a list of tokens. | [
{
"docid": "da4b2f6df8ade16e947826f6375f0122",
"score": "0.0",
"text": "def read_Dataset(self, name=\"text8\", path=\"data/text8\"):\n\t\tprint(\"Reading dataset ....\")\n\t\tif name == \"text8\":\n\t\t\tself.sentences = Text8Corpus(path)\n\t\telif name == \"OneBilCorpus\":\n\t\t\tself.sentences = OneBi... | [
{
"docid": "588216166c5e9382d18abc8829484081",
"score": "0.6711342",
"text": "def tokenize_sentences(self):\n data = []\n labels = []\n\n for sentence in self.sentences:\n data_tmp, label_tmp = self.get_data_from_sentence(sentence)\n\n if data_tmp != None and l... |
12ae536e1be853f36fd4bbaf1591d538 | Activate or deactivate the auto pop feature. | [
{
"docid": "3e636a39b7a2583660972c17883921ef",
"score": "0.76103395",
"text": "def toggle_auto_pop(self):\n self.__auto_pop = not self.__auto_pop",
"title": ""
}
] | [
{
"docid": "14e749386ee2783666321d92f9bc8f6d",
"score": "0.6201403",
"text": "def activate(self):\n self.is_activated = True",
"title": ""
},
{
"docid": "14e749386ee2783666321d92f9bc8f6d",
"score": "0.6201403",
"text": "def activate(self):\n self.is_activated = True",
... |
f3e40a2aca9f7f2de33ba0c5cf4d77a1 | test standard bindit mapping bind referenced dir, no other manual binds. | [
{
"docid": "56b3dc3796c0b29a77d78999e62a3ce7",
"score": "0.69535625",
"text": "def test_bind():\n with tempfile.TemporaryDirectory(prefix=TEMPFILE_PREFIX) as sourcedir:\n sourcedir_resolved = pathlib.Path(sourcedir).resolve()\n sourcefile = pathlib.Path(\n tempfile.mkstemp(di... | [
{
"docid": "685b1d1fbedb236e7d0adef4cef659fa",
"score": "0.6799697",
"text": "def test_manual_bind_mount():\n manual_bind_runner(bindit.docker.mount_bind_args)",
"title": ""
},
{
"docid": "45be098ba0da542a8d83bf55eafcb293",
"score": "0.67655706",
"text": "def test_bind_folder():\n... |
097d889d8c010a3f964bb2318d6fdc7d | Returns xTr,yTr,xTe,yTe xTr, xTe are in the form nxd yTr, yTe are in the form nx1 | [
{
"docid": "cd1a66d6dcdf54f7e6246d0554f449d8",
"score": "0.0",
"text": "def loaddata(filename):\n data = loadmat(filename)\n xTr = data[\"xTr\"] # load in Training data\n yTr = np.round(data[\"yTr\"]) # load in Training labels\n xTe = data[\"xTe\"] # load in Testing data\n yTe = np.round(... | [
{
"docid": "c368212fe8899d1fda158fbf08a31d89",
"score": "0.60926294",
"text": "def teax():",
"title": ""
},
{
"docid": "04917ac1922191ebcdd6f268d82c706f",
"score": "0.5846229",
"text": "def get_x_tuple(self):\n tup = (self.n_x,)\n if self.has_y:\n tup += (sel... |
4f938c74238954c3edbb785491861f2e | test_get_cinderclient_unknown_module check that we could not retrieve a Session client to work with cinder if there is no modules defined | [
{
"docid": "ab92b3bde7e9989429107596424a5dc4",
"score": "0.8516438",
"text": "def test_get_cinderclient_unknown_module(self):\n try:\n osclients = OpenStackClients(modules=\"\")\n osclients.get_cinderclient()\n except Exception as ex:\n self.assertRaises(ex... | [
{
"docid": "f7327525d1d6f0075e2207867225b3e4",
"score": "0.74871695",
"text": "def test_implement_client_with_unknown_module(self):\n\n try:\n OpenStackClients(modules=\"fakeOpenstackModule\")\n except Exception as ex:\n self.assertRaises(ex)",
"title": ""
},
... |
b2ac1bae8c15b61f857fd75e574ad723 | Monta a resposta com os bytes do arquivo | [
{
"docid": "9dcf6692d41eb6d24114a8c8facca69e",
"score": "0.0",
"text": "def read_file(file):\n\n\t\text = file.split('.')[1]\n\t\tcontent_type = 'text/html'\n\t\tif ext == 'jpg' or ext == 'jpeg':\n\t\t\tcontent_type = 'image/jpeg'\n\t\telif ext == 'ico':\n\t\t\tcontent_type = 'image/x-icon'\n\t\treturn ... | [
{
"docid": "4785d1e6c36afd80214ff7cc0687736b",
"score": "0.6302854",
"text": "def make_res_file(data):\r\n res_bytes = bytearray()\r\n magic_num = int.to_bytes(0x497E, 2, \"big\")\r\n mode_type = int.to_bytes(2, 1, \"big\")\r\n res_bytes.extend(magic_num)\r\n res_bytes.extend(mode_type)\r... |
be3d4e90ce876ad3c24fd666a0ec6399 | Takes file_id string. Initializes temp files. Returns tuple of vars. Called by grab_latest_file() | [
