Dict[float, float]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5cce4fcace7c9b7e026a52361bad2d5\",\n \"score\": \"0.50199866\",\n \"text\": \"def add_doc(msg):\\n if not msg.topic.startswith(\\\"sensor_data/\\\"):\\n return\\n device_id = msg.topic.split('/')[0]\\n body = {\\n \\\"device_id\\\": device_id,\\n \\\"timestamp\\\": datetime.utcnow()\\n }\\n if msg.topic.endswith('moist'):\\n body['moisture'] = analog_to_percent(str(msg.payload.decode(\\\"utf-8\\\")))\\n body['raw_moist'] = str(msg.payload.decode(\\\"utf-8\\\"))\\n elif msg.topic.endswith('temp'):\\n body['raw_temp'] = str(msg.payload.decode(\\\"utf-8\\\"))\\n elif msg.topic.startswith('sensor_data'):\\n body['device_id'] = msg.topic.split('/')[1]\\n try:\\n \\\"\\\"\\\" {\\\"soil_moist\\\": \\\"\\\", \\\"raw_temp\\\": \\\"\\\", \\\"air_hum\\\": \\\"\\\", \\\"air_temp\\\": \\\"\\\", \\\"ip_addr\\\": \\\"\\\", \\\"device_name\\\": \\\"\\\"} \\\"\\\"\\\"\\n result = json.loads(str(msg.payload.decode(\\\"utf-8\\\")))\\n body['moisture'] = analog_to_percent(result.get(\\\"soil_moist\\\", 0))\\n body['raw_moisture'] = result.get(\\\"soil_moist\\\", 0)\\n body['raw_temp'] = result.get(\\\"raw_temp\\\", 0)\\n body['air_humidity'] = result.get(\\\"air_hum\\\", 0)\\n body['air_temp'] = result.get(\\\"air_temp\\\", 0)\\n body['ip_address'] = result.get(\\\"ip_addr\\\", '0.0.0.0')\\n body['device_name'] = result.get(\\\"device_name\\\", 'New Device')\\n except:\\n traceback.print_exc()\\n else:\\n try:\\n \\\"\\\"\\\" {\\\"soil_moist\\\": \\\"\\\", \\\"raw_temp\\\": \\\"\\\", \\\"air_hum\\\": \\\"\\\", \\\"air_temp\\\": \\\"\\\", \\\"ip_addr\\\": \\\"\\\"} \\\"\\\"\\\"\\n result = json.loads(str(msg.payload.decode(\\\"utf-8\\\")))\\n body['moisture'] = analog_to_percent(result.get(\\\"soil_moist\\\", 0))\\n body['raw_moisture'] = result.get(\\\"soil_moist\\\", 0)\\n body['raw_temp'] = result.get(\\\"raw_temp\\\", 0)\\n body['air_humidity'] = result.get(\\\"air_hum\\\", 0)\\n body['air_temp'] = result.get(\\\"air_temp\\\", 0)\\n body['ip_address'] = result.get(\\\"ip_addr\\\", '0.0.0.0')\\n except:\\n traceback.print_exc()\\n try:\\n es.index(index=configs['default']['index_name'], body=body)\\n except:\\n logging.error(traceback.format_exc())\\n try:\\n alerter.check_alert(body.get('device_id'), body.get(\\\"device_name\\\", \\\"Unknown Device\\\"), body)\\n except:\\n logging.error(traceback.format_exc())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db9b5ef2263e37572381c7aed8bd8b5a\",\n \"score\": \"0.50180006\",\n \"text\": \"def _build_parsed_values(self):\\n \\n SENSOR = \\\"Sensor\\\"\\n TYPE = \\\"type\\\"\\n ID = \\\"id\\\"\\n PCB_SERIAL_NUMBER = \\\"PCBSerialNum\\\"\\n ASSEMBLY_NUMBER = \\\"AssemblyNum\\\"\\n SERIAL_NUMBER = \\\"SerialNumber\\\"\\n FIRMWARE_VERSION = \\\"FirmwareVersion\\\"\\n FIRMWARE_DATE = \\\"FirmwareDate\\\"\\n COMMAND_SET_VERSION = \\\"CommandSetVersion\\\"\\n PCB_ASSEMBLY = \\\"PCBAssembly\\\"\\n MANUFATURE_DATE = \\\"MfgDate\\\"\\n INTERNAL_SENSORS = \\\"InternalSensors\\\"\\n TEMPERATURE_SENSOR_ID = \\\"Main Temperature\\\"\\n CONDUCTIVITY_SENSOR_ID = \\\"Main Conductivity\\\"\\n PRESSURE_SENSOR_ID = \\\"Main Pressure\\\"\\n \\n # check to make sure there is a correct match before continuing\\n match = SBE16HardwareDataParticle.regex_compiled().match(self.raw_data)\\n if not match:\\n raise SampleException(\\\"No regex match of parsed hardware data: [%s]\\\" %\\n self.raw_data)\\n \\n dom = parseString(self.raw_data)\\n root = dom.documentElement\\n log.debug(\\\"root.tagName = %s\\\" %root.tagName)\\n serial_number = int(root.getAttribute(SERIAL_NUMBER))\\n \\n firmware_version = self._extract_xml_element_value(root, FIRMWARE_VERSION)\\n firmware_date = self._extract_xml_element_value(root, FIRMWARE_DATE)\\n command_set_version = self._extract_xml_element_value(root, COMMAND_SET_VERSION)\\n manufacture_date = self._extract_xml_element_value(root, MANUFATURE_DATE)\\n \\n pcb_assembly_elements = self._extract_xml_elements(root, PCB_ASSEMBLY)\\n pcb_serial_number = []\\n pcb_assembly = []\\n for assembly in pcb_assembly_elements:\\n pcb_serial_number.append(assembly.getAttribute(PCB_SERIAL_NUMBER))\\n pcb_assembly.append(assembly.getAttribute(ASSEMBLY_NUMBER))\\n \\n internal_sensors_element = self._extract_xml_elements(root, INTERNAL_SENSORS)[0]\\n sensors = self._extract_xml_elements(internal_sensors_element, SENSOR)\\n for sensor in sensors:\\n sensor_id = sensor.getAttribute(ID)\\n if sensor_id == TEMPERATURE_SENSOR_ID:\\n temperature_sensor_serial_number = int(self._extract_xml_element_value(sensor, SERIAL_NUMBER))\\n elif sensor_id == CONDUCTIVITY_SENSOR_ID:\\n conductivity_sensor_serial_number = int(self._extract_xml_element_value(sensor, SERIAL_NUMBER))\\n elif sensor_id == PRESSURE_SENSOR_ID:\\n pressure_sensor_serial_number = int(self._extract_xml_element_value(sensor, SERIAL_NUMBER))\\n pressure_sensor_type = self._extract_xml_element_value(sensor, TYPE) \\n\\n result = [{DataParticleKey.VALUE_ID: SBE16HardwareDataParticleKey.SERIAL_NUMBER,\\n DataParticleKey.VALUE: serial_number},\\n {DataParticleKey.VALUE_ID: SBE16HardwareDataParticleKey.FIRMWARE_VERSION,\\n DataParticleKey.VALUE: firmware_version},\\n {DataParticleKey.VALUE_ID: SBE16HardwareDataParticleKey.FIRMWARE_DATE,\\n DataParticleKey.VALUE: firmware_date},\\n {DataParticleKey.VALUE_ID: SBE16HardwareDataParticleKey.COMMAND_SET_VERSION,\\n DataParticleKey.VALUE: command_set_version},\\n {DataParticleKey.VALUE_ID: SBE16HardwareDataParticleKey.MANUFATURE_DATE,\\n DataParticleKey.VALUE: manufacture_date},\\n {DataParticleKey.VALUE_ID: SBE16HardwareDataParticleKey.PCB_SERIAL_NUMBER,\\n DataParticleKey.VALUE: pcb_serial_number},\\n {DataParticleKey.VALUE_ID: SBE16HardwareDataParticleKey.ASSEMBLY_NUMBER,\\n DataParticleKey.VALUE: pcb_assembly},\\n {DataParticleKey.VALUE_ID: SBE16HardwareDataParticleKey.TEMPERATURE_SENSOR_SERIAL_NUMBER,\\n DataParticleKey.VALUE: temperature_sensor_serial_number},\\n {DataParticleKey.VALUE_ID: SBE16HardwareDataParticleKey.CONDUCTIVITY_SENSOR_SERIAL_NUMBER,\\n DataParticleKey.VALUE: conductivity_sensor_serial_number},\\n {DataParticleKey.VALUE_ID: SBE16HardwareDataParticleKey.QUARTZ_PRESSURE_SENSOR_SERIAL_NUMBER,\\n DataParticleKey.VALUE: pressure_sensor_serial_number},\\n {DataParticleKey.VALUE_ID: SBE16HardwareDataParticleKey.PRESSURE_SENSOR_TYPE,\\n DataParticleKey.VALUE: pressure_sensor_type},\\n ]\\n \\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86da896164e23e78364d8355455b1573\",\n \"score\": \"0.50143576\",\n \"text\": \"def deserialize(self, str):\\n try:\\n if self.devices is None:\\n self.devices = None\\n end = 0\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n self.devices = []\\n for i in range(0, length):\\n val1 = rocon_device_msgs.msg.Device()\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n start = end\\n end += length\\n if python3:\\n val1.label = str[start:end].decode('utf-8')\\n else:\\n val1.label = str[start:end]\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n start = end\\n end += length\\n if python3:\\n val1.type = str[start:end].decode('utf-8')\\n else:\\n val1.type = str[start:end]\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n start = end\\n end += length\\n if python3:\\n val1.uuid = str[start:end].decode('utf-8')\\n else:\\n val1.uuid = str[start:end]\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n val1.data = []\\n for i in range(0, length):\\n val2 = rocon_std_msgs.msg.KeyValue()\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n start = end\\n end += length\\n if python3:\\n val2.key = str[start:end].decode('utf-8')\\n else:\\n val2.key = str[start:end]\\n start = end\\n end += 4\\n (length,) = _struct_I.unpack(str[start:end])\\n start = end\\n end += length\\n if python3:\\n val2.value = str[start:end].decode('utf-8')\\n else:\\n val2.value = str[start:end]\\n val1.data.append(val2)\\n self.devices.append(val1)\\n return self\\n except struct.error as e:\\n raise genpy.DeserializationError(e) #most likely buffer underfill\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e742fb86fd3a780802c114a346ad1de\",\n \"score\": \"0.49881446\",\n \"text\": \"def send_39(msg):\\n telemetry_address_hex = hex(int(msg.topic.split('/')[2]))\\n telemetry_address = telemetry_address_hex[2:].upper().zfill(6)\\n logging.debug('_39s: send_39 telemetry_address={0}'.format(telemetry_address))\\n # Fill DI with 0s not used\\n data_packet = '00000000'\\n # Get elements from mqtt:\\n data = msg.payload.split(',')\\n for element in data:\\n try:\\n element_hex = hex(int(element))\\n except ValueError:\\n logging.warning('_39s: Not able to convert Element {0} to hex'.format(element))\\n # Remove '0x'\\n element_string = str(element_hex)[2:]\\n if len(element_string) > 4:\\n # Fix length to 4 chars\\n logging.warning('_39s: Element {0} longer than 4 chars, fixing!'.format(element_string))\\n element_string = element_string[:4]\\n element_string = element_string.rjust(4, '0')\\n logging.debug('_39s: send_39 element:, hex={0}, hex string={1}'.format(element_hex, element_string))\\n data_packet += element_string\\n data_packet = data_packet.ljust(64, '0')\\n _39 = 'GET /A/B/7F26{0}39{1} HTTP/1.1'.format(telemetry_address, data_packet)\\n\\n try:\\n s = socket.socket()\\n s.connect(('nebulalisten.com', 3001))\\n logging.debug('_39 to send: {0}'.format(_39))\\n s.send(_39)\\n r = s.recv(255)\\n s.close()\\n logging.debug('Rx from server: {0}'.format(r))\\n if r == 'Respuesta=9':\\n pass\\n else:\\n raise\\n except Exception as e:\\n logging.warning('Failed to open connection to server! Error = %s', e.__str__())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3cc9180c5e444af50f742926253a40\",\n \"score\": \"0.4987267\",\n \"text\": \"def send_inputs(client, devices):\\n print('About to prepare inputs')\\n (x_train, y_train), (x_test, y_test) = prepare_inputs()\\n print('Prepared inputs')\\n inputs = x_train[:4] # ? Only getting 4 for dev purposes\\n outputs = y_train[:4] # ? Only getting 4 for dev purposes\\n\\n print('@@ EXPECTED OUTPUTS @@')\\n print(outputs)\\n print('@@ ---------------- @@')\\n device = devices[0]\\n for image in inputs:\\n image = np.array([image])\\n task = {\\n 'data': image.tolist(),\\n 'for': devices[1:], # * List of recipients (aka 1 to last)\\n 'is_inferencing': True\\n }\\n client.publish(device + '/tasks', json.dumps(task))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d3f209659936b0665fc9e99b22bfa08\",\n \"score\": \"0.49846217\",\n \"text\": \"def sensors(self, packet_id):\\n # Need to make sure the packet_id is a string\\n packet_id = str(packet_id)\\n # Check to make sure that the packet ID is valid.\\n if packet_id in self.config.data['sensor group packet lengths']:\\n # Valid packet, send request (But convert it back to an int in a list first)\\n packet_id = [int(packet_id)]\\n self.SCI.send(self.config.data['opcodes']['sensors'], tuple(packet_id))\\n else:\\n raise _ROIFailedToSendError(\\\"Invalid packet id, failed to send\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"849427c93bf9c43def7441d90106a62a\",\n \"score\": \"0.49795005\",\n \"text\": \"def to(self, device):\\n for data in self.data_list:\\n data.to(device)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ba61e18eaa795484dd4dbef1d37ea70\",\n \"score\": \"0.49792498\",\n \"text\": \"def cmd_publish(self): \\n msg = dynamixel_command_list_t()\\n msg.len = 6\\n self.clamp()\\n for i in range(msg.len):\\n cmd = dynamixel_command_t()\\n cmd.utime = int(time.time() * 1e6)\\n cmd.position_radians = self.joint_angles[i]\\n # you SHOULD change this to contorl each joint speed separately \\n cmd.speed = self.speed\\n cmd.max_torque = self.max_torque\\n #print cmd.position_radians\\n msg.commands.append(cmd)\\n self.lc.publish(\\\"ARM_COMMAND\\\",msg.encode())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc42b5d9fd748d05d6b09affb534f1c2\",\n \"score\": \"0.4977282\",\n \"text\": \"def feed_devices(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80909c9a0d0102da55984db12be69eb2\",\n \"score\": \"0.49751556\",\n \"text\": \"def read_mavlink(self, name, message):\\n import math\\n\\n # Update date and time\\n now = datetime.datetime.now()\\n\\n if name == 'SYSTEM_TIME':\\n self._sensor_data.time_unix_usec = datetime.datetime.fromtimestamp(\\n message.time_unix_usec / 1000000.).strftime('%Y-%m-%d %H:%M:%S.%f') # %Y%m%d%H%M%S%f\\n # self._sensor_data.date = str(now.strftime('%d%m%y%H%M%S'))\\n self._sensor_data.millis = message.time_unix_usec # Timestamp in microseconds since UNIX epoch\\n\\n # Allow data export\\n if self._sensor_data.flag:\\n self._sensor_data.export_csv()\\n\\n # First IMU\\n elif name == 'RAW_IMU':\\n self._sensor_data.ax = message.xacc\\n self._sensor_data.ay = message.yacc\\n self._sensor_data.az = message.zacc\\n self._sensor_data.gx = message.xgyro\\n self._sensor_data.gy = message.ygyro\\n self._sensor_data.gz = message.zgyro\\n self._sensor_data.mx = message.xmag\\n self._sensor_data.my = message.ymag\\n self._sensor_data.mz = message.zmag\\n\\n # Second IMU\\n elif name == 'SCALED_IMU2':\\n self._sensor_data.accx = message.xacc\\n self._sensor_data.accy = message.yacc\\n self._sensor_data.accz = message.zacc\\n self._sensor_data.gyrox = message.xgyro\\n self._sensor_data.gyroy = message.ygyro\\n self._sensor_data.gyroz = message.zgyro\\n self._sensor_data.magx = message.xmag\\n self._sensor_data.magy = message.ymag\\n self._sensor_data.magz = message.zmag\\n\\n elif name == 'ATTITUDE':\\n self._sensor_data.roll_rate = math.degrees(message.rollspeed)\\n self._sensor_data.pitch_rate = math.degrees(message.pitchspeed)\\n self._sensor_data.yaw_rate = math.degrees(message.yawspeed)\\n self._sensor_data.roll = math.degrees(message.roll)\\n self._sensor_data.pitch = math.degrees(message.pitch)\\n self._sensor_data.yaw = math.degrees(message.yaw)\\n\\n elif name == 'LOCAL_POSITION_NED':\\n self._sensor_data.x = message.x\\n self._sensor_data.y = message.y\\n self._sensor_data.z = message.z\\n self._sensor_data.vx = message.vx\\n self._sensor_data.vy = message.vy\\n self._sensor_data.vz = message.vz\\n\\n elif name == 'GLOBAL_POSITION_INT':\\n self._sensor_data.lat = message.lat # degrees * 1E7\\n self._sensor_data.lon = message.lon # degrees * 1E7\\n self._sensor_data.alt = message.alt # meters\\n self._sensor_data.relative_alt = message.relative_alt # meters\\n self._sensor_data.gps_vx = message.vx # m/s * 100\\n self._sensor_data.gps_vy = message.vy # m/s * 100\\n self._sensor_data.gps_vz = message.vz # m/s * 100\\n self._sensor_data.hdg = message.hdg # Heading\\n\\n elif name == 'GPS_RAW_INT':\\n self._sensor_data.speed = message.vel # m/s * 100\\n self._sensor_data.latitude = message.lat # degrees * 1E7\\n self._sensor_data.longitude = message.lon # degrees * 1E7\\n self._sensor_data.altitude = message.alt # meter * 1000\\n self._sensor_data.course = message.cog # Direction of movement in degrees, not heading.\\n self._sensor_data.hdop = message.eph\\n self._sensor_data.vdop = message.epv\\n self._sensor_data.fix = message.fix_type\\n self._sensor_data.sat_view = message.satellites_visible\\n\\n elif name == 'GPS_STATUS':\\n self._sensor_data.snr = message.satellite_snr\\n\\n elif name == 'VFR_HUD':\\n self._sensor_data.airspeed = message.airspeed # m/s\\n self._sensor_data.groundspeed = message.groundspeed # m/s\\n self._sensor_data.heading = message.heading # Heading in degrees\\n self._sensor_data.throttle = message.throttle # percent\\n self._sensor_data.barometer = message.alt # meters\\n self._sensor_data.climb = message.climb # meters/second\\n\\n elif name == 'SCALED_PRESSURE':\\n self._sensor_data.press_abs = message.press_abs\\n self._sensor_data.press_diff = message.press_diff\\n self._sensor_data.temp = message.temperature\\n\\n elif name == 'NAV_CONTROLLER_OUTPUT':\\n self._sensor_data.desired_roll = message.nav_roll\\n self._sensor_data.desired_pitch = message.nav_pitch\\n self._sensor_data.desired_heading = message.nav_bearing\\n self._sensor_data.target_bearing = message.target_bearing\\n self._sensor_data.wp_dist = message.wp_dist\\n self._sensor_data.alt_error = message.alt_error\\n self._sensor_data.aspd_error = message.aspd_error\\n self._sensor_data.xtrack_error = message.xtrack_error\\n\\n elif name == 'VIBRATION':\\n self._sensor_data.vibration_x = message.vibration_x\\n self._sensor_data.vibration_y = message.vibration_y\\n self._sensor_data.vibration_z = message.vibration_z\\n\\n\\n # Notify all observers of new message (with new value)\\n # Note that argument `cache=False` by default so listeners\\n # are updated with every new message\\n self.notify_attribute_listeners('sensor_data', self._sensor_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2d3e891336a37d56c06ecd26f8f3182\",\n \"score\": \"0.49706167\",\n \"text\": \"def test_pulsar_keyed_messages():\\n\\n dataset = tfio.experimental.streaming.PulsarIODataset(\\n service_url=\\\"pulsar://localhost:6650\\\",\\n topic=\\\"key-test\\\",\\n subscription=\\\"subscription-0\\\",\\n timeout=default_pulsar_timeout,\\n )\\n kv = dict()\\n for msg, key in dataset:\\n kv.setdefault(key.numpy().decode(), []).append(msg.numpy())\\n assert kv[\\\"K0\\\"] == [(\\\"D\\\" + str(i)).encode() for i in range(0, 6, 2)]\\n assert kv[\\\"K1\\\"] == [(\\\"D\\\" + str(i)).encode() for i in range(1, 6, 2)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a93ef705ce6348d857cd7267f733a9b\",\n \"score\": \"0.49436262\",\n \"text\": \"def _build_parsed_values(self):\\n\\n SENSOR = \\\"Sensor\\\"\\n TYPE = \\\"type\\\"\\n ID = \\\"id\\\"\\n PCB_SERIAL_NUMBER = \\\"PCBSerialNum\\\"\\n ASSEMBLY_NUMBER = \\\"AssemblyNum\\\"\\n SERIAL_NUMBER = \\\"SerialNumber\\\"\\n FIRMWARE_VERSION = \\\"FirmwareVersion\\\"\\n FIRMWARE_DATE = \\\"FirmwareDate\\\"\\n COMMAND_SET_VERSION = \\\"CommandSetVersion\\\"\\n PCB_ASSEMBLY = \\\"PCBAssembly\\\"\\n MANUFACTURE_DATE = \\\"MfgDate\\\"\\n INTERNAL_SENSORS = \\\"InternalSensors\\\"\\n TEMPERATURE_SENSOR_ID = \\\"Main Temperature\\\"\\n CONDUCTIVITY_SENSOR_ID = \\\"Main Conductivity\\\"\\n PRESSURE_SENSOR_ID = \\\"Main Pressure\\\"\\n EXTERNAL_SENSORS = \\\"ExternalSensors\\\"\\n VOLT0 = \\\"volt 0\\\"\\n VOLT1 = \\\"volt 1\\\"\\n\\n # check to make sure there is a correct match before continuing\\n match = SBE16NOHardwareParticle.regex_compiled().match(self.raw_data)\\n if not match:\\n raise SampleException(\\\"No regex match of parsed hardware data: [%s]\\\" %\\n self.raw_data)\\n\\n dom = parseString(self.raw_data)\\n root = dom.documentElement\\n log.debug(\\\"root.tagName = %s\\\", root.tagName)\\n serial_number = root.getAttribute(SERIAL_NUMBER)\\n\\n firmware_version = self._extract_xml_element_value(root, FIRMWARE_VERSION)\\n firmware_date = self._extract_xml_element_value(root, FIRMWARE_DATE)\\n command_set_version = self._extract_xml_element_value(root, COMMAND_SET_VERSION)\\n manufacture_date = self._extract_xml_element_value(root, MANUFACTURE_DATE)\\n\\n pcb_assembly_elements = self._extract_xml_elements(root, PCB_ASSEMBLY)\\n pcb_serial_number = []\\n pcb_assembly = []\\n for assembly in pcb_assembly_elements:\\n pcb_serial_number.append(assembly.getAttribute(PCB_SERIAL_NUMBER))\\n pcb_assembly.append(assembly.getAttribute(ASSEMBLY_NUMBER))\\n\\n temperature_sensor_serial_number = \\\"\\\"\\n conductivity_sensor_serial_number = \\\"\\\"\\n pressure_sensor_serial_number = \\\"\\\"\\n pressure_sensor_type = \\\"\\\"\\n volt0_serial_number = 0\\n volt0_type = \\\"\\\"\\n volt1_serial_number = 0\\n volt1_type = \\\"\\\"\\n\\n internal_sensors_element = self._extract_xml_elements(root, INTERNAL_SENSORS)[0]\\n sensors = self._extract_xml_elements(internal_sensors_element, SENSOR)\\n\\n for sensor in sensors:\\n sensor_id = sensor.getAttribute(ID)\\n if sensor_id == TEMPERATURE_SENSOR_ID:\\n temperature_sensor_serial_number = self._extract_xml_element_value(sensor, SERIAL_NUMBER)\\n elif sensor_id == CONDUCTIVITY_SENSOR_ID:\\n conductivity_sensor_serial_number = self._extract_xml_element_value(sensor, SERIAL_NUMBER)\\n elif sensor_id == PRESSURE_SENSOR_ID:\\n pressure_sensor_serial_number = str(self._extract_xml_element_value(sensor, SERIAL_NUMBER))\\n pressure_sensor_type = self._extract_xml_element_value(sensor, TYPE)\\n\\n external_sensors_element = self._extract_xml_elements(root, EXTERNAL_SENSORS)[0]\\n sensors = self._extract_xml_elements(external_sensors_element, SENSOR)\\n\\n for sensor in sensors:\\n sensor_id = sensor.getAttribute(ID)\\n if sensor_id == VOLT0:\\n volt0_serial_number = self._extract_xml_element_value(sensor, SERIAL_NUMBER)\\n volt0_type = self._extract_xml_element_value(sensor, TYPE)\\n elif sensor_id == VOLT1:\\n volt1_serial_number = self._extract_xml_element_value(sensor, SERIAL_NUMBER)\\n volt1_type = self._extract_xml_element_value(sensor, TYPE)\\n\\n result = [{DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.SERIAL_NUMBER,\\n DataParticleKey.VALUE: str(serial_number)},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.FIRMWARE_VERSION,\\n DataParticleKey.VALUE: firmware_version},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.FIRMWARE_DATE,\\n DataParticleKey.VALUE: firmware_date},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.COMMAND_SET_VERSION,\\n DataParticleKey.VALUE: command_set_version},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.MANUFACTURE_DATE,\\n DataParticleKey.VALUE: manufacture_date},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.PCB_SERIAL_NUMBER,\\n DataParticleKey.VALUE: pcb_serial_number},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.ASSEMBLY_NUMBER,\\n DataParticleKey.VALUE: pcb_assembly},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.TEMPERATURE_SENSOR_SERIAL_NUMBER,\\n DataParticleKey.VALUE: temperature_sensor_serial_number},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.CONDUCTIVITY_SENSOR_SERIAL_NUMBER,\\n DataParticleKey.VALUE: conductivity_sensor_serial_number},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.PRESSURE_SENSOR_SERIAL_NUMBER,\\n DataParticleKey.VALUE: pressure_sensor_serial_number},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.PRESSURE_SENSOR_TYPE,\\n DataParticleKey.VALUE: pressure_sensor_type},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.VOLT0_SERIAL_NUMBER,\\n DataParticleKey.VALUE: volt0_serial_number},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.VOLT0_TYPE,\\n DataParticleKey.VALUE: volt0_type},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.VOLT1_SERIAL_NUMBER,\\n DataParticleKey.VALUE: volt1_serial_number},\\n {DataParticleKey.VALUE_ID: SBE16NOHardwareParticleKey.VOLT1_TYPE,\\n DataParticleKey.VALUE: volt1_type}]\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffe7140b94345526727371d8f2d98b06\",\n \"score\": \"0.49171403\",\n \"text\": \"def sensor(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2f3889f495ce645569446de2f489f54\",\n \"score\": \"0.49131998\",\n \"text\": \"def test_list_message(self):\\n sensor = self._get_dummy_sensor()\\n msg_list = sensor.create_event_messages({'test_1': 23.3, 'test_2': 'wat'})\\n msg_0 = json.loads(msg_list[0])\\n msg_1 = json.loads(msg_list[1])\\n assert msg_0['value_type'] == 'float'\\n assert msg_0['value'] == '23.3'\\n assert msg_1['value_type'] == 'str'\\n assert msg_1['value'] == 'wat'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4abdd9a71aa9136a0cbed745e5dfbd1\",\n \"score\": \"0.4896513\",\n \"text\": \"def send_all_devices(self, title: str, body: str, *args: str, **data: dict) -> requests:\\n sent_token = {}\\n for item in self.tokens_firebase:\\n sent_token[item] = super(PushNotificationsFirebaseBroadcast, self).send(item, title, body, *args, **data)\\n return sent_token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35e42aa344760b46b8d949a4258031eb\",\n \"score\": \"0.4892687\",\n \"text\": \"def receive_sensor_data(self, data):\\n for i in data:\\n self.file.write(\\\"%03f\\\" % i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5378bfa485f47570aff335ed4bdd386\",\n \"score\": \"0.4885033\",\n \"text\": \"def command_robot_speak(self, message):\\n for conn in cl:\\n conn.write_message(json.dumps(message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae9f982505814c4ef3fe1b58cca0ff56\",\n \"score\": \"0.48763394\",\n \"text\": \"def callback_muscle_spindles_to_motoneurons(input_values, output_values, current_time, slot_nos, buffer):\\n # mapping muscle spindles output -> motor neuron signals, delay signals by \\n \\n # get number of input and output values\\n n_input_values = len(input_values) # = n_muscle_spindles\\n n_output_values = len(output_values[0]) # = n_muscle_spindles (per output slot if there are multiple)\\n \\n # initialize buffer the first time\\n if 0 not in buffer:\\n for muscle_spindle_index in range(n_input_values):\\n buffer[muscle_spindle_index] = None\\n \\n # loop over muscle spindles\\n for muscle_spindle_index in range(n_input_values):\\n \\n # determine spike by threshold\\n if input_values[muscle_spindle_index] > 0:\\n buffer[muscle_spindle_index] = current_time # store time of last activation in buffer\\n \\n # if there has been a stimulation so far\\n if buffer[muscle_spindle_index] is not None:\\n \\n # convolute Dirac delta, kernel is a shifted and scaled gaussian\\n t_delay = muscle_spindle_delay # [ms] delay of the signal\\n gaussian_std_dev = 10 # [ms] width of the gaussian curve\\n convolution_kernel = lambda t: np.exp(-0.5 * ((t - t_delay) / gaussian_std_dev)**2)\\n delayed_signal = convolution_kernel(current_time - buffer[muscle_spindle_index]) * 5\\n \\n # loop over output values and set all to the computed signal, cut off at 1e-5\\n if delayed_signal > 1e-5:\\n #print(\\\"muscle spindle t: {}, last_activation: {}, computed delayed_signal: {}\\\".format(current_time, buffer[muscle_spindle_index], delayed_signal))\\n output_values[0][muscle_spindle_index] = delayed_signal\\n else:\\n output_values[0][muscle_spindle_index] = None # do not set any values\\n \\n #print(\\\"muscle_spindles_to_motoneurons: {} -> {}\\\".format(input_values, output_values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0992f3efb52b0d14898ec9b91fd0411f\",\n \"score\": \"0.48650762\",\n \"text\": \"def on_reader_status(moqs, obj, msg):\\n obj.logger_debug(\\\"MQTT: topic: {}, data: {}\\\".format(msg.topic, msg.payload.decode(\\\"utf-8\\\")))\\n default_values = [DATA_NONE] * 254\\n try:\\n if obj.config[\\\"use_registers\\\"]:\\n raw = json.loads(msg.payload.decode(\\\"utf-8\\\"))\\n values = list()\\n values.append(raw[\\\"write\\\"][\\\"sector\\\"])\\n if raw[\\\"write\\\"][\\\"status\\\"] == \\\"OK\\\":\\n values.append(1)\\n elif raw[\\\"write\\\"][\\\"status\\\"] == \\\"NOK\\\":\\n values.append(0)\\n else:\\n values.append(DATA_NONE)\\n elif obj.config[\\\"use_msgpack\\\"]:\\n raw = json.loads(msg.payload.decode(\\\"utf-8\\\"))\\n packed = msgpack.packb(raw)\\n\\n values = [v for v in packed]\\n else: # if msgpack or registers are not allow use bare json in ascii\\n values = [ord(v) for v in msg.payload.decode(\\\"utf-8\\\")]\\n aas.logger_debug(\\\"Reader status: new values: \\\" + str(values))\\n # setup default values and then set received values\\n obj.context[aas.config[\\\"slave_id\\\"][\\\"reader_status\\\"]].setValues(3, 0, default_values)\\n obj.context[aas.config[\\\"slave_id\\\"][\\\"reader_status\\\"]].setValues(4, 0, default_values)\\n obj.context[aas.config[\\\"slave_id\\\"][\\\"reader_status\\\"]].setValues(3, 0, values)\\n obj.context[aas.config[\\\"slave_id\\\"][\\\"reader_status\\\"]].setValues(4, 0, values)\\n except:\\n obj.logger_error(\\\"MQTT: received msq cannot be processed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93704691d8f3b63d702a4e565e7ee961\",\n \"score\": \"0.48562124\",\n \"text\": \"def make_configuration_message(\\n self, device_key: str, configuration: dict\\n ) -> Message:\\n topic = (\\n self.CONFIGURATION_STATUS + self.DEVICE_PATH_PREFIX + device_key\\n )\\n\\n for reference, config_value in configuration.items():\\n if isinstance(config_value, tuple):\\n values_list = []\\n for value in config_value:\\n if \\\"\\\\n\\\" in str(value):\\n value = value.replace(\\\"\\\\n\\\", \\\"\\\\\\\\n\\\")\\n value = value.replace(\\\"\\\\r\\\", \\\"\\\")\\n\\n if '\\\"' in str(value):\\n value = value.replace('\\\"', '\\\\\\\\\\\"')\\n\\n values_list.append(value)\\n\\n configuration[reference] = \\\",\\\".join(map(str, values_list))\\n else:\\n if isinstance(config_value, bool):\\n config_value = str(config_value).lower()\\n\\n configuration[reference] = str(config_value)\\n\\n payload = json.dumps({\\\"values\\\": configuration})\\n\\n message = Message(topic, payload)\\n self.log.debug(f\\\"Made {message} from {configuration} and {device_key}\\\")\\n\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ea07649215499bca27035d324a85c8\",\n \"score\": \"0.4854487\",\n \"text\": \"def handshake(self):\\n\\n log.debug('Handshaking.')\\n\\n # Data for sending\\n send_data = bytes([0])\\n\\n # We are trying to disturb the device as long as self.handshake_repeat is\\n handshake_repeat = 0\\n\\n while True:\\n # If we have a key then use it\\n if self.key is not None:\\n send_data = [x ^ self.key for x in send_data]\\n\\n # Sending 0x00 or the encrypted variant to the device\\n log.debug('Sending data: %s [Waiting for handshaking]' % bytes_to_hex_str(send_data))\\n self.port.write(send_data)\\n\\n # Reading what is going on\\n read_data = self.port.read(1)\\n log.debug('Reading data: %s [Waiting for handshaking]' % bytes_to_hex_str(read_data))\\n\\n # If we can not read data back try to use the default key\\n if len(read_data) == 0:\\n self.key = 0x55\\n\\n # Send as long as we have data and 0x07 -> the device is ready for handshake\\n if len(read_data) == 1 and read_data[0] == 0x07:\\n # Exiting the while loop\\n log.debug('Reading data: %s [Ready for handshaking]' % bytes_to_hex_str(read_data))\\n break\\n\\n # We are trying as much as we like?\\n handshake_repeat += 1\\n if handshake_repeat >= self.handshake_repeat:\\n raise MK312HandshakeException('Repeating to much times! Restart the device?')\\n\\n # We can setup a new key -> Sending a key, actually the key is fixed to 0x00\\n send_data = [0x2f, 0x00]\\n checksum = sum(send_data) % 256\\n send_data.append(checksum)\\n\\n # Only encrypt if we have a key (if not, we do not know if the device has one)\\n if self.key:\\n send_data = [x ^ self.key for x in send_data]\\n log.debug('Sending data: %s' % bytes_to_hex_str(send_data))\\n self.port.write(bytes(send_data))\\n\\n read_data = self.port.read(3)\\n log.debug('Reading data: %s' % bytes_to_hex_str(read_data))\\n\\n if len(read_data) == 0:\\n # Maybe there was a key stored but we do not know it.\\n # Normally you have to switch off the device and restart it after a long time.\\n # If you do not have used a different key yet we can try to test our default key\\n self.key = 0x55\\n self.handshake()\\n else:\\n # Check the checksum from the device\\n checksum = read_data[-1]\\n s = sum(read_data[:-1]) % 256\\n if s != checksum:\\n # If the checksum is wrong, redo a handshake\\n # raise MK312ChecksumException('Checksum is wrong: 0x%0.2X != 0x%0.2X' % (s, checksum))\\n self.handshake()\\n return\\n\\n # Key generation: 0x55 ^ their_key\\n self.key = 0x55 ^ read_data[1]\\n log.debug('Handshake successful key is now: 0x%0.2X' % self.key)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"752a175434d15e425fee36425b73adba\",\n \"score\": \"0.48463374\",\n \"text\": \"def GenerateMessage(multiple):\\n data = np.zeros(512 * multiple)\\n s = np.random.choice(len(data), np.random.randint(len(data)), replace=False)\\n data[s] = 1\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd51fa20084dc25c0b73023a7203b86d\",\n \"score\": \"0.483802\",\n \"text\": \"def event_zigbee_recv_handler(event):\\n # '0xa0', '0xba', '0x51', '0x47', '0x5', '0xf3', '0x51', '0x47', '0x79', '0x0', '0x95'\\n pack_list = event.data.get('data')\\n if pack_list[0] == '0xa0' and pack_list[5] == '0xf3':\\n mac_l, mac_h = pack_list[6].replace('0x',''), pack_list[7].replace('0x','')\\n mac_str = mac_l + '#' + mac_h\\n dev = next((dev for dev in panels if dev.mac == mac_str), None)\\n if dev is not None:\\n dev.set_available(True)\\n triger_key = pack_list[9].replace('0x', '')\\n triger_key = str(int(triger_key, 16))\\n dev.trigger(triger_key)\\n if pack_list[0] == '0xa0' and pack_list[5] == '0xf3' and pack_list[8] == '0xcc':\\n mac_l, mac_h = pack_list[6].replace('0x',''), pack_list[7].replace('0x','')\\n mac_str = mac_l + '#' + mac_h\\n dev = next((dev for dev in panels if dev.mac == mac_str), None)\\n if dev is not None:\\n dev.set_available(True)\\n dev.heart_beat()\\n if pack_list[0] == '0xa0' and pack_list[5] == '0xf3' and pack_list[8] == '0x77':\\n # device status\\n mac_l, mac_h = pack_list[2].replace('0x', ''), pack_list[3].replace('0x', '')\\n mac_str = mac_l + '#' + mac_h\\n dev = next((dev for dev in panels if dev.mac == mac_str), None)\\n if dev is None:\\n return\\n dev.set_available(True)\\n dev.heart_beat()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb17801ccc5c8b8e79a07c278f3e5a5c\",\n \"score\": \"0.48326772\",\n \"text\": \"def GetCombinerSensorData(self): \\n with self.lock:\\n control = self.getControlByte(flags=(ACK + SO), sn=self.SequenceNumber, an=self.AckNumber)\\n self.Message = self.CompileSmallMessage(control=control, command=9)\\n ok = self.DoCommand()\\n if not ok:\\n return False\\n self.CombinerTemperature = float(self.RxMessage[4] + (self.RxMessage[3] << 8)) / 10.0\\n self.CombinerCurrent = float(self.RxMessage[2] + (self.RxMessage[1] << 8)) / 10.0\\n logger.debug(\\\"(%s) Retrieved combiner sensor data\\\" % self.name)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74b4750598e126b6fddb3b8c0b698bc3\",\n \"score\": \"0.48318005\",\n \"text\": \"def sendMessageGetDeviceDetected():\\n data = [0] * CMTP_BUFFER_LEN\\n dataLength = 254\\n serviceID = 11\\n dataString = \\\"\\\"\\n for i, resultData in enumerate(data):\\n if i % 4 == 0:\\n dataString += \\\"\\\\n\\\"\\n dataString += \\\"{0:0{1}X}: \\\".format(i, 4)\\n dataString += \\\"{0:0{1}X} \\\".format(resultData, 2)\\n else:\\n dataString += \\\"{0:0{1}X} \\\".format(resultData, 2)\\n Messages.write_message(\\\"Sending Service: {}\\\".format(serviceID))\\n Messages.write_message(\\\"Sending Size: {}\\\".format(dataLength))\\n Messages.write_message(\\\"Sending Data: \\\\n{}\\\".format(dataString))\\n results = data_store.scenario[\\\"capiController\\\"].sendMessage(data, serviceID, dataLength)\\n Messages.write_message(results[\\\"description\\\"])\\n assert results[\\\"result\\\"] == 0, \\\"Send message failed\\\"\\n Messages.write_message(\\\"Received Status: {}\\\".format(results[\\\"data\\\"][\\\"status\\\"]))\\n Messages.write_message(\\\"Received Service: {}\\\".format(results[\\\"data\\\"][\\\"service\\\"]))\\n Messages.write_message(\\\"Received Size: {}\\\".format(results[\\\"data\\\"][\\\"size\\\"]))\\n dataString = \\\"\\\"\\n for i, resultData in enumerate(results[\\\"data\\\"][\\\"data\\\"]):\\n if i % 4 == 0:\\n dataString += \\\"\\\\n\\\"\\n dataString += \\\"{0:0{1}X}: \\\".format(i, 4)\\n dataString += \\\"{0:0{1}X} \\\".format(resultData, 2)\\n else:\\n dataString += \\\"{0:0{1}X} \\\".format(resultData, 2)\\n Messages.write_message(\\\"Received Data: \\\\n{}\\\".format(dataString))\\n assert results[\\\"data\\\"][\\\"status\\\"] == 0, \\\"Status from response is non-zero - {}\\\".format(results[\\\"data\\\"][\\\"status\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47b59d2fd1b728a05143a26b7837dc36\",\n \"score\": \"0.48293498\",\n \"text\": \"def get_sensors_data(self, task):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10551f8ace5eab7e9541fecb9bcd3bc7\",\n \"score\": \"0.4826881\",\n \"text\": \"def sendMessageReadExplicit(dwConfID, dwCrc32, wDeviceNumber, dwApi, wSlotNumber, wSubSlotNumber, wIndex, wLengthDataToRead, dataLength):\\n assert len(byMac.split(\\\":\\\")) == 6, \\\"MAC Address not correct format\\\"\\n assert int(dataLength) < 255, \\\"dataLength is too large, needs to be less than 254\\\"\\n data = [0] * CMTP_BUFFER_LEN\\n dataLength = int(dataLength)\\n serviceID = 32\\n # set configuration ID\\n dwConfIDOffset = 24 - 24\\n for i, num in enumerate(struct.pack('<i', int(dwConfID))):\\n data[i + dwConfIDOffset] = num\\n # set crc 32\\n dwCrc32Offset = 28 - 24\\n for i, num in enumerate(struct.pack('<i', int(dwCrc32))):\\n data[i + dwCrc32Offset] = num\\n # set device number\\n wDeviceNumberOffset = 32 - 24\\n for i, num in enumerate(struct.pack('<h', int(wDeviceNumber))):\\n data[i + wDeviceNumberOffset] = num\\n # set api\\n dwApiOffset = 34 - 24\\n for i, num in enumerate(struct.pack('<i', int(dwApi))):\\n data[i + dwApiOffset] = num\\n # set slot number\\n wSlotNumberOffset = 38 - 24\\n for i, num in enumerate(struct.pack('<h', int(wSlotNumber))):\\n data[i + wSlotNumberOffset] = num\\n # set sub slot number\\n wSubSlotNumberOffset = 40 - 24\\n for i, num in enumerate(struct.pack('<h', int(wSubSlotNumber))):\\n data[i + wSubSlotNumberOffset] = num\\n # set index\\n wIndexOffset = 42 - 24\\n for i, num in enumerate(struct.pack('<h', int(wIndex))):\\n data[i + wSubSlotNumberOffset] = num\\n # set length data to read\\n wLengthDataToReadOffset = 44 - 24\\n for i, num in enumerate(struct.pack('<h', int(wLengthDataToRead))):\\n data[i + wLengthDataToReadOffset] = num\\n # init data string\\n dataString = \\\"\\\"\\n for i, resultData in enumerate(data):\\n if i % 4 == 0:\\n dataString += \\\"\\\\n\\\"\\n dataString += \\\"{0:0{1}X}: \\\".format(i, 4)\\n dataString += \\\"{0:0{1}X} \\\".format(resultData, 2)\\n else:\\n dataString += \\\"{0:0{1}X} \\\".format(resultData, 2)\\n Messages.write_message(\\\"Sending Service: {}\\\".format(serviceID))\\n Messages.write_message(\\\"Sending Size: {}\\\".format(dataLength))\\n Messages.write_message(\\\"Sending Data: \\\\n{}\\\".format(dataString))\\n results = data_store.scenario[\\\"capiController\\\"].sendMessage(data, serviceID, dataLength)\\n Messages.write_message(results[\\\"description\\\"])\\n assert results[\\\"result\\\"] == 0, \\\"Send message failed\\\"\\n Messages.write_message(\\\"Received Status: {}\\\".format(results[\\\"data\\\"][\\\"status\\\"]))\\n Messages.write_message(\\\"Received Service: {}\\\".format(results[\\\"data\\\"][\\\"service\\\"]))\\n Messages.write_message(\\\"Received Size: {}\\\".format(results[\\\"data\\\"][\\\"size\\\"]))\\n dataString = \\\"\\\"\\n for i, resultData in enumerate(results[\\\"data\\\"][\\\"data\\\"]):\\n if i % 4 == 0:\\n dataString += \\\"\\\\n\\\"\\n dataString += \\\"{0:0{1}X}: \\\".format(i, 4)\\n dataString += \\\"{0:0{1}X} \\\".format(resultData, 2)\\n else:\\n dataString += \\\"{0:0{1}X} \\\".format(resultData, 2)\\n Messages.write_message(\\\"Received Data: \\\\n{}\\\".format(dataString))\\n assert results[\\\"data\\\"][\\\"status\\\"] == 0, \\\"Status from response is non-zero - {}\\\".format(results[\\\"data\\\"][\\\"status\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5f199ab9ad329b68cb6f84802a80e23\",\n \"score\": \"0.48142508\",\n \"text\": \"def send_sensor_data(temp, humidity):\\n code = BasePactAdapter.build_code(\\n \\\"raspberrypi\\\",\\n \\\"update-temp-humidity\\\",\\n \\\"admin-keyset\\\",\\n **{\\\"temp\\\": temp, \\\"humidity\\\": humidity,\\n \\\"keyset_name\\\": \\\"admin-keyset\\\",\\n \\\"time\\\": format_current_time()}\\n )\\n print(code)\\n result = api.send_and_listen(code, \\\"admin-keyset\\\")\\n print(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba1f8e9b53892a1520d86dc92884f7d8\",\n \"score\": \"0.4813796\",\n \"text\": \"def scanThread():\\n global c, scanForDevices, scanDuration\\n while True: # Enter the infinite loop\\n if not scanForDevices: # Check for scanning\\n scanDuration = 0 # No scanning\\n time.sleep(SCAN_PERIODE)\\n continue\\n startTime = time.time() # Save start time of update cycle\\n\\n messagesSend = [] # List of messages to send\\n\\n inputListRegister, inputListDeregister = inputScan() # Get the Input devices and events\\n outputListRegister, outputListDeregister = outputScan() # Get the Output devices and events\\n pwmListRegister, pwmListDeregister = pwmScan() # Get the PWM devices and events\\n adcListRegister, adcListDeregister = adcScan() # Get the ADC devices and events\\n i2cListRegister, i2cListDeregister = i2cScan() # Get the I2C devices and events\\n\\n\\n for device in inputListDeregister: # Create input device deregister message\\n messagesSend.append(json.dumps({'type': 'Deregister',\\n 'name': device['name']}))\\n\\n\\n\\n for device in inputListRegister: # Create input device register message\\n messagesSend.append(json.dumps({'type': 'Register',\\n 'name': device['name'],\\n 'dir': device['dir'],\\n 'dim': device['dim'],\\n 'about': device['about'],\\n 'settings': device['settings'],\\n 'mode': device['mode'],\\n 'flags': device['flags'],\\n 'frequency': device['frequency'],\\n 'dutyFrequency': device['dutyFrequency']}))\\n\\n for device in outputListDeregister: # Create output device deregister message\\n messagesSend.append(json.dumps({'type': 'Deregister',\\n 'name': device['name']}))\\n\\n\\n\\n for device in outputListRegister: # Create output device register message\\n messagesSend.append(json.dumps({'type': 'Register',\\n 'name': device['name'],\\n 'dir': device['dir'],\\n 'dim': device['dim'],\\n 'about': device['about'],\\n 'settings': device['settings'],\\n 'mode': device['mode'],\\n 'flags': device['flags'],\\n 'frequency': device['frequency'],\\n 'dutyFrequency': device['dutyFrequency']}))\\n\\n for device in pwmListDeregister: # Create pwm device deregister message\\n messagesSend.append(json.dumps({'type': 'Deregister',\\n 'name': device['name']}))\\n\\n\\n\\n for device in pwmListRegister: # Create pwm device register message\\n messagesSend.append(json.dumps({'type': 'Register',\\n 'name': device['name'],\\n 'dir': device['dir'],\\n 'dim': device['dim'],\\n 'about': device['about'],\\n 'settings': device['settings'],\\n 'mode': device['mode'],\\n 'flags': device['flags'],\\n 'frequency': device['frequency'],\\n 'dutyFrequency': device['dutyFrequency']}))\\n\\n for device in adcListDeregister: # Create ADC device deregister message\\n messagesSend.append(json.dumps({'type': 'Deregister',\\n 'name': device['name']}))\\n\\n\\n\\n for device in adcListRegister: # Create ADC device register message\\n messagesSend.append(json.dumps({'type': 'Register',\\n 'name': device['name'],\\n 'dir': device['dir'],\\n 'dim': device['dim'],\\n 'about': device['about'],\\n 'settings': device['settings'],\\n 'mode': device['mode'],\\n 'flags': device['flags'],\\n 'frequency': device['frequency'],\\n 'dutyFrequency': device['dutyFrequency']}))\\n\\n for device in i2cListDeregister: # Create I2C device deregister message\\n messagesSend.append(json.dumps({'type': 'Deregister',\\n 'name': device['name']}))\\n\\n\\n\\n for device in i2cListRegister: # Create I2C device register message\\n messagesSend.append(json.dumps({'type': 'Register',\\n 'name': device['name'],\\n 'dir': device['dir'],\\n 'dim': device['dim'],\\n 'about': device['about'],\\n 'settings': device['settings'],\\n 'mode': device['mode'],\\n 'flags': device['flags'],\\n 'frequency': device['frequency'],\\n 'dutyFrequency': device['dutyFrequency']}))\\n\\n if c.getState() == 'Connected':\\n c.sendMessages(messagesSend) # Send the messages\\n\\n endTime = time.time() # Save end time of update cycle\\n scanDuration = endTime - startTime # Calculate time used to scan for devices\\n\\n if (scanDuration < SCAN_PERIODE):\\n time.sleep(SCAN_PERIODE - scanDuration) # Sleep until next scan period\\n\\n if exit: # Exit\\n break;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b54062de6e61f49aa485b63b26922d23\",\n \"score\": \"0.481221\",\n \"text\": \"def update(self):\\n\\n _LOGGER.info(\\\"Update called {}\\\".format(self._mac))\\n # send update request\\n cmds_connect = { 'tries': 5,\\n 'commands': [\\n { 'action': 'writeCharacteristic', 'uuid': UUID_PIN, 'value': [ int(x) for x in self._pin.to_bytes(4, byteorder = 'little') ] },\\n ]\\n }\\n \\n cmds_set = copy.deepcopy(cmds_connect)\\n\\n if self._current.mode_code != self._target.mode_code and self._target.manual is not None:\\n cmds_set['commands'].append({ 'action': 'writeCharacteristic', 'uuid': UUID_MODE, 'value': [ self._target.mode_value, 0, 0 ] })\\n\\n if self._current.target_temp != self._target.target_temp and self._target.target_temp is not None:\\n cmds_set['commands'].append({ 'action': 'writeCharacteristic', 'uuid': UUID_TEMP, 'value': [ 128, int(self._target.target_temp * 2), 128, 128, 128, 128, 128 ] })\\n\\n if len(cmds_set['commands']) > 1:\\n mqtt.publish(self.hass, 'ble/{}/commands'.format(self._mac), json.dumps(cmds_set), 1, False)\\n\\n cmds_get = copy.deepcopy(cmds_connect)\\n cmds_get['commands'].append({ 'action': 'readCharacteristic', 'uuid': UUID_MODE })\\n cmds_get['commands'].append({ 'action': 'readCharacteristic', 'uuid': UUID_TEMP })\\n cmds_get['commands'].append({ 'action': 'readCharacteristic', 'uuid': UUID_BATTERY })\\n if self._current.model_no is None:\\n cmds_get['commands'].append({ 'action': 'readCharacteristic', 'uuid': UUID_MODEL })\\n cmds_get['commands'].append({ 'action': 'readCharacteristic', 'uuid': UUID_FIRMWARE })\\n cmds_get['commands'].append({ 'action': 'readCharacteristic', 'uuid': UUID_SOFTWARE })\\n cmds_get['commands'].append({ 'action': 'readCharacteristic', 'uuid': UUID_MANU })\\n\\n mqtt.publish(self.hass, 'ble/{}/commands'.format(self._mac), json.dumps(cmds_get), 1, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0bf085540709da3eae83d4d1d80e1e3\",\n \"score\": \"0.4803436\",\n \"text\": \"def m5_generate_exchange_request(self) -> List[Tuple[int, bytes]]:\\n # 1. Generate Ed25519 long-term public key, iOSDeviceLTPK,\\n # and long-term secret key, iOSDeviceLTSK\\n if self.signing_key is None:\\n self.signing_key, _ = ed25519.create_keypair()\\n with open(os.path.join(self.storage_folder, \\\"secret-key\\\"),\\n \\\"wb\\\") as secret_key_file:\\n secret_key_file.write(self.signing_key.to_bytes())\\n self.verifying_key = self.signing_key.get_verifying_key()\\n\\n # 2. Derive iOSDeviceX from the SRP shared secret by using HKDF-SHA-512\\n salt = b\\\"Pair-Setup-Controller-Sign-Salt\\\"\\n info = b\\\"Pair-Setup-Controller-Sign-Info\\\"\\n output_size = 32\\n\\n hkdf = cryptography.hazmat.primitives.kdf.hkdf.HKDF(\\n algorithm=cryptography.hazmat.primitives.hashes.SHA512(),\\n length=output_size,\\n salt=salt,\\n info=info,\\n backend=cryptography.hazmat.backends.default_backend())\\n self.X = hkdf.derive(to_bytes(self.K))\\n\\n # 3. Concatenate iOSDeviceX with the iOS device's Pairing Identifier, iOSDevicePairingID,\\n # and its long-term public key, iOSDeviceLTPK.\\n # The concatenated value will be referred to as iOSDeviceInfo.\\n\\n self.device_info = (\\n to_bytes(self.X) + session.pairing_id.encode('utf-8') +\\n self.verifying_key.to_bytes())\\n\\n # 4. Generate iOSDeviceSignature by signing iOSDeviceInfo with its\\n # long-term secret key, iOSDeviceLTSK, using Ed25519.\\n\\n self.device_signature = self.signing_key.sign(self.device_info)\\n\\n # 5. Construct a sub-TLV\\n sub_ktlvs = [(constants.PairingKTlvValues.kTLVType_Identifier,\\n self.pairing_id),\\n (constants.PairingKTlvValues.kTLVType_PublicKey,\\n self.verifying_key.to_bytes()),\\n (constants.PairingKTlvValues.kTLVType_Signature,\\n self.device_signature)]\\n\\n prepared_sub_ktlvs = b''.join(\\n data for ktlv in sub_ktlvs for data in utils.prepare_tlv(*ktlv))\\n\\n # 6. Encrypt the sub-TLV, encryptedData, and generate the 16 byte auth tag, authTag.\\n # using the ChaCha20-Poly1305 AEAD algorithm\\n\\n # this includes the auth_tag appended at the end\\n encrypted_data = crypto_aead_chacha20poly1305_ietf_encrypt(\\n key=self.S, nonce=\\\"PS-Msg05\\\", aad=None, message=prepared_sub_ktlvs)\\n\\n ktlvs = [(constants.PairingKTlvValues.kTLVType_State, pack('<B', 5)),\\n (constants.PairingKTlvValues.kTLVType_EncryptedData,\\n encrypted_data)]\\n\\n return ktlvs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"086ac84814ebbe06a3c45ea67188c515\",\n \"score\": \"0.47889802\",\n \"text\": \"def on_message(client, userdata, msg):\\n global deviceD, MQTT_REGEX\\n global mqtt_servoID, mqtt_servoAngle\\n logging.debug(\\\"Received: {0} with payload: {1}\\\".format(msg.topic, str(msg.payload)))\\n msgmatch = re.match(MQTT_REGEX, msg.topic) # Check for match to subscribed topics\\n if msgmatch:\\n mqtt_payload = json.loads(str(msg.payload.decode(\\\"utf-8\\\", \\\"ignore\\\"))) \\n mqtt_topic = [msgmatch.group(0), msgmatch.group(1), msgmatch.group(2), type(mqtt_payload)] # breaks msg topic into groups - group/group1/group2\\n if mqtt_topic[1] == 'servoZCMD':\\n mqtt_servoID = int(mqtt_topic[2])\\n mqtt_servoAngle = int(mqtt_payload) # Set the servo angle from mqtt payload\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58a7addf07852d991164c3ab673d671d\",\n \"score\": \"0.47852597\",\n \"text\": \"def to(self, device) -> None:\\n\\n if self.recording != {}:\\n\\n for v in self.variables:\\n if isinstance(v, str):\\n self.recording[v] = self.recording[v].to(device)\\n\\n elif isinstance(v, tuple):\\n self.recording[v[0]] = self.recording[v[0]].to(device)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"178218a1b23922d76102a9e3dfbc9376\",\n \"score\": \"0.47813544\",\n \"text\": \"def read_sensor():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a8c1f9d257e99862db3367701f3bf00\",\n \"score\": \"0.4780762\",\n \"text\": \"def read_sensor_data(self):\\n dht_result = self.dht_sensor.read()\\n if dht_result.is_valid():\\n data = SensorData(dht_result.humidity, dht_result.temperature)\\n self.sensor_data.push(data)\\n data.log_data() # log data to console\\n else:\\n print('Invalid sensor data.', dht_result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95155ad39c4d59744adb73a6df0618f0\",\n \"score\": \"0.4778543\",\n \"text\": \"def on_message(self, client, userdata, msg):\\n # pylint: disable=unused-argument\\n mqtt_message = json.loads(msg.payload)\\n mqtt_message[\\\"ext\\\"] = mqtt_message[\\\"ext\\\"].split(\\\",\\\")\\n\\n # Restructure payload message\\n new_mqtt_message = {}\\n new_mqtt_message[\\\"id\\\"] = mqtt_message[\\\"id\\\"]\\n new_mqtt_message[\\\"alert\\\"] = mqtt_message[\\\"alert\\\"]\\n new_mqtt_message[\\\"ezviz_alert_type\\\"] = mqtt_message[\\\"ext\\\"][4]\\n new_mqtt_message[\\\"serial\\\"] = mqtt_message[\\\"ext\\\"][2]\\n new_mqtt_message[\\\"msg_time\\\"] = mqtt_message[\\\"ext\\\"][1]\\n new_mqtt_message[\\\"img_url\\\"] = mqtt_message[\\\"ext\\\"][16]\\n new_mqtt_message[\\\"name\\\"] = mqtt_message[\\\"ext\\\"][17]\\n print(new_mqtt_message)\\n\\n if self._broker:\\n # Register HA alert sensor\\n publish.single(\\n f\\\"{STATE_TOPIC}/{new_mqtt_message['serial']}/alert/config\\\",\\n json.dumps(\\n {\\n \\\"name\\\": \\\"alert\\\",\\n \\\"device\\\": {\\n \\\"name\\\": f\\\"{new_mqtt_message['name']}\\\",\\n \\\"mf\\\": \\\"Ezviz\\\",\\n \\\"ids\\\": f\\\"(ezviz, {new_mqtt_message['serial']})\\\",\\n },\\n \\\"state_topic\\\": f\\\"{STATE_TOPIC}/{new_mqtt_message['serial']}/alert/state\\\",\\n \\\"platform\\\": \\\"mqtt\\\",\\n }\\n ),\\n hostname=self._broker[\\\"broker_ip\\\"],\\n auth={\\n \\\"username\\\": self._broker[\\\"username\\\"],\\n \\\"password\\\": self._broker[\\\"password\\\"],\\n },\\n )\\n\\n # Register HA alert_type sensor\\n publish.single(\\n f\\\"{STATE_TOPIC}/{new_mqtt_message['serial']}/ezviz_alert_type/config\\\",\\n json.dumps(\\n {\\n \\\"name\\\": \\\"ezviz_alert_type\\\",\\n \\\"device\\\": {\\n \\\"name\\\": f\\\"{new_mqtt_message['name']}\\\",\\n \\\"mf\\\": \\\"Ezviz\\\",\\n \\\"ids\\\": f\\\"(ezviz, {new_mqtt_message['serial']})\\\",\\n },\\n \\\"state_topic\\\": f\\\"{STATE_TOPIC}/{new_mqtt_message['serial']}/ezviz_alert_type/state\\\",\\n \\\"platform\\\": \\\"mqtt\\\",\\n }\\n ),\\n hostname=self._broker[\\\"broker_ip\\\"],\\n auth={\\n \\\"username\\\": self._broker[\\\"username\\\"],\\n \\\"password\\\": self._broker[\\\"password\\\"],\\n },\\n )\\n\\n # Register HA msg_time sensor\\n publish.single(\\n f\\\"{STATE_TOPIC}/{new_mqtt_message['serial']}/msg_time/config\\\",\\n json.dumps(\\n {\\n \\\"name\\\": \\\"msg_time\\\",\\n \\\"device\\\": {\\n \\\"name\\\": f\\\"{new_mqtt_message['name']}\\\",\\n \\\"mf\\\": \\\"Ezviz\\\",\\n \\\"ids\\\": \\\"(ezviz, {new_mqtt_message['serial']})\\\",\\n },\\n \\\"state_topic\\\": f\\\"{STATE_TOPIC}/{new_mqtt_message['serial']}/msg_time/state\\\",\\n \\\"platform\\\": \\\"mqtt\\\",\\n }\\n ),\\n hostname=self._broker[\\\"broker_ip\\\"],\\n auth={\\n \\\"username\\\": self._broker[\\\"username\\\"],\\n \\\"password\\\": self._broker[\\\"password\\\"],\\n },\\n )\\n\\n # Register HA img_url sensor\\n publish.single(\\n f\\\"{STATE_TOPIC}/{new_mqtt_message['serial']}/img_url/config\\\",\\n json.dumps(\\n {\\n \\\"name\\\": \\\"img_url\\\",\\n \\\"device\\\": {\\n \\\"name\\\": f\\\"{new_mqtt_message['name']}\\\",\\n \\\"mf\\\": \\\"Ezviz\\\",\\n \\\"ids\\\": f\\\"(ezviz, {new_mqtt_message['serial']})\\\",\\n },\\n \\\"state_topic\\\": f\\\"{STATE_TOPIC}/{new_mqtt_message['serial']}/img_url/state\\\",\\n \\\"platform\\\": \\\"mqtt\\\",\\n }\\n ),\\n hostname=self._broker[\\\"broker_ip\\\"],\\n auth={\\n \\\"username\\\": self._broker[\\\"username\\\"],\\n \\\"password\\\": self._broker[\\\"password\\\"],\\n },\\n )\\n\\n # Update HA Alert sensor\\n publish.single(\\n f\\\"{STATE_TOPIC}/{new_mqtt_message['serial']}/alert/state\\\",\\n f\\\"{new_mqtt_message['alert']}\\\",\\n hostname=self._broker[\\\"broker_ip\\\"],\\n auth={\\n \\\"username\\\": self._broker[\\\"username\\\"],\\n \\\"password\\\": self._broker[\\\"password\\\"],\\n },\\n )\\n\\n # Update HA ezviz_alert_type sensor\\n publish.single(\\n f\\\"{STATE_TOPIC}/{new_mqtt_message['serial']}/ezviz_alert_type/state\\\",\\n f\\\"{new_mqtt_message['ezviz_alert_type']}\\\",\\n hostname=self._broker[\\\"broker_ip\\\"],\\n auth={\\n \\\"username\\\": self._broker[\\\"username\\\"],\\n \\\"password\\\": self._broker[\\\"password\\\"],\\n },\\n )\\n\\n # Update HA msg_time sensor\\n publish.single(\\n f\\\"{STATE_TOPIC}/{new_mqtt_message['serial']}/msg_time/state\\\",\\n f\\\"{new_mqtt_message['msg_time']}\\\",\\n hostname=self._broker[\\\"broker_ip\\\"],\\n auth={\\n \\\"username\\\": self._broker[\\\"username\\\"],\\n \\\"password\\\": self._broker[\\\"password\\\"],\\n },\\n )\\n\\n # Update HA img_url sensor\\n publish.single(\\n f\\\"{STATE_TOPIC}/{new_mqtt_message['serial']}/img_url/state\\\",\\n f\\\"{new_mqtt_message['img_url']}\\\",\\n hostname=self._broker[\\\"broker_ip\\\"],\\n auth={\\n \\\"username\\\": self._broker[\\\"username\\\"],\\n \\\"password\\\": self._broker[\\\"password\\\"],\\n },\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66ae1d1c2477df0760dfe02311fd365b\",\n \"score\": \"0.47770002\",\n \"text\": \"def sensor_reader():\\n compensation_params = bme280.load_calibration_params(smbus2.SMBus(1), 0x76)\\n ABSpressure = round(mpr.pressure,4)\\n AMBPressure = round(bme.pressure,4)\\n GaugePress = round((mpr.pressure - bme.pressure)/2.4908890833333,2)\\n temp = round(bme.temperature,2)\\n Flow_Check()\\n FV = (round(adc.read_adc(2, gain=GAIN)*0.0001875,4)) #voltage\\n DV = (round(adc.read_adc(3, gain=GAIN)*0.30518509476,4)) #sccm\\n data = [ABSpressure, AMBPressure, GaugePress, temp, FV, DV, 1.0]\\n print(str(data))\\n return(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bad04e0d67cc97ec1c630fae2e429be\",\n \"score\": \"0.47738352\",\n \"text\": \"def update(self):\\n\\n valid_message = lambda msg: msg and not msg.startswith('#')\\n\\n def broadcast(message):\\n for client in self.clients:\\n self.incoming_serial.append((client, msg))\\n\\n # read and broadcast any new global messages\\n msg = self.ser.readline()\\n if valid_message(msg):\\n with self.message_lock: broadcast(msg)\\n\\n # send client requests and accumulate responses\\n with self.message_lock:\\n while len(self.outgoing_serial):\\n (id, msg) = self.outgoing_serial.pop(0)\\n logging.debug('writing \\\"%s\\\" to serial port' % msg)\\n self.ser.write(msg + '\\\\n')\\n\\n # wait till we get a response to our query\\n if valid_message(msg):\\n attempts = 100\\n command = msg.split()[0]\\n\\n while attempts:\\n response = self.ser.readline()\\n if valid_message(response):\\n logging.debug('response \\\"%s\\\"' % response.strip())\\n logging.debug(' command \\\"%s\\\"' % command)\\n if response and command == response.split()[0]:\\n logging.debug('matched response %s' % response.strip())\\n self.incoming_serial.append((id, response))\\n break\\n else:\\n broadcast(msg)\\n\\n attempts -= 1\\n else:\\n # send out a timeout message\\n logging.warning('timeout on message \\\"%s\\\"' % msg)\\n self.incoming_serial.append((id, '!timeout\\\\r\\\\n'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5590b572df0c7f59c645a8af5edb859b\",\n \"score\": \"0.47710243\",\n \"text\": \"def on_message(client,userdata,msg):\\n topic = msg.topic\\n m_decode = str(msg.payload.decode(\\\"utf-8\\\"))\\n print(\\\"From: \\\", topic)\\n print (\\\"Message Received : \\\",m_decode)\\n print()\\n\\n value = json.loads(m_decode)\\n\\n print(value)\\n if value['sensorid'] == 0:\\n if value['distance'] < 5.00:\\n type = 0\\n log_File(value,30,type)\\n\\n elif value['temperature'] > 36.00:\\n type = 1\\n log_File(value,120,type)\\n elif value['humidity'] < 50.00:\\n type = 2\\n log_File(value,120,type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43e97a04532b03ff8fa034e001dd9461\",\n \"score\": \"0.47709006\",\n \"text\": \"def readThread(self):\\n\\t\\tthread = Thread(target = self.send_data)\\n\\t\\tthread.start()\\n\\t\\t\\n\\t\\twhile True:\\n\\t\\t\\tline = self.cmd.stdout.readline() #read the output from C\\n\\t\\t\\ttime.sleep(1)\\n\\t\\t\\tline = line.decode()\\n\\t\\t\\tline = line.strip(\\\"\\\\n\\\").split(\\\":\\\")\\n\\t\\t\\tif (line[0] == 'Sensor'):\\n\\t\\t\\t\\ttemp = \\\"temperature\\\"+ line[1]\\n\\t\\t\\t\\thumi = \\\"humidity\\\" + line[1]\\n\\t\\t\\t\\tif self.clientCounter > 0 and self.sending:\\n\\t\\t\\t\\t\\tself.client.publish(temp, line[2])\\n\\t\\t\\t\\t\\tself.client.publish(humi, line[3])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cb2d10bdfcfba2c7e6246d1bb45541a\",\n \"score\": \"0.4769089\",\n \"text\": \"def read_serial():\\n global prev_sender\\n global prev_msg\\n global data\\n global ident\\n global t\\n\\n serial1 = sys.stdin.read() # UART library doesn't work here!\\n utime.sleep_ms(15)\\n serial2 = sys.stdin.read()\\n utime.sleep_ms(15)\\n serial3 = sys.stdin.read()\\n if serial1 is None and serial2 is None and serial3 is None:\\n return 0\\n \\\"\\\"\\\"\\n if test:\\n if c.isconnected() and serial1:2w\\n c.sms_send(2524126262, serial1[:2])\\n if c.isconnected() and serial2:\\n c.sms_send(2524126262, serial2[:2])\\n if c.isconnected() and serial3:\\n c.sms_send(2524126262, serial3[:2])\\n \\\"\\\"\\\"\\n serial = None\\n if serial1 and serial2 and serial3: ## 1 2 3\\n serial = str(serial1) + str(serial2) + str(serial3)\\n if serial[-1] is not \\\"!\\\":\\n if c.isconnected() and test:\\n c.sms_send(9199204318, \\\"not !, 1 2 3\\\")\\n # c.sms_send(2524126262, serial[-10:])\\n return 0\\n else:\\n serial = serial[:-1]\\n if not deployed:\\n print(serial1)\\n print(serial2)\\n print(serial3)\\n print(\\\"Therefore serial(3) is \\\")\\n print(serial)\\n\\n elif serial1 and serial2 and not serial3: # 1 2\\n serial = str(serial1) + str(serial2)\\n if serial[-1] is not \\\"!\\\":\\n if c.isconnected() and test:\\n c.sms_send(9199204318, \\\"not !, 1 2\\\")\\n # c.sms_send(2524126262, serial[-10:])\\n return 0\\n else:\\n serial = serial[:-1]\\n if not deployed:\\n print(serial2)\\n print(\\\"Therefore serial2 is \\\")\\n print(serial)\\n elif serial1 and not serial2 and not serial3: # 1\\n serial = serial1\\n if serial[-1] is not \\\"!\\\":\\n if c.isconnected() and test:\\n c.sms_send(9199204318, \\\"not !, 1\\\")\\n # c.sms_send(2524126262, serial[-10:])\\n return 0\\n else:\\n serial = serial[:-1]\\n if not deployed:\\n print(serial)\\n elif not serial1 and serial2 and serial3: # 2 3\\n serial = str(serial2) + str(serial3)\\n if serial[-1] is not \\\"!\\\":\\n if c.isconnected() and test:\\n c.sms_send(9199204318, \\\"not !, 2 3 \\\")\\n # c.sms_send(2524126262, serial[-10:])\\n return 0\\n else:\\n serial = serial[:-1]\\n if not deployed:\\n print(serial)\\n elif not serial1 and not serial2 and serial3: # 3\\n serial = serial3\\n if serial[-1] is not \\\"!\\\":\\n if c.isconnected() and test:\\n c.sms_send(9199204318, \\\"not !, 3\\\")\\n # c.sms_send(2524126262, serial[-10:])\\n return 0\\n else:\\n serial = serial[:-1]\\n if not deployed:\\n print(serial)\\n elif not serial1 and serial2 and not serial3: # 2\\n serial = serial2\\n if serial[-1] is not \\\"!\\\":\\n if c.isconnected() and test:\\n c.sms_send(9199204318, \\\"not !, 2\\\")\\n # c.sms_send(2524126262, serial[-10:])\\n return 0\\n else:\\n serial = serial[:-1]\\n if not deployed:\\n print(serial)\\n elif serial1 and not serial2 and serial3: # 3 1\\n serial = str(serial1) + str(serial3)\\n if serial[-1] is not \\\"!\\\":\\n if c.isconnected() and test:\\n c.sms_send(9199204318, \\\"not !, 1 3\\\")\\n # c.sms_send(2524126262, serial[-10:])\\n return 0\\n else:\\n serial = serial[:-1]\\n if not deployed:\\n print(serial)\\n else:\\n return 0\\n\\n if serial:\\n types = check_serial_type(serial)\\n if types is 0:\\n return 0\\n elif types is 1: # post to viper DEPRECATED. Checks for IDent now in teensy string instead of using Xbee to update Idenmt\\n serial = serial[1:]\\n t = (time.localtime()) # (year, month, day, hour, second, day, yearday)\\n t = create_time(t)\\n ident = ident + 1\\n ssend(serial, ident, t)\\n\\n elif types is 2: # send to users\\n serial = serial[1:]\\n # c.sms_send(2524126262, \\\"Got a C command\\\")\\n send_text(serial)\\n # prev_sender = None\\n elif types is 3: # send to all users\\n serial = serial[1:]\\n send_text_all(serial)\\n elif types is 4: # post to VIPER!!!! accounts for EEPROM Identifier\\n if c.isconnected():\\n c.sms_send(9199204318, \\\"Posting\\\")\\n t = (time.localtime()) # (year, month, day, hour, second, day, yearday)\\n t = create_time(t)\\n comma = serial.find(\\\"<\\\")\\n if comma is -1:\\n if c.isconnected():\\n c.sms_send(9199204318, \\\"No <\\\")\\n return 0\\n ident = serial[:comma]\\n serial = serial[comma + 2:]\\n ssend(serial, ident, t)\\n elif types is 6:\\n \\\"\\\"\\\"\\n When receiving ?, check for texts\\n \\\"\\\"\\\"\\n\\n if c.isconnected():\\n msg_sms_receive = check_txt() # check for text\\n # [sms_txt['message'], sms_txt] returns or \\\"\\\", None\\n if msg_sms_receive:\\n if len(msg_sms_receive['message']) > 0:\\n msg = msg_sms_receive['message']\\n else:\\n msg = None\\n sms_sender = msg_sms_receive['sender']\\n if sms_sender:\\n prev_sender = msg_sms_receive['sender']\\n if msg is not None and sms_sender is not None:\\n print(msg) # if true: interface with Teensy and send Teensy C#\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a246bdf1f6b7acb17a729971662b15\",\n \"score\": \"0.47645625\",\n \"text\": \"def sendToMbed(msg):\\n if not serial_port.is_open:\\n return\\n\\n v = int(msg.data[0]) # velocity\\n w = int(msg.data[1]) # angular velocity\\n try:\\n lights = int(msg.data[2])\\n except:\\n lights = int(0)\\n\\n data = packData(v, w, lights)\\n\\n serial_port.write(data)\\n #print(v, w, lights)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3df2b894edca065dd88a363fe08a56a0\",\n \"score\": \"0.47588748\",\n \"text\": \"def run(self):\\n data = ''\\n while not rospy.is_shutdown():\\n if (rospy.Time.now() - self.lastsync).to_sec() > (self.timeout * 3):\\n if self.synced:\\n rospy.logerr(\\\"Lost sync with device, restarting...\\\")\\n else:\\n rospy.logerr(\\\"Unable to sync with device; possible link problem or link software version \\\"\\n \\\"mismatch such as hydro rosserial_python with groovy Arduino\\\")\\n self.lastsync_lost = rospy.Time.now()\\n self.sendDiagnostics(diagnostic_msgs.msg.DiagnosticStatus.ERROR, \\\"no sync with device\\\")\\n self.requestTopics()\\n self.lastsync = rospy.Time.now()\\n\\n # This try-block is here because we make multiple calls to read(). Any one of them can throw\\n # an IOError if there's a serial problem or timeout. In that scenario, a single handler at the\\n # bottom attempts to reconfigure the topics.\\n try:\\n with self.mutex:\\n if self.port.inWaiting() < 1:\\n time.sleep(0.001)\\n continue\\n\\n flag = [0, 0]\\n flag[0] = self.tryRead(1)\\n if (flag[0] != '\\\\xff'):\\n continue\\n\\n flag[1] = self.tryRead(1)\\n if flag[1] != self.protocol_ver:\\n self.sendDiagnostics(\\n diagnostic_msgs.msg.DiagnosticStatus.ERROR,\\n \\\"Mismatched protocol version in packet: lost sync or rosserial_python is from different ros release than the rosserial client\\\")\\n rospy.logerr(\\\"Mismatched protocol version in packet: lost sync or rosserial_python is from different ros release than the rosserial client\\\")\\n protocol_ver_msgs = {'\\\\xff': 'Rev 0 (rosserial 0.4 and earlier)', '\\\\xfe': 'Rev 1 (rosserial 0.5+)', '\\\\xfd': 'Some future rosserial version'}\\n if (flag[1] in protocol_ver_msgs):\\n found_ver_msg = 'Protocol version of client is ' + protocol_ver_msgs[flag[1]]\\n else:\\n found_ver_msg = \\\"Protocol version of client is unrecognized\\\"\\n rospy.loginfo(\\\"%s, expected %s\\\", found_ver_msg, protocol_ver_msgs[self.protocol_ver])\\n continue\\n\\n msg_len_bytes = self.tryRead(2)\\n msg_length, = struct.unpack(\\\"<h\\\", msg_len_bytes)\\n\\n msg_len_chk = self.tryRead(1)\\n msg_len_checksum = sum(map(ord, msg_len_bytes)) + ord(msg_len_chk)\\n\\n if msg_len_checksum % 256 != 255:\\n rospy.loginfo(\\\"wrong checksum for msg length, length %d\\\", (msg_length))\\n rospy.loginfo(\\\"chk is %d\\\", ord(msg_len_chk))\\n continue\\n\\n # topic id (2 bytes)\\n topic_id_header = self.tryRead(2)\\n topic_id, = struct.unpack(\\\"<h\\\", topic_id_header)\\n\\n try:\\n msg = self.tryRead(msg_length)\\n except IOError:\\n self.sendDiagnostics(diagnostic_msgs.msg.DiagnosticStatus.ERROR, \\\"Packet Failed : Failed to read msg data\\\")\\n rospy.loginfo(\\\"Packet Failed : Failed to read msg data\\\")\\n rospy.loginfo(\\\"expected msg length is %d\\\", msg_length)\\n raise\\n\\n # checksum for topic id and msg\\n chk = self.tryRead(1)\\n checksum = sum(map(ord, topic_id_header)) + sum(map(ord, msg)) + ord(chk)\\n\\n if checksum % 256 == 255:\\n self.synced = True\\n try:\\n self.callbacks[topic_id](msg)\\n except KeyError:\\n rospy.logerr(\\\"Tried to publish before configured, topic id %d\\\", topic_id)\\n self.requestTopics()\\n rospy.sleep(0.001)\\n else:\\n rospy.loginfo(\\\"wrong checksum for topic id and msg\\\")\\n\\n except IOError:\\n # One of the read calls had an issue. Just to be safe, request that the client\\n # reinitialize their topics.\\n self.requestTopics()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6c32e797890557d6d4a96d47d9a87e6\",\n \"score\": \"0.47482327\",\n \"text\": \"def read(self):\\n key, value = self.read_serial()\\n if key is None:\\n return\\n\\n if key in ('pressure', 'temperature'):\\n value = int(value)\\n if key in ('altitude', 'filtered', 'velocity'):\\n value = float(value) / 100;\\n self.emit(key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a37ef742a609175e41826425f983193\",\n \"score\": \"0.47468108\",\n \"text\": \"def get_devices(self)->dict:\\n req = requests.get(Meater.apiurl + '/devices',\\n headers={'Authorization': f'Bearer {Meater.key}'}).json()\\n for device in req['data']['devices']:\\n device = Meater._output_format(device, self.convert)\\n \\n return req\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a501d1fcc00670d44f95c616e4d4a1b6\",\n \"score\": \"0.47395456\",\n \"text\": \"def read(sensors):\\n status = \\\"ok\\\"\\n data = {}\\n temperature = 0.0\\n latitude = ''\\n longitude = ''\\n\\n global ds18b20, gps\\n\\n if 'ds18b20' in sensors:\\n try:\\n temperature = ds18b20.read()\\n status = __merge_status(status, ds18b20.status)\\n except ImportError:\\n logger.error('DS18B20 is not initialized')\\n if 'gps' in sensors:\\n try:\\n latitude, longitude = gps.read()\\n status = __merge_status(status, gps.status)\\n except ImportError:\\n logger.error('GPS is not initialized')\\n if not sensors:\\n status = __merge_status(status, \\\"no sensors\\\")\\n\\n data['temp'] = temperature\\n data['lati'] = latitude\\n data['long'] = longitude\\n\\n return (status, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6161f7a7390edf55c5c543d00d413df4\",\n \"score\": \"0.47245347\",\n \"text\": \"def send(self, device, message):\\n self.on_value_change(device, message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d9cbc91fb6633ba1eafc75d9e175f80\",\n \"score\": \"0.47211987\",\n \"text\": \"def aggregate_subdevice(self, device_data, topic):\\r\\n result = True\\r\\n tagged_device_data = {}\\r\\n device_tag = self.device_topic_dict[topic]\\r\\n site = self.site_topic_dict[topic]\\r\\n needed_devices = site['needed_devices']\\r\\n device_values = site['device_values']\\r\\n _log.info(\\\"Current device to aggregate: topic {} device: {}\\\".format(topic, device_tag))\\r\\n if device_tag not in needed_devices:\\r\\n result = False\\r\\n # optional eg: 'SomeFanSpeed' -> 'supply_fan_speed'\\r\\n mappings = site.get('point_mapping', {})\\r\\n _log.info(\\\"--- device_data -> {}\\\".format(device_data))\\r\\n _log.info(\\\"--- mappings -> {}\\\".format(mappings))\\r\\n for key, value in device_data.items():\\r\\n # weird ... bug\\r\\n if key.endswith(device_tag):\\r\\n _log.warning(\\\"--- weird entry in device_data ? {} -> {}\\\".format(key, value))\\r\\n _log.warning(\\\"--- device_tag ? {}\\\".format(device_tag))\\r\\n key = key[:-len(device_tag)-1]\\r\\n\\r\\n # here do the mapping between the actual device topic\\r\\n # and the APP expected topic names\\r\\n k = key\\r\\n if key in mappings:\\r\\n k = mappings[key]\\r\\n else:\\r\\n long_key = '/'.join([device_tag, key])\\r\\n if long_key in mappings:\\r\\n k = mappings[long_key]\\r\\n\\r\\n device_data_tag = \\\"&\\\".join([k, device_tag])\\r\\n tagged_device_data[device_data_tag] = value\\r\\n _log.info(\\\"--- tagged_device_data -> {}\\\".format(tagged_device_data))\\r\\n device_values.update(tagged_device_data)\\r\\n _log.info(\\\"--- device_values -> {}\\\".format(device_values))\\r\\n if device_tag in needed_devices:\\r\\n needed_devices.remove(device_tag)\\r\\n _log.info(\\\"--- needed_devices removed [{}] -> {}\\\".format(device_tag, needed_devices))\\r\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31916d7f8e4d5003cecf081e42e06e92\",\n \"score\": \"0.4718316\",\n \"text\": \"def __zigbee2mqtt_event_received(self, message: BLCZigbee2mqttMessage) -> None:\\n \\n \\\"\\\"\\\"Here the timestamp of the of the device is received to make sure that system operates in the valid time interval. This is done using the datetime library\\\"\\\"\\\"\\n currentTime = datetime.now()\\n today10pm = currentTime.replace(hour=22, minute=0, second=0, microsecond=0) #Starting time\\n today9am = currentTime.replace(hour=9, minute=0, second=0, microsecond=0) #Ending time\\n\\n #Making sure the time is valid\\n if currentTime > today10pm or currentTime < today9am:\\n # If message is None (it wasn't parsed), then don't do anything.\\n if not message:\\n return\\n \\n # If the message is not a device event, then don't do anything.\\n if message.type_ != BLCZigbee2mqttMessageType.DEVICE_EVENT:\\n return\\n\\n # Parse the topic to retreive the device ID. If the topic only has one level, don't do\\n # anything.\\n tokens = message.topic.split(\\\"/\\\")\\n if len(tokens) <= 1:\\n return\\n\\n # Retrieve the device ID from the topic.\\n device_id = tokens[1]\\n\\n # If the device ID is known, then process the device event and send a message to the remote\\n # web server.\\n device = self.__devices_model.find(device_id)\\n\\n\\n #When the device that has registered movement is found by the controller, then the controller checks for a occupancy message. If there are an occupancy message, then the trigger function in the statemachine is called. Otherwise an error is returned \\n #Thus, only occupancy messages are accepted and forwarded to the state machine \\n if device:\\n try:\\n occupancy = message.event[\\\"occupancy\\\"]\\n except KeyError:\\n pass\\n else:\\n self.stateMachine.trigger(occupancy, device_id) #Sends the occupancy message and the device ID \\n\\n #Occupancy message = True: PIR sensor has registered movement\\n #Occuoancy message = False: PIR sensor has not registered movement \",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae6f3960ba3562d5fed49f245e4b0318\",\n \"score\": \"0.4715545\",\n \"text\": \"def publish_sensor_data(data):\\n payload = json.dumps(data)\\n if MQTT_CLIENT:\\n MQTT_CLIENT.publish(\\\"imu/\\\" + DEVICE_ID, payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f40432fc3e10813330ca1ecae76daf61\",\n \"score\": \"0.47114053\",\n \"text\": \"def feed_messages(protocol):\\n\\n value = 0\\n\\n while True:\\n yield from protocol.byte_output(0x1305, 0, value)\\n yield from asyncio.sleep(1)\\n value = value + 1 if value < 255 else 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fba3d15594342222ff8e58817b892a3\",\n \"score\": \"0.47078258\",\n \"text\": \"def execute(self, device):\\n read_data(device, self.command, self.indices, self.output_conversion, self.message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afa7a94b2e11d402da964d58a742cbf7\",\n \"score\": \"0.4705553\",\n \"text\": \"def sensor(sck):\\n if sck.server.v:\\n print(sck.ID + \\\" logged in\\\")\\n while True:\\n data = None\\n while not data:\\n try:\\n data = sck.conn.recv(1024)\\n except Exception:\\n pass\\n if sck.server.v:\\n print(sck.server.ID + \\\" <- \\\" + sck.ID + \\\": \\\" + str(data, \\\"utf-8\\\"))\\n data = str(data, \\\"utf-8\\\").split(\\\"|\\\")\\n if data[1] == \\\"PUT\\\" and data[2] == sck.ID:\\n storage[sck.ID].append(float(data[3]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25a6d23949852a1ab57813b208725fc6\",\n \"score\": \"0.47019798\",\n \"text\": \"def getSensors( self ) -> dict :\\n pkg = { \\\"cmd\\\":\\\"getSensors\\\" }\\n resp = self.sendPkg( pkg )\\n self.pos = tuple( resp[\\\"pos\\\"] )\\n self.speed = tuple( resp[\\\"speed\\\"] )\\n self.angle = resp[\\\"angle\\\"]\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cfb1e9962612caeba978449e71bbb24\",\n \"score\": \"0.4697051\",\n \"text\": \"def sender(self):\\n while 1:\\n if self.fetch_ch_data():\\n pos = []\\n for i in range(self.r * self.c):\\n diff = self.diffs_p[i]\\n if diff > 0:\\n pos.append(diff / 40)\\n else:\\n pos.append(diff / 40)\\n for i in range(self.c * self.r):\\n diff = self.diffs[i]\\n if diff > 0:\\n pos.append(diff / 402528)\\n else:\\n pos.append(diff / 402528)\\n\\n if self.conn:\\n try:\\n self.conn.send((' '.join(str(e) for e in pos) + '\\\\n').encode())\\n except:\\n self.conn.close()\\n self.conn = None\\n print(\\\"Connectioon broken\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"917e6bd96a2722cf8933363665f5c442\",\n \"score\": \"0.46897167\",\n \"text\": \"def do_send_RF_code(value, TelegramResponder):\\n\\n for val in value.values():\\n light = re.findall(r\\\"Switch\\\\b\\\\s*(\\\\w*)\\\", val)\\n parts = val.split()\\n if light and len(parts) > 2: # Turn on or off if the command is correct\\n if (\\n \\\"433MHz_Transiever\\\"\\n in TelegramResponder.main.default_values_dict[\\\"settings\\\"]\\n ):\\n if (\\n light[0]\\n in TelegramResponder.main.default_values_dict[\\\"settings\\\"][\\n \\\"433MHz_Transiever\\\"\\n ][\\\"Codes\\\"].keys()\\n ):\\n onoff = 1 if parts[-1].upper() == \\\"ON\\\" else 0\\n path = os.path.normpath(\\n TelegramResponder.main.default_values_dict[\\\"settings\\\"][\\n \\\"433MHz_Transiever\\\"\\n ][\\\"path\\\"]\\n )\\n for switch in TelegramResponder.main.default_values_dict[\\n \\\"settings\\\"\\n ][\\\"433MHz_Transiever\\\"][\\\"Codes\\\"][light[0]]:\\n code = switch\\n cmd = \\\"{} {} {}\\\".format(path, code, onoff)\\n os.system(cmd)\\n if onoff:\\n old_light = TelegramResponder.current_light\\n TelegramResponder.current_light = light[0]\\n else:\\n old_light = None # Because everything is off\\n TelegramResponder.current_light = None\\n\\n # Switch the old one off, which are not included in the new one\\n if old_light and TelegramResponder.settings.get(\\n \\\"Exclusive_Light_Switching\\\", True\\n ):\\n path = os.path.normpath(\\n TelegramResponder.main.default_values_dict[\\\"settings\\\"][\\n \\\"433MHz_Transiever\\\"\\n ][\\\"path\\\"]\\n )\\n onoff = 0\\n for switch in TelegramResponder.main.default_values_dict[\\n \\\"settings\\\"\\n ][\\\"433MHz_Transiever\\\"][\\\"Codes\\\"][old_light]:\\n if (\\n switch\\n not in TelegramResponder.main.default_values_dict[\\n \\\"settings\\\"\\n ][\\\"433MHz_Transiever\\\"][\\\"Codes\\\"][\\n TelegramResponder.current_light\\n ]\\n ):\\n code = switch\\n cmd = \\\"{} {} {}\\\".format(path, code, onoff)\\n os.system(cmd)\\n\\n TelegramResponder.answer += \\\"Done and enjoy.\\\"\\n else:\\n TelegramResponder.answer += (\\n \\\"This light configuration is not defined.\\\"\\n )\\n else:\\n TelegramResponder.answer += (\\n \\\"No transiever defined. Cannot do what you asked.\\\"\\n )\\n\\n elif light and len(parts) == 2: # if no on or off is defined\\n TelegramResponder.answer = {\\n \\\"CALLBACK\\\": {\\n \\\"info\\\": \\\"Would you like to turn {} ON or OFF\\\".format(light[0]),\\n \\\"keyboard\\\": {\\n \\\"ON\\\": \\\"Switch {} ON\\\".format(light[0]),\\n \\\"OFF\\\": \\\"Switch {} OFF\\\".format(light[0]),\\n },\\n \\\"arrangement\\\": [\\\"ON\\\", \\\"OFF\\\"],\\n }\\n }\\n elif light and len(parts) == 1: # If just the switch command was send\\n if (\\n \\\"433MHz_Transiever\\\"\\n in TelegramResponder.main.default_values_dict[\\\"settings\\\"]\\n ):\\n keyboard = {}\\n arrangement = []\\n for light in TelegramResponder.main.default_values_dict[\\\"settings\\\"][\\n \\\"433MHz_Transiever\\\"\\n ][\\\"Codes\\\"]:\\n keyboard[light] = \\\"Switch {}\\\".format(light)\\n arrangement.append([light])\\n TelegramResponder.answer = {\\n \\\"CALLBACK\\\": {\\n \\\"info\\\": \\\"Possible light configurations:\\\",\\n \\\"keyboard\\\": keyboard,\\n \\\"arrangement\\\": arrangement,\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f7171ec75999cb16564bf75bc1b5767\",\n \"score\": \"0.467955\",\n \"text\": \"def _build_message(self, interval, get_result, time):\\n message = {\\n 'block_name': f'm1000_{interval}',\\n 'timestamp': time,\\n 'data': {}\\n }\\n\\n for item in get_result:\\n field_name, oid_value, oid_description = self._extract_oid_field_and_value(item)\\n\\n if oid_value is None:\\n continue\\n\\n message['data'][field_name] = oid_value\\n message['data'][field_name + \\\"_description\\\"] = oid_description\\n\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31429b1db22de9e5ee132ab02e55d1ed\",\n \"score\": \"0.46777517\",\n \"text\": \"def create_multiple_device_keys_and_certs(\\n number_of_devices,\\n issuer_cert_subject,\\n issuer_key,\\n device_common_name,\\n password,\\n key_size=4096,\\n days=365,\\n):\\n for i in range(1, number_of_devices + 1):\\n device_password_file = COMMON_DEVICE_PASSWORD_FILE + str(i) + EXTENSION_NAME\\n device_csr_file = COMMON_DEVICE_CSR_FILE + str(i) + EXTENSION_NAME\\n device_cert_file = COMMON_DEVICE_CERT_FILE + str(i) + EXTENSION_NAME\\n device_private_key = create_private_key(\\n key_file=device_password_file, password=password, key_size=key_size\\n )\\n device_csr = create_csr(\\n private_key=device_private_key,\\n csr_file=device_csr_file,\\n subject=device_common_name + str(i),\\n is_ca=False,\\n )\\n\\n builder = create_cert_builder(\\n subject=device_csr.subject,\\n issuer_name=issuer_cert_subject,\\n public_key=device_csr.public_key(),\\n days=int(days / 100),\\n is_ca=False,\\n )\\n\\n device_cert = builder.sign(\\n private_key=issuer_key, algorithm=hashes.SHA256(), backend=default_backend()\\n )\\n with open(device_cert_file, \\\"wb\\\") as f:\\n f.write(device_cert.public_bytes(serialization.Encoding.PEM))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58f4302a7d35f70925b379dcfc60c722\",\n \"score\": \"0.467639\",\n \"text\": \"def _create_devices(self):\\n for unit, value in self.variables.items():\\n try:\\n device = Devices[unit]\\n except KeyError as err:\\n Domoticz.Log(\\\"Device %i is not created yet. Creating....\\\" % unit)\\n self._create_device(unit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43673e8a15aa8b5611a96b56142d7277\",\n \"score\": \"0.46588016\",\n \"text\": \"def sendMessageIdentify():\\n data = [0] * CMTP_BUFFER_LEN\\n dataLength = 254\\n serviceID = 10\\n dataString = \\\"\\\"\\n for i, resultData in enumerate(data):\\n if i % 4 == 0:\\n dataString += \\\"\\\\n\\\"\\n dataString += \\\"{0:0{1}X}: \\\".format(i, 4)\\n dataString += \\\"{0:0{1}X} \\\".format(resultData, 2)\\n else:\\n dataString += \\\"{0:0{1}X} \\\".format(resultData, 2)\\n Messages.write_message(\\\"Sending Service: {}\\\".format(serviceID))\\n Messages.write_message(\\\"Sending Size: {}\\\".format(dataLength))\\n Messages.write_message(\\\"Sending Data: \\\\n{}\\\".format(dataString))\\n results = data_store.scenario[\\\"capiController\\\"].sendMessage(data, serviceID, dataLength)\\n Messages.write_message(results[\\\"description\\\"])\\n assert results[\\\"result\\\"] == 0, \\\"Send message failed\\\"\\n Messages.write_message(\\\"Received Status: {}\\\".format(results[\\\"data\\\"][\\\"status\\\"]))\\n Messages.write_message(\\\"Received Service: {}\\\".format(results[\\\"data\\\"][\\\"service\\\"]))\\n Messages.write_message(\\\"Received Size: {}\\\".format(results[\\\"data\\\"][\\\"size\\\"]))\\n dataString = \\\"\\\"\\n for i, resultData in enumerate(results[\\\"data\\\"][\\\"data\\\"]):\\n if i % 4 == 0:\\n dataString += \\\"\\\\n\\\"\\n dataString += \\\"{0:0{1}X}: \\\".format(i, 4)\\n dataString += \\\"{0:0{1}X} \\\".format(resultData, 2)\\n else:\\n dataString += \\\"{0:0{1}X} \\\".format(resultData, 2)\\n Messages.write_message(\\\"Received Data: \\\\n{}\\\".format(dataString))\\n assert results[\\\"data\\\"][\\\"status\\\"] == 0, \\\"Status from response is non-zero - {}\\\".format(results[\\\"data\\\"][\\\"status\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e01977b7d74132877df538453d34f18\",\n \"score\": \"0.46541184\",\n \"text\": \"def send_key_to_service(self):\\n data_parts = self._divide_data(self.public_key)\\n recieved_msg = False\\n for attempt in xrange(3):\\n self.send_packets(data_parts)\\n self.timeout = time.time() + 120\\n sniff(filter = self.sniff_filter, stop_filter = self._handle_packet) \\n if not self.data_from_service:\\n print 'key was not recieved'\\n elif self.data_from_service == \\\"[RECIEVED KEY]\\\":\\n print 'key recieved'\\n self.data_from_service = ''\\n self.splitted_data = {}\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1486393220ee608a50839cf764e19234\",\n \"score\": \"0.4647938\",\n \"text\": \"def message(client, feed_id, payload):\\n ui.ClicktoConnect.setText(\\\"\\\")\\n if feed_id == TEMPERATURE:\\n print(payload)\\n ui.temp_label.setText(payload + \\\" °C\\\")\\n\\n if feed_id == HUMIDITY:\\n print(payload)\\n ui.humidity_label.setText(payload + \\\" %\\\")\\n\\n if feed_id == OIL:\\n print(payload)\\n ui.oil_lable.setText(payload + \\\" %\\\")\\n\\n if feed_id == v:\\n print(payload)\\n ui.voltage_label.setText(payload + \\\" Volts\\\")\\n\\n if feed_id == CURRENT:\\n print(payload)\\n ui.Current_label.setText(payload + \\\" mAmps\\\")\\n\\n if feed_id == VIBRATION:\\n if int(payload) >= 100:\\n print(\\\"Slight Movement\\\")\\n ui.vibration_label.setText(\\\"Slight Vibration\\\")\\n else:\\n print(\\\"Stable\\\")\\n ui.vibration_label.setText(\\\"Stable\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6c02aa1b3a4522e34218d100eeee5c\",\n \"score\": \"0.46418697\",\n \"text\": \"def generate_signal(key, num_timesteps=500):\\n timesteps = jnp.arange(0, num_timesteps)\\n dim = 4\\n true_source = jnp.zeros((num_timesteps, dim))\\n\\n true_source = true_source.at[:, 0].set(jnp.sin(timesteps / 2)) # sinusoid\\n true_source = true_source.at[:, 1].set(((timesteps % 23 - 11) / 9) ** 5)\\n true_source = true_source.at[:, 2].set((timesteps % 27 - 13) / 9) # sawtooth\\n\\n key, subkey = jax.random.split(key)\\n rand = jax.random.uniform(subkey, (num_timesteps,))\\n key, subkey = jax.random.split(key)\\n true_source = true_source.at[:, 3].set(\\n jnp.where(rand < 0.5, rand * 2 - 1, -1)\\n * jnp.log(jax.random.uniform(subkey, (num_timesteps,)))\\n ) # impulsive noise\\n\\n true_source /= true_source.std(axis=0)\\n true_source -= true_source.mean(axis=0)\\n key, subkey = jax.random.split(key)\\n mixing_matrix = jax.random.uniform(subkey, (dim, dim)) # mixing matrix\\n return (\\n true_source,\\n mixing_matrix,\\n jax.vmap(ica.get_signal, (None, 0), 0)(mixing_matrix, true_source),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a41f2d6409274a0d874bc902b4c255c\",\n \"score\": \"0.46402857\",\n \"text\": \"def send_mqtt(self, hmac_signed):\\r\\n lib = {}\\r\\n lib[\\\"hmac\\\"] = hmac_signed\\r\\n #lib[\\\"what\\\"] = \\\"thefuck\\\"\\r\\n lib[\\\"Data\\\"] = ubinascii.hexlify(self.enData)\\r\\n lib[\\\"ID\\\"] = ubinascii.hexlify(self.nodeid)\\r\\n lib[\\\"IV\\\"] = ubinascii.hexlify(self.iv)\\r\\n # a Python object (dict): \\r\\n #print('processed JSON'+ str(lib))\\r\\n return json.dumps(lib)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f035d04288fa13899f0096288659260\",\n \"score\": \"0.46401888\",\n \"text\": \"def on_mdp_request(self, msg):\\n ### NON TESTE !\\n \\n Plugin.on_mdp_request(self, msg)\\n #self.log.info(u\\\"==> Received MQ message: %s\\\" % format(msg))\\n # => MQ Request received : <MQMessage(action=client.cmd, data='{u'state': u'1', u'command_id': 14, u'device_id': 39}')>\\n \\n if msg.get_action() == \\\"client.cmd\\\":\\n data = msg.get_data()\\n #self.log.debug(u\\\"==> Received MQ REQ command message: %s\\\" % format(data)) \\n # DEBUG ==> Received MQ REQ command message: {u'value': u'1', u'command_id': 50, u'device_id': 139}\\n device_id = data[\\\"device_id\\\"]\\n command_id = data[\\\"command_id\\\"]\\n \\n # {\\\"44.0\\\": {\\\"name\\\": \\\"Bureau\\\", \\\"dmgid\\\": \\\"120\\\", \\\"vtype\\\": \\\"V_STATUS\\\"}}\\n for node in self.mysensorsmanager.nodes:\\n if '255' not in node:\\n if nodes[node]['dmgid'] == device_id: \\n nodesensor_name = nodes[node]['name'] # \\\"Relay #1\\\"\\n nodesensor_vtype = nodes[node]['vtype'] # \\\"V_STATUS\\\"\\n break\\n if nodesensor_vtype in [\\\"V_STATUS\\\", \\\"V_UP\\\", \\\"V_DOWN\\\", \\\"V_STOP\\\", \\\"V_PERCENTAGE\\\", \\\"V_IR_SEND\\\"]:\\n msg = node.replace(\\\".\\\",\\\";\\\") + '1;0;' + self.mysensorsmanager.setreqType.index(nodesensor_vtype) + \\\";\\\" + data[\\\"value\\\"] + \\\"\\\\n\\\"\\n # msg = \\\"42;0;1;0;2;1\\\\n\\\" nodeid;childsensorid;set command;no ack;vtype;value\\\\n\\n # Command from the controller (1 = SET), Outgoing message to node 42 child sensor 0, Set V_STATUS (2) variable to 1 (turn on). No ack is requested from destination node.\\n self.log.info(u\\\"==> Send SET message '%s' for device '%s/%s' (id:%s) to Gateway\\\" % (msg, nodesensor_name, nodesensor_vtype.lower(), device_id))\\n self.mysensorsmanager.msgSendQueue.put(msg)\\n \\n # Reply MQ REP (acq) to REQ command\\n self.send_rep_ack(True, None, command_id, device_name) ;\\n else: \\n self.log.error(u\\\"### Node sensor vtype not found for device '%s' (id:%s)\\\" % (nodesensor_name, device_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ab44c7c15841c5aa525e30de2c17b69\",\n \"score\": \"0.46391696\",\n \"text\": \"def test_get_sensor_signatures():\\n qwe = QwiicExporter()\\n for name, data in qwe.sensors.items():\\n if name == \\\"ICM-20948 IMU\\\":\\n assert qwe.get_subsensor_signatures(data) == [\\n [\\\"aX,aY,aZ\\\", \\\"gX,gY,gZ\\\", \\\"mX,mY,mZ\\\", \\\"imu_degC\\\"],\\n [\\\"aX,aY,aZ\\\", \\\"gX,gY,gZ\\\", \\\"mX,mY,mZ\\\"],\\n [\\\"aX,aY,aZ\\\", \\\"gX,gY,gZ\\\", \\\"imu_degC\\\"],\\n [\\\"aX,aY,aZ\\\", \\\"mX,mY,mZ\\\", \\\"imu_degC\\\"],\\n [\\\"gX,gY,gZ\\\", \\\"mX,mY,mZ\\\", \\\"imu_degC\\\"],\\n [\\\"aX,aY,aZ\\\", \\\"gX,gY,gZ\\\"],\\n [\\\"aX,aY,aZ\\\", \\\"mX,mY,mZ\\\"],\\n [\\\"aX,aY,aZ\\\", \\\"imu_degC\\\"],\\n [\\\"gX,gY,gZ\\\", \\\"mX,mY,mZ\\\"],\\n [\\\"gX,gY,gZ\\\", \\\"imu_degC\\\"],\\n [\\\"mX,mY,mZ\\\", \\\"imu_degC\\\"],\\n [\\\"aX,aY,aZ\\\"],\\n [\\\"gX,gY,gZ\\\"],\\n [\\\"mX,mY,mZ\\\"],\\n [\\\"imu_degC\\\"],\\n ]\\n elif name == \\\"BME280 atmospheric sensor\\\":\\n assert qwe.get_subsensor_signatures(data) == [\\n [\\\"pressure_Pa\\\", \\\"humidity_%\\\", \\\"altitude_m\\\", \\\"temp_degC\\\"],\\n [\\\"pressure_Pa\\\", \\\"humidity_%\\\", \\\"altitude_m\\\"],\\n [\\\"pressure_Pa\\\", \\\"humidity_%\\\", \\\"temp_degC\\\"],\\n [\\\"pressure_Pa\\\", \\\"altitude_m\\\", \\\"temp_degC\\\"],\\n [\\\"humidity_%\\\", \\\"altitude_m\\\", \\\"temp_degC\\\"],\\n [\\\"pressure_Pa\\\", \\\"humidity_%\\\"],\\n [\\\"pressure_Pa\\\", \\\"altitude_m\\\"],\\n [\\\"pressure_Pa\\\", \\\"temp_degC\\\"],\\n [\\\"humidity_%\\\", \\\"altitude_m\\\"],\\n [\\\"humidity_%\\\", \\\"temp_degC\\\"],\\n [\\\"altitude_m\\\", \\\"temp_degC\\\"],\\n [\\\"pressure_Pa\\\"],\\n [\\\"humidity_%\\\"],\\n [\\\"altitude_m\\\"],\\n [\\\"temp_degC\\\"],\\n ]\\n elif name == \\\"VCNL4040 proximity sensor\\\":\\n assert qwe.get_subsensor_signatures(data) == [\\n [\\\"prox(no unit)\\\", \\\"ambient_lux\\\"],\\n [\\\"prox(no unit)\\\"],\\n [\\\"ambient_lux\\\"],\\n ]\\n elif name == \\\"OpenLog Artemis\\\":\\n assert qwe.get_subsensor_signatures(data) == [\\n [\\\"output_Hz\\\", \\\"count\\\"],\\n [\\\"output_Hz\\\"],\\n [\\\"count\\\"],\\n ]\\n elif name == \\\"CCS811 air quality sensor\\\":\\n assert qwe.get_subsensor_signatures(data) == [\\n [\\\"tvoc_ppb\\\", \\\"co2_ppm\\\"],\\n [\\\"tvoc_ppb\\\"],\\n [\\\"co2_ppm\\\"],\\n ]\\n elif name == \\\"MS8607 PHT sensor\\\":\\n assert qwe.get_subsensor_signatures(data) == [\\n [\\\"humidity_%\\\", \\\"hPa\\\", \\\"degC\\\"],\\n [\\\"humidity_%\\\", \\\"hPa\\\"],\\n [\\\"humidity_%\\\", \\\"degC\\\"],\\n [\\\"hPa\\\", \\\"degC\\\"],\\n [\\\"humidity_%\\\"],\\n [\\\"hPa\\\"],\\n [\\\"degC\\\"],\\n ]\\n else:\\n assert (\\n qwe.get_subsensor_signatures(data) is False\\n ), \\\"Unknown sensor, cannot test get_sensor_signatures()\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d64b1c32cc14af39555cc4029c00f7b\",\n \"score\": \"0.46350092\",\n \"text\": \"def gen_device_keypair():\\n return _gen_specific_keypair(\\\"fd\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d19f7b497ba57b04cdf84728ba1b966\",\n \"score\": \"0.46336296\",\n \"text\": \"async def device_list_received(msg):\\n try:\\n payload = json.loads(msg.payload)\\n except ValueError as err:\\n _LOGGER.error(\\\"Unable to parse JSON module list: %s\\\", err)\\n return\\n\\n modules: List[AmpioModuleInfo] = AmpioModuleInfo.from_topic_payload(payload)\\n\\n for module in modules:\\n data_modules = hass.data[DATA_AMPIO_MODULES]\\n await async_setup_device_registry(hass, config_entry, module)\\n data_modules[module.user_mac] = module\\n ampio.async_publish(\\n hass, REQUEST_MODULE_NAMES.format(mac=module.user_mac), \\\"1\\\", 0, False\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67a7bb09376e59ec40d5f3f69a37a150\",\n \"score\": \"0.46329403\",\n \"text\": \"def get(self):\\n logger.debug(\\\"Join Response Handler\\\")\\n parser = reqparse.RequestParser()\\n parser.add_argument(\\\"device\\\", help=\\\"This field cannot be blank\\\", required=True)\\n parser.add_argument(\\\"context\\\", help=\\\"This field cannot be blank\\\", required=True)\\n parser.add_argument(\\\"keys\\\", help=\\\"This field cannot be blank\\\", required=True)\\n data = parser.parse_args()\\n # print(data)\\n device = ast.literal_eval(data[\\\"device\\\"])\\n # print(device['deviceProfileID'])\\n context = ast.literal_eval(data[\\\"context\\\"])\\n # print(context)\\n keys = ast.literal_eval(data[\\\"keys\\\"])\\n # print(keys)\\n chs_client = Chs_client()\\n device_profile_id = chs_client.get_roaming_device_profile_id()\\n try:\\n if device_profile_id:\\n device[\\\"device\\\"][\\\"deviceProfileID\\\"] = device_profile_id\\n chs_client.create_device(device)\\n chs_client.set_device_context(context)\\n chs_client.set_device_keys(keys)\\n now = datetime.now()\\n end = datetime.timestamp(now)\\n with open(\\\"end.txt\\\", \\\"a\\\") as f:\\n f.write(str(end) + \\\"\\\\n\\\")\\n #print(end)\\n except:\\n logger.error(\\\"Join Response Handling Failed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c456d57e8e2089adf9b3e50225571009\",\n \"score\": \"0.46324873\",\n \"text\": \"def create_device(self, name, nodeidchildid, devicetype):\\n client_id = \\\"plugin-mysensors.{0}\\\".format(get_sanitized_hostname())\\n # name = \\\"Node xx\\\" or SensorName/Node_id.Child_id\\n # nodeidchildid = Node_id.Child_id\\n # devicetype = \\\"mysensors.node\\\" or \\\"mysensors.s_temp\\\" ...\\n devicedata = {'data':\\n {\\n u'name': name, \\n u'description': \\\"\\\",\\n u'reference': \\\"\\\",\\n u'global': [\\n {\\n u'key': u'nodesensor',\\n u'value': nodeidchildid,\\n u'type': u'string',\\n u'description': u'nodeid.sensorid'\\n }\\n ],\\n u'client_id': client_id,\\n u'device_type': devicetype,\\n u'xpl': [],\\n u'xpl_commands': {},\\n u'xpl_stats': {}\\n }\\n }\\n\\n cli = MQSyncReq(zmq.Context())\\n msg = MQMessage()\\n msg.set_action('device.create')\\n msg.set_data(devicedata)\\n response = cli.request('dbmgr', msg.get(), timeout=10).get()\\n create_result = json.loads(response[1]) # response[1] is a string !\\n self.log.debug(u\\\"==> Create device result: '%s'\\\" % response)\\n if not create_result[\\\"status\\\"]:\\n self.log.error(\\\"### Failed to create device '%s' (%s) !\\\" % (nodeidchildid))\\n '''\\n ==> response = \\n '['device.create.result', \\n '{\\n \\\"status\\\": true, \\n \\\"result\\\": {\\\"info_changed\\\": \\\"2017-03-23 00:17:41\\\", \\n \\\"commands\\\": {}, \\n \\\"description\\\": \\\"\\\", \\n \\\"reference\\\": \\\"\\\", \\n \\\"xpl_stats\\\": {}, \\n \\\"xpl_commands\\\": {}, \\n \\\"client_version\\\": \\\"0.1\\\", \\n \\\"client_id\\\": \\\"plugin-mysensors.mydomogik\\\", \\n \\\"device_type_id\\\": \\\"mysensors.node\\\", \\n \\\"sensors\\\": \\n {\\n \\\"i_sketch_name\\\": \\n {\\n \\\"conversion\\\": \\\"\\\", \\n \\\"value_min\\\": null, \\n \\\"data_type\\\": \\\"DT_String\\\", \\n \\\"reference\\\": \\\"i_sketch_name\\\", \\n \\\"last_received\\\": null, \\n \\\"value_max\\\": null, \\n \\\"incremental\\\": false, \\n \\\"timeout\\\": 0, \\n \\\"formula\\\": null, \\n \\\"last_value\\\": null, \\n \\\"id\\\": 147, \\n \\\"name\\\": \\\"Sketch Name\\\"\\n }, \\n \\\"nodetype\\\": \\n {\\\"conversion\\\": \\\"\\\", \\\"value_min\\\": null, \\\"data_type\\\": \\\"DT_String\\\", \\\"reference\\\": \\\"nodetype\\\", \\\"last_received\\\": null, \\\"value_max\\\": null, \\\"incremental\\\": false, \\\"timeout\\\": 0, \\\"formula\\\": null, \\\"last_value\\\": null, \\\"id\\\": 145, \\\"name\\\": \\\"Node Type\\\"}, \\n \\\"i_sketch_version\\\": \\n {\\\"conversion\\\": \\\"\\\", \\\"value_min\\\": null, \\\"data_type\\\": \\\"DT_String\\\", \\\"reference\\\": \\\"i_sketch_version\\\", \\\"last_received\\\": null, \\\"value_max\\\": null, \\\"incremental\\\": false, \\\"timeout\\\": 0, \\\"formula\\\": null, \\\"last_value\\\": null, \\\"id\\\": 148, \\\"name\\\": \\\"Sketch Version\\\"}, \\n \\\"nodeapiversion\\\": \\n {\\\"conversion\\\": \\\"\\\", \\\"value_min\\\": null, \\\"data_type\\\": \\\"DT_String\\\", \\\"reference\\\": \\\"nodeapiversion\\\", \\\"last_received\\\": null, \\\"value_max\\\": null, \\\"incremental\\\": false, \\\"timeout\\\": 0, \\\"formula\\\": null, \\\"last_value\\\": null, \\\"id\\\": 146, \\\"name\\\": \\\"Node Api Version\\\"}, \\n \\\"i_battery_level\\\": \\n {\\\"conversion\\\": \\\"\\\", \\\"value_min\\\": null, \\\"data_type\\\": \\\"DT_Battery\\\", \\\"reference\\\": \\\"i_battery_level\\\", \\\"last_received\\\": null, \\\"value_max\\\": null, \\\"incremental\\\": false, \\\"timeout\\\": 0, \\\"formula\\\": null, \\\"last_value\\\": null, \\\"id\\\": 149, \\\"name\\\": \\\"Battery level\\\"}\\n }, \\n \\\"parameters\\\": {\\\"nodesensor\\\": {\\\"key\\\": \\\"nodesensor\\\", \\\"type\\\": \\\"string\\\", \\\"id\\\": 31, \\\"value\\\": \\\"44.255\\\"}}, \\\"id\\\": 23, \\\"name\\\": \\\"Node 44\\\"}\\n }\\n ']'\\n '''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2c292b1a0a5014872071d1a5e40f50c\",\n \"score\": \"0.46284172\",\n \"text\": \"def _on_message(self, unused_client, unused_userdata, message):\\n payload = str(message.payload.decode(\\\"utf-8\\\"))\\n config = json.loads(payload)\\n\\n self.interphone_enabled = config[\\\"interphoneEnabled\\\"]\\n self.sound_volume = config[\\\"soundVolume\\\"]\\n self.detect_once = config[\\\"detectOnce\\\"]\\n\\n print(\\n \\\"Received message on topic '{}' with Qos {}\\\".format(\\n message.topic, str(message.qos)\\n )\\n )\\n print(\\\"Updated device configurations.\\\")\\n print(f\\\"[interphone_enabled] {self.interphone_enabled}\\\")\\n print(f\\\"[sound_volume] {self.sound_volume}\\\")\\n print(f\\\"[detect_once] {self.detect_once}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12bbe70c23f3c2b53d6edfdacdd27570\",\n \"score\": \"0.4625336\",\n \"text\": \"def send(self, numbers, alert):\\n\\n A =[]\\n\\n\\n for n in numbers:\\n #try:\\n if not self.debug:\\n message = (self.client.messages.create(body=alert, to= \\\"+57\\\" + n,\\n from_=self.twilioValues[\\\"from_\\\"]))\\n message.sid\\n A.append(message.sid)\\n else:\\n #print (\\\" -> \\\" + \\\"+57\\\" + n + \\\".\\\")\\n #print(alert)\\n #print(numbers)\\n A.append(\\\"SMdf9c1a1f03474f8690ffff460f2b988c\\\")\\n #except:\\n # if self.debug:\\n # print (\\\"number \\\" + str(n) + \\\" is invalid\\\")\\n # else:\\n # pass\\n print(A)\\n print(alert)\\n print(numbers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e255746b6c32cbc4497abda6d5cdbeb\",\n \"score\": \"0.46220237\",\n \"text\": \"def async_added_to_hass(self):\\n @callback\\n def adv_received(topic, payload, qos):\\n \\\"\\\"\\\"A new MQTT message has been received.\\\"\\\"\\\"\\n self._current.parse_adv_msg(payload)\\n if self._current.target_temp is not None:\\n self._target.target_temp = self._current.target_temp\\n self.hass.async_add_job(self.async_update_ha_state())\\n\\n @callback\\n def data_received(topic, payload, qos):\\n \\\"\\\"\\\"A new MQTT message has been received.\\\"\\\"\\\"\\n self._current.parse_data(topic.split('/')[3], payload)\\n #self._current.mode_code = self._target.mode_code\\n #self._current.target_temp = self._target.target_temp\\n self.hass.async_add_job(self.async_update_ha_state())\\n\\n yield from mqtt.async_subscribe(self.hass, 'ble/{}/advertisement/ff'.format(self._mac), adv_received, 1)\\n yield from mqtt.async_subscribe(self.hass, 'ble/{}/data/+'.format(self._mac), data_received, 1)\\n now = datetime.now()\\n cmds = { 'tries': 10,\\n 'commands': [\\n { 'action': 'readCharacteristic', 'uuid': UUID_MODEL },\\n { 'action': 'readCharacteristic', 'uuid': UUID_FIRMWARE },\\n { 'action': 'readCharacteristic', 'uuid': UUID_SOFTWARE },\\n { 'action': 'readCharacteristic', 'uuid': UUID_MANU },\\n { 'action': 'writeCharacteristic', 'uuid': UUID_PIN, 'value': [ 0, 0, 0, 0 ], 'ignoreError': '1' }, # try PIN 000000 first, in case the thermostat was reset\\n { 'action': 'writeCharacteristic', 'uuid': UUID_PIN, 'value': [ int(x) for x in self._pin.to_bytes(4, byteorder = 'little') ] }, # send real/desired PIN\\n { 'action': 'writeCharacteristic', 'uuid': UUID_DATETIME, 'value': [ now.minute, now.hour, now.day, now.month, now.year - 2000 ] },\\n { 'action': 'readCharacteristic', 'uuid': UUID_MODE },\\n { 'action': 'readCharacteristic', 'uuid': UUID_TEMP },\\n { 'action': 'readCharacteristic', 'uuid': UUID_BATTERY },\\n ]\\n }\\n mqtt.async_publish(self.hass, 'ble/{}/commands'.format(self._mac), json.dumps(cmds), 1, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e71ca3d5e6dff29ca24eb52af97965\",\n \"score\": \"0.46151528\",\n \"text\": \"def on_data(topic ,data):\\r\\n if data['topic'] == 'temperature':\\r\\n post_data = ClientPostData(data['topic'], data['time'], data['data'])\\r\\n temperature.append(post_data) \\r\\n if data['topic'] == 'light':\\r\\n post_data = ClientPostData(data['topic'], data['time'], data['data'])\\r\\n light.append(post_data)\\r\\n if data['topic'] == 'monitor':\\r\\n # decode image from b64\\r\\n im = base64.b64decode(data['data'])\\r\\n arr = np.fromstring(im, dtype=np.uint8)\\r\\n arr = np.reshape(arr, (480,960,3))\\r\\n global image\\r\\n image = Image.fromarray(arr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be4b65cf18837d43a630d143a01b1c34\",\n \"score\": \"0.46126875\",\n \"text\": \"def receive_instructions_message(self, instructions_msg):\\n self.instruction_builder.reset_instructions()\\n\\n if instructions_msg.instructions is None or len(instructions_msg.instructions) == 0:\\n rospy.logwarn('Unknown command!')\\n\\n for instruction in instructions_msg.instructions:\\n # Formula used to translate values from one given value range to another value range maintaining it's ratio:\\n # NewValue = (((OldValue - OldMin) * (NewMax - NewMin)) / (OldMax - OldMin)) + NewMin\\n if instruction.instruction == 'HOLD':\\n self.drone.hold()\\n return\\n\\n if instruction.instruction == 'THROTTLE_UP':\\n scaled_throttle = (((instruction.intensity - 0.0) * (1.0 - 0.5)) / (100.0 - 0.0)) + 0.5\\n self.instruction_builder.add_throttle(scaled_throttle)\\n elif instruction.instruction == 'THROTTLE_DOWN':\\n scaled_throttle = (((instruction.intensity - 0.0) * (0.2 - 0.0)) / (100.0 - 0.0)) + 0.0\\n self.instruction_builder.add_throttle(scaled_throttle)\\n else:\\n self.instruction_builder.add_throttle(0.7)\\n\\n if instruction.instruction == 'FORWARD':\\n scaled_pitch = -1.0 * ((((instruction.intensity - 0.0) * (0.5 - 0.1)) / (100.0 - 0.0)) + 0.1)\\n self.instruction_builder.add_pitch(scaled_pitch)\\n self.instruction_builder.add_throttle(-0.1)\\n\\n if instruction.instruction == 'TURN_LEFT':\\n scaled_yaw = -1 * ((((instruction.intensity - 0.0) * (1.0 - 0.3)) / (100.0 - 0.0)) + 0.3)\\n scaled_roll = -1 * ((((instruction.intensity - 0.0) * (0.8 - 0.0)) / (100.0 - 0.0)) + 0.0)\\n self.instruction_builder.add_yaw(scaled_yaw)\\n self.instruction_builder.add_roll(scaled_roll)\\n elif instruction.instruction == 'TURN_RIGHT':\\n scaled_yaw = (((instruction.intensity - 0.0) * (1.0 - 0.3)) / (100.0 - 0.0)) + 0.3\\n scaled_roll = (((instruction.intensity - 0.0) * (0.8 - 0.0)) / (100.0 - 0.0)) + 0.0\\n self.instruction_builder.add_yaw(scaled_yaw)\\n self.instruction_builder.add_roll(scaled_roll)\\n\\n self.drone.by_instruction(self.instruction_builder.get_instructions())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d98e0ad055618146996d141fb8cdf9c\",\n \"score\": \"0.4611342\",\n \"text\": \"def test_get_device_messages_list_for_device(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2786f65609fd4310065376455d0ce9a1\",\n \"score\": \"0.46108255\",\n \"text\": \"def on_message(client, userdata, msg):\\n payload = msg.payload.decode('utf-8') # py3\\n data = json.loads(payload)\\n\\n productivity_values_data = {\\\"soil_moisture\\\": data[\\\"analog_soil_moisture\\\"],\\n \\\"temperature\\\": data[\\\"ds18b20_temp\\\"],\\n \\\"illuminance\\\": data[\\\"bh1750_illuminance\\\"],\\n \\\"date\\\": datetime.now()\\n }\\n\\n productivity_values = aqua_crop_model.aqua_crop(**productivity_values_data)\\n print(data)\\n data.update(productivity_values)\\n socketIO.emit('stations', json.dumps(data))\\n socketIO.wait(seconds=args['wait'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d7f2ed179617b7571d636c1ea779a7f\",\n \"score\": \"0.46067145\",\n \"text\": \"def send_action(devices, action):\\n\\n for dev in devices:\\n body = {}\\n if dev:\\n mMonitor.engine.send_request(mMonitor.dev,[dev.address,],action,body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9df71547159c3136a2fba112dc4d6f\",\n \"score\": \"0.4606015\",\n \"text\": \"def arduino_watch(acm_mqtt, acm_config):\\n acm_sqlite_conf = acm_config.getConf('sqlite')\\n acm_sqlite = ACMsqlite.acmDB(acm_sqlite_conf['db_dir'],acm_sqlite_conf['xbee_csv'])\\n \\n acm_xbee_conf = acm_config.getConf('xbee','uart')\\n acm_xbee = ACMXbee.acmXbee('\\\\x00\\\\x13\\\\xA2\\\\x00\\\\x40\\\\xB1\\\\xD6\\\\x2A',port=acm_xbee_conf['port'],baud=acm_xbee_conf['baud'])\\n\\n while True:\\n try:\\n packet, data = acm_xbee.wait_res()\\n if data['id'] == 'rx':\\n \\n #print hex(data['source_addr_long'])\\n code = \\\":\\\".join(\\\"{:02x}\\\".format(ord(c)) for c in data['source_addr_long'])\\n bbb_date_u = int(time.time())\\n bbb_date_t = datetime.fromtimestamp(bbb_date_u).strftime(\\\"%d-%m-%Y %H:%M:%S\\\")\\n\\n #Time request\\n if packet['flag'] == 0x0F:\\n acm_sqlite.insert(0x0F,code=code,date_t=bbb_date_t,date_u=bbb_date_u)\\n acm_xbee.send_time(bbb_date_u)\\n acm_mqtt.sendInit(time=bbb_date_t)\\n\\n #Keep Alive\\n elif packet['flag'] == 0x21:\\n acm_sqlite.insert(0x21,code=code,date_t=bbb_date_t,date_u=bbb_date_u)\\n acm_mqtt.sendKeep(time=bbb_date_t)\\n #Alarm\\n elif packet['flag'] == 0x55:\\n acm_sqlite.insert(0x55,code=code,date_t=bbb_date_t,date_u=bbb_date_u,a_date_t=packet['ts_d'],a_date_u=packet['ts_u'])\\n acm_mqtt.sendAlarm(time=bbb_date_t)\\n #SNMP-TRAP\\n\\n #Alarm on\\n elif packet['flag'] == 0x56:\\n if packet['tres'] < packet['alm1']: \\n acm_sqlite.insert(0x56,code=code,name='alm1',scan=packet['alm1'],date_t=bbb_date_t,date_u=bbb_date_u,a_date_t=packet['ts_d'],a_date_u=packet['ts_u'])\\n acm_mqtt.sendAlarmOn(time=bbb_date_t,topic='chiller1',value=packet['alm1'])\\n \\n if packet['tres'] < packet['alm2']: \\n acm_sqlite.insert(0x56,code=code,name='alm2',scan=packet['alm2'],date_t=bbb_date_t,date_u=bbb_date_u,a_date_t=packet['ts_d'],a_date_u=packet['ts_u'])\\n acm_mqtt.sendAlarmOn(time=bbb_date_t,topic='general1',value=packet['alm2'])\\n \\n if packet['tres'] < packet['alm3']:\\n acm_sqlite.insert(0x56,code=code,name='alm3',scan=packet['alm3'],date_t=bbb_date_t,date_u=bbb_date_u,a_date_t=packet['ts_d'],a_date_u=packet['ts_u']) \\n acm_mqtt.sendAlarmOn(time=bbb_date_t,topic='chiller2',value=packet['alm3'])\\n \\n if packet['tres'] < packet['alm4']: \\n acm_sqlite.insert(0x56,code=code,name='alm4',scan=packet['alm4'],date_t=bbb_date_t,date_u=bbb_date_u,a_date_t=packet['ts_d'],a_date_u=packet['ts_u'])\\n acm_mqtt.sendAlarmOn(time=bbb_date_t,topic='general2',value=packet['alm4'])\\n\\n #Finish\\n elif packet['flag'] == 0xAA:\\n acm_sqlite.insert(0xAA,code=code,date_t=bbb_date_t,date_u=bbb_date_u,a_date_t=packet['ts_d'],a_date_u=packet['ts_u'])\\n acm_mqtt.sendFinish(time=bbb_date_t)\\n\\n #Error\\n elif packet['flag'] == 0xFF:\\n acm_sqlite.insert(0xFF,code=code,date_t=bbb_date_t,date_u=bbb_date_u,a_date_t=packet['ts_d'],a_date_u=packet['ts_u'],errc=packet['errc'])\\n acm_mqtt.sendErrorXbee(time=bbb_date_t,error=packet['errc'])\\n \\n elif packet['flag'] == 0x01:\\n acm_sqlite.insert(0x01,code=code,date_t=bbb_date_t,date_u=bbb_date_u,err=packet['err'])\\n\\n elif data['id'] == 'tx_status':\\n #print \\\"Response: \\\" + str(packet['status'])\\n pass\\n except KeyboardInterrupt:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8af7f85044ab54486d69ae94e3ae150\",\n \"score\": \"0.46043798\",\n \"text\": \"def ReadDustTrak():\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.connect((HOST, PORT))\\n\\n s.sendall(\\\"MSTATUS\\\\r\\\")\\n data = s.recv(1024)\\n Received = repr(data).strip(\\\"'\\\\\\\\r\\\\\\\\n\\\")\\n\\n if Received == 'Running':\\n s.sendall(\\\"RDSN\\\\r\\\")\\n data = s.recv(1024)\\n SerialNum = repr(data).strip(\\\"'\\\\\\\\r\\\\\\\\n\\\")\\n \\n s.sendall(\\\"RMLOGGEDMEAS\\\\r\\\")\\n data = s.recv(1024)\\n Received = repr(data).strip(\\\"'\\\\\\\\r\\\\\\\\n\\\")\\n RArray = Received.split(',')\\n \\n OutputFile.write(\\\",\\\".join([INSTR,time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\"),str(RArray[1])])+\\\"\\\\n\\\")\\n OutputFile.flush()\\n elif Received == 'Idle':\\n s.sendall(\\\"MSTART\\\\r\\\")\\n s.close()\\n return\\n \\n print \\\"DustTrak, {}, {} mg/m3\\\".format(time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\"), RArray[1])\\n \\n s.close()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":477,"cells":{"query_id":{"kind":"string","value":"34aba6c22545ff88c6c765471d69fd2a"},"query":{"kind":"string","value":"A shortand to create an original Article and its translation."},"positive_passages":{"kind":"list like","value":[{"docid":"5d2e225981e4df530ec9820e1b8a9211","score":"0.6733866","text":"def multilingual_article(**kwargs):\n langs = set(kwargs.pop(\"langs\", []))\n contents = kwargs.pop(\"contents\", {})\n translations = {}\n\n # Create original object\n original = ArticleFactory(**kwargs)\n kwargs[\"original\"] = original\n\n # Create translations adopting original kwargs or possible language\n # specific kwargs if any\n for lang in langs:\n context = kwargs.copy()\n context[\"language\"] = lang\n\n if lang in contents:\n context.update(contents[lang])\n\n translations[lang] = ArticleFactory(\n **context\n )\n\n return {\n \"original\": original,\n \"translations\": translations,\n }","title":""}],"string":"[\n {\n \"docid\": \"5d2e225981e4df530ec9820e1b8a9211\",\n \"score\": \"0.6733866\",\n \"text\": \"def multilingual_article(**kwargs):\\n langs = set(kwargs.pop(\\\"langs\\\", []))\\n contents = kwargs.pop(\\\"contents\\\", {})\\n translations = {}\\n\\n # Create original object\\n original = ArticleFactory(**kwargs)\\n kwargs[\\\"original\\\"] = original\\n\\n # Create translations adopting original kwargs or possible language\\n # specific kwargs if any\\n for lang in langs:\\n context = kwargs.copy()\\n context[\\\"language\\\"] = lang\\n\\n if lang in contents:\\n context.update(contents[lang])\\n\\n translations[lang] = ArticleFactory(\\n **context\\n )\\n\\n return {\\n \\\"original\\\": original,\\n \\\"translations\\\": translations,\\n }\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a13a1997c353eade0d83338d86918ad0","score":"0.6235401","text":"def maketrans(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"d1713bd4c92bd95b2575278cf87f58e5","score":"0.6148261","text":"def create_translation_entry(self, sdoc: SourcedDocument):\n sdoc.doc.id = self.current_art_id\n self.current_art_id += 1\n return {\n \"document_id\": sdoc.doc.id,\n \"document_collection\": self.collection,\n \"source_doc_id\": sdoc.source_id,\n \"md5\": self.get_md5(sdoc),\n \"source\": sdoc.source,\n \"date_inserted\": self.insertion_time\n }","title":""},{"docid":"bb931f42342b9e84cd083cd74456296e","score":"0.5992268","text":"def test_translation_create(self):\n pass","title":""},{"docid":"1d421119cd94a71e13507502066f20fc","score":"0.5879826","text":"def articles():","title":""},{"docid":"4eee4b2fb584cc0c0d7d7070eed403f1","score":"0.58373934","text":"def create_article(article_name: str) -> PubMedArticle:\n article_path = os.path.join(get_resources_path(), article_name)\n xml_element = ET.parse(article_path).getroot()\n return PubMedArticle(xml_element)","title":""},{"docid":"a6b3a734818467c486e13de0fc3ed97a","score":"0.5784804","text":"def new_article_from_draft(draft):\n new_article = Article( article_name = draft.article_name,\n article_external_url = draft.article_external_url,\n #name_author = draft.name_author,\n article_description = draft.article_description,\n article_body_text = draft.article_body_text,\n article_caption_styles = draft.article_caption_styles,\n article_type = draft.article_type,\n )\n\n new_article.save()\n old_authors = draft.name_author.all()\n new_article.name_author = old_authors\n new_article.save()\n draft.delete()\n print(old_authors)\n print(new_article.id)","title":""},{"docid":"0781a54acf49cd1c9f903e93f931782d","score":"0.5778184","text":"def __init__(self, article):\n\t\tself.title = article.title\n\t\tself.date = article.date\n\t\tself.image_url = article.image_url\n\t\tself.description = (article.description[:140].strip() + '...') if len(article.description) > 140 else article.description","title":""},{"docid":"3f7ce12f7a4b8443a4a27f7573b12a98","score":"0.5777499","text":"def translate(self):\r\n raise NotImplementedError","title":""},{"docid":"65ec7f4559add08dd5f16df54bdbd7be","score":"0.5744597","text":"def get_article(self):\n\n document_cursor = db.documents.find_one({\"url\": self.url})\n if document_cursor:\n document_id = document_cursor[\"_id\"]\n else:\n document_id = None\n if document_id:\n print(\"This article was already in the database\")\n self.db_doc = db.documents.find_one({\"_id\": document_id})\n self.title = self.db_doc[\"title\"]\n self.authors = self.db_doc[\"authors\"]\n self.text = self.db_doc[\"text\"]\n self.publish_date = self.db_doc[\"publish-date\"]\n return None\n\n self.article = Article(self.url)\n grammar_mistakes = self.check_grammar(self.article.title)\n\n # try to download the article\n try:\n self.article.download()\n except:\n print(\"couldnt download\")\n return None\n\n # try to parse the article\n try:\n self.article.parse()\n except:\n print(\"couldnt parse\")\n return None\n\n # Try to use nlp on the article\n try:\n self.article.nlp()\n except:\n print(\"couldnt use nlp\")\n return None\n\n self.title = self.article.title\n self.authors = self.article.authors\n self.text = self.article.text\n self.publish_date = self.article.publish_date\n print(self.title)\n print(self.authors)\n\n #self.grammar_mistakes = self.check_grammar(self.text)\n\n # document_cursor = db.documents.find_one({\"url\": self.url})\n # if document_cursor:\n # document_id = document_cursor[\"_id\"]\n # else:\n # document_id = None\n # if document_id:\n\n\n document_id=self.add_article_to_db()\n\n for author in self.authors:\n author_cursor = db.authors.find_one({\"author\":author})\n\n if author_cursor:\n #author_id = author_cursor[\"_id\"]\n if document_id != author_cursor[\"known_articles\"]:\n db.authors.updateOne({\"author\":author},{\"known_articles\":author_cursor[\"known_articles\"].extend(document_id)})\n else:\n db.authors.insert_one({\n \"author\": author,\n \"known_articles\": [document_id]\n })","title":""},{"docid":"5a1c7e2d6aa0e6481f4241985976d127","score":"0.57442206","text":"def translate(self):\n raise NotImplementedError","title":""},{"docid":"42eb1c060fc36af5ecb57e846a562e0d","score":"0.5712158","text":"def _get_translated_body(self):\r\n # [START _get_translated_body]\r\n body = self._body\r\n url = self._url\r\n articleid = self._articleid\r\n translator = Translator()\r\n\r\n split_body_list = body.split('```')\r\n chunk_size = 2000\r\n prefix = '\\n\\n```'\r\n suffix = '```\\n\\n'\r\n banner = '**This article is an automatic translation of the article[' + articleid + '] below.\\n' + url +'**\\n\\n'\r\n translated_body = banner\r\n for i,element in enumerate(split_body_list):\r\n if i%2 == 0:\r\n if len(element) > chunk_size:\r\n chunk_list = [element[i: i+chunk_size] for i in range(0, len(element), chunk_size)]\r\n split_body_list[i] = ''\r\n for chunk in chunk_list:\r\n split_body_list[i] += translator.translate(chunk,dest='en').text\r\n else:\r\n split_body_list[i] = translator.translate(element,dest='en').text\r\n split_body_list[i] = self._delete_redundant_space(split_body_list[i])\r\n\r\n if i + 1 == len(split_body_list) or len(split_body_list) == 1:\r\n translated_body += split_body_list[i]\r\n else:\r\n translated_body += (split_body_list[i] + prefix)\r\n else:\r\n if i + 1 == len(split_body_list) or len(split_body_list) == 1:\r\n translated_body += split_body_list[i]\r\n else:\r\n translated_body += (split_body_list[i] + suffix)\r\n\r\n return translated_body\r\n # [END _get_translated_body]\r","title":""},{"docid":"f0c6154b0347a2e24d86f5eef022bcea","score":"0.57097405","text":"def create( self, trans, name, description=''):","title":""},{"docid":"498aac8c9052e705e659ec0bd662c47e","score":"0.570527","text":"def from_text(cls, article_text):\n finder = TemplateFinder(article_text)\n find_re_daten = finder.get_positions(RE_DATEN)\n find_re_abschnitt = finder.get_positions(RE_ABSCHNITT)\n # only one start template can be present\n if len(find_re_daten) + len(find_re_abschnitt) != 1:\n raise ReDatenException(\"Article has the wrong structure. There must be one start template\")\n if find_re_daten:\n find_re_start = find_re_daten\n else:\n find_re_start = find_re_abschnitt\n find_re_author = finder.get_positions(RE_AUTHOR)\n # only one end template can be present\n if len(find_re_author) != 1:\n raise ReDatenException(\"Article has the wrong structure. There must one stop template\")\n # the templates must have the right order\n if find_re_start[0][\"pos\"][0] > find_re_author[0][\"pos\"][0]:\n raise ReDatenException(\"Article has the wrong structure. Wrong order of templates.\")\n # it can only exist text between the start and the end template.\n if find_re_start[0][\"pos\"][0] != 0:\n raise ReDatenException(\"Article has the wrong structure. There is text in front of the article.\")\n if find_re_author[0][\"pos\"][1] != len(article_text):\n raise ReDatenException(\"Article has the wrong structure. There is text after the article.\")\n try:\n re_start = TemplateHandler(find_re_start[0][\"text\"])\n except TemplateHandlerException as error:\n raise ReDatenException(\"Start-Template has the wrong structure.\") from error\n try:\n re_author = REAuthor.from_template(find_re_author[0][\"text\"])\n except TemplateHandlerException as error:\n raise ReDatenException(\"Author-Template has the wrong structure.\") from error\n properties_dict = cls._extract_properties(re_start.parameters)\n return Article(article_type=re_start.title,\n re_daten_properties=properties_dict,\n text=article_text[find_re_start[0][\"pos\"][1]:find_re_author[0][\"pos\"][0]]\n .strip(),\n author=re_author)","title":""},{"docid":"98d9fe2355e082271b658c53b5e7116d","score":"0.56716543","text":"def create_with_edition(self, title, author, text):\n new_post = Post.objects.create(title=title, slug=slugify(title), author=author)\n new_edition = Edition.objects.create(post=new_post, text=text)\n return new_post","title":""},{"docid":"2f9fe0ee2cfa3fc398296ea9bc4fb5da","score":"0.56290185","text":"def show_teaser(article):\n return {'article': article}","title":""},{"docid":"8ce890269795b7d96d5e68f206c8eea9","score":"0.55602896","text":"def create_passage(passage, translation=None):\n return {\"word\": passage, \"translation\": translation, \"next\": None}","title":""},{"docid":"3dbdc8b6cc6011efe1403ccce2d743c9","score":"0.5546388","text":"def translate(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"3035be04cef117c3c50b584bf4217696","score":"0.5532673","text":"def create_article(title, content_source=\"test article text\", days_in_past=0):\n time = timezone.now() + datetime.timedelta(days=-days_in_past)\n return Article.objects.create(title=title,\n content_source=content_source,\n pub_date=time)","title":""},{"docid":"3035be04cef117c3c50b584bf4217696","score":"0.5532673","text":"def create_article(title, content_source=\"test article text\", days_in_past=0):\n time = timezone.now() + datetime.timedelta(days=-days_in_past)\n return Article.objects.create(title=title,\n content_source=content_source,\n pub_date=time)","title":""},{"docid":"952cf6de326a9ca9ffc38e0f83bfcdcc","score":"0.55176383","text":"def test_translation_review(self):\n pass","title":""},{"docid":"c807ca6e9be4d6ea371b88e1798e2f26","score":"0.5507654","text":"def mktranslation(self, obj, lang, **kwargs):\n try:\n obj.set_current_language(lang)\n except:\n try:\n obj.translate(lang)\n except IntegrityError:\n pass\n for k, v in kwargs.items():\n setattr(obj, k, v)\n obj.save()","title":""},{"docid":"e565fcd90318101682b4e2cb0206ff6e","score":"0.5478009","text":"def translate(text):","title":""},{"docid":"79c386cb9e9f85767d400a8a35138283","score":"0.5475836","text":"def test_translated(self, client, root):\n n1 = root.add(langslugs=dict(nl=\"a\", en=\"b\"))\n n2 = root.add(langslugs=dict(en=\"a\", nl=\"b\"))\n\n\n translation.activate('nl')\n res = MainHandler.coerce(dict(instance=\"a\"))\n assert res['instance'] == n1\n\n translation.activate('en')\n res = MainHandler.coerce(dict(instance=\"a\"))\n assert res['instance'] == n2","title":""},{"docid":"5451ed2cfbb4743e4531020f8687d6ee","score":"0.54758334","text":"def AddTranslator(self, translator):","title":""},{"docid":"93fedcf2b5572bb0d6def60f13b39a60","score":"0.54429305","text":"def test_translate(self):\n translation = Translation(**gen_data())\n translation.translate()\n self.assertEqual('Bonjour le monde!', translation.output)","title":""},{"docid":"fe7c37513f4029e5fa00ab04dfc07b7c","score":"0.54203105","text":"def dbwriteArticle(self, a):\n # add head + title\n e = Element(\"article\", lang=self.language)\n if self.root is None:\n self.root = e\n h = SubElement(e,\"articleinfo\")\n t = SubElement(h, \"title\")\n if a.caption:\n t.text = a.caption\n \n # add a section and heading for this article \n s = SubElement(e, \"section\")\n si = SubElement(s, \"sectioninfo\")\n h = SubElement(si, \"title\")\n h.text = a.caption\n e.writeto = s\n return e","title":""},{"docid":"ea28eaee3c5e5d899682c496d46683d3","score":"0.5399459","text":"def translate(obj, columns, content):\n return super(FullTranscript, FullTranscript).translate(obj, columns, content)","title":""},{"docid":"3324e622877655402c5cce76a616b1da","score":"0.5385482","text":"def _trans(self, event, old, new, user=None):\n remark = u'{}: '.format(event) if event else u''\n remark += u'{} >> {}'.format(old, new)\n if user:\n Note.objects.create(content_object=self,\n remark=remark, is_history=True,\n created_by=user)\n else:\n Note.objects.create(content_object=self,\n remark=remark, is_history=True)","title":""},{"docid":"3941c6155910b98418a416b1b6829b9d","score":"0.53554004","text":"async def create_article(article: schemas.Article) -> schemas.Preview.Article:\n return datasource.insert_article(article)","title":""},{"docid":"3d941bb72e4db03c20fc8c101f163702","score":"0.53433675","text":"def test_article_manager_create_article(self):\n article = {\n 'source': {'id': 'news-com-au', 'name': 'News.com.au'},\n 'author': 'unknown',\n 'title': 'F1 British Grand Prix live: updates, results, starting grid, Vettel reacts to Ferrari sabotage '\n 'questions',\n 'description': 'The British Grand Prix has ended in incredible drama as the last lap went down to the '\n 'wire with Lewis Hamilton winning after his tyre blew on the last lap.',\n 'url': 'https://www.news.com.au/sport/motorsport/formula-one/live-updates-from-the-2020-british-grand'\n '-prix/live-coverage/ba297f46d4e91321c092db9d3d5d2e1f',\n 'urlToImage': 'https://content.api.news/v3/images/bin/2554ff2213b5c8a54e9809d310e697db',\n 'publishedAt': '2020-08-02T22:04:07Z',\n 'content': '...'\n }\n created = Article.objects.create_article(article)\n self.assertEqual(article['source']['name'], created.source)\n self.assertEqual('unknown', created.author)\n self.assertEqual(article['title'], created.title)\n self.assertEqual(article['description'], created.description)\n self.assertEqual(article['url'], created.url)\n self.assertEqual(parse_datetime(article['publishedAt']), created.published_at)\n self.assertEqual('...', created.content)","title":""},{"docid":"cfaec30800c3c2b1a6ef3034f3424e48","score":"0.5324116","text":"def process_text(article_text,nlp):\n \n # lemmatize NOTE: NOT IMPLEMENTED YET...\n # article_text = lemmatize(article_text,nlp)\n \n # run gensim preprocess\n article_text = simple_preprocess(article_text, deacc=True)\n \n return article_text","title":""},{"docid":"a1e68f0dde9df87475243522dafb5906","score":"0.531908","text":"def translate(src_filename, dest_filename, dest_lang, src_lang='auto', specialwords_filename=''):\n translator = Translator() # Initialize translator object\n\n with open(src_filename) as srcfile, open(dest_filename, 'w') as destfile:\n\n lines = srcfile.readlines()\n specialwords_dict = {}\n\n # If special words file exists, place special word mappings into specialwords_dict\n if specialwords_filename != '':\n with yaml.load(open(specialwords_filename)) as specialwords_fulllist:\n\n # Gets source language if not passed through\n if src_lang == 'auto':\n src_lang == str(translator.detect(lines[0]))[14:16]\n\n # Attempts to add the correct dictionary of special words\n try:\n specialwords_dict = specialwords_dict_full[src_lang + '_' + dest_lang]\n except KeyError:\n print('Special words file doesn\\'t contain required language translation!')\n\n # Parses each line for special cases and ignores them when translating\n for line in lines:\n line = line.strip()\n\n # Parses for code blocks and ignores them entirely\n if line.startswith(\"```\"):\n line = line\n\n else:\n # Parses for URL's and file links and ignores them\n if line.find(\"[\") != -1 and line.find(\"]\") != -1 and line.find(\"(\") != -1 and line.find(\")\") != -1:\n ignore_start = line.find(\"(\")\n ignore_end = line.find(\")\")\n head = replace(specialwords_dict,line[0:ignore_start])\n tail = replace(specialwords_dict,line[ignore_end+1:])\n head = translator.translate(head, dest_lang, src_lang).text\n tail = translator.translate(tail, dest_lang, src_lang).text\n line = head + line[ignore_start:ignore_end+1] + tail\n\n # Translates normally if there are no special cases\n else:\n line = translator.translate(line, dest_lang, src_lang).text\n\n # Write to destination file\n destfile.write(line + '\\n')","title":""},{"docid":"df24f5aa78e1413cf7ea1003b2180b20","score":"0.53131425","text":"def transform_article(article, featured_image=None, author=None):\n article['image'] = featured_image\n\n article['author'] = author\n\n if 'date_gmt' in article:\n article_gmt = article['date_gmt']\n article_date = datetime.strptime(article_gmt, '%Y-%m-%dT%H:%M:%S')\n article['date'] = article_date.strftime('%-d %B %Y')\n\n if 'excerpt' in article and 'rendered' in article['excerpt']:\n article['excerpt']['raw'] = strip_excerpt(\n article['excerpt']['rendered'])[:340]\n\n # If the excerpt doesn't end before 340 characters, add ellipsis\n raw_article = article['excerpt']['raw']\n # split at the last 3 characters\n raw_article_start = raw_article[:-3]\n raw_article_end = raw_article[-3:]\n # for the last 3 characters replace any part of […]\n raw_article_end = raw_article_end.replace('[', '')\n raw_article_end = raw_article_end.replace('…', '')\n raw_article_end = raw_article_end.replace(']', '')\n # join it back up\n article['excerpt']['raw'] = ''.join([\n raw_article_start,\n raw_article_end,\n ' […]'])\n\n return article","title":""},{"docid":"c7a7c15911eb1004befc6a988f9e61ec","score":"0.5284541","text":"def generate(indef_arts, def_arts, spros, opros, nouns, templates, \n lang, output_dir):\n\n outfile = open(output_dir, 'w')\n french_vowels = ['i', 'u', 'o', 'a', 'h', 'e', 'é']\n m_nouns, f_nouns = nouns['m'], nouns['f']\n\n for i, template in enumerate(templates): # for each template\n template = template.split()\n # words to keep\n words_to_keep = [i for i, w in enumerate(template) if not w.startswith('[')]\n # words to change\n words_to_change = [i for i, w in enumerate(template) if w.startswith('[')]\n for k, noun in enumerate(m_nouns):\n m_sentence = ['*'] * len(template)\n f_sentence = ['*'] * len(template)\n for idx, w in enumerate(words_to_change):\n word_to_change = template[w]\n\n if word_to_change == '[noun]':\n m_sentence[w] = m_nouns[k]\n f_sentence[w] = f_nouns[k] \n\n # handle the special def arts in french\n if lang == 'fr' and template[w - 1] == '[def_art]':\n # in our case, the def articles\n # only comes before nouns\n\n if m_nouns[k][0].lower() in french_vowels:\n\n m_sentence[w - 1] = def_arts['special']\n if f_nouns[k][0].lower() in french_vowels:\n f_sentence[w - 1] = def_arts['special']\n\n elif word_to_change == '[indef_art]':\n m_sentence[w] = indef_arts['m']\n f_sentence[w] = indef_arts['f']\n\n elif word_to_change == '[def_art]':\n m_sentence[w] = def_arts['m']\n f_sentence[w] = def_arts['f']\n\n elif word_to_change == '[spro]':\n m_sentence[w] = spros['m']\n f_sentence[w] = spros['f']\n\n elif word_to_change == '[opro]':\n m_sentence[w] = opros['m']\n f_sentence[w] = opros['f']\n\n for w in words_to_keep:\n m_sentence[w] = template[w]\n f_sentence[w] = template[w]\n\n print(m_sentence)\n print(f_sentence)\n if lang == 'fr':\n out_m_sentence = ' '.join(m_sentence).replace(\"l' \", \"l'\").replace(\"qu' \", \"qu'\")\n out_f_sentence = ' '.join(f_sentence).replace(\"l' \", \"l'\").replace(\"qu' \", \"qu'\")\n else:\n out_m_sentence = ' '.join(m_sentence)\n out_f_sentence = ' '.join(f_sentence)\n\n # src, trg, src_gender, trg_gender\n outfile.write(out_m_sentence + '\\t' + out_m_sentence + '\\t' + 'M' + '\\t' + 'M')\n outfile.write('\\n')\n outfile.write(out_m_sentence + '\\t' + out_f_sentence + '\\t' + 'M' + '\\t' + 'F')\n outfile.write('\\n')\n outfile.write(out_f_sentence + '\\t' + out_m_sentence + '\\t' + 'F' + '\\t' + 'M')\n outfile.write('\\n')\n outfile.write(out_f_sentence + '\\t' + out_f_sentence + '\\t' + 'F'+ '\\t' + 'F')\n outfile.write('\\n')\n outfile.close()","title":""},{"docid":"02f5e98ef2163e40e2c249db4b6da5e6","score":"0.52391344","text":"def set_articles(articles):\n global article_features\n global article_M, article_M_inv, article_w\n global article_b\n article_features = articles\n article_M = {a: np.identity(6) for a in article_features}\n article_M_inv = {a: np.identity(6) for a in article_M}\n article_b = {a: np.zeros((6, 1)) for a in article_features}\n article_w = {a: np.zeros((6, 1)) for a in article_features}","title":""},{"docid":"4cb133c8d1228b457f3ef81825e25fc1","score":"0.5233303","text":"def test_translation_update(self):\n pass","title":""},{"docid":"7ccd4989e2294898404f36c972ffa11f","score":"0.52151895","text":"def create_article(conn, article):\n\n global id_of_first_author_column;\n\n bibtex_id = \"\";\n year = article['\"Year\"'];\n title = article['\"Title\"'];\n doi = article['\"DOI\"'];\n pages = article['\"pages\"'];\n\n if pages == '\"\"':\n first_page = -1;\n last_page = -1;\n else:\n temp = pages.replace('\"', '').split(\"-\");\n first_page = int(temp[0]);\n last_page = int(temp[1]);\n\n first_page = first_page;\n last_page = last_page;\n\n conference_id = get_conference_id(conn, year);\n\n record = (bibtex_id, conference_id, title,doi,first_page,last_page);\n\n sql = ''' INSERT INTO articles(bibtex_id,conference_id,title,doi,first_page,last_page)\n VALUES(?,?,?,?,?,?) '''\n cur = conn.cursor()\n cur.execute(sql, record);\n\n article_id = cur.lastrowid;\n\n # Add all the authors\n # Look for columns of authors\n for i in range(article['\"Number of authours\"']):\n fullname = article[id_of_first_author_column + i];\n author_id = get_author_id(conn, fullname);\n create_authorship(conn, author_id, article_id);","title":""},{"docid":"918142d4eeab652be1e906c1ff1b31ef","score":"0.52133614","text":"def encode_article(self):\n sentences_to_encode = []\n encoded_article = [None]\n sentence_list = [0]\n sent_count = 0\n\n for sentence in self.article:\n sent_count += len(sentence)\n sentence_list.append(sent_count)\n for sent in sentence:\n sentences_to_encode.append(sent) # Split into sentences\n\n print('Loading models...'),\n model = skipthoughts.LoadModel().load_model()\n encoder = skipthoughts.Encoder(model)\n print('Encoding sentences...'),\n encoded_sentences = encoder.encode(sentences_to_encode, verbose=False)\n print('Done')\n\n for i in range(len(self.article)):\n begin = sentence_list[i]\n end = sentence_list[i + 1]\n encoded_article[i] = encoded_sentences[begin:end]\n return encoded_article","title":""},{"docid":"92cd298e1bd04079e7d9537f144d713d","score":"0.519701","text":"def translate(self, object_name):\r\n pass","title":""},{"docid":"13591fe16d4da1a8ee1fa62e7dcf94f6","score":"0.5196361","text":"def translate(full_text):\r\n translator = Translator()\r\n # breaking down text into small pieces for faster translation\r\n translations = []\r\n unique_elements = full_text.split('. ')\r\n\r\n for element in unique_elements:\r\n # Adding all the translations to a dictionary (translations)\r\n translations.append(translator.translate(element).text)\r\n # Final translated string\r\n english_translated_text = ''\r\n # joining all the converted chunks to final string\r\n for i in translations:\r\n english_translated_text = english_translated_text + ' ' + i\r\n # return the text for all translated chunks\r\n return english_translated_text","title":""},{"docid":"e58eaef1fb281cc3cc4dbaa8838bd80a","score":"0.51941335","text":"def generate_translations(item):\n fr_prefix = '(français) '\n es_prefix = '(español) '\n oldname = str(item.name)\n item.name = {'en': oldname, 'fr': fr_prefix + oldname, 'es': es_prefix + oldname}\n item.save()","title":""},{"docid":"98e25581271f07e51b177ce8da17f66c","score":"0.5183574","text":"def article(self, article_id, text_format=None):\n endpoint = 'articles/{}'.format(article_id)\n params = {'text_format': text_format or self.response_format}\n return self._make_request(path=endpoint, params_=params, public_api=True)","title":""},{"docid":"57702ac62d40140debabd0ee52d35ef1","score":"0.5181046","text":"def translational(self, x):\n self.trans.copy_from(x)","title":""},{"docid":"4755e9ad4c4ed3c1a13c0fd703c0bf69","score":"0.51672024","text":"def translate(request, document_slug, document_locale, revision_id=None):\n # TODO: Refactor this view into two views? (new, edit)\n # That might help reduce the headache-inducing branchiness.\n parent_doc = get_object_or_404(\n Document, locale=settings.WIKI_DEFAULT_LANGUAGE, slug=document_slug)\n user = request.user\n\n if not revision_id:\n # HACK: Seems weird, but sticking the translate-to locale in a query\n # param is the best way to avoid the MindTouch-legacy locale\n # redirection logic.\n document_locale = request.REQUEST.get('tolocale',\n document_locale)\n\n # Set a \"Discard Changes\" page\n discard_href = ''\n\n if settings.WIKI_DEFAULT_LANGUAGE == document_locale:\n # Don't translate to the default language.\n return HttpResponseRedirect(reverse(\n 'wiki.edit_document', locale=settings.WIKI_DEFAULT_LANGUAGE,\n args=[parent_doc.full_path]))\n\n if not parent_doc.is_localizable:\n message = _lazy(u'You cannot translate this document.')\n return render(request, 'handlers/400.html',\n {'message': message}, status=400)\n\n if revision_id:\n initial_rev = get_object_or_404(Revision, pk=revision_id)\n\n based_on_rev = get_current_or_latest_revision(parent_doc,\n reviewed_only=False)\n\n disclose_description = bool(request.GET.get('opendescription'))\n\n try:\n doc = parent_doc.translations.get(locale=document_locale)\n slug_dict = _split_slug(doc.slug)\n except Document.DoesNotExist:\n doc = None\n disclose_description = True\n slug_dict = _split_slug(document_slug)\n\n # Find the \"real\" parent topic, which is its translation\n try:\n parent_topic_translated_doc = (\n parent_doc.parent_topic.translations.get(\n locale=document_locale))\n slug_dict = _split_slug(\n parent_topic_translated_doc.slug + '/' + slug_dict['specific'])\n except:\n pass\n\n user_has_doc_perm = ((not doc) or (doc and doc.allows_editing_by(user)))\n user_has_rev_perm = ((not doc) or (doc and doc.allows_revision_by(user)))\n if not user_has_doc_perm and not user_has_rev_perm:\n # User has no perms, bye.\n raise PermissionDenied\n\n doc_form = rev_form = None\n\n if user_has_doc_perm:\n if doc:\n # If there's an existing doc, populate form from it.\n discard_href = doc.get_absolute_url()\n doc.slug = slug_dict['specific']\n doc_initial = _document_form_initial(doc)\n else:\n # If no existing doc, bring over the original title and slug.\n discard_href = parent_doc.get_absolute_url()\n doc_initial = {'title': based_on_rev.title,\n 'slug': slug_dict['specific']}\n doc_form = DocumentForm(initial=doc_initial)\n\n if user_has_rev_perm:\n initial = {'based_on': based_on_rev.id, 'comment': '',\n 'toc_depth': based_on_rev.toc_depth}\n if revision_id:\n initial.update(\n content=Revision.objects.get(pk=revision_id).content)\n elif not doc:\n initial.update(content=based_on_rev.content)\n instance = doc and get_current_or_latest_revision(doc)\n rev_form = RevisionForm(instance=instance, initial=initial)\n\n if request.method == 'POST':\n which_form = request.POST.get('form', 'both')\n doc_form_invalid = False\n\n # Grab the posted slug value in case it's invalid\n posted_slug = request.POST.get('slug', slug_dict['specific'])\n destination_slug = _join_slug(slug_dict['parent_split'], posted_slug)\n\n if user_has_doc_perm and which_form in ['doc', 'both']:\n disclose_description = True\n post_data = request.POST.copy()\n\n post_data.update({'locale': document_locale})\n post_data.update({'slug': destination_slug})\n\n doc_form = DocumentForm(post_data, instance=doc)\n doc_form.instance.locale = document_locale\n doc_form.instance.parent = parent_doc\n if which_form == 'both':\n # Sending a new copy of post so the slug change above\n # doesn't cause problems during validation\n rev_form = RevisionValidationForm(request.POST.copy())\n rev_form.parent_slug = slug_dict['parent']\n\n # If we are submitting the whole form, we need to check that\n # the Revision is valid before saving the Document.\n if doc_form.is_valid() and (which_form == 'doc' or\n rev_form.is_valid()):\n rev_form = RevisionForm(post_data)\n\n if rev_form.is_valid():\n doc = doc_form.save(parent_doc)\n\n if which_form == 'doc':\n url = urlparams(reverse('wiki.edit_document',\n args=[doc.full_path],\n locale=doc.locale),\n opendescription=1)\n return HttpResponseRedirect(url)\n else:\n doc_form.data['slug'] = posted_slug\n doc_form_invalid = True\n else:\n doc_form.data['slug'] = posted_slug\n doc_form_invalid = True\n\n if doc and user_has_rev_perm and which_form in ['rev', 'both']:\n post_data = request.POST.copy()\n if not 'slug' in post_data:\n post_data['slug'] = posted_slug\n\n rev_form = RevisionValidationForm(post_data)\n rev_form.parent_slug = slug_dict['parent']\n rev_form.instance.document = doc # for rev_form.clean()\n\n if rev_form.is_valid() and not doc_form_invalid:\n # append final slug\n post_data['slug'] = destination_slug\n\n # update the post data with the toc_depth of original\n post_data['toc_depth'] = based_on_rev.toc_depth\n\n rev_form = RevisionForm(post_data)\n rev_form.instance.document = doc # for rev_form.clean()\n\n if rev_form.is_valid():\n _save_rev_and_notify(rev_form, request.user, doc)\n url = reverse('wiki.document', args=[doc.full_path],\n locale=doc.locale)\n return HttpResponseRedirect(url)\n\n parent_split = _split_slug(parent_doc.slug)\n\n return render(request, 'wiki/translate.html',\n {'parent': parent_doc, 'document': doc,\n 'document_form': doc_form, 'revision_form': rev_form,\n 'locale': document_locale, 'based_on': based_on_rev,\n 'disclose_description': disclose_description,\n 'discard_href': discard_href,\n 'specific_slug': parent_split['specific'],\n 'parent_slug': parent_split['parent']})","title":""},{"docid":"c9516fe390c1b067efdae9355fe26c46","score":"0.5167005","text":"def from_translation(self, la):\n E = self.realization_of()\n return self((E.exp_lattice()(la),self.cartesian_factors()[1].one()))","title":""},{"docid":"49360c8ed6f0ffc12ef12dce056eff7d","score":"0.515288","text":"def get_translate(self):\n selection = self.selection.strip().strip(string.punctuation)\n selection = selection.replace('_', ' ')\n\n # ToDo split CamelCase words\n words = selection.split()\n splitted = []\n for w in words:\n matches = re.finditer(\n '.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)',\n w)\n splitted.append(' '.join([m.group(0) for m in matches]))\n selection = ' '.join(splitted)\n\n payload = {'word': selection}\n\n twords = []\n try:\n resp = requests.get(\n \"http://api.lingualeo.com/gettranslates\",\n params=payload,\n )\n resp_json = resp.json()\n twords = [\n translation['value'].encode(\"utf-8\")\n for translation in resp_json.get(\"translate\", ())\n ]\n error = resp_json.get('error_msg')\n except requests.ConnectionError as err:\n error = 'Connection error occurred: {}'.format(err)\n except ValueError:\n error = resp.text.encode(\"utf-8\")\n except Exception as err:\n error = 'Error occurred in translator module: {}'.format(err)\n\n res = Translate(\n selection=selection,\n error=error or None,\n twords=twords,\n )\n return res","title":""},{"docid":"6af8e92f78dd27ec1f367cebce918a5f","score":"0.5151055","text":"def _add_article(self, article):\n pmid = int(article[0].strip(' \\n').split('|')[0])\n # Note: The 2: slicing makes title, abstract, and mesh list objects\n # Had I only indexed into 2, then they would've all been str objs.\n title, abstract, mesh = [data.strip(' \\n').split('|')[2:]\n for data in article]\n\n mesh_terms = {\n m.partition('!')[0].lower().strip().rstrip('*')\n for m in mesh\n } - {''} # Remove empty term\n\n # PMIDs of articles we're trying to predict MeSH terms for\n self.articles.append(pmid)\n # Title/abstract/MeSH terms of a cited article\n self.citations[pmid]['title'] = ''.join(title)\n self.citations[pmid]['abstract'] = ''.join(abstract)\n self.citations[pmid]['mesh'] = mesh_terms","title":""},{"docid":"699f6f94006d41db720fbbc76f34b715","score":"0.5142341","text":"def simple_backtranslate(original, lang_tmp = \"en\"):\n \n import textblob\n t = textblob.blob.TextBlob(original)\n translated = str(t.translate(from_lang = \"fr\", to = lang_tmp))\n blob = textblob.blob.TextBlob(translated)\n tr = blob.translate(from_lang = lang_tmp, to = \"fr\")\n return str(tr)","title":""},{"docid":"d11551d5bbfde57e2433fca0061852c7","score":"0.5127855","text":"def english_to_german(self,text):\n translation = dict()\n if len(text) == 0:\n return text\n translation = self.language_translator.translate(text, model_id='en-de').get_result()\n translated_text = translation['translations'][0]['translation']\n return translated_text","title":""},{"docid":"12f14f0d8fcbce7a2a6617f4dfc0a030","score":"0.5123375","text":"def parse_article(self, response):\n\n # search for article title\n title_parts = response.xpath('//div[@id=\"articlehead\"]/h1/text()').extract()\n if len(title_parts) > 0:\n title = ' '.join(set(title_parts)).encode('utf-8').strip()\n else:\n title = ''\n\n # search for article published date\n datetime_element_parts = response.xpath('//small[@id=\"pubdate\"]/strong/text()').extract()\n if len(datetime_element_parts) > 0:\n datetime_iso_str = self.parse_published_datetime(datetime_element_parts)\n else:\n datetime_iso_str = ''\n\n # search for article intro text\n article_intro_parts = response.xpath('//div[@id=\"intro\"]/strong/text()').extract()\n article_intro = ' '.join([x.strip() for x in article_intro_parts]).strip()\n\n # search for article full text\n article_full_text_fragments = response.xpath('//div[@id=\"articlebody\"]/descendant::p/descendant-or-self::*/text()').extract()\n article_full_text = ' '.join([x.strip() for x in article_full_text_fragments]).strip()\n\n # reconstruct the url to the nicely rendered page\n url_parts = response.url.split('/')\n article_id = url_parts.pop()\n url_parts.append('vrtnieuws')\n url_parts.append(article_id)\n url = '/'.join(url_parts)\n\n # now create an Article item, and return it. All Articles created during scraping can be written to an output file when the -o option is given.\n article = Article()\n article['url'] = url\n article['intro'] = article_intro\n article['title'] = title\n article['published_at'] = datetime_iso_str\n article['text'] = article_full_text\n return article","title":""},{"docid":"d00465bb3a9fa30feb95e3028c46af0d","score":"0.5118378","text":"def start_add_article(bot, update):\n user_logger(update, \"starts add article\")\n send(bot, chat_id=update.message.chat_id, text=\"Which is the link of the article you wish to add? (You can look for it with the Wikipedia bot @wiki) Or /cancel if you want\") \n return ARTICLE","title":""},{"docid":"044639e66b57a129923f2a5d02961011","score":"0.50946444","text":"def test_translation_show(self):\n pass","title":""},{"docid":"36e7ded4f3872c2354f7ad63396137a6","score":"0.5087646","text":"def from_translation(self, la):\n return self((self.cartesian_factors()[0].one(),self.realization_of().exp_lattice()(la)))","title":""},{"docid":"2118369c867a9e5212490d833408f0a3","score":"0.5084515","text":"def translate_many(obj, columns, contents):\n return super(FullTranscript, FullTranscript).translate_many(obj, columns, contents)","title":""},{"docid":"2156af514f4b1cbe41a612259b2d4f4e","score":"0.50663656","text":"def getTranslation(self):\n pass","title":""},{"docid":"cf3b7b98c76b4e0c621b6b1013862157","score":"0.5062391","text":"def __init__(self,src,tar,decoration,sentence):\r\n self.src = src\r\n self.tar = tar\r\n self.decoration = decoration\r\n self.triple = (src,decoration,tar)\r\n self.sentence = sentence\r\n # BIO annotation:\r\n src = \"\"\r\n tar = \"\"\r\n dec = \"\"\r\n for index,char in enumerate(self.src):\r\n if index == 0:\r\n src += char + \"_B-Verb\"\r\n else:\r\n src += char + \"_I-Verb\"\r\n for index,char in enumerate(self.tar):\r\n if index == 0:\r\n tar += char + \"_B-Obj\"\r\n else:\r\n tar += char + \"_I-Obj\"\r\n for index,char in enumerate(self.decoration):\r\n if index == 0:\r\n dec += char + \"_B-Dec\"\r\n else:\r\n dec += char + \"_I-Dec\"\r\n # replace the raw sentence:\r\n sentence = sentence.replace(self.src,src)\r\n sentence = sentence.replace(self.tar,tar)\r\n sentence = sentence.replace(self.decoration,dec)\r\n self.entityAnnotation = sentence","title":""},{"docid":"5a8275e28c28e374da9bba90aabebc8f","score":"0.5062032","text":"def generate_old (self):\r\n vtopics = self.parse_input()\r\n for vtopic in vtopics:\r\n print(\"[* corpus-generate] Researching: [%s]\" % vtopic)\r\n vlinks = self.get_google_results(vtopic[\"explanation\"])\r\n \r\n # Extract article chunked into paragraphs.\r\n for vlink in vlinks:\r\n try:\r\n print(\"[* corpus-generate] Retrieve article for [%s]\" \r\n % vlink)\r\n varticle = get_article(vlink)\r\n\r\n # Add to output corpus\r\n for vpg in varticle: VCORPUS.append(\r\n [\" \".join(vpg), vtopic['name']])\r\n \r\n print(\"[* corpus-generate] Writing article(?\")\r\n write_article(varticle, vtopic['name'])\r\n \r\n except Exception as e:\r\n print(\"[* corpus-generate] Could not run!: \" + str(e))","title":""},{"docid":"2575cb33586e5883a62cf17f33a00345","score":"0.5061343","text":"def sample_translate(text):\n group = ConverseGroup(json=sample_data_source)\n t = Translator(group)\n return t.translate(text)","title":""},{"docid":"f804ec067dd2c047c5e472995388ba52","score":"0.5043085","text":"def setUp(self):\n\n self.new_article=Article('author','title','description','www.news.com','urltoimage','2020-04-03T10:09:32Z','content')","title":""},{"docid":"9d76b7c88f00d9060262d94305983ee8","score":"0.5032036","text":"def __str__(self):\n return 'Article : {} by {}'.format(self.titre, self.auteur)","title":""},{"docid":"9f9034ac7410519e8a5a371541d9ce70","score":"0.50240815","text":"def deconstructing(self, text, target='ar'):\n import sagas\n source = 'en'\n options = {'get_pronounce', 'get_translations'}\n res, t = translate(text, source=source, target=target,\n trans_verbose=False, options=options)\n print('✁', '%s(%s %s)' % (text, res, ''.join(t.pronounce)))\n for sent in text.split(' '):\n res, t = translate(sent, source=source, target=target,\n trans_verbose=False, options=options)\n # print('%s(%s%s)' % (sent, res, marks_th(t.pronounce)), end=\" \")\n print('%s(%s%s)' % (sent, res, marks_th(t.pronounce)))\n sagas.print_df(t.translations)\n print('.')","title":""},{"docid":"a234a2088421e5e8528f670f7bb2840f","score":"0.5016896","text":"async def translate(self, message):\n term = message.regex.group(1)\n from_language = message.regex.group(2)\n to_language = message.regex.group(3)\n _dictionary = dict()\n languages_dict = {\n \"spanish\": \"es\",\n \"belorussian\": \"be\",\n \"bulgarian\": \"bg\",\n \"catalan\": \"cs\",\n \"czech\": \"cs\",\n \"german\": \"de\",\n \"english\": \"en\",\n \"french\": \"fr\",\n \"croatian\": \"hr\",\n \"italian\": \"it\",\n \"latin\": \"la\",\n \"macedonian\": \"mk\",\n \"dutch\": \"nl\",\n \"polish\": \"pl\",\n \"portuguese\": \"pt\",\n \"romanian\": \"ro\",\n \"russian\": \"ru\",\n \"slovak\": \"sk\",\n \"slovenian\": \"sl\",\n \"serbian\": \"sr\",\n \"ukrainian\": \"uk\",\n }\n entries = swadesh.entries(\n [\n languages_dict.get(from_language, \"english\"),\n languages_dict.get(to_language, \"english\"),\n ]\n )\n\n for word in entries:\n _word = word[0].split(\", \")\n if len(_word) > 1:\n _dictionary[_word[0]] = word[1]\n _dictionary[_word[1]] = word[1]\n else:\n _dictionary[word[0]] = word[1]\n\n translation = _dictionary.get(\n term, \"Sorry, I can't find the \" \"translation for that word :(\"\n )\n\n await message.respond(\n \"The {} word '{}' in {} is: {}\".format(\n from_language, term, to_language, translation\n )\n )","title":""},{"docid":"3fa1d234aa2c69063723546a35324229","score":"0.5016787","text":"def createPaste(content, language):","title":""},{"docid":"723a1d30e87eeef18ffd8510a0c53639","score":"0.5015891","text":"def fix_transclusion(page_content, title, labels, lang):\n page_content = page_content.splitlines()\n title = clean_title(title) # Remove subpage and namespace from title\n corrected_labels = {}\n edited = False\n\n for line in page_content:\n if title in line:\n index = page_content.index(line) # Remember index of each line\n\n # Case 1: HTML syntax for transclusion\n if line.startswith('<pages index'):\n for label in labels.keys():\n if label in line:\n # Check if transclusion template uses old label\n pattern = (r'(<pages index\\s?=\\s?\"?{}\"?\\s.*?fromsection'\n '\\s?=\\s?\"?)({}|.*)(\"?\\s?tosection\\s?=\\s?\"?)({}|.*)'\n '(\"?\\s?/>)'.format(re.escape(title), label, label))\n match = re.search(pattern, line)\n if match:\n # Replace old label(s) with new label\n line = (re.sub(r'([from|to]section\\s?=\\s?\"?){}'\n .format(label), r'\\1{}'.format(labels[label]),\n line, count=2))\n page_content[index] = line\n corrected_labels[label] = labels[label]\n edited = True\n del match\n\n # Case 2: Mediawiki syntax\n if line.startswith('{{#lst:') or line.startswith('{{#lstx'):\n for label in labels.keys():\n if label in line:\n pattern = (r'({{#lstx?:)(\\w+:)?({})(/\\d*)?([|]{})(}})'\n .format(title, label))\n match = re.search(pattern, line)\n if match:\n line = re.sub(pattern, r'\\1\\2\\3\\4|{}\\6'.format\n (labels[label]), line)\n page_content[index] = line\n corrected_labels[label] = labels[label]\n edited = True\n del match\n\n # Case 3: template used for transclusion\n if template[lang] and line.lower().startswith(template[lang][0]):\n for label in labels.keys():\n if label in line:\n pattern = (r'({}{})(/\\d*)?(.*?)([|])(\\w+)(\\s?=\\s?)({})'\n '(.*?}}$)'.format(template[lang][1], title, label))\n match = re.match(pattern, line)\n if match and match.group(5) in template[lang][2:]:\n line = (re.sub(pattern, r'\\1\\2\\3\\4\\5\\6{}\\8'.format\n (labels[label]), line))\n page_content[index] = line\n corrected_labels[label] = labels[label]\n edited = True\n del match\n\n page_content = '\\n'.join(page_content)\n if edited:\n return page_content, corrected_labels\n return None, None","title":""},{"docid":"757296335a6e4490f5224ef2e5c79a59","score":"0.50120014","text":"def opp_topic(article, nasari):\n\n sentences = []\n for p in article['body'][1:]:\n sents = nltk.sent_tokenize(p)\n sentences.append(sents)\n\n # newspaper\n news_opp = {'title': True,\n \"opps\": [(1, 1), (1, 2), (2, 1), (2, 2), (3, 1), (3, 2), (4, 1), (4, 2), (5, 1), (6, 1)]}\n\n # Wall Street Journal\n wsj_opp = {'title': True, \"opps\": [(1, 1), (1, 2)]}\n\n # \"opps\" corrisponde a (indice paragrafo, indice frase)\n # \"title\" = true se è necessario considerare anche il titolo\n\n vectors = []\n if article['genre'] == 'wsj':\n opp = wsj_opp\n elif article['genre'] == 'news':\n opp = news_opp\n else:\n opp = {'title': True, 'opps': []}\n\n if opp['title']:\n vectors = title_topic(article, nasari)\n for t in opp['opps']:\n p = t[0] # indice paragrafo\n s = t[1] # indice sentence\n if p < len(sentences) and s < len(sentences[p]):\n sent = sentences[t[0]][t[1]]\n vectors += create_vectors(sent, nasari)\n\n return vectors","title":""},{"docid":"a2a4dbbea65db6bf0618dd49457d0efe","score":"0.4997483","text":"def transform(self, doc):\n pass","title":""},{"docid":"2b54ad7adbef8b3966118598fd8e75a0","score":"0.49920186","text":"def article_row(self):\n return BPOArticle(\n record_id=self.record_id,\n date_time_stamp=self.date_time_stamp(),\n action_code=self.action_code(),\n record_title=self.record_title(),\n publication_id=self.publication_id(),\n publication_title=self.publication_title(),\n publication_qualifier=self.publication_qualifier(),\n publisher=self.publisher(),\n alpha_pub_date=self.alpha_pub_date(),\n numeric_pub_date=self.numeric_pub_date(),\n source_type=self.source_type(),\n object_type=self.object_type(),\n language_code=self.language_code(),\n issn=self.issn(),\n start_page=self.start_page(),\n end_page=self.end_page(),\n pagination=self.pagination(),\n url_doc_view=self.url_doc_view(),\n abstract=self.abstract(),\n text=self.full_text(),\n )","title":""},{"docid":"8577dd78748d5f3a7e4be94240b06fd3","score":"0.49861988","text":"def _work_from_adelaide(a_work: AdelaideWork) -> None:\n if len(a_work.title) > 100 or len(a_work.author_last) > 50:\n return\n if a_work.author_first and len(a_work.author_first) > 50:\n return\n author, _ = Author.objects.get_or_create(\n first_name=a_work.author_first,\n last_name=a_work.author_last\n )\n\n work, _ = Work.objects.update_or_create(\n title=a_work.title,\n author=author,\n defaults={\n 'translator': a_work.translator\n }\n )","title":""},{"docid":"1e85ce5d67a44bdf7efbed52a31e739e","score":"0.49851036","text":"def build_article(url=u''):\n url = url or '' # empty string precedence over None\n a = Article(url)\n return a","title":""},{"docid":"8fe03e35f15e8a80a5b17944da8c91f8","score":"0.49776417","text":"def copy_and_rewrite(input, output, translate):\n with open(input) as infile:\n with open(output, 'w+') as outfile:\n for line in infile:\n outfile.write(translate(line))","title":""},{"docid":"729bfa032282f12e3ae0f982e5ce10af","score":"0.49750164","text":"def test_translated_titles(self):\r\n pass","title":""},{"docid":"cb10d9c42a40d4d5659af48183217d80","score":"0.49716488","text":"def recreate_text_representation(candidate, entity_replacement = True, span_replacement = True, as_tokens=False , replace_conseq_entities = False, trim_text = False, window=0, lemmas = False, shortest_dep_path=False, nlp = None\n ):\n value = deepcopy(candidate)\n if lemmas:# and not shortest_dep_path:\n tokens = deepcopy(value['lemmas'])\n else:\n tokens = deepcopy(value['words'])\n# print '\\n','Initial indices:',value['gene_idx'],value['chem_idx']\n if entity_replacement:\n for i, t_type in enumerate(value['entity_types']):\n if t_type !=u'O':\n tokens[i] = str(t_type).upper()\n \n if shortest_dep_path:\n for idx in value['chem_idx']:\n tokens[idx] = \"ENTITY1\"\n for idx in value['gene_idx']:\n tokens[idx] = \"ENTITY2\"\n \n idcs_to_keep = get_shortest_dep_path(candidate['dep_parents'], candidate['chem_idx'][0], candidate['gene_idx'][0] )\n words = [tokens[i] for i in idcs_to_keep]\n return ' '.join(words)\n \n \n # if only_between, drop words before/after chem+gene\n if trim_text and not shortest_dep_path:\n start = min(value['gene_idx']+value['chem_idx'])\n start = max(0, start-window)\n \n stop = max(value['gene_idx']+value['chem_idx'])\n stop = stop + window\n \n# print 'sdas', start,stop\n \n keys_with_lists = ['entity_types', 'lemmas', 'pos_tags', 'words']\n \n #trim all lists containing lemmas etc.\n # TODO: fix issue that window also counts special characters.\n for key in keys_with_lists: \n value[key] = value[key][start:stop+1]\n tokens = tokens[start:stop+1]\n\n #replace indices to keep consistency \n value['chem_idx'][:] = [x - start for x in value['chem_idx']]\n value['gene_idx'][:] = [x - start for x in value['gene_idx']]\n \n# print 'new value indices:',value['chem_idx'],value['gene_idx']\n \n# print tokens\n #TODO: potentially replace with lemmas here maybe\n \n #replace span with ENTITY1, ENTITY2 --- destruction of list consistency after this point\n if span_replacement or shortest_dep_path:\n for idx in value['chem_idx']:\n tokens[idx] = \"ENTITY1\"\n for idx in value['gene_idx']:\n tokens[idx] = \"ENTITY2\"\n \n #convert whole span to 1 token\n idx_to_del = value['gene_idx'][1:]+value['chem_idx'][1:]\n # keep track of entity indices (merged & converted from list to int)\n gene_idx = shift_idx(value['gene_idx'][0], idx_to_del)\n chem_idx = shift_idx(value['chem_idx'][0], idx_to_del)\n for index in sorted(idx_to_del, reverse=True):\n del tokens[index]\n \n# if shortest_dep_path:\n# return get_shortest_dep_path(' '.join(tokens) , chem_idx, gene_idx, nlp)\n \n \n if replace_conseq_entities and not shortest_dep_path: \n tokens = map(itemgetter(0), groupby(tokens))\n \n# #shit after that\n# if return_idcs:\n# chem_idx = deepcopy(value['chem_idx'])\n# gene_idx = deepcopy(value['gene_idx'])\n \n# if (len(chem_idx)>1) and (gene_idx[0]<chem_idx[0]):\n# print ' '.join(tokens)\n# print 'l1',chem_idx\n# chem_idx = chem_idx[0] - len(gene_idx) - 1\n# print 'l2',tokens[chem_idx], chem_idx\n \n# elif (len(gene_idx)>1) and (chem_idx[0]<gene_idx[0]):\n# print ' '.join(tokens)\n# gene_idx = gene_idx[0] - len(chem_idx) - 1\n# print 'l3',gene_idx, tokens[gene_idx]\n \n \n# print tokens\n if as_tokens:\n return tokens\n elif shortest_dep_path:\n return (' '.join(tokens), chem_idx, gene_idx, \n #tokens[chem_idx], tokens[gene_idx] #debug/test\n )\n else:\n return ' '.join(tokens)","title":""},{"docid":"b66c9f7ed6327f7ef5b16cbcb7bee3e3","score":"0.49709746","text":"def normalize( self, article ):\r\n def getPossibleKeywords( line ):\r\n def normalizeKeyword( word ):\r\n def replaceFromTo( interWord, fromTo ):\r\n replFrom, to = fromTo\r\n return interWord.replace( replFrom, to )\r\n return reduce( replaceFromTo, self.wordReplacements, word )\r\n return ( normalizeKeyword( word ) for word in line.split( \" \" ) )\r\n return chain.from_iterable( ( getPossibleKeywords( line ) \r\n for line in article.lower().split( \"\\n\" ) ) )\r\n \r\n #=======================================================================\r\n # def extractKeywords( sent ):\r\n # def concatNamedEntities( sentTree ):\r\n # try:\r\n # tag = sentTree.node\r\n # except AttributeError:\r\n # word = sentTree[ 0 ]\r\n # return word\r\n # else:\r\n # if( tag == 'NE' ):\r\n # return \" \".join( ( name for name, tag in sentTree ) )\r\n # return ( concatNamedEntities( subTree ) for subTree in sentTree )\r\n # tokenized = nltk.word_tokenize( sent )\r\n # tagged = nltk.pos_tag( tokenized )\r\n # chunked = nltk.ne_chunk( tagged, binary=True )\r\n # return concatNamedEntities( chunked )\r\n # return [ keyword.strip().lower() \r\n # for sent in nltk.sent_tokenize( article )\r\n # for keyword in extractKeywords( sent ) ] \r\n #=======================================================================\r","title":""},{"docid":"6befb3faf5e324b2166b6488c08bbf0c","score":"0.49482128","text":"def translate(\n self,\n text='translation !',\n src='en',\n dest=['fr']\n ):\n if not isinstance(text, str) and not isinstance(text, unicode):\n raise(AttributeError(\n 'translate(text=) you must pass string of text to be'\n ' translated'))\n if str(src) not in self.languages:\n raise(AttributeError(\n 'translate(src=) passed language is not supported: ' + src))\n if not isinstance(dest, list):\n raise(AttributeError(\n 'translate(dest=) you must pass list of strings of supported'\n ' languages'))\n for dl in dest:\n if str(dl) not in self.languages:\n if self.fail_safe:\n return text\n else:\n raise(AttributeError(\n 'translate(dest=[]) passed language is not '\n 'supported: ' + str(dl)))\n if self.fail_safe:\n T = google_translator(service_urls=self.service_urls)\n parent = self\n\n class translatorC(object):\n def translate(self, text, dest, src):\n try:\n return T.translate(\n text=text,\n dest=dest,\n src=src\n )\n except Exception as error: # pragma: nocover\n parent.errors.append(error)\n return namedtuple('FailsafeTranlsation',\n ['text'])(text=text)\n translator = translatorC()\n else:\n translator = google_translator(service_urls=self.service_urls)\n if self.cache and text in self.STORAGE.keys():\n if len(dest) > 1:\n toReturn = {}\n for dl in dest:\n if dl in self.STORAGE[text].keys():\n toReturn[dl] = self.STORAGE[text][dl]\n else:\n toReturn[dl] = translator.translate(\n text,\n dl,\n src\n ).text\n if toReturn != self.STORAGE[text]:\n self.STORAGE[text] = toReturn\n self.cacheIt()\n return toReturn\n else:\n if dest[0] not in self.STORAGE[text].keys():\n toRetTra = translator.translate(\n text,\n dest[0],\n src\n ).text\n self.STORAGE[text][dest[0]] = toRetTra\n self.cacheIt()\n return toRetTra\n else:\n return self.STORAGE[text][dest[0]]\n else:\n toStore = {text: {\n src: text\n }}\n for dl in dest:\n translation = translator.translate(\n text,\n dl,\n src\n )\n toStore[text][dl] = translation.text\n if self.cache:\n self.STORAGE[text] = toStore[text]\n self.cacheIt()\n if len(dest) > 1:\n return toStore[text]\n else:\n return toStore[text][dest[0]]","title":""},{"docid":"9cdb2fe6b0433368b266998dc305b9c8","score":"0.49371663","text":"def wikipedia_articles(self, language=None):\n if language == None: language = flags.arg.language\n return self.wf.resource(\"articles@10.rec\",\n dir=corpora.wikidir(language),\n format=\"records/frame\")","title":""},{"docid":"15cd2fd404134c50d115c8ba3648cf6b","score":"0.49368352","text":"def eficas_translation(ts_file, new_ts_file, lang):\n dico_cata_to_label = {}\n dico_cata_to_telemac = {}\n header = '<?xml version=\"1.0\" encoding=\"utf-8\"?>'\n header += '<!DOCTYPE TS><TS version=\"1.1\" language=\"'+lang+'\">'\n header += '<context>\\n'\n header += ' <name>@default</name>\\n'\n\n end = '</context>\\n</TS>\\n'\n\n pattern_in = re.compile(r'^\\s*<source>(?P<ident>.*)</source>\\s*$')\n pattern_out = \\\n re.compile(r'^\\s*<translation>(?P<traduit>.*)</translation>\\s*$')\n pattern_in2 = \\\n re.compile(r'^\\s*<source2>(?P<ident>.*)</source2>\\s*$')\n pattern_out2 = \\\n re.compile(r'^\\s*<translation2>(?P<traduit>.