{
"docid": "5553c248c1e9126d396d91c0abc1ea0c",
"score": "0.68019724",
"text": "def _setup_grab_files( self, file_id ):\n ( temp_stdout_filepath, temp_stderr_filepath ) = ( self._make_temp_filepath(u'stdout_vcs'), self._make_temp_filepath(u'stderr_vcs') )\n f_stdout = open( temp_stdout_file... | [
{
"docid": "3d6872523ef015b79e75b7b6e6599e0d",
"score": "0.60206676",
"text": "def initialize_tmpfile(data, directory=None):",
"title": ""
},
{
"docid": "d2e9181334398851a16b4029dd7632b8",
"score": "0.5896506",
"text": "def _prepare_temp_file(self):\n if not self._details.__co... |
834422ee3835d1f972850906bdfdc885 | Save frame data as an HDF5 file. | [
{
"docid": "1b00b556c8358c176a6a9ae32ecffd8a",
"score": "0.74281675",
"text": "def save_h5(self, filename, max_load=1):\n self.save_hdf5(filename, max_load=max_load)",
"title": ""
}
] | [
{
"docid": "2240ae2fa11185698b2a1d51bc73e042",
"score": "0.77490896",
"text": "def save_H5(self, avg=False):\n if not os.path.isfile(self.get_H5_path()):\n self.df.to_hdf(self.get_H5_path(), key=\"df\", mode=\"w\")\n if avg:\n self.df_avg.to_hdf(self.get_H5_avg_path()... |
49158efcf16c9ac85c88c1f9d2ba429c | Get names of attributes (without protected or private sign) and theirs values. | [
{
"docid": "a72c105d48731a8c6de6203d97053c34",
"score": "0.6518105",
"text": "def get_attributes(self):\n\n attributes_info = basic_backend.get_attributes(self)\n return attributes_info",
"title": ""
}
] | [
{
"docid": "4fa03f4879e3a499790c4f0f0ae91d6a",
"score": "0.7723248",
"text": "def get_attribute_names():\n\n _round = Round()\n return _round.get_attributes().keys()",
"title": ""
},
{
"docid": "84e9ad410474b850e8ecf40557e65ca6",
"score": "0.7629509",
"text": "def get_a... |
d141728462746520515df7079f0b42f9 | Return a list of documented arguments with arguments metadata for specific method. | [
{
"docid": "c613af79aedd77a70e66d5666c23d4d0",
"score": "0.6292819",
"text": "def get_arguments_documentation(method_ref, as_params_collection=False, spilt_or=False, ignore_non_args=False):\n\n PARAM_ANNOTATION = '@param'\n IVAR_ANNOTATION = '@ivar'\n params_list = []\n ivars... | [
{
"docid": "7cb01420f7c7a316d7caf8d70c0e9a07",
"score": "0.7419464",
"text": "def get_documented_arguments(method_ref, as_params_collection=False, spilt_or=False):\n return MethodHelper.get_arguments_documentation(method_ref, as_params_collection, spilt_or, ignore_non_args=True)",
"title": ""... |
5e214a171ac431e3c9503bf27268fa04 | limit_labmda Cheesy as fuck hook to pass limit to a list newer_than Stream items greater than this BACKSTORY You probably want to deliver all unacknowledged but you might not want the entire history of everything they've already seen. | [
{
"docid": "8a477347b9b0440842084615bfcf4918",
"score": "0.0",
"text": "def user_stream(user, limit=None, newer_than=None):\n conn = get_cache_client()\n return get_stream(\n user.pk, conn,\n limit=limit, newer_than=newer_than, renderer=compile_stream)",
"title": ""
}
] | [
{
"docid": "ddf21283e58c1f92eb4c910709521cc2",
"score": "0.6336143",
"text": "def get_latest_spam(self, limit: int) -> list:",
"title": ""
},
{
"docid": "d193da18897c79e4b6729ddc3832bcc3",
"score": "0.6221573",
"text": "def on_limit(self, track):\r\n return",
"title": ""
... |
f6e3df0a207abdfe02e591d71511ee1a | Get function that returns distances between examples in learned space. | [
{
"docid": "81e5a0405eb8930cf0ef7fefed8254f7",
"score": "0.66239303",
"text": "def get_dist_func(data : Array[np.float64], target : Array[np.float64], n : np.int) -> Callable[[Callable[[np.float64, np.float64], np.float64], np.int, np.int], np.float64]:\n\n # Get transformed data.\n data_trans : A... | [
{
"docid": "5b90c6db1c4c7a51d2b69c6d61c11091",
"score": "0.7252572",
"text": "def get_distances():",
"title": ""
},
{
"docid": "784bc0b08043ecf1f5b80dbff811c330",
"score": "0.6990197",
"text": "def dist(self) -> Distance:",
"title": ""
},
{
"docid": "8269db68ee68dc5cddccb... |
a9bfebd5f0f713c108a4f8f2102afde3 | Get path to write and attach corresponding file extension. | [