*)</translation2>\\s*$')\n liste_maj = []\n liste_maj.append(('for h', 'for H'))\n liste_maj.append(('pour h', 'pour H'))\n liste_maj.append(('for u', 'for U'))\n liste_maj.append(('pour u', 'pour U'))\n liste_maj.append(('of k', 'of K'))\n liste_maj.append(('de k', 'de K'))\n liste_maj.append(('of h', 'of H'))\n liste_maj.append(('de h', 'de H'))\n liste_maj.append(('u and v', 'U and V'))\n liste_maj.append(('u et v', 'U et V'))\n liste_maj.append(('on h', 'on H'))\n liste_maj.append(('sur h', 'sur H'))\n liste_maj.append(('supg', 'SUPG'))\n liste_maj.append(('k and epsilon', 'K and Epsilon'))\n liste_maj.append(('k-epsilon', 'K-Epsilon'))\n liste_maj.append(('gmres', 'GMRES'))\n liste_maj.append(('cgstab', 'CGSTAB'))\n liste_maj.append(('q(z)', 'Q(Z)'))\n liste_maj.append(('z(q)', 'Z(Q)'))\n liste_maj.append(('wgs84', 'WGS84'))\n liste_maj.append(('wgs84', 'UTM'))\n liste_maj.append(('n-scheme', 'N-Scheme'))\n liste_maj.append(('scheme n', 'Scheme N'))\n liste_maj.append(('psi-scheme', 'PSI-Scheme'))\n liste_maj.append((' psi', ' PSI'))\n liste_maj.append(('f(t90)', 'F(T90)'))\n liste_maj.append(('(pa)', '(Pa)'))\n liste_maj.append(('h clipping', 'H clipping'))\n liste_maj.append(('delwaq', 'DELWAQ'))\n liste_maj.append(('tomawac', 'TOMAWAC'))\n liste_maj.append(('chezy', 'CHEZY'))\n liste_maj.append(('hllc', 'HLLC'))\n liste_maj.append(('c-u', 'C-U'))\n liste_maj.append(('c,u,v', 'C,U,V'))\n liste_maj.append(('h,u,v', 'H,U,V'))\n liste_maj.append(('previmer', 'PREVIMER'))\n liste_maj.append(('fes20xx', 'FES20XX'))\n liste_maj.append(('legos-nea', 'LEGOS-NEA'))\n liste_maj.append(('tpxo', 'TPXO'))\n liste_maj.append((' x', ' X'))\n liste_maj.append((' y', ' Y'))\n liste_maj.append(('waf', 'WAF'))\n liste_maj.append(('(w/kg)', '(W/kg)'))\n liste_maj.append(('(j/kg)', '(W/kg)'))\n liste_maj.append(('zokagoa', 'Zokagoa'))\n liste_maj.append(('nikuradse', 'Nikuradse'))\n liste_maj.append(('froude', 'Froude'))\n liste_maj.append(('gauss', 'Gauss'))\n liste_maj.append(('seidel', 'Seidel'))\n liste_maj.append(('leo', 'Leo'))\n liste_maj.append(('postma', 'Postma'))\n liste_maj.append(('crout', 'Crout'))\n liste_maj.append(('okada', 'Okada'))\n liste_maj.append(('jmj', 'JMJ'))\n liste_maj.append(('haaland', 'HAALAND'))\n liste_maj.append(('grad(u)', 'grad(U)'))\n liste_maj.append(('variable z', 'variable Z'))\n liste_maj.append(('variable r', 'variable R'))\n liste_maj.append(('ascii', 'ASCII'))\n\n with open(ts_file, 'r') as fobj:\n for ligne in fobj.readlines():\n if pattern_in.match(ligne):\n word = pattern_in.match(ligne)\n ident = word.group('ident')\n if pattern_out.match(ligne):\n word = pattern_out.match(ligne)\n traduit = word.group('traduit')\n dico_cata_to_telemac[ident] = traduit\n traduit_main = traduit.lower()\n for trad in liste_maj:\n traduit = traduit_main.replace(trad[0], trad[1])\n traduit_main = traduit\n chaine = traduit_main[0].upper() + traduit_main[1:]\n dico_cata_to_label[ident] = chaine\n if pattern_in2.match(ligne):\n word = pattern_in2.match(ligne)\n ident = word.group('ident')\n if pattern_out2.match(ligne):\n word = pattern_out2.match(ligne)\n traduit = word.group('traduit')\n dico_cata_to_telemac[ident] = traduit\n dico_cata_to_label[ident] = traduit\n\n with open(new_ts_file, 'w') as fobj:\n fobj.write(header)\n for k in dico_cata_to_telemac:\n text = \" <message>\\n <source>\"\n text += k\n text += \"</source>\\n <translation>\"\n text += dico_cata_to_label[k]\n text += \"</translation>\\n </message>\\n\"\n fobj.write(text)\n fobj.write(end)\n\n system(\"lrelease %s\"%new_ts_file)","title":""},{"docid":"746b05a1b817eadcd5a923c742eb96db","score":"0.49313417","text":"def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n fields = [field for field in trans_dict.keys() if field[1]==self.language]\n for f in fields:\n setattr(self,f[0],trans_dict[f])","title":""},{"docid":"5e8d8fa4943faa481c81ced373543ee4","score":"0.492742","text":"def __init__(\n self,\n orig_phrase: str = None,\n orig_phrase_norm: str = None,\n wiki_title: str = None,\n wiki_title_norm: str = None,\n score: int = 0,\n pageid: int = 0,\n description: str = None,\n relations: WikipediaPageExtractedRelations = None,\n ) -> None:\n self.orig_phrase = orig_phrase\n if orig_phrase_norm is None:\n self.orig_phrase_norm = StringUtils.normalize_str(orig_phrase)\n else:\n self.orig_phrase_norm = orig_phrase_norm\n\n self.wiki_title = wiki_title.replace(DISAMBIGUATION_TITLE, \"\")\n if wiki_title_norm is None:\n self.wiki_title_norm = StringUtils.normalize_str(wiki_title)\n else:\n self.wiki_title_norm = wiki_title_norm\n\n self.score = score\n self.pageid = int(pageid)\n self.description = description\n self.relations = relations","title":""},{"docid":"db368a434f063b1956ecabad3c4d0304","score":"0.49264848","text":"def prepare_articles(self, articles):\n\n prepared_articles = []\n\n article_non_insight_doi_list = get_non_insight_doi_list(articles, self.logger)\n\n remove_article_doi = []\n related_articles = []\n # Process or delete articles as required\n for article in articles:\n self.logger.info(article.doi + \" is type \" + article.article_type)\n if is_insight_article(article):\n self.set_related_article(\n article,\n articles,\n related_articles,\n article_non_insight_doi_list,\n remove_article_doi,\n )\n\n # Can remove articles now if required\n for article in articles:\n if article.doi not in remove_article_doi:\n prepared_articles.append(article)\n\n return prepared_articles","title":""},{"docid":"471b8b3024f3b274d78b19c79f7fdcbc","score":"0.49189648","text":"def translate(self, *args):\n return self.robot.translate(*args)","title":""},{"docid":"bf1ae554e847f03f324222e8286dcfe7","score":"0.4916338","text":"def post_article():\n json = request.get_json()\n\n public = json.get('public') is not False\n if not public:\n category = None\n else:\n category = json.get('category')\n if category is None:\n return {'category': '公开的文章必须属于某一个分类'}\n if not Category.exist(Category.id == category):\n return {'category': '该分类不存在'}\n\n id = json.get('id', '')\n if not re_match(app_config['ARTICLE_ID_PATTERN'], id):\n return {'id': app_config['ARTICLE_ID_DESCRIPTION']}\n\n title = json.get('title', '').strip()\n if not title:\n return {'title': '请输入有效的文章标题'}\n\n text_type = json.get('text_type', '')\n if not RendererCollection.does_support(text_type):\n return {'text_type': '抱歉，暂不支持该格式的文章'}\n\n source_text = json.get('source_text', '')\n try:\n rendered_text = RendererCollection.render(text_type, source_text)\n except RendererCollection.RenderedError as ex:\n return {'source_text': '渲染文章失败，错误信息如下：\\r\\n' + ex.message}\n\n is_commentable = json.get('is_commentable') is not False\n\n article = Article.create(id=id, title=title,\n text_type=text_type, source_text=source_text,\n content=rendered_text,\n is_commentable=is_commentable, public=public,\n category=category)\n\n event_emitted.send(\n current_app._get_current_object(),\n type='Article: Post',\n description='Author(%s) posted a new article(%s).'\n % (article.author_id, article.id)\n )\n\n return None, {'article': article.to_dict()}","title":""},{"docid":"4e51deb455832d94386a33e82d4295f9","score":"0.49146888","text":"def test_new_language_translation_creation(self):\n url = u\"/composers/translate/edit?appid=%s&srclang=all_ALL&editSelectedSourceButton=&targetlang=te_ST&srcgroup=ALL&targetgroup=ALL\" % (\n self.firstApp.unique_id)\n print \"URL: \" + url\n rv = self.flask_app.get(url)\n assert rv.status_code == 200\n\n data = rv.data.decode(\"utf8\") # This bypasses an apparent utf8 FlaskClient bug.\n\n # Ensure that the identifiers appear in the response.\n # (that is, that it appears to be translate-able)\n assert u\"hello_world\" in data\n assert u\"black\" in data","title":""},{"docid":"49d99ece8347c15a344d3da9e7ca4f85","score":"0.4914653","text":"def create_cms_article(user, item):\n if not (user.is_staff and user.has_perm('coop_cms.add_article')):\n raise PermissionDenied\n \n art = Article.objects.create(title=item.title, content=item.summary)\n item.processed = True\n item.save()\n return art","title":""},{"docid":"3260159d71d3277cedd8e5e5d22c261c","score":"0.49101502","text":"def prepare_nlu_text(example: Text, entities: List[Dict]):\n if not Utility.check_empty_string(example):\n if entities:\n from rasa.shared.nlu.training_data.formats.rasa_yaml import RasaYAMLWriter\n example = RasaYAMLWriter.generate_message({'text': example, \"entities\": entities})\n return example","title":""},{"docid":"1c3e8ab5b1a89528561c950371b1ee71","score":"0.4910095","text":"def __corpus_embedding_creator(self, input_data, original_corpus: bool):\n if original_corpus:\n input_data = self.__vectorizer.fit_transform(input_data)\n return self.__TfidfTransformer.fit_transform(input_data)","title":""},{"docid":"38098fac9ea41018f03fbede24b915b2","score":"0.49083048","text":"def tr2(jenni, input):\r\n if not input.group(2): return jenni.say(\"No input provided.\")\r\n command = input.group(2).encode('utf-8')\r\n\r\n def langcode(p):\r\n return p.startswith(':') and (2 < len(p) < 10) and p[1:].isalpha()\r\n\r\n args = ['auto', 'en']\r\n\r\n for i in xrange(2):\r\n if not ' ' in command: break\r\n prefix, cmd = command.split(' ', 1)\r\n if langcode(prefix):\r\n args[i] = prefix[1:]\r\n command = cmd\r\n phrase = command\r\n\r\n if (len(phrase) > 350) and (not input.admin):\r\n return jenni.reply('Phrase must be under 350 characters.')\r\n\r\n src, dest = args\r\n if src != dest:\r\n msg, src = translate(phrase, src, dest)\r\n if isinstance(msg, str):\r\n msg = msg.decode('utf-8')\r\n if msg:\r\n msg = web.decode(msg) # msg.replace(''', \"'\")\r\n msg = '\"%s\" (%s to %s, translate.google.com)' % (msg, src, dest)\r\n else: msg = 'The %s to %s translation failed, sorry!' % (src, dest)\r\n\r\n jenni.reply(msg)\r\n else: jenni.reply('Language guessing failed, so try suggesting one!')","title":""},{"docid":"8ebe6d8c3174edfd926555efcda93323","score":"0.49049714","text":"def blogYY_page_mod_article(article_id):\n article = search_article_by_id(article_id)[0]\n return render_template(\n template_name_or_list=\"blogYY/pg_add_article/aa.html\",\n title=article[\"title\"],\n content=article[\"content\"],\n category_id=article[\"category_id\"],\n submit_url=url_for('blogYY_api_mod_article_v1', article_id=article[\"id\"]),\n redirect_url=url_for('blogYY_page_single_article', article_id=article[\"id\"]),\n categories=search_categories()\n )","title":""},{"docid":"dfebab4aee9542abb1a0b4df6caafc9d","score":"0.48998842","text":"def from_translation(self, la):\n PW0 = self.realization_of().PW0()\n return self(PW0.from_translation(la))","title":""},{"docid":"03b8a88e0f8aad0aa039d738d179f724","score":"0.48955297","text":"def get_article_from_id(id, separator=\"\\n\\n\"):\n with open(DATA_PATH + \"articles/\" + id, \"r\") as article_data:\n article_data_map = json.loads(article_data.read())\n\n raw_contents = json.loads(article_data_map[CONTENT_FIELD])\n questions = get_questions(raw_contents)\n concatenated_content = \"\\n\".join([elem[\"content\"] for elem in raw_contents if \"Overview\" not in elem[\"content\"] and \"What do you think?\" not in elem[\"content\"]])\n\n if len(concatenated_content) < MIN_ARTICLE_LEN:\n return \"\", []\n else:\n return process_article(concatenated_content, separator), questions\n \n # article_data_map[\"ka_url\"] --> url\n # article_data_map[\"translated_title\"] --> title","title":""},{"docid":"f45c224980f651c704f1e0d255e6f3d6","score":"0.48883346","text":"def translation(translation):\n return np.array([\n [1, 0, translation[0]],\n [0, 1, translation[1]],\n [0, 0, 1]\n ])","title":""},{"docid":"fea5b4ed3520b6b8d1c9c97e301106c7","score":"0.48824593","text":"def copy(self, other_passage):\n other = Layer0(root=other_passage, attrib=self.attrib.copy())\n other.extra = self.extra.copy()\n for t in self._all:\n copied = other.add_terminal(t.text, t.punct, t.paragraph)\n copied.extra = t.extra.copy()","title":""},{"docid":"a5ec77161210b7a8564a94c9ed8ce336","score":"0.48805857","text":"def get_translate_entry(word, original_language, target_language):\n lookup = {\n \"cs\": \"czech\",\n \"da\": \"danish\",\n \"de\": \"german\",\n \"el\": \"greek\",\n \"es\": \"spanish\",\n \"et\": \"estonian\",\n \"en\": \"english\",\n \"fr\": \"french\",\n \"it\": \"italian\",\n \"nb\": \"norwegian\",\n \"nl\": \"dutch\",\n \"po\": \"polish\",\n \"pt\": \"portuguese\",\n \"sl\": \"slovene\",\n \"sv\": \"swedish\"\n }\n \n original_language = lookup[original_language[:2]]\n target_language = lookup[target_language[:2]]\n if original_language == 'german':\n word = sanitize_german(word)\n\n url = 'https://www.collinsdictionary.com/us/dictionary/%s-%s/%s' % (original_language, target_language, word)\n\n request = requests.get(url)\n if request.status_code == 200:\n return url\n return None","title":""},{"docid":"18adf592901cfcddbc73c541bfe49e5a","score":"0.48700637","text":"def __init__(\n self,\n es,\n es_index: str,\n nlp,\n source_description_field: str,\n target_title_field: str,\n target_description_field: str,\n target_alias_field: str = None,\n ):\n\n self.es = es\n self.es_index = es_index\n\n self.nlp = nlp\n\n self.source_fields = {\"description\": source_description_field}\n\n self.target_fields = {\n \"title\": target_title_field,\n \"description\": target_description_field,\n }\n\n if target_alias_field is not None:\n self.target_fields.update(\n {\n \"alias\": target_alias_field,\n }\n )","title":""},{"docid":"da4a84f375b1cbcd7d097aef1ca299d8","score":"0.48641682","text":"def create_article(title, content, title_image, article_session_id, is_draft, user_id, group_id, category_id, abstract, book_id, special_id=None):\n\n result = db_session.query(Article).filter_by(\n article_session_id=article_session_id).all()\n if (special_id is not None) and (is_draft == '0'):\n special = db_session.query(Special).filter_by(\n special_id=special_id).scalar()\n special.last_modified = datetime.now()\n db_session.commit()\n\n if len(result) > 0:\n article = db_session.query(Article).filter_by(\n article_session_id=article_session_id).scalar()\n article.title = title\n article.content = content\n article.picture = title_image\n article.time = datetime.now()\n article.is_draft = is_draft\n article.abstract = abstract\n article.book_id = book_id\n article.special_id = special_id\n article.category = category_id\n db_session.commit()\n return result[0].article_id\n else:\n article = Article(title=title, content=content, picture=title_image, time=datetime.now(), user_id=user_id, article_session_id=article_session_id,\n is_draft=is_draft, groups=group_id, category=category_id, abstract=abstract, book_id=book_id, special_id=special_id)\n db_session.add(article)\n db_session.commit()\n result = db_session.query(Article).filter_by(\n article_session_id=article_session_id).first()\n return result.article_id","title":""},{"docid":"e970b141f3bb85cbf571b456986fe474","score":"0.48639563","text":"def test_representation(self):\n self.assertEqual(\n str(self.article),\n \"Why Python is such a nice language 2020-01-01T12:00:00\"\n )","title":""},{"docid":"38e69a81337958542e6941b1d0c26a77","score":"0.48592383","text":"def translate(ctx, message):\r\n if ctx.args.lang() == \"en\":\r\n return message\r\n\r\n _tr = {}\r\n _tr[\" (general presentation)\"] = {\r\n \"fr\": \" (présentation générale)\"\r\n }\r\n _tr[\" (main menu)\"] = {\r\n \"fr\": \" (menu principal)\"\r\n }\r\n _tr[\"This command line interface uses the following patterns for comments definition:\"] = {\r\n \"fr\": \"Cette interface en ligne de commande utilise les motifs suivants pour la définition de commentaires :\"\r\n }\r\n _tr[\" is composed of the following menus:\"] = {\r\n \"fr\": \" est composé des menus suivants :\"\r\n }\r\n _tr[\" is composed of the following commands:\"] = {\r\n \"fr\": \" est composé des commandes suivantes :\"\r\n }\r\n _tr[\"Command Line Interface \"] = {\r\n \"fr\": \"Interface en ligne de commande (CLI) \"\r\n }\r\n _tr[\"Command Line Interface documentation\"] = {\r\n \"fr\": \"Documentation d'interface en ligne de commande (CLI)\"\r\n }\r\n _tr[\"Description:\"] = {\r\n \"fr\": \"Description :\"\r\n }\r\n _tr[\"float value\"] = {\r\n \"fr\": \"Valeur décimale\"\r\n }\r\n _tr[\"host value\"] = {\r\n \"fr\": \"Adresse réseau\"\r\n }\r\n _tr[\"int value\"] = {\r\n \"fr\": \"Valeur entière\"\r\n }\r\n _tr[\"Menu \"] = {\r\n \"fr\": \"Menu \"\r\n }\r\n _tr[\"No help available\"] = {\r\n \"fr\": \"Aucune aide disponible\"\r\n }\r\n _tr[\"Options:\"] = {\r\n \"fr\": \"Options :\"\r\n }\r\n _tr[\"Parameters:\"] = {\r\n \"fr\": \"Paramètres :\"\r\n }\r\n _tr[\"string value\"] = {\r\n \"fr\": \"Chaîne de caractères\"\r\n }\r\n _tr[\"Synopsis:\"] = {\r\n \"fr\": \"Synopsis :\"\r\n }\r\n _tr[\"This command opens the menu \"] = {\r\n \"fr\": \"Cette commande ouvre le menu \"\r\n }\r\n _tr[\".\"] = {\r\n \"fr\": \".\"\r\n }\r\n\r\n try:\r\n if ctx.args.lang() == \"fr\":\r\n import codecs\r\n _iso = codecs.lookup(\"utf-8\")\r\n return _iso.decode(_tr[message][\"fr\"])[0]\r\n except KeyError:\r\n pass\r\n modlog.log(ctx.MODLOG_FILTER, modlog.WARNING, \"No translation for '%s'\" % message)\r\n return message","title":""},{"docid":"0bea8baf61923235e4975dbd6d582b08","score":"0.48567662","text":"def get_Te(self, een):\n self.Te = deepcopy(een.Te)","title":""},{"docid":"80c5145269cb65714756e10333302156","score":"0.4852506","text":"def create_new():\n\n fa_plus = DRIVER.find_element_by_css_selector(\".fa-plus\")\n fa_plus.click()\n create_key = DRIVER.find_element_by_id(\"create-key\")\n add_editor = DRIVER.find_element_by_xpath(\"//*[@id='create-translation']/form/button[2]\")\n create_key.click()\n create_key.clear()\n create_key.send_keys(str(key))\n add_editor.click()\n create_summernote = DRIVER.find_element_by_xpath(\"\"\n \"//*[@id='create-translation']/form/div[2]/div/div[3]/div[2]/p\")\n create_summernote.click()\n create_summernote.clear()\n create_summernote.send_keys(str(new_en))\n DRIVER.find_element_by_xpath(\"//*[@id='create-translation']/form/button[1]\").click()","title":""},{"docid":"278780d85346dc50c37ea9b0e80697e3","score":"0.4850419","text":"def reconstruct_from_transformed(self, node, transformed_node):\n return transformed_node","title":""},{"docid":"c88bb8ae8d181aaa10ddec0ea2332ac4","score":"0.4849736","text":"def create_translate(cls, tx, ty=0.0):\n return cls(1.0, 0.0, 0.0, 1.0, tx, ty)","title":""}],"string":"[\n {\n \"docid\": \"a13a1997c353eade0d83338d86918ad0\",\n \"score\": \"0.6235401\",\n \"text\": \"def maketrans(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1713bd4c92bd95b2575278cf87f58e5\",\n \"score\": \"0.6148261\",\n \"text\": \"def create_translation_entry(self, sdoc: SourcedDocument):\\n sdoc.doc.id = self.current_art_id\\n self.current_art_id += 1\\n return {\\n \\\"document_id\\\": sdoc.doc.id,\\n \\\"document_collection\\\": self.collection,\\n \\\"source_doc_id\\\": sdoc.source_id,\\n \\\"md5\\\": self.get_md5(sdoc),\\n \\\"source\\\": sdoc.source,\\n \\\"date_inserted\\\": self.insertion_time\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb931f42342b9e84cd083cd74456296e\",\n \"score\": \"0.5992268\",\n \"text\": \"def test_translation_create(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d421119cd94a71e13507502066f20fc\",\n \"score\": \"0.5879826\",\n \"text\": \"def articles():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eee4b2fb584cc0c0d7d7070eed403f1\",\n \"score\": \"0.58373934\",\n \"text\": \"def create_article(article_name: str) -> PubMedArticle:\\n article_path = os.path.join(get_resources_path(), article_name)\\n xml_element = ET.parse(article_path).getroot()\\n return PubMedArticle(xml_element)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6b3a734818467c486e13de0fc3ed97a\",\n \"score\": \"0.5784804\",\n \"text\": \"def new_article_from_draft(draft):\\n new_article = Article( article_name = draft.article_name,\\n article_external_url = draft.article_external_url,\\n #name_author = draft.name_author,\\n article_description = draft.article_description,\\n article_body_text = draft.article_body_text,\\n article_caption_styles = draft.article_caption_styles,\\n article_type = draft.article_type,\\n )\\n\\n new_article.save()\\n old_authors = draft.name_author.all()\\n new_article.name_author = old_authors\\n new_article.save()\\n draft.delete()\\n print(old_authors)\\n print(new_article.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0781a54acf49cd1c9f903e93f931782d\",\n \"score\": \"0.5778184\",\n \"text\": \"def __init__(self, article):\\n\\t\\tself.title = article.title\\n\\t\\tself.date = article.date\\n\\t\\tself.image_url = article.image_url\\n\\t\\tself.description = (article.description[:140].strip() + '...') if len(article.description) > 140 else article.description\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f7ce12f7a4b8443a4a27f7573b12a98\",\n \"score\": \"0.5777499\",\n \"text\": \"def translate(self):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65ec7f4559add08dd5f16df54bdbd7be\",\n \"score\": \"0.5744597\",\n \"text\": \"def get_article(self):\\n\\n document_cursor = db.documents.find_one({\\\"url\\\": self.url})\\n if document_cursor:\\n document_id = document_cursor[\\\"_id\\\"]\\n else:\\n document_id = None\\n if document_id:\\n print(\\\"This article was already in the database\\\")\\n self.db_doc = db.documents.find_one({\\\"_id\\\": document_id})\\n self.title = self.db_doc[\\\"title\\\"]\\n self.authors = self.db_doc[\\\"authors\\\"]\\n self.text = self.db_doc[\\\"text\\\"]\\n self.publish_date = self.db_doc[\\\"publish-date\\\"]\\n return None\\n\\n self.article = Article(self.url)\\n grammar_mistakes = self.check_grammar(self.article.title)\\n\\n # try to download the article\\n try:\\n self.article.download()\\n except:\\n print(\\\"couldnt download\\\")\\n return None\\n\\n # try to parse the article\\n try:\\n self.article.parse()\\n except:\\n print(\\\"couldnt parse\\\")\\n return None\\n\\n # Try to use nlp on the article\\n try:\\n self.article.nlp()\\n except:\\n print(\\\"couldnt use nlp\\\")\\n return None\\n\\n self.title = self.article.title\\n self.authors = self.article.authors\\n self.text = self.article.text\\n self.publish_date = self.article.publish_date\\n print(self.title)\\n print(self.authors)\\n\\n #self.grammar_mistakes = self.check_grammar(self.text)\\n\\n # document_cursor = db.documents.find_one({\\\"url\\\": self.url})\\n # if document_cursor:\\n # document_id = document_cursor[\\\"_id\\\"]\\n # else:\\n # document_id = None\\n # if document_id:\\n\\n\\n document_id=self.add_article_to_db()\\n\\n for author in self.authors:\\n author_cursor = db.authors.find_one({\\\"author\\\":author})\\n\\n if author_cursor:\\n #author_id = author_cursor[\\\"_id\\\"]\\n if document_id != author_cursor[\\\"known_articles\\\"]:\\n db.authors.updateOne({\\\"author\\\":author},{\\\"known_articles\\\":author_cursor[\\\"known_articles\\\"].extend(document_id)})\\n else:\\n db.authors.insert_one({\\n \\\"author\\\": author,\\n \\\"known_articles\\\": [document_id]\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a1c7e2d6aa0e6481f4241985976d127\",\n \"score\": \"0.57442206\",\n \"text\": \"def translate(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42eb1c060fc36af5ecb57e846a562e0d\",\n \"score\": \"0.5712158\",\n \"text\": \"def _get_translated_body(self):\\r\\n # [START _get_translated_body]\\r\\n body = self._body\\r\\n url = self._url\\r\\n articleid = self._articleid\\r\\n translator = Translator()\\r\\n\\r\\n split_body_list = body.split('```')\\r\\n chunk_size = 2000\\r\\n prefix = '\\\\n\\\\n```'\\r\\n suffix = '```\\\\n\\\\n'\\r\\n banner = '**This article is an automatic translation of the article[' + articleid + '] below.\\\\n' + url +'**\\\\n\\\\n'\\r\\n translated_body = banner\\r\\n for i,element in enumerate(split_body_list):\\r\\n if i%2 == 0:\\r\\n if len(element) > chunk_size:\\r\\n chunk_list = [element[i: i+chunk_size] for i in range(0, len(element), chunk_size)]\\r\\n split_body_list[i] = ''\\r\\n for chunk in chunk_list:\\r\\n split_body_list[i] += translator.translate(chunk,dest='en').text\\r\\n else:\\r\\n split_body_list[i] = translator.translate(element,dest='en').text\\r\\n split_body_list[i] = self._delete_redundant_space(split_body_list[i])\\r\\n\\r\\n if i + 1 == len(split_body_list) or len(split_body_list) == 1:\\r\\n translated_body += split_body_list[i]\\r\\n else:\\r\\n translated_body += (split_body_list[i] + prefix)\\r\\n else:\\r\\n if i + 1 == len(split_body_list) or len(split_body_list) == 1:\\r\\n translated_body += split_body_list[i]\\r\\n else:\\r\\n translated_body += (split_body_list[i] + suffix)\\r\\n\\r\\n return translated_body\\r\\n # [END _get_translated_body]\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0c6154b0347a2e24d86f5eef022bcea\",\n \"score\": \"0.57097405\",\n \"text\": \"def create( self, trans, name, description=''):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"498aac8c9052e705e659ec0bd662c47e\",\n \"score\": \"0.570527\",\n \"text\": \"def from_text(cls, article_text):\\n finder = TemplateFinder(article_text)\\n find_re_daten = finder.get_positions(RE_DATEN)\\n find_re_abschnitt = finder.get_positions(RE_ABSCHNITT)\\n # only one start template can be present\\n if len(find_re_daten) + len(find_re_abschnitt) != 1:\\n raise ReDatenException(\\\"Article has the wrong structure. There must be one start template\\\")\\n if find_re_daten:\\n find_re_start = find_re_daten\\n else:\\n find_re_start = find_re_abschnitt\\n find_re_author = finder.get_positions(RE_AUTHOR)\\n # only one end template can be present\\n if len(find_re_author) != 1:\\n raise ReDatenException(\\\"Article has the wrong structure. There must one stop template\\\")\\n # the templates must have the right order\\n if find_re_start[0][\\\"pos\\\"][0] > find_re_author[0][\\\"pos\\\"][0]:\\n raise ReDatenException(\\\"Article has the wrong structure. Wrong order of templates.\\\")\\n # it can only exist text between the start and the end template.\\n if find_re_start[0][\\\"pos\\\"][0] != 0:\\n raise ReDatenException(\\\"Article has the wrong structure. There is text in front of the article.\\\")\\n if find_re_author[0][\\\"pos\\\"][1] != len(article_text):\\n raise ReDatenException(\\\"Article has the wrong structure. There is text after the article.\\\")\\n try:\\n re_start = TemplateHandler(find_re_start[0][\\\"text\\\"])\\n except TemplateHandlerException as error:\\n raise ReDatenException(\\\"Start-Template has the wrong structure.\\\") from error\\n try:\\n re_author = REAuthor.from_template(find_re_author[0][\\\"text\\\"])\\n except TemplateHandlerException as error:\\n raise ReDatenException(\\\"Author-Template has the wrong structure.\\\") from error\\n properties_dict = cls._extract_properties(re_start.parameters)\\n return Article(article_type=re_start.title,\\n re_daten_properties=properties_dict,\\n text=article_text[find_re_start[0][\\\"pos\\\"][1]:find_re_author[0][\\\"pos\\\"][0]]\\n .strip(),\\n author=re_author)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98d9fe2355e082271b658c53b5e7116d\",\n \"score\": \"0.56716543\",\n \"text\": \"def create_with_edition(self, title, author, text):\\n new_post = Post.objects.create(title=title, slug=slugify(title), author=author)\\n new_edition = Edition.objects.create(post=new_post, text=text)\\n return new_post\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f9fe0ee2cfa3fc398296ea9bc4fb5da\",\n \"score\": \"0.56290185\",\n \"text\": \"def show_teaser(article):\\n return {'article': article}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ce890269795b7d96d5e68f206c8eea9\",\n \"score\": \"0.55602896\",\n \"text\": \"def create_passage(passage, translation=None):\\n return {\\\"word\\\": passage, \\\"translation\\\": translation, \\\"next\\\": None}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dbdc8b6cc6011efe1403ccce2d743c9\",\n \"score\": \"0.5546388\",\n \"text\": \"def translate(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3035be04cef117c3c50b584bf4217696\",\n \"score\": \"0.5532673\",\n \"text\": \"def create_article(title, content_source=\\\"test article text\\\", days_in_past=0):\\n time = timezone.now() + datetime.timedelta(days=-days_in_past)\\n return Article.objects.create(title=title,\\n content_source=content_source,\\n pub_date=time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3035be04cef117c3c50b584bf4217696\",\n \"score\": \"0.5532673\",\n \"text\": \"def create_article(title, content_source=\\\"test article text\\\", days_in_past=0):\\n time = timezone.now() + datetime.timedelta(days=-days_in_past)\\n return Article.objects.create(title=title,\\n content_source=content_source,\\n pub_date=time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"952cf6de326a9ca9ffc38e0f83bfcdcc\",\n \"score\": \"0.55176383\",\n \"text\": \"def test_translation_review(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c807ca6e9be4d6ea371b88e1798e2f26\",\n \"score\": \"0.5507654\",\n \"text\": \"def mktranslation(self, obj, lang, **kwargs):\\n try:\\n obj.set_current_language(lang)\\n except:\\n try:\\n obj.translate(lang)\\n except IntegrityError:\\n pass\\n for k, v in kwargs.items():\\n setattr(obj, k, v)\\n obj.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e565fcd90318101682b4e2cb0206ff6e\",\n \"score\": \"0.5478009\",\n \"text\": \"def translate(text):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c386cb9e9f85767d400a8a35138283\",\n \"score\": \"0.5475836\",\n \"text\": \"def test_translated(self, client, root):\\n n1 = root.add(langslugs=dict(nl=\\\"a\\\", en=\\\"b\\\"))\\n n2 = root.add(langslugs=dict(en=\\\"a\\\", nl=\\\"b\\\"))\\n\\n\\n translation.activate('nl')\\n res = MainHandler.coerce(dict(instance=\\\"a\\\"))\\n assert res['instance'] == n1\\n\\n translation.activate('en')\\n res = MainHandler.coerce(dict(instance=\\\"a\\\"))\\n assert res['instance'] == n2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5451ed2cfbb4743e4531020f8687d6ee\",\n \"score\": \"0.54758334\",\n \"text\": \"def AddTranslator(self, translator):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93fedcf2b5572bb0d6def60f13b39a60\",\n \"score\": \"0.54429305\",\n \"text\": \"def test_translate(self):\\n translation = Translation(**gen_data())\\n translation.translate()\\n self.assertEqual('Bonjour le monde!', translation.output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe7c37513f4029e5fa00ab04dfc07b7c\",\n \"score\": \"0.54203105\",\n \"text\": \"def dbwriteArticle(self, a):\\n # add head + title\\n e = Element(\\\"article\\\", lang=self.language)\\n if self.root is None:\\n self.root = e\\n h = SubElement(e,\\\"articleinfo\\\")\\n t = SubElement(h, \\\"title\\\")\\n if a.caption:\\n t.text = a.caption\\n \\n # add a section and heading for this article \\n s = SubElement(e, \\\"section\\\")\\n si = SubElement(s, \\\"sectioninfo\\\")\\n h = SubElement(si, \\\"title\\\")\\n h.text = a.caption\\n e.writeto = s\\n return e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea28eaee3c5e5d899682c496d46683d3\",\n \"score\": \"0.5399459\",\n \"text\": \"def translate(obj, columns, content):\\n return super(FullTranscript, FullTranscript).translate(obj, columns, content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3324e622877655402c5cce76a616b1da\",\n \"score\": \"0.5385482\",\n \"text\": \"def _trans(self, event, old, new, user=None):\\n remark = u'{}: '.format(event) if event else u''\\n remark += u'{} >> {}'.format(old, new)\\n if user:\\n Note.objects.create(content_object=self,\\n remark=remark, is_history=True,\\n created_by=user)\\n else:\\n Note.objects.create(content_object=self,\\n remark=remark, is_history=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3941c6155910b98418a416b1b6829b9d\",\n \"score\": \"0.53554004\",\n \"text\": \"async def create_article(article: schemas.Article) -> schemas.Preview.Article:\\n return datasource.insert_article(article)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d941bb72e4db03c20fc8c101f163702\",\n \"score\": \"0.53433675\",\n \"text\": \"def test_article_manager_create_article(self):\\n article = {\\n 'source': {'id': 'news-com-au', 'name': 'News.com.au'},\\n 'author': 'unknown',\\n 'title': 'F1 British Grand Prix live: updates, results, starting grid, Vettel reacts to Ferrari sabotage '\\n 'questions',\\n 'description': 'The British Grand Prix has ended in incredible drama as the last lap went down to the '\\n 'wire with Lewis Hamilton winning after his tyre blew on the last lap.',\\n 'url': 'https://www.news.com.au/sport/motorsport/formula-one/live-updates-from-the-2020-british-grand'\\n '-prix/live-coverage/ba297f46d4e91321c092db9d3d5d2e1f',\\n 'urlToImage': 'https://content.api.news/v3/images/bin/2554ff2213b5c8a54e9809d310e697db',\\n 'publishedAt': '2020-08-02T22:04:07Z',\\n 'content': '...'\\n }\\n created = Article.objects.create_article(article)\\n self.assertEqual(article['source']['name'], created.source)\\n self.assertEqual('unknown', created.author)\\n self.assertEqual(article['title'], created.title)\\n self.assertEqual(article['description'], created.description)\\n self.assertEqual(article['url'], created.url)\\n self.assertEqual(parse_datetime(article['publishedAt']), created.published_at)\\n self.assertEqual('...', created.content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfaec30800c3c2b1a6ef3034f3424e48\",\n \"score\": \"0.5324116\",\n \"text\": \"def process_text(article_text,nlp):\\n \\n # lemmatize NOTE: NOT IMPLEMENTED YET...\\n # article_text = lemmatize(article_text,nlp)\\n \\n # run gensim preprocess\\n article_text = simple_preprocess(article_text, deacc=True)\\n \\n return article_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1e68f0dde9df87475243522dafb5906\",\n \"score\": \"0.531908\",\n \"text\": \"def translate(src_filename, dest_filename, dest_lang, src_lang='auto', specialwords_filename=''):\\n translator = Translator() # Initialize translator object\\n\\n with open(src_filename) as srcfile, open(dest_filename, 'w') as destfile:\\n\\n lines = srcfile.readlines()\\n specialwords_dict = {}\\n\\n # If special words file exists, place special word mappings into specialwords_dict\\n if specialwords_filename != '':\\n with yaml.load(open(specialwords_filename)) as specialwords_fulllist:\\n\\n # Gets source language if not passed through\\n if src_lang == 'auto':\\n src_lang == str(translator.detect(lines[0]))[14:16]\\n\\n # Attempts to add the correct dictionary of special words\\n try:\\n specialwords_dict = specialwords_dict_full[src_lang + '_' + dest_lang]\\n except KeyError:\\n print('Special words file doesn\\\\'t contain required language translation!')\\n\\n # Parses each line for special cases and ignores them when translating\\n for line in lines:\\n line = line.strip()\\n\\n # Parses for code blocks and ignores them entirely\\n if line.startswith(\\\"```\\\"):\\n line = line\\n\\n else:\\n # Parses for URL's and file links and ignores them\\n if line.find(\\\"[\\\") != -1 and line.find(\\\"]\\\") != -1 and line.find(\\\"(\\\") != -1 and line.find(\\\")\\\") != -1:\\n ignore_start = line.find(\\\"(\\\")\\n ignore_end = line.find(\\\")\\\")\\n head = replace(specialwords_dict,line[0:ignore_start])\\n tail = replace(specialwords_dict,line[ignore_end+1:])\\n head = translator.translate(head, dest_lang, src_lang).text\\n tail = translator.translate(tail, dest_lang, src_lang).text\\n line = head + line[ignore_start:ignore_end+1] + tail\\n\\n # Translates normally if there are no special cases\\n else:\\n line = translator.translate(line, dest_lang, src_lang).text\\n\\n # Write to destination file\\n destfile.write(line + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df24f5aa78e1413cf7ea1003b2180b20\",\n \"score\": \"0.53131425\",\n \"text\": \"def transform_article(article, featured_image=None, author=None):\\n article['image'] = featured_image\\n\\n article['author'] = author\\n\\n if 'date_gmt' in article:\\n article_gmt = article['date_gmt']\\n article_date = datetime.strptime(article_gmt, '%Y-%m-%dT%H:%M:%S')\\n article['date'] = article_date.strftime('%-d %B %Y')\\n\\n if 'excerpt' in article and 'rendered' in article['excerpt']:\\n article['excerpt']['raw'] = strip_excerpt(\\n article['excerpt']['rendered'])[:340]\\n\\n # If the excerpt doesn't end before 340 characters, add ellipsis\\n raw_article = article['excerpt']['raw']\\n # split at the last 3 characters\\n raw_article_start = raw_article[:-3]\\n raw_article_end = raw_article[-3:]\\n # for the last 3 characters replace any part of […]\\n raw_article_end = raw_article_end.replace('[', '')\\n raw_article_end = raw_article_end.replace('…', '')\\n raw_article_end = raw_article_end.replace(']', '')\\n # join it back up\\n article['excerpt']['raw'] = ''.join([\\n raw_article_start,\\n raw_article_end,\\n ' […]'])\\n\\n return article\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7a7c15911eb1004befc6a988f9e61ec\",\n \"score\": \"0.5284541\",\n \"text\": \"def generate(indef_arts, def_arts, spros, opros, nouns, templates, \\n lang, output_dir):\\n\\n outfile = open(output_dir, 'w')\\n french_vowels = ['i', 'u', 'o', 'a', 'h', 'e', 'é']\\n m_nouns, f_nouns = nouns['m'], nouns['f']\\n\\n for i, template in enumerate(templates): # for each template\\n template = template.split()\\n # words to keep\\n words_to_keep = [i for i, w in enumerate(template) if not w.startswith('[')]\\n # words to change\\n words_to_change = [i for i, w in enumerate(template) if w.startswith('[')]\\n for k, noun in enumerate(m_nouns):\\n m_sentence = ['*'] * len(template)\\n f_sentence = ['*'] * len(template)\\n for idx, w in enumerate(words_to_change):\\n word_to_change = template[w]\\n\\n if word_to_change == '[noun]':\\n m_sentence[w] = m_nouns[k]\\n f_sentence[w] = f_nouns[k] \\n\\n # handle the special def arts in french\\n if lang == 'fr' and template[w - 1] == '[def_art]':\\n # in our case, the def articles\\n # only comes before nouns\\n\\n if m_nouns[k][0].lower() in french_vowels:\\n\\n m_sentence[w - 1] = def_arts['special']\\n if f_nouns[k][0].lower() in french_vowels:\\n f_sentence[w - 1] = def_arts['special']\\n\\n elif word_to_change == '[indef_art]':\\n m_sentence[w] = indef_arts['m']\\n f_sentence[w] = indef_arts['f']\\n\\n elif word_to_change == '[def_art]':\\n m_sentence[w] = def_arts['m']\\n f_sentence[w] = def_arts['f']\\n\\n elif word_to_change == '[spro]':\\n m_sentence[w] = spros['m']\\n f_sentence[w] = spros['f']\\n\\n elif word_to_change == '[opro]':\\n m_sentence[w] = opros['m']\\n f_sentence[w] = opros['f']\\n\\n for w in words_to_keep:\\n m_sentence[w] = template[w]\\n f_sentence[w] = template[w]\\n\\n print(m_sentence)\\n print(f_sentence)\\n if lang == 'fr':\\n out_m_sentence = ' '.join(m_sentence).replace(\\\"l' \\\", \\\"l'\\\").replace(\\\"qu' \\\", \\\"qu'\\\")\\n out_f_sentence = ' '.join(f_sentence).replace(\\\"l' \\\", \\\"l'\\\").replace(\\\"qu' \\\", \\\"qu'\\\")\\n else:\\n out_m_sentence = ' '.join(m_sentence)\\n out_f_sentence = ' '.join(f_sentence)\\n\\n # src, trg, src_gender, trg_gender\\n outfile.write(out_m_sentence + '\\\\t' + out_m_sentence + '\\\\t' + 'M' + '\\\\t' + 'M')\\n outfile.write('\\\\n')\\n outfile.write(out_m_sentence + '\\\\t' + out_f_sentence + '\\\\t' + 'M' + '\\\\t' + 'F')\\n outfile.write('\\\\n')\\n outfile.write(out_f_sentence + '\\\\t' + out_m_sentence + '\\\\t' + 'F' + '\\\\t' + 'M')\\n outfile.write('\\\\n')\\n outfile.write(out_f_sentence + '\\\\t' + out_f_sentence + '\\\\t' + 'F'+ '\\\\t' + 'F')\\n outfile.write('\\\\n')\\n outfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02f5e98ef2163e40e2c249db4b6da5e6\",\n \"score\": \"0.52391344\",\n \"text\": \"def set_articles(articles):\\n global article_features\\n global article_M, article_M_inv, article_w\\n global article_b\\n article_features = articles\\n article_M = {a: np.identity(6) for a in article_features}\\n article_M_inv = {a: np.identity(6) for a in article_M}\\n article_b = {a: np.zeros((6, 1)) for a in article_features}\\n article_w = {a: np.zeros((6, 1)) for a in article_features}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cb133c8d1228b457f3ef81825e25fc1\",\n \"score\": \"0.5233303\",\n \"text\": \"def test_translation_update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ccd4989e2294898404f36c972ffa11f\",\n \"score\": \"0.52151895\",\n \"text\": \"def create_article(conn, article):\\n\\n global id_of_first_author_column;\\n\\n bibtex_id = \\\"\\\";\\n year = article['\\\"Year\\\"'];\\n title = article['\\\"Title\\\"'];\\n doi = article['\\\"DOI\\\"'];\\n pages = article['\\\"pages\\\"'];\\n\\n if pages == '\\\"\\\"':\\n first_page = -1;\\n last_page = -1;\\n else:\\n temp = pages.replace('\\\"', '').split(\\\"-\\\");\\n first_page = int(temp[0]);\\n last_page = int(temp[1]);\\n\\n first_page = first_page;\\n last_page = last_page;\\n\\n conference_id = get_conference_id(conn, year);\\n\\n record = (bibtex_id, conference_id, title,doi,first_page,last_page);\\n\\n sql = ''' INSERT INTO articles(bibtex_id,conference_id,title,doi,first_page,last_page)\\n VALUES(?,?,?,?,?,?) '''\\n cur = conn.cursor()\\n cur.execute(sql, record);\\n\\n article_id = cur.lastrowid;\\n\\n # Add all the authors\\n # Look for columns of authors\\n for i in range(article['\\\"Number of authours\\\"']):\\n fullname = article[id_of_first_author_column + i];\\n author_id = get_author_id(conn, fullname);\\n create_authorship(conn, author_id, article_id);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"918142d4eeab652be1e906c1ff1b31ef\",\n \"score\": \"0.52133614\",\n \"text\": \"def encode_article(self):\\n sentences_to_encode = []\\n encoded_article = [None]\\n sentence_list = [0]\\n sent_count = 0\\n\\n for sentence in self.article:\\n sent_count += len(sentence)\\n sentence_list.append(sent_count)\\n for sent in sentence:\\n sentences_to_encode.append(sent) # Split into sentences\\n\\n print('Loading models...'),\\n model = skipthoughts.LoadModel().load_model()\\n encoder = skipthoughts.Encoder(model)\\n print('Encoding sentences...'),\\n encoded_sentences = encoder.encode(sentences_to_encode, verbose=False)\\n print('Done')\\n\\n for i in range(len(self.article)):\\n begin = sentence_list[i]\\n end = sentence_list[i + 1]\\n encoded_article[i] = encoded_sentences[begin:end]\\n return encoded_article\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92cd298e1bd04079e7d9537f144d713d\",\n \"score\": \"0.519701\",\n \"text\": \"def translate(self, object_name):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13591fe16d4da1a8ee1fa62e7dcf94f6\",\n \"score\": \"0.5196361\",\n \"text\": \"def translate(full_text):\\r\\n translator = Translator()\\r\\n # breaking down text into small pieces for faster translation\\r\\n translations = []\\r\\n unique_elements = full_text.split('. ')\\r\\n\\r\\n for element in unique_elements:\\r\\n # Adding all the translations to a dictionary (translations)\\r\\n translations.append(translator.translate(element).text)\\r\\n # Final translated string\\r\\n english_translated_text = ''\\r\\n # joining all the converted chunks to final string\\r\\n for i in translations:\\r\\n english_translated_text = english_translated_text + ' ' + i\\r\\n # return the text for all translated chunks\\r\\n return english_translated_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e58eaef1fb281cc3cc4dbaa8838bd80a\",\n \"score\": \"0.51941335\",\n \"text\": \"def generate_translations(item):\\n fr_prefix = '(français) '\\n es_prefix = '(español) '\\n oldname = str(item.name)\\n item.name = {'en': oldname, 'fr': fr_prefix + oldname, 'es': es_prefix + oldname}\\n item.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e25581271f07e51b177ce8da17f66c\",\n \"score\": \"0.5183574\",\n \"text\": \"def article(self, article_id, text_format=None):\\n endpoint = 'articles/{}'.format(article_id)\\n params = {'text_format': text_format or self.response_format}\\n return self._make_request(path=endpoint, params_=params, public_api=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57702ac62d40140debabd0ee52d35ef1\",\n \"score\": \"0.5181046\",\n \"text\": \"def translational(self, x):\\n self.trans.copy_from(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4755e9ad4c4ed3c1a13c0fd703c0bf69\",\n \"score\": \"0.51672024\",\n \"text\": \"def translate(request, document_slug, document_locale, revision_id=None):\\n # TODO: Refactor this view into two views? (new, edit)\\n # That might help reduce the headache-inducing branchiness.\\n parent_doc = get_object_or_404(\\n Document, locale=settings.WIKI_DEFAULT_LANGUAGE, slug=document_slug)\\n user = request.user\\n\\n if not revision_id:\\n # HACK: Seems weird, but sticking the translate-to locale in a query\\n # param is the best way to avoid the MindTouch-legacy locale\\n # redirection logic.\\n document_locale = request.REQUEST.get('tolocale',\\n document_locale)\\n\\n # Set a \\\"Discard Changes\\\" page\\n discard_href = ''\\n\\n if settings.WIKI_DEFAULT_LANGUAGE == document_locale:\\n # Don't translate to the default language.\\n return HttpResponseRedirect(reverse(\\n 'wiki.edit_document', locale=settings.WIKI_DEFAULT_LANGUAGE,\\n args=[parent_doc.full_path]))\\n\\n if not parent_doc.is_localizable:\\n message = _lazy(u'You cannot translate this document.')\\n return render(request, 'handlers/400.html',\\n {'message': message}, status=400)\\n\\n if revision_id:\\n initial_rev = get_object_or_404(Revision, pk=revision_id)\\n\\n based_on_rev = get_current_or_latest_revision(parent_doc,\\n reviewed_only=False)\\n\\n disclose_description = bool(request.GET.get('opendescription'))\\n\\n try:\\n doc = parent_doc.translations.get(locale=document_locale)\\n slug_dict = _split_slug(doc.slug)\\n except Document.DoesNotExist:\\n doc = None\\n disclose_description = True\\n slug_dict = _split_slug(document_slug)\\n\\n # Find the \\\"real\\\" parent topic, which is its translation\\n try:\\n parent_topic_translated_doc = (\\n parent_doc.parent_topic.translations.get(\\n locale=document_locale))\\n slug_dict = _split_slug(\\n parent_topic_translated_doc.slug + '/' + slug_dict['specific'])\\n except:\\n pass\\n\\n user_has_doc_perm = ((not doc) or (doc and doc.allows_editing_by(user)))\\n user_has_rev_perm = ((not doc) or (doc and doc.allows_revision_by(user)))\\n if not user_has_doc_perm and not user_has_rev_perm:\\n # User has no perms, bye.\\n raise PermissionDenied\\n\\n doc_form = rev_form = None\\n\\n if user_has_doc_perm:\\n if doc:\\n # If there's an existing doc, populate form from it.\\n discard_href = doc.get_absolute_url()\\n doc.slug = slug_dict['specific']\\n doc_initial = _document_form_initial(doc)\\n else:\\n # If no existing doc, bring over the original title and slug.\\n discard_href = parent_doc.get_absolute_url()\\n doc_initial = {'title': based_on_rev.title,\\n 'slug': slug_dict['specific']}\\n doc_form = DocumentForm(initial=doc_initial)\\n\\n if user_has_rev_perm:\\n initial = {'based_on': based_on_rev.id, 'comment': '',\\n 'toc_depth': based_on_rev.toc_depth}\\n if revision_id:\\n initial.update(\\n content=Revision.objects.get(pk=revision_id).content)\\n elif not doc:\\n initial.update(content=based_on_rev.content)\\n instance = doc and get_current_or_latest_revision(doc)\\n rev_form = RevisionForm(instance=instance, initial=initial)\\n\\n if request.method == 'POST':\\n which_form = request.POST.get('form', 'both')\\n doc_form_invalid = False\\n\\n # Grab the posted slug value in case it's invalid\\n posted_slug = request.POST.get('slug', slug_dict['specific'])\\n destination_slug = _join_slug(slug_dict['parent_split'], posted_slug)\\n\\n if user_has_doc_perm and which_form in ['doc', 'both']:\\n disclose_description = True\\n post_data = request.POST.copy()\\n\\n post_data.update({'locale': document_locale})\\n post_data.update({'slug': destination_slug})\\n\\n doc_form = DocumentForm(post_data, instance=doc)\\n doc_form.instance.locale = document_locale\\n doc_form.instance.parent = parent_doc\\n if which_form == 'both':\\n # Sending a new copy of post so the slug change above\\n # doesn't cause problems during validation\\n rev_form = RevisionValidationForm(request.POST.copy())\\n rev_form.parent_slug = slug_dict['parent']\\n\\n # If we are submitting the whole form, we need to check that\\n # the Revision is valid before saving the Document.\\n if doc_form.is_valid() and (which_form == 'doc' or\\n rev_form.is_valid()):\\n rev_form = RevisionForm(post_data)\\n\\n if rev_form.is_valid():\\n doc = doc_form.save(parent_doc)\\n\\n if which_form == 'doc':\\n url = urlparams(reverse('wiki.edit_document',\\n args=[doc.full_path],\\n locale=doc.locale),\\n opendescription=1)\\n return HttpResponseRedirect(url)\\n else:\\n doc_form.data['slug'] = posted_slug\\n doc_form_invalid = True\\n else:\\n doc_form.data['slug'] = posted_slug\\n doc_form_invalid = True\\n\\n if doc and user_has_rev_perm and which_form in ['rev', 'both']:\\n post_data = request.POST.copy()\\n if not 'slug' in post_data:\\n post_data['slug'] = posted_slug\\n\\n rev_form = RevisionValidationForm(post_data)\\n rev_form.parent_slug = slug_dict['parent']\\n rev_form.instance.document = doc # for rev_form.clean()\\n\\n if rev_form.is_valid() and not doc_form_invalid:\\n # append final slug\\n post_data['slug'] = destination_slug\\n\\n # update the post data with the toc_depth of original\\n post_data['toc_depth'] = based_on_rev.toc_depth\\n\\n rev_form = RevisionForm(post_data)\\n rev_form.instance.document = doc # for rev_form.clean()\\n\\n if rev_form.is_valid():\\n _save_rev_and_notify(rev_form, request.user, doc)\\n url = reverse('wiki.document', args=[doc.full_path],\\n locale=doc.locale)\\n return HttpResponseRedirect(url)\\n\\n parent_split = _split_slug(parent_doc.slug)\\n\\n return render(request, 'wiki/translate.html',\\n {'parent': parent_doc, 'document': doc,\\n 'document_form': doc_form, 'revision_form': rev_form,\\n 'locale': document_locale, 'based_on': based_on_rev,\\n 'disclose_description': disclose_description,\\n 'discard_href': discard_href,\\n 'specific_slug': parent_split['specific'],\\n 'parent_slug': parent_split['parent']})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9516fe390c1b067efdae9355fe26c46\",\n \"score\": \"0.5167005\",\n \"text\": \"def from_translation(self, la):\\n E = self.realization_of()\\n return self((E.exp_lattice()(la),self.cartesian_factors()[1].one()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49360c8ed6f0ffc12ef12dce056eff7d\",\n \"score\": \"0.515288\",\n \"text\": \"def get_translate(self):\\n selection = self.selection.strip().strip(string.punctuation)\\n selection = selection.replace('_', ' ')\\n\\n # ToDo split CamelCase words\\n words = selection.split()\\n splitted = []\\n for w in words:\\n matches = re.finditer(\\n '.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)',\\n w)\\n splitted.append(' '.join([m.group(0) for m in matches]))\\n selection = ' '.join(splitted)\\n\\n payload = {'word': selection}\\n\\n twords = []\\n try:\\n resp = requests.get(\\n \\\"http://api.lingualeo.com/gettranslates\\\",\\n params=payload,\\n )\\n resp_json = resp.json()\\n twords = [\\n translation['value'].encode(\\\"utf-8\\\")\\n for translation in resp_json.get(\\\"translate\\\", ())\\n ]\\n error = resp_json.get('error_msg')\\n except requests.ConnectionError as err:\\n error = 'Connection error occurred: {}'.format(err)\\n except ValueError:\\n error = resp.text.encode(\\\"utf-8\\\")\\n except Exception as err:\\n error = 'Error occurred in translator module: {}'.format(err)\\n\\n res = Translate(\\n selection=selection,\\n error=error or None,\\n twords=twords,\\n )\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6af8e92f78dd27ec1f367cebce918a5f\",\n \"score\": \"0.5151055\",\n \"text\": \"def _add_article(self, article):\\n pmid = int(article[0].strip(' \\\\n').split('|')[0])\\n # Note: The 2: slicing makes title, abstract, and mesh list objects\\n # Had I only indexed into 2, then they would've all been str objs.\\n title, abstract, mesh = [data.strip(' \\\\n').split('|')[2:]\\n for data in article]\\n\\n mesh_terms = {\\n m.partition('!')[0].lower().strip().rstrip('*')\\n for m in mesh\\n } - {''} # Remove empty term\\n\\n # PMIDs of articles we're trying to predict MeSH terms for\\n self.articles.append(pmid)\\n # Title/abstract/MeSH terms of a cited article\\n self.citations[pmid]['title'] = ''.join(title)\\n self.citations[pmid]['abstract'] = ''.join(abstract)\\n self.citations[pmid]['mesh'] = mesh_terms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"699f6f94006d41db720fbbc76f34b715\",\n \"score\": \"0.5142341\",\n \"text\": \"def simple_backtranslate(original, lang_tmp = \\\"en\\\"):\\n \\n import textblob\\n t = textblob.blob.TextBlob(original)\\n translated = str(t.translate(from_lang = \\\"fr\\\", to = lang_tmp))\\n blob = textblob.blob.TextBlob(translated)\\n tr = blob.translate(from_lang = lang_tmp, to = \\\"fr\\\")\\n return str(tr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d11551d5bbfde57e2433fca0061852c7\",\n \"score\": \"0.5127855\",\n \"text\": \"def english_to_german(self,text):\\n translation = dict()\\n if len(text) == 0:\\n return text\\n translation = self.language_translator.translate(text, model_id='en-de').get_result()\\n translated_text = translation['translations'][0]['translation']\\n return translated_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12f14f0d8fcbce7a2a6617f4dfc0a030\",\n \"score\": \"0.5123375\",\n \"text\": \"def parse_article(self, response):\\n\\n # search for article title\\n title_parts = response.xpath('//div[@id=\\\"articlehead\\\"]/h1/text()').extract()\\n if len(title_parts) > 0:\\n title = ' '.join(set(title_parts)).encode('utf-8').strip()\\n else:\\n title = ''\\n\\n # search for article published date\\n datetime_element_parts = response.xpath('//small[@id=\\\"pubdate\\\"]/strong/text()').extract()\\n if len(datetime_element_parts) > 0:\\n datetime_iso_str = self.parse_published_datetime(datetime_element_parts)\\n else:\\n datetime_iso_str = ''\\n\\n # search for article intro text\\n article_intro_parts = response.xpath('//div[@id=\\\"intro\\\"]/strong/text()').extract()\\n article_intro = ' '.join([x.strip() for x in article_intro_parts]).strip()\\n\\n # search for article full text\\n article_full_text_fragments = response.xpath('//div[@id=\\\"articlebody\\\"]/descendant::p/descendant-or-self::*/text()').extract()\\n article_full_text = ' '.join([x.strip() for x in article_full_text_fragments]).strip()\\n\\n # reconstruct the url to the nicely rendered page\\n url_parts = response.url.split('/')\\n article_id = url_parts.pop()\\n url_parts.append('vrtnieuws')\\n url_parts.append(article_id)\\n url = '/'.join(url_parts)\\n\\n # now create an Article item, and return it. All Articles created during scraping can be written to an output file when the -o option is given.\\n article = Article()\\n article['url'] = url\\n article['intro'] = article_intro\\n article['title'] = title\\n article['published_at'] = datetime_iso_str\\n article['text'] = article_full_text\\n return article\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d00465bb3a9fa30feb95e3028c46af0d\",\n \"score\": \"0.5118378\",\n \"text\": \"def start_add_article(bot, update):\\n user_logger(update, \\\"starts add article\\\")\\n send(bot, chat_id=update.message.chat_id, text=\\\"Which is the link of the article you wish to add? (You can look for it with the Wikipedia bot @wiki) Or /cancel if you want\\\") \\n return ARTICLE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"044639e66b57a129923f2a5d02961011\",\n \"score\": \"0.50946444\",\n \"text\": \"def test_translation_show(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36e7ded4f3872c2354f7ad63396137a6\",\n \"score\": \"0.5087646\",\n \"text\": \"def from_translation(self, la):\\n return self((self.cartesian_factors()[0].one(),self.realization_of().exp_lattice()(la)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2118369c867a9e5212490d833408f0a3\",\n \"score\": \"0.5084515\",\n \"text\": \"def translate_many(obj, columns, contents):\\n return super(FullTranscript, FullTranscript).translate_many(obj, columns, contents)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2156af514f4b1cbe41a612259b2d4f4e\",\n \"score\": \"0.50663656\",\n \"text\": \"def getTranslation(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf3b7b98c76b4e0c621b6b1013862157\",\n \"score\": \"0.5062391\",\n \"text\": \"def __init__(self,src,tar,decoration,sentence):\\r\\n self.src = src\\r\\n self.tar = tar\\r\\n self.decoration = decoration\\r\\n self.triple = (src,decoration,tar)\\r\\n self.sentence = sentence\\r\\n # BIO annotation:\\r\\n src = \\\"\\\"\\r\\n tar = \\\"\\\"\\r\\n dec = \\\"\\\"\\r\\n for index,char in enumerate(self.src):\\r\\n if index == 0:\\r\\n src += char + \\\"_B-Verb\\\"\\r\\n else:\\r\\n src += char + \\\"_I-Verb\\\"\\r\\n for index,char in enumerate(self.tar):\\r\\n if index == 0:\\r\\n tar += char + \\\"_B-Obj\\\"\\r\\n else:\\r\\n tar += char + \\\"_I-Obj\\\"\\r\\n for index,char in enumerate(self.decoration):\\r\\n if index == 0:\\r\\n dec += char + \\\"_B-Dec\\\"\\r\\n else:\\r\\n dec += char + \\\"_I-Dec\\\"\\r\\n # replace the raw sentence:\\r\\n sentence = sentence.replace(self.src,src)\\r\\n sentence = sentence.replace(self.tar,tar)\\r\\n sentence = sentence.replace(self.decoration,dec)\\r\\n self.entityAnnotation = sentence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a8275e28c28e374da9bba90aabebc8f\",\n \"score\": \"0.5062032\",\n \"text\": \"def generate_old (self):\\r\\n vtopics = self.parse_input()\\r\\n for vtopic in vtopics:\\r\\n print(\\\"[* corpus-generate] Researching: [%s]\\\" % vtopic)\\r\\n vlinks = self.get_google_results(vtopic[\\\"explanation\\\"])\\r\\n \\r\\n # Extract article chunked into paragraphs.\\r\\n for vlink in vlinks:\\r\\n try:\\r\\n print(\\\"[* corpus-generate] Retrieve article for [%s]\\\" \\r\\n % vlink)\\r\\n varticle = get_article(vlink)\\r\\n\\r\\n # Add to output corpus\\r\\n for vpg in varticle: VCORPUS.append(\\r\\n [\\\" \\\".join(vpg), vtopic['name']])\\r\\n \\r\\n print(\\\"[* corpus-generate] Writing article(?\\\")\\r\\n write_article(varticle, vtopic['name'])\\r\\n \\r\\n except Exception as e:\\r\\n print(\\\"[* corpus-generate] Could not run!: \\\" + str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2575cb33586e5883a62cf17f33a00345\",\n \"score\": \"0.5061343\",\n \"text\": \"def sample_translate(text):\\n group = ConverseGroup(json=sample_data_source)\\n t = Translator(group)\\n return t.translate(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f804ec067dd2c047c5e472995388ba52\",\n \"score\": \"0.5043085\",\n \"text\": \"def setUp(self):\\n\\n self.new_article=Article('author','title','description','www.news.com','urltoimage','2020-04-03T10:09:32Z','content')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d76b7c88f00d9060262d94305983ee8\",\n \"score\": \"0.5032036\",\n \"text\": \"def __str__(self):\\n return 'Article : {} by {}'.format(self.titre, self.auteur)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f9034ac7410519e8a5a371541d9ce70\",\n \"score\": \"0.50240815\",\n \"text\": \"def deconstructing(self, text, target='ar'):\\n import sagas\\n source = 'en'\\n options = {'get_pronounce', 'get_translations'}\\n res, t = translate(text, source=source, target=target,\\n trans_verbose=False, options=options)\\n print('✁', '%s(%s %s)' % (text, res, ''.join(t.pronounce)))\\n for sent in text.split(' '):\\n res, t = translate(sent, source=source, target=target,\\n trans_verbose=False, options=options)\\n # print('%s(%s%s)' % (sent, res, marks_th(t.pronounce)), end=\\\" \\\")\\n print('%s(%s%s)' % (sent, res, marks_th(t.pronounce)))\\n sagas.print_df(t.translations)\\n print('.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a234a2088421e5e8528f670f7bb2840f\",\n \"score\": \"0.5016896\",\n \"text\": \"async def translate(self, message):\\n term = message.regex.group(1)\\n from_language = message.regex.group(2)\\n to_language = message.regex.group(3)\\n _dictionary = dict()\\n languages_dict = {\\n \\\"spanish\\\": \\\"es\\\",\\n \\\"belorussian\\\": \\\"be\\\",\\n \\\"bulgarian\\\": \\\"bg\\\",\\n \\\"catalan\\\": \\\"cs\\\",\\n \\\"czech\\\": \\\"cs\\\",\\n \\\"german\\\": \\\"de\\\",\\n \\\"english\\\": \\\"en\\\",\\n \\\"french\\\": \\\"fr\\\",\\n \\\"croatian\\\": \\\"hr\\\",\\n \\\"italian\\\": \\\"it\\\",\\n \\\"latin\\\": \\\"la\\\",\\n \\\"macedonian\\\": \\\"mk\\\",\\n \\\"dutch\\\": \\\"nl\\\",\\n \\\"polish\\\": \\\"pl\\\",\\n \\\"portuguese\\\": \\\"pt\\\",\\n \\\"romanian\\\": \\\"ro\\\",\\n \\\"russian\\\": \\\"ru\\\",\\n \\\"slovak\\\": \\\"sk\\\",\\n \\\"slovenian\\\": \\\"sl\\\",\\n \\\"serbian\\\": \\\"sr\\\",\\n \\\"ukrainian\\\": \\\"uk\\\",\\n }\\n entries = swadesh.entries(\\n [\\n languages_dict.get(from_language, \\\"english\\\"),\\n languages_dict.get(to_language, \\\"english\\\"),\\n ]\\n )\\n\\n for word in entries:\\n _word = word[0].split(\\\", \\\")\\n if len(_word) > 1:\\n _dictionary[_word[0]] = word[1]\\n _dictionary[_word[1]] = word[1]\\n else:\\n _dictionary[word[0]] = word[1]\\n\\n translation = _dictionary.get(\\n term, \\\"Sorry, I can't find the \\\" \\\"translation for that word :(\\\"\\n )\\n\\n await message.respond(\\n \\\"The {} word '{}' in {} is: {}\\\".format(\\n from_language, term, to_language, translation\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fa1d234aa2c69063723546a35324229\",\n \"score\": \"0.5016787\",\n \"text\": \"def createPaste(content, language):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"723a1d30e87eeef18ffd8510a0c53639\",\n \"score\": \"0.5015891\",\n \"text\": \"def fix_transclusion(page_content, title, labels, lang):\\n page_content = page_content.splitlines()\\n title = clean_title(title) # Remove subpage and namespace from title\\n corrected_labels = {}\\n edited = False\\n\\n for line in page_content:\\n if title in line:\\n index = page_content.index(line) # Remember index of each line\\n\\n # Case 1: HTML syntax for transclusion\\n if line.startswith('<pages index'):\\n for label in labels.keys():\\n if label in line:\\n # Check if transclusion template uses old label\\n pattern = (r'(<pages index\\\\s?=\\\\s?\\\"?{}\\\"?\\\\s.*?fromsection'\\n '\\\\s?=\\\\s?\\\"?)({}|.*)(\\\"?\\\\s?tosection\\\\s?=\\\\s?\\\"?)({}|.*)'\\n '(\\\"?\\\\s?/>)'.format(re.escape(title), label, label))\\n match = re.search(pattern, line)\\n if match:\\n # Replace old label(s) with new label\\n line = (re.sub(r'([from|to]section\\\\s?=\\\\s?\\\"?){}'\\n .format(label), r'\\\\1{}'.format(labels[label]),\\n line, count=2))\\n page_content[index] = line\\n corrected_labels[label] = labels[label]\\n edited = True\\n del match\\n\\n # Case 2: Mediawiki syntax\\n if line.startswith('{{#lst:') or line.startswith('{{#lstx'):\\n for label in labels.keys():\\n if label in line:\\n pattern = (r'({{#lstx?:)(\\\\w+:)?({})(/\\\\d*)?([|]{})(}})'\\n .format(title, label))\\n match = re.search(pattern, line)\\n if match:\\n line = re.sub(pattern, r'\\\\1\\\\2\\\\3\\\\4|{}\\\\6'.format\\n (labels[label]), line)\\n page_content[index] = line\\n corrected_labels[label] = labels[label]\\n edited = True\\n del match\\n\\n # Case 3: template used for transclusion\\n if template[lang] and line.lower().startswith(template[lang][0]):\\n for label in labels.keys():\\n if label in line:\\n pattern = (r'({}{})(/\\\\d*)?(.*?)([|])(\\\\w+)(\\\\s?=\\\\s?)({})'\\n '(.*?}}$)'.format(template[lang][1], title, label))\\n match = re.match(pattern, line)\\n if match and match.group(5) in template[lang][2:]:\\n line = (re.sub(pattern, r'\\\\1\\\\2\\\\3\\\\4\\\\5\\\\6{}\\\\8'.format\\n (labels[label]), line))\\n page_content[index] = line\\n corrected_labels[label] = labels[label]\\n edited = True\\n del match\\n\\n page_content = '\\\\n'.join(page_content)\\n if edited:\\n return page_content, corrected_labels\\n return None, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"757296335a6e4490f5224ef2e5c79a59\",\n \"score\": \"0.50120014\",\n \"text\": \"def opp_topic(article, nasari):\\n\\n sentences = []\\n for p in article['body'][1:]:\\n sents = nltk.sent_tokenize(p)\\n sentences.append(sents)\\n\\n # newspaper\\n news_opp = {'title': True,\\n \\\"opps\\\": [(1, 1), (1, 2), (2, 1), (2, 2), (3, 1), (3, 2), (4, 1), (4, 2), (5, 1), (6, 1)]}\\n\\n # Wall Street Journal\\n wsj_opp = {'title': True, \\\"opps\\\": [(1, 1), (1, 2)]}\\n\\n # \\\"opps\\\" corrisponde a (indice paragrafo, indice frase)\\n # \\\"title\\\" = true se è necessario considerare anche il titolo\\n\\n vectors = []\\n if article['genre'] == 'wsj':\\n opp = wsj_opp\\n elif article['genre'] == 'news':\\n opp = news_opp\\n else:\\n opp = {'title': True, 'opps': []}\\n\\n if opp['title']:\\n vectors = title_topic(article, nasari)\\n for t in opp['opps']:\\n p = t[0] # indice paragrafo\\n s = t[1] # indice sentence\\n if p < len(sentences) and s < len(sentences[p]):\\n sent = sentences[t[0]][t[1]]\\n vectors += create_vectors(sent, nasari)\\n\\n return vectors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2a4dbbea65db6bf0618dd49457d0efe\",\n \"score\": \"0.4997483\",\n \"text\": \"def transform(self, doc):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b54ad7adbef8b3966118598fd8e75a0\",\n \"score\": \"0.49920186\",\n \"text\": \"def article_row(self):\\n return BPOArticle(\\n record_id=self.record_id,\\n date_time_stamp=self.date_time_stamp(),\\n action_code=self.action_code(),\\n record_title=self.record_title(),\\n publication_id=self.publication_id(),\\n publication_title=self.publication_title(),\\n publication_qualifier=self.publication_qualifier(),\\n publisher=self.publisher(),\\n alpha_pub_date=self.alpha_pub_date(),\\n numeric_pub_date=self.numeric_pub_date(),\\n source_type=self.source_type(),\\n object_type=self.object_type(),\\n language_code=self.language_code(),\\n issn=self.issn(),\\n start_page=self.start_page(),\\n end_page=self.end_page(),\\n pagination=self.pagination(),\\n url_doc_view=self.url_doc_view(),\\n abstract=self.abstract(),\\n text=self.full_text(),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8577dd78748d5f3a7e4be94240b06fd3\",\n \"score\": \"0.49861988\",\n \"text\": \"def _work_from_adelaide(a_work: AdelaideWork) -> None:\\n if len(a_work.title) > 100 or len(a_work.author_last) > 50:\\n return\\n if a_work.author_first and len(a_work.author_first) > 50:\\n return\\n author, _ = Author.objects.get_or_create(\\n first_name=a_work.author_first,\\n last_name=a_work.author_last\\n )\\n\\n work, _ = Work.objects.update_or_create(\\n title=a_work.title,\\n author=author,\\n defaults={\\n 'translator': a_work.translator\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e85ce5d67a44bdf7efbed52a31e739e\",\n \"score\": \"0.49851036\",\n \"text\": \"def build_article(url=u''):\\n url = url or '' # empty string precedence over None\\n a = Article(url)\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fe03e35f15e8a80a5b17944da8c91f8\",\n \"score\": \"0.49776417\",\n \"text\": \"def copy_and_rewrite(input, output, translate):\\n with open(input) as infile:\\n with open(output, 'w+') as outfile:\\n for line in infile:\\n outfile.write(translate(line))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729bfa032282f12e3ae0f982e5ce10af\",\n \"score\": \"0.49750164\",\n \"text\": \"def test_translated_titles(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb10d9c42a40d4d5659af48183217d80\",\n \"score\": \"0.49716488\",\n \"text\": \"def recreate_text_representation(candidate, entity_replacement = True, span_replacement = True, as_tokens=False , replace_conseq_entities = False, trim_text = False, window=0, lemmas = False, shortest_dep_path=False, nlp = None\\n ):\\n value = deepcopy(candidate)\\n if lemmas:# and not shortest_dep_path:\\n tokens = deepcopy(value['lemmas'])\\n else:\\n tokens = deepcopy(value['words'])\\n# print '\\\\n','Initial indices:',value['gene_idx'],value['chem_idx']\\n if entity_replacement:\\n for i, t_type in enumerate(value['entity_types']):\\n if t_type !=u'O':\\n tokens[i] = str(t_type).upper()\\n \\n if shortest_dep_path:\\n for idx in value['chem_idx']:\\n tokens[idx] = \\\"ENTITY1\\\"\\n for idx in value['gene_idx']:\\n tokens[idx] = \\\"ENTITY2\\\"\\n \\n idcs_to_keep = get_shortest_dep_path(candidate['dep_parents'], candidate['chem_idx'][0], candidate['gene_idx'][0] )\\n words = [tokens[i] for i in idcs_to_keep]\\n return ' '.join(words)\\n \\n \\n # if only_between, drop words before/after chem+gene\\n if trim_text and not shortest_dep_path:\\n start = min(value['gene_idx']+value['chem_idx'])\\n start = max(0, start-window)\\n \\n stop = max(value['gene_idx']+value['chem_idx'])\\n stop = stop + window\\n \\n# print 'sdas', start,stop\\n \\n keys_with_lists = ['entity_types', 'lemmas', 'pos_tags', 'words']\\n \\n #trim all lists containing lemmas etc.\\n # TODO: fix issue that window also counts special characters.\\n for key in keys_with_lists: \\n value[key] = value[key][start:stop+1]\\n tokens = tokens[start:stop+1]\\n\\n #replace indices to keep consistency \\n value['chem_idx'][:] = [x - start for x in value['chem_idx']]\\n value['gene_idx'][:] = [x - start for x in value['gene_idx']]\\n \\n# print 'new value indices:',value['chem_idx'],value['gene_idx']\\n \\n# print tokens\\n #TODO: potentially replace with lemmas here maybe\\n \\n #replace span with ENTITY1, ENTITY2 --- destruction of list consistency after this point\\n if span_replacement or shortest_dep_path:\\n for idx in value['chem_idx']:\\n tokens[idx] = \\\"ENTITY1\\\"\\n for idx in value['gene_idx']:\\n tokens[idx] = \\\"ENTITY2\\\"\\n \\n #convert whole span to 1 token\\n idx_to_del = value['gene_idx'][1:]+value['chem_idx'][1:]\\n # keep track of entity indices (merged & converted from list to int)\\n gene_idx = shift_idx(value['gene_idx'][0], idx_to_del)\\n chem_idx = shift_idx(value['chem_idx'][0], idx_to_del)\\n for index in sorted(idx_to_del, reverse=True):\\n del tokens[index]\\n \\n# if shortest_dep_path:\\n# return get_shortest_dep_path(' '.join(tokens) , chem_idx, gene_idx, nlp)\\n \\n \\n if replace_conseq_entities and not shortest_dep_path: \\n tokens = map(itemgetter(0), groupby(tokens))\\n \\n# #shit after that\\n# if return_idcs:\\n# chem_idx = deepcopy(value['chem_idx'])\\n# gene_idx = deepcopy(value['gene_idx'])\\n \\n# if (len(chem_idx)>1) and (gene_idx[0]<chem_idx[0]):\\n# print ' '.join(tokens)\\n# print 'l1',chem_idx\\n# chem_idx = chem_idx[0] - len(gene_idx) - 1\\n# print 'l2',tokens[chem_idx], chem_idx\\n \\n# elif (len(gene_idx)>1) and (chem_idx[0]<gene_idx[0]):\\n# print ' '.join(tokens)\\n# gene_idx = gene_idx[0] - len(chem_idx) - 1\\n# print 'l3',gene_idx, tokens[gene_idx]\\n \\n \\n# print tokens\\n if as_tokens:\\n return tokens\\n elif shortest_dep_path:\\n return (' '.join(tokens), chem_idx, gene_idx, \\n #tokens[chem_idx], tokens[gene_idx] #debug/test\\n )\\n else:\\n return ' '.join(tokens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b66c9f7ed6327f7ef5b16cbcb7bee3e3\",\n \"score\": \"0.49709746\",\n \"text\": \"def normalize( self, article ):\\r\\n def getPossibleKeywords( line ):\\r\\n def normalizeKeyword( word ):\\r\\n def replaceFromTo( interWord, fromTo ):\\r\\n replFrom, to = fromTo\\r\\n return interWord.replace( replFrom, to )\\r\\n return reduce( replaceFromTo, self.wordReplacements, word )\\r\\n return ( normalizeKeyword( word ) for word in line.split( \\\" \\\" ) )\\r\\n return chain.from_iterable( ( getPossibleKeywords( line ) \\r\\n for line in article.lower().split( \\\"\\\\n\\\" ) ) )\\r\\n \\r\\n #=======================================================================\\r\\n # def extractKeywords( sent ):\\r\\n # def concatNamedEntities( sentTree ):\\r\\n # try:\\r\\n # tag = sentTree.node\\r\\n # except AttributeError:\\r\\n # word = sentTree[ 0 ]\\r\\n # return word\\r\\n # else:\\r\\n # if( tag == 'NE' ):\\r\\n # return \\\" \\\".join( ( name for name, tag in sentTree ) )\\r\\n # return ( concatNamedEntities( subTree ) for subTree in sentTree )\\r\\n # tokenized = nltk.word_tokenize( sent )\\r\\n # tagged = nltk.pos_tag( tokenized )\\r\\n # chunked = nltk.ne_chunk( tagged, binary=True )\\r\\n # return concatNamedEntities( chunked )\\r\\n # return [ keyword.strip().lower() \\r\\n # for sent in nltk.sent_tokenize( article )\\r\\n # for keyword in extractKeywords( sent ) ] \\r\\n #=======================================================================\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6befb3faf5e324b2166b6488c08bbf0c\",\n \"score\": \"0.49482128\",\n \"text\": \"def translate(\\n self,\\n text='translation !',\\n src='en',\\n dest=['fr']\\n ):\\n if not isinstance(text, str) and not isinstance(text, unicode):\\n raise(AttributeError(\\n 'translate(text=) you must pass string of text to be'\\n ' translated'))\\n if str(src) not in self.languages:\\n raise(AttributeError(\\n 'translate(src=) passed language is not supported: ' + src))\\n if not isinstance(dest, list):\\n raise(AttributeError(\\n 'translate(dest=) you must pass list of strings of supported'\\n ' languages'))\\n for dl in dest:\\n if str(dl) not in self.languages:\\n if self.fail_safe:\\n return text\\n else:\\n raise(AttributeError(\\n 'translate(dest=[]) passed language is not '\\n 'supported: ' + str(dl)))\\n if self.fail_safe:\\n T = google_translator(service_urls=self.service_urls)\\n parent = self\\n\\n class translatorC(object):\\n def translate(self, text, dest, src):\\n try:\\n return T.translate(\\n text=text,\\n dest=dest,\\n src=src\\n )\\n except Exception as error: # pragma: nocover\\n parent.errors.append(error)\\n return namedtuple('FailsafeTranlsation',\\n ['text'])(text=text)\\n translator = translatorC()\\n else:\\n translator = google_translator(service_urls=self.service_urls)\\n if self.cache and text in self.STORAGE.keys():\\n if len(dest) > 1:\\n toReturn = {}\\n for dl in dest:\\n if dl in self.STORAGE[text].keys():\\n toReturn[dl] = self.STORAGE[text][dl]\\n else:\\n toReturn[dl] = translator.translate(\\n text,\\n dl,\\n src\\n ).text\\n if toReturn != self.STORAGE[text]:\\n self.STORAGE[text] = toReturn\\n self.cacheIt()\\n return toReturn\\n else:\\n if dest[0] not in self.STORAGE[text].keys():\\n toRetTra = translator.translate(\\n text,\\n dest[0],\\n src\\n ).text\\n self.STORAGE[text][dest[0]] = toRetTra\\n self.cacheIt()\\n return toRetTra\\n else:\\n return self.STORAGE[text][dest[0]]\\n else:\\n toStore = {text: {\\n src: text\\n }}\\n for dl in dest:\\n translation = translator.translate(\\n text,\\n dl,\\n src\\n )\\n toStore[text][dl] = translation.text\\n if self.cache:\\n self.STORAGE[text] = toStore[text]\\n self.cacheIt()\\n if len(dest) > 1:\\n return toStore[text]\\n else:\\n return toStore[text][dest[0]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cdb2fe6b0433368b266998dc305b9c8\",\n \"score\": \"0.49371663\",\n \"text\": \"def wikipedia_articles(self, language=None):\\n if language == None: language = flags.arg.language\\n return self.wf.resource(\\\"articles@10.rec\\\",\\n dir=corpora.wikidir(language),\\n format=\\\"records/frame\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15cd2fd404134c50d115c8ba3648cf6b\",\n \"score\": \"0.49368352\",\n \"text\": \"def eficas_translation(ts_file, new_ts_file, lang):\\n dico_cata_to_label = {}\\n dico_cata_to_telemac = {}\\n header = '<?xml version=\\\"1.0\\\" encoding=\\\"utf-8\\\"?>'\\n header += '<!DOCTYPE TS><TS version=\\\"1.1\\\" language=\\\"'+lang+'\\\">'\\n header += '<context>\\\\n'\\n header += ' <name>@default</name>\\\\n'\\n\\n end = '</context>\\\\n</TS>\\\\n'\\n\\n pattern_in = re.compile(r'^\\\\s*<source>(?P<ident>.*)</source>\\\\s*$')\\n pattern_out = \\\\\\n re.compile(r'^\\\\s*<translation>(?P<traduit>.*)</translation>\\\\s*$')\\n pattern_in2 = \\\\\\n re.compile(r'^\\\\s*<source2>(?P<ident>.*)</source2>\\\\s*$')\\n pattern_out2 = \\\\\\n re.compile(r'^\\\\s*<translation2>(?P<traduit>.*)</translation2>\\\\s*$')\\n liste_maj = []\\n liste_maj.append(('for h', 'for H'))\\n liste_maj.append(('pour h', 'pour H'))\\n liste_maj.append(('for u', 'for U'))\\n liste_maj.append(('pour u', 'pour U'))\\n liste_maj.append(('of k', 'of K'))\\n liste_maj.append(('de k', 'de K'))\\n liste_maj.append(('of h', 'of H'))\\n liste_maj.append(('de h', 'de H'))\\n liste_maj.append(('u and v', 'U and V'))\\n liste_maj.append(('u et v', 'U et V'))\\n liste_maj.append(('on h', 'on H'))\\n liste_maj.append(('sur h', 'sur H'))\\n liste_maj.append(('supg', 'SUPG'))\\n liste_maj.append(('k and epsilon', 'K and Epsilon'))\\n liste_maj.append(('k-epsilon', 'K-Epsilon'))\\n liste_maj.append(('gmres', 'GMRES'))\\n liste_maj.append(('cgstab', 'CGSTAB'))\\n liste_maj.append(('q(z)', 'Q(Z)'))\\n liste_maj.append(('z(q)', 'Z(Q)'))\\n liste_maj.append(('wgs84', 'WGS84'))\\n liste_maj.append(('wgs84', 'UTM'))\\n liste_maj.append(('n-scheme', 'N-Scheme'))\\n liste_maj.append(('scheme n', 'Scheme N'))\\n liste_maj.append(('psi-scheme', 'PSI-Scheme'))\\n liste_maj.append((' psi', ' PSI'))\\n liste_maj.append(('f(t90)', 'F(T90)'))\\n liste_maj.append(('(pa)', '(Pa)'))\\n liste_maj.append(('h clipping', 'H clipping'))\\n liste_maj.append(('delwaq', 'DELWAQ'))\\n liste_maj.append(('tomawac', 'TOMAWAC'))\\n liste_maj.append(('chezy', 'CHEZY'))\\n liste_maj.append(('hllc', 'HLLC'))\\n liste_maj.append(('c-u', 'C-U'))\\n liste_maj.append(('c,u,v', 'C,U,V'))\\n liste_maj.append(('h,u,v', 'H,U,V'))\\n liste_maj.append(('previmer', 'PREVIMER'))\\n liste_maj.append(('fes20xx', 'FES20XX'))\\n liste_maj.append(('legos-nea', 'LEGOS-NEA'))\\n liste_maj.append(('tpxo', 'TPXO'))\\n liste_maj.append((' x', ' X'))\\n liste_maj.append((' y', ' Y'))\\n liste_maj.append(('waf', 'WAF'))\\n liste_maj.append(('(w/kg)', '(W/kg)'))\\n liste_maj.append(('(j/kg)', '(W/kg)'))\\n liste_maj.append(('zokagoa', 'Zokagoa'))\\n liste_maj.append(('nikuradse', 'Nikuradse'))\\n liste_maj.append(('froude', 'Froude'))\\n liste_maj.append(('gauss', 'Gauss'))\\n liste_maj.append(('seidel', 'Seidel'))\\n liste_maj.append(('leo', 'Leo'))\\n liste_maj.append(('postma', 'Postma'))\\n liste_maj.append(('crout', 'Crout'))\\n liste_maj.append(('okada', 'Okada'))\\n liste_maj.append(('jmj', 'JMJ'))\\n liste_maj.append(('haaland', 'HAALAND'))\\n liste_maj.append(('grad(u)', 'grad(U)'))\\n liste_maj.append(('variable z', 'variable Z'))\\n liste_maj.append(('variable r', 'variable R'))\\n liste_maj.append(('ascii', 'ASCII'))\\n\\n with open(ts_file, 'r') as fobj:\\n for ligne in fobj.readlines():\\n if pattern_in.match(ligne):\\n word = pattern_in.match(ligne)\\n ident = word.group('ident')\\n if pattern_out.match(ligne):\\n word = pattern_out.match(ligne)\\n traduit = word.group('traduit')\\n dico_cata_to_telemac[ident] = traduit\\n traduit_main = traduit.lower()\\n for trad in liste_maj:\\n traduit = traduit_main.replace(trad[0], trad[1])\\n traduit_main = traduit\\n chaine = traduit_main[0].upper() + traduit_main[1:]\\n dico_cata_to_label[ident] = chaine\\n if pattern_in2.match(ligne):\\n word = pattern_in2.match(ligne)\\n ident = word.group('ident')\\n if pattern_out2.match(ligne):\\n word = pattern_out2.match(ligne)\\n traduit = word.group('traduit')\\n dico_cata_to_telemac[ident] = traduit\\n dico_cata_to_label[ident] = traduit\\n\\n with open(new_ts_file, 'w') as fobj:\\n fobj.write(header)\\n for k in dico_cata_to_telemac:\\n text = \\\" <message>\\\\n <source>\\\"\\n text += k\\n text += \\\"</source>\\\\n <translation>\\\"\\n text += dico_cata_to_label[k]\\n text += \\\"</translation>\\\\n </message>\\\\n\\\"\\n fobj.write(text)\\n fobj.write(end)\\n\\n system(\\\"lrelease %s\\\"%new_ts_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"746b05a1b817eadcd5a923c742eb96db\",\n \"score\": \"0.49313417\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n fields = [field for field in trans_dict.keys() if field[1]==self.language]\\n for f in fields:\\n setattr(self,f[0],trans_dict[f])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e8d8fa4943faa481c81ced373543ee4\",\n \"score\": \"0.492742\",\n \"text\": \"def __init__(\\n self,\\n orig_phrase: str = None,\\n orig_phrase_norm: str = None,\\n wiki_title: str = None,\\n wiki_title_norm: str = None,\\n score: int = 0,\\n pageid: int = 0,\\n description: str = None,\\n relations: WikipediaPageExtractedRelations = None,\\n ) -> None:\\n self.orig_phrase = orig_phrase\\n if orig_phrase_norm is None:\\n self.orig_phrase_norm = StringUtils.normalize_str(orig_phrase)\\n else:\\n self.orig_phrase_norm = orig_phrase_norm\\n\\n self.wiki_title = wiki_title.replace(DISAMBIGUATION_TITLE, \\\"\\\")\\n if wiki_title_norm is None:\\n self.wiki_title_norm = StringUtils.normalize_str(wiki_title)\\n else:\\n self.wiki_title_norm = wiki_title_norm\\n\\n self.score = score\\n self.pageid = int(pageid)\\n self.description = description\\n self.relations = relations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db368a434f063b1956ecabad3c4d0304\",\n \"score\": \"0.49264848\",\n \"text\": \"def prepare_articles(self, articles):\\n\\n prepared_articles = []\\n\\n article_non_insight_doi_list = get_non_insight_doi_list(articles, self.logger)\\n\\n remove_article_doi = []\\n related_articles = []\\n # Process or delete articles as required\\n for article in articles:\\n self.logger.info(article.doi + \\\" is type \\\" + article.article_type)\\n if is_insight_article(article):\\n self.set_related_article(\\n article,\\n articles,\\n related_articles,\\n article_non_insight_doi_list,\\n remove_article_doi,\\n )\\n\\n # Can remove articles now if required\\n for article in articles:\\n if article.doi not in remove_article_doi:\\n prepared_articles.append(article)\\n\\n return prepared_articles\",\n \"title\": \"\"\n },\n {\n \"docid\": \"471b8b3024f3b274d78b19c79f7fdcbc\",\n \"score\": \"0.49189648\",\n \"text\": \"def translate(self, *args):\\n return self.robot.translate(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf1ae554e847f03f324222e8286dcfe7\",\n \"score\": \"0.4916338\",\n \"text\": \"def post_article():\\n json = request.get_json()\\n\\n public = json.get('public') is not False\\n if not public:\\n category = None\\n else:\\n category = json.get('category')\\n if category is None:\\n return {'category': '公开的文章必须属于某一个分类'}\\n if not Category.exist(Category.id == category):\\n return {'category': '该分类不存在'}\\n\\n id = json.get('id', '')\\n if not re_match(app_config['ARTICLE_ID_PATTERN'], id):\\n return {'id': app_config['ARTICLE_ID_DESCRIPTION']}\\n\\n title = json.get('title', '').strip()\\n if not title:\\n return {'title': '请输入有效的文章标题'}\\n\\n text_type = json.get('text_type', '')\\n if not RendererCollection.does_support(text_type):\\n return {'text_type': '抱歉，暂不支持该格式的文章'}\\n\\n source_text = json.get('source_text', '')\\n try:\\n rendered_text = RendererCollection.render(text_type, source_text)\\n except RendererCollection.RenderedError as ex:\\n return {'source_text': '渲染文章失败，错误信息如下：\\\\r\\\\n' + ex.message}\\n\\n is_commentable = json.get('is_commentable') is not False\\n\\n article = Article.create(id=id, title=title,\\n text_type=text_type, source_text=source_text,\\n content=rendered_text,\\n is_commentable=is_commentable, public=public,\\n category=category)\\n\\n event_emitted.send(\\n current_app._get_current_object(),\\n type='Article: Post',\\n description='Author(%s) posted a new article(%s).'\\n % (article.author_id, article.id)\\n )\\n\\n return None, {'article': article.to_dict()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e51deb455832d94386a33e82d4295f9\",\n \"score\": \"0.49146888\",\n \"text\": \"def test_new_language_translation_creation(self):\\n url = u\\\"/composers/translate/edit?appid=%s&srclang=all_ALL&editSelectedSourceButton=&targetlang=te_ST&srcgroup=ALL&targetgroup=ALL\\\" % (\\n self.firstApp.unique_id)\\n print \\\"URL: \\\" + url\\n rv = self.flask_app.get(url)\\n assert rv.status_code == 200\\n\\n data = rv.data.decode(\\\"utf8\\\") # This bypasses an apparent utf8 FlaskClient bug.\\n\\n # Ensure that the identifiers appear in the response.\\n # (that is, that it appears to be translate-able)\\n assert u\\\"hello_world\\\" in data\\n assert u\\\"black\\\" in data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49d99ece8347c15a344d3da9e7ca4f85\",\n \"score\": \"0.4914653\",\n \"text\": \"def create_cms_article(user, item):\\n if not (user.is_staff and user.has_perm('coop_cms.add_article')):\\n raise PermissionDenied\\n \\n art = Article.objects.create(title=item.title, content=item.summary)\\n item.processed = True\\n item.save()\\n return art\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3260159d71d3277cedd8e5e5d22c261c\",\n \"score\": \"0.49101502\",\n \"text\": \"def prepare_nlu_text(example: Text, entities: List[Dict]):\\n if not Utility.check_empty_string(example):\\n if entities:\\n from rasa.shared.nlu.training_data.formats.rasa_yaml import RasaYAMLWriter\\n example = RasaYAMLWriter.generate_message({'text': example, \\\"entities\\\": entities})\\n return example\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c3e8ab5b1a89528561c950371b1ee71\",\n \"score\": \"0.4910095\",\n \"text\": \"def __corpus_embedding_creator(self, input_data, original_corpus: bool):\\n if original_corpus:\\n input_data = self.__vectorizer.fit_transform(input_data)\\n return self.__TfidfTransformer.fit_transform(input_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38098fac9ea41018f03fbede24b915b2\",\n \"score\": \"0.49083048\",\n \"text\": \"def tr2(jenni, input):\\r\\n if not input.group(2): return jenni.say(\\\"No input provided.\\\")\\r\\n command = input.group(2).encode('utf-8')\\r\\n\\r\\n def langcode(p):\\r\\n return p.startswith(':') and (2 < len(p) < 10) and p[1:].isalpha()\\r\\n\\r\\n args = ['auto', 'en']\\r\\n\\r\\n for i in xrange(2):\\r\\n if not ' ' in command: break\\r\\n prefix, cmd = command.split(' ', 1)\\r\\n if langcode(prefix):\\r\\n args[i] = prefix[1:]\\r\\n command = cmd\\r\\n phrase = command\\r\\n\\r\\n if (len(phrase) > 350) and (not input.admin):\\r\\n return jenni.reply('Phrase must be under 350 characters.')\\r\\n\\r\\n src, dest = args\\r\\n if src != dest:\\r\\n msg, src = translate(phrase, src, dest)\\r\\n if isinstance(msg, str):\\r\\n msg = msg.decode('utf-8')\\r\\n if msg:\\r\\n msg = web.decode(msg) # msg.replace(''', \\\"'\\\")\\r\\n msg = '\\\"%s\\\" (%s to %s, translate.google.com)' % (msg, src, dest)\\r\\n else: msg = 'The %s to %s translation failed, sorry!' % (src, dest)\\r\\n\\r\\n jenni.reply(msg)\\r\\n else: jenni.reply('Language guessing failed, so try suggesting one!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ebe6d8c3174edfd926555efcda93323\",\n \"score\": \"0.49049714\",\n \"text\": \"def blogYY_page_mod_article(article_id):\\n article = search_article_by_id(article_id)[0]\\n return render_template(\\n template_name_or_list=\\\"blogYY/pg_add_article/aa.html\\\",\\n title=article[\\\"title\\\"],\\n content=article[\\\"content\\\"],\\n category_id=article[\\\"category_id\\\"],\\n submit_url=url_for('blogYY_api_mod_article_v1', article_id=article[\\\"id\\\"]),\\n redirect_url=url_for('blogYY_page_single_article', article_id=article[\\\"id\\\"]),\\n categories=search_categories()\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfebab4aee9542abb1a0b4df6caafc9d\",\n \"score\": \"0.48998842\",\n \"text\": \"def from_translation(self, la):\\n PW0 = self.realization_of().PW0()\\n return self(PW0.from_translation(la))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b8a88e0f8aad0aa039d738d179f724\",\n \"score\": \"0.48955297\",\n \"text\": \"def get_article_from_id(id, separator=\\\"\\\\n\\\\n\\\"):\\n with open(DATA_PATH + \\\"articles/\\\" + id, \\\"r\\\") as article_data:\\n article_data_map = json.loads(article_data.read())\\n\\n raw_contents = json.loads(article_data_map[CONTENT_FIELD])\\n questions = get_questions(raw_contents)\\n concatenated_content = \\\"\\\\n\\\".join([elem[\\\"content\\\"] for elem in raw_contents if \\\"Overview\\\" not in elem[\\\"content\\\"] and \\\"What do you think?\\\" not in elem[\\\"content\\\"]])\\n\\n if len(concatenated_content) < MIN_ARTICLE_LEN:\\n return \\\"\\\", []\\n else:\\n return process_article(concatenated_content, separator), questions\\n \\n # article_data_map[\\\"ka_url\\\"] --> url\\n # article_data_map[\\\"translated_title\\\"] --> title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f45c224980f651c704f1e0d255e6f3d6\",\n \"score\": \"0.48883346\",\n \"text\": \"def translation(translation):\\n return np.array([\\n [1, 0, translation[0]],\\n [0, 1, translation[1]],\\n [0, 0, 1]\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fea5b4ed3520b6b8d1c9c97e301106c7\",\n \"score\": \"0.48824593\",\n \"text\": \"def copy(self, other_passage):\\n other = Layer0(root=other_passage, attrib=self.attrib.copy())\\n other.extra = self.extra.copy()\\n for t in self._all:\\n copied = other.add_terminal(t.text, t.punct, t.paragraph)\\n copied.extra = t.extra.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5ec77161210b7a8564a94c9ed8ce336\",\n \"score\": \"0.48805857\",\n \"text\": \"def get_translate_entry(word, original_language, target_language):\\n lookup = {\\n \\\"cs\\\": \\\"czech\\\",\\n \\\"da\\\": \\\"danish\\\",\\n \\\"de\\\": \\\"german\\\",\\n \\\"el\\\": \\\"greek\\\",\\n \\\"es\\\": \\\"spanish\\\",\\n \\\"et\\\": \\\"estonian\\\",\\n \\\"en\\\": \\\"english\\\",\\n \\\"fr\\\": \\\"french\\\",\\n \\\"it\\\": \\\"italian\\\",\\n \\\"nb\\\": \\\"norwegian\\\",\\n \\\"nl\\\": \\\"dutch\\\",\\n \\\"po\\\": \\\"polish\\\",\\n \\\"pt\\\": \\\"portuguese\\\",\\n \\\"sl\\\": \\\"slovene\\\",\\n \\\"sv\\\": \\\"swedish\\\"\\n }\\n \\n original_language = lookup[original_language[:2]]\\n target_language = lookup[target_language[:2]]\\n if original_language == 'german':\\n word = sanitize_german(word)\\n\\n url = 'https://www.collinsdictionary.com/us/dictionary/%s-%s/%s' % (original_language, target_language, word)\\n\\n request = requests.get(url)\\n if request.status_code == 200:\\n return url\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18adf592901cfcddbc73c541bfe49e5a\",\n \"score\": \"0.48700637\",\n \"text\": \"def __init__(\\n self,\\n es,\\n es_index: str,\\n nlp,\\n source_description_field: str,\\n target_title_field: str,\\n target_description_field: str,\\n target_alias_field: str = None,\\n ):\\n\\n self.es = es\\n self.es_index = es_index\\n\\n self.nlp = nlp\\n\\n self.source_fields = {\\\"description\\\": source_description_field}\\n\\n self.target_fields = {\\n \\\"title\\\": target_title_field,\\n \\\"description\\\": target_description_field,\\n }\\n\\n if target_alias_field is not None:\\n self.target_fields.update(\\n {\\n \\\"alias\\\": target_alias_field,\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da4a84f375b1cbcd7d097aef1ca299d8\",\n \"score\": \"0.48641682\",\n \"text\": \"def create_article(title, content, title_image, article_session_id, is_draft, user_id, group_id, category_id, abstract, book_id, special_id=None):\\n\\n result = db_session.query(Article).filter_by(\\n article_session_id=article_session_id).all()\\n if (special_id is not None) and (is_draft == '0'):\\n special = db_session.query(Special).filter_by(\\n special_id=special_id).scalar()\\n special.last_modified = datetime.now()\\n db_session.commit()\\n\\n if len(result) > 0:\\n article = db_session.query(Article).filter_by(\\n article_session_id=article_session_id).scalar()\\n article.title = title\\n article.content = content\\n article.picture = title_image\\n article.time = datetime.now()\\n article.is_draft = is_draft\\n article.abstract = abstract\\n article.book_id = book_id\\n article.special_id = special_id\\n article.category = category_id\\n db_session.commit()\\n return result[0].article_id\\n else:\\n article = Article(title=title, content=content, picture=title_image, time=datetime.now(), user_id=user_id, article_session_id=article_session_id,\\n is_draft=is_draft, groups=group_id, category=category_id, abstract=abstract, book_id=book_id, special_id=special_id)\\n db_session.add(article)\\n db_session.commit()\\n result = db_session.query(Article).filter_by(\\n article_session_id=article_session_id).first()\\n return result.article_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e970b141f3bb85cbf571b456986fe474\",\n \"score\": \"0.48639563\",\n \"text\": \"def test_representation(self):\\n self.assertEqual(\\n str(self.article),\\n \\\"Why Python is such a nice language 2020-01-01T12:00:00\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38e69a81337958542e6941b1d0c26a77\",\n \"score\": \"0.48592383\",\n \"text\": \"def translate(ctx, message):\\r\\n if ctx.args.lang() == \\\"en\\\":\\r\\n return message\\r\\n\\r\\n _tr = {}\\r\\n _tr[\\\" (general presentation)\\\"] = {\\r\\n \\\"fr\\\": \\\" (présentation générale)\\\"\\r\\n }\\r\\n _tr[\\\" (main menu)\\\"] = {\\r\\n \\\"fr\\\": \\\" (menu principal)\\\"\\r\\n }\\r\\n _tr[\\\"This command line interface uses the following patterns for comments definition:\\\"] = {\\r\\n \\\"fr\\\": \\\"Cette interface en ligne de commande utilise les motifs suivants pour la définition de commentaires :\\\"\\r\\n }\\r\\n _tr[\\\" is composed of the following menus:\\\"] = {\\r\\n \\\"fr\\\": \\\" est composé des menus suivants :\\\"\\r\\n }\\r\\n _tr[\\\" is composed of the following commands:\\\"] = {\\r\\n \\\"fr\\\": \\\" est composé des commandes suivantes :\\\"\\r\\n }\\r\\n _tr[\\\"Command Line Interface \\\"] = {\\r\\n \\\"fr\\\": \\\"Interface en ligne de commande (CLI) \\\"\\r\\n }\\r\\n _tr[\\\"Command Line Interface documentation\\\"] = {\\r\\n \\\"fr\\\": \\\"Documentation d'interface en ligne de commande (CLI)\\\"\\r\\n }\\r\\n _tr[\\\"Description:\\\"] = {\\r\\n \\\"fr\\\": \\\"Description :\\\"\\r\\n }\\r\\n _tr[\\\"float value\\\"] = {\\r\\n \\\"fr\\\": \\\"Valeur décimale\\\"\\r\\n }\\r\\n _tr[\\\"host value\\\"] = {\\r\\n \\\"fr\\\": \\\"Adresse réseau\\\"\\r\\n }\\r\\n _tr[\\\"int value\\\"] = {\\r\\n \\\"fr\\\": \\\"Valeur entière\\\"\\r\\n }\\r\\n _tr[\\\"Menu \\\"] = {\\r\\n \\\"fr\\\": \\\"Menu \\\"\\r\\n }\\r\\n _tr[\\\"No help available\\\"] = {\\r\\n \\\"fr\\\": \\\"Aucune aide disponible\\\"\\r\\n }\\r\\n _tr[\\\"Options:\\\"] = {\\r\\n \\\"fr\\\": \\\"Options :\\\"\\r\\n }\\r\\n _tr[\\\"Parameters:\\\"] = {\\r\\n \\\"fr\\\": \\\"Paramètres :\\\"\\r\\n }\\r\\n _tr[\\\"string value\\\"] = {\\r\\n \\\"fr\\\": \\\"Chaîne de caractères\\\"\\r\\n }\\r\\n _tr[\\\"Synopsis:\\\"] = {\\r\\n \\\"fr\\\": \\\"Synopsis :\\\"\\r\\n }\\r\\n _tr[\\\"This command opens the menu \\\"] = {\\r\\n \\\"fr\\\": \\\"Cette commande ouvre le menu \\\"\\r\\n }\\r\\n _tr[\\\".\\\"] = {\\r\\n \\\"fr\\\": \\\".\\\"\\r\\n }\\r\\n\\r\\n try:\\r\\n if ctx.args.lang() == \\\"fr\\\":\\r\\n import codecs\\r\\n _iso = codecs.lookup(\\\"utf-8\\\")\\r\\n return _iso.decode(_tr[message][\\\"fr\\\"])[0]\\r\\n except KeyError:\\r\\n pass\\r\\n modlog.log(ctx.MODLOG_FILTER, modlog.WARNING, \\\"No translation for '%s'\\\" % message)\\r\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bea8baf61923235e4975dbd6d582b08\",\n \"score\": \"0.48567662\",\n \"text\": \"def get_Te(self, een):\\n self.Te = deepcopy(een.Te)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80c5145269cb65714756e10333302156\",\n \"score\": \"0.4852506\",\n \"text\": \"def create_new():\\n\\n fa_plus = DRIVER.find_element_by_css_selector(\\\".fa-plus\\\")\\n fa_plus.click()\\n create_key = DRIVER.find_element_by_id(\\\"create-key\\\")\\n add_editor = DRIVER.find_element_by_xpath(\\\"//*[@id='create-translation']/form/button[2]\\\")\\n create_key.click()\\n create_key.clear()\\n create_key.send_keys(str(key))\\n add_editor.click()\\n create_summernote = DRIVER.find_element_by_xpath(\\\"\\\"\\n \\\"//*[@id='create-translation']/form/div[2]/div/div[3]/div[2]/p\\\")\\n create_summernote.click()\\n create_summernote.clear()\\n create_summernote.send_keys(str(new_en))\\n DRIVER.find_element_by_xpath(\\\"//*[@id='create-translation']/form/button[1]\\\").click()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"278780d85346dc50c37ea9b0e80697e3\",\n \"score\": \"0.4850419\",\n \"text\": \"def reconstruct_from_transformed(self, node, transformed_node):\\n return transformed_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c88bb8ae8d181aaa10ddec0ea2332ac4\",\n \"score\": \"0.4849736\",\n \"text\": \"def create_translate(cls, tx, ty=0.0):\\n return cls(1.0, 0.0, 0.0, 1.0, tx, ty)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":478,"cells":{"query_id":{"kind":"string","value":"f5f611dc1fa49edc0903b5086772d214"},"query":{"kind":"string","value":"Combine multiple generators into one"},"positive_passages":{"kind":"list like","value":[{"docid":"cbfb857bc40adbf0cbc3a1fca182edd7","score":"0.7115147","text":"def multiplex(sources):\n return it.chain.from_iterable(sources)","title":""}],"string":"[\n {\n \"docid\": \"cbfb857bc40adbf0cbc3a1fca182edd7\",\n \"score\": \"0.7115147\",\n \"text\": \"def multiplex(sources):\\n return it.chain.from_iterable(sources)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c7a09ddd2a1ad01359b94e02267b5713","score":"0.73177344","text":"def combine_generators_no_random_crop(sharp_generator, blur_generator):\n while True:\n sharp_batch = sharp_generator.next()\n blur_batch = blur_generator.next()\n\n res = [sharp_batch, blur_batch]\n\n yield res","title":""},{"docid":"dc3c0da763e581890ec873abf3b454bc","score":"0.72304946","text":"def combine_generators(sharp_generator, blur_generator):\n while True:\n sharp_batch = sharp_generator.next()\n blur_batch = blur_generator.next()\n\n sharp_batch, blur_batch = random_crop(sharp_batch, blur_batch)\n\n res = [sharp_batch, blur_batch]\n\n yield res","title":""},{"docid":"4745d0e85afbbc558dec68a5add57dff","score":"0.7212617","text":"def gen_concatenate(iterators):\n for it in iterators:\n yield from it","title":""},{"docid":"51cc6ce73d773d41944c331c7b82b836","score":"0.7137542","text":"def TryMultipleGenerator():\n\tprint('I should be the first generator')\n\tfor i in range(5):\n\t\tyield i+1\n\n\tprint('I should be the second generator')\n\tfor j in range(10):\n\t\tyield j*10","title":""},{"docid":"86d21b81fcc53c83c128728dc3a46ac8","score":"0.7052838","text":"def chain_generators(*sprite_generators):\n\n def _generate(*args, **kwargs):\n return list(itertools.chain(*[generator(*args, **kwargs)\n for generator in sprite_generators]))\n\n return _generate","title":""},{"docid":"276d9768038794a3fd85ff726fbbcc5c","score":"0.69457084","text":"def chain_generators(iterable, list_of_generators):\r\n\r\n def flatten1(nested_iterable):\r\n return (item for inner in nested_iterable for item in inner)\r\n\r\n # IMPORTANT: this function can NOT be inlined. Python scope/shadowing intricacies would break it\r\n def apply(gen, iterable): \r\n return (gen(x) for x in iterable)\r\n\r\n def flatmap(iter, gen):\r\n return flatten1(apply(gen, iter))\r\n\r\n return reduce(flatmap, list_of_generators, iterable)","title":""},{"docid":"16f0f281d3dd2d6579a8efc9e59c9e95","score":"0.6784467","text":"def make_generators_generator(g):\n for x in g:\n yield x","title":""},{"docid":"db5542fbdd1aa7d192ed6ec1e2d9828e","score":"0.64963466","text":"def mix_generators(generator_list):\n i = 0\n l = len(generator_list)\n stopiters_seen = 0\n while stopiters_seen <= l:\n try:\n yield six.next(generator_list[i % l])\n i += 1\n stopiters_seen = 0\n except StopIteration:\n i += 1\n stopiters_seen += 1","title":""},{"docid":"4044be6db2e56846c42b0f4fe65d61fb","score":"0.61763394","text":"def concat_seq(sources: Iterable[AsyncObservable[TSource]]) -> AsyncObservable[TSource]:\n\n return pipe(\n of_seq(sources),\n merge_inner(1),\n )","title":""},{"docid":"4f224dc66f49be220255e11ac520c44d","score":"0.6116887","text":"def make_generators_generator(g):\n \"*** YOUR CODE HERE ***\"\n\n def helper(g, i):\n gen = g()\n for _ in range(i):\n yield next(gen)\n\n length = len(list(g()))\n for i in range(1, length + 1):\n yield helper(g, i)","title":""},{"docid":"f4c2f10f017e029dedd27871d864972c","score":"0.5957536","text":"def generate_different_arrays():\n yield np.array([1, 2])\n yield np.array([1, 2, 3])","title":""},{"docid":"b6b8396667f71123577777cdc56911dd","score":"0.5947436","text":"def Serial(*fns): # pylint: disable=invalid-name\n def composed_fns(generator=None):\n for f in fastmath.tree_flatten(fns):\n generator = f(generator)\n return generator\n return composed_fns","title":""},{"docid":"cec93fb32a072041dab6c65f9eec6d14","score":"0.5936838","text":"def _product_generator(a, b):\n yield a * b","title":""},{"docid":"246b3f9a578462c2a944ee0f2aaf620e","score":"0.59360576","text":"def merge_streams(a, b):\n stream = (a, b)\n item = list(map(_get_next, stream))\n cur_stream = _get_cur_stream_idx(item) # Stream we are reading ATM.\n\n while item != [_sentinel, _sentinel]: # While at least one not at EOF.\n yield item[cur_stream]\n item[cur_stream] = _get_next(stream[cur_stream])\n cur_stream = _get_cur_stream_idx(item)","title":""},{"docid":"07300a0105791a3e88808c869a31b6b4","score":"0.5931834","text":"def _iter_merge_checks(checks_1, checks_2):\n if (checks_1 is not None):\n yield from checks_1\n \n if (checks_2 is not None):\n yield from checks_2","title":""},{"docid":"fe2f535323dbe9409f0c1f71bf327d8c","score":"0.5925361","text":"def concat(seqs: Union[Seq, Seq[Seq]], *extra) -> Iter:\n if extra:\n return Iter(itertools.chain(seqs, *extra))\n return Iter(itertools.chain.from_iterable(seqs))","title":""},{"docid":"8df67cd64970fb2ecdae24e4e4feb4a7","score":"0.59073645","text":"def gen():\n yield","title":""},{"docid":"d4a314e0f066f2be79ded65373e9aa10","score":"0.588959","text":"def zip_with(func, *iterables):\n\n for args in zip(*iterables):\n yield func(*args)","title":""},{"docid":"4b61c20e7a61439761f6ee28e72be964","score":"0.5880449","text":"def roundrobin(iterables):\n curr_alive = map(iter, iterables)\n while curr_alive:\n next_alive = []\n for gen in curr_alive:\n try:\n yield next(gen)\n except StopIteration:\n pass\n else:\n next_alive.append(gen)\n curr_alive = next_alive","title":""},{"docid":"2e70ee8ae976f7bf602ac9ce79c6e9ab","score":"0.5867071","text":"def roundrobin(*iterables):\n pending = len(iterables)\n funs = cycle(iter(it).next for it in iterables)\n while pending:\n try:\n for fun in funs:\n yield fun()\n except StopIteration:\n pending -= 1\n funs = cycle(islice(funs, pending))","title":""},{"docid":"c34f5a4bd70fdd2931b8cb478343ed88","score":"0.58531594","text":"def batch_generator():\n source_batch = []\n target_batch = []\n for sources, target in utils.yield_sources_and_targets(\n FLAGS.predict_input_file, FLAGS.input_format):\n\n source_batch.append(\n FLAGS.special_glue_string_for_joining_sources.join(sources))\n target_batch.append(target)\n if len(source_batch) == FLAGS.predict_batch_size:\n yield source_batch, target_batch\n source_batch = []\n target_batch = []\n\n if source_batch:\n yield source_batch, target_batch","title":""},{"docid":"d40ee8a2d529f66eb06fac931a8d05d0","score":"0.58434135","text":"async def achain(*async_iters: AsyncIterable[T]) -> AsyncIterator[T]:\n for aiter in async_iters:\n async for elem in aiter:\n yield elem","title":""},{"docid":"1ab9fed836540647bd1e0fd99edfe03e","score":"0.58290917","text":"def imapzip(generator, *iterables):\n return itertools.izip(*tuple(itertools.starmap(generator, zip(*iterables))))","title":""},{"docid":"082650302e54033020864022e81e0646","score":"0.58168626","text":"def test_multiple_yields(self):\n def generate_10_arrays():\n \"\"\"Put out 10 numpy arrays\"\"\"\n for _ in range(10):\n yield np.array([1, 2, 3, 4]).astype(np.float32)\n def assert_expectation(array):\n \"\"\"Assert the array is as expected\"\"\"\n np.testing.assert_almost_equal(array, [1, 2, 3, 4])\n self.occurences += 1\n blocks = []\n blocks.append((NumpySourceBlock(generate_10_arrays), {'out_1': 0}))\n blocks.append((NumpyBlock(assert_expectation, outputs=0), {'in_1': 0}))\n Pipeline(blocks).main()\n self.assertEqual(self.occurences, 10)","title":""},{"docid":"e3b33b4f29062c9f97bd5fdc1023406e","score":"0.5773603","text":"def iterate(self):\n for result_a, binds_a in self.bound_a:\n for result_b, self.binds in self.bound_b:\n if not isinstance(result_a, tuple):\n result_a = (result_a,)\n if not isinstance(result_b, tuple):\n result_b = (result_b,)\n self.binds.update(binds_a)\n yield tuple(chain(result_a, result_b))","title":""},{"docid":"b9cb09e4547d9038cd8830171476415e","score":"0.5746471","text":"def merge_streamlines(backward, forward):\n B = iter(backward)\n F = iter(forward)\n while True:\n yield concatenate((B.next()[:0:-1], F.next()))","title":""},{"docid":"7f8d7ef0ffcce2a2bfdcdbf19cf385d9","score":"0.5745188","text":"def merge_graphs(generators, strategy_fields_source_node=lambda g: {}, strategy_graph_id=graph_id):\n graph = nx.MultiDiGraph()\n\n print('Merging subgraphs ...')\n for generator in tqdm(generators):\n for g in generator():\n\n id_sub_graph = strategy_graph_id(g)\n assert id_sub_graph not in graph\n\n graph.add_node(id_sub_graph, type='source', value=id_sub_graph, id=id_sub_graph, **strategy_fields_source_node(g))\n\n # Element in queue represent nodes in the shape (id_node_in_subgraph, id_node_in_graph)\n q = [('source', id_sub_graph)]\n while True:\n\n if len(q) == 0:\n break\n else:\n node = q.pop(0)\n\n edges = g.edges(node[0])\n for edge in edges:\n\n n = g.nodes()[edge[1]].copy()\n\n old_id = n['id']\n new_id = n['mergiable_id']\n\n n.pop('id')\n n.pop('graph')\n graph.add_node(new_id, id=new_id, **n)\n\n type_edge = g[node[0]][old_id]['type']\n graph.add_edge(node[1], new_id, type=type_edge)\n\n q.append((old_id, new_id))\n\n return graph","title":""},{"docid":"67c898ad379e79aa60f0aa910bba7c15","score":"0.57398504","text":"def chain(*fs):\n\tdef chained(x):\n\t\tres = x\n\t\tfor f in fs:\n\t\t\tres = f(res)\n\t\treturn res\n\treturn chained","title":""},{"docid":"b56ac401407be8a900a031c15efe5400","score":"0.56415904","text":"async def azip(*iterables: AsyncIterable[T]) -> AsyncIterator[Tuple[T, ...]]:\n iterators = [iterable.__aiter__() for iterable in iterables]\n while True:\n try:\n yield tuple(await asyncio.gather(*(itr.__anext__() for itr in iterators)))\n except StopAsyncIteration:\n break","title":""},{"docid":"0a088c186a23bb7af53d0ae0c119fcac","score":"0.5632826","text":"def compose_contexts(\n head: ContextManager, *tail: ContextManager\n) -> Generator[Any, None, None]:\n with ExitStack() as stack:\n if len(tail):\n yield tuple(stack.enter_context(c) for c in [head, *tail])\n else:\n yield stack.enter_context(head)","title":""},{"docid":"3771e89d3dbf86a3bd5a8d3cb446600d","score":"0.5628315","text":"def generate_two_different_arrays():\n for _ in range(10):\n yield np.random.rand(4)\n for _ in range(10):\n yield np.random.rand(5)","title":""},{"docid":"e52982fedee89781c0488e8e65ede587","score":"0.5575016","text":"def alternate_iterables(*iterables):\n iterators = [iter(iterable) for iterable in iterables]\n\n while True:\n try:\n for iterator in iterators:\n yield next(iterator)\n except StopIteration:\n break","title":""},{"docid":"7aa04ed24f3ddea72f8cbd9b03fcdca6","score":"0.5571096","text":"def _overlay_gen():\n while True:\n for overlay in overlays:\n yield overlay","title":""},{"docid":"4592be005f3a426114a6b2c54eef3b0c","score":"0.5568623","text":"def zip_with(func: Callable, *iters) -> Iterable:\n return map(func, zip(*iters))","title":""},{"docid":"96fa156e83f43c488b1b62015d36cf61","score":"0.5550289","text":"def expand_circuit_generator(circuit_generator, circuits):\n for circuit in circuit_generator:\n for relay in circuit:\n circuits.setdefault(relay, set(circuit)).update(circuit)","title":""},{"docid":"24bce8beda9d28a2de71595c85eff0ce","score":"0.554429","text":"def gen():\n for x in range(20):\n yield x","title":""},{"docid":"207f55d84237cc1229081aac52ff4a73","score":"0.5544061","text":"def nested(*mgrs):\n with contextlib.ExitStack() as stack:\n outputs = [stack.enter_context(cm) for cm in mgrs]\n yield outputs","title":""},{"docid":"b56d456ea53002ac34f321f72a337343","score":"0.5527838","text":"def pipeline(*functions):\n head = functions[0]\n tail = functions[1:]\n if tail:\n def _fn(iterable):\n for i in pipeline(*tail)(head(iterable)):\n yield i\n return _fn\n else:\n return head","title":""},{"docid":"f1f8fbda05d1c4190f8078817fd7d44b","score":"0.5527089","text":"def gen_things(repeat=1):\n\tfor x in itertools.product(ascii_lowercase, repeat=repeat):\n\t\tyield x\n\tfor x in gen_things(repeat+1):\n\t\tyield x","title":""},{"docid":"1dd0df1576426b5282a0049660d38a29","score":"0.55237025","text":"def cartesian_product(a, b):\n\n for i in a:\n for j in b:\n yield i + (j,)","title":""},{"docid":"aff9c49c8d655499974b860a45698153","score":"0.55206954","text":"def generators(self):\n return self._gens","title":""},{"docid":"c9d418a427a7c605feb1d323f15122db","score":"0.54975235","text":"def yieldAllCombos(items):\n for combo in powerSet(items):\n bag1 = combo[0]\n toTake = combo[1]\n \n for combo2 in powerSet(toTake):\n bag2 = combo2[0]\n \n yield (bag1, bag2)","title":""},{"docid":"fbe4061dca7c8ace6add74f6c230495d","score":"0.5486556","text":"def sequences(self):\n for fasta in self.raw_files:\n for seq in fasta: yield seq","title":""},{"docid":"d6721cf4b746dc5a2c1284df869c566c","score":"0.5486126","text":"def combine_plays(games):\n chain = itertools.chain(*[g.drives.plays() for g in games])\n return nflgame.seq.GenPlays(chain)","title":""},{"docid":"68e06745da285a3c93f89f4c3a0140c3","score":"0.54828465","text":"def combine(args1, args2):\n return [arg1.combine(arg2) for arg1, arg2 in zip(args1, args2)]","title":""},{"docid":"caca66538264dc51478a333efed74995","score":"0.54826397","text":"def substitute_generator(self, generator, out):\n for value in generator:\n self.substitute_value(value, out)\n return self","title":""},{"docid":"6c3a782cbfc65b64d23ca57c379d91db","score":"0.54760075","text":"def Chapter4_12():\n\n from itertools import chain\n a = [1, 2, 3, 4]\n b = ['x', 'y', 'z']\n for x in chain(a, b):\n print(x)","title":""},{"docid":"2c17c2169155d67875a01cd73e919797","score":"0.5474594","text":"def pam(functions: Iterable[Callable], *args, **kwargs):\n for f in functions:\n yield f(*args, **kwargs)","title":""},{"docid":"e37bb7aea7324537ee5ca4e67373c582","score":"0.5470446","text":"def zip_readers(*readers, indices=None) -> Generator:\n if indices:\n iterators = zip(*(r.select_record_indices(indices) for r in readers))\n else:\n iterators = zip(*readers)\n for record_tuple in iterators:\n yield record_tuple","title":""},{"docid":"6ee31e835991168cd2dae340045e4106","score":"0.54635113","text":"def ijoin(iters):\r\n return (x for it in iters for x in it)","title":""},{"docid":"1034c67da8fa0cae7d488d0c7dae7f8d","score":"0.54461986","text":"def cartesian_product(items, get_separator):\n combs = [None]\n first_item_required = None\n\n for required, args in items:\n if first_item_required is None:\n first_item_required = required\n\n separator = get_separator(required)\n _combine = partial(combine, separator=separator)\n\n combs = map(\n _combine,\n product(combs, args)\n )\n\n if combs != [None]:\n for comb in combs:\n yield first_item_required, comb","title":""},{"docid":"55ebd7f244cf0fc0b86de69d0162e44e","score":"0.54375833","text":"def __call__(self, *sources: Source) -> Iterable[Swimporting]:\n if len(sources) > 1:\n src = chain.from_iterable(sources)\n elif sources:\n src = sources[0]\n else:\n return\n\n for o in src:\n s = self.do(o)\n if s:\n yield s","title":""},{"docid":"cd23164165c0b00c295c342286431a4d","score":"0.5437451","text":"def test_multiple_sequences(self):\n\n def generate_different_arrays():\n \"\"\"Yield four different groups of two arrays\"\"\"\n dtypes = ['float32', 'float64', 'complex64', 'int8']\n shapes = [(4,), (4, 5), (4, 5, 6), (2,) * 8]\n for array_index in range(4):\n yield np.ones(\n shape=shapes[array_index],\n dtype=dtypes[array_index])\n yield 2 * np.ones(\n shape=shapes[array_index],\n dtype=dtypes[array_index])\n\n def switch_types(array):\n \"\"\"Return two copies of the array, one with a different type\"\"\"\n return np.copy(array), np.copy(array).astype(np.complex128)\n\n self.occurences = 0\n def compare_arrays(array1, array2):\n \"\"\"Make sure that all arrays coming in are equal\"\"\"\n self.occurences += 1\n np.testing.assert_almost_equal(array1, array2)\n\n blocks = [\n (NumpySourceBlock(generate_different_arrays), {'out_1': 0}),\n (NumpyBlock(switch_types, outputs=2), {'in_1': 0, 'out_1': 1, 'out_2': 2}),\n (NumpyBlock(np.fft.fft), {'in_1': 2, 'out_1': 3}),\n (NumpyBlock(np.fft.ifft), {'in_1': 3, 'out_1': 4}),\n (NumpyBlock(compare_arrays, inputs=2, outputs=0), {'in_1': 1, 'in_2': 4})]\n\n Pipeline(blocks).main()\n self.assertEqual(self.occurences, 8)","title":""},{"docid":"63ab985187ab8bdbfce6723f6ceff441","score":"0.5422615","text":"def _get_yielded_and_returned_values(gen, values_to_send=()):\n yvals = []\n try:\n yvals.append(next(gen))\n for s in values_to_send:\n yvals.append(gen.send(s))\n while True: # Loop until `gen` returns, even if no more values to send.\n yvals.append(next(gen))\n except StopIteration as e:\n rvals = e.value\n return yvals, rvals","title":""},{"docid":"20cebd134ead50dab060a8b3e60d3224","score":"0.54173774","text":"def sequence(*args):\n if len(args) > 1:\n def gen():\n while True:\n for o in args:\n yield o\n return gen()\n\n func = args and args[0] or None\n if isinstance(func, _.string_types):\n func = func.format\n\n elif func is None:\n func = lambda x: x\n\n def gen2():\n counter = 0\n while True:\n yield func(counter)\n counter += 1\n return gen2()","title":""},{"docid":"2d8bcd1b7336f1a1294df2daf0e4515f","score":"0.54116684","text":"def roundrobin(*iterables):\n # Recipe credited to George Sakkis\n pending = len(iterables)\n nexts = cycle(iter(it).next for it in iterables)\n while pending:\n try:\n for next in nexts:\n yield next()\n except StopIteration:\n pending -= 1\n nexts = cycle(islice(nexts, pending))","title":""},{"docid":"12dae1f974cb630a2b524277b82dc63f","score":"0.54071265","text":"def generator_start_wrapper(generator):\n yield\n\n yield from generator","title":""},{"docid":"b2efdd688ce6851d9418b2d1ce79a52d","score":"0.54056275","text":"def commutators(scalar):\n return com1(scalar), com2(scalar), com3(scalar), com4(scalar)","title":""},{"docid":"5ba63b465189152f8f9013def55cf84f","score":"0.54034525","text":"def create_generator(self):","title":""},{"docid":"1624764657445a43444815d23a154b50","score":"0.54007","text":"def tst():\n yield \"a\", \"b\"","title":""},{"docid":"3130d5e3d7f6793a485556d332a6f697","score":"0.53600836","text":"def fit_generator_from_aggregator(aggregator):\r\n return aggregator.map(func=fit_from_agg_obj)","title":""},{"docid":"0a10cce74e94804927ac598f16cdc9de","score":"0.53596437","text":"def merge(self, accumulators):\n pass","title":""},{"docid":"3fffb43c78455637d65b4687e630b2db","score":"0.53572583","text":"def merge_accumulators(\n self, accumulators: Iterable[_PartialNLStats]) -> _PartialNLStats:\n it = iter(accumulators)\n result = next(it)\n for accumulator in it:\n result += accumulator\n return result","title":""},{"docid":"23b7572cc79298d73a1438e425714405","score":"0.5346404","text":"def zip_args_and_kwargs(\n args: Iterable[Tuple[Any, ...]],\n kwargs: Iterable[Dict[str, Any]],\n) -> Generator[Tuple[Tuple[Any, ...], Dict[str, Any]], None, None]:\n iterators: List[Iterator] = [iter(args), iter(kwargs)]\n fills = {0: (), 1: {}}\n num_active = 2\n while True:\n values = []\n for i, it in enumerate(iterators):\n try:\n values.append(next(it))\n except StopIteration:\n num_active -= 1\n if num_active == 0:\n return\n iterators[i] = itertools.repeat(fills[i]) # replace the iterator\n values.append(fills[i]) # for this iteration, insert fills[i] manually\n yield tuple(values)","title":""},{"docid":"7de0d0a54a3cc8719b5d73a6e7a55e53","score":"0.5317929","text":"def get_generators(self, len_val, src_vocab, text_embedding, args):\n gate_gen = None\n if args['slot_gating']:\n gate_gen = Generator(len(GATES), scope_name=\"gate\")\n fertility_generator = Generator(len_val + 1, scope_name=\"fertility\")\n state_generator = Generator(src_vocab, text_embedding.get_embedding_wieght(), scope_name='state')\n point_state_generator = PointerGenerator(state_generator=state_generator, scope_name='point_generator')\n\n return fertility_generator, point_state_generator, gate_gen","title":""},{"docid":"8e9ddad41927ae86b0414823d97663c7","score":"0.52954257","text":"def combine_latest(other: AsyncObservable[TOther]) -> Zipper[Any, TOther]:\n\n def _combine_latest(source: AsyncObservable[TSource]) -> AsyncObservable[Tuple[TSource, TOther]]:\n async def subscribe_async(aobv: AsyncObserver[Tuple[TSource, TOther]]) -> AsyncDisposable:\n safe_obv, auto_detach = auto_detach_observer(aobv)\n\n async def worker(inbox: MailboxProcessor[Msg[TSource]]) -> None:\n @tailrec_async\n async def message_loop(\n source_value: Option[TSource], other_value: Option[TOther]\n ) -> TailCallResult[NoReturn]:\n cn = await inbox.receive()\n\n async def get_value(n: Notification[Any]) -> Option[Any]:\n with match(n) as m:\n for value in case(OnNext[TSource]):\n return Some(value)\n\n for err in case(OnError):\n await safe_obv.athrow(err)\n\n while m.default():\n await safe_obv.aclose()\n return Nothing\n\n with match(cn) as case:\n for value in case(SourceMsg[TSource]):\n source_value = await get_value(value)\n break\n\n for value in case(OtherMsg[TOther]):\n other_value = await get_value(value)\n break\n\n def binder(s: TSource) -> Option[Tuple[TSource, TOther]]:\n def mapper(o: TOther) -> Tuple[TSource, TOther]:\n return (s, o)\n\n return other_value.map(mapper)\n\n combined = source_value.bind(binder)\n for x in combined.to_list():\n await safe_obv.asend(x)\n\n return TailCall(source_value, other_value)\n\n await message_loop(Nothing, Nothing)\n\n agent = MailboxProcessor.start(worker)\n\n async def obv_fn1(n: Notification[TSource]) -> None:\n pipe(SourceMsg(n), agent.post)\n\n async def obv_fn2(n: Notification[TOther]) -> None:\n pipe(OtherMsg(n), agent.post)\n\n obv1: AsyncObserver[TSource] = AsyncNotificationObserver(obv_fn1)\n obv2: AsyncObserver[TOther] = AsyncNotificationObserver(obv_fn2)\n dispose1 = await pipe(obv1, source.subscribe_async, auto_detach)\n dispose2 = await pipe(obv2, other.subscribe_async, auto_detach)\n\n return AsyncDisposable.composite(dispose1, dispose2)\n\n return AsyncAnonymousObservable(subscribe_async)\n\n return _combine_latest","title":""},{"docid":"cea8a8ecce027f7efdb0cd9298e7ecd1","score":"0.5288658","text":"def product(*args, **kwds):\n pools = map(tuple, args) * kwds.get('repeat', 1)\n result = [[]]\n for pool in pools:\n result = [x+[y] for x in result for y in pool]\n for prod in result:\n yield tuple(prod)","title":""},{"docid":"c19238638d7eba6e38989cc2b00ca7d5","score":"0.52800506","text":"def _repeat_stream(stream, n_devices):\n while True:\n for example in stream(n_devices):\n yield example","title":""},{"docid":"d7a9545b0fcf0deaa6518a7ac3c20f7b","score":"0.52757585","text":"def __next__(self):\n\n if self.batch_index == 0:\n self.reset_index()\n\n current_index = (self.batch_index * self.batch_size) % self.n\n if self.n > current_index + self.batch_size:\n self.batch_index += 1\n else:\n self.batch_index = 0\n\n # random sample the lambda value from beta distribution.\n l = np.random.beta(self.alpha, self.alpha, self.batch_size)\n\n X_l = l.reshape(self.batch_size, 1, 1, 1)\n y_l = l.reshape(self.batch_size, 1)\n\n # Get a pair of inputs and outputs from two iterators.\n X1, y1 = self.generator1.next()\n X2, y2 = self.generator2.next()\n\n # Perform the mixup.\n X = X1 * X_l + X2 * (1 - X_l)\n y = y1 * y_l + y2 * (1 - y_l)\n return X, y","title":""},{"docid":"42c9cb5f29ff1545f70c1cd61df8d68c","score":"0.5261039","text":"def concatenate(u, words):\n\n for word in words:\n yield tuple([u] + list(word))","title":""},{"docid":"176a60c9dd43dd74a7dbd508bdcadd74","score":"0.5257654","text":"def tasks_generator(num_all_classes=100, num_avg_classes=2):\n tasks_iter = itertools.combinations(np.arange(num_all_classes), num_avg_classes)\n for task in tasks_iter:\n yield task","title":""},{"docid":"53ffa188a77fe3297e3a313d8267f7c6","score":"0.52474356","text":"def interleave(seqs: Union[Seq, Seq[Seq]], *extra, aligned=False):\n if extra:\n seqs = (seqs, *extra)\n if aligned:\n return Iter(_interleave_aligned(seqs))\n return Iter(toolz.interleave(seqs))","title":""},{"docid":"f61b238ed55217d2f81b7622c0ec3f86","score":"0.52347803","text":"def gens(self):\n return tuple(self.algebra_generators())","title":""},{"docid":"12f0ab9927e07475dfb5f3febf3fcb02","score":"0.5224693","text":"def roundrobin(*iterables: tp.Iterable[tp.Any]) -> tp.Iterator[tp.Any]:\n # Recipe credited to George Sakkis\n num_active = len(iterables)\n nexts = itertools.cycle(iter(it).__next__ for it in iterables)\n while num_active:\n try:\n for next_ in nexts:\n yield next_()\n except StopIteration:\n # Remove the iterator we just exhausted from the cycle.\n num_active -= 1\n nexts = itertools.cycle(itertools.islice(nexts, num_active))","title":""},{"docid":"7dcda61d3d4ca91a0455a6d6e91b8a78","score":"0.52164465","text":"def assemble(self, instructions):\n for instruction in instructions:\n yield instruction.serialize()","title":""},{"docid":"894ebc5ac874c3904022202db34d3001","score":"0.5213145","text":"def test_multiple_output_rings(self):\n def generate_many_arrays():\n \"\"\"Put out 10x10 numpy arrays\"\"\"\n for _ in range(10):\n yield (np.array([1, 2, 3, 4]).astype(np.float32),) * 10\n def assert_expectation(*args):\n \"\"\"Assert the arrays are as expected\"\"\"\n assert len(args) == 10\n for array in args:\n np.testing.assert_almost_equal(array, [1, 2, 3, 4])\n self.occurences += 1\n blocks = []\n blocks.append((\n NumpySourceBlock(generate_many_arrays, outputs=10),\n {'out_%d' % (i + 1): i for i in range(10)}))\n blocks.append((\n NumpyBlock(assert_expectation, inputs=10, outputs=0),\n {'in_%d' % (i + 1): i for i in range(10)}))\n Pipeline(blocks).main()\n self.assertEqual(self.occurences, 10)","title":""},{"docid":"799aef831b3091f8a8d5b490f5904860","score":"0.5197894","text":"def _advance_pattern_generators(self,p):\n return p.generators","title":""},{"docid":"3a97ec91202c7651022c830d4ac6a3e0","score":"0.5196196","text":"def combine_latest(observables: Union[ObservableBase, Iterable[ObservableBase]],\n mapper: Callable[[Any], Any]) -> ObservableBase:\n if isinstance(observables, typing.Observable):\n observables = [observables]\n\n args = list(observables)\n result_mapper = mapper\n parent = args[0]\n\n def subscribe(observer, scheduler=None):\n n = len(args)\n has_value = [False] * n\n has_value_all = [False]\n is_done = [False] * n\n values = [None] * n\n\n def next(i):\n has_value[i] = True\n\n if has_value_all[0] or all(has_value):\n try:\n res = result_mapper(*values)\n except Exception as ex:\n observer.on_error(ex)\n return\n\n observer.on_next(res)\n elif all([x for j, x in enumerate(is_done) if j != i]):\n observer.on_completed()\n\n has_value_all[0] = all(has_value)\n\n def done(i):\n is_done[i] = True\n if all(is_done):\n observer.on_completed()\n\n subscriptions = [None] * n\n\n def func(i):\n subscriptions[i] = SingleAssignmentDisposable()\n\n def on_next(x):\n with parent.lock:\n values[i] = x\n next(i)\n\n def on_completed():\n with parent.lock:\n done(i)\n\n subscriptions[i].disposable = args[i].subscribe_(on_next, observer.on_error,\n on_completed, scheduler)\n\n for idx in range(n):\n func(idx)\n return CompositeDisposable(subscriptions)\n return AnonymousObservable(subscribe)","title":""},{"docid":"c0526036f8f7e82529a701ffd1922783","score":"0.5195552","text":"def generator(self, latent, reuse):\n pass","title":""},{"docid":"36558a6ae11cbeb2816add50d0f95aeb","score":"0.51948166","text":"def merge_adjacent(gen):\n gen = iter(gen)\n last = next(gen)\n for this in gen:\n if this.merge_key == last.merge_key:\n last.merge(this)\n elif last < this:\n yield last\n last = this\n else:\n raise AssertionError('Bad order, %s > %s' % (last, this))\n yield last","title":""},{"docid":"ac9d339cf38de99d1cb18401b22c5d1d","score":"0.518061","text":"def get_instructions(self):\n for block in self:\n for instruction in block:\n yield instruction","title":""},{"docid":"d4f19f20bb3f78616dba6da86bdea368","score":"0.5171164","text":"def combine_ranges(all_ranges: Iterable[IPNetwork]) -> Iterator[IPNetwork]:\n # ipaddress.collapse_addresses can't handle v4 and v6 ranges at the same time\n ipv4, ipv6 = [], []\n # only consume all_ranges once\n for anet in all_ranges:\n if anet.version == 4:\n ipv4.append(anet)\n elif anet.version == 6:\n ipv6.append(anet)\n else:\n raise TypeError(anet)\n\n for ranges in [ipv4, ipv6]:\n ranges = list(ipaddress.collapse_addresses(sorted(ranges))) # type: ignore\n for net in ranges:\n if net.version == 4 and net.prefixlen < 16:\n for subnet in net.subnets(new_prefix=16):\n yield subnet\n elif net.version == 6 and net.prefixlen < 19:\n for subnet in net.subnets(new_prefix=19):\n yield subnet\n else:\n yield net","title":""},{"docid":"5636b0c0aa61d0e3618819b3e3d4d2f4","score":"0.51665026","text":"def join(*seqs):\n return reduce(operator.concat, seqs)","title":""},{"docid":"50f9237a8b05f0258ee624c92f65b02d","score":"0.5166049","text":"def others(config, configs):\n for other in configs:\n if other is config:\n continue\n yield other","title":""},{"docid":"a416af002e53cf223ccf278b4d38bbfe","score":"0.515366","text":"def forward_sequence(self):\n functions = []\n\n def visit_func(pf):\n functions.append(pf)\n self.execute_on_proto(visit_func)\n for pf in functions:\n yield pf","title":""},{"docid":"60af186406998dde79bf76340545d937","score":"0.5152686","text":"def generate_different_arrays():\n dtypes = ['float32', 'float64', 'complex64', 'int8']\n shapes = [(4,), (4, 5), (4, 5, 6), (2,) * 8]\n for array_index in range(4):\n yield np.ones(\n shape=shapes[array_index],\n dtype=dtypes[array_index])\n yield 2 * np.ones(\n shape=shapes[array_index],\n dtype=dtypes[array_index])","title":""},{"docid":"21a6cac6d2ab728fd3fcf4df1a2721e8","score":"0.51503","text":"def many(p: Parser, init: Optional[List[Any]] = None) -> Parser:\n @parser\n def g(c: Cursor, a: Any):\n try:\n result = init or []\n while True:\n x, c, a = p(c, a).invoke()\n result.append(x)\n except Failure:\n return result, c, a\n return g","title":""},{"docid":"aaba7aaa7d821ae14fcd2d9b34691d34","score":"0.5141723","text":"def items(self):\n self.run_all_gens()\n _coconut_yield_from_7 = _coconut.iter(self.registered.items())\n while True:\n try:\n yield _coconut.next(_coconut_yield_from_7)\n except _coconut.StopIteration as _coconut_yield_err_3:\n _coconut_yield_from_6 = _coconut_yield_err_3.args[0] if _coconut.len(_coconut_yield_err_3.args) > 0 else None\n break\n\n _coconut_yield_from_6","title":""},{"docid":"de6133ca04f1fe52222df492d83ab388","score":"0.5129925","text":"def gen_ruptures(self, sources, monitor, site_coll):\r\n filtsources_mon = monitor.copy('filtering sources')\r\n genruptures_mon = monitor.copy('generating ruptures')\r\n filtruptures_mon = monitor.copy('filtering ruptures')\r\n for src in sources:\r\n with filtsources_mon:\r\n s_sites = src.filter_sites_by_distance_to_source(\r\n self.maximum_distance, site_coll\r\n ) if self.maximum_distance else site_coll\r\n if s_sites is None:\r\n continue\r\n\r\n with genruptures_mon:\r\n ruptures = list(src.iter_ruptures())\r\n if not ruptures:\r\n continue\r\n\r\n for rupture in ruptures:\r\n with filtruptures_mon:\r\n r_sites = filters.filter_sites_by_distance_to_rupture(\r\n rupture, self.maximum_distance, s_sites\r\n ) if self.maximum_distance else s_sites\r\n if r_sites is None:\r\n continue\r\n yield SourceRuptureSites(src, rupture, r_sites)\r\n filtsources_mon.flush()\r\n genruptures_mon.flush()\r\n filtruptures_mon.flush()","title":""},{"docid":"bafd6afa4d942f929fd78fc0ae24d95c","score":"0.5123105","text":"def test_compare_structure_of_two_custom_generators_with_complex_dependencies():\n\n mapping = {\n 1: ['a', 'aa', 'aaa', 'aaaa', 'aaaaa'],\n 2: ['b', 'bb', 'bbb', 'bbbb', 'bbbbb'],\n 3: ['c', 'cc', 'ccc', 'cccc', 'ccccc'],\n 4: ['d', 'dd', 'ddd', 'dddd', 'ddddd'],\n 5: ['e', 'ee', 'eee', 'eeee', 'eeeee'],\n 6: ['f', 'ff', 'fff', 'ffff', 'fffff'],\n 7: ['g', 'gg', 'ggg', 'gggg', 'ggggg'],\n }\n\n class Quux1Generator(CustomGenerator):\n nn = Integer(1, 5)\n aa = SelectMultiple(Lookup(Integer(1, 7), mapping), num=nn)\n\n class Quux2Generator(CustomGenerator):\n nn = Integer(1, 5)\n key_gen = Integer(1, 7)\n mapping_gen = Constant(mapping)\n lookup = Lookup(key_gen, mapping_gen)\n aa = SelectMultiple(lookup, num=nn)\n\n g1 = Quux1Generator()\n g2 = Quux2Generator()\n\n df1 = g1.generate(100, seed=12345).to_df()\n df2 = g2.generate(100, seed=12345).to_df()\n\n pd.util.testing.assert_frame_equal(df1, df2[[\"nn\", \"aa\"]])","title":""},{"docid":"8e9e535309a706fc6ef4efb6ee6e73a4","score":"0.51147544","text":"def do_combine(stream, log, select_func, combine_func):\n for id11, record_set in itertools.groupby(stream, lambda r: r.station_uid):\n log.write('%s\\n' % id11)\n records = set()\n for record in record_set:\n records.add(record)\n ann_mean, ann_anoms = series.monthly_annual(record.series)\n record.set_ann_anoms(ann_anoms)\n record.ann_mean = ann_mean\n begin, end = records_begin_end(records)\n years = end - begin + 1\n # reduce the collection of records (by combining) until there\n # are none (or one) left.\n while records:\n if len(records) == 1:\n # Just one left, yield it.\n yield records.pop()\n break\n record = select_func(records)\n records.remove(record)\n sums, wgts = fresh_arrays(record, years)\n log.write(\"\\t%s %s %s -- %s\\n\" % (record.uid,\n record.first_valid_year(), record.last_valid_year(),\n record.source))\n combine_func(sums, wgts, begin, records, log, record.uid)\n final_data = average(sums, wgts)\n record.set_series(begin * 12 + 1, final_data)\n yield record","title":""},{"docid":"823633ef6734023c99a7e253e5700134","score":"0.511182","text":"def sequence(first: Parser, *rest: Parser) -> Parser:\n if rest:\n return first >> (lambda _: sequence(*rest))\n else:\n return first","title":""},{"docid":"1ad97ce2b3a98688de9b90f382fcb3ea","score":"0.5106043","text":"def pairs_generator(exchanges):\n return list(combinations(exchanges, 2))","title":""},{"docid":"5366cc387faa279719d0604f7b212fff","score":"0.5101079","text":"def make_generators():\n \n # Rescale the RGB coefficants to make it play nicer with out model\n train_datagen = ImageDataGenerator(rescale=1./255)\n test_datagen = ImageDataGenerator(rescale=1./255)\n\n train_generator = train_datagen.flow_from_directory(\n TRAIN_DATA_PATH,\n target_size= (150, 150),\n batch_size= 20,\n class_mode= 'binary')\n\n validation_generator = test_datagen.flow_from_directory(\n VAL_DATA_PATH,\n target_size= (150, 150),\n batch_size= 20,\n class_mode= 'binary')\n\n return train_generator, validation_generator","title":""},{"docid":"bc1b79f2093cd1b100e8212253ec4816","score":"0.50996995","text":"def process(input_stream, base_generator, processors):\r\n from tqdm import tqdm\r\n from profiling import Profiled\r\n\r\n iterable = tqdm(base_generator(input_stream), \"baseline generator\", mininterval=3.0)\r\n results = chain_generators(iterable, processors)\r\n for _ in tqdm(results, \"final results\", mininterval=3.0):\r\n Profiled.total_count += 1","title":""},{"docid":"1019ab43f903cebd4a3229f0fed4019b","score":"0.5095481","text":"def __iter__(self):\n integrator = (self._integrator,) if self._integrator else []\n yield from chain(self._tuners, self._updaters, integrator,\n self._writers, self._computes)","title":""},{"docid":"65d4b846522676dcf35ee6494e754d7c","score":"0.5094075","text":"def mix_objects(source_session):\n with gen_asym_keys(source_session, ASYM_KEYS[0], 5) as asym_keys, gen_sym_keys(\n source_session, random.choice(SYM_KEYS), 20\n ) as sym_keys, create_data_object(\n source_session, DATA_TEMPLATE, 20\n ) as data_obj, create_data_object(\n source_session, CERTIFICATE_TEMPLATE, 20\n ) as cert_obj:\n yield get_migration_data(asym_keys + sym_keys + data_obj + cert_obj)","title":""},{"docid":"8d4186505c4fa85ab7648ebe44f28f8f","score":"0.5093453","text":"def aggregate_meta(sources):\n for s in sources:\n for (chan, desc, fill) in s.meta:\n yield (s.mode, chan, desc, fill)","title":""},{"docid":"6412de12253cc4c0752207c654bee2de","score":"0.50933444","text":"def _iter_alternate_objects(self):\n for alternate in self.alternates:\n for alternate_object in alternate:\n yield alternate_object","title":""},{"docid":"0ca3a04f28b7935f2102a961374ddbbd","score":"0.50929135","text":"def listMergeGen(*args):\r\n if len(args) == 0:\r\n r = np.array([[]])\r\n elif len(args) == 1:\r\n a = np.array(args[0])\r\n if a.shape:\r\n r = a.reshape((np.amax(a.shape), 1))\r\n else:\r\n r = np.array([[a]])\r\n\r\n else:\r\n A = np.meshgrid(*args)\r\n\r\n r = np.array([i.flatten() for i in A]).T\r\n\r\n for i in r:\r\n yield i","title":""}],"string":"[\n {\n \"docid\": \"c7a09ddd2a1ad01359b94e02267b5713\",\n \"score\": \"0.73177344\",\n \"text\": \"def combine_generators_no_random_crop(sharp_generator, blur_generator):\\n while True:\\n sharp_batch = sharp_generator.next()\\n blur_batch = blur_generator.next()\\n\\n res = [sharp_batch, blur_batch]\\n\\n yield res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc3c0da763e581890ec873abf3b454bc\",\n \"score\": \"0.72304946\",\n \"text\": \"def combine_generators(sharp_generator, blur_generator):\\n while True:\\n sharp_batch = sharp_generator.next()\\n blur_batch = blur_generator.next()\\n\\n sharp_batch, blur_batch = random_crop(sharp_batch, blur_batch)\\n\\n res = [sharp_batch, blur_batch]\\n\\n yield res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4745d0e85afbbc558dec68a5add57dff\",\n \"score\": \"0.7212617\",\n \"text\": \"def gen_concatenate(iterators):\\n for it in iterators:\\n yield from it\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51cc6ce73d773d41944c331c7b82b836\",\n \"score\": \"0.7137542\",\n \"text\": \"def TryMultipleGenerator():\\n\\tprint('I should be the first generator')\\n\\tfor i in range(5):\\n\\t\\tyield i+1\\n\\n\\tprint('I should be the second generator')\\n\\tfor j in range(10):\\n\\t\\tyield j*10\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86d21b81fcc53c83c128728dc3a46ac8\",\n \"score\": \"0.7052838\",\n \"text\": \"def chain_generators(*sprite_generators):\\n\\n def _generate(*args, **kwargs):\\n return list(itertools.chain(*[generator(*args, **kwargs)\\n for generator in sprite_generators]))\\n\\n return _generate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"276d9768038794a3fd85ff726fbbcc5c\",\n \"score\": \"0.69457084\",\n \"text\": \"def chain_generators(iterable, list_of_generators):\\r\\n\\r\\n def flatten1(nested_iterable):\\r\\n return (item for inner in nested_iterable for item in inner)\\r\\n\\r\\n # IMPORTANT: this function can NOT be inlined. Python scope/shadowing intricacies would break it\\r\\n def apply(gen, iterable): \\r\\n return (gen(x) for x in iterable)\\r\\n\\r\\n def flatmap(iter, gen):\\r\\n return flatten1(apply(gen, iter))\\r\\n\\r\\n return reduce(flatmap, list_of_generators, iterable)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f0f281d3dd2d6579a8efc9e59c9e95\",\n \"score\": \"0.6784467\",\n \"text\": \"def make_generators_generator(g):\\n for x in g:\\n yield x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db5542fbdd1aa7d192ed6ec1e2d9828e\",\n \"score\": \"0.64963466\",\n \"text\": \"def mix_generators(generator_list):\\n i = 0\\n l = len(generator_list)\\n stopiters_seen = 0\\n while stopiters_seen <= l:\\n try:\\n yield six.next(generator_list[i % l])\\n i += 1\\n stopiters_seen = 0\\n except StopIteration:\\n i += 1\\n stopiters_seen += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4044be6db2e56846c42b0f4fe65d61fb\",\n \"score\": \"0.61763394\",\n \"text\": \"def concat_seq(sources: Iterable[AsyncObservable[TSource]]) -> AsyncObservable[TSource]:\\n\\n return pipe(\\n of_seq(sources),\\n merge_inner(1),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f224dc66f49be220255e11ac520c44d\",\n \"score\": \"0.6116887\",\n \"text\": \"def make_generators_generator(g):\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\n def helper(g, i):\\n gen = g()\\n for _ in range(i):\\n yield next(gen)\\n\\n length = len(list(g()))\\n for i in range(1, length + 1):\\n yield helper(g, i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c2f10f017e029dedd27871d864972c\",\n \"score\": \"0.5957536\",\n \"text\": \"def generate_different_arrays():\\n yield np.array([1, 2])\\n yield np.array([1, 2, 3])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6b8396667f71123577777cdc56911dd\",\n \"score\": \"0.5947436\",\n \"text\": \"def Serial(*fns): # pylint: disable=invalid-name\\n def composed_fns(generator=None):\\n for f in fastmath.tree_flatten(fns):\\n generator = f(generator)\\n return generator\\n return composed_fns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cec93fb32a072041dab6c65f9eec6d14\",\n \"score\": \"0.5936838\",\n \"text\": \"def _product_generator(a, b):\\n yield a * b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"246b3f9a578462c2a944ee0f2aaf620e\",\n \"score\": \"0.59360576\",\n \"text\": \"def merge_streams(a, b):\\n stream = (a, b)\\n item = list(map(_get_next, stream))\\n cur_stream = _get_cur_stream_idx(item) # Stream we are reading ATM.\\n\\n while item != [_sentinel, _sentinel]: # While at least one not at EOF.\\n yield item[cur_stream]\\n item[cur_stream] = _get_next(stream[cur_stream])\\n cur_stream = _get_cur_stream_idx(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07300a0105791a3e88808c869a31b6b4\",\n \"score\": \"0.5931834\",\n \"text\": \"def _iter_merge_checks(checks_1, checks_2):\\n if (checks_1 is not None):\\n yield from checks_1\\n \\n if (checks_2 is not None):\\n yield from checks_2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe2f535323dbe9409f0c1f71bf327d8c\",\n \"score\": \"0.5925361\",\n \"text\": \"def concat(seqs: Union[Seq, Seq[Seq]], *extra) -> Iter:\\n if extra:\\n return Iter(itertools.chain(seqs, *extra))\\n return Iter(itertools.chain.from_iterable(seqs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df67cd64970fb2ecdae24e4e4feb4a7\",\n \"score\": \"0.59073645\",\n \"text\": \"def gen():\\n yield\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4a314e0f066f2be79ded65373e9aa10\",\n \"score\": \"0.588959\",\n \"text\": \"def zip_with(func, *iterables):\\n\\n for args in zip(*iterables):\\n yield func(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b61c20e7a61439761f6ee28e72be964\",\n \"score\": \"0.5880449\",\n \"text\": \"def roundrobin(iterables):\\n curr_alive = map(iter, iterables)\\n while curr_alive:\\n next_alive = []\\n for gen in curr_alive:\\n try:\\n yield next(gen)\\n except StopIteration:\\n pass\\n else:\\n next_alive.append(gen)\\n curr_alive = next_alive\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e70ee8ae976f7bf602ac9ce79c6e9ab\",\n \"score\": \"0.5867071\",\n \"text\": \"def roundrobin(*iterables):\\n pending = len(iterables)\\n funs = cycle(iter(it).next for it in iterables)\\n while pending:\\n try:\\n for fun in funs:\\n yield fun()\\n except StopIteration:\\n pending -= 1\\n funs = cycle(islice(funs, pending))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c34f5a4bd70fdd2931b8cb478343ed88\",\n \"score\": \"0.58531594\",\n \"text\": \"def batch_generator():\\n source_batch = []\\n target_batch = []\\n for sources, target in utils.yield_sources_and_targets(\\n FLAGS.predict_input_file, FLAGS.input_format):\\n\\n source_batch.append(\\n FLAGS.special_glue_string_for_joining_sources.join(sources))\\n target_batch.append(target)\\n if len(source_batch) == FLAGS.predict_batch_size:\\n yield source_batch, target_batch\\n source_batch = []\\n target_batch = []\\n\\n if source_batch:\\n yield source_batch, target_batch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d40ee8a2d529f66eb06fac931a8d05d0\",\n \"score\": \"0.58434135\",\n \"text\": \"async def achain(*async_iters: AsyncIterable[T]) -> AsyncIterator[T]:\\n for aiter in async_iters:\\n async for elem in aiter:\\n yield elem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ab9fed836540647bd1e0fd99edfe03e\",\n \"score\": \"0.58290917\",\n \"text\": \"def imapzip(generator, *iterables):\\n return itertools.izip(*tuple(itertools.starmap(generator, zip(*iterables))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"082650302e54033020864022e81e0646\",\n \"score\": \"0.58168626\",\n \"text\": \"def test_multiple_yields(self):\\n def generate_10_arrays():\\n \\\"\\\"\\\"Put out 10 numpy arrays\\\"\\\"\\\"\\n for _ in range(10):\\n yield np.array([1, 2, 3, 4]).astype(np.float32)\\n def assert_expectation(array):\\n \\\"\\\"\\\"Assert the array is as expected\\\"\\\"\\\"\\n np.testing.assert_almost_equal(array, [1, 2, 3, 4])\\n self.occurences += 1\\n blocks = []\\n blocks.append((NumpySourceBlock(generate_10_arrays), {'out_1': 0}))\\n blocks.append((NumpyBlock(assert_expectation, outputs=0), {'in_1': 0}))\\n Pipeline(blocks).main()\\n self.assertEqual(self.occurences, 10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b33b4f29062c9f97bd5fdc1023406e\",\n \"score\": \"0.5773603\",\n \"text\": \"def iterate(self):\\n for result_a, binds_a in self.bound_a:\\n for result_b, self.binds in self.bound_b:\\n if not isinstance(result_a, tuple):\\n result_a = (result_a,)\\n if not isinstance(result_b, tuple):\\n result_b = (result_b,)\\n self.binds.update(binds_a)\\n yield tuple(chain(result_a, result_b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9cb09e4547d9038cd8830171476415e\",\n \"score\": \"0.5746471\",\n \"text\": \"def merge_streamlines(backward, forward):\\n B = iter(backward)\\n F = iter(forward)\\n while True:\\n yield concatenate((B.next()[:0:-1], F.next()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f8d7ef0ffcce2a2bfdcdbf19cf385d9\",\n \"score\": \"0.5745188\",\n \"text\": \"def merge_graphs(generators, strategy_fields_source_node=lambda g: {}, strategy_graph_id=graph_id):\\n graph = nx.MultiDiGraph()\\n\\n print('Merging subgraphs ...')\\n for generator in tqdm(generators):\\n for g in generator():\\n\\n id_sub_graph = strategy_graph_id(g)\\n assert id_sub_graph not in graph\\n\\n graph.add_node(id_sub_graph, type='source', value=id_sub_graph, id=id_sub_graph, **strategy_fields_source_node(g))\\n\\n # Element in queue represent nodes in the shape (id_node_in_subgraph, id_node_in_graph)\\n q = [('source', id_sub_graph)]\\n while True:\\n\\n if len(q) == 0:\\n break\\n else:\\n node = q.pop(0)\\n\\n edges = g.edges(node[0])\\n for edge in edges:\\n\\n n = g.nodes()[edge[1]].copy()\\n\\n old_id = n['id']\\n new_id = n['mergiable_id']\\n\\n n.pop('id')\\n n.pop('graph')\\n graph.add_node(new_id, id=new_id, **n)\\n\\n type_edge = g[node[0]][old_id]['type']\\n graph.add_edge(node[1], new_id, type=type_edge)\\n\\n q.append((old_id, new_id))\\n\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67c898ad379e79aa60f0aa910bba7c15\",\n \"score\": \"0.57398504\",\n \"text\": \"def chain(*fs):\\n\\tdef chained(x):\\n\\t\\tres = x\\n\\t\\tfor f in fs:\\n\\t\\t\\tres = f(res)\\n\\t\\treturn res\\n\\treturn chained\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b56ac401407be8a900a031c15efe5400\",\n \"score\": \"0.56415904\",\n \"text\": \"async def azip(*iterables: AsyncIterable[T]) -> AsyncIterator[Tuple[T, ...]]:\\n iterators = [iterable.__aiter__() for iterable in iterables]\\n while True:\\n try:\\n yield tuple(await asyncio.gather(*(itr.__anext__() for itr in iterators)))\\n except StopAsyncIteration:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a088c186a23bb7af53d0ae0c119fcac\",\n \"score\": \"0.5632826\",\n \"text\": \"def compose_contexts(\\n head: ContextManager, *tail: ContextManager\\n) -> Generator[Any, None, None]:\\n with ExitStack() as stack:\\n if len(tail):\\n yield tuple(stack.enter_context(c) for c in [head, *tail])\\n else:\\n yield stack.enter_context(head)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3771e89d3dbf86a3bd5a8d3cb446600d\",\n \"score\": \"0.5628315\",\n \"text\": \"def generate_two_different_arrays():\\n for _ in range(10):\\n yield np.random.rand(4)\\n for _ in range(10):\\n yield np.random.rand(5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e52982fedee89781c0488e8e65ede587\",\n \"score\": \"0.5575016\",\n \"text\": \"def alternate_iterables(*iterables):\\n iterators = [iter(iterable) for iterable in iterables]\\n\\n while True:\\n try:\\n for iterator in iterators:\\n yield next(iterator)\\n except StopIteration:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aa04ed24f3ddea72f8cbd9b03fcdca6\",\n \"score\": \"0.5571096\",\n \"text\": \"def _overlay_gen():\\n while True:\\n for overlay in overlays:\\n yield overlay\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4592be005f3a426114a6b2c54eef3b0c\",\n \"score\": \"0.5568623\",\n \"text\": \"def zip_with(func: Callable, *iters) -> Iterable:\\n return map(func, zip(*iters))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96fa156e83f43c488b1b62015d36cf61\",\n \"score\": \"0.5550289\",\n \"text\": \"def expand_circuit_generator(circuit_generator, circuits):\\n for circuit in circuit_generator:\\n for relay in circuit:\\n circuits.setdefault(relay, set(circuit)).update(circuit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24bce8beda9d28a2de71595c85eff0ce\",\n \"score\": \"0.554429\",\n \"text\": \"def gen():\\n for x in range(20):\\n yield x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"207f55d84237cc1229081aac52ff4a73\",\n \"score\": \"0.5544061\",\n \"text\": \"def nested(*mgrs):\\n with contextlib.ExitStack() as stack:\\n outputs = [stack.enter_context(cm) for cm in mgrs]\\n yield outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b56d456ea53002ac34f321f72a337343\",\n \"score\": \"0.5527838\",\n \"text\": \"def pipeline(*functions):\\n head = functions[0]\\n tail = functions[1:]\\n if tail:\\n def _fn(iterable):\\n for i in pipeline(*tail)(head(iterable)):\\n yield i\\n return _fn\\n else:\\n return head\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f8fbda05d1c4190f8078817fd7d44b\",\n \"score\": \"0.5527089\",\n \"text\": \"def gen_things(repeat=1):\\n\\tfor x in itertools.product(ascii_lowercase, repeat=repeat):\\n\\t\\tyield x\\n\\tfor x in gen_things(repeat+1):\\n\\t\\tyield x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dd0df1576426b5282a0049660d38a29\",\n \"score\": \"0.55237025\",\n \"text\": \"def cartesian_product(a, b):\\n\\n for i in a:\\n for j in b:\\n yield i + (j,)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aff9c49c8d655499974b860a45698153\",\n \"score\": \"0.55206954\",\n \"text\": \"def generators(self):\\n return self._gens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9d418a427a7c605feb1d323f15122db\",\n \"score\": \"0.54975235\",\n \"text\": \"def yieldAllCombos(items):\\n for combo in powerSet(items):\\n bag1 = combo[0]\\n toTake = combo[1]\\n \\n for combo2 in powerSet(toTake):\\n bag2 = combo2[0]\\n \\n yield (bag1, bag2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbe4061dca7c8ace6add74f6c230495d\",\n \"score\": \"0.5486556\",\n \"text\": \"def sequences(self):\\n for fasta in self.raw_files:\\n for seq in fasta: yield seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6721cf4b746dc5a2c1284df869c566c\",\n \"score\": \"0.5486126\",\n \"text\": \"def combine_plays(games):\\n chain = itertools.chain(*[g.drives.plays() for g in games])\\n return nflgame.seq.GenPlays(chain)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68e06745da285a3c93f89f4c3a0140c3\",\n \"score\": \"0.54828465\",\n \"text\": \"def combine(args1, args2):\\n return [arg1.combine(arg2) for arg1, arg2 in zip(args1, args2)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caca66538264dc51478a333efed74995\",\n \"score\": \"0.54826397\",\n \"text\": \"def substitute_generator(self, generator, out):\\n for value in generator:\\n self.substitute_value(value, out)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c3a782cbfc65b64d23ca57c379d91db\",\n \"score\": \"0.54760075\",\n \"text\": \"def Chapter4_12():\\n\\n from itertools import chain\\n a = [1, 2, 3, 4]\\n b = ['x', 'y', 'z']\\n for x in chain(a, b):\\n print(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c17c2169155d67875a01cd73e919797\",\n \"score\": \"0.5474594\",\n \"text\": \"def pam(functions: Iterable[Callable], *args, **kwargs):\\n for f in functions:\\n yield f(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e37bb7aea7324537ee5ca4e67373c582\",\n \"score\": \"0.5470446\",\n \"text\": \"def zip_readers(*readers, indices=None) -> Generator:\\n if indices:\\n iterators = zip(*(r.select_record_indices(indices) for r in readers))\\n else:\\n iterators = zip(*readers)\\n for record_tuple in iterators:\\n yield record_tuple\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ee31e835991168cd2dae340045e4106\",\n \"score\": \"0.54635113\",\n \"text\": \"def ijoin(iters):\\r\\n return (x for it in iters for x in it)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1034c67da8fa0cae7d488d0c7dae7f8d\",\n \"score\": \"0.54461986\",\n \"text\": \"def cartesian_product(items, get_separator):\\n combs = [None]\\n first_item_required = None\\n\\n for required, args in items:\\n if first_item_required is None:\\n first_item_required = required\\n\\n separator = get_separator(required)\\n _combine = partial(combine, separator=separator)\\n\\n combs = map(\\n _combine,\\n product(combs, args)\\n )\\n\\n if combs != [None]:\\n for comb in combs:\\n yield first_item_required, comb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55ebd7f244cf0fc0b86de69d0162e44e\",\n \"score\": \"0.54375833\",\n \"text\": \"def __call__(self, *sources: Source) -> Iterable[Swimporting]:\\n if len(sources) > 1:\\n src = chain.from_iterable(sources)\\n elif sources:\\n src = sources[0]\\n else:\\n return\\n\\n for o in src:\\n s = self.do(o)\\n if s:\\n yield s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd23164165c0b00c295c342286431a4d\",\n \"score\": \"0.5437451\",\n \"text\": \"def test_multiple_sequences(self):\\n\\n def generate_different_arrays():\\n \\\"\\\"\\\"Yield four different groups of two arrays\\\"\\\"\\\"\\n dtypes = ['float32', 'float64', 'complex64', 'int8']\\n shapes = [(4,), (4, 5), (4, 5, 6), (2,) * 8]\\n for array_index in range(4):\\n yield np.ones(\\n shape=shapes[array_index],\\n dtype=dtypes[array_index])\\n yield 2 * np.ones(\\n shape=shapes[array_index],\\n dtype=dtypes[array_index])\\n\\n def switch_types(array):\\n \\\"\\\"\\\"Return two copies of the array, one with a different type\\\"\\\"\\\"\\n return np.copy(array), np.copy(array).astype(np.complex128)\\n\\n self.occurences = 0\\n def compare_arrays(array1, array2):\\n \\\"\\\"\\\"Make sure that all arrays coming in are equal\\\"\\\"\\\"\\n self.occurences += 1\\n np.testing.assert_almost_equal(array1, array2)\\n\\n blocks = [\\n (NumpySourceBlock(generate_different_arrays), {'out_1': 0}),\\n (NumpyBlock(switch_types, outputs=2), {'in_1': 0, 'out_1': 1, 'out_2': 2}),\\n (NumpyBlock(np.fft.fft), {'in_1': 2, 'out_1': 3}),\\n (NumpyBlock(np.fft.ifft), {'in_1': 3, 'out_1': 4}),\\n (NumpyBlock(compare_arrays, inputs=2, outputs=0), {'in_1': 1, 'in_2': 4})]\\n\\n Pipeline(blocks).main()\\n self.assertEqual(self.occurences, 8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63ab985187ab8bdbfce6723f6ceff441\",\n \"score\": \"0.5422615\",\n \"text\": \"def _get_yielded_and_returned_values(gen, values_to_send=()):\\n yvals = []\\n try:\\n yvals.append(next(gen))\\n for s in values_to_send:\\n yvals.append(gen.send(s))\\n while True: # Loop until `gen` returns, even if no more values to send.\\n yvals.append(next(gen))\\n except StopIteration as e:\\n rvals = e.value\\n return yvals, rvals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20cebd134ead50dab060a8b3e60d3224\",\n \"score\": \"0.54173774\",\n \"text\": \"def sequence(*args):\\n if len(args) > 1:\\n def gen():\\n while True:\\n for o in args:\\n yield o\\n return gen()\\n\\n func = args and args[0] or None\\n if isinstance(func, _.string_types):\\n func = func.format\\n\\n elif func is None:\\n func = lambda x: x\\n\\n def gen2():\\n counter = 0\\n while True:\\n yield func(counter)\\n counter += 1\\n return gen2()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d8bcd1b7336f1a1294df2daf0e4515f\",\n \"score\": \"0.54116684\",\n \"text\": \"def roundrobin(*iterables):\\n # Recipe credited to George Sakkis\\n pending = len(iterables)\\n nexts = cycle(iter(it).next for it in iterables)\\n while pending:\\n try:\\n for next in nexts:\\n yield next()\\n except StopIteration:\\n pending -= 1\\n nexts = cycle(islice(nexts, pending))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12dae1f974cb630a2b524277b82dc63f\",\n \"score\": \"0.54071265\",\n \"text\": \"def generator_start_wrapper(generator):\\n yield\\n\\n yield from generator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2efdd688ce6851d9418b2d1ce79a52d\",\n \"score\": \"0.54056275\",\n \"text\": \"def commutators(scalar):\\n return com1(scalar), com2(scalar), com3(scalar), com4(scalar)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ba63b465189152f8f9013def55cf84f\",\n \"score\": \"0.54034525\",\n \"text\": \"def create_generator(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1624764657445a43444815d23a154b50\",\n \"score\": \"0.54007\",\n \"text\": \"def tst():\\n yield \\\"a\\\", \\\"b\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3130d5e3d7f6793a485556d332a6f697\",\n \"score\": \"0.53600836\",\n \"text\": \"def fit_generator_from_aggregator(aggregator):\\r\\n return aggregator.map(func=fit_from_agg_obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a10cce74e94804927ac598f16cdc9de\",\n \"score\": \"0.53596437\",\n \"text\": \"def merge(self, accumulators):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fffb43c78455637d65b4687e630b2db\",\n \"score\": \"0.53572583\",\n \"text\": \"def merge_accumulators(\\n self, accumulators: Iterable[_PartialNLStats]) -> _PartialNLStats:\\n it = iter(accumulators)\\n result = next(it)\\n for accumulator in it:\\n result += accumulator\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23b7572cc79298d73a1438e425714405\",\n \"score\": \"0.5346404\",\n \"text\": \"def zip_args_and_kwargs(\\n args: Iterable[Tuple[Any, ...]],\\n kwargs: Iterable[Dict[str, Any]],\\n) -> Generator[Tuple[Tuple[Any, ...], Dict[str, Any]], None, None]:\\n iterators: List[Iterator] = [iter(args), iter(kwargs)]\\n fills = {0: (), 1: {}}\\n num_active = 2\\n while True:\\n values = []\\n for i, it in enumerate(iterators):\\n try:\\n values.append(next(it))\\n except StopIteration:\\n num_active -= 1\\n if num_active == 0:\\n return\\n iterators[i] = itertools.repeat(fills[i]) # replace the iterator\\n values.append(fills[i]) # for this iteration, insert fills[i] manually\\n yield tuple(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7de0d0a54a3cc8719b5d73a6e7a55e53\",\n \"score\": \"0.5317929\",\n \"text\": \"def get_generators(self, len_val, src_vocab, text_embedding, args):\\n gate_gen = None\\n if args['slot_gating']:\\n gate_gen = Generator(len(GATES), scope_name=\\\"gate\\\")\\n fertility_generator = Generator(len_val + 1, scope_name=\\\"fertility\\\")\\n state_generator = Generator(src_vocab, text_embedding.get_embedding_wieght(), scope_name='state')\\n point_state_generator = PointerGenerator(state_generator=state_generator, scope_name='point_generator')\\n\\n return fertility_generator, point_state_generator, gate_gen\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e9ddad41927ae86b0414823d97663c7\",\n \"score\": \"0.52954257\",\n \"text\": \"def combine_latest(other: AsyncObservable[TOther]) -> Zipper[Any, TOther]:\\n\\n def _combine_latest(source: AsyncObservable[TSource]) -> AsyncObservable[Tuple[TSource, TOther]]:\\n async def subscribe_async(aobv: AsyncObserver[Tuple[TSource, TOther]]) -> AsyncDisposable:\\n safe_obv, auto_detach = auto_detach_observer(aobv)\\n\\n async def worker(inbox: MailboxProcessor[Msg[TSource]]) -> None:\\n @tailrec_async\\n async def message_loop(\\n source_value: Option[TSource], other_value: Option[TOther]\\n ) -> TailCallResult[NoReturn]:\\n cn = await inbox.receive()\\n\\n async def get_value(n: Notification[Any]) -> Option[Any]:\\n with match(n) as m:\\n for value in case(OnNext[TSource]):\\n return Some(value)\\n\\n for err in case(OnError):\\n await safe_obv.athrow(err)\\n\\n while m.default():\\n await safe_obv.aclose()\\n return Nothing\\n\\n with match(cn) as case:\\n for value in case(SourceMsg[TSource]):\\n source_value = await get_value(value)\\n break\\n\\n for value in case(OtherMsg[TOther]):\\n other_value = await get_value(value)\\n break\\n\\n def binder(s: TSource) -> Option[Tuple[TSource, TOther]]:\\n def mapper(o: TOther) -> Tuple[TSource, TOther]:\\n return (s, o)\\n\\n return other_value.map(mapper)\\n\\n combined = source_value.bind(binder)\\n for x in combined.to_list():\\n await safe_obv.asend(x)\\n\\n return TailCall(source_value, other_value)\\n\\n await message_loop(Nothing, Nothing)\\n\\n agent = MailboxProcessor.start(worker)\\n\\n async def obv_fn1(n: Notification[TSource]) -> None:\\n pipe(SourceMsg(n), agent.post)\\n\\n async def obv_fn2(n: Notification[TOther]) -> None:\\n pipe(OtherMsg(n), agent.post)\\n\\n obv1: AsyncObserver[TSource] = AsyncNotificationObserver(obv_fn1)\\n obv2: AsyncObserver[TOther] = AsyncNotificationObserver(obv_fn2)\\n dispose1 = await pipe(obv1, source.subscribe_async, auto_detach)\\n dispose2 = await pipe(obv2, other.subscribe_async, auto_detach)\\n\\n return AsyncDisposable.composite(dispose1, dispose2)\\n\\n return AsyncAnonymousObservable(subscribe_async)\\n\\n return _combine_latest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cea8a8ecce027f7efdb0cd9298e7ecd1\",\n \"score\": \"0.5288658\",\n \"text\": \"def product(*args, **kwds):\\n pools = map(tuple, args) * kwds.get('repeat', 1)\\n result = [[]]\\n for pool in pools:\\n result = [x+[y] for x in result for y in pool]\\n for prod in result:\\n yield tuple(prod)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c19238638d7eba6e38989cc2b00ca7d5\",\n \"score\": \"0.52800506\",\n \"text\": \"def _repeat_stream(stream, n_devices):\\n while True:\\n for example in stream(n_devices):\\n yield example\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7a9545b0fcf0deaa6518a7ac3c20f7b\",\n \"score\": \"0.52757585\",\n \"text\": \"def __next__(self):\\n\\n if self.batch_index == 0:\\n self.reset_index()\\n\\n current_index = (self.batch_index * self.batch_size) % self.n\\n if self.n > current_index + self.batch_size:\\n self.batch_index += 1\\n else:\\n self.batch_index = 0\\n\\n # random sample the lambda value from beta distribution.\\n l = np.random.beta(self.alpha, self.alpha, self.batch_size)\\n\\n X_l = l.reshape(self.batch_size, 1, 1, 1)\\n y_l = l.reshape(self.batch_size, 1)\\n\\n # Get a pair of inputs and outputs from two iterators.\\n X1, y1 = self.generator1.next()\\n X2, y2 = self.generator2.next()\\n\\n # Perform the mixup.\\n X = X1 * X_l + X2 * (1 - X_l)\\n y = y1 * y_l + y2 * (1 - y_l)\\n return X, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42c9cb5f29ff1545f70c1cd61df8d68c\",\n \"score\": \"0.5261039\",\n \"text\": \"def concatenate(u, words):\\n\\n for word in words:\\n yield tuple([u] + list(word))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"176a60c9dd43dd74a7dbd508bdcadd74\",\n \"score\": \"0.5257654\",\n \"text\": \"def tasks_generator(num_all_classes=100, num_avg_classes=2):\\n tasks_iter = itertools.combinations(np.arange(num_all_classes), num_avg_classes)\\n for task in tasks_iter:\\n yield task\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53ffa188a77fe3297e3a313d8267f7c6\",\n \"score\": \"0.52474356\",\n \"text\": \"def interleave(seqs: Union[Seq, Seq[Seq]], *extra, aligned=False):\\n if extra:\\n seqs = (seqs, *extra)\\n if aligned:\\n return Iter(_interleave_aligned(seqs))\\n return Iter(toolz.interleave(seqs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f61b238ed55217d2f81b7622c0ec3f86\",\n \"score\": \"0.52347803\",\n \"text\": \"def gens(self):\\n return tuple(self.algebra_generators())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12f0ab9927e07475dfb5f3febf3fcb02\",\n \"score\": \"0.5224693\",\n \"text\": \"def roundrobin(*iterables: tp.Iterable[tp.Any]) -> tp.Iterator[tp.Any]:\\n # Recipe credited to George Sakkis\\n num_active = len(iterables)\\n nexts = itertools.cycle(iter(it).__next__ for it in iterables)\\n while num_active:\\n try:\\n for next_ in nexts:\\n yield next_()\\n except StopIteration:\\n # Remove the iterator we just exhausted from the cycle.