{
"docid": "bf1f77f0ec7a3a829b392792fd70b7e0",
"score": "0.71833307",
"text": "def _get_write_path(self, file_extension: str):\n if file_extension[0] != \".\":\n file_extension += \".\"\n return os.path.join(self._result_dir, self._filename) + file_extension",
"title": ""
... | [
{
"docid": "ec57b0d5838d03e7f48a93fb1846bc4c",
"score": "0.6766839",
"text": "def append_file_extension(path: str, ext: Optional[str]) -> str:\n\n if ext is not None:\n return '%s.%s' % (path, ext)\n return path",
"title": ""
},
{
"docid": "fae1c0631c9baf21a2c2eaa6294f514c",
... |
8c1c0015e095064e7a47460cfae784b1 | Decrement the integer value of a key by the given number | [
{
"docid": "f508a6af265ff36873fd63de38632270",
"score": "0.74317914",
"text": "def decrby(self, key:NativeType, increment:int) -> int:\n return self._query(b'decrby', self.encode_from_native(key), self._encode_int(increment))",
"title": ""
}
] | [
{
"docid": "16da0a9d2e42a401a0be02abacf3724c",
"score": "0.82384324",
"text": "def decrement_int_value(self, key):\n return self._redis.decr(name=key)",
"title": ""
},
{
"docid": "f1cd9fbc3a0a8519344f377e45eb3e85",
"score": "0.7788498",
"text": "def dec(self, key):\r\n ... |
1d8935b2d9ad8819e0c4381d50f0d81e | Initializes hidden layers, based on defined number of layers. | [
{
"docid": "cc63545b27bc51814908aea07aabddd9",
"score": "0.7333883",
"text": "def _initialize_hidden_layers(self, neural_network):\n \n number_of_layers = randint(self.init_minimum_layers, self.layers)\n neurons_per_layer = [randint(1, self.init_maximum_neurons_per_layer) for i in r... | [
{
"docid": "4fa5825f794dd872fe20c41cb694ed3f",
"score": "0.7226314",
"text": "def init_hidden(self):\n return torch.zeros(self.num_layers, self.batch_size, self.hidden_size)",
"title": ""
},
{
"docid": "68b498dd3486faea72dc3a9779706daf",
"score": "0.7152341",
"text": "def init... |
41f1216a5d7b8ef4758b1bc9d568f4f2 | Mix species of different families. All possible combinations are extracted. | [
{
"docid": "04d8761f1f3aa61edccb2ca7ddeca5a0",
"score": "0.72845006",
"text": "def mix_species(self, species, optional=False):\n family_numbers = np.unique([s.family_number for s in species])\n if len(family_numbers) == 0:\n return []\n families = [ [s for s in species if... | [
{
"docid": "f4baea9f29fddc8c0948f2b7ca1e9cfc",
"score": "0.59810746",
"text": "def expand_mix(self, mix):\n new_dict = mix[0].species()\n for i in range(1, len(mix)):\n new_dict = self.merge_dicts_mz(new_dict, mix[i].species())\n return new_dict",
"title": ""
},
{... |
1212537f420017b9738fde435fa396fa | r"""Return a list of sequences to run to perform gate set tomography according to the given spec, with standard poweroftwo sequence lengths up to (approximately) | [
{
"docid": "f64510c4db40c41c7c3c5ba2f27316cf",
"score": "0.57117736",
"text": "def generate_std_gst_sequences(spec: GSTSpec,\n max_len_exponent: int) -> List[GateSequence]:\n return generate_gst_sequences(spec, (2**i for i in range(max_len_exponent + 1)))",
"title": ... | [
{
"docid": "2a8461217b4174229a6b47de662dc87f",
"score": "0.62755734",
"text": "def generate_gst_sequences(spec: GSTSpec,\n target_lens: Sequence[int]) -> List[GateSequence]:\n # Collect sequences in set for deduplication.\n seqs = set()\n for target_len in target_lens:... |
2376dd75aefe8d1ed378c729882d320a | Create a list of windows to be slid over / searched on, for a given image Takes an image, start and stop positions in both x and y, window size (x and y dimensions), and overlap fraction (for both x and y) | [
{
"docid": "0bcaaf2bb4003482140a814aa8362ab7",
"score": "0.7977463",
"text": "def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None],\n xy_window=(64, 64), xy_overlap=(0.5, 0.5)):\n # If x and/or y start/stop positions not defined, set to image size\n if x_start... | [
{
"docid": "cf9ffb3c2b3f1cbe7a7e2ac9920c7980",
"score": "0.8306893",
"text": "def generate_windows_list(img,\n x_start_stop,\n y_start_stop,\n xy_window,\n xy_overlap):\n if x_start_stop[0] is None:\n ... |