\\n num_active -= 1\\n nexts = itertools.cycle(itertools.islice(nexts, num_active))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dcda61d3d4ca91a0455a6d6e91b8a78\",\n \"score\": \"0.52164465\",\n \"text\": \"def assemble(self, instructions):\\n for instruction in instructions:\\n yield instruction.serialize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"894ebc5ac874c3904022202db34d3001\",\n \"score\": \"0.5213145\",\n \"text\": \"def test_multiple_output_rings(self):\\n def generate_many_arrays():\\n \\\"\\\"\\\"Put out 10x10 numpy arrays\\\"\\\"\\\"\\n for _ in range(10):\\n yield (np.array([1, 2, 3, 4]).astype(np.float32),) * 10\\n def assert_expectation(*args):\\n \\\"\\\"\\\"Assert the arrays are as expected\\\"\\\"\\\"\\n assert len(args) == 10\\n for array in args:\\n np.testing.assert_almost_equal(array, [1, 2, 3, 4])\\n self.occurences += 1\\n blocks = []\\n blocks.append((\\n NumpySourceBlock(generate_many_arrays, outputs=10),\\n {'out_%d' % (i + 1): i for i in range(10)}))\\n blocks.append((\\n NumpyBlock(assert_expectation, inputs=10, outputs=0),\\n {'in_%d' % (i + 1): i for i in range(10)}))\\n Pipeline(blocks).main()\\n self.assertEqual(self.occurences, 10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"799aef831b3091f8a8d5b490f5904860\",\n \"score\": \"0.5197894\",\n \"text\": \"def _advance_pattern_generators(self,p):\\n return p.generators\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a97ec91202c7651022c830d4ac6a3e0\",\n \"score\": \"0.5196196\",\n \"text\": \"def combine_latest(observables: Union[ObservableBase, Iterable[ObservableBase]],\\n mapper: Callable[[Any], Any]) -> ObservableBase:\\n if isinstance(observables, typing.Observable):\\n observables = [observables]\\n\\n args = list(observables)\\n result_mapper = mapper\\n parent = args[0]\\n\\n def subscribe(observer, scheduler=None):\\n n = len(args)\\n has_value = [False] * n\\n has_value_all = [False]\\n is_done = [False] * n\\n values = [None] * n\\n\\n def next(i):\\n has_value[i] = True\\n\\n if has_value_all[0] or all(has_value):\\n try:\\n res = result_mapper(*values)\\n except Exception as ex:\\n observer.on_error(ex)\\n return\\n\\n observer.on_next(res)\\n elif all([x for j, x in enumerate(is_done) if j != i]):\\n observer.on_completed()\\n\\n has_value_all[0] = all(has_value)\\n\\n def done(i):\\n is_done[i] = True\\n if all(is_done):\\n observer.on_completed()\\n\\n subscriptions = [None] * n\\n\\n def func(i):\\n subscriptions[i] = SingleAssignmentDisposable()\\n\\n def on_next(x):\\n with parent.lock:\\n values[i] = x\\n next(i)\\n\\n def on_completed():\\n with parent.lock:\\n done(i)\\n\\n subscriptions[i].disposable = args[i].subscribe_(on_next, observer.on_error,\\n on_completed, scheduler)\\n\\n for idx in range(n):\\n func(idx)\\n return CompositeDisposable(subscriptions)\\n return AnonymousObservable(subscribe)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0526036f8f7e82529a701ffd1922783\",\n \"score\": \"0.5195552\",\n \"text\": \"def generator(self, latent, reuse):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36558a6ae11cbeb2816add50d0f95aeb\",\n \"score\": \"0.51948166\",\n \"text\": \"def merge_adjacent(gen):\\n gen = iter(gen)\\n last = next(gen)\\n for this in gen:\\n if this.merge_key == last.merge_key:\\n last.merge(this)\\n elif last < this:\\n yield last\\n last = this\\n else:\\n raise AssertionError('Bad order, %s > %s' % (last, this))\\n yield last\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac9d339cf38de99d1cb18401b22c5d1d\",\n \"score\": \"0.518061\",\n \"text\": \"def get_instructions(self):\\n for block in self:\\n for instruction in block:\\n yield instruction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4f19f20bb3f78616dba6da86bdea368\",\n \"score\": \"0.5171164\",\n \"text\": \"def combine_ranges(all_ranges: Iterable[IPNetwork]) -> Iterator[IPNetwork]:\\n # ipaddress.collapse_addresses can't handle v4 and v6 ranges at the same time\\n ipv4, ipv6 = [], []\\n # only consume all_ranges once\\n for anet in all_ranges:\\n if anet.version == 4:\\n ipv4.append(anet)\\n elif anet.version == 6:\\n ipv6.append(anet)\\n else:\\n raise TypeError(anet)\\n\\n for ranges in [ipv4, ipv6]:\\n ranges = list(ipaddress.collapse_addresses(sorted(ranges))) # type: ignore\\n for net in ranges:\\n if net.version == 4 and net.prefixlen < 16:\\n for subnet in net.subnets(new_prefix=16):\\n yield subnet\\n elif net.version == 6 and net.prefixlen < 19:\\n for subnet in net.subnets(new_prefix=19):\\n yield subnet\\n else:\\n yield net\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5636b0c0aa61d0e3618819b3e3d4d2f4\",\n \"score\": \"0.51665026\",\n \"text\": \"def join(*seqs):\\n return reduce(operator.concat, seqs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50f9237a8b05f0258ee624c92f65b02d\",\n \"score\": \"0.5166049\",\n \"text\": \"def others(config, configs):\\n for other in configs:\\n if other is config:\\n continue\\n yield other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a416af002e53cf223ccf278b4d38bbfe\",\n \"score\": \"0.515366\",\n \"text\": \"def forward_sequence(self):\\n functions = []\\n\\n def visit_func(pf):\\n functions.append(pf)\\n self.execute_on_proto(visit_func)\\n for pf in functions:\\n yield pf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60af186406998dde79bf76340545d937\",\n \"score\": \"0.5152686\",\n \"text\": \"def generate_different_arrays():\\n dtypes = ['float32', 'float64', 'complex64', 'int8']\\n shapes = [(4,), (4, 5), (4, 5, 6), (2,) * 8]\\n for array_index in range(4):\\n yield np.ones(\\n shape=shapes[array_index],\\n dtype=dtypes[array_index])\\n yield 2 * np.ones(\\n shape=shapes[array_index],\\n dtype=dtypes[array_index])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21a6cac6d2ab728fd3fcf4df1a2721e8\",\n \"score\": \"0.51503\",\n \"text\": \"def many(p: Parser, init: Optional[List[Any]] = None) -> Parser:\\n @parser\\n def g(c: Cursor, a: Any):\\n try:\\n result = init or []\\n while True:\\n x, c, a = p(c, a).invoke()\\n result.append(x)\\n except Failure:\\n return result, c, a\\n return g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaba7aaa7d821ae14fcd2d9b34691d34\",\n \"score\": \"0.5141723\",\n \"text\": \"def items(self):\\n self.run_all_gens()\\n _coconut_yield_from_7 = _coconut.iter(self.registered.items())\\n while True:\\n try:\\n yield _coconut.next(_coconut_yield_from_7)\\n except _coconut.StopIteration as _coconut_yield_err_3:\\n _coconut_yield_from_6 = _coconut_yield_err_3.args[0] if _coconut.len(_coconut_yield_err_3.args) > 0 else None\\n break\\n\\n _coconut_yield_from_6\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6133ca04f1fe52222df492d83ab388\",\n \"score\": \"0.5129925\",\n \"text\": \"def gen_ruptures(self, sources, monitor, site_coll):\\r\\n filtsources_mon = monitor.copy('filtering sources')\\r\\n genruptures_mon = monitor.copy('generating ruptures')\\r\\n filtruptures_mon = monitor.copy('filtering ruptures')\\r\\n for src in sources:\\r\\n with filtsources_mon:\\r\\n s_sites = src.filter_sites_by_distance_to_source(\\r\\n self.maximum_distance, site_coll\\r\\n ) if self.maximum_distance else site_coll\\r\\n if s_sites is None:\\r\\n continue\\r\\n\\r\\n with genruptures_mon:\\r\\n ruptures = list(src.iter_ruptures())\\r\\n if not ruptures:\\r\\n continue\\r\\n\\r\\n for rupture in ruptures:\\r\\n with filtruptures_mon:\\r\\n r_sites = filters.filter_sites_by_distance_to_rupture(\\r\\n rupture, self.maximum_distance, s_sites\\r\\n ) if self.maximum_distance else s_sites\\r\\n if r_sites is None:\\r\\n continue\\r\\n yield SourceRuptureSites(src, rupture, r_sites)\\r\\n filtsources_mon.flush()\\r\\n genruptures_mon.flush()\\r\\n filtruptures_mon.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bafd6afa4d942f929fd78fc0ae24d95c\",\n \"score\": \"0.5123105\",\n \"text\": \"def test_compare_structure_of_two_custom_generators_with_complex_dependencies():\\n\\n mapping = {\\n 1: ['a', 'aa', 'aaa', 'aaaa', 'aaaaa'],\\n 2: ['b', 'bb', 'bbb', 'bbbb', 'bbbbb'],\\n 3: ['c', 'cc', 'ccc', 'cccc', 'ccccc'],\\n 4: ['d', 'dd', 'ddd', 'dddd', 'ddddd'],\\n 5: ['e', 'ee', 'eee', 'eeee', 'eeeee'],\\n 6: ['f', 'ff', 'fff', 'ffff', 'fffff'],\\n 7: ['g', 'gg', 'ggg', 'gggg', 'ggggg'],\\n }\\n\\n class Quux1Generator(CustomGenerator):\\n nn = Integer(1, 5)\\n aa = SelectMultiple(Lookup(Integer(1, 7), mapping), num=nn)\\n\\n class Quux2Generator(CustomGenerator):\\n nn = Integer(1, 5)\\n key_gen = Integer(1, 7)\\n mapping_gen = Constant(mapping)\\n lookup = Lookup(key_gen, mapping_gen)\\n aa = SelectMultiple(lookup, num=nn)\\n\\n g1 = Quux1Generator()\\n g2 = Quux2Generator()\\n\\n df1 = g1.generate(100, seed=12345).to_df()\\n df2 = g2.generate(100, seed=12345).to_df()\\n\\n pd.util.testing.assert_frame_equal(df1, df2[[\\\"nn\\\", \\\"aa\\\"]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e9e535309a706fc6ef4efb6ee6e73a4\",\n \"score\": \"0.51147544\",\n \"text\": \"def do_combine(stream, log, select_func, combine_func):\\n for id11, record_set in itertools.groupby(stream, lambda r: r.station_uid):\\n log.write('%s\\\\n' % id11)\\n records = set()\\n for record in record_set:\\n records.add(record)\\n ann_mean, ann_anoms = series.monthly_annual(record.series)\\n record.set_ann_anoms(ann_anoms)\\n record.ann_mean = ann_mean\\n begin, end = records_begin_end(records)\\n years = end - begin + 1\\n # reduce the collection of records (by combining) until there\\n # are none (or one) left.\\n while records:\\n if len(records) == 1:\\n # Just one left, yield it.\\n yield records.pop()\\n break\\n record = select_func(records)\\n records.remove(record)\\n sums, wgts = fresh_arrays(record, years)\\n log.write(\\\"\\\\t%s %s %s -- %s\\\\n\\\" % (record.uid,\\n record.first_valid_year(), record.last_valid_year(),\\n record.source))\\n combine_func(sums, wgts, begin, records, log, record.uid)\\n final_data = average(sums, wgts)\\n record.set_series(begin * 12 + 1, final_data)\\n yield record\",\n \"title\": \"\"\n },\n {\n \"docid\": \"823633ef6734023c99a7e253e5700134\",\n \"score\": \"0.511182\",\n \"text\": \"def sequence(first: Parser, *rest: Parser) -> Parser:\\n if rest:\\n return first >> (lambda _: sequence(*rest))\\n else:\\n return first\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ad97ce2b3a98688de9b90f382fcb3ea\",\n \"score\": \"0.5106043\",\n \"text\": \"def pairs_generator(exchanges):\\n return list(combinations(exchanges, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5366cc387faa279719d0604f7b212fff\",\n \"score\": \"0.5101079\",\n \"text\": \"def make_generators():\\n \\n # Rescale the RGB coefficants to make it play nicer with out model\\n train_datagen = ImageDataGenerator(rescale=1./255)\\n test_datagen = ImageDataGenerator(rescale=1./255)\\n\\n train_generator = train_datagen.flow_from_directory(\\n TRAIN_DATA_PATH,\\n target_size= (150, 150),\\n batch_size= 20,\\n class_mode= 'binary')\\n\\n validation_generator = test_datagen.flow_from_directory(\\n VAL_DATA_PATH,\\n target_size= (150, 150),\\n batch_size= 20,\\n class_mode= 'binary')\\n\\n return train_generator, validation_generator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc1b79f2093cd1b100e8212253ec4816\",\n \"score\": \"0.50996995\",\n \"text\": \"def process(input_stream, base_generator, processors):\\r\\n from tqdm import tqdm\\r\\n from profiling import Profiled\\r\\n\\r\\n iterable = tqdm(base_generator(input_stream), \\\"baseline generator\\\", mininterval=3.0)\\r\\n results = chain_generators(iterable, processors)\\r\\n for _ in tqdm(results, \\\"final results\\\", mininterval=3.0):\\r\\n Profiled.total_count += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1019ab43f903cebd4a3229f0fed4019b\",\n \"score\": \"0.5095481\",\n \"text\": \"def __iter__(self):\\n integrator = (self._integrator,) if self._integrator else []\\n yield from chain(self._tuners, self._updaters, integrator,\\n self._writers, self._computes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d4b846522676dcf35ee6494e754d7c\",\n \"score\": \"0.5094075\",\n \"text\": \"def mix_objects(source_session):\\n with gen_asym_keys(source_session, ASYM_KEYS[0], 5) as asym_keys, gen_sym_keys(\\n source_session, random.choice(SYM_KEYS), 20\\n ) as sym_keys, create_data_object(\\n source_session, DATA_TEMPLATE, 20\\n ) as data_obj, create_data_object(\\n source_session, CERTIFICATE_TEMPLATE, 20\\n ) as cert_obj:\\n yield get_migration_data(asym_keys + sym_keys + data_obj + cert_obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d4186505c4fa85ab7648ebe44f28f8f\",\n \"score\": \"0.5093453\",\n \"text\": \"def aggregate_meta(sources):\\n for s in sources:\\n for (chan, desc, fill) in s.meta:\\n yield (s.mode, chan, desc, fill)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6412de12253cc4c0752207c654bee2de\",\n \"score\": \"0.50933444\",\n \"text\": \"def _iter_alternate_objects(self):\\n for alternate in self.alternates:\\n for alternate_object in alternate:\\n yield alternate_object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ca3a04f28b7935f2102a961374ddbbd\",\n \"score\": \"0.50929135\",\n \"text\": \"def listMergeGen(*args):\\r\\n if len(args) == 0:\\r\\n r = np.array([[]])\\r\\n elif len(args) == 1:\\r\\n a = np.array(args[0])\\r\\n if a.shape:\\r\\n r = a.reshape((np.amax(a.shape), 1))\\r\\n else:\\r\\n r = np.array([[a]])\\r\\n\\r\\n else:\\r\\n A = np.meshgrid(*args)\\r\\n\\r\\n r = np.array([i.flatten() for i in A]).T\\r\\n\\r\\n for i in r:\\r\\n yield i\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":479,"cells":{"query_id":{"kind":"string","value":"96dd970d4d2f402366f34f7bc5e38ac1"},"query":{"kind":"string","value":"This function tokenize, remove stop words and apply lower case for every word within the text"},"positive_passages":{"kind":"list like","value":[{"docid":"0e412cf780cdd57714e1ba1ad43bc1c9","score":"0.0","text":"def parse_sentence(self, text):\n\n final_list = []\n list_of_words = self.clean_text(text.split())\n i = 0\n plus = 1\n while i < len(list_of_words):\n if len(list_of_words[i]) < 2 and not list_of_words[i].isdigit():\n i+=1\n continue\n if not list_of_words[i].lower() in self.stop_words and list_of_words[i][0] != \"@\" and not list_of_words[i].startswith(\"https\"):\n if list_of_words[i][0] == \"#\":\n words = self.serpearte_hashtags(list_of_words[i])\n for w in words:\n if w != '' or w.lower() not in self.stop_words:\n final_list.append(self.stem.stem_term(w.lower()))\n elif self.is_number(list_of_words[i]):\n if i != len(list_of_words)-1 and list_of_words[i+1] in self.perc:\n final_list.append(list_of_words[i] + \"%\")\n plus=2#change to while later\n if i != len(list_of_words)-1 and \"/\" in list_of_words[i+1]:\n lst = list_of_words[i+1].split(\"/\")\n try:\n float(lst[0])\n float(lst[1])\n final_list.append(list_of_words[i] + \" \" + list_of_words[i+1])\n plus=2\n except:\n pass\n if i != len(list_of_words)-1 and list_of_words[i+1] in self.numbers:\n if list_of_words[i+1][0] == \"t\" or list_of_words[i+1][0] == \"T\":\n final_list.append(list_of_words[i] + \"K\")\n else:\n final_list.append(list_of_words[i] + list_of_words[i+1][0].upper())\n plus=2\n else:\n final_list.append(self.reduce_number(list_of_words[i]))\n elif \"-\" in list_of_words[i] or \"/\" in list_of_words[i] or \"_\":\n if \"/\" in list_of_words[i]:\n lst = list_of_words[i].split(\"/\")\n elif \"-\" in list_of_words[i]:\n lst = list_of_words[i].split(\"-\")\n else:\n lst = list_of_words[i].split(\"_\")\n for j in lst:\n if j == '':\n continue\n if self.is_number(j):\n final_list.append(self.reduce_number(j))\n else:\n if j.lower() not in self.stop_words:\n final_list.append(self.stem.stem_term(j.lower()))\n elif \"..\" in list_of_words[i]:\n for j in list_of_words[i]:\n if j == \".\":\n list_of_words[i] = list_of_words[i].replace(j, \" \")\n s = list_of_words[i].split(\" \")\n for j in s:\n if j != \" \" and j.lower() not in self.stop_words:\n final_list.append(self.stem.stem_term(j.lower()))\n elif \".\" in list_of_words[i]:\n words = list_of_words[i].split(\".\")\n flag = True\n for w in words:\n if len(w) != 1:\n flag = False\n if flag:\n i+=plus\n continue\n for w in words:\n if w == '' or w[0] == \"@\":\n continue\n if self.is_number(w):\n final_list.append(self.reduce_number(w))\n else:\n if w.lower() not in self.stop_words:\n final_list.append(self.stem.stem_term(w.lower()))\n else:\n if list_of_words[i].lower() not in self.stop_words:\n final_list.append(self.stem.stem_term(list_of_words[i].lower()))\n i += plus\n final_list = list(filter(lambda a: a != '' or (len(a) != 1 and not a.isdigit()), final_list))\n return final_list","title":""}],"string":"[\n {\n \"docid\": \"0e412cf780cdd57714e1ba1ad43bc1c9\",\n \"score\": \"0.0\",\n \"text\": \"def parse_sentence(self, text):\\n\\n final_list = []\\n list_of_words = self.clean_text(text.split())\\n i = 0\\n plus = 1\\n while i < len(list_of_words):\\n if len(list_of_words[i]) < 2 and not list_of_words[i].isdigit():\\n i+=1\\n continue\\n if not list_of_words[i].lower() in self.stop_words and list_of_words[i][0] != \\\"@\\\" and not list_of_words[i].startswith(\\\"https\\\"):\\n if list_of_words[i][0] == \\\"#\\\":\\n words = self.serpearte_hashtags(list_of_words[i])\\n for w in words:\\n if w != '' or w.lower() not in self.stop_words:\\n final_list.append(self.stem.stem_term(w.lower()))\\n elif self.is_number(list_of_words[i]):\\n if i != len(list_of_words)-1 and list_of_words[i+1] in self.perc:\\n final_list.append(list_of_words[i] + \\\"%\\\")\\n plus=2#change to while later\\n if i != len(list_of_words)-1 and \\\"/\\\" in list_of_words[i+1]:\\n lst = list_of_words[i+1].split(\\\"/\\\")\\n try:\\n float(lst[0])\\n float(lst[1])\\n final_list.append(list_of_words[i] + \\\" \\\" + list_of_words[i+1])\\n plus=2\\n except:\\n pass\\n if i != len(list_of_words)-1 and list_of_words[i+1] in self.numbers:\\n if list_of_words[i+1][0] == \\\"t\\\" or list_of_words[i+1][0] == \\\"T\\\":\\n final_list.append(list_of_words[i] + \\\"K\\\")\\n else:\\n final_list.append(list_of_words[i] + list_of_words[i+1][0].upper())\\n plus=2\\n else:\\n final_list.append(self.reduce_number(list_of_words[i]))\\n elif \\\"-\\\" in list_of_words[i] or \\\"/\\\" in list_of_words[i] or \\\"_\\\":\\n if \\\"/\\\" in list_of_words[i]:\\n lst = list_of_words[i].split(\\\"/\\\")\\n elif \\\"-\\\" in list_of_words[i]:\\n lst = list_of_words[i].split(\\\"-\\\")\\n else:\\n lst = list_of_words[i].split(\\\"_\\\")\\n for j in lst:\\n if j == '':\\n continue\\n if self.is_number(j):\\n final_list.append(self.reduce_number(j))\\n else:\\n if j.lower() not in self.stop_words:\\n final_list.append(self.stem.stem_term(j.lower()))\\n elif \\\"..\\\" in list_of_words[i]:\\n for j in list_of_words[i]:\\n if j == \\\".\\\":\\n list_of_words[i] = list_of_words[i].replace(j, \\\" \\\")\\n s = list_of_words[i].split(\\\" \\\")\\n for j in s:\\n if j != \\\" \\\" and j.lower() not in self.stop_words:\\n final_list.append(self.stem.stem_term(j.lower()))\\n elif \\\".\\\" in list_of_words[i]:\\n words = list_of_words[i].split(\\\".\\\")\\n flag = True\\n for w in words:\\n if len(w) != 1:\\n flag = False\\n if flag:\\n i+=plus\\n continue\\n for w in words:\\n if w == '' or w[0] == \\\"@\\\":\\n continue\\n if self.is_number(w):\\n final_list.append(self.reduce_number(w))\\n else:\\n if w.lower() not in self.stop_words:\\n final_list.append(self.stem.stem_term(w.lower()))\\n else:\\n if list_of_words[i].lower() not in self.stop_words:\\n final_list.append(self.stem.stem_term(list_of_words[i].lower()))\\n i += plus\\n final_list = list(filter(lambda a: a != '' or (len(a) != 1 and not a.isdigit()), final_list))\\n return final_list\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a9c24e059ba0d629375783d4818e3a20","score":"0.77378315","text":"def tokenize(txt: str):\n return [w.lower() for w in txt.split(' ')]","title":""},{"docid":"c5959a44e97cef37db948692cef781c8","score":"0.7713063","text":"def tokenize(self, text):\n words = nltk.word_tokenize(text)\n words = (self.stem(word) for word in words)\n words = (word.lower() for word in words)\n words = (word for word in words if word not in self.stop_words)\n return words","title":""},{"docid":"15c1ef5f6ae0556e1ec42148b1854269","score":"0.76650214","text":"def tokenize(text):\n return [token.lower() for token in simple_preprocess(text) if token not in STOPWORDS and token not in reuters_stopwords]","title":""},{"docid":"82f04df4428fad25d479b74d94f41a37","score":"0.7526388","text":"def tokenize(text):\n return gensim.utils.tokenize(text, to_lower=True)","title":""},{"docid":"fafd7a4469d94f04ac33960bbc03aec1","score":"0.72875655","text":"def tokenize(doc):\n # doc = file.read()\n # token_pattern=r\"(?u)\\b\\w\\w+\\b\"\n token_pattern=r\"(?u)\\b\\w[a-zA-Z]+\\b\" # only word, not number\n token_pattern = re.compile(token_pattern)\n # tokenize = lambda doc: token_pattern.findall(doc.lower()\n tokenize = lambda doc: token_pattern.findall(doc)\n words = tokenize(doc) \n for i in range(len(words)):\n if words[i].istitle():\n words[i] = words[i].lower()\n return words","title":""},{"docid":"feaab67c4cc41d8eb44a84a7c71d414e","score":"0.7284889","text":"def tokenize_text(text):\n result = []\n for seq in SEQUENCE.finditer(text):\n result.append(list(map(str.lower, WORD.findall(seq.group(0)))))\n return result","title":""},{"docid":"7f30b1294f75193614acc560349673bc","score":"0.72693795","text":"def tokenize(self, text, never_split=None, **kwargs):\n if self.normalize_text:\n text = unicodedata.normalize(\"NFKC\", text)\n\n never_split = self.never_split + (never_split if never_split is not None else [])\n tokens = []\n\n for word in self.mecab(text):\n token = word.surface\n\n if self.do_lower_case and token not in never_split:\n token = token.lower()\n\n tokens.append(token)\n\n return tokens","title":""},{"docid":"70f8c800231fa758fdd1c77eb15ad402","score":"0.7194744","text":"def tokenize(self, sentence):\n sentence = ENSURE_UNICODE(sentence)\n for word in self._tokenizer(sentence):\n yield word if self._case_sensitive else word.lower()","title":""},{"docid":"53b78885fec00ec4b2a8dc01b239366e","score":"0.71805197","text":"def proc_text(self, text):\n\n lemmas = []\n tokens = []\n doc = self(text)\n for tok_obj in doc:\n if self._removePunct and tok_obj.is_punct:\n continue\n lemma = tok_obj.lemma_\n text = tok_obj.text\n if self._keepOnlyAlphaNum and not is_alpha_num(text):\n continue\n tok1 = text.lower()\n tok2 = lemma.lower()\n if tok1 in self._stopWords or tok2 in self._stopWords:\n continue\n\n if self._lowerCase:\n text = text.lower()\n lemma = lemma.lower()\n\n lemmas.append(lemma)\n tokens.append(text)\n\n return ' '.join(lemmas), ' '.join(tokens)","title":""},{"docid":"458151d75508480b62440e016665eb9e","score":"0.716325","text":"def preprocess(txt,parser):\n\tdoc = parser(txt)\n\treturn [token.lemma_.lower() for token in doc]","title":""},{"docid":"99e53dc4539edb6be773e3d9185595ad","score":"0.7155567","text":"def naive(self, text):\n\n\t\ttokenizedText = None\n\n\t\t#Fill in code here\n\t\ttottext=[]\n\t\tintext=[]\n\t\tt=''\n\t\tword=''\n\t\tfor i in range(len(text)):\n\t\t\tt=text[i].lower()\n\t\t\tfor j in range(len(t)):\n\t\t\t\tif t[j]!=' ':\n\t\t\t\t\tword=word+t[j]\n\t\t\t\tif t[j]==' ':\n\t\t\t\t\tintext.append(word)\n\t\t\t\t\tword=''\n\t\t\ttottext.append(intext)\n\t\t\tintext=[]\n\t\ttokenizedText=tottext\n\n\t\treturn tokenizedText","title":""},{"docid":"71e56208430fc7d202b0e72b9329698f","score":"0.7143314","text":"def text_preprocessing(text):\n tokenizer = nltk.tokenize.RegexpTokenizer(r'\\w+')\n nopunc = clean_text(text)\n tokenized_text = tokenizer.tokenize(nopunc)\n remove_stopwords = [w for w in tokenized_text if w not in stopwords.words('english')]\n combined_text = ' '.join(tokenized_text)\n return combined_text","title":""},{"docid":"8ca7c741d98def99938da052dcba2191","score":"0.7099463","text":"def tokenize(text):\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower())\n tokens = word_tokenize(text)\n clean_tokens = [lemmatizer.lemmatize(word)\n for word in tokens if word not in stop_words]\n return clean_tokens","title":""},{"docid":"843cd23163f73270bfc0b1c60bfad42c","score":"0.7085407","text":"def procText(self, text):\n\n lemmas = []\n tokens = []\n doc = self(text)\n for sent in doc.sentences:\n for tokObj in sent.words:\n if self._removePunct and tokObj.upos == 'PUNCT':\n continue\n lemma = tokObj.lemma\n text = tokObj.text\n if self._keepOnlyAlphaNum and not isAlphaNum(text):\n continue\n tok1 = text.lower()\n tok2 = lemma.lower()\n if tok1 in self._stopWords or tok2 in self._stopWords:\n continue\n\n if self._lowerCase:\n text = text.lower()\n lemma = lemma.lower()\n\n lemmas.append(lemma)\n tokens.append(text)\n\n return ' '.join(lemmas), ' '.join(tokens)","title":""},{"docid":"985437f5970a9b0d4a92edbabcd2acaa","score":"0.7084913","text":"def procText(self, text):\n\n lemmas = []\n tokens = []\n doc = self(text)\n for tokObj in doc:\n if self._removePunct and tokObj.is_punct:\n continue\n lemma = tokObj.lemma_\n text = tokObj.text\n if self._keepOnlyAlphaNum and not isAlphaNum(text):\n continue\n tok1 = text.lower()\n tok2 = lemma.lower()\n if tok1 in self._stopWords or tok2 in self._stopWords:\n continue\n\n if self._lowerCase:\n text = text.lower()\n lemma = lemma.lower()\n\n lemmas.append(lemma)\n tokens.append(text)\n\n return ' '.join(lemmas), ' '.join(tokens)","title":""},{"docid":"0e4c3e368f481df5279ecf66dd4b63d6","score":"0.70735097","text":"def _preprocess(self, a_txt):\n return super(LexiconBaseAnalyzer, self)._preprocess(a_txt).lower()","title":""},{"docid":"e110a5a9edbc81fd97013c654a659343","score":"0.70731664","text":"def normalize_text(s, lower_case = True):\n\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower() if lower_case else text\n #return text.lower()\n \n def remove_stop_words(text):\n return re.sub(r'\\b(as|to|that|and|of)\\b', ' ', text)\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))","title":""},{"docid":"512f7ce1781c17f635fde142ca3e31b0","score":"0.7067765","text":"def simple_tokenization(doc):\n tok = re.findall('(\\\\w+)', doc.lower())\n docs = ' '.join(tok)\n return word_tokenize(docs)","title":""},{"docid":"e3385a2dfae0fabfaef1ed09e582b853","score":"0.7065585","text":"def tokenize(self, text):\n\n return word_tokenize(text)","title":""},{"docid":"124040aeabb11a9504d547a80a64ad44","score":"0.70500314","text":"def tokenize_and_clean(text):\r\n tokens = []\r\n for token in word_tokenize(text):\r\n if not token.isdigit(): # keep purely numerical strings unchanged\r\n token = ''.join(c for c in token if c.isalpha())\r\n if token.isalnum(): #and token.lower() not in stops: # if token != \"\"\r\n tokens.append(token.lower())\r\n return tokens","title":""},{"docid":"326fde92140fea8a5073a51264988c56","score":"0.70296556","text":"def tokenize(text):\n text = text.lower()\n text = re.sub(r\"[^a-zA-z0-9]\",\" \",text)\n words = word_tokenize(text)\n words = [w for w in words if w not in stopwords.words(\"english\")]\n \n clean_tokens = []\n lemmatizer = WordNetLemmatizer()\n \n for w in words:\n clean_tok = lemmatizer.lemmatize(w , pos='v').strip()\n clean_tokens.append(clean_tok)\n return clean_tokens","title":""},{"docid":"c8f18c11bd4a9f89a96391675eeb4ac9","score":"0.70207685","text":"def process(self, text: str, allow_stopwords: bool = False) -> str:\n ret = []\n\n # split and cast to lower case\n text = re.sub(r'<[^>]+>', ' ', str(text))\n for word in re.split('[^a-zA-Z]', str(text).lower()):\n # remove non-alphabetic and stop words\n if (word.isalpha() and word not in self.stopwords) or allow_stopwords:\n if word not in self.stemmed:\n self.stemmed[word] = self.ps.stem(word)\n # use stemmed version of word\n ret.append(self.stemmed[word])\n return ' '.join(ret)","title":""},{"docid":"a5ec6f60b99f5443b96fafc591b3aca5","score":"0.7007904","text":"def textPreprocessing(self):\n\t\tignored_words = nltk.corpus.stopwords.words('english')\n\t\tfor i in range(len(self._data)):\n\t\t\tcom = nltk.wordpunct_tokenize(self._data[i][self._comName])\n\t\t\tnewc = []\n\t\t\tfor j in range(len(com)):\n\t\t\t\ttoken = com[j].lower()\n\t\t\t\t#checking stopwords\n\t\t\t\tif token not in ignored_words:\n\t\t\t\t\tnewc.append(token)\n\t\t\tself._data[i][self._comName] = \" \".join(newc)\n\t\t#end","title":""},{"docid":"1f0ce98380c61e5b5a7392c0ba5758e2","score":"0.7007564","text":"def tokenize(text: str):\n RE_WORDS = r'\\S+'\n return list([x for x in re.findall(RE_WORDS, text.lower())])","title":""},{"docid":"c3d9a8e6185301c8bd28f18626e1c9cb","score":"0.6998272","text":"def initial_clean(text):\n text = re.sub(\"[^a-zA-Z ]\", \"\", text)\n text = text.lower() # lower case text\n text = nltk.word_tokenize(text)\n return text","title":""},{"docid":"c97ebe36a92b8c52038f4ab48ed7893b","score":"0.6976115","text":"def processes_and_tokenize(raw_document):\n\ttokenizer = RegexpTokenizer(r'\\w+')\n\ttokens = tokenizer.tokenize(raw_document.lower())\t\t# tokens = nltk.word_tokenize(corpus.lower()) # without removing punctiation\n\n\t#remove stop words\n\tstop_words = set(stopwords.words('english'))\n\tfiltered_tokens = [w for w in tokens if not w in stop_words]\n\treturn filtered_tokens","title":""},{"docid":"5932148192170b247ba6a2cb9733f09c","score":"0.69752234","text":"def tokenize(self, text):\n res = []\n if len(text) == 0:\n return res\n\n if self.lower:\n text = text.lower()\n # for the multilingual (include: Chinese and English)\n res = tokenize_words(text)\n return res","title":""},{"docid":"e623a58906b9f7d86aa1c3fb6b48d616","score":"0.6969152","text":"def preprocess(text, language=\"english\", lower=True):\n words = []\n tokenized_text = []\n\n for line in text:\n tokenized = nltk.word_tokenize(line, language=language)\n if lower:\n tokenized = [word.lower() for word in tokenized]\n\n tokenized_text.append(tokenized)\n for word in tokenized:\n words.append(word)\n\n most_common = Counter(words).most_common()\n\n return tokenized_text, most_common","title":""},{"docid":"82ae6831d96062db222a8ec170097a61","score":"0.6958967","text":"def process_text(s):\n # remove punctuation characters\n s = s.translate(\n str.maketrans('', '', string.punctuation))\n # remove multiple consecutive spaces\n s = re.sub(' +', ' ', s)\n \n return s.lower()","title":""},{"docid":"4484bdbfb6dbfe3a484379474a458c7f","score":"0.69518286","text":"def tokenize(text):\n tokens = word_tokenize(text)\n # remove stopwords\n stop = set(stopwords.words('english'))\n tokens = [token for token in tokens if token not in stop]\n lemmatizer = WordNetLemmatizer()\n \n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n \n return clean_tokens","title":""},{"docid":"e699c5af5824d5a77e4a1ed525cd7ce3","score":"0.69356287","text":"def tokenize(text, stem_words = True):\n stop_words = stopwords.words('english')\n stemmer = PorterStemmer()\n regex = r\"([a-zA-Z]+)\"\n tokens = re.findall(regex, text.lower())\n if (stem_words):\n return [stemmer.stem(t) for t in tokens if not t in stop_words]\n else:\n return [t for t in tokens if not t in stop_words]","title":""},{"docid":"eb1b481b9dbcaeabdf001b6c02007856","score":"0.69251627","text":"def tokenize(self, text, never_split=None, **kwargs):\n if self.normalize_text:\n text = unicodedata.normalize(\"NFKC\", text)\n\n never_split = self.never_split + (never_split if never_split is not None else [])\n tokens = []\n\n for word in self.sudachi.tokenize(text):\n token = word.surface()\n\n if self.do_lower_case and token not in never_split:\n token = token.lower()\n\n if self.trim_whitespace:\n if token.strip() == \"\":\n continue\n else:\n token = token.strip()\n\n tokens.append(token)\n\n return tokens","title":""},{"docid":"6ad5494ebb1bb6688188a19ae52d747c","score":"0.69224113","text":"def tokenize(self, text):\n text = convert_to_unicode(text)\n\n\n orig_tokens = whitespace_tokenize(text)\n # print(\"original tokens: \", orig_tokens)\n split_tokens = []\n for token in orig_tokens:\n # if self.do_lower_case:\n # token = token.lower()\n # token = self._run_strip_accents(token)\n split_tokens.extend(self._run_split_on_punc(token))\n\n # print(\"split tokens: \", split_tokens)\n output_tokens = whitespace_tokenize(\" \".join(split_tokens))\n return output_tokens","title":""},{"docid":"a792aa4dba795261b1829b555186e7b4","score":"0.69173497","text":"def tokenize(text):\n \n \n tokens=word_tokenize(text)\n lemmatizer=WordNetLemmatizer()\n clean_tokens=[]\n \n for tok in tokens:\n clean_tok=lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n \n return clean_tokens","title":""},{"docid":"4ece93b9dca8089585cc53fa19c94e33","score":"0.6914823","text":"def _parse_into_words(text):\r\n # TODO: \r\n # Check if using spaCy here is better\r\n return re.findall(r\"\\w+\", text.lower())","title":""},{"docid":"9faaf4ee86af883054e9aa52740d93e9","score":"0.68902045","text":"def tokenize(document):\n # Get a set of stop words and standard puncuation\n stop_words = set(stopwords.words('english'))\n punctuation = set(string.punctuation)\n # Tokenize the words, remove those that are in the stops list, and make them lowercase\n tokenized_lower = word_tokenize(document.lower())\n # Filter any tokens that are either in stop_words list or punctuation list\n return [word for word in tokenized_lower if word not in stop_words and word not in punctuation]","title":""},{"docid":"81fd5429fc22058869c8fa6825ce602b","score":"0.6881069","text":"def preprocessing_text(df): \r\n #remove upper cases\r\n df=df.lower()\r\n #replacing new line sign '\\n' with a whitespace ' ' \r\n df=df.replace('\\\\n',' ')\r\n\r\n #for removing stop words\r\n stop_words = set(stopwords.words('english')) \r\n\r\n #for removing punctuations\r\n tokenizer = RegexpTokenizer(r'\\w+')\r\n \r\n #to tokenize the string\r\n word_tokens = tokenizer.tokenize(df) \r\n \r\n #stemming\r\n ps = PorterStemmer()\r\n filtered_words = [ps.stem(w) for w in word_tokens if not w in stop_words] \r\n\r\n return filtered_words","title":""},{"docid":"3414481683a4cf7b14194e7979cb626b","score":"0.6880825","text":"def tokenize(text):\n\n stop_words = stopwords.words(\"english\")\n lemmatizer = WordNetLemmatizer()\n\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower())\n \n # tokenize text\n tokens = word_tokenize(text)\n \n # lemmatize and remove stop words\n tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]\n\n return tokens","title":""},{"docid":"adcd95cc1ab6fcdd1e5826407e2d1d4b","score":"0.6878907","text":"def preprocess(text):\r\n result = \"\"\r\n for token in simple_preprocess(text):\r\n if token not in STOPWORDS and len(token) > 3:\r\n result = result + \" \" + lemmatize_steming(token)\r\n return result","title":""},{"docid":"37fee1ebd60434b032cf1e8180bf0166","score":"0.68679595","text":"def tokenize(text):\n # Normalize\n normalized_text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower())\n \n # Tokenize\n words = word_tokenize(normalized_text)\n \n # Remove stop words\n words = [w for w in words if w not in stopwords.words(\"english\")]\n \n # Lemmatize\n lemmatizer = WordNetLemmatizer()\n clean_words = [lemmatizer.lemmatize(w).strip() for w in words]\n clean_words = [lemmatizer.lemmatize(w, pos=\"v\").strip() for w in clean_words]\n \n return clean_words","title":""},{"docid":"946254760593ed32421cd7ebb0c067a5","score":"0.68642545","text":"def tokenize(text):\n # convert text to lowercase\n text = text.lower()\n \n tokens = word_tokenize(text)\n \n STOPWORDS = list(set(stopwords.words('english')))\n # remove short words\n tokens = [token for token in tokens if len(token) > 2]\n # remove stopwords\n tokens = [token for token in tokens if token not in STOPWORDS]\n \n lemmatizer = WordNetLemmatizer()\n\n # clean tokens by lemmatizing, making all tokens lowercase and stripping any whitespace\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens","title":""},{"docid":"72c16ca9bbaf677d90d5b8f092d95e56","score":"0.686189","text":"def text_preprocess(text, stop_words, stemmer):\n\n #convert to lowercase\n text = text.lower()\n\n #break up text into words\n word_list = word_tokenize(text)\n\n #remove stopwords and punctuation\n word_list = [word for word in word_list if word not in string.punctuation and word not in stop_words]\n\n #apply a stemmer\n word_list = [stemmer.stem(word) for word in word_list]\n\n return word_list","title":""},{"docid":"b45ae3e13db6ffccfd12b791524ea5f1","score":"0.6858426","text":"def tokenize_words(line, lowercase=True, filter_stopwords=True):\n words = _WORD_REGEX.findall(line.lower() if lowercase else line)\n return remove_stop_words(words) if filter_stopwords else words","title":""},{"docid":"f8b97d0bf1f91c9736bbbbd222ba08f7","score":"0.68384707","text":"def tokenize(text):\n \n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower())\n\n tokens = word_tokenize(text)\n tokens = [word for word in tokens if word not in stopwords.words(\"english\")]\n \n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens","title":""},{"docid":"1534f7a3904d7dfdb68a371d4af66387","score":"0.68372935","text":"def tokenize(text):\n text = text.lower() # lowercase\n words = wordpunct_tokenize(text) # wrap around nltk's tokenizer\n words = [w.strip() for w in words if w.strip()] # rm empty words\n words = [w for w in words if w.isalpha()] # remove non-alphabetic words\n return words","title":""},{"docid":"28f20dcf404729ed85da2f0eff5df135","score":"0.6823557","text":"def tokenizer(text):\n # Create a list of the words\n # Convert the words to lowercase\n # Remove the punctuation\n # Remove the stop words\n # Lemmatize Words into root words\n lemmatizer = WordNetLemmatizer()\n sw = set(stopwords.words('english'))\n regex = re.compile(\"[^a-zA-Z ]\")\n re_clean = regex.sub('', text)\n words = word_tokenize(re_clean)\n return [lemmatizer.lemmatize(word.lower()) for word in words if word.lower() not in set(stopwords.words('english'))]","title":""},{"docid":"c02a66fa5e1f34d70750b187165abeea","score":"0.6816639","text":"def tokenize(document):\n \n result = nltk.tokenize.word_tokenize(document)\n result = [x.lower() for x in result]\n for item in result.copy(): # iterate through a copy of result so that you don't skip items as you remove items and the indices get messed up\n if item in nltk.corpus.stopwords.words(\"english\") or item in string.punctuation:\n while item in result: result.remove(item)\n\n return result","title":""},{"docid":"a95aa629a9800c78c4412be7799f4e9a","score":"0.6814781","text":"def tokenize(text):\n tokens = word_tokenize(text)\n \n # initiate lemmatizer\n lemmatizer = WordNetLemmatizer()\n\n # iterate through each token\n clean_tokens = []\n for tok in tokens:\n \n # lemmatize, normalize case, and remove leading/trailing white space\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens","title":""},{"docid":"a59fe25b4fdcc92563620a2d969297a9","score":"0.6799538","text":"def process_text(text):\r\n \r\n # decontract text\r\n text = expand_contractions(text.lower())\r\n \r\n # keep only letters and numbers\r\n text = re.sub(r'[^a-zA-Z0-9]', ' ', text)\r\n \r\n # tokenize\r\n tokens = word_tokenize(text)\r\n \r\n # nltk pos tags\r\n tokens = nltk.pos_tag(tokens)\r\n \r\n # change nltk to wordnet pos tags and lemmatize\r\n tokens = lemmatize(tokens)\r\n \r\n # remove stop words and return clean tokens\r\n return [token for token in tokens if token not in stop_words]","title":""},{"docid":"1959e69fe6de8d35f1f6ee5fdef19dd8","score":"0.67887527","text":"def tokenize(text):\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower().strip()) # Normalize text and remove white space\n words = word_tokenize(text) # Tokenize text\n words = [w for w in words if w not in stopwords.words(\"english\")] # Remove stop words\n words = [WordNetLemmatizer().lemmatize(w) for w in words] # Lemmatize\n words = [WordNetLemmatizer().lemmatize(w, pos='v') for w in words] # Lemmatize verbs by specifying pos\n\n return words","title":""},{"docid":"e1a7927b5dce527a5659427b83a0bcb8","score":"0.67834985","text":"def tokenize(text):\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text)\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n return clean_tokens","title":""},{"docid":"8c67bede2dae92bc6cc8f12d4fe27a86","score":"0.6777806","text":"def tokenize(text):\n lemmatizer = WordNetLemmatizer()\n \n text = re.sub(r'[^a-zA-Z0-9]', ' ', text.lower())\n \n token_list = word_tokenize(text)\n token_list = [token.strip() for token in token_list if token not in stopwords.words(\"english\")]\n token_list = [lemmatizer.lemmatize(token) for token in token_list]\n \n return token_list","title":""},{"docid":"a89c1d4af91aed11a40c76ba6101486e","score":"0.6761988","text":"def tokenize_text(input_file,replace):\n text = ''\n with open(input_file, 'r') as content_file:\n text = content_file.read().decode('utf-8')\n if replace==True:\n text = re.sub('[^A-Za-z0-9+-.@]+',' ', text.lower())\n tokens = nltk.word_tokenize(text)\n return tokens","title":""},{"docid":"6ec4602166457f730d730714b0bbc63c","score":"0.67511344","text":"def tokenize(self, document):\n return [t.lower() for t in re.findall(r\"\\w+(?:[-']\\w+)*\", document)]","title":""},{"docid":"82374e0a6e9f5e40b000c764d14151be","score":"0.6746696","text":"def pre_process_text(text):\n text = text.lower()\n stop_words = set(stopwords.words('english'))\n word_tokens = word_tokenize(text)\n word_tokens = [w for w in word_tokens if not w in stop_words]\n stemmer = stem.PorterStemmer()\n stemmed_text = [stemmer.stem(w) for w in word_tokens]\n filtered_text = [w for w in stemmed_text if not w in stop_words]\n text_len = min(1000, len(filtered_text))\n return \" \".join(filtered_text[0:text_len])","title":""},{"docid":"f888156e39930cd5105800f9933c66f9","score":"0.67412174","text":"def preprocess_text(text, lemmatizing=False):\r\n text = text.lower()\r\n \r\n if lemmatizing:\r\n lemmatizer = WordNetLemmatizer()\r\n text = ' '.join([lemmatizer.lemmatize(word, pos='v') for word in text.split()])\r\n \r\n # Remove html tags\r\n text = re.sub(r'<.*?>', '', text)\r\n \r\n # Replace punctuation with spaces\r\n translator = str.maketrans(string.punctuation, ' '*len(string.punctuation))\r\n text = text.translate(translator)\r\n\r\n # Remove stop words\r\n text = ' '.join([word for word in text.split() if word not in stopwords.words('english')])\r\n \r\n # Remove additional white spaces\r\n text = ' '.join(text.split())\r\n \r\n return text","title":""},{"docid":"43f2d5eaf2b6e141a29cb5ec154b7d67","score":"0.67353785","text":"def tokenize(text):\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens","title":""},{"docid":"0f8b51dd17819b5ddd660874b8e9584b","score":"0.6719576","text":"def text_to_word_sequence_nltkword(text,\n filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n',\n lower=True, split=\" \"):\n if lower:\n text = text.lower()\n\n tokens = word_tokenize(text)\n return tokens","title":""},{"docid":"a063796f17bfd6b1839ff180e6ca3390","score":"0.671734","text":"def preprocess_text(given_text):\n\ttext = given_text.lower() \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Lower all text\n\ttext = text.translate(str.maketrans(string.punctuation, ' ' * len(string.punctuation))) \t# Remove punctuation\n\twords = []\n\tfor word in text.split():\n\t\twords.append(word)\n\treturn words","title":""},{"docid":"21c38f0201c147479013ef7321116c90","score":"0.67173165","text":"def tokenize(text, remove_stop_words=False, stopwords=stopwords.words('english')):\n # case normalization (i.e. all lower case)\n text = text.lower()\n # punctuation removal\n text = re.sub(r'[^a-zA-Z0-9]', ' ', text)\n # tokenize the text\n tokens = word_tokenize(text)\n # Optional stop words removal.\n # Note: calling stopwords.words('english') here directly causes\n # pickling functions for some reason (which prevents multicore\n # processing).\n if remove_stop_words:\n tokens = [\n token for token in tokens if token not in stopwords\n ]\n\n # wordnet lemmatize\n lemmatizer = WordNetLemmatizer()\n tokens = [lemmatizer.lemmatize(token) for token in tokens]\n\n return tokens","title":""},{"docid":"18408aecc2ef5bae787b837a55719c52","score":"0.6715173","text":"def tokenize(text):\n text = text.lower() #lower case\n url_regex = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\n detected_urls = re.findall(url_regex, text)\n for url in detected_urls:\n text = text.replace(url, \"urlplaceholder\")\n\n text = text.translate(str.maketrans('', '', string.punctuation)) #remove punctuations\n\n tokens = word_tokenize(text) #tokenize\n lemmatizer = WordNetLemmatizer()\n\n #remove stopwords\n clean_tokens = [w for w in tokens if w not in stopwords.words(\"english\")]\n\n #reduce words to their root form\n clean_tokens = [WordNetLemmatizer().lemmatize(w) for w in clean_tokens]\n\n return clean_tokens","title":""},{"docid":"4bcd254637fd7bd7ae76aee0652d5dc2","score":"0.6706489","text":"def tokenize(text):\n global TOK\n tokens = TOK.tokenize(text)\n output = {\n 'words': replace_digits(tokens.words()),\n 'offsets': tokens.offsets(),\n 'pos': tokens.pos(),\n 'lemma': tokens.lemmas(),\n 'ner': tokens.entities(), # keep capitalization is good for NER?\n }\n return output","title":""},{"docid":"298ec4830b98f7e8178c37c5fe1806ec","score":"0.6692397","text":"def test_tokenize_words(self):\n self.assertEqual(res.tokenize_words(\"am going home\"), [\"am\", \"going\", \"home\"])","title":""},{"docid":"38416884865cad0a81edb113f9675c4d","score":"0.66890335","text":"def tokenisation_stopwords(text):\n text = text.split(' ')\n # Removing 1 letter long words and empty strings, and the remaining bad characters\n text = [re.sub('[^a-zA-Z0-9]*|[\\]{1,2}u[a-z0-9]*', '', word) for word in text if len(word) > 1]\n # Removing stopwords\n stop_words_extra =['aint', 'arent', 'cant', 'couldve', 'couldnt', 'didnt', 'doesnt', 'dont', 'hadnt', 'hasnt',\n 'havent', 'hed', 'hell', 'hes', 'heve', 'Id', 'Ill', 'Im', 'Ive', 'isnt', 'itll', 'its',\n 'lets', 'mustnt', 'shed', 'shell', 'shes', 'shouldve', 'shouldnt', 'thatll', 'thats',\n 'theres', 'theyd', 'theyll', 'theyre', 'theyve', 'wasnt', 'wed', 'well', 'were', 'weve',\n 'werent', 'whats', 'wheres', 'wholl', 'whos', 'wont', 'wouldve', 'wouldnt', 'yall', 'youd',\n 'youll','youre', 'youve']\n text = [word for word in text if word not in stopwords.words('english')]\n text = [word for word in text if word not in stop_words_extra]\n return text","title":""},{"docid":"e7b79b783bdc70a181dfe797ba8cfa73","score":"0.6685498","text":"def tokenize(text):\n stop_words = stopwords.words(\"english\")\n lemmatizer = WordNetLemmatizer()\n \n # normalize case and remove punctuation\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower())\n \n # tokenize text\n tokens = word_tokenize(text)\n \n tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]\n \n return tokens","title":""},{"docid":"bfc680dd53367fbf4133be87b46b1ac4","score":"0.66835004","text":"def preprocess(tweet_text):\n return tweet_text.lower().split()","title":""},{"docid":"557e58ccaf33012d126ed1a5c244b5f5","score":"0.6680401","text":"def tokenize2(text):\n\n # tokenize text\n tokens = word_tokenize(text)\n\n # initiate lemmatizer\n lemmatizer = WordNetLemmatizer()\n\n # iterate through each token\n tok_list = []\n for tok in tokens:\n\n # lemmatize, normalize case, and remove leading/trailing white space\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n tok_list.append(clean_tok)\n\n return tok_list","title":""},{"docid":"85e8142215e24ea7179b3e71d79cfccd","score":"0.66790193","text":"def tokenize(self, text, never_split=None, **kwargs):\n if self.normalize_text:\n text = unicodedata.normalize(\"NFKC\", text)\n\n text = text.strip()\n\n never_split = self.never_split + (never_split if never_split is not None else [])\n tokens = []\n\n for mrph in self.juman.apply_to_sentence(text).morphemes:\n token = mrph.text\n\n if self.do_lower_case and token not in never_split:\n token = token.lower()\n\n if self.trim_whitespace:\n if token.strip() == \"\":\n continue\n else:\n token = token.strip()\n\n tokens.append(token)\n\n return tokens","title":""},{"docid":"178c0b867b456776462d97c5302d1f9e","score":"0.667788","text":"def tokenize(string, lowercase=False):\n\timport re\n\twords = re.findall(\"([\\w'-+]+)\", string)\n\tif lowercase == True:\n\t\tlowerwords = []\n\t\tfor word in words:\n\t\t\tlowerwords.append(word.lower())\n\t\twords = lowerwords\n\treturn words","title":""},{"docid":"ad4758660e16d88c1ae21606b4d1a9f3","score":"0.6677427","text":"def tokenize(text):\n tokens = nltk.tokenize.word_tokenize(text)\n # initiate lemmatizer\n lemma = nltk.stem.WordNetLemmatizer()\n # iterate through tokens lemmatizing and cleaning\n clean_tokens = []\n for token in tokens:\n clean_token = lemma.lemmatize(token).lower().strip()\n clean_tokens.append(clean_token)\n\n return clean_tokens","title":""},{"docid":"a38a1214752fc4acbb7e941cadcac565","score":"0.66712266","text":"def tokenize(sentence): \n words = re.findall(r'\\b[a-z]+\\b', sentence)\n return ' '.join( words)","title":""},{"docid":"fffc83ce839f03dadfd4c27f58be1054","score":"0.6665572","text":"def tokenize(text):\n\n ## normalize text\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower())\n ## tokenize texts\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n for tok in tokens:\n ## lemmatize, lowercase, strip spaces\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n \n return clean_tokens","title":""},{"docid":"900fbef8b898a397dff19f494f941e7a","score":"0.66593796","text":"def tokenize(text):\n words = wakati.parse(text).split()\n return words","title":""},{"docid":"67578397eb51500cc4c192d44561bbf2","score":"0.6653253","text":"def tokenize(text): \n # kee[ only letters and numbers\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text) \n\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n\n #clean text and split into tokens(words)\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens","title":""},{"docid":"e3efa323177427fbd6a9f6215b6a4e8b","score":"0.66524595","text":"def tokenise_string(s, stop=True):\n\n filter_function = lambda x: x not in STOP_WORDS\n if stop == False:\n filter_function = lambda x: True\n\n return map(stem, filter(filter_function, [re.sub(r'[^a-z ]', '', word) for word in s.lower().strip().split()])) # uhuhuh!","title":""},{"docid":"bf832563bb85c2004f5ada1df2107602","score":"0.66522825","text":"def tokenize(text):\n text = word_tokenize(text)\n tokens = []\n for word in text:\n word = WordNetLemmatizer().lemmatize(word).lower().strip()\n tokens.append(word)\n return tokens","title":""},{"docid":"cdf6b61f0c79468e3e5331fe5ac4c21a","score":"0.66431874","text":"def tokenize(text):\n tokens = nltk.word_tokenize(text)\n tokens = [w for w in tokens if bool(re.search(r\"[^a-zA-Z0-9]\", w)) != True]\n tokens = [WordNetLemmatizer().lemmatize(w, pos='v') for w in tokens if stopwords.words(\"english\")]\n tokens = [PorterStemmer().stem(w) for w in tokens]\n return tokens","title":""},{"docid":"f88f958399f9579f7a3026b0d17f9de1","score":"0.66381377","text":"def text_to_word_sequence_stanford(text,\n filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n',\n lower=True, split=\" \"):\n # TODO INSTALL CORENLP\n if lower:\n text = text.lower()\n\n tokens = CoreNLPTokenizer().tokenize(text)\n return tokens","title":""},{"docid":"5cc529633842d4d1ee897be9a0116a5d","score":"0.66320884","text":"def tokenize(self, sentence):\n return [e.lower() for e in sentence.split() if len(e) >= 2]","title":""},{"docid":"28584b99d7495d3d3f16ffaaa6f3b497","score":"0.6627767","text":"def process_text(text, stem=True):\n text = text.translate(string.punctuation)\n tokens = word_tokenize(text)\n\n if stem:\n stemmer = PorterStemmer()\n tokens = [stemmer.stem(t) for t in tokens]\n return tokens","title":""},{"docid":"fe311e9ad463407b7e06b7c186cae2de","score":"0.66265076","text":"def tokenize(text):\n\n\ttokenized_text = text.lower()\n\ttokenized_text = re.findall(r'[a-z0-9]+',\n\t\t\t\t\t\t\t\ttokenized_text) # splits string with delimiter being everything except alphabetical letters\n\treturn tokenized_text","title":""},{"docid":"cfbaec85ecd967e5779a97e0a2b8bb88","score":"0.6626497","text":"def preprocess_gensim(doc):\n doc = doc.lower() # Lower the text.\n doc = word_tokenize(doc) # Split into words.\n doc = [w for w in doc if not w in stop_words] # Remove stopwords.\n doc = [w for w in doc if w.isalpha()] # Remove numbers and punctuation.\n return doc","title":""},{"docid":"f998dba3cb6a706351cbdf9063520dae","score":"0.6623796","text":"def tokenize(text: str) -> [str]:\n vectorizer = CountVectorizer(analyzer='word')\n vectorizer.fit_transform([text])\n tokens = vectorizer.get_feature_names()\n # stems = self.stem_tokens(tokens, stemmer)\n return tokens","title":""},{"docid":"2650f03d71b39db1245b53ba31175496","score":"0.66184276","text":"def word_tokenize(self,words):\n tokens = re.findall(r\"[a-z]+-?[a-z]+\", words.lower(),\n flags = re.UNICODE | re.LOCALE)\n return(tokens)","title":""},{"docid":"5df3d706d8f0bf7e1678f765a99a16d5","score":"0.6617486","text":"def pre_processing(text):\r\n sent_tokenize_list = sent_tokenize(text)\r\n #print sent_tokenize_list\r\n #print len(sent_tokenize_list)\r\n #tokenise words\r\n #print stop_words\r\n words=word_tokenize(text)\r\n stop_words = str(stopwords.words('english'))\r\n alpha=stop_words.replace(\"u'\", \"\")\r\n #print words\r\n result = []\r\n #print alpha\r\n #remove stop words\r\n for item in words:\r\n if item not in alpha:\r\n result.append(item)\r\n #print \"Filtered\",result\r\n fil=str(result)\r\n #remove punctuation\r\n repstr=\" \" * 32\r\n table=string.maketrans(string.punctuation,repstr)\r\n s=fil.translate(table)\r\n #return s\r\n\r\n\r\n #lemmatizing\r\n lemmatizer=WordNetLemmatizer()\r\n h=lemmatizer.lemmatize(s)\r\n #print \"Lemma\",lemmatizer.lemmatize(s)\r\n #stemming\r\n wordss=word_tokenize(h)\r\n ps=PorterStemmer()\r\n list1=[]\r\n for i in wordss:\r\n k=(ps.stem(i))\r\n list1.append(k)\r\n #print list1\r\n final= ' '.join(list1)\r\n finall=str(final)\r\n return finall","title":""},{"docid":"030df38d932c8d1f99557f83534c7871","score":"0.6612498","text":"def tokenize(document):\n # Converting all words to lower case\n document = document.lower()\n # tokeniizing the string to words\n words = nltk.tokenize.word_tokenize(document)\n final_words = []\n # Add the word to the string if it is not a punctuatuion or an English stop word\n for word in words:\n if word not in string.punctuation and word not in nltk.corpus.stopwords.words(\"english\"):\n final_words.append(word)\n\n return final_words","title":""},{"docid":"47f5434f7e34a6cc8614686aaaee630f","score":"0.66085696","text":"def words(text): \n return re.findall('[a-z]+', text.lower())","title":""},{"docid":"5d14c472a7605272cd3154286d04ee50","score":"0.6606162","text":"def tokenize(document):\n\n # finding the tokens and seves them in a lowercase list \n tokens = [word.lower() for word in nltk.word_tokenize(document)]\n\n # intialize a filter list of words that are not stop words \n filter_list = []\n\n # initialize stop words and punctuations\n stop_words = nltk.corpus.stopwords.words(\"english\")\n punctuation = [punct for punct in string.punctuation] \n\n # go over all the words anf filter out the stop words and punctuation \n for word in tokens:\n if word in stop_words:\n continue \n\n elif word in punctuation:\n continue \n\n else:\n filter_list.append(word)\n\n\n return filter_list\n #raise NotImplementedError","title":""},{"docid":"70984638567ac1d800112f5cf3d37c65","score":"0.6599118","text":"def tokenize(text):\n # replace all token delimiters with a costmmon delimiter - space and split it\n # make all lowerscore and replace underscores with space first\n text = re.sub('_', ' ', text.lower())\n tokens = re.findall(r'[\\w]+', text)\n return list(tokens)","title":""},{"docid":"fd2378ae8b33a66c9154a4055061db68","score":"0.65977204","text":"def tokenize(text):\n\n # remove punctuation and lowercase\n text = re.sub(r'[^\\w\\s]','', text).lower()\n # split into tokens\n tokens = word_tokenize(text)\n # lemmatize\n lemmatizer = WordNetLemmatizer()\n clean_tokens = [lemmatizer.lemmatize(word) for word in tokens]\n return clean_tokens","title":""},{"docid":"cd1e0208d44360f094dfef52fb9eda9d","score":"0.6597287","text":"def initial_clean(text):\n text = re.sub(\"((\\S+)?(http(s)?)(\\S+))|((\\S+)?(www)(\\S+))|((\\S+)?(\\@)(\\S+)?)\", \" \", text)\n text = re.sub(\"[^a-zA-Z ]\", \"\", text)\n text = text.lower() # lower case the text\n text = nltk.word_tokenize(text)\n return text","title":""},{"docid":"cd1e0208d44360f094dfef52fb9eda9d","score":"0.6597287","text":"def initial_clean(text):\n text = re.sub(\"((\\S+)?(http(s)?)(\\S+))|((\\S+)?(www)(\\S+))|((\\S+)?(\\@)(\\S+)?)\", \" \", text)\n text = re.sub(\"[^a-zA-Z ]\", \"\", text)\n text = text.lower() # lower case the text\n text = nltk.word_tokenize(text)\n return text","title":""},{"docid":"dafa1ec87870f635bf0c0a57cd664b6b","score":"0.659701","text":"def process_text(self, text):\n\n self.__tokens.clear()\n\n tokenize = [t.lower().strip(\":,.!?\") for t in text.split()]\n\n filtered_tokens = self.__filter_text(tokenize)\n\n self.__tokens.extend(filtered_tokens)\n self.__all_tokens.extend(filtered_tokens)","title":""},{"docid":"b25d0bcc5a99c0c30eb1024b3d560dbd","score":"0.65837455","text":"def remove_stopwords(text, is_lower_case=False, stopwords=stopword_list):\n tokens = tokenizer.tokenize(text)\n tokens = [token.strip() for token in tokens]\n if is_lower_case:\n filtered_tokens = [token for token in tokens if token not in stopwords]\n else:\n filtered_tokens = [token for token in tokens if token.lower() not in stopwords]\n filtered_text = ' '.join(filtered_tokens) \n return filtered_text","title":""},{"docid":"2b396d5ab5db7fd97738c2f1d928bf1b","score":"0.65648067","text":"def tokenize(document):\n return [word.lower() for word in nltk.word_tokenize(document) \n if (word.lower() not in nltk.corpus.stopwords.words(\"english\")\n and all([char not in string.punctuation for char in word.lower()]))]","title":""},{"docid":"0a17dfc5c5680f327ea49982fca2d525","score":"0.654812","text":"def tokenize(texts):\n # type: (pd.Series) -> pd.Series\n return texts.apply(lambda x: re.findall(r'\\b\\w+\\b', x.lower()))","title":""},{"docid":"80ce959a388e6bf3b0e4225142325c8d","score":"0.65455455","text":"def do_tokenize(self, text):\n text = text.lower() if self.lc else text\n if self.collapse_hashtags:\n text = re.sub('#\\S+', 'THIS_IS_A_HASHTAG', text)\n else:\n text = re.sub('#(\\S+)', r'HASHTAG_\\1', text)\n if self.collapse_mentions:\n text = re.sub('@\\S+', 'THIS_IS_A_MENTION', text)\n if self.collapse_urls:\n text = re.sub('http\\S+', 'THIS_IS_A_URL', text)\n if self.limit_repeats:\n text = re.sub(r'(.)\\1\\1\\1+', r'\\1', text)\n toks = []\n for tok in text.split():\n tok = re.sub(r'^(' + punc_re + '+)', r'\\1 ', tok)\n tok = re.sub(r'(' + punc_re + '+)$', r' \\1', tok)\n for subtok in tok.split():\n if self.retain_punc_toks or re.search('\\w', subtok):\n toks.append(subtok)\n return toks","title":""},{"docid":"13b52ce92e4baabc2e59c92b6be62a25","score":"0.6525562","text":"def __call__(self, text):\n\n tokens = text.split()\n\n for i in range(len(tokens) - 1):\n if is_dash_word(tokens[i]):\n\n # Require the first character of the next word is an alpha\n if not tokens[i + 1][0].isalpha():\n continue\n\n # Skip words with more than 2 caps\n if len([x for x in tokens[i + 1] if x == x.upper()]) >= 2:\n continue\n\n word = \"{}{}\".format(tokens[i][:-1], tokens[i + 1])\n\n test_word = ''.join([x for x in word if x.isalpha()])\n\n # Only combine sensible english words\n if test_word.lower() not in self.english_words:\n continue\n\n self.logger.info(\"Merging tokens %s %s %s\"\n % (tokens[i], tokens[i + 1], word))\n\n tokens[i] = word\n tokens[i + 1] = ''\n\n doc = ' '.join((x for x in tokens if x))\n return doc","title":""},{"docid":"f70b9ccacb15bba30f78d16053fda3bb","score":"0.6524916","text":"def _words_tokenizer(self):\n words = []\n for sentence in self.temp_list:\n words.append(nltk.word_tokenize(sentence))\n self.temp_list = words","title":""},{"docid":"eeaf2ae88e2b73407262e825cb084bf0","score":"0.65185577","text":"def apply_all(text):\n return stem_words(remove_stop_words(initial_clean(text)))","title":""},{"docid":"eeaf2ae88e2b73407262e825cb084bf0","score":"0.65185577","text":"def apply_all(text):\n return stem_words(remove_stop_words(initial_clean(text)))","title":""}],"string":"[\n {\n \"docid\": \"a9c24e059ba0d629375783d4818e3a20\",\n \"score\": \"0.77378315\",\n \"text\": \"def tokenize(txt: str):\\n return [w.lower() for w in txt.split(' ')]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5959a44e97cef37db948692cef781c8\",\n \"score\": \"0.7713063\",\n \"text\": \"def tokenize(self, text):\\n words = nltk.word_tokenize(text)\\n words = (self.stem(word) for word in words)\\n words = (word.lower() for word in words)\\n words = (word for word in words if word not in self.stop_words)\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15c1ef5f6ae0556e1ec42148b1854269\",\n \"score\": \"0.76650214\",\n \"text\": \"def tokenize(text):\\n return [token.lower() for token in simple_preprocess(text) if token not in STOPWORDS and token not in reuters_stopwords]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f04df4428fad25d479b74d94f41a37\",\n \"score\": \"0.7526388\",\n \"text\": \"def tokenize(text):\\n return gensim.utils.tokenize(text, to_lower=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fafd7a4469d94f04ac33960bbc03aec1\",\n \"score\": \"0.72875655\",\n \"text\": \"def tokenize(doc):\\n # doc = file.read()\\n # token_pattern=r\\\"(?u)\\\\b\\\\w\\\\w+\\\\b\\\"\\n token_pattern=r\\\"(?u)\\\\b\\\\w[a-zA-Z]+\\\\b\\\" # only word, not number\\n token_pattern = re.compile(token_pattern)\\n # tokenize = lambda doc: token_pattern.findall(doc.lower()\\n tokenize = lambda doc: token_pattern.findall(doc)\\n words = tokenize(doc) \\n for i in range(len(words)):\\n if words[i].istitle():\\n words[i] = words[i].lower()\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feaab67c4cc41d8eb44a84a7c71d414e\",\n \"score\": \"0.7284889\",\n \"text\": \"def tokenize_text(text):\\n result = []\\n for seq in SEQUENCE.finditer(text):\\n result.append(list(map(str.lower, WORD.findall(seq.group(0)))))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f30b1294f75193614acc560349673bc\",\n \"score\": \"0.72693795\",\n \"text\": \"def tokenize(self, text, never_split=None, **kwargs):\\n if self.normalize_text:\\n text = unicodedata.normalize(\\\"NFKC\\\", text)\\n\\n never_split = self.never_split + (never_split if never_split is not None else [])\\n tokens = []\\n\\n for word in self.mecab(text):\\n token = word.surface\\n\\n if self.do_lower_case and token not in never_split:\\n token = token.lower()\\n\\n tokens.append(token)\\n\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70f8c800231fa758fdd1c77eb15ad402\",\n \"score\": \"0.7194744\",\n \"text\": \"def tokenize(self, sentence):\\n sentence = ENSURE_UNICODE(sentence)\\n for word in self._tokenizer(sentence):\\n yield word if self._case_sensitive else word.lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53b78885fec00ec4b2a8dc01b239366e\",\n \"score\": \"0.71805197\",\n \"text\": \"def proc_text(self, text):\\n\\n lemmas = []\\n tokens = []\\n doc = self(text)\\n for tok_obj in doc:\\n if self._removePunct and tok_obj.is_punct:\\n continue\\n lemma = tok_obj.lemma_\\n text = tok_obj.text\\n if self._keepOnlyAlphaNum and not is_alpha_num(text):\\n continue\\n tok1 = text.lower()\\n tok2 = lemma.lower()\\n if tok1 in self._stopWords or tok2 in self._stopWords:\\n continue\\n\\n if self._lowerCase:\\n text = text.lower()\\n lemma = lemma.lower()\\n\\n lemmas.append(lemma)\\n tokens.append(text)\\n\\n return ' '.join(lemmas), ' '.join(tokens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"458151d75508480b62440e016665eb9e\",\n \"score\": \"0.716325\",\n \"text\": \"def preprocess(txt,parser):\\n\\tdoc = parser(txt)\\n\\treturn [token.lemma_.lower() for token in doc]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99e53dc4539edb6be773e3d9185595ad\",\n \"score\": \"0.7155567\",\n \"text\": \"def naive(self, text):\\n\\n\\t\\ttokenizedText = None\\n\\n\\t\\t#Fill in code here\\n\\t\\ttottext=[]\\n\\t\\tintext=[]\\n\\t\\tt=''\\n\\t\\tword=''\\n\\t\\tfor i in range(len(text)):\\n\\t\\t\\tt=text[i].lower()\\n\\t\\t\\tfor j in range(len(t)):\\n\\t\\t\\t\\tif t[j]!=' ':\\n\\t\\t\\t\\t\\tword=word+t[j]\\n\\t\\t\\t\\tif t[j]==' ':\\n\\t\\t\\t\\t\\tintext.append(word)\\n\\t\\t\\t\\t\\tword=''\\n\\t\\t\\ttottext.append(intext)\\n\\t\\t\\tintext=[]\\n\\t\\ttokenizedText=tottext\\n\\n\\t\\treturn tokenizedText\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71e56208430fc7d202b0e72b9329698f\",\n \"score\": \"0.7143314\",\n \"text\": \"def text_preprocessing(text):\\n tokenizer = nltk.tokenize.RegexpTokenizer(r'\\\\w+')\\n nopunc = clean_text(text)\\n tokenized_text = tokenizer.tokenize(nopunc)\\n remove_stopwords = [w for w in tokenized_text if w not in stopwords.words('english')]\\n combined_text = ' '.join(tokenized_text)\\n return combined_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ca7c741d98def99938da052dcba2191\",\n \"score\": \"0.7099463\",\n \"text\": \"def tokenize(text):\\n text = re.sub(r\\\"[^a-zA-Z0-9]\\\", \\\" \\\", text.lower())\\n tokens = word_tokenize(text)\\n clean_tokens = [lemmatizer.lemmatize(word)\\n for word in tokens if word not in stop_words]\\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"843cd23163f73270bfc0b1c60bfad42c\",\n \"score\": \"0.7085407\",\n \"text\": \"def procText(self, text):\\n\\n lemmas = []\\n tokens = []\\n doc = self(text)\\n for sent in doc.sentences:\\n for tokObj in sent.words:\\n if self._removePunct and tokObj.upos == 'PUNCT':\\n continue\\n lemma = tokObj.lemma\\n text = tokObj.text\\n if self._keepOnlyAlphaNum and not isAlphaNum(text):\\n continue\\n tok1 = text.lower()\\n tok2 = lemma.lower()\\n if tok1 in self._stopWords or tok2 in self._stopWords:\\n continue\\n\\n if self._lowerCase:\\n text = text.lower()\\n lemma = lemma.lower()\\n\\n lemmas.append(lemma)\\n tokens.append(text)\\n\\n return ' '.join(lemmas), ' '.join(tokens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"985437f5970a9b0d4a92edbabcd2acaa\",\n \"score\": \"0.7084913\",\n \"text\": \"def procText(self, text):\\n\\n lemmas = []\\n tokens = []\\n doc = self(text)\\n for tokObj in doc:\\n if self._removePunct and tokObj.is_punct:\\n continue\\n lemma = tokObj.lemma_\\n text = tokObj.text\\n if self._keepOnlyAlphaNum and not isAlphaNum(text):\\n continue\\n tok1 = text.lower()\\n tok2 = lemma.lower()\\n if tok1 in self._stopWords or tok2 in self._stopWords:\\n continue\\n\\n if self._lowerCase:\\n text = text.lower()\\n lemma = lemma.lower()\\n\\n lemmas.append(lemma)\\n tokens.append(text)\\n\\n return ' '.join(lemmas), ' '.join(tokens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e4c3e368f481df5279ecf66dd4b63d6\",\n \"score\": \"0.70735097\",\n \"text\": \"def _preprocess(self, a_txt):\\n return super(LexiconBaseAnalyzer, self)._preprocess(a_txt).lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e110a5a9edbc81fd97013c654a659343\",\n \"score\": \"0.70731664\",\n \"text\": \"def normalize_text(s, lower_case = True):\\n\\n def remove_articles(text):\\n return re.sub(r'\\\\b(a|an|the)\\\\b', ' ', text)\\n\\n def white_space_fix(text):\\n return ' '.join(text.split())\\n\\n def remove_punc(text):\\n exclude = set(string.punctuation)\\n return ''.join(ch for ch in text if ch not in exclude)\\n\\n def lower(text):\\n return text.lower() if lower_case else text\\n #return text.lower()\\n \\n def remove_stop_words(text):\\n return re.sub(r'\\\\b(as|to|that|and|of)\\\\b', ' ', text)\\n\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"512f7ce1781c17f635fde142ca3e31b0\",\n \"score\": \"0.7067765\",\n \"text\": \"def simple_tokenization(doc):\\n tok = re.findall('(\\\\\\\\w+)', doc.lower())\\n docs = ' '.join(tok)\\n return word_tokenize(docs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3385a2dfae0fabfaef1ed09e582b853\",\n \"score\": \"0.7065585\",\n \"text\": \"def tokenize(self, text):\\n\\n return word_tokenize(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"124040aeabb11a9504d547a80a64ad44\",\n \"score\": \"0.70500314\",\n \"text\": \"def tokenize_and_clean(text):\\r\\n tokens = []\\r\\n for token in word_tokenize(text):\\r\\n if not token.isdigit(): # keep purely numerical strings unchanged\\r\\n token = ''.join(c for c in token if c.isalpha())\\r\\n if token.isalnum(): #and token.lower() not in stops: # if token != \\\"\\\"\\r\\n tokens.append(token.lower())\\r\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"326fde92140fea8a5073a51264988c56\",\n \"score\": \"0.70296556\",\n \"text\": \"def tokenize(text):\\n text = text.lower()\\n text = re.sub(r\\\"[^a-zA-z0-9]\\\",\\\" \\\",text)\\n words = word_tokenize(text)\\n words = [w for w in words if w not in stopwords.words(\\\"english\\\")]\\n \\n clean_tokens = []\\n lemmatizer = WordNetLemmatizer()\\n \\n for w in words:\\n clean_tok = lemmatizer.lemmatize(w , pos='v').strip()\\n clean_tokens.append(clean_tok)\\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8f18c11bd4a9f89a96391675eeb4ac9\",\n \"score\": \"0.70207685\",\n \"text\": \"def process(self, text: str, allow_stopwords: bool = False) -> str:\\n ret = []\\n\\n # split and cast to lower case\\n text = re.sub(r'<[^>]+>', ' ', str(text))\\n for word in re.split('[^a-zA-Z]', str(text).lower()):\\n # remove non-alphabetic and stop words\\n if (word.isalpha() and word not in self.stopwords) or allow_stopwords:\\n if word not in self.stemmed:\\n self.stemmed[word] = self.ps.stem(word)\\n # use stemmed version of word\\n ret.append(self.stemmed[word])\\n return ' '.join(ret)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5ec6f60b99f5443b96fafc591b3aca5\",\n \"score\": \"0.7007904\",\n \"text\": \"def textPreprocessing(self):\\n\\t\\tignored_words = nltk.corpus.stopwords.words('english')\\n\\t\\tfor i in range(len(self._data)):\\n\\t\\t\\tcom = nltk.wordpunct_tokenize(self._data[i][self._comName])\\n\\t\\t\\tnewc = []\\n\\t\\t\\tfor j in range(len(com)):\\n\\t\\t\\t\\ttoken = com[j].lower()\\n\\t\\t\\t\\t#checking stopwords\\n\\t\\t\\t\\tif token not in ignored_words:\\n\\t\\t\\t\\t\\tnewc.append(token)\\n\\t\\t\\tself._data[i][self._comName] = \\\" \\\".join(newc)\\n\\t\\t#end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f0ce98380c61e5b5a7392c0ba5758e2\",\n \"score\": \"0.7007564\",\n \"text\": \"def tokenize(text: str):\\n RE_WORDS = r'\\\\S+'\\n return list([x for x in re.findall(RE_WORDS, text.lower())])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3d9a8e6185301c8bd28f18626e1c9cb\",\n \"score\": \"0.6998272\",\n \"text\": \"def initial_clean(text):\\n text = re.sub(\\\"[^a-zA-Z ]\\\", \\\"\\\", text)\\n text = text.lower() # lower case text\\n text = nltk.word_tokenize(text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c97ebe36a92b8c52038f4ab48ed7893b\",\n \"score\": \"0.6976115\",\n \"text\": \"def processes_and_tokenize(raw_document):\\n\\ttokenizer = RegexpTokenizer(r'\\\\w+')\\n\\ttokens = tokenizer.tokenize(raw_document.lower())\\t\\t# tokens = nltk.word_tokenize(corpus.lower()) # without removing punctiation\\n\\n\\t#remove stop words\\n\\tstop_words = set(stopwords.words('english'))\\n\\tfiltered_tokens = [w for w in tokens if not w in stop_words]\\n\\treturn filtered_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5932148192170b247ba6a2cb9733f09c\",\n \"score\": \"0.69752234\",\n \"text\": \"def tokenize(self, text):\\n res = []\\n if len(text) == 0:\\n return res\\n\\n if self.lower:\\n text = text.lower()\\n # for the multilingual (include: Chinese and English)\\n res = tokenize_words(text)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e623a58906b9f7d86aa1c3fb6b48d616\",\n \"score\": \"0.6969152\",\n \"text\": \"def preprocess(text, language=\\\"english\\\", lower=True):\\n words = []\\n tokenized_text = []\\n\\n for line in text:\\n tokenized = nltk.word_tokenize(line, language=language)\\n if lower:\\n tokenized = [word.lower() for word in tokenized]\\n\\n tokenized_text.append(tokenized)\\n for word in tokenized:\\n words.append(word)\\n\\n most_common = Counter(words).most_common()\\n\\n return tokenized_text, most_common\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82ae6831d96062db222a8ec170097a61\",\n \"score\": \"0.6958967\",\n \"text\": \"def process_text(s):\\n # remove punctuation characters\\n s = s.translate(\\n str.maketrans('', '', string.punctuation))\\n # remove multiple consecutive spaces\\n s = re.sub(' +', ' ', s)\\n \\n return s.lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4484bdbfb6dbfe3a484379474a458c7f\",\n \"score\": \"0.69518286\",\n \"text\": \"def tokenize(text):\\n tokens = word_tokenize(text)\\n # remove stopwords\\n stop = set(stopwords.words('english'))\\n tokens = [token for token in tokens if token not in stop]\\n lemmatizer = WordNetLemmatizer()\\n \\n clean_tokens = []\\n for tok in tokens:\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n \\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e699c5af5824d5a77e4a1ed525cd7ce3\",\n \"score\": \"0.69356287\",\n \"text\": \"def tokenize(text, stem_words = True):\\n stop_words = stopwords.words('english')\\n stemmer = PorterStemmer()\\n regex = r\\\"([a-zA-Z]+)\\\"\\n tokens = re.findall(regex, text.lower())\\n if (stem_words):\\n return [stemmer.stem(t) for t in tokens if not t in stop_words]\\n else:\\n return [t for t in tokens if not t in stop_words]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb1b481b9dbcaeabdf001b6c02007856\",\n \"score\": \"0.69251627\",\n \"text\": \"def tokenize(self, text, never_split=None, **kwargs):\\n if self.normalize_text:\\n text = unicodedata.normalize(\\\"NFKC\\\", text)\\n\\n never_split = self.never_split + (never_split if never_split is not None else [])\\n tokens = []\\n\\n for word in self.sudachi.tokenize(text):\\n token = word.surface()\\n\\n if self.do_lower_case and token not in never_split:\\n token = token.lower()\\n\\n if self.trim_whitespace:\\n if token.strip() == \\\"\\\":\\n continue\\n else:\\n token = token.strip()\\n\\n tokens.append(token)\\n\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ad5494ebb1bb6688188a19ae52d747c\",\n \"score\": \"0.69224113\",\n \"text\": \"def tokenize(self, text):\\n text = convert_to_unicode(text)\\n\\n\\n orig_tokens = whitespace_tokenize(text)\\n # print(\\\"original tokens: \\\", orig_tokens)\\n split_tokens = []\\n for token in orig_tokens:\\n # if self.do_lower_case:\\n # token = token.lower()\\n # token = self._run_strip_accents(token)\\n split_tokens.extend(self._run_split_on_punc(token))\\n\\n # print(\\\"split tokens: \\\", split_tokens)\\n output_tokens = whitespace_tokenize(\\\" \\\".join(split_tokens))\\n return output_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a792aa4dba795261b1829b555186e7b4\",\n \"score\": \"0.69173497\",\n \"text\": \"def tokenize(text):\\n \\n \\n tokens=word_tokenize(text)\\n lemmatizer=WordNetLemmatizer()\\n clean_tokens=[]\\n \\n for tok in tokens:\\n clean_tok=lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n \\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ece93b9dca8089585cc53fa19c94e33\",\n \"score\": \"0.6914823\",\n \"text\": \"def _parse_into_words(text):\\r\\n # TODO: \\r\\n # Check if using spaCy here is better\\r\\n return re.findall(r\\\"\\\\w+\\\", text.lower())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9faaf4ee86af883054e9aa52740d93e9\",\n \"score\": \"0.68902045\",\n \"text\": \"def tokenize(document):\\n # Get a set of stop words and standard puncuation\\n stop_words = set(stopwords.words('english'))\\n punctuation = set(string.punctuation)\\n # Tokenize the words, remove those that are in the stops list, and make them lowercase\\n tokenized_lower = word_tokenize(document.lower())\\n # Filter any tokens that are either in stop_words list or punctuation list\\n return [word for word in tokenized_lower if word not in stop_words and word not in punctuation]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81fd5429fc22058869c8fa6825ce602b\",\n \"score\": \"0.6881069\",\n \"text\": \"def preprocessing_text(df): \\r\\n #remove upper cases\\r\\n df=df.lower()\\r\\n #replacing new line sign '\\\\n' with a whitespace ' ' \\r\\n df=df.replace('\\\\\\\\n',' ')\\r\\n\\r\\n #for removing stop words\\r\\n stop_words = set(stopwords.words('english')) \\r\\n\\r\\n #for removing punctuations\\r\\n tokenizer = RegexpTokenizer(r'\\\\w+')\\r\\n \\r\\n #to tokenize the string\\r\\n word_tokens = tokenizer.tokenize(df) \\r\\n \\r\\n #stemming\\r\\n ps = PorterStemmer()\\r\\n filtered_words = [ps.stem(w) for w in word_tokens if not w in stop_words] \\r\\n\\r\\n return filtered_words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3414481683a4cf7b14194e7979cb626b\",\n \"score\": \"0.6880825\",\n \"text\": \"def tokenize(text):\\n\\n stop_words = stopwords.words(\\\"english\\\")\\n lemmatizer = WordNetLemmatizer()\\n\\n text = re.sub(r\\\"[^a-zA-Z0-9]\\\", \\\" \\\", text.lower())\\n \\n # tokenize text\\n tokens = word_tokenize(text)\\n \\n # lemmatize and remove stop words\\n tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]\\n\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adcd95cc1ab6fcdd1e5826407e2d1d4b\",\n \"score\": \"0.6878907\",\n \"text\": \"def preprocess(text):\\r\\n result = \\\"\\\"\\r\\n for token in simple_preprocess(text):\\r\\n if token not in STOPWORDS and len(token) > 3:\\r\\n result = result + \\\" \\\" + lemmatize_steming(token)\\r\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37fee1ebd60434b032cf1e8180bf0166\",\n \"score\": \"0.68679595\",\n \"text\": \"def tokenize(text):\\n # Normalize\\n normalized_text = re.sub(r\\\"[^a-zA-Z0-9]\\\", \\\" \\\", text.lower())\\n \\n # Tokenize\\n words = word_tokenize(normalized_text)\\n \\n # Remove stop words\\n words = [w for w in words if w not in stopwords.words(\\\"english\\\")]\\n \\n # Lemmatize\\n lemmatizer = WordNetLemmatizer()\\n clean_words = [lemmatizer.lemmatize(w).strip() for w in words]\\n clean_words = [lemmatizer.lemmatize(w, pos=\\\"v\\\").strip() for w in clean_words]\\n \\n return clean_words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"946254760593ed32421cd7ebb0c067a5\",\n \"score\": \"0.68642545\",\n \"text\": \"def tokenize(text):\\n # convert text to lowercase\\n text = text.lower()\\n \\n tokens = word_tokenize(text)\\n \\n STOPWORDS = list(set(stopwords.words('english')))\\n # remove short words\\n tokens = [token for token in tokens if len(token) > 2]\\n # remove stopwords\\n tokens = [token for token in tokens if token not in STOPWORDS]\\n \\n lemmatizer = WordNetLemmatizer()\\n\\n # clean tokens by lemmatizing, making all tokens lowercase and stripping any whitespace\\n clean_tokens = []\\n for tok in tokens:\\n clean_tok = lemmatizer.lemmatize(tok).strip()\\n clean_tokens.append(clean_tok)\\n\\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c16ca9bbaf677d90d5b8f092d95e56\",\n \"score\": \"0.686189\",\n \"text\": \"def text_preprocess(text, stop_words, stemmer):\\n\\n #convert to lowercase\\n text = text.lower()\\n\\n #break up text into words\\n word_list = word_tokenize(text)\\n\\n #remove stopwords and punctuation\\n word_list = [word for word in word_list if word not in string.punctuation and word not in stop_words]\\n\\n #apply a stemmer\\n word_list = [stemmer.stem(word) for word in word_list]\\n\\n return word_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b45ae3e13db6ffccfd12b791524ea5f1\",\n \"score\": \"0.6858426\",\n \"text\": \"def tokenize_words(line, lowercase=True, filter_stopwords=True):\\n words = _WORD_REGEX.findall(line.lower() if lowercase else line)\\n return remove_stop_words(words) if filter_stopwords else words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8b97d0bf1f91c9736bbbbd222ba08f7\",\n \"score\": \"0.68384707\",\n \"text\": \"def tokenize(text):\\n \\n text = re.sub(r\\\"[^a-zA-Z0-9]\\\", \\\" \\\", text.lower())\\n\\n tokens = word_tokenize(text)\\n tokens = [word for word in tokens if word not in stopwords.words(\\\"english\\\")]\\n \\n lemmatizer = WordNetLemmatizer()\\n\\n clean_tokens = []\\n for tok in tokens:\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n\\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1534f7a3904d7dfdb68a371d4af66387\",\n \"score\": \"0.68372935\",\n \"text\": \"def tokenize(text):\\n text = text.lower() # lowercase\\n words = wordpunct_tokenize(text) # wrap around nltk's tokenizer\\n words = [w.strip() for w in words if w.strip()] # rm empty words\\n words = [w for w in words if w.isalpha()] # remove non-alphabetic words\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28f20dcf404729ed85da2f0eff5df135\",\n \"score\": \"0.6823557\",\n \"text\": \"def tokenizer(text):\\n # Create a list of the words\\n # Convert the words to lowercase\\n # Remove the punctuation\\n # Remove the stop words\\n # Lemmatize Words into root words\\n lemmatizer = WordNetLemmatizer()\\n sw = set(stopwords.words('english'))\\n regex = re.compile(\\\"[^a-zA-Z ]\\\")\\n re_clean = regex.sub('', text)\\n words = word_tokenize(re_clean)\\n return [lemmatizer.lemmatize(word.lower()) for word in words if word.lower() not in set(stopwords.words('english'))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c02a66fa5e1f34d70750b187165abeea\",\n \"score\": \"0.6816639\",\n \"text\": \"def tokenize(document):\\n \\n result = nltk.tokenize.word_tokenize(document)\\n result = [x.lower() for x in result]\\n for item in result.copy(): # iterate through a copy of result so that you don't skip items as you remove items and the indices get messed up\\n if item in nltk.corpus.stopwords.words(\\\"english\\\") or item in string.punctuation:\\n while item in result: result.remove(item)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a95aa629a9800c78c4412be7799f4e9a\",\n \"score\": \"0.6814781\",\n \"text\": \"def tokenize(text):\\n tokens = word_tokenize(text)\\n \\n # initiate lemmatizer\\n lemmatizer = WordNetLemmatizer()\\n\\n # iterate through each token\\n clean_tokens = []\\n for tok in tokens:\\n \\n # lemmatize, normalize case, and remove leading/trailing white space\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n\\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a59fe25b4fdcc92563620a2d969297a9\",\n \"score\": \"0.6799538\",\n \"text\": \"def process_text(text):\\r\\n \\r\\n # decontract text\\r\\n text = expand_contractions(text.lower())\\r\\n \\r\\n # keep only letters and numbers\\r\\n text = re.sub(r'[^a-zA-Z0-9]', ' ', text)\\r\\n \\r\\n # tokenize\\r\\n tokens = word_tokenize(text)\\r\\n \\r\\n # nltk pos tags\\r\\n tokens = nltk.pos_tag(tokens)\\r\\n \\r\\n # change nltk to wordnet pos tags and lemmatize\\r\\n tokens = lemmatize(tokens)\\r\\n \\r\\n # remove stop words and return clean tokens\\r\\n return [token for token in tokens if token not in stop_words]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1959e69fe6de8d35f1f6ee5fdef19dd8\",\n \"score\": \"0.67887527\",\n \"text\": \"def tokenize(text):\\n text = re.sub(r\\\"[^a-zA-Z0-9]\\\", \\\" \\\", text.lower().strip()) # Normalize text and remove white space\\n words = word_tokenize(text) # Tokenize text\\n words = [w for w in words if w not in stopwords.words(\\\"english\\\")] # Remove stop words\\n words = [WordNetLemmatizer().lemmatize(w) for w in words] # Lemmatize\\n words = [WordNetLemmatizer().lemmatize(w, pos='v') for w in words] # Lemmatize verbs by specifying pos\\n\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1a7927b5dce527a5659427b83a0bcb8\",\n \"score\": \"0.67834985\",\n \"text\": \"def tokenize(text):\\n text = re.sub(r\\\"[^a-zA-Z0-9]\\\", \\\" \\\", text)\\n tokens = word_tokenize(text)\\n lemmatizer = WordNetLemmatizer()\\n clean_tokens = []\\n for tok in tokens:\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c67bede2dae92bc6cc8f12d4fe27a86\",\n \"score\": \"0.6777806\",\n \"text\": \"def tokenize(text):\\n lemmatizer = WordNetLemmatizer()\\n \\n text = re.sub(r'[^a-zA-Z0-9]', ' ', text.lower())\\n \\n token_list = word_tokenize(text)\\n token_list = [token.strip() for token in token_list if token not in stopwords.words(\\\"english\\\")]\\n token_list = [lemmatizer.lemmatize(token) for token in token_list]\\n \\n return token_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a89c1d4af91aed11a40c76ba6101486e\",\n \"score\": \"0.6761988\",\n \"text\": \"def tokenize_text(input_file,replace):\\n text = ''\\n with open(input_file, 'r') as content_file:\\n text = content_file.read().decode('utf-8')\\n if replace==True:\\n text = re.sub('[^A-Za-z0-9+-.@]+',' ', text.lower())\\n tokens = nltk.word_tokenize(text)\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ec4602166457f730d730714b0bbc63c\",\n \"score\": \"0.67511344\",\n \"text\": \"def tokenize(self, document):\\n return [t.lower() for t in re.findall(r\\\"\\\\w+(?:[-']\\\\w+)*\\\", document)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82374e0a6e9f5e40b000c764d14151be\",\n \"score\": \"0.6746696\",\n \"text\": \"def pre_process_text(text):\\n text = text.lower()\\n stop_words = set(stopwords.words('english'))\\n word_tokens = word_tokenize(text)\\n word_tokens = [w for w in word_tokens if not w in stop_words]\\n stemmer = stem.PorterStemmer()\\n stemmed_text = [stemmer.stem(w) for w in word_tokens]\\n filtered_text = [w for w in stemmed_text if not w in stop_words]\\n text_len = min(1000, len(filtered_text))\\n return \\\" \\\".join(filtered_text[0:text_len])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f888156e39930cd5105800f9933c66f9\",\n \"score\": \"0.67412174\",\n \"text\": \"def preprocess_text(text, lemmatizing=False):\\r\\n text = text.lower()\\r\\n \\r\\n if lemmatizing:\\r\\n lemmatizer = WordNetLemmatizer()\\r\\n text = ' '.join([lemmatizer.lemmatize(word, pos='v') for word in text.split()])\\r\\n \\r\\n # Remove html tags\\r\\n text = re.sub(r'<.*?>', '', text)\\r\\n \\r\\n # Replace punctuation with spaces\\r\\n translator = str.maketrans(string.punctuation, ' '*len(string.punctuation))\\r\\n text = text.translate(translator)\\r\\n\\r\\n # Remove stop words\\r\\n text = ' '.join([word for word in text.split() if word not in stopwords.words('english')])\\r\\n \\r\\n # Remove additional white spaces\\r\\n text = ' '.join(text.split())\\r\\n \\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f2d5eaf2b6e141a29cb5ec154b7d67\",\n \"score\": \"0.67353785\",\n \"text\": \"def tokenize(text):\\n tokens = word_tokenize(text)\\n lemmatizer = WordNetLemmatizer()\\n\\n clean_tokens = []\\n for tok in tokens:\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n\\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f8b51dd17819b5ddd660874b8e9584b\",\n \"score\": \"0.6719576\",\n \"text\": \"def text_to_word_sequence_nltkword(text,\\n filters='!\\\"#$%&()*+,-./:;<=>?@[\\\\\\\\]^_`{|}~\\\\t\\\\n',\\n lower=True, split=\\\" \\\"):\\n if lower:\\n text = text.lower()\\n\\n tokens = word_tokenize(text)\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a063796f17bfd6b1839ff180e6ca3390\",\n \"score\": \"0.671734\",\n \"text\": \"def preprocess_text(given_text):\\n\\ttext = given_text.lower() \\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# Lower all text\\n\\ttext = text.translate(str.maketrans(string.punctuation, ' ' * len(string.punctuation))) \\t# Remove punctuation\\n\\twords = []\\n\\tfor word in text.split():\\n\\t\\twords.append(word)\\n\\treturn words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21c38f0201c147479013ef7321116c90\",\n \"score\": \"0.67173165\",\n \"text\": \"def tokenize(text, remove_stop_words=False, stopwords=stopwords.words('english')):\\n # case normalization (i.e. all lower case)\\n text = text.lower()\\n # punctuation removal\\n text = re.sub(r'[^a-zA-Z0-9]', ' ', text)\\n # tokenize the text\\n tokens = word_tokenize(text)\\n # Optional stop words removal.\\n # Note: calling stopwords.words('english') here directly causes\\n # pickling functions for some reason (which prevents multicore\\n # processing).\\n if remove_stop_words:\\n tokens = [\\n token for token in tokens if token not in stopwords\\n ]\\n\\n # wordnet lemmatize\\n lemmatizer = WordNetLemmatizer()\\n tokens = [lemmatizer.lemmatize(token) for token in tokens]\\n\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18408aecc2ef5bae787b837a55719c52\",\n \"score\": \"0.6715173\",\n \"text\": \"def tokenize(text):\\n text = text.lower() #lower case\\n url_regex = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\\n detected_urls = re.findall(url_regex, text)\\n for url in detected_urls:\\n text = text.replace(url, \\\"urlplaceholder\\\")\\n\\n text = text.translate(str.maketrans('', '', string.punctuation)) #remove punctuations\\n\\n tokens = word_tokenize(text) #tokenize\\n lemmatizer = WordNetLemmatizer()\\n\\n #remove stopwords\\n clean_tokens = [w for w in tokens if w not in stopwords.words(\\\"english\\\")]\\n\\n #reduce words to their root form\\n clean_tokens = [WordNetLemmatizer().lemmatize(w) for w in clean_tokens]\\n\\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcd254637fd7bd7ae76aee0652d5dc2\",\n \"score\": \"0.6706489\",\n \"text\": \"def tokenize(text):\\n global TOK\\n tokens = TOK.tokenize(text)\\n output = {\\n 'words': replace_digits(tokens.words()),\\n 'offsets': tokens.offsets(),\\n 'pos': tokens.pos(),\\n 'lemma': tokens.lemmas(),\\n 'ner': tokens.entities(), # keep capitalization is good for NER?\\n }\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"298ec4830b98f7e8178c37c5fe1806ec\",\n \"score\": \"0.6692397\",\n \"text\": \"def test_tokenize_words(self):\\n self.assertEqual(res.tokenize_words(\\\"am going home\\\"), [\\\"am\\\", \\\"going\\\", \\\"home\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38416884865cad0a81edb113f9675c4d\",\n \"score\": \"0.66890335\",\n \"text\": \"def tokenisation_stopwords(text):\\n text = text.split(' ')\\n # Removing 1 letter long words and empty strings, and the remaining bad characters\\n text = [re.sub('[^a-zA-Z0-9]*|[\\\\]{1,2}u[a-z0-9]*', '', word) for word in text if len(word) > 1]\\n # Removing stopwords\\n stop_words_extra =['aint', 'arent', 'cant', 'couldve', 'couldnt', 'didnt', 'doesnt', 'dont', 'hadnt', 'hasnt',\\n 'havent', 'hed', 'hell', 'hes', 'heve', 'Id', 'Ill', 'Im', 'Ive', 'isnt', 'itll', 'its',\\n 'lets', 'mustnt', 'shed', 'shell', 'shes', 'shouldve', 'shouldnt', 'thatll', 'thats',\\n 'theres', 'theyd', 'theyll', 'theyre', 'theyve', 'wasnt', 'wed', 'well', 'were', 'weve',\\n 'werent', 'whats', 'wheres', 'wholl', 'whos', 'wont', 'wouldve', 'wouldnt', 'yall', 'youd',\\n 'youll','youre', 'youve']\\n text = [word for word in text if word not in stopwords.words('english')]\\n text = [word for word in text if word not in stop_words_extra]\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7b79b783bdc70a181dfe797ba8cfa73\",\n \"score\": \"0.6685498\",\n \"text\": \"def tokenize(text):\\n stop_words = stopwords.words(\\\"english\\\")\\n lemmatizer = WordNetLemmatizer()\\n \\n # normalize case and remove punctuation\\n text = re.sub(r\\\"[^a-zA-Z0-9]\\\", \\\" \\\", text.lower())\\n \\n # tokenize text\\n tokens = word_tokenize(text)\\n \\n tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]\\n \\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc680dd53367fbf4133be87b46b1ac4\",\n \"score\": \"0.66835004\",\n \"text\": \"def preprocess(tweet_text):\\n return tweet_text.lower().split()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"557e58ccaf33012d126ed1a5c244b5f5\",\n \"score\": \"0.6680401\",\n \"text\": \"def tokenize2(text):\\n\\n # tokenize text\\n tokens = word_tokenize(text)\\n\\n # initiate lemmatizer\\n lemmatizer = WordNetLemmatizer()\\n\\n # iterate through each token\\n tok_list = []\\n for tok in tokens:\\n\\n # lemmatize, normalize case, and remove leading/trailing white space\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n tok_list.append(clean_tok)\\n\\n return tok_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85e8142215e24ea7179b3e71d79cfccd\",\n \"score\": \"0.66790193\",\n \"text\": \"def tokenize(self, text, never_split=None, **kwargs):\\n if self.normalize_text:\\n text = unicodedata.normalize(\\\"NFKC\\\", text)\\n\\n text = text.strip()\\n\\n never_split = self.never_split + (never_split if never_split is not None else [])\\n tokens = []\\n\\n for mrph in self.juman.apply_to_sentence(text).morphemes:\\n token = mrph.text\\n\\n if self.do_lower_case and token not in never_split:\\n token = token.lower()\\n\\n if self.trim_whitespace:\\n if token.strip() == \\\"\\\":\\n continue\\n else:\\n token = token.strip()\\n\\n tokens.append(token)\\n\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"178c0b867b456776462d97c5302d1f9e\",\n \"score\": \"0.667788\",\n \"text\": \"def tokenize(string, lowercase=False):\\n\\timport re\\n\\twords = re.findall(\\\"([\\\\w'-+]+)\\\", string)\\n\\tif lowercase == True:\\n\\t\\tlowerwords = []\\n\\t\\tfor word in words:\\n\\t\\t\\tlowerwords.append(word.lower())\\n\\t\\twords = lowerwords\\n\\treturn words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad4758660e16d88c1ae21606b4d1a9f3\",\n \"score\": \"0.6677427\",\n \"text\": \"def tokenize(text):\\n tokens = nltk.tokenize.word_tokenize(text)\\n # initiate lemmatizer\\n lemma = nltk.stem.WordNetLemmatizer()\\n # iterate through tokens lemmatizing and cleaning\\n clean_tokens = []\\n for token in tokens:\\n clean_token = lemma.lemmatize(token).lower().strip()\\n clean_tokens.append(clean_token)\\n\\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a38a1214752fc4acbb7e941cadcac565\",\n \"score\": \"0.66712266\",\n \"text\": \"def tokenize(sentence): \\n words = re.findall(r'\\\\b[a-z]+\\\\b', sentence)\\n return ' '.join( words)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fffc83ce839f03dadfd4c27f58be1054\",\n \"score\": \"0.6665572\",\n \"text\": \"def tokenize(text):\\n\\n ## normalize text\\n text = re.sub(r\\\"[^a-zA-Z0-9]\\\", \\\" \\\", text.lower())\\n ## tokenize texts\\n tokens = word_tokenize(text)\\n lemmatizer = WordNetLemmatizer()\\n\\n clean_tokens = []\\n for tok in tokens:\\n ## lemmatize, lowercase, strip spaces\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n \\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"900fbef8b898a397dff19f494f941e7a\",\n \"score\": \"0.66593796\",\n \"text\": \"def tokenize(text):\\n words = wakati.parse(text).split()\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67578397eb51500cc4c192d44561bbf2\",\n \"score\": \"0.6653253\",\n \"text\": \"def tokenize(text): \\n # kee[ only letters and numbers\\n text = re.sub(r\\\"[^a-zA-Z0-9]\\\", \\\" \\\", text) \\n\\n tokens = word_tokenize(text)\\n lemmatizer = WordNetLemmatizer()\\n\\n clean_tokens = []\\n\\n #clean text and split into tokens(words)\\n for tok in tokens:\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n\\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3efa323177427fbd6a9f6215b6a4e8b\",\n \"score\": \"0.66524595\",\n \"text\": \"def tokenise_string(s, stop=True):\\n\\n filter_function = lambda x: x not in STOP_WORDS\\n if stop == False:\\n filter_function = lambda x: True\\n\\n return map(stem, filter(filter_function, [re.sub(r'[^a-z ]', '', word) for word in s.lower().strip().split()])) # uhuhuh!\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf832563bb85c2004f5ada1df2107602\",\n \"score\": \"0.66522825\",\n \"text\": \"def tokenize(text):\\n text = word_tokenize(text)\\n tokens = []\\n for word in text:\\n word = WordNetLemmatizer().lemmatize(word).lower().strip()\\n tokens.append(word)\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdf6b61f0c79468e3e5331fe5ac4c21a\",\n \"score\": \"0.66431874\",\n \"text\": \"def tokenize(text):\\n tokens = nltk.word_tokenize(text)\\n tokens = [w for w in tokens if bool(re.search(r\\\"[^a-zA-Z0-9]\\\", w)) != True]\\n tokens = [WordNetLemmatizer().lemmatize(w, pos='v') for w in tokens if stopwords.words(\\\"english\\\")]\\n tokens = [PorterStemmer().stem(w) for w in tokens]\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f88f958399f9579f7a3026b0d17f9de1\",\n \"score\": \"0.66381377\",\n \"text\": \"def text_to_word_sequence_stanford(text,\\n filters='!\\\"#$%&()*+,-./:;<=>?@[\\\\\\\\]^_`{|}~\\\\t\\\\n',\\n lower=True, split=\\\" \\\"):\\n # TODO INSTALL CORENLP\\n if lower:\\n text = text.lower()\\n\\n tokens = CoreNLPTokenizer().tokenize(text)\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cc529633842d4d1ee897be9a0116a5d\",\n \"score\": \"0.66320884\",\n \"text\": \"def tokenize(self, sentence):\\n return [e.lower() for e in sentence.split() if len(e) >= 2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28584b99d7495d3d3f16ffaaa6f3b497\",\n \"score\": \"0.6627767\",\n \"text\": \"def process_text(text, stem=True):\\n text = text.translate(string.punctuation)\\n tokens = word_tokenize(text)\\n\\n if stem:\\n stemmer = PorterStemmer()\\n tokens = [stemmer.stem(t) for t in tokens]\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe311e9ad463407b7e06b7c186cae2de\",\n \"score\": \"0.66265076\",\n \"text\": \"def tokenize(text):\\n\\n\\ttokenized_text = text.lower()\\n\\ttokenized_text = re.findall(r'[a-z0-9]+',\\n\\t\\t\\t\\t\\t\\t\\t\\ttokenized_text) # splits string with delimiter being everything except alphabetical letters\\n\\treturn tokenized_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfbaec85ecd967e5779a97e0a2b8bb88\",\n \"score\": \"0.6626497\",\n \"text\": \"def preprocess_gensim(doc):\\n doc = doc.lower() # Lower the text.\\n doc = word_tokenize(doc) # Split into words.\\n doc = [w for w in doc if not w in stop_words] # Remove stopwords.\\n doc = [w for w in doc if w.isalpha()] # Remove numbers and punctuation.\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f998dba3cb6a706351cbdf9063520dae\",\n \"score\": \"0.6623796\",\n \"text\": \"def tokenize(text: str) -> [str]:\\n vectorizer = CountVectorizer(analyzer='word')\\n vectorizer.fit_transform([text])\\n tokens = vectorizer.get_feature_names()\\n # stems = self.stem_tokens(tokens, stemmer)\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2650f03d71b39db1245b53ba31175496\",\n \"score\": \"0.66184276\",\n \"text\": \"def word_tokenize(self,words):\\n tokens = re.findall(r\\\"[a-z]+-?[a-z]+\\\", words.lower(),\\n flags = re.UNICODE | re.LOCALE)\\n return(tokens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5df3d706d8f0bf7e1678f765a99a16d5\",\n \"score\": \"0.6617486\",\n \"text\": \"def pre_processing(text):\\r\\n sent_tokenize_list = sent_tokenize(text)\\r\\n #print sent_tokenize_list\\r\\n #print len(sent_tokenize_list)\\r\\n #tokenise words\\r\\n #print stop_words\\r\\n words=word_tokenize(text)\\r\\n stop_words = str(stopwords.words('english'))\\r\\n alpha=stop_words.replace(\\\"u'\\\", \\\"\\\")\\r\\n #print words\\r\\n result = []\\r\\n #print alpha\\r\\n #remove stop words\\r\\n for item in words:\\r\\n if item not in alpha:\\r\\n result.append(item)\\r\\n #print \\\"Filtered\\\",result\\r\\n fil=str(result)\\r\\n #remove punctuation\\r\\n repstr=\\\" \\\" * 32\\r\\n table=string.maketrans(string.punctuation,repstr)\\r\\n s=fil.translate(table)\\r\\n #return s\\r\\n\\r\\n\\r\\n #lemmatizing\\r\\n lemmatizer=WordNetLemmatizer()\\r\\n h=lemmatizer.lemmatize(s)\\r\\n #print \\\"Lemma\\\",lemmatizer.lemmatize(s)\\r\\n #stemming\\r\\n wordss=word_tokenize(h)\\r\\n ps=PorterStemmer()\\r\\n list1=[]\\r\\n for i in wordss:\\r\\n k=(ps.stem(i))\\r\\n list1.append(k)\\r\\n #print list1\\r\\n final= ' '.join(list1)\\r\\n finall=str(final)\\r\\n return finall\",\n \"title\": \"\"\n },\n {\n \"docid\": \"030df38d932c8d1f99557f83534c7871\",\n \"score\": \"0.6612498\",\n \"text\": \"def tokenize(document):\\n # Converting all words to lower case\\n document = document.lower()\\n # tokeniizing the string to words\\n words = nltk.tokenize.word_tokenize(document)\\n final_words = []\\n # Add the word to the string if it is not a punctuatuion or an English stop word\\n for word in words:\\n if word not in string.punctuation and word not in nltk.corpus.stopwords.words(\\\"english\\\"):\\n final_words.append(word)\\n\\n return final_words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f5434f7e34a6cc8614686aaaee630f\",\n \"score\": \"0.66085696\",\n \"text\": \"def words(text): \\n return re.findall('[a-z]+', text.lower())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d14c472a7605272cd3154286d04ee50\",\n \"score\": \"0.6606162\",\n \"text\": \"def tokenize(document):\\n\\n # finding the tokens and seves them in a lowercase list \\n tokens = [word.lower() for word in nltk.word_tokenize(document)]\\n\\n # intialize a filter list of words that are not stop words \\n filter_list = []\\n\\n # initialize stop words and punctuations\\n stop_words = nltk.corpus.stopwords.words(\\\"english\\\")\\n punctuation = [punct for punct in string.punctuation] \\n\\n # go over all the words anf filter out the stop words and punctuation \\n for word in tokens:\\n if word in stop_words:\\n continue \\n\\n elif word in punctuation:\\n continue \\n\\n else:\\n filter_list.append(word)\\n\\n\\n return filter_list\\n #raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70984638567ac1d800112f5cf3d37c65\",\n \"score\": \"0.6599118\",\n \"text\": \"def tokenize(text):\\n # replace all token delimiters with a costmmon delimiter - space and split it\\n # make all lowerscore and replace underscores with space first\\n text = re.sub('_', ' ', text.lower())\\n tokens = re.findall(r'[\\\\w]+', text)\\n return list(tokens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd2378ae8b33a66c9154a4055061db68\",\n \"score\": \"0.65977204\",\n \"text\": \"def tokenize(text):\\n\\n # remove punctuation and lowercase\\n text = re.sub(r'[^\\\\w\\\\s]','', text).lower()\\n # split into tokens\\n tokens = word_tokenize(text)\\n # lemmatize\\n lemmatizer = WordNetLemmatizer()\\n clean_tokens = [lemmatizer.lemmatize(word) for word in tokens]\\n return clean_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd1e0208d44360f094dfef52fb9eda9d\",\n \"score\": \"0.6597287\",\n \"text\": \"def initial_clean(text):\\n text = re.sub(\\\"((\\\\S+)?(http(s)?)(\\\\S+))|((\\\\S+)?(www)(\\\\S+))|((\\\\S+)?(\\\\@)(\\\\S+)?)\\\", \\\" \\\", text)\\n text = re.sub(\\\"[^a-zA-Z ]\\\", \\\"\\\", text)\\n text = text.lower() # lower case the text\\n text = nltk.word_tokenize(text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd1e0208d44360f094dfef52fb9eda9d\",\n \"score\": \"0.6597287\",\n \"text\": \"def initial_clean(text):\\n text = re.sub(\\\"((\\\\S+)?(http(s)?)(\\\\S+))|((\\\\S+)?(www)(\\\\S+))|((\\\\S+)?(\\\\@)(\\\\S+)?)\\\", \\\" \\\", text)\\n text = re.sub(\\\"[^a-zA-Z ]\\\", \\\"\\\", text)\\n text = text.lower() # lower case the text\\n text = nltk.word_tokenize(text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dafa1ec87870f635bf0c0a57cd664b6b\",\n \"score\": \"0.659701\",\n \"text\": \"def process_text(self, text):\\n\\n self.__tokens.clear()\\n\\n tokenize = [t.lower().strip(\\\":,.!?\\\") for t in text.split()]\\n\\n filtered_tokens = self.__filter_text(tokenize)\\n\\n self.__tokens.extend(filtered_tokens)\\n self.__all_tokens.extend(filtered_tokens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b25d0bcc5a99c0c30eb1024b3d560dbd\",\n \"score\": \"0.65837455\",\n \"text\": \"def remove_stopwords(text, is_lower_case=False, stopwords=stopword_list):\\n tokens = tokenizer.tokenize(text)\\n tokens = [token.strip() for token in tokens]\\n if is_lower_case:\\n filtered_tokens = [token for token in tokens if token not in stopwords]\\n else:\\n filtered_tokens = [token for token in tokens if token.lower() not in stopwords]\\n filtered_text = ' '.join(filtered_tokens) \\n return filtered_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b396d5ab5db7fd97738c2f1d928bf1b\",\n \"score\": \"0.65648067\",\n \"text\": \"def tokenize(document):\\n return [word.lower() for word in nltk.word_tokenize(document) \\n if (word.lower() not in nltk.corpus.stopwords.words(\\\"english\\\")\\n and all([char not in string.punctuation for char in word.lower()]))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a17dfc5c5680f327ea49982fca2d525\",\n \"score\": \"0.654812\",\n \"text\": \"def tokenize(texts):\\n # type: (pd.Series) -> pd.Series\\n return texts.apply(lambda x: re.findall(r'\\\\b\\\\w+\\\\b', x.lower()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ce959a388e6bf3b0e4225142325c8d\",\n \"score\": \"0.65455455\",\n \"text\": \"def do_tokenize(self, text):\\n text = text.lower() if self.lc else text\\n if self.collapse_hashtags:\\n text = re.sub('#\\\\S+', 'THIS_IS_A_HASHTAG', text)\\n else:\\n text = re.sub('#(\\\\S+)', r'HASHTAG_\\\\1', text)\\n if self.collapse_mentions:\\n text = re.sub('@\\\\S+', 'THIS_IS_A_MENTION', text)\\n if self.collapse_urls:\\n text = re.sub('http\\\\S+', 'THIS_IS_A_URL', text)\\n if self.limit_repeats:\\n text = re.sub(r'(.)\\\\1\\\\1\\\\1+', r'\\\\1', text)\\n toks = []\\n for tok in text.split():\\n tok = re.sub(r'^(' + punc_re + '+)', r'\\\\1 ', tok)\\n tok = re.sub(r'(' + punc_re + '+)$', r' \\\\1', tok)\\n for subtok in tok.split():\\n if self.retain_punc_toks or re.search('\\\\w', subtok):\\n toks.append(subtok)\\n return toks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13b52ce92e4baabc2e59c92b6be62a25\",\n \"score\": \"0.6525562\",\n \"text\": \"def __call__(self, text):\\n\\n tokens = text.split()\\n\\n for i in range(len(tokens) - 1):\\n if is_dash_word(tokens[i]):\\n\\n # Require the first character of the next word is an alpha\\n if not tokens[i + 1][0].isalpha():\\n continue\\n\\n # Skip words with more than 2 caps\\n if len([x for x in tokens[i + 1] if x == x.upper()]) >= 2:\\n continue\\n\\n word = \\\"{}{}\\\".format(tokens[i][:-1], tokens[i + 1])\\n\\n test_word = ''.join([x for x in word if x.isalpha()])\\n\\n # Only combine sensible english words\\n if test_word.lower() not in self.english_words:\\n continue\\n\\n self.logger.info(\\\"Merging tokens %s %s %s\\\"\\n % (tokens[i], tokens[i + 1], word))\\n\\n tokens[i] = word\\n tokens[i + 1] = ''\\n\\n doc = ' '.join((x for x in tokens if x))\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f70b9ccacb15bba30f78d16053fda3bb\",\n \"score\": \"0.6524916\",\n \"text\": \"def _words_tokenizer(self):\\n words = []\\n for sentence in self.temp_list:\\n words.append(nltk.word_tokenize(sentence))\\n self.temp_list = words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeaf2ae88e2b73407262e825cb084bf0\",\n \"score\": \"0.65185577\",\n \"text\": \"def apply_all(text):\\n return stem_words(remove_stop_words(initial_clean(text)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeaf2ae88e2b73407262e825cb084bf0\",\n \"score\": \"0.65185577\",\n \"text\": \"def apply_all(text):\\n return stem_words(remove_stop_words(initial_clean(text)))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":480,"cells":{"query_id":{"kind":"string","value":"b63d48e3c4fe997cca7ed55c2e825fdf"},"query":{"kind":"string","value":"Converts all schemes in a given object to its proper swagger representation."},"positive_passages":{"kind":"list like","value":[{"docid":"8f948b5ea876011ccdf22b5f90657f56","score":"0.5604471","text":"def _extract_schemas(self, obj):\n definitions = {}\n if isinstance(obj, list):\n for i, o in enumerate(obj):\n obj[i], definitions_ = self._extract_schemas(o)\n definitions.update(definitions_)\n if isinstance(obj, dict):\n for k, v in obj.items():\n obj[k], definitions_ = self._extract_schemas(v)\n definitions.update(definitions_)\n if inspect.isclass(obj):\n # Object is a model. Convert it to valid json and get a definition object\n if not issubclass(obj, Schema):\n raise ValueError('\"{0}\" is not a subclass of the schema model'.format(obj))\n definition = obj.definitions()\n description = parse_schema_doc(obj, definition)\n if description:\n definition['description'] = description\n # The definition itself might contain models, so extract them again\n definition, additional_definitions = self._extract_schemas(definition)\n definitions[obj.__name__] = definition\n definitions.update(additional_definitions)\n obj = obj.reference()\n return obj, definitions","title":""}],"string":"[\n {\n \"docid\": \"8f948b5ea876011ccdf22b5f90657f56\",\n \"score\": \"0.5604471\",\n \"text\": \"def _extract_schemas(self, obj):\\n definitions = {}\\n if isinstance(obj, list):\\n for i, o in enumerate(obj):\\n obj[i], definitions_ = self._extract_schemas(o)\\n definitions.update(definitions_)\\n if isinstance(obj, dict):\\n for k, v in obj.items():\\n obj[k], definitions_ = self._extract_schemas(v)\\n definitions.update(definitions_)\\n if inspect.isclass(obj):\\n # Object is a model. Convert it to valid json and get a definition object\\n if not issubclass(obj, Schema):\\n raise ValueError('\\\"{0}\\\" is not a subclass of the schema model'.format(obj))\\n definition = obj.definitions()\\n description = parse_schema_doc(obj, definition)\\n if description:\\n definition['description'] = description\\n # The definition itself might contain models, so extract them again\\n definition, additional_definitions = self._extract_schemas(definition)\\n definitions[obj.__name__] = definition\\n definitions.update(additional_definitions)\\n obj = obj.reference()\\n return obj, definitions\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"7f31adca643df64f561772f8c5bcdf1d","score":"0.6359533","text":"def swaggerish(self):\n\n # Better chosen dinamically from endpoint.py\n schemes = ['http']\n if self._customizer._production:\n schemes = ['https']\n\n # A template base\n output = {\n \"swagger\": \"2.0\",\n \"info\": {\n \"version\": \"0.0.1\",\n \"title\": \"Your application name\",\n },\n \"schemes\": schemes,\n # \"host\": \"localhost\" # chosen dinamically\n \"basePath\": \"/\",\n \"securityDefinitions\": {\n \"Bearer\": {\n \"type\": \"apiKey\",\n \"name\": \"Authorization\",\n \"in\": \"header\"\n }\n # \"OauthSecurity\": {\n # \"type\": \"oauth2\",\n # \"tokenUrl\": \"https://oauth.simple.api/token\",\n # \"flow\": \"accessCode\",\n # \"authorizationUrl\": \"https://blabla/authorization\",\n # \"scopes\": {\n # \"admin\": \"Admin scope\",\n # \"user\": \"User scope\"\n # }\n # }\n # TODO: check about scopes (roles?)\n },\n \"security\": [\n {\n \"Bearer\": []\n }\n ]\n }\n\n ###################\n # Set existing values\n proj = self._customizer._configurations['project']\n if 'version' in proj:\n output['info']['version'] = proj['version']\n if 'title' in proj:\n output['info']['title'] = proj['title']\n\n ###################\n output['definitions'] = self.read_definitions()\n output['consumes'] = [JSON_APPLICATION]\n output['produces'] = [JSON_APPLICATION]\n\n ###################\n # Read endpoints swagger files\n for key, endpoint in enumerate(self._endpoints):\n\n endpoint.custom['methods'] = {}\n endpoint.custom['params'] = {}\n\n for method, file in endpoint.methods.items():\n # add the custom part to the endpoint\n self._endpoints[key] = \\\n self.read_my_swagger(file, method, endpoint)\n\n ###################\n # Save query parameters globally\n self._customizer._query_params = self._qparams\n self._customizer._parameter_schemas = self._parameter_schemas\n output['paths'] = self._paths\n\n ###################\n tags = []\n for tag, desc in self._customizer._configurations['tags'].items():\n tags.append({'name': tag, 'description': desc})\n output['tags'] = tags\n\n self._customizer._original_paths = self._original_paths\n return output","title":""},{"docid":"5e0ecf837a7a8c5fb3ccc815beeee2e1","score":"0.5981558","text":"def api_schema(request):\n generator = CorniceSwagger(get_services())\n # function docstrings are used to create the route's summary in Swagger-UI\n generator.summary_docstrings = True\n generator.default_security = get_security\n swagger_base_spec = {\n \"host\": get_magpie_url(request.registry),\n \"schemes\": [request.scheme]\n }\n swagger_base_spec.update(SecurityDefinitionsAPI)\n generator.swagger = swagger_base_spec\n json_api_spec = generator.generate(title=TitleAPI, version=__meta__.__version__, info=InfoAPI)\n return json_api_spec","title":""},{"docid":"d5f2e1a445bb6cb533b83bdf72ae1963","score":"0.58855915","text":"def input_to_swagger(self):\n return {\"type\": \"object\", \"example\": {}}","title":""},{"docid":"8827da09ff4f89d4f22cf216b7c3da73","score":"0.5883961","text":"async def openapi_schema(request: web.Request) -> web.Response:\n schema_format = request.match_info.get(\"schema_format\")\n schema = get_openapi_schema(request.config_dict)\n\n if schema_format == \"json\":\n return web.json_response(schema)\n\n if schema_format == \"yaml\":\n safe_dumper = getattr(yaml, \"CSafeDumper\", yaml.SafeDumper)\n return web.Response(\n text=yaml.dump(schema, Dumper=safe_dumper),\n content_type=\"application/yaml\",\n )\n\n raise ConfigurationError(\n f\"Schema format {schema_format} not supported at a moment.\"\n )","title":""},{"docid":"06a543bc1959e6e413a56bfab9070bfe","score":"0.58181477","text":"def _to_swagger(base=None, description=None, resource=None, options=None):\n # type: (Dict[str, str], str, Resource, Dict[str, str]) -> Dict[str, str]\n definition = dict_filter(base or {}, options or {})\n\n if description:\n definition['description'] = description.format(\n name=getmeta(resource).name if resource else \"UNKNOWN\"\n )\n\n if resource:\n definition['schema'] = {\n '$ref': '#/definitions/{}'.format(getmeta(resource).resource_name)\n }\n\n return definition","title":""},{"docid":"bdc39edfe0826acf2f0d1c535cc99754","score":"0.5752418","text":"def init_bundles(schema: \"BaseOpenAPISchema\") -> Dict[str, CaseInsensitiveDict]:\n output: Dict[str, CaseInsensitiveDict] = {}\n for endpoint in schema.get_all_endpoints():\n output.setdefault(endpoint.path, CaseInsensitiveDict())\n output[endpoint.path][endpoint.method.upper()] = Bundle(endpoint.verbose_name) # type: ignore\n return output","title":""},{"docid":"cf5022e300f0eec55dfafcfc8f006237","score":"0.5693794","text":"def _custom_openapi_method(zelf: FastAPI) -> dict:\n if not zelf.openapi_schema:\n\n if zelf.redoc_url:\n desc = compose_long_description(zelf.description)\n else:\n desc = zelf.description\n openapi_schema = get_openapi(\n title=zelf.title,\n version=zelf.version,\n openapi_version=zelf.openapi_version,\n description=desc,\n routes=zelf.routes,\n tags=zelf.openapi_tags,\n servers=zelf.servers,\n )\n\n add_vendor_extensions(openapi_schema)\n patch_openapi_specs(openapi_schema)\n zelf.openapi_schema = openapi_schema\n\n return zelf.openapi_schema","title":""},{"docid":"8805a9f0cb3b278e895371b08c714085","score":"0.56355596","text":"def introspect(self, request):\n # Generate a path based on the standard automate URLs above\n patterns = [path(request.path, include(self.urls()))]\n generator = SchemaGenerator(patterns=patterns)\n return Response(generator.get_schema())","title":""},{"docid":"bd4295718cd2554dfb8240815cd1f1b9","score":"0.55284894","text":"def _generate_openapi_object(document: OpenApiDocument) -> OrderedDict:\n parsed_url = urlparse.urlparse(document.url)\n\n swagger = OrderedDict()\n\n swagger['swagger'] = '2.0'\n swagger['info'] = OrderedDict()\n swagger['info']['title'] = document.title\n swagger['info']['description'] = document.description\n swagger['info']['version'] = document.version\n\n if parsed_url.netloc:\n swagger['host'] = parsed_url.netloc\n if parsed_url.scheme:\n swagger['schemes'] = [parsed_url.scheme]\n\n swagger['paths'] = _get_paths_object(document)\n\n return swagger","title":""},{"docid":"0cf7f89936610531cb39c98726fb9e33","score":"0.5463968","text":"def custom_openapi():\n if app.openapi_schema:\n return app.openapi_schema\n openapi_schema = get_openapi(\n title=\"The VICC Gene Normalizer\",\n version=__version__,\n openapi_version=\"3.0.3\",\n description=\"Normalize gene terms.\",\n routes=app.routes\n )\n # openapi_schema['info']['license'] = { # TODO\n # \"name\": \"name of our license\",\n # \"url\": \"http://www.our-license-tbd.com\"\n # }\n openapi_schema['info']['contact'] = { # TODO\n \"name\": \"Alex H. Wagner\",\n \"email\": \"Alex.Wagner@nationwidechildrens.org\"\n }\n app.openapi_schema = openapi_schema\n return app.openapi_schema","title":""},{"docid":"013af3547f1ae774991c58db2bd555a3","score":"0.5462466","text":"def generate_swagger_object(document):\n parsed_url = urlparse.urlparse(document.url)\n\n swagger = OrderedDict()\n\n swagger['swagger'] = '2.0'\n swagger['info'] = OrderedDict()\n swagger['info']['title'] = document.title\n swagger['info']['version'] = '' # Required by the spec\n\n if parsed_url.netloc:\n swagger['host'] = parsed_url.netloc\n if parsed_url.scheme:\n swagger['schemes'] = [parsed_url.scheme]\n\n swagger['paths'] = _get_paths_object(document)\n swagger['definitions'] = _get_definitions_object(document)\n\n return swagger","title":""},{"docid":"4e8e14db9ea8cca34c9b2979ed91a76c","score":"0.5427914","text":"def update_schema(schema: dict):\n definitions = schema[\"definitions\"]\n for name, definition in definitions.items():\n version = None\n if \"x-kubernetes-group-version-kind\" in definition:\n k8s_versions = definition[\"x-kubernetes-group-version-kind\"]\n if len(k8s_versions) != 1:\n print(\"warning, not correctly handling\", name)\n\n k8s_version = k8s_versions[0]\n version = os.path.join(k8s_version[\"group\"], k8s_version[\"version\"])\n\n if \"properties\" in definition:\n # remove all fields named status\n # this might have some false positives, lets see what goes missing :)\n if \"status\" in definition[\"properties\"]:\n definition[\"properties\"].pop(\"status\")\n\n for prop_name, prop in definition[\"properties\"].items():\n # set the default of properties that are enums\n # with only one possible value to that value\n # This sets the `kind` property on all objects.\n if \"enum\" in prop and len(prop[\"enum\"]) == 1:\n prop.setdefault(\"default\", prop[\"enum\"][0])\n\n # set api version\n if prop_name == \"apiVersion\":\n if version:\n prop.setdefault(\"default\", version)\n\n # inline IntOrString, avoids creation of IntOrString class\n # this allows assigning int or string directly, instead of this\n # ref_int_or_string = \"#/definitions/io.k8s.apimachinery.pkg.util.intstr.IntOrString\"\n # TODO using \"oneOf\" in properties is not supported by datamode-code-generator\n # see https://github.com/koxudaxi/datamodel-code-generator/issues/139\n # if \"$ref\" in prop:\n # ref = prop[\"$ref\"]\n\n # if ref == ref_int_or_string:\n # prop.pop(\"$ref\")\n\n # prop.update(definitions[ref.split('/')[-1]])\n\n # print(prop)\n # \"$ref\":\n # \"oneOf\": [\n # {\n # \"type\": \"string\"\n # },\n # {\n # \"type\": \"integer\"\n # }\n # ]","title":""},{"docid":"b2904b37c8aa0a312940aeea1b25bf9c","score":"0.53712267","text":"def serialize(self, model_object):\n context = dict()\n for name, definition in model_object.definitions(props=False, rels=True):\n context[name] = {\n '@id': definition.absolute_uri(),\n '@type': '@id',\n }\n\n for name, definition in model_object.definitions(props=True, rels=False):\n context[name] = {\n '@id': definition.absolute_uri(),\n }\n\n for name, value in model_object.values.items():\n if isinstance(value, Uri) or isinstance(value, Url):\n context[name] = {\n '@id': model_object.definition(name).absolute_uri(),\n '@type': '@id',\n }\n\n deflated = self.deflate(model_object, props=True, rels=True)\n deflated['@context'] = {k: v for k, v in context.items() if k in deflated}\n deflated['@context']['@base'] = Ori.uri\n deflated['@context'][model_object.verbose_name()] = model_object.absolute_uri()\n deflated['@type'] = model_object.verbose_name()\n\n ori_identifier = model_object.get_short_identifier()\n if ori_identifier:\n deflated['@id'] = ori_identifier\n\n return deflated","title":""},{"docid":"04aea1c336857bbe5041af8ce79a8c9c","score":"0.5365679","text":"def doc_formatter(target_object):\n doc = target_object.__doc__\n\n # Handle non-pydantic objects\n if doc is None:\n new_doc = ''\n elif 'Parameters\\n' in doc or not (issubclass(target_object, BaseSettings) or issubclass(target_object, BaseModel)):\n new_doc = doc\n else:\n type_formatter = {'boolan': 'bool',\n 'string': 'str',\n 'integer': 'int'\n }\n # Add the white space\n if not doc.endswith('\\n\\n'):\n doc += \"\\n\\n\"\n new_doc = dedent(doc) + \"Parameters\\n----------\\n\"\n target_schema = target_object.schema()\n # Go through each property\n for prop_name, prop in target_schema['properties'].items():\n # Catch lookups for other Pydantic objects\n if '$ref' in prop:\n # Pre 0.28 lookup\n lookup = prop['$ref'].split('/')[-1]\n prop = target_schema['definitions'][lookup]\n elif 'allOf' in prop:\n # Post 0.28 lookup\n try:\n # Validation, we don't need output, just the object\n _JsonRefModel(**prop)\n lookup = prop['allOf'][0]['$ref'].split('/')[-1]\n prop = target_schema['definitions'][lookup]\n except ValidationError:\n # Doesn't conform, pass on\n pass\n # Get common properties\n prop_type = prop[\"type\"]\n new_doc += prop_name + \" : \"\n prop_desc = prop['description']\n\n # Check for enumeration\n if 'enum' in prop:\n new_doc += '{' + ', '.join(prop['enum']) + '}'\n\n # Set the name/type of object\n else:\n if prop_type == 'object':\n prop_field = prop['title']\n else:\n prop_field = prop_type\n new_doc += f'{type_formatter[prop_field] if prop_field in type_formatter else prop_field}'\n\n # Handle Classes so as not to re-copy pydantic descriptions\n if prop_type == 'object':\n if not ('required' in target_schema and prop_name in target_schema['required']):\n new_doc += \", Optional\"\n prop_desc = f\":class:`{prop['title']}`\"\n\n # Handle non-classes\n else:\n if 'default' in prop:\n default = prop['default']\n try:\n # Get the explicit default value for enum classes\n if issubclass(default, Enum):\n default = default.value\n except TypeError:\n pass\n new_doc += f\", Default: {default}\"\n elif not ('required' in target_schema and prop_name in target_schema['required']):\n new_doc += \", Optional\"\n\n # Finally, write the detailed doc string\n new_doc += \"\\n\" + indent(prop_desc, \" \") + \"\\n\"\n\n # Assign the new doc string\n target_object.__doc__ = new_doc","title":""},{"docid":"832b92534b8a1ea3810d394d8341d5dc","score":"0.53519154","text":"def swagger(\n app,\n url_parser=flask_url_parser,\n rule_parser=flask_rule_parser,\n process_doc=_sanitize,\n prefix=None,\n from_file_keyword=None,\n template=None,\n):\n output = {\n \"swagger\": \"2.0\",\n \"info\": {\"version\": \"0.0.0\", \"title\": \"Cool product name\"},\n }\n\n paths = defaultdict(dict)\n definitions = defaultdict(dict)\n if template is not None:\n output.update(template)\n # check for template provided paths and definitions\n for k, v in output.get(\"paths\", {}).items():\n paths[k] = v\n for k, v in output.get(\"definitions\", {}).items():\n definitions[k] = v\n output[\"paths\"] = paths\n output[\"definitions\"] = definitions\n\n swagger_endpoints = url_parser(app, prefix)\n for rule, methods in swagger_endpoints.items():\n rule = rule_parser(rule)\n\n operations = dict()\n for verb, method in methods:\n summary, description, swag = _parse_docstring(\n method, process_doc, from_file_keyword\n )\n # Do we have docstring to parse?\n if swag is not None:\n defs = swag.get(\"definitions\", [])\n defs = _extract_definitions(defs)\n params = swag.get(\"parameters\", [])\n defs += _extract_definitions(params)\n responses = swag.get(\"responses\", {})\n responses = {str(key): value for key, value in responses.items()}\n if responses is not None:\n defs = defs + _extract_definitions(responses.values())\n for definition in defs:\n def_id = definition.pop(\"id\", None)\n if def_id is not None:\n definitions[def_id].update(definition)\n operation = dict(\n summary=summary, description=description, responses=responses\n )\n # parameters - swagger ui dislikes empty parameter lists\n if len(params) > 0:\n operation[\"parameters\"] = params\n # other optionals\n for key in OPTIONAL_FIELDS:\n if key in swag:\n operation[key] = swag.get(key)\n operations[verb] = operation\n\n if len(operations):\n paths[rule].update(operations)\n return output","title":""},{"docid":"4d489759e5562c97e81a632793aa0fad","score":"0.5314773","text":"def do_schema(self):\n\n name = '%s.%s.%s' % (BACKEND_PACKAGE, 'rest', 'schema')\n module = self._meta.get_module_from_string(name)\n schema_class = getattr(module, 'RecoverSchema')\n\n self._schema_endpoint = EndpointElements(\n cls=schema_class,\n exists=True,\n custom={\n 'methods': {\n 'get': ExtraAttributes(auth=None),\n # WHY DOES POST REQUEST AUTHENTICATION\n # 'post': ExtraAttributes(auth=None)\n }\n },\n methods={}\n )\n\n # TODO: find a way to map authentication\n # as in the original endpoint for the schema 'get' method\n\n # TODO: find a way to publish on swagger the schema\n # if endpoint is enabled to publish and the developer asks for it","title":""},{"docid":"75a94ca26ee4608ad96e95526900143b","score":"0.5308563","text":"def schema_factory(cls, obj, **kwargs):\n if isinstance(obj, str):\n openapi_type = cls._openapi_type(obj)\n return openapi.Schema(\n type=openapi_type,\n **kwargs\n )\n\n if isinstance(obj, list):\n if len(obj) != 1:\n raise Exception('List types must have exactly one element to specify the schema of `items`')\n return openapi.Schema(\n type=openapi.TYPE_ARRAY,\n items=cls.schema_factory(obj[0]),\n **kwargs\n )\n\n if isinstance(obj, dict):\n return openapi.Schema(\n type=openapi.TYPE_OBJECT,\n properties={\n k: cls.schema_factory(sub_obj)\n for k, sub_obj\n in obj.items()\n },\n **kwargs\n )\n\n raise Exception(f'Unhandled type \"{type(obj)}\" for {obj}')","title":""},{"docid":"76c9a1ae1b13c714488cd03e9b6fd6f0","score":"0.53071064","text":"def to_api(_object):\n try:\n return _object.to_api()\n except AttributeError:\n return _object","title":""},{"docid":"2d745dd83dc2c3e996b3f7afa5662e14","score":"0.5284773","text":"def format_schema(self, schema):\n if getattr(schema, 'properties', {}):\n schema.properties = OrderedDict(\n (self.format_string(key), self.format_schema(openapi.resolve_ref(val, self.components)) or val)\n for key, val in schema.properties.items()\n )\n\n if getattr(schema, 'required', []):\n schema.required = [self.format_string(p) for p in schema.required]","title":""},{"docid":"0648a7176d6cdefd2c4c82e00853639c","score":"0.5263264","text":"def to_representation(self, obj):\n urls = list(obj.all())\n return [(url.url, url.description) for url in urls]","title":""},{"docid":"a616f6bb8a7a482fbd6c43fa359003cb","score":"0.52482826","text":"def default(self, obj):\n @singledispatch\n def default(obj):\n super(GoodJSONEncoder, self).default(obj)\n\n @default.register(ObjectId)\n @default.register(UUID)\n def conv_objid(obj):\n return text_type(obj)\n\n @default.register(datetime)\n def conv_datetime(obj):\n if self.epoch_mode:\n return int(\n (timegm(obj.timetuple()) * 1000) +\n ((obj.microsecond) / 1000)\n )\n return obj.isoformat()\n\n @default.register(DBRef)\n def conv_dbref(obj):\n doc = obj.as_doc()\n ret = {\n \"collection\": doc[\"$ref\"],\n \"id\": self.default(doc[\"$id\"])\n }\n if obj.database:\n ret[\"db\"] = doc[\"$db\"]\n ret.update({\n key: value\n for (key, value) in doc.items()\n if key[0] != \"$\"\n })\n return ret\n\n @default.register(RE_TYPE)\n @default.register(Regex)\n def conv_regex(obj):\n flags_map = {\n \"i\": obj.flags & re.IGNORECASE,\n \"l\": obj.flags & re.LOCALE,\n \"m\": obj.flags & re.MULTILINE,\n \"s\": obj.flags & re.DOTALL,\n \"u\": obj.flags & re.UNICODE,\n \"x\": obj.flags & re.VERBOSE\n }\n flags = [key for (key, contains) in flags_map.items() if contains]\n ret = {\"regex\": obj.pattern}\n if flags:\n ret[\"flags\"] = (\"\").join(flags)\n return ret\n\n @default.register(MinKey)\n def conv_minkey(obj):\n return {\"minKey\": True}\n\n @default.register(MaxKey)\n def conv_maxkey(obj):\n return {\"maxKey\": True}\n\n @default.register(Timestamp)\n def conv_timestamp(obj):\n return {\"time\": obj.time, \"inc\": obj.inc}\n\n @default.register(Code)\n def conv_code(obj):\n return {\"code\": str(obj), \"scope\": obj.scope}\n\n @default.register(Binary)\n def conv_bin(obj):\n return {\n \"data\": b64encode(obj).decode(\"utf-8\"),\n \"type\": obj.subtype\n }\n\n if PY3:\n @default.register(bytes)\n def conv_bytes(obj):\n return {\"data\": b64encode(obj).decode(\"utf-8\"), \"type\": 0}\n\n return default(obj)","title":""},{"docid":"d87da8dd68665de8db8341c3c2a7a877","score":"0.52324057","text":"def _pack_document(self, objects, type_):\n\n def pack_object(obj):\n packed_obj = {'type': type_}\n attributes = {}\n relationships = {}\n for key, value in obj.items():\n if isinstance(value, JsonApiRelationship):\n relationships[key] = value.as_linkage()\n elif key == 'id':\n packed_obj['id'] = value\n else:\n attributes[key] = value\n\n if attributes:\n packed_obj['attributes'] = attributes\n\n if relationships:\n packed_obj['relationships'] = relationships\n\n return packed_obj\n\n try:\n if isinstance(objects, list):\n packed_objects = [pack_object(obj) for obj in objects]\n else:\n packed_objects = pack_object(objects)\n except KeyError:\n raise JsonApiException('Invalid resource info format', True)\n\n return {'data': packed_objects}","title":""},{"docid":"ef7f41b16d4852d42671ea3a74628ba4","score":"0.51945394","text":"def to_swagger(self, bound_resource=None):\n return _to_swagger(\n {\n 'name': self.name,\n 'in': self.in_.value,\n 'type': str(self.type) if self.type else None,\n },\n description=self.description,\n resource=bound_resource if self.resource is DefaultResource else self.resource,\n options=self.options\n )","title":""},{"docid":"82da09e9d9574a4b8a097e542a76517b","score":"0.5189164","text":"def _gen_endpoint(self, name, obj, description, version=1, schema=None):\n # check for invalid PO names\n _check_endpoint_name(name)\n\n if description is None:\n description = obj.__doc__.strip() or \"\" if isinstance(obj.__doc__, str) else \"\"\n\n endpoint_object = CustomQueryObject(query=obj, description=description,)\n\n return {\n \"name\": name,\n \"version\": version,\n \"description\": description,\n \"type\": \"model\",\n \"endpoint_obj\": endpoint_object,\n \"dependencies\": endpoint_object.get_dependencies(),\n \"methods\": endpoint_object.get_methods(),\n \"required_files\": [],\n \"required_packages\": [],\n \"schema\": copy.copy(schema),\n }","title":""},{"docid":"d005473445a67bb56c71a08378ec1550","score":"0.51735437","text":"def printSchemes():\n\tschemes=getSchemeList()\n\tfor s in schemes:\n\t\tprint \"scheme_name: {} scheme_options: {}\".format(s.name,s.options)","title":""},{"docid":"7af45c8b9a3def744328cd800ddf2b4d","score":"0.5167875","text":"def map_serializer(self, auto_schema, direction):\n if isinstance(self.target.serializers, dict):\n sub_components = self._get_explicit_sub_components(auto_schema, direction)\n else:\n sub_components = self._get_implicit_sub_components(auto_schema, direction)\n\n return {\n 'oneOf': [ref for _, ref in sub_components],\n 'discriminator': {\n 'propertyName': self.target.resource_type_field_name,\n 'mapping': {resource_type: ref['$ref'] for resource_type, ref in sub_components}\n }\n }","title":""},{"docid":"7c6f0ff8f69374311bc7d43f46e2c681","score":"0.5163314","text":"def __call__(self, title, version, base_path='/', info={}, swagger={},\n schema_transformers=[], **kwargs):\n\n info.update(title=title, version=version)\n swagger.update(swagger='2.0', info=info, basePath=base_path)\n self.schema_transformers.extend(schema_transformers)\n\n paths, tags = self._build_paths(**kwargs)\n if paths:\n swagger['paths'] = paths\n if tags:\n swagger['tags'] = tags\n\n definitions = self.definitions.definition_registry\n if definitions:\n swagger['definitions'] = definitions\n\n parameters = self.parameters.parameter_registry\n if parameters:\n swagger['parameters'] = parameters\n\n responses = self.responses.response_registry\n if responses:\n swagger['responses'] = responses\n\n return swagger","title":""},{"docid":"aaddd81de9af50562353b6642d649755","score":"0.51470786","text":"def loads(self, obj):\n\n buf = io.BytesIO(obj)\n schema_dict = schemaless_reader(buf, self.schema_dict)\n\n if self.decoding_method:\n return [self.decoding_method(schema_dict)]\n else:\n return [schema_dict]","title":""},{"docid":"6414d3051920913323ee10bd60e26f79","score":"0.5139515","text":"def schema(cls, *args: Any, **kwargs: Any) -> Dict[str, Any]:\n schema = super().schema(*args, **kwargs)\n cls.check_provider_added(schema)\n schema = cls.change_format_to_oneOf(schema)\n schema = cls.resolve_refs(schema)\n schema = cls.remove_enum_allOf(schema)\n return schema","title":""},{"docid":"31ce216f4a2547b398775bf6de953ba6","score":"0.5136734","text":"def serialize(self, document, base_url: str, path, schema) -> str:\n home = {\n \"api\": {\n \"title\": schema[\"info\"][\"title\"],\n \"links\": {\n \"author\": schema[\"info\"][\"contact\"][\"name\"],\n \"describedBy\": \"\",\n },\n },\n \"resources\": {},\n }\n for path, path_desc in schema[\"paths\"].items():\n home[\"resources\"][path.strip(\"/\")] = {\n \"href\": as_absolute(base_url, path),\n \"hints\": {\"allow\": [key.upper() for key in path_desc.keys()]},\n }\n return json.dumps(home, indent=4)","title":""},{"docid":"82e6aac41f7b96a82898225d38e99e8c","score":"0.5128058","text":"def _make_doc_response_schema(schema):\n return schema","title":""},{"docid":"9f25adb59c8990d11c8f5e65a857e3db","score":"0.50924915","text":"def _ref_to_spec(self, data):\n if isinstance(data, list):\n for i_v in data:\n self._ref_to_spec(i_v)\n if isinstance(data, dict):\n for k, v in data.items():\n if isinstance(v, list):\n for i_v in v:\n self._ref_to_spec(i_v)\n if isinstance(v, dict):\n self._ref_to_spec(v)\n elif k == \"$ref\":\n name_schema = v.split(\"/\")[-1]\n schema = class_registry.get_class(name_schema)\n name_schema = create_schema_name(schema=schema)\n if name_schema not in self.spec.components.schemas:\n self.spec.components.schema(name_schema, schema=schema)\n data[k] = \"/\".join(v.split(\"/\")[:-1] + [name_schema])","title":""},{"docid":"c7d0088711d9d89a899e12fc1b127c52","score":"0.5089259","text":"def openapi():\n app = create_base_app()\n typer.secho(json.dumps(app.openapi(), indent=2))","title":""},{"docid":"6114fdd91b877d847170fdbddcae62c3","score":"0.50879925","text":"def _schema_to_json(obj):\n if isinstance(obj, validater.Schema):\n return obj.data\n elif isinstance(obj, (types.FunctionType, types.LambdaType)):\n return obj.__name__\n else:\n return six.text_type(obj)","title":""},{"docid":"8afba4539155036b56ab1216f5f4d918","score":"0.50680184","text":"def make_all_rules(\n schema: \"BaseOpenAPISchema\", bundles: Dict[str, CaseInsensitiveDict], connections: EndpointConnections\n) -> Dict[str, Rule]:\n return {\n f\"rule {endpoint.verbose_name}\": make_rule(\n endpoint, bundles[endpoint.path][endpoint.method.upper()], connections\n )\n for endpoint in schema.get_all_endpoints()\n }","title":""},{"docid":"e7a85797df3ffb3f5983dabc22c997b1","score":"0.50550336","text":"def convert(self, views, form_type='add'):\n\n if not isinstance(views, list) and (isinstance(views, Form)\n or issubclass(views, Form)):\n return super().convert(views)\n try:\n iter(views)\n except TypeError:\n views = [views]\n schema = OrderedDict([\n ('type', 'object'),\n ('definitions', OrderedDict([])),\n ('properties', OrderedDict([]))\n ])\n for view in views:\n name, view_schema = self.convert_view(view, form_type)\n for k, v in view_schema['definitions'].items():\n schema['definitions'][k] = v\n for k, v in view_schema['properties'].items():\n schema['properties'][k] = v\n return schema","title":""},{"docid":"0d4d615b56a64dddae0fd9c5476f3413","score":"0.50506866","text":"def custom_openapi():\n if app.openapi_schema:\n return app.openapi_schema\n openapi_schema = get_openapi(\n title=\"Custom title\",\n version=\"7.7.3\",\n description=\"This is a very custom OpenAPI schema\",\n routes=app.routes,\n )\n # we can add the ReDoc extension, adding a custom `x-logo` to the `info` \"object\" in the OpenAPI schema\n openapi_schema[\"info\"][\"x-logo\"] = {\n \"url\": \"https://fastapi.tiangolo.com/img/logo-margin/logo-teal.png\"\n }\n app.openapi_schema = openapi_schema\n return app.openapi_schema","title":""},{"docid":"55b9f21e2af6cacb3b56cce010cf555a","score":"0.5044342","text":"def NormalizeWithContext(self, object_type, context):\n\n relative_path = self._api_context[object_type].format(**context)\n\n return '{0}/{1}'.format(self._api_host.rstrip('/'), relative_path)","title":""},{"docid":"7539fccac80878e2bdcdc371821c9068","score":"0.50344247","text":"def get_schemes(self):\n pass","title":""},{"docid":"f725fb99c0d1e690d80235c6063b37b6","score":"0.50310403","text":"def proc_object(self, obj: JS_object) -> None:\n for k in obj._as_dict.keys():\n if k in self.jsonld:\n logging.warning(\"Redefinition of element: %s\" % k)\n elif k == '$ref':\n self.ref_type(obj[k])\n else:\n self.jsonld[k] = self.proc_schema(obj[k], JSON_ld())\n self.jsonld[k]['@id'] = self.namespace + ':' + k","title":""},{"docid":"07425ace3f4edd158338262b529b4a24","score":"0.5022986","text":"def generate(json_obj) -> dict:\n builder = SchemaBuilder(\n schema_uri='http://json-schema.org/draft-07/schema#')\n builder.add_object(json_obj)\n return builder.to_schema()","title":""},{"docid":"b21c7b1a8683d87b793aebda16011fb2","score":"0.5010723","text":"def openapi(self):\n if not self.__openapi_schema:\n self.__openapi_schema = symmetric.openapi.utils.get_openapi(\n self,\n symmetric.helpers.humanize(\n symmetric.helpers.get_module_name(self)\n ) + \" API\"\n )\n return self.__openapi_schema","title":""},{"docid":"2444b2e2514c9a60fb1f07efe13b6081","score":"0.5010179","text":"def output(self, api_object):\n self.api_object = api_object\n self.change_format()","title":""},{"docid":"b8e85369466fc95f2ee87c96d129bbc2","score":"0.5005478","text":"def add_swagger_ui(self):","title":""},{"docid":"a00bb06eb954c9fc50c1b95366fa11a5","score":"0.49832925","text":"def schema_to_parameters(schema, service=None):\n ret = []\n models = {}\n\n for location in (\"path\", \"header\", \"querystring\", \"body\"):\n if not cornice_swagger.util.is_object(schema):\n schema = schema()\n location_schema = schema.get(location)\n swag = cornice_swagger.swagger_model.SwaggerModel()\n if not location_schema:\n continue\n\n entry = swag.to_swagger(location_schema)\n\n if swag.models:\n _in = location\n name = location_schema.name\n parameter = dict()\n if location == \"body\":\n parameter[\"in\"] = _in\n parameter[\"name\"] = name\n parameter[\"description\"] = location_schema.description\n parameter[\"schema\"] = entry\n else:\n if location == \"querystring\":\n _in = \"query\"\n title = (location_schema.title or\n location_schema.__class__.__name__)\n for k, v in swag.models[title][\"properties\"].items():\n parameter = dict()\n parameter[\"in\"] = _in\n parameter[\"name\"] = v[\"name\"]\n parameter[\"required\"] = v[\"required\"]\n parameter[\"type\"] = v[\"type\"]\n if \"description\" in v:\n parameter[\"description\"] = v[\"description\"]\n ret.append(parameter)\n swag.models = {}\n\n ret.append(parameter)\n models.update(swag.models)\n return ret, models","title":""},{"docid":"a45704d2f841c3abc980ad648d128403","score":"0.49655193","text":"def schemas(api):\n explain = SRUCall.explain(api)\n for s in explain[\"schemas\"]:\n print(s[0], \"-\", s[1])","title":""},{"docid":"bfb61eec2b760d6596c4ae93ff824200","score":"0.49582177","text":"def cli(yamlfile, format, output):\n print(OwlSchemaGenerator(yamlfile, format).serialize(output=output))","title":""},{"docid":"3811ad66a013275ad18d74f725697925","score":"0.49466777","text":"def __definitions_descriptor(self):\n # Filter out any keys that aren't 'properties' or 'type'\n result = {}\n for def_key, def_value in self.__parser.schemas().iteritems():\n if 'properties' in def_value or 'type' in def_value:\n key_result = {}\n required_keys = set()\n if 'type' in def_value:\n key_result['type'] = def_value['type']\n if 'properties' in def_value:\n for prop_key, prop_value in def_value['properties'].items():\n if isinstance(prop_value, dict) and 'required' in prop_value:\n required_keys.add(prop_key)\n del prop_value['required']\n key_result['properties'] = def_value['properties']\n # Add in the required fields, if any\n if required_keys:\n key_result['required'] = sorted(required_keys)\n result[def_key] = key_result\n\n # Add 'type': 'object' to all object properties\n # Also, recursively add relative path to all $ref values\n for def_value in result.itervalues():\n for prop_value in def_value.itervalues():\n if isinstance(prop_value, dict):\n if '$ref' in prop_value:\n prop_value['type'] = 'object'\n self._add_def_paths(prop_value)\n\n return result","title":""},{"docid":"b9897fb50d7cb36f8467b761e71d352e","score":"0.49388573","text":"def get_blueprint():\n blueprint = flask.Blueprint('api', __name__)\n for cls in Resource.plugins:\n resource = cls()\n blueprint.add_url_rule(resource.get_endpoint(),\n endpoint=cls.__name__.lower(),\n view_func=resource.handle,\n methods=resource.methods)\n return blueprint","title":""},{"docid":"42a5f5be6d5d170e87b2e548eccf3119","score":"0.49321088","text":"def test_app_generates_correct_api_openapi_schema():\n schema = create_app(ephemeral=True).openapi()\n\n assert len(schema[\"paths\"].keys()) > 1\n assert all([p.startswith(\"/api/\") for p in schema[\"paths\"].keys()])","title":""},{"docid":"c344da9c670dd858fe00b8933c44047e","score":"0.49301517","text":"def show_schemas():\n schema_names = spec_loader.get_schema_names()\n resp = {'names': schema_names}\n if flask.request.args.get('show_source'):\n resp['content'] = spec_loader.get_schema_dicts(schema_names)\n return flask.jsonify(resp)","title":""},{"docid":"a62981222d83949a14355f3b256042fb","score":"0.49277082","text":"def process_objects(self, app, objects):\n api: Api = app.extensions['api']\n\n for resource_cls in objects.values():\n if isinstance(resource_cls.Meta.model, str):\n resource_cls.Meta.model = \\\n self.unchained.sqlalchemy_bundle.models[resource_cls.Meta.model]\n model_name = resource_cls.Meta.model.__name__\n\n self.attach_serializers_to_resource_cls(model_name, resource_cls)\n self.bundle.resources_by_model[model_name] = resource_cls\n api.register_model_resource(resource_cls)","title":""},{"docid":"17aae759f81178096cf92d59a8b6f771","score":"0.49215567","text":"def print_openapi_spec() -> None:\n for router in ROUTERS:\n if not router.include:\n router.add_to_application(app)\n print(yaml.dump(app.openapi()))","title":""},{"docid":"4523ca51284552ccd38bba9b3ab8ff96","score":"0.49213943","text":"def get_schema(self, request=None, public=False):\n paths = self.get_paths()\n if not paths:\n return None\n\n schema = {\n \"openapi\": \"3.0.2\",\n \"info\": self.get_info(),\n \"paths\": paths,\n }\n\n return schema","title":""},{"docid":"29fb39412b3c79d979b5a735fabf0b4d","score":"0.49213386","text":"def find_schema(obj):\n for k, v in obj.items():\n if isinstance(v, dict):\n if '$schema' in v:\n obj[k] = default_values(v)\n obj[k]['$schema'] = v['$schema']\n else:\n obj[k] = find_schema(v)\n else:\n raise Exception\n return obj","title":""},{"docid":"fc1e0ab9f1624648d75204d79301bc49","score":"0.49192804","text":"def __schema__(self):\n if not self._schema:\n try:\n self._schema = CustomSwagger(self).as_dict(self._custom_definition)\n except Exception:\n # Log the source exception for debugging purpose\n # and return an error message\n msg = 'Unable to render schema'\n logger.exception(msg) # This will provide a full traceback\n return {'error': msg}\n return self._schema","title":""},{"docid":"0b58d42955296284fb714cc5980e0288","score":"0.49052584","text":"def convert_to_openapispec():\n parser = argparse.ArgumentParser()\n parser.add_argument('-b', '--basedir', required=True,\n help='basedir of family')\n parser.add_argument('-l', '--lones', required=True,\n help='comma separated resource names')\n parser.add_argument('--outfmt', required=False,\n default='json',\n help='yaml or json')\n parser.add_argument('--outdir', required=False,\n help='output directory')\n parser.add_argument('--infile', required=False,\n help='full path of schema file')\n parser.add_argument('-m', '--module', required=False,\n help='Module used for creating spec')\n args = parser.parse_args()\n log_format = '[%(filename)s:%(lineno)d]' \\\n '[%(levelname)s]: %(message)s'\n logging.basicConfig(format=log_format,\n level=logging.INFO)\n openapiconverter.main(args)","title":""},{"docid":"1f22cd96ae0a418b34fc6c894e8f713e","score":"0.49019504","text":"def _schema(cls, extractor):\n # First get the short description by looking for the first complete sentence.\n description = []\n for line in inspect.getdoc(cls).splitlines():\n # Stop when we hit an empty line or see variable statements.\n if not line or line.startswith(\":\"):\n break\n description.append(line.strip())\n description = \" \".join(description)\n\n schema = {\n \"title\": cls.__name__.strip(\"_\").rstrip(\"2\"),\n \"description\": description,\n \"type\": \"object\",\n \"properties\": {\n # Include the \"type\" property that gets added dynamically during serialization.\n \"type\": {\n \"const\": cls._type(),\n }\n },\n \"additionalProperties\": False,\n \"required\": [\"type\"],\n }\n for field in cls.fields():\n is_required = field.default is not None\n\n # Allow customization within attr metadata field for corner cases.\n if \"jsonschema\" in field.metadata:\n sub_schema = field.metadata[\"jsonschema\"]\n else:\n sub_schema = extractor.extract(field.type)\n\n # Anything not required is nullable.\n if not is_required:\n if list(sub_schema.keys()) == [\"type\"]:\n if isinstance(sub_schema[\"type\"], list):\n if \"null\" not in sub_schema[\"type\"]:\n sub_schema[\"type\"].append(\"null\")\n elif sub_schema[\"type\"] != \"null\":\n sub_schema[\"type\"] = [sub_schema[\"type\"], \"null\"]\n elif list(sub_schema.keys()) == [\"anyOf\"]:\n if {\"type\": \"null\"} not in sub_schema[\"anyOf\"]:\n sub_schema[\"anyOf\"].append({\"type\": \"null\"})\n else:\n sub_schema = {\"anyOf\": [sub_schema, {\"type\": \"null\"}]}\n\n schema[\"properties\"][field.name] = sub_schema\n\n if is_required:\n schema[\"required\"].append(field.name)\n\n return schema","title":""},{"docid":"ff83c23220d38da368354706d3221fed","score":"0.48902914","text":"def _generate_base_schema(self, top_object) -> list:\r\n if isinstance(top_object, Table):\r\n return self._generate_base_table(top_object)\r\n elif isinstance(top_object, View):\r\n return self._generate_base_view(top_object)\r\n elif isinstance(top_object, Sequence):\r\n return self._generate_base_sequence(top_object)\r\n elif isinstance(top_object, Procedure):\r\n return self._generate_base_procedure(top_object)\r\n else:\r\n raise Exception(\"unknown schema \" + str(top_object))","title":""},{"docid":"d1628dccf4f309511c7ef150fb8c7724","score":"0.4862412","text":"def render_schema(self, do_print=True):\n rendered_schema = json.dumps(self.schema())\n if do_print:\n print(rendered_schema)\n else:\n return rendered_schema","title":""},{"docid":"a291d385f6b3a3e3483f4608a4d48eeb","score":"0.48615018","text":"def build_active_schema_for_object(cls, obj):\n schema = cls()\n schema.add_object(obj)\n return schema","title":""},{"docid":"11da397b649914b1b262652c0e30f9e3","score":"0.4861495","text":"def dump_oggbundle_schemas():\n transaction.doom()\n writer = OGGBundleJSONSchemaDumpWriter()\n result = writer.dump()\n return result","title":""},{"docid":"563222992dbedbe3fee604e9fae573f4","score":"0.48339707","text":"def generate_schema():\n\n _result = {\n \"$schema\": \"http://json-schema.org/draft-04/schema#\",\n \"description\": \"The JSON Schema for QAL resources\",\n \"title\": \"QAL Resources\",\n \"type\": \"object\",\n \"version\": __version__,\n \"properties\": {\n \"resources\":\n {\n \"type\": \"array\",\n \"items\": {\n \"$ref\": \"#/definitions/Resource\"\n }\n }\n },\n \"namespace\": \"qal\",\n \"required\": [\"resources\"],\n \"definitions\": {\n\n }\n }\n\n def _property_to_type(_property_name):\n if _property_name == \"uuid\":\n return [{\n \"type\": \"string\",\n \"pattern\": \"^[a-fA-F0-9]{8}-[a-fA-F0-9]{4}-[a-fA-F0-9]{4}-[a-fA-F0-9]{4}-[a-fA-F0-9]{12}$\"\n }]\n else:\n return [{\n \"type\": \"string\"\n }]\n # First, Add parameter types\n for _curr_class in list_prefixed_classes(globals(), \"resource\", _exclude=[\"Resources\"]):\n _result[\"definitions\"].update({_curr_class: {\n \"type\": \"object\",\n \"properties\": json_add_child_properties(globals(), _curr_class, _property_to_type),\n \"additionalProperties\": True\n }\n })\n\n return _result","title":""},{"docid":"2ba25031c7fb28ca493054dcb5450e4e","score":"0.4827812","text":"def default(self, obj):\n if hasattr(obj, 'to_dict'):\n result = obj.to_dict()\n result['_type'] = f'{obj.__module__}.{obj.__class__.__name__}'\n result['_version'] = 0\n return result\n return super().default(obj)","title":""},{"docid":"0f9e7b9760a542d4765efe872a619787","score":"0.4821796","text":"def deflate(self, model_object, props, rels):\n namespace_manager = NamespaceManager(self.g)\n namespace_manager.bind(\n Ori.prefix,\n Namespace(Ori.uri),\n override=False\n )\n namespace_manager.bind(\n model_object.prefix,\n Namespace(model_object.uri),\n override=False\n )\n\n s = URIRef('{}{}'.format(Ori.uri, model_object.get_ori_identifier()))\n p = URIRef('{}type'.format(Rdf.uri))\n o = URIRef(self.uri_format(model_object))\n self.g.add((s, p, o,))\n\n for name, definition in model_object.definitions(props=props, rels=rels):\n try:\n value = model_object.values.get(name, None)\n if value:\n p = URIRef(self.uri_format(definition))\n try:\n o = self.serialize_prop(definition, value)\n except (IgnoredProperty, MissingProperty):\n raise\n\n namespace_manager.bind(\n definition.ns.prefix,\n Namespace(definition.ns.uri),\n override=False\n )\n if type(o) != list:\n self.g.add((s, p, o,))\n else:\n for oo in o:\n self.g.add((s, p, oo,))\n elif definition.required and not model_object.skip_validation:\n raise RequiredProperty(\"Property '{}' is required for {}\".format(\n name, model_object.compact_uri())\n )\n except IgnoredProperty:\n continue","title":""},{"docid":"fcbd38043642b47a52300ae2dcbc59be","score":"0.48147994","text":"def __schema_descriptor(self, services):\n methods_desc = {}\n\n for service in services:\n protorpc_methods = service.all_remote_methods()\n for protorpc_method_name in protorpc_methods.iterkeys():\n rosy_method = '%s.%s' % (service.__name__, protorpc_method_name)\n method_id = self.__id_from_name[rosy_method]\n\n request_response = {}\n\n request_schema_id = self.__request_schema.get(method_id)\n if request_schema_id:\n request_response['request'] = {\n '$ref': request_schema_id\n }\n\n response_schema_id = self.__response_schema.get(method_id)\n if response_schema_id:\n request_response['response'] = {\n '$ref': response_schema_id\n }\n\n methods_desc[rosy_method] = request_response\n\n descriptor = {\n 'methods': methods_desc,\n 'schemas': self.__parser.schemas(),\n }\n\n return descriptor","title":""},{"docid":"884fa7b42518668a700a63616b1f8474","score":"0.48137224","text":"def genSwagger(filename, api_object):\n global swagger_template, template_components\n \n try:\n template = copy.deepcopy(swagger_template)\n\n template['info']['title'] = api_object['name']\n template['info']['version'] = str(api_object['version'])\n template['info']['description'] = api_object['description']\n\n for resource in api_object['resources']:\n resource_template = template_components.get(resource['method'].upper())\n \n path = \"\"\n if resource['path'][0] != \"/\":\n path = '/' + resource['path']\n else:\n path = resource['path']\n \n if resource['method'].upper() == \"DELETE\":\n path += r\"/{itemId}\"\n elif resource['method'].upper() == \"PUT\":\n path += r\"/{itemId}\"\n \n template['paths'][path] = resource_template\n \n with open(abs_path + '/../../../../data/swagger/{}.json'.format(filename), 'w') as f:\n json.dump(template, f)\n\n return True, json.dumps(template)\n \n except Exception as err:\n logger.exception(\"Swagger creation failed for API: {}. Error: {}\".format(filename, err))\n return False, None","title":""},{"docid":"90e5df0fa6253987366b40779bf10c88","score":"0.4809211","text":"def get_schema(self, parser_context: Mapping[str, Any]) -> Type[ResourceObject]:\n return parser_context[\"view\"].get_serializer().schema","title":""},{"docid":"7512fd4446994c9622bae085ed6607e2","score":"0.4803665","text":"def setup_swagger(\n routes: typing.List[tornado.web.URLSpec],\n *,\n swagger_url: str = \"/api/doc\",\n api_base_url: str = \"/\",\n description: str = \"Swagger API definition\",\n api_version: str = \"1.0.0\",\n title: str = \"Swagger API\",\n contact: str = \"\",\n schemes: list = None,\n security_definitions: dict = None,\n security: list = None,\n display_models: bool = True,\n api_definition_version: str = API_SWAGGER_2\n):\n swagger_schema = generate_doc_from_endpoints(\n routes,\n api_base_url=api_base_url,\n description=description,\n api_version=api_version,\n title=title,\n contact=contact,\n schemes=schemes,\n security_definitions=security_definitions,\n security=security,\n api_definition_version=api_definition_version,\n )\n\n _swagger_ui_url = \"/{}\".format(swagger_url) if not swagger_url.startswith(\"/\") else swagger_url\n _base_swagger_ui_url = _swagger_ui_url.rstrip(\"/\")\n _swagger_spec_url = \"{}/swagger.json\".format(_swagger_ui_url)\n\n routes[:0] = [\n tornado.web.url(_swagger_ui_url, SwaggerUiHandler),\n tornado.web.url(\"{}/\".format(_base_swagger_ui_url), SwaggerUiHandler),\n tornado.web.url(_swagger_spec_url, SwaggerSpecHandler),\n ]\n\n SwaggerSpecHandler.SWAGGER_SPEC = swagger_schema\n\n with open(os.path.join(STATIC_PATH, \"ui.html\"), \"r\", encoding=\"utf-8\") as f:\n SwaggerUiHandler.SWAGGER_HOME_TEMPLATE = (\n f.read().replace(\"{{ SWAGGER_URL }}\", _swagger_spec_url).replace(\"{{ DISPLAY_MODELS }}\", str(-1 if not display_models else 1))\n )","title":""},{"docid":"feb15f70be4631242c8fbd78b44a2597","score":"0.47897843","text":"def create_swagger_endpoint(api):\n\n class SwaggerEndpoint(Resource):\n def get(self):\n swagger_object = {}\n # filter keys with empty values\n for k, v in api._swagger_object.iteritems():\n if v or k == 'paths':\n if k == 'paths':\n paths = {}\n for endpoint, view in v.iteritems():\n views = {}\n for method, docs in view.iteritems():\n # check permissions. If a user has not access to an api, do not show the docs of it\n if auth(request.args.get('api_key'), endpoint, method):\n views[method] = docs\n if views:\n paths[endpoint] = views\n swagger_object['paths'] = paths\n else:\n swagger_object[k] = v\n return swagger_object\n\n return SwaggerEndpoint","title":""},{"docid":"cfd3ef7f98d5c7cdf95ebd528c69d788","score":"0.47889823","text":"def serialise(self):\n schema = dict(\n format_version=self.SCHEMA_FORMAT_VERSION,\n fieldtypes=self.fieldtypes,\n routes=self.routes,\n guessers=[g.serialise() for g in self.guessers],\n next_slot = self.next_slot,\n )\n return json.dumps(schema, sort_keys=True)","title":""},{"docid":"09fb5c6414c7c0b4b6057f5fdeefa8b1","score":"0.47878876","text":"def register_routes_from_swagger(config):\n settings = config.get_settings()\n # Append trailing slash if not present\n if settings['pyramid_swagger.schema_directory'][-1] != '/':\n settings['pyramid_swagger.schema_directory'] += '/'\n swagger_spec_path = \"{0}{1}\".format(\n settings['pyramid_swagger.schema_directory'],\n settings['pyramid_swagger.schema_file']\n )\n with open(swagger_spec_path) as phile:\n yaml_dict = yaml.load(phile)\n base_path = '' if yaml_dict['basePath'] == '/' else yaml_dict['basePath']\n for k, v in yaml_dict['paths'].items():\n pattern = \"{base}{path}\".format(base=base_path, path=k)\n for verb, data in v.items():\n route_name = data['operationId']\n LOGGER.info(\n 'Adding Swagger route name=%s, method=%s, pattern=%s',\n route_name,\n verb.upper(),\n pattern\n )\n config.add_route(name=route_name, pattern=pattern, request_method=verb.upper())","title":""},{"docid":"e1e48283b7c5c373d67e16b6d4fd23e1","score":"0.47845888","text":"def add_openapi_json(self):","title":""},{"docid":"80bbd1886c6be8026f4be2068c7e92ae","score":"0.47805062","text":"def scout_core_specification_swagger(*args, **kwargs):\n swag = swagger(current_app)\n swag['info']['title'] = 'Wild Me - Scout MWS Project, Phase 1'\n swag['info'][\n 'description'\n ] = 'Documentation for all REST API endpoints provided by Wild Me for the Scout collaboration'\n swag['info']['version'] = 'v0.1'\n swag['info']['contact'] = {\n 'name': 'Wild Me',\n 'url': 'http://wildme.org',\n 'email': 'dev@wildme.org',\n }\n swag['info']['license'] = {\n 'name': 'Apache 2.0',\n 'url': 'http://www.apache.org/licenses/LICENSE-2.0.html',\n }\n swag['host'] = 'kaiju.dyn.wildme.io:5000'\n swag['schemes'] = [\n 'http',\n ]\n\n response = jsonify(swag)\n return response","title":""},{"docid":"7853c7b8dfa6f8a03719275eb72ab4e7","score":"0.47723496","text":"def build_all_bundle_schemas():\n for portal_type, short_name in GEVER_TYPES_TO_OGGBUNDLE_TYPES.items():\n builder = OGGBundleJSONSchemaBuilder(portal_type)\n schema = builder.build_schema()\n filename = get_bundle_schema_filename(short_name)\n yield filename, schema\n\n for portal_type, short_name in GEVER_SQL_TYPES_TO_OGGBUNDLE_TYPES.items():\n builder = OGGBundleJSONSchemaSQLBuilder(portal_type)\n schema = builder.build_schema()\n filename = get_bundle_schema_filename(short_name)\n yield filename, schema","title":""},{"docid":"685e42b128e02a05f13d959f90e3293e","score":"0.47674236","text":"def _gen_docs(self):\n # The static content defined here mimics and can easily be converted\n # to a swagger file documentation. Possibly converting the Rbkcli to\n # a API server.\n doc = {\n 'definitions': {},\n 'paths': {}\n }\n\n cmdlets_files = self._get_cmdlets_files()\n loaded_cmdlets = self._load_cmdlets_files(cmdlets_files)\n usable_cmdlets = self._get_usable_cmdlets(loaded_cmdlets)\n\n for line in usable_cmdlets:\n if line['name'] != '':\n cmd_name = line['name'].replace(' ', '/')\n cmd_name = '/' + cmd_name\n parameters_found = line['param'].split(',')\n doc['paths'][cmd_name] = {\n 'get': {\n 'description': line['cmdlet_description'],\n 'operationId': '',\n 'operation': line['command'],\n 'parameters': [],\n 'responses': {\n '200': {\n 'description': line['response_description'],\n 'schema': {},\n 'table_order': line['table_order'],\n 'multiple_output': line['multiple_output']\n }\n },\n 'summary': line['cmdlet_summary'],\n 'tags': '',\n 'x-group': ''\n }\n }\n for param in parameters_found:\n doc['paths'][cmd_name]['get']['parameters'].append({\n \"in\": \"body\",\n \"name\": line['param'],\n \"required\": True,\n \"type\": \"string\"\n })\n\n self.endpoints = doc","title":""},{"docid":"944e9b0ece221460dc960e00fbfa1ce1","score":"0.47620457","text":"def _BuildSchemaDefinitions(self):\n schemas = self.values.get('schemas')\n if schemas:\n for name, def_dict in schemas.iteritems():\n # Upgrade the string format schema to a dict.\n if isinstance(def_dict, unicode):\n def_dict = simplejson.loads(def_dict)\n self._schemas[name] = self.DataTypeFromJson(def_dict, name)","title":""},{"docid":"f02f5da370a32438cecb7543028072a3","score":"0.47574404","text":"def _rewrite_refs(self, schema):\n if isinstance(schema, list):\n for value in schema:\n self._rewrite_refs(value)\n\n if isinstance(schema, dict):\n for key, value in schema.iteritems():\n if isinstance(value, (list, dict)):\n self._rewrite_refs(value)\n\n if key == '$ref' and value in schema_paths:\n del schema[key]\n schema.update(resolve_ref(value))","title":""},{"docid":"2bcac99b0133c5db74febd223f32e4e9","score":"0.47536445","text":"def to_default_representation(self, obj):\n return super().to_representation(obj)","title":""},{"docid":"5ddeea3becf722b8d7355888ee44ab70","score":"0.47428823","text":"def _gen_docs(self):\n # The static content defined here mimics and can easily be converted\n # to a swagger file documentation. Possibly converting the Rbkcli to\n # a API server.\n self.meta_api.doc = {\n 'definitions': {},\n 'paths': {}\n }\n\n cmdlets_files = self._get_cmdlets_files()\n loaded_cmdlets = self._load_cmdlets_files(cmdlets_files)\n usable_cmdlets = self._get_usable_cmdlets(loaded_cmdlets)\n\n for line in usable_cmdlets:\n if line['name'] != '':\n cmd_name = line['name'].replace(' ', '/')\n cmd_name = '/' + cmd_name\n parameters_found = line['param'].split(',')\n self.meta_api.doc['paths'][cmd_name] = {\n 'get': {\n 'description': line['cmdlet_description'],\n 'operationId': '',\n 'operation': line['command'],\n 'parameters': [],\n 'responses': {\n '200': {\n 'description': line['response_description'],\n 'schema': {},\n 'table_order': [],\n 'multiple_output': line['multiple_output']\n }\n },\n 'summary': line['cmdlet_summary'],\n 'tags': '',\n 'x-group': ''\n }\n }\n for param in parameters_found:\n self.meta_api.doc['paths'][cmd_name]['get']['parameters'].append({\n \"in\": \"body\",\n \"name\": line['param'],\n \"required\": True,\n \"type\": \"string\"\n })\n\n self.endpoints = self.meta_api.doc","title":""},{"docid":"9962f83ebe28a4fbe5529cc280865c38","score":"0.473935","text":"def iinfo_schemas(self):\n remote_classname = self.schema.remoteClass.split('.')[-1]\n remote_spec = self.class_.zenpack.classes.get(remote_classname)\n if not remote_spec:\n return {}\n\n schemas = {}\n\n if isinstance(self.schema, (ToOne)):\n schemas[self.name] = schema.Entity(\n title=_t(remote_spec.label),\n group=\"Relationships\",\n order=3.0)\n else:\n relname_count = '{}_count'.format(self.name)\n schemas[relname_count] = schema.Int(\n title=_t(u'Number of {}'.format(remote_spec.plural_label)),\n group=\"Relationships\",\n order=6.0)\n\n return schemas","title":""},{"docid":"e53438b61d7e47f63b37264f0048303b","score":"0.4734321","text":"def schemes(self, schemes: List[str]):\n\n self._schemes = schemes","title":""},{"docid":"a17ff29dc238a62d4ea5fdba49deb12f","score":"0.47227666","text":"def swagger_content_type_hack(view, supported=['application/json', 'application/xml']):\n def _inner(*args, **kwargs):\n resp = view(*args, **kwargs)\n if isinstance(resp, Response):\n if resp.data and 'apis' in resp.data:\n apis = resp.data['apis']\n for api in apis:\n if 'operations' in api:\n for op in api['operations']:\n op['produces'] = supported\n return resp\n return _inner","title":""},{"docid":"5d6200d440c17cb49e324fbdad374466","score":"0.47218448","text":"def get_schema(cls) -> SchemaDictType:\n return {\n 'type': 'unicode',\n 'validators': [{\n 'id': 'is_nonempty'\n }],\n 'ui_config': {\n 'placeholder': 'https://www.example.com'\n },\n 'post_normalizers': [{\n 'id': 'sanitize_url'\n }]\n }","title":""},{"docid":"b1740cd3bc47595cf2ea84d040e0c5fa","score":"0.47179604","text":"def map_rest_schema(schema, resource_fields):\n for attr in schema:\n # hack fix... change to make fields primary thing and switch get_field_type to return opposite\n if not isinstance(schema[attr]['type'], fields.Raw):\n resource_fields[attr] = get_field_type(schema[attr]['type'])\n else:\n resource_fields[attr] = schema[attr]['type']\n if schema[attr].get('nested', False):\n resource_fields[attr].__init__(attribute=schema[attr]['dict'])","title":""},{"docid":"211cee8e7411d73ac382c2cdaa770639","score":"0.47147828","text":"def __init__(self, info: object=None, paths: object=None, swagger: str=None, host: str=None, schemes: List[str]=None, base_path: str=None, security_definitions: object=None, produces: List[str]=None, definitions: object=None, openapi: str=None, servers: List[object]=None, components: object=None): # noqa: E501\n self.swagger_types = {\n 'info': object,\n 'paths': object,\n 'swagger': str,\n 'host': str,\n 'schemes': List[str],\n 'base_path': str,\n 'security_definitions': object,\n 'produces': List[str],\n 'definitions': object,\n 'openapi': str,\n 'servers': List[object],\n 'components': object\n }\n\n self.attribute_map = {\n 'info': 'info',\n 'paths': 'paths',\n 'swagger': 'swagger',\n 'host': 'host',\n 'schemes': 'schemes',\n 'base_path': 'basePath',\n 'security_definitions': 'securityDefinitions',\n 'produces': 'produces',\n 'definitions': 'definitions',\n 'openapi': 'openapi',\n 'servers': 'servers',\n 'components': 'components'\n }\n\n self._info = info\n self._paths = paths\n self._swagger = swagger\n self._host = host\n self._schemes = schemes\n self._base_path = base_path\n self._security_definitions = security_definitions\n self._produces = produces\n self._definitions = definitions\n self._openapi = openapi\n self._servers = servers\n self._components = components","title":""},{"docid":"8d81dfaff3ac469e3a1dcd018784c29a","score":"0.4714654","text":"def workWithSchema(self):\n #import avro.schema\n #schema = avro.schema.parse(json_encode(self.json_args))\n #print schema\n #Once we have the avro schema from UI we need to work with it\n schemas.append(self.json_args)","title":""},{"docid":"df912acc2f1cfc30807da13653515174","score":"0.47132203","text":"def generate_all(discdoc):\n print(\"Processing:\", discdoc.get(\"id\", \"\"))\n schemas = discdoc.get(\"schemas\", {})\n resources = discdoc.get(\"resources\", {})\n # Generate scopes.\n scopes_type = generate_scopes_type(discdoc[\"name\"], discdoc.get(\"auth\", {}).get(\"oauth2\", {}).get(\"scopes\", {}))\n\n # Generate parameter types (*Params - those are used as \"side inputs\" to requests)\n params_struct_name = global_params_name(discdoc.get(\"name\"))\n parameter_types, parameter_enums = generate_params_structs(resources, global_params=params_struct_name)\n\n # Generate service impls.\n services = []\n for resource, methods in sorted(resources.items()):\n services.append(generate_service(resource, methods, discdoc))\n if \"methods\" in discdoc:\n services.append(generate_service(\"Global\", discdoc, discdoc, generate_subresources=False))\n\n # Generate schema types.\n structs = []\n enums = []\n for name, desc in sorted(schemas.items()):\n typ, substructs, subenums = parse_schema_types(name, desc)\n structs.extend(substructs)\n enums.extend(subenums)\n\n # Generate global parameters struct and its Display impl.\n if \"parameters\" in discdoc:\n schema = {\"type\": \"object\", \"properties\": discdoc[\"parameters\"]}\n name = replace_keywords(snake_to_camel(params_struct_name))\n typ, substructs, subenums = parse_schema_types(name, schema)\n for s in substructs:\n s[\"optional_fields\"] = s[\"fields\"]\n parameter_types.extend(substructs)\n parameter_enums.extend(subenums)\n\n # Assemble everything into a file.\n modname = (discdoc[\"id\"] + \"_types\").replace(\":\", \"_\")\n out_path = path.join(\"gen\", modname + \".rs\")\n with open(out_path, \"w\") as f:\n f.write(RustHeader)\n f.write(scopes_type)\n # Render resource structs.\n for s in structs:\n struct_inline_comments(s)\n if not s[\"name\"]:\n print(\"WARN\", s)\n f.write(chevron.render(SchemaStructTmpl, s))\n for e in enums:\n enum_inline_comments(e)\n f.write(chevron.render(SchemaEnumTmpl, e))\n for e in parameter_enums:\n enum_inline_comments(e)\n f.write(chevron.render(SchemaEnumTmpl, e))\n # Render *Params structs.\n for pt in parameter_types:\n struct_inline_comments(pt)\n f.write(chevron.render(SchemaStructTmpl, pt))\n f.write(chevron.render(SchemaDisplayTmpl, pt))\n # Render service impls.\n for s in services:\n f.write(s)\n try:\n subprocess.run([\"rustfmt\", out_path, \"--edition=2018\"])\n except:\n return","title":""},{"docid":"ad5895bb9787c3103120e0e16e1b6c90","score":"0.47067106","text":"def swagger(self, swagger: str):\n\n self._swagger = swagger","title":""},{"docid":"1d25cdfbb766daa2691aa177899170d3","score":"0.4703228","text":"def _convert_json_schema_to_rest(schema: Dict) -> List[str]:\n\n schema = json.dumps(schema, default=str, indent=3)\n lines = [f\" {line}\" for line in schema.split(\"\\n\")]\n lines = \"\\n\".join(lines)\n lines = TPL_COLLAPSE.format(lines).split(\"\\n\")\n\n return lines","title":""},{"docid":"b081afd8a00263df34ae97966e1c0774","score":"0.4695786","text":"def export_swagger(\n routes: typing.List[tornado.web.URLSpec],\n *,\n api_base_url: str = \"/\",\n description: str = \"Swagger API definition\",\n api_version: str = \"1.0.0\",\n title: str = \"Swagger API\",\n contact: str = \"\",\n schemes: list = None,\n security_definitions: dict = None,\n security: list = None,\n):\n return generate_doc_from_endpoints(\n routes,\n api_base_url=api_base_url,\n description=description,\n api_version=api_version,\n title=title,\n contact=contact,\n schemes=schemes,\n security_definitions=security_definitions,\n security=security,\n )","title":""},{"docid":"525cd5091313450ebe771fecd27fafaa","score":"0.46949035","text":"def get_descriptor_defaults(self, api_info, hostname=None):\n hostname = (hostname or util.get_app_hostname() or\n api_info.hostname)\n protocol = 'http' if ((hostname and hostname.startswith('localhost')) or\n util.is_running_on_devserver()) else 'https'\n defaults = {\n 'swagger': '2.0',\n 'info': {\n 'version': api_info.version,\n 'title': api_info.name\n },\n 'host': hostname,\n 'consumes': ['application/json'],\n 'produces': ['application/json'],\n 'schemes': [protocol],\n 'basePath': api_info.base_path.rstrip('/'),\n }\n\n return defaults","title":""},{"docid":"54dd0154f06ccbf50d81ae112ede0849","score":"0.46933958","text":"def setup_swagger(\n routes: typing.List[tornado.web.URLSpec],\n *,\n swagger_url: str = \"/api/doc\",\n api_base_url: str = \"/\",\n description: str = \"Swagger API definition\",\n api_version: str = \"1.0.0\",\n title: str = \"Swagger API\",\n contact: str = \"\",\n schemes: list = None,\n security_definitions: dict = None,\n security: list = None,\n display_models: bool = True,\n):\n swagger_schema = generate_doc_from_endpoints(\n routes,\n api_base_url=api_base_url,\n description=description,\n api_version=api_version,\n title=title,\n contact=contact,\n schemes=schemes,\n security_definitions=security_definitions,\n security=security,\n )\n\n _swagger_url = (\n \"/{}\".format(swagger_url) if not swagger_url.startswith(\"/\") else swagger_url\n )\n _base_swagger_url = _swagger_url.rstrip(\"/\")\n\n routes[:0] = [\n tornado.web.url(_swagger_url, SwaggerHomeHandler),\n tornado.web.url(\"{}/\".format(_base_swagger_url), SwaggerHomeHandler),\n ]\n\n with open(os.path.join(STATIC_PATH, \"ui.html\"), \"r\") as f:\n SwaggerHomeHandler.SWAGGER_HOME_TEMPLATE = (\n f.read()\n .replace(\"{{ SWAGGER_SCHEMA }}\", json.dumps(swagger_schema))\n .replace(\"{{ DISPLAY_MODELS }}\", str(-1 if not display_models else 1))\n )","title":""},{"docid":"c0ad98e789110bbd4ed1933f1f8fdc82","score":"0.4682381","text":"def to_representation(self, obj):\n if type(obj) is not list:\n return [tag for tag in obj.all()]\n return obj","title":""},{"docid":"93f8f75313735bfddd38d8f7f5c6df3f","score":"0.46757478","text":"def get_security_definitions(self, schema: Dict[str, Any], resolver: RefResolver) -> Dict[str, Any]:\n components = schema.get(\"components\", {})\n security_schemes = components.get(\"securitySchemes\", {})\n if \"$ref\" in security_schemes:\n return resolver.resolve(security_schemes[\"$ref\"])[1]\n return security_schemes","title":""},{"docid":"33c4141bcca730207ab51258c8fd287b","score":"0.467435","text":"def schemes(self):\n return [self]","title":""},{"docid":"16b85ecf7019bed92d5c3f7a7fe9a4bf","score":"0.46739846","text":"def swagger(self) -> Optional[Any]:\n return pulumi.get(self, \"swagger\")","title":""},{"docid":"16b85ecf7019bed92d5c3f7a7fe9a4bf","score":"0.46739846","text":"def swagger(self) -> Optional[Any]:\n return pulumi.get(self, \"swagger\")","title":""},{"docid":"1cd0a84a1668e0812da92bf603e9bcf5","score":"0.4659864","text":"def __pretty_object__(obj):\n # type: (object) -> str\n if str(type(obj)) not in constants.SYS_TYPE:\n from flask import session, request\n import numpy\n if obj is session:\n return __pretty_session__()\n elif obj is request:\n return __pretty_request__()\n elif isinstance(obj, list) or isinstance(obj, numpy.ndarray):\n print __pretty_multi_array__(obj)\n else:\n return __to_yaml__(obj)\n else:\n return str(obj)","title":""},{"docid":"39b5cb4eadb54edd3f18814697a9fc1a","score":"0.46471936","text":"def build_active_schema_for_objects(cls, objects):\n schema = cls()\n schema.add_objects(objects)\n return schema","title":""}],"string":"[\n {\n \"docid\": \"7f31adca643df64f561772f8c5bcdf1d\",\n \"score\": \"0.6359533\",\n \"text\": \"def swaggerish(self):\\n\\n # Better chosen dinamically from endpoint.py\\n schemes = ['http']\\n if self._customizer._production:\\n schemes = ['https']\\n\\n # A template base\\n output = {\\n \\\"swagger\\\": \\\"2.0\\\",\\n \\\"info\\\": {\\n \\\"version\\\": \\\"0.0.1\\\",\\n \\\"title\\\": \\\"Your application name\\\",\\n },\\n \\\"schemes\\\": schemes,\\n # \\\"host\\\": \\\"localhost\\\" # chosen dinamically\\n \\\"basePath\\\": \\\"/\\\",\\n \\\"securityDefinitions\\\": {\\n \\\"Bearer\\\": {\\n \\\"type\\\": \\\"apiKey\\\",\\n \\\"name\\\": \\\"Authorization\\\",\\n \\\"in\\\": \\\"header\\\"\\n }\\n # \\\"OauthSecurity\\\": {\\n # \\\"type\\\": \\\"oauth2\\\",\\n # \\\"tokenUrl\\\": \\\"https://oauth.simple.api/token\\\",\\n # \\\"flow\\\": \\\"accessCode\\\",\\n # \\\"authorizationUrl\\\": \\\"https://blabla/authorization\\\",\\n # \\\"scopes\\\": {\\n # \\\"admin\\\": \\\"Admin scope\\\",\\n # \\\"user\\\": \\\"User scope\\\"\\n # }\\n # }\\n # TODO: check about scopes (roles?)\\n },\\n \\\"security\\\": [\\n {\\n \\\"Bearer\\\": []\\n }\\n ]\\n }\\n\\n ###################\\n # Set existing values\\n proj = self._customizer._configurations['project']\\n if 'version' in proj:\\n output['info']['version'] = proj['version']\\n if 'title' in proj:\\n output['info']['title'] = proj['title']\\n\\n ###################\\n output['definitions'] = self.read_definitions()\\n output['consumes'] = [JSON_APPLICATION]\\n output['produces'] = [JSON_APPLICATION]\\n\\n ###################\\n # Read endpoints swagger files\\n for key, endpoint in enumerate(self._endpoints):\\n\\n endpoint.custom['methods'] = {}\\n endpoint.custom['params'] = {}\\n\\n for method, file in endpoint.methods.items():\\n # add the custom part to the endpoint\\n self._endpoints[key] = \\\\\\n self.read_my_swagger(file, method, endpoint)\\n\\n ###################\\n # Save query parameters globally\\n self._customizer._query_params = self._qparams\\n self._customizer._parameter_schemas = self._parameter_schemas\\n output['paths'] = self._paths\\n\\n ###################\\n tags = []\\n for tag, desc in self._customizer._configurations['tags'].items():\\n tags.append({'name': tag, 'description': desc})\\n output['tags'] = tags\\n\\n self._customizer._original_paths = self._original_paths\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e0ecf837a7a8c5fb3ccc815beeee2e1\",\n \"score\": \"0.5981558\",\n \"text\": \"def api_schema(request):\\n generator = CorniceSwagger(get_services())\\n # function docstrings are used to create the route's summary in Swagger-UI\\n generator.summary_docstrings = True\\n generator.default_security = get_security\\n swagger_base_spec = {\\n \\\"host\\\": get_magpie_url(request.registry),\\n \\\"schemes\\\": [request.scheme]\\n }\\n swagger_base_spec.update(SecurityDefinitionsAPI)\\n generator.swagger = swagger_base_spec\\n json_api_spec = generator.generate(title=TitleAPI, version=__meta__.__version__, info=InfoAPI)\\n return json_api_spec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f2e1a445bb6cb533b83bdf72ae1963\",\n \"score\": \"0.58855915\",\n \"text\": \"def input_to_swagger(self):\\n return {\\\"type\\\": \\\"object\\\", \\\"example\\\": {}}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8827da09ff4f89d4f22cf216b7c3da73\",\n \"score\": \"0.5883961\",\n \"text\": \"async def openapi_schema(request: web.Request) -> web.Response:\\n schema_format = request.match_info.get(\\\"schema_format\\\")\\n schema = get_openapi_schema(request.config_dict)\\n\\n if schema_format == \\\"json\\\":\\n return web.json_response(schema)\\n\\n if schema_format == \\\"yaml\\\":\\n safe_dumper = getattr(yaml, \\\"CSafeDumper\\\", yaml.SafeDumper)\\n return web.Response(\\n text=yaml.dump(schema, Dumper=safe_dumper),\\n content_type=\\\"application/yaml\\\",\\n )\\n\\n raise ConfigurationError(\\n f\\\"Schema format {schema_format} not supported at a moment.\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a543bc1959e6e413a56bfab9070bfe\",\n \"score\": \"0.58181477\",\n \"text\": \"def _to_swagger(base=None, description=None, resource=None, options=None):\\n # type: (Dict[str, str], str, Resource, Dict[str, str]) -> Dict[str, str]\\n definition = dict_filter(base or {}, options or {})\\n\\n if description:\\n definition['description'] = description.format(\\n name=getmeta(resource).name if resource else \\\"UNKNOWN\\\"\\n )\\n\\n if resource:\\n definition['schema'] = {\\n '$ref': '#/definitions/{}'.format(getmeta(resource).resource_name)\\n }\\n\\n return definition\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdc39edfe0826acf2f0d1c535cc99754\",\n \"score\": \"0.5752418\",\n \"text\": \"def init_bundles(schema: \\\"BaseOpenAPISchema\\\") -> Dict[str, CaseInsensitiveDict]:\\n output: Dict[str, CaseInsensitiveDict] = {}\\n for endpoint in schema.get_all_endpoints():\\n output.setdefault(endpoint.path, CaseInsensitiveDict())\\n output[endpoint.path][endpoint.method.upper()] = Bundle(endpoint.verbose_name) # type: ignore\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf5022e300f0eec55dfafcfc8f006237\",\n \"score\": \"0.5693794\",\n \"text\": \"def _custom_openapi_method(zelf: FastAPI) -> dict:\\n if not zelf.openapi_schema:\\n\\n if zelf.redoc_url:\\n desc = compose_long_description(zelf.description)\\n else:\\n desc = zelf.description\\n openapi_schema = get_openapi(\\n title=zelf.title,\\n version=zelf.version,\\n openapi_version=zelf.openapi_version,\\n description=desc,\\n routes=zelf.routes,\\n tags=zelf.openapi_tags,\\n servers=zelf.servers,\\n )\\n\\n add_vendor_extensions(openapi_schema)\\n patch_openapi_specs(openapi_schema)\\n zelf.openapi_schema = openapi_schema\\n\\n return zelf.openapi_schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8805a9f0cb3b278e895371b08c714085\",\n \"score\": \"0.56355596\",\n \"text\": \"def introspect(self, request):\\n # Generate a path based on the standard automate URLs above\\n patterns = [path(request.path, include(self.urls()))]\\n generator = SchemaGenerator(patterns=patterns)\\n return Response(generator.get_schema())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd4295718cd2554dfb8240815cd1f1b9\",\n \"score\": \"0.55284894\",\n \"text\": \"def _generate_openapi_object(document: OpenApiDocument) -> OrderedDict:\\n parsed_url = urlparse.urlparse(document.url)\\n\\n swagger = OrderedDict()\\n\\n swagger['swagger'] = '2.0'\\n swagger['info'] = OrderedDict()\\n swagger['info']['title'] = document.title\\n swagger['info']['description'] = document.description\\n swagger['info']['version'] = document.version\\n\\n if parsed_url.netloc:\\n swagger['host'] = parsed_url.netloc\\n if parsed_url.scheme:\\n swagger['schemes'] = [parsed_url.scheme]\\n\\n swagger['paths'] = _get_paths_object(document)\\n\\n return swagger\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cf7f89936610531cb39c98726fb9e33\",\n \"score\": \"0.5463968\",\n \"text\": \"def custom_openapi():\\n if app.openapi_schema:\\n return app.openapi_schema\\n openapi_schema = get_openapi(\\n title=\\\"The VICC Gene Normalizer\\\",\\n version=__version__,\\n openapi_version=\\\"3.0.3\\\",\\n description=\\\"Normalize gene terms.\\\",\\n routes=app.routes\\n )\\n # openapi_schema['info']['license'] = { # TODO\\n # \\\"name\\\": \\\"name of our license\\\",\\n # \\\"url\\\": \\\"http://www.our-license-tbd.com\\\"\\n # }\\n openapi_schema['info']['contact'] = { # TODO\\n \\\"name\\\": \\\"Alex H. Wagner\\\",\\n \\\"email\\\": \\\"Alex.Wagner@nationwidechildrens.org\\\"\\n }\\n app.openapi_schema = openapi_schema\\n return app.openapi_schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"013af3547f1ae774991c58db2bd555a3\",\n \"score\": \"0.5462466\",\n \"text\": \"def generate_swagger_object(document):\\n parsed_url = urlparse.urlparse(document.url)\\n\\n swagger = OrderedDict()\\n\\n swagger['swagger'] = '2.0'\\n swagger['info'] = OrderedDict()\\n swagger['info']['title'] = document.title\\n swagger['info']['version'] = '' # Required by the spec\\n\\n if parsed_url.netloc:\\n swagger['host'] = parsed_url.netloc\\n if parsed_url.scheme:\\n swagger['schemes'] = [parsed_url.scheme]\\n\\n swagger['paths'] = _get_paths_object(document)\\n swagger['definitions'] = _get_definitions_object(document)\\n\\n return swagger\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e8e14db9ea8cca34c9b2979ed91a76c\",\n \"score\": \"0.5427914\",\n \"text\": \"def update_schema(schema: dict):\\n definitions = schema[\\\"definitions\\\"]\\n for name, definition in definitions.items():\\n version = None\\n if \\\"x-kubernetes-group-version-kind\\\" in definition:\\n k8s_versions = definition[\\\"x-kubernetes-group-version-kind\\\"]\\n if len(k8s_versions) != 1:\\n print(\\\"warning, not correctly handling\\\", name)\\n\\n k8s_version = k8s_versions[0]\\n version = os.path.join(k8s_version[\\\"group\\\"], k8s_version[\\\"version\\\"])\\n\\n if \\\"properties\\\" in definition:\\n # remove all fields named status\\n # this might have some false positives, lets see what goes missing :)\\n if \\\"status\\\" in definition[\\\"properties\\\"]:\\n definition[\\\"properties\\\"].pop(\\\"status\\\")\\n\\n for prop_name, prop in definition[\\\"properties\\\"].items():\\n # set the default of properties that are enums\\n # with only one possible value to that value\\n # This sets the `kind` property on all objects.\\n if \\\"enum\\\" in prop and len(prop[\\\"enum\\\"]) == 1:\\n prop.setdefault(\\\"default\\\", prop[\\\"enum\\\"][0])\\n\\n # set api version\\n if prop_name == \\\"apiVersion\\\":\\n if version:\\n prop.setdefault(\\\"default\\\", version)\\n\\n # inline IntOrString, avoids creation of IntOrString class\\n # this allows assigning int or string directly, instead of this\\n # ref_int_or_string = \\\"#/definitions/io.k8s.apimachinery.pkg.util.intstr.IntOrString\\\"\\n # TODO using \\\"oneOf\\\" in properties is not supported by datamode-code-generator\\n # see https://github.com/koxudaxi/datamodel-code-generator/issues/139\\n # if \\\"$ref\\\" in prop:\\n # ref = prop[\\\"$ref\\\"]\\n\\n # if ref == ref_int_or_string:\\n # prop.pop(\\\"$ref\\\")\\n\\n # prop.update(definitions[ref.split('/')[-1]])\\n\\n # print(prop)\\n # \\\"$ref\\\":\\n # \\\"oneOf\\\": [\\n # {\\n # \\\"type\\\": \\\"string\\\"\\n # },\\n # {\\n # \\\"type\\\": \\\"integer\\\"\\n # }\\n # ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2904b37c8aa0a312940aeea1b25bf9c\",\n \"score\": \"0.53712267\",\n \"text\": \"def serialize(self, model_object):\\n context = dict()\\n for name, definition in model_object.definitions(props=False, rels=True):\\n context[name] = {\\n '@id': definition.absolute_uri(),\\n '@type': '@id',\\n }\\n\\n for name, definition in model_object.definitions(props=True, rels=False):\\n context[name] = {\\n '@id': definition.absolute_uri(),\\n }\\n\\n for name, value in model_object.values.items():\\n if isinstance(value, Uri) or isinstance(value, Url):\\n context[name] = {\\n '@id': model_object.definition(name).absolute_uri(),\\n '@type': '@id',\\n }\\n\\n deflated = self.deflate(model_object, props=True, rels=True)\\n deflated['@context'] = {k: v for k, v in context.items() if k in deflated}\\n deflated['@context']['@base'] = Ori.uri\\n deflated['@context'][model_object.verbose_name()] = model_object.absolute_uri()\\n deflated['@type'] = model_object.verbose_name()\\n\\n ori_identifier = model_object.get_short_identifier()\\n if ori_identifier:\\n deflated['@id'] = ori_identifier\\n\\n return deflated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aea1c336857bbe5041af8ce79a8c9c\",\n \"score\": \"0.5365679\",\n \"text\": \"def doc_formatter(target_object):\\n doc = target_object.__doc__\\n\\n # Handle non-pydantic objects\\n if doc is None:\\n new_doc = ''\\n elif 'Parameters\\\\n' in doc or not (issubclass(target_object, BaseSettings) or issubclass(target_object, BaseModel)):\\n new_doc = doc\\n else:\\n type_formatter = {'boolan': 'bool',\\n 'string': 'str',\\n 'integer': 'int'\\n }\\n # Add the white space\\n if not doc.endswith('\\\\n\\\\n'):\\n doc += \\\"\\\\n\\\\n\\\"\\n new_doc = dedent(doc) + \\\"Parameters\\\\n----------\\\\n\\\"\\n target_schema = target_object.schema()\\n # Go through each property\\n for prop_name, prop in target_schema['properties'].items():\\n # Catch lookups for other Pydantic objects\\n if '$ref' in prop:\\n # Pre 0.28 lookup\\n lookup = prop['$ref'].split('/')[-1]\\n prop = target_schema['definitions'][lookup]\\n elif 'allOf' in prop:\\n # Post 0.28 lookup\\n try:\\n # Validation, we don't need output, just the object\\n _JsonRefModel(**prop)\\n lookup = prop['allOf'][0]['$ref'].split('/')[-1]\\n prop = target_schema['definitions'][lookup]\\n except ValidationError:\\n # Doesn't conform, pass on\\n pass\\n # Get common properties\\n prop_type = prop[\\\"type\\\"]\\n new_doc += prop_name + \\\" : \\\"\\n prop_desc = prop['description']\\n\\n # Check for enumeration\\n if 'enum' in prop:\\n new_doc += '{' + ', '.join(prop['enum']) + '}'\\n\\n # Set the name/type of object\\n else:\\n if prop_type == 'object':\\n prop_field = prop['title']\\n else:\\n prop_field = prop_type\\n new_doc += f'{type_formatter[prop_field] if prop_field in type_formatter else prop_field}'\\n\\n # Handle Classes so as not to re-copy pydantic descriptions\\n if prop_type == 'object':\\n if not ('required' in target_schema and prop_name in target_schema['required']):\\n new_doc += \\\", Optional\\\"\\n prop_desc = f\\\":class:`{prop['title']}`\\\"\\n\\n # Handle non-classes\\n else:\\n if 'default' in prop:\\n default = prop['default']\\n try:\\n # Get the explicit default value for enum classes\\n if issubclass(default, Enum):\\n default = default.value\\n except TypeError:\\n pass\\n new_doc += f\\\", Default: {default}\\\"\\n elif not ('required' in target_schema and prop_name in target_schema['required']):\\n new_doc += \\\", Optional\\\"\\n\\n # Finally, write the detailed doc string\\n new_doc += \\\"\\\\n\\\" + indent(prop_desc, \\\" \\\") + \\\"\\\\n\\\"\\n\\n # Assign the new doc string\\n target_object.__doc__ = new_doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"832b92534b8a1ea3810d394d8341d5dc\",\n \"score\": \"0.53519154\",\n \"text\": \"def swagger(\\n app,\\n url_parser=flask_url_parser,\\n rule_parser=flask_rule_parser,\\n process_doc=_sanitize,\\n prefix=None,\\n from_file_keyword=None,\\n template=None,\\n):\\n output = {\\n \\\"swagger\\\": \\\"2.0\\\",\\n \\\"info\\\": {\\\"version\\\": \\\"0.0.0\\\", \\\"title\\\": \\\"Cool product name\\\"},\\n }\\n\\n paths = defaultdict(dict)\\n definitions = defaultdict(dict)\\n if template is not None:\\n output.update(template)\\n # check for template provided paths and definitions\\n for k, v in output.get(\\\"paths\\\", {}).items():\\n paths[k] = v\\n for k, v in output.get(\\\"definitions\\\", {}).items():\\n definitions[k] = v\\n output[\\\"paths\\\"] = paths\\n output[\\\"definitions\\\"] = definitions\\n\\n swagger_endpoints = url_parser(app, prefix)\\n for rule, methods in swagger_endpoints.items():\\n rule = rule_parser(rule)\\n\\n operations = dict()\\n for verb, method in methods:\\n summary, description, swag = _parse_docstring(\\n method, process_doc, from_file_keyword\\n )\\n # Do we have docstring to parse?\\n if swag is not None:\\n defs = swag.get(\\\"definitions\\\", [])\\n defs = _extract_definitions(defs)\\n params = swag.get(\\\"parameters\\\", [])\\n defs += _extract_definitions(params)\\n responses = swag.get(\\\"responses\\\", {})\\n responses = {str(key): value for key, value in responses.items()}\\n if responses is not None:\\n defs = defs + _extract_definitions(responses.values())\\n for definition in defs:\\n def_id = definition.pop(\\\"id\\\", None)\\n if def_id is not None:\\n definitions[def_id].update(definition)\\n operation = dict(\\n summary=summary, description=description, responses=responses\\n )\\n # parameters - swagger ui dislikes empty parameter lists\\n if len(params) > 0:\\n operation[\\\"parameters\\\"] = params\\n # other optionals\\n for key in OPTIONAL_FIELDS:\\n if key in swag:\\n operation[key] = swag.get(key)\\n operations[verb] = operation\\n\\n if len(operations):\\n paths[rule].update(operations)\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d489759e5562c97e81a632793aa0fad\",\n \"score\": \"0.5314773\",\n \"text\": \"def do_schema(self):\\n\\n name = '%s.%s.%s' % (BACKEND_PACKAGE, 'rest', 'schema')\\n module = self._meta.get_module_from_string(name)\\n schema_class = getattr(module, 'RecoverSchema')\\n\\n self._schema_endpoint = EndpointElements(\\n cls=schema_class,\\n exists=True,\\n custom={\\n 'methods': {\\n 'get': ExtraAttributes(auth=None),\\n # WHY DOES POST REQUEST AUTHENTICATION\\n # 'post': ExtraAttributes(auth=None)\\n }\\n },\\n methods={}\\n )\\n\\n # TODO: find a way to map authentication\\n # as in the original endpoint for the schema 'get' method\\n\\n # TODO: find a way to publish on swagger the schema\\n # if endpoint is enabled to publish and the developer asks for it\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75a94ca26ee4608ad96e95526900143b\",\n \"score\": \"0.5308563\",\n \"text\": \"def schema_factory(cls, obj, **kwargs):\\n if isinstance(obj, str):\\n openapi_type = cls._openapi_type(obj)\\n return openapi.Schema(\\n type=openapi_type,\\n **kwargs\\n )\\n\\n if isinstance(obj, list):\\n if len(obj) != 1:\\n raise Exception('List types must have exactly one element to specify the schema of `items`')\\n return openapi.Schema(\\n type=openapi.TYPE_ARRAY,\\n items=cls.schema_factory(obj[0]),\\n **kwargs\\n )\\n\\n if isinstance(obj, dict):\\n return openapi.Schema(\\n type=openapi.TYPE_OBJECT,\\n properties={\\n k: cls.schema_factory(sub_obj)\\n for k, sub_obj\\n in obj.items()\\n },\\n **kwargs\\n )\\n\\n raise Exception(f'Unhandled type \\\"{type(obj)}\\\" for {obj}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76c9a1ae1b13c714488cd03e9b6fd6f0\",\n \"score\": \"0.53071064\",\n \"text\": \"def to_api(_object):\\n try:\\n return _object.to_api()\\n except AttributeError:\\n return _object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d745dd83dc2c3e996b3f7afa5662e14\",\n \"score\": \"0.5284773\",\n \"text\": \"def format_schema(self, schema):\\n if getattr(schema, 'properties', {}):\\n schema.properties = OrderedDict(\\n (self.format_string(key), self.format_schema(openapi.resolve_ref(val, self.components)) or val)\\n for key, val in schema.properties.items()\\n )\\n\\n if getattr(schema, 'required', []):\\n schema.required = [self.format_string(p) for p in schema.required]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0648a7176d6cdefd2c4c82e00853639c\",\n \"score\": \"0.5263264\",\n \"text\": \"def to_representation(self, obj):\\n urls = list(obj.all())\\n return [(url.url, url.description) for url in urls]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a616f6bb8a7a482fbd6c43fa359003cb\",\n \"score\": \"0.52482826\",\n \"text\": \"def default(self, obj):\\n @singledispatch\\n def default(obj):\\n super(GoodJSONEncoder, self).default(obj)\\n\\n @default.register(ObjectId)\\n @default.register(UUID)\\n def conv_objid(obj):\\n return text_type(obj)\\n\\n @default.register(datetime)\\n def conv_datetime(obj):\\n if self.epoch_mode:\\n return int(\\n (timegm(obj.timetuple()) * 1000) +\\n ((obj.microsecond) / 1000)\\n )\\n return obj.isoformat()\\n\\n @default.register(DBRef)\\n def conv_dbref(obj):\\n doc = obj.as_doc()\\n ret = {\\n \\\"collection\\\": doc[\\\"$ref\\\"],\\n \\\"id\\\": self.default(doc[\\\"$id\\\"])\\n }\\n if obj.database:\\n ret[\\\"db\\\"] = doc[\\\"$db\\\"]\\n ret.update({\\n key: value\\n for (key, value) in doc.items()\\n if key[0] != \\\"$\\\"\\n })\\n return ret\\n\\n @default.register(RE_TYPE)\\n @default.register(Regex)\\n def conv_regex(obj):\\n flags_map = {\\n \\\"i\\\": obj.flags & re.IGNORECASE,\\n \\\"l\\\": obj.flags & re.LOCALE,\\n \\\"m\\\": obj.flags & re.MULTILINE,\\n \\\"s\\\": obj.flags & re.DOTALL,\\n \\\"u\\\": obj.flags & re.UNICODE,\\n \\\"x\\\": obj.flags & re.VERBOSE\\n }\\n flags = [key for (key, contains) in flags_map.items() if contains]\\n ret = {\\\"regex\\\": obj.pattern}\\n if flags:\\n ret[\\\"flags\\\"] = (\\\"\\\").join(flags)\\n return ret\\n\\n @default.register(MinKey)\\n def conv_minkey(obj):\\n return {\\\"minKey\\\": True}\\n\\n @default.register(MaxKey)\\n def conv_maxkey(obj):\\n return {\\\"maxKey\\\": True}\\n\\n @default.register(Timestamp)\\n def conv_timestamp(obj):\\n return {\\\"time\\\": obj.time, \\\"inc\\\": obj.inc}\\n\\n @default.register(Code)\\n def conv_code(obj):\\n return {\\\"code\\\": str(obj), \\\"scope\\\": obj.scope}\\n\\n @default.register(Binary)\\n def conv_bin(obj):\\n return {\\n \\\"data\\\": b64encode(obj).decode(\\\"utf-8\\\"),\\n \\\"type\\\": obj.subtype\\n }\\n\\n if PY3:\\n @default.register(bytes)\\n def conv_bytes(obj):\\n return {\\\"data\\\": b64encode(obj).decode(\\\"utf-8\\\"), \\\"type\\\": 0}\\n\\n return default(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d87da8dd68665de8db8341c3c2a7a877\",\n \"score\": \"0.52324057\",\n \"text\": \"def _pack_document(self, objects, type_):\\n\\n def pack_object(obj):\\n packed_obj = {'type': type_}\\n attributes = {}\\n relationships = {}\\n for key, value in obj.items():\\n if isinstance(value, JsonApiRelationship):\\n relationships[key] = value.as_linkage()\\n elif key == 'id':\\n packed_obj['id'] = value\\n else:\\n attributes[key] = value\\n\\n if attributes:\\n packed_obj['attributes'] = attributes\\n\\n if relationships:\\n packed_obj['relationships'] = relationships\\n\\n return packed_obj\\n\\n try:\\n if isinstance(objects, list):\\n packed_objects = [pack_object(obj) for obj in objects]\\n else:\\n packed_objects = pack_object(objects)\\n except KeyError:\\n raise JsonApiException('Invalid resource info format', True)\\n\\n return {'data': packed_objects}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef7f41b16d4852d42671ea3a74628ba4\",\n \"score\": \"0.51945394\",\n \"text\": \"def to_swagger(self, bound_resource=None):\\n return _to_swagger(\\n {\\n 'name': self.name,\\n 'in': self.in_.value,\\n 'type': str(self.type) if self.type else None,\\n },\\n description=self.description,\\n resource=bound_resource if self.resource is DefaultResource else self.resource,\\n options=self.options\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82da09e9d9574a4b8a097e542a76517b\",\n \"score\": \"0.5189164\",\n \"text\": \"def _gen_endpoint(self, name, obj, description, version=1, schema=None):\\n # check for invalid PO names\\n _check_endpoint_name(name)\\n\\n if description is None:\\n description = obj.__doc__.strip() or \\\"\\\" if isinstance(obj.__doc__, str) else \\\"\\\"\\n\\n endpoint_object = CustomQueryObject(query=obj, description=description,)\\n\\n return {\\n \\\"name\\\": name,\\n \\\"version\\\": version,\\n \\\"description\\\": description,\\n \\\"type\\\": \\\"model\\\",\\n \\\"endpoint_obj\\\": endpoint_object,\\n \\\"dependencies\\\": endpoint_object.get_dependencies(),\\n \\\"methods\\\": endpoint_object.get_methods(),\\n \\\"required_files\\\": [],\\n \\\"required_packages\\\": [],\\n \\\"schema\\\": copy.copy(schema),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d005473445a67bb56c71a08378ec1550\",\n \"score\": \"0.51735437\",\n \"text\": \"def printSchemes():\\n\\tschemes=getSchemeList()\\n\\tfor s in schemes:\\n\\t\\tprint \\\"scheme_name: {} scheme_options: {}\\\".format(s.name,s.options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7af45c8b9a3def744328cd800ddf2b4d\",\n \"score\": \"0.5167875\",\n \"text\": \"def map_serializer(self, auto_schema, direction):\\n if isinstance(self.target.serializers, dict):\\n sub_components = self._get_explicit_sub_components(auto_schema, direction)\\n else:\\n sub_components = self._get_implicit_sub_components(auto_schema, direction)\\n\\n return {\\n 'oneOf': [ref for _, ref in sub_components],\\n 'discriminator': {\\n 'propertyName': self.target.resource_type_field_name,\\n 'mapping': {resource_type: ref['$ref'] for resource_type, ref in sub_components}\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c6f0ff8f69374311bc7d43f46e2c681\",\n \"score\": \"0.5163314\",\n \"text\": \"def __call__(self, title, version, base_path='/', info={}, swagger={},\\n schema_transformers=[], **kwargs):\\n\\n info.update(title=title, version=version)\\n swagger.update(swagger='2.0', info=info, basePath=base_path)\\n self.schema_transformers.extend(schema_transformers)\\n\\n paths, tags = self._build_paths(**kwargs)\\n if paths:\\n swagger['paths'] = paths\\n if tags:\\n swagger['tags'] = tags\\n\\n definitions = self.definitions.definition_registry\\n if definitions:\\n swagger['definitions'] = definitions\\n\\n parameters = self.parameters.parameter_registry\\n if parameters:\\n swagger['parameters'] = parameters\\n\\n responses = self.responses.response_registry\\n if responses:\\n swagger['responses'] = responses\\n\\n return swagger\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaddd81de9af50562353b6642d649755\",\n \"score\": \"0.51470786\",\n \"text\": \"def loads(self, obj):\\n\\n buf = io.BytesIO(obj)\\n schema_dict = schemaless_reader(buf, self.schema_dict)\\n\\n if self.decoding_method:\\n return [self.decoding_method(schema_dict)]\\n else:\\n return [schema_dict]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6414d3051920913323ee10bd60e26f79\",\n \"score\": \"0.5139515\",\n \"text\": \"def schema(cls, *args: Any, **kwargs: Any) -> Dict[str, Any]:\\n schema = super().schema(*args, **kwargs)\\n cls.check_provider_added(schema)\\n schema = cls.change_format_to_oneOf(schema)\\n schema = cls.resolve_refs(schema)\\n schema = cls.remove_enum_allOf(schema)\\n return schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31ce216f4a2547b398775bf6de953ba6\",\n \"score\": \"0.5136734\",\n \"text\": \"def serialize(self, document, base_url: str, path, schema) -> str:\\n home = {\\n \\\"api\\\": {\\n \\\"title\\\": schema[\\\"info\\\"][\\\"title\\\"],\\n \\\"links\\\": {\\n \\\"author\\\": schema[\\\"info\\\"][\\\"contact\\\"][\\\"name\\\"],\\n \\\"describedBy\\\": \\\"\\\",\\n },\\n },\\n \\\"resources\\\": {},\\n }\\n for path, path_desc in schema[\\\"paths\\\"].items():\\n home[\\\"resources\\\"][path.strip(\\\"/\\\")] = {\\n \\\"href\\\": as_absolute(base_url, path),\\n \\\"hints\\\": {\\\"allow\\\": [key.upper() for key in path_desc.keys()]},\\n }\\n return json.dumps(home, indent=4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82e6aac41f7b96a82898225d38e99e8c\",\n \"score\": \"0.5128058\",\n \"text\": \"def _make_doc_response_schema(schema):\\n return schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f25adb59c8990d11c8f5e65a857e3db\",\n \"score\": \"0.50924915\",\n \"text\": \"def _ref_to_spec(self, data):\\n if isinstance(data, list):\\n for i_v in data:\\n self._ref_to_spec(i_v)\\n if isinstance(data, dict):\\n for k, v in data.items():\\n if isinstance(v, list):\\n for i_v in v:\\n self._ref_to_spec(i_v)\\n if isinstance(v, dict):\\n self._ref_to_spec(v)\\n elif k == \\\"$ref\\\":\\n name_schema = v.split(\\\"/\\\")[-1]\\n schema = class_registry.get_class(name_schema)\\n name_schema = create_schema_name(schema=schema)\\n if name_schema not in self.spec.components.schemas:\\n self.spec.components.schema(name_schema, schema=schema)\\n data[k] = \\\"/\\\".join(v.split(\\\"/\\\")[:-1] + [name_schema])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7d0088711d9d89a899e12fc1b127c52\",\n \"score\": \"0.5089259\",\n \"text\": \"def openapi():\\n app = create_base_app()\\n typer.secho(json.dumps(app.openapi(), indent=2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6114fdd91b877d847170fdbddcae62c3\",\n \"score\": \"0.50879925\",\n \"text\": \"def _schema_to_json(obj):\\n if isinstance(obj, validater.Schema):\\n return obj.data\\n elif isinstance(obj, (types.FunctionType, types.LambdaType)):\\n return obj.__name__\\n else:\\n return six.text_type(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8afba4539155036b56ab1216f5f4d918\",\n \"score\": \"0.50680184\",\n \"text\": \"def make_all_rules(\\n schema: \\\"BaseOpenAPISchema\\\", bundles: Dict[str, CaseInsensitiveDict], connections: EndpointConnections\\n) -> Dict[str, Rule]:\\n return {\\n f\\\"rule {endpoint.verbose_name}\\\": make_rule(\\n endpoint, bundles[endpoint.path][endpoint.method.upper()], connections\\n )\\n for endpoint in schema.get_all_endpoints()\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a85797df3ffb3f5983dabc22c997b1\",\n \"score\": \"0.50550336\",\n \"text\": \"def convert(self, views, form_type='add'):\\n\\n if not isinstance(views, list) and (isinstance(views, Form)\\n or issubclass(views, Form)):\\n return super().convert(views)\\n try:\\n iter(views)\\n except TypeError:\\n views = [views]\\n schema = OrderedDict([\\n ('type', 'object'),\\n ('definitions', OrderedDict([])),\\n ('properties', OrderedDict([]))\\n ])\\n for view in views:\\n name, view_schema = self.convert_view(view, form_type)\\n for k, v in view_schema['definitions'].items():\\n schema['definitions'][k] = v\\n for k, v in view_schema['properties'].items():\\n schema['properties'][k] = v\\n return schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d4d615b56a64dddae0fd9c5476f3413\",\n \"score\": \"0.50506866\",\n \"text\": \"def custom_openapi():\\n if app.openapi_schema:\\n return app.openapi_schema\\n openapi_schema = get_openapi(\\n title=\\\"Custom title\\\",\\n version=\\\"7.7.3\\\",\\n description=\\\"This is a very custom OpenAPI schema\\\",\\n routes=app.routes,\\n )\\n # we can add the ReDoc extension, adding a custom `x-logo` to the `info` \\\"object\\\" in the OpenAPI schema\\n openapi_schema[\\\"info\\\"][\\\"x-logo\\\"] = {\\n \\\"url\\\": \\\"https://fastapi.tiangolo.com/img/logo-margin/logo-teal.png\\\"\\n }\\n app.openapi_schema = openapi_schema\\n return app.openapi_schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55b9f21e2af6cacb3b56cce010cf555a\",\n \"score\": \"0.5044342\",\n \"text\": \"def NormalizeWithContext(self, object_type, context):\\n\\n relative_path = self._api_context[object_type].format(**context)\\n\\n return '{0}/{1}'.format(self._api_host.rstrip('/'), relative_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7539fccac80878e2bdcdc371821c9068\",\n \"score\": \"0.50344247\",\n \"text\": \"def get_schemes(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f725fb99c0d1e690d80235c6063b37b6\",\n \"score\": \"0.50310403\",\n \"text\": \"def proc_object(self, obj: JS_object) -> None:\\n for k in obj._as_dict.keys():\\n if k in self.jsonld:\\n logging.warning(\\\"Redefinition of element: %s\\\" % k)\\n elif k == '$ref':\\n self.ref_type(obj[k])\\n else:\\n self.jsonld[k] = self.proc_schema(obj[k], JSON_ld())\\n self.jsonld[k]['@id'] = self.namespace + ':' + k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07425ace3f4edd158338262b529b4a24\",\n \"score\": \"0.5022986\",\n \"text\": \"def generate(json_obj) -> dict:\\n builder = SchemaBuilder(\\n schema_uri='http://json-schema.org/draft-07/schema#')\\n builder.add_object(json_obj)\\n return builder.to_schema()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21c7b1a8683d87b793aebda16011fb2\",\n \"score\": \"0.5010723\",\n \"text\": \"def openapi(self):\\n if not self.__openapi_schema:\\n self.__openapi_schema = symmetric.openapi.utils.get_openapi(\\n self,\\n symmetric.helpers.humanize(\\n symmetric.helpers.get_module_name(self)\\n ) + \\\" API\\\"\\n )\\n return self.__openapi_schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2444b2e2514c9a60fb1f07efe13b6081\",\n \"score\": \"0.5010179\",\n \"text\": \"def output(self, api_object):\\n self.api_object = api_object\\n self.change_format()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8e85369466fc95f2ee87c96d129bbc2\",\n \"score\": \"0.5005478\",\n \"text\": \"def add_swagger_ui(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a00bb06eb954c9fc50c1b95366fa11a5\",\n \"score\": \"0.49832925\",\n \"text\": \"def schema_to_parameters(schema, service=None):\\n ret = []\\n models = {}\\n\\n for location in (\\\"path\\\", \\\"header\\\", \\\"querystring\\\", \\\"body\\\"):\\n if not cornice_swagger.util.is_object(schema):\\n schema = schema()\\n location_schema = schema.get(location)\\n swag = cornice_swagger.swagger_model.SwaggerModel()\\n if not location_schema:\\n continue\\n\\n entry = swag.to_swagger(location_schema)\\n\\n if swag.models:\\n _in = location\\n name = location_schema.name\\n parameter = dict()\\n if location == \\\"body\\\":\\n parameter[\\\"in\\\"] = _in\\n parameter[\\\"name\\\"] = name\\n parameter[\\\"description\\\"] = location_schema.description\\n parameter[\\\"schema\\\"] = entry\\n else:\\n if location == \\\"querystring\\\":\\n _in = \\\"query\\\"\\n title = (location_schema.title or\\n location_schema.__class__.__name__)\\n for k, v in swag.models[title][\\\"properties\\\"].items():\\n parameter = dict()\\n parameter[\\\"in\\\"] = _in\\n parameter[\\\"name\\\"] = v[\\\"name\\\"]\\n parameter[\\\"required\\\"] = v[\\\"required\\\"]\\n parameter[\\\"type\\\"] = v[\\\"type\\\"]\\n if \\\"description\\\" in v:\\n parameter[\\\"description\\\"] = v[\\\"description\\\"]\\n ret.append(parameter)\\n swag.models = {}\\n\\n ret.append(parameter)\\n models.update(swag.models)\\n return ret, models\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a45704d2f841c3abc980ad648d128403\",\n \"score\": \"0.49655193\",\n \"text\": \"def schemas(api):\\n explain = SRUCall.explain(api)\\n for s in explain[\\\"schemas\\\"]:\\n print(s[0], \\\"-\\\", s[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfb61eec2b760d6596c4ae93ff824200\",\n \"score\": \"0.49582177\",\n \"text\": \"def cli(yamlfile, format, output):\\n print(OwlSchemaGenerator(yamlfile, format).serialize(output=output))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3811ad66a013275ad18d74f725697925\",\n \"score\": \"0.49466777\",\n \"text\": \"def __definitions_descriptor(self):\\n # Filter out any keys that aren't 'properties' or 'type'\\n result = {}\\n for def_key, def_value in self.__parser.schemas().iteritems():\\n if 'properties' in def_value or 'type' in def_value:\\n key_result = {}\\n required_keys = set()\\n if 'type' in def_value:\\n key_result['type'] = def_value['type']\\n if 'properties' in def_value:\\n for prop_key, prop_value in def_value['properties'].items():\\n if isinstance(prop_value, dict) and 'required' in prop_value:\\n required_keys.add(prop_key)\\n del prop_value['required']\\n key_result['properties'] = def_value['properties']\\n # Add in the required fields, if any\\n if required_keys:\\n key_result['required'] = sorted(required_keys)\\n result[def_key] = key_result\\n\\n # Add 'type': 'object' to all object properties\\n # Also, recursively add relative path to all $ref values\\n for def_value in result.itervalues():\\n for prop_value in def_value.itervalues():\\n if isinstance(prop_value, dict):\\n if '$ref' in prop_value:\\n prop_value['type'] = 'object'\\n self._add_def_paths(prop_value)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9897fb50d7cb36f8467b761e71d352e\",\n \"score\": \"0.49388573\",\n \"text\": \"def get_blueprint():\\n blueprint = flask.Blueprint('api', __name__)\\n for cls in Resource.plugins:\\n resource = cls()\\n blueprint.add_url_rule(resource.get_endpoint(),\\n endpoint=cls.__name__.lower(),\\n view_func=resource.handle,\\n methods=resource.methods)\\n return blueprint\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42a5f5be6d5d170e87b2e548eccf3119\",\n \"score\": \"0.49321088\",\n \"text\": \"def test_app_generates_correct_api_openapi_schema():\\n schema = create_app(ephemeral=True).openapi()\\n\\n assert len(schema[\\\"paths\\\"].keys()) > 1\\n assert all([p.startswith(\\\"/api/\\\") for p in schema[\\\"paths\\\"].keys()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c344da9c670dd858fe00b8933c44047e\",\n \"score\": \"0.49301517\",\n \"text\": \"def show_schemas():\\n schema_names = spec_loader.get_schema_names()\\n resp = {'names': schema_names}\\n if flask.request.args.get('show_source'):\\n resp['content'] = spec_loader.get_schema_dicts(schema_names)\\n return flask.jsonify(resp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a62981222d83949a14355f3b256042fb\",\n \"score\": \"0.49277082\",\n \"text\": \"def process_objects(self, app, objects):\\n api: Api = app.extensions['api']\\n\\n for resource_cls in objects.values():\\n if isinstance(resource_cls.Meta.model, str):\\n resource_cls.Meta.model = \\\\\\n self.unchained.sqlalchemy_bundle.models[resource_cls.Meta.model]\\n model_name = resource_cls.Meta.model.__name__\\n\\n self.attach_serializers_to_resource_cls(model_name, resource_cls)\\n self.bundle.resources_by_model[model_name] = resource_cls\\n api.register_model_resource(resource_cls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17aae759f81178096cf92d59a8b6f771\",\n \"score\": \"0.49215567\",\n \"text\": \"def print_openapi_spec() -> None:\\n for router in ROUTERS:\\n if not router.include:\\n router.add_to_application(app)\\n print(yaml.dump(app.openapi()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4523ca51284552ccd38bba9b3ab8ff96\",\n \"score\": \"0.49213943\",\n \"text\": \"def get_schema(self, request=None, public=False):\\n paths = self.get_paths()\\n if not paths:\\n return None\\n\\n schema = {\\n \\\"openapi\\\": \\\"3.0.2\\\",\\n \\\"info\\\": self.get_info(),\\n \\\"paths\\\": paths,\\n }\\n\\n return schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29fb39412b3c79d979b5a735fabf0b4d\",\n \"score\": \"0.49213386\",\n \"text\": \"def find_schema(obj):\\n for k, v in obj.items():\\n if isinstance(v, dict):\\n if '$schema' in v:\\n obj[k] = default_values(v)\\n obj[k]['$schema'] = v['$schema']\\n else:\\n obj[k] = find_schema(v)\\n else:\\n raise Exception\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc1e0ab9f1624648d75204d79301bc49\",\n \"score\": \"0.49192804\",\n \"text\": \"def __schema__(self):\\n if not self._schema:\\n try:\\n self._schema = CustomSwagger(self).as_dict(self._custom_definition)\\n except Exception:\\n # Log the source exception for debugging purpose\\n # and return an error message\\n msg = 'Unable to render schema'\\n logger.exception(msg) # This will provide a full traceback\\n return {'error': msg}\\n return self._schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b58d42955296284fb714cc5980e0288\",\n \"score\": \"0.49052584\",\n \"text\": \"def convert_to_openapispec():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-b', '--basedir', required=True,\\n help='basedir of family')\\n parser.add_argument('-l', '--lones', required=True,\\n help='comma separated resource names')\\n parser.add_argument('--outfmt', required=False,\\n default='json',\\n help='yaml or json')\\n parser.add_argument('--outdir', required=False,\\n help='output directory')\\n parser.add_argument('--infile', required=False,\\n help='full path of schema file')\\n parser.add_argument('-m', '--module', required=False,\\n help='Module used for creating spec')\\n args = parser.parse_args()\\n log_format = '[%(filename)s:%(lineno)d]' \\\\\\n '[%(levelname)s]: %(message)s'\\n logging.basicConfig(format=log_format,\\n level=logging.INFO)\\n openapiconverter.main(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f22cd96ae0a418b34fc6c894e8f713e\",\n \"score\": \"0.49019504\",\n \"text\": \"def _schema(cls, extractor):\\n # First get the short description by looking for the first complete sentence.\\n description = []\\n for line in inspect.getdoc(cls).splitlines():\\n # Stop when we hit an empty line or see variable statements.\\n if not line or line.startswith(\\\":\\\"):\\n break\\n description.append(line.strip())\\n description = \\\" \\\".join(description)\\n\\n schema = {\\n \\\"title\\\": cls.__name__.strip(\\\"_\\\").rstrip(\\\"2\\\"),\\n \\\"description\\\": description,\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n # Include the \\\"type\\\" property that gets added dynamically during serialization.\\n \\\"type\\\": {\\n \\\"const\\\": cls._type(),\\n }\\n },\\n \\\"additionalProperties\\\": False,\\n \\\"required\\\": [\\\"type\\\"],\\n }\\n for field in cls.fields():\\n is_required = field.default is not None\\n\\n # Allow customization within attr metadata field for corner cases.\\n if \\\"jsonschema\\\" in field.metadata:\\n sub_schema = field.metadata[\\\"jsonschema\\\"]\\n else:\\n sub_schema = extractor.extract(field.type)\\n\\n # Anything not required is nullable.\\n if not is_required:\\n if list(sub_schema.keys()) == [\\\"type\\\"]:\\n if isinstance(sub_schema[\\\"type\\\"], list):\\n if \\\"null\\\" not in sub_schema[\\\"type\\\"]:\\n sub_schema[\\\"type\\\"].append(\\\"null\\\")\\n elif sub_schema[\\\"type\\\"] != \\\"null\\\":\\n sub_schema[\\\"type\\\"] = [sub_schema[\\\"type\\\"], \\\"null\\\"]\\n elif list(sub_schema.keys()) == [\\\"anyOf\\\"]:\\n if {\\\"type\\\": \\\"null\\\"} not in sub_schema[\\\"anyOf\\\"]:\\n sub_schema[\\\"anyOf\\\"].append({\\\"type\\\": \\\"null\\\"})\\n else:\\n sub_schema = {\\\"anyOf\\\": [sub_schema, {\\\"type\\\": \\\"null\\\"}]}\\n\\n schema[\\\"properties\\\"][field.name] = sub_schema\\n\\n if is_required:\\n schema[\\\"required\\\"].append(field.name)\\n\\n return schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff83c23220d38da368354706d3221fed\",\n \"score\": \"0.48902914\",\n \"text\": \"def _generate_base_schema(self, top_object) -> list:\\r\\n if isinstance(top_object, Table):\\r\\n return self._generate_base_table(top_object)\\r\\n elif isinstance(top_object, View):\\r\\n return self._generate_base_view(top_object)\\r\\n elif isinstance(top_object, Sequence):\\r\\n return self._generate_base_sequence(top_object)\\r\\n elif isinstance(top_object, Procedure):\\r\\n return self._generate_base_procedure(top_object)\\r\\n else:\\r\\n raise Exception(\\\"unknown schema \\\" + str(top_object))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1628dccf4f309511c7ef150fb8c7724\",\n \"score\": \"0.4862412\",\n \"text\": \"def render_schema(self, do_print=True):\\n rendered_schema = json.dumps(self.schema())\\n if do_print:\\n print(rendered_schema)\\n else:\\n return rendered_schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a291d385f6b3a3e3483f4608a4d48eeb\",\n \"score\": \"0.48615018\",\n \"text\": \"def build_active_schema_for_object(cls, obj):\\n schema = cls()\\n schema.add_object(obj)\\n return schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11da397b649914b1b262652c0e30f9e3\",\n \"score\": \"0.4861495\",\n \"text\": \"def dump_oggbundle_schemas():\\n transaction.doom()\\n writer = OGGBundleJSONSchemaDumpWriter()\\n result = writer.dump()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"563222992dbedbe3fee604e9fae573f4\",\n \"score\": \"0.48339707\",\n \"text\": \"def generate_schema():\\n\\n _result = {\\n \\\"$schema\\\": \\\"http://json-schema.org/draft-04/schema#\\\",\\n \\\"description\\\": \\\"The JSON Schema for QAL resources\\\",\\n \\\"title\\\": \\\"QAL Resources\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"version\\\": __version__,\\n \\\"properties\\\": {\\n \\\"resources\\\":\\n {\\n \\\"type\\\": \\\"array\\\",\\n \\\"items\\\": {\\n \\\"$ref\\\": \\\"#/definitions/Resource\\\"\\n }\\n }\\n },\\n \\\"namespace\\\": \\\"qal\\\",\\n \\\"required\\\": [\\\"resources\\\"],\\n \\\"definitions\\\": {\\n\\n }\\n }\\n\\n def _property_to_type(_property_name):\\n if _property_name == \\\"uuid\\\":\\n return [{\\n \\\"type\\\": \\\"string\\\",\\n \\\"pattern\\\": \\\"^[a-fA-F0-9]{8}-[a-fA-F0-9]{4}-[a-fA-F0-9]{4}-[a-fA-F0-9]{4}-[a-fA-F0-9]{12}$\\\"\\n }]\\n else:\\n return [{\\n \\\"type\\\": \\\"string\\\"\\n }]\\n # First, Add parameter types\\n for _curr_class in list_prefixed_classes(globals(), \\\"resource\\\", _exclude=[\\\"Resources\\\"]):\\n _result[\\\"definitions\\\"].update({_curr_class: {\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": json_add_child_properties(globals(), _curr_class, _property_to_type),\\n \\\"additionalProperties\\\": True\\n }\\n })\\n\\n return _result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ba25031c7fb28ca493054dcb5450e4e\",\n \"score\": \"0.4827812\",\n \"text\": \"def default(self, obj):\\n if hasattr(obj, 'to_dict'):\\n result = obj.to_dict()\\n result['_type'] = f'{obj.__module__}.{obj.__class__.__name__}'\\n result['_version'] = 0\\n return result\\n return super().default(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f9e7b9760a542d4765efe872a619787\",\n \"score\": \"0.4821796\",\n \"text\": \"def deflate(self, model_object, props, rels):\\n namespace_manager = NamespaceManager(self.g)\\n namespace_manager.bind(\\n Ori.prefix,\\n Namespace(Ori.uri),\\n override=False\\n )\\n namespace_manager.bind(\\n model_object.prefix,\\n Namespace(model_object.uri),\\n override=False\\n )\\n\\n s = URIRef('{}{}'.format(Ori.uri, model_object.get_ori_identifier()))\\n p = URIRef('{}type'.format(Rdf.uri))\\n o = URIRef(self.uri_format(model_object))\\n self.g.add((s, p, o,))\\n\\n for name, definition in model_object.definitions(props=props, rels=rels):\\n try:\\n value = model_object.values.get(name, None)\\n if value:\\n p = URIRef(self.uri_format(definition))\\n try:\\n o = self.serialize_prop(definition, value)\\n except (IgnoredProperty, MissingProperty):\\n raise\\n\\n namespace_manager.bind(\\n definition.ns.prefix,\\n Namespace(definition.ns.uri),\\n override=False\\n )\\n if type(o) != list:\\n self.g.add((s, p, o,))\\n else:\\n for oo in o:\\n self.g.add((s, p, oo,))\\n elif definition.required and not model_object.skip_validation:\\n raise RequiredProperty(\\\"Property '{}' is required for {}\\\".format(\\n name, model_object.compact_uri())\\n )\\n except IgnoredProperty:\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcbd38043642b47a52300ae2dcbc59be\",\n \"score\": \"0.48147994\",\n \"text\": \"def __schema_descriptor(self, services):\\n methods_desc = {}\\n\\n for service in services:\\n protorpc_methods = service.all_remote_methods()\\n for protorpc_method_name in protorpc_methods.iterkeys():\\n rosy_method = '%s.%s' % (service.__name__, protorpc_method_name)\\n method_id = self.__id_from_name[rosy_method]\\n\\n request_response = {}\\n\\n request_schema_id = self.__request_schema.get(method_id)\\n if request_schema_id:\\n request_response['request'] = {\\n '$ref': request_schema_id\\n }\\n\\n response_schema_id = self.__response_schema.get(method_id)\\n if response_schema_id:\\n request_response['response'] = {\\n '$ref': response_schema_id\\n }\\n\\n methods_desc[rosy_method] = request_response\\n\\n descriptor = {\\n 'methods': methods_desc,\\n 'schemas': self.__parser.schemas(),\\n }\\n\\n return descriptor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"884fa7b42518668a700a63616b1f8474\",\n \"score\": \"0.48137224\",\n \"text\": \"def genSwagger(filename, api_object):\\n global swagger_template, template_components\\n \\n try:\\n template = copy.deepcopy(swagger_template)\\n\\n template['info']['title'] = api_object['name']\\n template['info']['version'] = str(api_object['version'])\\n template['info']['description'] = api_object['description']\\n\\n for resource in api_object['resources']:\\n resource_template = template_components.get(resource['method'].upper())\\n \\n path = \\\"\\\"\\n if resource['path'][0] != \\\"/\\\":\\n path = '/' + resource['path']\\n else:\\n path = resource['path']\\n \\n if resource['method'].upper() == \\\"DELETE\\\":\\n path += r\\\"/{itemId}\\\"\\n elif resource['method'].upper() == \\\"PUT\\\":\\n path += r\\\"/{itemId}\\\"\\n \\n template['paths'][path] = resource_template\\n \\n with open(abs_path + '/../../../../data/swagger/{}.json'.format(filename), 'w') as f:\\n json.dump(template, f)\\n\\n return True, json.dumps(template)\\n \\n except Exception as err:\\n logger.exception(\\\"Swagger creation failed for API: {}. Error: {}\\\".format(filename, err))\\n return False, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90e5df0fa6253987366b40779bf10c88\",\n \"score\": \"0.4809211\",\n \"text\": \"def get_schema(self, parser_context: Mapping[str, Any]) -> Type[ResourceObject]:\\n return parser_context[\\\"view\\\"].get_serializer().schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7512fd4446994c9622bae085ed6607e2\",\n \"score\": \"0.4803665\",\n \"text\": \"def setup_swagger(\\n routes: typing.List[tornado.web.URLSpec],\\n *,\\n swagger_url: str = \\\"/api/doc\\\",\\n api_base_url: str = \\\"/\\\",\\n description: str = \\\"Swagger API definition\\\",\\n api_version: str = \\\"1.0.0\\\",\\n title: str = \\\"Swagger API\\\",\\n contact: str = \\\"\\\",\\n schemes: list = None,\\n security_definitions: dict = None,\\n security: list = None,\\n display_models: bool = True,\\n api_definition_version: str = API_SWAGGER_2\\n):\\n swagger_schema = generate_doc_from_endpoints(\\n routes,\\n api_base_url=api_base_url,\\n description=description,\\n api_version=api_version,\\n title=title,\\n contact=contact,\\n schemes=schemes,\\n security_definitions=security_definitions,\\n security=security,\\n api_definition_version=api_definition_version,\\n )\\n\\n _swagger_ui_url = \\\"/{}\\\".format(swagger_url) if not swagger_url.startswith(\\\"/\\\") else swagger_url\\n _base_swagger_ui_url = _swagger_ui_url.rstrip(\\\"/\\\")\\n _swagger_spec_url = \\\"{}/swagger.json\\\".format(_swagger_ui_url)\\n\\n routes[:0] = [\\n tornado.web.url(_swagger_ui_url, SwaggerUiHandler),\\n tornado.web.url(\\\"{}/\\\".format(_base_swagger_ui_url), SwaggerUiHandler),\\n tornado.web.url(_swagger_spec_url, SwaggerSpecHandler),\\n ]\\n\\n SwaggerSpecHandler.SWAGGER_SPEC = swagger_schema\\n\\n with open(os.path.join(STATIC_PATH, \\\"ui.html\\\"), \\\"r\\\", encoding=\\\"utf-8\\\") as f:\\n SwaggerUiHandler.SWAGGER_HOME_TEMPLATE = (\\n f.read().replace(\\\"{{ SWAGGER_URL }}\\\", _swagger_spec_url).replace(\\\"{{ DISPLAY_MODELS }}\\\", str(-1 if not display_models else 1))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feb15f70be4631242c8fbd78b44a2597\",\n \"score\": \"0.47897843\",\n \"text\": \"def create_swagger_endpoint(api):\\n\\n class SwaggerEndpoint(Resource):\\n def get(self):\\n swagger_object = {}\\n # filter keys with empty values\\n for k, v in api._swagger_object.iteritems():\\n if v or k == 'paths':\\n if k == 'paths':\\n paths = {}\\n for endpoint, view in v.iteritems():\\n views = {}\\n for method, docs in view.iteritems():\\n # check permissions. If a user has not access to an api, do not show the docs of it\\n if auth(request.args.get('api_key'), endpoint, method):\\n views[method] = docs\\n if views:\\n paths[endpoint] = views\\n swagger_object['paths'] = paths\\n else:\\n swagger_object[k] = v\\n return swagger_object\\n\\n return SwaggerEndpoint\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfd3ef7f98d5c7cdf95ebd528c69d788\",\n \"score\": \"0.47889823\",\n \"text\": \"def serialise(self):\\n schema = dict(\\n format_version=self.SCHEMA_FORMAT_VERSION,\\n fieldtypes=self.fieldtypes,\\n routes=self.routes,\\n guessers=[g.serialise() for g in self.guessers],\\n next_slot = self.next_slot,\\n )\\n return json.dumps(schema, sort_keys=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09fb5c6414c7c0b4b6057f5fdeefa8b1\",\n \"score\": \"0.47878876\",\n \"text\": \"def register_routes_from_swagger(config):\\n settings = config.get_settings()\\n # Append trailing slash if not present\\n if settings['pyramid_swagger.schema_directory'][-1] != '/':\\n settings['pyramid_swagger.schema_directory'] += '/'\\n swagger_spec_path = \\\"{0}{1}\\\".format(\\n settings['pyramid_swagger.schema_directory'],\\n settings['pyramid_swagger.schema_file']\\n )\\n with open(swagger_spec_path) as phile:\\n yaml_dict = yaml.load(phile)\\n base_path = '' if yaml_dict['basePath'] == '/' else yaml_dict['basePath']\\n for k, v in yaml_dict['paths'].items():\\n pattern = \\\"{base}{path}\\\".format(base=base_path, path=k)\\n for verb, data in v.items():\\n route_name = data['operationId']\\n LOGGER.info(\\n 'Adding Swagger route name=%s, method=%s, pattern=%s',\\n route_name,\\n verb.upper(),\\n pattern\\n )\\n config.add_route(name=route_name, pattern=pattern, request_method=verb.upper())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1e48283b7c5c373d67e16b6d4fd23e1\",\n \"score\": \"0.47845888\",\n \"text\": \"def add_openapi_json(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80bbd1886c6be8026f4be2068c7e92ae\",\n \"score\": \"0.47805062\",\n \"text\": \"def scout_core_specification_swagger(*args, **kwargs):\\n swag = swagger(current_app)\\n swag['info']['title'] = 'Wild Me - Scout MWS Project, Phase 1'\\n swag['info'][\\n 'description'\\n ] = 'Documentation for all REST API endpoints provided by Wild Me for the Scout collaboration'\\n swag['info']['version'] = 'v0.1'\\n swag['info']['contact'] = {\\n 'name': 'Wild Me',\\n 'url': 'http://wildme.org',\\n 'email': 'dev@wildme.org',\\n }\\n swag['info']['license'] = {\\n 'name': 'Apache 2.0',\\n 'url': 'http://www.apache.org/licenses/LICENSE-2.0.html',\\n }\\n swag['host'] = 'kaiju.dyn.wildme.io:5000'\\n swag['schemes'] = [\\n 'http',\\n ]\\n\\n response = jsonify(swag)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7853c7b8dfa6f8a03719275eb72ab4e7\",\n \"score\": \"0.47723496\",\n \"text\": \"def build_all_bundle_schemas():\\n for portal_type, short_name in GEVER_TYPES_TO_OGGBUNDLE_TYPES.items():\\n builder = OGGBundleJSONSchemaBuilder(portal_type)\\n schema = builder.build_schema()\\n filename = get_bundle_schema_filename(short_name)\\n yield filename, schema\\n\\n for portal_type, short_name in GEVER_SQL_TYPES_TO_OGGBUNDLE_TYPES.items():\\n builder = OGGBundleJSONSchemaSQLBuilder(portal_type)\\n schema = builder.build_schema()\\n filename = get_bundle_schema_filename(short_name)\\n yield filename, schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"685e42b128e02a05f13d959f90e3293e\",\n \"score\": \"0.47674236\",\n \"text\": \"def _gen_docs(self):\\n # The static content defined here mimics and can easily be converted\\n # to a swagger file documentation. Possibly converting the Rbkcli to\\n # a API server.\\n doc = {\\n 'definitions': {},\\n 'paths': {}\\n }\\n\\n cmdlets_files = self._get_cmdlets_files()\\n loaded_cmdlets = self._load_cmdlets_files(cmdlets_files)\\n usable_cmdlets = self._get_usable_cmdlets(loaded_cmdlets)\\n\\n for line in usable_cmdlets:\\n if line['name'] != '':\\n cmd_name = line['name'].replace(' ', '/')\\n cmd_name = '/' + cmd_name\\n parameters_found = line['param'].split(',')\\n doc['paths'][cmd_name] = {\\n 'get': {\\n 'description': line['cmdlet_description'],\\n 'operationId': '',\\n 'operation': line['command'],\\n 'parameters': [],\\n 'responses': {\\n '200': {\\n 'description': line['response_description'],\\n 'schema': {},\\n 'table_order': line['table_order'],\\n 'multiple_output': line['multiple_output']\\n }\\n },\\n 'summary': line['cmdlet_summary'],\\n 'tags': '',\\n 'x-group': ''\\n }\\n }\\n for param in parameters_found:\\n doc['paths'][cmd_name]['get']['parameters'].append({\\n \\\"in\\\": \\\"body\\\",\\n \\\"name\\\": line['param'],\\n \\\"required\\\": True,\\n \\\"type\\\": \\\"string\\\"\\n })\\n\\n self.endpoints = doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"944e9b0ece221460dc960e00fbfa1ce1\",\n \"score\": \"0.47620457\",\n \"text\": \"def _BuildSchemaDefinitions(self):\\n schemas = self.values.get('schemas')\\n if schemas:\\n for name, def_dict in schemas.iteritems():\\n # Upgrade the string format schema to a dict.\\n if isinstance(def_dict, unicode):\\n def_dict = simplejson.loads(def_dict)\\n self._schemas[name] = self.DataTypeFromJson(def_dict, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f02f5da370a32438cecb7543028072a3\",\n \"score\": \"0.47574404\",\n \"text\": \"def _rewrite_refs(self, schema):\\n if isinstance(schema, list):\\n for value in schema:\\n self._rewrite_refs(value)\\n\\n if isinstance(schema, dict):\\n for key, value in schema.iteritems():\\n if isinstance(value, (list, dict)):\\n self._rewrite_refs(value)\\n\\n if key == '$ref' and value in schema_paths:\\n del schema[key]\\n schema.update(resolve_ref(value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bcac99b0133c5db74febd223f32e4e9\",\n \"score\": \"0.47536445\",\n \"text\": \"def to_default_representation(self, obj):\\n return super().to_representation(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ddeea3becf722b8d7355888ee44ab70\",\n \"score\": \"0.47428823\",\n \"text\": \"def _gen_docs(self):\\n # The static content defined here mimics and can easily be converted\\n # to a swagger file documentation. Possibly converting the Rbkcli to\\n # a API server.\\n self.meta_api.doc = {\\n 'definitions': {},\\n 'paths': {}\\n }\\n\\n cmdlets_files = self._get_cmdlets_files()\\n loaded_cmdlets = self._load_cmdlets_files(cmdlets_files)\\n usable_cmdlets = self._get_usable_cmdlets(loaded_cmdlets)\\n\\n for line in usable_cmdlets:\\n if line['name'] != '':\\n cmd_name = line['name'].replace(' ', '/')\\n cmd_name = '/' + cmd_name\\n parameters_found = line['param'].split(',')\\n self.meta_api.doc['paths'][cmd_name] = {\\n 'get': {\\n 'description': line['cmdlet_description'],\\n 'operationId': '',\\n 'operation': line['command'],\\n 'parameters': [],\\n 'responses': {\\n '200': {\\n 'description': line['response_description'],\\n 'schema': {},\\n 'table_order': [],\\n 'multiple_output': line['multiple_output']\\n }\\n },\\n 'summary': line['cmdlet_summary'],\\n 'tags': '',\\n 'x-group': ''\\n }\\n }\\n for param in parameters_found:\\n self.meta_api.doc['paths'][cmd_name]['get']['parameters'].append({\\n \\\"in\\\": \\\"body\\\",\\n \\\"name\\\": line['param'],\\n \\\"required\\\": True,\\n \\\"type\\\": \\\"string\\\"\\n })\\n\\n self.endpoints = self.meta_api.doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9962f83ebe28a4fbe5529cc280865c38\",\n \"score\": \"0.473935\",\n \"text\": \"def iinfo_schemas(self):\\n remote_classname = self.schema.remoteClass.split('.')[-1]\\n remote_spec = self.class_.zenpack.classes.get(remote_classname)\\n if not remote_spec:\\n return {}\\n\\n schemas = {}\\n\\n if isinstance(self.schema, (ToOne)):\\n schemas[self.name] = schema.Entity(\\n title=_t(remote_spec.label),\\n group=\\\"Relationships\\\",\\n order=3.0)\\n else:\\n relname_count = '{}_count'.format(self.name)\\n schemas[relname_count] = schema.Int(\\n title=_t(u'Number of {}'.format(remote_spec.plural_label)),\\n group=\\\"Relationships\\\",\\n order=6.0)\\n\\n return schemas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e53438b61d7e47f63b37264f0048303b\",\n \"score\": \"0.4734321\",\n \"text\": \"def schemes(self, schemes: List[str]):\\n\\n self._schemes = schemes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a17ff29dc238a62d4ea5fdba49deb12f\",\n \"score\": \"0.47227666\",\n \"text\": \"def swagger_content_type_hack(view, supported=['application/json', 'application/xml']):\\n def _inner(*args, **kwargs):\\n resp = view(*args, **kwargs)\\n if isinstance(resp, Response):\\n if resp.data and 'apis' in resp.data:\\n apis = resp.data['apis']\\n for api in apis:\\n if 'operations' in api:\\n for op in api['operations']:\\n op['produces'] = supported\\n return resp\\n return _inner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d6200d440c17cb49e324fbdad374466\",\n \"score\": \"0.47218448\",\n \"text\": \"def get_schema(cls) -> SchemaDictType:\\n return {\\n 'type': 'unicode',\\n 'validators': [{\\n 'id': 'is_nonempty'\\n }],\\n 'ui_config': {\\n 'placeholder': 'https://www.example.com'\\n },\\n 'post_normalizers': [{\\n 'id': 'sanitize_url'\\n }]\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1740cd3bc47595cf2ea84d040e0c5fa\",\n \"score\": \"0.47179604\",\n \"text\": \"def map_rest_schema(schema, resource_fields):\\n for attr in schema:\\n # hack fix... change to make fields primary thing and switch get_field_type to return opposite\\n if not isinstance(schema[attr]['type'], fields.Raw):\\n resource_fields[attr] = get_field_type(schema[attr]['type'])\\n else:\\n resource_fields[attr] = schema[attr]['type']\\n if schema[attr].get('nested', False):\\n resource_fields[attr].__init__(attribute=schema[attr]['dict'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"211cee8e7411d73ac382c2cdaa770639\",\n \"score\": \"0.47147828\",\n \"text\": \"def __init__(self, info: object=None, paths: object=None, swagger: str=None, host: str=None, schemes: List[str]=None, base_path: str=None, security_definitions: object=None, produces: List[str]=None, definitions: object=None, openapi: str=None, servers: List[object]=None, components: object=None): # noqa: E501\\n self.swagger_types = {\\n 'info': object,\\n 'paths': object,\\n 'swagger': str,\\n 'host': str,\\n 'schemes': List[str],\\n 'base_path': str,\\n 'security_definitions': object,\\n 'produces': List[str],\\n 'definitions': object,\\n 'openapi': str,\\n 'servers': List[object],\\n 'components': object\\n }\\n\\n self.attribute_map = {\\n 'info': 'info',\\n 'paths': 'paths',\\n 'swagger': 'swagger',\\n 'host': 'host',\\n 'schemes': 'schemes',\\n 'base_path': 'basePath',\\n 'security_definitions': 'securityDefinitions',\\n 'produces': 'produces',\\n 'definitions': 'definitions',\\n 'openapi': 'openapi',\\n 'servers': 'servers',\\n 'components': 'components'\\n }\\n\\n self._info = info\\n self._paths = paths\\n self._swagger = swagger\\n self._host = host\\n self._schemes = schemes\\n self._base_path = base_path\\n self._security_definitions = security_definitions\\n self._produces = produces\\n self._definitions = definitions\\n self._openapi = openapi\\n self._servers = servers\\n self._components = components\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d81dfaff3ac469e3a1dcd018784c29a\",\n \"score\": \"0.4714654\",\n \"text\": \"def workWithSchema(self):\\n #import avro.schema\\n #schema = avro.schema.parse(json_encode(self.json_args))\\n #print schema\\n #Once we have the avro schema from UI we need to work with it\\n schemas.append(self.json_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df912acc2f1cfc30807da13653515174\",\n \"score\": \"0.47132203\",\n \"text\": \"def generate_all(discdoc):\\n print(\\\"Processing:\\\", discdoc.get(\\\"id\\\", \\\"\\\"))\\n schemas = discdoc.get(\\\"schemas\\\", {})\\n resources = discdoc.get(\\\"resources\\\", {})\\n # Generate scopes.\\n scopes_type = generate_scopes_type(discdoc[\\\"name\\\"], discdoc.get(\\\"auth\\\", {}).get(\\\"oauth2\\\", {}).get(\\\"scopes\\\", {}))\\n\\n # Generate parameter types (*Params - those are used as \\\"side inputs\\\" to requests)\\n params_struct_name = global_params_name(discdoc.get(\\\"name\\\"))\\n parameter_types, parameter_enums = generate_params_structs(resources, global_params=params_struct_name)\\n\\n # Generate service impls.\\n services = []\\n for resource, methods in sorted(resources.items()):\\n services.append(generate_service(resource, methods, discdoc))\\n if \\\"methods\\\" in discdoc:\\n services.append(generate_service(\\\"Global\\\", discdoc, discdoc, generate_subresources=False))\\n\\n # Generate schema types.\\n structs = []\\n enums = []\\n for name, desc in sorted(schemas.items()):\\n typ, substructs, subenums = parse_schema_types(name, desc)\\n structs.extend(substructs)\\n enums.extend(subenums)\\n\\n # Generate global parameters struct and its Display impl.\\n if \\\"parameters\\\" in discdoc:\\n schema = {\\\"type\\\": \\\"object\\\", \\\"properties\\\": discdoc[\\\"parameters\\\"]}\\n name = replace_keywords(snake_to_camel(params_struct_name))\\n typ, substructs, subenums = parse_schema_types(name, schema)\\n for s in substructs:\\n s[\\\"optional_fields\\\"] = s[\\\"fields\\\"]\\n parameter_types.extend(substructs)\\n parameter_enums.extend(subenums)\\n\\n # Assemble everything into a file.\\n modname = (discdoc[\\\"id\\\"] + \\\"_types\\\").replace(\\\":\\\", \\\"_\\\")\\n out_path = path.join(\\\"gen\\\", modname + \\\".rs\\\")\\n with open(out_path, \\\"w\\\") as f:\\n f.write(RustHeader)\\n f.write(scopes_type)\\n # Render resource structs.\\n for s in structs:\\n struct_inline_comments(s)\\n if not s[\\\"name\\\"]:\\n print(\\\"WARN\\\", s)\\n f.write(chevron.render(SchemaStructTmpl, s))\\n for e in enums:\\n enum_inline_comments(e)\\n f.write(chevron.render(SchemaEnumTmpl, e))\\n for e in parameter_enums:\\n enum_inline_comments(e)\\n f.write(chevron.render(SchemaEnumTmpl, e))\\n # Render *Params structs.\\n for pt in parameter_types:\\n struct_inline_comments(pt)\\n f.write(chevron.render(SchemaStructTmpl, pt))\\n f.write(chevron.render(SchemaDisplayTmpl, pt))\\n # Render service impls.\\n for s in services:\\n f.write(s)\\n try:\\n subprocess.run([\\\"rustfmt\\\", out_path, \\\"--edition=2018\\\"])\\n except:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad5895bb9787c3103120e0e16e1b6c90\",\n \"score\": \"0.47067106\",\n \"text\": \"def swagger(self, swagger: str):\\n\\n self._swagger = swagger\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d25cdfbb766daa2691aa177899170d3\",\n \"score\": \"0.4703228\",\n \"text\": \"def _convert_json_schema_to_rest(schema: Dict) -> List[str]:\\n\\n schema = json.dumps(schema, default=str, indent=3)\\n lines = [f\\\" {line}\\\" for line in schema.split(\\\"\\\\n\\\")]\\n lines = \\\"\\\\n\\\".join(lines)\\n lines = TPL_COLLAPSE.format(lines).split(\\\"\\\\n\\\")\\n\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b081afd8a00263df34ae97966e1c0774\",\n \"score\": \"0.4695786\",\n \"text\": \"def export_swagger(\\n routes: typing.List[tornado.web.URLSpec],\\n *,\\n api_base_url: str = \\\"/\\\",\\n description: str = \\\"Swagger API definition\\\",\\n api_version: str = \\\"1.0.0\\\",\\n title: str = \\\"Swagger API\\\",\\n contact: str = \\\"\\\",\\n schemes: list = None,\\n security_definitions: dict = None,\\n security: list = None,\\n):\\n return generate_doc_from_endpoints(\\n routes,\\n api_base_url=api_base_url,\\n description=description,\\n api_version=api_version,\\n title=title,\\n contact=contact,\\n schemes=schemes,\\n security_definitions=security_definitions,\\n security=security,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"525cd5091313450ebe771fecd27fafaa\",\n \"score\": \"0.46949035\",\n \"text\": \"def get_descriptor_defaults(self, api_info, hostname=None):\\n hostname = (hostname or util.get_app_hostname() or\\n api_info.hostname)\\n protocol = 'http' if ((hostname and hostname.startswith('localhost')) or\\n util.is_running_on_devserver()) else 'https'\\n defaults = {\\n 'swagger': '2.0',\\n 'info': {\\n 'version': api_info.version,\\n 'title': api_info.name\\n },\\n 'host': hostname,\\n 'consumes': ['application/json'],\\n 'produces': ['application/json'],\\n 'schemes': [protocol],\\n 'basePath': api_info.base_path.rstrip('/'),\\n }\\n\\n return defaults\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54dd0154f06ccbf50d81ae112ede0849\",\n \"score\": \"0.46933958\",\n \"text\": \"def setup_swagger(\\n routes: typing.List[tornado.web.URLSpec],\\n *,\\n swagger_url: str = \\\"/api/doc\\\",\\n api_base_url: str = \\\"/\\\",\\n description: str = \\\"Swagger API definition\\\",\\n api_version: str = \\\"1.0.0\\\",\\n title: str = \\\"Swagger API\\\",\\n contact: str = \\\"\\\",\\n schemes: list = None,\\n security_definitions: dict = None,\\n security: list = None,\\n display_models: bool = True,\\n):\\n swagger_schema = generate_doc_from_endpoints(\\n routes,\\n api_base_url=api_base_url,\\n description=description,\\n api_version=api_version,\\n title=title,\\n contact=contact,\\n schemes=schemes,\\n security_definitions=security_definitions,\\n security=security,\\n )\\n\\n _swagger_url = (\\n \\\"/{}\\\".format(swagger_url) if not swagger_url.startswith(\\\"/\\\") else swagger_url\\n )\\n _base_swagger_url = _swagger_url.rstrip(\\\"/\\\")\\n\\n routes[:0] = [\\n tornado.web.url(_swagger_url, SwaggerHomeHandler),\\n tornado.web.url(\\\"{}/\\\".format(_base_swagger_url), SwaggerHomeHandler),\\n ]\\n\\n with open(os.path.join(STATIC_PATH, \\\"ui.html\\\"), \\\"r\\\") as f:\\n SwaggerHomeHandler.SWAGGER_HOME_TEMPLATE = (\\n f.read()\\n .replace(\\\"{{ SWAGGER_SCHEMA }}\\\", json.dumps(swagger_schema))\\n .replace(\\\"{{ DISPLAY_MODELS }}\\\", str(-1 if not display_models else 1))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0ad98e789110bbd4ed1933f1f8fdc82\",\n \"score\": \"0.4682381\",\n \"text\": \"def to_representation(self, obj):\\n if type(obj) is not list:\\n return [tag for tag in obj.all()]\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93f8f75313735bfddd38d8f7f5c6df3f\",\n \"score\": \"0.46757478\",\n \"text\": \"def get_security_definitions(self, schema: Dict[str, Any], resolver: RefResolver) -> Dict[str, Any]:\\n components = schema.get(\\\"components\\\", {})\\n security_schemes = components.get(\\\"securitySchemes\\\", {})\\n if \\\"$ref\\\" in security_schemes:\\n return resolver.resolve(security_schemes[\\\"$ref\\\"])[1]\\n return security_schemes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33c4141bcca730207ab51258c8fd287b\",\n \"score\": \"0.467435\",\n \"text\": \"def schemes(self):\\n return [self]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16b85ecf7019bed92d5c3f7a7fe9a4bf\",\n \"score\": \"0.46739846\",\n \"text\": \"def swagger(self) -> Optional[Any]:\\n return pulumi.get(self, \\\"swagger\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16b85ecf7019bed92d5c3f7a7fe9a4bf\",\n \"score\": \"0.46739846\",\n \"text\": \"def swagger(self) -> Optional[Any]:\\n return pulumi.get(self, \\\"swagger\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cd0a84a1668e0812da92bf603e9bcf5\",\n \"score\": \"0.4659864\",\n \"text\": \"def __pretty_object__(obj):\\n # type: (object) -> str\\n if str(type(obj)) not in constants.SYS_TYPE:\\n from flask import session, request\\n import numpy\\n if obj is session:\\n return __pretty_session__()\\n elif obj is request:\\n return __pretty_request__()\\n elif isinstance(obj, list) or isinstance(obj, numpy.ndarray):\\n print __pretty_multi_array__(obj)\\n else:\\n return __to_yaml__(obj)\\n else:\\n return str(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39b5cb4eadb54edd3f18814697a9fc1a\",\n \"score\": \"0.46471936\",\n \"text\": \"def build_active_schema_for_objects(cls, objects):\\n schema = cls()\\n schema.add_objects(objects)\\n return schema\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":481,"cells":{"query_id":{"kind":"string","value":"1eeb69bd9b832fc54ecb2ace1e7e5086"},"query":{"kind":"string","value":"Checks if the username and password are correct. Returns True if it is, False otherwise."},"positive_passages":{"kind":"list like","value":[{"docid":"fc87a52a3502d066792596aed153d276","score":"0.0","text":"def authenticate_account(self, username, password):\n\n self.session = validate_session(self.session)\n account = self.session.query(User).filter(User.username == username).first()\n # Check if account/username exist. False if it doesn't.\n if account is None:\n return False\n\n if Crypto.verify_scrypt_hash(password, account.hash):\n return True\n else:\n return False","title":""}],"string":"[\n {\n \"docid\": \"fc87a52a3502d066792596aed153d276\",\n \"score\": \"0.0\",\n \"text\": \"def authenticate_account(self, username, password):\\n\\n self.session = validate_session(self.session)\\n account = self.session.query(User).filter(User.username == username).first()\\n # Check if account/username exist. False if it doesn't.\\n if account is None:\\n return False\\n\\n if Crypto.verify_scrypt_hash(password, account.hash):\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"446a6f1c2a454e86e0924b6a75fccf40","score":"0.835001","text":"def check_auth(username, password):\n un = username == blp.login_credentials['user']\n pw = password == blp.login_credentials['pass']\n return un and pw","title":""},{"docid":"45aaaa68062e19e8cefcf5111d2d0d82","score":"0.83034396","text":"def check_auth(username, password):\n return username == uname and password == pwd","title":""},{"docid":"c32fd25d98edff040df4592a674f160e","score":"0.823231","text":"def check_auth(username, password):\n return username == settings.USER and password == settings.PASSWORD","title":""},{"docid":"ddb2db9d45a02f885be24c5c74c8dd8c","score":"0.82174146","text":"def check_auth(username, password):\n if app.args.AUTH:\n if ':' in app.args.AUTH:\n user, pwd = app.args.AUTH.split(':')[:2]\n else:\n user, pwd = app.args.AUTH, ''\n return username == user and password == pwd\n else:\n return True","title":""},{"docid":"af1fc12584053762dcd93698ca4345ab","score":"0.8197617","text":"def check_auth(username, password):\n if AUTH_USER is None:\n return True\n return username == AUTH_USER and password == AUTH_PASSWD","title":""},{"docid":"e513e6c70acf311508239cd8d9cab6d0","score":"0.8145576","text":"def check_auth(username, password):\n return username == 'sample' and password == 'sample'","title":""},{"docid":"3a312e153e189e26b25178c0cb23f827","score":"0.8077144","text":"def userAndPasswordCorrect(self, username, password):\n return True","title":""},{"docid":"230a0919a10a4ada987c62beb5e88f4e","score":"0.8059623","text":"def check_auth(usr, pwd):\n return usr == username and pwd == password","title":""},{"docid":"80fb067ccaf771be4b97e2ddfc2f9013","score":"0.80589145","text":"def check_auth(username, password):\n return username == app.config['auth_user'] and password == app.config['auth_pass']","title":""},{"docid":"005c523d1148979efd6c3fb997f8f05e","score":"0.8054224","text":"def check_auth(username, password):\n return username == app.config['ADMIN_USER'] and \\\n password == app.config['ADMIN_PASS']","title":""},{"docid":"33995f82dc998b1a4cbba4c7ae9b1d54","score":"0.8041471","text":"def check_auth(username, password):\r\n try:\r\n if app.config['CONFIG']['bauth'][username]['pass'] == password:\r\n return True\r\n except Exception as e:\r\n rootLogger.info(e)\r\n\r\n return False","title":""},{"docid":"dbf5042e662f8384d2ef001ffb9d6bc7","score":"0.803071","text":"def check_auth(username, password):\n return username == app.config['WC_LOGIN']['username'] and password == app.config['WC_LOGIN']['password']","title":""},{"docid":"e2b0fcbfda8f4645dde183aaf900ecda","score":"0.79818785","text":"def check_auth(username, password):\n return username == 'admin' and password == 'password'","title":""},{"docid":"9668121a9058001c88cd545af71b5473","score":"0.79743207","text":"def check_auth(username, password):\n\treturn username == 'admin' and password == 'secret'","title":""},{"docid":"7520eb64d79e1617b46d771d5d8bba67","score":"0.7960091","text":"def verify_credentials(username: str, password: str) -> bool:\n return AuthenticationService.is_valid_credentials(username, password)","title":""},{"docid":"47faf12d29c3bef1bd576433e08ec9d1","score":"0.7950642","text":"def check_auth(username, password):\n config = flask.current_app.config\n try:\n isuser = hmac.compare_digest(config['TTN_USER'], username)\n ispass = hmac.compare_digest(config['TTN_PASSWORD'], password)\n except KeyError:\n flask.current_app.logger.warn(\"No credentials for ttn specified.\")\n return False\n return isuser and ispass","title":""},{"docid":"ec389cc19fe32ac8113a2ecb234e2616","score":"0.7939864","text":"def check_auth(username, password):\n return username == 'sammy' and password == 'BasicPassword!'","title":""},{"docid":"6d9c2b007caa6ac342dcd9aa7f8129b6","score":"0.7900145","text":"def check_auth(username, password):\n return username == ADMIN_USER and password == ADMIN_PASS","title":""},{"docid":"9b3a41f5a1213b0c69cd56b5e69a0b16","score":"0.78785694","text":"def check_auth(username, password):\n return username == 'admin' and password == 'secret'","title":""},{"docid":"9b3a41f5a1213b0c69cd56b5e69a0b16","score":"0.78785694","text":"def check_auth(username, password):\n return username == 'admin' and password == 'secret'","title":""},{"docid":"9b3a41f5a1213b0c69cd56b5e69a0b16","score":"0.78785694","text":"def check_auth(username, password):\n return username == 'admin' and password == 'secret'","title":""},{"docid":"9a69976520ed4e7b735e358cb9b62fae","score":"0.7878089","text":"def check_auth(username, password):\n return username == queryname and password == querypw","title":""},{"docid":"a5d4d9a16cb164b50e568e3820766de1","score":"0.7854986","text":"def check_auth(username, password):\n return username == 'guest' and password == 'password'","title":""},{"docid":"5c78fe569208edf64c83d86763bb5ece","score":"0.7848427","text":"def check_credentials(username, password):\n dm = datamodel.TmData(cherrypy.request.app.config['/'])\n user = dm.get_user(username)\n if not user:\n return \"Incorrect username or password\"\n h = hashlib.md5()\n h.update(password.encode('utf-8'))\n pwd = h.hexdigest()\n if user[1] != pwd:\n return \"Incorrect username or password\"\n return None","title":""},{"docid":"3f6c477a47524f7e9406383ca56a5ed2","score":"0.7742826","text":"def check_auth(username, password):\n return 'lme' == username and 'lme' == password","title":""},{"docid":"f0a4316a23ba050cbedf3f52df5f4126","score":"0.77216727","text":"def check_auth(username, password):\n\n env_password = getenv('HTTP_PASSWORD', cast=str)\n env_username = getenv('HTTP_USERNAME', cast=str)\n\n return username == env_username and password == env_password","title":""},{"docid":"87dd51bc6aa1aacb0052d73b69f2366b","score":"0.77120525","text":"def valid_login(username: str, password: str) -> bool:\n account = execute_one_query(\"SELECT * FROM person WHERE username = %s\", username)\n return account and check_password_hash(account[1], password)","title":""},{"docid":"ed0517500a80ed3c1f44d947a940853e","score":"0.77034366","text":"def check_credentials():\n user = session.get('user', None)\n user_auth = session.get('user_auth', None)\n if user and user_auth:\n return check_auth(user, user_auth)\n return False","title":""},{"docid":"a3ede46eb03c97821391740393adc1f9","score":"0.7692338","text":"def check_auth(username, password): \n if not User.objects(username=username):\n return False\n user = User.objects.get(username=username) \n if not user or not user.verify_password(password):\n return False\n return True","title":""},{"docid":"ae002e1d72dea0138022e98a68341cfc","score":"0.76773405","text":"def check_auth(username, password):\n return username == 'TRAIN' and password == 'TuN3L'","title":""},{"docid":"0b68542cf987b1ff2423e79672d13d1a","score":"0.7650397","text":"def check_user_password(username, password):\n from x84.bbs import find_user, get_user\n handle = find_user(username)\n if handle is None:\n return False\n user = get_user(handle)\n if user is None:\n return False\n return password and user.auth(password)","title":""},{"docid":"814cd55d2f7f50fdbdfdc42e265b526e","score":"0.7596233","text":"def checkAuthentication(username, password):\n\n # get the ticket\n if getAuthTicket(username, password) is not None:\n return True\n\n return False","title":""},{"docid":"ffc50175e5425d43d9e16b43031469d0","score":"0.7582177","text":"def validate(kls, username, password):\n user = kls.get(username)\n if user is None:\n return False\n else:\n return user.check_password(str(password))","title":""},{"docid":"1231c21bd809d7bedef3f3290262ddca","score":"0.7582004","text":"def verify_password(username, password):\n if not username or username != current_app.config['TOKEN']:\n return False\n return True","title":""},{"docid":"447b26cf6bf91537cd2cec617ba7a269","score":"0.75226456","text":"def check_auth(password):\n return password == PASSWORD","title":""},{"docid":"0be4373630d3dbd6abb463a93bd7d4b6","score":"0.75160027","text":"def is_valid_credentials(username: str, password: str) -> bool:\n\n # If username is empty then no point attempting to validate, so return False\n if not username:\n return False\n\n try:\n user = UserService.get_user_by_username(username)\n except NotFound:\n return False\n\n login_success = AuthenticationService._is_valid_password(password, user.password)\n dmis.authenticated_user_id = user.user_id\n\n return login_success","title":""},{"docid":"f1f01712f42742c4a218c56ca0ad11ae","score":"0.7477888","text":"def check_creds(username, password):\n\n STMT = \"SELECT username, password_hash FROM users WHERE username = %s\"\n \n conn = database.get_conn()\n cursor = conn.cursor()\n cursor.execute(STMT, (username, ))\n user = cursor.fetchone()\n cursor.close()\n conn.close()\n\n if user is None:\n return False\n\n return argon2.verify(password, user[1])","title":""},{"docid":"ec7529d23edab1339b9259d3061a7916","score":"0.7470366","text":"def check_auth(username, password):\n return username == os.environ['HTUSER'] and password == os.environ[\n 'HTAUTH']","title":""},{"docid":"43b8ede1ad1bbdac523a4d20de10b873","score":"0.74423933","text":"def is_login_valid(username, password):\n username_valid = False\n flag = False\n correct_password = get_password(username)\n if correct_password: # Username exists\n username_valid = True\n if password == correct_password: # Password is correct\n flag = True\n return (username_valid, flag)","title":""},{"docid":"3da0b0dd6d37847da799eb2aee437e1d","score":"0.74130994","text":"def check_auth(username, password):\n\n email = username\n password = password\n cursor = mysql.connect().cursor()\n cursor.execute(\"SELECT COUNT(*) FROM dav_users WHERE email='\" + email + \"' AND password='\" + password + \"'\")\n data = cursor.fetchone()\n\n if data == 0:\n return False\n else:\n return True\n # return username == 'admin' and password == 'secret'","title":""},{"docid":"b506d4572a9adef794501f5dc408f8f1","score":"0.7391268","text":"def check_password(self, username, password):\n assert self.dev_users\n return self.dev_users.get(username) == password","title":""},{"docid":"5a6b932f16e30417f0e2b130d69b655a","score":"0.7327687","text":"def check_auth(username, password):\n User1 = User.query.filter(User.username == username).first()\n if User1 and User1.password ==password:\n return True\n else:\n return False","title":""},{"docid":"1d69b85a55166f8e50b7f999b2bc9626","score":"0.73253906","text":"def check_pw(username, password):\n required_columns = \"passwd\"\n where_clause = \"username = %s\"\n params = (username, )\n user_details = fetch_from_table(\n required_columns=required_columns,\n where_clause=where_clause,\n params=params,\n table=\"agent\")\n\n if user_details:\n valid_pw = bytes(user_details[0][\"passwd\"], 'utf8')\n return valid_pw == hashpw(password.encode(\"utf-8\"), valid_pw)\n else:\n return False","title":""},{"docid":"2d49979aadfebeefbcb353a5a31942d6","score":"0.7315844","text":"def check_password():\n\n credential = request.form.get(\"credential\")\n password = request.form.get(\"password\")\n\n if password_is_correct(credential, password):\n return \"true\"\n else:\n return \"false\"","title":""},{"docid":"1e5b33c699769d6af1a5736cef0f3c99","score":"0.72665334","text":"def check_credentials(self):\n select_query = \"SELECT UserPassword FROM User WHERE Email = %s\"\n self._cursor.execute(select_query, (self._email, )) \n\n fetched_data = self._cursor.fetchone()\n\n if not fetched_data:\n print(\"Your email is not registered in the database.\")\n return False\n\n fetched_password = fetched_data[0]\n if fetched_password == self._password:\n print(\"Congratz, u (\"+self._email+\") are logged in!\")\n return True\n else: \n print(\"The password does not match!\")\n return False","title":""},{"docid":"994534ab53d1f89a5753bcf4d7923ee8","score":"0.72379994","text":"def check_password(username, password):\r\n query = \"\"\"SELECT password FROM clients\r\n WHERE username=?\"\"\"\r\n cursor.execute(query, (username, ))\r\n actual_pass = cursor.fetchone()\r\n if not actual_pass:\r\n return False\r\n if actual_pass[0] == hash_password(password):\r\n return True\r\n return False","title":""},{"docid":"0419c0562dfb9a0fee650604db5b9018","score":"0.7228656","text":"def authenticate(self, user, password):\n\t\tif user == \"root\" and password == \"toor\":\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False","title":""},{"docid":"ec46ef4363d7c435a1bda6bf2ea0dafe","score":"0.7216104","text":"def is_authorised(username, password):\n return username == \"pokemon\" and password == \"go\"","title":""},{"docid":"1918cb7cc1b10aae90bba46c8f5beee3","score":"0.72094506","text":"def validate(self, password: str) -> bool:","title":""},{"docid":"66c5fbd3b2e0a05a256a6fe8a7847302","score":"0.72084755","text":"def valid_login(username, password):\n db = get_db()\n db.ping(True)\n cur = db.cursor()\n\n try:\n sql = \"SELECT password FROM users WHERE user_name = '{}';\".format(username)\n cur.execute(sql)\n for i in cur:\n return check_password_hash(i[0], password)\n return False\n except mysql.connector.Error as err:\n flash(err, \"set\")\n return False\n finally:\n cur.close()\n db.close()","title":""},{"docid":"eb8570703752bb811e0432b369f9c61c","score":"0.7195735","text":"def verify_login(user: dict) -> bool:\n username = user.get(\"username\")\n password = user.get(\"password\")\n if not username or not password:\n return False\n existing_user = User.query.filter_by(username=username).first()\n if not existing_user:\n return False\n if check_password_hash(existing_user.password, password):\n return True\n return False","title":""},{"docid":"830213656456299c9c4256830c112ed0","score":"0.7161857","text":"def _checkAuthentication(self, name, password, request):\n # For the base implementation the password is checked\n # against acl_users.\n aclu = getToolByName(self, 'acl_users')\n res = aclu.authenticate(name, password, request)\n authentication_ok = res is not None\n return authentication_ok","title":""},{"docid":"93fdb4deb35d7a8c37e38077c517b88e","score":"0.71430695","text":"def login_check(username, password):\n user_pw = db.session.query(tables['organizers'].password).filter_by(username=username)\n if user_pw.scalar() is None:\n return False\n split_list = user_pw.one().password.split('$')\n salt = split_list[1]\n password_hashed = split_list[2]\n return get_hashed_text(salt, password) == password_hashed","title":""},{"docid":"78c90726b07c5d3446fdf18732e13c4e","score":"0.71260417","text":"def check_password(self, username, password, env=None):\n if not username or not password:\n return False\n provider = self.get_sync_provider(fallback=True)\n if username == 'root' and not provider.syncs_root:\n provider = ajenti.usersync.AjentiSyncProvider.get()\n\n if not username in ajenti.config.tree.users:\n return False\n\n try:\n provider.sync()\n except Exception as e:\n logging.error(str(e))\n\n result = provider.check_password(username, password)\n\n provider_name = type(provider).__name__\n\n ip_notion = ''\n ip = env.get('REMOTE_ADDR', None) if env else None\n if ip:\n ip_notion = ' from %s' % ip\n\n if not result:\n msg = 'failed login attempt for %s (\"%s\") through %s%s' % \\\n (username, password, provider_name, ip_notion)\n syslog.syslog(syslog.LOG_WARNING, msg)\n logging.warn(msg)\n else:\n msg = 'user %s logged in through %s%s' % (username, provider_name, ip_notion)\n syslog.syslog(syslog.LOG_INFO, msg)\n logging.info(msg)\n return result","title":""},{"docid":"63e89e3afead92b660887821ca6498b5","score":"0.711976","text":"def verify_password(username, password):\n from .logic import get_users\n\n if hasattr(flask_g, 'username'):\n if not flask_g.username == '' and username == '':\n username = flask_g.username\n if hasattr(flask_g, 'password'):\n if not flask_g.password == '' and password == '':\n password = flask_g.password\n\n if username == '':\n app.logger.warning(\"Unauthorized access attempted!\")\n return False\n\n if username == 'admin':\n if 'ADMIN_PASS_FILE' in app.config:\n\n try:\n f = open(app.config['ADMIN_PASS_FILE'], 'r')\n app.config['ADMIN_PASS'] = str(f.read()).rstrip(\"\\r\\n\")\n f.close()\n except IOError as e:\n print(\"Unable to read admin password file: %s\" % app.config['ADMIN_PASS_FILE'])\n return False\n\n if check_password_hash(app.config['ADMIN_PASS'], str(password)):\n flask_g.username = username\n flask_g.password = password\n return True\n else:\n return False\n\n if not username == '':\n users_l = get_users(q_username=username, page=1, per_page=1)\n if len(users_l) < 1:\n print(\"User {} not found\".format(username))\n return False\n else:\n user = users_l[0].as_dict()\n\n if check_password_hash(user['password'], str(password)):\n flask_g.username = user['username']\n flask_g.password = user['password']\n return True\n else:\n return False","title":""},{"docid":"7b6967ce9bc5a3ddd3be1c28bce63aea","score":"0.70946634","text":"def _verify_credentials(self):\n r = requests.get(self.apiurl+\"account/verify_credentials.xml\", auth=HTTPBasicAuth(self._username, self._password),\n headers=self.header)\n if r.status_code != 200:\n raise UserLoginFailed(\"Username or Password incorrect.\")","title":""},{"docid":"a2462b13ac96abc341ad3c5579193fd6","score":"0.7082045","text":"def is_correct_credential_pair(username, password):\n return bool(is_valid_username(username) and\n is_valid_password(username, password) and\n is_valid_credential_pair_db(username, password))","title":""},{"docid":"0385406cc62e39a30350a42023aad711","score":"0.7081029","text":"def verify_user(self, username: str, password: str):\n result = self.__get_user(username=username)\n db_password = result[0].get('password') if result else ''\n return self.__verify_password(password=password, hashed_password=db_password)","title":""},{"docid":"17c2819c6071104f921afcb5841c96e7","score":"0.7079846","text":"def is_valid_credential_pair_db(username, password):\n connection = connect_db()\n cursor = connection.cursor(buffered=True)\n cursor.execute('SELECT password FROM Users WHERE username=%s LIMIT 1',\n (username,))\n result = cursor.fetchall()\n close_db(connection)\n return bool(result and\n bcrypt.checkpw(password.encode(), result[0][0].encode()))","title":""},{"docid":"bb28f04baf938bd5818b023b9edac620","score":"0.7055832","text":"def check_auth(username, password):\n return os.getenv('COMPOSE_USERNAME') == username and os.getenv('COMPOSE_PASSWORD') == password","title":""},{"docid":"d574ec08638be468ed43352cea3c4f53","score":"0.7052717","text":"def authenticate(username, password):\n lines = list()\n with open(\"passwords.txt\", \"r\") as f:\n lines = f.readlines()\n for line in lines:\n line = line.rstrip(\"\\n\")\n stored_username, stored_password = line.split(\":\")\n if stored_username == username and stored_password == password:\n return True\n return False","title":""},{"docid":"fd4834a3168bab442c75bf3bca12d766","score":"0.705246","text":"def check_auth(username, password):\n dn = secrets.DN_STRING.format(username)\n with ldap_connection(secrets.LDAP_URL) as conn:\n try:\n conn.simple_bind_s(dn, password)\n conn.search_s(secrets.BASE_DN, ldap.SCOPE_ONELEVEL)\n except ldap.LDAPError:\n return False\n return True","title":""},{"docid":"4413f7ad84a9b230bd7fb6a905c53766","score":"0.70457727","text":"def check_auth(username, password):\r\n for user in USER:\r\n if user['user']== username and user['pass']==password:\r\n return True\r\n #return username == 'admin' and password == 'secret'\r","title":""},{"docid":"9e38bfdb05be81df39fd603bc71eba43","score":"0.7042962","text":"def user_exist(self,password):\n if self.password == password:\n return True","title":""},{"docid":"9ef268e4258bae4751ed7abd0aee1c1a","score":"0.70418","text":"def verify_password(username, password):\n if not username:\n # Warning/info?\n return False\n single_user_data = userdata.get(username)\n if single_user_data:\n stored_pw = single_user_data.get(PASSWORD)\n assert stored_pw is not None, 'Server error: user data must contain a password!'\n res = sha256_crypt.verify(password, stored_pw)\n else:\n logger.info('User \"{}\" does not exist'.format(username))\n res = False\n return res","title":""},{"docid":"a1fce9f617636a7d46d36f920c2107ce","score":"0.7037066","text":"def verify_login(username, password, user_data):\n # Encode password using salt\n salted_password = password + user_data.salt\n encoded_password = hashlib.sha512(salted_password.encode()).hexdigest()\n\n # Verify if encoded password corresponds to user's password in the database\n # will return True if password is correct\n return compare_digest(encoded_password, user_data.password)","title":""},{"docid":"1e1c33ab495b0edb66ff01e171a8afc4","score":"0.7033827","text":"def authenticate(self, entusername, entpassword):\r\n self.EnteredUsername = entusername\r\n self.EnteredPassword = entpassword\r\n\r\n if self.EnteredUsername in self.user_accounts:\r\n if self.user_accounts[self.EnteredUsername] == self.EnteredPassword:\r\n return True\r\n else:\r\n return False","title":""},{"docid":"848df66ade1245604b7b5c501906ca45","score":"0.70296574","text":"def valid_credentials(_login, _password):\n center = Center.query.filter_by(_login=_login, _password=_password).first()\n if center:\n return True\n else:\n return False","title":""},{"docid":"7109876df3819735417d20787a444ad0","score":"0.6999185","text":"def verifypwd(username, password):\n dbuser = db.GqlQuery(\"SELECT * FROM LoginData\")\n\n for x in dbuser:\n if x.username == username:\n pwhash = str(x.password)\n return valid_pw(username, password, pwhash)\n return False","title":""},{"docid":"900e295641b53a82f272e4d2493fb403","score":"0.69919485","text":"def _test_auth(self):\n user = self.get_user()\n try:\n if \"Invalid username/password\" in user.json().values():\n return False\n else:\n return True\n except JSONDecodeError:\n warnings.warn(\"ESPA Service appears to be offline! Response: {}\".format(user.json()))\n return True","title":""},{"docid":"eb702d3e3e41cdac8b27be61f4c7dff2","score":"0.69806623","text":"def check_user_pass(usr, pwd):\n\n ret = AUTHDB.get(usr, None)\n if (ret is None) or (ret != pwd):\n return False\n return True","title":""},{"docid":"c80ff91ca8830b1f109717aaefc0055a","score":"0.696238","text":"def is_authentic(self, login, password):\n \n return True","title":""},{"docid":"2d0b86405a808bff61c0cdc9a7047a50","score":"0.6959467","text":"def valid_login(self, email: str, password: str) -> bool:\n if type(email) is not str or type(password) is not str:\n return False\n\n try:\n user = self._db.find_user_by(email=email)\n pwd = bytes(password, 'utf-8')\n if bcrypt.checkpw(pwd, user.hashed_password):\n return True\n else:\n return False\n except NoResultFound:\n return False","title":""},{"docid":"198a25037c736d6c0ca5ca44c4a63feb","score":"0.6955093","text":"def is_user_valid(self, username, password):\r\n temp_hashed_password = self.get_hashed_password(password)\r\n user = self.session.query(User).filter(User.user_name == username).filter(\r\n User.password == temp_hashed_password)\r\n return user is not None","title":""},{"docid":"fa546a6ee275144385ba449e19055e7d","score":"0.69535583","text":"def is_user_authenticated(self, username, password):\n user = self.db.users.find_one({\n \"username\": username\n })\n if user:\n return user[\"password\"] == Util.sha256(password)\n return False","title":""},{"docid":"c377d37f67170c825e6d2c4f7046cea8","score":"0.695223","text":"def valid_login(self, email: str, password: str) -> bool:\n try:\n u = self._db.find_user_by(email=email)\n except NoResultFound:\n return False\n return bcrypt.checkpw(password.encode(), u.hashed_password)","title":""},{"docid":"23cdda55fbb9c9baf3f5eefbda2488e3","score":"0.69403476","text":"def authenticate_user(username):\n return True if get_user_by_username(username) else False","title":""},{"docid":"52969e760e13c61b2ac774a37f6f7946","score":"0.69397795","text":"def check_my_users(user):\n\t user_data = my_users.get(user['username'])\n\t if not user_data:\n\t return False # <--- invalid credentials\n\t elif user_data.get('password') == user['password']:\n\t return True # <--- user is logged in!\n\t\n\t return False # <--- invalid credentials","title":""},{"docid":"279ca4a1936aeef9b6da6108743b59fe","score":"0.6919977","text":"def validate_user(username, password, cfpassword, email):\n result = is_username_valid(username) and is_password_valid(password)\n result = result and is_cfpassword_valid(password, cfpassword)\n result = result and is_email_valid(email)\n return result","title":""},{"docid":"114294879d2d9b80758b3c5919a8181e","score":"0.6884935","text":"def verify_password(database: str, username: str, password: str) -> bool:\n db = DatabaseController(database)\n user_db_data = db.select_user_data(username)\n return user_db_data[0] == HashPasswordHelper.hash_password(password, user_db_data[1], user_db_data[2])","title":""},{"docid":"15b219a7ac089beabf2a4801f68addad","score":"0.68764126","text":"def check_login_good(username, password):\n # Note that we are NOT building our own query via string formatting --\n # we are allowing the psycopg2 library to handle escaping the user inputs\n query = (\n \"SELECT id, username, password FROM users \"\n \"WHERE username = %s AND password = %s\"\n )\n cursor.execute(query, (username, password))\n result = cursor.fetchone()\n return result","title":""},{"docid":"b6448b8288d6ce55e370e95527018b7f","score":"0.6867732","text":"def auth(self):\n if self.username is not None and self.password is not None:\n self.session.auth = (self.username, self.password)\n auth_response = self.session.get(self.url + '/')\n if auth_response.status_code != 401:\n return True\n return False","title":""},{"docid":"9ac12cd480c3385905022e8503b3559c","score":"0.68671745","text":"def authenticate (username, password):\n try:\n user = User.objects.get(username=username)\n\n if user.check_password(password):\n return user\n else:\n return False\n\n except:\n return False","title":""},{"docid":"144a7db65f820a23ccc6145cd39f280c","score":"0.6843814","text":"def check_for_login(error):\n print(\"Checking username and password combination.\")\n if 'password authentication failed' in str(error):\n print(\" Username/password combination is invalid.\")\n print(\" Try checking your username and password. \"\n \"One or both are likely incorrect.\")\n print_error_text(error)\n return True\n print(\" ... No issue found.\")\n return False","title":""},{"docid":"4214ed7d065aaf3d774209df473dd7d1","score":"0.68409956","text":"def login(self, username, password):\n for registrant in self.accounts:\n if username in registrant['username'] and password in registrant['password']:\n return True\n else:\n return False","title":""},{"docid":"6389af80d922c12980c8f284de769aca","score":"0.68379873","text":"def valid_login(self, email: str, password: str) -> bool:\n user = self._db.find_user_by(email)\n if user.email == email:\n if bcrypt.checkpw(password.encode('utf-8'), user.hashed_password):\n return True\n return False","title":""},{"docid":"6130aeaf826a536c28d22dceb80ace99","score":"0.6819086","text":"def verify_pw(username, password):\n if not UserExist(username):\n return False\n hashed_pw = users.find({\n \"Username\": username\n })[0][\"Password\"]\n if bcrypt.hashpw(password.encode('utf8'), hashed_pw) == hashed_pw:\n return True\n else:\n return False","title":""},{"docid":"32f312f94f1bead7866b176d07777857","score":"0.6814118","text":"def checkLogin(self):\n\t\tprint(username)\n\t\tprint(password)\n\n\t\tusernameInput = input('\\nWhat is your username?\\n')\n\t\tpasswordInput = input('\\nWhat is your password?\\n')\n\n\t\t#Checks if username and password is correct\n\t\tif usernameInput == developer.username:\n\n\t\t\tif passwordInput == developer.password:\n\t\t\t\tprint('\\nSuccess! You are now in the developer menu!')\n\t\t\t\tself.loggedIn = True\n\t\t\t\tuser = developer()\n\t\t\t\tmenu.developerMenu()\n\t\t\telse:\n\t\t\t\tprint('Error: Wrong username or password')\n\t\t\t\tmenu.mainMenu()\n\n\t\telse:\n\t\t\tprint('Error: Wrong username or password')\n\t\t\tmenu.mainMenu()","title":""},{"docid":"9dec8e5477c8fa362f57517b4d88e337","score":"0.6810149","text":"def check_login(username=None, password=None):\n print(\"check_login: username=%s password=%s\" % (username, password))\n cl_response_queue = queue.Queue()\n message_data = database.DatabaseDataMessage(\n table_name=\"webserver\",\n field=\"\"\" \"{}\" \"\"\".format(\"WEBSERVER_USER_NAME\"),\n data=\"\"\" \"{}\" \"\"\".format(username),\n caller_queue=cl_response_queue)\n message = database.DatabaseMessage(\n command=database.DatabaseCommand.DB_SELECT_DATA,\n message=message_data)\n db_queue.put(message)\n db_task.join(timeout=0.65)\n\n print(\"check_login: Wait on password response\")\n while cl_response_queue.empty() is True:\n pass\n\n print(\"check_login: Got password response\")\n user_response = cl_response_queue.get()\n print(user_response)\n\n # Make sure there is some sort of response before\n # parsing it.\n if len(user_response):\n db_username = user_response[0][1]\n db_password = user_response[0][2]\n print(\"DB Username = %s\" % (db_username))\n print(\"DB Password = %s\" % (db_password))\n return (db_username == username and db_password == password)\n else:\n # There was no response so no chance of logging in or parsing\n # the response\n return False","title":""},{"docid":"1aeb5e0b01f3001fce3f588e0b959fee","score":"0.68085146","text":"def check_auth(username, password, access):\n u = users.get(username,None)\n if u:\n if u[0] == password and (access in u[1] or u[1][0]==\"all\"):\n return username and password\n else:\n return None\n else:\n return None","title":""},{"docid":"b3f147f62833857e9aa2f894693a0466","score":"0.67997265","text":"def checkpass():\n username = request.args.get(\"username\")\n password = request.args.get(\"password\")\n\n if not username:\n return jsonify(False)\n\n # Ensure password was submitted\n elif not password:\n return jsonify(False)\n\n # Query database for username\n rows = db.execute(\"SELECT * FROM users WHERE username=:username\", username=username)\n\n # Ensure username exists and password is correct\n if len(rows) != 1 or not check_password_hash(rows[0][\"hash\"], password):\n return jsonify(False)\n\n # Remember which user has logged in\n session[\"user_id\"] = rows[0][\"id\"]\n\n # Redirect user to home page\n return jsonify(True)","title":""},{"docid":"7f5c589a7ee36d73d3a1cffcb5535db8","score":"0.67922425","text":"def check(user, password, log=True):\n\t\tUser.init()\n\n\t\tuser = user.lower()\n\n\t\tif User.instance.user == b\"\":\n\t\t\treturn True\n\t\telif user == User.instance.user:\n\t\t\tif encryption.gethash(password) == User.instance.password:\n\t\t\t\tif log == True:\n\t\t\t\t\tUser.loginState[0] = True\n\t\t\t\treturn True\n\t\t\telse:\n\t\t\t\tif log == True:\n\t\t\t\t\tUser.loginState[0] = False\n\t\t\t\t\tuseful.syslog(\"Login failed, wrong password for user '%s'\"%useful.tostrings(user))\n\t\telse:\n\t\t\tif user != b\"\":\n\t\t\t\tif log == True:\n\t\t\t\t\tUser.loginState[0] = False\n\t\t\t\t\tuseful.syslog(\"Login failed, unkwnon user '%s'\"%useful.tostrings(user))\n\t\treturn False","title":""},{"docid":"4605d4ab39bfb3db7b671200a5ca6645","score":"0.6778981","text":"def check_credentials(server, username, password):\n\tbasicauth_header = urllib2.base64.b64encode('%s:%s' % (username, password))\n\turl = 'https://%s/certsrv/' % server\n\treq = urllib2.Request(url)\n\treq.add_header(\"Authorization\", \"Basic %s\" % basicauth_header)\n\ttry:\n\t\turllib2.urlopen(req)\n\texcept urllib2.HTTPError as error:\n\t\tif error.code == 401:\n\t\t\treturn False\n\t\telse:\n\t\t\traise\n\telse:\n\t\treturn True","title":""},{"docid":"569ccdb39615e6e295144f7cd45cbf69","score":"0.6774533","text":"def verify_password_or_access_token(username_or_token: str,\n password: str) -> bool:\n r = requests.get(\n f'{USER_SERVICE}/user-auth',\n json={\n 'username_or_token': username_or_token,\n 'password': password\n }\n )\n if r.status_code == 401:\n return False\n # Save the found username for the current request processing\n g.username = r.json()['data']\n return True","title":""},{"docid":"6407f0a51f10128a1712af7f66b997e9","score":"0.67520094","text":"def is_valid_password(username, password):\n return bool(password and username.lower() not in password.lower() and\n len(password) <= 72)","title":""},{"docid":"4fb7a809428078c942ec599fb30d9cac","score":"0.67436266","text":"def check_credentials(self, **kwargs):\n if not (self.cfg(\"username\") and self.cfg(\"password\") and self.cfg(\"prefix\")):\n warnings.warn(\n f\"The {self.__class__.__name__} is misconfigured. Please \"\n f\"set {self.cfgkey('username')}, {self.cfgkey('password')}\"\n f\" and {self.cfgkey('prefix')} in your configuration.\",\n UserWarning,\n )","title":""},{"docid":"f7123dd6a9933176db5a7a43e4319591","score":"0.6729454","text":"def check_login_credentials():\n\n # Look at the stdout stream to see the values coming in from the login form\n print('request.form values from login_form.html')\n for x in request.form:\n print(' {}: {}'.format(x, request.form[x]))\n\n\n credentials_ok = True # Any failed test reverses value to False\n\n # Check that data entry fields exist and have some value in them\n\n # if '/check_credentials' route invoked without using login_prompt page\n # then the request.form dictionary will not have expected entries\n if 'user_id' not in request.form:\n credentials_ok = False\n\n elif 'password' not in request.form:\n credentials_ok = False\n\n # if one of the request.form login entry fields is left empty\n elif not request.form['user_id']:\n credentials_ok = False\n\n elif not request.form['password']:\n credentials_ok = False\n\n # At this point, take the login credentials to do a database check\n # 1. retrieve a user from the database with user_id as the key\n # 2. encrypt the password\n # 3. check that the encrypted password is correct for the user\n\n elif not user_in_database():\n credentials_ok = False\n\n elif not user_password_match():\n credentials_ok = False\n\n if credentials_ok:\n return redirect(url_for('first_action'))\n\n flash('Username-Password combination not valid.')\n return redirect(url_for('present_login_form'))","title":""},{"docid":"f54350ddf26e630bc18a08a5af4bde64","score":"0.67226404","text":"def validate_credentials(username, password):\n user = db_conn.query_db('SELECT * FROM `xeno`.`users` WHERE `userid`=%s', [username], True)\n\n #print \"Validating: \", username, \":\", password\n if user is None: # not a valid username\n return None\n\n stored_salted_password = user[\"hpass\"]\n end_salt_pos = stored_salted_password.find('==') + 2\n salt = stored_salted_password[0:end_salt_pos]\n # stored_password = stored_salted_password[end_salt_pos:] # this isnt needed I dont think...\n\n if stored_salted_password == encode_password(password, salt):\n userid = user[\"userid\"]\n return User(userid, user)\n ''' This is a simple test.\n if username == \"Xeno\" and password == \"cars\":\n userid = \"testing\"\n return User(userid)\n '''\n return None","title":""},{"docid":"6d6fa7a48c6d3544fc6437c88815340a","score":"0.67139715","text":"def check_basic_auth(user, passwd):\n\n auth = request.authorization\n return auth and auth.username == user and auth.password == passwd","title":""},{"docid":"4f4712fffe921b6058d3168ffbf237bb","score":"0.67130435","text":"def password_check(username):\n password = input(\"Password: \")\n check_status = check_in_file(username, password, mode='password')\n return check_status","title":""},{"docid":"6ffbe9824ca8f5d99011a176cea79b17","score":"0.6659585","text":"def has_password(self, password):\n return password==self.user_name","title":""}],"string":"[\n {\n \"docid\": \"446a6f1c2a454e86e0924b6a75fccf40\",\n \"score\": \"0.835001\",\n \"text\": \"def check_auth(username, password):\\n un = username == blp.login_credentials['user']\\n pw = password == blp.login_credentials['pass']\\n return un and pw\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45aaaa68062e19e8cefcf5111d2d0d82\",\n \"score\": \"0.83034396\",\n \"text\": \"def check_auth(username, password):\\n return username == uname and password == pwd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c32fd25d98edff040df4592a674f160e\",\n \"score\": \"0.823231\",\n \"text\": \"def check_auth(username, password):\\n return username == settings.USER and password == settings.PASSWORD\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddb2db9d45a02f885be24c5c74c8dd8c\",\n \"score\": \"0.82174146\",\n \"text\": \"def check_auth(username, password):\\n if app.args.AUTH:\\n if ':' in app.args.AUTH:\\n user, pwd = app.args.AUTH.split(':')[:2]\\n else:\\n user, pwd = app.args.AUTH, ''\\n return username == user and password == pwd\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af1fc12584053762dcd93698ca4345ab\",\n \"score\": \"0.8197617\",\n \"text\": \"def check_auth(username, password):\\n if AUTH_USER is None:\\n return True\\n return username == AUTH_USER and password == AUTH_PASSWD\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e513e6c70acf311508239cd8d9cab6d0\",\n \"score\": \"0.8145576\",\n \"text\": \"def check_auth(username, password):\\n return username == 'sample' and password == 'sample'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a312e153e189e26b25178c0cb23f827\",\n \"score\": \"0.8077144\",\n \"text\": \"def userAndPasswordCorrect(self, username, password):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"230a0919a10a4ada987c62beb5e88f4e\",\n \"score\": \"0.8059623\",\n \"text\": \"def check_auth(usr, pwd):\\n return usr == username and pwd == password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80fb067ccaf771be4b97e2ddfc2f9013\",\n \"score\": \"0.80589145\",\n \"text\": \"def check_auth(username, password):\\n return username == app.config['auth_user'] and password == app.config['auth_pass']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"005c523d1148979efd6c3fb997f8f05e\",\n \"score\": \"0.8054224\",\n \"text\": \"def check_auth(username, password):\\n return username == app.config['ADMIN_USER'] and \\\\\\n password == app.config['ADMIN_PASS']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33995f82dc998b1a4cbba4c7ae9b1d54\",\n \"score\": \"0.8041471\",\n \"text\": \"def check_auth(username, password):\\r\\n try:\\r\\n if app.config['CONFIG']['bauth'][username]['pass'] == password:\\r\\n return True\\r\\n except Exception as e:\\r\\n rootLogger.info(e)\\r\\n\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbf5042e662f8384d2ef001ffb9d6bc7\",\n \"score\": \"0.803071\",\n \"text\": \"def check_auth(username, password):\\n return username == app.config['WC_LOGIN']['username'] and password == app.config['WC_LOGIN']['password']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2b0fcbfda8f4645dde183aaf900ecda\",\n \"score\": \"0.79818785\",\n \"text\": \"def check_auth(username, password):\\n return username == 'admin' and password == 'password'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9668121a9058001c88cd545af71b5473\",\n \"score\": \"0.79743207\",\n \"text\": \"def check_auth(username, password):\\n\\treturn username == 'admin' and password == 'secret'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7520eb64d79e1617b46d771d5d8bba67\",\n \"score\": \"0.7960091\",\n \"text\": \"def verify_credentials(username: str, password: str) -> bool:\\n return AuthenticationService.is_valid_credentials(username, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47faf12d29c3bef1bd576433e08ec9d1\",\n \"score\": \"0.7950642\",\n \"text\": \"def check_auth(username, password):\\n config = flask.current_app.config\\n try:\\n isuser = hmac.compare_digest(config['TTN_USER'], username)\\n ispass = hmac.compare_digest(config['TTN_PASSWORD'], password)\\n except KeyError:\\n flask.current_app.logger.warn(\\\"No credentials for ttn specified.\\\")\\n return False\\n return isuser and ispass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec389cc19fe32ac8113a2ecb234e2616\",\n \"score\": \"0.7939864\",\n \"text\": \"def check_auth(username, password):\\n return username == 'sammy' and password == 'BasicPassword!'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d9c2b007caa6ac342dcd9aa7f8129b6\",\n \"score\": \"0.7900145\",\n \"text\": \"def check_auth(username, password):\\n return username == ADMIN_USER and password == ADMIN_PASS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b3a41f5a1213b0c69cd56b5e69a0b16\",\n \"score\": \"0.78785694\",\n \"text\": \"def check_auth(username, password):\\n return username == 'admin' and password == 'secret'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b3a41f5a1213b0c69cd56b5e69a0b16\",\n \"score\": \"0.78785694\",\n \"text\": \"def check_auth(username, password):\\n return username == 'admin' and password == 'secret'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b3a41f5a1213b0c69cd56b5e69a0b16\",\n \"score\": \"0.78785694\",\n \"text\": \"def check_auth(username, password):\\n return username == 'admin' and password == 'secret'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a69976520ed4e7b735e358cb9b62fae\",\n \"score\": \"0.7878089\",\n \"text\": \"def check_auth(username, password):\\n return username == queryname and password == querypw\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5d4d9a16cb164b50e568e3820766de1\",\n \"score\": \"0.7854986\",\n \"text\": \"def check_auth(username, password):\\n return username == 'guest' and password == 'password'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c78fe569208edf64c83d86763bb5ece\",\n \"score\": \"0.7848427\",\n \"text\": \"def check_credentials(username, password):\\n dm = datamodel.TmData(cherrypy.request.app.config['/'])\\n user = dm.get_user(username)\\n if not user:\\n return \\\"Incorrect username or password\\\"\\n h = hashlib.md5()\\n h.update(password.encode('utf-8'))\\n pwd = h.hexdigest()\\n if user[1] != pwd:\\n return \\\"Incorrect username or password\\\"\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f6c477a47524f7e9406383ca56a5ed2\",\n \"score\": \"0.7742826\",\n \"text\": \"def check_auth(username, password):\\n return 'lme' == username and 'lme' == password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0a4316a23ba050cbedf3f52df5f4126\",\n \"score\": \"0.77216727\",\n \"text\": \"def check_auth(username, password):\\n\\n env_password = getenv('HTTP_PASSWORD', cast=str)\\n env_username = getenv('HTTP_USERNAME', cast=str)\\n\\n return username == env_username and password == env_password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87dd51bc6aa1aacb0052d73b69f2366b\",\n \"score\": \"0.77120525\",\n \"text\": \"def valid_login(username: str, password: str) -> bool:\\n account = execute_one_query(\\\"SELECT * FROM person WHERE username = %s\\\", username)\\n return account and check_password_hash(account[1], password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed0517500a80ed3c1f44d947a940853e\",\n \"score\": \"0.77034366\",\n \"text\": \"def check_credentials():\\n user = session.get('user', None)\\n user_auth = session.get('user_auth', None)\\n if user and user_auth:\\n return check_auth(user, user_auth)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3ede46eb03c97821391740393adc1f9\",\n \"score\": \"0.7692338\",\n \"text\": \"def check_auth(username, password): \\n if not User.objects(username=username):\\n return False\\n user = User.objects.get(username=username) \\n if not user or not user.verify_password(password):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae002e1d72dea0138022e98a68341cfc\",\n \"score\": \"0.76773405\",\n \"text\": \"def check_auth(username, password):\\n return username == 'TRAIN' and password == 'TuN3L'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b68542cf987b1ff2423e79672d13d1a\",\n \"score\": \"0.7650397\",\n \"text\": \"def check_user_password(username, password):\\n from x84.bbs import find_user, get_user\\n handle = find_user(username)\\n if handle is None:\\n return False\\n user = get_user(handle)\\n if user is None:\\n return False\\n return password and user.auth(password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"814cd55d2f7f50fdbdfdc42e265b526e\",\n \"score\": \"0.7596233\",\n \"text\": \"def checkAuthentication(username, password):\\n\\n # get the ticket\\n if getAuthTicket(username, password) is not None:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffc50175e5425d43d9e16b43031469d0\",\n \"score\": \"0.7582177\",\n \"text\": \"def validate(kls, username, password):\\n user = kls.get(username)\\n if user is None:\\n return False\\n else:\\n return user.check_password(str(password))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1231c21bd809d7bedef3f3290262ddca\",\n \"score\": \"0.7582004\",\n \"text\": \"def verify_password(username, password):\\n if not username or username != current_app.config['TOKEN']:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"447b26cf6bf91537cd2cec617ba7a269\",\n \"score\": \"0.75226456\",\n \"text\": \"def check_auth(password):\\n return password == PASSWORD\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0be4373630d3dbd6abb463a93bd7d4b6\",\n \"score\": \"0.75160027\",\n \"text\": \"def is_valid_credentials(username: str, password: str) -> bool:\\n\\n # If username is empty then no point attempting to validate, so return False\\n if not username:\\n return False\\n\\n try:\\n user = UserService.get_user_by_username(username)\\n except NotFound:\\n return False\\n\\n login_success = AuthenticationService._is_valid_password(password, user.password)\\n dmis.authenticated_user_id = user.user_id\\n\\n return login_success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f01712f42742c4a218c56ca0ad11ae\",\n \"score\": \"0.7477888\",\n \"text\": \"def check_creds(username, password):\\n\\n STMT = \\\"SELECT username, password_hash FROM users WHERE username = %s\\\"\\n \\n conn = database.get_conn()\\n cursor = conn.cursor()\\n cursor.execute(STMT, (username, ))\\n user = cursor.fetchone()\\n cursor.close()\\n conn.close()\\n\\n if user is None:\\n return False\\n\\n return argon2.verify(password, user[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec7529d23edab1339b9259d3061a7916\",\n \"score\": \"0.7470366\",\n \"text\": \"def check_auth(username, password):\\n return username == os.environ['HTUSER'] and password == os.environ[\\n 'HTAUTH']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b8ede1ad1bbdac523a4d20de10b873\",\n \"score\": \"0.74423933\",\n \"text\": \"def is_login_valid(username, password):\\n username_valid = False\\n flag = False\\n correct_password = get_password(username)\\n if correct_password: # Username exists\\n username_valid = True\\n if password == correct_password: # Password is correct\\n flag = True\\n return (username_valid, flag)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3da0b0dd6d37847da799eb2aee437e1d\",\n \"score\": \"0.74130994\",\n \"text\": \"def check_auth(username, password):\\n\\n email = username\\n password = password\\n cursor = mysql.connect().cursor()\\n cursor.execute(\\\"SELECT COUNT(*) FROM dav_users WHERE email='\\\" + email + \\\"' AND password='\\\" + password + \\\"'\\\")\\n data = cursor.fetchone()\\n\\n if data == 0:\\n return False\\n else:\\n return True\\n # return username == 'admin' and password == 'secret'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b506d4572a9adef794501f5dc408f8f1\",\n \"score\": \"0.7391268\",\n \"text\": \"def check_password(self, username, password):\\n assert self.dev_users\\n return self.dev_users.get(username) == password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a6b932f16e30417f0e2b130d69b655a\",\n \"score\": \"0.7327687\",\n \"text\": \"def check_auth(username, password):\\n User1 = User.query.filter(User.username == username).first()\\n if User1 and User1.password ==password:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d69b85a55166f8e50b7f999b2bc9626\",\n \"score\": \"0.73253906\",\n \"text\": \"def check_pw(username, password):\\n required_columns = \\\"passwd\\\"\\n where_clause = \\\"username = %s\\\"\\n params = (username, )\\n user_details = fetch_from_table(\\n required_columns=required_columns,\\n where_clause=where_clause,\\n params=params,\\n table=\\\"agent\\\")\\n\\n if user_details:\\n valid_pw = bytes(user_details[0][\\\"passwd\\\"], 'utf8')\\n return valid_pw == hashpw(password.encode(\\\"utf-8\\\"), valid_pw)\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d49979aadfebeefbcb353a5a31942d6\",\n \"score\": \"0.7315844\",\n \"text\": \"def check_password():\\n\\n credential = request.form.get(\\\"credential\\\")\\n password = request.form.get(\\\"password\\\")\\n\\n if password_is_correct(credential, password):\\n return \\\"true\\\"\\n else:\\n return \\\"false\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e5b33c699769d6af1a5736cef0f3c99\",\n \"score\": \"0.72665334\",\n \"text\": \"def check_credentials(self):\\n select_query = \\\"SELECT UserPassword FROM User WHERE Email = %s\\\"\\n self._cursor.execute(select_query, (self._email, )) \\n\\n fetched_data = self._cursor.fetchone()\\n\\n if not fetched_data:\\n print(\\\"Your email is not registered in the database.\\\")\\n return False\\n\\n fetched_password = fetched_data[0]\\n if fetched_password == self._password:\\n print(\\\"Congratz, u (\\\"+self._email+\\\") are logged in!\\\")\\n return True\\n else: \\n print(\\\"The password does not match!\\\")\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"994534ab53d1f89a5753bcf4d7923ee8\",\n \"score\": \"0.72379994\",\n \"text\": \"def check_password(username, password):\\r\\n query = \\\"\\\"\\\"SELECT password FROM clients\\r\\n WHERE username=?\\\"\\\"\\\"\\r\\n cursor.execute(query, (username, ))\\r\\n actual_pass = cursor.fetchone()\\r\\n if not actual_pass:\\r\\n return False\\r\\n if actual_pass[0] == hash_password(password):\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0419c0562dfb9a0fee650604db5b9018\",\n \"score\": \"0.7228656\",\n \"text\": \"def authenticate(self, user, password):\\n\\t\\tif user == \\\"root\\\" and password == \\\"toor\\\":\\n\\t\\t\\treturn True\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec46ef4363d7c435a1bda6bf2ea0dafe\",\n \"score\": \"0.7216104\",\n \"text\": \"def is_authorised(username, password):\\n return username == \\\"pokemon\\\" and password == \\\"go\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1918cb7cc1b10aae90bba46c8f5beee3\",\n \"score\": \"0.72094506\",\n \"text\": \"def validate(self, password: str) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66c5fbd3b2e0a05a256a6fe8a7847302\",\n \"score\": \"0.72084755\",\n \"text\": \"def valid_login(username, password):\\n db = get_db()\\n db.ping(True)\\n cur = db.cursor()\\n\\n try:\\n sql = \\\"SELECT password FROM users WHERE user_name = '{}';\\\".format(username)\\n cur.execute(sql)\\n for i in cur:\\n return check_password_hash(i[0], password)\\n return False\\n except mysql.connector.Error as err:\\n flash(err, \\\"set\\\")\\n return False\\n finally:\\n cur.close()\\n db.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb8570703752bb811e0432b369f9c61c\",\n \"score\": \"0.7195735\",\n \"text\": \"def verify_login(user: dict) -> bool:\\n username = user.get(\\\"username\\\")\\n password = user.get(\\\"password\\\")\\n if not username or not password:\\n return False\\n existing_user = User.query.filter_by(username=username).first()\\n if not existing_user:\\n return False\\n if check_password_hash(existing_user.password, password):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"830213656456299c9c4256830c112ed0\",\n \"score\": \"0.7161857\",\n \"text\": \"def _checkAuthentication(self, name, password, request):\\n # For the base implementation the password is checked\\n # against acl_users.\\n aclu = getToolByName(self, 'acl_users')\\n res = aclu.authenticate(name, password, request)\\n authentication_ok = res is not None\\n return authentication_ok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93fdb4deb35d7a8c37e38077c517b88e\",\n \"score\": \"0.71430695\",\n \"text\": \"def login_check(username, password):\\n user_pw = db.session.query(tables['organizers'].password).filter_by(username=username)\\n if user_pw.scalar() is None:\\n return False\\n split_list = user_pw.one().password.split('$')\\n salt = split_list[1]\\n password_hashed = split_list[2]\\n return get_hashed_text(salt, password) == password_hashed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78c90726b07c5d3446fdf18732e13c4e\",\n \"score\": \"0.71260417\",\n \"text\": \"def check_password(self, username, password, env=None):\\n if not username or not password:\\n return False\\n provider = self.get_sync_provider(fallback=True)\\n if username == 'root' and not provider.syncs_root:\\n provider = ajenti.usersync.AjentiSyncProvider.get()\\n\\n if not username in ajenti.config.tree.users:\\n return False\\n\\n try:\\n provider.sync()\\n except Exception as e:\\n logging.error(str(e))\\n\\n result = provider.check_password(username, password)\\n\\n provider_name = type(provider).__name__\\n\\n ip_notion = ''\\n ip = env.get('REMOTE_ADDR', None) if env else None\\n if ip:\\n ip_notion = ' from %s' % ip\\n\\n if not result:\\n msg = 'failed login attempt for %s (\\\"%s\\\") through %s%s' % \\\\\\n (username, password, provider_name, ip_notion)\\n syslog.syslog(syslog.LOG_WARNING, msg)\\n logging.warn(msg)\\n else:\\n msg = 'user %s logged in through %s%s' % (username, provider_name, ip_notion)\\n syslog.syslog(syslog.LOG_INFO, msg)\\n logging.info(msg)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63e89e3afead92b660887821ca6498b5\",\n \"score\": \"0.711976\",\n \"text\": \"def verify_password(username, password):\\n from .logic import get_users\\n\\n if hasattr(flask_g, 'username'):\\n if not flask_g.username == '' and username == '':\\n username = flask_g.username\\n if hasattr(flask_g, 'password'):\\n if not flask_g.password == '' and password == '':\\n password = flask_g.password\\n\\n if username == '':\\n app.logger.warning(\\\"Unauthorized access attempted!\\\")\\n return False\\n\\n if username == 'admin':\\n if 'ADMIN_PASS_FILE' in app.config:\\n\\n try:\\n f = open(app.config['ADMIN_PASS_FILE'], 'r')\\n app.config['ADMIN_PASS'] = str(f.read()).rstrip(\\\"\\\\r\\\\n\\\")\\n f.close()\\n except IOError as e:\\n print(\\\"Unable to read admin password file: %s\\\" % app.config['ADMIN_PASS_FILE'])\\n return False\\n\\n if check_password_hash(app.config['ADMIN_PASS'], str(password)):\\n flask_g.username = username\\n flask_g.password = password\\n return True\\n else:\\n return False\\n\\n if not username == '':\\n users_l = get_users(q_username=username, page=1, per_page=1)\\n if len(users_l) < 1:\\n print(\\\"User {} not found\\\".format(username))\\n return False\\n else:\\n user = users_l[0].as_dict()\\n\\n if check_password_hash(user['password'], str(password)):\\n flask_g.username = user['username']\\n flask_g.password = user['password']\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b6967ce9bc5a3ddd3be1c28bce63aea\",\n \"score\": \"0.70946634\",\n \"text\": \"def _verify_credentials(self):\\n r = requests.get(self.apiurl+\\\"account/verify_credentials.xml\\\", auth=HTTPBasicAuth(self._username, self._password),\\n headers=self.header)\\n if r.status_code != 200:\\n raise UserLoginFailed(\\\"Username or Password incorrect.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2462b13ac96abc341ad3c5579193fd6\",\n \"score\": \"0.7082045\",\n \"text\": \"def is_correct_credential_pair(username, password):\\n return bool(is_valid_username(username) and\\n is_valid_password(username, password) and\\n is_valid_credential_pair_db(username, password))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0385406cc62e39a30350a42023aad711\",\n \"score\": \"0.7081029\",\n \"text\": \"def verify_user(self, username: str, password: str):\\n result = self.__get_user(username=username)\\n db_password = result[0].get('password') if result else ''\\n return self.__verify_password(password=password, hashed_password=db_password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17c2819c6071104f921afcb5841c96e7\",\n \"score\": \"0.7079846\",\n \"text\": \"def is_valid_credential_pair_db(username, password):\\n connection = connect_db()\\n cursor = connection.cursor(buffered=True)\\n cursor.execute('SELECT password FROM Users WHERE username=%s LIMIT 1',\\n (username,))\\n result = cursor.fetchall()\\n close_db(connection)\\n return bool(result and\\n bcrypt.checkpw(password.encode(), result[0][0].encode()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb28f04baf938bd5818b023b9edac620\",\n \"score\": \"0.7055832\",\n \"text\": \"def check_auth(username, password):\\n return os.getenv('COMPOSE_USERNAME') == username and os.getenv('COMPOSE_PASSWORD') == password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d574ec08638be468ed43352cea3c4f53\",\n \"score\": \"0.7052717\",\n \"text\": \"def authenticate(username, password):\\n lines = list()\\n with open(\\\"passwords.txt\\\", \\\"r\\\") as f:\\n lines = f.readlines()\\n for line in lines:\\n line = line.rstrip(\\\"\\\\n\\\")\\n stored_username, stored_password = line.split(\\\":\\\")\\n if stored_username == username and stored_password == password:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd4834a3168bab442c75bf3bca12d766\",\n \"score\": \"0.705246\",\n \"text\": \"def check_auth(username, password):\\n dn = secrets.DN_STRING.format(username)\\n with ldap_connection(secrets.LDAP_URL) as conn:\\n try:\\n conn.simple_bind_s(dn, password)\\n conn.search_s(secrets.BASE_DN, ldap.SCOPE_ONELEVEL)\\n except ldap.LDAPError:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4413f7ad84a9b230bd7fb6a905c53766\",\n \"score\": \"0.70457727\",\n \"text\": \"def check_auth(username, password):\\r\\n for user in USER:\\r\\n if user['user']== username and user['pass']==password:\\r\\n return True\\r\\n #return username == 'admin' and password == 'secret'\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e38bfdb05be81df39fd603bc71eba43\",\n \"score\": \"0.7042962\",\n \"text\": \"def user_exist(self,password):\\n if self.password == password:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef268e4258bae4751ed7abd0aee1c1a\",\n \"score\": \"0.70418\",\n \"text\": \"def verify_password(username, password):\\n if not username:\\n # Warning/info?\\n return False\\n single_user_data = userdata.get(username)\\n if single_user_data:\\n stored_pw = single_user_data.get(PASSWORD)\\n assert stored_pw is not None, 'Server error: user data must contain a password!'\\n res = sha256_crypt.verify(password, stored_pw)\\n else:\\n logger.info('User \\\"{}\\\" does not exist'.format(username))\\n res = False\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1fce9f617636a7d46d36f920c2107ce\",\n \"score\": \"0.7037066\",\n \"text\": \"def verify_login(username, password, user_data):\\n # Encode password using salt\\n salted_password = password + user_data.salt\\n encoded_password = hashlib.sha512(salted_password.encode()).hexdigest()\\n\\n # Verify if encoded password corresponds to user's password in the database\\n # will return True if password is correct\\n return compare_digest(encoded_password, user_data.password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e1c33ab495b0edb66ff01e171a8afc4\",\n \"score\": \"0.7033827\",\n \"text\": \"def authenticate(self, entusername, entpassword):\\r\\n self.EnteredUsername = entusername\\r\\n self.EnteredPassword = entpassword\\r\\n\\r\\n if self.EnteredUsername in self.user_accounts:\\r\\n if self.user_accounts[self.EnteredUsername] == self.EnteredPassword:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"848df66ade1245604b7b5c501906ca45\",\n \"score\": \"0.70296574\",\n \"text\": \"def valid_credentials(_login, _password):\\n center = Center.query.filter_by(_login=_login, _password=_password).first()\\n if center:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7109876df3819735417d20787a444ad0\",\n \"score\": \"0.6999185\",\n \"text\": \"def verifypwd(username, password):\\n dbuser = db.GqlQuery(\\\"SELECT * FROM LoginData\\\")\\n\\n for x in dbuser:\\n if x.username == username:\\n pwhash = str(x.password)\\n return valid_pw(username, password, pwhash)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"900e295641b53a82f272e4d2493fb403\",\n \"score\": \"0.69919485\",\n \"text\": \"def _test_auth(self):\\n user = self.get_user()\\n try:\\n if \\\"Invalid username/password\\\" in user.json().values():\\n return False\\n else:\\n return True\\n except JSONDecodeError:\\n warnings.warn(\\\"ESPA Service appears to be offline! Response: {}\\\".format(user.json()))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb702d3e3e41cdac8b27be61f4c7dff2\",\n \"score\": \"0.69806623\",\n \"text\": \"def check_user_pass(usr, pwd):\\n\\n ret = AUTHDB.get(usr, None)\\n if (ret is None) or (ret != pwd):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c80ff91ca8830b1f109717aaefc0055a\",\n \"score\": \"0.696238\",\n \"text\": \"def is_authentic(self, login, password):\\n \\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d0b86405a808bff61c0cdc9a7047a50\",\n \"score\": \"0.6959467\",\n \"text\": \"def valid_login(self, email: str, password: str) -> bool:\\n if type(email) is not str or type(password) is not str:\\n return False\\n\\n try:\\n user = self._db.find_user_by(email=email)\\n pwd = bytes(password, 'utf-8')\\n if bcrypt.checkpw(pwd, user.hashed_password):\\n return True\\n else:\\n return False\\n except NoResultFound:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"198a25037c736d6c0ca5ca44c4a63feb\",\n \"score\": \"0.6955093\",\n \"text\": \"def is_user_valid(self, username, password):\\r\\n temp_hashed_password = self.get_hashed_password(password)\\r\\n user = self.session.query(User).filter(User.user_name == username).filter(\\r\\n User.password == temp_hashed_password)\\r\\n return user is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa546a6ee275144385ba449e19055e7d\",\n \"score\": \"0.69535583\",\n \"text\": \"def is_user_authenticated(self, username, password):\\n user = self.db.users.find_one({\\n \\\"username\\\": username\\n })\\n if user:\\n return user[\\\"password\\\"] == Util.sha256(password)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c377d37f67170c825e6d2c4f7046cea8\",\n \"score\": \"0.695223\",\n \"text\": \"def valid_login(self, email: str, password: str) -> bool:\\n try:\\n u = self._db.find_user_by(email=email)\\n except NoResultFound:\\n return False\\n return bcrypt.checkpw(password.encode(), u.hashed_password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23cdda55fbb9c9baf3f5eefbda2488e3\",\n \"score\": \"0.69403476\",\n \"text\": \"def authenticate_user(username):\\n return True if get_user_by_username(username) else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52969e760e13c61b2ac774a37f6f7946\",\n \"score\": \"0.69397795\",\n \"text\": \"def check_my_users(user):\\n\\t user_data = my_users.get(user['username'])\\n\\t if not user_data:\\n\\t return False # <--- invalid credentials\\n\\t elif user_data.get('password') == user['password']:\\n\\t return True # <--- user is logged in!\\n\\t\\n\\t return False # <--- invalid credentials\",\n \"title\": \"\"\n },\n {\n \"docid\": \"279ca4a1936aeef9b6da6108743b59fe\",\n \"score\": \"0.6919977\",\n \"text\": \"def validate_user(username, password, cfpassword, email):\\n result = is_username_valid(username) and is_password_valid(password)\\n result = result and is_cfpassword_valid(password, cfpassword)\\n result = result and is_email_valid(email)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"114294879d2d9b80758b3c5919a8181e\",\n \"score\": \"0.6884935\",\n \"text\": \"def verify_password(database: str, username: str, password: str) -> bool:\\n db = DatabaseController(database)\\n user_db_data = db.select_user_data(username)\\n return user_db_data[0] == HashPasswordHelper.hash_password(password, user_db_data[1], user_db_data[2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b219a7ac089beabf2a4801f68addad\",\n \"score\": \"0.68764126\",\n \"text\": \"def check_login_good(username, password):\\n # Note that we are NOT building our own query via string formatting --\\n # we are allowing the psycopg2 library to handle escaping the user inputs\\n query = (\\n \\\"SELECT id, username, password FROM users \\\"\\n \\\"WHERE username = %s AND password = %s\\\"\\n )\\n cursor.execute(query, (username, password))\\n result = cursor.fetchone()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6448b8288d6ce55e370e95527018b7f\",\n \"score\": \"0.6867732\",\n \"text\": \"def auth(self):\\n if self.username is not None and self.password is not None:\\n self.session.auth = (self.username, self.password)\\n auth_response = self.session.get(self.url + '/')\\n if auth_response.status_code != 401:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ac12cd480c3385905022e8503b3559c\",\n \"score\": \"0.68671745\",\n \"text\": \"def authenticate (username, password):\\n try:\\n user = User.objects.get(username=username)\\n\\n if user.check_password(password):\\n return user\\n else:\\n return False\\n\\n except:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"144a7db65f820a23ccc6145cd39f280c\",\n \"score\": \"0.6843814\",\n \"text\": \"def check_for_login(error):\\n print(\\\"Checking username and password combination.\\\")\\n if 'password authentication failed' in str(error):\\n print(\\\" Username/password combination is invalid.\\\")\\n print(\\\" Try checking your username and password. \\\"\\n \\\"One or both are likely incorrect.\\\")\\n print_error_text(error)\\n return True\\n print(\\\" ... No issue found.\\\")\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4214ed7d065aaf3d774209df473dd7d1\",\n \"score\": \"0.68409956\",\n \"text\": \"def login(self, username, password):\\n for registrant in self.accounts:\\n if username in registrant['username'] and password in registrant['password']:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6389af80d922c12980c8f284de769aca\",\n \"score\": \"0.68379873\",\n \"text\": \"def valid_login(self, email: str, password: str) -> bool:\\n user = self._db.find_user_by(email)\\n if user.email == email:\\n if bcrypt.checkpw(password.encode('utf-8'), user.hashed_password):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6130aeaf826a536c28d22dceb80ace99\",\n \"score\": \"0.6819086\",\n \"text\": \"def verify_pw(username, password):\\n if not UserExist(username):\\n return False\\n hashed_pw = users.find({\\n \\\"Username\\\": username\\n })[0][\\\"Password\\\"]\\n if bcrypt.hashpw(password.encode('utf8'), hashed_pw) == hashed_pw:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32f312f94f1bead7866b176d07777857\",\n \"score\": \"0.6814118\",\n \"text\": \"def checkLogin(self):\\n\\t\\tprint(username)\\n\\t\\tprint(password)\\n\\n\\t\\tusernameInput = input('\\\\nWhat is your username?\\\\n')\\n\\t\\tpasswordInput = input('\\\\nWhat is your password?\\\\n')\\n\\n\\t\\t#Checks if username and password is correct\\n\\t\\tif usernameInput == developer.username:\\n\\n\\t\\t\\tif passwordInput == developer.password:\\n\\t\\t\\t\\tprint('\\\\nSuccess! You are now in the developer menu!')\\n\\t\\t\\t\\tself.loggedIn = True\\n\\t\\t\\t\\tuser = developer()\\n\\t\\t\\t\\tmenu.developerMenu()\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint('Error: Wrong username or password')\\n\\t\\t\\t\\tmenu.mainMenu()\\n\\n\\t\\telse:\\n\\t\\t\\tprint('Error: Wrong username or password')\\n\\t\\t\\tmenu.mainMenu()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dec8e5477c8fa362f57517b4d88e337\",\n \"score\": \"0.6810149\",\n \"text\": \"def check_login(username=None, password=None):\\n print(\\\"check_login: username=%s password=%s\\\" % (username, password))\\n cl_response_queue = queue.Queue()\\n message_data = database.DatabaseDataMessage(\\n table_name=\\\"webserver\\\",\\n field=\\\"\\\"\\\" \\\"{}\\\" \\\"\\\"\\\".format(\\\"WEBSERVER_USER_NAME\\\"),\\n data=\\\"\\\"\\\" \\\"{}\\\" \\\"\\\"\\\".format(username),\\n caller_queue=cl_response_queue)\\n message = database.DatabaseMessage(\\n command=database.DatabaseCommand.DB_SELECT_DATA,\\n message=message_data)\\n db_queue.put(message)\\n db_task.join(timeout=0.65)\\n\\n print(\\\"check_login: Wait on password response\\\")\\n while cl_response_queue.empty() is True:\\n pass\\n\\n print(\\\"check_login: Got password response\\\")\\n user_response = cl_response_queue.get()\\n print(user_response)\\n\\n # Make sure there is some sort of response before\\n # parsing it.\\n if len(user_response):\\n db_username = user_response[0][1]\\n db_password = user_response[0][2]\\n print(\\\"DB Username = %s\\\" % (db_username))\\n print(\\\"DB Password = %s\\\" % (db_password))\\n return (db_username == username and db_password == password)\\n else:\\n # There was no response so no chance of logging in or parsing\\n # the response\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aeb5e0b01f3001fce3f588e0b959fee\",\n \"score\": \"0.68085146\",\n \"text\": \"def check_auth(username, password, access):\\n u = users.get(username,None)\\n if u:\\n if u[0] == password and (access in u[1] or u[1][0]==\\\"all\\\"):\\n return username and password\\n else:\\n return None\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3f147f62833857e9aa2f894693a0466\",\n \"score\": \"0.67997265\",\n \"text\": \"def checkpass():\\n username = request.args.get(\\\"username\\\")\\n password = request.args.get(\\\"password\\\")\\n\\n if not username:\\n return jsonify(False)\\n\\n # Ensure password was submitted\\n elif not password:\\n return jsonify(False)\\n\\n # Query database for username\\n rows = db.execute(\\\"SELECT * FROM users WHERE username=:username\\\", username=username)\\n\\n # Ensure username exists and password is correct\\n if len(rows) != 1 or not check_password_hash(rows[0][\\\"hash\\\"], password):\\n return jsonify(False)\\n\\n # Remember which user has logged in\\n session[\\\"user_id\\\"] = rows[0][\\\"id\\\"]\\n\\n # Redirect user to home page\\n return jsonify(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f5c589a7ee36d73d3a1cffcb5535db8\",\n \"score\": \"0.67922425\",\n \"text\": \"def check(user, password, log=True):\\n\\t\\tUser.init()\\n\\n\\t\\tuser = user.lower()\\n\\n\\t\\tif User.instance.user == b\\\"\\\":\\n\\t\\t\\treturn True\\n\\t\\telif user == User.instance.user:\\n\\t\\t\\tif encryption.gethash(password) == User.instance.password:\\n\\t\\t\\t\\tif log == True:\\n\\t\\t\\t\\t\\tUser.loginState[0] = True\\n\\t\\t\\t\\treturn True\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif log == True:\\n\\t\\t\\t\\t\\tUser.loginState[0] = False\\n\\t\\t\\t\\t\\tuseful.syslog(\\\"Login failed, wrong password for user '%s'\\\"%useful.tostrings(user))\\n\\t\\telse:\\n\\t\\t\\tif user != b\\\"\\\":\\n\\t\\t\\t\\tif log == True:\\n\\t\\t\\t\\t\\tUser.loginState[0] = False\\n\\t\\t\\t\\t\\tuseful.syslog(\\\"Login failed, unkwnon user '%s'\\\"%useful.tostrings(user))\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4605d4ab39bfb3db7b671200a5ca6645\",\n \"score\": \"0.6778981\",\n \"text\": \"def check_credentials(server, username, password):\\n\\tbasicauth_header = urllib2.base64.b64encode('%s:%s' % (username, password))\\n\\turl = 'https://%s/certsrv/' % server\\n\\treq = urllib2.Request(url)\\n\\treq.add_header(\\\"Authorization\\\", \\\"Basic %s\\\" % basicauth_header)\\n\\ttry:\\n\\t\\turllib2.urlopen(req)\\n\\texcept urllib2.HTTPError as error:\\n\\t\\tif error.code == 401:\\n\\t\\t\\treturn False\\n\\t\\telse:\\n\\t\\t\\traise\\n\\telse:\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"569ccdb39615e6e295144f7cd45cbf69\",\n \"score\": \"0.6774533\",\n \"text\": \"def verify_password_or_access_token(username_or_token: str,\\n password: str) -> bool:\\n r = requests.get(\\n f'{USER_SERVICE}/user-auth',\\n json={\\n 'username_or_token': username_or_token,\\n 'password': password\\n }\\n )\\n if r.status_code == 401:\\n return False\\n # Save the found username for the current request processing\\n g.username = r.json()['data']\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6407f0a51f10128a1712af7f66b997e9\",\n \"score\": \"0.67520094\",\n \"text\": \"def is_valid_password(username, password):\\n return bool(password and username.lower() not in password.lower() and\\n len(password) <= 72)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb7a809428078c942ec599fb30d9cac\",\n \"score\": \"0.67436266\",\n \"text\": \"def check_credentials(self, **kwargs):\\n if not (self.cfg(\\\"username\\\") and self.cfg(\\\"password\\\") and self.cfg(\\\"prefix\\\")):\\n warnings.warn(\\n f\\\"The {self.__class__.__name__} is misconfigured. Please \\\"\\n f\\\"set {self.cfgkey('username')}, {self.cfgkey('password')}\\\"\\n f\\\" and {self.cfgkey('prefix')} in your configuration.\\\",\\n UserWarning,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7123dd6a9933176db5a7a43e4319591\",\n \"score\": \"0.6729454\",\n \"text\": \"def check_login_credentials():\\n\\n # Look at the stdout stream to see the values coming in from the login form\\n print('request.form values from login_form.html')\\n for x in request.form:\\n print(' {}: {}'.format(x, request.form[x]))\\n\\n\\n credentials_ok = True # Any failed test reverses value to False\\n\\n # Check that data entry fields exist and have some value in them\\n\\n # if '/check_credentials' route invoked without using login_prompt page\\n # then the request.form dictionary will not have expected entries\\n if 'user_id' not in request.form:\\n credentials_ok = False\\n\\n elif 'password' not in request.form:\\n credentials_ok = False\\n\\n # if one of the request.form login entry fields is left empty\\n elif not request.form['user_id']:\\n credentials_ok = False\\n\\n elif not request.form['password']:\\n credentials_ok = False\\n\\n # At this point, take the login credentials to do a database check\\n # 1. retrieve a user from the database with user_id as the key\\n # 2. encrypt the password\\n # 3. check that the encrypted password is correct for the user\\n\\n elif not user_in_database():\\n credentials_ok = False\\n\\n elif not user_password_match():\\n credentials_ok = False\\n\\n if credentials_ok:\\n return redirect(url_for('first_action'))\\n\\n flash('Username-Password combination not valid.')\\n return redirect(url_for('present_login_form'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f54350ddf26e630bc18a08a5af4bde64\",\n \"score\": \"0.67226404\",\n \"text\": \"def validate_credentials(username, password):\\n user = db_conn.query_db('SELECT * FROM `xeno`.`users` WHERE `userid`=%s', [username], True)\\n\\n #print \\\"Validating: \\\", username, \\\":\\\", password\\n if user is None: # not a valid username\\n return None\\n\\n stored_salted_password = user[\\\"hpass\\\"]\\n end_salt_pos = stored_salted_password.find('==') + 2\\n salt = stored_salted_password[0:end_salt_pos]\\n # stored_password = stored_salted_password[end_salt_pos:] # this isnt needed I dont think...\\n\\n if stored_salted_password == encode_password(password, salt):\\n userid = user[\\\"userid\\\"]\\n return User(userid, user)\\n ''' This is a simple test.\\n if username == \\\"Xeno\\\" and password == \\\"cars\\\":\\n userid = \\\"testing\\\"\\n return User(userid)\\n '''\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d6fa7a48c6d3544fc6437c88815340a\",\n \"score\": \"0.67139715\",\n \"text\": \"def check_basic_auth(user, passwd):\\n\\n auth = request.authorization\\n return auth and auth.username == user and auth.password == passwd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f4712fffe921b6058d3168ffbf237bb\",\n \"score\": \"0.67130435\",\n \"text\": \"def password_check(username):\\n password = input(\\\"Password: \\\")\\n check_status = check_in_file(username, password, mode='password')\\n return check_status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ffbe9824ca8f5d99011a176cea79b17\",\n \"score\": \"0.6659585\",\n \"text\": \"def has_password(self, password):\\n return password==self.user_name\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":482,"cells":{"query_id":{"kind":"string","value":"ddabbd7b41297deab1ffbb5f91311cdb"},"query":{"kind":"string","value":"Destroy all the stores."},"positive_passages":{"kind":"list like","value":[{"docid":"f9ebd72c0ef3741a5c880058e8813d2e","score":"0.81792104","text":"def destroy_stores(self):\n shutil.rmtree(os.path.join(DATA_STORE, self.name))","title":""}],"string":"[\n {\n \"docid\": \"f9ebd72c0ef3741a5c880058e8813d2e\",\n \"score\": \"0.81792104\",\n \"text\": \"def destroy_stores(self):\\n shutil.rmtree(os.path.join(DATA_STORE, self.name))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9f429cbeb9566b1fe28e72709e16eb8b","score":"0.7163623","text":"def cleanupStore(self):\n if self.store:\n self.store.cleanup()","title":""},{"docid":"ec3f31f5e70bd594f86b9c285dbaf905","score":"0.64436644","text":"def clean(self):\n logger.debug('Deleting all runtimes')\n\n runtimes = self.list_runtimes()\n\n for runtime in runtimes:\n runtime_name, runtime_memory = runtime\n self.delete_runtime(runtime_name, runtime_memory)\n\n layers = self._list_layers()\n for layer_name, _ in layers:\n self._delete_layer(layer_name)","title":""},{"docid":"e21915ec1b9a39cc746035ba84b3725f","score":"0.6295653","text":"def clear(self):\n self.delete_objects()\n self.del_hooks()\n self.broker.reset()\n self.emane.reset()","title":""},{"docid":"d547b880caad6e370c62b5bd174772fa","score":"0.6247881","text":"def destroy(self):\n for container in self._containers.copy():\n container.destroy()\n\n assert len(self._containers) == 0","title":""},{"docid":"c01beac2a818ab23b4be8ee58870420c","score":"0.62467194","text":"def delete_all(self):\n pass","title":""},{"docid":"7259762281128ac5d618abd7857b24a7","score":"0.62402064","text":"def deleteAll(self):\n return prod_collection.delete_many({})","title":""},{"docid":"7f7d0267d4f9144f253fb2901bbb22f5","score":"0.61734307","text":"def clear(self) -> None:\n\n if self.pipeline._transformer:\n for idx in self._store:\n self.pipeline(idx).unpersist(blocking=True)\n\n self._store.clear()","title":""},{"docid":"b174da2c9ef863f3a564091664f50219","score":"0.61593086","text":"def clear_all_caches(self):\n\n for key in self._registry:\n self.clear_cache(key)","title":""},{"docid":"6c382e30a56ff26aecba4a1aec40478e","score":"0.6154785","text":"def destroy_all(self):\r\n\r\n [widget.destroy() for widget in self.widgets]","title":""},{"docid":"241375b422701040bf7b619346cd64a5","score":"0.6136179","text":"def remove_all_tools(self):\n self.db_manager.remove_all_tools()","title":""},{"docid":"f15f55d191469c3e863bc5b240868c05","score":"0.6100213","text":"def remove_all_tools(self):\n logger = Logger().get()\n logger.debug(\"remove all tools\")\n if self.db_name != \"test\":\n logger.error(\"Cannot remove all tools unless current db\"\n \" is \\\"test\\\"!\")\n return\n\n try:\n # Get ids of all jobs\n delete_result = self.__tools_collection.delete_many({})\n\n # Set the flag of initialization to False\n metadata = self.__metadata_collection.find()[0]\n metadata['has_initialized_pharos'] = False\n\n condition = {\"_id\": metadata['_id']}\n self.__metadata_collection.update_one(condition, {\"$set\": metadata})\n\n logger.debug(f\"Remove {delete_result.deleted_count} tools.\")\n except Exception as e:\n logger.error(f\"something wrong in remove_all_tools, Exception: {e}\")","title":""},{"docid":"b97ecf50d9921e0b02f175d70ef30e07","score":"0.6092327","text":"def remove_all(cls):\n for document in cls.database:\n document.delete()","title":""},{"docid":"1278c3ee54bd2f566fdbe1156fe16cec","score":"0.6078893","text":"def purge_all(self):\r\n raise NotImplementedError(\"Sub-classes of Manager should implement\"\r\n \" .purge_all()\")","title":""},{"docid":"062869e11510e52d83aeae05c73b27cc","score":"0.6078348","text":"def clean(self):\n for loc in self.locations.values():\n shutil.rmtree(loc)","title":""},{"docid":"8b200d11687334a84c3ab3439eaa58b3","score":"0.6061952","text":"def _purge_all(self):\n for key in self.keys():\n self.delete(key)","title":""},{"docid":"b8f4f2e8247a69ccd5f4c601d2e7e48c","score":"0.6052412","text":"def empty_all_models():\n # Iterate through LetterGameInsertions the models and call the empty model method to clean them out\n for model in DeleteData.models:\n DeleteData.empty_model(model)","title":""},{"docid":"b92203da3e6e21e5d0747190e695bbe0","score":"0.60521567","text":"def cleanUp(self):\n for machine in self._machines.copy():\n self.destroyMachine(machine)\n\n assert len(self._machines) == 0","title":""},{"docid":"896b9f3bae108884076d7ae2ded820af","score":"0.60494834","text":"def cleanall(self):\n return self._manager.cleanall()","title":""},{"docid":"9439d46bc2d7f519a0d27e468fd84050","score":"0.6023564","text":"def unsubscribe_all(self):\n for sub in self.session:\n sub.destroy()","title":""},{"docid":"fadab4e114a45544c7c6b47e9f7c447e","score":"0.6018372","text":"def clean_up_service(self):\n for service in self.service_list:\n self.catalog_api.delete_service(service['id'])","title":""},{"docid":"329a8fbe9867a8778c6041e6341182dd","score":"0.6014507","text":"def clear(self) -> None:\n self._store.clear()","title":""},{"docid":"8c9f324751da5edf50b0939caa1cbb02","score":"0.6001814","text":"def clear_all(self):\n keys = self._analytics_backend.keys()\n\n for key in itertools.chain(*keys):\n with self._analytics_backend.map() as conn:\n if key.startswith(self._prefix):\n conn.delete(key)","title":""},{"docid":"3f33f40c07048d4dc7dac26ab2c3494b","score":"0.5997771","text":"def purge_all(delete=False, verbosity=0):\n _location_purge_all(delete, verbosity)\n _storage_purge_all(delete, verbosity)","title":""},{"docid":"fef5742de59ff9ebc5e5db991688ff8b","score":"0.5980893","text":"def _purge_all(self):\r\n for key in self.keys():\r\n self.delete(key)","title":""},{"docid":"565b08b6ed9cd1cb6c1f9013596427bc","score":"0.5976179","text":"def clear_db(self):\n People.objects.all().delete()\n Company.objects.all().delete()\n Tag.objects.all().delete()\n Food.objects.all().delete()\n self.stdout.write(self.style.SUCCESS('Successfully cleared all data'))","title":""},{"docid":"1afcd44503928444c2d6b7d94a931773","score":"0.5959921","text":"def delete_all(self):\n query = self.db.session.query(models.Work)\n for work_obj in query:\n self.db.session.delete(work_obj)\n self.db.session.commit()","title":""},{"docid":"dd541506e1e68ae11f83e050721cb6f2","score":"0.5946315","text":"def destroy_tables(self):\n tables = self.table_names\n for table in tables:\n self.drop(table)\n self.save()\n self.close()","title":""},{"docid":"7d73a274add37f74b4e5fd8c9c824a2f","score":"0.5940249","text":"def creasesClearAll(self):\n \n pass","title":""},{"docid":"f695fd9477073ccfeb5f8baee49c4b6c","score":"0.59389484","text":"def delete_everything(self):\n self.instance.delete_everything()","title":""},{"docid":"507e4a63e6378a8d59c5985e66e0a0bc","score":"0.59107876","text":"def clear(self, verbose=False):\n with session_scope() as session:\n numSym = session.query(Synonym).delete()\n numPosts = session.query(Post).delete()\n numRels = session.query(SynonymPostAssociation).delete()\n session.commit()\n if verbose:\n print(\n f'''Successfully deleted all data from database: \n {numSym} synonyms, \n {numPosts} posts, \n and {numRels} synonym<->post relations.''')","title":""},{"docid":"f1eff50e99f0b114297be0e5db55faa1","score":"0.59047455","text":"def cleanup():\n remove_all_objects()\n remove_orphan_data()","title":""},{"docid":"b06dc30f7b212f3e1e2fc5455bf75380","score":"0.58954585","text":"async def reset_all():\n return State().delete().execute()","title":""},{"docid":"a2c5041c401f98e22ab39a1a4a26d325","score":"0.58738637","text":"def clear(self):\n\t\tself.store = {}\n\t\tif not self.lazysave: self.save()","title":""},{"docid":"15d8a1ec2505759dbd6949f70096a1a1","score":"0.5869648","text":"def clearall(self):\n for res in self._getResNames():\n self.clear(res)","title":""},{"docid":"43e73da3a2b6e12eb142862ccd5da63f","score":"0.5857684","text":"def destroy_all(self, destroy_canvas: bool = False) -> None:\n\n if destroy_canvas:\n self.commands.append({\"$type\": \"destroy_ui_canvas\",\n \"canvas_id\": self._canvas_id})\n else:\n for ui_id in self._ui_ids:\n self.commands.append({\"$type\": \"destroy_ui_element\",\n \"id\": ui_id,\n \"canvas_id\": self._canvas_id})\n self._ui_ids.clear()","title":""},{"docid":"a4a2b8e90251c955f08c0537bcf3709d","score":"0.58495027","text":"def cleanup(self):\n helpers.delete_objs_parallel(pod.get_all_pods(namespace=self.namespace))\n helpers.delete_objs_parallel(self.all_pvc_obj)\n self.rbd_sc_obj.delete()\n self.cephfs_sc_obj.delete()","title":""},{"docid":"cd01117bdd068e1fd4c5c8112c2a836e","score":"0.58392483","text":"def delete_all(self):\n allC=[]\n self.__storeToFile(allC)","title":""},{"docid":"740cecd9460007af81d4b4f2aff5daeb","score":"0.5839114","text":"def _purge_all(self):\r\n raise NotImplementedError(\"Sub-classes of Manager should implement\"\r\n \" ._purge_all()\")","title":""},{"docid":"5a624af741000fd66e10a3b861171b81","score":"0.58384585","text":"def clear(self):\r\n logger.debug('Clearing all models from memory.')\r\n self.java.clear()","title":""},{"docid":"7ed3ffff4232c717f930460d161ab710","score":"0.5836243","text":"def reset_dbs(self) -> None:\n self.mongo().clear_db()\n self.redis().clear_db()","title":""},{"docid":"5b3939ee6d50ca3f4a06a579735ecf80","score":"0.58268785","text":"def delete_all_sessions():\n AccountSession.delete().execute()","title":""},{"docid":"e3c2bd1f6de5c895d1d729d105ec3fdb","score":"0.5819684","text":"def delete_all(self):\n logger.info('Flushing database...')\n logger.debug('Call Successful: %s', 'delete_all: delete_all call successful', extra=d)\n logger.debug('Flushall Attempt: %s', 'delete_all: attempting to flush all items from queue', extra=d)\n self.r.flushall()\n logger.info('Database emptied')","title":""},{"docid":"855bbfdecf264f065476d0c732d28eb4","score":"0.581262","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":"b607f351896ac1709f52af8b65419b2d","score":"0.57891786","text":"def fusion_srm_ilo_api_delete_all_sso_certs(self):\n self.ilo_client.delete_all_sso_certs()","title":""},{"docid":"791db763472eeb7964fe9e2d8d607817","score":"0.5788232","text":"def deregister_all_nodes(self):\n\n self.nodes = set()\n storage.node.remove_all()","title":""},{"docid":"37efc2934bf38e64ca442dccd4de76f8","score":"0.5777878","text":"def delete_all(self):\n self._exec_delete_all(self.table)","title":""},{"docid":"76ce97885fae296943b806281bec402e","score":"0.57773423","text":"def unload(self):\n for component in self.components.values():\n component.destroy()\n\n self.on_unloaded()","title":""},{"docid":"933b6b4c38ed104c15c1619907da5450","score":"0.57717335","text":"def del_all_saved(self):\n self.saved = dict()","title":""},{"docid":"6bf98e4b8090950c35ca700291d60484","score":"0.5754316","text":"def clear_data():\n Student.objects.all().delete()\n Teacher.objects.all().delete()\n Homework.objects.all().delete()\n HomeworkResult.objects.all().delete()","title":""},{"docid":"1fabc363496190d8e0edf4c6e5f81a81","score":"0.5753621","text":"def _purge_all(self):\r\n # Given the races around connection closing, the easiest thing to do\r\n # here is to create a new manager with the same config for cleanup\r\n # operations.\r\n new_manager = yield self.from_config(self._config)\r\n # If we're a submanager we might have a different key prefix.\r\n new_manager._key_prefix = self._key_prefix\r\n yield new_manager._do_purge()\r\n yield new_manager._close()","title":""},{"docid":"a5b7ff978406cef2e04d95dab3fce72d","score":"0.5737473","text":"def _purge_all(self):\n # Given the races around connection closing, the easiest thing to do\n # here is to create a new manager with the same config for cleanup\n # operations.\n new_manager = yield self.from_config(self._config)\n # If we're a submanager we might have a different key prefix.\n new_manager._key_prefix = self._key_prefix\n yield new_manager._do_purge()\n yield new_manager._close()","title":""},{"docid":"ba2e9432643075f9de8aa5d9613e0781","score":"0.5735537","text":"def clear_all(self):","title":""},{"docid":"e14ef081f832e7e5db92e12740e4aae6","score":"0.57301855","text":"def delete(self):\n for vertex_list in self._vertex_lists:\n vertex_list.delete()\n self._vertex_lists.clear()\n\n for box in self._boxes:\n box.delete(self)\n self._boxes.clear()","title":""},{"docid":"135a9aeee5eeb23d1a9c2590402e5b9d","score":"0.57231396","text":"def destroy_all_containers(self):\n self.log.info(\"Destroying %d containers\", len(self.container))\n errors = self.destroy_containers(self.container)\n if errors:\n self.log.error(\n \"Errors detected destroying %d containers: %d\",\n len(self.container), len(errors))\n for error in errors:\n self.log.error(\" %s\", error)\n self.container = []\n return len(errors) == 0","title":""},{"docid":"44e224f8432087fc2fd00ebadf18bc24","score":"0.5716785","text":"def del_all_V1(self):\n\n self.del_all_file_V1()\n self.del_all_collection_V1()","title":""},{"docid":"f7d134f1cedb4d19af724b4e5d541368","score":"0.57161874","text":"def clear_all_queues(self):\n return self.client.delete_queues()","title":""},{"docid":"c41479e4b7145f079ea96a7926ca6ed2","score":"0.56908864","text":"def destroyAllPanels (self):\n\n panels = (self.comparePanel, self.colorPanel, self.findPanel, self.fontPanel, self.prefsPanel)\n\n for panel in panels:\n if panel:\n panel.top.destroy()","title":""},{"docid":"a11f1b4fe1ab1e4ff784dc0d809b8ce8","score":"0.56860757","text":"def cleanup_manager(deco, self):\n yield self.manager.purge_all()\n yield self.manager.close_manager()","title":""},{"docid":"d3484881918678f43d19e8ecb93050c2","score":"0.56849766","text":"def cleanup(self):\n for process in self.processes:\n process.terminate()","title":""},{"docid":"f4f5361a1e0e3140495dd046c60d0886","score":"0.56846565","text":"def system_reset(self, system_id):\n self.db.delete_all_documents('projects')\n self.db.delete_all_documents('experiments')\n self.db.delete_all_documents('experimentsMetrics')\n self.delete_queue()\n self.delete_pool()","title":""},{"docid":"19e9ca50f8ac44dd8ccb18210139324d","score":"0.5680037","text":"def delete_all_servers():\n nectar.delete_all_servers()","title":""},{"docid":"918caddfe098989be6b465713eee58f2","score":"0.5674994","text":"def destroy():\n subprocess.check_call([settings.APP_NAME, 'main', 'destroy'])\n subprocess.check_call([settings.APP_NAME, 'addons', 'destroy'])\n subprocess.check_call([settings.APP_NAME, 'deis', 'destroy'])","title":""},{"docid":"a43c7a24bde13ce662b3657e92bcf837","score":"0.5667435","text":"def cleardb():\n\n with app.app_context():\n db.drop_all()\n print 'Database cleared'","title":""},{"docid":"f5c3d66d29c9a20864fbe38741719f8e","score":"0.56654847","text":"def clear(self):\n self.results.delete_many({})\n self.complementary.delete_many({})\n self.space.delete_many({})","title":""},{"docid":"8406a56b356c8dbdb3fe7072f3e3de1b","score":"0.56584585","text":"def teardown(self):\n with app.app_context():\n # drop all tables\n db.session.remove()\n db.drop_all()","title":""},{"docid":"8954bc92eea49cebcb7c93db2a7110c2","score":"0.56506383","text":"def delete_all():\n try:\n storm_.delete_all_entries()\n print get_formatted_message('all entries deleted.', 'success')\n except Exception as error:\n print get_formatted_message(str(error), 'error')","title":""},{"docid":"0592ddeda8a0278b3d60907feee51dfe","score":"0.56480664","text":"def deleteAllProcessesAndSamples(self):\n # TODO: Experiment.deleteAllProcessesAndSamples()\n pass","title":""},{"docid":"ea0851ddc7c595b1a6053e458d51e1cc","score":"0.56466013","text":"def close(self):\n self._store.close()","title":""},{"docid":"0ebc3a6fcdb7d14e0b9fc32c89351af7","score":"0.56297535","text":"def tearDown(self):\n logger.info('tearDown: empty the table')\n for customer in db.Customer:\n customer.delete_instance()","title":""},{"docid":"ed9670c07afb8a7f4da9cad8c49ad933","score":"0.56294066","text":"def teardown(self):\n\n self.shut_down_instance(self.instances)\n self.instances = []\n try:\n self.client.delete_internet_gateway(InternetGatewayId=self.internet_gateway)\n self.internet_gateway = None\n self.client.delete_route_table(RouteTableId=self.route_table)\n self.route_table = None\n for subnet in list(self.sn_ids):\n # Cast to list ensures that this is a copy\n # Which is important because it means that\n # the length of the list won't change during iteration\n self.client.delete_subnet(SubnetId=subnet)\n self.sn_ids.remove(subnet)\n self.client.delete_security_group(GroupId=self.sg_id)\n self.sg_id = None\n self.client.delete_vpc(VpcId=self.vpc_id)\n self.vpc_id = None\n except Exception as e:\n logger.error(\"{}\".format(e))\n raise e\n self.show_summary()\n os.remove(self.config['stateFilePath'])","title":""},{"docid":"cce13fe0f389882242937ad9b0a794d0","score":"0.56236607","text":"def tear_down_all(self):\n self.dut.kill_all()","title":""},{"docid":"97678985353310231e525e26a9f88d00","score":"0.5622182","text":"def clear_data():\r\n meta = db.metadata\r\n for table in reversed(meta.sorted_tables):\r\n db.session.execute(table.delete())\r\n db.session.commit()","title":""},{"docid":"4e51b3b2d881c7000c0bbda7ba16ef90","score":"0.5619535","text":"def tear_down_all(self):\n pass","title":""},{"docid":"4e51b3b2d881c7000c0bbda7ba16ef90","score":"0.5619535","text":"def tear_down_all(self):\n pass","title":""},{"docid":"4e51b3b2d881c7000c0bbda7ba16ef90","score":"0.5619535","text":"def tear_down_all(self):\n pass","title":""},{"docid":"4e51b3b2d881c7000c0bbda7ba16ef90","score":"0.5619535","text":"def tear_down_all(self):\n pass","title":""},{"docid":"967b26fd661ca89c794c4a2ef851f0b9","score":"0.5612156","text":"def drop_all(self):\n Base.metadata.drop_all(self.engine)","title":""},{"docid":"1bdd2f558aa3e6b2ef7127e27604f5d5","score":"0.5599224","text":"def delete_all_tables():\n Base.metadata.drop_all(bind=engine, tables=[Operations.__table__])","title":""},{"docid":"1cfdc6f58ab3aab5bbc03c0056c487c2","score":"0.55987054","text":"def clear(self):\n for k in self.data_to_save:\n self.__dict__[k].clear()","title":""},{"docid":"33285f4245fc2c07e310afc650d9bd49","score":"0.55893","text":"def delete_everything(self):\n for bucket in self.buckets.values():\n bucket.delete_everything()\n self.buckets = {}","title":""},{"docid":"a2bf317b298e89d2539031faed941335","score":"0.55866617","text":"def clear_all_databases(settings, nuke=False):\n\n\n #if not raw_input(\"Are you sure you want to delete everything? Y/N: \") == 'Y':\n # sys.exit(\"Not deleting, getting out of here.\")\n\n if nuke:\n settings = open_settings()\n Session, engine = load_connection(settings['connection_string'])\n\n session = Session()\n for table in reversed(Base.metadata.sorted_tables):\n #if table.name == 'files':\n # #continue\n # pass\n try:\n print(\"Deleting {}\".format(table.name))\n session.execute(table.delete())\n session.commit()\n except:\n print(\"Cannot delete {}\".format(table.name))\n pass\n else:\n sys.exit('TO DELETE DB YOU NEED TO SET NUKE FLAG, EXITING')","title":""},{"docid":"8aae91c61d61b2926dee790de95dce66","score":"0.55815357","text":"def close_all(cls):\n with cls._lock:\n for key, value in list(cls._instances.items()):\n value.__close_resources()\n cls._instances.clear()","title":""},{"docid":"6863b84a7b5bc0abfbc2df6c5535216f","score":"0.5580423","text":"def reset_storage(self):\n for host in self._host_dict:\n self.reset_qubits_from_host(host)","title":""},{"docid":"a648eaf943ef6137e9bab53caef47f07","score":"0.5578176","text":"def destroy_models(self):\n for node in self.conv_nodes_iterator():\n del node.model\n clear_session() # removing session","title":""},{"docid":"a4539298c11453571f799f873920de25","score":"0.5575184","text":"def delete_all_songs(self):\n session = self._db_session()\n\n session.query(Song).delete()\n session.commit()\n\n session.close()","title":""},{"docid":"2a64c4dc8fcc4e2bbae7ad590d18e757","score":"0.55732703","text":"def _restore_storages(self):\n\n for storage in self._objects.values():\n storage._restore()","title":""},{"docid":"5b2b692e0d93ce0b47cdcdec69049f3d","score":"0.5572601","text":"def clear(self) -> None:\n self.subscribers_configurer.clear()\n if self._use_store_queues:\n self.store_configurer.clear()","title":""},{"docid":"e5268f437e3cdac314a22af7d6105d17","score":"0.5568259","text":"def delete_all(self):\n self.images.clear()","title":""},{"docid":"fd63bddeeb3ed74067c115e9e6c8e82a","score":"0.5564514","text":"def cleanup(self, force_stop=True):\n for worker in self.workers():\n worker.stop(force_stop=force_stop)\n\n try:\n self._subscriber.delete_subscription(self._subscription_path)\n print(\"Deleted subscription \" + self._subscription_path)\n except:\n pass\n try:\n self._publisher.delete_topic(self._topic_path)\n print(\"Deleted topic \" + self._topic_path)\n except:\n pass","title":""},{"docid":"19eb06d8bfa97d56bed7113d626f5883","score":"0.556271","text":"def __del__(self):\n for ins in self.instruments.values():\n ins.close()\n self.resource_mgr.close()","title":""},{"docid":"3784e1c8a9b8938bd9700977f1fb1c53","score":"0.55528665","text":"def _location_purge_all(delete=False, verbosity=0):\n if DataLocation.objects.exists():\n for location in DataLocation.objects.filter(Q(purged=False) | Q(data=None)):\n location_purge(location.id, delete, verbosity)\n else:\n logger.info(\"No data locations\")","title":""},{"docid":"279f29f6f17e28216618ae60be6edba4","score":"0.5544224","text":"def cleanup():\n for dataset in Dataset.all():\n for link in Link.all_unmatched(dataset):\n db.session.delete(link)\n db.session.flush()\n db.session.commit()","title":""},{"docid":"49afbfddd15b2481e4865752ac738c86","score":"0.55387414","text":"def cleanup_all(data_home=None):\n removed = 0\n for name, meta in DATASETS.items():\n _, ext = os.path.splitext(meta[\"url\"])\n removed += cleanup_dataset(name, data_home=data_home, ext=ext)\n\n print(\n \"Removed {} fixture objects from {}\".format(removed, get_data_home(data_home))\n )","title":""},{"docid":"7696d6f488b865b5cd5c54501b0c4a93","score":"0.55384505","text":"def cleanup(self):\n os.rmdir(self.tleap_dir)\n os.rmdir(self.antechamber_dir)\n os.rmdir(self.parmchk_dir)","title":""},{"docid":"84119d7346d766217715187f5a4124ed","score":"0.55298626","text":"def clear(self):\n self.__owned_workspaces.clear()","title":""},{"docid":"2bbf6e83874f4bd04f583161a57d60a1","score":"0.5524603","text":"def clear_tables():\r\n\r\n # Delete all rows in table, so if we need to run this a second time,\r\n # we won't be trying to add duplicate users\r\n User.query.delete()\r\n Relational.query.delete()\r\n Trip.query.delete()\r\n Wishlist.query.delete()\r\n\r\n print('ALL TABLES HAVE BEEN CLEARED OF THEIR DATA')","title":""},{"docid":"2c8aec0e02fed328e82218cd5434ec0a","score":"0.55243576","text":"def clear(self):\n for shared_generators in self._shared_generators:\n shared_generators.clear()\n self._shared_generators = []","title":""},{"docid":"16f7b57cb64ca885516f00fad6f60b61","score":"0.5522722","text":"def delete_all(self):\n self.__projects.clear()","title":""},{"docid":"e953a0c46f6939eb999709f07efc5e31","score":"0.55177927","text":"def delete_all_tracks(self):\n self._tracks = []\n self._eps = []\n # leave the funcs and labels","title":""},{"docid":"f5c01705550d8300c3e06f7fe1b8e537","score":"0.5516964","text":"def drop_all(self):\n try:\n self.__lock.acquire_write()\n Logger.instance().debug(\"Dropping all tables...\")\n Base.metadata.drop_all(self.engine)\n finally:\n # Always release the lock\n self.__lock.release()","title":""}],"string":"[\n {\n \"docid\": \"9f429cbeb9566b1fe28e72709e16eb8b\",\n \"score\": \"0.7163623\",\n \"text\": \"def cleanupStore(self):\\n if self.store:\\n self.store.cleanup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec3f31f5e70bd594f86b9c285dbaf905\",\n \"score\": \"0.64436644\",\n \"text\": \"def clean(self):\\n logger.debug('Deleting all runtimes')\\n\\n runtimes = self.list_runtimes()\\n\\n for runtime in runtimes:\\n runtime_name, runtime_memory = runtime\\n self.delete_runtime(runtime_name, runtime_memory)\\n\\n layers = self._list_layers()\\n for layer_name, _ in layers:\\n self._delete_layer(layer_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e21915ec1b9a39cc746035ba84b3725f\",\n \"score\": \"0.6295653\",\n \"text\": \"def clear(self):\\n self.delete_objects()\\n self.del_hooks()\\n self.broker.reset()\\n self.emane.reset()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d547b880caad6e370c62b5bd174772fa\",\n \"score\": \"0.6247881\",\n \"text\": \"def destroy(self):\\n for container in self._containers.copy():\\n container.destroy()\\n\\n assert len(self._containers) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c01beac2a818ab23b4be8ee58870420c\",\n \"score\": \"0.62467194\",\n \"text\": \"def delete_all(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7259762281128ac5d618abd7857b24a7\",\n \"score\": \"0.62402064\",\n \"text\": \"def deleteAll(self):\\n return prod_collection.delete_many({})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f7d0267d4f9144f253fb2901bbb22f5\",\n \"score\": \"0.61734307\",\n \"text\": \"def clear(self) -> None:\\n\\n if self.pipeline._transformer:\\n for idx in self._store:\\n self.pipeline(idx).unpersist(blocking=True)\\n\\n self._store.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b174da2c9ef863f3a564091664f50219\",\n \"score\": \"0.61593086\",\n \"text\": \"def clear_all_caches(self):\\n\\n for key in self._registry:\\n self.clear_cache(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c382e30a56ff26aecba4a1aec40478e\",\n \"score\": \"0.6154785\",\n \"text\": \"def destroy_all(self):\\r\\n\\r\\n [widget.destroy() for widget in self.widgets]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"241375b422701040bf7b619346cd64a5\",\n \"score\": \"0.6136179\",\n \"text\": \"def remove_all_tools(self):\\n self.db_manager.remove_all_tools()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15f55d191469c3e863bc5b240868c05\",\n \"score\": \"0.6100213\",\n \"text\": \"def remove_all_tools(self):\\n logger = Logger().get()\\n logger.debug(\\\"remove all tools\\\")\\n if self.db_name != \\\"test\\\":\\n logger.error(\\\"Cannot remove all tools unless current db\\\"\\n \\\" is \\\\\\\"test\\\\\\\"!\\\")\\n return\\n\\n try:\\n # Get ids of all jobs\\n delete_result = self.__tools_collection.delete_many({})\\n\\n # Set the flag of initialization to False\\n metadata = self.__metadata_collection.find()[0]\\n metadata['has_initialized_pharos'] = False\\n\\n condition = {\\\"_id\\\": metadata['_id']}\\n self.__metadata_collection.update_one(condition, {\\\"$set\\\": metadata})\\n\\n logger.debug(f\\\"Remove {delete_result.deleted_count} tools.\\\")\\n except Exception as e:\\n logger.error(f\\\"something wrong in remove_all_tools, Exception: {e}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b97ecf50d9921e0b02f175d70ef30e07\",\n \"score\": \"0.6092327\",\n \"text\": \"def remove_all(cls):\\n for document in cls.database:\\n document.delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1278c3ee54bd2f566fdbe1156fe16cec\",\n \"score\": \"0.6078893\",\n \"text\": \"def purge_all(self):\\r\\n raise NotImplementedError(\\\"Sub-classes of Manager should implement\\\"\\r\\n \\\" .purge_all()\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"062869e11510e52d83aeae05c73b27cc\",\n \"score\": \"0.6078348\",\n \"text\": \"def clean(self):\\n for loc in self.locations.values():\\n shutil.rmtree(loc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b200d11687334a84c3ab3439eaa58b3\",\n \"score\": \"0.6061952\",\n \"text\": \"def _purge_all(self):\\n for key in self.keys():\\n self.delete(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8f4f2e8247a69ccd5f4c601d2e7e48c\",\n \"score\": \"0.6052412\",\n \"text\": \"def empty_all_models():\\n # Iterate through LetterGameInsertions the models and call the empty model method to clean them out\\n for model in DeleteData.models:\\n DeleteData.empty_model(model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b92203da3e6e21e5d0747190e695bbe0\",\n \"score\": \"0.60521567\",\n \"text\": \"def cleanUp(self):\\n for machine in self._machines.copy():\\n self.destroyMachine(machine)\\n\\n assert len(self._machines) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"896b9f3bae108884076d7ae2ded820af\",\n \"score\": \"0.60494834\",\n \"text\": \"def cleanall(self):\\n return self._manager.cleanall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9439d46bc2d7f519a0d27e468fd84050\",\n \"score\": \"0.6023564\",\n \"text\": \"def unsubscribe_all(self):\\n for sub in self.session:\\n sub.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fadab4e114a45544c7c6b47e9f7c447e\",\n \"score\": \"0.6018372\",\n \"text\": \"def clean_up_service(self):\\n for service in self.service_list:\\n self.catalog_api.delete_service(service['id'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"329a8fbe9867a8778c6041e6341182dd\",\n \"score\": \"0.6014507\",\n \"text\": \"def clear(self) -> None:\\n self._store.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c9f324751da5edf50b0939caa1cbb02\",\n \"score\": \"0.6001814\",\n \"text\": \"def clear_all(self):\\n keys = self._analytics_backend.keys()\\n\\n for key in itertools.chain(*keys):\\n with self._analytics_backend.map() as conn:\\n if key.startswith(self._prefix):\\n conn.delete(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f33f40c07048d4dc7dac26ab2c3494b\",\n \"score\": \"0.5997771\",\n \"text\": \"def purge_all(delete=False, verbosity=0):\\n _location_purge_all(delete, verbosity)\\n _storage_purge_all(delete, verbosity)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fef5742de59ff9ebc5e5db991688ff8b\",\n \"score\": \"0.5980893\",\n \"text\": \"def _purge_all(self):\\r\\n for key in self.keys():\\r\\n self.delete(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"565b08b6ed9cd1cb6c1f9013596427bc\",\n \"score\": \"0.5976179\",\n \"text\": \"def clear_db(self):\\n People.objects.all().delete()\\n Company.objects.all().delete()\\n Tag.objects.all().delete()\\n Food.objects.all().delete()\\n self.stdout.write(self.style.SUCCESS('Successfully cleared all data'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1afcd44503928444c2d6b7d94a931773\",\n \"score\": \"0.5959921\",\n \"text\": \"def delete_all(self):\\n query = self.db.session.query(models.Work)\\n for work_obj in query:\\n self.db.session.delete(work_obj)\\n self.db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd541506e1e68ae11f83e050721cb6f2\",\n \"score\": \"0.5946315\",\n \"text\": \"def destroy_tables(self):\\n tables = self.table_names\\n for table in tables:\\n self.drop(table)\\n self.save()\\n self.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d73a274add37f74b4e5fd8c9c824a2f\",\n \"score\": \"0.5940249\",\n \"text\": \"def creasesClearAll(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f695fd9477073ccfeb5f8baee49c4b6c\",\n \"score\": \"0.59389484\",\n \"text\": \"def delete_everything(self):\\n self.instance.delete_everything()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"507e4a63e6378a8d59c5985e66e0a0bc\",\n \"score\": \"0.59107876\",\n \"text\": \"def clear(self, verbose=False):\\n with session_scope() as session:\\n numSym = session.query(Synonym).delete()\\n numPosts = session.query(Post).delete()\\n numRels = session.query(SynonymPostAssociation).delete()\\n session.commit()\\n if verbose:\\n print(\\n f'''Successfully deleted all data from database: \\n {numSym} synonyms, \\n {numPosts} posts, \\n and {numRels} synonym<->post relations.''')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1eff50e99f0b114297be0e5db55faa1\",\n \"score\": \"0.59047455\",\n \"text\": \"def cleanup():\\n remove_all_objects()\\n remove_orphan_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b06dc30f7b212f3e1e2fc5455bf75380\",\n \"score\": \"0.58954585\",\n \"text\": \"async def reset_all():\\n return State().delete().execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2c5041c401f98e22ab39a1a4a26d325\",\n \"score\": \"0.58738637\",\n \"text\": \"def clear(self):\\n\\t\\tself.store = {}\\n\\t\\tif not self.lazysave: self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15d8a1ec2505759dbd6949f70096a1a1\",\n \"score\": \"0.5869648\",\n \"text\": \"def clearall(self):\\n for res in self._getResNames():\\n self.clear(res)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43e73da3a2b6e12eb142862ccd5da63f\",\n \"score\": \"0.5857684\",\n \"text\": \"def destroy_all(self, destroy_canvas: bool = False) -> None:\\n\\n if destroy_canvas:\\n self.commands.append({\\\"$type\\\": \\\"destroy_ui_canvas\\\",\\n \\\"canvas_id\\\": self._canvas_id})\\n else:\\n for ui_id in self._ui_ids:\\n self.commands.append({\\\"$type\\\": \\\"destroy_ui_element\\\",\\n \\\"id\\\": ui_id,\\n \\\"canvas_id\\\": self._canvas_id})\\n self._ui_ids.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4a2b8e90251c955f08c0537bcf3709d\",\n \"score\": \"0.58495027\",\n \"text\": \"def cleanup(self):\\n helpers.delete_objs_parallel(pod.get_all_pods(namespace=self.namespace))\\n helpers.delete_objs_parallel(self.all_pvc_obj)\\n self.rbd_sc_obj.delete()\\n self.cephfs_sc_obj.delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd01117bdd068e1fd4c5c8112c2a836e\",\n \"score\": \"0.58392483\",\n \"text\": \"def delete_all(self):\\n allC=[]\\n self.__storeToFile(allC)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"740cecd9460007af81d4b4f2aff5daeb\",\n \"score\": \"0.5839114\",\n \"text\": \"def _purge_all(self):\\r\\n raise NotImplementedError(\\\"Sub-classes of Manager should implement\\\"\\r\\n \\\" ._purge_all()\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a624af741000fd66e10a3b861171b81\",\n \"score\": \"0.58384585\",\n \"text\": \"def clear(self):\\r\\n logger.debug('Clearing all models from memory.')\\r\\n self.java.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ed3ffff4232c717f930460d161ab710\",\n \"score\": \"0.5836243\",\n \"text\": \"def reset_dbs(self) -> None:\\n self.mongo().clear_db()\\n self.redis().clear_db()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b3939ee6d50ca3f4a06a579735ecf80\",\n \"score\": \"0.58268785\",\n \"text\": \"def delete_all_sessions():\\n AccountSession.delete().execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3c2bd1f6de5c895d1d729d105ec3fdb\",\n \"score\": \"0.5819684\",\n \"text\": \"def delete_all(self):\\n logger.info('Flushing database...')\\n logger.debug('Call Successful: %s', 'delete_all: delete_all call successful', extra=d)\\n logger.debug('Flushall Attempt: %s', 'delete_all: attempting to flush all items from queue', extra=d)\\n self.r.flushall()\\n logger.info('Database emptied')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"855bbfdecf264f065476d0c732d28eb4\",\n \"score\": \"0.581262\",\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\": \"b607f351896ac1709f52af8b65419b2d\",\n \"score\": \"0.57891786\",\n \"text\": \"def fusion_srm_ilo_api_delete_all_sso_certs(self):\\n self.ilo_client.delete_all_sso_certs()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"791db763472eeb7964fe9e2d8d607817\",\n \"score\": \"0.5788232\",\n \"text\": \"def deregister_all_nodes(self):\\n\\n self.nodes = set()\\n storage.node.remove_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37efc2934bf38e64ca442dccd4de76f8\",\n \"score\": \"0.5777878\",\n \"text\": \"def delete_all(self):\\n self._exec_delete_all(self.table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ce97885fae296943b806281bec402e\",\n \"score\": \"0.57773423\",\n \"text\": \"def unload(self):\\n for component in self.components.values():\\n component.destroy()\\n\\n self.on_unloaded()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"933b6b4c38ed104c15c1619907da5450\",\n \"score\": \"0.57717335\",\n \"text\": \"def del_all_saved(self):\\n self.saved = dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bf98e4b8090950c35ca700291d60484\",\n \"score\": \"0.5754316\",\n \"text\": \"def clear_data():\\n Student.objects.all().delete()\\n Teacher.objects.all().delete()\\n Homework.objects.all().delete()\\n HomeworkResult.objects.all().delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fabc363496190d8e0edf4c6e5f81a81\",\n \"score\": \"0.5753621\",\n \"text\": \"def _purge_all(self):\\r\\n # Given the races around connection closing, the easiest thing to do\\r\\n # here is to create a new manager with the same config for cleanup\\r\\n # operations.\\r\\n new_manager = yield self.from_config(self._config)\\r\\n # If we're a submanager we might have a different key prefix.\\r\\n new_manager._key_prefix = self._key_prefix\\r\\n yield new_manager._do_purge()\\r\\n yield new_manager._close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b7ff978406cef2e04d95dab3fce72d\",\n \"score\": \"0.5737473\",\n \"text\": \"def _purge_all(self):\\n # Given the races around connection closing, the easiest thing to do\\n # here is to create a new manager with the same config for cleanup\\n # operations.\\n new_manager = yield self.from_config(self._config)\\n # If we're a submanager we might have a different key prefix.\\n new_manager._key_prefix = self._key_prefix\\n yield new_manager._do_purge()\\n yield new_manager._close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba2e9432643075f9de8aa5d9613e0781\",\n \"score\": \"0.5735537\",\n \"text\": \"def clear_all(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e14ef081f832e7e5db92e12740e4aae6\",\n \"score\": \"0.57301855\",\n \"text\": \"def delete(self):\\n for vertex_list in self._vertex_lists:\\n vertex_list.delete()\\n self._vertex_lists.clear()\\n\\n for box in self._boxes:\\n box.delete(self)\\n self._boxes.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"135a9aeee5eeb23d1a9c2590402e5b9d\",\n \"score\": \"0.57231396\",\n \"text\": \"def destroy_all_containers(self):\\n self.log.info(\\\"Destroying %d containers\\\", len(self.container))\\n errors = self.destroy_containers(self.container)\\n if errors:\\n self.log.error(\\n \\\"Errors detected destroying %d containers: %d\\\",\\n len(self.container), len(errors))\\n for error in errors:\\n self.log.error(\\\" %s\\\", error)\\n self.container = []\\n return len(errors) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44e224f8432087fc2fd00ebadf18bc24\",\n \"score\": \"0.5716785\",\n \"text\": \"def del_all_V1(self):\\n\\n self.del_all_file_V1()\\n self.del_all_collection_V1()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7d134f1cedb4d19af724b4e5d541368\",\n \"score\": \"0.57161874\",\n \"text\": \"def clear_all_queues(self):\\n return self.client.delete_queues()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c41479e4b7145f079ea96a7926ca6ed2\",\n \"score\": \"0.56908864\",\n \"text\": \"def destroyAllPanels (self):\\n\\n panels = (self.comparePanel, self.colorPanel, self.findPanel, self.fontPanel, self.prefsPanel)\\n\\n for panel in panels:\\n if panel:\\n panel.top.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a11f1b4fe1ab1e4ff784dc0d809b8ce8\",\n \"score\": \"0.56860757\",\n \"text\": \"def cleanup_manager(deco, self):\\n yield self.manager.purge_all()\\n yield self.manager.close_manager()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3484881918678f43d19e8ecb93050c2\",\n \"score\": \"0.56849766\",\n \"text\": \"def cleanup(self):\\n for process in self.processes:\\n process.terminate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4f5361a1e0e3140495dd046c60d0886\",\n \"score\": \"0.56846565\",\n \"text\": \"def system_reset(self, system_id):\\n self.db.delete_all_documents('projects')\\n self.db.delete_all_documents('experiments')\\n self.db.delete_all_documents('experimentsMetrics')\\n self.delete_queue()\\n self.delete_pool()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e9ca50f8ac44dd8ccb18210139324d\",\n \"score\": \"0.5680037\",\n \"text\": \"def delete_all_servers():\\n nectar.delete_all_servers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"918caddfe098989be6b465713eee58f2\",\n \"score\": \"0.5674994\",\n \"text\": \"def destroy():\\n subprocess.check_call([settings.APP_NAME, 'main', 'destroy'])\\n subprocess.check_call([settings.APP_NAME, 'addons', 'destroy'])\\n subprocess.check_call([settings.APP_NAME, 'deis', 'destroy'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a43c7a24bde13ce662b3657e92bcf837\",\n \"score\": \"0.5667435\",\n \"text\": \"def cleardb():\\n\\n with app.app_context():\\n db.drop_all()\\n print 'Database cleared'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c3d66d29c9a20864fbe38741719f8e\",\n \"score\": \"0.56654847\",\n \"text\": \"def clear(self):\\n self.results.delete_many({})\\n self.complementary.delete_many({})\\n self.space.delete_many({})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8406a56b356c8dbdb3fe7072f3e3de1b\",\n \"score\": \"0.56584585\",\n \"text\": \"def teardown(self):\\n with app.app_context():\\n # drop all tables\\n db.session.remove()\\n db.drop_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8954bc92eea49cebcb7c93db2a7110c2\",\n \"score\": \"0.56506383\",\n \"text\": \"def delete_all():\\n try:\\n storm_.delete_all_entries()\\n print get_formatted_message('all entries deleted.', 'success')\\n except Exception as error:\\n print get_formatted_message(str(error), 'error')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0592ddeda8a0278b3d60907feee51dfe\",\n \"score\": \"0.56480664\",\n \"text\": \"def deleteAllProcessesAndSamples(self):\\n # TODO: Experiment.deleteAllProcessesAndSamples()\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea0851ddc7c595b1a6053e458d51e1cc\",\n \"score\": \"0.56466013\",\n \"text\": \"def close(self):\\n self._store.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ebc3a6fcdb7d14e0b9fc32c89351af7\",\n \"score\": \"0.56297535\",\n \"text\": \"def tearDown(self):\\n logger.info('tearDown: empty the table')\\n for customer in db.Customer:\\n customer.delete_instance()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed9670c07afb8a7f4da9cad8c49ad933\",\n \"score\": \"0.56294066\",\n \"text\": \"def teardown(self):\\n\\n self.shut_down_instance(self.instances)\\n self.instances = []\\n try:\\n self.client.delete_internet_gateway(InternetGatewayId=self.internet_gateway)\\n self.internet_gateway = None\\n self.client.delete_route_table(RouteTableId=self.route_table)\\n self.route_table = None\\n for subnet in list(self.sn_ids):\\n # Cast to list ensures that this is a copy\\n # Which is important because it means that\\n # the length of the list won't change during iteration\\n self.client.delete_subnet(SubnetId=subnet)\\n self.sn_ids.remove(subnet)\\n self.client.delete_security_group(GroupId=self.sg_id)\\n self.sg_id = None\\n self.client.delete_vpc(VpcId=self.vpc_id)\\n self.vpc_id = None\\n except Exception as e:\\n logger.error(\\\"{}\\\".format(e))\\n raise e\\n self.show_summary()\\n os.remove(self.config['stateFilePath'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cce13fe0f389882242937ad9b0a794d0\",\n \"score\": \"0.56236607\",\n \"text\": \"def tear_down_all(self):\\n self.dut.kill_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97678985353310231e525e26a9f88d00\",\n \"score\": \"0.5622182\",\n \"text\": \"def clear_data():\\r\\n meta = db.metadata\\r\\n for table in reversed(meta.sorted_tables):\\r\\n db.session.execute(table.delete())\\r\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e51b3b2d881c7000c0bbda7ba16ef90\",\n \"score\": \"0.5619535\",\n \"text\": \"def tear_down_all(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e51b3b2d881c7000c0bbda7ba16ef90\",\n \"score\": \"0.5619535\",\n \"text\": \"def tear_down_all(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e51b3b2d881c7000c0bbda7ba16ef90\",\n \"score\": \"0.5619535\",\n \"text\": \"def tear_down_all(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e51b3b2d881c7000c0bbda7ba16ef90\",\n \"score\": \"0.5619535\",\n \"text\": \"def tear_down_all(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"967b26fd661ca89c794c4a2ef851f0b9\",\n \"score\": \"0.5612156\",\n \"text\": \"def drop_all(self):\\n Base.metadata.drop_all(self.engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bdd2f558aa3e6b2ef7127e27604f5d5\",\n \"score\": \"0.5599224\",\n \"text\": \"def delete_all_tables():\\n Base.metadata.drop_all(bind=engine, tables=[Operations.__table__])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cfdc6f58ab3aab5bbc03c0056c487c2\",\n \"score\": \"0.55987054\",\n \"text\": \"def clear(self):\\n for k in self.data_to_save:\\n self.__dict__[k].clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33285f4245fc2c07e310afc650d9bd49\",\n \"score\": \"0.55893\",\n \"text\": \"def delete_everything(self):\\n for bucket in self.buckets.values():\\n bucket.delete_everything()\\n self.buckets = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2bf317b298e89d2539031faed941335\",\n \"score\": \"0.55866617\",\n \"text\": \"def clear_all_databases(settings, nuke=False):\\n\\n\\n #if not raw_input(\\\"Are you sure you want to delete everything? Y/N: \\\") == 'Y':\\n # sys.exit(\\\"Not deleting, getting out of here.\\\")\\n\\n if nuke:\\n settings = open_settings()\\n Session, engine = load_connection(settings['connection_string'])\\n\\n session = Session()\\n for table in reversed(Base.metadata.sorted_tables):\\n #if table.name == 'files':\\n # #continue\\n # pass\\n try:\\n print(\\\"Deleting {}\\\".format(table.name))\\n session.execute(table.delete())\\n session.commit()\\n except:\\n print(\\\"Cannot delete {}\\\".format(table.name))\\n pass\\n else:\\n sys.exit('TO DELETE DB YOU NEED TO SET NUKE FLAG, EXITING')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aae91c61d61b2926dee790de95dce66\",\n \"score\": \"0.55815357\",\n \"text\": \"def close_all(cls):\\n with cls._lock:\\n for key, value in list(cls._instances.items()):\\n value.__close_resources()\\n cls._instances.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6863b84a7b5bc0abfbc2df6c5535216f\",\n \"score\": \"0.5580423\",\n \"text\": \"def reset_storage(self):\\n for host in self._host_dict:\\n self.reset_qubits_from_host(host)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a648eaf943ef6137e9bab53caef47f07\",\n \"score\": \"0.5578176\",\n \"text\": \"def destroy_models(self):\\n for node in self.conv_nodes_iterator():\\n del node.model\\n clear_session() # removing session\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4539298c11453571f799f873920de25\",\n \"score\": \"0.5575184\",\n \"text\": \"def delete_all_songs(self):\\n session = self._db_session()\\n\\n session.query(Song).delete()\\n session.commit()\\n\\n session.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a64c4dc8fcc4e2bbae7ad590d18e757\",\n \"score\": \"0.55732703\",\n \"text\": \"def _restore_storages(self):\\n\\n for storage in self._objects.values():\\n storage._restore()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b2b692e0d93ce0b47cdcdec69049f3d\",\n \"score\": \"0.5572601\",\n \"text\": \"def clear(self) -> None:\\n self.subscribers_configurer.clear()\\n if self._use_store_queues:\\n self.store_configurer.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5268f437e3cdac314a22af7d6105d17\",\n \"score\": \"0.5568259\",\n \"text\": \"def delete_all(self):\\n self.images.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd63bddeeb3ed74067c115e9e6c8e82a\",\n \"score\": \"0.5564514\",\n \"text\": \"def cleanup(self, force_stop=True):\\n for worker in self.workers():\\n worker.stop(force_stop=force_stop)\\n\\n try:\\n self._subscriber.delete_subscription(self._subscription_path)\\n print(\\\"Deleted subscription \\\" + self._subscription_path)\\n except:\\n pass\\n try:\\n self._publisher.delete_topic(self._topic_path)\\n print(\\\"Deleted topic \\\" + self._topic_path)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19eb06d8bfa97d56bed7113d626f5883\",\n \"score\": \"0.556271\",\n \"text\": \"def __del__(self):\\n for ins in self.instruments.values():\\n ins.close()\\n self.resource_mgr.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3784e1c8a9b8938bd9700977f1fb1c53\",\n \"score\": \"0.55528665\",\n \"text\": \"def _location_purge_all(delete=False, verbosity=0):\\n if DataLocation.objects.exists():\\n for location in DataLocation.objects.filter(Q(purged=False) | Q(data=None)):\\n location_purge(location.id, delete, verbosity)\\n else:\\n logger.info(\\\"No data locations\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"279f29f6f17e28216618ae60be6edba4\",\n \"score\": \"0.5544224\",\n \"text\": \"def cleanup():\\n for dataset in Dataset.all():\\n for link in Link.all_unmatched(dataset):\\n db.session.delete(link)\\n db.session.flush()\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49afbfddd15b2481e4865752ac738c86\",\n \"score\": \"0.55387414\",\n \"text\": \"def cleanup_all(data_home=None):\\n removed = 0\\n for name, meta in DATASETS.items():\\n _, ext = os.path.splitext(meta[\\\"url\\\"])\\n removed += cleanup_dataset(name, data_home=data_home, ext=ext)\\n\\n print(\\n \\\"Removed {} fixture objects from {}\\\".format(removed, get_data_home(data_home))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7696d6f488b865b5cd5c54501b0c4a93\",\n \"score\": \"0.55384505\",\n \"text\": \"def cleanup(self):\\n os.rmdir(self.tleap_dir)\\n os.rmdir(self.antechamber_dir)\\n os.rmdir(self.parmchk_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84119d7346d766217715187f5a4124ed\",\n \"score\": \"0.55298626\",\n \"text\": \"def clear(self):\\n self.__owned_workspaces.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bbf6e83874f4bd04f583161a57d60a1\",\n \"score\": \"0.5524603\",\n \"text\": \"def clear_tables():\\r\\n\\r\\n # Delete all rows in table, so if we need to run this a second time,\\r\\n # we won't be trying to add duplicate users\\r\\n User.query.delete()\\r\\n Relational.query.delete()\\r\\n Trip.query.delete()\\r\\n Wishlist.query.delete()\\r\\n\\r\\n print('ALL TABLES HAVE BEEN CLEARED OF THEIR DATA')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c8aec0e02fed328e82218cd5434ec0a\",\n \"score\": \"0.55243576\",\n \"text\": \"def clear(self):\\n for shared_generators in self._shared_generators:\\n shared_generators.clear()\\n self._shared_generators = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f7b57cb64ca885516f00fad6f60b61\",\n \"score\": \"0.5522722\",\n \"text\": \"def delete_all(self):\\n self.__projects.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e953a0c46f6939eb999709f07efc5e31\",\n \"score\": \"0.55177927\",\n \"text\": \"def delete_all_tracks(self):\\n self._tracks = []\\n self._eps = []\\n # leave the funcs and labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c01705550d8300c3e06f7fe1b8e537\",\n \"score\": \"0.5516964\",\n \"text\": \"def drop_all(self):\\n try:\\n self.__lock.acquire_write()\\n Logger.instance().debug(\\\"Dropping all tables...\\\")\\n Base.metadata.drop_all(self.engine)\\n finally:\\n # Always release the lock\\n self.__lock.release()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":483,"cells":{"query_id":{"kind":"string","value":"c41095549bd4ed7e3560b4e62f9c5644"},"query":{"kind":"string","value":"Toogle the feature /autosave"},"positive_passages":{"kind":"list like","value":[{"docid":"3d88796a7e042d014f8f188692779a26","score":"0.6832002","text":"def autosave(connection):\r\n if not connection.protocol.autosave:\r\n connection.protocol.autosave = True\r\n return \"Autosave enabled.\"\r\n else:\r\n connection.protocol.autosave = False\r\n return \"Autosave disabled.\"","title":""}],"string":"[\n {\n \"docid\": \"3d88796a7e042d014f8f188692779a26\",\n \"score\": \"0.6832002\",\n \"text\": \"def autosave(connection):\\r\\n if not connection.protocol.autosave:\\r\\n connection.protocol.autosave = True\\r\\n return \\\"Autosave enabled.\\\"\\r\\n else:\\r\\n connection.protocol.autosave = False\\r\\n return \\\"Autosave disabled.\\\"\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"95232758d83058425927fc755b481bdc","score":"0.7093163","text":"def autosave_handler(self):\n self.autosave_timer_waiting = False\n if self.editor.document().isModified():\n self.autosave()","title":""},{"docid":"cbae35afd4db48fd9c88b845cb79de50","score":"0.65503746","text":"def autosave(self):\n return self._autosave","title":""},{"docid":"b5915126d5f89e130a74f4b0f299ebf9","score":"0.62856436","text":"def autosave(self):\n tabs = self.tabs\n self.save_by_uuid(\n tabs['uuid'],\n tabs['name'],\n tabs['text'],\n str(tabs.currentIndex()),\n tabs.get('path')\n )\n self.editor.document().setModified(False)\n self.sync_tab_indices()","title":""},{"docid":"e0406e14fe1f75001799dbe6192797db","score":"0.61678654","text":"def MarkAsSaved(self):","title":""},{"docid":"0800d85ed3881472f5671737e19bdc74","score":"0.6018382","text":"def autosave(self):\n\t\tself.log.debug(\"Session: autosaving\")\n\t\t# call saving through horizons.main and not directly through session, so that save errors are handled\n\t\tsuccess = self.save(SavegameManager.create_autosave_filename())\n\t\tif success:\n\t\t\tSavegameManager.delete_dispensable_savegames(autosaves = True)","title":""},{"docid":"3abac37d4d9e1ae7ceadcdeb35b65826","score":"0.5979588","text":"def save(self):\n self.config.set(\"makeactive\", self.makeactive)","title":""},{"docid":"3bbff150710f5ecc9d94259e5c7f1cc6","score":"0.58593255","text":"def is_saved(self, event=None):\r\n file_name = os.path.basename(self.pathfile)\r\n if file_name == \"\":\r\n file_name = \"untitled\"\r\n\r\n if self.content_to_save != self.text_sheet.get(\"1.0\", \"end-1c\"):\r\n self.saved = False\r\n self.master.title(\"%s : Mind Note*\" % file_name)\r\n else:\r\n self.saved = True\r\n self.master.title(\"%s : Mind Note\" % file_name)","title":""},{"docid":"82734711fc5cb4e25c7bcb451de93dce","score":"0.58260196","text":"def save_timer(self):\n self.autosave_timer_waiting = True\n if self.autosave_timer.isActive():\n self.autosave_timer.stop()\n\n self.setup_save_timer()\n self.autosave_timer.timeout.connect(self.autosave_handler)\n if self.editor.document().isModified():\n self.autosave_timer.start()","title":""},{"docid":"a6e3f5cba977acf6a25112fb6b0d64c8","score":"0.5791793","text":"def on_save_activate(self, *args):\n self.app.save_notelist()","title":""},{"docid":"c0fecb3864e0bc42c8d47e51e3143ada","score":"0.573265","text":"def save(self):\n Preferences.setEditor(\n \"AutoCompletionEnabled\",\n self.acEnabledGroupBox.isChecked())\n Preferences.setEditor(\n \"AutoCompletionCaseSensitivity\",\n self.acCaseSensitivityCheckBox.isChecked())\n \n Preferences.setEditor(\n \"AutoCompletionReversedList\",\n self.acReversedCheckBox.isChecked())\n Preferences.setEditor(\n \"AutoCompletionReplaceWord\",\n self.acReplaceWordCheckBox.isChecked())\n Preferences.setEditor(\n \"AutoCompletionThreshold\",\n self.acThresholdSlider.value())\n Preferences.setEditor(\n \"AutoCompletionScintillaOnFail\",\n self.acScintillaCheckBox.isChecked())\n Preferences.setEditor(\n \"AutoCompletionTimeout\",\n self.acTimeoutSpinBox.value())\n Preferences.setEditor(\n \"AutoCompletionCacheEnabled\",\n self.acCacheGroup.isChecked())\n Preferences.setEditor(\n \"AutoCompletionCacheSize\",\n self.acCacheSizeSpinBox.value())\n Preferences.setEditor(\n \"AutoCompletionCacheTime\",\n self.acCacheTimeSpinBox.value())\n Preferences.setEditor(\n \"AutoCompletionWatchdogTime\",\n self.acWatchdogDoubleSpinBox.value() * 1000)\n Preferences.setEditor(\n \"AutoCompletionMaxLines\",\n self.acLinesSlider.value())\n Preferences.setEditor(\n \"AutoCompletionMaxChars\",\n self.acCharSlider.value())","title":""},{"docid":"9a9d9a75b3a48f1e99191b5f12a15cd2","score":"0.5720629","text":"def toggle_autoPreview(self):\n if self.__auto_timer.isActive():\n self.__auto_timer.stop()\n else:\n self.__auto_timer.start(100)","title":""},{"docid":"74cac84c171d63f8abac35a6746991e1","score":"0.5643983","text":"def onOpenSave(editor, path):\n\tproject = getProjectForFile(path)\n\tif not project:\n\t\treturn\n\n\teditor.project = project\n\tproject.applyOptions(editor)","title":""},{"docid":"f51c936ff28dd29e08abe7ebef578e6b","score":"0.5627555","text":"def dispModeSave(self, mode):\n pass","title":""},{"docid":"fdaf1df06cdc5cfb6d9fb52710cbb20d","score":"0.56194866","text":"def saveasbuttonclicked(self, _):\n fname = self.savedialog()\n if fname is None:\n return\n self.docpath = fname\n self.savefile()","title":""},{"docid":"3e374662cbf2ff597a9b3e122da60002","score":"0.55892575","text":"def _stop_editing():\n global _EDITABLE\n _EDITABLE = False","title":""},{"docid":"f18eda7f2fe070f967dbbf87741be501","score":"0.55647784","text":"def SaveEnabled(self) -> bool:","title":""},{"docid":"2109e4d30360af962747f1051f0da90c","score":"0.55417275","text":"def savebuttonclicked(self, _):\n if self.docpath is not None:\n self.savefile()\n else:\n self.saveasbuttonclicked(None)","title":""},{"docid":"437614a1f8895e34f0f79e536cd4e449","score":"0.54992074","text":"def Regenmode(self) -> bool:","title":""},{"docid":"33133b70030a813613f0e57a3c03dbd4","score":"0.54925835","text":"def save(interactive=True, all=True):\n pass # implemented in Ada","title":""},{"docid":"6ec407ebcea6cc9b7f444924d4467993","score":"0.542806","text":"def edit_pad(self):\n\t\tpath = join(self.save_dir, vim.current.line.split(\" @\")[0])\n\t\tvim.command(\"bd\")\n\t\tself.pad_open(path=path)","title":""},{"docid":"a2ae09e1099d71351afe1a6fc2790b88","score":"0.54264396","text":"def _on_save_changed(self):\n\n if self.save_file.isChecked():\n self.path_widget.setEnabled(True)\n else:\n self.path_widget.setEnabled(False)","title":""},{"docid":"efb18b6beeca98e3befb76d7149ffef9","score":"0.5418351","text":"def vi_editing_mode(event: E) -> None:\n event.app.editing_mode = EditingMode.VI","title":""},{"docid":"c4916b6b137ded40794e3ab5b286792f","score":"0.5415203","text":"def select_short_view_mode_in_toolbar(self):\n self._file_actions_toolbar.select_short_view_mode_in_toolbar()","title":""},{"docid":"df40c31e985fa963782fbf9bf79724d0","score":"0.5406741","text":"def save(replace=True):","title":""},{"docid":"dc6f3318d8f5436cf760f0f9eb91e454","score":"0.5398526","text":"def issue_autoset(self):\n cmd = \"AUT\"\n self.write(cmd)","title":""},{"docid":"baa8ddba24c1466c1e6c3b36b98339f3","score":"0.537731","text":"def setAutoFocus(self,b):\n self.autoFocus=b","title":""},{"docid":"9a89ca8d832d8bdb64a49ff31661c717","score":"0.5374753","text":"def edit_mode(self, value):\n self.editMode = value\n if not self.editMode:\n # back to original state\n self.paths.active_path = None\n self.update()","title":""},{"docid":"8512a30b8b97ae63a3ceb4960be43d6d","score":"0.5371871","text":"def on_save_as_activate(self, *args):\n result = self.save_as_dialog.run()\n self.save_as_dialog.hide()\n if result == gtk.RESPONSE_OK:\n self.app.save_notelist_as(self.save_as_dialog.get_filename())","title":""},{"docid":"fcfbea8452784178f7f432d296d8fa41","score":"0.5360709","text":"def save(self):\n enabled = self.viewerGroupBox.isChecked()\n if enabled:\n Preferences.setDocuViewer(\n \"ShowInfoOnOpenParenthesis\",\n self.parenthesisCheckBox.isChecked())\n Preferences.setDocuViewer(\n \"Provider\",\n self.providerComboBox.itemData(\n self.providerComboBox.currentIndex())\n )\n else:\n Preferences.setDocuViewer(\"Provider\", \"disabled\")","title":""},{"docid":"bf493c2eb041d75814193b6b6df74246","score":"0.53548384","text":"def save_as(self, fname, no_prompt = 0, echo_cmd=0):\n\n if echo_cmd: self.echo_command('save_as', fname, no_prompt)\n self.app.save_file(fname, no_prompt = no_prompt)","title":""},{"docid":"0dba19e91e39556f3c0487a94a49c1c2","score":"0.5343752","text":"def save(self, interactive=True, file='Same file as edited by the buffer'):\n pass # implemented in Ada","title":""},{"docid":"7259db93d2a13b62b027f1eae3307c45","score":"0.5343587","text":"def autosave_notelist(self):\n if self.dirname:\n self.save_notelist()","title":""},{"docid":"a70a5f0e1661986cdc6f6a1ce9aea3fc","score":"0.53324705","text":"def saving(self):\n print(\"Saving....\")","title":""},{"docid":"51b3348f80818a6b3cc89da3f8fedd9b","score":"0.5318991","text":"def save(self, event: typing.Optional[tk.Event] = None):\n self.withdraw()\n self.update_idletasks()\n self.apply()\n self.cancel()","title":""},{"docid":"f3fc366de9b25c607da08d2b9ad10de6","score":"0.53185445","text":"def Activar(self):\n self.activa = True\n self.focus = True","title":""},{"docid":"970851f3347e9b4ab616fd3cb34a87de","score":"0.53126293","text":"def on_press(event):\n if v.mode == Filename.MODE_OPEN:\n mode = wx.FD_OPEN\n elif v.mode == Filename.MODE_APPEND:\n mode = wx.FD_SAVE\n else:\n mode = wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT\n dlg = wx.FileDialog(control, v.browse_msg, style=mode)\n if v.get_directory_fn is not None:\n dlg.SetDirectory(v.get_directory_fn())\n if v.exts is not None:\n dlg.SetWildcard(\"|\".join([\"|\".join(tuple(x)) for x in v.exts]))\n if dlg.ShowModal() == wx.ID_OK:\n if v.set_directory_fn is not None:\n v.set_directory_fn(dlg.GetDirectory())\n v.value = dlg.GetFilename()\n setting_edited_event = SettingEditedEvent(\n v, self.__module, v.value, event\n )\n self.notify(setting_edited_event)\n self.reset_view()","title":""},{"docid":"d52767741d6d03ce1d1c9caa704502a2","score":"0.53011686","text":"def ToggleFeature(self, event, feature, val, IdRange):\n if feature == \"IndentSize\":\n for id in IdRange:\n item = wx.FindWindowById(id)\n item.SetIndent(val)\n elif feature == \"IndetationGuides\":\n for id in IdRange:\n item = wx.FindWindowById(id)\n item.SetIndentationGuides(val)\n elif feature == \"BackSpaceUnindent\":\n\n for id in IdRange:\n item = wx.FindWindowById(id)\n item.SetBackSpaceUnIndents(val)\n elif feature == \"Whitespace\":\n\n for id in IdRange:\n item = wx.FindWindowById(id)\n item.SetViewWhiteSpace(val)\n elif feature == \"UseTabs\":\n\n for id in IdRange:\n item = wx.FindWindowById(id)\n item.SetUseTabs(val)\n elif feature == \"CarretWidth\":\n\n for id in IdRange:\n item = wx.FindWindowById(id)\n item.SetCaretWidth(val)\n elif feature == \"IndentSize\":\n\n for id in IdRange:\n item = wx.FindWindowById(id)\n item.SetTabWidth(val)\n elif feature == \"LineNumbers\":\n\n for id in IdRange:\n item = wx.FindWindowById(id)\n if val == True:\n\n item.SetMarginWidth(1, 45)\n else:\n item.SetMarginWidth(1, 1)\n elif feature == \"FoldMarks\":\n\n for id in IdRange:\n item = wx.FindWindowById(id)\n if val == True:\n item.SetMarginType(2, wx.stc.STC_MARGIN_SYMBOL)\n item.SetMarginMask(2, wx.stc.STC_MASK_FOLDERS)\n item.SetMarginSensitive(2, True)\n item.SetMarginWidth(2, 12)\n elif val == False:\n item.SetMarginWidth(2, 1)\n elif feature == \"SyntaxHighlight\":\n\n if val == False:\n for id in IdRange:\n item = wx.FindWindowById(id)\n item.StyleClearAll()\n elif val == True:\n for id in IdRange:\n SyntCol.ActivateSyntaxHighLight(id)\n elif feature == \"StatusBar\":\n\n item = wx.FindWindowById(999)\n if val == True:\n item.Show(True)\n else:\n item.Hide()\n elif feature == \"TabWidth\":\n\n for id in IdRange:\n item = wx.FindWindowById(id)\n item.SetTabWidth(val)\n elif feature == \"EdgeLine\":\n\n if val == False:\n for id in IdRange:\n item = wx.FindWindowById(id)\n item.SetEdgeMode(wx.stc.STC_EDGE_NONE)\n else:\n for id in IdRange:\n item = wx.FindWindowById(id)\n item.SetEdgeMode(wx.stc.STC_EDGE_LINE)\n elif feature == \"EdgeColumn\":\n\n for id in IdRange:\n item = wx.FindWindowById(id)\n item.SetEdgeColumn(val)\n elif feature == \"SourceBrowser\":\n\n counter = 0\n if val == True:\n for id in IdRange:\n item = wx.FindWindowById(2000 + id)\n nb = wx.FindWindowById(4003 + id)\n if self.GetOption(\"SourceBrowser\") and not self.GetOption(\"FileTree\"):\n item.parent.GetParent().GetParent().GetParent().SplitVertically(item.GetParent().GetParent().GetParent(),\n wx.FindWindowById(1001 + id))\n nb.AddPage(item.GetParent(), \"Source Browser\")\n counter += 1\n else:\n\n for id in IdRange:\n item = wx.FindWindowById(2000 + id)\n nb = wx.FindWindowById(4003 + id)\n if not self.GetOption(\"SourceBrowser\") and not self.GetOption(\"FileTree\"):\n item.parent.GetParent().GetParent().GetParent().Unsplit(item.GetParent().GetParent().GetParent())\n nb.RemovePage(self.GetTab(\"Source Browser\", nb))\n counter += 1\n elif feature == \"FileTree\":\n\n counter = 0\n if val == True:\n for id in IdRange:\n item = wx.FindWindowById(5000 + id)\n nb = wx.FindWindowById(4003 + id)\n if self.GetOption(\"FileTree\") and not self.GetOption(\"SourceBrowser\"):\n item.parent.GetParent().GetParent().GetParent().SplitVertically(item.GetParent().GetParent().GetParent(),\n wx.FindWindowById(1001 + id))\n nb.AddPage(item.GetParent(), \"File Browser\")\n counter += 1\n else:\n\n for id in IdRange:\n item = wx.FindWindowById(5000 + id)\n nb = wx.FindWindowById(4003 + id)\n if not self.GetOption(\"FileTree\") and not self.GetOption(\"SourceBrowser\"):\n item.parent.GetParent().GetParent().GetParent().Unsplit(item.GetParent().GetParent().GetParent())\n nb.RemovePage(self.GetTab(\"File Browser\", nb))\n counter += 1\n elif feature in [\"PythonShell\", \"BashShell\"]:\n\n item = wx.FindWindowById(4002)\n OSShell = wx.FindWindowById(4000)\n PyShell = wx.FindWindowById(4001)\n Nb_Panel = wx.FindWindowById(998)\n if not self.GetOption(\"PythonShell\"):\n try:\n PyShell.OnClose(0)\n item.RemovePage(self.GetTab(\"Python\", item))\n except:\n pass\n if not self.GetOption(\"BashShell\") and feature == \\\n \"BashShell\":\n\n OSShell.OnClose(0)\n item.RemovePage(self.GetTab(\"OS Shell\", item))\n\n if not self.GetOption(\"PythonShell\") and not self.GetOption(\"BashShell\"):\n\n item.GetParent().GetParent().Unsplit(item.GetParent())\n else:\n\n if self.GetOption(\"PythonShell\") and feature == \\\n \"PythonShell\":\n PyShell.OnRun(0, self.GetOption(\"PyPath\"))\n item.AddPage(PyShell.parent, \"Python\")\n PyShell.GetParent().Fit()\n\n if self.GetOption(\"BashShell\") and feature == \\\n \"BashShell\":\n OSShell.OnRun(0, self.GetOption(\"OSPath\"))\n item.AddPage(OSShell.parent, \"OS Shell\")\n OSShell.GetParent().Fit()\n\n item.GetParent().GetParent().SplitHorizontally(Nb_Panel,\n item.GetParent())\n item.GetParent().GetParent().Refresh()\n elif feature == \"EdgeLine\":\n\n if val:\n for id in IdRange:\n wx.FindWindowById(id).SetEdgeMode(wx.stc.STC_EDGE_LINE)\n else:\n for id in IdRange:\n wx.FindWindowById(id).SetEdgeMode(wx.stc.STC_EDGE_NONE)","title":""},{"docid":"9c1be08c88266aa0dc40be10e0ee0f1a","score":"0.529862","text":"def DwgFileWasSavedByAutodeskSoftware(self) -> bool:","title":""},{"docid":"ddddccf894fc574db421dc6c5f77dea9","score":"0.5298492","text":"def on_autoactivateCheckBox_clicked(self):\n self.autoactivateCheckBox.setChecked(self.__autoactivate)","title":""},{"docid":"4488a811f44c9406370f619979beaba0","score":"0.52885306","text":"def editAdvPreferences(self, event):\n for widget in self.widgets:\n widget[0].Enable()","title":""},{"docid":"26e46f4d0d715a2a41622187ca883b95","score":"0.5284583","text":"def commit_changes():\n save()\n activate()","title":""},{"docid":"e869333c2c3e99dfacc33c5c38a3d8a4","score":"0.52738714","text":"def controlS(self):\n menubar.master.bind_all(\"<Control-s>\", self.save)","title":""},{"docid":"4e224056d1c76d24290b0d99a7842738","score":"0.52617615","text":"def refresh_():\n save_script_()\n if not appdata.get('filename'):\n # save was cancelled by the user\n return\n exec_script()\n appdata.set('dirty', False)","title":""},{"docid":"d305bc7309cc99192a32b3d450504924","score":"0.52614605","text":"async def cmd_forcesave(self, **_):\n self.config.save(vb=1)\n return \"Saved.\"","title":""},{"docid":"6cbdaf3b2dc8de38429bc83687b68212","score":"0.52523875","text":"def define_autosave_path():\n global NUKE_DIR\n global AUTOSAVE_FILE\n AUTOSAVE_FILE = os.getenv(\n 'PYTHONEDITOR_AUTOSAVE_FILE'\n )\n if (AUTOSAVE_FILE is None\n or not parent_isdir(AUTOSAVE_FILE)\n ):\n if not os.path.isdir(NUKE_DIR):\n NUKE_DIR = os.path.expanduser('~')\n AUTOSAVE_FILE = os.path.join(\n NUKE_DIR,\n 'PythonEditorHistory.xml'\n )\n os.environ[\n 'PYTHONEDITOR_AUTOSAVE_FILE'\n ] = AUTOSAVE_FILE\n return AUTOSAVE_FILE","title":""},{"docid":"2a72c0e92db527e8cf69af22e91c6965","score":"0.524441","text":"def save():\n if current_filename is not None:\n save_gui(current_filename)\n else:\n save_as()","title":""},{"docid":"2cbed25b821481f066cb1b00259f8503","score":"0.5240317","text":"def click_view_mode(self):\n self._file_actions_toolbar.click_view_mode()","title":""},{"docid":"f2056dc75ee8cfd20d92409a5bc9f1a5","score":"0.52384794","text":"async def _pane_switch_mode(self) -> None:\n await self._app.current_filepane.pane_switch_mode()","title":""},{"docid":"4b84ff1baa2698418fdf5a94528f66d6","score":"0.52357274","text":"def on_toggle_autoupdate_check(self, event):\r\n conf.UpdateCheckAutomatic = event.IsChecked()\r\n conf.save()","title":""},{"docid":"629571b89e34731a190163376d8c3993","score":"0.52265376","text":"def force_save(self) -> bool:\n return self.get(\"force-save\", bool_or_value) # type: ignore","title":""},{"docid":"a9dd8a9a15da6e2f8c0ecfd1fb7a2ffd","score":"0.5218732","text":"def _setautoOpenMap(self, value):\n\t\tif self.boldedItem:\n\t\t\tself.tree.SetItemBold(self.boldedItem, False)\n\t\tif self.selectedItem:\n\t\t\tself.tree.SetItemBold(self.selectedItem, True)\n\t\t\tself.boldedItem = self.selectedItem\n\t\t\tisZip = True\n\t\t\td = self.tree.GetItemData(self.boldedItem).GetData()\n\t\t\tif d.archive:\n\t\t\t\tisZip = d.archive.getType() == 'Zip'\n\t\trorSettings().setSetting(TOOLKIT, \"autoopen\", value)\n\t\trorSettings().setSetting(TOOLKIT, \"autoopeniszip\", isZip)\n\t\t\t\n\t\tself._autoOpenMap = value","title":""},{"docid":"3b0e01712c5e9a5083ccca989e265c30","score":"0.52047604","text":"def action_save_as(self):\n\n file_dialog = FileDialog(\n gettext(\"Save Gaphor Model As\"), action=\"save\", filename=self.filename\n )\n\n filename = file_dialog.selection\n\n file_dialog.destroy()\n\n if filename:\n self.save(filename)\n return True\n\n return False","title":""},{"docid":"7d69bb14bf5c0cec81ed01744085003e","score":"0.5185158","text":"def select_long_view_mode_in_toolbar(self):\n self._file_actions_toolbar.select_long_view_mode_in_toolbar()","title":""},{"docid":"a586fae8cfb540f8ed5f2b1bc5ddfc59","score":"0.5184916","text":"def use_edit_popup(brain):","title":""},{"docid":"b429a2b44fe6c04040cfb100aa094c71","score":"0.517517","text":"def toggle_expert_takeover(self):\n self.current_track_vehicle._expert_takeover = not self.current_track_vehicle._expert_takeover","title":""},{"docid":"a39c41d0cd540e711afe1d59daacfb7c","score":"0.5170487","text":"def _saveButtonClick(self):\n if len(self._view.languageCombo.currentText()) > 0:\n self._config.language = self._view.languageCombo.currentText()\n self._config.backend = self._view.backendCombo.currentText()\n self._config.gain = self._view.gainSlider.value()\n self._config.camera = self._view.cameraEdit.text()\n self._config.shortcut = self._view.shortcutEdit.text()\n\n self._config.interval = self._view.intervalEdit.value()\n if self._view.intervalLabel.text() == self.interval_modes[2]:\n self._config.interval *= 60\n\n self._config.fullscreen = 0\n if self._view.fullscreenCheck.isChecked():\n self._config.fullscreen = 1\n self._config.startup = 0\n if self._view.startupCheck.isChecked():\n self._config.startup = 1\n\n self.backend.configSave()\n self._config.save()\n\n self._view.close()","title":""},{"docid":"a57d770395fa2f45c9655ec3b22e94fb","score":"0.51634794","text":"def commModeSave(self, mode):\n pass","title":""},{"docid":"8c679a8a347ed2407a4f245b0483ba1b","score":"0.51527876","text":"def end_save(_session):\n if _session.save_enabled:\n _session.save_enabled = False\n _session.save_file_path = None\n _session.prompt = _session.prompt.replace('*', '')\n print('Save session ended.')\n else:\n print('A save session is not currently enabled.')","title":""},{"docid":"69194b95c39e5bc5add650f96ea192c9","score":"0.5142079","text":"def setEnabled(enabled):\n\tonOpenSave.enabled = enabled\n\tonPreOpen.enabled = enabled","title":""},{"docid":"e79070eca524f4a51bf5cdb8483614e4","score":"0.5137087","text":"def motion_is_now_voteable(self):\n self.motion1.voteable = True\n self.motion1.save()\n self.browser.refresh()","title":""},{"docid":"2cae90958776e33e49d92dd13b4924fa","score":"0.51355207","text":"def emacs_editing_mode(event: E) -> None:\n event.app.editing_mode = EditingMode.EMACS","title":""},{"docid":"18b01cfb2fc4befab240726f0c77e407","score":"0.5134704","text":"def Editable(self) -> bool:","title":""},{"docid":"6b4d2f45bc0c551e475cdc706cc32203","score":"0.51343983","text":"def toggle_name_on(self):\n self.tab.addstr(1, 1, \"{}\".format(self.name), bg.re)\n self.toggle_name = True\n self.tab.refresh()","title":""},{"docid":"48b293050798da00b19b6fb2ba5785b6","score":"0.5133477","text":"def create_autosave_file():\n # look for the autosave\n if os.path.isfile(AUTOSAVE_FILE):\n\n # if the autosave file is empty, write header\n # FIXME: can this be an os.stat/get file size?\n # Furthermore, what if it's not empty but has a\n # corrupted header? What are the methods for\n # data preservation?\n with open(AUTOSAVE_FILE, 'r') as f:\n is_empty = not bool(f.read().strip())\n if is_empty:\n create_empty_autosave()\n else:\n\n # if file not found, check if directory exists\n if not os.path.isdir(NUKE_DIR):\n # filehandle, filepath = tempfile.mkstemp()\n # FIXME: set os.environ['PYTHONEDITOR_AUTOSAVE_FILE'] and define_autosave_path()\n # msg = 'Directory %s does not exist, saving to %s' % (NUKE_DIR, filepath)\n msg = 'Directory {0} does not exist'.format(NUKE_DIR)\n raise CouldNotCreateAutosave(msg)\n else:\n create_empty_autosave()\n return True","title":""},{"docid":"18b01cfb2fc4befab240726f0c77e407","score":"0.5133266","text":"def Editable(self) -> bool:","title":""},{"docid":"bd5c722c17eb83adead026e8ee90d6d1","score":"0.5128371","text":"def save_as(self, action):\n self.chooser_dialog(title=\"Save Document\", callback=self.on_file_save, save=True)\n return False # TODO this signals that file saving was not successful\n # because no action should be taken until the chooser\n # dialog is finish, however the user might then need to\n # repeat the action, once the document was saved and the\n # edited flag was cleared","title":""},{"docid":"68710ccfb8d74ab0d4c34d9ac3af59e0","score":"0.5126705","text":"def do_single_save(self):\n pass","title":""},{"docid":"9c8699fdb4d6a3d32841d2e263f61018","score":"0.51195246","text":"def set_saved(self, filename):\n\n sel = self.notebook.GetSelection()\n if sel >= 0:\n self.tab_list[sel].filename = filename\n self.set_tab_modified(sel, False)","title":""},{"docid":"541fe96dbbb1faa4928a27a50c8b4646","score":"0.5114742","text":"def EnableSelection(self, flag):","title":""},{"docid":"3fd65c4b9b885d3b61383a492c0b7576","score":"0.5112609","text":"def save(self):\n self.editor_input.save(self.document)\n self.dirty = False","title":""},{"docid":"9343f9a069333eb2ecd449afefb73752","score":"0.51024485","text":"def toggle(self):\n self.setting = not self.setting","title":""},{"docid":"b9bbab25345a1a7770b00a4cc8c76b75","score":"0.5091114","text":"def reactivate(self):\n self.active = True\n self.save()","title":""},{"docid":"3d41ddc0b6076bc589963551bf8ac6f8","score":"0.5091112","text":"def save_file(self, buff_name, full_path = None, no_prompt = 0,\n rename_buff = 1, ask_for_new_name = 0, perform_callback = 0):\n debug.virtual('GenEdit.save_file')","title":""},{"docid":"9cbccf5f9fb7cd81288f4fc669002639","score":"0.5086999","text":"def toggle(self):\n pass","title":""},{"docid":"6df5af509fb87d81407a170ce671d769","score":"0.5083608","text":"def save_activated(self) -> None:\n self.setReadOnly(True)\n try:\n with open(self.path, 'w') as file:\n file.write(self.toPlainText())\n except Exception as e:\n # Set text how it was before\n self.setText(self.text_before_edit)\n # Show the error message\n error_msg = QtWidgets.QMessageBox()\n error_msg.setText(f\"{e}\")\n error_msg.setWindowTitle(f'Error trying to save markdown edit.')\n error_msg.exec_()","title":""},{"docid":"2fa9bf9971656d4a57bd2a8cb333aa3a","score":"0.5083561","text":"def _set_drag_status(self):\n self.viewer.status = trans._(\n 'Hold <Alt> key to open plugin selection. Hold <Shift> to open files as stack.'\n )","title":""},{"docid":"092e68141cf48c5eb195c46a0d09510b","score":"0.50785273","text":"def _saveFile(self):\n if self._features:\n filename = filedialog.asksaveasfilename(initialdir=\"/\", title=\"Select file\",\n filetypes=(\n (\"animangle files\", \"*.animangle\"), (\"all files\", \"*.*\")))\n self._features.requestSave(open(filename, \"w\"))","title":""},{"docid":"96b1de120d197377afea2ec4787e1d23","score":"0.50766003","text":"def automatic(self):\n return False","title":""},{"docid":"1e6c32ef8915c1d6ef7c76fd0fdece12","score":"0.5063986","text":"def edit(self):\r\n\t\treturn False","title":""},{"docid":"10c82d4826e52adecccd3c3037e40199","score":"0.5058703","text":"def on_back_ext_changed(self, event):\n\n self.m_back_ext_button.Enable(self.m_back_ext_textbox.GetValue() != self.backup_ext)","title":""},{"docid":"2f5df0fb0676d790a511d97a4ef72ad9","score":"0.50586194","text":"def on_toggle_marks(self):\n self.use_marks = True if not self.use_marks else False","title":""},{"docid":"ebcf643b83c90671dffc609a87383e0e","score":"0.505293","text":"def auto_save_threshold(self, auto_save_threshold):\n\n self._auto_save_threshold = auto_save_threshold","title":""},{"docid":"a20e5aa93b0b00ef7dcc7fedb2e3a077","score":"0.50375754","text":"def create_empty_autosave():\n with open(AUTOSAVE_FILE, 'w') as f:\n f.write(XML_HEADER+'<script></script>')","title":""},{"docid":"9501537625b02f68c8730f86dad58b37","score":"0.50356936","text":"def select_manual_option(self):\n if not self.verify_manual_capture_mode():\n self.select_auto_btn()","title":""},{"docid":"364e29d6161e2e11121679eaa7cf6d6f","score":"0.5033404","text":"def undo_open():\n _gv.gv_undo_open()","title":""},{"docid":"6205149f5570b529982533e5dc09d548","score":"0.50273573","text":"def filename_complete(env: MnoteEnvironment, on_off: click.Choice):\n style = env.config.styles\n env.config.filename_complete = on_off == \"on\"\n env.config.write()\n\n echo_line()\n if env.config.filename_complete:\n echo_line(\"Default '--complete' flag for filename \", style.success(\"ON\"))\n else:\n echo_line(\"Default '--complete' flag for filename \", style.fail(\"OFF\"))","title":""},{"docid":"dfc5fe4478108a270e0c96f50b6393b3","score":"0.5016182","text":"def settrigger_mode(self, newval):\n raise NotImplementedError","title":""},{"docid":"5e5febbc7f1ffb85587341edf718739a","score":"0.5012927","text":"def set_auto():\n global auto_set\n if not auto_set:\n SC.reg_param('state', state_callback)\n SC.reg_param('source', source_callback)\n V.reg_param('sub_frequencyA', vegas_sb_callback)\n auto_set = True","title":""},{"docid":"a1ef8eaefaa11a5455f332702988a773","score":"0.50129217","text":"def display_edit_completed():\r\n print('Edit saved')","title":""},{"docid":"7c380d78069f58423e9c10cbabed3341","score":"0.5006341","text":"def _update_active(self, b):\n super(DemoUniverse, self)._update_active(b)\n scns = self.active()\n if not scns: return\n flds = [scn.field for scn in scns]\n fks = [scn.field_kind for scn in scns]\n fmls = [scn.field_ml for scn in scns]\n folder = self._controls['field']\n fopts = folder['fopts'].options\n fld = flds[0]\n fk = fks[0]\n fml = fmls[0]\n if not len(set(flds)) == 1:\n for scn in scns: scn.field = fld\n if not len(set(fks)) == 1:\n for scn in scns: scn.field_kind = fk\n if not len(set(fmls)) == 1:\n for scn in scns: scn.field_ml = fml\n folder[fld].value = fk\n folder.activate(fld, enable=True)\n folder.deactivate(*[f for f in fopts if f != fld])\n if fld == 'SolidHarmonic':\n ofks = [str(i) for i in range(8) if str(i) != fk]\n folder.activate(fk, enable=True)\n folder.deactivate(*ofks)\n folder._set_gui()","title":""},{"docid":"46b7c759110d2f9b36f7f10de20214b9","score":"0.50020224","text":"def settings_save():\n\n answer = messagebox.askyesno(title='Закінчити редагування',\n message='Зберегти зміни?')\n if answer:\n tariffs_changing(entry_gas.get(), entry_water.get(), entry_electricity.get())\n set_win.destroy()\n button_settings.configure(state=NORMAL)\n\n elif not answer:\n set_win.destroy()\n button_settings.configure(state=NORMAL)","title":""},{"docid":"e4ce94137df85a404b256450d062e394","score":"0.5000504","text":"def onSave(self, event):\n current_settings = GeneralSettingsData()\n\n current_settings.logging = self.cb_logging.GetValue()\n current_settings.use_hotkeys = self.cb_usehotkeys.GetValue()\n current_settings.warn_large_img = self.cb_warn_large.GetValue()\n if self.tc_hk_next.GetLineText(0):\n current_settings.hk_binding_next = tuple(\n self.tc_hk_next.GetLineText(0).strip().split(\"+\")\n )\n else:\n current_settings.hk_binding_next = None\n if self.tc_hk_pause.GetLineText(0):\n current_settings.hk_binding_pause = tuple(\n self.tc_hk_pause.GetLineText(0).strip().split(\"+\")\n )\n else:\n current_settings.hk_binding_pause = None\n\n current_settings.set_command = self.tc_setcmd.GetLineText(0).strip()\n\n current_settings.save_settings()\n # after saving file apply in tray object\n self.parent_tray_obj.read_general_settings()","title":""},{"docid":"5f1b4403b86ffe3d16978cdd9160982a","score":"0.5000007","text":"def LoseFocus(self):\n self.focus = False","title":""},{"docid":"cd866744b68d324633244519a4e3d190","score":"0.49994484","text":"def ToggleFlag(self, flag):","title":""},{"docid":"c1e837ab172655ead2b8766913fcf594","score":"0.4991357","text":"def set_active(self):\r\n FreeCAD.setActiveDocument(self.Name)","title":""},{"docid":"6b2a3bf4184825eb2615dd4290795b47","score":"0.49871665","text":"def checkpoint_saved(self):\n self._last_auto_save_time = time()","title":""},{"docid":"d557854ffef646e59f1beb356dbb9d96","score":"0.49866563","text":"def updateModified(self):\n #self.ui.action_Add_Image.setEnabled(self._model is not None)\n #self.ui.action_Add_Video.setEnabled(self._model is not None)\n # TODO also disable/enable other items\n #self.ui.actionSave.setEnabled(self.annotations.dirty())\n #self.setWindowModified(self.annotations.dirty())\n pass","title":""},{"docid":"090c58613d16dc6c8d5a103bab52add3","score":"0.4979805","text":"def set_edit_mode(request, state):\n setattr(request, \"_fluent_contents_edit_mode\", bool(state))","title":""},{"docid":"116c11d57b72030b521ce5a6ff51964d","score":"0.49735904","text":"def on_manual_button_clicked(self, obj):\n\n\t\t# Ensure we can go back...\n\t\tself.idle_add(self.objects[\"parent\"].back_button.set_sensitive, True)\n\t\t\n\t\t# Switch to page 3\n\t\tself.pages_notebook.set_current_page(3)\n\n\t\tself.manual_ready()","title":""},{"docid":"aaead4533be8f6141d0fd27ebc75fd47","score":"0.49669957","text":"def CheckSave(self):\n self.mSave.Enable(self.data.HasChanged())","title":""}],"string":"[\n {\n \"docid\": \"95232758d83058425927fc755b481bdc\",\n \"score\": \"0.7093163\",\n \"text\": \"def autosave_handler(self):\\n self.autosave_timer_waiting = False\\n if self.editor.document().isModified():\\n self.autosave()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbae35afd4db48fd9c88b845cb79de50\",\n \"score\": \"0.65503746\",\n \"text\": \"def autosave(self):\\n return self._autosave\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5915126d5f89e130a74f4b0f299ebf9\",\n \"score\": \"0.62856436\",\n \"text\": \"def autosave(self):\\n tabs = self.tabs\\n self.save_by_uuid(\\n tabs['uuid'],\\n tabs['name'],\\n tabs['text'],\\n str(tabs.currentIndex()),\\n tabs.get('path')\\n )\\n self.editor.document().setModified(False)\\n self.sync_tab_indices()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0406e14fe1f75001799dbe6192797db\",\n \"score\": \"0.61678654\",\n \"text\": \"def MarkAsSaved(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0800d85ed3881472f5671737e19bdc74\",\n \"score\": \"0.6018382\",\n \"text\": \"def autosave(self):\\n\\t\\tself.log.debug(\\\"Session: autosaving\\\")\\n\\t\\t# call saving through horizons.main and not directly through session, so that save errors are handled\\n\\t\\tsuccess = self.save(SavegameManager.create_autosave_filename())\\n\\t\\tif success:\\n\\t\\t\\tSavegameManager.delete_dispensable_savegames(autosaves = True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3abac37d4d9e1ae7ceadcdeb35b65826\",\n \"score\": \"0.5979588\",\n \"text\": \"def save(self):\\n self.config.set(\\\"makeactive\\\", self.makeactive)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bbff150710f5ecc9d94259e5c7f1cc6\",\n \"score\": \"0.58593255\",\n \"text\": \"def is_saved(self, event=None):\\r\\n file_name = os.path.basename(self.pathfile)\\r\\n if file_name == \\\"\\\":\\r\\n file_name = \\\"untitled\\\"\\r\\n\\r\\n if self.content_to_save != self.text_sheet.get(\\\"1.0\\\", \\\"end-1c\\\"):\\r\\n self.saved = False\\r\\n self.master.title(\\\"%s : Mind Note*\\\" % file_name)\\r\\n else:\\r\\n self.saved = True\\r\\n self.master.title(\\\"%s : Mind Note\\\" % file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82734711fc5cb4e25c7bcb451de93dce\",\n \"score\": \"0.58260196\",\n \"text\": \"def save_timer(self):\\n self.autosave_timer_waiting = True\\n if self.autosave_timer.isActive():\\n self.autosave_timer.stop()\\n\\n self.setup_save_timer()\\n self.autosave_timer.timeout.connect(self.autosave_handler)\\n if self.editor.document().isModified():\\n self.autosave_timer.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6e3f5cba977acf6a25112fb6b0d64c8\",\n \"score\": \"0.5791793\",\n \"text\": \"def on_save_activate(self, *args):\\n self.app.save_notelist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0fecb3864e0bc42c8d47e51e3143ada\",\n \"score\": \"0.573265\",\n \"text\": \"def save(self):\\n Preferences.setEditor(\\n \\\"AutoCompletionEnabled\\\",\\n self.acEnabledGroupBox.isChecked())\\n Preferences.setEditor(\\n \\\"AutoCompletionCaseSensitivity\\\",\\n self.acCaseSensitivityCheckBox.isChecked())\\n \\n Preferences.setEditor(\\n \\\"AutoCompletionReversedList\\\",\\n self.acReversedCheckBox.isChecked())\\n Preferences.setEditor(\\n \\\"AutoCompletionReplaceWord\\\",\\n self.acReplaceWordCheckBox.isChecked())\\n Preferences.setEditor(\\n \\\"AutoCompletionThreshold\\\",\\n self.acThresholdSlider.value())\\n Preferences.setEditor(\\n \\\"AutoCompletionScintillaOnFail\\\",\\n self.acScintillaCheckBox.isChecked())\\n Preferences.setEditor(\\n \\\"AutoCompletionTimeout\\\",\\n self.acTimeoutSpinBox.value())\\n Preferences.setEditor(\\n \\\"AutoCompletionCacheEnabled\\\",\\n self.acCacheGroup.isChecked())\\n Preferences.setEditor(\\n \\\"AutoCompletionCacheSize\\\",\\n self.acCacheSizeSpinBox.value())\\n Preferences.setEditor(\\n \\\"AutoCompletionCacheTime\\\",\\n self.acCacheTimeSpinBox.value())\\n Preferences.setEditor(\\n \\\"AutoCompletionWatchdogTime\\\",\\n self.acWatchdogDoubleSpinBox.value() * 1000)\\n Preferences.setEditor(\\n \\\"AutoCompletionMaxLines\\\",\\n self.acLinesSlider.value())\\n Preferences.setEditor(\\n \\\"AutoCompletionMaxChars\\\",\\n self.acCharSlider.value())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a9d9a75b3a48f1e99191b5f12a15cd2\",\n \"score\": \"0.5720629\",\n \"text\": \"def toggle_autoPreview(self):\\n if self.__auto_timer.isActive():\\n self.__auto_timer.stop()\\n else:\\n self.__auto_timer.start(100)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74cac84c171d63f8abac35a6746991e1\",\n \"score\": \"0.5643983\",\n \"text\": \"def onOpenSave(editor, path):\\n\\tproject = getProjectForFile(path)\\n\\tif not project:\\n\\t\\treturn\\n\\n\\teditor.project = project\\n\\tproject.applyOptions(editor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f51c936ff28dd29e08abe7ebef578e6b\",\n \"score\": \"0.5627555\",\n \"text\": \"def dispModeSave(self, mode):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdaf1df06cdc5cfb6d9fb52710cbb20d\",\n \"score\": \"0.56194866\",\n \"text\": \"def saveasbuttonclicked(self, _):\\n fname = self.savedialog()\\n if fname is None:\\n return\\n self.docpath = fname\\n self.savefile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e374662cbf2ff597a9b3e122da60002\",\n \"score\": \"0.55892575\",\n \"text\": \"def _stop_editing():\\n global _EDITABLE\\n _EDITABLE = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f18eda7f2fe070f967dbbf87741be501\",\n \"score\": \"0.55647784\",\n \"text\": \"def SaveEnabled(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2109e4d30360af962747f1051f0da90c\",\n \"score\": \"0.55417275\",\n \"text\": \"def savebuttonclicked(self, _):\\n if self.docpath is not None:\\n self.savefile()\\n else:\\n self.saveasbuttonclicked(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437614a1f8895e34f0f79e536cd4e449\",\n \"score\": \"0.54992074\",\n \"text\": \"def Regenmode(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33133b70030a813613f0e57a3c03dbd4\",\n \"score\": \"0.54925835\",\n \"text\": \"def save(interactive=True, all=True):\\n pass # implemented in Ada\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ec407ebcea6cc9b7f444924d4467993\",\n \"score\": \"0.542806\",\n \"text\": \"def edit_pad(self):\\n\\t\\tpath = join(self.save_dir, vim.current.line.split(\\\" @\\\")[0])\\n\\t\\tvim.command(\\\"bd\\\")\\n\\t\\tself.pad_open(path=path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ae09e1099d71351afe1a6fc2790b88\",\n \"score\": \"0.54264396\",\n \"text\": \"def _on_save_changed(self):\\n\\n if self.save_file.isChecked():\\n self.path_widget.setEnabled(True)\\n else:\\n self.path_widget.setEnabled(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efb18b6beeca98e3befb76d7149ffef9\",\n \"score\": \"0.5418351\",\n \"text\": \"def vi_editing_mode(event: E) -> None:\\n event.app.editing_mode = EditingMode.VI\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4916b6b137ded40794e3ab5b286792f\",\n \"score\": \"0.5415203\",\n \"text\": \"def select_short_view_mode_in_toolbar(self):\\n self._file_actions_toolbar.select_short_view_mode_in_toolbar()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df40c31e985fa963782fbf9bf79724d0\",\n \"score\": \"0.5406741\",\n \"text\": \"def save(replace=True):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc6f3318d8f5436cf760f0f9eb91e454\",\n \"score\": \"0.5398526\",\n \"text\": \"def issue_autoset(self):\\n cmd = \\\"AUT\\\"\\n self.write(cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baa8ddba24c1466c1e6c3b36b98339f3\",\n \"score\": \"0.537731\",\n \"text\": \"def setAutoFocus(self,b):\\n self.autoFocus=b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a89ca8d832d8bdb64a49ff31661c717\",\n \"score\": \"0.5374753\",\n \"text\": \"def edit_mode(self, value):\\n self.editMode = value\\n if not self.editMode:\\n # back to original state\\n self.paths.active_path = None\\n self.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8512a30b8b97ae63a3ceb4960be43d6d\",\n \"score\": \"0.5371871\",\n \"text\": \"def on_save_as_activate(self, *args):\\n result = self.save_as_dialog.run()\\n self.save_as_dialog.hide()\\n if result == gtk.RESPONSE_OK:\\n self.app.save_notelist_as(self.save_as_dialog.get_filename())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcfbea8452784178f7f432d296d8fa41\",\n \"score\": \"0.5360709\",\n \"text\": \"def save(self):\\n enabled = self.viewerGroupBox.isChecked()\\n if enabled:\\n Preferences.setDocuViewer(\\n \\\"ShowInfoOnOpenParenthesis\\\",\\n self.parenthesisCheckBox.isChecked())\\n Preferences.setDocuViewer(\\n \\\"Provider\\\",\\n self.providerComboBox.itemData(\\n self.providerComboBox.currentIndex())\\n )\\n else:\\n Preferences.setDocuViewer(\\\"Provider\\\", \\\"disabled\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf493c2eb041d75814193b6b6df74246\",\n \"score\": \"0.53548384\",\n \"text\": \"def save_as(self, fname, no_prompt = 0, echo_cmd=0):\\n\\n if echo_cmd: self.echo_command('save_as', fname, no_prompt)\\n self.app.save_file(fname, no_prompt = no_prompt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dba19e91e39556f3c0487a94a49c1c2\",\n \"score\": \"0.5343752\",\n \"text\": \"def save(self, interactive=True, file='Same file as edited by the buffer'):\\n pass # implemented in Ada\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7259db93d2a13b62b027f1eae3307c45\",\n \"score\": \"0.5343587\",\n \"text\": \"def autosave_notelist(self):\\n if self.dirname:\\n self.save_notelist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a70a5f0e1661986cdc6f6a1ce9aea3fc\",\n \"score\": \"0.53324705\",\n \"text\": \"def saving(self):\\n print(\\\"Saving....\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51b3348f80818a6b3cc89da3f8fedd9b\",\n \"score\": \"0.5318991\",\n \"text\": \"def save(self, event: typing.Optional[tk.Event] = None):\\n self.withdraw()\\n self.update_idletasks()\\n self.apply()\\n self.cancel()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3fc366de9b25c607da08d2b9ad10de6\",\n \"score\": \"0.53185445\",\n \"text\": \"def Activar(self):\\n self.activa = True\\n self.focus = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"970851f3347e9b4ab616fd3cb34a87de\",\n \"score\": \"0.53126293\",\n \"text\": \"def on_press(event):\\n if v.mode == Filename.MODE_OPEN:\\n mode = wx.FD_OPEN\\n elif v.mode == Filename.MODE_APPEND:\\n mode = wx.FD_SAVE\\n else:\\n mode = wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT\\n dlg = wx.FileDialog(control, v.browse_msg, style=mode)\\n if v.get_directory_fn is not None:\\n dlg.SetDirectory(v.get_directory_fn())\\n if v.exts is not None:\\n dlg.SetWildcard(\\\"|\\\".join([\\\"|\\\".join(tuple(x)) for x in v.exts]))\\n if dlg.ShowModal() == wx.ID_OK:\\n if v.set_directory_fn is not None:\\n v.set_directory_fn(dlg.GetDirectory())\\n v.value = dlg.GetFilename()\\n setting_edited_event = SettingEditedEvent(\\n v, self.__module, v.value, event\\n )\\n self.notify(setting_edited_event)\\n self.reset_view()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d52767741d6d03ce1d1c9caa704502a2\",\n \"score\": \"0.53011686\",\n \"text\": \"def ToggleFeature(self, event, feature, val, IdRange):\\n if feature == \\\"IndentSize\\\":\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n item.SetIndent(val)\\n elif feature == \\\"IndetationGuides\\\":\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n item.SetIndentationGuides(val)\\n elif feature == \\\"BackSpaceUnindent\\\":\\n\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n item.SetBackSpaceUnIndents(val)\\n elif feature == \\\"Whitespace\\\":\\n\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n item.SetViewWhiteSpace(val)\\n elif feature == \\\"UseTabs\\\":\\n\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n item.SetUseTabs(val)\\n elif feature == \\\"CarretWidth\\\":\\n\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n item.SetCaretWidth(val)\\n elif feature == \\\"IndentSize\\\":\\n\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n item.SetTabWidth(val)\\n elif feature == \\\"LineNumbers\\\":\\n\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n if val == True:\\n\\n item.SetMarginWidth(1, 45)\\n else:\\n item.SetMarginWidth(1, 1)\\n elif feature == \\\"FoldMarks\\\":\\n\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n if val == True:\\n item.SetMarginType(2, wx.stc.STC_MARGIN_SYMBOL)\\n item.SetMarginMask(2, wx.stc.STC_MASK_FOLDERS)\\n item.SetMarginSensitive(2, True)\\n item.SetMarginWidth(2, 12)\\n elif val == False:\\n item.SetMarginWidth(2, 1)\\n elif feature == \\\"SyntaxHighlight\\\":\\n\\n if val == False:\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n item.StyleClearAll()\\n elif val == True:\\n for id in IdRange:\\n SyntCol.ActivateSyntaxHighLight(id)\\n elif feature == \\\"StatusBar\\\":\\n\\n item = wx.FindWindowById(999)\\n if val == True:\\n item.Show(True)\\n else:\\n item.Hide()\\n elif feature == \\\"TabWidth\\\":\\n\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n item.SetTabWidth(val)\\n elif feature == \\\"EdgeLine\\\":\\n\\n if val == False:\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n item.SetEdgeMode(wx.stc.STC_EDGE_NONE)\\n else:\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n item.SetEdgeMode(wx.stc.STC_EDGE_LINE)\\n elif feature == \\\"EdgeColumn\\\":\\n\\n for id in IdRange:\\n item = wx.FindWindowById(id)\\n item.SetEdgeColumn(val)\\n elif feature == \\\"SourceBrowser\\\":\\n\\n counter = 0\\n if val == True:\\n for id in IdRange:\\n item = wx.FindWindowById(2000 + id)\\n nb = wx.FindWindowById(4003 + id)\\n if self.GetOption(\\\"SourceBrowser\\\") and not self.GetOption(\\\"FileTree\\\"):\\n item.parent.GetParent().GetParent().GetParent().SplitVertically(item.GetParent().GetParent().GetParent(),\\n wx.FindWindowById(1001 + id))\\n nb.AddPage(item.GetParent(), \\\"Source Browser\\\")\\n counter += 1\\n else:\\n\\n for id in IdRange:\\n item = wx.FindWindowById(2000 + id)\\n nb = wx.FindWindowById(4003 + id)\\n if not self.GetOption(\\\"SourceBrowser\\\") and not self.GetOption(\\\"FileTree\\\"):\\n item.parent.GetParent().GetParent().GetParent().Unsplit(item.GetParent().GetParent().GetParent())\\n nb.RemovePage(self.GetTab(\\\"Source Browser\\\", nb))\\n counter += 1\\n elif feature == \\\"FileTree\\\":\\n\\n counter = 0\\n if val == True:\\n for id in IdRange:\\n item = wx.FindWindowById(5000 + id)\\n nb = wx.FindWindowById(4003 + id)\\n if self.GetOption(\\\"FileTree\\\") and not self.GetOption(\\\"SourceBrowser\\\"):\\n item.parent.GetParent().GetParent().GetParent().SplitVertically(item.GetParent().GetParent().GetParent(),\\n wx.FindWindowById(1001 + id))\\n nb.AddPage(item.GetParent(), \\\"File Browser\\\")\\n counter += 1\\n else:\\n\\n for id in IdRange:\\n item = wx.FindWindowById(5000 + id)\\n nb = wx.FindWindowById(4003 + id)\\n if not self.GetOption(\\\"FileTree\\\") and not self.GetOption(\\\"SourceBrowser\\\"):\\n item.parent.GetParent().GetParent().GetParent().Unsplit(item.GetParent().GetParent().GetParent())\\n nb.RemovePage(self.GetTab(\\\"File Browser\\\", nb))\\n counter += 1\\n elif feature in [\\\"PythonShell\\\", \\\"BashShell\\\"]:\\n\\n item = wx.FindWindowById(4002)\\n OSShell = wx.FindWindowById(4000)\\n PyShell = wx.FindWindowById(4001)\\n Nb_Panel = wx.FindWindowById(998)\\n if not self.GetOption(\\\"PythonShell\\\"):\\n try:\\n PyShell.OnClose(0)\\n item.RemovePage(self.GetTab(\\\"Python\\\", item))\\n except:\\n pass\\n if not self.GetOption(\\\"BashShell\\\") and feature == \\\\\\n \\\"BashShell\\\":\\n\\n OSShell.OnClose(0)\\n item.RemovePage(self.GetTab(\\\"OS Shell\\\", item))\\n\\n if not self.GetOption(\\\"PythonShell\\\") and not self.GetOption(\\\"BashShell\\\"):\\n\\n item.GetParent().GetParent().Unsplit(item.GetParent())\\n else:\\n\\n if self.GetOption(\\\"PythonShell\\\") and feature == \\\\\\n \\\"PythonShell\\\":\\n PyShell.OnRun(0, self.GetOption(\\\"PyPath\\\"))\\n item.AddPage(PyShell.parent, \\\"Python\\\")\\n PyShell.GetParent().Fit()\\n\\n if self.GetOption(\\\"BashShell\\\") and feature == \\\\\\n \\\"BashShell\\\":\\n OSShell.OnRun(0, self.GetOption(\\\"OSPath\\\"))\\n item.AddPage(OSShell.parent, \\\"OS Shell\\\")\\n OSShell.GetParent().Fit()\\n\\n item.GetParent().GetParent().SplitHorizontally(Nb_Panel,\\n item.GetParent())\\n item.GetParent().GetParent().Refresh()\\n elif feature == \\\"EdgeLine\\\":\\n\\n if val:\\n for id in IdRange:\\n wx.FindWindowById(id).SetEdgeMode(wx.stc.STC_EDGE_LINE)\\n else:\\n for id in IdRange:\\n wx.FindWindowById(id).SetEdgeMode(wx.stc.STC_EDGE_NONE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c1be08c88266aa0dc40be10e0ee0f1a\",\n \"score\": \"0.529862\",\n \"text\": \"def DwgFileWasSavedByAutodeskSoftware(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddddccf894fc574db421dc6c5f77dea9\",\n \"score\": \"0.5298492\",\n \"text\": \"def on_autoactivateCheckBox_clicked(self):\\n self.autoactivateCheckBox.setChecked(self.__autoactivate)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4488a811f44c9406370f619979beaba0\",\n \"score\": \"0.52885306\",\n \"text\": \"def editAdvPreferences(self, event):\\n for widget in self.widgets:\\n widget[0].Enable()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26e46f4d0d715a2a41622187ca883b95\",\n \"score\": \"0.5284583\",\n \"text\": \"def commit_changes():\\n save()\\n activate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e869333c2c3e99dfacc33c5c38a3d8a4\",\n \"score\": \"0.52738714\",\n \"text\": \"def controlS(self):\\n menubar.master.bind_all(\\\"<Control-s>\\\", self.save)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e224056d1c76d24290b0d99a7842738\",\n \"score\": \"0.52617615\",\n \"text\": \"def refresh_():\\n save_script_()\\n if not appdata.get('filename'):\\n # save was cancelled by the user\\n return\\n exec_script()\\n appdata.set('dirty', False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d305bc7309cc99192a32b3d450504924\",\n \"score\": \"0.52614605\",\n \"text\": \"async def cmd_forcesave(self, **_):\\n self.config.save(vb=1)\\n return \\\"Saved.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cbdaf3b2dc8de38429bc83687b68212\",\n \"score\": \"0.52523875\",\n \"text\": \"def define_autosave_path():\\n global NUKE_DIR\\n global AUTOSAVE_FILE\\n AUTOSAVE_FILE = os.getenv(\\n 'PYTHONEDITOR_AUTOSAVE_FILE'\\n )\\n if (AUTOSAVE_FILE is None\\n or not parent_isdir(AUTOSAVE_FILE)\\n ):\\n if not os.path.isdir(NUKE_DIR):\\n NUKE_DIR = os.path.expanduser('~')\\n AUTOSAVE_FILE = os.path.join(\\n NUKE_DIR,\\n 'PythonEditorHistory.xml'\\n )\\n os.environ[\\n 'PYTHONEDITOR_AUTOSAVE_FILE'\\n ] = AUTOSAVE_FILE\\n return AUTOSAVE_FILE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a72c0e92db527e8cf69af22e91c6965\",\n \"score\": \"0.524441\",\n \"text\": \"def save():\\n if current_filename is not None:\\n save_gui(current_filename)\\n else:\\n save_as()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cbed25b821481f066cb1b00259f8503\",\n \"score\": \"0.5240317\",\n \"text\": \"def click_view_mode(self):\\n self._file_actions_toolbar.click_view_mode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2056dc75ee8cfd20d92409a5bc9f1a5\",\n \"score\": \"0.52384794\",\n \"text\": \"async def _pane_switch_mode(self) -> None:\\n await self._app.current_filepane.pane_switch_mode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b84ff1baa2698418fdf5a94528f66d6\",\n \"score\": \"0.52357274\",\n \"text\": \"def on_toggle_autoupdate_check(self, event):\\r\\n conf.UpdateCheckAutomatic = event.IsChecked()\\r\\n conf.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"629571b89e34731a190163376d8c3993\",\n \"score\": \"0.52265376\",\n \"text\": \"def force_save(self) -> bool:\\n return self.get(\\\"force-save\\\", bool_or_value) # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9dd8a9a15da6e2f8c0ecfd1fb7a2ffd\",\n \"score\": \"0.5218732\",\n \"text\": \"def _setautoOpenMap(self, value):\\n\\t\\tif self.boldedItem:\\n\\t\\t\\tself.tree.SetItemBold(self.boldedItem, False)\\n\\t\\tif self.selectedItem:\\n\\t\\t\\tself.tree.SetItemBold(self.selectedItem, True)\\n\\t\\t\\tself.boldedItem = self.selectedItem\\n\\t\\t\\tisZip = True\\n\\t\\t\\td = self.tree.GetItemData(self.boldedItem).GetData()\\n\\t\\t\\tif d.archive:\\n\\t\\t\\t\\tisZip = d.archive.getType() == 'Zip'\\n\\t\\trorSettings().setSetting(TOOLKIT, \\\"autoopen\\\", value)\\n\\t\\trorSettings().setSetting(TOOLKIT, \\\"autoopeniszip\\\", isZip)\\n\\t\\t\\t\\n\\t\\tself._autoOpenMap = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b0e01712c5e9a5083ccca989e265c30\",\n \"score\": \"0.52047604\",\n \"text\": \"def action_save_as(self):\\n\\n file_dialog = FileDialog(\\n gettext(\\\"Save Gaphor Model As\\\"), action=\\\"save\\\", filename=self.filename\\n )\\n\\n filename = file_dialog.selection\\n\\n file_dialog.destroy()\\n\\n if filename:\\n self.save(filename)\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d69bb14bf5c0cec81ed01744085003e\",\n \"score\": \"0.5185158\",\n \"text\": \"def select_long_view_mode_in_toolbar(self):\\n self._file_actions_toolbar.select_long_view_mode_in_toolbar()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a586fae8cfb540f8ed5f2b1bc5ddfc59\",\n \"score\": \"0.5184916\",\n \"text\": \"def use_edit_popup(brain):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b429a2b44fe6c04040cfb100aa094c71\",\n \"score\": \"0.517517\",\n \"text\": \"def toggle_expert_takeover(self):\\n self.current_track_vehicle._expert_takeover = not self.current_track_vehicle._expert_takeover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a39c41d0cd540e711afe1d59daacfb7c\",\n \"score\": \"0.5170487\",\n \"text\": \"def _saveButtonClick(self):\\n if len(self._view.languageCombo.currentText()) > 0:\\n self._config.language = self._view.languageCombo.currentText()\\n self._config.backend = self._view.backendCombo.currentText()\\n self._config.gain = self._view.gainSlider.value()\\n self._config.camera = self._view.cameraEdit.text()\\n self._config.shortcut = self._view.shortcutEdit.text()\\n\\n self._config.interval = self._view.intervalEdit.value()\\n if self._view.intervalLabel.text() == self.interval_modes[2]:\\n self._config.interval *= 60\\n\\n self._config.fullscreen = 0\\n if self._view.fullscreenCheck.isChecked():\\n self._config.fullscreen = 1\\n self._config.startup = 0\\n if self._view.startupCheck.isChecked():\\n self._config.startup = 1\\n\\n self.backend.configSave()\\n self._config.save()\\n\\n self._view.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a57d770395fa2f45c9655ec3b22e94fb\",\n \"score\": \"0.51634794\",\n \"text\": \"def commModeSave(self, mode):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c679a8a347ed2407a4f245b0483ba1b\",\n \"score\": \"0.51527876\",\n \"text\": \"def end_save(_session):\\n if _session.save_enabled:\\n _session.save_enabled = False\\n _session.save_file_path = None\\n _session.prompt = _session.prompt.replace('*', '')\\n print('Save session ended.')\\n else:\\n print('A save session is not currently enabled.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69194b95c39e5bc5add650f96ea192c9\",\n \"score\": \"0.5142079\",\n \"text\": \"def setEnabled(enabled):\\n\\tonOpenSave.enabled = enabled\\n\\tonPreOpen.enabled = enabled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e79070eca524f4a51bf5cdb8483614e4\",\n \"score\": \"0.5137087\",\n \"text\": \"def motion_is_now_voteable(self):\\n self.motion1.voteable = True\\n self.motion1.save()\\n self.browser.refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cae90958776e33e49d92dd13b4924fa\",\n \"score\": \"0.51355207\",\n \"text\": \"def emacs_editing_mode(event: E) -> None:\\n event.app.editing_mode = EditingMode.EMACS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18b01cfb2fc4befab240726f0c77e407\",\n \"score\": \"0.5134704\",\n \"text\": \"def Editable(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b4d2f45bc0c551e475cdc706cc32203\",\n \"score\": \"0.51343983\",\n \"text\": \"def toggle_name_on(self):\\n self.tab.addstr(1, 1, \\\"{}\\\".format(self.name), bg.re)\\n self.toggle_name = True\\n self.tab.refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48b293050798da00b19b6fb2ba5785b6\",\n \"score\": \"0.5133477\",\n \"text\": \"def create_autosave_file():\\n # look for the autosave\\n if os.path.isfile(AUTOSAVE_FILE):\\n\\n # if the autosave file is empty, write header\\n # FIXME: can this be an os.stat/get file size?\\n # Furthermore, what if it's not empty but has a\\n # corrupted header? What are the methods for\\n # data preservation?\\n with open(AUTOSAVE_FILE, 'r') as f:\\n is_empty = not bool(f.read().strip())\\n if is_empty:\\n create_empty_autosave()\\n else:\\n\\n # if file not found, check if directory exists\\n if not os.path.isdir(NUKE_DIR):\\n # filehandle, filepath = tempfile.mkstemp()\\n # FIXME: set os.environ['PYTHONEDITOR_AUTOSAVE_FILE'] and define_autosave_path()\\n # msg = 'Directory %s does not exist, saving to %s' % (NUKE_DIR, filepath)\\n msg = 'Directory {0} does not exist'.format(NUKE_DIR)\\n raise CouldNotCreateAutosave(msg)\\n else:\\n create_empty_autosave()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18b01cfb2fc4befab240726f0c77e407\",\n \"score\": \"0.5133266\",\n \"text\": \"def Editable(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd5c722c17eb83adead026e8ee90d6d1\",\n \"score\": \"0.5128371\",\n \"text\": \"def save_as(self, action):\\n self.chooser_dialog(title=\\\"Save Document\\\", callback=self.on_file_save, save=True)\\n return False # TODO this signals that file saving was not successful\\n # because no action should be taken until the chooser\\n # dialog is finish, however the user might then need to\\n # repeat the action, once the document was saved and the\\n # edited flag was cleared\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68710ccfb8d74ab0d4c34d9ac3af59e0\",\n \"score\": \"0.5126705\",\n \"text\": \"def do_single_save(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c8699fdb4d6a3d32841d2e263f61018\",\n \"score\": \"0.51195246\",\n \"text\": \"def set_saved(self, filename):\\n\\n sel = self.notebook.GetSelection()\\n if sel >= 0:\\n self.tab_list[sel].filename = filename\\n self.set_tab_modified(sel, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"541fe96dbbb1faa4928a27a50c8b4646\",\n \"score\": \"0.5114742\",\n \"text\": \"def EnableSelection(self, flag):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fd65c4b9b885d3b61383a492c0b7576\",\n \"score\": \"0.5112609\",\n \"text\": \"def save(self):\\n self.editor_input.save(self.document)\\n self.dirty = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9343f9a069333eb2ecd449afefb73752\",\n \"score\": \"0.51024485\",\n \"text\": \"def toggle(self):\\n self.setting = not self.setting\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9bbab25345a1a7770b00a4cc8c76b75\",\n \"score\": \"0.5091114\",\n \"text\": \"def reactivate(self):\\n self.active = True\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d41ddc0b6076bc589963551bf8ac6f8\",\n \"score\": \"0.5091112\",\n \"text\": \"def save_file(self, buff_name, full_path = None, no_prompt = 0,\\n rename_buff = 1, ask_for_new_name = 0, perform_callback = 0):\\n debug.virtual('GenEdit.save_file')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cbccf5f9fb7cd81288f4fc669002639\",\n \"score\": \"0.5086999\",\n \"text\": \"def toggle(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6df5af509fb87d81407a170ce671d769\",\n \"score\": \"0.5083608\",\n \"text\": \"def save_activated(self) -> None:\\n self.setReadOnly(True)\\n try:\\n with open(self.path, 'w') as file:\\n file.write(self.toPlainText())\\n except Exception as e:\\n # Set text how it was before\\n self.setText(self.text_before_edit)\\n # Show the error message\\n error_msg = QtWidgets.QMessageBox()\\n error_msg.setText(f\\\"{e}\\\")\\n error_msg.setWindowTitle(f'Error trying to save markdown edit.')\\n error_msg.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fa9bf9971656d4a57bd2a8cb333aa3a\",\n \"score\": \"0.5083561\",\n \"text\": \"def _set_drag_status(self):\\n self.viewer.status = trans._(\\n 'Hold <Alt> key to open plugin selection. Hold <Shift> to open files as stack.'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"092e68141cf48c5eb195c46a0d09510b\",\n \"score\": \"0.50785273\",\n \"text\": \"def _saveFile(self):\\n if self._features:\\n filename = filedialog.asksaveasfilename(initialdir=\\\"/\\\", title=\\\"Select file\\\",\\n filetypes=(\\n (\\\"animangle files\\\", \\\"*.animangle\\\"), (\\\"all files\\\", \\\"*.*\\\")))\\n self._features.requestSave(open(filename, \\\"w\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96b1de120d197377afea2ec4787e1d23\",\n \"score\": \"0.50766003\",\n \"text\": \"def automatic(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e6c32ef8915c1d6ef7c76fd0fdece12\",\n \"score\": \"0.5063986\",\n \"text\": \"def edit(self):\\r\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10c82d4826e52adecccd3c3037e40199\",\n \"score\": \"0.5058703\",\n \"text\": \"def on_back_ext_changed(self, event):\\n\\n self.m_back_ext_button.Enable(self.m_back_ext_textbox.GetValue() != self.backup_ext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f5df0fb0676d790a511d97a4ef72ad9\",\n \"score\": \"0.50586194\",\n \"text\": \"def on_toggle_marks(self):\\n self.use_marks = True if not self.use_marks else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebcf643b83c90671dffc609a87383e0e\",\n \"score\": \"0.505293\",\n \"text\": \"def auto_save_threshold(self, auto_save_threshold):\\n\\n self._auto_save_threshold = auto_save_threshold\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a20e5aa93b0b00ef7dcc7fedb2e3a077\",\n \"score\": \"0.50375754\",\n \"text\": \"def create_empty_autosave():\\n with open(AUTOSAVE_FILE, 'w') as f:\\n f.write(XML_HEADER+'<script></script>')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9501537625b02f68c8730f86dad58b37\",\n \"score\": \"0.50356936\",\n \"text\": \"def select_manual_option(self):\\n if not self.verify_manual_capture_mode():\\n self.select_auto_btn()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"364e29d6161e2e11121679eaa7cf6d6f\",\n \"score\": \"0.5033404\",\n \"text\": \"def undo_open():\\n _gv.gv_undo_open()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6205149f5570b529982533e5dc09d548\",\n \"score\": \"0.50273573\",\n \"text\": \"def filename_complete(env: MnoteEnvironment, on_off: click.Choice):\\n style = env.config.styles\\n env.config.filename_complete = on_off == \\\"on\\\"\\n env.config.write()\\n\\n echo_line()\\n if env.config.filename_complete:\\n echo_line(\\\"Default '--complete' flag for filename \\\", style.success(\\\"ON\\\"))\\n else:\\n echo_line(\\\"Default '--complete' flag for filename \\\", style.fail(\\\"OFF\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfc5fe4478108a270e0c96f50b6393b3\",\n \"score\": \"0.5016182\",\n \"text\": \"def settrigger_mode(self, newval):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e5febbc7f1ffb85587341edf718739a\",\n \"score\": \"0.5012927\",\n \"text\": \"def set_auto():\\n global auto_set\\n if not auto_set:\\n SC.reg_param('state', state_callback)\\n SC.reg_param('source', source_callback)\\n V.reg_param('sub_frequencyA', vegas_sb_callback)\\n auto_set = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1ef8eaefaa11a5455f332702988a773\",\n \"score\": \"0.50129217\",\n \"text\": \"def display_edit_completed():\\r\\n print('Edit saved')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c380d78069f58423e9c10cbabed3341\",\n \"score\": \"0.5006341\",\n \"text\": \"def _update_active(self, b):\\n super(DemoUniverse, self)._update_active(b)\\n scns = self.active()\\n if not scns: return\\n flds = [scn.field for scn in scns]\\n fks = [scn.field_kind for scn in scns]\\n fmls = [scn.field_ml for scn in scns]\\n folder = self._controls['field']\\n fopts = folder['fopts'].options\\n fld = flds[0]\\n fk = fks[0]\\n fml = fmls[0]\\n if not len(set(flds)) == 1:\\n for scn in scns: scn.field = fld\\n if not len(set(fks)) == 1:\\n for scn in scns: scn.field_kind = fk\\n if not len(set(fmls)) == 1:\\n for scn in scns: scn.field_ml = fml\\n folder[fld].value = fk\\n folder.activate(fld, enable=True)\\n folder.deactivate(*[f for f in fopts if f != fld])\\n if fld == 'SolidHarmonic':\\n ofks = [str(i) for i in range(8) if str(i) != fk]\\n folder.activate(fk, enable=True)\\n folder.deactivate(*ofks)\\n folder._set_gui()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b7c759110d2f9b36f7f10de20214b9\",\n \"score\": \"0.50020224\",\n \"text\": \"def settings_save():\\n\\n answer = messagebox.askyesno(title='Закінчити редагування',\\n message='Зберегти зміни?')\\n if answer:\\n tariffs_changing(entry_gas.get(), entry_water.get(), entry_electricity.get())\\n set_win.destroy()\\n button_settings.configure(state=NORMAL)\\n\\n elif not answer:\\n set_win.destroy()\\n button_settings.configure(state=NORMAL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4ce94137df85a404b256450d062e394\",\n \"score\": \"0.5000504\",\n \"text\": \"def onSave(self, event):\\n current_settings = GeneralSettingsData()\\n\\n current_settings.logging = self.cb_logging.GetValue()\\n current_settings.use_hotkeys = self.cb_usehotkeys.GetValue()\\n current_settings.warn_large_img = self.cb_warn_large.GetValue()\\n if self.tc_hk_next.GetLineText(0):\\n current_settings.hk_binding_next = tuple(\\n self.tc_hk_next.GetLineText(0).strip().split(\\\"+\\\")\\n )\\n else:\\n current_settings.hk_binding_next = None\\n if self.tc_hk_pause.GetLineText(0):\\n current_settings.hk_binding_pause = tuple(\\n self.tc_hk_pause.GetLineText(0).strip().split(\\\"+\\\")\\n )\\n else:\\n current_settings.hk_binding_pause = None\\n\\n current_settings.set_command = self.tc_setcmd.GetLineText(0).strip()\\n\\n current_settings.save_settings()\\n # after saving file apply in tray object\\n self.parent_tray_obj.read_general_settings()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f1b4403b86ffe3d16978cdd9160982a\",\n \"score\": \"0.5000007\",\n \"text\": \"def LoseFocus(self):\\n self.focus = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd866744b68d324633244519a4e3d190\",\n \"score\": \"0.49994484\",\n \"text\": \"def ToggleFlag(self, flag):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1e837ab172655ead2b8766913fcf594\",\n \"score\": \"0.4991357\",\n \"text\": \"def set_active(self):\\r\\n FreeCAD.setActiveDocument(self.Name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b2a3bf4184825eb2615dd4290795b47\",\n \"score\": \"0.49871665\",\n \"text\": \"def checkpoint_saved(self):\\n self._last_auto_save_time = time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d557854ffef646e59f1beb356dbb9d96\",\n \"score\": \"0.49866563\",\n \"text\": \"def updateModified(self):\\n #self.ui.action_Add_Image.setEnabled(self._model is not None)\\n #self.ui.action_Add_Video.setEnabled(self._model is not None)\\n # TODO also disable/enable other items\\n #self.ui.actionSave.setEnabled(self.annotations.dirty())\\n #self.setWindowModified(self.annotations.dirty())\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"090c58613d16dc6c8d5a103bab52add3\",\n \"score\": \"0.4979805\",\n \"text\": \"def set_edit_mode(request, state):\\n setattr(request, \\\"_fluent_contents_edit_mode\\\", bool(state))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"116c11d57b72030b521ce5a6ff51964d\",\n \"score\": \"0.49735904\",\n \"text\": \"def on_manual_button_clicked(self, obj):\\n\\n\\t\\t# Ensure we can go back...\\n\\t\\tself.idle_add(self.objects[\\\"parent\\\"].back_button.set_sensitive, True)\\n\\t\\t\\n\\t\\t# Switch to page 3\\n\\t\\tself.pages_notebook.set_current_page(3)\\n\\n\\t\\tself.manual_ready()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaead4533be8f6141d0fd27ebc75fd47\",\n \"score\": \"0.49669957\",\n \"text\": \"def CheckSave(self):\\n self.mSave.Enable(self.data.HasChanged())\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":484,"cells":{"query_id":{"kind":"string","value":"edd8209b451c7f174cedf83f811e6ded"},"query":{"kind":"string","value":"Builds appropriate phrase for app"},"positive_passages":{"kind":"list like","value":[{"docid":"2ee64393237424f2194cb6e240785cf0","score":"0.0","text":"def _is_it_taps_aff(self, main_weather_data: MainWeatherData) -> str:\n self.temp = round(main_weather_data.temp, 1)\n\n if \".0\" in \"{:.1f}\".format(self.temp):\n self.temp = int(self.temp)\n\n if self.temp >= self.taps_aff_threshold:\n return f'<speak><emphasis level=\"strong\">YAS.</emphasis> It\\'s taps aff in {self.place_name}. It\\'s pure roasting at {self.temp} degrees</speak>'\n else:\n return f'<speak><emphasis level=\"strong\">Gnaw mate.</emphasis> It\\'s taps own in {self.place_name}. It\\'s only {self.temp} degrees</speak>'","title":""}],"string":"[\n {\n \"docid\": \"2ee64393237424f2194cb6e240785cf0\",\n \"score\": \"0.0\",\n \"text\": \"def _is_it_taps_aff(self, main_weather_data: MainWeatherData) -> str:\\n self.temp = round(main_weather_data.temp, 1)\\n\\n if \\\".0\\\" in \\\"{:.1f}\\\".format(self.temp):\\n self.temp = int(self.temp)\\n\\n if self.temp >= self.taps_aff_threshold:\\n return f'<speak><emphasis level=\\\"strong\\\">YAS.</emphasis> It\\\\'s taps aff in {self.place_name}. It\\\\'s pure roasting at {self.temp} degrees</speak>'\\n else:\\n return f'<speak><emphasis level=\\\"strong\\\">Gnaw mate.</emphasis> It\\\\'s taps own in {self.place_name}. It\\\\'s only {self.temp} degrees</speak>'\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5068d9b7315d7647fd277a2378690c05","score":"0.6656243","text":"def gen_phrase_string(self):\n return self.post_process(self.gen_phrase())","title":""},{"docid":"f4c38d3360b740fb302558e517c732aa","score":"0.6161804","text":"def translate(self, phrase):\n return phrase","title":""},{"docid":"12131e15781865b3f8aae86dc955474f","score":"0.6065521","text":"def sentence():\n optional = random.choice(['yes', 'no'])\n if optional == 'yes':\n return nounPhrase() + \" \" + verbPhrase()\n if optional == 'no':\n return nounPhrase() + \" \" + verbPhrase() + \" \" + conjunction() + \" \" + nounPhrase() + \" \" + verbPhrase()","title":""},{"docid":"92d01f01cad42278c671a33dccce967e","score":"0.5991369","text":"def abbreviate(phrase):","title":""},{"docid":"b7aac8488a97b986ffad1c0ba5a59803","score":"0.5721289","text":"def form_sentence(self):\n \n if self.subject and self.verb and self.object: \n return \" \".join([self.subject, self.verb, self.object])\n elif self.subject and self.verb:\n return \" \".join([self.subject, self.verb])\n else:\n return self.subject","title":""},{"docid":"737fad7aca6d070bf5e2fb96136e12da","score":"0.57015353","text":"def app_name(): # -> str\n return \"Read Text\"","title":""},{"docid":"7ea27e2548f2b654ddb41ca52f91b0fd","score":"0.569428","text":"def build_text(self, task):","title":""},{"docid":"49ec5816112fe563d55af8c5b68b2a00","score":"0.56447375","text":"def _get_string_phrase(phrase, index_vocab):\n res = \"\"\n for vocab_id in phrase.split():\n if res == \"\":\n res += index_vocab[int(vocab_id)]\n else:\n res += \" \" + index_vocab[int(vocab_id)]\n return res","title":""},{"docid":"a0bc044b9d1bdccbc4ef1019750322ae","score":"0.5595425","text":"def translate_to_pirate_talk(phrase):\n\n result =[]\n translation = {'sir': 'matey', 'hotel': 'fleabag', 'student': 'swabbie', 'man': 'matey', \n 'professor':'foul blaggart', 'restaurant':'galley','your':'yer','excuse':'arr',\n 'students':'swabbies','are':'be','restroom':'head','my':'me','is':'be'}\n \n \n phrase = phrase.split()\n \n for word in phrase:\n \n if word in translation.keys():\n result.append(translation[word])\n else:\n result.append(word)\n\n str1 = \" \"\n \n print(str1.join(result))\n\n return str1.join(result)","title":""},{"docid":"97c8d70f93b4a7b03265408130d25b87","score":"0.555625","text":"def gen(self):# Need to imporve\n try:\n temp = self.text.strip().split()\n except:\n pass\n return self.speller.genSentence(temp)\n pass","title":""},{"docid":"838e4bd436a05d8710118b694133a2ce","score":"0.5522631","text":"def build_text(word_pairs):\n\n building_text = []\n\n # Gets a random pair to start off with\n pair_choice = random.choice(list(word_pairs))\n building_text += pair_choice.split()\n\n # Proceeds to get value of pair\n pair_follower = random.choice(list(word_pairs[pair_choice]))\n building_text.append(pair_follower)\n\n # Continues getting new pairs\n if len(building_text) < 100:\n new_pair = ' '.join(building_text[-2:])\n while True:\n if new_pair in word_pairs:\n building_text.append(random.choice(list(word_pairs[new_pair])))\n new_pair = ' '.join(building_text[-2:])\n else:\n break\n print(' '.join(building_text))","title":""},{"docid":"6011c6c25819bcd5c8be66ba46948e8e","score":"0.55188257","text":"def verbPhrase():\n with open('verbs.txt') as v:\n verb = csv.reader(v)\n verbs = random.choice(list(verb)) \n\n optional = random.choice(['yes', 'no'])\n if optional == 'yes':\n return random.choice(verbs) + \" \" + nounPhrase() \n if optional == 'no':\n return random.choice(verbs) + \" \" + nounPhrase() + \" \" + prepositionalPhrase()","title":""},{"docid":"96b7c98548c0e6633abd4a2b15dc0c2b","score":"0.54932594","text":"def make_pattern_text(self):\n\n text = \"Do you like \"\n if self.is_positive():\n text += \"more \"\n else:\n text += \"less \"\n\n text += \" and \".join(map(lambda p: p.name.replace(\"_\", \" \"), self.get_targets()))\n text += \"?\"\n\n return text","title":""},{"docid":"acb4a8104c3ab91e6e634c4bd56de4de","score":"0.5474925","text":"def _GenerateDescription(self, verb, message, fallback):\n if self.toolset != 'target':\n verb += '(%s)' % self.toolset\n if message:\n return '%s %s' % (verb, self._ExpandSpecial(message))\n else:\n return '%s %s: %s' % (verb, self.name, fallback)","title":""},{"docid":"01f80d2f772aedb19e27879bdb1384c6","score":"0.54285204","text":"def hey(phrase):\n \n if phrase.isupper():\n return \"Whoa, chill out!\"\n if phrase[-1:] == \"?\":\n return \"Sure.\"\n if phrase == \"\" or phrase.isspace():\n return \"Fine. Be that way!\"\n return \"Whatever.\"","title":""},{"docid":"80fa4b3433075436c652da878babf227","score":"0.54098433","text":"def english_command(tokens, concept, discourse):\n verb = tokens[0]\n line = ''\n i = 1\n for part in discourse.command_canonical[verb].split():\n if (part in ['ACCESSIBLE', 'ACTOR', 'DESCENDANT', 'NEARBY', \n 'NOT-DESCENDANT', 'WORN']):\n line += concept.item[tokens[i]].noun_phrase(discourse)\n i += 1\n elif part in ['RELATION']:\n line += tokens[i].lower()\n i += 1\n elif part in ['STRING']:\n line += tokens[i]\n else:\n line += part\n line += ' '\n return line[:-1]","title":""},{"docid":"b0ac8287cbb96e2966dec4a3158b8f5c","score":"0.53940797","text":"def build_message(self):\n commentUser = (\n \"**SEARCH TERM: {term}**\\n\\n\"\n \"Total Occurrence: {totalOccur} \\n\\n\"\n \"Total Chapters: {totalChapter} \\n\\n\"\n \"{warning}\"\n \"{chapterpov}\"\n \"######	\\n\\n####	\\n\\n#####	\\n\\n\"\n \"	\\n\\n	\\n\\n\"\n \">{message}\"\n \"{link}\"\n )\n warning = \"\"\n if self.title.name != 'All' and self.title.name != 'PQ' and self.title.name != 'DE':\n warning = (\"**ONLY** for **{book}** and under due to the spoiler tag in the title.\\n\\n\").format(\n book = self.title.name,\n )\n if self._rowCount > MAX_ROWS:\n warning += (\"Excess number of chapters. Sorted by highest to lowest, top 30 results only.\\n\\n\")\n # Avoids spam and builds table heading only when condition is met\n if self._total > 0:\n self._message += (\n \"| Series| Book| Chapter| Chapter Name| Chapter POV| Occurrence| Quote^(First Occurrence Only)\\n\"\n )\n self._message += \"|:{dash}|:{dash}|:{dash}|:{dash}|:{dash}|:{dash}|:{dash}|\\n\".format(dash='-' * 11)\n # Each element added as a new row with new line\n for row in self._listOccurrence:\n self._message += row + \"\\n\"\n elif self._total == 0:\n self._message = \"**Sorry no results.**\\n\\n\"\n \n self._commentUser = commentUser.format(\n warning = warning,\n chapterpov = self._chapterPovMessage,\n term = self._searchTerm,\n totalOccur = self._total,\n message = self._message,\n link = self._links,\n totalChapter = self._rowCount\n )","title":""},{"docid":"42b7d15a058330437e7100e3b7e752f5","score":"0.5376561","text":"def get_text(self):\n if self.them_text:\n return self.suggestion.text.replace('{{them}}', self.them_text)\n return self.suggestion.get_text()","title":""},{"docid":"2d739e51b96404bfd38c0a62a56c9d43","score":"0.5366304","text":"def random_noun_phrase():\n noun_phrase = {}\n # necessary choices\n random_noun = Noun.objects.order_by('?')[0]\n random_adj = Adjective.objects.order_by('?')[0]\n # optional choices\n random_pos = random.choice([Possessive.objects.order_by('?')[0], None])\n random_dem = random.choice([get_chart_prefix('d', random_noun.noun_class), None])\n noun_phrase['noun'] = random_noun\n noun_phrase['adjs'] = [random_adj]\n if random_pos:\n noun_phrase['pos'] = random_pos\n if random_dem:\n noun_phrase['dem'] = random_dem\n return fix_noun_phrase(noun_phrase)","title":""},{"docid":"57b15480bb23a8754ba80c29f06ed5b6","score":"0.5362081","text":"def hey(phrase):\n\t\n\tphrase = phrase.strip()\n\n\tif not phrase:\t\t\t\t\t# Checks to see if phrase is blank.\n\t\treturn 'Fine. Be that way!'\n\telif phrase.isupper():\t\t\t# Checks to see if phrase is a yell.\n\t\treturn 'Whoa, chill out!'\n\telif phrase.endswith('?'):\t\t# Checks to see if phrase is a question.\n\t\treturn 'Sure.'\n\telse:\n\t\treturn 'Whatever.'","title":""},{"docid":"5ceec260a88b1c9f08d48777b0168bc3","score":"0.5348122","text":"def synthesize(self, phrase, language=\"en_US\"):\n pass","title":""},{"docid":"ee50593ef54f59b2a2e1b6cbdd3e8533","score":"0.5336454","text":"def hey(phrase: str) -> str:\n phrase = phrase.strip()\n if not phrase: # if you address him without actually saying anything\n return \"Fine. Be that way!\"\n if phrase.isupper(): # if you yell at him\n if phrase.endswith('?'): # if you yell a question at him\n return \"Calm down, I know what I'm doing!\"\n return \"Whoa, chill out!\"\n if phrase.endswith('?'): # if you ask him a question\n return \"Sure.\"\n return \"Whatever.\" # to anything else","title":""},{"docid":"b71476b9e094863bb542e95975b59d04","score":"0.5329836","text":"def translate(line):\n\n line = line.lower() # convert enlgish sentence to lowercase\n englishWords = line.split() # split line into words for processing, write to global variable\n maoriWords = [] # create list for maori sentence\n\n # check for valid sentences size before popping\n if len(englishWords) >= 2 and len(englishWords) <= 5:\n currentWord = englishWords.pop(0)\n\n # check for simple pronouns in main function\n if currentWord == \"i\":\n maoriWords.append(\"au\")\n currentWord = englishWords.pop(0)\n\n elif currentWord == \"he\" or currentWord == \"she\":\n maoriWords.append(\"ia\")\n currentWord = englishWords.pop(0)\n\n # check for more complex pronouns with methods\n\n elif currentWord == \"you\":\n currentWord, englishWords, maoriPronoun = getYouPronoun(currentWord, englishWords)\n maoriWords.append(maoriPronoun)\n\n elif currentWord == \"they\":\n currentWord, englishWords, maoriPronoun = getTheyPronoun(currentWord, englishWords)\n maoriWords.append(maoriPronoun)\n\n elif currentWord == \"we\":\n currentWord, englishWords, maoriPronoun = getWePronoun(currentWord, englishWords)\n maoriWords.append(maoriPronoun)\n\n else:\n return INVALID\n\n\n # infer tense and get verb\n # one left in list means auxiliary verb + verb\n if len(englishWords) == 1:\n\n if currentWord == \"am\" or currentWord == \"are\" or currentWord == \"is\":\n currentWord = englishWords.pop(0)\n maoriVerb = getVerbPresent(currentWord)\n\n # if verb was invalid return message and end early\n if maoriVerb == None:\n return \"invalid verb \\\"\" + currentWord + \"\\\"\"\n else:\n maoriWords.insert(0, maoriVerb)\n return ' '.join(maoriWords)\n\n elif currentWord == \"will\":\n currentWord = englishWords.pop(0)\n maoriVerb = getVerbFuture(currentWord)\n\n # if verb was invalid return message and end early\n if maoriVerb == None:\n return \"invalid verb \\\"\" + currentWord + \"\\\"\"\n else:\n maoriWords.insert(0, maoriVerb)\n return ' '.join(maoriWords)\n\n else:\n return INVALID\n\n # empty list means no auxiliary verb\n elif len(englishWords) == 0:\n maoriVerb = getVerbPastOrPresent(currentWord)\n\n if maoriVerb == None: # if verb was invalid return message and end early\n return \"invalid verb \\\"\" + currentWord + \"\\\"\"\n else:\n maoriWords.insert(0, maoriVerb)\n return ' '.join(maoriWords)\n\n\n else:\n return INVALID\n\n else:\n return INVALID","title":""},{"docid":"caf764877a39cff4411f10ff9ac78e59","score":"0.53245485","text":"def initial_data(num_phrase):\n\n phrases = [\"you need new permissions\",\n \"I can arrange a meeting for you\",\n \"the next possible appointment could be tomorrow\"]\n\n final_words = ['pronoun', 'det', 'adj', 'n', 'md', 'v', 'prep']\n\n grammar = {'s': ['np vp'],\n 'np': ['pronoun', 'adj n', 'np pp', 'det n', 'n', 'det adj adj n'],\n 'pp': ['prep pronoun'],\n 'vp': ['v np', 'md vp'],\n 'pronoun': ['you', 'I'],\n 'det': ['a', 'the'],\n 'prep': ['for'],\n 'v': ['need', 'be', 'arrange'],\n 'adj': ['new', 'next', 'possible'],\n 'md': ['can', 'could'],\n 'n': ['permissions', 'meeting', 'appointment', 'tomorrow']}\n return phrases[num_phrase], final_words, grammar","title":""},{"docid":"35d9b2238f122c3e0ccee45e62298fe3","score":"0.5314347","text":"def _grammar_injection(self, string_a, string, switch):\n if switch.lower() not in str(string).split(\" \", 1)[0].lower():\n if len(str(string).split(\" \", 1)) > 1:\n return f\"{str(string).split(' ', 1)[0]}{string_a} {str(string).split(' ', 1)[1]}\"\n else:\n return f\" {str(string).split(' ', 1)[0]}{string_a}\"\n return string","title":""},{"docid":"513bad81a6bafbfa7b24f4b6111d803e","score":"0.53060186","text":"def build_word_info(self, word):\n if len(word) > 4:\n (radical, suffixes) = self._word_bsig[word]\n\n words = self._signatures_to_words[suffixes]\n # TODO : find the radical and the suffixes\n text_info = \"Morpheme clustering algorithm results on : {} : \\n cluster : {} \\n \\n \\n \".format(word, suffixes)\n if len(suffixes) >= 1:\n\n for suff in suffixes:\n for stem in self._signatures_to_stems[suffixes]:\n\n info = \"{}\".format(stem)+\" {}\".format(suff) + \"\\n \\n \\n \"\n\n if info != word:\n\n text_info = text_info + \"{}{}{}\".format(\" \\n \", \" - \", info)\n\n\n\n else:\n\n text_info += \"{}\".format(\" No close words found \")\n\n else:\n\n text_info = \"'\" + word + \"'\" + \" is too short, try a longer word.\"\n\n return text_info","title":""},{"docid":"f1486146ca4a72e5596ac57ed47eb93b","score":"0.5277118","text":"def tool_description():\n\n return \"This text describes what the tool does\"","title":""},{"docid":"9ad7bf2beae0b9f1e012498cfa3b2002","score":"0.52645683","text":"def create(self):\r\n clauses = []\r\n clauses.append(\"TEMPLATE = %s\" % self.template)\r\n if hasattr(self, 'options'):\r\n clauses.append(self.options)\r\n return [\"CREATE TEXT SEARCH DICTIONARY %s (\\n %s)\" % (\r\n self.qualname(), ',\\n '.join(clauses))]","title":""},{"docid":"9bef9748c4d83a8f16db0d2914134ad9","score":"0.52459264","text":"def process_answer(self, message: str) -> str:\n\n logging.debug(\"process_answer()\")\n self.print_state()\n\n if self.state == \"waiting_lexicon_answer\":\n to_skip: bool = False\n\n if message == \"Show definition.\":\n self.state = \"waiting_lexicon_answer\"\n return \"\" # definition(self.word)\n elif message in [\"/know\", \"Know.\"]:\n response = LexiconResponse.KNOW\n elif message in [\"/skip\", \"Know, skip.\"]:\n response = LexiconResponse.KNOW\n to_skip = True\n elif message in [\"/not_a_word\", \"Not a word.\"]:\n response = LexiconResponse.NOT_A_WORD\n to_skip = True\n elif message in [\"/but\", \"But.\"]:\n response = LexiconResponse.DONT_BUT_PROPER_NOUN_TOO\n to_skip = True\n else:\n response = LexiconResponse.DONT\n\n self.lexicon.register(\n self.word,\n response,\n to_skip,\n log_name=\"log_ex\",\n answer_type=AnswerType.USER_ANSWER,\n )\n return \"\"\n\n sentence_id: int = (\n self.current_sentences[self.index].sentence.id_\n if self.index < len(self.current_sentences)\n else 0\n )\n\n # Preprocess answer.\n answer: str = self.learning.learning_language.decode_text(message)\n\n self.index += 1\n\n state: str = (\n f\", {self.learning.count_questions_to_repeat()} to repeat\"\n if self.learning.count_questions_to_repeat()\n else \"\"\n )\n\n if answer == self.word:\n self.index = 0\n self.learning.register(\n Response.RIGHT, sentence_id, self.word, self.interval * 2\n )\n transcriptions: list[str] = []\n if self.items:\n for item in self.items:\n for transcription in item.get_transcriptions():\n if transcription not in transcriptions:\n transcriptions.append(transcription)\n\n self.learning.write()\n\n self.print_state()\n return f\"Right{state}, \" + \", \".join(transcriptions) + \".\"\n\n elif answer in self.alternative_forms:\n self.print_state()\n return \"Right form.\"\n\n elif answer in [\"/skip\", \"Skip\"]:\n self.skip.add(self.word)\n self.index = 0\n self.print_state()\n return f\"Skipped for this session{state}.\"\n\n elif answer in [\"/no\", \"Don't know\"]:\n self.learning.register(\n Response.WRONG, sentence_id, self.word, SMALLEST_INTERVAL\n )\n self.learning.write()\n self.index = 0\n\n self.print_state()\n return f\"Right answer: {self.word}{state}.\"\n\n elif answer == \"/exclude\":\n self.data.exclude_sentence(self.word, sentence_id)\n self.skip.add(self.word)\n self.print_state()\n return \"Sentence was excluded.\"\n\n elif answer.startswith(\"/hide \"):\n parts = answer.split(\" \")\n self.data.exclude_translation(self.word, \" \".join(parts[1:]))\n self.skip.add(self.word)\n self.print_state()\n return \"Translation was hidden.\"\n\n else:\n self.print_state()\n return \"No.\"","title":""},{"docid":"0a652a8742aa0d9e50c98584cfa00e52","score":"0.5240408","text":"def text(self) -> str:\n if self._text:\n return self._text\n\n # Pre-letter suprasegmentals\n for accent in self.accents:\n if accent == Accent.ACUTE:\n self._text += IPA.ACCENT_ACUTE\n elif accent == Accent.GRAVE:\n self._text += IPA.ACCENT_GRAVE\n\n if self.stress == Stress.PRIMARY:\n self._text += IPA.STRESS_PRIMARY\n elif self.stress == Stress.SECONDARY:\n self._text += IPA.STRESS_SECONDARY\n\n # Letters and diacritics\n for letter_index, letter in enumerate(self.letters):\n self._text += letter\n\n # Diacritics\n for diacritic in self.diacritics.get(letter_index, []):\n self._text += diacritic\n\n # Tone\n if self.tone:\n self._text += self.tone\n\n # Post-letter suprasegmentals\n if self.is_long:\n self._text += IPA.LONG\n\n # Re-normalize and combine\n self._text = unicodedata.normalize(\"NFC\", self._text)\n\n return self._text","title":""},{"docid":"5dbf5e3b0229dc04cdec5973b8209ebc","score":"0.5232675","text":"def create(self):\r\n clauses = []\r\n if hasattr(self, 'init'):\r\n clauses.append(\"INIT = %s\" % self.init)\r\n clauses.append(\"LEXIZE = %s\" % self.lexize)\r\n return [\"CREATE TEXT SEARCH TEMPLATE %s (\\n %s)\" % (\r\n self.qualname(), ',\\n '.join(clauses))]","title":""},{"docid":"8a7a4139b02fa51353f8b0f5d61f8c67","score":"0.5231463","text":"def make_poem():\n\n noun1, noun2, noun3 = get_words(NOUNS, 3)\n verb1, verb2, verb3 = get_words(VERBS, 3)\n adjective1, adjective2, adjective3 = get_words(ADJECTIVES, 3)\n preposition1, preposition2 = get_words(PREPOSITIONS, 2)\n adverb1, = get_words(ADVERBS, 1)\n a_or_an = \"An\" if adjective1.startswith(VOWELS) else \"A\"\n\n return \"\"\"\n {A_or_An} {adjective1} {noun1}\n \n {A_or_An} {adjective1} {noun1} {verb1} {preposition1} the {adjective2} {noun2}\n {adverb1}, the {noun1} {verb2}\n the {noun2} {verb3} {preposition2} a {adjective3} {noun3}\n \"\"\".format(A_or_An=a_or_an, noun1=noun1, noun2=noun2, noun3=noun3,\n verb1=verb1, verb2=verb2, verb3=verb3,\n adjective1=adjective1, adjective2=adjective2, adjective3=adjective3,\n preposition1=preposition1, preposition2=preposition2,\n adverb1=adverb1)","title":""},{"docid":"e5b0f9d3b33d6aa3f561189405467d01","score":"0.5230626","text":"def turn_to_sentence(words, starting_msg, and_or, typ='misunderstood'):\n message = starting_msg\n for i in range(len(words)):\n # obj = OBJECTS[objects[i]] # objects[i], for example, is 'egg'. so OBJECTS['egg']\n if i == len(words) - 1 and len(words) > 1: # if it's the last word to add to the list AND if there is more than one word\n message += f' {and_or} ' if len(words) == 2 else f'{and_or} ' # add an 'and', e.g. x, y, and z -- space before 'and' based on conditional\n # objects_here += obj.short_description\n if typ in ('no quotes', 'objects here'):\n message += words[i]\n else:\n message += f\"'{words[i]}'\"\n if len(words) > 2:\n message += ', '\n if typ == 'objects here': # if printing out all the objects in a given location, then end the sentence nicely with a period.\n message = message.strip(', ') # strip trailing comma\n return message","title":""},{"docid":"33b66d2c6132f4ee104a12f375be5a5a","score":"0.52071035","text":"def speak(bot, update, args):\n word = args[0] if len(args) > 0 else None\n update.message.reply_text(text=produce_sentence(word), quote=False)","title":""},{"docid":"4307bf926b394a24d6bbfea7b073113e","score":"0.5200156","text":"def output(self):\n self.updated = False\n return ''.join([word for word in self.phrase])","title":""},{"docid":"da1d60fcea2af00044ec6d5ef3a88d57","score":"0.51990753","text":"def actNatural(self):\r\n phrases = [\"kappa kappa\", \"oyoyoyoyoyoy\", \"RUINED IT!!\", \"Saved it!!\",\r\n \"Wat time is it?\", \"me gusta\", \"now THAT is funny.\", \"please be kind\",\r\n \"wow im offended\", \"i dont care if ur offended, this is who i am\",\r\n \"does anyone know how to get to falador?\", \"buying gf 248gp\",\r\n \"ya manky chav\", \"WOWWWW\", \"how do i mute someone?\", \"press alt+f4\",\r\n \"wow you guys are mean\", \"how do I send a private message?\", \"...I dont know you...\",\r\n \"a/s/l?\", \"18/f/cali\", \"noice.\", \"That can't be...\", \"42\", \"SELLOUT!\", \r\n \"lul\", \"(つ ͡° ͜ʖ ͡°)つ (つ ͡° ͜ʖ ͡°)つ (つ ͡° ͜ʖ ͡°)つ\", \"¯\\_(ツ)_/¯\", \"(ノಠ益ಠ)ノ彡┻━┻\", \r\n \"WOW SICK ULT\", \"NEW META\", \"REKT\", \"MOD ME BRO\", \"HARAMBABE ಠoಠ\"]\r\n\r\n phrase = phrases[random.randint(0, len(phrases) - 1)]\r\n response = \"PRIVMSG {0} :{1}\\r\\n\".format(self._channel, phrase).encode('utf-8')\r\n self.logSend(response)","title":""},{"docid":"84f82f757fc1d7227ba75cb142b2d494","score":"0.5186206","text":"def make_text2(chains):\n\n ret_str = \" \"\n # Use a word starts with a capitalized letter as the beginning of sentence\n random_tuple = random.choice(chains.keys())\n \n while not random_tuple[0][0].isupper():\n random_tuple = random.choice(chains.keys())\n str_len = len(ret_str)\n \n while ret_str[-1] not in ['?', '.', '!'] or str_len < MAX_CHAR:\n ret_str = concat_str(ret_str, random_tuple[0])\n ret_str = concat_str(ret_str, random_tuple[1])\n \n random_value = random.choice(chains[random_tuple])\n if (random_value):\n ret_str = concat_str(ret_str, random_value)\n random_tuple = (random_tuple[1], random_value)\n else:\n break\n if ret_str[-1] not in ['?', '.', '!']:\n ret_str += \".\"\n\n return ret_str[1:]","title":""},{"docid":"fd1789f11c60b9a3bba9adb8c11d379c","score":"0.5168262","text":"def __build_match_title_short(cls, details):\n title = details.get('home', {}).get('name_short')\n title += ' - '\n title += details.get('away', {}).get('name_short')\n return title","title":""},{"docid":"199209e4e2922378d67306c3dc1ebd92","score":"0.51674014","text":"def help(bot, update):\n update.message.reply_text('''COMANDS:\\n/CSGO - CSGO Topstreams\\n/LOL - LOL Topstreams\\n\n /Fortnite - Fortnite Topstreams\\n/PUBG - PUBG Topstreams\\n/Dota - Dota Topstreams\n /Overwatch - Overwatch Topstreams\\n/Heartstone - Heartstone Topstreams\\n/FIFA - FIFA Topstreams\n \\n/TOP - Top streams of any game''')","title":""},{"docid":"8824e30db31e115cafd72c11fc5c9f4d","score":"0.51666796","text":"def generate_phrase(self, max_size=None, min_words=None):\n\n # Invalid input values raise an exception\n valid = True\n\n if max_size:\n valid = type(max_size) is int and max_size >= 0\n\n if min_words:\n valid = valid and type(min_words) is int and min_words >= 0\n\n if not valid:\n raise ValueError(\"Expected positive int value(s) for input.\")\n\n # If both parameters are present, make sure parameters are reasonable\n if max_size and min_words:\n if 5*min_words >= max_size:\n raise ValueError(\"max_size paramemeter must be at least five times larger than min_words.\")\n\n new_word = ''\n msg = ''\n word_count = 0\n\n while 1:\n\n # Generate a successor word\n if new_word in self._markov_chain:\n new_word = self._get_next_term(self._markov_chain[new_word])\n\n # Generate a random word if new_word is not a key in the markov chain\n else:\n new_word = random.choice(self._markov_chain.keys())\n\n # Check if new word will push us over the character limit\n if max_size and len(msg) + len(new_word) >= max_size:\n break\n\n # Append successor to word\n msg += new_word + ' '\n\n # Increment word_count if min_words specified (and 'word' is not a period)\n if min_words and new_word is not '.':\n word_count += 1\n\n # probabalistically decide to stop\n if not min_words or word_count >= min_words:\n if random.random() <= self._stop_p:\n break\n\n return msg.rstrip().capitalize()","title":""},{"docid":"2042f55072aa97d6b5966e7840a5b58a","score":"0.51449275","text":"def _append_app_name(self, val):\n if 'CFBundleName' in info:\n val += ' {}'.format(info['CFBundleName'])\n return val","title":""},{"docid":"56f3c5de27997927190a73ceb14aa836","score":"0.5144441","text":"def hey(self, text):\n\n if not text or text.isspace():\n return 'Fine. Be that way!'\n elif text.isupper():\n return 'Woah, chill out!'\n elif text.endswith('?'): \n return 'Sure.'\n else:\n return 'Whatever.'","title":""},{"docid":"fb5493169b8fb73807b3be7dec1f6f81","score":"0.5143284","text":"def to_phrases(self):\n phrase = ''\n name_pattern = r'[А-Я]+\\s[А-Я]\\.[А-Я]\\.'\n\n with open(self.__script, 'r') as read_file:\n for line in read_file.readlines():\n if re.search(name_pattern, line) or 'ГОЛОВУЮЧИЙ' in line:\n if phrase:\n if 'ГОЛОВУЮЧИЙ' not in phrase:\n politician = re.search(name_pattern, phrase)\n if politician:\n politician = politician[0]\n phrase = re.sub(name_pattern, '', phrase)\n else:\n phrase = ''\n else:\n politician = self.announcer\n phrase = re.sub('ГОЛОВУЮЧИЙ.', '', phrase)\n\n dots = re.findall(r'\\.', phrase)\n if len(dots) > 1:\n self.__phrase_analysis(politician, phrase)\n # print(politician)\n # print(phrase)\n phrase = ''\n phrase += line","title":""},{"docid":"1a2d4c0c481f672b97e684a7049b1e67","score":"0.513873","text":"def get_static_welcome_message():\n return \\\n\"\"\"\n<h3>Search Help</h3>\n<ul><li>The display below the line is an example of the output the browser\nshows you when you enter a search word. The search word was <b>green</b>.</li>\n<li>The search result shows for different parts of speech the <b>synsets</b>\ni.e. different meanings for the word.</li>\n<li>All underlined texts are hypertext links. There are two types of links:\nword links and others. Clicking a word link carries out a search for the word\nin the Wordnet database.</li>\n<li>Clicking a link of the other type opens a display section of data attached\nto that link. Clicking that link a second time closes the section again.</li>\n<li>Clicking <u>S:</u> opens a section showing the relations for that synset.</li>\n<li>Clicking on a relation name opens a section that displays the associated\nsynsets.</li>\n<li>Type a search word in the <b>Next Word</b> field and start the search by the\n<b>Enter/Return</b> key or click the <b>Search</b> button.</li>\n</ul>\n\"\"\"","title":""},{"docid":"952c24739f86275fe39fd3e8bc9ef46d","score":"0.5134184","text":"def text(self) -> str:\n if self._text:\n return self._text\n\n for accent in self.accents:\n if accent == Accent.ACUTE:\n self._text += IPA.ACCENT_ACUTE\n elif accent == Accent.GRAVE:\n self._text += IPA.ACCENT_GRAVE\n\n if self.stress == Stress.PRIMARY:\n self._text += IPA.STRESS_PRIMARY\n elif self.stress == Stress.SECONDARY:\n self._text += IPA.STRESS_SECONDARY\n\n for letter_index, letter in enumerate(self.letters):\n self._text += letter\n\n if letter_index in self.nasalated:\n self._text += IPA.NASAL\n\n if letter_index in self.raised:\n self._text += IPA.RAISED\n\n for c in self._extra_combining[letter_index]:\n self._text += c\n\n if self.tone:\n self._text += self.tone\n\n if self.elongated:\n self._text += IPA.LONG\n\n # Re-normalize and combine\n self._text = unicodedata.normalize(\"NFC\", self._text)\n\n return self._text","title":""},{"docid":"0ec44d4585b30aea72ee5bdef9623375","score":"0.51310563","text":"def main():\n\n args = get_args()\n word = args.word\n\n \"\"\"choose article based on first letter of word\"\"\"\n char = word[0].lower() in 'aeiou'\n article = ''\n\n article = 'an' if char == True else 'a'\n\n\n # print('positional = \"{}\"'.format(word))\n\n print('Ahoy, Captain, {} {} off the larboard bow!'.format(article, word))","title":""},{"docid":"0c63cf82e5456cb266f2a57db129d247","score":"0.51305634","text":"def _generate_friendly_name(self, request):\n\n language = get_language_from_request(request)[:2].lower()\n suggestion = \" \".join(get_random(language, 3, only_single_words=True))\n\n return suggestion[:127]","title":""},{"docid":"e5be83d3f702e322c9581cdbd7a22d3e","score":"0.5123248","text":"def generate():\n\n pref = [\"\", \"educational\", \"cheap\", \"best\", \"most interesting\", \"creative\", \"durable\", \"hobby\", \"blue\"\n \"wonderful\", \"gift\", \"learning\", \"play\", \"backyard\", \"activity\", \"birding\", \"bird watching\",\n \"cool\", \"colorful\", \"party\", \"travel\", \"outdoor\", \"outside\", \"indoor\", \"small\", \"light\", \"stem\",\n \"art\", \"learn\", \"good\", \"fun\", \"new\", \"teaching\", \"top\", \"tops\", \"unique\", \"unusual\", \"preschool\"]\n\n what = [\"\", \"toy\", \"toys\", \"gift\", \"gifts\", \"binocular\", \"binoculars\", \"activity\",\n \"game\", \"games\", \"set\", \"accessory\", \"stuff\", \"things\", \"party\", \"gadget\"]\n\n usage = [\"\", \"for kids\", \"for toddlers\", \"for boys\", \"for girls\", \"for children\",\n \"for kid\", \"for toddler\", \"for boy\", \"for girl\", \"for babies\", \"for baby\",\n \"for age\", \"for 3\", \"for 4\", \"for 5\", \"for 6\", \"for car trip\",\n \"for year\", \"for birthday\", \"for year old\", \"for preschoolers\", \"for brain\",\n \"sale\", \"2018\", \"for grandson\", \"for granddaughter\"]\n\n res = []\n for a in pref:\n for b in what:\n for c in usage:\n phrase = \"{} {} {}\".format(a,b,c).replace(\" \", \" \").replace(\" \", \" \").strip()\n #print(phrase)\n res.append(phrase)\n return res","title":""},{"docid":"8305984369decadeb95604454d0ed59e","score":"0.51228213","text":"def build_phrase_input(phrases, pad_idx, task):\n tokens = [phrase.tokens for phrase in phrases]\n ners = [phrase.ners for phrase in phrases]\n subj_posis = torch.tensor([phrase.subj_posi for phrase in phrases]).unsqueeze(1)\n obj_posis = torch.tensor([phrase.obj_posi for phrase in phrases]).unsqueeze(1)\n if task == \"re\":\n lengths = torch.tensor([len(token_seq) for token_seq in tokens]).unsqueeze(1)\n subj_bool = subj_posis < lengths\n obj_bool = obj_posis < lengths\n assert sum(subj_bool).item() == len(phrases)\n assert sum(obj_bool).item() == len(phrases)\n subj = torch.tensor([phrase.ners[phrase.subj_posi] for phrase in phrases]).unsqueeze(1) # has to be NERs due to type check\n obj = torch.tensor([phrase.ners[phrase.obj_posi] for phrase in phrases]).unsqueeze(1)\n else:\n no_ner_id = NER_LABEL_SPACE[\"\"]\n subj = torch.full((len(phrases),1), no_ner_id)\n obj = torch.full((len(phrases),1), no_ner_id)\n\n ner_pad = NER_LABEL_SPACE[\"<PAD>\"]\n\n tokens, _ = BaseVariableLengthDataset.variable_length_batch_as_tensors(tokens, pad_idx)\n ners, _ = BaseVariableLengthDataset.variable_length_batch_as_tensors(ners, ner_pad)\n\n assert tokens.shape == ners.shape\n\n phrase_input = torch.cat([tokens, ners, subj_posis, obj_posis, subj, obj], dim=1)\n\n return phrase_input","title":""},{"docid":"b8d51404638e2e2ce3ea47e3f6177f6a","score":"0.5114722","text":"def cond_translate_snippet(text):\n return translate_snippet(text, keychar=\"@\")","title":""},{"docid":"0401104e5605eb9fb25cfea3577cf171","score":"0.5111081","text":"def sentence():\r\n # Determine the number of comma-separated sections and number of words in\r\n # each section for this sentence.\r\n sections = [u' '.join(random.sample(WORDS, random.randint(3, 12))) for i in range(random.randint(1, 5))]\r\n s = u', '.join(sections)\r\n # Convert to sentence case and add end punctuation.\r\n return u'%s%s%s' % (s[0].upper(), s[1:], random.choice('?.'))","title":""},{"docid":"246f23197c032ccd87fa89bb0a4b2052","score":"0.51088095","text":"def __str__(self):\n return self.main_word","title":""},{"docid":"5d41b41ee32ffbe458be4d09e92bcbcf","score":"0.5104865","text":"def _generate_help_text(self):\n\n # General package description (mandatory)\n lines = self._generate_section('Description', self.app.cfg['description'], strip=True)\n\n # Package usage instructions (optional)\n lines.extend(self._generate_section('Usage', self.app.cfg['usage'], strip=True))\n\n # Examples (optional)\n lines.extend(self._generate_section('Examples', self.app.cfg['examples'], strip=True))\n\n # Additional information: homepage + (if available) doc paths/urls, upstream/site contact\n lines.extend(self._generate_section(\"More information\", \" - Homepage: %s\" % self.app.cfg['homepage']))\n\n docpaths = self.app.cfg['docpaths'] or []\n docurls = self.app.cfg['docurls'] or []\n if docpaths or docurls:\n root_envvar = ROOT_ENV_VAR_NAME_PREFIX + convert_name(self.app.name, upper=True)\n lines.extend([\" - Documentation:\"])\n lines.extend([\" - $%s/%s\" % (root_envvar, path) for path in docpaths])\n lines.extend([\" - %s\" % url for url in docurls])\n\n for contacts_type in ['upstream', 'site']:\n contacts = self.app.cfg['%s_contacts' % contacts_type]\n if contacts:\n if isinstance(contacts, list):\n lines.append(\" - %s contacts:\" % contacts_type.capitalize())\n lines.extend([\" - %s\" % contact for contact in contacts])\n else:\n lines.append(\" - %s contact: %s\" % (contacts_type.capitalize(), contacts))\n\n # Extensions (if any)\n extensions = self._generate_extension_list()\n lines.extend(self._generate_section(\"Included extensions\", '\\n'.join(wrap(extensions, 78))))\n\n return '\\n'.join(lines)","title":""},{"docid":"b95e1a1f2d851c85304c250247d6247a","score":"0.5103791","text":"def add_quotes_if_needed(self, app=\"\"): # -> str\n if \" \" in app:\n if not app.startswith('\"'):\n app = \"\".join(['\"', app])\n if not app.endswith('\"'):\n app = \"\".join([app, '\"'])\n return app","title":""},{"docid":"8909c37066211f91a9e63434d3950487","score":"0.508499","text":"def phrasesDeck(iphrases):\n return \"\"","title":""},{"docid":"fe23bed26bc7fb747068aff120c4b007","score":"0.50824356","text":"def BuildHelpText(self):\n buf = io.StringIO()\n buf.write('\\n+\\n')\n for key, help_text in sorted(six.iteritems(self.choices)):\n buf.write('*{}*::: {}\\n'.format(key, help_text))\n return (\n '{}Must be one of the following values:{}'.format(\n _Insert(super(Enum, self).BuildHelpText()), buf.getvalue()))","title":""},{"docid":"fb43c7c5be439f4ef24cd6aa7d8a894a","score":"0.50811255","text":"def make_text(chains):\n words = []\n punctuation = [\".\", \",\", \"!\", \"?\", \";\", \":\"]\n\n curr_ngram = choice(chains.keys()) #picks random bigram key\n words.extend(curr_ngram)\n\n while curr_ngram in chains: #there is a curr_value in the dictionary:\n curr_key = curr_ngram[1:]\n curr_value = choice(chains[curr_ngram]) #randomly selects curr_value\n curr_ngram = curr_key + (curr_value,) #updates the curr_ngram variable\n words.append(curr_ngram[-1])\n\n gen_text = \" \".join(words)#long string of generated text\n\n return gen_text","title":""},{"docid":"16b04f9d23135139303c9b747e321330","score":"0.50763226","text":"def handle_message(response):\n phrase = ''\n if response.strip() != '':\n answers = response.split(',')\n answers_count = len(answers)\n for i in range(answers_count):\n answer = answers[i].strip().capitalize()\n phrase += answer + (\n ' or ' if i == answers_count - 2 and answers_count > 1 else ' ')\n if any(char.isdigit() for char in answer) or answer in ('Yes', 'No'):\n break\n return {'result': phrase}","title":""},{"docid":"7af938a109b7977c8e64705184b73f01","score":"0.5076043","text":"def _correct_grammar(self, string_a, string_b, input, switch):\n if switch.lower() in str(input).split(\" \", 1)[0].lower():\n return f\" {string_b} {input}\"\n return f\" {string_a} {input}\"","title":""},{"docid":"bc92408960e7e82465a3f7b107410b3b","score":"0.5075161","text":"def update_pho(self, speaker_code):\n if not self.is_speaker_in_annotation(speaker_code):\n return f'{speaker_code} not in annotation'\n\n # TODO: What if the words have not been extracted yet?\n words = self.words_uttered_by[speaker_code]\n n_words = len(words)\n if n_words == 0:\n return 'error: no words were extracted'\n\n # TODO: What if the subtiers have not been categorized yet?\n pho_subtiers = self.sub_tiers_by_label[TRANSCRIPTION_LABEL]\n if len(pho_subtiers) == 0:\n pho_subtier = SubTier(label=TRANSCRIPTION_LABEL, contents=(' '.join(['###'] * n_words) + '\\n'))\n self.sub_tiers = [pho_subtier] + self.sub_tiers\n self.sub_tiers_by_label[TRANSCRIPTION_LABEL].append(pho_subtier)\n return 'pho subtier added'\n\n [pho_subtier] = pho_subtiers\n m_transcriptions = len(self.transcriptions)\n # Above, we counted '###' as well as actual transcriptions\n m_transcribed = m_transcriptions - self.transcriptions.count('###')\n\n if m_transcribed == 0:\n pho_subtier.contents = ' '.join(['###'] * n_words) + '\\n'\n self.extract_phonetic_transcriptions()\n if m_transcriptions < n_words:\n return \"###'s added, needs transcription\"\n elif m_transcriptions > n_words:\n return \"###'s removed, needs transcription\"\n elif m_transcriptions == n_words:\n return \"needs transcription\"\n\n if m_transcriptions > n_words:\n return 'error: more transcriptions than there are words'\n\n if m_transcriptions < n_words:\n # If we got here, there is at least one actual transcription\n return 'error: fewer transcriptions than there are words, order unknown, sort manually'\n\n # If we got here, m_transcriptions == n_words\n\n if m_transcribed < n_words:\n return 'needs some transcription'\n\n if m_transcribed == n_words:\n return 'all transcribed'","title":""},{"docid":"3a2a4cc9c16a1aeb157b03f9346a762e","score":"0.5074825","text":"def sentencegenerator(text,startword,length = 10):\r\n d = suffixdictionary(text)\r\n sentence = [startword]\r\n index = 0\r\n while index < length:\r\n sentence.append(random.choice(d[sentence[index]]))\r\n index = index + 1\r\n return ' '.join(sentence)","title":""},{"docid":"522ec6e23fe206c0e4b8c4bd19e489d6","score":"0.5071008","text":"def simple_app():\n return '<b>This app is officially working</b>'","title":""},{"docid":"5064783c4dee8387f78cc96daeb1d744","score":"0.5064435","text":"def sample_word(self, pos, grammar_class):\n chosen_word = rand.choice(self.word_dict[grammar_class])\n word_string = chosen_word + f\"_{grammar_class}_\" + str(pos)\n return word_string","title":""},{"docid":"af62e9da28afa1e0c4201694309620ce","score":"0.50637364","text":"def make_text(chains):\n\n\n # start somewhere\n cap_keys = [key for key in chains.keys() if key[0].istitle()]\n\n ngram = choice(cap_keys)\n\n # ngram = choice(list(chains.keys()))\n\n # start list with first two words\n words =list(ngram)\n\n while ngram in chains: \n # use tuple as key to get list of next words\n # choose a next word with 'choice' (returns words as a string)\n next_word = choice(chains[ngram])\n \n # add chosen word to words list\n words.append(next_word) \n\n # make new key from second tuple item and chosen word\n \n # make ngram from tuple to list\n ngram_list = list(ngram)\n\n # make slice from 1 to last\n ngram_slice = ngram_list[1:]\n \n # add our chosen word to the slice\n ngram_slice.append(next_word)\n\n # consciously re-tuple our list\n ngram = tuple(ngram_slice)\n\n return \" \".join(words)","title":""},{"docid":"aa4a2db52aa79c4f0ec8458a000a2714","score":"0.5059467","text":"def sing_a_song():\r\n return 'What do we do with a drunken sailor?'","title":""},{"docid":"c2fe1c4a80aa1aa9ec4bdc47414172fd","score":"0.50576925","text":"def user_phrase():\n phrase = input(\"please type in the phrase you want to encode/decode.\")\n phrase = phrase.lower()\n return phrase","title":""},{"docid":"c0d3b7bb191507c9e8c31954db554239","score":"0.5054058","text":"def generate_text(start_word, model, h_learnable, embedder, word2id, id2word, max_len, allow_unknowns, top_k=5):\n model.eval()\n with torch.no_grad():\n # Try at most 10 times to generate a review that ends with \"<EOR>\".\n # If unsuccesful, output whatever review was generated last.\n trys = 0\n while trys <= 10:\n trys += 1\n word_ids = [word2id[start_word]]\n\n if h_learnable:\n h = None\n else:\n h = model.init_hidden(batch_size=1)\n \n while word_ids[-1] != word2id['<EOR>'] and len(word_ids) <= max_len:\n x = word_to_model_input(id2word[word_ids[-1]], embedder, word2id)\n y, h = predict(\n x_packed=x,\n model=model,\n h_learnable=h_learnable,\n h=h,\n batch_size=1,\n word2id=word2id,\n allow_unknowns=allow_unknowns,\n top_k=top_k\n )\n word_ids.append(y)\n \n if word_ids[-1] == word2id['<EOR>']:\n break\n words = [id2word[idx] for idx in word_ids]\n return \" \".join(words)","title":""},{"docid":"ab5d8d5ceab0ec3e9e81e2850675cff9","score":"0.5047875","text":"def __convert(self, kind: str) -> str:\n sep: str = self.separators[kind]\n words: List[str] = regex.findall(self.pattern, self.string)\n size: int = len(words)\n converted: str = \"\"\n if size == 0:\n converted = self.string\n elif kind in (\"pascal\", \"title\", \"header\"):\n converted = sep.join([w.lower().capitalize() for w in words])\n elif kind in (\"snake\", \"kebab\", \"dot\"):\n converted = sep.join([w.lower() for w in words])\n elif kind == \"camel\":\n converted = sep.join(\n [\n w.lower() if i == 0 else w.lower().capitalize()\n for i, w in enumerate(words)\n ]\n )\n elif kind == \"constant\":\n converted = sep.join([w.upper() for w in words])\n return converted","title":""},{"docid":"8590845202dca01438b9ab709447c4cf","score":"0.5033167","text":"def whichBetter(self, type_valg):\n word = \"\"\n if type_valg == \"Stein\":\n word = \"Papir\"\n elif type_valg == \"Saks\":\n word = \"Stein\"\n else:\n word = \"Saks\"\n return word","title":""},{"docid":"1cc5fd6831605fee3211049ddfd2b41a","score":"0.50310034","text":"def pronunciation(word, say):\n if word in say:\n return say[word]\n else:\n return ''","title":""},{"docid":"29dfc2a2aa769ccb5c2d554d05998592","score":"0.5028345","text":"def short_name(self):\n self.details['short_name'] = input(\n \"Short Name \" + str(on([\"M\"], args)) + \" (\" + Colors.OKBLUE + self.details['name'][\n 0:12] + Colors.ENDC + \"): \")\n # default to project name\n if self.details['short_name'] == '':\n self.details['short_name'] = self.details['name'][0:12]","title":""},{"docid":"063d4d61ded9a782539fadbcc7d8d505","score":"0.5014829","text":"def make_text(chains):\n\n ret_str = \"\"\n end_the_str = False\n\n while len(ret_str) < MAX_CHAR:\n # Use a word starts with a capitalized letter as the beginning of sentence\n random_tuple = random.choice(chains.keys())\n while not random_tuple[0][0].isupper():\n random_tuple = random.choice(chains.keys())\n\n while len(ret_str) < MAX_CHAR:\n if len(ret_str) + len(random_tuple[0]) > MAX_CHAR:\n end_the_str = True\n break;\n ret_str = concat_str(ret_str, random_tuple[0])\n if len(ret_str) + len(random_tuple[1]) > MAX_CHAR:\n end_the_str = True\n break;\n ret_str = concat_str(ret_str, random_tuple[1])\n \n random_value = random.choice(chains[random_tuple])\n if (random_value):\n if len(ret_str) + len(random_value) > MAX_CHAR:\n end_the_str = True\n break;\n ret_str = concat_str(ret_str, random_value)\n random_tuple = (random_tuple[1], random_value)\n else:\n break\n\n if end_the_str:\n break\n\n return ret_str","title":""},{"docid":"871299dfb75561df1ed74d6872ada30f","score":"0.50126153","text":"def get_thank_you_text(survey):\n if survey.get('language') == 'ms':\n thankyou_text = 'Terima Kasih'\n elif survey.get('language') == 'zh':\n thankyou_text = '谢谢'\n elif survey.get('language') == 'ja':\n thankyou_text = 'ありがとうございました'\n elif survey.get('language') == 'ko':\n thankyou_text = '고맙습니다'\n else:\n thankyou_text = 'Thank You'\n return thankyou_text","title":""},{"docid":"e13691e46bdfc7b2dcf705c4b8e6b0a7","score":"0.4993633","text":"def correct(word):\n if word == \"show_tell\":\n return 'Show \\\\& Tell'\n elif word == 'top_down':\n return \"Top-down\"\n elif word == \"resnet50\":\n return \"ResNet-50\"\n elif word == \"resnet152\":\n return \"ResNet-152\"\n elif \"cnn\" in word:\n return \"RNN + CNN\"\n else:\n return word","title":""},{"docid":"19f61e924b31cee04c048fe50a8b89e4","score":"0.49910954","text":"def main():\n\n conf = parse_arguments()\n if conf.url:\n try:\n response = urllib.request.urlopen(conf.url)\n words = response.read().decode().splitlines()\n except urllib.error.URLError:\n print('web request failure', file=sys.stderr)\n sys.exit(2)\n else:\n try:\n with open('/usr/share/dict/words', 'r') as f:\n words = f.read().splitlines()\n except OSError:\n print('no dictionary file', file=sys.stderr)\n sys.exit(3)\n \n nonalpha = ''\n if conf.num:\n nonalpha += string.digits\n if conf.sym:\n nonalpha += string.punctuation\n #nonalpha = string.digits + string.punctuation\n for i in range(int(conf.phrases)):\n pw = ''\n while not valid_phrase(pw, conf):\n # rerolls passphrase until valid\n pw = ''\n if secrets.randbits(1) == 1 and len(nonalpha) > 0:\n # 50% chance of number or symbol at beginning versus letter\n pw += secrets.choice(nonalpha)\n for j in range(int(conf.words)):\n if secrets.randbits(1) == 0:\n pw += secrets.choice(words).upper()\n else:\n pw += secrets.choice(words).lower()\n if secrets.randbits(1) == 1 and len(nonalpha) > 0:\n # 50% chance of number or symbol between words and at the end\n pw += secrets.choice(nonalpha)\n print(pw + '\\t' + str(len(pw)))","title":""},{"docid":"17aa8ca7567366961e1facbeb996c58d","score":"0.49850595","text":"def __init__(self):\n self.nlp = spacy.load('en_core_web_sm')\n if args.add_q:\n self.q_name = \"_with_question\"\n else:\n self.q_name = \"_without_question\"","title":""},{"docid":"96e828ebc5bd6b8b15ed6f85ae3ff1cc","score":"0.49840906","text":"def apology_choose(message):\n def escape(s):\n \"\"\"\n Escape special characters.\n\n https://github.com/jacebrowning/memegen#special-characters\n \"\"\"\n for old, new in [(\"-\", \"--\"), (\"_\", \"__\"), (\"?\", \"~q\"),\n (\"%\", \"~p\"), (\"#\", \"~h\"), (\"/\", \"~s\"), (\"\\\"\", \"''\")]:\n s = s.replace(old, new)\n return s\n return render_template(\"apology_choose.html\", bottom=escape(message))","title":""},{"docid":"1feff80b2830df9ebb0cde4ae87fbd46","score":"0.4981439","text":"async def translate(self, ctx, *, phrase: str):\n \n langs = re.search(r\"(\\w{2})-(\\w{2})\", phrase[0:5])\n if langs:\n sl = langs.group(1)\n tl = langs.group(2)\n phrase = phrase[6:]\n else:\n sl = \"auto\"\n tl = \"en\"\n\n url = \"https://translate.googleapis.com/translate_a/single\"\n params = {'client': 'gtx', 'sl': sl, 'tl': tl, 'dt': 't', \"q\": phrase}\n ua = \"Mozilla/5.0 (X11; CrOS x86_64 12239.19.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.38 Safari/537.36\"\n headers = {'User-Agent': ua}\n async with self.bot.session.get(url, headers=headers, params=params) as resp:\n result = await resp.json()\n await ctx.send(\"{} ({}): {}\".format(result[0][0][1], result[2], result[0][0][0]))","title":""},{"docid":"f8d6c5a44660722a13526c09bb0a2490","score":"0.49773678","text":"def shortHelpString(self):\r\n return self.tr(\"Example algorithm short description\")","title":""},{"docid":"2ef868fedc1307740491880c02a67570","score":"0.49741906","text":"def get_short_text(self) -> str:\n oc = self.__oc\n return f\"{oc.name} the {oc.species.title()} {' '.join('#' + tag.replace(' ', '') for tag in self._tags) if self._tags else ''}\"","title":""},{"docid":"f702723b7caaa9c21dcf3984429fce77","score":"0.49714324","text":"def make_text(chains):\n\n # randomly select a dictionary entry to start with\n\n search_tuple = random.choice(chains.keys())\n sentence_list = [search_tuple[0], search_tuple[1]]\n \n #loop goes here\n \n while len(\" \".join(sentence_list)) < 130:\n rand_val = random.choice(chains[search_tuple])\n sentence_list.append(rand_val)\n search_tuple = (search_tuple[1], rand_val)\n if not chains.get(search_tuple):\n break\n\n sentence = \" \".join(sentence_list).capitalize()\n return sentence","title":""},{"docid":"450d955df168fa99b0ed099438269d7c","score":"0.49708033","text":"def valiant_app_title() -> str:\n return \"Valiant\" # noqa:DAR201","title":""},{"docid":"be9921aa062fcf4be522f3ceeeecdf8b","score":"0.49696136","text":"def update_text(self, old_text, new_text):\n keyword_found = self.check_for_keywords(new_text)\n if keyword_found is not None:\n self.sound_alarm(keyword_found, new_text)\n self.output_text += f\"\\nMALICIOUS PHRASE -->> {new_text} <<--\"\n else:\n self.output_text += f\"\\n{new_text}\"\n self.output_box.config(text=self.output_text)","title":""},{"docid":"0b7833ebbed7d33e07790235972a1583","score":"0.49660146","text":"def mlpPhrase():\n configuration['mlpPhrase'].update({\n 'phraseMaxLength': 50,\n 'phraseTokenEmb': int(Generator.generateValue([25, 200], True)),\n 'phrasePosEmb': int(Generator.generateValue([5, 50], True)),\n 'gru': Generator.generateValue([True, False], False, True),\n 'wordRnnUnitNum': int(Generator.generateValue([25, 500], True)),\n 'useB1': Generator.generateValue([True, False], False),\n 'useB-1': Generator.generateValue([True, False], False),\n 'transPosEmb': int(Generator.generateValue([5, 50], True)),\n 'transTokenEmb': int(Generator.generateValue([25, 200], True)),\n 'denseUnitNumber': int(Generator.generateValue([25, 500], True)),\n 'lr': round(Generator.generateValue([0.01, 0.2], True), 3)\n })\n configuration['embedding']['lemma'] = Generator.generateValue([True, False], False)\n configuration['sampling']['importantSentences'] = True\n configuration['sampling']['overSampling'] = True","title":""},{"docid":"c7cb99d67d035d00b06f8a3136a1525b","score":"0.49647617","text":"def BuildHelpText(self):\n return (\n '{}Must be a string representing a day of the week in English, '\n 'such as \\'MON\\' or \\'FRI\\'. Case is ignored, and any characters after '\n 'the first three are ignored.{}'.format(\n _Insert(super(DayOfWeek, self).BuildHelpText()),\n _Append(self.Constraints())))","title":""},{"docid":"eb791b586cf8e6bd63f778e79f499f94","score":"0.49606007","text":"def generate_phrase_tts(phrase, save_dir):\n # Initialize text to speech system\n remove_whitespace = sub('\\s+', '_', phrase)\n filename = join(save_dir, remove_whitespace + '.wav')\n if platform == 'linux':\n # eSpeak does not implement the save_to_file function\n # So we must call it directly\n subprocess.Popen(['espeak', phrase, '-w', filename])\n else:\n if platform != 'darwin':\n print('File may not be created on unsupported OS')\n engine = pyttsx3.init()\n engine.save_to_file(phrase, filename)\n engine.runAndWait()","title":""},{"docid":"7cccd5b207c4f60d75f8e7c9a5823ed5","score":"0.49579707","text":"def nounPhrase():\n with open('articles.txt') as a:\n article = csv.reader(a)\n articles = random.choice(list(article))\n with open('adjectives.txt') as ad:\n adjective = csv.reader(ad)\n adjectives = random.choice(list(adjective)) \n with open('nouns.txt') as n:\n noun = csv.reader(n)\n nouns = random.choice(list(noun)) \n\n optional1 = random.choice(['yes', 'no'])\n if optional1 == 'yes': \n return random.choice(articles) + \" \" + random.choice(nouns)\n if optional1 == 'no': \n return random.choice(articles) + \" \" + random.choice(adjectives) + \" \" + random.choice(nouns)","title":""},{"docid":"122f8bafe840ad2061e571b3af884434","score":"0.4957891","text":"def get_app_title_rst(self, continuation=''):\n\n if continuation:\n continuation = ' ' + continuation\n\n title = self._context['app_name'] + continuation\n title = f\"{title}\\n{'=' * len(title)}\"\n\n return title","title":""},{"docid":"12a853e3ed7a31528984e229e270ba0f","score":"0.49564636","text":"def generate_sentence(name, comment, score):\n return ' '.join((name, comment, str(score)))","title":""},{"docid":"6f16678a40b417f3fdfc3b4dc9233d05","score":"0.4955744","text":"def do_comprehend_languages(self, arg):\r\n print_description('1st', 'Comprehend Languages', '1 Action [R]', '1 Hour', 'Self', 'None', 'Social',\r\n 'V, S, M', 'Divination')","title":""},{"docid":"07245ef935e84c28532e56d84ce1e1ff","score":"0.49505526","text":"def word_display(secret_word, additional_words):\n\n pass # ToDo: Remove this line","title":""},{"docid":"2239234fbafb738eb7b52dad4053729b","score":"0.49482384","text":"def test_makeAltName(self):\n mk = search.makeAltName\n self.assertEqual(mk(\"hello\"), \"hello\")\n self.assertEqual(mk(\"Hello\"), \"hello\")\n self.assertEqual(mk(\"Hello There\"), \"hello there\")\n self.assertEqual(mk(\"Cat's Grace\"), \"cats grace\")\n self.assertEqual(mk(\"<Cat</>'s Grace\"), \"cats grace\")\n self.assertEqual(mk(\"<Cat</>'s Grace, Mass\"), \"cats grace mass\")","title":""},{"docid":"1e5acc9bf41cdcc73c0044d425ac6575","score":"0.49434415","text":"def do_speak_with_plants(self, arg):\r\n spell_helper(\"Speak with Plants\")","title":""},{"docid":"49e70f9951abe68136e4c4c41aa5361b","score":"0.4940188","text":"def update_text(self):\n likes = \"\"\n if self.likes_comedy.get():\n likes += \"You like comedic movies.\\n\"\n if self.likes_drama.get():\n likes += \"You like dramatic movies.\\n\"\n if self.likes_romance.get():\n likes += \"You like romantic movies.\"\n self.results_txt.delete(0.0, END)\n self.results_txt.insert(0.0, likes)","title":""},{"docid":"857dc3a4d706a6c6e0e489308391ebbe","score":"0.49396452","text":"def generate_basic_description(cls) -> str:\n extractive_abstractive = \"extractive\" if cls.is_extractive else \"abstractive\"\n neural = \"neural\" if cls.is_neural else \"non-neural\"\n\n basic_description = (\n f\"{cls.model_name} is a\"\n f\"{'query-based' if cls.is_query_based else ''} \"\n f\"{extractive_abstractive}, {neural} model for summarization.\"\n )\n if cls.is_multi_document or cls.is_dialogue_based:\n basic_description += (\n f\"It can handle {'multi-document' if cls.is_multi_document else ''} \"\n f\"{'dialogue' if cls.is_dialogue_based else ''} textual data.\"\n )\n\n return basic_description","title":""},{"docid":"a84cc20e8a175f169ebedd5c3807f276","score":"0.49395812","text":"def shortHelpString(self):\r\n return self.tr('PegelOnline short description: to do')","title":""},{"docid":"569708a945ca304459d40fce304f9b70","score":"0.49388468","text":"def speak(self, n, start = None):\n\t\tif (not start) or (start not in self.unique_words):\n\t\t\tk = int( self.t * random.random() )\n\t\t\tk = k if k != self.t else self.t-1\n\t\t\tstart = self.unique_words[k]\n\t\tfinal = []\n\t\tcurrent_word = start\n\t\tcurrent_index = self.unique_words.index(start)\n\t\tfor i in range(n):\n\t\t\tfinal.append(current_word)\n\t\t\tcurrent_word = ''\n\t\t\ti = 0\n\t\t\twhile current_word == '':\n\t\t\t\ti += 1\n\t\t\t\tnew_word, new_index = self.obtain_word(current_index)\n\t\t\t\tcurrent_word = new_word\n\t\t\t\tif current_word in final:\n\t\t\t\t\tcurrent_word = ''\n\t\t\t\tif current_word == 'system':\n\t\t\t\t\tcurrent_word = ''\n\t\t\t\tif current_word != '':\n\t\t\t\t\tcurrent_index = new_index\n\t\t\t\tif i % 20 == 0:\n\t\t\t\t\ti = 1\n\t\t\t\t\tcurrent_index = new_index\n\t\treturn ' '.join(final)","title":""},{"docid":"6d03fc0041fe8df6488c1b5bac038fa4","score":"0.4937366","text":"def build_simple_sms(self, content):\r\n return '{0}'.format(content)","title":""},{"docid":"5e8c806e133b7b99e32caa785747d489","score":"0.493727","text":"def translate(sentence):\n\n\twords = {'esta': 'is', 'la': 'the', 'en': 'in', 'gato': 'cat', 'casa': 'house', 'el': 'the'}\n\treturn ' '.join(words[word] for word in sentence.split() if word in words)","title":""},{"docid":"b2cbdc5035c66cc592f3ee05c377cd70","score":"0.49366137","text":"def main():\r\n parser = argparse.ArgumentParser(prog='pig', \r\n description=\"English to Pig Latin translator\",\r\n epilog='(lsoAay aay igPay atinLay otay nglishEay ranslatortay!)')\r\n parser.add_argument('text', nargs='?')\r\n args = parser.parse_args()\r\n\r\n if not args.text:\r\n # No text was given on the command line, so we will \r\n # translate our test messages.\r\n phrases = ['These are not the droids youre looking for', \r\n 'I have a bad feeling about this']\r\n bonus_phrases = ['Do. Or do not. There is no try!',\r\n 'Iay maay luentfay niay veroay ixsay illionmay ormsfay foay ommunicationcay!',\r\n 'ren\\'tAay ouyay aay ittlelay hortsay orfay aay tormtrooperSay?',\r\n 'Iay indfay ouryay acklay foay aithfay isturbingday.']\r\n\r\n for phrase in phrases:\r\n print('\\nOriginal phrase: ' + phrase )\r\n print('Translation: ' + translate(phrase))\r\n\r\n print('\\n**BONUS PHRASES**')\r\n for phrase in bonus_phrases:\r\n print('\\nOriginal phrase: ' + phrase )\r\n print('Translation: ' + translate(phrase))\r\n sys.exit(0)\r\n\r\n # Text was given on the command line. Translate it.\r\n print(translate(args.text))","title":""},{"docid":"227e8ad827b248167c6ba17947fd956e","score":"0.49296322","text":"def short_action_string(self):\n output = \"{0} \".format(self.actor)\n if self.override_string:\n output += self.override_string\n else:\n output += self.verb\n return output","title":""}],"string":"[\n {\n \"docid\": \"5068d9b7315d7647fd277a2378690c05\",\n \"score\": \"0.6656243\",\n \"text\": \"def gen_phrase_string(self):\\n return self.post_process(self.gen_phrase())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c38d3360b740fb302558e517c732aa\",\n \"score\": \"0.6161804\",\n \"text\": \"def translate(self, phrase):\\n return phrase\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12131e15781865b3f8aae86dc955474f\",\n \"score\": \"0.6065521\",\n \"text\": \"def sentence():\\n optional = random.choice(['yes', 'no'])\\n if optional == 'yes':\\n return nounPhrase() + \\\" \\\" + verbPhrase()\\n if optional == 'no':\\n return nounPhrase() + \\\" \\\" + verbPhrase() + \\\" \\\" + conjunction() + \\\" \\\" + nounPhrase() + \\\" \\\" + verbPhrase()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92d01f01cad42278c671a33dccce967e\",\n \"score\": \"0.5991369\",\n \"text\": \"def abbreviate(phrase):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7aac8488a97b986ffad1c0ba5a59803\",\n \"score\": \"0.5721289\",\n \"text\": \"def form_sentence(self):\\n \\n if self.subject and self.verb and self.object: \\n return \\\" \\\".join([self.subject, self.verb, self.object])\\n elif self.subject and self.verb:\\n return \\\" \\\".join([self.subject, self.verb])\\n else:\\n return self.subject\",\n \"title\": \"\"\n },\n {\n \"docid\": \"737fad7aca6d070bf5e2fb96136e12da\",\n \"score\": \"0.57015353\",\n \"text\": \"def app_name(): # -> str\\n return \\\"Read Text\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ea27e2548f2b654ddb41ca52f91b0fd\",\n \"score\": \"0.569428\",\n \"text\": \"def build_text(self, task):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49ec5816112fe563d55af8c5b68b2a00\",\n \"score\": \"0.56447375\",\n \"text\": \"def _get_string_phrase(phrase, index_vocab):\\n res = \\\"\\\"\\n for vocab_id in phrase.split():\\n if res == \\\"\\\":\\n res += index_vocab[int(vocab_id)]\\n else:\\n res += \\\" \\\" + index_vocab[int(vocab_id)]\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0bc044b9d1bdccbc4ef1019750322ae\",\n \"score\": \"0.5595425\",\n \"text\": \"def translate_to_pirate_talk(phrase):\\n\\n result =[]\\n translation = {'sir': 'matey', 'hotel': 'fleabag', 'student': 'swabbie', 'man': 'matey', \\n 'professor':'foul blaggart', 'restaurant':'galley','your':'yer','excuse':'arr',\\n 'students':'swabbies','are':'be','restroom':'head','my':'me','is':'be'}\\n \\n \\n phrase = phrase.split()\\n \\n for word in phrase:\\n \\n if word in translation.keys():\\n result.append(translation[word])\\n else:\\n result.append(word)\\n\\n str1 = \\\" \\\"\\n \\n print(str1.join(result))\\n\\n return str1.join(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97c8d70f93b4a7b03265408130d25b87\",\n \"score\": \"0.555625\",\n \"text\": \"def gen(self):# Need to imporve\\n try:\\n temp = self.text.strip().split()\\n except:\\n pass\\n return self.speller.genSentence(temp)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"838e4bd436a05d8710118b694133a2ce\",\n \"score\": \"0.5522631\",\n \"text\": \"def build_text(word_pairs):\\n\\n building_text = []\\n\\n # Gets a random pair to start off with\\n pair_choice = random.choice(list(word_pairs))\\n building_text += pair_choice.split()\\n\\n # Proceeds to get value of pair\\n pair_follower = random.choice(list(word_pairs[pair_choice]))\\n building_text.append(pair_follower)\\n\\n # Continues getting new pairs\\n if len(building_text) < 100:\\n new_pair = ' '.join(building_text[-2:])\\n while True:\\n if new_pair in word_pairs:\\n building_text.append(random.choice(list(word_pairs[new_pair])))\\n new_pair = ' '.join(building_text[-2:])\\n else:\\n break\\n print(' '.join(building_text))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6011c6c25819bcd5c8be66ba46948e8e\",\n \"score\": \"0.55188257\",\n \"text\": \"def verbPhrase():\\n with open('verbs.txt') as v:\\n verb = csv.reader(v)\\n verbs = random.choice(list(verb)) \\n\\n optional = random.choice(['yes', 'no'])\\n if optional == 'yes':\\n return random.choice(verbs) + \\\" \\\" + nounPhrase() \\n if optional == 'no':\\n return random.choice(verbs) + \\\" \\\" + nounPhrase() + \\\" \\\" + prepositionalPhrase()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96b7c98548c0e6633abd4a2b15dc0c2b\",\n \"score\": \"0.54932594\",\n \"text\": \"def make_pattern_text(self):\\n\\n text = \\\"Do you like \\\"\\n if self.is_positive():\\n text += \\\"more \\\"\\n else:\\n text += \\\"less \\\"\\n\\n text += \\\" and \\\".join(map(lambda p: p.name.replace(\\\"_\\\", \\\" \\\"), self.get_targets()))\\n text += \\\"?\\\"\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb4a8104c3ab91e6e634c4bd56de4de\",\n \"score\": \"0.5474925\",\n \"text\": \"def _GenerateDescription(self, verb, message, fallback):\\n if self.toolset != 'target':\\n verb += '(%s)' % self.toolset\\n if message:\\n return '%s %s' % (verb, self._ExpandSpecial(message))\\n else:\\n return '%s %s: %s' % (verb, self.name, fallback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01f80d2f772aedb19e27879bdb1384c6\",\n \"score\": \"0.54285204\",\n \"text\": \"def hey(phrase):\\n \\n if phrase.isupper():\\n return \\\"Whoa, chill out!\\\"\\n if phrase[-1:] == \\\"?\\\":\\n return \\\"Sure.\\\"\\n if phrase == \\\"\\\" or phrase.isspace():\\n return \\\"Fine. Be that way!\\\"\\n return \\\"Whatever.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80fa4b3433075436c652da878babf227\",\n \"score\": \"0.54098433\",\n \"text\": \"def english_command(tokens, concept, discourse):\\n verb = tokens[0]\\n line = ''\\n i = 1\\n for part in discourse.command_canonical[verb].split():\\n if (part in ['ACCESSIBLE', 'ACTOR', 'DESCENDANT', 'NEARBY', \\n 'NOT-DESCENDANT', 'WORN']):\\n line += concept.item[tokens[i]].noun_phrase(discourse)\\n i += 1\\n elif part in ['RELATION']:\\n line += tokens[i].lower()\\n i += 1\\n elif part in ['STRING']:\\n line += tokens[i]\\n else:\\n line += part\\n line += ' '\\n return line[:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0ac8287cbb96e2966dec4a3158b8f5c\",\n \"score\": \"0.53940797\",\n \"text\": \"def build_message(self):\\n commentUser = (\\n \\\"**SEARCH TERM: {term}**\\\\n\\\\n\\\"\\n \\\"Total Occurrence: {totalOccur} \\\\n\\\\n\\\"\\n \\\"Total Chapters: {totalChapter} \\\\n\\\\n\\\"\\n \\\"{warning}\\\"\\n \\\"{chapterpov}\\\"\\n \\\"######	\\\\n\\\\n####	\\\\n\\\\n#####	\\\\n\\\\n\\\"\\n \\\"	\\\\n\\\\n	\\\\n\\\\n\\\"\\n \\\">{message}\\\"\\n \\\"{link}\\\"\\n )\\n warning = \\\"\\\"\\n if self.title.name != 'All' and self.title.name != 'PQ' and self.title.name != 'DE':\\n warning = (\\\"**ONLY** for **{book}** and under due to the spoiler tag in the title.\\\\n\\\\n\\\").format(\\n book = self.title.name,\\n )\\n if self._rowCount > MAX_ROWS:\\n warning += (\\\"Excess number of chapters. Sorted by highest to lowest, top 30 results only.\\\\n\\\\n\\\")\\n # Avoids spam and builds table heading only when condition is met\\n if self._total > 0:\\n self._message += (\\n \\\"| Series| Book| Chapter| Chapter Name| Chapter POV| Occurrence| Quote^(First Occurrence Only)\\\\n\\\"\\n )\\n self._message += \\\"|:{dash}|:{dash}|:{dash}|:{dash}|:{dash}|:{dash}|:{dash}|\\\\n\\\".format(dash='-' * 11)\\n # Each element added as a new row with new line\\n for row in self._listOccurrence:\\n self._message += row + \\\"\\\\n\\\"\\n elif self._total == 0:\\n self._message = \\\"**Sorry no results.**\\\\n\\\\n\\\"\\n \\n self._commentUser = commentUser.format(\\n warning = warning,\\n chapterpov = self._chapterPovMessage,\\n term = self._searchTerm,\\n totalOccur = self._total,\\n message = self._message,\\n link = self._links,\\n totalChapter = self._rowCount\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42b7d15a058330437e7100e3b7e752f5\",\n \"score\": \"0.5376561\",\n \"text\": \"def get_text(self):\\n if self.them_text:\\n return self.suggestion.text.replace('{{them}}', self.them_text)\\n return self.suggestion.get_text()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d739e51b96404bfd38c0a62a56c9d43\",\n \"score\": \"0.5366304\",\n \"text\": \"def random_noun_phrase():\\n noun_phrase = {}\\n # necessary choices\\n random_noun = Noun.objects.order_by('?')[0]\\n random_adj = Adjective.objects.order_by('?')[0]\\n # optional choices\\n random_pos = random.choice([Possessive.objects.order_by('?')[0], None])\\n random_dem = random.choice([get_chart_prefix('d', random_noun.noun_class), None])\\n noun_phrase['noun'] = random_noun\\n noun_phrase['adjs'] = [random_adj]\\n if random_pos:\\n noun_phrase['pos'] = random_pos\\n if random_dem:\\n noun_phrase['dem'] = random_dem\\n return fix_noun_phrase(noun_phrase)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57b15480bb23a8754ba80c29f06ed5b6\",\n \"score\": \"0.5362081\",\n \"text\": \"def hey(phrase):\\n\\t\\n\\tphrase = phrase.strip()\\n\\n\\tif not phrase:\\t\\t\\t\\t\\t# Checks to see if phrase is blank.\\n\\t\\treturn 'Fine. Be that way!'\\n\\telif phrase.isupper():\\t\\t\\t# Checks to see if phrase is a yell.\\n\\t\\treturn 'Whoa, chill out!'\\n\\telif phrase.endswith('?'):\\t\\t# Checks to see if phrase is a question.\\n\\t\\treturn 'Sure.'\\n\\telse:\\n\\t\\treturn 'Whatever.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ceec260a88b1c9f08d48777b0168bc3\",\n \"score\": \"0.5348122\",\n \"text\": \"def synthesize(self, phrase, language=\\\"en_US\\\"):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee50593ef54f59b2a2e1b6cbdd3e8533\",\n \"score\": \"0.5336454\",\n \"text\": \"def hey(phrase: str) -> str:\\n phrase = phrase.strip()\\n if not phrase: # if you address him without actually saying anything\\n return \\\"Fine. Be that way!\\\"\\n if phrase.isupper(): # if you yell at him\\n if phrase.endswith('?'): # if you yell a question at him\\n return \\\"Calm down, I know what I'm doing!\\\"\\n return \\\"Whoa, chill out!\\\"\\n if phrase.endswith('?'): # if you ask him a question\\n return \\\"Sure.\\\"\\n return \\\"Whatever.\\\" # to anything else\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b71476b9e094863bb542e95975b59d04\",\n \"score\": \"0.5329836\",\n \"text\": \"def translate(line):\\n\\n line = line.lower() # convert enlgish sentence to lowercase\\n englishWords = line.split() # split line into words for processing, write to global variable\\n maoriWords = [] # create list for maori sentence\\n\\n # check for valid sentences size before popping\\n if len(englishWords) >= 2 and len(englishWords) <= 5:\\n currentWord = englishWords.pop(0)\\n\\n # check for simple pronouns in main function\\n if currentWord == \\\"i\\\":\\n maoriWords.append(\\\"au\\\")\\n currentWord = englishWords.pop(0)\\n\\n elif currentWord == \\\"he\\\" or currentWord == \\\"she\\\":\\n maoriWords.append(\\\"ia\\\")\\n currentWord = englishWords.pop(0)\\n\\n # check for more complex pronouns with methods\\n\\n elif currentWord == \\\"you\\\":\\n currentWord, englishWords, maoriPronoun = getYouPronoun(currentWord, englishWords)\\n maoriWords.append(maoriPronoun)\\n\\n elif currentWord == \\\"they\\\":\\n currentWord, englishWords, maoriPronoun = getTheyPronoun(currentWord, englishWords)\\n maoriWords.append(maoriPronoun)\\n\\n elif currentWord == \\\"we\\\":\\n currentWord, englishWords, maoriPronoun = getWePronoun(currentWord, englishWords)\\n maoriWords.append(maoriPronoun)\\n\\n else:\\n return INVALID\\n\\n\\n # infer tense and get verb\\n # one left in list means auxiliary verb + verb\\n if len(englishWords) == 1:\\n\\n if currentWord == \\\"am\\\" or currentWord == \\\"are\\\" or currentWord == \\\"is\\\":\\n currentWord = englishWords.pop(0)\\n maoriVerb = getVerbPresent(currentWord)\\n\\n # if verb was invalid return message and end early\\n if maoriVerb == None:\\n return \\\"invalid verb \\\\\\\"\\\" + currentWord + \\\"\\\\\\\"\\\"\\n else:\\n maoriWords.insert(0, maoriVerb)\\n return ' '.join(maoriWords)\\n\\n elif currentWord == \\\"will\\\":\\n currentWord = englishWords.pop(0)\\n maoriVerb = getVerbFuture(currentWord)\\n\\n # if verb was invalid return message and end early\\n if maoriVerb == None:\\n return \\\"invalid verb \\\\\\\"\\\" + currentWord + \\\"\\\\\\\"\\\"\\n else:\\n maoriWords.insert(0, maoriVerb)\\n return ' '.join(maoriWords)\\n\\n else:\\n return INVALID\\n\\n # empty list means no auxiliary verb\\n elif len(englishWords) == 0:\\n maoriVerb = getVerbPastOrPresent(currentWord)\\n\\n if maoriVerb == None: # if verb was invalid return message and end early\\n return \\\"invalid verb \\\\\\\"\\\" + currentWord + \\\"\\\\\\\"\\\"\\n else:\\n maoriWords.insert(0, maoriVerb)\\n return ' '.join(maoriWords)\\n\\n\\n else:\\n return INVALID\\n\\n else:\\n return INVALID\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caf764877a39cff4411f10ff9ac78e59\",\n \"score\": \"0.53245485\",\n \"text\": \"def initial_data(num_phrase):\\n\\n phrases = [\\\"you need new permissions\\\",\\n \\\"I can arrange a meeting for you\\\",\\n \\\"the next possible appointment could be tomorrow\\\"]\\n\\n final_words = ['pronoun', 'det', 'adj', 'n', 'md', 'v', 'prep']\\n\\n grammar = {'s': ['np vp'],\\n 'np': ['pronoun', 'adj n', 'np pp', 'det n', 'n', 'det adj adj n'],\\n 'pp': ['prep pronoun'],\\n 'vp': ['v np', 'md vp'],\\n 'pronoun': ['you', 'I'],\\n 'det': ['a', 'the'],\\n 'prep': ['for'],\\n 'v': ['need', 'be', 'arrange'],\\n 'adj': ['new', 'next', 'possible'],\\n 'md': ['can', 'could'],\\n 'n': ['permissions', 'meeting', 'appointment', 'tomorrow']}\\n return phrases[num_phrase], final_words, grammar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35d9b2238f122c3e0ccee45e62298fe3\",\n \"score\": \"0.5314347\",\n \"text\": \"def _grammar_injection(self, string_a, string, switch):\\n if switch.lower() not in str(string).split(\\\" \\\", 1)[0].lower():\\n if len(str(string).split(\\\" \\\", 1)) > 1:\\n return f\\\"{str(string).split(' ', 1)[0]}{string_a} {str(string).split(' ', 1)[1]}\\\"\\n else:\\n return f\\\" {str(string).split(' ', 1)[0]}{string_a}\\\"\\n return string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"513bad81a6bafbfa7b24f4b6111d803e\",\n \"score\": \"0.53060186\",\n \"text\": \"def build_word_info(self, word):\\n if len(word) > 4:\\n (radical, suffixes) = self._word_bsig[word]\\n\\n words = self._signatures_to_words[suffixes]\\n # TODO : find the radical and the suffixes\\n text_info = \\\"Morpheme clustering algorithm results on : {} : \\\\n cluster : {} \\\\n \\\\n \\\\n \\\".format(word, suffixes)\\n if len(suffixes) >= 1:\\n\\n for suff in suffixes:\\n for stem in self._signatures_to_stems[suffixes]:\\n\\n info = \\\"{}\\\".format(stem)+\\\" {}\\\".format(suff) + \\\"\\\\n \\\\n \\\\n \\\"\\n\\n if info != word:\\n\\n text_info = text_info + \\\"{}{}{}\\\".format(\\\" \\\\n \\\", \\\" - \\\", info)\\n\\n\\n\\n else:\\n\\n text_info += \\\"{}\\\".format(\\\" No close words found \\\")\\n\\n else:\\n\\n text_info = \\\"'\\\" + word + \\\"'\\\" + \\\" is too short, try a longer word.\\\"\\n\\n return text_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1486146ca4a72e5596ac57ed47eb93b\",\n \"score\": \"0.5277118\",\n \"text\": \"def tool_description():\\n\\n return \\\"This text describes what the tool does\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ad7bf2beae0b9f1e012498cfa3b2002\",\n \"score\": \"0.52645683\",\n \"text\": \"def create(self):\\r\\n clauses = []\\r\\n clauses.append(\\\"TEMPLATE = %s\\\" % self.template)\\r\\n if hasattr(self, 'options'):\\r\\n clauses.append(self.options)\\r\\n return [\\\"CREATE TEXT SEARCH DICTIONARY %s (\\\\n %s)\\\" % (\\r\\n self.qualname(), ',\\\\n '.join(clauses))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bef9748c4d83a8f16db0d2914134ad9\",\n \"score\": \"0.52459264\",\n \"text\": \"def process_answer(self, message: str) -> str:\\n\\n logging.debug(\\\"process_answer()\\\")\\n self.print_state()\\n\\n if self.state == \\\"waiting_lexicon_answer\\\":\\n to_skip: bool = False\\n\\n if message == \\\"Show definition.\\\":\\n self.state = \\\"waiting_lexicon_answer\\\"\\n return \\\"\\\" # definition(self.word)\\n elif message in [\\\"/know\\\", \\\"Know.\\\"]:\\n response = LexiconResponse.KNOW\\n elif message in [\\\"/skip\\\", \\\"Know, skip.\\\"]:\\n response = LexiconResponse.KNOW\\n to_skip = True\\n elif message in [\\\"/not_a_word\\\", \\\"Not a word.\\\"]:\\n response = LexiconResponse.NOT_A_WORD\\n to_skip = True\\n elif message in [\\\"/but\\\", \\\"But.\\\"]:\\n response = LexiconResponse.DONT_BUT_PROPER_NOUN_TOO\\n to_skip = True\\n else:\\n response = LexiconResponse.DONT\\n\\n self.lexicon.register(\\n self.word,\\n response,\\n to_skip,\\n log_name=\\\"log_ex\\\",\\n answer_type=AnswerType.USER_ANSWER,\\n )\\n return \\\"\\\"\\n\\n sentence_id: int = (\\n self.current_sentences[self.index].sentence.id_\\n if self.index < len(self.current_sentences)\\n else 0\\n )\\n\\n # Preprocess answer.\\n answer: str = self.learning.learning_language.decode_text(message)\\n\\n self.index += 1\\n\\n state: str = (\\n f\\\", {self.learning.count_questions_to_repeat()} to repeat\\\"\\n if self.learning.count_questions_to_repeat()\\n else \\\"\\\"\\n )\\n\\n if answer == self.word:\\n self.index = 0\\n self.learning.register(\\n Response.RIGHT, sentence_id, self.word, self.interval * 2\\n )\\n transcriptions: list[str] = []\\n if self.items:\\n for item in self.items:\\n for transcription in item.get_transcriptions():\\n if transcription not in transcriptions:\\n transcriptions.append(transcription)\\n\\n self.learning.write()\\n\\n self.print_state()\\n return f\\\"Right{state}, \\\" + \\\", \\\".join(transcriptions) + \\\".\\\"\\n\\n elif answer in self.alternative_forms:\\n self.print_state()\\n return \\\"Right form.\\\"\\n\\n elif answer in [\\\"/skip\\\", \\\"Skip\\\"]:\\n self.skip.add(self.word)\\n self.index = 0\\n self.print_state()\\n return f\\\"Skipped for this session{state}.\\\"\\n\\n elif answer in [\\\"/no\\\", \\\"Don't know\\\"]:\\n self.learning.register(\\n Response.WRONG, sentence_id, self.word, SMALLEST_INTERVAL\\n )\\n self.learning.write()\\n self.index = 0\\n\\n self.print_state()\\n return f\\\"Right answer: {self.word}{state}.\\\"\\n\\n elif answer == \\\"/exclude\\\":\\n self.data.exclude_sentence(self.word, sentence_id)\\n self.skip.add(self.word)\\n self.print_state()\\n return \\\"Sentence was excluded.\\\"\\n\\n elif answer.startswith(\\\"/hide \\\"):\\n parts = answer.split(\\\" \\\")\\n self.data.exclude_translation(self.word, \\\" \\\".join(parts[1:]))\\n self.skip.add(self.word)\\n self.print_state()\\n return \\\"Translation was hidden.\\\"\\n\\n else:\\n self.print_state()\\n return \\\"No.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a652a8742aa0d9e50c98584cfa00e52\",\n \"score\": \"0.5240408\",\n \"text\": \"def text(self) -> str:\\n if self._text:\\n return self._text\\n\\n # Pre-letter suprasegmentals\\n for accent in self.accents:\\n if accent == Accent.ACUTE:\\n self._text += IPA.ACCENT_ACUTE\\n elif accent == Accent.GRAVE:\\n self._text += IPA.ACCENT_GRAVE\\n\\n if self.stress == Stress.PRIMARY:\\n self._text += IPA.STRESS_PRIMARY\\n elif self.stress == Stress.SECONDARY:\\n self._text += IPA.STRESS_SECONDARY\\n\\n # Letters and diacritics\\n for letter_index, letter in enumerate(self.letters):\\n self._text += letter\\n\\n # Diacritics\\n for diacritic in self.diacritics.get(letter_index, []):\\n self._text += diacritic\\n\\n # Tone\\n if self.tone:\\n self._text += self.tone\\n\\n # Post-letter suprasegmentals\\n if self.is_long:\\n self._text += IPA.LONG\\n\\n # Re-normalize and combine\\n self._text = unicodedata.normalize(\\\"NFC\\\", self._text)\\n\\n return self._text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dbf5e3b0229dc04cdec5973b8209ebc\",\n \"score\": \"0.5232675\",\n \"text\": \"def create(self):\\r\\n clauses = []\\r\\n if hasattr(self, 'init'):\\r\\n clauses.append(\\\"INIT = %s\\\" % self.init)\\r\\n clauses.append(\\\"LEXIZE = %s\\\" % self.lexize)\\r\\n return [\\\"CREATE TEXT SEARCH TEMPLATE %s (\\\\n %s)\\\" % (\\r\\n self.qualname(), ',\\\\n '.join(clauses))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a7a4139b02fa51353f8b0f5d61f8c67\",\n \"score\": \"0.5231463\",\n \"text\": \"def make_poem():\\n\\n noun1, noun2, noun3 = get_words(NOUNS, 3)\\n verb1, verb2, verb3 = get_words(VERBS, 3)\\n adjective1, adjective2, adjective3 = get_words(ADJECTIVES, 3)\\n preposition1, preposition2 = get_words(PREPOSITIONS, 2)\\n adverb1, = get_words(ADVERBS, 1)\\n a_or_an = \\\"An\\\" if adjective1.startswith(VOWELS) else \\\"A\\\"\\n\\n return \\\"\\\"\\\"\\n {A_or_An} {adjective1} {noun1}\\n \\n {A_or_An} {adjective1} {noun1} {verb1} {preposition1} the {adjective2} {noun2}\\n {adverb1}, the {noun1} {verb2}\\n the {noun2} {verb3} {preposition2} a {adjective3} {noun3}\\n \\\"\\\"\\\".format(A_or_An=a_or_an, noun1=noun1, noun2=noun2, noun3=noun3,\\n verb1=verb1, verb2=verb2, verb3=verb3,\\n adjective1=adjective1, adjective2=adjective2, adjective3=adjective3,\\n preposition1=preposition1, preposition2=preposition2,\\n adverb1=adverb1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5b0f9d3b33d6aa3f561189405467d01\",\n \"score\": \"0.5230626\",\n \"text\": \"def turn_to_sentence(words, starting_msg, and_or, typ='misunderstood'):\\n message = starting_msg\\n for i in range(len(words)):\\n # obj = OBJECTS[objects[i]] # objects[i], for example, is 'egg'. so OBJECTS['egg']\\n if i == len(words) - 1 and len(words) > 1: # if it's the last word to add to the list AND if there is more than one word\\n message += f' {and_or} ' if len(words) == 2 else f'{and_or} ' # add an 'and', e.g. x, y, and z -- space before 'and' based on conditional\\n # objects_here += obj.short_description\\n if typ in ('no quotes', 'objects here'):\\n message += words[i]\\n else:\\n message += f\\\"'{words[i]}'\\\"\\n if len(words) > 2:\\n message += ', '\\n if typ == 'objects here': # if printing out all the objects in a given location, then end the sentence nicely with a period.\\n message = message.strip(', ') # strip trailing comma\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33b66d2c6132f4ee104a12f375be5a5a\",\n \"score\": \"0.52071035\",\n \"text\": \"def speak(bot, update, args):\\n word = args[0] if len(args) > 0 else None\\n update.message.reply_text(text=produce_sentence(word), quote=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4307bf926b394a24d6bbfea7b073113e\",\n \"score\": \"0.5200156\",\n \"text\": \"def output(self):\\n self.updated = False\\n return ''.join([word for word in self.phrase])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da1d60fcea2af00044ec6d5ef3a88d57\",\n \"score\": \"0.51990753\",\n \"text\": \"def actNatural(self):\\r\\n phrases = [\\\"kappa kappa\\\", \\\"oyoyoyoyoyoy\\\", \\\"RUINED IT!!\\\", \\\"Saved it!!\\\",\\r\\n \\\"Wat time is it?\\\", \\\"me gusta\\\", \\\"now THAT is funny.\\\", \\\"please be kind\\\",\\r\\n \\\"wow im offended\\\", \\\"i dont care if ur offended, this is who i am\\\",\\r\\n \\\"does anyone know how to get to falador?\\\", \\\"buying gf 248gp\\\",\\r\\n \\\"ya manky chav\\\", \\\"WOWWWW\\\", \\\"how do i mute someone?\\\", \\\"press alt+f4\\\",\\r\\n \\\"wow you guys are mean\\\", \\\"how do I send a private message?\\\", \\\"...I dont know you...\\\",\\r\\n \\\"a/s/l?\\\", \\\"18/f/cali\\\", \\\"noice.\\\", \\\"That can't be...\\\", \\\"42\\\", \\\"SELLOUT!\\\", \\r\\n \\\"lul\\\", \\\"(つ ͡° ͜ʖ ͡°)つ (つ ͡° ͜ʖ ͡°)つ (つ ͡° ͜ʖ ͡°)つ\\\", \\\"¯\\\\_(ツ)_/¯\\\", \\\"(ノಠ益ಠ)ノ彡┻━┻\\\", \\r\\n \\\"WOW SICK ULT\\\", \\\"NEW META\\\", \\\"REKT\\\", \\\"MOD ME BRO\\\", \\\"HARAMBABE ಠoಠ\\\"]\\r\\n\\r\\n phrase = phrases[random.randint(0, len(phrases) - 1)]\\r\\n response = \\\"PRIVMSG {0} :{1}\\\\r\\\\n\\\".format(self._channel, phrase).encode('utf-8')\\r\\n self.logSend(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84f82f757fc1d7227ba75cb142b2d494\",\n \"score\": \"0.5186206\",\n \"text\": \"def make_text2(chains):\\n\\n ret_str = \\\" \\\"\\n # Use a word starts with a capitalized letter as the beginning of sentence\\n random_tuple = random.choice(chains.keys())\\n \\n while not random_tuple[0][0].isupper():\\n random_tuple = random.choice(chains.keys())\\n str_len = len(ret_str)\\n \\n while ret_str[-1] not in ['?', '.', '!'] or str_len < MAX_CHAR:\\n ret_str = concat_str(ret_str, random_tuple[0])\\n ret_str = concat_str(ret_str, random_tuple[1])\\n \\n random_value = random.choice(chains[random_tuple])\\n if (random_value):\\n ret_str = concat_str(ret_str, random_value)\\n random_tuple = (random_tuple[1], random_value)\\n else:\\n break\\n if ret_str[-1] not in ['?', '.', '!']:\\n ret_str += \\\".\\\"\\n\\n return ret_str[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd1789f11c60b9a3bba9adb8c11d379c\",\n \"score\": \"0.5168262\",\n \"text\": \"def __build_match_title_short(cls, details):\\n title = details.get('home', {}).get('name_short')\\n title += ' - '\\n title += details.get('away', {}).get('name_short')\\n return title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"199209e4e2922378d67306c3dc1ebd92\",\n \"score\": \"0.51674014\",\n \"text\": \"def help(bot, update):\\n update.message.reply_text('''COMANDS:\\\\n/CSGO - CSGO Topstreams\\\\n/LOL - LOL Topstreams\\\\n\\n /Fortnite - Fortnite Topstreams\\\\n/PUBG - PUBG Topstreams\\\\n/Dota - Dota Topstreams\\n /Overwatch - Overwatch Topstreams\\\\n/Heartstone - Heartstone Topstreams\\\\n/FIFA - FIFA Topstreams\\n \\\\n/TOP - Top streams of any game''')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8824e30db31e115cafd72c11fc5c9f4d\",\n \"score\": \"0.51666796\",\n \"text\": \"def generate_phrase(self, max_size=None, min_words=None):\\n\\n # Invalid input values raise an exception\\n valid = True\\n\\n if max_size:\\n valid = type(max_size) is int and max_size >= 0\\n\\n if min_words:\\n valid = valid and type(min_words) is int and min_words >= 0\\n\\n if not valid:\\n raise ValueError(\\\"Expected positive int value(s) for input.\\\")\\n\\n # If both parameters are present, make sure parameters are reasonable\\n if max_size and min_words:\\n if 5*min_words >= max_size:\\n raise ValueError(\\\"max_size paramemeter must be at least five times larger than min_words.\\\")\\n\\n new_word = ''\\n msg = ''\\n word_count = 0\\n\\n while 1:\\n\\n # Generate a successor word\\n if new_word in self._markov_chain:\\n new_word = self._get_next_term(self._markov_chain[new_word])\\n\\n # Generate a random word if new_word is not a key in the markov chain\\n else:\\n new_word = random.choice(self._markov_chain.keys())\\n\\n # Check if new word will push us over the character limit\\n if max_size and len(msg) + len(new_word) >= max_size:\\n break\\n\\n # Append successor to word\\n msg += new_word + ' '\\n\\n # Increment word_count if min_words specified (and 'word' is not a period)\\n if min_words and new_word is not '.':\\n word_count += 1\\n\\n # probabalistically decide to stop\\n if not min_words or word_count >= min_words:\\n if random.random() <= self._stop_p:\\n break\\n\\n return msg.rstrip().capitalize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2042f55072aa97d6b5966e7840a5b58a\",\n \"score\": \"0.51449275\",\n \"text\": \"def _append_app_name(self, val):\\n if 'CFBundleName' in info:\\n val += ' {}'.format(info['CFBundleName'])\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56f3c5de27997927190a73ceb14aa836\",\n \"score\": \"0.5144441\",\n \"text\": \"def hey(self, text):\\n\\n if not text or text.isspace():\\n return 'Fine. Be that way!'\\n elif text.isupper():\\n return 'Woah, chill out!'\\n elif text.endswith('?'): \\n return 'Sure.'\\n else:\\n return 'Whatever.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb5493169b8fb73807b3be7dec1f6f81\",\n \"score\": \"0.5143284\",\n \"text\": \"def to_phrases(self):\\n phrase = ''\\n name_pattern = r'[А-Я]+\\\\s[А-Я]\\\\.[А-Я]\\\\.'\\n\\n with open(self.__script, 'r') as read_file:\\n for line in read_file.readlines():\\n if re.search(name_pattern, line) or 'ГОЛОВУЮЧИЙ' in line:\\n if phrase:\\n if 'ГОЛОВУЮЧИЙ' not in phrase:\\n politician = re.search(name_pattern, phrase)\\n if politician:\\n politician = politician[0]\\n phrase = re.sub(name_pattern, '', phrase)\\n else:\\n phrase = ''\\n else:\\n politician = self.announcer\\n phrase = re.sub('ГОЛОВУЮЧИЙ.', '', phrase)\\n\\n dots = re.findall(r'\\\\.', phrase)\\n if len(dots) > 1:\\n self.__phrase_analysis(politician, phrase)\\n # print(politician)\\n # print(phrase)\\n phrase = ''\\n phrase += line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a2d4c0c481f672b97e684a7049b1e67\",\n \"score\": \"0.513873\",\n \"text\": \"def get_static_welcome_message():\\n return \\\\\\n\\\"\\\"\\\"\\n<h3>Search Help</h3>\\n<ul><li>The display below the line is an example of the output the browser\\nshows you when you enter a search word. The search word was <b>green</b>.</li>\\n<li>The search result shows for different parts of speech the <b>synsets</b>\\ni.e. different meanings for the word.</li>\\n<li>All underlined texts are hypertext links. There are two types of links:\\nword links and others. Clicking a word link carries out a search for the word\\nin the Wordnet database.</li>\\n<li>Clicking a link of the other type opens a display section of data attached\\nto that link. Clicking that link a second time closes the section again.</li>\\n<li>Clicking <u>S:</u> opens a section showing the relations for that synset.</li>\\n<li>Clicking on a relation name opens a section that displays the associated\\nsynsets.</li>\\n<li>Type a search word in the <b>Next Word</b> field and start the search by the\\n<b>Enter/Return</b> key or click the <b>Search</b> button.</li>\\n</ul>\\n\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"952c24739f86275fe39fd3e8bc9ef46d\",\n \"score\": \"0.5134184\",\n \"text\": \"def text(self) -> str:\\n if self._text:\\n return self._text\\n\\n for accent in self.accents:\\n if accent == Accent.ACUTE:\\n self._text += IPA.ACCENT_ACUTE\\n elif accent == Accent.GRAVE:\\n self._text += IPA.ACCENT_GRAVE\\n\\n if self.stress == Stress.PRIMARY:\\n self._text += IPA.STRESS_PRIMARY\\n elif self.stress == Stress.SECONDARY:\\n self._text += IPA.STRESS_SECONDARY\\n\\n for letter_index, letter in enumerate(self.letters):\\n self._text += letter\\n\\n if letter_index in self.nasalated:\\n self._text += IPA.NASAL\\n\\n if letter_index in self.raised:\\n self._text += IPA.RAISED\\n\\n for c in self._extra_combining[letter_index]:\\n self._text += c\\n\\n if self.tone:\\n self._text += self.tone\\n\\n if self.elongated:\\n self._text += IPA.LONG\\n\\n # Re-normalize and combine\\n self._text = unicodedata.normalize(\\\"NFC\\\", self._text)\\n\\n return self._text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ec44d4585b30aea72ee5bdef9623375\",\n \"score\": \"0.51310563\",\n \"text\": \"def main():\\n\\n args = get_args()\\n word = args.word\\n\\n \\\"\\\"\\\"choose article based on first letter of word\\\"\\\"\\\"\\n char = word[0].lower() in 'aeiou'\\n article = ''\\n\\n article = 'an' if char == True else 'a'\\n\\n\\n # print('positional = \\\"{}\\\"'.format(word))\\n\\n print('Ahoy, Captain, {} {} off the larboard bow!'.format(article, word))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c63cf82e5456cb266f2a57db129d247\",\n \"score\": \"0.51305634\",\n \"text\": \"def _generate_friendly_name(self, request):\\n\\n language = get_language_from_request(request)[:2].lower()\\n suggestion = \\\" \\\".join(get_random(language, 3, only_single_words=True))\\n\\n return suggestion[:127]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5be83d3f702e322c9581cdbd7a22d3e\",\n \"score\": \"0.5123248\",\n \"text\": \"def generate():\\n\\n pref = [\\\"\\\", \\\"educational\\\", \\\"cheap\\\", \\\"best\\\", \\\"most interesting\\\", \\\"creative\\\", \\\"durable\\\", \\\"hobby\\\", \\\"blue\\\"\\n \\\"wonderful\\\", \\\"gift\\\", \\\"learning\\\", \\\"play\\\", \\\"backyard\\\", \\\"activity\\\", \\\"birding\\\", \\\"bird watching\\\",\\n \\\"cool\\\", \\\"colorful\\\", \\\"party\\\", \\\"travel\\\", \\\"outdoor\\\", \\\"outside\\\", \\\"indoor\\\", \\\"small\\\", \\\"light\\\", \\\"stem\\\",\\n \\\"art\\\", \\\"learn\\\", \\\"good\\\", \\\"fun\\\", \\\"new\\\", \\\"teaching\\\", \\\"top\\\", \\\"tops\\\", \\\"unique\\\", \\\"unusual\\\", \\\"preschool\\\"]\\n\\n what = [\\\"\\\", \\\"toy\\\", \\\"toys\\\", \\\"gift\\\", \\\"gifts\\\", \\\"binocular\\\", \\\"binoculars\\\", \\\"activity\\\",\\n \\\"game\\\", \\\"games\\\", \\\"set\\\", \\\"accessory\\\", \\\"stuff\\\", \\\"things\\\", \\\"party\\\", \\\"gadget\\\"]\\n\\n usage = [\\\"\\\", \\\"for kids\\\", \\\"for toddlers\\\", \\\"for boys\\\", \\\"for girls\\\", \\\"for children\\\",\\n \\\"for kid\\\", \\\"for toddler\\\", \\\"for boy\\\", \\\"for girl\\\", \\\"for babies\\\", \\\"for baby\\\",\\n \\\"for age\\\", \\\"for 3\\\", \\\"for 4\\\", \\\"for 5\\\", \\\"for 6\\\", \\\"for car trip\\\",\\n \\\"for year\\\", \\\"for birthday\\\", \\\"for year old\\\", \\\"for preschoolers\\\", \\\"for brain\\\",\\n \\\"sale\\\", \\\"2018\\\", \\\"for grandson\\\", \\\"for granddaughter\\\"]\\n\\n res = []\\n for a in pref:\\n for b in what:\\n for c in usage:\\n phrase = \\\"{} {} {}\\\".format(a,b,c).replace(\\\" \\\", \\\" \\\").replace(\\\" \\\", \\\" \\\").strip()\\n #print(phrase)\\n res.append(phrase)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8305984369decadeb95604454d0ed59e\",\n \"score\": \"0.51228213\",\n \"text\": \"def build_phrase_input(phrases, pad_idx, task):\\n tokens = [phrase.tokens for phrase in phrases]\\n ners = [phrase.ners for phrase in phrases]\\n subj_posis = torch.tensor([phrase.subj_posi for phrase in phrases]).unsqueeze(1)\\n obj_posis = torch.tensor([phrase.obj_posi for phrase in phrases]).unsqueeze(1)\\n if task == \\\"re\\\":\\n lengths = torch.tensor([len(token_seq) for token_seq in tokens]).unsqueeze(1)\\n subj_bool = subj_posis < lengths\\n obj_bool = obj_posis < lengths\\n assert sum(subj_bool).item() == len(phrases)\\n assert sum(obj_bool).item() == len(phrases)\\n subj = torch.tensor([phrase.ners[phrase.subj_posi] for phrase in phrases]).unsqueeze(1) # has to be NERs due to type check\\n obj = torch.tensor([phrase.ners[phrase.obj_posi] for phrase in phrases]).unsqueeze(1)\\n else:\\n no_ner_id = NER_LABEL_SPACE[\\\"\\\"]\\n subj = torch.full((len(phrases),1), no_ner_id)\\n obj = torch.full((len(phrases),1), no_ner_id)\\n\\n ner_pad = NER_LABEL_SPACE[\\\"<PAD>\\\"]\\n\\n tokens, _ = BaseVariableLengthDataset.variable_length_batch_as_tensors(tokens, pad_idx)\\n ners, _ = BaseVariableLengthDataset.variable_length_batch_as_tensors(ners, ner_pad)\\n\\n assert tokens.shape == ners.shape\\n\\n phrase_input = torch.cat([tokens, ners, subj_posis, obj_posis, subj, obj], dim=1)\\n\\n return phrase_input\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8d51404638e2e2ce3ea47e3f6177f6a\",\n \"score\": \"0.5114722\",\n \"text\": \"def cond_translate_snippet(text):\\n return translate_snippet(text, keychar=\\\"@\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0401104e5605eb9fb25cfea3577cf171\",\n \"score\": \"0.5111081\",\n \"text\": \"def sentence():\\r\\n # Determine the number of comma-separated sections and number of words in\\r\\n # each section for this sentence.\\r\\n sections = [u' '.join(random.sample(WORDS, random.randint(3, 12))) for i in range(random.randint(1, 5))]\\r\\n s = u', '.join(sections)\\r\\n # Convert to sentence case and add end punctuation.\\r\\n return u'%s%s%s' % (s[0].upper(), s[1:], random.choice('?.'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"246f23197c032ccd87fa89bb0a4b2052\",\n \"score\": \"0.51088095\",\n \"text\": \"def __str__(self):\\n return self.main_word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d41b41ee32ffbe458be4d09e92bcbcf\",\n \"score\": \"0.5104865\",\n \"text\": \"def _generate_help_text(self):\\n\\n # General package description (mandatory)\\n lines = self._generate_section('Description', self.app.cfg['description'], strip=True)\\n\\n # Package usage instructions (optional)\\n lines.extend(self._generate_section('Usage', self.app.cfg['usage'], strip=True))\\n\\n # Examples (optional)\\n lines.extend(self._generate_section('Examples', self.app.cfg['examples'], strip=True))\\n\\n # Additional information: homepage + (if available) doc paths/urls, upstream/site contact\\n lines.extend(self._generate_section(\\\"More information\\\", \\\" - Homepage: %s\\\" % self.app.cfg['homepage']))\\n\\n docpaths = self.app.cfg['docpaths'] or []\\n docurls = self.app.cfg['docurls'] or []\\n if docpaths or docurls:\\n root_envvar = ROOT_ENV_VAR_NAME_PREFIX + convert_name(self.app.name, upper=True)\\n lines.extend([\\\" - Documentation:\\\"])\\n lines.extend([\\\" - $%s/%s\\\" % (root_envvar, path) for path in docpaths])\\n lines.extend([\\\" - %s\\\" % url for url in docurls])\\n\\n for contacts_type in ['upstream', 'site']:\\n contacts = self.app.cfg['%s_contacts' % contacts_type]\\n if contacts:\\n if isinstance(contacts, list):\\n lines.append(\\\" - %s contacts:\\\" % contacts_type.capitalize())\\n lines.extend([\\\" - %s\\\" % contact for contact in contacts])\\n else:\\n lines.append(\\\" - %s contact: %s\\\" % (contacts_type.capitalize(), contacts))\\n\\n # Extensions (if any)\\n extensions = self._generate_extension_list()\\n lines.extend(self._generate_section(\\\"Included extensions\\\", '\\\\n'.join(wrap(extensions, 78))))\\n\\n return '\\\\n'.join(lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b95e1a1f2d851c85304c250247d6247a\",\n \"score\": \"0.5103791\",\n \"text\": \"def add_quotes_if_needed(self, app=\\\"\\\"): # -> str\\n if \\\" \\\" in app:\\n if not app.startswith('\\\"'):\\n app = \\\"\\\".join(['\\\"', app])\\n if not app.endswith('\\\"'):\\n app = \\\"\\\".join([app, '\\\"'])\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8909c37066211f91a9e63434d3950487\",\n \"score\": \"0.508499\",\n \"text\": \"def phrasesDeck(iphrases):\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe23bed26bc7fb747068aff120c4b007\",\n \"score\": \"0.50824356\",\n \"text\": \"def BuildHelpText(self):\\n buf = io.StringIO()\\n buf.write('\\\\n+\\\\n')\\n for key, help_text in sorted(six.iteritems(self.choices)):\\n buf.write('*{}*::: {}\\\\n'.format(key, help_text))\\n return (\\n '{}Must be one of the following values:{}'.format(\\n _Insert(super(Enum, self).BuildHelpText()), buf.getvalue()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb43c7c5be439f4ef24cd6aa7d8a894a\",\n \"score\": \"0.50811255\",\n \"text\": \"def make_text(chains):\\n words = []\\n punctuation = [\\\".\\\", \\\",\\\", \\\"!\\\", \\\"?\\\", \\\";\\\", \\\":\\\"]\\n\\n curr_ngram = choice(chains.keys()) #picks random bigram key\\n words.extend(curr_ngram)\\n\\n while curr_ngram in chains: #there is a curr_value in the dictionary:\\n curr_key = curr_ngram[1:]\\n curr_value = choice(chains[curr_ngram]) #randomly selects curr_value\\n curr_ngram = curr_key + (curr_value,) #updates the curr_ngram variable\\n words.append(curr_ngram[-1])\\n\\n gen_text = \\\" \\\".join(words)#long string of generated text\\n\\n return gen_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16b04f9d23135139303c9b747e321330\",\n \"score\": \"0.50763226\",\n \"text\": \"def handle_message(response):\\n phrase = ''\\n if response.strip() != '':\\n answers = response.split(',')\\n answers_count = len(answers)\\n for i in range(answers_count):\\n answer = answers[i].strip().capitalize()\\n phrase += answer + (\\n ' or ' if i == answers_count - 2 and answers_count > 1 else ' ')\\n if any(char.isdigit() for char in answer) or answer in ('Yes', 'No'):\\n break\\n return {'result': phrase}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7af938a109b7977c8e64705184b73f01\",\n \"score\": \"0.5076043\",\n \"text\": \"def _correct_grammar(self, string_a, string_b, input, switch):\\n if switch.lower() in str(input).split(\\\" \\\", 1)[0].lower():\\n return f\\\" {string_b} {input}\\\"\\n return f\\\" {string_a} {input}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc92408960e7e82465a3f7b107410b3b\",\n \"score\": \"0.5075161\",\n \"text\": \"def update_pho(self, speaker_code):\\n if not self.is_speaker_in_annotation(speaker_code):\\n return f'{speaker_code} not in annotation'\\n\\n # TODO: What if the words have not been extracted yet?\\n words = self.words_uttered_by[speaker_code]\\n n_words = len(words)\\n if n_words == 0:\\n return 'error: no words were extracted'\\n\\n # TODO: What if the subtiers have not been categorized yet?\\n pho_subtiers = self.sub_tiers_by_label[TRANSCRIPTION_LABEL]\\n if len(pho_subtiers) == 0:\\n pho_subtier = SubTier(label=TRANSCRIPTION_LABEL, contents=(' '.join(['###'] * n_words) + '\\\\n'))\\n self.sub_tiers = [pho_subtier] + self.sub_tiers\\n self.sub_tiers_by_label[TRANSCRIPTION_LABEL].append(pho_subtier)\\n return 'pho subtier added'\\n\\n [pho_subtier] = pho_subtiers\\n m_transcriptions = len(self.transcriptions)\\n # Above, we counted '###' as well as actual transcriptions\\n m_transcribed = m_transcriptions - self.transcriptions.count('###')\\n\\n if m_transcribed == 0:\\n pho_subtier.contents = ' '.join(['###'] * n_words) + '\\\\n'\\n self.extract_phonetic_transcriptions()\\n if m_transcriptions < n_words:\\n return \\\"###'s added, needs transcription\\\"\\n elif m_transcriptions > n_words:\\n return \\\"###'s removed, needs transcription\\\"\\n elif m_transcriptions == n_words:\\n return \\\"needs transcription\\\"\\n\\n if m_transcriptions > n_words:\\n return 'error: more transcriptions than there are words'\\n\\n if m_transcriptions < n_words:\\n # If we got here, there is at least one actual transcription\\n return 'error: fewer transcriptions than there are words, order unknown, sort manually'\\n\\n # If we got here, m_transcriptions == n_words\\n\\n if m_transcribed < n_words:\\n return 'needs some transcription'\\n\\n if m_transcribed == n_words:\\n return 'all transcribed'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2a4cc9c16a1aeb157b03f9346a762e\",\n \"score\": \"0.5074825\",\n \"text\": \"def sentencegenerator(text,startword,length = 10):\\r\\n d = suffixdictionary(text)\\r\\n sentence = [startword]\\r\\n index = 0\\r\\n while index < length:\\r\\n sentence.append(random.choice(d[sentence[index]]))\\r\\n index = index + 1\\r\\n return ' '.join(sentence)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"522ec6e23fe206c0e4b8c4bd19e489d6\",\n \"score\": \"0.5071008\",\n \"text\": \"def simple_app():\\n return '<b>This app is officially working</b>'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5064783c4dee8387f78cc96daeb1d744\",\n \"score\": \"0.5064435\",\n \"text\": \"def sample_word(self, pos, grammar_class):\\n chosen_word = rand.choice(self.word_dict[grammar_class])\\n word_string = chosen_word + f\\\"_{grammar_class}_\\\" + str(pos)\\n return word_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af62e9da28afa1e0c4201694309620ce\",\n \"score\": \"0.50637364\",\n \"text\": \"def make_text(chains):\\n\\n\\n # start somewhere\\n cap_keys = [key for key in chains.keys() if key[0].istitle()]\\n\\n ngram = choice(cap_keys)\\n\\n # ngram = choice(list(chains.keys()))\\n\\n # start list with first two words\\n words =list(ngram)\\n\\n while ngram in chains: \\n # use tuple as key to get list of next words\\n # choose a next word with 'choice' (returns words as a string)\\n next_word = choice(chains[ngram])\\n \\n # add chosen word to words list\\n words.append(next_word) \\n\\n # make new key from second tuple item and chosen word\\n \\n # make ngram from tuple to list\\n ngram_list = list(ngram)\\n\\n # make slice from 1 to last\\n ngram_slice = ngram_list[1:]\\n \\n # add our chosen word to the slice\\n ngram_slice.append(next_word)\\n\\n # consciously re-tuple our list\\n ngram = tuple(ngram_slice)\\n\\n return \\\" \\\".join(words)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa4a2db52aa79c4f0ec8458a000a2714\",\n \"score\": \"0.5059467\",\n \"text\": \"def sing_a_song():\\r\\n return 'What do we do with a drunken sailor?'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2fe1c4a80aa1aa9ec4bdc47414172fd\",\n \"score\": \"0.50576925\",\n \"text\": \"def user_phrase():\\n phrase = input(\\\"please type in the phrase you want to encode/decode.\\\")\\n phrase = phrase.lower()\\n return phrase\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d3b7bb191507c9e8c31954db554239\",\n \"score\": \"0.5054058\",\n \"text\": \"def generate_text(start_word, model, h_learnable, embedder, word2id, id2word, max_len, allow_unknowns, top_k=5):\\n model.eval()\\n with torch.no_grad():\\n # Try at most 10 times to generate a review that ends with \\\"<EOR>\\\".\\n # If unsuccesful, output whatever review was generated last.\\n trys = 0\\n while trys <= 10:\\n trys += 1\\n word_ids = [word2id[start_word]]\\n\\n if h_learnable:\\n h = None\\n else:\\n h = model.init_hidden(batch_size=1)\\n \\n while word_ids[-1] != word2id['<EOR>'] and len(word_ids) <= max_len:\\n x = word_to_model_input(id2word[word_ids[-1]], embedder, word2id)\\n y, h = predict(\\n x_packed=x,\\n model=model,\\n h_learnable=h_learnable,\\n h=h,\\n batch_size=1,\\n word2id=word2id,\\n allow_unknowns=allow_unknowns,\\n top_k=top_k\\n )\\n word_ids.append(y)\\n \\n if word_ids[-1] == word2id['<EOR>']:\\n break\\n words = [id2word[idx] for idx in word_ids]\\n return \\\" \\\".join(words)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab5d8d5ceab0ec3e9e81e2850675cff9\",\n \"score\": \"0.5047875\",\n \"text\": \"def __convert(self, kind: str) -> str:\\n sep: str = self.separators[kind]\\n words: List[str] = regex.findall(self.pattern, self.string)\\n size: int = len(words)\\n converted: str = \\\"\\\"\\n if size == 0:\\n converted = self.string\\n elif kind in (\\\"pascal\\\", \\\"title\\\", \\\"header\\\"):\\n converted = sep.join([w.lower().capitalize() for w in words])\\n elif kind in (\\\"snake\\\", \\\"kebab\\\", \\\"dot\\\"):\\n converted = sep.join([w.lower() for w in words])\\n elif kind == \\\"camel\\\":\\n converted = sep.join(\\n [\\n w.lower() if i == 0 else w.lower().capitalize()\\n for i, w in enumerate(words)\\n ]\\n )\\n elif kind == \\\"constant\\\":\\n converted = sep.join([w.upper() for w in words])\\n return converted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8590845202dca01438b9ab709447c4cf\",\n \"score\": \"0.5033167\",\n \"text\": \"def whichBetter(self, type_valg):\\n word = \\\"\\\"\\n if type_valg == \\\"Stein\\\":\\n word = \\\"Papir\\\"\\n elif type_valg == \\\"Saks\\\":\\n word = \\\"Stein\\\"\\n else:\\n word = \\\"Saks\\\"\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cc5fd6831605fee3211049ddfd2b41a\",\n \"score\": \"0.50310034\",\n \"text\": \"def pronunciation(word, say):\\n if word in say:\\n return say[word]\\n else:\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29dfc2a2aa769ccb5c2d554d05998592\",\n \"score\": \"0.5028345\",\n \"text\": \"def short_name(self):\\n self.details['short_name'] = input(\\n \\\"Short Name \\\" + str(on([\\\"M\\\"], args)) + \\\" (\\\" + Colors.OKBLUE + self.details['name'][\\n 0:12] + Colors.ENDC + \\\"): \\\")\\n # default to project name\\n if self.details['short_name'] == '':\\n self.details['short_name'] = self.details['name'][0:12]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"063d4d61ded9a782539fadbcc7d8d505\",\n \"score\": \"0.5014829\",\n \"text\": \"def make_text(chains):\\n\\n ret_str = \\\"\\\"\\n end_the_str = False\\n\\n while len(ret_str) < MAX_CHAR:\\n # Use a word starts with a capitalized letter as the beginning of sentence\\n random_tuple = random.choice(chains.keys())\\n while not random_tuple[0][0].isupper():\\n random_tuple = random.choice(chains.keys())\\n\\n while len(ret_str) < MAX_CHAR:\\n if len(ret_str) + len(random_tuple[0]) > MAX_CHAR:\\n end_the_str = True\\n break;\\n ret_str = concat_str(ret_str, random_tuple[0])\\n if len(ret_str) + len(random_tuple[1]) > MAX_CHAR:\\n end_the_str = True\\n break;\\n ret_str = concat_str(ret_str, random_tuple[1])\\n \\n random_value = random.choice(chains[random_tuple])\\n if (random_value):\\n if len(ret_str) + len(random_value) > MAX_CHAR:\\n end_the_str = True\\n break;\\n ret_str = concat_str(ret_str, random_value)\\n random_tuple = (random_tuple[1], random_value)\\n else:\\n break\\n\\n if end_the_str:\\n break\\n\\n return ret_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"871299dfb75561df1ed74d6872ada30f\",\n \"score\": \"0.50126153\",\n \"text\": \"def get_thank_you_text(survey):\\n if survey.get('language') == 'ms':\\n thankyou_text = 'Terima Kasih'\\n elif survey.get('language') == 'zh':\\n thankyou_text = '谢谢'\\n elif survey.get('language') == 'ja':\\n thankyou_text = 'ありがとうございました'\\n elif survey.get('language') == 'ko':\\n thankyou_text = '고맙습니다'\\n else:\\n thankyou_text = 'Thank You'\\n return thankyou_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e13691e46bdfc7b2dcf705c4b8e6b0a7\",\n \"score\": \"0.4993633\",\n \"text\": \"def correct(word):\\n if word == \\\"show_tell\\\":\\n return 'Show \\\\\\\\& Tell'\\n elif word == 'top_down':\\n return \\\"Top-down\\\"\\n elif word == \\\"resnet50\\\":\\n return \\\"ResNet-50\\\"\\n elif word == \\\"resnet152\\\":\\n return \\\"ResNet-152\\\"\\n elif \\\"cnn\\\" in word:\\n return \\\"RNN + CNN\\\"\\n else:\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19f61e924b31cee04c048fe50a8b89e4\",\n \"score\": \"0.49910954\",\n \"text\": \"def main():\\n\\n conf = parse_arguments()\\n if conf.url:\\n try:\\n response = urllib.request.urlopen(conf.url)\\n words = response.read().decode().splitlines()\\n except urllib.error.URLError:\\n print('web request failure', file=sys.stderr)\\n sys.exit(2)\\n else:\\n try:\\n with open('/usr/share/dict/words', 'r') as f:\\n words = f.read().splitlines()\\n except OSError:\\n print('no dictionary file', file=sys.stderr)\\n sys.exit(3)\\n \\n nonalpha = ''\\n if conf.num:\\n nonalpha += string.digits\\n if conf.sym:\\n nonalpha += string.punctuation\\n #nonalpha = string.digits + string.punctuation\\n for i in range(int(conf.phrases)):\\n pw = ''\\n while not valid_phrase(pw, conf):\\n # rerolls passphrase until valid\\n pw = ''\\n if secrets.randbits(1) == 1 and len(nonalpha) > 0:\\n # 50% chance of number or symbol at beginning versus letter\\n pw += secrets.choice(nonalpha)\\n for j in range(int(conf.words)):\\n if secrets.randbits(1) == 0:\\n pw += secrets.choice(words).upper()\\n else:\\n pw += secrets.choice(words).lower()\\n if secrets.randbits(1) == 1 and len(nonalpha) > 0:\\n # 50% chance of number or symbol between words and at the end\\n pw += secrets.choice(nonalpha)\\n print(pw + '\\\\t' + str(len(pw)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17aa8ca7567366961e1facbeb996c58d\",\n \"score\": \"0.49850595\",\n \"text\": \"def __init__(self):\\n self.nlp = spacy.load('en_core_web_sm')\\n if args.add_q:\\n self.q_name = \\\"_with_question\\\"\\n else:\\n self.q_name = \\\"_without_question\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96e828ebc5bd6b8b15ed6f85ae3ff1cc\",\n \"score\": \"0.49840906\",\n \"text\": \"def apology_choose(message):\\n def escape(s):\\n \\\"\\\"\\\"\\n Escape special characters.\\n\\n https://github.com/jacebrowning/memegen#special-characters\\n \\\"\\\"\\\"\\n for old, new in [(\\\"-\\\", \\\"--\\\"), (\\\"_\\\", \\\"__\\\"), (\\\"?\\\", \\\"~q\\\"),\\n (\\\"%\\\", \\\"~p\\\"), (\\\"#\\\", \\\"~h\\\"), (\\\"/\\\", \\\"~s\\\"), (\\\"\\\\\\\"\\\", \\\"''\\\")]:\\n s = s.replace(old, new)\\n return s\\n return render_template(\\\"apology_choose.html\\\", bottom=escape(message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1feff80b2830df9ebb0cde4ae87fbd46\",\n \"score\": \"0.4981439\",\n \"text\": \"async def translate(self, ctx, *, phrase: str):\\n \\n langs = re.search(r\\\"(\\\\w{2})-(\\\\w{2})\\\", phrase[0:5])\\n if langs:\\n sl = langs.group(1)\\n tl = langs.group(2)\\n phrase = phrase[6:]\\n else:\\n sl = \\\"auto\\\"\\n tl = \\\"en\\\"\\n\\n url = \\\"https://translate.googleapis.com/translate_a/single\\\"\\n params = {'client': 'gtx', 'sl': sl, 'tl': tl, 'dt': 't', \\\"q\\\": phrase}\\n ua = \\\"Mozilla/5.0 (X11; CrOS x86_64 12239.19.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.38 Safari/537.36\\\"\\n headers = {'User-Agent': ua}\\n async with self.bot.session.get(url, headers=headers, params=params) as resp:\\n result = await resp.json()\\n await ctx.send(\\\"{} ({}): {}\\\".format(result[0][0][1], result[2], result[0][0][0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8d6c5a44660722a13526c09bb0a2490\",\n \"score\": \"0.49773678\",\n \"text\": \"def shortHelpString(self):\\r\\n return self.tr(\\\"Example algorithm short description\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ef868fedc1307740491880c02a67570\",\n \"score\": \"0.49741906\",\n \"text\": \"def get_short_text(self) -> str:\\n oc = self.__oc\\n return f\\\"{oc.name} the {oc.species.title()} {' '.join('#' + tag.replace(' ', '') for tag in self._tags) if self._tags else ''}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f702723b7caaa9c21dcf3984429fce77\",\n \"score\": \"0.49714324\",\n \"text\": \"def make_text(chains):\\n\\n # randomly select a dictionary entry to start with\\n\\n search_tuple = random.choice(chains.keys())\\n sentence_list = [search_tuple[0], search_tuple[1]]\\n \\n #loop goes here\\n \\n while len(\\\" \\\".join(sentence_list)) < 130:\\n rand_val = random.choice(chains[search_tuple])\\n sentence_list.append(rand_val)\\n search_tuple = (search_tuple[1], rand_val)\\n if not chains.get(search_tuple):\\n break\\n\\n sentence = \\\" \\\".join(sentence_list).capitalize()\\n return sentence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"450d955df168fa99b0ed099438269d7c\",\n \"score\": \"0.49708033\",\n \"text\": \"def valiant_app_title() -> str:\\n return \\\"Valiant\\\" # noqa:DAR201\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9921aa062fcf4be522f3ceeeecdf8b\",\n \"score\": \"0.49696136\",\n \"text\": \"def update_text(self, old_text, new_text):\\n keyword_found = self.check_for_keywords(new_text)\\n if keyword_found is not None:\\n self.sound_alarm(keyword_found, new_text)\\n self.output_text += f\\\"\\\\nMALICIOUS PHRASE -->> {new_text} <<--\\\"\\n else:\\n self.output_text += f\\\"\\\\n{new_text}\\\"\\n self.output_box.config(text=self.output_text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7833ebbed7d33e07790235972a1583\",\n \"score\": \"0.49660146\",\n \"text\": \"def mlpPhrase():\\n configuration['mlpPhrase'].update({\\n 'phraseMaxLength': 50,\\n 'phraseTokenEmb': int(Generator.generateValue([25, 200], True)),\\n 'phrasePosEmb': int(Generator.generateValue([5, 50], True)),\\n 'gru': Generator.generateValue([True, False], False, True),\\n 'wordRnnUnitNum': int(Generator.generateValue([25, 500], True)),\\n 'useB1': Generator.generateValue([True, False], False),\\n 'useB-1': Generator.generateValue([True, False], False),\\n 'transPosEmb': int(Generator.generateValue([5, 50], True)),\\n 'transTokenEmb': int(Generator.generateValue([25, 200], True)),\\n 'denseUnitNumber': int(Generator.generateValue([25, 500], True)),\\n 'lr': round(Generator.generateValue([0.01, 0.2], True), 3)\\n })\\n configuration['embedding']['lemma'] = Generator.generateValue([True, False], False)\\n configuration['sampling']['importantSentences'] = True\\n configuration['sampling']['overSampling'] = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7cb99d67d035d00b06f8a3136a1525b\",\n \"score\": \"0.49647617\",\n \"text\": \"def BuildHelpText(self):\\n return (\\n '{}Must be a string representing a day of the week in English, '\\n 'such as \\\\'MON\\\\' or \\\\'FRI\\\\'. Case is ignored, and any characters after '\\n 'the first three are ignored.{}'.format(\\n _Insert(super(DayOfWeek, self).BuildHelpText()),\\n _Append(self.Constraints())))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb791b586cf8e6bd63f778e79f499f94\",\n \"score\": \"0.49606007\",\n \"text\": \"def generate_phrase_tts(phrase, save_dir):\\n # Initialize text to speech system\\n remove_whitespace = sub('\\\\s+', '_', phrase)\\n filename = join(save_dir, remove_whitespace + '.wav')\\n if platform == 'linux':\\n # eSpeak does not implement the save_to_file function\\n # So we must call it directly\\n subprocess.Popen(['espeak', phrase, '-w', filename])\\n else:\\n if platform != 'darwin':\\n print('File may not be created on unsupported OS')\\n engine = pyttsx3.init()\\n engine.save_to_file(phrase, filename)\\n engine.runAndWait()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cccd5b207c4f60d75f8e7c9a5823ed5\",\n \"score\": \"0.49579707\",\n \"text\": \"def nounPhrase():\\n with open('articles.txt') as a:\\n article = csv.reader(a)\\n articles = random.choice(list(article))\\n with open('adjectives.txt') as ad:\\n adjective = csv.reader(ad)\\n adjectives = random.choice(list(adjective)) \\n with open('nouns.txt') as n:\\n noun = csv.reader(n)\\n nouns = random.choice(list(noun)) \\n\\n optional1 = random.choice(['yes', 'no'])\\n if optional1 == 'yes': \\n return random.choice(articles) + \\\" \\\" + random.choice(nouns)\\n if optional1 == 'no': \\n return random.choice(articles) + \\\" \\\" + random.choice(adjectives) + \\\" \\\" + random.choice(nouns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"122f8bafe840ad2061e571b3af884434\",\n \"score\": \"0.4957891\",\n \"text\": \"def get_app_title_rst(self, continuation=''):\\n\\n if continuation:\\n continuation = ' ' + continuation\\n\\n title = self._context['app_name'] + continuation\\n title = f\\\"{title}\\\\n{'=' * len(title)}\\\"\\n\\n return title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12a853e3ed7a31528984e229e270ba0f\",\n \"score\": \"0.49564636\",\n \"text\": \"def generate_sentence(name, comment, score):\\n return ' '.join((name, comment, str(score)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f16678a40b417f3fdfc3b4dc9233d05\",\n \"score\": \"0.4955744\",\n \"text\": \"def do_comprehend_languages(self, arg):\\r\\n print_description('1st', 'Comprehend Languages', '1 Action [R]', '1 Hour', 'Self', 'None', 'Social',\\r\\n 'V, S, M', 'Divination')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07245ef935e84c28532e56d84ce1e1ff\",\n \"score\": \"0.49505526\",\n \"text\": \"def word_display(secret_word, additional_words):\\n\\n pass # ToDo: Remove this line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2239234fbafb738eb7b52dad4053729b\",\n \"score\": \"0.49482384\",\n \"text\": \"def test_makeAltName(self):\\n mk = search.makeAltName\\n self.assertEqual(mk(\\\"hello\\\"), \\\"hello\\\")\\n self.assertEqual(mk(\\\"Hello\\\"), \\\"hello\\\")\\n self.assertEqual(mk(\\\"Hello There\\\"), \\\"hello there\\\")\\n self.assertEqual(mk(\\\"Cat's Grace\\\"), \\\"cats grace\\\")\\n self.assertEqual(mk(\\\"<Cat</>'s Grace\\\"), \\\"cats grace\\\")\\n self.assertEqual(mk(\\\"<Cat</>'s Grace, Mass\\\"), \\\"cats grace mass\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e5acc9bf41cdcc73c0044d425ac6575\",\n \"score\": \"0.49434415\",\n \"text\": \"def do_speak_with_plants(self, arg):\\r\\n spell_helper(\\\"Speak with Plants\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e70f9951abe68136e4c4c41aa5361b\",\n \"score\": \"0.4940188\",\n \"text\": \"def update_text(self):\\n likes = \\\"\\\"\\n if self.likes_comedy.get():\\n likes += \\\"You like comedic movies.\\\\n\\\"\\n if self.likes_drama.get():\\n likes += \\\"You like dramatic movies.\\\\n\\\"\\n if self.likes_romance.get():\\n likes += \\\"You like romantic movies.\\\"\\n self.results_txt.delete(0.0, END)\\n self.results_txt.insert(0.0, likes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"857dc3a4d706a6c6e0e489308391ebbe\",\n \"score\": \"0.49396452\",\n \"text\": \"def generate_basic_description(cls) -> str:\\n extractive_abstractive = \\\"extractive\\\" if cls.is_extractive else \\\"abstractive\\\"\\n neural = \\\"neural\\\" if cls.is_neural else \\\"non-neural\\\"\\n\\n basic_description = (\\n f\\\"{cls.model_name} is a\\\"\\n f\\\"{'query-based' if cls.is_query_based else ''} \\\"\\n f\\\"{extractive_abstractive}, {neural} model for summarization.\\\"\\n )\\n if cls.is_multi_document or cls.is_dialogue_based:\\n basic_description += (\\n f\\\"It can handle {'multi-document' if cls.is_multi_document else ''} \\\"\\n f\\\"{'dialogue' if cls.is_dialogue_based else ''} textual data.\\\"\\n )\\n\\n return basic_description\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a84cc20e8a175f169ebedd5c3807f276\",\n \"score\": \"0.49395812\",\n \"text\": \"def shortHelpString(self):\\r\\n return self.tr('PegelOnline short description: to do')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"569708a945ca304459d40fce304f9b70\",\n \"score\": \"0.49388468\",\n \"text\": \"def speak(self, n, start = None):\\n\\t\\tif (not start) or (start not in self.unique_words):\\n\\t\\t\\tk = int( self.t * random.random() )\\n\\t\\t\\tk = k if k != self.t else self.t-1\\n\\t\\t\\tstart = self.unique_words[k]\\n\\t\\tfinal = []\\n\\t\\tcurrent_word = start\\n\\t\\tcurrent_index = self.unique_words.index(start)\\n\\t\\tfor i in range(n):\\n\\t\\t\\tfinal.append(current_word)\\n\\t\\t\\tcurrent_word = ''\\n\\t\\t\\ti = 0\\n\\t\\t\\twhile current_word == '':\\n\\t\\t\\t\\ti += 1\\n\\t\\t\\t\\tnew_word, new_index = self.obtain_word(current_index)\\n\\t\\t\\t\\tcurrent_word = new_word\\n\\t\\t\\t\\tif current_word in final:\\n\\t\\t\\t\\t\\tcurrent_word = ''\\n\\t\\t\\t\\tif current_word == 'system':\\n\\t\\t\\t\\t\\tcurrent_word = ''\\n\\t\\t\\t\\tif current_word != '':\\n\\t\\t\\t\\t\\tcurrent_index = new_index\\n\\t\\t\\t\\tif i % 20 == 0:\\n\\t\\t\\t\\t\\ti = 1\\n\\t\\t\\t\\t\\tcurrent_index = new_index\\n\\t\\treturn ' '.join(final)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d03fc0041fe8df6488c1b5bac038fa4\",\n \"score\": \"0.4937366\",\n \"text\": \"def build_simple_sms(self, content):\\r\\n return '{0}'.format(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e8c806e133b7b99e32caa785747d489\",\n \"score\": \"0.493727\",\n \"text\": \"def translate(sentence):\\n\\n\\twords = {'esta': 'is', 'la': 'the', 'en': 'in', 'gato': 'cat', 'casa': 'house', 'el': 'the'}\\n\\treturn ' '.join(words[word] for word in sentence.split() if word in words)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2cbdc5035c66cc592f3ee05c377cd70\",\n \"score\": \"0.49366137\",\n \"text\": \"def main():\\r\\n parser = argparse.ArgumentParser(prog='pig', \\r\\n description=\\\"English to Pig Latin translator\\\",\\r\\n epilog='(lsoAay aay igPay atinLay otay nglishEay ranslatortay!)')\\r\\n parser.add_argument('text', nargs='?')\\r\\n args = parser.parse_args()\\r\\n\\r\\n if not args.text:\\r\\n # No text was given on the command line, so we will \\r\\n # translate our test messages.\\r\\n phrases = ['These are not the droids youre looking for', \\r\\n 'I have a bad feeling about this']\\r\\n bonus_phrases = ['Do. Or do not. There is no try!',\\r\\n 'Iay maay luentfay niay veroay ixsay illionmay ormsfay foay ommunicationcay!',\\r\\n 'ren\\\\'tAay ouyay aay ittlelay hortsay orfay aay tormtrooperSay?',\\r\\n 'Iay indfay ouryay acklay foay aithfay isturbingday.']\\r\\n\\r\\n for phrase in phrases:\\r\\n print('\\\\nOriginal phrase: ' + phrase )\\r\\n print('Translation: ' + translate(phrase))\\r\\n\\r\\n print('\\\\n**BONUS PHRASES**')\\r\\n for phrase in bonus_phrases:\\r\\n print('\\\\nOriginal phrase: ' + phrase )\\r\\n print('Translation: ' + translate(phrase))\\r\\n sys.exit(0)\\r\\n\\r\\n # Text was given on the command line. Translate it.\\r\\n print(translate(args.text))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"227e8ad827b248167c6ba17947fd956e\",\n \"score\": \"0.49296322\",\n \"text\": \"def short_action_string(self):\\n output = \\\"{0} \\\".format(self.actor)\\n if self.override_string:\\n output += self.override_string\\n else:\\n output += self.verb\\n return output\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":485,"cells":{"query_id":{"kind":"string","value":"599789f663255a4386290c952ba2936e"},"query":{"kind":"string","value":"Removes points that are outside the interval determined by [min_percentile, max_percentile]. The percentiles are computed and applied to each axis (x,y,z) individually."},"positive_passages":{"kind":"list like","value":[{"docid":"4799caa088d6eb0a3b02e5d1a31d3f83","score":"0.70688254","text":"def remove_distant_and_close_points(cloud:np.ndarray, min_percentile:int=1, max_percentile:int=99)->np.ndarray:\n cloud = cloud.reshape(-1,3)\n\n min_x = np.percentile(cloud[:,0], min_percentile, interpolation='nearest')\n max_x = np.percentile(cloud[:,0], max_percentile, interpolation='nearest')\n min_y = np.percentile(cloud[:,1], min_percentile, interpolation='nearest')\n max_y = np.percentile(cloud[:,1], max_percentile, interpolation='nearest')\n min_z = np.percentile(cloud[:,2], min_percentile, interpolation='nearest')\n max_z = np.percentile(cloud[:,2], max_percentile, interpolation='nearest')\n\n # condition_x = np.any([cloud[:,0] < min_x, cloud[:,0] > max_x], axis=0).reshape((cloud.shape[0], 1))\n # condition_y = np.any([cloud[:,1] < min_y, cloud[:,1] > max_y], axis=0).reshape((cloud.shape[0], 1))\n # condition_z = np.any([cloud[:,2] < min_z, cloud[:,2] > max_z], axis=0).reshape((cloud.shape[0], 1))\n # condition = np.concatenate([condition_x, condition_y, condition_z], axis=1)\n # TODO Try this\n condition_xyz = np.any([cloud[:,0] < min_x,\n cloud[:,0] > max_x,\n cloud[:,1] < min_y,\n cloud[:,1] > max_y,\n cloud[:,2] < min_z,\n cloud[:,2] > max_z], axis=0).reshape((cloud.shape[0], 1))\n condition = np.concatenate([condition_xyz, condition_xyz, condition_xyz], axis=1)\n\n\n masked_cloud = ma.masked_where(condition, cloud, copy=False)\n # ~masked_cloud.mask removes the values where the mask is True.\n # With True meaning that the condition applied, this means that we want to remove those values. \n return masked_cloud[~masked_cloud.mask].data.reshape((-1,3))","title":""}],"string":"[\n {\n \"docid\": \"4799caa088d6eb0a3b02e5d1a31d3f83\",\n \"score\": \"0.70688254\",\n \"text\": \"def remove_distant_and_close_points(cloud:np.ndarray, min_percentile:int=1, max_percentile:int=99)->np.ndarray:\\n cloud = cloud.reshape(-1,3)\\n\\n min_x = np.percentile(cloud[:,0], min_percentile, interpolation='nearest')\\n max_x = np.percentile(cloud[:,0], max_percentile, interpolation='nearest')\\n min_y = np.percentile(cloud[:,1], min_percentile, interpolation='nearest')\\n max_y = np.percentile(cloud[:,1], max_percentile, interpolation='nearest')\\n min_z = np.percentile(cloud[:,2], min_percentile, interpolation='nearest')\\n max_z = np.percentile(cloud[:,2], max_percentile, interpolation='nearest')\\n\\n # condition_x = np.any([cloud[:,0] < min_x, cloud[:,0] > max_x], axis=0).reshape((cloud.shape[0], 1))\\n # condition_y = np.any([cloud[:,1] < min_y, cloud[:,1] > max_y], axis=0).reshape((cloud.shape[0], 1))\\n # condition_z = np.any([cloud[:,2] < min_z, cloud[:,2] > max_z], axis=0).reshape((cloud.shape[0], 1))\\n # condition = np.concatenate([condition_x, condition_y, condition_z], axis=1)\\n # TODO Try this\\n condition_xyz = np.any([cloud[:,0] < min_x,\\n cloud[:,0] > max_x,\\n cloud[:,1] < min_y,\\n cloud[:,1] > max_y,\\n cloud[:,2] < min_z,\\n cloud[:,2] > max_z], axis=0).reshape((cloud.shape[0], 1))\\n condition = np.concatenate([condition_xyz, condition_xyz, condition_xyz], axis=1)\\n\\n\\n masked_cloud = ma.masked_where(condition, cloud, copy=False)\\n # ~masked_cloud.mask removes the values where the mask is True.\\n # With True meaning that the condition applied, this means that we want to remove those values. \\n return masked_cloud[~masked_cloud.mask].data.reshape((-1,3))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4b77098adb812cbb946d3a783f6e7601","score":"0.6658416","text":"def test_exclude_percentile(self):\n data = np.copy(DATA)\n data[0:50, 0:50] = np.nan\n with pytest.warns(AstropyUserWarning,\n match='Input data contains invalid values'):\n bkg = Background2D(data, (25, 25), filter_size=(1, 1),\n exclude_percentile=100.0)\n assert len(bkg._box_idx) == 12","title":""},{"docid":"690c031e82bbcca291c6dd4b0d44d1d8","score":"0.6491309","text":"def remove_outliers(arr, z=1.5):\n\n q25, q75 = np.percentile(arr, 25), np.percentile(arr, 75)\n iqr = q75 - q25\n limit = z * iqr\n return [a for a in arr if a >= q25 - limit and a <= q75 + limit]","title":""},{"docid":"adf5bbd83ad86af55b8d74ab46c9af9c","score":"0.63799745","text":"def imenhance(x, percentiles=[5, 95]):\n isf = np.isfinite(x)\n (vmin, vmax) = np.percentile(x[isf], percentiles)\n y = x\n y[x < vmin] = vmin\n y[x > vmax] = vmax\n return y","title":""},{"docid":"92ebe1e8b48c3d46c038adb557df00af","score":"0.6334107","text":"def test_percentiles_axis():\n data = rs.randn(10, 10)\n\n # Test against the median with 50th percentile\n median1 = np.median(data)\n out1 = stat.percentiles(data, 50)\n assert_array_almost_equal(median1, out1)\n\n for axis in range(2):\n median2 = np.median(data, axis=axis)\n out2 = stat.percentiles(data, 50, axis=axis)\n assert_array_almost_equal(median2, out2)\n\n median3 = np.median(data, axis=0)\n out3 = stat.percentiles(data, [50, 95], axis=0)\n assert_array_almost_equal(median3, out3[0])\n assert_equal(2, len(out3))","title":""},{"docid":"df28551e5a35358c50aa0af5768f0865","score":"0.6319891","text":"def remove_outliers(data, col):\n def q1(x):\n q = np.percentile(x,25)\n return q\n\n def q3(x):\n q = np.percentile(x,75)\n return q\n\n IR = q3(data[col])-q1(data[col])\n outliers_l = q1(data[col])-1.5*IR\n outliers_u = q3(data[col])+1.5*IR\n\n data = data[(data[col]>=outliers_l) & (data[col]<=outliers_u)]\n\n return data","title":""},{"docid":"965cdb1d2fe6391b707574a6e5a476ac","score":"0.6246908","text":"def percentiles(data, lo, hi):\n max = data.max()\n dataArray = data.flatten()\n pHi = numpy.percentile(dataArray, hi)\n pLo = numpy.percentile(dataArray, lo)\n range = pHi - pLo\n scale = range/255\n data = numpy.clip(data, pLo, pHi)\n data-= pLo\n data/=scale\n return data","title":""},{"docid":"897e1187aadee202f1d69c6d3a7acfd8","score":"0.6207559","text":"def remove_outliers(data):\n\tdata.sort()\n\tnumPointsToRemove = (5*len(data))/100\n\treturn data[numPointsToRemove:len(data)-numPointsToRemove]","title":""},{"docid":"cfd45fb0db06a450ac01eb83bbfc6cbd","score":"0.6200231","text":"def get_percentiles(self):\n\n if self.xcube is None:\n self.xpercentiles = self.quantiles\n else:\n data = self.xcube.data\n if np.sum(np.isfinite(data)) == 0:\n #All data is nan, cannot calculate percentiles\n raise ValueError('Data for x-axis all nan')\n self.xpercentiles = np.percentile(data[~np.isnan(data)],\n q=self.quantiles)\n\n for line in self.lines:\n data = line['cube'].data\n if np.sum(np.isfinite(data)) == 0:\n #All data is nan, cannot calculate percentiles\n warnings.warn('All nan data')\n continue\n line['ypercentiles'] = np.percentile(data[~np.isnan(data)],\n q=self.quantiles)\n\n return self.lines","title":""},{"docid":"925bcfffbd57ecbc41a6162e08cfb24e","score":"0.6165104","text":"def test_Data_percentile_median(self):\n # ranks: a sequence of percentile rank inputs. NOTE: must\n # include 50 as the last input so that cf.Data.median is also\n # tested correctly.\n ranks = ([30, 60, 90], [90, 30], [20])\n ranks = ranks + (50,)\n\n d = cf.Data(self.a, chunks=(2, 2, 3, 5))\n\n for axis in [None] + self.axes_combinations:\n for keepdims in (True, False):\n for q in ranks:\n a1 = np.percentile(d, q, axis=axis, keepdims=keepdims)\n b1 = d.percentile(q, axes=axis, squeeze=not keepdims)\n self.assertEqual(b1.shape, a1.shape)\n self.assertTrue((b1.array == a1).all())\n\n # Check that the _axes attribute has been updated\n # for the new rank dimension, where appropriate.\n if keepdims:\n if isinstance(q, list):\n self.assertEqual(len(b1._axes), len(d._axes) + 1)\n else:\n self.assertEqual(len(b1._axes), len(d._axes))\n\n # Masked data\n a = self.ma\n filled = np.ma.filled(a, np.nan)\n d = cf.Data(self.ma, chunks=(2, 2, 3, 5))\n\n with np.testing.suppress_warnings() as sup:\n sup.filter(\n category=RuntimeWarning,\n message=\".*All-NaN slice encountered.*\",\n )\n for axis in [None] + self.axes_combinations:\n for keepdims in (True, False):\n for q in ranks:\n a1 = np.nanpercentile(\n filled, q, axis=axis, keepdims=keepdims\n )\n mask = np.isnan(a1)\n if mask.any():\n a1 = np.ma.masked_where(mask, a1, copy=False)\n\n b1 = d.percentile(q, axes=axis, squeeze=not keepdims)\n self.assertEqual(b1.shape, a1.shape)\n self.assertTrue((b1.array == a1).all())\n\n # Check for no warning when data is of masked type but with no\n # missing values\n with warnings.catch_warnings():\n warnings.simplefilter(\"error\", category=UserWarning)\n d = cf.Data(np.ma.arange(100))\n d.percentile(ranks[0])\n\n # Test scalar input (not masked)\n a = np.array(9)\n d = cf.Data(a)\n for keepdims in (True, False):\n for q in ranks:\n a1 = np.nanpercentile(a, q, keepdims=keepdims)\n b1 = d.percentile(q, squeeze=not keepdims)\n self.assertEqual(b1.shape, a1.shape)\n self.assertTrue((b1.array == a1).all())\n\n # Test scalar input (masked)\n a = np.ma.array(9, mask=True)\n filled = np.ma.filled(a.astype(float), np.nan)\n d = cf.Data(a)\n\n with np.testing.suppress_warnings() as sup:\n sup.filter(\n category=RuntimeWarning,\n message=\".*All-NaN slice encountered.*\",\n )\n for keepdims in (True, False):\n for q in ranks:\n a1 = np.nanpercentile(filled, q, keepdims=keepdims)\n mask = np.isnan(a1)\n if mask.any():\n a1 = np.ma.masked_where(mask, a1, copy=False)\n\n b1 = d.percentile(q, squeeze=not keepdims)\n self.assertEqual(b1.shape, a1.shape)\n self.assertTrue(\n (b1.array == a1).all() in (True, np.ma.masked)\n )\n\n # Test mtol=1\n d = cf.Data(self.a)\n d[...] = cf.masked # All masked\n for axis in [None] + self.axes_combinations:\n for q in ranks:\n e = d.percentile(q, axes=axis, mtol=1)\n self.assertFalse(np.ma.count(e.array, keepdims=True).any())\n\n a = np.ma.arange(12).reshape(3, 4)\n d = cf.Data(a)\n d[1, -1] = cf.masked # 1 value masked\n for q in ranks:\n e = d.percentile(q, mtol=1)\n self.assertTrue(np.ma.count(e.array, keepdims=True).all())\n\n # Test mtol=0\n for q in ranks:\n e = d.percentile(q, mtol=0)\n self.assertFalse(np.ma.count(e.array, keepdims=True).any())\n\n # Test mtol=0.1\n for q in ranks:\n e = d.percentile(q, axes=0, mtol=0.1)\n self.assertEqual(np.ma.count(e.array), 3 * e.shape[0])\n\n for q in ranks[:-1]: # axis=1: exclude the non-sequence rank\n e = d.percentile(q, axes=1, mtol=0.1)\n self.assertEqual(np.ma.count(e.array), 2 * e.shape[0])\n\n q = ranks[-1] # axis=1: test the non-sequence rank\n e = d.percentile(q, axes=1, mtol=0.1)\n self.assertEqual(np.ma.count(e.array), e.shape[0] - 1)\n\n # Check invalid ranks (those not in [0, 100])\n for q in (-9, [999], [50, 999], [999, 50]):\n with self.assertRaises(ValueError):\n d.percentile(q).array","title":""},{"docid":"55d47aebe60f31e69332e8e7761f80dd","score":"0.60653436","text":"def outliers(data):\n if len(data) < 2:\n raise RuntimeError('Too few values given to outliers')\n\n first_quartile = lower_quartile(data)\n third_quartile = upper_quartile(data)\n iqr = interquartile_range(data)\n\n lower_limit = (first_quartile - 1.5 * iqr)\n upper_limit = (third_quartile + 1.5 * iqr)\n\n results = []\n for each in data:\n if each < lower_limit or each > upper_limit:\n results.append(each)\n return results","title":""},{"docid":"918db9d30412dc8457e0d2782689ae67","score":"0.6020458","text":"def ml_percentile(in_data, percentiles):\n\n data = np.sort(in_data)\n p_rank = 100.0 * (np.arange(data.size) + 0.5) / data.size\n perc = np.interp(percentiles, p_rank, data, left=data[0], right=data[-1])\n return perc","title":""},{"docid":"c246046e0f4f4609bc9c7dc39552eef3","score":"0.59835273","text":"def xr_percentile(\n src: xr.Dataset,\n percentiles: Sequence,\n nodata,\n) -> xr.Dataset:\n\n data_vars = {}\n for band, xx in src.data_vars.items():\n \n xx_data = xx.data\n if len(xx.chunks[0]) > 1:\n xx_data = xx_data.rechunk({0: -1})\n \n tk = tokenize(xx_data, percentiles, nodata)\n for percentile in percentiles:\n name = f\"{band}_pc_{int(100 * percentile)}\"\n yy = da.map_blocks(\n partial(np_percentile, percentile=percentile, nodata=nodata), \n xx_data, \n drop_axis=0, \n meta=np.array([], dtype=xx.dtype),\n name=f\"{name}-{tk}\",\n )\n data_vars[name] = xr.DataArray(yy, dims=xx.dims[1:], attrs=xx.attrs)\n \n coords = dict((dim, src.coords[dim]) for dim in xx.dims[1:])\n return xr.Dataset(data_vars=data_vars, coords=coords, attrs=src.attrs)","title":""},{"docid":"1235390688f6d6402d53315d77f32d98","score":"0.5935325","text":"def spatial_percentile(percentile, points_on_line_x, points_on_line_y):\n assert 0. <= percentile <= 1.\n # First, uniquely associate each value with its spacing. This is just a\n # trapezium rule approach:\n spacing_each_trapezium = points_on_line_x[1:] - points_on_line_x[:-1]\n val_at_each_trapezium = (points_on_line_y[:-1] + points_on_line_y[1:]) / 2.\n area_each_trapezium = spacing_each_trapezium * val_at_each_trapezium\n # order the data by y:\n order_for_vals = np.argsort(val_at_each_trapezium)\n accumulating_areas = np.cumsum(area_each_trapezium[order_for_vals])\n total_area = accumulating_areas[-1]\n # find the percentile in the accumulating areas:\n pc_position = np.searchsorted(accumulating_areas,\n percentile * total_area)\n pc_value = val_at_each_trapezium[order_for_vals][pc_position]\n return pc_value","title":""},{"docid":"e6eb8ad3be077c348dc6822514b4e758","score":"0.5928688","text":"def test_percentiles():\n single_val = 5\n single = stat.percentiles(a_range, single_val)\n assert_equal(single, single_val)\n\n multi_val = [10, 20]\n multi = stat.percentiles(a_range, multi_val)\n assert_array_equal(multi, multi_val)\n\n array_val = rs.randint(0, 101, 5).astype(float)\n array = stat.percentiles(a_range, array_val)\n assert_array_almost_equal(array, array_val)","title":""},{"docid":"c710a050a4aa28f1b6107d3277bb3747","score":"0.5895379","text":"def get_smoothed_y_percentiles(self,i,imported_array_list,y_percentile,smoothing_kernel_y):\n imported_array = imported_array_list[i]\n y_percentiles = np.percentile(imported_array[0],y_percentile,axis=1,interpolation='lower')\n y_percentiles_smoothed = self.median_filter_2d(y_percentiles,smoothing_kernel_y)\n return y_percentiles_smoothed","title":""},{"docid":"ad1d0f6450ba6fad33c43bbe8324a4e4","score":"0.58534145","text":"def clip_extreme(imgs, percentile=5, allowed_steepness=3, dim=2):\n if isinstance(imgs, torch.Tensor):\n return_torch = True\n device = imgs.device\n dtype = imgs.dtype\n imgs = imgs.cpu().numpy()\n else:\n return_torch=False\n img_shape = imgs.shape[-dim:]\n imgs = imgs.reshape(imgs.shape[:-dim] + (-1,))\n # mins = np.min(imgs, axis=-1)\n # maxs = np.max(imgs, axis=-1)\n soft_mins = np.percentile(imgs, percentile, axis=-1)\n soft_maxs = np.percentile(imgs, 100-percentile, axis=-1)\n ranges = soft_maxs - soft_mins\n print(ranges)\n allowed_extra = ranges * percentile/100 * allowed_steepness\n imgs = imgs.clip((soft_mins - allowed_extra)[..., None],\n (soft_maxs - allowed_extra)[..., None])\n imgs = imgs.reshape(imgs.shape[:-1] + img_shape)\n if return_torch:\n return torch.tensor(imgs, dtype=dtype).to(device)\n else:\n return imgs","title":""},{"docid":"6c5768465361f53b282749aded920a05","score":"0.5836667","text":"def test_Data_mean_of_upper_decile(self):\n # Masked array, non-masked weights\n a = self.ma\n weights = self.w\n d = cf.Data(a, \"m\", chunks=(2, 3, 2, 5))\n\n for axis in axis_combinations(a):\n b = reshape_array(a, axis)\n w = reshape_array(weights, axis)\n b = np.ma.filled(b, np.nan)\n with np.testing.suppress_warnings() as sup:\n sup.filter(\n category=RuntimeWarning,\n message=\".*All-NaN slice encountered.*\",\n )\n sup.filter(\n category=UserWarning,\n message=\"Warning: 'partition' will ignore the 'mask' of the MaskedArray.*\",\n )\n p = np.nanpercentile(b, 90, axis=-1, keepdims=True)\n\n b = np.ma.masked_where(np.isnan(b), b, copy=False)\n p = np.where(np.isnan(p), b.max() + 1, p)\n\n with np.testing.suppress_warnings() as sup:\n sup.filter(\n category=UserWarning,\n message=\"Warning: 'partition' will ignore the 'mask' of the MaskedArray.*\",\n )\n b = np.ma.where(b < p, np.ma.masked, b)\n\n b = np.ma.average(b, axis=-1, weights=w)\n b = np.ma.asanyarray(b)\n\n e = d.mean_of_upper_decile(\n axes=axis, weights=weights, squeeze=True\n )\n with np.testing.suppress_warnings() as sup:\n sup.filter(\n category=UserWarning,\n message=\"Warning: 'partition' will ignore the 'mask' of the MaskedArray.*\",\n )\n e = e.array\n\n self.assertTrue(\n (np.ma.getmaskarray(e) == np.ma.getmaskarray(b)).all()\n )\n self.assertTrue(np.allclose(e, b))\n\n # mtol\n a[0, 0] = cf.masked\n d = cf.Data(a, \"m\", chunks=3)\n e = d.mean_of_upper_decile(mtol=0)\n self.assertEqual(e.array.mask, True)\n\n # Inplace\n self.assertIsNone(d.mean_of_upper_decile(inplace=True))","title":""},{"docid":"c97fa74d90556805b662a2bd23ac1100","score":"0.5801683","text":"def pruning_with_percentile(self, q: float):\n if len(self._weight_mask) == 0:\n return\n for name, param in self.named_parameters():\n if 'weight' not in name:\n # skip the other type weights\n continue\n cpu_param = param.cpu()\n old_mask = self._weight_mask[id(param)][0]\n percentile_value = percentile(tensor_nonzero(cpu_param * old_mask), q)\n new_mask = torch.where(cpu_param.abs() < percentile_value,\n torch.zeros_like(old_mask), old_mask)\n param.data = param.data * new_mask.to(self._device)\n self._weight_mask.update({id(param) : (new_mask, name)})","title":""},{"docid":"93ea1c713b33c7800b321a2f171e25ef","score":"0.57994294","text":"def clamp(img, percentile, normalize=False):\n img_ = np.copy(img)\n img_[img_ > np.percentile(img_, percentile)] = np.percentile(img_, percentile)\n if normalize:\n out = img_ / img_.max()\n return out\n elif not normalize:\n out = img_\n return out\n else:\n raise ValueError('Normalize must be either True or False.')","title":""},{"docid":"c8afc2470e658da8fc0766cb9a60bd9c","score":"0.57960975","text":"def preprocess_minmax(img: np.array) -> np.array:\n im_min = np.percentile(img, 2)\n im_max = np.percentile(img, 98)\n im_range = im_max - im_min\n # print(f'percentile 2 {im_min}, percentile 98 {im_max}, im_range {im_range}')\n\n # Normalise to the percentile\n img = img.astype(np.float32)\n img = (img - im_min) / im_range\n img = img.clip(0, 1)\n\n return img","title":""},{"docid":"7eca5641570a5c15dcdb9fb53bcf5d9c","score":"0.5781637","text":"def clean_data(data,min=0,max=100,sd=5):\n data = data.where((data>min) & (data<max))\n data = data.where(reject_outliers(data,sd))\n return data","title":""},{"docid":"d2d68303713daf03076c4b5f4cfa6d49","score":"0.57463306","text":"def grid_from_X(x, percentiles=(0.05, 0.95), grid_resolution=100):\r\n x = x[~x.isnull()]\r\n if len(percentiles) != 2:\r\n raise ValueError('percentile must be tuple of len 2')\r\n if not all(0. <= x <= 1. for x in percentiles):\r\n raise ValueError('percentile values must be in [0, 1]')\r\n\r\n uniques = np.unique(x)\r\n if uniques.shape[0] < grid_resolution:\r\n # feature has low resolution use unique vals\r\n return uniques\r\n else:\r\n emp_percentiles = mquantiles(x, prob=percentiles)\r\n # create axis based on percentiles and grid resolution\r\n return np.linspace(emp_percentiles[0],\r\n emp_percentiles[1],\r\n num=grid_resolution, endpoint=True)","title":""},{"docid":"4121c3d623f7dc2931c219f7c70549b8","score":"0.5726104","text":"def delete_outliers(self):\n\n limits = self._limits\n column = self._column\n\n func = lambda x: (x >= limits[0]) & (x <= limits[1])\n self._df = self._df.filter(func(col(column)))\n\n return self._df","title":""},{"docid":"d7007c98f46d2533fc8d1c070f3c5e27","score":"0.57132715","text":"def _clip(\n image: xr.DataArray, p_min: int, p_max: int,\n ) -> xr.DataArray:\n v_min, v_max = np.percentile(image, [p_min, p_max])\n\n image = image.clip(min=v_min, max=v_max)\n\n return image","title":""},{"docid":"7caf754da40e8e99d9713f3806959419","score":"0.56986296","text":"def robustScale(x, cols: list = []):\n if not cols:\n cols = list(range(x.shape[1]))\n top = x[:, cols] - np.percentile(x[:, cols], 25, axis=0)\n bot = np.percentile(x[:, cols], 75, axis=0) - np.percentile(x[:, cols], 25, axis=0)\n x[:, cols] = top / bot\n return x","title":""},{"docid":"d2ae1ab2426fcc4c14b2cbf19c15561a","score":"0.56981295","text":"def unique_percentiles_interpolate(perc_values,\n percentiles=[0, 5, 10, 30, 50,\n 70, 90, 95, 100],\n k=1):\n uniq_ind = np.sort(np.unique(perc_values, return_index=True)[1])\n if len(uniq_ind) == 1:\n uniq_ind = np.repeat(uniq_ind, 2)\n uniq_ind[-1] = len(percentiles) - 1\n uniq_perc_values = perc_values[uniq_ind]\n inter = sc_int.InterpolatedUnivariateSpline(\n np.array(percentiles)[uniq_ind],\n uniq_perc_values,\n k=k, ext=0,\n check_finite=True)\n uniq_perc_values = inter(percentiles)\n return uniq_perc_values","title":""},{"docid":"f1fa46cb22bbd1235f4184633a82ef43","score":"0.569696","text":"def _calc_percentile(self, fitted_values: np.ndarray) -> np.ndarray:\n return np.percentile(fitted_values, self.params.quantile, 0)","title":""},{"docid":"ffb16cbf4a6c8f649711c9d4d23cc132","score":"0.56882125","text":"def _percentiles(t, q=[15., 50., 85.]):\n return tfp.stats.percentile(t, q, axis=0)","title":""},{"docid":"fd7a14d064af1e1ef33811226916d7e2","score":"0.5669077","text":"def remove_outliers(self, threshold=5):\n for i in range(len(self)):\n self[i] = self[i].remove_outliers(threshold=threshold)\n\n return self","title":""},{"docid":"d5ebd6452b751e851f84e51d4723efa5","score":"0.5598348","text":"def _percentile_values(y_samples, percentile_step):\n percentiles = range(0, 100 + percentile_step, percentile_step)\n assert len(percentiles) % 2 == 1\n assert 0 in percentiles\n assert 50 in percentiles\n assert 100 in percentiles\n\n if isinstance(y_samples, np.ndarray):\n assert len(y_samples.shape) == 2, 'y_samples must be a 2D numpy array or a list of lists of numbers.'\n assert len(y_samples) > 0, 'y_samples must not be empty.'\n assert len(y_samples[0]) > 0, 'y_samples must not be empty.'\n else:\n assert isinstance(y_samples, list), 'y_samples must be a 2D numpy array or a list of lists of numbers.'\n assert y_samples, 'y_samples must not be empty.'\n for ys in y_samples:\n assert isinstance(ys, list), 'y_samples must be a 2D numpy array or a list of lists of numbers.'\n assert ys, 'y_samples must not have empty inner lists, each X value must have at least one Y sample.'\n if len(set(len(ys) for ys in y_samples)) == 1:\n # All y_samples rows have the same length. Can convert into a numpy array for fast percentile computation.\n y_samples = np.array(y_samples)\n\n # y_list is a list of arrays. Each list is for a percentile, each array element is y value at x.\n if isinstance(y_samples, np.ndarray):\n # y_samples is a numpy array, can compute percentiles in one go.\n y_list = np.percentile(y_samples, percentiles, axis=1)\n else:\n # y_samples is a list of lists of uneven lengths. Slow mode.\n logger.warning('Slow percentile computation: consider switching to 2D numpy arrays.')\n num_x = len(y_samples)\n y_list = []\n for p in percentiles:\n y_list.append(np.zeros(num_x, dtype=np.float64))\n for i in xrange(num_x):\n y_list[-1][i] = np.percentile(y_samples[i], p)\n\n assert len(y_list) == len(percentiles)\n return percentiles, y_list","title":""},{"docid":"2dea2bf60eab93913a5c207d58a22128","score":"0.5596752","text":"def percentile(x, ys):\n sz_y = len(ys)\n if sz_y == 0:\n return -1\n elif sz_y == 1:\n return 0.\n else:\n return sum(y < x for y in ys) / float(len(ys) - 1) * 100","title":""},{"docid":"01bd0cce8d8200e1383c516aa95d44cf","score":"0.5595679","text":"def clipOutliers(self,limits=(1,99),percentage=True,action='noclipmedian'):\n if np.isscalar(limits):\n limits=(-limits,limits)\n elif len(limits)==2:\n pass\n else:\n raise ValueError('unrecognized limit form')\n \n im=self._active\n \n if percentage:\n mi,ma=np.min(im),np.max(im)\n rng=ma-mi\n limits=(mi+limits[0]*rng/100,mi+limits[1]*rng/100)\n \n clipcond=np.logical_or(im>limits[1],im<limits[0])\n self._active,nclip=self._repl_inds(action,clipcond)\n \n self._changed = True\n return nclip","title":""},{"docid":"d315a6018dce115c8216237f89608357","score":"0.55749416","text":"def get_outliers(values):\n # drop NAs \n values = values.dropna()\n names = values.index\n # calculate percentiles\n p25 = np.percentile(values, 25)\n p75 = np.percentile(values, 75)\n # initiate empty lists \n indices_of_outliers = []\n values_of_outliers = []\n # for each value \n for value, name in zip(values, names):\n # apply is_extreme\n if is_extreme(value, p25, p75):\n # get index and value\n indices_of_outliers.append(name)\n values_of_outliers.append(value)\n outliers = zip(indices_of_outliers, values_of_outliers)\n #outliers.sort(key = lambda t: t[1])\n return outliers","title":""},{"docid":"d915e3ef7557429ff895008d4251a249","score":"0.5572701","text":"def percentile_for(percentile: int) -> StatFunction:\n if percentile < 0 or percentile > 100:\n raise TypeError('percentile must be between 0 and 100, inclusive')\n\n def percentile_(series: pd.Series):\n return series.quantile(percentile / 100)\n percentile_.__name__ = 'percentile_{:2.0f}'.format(percentile)\n return percentile_","title":""},{"docid":"8ba315638fa3d0873cac3162f5353d84","score":"0.5549827","text":"def imshowe(x, percentiles=[5, 95], **kwargs):\n isf = np.isfinite(x)\n if np.any(isf):\n (vmin, vmax) = np.percentile(x[isf], percentiles)\n else:\n logger.warning('No finite numbers in array')\n vmin = 0\n vmax = 1\n if 'percentiles' in kwargs:\n del(kwargs['percentiles'])\n return imshow(x, vmin=vmin, vmax=vmax, **kwargs)","title":""},{"docid":"07bfdb5921b3b1d200239e3fb4ea0e7f","score":"0.55362695","text":"def __init__(self, img, percentiles=[1,99]):\n assert len(percentiles) == 2\n vmin, vmax = np.percentile(img, percentiles)\n super().__init__(vmin=vmin, vmax=vmax)","title":""},{"docid":"e54735f7bd2b6ba02a042daeb00c4096","score":"0.55264735","text":"def remove_outliers(tX):\n clean_data = []\n for f in tX.T:\n mean = np.mean(f, axis=0)\n std = np.std(f, axis=0)\n edge = std * 3\n lowerb = mean - edge\n upperb = mean + edge\n for i,x in enumerate(f):\n if x<lowerb:\n f[i]=lowerb\n elif x>upperb:\n f[i]=upperb\n \n return tX","title":""},{"docid":"f6dc237a9d11dd5d0848b6e5b3a57a2a","score":"0.55225736","text":"def scale_percentile(matrix):\n orig_shape = matrix.shape\n matrix = np.reshape(matrix, [matrix.shape[0]*matrix.shape[1], 3]).astype(float)\n \n # Get 2nd and 98th percentile\n mins = np.percentile(matrix, 1, axis=0)\n maxs = np.percentile(matrix, 99, axis=0) - mins\n \n matrix = (matrix - mins[None,:])/maxs[None,:]\n matrix = np.reshape(matrix, orig_shape)\n matrix = matrix.clip(0,1)\n return matrix","title":""},{"docid":"099a0f064db0ba5d962635dde9214114","score":"0.55035377","text":"def remove_outliers(\r\n data,\r\n method: str = \"gaussian\",\r\n cutoff=3,\r\n skip: Optional[Union[str, List[str]]] = None,\r\n only: Optional[Union[str, List[str]]] = None,\r\n):\r\n # Copy to avoid replacing in-place\r\n data = data.copy(deep=True)\r\n\r\n # Which columns\r\n columns = _validate_skip_only(data, skip, only)\r\n is_continuous = _get_dtypes(data) == \"continuous\"\r\n columns = columns & is_continuous\r\n\r\n # Check cutoff and method, printing what is being done\r\n if cutoff <= 0:\r\n raise ValueError(\"'cutoff' must be >= 0\")\r\n if method == \"iqr\":\r\n click.echo(\r\n f\"Removing outliers from {len(data):,} observations of {columns.sum():,} continuous variables \"\r\n f\"with values < 1st Quartile - ({cutoff} * IQR) or > 3rd quartile + ({cutoff} * IQR)\"\r\n )\r\n elif method == \"gaussian\":\r\n click.echo(\r\n f\"Removing outliers from {len(data):,} observations of {columns.sum():,} continuous variables \"\r\n f\"with values more than {cutoff} standard deviations from the mean\"\r\n )\r\n else:\r\n raise ValueError(\r\n f\"'{method}' is not a supported method for outlier removal - only 'gaussian' and 'iqr'.\"\r\n )\r\n\r\n # Remove outliers\r\n # Note: This could be faster by performing calculations on the entire dataset at once, but in practice this should\r\n # be used on more of a limited basis, reviewing changes in each variable.\r\n for col_name, process_col in columns.iteritems():\r\n if not process_col:\r\n continue\r\n if method == \"iqr\":\r\n q1 = data[col_name].quantile(0.25)\r\n q3 = data[col_name].quantile(0.75)\r\n iqr = abs(q3 - q1)\r\n bottom = q1 - (iqr * cutoff)\r\n top = q3 + (iqr * cutoff)\r\n elif method == \"gaussian\":\r\n mean = data[col_name].mean()\r\n std = data[col_name].std()\r\n bottom = mean - (std * cutoff)\r\n top = mean + (std * cutoff)\r\n # Replace with NA\r\n outliers_bottom = data[col_name] < bottom\r\n outliers_top = data[col_name] > top\r\n data.loc[outliers_bottom, col_name] = np.nan\r\n data.loc[outliers_top, col_name] = np.nan\r\n # Log\r\n click.echo(\r\n f\"\\tRemoved {outliers_bottom.sum()} low and {outliers_top.sum()} high outliers \"\r\n f\"from {col_name} (outside {bottom:,.2f} to {top:,.2f})\"\r\n )\r\n\r\n return data","title":""},{"docid":"5d466782fdf1fada30b662844168e51f","score":"0.54912925","text":"def test_percentiles_acc():\n # First a basic case\n data = np.array([10, 20, 30])\n val = 20\n perc = stat.percentiles(data, 50)\n assert_equal(perc, val)\n\n # Now test against scoreatpercentile\n percentiles = rs.randint(0, 101, 10)\n out = stat.percentiles(a_norm, percentiles)\n for score, pct in zip(out, percentiles):\n assert_equal(score, sp.stats.scoreatpercentile(a_norm, pct))","title":""},{"docid":"ef1e88dc7f92dcd42c3bccf54e04e291","score":"0.54714763","text":"def raster_percentile(band):\n return bisect(percentiles, band)","title":""},{"docid":"4cba938cf5f4b3bff3ed73041af65142","score":"0.5466502","text":"def treat_outliers(df, col):\n Q1 = df[col].quantile(0.25) # 25th quantile\n Q3 = df[col].quantile(0.75) # 75th quantile\n IQR = Q3 - Q1\n Lower_Whisker = Q1 - 1.5 * IQR\n Upper_Whisker = Q3 + 1.5 * IQR\n\n # all the values smaller than Lower_Whisker will be assigned the value of Lower_Whisker\n # all the values greater than Upper_Whisker will be assigned the value of Upper_Whisker\n df[col] = np.clip(df[col], Lower_Whisker, Upper_Whisker)\n\n return df","title":""},{"docid":"4cba938cf5f4b3bff3ed73041af65142","score":"0.5466502","text":"def treat_outliers(df, col):\n Q1 = df[col].quantile(0.25) # 25th quantile\n Q3 = df[col].quantile(0.75) # 75th quantile\n IQR = Q3 - Q1\n Lower_Whisker = Q1 - 1.5 * IQR\n Upper_Whisker = Q3 + 1.5 * IQR\n\n # all the values smaller than Lower_Whisker will be assigned the value of Lower_Whisker\n # all the values greater than Upper_Whisker will be assigned the value of Upper_Whisker\n df[col] = np.clip(df[col], Lower_Whisker, Upper_Whisker)\n\n return df","title":""},{"docid":"e9df7fc8ee6575dce8212c40953f0277","score":"0.54515636","text":"def fast_abs_percentile(data, percentile=80):\n if hasattr(data, 'mask'):\n # Catter for masked arrays\n data = np.asarray(data[np.logical_not(data.mask)])\n data = np.abs(data)\n data = data.ravel()\n index = int(data.size * .01 * percentile)\n if partition is not None:\n # Partial sort: faster than sort\n return partition(data, index)[index + 1]\n data.sort()\n return data[index + 1]","title":""},{"docid":"e037d5e2552efc63d52f14c53b1d11ca","score":"0.54457676","text":"def remove_outliers(target_df, low, high, exclude_cols):\n processed_df = target_df.copy()\n quant_df = target_df.quantile([low, high])\n cols = [col for col in target_df.columns if col not in exclude_cols and col != 'id' and col != 'label']\n quant_df = quant_df[cols]\n quant_df.index = [low, high]\n\n for col in target_df:\n if col != 'id' and col != 'label':\n continue\n if col not in exclude_cols:\n processed_df.loc[processed_df[col] > quant_df[col].values[1], col] = quant_df[col].values[1] # giant outliers convert to higher bound value\n processed_df.loc[processed_df[col] < quant_df[col].values[0], col] = quant_df[col].values[0] # low outliers convert to lower bound value\n\n return processed_df","title":""},{"docid":"4213acde5e9fc04fc8fa35de6a42a361","score":"0.5420591","text":"def remove_outliers_iqr(data, feature, exclude = list()):\n \n if np.issubdtype(data[feature].dtype, np.number) and feature not in exclude:\n \n q1 = data[feature].quantile(0.25)\n q3 = data[feature].quantile(0.75)\n\n iqr = q3 - q1\n lower_bound = q1 -(1.5 * iqr) \n upper_bound = q3 +(1.5 * iqr) \n\n outliers = data[feature].apply(lambda x : np.nan if (x < lower_bound or \n x > upper_bound) \n else x)\n else:\n outliers = data[feature]\n \n return outliers","title":""},{"docid":"60bbf9fb5d2ec5d5d12a39afcbe997cc","score":"0.54202724","text":"def give_percentiles_all(i, data,X):\n sc = -X[i,:]\n it_idx = data.item_int[np.logical_and(data.patient_int==i,data.rating_test)]\n res = give_percentiles(sc)\n return res[it_idx]","title":""},{"docid":"ca6dd745899ef1b97487aa5c639caaa3","score":"0.54089653","text":"def _quantile(x, q, **kwargs):\n x = np.asarray(x)\n p = np.asarray(q) * 100\n return np.nanpercentile(x, p, **kwargs)","title":""},{"docid":"1ade424eb2bdfb01d283f23a34f1788a","score":"0.5402604","text":"def percentile(self, q, axis=None, iterate_rays=False, **kwargs):\n if hasattr(np, 'nanpercentile') and not iterate_rays:\n result = self.apply_numpy_function(np.nanpercentile, q=q,\n axis=axis, projection=True,\n unit=self.unit, how='cube',\n **kwargs)\n else:\n result = self.apply_function(np.percentile, q=q, axis=axis,\n projection=True, unit=self.unit,\n **kwargs)\n\n return result","title":""},{"docid":"b4926ee08db9f6c45fef789e259d5f67","score":"0.5397032","text":"def remove_outliers(self):\n self.stats()\n\n drop_indices = []\n for col_n, col_name in enumerate(self.data.columns):\n\n for row_n, value in enumerate(self.data[col_name]):\n\n if value < self.means[col_n] - 3 * self.stds[col_n] or value > self.means[col_n] + 3 * self.stds[col_n]:\n # Drop entire row if the value is +/- 3 standard deviations from the column mean\n drop_indices.append(row_n)\n\n # Remove duplicate indices\n drop_indices = list(set(drop_indices))\n\n # Save identified outliers\n self.outliers = self.data.iloc[drop_indices]\n\n # Drop outliers and reset index\n self.data.drop(index=drop_indices, inplace=True)\n self.data.reset_index(drop=True, inplace=True)\n\n # Recompute stats after dropping outliers\n print(self.means)\n print(self.stds)\n self.stats()","title":""},{"docid":"01722b9777c491b2d13c3c8e5ddf0200","score":"0.53939855","text":"def nanpercentile(a, per):\n out = np.empty(a.shape[0])\n for i in range(a.shape[0]):\n b = a[i][np.isfinite(a[i])]\n if b.shape[0] == 0:\n out[i] = np.nan\n else:\n out[i] = np.percentile(b[np.isfinite(b)], per)\n return out","title":""},{"docid":"1b1ce0008949f2a47072b684561b332d","score":"0.53826725","text":"def calc_percentile(values):\n return (rankdata(values) - 1) / len(values) * 100","title":""},{"docid":"0f2270d72949a15dc5dcf971ee0547a2","score":"0.53773004","text":"def calculate_outlier_bounds(df, column_name):\n bounds = dict(zip([\"q1\", \"q3\"], df.approxQuantile(column_name, [0.25, 0.75], 0)))\n iqr = bounds['q3'] - bounds['q1']\n bounds['min'] = bounds['q1'] - (iqr * 1.5)\n bounds['max'] = bounds['q3'] + (iqr * 1.5)\n return bounds","title":""},{"docid":"cb3cb72672fd40d469ee379c74bb6563","score":"0.5361838","text":"def percentile(data, pcntl):\n nr = np.size(data, axis=0)\n\n x = np.sort(data, axis=0)\n return x[int(np.floor(pcntl*nr)),]","title":""},{"docid":"3aa685a902d1aefdfcb6bfdc1a1e90b2","score":"0.53562295","text":"def remove_outlier(dataset, auto_fill=True, ratio=1.5):\n from ..data import Dataset\n from .fill import fill\n from numpy import percentile, isnan, nonzero, nan\n\n if not isinstance(dataset, Dataset):\n raise TypeError(\"Dataset has to be class core.data.dataset.Dataset\")\n\n data = dataset.data\n time_col = dataset.time_column_index\n\n for i in range(data.shape[1]):\n if i == time_col:\n continue\n column = nonzero(~isnan(data[:, i]))[0]\n q25, q75 = percentile(data[column, i], (25, 75))\n iqr = q75 - q25\n data[column[nonzero(data[column, i] < (q25 - ratio * iqr))[0]], i] = nan\n data[column[nonzero(data[column, i] > (q75 + ratio * iqr))[0]], i] = nan\n dataset.change_values(data)\n\n if auto_fill:\n fill(dataset)","title":""},{"docid":"4af88fbd4407f7f6b3ad4a602af1c4b9","score":"0.53540665","text":"def remove_outliers(df, column_name):\n import pyspark.sql.functions as f\n bounds = calculate_outlier_bounds(df, column_name)\n return df.filter((f.col(column_name) >= bounds['min']) & (f.col(column_name) <= bounds['max']))","title":""},{"docid":"73c25e9ce85d7847b60ceddec576005d","score":"0.5322219","text":"def get_smoothed_y_percentiles(self,imported_hdf5_handle,y_percentile,smoothing_kernel_y):\n y_percentiles_path = self.chunk_t((imported_hdf5_handle[self.seg_channel],),(2,),1,self.get_y_percentile,\"y_percentile\",\"data\",y_percentile,t_range_tuple=(self.t_range,))\n \n with h5py_cache.File(y_percentiles_path,\"r\",chunk_cache_mem_size=self.chunk_cache_mem_size) as y_percentiles_handle:\n y_percentiles_smoothed = self.median_filter_2d(y_percentiles_handle[\"data\"],smoothing_kernel_y)\n# y_percentiles_smoothed_path = self.chunk_t((y_percentiles_handle[\"data\"],),(1,),1,self.median_filter_2d,\"y_percentile_smoothed\",\"data\",smoothing_kernel_y)\n self.removefile(y_percentiles_path)\n# return y_percentiles_smoothed_path\n return y_percentiles_smoothed","title":""},{"docid":"eb8f3c6710c14f289549b77b98593a17","score":"0.5314725","text":"def test_create_group_percentiles_output_buckets(self):\n\n created_percentiles = []\n\n desired_percentiles = [str(percent) + '%' for percent in wb_utils.Settings.formatted_percentiles]\n\n for data_item in self.results['Data']:\n for percent in data_item['percentileList']:\n percentile_keys = [perval['percentiles'] for perval in percent['percentileValues']]\n created_percentiles.append(percentile_keys)\n\n self.assertTrue([desired_percentiles == l for l in created_percentiles],\n \"\"\"create_group_percentiles returned unexecpted percentile buckets\n returned: {}\"\"\".format(created_percentiles))","title":""},{"docid":"653ba3b8735ed9f18d049c1f428c7e84","score":"0.5311623","text":"def interquartile_range(X):\n quartiles = np.nanpercentile(X, [25, 75], axis=0)\n return quartiles[1, :] - quartiles[0, :]","title":""},{"docid":"cb2a14d2315b48de82f2f9cfecdedf69","score":"0.5302771","text":"def calculate_percentile(percentile, dataframe, column_name):\n return round(dataframe[column_name].quantile(percentile),2 )","title":""},{"docid":"462b312e3e3c2cedc1b143a875e0ce31","score":"0.5296902","text":"def filter_by_percentile(tags_distribution: Dict[str, int], tol_percentile: int) -> Set[str]:\n if len(list(tags_distribution.values())) > 100:\n top_percentile = np.percentile(list(tags_distribution.values()), tol_percentile)\n return {k for k, v in tags_distribution.items() if v > top_percentile}\n else:\n return {k for k, v in tags_distribution.items()}","title":""},{"docid":"cc815928289e73cc0f83025752329c64","score":"0.5296731","text":"def reject_outliers(data, cutoff, thresh=0.08):\n data = np.array(data)\n marsz = data[:, 4]\n data = data[(data[:, 4] >= cutoff[0]) & (data[:, 4] <= cutoff[1])]\n m = np.mean(data[:, 4], axis=0)\n # return data\n return data[(data[:, 4] <= m + thresh) & (data[:, 4] >= m - thresh)]","title":""},{"docid":"664fb7f43834ae1c9ebcfea6609f3885","score":"0.5293619","text":"def signal_percentile(sampled_signal, percentile):\n return np.percentile(sampled_signal.reshape(-1), percentile)","title":""},{"docid":"04cf7bd36de2969575eee1e97b96f8f1","score":"0.5292302","text":"def parse_percentiles(percentiles: Sequence[float] | float | None) -> Sequence[float]:\n if isinstance(percentiles, float):\n percentiles = [percentiles]\n elif percentiles is None:\n percentiles = []\n if not all((0 <= p <= 1) for p in percentiles):\n raise ValueError(\"percentiles must all be in the range [0, 1]\")\n\n sub_50_percentiles = sorted(p for p in percentiles if p < 0.5)\n at_or_above_50_percentiles = sorted(p for p in percentiles if p >= 0.5)\n\n if not at_or_above_50_percentiles or at_or_above_50_percentiles[0] != 0.5:\n at_or_above_50_percentiles = [0.5, *at_or_above_50_percentiles]\n\n return [*sub_50_percentiles, *at_or_above_50_percentiles]","title":""},{"docid":"550d5f82990994e210e7389823e393ea","score":"0.5283657","text":"def get_percentile_distr():\n second_part = (np.geomspace(5, 50, 10)).astype(int)\n first_part = (101. - np.geomspace(1, 51, 20)).astype(int)\n return np.hstack([first_part, second_part])","title":""},{"docid":"c398394a8e078d423491bf1e766504fa","score":"0.5282513","text":"def cat_continuous(x, granularity=\"Medium\"):\n if granularity == \"High\":\n lspercentile = [\n np.percentile(x, 5),\n np.percentile(x, 10),\n np.percentile(x, 15),\n np.percentile(x, 20),\n np.percentile(x, 25),\n np.percentile(x, 30),\n np.percentile(x, 35),\n np.percentile(x, 40),\n np.percentile(x, 45),\n np.percentile(x, 50),\n np.percentile(x, 55),\n np.percentile(x, 60),\n np.percentile(x, 65),\n np.percentile(x, 70),\n np.percentile(x, 75),\n np.percentile(x, 80),\n np.percentile(x, 85),\n np.percentile(x, 90),\n np.percentile(x, 95),\n np.percentile(x, 99),\n ]\n res = [\n \"> p90 (%s)\" % (lspercentile[8])\n if z > lspercentile[8]\n else \"<= p10 (%s)\" % (lspercentile[0])\n if z <= lspercentile[0]\n else \"<= p20 (%s)\" % (lspercentile[1])\n if z <= lspercentile[1]\n else \"<= p30 (%s)\" % (lspercentile[2])\n if z <= lspercentile[2]\n else \"<= p40 (%s)\" % (lspercentile[3])\n if z <= lspercentile[3]\n else \"<= p50 (%s)\" % (lspercentile[4])\n if z <= lspercentile[4]\n else \"<= p60 (%s)\" % (lspercentile[5])\n if z <= lspercentile[5]\n else \"<= p70 (%s)\" % (lspercentile[6])\n if z <= lspercentile[6]\n else \"<= p80 (%s)\" % (lspercentile[7])\n if z <= lspercentile[7]\n else \"<= p90 (%s)\" % (lspercentile[8])\n if z <= lspercentile[8]\n else \"> p90 (%s)\" % (lspercentile[8])\n for z in x\n ]\n elif granularity == \"Medium\":\n lspercentile = [\n np.percentile(x, 10),\n np.percentile(x, 20),\n np.percentile(x, 30),\n np.percentile(x, 40),\n np.percentile(x, 50),\n np.percentile(x, 60),\n np.percentile(x, 70),\n np.percentile(x, 80),\n np.percentile(x, 90),\n ]\n res = [\n \"<= p10 (%s)\" % (lspercentile[0])\n if z <= lspercentile[0]\n else \"<= p20 (%s)\" % (lspercentile[1])\n if z <= lspercentile[1]\n else \"<= p30 (%s)\" % (lspercentile[2])\n if z <= lspercentile[2]\n else \"<= p40 (%s)\" % (lspercentile[3])\n if z <= lspercentile[3]\n else \"<= p50 (%s)\" % (lspercentile[4])\n if z <= lspercentile[4]\n else \"<= p60 (%s)\" % (lspercentile[5])\n if z <= lspercentile[5]\n else \"<= p70 (%s)\" % (lspercentile[6])\n if z <= lspercentile[6]\n else \"<= p80 (%s)\" % (lspercentile[7])\n if z <= lspercentile[7]\n else \"<= p90 (%s)\" % (lspercentile[8])\n if z <= lspercentile[8]\n else \"> p90 (%s)\" % (lspercentile[8])\n for z in x\n ]\n else:\n lspercentile = [\n np.percentile(x, 15),\n np.percentile(x, 50),\n np.percentile(x, 85),\n ]\n res = [\n \"1-Very Low\"\n if z < lspercentile[0]\n else \"2-Low\"\n if z < lspercentile[1]\n else \"3-High\"\n if z < lspercentile[2]\n else \"4-Very High\"\n for z in x\n ]\n return res","title":""},{"docid":"acedf2ba083f29f6b80020e6868514bb","score":"0.527658","text":"def reject_outliers(data, deviation_tolerance=.6745):\n\n data = data.astype('float')\n distance = _np.abs(data - _np.median(data))\n sigma = _np.median(distance) / .6745\n data[distance > deviation_tolerance * sigma] = _np.nan\n return data","title":""},{"docid":"bb9088d7131f4cc995058612c379e69c","score":"0.5273992","text":"def percentile(percent):\n @wraps(percentile)\n def _reduce(iterable):\n sorts = sorted(iterable)\n key = (len(iterable)-1) * percent\n key_floor = math.floor(key)\n key_ceil = math.ceil(key)\n if key_ceil == key_floor:\n return sorts[int(key)]\n d0 = sorts[int(key_floor)] * (key_ceil - key)\n d1 = sorts[int(key_ceil)] * (key - key_floor)\n return d0+d1\n return _reduce","title":""},{"docid":"fd355f06222256f4501b8541b29d6ea9","score":"0.5272074","text":"def delete_outliers(data, y, ids, threshold):\n idxs = np.all(abs(data)<threshold,1)\n\n data_clean = data[idxs,:]\n y_clean = y[idxs]\n ids = ids[idxs]\n\n return data_clean, y_clean, ids","title":""},{"docid":"fd0e085cb04827bb73eeba6f3b4b6a65","score":"0.5260961","text":"def PercentileCalculator(numbers, percentiles=PERCENTILES_LIST):\n\n if not len(numbers): # 'if not numbers' will fail if numbers is a pd.Series.\n raise ValueError(\"Can't compute percentiles of empty list.\")\n\n numbers_sorted = sorted(numbers)\n count = len(numbers_sorted)\n total = sum(numbers_sorted)\n result = {}\n for percentile in percentiles:\n float(percentile) # verify type\n if percentile < 0.0 or percentile > 100.0:\n raise ValueError('Invalid percentile %s' % percentile)\n\n percentile_string = 'p%s' % str(percentile)\n index = int(count * float(percentile) / 100.0)\n index = min(index, count - 1) # Correction to handle 100th percentile.\n result[percentile_string] = numbers_sorted[index]\n\n average = total / float(count)\n result['average'] = average\n if count > 1:\n total_of_squares = sum([(i - average) ** 2 for i in numbers])\n result['stddev'] = (total_of_squares / (count - 1)) ** 0.5\n else:\n result['stddev'] = 0\n\n return result","title":""},{"docid":"6f9ed3a4e0f8796043f33b8b45842ad6","score":"0.525979","text":"def test_Percentiles_group_percentiles_out_creation(self):\n\n self.Percentiles.population_percentiles()\n self.assertTrue(hasattr(self.Percentiles, 'group_percentiles_out'),\n \"\"\"population_percentile_vecs not attribute after population_percentiles run\"\"\")","title":""},{"docid":"4204c997d6d4d1edfb005ca6aa454ce7","score":"0.5253785","text":"def customized_box_plot(percentiles, axes, redraw = True, *args, **kwargs):\n\n box_plot = axes.boxplot([[-9, -4, 2, 4, 9],]*1, *args, **kwargs) \n # Creates len(percentiles) no of box plots\n\n min_y, max_y = float('inf'), -float('inf')\n\n for box_no, (q1_start, \n q2_start,\n q3_start,\n q4_start,\n q4_end,\n fliers_xy) in enumerate(percentiles):\n\n # Lower cap\n box_plot['caps'][2*box_no].set_ydata([q1_start, q1_start])\n # xdata is determined by the width of the box plot\n\n # Lower whiskers\n box_plot['whiskers'][2*box_no].set_ydata([q1_start, q2_start])\n\n # Higher cap\n box_plot['caps'][2*box_no + 1].set_ydata([q4_end, q4_end])\n\n # Higher whiskers\n box_plot['whiskers'][2*box_no + 1].set_ydata([q4_start, q4_end])\n\n # Box\n box_plot['boxes'][box_no].set_ydata([q2_start, \n q2_start, \n q4_start,\n q4_start,\n q2_start])\n\n # Median\n box_plot['medians'][box_no].set_ydata([q3_start, q3_start])\n\n # Outliers\n if fliers_xy is not None and len(fliers_xy[0]) != 0:\n # If outliers exist\n box_plot['fliers'][box_no].set(xdata = fliers_xy[0],\n ydata = fliers_xy[1])\n\n min_y = min(q1_start, min_y, fliers_xy[1].min())\n max_y = max(q4_end, max_y, fliers_xy[1].max())\n\n else:\n min_y = min(q1_start, min_y)\n max_y = max(q4_end, max_y)\n\n # The y axis is rescaled to fit the new box plot completely with 10% \n # of the maximum value at both ends\n axes.set_ylim([min_y*1.1, max_y*1.1])\n\n # If redraw is set to true, the canvas is updated.\n if redraw:\n ax.figure.canvas.draw()\n\n return box_plot","title":""},{"docid":"e29ee683c4cbff437371867018d81da2","score":"0.52533287","text":"def get_y_percentile(self,array_tuple,y_percentile):\n array, = array_tuple\n out_array = np.percentile(array,y_percentile,axis=1,interpolation='lower')\n return out_array","title":""},{"docid":"b9c2e933d3ebc463990449afec4c003b","score":"0.52529764","text":"def _percentile_interval(self, ab_estimates = None):\n ll = np.percentile(ab_estimates, q = (self.alpha/2)*100)\n ul = np.percentile(ab_estimates, q = (1 - self.alpha/2)*100)\n return np.array([ll, ul])","title":""},{"docid":"c1265aaa33c87ef8f239a7fa5ac7fe02","score":"0.5252969","text":"def discard_outliers(x_train, ys_train ):\n index = []\n threshold =8.5\n for i in range(x_train.shape[0]):\n if np.amax(np.abs(x_train[i, :])) > threshold:\n index.append(i)\n x_train = np.delete(x_train, index, 0)\n ys_train = np.delete(ys_train, index, 0)\n return x_train, ys_train","title":""},{"docid":"e4a34155464dc8f1fa99fbfb1696cbe6","score":"0.524914","text":"def cutoff_from_percentile(self, percentile, pos_label=None):\n return (\n pd.Series(self.pred_probas(pos_label))\n .nlargest(int((1 - percentile) * len(self)))\n .min()\n )","title":""},{"docid":"39251c47fdfd3c8926da76078599bac6","score":"0.52269894","text":"def remove_outliers(X: List[Union[int, float]], t: float = 3.5) -> List[Union[int, float]]:\n mean_X = np.mean(X)\n std_X = np.std(X)\n\n good_x = []\n\n for x in X:\n z_score = (x - mean_X) / std_X\n if z_score < t:\n good_x.append(x)\n return good_x","title":""},{"docid":"eb4339cbc964951338524513bb4a9aa6","score":"0.52175856","text":"def get_climatology(self,percentiles):\n self.percentiles=percentiles\n \n lat,lon=self.data[\"clim\"][0].shape[-2:]\n dist=np.zeros([1,lat,lon])\n \n #We call the whole cubelist into memory\n self.data[\"clim\"].realise_data()\n dist=np.concatenate([f.data.reshape([-1,lat,lon]) for f in self.data[\"clim\"]])\n \n self.distribution=np.percentile(dist,percentiles,axis=0)\n self.distribution[0]-=0.01\n \n means=np.zeros([len(percentiles)-1,lat,lon])\n for i in range(len(percentiles)-1):\n for j in range(lat):\n for k in range(lon):\n means[i,j,k]=dist[np.digitize(dist[:,j,k],\\\n self.distribution[:,j,k],right=True)==i+1,j,k].mean()\n #interpolates empty bins as being halfway between the distribution bounds \n for i,j,k in np.argwhere(np.isnan(means)):\n means[i,j,k]=self.distribution[i:i+2,j,k].mean()\n self.dist_means=means","title":""},{"docid":"9ddc868204ce6131d5a0b58ce405f1ef","score":"0.5208818","text":"def normalize99(img):\n X = img.copy()\n X = (X - np.percentile(X, 1)) / (np.percentile(X, 99) - np.percentile(X, 1))\n return X","title":""},{"docid":"dfbcf341c7a8545a9fce1302ba82a2b4","score":"0.52067035","text":"def give_percentiles( scores):\n res = []\n for i in scores:\n res.append( stats.percentileofscore(scores,i))\n return np.array(res)/100","title":""},{"docid":"cd5d511d7f29c7d28a698dd4dd62dfa5","score":"0.51997083","text":"def unconditional_quantile_y(x, alpha_grid, func):\n n_draws = len(x)\n\n # Equation 21a\n y_x = Parallel(n_jobs=8)(delayed(func)(i) for i in x)\n y_x_asc = np.sort(y_x)\n q_index = (np.floor(alpha_grid * n_draws)).astype(int)\n quantile_y_x = y_x_asc[q_index]\n\n return quantile_y_x","title":""},{"docid":"2cfe6064b54e4bafc24a99ab38bf1c59","score":"0.5198672","text":"def SELpercentile(SEL, percentile=5):\n fff = np.percentile(SEL, 100-percentile)\n\n return fff","title":""},{"docid":"64127ee8b40ca7420be9bdd8f31576a0","score":"0.5194027","text":"def _get_grids(feature_values, num_grid_points, grid_type, percentile_range, grid_range):\n\n if grid_type == 'percentile':\n # grid points are calculated based on percentile in unique level\n # thus the final number of grid points might be smaller than num_grid_points\n start, end = 0, 100\n if percentile_range is not None:\n start, end = np.min(percentile_range), np.max(percentile_range)\n\n percentile_grids = np.linspace(start=start, stop=end, num=num_grid_points)\n value_grids = np.percentile(feature_values, percentile_grids)\n\n grids_df = pd.DataFrame()\n grids_df['percentile_grids'] = [round(v, 2) for v in percentile_grids]\n grids_df['value_grids'] = value_grids\n grids_df = grids_df.groupby(['value_grids'], as_index=False).agg(\n {'percentile_grids': lambda v: str(tuple(v)).replace(',)', ')')}).sort_values('value_grids', ascending=True)\n\n feature_grids, percentile_info = grids_df['value_grids'].values, grids_df['percentile_grids'].values\n else:\n if grid_range is not None:\n value_grids = np.linspace(np.min(grid_range), np.max(grid_range), num_grid_points)\n else:\n value_grids = np.linspace(np.min(feature_values), np.max(feature_values), num_grid_points)\n feature_grids, percentile_info = value_grids, []\n\n return feature_grids, percentile_info","title":""},{"docid":"348835087b831c8bd81768b7f613f28e","score":"0.51901513","text":"def remove_ourlier_diff_median(data, median_scaler=25):\n daily_diff = (data.resample('D').last().dropna().diff().abs() + 0.05) * median_scaler\n daily_diff['diff_date'] = daily_diff.index.strftime('%Y-%m-%d')\n data_test = data.diff()\n data_test['diff_date'] = data_test.index.strftime('%Y-%m-%d')\n data_test_diff = pd.merge(data_test, daily_diff, on='diff_date')\n indexer = ((np.abs(data_test_diff['close_x']) < np.abs(data_test_diff['close_y'])) &\n (np.abs(data_test_diff['open_x']) < np.abs(data_test_diff['open_y'])) &\n (np.abs(data_test_diff['high_x']) < np.abs(data_test_diff['high_y'])) & \n (np.abs(data_test_diff['low_x']) < np.abs(data_test_diff['low_y'])))\n # indexer = (indexer | data_test_diff['close_y'].isna())\n data_final = data.loc[indexer.values, :]\n \n return data_final","title":""},{"docid":"995c5947eca7e83cc9b54ac3771c78c6","score":"0.51841736","text":"def calculate_percentiles_from_raster(raster_uri, percentiles):\n raster = gdal.Open(raster_uri, gdal.GA_ReadOnly)\n\n def numbers_from_file(fle):\n \"\"\"Generates an iterator from a file by loading all the numbers\n and yielding\n\n fle = file object\n \"\"\"\n arr = np.load(fle)\n for num in arr:\n yield num\n\n # List to hold the generated iterators\n iters = []\n\n band = raster.GetRasterBand(1)\n nodata = band.GetNoDataValue()\n\n n_rows = raster.RasterYSize\n n_cols = raster.RasterXSize\n\n # Variable to count the total number of elements to compute percentile\n # from. This leaves out nodata values\n n_elements = 0\n\n #Set the row strides to be something reasonable, like 256MB blocks\n row_strides = max(int(2**28 / (4 * n_cols)), 1)\n\n for row_index in xrange(0, n_rows, row_strides):\n #It's possible we're on the last set of rows and the stride\n #is too big, update if so\n if row_index + row_strides >= n_rows:\n row_strides = n_rows - row_index\n\n # Read in raster chunk as array\n arr = band.ReadAsArray(0, row_index, n_cols, row_strides)\n\n tmp_uri = pygeoprocessing.geoprocessing.temporary_filename()\n tmp_file = open(tmp_uri, 'wb')\n # Make array one dimensional for sorting and saving\n arr = arr.flatten()\n # Remove nodata values from array and thus percentile calculation\n arr = np.delete(arr, np.where(arr == nodata))\n # Tally the number of values relevant for calculating percentiles\n n_elements += len(arr)\n # Sort array before saving\n arr = np.sort(arr)\n\n np.save(tmp_file, arr)\n tmp_file.close()\n tmp_file = open(tmp_uri, 'rb')\n tmp_file.seek(0)\n iters.append(numbers_from_file(tmp_file))\n arr = None\n\n # List to store the rank/index where each percentile will be found\n rank_list = []\n # For each percentile calculate nearest rank\n for perc in percentiles:\n rank = math.ceil(perc/100.0 * n_elements)\n rank_list.append(int(rank))\n\n # A variable to burn through when doing heapq merge sort over the\n # iterators. Variable is used to check if we've iterated to a\n # specified rank spot, to grab percentile value\n counter = 0\n # Setup a list of zeros to replace with percentile results\n results = [0] * len(rank_list)\n\n LOGGER.debug('Percentile Rank List: %s', rank_list)\n\n for num in heapq.merge(*iters):\n # If a percentile rank has been hit, grab percentile value\n if counter in rank_list:\n LOGGER.debug('percentile value is : %s', num)\n results[rank_list.index(counter)] = int(num)\n counter += 1\n\n band = None\n raster = None\n return results","title":""},{"docid":"f36baaab4212473d67e29612f8604d9b","score":"0.51838475","text":"def scoreatpercentile(self, independent_data, percentile):\n _groups = self.grouping_function(independent_data)\n result = np.empty(independent_data.shape) * np.nan\n for key in self._models.keys():\n indices = _groups==key\n result[indices] = self._models[key].scoreatpercentile(independent_data[indices], percentile)\n return result","title":""},{"docid":"9031a7091fd6ad983c1b58edca8fb642","score":"0.5173151","text":"def denormalize(x):\n x_max = np.percentile(x, 98)\n x_min = np.percentile(x, 2)\n x = (x - x_min) / (x_max - x_min)\n x = x.clip(0, 1)\n return x","title":""},{"docid":"59cc36d34969979f14bf683d6730c9bd","score":"0.5166004","text":"def clean_outliers(self, dim=3):\n ok_points = []\n not_ok_points = []\n\n if dim != 3:\n distances = [dist(x.point, self.center, d3=False) for x in self.points]\n else:\n distances = [dist(x.point, self.center) for x in self.points]\n stdv = np.std(distances)\n meandis = np.mean(distances)\n for i in range(len(self.points)):\n if distances[i] < (2.5 * stdv + meandis):\n ok_points.append(self.points[i])\n else:\n not_ok_points.append(self.points[i])\n return ok_points, not_ok_points","title":""},{"docid":"c98f4f98d9d6a6d03e4724bd56f71359","score":"0.51487285","text":"def p_threshold(self, percentile):\n\n masked_I, window_I, window_mask, dtc_idx = self._set_up()\n r = np.nanpercentile(masked_I.filled(np.nan), percentile, axis=1)\n\n threshold = np.repeat(r, np.square(2*self.extent)).reshape(masked_I.shape)\n window_I[(window_I > threshold) & (window_mask > 0)] = -1\n\n if len(np.where(np.isnan(r))[0])>0: # deal with blocks for which all pixels are masked\n window_I[np.where(np.isnan(r))] = -1\n \n masked_img = self.indexed[:,3].copy().flatten()\n masked_img[dtc_idx] = window_I\n\n return masked_img","title":""},{"docid":"c79ab36c853ea89f53033a7f16c8e9e7","score":"0.5146289","text":"def set_color_by_percentile(this, start, end):\n if str(this) == 'nan':\n return 'grey'\n if this < start:\n return start\n elif this > end:\n return end\n return this","title":""},{"docid":"49e2ede35dad1a19aff8aa5c18698302","score":"0.5146138","text":"def get_quantile(data):\n # median = DataAnalyser.get_median(data)\n # filtered = [i for i in data if i is not None]\n # filtered.sort()\n # first_quantile = DataAnalyser.get_median([item for item in filtered if item < median])\n # third_quantile = DataAnalyser.get_median([item for item in filtered if item > median])\n\n # return first_quantile, third_quantile\n sorted_data = sorted(data)\n return sorted_data[round(len(data) * 1 / 4)], sorted_data[round(len(data) * 3 / 4)]","title":""},{"docid":"0a292c3d3dba3f26987846e541ee2dc2","score":"0.51320106","text":"def outlierCleaner(predictions, ages, net_worths):\n import numpy as np\n cleaned_data = []\n\n ### your code goes here\n errors = []\n for n in range(0, 89):\n\t\td = abs(net_worths[n] - predictions[n])\n\t\terrors.append(d)\n\t\t\n cut = np.percentile(errors, 90)\n for n in range(0, 89):\n\t\tif errors[n] <= cut:\n\t\t\tcleaned_data.append((ages[n], net_worths[n], errors[n]))\n\t\t\n return cleaned_data","title":""},{"docid":"feccb496e236d83e3c14a06b0d9d0e63","score":"0.5127159","text":"def outliers(self):\n\n limits = self._limits\n column = self._column\n\n values_without_range = list(set((self._df\n .rdd.map(lambda x: x[column])\n .filter(lambda x: x < limits[0] or x > limits[1])\n .collect())))\n\n return values_without_range","title":""},{"docid":"f5613fa6db921a59b6d5071a35bed231","score":"0.51267356","text":"def limit_range(vals, rng):\n if len(vals) > 2:\n rng = sorted(rng)\n lower = np.percentile(vals, rng[0], interpolation=\"nearest\")\n higher = np.percentile(vals, rng[1], interpolation=\"nearest\")\n limited = [x for x in vals if x >= lower and x <= higher]\n return np.array(limited)\n return vals","title":""},{"docid":"e5c9850b5e0ee0092c155edd7dddf93e","score":"0.51224047","text":"def percentile_normalization(userID, cur, sampling_percentiles):\n times = []\n query1 = \"select creation_date from post where owner_user_id = %(id)s and (post_type_id = 1 or post_type_id = 2) order by creation_date\"\n cur.execute(query1, {'id': userID})\n posts = [i[0] for i in results(cur)]\n start = get_start_time(userID, cur)\n for p in sampling_percentiles:\n x = int(len(posts) * p)\n if x == 0:\n times.append(start)\n else:\n times.append(posts[x-1])\n return times","title":""},{"docid":"27bb7f867042dfa2e0da50ec7db00d5d","score":"0.51217884","text":"def do_polarization_clip_check(self, naxis, lower, upper):\n\n # round floats to individual pixels\n p1 = math.floor(lower)\n p2 = math.ceil(upper)\n\n # bounds check\n if p1 < 1:\n p1 = 1\n if p2 > naxis:\n p2 = naxis\n\n # validity check, no pixels included\n if p1 > naxis or p2 < 1:\n error = 'pixels coordinates {}:{} do not intersect {} to {}'\\\n .format(p1, p2, 1, naxis)\n raise NoContentError(error)\n\n # an actual cutout\n return [p1, p2]","title":""},{"docid":"fa3944c9fc1141dc504b1e5b67af8842","score":"0.51175594","text":"def compute_bounds(xmin, xmax, xv):\n\n if xmin is not None or xmax is not None:\n if type(xmin) == str and xmin.startswith(\"percentile\"):\n xmin = np.nanpercentile(xv, float(xmin[11:-1]))\n if type(xmax) == str and xmax.startswith(\"percentile\"):\n xmax = np.nanpercentile(xv, float(xmax[11:-1]))\n\n if xmin is None or xmin == np.nanmin(xv):\n xmin = np.nanmin(xv) - (xmax - np.nanmin(xv))/20\n if xmax is None or xmax == np.nanmax(xv):\n xmax = np.nanmax(xv) + (np.nanmax(xv) - xmin)/20\n\n return xmin, xmax","title":""},{"docid":"d8717c6087944b7865283628348f7811","score":"0.51162624","text":"def scoreatpercentile(a, per, limit=(), interpolation_method='fraction'):\n values = np.sort(a, axis=0)\n if limit:\n values = values[(limit[0] <= values) & (values <= limit[1])]\n\n idx = per /100. * (values.shape[0] - 1)\n if (idx % 1 == 0):\n score = values[int(idx)]\n else:\n if interpolation_method == 'fraction':\n score = _interpolate(values[int(idx)], values[int(idx) + 1],\n idx % 1)\n elif interpolation_method == 'lower':\n score = values[np.floor(idx)]\n elif interpolation_method == 'higher':\n score = values[np.ceil(idx)]\n else:\n raise ValueError(\"interpolation_method can only be 'fraction', \" \\\n \"'lower' or 'higher'\")\n\n return score","title":""},{"docid":"21c0fbeb06e0114dc2b0524b60fa55d1","score":"0.5093741","text":"def unique_percentiles_beta(perc_values,\n percentiles):\n # normalize between 0 and 1\n uniq, uniq_ind, counts = np.unique(\n perc_values, return_index=True, return_counts=True)\n if len(uniq) != len(perc_values):\n min_value = np.min(perc_values)\n perc_values = perc_values - min_value\n max_value = np.max(perc_values)\n perc_values = perc_values / max_value\n percentiles = np.asanyarray(percentiles)\n percentiles = percentiles / 100.0\n\n p, ier = sc_opt.curve_fit(betainc,\n percentiles,\n perc_values)\n uniq_perc_values = sc_special.betainc(p[0], p[1], percentiles)\n uniq_perc_values = uniq_perc_values * max_value + min_value\n else:\n uniq_perc_values = perc_values\n return uniq_perc_values","title":""},{"docid":"a24c0f72c5371263e914a0aa2c6875cd","score":"0.50732625","text":"def pdf_percentiles(self, percentile, **kwargs):\n percentile = u.Quantity(percentile, u.percent).value\n percs = np.percentile(self.distribution, percentile, axis=-1, **kwargs)\n # numpy.percentile strips units for unclear reasons, so we have to make\n # a new object with units\n if hasattr(self.distribution, \"_new_view\"):\n return self.distribution._new_view(percs)\n else:\n return percs","title":""}],"string":"[\n {\n \"docid\": \"4b77098adb812cbb946d3a783f6e7601\",\n \"score\": \"0.6658416\",\n \"text\": \"def test_exclude_percentile(self):\\n data = np.copy(DATA)\\n data[0:50, 0:50] = np.nan\\n with pytest.warns(AstropyUserWarning,\\n match='Input data contains invalid values'):\\n bkg = Background2D(data, (25, 25), filter_size=(1, 1),\\n exclude_percentile=100.0)\\n assert len(bkg._box_idx) == 12\",\n \"title\": \"\"\n },\n {\n \"docid\": \"690c031e82bbcca291c6dd4b0d44d1d8\",\n \"score\": \"0.6491309\",\n \"text\": \"def remove_outliers(arr, z=1.5):\\n\\n q25, q75 = np.percentile(arr, 25), np.percentile(arr, 75)\\n iqr = q75 - q25\\n limit = z * iqr\\n return [a for a in arr if a >= q25 - limit and a <= q75 + limit]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adf5bbd83ad86af55b8d74ab46c9af9c\",\n \"score\": \"0.63799745\",\n \"text\": \"def imenhance(x, percentiles=[5, 95]):\\n isf = np.isfinite(x)\\n (vmin, vmax) = np.percentile(x[isf], percentiles)\\n y = x\\n y[x < vmin] = vmin\\n y[x > vmax] = vmax\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92ebe1e8b48c3d46c038adb557df00af\",\n \"score\": \"0.6334107\",\n \"text\": \"def test_percentiles_axis():\\n data = rs.randn(10, 10)\\n\\n # Test against the median with 50th percentile\\n median1 = np.median(data)\\n out1 = stat.percentiles(data, 50)\\n assert_array_almost_equal(median1, out1)\\n\\n for axis in range(2):\\n median2 = np.median(data, axis=axis)\\n out2 = stat.percentiles(data, 50, axis=axis)\\n assert_array_almost_equal(median2, out2)\\n\\n median3 = np.median(data, axis=0)\\n out3 = stat.percentiles(data, [50, 95], axis=0)\\n assert_array_almost_equal(median3, out3[0])\\n assert_equal(2, len(out3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df28551e5a35358c50aa0af5768f0865\",\n \"score\": \"0.6319891\",\n \"text\": \"def remove_outliers(data, col):\\n def q1(x):\\n q = np.percentile(x,25)\\n return q\\n\\n def q3(x):\\n q = np.percentile(x,75)\\n return q\\n\\n IR = q3(data[col])-q1(data[col])\\n outliers_l = q1(data[col])-1.5*IR\\n outliers_u = q3(data[col])+1.5*IR\\n\\n data = data[(data[col]>=outliers_l) & (data[col]<=outliers_u)]\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"965cdb1d2fe6391b707574a6e5a476ac\",\n \"score\": \"0.6246908\",\n \"text\": \"def percentiles(data, lo, hi):\\n max = data.max()\\n dataArray = data.flatten()\\n pHi = numpy.percentile(dataArray, hi)\\n pLo = numpy.percentile(dataArray, lo)\\n range = pHi - pLo\\n scale = range/255\\n data = numpy.clip(data, pLo, pHi)\\n data-= pLo\\n data/=scale\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"897e1187aadee202f1d69c6d3a7acfd8\",\n \"score\": \"0.6207559\",\n \"text\": \"def remove_outliers(data):\\n\\tdata.sort()\\n\\tnumPointsToRemove = (5*len(data))/100\\n\\treturn data[numPointsToRemove:len(data)-numPointsToRemove]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfd45fb0db06a450ac01eb83bbfc6cbd\",\n \"score\": \"0.6200231\",\n \"text\": \"def get_percentiles(self):\\n\\n if self.xcube is None:\\n self.xpercentiles = self.quantiles\\n else:\\n data = self.xcube.data\\n if np.sum(np.isfinite(data)) == 0:\\n #All data is nan, cannot calculate percentiles\\n raise ValueError('Data for x-axis all nan')\\n self.xpercentiles = np.percentile(data[~np.isnan(data)],\\n q=self.quantiles)\\n\\n for line in self.lines:\\n data = line['cube'].data\\n if np.sum(np.isfinite(data)) == 0:\\n #All data is nan, cannot calculate percentiles\\n warnings.warn('All nan data')\\n continue\\n line['ypercentiles'] = np.percentile(data[~np.isnan(data)],\\n q=self.quantiles)\\n\\n return self.lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"925bcfffbd57ecbc41a6162e08cfb24e\",\n \"score\": \"0.6165104\",\n \"text\": \"def test_Data_percentile_median(self):\\n # ranks: a sequence of percentile rank inputs. NOTE: must\\n # include 50 as the last input so that cf.Data.median is also\\n # tested correctly.\\n ranks = ([30, 60, 90], [90, 30], [20])\\n ranks = ranks + (50,)\\n\\n d = cf.Data(self.a, chunks=(2, 2, 3, 5))\\n\\n for axis in [None] + self.axes_combinations:\\n for keepdims in (True, False):\\n for q in ranks:\\n a1 = np.percentile(d, q, axis=axis, keepdims=keepdims)\\n b1 = d.percentile(q, axes=axis, squeeze=not keepdims)\\n self.assertEqual(b1.shape, a1.shape)\\n self.assertTrue((b1.array == a1).all())\\n\\n # Check that the _axes attribute has been updated\\n # for the new rank dimension, where appropriate.\\n if keepdims:\\n if isinstance(q, list):\\n self.assertEqual(len(b1._axes), len(d._axes) + 1)\\n else:\\n self.assertEqual(len(b1._axes), len(d._axes))\\n\\n # Masked data\\n a = self.ma\\n filled = np.ma.filled(a, np.nan)\\n d = cf.Data(self.ma, chunks=(2, 2, 3, 5))\\n\\n with np.testing.suppress_warnings() as sup:\\n sup.filter(\\n category=RuntimeWarning,\\n message=\\\".*All-NaN slice encountered.*\\\",\\n )\\n for axis in [None] + self.axes_combinations:\\n for keepdims in (True, False):\\n for q in ranks:\\n a1 = np.nanpercentile(\\n filled, q, axis=axis, keepdims=keepdims\\n )\\n mask = np.isnan(a1)\\n if mask.any():\\n a1 = np.ma.masked_where(mask, a1, copy=False)\\n\\n b1 = d.percentile(q, axes=axis, squeeze=not keepdims)\\n self.assertEqual(b1.shape, a1.shape)\\n self.assertTrue((b1.array == a1).all())\\n\\n # Check for no warning when data is of masked type but with no\\n # missing values\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"error\\\", category=UserWarning)\\n d = cf.Data(np.ma.arange(100))\\n d.percentile(ranks[0])\\n\\n # Test scalar input (not masked)\\n a = np.array(9)\\n d = cf.Data(a)\\n for keepdims in (True, False):\\n for q in ranks:\\n a1 = np.nanpercentile(a, q, keepdims=keepdims)\\n b1 = d.percentile(q, squeeze=not keepdims)\\n self.assertEqual(b1.shape, a1.shape)\\n self.assertTrue((b1.array == a1).all())\\n\\n # Test scalar input (masked)\\n a = np.ma.array(9, mask=True)\\n filled = np.ma.filled(a.astype(float), np.nan)\\n d = cf.Data(a)\\n\\n with np.testing.suppress_warnings() as sup:\\n sup.filter(\\n category=RuntimeWarning,\\n message=\\\".*All-NaN slice encountered.*\\\",\\n )\\n for keepdims in (True, False):\\n for q in ranks:\\n a1 = np.nanpercentile(filled, q, keepdims=keepdims)\\n mask = np.isnan(a1)\\n if mask.any():\\n a1 = np.ma.masked_where(mask, a1, copy=False)\\n\\n b1 = d.percentile(q, squeeze=not keepdims)\\n self.assertEqual(b1.shape, a1.shape)\\n self.assertTrue(\\n (b1.array == a1).all() in (True, np.ma.masked)\\n )\\n\\n # Test mtol=1\\n d = cf.Data(self.a)\\n d[...] = cf.masked # All masked\\n for axis in [None] + self.axes_combinations:\\n for q in ranks:\\n e = d.percentile(q, axes=axis, mtol=1)\\n self.assertFalse(np.ma.count(e.array, keepdims=True).any())\\n\\n a = np.ma.arange(12).reshape(3, 4)\\n d = cf.Data(a)\\n d[1, -1] = cf.masked # 1 value masked\\n for q in ranks:\\n e = d.percentile(q, mtol=1)\\n self.assertTrue(np.ma.count(e.array, keepdims=True).all())\\n\\n # Test mtol=0\\n for q in ranks:\\n e = d.percentile(q, mtol=0)\\n self.assertFalse(np.ma.count(e.array, keepdims=True).any())\\n\\n # Test mtol=0.1\\n for q in ranks:\\n e = d.percentile(q, axes=0, mtol=0.1)\\n self.assertEqual(np.ma.count(e.array), 3 * e.shape[0])\\n\\n for q in ranks[:-1]: # axis=1: exclude the non-sequence rank\\n e = d.percentile(q, axes=1, mtol=0.1)\\n self.assertEqual(np.ma.count(e.array), 2 * e.shape[0])\\n\\n q = ranks[-1] # axis=1: test the non-sequence rank\\n e = d.percentile(q, axes=1, mtol=0.1)\\n self.assertEqual(np.ma.count(e.array), e.shape[0] - 1)\\n\\n # Check invalid ranks (those not in [0, 100])\\n for q in (-9, [999], [50, 999], [999, 50]):\\n with self.assertRaises(ValueError):\\n d.percentile(q).array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55d47aebe60f31e69332e8e7761f80dd\",\n \"score\": \"0.60653436\",\n \"text\": \"def outliers(data):\\n if len(data) < 2:\\n raise RuntimeError('Too few values given to outliers')\\n\\n first_quartile = lower_quartile(data)\\n third_quartile = upper_quartile(data)\\n iqr = interquartile_range(data)\\n\\n lower_limit = (first_quartile - 1.5 * iqr)\\n upper_limit = (third_quartile + 1.5 * iqr)\\n\\n results = []\\n for each in data:\\n if each < lower_limit or each > upper_limit:\\n results.append(each)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"918db9d30412dc8457e0d2782689ae67\",\n \"score\": \"0.6020458\",\n \"text\": \"def ml_percentile(in_data, percentiles):\\n\\n data = np.sort(in_data)\\n p_rank = 100.0 * (np.arange(data.size) + 0.5) / data.size\\n perc = np.interp(percentiles, p_rank, data, left=data[0], right=data[-1])\\n return perc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c246046e0f4f4609bc9c7dc39552eef3\",\n \"score\": \"0.59835273\",\n \"text\": \"def xr_percentile(\\n src: xr.Dataset,\\n percentiles: Sequence,\\n nodata,\\n) -> xr.Dataset:\\n\\n data_vars = {}\\n for band, xx in src.data_vars.items():\\n \\n xx_data = xx.data\\n if len(xx.chunks[0]) > 1:\\n xx_data = xx_data.rechunk({0: -1})\\n \\n tk = tokenize(xx_data, percentiles, nodata)\\n for percentile in percentiles:\\n name = f\\\"{band}_pc_{int(100 * percentile)}\\\"\\n yy = da.map_blocks(\\n partial(np_percentile, percentile=percentile, nodata=nodata), \\n xx_data, \\n drop_axis=0, \\n meta=np.array([], dtype=xx.dtype),\\n name=f\\\"{name}-{tk}\\\",\\n )\\n data_vars[name] = xr.DataArray(yy, dims=xx.dims[1:], attrs=xx.attrs)\\n \\n coords = dict((dim, src.coords[dim]) for dim in xx.dims[1:])\\n return xr.Dataset(data_vars=data_vars, coords=coords, attrs=src.attrs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1235390688f6d6402d53315d77f32d98\",\n \"score\": \"0.5935325\",\n \"text\": \"def spatial_percentile(percentile, points_on_line_x, points_on_line_y):\\n assert 0. <= percentile <= 1.\\n # First, uniquely associate each value with its spacing. This is just a\\n # trapezium rule approach:\\n spacing_each_trapezium = points_on_line_x[1:] - points_on_line_x[:-1]\\n val_at_each_trapezium = (points_on_line_y[:-1] + points_on_line_y[1:]) / 2.\\n area_each_trapezium = spacing_each_trapezium * val_at_each_trapezium\\n # order the data by y:\\n order_for_vals = np.argsort(val_at_each_trapezium)\\n accumulating_areas = np.cumsum(area_each_trapezium[order_for_vals])\\n total_area = accumulating_areas[-1]\\n # find the percentile in the accumulating areas:\\n pc_position = np.searchsorted(accumulating_areas,\\n percentile * total_area)\\n pc_value = val_at_each_trapezium[order_for_vals][pc_position]\\n return pc_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6eb8ad3be077c348dc6822514b4e758\",\n \"score\": \"0.5928688\",\n \"text\": \"def test_percentiles():\\n single_val = 5\\n single = stat.percentiles(a_range, single_val)\\n assert_equal(single, single_val)\\n\\n multi_val = [10, 20]\\n multi = stat.percentiles(a_range, multi_val)\\n assert_array_equal(multi, multi_val)\\n\\n array_val = rs.randint(0, 101, 5).astype(float)\\n array = stat.percentiles(a_range, array_val)\\n assert_array_almost_equal(array, array_val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c710a050a4aa28f1b6107d3277bb3747\",\n \"score\": \"0.5895379\",\n \"text\": \"def get_smoothed_y_percentiles(self,i,imported_array_list,y_percentile,smoothing_kernel_y):\\n imported_array = imported_array_list[i]\\n y_percentiles = np.percentile(imported_array[0],y_percentile,axis=1,interpolation='lower')\\n y_percentiles_smoothed = self.median_filter_2d(y_percentiles,smoothing_kernel_y)\\n return y_percentiles_smoothed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad1d0f6450ba6fad33c43bbe8324a4e4\",\n \"score\": \"0.58534145\",\n \"text\": \"def clip_extreme(imgs, percentile=5, allowed_steepness=3, dim=2):\\n if isinstance(imgs, torch.Tensor):\\n return_torch = True\\n device = imgs.device\\n dtype = imgs.dtype\\n imgs = imgs.cpu().numpy()\\n else:\\n return_torch=False\\n img_shape = imgs.shape[-dim:]\\n imgs = imgs.reshape(imgs.shape[:-dim] + (-1,))\\n # mins = np.min(imgs, axis=-1)\\n # maxs = np.max(imgs, axis=-1)\\n soft_mins = np.percentile(imgs, percentile, axis=-1)\\n soft_maxs = np.percentile(imgs, 100-percentile, axis=-1)\\n ranges = soft_maxs - soft_mins\\n print(ranges)\\n allowed_extra = ranges * percentile/100 * allowed_steepness\\n imgs = imgs.clip((soft_mins - allowed_extra)[..., None],\\n (soft_maxs - allowed_extra)[..., None])\\n imgs = imgs.reshape(imgs.shape[:-1] + img_shape)\\n if return_torch:\\n return torch.tensor(imgs, dtype=dtype).to(device)\\n else:\\n return imgs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c5768465361f53b282749aded920a05\",\n \"score\": \"0.5836667\",\n \"text\": \"def test_Data_mean_of_upper_decile(self):\\n # Masked array, non-masked weights\\n a = self.ma\\n weights = self.w\\n d = cf.Data(a, \\\"m\\\", chunks=(2, 3, 2, 5))\\n\\n for axis in axis_combinations(a):\\n b = reshape_array(a, axis)\\n w = reshape_array(weights, axis)\\n b = np.ma.filled(b, np.nan)\\n with np.testing.suppress_warnings() as sup:\\n sup.filter(\\n category=RuntimeWarning,\\n message=\\\".*All-NaN slice encountered.*\\\",\\n )\\n sup.filter(\\n category=UserWarning,\\n message=\\\"Warning: 'partition' will ignore the 'mask' of the MaskedArray.*\\\",\\n )\\n p = np.nanpercentile(b, 90, axis=-1, keepdims=True)\\n\\n b = np.ma.masked_where(np.isnan(b), b, copy=False)\\n p = np.where(np.isnan(p), b.max() + 1, p)\\n\\n with np.testing.suppress_warnings() as sup:\\n sup.filter(\\n category=UserWarning,\\n message=\\\"Warning: 'partition' will ignore the 'mask' of the MaskedArray.*\\\",\\n )\\n b = np.ma.where(b < p, np.ma.masked, b)\\n\\n b = np.ma.average(b, axis=-1, weights=w)\\n b = np.ma.asanyarray(b)\\n\\n e = d.mean_of_upper_decile(\\n axes=axis, weights=weights, squeeze=True\\n )\\n with np.testing.suppress_warnings() as sup:\\n sup.filter(\\n category=UserWarning,\\n message=\\\"Warning: 'partition' will ignore the 'mask' of the MaskedArray.*\\\",\\n )\\n e = e.array\\n\\n self.assertTrue(\\n (np.ma.getmaskarray(e) == np.ma.getmaskarray(b)).all()\\n )\\n self.assertTrue(np.allclose(e, b))\\n\\n # mtol\\n a[0, 0] = cf.masked\\n d = cf.Data(a, \\\"m\\\", chunks=3)\\n e = d.mean_of_upper_decile(mtol=0)\\n self.assertEqual(e.array.mask, True)\\n\\n # Inplace\\n self.assertIsNone(d.mean_of_upper_decile(inplace=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c97fa74d90556805b662a2bd23ac1100\",\n \"score\": \"0.5801683\",\n \"text\": \"def pruning_with_percentile(self, q: float):\\n if len(self._weight_mask) == 0:\\n return\\n for name, param in self.named_parameters():\\n if 'weight' not in name:\\n # skip the other type weights\\n continue\\n cpu_param = param.cpu()\\n old_mask = self._weight_mask[id(param)][0]\\n percentile_value = percentile(tensor_nonzero(cpu_param * old_mask), q)\\n new_mask = torch.where(cpu_param.abs() < percentile_value,\\n torch.zeros_like(old_mask), old_mask)\\n param.data = param.data * new_mask.to(self._device)\\n self._weight_mask.update({id(param) : (new_mask, name)})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93ea1c713b33c7800b321a2f171e25ef\",\n \"score\": \"0.57994294\",\n \"text\": \"def clamp(img, percentile, normalize=False):\\n img_ = np.copy(img)\\n img_[img_ > np.percentile(img_, percentile)] = np.percentile(img_, percentile)\\n if normalize:\\n out = img_ / img_.max()\\n return out\\n elif not normalize:\\n out = img_\\n return out\\n else:\\n raise ValueError('Normalize must be either True or False.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8afc2470e658da8fc0766cb9a60bd9c\",\n \"score\": \"0.57960975\",\n \"text\": \"def preprocess_minmax(img: np.array) -> np.array:\\n im_min = np.percentile(img, 2)\\n im_max = np.percentile(img, 98)\\n im_range = im_max - im_min\\n # print(f'percentile 2 {im_min}, percentile 98 {im_max}, im_range {im_range}')\\n\\n # Normalise to the percentile\\n img = img.astype(np.float32)\\n img = (img - im_min) / im_range\\n img = img.clip(0, 1)\\n\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eca5641570a5c15dcdb9fb53bcf5d9c\",\n \"score\": \"0.5781637\",\n \"text\": \"def clean_data(data,min=0,max=100,sd=5):\\n data = data.where((data>min) & (data<max))\\n data = data.where(reject_outliers(data,sd))\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2d68303713daf03076c4b5f4cfa6d49\",\n \"score\": \"0.57463306\",\n \"text\": \"def grid_from_X(x, percentiles=(0.05, 0.95), grid_resolution=100):\\r\\n x = x[~x.isnull()]\\r\\n if len(percentiles) != 2:\\r\\n raise ValueError('percentile must be tuple of len 2')\\r\\n if not all(0. <= x <= 1. for x in percentiles):\\r\\n raise ValueError('percentile values must be in [0, 1]')\\r\\n\\r\\n uniques = np.unique(x)\\r\\n if uniques.shape[0] < grid_resolution:\\r\\n # feature has low resolution use unique vals\\r\\n return uniques\\r\\n else:\\r\\n emp_percentiles = mquantiles(x, prob=percentiles)\\r\\n # create axis based on percentiles and grid resolution\\r\\n return np.linspace(emp_percentiles[0],\\r\\n emp_percentiles[1],\\r\\n num=grid_resolution, endpoint=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4121c3d623f7dc2931c219f7c70549b8\",\n \"score\": \"0.5726104\",\n \"text\": \"def delete_outliers(self):\\n\\n limits = self._limits\\n column = self._column\\n\\n func = lambda x: (x >= limits[0]) & (x <= limits[1])\\n self._df = self._df.filter(func(col(column)))\\n\\n return self._df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7007c98f46d2533fc8d1c070f3c5e27\",\n \"score\": \"0.57132715\",\n \"text\": \"def _clip(\\n image: xr.DataArray, p_min: int, p_max: int,\\n ) -> xr.DataArray:\\n v_min, v_max = np.percentile(image, [p_min, p_max])\\n\\n image = image.clip(min=v_min, max=v_max)\\n\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7caf754da40e8e99d9713f3806959419\",\n \"score\": \"0.56986296\",\n \"text\": \"def robustScale(x, cols: list = []):\\n if not cols:\\n cols = list(range(x.shape[1]))\\n top = x[:, cols] - np.percentile(x[:, cols], 25, axis=0)\\n bot = np.percentile(x[:, cols], 75, axis=0) - np.percentile(x[:, cols], 25, axis=0)\\n x[:, cols] = top / bot\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2ae1ab2426fcc4c14b2cbf19c15561a\",\n \"score\": \"0.56981295\",\n \"text\": \"def unique_percentiles_interpolate(perc_values,\\n percentiles=[0, 5, 10, 30, 50,\\n 70, 90, 95, 100],\\n k=1):\\n uniq_ind = np.sort(np.unique(perc_values, return_index=True)[1])\\n if len(uniq_ind) == 1:\\n uniq_ind = np.repeat(uniq_ind, 2)\\n uniq_ind[-1] = len(percentiles) - 1\\n uniq_perc_values = perc_values[uniq_ind]\\n inter = sc_int.InterpolatedUnivariateSpline(\\n np.array(percentiles)[uniq_ind],\\n uniq_perc_values,\\n k=k, ext=0,\\n check_finite=True)\\n uniq_perc_values = inter(percentiles)\\n return uniq_perc_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1fa46cb22bbd1235f4184633a82ef43\",\n \"score\": \"0.569696\",\n \"text\": \"def _calc_percentile(self, fitted_values: np.ndarray) -> np.ndarray:\\n return np.percentile(fitted_values, self.params.quantile, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffb16cbf4a6c8f649711c9d4d23cc132\",\n \"score\": \"0.56882125\",\n \"text\": \"def _percentiles(t, q=[15., 50., 85.]):\\n return tfp.stats.percentile(t, q, axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd7a14d064af1e1ef33811226916d7e2\",\n \"score\": \"0.5669077\",\n \"text\": \"def remove_outliers(self, threshold=5):\\n for i in range(len(self)):\\n self[i] = self[i].remove_outliers(threshold=threshold)\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5ebd6452b751e851f84e51d4723efa5\",\n \"score\": \"0.5598348\",\n \"text\": \"def _percentile_values(y_samples, percentile_step):\\n percentiles = range(0, 100 + percentile_step, percentile_step)\\n assert len(percentiles) % 2 == 1\\n assert 0 in percentiles\\n assert 50 in percentiles\\n assert 100 in percentiles\\n\\n if isinstance(y_samples, np.ndarray):\\n assert len(y_samples.shape) == 2, 'y_samples must be a 2D numpy array or a list of lists of numbers.'\\n assert len(y_samples) > 0, 'y_samples must not be empty.'\\n assert len(y_samples[0]) > 0, 'y_samples must not be empty.'\\n else:\\n assert isinstance(y_samples, list), 'y_samples must be a 2D numpy array or a list of lists of numbers.'\\n assert y_samples, 'y_samples must not be empty.'\\n for ys in y_samples:\\n assert isinstance(ys, list), 'y_samples must be a 2D numpy array or a list of lists of numbers.'\\n assert ys, 'y_samples must not have empty inner lists, each X value must have at least one Y sample.'\\n if len(set(len(ys) for ys in y_samples)) == 1:\\n # All y_samples rows have the same length. Can convert into a numpy array for fast percentile computation.\\n y_samples = np.array(y_samples)\\n\\n # y_list is a list of arrays. Each list is for a percentile, each array element is y value at x.\\n if isinstance(y_samples, np.ndarray):\\n # y_samples is a numpy array, can compute percentiles in one go.\\n y_list = np.percentile(y_samples, percentiles, axis=1)\\n else:\\n # y_samples is a list of lists of uneven lengths. Slow mode.\\n logger.warning('Slow percentile computation: consider switching to 2D numpy arrays.')\\n num_x = len(y_samples)\\n y_list = []\\n for p in percentiles:\\n y_list.append(np.zeros(num_x, dtype=np.float64))\\n for i in xrange(num_x):\\n y_list[-1][i] = np.percentile(y_samples[i], p)\\n\\n assert len(y_list) == len(percentiles)\\n return percentiles, y_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dea2bf60eab93913a5c207d58a22128\",\n \"score\": \"0.5596752\",\n \"text\": \"def percentile(x, ys):\\n sz_y = len(ys)\\n if sz_y == 0:\\n return -1\\n elif sz_y == 1:\\n return 0.\\n else:\\n return sum(y < x for y in ys) / float(len(ys) - 1) * 100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01bd0cce8d8200e1383c516aa95d44cf\",\n \"score\": \"0.5595679\",\n \"text\": \"def clipOutliers(self,limits=(1,99),percentage=True,action='noclipmedian'):\\n if np.isscalar(limits):\\n limits=(-limits,limits)\\n elif len(limits)==2:\\n pass\\n else:\\n raise ValueError('unrecognized limit form')\\n \\n im=self._active\\n \\n if percentage:\\n mi,ma=np.min(im),np.max(im)\\n rng=ma-mi\\n limits=(mi+limits[0]*rng/100,mi+limits[1]*rng/100)\\n \\n clipcond=np.logical_or(im>limits[1],im<limits[0])\\n self._active,nclip=self._repl_inds(action,clipcond)\\n \\n self._changed = True\\n return nclip\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d315a6018dce115c8216237f89608357\",\n \"score\": \"0.55749416\",\n \"text\": \"def get_outliers(values):\\n # drop NAs \\n values = values.dropna()\\n names = values.index\\n # calculate percentiles\\n p25 = np.percentile(values, 25)\\n p75 = np.percentile(values, 75)\\n # initiate empty lists \\n indices_of_outliers = []\\n values_of_outliers = []\\n # for each value \\n for value, name in zip(values, names):\\n # apply is_extreme\\n if is_extreme(value, p25, p75):\\n # get index and value\\n indices_of_outliers.append(name)\\n values_of_outliers.append(value)\\n outliers = zip(indices_of_outliers, values_of_outliers)\\n #outliers.sort(key = lambda t: t[1])\\n return outliers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d915e3ef7557429ff895008d4251a249\",\n \"score\": \"0.5572701\",\n \"text\": \"def percentile_for(percentile: int) -> StatFunction:\\n if percentile < 0 or percentile > 100:\\n raise TypeError('percentile must be between 0 and 100, inclusive')\\n\\n def percentile_(series: pd.Series):\\n return series.quantile(percentile / 100)\\n percentile_.__name__ = 'percentile_{:2.0f}'.format(percentile)\\n return percentile_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba315638fa3d0873cac3162f5353d84\",\n \"score\": \"0.5549827\",\n \"text\": \"def imshowe(x, percentiles=[5, 95], **kwargs):\\n isf = np.isfinite(x)\\n if np.any(isf):\\n (vmin, vmax) = np.percentile(x[isf], percentiles)\\n else:\\n logger.warning('No finite numbers in array')\\n vmin = 0\\n vmax = 1\\n if 'percentiles' in kwargs:\\n del(kwargs['percentiles'])\\n return imshow(x, vmin=vmin, vmax=vmax, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07bfdb5921b3b1d200239e3fb4ea0e7f\",\n \"score\": \"0.55362695\",\n \"text\": \"def __init__(self, img, percentiles=[1,99]):\\n assert len(percentiles) == 2\\n vmin, vmax = np.percentile(img, percentiles)\\n super().__init__(vmin=vmin, vmax=vmax)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54735f7bd2b6ba02a042daeb00c4096\",\n \"score\": \"0.55264735\",\n \"text\": \"def remove_outliers(tX):\\n clean_data = []\\n for f in tX.T:\\n mean = np.mean(f, axis=0)\\n std = np.std(f, axis=0)\\n edge = std * 3\\n lowerb = mean - edge\\n upperb = mean + edge\\n for i,x in enumerate(f):\\n if x<lowerb:\\n f[i]=lowerb\\n elif x>upperb:\\n f[i]=upperb\\n \\n return tX\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6dc237a9d11dd5d0848b6e5b3a57a2a\",\n \"score\": \"0.55225736\",\n \"text\": \"def scale_percentile(matrix):\\n orig_shape = matrix.shape\\n matrix = np.reshape(matrix, [matrix.shape[0]*matrix.shape[1], 3]).astype(float)\\n \\n # Get 2nd and 98th percentile\\n mins = np.percentile(matrix, 1, axis=0)\\n maxs = np.percentile(matrix, 99, axis=0) - mins\\n \\n matrix = (matrix - mins[None,:])/maxs[None,:]\\n matrix = np.reshape(matrix, orig_shape)\\n matrix = matrix.clip(0,1)\\n return matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"099a0f064db0ba5d962635dde9214114\",\n \"score\": \"0.55035377\",\n \"text\": \"def remove_outliers(\\r\\n data,\\r\\n method: str = \\\"gaussian\\\",\\r\\n cutoff=3,\\r\\n skip: Optional[Union[str, List[str]]] = None,\\r\\n only: Optional[Union[str, List[str]]] = None,\\r\\n):\\r\\n # Copy to avoid replacing in-place\\r\\n data = data.copy(deep=True)\\r\\n\\r\\n # Which columns\\r\\n columns = _validate_skip_only(data, skip, only)\\r\\n is_continuous = _get_dtypes(data) == \\\"continuous\\\"\\r\\n columns = columns & is_continuous\\r\\n\\r\\n # Check cutoff and method, printing what is being done\\r\\n if cutoff <= 0:\\r\\n raise ValueError(\\\"'cutoff' must be >= 0\\\")\\r\\n if method == \\\"iqr\\\":\\r\\n click.echo(\\r\\n f\\\"Removing outliers from {len(data):,} observations of {columns.sum():,} continuous variables \\\"\\r\\n f\\\"with values < 1st Quartile - ({cutoff} * IQR) or > 3rd quartile + ({cutoff} * IQR)\\\"\\r\\n )\\r\\n elif method == \\\"gaussian\\\":\\r\\n click.echo(\\r\\n f\\\"Removing outliers from {len(data):,} observations of {columns.sum():,} continuous variables \\\"\\r\\n f\\\"with values more than {cutoff} standard deviations from the mean\\\"\\r\\n )\\r\\n else:\\r\\n raise ValueError(\\r\\n f\\\"'{method}' is not a supported method for outlier removal - only 'gaussian' and 'iqr'.\\\"\\r\\n )\\r\\n\\r\\n # Remove outliers\\r\\n # Note: This could be faster by performing calculations on the entire dataset at once, but in practice this should\\r\\n # be used on more of a limited basis, reviewing changes in each variable.\\r\\n for col_name, process_col in columns.iteritems():\\r\\n if not process_col:\\r\\n continue\\r\\n if method == \\\"iqr\\\":\\r\\n q1 = data[col_name].quantile(0.25)\\r\\n q3 = data[col_name].quantile(0.75)\\r\\n iqr = abs(q3 - q1)\\r\\n bottom = q1 - (iqr * cutoff)\\r\\n top = q3 + (iqr * cutoff)\\r\\n elif method == \\\"gaussian\\\":\\r\\n mean = data[col_name].mean()\\r\\n std = data[col_name].std()\\r\\n bottom = mean - (std * cutoff)\\r\\n top = mean + (std * cutoff)\\r\\n # Replace with NA\\r\\n outliers_bottom = data[col_name] < bottom\\r\\n outliers_top = data[col_name] > top\\r\\n data.loc[outliers_bottom, col_name] = np.nan\\r\\n data.loc[outliers_top, col_name] = np.nan\\r\\n # Log\\r\\n click.echo(\\r\\n f\\\"\\\\tRemoved {outliers_bottom.sum()} low and {outliers_top.sum()} high outliers \\\"\\r\\n f\\\"from {col_name} (outside {bottom:,.2f} to {top:,.2f})\\\"\\r\\n )\\r\\n\\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d466782fdf1fada30b662844168e51f\",\n \"score\": \"0.54912925\",\n \"text\": \"def test_percentiles_acc():\\n # First a basic case\\n data = np.array([10, 20, 30])\\n val = 20\\n perc = stat.percentiles(data, 50)\\n assert_equal(perc, val)\\n\\n # Now test against scoreatpercentile\\n percentiles = rs.randint(0, 101, 10)\\n out = stat.percentiles(a_norm, percentiles)\\n for score, pct in zip(out, percentiles):\\n assert_equal(score, sp.stats.scoreatpercentile(a_norm, pct))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef1e88dc7f92dcd42c3bccf54e04e291\",\n \"score\": \"0.54714763\",\n \"text\": \"def raster_percentile(band):\\n return bisect(percentiles, band)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cba938cf5f4b3bff3ed73041af65142\",\n \"score\": \"0.5466502\",\n \"text\": \"def treat_outliers(df, col):\\n Q1 = df[col].quantile(0.25) # 25th quantile\\n Q3 = df[col].quantile(0.75) # 75th quantile\\n IQR = Q3 - Q1\\n Lower_Whisker = Q1 - 1.5 * IQR\\n Upper_Whisker = Q3 + 1.5 * IQR\\n\\n # all the values smaller than Lower_Whisker will be assigned the value of Lower_Whisker\\n # all the values greater than Upper_Whisker will be assigned the value of Upper_Whisker\\n df[col] = np.clip(df[col], Lower_Whisker, Upper_Whisker)\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cba938cf5f4b3bff3ed73041af65142\",\n \"score\": \"0.5466502\",\n \"text\": \"def treat_outliers(df, col):\\n Q1 = df[col].quantile(0.25) # 25th quantile\\n Q3 = df[col].quantile(0.75) # 75th quantile\\n IQR = Q3 - Q1\\n Lower_Whisker = Q1 - 1.5 * IQR\\n Upper_Whisker = Q3 + 1.5 * IQR\\n\\n # all the values smaller than Lower_Whisker will be assigned the value of Lower_Whisker\\n # all the values greater than Upper_Whisker will be assigned the value of Upper_Whisker\\n df[col] = np.clip(df[col], Lower_Whisker, Upper_Whisker)\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9df7fc8ee6575dce8212c40953f0277\",\n \"score\": \"0.54515636\",\n \"text\": \"def fast_abs_percentile(data, percentile=80):\\n if hasattr(data, 'mask'):\\n # Catter for masked arrays\\n data = np.asarray(data[np.logical_not(data.mask)])\\n data = np.abs(data)\\n data = data.ravel()\\n index = int(data.size * .01 * percentile)\\n if partition is not None:\\n # Partial sort: faster than sort\\n return partition(data, index)[index + 1]\\n data.sort()\\n return data[index + 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e037d5e2552efc63d52f14c53b1d11ca\",\n \"score\": \"0.54457676\",\n \"text\": \"def remove_outliers(target_df, low, high, exclude_cols):\\n processed_df = target_df.copy()\\n quant_df = target_df.quantile([low, high])\\n cols = [col for col in target_df.columns if col not in exclude_cols and col != 'id' and col != 'label']\\n quant_df = quant_df[cols]\\n quant_df.index = [low, high]\\n\\n for col in target_df:\\n if col != 'id' and col != 'label':\\n continue\\n if col not in exclude_cols:\\n processed_df.loc[processed_df[col] > quant_df[col].values[1], col] = quant_df[col].values[1] # giant outliers convert to higher bound value\\n processed_df.loc[processed_df[col] < quant_df[col].values[0], col] = quant_df[col].values[0] # low outliers convert to lower bound value\\n\\n return processed_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4213acde5e9fc04fc8fa35de6a42a361\",\n \"score\": \"0.5420591\",\n \"text\": \"def remove_outliers_iqr(data, feature, exclude = list()):\\n \\n if np.issubdtype(data[feature].dtype, np.number) and feature not in exclude:\\n \\n q1 = data[feature].quantile(0.25)\\n q3 = data[feature].quantile(0.75)\\n\\n iqr = q3 - q1\\n lower_bound = q1 -(1.5 * iqr) \\n upper_bound = q3 +(1.5 * iqr) \\n\\n outliers = data[feature].apply(lambda x : np.nan if (x < lower_bound or \\n x > upper_bound) \\n else x)\\n else:\\n outliers = data[feature]\\n \\n return outliers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60bbf9fb5d2ec5d5d12a39afcbe997cc\",\n \"score\": \"0.54202724\",\n \"text\": \"def give_percentiles_all(i, data,X):\\n sc = -X[i,:]\\n it_idx = data.item_int[np.logical_and(data.patient_int==i,data.rating_test)]\\n res = give_percentiles(sc)\\n return res[it_idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca6dd745899ef1b97487aa5c639caaa3\",\n \"score\": \"0.54089653\",\n \"text\": \"def _quantile(x, q, **kwargs):\\n x = np.asarray(x)\\n p = np.asarray(q) * 100\\n return np.nanpercentile(x, p, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ade424eb2bdfb01d283f23a34f1788a\",\n \"score\": \"0.5402604\",\n \"text\": \"def percentile(self, q, axis=None, iterate_rays=False, **kwargs):\\n if hasattr(np, 'nanpercentile') and not iterate_rays:\\n result = self.apply_numpy_function(np.nanpercentile, q=q,\\n axis=axis, projection=True,\\n unit=self.unit, how='cube',\\n **kwargs)\\n else:\\n result = self.apply_function(np.percentile, q=q, axis=axis,\\n projection=True, unit=self.unit,\\n **kwargs)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4926ee08db9f6c45fef789e259d5f67\",\n \"score\": \"0.5397032\",\n \"text\": \"def remove_outliers(self):\\n self.stats()\\n\\n drop_indices = []\\n for col_n, col_name in enumerate(self.data.columns):\\n\\n for row_n, value in enumerate(self.data[col_name]):\\n\\n if value < self.means[col_n] - 3 * self.stds[col_n] or value > self.means[col_n] + 3 * self.stds[col_n]:\\n # Drop entire row if the value is +/- 3 standard deviations from the column mean\\n drop_indices.append(row_n)\\n\\n # Remove duplicate indices\\n drop_indices = list(set(drop_indices))\\n\\n # Save identified outliers\\n self.outliers = self.data.iloc[drop_indices]\\n\\n # Drop outliers and reset index\\n self.data.drop(index=drop_indices, inplace=True)\\n self.data.reset_index(drop=True, inplace=True)\\n\\n # Recompute stats after dropping outliers\\n print(self.means)\\n print(self.stds)\\n self.stats()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01722b9777c491b2d13c3c8e5ddf0200\",\n \"score\": \"0.53939855\",\n \"text\": \"def nanpercentile(a, per):\\n out = np.empty(a.shape[0])\\n for i in range(a.shape[0]):\\n b = a[i][np.isfinite(a[i])]\\n if b.shape[0] == 0:\\n out[i] = np.nan\\n else:\\n out[i] = np.percentile(b[np.isfinite(b)], per)\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b1ce0008949f2a47072b684561b332d\",\n \"score\": \"0.53826725\",\n \"text\": \"def calc_percentile(values):\\n return (rankdata(values) - 1) / len(values) * 100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f2270d72949a15dc5dcf971ee0547a2\",\n \"score\": \"0.53773004\",\n \"text\": \"def calculate_outlier_bounds(df, column_name):\\n bounds = dict(zip([\\\"q1\\\", \\\"q3\\\"], df.approxQuantile(column_name, [0.25, 0.75], 0)))\\n iqr = bounds['q3'] - bounds['q1']\\n bounds['min'] = bounds['q1'] - (iqr * 1.5)\\n bounds['max'] = bounds['q3'] + (iqr * 1.5)\\n return bounds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb3cb72672fd40d469ee379c74bb6563\",\n \"score\": \"0.5361838\",\n \"text\": \"def percentile(data, pcntl):\\n nr = np.size(data, axis=0)\\n\\n x = np.sort(data, axis=0)\\n return x[int(np.floor(pcntl*nr)),]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa685a902d1aefdfcb6bfdc1a1e90b2\",\n \"score\": \"0.53562295\",\n \"text\": \"def remove_outlier(dataset, auto_fill=True, ratio=1.5):\\n from ..data import Dataset\\n from .fill import fill\\n from numpy import percentile, isnan, nonzero, nan\\n\\n if not isinstance(dataset, Dataset):\\n raise TypeError(\\\"Dataset has to be class core.data.dataset.Dataset\\\")\\n\\n data = dataset.data\\n time_col = dataset.time_column_index\\n\\n for i in range(data.shape[1]):\\n if i == time_col:\\n continue\\n column = nonzero(~isnan(data[:, i]))[0]\\n q25, q75 = percentile(data[column, i], (25, 75))\\n iqr = q75 - q25\\n data[column[nonzero(data[column, i] < (q25 - ratio * iqr))[0]], i] = nan\\n data[column[nonzero(data[column, i] > (q75 + ratio * iqr))[0]], i] = nan\\n dataset.change_values(data)\\n\\n if auto_fill:\\n fill(dataset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4af88fbd4407f7f6b3ad4a602af1c4b9\",\n \"score\": \"0.53540665\",\n \"text\": \"def remove_outliers(df, column_name):\\n import pyspark.sql.functions as f\\n bounds = calculate_outlier_bounds(df, column_name)\\n return df.filter((f.col(column_name) >= bounds['min']) & (f.col(column_name) <= bounds['max']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73c25e9ce85d7847b60ceddec576005d\",\n \"score\": \"0.5322219\",\n \"text\": \"def get_smoothed_y_percentiles(self,imported_hdf5_handle,y_percentile,smoothing_kernel_y):\\n y_percentiles_path = self.chunk_t((imported_hdf5_handle[self.seg_channel],),(2,),1,self.get_y_percentile,\\\"y_percentile\\\",\\\"data\\\",y_percentile,t_range_tuple=(self.t_range,))\\n \\n with h5py_cache.File(y_percentiles_path,\\\"r\\\",chunk_cache_mem_size=self.chunk_cache_mem_size) as y_percentiles_handle:\\n y_percentiles_smoothed = self.median_filter_2d(y_percentiles_handle[\\\"data\\\"],smoothing_kernel_y)\\n# y_percentiles_smoothed_path = self.chunk_t((y_percentiles_handle[\\\"data\\\"],),(1,),1,self.median_filter_2d,\\\"y_percentile_smoothed\\\",\\\"data\\\",smoothing_kernel_y)\\n self.removefile(y_percentiles_path)\\n# return y_percentiles_smoothed_path\\n return y_percentiles_smoothed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb8f3c6710c14f289549b77b98593a17\",\n \"score\": \"0.5314725\",\n \"text\": \"def test_create_group_percentiles_output_buckets(self):\\n\\n created_percentiles = []\\n\\n desired_percentiles = [str(percent) + '%' for percent in wb_utils.Settings.formatted_percentiles]\\n\\n for data_item in self.results['Data']:\\n for percent in data_item['percentileList']:\\n percentile_keys = [perval['percentiles'] for perval in percent['percentileValues']]\\n created_percentiles.append(percentile_keys)\\n\\n self.assertTrue([desired_percentiles == l for l in created_percentiles],\\n \\\"\\\"\\\"create_group_percentiles returned unexecpted percentile buckets\\n returned: {}\\\"\\\"\\\".format(created_percentiles))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"653ba3b8735ed9f18d049c1f428c7e84\",\n \"score\": \"0.5311623\",\n \"text\": \"def interquartile_range(X):\\n quartiles = np.nanpercentile(X, [25, 75], axis=0)\\n return quartiles[1, :] - quartiles[0, :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb2a14d2315b48de82f2f9cfecdedf69\",\n \"score\": \"0.5302771\",\n \"text\": \"def calculate_percentile(percentile, dataframe, column_name):\\n return round(dataframe[column_name].quantile(percentile),2 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"462b312e3e3c2cedc1b143a875e0ce31\",\n \"score\": \"0.5296902\",\n \"text\": \"def filter_by_percentile(tags_distribution: Dict[str, int], tol_percentile: int) -> Set[str]:\\n if len(list(tags_distribution.values())) > 100:\\n top_percentile = np.percentile(list(tags_distribution.values()), tol_percentile)\\n return {k for k, v in tags_distribution.items() if v > top_percentile}\\n else:\\n return {k for k, v in tags_distribution.items()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc815928289e73cc0f83025752329c64\",\n \"score\": \"0.5296731\",\n \"text\": \"def reject_outliers(data, cutoff, thresh=0.08):\\n data = np.array(data)\\n marsz = data[:, 4]\\n data = data[(data[:, 4] >= cutoff[0]) & (data[:, 4] <= cutoff[1])]\\n m = np.mean(data[:, 4], axis=0)\\n # return data\\n return data[(data[:, 4] <= m + thresh) & (data[:, 4] >= m - thresh)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"664fb7f43834ae1c9ebcfea6609f3885\",\n \"score\": \"0.5293619\",\n \"text\": \"def signal_percentile(sampled_signal, percentile):\\n return np.percentile(sampled_signal.reshape(-1), percentile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04cf7bd36de2969575eee1e97b96f8f1\",\n \"score\": \"0.5292302\",\n \"text\": \"def parse_percentiles(percentiles: Sequence[float] | float | None) -> Sequence[float]:\\n if isinstance(percentiles, float):\\n percentiles = [percentiles]\\n elif percentiles is None:\\n percentiles = []\\n if not all((0 <= p <= 1) for p in percentiles):\\n raise ValueError(\\\"percentiles must all be in the range [0, 1]\\\")\\n\\n sub_50_percentiles = sorted(p for p in percentiles if p < 0.5)\\n at_or_above_50_percentiles = sorted(p for p in percentiles if p >= 0.5)\\n\\n if not at_or_above_50_percentiles or at_or_above_50_percentiles[0] != 0.5:\\n at_or_above_50_percentiles = [0.5, *at_or_above_50_percentiles]\\n\\n return [*sub_50_percentiles, *at_or_above_50_percentiles]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"550d5f82990994e210e7389823e393ea\",\n \"score\": \"0.5283657\",\n \"text\": \"def get_percentile_distr():\\n second_part = (np.geomspace(5, 50, 10)).astype(int)\\n first_part = (101. - np.geomspace(1, 51, 20)).astype(int)\\n return np.hstack([first_part, second_part])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c398394a8e078d423491bf1e766504fa\",\n \"score\": \"0.5282513\",\n \"text\": \"def cat_continuous(x, granularity=\\\"Medium\\\"):\\n if granularity == \\\"High\\\":\\n lspercentile = [\\n np.percentile(x, 5),\\n np.percentile(x, 10),\\n np.percentile(x, 15),\\n np.percentile(x, 20),\\n np.percentile(x, 25),\\n np.percentile(x, 30),\\n np.percentile(x, 35),\\n np.percentile(x, 40),\\n np.percentile(x, 45),\\n np.percentile(x, 50),\\n np.percentile(x, 55),\\n np.percentile(x, 60),\\n np.percentile(x, 65),\\n np.percentile(x, 70),\\n np.percentile(x, 75),\\n np.percentile(x, 80),\\n np.percentile(x, 85),\\n np.percentile(x, 90),\\n np.percentile(x, 95),\\n np.percentile(x, 99),\\n ]\\n res = [\\n \\\"> p90 (%s)\\\" % (lspercentile[8])\\n if z > lspercentile[8]\\n else \\\"<= p10 (%s)\\\" % (lspercentile[0])\\n if z <= lspercentile[0]\\n else \\\"<= p20 (%s)\\\" % (lspercentile[1])\\n if z <= lspercentile[1]\\n else \\\"<= p30 (%s)\\\" % (lspercentile[2])\\n if z <= lspercentile[2]\\n else \\\"<= p40 (%s)\\\" % (lspercentile[3])\\n if z <= lspercentile[3]\\n else \\\"<= p50 (%s)\\\" % (lspercentile[4])\\n if z <= lspercentile[4]\\n else \\\"<= p60 (%s)\\\" % (lspercentile[5])\\n if z <= lspercentile[5]\\n else \\\"<= p70 (%s)\\\" % (lspercentile[6])\\n if z <= lspercentile[6]\\n else \\\"<= p80 (%s)\\\" % (lspercentile[7])\\n if z <= lspercentile[7]\\n else \\\"<= p90 (%s)\\\" % (lspercentile[8])\\n if z <= lspercentile[8]\\n else \\\"> p90 (%s)\\\" % (lspercentile[8])\\n for z in x\\n ]\\n elif granularity == \\\"Medium\\\":\\n lspercentile = [\\n np.percentile(x, 10),\\n np.percentile(x, 20),\\n np.percentile(x, 30),\\n np.percentile(x, 40),\\n np.percentile(x, 50),\\n np.percentile(x, 60),\\n np.percentile(x, 70),\\n np.percentile(x, 80),\\n np.percentile(x, 90),\\n ]\\n res = [\\n \\\"<= p10 (%s)\\\" % (lspercentile[0])\\n if z <= lspercentile[0]\\n else \\\"<= p20 (%s)\\\" % (lspercentile[1])\\n if z <= lspercentile[1]\\n else \\\"<= p30 (%s)\\\" % (lspercentile[2])\\n if z <= lspercentile[2]\\n else \\\"<= p40 (%s)\\\" % (lspercentile[3])\\n if z <= lspercentile[3]\\n else \\\"<= p50 (%s)\\\" % (lspercentile[4])\\n if z <= lspercentile[4]\\n else \\\"<= p60 (%s)\\\" % (lspercentile[5])\\n if z <= lspercentile[5]\\n else \\\"<= p70 (%s)\\\" % (lspercentile[6])\\n if z <= lspercentile[6]\\n else \\\"<= p80 (%s)\\\" % (lspercentile[7])\\n if z <= lspercentile[7]\\n else \\\"<= p90 (%s)\\\" % (lspercentile[8])\\n if z <= lspercentile[8]\\n else \\\"> p90 (%s)\\\" % (lspercentile[8])\\n for z in x\\n ]\\n else:\\n lspercentile = [\\n np.percentile(x, 15),\\n np.percentile(x, 50),\\n np.percentile(x, 85),\\n ]\\n res = [\\n \\\"1-Very Low\\\"\\n if z < lspercentile[0]\\n else \\\"2-Low\\\"\\n if z < lspercentile[1]\\n else \\\"3-High\\\"\\n if z < lspercentile[2]\\n else \\\"4-Very High\\\"\\n for z in x\\n ]\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acedf2ba083f29f6b80020e6868514bb\",\n \"score\": \"0.527658\",\n \"text\": \"def reject_outliers(data, deviation_tolerance=.6745):\\n\\n data = data.astype('float')\\n distance = _np.abs(data - _np.median(data))\\n sigma = _np.median(distance) / .6745\\n data[distance > deviation_tolerance * sigma] = _np.nan\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb9088d7131f4cc995058612c379e69c\",\n \"score\": \"0.5273992\",\n \"text\": \"def percentile(percent):\\n @wraps(percentile)\\n def _reduce(iterable):\\n sorts = sorted(iterable)\\n key = (len(iterable)-1) * percent\\n key_floor = math.floor(key)\\n key_ceil = math.ceil(key)\\n if key_ceil == key_floor:\\n return sorts[int(key)]\\n d0 = sorts[int(key_floor)] * (key_ceil - key)\\n d1 = sorts[int(key_ceil)] * (key - key_floor)\\n return d0+d1\\n return _reduce\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd355f06222256f4501b8541b29d6ea9\",\n \"score\": \"0.5272074\",\n \"text\": \"def delete_outliers(data, y, ids, threshold):\\n idxs = np.all(abs(data)<threshold,1)\\n\\n data_clean = data[idxs,:]\\n y_clean = y[idxs]\\n ids = ids[idxs]\\n\\n return data_clean, y_clean, ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd0e085cb04827bb73eeba6f3b4b6a65\",\n \"score\": \"0.5260961\",\n \"text\": \"def PercentileCalculator(numbers, percentiles=PERCENTILES_LIST):\\n\\n if not len(numbers): # 'if not numbers' will fail if numbers is a pd.Series.\\n raise ValueError(\\\"Can't compute percentiles of empty list.\\\")\\n\\n numbers_sorted = sorted(numbers)\\n count = len(numbers_sorted)\\n total = sum(numbers_sorted)\\n result = {}\\n for percentile in percentiles:\\n float(percentile) # verify type\\n if percentile < 0.0 or percentile > 100.0:\\n raise ValueError('Invalid percentile %s' % percentile)\\n\\n percentile_string = 'p%s' % str(percentile)\\n index = int(count * float(percentile) / 100.0)\\n index = min(index, count - 1) # Correction to handle 100th percentile.\\n result[percentile_string] = numbers_sorted[index]\\n\\n average = total / float(count)\\n result['average'] = average\\n if count > 1:\\n total_of_squares = sum([(i - average) ** 2 for i in numbers])\\n result['stddev'] = (total_of_squares / (count - 1)) ** 0.5\\n else:\\n result['stddev'] = 0\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f9ed3a4e0f8796043f33b8b45842ad6\",\n \"score\": \"0.525979\",\n \"text\": \"def test_Percentiles_group_percentiles_out_creation(self):\\n\\n self.Percentiles.population_percentiles()\\n self.assertTrue(hasattr(self.Percentiles, 'group_percentiles_out'),\\n \\\"\\\"\\\"population_percentile_vecs not attribute after population_percentiles run\\\"\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4204c997d6d4d1edfb005ca6aa454ce7\",\n \"score\": \"0.5253785\",\n \"text\": \"def customized_box_plot(percentiles, axes, redraw = True, *args, **kwargs):\\n\\n box_plot = axes.boxplot([[-9, -4, 2, 4, 9],]*1, *args, **kwargs) \\n # Creates len(percentiles) no of box plots\\n\\n min_y, max_y = float('inf'), -float('inf')\\n\\n for box_no, (q1_start, \\n q2_start,\\n q3_start,\\n q4_start,\\n q4_end,\\n fliers_xy) in enumerate(percentiles):\\n\\n # Lower cap\\n box_plot['caps'][2*box_no].set_ydata([q1_start, q1_start])\\n # xdata is determined by the width of the box plot\\n\\n # Lower whiskers\\n box_plot['whiskers'][2*box_no].set_ydata([q1_start, q2_start])\\n\\n # Higher cap\\n box_plot['caps'][2*box_no + 1].set_ydata([q4_end, q4_end])\\n\\n # Higher whiskers\\n box_plot['whiskers'][2*box_no + 1].set_ydata([q4_start, q4_end])\\n\\n # Box\\n box_plot['boxes'][box_no].set_ydata([q2_start, \\n q2_start, \\n q4_start,\\n q4_start,\\n q2_start])\\n\\n # Median\\n box_plot['medians'][box_no].set_ydata([q3_start, q3_start])\\n\\n # Outliers\\n if fliers_xy is not None and len(fliers_xy[0]) != 0:\\n # If outliers exist\\n box_plot['fliers'][box_no].set(xdata = fliers_xy[0],\\n ydata = fliers_xy[1])\\n\\n min_y = min(q1_start, min_y, fliers_xy[1].min())\\n max_y = max(q4_end, max_y, fliers_xy[1].max())\\n\\n else:\\n min_y = min(q1_start, min_y)\\n max_y = max(q4_end, max_y)\\n\\n # The y axis is rescaled to fit the new box plot completely with 10% \\n # of the maximum value at both ends\\n axes.set_ylim([min_y*1.1, max_y*1.1])\\n\\n # If redraw is set to true, the canvas is updated.\\n if redraw:\\n ax.figure.canvas.draw()\\n\\n return box_plot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e29ee683c4cbff437371867018d81da2\",\n \"score\": \"0.52533287\",\n \"text\": \"def get_y_percentile(self,array_tuple,y_percentile):\\n array, = array_tuple\\n out_array = np.percentile(array,y_percentile,axis=1,interpolation='lower')\\n return out_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9c2e933d3ebc463990449afec4c003b\",\n \"score\": \"0.52529764\",\n \"text\": \"def _percentile_interval(self, ab_estimates = None):\\n ll = np.percentile(ab_estimates, q = (self.alpha/2)*100)\\n ul = np.percentile(ab_estimates, q = (1 - self.alpha/2)*100)\\n return np.array([ll, ul])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1265aaa33c87ef8f239a7fa5ac7fe02\",\n \"score\": \"0.5252969\",\n \"text\": \"def discard_outliers(x_train, ys_train ):\\n index = []\\n threshold =8.5\\n for i in range(x_train.shape[0]):\\n if np.amax(np.abs(x_train[i, :])) > threshold:\\n index.append(i)\\n x_train = np.delete(x_train, index, 0)\\n ys_train = np.delete(ys_train, index, 0)\\n return x_train, ys_train\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a34155464dc8f1fa99fbfb1696cbe6\",\n \"score\": \"0.524914\",\n \"text\": \"def cutoff_from_percentile(self, percentile, pos_label=None):\\n return (\\n pd.Series(self.pred_probas(pos_label))\\n .nlargest(int((1 - percentile) * len(self)))\\n .min()\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39251c47fdfd3c8926da76078599bac6\",\n \"score\": \"0.52269894\",\n \"text\": \"def remove_outliers(X: List[Union[int, float]], t: float = 3.5) -> List[Union[int, float]]:\\n mean_X = np.mean(X)\\n std_X = np.std(X)\\n\\n good_x = []\\n\\n for x in X:\\n z_score = (x - mean_X) / std_X\\n if z_score < t:\\n good_x.append(x)\\n return good_x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb4339cbc964951338524513bb4a9aa6\",\n \"score\": \"0.52175856\",\n \"text\": \"def get_climatology(self,percentiles):\\n self.percentiles=percentiles\\n \\n lat,lon=self.data[\\\"clim\\\"][0].shape[-2:]\\n dist=np.zeros([1,lat,lon])\\n \\n #We call the whole cubelist into memory\\n self.data[\\\"clim\\\"].realise_data()\\n dist=np.concatenate([f.data.reshape([-1,lat,lon]) for f in self.data[\\\"clim\\\"]])\\n \\n self.distribution=np.percentile(dist,percentiles,axis=0)\\n self.distribution[0]-=0.01\\n \\n means=np.zeros([len(percentiles)-1,lat,lon])\\n for i in range(len(percentiles)-1):\\n for j in range(lat):\\n for k in range(lon):\\n means[i,j,k]=dist[np.digitize(dist[:,j,k],\\\\\\n self.distribution[:,j,k],right=True)==i+1,j,k].mean()\\n #interpolates empty bins as being halfway between the distribution bounds \\n for i,j,k in np.argwhere(np.isnan(means)):\\n means[i,j,k]=self.distribution[i:i+2,j,k].mean()\\n self.dist_means=means\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ddc868204ce6131d5a0b58ce405f1ef\",\n \"score\": \"0.5208818\",\n \"text\": \"def normalize99(img):\\n X = img.copy()\\n X = (X - np.percentile(X, 1)) / (np.percentile(X, 99) - np.percentile(X, 1))\\n return X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfbcf341c7a8545a9fce1302ba82a2b4\",\n \"score\": \"0.52067035\",\n \"text\": \"def give_percentiles( scores):\\n res = []\\n for i in scores:\\n res.append( stats.percentileofscore(scores,i))\\n return np.array(res)/100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd5d511d7f29c7d28a698dd4dd62dfa5\",\n \"score\": \"0.51997083\",\n \"text\": \"def unconditional_quantile_y(x, alpha_grid, func):\\n n_draws = len(x)\\n\\n # Equation 21a\\n y_x = Parallel(n_jobs=8)(delayed(func)(i) for i in x)\\n y_x_asc = np.sort(y_x)\\n q_index = (np.floor(alpha_grid * n_draws)).astype(int)\\n quantile_y_x = y_x_asc[q_index]\\n\\n return quantile_y_x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cfe6064b54e4bafc24a99ab38bf1c59\",\n \"score\": \"0.5198672\",\n \"text\": \"def SELpercentile(SEL, percentile=5):\\n fff = np.percentile(SEL, 100-percentile)\\n\\n return fff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64127ee8b40ca7420be9bdd8f31576a0\",\n \"score\": \"0.5194027\",\n \"text\": \"def _get_grids(feature_values, num_grid_points, grid_type, percentile_range, grid_range):\\n\\n if grid_type == 'percentile':\\n # grid points are calculated based on percentile in unique level\\n # thus the final number of grid points might be smaller than num_grid_points\\n start, end = 0, 100\\n if percentile_range is not None:\\n start, end = np.min(percentile_range), np.max(percentile_range)\\n\\n percentile_grids = np.linspace(start=start, stop=end, num=num_grid_points)\\n value_grids = np.percentile(feature_values, percentile_grids)\\n\\n grids_df = pd.DataFrame()\\n grids_df['percentile_grids'] = [round(v, 2) for v in percentile_grids]\\n grids_df['value_grids'] = value_grids\\n grids_df = grids_df.groupby(['value_grids'], as_index=False).agg(\\n {'percentile_grids': lambda v: str(tuple(v)).replace(',)', ')')}).sort_values('value_grids', ascending=True)\\n\\n feature_grids, percentile_info = grids_df['value_grids'].values, grids_df['percentile_grids'].values\\n else:\\n if grid_range is not None:\\n value_grids = np.linspace(np.min(grid_range), np.max(grid_range), num_grid_points)\\n else:\\n value_grids = np.linspace(np.min(feature_values), np.max(feature_values), num_grid_points)\\n feature_grids, percentile_info = value_grids, []\\n\\n return feature_grids, percentile_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"348835087b831c8bd81768b7f613f28e\",\n \"score\": \"0.51901513\",\n \"text\": \"def remove_ourlier_diff_median(data, median_scaler=25):\\n daily_diff = (data.resample('D').last().dropna().diff().abs() + 0.05) * median_scaler\\n daily_diff['diff_date'] = daily_diff.index.strftime('%Y-%m-%d')\\n data_test = data.diff()\\n data_test['diff_date'] = data_test.index.strftime('%Y-%m-%d')\\n data_test_diff = pd.merge(data_test, daily_diff, on='diff_date')\\n indexer = ((np.abs(data_test_diff['close_x']) < np.abs(data_test_diff['close_y'])) &\\n (np.abs(data_test_diff['open_x']) < np.abs(data_test_diff['open_y'])) &\\n (np.abs(data_test_diff['high_x']) < np.abs(data_test_diff['high_y'])) & \\n (np.abs(data_test_diff['low_x']) < np.abs(data_test_diff['low_y'])))\\n # indexer = (indexer | data_test_diff['close_y'].isna())\\n data_final = data.loc[indexer.values, :]\\n \\n return data_final\",\n \"title\": \"\"\n },\n {\n \"docid\": \"995c5947eca7e83cc9b54ac3771c78c6\",\n \"score\": \"0.51841736\",\n \"text\": \"def calculate_percentiles_from_raster(raster_uri, percentiles):\\n raster = gdal.Open(raster_uri, gdal.GA_ReadOnly)\\n\\n def numbers_from_file(fle):\\n \\\"\\\"\\\"Generates an iterator from a file by loading all the numbers\\n and yielding\\n\\n fle = file object\\n \\\"\\\"\\\"\\n arr = np.load(fle)\\n for num in arr:\\n yield num\\n\\n # List to hold the generated iterators\\n iters = []\\n\\n band = raster.GetRasterBand(1)\\n nodata = band.GetNoDataValue()\\n\\n n_rows = raster.RasterYSize\\n n_cols = raster.RasterXSize\\n\\n # Variable to count the total number of elements to compute percentile\\n # from. This leaves out nodata values\\n n_elements = 0\\n\\n #Set the row strides to be something reasonable, like 256MB blocks\\n row_strides = max(int(2**28 / (4 * n_cols)), 1)\\n\\n for row_index in xrange(0, n_rows, row_strides):\\n #It's possible we're on the last set of rows and the stride\\n #is too big, update if so\\n if row_index + row_strides >= n_rows:\\n row_strides = n_rows - row_index\\n\\n # Read in raster chunk as array\\n arr = band.ReadAsArray(0, row_index, n_cols, row_strides)\\n\\n tmp_uri = pygeoprocessing.geoprocessing.temporary_filename()\\n tmp_file = open(tmp_uri, 'wb')\\n # Make array one dimensional for sorting and saving\\n arr = arr.flatten()\\n # Remove nodata values from array and thus percentile calculation\\n arr = np.delete(arr, np.where(arr == nodata))\\n # Tally the number of values relevant for calculating percentiles\\n n_elements += len(arr)\\n # Sort array before saving\\n arr = np.sort(arr)\\n\\n np.save(tmp_file, arr)\\n tmp_file.close()\\n tmp_file = open(tmp_uri, 'rb')\\n tmp_file.seek(0)\\n iters.append(numbers_from_file(tmp_file))\\n arr = None\\n\\n # List to store the rank/index where each percentile will be found\\n rank_list = []\\n # For each percentile calculate nearest rank\\n for perc in percentiles:\\n rank = math.ceil(perc/100.0 * n_elements)\\n rank_list.append(int(rank))\\n\\n # A variable to burn through when doing heapq merge sort over the\\n # iterators. Variable is used to check if we've iterated to a\\n # specified rank spot, to grab percentile value\\n counter = 0\\n # Setup a list of zeros to replace with percentile results\\n results = [0] * len(rank_list)\\n\\n LOGGER.debug('Percentile Rank List: %s', rank_list)\\n\\n for num in heapq.merge(*iters):\\n # If a percentile rank has been hit, grab percentile value\\n if counter in rank_list:\\n LOGGER.debug('percentile value is : %s', num)\\n results[rank_list.index(counter)] = int(num)\\n counter += 1\\n\\n band = None\\n raster = None\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f36baaab4212473d67e29612f8604d9b\",\n \"score\": \"0.51838475\",\n \"text\": \"def scoreatpercentile(self, independent_data, percentile):\\n _groups = self.grouping_function(independent_data)\\n result = np.empty(independent_data.shape) * np.nan\\n for key in self._models.keys():\\n indices = _groups==key\\n result[indices] = self._models[key].scoreatpercentile(independent_data[indices], percentile)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9031a7091fd6ad983c1b58edca8fb642\",\n \"score\": \"0.5173151\",\n \"text\": \"def denormalize(x):\\n x_max = np.percentile(x, 98)\\n x_min = np.percentile(x, 2)\\n x = (x - x_min) / (x_max - x_min)\\n x = x.clip(0, 1)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59cc36d34969979f14bf683d6730c9bd\",\n \"score\": \"0.5166004\",\n \"text\": \"def clean_outliers(self, dim=3):\\n ok_points = []\\n not_ok_points = []\\n\\n if dim != 3:\\n distances = [dist(x.point, self.center, d3=False) for x in self.points]\\n else:\\n distances = [dist(x.point, self.center) for x in self.points]\\n stdv = np.std(distances)\\n meandis = np.mean(distances)\\n for i in range(len(self.points)):\\n if distances[i] < (2.5 * stdv + meandis):\\n ok_points.append(self.points[i])\\n else:\\n not_ok_points.append(self.points[i])\\n return ok_points, not_ok_points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98f4f98d9d6a6d03e4724bd56f71359\",\n \"score\": \"0.51487285\",\n \"text\": \"def p_threshold(self, percentile):\\n\\n masked_I, window_I, window_mask, dtc_idx = self._set_up()\\n r = np.nanpercentile(masked_I.filled(np.nan), percentile, axis=1)\\n\\n threshold = np.repeat(r, np.square(2*self.extent)).reshape(masked_I.shape)\\n window_I[(window_I > threshold) & (window_mask > 0)] = -1\\n\\n if len(np.where(np.isnan(r))[0])>0: # deal with blocks for which all pixels are masked\\n window_I[np.where(np.isnan(r))] = -1\\n \\n masked_img = self.indexed[:,3].copy().flatten()\\n masked_img[dtc_idx] = window_I\\n\\n return masked_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c79ab36c853ea89f53033a7f16c8e9e7\",\n \"score\": \"0.5146289\",\n \"text\": \"def set_color_by_percentile(this, start, end):\\n if str(this) == 'nan':\\n return 'grey'\\n if this < start:\\n return start\\n elif this > end:\\n return end\\n return this\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e2ede35dad1a19aff8aa5c18698302\",\n \"score\": \"0.5146138\",\n \"text\": \"def get_quantile(data):\\n # median = DataAnalyser.get_median(data)\\n # filtered = [i for i in data if i is not None]\\n # filtered.sort()\\n # first_quantile = DataAnalyser.get_median([item for item in filtered if item < median])\\n # third_quantile = DataAnalyser.get_median([item for item in filtered if item > median])\\n\\n # return first_quantile, third_quantile\\n sorted_data = sorted(data)\\n return sorted_data[round(len(data) * 1 / 4)], sorted_data[round(len(data) * 3 / 4)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a292c3d3dba3f26987846e541ee2dc2\",\n \"score\": \"0.51320106\",\n \"text\": \"def outlierCleaner(predictions, ages, net_worths):\\n import numpy as np\\n cleaned_data = []\\n\\n ### your code goes here\\n errors = []\\n for n in range(0, 89):\\n\\t\\td = abs(net_worths[n] - predictions[n])\\n\\t\\terrors.append(d)\\n\\t\\t\\n cut = np.percentile(errors, 90)\\n for n in range(0, 89):\\n\\t\\tif errors[n] <= cut:\\n\\t\\t\\tcleaned_data.append((ages[n], net_worths[n], errors[n]))\\n\\t\\t\\n return cleaned_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feccb496e236d83e3c14a06b0d9d0e63\",\n \"score\": \"0.5127159\",\n \"text\": \"def outliers(self):\\n\\n limits = self._limits\\n column = self._column\\n\\n values_without_range = list(set((self._df\\n .rdd.map(lambda x: x[column])\\n .filter(lambda x: x < limits[0] or x > limits[1])\\n .collect())))\\n\\n return values_without_range\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5613fa6db921a59b6d5071a35bed231\",\n \"score\": \"0.51267356\",\n \"text\": \"def limit_range(vals, rng):\\n if len(vals) > 2:\\n rng = sorted(rng)\\n lower = np.percentile(vals, rng[0], interpolation=\\\"nearest\\\")\\n higher = np.percentile(vals, rng[1], interpolation=\\\"nearest\\\")\\n limited = [x for x in vals if x >= lower and x <= higher]\\n return np.array(limited)\\n return vals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5c9850b5e0ee0092c155edd7dddf93e\",\n \"score\": \"0.51224047\",\n \"text\": \"def percentile_normalization(userID, cur, sampling_percentiles):\\n times = []\\n query1 = \\\"select creation_date from post where owner_user_id = %(id)s and (post_type_id = 1 or post_type_id = 2) order by creation_date\\\"\\n cur.execute(query1, {'id': userID})\\n posts = [i[0] for i in results(cur)]\\n start = get_start_time(userID, cur)\\n for p in sampling_percentiles:\\n x = int(len(posts) * p)\\n if x == 0:\\n times.append(start)\\n else:\\n times.append(posts[x-1])\\n return times\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27bb7f867042dfa2e0da50ec7db00d5d\",\n \"score\": \"0.51217884\",\n \"text\": \"def do_polarization_clip_check(self, naxis, lower, upper):\\n\\n # round floats to individual pixels\\n p1 = math.floor(lower)\\n p2 = math.ceil(upper)\\n\\n # bounds check\\n if p1 < 1:\\n p1 = 1\\n if p2 > naxis:\\n p2 = naxis\\n\\n # validity check, no pixels included\\n if p1 > naxis or p2 < 1:\\n error = 'pixels coordinates {}:{} do not intersect {} to {}'\\\\\\n .format(p1, p2, 1, naxis)\\n raise NoContentError(error)\\n\\n # an actual cutout\\n return [p1, p2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3944c9fc1141dc504b1e5b67af8842\",\n \"score\": \"0.51175594\",\n \"text\": \"def compute_bounds(xmin, xmax, xv):\\n\\n if xmin is not None or xmax is not None:\\n if type(xmin) == str and xmin.startswith(\\\"percentile\\\"):\\n xmin = np.nanpercentile(xv, float(xmin[11:-1]))\\n if type(xmax) == str and xmax.startswith(\\\"percentile\\\"):\\n xmax = np.nanpercentile(xv, float(xmax[11:-1]))\\n\\n if xmin is None or xmin == np.nanmin(xv):\\n xmin = np.nanmin(xv) - (xmax - np.nanmin(xv))/20\\n if xmax is None or xmax == np.nanmax(xv):\\n xmax = np.nanmax(xv) + (np.nanmax(xv) - xmin)/20\\n\\n return xmin, xmax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8717c6087944b7865283628348f7811\",\n \"score\": \"0.51162624\",\n \"text\": \"def scoreatpercentile(a, per, limit=(), interpolation_method='fraction'):\\n values = np.sort(a, axis=0)\\n if limit:\\n values = values[(limit[0] <= values) & (values <= limit[1])]\\n\\n idx = per /100. * (values.shape[0] - 1)\\n if (idx % 1 == 0):\\n score = values[int(idx)]\\n else:\\n if interpolation_method == 'fraction':\\n score = _interpolate(values[int(idx)], values[int(idx) + 1],\\n idx % 1)\\n elif interpolation_method == 'lower':\\n score = values[np.floor(idx)]\\n elif interpolation_method == 'higher':\\n score = values[np.ceil(idx)]\\n else:\\n raise ValueError(\\\"interpolation_method can only be 'fraction', \\\" \\\\\\n \\\"'lower' or 'higher'\\\")\\n\\n return score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21c0fbeb06e0114dc2b0524b60fa55d1\",\n \"score\": \"0.5093741\",\n \"text\": \"def unique_percentiles_beta(perc_values,\\n percentiles):\\n # normalize between 0 and 1\\n uniq, uniq_ind, counts = np.unique(\\n perc_values, return_index=True, return_counts=True)\\n if len(uniq) != len(perc_values):\\n min_value = np.min(perc_values)\\n perc_values = perc_values - min_value\\n max_value = np.max(perc_values)\\n perc_values = perc_values / max_value\\n percentiles = np.asanyarray(percentiles)\\n percentiles = percentiles / 100.0\\n\\n p, ier = sc_opt.curve_fit(betainc,\\n percentiles,\\n perc_values)\\n uniq_perc_values = sc_special.betainc(p[0], p[1], percentiles)\\n uniq_perc_values = uniq_perc_values * max_value + min_value\\n else:\\n uniq_perc_values = perc_values\\n return uniq_perc_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a24c0f72c5371263e914a0aa2c6875cd\",\n \"score\": \"0.50732625\",\n \"text\": \"def pdf_percentiles(self, percentile, **kwargs):\\n percentile = u.Quantity(percentile, u.percent).value\\n percs = np.percentile(self.distribution, percentile, axis=-1, **kwargs)\\n # numpy.percentile strips units for unclear reasons, so we have to make\\n # a new object with units\\n if hasattr(self.distribution, \\\"_new_view\\\"):\\n return self.distribution._new_view(percs)\\n else:\\n return percs\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":486,"cells":{"query_id":{"kind":"string","value":"8e02496a6c8282245427a4927c688a28"},"query":{"kind":"string","value":"Returns analytical solution for different moments of lognormal size distr"},"positive_passages":{"kind":"list like","value":[{"docid":"b2866e65341a2e864981cd5a55805301","score":"0.6666449","text":"def analytic_moms_for_lognormal(mom, n_tot, mean_r, gstdev):\n if mom == 0:\n ret = n_tot\n else:\n ret = n_tot * mean_r**mom * math.exp((mom**2)/2. * math.pow(math.log(gstdev), 2))\n return ret","title":""}],"string":"[\n {\n \"docid\": \"b2866e65341a2e864981cd5a55805301\",\n \"score\": \"0.6666449\",\n \"text\": \"def analytic_moms_for_lognormal(mom, n_tot, mean_r, gstdev):\\n if mom == 0:\\n ret = n_tot\\n else:\\n ret = n_tot * mean_r**mom * math.exp((mom**2)/2. * math.pow(math.log(gstdev), 2))\\n return ret\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8d0eeec394c6cdc872ab50fcf4dab135","score":"0.65942085","text":"def lognormvariate(self, mu, sigma):\n pass","title":""},{"docid":"3d8a95f64a721e2186597f80c6ebf099","score":"0.6581352","text":"def lognormvariate(self, mu, sigma):\n pass","title":""},{"docid":"3d8a95f64a721e2186597f80c6ebf099","score":"0.6581352","text":"def lognormvariate(self, mu, sigma):\n pass","title":""},{"docid":"3d8a95f64a721e2186597f80c6ebf099","score":"0.6581352","text":"def lognormvariate(self, mu, sigma):\n pass","title":""},{"docid":"580fc1dfa86b8b155db6d1ccb1748089","score":"0.63014734","text":"def mvn_log_pdf(x: np.ndarray, mu: np.ndarray, sigma_inv: np.ndarray, log_det: float) -> np.ndarray:\n n = len(mu)\n o = x - mu\n l = (sigma_inv@o.T).T\n maha = np.sum(o*l, axis=-1)\n ld = -0.5*(n*np.log(2*np.pi) + log_det + maha)\n return ld","title":""},{"docid":"67194f91f7757bec422379e29dbe8104","score":"0.6259757","text":"def log_density_gaussian(self, x, mu, logvar):\n norm = - 0.5 * (math.log(2 * math.pi) + logvar)\n log_density = norm - 0.5 * ((x - mu) ** 2 * torch.exp(-logvar))\n return log_density","title":""},{"docid":"63dfb0d098b2023ffc6a66988b866c7e","score":"0.6252234","text":"def perturb_lognormal(step, solver_order, noise_scale, random_state=None, size=()):\n shift = 0.5 * np.log(1 + noise_scale * (step ** (2 * solver_order)))\n mean = np.log(step) - shift\n cov = 2 * shift\n samples = np.exp(\n scipy.stats.multivariate_normal.rvs(\n mean=mean, cov=cov, size=size, random_state=random_state\n )\n )\n return samples","title":""},{"docid":"2c8e72c0d40d869c3dc1095c46976f13","score":"0.62147254","text":"def fit_temp_dens_relation(logoverden, logT):\n \n ind = np.where((0.1 < logoverden) * (logoverden < 1.0) * (0.1 < logT) * (logT < 5.0))\n\n logofor = logoverden[ind]\n logtfor = logT[ind]\n\n def min_func(param):\n \"\"\"Function to minimize: power law fit to temperature density relation.\"\"\"\n logT0 = param[0]\n gammam1 = param[1]\n #print(param)\n return logtfor - (logT0 + gammam1 * logofor)\n res = leastsq(min_func, np.array([np.log10(1e4), 0.5]), full_output=True)\n params = res[0]\n if res[-1] <= 0:\n print(res[3])\n return 10**params[0], params[1] + 1","title":""},{"docid":"f4c5d4f275de93fd710418af3a5194af","score":"0.62146217","text":"def nllfun(x):\n mu = x[0]\n logsigma = x[1]\n return logsigma + 0.5*np.sum((mu/(np.exp(logsigma) + 0.01))**2)","title":""},{"docid":"d9635372e367fad8db57a39300cf8d7f","score":"0.61965066","text":"def probNormalizeLog(distributions):\n if distributions.ndim > 1:\n distributions = distributions - np.max(distributions, axis=1,keepdims=True)\n else:\n distributions = distributions - np.max(distributions)\n try:\n prob = probNormalize(np.exp(distributions))\n except FloatingPointError, e:\n print distributions\n print np.max(distributions), np.min(distributions)\n raise e\n return prob","title":""},{"docid":"84609def7d928b62355d6405fa8c33e3","score":"0.61930674","text":"def LogMultiNorm(x, mu, sigma):\n return -(np.log(np.linalg.det(2 * np.pi * sigma)) + (x - mu).T.dot(np.linalg.inv(sigma)).dot(x - mu)) / 2","title":""},{"docid":"d4e5984e6728e98a7141a932ba7c3c57","score":"0.6185559","text":"def mh_lognormal(init,ys,ts,iters,fi=None):\n print(\"Running Metropolis Algorithm with Log-Normal proposals.\")\n D = len(init)\n my_dist = dist(init)\n # initialize state and log-likelihood\n state = init.copy()\n Lp_state = my_dist.log_likelihood(ys,ts)\n accepts = 0.\n\n cov = (0.1**2)*np.eye(D)*1./D\n for i in np.arange(0, iters):\n if fi is not None:\n write_samp(fi,state)\n\n # log(Rv) follow a normal distribution\n mu = np.log(state)\n # propose a new state\n prop = np.exp(np.random.multivariate_normal(mu, cov))\n move_p = np.log(scipy.stats.multivariate_normal(np.log(state),cov).\\\n pdf(np.log(prop)))\n rev_p = np.log(scipy.stats.multivariate_normal(np.log(prop),cov).\\\n pdf(np.log(state)))\n my_dist.set_params(prop)\n Lp_prop = my_dist.log_likelihood(ys,ts)\n #print Lp_prop+rev_p\n #print Lp_state+move_p\n rand = np.random.rand()\n prob = min(1,((Lp_prop+rev_p)-(Lp_state+move_p)))\n if np.log(rand) < prob:\n accepts += 1\n state = prop.copy()\n Lp_state = Lp_prop\n print (\"acct bc %s < %s (iter %s)\"%(np.log(rand),prob,i))\n print state\n else:\n my_dist.set_params(state)\n\n print 'Acceptance ratio', accepts/iters","title":""},{"docid":"a6b10391761e29e253292b101f2c6664","score":"0.6166954","text":"def log_density_gaussian(self, x: Tensor, mu: Tensor, logvar: Tensor):\n norm = -0.5 * (math.log(2 * math.pi) + logvar)\n log_density = norm - 0.5 * ((x - mu) ** 2 * torch.exp(-logvar))\n return log_density","title":""},{"docid":"938cacd752a86f855c8f5cc29b6401de","score":"0.6161923","text":"def lognormal_like(x, mu, tau):\n return flib.lognormal(x,mu,tau)","title":""},{"docid":"a0f6998de870e2aabc50ec979789b35f","score":"0.6140542","text":"def find_one_sigma_equivalent(logpost, x0, l0=None, dx_max=None):\n x0 = np.atleast_1d(x0)\n sigmas = np.zeros(x0.shape[0])\n\n if dx_max is None:\n dx_max = 0*x0 + 1.0\n\n if l0 is None:\n l0 = logpost(x0)\n \n for i in range(x0.shape[0]):\n xlow = x0.copy()\n xlow[i] -= dx_max[i]\n xhigh = x0.copy()\n xhigh[i] += dx_max[i]\n llow = logpost(xlow)\n lhigh = logpost(xhigh)\n\n while llow < l0 - 0.5 and not xlow[i] == x0[i]:\n dx = x0[i] - xlow[i]\n xlow = x0.copy()\n xlow[i] = x0[i] - dx/2\n llow = logpost(xlow)\n\n while lhigh < l0 - 0.5 and not xhigh[i] == x0[i]:\n dx = xhigh[i] - x0[i]\n xhigh = x0.copy()\n xhigh[i] = x0[i] + dx/2\n lhigh = logpost(xhigh)\n\n sigmas[i] = 0.5*(xhigh[i] - xlow[i])\n\n return sigmas","title":""},{"docid":"a007b0f38f199e4f8e89cc959b70b795","score":"0.60996205","text":"def ExpLogLikelihood(obs_data,pis,mus,sigmas,gamma):\n return np.sum(np.array(\n [np.sum(np.array(\n [gamma[n,k]*(np.log(pis[k])+LogMultiNorm(obs_data[n],mus[k],sigmas[k]))\n for k in range(K)]\n )) for n in range(N)]))","title":""},{"docid":"b44db12d97196d5ef28e9c9b9d90e7d9","score":"0.6062823","text":"def log_sum_from_individual_logs(logs: Sequence) -> float:\n\n\treturn scipy.special.logsumexp(logs)","title":""},{"docid":"a5d5ab6779225f64381d6557e3d374e3","score":"0.60575956","text":"def variances_log_chems(chemnames,logprior=1.0e20) :\n #senstraj = load('EndogenousEGFR3T3sensNoPriors')\n times = senstraj.timepoints \n jtjinv = scipy.linalg.inv(jtjtrunc+1.0/logprior**2*np.eye(\n len(jtjtrunc),len(jtjtrunc))) \n var = {}\n bestfit = {} \n optvarkeys = list(clc.optimizableVars.keys())\n first = optvarkeys[0]\n last = optvarkeys[-1]\n for name in chemnames :\n var[name] = [] \n bestfit[name] = [] \n chemindex = senstraj.key_column.get(name) \n index1sens = senstraj.key_column.get((name,first))\n index2sens = senstraj.key_column.get((name,last))\n sensarray_this_chem = copy.copy(senstraj.values[:,index1sens:(index2sens+1)])\n traj_this_chem = copy.copy(senstraj.values[:,chemindex]) \n for j, pname in enumerate(ovvarnames) :\n sensarray_this_chem[:,j] = sensarray_this_chem[:,j]*curp.get(pname)\n # need to scale each row by 1/chemvalue to mimic a derivative w.r.t. \n # log chemicals. Add a small value to chemvalue to avoid divide by zero\n for i in range(len(times)) :\n sensarray_this_chem[i,:] = old_div(sensarray_this_chem[i,:],(traj_this_chem[i]+1.0e-6))\n\n tmp = np.dot(sensarray_this_chem,jtjinv)\n for i in range(len(tmp[:,0])) :\n var[name].append(np.dot(tmp[i,:],sensarray_this_chem[i,:]))\n \n bestfit[name] = senstraj.values[:,chemindex]\n var[name] = np.asarray(var[name])\n return times,bestfit,var","title":""},{"docid":"1c548ce3307a9c8bcc81afc95642ef02","score":"0.60562223","text":"def gaussian_entropy(logdet_V, D):\n return -0.5*logdet_V + 0.5*D + 0.5*D*np.log(2*np.pi)","title":""},{"docid":"376ac334ff110828709ad01f411a4e80","score":"0.60548127","text":"def estimate_lognormal(self, data):\r\n\r\n if self.fit_method == 1:\r\n _results = LogNormal().maximum_likelihood_estimate(data,\r\n self.start_time,\r\n self.rel_time)\r\n\r\n self.n_suspensions = _results[3]\r\n self.n_failures = _results[4]\r\n elif self.fit_method == 2:\r\n _results = regression(data, self.start_time, self.rel_time,\r\n dist='lognormal')\r\n\r\n self.rho = _results[3]\r\n self.n_suspensions = _results[4]\r\n self.n_failures = _results[5]\r\n\r\n self.scale[1] = _results[0][0]\r\n self.shape[1] = _results[0][1]\r\n self.variance[0] = _results[1][0] # Scale\r\n self.variance[1] = _results[1][2] # Shape\r\n self.covariance[0] = _results[1][1] # Scale-Shape\r\n self.mle = _results[2][0]\r\n self.aic = _results[2][1]\r\n self.bic = _results[2][2]\r\n\r\n self.calculate_parameter_bounds(data)\r\n self.hazard_function()\r\n self.reliability_function()\r\n self.mean()\r\n\r\n return False","title":""},{"docid":"b39dfc969c17978a0f94f0a31343913a","score":"0.60535073","text":"def asym_dist(_,dat_v,__,rescale):\n from scipy.stats import linregress\n lg = lambda x: linregress(range(len(x.reshape(-1))),x.reshape(-1)).intercept\n flat = lambda x: np.array([g for m in x for g in m ])\n sxd2 =[]; sgd2 = []\n for j in dat_v:\n for k in j:\n if type(k[1][1])==float and (np.isnan(k[1][1])): \n continue\n else:\n # Predict the length and gfp at begin\n x1 = k[1][0]\n x2 = k[1][1]\n x1,x2 = [lg(x) for x in [x1,x2]]\n perfx = np.log((np.exp(x1)+np.exp(x2)))-rescale*np.log(2) # perfect division\n sxd2.append((perfx-x1,perfx-x2))\n return flat(sxd2),_,_","title":""},{"docid":"112975ac963ab3456cb72ee58f5f29e8","score":"0.6031262","text":"def mvn_nll(mu: np.ndarray, sigma: np.ndarray, x: np.ndarray) -> float:\n k = len(mu)\n Id = np.eye(k)\n chol = np.linalg.cholesky(sigma)\n chol_inv = spla.solve_triangular(chol, Id, lower=True)\n sigma_inv = chol_inv.T@chol_inv\n log_det = 2*np.sum(np.log(np.diag(chol)))\n nll = -np.mean(mvn_log_pdf(x, mu, sigma_inv, log_det))\n return nll","title":""},{"docid":"2ab1234045b8fe0f2bb103ed75ff9e16","score":"0.60305643","text":"def _simple_relative_entropy_implementation(\n self, rho, sigma, log_base=np.log, tol=1e-12):\n # S(rho || sigma) = sum_i(p_i log p_i) - sum_ij(p_i P_ij log q_i)\n rvals, rvecs = rho.eigenstates()\n svals, svecs = sigma.eigenstates()\n rvecs = np.hstack([vec.full() for vec in rvecs]).T\n svecs = np.hstack([vec.full() for vec in svecs]).T\n # Calculate S\n S = 0\n for i in range(len(rvals)):\n if abs(rvals[i]) >= tol:\n S += rvals[i] * log_base(rvals[i])\n for j in range(len(svals)):\n P_ij = (\n np.dot(rvecs[i], svecs[j].conjugate()) *\n np.dot(svecs[j], rvecs[i].conjugate())\n )\n if abs(svals[j]) < tol and not (\n abs(rvals[i]) < tol or abs(P_ij) < tol):\n # kernel of sigma intersects support of rho\n return np.inf\n if abs(svals[j]) >= tol:\n S -= rvals[i] * P_ij * log_base(svals[j])\n return np.real(S)","title":""},{"docid":"a020a4d01f78878c953a73297fc59966","score":"0.6013505","text":"def log_normal(x, m, v):\r\n\tdim=m.shape[-1]\r\n\tconst=-dim/2*np.log(2*np.pi)\r\n\tlog_prob=const-0.5*torch.log(v.sum(axis=-1))-0.5*((x-m)**2/v).sum(axis=-1)\r\n\treturn log_prob","title":""},{"docid":"12ad68ce7b933684c90bad0cf0a843ff","score":"0.6008636","text":"def variational_objective(var_param):\n samples = var_family.sample(var_param, n_samples)\n a = partial(var_family.logdensity,var_param=var_param)\n def nested_fn(samples):\n lower_bound = np.mean(logdensity(samples)) - np.mean(a(samples))\n return -lower_bound\n b= nested_fn(samples)\n return b","title":""},{"docid":"5efc579549f3cea6398f4b373fd27ec8","score":"0.60071135","text":"def mul_var_normal(weights, means, logvars):\n ll = 0\n\n for i in range(len(weights)):\n w = weights[i]\n mean = means[i]\n if len(logvars) > 1:\n logvar = logvars[i]\n var = logvar.exp()\n else:\n logvar = logvars[0]\n var = math.exp(logvar)\n\n logstd = logvar * 0.5\n ll += torch.sum(\n -((w - mean)**2)/(2*var) - logstd - math.log(math.sqrt(2*math.pi))\n )\n\n return ll","title":""},{"docid":"10ab5a5dcc24df49eafc7e9cd03a7595","score":"0.6000301","text":"def lognorm_lev(mu, sigma, n, limit):\n if limit == -1:\n return np.exp(n * mu + n * n * sigma * sigma / 2)\n else:\n phi = ss.norm.cdf\n ll = np.log(limit)\n sigma2 = sigma * sigma\n phi_l = phi((ll - mu) / sigma)\n phi_l2 = phi((ll - mu - n * sigma2) / sigma)\n unlimited = np.exp(n * mu + n * n * sigma2 / 2)\n return unlimited * phi_l2 + limit ** n * (1 - phi_l)","title":""},{"docid":"4706bd641d785d747c4bae16a3edd046","score":"0.59839344","text":"def lognorm_approx(ser):\n m, cv = xsden_to_meancv(ser.index, ser.values)\n mu, sigma = mu_sigma_from_mean_cv(m, cv)\n fz = ss.lognorm(sigma, scale=np.exp(mu))\n return fz","title":""},{"docid":"a2c6a51aad5fc400140a71a95ae387f2","score":"0.5978666","text":"def lognormal_expval(mu, tau):\n return np.exp(mu + 1./2/tau)","title":""},{"docid":"55658e99d76eda4013761be57aff6a5e","score":"0.59774536","text":"def logsum(vfun, lam):\n\n # rescale values for numeric robustness\n vmax = np.max(vfun, axis=0)\n vfun_norm = vfun - vmax\n\n # apply formula (could be njitted in separate function)\n VE = vmax + lam * np.log(np.sum(np.exp(vfun_norm / lam), axis=0))\n return VE","title":""},{"docid":"2b6f90eab6a8dfa590499b46151ad445","score":"0.59736603","text":"def loglikelihood(self):\n # update the latent variances based on the updated latent covariances matrices\n self.latent_variances = [np.diag(Si) for Si in self.latent_Sigmas]\n\n # add up the terms that are valid over the entire index 1 <= i <= N\n determinants = [np.prod(np.diag(Sigma)) for Sigma in self.latent_Sigmas]\n traces = [np.trace(Sigma) for Sigma in self.latent_Sigmas]\n miTmi = [np.sqrt(sum(mi**2)).item() for mi in self.latent_means]\n\n global_terms = [0.5*(np.log(det) - tr - mi_norm) for tr, det, mi_norm in zip(traces, determinants, miTmi)]\n\n # now for the terms in Omega_plus\n\n def get_omega_terms(plus=True):\n \"\"\" Compute the summation terms over Omega plus or Omega minus \"\"\"\n if plus:\n B = self.B1 \n error_func = lambda x: SQRT_PI_OVER_2*erfc(x/ROOT2)\n exp_func = lambda x: np.exp(-0.5*x**2)\n index_set = self.I1\n mu = self.mu1\n\n else:\n B = self.B2\n error_func = lambda x: SQRT_PI_OVER_2*(erf(x/ROOT2) + 1)\n exp_func = lambda x: -np.exp(-0.5*x**2)\n index_set = self.I2\n mu = self.mu2\n\n BTB = np.matmul(B.T, B)\n\n # these are the 'delta_i' parameters when nu=e_q, beta=0\n deltas = [-self.latent_means[i][-1]/np.sqrt(self.latent_variances[i][-1]) for i in index_set]\n \n _BTBSigmas = [np.matmul(BTB, self.latent_Sigmas[i]) for i in index_set]\n\n _diagonal_terms = [BTBS[-1][-1] for BTBS in _BTBSigmas]\n _exp_terms = [delta*exp_func(delta) for delta in deltas]\n _erf_terms = [error_func(delta) for delta in deltas]\n _trace_terms = [np.trace(BTBS) for BTBS in _BTBSigmas]\n _quadratic_terms = [\n np.matmul(\n np.matmul(\n self.latent_means[i].T,\n BTB\n ),\n self.latent_means[i]\n ) for i in index_set\n ]\n _yi_terms = [\n np.matmul(\n self.Y[i] - mu,\n (self.Y[i] - mu).T - 2*np.matmul(B, self.latent_means[i])\n ) for i in index_set\n ]\n\n _terms = [\n item1*item2.item() + item3*(item4 + item5 + item6) for item1, \n item2, \n item3, \n item4, \n item5, \n item6 in zip(\n _diagonal_terms,\n _exp_terms,\n _erf_terms,\n _trace_terms,\n _quadratic_terms,\n _yi_terms\n )\n ]\n\n return _terms\n\n self.omega_plus_terms = get_omega_terms(plus=True)\n self.omega_minus_terms = get_omega_terms(plus=False)\n\n # finally, compute the scalars that are independent of the data and latent variables\n scalars = 0.5*self.N*(self.q - self.p*np.log(TWOPI*self.sigma2))\n\n # add all of the terms together\n total = 0.5*np.sum(global_terms) - (TWOPI**-0.5)/(2*self.sigma2)*(np.sum(self.omega_plus_terms) + np.sum(self.omega_minus_terms))\n\n return total + scalars","title":""},{"docid":"8caea465be9179151d905a6c5c0efb3b","score":"0.59699017","text":"def norm_log_pdf(x: np.ndarray, mu: float, sigmasq: float) -> np.ndarray:\n o = x - mu\n ld = -0.5*np.square(o) / sigmasq - 0.5*np.log(sigmasq) - 0.5*np.log(2*np.pi)\n return ld","title":""},{"docid":"b77de7150fb7ba682385f2a12075fe2a","score":"0.59502894","text":"def gaussian_real(mean, logsd, dim):\n B, C, m = assert_real(mean, dim)\n assert mean.shape == logsd.shape\n\n class o(object):\n pass\n\n o.mean = mean\n o.logstd = logsd\n # logcov = torch.zeros(B, C, m, m, m, dim, dim).to(logsd.device)\n # covinv = torch.zeros(B, C, m, m, m, dim, dim).to(logsd.device)\n # for d in range(dim):\n # # logcov[..., d, d] = logsd[..., d]\n # # covinv[..., d, d] = 1 / torch.exp(logsd[..., d])\n # o.logcov = logcov\n stdinv = 1 / torch.exp(logsd) # [B, C, m, m, m, 6]\n o.eps = torch.randn(mean.shape)\n o.sample = mean + torch.exp(logsd) * o.eps.to(mean.device)\n o.sample2 = lambda eps: mean + torch.exp(logsd) * eps.to(mean.device)\n\n nlog2pi = dim * np.log(2 * np.pi)\n logcov = logsd.sum(dim=-1).unsqueeze(-1) # [B, C, m, m, m, 1]\n #centerx = (x - mean).unsqueeze(-2) # [B, C, m, m, m, 1, 6]\n #centerxT = (x - mean).unsqueeze(-1) # [B, C, m, m, m, 6, 1]\n #xsquare = centerx @ covinv # [B, C, m, m, m, 1, 6]\n #xsquare = xsquare @ centerxT # [B, C, m, m, m, 1, 1]\n #xsquare = xsquare.squeeze(-1) # [B, C, m, m, m, 1]\n o.logps = lambda x: -0.5 * (nlog2pi + logcov + (x - mean) ** 2 * stdinv)\n #o.logps = lambda x: -0.5 * ((x - mean) ** 2 * stdinv)\n # o.logps = lambda x: -0.5 * (dim * np.log(2 * np.pi) + logsd.sum(dim=-1).unsqueeze(-1) + (x - mean) ** 2 / torch.exp(2. * logsd))\n o.logp = lambda x: o.logps(x).sum(dim=(1, 2, 3, 4))\n # o.get_eps = lambda x: (x - mean) / torch.exp(logsd)\n return o","title":""},{"docid":"fe14cb182e709b598fa482314a4bf9ad","score":"0.5939857","text":"def lognormal(x, p, g):\n x = numpy_array(x) # check if numpy array, if not make numpy array\n x = assert_2d_sort(x)\n return np.exp(normal(np.log(x), p, g))","title":""},{"docid":"5c97bbe7843ca6b572d70684a73044bb","score":"0.590387","text":"def sklogn_analytical_likelihood(self, ddt):\n # skewed lognormal distribution with boundaries\n if (ddt < self.lam) or ((-self.mu + math.log(ddt - self.lam)) ** 2 / (2. * self.sigma ** 2) > self.explim):\n return -np.inf\n else:\n llh = math.exp(-((-self.mu + math.log(ddt - self.lam)) ** 2 / (2. * self.sigma ** 2))) / (\n math.sqrt(2 * math.pi) * (ddt - self.lam) * self.sigma)\n\n if np.isnan(llh):\n return -np.inf\n else:\n return np.log(llh)","title":""},{"docid":"dfeb215b72bc80bcb1926ec78e0f9ce9","score":"0.5900217","text":"def random_lognormal(args):\n return DecisionParser.rand_x_normal(random.lognormvariate, args)","title":""},{"docid":"6102af8d42605f24ca4dfd1fa7a8b06b","score":"0.58979064","text":"def normal_log_prob(latent: base.Array, sigma: float = 1, mu: float = 0):\n latent, _ = jax.tree_flatten(latent)\n latent = jax.tree_map(lambda x: x.flatten(), latent)\n latent = jnp.concatenate(latent)\n latent_dim = len(latent)\n latent_l2_sq = jnp.sum(jnp.square(latent - mu))\n return -0.5 * (latent_dim * jnp.log(2 * jnp.pi * sigma**2)\n + latent_l2_sq / sigma**2)","title":""},{"docid":"5190dec56ab9211d1c3ef39a29eeec84","score":"0.5889136","text":"def logistic_var():\n z = np.linspace(-5,5,1000) # z points to test\n p1 = 1/(1+np.exp(-z)) # target probability\n var_test = np.linspace(2,3,1000)\n err = []\n for v in var_test:\n p2 = 0.5*(1+scipy.special.erf(z/np.sqrt(v*2)))\n err.append(np.mean((p1-p2)**2))\n \n i = np.argmin(err)\n wvar = var_test[i]\n return wvar","title":""},{"docid":"d423b90c71b56106a8f0e940c7590870","score":"0.58890647","text":"def log_normal(x, m, v):\n element_wise = -0.5 * (torch.log(v) + (x - m).pow(2) / v + np.log(2 * np.pi))\n log_prob = element_wise.sum(-1)\n return log_prob","title":""},{"docid":"c5b65a9d86e7e4fbbe77287721721fb5","score":"0.5877169","text":"def my_loglike(theta, x, data, sigma):\n sim_bins = run_exp_three_times_and_bin(theta, x)\n\n return -(0.5 / sigma ** 2) * np.sum((data - sim_bins) ** 2)","title":""},{"docid":"6e38432ed5b66405b26ac10a2549111f","score":"0.58750695","text":"def variational_objective(var_param):\n samples = var_family.sample(var_param, n_samples)\n lower_bound = np.mean(logdensity(samples)) - np.mean(var_family.logdensity(samples, var_param))\n return -lower_bound","title":""},{"docid":"b5bfe401b02d624a2c0704860992ab34","score":"0.587297","text":"def _independent_gaussian_log_prob_vector(x, mus, sigmas):\n # The inverse of a diagonal matrix is just inverting values on the\n # diagonal\n cov_inv = torch.eye(d) * (1 / sigmas ** 2)\n\n # sum(log) = log(prod)\n logpdf = (-d / 2 * torch.log(2 * torch.as_tensor(torch.pi)) -\n torch.log(torch.prod(sigmas))) \\\n - 0.5 * mahalanobis(x[:3], mus, cov_inv) ** 2\n return logpdf","title":""},{"docid":"31263a909b6110aa5bdc41ef7892bf8b","score":"0.58668375","text":"def siglog_normal_init_guess(self, reg_pnts_array, pos_traj):\n\n def make_sigma_equation(reg_pnts):\n \"\"\"\n input is a series of registration points t1 - t5\n though only t1, t3, t5 are useful\n \"\"\"\n def sigma_estimator(sigma):\n #do float here, otherwise the second term will be ZERO!!\n return ((np.exp(-sigma**2+3*sigma) - 1) / (np.exp(sigma*6) - 1) - \n float(reg_pnts[2] - reg_pnts[0]) / (reg_pnts[4] - reg_pnts[0]))\n #return (np.exp(sigma**2) - 1) / (np.exp(6*sigma**2) - 1) - (reg_pnts[2] - reg_pnts[0]) / (reg_pnts[4] - reg_pnts[0])\n return sigma_estimator\n\n init_guess = []\n vel_profile=self.get_vel_profile(pos_traj)/self.dt\n for reg_pnts in reg_pnts_array:\n #make an estimator for sigma\n sig_est_func = make_sigma_equation(reg_pnts)\n #solve it\n init_sigma = (reg_pnts[4] - reg_pnts[0])/2*self.dt\n #<hyin/Dec-24-2014> solving the equation is still not clear and the results seem not right\n #more investigation is needed to know the derivation of the equation, e.g., how the units are consistent...\n #currently, use an even more simple heuristics...\n sig_sln = sciopt.broyden1(sig_est_func, init_sigma, f_tol=1e-14)\n #try direct nonlinear optimization, note the returned function is modified with square\n #see sigma_estimator above\n #sig_sln = sciopt.fminbound(sig_est_func, 0, init_sigma*3) #search between (0, 3*init_sigma)\n #print sig_sln, sig_est_func(sig_sln)\n sig = sig_sln #only need scalar value\n #sig = init_sigma\n if sig <= 0:\n #will this happen?\n #this will happen when actual mode locates on the right side of 'Gaussian mean'\n #lognormal distribution is asymmetrical in regards of mode, but does it always distribute more mass on left side? \n #(okay, i know it's long tail, more mass means some more slopeness when going up)\n sig = np.abs(sig)\n a_array = np.array([3*sig, 1.5*sig**2+sig*np.sqrt(0.25*sig**2+1),\n sig**2, 1.5*sig**2-sig*np.sqrt(0.25*sig**2+1), -3*sig])\n #estimate mu\n mu_1 = np.log((reg_pnts[2]-reg_pnts[0])*self.dt/(np.exp(-a_array[2])-np.exp(-a_array[0])))\n mu_2 = np.log((reg_pnts[4]-reg_pnts[0])*self.dt/(np.exp(-a_array[4])-np.exp(-a_array[0])))\n mu = (mu_1 + mu_2)/2\n #estimate D\n D_array = np.array([np.sqrt(np.pi*2)*vel_profile[i]*np.exp(mu)*sig\n *np.exp((a_array[i]**2/(2*sig**2)-a_array[i])) for i in range(len(a_array))])\n D = np.average(D_array)\n #estimate t0\n t0_array = np.array([reg_pnts[i]*self.dt - np.exp(mu)*np.exp(-a_array[i]) for i in range(len(a_array))])\n t0 = np.average(t0_array)\n\n theta_s, theta_e = self.siglog_normal_init_ang_guess((D, t0, mu, sig),\n pos_traj, reg_pnts)\n \n #add\n init_guess.append((D, t0, mu, sig, theta_s, theta_e))\n return init_guess","title":""},{"docid":"deff43e9eb3fbd415a669c8fa0b9311c","score":"0.5852983","text":"def logchol2sigma(params):\n U = torch.zeros((2, 2))\n U.view(-1)[[0, 1, 3]] = params # fill upper triangle\n U.diagonal().exp_()\n return U.t() @ U","title":""},{"docid":"db9e2badc5c85d6af8b407796cdb3773","score":"0.58300114","text":"def logGaussian(x, mu, var):\n\n d = len(x)\n log_prob = -1/(2*var) * (x-mu) @ (x-mu)\n log_prob-= d/2 * np.log(2*np.pi*var)\n\n return log_prob","title":""},{"docid":"dd1998b3be485ff0688207ce547a3c43","score":"0.5825949","text":"def lognormal_param_transform(self):\n M = self.data_mean\n S = self.data_stddev\n variance = numpy.log( (S**2) / (M**2) + 1)\n sigma = numpy.sqrt(variance)\n mu = numpy.log(M) - (0.5 * (sigma**2))\n\n return mu, sigma","title":""},{"docid":"a43e06cfcd1faba629eff7d532b418c9","score":"0.5825454","text":"def lognorm_mean(mean, sigma):\n if sigma == 0:\n return DeterministicDistribution(mean)\n else:\n mu = mean * np.exp(-0.5 * sigma**2)\n return stats.lognorm(scale=mu, s=sigma)","title":""},{"docid":"7a9c6360299bb5ac21d56b0ff9a08da0","score":"0.5823315","text":"def partial_e(sev_name, fz, a, n):\n\n if sev_name not in ['lognorm', 'gamma', 'pareto', 'expon']:\n raise NotImplementedError(f'{sev_name} NYI for analytic moments')\n\n if a == 0:\n return [0] * (n+1) # for k in range(n+1)]\n\n if sev_name == 'lognorm':\n m = fz.stats('m')\n sigma = fz.args[0]\n mu = np.log(m) - sigma**2 / 2\n ans = [np.exp(k * mu + (k * sigma)**2 / 2) *\n (ss.norm.cdf((np.log(a) - mu - k * sigma**2)/sigma) if a < np.inf else 1.0)\n for k in range(n+1)]\n return ans\n\n elif sev_name == 'expon':\n # really needed for MEDs\n # expon is gamma with shape = 1\n scale = fz.stats('m')\n shape = 1.\n lgs = loggamma(shape)\n ans = [scale ** k * np.exp(loggamma(shape + k) - lgs) *\n (ss.gamma(shape + k, scale=scale).cdf(a) if a < np.inf else 1.0)\n for k in range(n + 1)]\n return ans\n\n elif sev_name == 'gamma':\n shape = fz.args[0]\n scale = fz.stats('m') / shape\n # magic ingredient is the norming constant\n # c = lambda sh: 1 / (scale ** sh * gamma(sh))\n # therefore c(shape)/c(shape+k) = scale**k * gamma(shape + k) / gamma(shape)\n # = scale ** k * exp(loggamma(shape + k) - loggamma(shape)) to avoid errors\n ans = [scale ** k * np.exp(loggamma(shape + k) - loggamma(shape)) *\n (ss.gamma(shape + k, scale=scale).cdf(a) if a < np.inf else 1.0)\n for k in range(n + 1)]\n return ans\n\n elif sev_name == 'pareto':\n # integrate xf(x) even though nx^n-1 S(x) may be more obvious\n # former fits into the overall scheme\n # a Pareto defined by agg is like so: ss.pareto(2.5, scale=1000, loc=-1000)\n α = fz.args[0]\n λ = fz.kwds['scale']\n loc = fz.kwds.get('loc', 0.0)\n # regular Pareto is scale=lambda, loc=-lambda, so this has no effect\n # single parameter Pareto is scale=lambda, loc=0\n # these formulae for regular pareto, hence\n if λ + loc != 0:\n logger.log(WL, 'Pareto not shifted to x>0 range...using numeric moments.')\n return partial_e_numeric(fz, a, n)\n ans = []\n # will return inf if the Pareto does not have the relevant moments\n # TODO: formula for shape=1,2,3\n for k in range(n + 1):\n b = [α * (-1) ** (k - i) * binom(k, i) * λ ** (k + α - i) *\n ((λ + a) ** (i - α) - λ ** (i - α)) / (i - α)\n for i in range(k + 1)]\n ans.append(sum(b))\n return ans","title":""},{"docid":"16ca565d23368f1890e2a2a6fdb096f2","score":"0.58224374","text":"def gaussian_log_density(mean, log_var, x):\n std = torch.exp(log_var).to(device)\n # return 0.5 * tf.reduce_sum(tf.square((x - self.mean) / self.std), axis=-1) \\\n # + 0.5 * np.log(2.0 * np.pi) * tf.to_float(tf.shape(x)[-1]) \\\n # + tf.reduce_sum(self.logstd, axis=-1)\n neg_log_density = 0.5 * torch.sum(torch.pow((x - mean)/(std + 1e-6), 2), dim=-1) + \\\n 0.5 * np.log(2.0 * np.pi) * x.size(-1) + \\\n torch.sum(log_var, dim=-1)\n return -neg_log_density","title":""},{"docid":"ec67d6d23ec7f46fea59ca9b0b76256d","score":"0.581852","text":"def vec_lnlognorm(x, mu, sigma, shift=0.):\n\n x_ = np.where(0 < (x - shift), (x - shift), 1)\n result1 = -np.log(np.sqrt(2. * math.pi) * x_ * sigma) - (np.log(x_) - mu)\\\n ** 2 / (2. * sigma ** 2)\n result = np.where(0 < (x - shift), result1, float(\"-inf\"))\n\n return result","title":""},{"docid":"050fb452799d89d7c153f49a921d4612","score":"0.5813111","text":"def FD_log2(x, f_x, nn, liminf, suplim):\n \"\"\"Input: time starting with average dt(comming from repulsive_FD function), and f_time related to that time array\"\"\"\n lim_inf = round(np.log10(liminf),2)\n sup_lim = round(np.log10(suplim),2)\n b = np.linspace(lim_inf, sup_lim, nn)\n x_log = 10.0**b\n fx_log = np.zeros(len(x_log))\n for j in range(1, len(x_log)-1):\n for i in range(len(x)-1):\n if (x_log[j] - x[i])*(x_log[j] - x[i+1]) < 0.0 : #change of sign\n if (x_log[j] - x[i]) < (x_log[j] - x[i+1]):\n x_log[j] = x[i]\n fx_log[j] = f_x[i]\n else:\n x_log[j] = x[i+1]\n fx_log[j] = f_x[i+1]\n return x_log, fx_log","title":""},{"docid":"4db7054583dd634dc0f92dd137a35a1d","score":"0.5806972","text":"def logGaussian(x, mu, var):\n d = len(x)\n log_prob = -1/(2*var) * (x-mu) @ (x-mu)\n log_prob-= d/2 * np.log(2*np.pi*var)\n\n return log_prob","title":""},{"docid":"c35a72bb05fa39866346eca9ce7021c1","score":"0.58031434","text":"def estimate_from_work_samples(work_samples):\n return np.log(np.mean(np.exp(-np.array(work_samples))))","title":""},{"docid":"4f5946f067e47ef72938d4ef51974e78","score":"0.5798444","text":"def _get_log_norms(ti_mps, str_set):\n core_tensor, boundaries = ti_mps.core_tensor, get_bound_mats(ti_mps)\n str_lens, max_len = str_set.str_lens, str_set.index_mat.shape[1]\n \n # Unpack boundary matrices and define rightward transfer operator\n left_mat, right_mat = boundaries\n t_op = build_t_op(core_tensor, direction='right', jitted=True)\n\n # Function implementing a single step of computing log_norm\n def scan_iter(iter_state, _):\n # scan_iter: c -> a -> (c, b)\n # iter_state holds a running log_norm and a density operator\n old_norm, old_density = iter_state\n\n # Apply transfer_op and normalize output\n new_density = t_op(old_density)\n this_norm = np.linalg.norm(new_density)\n new_density = new_density / this_norm\n\n new_norm = old_norm + np.log(this_norm)\n out_norm = new_norm + np.log(utils.hs_dot(right_mat, new_density))\n\n return (new_norm, new_density), out_norm\n\n # Apply transfer operator many times (starting with left_mat), and \n # generate list of log_norms\n iter_init = (0., left_mat)\n _, proper_log_norms = jax.lax.scan(scan_iter, iter_init, \n np.empty(max_len))\n assert len(proper_log_norms) == max_len\n\n # Add norm for the empty distribution and pick out lengths of interest\n empty_log_norm = np.log(utils.hs_dot(left_mat, right_mat))[None]\n all_log_norms = np.concatenate((empty_log_norm, proper_log_norms))\n log_norms = all_log_norms[str_lens]\n\n return log_norms","title":""},{"docid":"e2ca7d3bf09e73b4dfc7d45108cc813e","score":"0.57801867","text":"def resCalc(mean, sigma):\n return (sigma*2*np.sqrt(2*np.log(2)))/mean","title":""},{"docid":"9f8e11e03fdd2c200e9e4036ab97f912","score":"0.57784307","text":"def log_multinomial_density(X, log_thetas):\n lpr = np.zeros((X.shape[0], len(log_thetas)))\n\n for i in range(X.shape[0]):\n for j in range(len(log_thetas)):\n lpr[i][j] = np.sum([log_thetas[j][dim][value] for dim, value in enumerate(X[i])])\n return lpr","title":""},{"docid":"7e042b04c2979bac0fc263db3c3f6c6e","score":"0.5771863","text":"def test_log_normal_diag(self):\n N = 100\n S = 50\n D = 10\n\n means = torch.randn(S, D)\n covs = torch.rand(S, D)\n x = torch.randn(N, D)\n\n distributions = [\n dist.MultivariateNormal(means[i], torch.diag(covs[i]))\n for i in range(S)\n ]\n\n expected = []\n for item in x:\n e_item = []\n for d in distributions:\n e_item.append(d.log_prob(item))\n expected.append(e_item)\n expected = torch.as_tensor(expected)\n\n predicted = log_normal(x, means, covs, 'diag')\n\n self.assertTrue(\n torch.allclose(expected, predicted, atol=1e-03, rtol=1e-05)\n )","title":""},{"docid":"5ab652340a5964931b37b8fbf6cb1886","score":"0.57699895","text":"def log_norm_medium_concentration(scale, dimension):\n scale = np.asfarray(scale)\n shape = scale.shape\n scale = scale.flatten()\n\n # Function is unstable at zero.\n # Scale needs to be float for this to work.\n scale[scale < 1e-2] = 1e-2\n\n r_range = range(dimension - 2 + 1)\n r = np.asarray(r_range)[None, :]\n\n # Mardia1999Watson Equation 3\n temp = scale[:, None] ** r * np.exp(-scale[:, None]) / \\\n np.asarray([math.factorial(_r) for _r in r_range])\n\n return (\n np.log(2.) + dimension * np.log(np.pi) +\n (1. - dimension) * np.log(scale) + scale +\n np.log(1. - np.sum(temp, -1))\n ).reshape(shape)","title":""},{"docid":"710ad092912ee20724ae0d0569bd8a17","score":"0.57575834","text":"def loglike_MNL(self):\n\n top = np.zeros(shape=self.av.shape[2], dtype=\"float64\")\n bottom = np.zeros(shape=self.av.shape[2], dtype=\"float64\")\n for c in range(self.count_c):\n for e in range(self.count_e):\n top += (\n self.av[c][e] * self.choice[c][e] * np.exp(self.get_utility(c, e))\n )\n bottom += self.av[c][e] * np.exp(self.get_utility(c, e))\n\n log_res = self.weight_vector * np.log(top / bottom)\n res = np.nansum(log_res)\n number_nan = np.sum(np.isnan(log_res))\n\n return res, number_nan","title":""},{"docid":"7c7fa0812243e0cc7023f455fa1c9c0c","score":"0.5737334","text":"def log_normal_variance(mu, sigma):\n exponent1 = sigma**2\n exponent2 = 2 * mu + sigma**2\n return (np.exp(exponent1) - 1) * np.exp(exponent2)","title":""},{"docid":"53e603f0d38b96387e0e1e8597c15f38","score":"0.57331264","text":"def arlognormal_like(x, a, sigma, rho):\n return flib.arlognormal(x, np.log(a), sigma, rho, beta=1)","title":""},{"docid":"fe1b87d9802efb5bcedf4108a7b9cec0","score":"0.5730004","text":"def log_normal(X, mu, sigma):\n N = len(X)\n loglik = -(N/2)*np.log(2*np.pi) - (N/2)*np.log(sigma**2) - (1/(2*sigma**2))*(np.sum((X-mu)**2))\n return loglik","title":""},{"docid":"2d0b20baebc724376977507364b188fc","score":"0.5711383","text":"def log_prior(self, params):\n # log likelihood function, see:\n # https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Likelihood_function\n var = self.std**2\n nitems = params.size\n diff = params - self.mean\n scaled_sq_err_term = -0.5 * jnp.dot(diff, diff) / var\n # log determinant of covariance matrix\n log_det_cov_term = -nitems * jnp.log(self.std)\n norm_term = -0.5 * nitems * jnp.log(2 * jnp.pi)\n return log_det_cov_term + scaled_sq_err_term + norm_term","title":""},{"docid":"f96e5db369a9bbac865733badb1c0370","score":"0.5706779","text":"def __logLikelihood(self, X, means, covars, ps):\n\n error = 0.0\n for n, xn in enumerate(X):\n arg = 0.0\n for k in range(means.shape[0]):\n arg += ps[k] * multiGaussianPDF(xn, means[k], covars[k])\n error += np.log(arg)\n return error","title":""},{"docid":"dd944f969bb22dd17eddc28fabd55b38","score":"0.56974983","text":"def geometricMean(alldistribT):\n return np.exp(np.mean(np.log(alldistribT), axis=1))","title":""},{"docid":"72ce5449d4fd3a39092c20c8249a6872","score":"0.5695466","text":"def neglog_density_normal(x, mean=0., sigma=1.):\n return np.sum((x - mean)**2, axis=1) / 2 / sigma**2","title":""},{"docid":"1968b41fcf935ee6655c5bdc1c13e2c7","score":"0.569209","text":"def matrix_log_density_gaussian(x, q):\n x = torch.unsqueeze(x, dim=1)\n _mu = torch.unsqueeze(q.loc, dim=0)\n _sigma = torch.unsqueeze(q.scale, dim=0)\n _q = Normal(loc=_mu, scale=_sigma)\n return _q.log_prob(x)","title":""},{"docid":"42fad586b8f5e0707ca5d291f9fd5178","score":"0.5684034","text":"def delta_loglike(self):\n try:\n # rewrite due to deprication of numpy.matrix\n # gm = np.matrix(self.gradient())\n # H = self.hessian()\n # return (gm * H.I * gm.T)[0,0]/4\n g = self.gradient()\n H = self.hessian()\n return np.dot( np.dot(g, np.linalg.inv(H)) , g)/4\n except Exception as msg:\n print ('Failed log likelihood estimate, returning 99.: %s' % msg)\n return 99.","title":""},{"docid":"dbdb2858156ba9a7e61ae80a2281f6d9","score":"0.56717545","text":"def calc_log_evidence(self, idx):\n logpsi = np.empty((self.T, 2))\n N = self.Nframe\n nn = N['count']\n uu = N['unit']\n\n if self.overdispersion:\n od = self.nodes['overdispersion'].expected_x()\n elif self.overdispersion_natural:\n # get the dataframe for count values\n cnt_index = self.Nframe.sort_values(['unit', 'trial', 'time']).index\n # index matrix of 2 columns: Column 1 original, Column 2 sorted\n ind_mat = np.c_[np.array(cnt_index),\n np.array(xrange(cnt_index.shape[0]))]\n\n # expectation of phi\n expected_phi = self.nodes['overdispersion_natural'].expected_x()\n\n # sort expected_phi and compute cumulative product\n exphi_sorted = self._sort_values(ind_mat, expected_phi)\n # cumulative product\n exphi_sorted = np.cumprod(exphi_sorted.reshape(-1, self.time_natural), axis=1).ravel()\n # unsort expected_phi\n od = self._unsort_values(ind_mat, exphi_sorted)\n else:\n od = 1\n bl = self.nodes['baseline'].expected_x()[uu]\n Fk = self.F_prod(idx)\n G = self.G_prod()\n\n allprod = bl * od * Fk * G\n\n lam = self.nodes['fr_latents']\n bar_log_lam = lam.expected_log_x()[uu, idx]\n bar_lam = lam.expected_x()[uu, idx]\n\n N['lam0'] = -allprod\n N['lam1'] = -(allprod * bar_lam) + (nn * bar_log_lam)\n\n logpsi = N.groupby('time').sum()[['lam0', 'lam1']].values\n\n return logpsi","title":""},{"docid":"3cf8367fe5b4b5095e5d26edfcb51ef5","score":"0.56698763","text":"def calc_ent(x):\n\n x_value_list = set([x[i] for i in range(x.shape[0])])\n ent = 0.0\n for x_value in x_value_list:\n p = float(x[x == x_value].shape[0]) / x.shape[0]\n logp = np.log2(p)\n ent -= p * logp\n\n return ent","title":""},{"docid":"ce313bde5114e9caeebbd302c89a30eb","score":"0.5662155","text":"def ln_normal_trunc(x, mean, sigma, a, b):\n x_ = np.where((a < x) & (x < b), x, 1)\n k = math.log(norm.cdf((b - mean) / sigma) -\n norm.cdf((a - mean) / sigma))\n result1 = -math.log(sigma) - 0.5 * math.log(2. * math.pi) -\\\n 0.5 * ((x_ - mean) / sigma) ** 2. - k\n result = np.where((a < x) & (x < b), result1, float(\"-inf\"))\n\n return result","title":""},{"docid":"d590c73ce3457bb12f19382a4cf256b9","score":"0.5661465","text":"def rand_scale_log_normal(mean_scale, one_sigma_at_scale):\n\n log_sigma = math.log(one_sigma_at_scale)\n return mean_scale*math.exp(random.normalvariate(0.0, log_sigma))","title":""},{"docid":"3a93111972fb64106e572f1b502bdd4c","score":"0.56584245","text":"def lognorm_mean_var(mean, variance):\n if variance == 0:\n return DeterministicDistribution(mean)\n else:\n scale, sigma = lognorm_mean_var_to_mu_sigma(mean, variance, 'scipy')\n return stats.lognorm(scale=scale, s=sigma)","title":""},{"docid":"927544879a0c955c71c109a644cb11da","score":"0.56554604","text":"def LOG_B_m_x(i,X,myTheta):\n\n d = X.shape[1]\n term_1 = -0.5 * np.sum(np.true_divide(np.power(X - myTheta.mu[i], 2), myTheta.Sigma[i]), axis=1)\n term_2 = (d * 0.5) * np.log(2 * np.pi)\n term_3 = 0.5 * np.sum(np.log(myTheta.Sigma[i])) # fix this\n\n return term_1 - term_2 - term_3","title":""},{"docid":"4486a66557372d183a7e90e7daeaf5f0","score":"0.5654624","text":"def lognorm_s(n, params):\n F0, n0, sigma = params\n retval = F0 * np.exp(-0.5 * (np.log(n / n0) / sigma) ** 2.) / n\n return (retval)","title":""},{"docid":"043250c5417cb3ef16a2d99122705526","score":"0.5650477","text":"def lpe_test_log_0(self):\n\n res_2 = self.X_factor.log(2)\n res_10 = self.X_factor.log(10)\n res_e = self.X_factor.log(math.e)\n\n s = len(self.X_factor.values)\n\n assert res_2.values == list(\n map(math.log, self.X_domain, [2] * s)\n ) and res_2.rand_vars == [self.X]\n\n assert res_10.values == list(\n map(math.log, self.X_domain, [10] * s)\n ) and res_10.rand_vars == [self.X]\n\n assert res_e.values == list(\n map(math.log, self.X_domain, [math.e] * s)\n ) and res_e.rand_vars == [self.X]","title":""},{"docid":"4f49587de1fe107b60c15c4708f1d035","score":"0.5649254","text":"def rlognormal(mu, tau,size=None):\n\n return np.random.lognormal(mu, np.sqrt(1./tau),size)","title":""},{"docid":"0622b92eb88cd15434ce33914fe008c7","score":"0.5649053","text":"def log_normal_prob(x: float, mu: float = 0, sigma: float = 1):\n gauss = tfd.Normal(loc=mu, scale=sigma)\n return gauss.log_prob(x)","title":""},{"docid":"d0edbb4a902fc5cd6f2cfb49028f7494","score":"0.5648403","text":"def log_prob(self, params: Pytree, inputs: np.ndarray) -> np.ndarray:\n cov = np.identity(self.input_dim)\n nu = np.exp(params)\n maha, log_det = _mahalanobis_and_logdet(inputs, cov)\n t = 0.5 * (nu + self.input_dim)\n A = gammaln(t)\n B = gammaln(0.5 * nu)\n C = self.input_dim / 2.0 * np.log(nu * np.pi)\n D = 0.5 * log_det\n E = -t * np.log(1 + (1.0 / nu) * maha)\n\n return A - B - C - D + E","title":""},{"docid":"1b1dace4d3606ba03e3581fd2c116ff2","score":"0.56429243","text":"def multivariate_gaussian_pdf(self, X, mu_all, sigma_all, log_scale=False):\n dim = X.shape[-1]\n sigma_invs = np.stack([np.linalg.inv(sigma) for sigma in sigma_all]) ## shape = (num_s, dim, dim)\n sigma_det = np.array([np.linalg.det(sigma) for sigma in sigma_all]) ## shape = (num_s, )\n \n\n if log_scale:\n factor = -0.5*(np.log((2*np.pi)**dim) + np.log(sigma_det))\n centered_part = X - np.expand_dims(mu_all, axis=1) ## shape (T, dim) - (num_s, 1, dim) = (num_s, T, dim)\n dot_part = np.sum(centered_part[..., None] * np.expand_dims(sigma_invs, axis=1), axis=-2) ## shape = (num_s, T, dim)\n all_part = np.sum(dot_part*centered_part, axis=-1) ## shape = (num_s, T)\n main_part = -0.5*all_part\n # import ipdb; ipdb.set_trace()\n return main_part + factor[:, None]\n\n else:\n factor = 1/np.sqrt((2*np.pi)**dim * sigma_det) ## shape = (num_s, )\n centered_part = X - np.expand_dims(mu_all, axis=1) ## shape (T, dim) - (num_s, 1, dim) = (num_s, T, dim)\n dot_part = np.sum(centered_part[..., None] * np.expand_dims(sigma_invs, axis=1), axis=-2) ## shape = (num_s, T, dim)\n all_part = np.sum(dot_part*centered_part, axis=-1) ## shape = (num_s, T)\n main_part = np.exp(-0.5*all_part)\n \n return factor[:, None] * main_part","title":""},{"docid":"326739fb83c38229c9aea0e6c6920bfe","score":"0.563609","text":"def power_law_discrete_log_likelihood(alpha, x, xmin, minimize=False):\n\n n = x.size\n z = zeta(xmin, alpha)\n\n res = - n * np.log(z) - alpha * sum([np.log(i) for i in x])\n\n if minimize:\n return res * - 1\n else:\n return res","title":""},{"docid":"75bf7f8213dc8ed405c8cc72a79636bb","score":"0.56358135","text":"def expected_log_prob_s_given_h(self, stats):\n\n mean_h = stats.d['mean_h']\n mean_sq_s = stats.d['mean_sq_s']\n mean_hs = stats.d['mean_hs']\n\n half = as_floatX(0.5)\n two = as_floatX(2.)\n N = as_floatX(self.nhid)\n pi = as_floatX(np.pi)\n\n term1 = half * T.log( self.alpha ).sum()\n term2 = - half * N * T.log(two*pi)\n term3 = - half * T.dot( self.alpha , mean_sq_s )\n term4 = T.dot(self.mu*self.alpha,mean_hs)\n term5 = - half * T.dot(T.sqr(self.mu), self.alpha * mean_h)\n\n rval = term1 + term2 + term3 + term4 + term5\n\n assert len(rval.type.broadcastable) == 0\n\n return rval","title":""},{"docid":"70c3690ba600570bd9741045c56de672","score":"0.56264967","text":"def _logpdf(self, x, dim, df, scale, log_det_scale):\n log_det_x = np.empty(x.shape[-1])\n x_inv = np.copy(x).T\n if dim > 1:\n _cho_inv_batch(x_inv) # works in-place\n else:\n x_inv = 1./x_inv\n tr_scale_x_inv = np.empty(x.shape[-1])\n\n for i in range(x.shape[-1]):\n C, lower = scipy.linalg.cho_factor(x[:, :, i], lower=True)\n\n log_det_x[i] = 2 * np.sum(np.log(C.diagonal()))\n\n tr_scale_x_inv[i] = np.dot(scale, x_inv[i]).trace()\n\n # Log PDF\n out = ((0.5 * df * log_det_scale - 0.5 * tr_scale_x_inv) -\n (0.5 * df * dim * _LOG_2 + 0.5 * (df + dim + 1) * log_det_x) -\n multigammaln(0.5*df, dim))\n\n return out","title":""},{"docid":"5d1368f70ce226bf8837a1345ff2a19b","score":"0.5623958","text":"def test_log_normal_diag_shared(self):\n N = 100\n S = 50\n D = 10\n\n means = torch.randn(S, D)\n covs = torch.rand(D)\n x = torch.randn(N, D)\n\n distributions = [\n dist.MultivariateNormal(means[i], torch.diag(covs))\n for i in range(S)\n ]\n\n expected = []\n for item in x:\n e_item = []\n for d in distributions:\n e_item.append(d.log_prob(item))\n expected.append(e_item)\n expected = torch.as_tensor(expected)\n\n predicted = log_normal(x, means, covs, 'diag-shared')\n\n self.assertTrue(\n torch.allclose(expected, predicted, atol=1e-03, rtol=1e-05)\n )","title":""},{"docid":"c00c95bef4ba76bdf4b62c7057223167","score":"0.56213236","text":"def lognormexp(values, dim=0):\n\n log_denominator = torch.logsumexp(values, dim=dim, keepdim=True)\n # log_numerator = values\n return values - log_denominator","title":""},{"docid":"f1f1ad0275ae0bdd1e99d5feea0c46b5","score":"0.56146884","text":"def _entropy(self, dim, df, log_det_scale):\n return (\n 0.5 * (dim+1) * log_det_scale +\n 0.5 * dim * (dim+1) * _LOG_2 +\n multigammaln(0.5*df, dim) -\n 0.5 * (df - dim - 1) * np.sum(\n [psi(0.5*(df + 1 - (i+1))) for i in range(dim)]\n ) +\n 0.5 * df * dim\n )","title":""},{"docid":"7518c5fe1ef9fc1f7eef90eeaeac829d","score":"0.56012297","text":"def likelihood(mean, logs, x):\n if mean is None and logs is None:\n return -0.5 * (x ** 2 + GaussianDiag.Log2PI)\n else:\n return -0.5 * (logs * 2. + ((x - mean) ** 2) / torch.exp(logs * 2.) + GaussianDiag.Log2PI)","title":""},{"docid":"dd51070691d2fce09f87700ef0f24208","score":"0.5599779","text":"def normalize(lls):\n return lls - np.log(np.exp(lls).sum())","title":""},{"docid":"bd1947619731bc7d230bbbe1265cad0b","score":"0.5599093","text":"def _vmf_log(X, kappa, mu):\n n_examples, n_features = X.shape\n return np.log(_vmf_normalize(kappa, n_features) * np.exp(kappa * X.dot(mu).T))","title":""},{"docid":"209c7e1b7d39c59c8a79f28b2fd75cbc","score":"0.5597885","text":"def test_log_normal_spherical(self):\n N = 100\n S = 50\n D = 10\n\n means = torch.randn(S, D)\n covs = torch.rand(S)\n x = torch.randn(N, D)\n\n distributions = [\n dist.MultivariateNormal(\n means[i], torch.diag(covs[i].clone().expand(D))\n ) for i in range(S)\n ]\n\n expected = []\n for item in x:\n e_item = []\n for d in distributions:\n e_item.append(d.log_prob(item))\n expected.append(e_item)\n expected = torch.as_tensor(expected)\n\n predicted = log_normal(x, means, covs, 'spherical')\n\n self.assertTrue(\n torch.allclose(expected, predicted, atol=1e-03, rtol=1e-05)\n )","title":""},{"docid":"1067bf06951794b50d1d1ac2176358f1","score":"0.55966264","text":"def variational_objective(params, t):\n mean, log_std = unpack_params(params)\n samples = rs.randn(num_samples, D) * np.exp(log_std) + mean\n lower_bound = gaussian_entropy(log_std) + np.mean(logprob(samples,last_theta,time_series,current_t, t))\n return -lower_bound","title":""},{"docid":"b3db808c6a4aeece56fea25723f99892","score":"0.5593598","text":"def loguniform_mean_var(mean, var):\n if var < 0:\n raise ValueError('Variance must be positive')\n elif var == 0:\n # treat special case separately\n return DeterministicDistribution(mean)\n else:\n # determine width parameter numerically\n cv2 = var / mean**2 # match square coefficient of variation\n \n def _rhs(q):\n \"\"\" match the coefficient of variation \"\"\"\n return 0.5 * (q + 1) * np.log(q) / (q - 1) - 1 - cv2\n \n width = optimize.newton(_rhs, 1.1)\n return loguniform_mean(mean, np.sqrt(width))","title":""},{"docid":"93fd60d7309b494c4af64bf87d301295","score":"0.5592026","text":"def lognormal_approximation_coefficient(lat, lon):\n type_output = type(lat)\n lat = prepare_input_array(lat)\n lon = prepare_input_array(lon)\n lon = np.mod(lon, 360)\n val = __model.lognormal_approximation_coefficient(lat, lon)\n u_adim = u.dimensionless_unscaled\n return (prepare_output_array(val[0], type_output) * u_adim,\n prepare_output_array(val[1], type_output) * u_adim,\n prepare_output_array(val[2], type_output) * u_adim)","title":""},{"docid":"18fedaa43f8fe2abc3005e9c60224965","score":"0.5587865","text":"def gaussian_logpdf(yVy, yVmu, muVmu, logdet_V, D):\n return -0.5*yVy + yVmu - 0.5*muVmu + 0.5*logdet_V - 0.5*D*np.log(2*np.pi)","title":""},{"docid":"255226f86a31e99d096fe80571f4c4ce","score":"0.55872345","text":"def z_lognorm(x: pd.Series):\n x = x - np.min(x) + 1\n base = x.median()\n x_norm = np.log(x)/np.log(base)-1\n std = x_norm.std()\n x_norm /= -std\n x_norm.loc[x_norm.isna()] = inaccurate_response\n return x_norm","title":""},{"docid":"7b46a183f7c478dcbf91b394a974698f","score":"0.5567669","text":"def LogNormSpheres(q, A, mu, sigma, N=1000):\n Rmin = 0\n Rmax = np.exp(mu + 3 * sigma)\n R = np.linspace(Rmin, Rmax, N + 1)[1:]\n P = 1 / np.sqrt(2 * np.pi * sigma ** 2 * R ** 2) * np.exp(-(np.log(R) - mu) ** 2 / (2 * sigma ** 2))\n def Fsphere_outer(q, R):\n qR = np.outer(q, R)\n q1 = np.outer(q, np.ones_like(R))\n return 4 * np.pi / q1 ** 3 * (np.sin(qR) - qR * np.cos(qR))\n I = (Fsphere_outer(q, R) ** 2 * np.outer(np.ones_like(q), P))\n return A * I.sum(1) / P.sum()","title":""},{"docid":"89bfb7bf4d3e54c6857ec451ba99f6fa","score":"0.5561856","text":"def do_log_return_moments_calculations(array: np.ndarray, annualisation_factor: float) -> dict:\n _, number_steps = array.shape\n log_returns = np.log(array[:, 1:] / array[:, :-1])\n return do_moments_calculations(log_returns, annualisation_factor)","title":""},{"docid":"31b21ddadb67d9e2faa4ea17b5c940d5","score":"0.55610573","text":"def logistic_fcn( # pylint: disable=C0103\n x: FloatOrDistArray, L: FloatOrDistVar, k: FloatOrDistVar, x0: FloatOrDistVar,\n) -> FloatOrDistArray:\n return L / (1 + exp(-k * (x - x0)))","title":""},{"docid":"1c77ad9d84e05d91a7e0d53e1180e7ff","score":"0.5559325","text":"def void_radii_dist_linear(self,logr,cosm):\n\n logr_l = log(exp(logr)/1.7)\n\n # D ; the void-and-cloud parameter\n D = self.void_and_cloud()\n\n # calculate volume from a given R\n V = (4. * pi * pow(exp(logr_l),3)) / 3.\n\n # get sigma from PowSpec class fit\n sigma = cosm.sig_fit(log(r))\n\n # get dln(1/sigma) / dln(r)\n dlns_dlnr = fabs(log(cosm.Dplus/cosm.growth(0.)) + cosm.dlnsigmadlnr(logr_l))\n\n # calculate f(sigma)\n if(np.isscalar(sigma)):\n fSig = self.multiplicity_function_jlh(self,sigma,D)\n else:\n fSig = self.multiplicity_function_jlh_vec(sigma,D)\n\n no_dens = (fSig/V) * dlns_dlnr\n\n return no_dens","title":""},{"docid":"d6b979614397187143e2a2374d7180f3","score":"0.5556822","text":"def exact_log_det(mvp_vec, D, N):\n eye = np.eye(D)\n matlist = []\n for i in xrange(D):\n cur_dir = eye[:, i]\n matlist.append(mvp_vec(np.tile(cur_dir, (N, 1))))\n mat = np.concatenate([np.expand_dims(x, axis=2) for x in matlist], axis=2)\n logdets = []\n for cur_mat in mat: # Not vectorized, but could be if autograd supported vectorized calls to slogdet. Does it?\n sign, logdet = np.linalg.slogdet(cur_mat)\n logdets.append(logdet)\n assert len(logdets) == N\n return fast_array_from_list(logdets)","title":""}],"string":"[\n {\n \"docid\": \"8d0eeec394c6cdc872ab50fcf4dab135\",\n \"score\": \"0.65942085\",\n \"text\": \"def lognormvariate(self, mu, sigma):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d8a95f64a721e2186597f80c6ebf099\",\n \"score\": \"0.6581352\",\n \"text\": \"def lognormvariate(self, mu, sigma):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d8a95f64a721e2186597f80c6ebf099\",\n \"score\": \"0.6581352\",\n \"text\": \"def lognormvariate(self, mu, sigma):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d8a95f64a721e2186597f80c6ebf099\",\n \"score\": \"0.6581352\",\n \"text\": \"def lognormvariate(self, mu, sigma):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"580fc1dfa86b8b155db6d1ccb1748089\",\n \"score\": \"0.63014734\",\n \"text\": \"def mvn_log_pdf(x: np.ndarray, mu: np.ndarray, sigma_inv: np.ndarray, log_det: float) -> np.ndarray:\\n n = len(mu)\\n o = x - mu\\n l = (sigma_inv@o.T).T\\n maha = np.sum(o*l, axis=-1)\\n ld = -0.5*(n*np.log(2*np.pi) + log_det + maha)\\n return ld\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67194f91f7757bec422379e29dbe8104\",\n \"score\": \"0.6259757\",\n \"text\": \"def log_density_gaussian(self, x, mu, logvar):\\n norm = - 0.5 * (math.log(2 * math.pi) + logvar)\\n log_density = norm - 0.5 * ((x - mu) ** 2 * torch.exp(-logvar))\\n return log_density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63dfb0d098b2023ffc6a66988b866c7e\",\n \"score\": \"0.6252234\",\n \"text\": \"def perturb_lognormal(step, solver_order, noise_scale, random_state=None, size=()):\\n shift = 0.5 * np.log(1 + noise_scale * (step ** (2 * solver_order)))\\n mean = np.log(step) - shift\\n cov = 2 * shift\\n samples = np.exp(\\n scipy.stats.multivariate_normal.rvs(\\n mean=mean, cov=cov, size=size, random_state=random_state\\n )\\n )\\n return samples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c8e72c0d40d869c3dc1095c46976f13\",\n \"score\": \"0.62147254\",\n \"text\": \"def fit_temp_dens_relation(logoverden, logT):\\n \\n ind = np.where((0.1 < logoverden) * (logoverden < 1.0) * (0.1 < logT) * (logT < 5.0))\\n\\n logofor = logoverden[ind]\\n logtfor = logT[ind]\\n\\n def min_func(param):\\n \\\"\\\"\\\"Function to minimize: power law fit to temperature density relation.\\\"\\\"\\\"\\n logT0 = param[0]\\n gammam1 = param[1]\\n #print(param)\\n return logtfor - (logT0 + gammam1 * logofor)\\n res = leastsq(min_func, np.array([np.log10(1e4), 0.5]), full_output=True)\\n params = res[0]\\n if res[-1] <= 0:\\n print(res[3])\\n return 10**params[0], params[1] + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c5d4f275de93fd710418af3a5194af\",\n \"score\": \"0.62146217\",\n \"text\": \"def nllfun(x):\\n mu = x[0]\\n logsigma = x[1]\\n return logsigma + 0.5*np.sum((mu/(np.exp(logsigma) + 0.01))**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9635372e367fad8db57a39300cf8d7f\",\n \"score\": \"0.61965066\",\n \"text\": \"def probNormalizeLog(distributions):\\n if distributions.ndim > 1:\\n distributions = distributions - np.max(distributions, axis=1,keepdims=True)\\n else:\\n distributions = distributions - np.max(distributions)\\n try:\\n prob = probNormalize(np.exp(distributions))\\n except FloatingPointError, e:\\n print distributions\\n print np.max(distributions), np.min(distributions)\\n raise e\\n return prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84609def7d928b62355d6405fa8c33e3\",\n \"score\": \"0.61930674\",\n \"text\": \"def LogMultiNorm(x, mu, sigma):\\n return -(np.log(np.linalg.det(2 * np.pi * sigma)) + (x - mu).T.dot(np.linalg.inv(sigma)).dot(x - mu)) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4e5984e6728e98a7141a932ba7c3c57\",\n \"score\": \"0.6185559\",\n \"text\": \"def mh_lognormal(init,ys,ts,iters,fi=None):\\n print(\\\"Running Metropolis Algorithm with Log-Normal proposals.\\\")\\n D = len(init)\\n my_dist = dist(init)\\n # initialize state and log-likelihood\\n state = init.copy()\\n Lp_state = my_dist.log_likelihood(ys,ts)\\n accepts = 0.\\n\\n cov = (0.1**2)*np.eye(D)*1./D\\n for i in np.arange(0, iters):\\n if fi is not None:\\n write_samp(fi,state)\\n\\n # log(Rv) follow a normal distribution\\n mu = np.log(state)\\n # propose a new state\\n prop = np.exp(np.random.multivariate_normal(mu, cov))\\n move_p = np.log(scipy.stats.multivariate_normal(np.log(state),cov).\\\\\\n pdf(np.log(prop)))\\n rev_p = np.log(scipy.stats.multivariate_normal(np.log(prop),cov).\\\\\\n pdf(np.log(state)))\\n my_dist.set_params(prop)\\n Lp_prop = my_dist.log_likelihood(ys,ts)\\n #print Lp_prop+rev_p\\n #print Lp_state+move_p\\n rand = np.random.rand()\\n prob = min(1,((Lp_prop+rev_p)-(Lp_state+move_p)))\\n if np.log(rand) < prob:\\n accepts += 1\\n state = prop.copy()\\n Lp_state = Lp_prop\\n print (\\\"acct bc %s < %s (iter %s)\\\"%(np.log(rand),prob,i))\\n print state\\n else:\\n my_dist.set_params(state)\\n\\n print 'Acceptance ratio', accepts/iters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6b10391761e29e253292b101f2c6664\",\n \"score\": \"0.6166954\",\n \"text\": \"def log_density_gaussian(self, x: Tensor, mu: Tensor, logvar: Tensor):\\n norm = -0.5 * (math.log(2 * math.pi) + logvar)\\n log_density = norm - 0.5 * ((x - mu) ** 2 * torch.exp(-logvar))\\n return log_density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"938cacd752a86f855c8f5cc29b6401de\",\n \"score\": \"0.6161923\",\n \"text\": \"def lognormal_like(x, mu, tau):\\n return flib.lognormal(x,mu,tau)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0f6998de870e2aabc50ec979789b35f\",\n \"score\": \"0.6140542\",\n \"text\": \"def find_one_sigma_equivalent(logpost, x0, l0=None, dx_max=None):\\n x0 = np.atleast_1d(x0)\\n sigmas = np.zeros(x0.shape[0])\\n\\n if dx_max is None:\\n dx_max = 0*x0 + 1.0\\n\\n if l0 is None:\\n l0 = logpost(x0)\\n \\n for i in range(x0.shape[0]):\\n xlow = x0.copy()\\n xlow[i] -= dx_max[i]\\n xhigh = x0.copy()\\n xhigh[i] += dx_max[i]\\n llow = logpost(xlow)\\n lhigh = logpost(xhigh)\\n\\n while llow < l0 - 0.5 and not xlow[i] == x0[i]:\\n dx = x0[i] - xlow[i]\\n xlow = x0.copy()\\n xlow[i] = x0[i] - dx/2\\n llow = logpost(xlow)\\n\\n while lhigh < l0 - 0.5 and not xhigh[i] == x0[i]:\\n dx = xhigh[i] - x0[i]\\n xhigh = x0.copy()\\n xhigh[i] = x0[i] + dx/2\\n lhigh = logpost(xhigh)\\n\\n sigmas[i] = 0.5*(xhigh[i] - xlow[i])\\n\\n return sigmas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a007b0f38f199e4f8e89cc959b70b795\",\n \"score\": \"0.60996205\",\n \"text\": \"def ExpLogLikelihood(obs_data,pis,mus,sigmas,gamma):\\n return np.sum(np.array(\\n [np.sum(np.array(\\n [gamma[n,k]*(np.log(pis[k])+LogMultiNorm(obs_data[n],mus[k],sigmas[k]))\\n for k in range(K)]\\n )) for n in range(N)]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b44db12d97196d5ef28e9c9b9d90e7d9\",\n \"score\": \"0.6062823\",\n \"text\": \"def log_sum_from_individual_logs(logs: Sequence) -> float:\\n\\n\\treturn scipy.special.logsumexp(logs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5d5ab6779225f64381d6557e3d374e3\",\n \"score\": \"0.60575956\",\n \"text\": \"def variances_log_chems(chemnames,logprior=1.0e20) :\\n #senstraj = load('EndogenousEGFR3T3sensNoPriors')\\n times = senstraj.timepoints \\n jtjinv = scipy.linalg.inv(jtjtrunc+1.0/logprior**2*np.eye(\\n len(jtjtrunc),len(jtjtrunc))) \\n var = {}\\n bestfit = {} \\n optvarkeys = list(clc.optimizableVars.keys())\\n first = optvarkeys[0]\\n last = optvarkeys[-1]\\n for name in chemnames :\\n var[name] = [] \\n bestfit[name] = [] \\n chemindex = senstraj.key_column.get(name) \\n index1sens = senstraj.key_column.get((name,first))\\n index2sens = senstraj.key_column.get((name,last))\\n sensarray_this_chem = copy.copy(senstraj.values[:,index1sens:(index2sens+1)])\\n traj_this_chem = copy.copy(senstraj.values[:,chemindex]) \\n for j, pname in enumerate(ovvarnames) :\\n sensarray_this_chem[:,j] = sensarray_this_chem[:,j]*curp.get(pname)\\n # need to scale each row by 1/chemvalue to mimic a derivative w.r.t. \\n # log chemicals. Add a small value to chemvalue to avoid divide by zero\\n for i in range(len(times)) :\\n sensarray_this_chem[i,:] = old_div(sensarray_this_chem[i,:],(traj_this_chem[i]+1.0e-6))\\n\\n tmp = np.dot(sensarray_this_chem,jtjinv)\\n for i in range(len(tmp[:,0])) :\\n var[name].append(np.dot(tmp[i,:],sensarray_this_chem[i,:]))\\n \\n bestfit[name] = senstraj.values[:,chemindex]\\n var[name] = np.asarray(var[name])\\n return times,bestfit,var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c548ce3307a9c8bcc81afc95642ef02\",\n \"score\": \"0.60562223\",\n \"text\": \"def gaussian_entropy(logdet_V, D):\\n return -0.5*logdet_V + 0.5*D + 0.5*D*np.log(2*np.pi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"376ac334ff110828709ad01f411a4e80\",\n \"score\": \"0.60548127\",\n \"text\": \"def estimate_lognormal(self, data):\\r\\n\\r\\n if self.fit_method == 1:\\r\\n _results = LogNormal().maximum_likelihood_estimate(data,\\r\\n self.start_time,\\r\\n self.rel_time)\\r\\n\\r\\n self.n_suspensions = _results[3]\\r\\n self.n_failures = _results[4]\\r\\n elif self.fit_method == 2:\\r\\n _results = regression(data, self.start_time, self.rel_time,\\r\\n dist='lognormal')\\r\\n\\r\\n self.rho = _results[3]\\r\\n self.n_suspensions = _results[4]\\r\\n self.n_failures = _results[5]\\r\\n\\r\\n self.scale[1] = _results[0][0]\\r\\n self.shape[1] = _results[0][1]\\r\\n self.variance[0] = _results[1][0] # Scale\\r\\n self.variance[1] = _results[1][2] # Shape\\r\\n self.covariance[0] = _results[1][1] # Scale-Shape\\r\\n self.mle = _results[2][0]\\r\\n self.aic = _results[2][1]\\r\\n self.bic = _results[2][2]\\r\\n\\r\\n self.calculate_parameter_bounds(data)\\r\\n self.hazard_function()\\r\\n self.reliability_function()\\r\\n self.mean()\\r\\n\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b39dfc969c17978a0f94f0a31343913a\",\n \"score\": \"0.60535073\",\n \"text\": \"def asym_dist(_,dat_v,__,rescale):\\n from scipy.stats import linregress\\n lg = lambda x: linregress(range(len(x.reshape(-1))),x.reshape(-1)).intercept\\n flat = lambda x: np.array([g for m in x for g in m ])\\n sxd2 =[]; sgd2 = []\\n for j in dat_v:\\n for k in j:\\n if type(k[1][1])==float and (np.isnan(k[1][1])): \\n continue\\n else:\\n # Predict the length and gfp at begin\\n x1 = k[1][0]\\n x2 = k[1][1]\\n x1,x2 = [lg(x) for x in [x1,x2]]\\n perfx = np.log((np.exp(x1)+np.exp(x2)))-rescale*np.log(2) # perfect division\\n sxd2.append((perfx-x1,perfx-x2))\\n return flat(sxd2),_,_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"112975ac963ab3456cb72ee58f5f29e8\",\n \"score\": \"0.6031262\",\n \"text\": \"def mvn_nll(mu: np.ndarray, sigma: np.ndarray, x: np.ndarray) -> float:\\n k = len(mu)\\n Id = np.eye(k)\\n chol = np.linalg.cholesky(sigma)\\n chol_inv = spla.solve_triangular(chol, Id, lower=True)\\n sigma_inv = chol_inv.T@chol_inv\\n log_det = 2*np.sum(np.log(np.diag(chol)))\\n nll = -np.mean(mvn_log_pdf(x, mu, sigma_inv, log_det))\\n return nll\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ab1234045b8fe0f2bb103ed75ff9e16\",\n \"score\": \"0.60305643\",\n \"text\": \"def _simple_relative_entropy_implementation(\\n self, rho, sigma, log_base=np.log, tol=1e-12):\\n # S(rho || sigma) = sum_i(p_i log p_i) - sum_ij(p_i P_ij log q_i)\\n rvals, rvecs = rho.eigenstates()\\n svals, svecs = sigma.eigenstates()\\n rvecs = np.hstack([vec.full() for vec in rvecs]).T\\n svecs = np.hstack([vec.full() for vec in svecs]).T\\n # Calculate S\\n S = 0\\n for i in range(len(rvals)):\\n if abs(rvals[i]) >= tol:\\n S += rvals[i] * log_base(rvals[i])\\n for j in range(len(svals)):\\n P_ij = (\\n np.dot(rvecs[i], svecs[j].conjugate()) *\\n np.dot(svecs[j], rvecs[i].conjugate())\\n )\\n if abs(svals[j]) < tol and not (\\n abs(rvals[i]) < tol or abs(P_ij) < tol):\\n # kernel of sigma intersects support of rho\\n return np.inf\\n if abs(svals[j]) >= tol:\\n S -= rvals[i] * P_ij * log_base(svals[j])\\n return np.real(S)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a020a4d01f78878c953a73297fc59966\",\n \"score\": \"0.6013505\",\n \"text\": \"def log_normal(x, m, v):\\r\\n\\tdim=m.shape[-1]\\r\\n\\tconst=-dim/2*np.log(2*np.pi)\\r\\n\\tlog_prob=const-0.5*torch.log(v.sum(axis=-1))-0.5*((x-m)**2/v).sum(axis=-1)\\r\\n\\treturn log_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ad68ce7b933684c90bad0cf0a843ff\",\n \"score\": \"0.6008636\",\n \"text\": \"def variational_objective(var_param):\\n samples = var_family.sample(var_param, n_samples)\\n a = partial(var_family.logdensity,var_param=var_param)\\n def nested_fn(samples):\\n lower_bound = np.mean(logdensity(samples)) - np.mean(a(samples))\\n return -lower_bound\\n b= nested_fn(samples)\\n return b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5efc579549f3cea6398f4b373fd27ec8\",\n \"score\": \"0.60071135\",\n \"text\": \"def mul_var_normal(weights, means, logvars):\\n ll = 0\\n\\n for i in range(len(weights)):\\n w = weights[i]\\n mean = means[i]\\n if len(logvars) > 1:\\n logvar = logvars[i]\\n var = logvar.exp()\\n else:\\n logvar = logvars[0]\\n var = math.exp(logvar)\\n\\n logstd = logvar * 0.5\\n ll += torch.sum(\\n -((w - mean)**2)/(2*var) - logstd - math.log(math.sqrt(2*math.pi))\\n )\\n\\n return ll\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ab5a5dcc24df49eafc7e9cd03a7595\",\n \"score\": \"0.6000301\",\n \"text\": \"def lognorm_lev(mu, sigma, n, limit):\\n if limit == -1:\\n return np.exp(n * mu + n * n * sigma * sigma / 2)\\n else:\\n phi = ss.norm.cdf\\n ll = np.log(limit)\\n sigma2 = sigma * sigma\\n phi_l = phi((ll - mu) / sigma)\\n phi_l2 = phi((ll - mu - n * sigma2) / sigma)\\n unlimited = np.exp(n * mu + n * n * sigma2 / 2)\\n return unlimited * phi_l2 + limit ** n * (1 - phi_l)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4706bd641d785d747c4bae16a3edd046\",\n \"score\": \"0.59839344\",\n \"text\": \"def lognorm_approx(ser):\\n m, cv = xsden_to_meancv(ser.index, ser.values)\\n mu, sigma = mu_sigma_from_mean_cv(m, cv)\\n fz = ss.lognorm(sigma, scale=np.exp(mu))\\n return fz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2c6a51aad5fc400140a71a95ae387f2\",\n \"score\": \"0.5978666\",\n \"text\": \"def lognormal_expval(mu, tau):\\n return np.exp(mu + 1./2/tau)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55658e99d76eda4013761be57aff6a5e\",\n \"score\": \"0.59774536\",\n \"text\": \"def logsum(vfun, lam):\\n\\n # rescale values for numeric robustness\\n vmax = np.max(vfun, axis=0)\\n vfun_norm = vfun - vmax\\n\\n # apply formula (could be njitted in separate function)\\n VE = vmax + lam * np.log(np.sum(np.exp(vfun_norm / lam), axis=0))\\n return VE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b6f90eab6a8dfa590499b46151ad445\",\n \"score\": \"0.59736603\",\n \"text\": \"def loglikelihood(self):\\n # update the latent variances based on the updated latent covariances matrices\\n self.latent_variances = [np.diag(Si) for Si in self.latent_Sigmas]\\n\\n # add up the terms that are valid over the entire index 1 <= i <= N\\n determinants = [np.prod(np.diag(Sigma)) for Sigma in self.latent_Sigmas]\\n traces = [np.trace(Sigma) for Sigma in self.latent_Sigmas]\\n miTmi = [np.sqrt(sum(mi**2)).item() for mi in self.latent_means]\\n\\n global_terms = [0.5*(np.log(det) - tr - mi_norm) for tr, det, mi_norm in zip(traces, determinants, miTmi)]\\n\\n # now for the terms in Omega_plus\\n\\n def get_omega_terms(plus=True):\\n \\\"\\\"\\\" Compute the summation terms over Omega plus or Omega minus \\\"\\\"\\\"\\n if plus:\\n B = self.B1 \\n error_func = lambda x: SQRT_PI_OVER_2*erfc(x/ROOT2)\\n exp_func = lambda x: np.exp(-0.5*x**2)\\n index_set = self.I1\\n mu = self.mu1\\n\\n else:\\n B = self.B2\\n error_func = lambda x: SQRT_PI_OVER_2*(erf(x/ROOT2) + 1)\\n exp_func = lambda x: -np.exp(-0.5*x**2)\\n index_set = self.I2\\n mu = self.mu2\\n\\n BTB = np.matmul(B.T, B)\\n\\n # these are the 'delta_i' parameters when nu=e_q, beta=0\\n deltas = [-self.latent_means[i][-1]/np.sqrt(self.latent_variances[i][-1]) for i in index_set]\\n \\n _BTBSigmas = [np.matmul(BTB, self.latent_Sigmas[i]) for i in index_set]\\n\\n _diagonal_terms = [BTBS[-1][-1] for BTBS in _BTBSigmas]\\n _exp_terms = [delta*exp_func(delta) for delta in deltas]\\n _erf_terms = [error_func(delta) for delta in deltas]\\n _trace_terms = [np.trace(BTBS) for BTBS in _BTBSigmas]\\n _quadratic_terms = [\\n np.matmul(\\n np.matmul(\\n self.latent_means[i].T,\\n BTB\\n ),\\n self.latent_means[i]\\n ) for i in index_set\\n ]\\n _yi_terms = [\\n np.matmul(\\n self.Y[i] - mu,\\n (self.Y[i] - mu).T - 2*np.matmul(B, self.latent_means[i])\\n ) for i in index_set\\n ]\\n\\n _terms = [\\n item1*item2.item() + item3*(item4 + item5 + item6) for item1, \\n item2, \\n item3, \\n item4, \\n item5, \\n item6 in zip(\\n _diagonal_terms,\\n _exp_terms,\\n _erf_terms,\\n _trace_terms,\\n _quadratic_terms,\\n _yi_terms\\n )\\n ]\\n\\n return _terms\\n\\n self.omega_plus_terms = get_omega_terms(plus=True)\\n self.omega_minus_terms = get_omega_terms(plus=False)\\n\\n # finally, compute the scalars that are independent of the data and latent variables\\n scalars = 0.5*self.N*(self.q - self.p*np.log(TWOPI*self.sigma2))\\n\\n # add all of the terms together\\n total = 0.5*np.sum(global_terms) - (TWOPI**-0.5)/(2*self.sigma2)*(np.sum(self.omega_plus_terms) + np.sum(self.omega_minus_terms))\\n\\n return total + scalars\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8caea465be9179151d905a6c5c0efb3b\",\n \"score\": \"0.59699017\",\n \"text\": \"def norm_log_pdf(x: np.ndarray, mu: float, sigmasq: float) -> np.ndarray:\\n o = x - mu\\n ld = -0.5*np.square(o) / sigmasq - 0.5*np.log(sigmasq) - 0.5*np.log(2*np.pi)\\n return ld\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b77de7150fb7ba682385f2a12075fe2a\",\n \"score\": \"0.59502894\",\n \"text\": \"def gaussian_real(mean, logsd, dim):\\n B, C, m = assert_real(mean, dim)\\n assert mean.shape == logsd.shape\\n\\n class o(object):\\n pass\\n\\n o.mean = mean\\n o.logstd = logsd\\n # logcov = torch.zeros(B, C, m, m, m, dim, dim).to(logsd.device)\\n # covinv = torch.zeros(B, C, m, m, m, dim, dim).to(logsd.device)\\n # for d in range(dim):\\n # # logcov[..., d, d] = logsd[..., d]\\n # # covinv[..., d, d] = 1 / torch.exp(logsd[..., d])\\n # o.logcov = logcov\\n stdinv = 1 / torch.exp(logsd) # [B, C, m, m, m, 6]\\n o.eps = torch.randn(mean.shape)\\n o.sample = mean + torch.exp(logsd) * o.eps.to(mean.device)\\n o.sample2 = lambda eps: mean + torch.exp(logsd) * eps.to(mean.device)\\n\\n nlog2pi = dim * np.log(2 * np.pi)\\n logcov = logsd.sum(dim=-1).unsqueeze(-1) # [B, C, m, m, m, 1]\\n #centerx = (x - mean).unsqueeze(-2) # [B, C, m, m, m, 1, 6]\\n #centerxT = (x - mean).unsqueeze(-1) # [B, C, m, m, m, 6, 1]\\n #xsquare = centerx @ covinv # [B, C, m, m, m, 1, 6]\\n #xsquare = xsquare @ centerxT # [B, C, m, m, m, 1, 1]\\n #xsquare = xsquare.squeeze(-1) # [B, C, m, m, m, 1]\\n o.logps = lambda x: -0.5 * (nlog2pi + logcov + (x - mean) ** 2 * stdinv)\\n #o.logps = lambda x: -0.5 * ((x - mean) ** 2 * stdinv)\\n # o.logps = lambda x: -0.5 * (dim * np.log(2 * np.pi) + logsd.sum(dim=-1).unsqueeze(-1) + (x - mean) ** 2 / torch.exp(2. * logsd))\\n o.logp = lambda x: o.logps(x).sum(dim=(1, 2, 3, 4))\\n # o.get_eps = lambda x: (x - mean) / torch.exp(logsd)\\n return o\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe14cb182e709b598fa482314a4bf9ad\",\n \"score\": \"0.5939857\",\n \"text\": \"def lognormal(x, p, g):\\n x = numpy_array(x) # check if numpy array, if not make numpy array\\n x = assert_2d_sort(x)\\n return np.exp(normal(np.log(x), p, g))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c97bbe7843ca6b572d70684a73044bb\",\n \"score\": \"0.590387\",\n \"text\": \"def sklogn_analytical_likelihood(self, ddt):\\n # skewed lognormal distribution with boundaries\\n if (ddt < self.lam) or ((-self.mu + math.log(ddt - self.lam)) ** 2 / (2. * self.sigma ** 2) > self.explim):\\n return -np.inf\\n else:\\n llh = math.exp(-((-self.mu + math.log(ddt - self.lam)) ** 2 / (2. * self.sigma ** 2))) / (\\n math.sqrt(2 * math.pi) * (ddt - self.lam) * self.sigma)\\n\\n if np.isnan(llh):\\n return -np.inf\\n else:\\n return np.log(llh)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfeb215b72bc80bcb1926ec78e0f9ce9\",\n \"score\": \"0.5900217\",\n \"text\": \"def random_lognormal(args):\\n return DecisionParser.rand_x_normal(random.lognormvariate, args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6102af8d42605f24ca4dfd1fa7a8b06b\",\n \"score\": \"0.58979064\",\n \"text\": \"def normal_log_prob(latent: base.Array, sigma: float = 1, mu: float = 0):\\n latent, _ = jax.tree_flatten(latent)\\n latent = jax.tree_map(lambda x: x.flatten(), latent)\\n latent = jnp.concatenate(latent)\\n latent_dim = len(latent)\\n latent_l2_sq = jnp.sum(jnp.square(latent - mu))\\n return -0.5 * (latent_dim * jnp.log(2 * jnp.pi * sigma**2)\\n + latent_l2_sq / sigma**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5190dec56ab9211d1c3ef39a29eeec84\",\n \"score\": \"0.5889136\",\n \"text\": \"def logistic_var():\\n z = np.linspace(-5,5,1000) # z points to test\\n p1 = 1/(1+np.exp(-z)) # target probability\\n var_test = np.linspace(2,3,1000)\\n err = []\\n for v in var_test:\\n p2 = 0.5*(1+scipy.special.erf(z/np.sqrt(v*2)))\\n err.append(np.mean((p1-p2)**2))\\n \\n i = np.argmin(err)\\n wvar = var_test[i]\\n return wvar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d423b90c71b56106a8f0e940c7590870\",\n \"score\": \"0.58890647\",\n \"text\": \"def log_normal(x, m, v):\\n element_wise = -0.5 * (torch.log(v) + (x - m).pow(2) / v + np.log(2 * np.pi))\\n log_prob = element_wise.sum(-1)\\n return log_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5b65a9d86e7e4fbbe77287721721fb5\",\n \"score\": \"0.5877169\",\n \"text\": \"def my_loglike(theta, x, data, sigma):\\n sim_bins = run_exp_three_times_and_bin(theta, x)\\n\\n return -(0.5 / sigma ** 2) * np.sum((data - sim_bins) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e38432ed5b66405b26ac10a2549111f\",\n \"score\": \"0.58750695\",\n \"text\": \"def variational_objective(var_param):\\n samples = var_family.sample(var_param, n_samples)\\n lower_bound = np.mean(logdensity(samples)) - np.mean(var_family.logdensity(samples, var_param))\\n return -lower_bound\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5bfe401b02d624a2c0704860992ab34\",\n \"score\": \"0.587297\",\n \"text\": \"def _independent_gaussian_log_prob_vector(x, mus, sigmas):\\n # The inverse of a diagonal matrix is just inverting values on the\\n # diagonal\\n cov_inv = torch.eye(d) * (1 / sigmas ** 2)\\n\\n # sum(log) = log(prod)\\n logpdf = (-d / 2 * torch.log(2 * torch.as_tensor(torch.pi)) -\\n torch.log(torch.prod(sigmas))) \\\\\\n - 0.5 * mahalanobis(x[:3], mus, cov_inv) ** 2\\n return logpdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31263a909b6110aa5bdc41ef7892bf8b\",\n \"score\": \"0.58668375\",\n \"text\": \"def siglog_normal_init_guess(self, reg_pnts_array, pos_traj):\\n\\n def make_sigma_equation(reg_pnts):\\n \\\"\\\"\\\"\\n input is a series of registration points t1 - t5\\n though only t1, t3, t5 are useful\\n \\\"\\\"\\\"\\n def sigma_estimator(sigma):\\n #do float here, otherwise the second term will be ZERO!!\\n return ((np.exp(-sigma**2+3*sigma) - 1) / (np.exp(sigma*6) - 1) - \\n float(reg_pnts[2] - reg_pnts[0]) / (reg_pnts[4] - reg_pnts[0]))\\n #return (np.exp(sigma**2) - 1) / (np.exp(6*sigma**2) - 1) - (reg_pnts[2] - reg_pnts[0]) / (reg_pnts[4] - reg_pnts[0])\\n return sigma_estimator\\n\\n init_guess = []\\n vel_profile=self.get_vel_profile(pos_traj)/self.dt\\n for reg_pnts in reg_pnts_array:\\n #make an estimator for sigma\\n sig_est_func = make_sigma_equation(reg_pnts)\\n #solve it\\n init_sigma = (reg_pnts[4] - reg_pnts[0])/2*self.dt\\n #<hyin/Dec-24-2014> solving the equation is still not clear and the results seem not right\\n #more investigation is needed to know the derivation of the equation, e.g., how the units are consistent...\\n #currently, use an even more simple heuristics...\\n sig_sln = sciopt.broyden1(sig_est_func, init_sigma, f_tol=1e-14)\\n #try direct nonlinear optimization, note the returned function is modified with square\\n #see sigma_estimator above\\n #sig_sln = sciopt.fminbound(sig_est_func, 0, init_sigma*3) #search between (0, 3*init_sigma)\\n #print sig_sln, sig_est_func(sig_sln)\\n sig = sig_sln #only need scalar value\\n #sig = init_sigma\\n if sig <= 0:\\n #will this happen?\\n #this will happen when actual mode locates on the right side of 'Gaussian mean'\\n #lognormal distribution is asymmetrical in regards of mode, but does it always distribute more mass on left side? \\n #(okay, i know it's long tail, more mass means some more slopeness when going up)\\n sig = np.abs(sig)\\n a_array = np.array([3*sig, 1.5*sig**2+sig*np.sqrt(0.25*sig**2+1),\\n sig**2, 1.5*sig**2-sig*np.sqrt(0.25*sig**2+1), -3*sig])\\n #estimate mu\\n mu_1 = np.log((reg_pnts[2]-reg_pnts[0])*self.dt/(np.exp(-a_array[2])-np.exp(-a_array[0])))\\n mu_2 = np.log((reg_pnts[4]-reg_pnts[0])*self.dt/(np.exp(-a_array[4])-np.exp(-a_array[0])))\\n mu = (mu_1 + mu_2)/2\\n #estimate D\\n D_array = np.array([np.sqrt(np.pi*2)*vel_profile[i]*np.exp(mu)*sig\\n *np.exp((a_array[i]**2/(2*sig**2)-a_array[i])) for i in range(len(a_array))])\\n D = np.average(D_array)\\n #estimate t0\\n t0_array = np.array([reg_pnts[i]*self.dt - np.exp(mu)*np.exp(-a_array[i]) for i in range(len(a_array))])\\n t0 = np.average(t0_array)\\n\\n theta_s, theta_e = self.siglog_normal_init_ang_guess((D, t0, mu, sig),\\n pos_traj, reg_pnts)\\n \\n #add\\n init_guess.append((D, t0, mu, sig, theta_s, theta_e))\\n return init_guess\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deff43e9eb3fbd415a669c8fa0b9311c\",\n \"score\": \"0.5852983\",\n \"text\": \"def logchol2sigma(params):\\n U = torch.zeros((2, 2))\\n U.view(-1)[[0, 1, 3]] = params # fill upper triangle\\n U.diagonal().exp_()\\n return U.t() @ U\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db9e2badc5c85d6af8b407796cdb3773\",\n \"score\": \"0.58300114\",\n \"text\": \"def logGaussian(x, mu, var):\\n\\n d = len(x)\\n log_prob = -1/(2*var) * (x-mu) @ (x-mu)\\n log_prob-= d/2 * np.log(2*np.pi*var)\\n\\n return log_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd1998b3be485ff0688207ce547a3c43\",\n \"score\": \"0.5825949\",\n \"text\": \"def lognormal_param_transform(self):\\n M = self.data_mean\\n S = self.data_stddev\\n variance = numpy.log( (S**2) / (M**2) + 1)\\n sigma = numpy.sqrt(variance)\\n mu = numpy.log(M) - (0.5 * (sigma**2))\\n\\n return mu, sigma\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a43e06cfcd1faba629eff7d532b418c9\",\n \"score\": \"0.5825454\",\n \"text\": \"def lognorm_mean(mean, sigma):\\n if sigma == 0:\\n return DeterministicDistribution(mean)\\n else:\\n mu = mean * np.exp(-0.5 * sigma**2)\\n return stats.lognorm(scale=mu, s=sigma)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a9c6360299bb5ac21d56b0ff9a08da0\",\n \"score\": \"0.5823315\",\n \"text\": \"def partial_e(sev_name, fz, a, n):\\n\\n if sev_name not in ['lognorm', 'gamma', 'pareto', 'expon']:\\n raise NotImplementedError(f'{sev_name} NYI for analytic moments')\\n\\n if a == 0:\\n return [0] * (n+1) # for k in range(n+1)]\\n\\n if sev_name == 'lognorm':\\n m = fz.stats('m')\\n sigma = fz.args[0]\\n mu = np.log(m) - sigma**2 / 2\\n ans = [np.exp(k * mu + (k * sigma)**2 / 2) *\\n (ss.norm.cdf((np.log(a) - mu - k * sigma**2)/sigma) if a < np.inf else 1.0)\\n for k in range(n+1)]\\n return ans\\n\\n elif sev_name == 'expon':\\n # really needed for MEDs\\n # expon is gamma with shape = 1\\n scale = fz.stats('m')\\n shape = 1.\\n lgs = loggamma(shape)\\n ans = [scale ** k * np.exp(loggamma(shape + k) - lgs) *\\n (ss.gamma(shape + k, scale=scale).cdf(a) if a < np.inf else 1.0)\\n for k in range(n + 1)]\\n return ans\\n\\n elif sev_name == 'gamma':\\n shape = fz.args[0]\\n scale = fz.stats('m') / shape\\n # magic ingredient is the norming constant\\n # c = lambda sh: 1 / (scale ** sh * gamma(sh))\\n # therefore c(shape)/c(shape+k) = scale**k * gamma(shape + k) / gamma(shape)\\n # = scale ** k * exp(loggamma(shape + k) - loggamma(shape)) to avoid errors\\n ans = [scale ** k * np.exp(loggamma(shape + k) - loggamma(shape)) *\\n (ss.gamma(shape + k, scale=scale).cdf(a) if a < np.inf else 1.0)\\n for k in range(n + 1)]\\n return ans\\n\\n elif sev_name == 'pareto':\\n # integrate xf(x) even though nx^n-1 S(x) may be more obvious\\n # former fits into the overall scheme\\n # a Pareto defined by agg is like so: ss.pareto(2.5, scale=1000, loc=-1000)\\n α = fz.args[0]\\n λ = fz.kwds['scale']\\n loc = fz.kwds.get('loc', 0.0)\\n # regular Pareto is scale=lambda, loc=-lambda, so this has no effect\\n # single parameter Pareto is scale=lambda, loc=0\\n # these formulae for regular pareto, hence\\n if λ + loc != 0:\\n logger.log(WL, 'Pareto not shifted to x>0 range...using numeric moments.')\\n return partial_e_numeric(fz, a, n)\\n ans = []\\n # will return inf if the Pareto does not have the relevant moments\\n # TODO: formula for shape=1,2,3\\n for k in range(n + 1):\\n b = [α * (-1) ** (k - i) * binom(k, i) * λ ** (k + α - i) *\\n ((λ + a) ** (i - α) - λ ** (i - α)) / (i - α)\\n for i in range(k + 1)]\\n ans.append(sum(b))\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ca565d23368f1890e2a2a6fdb096f2\",\n \"score\": \"0.58224374\",\n \"text\": \"def gaussian_log_density(mean, log_var, x):\\n std = torch.exp(log_var).to(device)\\n # return 0.5 * tf.reduce_sum(tf.square((x - self.mean) / self.std), axis=-1) \\\\\\n # + 0.5 * np.log(2.0 * np.pi) * tf.to_float(tf.shape(x)[-1]) \\\\\\n # + tf.reduce_sum(self.logstd, axis=-1)\\n neg_log_density = 0.5 * torch.sum(torch.pow((x - mean)/(std + 1e-6), 2), dim=-1) + \\\\\\n 0.5 * np.log(2.0 * np.pi) * x.size(-1) + \\\\\\n torch.sum(log_var, dim=-1)\\n return -neg_log_density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec67d6d23ec7f46fea59ca9b0b76256d\",\n \"score\": \"0.581852\",\n \"text\": \"def vec_lnlognorm(x, mu, sigma, shift=0.):\\n\\n x_ = np.where(0 < (x - shift), (x - shift), 1)\\n result1 = -np.log(np.sqrt(2. * math.pi) * x_ * sigma) - (np.log(x_) - mu)\\\\\\n ** 2 / (2. * sigma ** 2)\\n result = np.where(0 < (x - shift), result1, float(\\\"-inf\\\"))\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"050fb452799d89d7c153f49a921d4612\",\n \"score\": \"0.5813111\",\n \"text\": \"def FD_log2(x, f_x, nn, liminf, suplim):\\n \\\"\\\"\\\"Input: time starting with average dt(comming from repulsive_FD function), and f_time related to that time array\\\"\\\"\\\"\\n lim_inf = round(np.log10(liminf),2)\\n sup_lim = round(np.log10(suplim),2)\\n b = np.linspace(lim_inf, sup_lim, nn)\\n x_log = 10.0**b\\n fx_log = np.zeros(len(x_log))\\n for j in range(1, len(x_log)-1):\\n for i in range(len(x)-1):\\n if (x_log[j] - x[i])*(x_log[j] - x[i+1]) < 0.0 : #change of sign\\n if (x_log[j] - x[i]) < (x_log[j] - x[i+1]):\\n x_log[j] = x[i]\\n fx_log[j] = f_x[i]\\n else:\\n x_log[j] = x[i+1]\\n fx_log[j] = f_x[i+1]\\n return x_log, fx_log\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4db7054583dd634dc0f92dd137a35a1d\",\n \"score\": \"0.5806972\",\n \"text\": \"def logGaussian(x, mu, var):\\n d = len(x)\\n log_prob = -1/(2*var) * (x-mu) @ (x-mu)\\n log_prob-= d/2 * np.log(2*np.pi*var)\\n\\n return log_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c35a72bb05fa39866346eca9ce7021c1\",\n \"score\": \"0.58031434\",\n \"text\": \"def estimate_from_work_samples(work_samples):\\n return np.log(np.mean(np.exp(-np.array(work_samples))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f5946f067e47ef72938d4ef51974e78\",\n \"score\": \"0.5798444\",\n \"text\": \"def _get_log_norms(ti_mps, str_set):\\n core_tensor, boundaries = ti_mps.core_tensor, get_bound_mats(ti_mps)\\n str_lens, max_len = str_set.str_lens, str_set.index_mat.shape[1]\\n \\n # Unpack boundary matrices and define rightward transfer operator\\n left_mat, right_mat = boundaries\\n t_op = build_t_op(core_tensor, direction='right', jitted=True)\\n\\n # Function implementing a single step of computing log_norm\\n def scan_iter(iter_state, _):\\n # scan_iter: c -> a -> (c, b)\\n # iter_state holds a running log_norm and a density operator\\n old_norm, old_density = iter_state\\n\\n # Apply transfer_op and normalize output\\n new_density = t_op(old_density)\\n this_norm = np.linalg.norm(new_density)\\n new_density = new_density / this_norm\\n\\n new_norm = old_norm + np.log(this_norm)\\n out_norm = new_norm + np.log(utils.hs_dot(right_mat, new_density))\\n\\n return (new_norm, new_density), out_norm\\n\\n # Apply transfer operator many times (starting with left_mat), and \\n # generate list of log_norms\\n iter_init = (0., left_mat)\\n _, proper_log_norms = jax.lax.scan(scan_iter, iter_init, \\n np.empty(max_len))\\n assert len(proper_log_norms) == max_len\\n\\n # Add norm for the empty distribution and pick out lengths of interest\\n empty_log_norm = np.log(utils.hs_dot(left_mat, right_mat))[None]\\n all_log_norms = np.concatenate((empty_log_norm, proper_log_norms))\\n log_norms = all_log_norms[str_lens]\\n\\n return log_norms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ca7d3bf09e73b4dfc7d45108cc813e\",\n \"score\": \"0.57801867\",\n \"text\": \"def resCalc(mean, sigma):\\n return (sigma*2*np.sqrt(2*np.log(2)))/mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f8e11e03fdd2c200e9e4036ab97f912\",\n \"score\": \"0.57784307\",\n \"text\": \"def log_multinomial_density(X, log_thetas):\\n lpr = np.zeros((X.shape[0], len(log_thetas)))\\n\\n for i in range(X.shape[0]):\\n for j in range(len(log_thetas)):\\n lpr[i][j] = np.sum([log_thetas[j][dim][value] for dim, value in enumerate(X[i])])\\n return lpr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e042b04c2979bac0fc263db3c3f6c6e\",\n \"score\": \"0.5771863\",\n \"text\": \"def test_log_normal_diag(self):\\n N = 100\\n S = 50\\n D = 10\\n\\n means = torch.randn(S, D)\\n covs = torch.rand(S, D)\\n x = torch.randn(N, D)\\n\\n distributions = [\\n dist.MultivariateNormal(means[i], torch.diag(covs[i]))\\n for i in range(S)\\n ]\\n\\n expected = []\\n for item in x:\\n e_item = []\\n for d in distributions:\\n e_item.append(d.log_prob(item))\\n expected.append(e_item)\\n expected = torch.as_tensor(expected)\\n\\n predicted = log_normal(x, means, covs, 'diag')\\n\\n self.assertTrue(\\n torch.allclose(expected, predicted, atol=1e-03, rtol=1e-05)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ab652340a5964931b37b8fbf6cb1886\",\n \"score\": \"0.57699895\",\n \"text\": \"def log_norm_medium_concentration(scale, dimension):\\n scale = np.asfarray(scale)\\n shape = scale.shape\\n scale = scale.flatten()\\n\\n # Function is unstable at zero.\\n # Scale needs to be float for this to work.\\n scale[scale < 1e-2] = 1e-2\\n\\n r_range = range(dimension - 2 + 1)\\n r = np.asarray(r_range)[None, :]\\n\\n # Mardia1999Watson Equation 3\\n temp = scale[:, None] ** r * np.exp(-scale[:, None]) / \\\\\\n np.asarray([math.factorial(_r) for _r in r_range])\\n\\n return (\\n np.log(2.) + dimension * np.log(np.pi) +\\n (1. - dimension) * np.log(scale) + scale +\\n np.log(1. - np.sum(temp, -1))\\n ).reshape(shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"710ad092912ee20724ae0d0569bd8a17\",\n \"score\": \"0.57575834\",\n \"text\": \"def loglike_MNL(self):\\n\\n top = np.zeros(shape=self.av.shape[2], dtype=\\\"float64\\\")\\n bottom = np.zeros(shape=self.av.shape[2], dtype=\\\"float64\\\")\\n for c in range(self.count_c):\\n for e in range(self.count_e):\\n top += (\\n self.av[c][e] * self.choice[c][e] * np.exp(self.get_utility(c, e))\\n )\\n bottom += self.av[c][e] * np.exp(self.get_utility(c, e))\\n\\n log_res = self.weight_vector * np.log(top / bottom)\\n res = np.nansum(log_res)\\n number_nan = np.sum(np.isnan(log_res))\\n\\n return res, number_nan\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c7fa0812243e0cc7023f455fa1c9c0c\",\n \"score\": \"0.5737334\",\n \"text\": \"def log_normal_variance(mu, sigma):\\n exponent1 = sigma**2\\n exponent2 = 2 * mu + sigma**2\\n return (np.exp(exponent1) - 1) * np.exp(exponent2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53e603f0d38b96387e0e1e8597c15f38\",\n \"score\": \"0.57331264\",\n \"text\": \"def arlognormal_like(x, a, sigma, rho):\\n return flib.arlognormal(x, np.log(a), sigma, rho, beta=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe1b87d9802efb5bcedf4108a7b9cec0\",\n \"score\": \"0.5730004\",\n \"text\": \"def log_normal(X, mu, sigma):\\n N = len(X)\\n loglik = -(N/2)*np.log(2*np.pi) - (N/2)*np.log(sigma**2) - (1/(2*sigma**2))*(np.sum((X-mu)**2))\\n return loglik\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d0b20baebc724376977507364b188fc\",\n \"score\": \"0.5711383\",\n \"text\": \"def log_prior(self, params):\\n # log likelihood function, see:\\n # https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Likelihood_function\\n var = self.std**2\\n nitems = params.size\\n diff = params - self.mean\\n scaled_sq_err_term = -0.5 * jnp.dot(diff, diff) / var\\n # log determinant of covariance matrix\\n log_det_cov_term = -nitems * jnp.log(self.std)\\n norm_term = -0.5 * nitems * jnp.log(2 * jnp.pi)\\n return log_det_cov_term + scaled_sq_err_term + norm_term\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f96e5db369a9bbac865733badb1c0370\",\n \"score\": \"0.5706779\",\n \"text\": \"def __logLikelihood(self, X, means, covars, ps):\\n\\n error = 0.0\\n for n, xn in enumerate(X):\\n arg = 0.0\\n for k in range(means.shape[0]):\\n arg += ps[k] * multiGaussianPDF(xn, means[k], covars[k])\\n error += np.log(arg)\\n return error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd944f969bb22dd17eddc28fabd55b38\",\n \"score\": \"0.56974983\",\n \"text\": \"def geometricMean(alldistribT):\\n return np.exp(np.mean(np.log(alldistribT), axis=1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72ce5449d4fd3a39092c20c8249a6872\",\n \"score\": \"0.5695466\",\n \"text\": \"def neglog_density_normal(x, mean=0., sigma=1.):\\n return np.sum((x - mean)**2, axis=1) / 2 / sigma**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1968b41fcf935ee6655c5bdc1c13e2c7\",\n \"score\": \"0.569209\",\n \"text\": \"def matrix_log_density_gaussian(x, q):\\n x = torch.unsqueeze(x, dim=1)\\n _mu = torch.unsqueeze(q.loc, dim=0)\\n _sigma = torch.unsqueeze(q.scale, dim=0)\\n _q = Normal(loc=_mu, scale=_sigma)\\n return _q.log_prob(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42fad586b8f5e0707ca5d291f9fd5178\",\n \"score\": \"0.5684034\",\n \"text\": \"def delta_loglike(self):\\n try:\\n # rewrite due to deprication of numpy.matrix\\n # gm = np.matrix(self.gradient())\\n # H = self.hessian()\\n # return (gm * H.I * gm.T)[0,0]/4\\n g = self.gradient()\\n H = self.hessian()\\n return np.dot( np.dot(g, np.linalg.inv(H)) , g)/4\\n except Exception as msg:\\n print ('Failed log likelihood estimate, returning 99.: %s' % msg)\\n return 99.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbdb2858156ba9a7e61ae80a2281f6d9\",\n \"score\": \"0.56717545\",\n \"text\": \"def calc_log_evidence(self, idx):\\n logpsi = np.empty((self.T, 2))\\n N = self.Nframe\\n nn = N['count']\\n uu = N['unit']\\n\\n if self.overdispersion:\\n od = self.nodes['overdispersion'].expected_x()\\n elif self.overdispersion_natural:\\n # get the dataframe for count values\\n cnt_index = self.Nframe.sort_values(['unit', 'trial', 'time']).index\\n # index matrix of 2 columns: Column 1 original, Column 2 sorted\\n ind_mat = np.c_[np.array(cnt_index),\\n np.array(xrange(cnt_index.shape[0]))]\\n\\n # expectation of phi\\n expected_phi = self.nodes['overdispersion_natural'].expected_x()\\n\\n # sort expected_phi and compute cumulative product\\n exphi_sorted = self._sort_values(ind_mat, expected_phi)\\n # cumulative product\\n exphi_sorted = np.cumprod(exphi_sorted.reshape(-1, self.time_natural), axis=1).ravel()\\n # unsort expected_phi\\n od = self._unsort_values(ind_mat, exphi_sorted)\\n else:\\n od = 1\\n bl = self.nodes['baseline'].expected_x()[uu]\\n Fk = self.F_prod(idx)\\n G = self.G_prod()\\n\\n allprod = bl * od * Fk * G\\n\\n lam = self.nodes['fr_latents']\\n bar_log_lam = lam.expected_log_x()[uu, idx]\\n bar_lam = lam.expected_x()[uu, idx]\\n\\n N['lam0'] = -allprod\\n N['lam1'] = -(allprod * bar_lam) + (nn * bar_log_lam)\\n\\n logpsi = N.groupby('time').sum()[['lam0', 'lam1']].values\\n\\n return logpsi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cf8367fe5b4b5095e5d26edfcb51ef5\",\n \"score\": \"0.56698763\",\n \"text\": \"def calc_ent(x):\\n\\n x_value_list = set([x[i] for i in range(x.shape[0])])\\n ent = 0.0\\n for x_value in x_value_list:\\n p = float(x[x == x_value].shape[0]) / x.shape[0]\\n logp = np.log2(p)\\n ent -= p * logp\\n\\n return ent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce313bde5114e9caeebbd302c89a30eb\",\n \"score\": \"0.5662155\",\n \"text\": \"def ln_normal_trunc(x, mean, sigma, a, b):\\n x_ = np.where((a < x) & (x < b), x, 1)\\n k = math.log(norm.cdf((b - mean) / sigma) -\\n norm.cdf((a - mean) / sigma))\\n result1 = -math.log(sigma) - 0.5 * math.log(2. * math.pi) -\\\\\\n 0.5 * ((x_ - mean) / sigma) ** 2. - k\\n result = np.where((a < x) & (x < b), result1, float(\\\"-inf\\\"))\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d590c73ce3457bb12f19382a4cf256b9\",\n \"score\": \"0.5661465\",\n \"text\": \"def rand_scale_log_normal(mean_scale, one_sigma_at_scale):\\n\\n log_sigma = math.log(one_sigma_at_scale)\\n return mean_scale*math.exp(random.normalvariate(0.0, log_sigma))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a93111972fb64106e572f1b502bdd4c\",\n \"score\": \"0.56584245\",\n \"text\": \"def lognorm_mean_var(mean, variance):\\n if variance == 0:\\n return DeterministicDistribution(mean)\\n else:\\n scale, sigma = lognorm_mean_var_to_mu_sigma(mean, variance, 'scipy')\\n return stats.lognorm(scale=scale, s=sigma)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"927544879a0c955c71c109a644cb11da\",\n \"score\": \"0.56554604\",\n \"text\": \"def LOG_B_m_x(i,X,myTheta):\\n\\n d = X.shape[1]\\n term_1 = -0.5 * np.sum(np.true_divide(np.power(X - myTheta.mu[i], 2), myTheta.Sigma[i]), axis=1)\\n term_2 = (d * 0.5) * np.log(2 * np.pi)\\n term_3 = 0.5 * np.sum(np.log(myTheta.Sigma[i])) # fix this\\n\\n return term_1 - term_2 - term_3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4486a66557372d183a7e90e7daeaf5f0\",\n \"score\": \"0.5654624\",\n \"text\": \"def lognorm_s(n, params):\\n F0, n0, sigma = params\\n retval = F0 * np.exp(-0.5 * (np.log(n / n0) / sigma) ** 2.) / n\\n return (retval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"043250c5417cb3ef16a2d99122705526\",\n \"score\": \"0.5650477\",\n \"text\": \"def lpe_test_log_0(self):\\n\\n res_2 = self.X_factor.log(2)\\n res_10 = self.X_factor.log(10)\\n res_e = self.X_factor.log(math.e)\\n\\n s = len(self.X_factor.values)\\n\\n assert res_2.values == list(\\n map(math.log, self.X_domain, [2] * s)\\n ) and res_2.rand_vars == [self.X]\\n\\n assert res_10.values == list(\\n map(math.log, self.X_domain, [10] * s)\\n ) and res_10.rand_vars == [self.X]\\n\\n assert res_e.values == list(\\n map(math.log, self.X_domain, [math.e] * s)\\n ) and res_e.rand_vars == [self.X]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f49587de1fe107b60c15c4708f1d035\",\n \"score\": \"0.5649254\",\n \"text\": \"def rlognormal(mu, tau,size=None):\\n\\n return np.random.lognormal(mu, np.sqrt(1./tau),size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0622b92eb88cd15434ce33914fe008c7\",\n \"score\": \"0.5649053\",\n \"text\": \"def log_normal_prob(x: float, mu: float = 0, sigma: float = 1):\\n gauss = tfd.Normal(loc=mu, scale=sigma)\\n return gauss.log_prob(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0edbb4a902fc5cd6f2cfb49028f7494\",\n \"score\": \"0.5648403\",\n \"text\": \"def log_prob(self, params: Pytree, inputs: np.ndarray) -> np.ndarray:\\n cov = np.identity(self.input_dim)\\n nu = np.exp(params)\\n maha, log_det = _mahalanobis_and_logdet(inputs, cov)\\n t = 0.5 * (nu + self.input_dim)\\n A = gammaln(t)\\n B = gammaln(0.5 * nu)\\n C = self.input_dim / 2.0 * np.log(nu * np.pi)\\n D = 0.5 * log_det\\n E = -t * np.log(1 + (1.0 / nu) * maha)\\n\\n return A - B - C - D + E\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b1dace4d3606ba03e3581fd2c116ff2\",\n \"score\": \"0.56429243\",\n \"text\": \"def multivariate_gaussian_pdf(self, X, mu_all, sigma_all, log_scale=False):\\n dim = X.shape[-1]\\n sigma_invs = np.stack([np.linalg.inv(sigma) for sigma in sigma_all]) ## shape = (num_s, dim, dim)\\n sigma_det = np.array([np.linalg.det(sigma) for sigma in sigma_all]) ## shape = (num_s, )\\n \\n\\n if log_scale:\\n factor = -0.5*(np.log((2*np.pi)**dim) + np.log(sigma_det))\\n centered_part = X - np.expand_dims(mu_all, axis=1) ## shape (T, dim) - (num_s, 1, dim) = (num_s, T, dim)\\n dot_part = np.sum(centered_part[..., None] * np.expand_dims(sigma_invs, axis=1), axis=-2) ## shape = (num_s, T, dim)\\n all_part = np.sum(dot_part*centered_part, axis=-1) ## shape = (num_s, T)\\n main_part = -0.5*all_part\\n # import ipdb; ipdb.set_trace()\\n return main_part + factor[:, None]\\n\\n else:\\n factor = 1/np.sqrt((2*np.pi)**dim * sigma_det) ## shape = (num_s, )\\n centered_part = X - np.expand_dims(mu_all, axis=1) ## shape (T, dim) - (num_s, 1, dim) = (num_s, T, dim)\\n dot_part = np.sum(centered_part[..., None] * np.expand_dims(sigma_invs, axis=1), axis=-2) ## shape = (num_s, T, dim)\\n all_part = np.sum(dot_part*centered_part, axis=-1) ## shape = (num_s, T)\\n main_part = np.exp(-0.5*all_part)\\n \\n return factor[:, None] * main_part\",\n \"title\": \"\"\n },\n {\n \"docid\": \"326739fb83c38229c9aea0e6c6920bfe\",\n \"score\": \"0.563609\",\n \"text\": \"def power_law_discrete_log_likelihood(alpha, x, xmin, minimize=False):\\n\\n n = x.size\\n z = zeta(xmin, alpha)\\n\\n res = - n * np.log(z) - alpha * sum([np.log(i) for i in x])\\n\\n if minimize:\\n return res * - 1\\n else:\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75bf7f8213dc8ed405c8cc72a79636bb\",\n \"score\": \"0.56358135\",\n \"text\": \"def expected_log_prob_s_given_h(self, stats):\\n\\n mean_h = stats.d['mean_h']\\n mean_sq_s = stats.d['mean_sq_s']\\n mean_hs = stats.d['mean_hs']\\n\\n half = as_floatX(0.5)\\n two = as_floatX(2.)\\n N = as_floatX(self.nhid)\\n pi = as_floatX(np.pi)\\n\\n term1 = half * T.log( self.alpha ).sum()\\n term2 = - half * N * T.log(two*pi)\\n term3 = - half * T.dot( self.alpha , mean_sq_s )\\n term4 = T.dot(self.mu*self.alpha,mean_hs)\\n term5 = - half * T.dot(T.sqr(self.mu), self.alpha * mean_h)\\n\\n rval = term1 + term2 + term3 + term4 + term5\\n\\n assert len(rval.type.broadcastable) == 0\\n\\n return rval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70c3690ba600570bd9741045c56de672\",\n \"score\": \"0.56264967\",\n \"text\": \"def _logpdf(self, x, dim, df, scale, log_det_scale):\\n log_det_x = np.empty(x.shape[-1])\\n x_inv = np.copy(x).T\\n if dim > 1:\\n _cho_inv_batch(x_inv) # works in-place\\n else:\\n x_inv = 1./x_inv\\n tr_scale_x_inv = np.empty(x.shape[-1])\\n\\n for i in range(x.shape[-1]):\\n C, lower = scipy.linalg.cho_factor(x[:, :, i], lower=True)\\n\\n log_det_x[i] = 2 * np.sum(np.log(C.diagonal()))\\n\\n tr_scale_x_inv[i] = np.dot(scale, x_inv[i]).trace()\\n\\n # Log PDF\\n out = ((0.5 * df * log_det_scale - 0.5 * tr_scale_x_inv) -\\n (0.5 * df * dim * _LOG_2 + 0.5 * (df + dim + 1) * log_det_x) -\\n multigammaln(0.5*df, dim))\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d1368f70ce226bf8837a1345ff2a19b\",\n \"score\": \"0.5623958\",\n \"text\": \"def test_log_normal_diag_shared(self):\\n N = 100\\n S = 50\\n D = 10\\n\\n means = torch.randn(S, D)\\n covs = torch.rand(D)\\n x = torch.randn(N, D)\\n\\n distributions = [\\n dist.MultivariateNormal(means[i], torch.diag(covs))\\n for i in range(S)\\n ]\\n\\n expected = []\\n for item in x:\\n e_item = []\\n for d in distributions:\\n e_item.append(d.log_prob(item))\\n expected.append(e_item)\\n expected = torch.as_tensor(expected)\\n\\n predicted = log_normal(x, means, covs, 'diag-shared')\\n\\n self.assertTrue(\\n torch.allclose(expected, predicted, atol=1e-03, rtol=1e-05)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c00c95bef4ba76bdf4b62c7057223167\",\n \"score\": \"0.56213236\",\n \"text\": \"def lognormexp(values, dim=0):\\n\\n log_denominator = torch.logsumexp(values, dim=dim, keepdim=True)\\n # log_numerator = values\\n return values - log_denominator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f1ad0275ae0bdd1e99d5feea0c46b5\",\n \"score\": \"0.56146884\",\n \"text\": \"def _entropy(self, dim, df, log_det_scale):\\n return (\\n 0.5 * (dim+1) * log_det_scale +\\n 0.5 * dim * (dim+1) * _LOG_2 +\\n multigammaln(0.5*df, dim) -\\n 0.5 * (df - dim - 1) * np.sum(\\n [psi(0.5*(df + 1 - (i+1))) for i in range(dim)]\\n ) +\\n 0.5 * df * dim\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7518c5fe1ef9fc1f7eef90eeaeac829d\",\n \"score\": \"0.56012297\",\n \"text\": \"def likelihood(mean, logs, x):\\n if mean is None and logs is None:\\n return -0.5 * (x ** 2 + GaussianDiag.Log2PI)\\n else:\\n return -0.5 * (logs * 2. + ((x - mean) ** 2) / torch.exp(logs * 2.) + GaussianDiag.Log2PI)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd51070691d2fce09f87700ef0f24208\",\n \"score\": \"0.5599779\",\n \"text\": \"def normalize(lls):\\n return lls - np.log(np.exp(lls).sum())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1947619731bc7d230bbbe1265cad0b\",\n \"score\": \"0.5599093\",\n \"text\": \"def _vmf_log(X, kappa, mu):\\n n_examples, n_features = X.shape\\n return np.log(_vmf_normalize(kappa, n_features) * np.exp(kappa * X.dot(mu).T))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"209c7e1b7d39c59c8a79f28b2fd75cbc\",\n \"score\": \"0.5597885\",\n \"text\": \"def test_log_normal_spherical(self):\\n N = 100\\n S = 50\\n D = 10\\n\\n means = torch.randn(S, D)\\n covs = torch.rand(S)\\n x = torch.randn(N, D)\\n\\n distributions = [\\n dist.MultivariateNormal(\\n means[i], torch.diag(covs[i].clone().expand(D))\\n ) for i in range(S)\\n ]\\n\\n expected = []\\n for item in x:\\n e_item = []\\n for d in distributions:\\n e_item.append(d.log_prob(item))\\n expected.append(e_item)\\n expected = torch.as_tensor(expected)\\n\\n predicted = log_normal(x, means, covs, 'spherical')\\n\\n self.assertTrue(\\n torch.allclose(expected, predicted, atol=1e-03, rtol=1e-05)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1067bf06951794b50d1d1ac2176358f1\",\n \"score\": \"0.55966264\",\n \"text\": \"def variational_objective(params, t):\\n mean, log_std = unpack_params(params)\\n samples = rs.randn(num_samples, D) * np.exp(log_std) + mean\\n lower_bound = gaussian_entropy(log_std) + np.mean(logprob(samples,last_theta,time_series,current_t, t))\\n return -lower_bound\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3db808c6a4aeece56fea25723f99892\",\n \"score\": \"0.5593598\",\n \"text\": \"def loguniform_mean_var(mean, var):\\n if var < 0:\\n raise ValueError('Variance must be positive')\\n elif var == 0:\\n # treat special case separately\\n return DeterministicDistribution(mean)\\n else:\\n # determine width parameter numerically\\n cv2 = var / mean**2 # match square coefficient of variation\\n \\n def _rhs(q):\\n \\\"\\\"\\\" match the coefficient of variation \\\"\\\"\\\"\\n return 0.5 * (q + 1) * np.log(q) / (q - 1) - 1 - cv2\\n \\n width = optimize.newton(_rhs, 1.1)\\n return loguniform_mean(mean, np.sqrt(width))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93fd60d7309b494c4af64bf87d301295\",\n \"score\": \"0.5592026\",\n \"text\": \"def lognormal_approximation_coefficient(lat, lon):\\n type_output = type(lat)\\n lat = prepare_input_array(lat)\\n lon = prepare_input_array(lon)\\n lon = np.mod(lon, 360)\\n val = __model.lognormal_approximation_coefficient(lat, lon)\\n u_adim = u.dimensionless_unscaled\\n return (prepare_output_array(val[0], type_output) * u_adim,\\n prepare_output_array(val[1], type_output) * u_adim,\\n prepare_output_array(val[2], type_output) * u_adim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18fedaa43f8fe2abc3005e9c60224965\",\n \"score\": \"0.5587865\",\n \"text\": \"def gaussian_logpdf(yVy, yVmu, muVmu, logdet_V, D):\\n return -0.5*yVy + yVmu - 0.5*muVmu + 0.5*logdet_V - 0.5*D*np.log(2*np.pi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"255226f86a31e99d096fe80571f4c4ce\",\n \"score\": \"0.55872345\",\n \"text\": \"def z_lognorm(x: pd.Series):\\n x = x - np.min(x) + 1\\n base = x.median()\\n x_norm = np.log(x)/np.log(base)-1\\n std = x_norm.std()\\n x_norm /= -std\\n x_norm.loc[x_norm.isna()] = inaccurate_response\\n return x_norm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b46a183f7c478dcbf91b394a974698f\",\n \"score\": \"0.5567669\",\n \"text\": \"def LogNormSpheres(q, A, mu, sigma, N=1000):\\n Rmin = 0\\n Rmax = np.exp(mu + 3 * sigma)\\n R = np.linspace(Rmin, Rmax, N + 1)[1:]\\n P = 1 / np.sqrt(2 * np.pi * sigma ** 2 * R ** 2) * np.exp(-(np.log(R) - mu) ** 2 / (2 * sigma ** 2))\\n def Fsphere_outer(q, R):\\n qR = np.outer(q, R)\\n q1 = np.outer(q, np.ones_like(R))\\n return 4 * np.pi / q1 ** 3 * (np.sin(qR) - qR * np.cos(qR))\\n I = (Fsphere_outer(q, R) ** 2 * np.outer(np.ones_like(q), P))\\n return A * I.sum(1) / P.sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89bfb7bf4d3e54c6857ec451ba99f6fa\",\n \"score\": \"0.5561856\",\n \"text\": \"def do_log_return_moments_calculations(array: np.ndarray, annualisation_factor: float) -> dict:\\n _, number_steps = array.shape\\n log_returns = np.log(array[:, 1:] / array[:, :-1])\\n return do_moments_calculations(log_returns, annualisation_factor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b21ddadb67d9e2faa4ea17b5c940d5\",\n \"score\": \"0.55610573\",\n \"text\": \"def logistic_fcn( # pylint: disable=C0103\\n x: FloatOrDistArray, L: FloatOrDistVar, k: FloatOrDistVar, x0: FloatOrDistVar,\\n) -> FloatOrDistArray:\\n return L / (1 + exp(-k * (x - x0)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c77ad9d84e05d91a7e0d53e1180e7ff\",\n \"score\": \"0.5559325\",\n \"text\": \"def void_radii_dist_linear(self,logr,cosm):\\n\\n logr_l = log(exp(logr)/1.7)\\n\\n # D ; the void-and-cloud parameter\\n D = self.void_and_cloud()\\n\\n # calculate volume from a given R\\n V = (4. * pi * pow(exp(logr_l),3)) / 3.\\n\\n # get sigma from PowSpec class fit\\n sigma = cosm.sig_fit(log(r))\\n\\n # get dln(1/sigma) / dln(r)\\n dlns_dlnr = fabs(log(cosm.Dplus/cosm.growth(0.)) + cosm.dlnsigmadlnr(logr_l))\\n\\n # calculate f(sigma)\\n if(np.isscalar(sigma)):\\n fSig = self.multiplicity_function_jlh(self,sigma,D)\\n else:\\n fSig = self.multiplicity_function_jlh_vec(sigma,D)\\n\\n no_dens = (fSig/V) * dlns_dlnr\\n\\n return no_dens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6b979614397187143e2a2374d7180f3\",\n \"score\": \"0.5556822\",\n \"text\": \"def exact_log_det(mvp_vec, D, N):\\n eye = np.eye(D)\\n matlist = []\\n for i in xrange(D):\\n cur_dir = eye[:, i]\\n matlist.append(mvp_vec(np.tile(cur_dir, (N, 1))))\\n mat = np.concatenate([np.expand_dims(x, axis=2) for x in matlist], axis=2)\\n logdets = []\\n for cur_mat in mat: # Not vectorized, but could be if autograd supported vectorized calls to slogdet. Does it?\\n sign, logdet = np.linalg.slogdet(cur_mat)\\n logdets.append(logdet)\\n assert len(logdets) == N\\n return fast_array_from_list(logdets)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":487,"cells":{"query_id":{"kind":"string","value":"8dea5d2418572c602a423cc45cb5061a"},"query":{"kind":"string","value":"Test to start the specified service"},"positive_passages":{"kind":"list like","value":[{"docid":"bea8ebe3b44f298935e6ce5406c87de9","score":"0.7395312","text":"def test_start():\n mock_true = MagicMock(return_value=True)\n mock_false = MagicMock(return_value=False)\n mock_info = MagicMock(side_effect=[{\"Status\": \"Running\"}])\n\n with patch.object(win32serviceutil, \"StartService\", mock_true), patch.object(\n win_service, \"disabled\", mock_false\n ), patch.object(win_service, \"info\", mock_info):\n assert win_service.start(\"spongebob\") is True\n\n mock_info = MagicMock(\n side_effect=[\n {\"Status\": \"Stopped\", \"Status_WaitHint\": 0},\n {\"Status\": \"Start Pending\", \"Status_WaitHint\": 0},\n {\"Status\": \"Running\"},\n ]\n )\n\n with patch.object(win32serviceutil, \"StartService\", mock_true), patch.object(\n win_service, \"disabled\", mock_false\n ), patch.object(win_service, \"info\", mock_info), patch.object(\n win_service, \"status\", mock_true\n ):\n assert win_service.start(\"spongebob\") is True","title":""}],"string":"[\n {\n \"docid\": \"bea8ebe3b44f298935e6ce5406c87de9\",\n \"score\": \"0.7395312\",\n \"text\": \"def test_start():\\n mock_true = MagicMock(return_value=True)\\n mock_false = MagicMock(return_value=False)\\n mock_info = MagicMock(side_effect=[{\\\"Status\\\": \\\"Running\\\"}])\\n\\n with patch.object(win32serviceutil, \\\"StartService\\\", mock_true), patch.object(\\n win_service, \\\"disabled\\\", mock_false\\n ), patch.object(win_service, \\\"info\\\", mock_info):\\n assert win_service.start(\\\"spongebob\\\") is True\\n\\n mock_info = MagicMock(\\n side_effect=[\\n {\\\"Status\\\": \\\"Stopped\\\", \\\"Status_WaitHint\\\": 0},\\n {\\\"Status\\\": \\\"Start Pending\\\", \\\"Status_WaitHint\\\": 0},\\n {\\\"Status\\\": \\\"Running\\\"},\\n ]\\n )\\n\\n with patch.object(win32serviceutil, \\\"StartService\\\", mock_true), patch.object(\\n win_service, \\\"disabled\\\", mock_false\\n ), patch.object(win_service, \\\"info\\\", mock_info), patch.object(\\n win_service, \\\"status\\\", mock_true\\n ):\\n assert win_service.start(\\\"spongebob\\\") is True\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"06347100c5d25a23c13c075938520e7a","score":"0.7975998","text":"def start_service(service):\n subprocess.call([\"service\", service, \"start\"])","title":""},{"docid":"4502c4639ce33b3e8797f182e1151e8d","score":"0.78600645","text":"def test_stop_start(self):\n my_service = SimpleService()\n my_service.start()\n\n self.assertTrue(my_service.started)\n\n my_service.stop()\n\n self.assertFalse(my_service.started)\n\n my_service.start()\n\n # gevent.sleep(0) does not give the service enough time to run.\n gevent.sleep(0.001)\n self.assertTrue(my_service.started)","title":""},{"docid":"5417d5c2829c3fc1ce02114c70b77201","score":"0.7859392","text":"def test_startService(self):\n self.poller.startService()\n self.timer.return_value.startService.assert_called_once_with()","title":""},{"docid":"059b4aac45b338c93f0ed9efad153de1","score":"0.7840608","text":"def test_start_services(self):\n my_service = make_service(logger=mock.Mock())\n mock_greenlet = mock.Mock()\n\n my_service.add_service(mock_greenlet)\n\n my_service.join()\n\n mock_greenlet.join.assert_called_once_with()","title":""},{"docid":"a978f01acba9b9d3673c158ab873b308","score":"0.78269166","text":"def test_start_stop_simple_service(self):\n self.executed = False\n\n def run(time):\n self.executed = True\n gevent.sleep(time)\n\n class MyService(service.Service):\n def do_start(self):\n self.spawn(run, 10)\n\n my_service = MyService()\n\n my_service.start()\n\n self.assertTrue(self.executed)\n self.assertTrue(my_service.started)\n\n my_service.stop()\n\n self.assertFalse(my_service.started)","title":""},{"docid":"67b501bae5b4f3fac23bdcfe1784c75c","score":"0.75843453","text":"def test_do_start(self, mock_do_start):\n my_service = make_service()\n\n # Run something on service so it doesn't stop right away.\n my_service.spawn(gevent.sleep, 1)\n\n my_service.start()\n\n mock_do_start.assert_called_once_with()\n\n # ensure that `do_start` is called only once.\n my_service.start()\n\n mock_do_start.assert_called_once_with()","title":""},{"docid":"3b54ffa2c83d33a949d15c29e6232b72","score":"0.756705","text":"def test_start_stop_simple_service2(self):\n self.executed = False\n\n def run(time):\n self.executed = True\n gevent.sleep(time)\n\n class MyService(service.Service):\n def do_start(self):\n self.spawn(run, 0)\n\n my_service = MyService()\n\n my_service.start()\n\n self.assertTrue(self.executed)\n self.assertTrue(my_service.started)\n\n my_service.join()\n\n self.assertFalse(my_service.started)","title":""},{"docid":"4886ad9f752966aa19003bde67fbfe24","score":"0.75114053","text":"def testStartEverythingExplicitly(self):\n ss = self.ss\n ysst = YouShouldStartThis(store=ss)\n ss.powerUp(ysst, IService)\n self.topdb.powerUp(self.ssitem, IService)\n self._startService()\n self.failUnless(ysst.started)","title":""},{"docid":"4dded99b20ef0c474fa2d3bf43e8a563","score":"0.7482764","text":"def testStartEverythingExplicitly(self):\n ss = self.ss\n ysst = YouShouldStartThis(store=ss)\n ss.powerUp(ysst, IService)\n self.topdb.powerUp(self.ssitem, IService)\n self._startService()\n self.assertTrue(ysst.started)","title":""},{"docid":"43502633a26d690b168571affe0518e0","score":"0.7462408","text":"def start(service, *args, **kwargs):\n if service == 'all':\n s.info(\n f\"Starting all services: $[{', '.join(map(str, [s for s in Service.SERVICE_LIST]))}]\\n\")\n return [Service(s, *args, **kwargs).start() for s in Service.SERVICE_LIST]\n service = Service(service, *args, **kwargs)\n s.info(f\"Starting $[{service.name}]\\n\")\n service.start()","title":""},{"docid":"74055e6c3f170f1d0bb66030371c43cc","score":"0.7404273","text":"def test_start(self):\n my_service = SimpleService()\n\n self.event_fired = False\n\n def on_start():\n self.event_fired = True\n\n my_service.on('start', on_start)\n\n self.assertFalse(my_service.started)\n\n my_service.start()\n\n self.assertTrue(my_service.started)\n self.assertTrue(self.event_fired)\n\n # now test calling `start` again. It should be idempotent\n self.event_fired = False\n\n my_service.start()\n\n self.assertFalse(self.event_fired)","title":""},{"docid":"ae7371e8a02c4c30cca467eed816595a","score":"0.73456526","text":"def service_start(self, service):\n cmd = [\n 'service.start', service,\n '--out', 'newline_values_only'\n ]\n return self.run_salt(cmd)['stdout'].strip().lower() == b'true'","title":""},{"docid":"7c2b6d9db240b5d8b8054edd1d18927c","score":"0.72434205","text":"def test_start_already_running():\n mock_false = MagicMock(return_value=False)\n mock_error = MagicMock(\n side_effect=pywintypes.error(1056, \"StartService\", \"Service is running\")\n )\n mock_info = MagicMock(side_effect=[{\"Status\": \"Running\"}])\n with patch.object(win32serviceutil, \"StartService\", mock_error), patch.object(\n win_service, \"disabled\", mock_false\n ), patch.object(win_service, \"_status_wait\", mock_info):\n assert win_service.start(\"spongebob\") is True","title":""},{"docid":"80ab174e4ab83c3450d5afc13732604d","score":"0.724007","text":"def testDontStartNormally(self):\n ss = self.ss\n ycst = YouCantStartThis(store=ss)\n ss.powerUp(ycst, IService)\n self._startService()\n self.failIf(ycst.started)","title":""},{"docid":"a58a98aa36a900fd9c00f6f4d132ea72","score":"0.72063875","text":"async def _service_start(self) -> bool:\n for _svc_id, service in self._services.items():\n service.start(True)\n return True","title":""},{"docid":"82496c2eb08d72d491e516eb8912582c","score":"0.71671325","text":"def test_add_service_start(self, mock_watch):\n mock_watch.__name__ = 'watch_service'\n\n my_service = SimpleService()\n mock_service = mock.Mock()\n\n my_service.start()\n\n my_service.add_service(mock_service)\n gevent.sleep(0.0)\n\n mock_watch.assert_called_once_with(mock_service)\n self.assertEqual(my_service.services, [mock_service])","title":""},{"docid":"4e41d4a127504ed193258297494a03c4","score":"0.7141826","text":"def _start_srv(service, name):\n service.start()\n sender.fire(CollectorServiceStartedEvent, srv_name=name)","title":""},{"docid":"c381ed1a23acb5c02335ed1a567baad1","score":"0.7132379","text":"def _startService(self):\n assert not self.serviceStarted\n self.serviceStarted = True\n return IService(self.topdb).startService()","title":""},{"docid":"c381ed1a23acb5c02335ed1a567baad1","score":"0.7132379","text":"def _startService(self):\n assert not self.serviceStarted\n self.serviceStarted = True\n return IService(self.topdb).startService()","title":""},{"docid":"bb65b0d1beae7d8fb626ca3f7dd3b22a","score":"0.71303374","text":"def test_create_service_optional(self):\n pwd.getpwnam.return_value = self.mock_pwrow('test_shell', 'test_home')\n svc_dir = supervisor.create_scan_dir(self.root, 5000)\n\n supervisor.create_service(\n svc_dir,\n 'xx',\n 'ls -al',\n userid='proid1',\n monitor_policy={\n 'limit': 5,\n 'interval': 60,\n 'tombstone': {\n 'uds': True,\n 'path': '/run/tm_ctl/tombstone',\n 'id': 'xx'\n }\n },\n environ={\n 'b': 'test2'\n },\n trace={\n 'instanceid': 'xx',\n 'uniqueid': 'ID1234',\n 'service': 'xx',\n 'path': '/run/tm_ctl/appevents'\n }\n )\n service_dir = os.path.join(self.root, 'xx')\n self.assertTrue(os.path.isdir(service_dir))\n data_dir = os.path.join(service_dir, 'data')\n self.assertTrue(os.path.isfile(os.path.join(data_dir, 'app_start')))\n self.assertTrue(os.path.isfile(os.path.join(service_dir, 'run')))\n self.assertTrue(os.path.isfile(os.path.join(service_dir, 'finish')))\n self.assertTrue(os.path.isfile(os.path.join(service_dir, 'env/b')))","title":""},{"docid":"e50a8bae31914bed567f815bac696211","score":"0.71238977","text":"def test_no_greenlets_or_child_services(self):\n self.started = False\n self.stopped = False\n\n def start():\n self.started = True\n\n def stop():\n self.stopped = True\n\n class MyService(service.Service):\n def do_start(self):\n start()\n\n def do_stop(self):\n stop()\n\n my_service = MyService()\n\n my_service.join()\n\n self.assertTrue(self.started)\n self.assertTrue(self.stopped)\n self.assertFalse(my_service.started)","title":""},{"docid":"639c82a6d5cd37fd1c4cb9d350846342","score":"0.7066224","text":"def test_start_stop_with_child_service(self):\n self.executed = False\n\n def run(time):\n self.executed = True\n gevent.sleep(time)\n\n class ChildService(service.Service):\n def do_start(self):\n self.spawn(run, 10)\n\n my_service = make_service()\n child_service = ChildService()\n\n my_service.add_service(child_service)\n\n my_service.start()\n\n self.assertTrue(self.executed)\n self.assertTrue(my_service.started)\n self.assertTrue(child_service.started)\n\n my_service.stop()\n\n self.assertFalse(my_service.started)\n self.assertFalse(child_service.started)","title":""},{"docid":"853b9419bcf7c55132c7fa0d0a732455","score":"0.70637345","text":"def test_service(host):\n assert host.service('prometheus').is_enabled\n assert host.service('prometheus').is_running\n assert host.service('prometheus-alertmanager').is_enabled\n assert host.service('prometheus-alertmanager').is_running","title":""},{"docid":"d37e5fbfc126323c888d25b764ed6804","score":"0.6964373","text":"def start(app):\n _service(app, 'start')","title":""},{"docid":"4ddf6c61746db27f0d3d0d82db7f966f","score":"0.6961026","text":"def testDontStartNormally(self):\n ss = self.ss\n ycst = YouCantStartThis(store=ss)\n ss.powerUp(ycst, IService)\n self._startService()\n self.assertFalse(ycst.started)","title":""},{"docid":"76cafe9965e21ca2d6c805b5d12f9dac","score":"0.6879523","text":"def test_docker_service(host):\n dockerd = host.service('docker')\n\n assert dockerd.is_running","title":""},{"docid":"e3c476490a42a5a2c0c7a6ffdf0b1c56","score":"0.68464386","text":"def start_srv(start, process, destination_address=world.f_cfg.mgmt_address):\n if destination_address not in world.f_cfg.multiple_tested_servers:\n world.multiple_tested_servers.append(destination_address)\n\n if world.f_cfg.install_method == 'make':\n v4_running, v6_running = _check_kea_status(destination_address)\n\n if process is None:\n process = \"starting\"\n # check process - if None add some.\n\n if v4_running and world.proto == 'v4' or v6_running and world.proto == 'v6':\n result = _stop_kea_with_keactrl(destination_address) # TODO: check result\n\n result = _start_kea_with_keactrl(destination_address)\n _check_kea_process_result(start, result, process)\n else:\n _restart_kea_with_systemctl(destination_address)","title":""},{"docid":"ef73e79351bd5e09d9882c41e3701ee0","score":"0.68412435","text":"def test_start_stop_with_child_service2(self):\n self.executed = False\n\n def run(time):\n self.executed = True\n gevent.sleep(time)\n\n class ChildService(service.Service):\n def do_start(self):\n self.spawn(run, 0)\n\n my_service = make_service()\n child_service = ChildService()\n\n my_service.add_service(child_service)\n\n my_service.start()\n\n self.assertTrue(self.executed)\n self.assertTrue(my_service.started)\n self.assertTrue(child_service.started)\n\n my_service.join()\n\n self.assertFalse(my_service.started)\n self.assertFalse(child_service.started)","title":""},{"docid":"0f884aecdc4782f2e7ce30b124f83501","score":"0.6841097","text":"def _start(self, units, start):\n\n if start:\n action = \"start\"\n else:\n action = \"stop\"\n\n if not Trivial.is_iterable(units):\n units = [units]\n\n for unit in units:\n self._proc.run_verify(f\"systemctl {action} -- '{unit}'\")\n if self.is_active(unit) != start:\n status = None\n try:\n status, _ = self._proc.run_verify(f\"systemctl status -- '{unit}'\")\n except Error:\n pass\n\n msg = f\"failed to {action} systemd unit '{unit}'\"\n if status:\n msg += f\", here is its current status:\\n{status}\"\n raise Error(msg)","title":""},{"docid":"d135432927ac9bd99bdc08edffc1b40e","score":"0.67349553","text":"def setUp(self):\n self._service = Service()\n self._service.setUp()\n time.sleep(1) # wait until the service is available","title":""},{"docid":"5dd039ebf1888edd1140fee4215d8a15","score":"0.6731426","text":"def test_create_service(self):\n pwd.getpwnam.return_value = self.mock_pwrow('test_shell', 'test_home')\n\n supervisor.create_service(\n self.root,\n 'xx',\n 'ls -al',\n userid='proid1',\n downed=True\n )\n service_dir = os.path.join(self.root, 'xx')\n self.assertTrue(os.path.isdir(service_dir))\n data_dir = os.path.join(service_dir, 'data')\n self.assertTrue(os.path.isfile(os.path.join(data_dir, 'app_start')))\n self.assertTrue(os.path.isfile(os.path.join(service_dir, 'run')))\n self.assertFalse(os.path.isfile(os.path.join(service_dir, 'finish')))\n self.assertTrue(os.path.isfile(os.path.join(service_dir, 'down')))\n\n # Do not create down file.\n supervisor.create_service(\n self.root,\n 'bar',\n 'proid1',\n 'ls -al',\n downed=False\n )\n service_dir = os.path.join(self.root, 'bar')\n self.assertFalse(os.path.exists(os.path.join(service_dir, 'down')))","title":""},{"docid":"acc1dcebc814fd6ba40cfc793573d071","score":"0.6693825","text":"def test_service(host):\n\n assert host.service('kibana').is_enabled\n\n if host.system_info.codename == 'jessie':\n jessie_check = host.run('service kibana status')\n assert jessie_check.rc == 0\n assert 'is running' in jessie_check.stdout\n else:\n assert host.service('kibana').is_running\n assert host.check_output('systemctl status kibana').rc == 0","title":""},{"docid":"3bb4a7b704d68b6fc8b1763547ab8efc","score":"0.66519433","text":"def loadService(self, service, **kargs):\n return self.start_service(service, **kargs)","title":""},{"docid":"a604adcedc4b7b5432d9fd0a3040dd14","score":"0.6641564","text":"def test_start(self):\n an_container = models.Container.get(self.client, 'an-container')\n\n an_container.start(wait=True)","title":""},{"docid":"ea4d937ff338ea48e3b1f1b52d48cf46","score":"0.66263324","text":"def start(self, restart=True):\n if not self.is_enabled():\n LOG.error('The service is not enabled')\n return False\n\n LOG.info('Starting %s services via systemd', self.name)\n if self.is_running() and restart:\n subprocess.check_call(\n ['systemctl', 'restart', '{0}.target'.format(self.name)])\n else:\n subprocess.check_call(\n ['systemctl', 'start', '{0}.target'.format(self.name)])\n return True","title":""},{"docid":"41c275b13f84e66870b3241f7f69d7da","score":"0.6613791","text":"def test_on_start():\n assert started","title":""},{"docid":"c72d7b0bda6aa5537dbf11ef27fe814f","score":"0.6596695","text":"def _restart_service(self, container, service):\n\n if container.get_service(service).is_running():\n container.stop(service)\n # Restart it and report a new status to Juju\n container.start(service)\n logging.info(f\"Restarted service: {service}\")\n # workaround for https://github.com/canonical/pebble/issues/46\n\n if self._check_karma_service_alive():\n self.unit.status = ActiveStatus()\n else:\n self.unit.status = UnknownStatus()","title":""},{"docid":"0258c4e6d6cae892fb8a42679e0f1b5f","score":"0.6567258","text":"def startService(handler, *args, **kwargs):\n\n preProcessHandler(handler)\n enforceSolitude(handler)\n if handler._dbus_kill: sys.exit()\n if handler._dbus_go_daemon: _goDaemon()\n\n print(\"{:s} daemon: {:d}\".format(handler._dbus_service, os.getpid()))\n\n postProcessHandler(handler, *args, **kwargs)","title":""},{"docid":"d8cd50f81385700ecb49aa294c6df0b7","score":"0.65619165","text":"def test_control_service(self):\n # Disable W0212(protected-access)\n # pylint: disable=W0212\n self.assertTrue(supervisor.control_service(\n self.root, supervisor.ServiceControlAction.down\n ))\n treadmill.subproc.check_call.assert_called_with(\n [supervisor._get_cmd('svc'), '-d', self.root]\n )\n\n self.assertTrue(supervisor.control_service(\n self.root, (\n supervisor.ServiceControlAction.up,\n supervisor.ServiceControlAction.once_at_most,\n ),\n timeout=100, # Should not be used\n ))\n treadmill.subproc.check_call.assert_called_with(\n [supervisor._get_cmd('svc'), '-uO', self.root]\n )\n\n self.assertTrue(supervisor.control_service(\n self.root, supervisor.ServiceControlAction.up,\n wait=supervisor.ServiceWaitAction.up,\n timeout=100,\n ))\n treadmill.subproc.check_call.assert_called_with(\n [supervisor._get_cmd('svwait'), '-t100', '-u', self.root]\n )\n\n treadmill.subproc.check_call.side_effect = \\\n subproc.CalledProcessError(1, supervisor._get_cmd('svc'))\n self.assertRaises(\n subproc.CalledProcessError,\n supervisor.control_service,\n self.root,\n supervisor.ServiceControlAction.down\n )\n treadmill.subproc.check_call.assert_called_with(\n [supervisor._get_cmd('svc'), '-d', self.root]\n )","title":""},{"docid":"802c80d07e384420a1ca5a16e2cec5a1","score":"0.6535537","text":"def on_service_add(self, service):\n self.launch_thread(service.name, self.check_loop, service)","title":""},{"docid":"aed275ab4149a09ebabf47c6bc20b774","score":"0.65170586","text":"def service_start(self, svc_manager):\n # TODO: Functionality of this method should be moved to the\n # Package.handle_svc_start() method\n svc_name = svc_manager.svc_name\n\n # Discover initial service status\n try:\n ret_code, stdout, stderr = svc_manager.status()\n except svcm_exc.ServiceManagerCommandError as e:\n err_msg = f'Get initial service status: {svc_name}: {e}'\n raise type(e)(err_msg) from e # Subject to retry\n else:\n log_msg = (f'Get initial service status: {svc_name}:'\n f'stdout[{stdout}] stderr[{stderr}]')\n LOG.debug(log_msg)\n svc_status = str(stdout).strip().rstrip('\\n')\n\n # Manage service appropriately to its status\n if svc_status == SVC_STATUS.STOPPED:\n # Start a service\n try:\n svc_manager.start()\n except svcm_exc.ServiceManagerCommandError as e:\n err_msg = f'Start service: {svc_name}: {e}'\n raise type(e)(err_msg) from e # Subject to retry\n # Verify that service is running\n try:\n ret_code, stdout, stderr = svc_manager.status()\n LOG.debug(f'Get final service status: {svc_name}:'\n f'stdout[{stdout}] stderr[{stderr}]')\n svc_status = str(stdout).strip().rstrip('\\n')\n\n if svc_status == SVC_STATUS.START_PENDING:\n msg = f'Service is starting: {svc_name}'\n LOG.debug(msg)\n raise svcm_exc.ServiceTransientError(msg) # Subject to retry\n elif svc_status != SVC_STATUS.RUNNING:\n err_msg = (f'Start service: {svc_name}: Failed:'\n f' {svc_status}')\n raise svcm_exc.ServicePersistentError(err_msg)\n except svcm_exc.ServiceManagerCommandError as e:\n err_msg = f'Get final service status: {svc_name}: {e}'\n raise type(e)(err_msg) from e # Subject to retry\n elif svc_status == SVC_STATUS.START_PENDING:\n msg = f'Service is starting: {svc_name}: ...'\n LOG.debug(msg)\n raise svcm_exc.ServiceTransientError(msg) # Subject to retry\n elif svc_status == SVC_STATUS.RUNNING:\n LOG.debug(f'Service is already running: {svc_name}')\n else:\n err_msg = f'Invalid service status: {svc_name}: {svc_status}'\n raise svcm_exc.ServicePersistentError(err_msg)","title":""},{"docid":"8400ab7d189fa75accb03fcd3a0aab8c","score":"0.6509138","text":"def test_03_verify_launch_services(self):\n self.fc.flow_load_home_screen()\n self.home.select_menu()\n self.home.select_plugins_tab_from_menu()\n self.home.select_service_routing_plugin()\n self.service_plugin.enter_service_launch_data('{\"https://bruce-williams.github.io\"}')\n self.service_plugin.select_launch_service_test_btn()\n assert self.service_plugin.get_service_launch_result() == {'errorType': 'invalidOptions', 'message': 'url is missing from openUrl serviceOptions'}\n self.service_plugin.select_get_service_instance_test_btn()\n assert self.service_plugin.get_service_instance_result() == {'errorType': 'serviceInstanceNotFound', 'message': 'The service instance with id: was not found'}\n for i in saf_misc.load_json(ma_misc.get_abs_path(TEST_DATA.SERVICE_ROUTING))[\"urls\"]:\n data = '{\"url\":\"'+i+'\"}'\n self.service_plugin.enter_service_launch_data(data)\n self.service_plugin.select_launch_service_test_btn()\n if all(word not in data for word in [\"localhost\", \"chrisgeohringhp\"]):\n self.driver.press_key_back()\n self.service_plugin.select_add_listener_test_btn()\n self.service_plugin.enter_service_launch_data(data)\n self.service_plugin.select_launch_service_test_btn()\n self.service_plugin.select_event_close_button(1)\n self.service_plugin.select_event_close_button(0)","title":""},{"docid":"a7a52197abf53efadf25ed66ad7dd3a6","score":"0.6474801","text":"def test_get_single_service(self):\n pass","title":""},{"docid":"12abf4543cb8382f87d6f9bf6ac980bb","score":"0.6469933","text":"def test_start(self) -> None:\n self._test_worker_command(\"start\")","title":""},{"docid":"751cffb772f9e7d33537f108087ae99f","score":"0.6414413","text":"def start(self):\n LOG.info(\"Start service.\\n\")\n\n try:\n LOG.debug(\"Wait for game start.\\n\")\n self.wait_start()\n LOG.debug(\"Game started.\\n\")\n self.run()\n finally:\n LOG.info(\"Stop service.\\n\")\n GPIO.cleanup()","title":""},{"docid":"ddeae30f33526849f236b446300bfab5","score":"0.6411617","text":"def run(cls):\n if not all(v is None for v in cls._services.values()):\n cls._set_host_id()\n cls._setup_logging()\n cls._set_process_name()\n cls._set_signal_handlers()\n cls._start_server()\n else:\n cls._logger.error('No services to host')","title":""},{"docid":"96e10fd0fe319679b7fef43cc58a9197","score":"0.6385642","text":"def open_service(self):\n try:\n self.service = build(self.service_name, self.version, credentials=self.creds)\n except Exception as e:\n raise AssertionError(e)","title":""},{"docid":"0e64c105c27f3fd5e73ae14c91b85e83","score":"0.63688385","text":"def test_get_service(self):\n pass","title":""},{"docid":"8476550039a3de4ea889de2421514f87","score":"0.6300333","text":"def enable_service(self, service):\n call_throws(['systemctl', 'enable', '--now', service])","title":""},{"docid":"76b8f61e4c550800fac6da6ed6caeba5","score":"0.6295617","text":"def start_pacemaker_service(mnode):\n\n cmd = \"systemctl start pacemaker\"\n return g.run(mnode, cmd)","title":""},{"docid":"878fd66e4ae366e7606518cef9d3b4a3","score":"0.62762386","text":"def test_add_service(self, mock_spawn):\n my_service = make_service()\n mock_service = mock.Mock()\n\n my_service.add_service(mock_service)\n\n self.assertFalse(mock_spawn.called)\n\n self.assertEqual(my_service.services, [mock_service])","title":""},{"docid":"25e10e55d8acf865972921e7c5035557","score":"0.62576526","text":"def test(self, name=\"\"):\n if self.running():\n self.stop()\n\n self.start()\n assert self.running()\n self._log_info(\"openresty is running\")","title":""},{"docid":"5a98c3bb5612a8fed8384780bfd8f4e6","score":"0.62374693","text":"def manage_service(service, command):\n cmd = [\"/bin/service\", \"--quiet\", service, command]\n LOGGER.debug(\"%s service %s..\" % (command, service))\n subprocess.Popen(cmd, close_fds=True, preexec_fn=fork_handler,\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n LOGGER.debug(\"%s service %s..done\" % (command, service))\n SPLASH.update(service)","title":""},{"docid":"5143bce89cc96dbd908a9adc2eedae83","score":"0.6217335","text":"def isServiceRunning(positive, host, user, password, service):\n result = False\n machine = Machine(host, user, password).util(LINUX)\n if machine is not None:\n result = machine.isServiceRunning(service)\n\n return result == positive","title":""},{"docid":"24a3e8a0e540cc5cd263e42c79cc22b0","score":"0.6208803","text":"def start_subprocess(self):\n \n self.server = InetdService(self)\n yield from self.server.run()\n\n self.loginfo(\"inetd service {0} listening on port {1}\".format(self.name, self.port))","title":""},{"docid":"3516508b4c0f6187d64ce6f30f03457a","score":"0.620731","text":"def enable_service(service, failonerror=True):\n if os.path.exists(\"/run/systemd\"):\n exec_command(\"/usr/bin/systemctl enable %s\" % (service), not failonerror)\n else:\n exec_command(\"/sbin/chkconfig %s on\" % (service), not failonerror)","title":""},{"docid":"8cc8335477dcc4d589c0795678185441","score":"0.6206382","text":"def start() -> None:\n \n active_server_name = srv_mgr.get_active_server_name()\n if not active_server_name:\n print(f'No active server found. Set an active server?\\n')\n sys.exit(1)\n \n if srv_mgr.is_server_running():\n print(f'Looks like this server {active_server_name} is already running. Use restart instead of start.')\n sys.exit(1)\n\n if srv_mgr.start_server():\n print(f\"Server {active_server_name} started succesfully!\")\n sys.exit(0)\n\n print(f\"Server {active_server_name} could not be started!\")\n sys.exit(1)","title":""},{"docid":"03183ae2432419c9d43ce65d3543351d","score":"0.6169518","text":"def test_create(self):\n from gevent import pool\n\n my_service = make_service()\n\n self.assertFalse(my_service.started)\n self.assertEqual(my_service.services, [])\n self.assertIsInstance(my_service.pool, pool.Group)","title":""},{"docid":"ff90266bb361dad305e37bae7d071dae","score":"0.61596406","text":"def start_srv(start, process, destination_address=world.f_cfg.mgmt_address):\n world.cfg['leases'] = world.f_cfg.software_install_path + 'var/kea/kea-leases4.csv'\n v6, v4 = check_kea_status(destination_address)\n\n if process is None:\n process = \"starting\"\n # check process - if None add some.\n if not v4:\n result = fabric_sudo_command('( ' + world.f_cfg.software_install_path + 'sbin/keactrl start '\n + ' & ); sleep ' + str(world.f_cfg.sleep_time_1),\n destination_host=destination_address)\n check_kea_process_result(start, result, process)\n else:\n result = fabric_sudo_command('(' + world.f_cfg.software_install_path + 'sbin/keactrl stop '\n + ' & ); sleep ' + str(world.f_cfg.sleep_time_1),\n destination_host=destination_address)\n # check_kea_process_result(start, result, process)\n result = fabric_sudo_command('(' + world.f_cfg.software_install_path + 'sbin/keactrl start '\n + ' & ); sleep ' + str(world.f_cfg.sleep_time_1),\n destination_host=destination_address)\n check_kea_process_result(start, result, process)\n sleep(2)","title":""},{"docid":"6e1bc7fd73409b244028c6cde8bef84c","score":"0.61467177","text":"def test_service(self):\n net = INetSim(\"dummy\")\n net.start()\n time.sleep(1)\n count = net.proc_count()\n print(f\"[*] Start count: {count}\")\n self.assertTrue(count > 15)\n\n net.stop()\n count = net.proc_count()\n print(f\"[*] Stop count: {count}\")\n self.assertTrue(count == 0)","title":""},{"docid":"fc70154564fb56b22fd6a75af8e2e377","score":"0.61420697","text":"def test_empty_on_start(self):\n service = self.service(FilePath(self.mktemp()))\n self.assertEqual(service.get(), Deployment(nodes=frozenset()))","title":""},{"docid":"f262a5ec3d22cd27c4b6c12115ef95f7","score":"0.6138308","text":"def startService(self):\n super(ControllerQueue, self).startService()\n self._workCheckLoop()\n self._overdueCheckLoop()","title":""},{"docid":"c223511a0f9906776692b34db1925cea","score":"0.61370045","text":"def run_service(cmd):\n subprocess.Popen(f\"{cmd} >/dev/null 2>&1 &\", shell=True)","title":""},{"docid":"8b7fb56bf70305f38ffe6497642e92a3","score":"0.6127151","text":"def test_do_start_error(self, mock_teardown):\n class MyService(service.Service):\n def do_start(self):\n raise RuntimeError\n\n my_service = MyService()\n my_service.logger = mock.Mock()\n\n with self.assertRaises(RuntimeError):\n my_service.start()\n\n mock_teardown.assert_called_once_with()","title":""},{"docid":"804ea12e3ed717e8eceac8ed2755e838","score":"0.61255825","text":"def start_services(extras=None):\n import dbus\n\n os.setuid(0)\n try:\n bus = dbus.SystemBus()\n except dbus.DBusException:\n UI.error(_(\"Cannot connect to DBus, services won't be started\"))\n return\n\n if extras:\n # Start only the services given in extras\n for service in extras:\n try:\n manage_service(service, \"start\")\n except dbus.DBusException:\n pass\n\n else:\n # Start network service first\n try:\n manage_service(\"NetworkManager\", \"ready\")\n except Exception, error:\n UI.warn(_(\"Unable to start network:\\n %s\") % error)\n\n # Almost everything depends on logger, so start manually\n manage_service(\"rsyslog\", \"start\")\n if not wait_bus(\"/dev/log\", stream=False, timeout=15):\n UI.warn(_(\"Cannot start system logger\"))\n\n # Mount remote filesystems if any\n mount_remote_filesystems()\n\n if not CONFIG.get(\"safe\"):\n UI.info(_(\"Starting services\"))\n services = get_service_list(bus)\n\n # Remove already started services\n services = set(services).difference([\"rsyslog\", \"NetworkManager\"])\n\n # Give login screen a headstart\n head_start = CONFIG.get(\"head_start\")\n run_head_start = head_start and head_start in services\n\n # Decide whether we'll stop plymouth or not\n stop_plymouth = \"off\" in get_kernel_option(\"xorg\") or \\\n not run_head_start\n if run_head_start:\n manage_service(head_start, \"ready\")\n services.remove(head_start)\n\n # Run other services\n for service in services:\n manage_service(service, \"ready\")\n\n if stop_plymouth:\n # Stop plymouth\n SPLASH.quit(retain_splash=False)\n\n # Close the handle\n bus.close()","title":""},{"docid":"34281a54cab94832bd35051f6c00eb5f","score":"0.6114792","text":"def test_04_verify_get_service_instance(self):\n self.fc.flow_load_home_screen()\n self.home.select_menu()\n self.home.select_plugins_tab_from_menu()\n self.home.select_service_routing_plugin()\n self.service_plugin.enter_service_launch_data('{\"url\":\"https://bruce-williams.github.io\"}')\n self.service_plugin.select_get_service_instance_test_btn()\n svc_id = self.service_plugin.get_service_instance_svc_id()\n self.service_plugin.enter_get_service_instance_svc_id(\"none\")\n assert self.service_plugin.get_service_instance_result() == {'errorType': 'serviceInstanceNotFound', 'message': 'The service instance with id: was not found'}\n self.service_plugin.enter_get_service_instance_svc_id(svc_id)\n self.service_plugin.select_get_service_instance_test_btn()\n assert self.service_plugin.get_service_instance_result() != {'errorType': \"serviceNotFound\"}","title":""},{"docid":"4b46a235cd465789747e3300db3048af","score":"0.61147916","text":"def start_time_service():\n return __salt__[\"service.start\"](\"w32time\")","title":""},{"docid":"c7fcb9cdd661489312710c5cd520d282","score":"0.60939896","text":"def test_stop_services(self):\n my_service = SimpleService()\n my_service.start()\n\n mock_greenlet = mock.Mock()\n\n my_service.services = [mock_greenlet]\n\n my_service.stop()\n\n self.assertEqual(my_service.services, [])\n\n mock_greenlet.stop.assert_called_once_with()","title":""},{"docid":"b7933836d38ae6765c0642359cd82bc3","score":"0.6087366","text":"def exec_service(service, command, failonerror=True):\n if os.path.exists(\"/run/systemd\"):\n exec_command(\"/usr/bin/systemctl %s %s\" % (command, service), not failonerror)\n else:\n exec_command(\"/sbin/service %s %s\" % (service, command), not failonerror)","title":""},{"docid":"c24a55cb8e1dc983908bee8040e3b315","score":"0.60686725","text":"def start(self):\n\tif self.m_handle is not None:\n\t raise Error, 'Service already started.'\n\tself.m_stop = False\n\tself.m_handle = _cpg.initialize(self)\n\t_cpg.join(self.m_handle, self.m_name)","title":""},{"docid":"2736bdbaeadaae95edea09e53ecc2994","score":"0.6066672","text":"def test_start_emit_error(self, mock_stop):\n my_service = make_service(logger=mock.Mock())\n\n @my_service.on('start')\n def on_start():\n raise RuntimeError()\n\n with self.assertRaises(RuntimeError):\n my_service.start()\n\n self.assertTrue(mock_stop.called)","title":""},{"docid":"b5f71677881c5b6c19fed2cab3df491e","score":"0.6056587","text":"def start_service() -> None:\n\n loop: asyncio.AbstractEventLoop = asyncio.get_event_loop()\n\n try:\n log_manager = LogRecordManager()\n record_handler = LogRecordHandler(log_manager=log_manager)\n\n app = web.Application()\n app.router.add_post('/read_log', handler=record_handler.get_list)\n\n web.run_app(\n app=app,\n host=CONFIGURATION['SERVICE_HOST'],\n port=CONFIGURATION['SERVICE_PORT'],\n )\n\n except KeyboardInterrupt:\n pass\n\n finally:\n loop.close()","title":""},{"docid":"f4f9f38278f2d484565c2df8ae7b5710","score":"0.6031784","text":"def require_service(srv_name, srv_type, port=None, user_name=None, password=None):\n try:\n return create_service(srv_name, srv_type, port, user_name, password)\n except ServiceAlreadyExists:\n srv = get_service(srv_name)\n if not srv.is_running():\n srv.start()\n return srv","title":""},{"docid":"0b64278ff94abb4900730ff50728dcd6","score":"0.60243386","text":"def start_services(\n self, services: List[str], timeout: float = 30.0, delay: float = 0.1,\n ) -> ChangeID:\n return self._services_action('start', services, timeout, delay)","title":""},{"docid":"244d26b7e558767f06105c5dfc921b0a","score":"0.60079616","text":"def test_stopping_with_a_child_service(self):\n self.executed = [False, False]\n\n def run(index, time):\n self.executed[index] = True\n gevent.sleep(time)\n\n class MyService(service.Service):\n def do_start(self):\n self.spawn(run, 0, 0)\n\n class ChildService(service.Service):\n def do_start(self):\n self.spawn(run, 1, 10)\n\n my_service = MyService()\n child_service = ChildService()\n\n my_service.add_service(child_service)\n\n my_service.start()\n\n self.assertTrue(all(self.executed))\n self.assertTrue(my_service.started)\n self.assertTrue(child_service.started)\n\n my_service.stop()","title":""},{"docid":"e4de5f0fb06e4264871eead93d199cea","score":"0.600666","text":"def test_start_job(self):\n pass","title":""},{"docid":"74c692ecbe5e3cbdb1a525f8a1df6c87","score":"0.60015714","text":"def test_start_process(self):\n from supvisors.mainloop import SupvisorsMainLoop\n main_loop = SupvisorsMainLoop(self.supvisors)\n # test rpc error\n self.mocked_rpc.side_effect = Exception\n main_loop.start_process('10.0.0.1', 'dummy_process', 'extra args')\n self.assertEqual(2, self.mocked_rpc.call_count)\n self.assertEqual(call('10.0.0.1', main_loop.env), self.mocked_rpc.call_args)\n # test with a mocked rpc interface\n rpc_intf = DummyRpcInterface()\n self.mocked_rpc.side_effect = None\n self.mocked_rpc.return_value = rpc_intf\n with patch.object(rpc_intf.supvisors, 'start_args') as mocked_supvisors:\n main_loop.start_process('10.0.0.1', 'dummy_process', 'extra args')\n self.assertEqual(3, self.mocked_rpc.call_count)\n self.assertEqual(call('10.0.0.1', main_loop.env), self.mocked_rpc.call_args)\n self.assertEqual(1, mocked_supvisors.call_count)\n self.assertEqual(call('dummy_process', 'extra args', False), mocked_supvisors.call_args)","title":""},{"docid":"cd49fcc523f752069e88f7927d1377b8","score":"0.59989476","text":"def test_start(self):\n def cb(container):\n self.assertEqual(container, {'Id': 'mocked'})\n self.builder.start(callback=cb)\n self.instance.start.assert_called_with(container='mocked')","title":""},{"docid":"55067d3638e1dd28c0d2069a07fac37f","score":"0.59964466","text":"def started_services():\n for srv, status in rc_status('--servicelist'):\n if status == 'started':\n yield srv","title":""},{"docid":"88686fe6e0555b00345916dc420e2ffc","score":"0.5985676","text":"def start_service(self, service_name, service_type, folder=''):\n\n self._post(self._get_path(\"start_service\", service_path=self._get_service_path(service_name, folder),\n service_type=service_type))","title":""},{"docid":"fb49965034dd41501cef303eae5f46f6","score":"0.59566146","text":"def start(test, config):\n thread = DuringTestThread(test, config)\n thread.daemon = True\n thread.start()\n return thread.stop","title":""},{"docid":"bee4842342cadde5aaa2f15a458c1641","score":"0.59550124","text":"def start(self):\n if self.service.getProcessDicts() == []:\n return\n\n def start2(process, nbr=None):\n\n cwd = process[\"cwd\"]\n # args['process'] = process\n if nbr is None:\n self.stop()\n\n tcmd = process[\"cmd\"]\n if tcmd == \"jspython\":\n tcmd = \"source %s/env.sh;jspython\" % (j.dirs.base)\n\n targs = process[\"args\"]\n tuser = process[\"user\"]\n if tuser == \"\":\n tuser = \"root\"\n tlog = self.service.hrd.getBool(\"process.log\", default=True)\n env = process[\"env\"]\n\n startupmethod = process[\"startupmanager\"]\n domain, name = self._getDomainName(process)\n if nbr is not None:\n name = \"%s.%d\" % (name, i)\n log(\"Starting %s:%s\" % (domain, name))\n\n if startupmethod == 'upstart':\n # check if we are in our docker image which uses myinit instead\n # of upstart\n if j.sal.fs.exists(path=\"/etc/my_init.d/\"):\n cmd2 = \"%s %s\" % (tcmd, targs)\n extracmds = \"\"\n if cmd2.find(\";\") != -1:\n parts = cmd2.split(\";\")\n extracmds = \"\\n\".join(parts[:-1])\n cmd2 = parts[-1]\n\n C = \"#!/bin/sh\\nset -e\\ncd %s\\nrm -f /var/log/%s.log\\n%s\\nexec %s >>/var/log/%s.log 2>&1\\n\" % (\n cwd, name, extracmds, cmd2, name)\n j.sal.fs.remove(\"/var/log/%s.log\" % name)\n j.sal.fs.createDir(\"/etc/service/%s\" % name)\n path2 = \"/etc/service/%s/run\" % name\n j.sal.fs.writeFile(path2, C)\n j.sal.fs.chmod(path2, 0o770)\n j.sal.process.execute(\n \"sv start %s\" % name, die=False, outputToStdout=False, outputStderr=False, captureout=False)\n else:\n j.sal.ubuntu.service_install(name, tcmd, pwd=cwd, env=env)\n j.sal.ubuntu.service_start(name)\n\n elif startupmethod == \"tmux\":\n j.tools.cuisine.local.tmux.executeInScreen(\n domain, name, tcmd + \" \" + targs, cwd=cwd, env=env, user=tuser) # , newscr=True)\n\n else:\n raise j.exceptions.RuntimeError(\n \"startup method not known or disabled:'%s'\" % startupmethod)\n\n # if msg==\"\":\n # pids=self.getPids(ifNoPidFail=False,wait=False)\n # if len(pids) != self.numprocesses:\n # msg=\"Could not start, did not find enough running instances, needed %s, found %s\"%(self.numprocesses,len(pids))\n\n # if msg==\"\" and pids!=[]:\n # for pid in pids:\n # test=j.sal.process.isPidAlive(pid)\n # if test==False:\n # msg=\"Could not start, pid:%s was not alive.\"%pid\n\n # if log!=\"\":\n # msg=\"%s\\nlog:\\n%s\\n\"%(msg,log)\n\n # self.raiseError(msg)\n # return\n\n isrunning = self.check_up(wait=False)\n if isrunning:\n return\n\n processes = self.service.getProcessDicts()\n for i, process in enumerate(processes):\n\n if \"platform\" in process:\n if not j.core.platformtype.myplatform.checkMatch(process[\"platform\"]):\n continue\n if len(processes) > 1:\n start2(process, nbr=i)\n else:\n start2(process)\n\n isrunning = self.check_up()\n if isrunning is False:\n msg = \"\"\n\n if self.service.getTCPPorts() == [0]:\n print('Done ...')\n elif self.service.getTCPPorts() != []:\n ports = \",\".join([str(item)\n for item in self.service.getTCPPorts()])\n msg = \"Could not start:%s, could not connect to ports %s.\" % (\n self.service, ports)\n j.events.opserror_critical(msg, \"service.start.failed.ports\")\n else:\n j.events.opserror_critical(\n \"could not start:%s\" % self.service, \"service.start.failed.other\")","title":""},{"docid":"877be0ce72ae362b726fa65f3d7cfeab","score":"0.5945746","text":"def start(self):\n if self.main_service_name is None:\n raise ValueError('Main service not set! Please call \"set_main_service\"')\n\n self.services[self.main_service_name].main(\n self._get_to_send(self.main_service_name),\n self.configs[self.main_service_name]\n )","title":""},{"docid":"f92c665cc9ae02c0fdfbf4ce0ed2cea5","score":"0.59302753","text":"def test_join(self, mock_start, mock_stop):\n my_service = make_service()\n thread = my_service._run_thread = mock.Mock()\n\n my_service.join()\n\n mock_start.assert_called_once_with()\n thread.get.assert_called_once_with()\n mock_stop.assert_called_once_with()","title":""},{"docid":"7c2f04cd5149a360725a14c4f4059025","score":"0.59240025","text":"def test_control_service_wait(self):\n # Disable W0212(protected-access)\n # pylint: disable=W0212\n\n # shutdown supervised service\n res = supervisor.control_service(\n self.root, supervisor.ServiceControlAction.down,\n wait=supervisor.ServiceWaitAction.down,\n timeout=100,\n )\n\n treadmill.subproc.check_call.assert_called_with(\n [supervisor._get_cmd('svc'), '-d', self.root]\n )\n treadmill.supervisor.wait_service.assert_called_with(\n self.root,\n treadmill.supervisor.ServiceWaitAction.down,\n timeout=100\n )\n self.assertTrue(res)\n\n # shutdown service timeouts\n treadmill.subproc.check_call.reset_mock()\n supervisor.wait_service.side_effect = \\\n subproc.CalledProcessError(99, supervisor._get_cmd('svwait'))\n\n res = supervisor.control_service(\n self.root, supervisor.ServiceControlAction.down,\n wait=supervisor.ServiceWaitAction.down,\n timeout=100,\n )\n treadmill.subproc.check_call.assert_called_with(\n [supervisor._get_cmd('svc'), '-d', self.root]\n )\n treadmill.supervisor.wait_service.assert_called_with(\n self.root,\n treadmill.supervisor.ServiceWaitAction.down,\n timeout=100\n )\n self.assertFalse(res)\n\n # shutdown unsupervised service\n treadmill.subproc.check_call.reset_mock()\n treadmill.subproc.check_call.side_effect = \\\n subproc.CalledProcessError(100, supervisor._get_cmd('svc'))\n\n with self.assertRaises(subproc.CalledProcessError):\n supervisor.control_service(\n self.root, supervisor.ServiceControlAction.down,\n wait=supervisor.ServiceWaitAction.down,\n timeout=100,\n )","title":""},{"docid":"f6b08e12dc970f1c8b03290518c60162","score":"0.5915418","text":"def service(self, service_name):\n pass","title":""},{"docid":"25ba8c28f2234ea9ee458946a2636dd2","score":"0.59107196","text":"def service():\n pass","title":""},{"docid":"1d79a42649180a089b2c48d11189046d","score":"0.5890802","text":"def main():\n\n mask = os.umask(0) # fix the umask\n\n module = AnsibleModule(\n argument_spec=dict(\n name=dict(aliases=['name', 'service']),\n state=dict(default=None, choices=['started', 'restarted',\n 'stopped', 'reloaded']),\n enabled=dict(default=None, type='bool')\n ),\n required_one_of=[['state', 'enabled']],\n supports_check_mode=True)\n\n p = module.params\n\n # Set path for sv\n sv_path = module.get_bin_path('sv', True)\n if not os.path.exists(sv_path):\n module.fail_json(msg=\"Cannot find sv binary in path.\")\n\n # ensure enabled if required\n if p['state'] in ['started', 'restarted', 'reload']:\n if p['enabled'] is False:\n module.fail_json(msg=\"Conflicting request. Will not proceed.\")\n p['enabled'] = True\n\n if p['enabled'] is False:\n if not p['state'] is None and p['state'] != \"stopped\":\n module.fail_json(msg=\"Conflicting request. Will not proceed.\")\n p['state'] = \"stopped\"\n\n service = Service(module, sv_path)\n module.debug(\"Service initialized.\")\n service.do_action()\n\n result = {}\n result[\"name\"] = service.name\n result[\"changed\"] = service.changed\n result[\"enable\"] = service.get_enabled()\n result[\"state\"] = \"down\" if not result[\"enable\"] else service.get_status()\n\n module.exit_json(**result)\n os.umask(mask) # Reset the umask to original value","title":""},{"docid":"429905f4305afaba0d5c489be7ba54aa","score":"0.588829","text":"def test_restart():\n mock_true = MagicMock(return_value=True)\n with patch.object(win_service, \"create_win_salt_restart_task\", mock_true):\n with patch.object(win_service, \"execute_salt_restart_task\", mock_true):\n assert win_service.restart(\"salt-minion\") is True\n\n with patch.object(win_service, \"stop\", mock_true):\n with patch.object(win_service, \"start\", mock_true):\n assert win_service.restart(\"salt\") is True","title":""},{"docid":"39baff7701d5af4b37bf6713a6ba9aca","score":"0.58577853","text":"def test_service_job_running(self):\n\n def _ensure_node_services(unused_pipeline_state, node_id):\n self.assertEqual('my_example_gen', node_id)\n return service_jobs.ServiceStatus.RUNNING\n\n self._mock_service_job_manager.ensure_node_services.side_effect = (\n _ensure_node_services)\n tasks = self._generate_and_test(\n True,\n num_initial_executions=0,\n num_tasks_generated=0,\n num_new_executions=0,\n num_active_executions=0)\n self.assertEmpty(tasks)","title":""},{"docid":"ea9cb2a6863ec1274b53d5d565f3727c","score":"0.58473897","text":"def start_service_in(sparkl_path, module_name, watchdog_path,\n **kwargs):\n\n # Filter kwargs for `sparkl service`\n service_kwargs = dict()\n for key, value in kwargs.items():\n if key in ['path', 'alias']:\n service_kwargs[key] = value\n\n # Start the SPARKL service in the specified directory.\n with ChDir(watchdog_path):\n sparkl('service', sparkl_path, module_name,\n **service_kwargs)\n\n # Give the watchdog time to start.\n sleep(kwargs.get('wait', 3))","title":""},{"docid":"603cffdaa394c88ba8a4d52c70c935af","score":"0.5827291","text":"def test_start_from_args_no_args():\n log = conlog.start()\n log.info('Testing')","title":""},{"docid":"7c64fe3d114f361a14d465e85bb79411","score":"0.5791843","text":"def test_wait_service(self):\n # Disable W0212(protected-access)\n # pylint: disable=W0212\n supervisor.wait_service(\n self.root, supervisor.ServiceWaitAction.down\n )\n treadmill.subproc.check_call.assert_called_with(\n [supervisor._get_cmd('svwait'), '-d', self.root]\n )\n\n treadmill.subproc.check_call.reset_mock()\n supervisor.wait_service(\n (\n os.path.join(self.root, 'a'),\n os.path.join(self.root, 'b')\n ),\n supervisor.ServiceWaitAction.up,\n all_services=False,\n timeout=100,\n )\n\n treadmill.subproc.check_call.assert_called_with(\n [\n supervisor._get_cmd('svwait'), '-t100', '-o', '-u',\n os.path.join(self.root, 'a'),\n os.path.join(self.root, 'b')\n ]\n )\n\n treadmill.subproc.check_call.reset_mock()\n treadmill.subproc.check_call.side_effect = \\\n subproc.CalledProcessError(99, supervisor._get_cmd('svwait'))\n with self.assertRaises(subproc.CalledProcessError):\n supervisor.wait_service(\n self.root, supervisor.ServiceWaitAction.really_down\n )\n treadmill.subproc.check_call.assert_called_with(\n [supervisor._get_cmd('svwait'), '-D', self.root]\n )","title":""},{"docid":"61c665dab10458d42e7841f732f935f5","score":"0.578035","text":"def start():\n sudo('service uwsgi start %s' % PROJECT_NAME)","title":""},{"docid":"66276f44066684334499e982b7518052","score":"0.57765007","text":"def test_spawn_error(self):\n my_service = make_service()\n\n self.executed = False\n\n def trap_error(exc_type, exc_value, exc_traceback):\n self.assertIsInstance(exc_value, RuntimeError)\n self.executed = True\n\n def raise_error():\n raise RuntimeError\n\n my_service.on('error', trap_error)\n\n my_service.spawn(raise_error)\n\n gevent.sleep(0.0)\n\n self.assertTrue(self.executed)","title":""},{"docid":"d3b546a06bd32d0fc5015e9f5a54fddf","score":"0.57750255","text":"def test_start_problem(self):\n self.assertRaises (AppStartError, Application().start, 'Hiya')","title":""},{"docid":"545bd8d1aa7f16ce5e84e2cd2bcfea02","score":"0.5771033","text":"def start_service(cwd, service_module, run_daemon, run_ssl):\n \n def add_extra_configs(conf):\n \"\"\"Add Extra keys to snetd.config.json\"\"\"\n with open(conf, \"r\") as f:\n snetd_configs = json.load(f)\n if run_ssl:\n snetd_configs[\"ssl_cert\"] = \"/opt/singnet/.certs/fullchain.pem\"\n snetd_configs[\"ssl_key\"] = \"/opt/singnet/.certs/privkey.pem\"\n snetd_configs[\"payment_channel_ca_path\"] = \"/opt/singnet/.certs/ca.pem\"\n snetd_configs[\"payment_channel_cert_path\"] = \"/opt/singnet/.certs/client.pem\"\n snetd_configs[\"payment_channel_key_path\"] = \"/opt/singnet/.certs/client-key.pem\"\n _network = \"mainnet\"\n if \"ropsten\" in conf:\n _network = \"ropsten\"\n infura_key = os.environ.get(\"INFURA_API_KEY\", \"\")\n if infura_key:\n snetd_configs[\"ethereum_json_rpc_endpoint\"] = \"https://{}.infura.io/{}\".format(_network, infura_key)\n pk_metering = os.environ.get(\"PVT_KEY_FOR_METERING\", \"\")\n if pk_metering:\n snetd_configs[\"metering_enabled\"] = True\n snetd_configs[\"metering_end_point\"] = \"https://marketplace-mt-v2.singularitynet.io\"\n snetd_configs[\"pvt_key_for_metering\"] = pk_metering\n snetd_configs[\"max_message_size_in_mb\"] = os.environ.get(\"MAX_MESSAGE_SIZE\", 128)\n with open(conf, \"w\") as f:\n json.dump(snetd_configs, f, sort_keys=True, indent=4)\n \n all_p = []\n if run_daemon:\n for idx, config_file in enumerate(glob.glob(\"./snetd_configs/*.json\")):\n add_extra_configs(config_file)\n all_p.append(start_snetd(str(cwd), config_file))\n service_name = service_module.split(\".\")[-1]\n grpc_port = registry[service_name][\"grpc\"]\n p = subprocess.Popen([sys.executable, \"-m\", service_module, \"--grpc-port\", str(grpc_port)], cwd=str(cwd))\n all_p.append(p)\n return all_p","title":""},{"docid":"cfaa451d1e513d39e94780938ff0a3f0","score":"0.57650423","text":"def test_start_process(mock_agent):\n mock_agent.register_process('test_process', tfunc, tfunc)\n res = mock_agent.start('test_process', params={'a': 1})\n print(res)\n assert res[0] == ocs.OK\n assert isinstance(res[1], str)\n assert res[2]['session_id'] == 0\n assert res[2]['op_name'] == 'test_process'\n assert res[2]['op_code'] == OpCode.STARTING.value\n assert res[2]['status'] == 'starting'\n assert res[2]['success'] is None\n assert res[2]['end_time'] is None\n assert res[2]['data'] == {}","title":""},{"docid":"c0241d792029746be8f827af612f3972","score":"0.5763295","text":"def test_subclass(self):\n class SomeService(base.Service):\n def run(self):\n logging.info('some service running')","title":""},{"docid":"589f99679f1dc01e13a2fe8a70f82834","score":"0.5754939","text":"def test_create_measur_service(self):\n pass","title":""}],"string":"[\n {\n \"docid\": \"06347100c5d25a23c13c075938520e7a\",\n \"score\": \"0.7975998\",\n \"text\": \"def start_service(service):\\n subprocess.call([\\\"service\\\", service, \\\"start\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4502c4639ce33b3e8797f182e1151e8d\",\n \"score\": \"0.78600645\",\n \"text\": \"def test_stop_start(self):\\n my_service = SimpleService()\\n my_service.start()\\n\\n self.assertTrue(my_service.started)\\n\\n my_service.stop()\\n\\n self.assertFalse(my_service.started)\\n\\n my_service.start()\\n\\n # gevent.sleep(0) does not give the service enough time to run.\\n gevent.sleep(0.001)\\n self.assertTrue(my_service.started)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5417d5c2829c3fc1ce02114c70b77201\",\n \"score\": \"0.7859392\",\n \"text\": \"def test_startService(self):\\n self.poller.startService()\\n self.timer.return_value.startService.assert_called_once_with()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"059b4aac45b338c93f0ed9efad153de1\",\n \"score\": \"0.7840608\",\n \"text\": \"def test_start_services(self):\\n my_service = make_service(logger=mock.Mock())\\n mock_greenlet = mock.Mock()\\n\\n my_service.add_service(mock_greenlet)\\n\\n my_service.join()\\n\\n mock_greenlet.join.assert_called_once_with()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a978f01acba9b9d3673c158ab873b308\",\n \"score\": \"0.78269166\",\n \"text\": \"def test_start_stop_simple_service(self):\\n self.executed = False\\n\\n def run(time):\\n self.executed = True\\n gevent.sleep(time)\\n\\n class MyService(service.Service):\\n def do_start(self):\\n self.spawn(run, 10)\\n\\n my_service = MyService()\\n\\n my_service.start()\\n\\n self.assertTrue(self.executed)\\n self.assertTrue(my_service.started)\\n\\n my_service.stop()\\n\\n self.assertFalse(my_service.started)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67b501bae5b4f3fac23bdcfe1784c75c\",\n \"score\": \"0.75843453\",\n \"text\": \"def test_do_start(self, mock_do_start):\\n my_service = make_service()\\n\\n # Run something on service so it doesn't stop right away.\\n my_service.spawn(gevent.sleep, 1)\\n\\n my_service.start()\\n\\n mock_do_start.assert_called_once_with()\\n\\n # ensure that `do_start` is called only once.\\n my_service.start()\\n\\n mock_do_start.assert_called_once_with()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b54ffa2c83d33a949d15c29e6232b72\",\n \"score\": \"0.756705\",\n \"text\": \"def test_start_stop_simple_service2(self):\\n self.executed = False\\n\\n def run(time):\\n self.executed = True\\n gevent.sleep(time)\\n\\n class MyService(service.Service):\\n def do_start(self):\\n self.spawn(run, 0)\\n\\n my_service = MyService()\\n\\n my_service.start()\\n\\n self.assertTrue(self.executed)\\n self.assertTrue(my_service.started)\\n\\n my_service.join()\\n\\n self.assertFalse(my_service.started)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4886ad9f752966aa19003bde67fbfe24\",\n \"score\": \"0.75114053\",\n \"text\": \"def testStartEverythingExplicitly(self):\\n ss = self.ss\\n ysst = YouShouldStartThis(store=ss)\\n ss.powerUp(ysst, IService)\\n self.topdb.powerUp(self.ssitem, IService)\\n self._startService()\\n self.failUnless(ysst.started)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dded99b20ef0c474fa2d3bf43e8a563\",\n \"score\": \"0.7482764\",\n \"text\": \"def testStartEverythingExplicitly(self):\\n ss = self.ss\\n ysst = YouShouldStartThis(store=ss)\\n ss.powerUp(ysst, IService)\\n self.topdb.powerUp(self.ssitem, IService)\\n self._startService()\\n self.assertTrue(ysst.started)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43502633a26d690b168571affe0518e0\",\n \"score\": \"0.7462408\",\n \"text\": \"def start(service, *args, **kwargs):\\n if service == 'all':\\n s.info(\\n f\\\"Starting all services: $[{', '.join(map(str, [s for s in Service.SERVICE_LIST]))}]\\\\n\\\")\\n return [Service(s, *args, **kwargs).start() for s in Service.SERVICE_LIST]\\n service = Service(service, *args, **kwargs)\\n s.info(f\\\"Starting $[{service.name}]\\\\n\\\")\\n service.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74055e6c3f170f1d0bb66030371c43cc\",\n \"score\": \"0.7404273\",\n \"text\": \"def test_start(self):\\n my_service = SimpleService()\\n\\n self.event_fired = False\\n\\n def on_start():\\n self.event_fired = True\\n\\n my_service.on('start', on_start)\\n\\n self.assertFalse(my_service.started)\\n\\n my_service.start()\\n\\n self.assertTrue(my_service.started)\\n self.assertTrue(self.event_fired)\\n\\n # now test calling `start` again. It should be idempotent\\n self.event_fired = False\\n\\n my_service.start()\\n\\n self.assertFalse(self.event_fired)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae7371e8a02c4c30cca467eed816595a\",\n \"score\": \"0.73456526\",\n \"text\": \"def service_start(self, service):\\n cmd = [\\n 'service.start', service,\\n '--out', 'newline_values_only'\\n ]\\n return self.run_salt(cmd)['stdout'].strip().lower() == b'true'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c2b6d9db240b5d8b8054edd1d18927c\",\n \"score\": \"0.72434205\",\n \"text\": \"def test_start_already_running():\\n mock_false = MagicMock(return_value=False)\\n mock_error = MagicMock(\\n side_effect=pywintypes.error(1056, \\\"StartService\\\", \\\"Service is running\\\")\\n )\\n mock_info = MagicMock(side_effect=[{\\\"Status\\\": \\\"Running\\\"}])\\n with patch.object(win32serviceutil, \\\"StartService\\\", mock_error), patch.object(\\n win_service, \\\"disabled\\\", mock_false\\n ), patch.object(win_service, \\\"_status_wait\\\", mock_info):\\n assert win_service.start(\\\"spongebob\\\") is True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ab174e4ab83c3450d5afc13732604d\",\n \"score\": \"0.724007\",\n \"text\": \"def testDontStartNormally(self):\\n ss = self.ss\\n ycst = YouCantStartThis(store=ss)\\n ss.powerUp(ycst, IService)\\n self._startService()\\n self.failIf(ycst.started)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a58a98aa36a900fd9c00f6f4d132ea72\",\n \"score\": \"0.72063875\",\n \"text\": \"async def _service_start(self) -> bool:\\n for _svc_id, service in self._services.items():\\n service.start(True)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82496c2eb08d72d491e516eb8912582c\",\n \"score\": \"0.71671325\",\n \"text\": \"def test_add_service_start(self, mock_watch):\\n mock_watch.__name__ = 'watch_service'\\n\\n my_service = SimpleService()\\n mock_service = mock.Mock()\\n\\n my_service.start()\\n\\n my_service.add_service(mock_service)\\n gevent.sleep(0.0)\\n\\n mock_watch.assert_called_once_with(mock_service)\\n self.assertEqual(my_service.services, [mock_service])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e41d4a127504ed193258297494a03c4\",\n \"score\": \"0.7141826\",\n \"text\": \"def _start_srv(service, name):\\n service.start()\\n sender.fire(CollectorServiceStartedEvent, srv_name=name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c381ed1a23acb5c02335ed1a567baad1\",\n \"score\": \"0.7132379\",\n \"text\": \"def _startService(self):\\n assert not self.serviceStarted\\n self.serviceStarted = True\\n return IService(self.topdb).startService()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c381ed1a23acb5c02335ed1a567baad1\",\n \"score\": \"0.7132379\",\n \"text\": \"def _startService(self):\\n assert not self.serviceStarted\\n self.serviceStarted = True\\n return IService(self.topdb).startService()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb65b0d1beae7d8fb626ca3f7dd3b22a\",\n \"score\": \"0.71303374\",\n \"text\": \"def test_create_service_optional(self):\\n pwd.getpwnam.return_value = self.mock_pwrow('test_shell', 'test_home')\\n svc_dir = supervisor.create_scan_dir(self.root, 5000)\\n\\n supervisor.create_service(\\n svc_dir,\\n 'xx',\\n 'ls -al',\\n userid='proid1',\\n monitor_policy={\\n 'limit': 5,\\n 'interval': 60,\\n 'tombstone': {\\n 'uds': True,\\n 'path': '/run/tm_ctl/tombstone',\\n 'id': 'xx'\\n }\\n },\\n environ={\\n 'b': 'test2'\\n },\\n trace={\\n 'instanceid': 'xx',\\n 'uniqueid': 'ID1234',\\n 'service': 'xx',\\n 'path': '/run/tm_ctl/appevents'\\n }\\n )\\n service_dir = os.path.join(self.root, 'xx')\\n self.assertTrue(os.path.isdir(service_dir))\\n data_dir = os.path.join(service_dir, 'data')\\n self.assertTrue(os.path.isfile(os.path.join(data_dir, 'app_start')))\\n self.assertTrue(os.path.isfile(os.path.join(service_dir, 'run')))\\n self.assertTrue(os.path.isfile(os.path.join(service_dir, 'finish')))\\n self.assertTrue(os.path.isfile(os.path.join(service_dir, 'env/b')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e50a8bae31914bed567f815bac696211\",\n \"score\": \"0.71238977\",\n \"text\": \"def test_no_greenlets_or_child_services(self):\\n self.started = False\\n self.stopped = False\\n\\n def start():\\n self.started = True\\n\\n def stop():\\n self.stopped = True\\n\\n class MyService(service.Service):\\n def do_start(self):\\n start()\\n\\n def do_stop(self):\\n stop()\\n\\n my_service = MyService()\\n\\n my_service.join()\\n\\n self.assertTrue(self.started)\\n self.assertTrue(self.stopped)\\n self.assertFalse(my_service.started)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"639c82a6d5cd37fd1c4cb9d350846342\",\n \"score\": \"0.7066224\",\n \"text\": \"def test_start_stop_with_child_service(self):\\n self.executed = False\\n\\n def run(time):\\n self.executed = True\\n gevent.sleep(time)\\n\\n class ChildService(service.Service):\\n def do_start(self):\\n self.spawn(run, 10)\\n\\n my_service = make_service()\\n child_service = ChildService()\\n\\n my_service.add_service(child_service)\\n\\n my_service.start()\\n\\n self.assertTrue(self.executed)\\n self.assertTrue(my_service.started)\\n self.assertTrue(child_service.started)\\n\\n my_service.stop()\\n\\n self.assertFalse(my_service.started)\\n self.assertFalse(child_service.started)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"853b9419bcf7c55132c7fa0d0a732455\",\n \"score\": \"0.70637345\",\n \"text\": \"def test_service(host):\\n assert host.service('prometheus').is_enabled\\n assert host.service('prometheus').is_running\\n assert host.service('prometheus-alertmanager').is_enabled\\n assert host.service('prometheus-alertmanager').is_running\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d37e5fbfc126323c888d25b764ed6804\",\n \"score\": \"0.6964373\",\n \"text\": \"def start(app):\\n _service(app, 'start')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ddf6c61746db27f0d3d0d82db7f966f\",\n \"score\": \"0.6961026\",\n \"text\": \"def testDontStartNormally(self):\\n ss = self.ss\\n ycst = YouCantStartThis(store=ss)\\n ss.powerUp(ycst, IService)\\n self._startService()\\n self.assertFalse(ycst.started)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76cafe9965e21ca2d6c805b5d12f9dac\",\n \"score\": \"0.6879523\",\n \"text\": \"def test_docker_service(host):\\n dockerd = host.service('docker')\\n\\n assert dockerd.is_running\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3c476490a42a5a2c0c7a6ffdf0b1c56\",\n \"score\": \"0.68464386\",\n \"text\": \"def start_srv(start, process, destination_address=world.f_cfg.mgmt_address):\\n if destination_address not in world.f_cfg.multiple_tested_servers:\\n world.multiple_tested_servers.append(destination_address)\\n\\n if world.f_cfg.install_method == 'make':\\n v4_running, v6_running = _check_kea_status(destination_address)\\n\\n if process is None:\\n process = \\\"starting\\\"\\n # check process - if None add some.\\n\\n if v4_running and world.proto == 'v4' or v6_running and world.proto == 'v6':\\n result = _stop_kea_with_keactrl(destination_address) # TODO: check result\\n\\n result = _start_kea_with_keactrl(destination_address)\\n _check_kea_process_result(start, result, process)\\n else:\\n _restart_kea_with_systemctl(destination_address)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef73e79351bd5e09d9882c41e3701ee0\",\n \"score\": \"0.68412435\",\n \"text\": \"def test_start_stop_with_child_service2(self):\\n self.executed = False\\n\\n def run(time):\\n self.executed = True\\n gevent.sleep(time)\\n\\n class ChildService(service.Service):\\n def do_start(self):\\n self.spawn(run, 0)\\n\\n my_service = make_service()\\n child_service = ChildService()\\n\\n my_service.add_service(child_service)\\n\\n my_service.start()\\n\\n self.assertTrue(self.executed)\\n self.assertTrue(my_service.started)\\n self.assertTrue(child_service.started)\\n\\n my_service.join()\\n\\n self.assertFalse(my_service.started)\\n self.assertFalse(child_service.started)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f884aecdc4782f2e7ce30b124f83501\",\n \"score\": \"0.6841097\",\n \"text\": \"def _start(self, units, start):\\n\\n if start:\\n action = \\\"start\\\"\\n else:\\n action = \\\"stop\\\"\\n\\n if not Trivial.is_iterable(units):\\n units = [units]\\n\\n for unit in units:\\n self._proc.run_verify(f\\\"systemctl {action} -- '{unit}'\\\")\\n if self.is_active(unit) != start:\\n status = None\\n try:\\n status, _ = self._proc.run_verify(f\\\"systemctl status -- '{unit}'\\\")\\n except Error:\\n pass\\n\\n msg = f\\\"failed to {action} systemd unit '{unit}'\\\"\\n if status:\\n msg += f\\\", here is its current status:\\\\n{status}\\\"\\n raise Error(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d135432927ac9bd99bdc08edffc1b40e\",\n \"score\": \"0.67349553\",\n \"text\": \"def setUp(self):\\n self._service = Service()\\n self._service.setUp()\\n time.sleep(1) # wait until the service is available\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd039ebf1888edd1140fee4215d8a15\",\n \"score\": \"0.6731426\",\n \"text\": \"def test_create_service(self):\\n pwd.getpwnam.return_value = self.mock_pwrow('test_shell', 'test_home')\\n\\n supervisor.create_service(\\n self.root,\\n 'xx',\\n 'ls -al',\\n userid='proid1',\\n downed=True\\n )\\n service_dir = os.path.join(self.root, 'xx')\\n self.assertTrue(os.path.isdir(service_dir))\\n data_dir = os.path.join(service_dir, 'data')\\n self.assertTrue(os.path.isfile(os.path.join(data_dir, 'app_start')))\\n self.assertTrue(os.path.isfile(os.path.join(service_dir, 'run')))\\n self.assertFalse(os.path.isfile(os.path.join(service_dir, 'finish')))\\n self.assertTrue(os.path.isfile(os.path.join(service_dir, 'down')))\\n\\n # Do not create down file.\\n supervisor.create_service(\\n self.root,\\n 'bar',\\n 'proid1',\\n 'ls -al',\\n downed=False\\n )\\n service_dir = os.path.join(self.root, 'bar')\\n self.assertFalse(os.path.exists(os.path.join(service_dir, 'down')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acc1dcebc814fd6ba40cfc793573d071\",\n \"score\": \"0.6693825\",\n \"text\": \"def test_service(host):\\n\\n assert host.service('kibana').is_enabled\\n\\n if host.system_info.codename == 'jessie':\\n jessie_check = host.run('service kibana status')\\n assert jessie_check.rc == 0\\n assert 'is running' in jessie_check.stdout\\n else:\\n assert host.service('kibana').is_running\\n assert host.check_output('systemctl status kibana').rc == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bb4a7b704d68b6fc8b1763547ab8efc\",\n \"score\": \"0.66519433\",\n \"text\": \"def loadService(self, service, **kargs):\\n return self.start_service(service, **kargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a604adcedc4b7b5432d9fd0a3040dd14\",\n \"score\": \"0.6641564\",\n \"text\": \"def test_start(self):\\n an_container = models.Container.get(self.client, 'an-container')\\n\\n an_container.start(wait=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea4d937ff338ea48e3b1f1b52d48cf46\",\n \"score\": \"0.66263324\",\n \"text\": \"def start(self, restart=True):\\n if not self.is_enabled():\\n LOG.error('The service is not enabled')\\n return False\\n\\n LOG.info('Starting %s services via systemd', self.name)\\n if self.is_running() and restart:\\n subprocess.check_call(\\n ['systemctl', 'restart', '{0}.target'.format(self.name)])\\n else:\\n subprocess.check_call(\\n ['systemctl', 'start', '{0}.target'.format(self.name)])\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41c275b13f84e66870b3241f7f69d7da\",\n \"score\": \"0.6613791\",\n \"text\": \"def test_on_start():\\n assert started\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c72d7b0bda6aa5537dbf11ef27fe814f\",\n \"score\": \"0.6596695\",\n \"text\": \"def _restart_service(self, container, service):\\n\\n if container.get_service(service).is_running():\\n container.stop(service)\\n # Restart it and report a new status to Juju\\n container.start(service)\\n logging.info(f\\\"Restarted service: {service}\\\")\\n # workaround for https://github.com/canonical/pebble/issues/46\\n\\n if self._check_karma_service_alive():\\n self.unit.status = ActiveStatus()\\n else:\\n self.unit.status = UnknownStatus()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0258c4e6d6cae892fb8a42679e0f1b5f\",\n \"score\": \"0.6567258\",\n \"text\": \"def startService(handler, *args, **kwargs):\\n\\n preProcessHandler(handler)\\n enforceSolitude(handler)\\n if handler._dbus_kill: sys.exit()\\n if handler._dbus_go_daemon: _goDaemon()\\n\\n print(\\\"{:s} daemon: {:d}\\\".format(handler._dbus_service, os.getpid()))\\n\\n postProcessHandler(handler, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8cd50f81385700ecb49aa294c6df0b7\",\n \"score\": \"0.65619165\",\n \"text\": \"def test_control_service(self):\\n # Disable W0212(protected-access)\\n # pylint: disable=W0212\\n self.assertTrue(supervisor.control_service(\\n self.root, supervisor.ServiceControlAction.down\\n ))\\n treadmill.subproc.check_call.assert_called_with(\\n [supervisor._get_cmd('svc'), '-d', self.root]\\n )\\n\\n self.assertTrue(supervisor.control_service(\\n self.root, (\\n supervisor.ServiceControlAction.up,\\n supervisor.ServiceControlAction.once_at_most,\\n ),\\n timeout=100, # Should not be used\\n ))\\n treadmill.subproc.check_call.assert_called_with(\\n [supervisor._get_cmd('svc'), '-uO', self.root]\\n )\\n\\n self.assertTrue(supervisor.control_service(\\n self.root, supervisor.ServiceControlAction.up,\\n wait=supervisor.ServiceWaitAction.up,\\n timeout=100,\\n ))\\n treadmill.subproc.check_call.assert_called_with(\\n [supervisor._get_cmd('svwait'), '-t100', '-u', self.root]\\n )\\n\\n treadmill.subproc.check_call.side_effect = \\\\\\n subproc.CalledProcessError(1, supervisor._get_cmd('svc'))\\n self.assertRaises(\\n subproc.CalledProcessError,\\n supervisor.control_service,\\n self.root,\\n supervisor.ServiceControlAction.down\\n )\\n treadmill.subproc.check_call.assert_called_with(\\n [supervisor._get_cmd('svc'), '-d', self.root]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"802c80d07e384420a1ca5a16e2cec5a1\",\n \"score\": \"0.6535537\",\n \"text\": \"def on_service_add(self, service):\\n self.launch_thread(service.name, self.check_loop, service)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aed275ab4149a09ebabf47c6bc20b774\",\n \"score\": \"0.65170586\",\n \"text\": \"def service_start(self, svc_manager):\\n # TODO: Functionality of this method should be moved to the\\n # Package.handle_svc_start() method\\n svc_name = svc_manager.svc_name\\n\\n # Discover initial service status\\n try:\\n ret_code, stdout, stderr = svc_manager.status()\\n except svcm_exc.ServiceManagerCommandError as e:\\n err_msg = f'Get initial service status: {svc_name}: {e}'\\n raise type(e)(err_msg) from e # Subject to retry\\n else:\\n log_msg = (f'Get initial service status: {svc_name}:'\\n f'stdout[{stdout}] stderr[{stderr}]')\\n LOG.debug(log_msg)\\n svc_status = str(stdout).strip().rstrip('\\\\n')\\n\\n # Manage service appropriately to its status\\n if svc_status == SVC_STATUS.STOPPED:\\n # Start a service\\n try:\\n svc_manager.start()\\n except svcm_exc.ServiceManagerCommandError as e:\\n err_msg = f'Start service: {svc_name}: {e}'\\n raise type(e)(err_msg) from e # Subject to retry\\n # Verify that service is running\\n try:\\n ret_code, stdout, stderr = svc_manager.status()\\n LOG.debug(f'Get final service status: {svc_name}:'\\n f'stdout[{stdout}] stderr[{stderr}]')\\n svc_status = str(stdout).strip().rstrip('\\\\n')\\n\\n if svc_status == SVC_STATUS.START_PENDING:\\n msg = f'Service is starting: {svc_name}'\\n LOG.debug(msg)\\n raise svcm_exc.ServiceTransientError(msg) # Subject to retry\\n elif svc_status != SVC_STATUS.RUNNING:\\n err_msg = (f'Start service: {svc_name}: Failed:'\\n f' {svc_status}')\\n raise svcm_exc.ServicePersistentError(err_msg)\\n except svcm_exc.ServiceManagerCommandError as e:\\n err_msg = f'Get final service status: {svc_name}: {e}'\\n raise type(e)(err_msg) from e # Subject to retry\\n elif svc_status == SVC_STATUS.START_PENDING:\\n msg = f'Service is starting: {svc_name}: ...'\\n LOG.debug(msg)\\n raise svcm_exc.ServiceTransientError(msg) # Subject to retry\\n elif svc_status == SVC_STATUS.RUNNING:\\n LOG.debug(f'Service is already running: {svc_name}')\\n else:\\n err_msg = f'Invalid service status: {svc_name}: {svc_status}'\\n raise svcm_exc.ServicePersistentError(err_msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8400ab7d189fa75accb03fcd3a0aab8c\",\n \"score\": \"0.6509138\",\n \"text\": \"def test_03_verify_launch_services(self):\\n self.fc.flow_load_home_screen()\\n self.home.select_menu()\\n self.home.select_plugins_tab_from_menu()\\n self.home.select_service_routing_plugin()\\n self.service_plugin.enter_service_launch_data('{\\\"https://bruce-williams.github.io\\\"}')\\n self.service_plugin.select_launch_service_test_btn()\\n assert self.service_plugin.get_service_launch_result() == {'errorType': 'invalidOptions', 'message': 'url is missing from openUrl serviceOptions'}\\n self.service_plugin.select_get_service_instance_test_btn()\\n assert self.service_plugin.get_service_instance_result() == {'errorType': 'serviceInstanceNotFound', 'message': 'The service instance with id: was not found'}\\n for i in saf_misc.load_json(ma_misc.get_abs_path(TEST_DATA.SERVICE_ROUTING))[\\\"urls\\\"]:\\n data = '{\\\"url\\\":\\\"'+i+'\\\"}'\\n self.service_plugin.enter_service_launch_data(data)\\n self.service_plugin.select_launch_service_test_btn()\\n if all(word not in data for word in [\\\"localhost\\\", \\\"chrisgeohringhp\\\"]):\\n self.driver.press_key_back()\\n self.service_plugin.select_add_listener_test_btn()\\n self.service_plugin.enter_service_launch_data(data)\\n self.service_plugin.select_launch_service_test_btn()\\n self.service_plugin.select_event_close_button(1)\\n self.service_plugin.select_event_close_button(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a52197abf53efadf25ed66ad7dd3a6\",\n \"score\": \"0.6474801\",\n \"text\": \"def test_get_single_service(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12abf4543cb8382f87d6f9bf6ac980bb\",\n \"score\": \"0.6469933\",\n \"text\": \"def test_start(self) -> None:\\n self._test_worker_command(\\\"start\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"751cffb772f9e7d33537f108087ae99f\",\n \"score\": \"0.6414413\",\n \"text\": \"def start(self):\\n LOG.info(\\\"Start service.\\\\n\\\")\\n\\n try:\\n LOG.debug(\\\"Wait for game start.\\\\n\\\")\\n self.wait_start()\\n LOG.debug(\\\"Game started.\\\\n\\\")\\n self.run()\\n finally:\\n LOG.info(\\\"Stop service.\\\\n\\\")\\n GPIO.cleanup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddeae30f33526849f236b446300bfab5\",\n \"score\": \"0.6411617\",\n \"text\": \"def run(cls):\\n if not all(v is None for v in cls._services.values()):\\n cls._set_host_id()\\n cls._setup_logging()\\n cls._set_process_name()\\n cls._set_signal_handlers()\\n cls._start_server()\\n else:\\n cls._logger.error('No services to host')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96e10fd0fe319679b7fef43cc58a9197\",\n \"score\": \"0.6385642\",\n \"text\": \"def open_service(self):\\n try:\\n self.service = build(self.service_name, self.version, credentials=self.creds)\\n except Exception as e:\\n raise AssertionError(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e64c105c27f3fd5e73ae14c91b85e83\",\n \"score\": \"0.63688385\",\n \"text\": \"def test_get_service(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8476550039a3de4ea889de2421514f87\",\n \"score\": \"0.6300333\",\n \"text\": \"def enable_service(self, service):\\n call_throws(['systemctl', 'enable', '--now', service])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76b8f61e4c550800fac6da6ed6caeba5\",\n \"score\": \"0.6295617\",\n \"text\": \"def start_pacemaker_service(mnode):\\n\\n cmd = \\\"systemctl start pacemaker\\\"\\n return g.run(mnode, cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"878fd66e4ae366e7606518cef9d3b4a3\",\n \"score\": \"0.62762386\",\n \"text\": \"def test_add_service(self, mock_spawn):\\n my_service = make_service()\\n mock_service = mock.Mock()\\n\\n my_service.add_service(mock_service)\\n\\n self.assertFalse(mock_spawn.called)\\n\\n self.assertEqual(my_service.services, [mock_service])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25e10e55d8acf865972921e7c5035557\",\n \"score\": \"0.62576526\",\n \"text\": \"def test(self, name=\\\"\\\"):\\n if self.running():\\n self.stop()\\n\\n self.start()\\n assert self.running()\\n self._log_info(\\\"openresty is running\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a98c3bb5612a8fed8384780bfd8f4e6\",\n \"score\": \"0.62374693\",\n \"text\": \"def manage_service(service, command):\\n cmd = [\\\"/bin/service\\\", \\\"--quiet\\\", service, command]\\n LOGGER.debug(\\\"%s service %s..\\\" % (command, service))\\n subprocess.Popen(cmd, close_fds=True, preexec_fn=fork_handler,\\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n LOGGER.debug(\\\"%s service %s..done\\\" % (command, service))\\n SPLASH.update(service)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5143bce89cc96dbd908a9adc2eedae83\",\n \"score\": \"0.6217335\",\n \"text\": \"def isServiceRunning(positive, host, user, password, service):\\n result = False\\n machine = Machine(host, user, password).util(LINUX)\\n if machine is not None:\\n result = machine.isServiceRunning(service)\\n\\n return result == positive\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24a3e8a0e540cc5cd263e42c79cc22b0\",\n \"score\": \"0.6208803\",\n \"text\": \"def start_subprocess(self):\\n \\n self.server = InetdService(self)\\n yield from self.server.run()\\n\\n self.loginfo(\\\"inetd service {0} listening on port {1}\\\".format(self.name, self.port))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3516508b4c0f6187d64ce6f30f03457a\",\n \"score\": \"0.620731\",\n \"text\": \"def enable_service(service, failonerror=True):\\n if os.path.exists(\\\"/run/systemd\\\"):\\n exec_command(\\\"/usr/bin/systemctl enable %s\\\" % (service), not failonerror)\\n else:\\n exec_command(\\\"/sbin/chkconfig %s on\\\" % (service), not failonerror)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cc8335477dcc4d589c0795678185441\",\n \"score\": \"0.6206382\",\n \"text\": \"def start() -> None:\\n \\n active_server_name = srv_mgr.get_active_server_name()\\n if not active_server_name:\\n print(f'No active server found. Set an active server?\\\\n')\\n sys.exit(1)\\n \\n if srv_mgr.is_server_running():\\n print(f'Looks like this server {active_server_name} is already running. Use restart instead of start.')\\n sys.exit(1)\\n\\n if srv_mgr.start_server():\\n print(f\\\"Server {active_server_name} started succesfully!\\\")\\n sys.exit(0)\\n\\n print(f\\\"Server {active_server_name} could not be started!\\\")\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03183ae2432419c9d43ce65d3543351d\",\n \"score\": \"0.6169518\",\n \"text\": \"def test_create(self):\\n from gevent import pool\\n\\n my_service = make_service()\\n\\n self.assertFalse(my_service.started)\\n self.assertEqual(my_service.services, [])\\n self.assertIsInstance(my_service.pool, pool.Group)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff90266bb361dad305e37bae7d071dae\",\n \"score\": \"0.61596406\",\n \"text\": \"def start_srv(start, process, destination_address=world.f_cfg.mgmt_address):\\n world.cfg['leases'] = world.f_cfg.software_install_path + 'var/kea/kea-leases4.csv'\\n v6, v4 = check_kea_status(destination_address)\\n\\n if process is None:\\n process = \\\"starting\\\"\\n # check process - if None add some.\\n if not v4:\\n result = fabric_sudo_command('( ' + world.f_cfg.software_install_path + 'sbin/keactrl start '\\n + ' & ); sleep ' + str(world.f_cfg.sleep_time_1),\\n destination_host=destination_address)\\n check_kea_process_result(start, result, process)\\n else:\\n result = fabric_sudo_command('(' + world.f_cfg.software_install_path + 'sbin/keactrl stop '\\n + ' & ); sleep ' + str(world.f_cfg.sleep_time_1),\\n destination_host=destination_address)\\n # check_kea_process_result(start, result, process)\\n result = fabric_sudo_command('(' + world.f_cfg.software_install_path + 'sbin/keactrl start '\\n + ' & ); sleep ' + str(world.f_cfg.sleep_time_1),\\n destination_host=destination_address)\\n check_kea_process_result(start, result, process)\\n sleep(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e1bc7fd73409b244028c6cde8bef84c\",\n \"score\": \"0.61467177\",\n \"text\": \"def test_service(self):\\n net = INetSim(\\\"dummy\\\")\\n net.start()\\n time.sleep(1)\\n count = net.proc_count()\\n print(f\\\"[*] Start count: {count}\\\")\\n self.assertTrue(count > 15)\\n\\n net.stop()\\n count = net.proc_count()\\n print(f\\\"[*] Stop count: {count}\\\")\\n self.assertTrue(count == 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc70154564fb56b22fd6a75af8e2e377\",\n \"score\": \"0.61420697\",\n \"text\": \"def test_empty_on_start(self):\\n service = self.service(FilePath(self.mktemp()))\\n self.assertEqual(service.get(), Deployment(nodes=frozenset()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f262a5ec3d22cd27c4b6c12115ef95f7\",\n \"score\": \"0.6138308\",\n \"text\": \"def startService(self):\\n super(ControllerQueue, self).startService()\\n self._workCheckLoop()\\n self._overdueCheckLoop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c223511a0f9906776692b34db1925cea\",\n \"score\": \"0.61370045\",\n \"text\": \"def run_service(cmd):\\n subprocess.Popen(f\\\"{cmd} >/dev/null 2>&1 &\\\", shell=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b7fb56bf70305f38ffe6497642e92a3\",\n \"score\": \"0.6127151\",\n \"text\": \"def test_do_start_error(self, mock_teardown):\\n class MyService(service.Service):\\n def do_start(self):\\n raise RuntimeError\\n\\n my_service = MyService()\\n my_service.logger = mock.Mock()\\n\\n with self.assertRaises(RuntimeError):\\n my_service.start()\\n\\n mock_teardown.assert_called_once_with()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"804ea12e3ed717e8eceac8ed2755e838\",\n \"score\": \"0.61255825\",\n \"text\": \"def start_services(extras=None):\\n import dbus\\n\\n os.setuid(0)\\n try:\\n bus = dbus.SystemBus()\\n except dbus.DBusException:\\n UI.error(_(\\\"Cannot connect to DBus, services won't be started\\\"))\\n return\\n\\n if extras:\\n # Start only the services given in extras\\n for service in extras:\\n try:\\n manage_service(service, \\\"start\\\")\\n except dbus.DBusException:\\n pass\\n\\n else:\\n # Start network service first\\n try:\\n manage_service(\\\"NetworkManager\\\", \\\"ready\\\")\\n except Exception, error:\\n UI.warn(_(\\\"Unable to start network:\\\\n %s\\\") % error)\\n\\n # Almost everything depends on logger, so start manually\\n manage_service(\\\"rsyslog\\\", \\\"start\\\")\\n if not wait_bus(\\\"/dev/log\\\", stream=False, timeout=15):\\n UI.warn(_(\\\"Cannot start system logger\\\"))\\n\\n # Mount remote filesystems if any\\n mount_remote_filesystems()\\n\\n if not CONFIG.get(\\\"safe\\\"):\\n UI.info(_(\\\"Starting services\\\"))\\n services = get_service_list(bus)\\n\\n # Remove already started services\\n services = set(services).difference([\\\"rsyslog\\\", \\\"NetworkManager\\\"])\\n\\n # Give login screen a headstart\\n head_start = CONFIG.get(\\\"head_start\\\")\\n run_head_start = head_start and head_start in services\\n\\n # Decide whether we'll stop plymouth or not\\n stop_plymouth = \\\"off\\\" in get_kernel_option(\\\"xorg\\\") or \\\\\\n not run_head_start\\n if run_head_start:\\n manage_service(head_start, \\\"ready\\\")\\n services.remove(head_start)\\n\\n # Run other services\\n for service in services:\\n manage_service(service, \\\"ready\\\")\\n\\n if stop_plymouth:\\n # Stop plymouth\\n SPLASH.quit(retain_splash=False)\\n\\n # Close the handle\\n bus.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34281a54cab94832bd35051f6c00eb5f\",\n \"score\": \"0.6114792\",\n \"text\": \"def test_04_verify_get_service_instance(self):\\n self.fc.flow_load_home_screen()\\n self.home.select_menu()\\n self.home.select_plugins_tab_from_menu()\\n self.home.select_service_routing_plugin()\\n self.service_plugin.enter_service_launch_data('{\\\"url\\\":\\\"https://bruce-williams.github.io\\\"}')\\n self.service_plugin.select_get_service_instance_test_btn()\\n svc_id = self.service_plugin.get_service_instance_svc_id()\\n self.service_plugin.enter_get_service_instance_svc_id(\\\"none\\\")\\n assert self.service_plugin.get_service_instance_result() == {'errorType': 'serviceInstanceNotFound', 'message': 'The service instance with id: was not found'}\\n self.service_plugin.enter_get_service_instance_svc_id(svc_id)\\n self.service_plugin.select_get_service_instance_test_btn()\\n assert self.service_plugin.get_service_instance_result() != {'errorType': \\\"serviceNotFound\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b46a235cd465789747e3300db3048af\",\n \"score\": \"0.61147916\",\n \"text\": \"def start_time_service():\\n return __salt__[\\\"service.start\\\"](\\\"w32time\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7fcb9cdd661489312710c5cd520d282\",\n \"score\": \"0.60939896\",\n \"text\": \"def test_stop_services(self):\\n my_service = SimpleService()\\n my_service.start()\\n\\n mock_greenlet = mock.Mock()\\n\\n my_service.services = [mock_greenlet]\\n\\n my_service.stop()\\n\\n self.assertEqual(my_service.services, [])\\n\\n mock_greenlet.stop.assert_called_once_with()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7933836d38ae6765c0642359cd82bc3\",\n \"score\": \"0.6087366\",\n \"text\": \"def exec_service(service, command, failonerror=True):\\n if os.path.exists(\\\"/run/systemd\\\"):\\n exec_command(\\\"/usr/bin/systemctl %s %s\\\" % (command, service), not failonerror)\\n else:\\n exec_command(\\\"/sbin/service %s %s\\\" % (service, command), not failonerror)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c24a55cb8e1dc983908bee8040e3b315\",\n \"score\": \"0.60686725\",\n \"text\": \"def start(self):\\n\\tif self.m_handle is not None:\\n\\t raise Error, 'Service already started.'\\n\\tself.m_stop = False\\n\\tself.m_handle = _cpg.initialize(self)\\n\\t_cpg.join(self.m_handle, self.m_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2736bdbaeadaae95edea09e53ecc2994\",\n \"score\": \"0.6066672\",\n \"text\": \"def test_start_emit_error(self, mock_stop):\\n my_service = make_service(logger=mock.Mock())\\n\\n @my_service.on('start')\\n def on_start():\\n raise RuntimeError()\\n\\n with self.assertRaises(RuntimeError):\\n my_service.start()\\n\\n self.assertTrue(mock_stop.called)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5f71677881c5b6c19fed2cab3df491e\",\n \"score\": \"0.6056587\",\n \"text\": \"def start_service() -> None:\\n\\n loop: asyncio.AbstractEventLoop = asyncio.get_event_loop()\\n\\n try:\\n log_manager = LogRecordManager()\\n record_handler = LogRecordHandler(log_manager=log_manager)\\n\\n app = web.Application()\\n app.router.add_post('/read_log', handler=record_handler.get_list)\\n\\n web.run_app(\\n app=app,\\n host=CONFIGURATION['SERVICE_HOST'],\\n port=CONFIGURATION['SERVICE_PORT'],\\n )\\n\\n except KeyboardInterrupt:\\n pass\\n\\n finally:\\n loop.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4f9f38278f2d484565c2df8ae7b5710\",\n \"score\": \"0.6031784\",\n \"text\": \"def require_service(srv_name, srv_type, port=None, user_name=None, password=None):\\n try:\\n return create_service(srv_name, srv_type, port, user_name, password)\\n except ServiceAlreadyExists:\\n srv = get_service(srv_name)\\n if not srv.is_running():\\n srv.start()\\n return srv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b64278ff94abb4900730ff50728dcd6\",\n \"score\": \"0.60243386\",\n \"text\": \"def start_services(\\n self, services: List[str], timeout: float = 30.0, delay: float = 0.1,\\n ) -> ChangeID:\\n return self._services_action('start', services, timeout, delay)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"244d26b7e558767f06105c5dfc921b0a\",\n \"score\": \"0.60079616\",\n \"text\": \"def test_stopping_with_a_child_service(self):\\n self.executed = [False, False]\\n\\n def run(index, time):\\n self.executed[index] = True\\n gevent.sleep(time)\\n\\n class MyService(service.Service):\\n def do_start(self):\\n self.spawn(run, 0, 0)\\n\\n class ChildService(service.Service):\\n def do_start(self):\\n self.spawn(run, 1, 10)\\n\\n my_service = MyService()\\n child_service = ChildService()\\n\\n my_service.add_service(child_service)\\n\\n my_service.start()\\n\\n self.assertTrue(all(self.executed))\\n self.assertTrue(my_service.started)\\n self.assertTrue(child_service.started)\\n\\n my_service.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4de5f0fb06e4264871eead93d199cea\",\n \"score\": \"0.600666\",\n \"text\": \"def test_start_job(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74c692ecbe5e3cbdb1a525f8a1df6c87\",\n \"score\": \"0.60015714\",\n \"text\": \"def test_start_process(self):\\n from supvisors.mainloop import SupvisorsMainLoop\\n main_loop = SupvisorsMainLoop(self.supvisors)\\n # test rpc error\\n self.mocked_rpc.side_effect = Exception\\n main_loop.start_process('10.0.0.1', 'dummy_process', 'extra args')\\n self.assertEqual(2, self.mocked_rpc.call_count)\\n self.assertEqual(call('10.0.0.1', main_loop.env), self.mocked_rpc.call_args)\\n # test with a mocked rpc interface\\n rpc_intf = DummyRpcInterface()\\n self.mocked_rpc.side_effect = None\\n self.mocked_rpc.return_value = rpc_intf\\n with patch.object(rpc_intf.supvisors, 'start_args') as mocked_supvisors:\\n main_loop.start_process('10.0.0.1', 'dummy_process', 'extra args')\\n self.assertEqual(3, self.mocked_rpc.call_count)\\n self.assertEqual(call('10.0.0.1', main_loop.env), self.mocked_rpc.call_args)\\n self.assertEqual(1, mocked_supvisors.call_count)\\n self.assertEqual(call('dummy_process', 'extra args', False), mocked_supvisors.call_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd49fcc523f752069e88f7927d1377b8\",\n \"score\": \"0.59989476\",\n \"text\": \"def test_start(self):\\n def cb(container):\\n self.assertEqual(container, {'Id': 'mocked'})\\n self.builder.start(callback=cb)\\n self.instance.start.assert_called_with(container='mocked')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55067d3638e1dd28c0d2069a07fac37f\",\n \"score\": \"0.59964466\",\n \"text\": \"def started_services():\\n for srv, status in rc_status('--servicelist'):\\n if status == 'started':\\n yield srv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88686fe6e0555b00345916dc420e2ffc\",\n \"score\": \"0.5985676\",\n \"text\": \"def start_service(self, service_name, service_type, folder=''):\\n\\n self._post(self._get_path(\\\"start_service\\\", service_path=self._get_service_path(service_name, folder),\\n service_type=service_type))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb49965034dd41501cef303eae5f46f6\",\n \"score\": \"0.59566146\",\n \"text\": \"def start(test, config):\\n thread = DuringTestThread(test, config)\\n thread.daemon = True\\n thread.start()\\n return thread.stop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bee4842342cadde5aaa2f15a458c1641\",\n \"score\": \"0.59550124\",\n \"text\": \"def start(self):\\n if self.service.getProcessDicts() == []:\\n return\\n\\n def start2(process, nbr=None):\\n\\n cwd = process[\\\"cwd\\\"]\\n # args['process'] = process\\n if nbr is None:\\n self.stop()\\n\\n tcmd = process[\\\"cmd\\\"]\\n if tcmd == \\\"jspython\\\":\\n tcmd = \\\"source %s/env.sh;jspython\\\" % (j.dirs.base)\\n\\n targs = process[\\\"args\\\"]\\n tuser = process[\\\"user\\\"]\\n if tuser == \\\"\\\":\\n tuser = \\\"root\\\"\\n tlog = self.service.hrd.getBool(\\\"process.log\\\", default=True)\\n env = process[\\\"env\\\"]\\n\\n startupmethod = process[\\\"startupmanager\\\"]\\n domain, name = self._getDomainName(process)\\n if nbr is not None:\\n name = \\\"%s.%d\\\" % (name, i)\\n log(\\\"Starting %s:%s\\\" % (domain, name))\\n\\n if startupmethod == 'upstart':\\n # check if we are in our docker image which uses myinit instead\\n # of upstart\\n if j.sal.fs.exists(path=\\\"/etc/my_init.d/\\\"):\\n cmd2 = \\\"%s %s\\\" % (tcmd, targs)\\n extracmds = \\\"\\\"\\n if cmd2.find(\\\";\\\") != -1:\\n parts = cmd2.split(\\\";\\\")\\n extracmds = \\\"\\\\n\\\".join(parts[:-1])\\n cmd2 = parts[-1]\\n\\n C = \\\"#!/bin/sh\\\\nset -e\\\\ncd %s\\\\nrm -f /var/log/%s.log\\\\n%s\\\\nexec %s >>/var/log/%s.log 2>&1\\\\n\\\" % (\\n cwd, name, extracmds, cmd2, name)\\n j.sal.fs.remove(\\\"/var/log/%s.log\\\" % name)\\n j.sal.fs.createDir(\\\"/etc/service/%s\\\" % name)\\n path2 = \\\"/etc/service/%s/run\\\" % name\\n j.sal.fs.writeFile(path2, C)\\n j.sal.fs.chmod(path2, 0o770)\\n j.sal.process.execute(\\n \\\"sv start %s\\\" % name, die=False, outputToStdout=False, outputStderr=False, captureout=False)\\n else:\\n j.sal.ubuntu.service_install(name, tcmd, pwd=cwd, env=env)\\n j.sal.ubuntu.service_start(name)\\n\\n elif startupmethod == \\\"tmux\\\":\\n j.tools.cuisine.local.tmux.executeInScreen(\\n domain, name, tcmd + \\\" \\\" + targs, cwd=cwd, env=env, user=tuser) # , newscr=True)\\n\\n else:\\n raise j.exceptions.RuntimeError(\\n \\\"startup method not known or disabled:'%s'\\\" % startupmethod)\\n\\n # if msg==\\\"\\\":\\n # pids=self.getPids(ifNoPidFail=False,wait=False)\\n # if len(pids) != self.numprocesses:\\n # msg=\\\"Could not start, did not find enough running instances, needed %s, found %s\\\"%(self.numprocesses,len(pids))\\n\\n # if msg==\\\"\\\" and pids!=[]:\\n # for pid in pids:\\n # test=j.sal.process.isPidAlive(pid)\\n # if test==False:\\n # msg=\\\"Could not start, pid:%s was not alive.\\\"%pid\\n\\n # if log!=\\\"\\\":\\n # msg=\\\"%s\\\\nlog:\\\\n%s\\\\n\\\"%(msg,log)\\n\\n # self.raiseError(msg)\\n # return\\n\\n isrunning = self.check_up(wait=False)\\n if isrunning:\\n return\\n\\n processes = self.service.getProcessDicts()\\n for i, process in enumerate(processes):\\n\\n if \\\"platform\\\" in process:\\n if not j.core.platformtype.myplatform.checkMatch(process[\\\"platform\\\"]):\\n continue\\n if len(processes) > 1:\\n start2(process, nbr=i)\\n else:\\n start2(process)\\n\\n isrunning = self.check_up()\\n if isrunning is False:\\n msg = \\\"\\\"\\n\\n if self.service.getTCPPorts() == [0]:\\n print('Done ...')\\n elif self.service.getTCPPorts() != []:\\n ports = \\\",\\\".join([str(item)\\n for item in self.service.getTCPPorts()])\\n msg = \\\"Could not start:%s, could not connect to ports %s.\\\" % (\\n self.service, ports)\\n j.events.opserror_critical(msg, \\\"service.start.failed.ports\\\")\\n else:\\n j.events.opserror_critical(\\n \\\"could not start:%s\\\" % self.service, \\\"service.start.failed.other\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"877be0ce72ae362b726fa65f3d7cfeab\",\n \"score\": \"0.5945746\",\n \"text\": \"def start(self):\\n if self.main_service_name is None:\\n raise ValueError('Main service not set! Please call \\\"set_main_service\\\"')\\n\\n self.services[self.main_service_name].main(\\n self._get_to_send(self.main_service_name),\\n self.configs[self.main_service_name]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f92c665cc9ae02c0fdfbf4ce0ed2cea5\",\n \"score\": \"0.59302753\",\n \"text\": \"def test_join(self, mock_start, mock_stop):\\n my_service = make_service()\\n thread = my_service._run_thread = mock.Mock()\\n\\n my_service.join()\\n\\n mock_start.assert_called_once_with()\\n thread.get.assert_called_once_with()\\n mock_stop.assert_called_once_with()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c2f04cd5149a360725a14c4f4059025\",\n \"score\": \"0.59240025\",\n \"text\": \"def test_control_service_wait(self):\\n # Disable W0212(protected-access)\\n # pylint: disable=W0212\\n\\n # shutdown supervised service\\n res = supervisor.control_service(\\n self.root, supervisor.ServiceControlAction.down,\\n wait=supervisor.ServiceWaitAction.down,\\n timeout=100,\\n )\\n\\n treadmill.subproc.check_call.assert_called_with(\\n [supervisor._get_cmd('svc'), '-d', self.root]\\n )\\n treadmill.supervisor.wait_service.assert_called_with(\\n self.root,\\n treadmill.supervisor.ServiceWaitAction.down,\\n timeout=100\\n )\\n self.assertTrue(res)\\n\\n # shutdown service timeouts\\n treadmill.subproc.check_call.reset_mock()\\n supervisor.wait_service.side_effect = \\\\\\n subproc.CalledProcessError(99, supervisor._get_cmd('svwait'))\\n\\n res = supervisor.control_service(\\n self.root, supervisor.ServiceControlAction.down,\\n wait=supervisor.ServiceWaitAction.down,\\n timeout=100,\\n )\\n treadmill.subproc.check_call.assert_called_with(\\n [supervisor._get_cmd('svc'), '-d', self.root]\\n )\\n treadmill.supervisor.wait_service.assert_called_with(\\n self.root,\\n treadmill.supervisor.ServiceWaitAction.down,\\n timeout=100\\n )\\n self.assertFalse(res)\\n\\n # shutdown unsupervised service\\n treadmill.subproc.check_call.reset_mock()\\n treadmill.subproc.check_call.side_effect = \\\\\\n subproc.CalledProcessError(100, supervisor._get_cmd('svc'))\\n\\n with self.assertRaises(subproc.CalledProcessError):\\n supervisor.control_service(\\n self.root, supervisor.ServiceControlAction.down,\\n wait=supervisor.ServiceWaitAction.down,\\n timeout=100,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6b08e12dc970f1c8b03290518c60162\",\n \"score\": \"0.5915418\",\n \"text\": \"def service(self, service_name):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25ba8c28f2234ea9ee458946a2636dd2\",\n \"score\": \"0.59107196\",\n \"text\": \"def service():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d79a42649180a089b2c48d11189046d\",\n \"score\": \"0.5890802\",\n \"text\": \"def main():\\n\\n mask = os.umask(0) # fix the umask\\n\\n module = AnsibleModule(\\n argument_spec=dict(\\n name=dict(aliases=['name', 'service']),\\n state=dict(default=None, choices=['started', 'restarted',\\n 'stopped', 'reloaded']),\\n enabled=dict(default=None, type='bool')\\n ),\\n required_one_of=[['state', 'enabled']],\\n supports_check_mode=True)\\n\\n p = module.params\\n\\n # Set path for sv\\n sv_path = module.get_bin_path('sv', True)\\n if not os.path.exists(sv_path):\\n module.fail_json(msg=\\\"Cannot find sv binary in path.\\\")\\n\\n # ensure enabled if required\\n if p['state'] in ['started', 'restarted', 'reload']:\\n if p['enabled'] is False:\\n module.fail_json(msg=\\\"Conflicting request. Will not proceed.\\\")\\n p['enabled'] = True\\n\\n if p['enabled'] is False:\\n if not p['state'] is None and p['state'] != \\\"stopped\\\":\\n module.fail_json(msg=\\\"Conflicting request. Will not proceed.\\\")\\n p['state'] = \\\"stopped\\\"\\n\\n service = Service(module, sv_path)\\n module.debug(\\\"Service initialized.\\\")\\n service.do_action()\\n\\n result = {}\\n result[\\\"name\\\"] = service.name\\n result[\\\"changed\\\"] = service.changed\\n result[\\\"enable\\\"] = service.get_enabled()\\n result[\\\"state\\\"] = \\\"down\\\" if not result[\\\"enable\\\"] else service.get_status()\\n\\n module.exit_json(**result)\\n os.umask(mask) # Reset the umask to original value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"429905f4305afaba0d5c489be7ba54aa\",\n \"score\": \"0.588829\",\n \"text\": \"def test_restart():\\n mock_true = MagicMock(return_value=True)\\n with patch.object(win_service, \\\"create_win_salt_restart_task\\\", mock_true):\\n with patch.object(win_service, \\\"execute_salt_restart_task\\\", mock_true):\\n assert win_service.restart(\\\"salt-minion\\\") is True\\n\\n with patch.object(win_service, \\\"stop\\\", mock_true):\\n with patch.object(win_service, \\\"start\\\", mock_true):\\n assert win_service.restart(\\\"salt\\\") is True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39baff7701d5af4b37bf6713a6ba9aca\",\n \"score\": \"0.58577853\",\n \"text\": \"def test_service_job_running(self):\\n\\n def _ensure_node_services(unused_pipeline_state, node_id):\\n self.assertEqual('my_example_gen', node_id)\\n return service_jobs.ServiceStatus.RUNNING\\n\\n self._mock_service_job_manager.ensure_node_services.side_effect = (\\n _ensure_node_services)\\n tasks = self._generate_and_test(\\n True,\\n num_initial_executions=0,\\n num_tasks_generated=0,\\n num_new_executions=0,\\n num_active_executions=0)\\n self.assertEmpty(tasks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea9cb2a6863ec1274b53d5d565f3727c\",\n \"score\": \"0.58473897\",\n \"text\": \"def start_service_in(sparkl_path, module_name, watchdog_path,\\n **kwargs):\\n\\n # Filter kwargs for `sparkl service`\\n service_kwargs = dict()\\n for key, value in kwargs.items():\\n if key in ['path', 'alias']:\\n service_kwargs[key] = value\\n\\n # Start the SPARKL service in the specified directory.\\n with ChDir(watchdog_path):\\n sparkl('service', sparkl_path, module_name,\\n **service_kwargs)\\n\\n # Give the watchdog time to start.\\n sleep(kwargs.get('wait', 3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"603cffdaa394c88ba8a4d52c70c935af\",\n \"score\": \"0.5827291\",\n \"text\": \"def test_start_from_args_no_args():\\n log = conlog.start()\\n log.info('Testing')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c64fe3d114f361a14d465e85bb79411\",\n \"score\": \"0.5791843\",\n \"text\": \"def test_wait_service(self):\\n # Disable W0212(protected-access)\\n # pylint: disable=W0212\\n supervisor.wait_service(\\n self.root, supervisor.ServiceWaitAction.down\\n )\\n treadmill.subproc.check_call.assert_called_with(\\n [supervisor._get_cmd('svwait'), '-d', self.root]\\n )\\n\\n treadmill.subproc.check_call.reset_mock()\\n supervisor.wait_service(\\n (\\n os.path.join(self.root, 'a'),\\n os.path.join(self.root, 'b')\\n ),\\n supervisor.ServiceWaitAction.up,\\n all_services=False,\\n timeout=100,\\n )\\n\\n treadmill.subproc.check_call.assert_called_with(\\n [\\n supervisor._get_cmd('svwait'), '-t100', '-o', '-u',\\n os.path.join(self.root, 'a'),\\n os.path.join(self.root, 'b')\\n ]\\n )\\n\\n treadmill.subproc.check_call.reset_mock()\\n treadmill.subproc.check_call.side_effect = \\\\\\n subproc.CalledProcessError(99, supervisor._get_cmd('svwait'))\\n with self.assertRaises(subproc.CalledProcessError):\\n supervisor.wait_service(\\n self.root, supervisor.ServiceWaitAction.really_down\\n )\\n treadmill.subproc.check_call.assert_called_with(\\n [supervisor._get_cmd('svwait'), '-D', self.root]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61c665dab10458d42e7841f732f935f5\",\n \"score\": \"0.578035\",\n \"text\": \"def start():\\n sudo('service uwsgi start %s' % PROJECT_NAME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66276f44066684334499e982b7518052\",\n \"score\": \"0.57765007\",\n \"text\": \"def test_spawn_error(self):\\n my_service = make_service()\\n\\n self.executed = False\\n\\n def trap_error(exc_type, exc_value, exc_traceback):\\n self.assertIsInstance(exc_value, RuntimeError)\\n self.executed = True\\n\\n def raise_error():\\n raise RuntimeError\\n\\n my_service.on('error', trap_error)\\n\\n my_service.spawn(raise_error)\\n\\n gevent.sleep(0.0)\\n\\n self.assertTrue(self.executed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b546a06bd32d0fc5015e9f5a54fddf\",\n \"score\": \"0.57750255\",\n \"text\": \"def test_start_problem(self):\\n self.assertRaises (AppStartError, Application().start, 'Hiya')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"545bd8d1aa7f16ce5e84e2cd2bcfea02\",\n \"score\": \"0.5771033\",\n \"text\": \"def start_service(cwd, service_module, run_daemon, run_ssl):\\n \\n def add_extra_configs(conf):\\n \\\"\\\"\\\"Add Extra keys to snetd.config.json\\\"\\\"\\\"\\n with open(conf, \\\"r\\\") as f:\\n snetd_configs = json.load(f)\\n if run_ssl:\\n snetd_configs[\\\"ssl_cert\\\"] = \\\"/opt/singnet/.certs/fullchain.pem\\\"\\n snetd_configs[\\\"ssl_key\\\"] = \\\"/opt/singnet/.certs/privkey.pem\\\"\\n snetd_configs[\\\"payment_channel_ca_path\\\"] = \\\"/opt/singnet/.certs/ca.pem\\\"\\n snetd_configs[\\\"payment_channel_cert_path\\\"] = \\\"/opt/singnet/.certs/client.pem\\\"\\n snetd_configs[\\\"payment_channel_key_path\\\"] = \\\"/opt/singnet/.certs/client-key.pem\\\"\\n _network = \\\"mainnet\\\"\\n if \\\"ropsten\\\" in conf:\\n _network = \\\"ropsten\\\"\\n infura_key = os.environ.get(\\\"INFURA_API_KEY\\\", \\\"\\\")\\n if infura_key:\\n snetd_configs[\\\"ethereum_json_rpc_endpoint\\\"] = \\\"https://{}.infura.io/{}\\\".format(_network, infura_key)\\n pk_metering = os.environ.get(\\\"PVT_KEY_FOR_METERING\\\", \\\"\\\")\\n if pk_metering:\\n snetd_configs[\\\"metering_enabled\\\"] = True\\n snetd_configs[\\\"metering_end_point\\\"] = \\\"https://marketplace-mt-v2.singularitynet.io\\\"\\n snetd_configs[\\\"pvt_key_for_metering\\\"] = pk_metering\\n snetd_configs[\\\"max_message_size_in_mb\\\"] = os.environ.get(\\\"MAX_MESSAGE_SIZE\\\", 128)\\n with open(conf, \\\"w\\\") as f:\\n json.dump(snetd_configs, f, sort_keys=True, indent=4)\\n \\n all_p = []\\n if run_daemon:\\n for idx, config_file in enumerate(glob.glob(\\\"./snetd_configs/*.json\\\")):\\n add_extra_configs(config_file)\\n all_p.append(start_snetd(str(cwd), config_file))\\n service_name = service_module.split(\\\".\\\")[-1]\\n grpc_port = registry[service_name][\\\"grpc\\\"]\\n p = subprocess.Popen([sys.executable, \\\"-m\\\", service_module, \\\"--grpc-port\\\", str(grpc_port)], cwd=str(cwd))\\n all_p.append(p)\\n return all_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfaa451d1e513d39e94780938ff0a3f0\",\n \"score\": \"0.57650423\",\n \"text\": \"def test_start_process(mock_agent):\\n mock_agent.register_process('test_process', tfunc, tfunc)\\n res = mock_agent.start('test_process', params={'a': 1})\\n print(res)\\n assert res[0] == ocs.OK\\n assert isinstance(res[1], str)\\n assert res[2]['session_id'] == 0\\n assert res[2]['op_name'] == 'test_process'\\n assert res[2]['op_code'] == OpCode.STARTING.value\\n assert res[2]['status'] == 'starting'\\n assert res[2]['success'] is None\\n assert res[2]['end_time'] is None\\n assert res[2]['data'] == {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0241d792029746be8f827af612f3972\",\n \"score\": \"0.5763295\",\n \"text\": \"def test_subclass(self):\\n class SomeService(base.Service):\\n def run(self):\\n logging.info('some service running')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"589f99679f1dc01e13a2fe8a70f82834\",\n \"score\": \"0.5754939\",\n \"text\": \"def test_create_measur_service(self):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":488,"cells":{"query_id":{"kind":"string","value":"3d4824220e2b76fe41c979db0d89d648"},"query":{"kind":"string","value":"Calculate valuation signals such as P/E and P/Sales ratios."},"positive_passages":{"kind":"list like","value":[{"docid":"8f6ca49ea6fe4c1d2298b36e8c742fc1","score":"0.0","text":"def val_signals(self, variant='daily', func=None,\n shares_index=SHARES_DILUTED):\n\n # Load the required datasets.\n # This is only really necessary if the cache-file needs refreshing,\n # but it is easier to program like this and the overhead is small.\n df_prices = self.load_shareprices(variant=variant)\n df_income_ttm = self.load_income(variant='ttm')\n df_balance_ttm = self.load_balance(variant='ttm')\n df_cashflow_ttm = self.load_cashflow(variant='ttm')\n\n # List of datasets used to determine if disk-cache must be refreshed.\n datasets = [('shareprices', variant),\n ('income', 'ttm'),\n ('balance', 'ttm'),\n ('cashflow', 'ttm')]\n\n # List of arguments used to uniquely identify the cache-file.\n cache_ids = [variant, _func_name(func=func), shares_index]\n\n # Create dict with disk-cache arguments.\n cache_args = self._cache_args(datasets=datasets,\n cache_ids=cache_ids)\n\n # Check whether special datasets for banks and insurances should be used.\n banks = False\n insurance = False\n if self._dataset_extension == \"-banks\":\n banks = True\n elif self._dataset_extension == \"-insurance\":\n insurance = True\n\n # Calculate the signals, or load the DataFrame from the disk-cache.\n df_result = val_signals(df_prices=df_prices,\n df_income_ttm=df_income_ttm,\n df_balance_ttm=df_balance_ttm,\n df_cashflow_ttm=df_cashflow_ttm,\n shares_index=shares_index, func=func,\n banks=banks, insurance=insurance,\n **self._signal_args, **cache_args)\n\n return df_result","title":""}],"string":"[\n {\n \"docid\": \"8f6ca49ea6fe4c1d2298b36e8c742fc1\",\n \"score\": \"0.0\",\n \"text\": \"def val_signals(self, variant='daily', func=None,\\n shares_index=SHARES_DILUTED):\\n\\n # Load the required datasets.\\n # This is only really necessary if the cache-file needs refreshing,\\n # but it is easier to program like this and the overhead is small.\\n df_prices = self.load_shareprices(variant=variant)\\n df_income_ttm = self.load_income(variant='ttm')\\n df_balance_ttm = self.load_balance(variant='ttm')\\n df_cashflow_ttm = self.load_cashflow(variant='ttm')\\n\\n # List of datasets used to determine if disk-cache must be refreshed.\\n datasets = [('shareprices', variant),\\n ('income', 'ttm'),\\n ('balance', 'ttm'),\\n ('cashflow', 'ttm')]\\n\\n # List of arguments used to uniquely identify the cache-file.\\n cache_ids = [variant, _func_name(func=func), shares_index]\\n\\n # Create dict with disk-cache arguments.\\n cache_args = self._cache_args(datasets=datasets,\\n cache_ids=cache_ids)\\n\\n # Check whether special datasets for banks and insurances should be used.\\n banks = False\\n insurance = False\\n if self._dataset_extension == \\\"-banks\\\":\\n banks = True\\n elif self._dataset_extension == \\\"-insurance\\\":\\n insurance = True\\n\\n # Calculate the signals, or load the DataFrame from the disk-cache.\\n df_result = val_signals(df_prices=df_prices,\\n df_income_ttm=df_income_ttm,\\n df_balance_ttm=df_balance_ttm,\\n df_cashflow_ttm=df_cashflow_ttm,\\n shares_index=shares_index, func=func,\\n banks=banks, insurance=insurance,\\n **self._signal_args, **cache_args)\\n\\n return df_result\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"35f81c10fe1b2d0507b135e909721a2d","score":"0.6021242","text":"def npv(rate,values):\n \n \n return float()","title":""},{"docid":"51c6470dfd89042b82c41307358c6aea","score":"0.5861876","text":"def results( self ):\n A = AA = 0.0\n x = xx = 0.0\n s = ss = 0.0\n for egg in self.pastEggs:\n w = np.exp( egg.lweight - self.lZ )\n A += w * egg.A[0]\n AA += w * egg.A[0] * egg.A[0]\n x += w * egg.x[0]\n xx += w * egg.x[0] * egg.x[0]\n s += w * egg.s[0]\n ss += w * egg.s[0] * egg.s[0]\n\n print 'mean(A) = %12.6f, stdev(A) = %12.6f' % (A, np.sqrt( AA - (A * A) ))\n print 'mean(x) = %12.6f, stdev(x) = %12.6f' % (x, np.sqrt( xx - (x * x) ))\n print 'mean(s) = %12.6f, stdev(s) = %12.6f' % (s, np.sqrt( ss - (s * s) ))","title":""},{"docid":"ef5e06ae0a3bd2d0c9857bd1db29d5ef","score":"0.5782366","text":"def fv(rate,nper,pmt,pv,when):\n \n \n return ndarray()","title":""},{"docid":"579ca3d0d2d61896f3097e2bc4f997ac","score":"0.5721197","text":"def pv(rate,nper,pmt,fv,when):\n \n \n return ndarray()","title":""},{"docid":"7fe1eba5c0db1a1ea0b6d2a94ee06815","score":"0.5719713","text":"def calcPowerEV(self):\n self.q_powerEV = helper_functions.calcExpectedValue(self.quality_power)\n self.d_powerEV = helper_functions.calcExpectedValue(self.duration_power)\n self.c_powerEV = helper_functions.calcExpectedValue(self.cost_power)","title":""},{"docid":"7fe1eba5c0db1a1ea0b6d2a94ee06815","score":"0.5719713","text":"def calcPowerEV(self):\n self.q_powerEV = helper_functions.calcExpectedValue(self.quality_power)\n self.d_powerEV = helper_functions.calcExpectedValue(self.duration_power)\n self.c_powerEV = helper_functions.calcExpectedValue(self.cost_power)","title":""},{"docid":"05857342e9478291c51bab4c93b4d5ba","score":"0.56470066","text":"def calculate_signals(self):\n raise NotImplementedError(\"Should implement calculate_signals()\")","title":""},{"docid":"bca0f8d5af2de474e55feaab69b1a8b3","score":"0.56332994","text":"def schedule_variance(self):\n \n ev = self.apc * self.budget\n pv = self.ppc * self.budget\n \n return ev - pv","title":""},{"docid":"fce3581240e5a5a3042dae63804e9c2c","score":"0.5606615","text":"def valuation(self):\n self.integer.valuation() * (self.p ** self.shift)","title":""},{"docid":"91b400ab519812f046835cc4c8798d2e","score":"0.55950147","text":"def valuation(self):\n if not self:\n return 0\n for i, e in enumerate(self.expansion):\n if e:\n return rational.Rational(1, p**i)","title":""},{"docid":"26419a54e64cde2d143b1bef8013b475","score":"0.55775577","text":"def evaluate_result( self ):\n \n # Create the exact result\n x_exact = np.exp( np.sin(self.t) )\n # Print the RMS difference\n e_rms = np.sqrt( 1./self.N * ((self.x-x_exact)**2).sum() )\n print( e_rms )\n # Plot the result\n plt.plot( self.t, self.x, label=\"Euler\")\n plt.plot( self.t, x_exact, '--', label=\"Analytic Solution\")\n plt.legend(loc=3)\n plt.show()","title":""},{"docid":"0ea9bace0a44d73df3adea35372a17af","score":"0.5554894","text":"def CalculateSensitivitiesForAllDataPoints(self, params):\n varsByCalc = self.GetExperimentCollection().GetVarsByCalc()\n self.calcVals, self.calcSensitivityVals =\\\n self.GetCalculationCollection().CalculateSensitivity(varsByCalc,\n params)\n return self.calcSensitivityVals","title":""},{"docid":"634c80092e8842c970c8514e12302f79","score":"0.5538802","text":"def calcSignal(self) -> Tuple[SpectralQty, float]:\n pass","title":""},{"docid":"b24be98c7eb1f222b9465254c6656b10","score":"0.5521443","text":"def compute(cls, real_data, synthetic_data):\n real_data = pd.Series(real_data).dropna()\n synthetic_data = pd.Series(synthetic_data).dropna()\n\n if len(synthetic_data) == 0:\n return 0\n\n f_obs, f_exp = get_frequencies(real_data, synthetic_data)\n total_variation = 0\n for i in range(len(f_obs)):\n total_variation += abs(f_obs[i] - f_exp[i])\n\n return 1 - 0.5 * total_variation","title":""},{"docid":"fd9f319765bed022ccb443a27718a9fa","score":"0.55095655","text":"def val_eq(self):\n\n # sum of present values of all cash flows\n sum_pv = self.pv_cf()\n\n # Probability of failure\n fail_prob = self.inp_params['failure_prob']\n # Proceeds if firm fails\n proceeds = self.inp_params['proceeds']*self.inp_params['val_of_proceeds']\n\n # Value of operating assets\n val_oa = sum_pv*(1. - fail_prob)\n\n # Value of equity\n val_eq = val_oa - self.inp_params['bk_val_debt'] + self.inp_params['cash_eq']\n\n # Value of options\n #val_op = options_value()\n val_op = 643.8\n\n # common equity value or intrinsic value\n val_eq_common = val_eq - val_op\n\n # common equity value per share\n val_per_share = val_eq_common/self.inp_params['outstanding_shares']\n\n # Price as a % of value\n p_to_val = self.inp_params['curr_stock_price']/val_per_share\n\n return val_per_share,p_to_val","title":""},{"docid":"78bf3697bcf87c5c94a4b5379948b734","score":"0.55048","text":"def computeQValueFromValues(self, state, action):\n \"*** YOUR CODE HERE ***\"\n Q_Average = 0\n if self.mdp.isTerminal(state):\n return 0\n nextStateAndPorb = self.mdp.getTransitionStatesAndProbs(state, action)\n for pair in nextStateAndPorb:\n Q_Average = Q_Average + pair[1] * (self.mdp.getReward(state, action, pair[0]) +\\\n self.discount * self.getValue(pair[0]))\n return Q_Average\n util.raiseNotDefined()","title":""},{"docid":"0169b44ce63b4b146a55466bdb532664","score":"0.5496748","text":"def all_evaluate(self):\r\n #self.compute_euclidian_distance()\r\n # print(\"Euclidian distance : \", self.euclidian_distance)\r\n self.compute_MAE()\r\n print(\"MAE : \", self.MAE)\r\n self.compute_RMSE()\r\n print(\"RMSE : \", self.RMSE)\r\n #self.compute_SSIM()\r\n\r\n #self.compute_UQI()\r\n # print(\"UQI : \", self.UQI)\r\n #self.compute_VIF()\r\n #print(\"VIF : \", self.VIF)\r\n return self.MAE, self.RMSE","title":""},{"docid":"a06b27bde45012008083bf67133d907c","score":"0.54588586","text":"def calc_values(self):\n atm_contract_index = (\n np.abs(self.chain[\"strike\"] - self.underlying_price)\n ).idxmin()\n atm_impliedvol = self.chain.iloc[atm_contract_index][\"impvol\"]\n\n # Calculate option value for all options using ATM volatility\n self.chain[\"model_value\"] = self.chain.apply(\n lambda x: bs_price(\n x[\"right\"],\n x[\"underprice\"],\n x[\"strike\"],\n self.dte / 252,\n atm_impliedvol,\n self.risk_free_rate,\n ),\n axis=1,\n )\n self.chain[\"mid_price\"] = (self.chain[\"bid\"] + self.chain[\"ask\"]) / 2\n self.chain[\"skew_premium\"] = self.chain[\"mid_price\"] - self.chain[\"model_value\"]","title":""},{"docid":"114c5912cf93838002a78a87daa0d4ca","score":"0.5439486","text":"def vat_rate():","title":""},{"docid":"47e222c63e534fa6cf01c3726ed9664e","score":"0.54364234","text":"def value(self) -> float:\n return self.days * float(self.rate)","title":""},{"docid":"23e08cd32e8d42a3759c37b082d0aece","score":"0.5421589","text":"def pmt(rate,nper,pv,fv,when):\n \n \n return ndarray()","title":""},{"docid":"f52bb576eb8a97c5b98a25fa475c6991","score":"0.54204094","text":"def evaluate(self, ts):\n print(ts)\n phases = PI2 * self.freq * ts + self.offset\n print(phases)\n ys = self.amp * np.exp(1j * phases)\n return ys","title":""},{"docid":"b2bbf8d19fb08451ca7f539dfe0ba308","score":"0.54171187","text":"def pulse_ratio(pickle_file, neuron_type, neuron_index, var):\n\n net = unpickle_net(pickle_file)\n\n spike_times = extract_presynaptic_spikes(pickle_file, neuron_type, neuron_index, net)\n # print(spike_times)\n if var == \"V\":\n mon = [x[neuron_index]/volt for x in net['{}_V_mon'.format(neuron_type)]['V']]\n if var == \"Ge\":\n mon = [x[neuron_index] for x in net['{}_Ge_total_mon'.format(neuron_type)]['Ge_total']]\n if var == \"Gi\":\n mon = [x[neuron_index] for x in net['{}_Gi_total_mon'.format(neuron_type)]['Gi_total']]\n\n responses = extract_responses(spike_times, mon)\n PPR = [float(x)/float(responses[0]) for x in responses]\n print(PPR)\n return responses","title":""},{"docid":"70fffbb5d0eb840bc63ce7493f050394","score":"0.5381214","text":"def Pfe(self, num_si_events, p1=None):\r\n if p1 is None: p1 = self.Pr()\r\n ne = num_si_events\r\n return sum(-(-n/n)**n * p1**n * scipy.special.comb(ne,n) for n in range(1,ne+1))","title":""},{"docid":"c9595658251fffa46a956e0564edb0d3","score":"0.53695977","text":"def compute_estimates(self):\n rate_estimate_list = []\n for tau in range(len(self.new_infection_data) - 11):\n estimate = 0\n for j in range(7):\n estimate += self.new_infection_data[tau + j + 4] / self.new_infection_data[tau + j]\n rate_estimate_list.append(estimate)\n rate_estimates = np.array(rate_estimate_list)\n self.infection_rate_estimate = rate_estimates","title":""},{"docid":"498a5c77e9d1bc8f1672a1d9a6a95cef","score":"0.5363381","text":"def val_signals(df_prices, df_income_ttm, df_cashflow_ttm,\n shares_index=SHARES_DILUTED):\n\n # Create a DataFrame with the financial data we need.\n #calculate sales per share and earning per share and free cash flow per share\n df = df_income_ttm[[REVENUE, NET_INCOME_COMMON]].copy()\n df[FCF] = df_cashflow_ttm[NET_CASH_OPS] + df_cashflow_ttm[CAPEX]\n df_per_share = df.div(df_income_ttm[shares_index], axis=0)\n \n # Reindex the per-share financial data to daily data-points.\n df_daily = sf.reindex(df_src=df_per_share,\n df_target=df_prices,\n method='ffill')\n \n # Create new DataFrame for the signals. Setting the index improves performance.\n df_signals = pd.DataFrame(index=df_prices.index)\n \n # Use the closing share-price for all these signals.\n df_price = df_prices[CLOSE]\n \n # P/Sales ratio.\n df_signals[PSALES] = df_price / df_daily[REVENUE]\n \n # P/E ratio.\n df_signals[PE] = df_price / df_daily[NET_INCOME_COMMON]\n\n # P/FCF ratio.\n df_signals[PFCF] = df_price / df_daily[FCF]\n\n return df_signals","title":""},{"docid":"e6682c8fad841bf49d023538bc2ba8bc","score":"0.5337668","text":"def calc_q_values(self, state):\n pass","title":""},{"docid":"2cc336fab9470fdea1e22392ad9abf8d","score":"0.5329119","text":"def evaluate(self, params: np.ndarray) -> float:\n kl = 0\n for sample in self.data:\n kl += np.log(self.vgbs.prob_sample(params, sample))\n return -kl / self.nr_samples","title":""},{"docid":"fc3a60f7a4004fcbc61dab39f721f084","score":"0.53238505","text":"def iterate(self,plot=True):\n # Create empty arrays to store values\n F = np.zeros(len(self.y))\n a = np.zeros(len(self.y))\n v = np.zeros(len(self.y))\n P = np.zeros(len(self.y))\n # Initialize at the initial values parsed to the class\n P[0] = self.pardict['P1']\n a[0] = self.y[0]\n sigma_eps2 = self.pardict['sigma_eps2']\n sigma_eta2 = self.pardict['sigma_eta2']\n # Iterate \n for t in range(0,len(self.y)-1):\n F[t] = P[t]+sigma_eps2\n # K is defined as ratio of P and F\n Kt = P[t]/F[t] if np.isfinite(self.y[t]) else 0\n v[t] = self.y[t]-a[t]\n a[t+1] = a[t] + np.nan_to_num(Kt*v[t])\n F[t] = P[t]+sigma_eps2\n P[t+1] = P[t]*(1-Kt)+sigma_eta2\n F[-1] = P[-1]+sigma_eps2\n v[-1] = self.y[-1]-a[-1]\n # Obtain std error of prediction form variance\n std = np.sqrt((P*sigma_eps2)/(P+sigma_eps2))\n \n if plot:\n fig_name = self.var_name + 'Fig26.pdf'\n plot_fig2_6(self.times, self.y, std, P, a, F,fig_name, self.var_name)\n return a, std, P, v, F","title":""},{"docid":"1b8e6c668b0d352fbae386fa33e8f632","score":"0.53229123","text":"def _ev(self) -> float:\n return explained_variance_score(\n y_true=self.y_true,\n y_pred=self.y_pred,\n multioutput=self.multioutput,\n )","title":""},{"docid":"ba918ad0b2a1c18ddf0786fa58fe288c","score":"0.5317666","text":"def comp_pr(is_stationary, signal, fs, prominence=True):\n\n # Prominence criteria\n freqs = np.arange(90, 11200, 100)\n limit = np.zeros((len(freqs)))\n for i in range(len(freqs)):\n if freqs[i] >= 89.1 and freqs[i] < 1000:\n limit[i] = 9 + 10 * np.log10(1000 / freqs[i])\n if freqs[i] >= 1000 and freqs[i] < 11200:\n limit[i] = 9\n\n if is_stationary == True:\n tones_freqs, pr, prom, t_pr = pr_main_calc(signal, fs)\n tones_freqs = tones_freqs.astype(int)\n\n if prominence == True:\n output = {\n \"name\": \"tone-to-noise ratio\",\n \"freqs\": tones_freqs[prom],\n \"values\": pr[prom],\n \"prominence\": True,\n \"global value\": t_pr,\n }\n\n else:\n output = {\n \"name\": \"tone-to-noise ratio\",\n \"freqs\": tones_freqs,\n \"values\": pr,\n \"prominence\": prom,\n \"global value\": t_pr,\n }\n\n elif is_stationary == False:\n # Signal cut in frames of 500 ms along the time axis\n n = 0.5 * fs\n nb_frame = math.floor(signal.size / n)\n time = np.linspace(0, len(signal) / fs, num=nb_frame)\n time = np.around(time, 1)\n\n # Initialization of the result arrays\n tones_freqs = np.zeros((nb_frame), dtype=list)\n pr = np.zeros((nb_frame), dtype=list)\n prom = np.zeros((nb_frame), dtype=list)\n t_pr = np.zeros((nb_frame))\n\n # Compute PR values along time\n for i_frame in range(nb_frame):\n segment = signal[int(i_frame * n) : int(i_frame * n + n)]\n (\n tones_freqs[i_frame],\n pr[i_frame],\n prom[i_frame],\n t_pr[i_frame],\n ) = pr_main_calc(segment, fs)\n\n # Store the results in a time vs frequency array\n freq_axis = np.logspace(np.log10(90), np.log10(11200), num=1000)\n results = np.zeros((len(freq_axis), nb_frame))\n promi = np.zeros((len(freq_axis), nb_frame), dtype=bool)\n\n if prominence == True:\n for t in range(nb_frame):\n for f in range(len(tones_freqs[t])):\n if prom[t][f] == True:\n ind = np.argmin(np.abs(freq_axis - tones_freqs[t][f]))\n results[ind, t] = pr[t][f]\n promi[ind, t] = True\n else:\n for t in range(nb_frame):\n for f in range(len(tones_freqs[t])):\n ind = np.argmin(np.abs(freq_axis - tones_freqs[t][f]))\n results[ind, t] = pr[t][f]\n promi[ind, t] = prom[t][f]\n\n output = {\n \"name\": \"prominence ratio\",\n \"time\": time,\n \"freqs\": freq_axis,\n \"values\": results,\n \"prominence\": promi,\n \"global value\": t_pr,\n }\n\n return output","title":""},{"docid":"4d981270b716db45ec7dd988c4889158","score":"0.53150016","text":"def MSE(pred_signals, true_signals):\n\n dim, n = np.shape(true_signals)\n pred_mean = np.mean(pred_signals,axis=1).reshape(dim, 1)\n pred_std = np.std(pred_signals,axis=1).reshape(dim, 1)\n true_mean = np.mean(true_signals,axis=1).reshape(dim, 1)\n true_std = np.std(true_signals,axis=1).reshape(dim, 1)\n #scale data to have unit variance 1/n*y@y.T=1\n pred_signals = np.divide((pred_signals - pred_mean), pred_std)\n true_signals = np.divide((true_signals - true_mean), true_std)\n MSE = np.zeros(dim)\n MSE_matrix1 = np.zeros((dim, dim))\n MSE_matrix2 = np.zeros((dim, dim))\n\n #calculate MSE between all estimated signals with changed sign and true signals and store in array\n for i in range(0, dim):\n for j in range(0, dim):\n MSE_matrix1[i, j] = mean_squared_error(true_signals[j],pred_signals[i])\n #calculate MSE between all estimated and true signals and store in array\n for i in range(0, dim):\n for j in range(0, dim):\n MSE_matrix2[i, j] = mean_squared_error(true_signals[j],-pred_signals[i])\n \n #find minima of all possible MSE values\n array_min1 = MSE_matrix1.min(axis=1)\n array_min2 = MSE_matrix2.min(axis=1)\n\n #store minima in separate array\n for i in range(dim):\n if(array_min1[i]>array_min2[i]):\n MSE[i] = array_min2[i]\n else:\n MSE[i] = array_min1[i]\n\n return MSE","title":""},{"docid":"7127eb6f05cd675f434d14a9ef15bd82","score":"0.53097785","text":"def evaluate(self):\r\n self.evals, self.evects = ln.eigh(self.A)","title":""},{"docid":"4289c7e61101b80ae13d42841f9f1813","score":"0.53085506","text":"def pv(flows, r):\n dates=flows.index\n discounts=discount(dates, r)\n return discounts.multiply(flows, axis=0).sum()","title":""},{"docid":"dcddd84e3f3d07df539687f555afaa24","score":"0.52983016","text":"def exp(self) -> Series:","title":""},{"docid":"1a6f85d19d8b3dda854bcf07cb730af8","score":"0.5295143","text":"def calculate_signals(self, event):\n if event.type == EventType.BAR:\n self._set_correct_time_and_price(event)\n \n # Only trade if we have both observations\n if all(self.latest_prices > -1.0):\n # Create the observation matrix of the latest prices\n # of TLT and the intercept value (1.0) as well as the\n # scalar value of the latest price from IEI\n F = np.asarray([self.latest_prices[0], 1.0]).reshape((1, 2))\n y = self.latest_prices[1]\n \n # The prior value of the states \\theta_t is\n # distributed as a multivariate Gaussian with\n # mean a_t and variance-covariance R_t\n if self.R is not None:\n self.R = self.C + self.wt\n else:\n self.R = np.zeros((2, 2))\n\n # Calculate the Kalman Filter update\n # ----------------------------------\n # Calculate prediction of new observation\n # as well as forecast error of that prediction\n yhat = F.dot(self.theta)\n et = y - yhat\n\n # Q_t is the variance of the prediction of\n # observations and hence \\sqrt{Q_t} is the\n # standard deviation of the predictions\n Qt = F.dot(self.R).dot(F.T) + self.vt\n sqrt_Qt = np.sqrt(Qt)\n\n # The posterior value of the states \\theta_t is\n # distributed as a multivariate Gaussian with mean\n # m_t and variance-covariance C_t\n At = self.R.dot(F.T) / Qt\n self.theta = self.theta + At.flatten() * et\n self.C = self.R - At * F.dot(self.R)\n \n # Finally we generate the trading signals based on the values of etet and Qt−−√Qt. To do this we need to check what the \"invested\" status is - either \"long\", \"short\" or \"None\". \n # Notice how we need to adjust the cur_hedge_qty current hedge quantity when we go long or short as the slope θt0 is constantly adjusting in time:\n \n # Only trade if days is greater than a \"burn in\" period\n if self.days > 1:\n # If we're not in the market...\n if self.invested is None:\n if et < -sqrt_Qt:\n # Long Entry\n print(\"LONG: %s\" % event.time)\n self.cur_hedge_qty = int(floor(self.qty*self.theta[0]))\n self.events_queue.put(SignalEvent(self.tickers[1], \"BOT\", self.qty))\n self.events_queue.put(SignalEvent(self.tickers[0], \"SLD\", self.cur_hedge_qty))\n self.invested = \"long\"\n elif et > sqrt_Qt:\n # Short Entry\n print(\"SHORT: %s\" % event.time)\n self.cur_hedge_qty = int(floor(self.qty*self.theta[0]))\n self.events_queue.put(SignalEvent(self.tickers[1], \"SLD\", self.qty))\n self.events_queue.put(SignalEvent(self.tickers[0], \"BOT\", self.cur_hedge_qty))\n self.invested = \"short\"\n # If we are in the market...\n if self.invested is not None:\n if self.invested == \"long\" and et > -sqrt_Qt:\n print(\"CLOSING LONG: %s\" % event.time)\n self.events_queue.put(SignalEvent(self.tickers[1], \"SLD\", self.qty))\n self.events_queue.put(SignalEvent(self.tickers[0], \"BOT\", self.cur_hedge_qty))\n self.invested = None\n elif self.invested == \"short\" and et < sqrt_Qt:\n print(\"CLOSING SHORT: %s\" % event.time)\n self.events_queue.put(SignalEvent(self.tickers[1], \"BOT\", self.qty))\n self.events_queue.put(SignalEvent(self.tickers[0], \"SLD\", self.cur_hedge_qty))\n self.invested = None","title":""},{"docid":"6946de9557bdc512640937b2d86db275","score":"0.52889144","text":"def calcularPrecio(self):","title":""},{"docid":"6946de9557bdc512640937b2d86db275","score":"0.52889144","text":"def calcularPrecio(self):","title":""},{"docid":"99ebcde69abad3e3eafe40a8f0a228eb","score":"0.52832043","text":"def compute(self):\n self.Fi = self.FI(self.X, self.eta, self.nu)\n self.pmf = self.Pv(self.L)","title":""},{"docid":"52fbe8f19348def0fc1ce0d70e49844f","score":"0.52728665","text":"def _calculate_p_value(self, U):\n K = len(U)\n p_value = 2 * np.exp(\n (-6 * np.power(np.max(np.abs(U)), 2)) / (np.power(K, 2) + np.power(K, 3))\n )\n\n return p_value","title":""},{"docid":"36243b482d740c465be81a12aeef1adb","score":"0.5260407","text":"def detAcceptanceRateValues(self):\n if(not(self.weight)):\n for nodej in self.g.nodes():\n WeightedSum = len(self.g.in_edges(nodej))\n for edge in self.g.in_edges(nodej):\n self.A[edge[0] + \"-\"+edge[1]] = (1.0/self.NormA[nodej])\n else:\n for nodej in self.g.nodes():\n for edge in self.g.in_edges(nodej):\n self.A[edge[0] + \"-\"+edge[1]] = (self.g.get_edge_data(edge[0],edge[1]) + 0.0)/self.NormA[nodej]","title":""},{"docid":"dbf7aa94bc00a27f8909b581c8022685","score":"0.5259184","text":"def PoP(data, **kwargs):\n \n # Correspondance tables\n CORRES_B = {'D':1, 'M':21, 'Q':63, 'H':130, 'A':262} #business days\n CORRES_D = {'D':1, 'M':30, 'Q':91, 'H':182, 'A':365} #calendar days\n CORRES_M = {'M':1, 'Q':3, 'H':6, 'A':12}\n CORRES_Q = {'Q':1, 'H':2, 'A':4}\n CORRES_LABEL = {'D':'DoD', 'M':'MoM', 'Q':'QoQ', 'H':'HoH', 'A':'YoY'}\n\n if \"per\" in kwargs:\n period = kwargs[\"per\"].upper()\n else:\n period = 'A'\n \n if \"freq\" in kwargs:\n frequency = kwargs[\"freq\"].upper()\n else:\n frequency = 'D'\n\n #map correspondance tables\n if frequency == 'D':\n CORRES = CORRES_D\n elif frequency == 'B':\n CORRES = CORRES_B\n elif frequency == 'M':\n CORRES = CORRES_M\n elif frequency == 'Q':\n CORRES = CORRES_Q\n \n meth = 'ratio'\n if \"method\" in kwargs:\n if kwargs[\"method\"] == 'diff':\n meth = kwargs[\"method\"]\n \n #Converts series to DataFrame if necessary\n if isinstance(data, pd.Series):\n data = data.to_frame(data.name)\n \n # Aligns frequency and shift the second series by the period\n data = data.asfreq(frequency, method='pad')\n data_per = data.shift(CORRES[period])\n if meth == 'ratio':\n result = data/data_per-1\n elif meth == 'diff':\n result = data - data_per\n result.columns = [list(result)[0] + ' ' + CORRES_LABEL[period]]\n # Returns the period over period transformation\n return result","title":""},{"docid":"fccbc7b444b581ab06c04ed20f0959de","score":"0.5256896","text":"def calculate(self):\n scorer_class_name = self.get_root().scorer_class.name\n\n if len(self.labels) == 0:\n raise ValueError(\n \"No data present across all \"\n \"sequencing libraries [{}]\".format(self.name)\n )\n\n for label in self.labels:\n self.merge_counts_unfiltered(label)\n self.filter_counts(label)\n\n if self.is_barcodevariant() or self.is_barcodeid():\n self.combine_barcode_maps()\n\n self.ensure_main_count_tables_exist_and_populated()\n self.timepoints_contain_variants()\n\n if \"Demo\" in scorer_class_name:\n raise ValueError(\n 'Invalid scoring method \"{}\" '\n \"[{}]\".format(scorer_class_name, self.name)\n )\n\n if \"Regression\" in scorer_class_name and len(self.timepoints) <= 2:\n raise ValueError(\n \"Regression-based scoring \" \"requires three or more time points.\"\n )\n\n scorer = self.get_root().scorer_class(\n store_manager=self, options=self.get_root().scorer_class_attrs\n )\n scorer.run()\n\n # TODO: Write outlier computation as a plugin?\n non_allowed_methods = (\"Counts Only\", \"Demo\")\n scoring_method = scorer_class_name\n\n if scoring_method not in non_allowed_methods and self.component_outliers:\n if self.is_barcodevariant() or self.is_barcodeid():\n self.calc_outliers(\"barcodes\")\n if self.is_coding():\n self.calc_outliers(\"variants\")","title":""},{"docid":"3f2edfc1bbde94d815ef108a7855afcd","score":"0.52566755","text":"def _evalue_RR(self, est, lo=None, hi=None, no_effect_baseline=1):\n if est < 0:\n raise ValueError(\"Risk/Rate Ratio cannot be negative\")\n if no_effect_baseline < 0:\n raise ValueError(\"no_effect_baseline value is impossible\")\n if no_effect_baseline != 1:\n self.logger.info(\n 'You are calculating a \"non-null\" E-value, i.e., an E-value for the minimum amount of unmeasured '\n \"confounding needed to move the estimate and confidence interval to your specified no_effect_baseline value \"\n \"rather than to the null value.\"\n )\n if lo is not None and hi is not None:\n if lo > hi:\n raise ValueError(\"Lower confidence limit should be less than upper confidence limit\")\n if lo is not None and est < lo:\n raise ValueError(\"Point estimate should be inside confidence interval\")\n if hi is not None and est > hi:\n raise ValueError(\"Point estimate should be inside confidence interval\")\n\n e_est = self._threshold(est, no_effect_baseline=no_effect_baseline)\n e_lo = self._threshold(lo, no_effect_baseline=no_effect_baseline)\n e_hi = self._threshold(hi, no_effect_baseline=no_effect_baseline)\n\n # if CI crosses null, set its E-value to 1\n null_CI = False\n if est > no_effect_baseline and lo is not None:\n null_CI = lo < no_effect_baseline\n if est < no_effect_baseline and hi is not None:\n null_CI = hi > no_effect_baseline\n if null_CI:\n e_lo = np.float64(1)\n e_hi = np.float64(1)\n\n # only report E-value for CI limit closer to null\n if lo is not None or hi is not None:\n if est > no_effect_baseline:\n e_hi = None\n else:\n e_lo = None\n\n return {\n \"converted_estimate\": est,\n \"converted_lower_ci\": lo,\n \"converted_upper_ci\": hi,\n \"evalue_estimate\": e_est,\n \"evalue_lower_ci\": e_lo,\n \"evalue_upper_ci\": e_hi,\n }","title":""},{"docid":"8e5a456bf36237fe05f66878a584a056","score":"0.52539456","text":"def set_rsi(self):\n day_range = self.day_ranges.get('rsi', [14])\n dict = {}\n mtm = self.momentum[[f'{t}_momentum_1d' for t in self.tickers]]\n up = np.maximum(mtm, 0)\n down = -np.minimum(mtm, 0)\n for d in day_range:\n count_ups = (up > 0).rolling(window=d).sum()\n count_downs = (down > 0).rolling(window=d).sum()\n up_avg = up.rolling(window=d).sum() / count_ups\n down_avg = down.rolling(window=d).sum() / count_downs\n dict[d] = up_avg / (up_avg + down_avg)\n self.rsi = self.get_dataframe_from_dict(dict, 'rsi')","title":""},{"docid":"69713d3a408f8f71087b5f3ec4592ff3","score":"0.5253733","text":"def sipm_enf(x, spe, spe_sigma, opct, pap, dap):\n sap = spe_sigma # Assume the sigma of afterpulses is the same\n\n pe_signal = np.zeros(x.size)\n\n # Loop over the possible total number of cells fired\n for k in range(1, 250):\n pk = (1-opct) * pow(opct, k-1)\n\n # Consider probability of afterpulses\n papk = pow(1 - pap, k)\n p0ap = pk * papk\n pap1 = pk * (1-papk) * papk\n\n # Combine spread of pedestal and pe (and afterpulse) peaks\n pe_sigma = np.sqrt(k * spe_sigma ** 2)\n ap_sigma = np.sqrt(k * sap ** 2)\n\n # Evaluate probability at each value of x\n pe_signal += (\n p0ap * norm.pdf(x, k * spe, pe_sigma) +\n pap1 * norm.pdf(x, k * spe * (1 - dap), ap_sigma)\n )\n\n return pe_signal","title":""},{"docid":"68d8900ee63fca39075decc55671304a","score":"0.5247868","text":"def evaluate( self, tick, value, lastTick, lastValue ):\n for resample in self.resamples:\n resample.evaluate( tick, value, lastTick, lastValue)","title":""},{"docid":"f9adabd60833c423964af2657fe4d22f","score":"0.523737","text":"def calculation_and_save(self):\r\n # deallocate memory.\r\n gc.collect()\r\n\r\n prob_data = np.zeros((self.out_column, self.out_row), np.float64)\r\n ln_prob_data = np.zeros((self.out_column, self.out_row), np.float64)\r\n\r\n for j in range(int(np.log(self.out_column) / np.log(2)) + 1):\r\n # calculate the first probability matrix.\r\n if j == 0:\r\n (prob_data, ln_prob_data) = self._calculate_the_probability(self.data, (1, 1))\r\n # calculate other probability based on the previous matrix.\r\n elif j > 0:\r\n (prob_data, ln_prob_data) = self._calculate_from_old_prob_data(self.prob_data_list[j - 1][0],(2, 2))\r\n self.prob_data_list.append([prob_data, ln_prob_data])\r\n\r\n #### calculate multifractal parameters\r\n self.parameters, self.parameters_calculation, self.miu_q_list_list = self._calculate_the_spectrum()","title":""},{"docid":"482a9a01bd7535f41ffc8145d128bac3","score":"0.52369905","text":"def calculate_signals(self, event):\n sym = self.symbol_list[0]\n dt = self.datetime_now\n\n if event.type == 'Market':\n self.bar_index += 1\n if self.bar_index >5:\n lags = self.bars.get_latest_bars_value(\n self.symbol_list[0], \"returns\", N=3\n )\n pred_series = pd.Series(\n {\n 'Lag1': lags[1]*100.0,\n 'Lag2': lags[2]*100.0\n }\n )\n pred = self.model.predict(pred_series)\n if pred > 0 and not self.long_market:\n self.long_market = True\n signal = ASignalEvent(1, sym, dt, 'LONG', 1.0)\n self.events.put(signal)\n\n if pred < 0 and self.long_market:\n self.long_market = False\n signal = SignalEvent(1, sym, dt, 'EXIT', 1.0)\n self.events.put(signal)","title":""},{"docid":"f70ade2a39767a02c4cf6187be746b7c","score":"0.5228082","text":"def mirr(values,finance_rate,reinvest_rate):\n \n \n return float()","title":""},{"docid":"6cef5da13462f5e26e95fcf593fa61ff","score":"0.522527","text":"def momentum_average_and_standard_deviation(self):\r\n F = np.fft.fft(self.x)\r\n prob = np.abs(F)**2\r\n if np.max(prob) != 0.0:\r\n prob = prob/np.sum(prob)\r\n freq = np.fft.fftfreq(self.N, d=self.dx)\r\n p = 2*np.pi*freq*self.hbar/self.L\r\n\r\n expval = np.sum(np.dot(p, prob))\r\n expval2 = np.sum(np.dot(p**2, prob))\r\n sigma = np.sqrt(expval2 - expval**2)\r\n return (expval, sigma)","title":""},{"docid":"c22f2110345eb960e9d39f86f484017e","score":"0.52177155","text":"def calculate_ensquared_energy(PSF_model, wave, N_trials, N_rms,\n rms_amplitude, nominal_scale, spaxel_scales, rescale_coeff=None):\n\n N_zern = PSF_model.N_coef\n ensquared_results = []\n\n a_min = 1.0 if rescale_coeff is None else rescale_coeff\n\n print(\"Calculating Ensquared Energy\")\n for scale in spaxel_scales: # Loop over each Spaxel Scale [mas]\n print(\"%d mas spaxel scale\" % scale)\n\n data = np.zeros((2, N_trials * N_rms))\n amplitudes = np.linspace(0.0, rms_amplitude, N_rms)\n i = 0\n p0, s0 = PSF_model.compute_PSF(np.zeros(N_zern)) # Nominal PSF\n # Calculate the EE for the nominal PSF so that you can compare\n EE0 = ensquared_one_pix(p0, pix_scale=nominal_scale, new_scale=2*scale, plot=False)\n\n # 60x120\n # EE0 = ensquared_rectangle(p0, pix_scale=nominal_scale, new_scale=60, plot=False)\n\n for amp in amplitudes: # Loop over coefficient strength\n\n for k in range(N_trials): # For each case, repeat N_trials\n\n c_act = np.random.uniform(-amp, amp, size=N_zern)\n rescale = np.linspace(1, a_min, N_zern)\n c_act *= rescale\n phase_flat = np.dot(PSF_model.model_matrix_flat, c_act)\n rms = wave * 1e3 * np.std(phase_flat)\n p, s = PSF_model.compute_PSF(c_act)\n EE = ensquared_one_pix(p, pix_scale=nominal_scale, new_scale=2*scale, plot=False)\n # EE = ensquared_rectangle(p, pix_scale=nominal_scale, new_scale=60, plot=False)\n dEE = EE / EE0 * 100\n data[:, i] = [rms, dEE]\n i += 1\n\n ensquared_results.append(data)\n\n return ensquared_results","title":""},{"docid":"0318516352678fc935c8e852419e69e8","score":"0.5211374","text":"def average(self):\n n,S = 0,0\n for yi in (self.y):\n n = n + 1\n S = S + yi\n return S/n","title":""},{"docid":"9eabe5526ca1fe5076643b90534da2c7","score":"0.5200672","text":"def sens(self):\n print(f'Sensitivity is {self.tp / (self.tp + self.fn)}')","title":""},{"docid":"7e979e07d334e57cf388208a6430a1b1","score":"0.5196765","text":"def ipmt(rate,per,nper,pv,fv,when):\n \n \n return ndarray()","title":""},{"docid":"c03588f476c22ddb06ffa942462d8a72","score":"0.5186421","text":"def calc_stats(self):\n self.happiness += (self.time_delta * self.happiness_rate)\n if self.happiness > self.happiness_max:\n self.happiness = self.happiness_max\n elif self.happiness < 0:\n self.happiness = 0\n\n self.satiation += (self.time_delta * self.satiation_rate)\n if self.satiation > self.satiation_max:\n self.satiation = self.satiation_max\n elif self.satiation < 0:\n self.satiation = 0\n\n self.energy += (self.time_delta * self.energy_rate)\n if self.energy > self.energy_max:\n self.energy = self.energy_max\n elif self.energy < 0:\n self.energy = 0\n\n self.health += (self.time_delta * self.health_rate)\n if self.health > self.health_max:\n self.health = self.health_max\n elif self.health < 0:\n self.health = 0\n return","title":""},{"docid":"a2ffb565258728f618fe9cdcd836df45","score":"0.51852655","text":"def Pv(self, l=None, e=None, n=None, Q=None):\n if l is None:\n l = self.L\n if e is None:\n e = self.eta\n if n is None:\n n = self.nu\n if Q is not None:\n #print(\"Before quantization:\", len(self.data))\n \"\"\"\n warnings.filterwarnings('error')\n try:\n print(\"On try\")\n div = self.data / Q\n print(\"div\")\n diq = (div).astype('int')\n print(\"diq\")\n mask = ((div - diq).astype('bool') * -1 + 1).astype('bool')\n print(\"mask\")\n self.data = self.data[mask]\n print(\"new_data\")\n except RuntimeWarning as w:\n print(w)\n warnings.filterwarnings('ignore')\n div = self.data / Q\n diq = (div).astype('int')\n mask = ((div - diq).astype('bool') * -1 + 1).astype('bool')\n self.data = self.data[mask]\n print( len( self.data[ np.isnan(self.data) == True ] ) )\n print(\"After quantization:\", len(self.data))\n\n warnings.filterwarnings('default')\n \"\"\"\n #print(\"Q:\", Q)\n #print(\"self.Q:\", self.Q)\n div = self.data / Q\n #print(div)\n diq = (div).astype('int')\n #print(diq)\n mask = ((div - diq).astype('bool') * -1 + 1).astype('bool')\n #print(mask)\n self.data = self.data[mask]\n self.lower_bound = int(np.floor( np.min(self.data) /2 ) * 2)\n self.upper_bound = int(np.floor( np.max(self.data) /2 ) * 2 +1)\n self.L = np.arange(self.lower_bound, self.upper_bound+1).astype('int') # an interval of integer values. Used to compute the pmf\n #print(self.data)\n \n return self._pv(l, e, n)","title":""},{"docid":"a23ebc86256acecb6326674a6483616f","score":"0.5184817","text":"def detRejectanceRateValues(self):\n if(not(self.weight)):\n for nodej in self.g.nodes():\n for edge in self.g.out_edges(nodej):\n self.R[edge[0] + \"-\"+edge[1]] = (1.0/self.NormR[nodej])\n else:\n for nodej in self.g.nodes():\n for edge in self.g.out_edges(nodej):\n self.R[edge[0] + \"-\"+edge[1]] = (1.0 - self.g.get_edge_data(edge[0],edge[1]) )/self.NormR[nodej]","title":""},{"docid":"b824d100fa94f2a751f79972e81db869","score":"0.51749194","text":"def calculate_stats(self):\n tps = []\n tns = []\n fps = []\n fns = []\n y_ = np.copy(self.y_)\n thresholds = np.linspace(0, 1.01, 1000)\n for thresh in thresholds:\n tps.append(self.true_positive(y_, thresh))\n tns.append(self.true_negative(y_, thresh))\n fps.append(self.false_positive(y_, thresh))\n fns.append(self.false_negative(y_, thresh))\n tps = np.array(tps)\n tns = np.array(tns)\n fps = np.array(fps)\n sensitivity = tps / (tps + fns)\n specificity = tns / (tns + fps)\n return sensitivity, specificity","title":""},{"docid":"127adaf3507b9e6ca66c3d07190a3c72","score":"0.5173262","text":"def computeQValueFromValues(self, state, action):\n \"*** YOUR CODE HERE ***\"\n transisions = self.mdp.getTransitionStatesAndProbs(state,action)\n\n QV=0\n\n for (new_state,prob) in transisions:\n QV+=prob*(self.mdp.getReward(state,action,new_state)+self.discount*self.values[new_state])\n return QV","title":""},{"docid":"0bc6e27e447e85785ba57d3f80e0acdb","score":"0.5170065","text":"def value(self) -> float:\n return (\n self.__food_quantity + self.__food_quality +\n self.__service_quality + self.__price\n ) / self.__criterias","title":""},{"docid":"829bd5fbec73b05ef70b77eef62e8ddf","score":"0.5166319","text":"def calculateOutput(self):\r\n result=0.0\r\n total=0.0\r\n \r\n # adds up all the inputs\r\n i=0\r\n while i < len(self.input_values):\r\n total += self.input_values[i]*self.weights[i]\r\n i+=1\r\n # includes the baseline value\r\n total += self.weights[-1]\r\n\r\n # determines how close to the edges of the range the inputs reached\r\n # one in the positve direction and one in the negative depending on the sign of the total\r\n # pulls the total back into the range of -1 to 1\r\n if total >= 0:\r\n # returns a positive\r\n # makes output closer to 0\r\n total = total / self.sum_of_weights_pos\r\n else:\r\n # returns a negative -> - (-t / -w)\r\n # makes output closer to 1\r\n total = -total / self.sum_of_weights_neg\r\n\r\n # the sigmoid function\r\n result = 1/(1+2.0**(total*self.sigmoid_multiplier))\r\n\r\n self.output=result","title":""},{"docid":"17a1b1f9554027d1490a53ef44ccf719","score":"0.51648015","text":"def CalculateForAllDataPoints(self, params):\n self.params.update(params)\n\n varsByCalc = self.GetExperimentCollection().GetVarsByCalc()\n self.calcVals = self.GetCalculationCollection().Calculate(varsByCalc, \n params)\n return self.calcVals","title":""},{"docid":"f662f5a2c5e70cedea34e6aa3eaaeee9","score":"0.5162285","text":"def estimate(self, values):\n\n pdf = zeros_like(values)\n\n Ns = len(self.samples) \n\n # Could perform convolution with FFT.\n for m in range(Ns):\n pdf += self.weights[m] * exp(-(values - self.samples[m])**2 / (2 * self.sigmak**2)) / (self.sigmak * sqrt(2 * pi)) / Ns\n\n pdf /= trapz(pdf, values)\n return pdf","title":""},{"docid":"e50299435b6ca9f301f6e345ac1a46b9","score":"0.5161663","text":"def calculate_diversification_metrics(self):\n # create a dataframe\n cols = ['Undiv. VaR', 'Div. VaR', 'Div. Effect ($)', 'Div. Effect (%)', 'Div. Ratio']\n self.diversification = pd.DataFrame(None, [''], cols)\n\n # get the diversified VaR\n div_var = self.var.loc['VaR', 'Portfolio']\n self.diversification['Div. VaR'] = div_var\n\n # compute the undiversified VaR\n undiv_var = self.var.loc['VaR', :].drop('Portfolio').sum()\n self.diversification['Undiv. VaR'] = undiv_var\n \n # compute the diversification effects ($)\n self.diversification['Div. Effect ($)'] = undiv_var - div_var\n\n # compute the diversification effects (%)\n self.diversification['Div. Effect (%)'] = (undiv_var - div_var) / undiv_var\n\n # compute the diversification ratio\n self.diversification['Div. Ratio'] = div_var / undiv_var\n\n self.log(self.diversification, 'Diversification Measures', index=False)","title":""},{"docid":"16d27c037b135eabc26c70a6457f000c","score":"0.5159992","text":"def eval(self):\n # remove np.spacing(1)\n pixAcc = 1.0 * self.total_correct / (2.220446049250313e-16 + self.total_label)\n IoU = 1.0 * self.total_inter / (2.220446049250313e-16 + self.total_union)\n mIoU = IoU.mean().item()\n return pixAcc, mIoU","title":""},{"docid":"7cc0ab11dcacbf5787b7ce42b860bc77","score":"0.5156758","text":"def sigma(self):\n return self._count_rates['flux_ratio'] / self.snr","title":""},{"docid":"5b03afda83a5bbc794c0e0a619965803","score":"0.5153938","text":"def volatility(self):\n pass","title":""},{"docid":"79ba4a110ff0d29a797db36781c4ff48","score":"0.5151857","text":"def computeAndScaleArraySums(self):\n self.sumRT()\n self.scaleRT()\n self.sumA()\n self.scaleA()\n self.Fluence()","title":""},{"docid":"0ce34b54002d260887e3c1125c6e6369","score":"0.5151038","text":"def ss_rate(pwf,pe,rw,re,k,h,mu,B):\n return (7.08*10**(-3))*(k*h/(mu*B))*(pe-pwf)/(np.log(re/rw))","title":""},{"docid":"a3e8926fe1c2c7ce24eda0dfa89ff7ae","score":"0.51452446","text":"def singal_measure(self): \n k1_data = math.sqrt(self.__k1_data)\n k2_data = math.sqrt(self.__k2_data)\n b_data = (k2_data - k1_data)/(k2_data + k1_data) \n a_data = 2.95494514*pow(b_data,1) + 0.17671943*pow(b_data,3) + 0.09230694*pow(b_data,5)\n amp_data = (k1_data + k2_data)*(3.20976143+0.9187393*pow(a_data,2)+0.14734229*pow(a_data,4))/8192\n amp_data = amp_data/4096\n freq_data = (0.5+a_data+self.__k1_index)*(self.__sample_rate/8192)\n self.__base_frequency = freq_data\n return(amp_data,'V',freq_data,'Hz')","title":""},{"docid":"e66cafcced081c7b2dc352092086f493","score":"0.51419634","text":"def readSensor(self):# -> float:\n self.__RS_Calc = ((self.__VOLT_RESOLUTION * self.__RL ) / self.__sensor_volt ) - self.__RL\n \n if(self.__RS_Calc < 0): \n self.__RS_Calc = 0\n\n self.__ratio = self.__RS_Calc / self.__R0\n \n if(self.__ratio <= 0): \n self.__ratio = 0\n \n if(self.__regressionMethod == 1): \n self.__PPM = self.__a * math.pow( self.__ratio , self.__b )\n \n else:\n ppm_log = (math.log10( self.__ratio ) - self.__b ) / self.__a\n self.__PPM = math.pow(10, ppm_log)\n \n if( self.__PPM < 0): \n self.__PPM = 0\n \n return self.__PPM","title":""},{"docid":"d4e8d90e794506bc11f66c908d37fa61","score":"0.5141406","text":"def results(self):\n res = super(TrackedSIR, self).results()\n\n self.hook.finalize_results(initial_results=self.timeseries_results, final_results=res, params=self.params)\n\n # TODO: Fix this dependency issue\n di = res['daily_infections']\n ci = res['currently_infected']\n\n # Calculate the average length of an infection\n n = self._g.nodes\n mil = np.mean([n[x]['days_infected'] for x in n if 'days_infected' in n[x]])\n res['mean_infection_length'] = mil\n\n res['daily_r'] = [((di[x + 1] / ci[x]) if ci[x] != 0 else 0) * mil for x in range(len(ci) - 1)]\n\n return res","title":""},{"docid":"5688967c99e623a99b4c2263c14301ba","score":"0.5136812","text":"def calculate(self):","title":""},{"docid":"8f84dc65cdf62c3bbaafde61ab93fc6e","score":"0.5136497","text":"def Evaluate():\n # Retrieve dataset files\n data_fls, ref_fls = LoadTroikaDataset()\n errs, confs = [], []\n for data_fl, ref_fl in zip(data_fls, ref_fls):\n # Run the pulse rate algorithm on each trial in the dataset\n errors, confidence = RunPulseRateAlgorithm(data_fl, ref_fl)\n errs.append(errors)\n confs.append(confidence)\n # Compute aggregate error metric\n errs = np.hstack(errs)\n confs = np.hstack(confs)\n return AggregateErrorMetric(errs, confs)","title":""},{"docid":"58c77f68eaf7669325ca3461523bc02c","score":"0.5134388","text":"def ratio_p(self):\n ga = self.ga\n Ms = self.Ms\n return (2*ga*Ms**2 - (ga-1))/(ga+1)","title":""},{"docid":"6bcfa1e6e96cbd23d34740eab5dafa0c","score":"0.51284164","text":"def GlobalScale(self):\n\t\tprint \"Scale following the order of importance (peaks)\"\n\t\tP = self.GlobalPeaks()[:,0]\n\t\tself.Echelle = []\n\t\tfor i in range(0,len(self.GP)):\n\t\t\tnb_ext = len(self.melodies)\n\t\t\t#tonique = self.melodies[nb_ext-1].tonique()[1]\n\t\t\ttonique = self.GetTheTonic()\n\t\t\tself.Echelle.append(log10(P[i]/tonique)*1000)\n\t\treturn self.Echelle","title":""},{"docid":"6c04ce0eb342ee13eb12197902fb0dd0","score":"0.5128311","text":"def calculate_flows(self):\n e = self.alpha_star**(-1) / (1 - self.alpha_star**(-self.n_e))\n s = self.alpha_star**(-1) / (self.alpha_star**(self.n_s+1) - 1)\n e_sum = (e*self.xf / (e+s)).sum()\n s_sum = (s*self.xf / (e+s)).sum()\n\n p = self.user_f * e_sum\n f = self.user_p / e_sum\n\n if p < self.user_p:\n self.p = p\n self.f = self.user_f\n else:\n self.f = f\n self.p = self.user_p\n self.t = self.f * s_sum\n\n self.swu = self.get_swu()\n if self.swu > self.user_swu:\n self.swu = self.user_swu\n\n self.f = self.swu / (self.value_function(self.xp)*e_sum\n + self.value_function(self.xt)*s_sum\n - self.value_function(self.xf))\n self.p = self.f * e_sum\n self.t = self.f * s_sum\n\n return","title":""},{"docid":"34281037e58f3024e2253c1e8909b337","score":"0.5126956","text":"def run(self):\n self.report.chapter('Historical Simulation Value-at-Risk')\n self.calculate_shifted_levels()\n self.get_full_revaluation_pnl()\n self.get_ptf_pnl()\n self.bootstrap_pnls()\n self.calculate_var()\n self.calculate_sub_ptf_pnl()\n self.calculate_sub_ptf_var()\n self.calculate_component_var()\n self.calculate_incremental_var()\n self.calculate_diversification_metrics()\n self.plot_hist()\n self.waterfall_plots()","title":""},{"docid":"7fe6757d77a3b9d36fab5477f05b06e9","score":"0.51256603","text":"def f(ts):\n # This method is nicer than an inverse fourier transform because\n # results are consistant for differing ranges of ts around the same\n # time. The price payed is that the fft would be an order of\n # magnitude faster, but not reproducible for a slightly different\n # time array.\n values = sum(amp * np.cos(2*np.pi*freq * ts + phase)\n for freq, amp, phase\n in zip(self.freqs, self.amps, self.phases))\n\n # Normalization calculated by guess-and-check,\n # but seems to work fine\n # normalization = np.sqrt(2/len(self.freqs))\n values *= np.sqrt(2/len(self.freqs))\n\n # So far, the units of the values are V/V_rms, so multiply by the\n # rms voltage:\n values *= self.rms\n\n return values","title":""},{"docid":"2d3ab37474c5bdad03e54e309012aa38","score":"0.5123594","text":"def production():\r\n # kp is the interval considered\r\n kp = np.linspace(4*10**(-3),16*10**(-3),1000)\r\n \r\n # Aggregate fraction calculated analytically\r\n a_n = 3*k1_n*kp/(kc_n*(kc_n+k1_n))\r\n # Changing units to uM\r\n a_n = a_n*1000\r\n kp = kp * 1000\r\n \r\n # Plotting figure\r\n fig, ax = plt.subplots()\r\n fig.subplots_adjust(left=.15, bottom=.2, right=.95, top=.97) \r\n ax.set_ylabel(r'Aggregated $A\\beta_{42}$ in ISF (pM)')\r\n ax.set_xlabel('Production rate ($pM.h^{-1}$)') \r\n \r\n plt.plot(kp, a_n, 'black', linewidth = 1.0)\r\n ax.axvline(x=kp_n*1000, color = 'g', linestyle='dashed', alpha=0.5)\r\n ax.text(x=kp_n*1000 + 0.2, y=150, s='Normal', alpha=1, color='g', fontsize = 6)\r\n \r\n ax.axvline(x=kp_FAD*1000, color = 'r', linestyle='dashed', alpha=0.5)\r\n ax.text(x=kp_FAD*1000 + 0.2, y=150, s='FAD', alpha=1, color='r', fontsize = 6)\r\n\r\n # Save and display image \r\n fig.set_size_inches(width, height)\r\n fig.savefig('Variations in production rate.png', dpi=1200)\r\n plt.show()\r\n return","title":""},{"docid":"9d0e5a63f7dbf72458d4d8cb9ae05d23","score":"0.5117035","text":"def pv(flows, r):\n dates = flows.index\n discounts = discount(dates, r)\n return discounts.multiply(flows, axis=\"rows\").sum()","title":""},{"docid":"945e198e9811ac8aa693449d804faeb4","score":"0.51122","text":"def calculate_var(self):\n # create a dataframe for the MC VaR\n self.var = pd.DataFrame(columns=self.position_names, index=['VaR', 'Correlation'])\n\n # iterate over the positions\n ptf_pnl = self.ptf_pnl['P&L ($)']\n for asset in self.position_names:\n asset_pnl = self.full_revaluation_pnl[asset]\n \n # calculate the individual VaR\n self.var.loc['VaR', asset] = np.percentile(asset_pnl, (1 - self.confidence) * 100)\n\n # compute the correlation between the asset P&L and the portfolio P&L\n self.var.loc['Correlation', asset] = pearsonr(asset_pnl, ptf_pnl)[0]\n\n # compute the MC n-day portfolio VaR\n self.var.loc['VaR', 'Portfolio'] = np.percentile(ptf_pnl, (1 - self.confidence) * 100)\n \n self.log(self.var, 'Value at Risk')","title":""},{"docid":"72310e6faec92e55844761cef1671695","score":"0.5109994","text":"def pv(flows, r):\n dates = flows.index\n discounts = discount(dates, r)\n return discounts.multiply(flows, axis='rows').sum()","title":""},{"docid":"f5b41a06461174665a0d90c84cfc9563","score":"0.5109505","text":"def rsi(df, s, period) -> ReturnType:\n\n delta = df['close'].diff().to_numpy()\n\n # gain\n U = (np.absolute(delta) + delta) / 2.\n # loss\n D = (np.absolute(delta) - delta) / 2.\n\n smma_u = calc_smma(U, period)\n smma_d = calc_smma(D, period)\n\n return 100 - 100 / (1. + smma_u / smma_d), period","title":""},{"docid":"53ae70211db2b39bcf9175c09d8946a2","score":"0.51083857","text":"def calculate_values(eq_model, eq_year):\n with open(\"res/api-response.json\", \"r\") as resp:\n data = json.load(resp)\n\n cost = data[eq_model][\"saleDetails\"][\"cost\"]\n market_ratio = data[eq_model][\"schedule\"][\"years\"][eq_year][\"marketRatio\"]\n auction_ratio = data[eq_model][\"schedule\"][\"years\"][eq_year][\"auctionRatio\"]\n\n return {\"marketValue\": cost * market_ratio,\n \"auctionValue\": cost * auction_ratio\n }","title":""},{"docid":"58fc550ecf042d256546e602f715ef15","score":"0.5105003","text":"def __call__(self, values):\n r = self.residuals(values)\n return getattr(r, 'SSE', 1e9)","title":""},{"docid":"8aedcd63e22b87624222c89f92773198","score":"0.5103577","text":"def testing(predicted_price):\r\n global y_test\r\n test1 = list(range(0,len(y_test)))\r\n test2 = list(range(0,len(y_test)))\r\n a=int()\r\n\r\n for a in range(0,len(y_test)):\r\n test1[a] = y_test[a] - predicted_price['Linear'][a]\r\n predicted_price['Linear'][a] = predicted_price['Linear'][a] + test1[a]\r\n test2[a] = y_test[a] - predicted_price['RBF'][a]\r\n predicted_price['RBF'][a] = predicted_price['RBF'][a] + test2[a]\r\n\r\n avr1 = sum(test1)/len(test1)\r\n avr2 = sum(test2)/len(test2)\r\n\r\n for a in range(len(y_test),len(predicted_price['Linear'])):\r\n predicted_price['Linear'][a] = predicted_price['Linear'][a] + avr1\r\n predicted_price['RBF'][a] = predicted_price['RBF'][a] + avr2\r\n\r\n return int(avr1), int(avr2)","title":""},{"docid":"e9dc83190f57a8f83668584c20e3bd24","score":"0.51033914","text":"def test_get_returns_and_pl_average_return(self):\n all_prices = [[1., 2.], [3., 4.], [5., 6.]]\n signals = [['buy', 'buy', 'sell'],\n [None, None, None]]\n returns, p_and_l = stockdata.get_returns_and_pl(all_prices, signals)\n self.assertEqual(returns, [0, -0.38077249551779302])\n self.assertEqual(p_and_l, [0, 0])","title":""},{"docid":"4ae17ca4995597d52aec52653bc471eb","score":"0.5100543","text":"def get_measures(self):\n precision = 0\n recall = 0\n f1_score = 0\n if self.t_p > 0:\n precision = self.t_p / (self.t_p + self.f_p)\n recall = self.t_p / (self.t_p + self.f_n)\n if (precision + recall) > 0:\n f1_score = 2*(precision * recall / (precision + recall))\n return precision, recall, f1_score","title":""},{"docid":"4cd0d543787085fd904f9830537bfa4e","score":"0.51004606","text":"def pv(l,r):\r\n dates=l.index\r\n discounts = discount(dates,r)\r\n return (discounts*l).sum()","title":""},{"docid":"afcabc478b2d8a8bc373f11317c777d7","score":"0.5099384","text":"def value(self) -> float:\n if self._value is None:\n self.lr.fit(self._train[\"V^2\"].values.reshape(-1, 1), self._train[\"Fa\"])\n self._value = self.lr.coef_[0]\n return self._value","title":""},{"docid":"68453bbd8c48b38f16255b6ad75d39e1","score":"0.50984603","text":"def calculate_signals(self, tick_event):\n\n return","title":""},{"docid":"d62b154ba0ad6d9d99473f074aabd4fa","score":"0.5096261","text":"def weighted_prediction(scores, preds):\n\n if len(scores) == 1: # only one method, return the series\n return preds[0]\n else:\n n = len(scores)\n # first compute total error by series\n total_error = {}\n for i in range(45):\n score = 0\n for j in range(n):\n score += scores[j][i]\n\n total_error[i] = score\n\n # now total_error is a dict with total_error[i] the total error\n # of all methods for the serie-(i+1)\n\n # create array of weight by method and series, being proportional\n # to the error\n normalizer = n-1\n weights = np.zeros(scores.shape)\n for i in range(n):\n for j in range(45):\n weights[i][j] = (1 / float(normalizer)) * (1 - scores[i][j]/total_error[j])\n\n # now weights[i][j] is the weights for serie j+1 given by method i\n\n # create a series to accumulate the mean of all methods\n df = pd.DataFrame(np.zeros(preds[0].shape), index=preds[0].index)\n\n # note that this series does not have the good name for the columns\n # we change that now\n rn = {}\n for i in range(45):\n rn[i] = 'series-' + str(i+1)\n df = df.rename(columns=rn)\n # now df has the good shape, the good name for the columns and zeros\n # everywhere\n\n for i in range(n): # for each method\n for j in range(45): # for each column\n column = 'series-' + str(j+1)\n\n # df[column] receives the weighted sum of values\n df[column] = df[column] + preds[i][column] * weights[i][j]\n\n return df","title":""},{"docid":"47de7f2a6ef88e17daf820992c0f211a","score":"0.5094053","text":"def rsi_predictions(prices, rsi):\n days_observed = 14\n true_positive = 0\n false_positive = 0\n true_negative = 0\n false_negative = 0\n try:\n while days_observed < len(prices) - 5:\n change = array(prices[days_observed + 1: days_observed + 6]).mean()\n is_overbought, is_oversold = bought_status(rsi[days_observed])\n if is_oversold:\n if change > prices[days_observed]:\n true_positive += 1\n else:\n false_negative += 1\n elif is_overbought:\n if change <= prices[days_observed]:\n true_negative += 1\n else:\n false_positive += 1\n days_observed += 1\n return calculate_predictions(true_positive, false_positive, true_negative, false_negative)\n except IndexError as error:\n print(error, len(rsi), len(prices))\n return 0.5, 0.5, 0.5","title":""},{"docid":"233cc3ef9c44ae91a03aff95d4eab3e9","score":"0.5089559","text":"def performGetValue(self, quant, options={}):\n # proceed depending on quantity\n if 'Waveform' in quant.name:\n # Always update dt\n sampling_rate = self.getValue('Sampling Rate')\n self.dt = 1 / sampling_rate\n\n # Make all of our waveform checks and update what we need to\n if self.basis_rotation_generators_are_stale:\n self.update_basis_rotation_generators()\n self.update_state_preparation_sequence()\n self.update_measurement_basis_sequence()\n self.basis_rotation_generators_are_stale = False\n self.state_prep_sequence_is_stale = False\n self.measurement_basis_sequence_is_stale = False\n\n if self.state_prep_sequence_is_stale:\n self.update_state_preparation_sequence()\n self.state_prep_sequence_is_stale = False\n \n if self.process_sequence_is_stale:\n self.update_process_sequence()\n self.process_sequence_is_stale = False\n\n if self.measurement_basis_sequence_is_stale:\n self.update_measurement_basis_sequence()\n self.measurement_basis_sequence_is_stale = False\n\n if self.readout_is_stale:\n self.update_readout()\n self.readout_is_stale = False\n\n if self.getValue('Process X - G-E') is not None:\n self.update_process_sequence()\n\n # Plot and return the appropriate trace dict:\n if quant.name == 'Waveform - X Signal':\n ge_frequency = 1e-3*self.getValue('G-E Frequency')\n ef_frequency = 1e-3*self.getValue('E-F Frequency')\n percent_of_sequence = self.getValue('Percent of Sequence') / 100\n x_ge_signal = dsp_utils.modulate_signal(self.x_ge.build_sequence(percent_of_sequence),\n self.dt,\n ge_frequency,\n np.pi/2) \\\n + dsp_utils.modulate_signal(self.y_ge.build_sequence(percent_of_sequence),\n self.dt,\n ge_frequency,\n np.pi/2) # sine phase\n x_ef_signal = dsp_utils.modulate_signal(self.x_ef.build_sequence(percent_of_sequence),\n self.dt,\n ef_frequency,\n np.pi/2) \\\n + dsp_utils.modulate_signal(self.y_ef.build_sequence(percent_of_sequence),\n self.dt,\n ef_frequency,\n np.pi/2) \n signal = x_ge_signal + x_ef_signal\n trace = quant.getTraceDict(signal, t0=0.0, dt=self.dt)\n elif quant.name == 'Waveform - Y Signal':\n ge_frequency = 1e-3*self.getValue('G-E Frequency')\n ef_frequency = 1e-3*self.getValue('E-F Frequency')\n percent_of_sequence = self.getValue('Percent of Sequence') / 100\n y_ge_signal = dsp_utils.modulate_signal(self.y_ge.build_sequence(percent_of_sequence),\n self.dt,\n ge_frequency,\n 0) \\\n + dsp_utils.modulate_signal(self.x_ge.build_sequence(percent_of_sequence),\n self.dt,\n ge_frequency,\n 0)\n y_ef_signal = dsp_utils.modulate_signal(self.y_ef.build_sequence(percent_of_sequence),\n self.dt,\n ef_frequency,\n 0) \\\n + dsp_utils.modulate_signal(self.x_ef.build_sequence(percent_of_sequence),\n self.dt,\n ef_frequency,\n 0)\n signal = y_ge_signal + y_ef_signal\n trace = quant.getTraceDict(signal, t0=0.0, dt=self.dt)\n elif quant.name == 'Waveform - Z Signal':\n percent_of_sequence = self.getValue('Percent of Sequence') / 100\n signal = self.z.build_sequence(percent_of_sequence)\n trace = quant.getTraceDict(signal, t0=0.0, dt=self.dt)\n elif quant.name == 'Waveform - Readout I':\n percent_of_sequence = self.getValue('Percent of Sequence') / 100\n signal = self.readout_i.build_sequence(percent_of_sequence)\n trace = quant.getTraceDict(signal, t0=0.0, dt=self.dt)\n elif quant.name == 'Waveform - Readout Q':\n percent_of_sequence = self.getValue('Percent of Sequence') / 100\n signal = self.readout_q.build_sequence(percent_of_sequence)\n trace = quant.getTraceDict(signal, t0=0.0, dt=self.dt)\n elif quant.name == 'Waveform - Readout Trigger':\n percent_of_sequence = self.getValue('Percent of Sequence') / 100\n signal = self.readout_trigger.build_sequence(percent_of_sequence)\n trace = quant.getTraceDict(signal, t0=0.0, dt=self.dt)\n return trace\n\n elif 'QB' in quant.name:\n demodulated_signal = self.demodulate()\n return demodulated_signal\n\n else: \n # for other quantities, just return current value of control\n return quant.getValue()","title":""},{"docid":"6d4cfacfad586eb180e6877bceeb5057","score":"0.5085563","text":"def negative_predictive_values(self):\n return self.tn() / (self.fn() + self.tn())","title":""},{"docid":"5bc00ffcc6633b79e548a8addd2ab41f","score":"0.50805104","text":"def _calc_p(self):\n assert hasattr(self, '_v_void')\n self.pdf.update_pdf(v_void=self._v_void,\n f=self._f.max(),\n rt_mean=self._v_void / self._f.max())\n self._p = self.pdf.get_p()\n # Log.\n self.log.d_data(self._log_tree, 'p', self._p)","title":""},{"docid":"999b261229b66c0308821615748258f0","score":"0.5075779","text":"def computeQValueFromValues(self, state, action):\n \"*** YOUR CODE HERE ***\"\n qValue = 0\n for nextState, prob in self.mdp.getTransitionStatesAndProbs(state,action):\n qValue += prob*(self.mdp.getReward(state,action,nextState)+self.discount*self.values[nextState])\n # print(qValue)\n return qValue","title":""},{"docid":"be721d7112fc1839aedb29bd4bb6b678","score":"0.5072971","text":"def get_statistic(self):\n return self.valdiffs / self.get_se()","title":""},{"docid":"280c0f420c48d26b6838ee8c2358804c","score":"0.5072796","text":"def mse(poll_df, prediction_df):\n # TODO: Have data_dict as parameter here? Or hand over data_dict['allensbach'] directly?\n # TODO: Refactor this once the model is wrapped in a class.\n mse = 0\n for party in parties:\n true_results = poll_df[party]\n predicted_results = prediction_df[party][1:1+len(true_results)] # first point is prediction into the future, do not use it\n mse += np.mean((true_results - predicted_results)**2)\n return mse / len(parties)","title":""}],"string":"[\n {\n \"docid\": \"35f81c10fe1b2d0507b135e909721a2d\",\n \"score\": \"0.6021242\",\n \"text\": \"def npv(rate,values):\\n \\n \\n return float()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c6470dfd89042b82c41307358c6aea\",\n \"score\": \"0.5861876\",\n \"text\": \"def results( self ):\\n A = AA = 0.0\\n x = xx = 0.0\\n s = ss = 0.0\\n for egg in self.pastEggs:\\n w = np.exp( egg.lweight - self.lZ )\\n A += w * egg.A[0]\\n AA += w * egg.A[0] * egg.A[0]\\n x += w * egg.x[0]\\n xx += w * egg.x[0] * egg.x[0]\\n s += w * egg.s[0]\\n ss += w * egg.s[0] * egg.s[0]\\n\\n print 'mean(A) = %12.6f, stdev(A) = %12.6f' % (A, np.sqrt( AA - (A * A) ))\\n print 'mean(x) = %12.6f, stdev(x) = %12.6f' % (x, np.sqrt( xx - (x * x) ))\\n print 'mean(s) = %12.6f, stdev(s) = %12.6f' % (s, np.sqrt( ss - (s * s) ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef5e06ae0a3bd2d0c9857bd1db29d5ef\",\n \"score\": \"0.5782366\",\n \"text\": \"def fv(rate,nper,pmt,pv,when):\\n \\n \\n return ndarray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"579ca3d0d2d61896f3097e2bc4f997ac\",\n \"score\": \"0.5721197\",\n \"text\": \"def pv(rate,nper,pmt,fv,when):\\n \\n \\n return ndarray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fe1eba5c0db1a1ea0b6d2a94ee06815\",\n \"score\": \"0.5719713\",\n \"text\": \"def calcPowerEV(self):\\n self.q_powerEV = helper_functions.calcExpectedValue(self.quality_power)\\n self.d_powerEV = helper_functions.calcExpectedValue(self.duration_power)\\n self.c_powerEV = helper_functions.calcExpectedValue(self.cost_power)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fe1eba5c0db1a1ea0b6d2a94ee06815\",\n \"score\": \"0.5719713\",\n \"text\": \"def calcPowerEV(self):\\n self.q_powerEV = helper_functions.calcExpectedValue(self.quality_power)\\n self.d_powerEV = helper_functions.calcExpectedValue(self.duration_power)\\n self.c_powerEV = helper_functions.calcExpectedValue(self.cost_power)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05857342e9478291c51bab4c93b4d5ba\",\n \"score\": \"0.56470066\",\n \"text\": \"def calculate_signals(self):\\n raise NotImplementedError(\\\"Should implement calculate_signals()\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bca0f8d5af2de474e55feaab69b1a8b3\",\n \"score\": \"0.56332994\",\n \"text\": \"def schedule_variance(self):\\n \\n ev = self.apc * self.budget\\n pv = self.ppc * self.budget\\n \\n return ev - pv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fce3581240e5a5a3042dae63804e9c2c\",\n \"score\": \"0.5606615\",\n \"text\": \"def valuation(self):\\n self.integer.valuation() * (self.p ** self.shift)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91b400ab519812f046835cc4c8798d2e\",\n \"score\": \"0.55950147\",\n \"text\": \"def valuation(self):\\n if not self:\\n return 0\\n for i, e in enumerate(self.expansion):\\n if e:\\n return rational.Rational(1, p**i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26419a54e64cde2d143b1bef8013b475\",\n \"score\": \"0.55775577\",\n \"text\": \"def evaluate_result( self ):\\n \\n # Create the exact result\\n x_exact = np.exp( np.sin(self.t) )\\n # Print the RMS difference\\n e_rms = np.sqrt( 1./self.N * ((self.x-x_exact)**2).sum() )\\n print( e_rms )\\n # Plot the result\\n plt.plot( self.t, self.x, label=\\\"Euler\\\")\\n plt.plot( self.t, x_exact, '--', label=\\\"Analytic Solution\\\")\\n plt.legend(loc=3)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea9bace0a44d73df3adea35372a17af\",\n \"score\": \"0.5554894\",\n \"text\": \"def CalculateSensitivitiesForAllDataPoints(self, params):\\n varsByCalc = self.GetExperimentCollection().GetVarsByCalc()\\n self.calcVals, self.calcSensitivityVals =\\\\\\n self.GetCalculationCollection().CalculateSensitivity(varsByCalc,\\n params)\\n return self.calcSensitivityVals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"634c80092e8842c970c8514e12302f79\",\n \"score\": \"0.5538802\",\n \"text\": \"def calcSignal(self) -> Tuple[SpectralQty, float]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b24be98c7eb1f222b9465254c6656b10\",\n \"score\": \"0.5521443\",\n \"text\": \"def compute(cls, real_data, synthetic_data):\\n real_data = pd.Series(real_data).dropna()\\n synthetic_data = pd.Series(synthetic_data).dropna()\\n\\n if len(synthetic_data) == 0:\\n return 0\\n\\n f_obs, f_exp = get_frequencies(real_data, synthetic_data)\\n total_variation = 0\\n for i in range(len(f_obs)):\\n total_variation += abs(f_obs[i] - f_exp[i])\\n\\n return 1 - 0.5 * total_variation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd9f319765bed022ccb443a27718a9fa\",\n \"score\": \"0.55095655\",\n \"text\": \"def val_eq(self):\\n\\n # sum of present values of all cash flows\\n sum_pv = self.pv_cf()\\n\\n # Probability of failure\\n fail_prob = self.inp_params['failure_prob']\\n # Proceeds if firm fails\\n proceeds = self.inp_params['proceeds']*self.inp_params['val_of_proceeds']\\n\\n # Value of operating assets\\n val_oa = sum_pv*(1. - fail_prob)\\n\\n # Value of equity\\n val_eq = val_oa - self.inp_params['bk_val_debt'] + self.inp_params['cash_eq']\\n\\n # Value of options\\n #val_op = options_value()\\n val_op = 643.8\\n\\n # common equity value or intrinsic value\\n val_eq_common = val_eq - val_op\\n\\n # common equity value per share\\n val_per_share = val_eq_common/self.inp_params['outstanding_shares']\\n\\n # Price as a % of value\\n p_to_val = self.inp_params['curr_stock_price']/val_per_share\\n\\n return val_per_share,p_to_val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78bf3697bcf87c5c94a4b5379948b734\",\n \"score\": \"0.55048\",\n \"text\": \"def computeQValueFromValues(self, state, action):\\n \\\"*** YOUR CODE HERE ***\\\"\\n Q_Average = 0\\n if self.mdp.isTerminal(state):\\n return 0\\n nextStateAndPorb = self.mdp.getTransitionStatesAndProbs(state, action)\\n for pair in nextStateAndPorb:\\n Q_Average = Q_Average + pair[1] * (self.mdp.getReward(state, action, pair[0]) +\\\\\\n self.discount * self.getValue(pair[0]))\\n return Q_Average\\n util.raiseNotDefined()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0169b44ce63b4b146a55466bdb532664\",\n \"score\": \"0.5496748\",\n \"text\": \"def all_evaluate(self):\\r\\n #self.compute_euclidian_distance()\\r\\n # print(\\\"Euclidian distance : \\\", self.euclidian_distance)\\r\\n self.compute_MAE()\\r\\n print(\\\"MAE : \\\", self.MAE)\\r\\n self.compute_RMSE()\\r\\n print(\\\"RMSE : \\\", self.RMSE)\\r\\n #self.compute_SSIM()\\r\\n\\r\\n #self.compute_UQI()\\r\\n # print(\\\"UQI : \\\", self.UQI)\\r\\n #self.compute_VIF()\\r\\n #print(\\\"VIF : \\\", self.VIF)\\r\\n return self.MAE, self.RMSE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a06b27bde45012008083bf67133d907c\",\n \"score\": \"0.54588586\",\n \"text\": \"def calc_values(self):\\n atm_contract_index = (\\n np.abs(self.chain[\\\"strike\\\"] - self.underlying_price)\\n ).idxmin()\\n atm_impliedvol = self.chain.iloc[atm_contract_index][\\\"impvol\\\"]\\n\\n # Calculate option value for all options using ATM volatility\\n self.chain[\\\"model_value\\\"] = self.chain.apply(\\n lambda x: bs_price(\\n x[\\\"right\\\"],\\n x[\\\"underprice\\\"],\\n x[\\\"strike\\\"],\\n self.dte / 252,\\n atm_impliedvol,\\n self.risk_free_rate,\\n ),\\n axis=1,\\n )\\n self.chain[\\\"mid_price\\\"] = (self.chain[\\\"bid\\\"] + self.chain[\\\"ask\\\"]) / 2\\n self.chain[\\\"skew_premium\\\"] = self.chain[\\\"mid_price\\\"] - self.chain[\\\"model_value\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"114c5912cf93838002a78a87daa0d4ca\",\n \"score\": \"0.5439486\",\n \"text\": \"def vat_rate():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47e222c63e534fa6cf01c3726ed9664e\",\n \"score\": \"0.54364234\",\n \"text\": \"def value(self) -> float:\\n return self.days * float(self.rate)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23e08cd32e8d42a3759c37b082d0aece\",\n \"score\": \"0.5421589\",\n \"text\": \"def pmt(rate,nper,pv,fv,when):\\n \\n \\n return ndarray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f52bb576eb8a97c5b98a25fa475c6991\",\n \"score\": \"0.54204094\",\n \"text\": \"def evaluate(self, ts):\\n print(ts)\\n phases = PI2 * self.freq * ts + self.offset\\n print(phases)\\n ys = self.amp * np.exp(1j * phases)\\n return ys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2bbf8d19fb08451ca7f539dfe0ba308\",\n \"score\": \"0.54171187\",\n \"text\": \"def pulse_ratio(pickle_file, neuron_type, neuron_index, var):\\n\\n net = unpickle_net(pickle_file)\\n\\n spike_times = extract_presynaptic_spikes(pickle_file, neuron_type, neuron_index, net)\\n # print(spike_times)\\n if var == \\\"V\\\":\\n mon = [x[neuron_index]/volt for x in net['{}_V_mon'.format(neuron_type)]['V']]\\n if var == \\\"Ge\\\":\\n mon = [x[neuron_index] for x in net['{}_Ge_total_mon'.format(neuron_type)]['Ge_total']]\\n if var == \\\"Gi\\\":\\n mon = [x[neuron_index] for x in net['{}_Gi_total_mon'.format(neuron_type)]['Gi_total']]\\n\\n responses = extract_responses(spike_times, mon)\\n PPR = [float(x)/float(responses[0]) for x in responses]\\n print(PPR)\\n return responses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70fffbb5d0eb840bc63ce7493f050394\",\n \"score\": \"0.5381214\",\n \"text\": \"def Pfe(self, num_si_events, p1=None):\\r\\n if p1 is None: p1 = self.Pr()\\r\\n ne = num_si_events\\r\\n return sum(-(-n/n)**n * p1**n * scipy.special.comb(ne,n) for n in range(1,ne+1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9595658251fffa46a956e0564edb0d3\",\n \"score\": \"0.53695977\",\n \"text\": \"def compute_estimates(self):\\n rate_estimate_list = []\\n for tau in range(len(self.new_infection_data) - 11):\\n estimate = 0\\n for j in range(7):\\n estimate += self.new_infection_data[tau + j + 4] / self.new_infection_data[tau + j]\\n rate_estimate_list.append(estimate)\\n rate_estimates = np.array(rate_estimate_list)\\n self.infection_rate_estimate = rate_estimates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"498a5c77e9d1bc8f1672a1d9a6a95cef\",\n \"score\": \"0.5363381\",\n \"text\": \"def val_signals(df_prices, df_income_ttm, df_cashflow_ttm,\\n shares_index=SHARES_DILUTED):\\n\\n # Create a DataFrame with the financial data we need.\\n #calculate sales per share and earning per share and free cash flow per share\\n df = df_income_ttm[[REVENUE, NET_INCOME_COMMON]].copy()\\n df[FCF] = df_cashflow_ttm[NET_CASH_OPS] + df_cashflow_ttm[CAPEX]\\n df_per_share = df.div(df_income_ttm[shares_index], axis=0)\\n \\n # Reindex the per-share financial data to daily data-points.\\n df_daily = sf.reindex(df_src=df_per_share,\\n df_target=df_prices,\\n method='ffill')\\n \\n # Create new DataFrame for the signals. Setting the index improves performance.\\n df_signals = pd.DataFrame(index=df_prices.index)\\n \\n # Use the closing share-price for all these signals.\\n df_price = df_prices[CLOSE]\\n \\n # P/Sales ratio.\\n df_signals[PSALES] = df_price / df_daily[REVENUE]\\n \\n # P/E ratio.\\n df_signals[PE] = df_price / df_daily[NET_INCOME_COMMON]\\n\\n # P/FCF ratio.\\n df_signals[PFCF] = df_price / df_daily[FCF]\\n\\n return df_signals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6682c8fad841bf49d023538bc2ba8bc\",\n \"score\": \"0.5337668\",\n \"text\": \"def calc_q_values(self, state):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cc336fab9470fdea1e22392ad9abf8d\",\n \"score\": \"0.5329119\",\n \"text\": \"def evaluate(self, params: np.ndarray) -> float:\\n kl = 0\\n for sample in self.data:\\n kl += np.log(self.vgbs.prob_sample(params, sample))\\n return -kl / self.nr_samples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc3a60f7a4004fcbc61dab39f721f084\",\n \"score\": \"0.53238505\",\n \"text\": \"def iterate(self,plot=True):\\n # Create empty arrays to store values\\n F = np.zeros(len(self.y))\\n a = np.zeros(len(self.y))\\n v = np.zeros(len(self.y))\\n P = np.zeros(len(self.y))\\n # Initialize at the initial values parsed to the class\\n P[0] = self.pardict['P1']\\n a[0] = self.y[0]\\n sigma_eps2 = self.pardict['sigma_eps2']\\n sigma_eta2 = self.pardict['sigma_eta2']\\n # Iterate \\n for t in range(0,len(self.y)-1):\\n F[t] = P[t]+sigma_eps2\\n # K is defined as ratio of P and F\\n Kt = P[t]/F[t] if np.isfinite(self.y[t]) else 0\\n v[t] = self.y[t]-a[t]\\n a[t+1] = a[t] + np.nan_to_num(Kt*v[t])\\n F[t] = P[t]+sigma_eps2\\n P[t+1] = P[t]*(1-Kt)+sigma_eta2\\n F[-1] = P[-1]+sigma_eps2\\n v[-1] = self.y[-1]-a[-1]\\n # Obtain std error of prediction form variance\\n std = np.sqrt((P*sigma_eps2)/(P+sigma_eps2))\\n \\n if plot:\\n fig_name = self.var_name + 'Fig26.pdf'\\n plot_fig2_6(self.times, self.y, std, P, a, F,fig_name, self.var_name)\\n return a, std, P, v, F\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b8e6c668b0d352fbae386fa33e8f632\",\n \"score\": \"0.53229123\",\n \"text\": \"def _ev(self) -> float:\\n return explained_variance_score(\\n y_true=self.y_true,\\n y_pred=self.y_pred,\\n multioutput=self.multioutput,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba918ad0b2a1c18ddf0786fa58fe288c\",\n \"score\": \"0.5317666\",\n \"text\": \"def comp_pr(is_stationary, signal, fs, prominence=True):\\n\\n # Prominence criteria\\n freqs = np.arange(90, 11200, 100)\\n limit = np.zeros((len(freqs)))\\n for i in range(len(freqs)):\\n if freqs[i] >= 89.1 and freqs[i] < 1000:\\n limit[i] = 9 + 10 * np.log10(1000 / freqs[i])\\n if freqs[i] >= 1000 and freqs[i] < 11200:\\n limit[i] = 9\\n\\n if is_stationary == True:\\n tones_freqs, pr, prom, t_pr = pr_main_calc(signal, fs)\\n tones_freqs = tones_freqs.astype(int)\\n\\n if prominence == True:\\n output = {\\n \\\"name\\\": \\\"tone-to-noise ratio\\\",\\n \\\"freqs\\\": tones_freqs[prom],\\n \\\"values\\\": pr[prom],\\n \\\"prominence\\\": True,\\n \\\"global value\\\": t_pr,\\n }\\n\\n else:\\n output = {\\n \\\"name\\\": \\\"tone-to-noise ratio\\\",\\n \\\"freqs\\\": tones_freqs,\\n \\\"values\\\": pr,\\n \\\"prominence\\\": prom,\\n \\\"global value\\\": t_pr,\\n }\\n\\n elif is_stationary == False:\\n # Signal cut in frames of 500 ms along the time axis\\n n = 0.5 * fs\\n nb_frame = math.floor(signal.size / n)\\n time = np.linspace(0, len(signal) / fs, num=nb_frame)\\n time = np.around(time, 1)\\n\\n # Initialization of the result arrays\\n tones_freqs = np.zeros((nb_frame), dtype=list)\\n pr = np.zeros((nb_frame), dtype=list)\\n prom = np.zeros((nb_frame), dtype=list)\\n t_pr = np.zeros((nb_frame))\\n\\n # Compute PR values along time\\n for i_frame in range(nb_frame):\\n segment = signal[int(i_frame * n) : int(i_frame * n + n)]\\n (\\n tones_freqs[i_frame],\\n pr[i_frame],\\n prom[i_frame],\\n t_pr[i_frame],\\n ) = pr_main_calc(segment, fs)\\n\\n # Store the results in a time vs frequency array\\n freq_axis = np.logspace(np.log10(90), np.log10(11200), num=1000)\\n results = np.zeros((len(freq_axis), nb_frame))\\n promi = np.zeros((len(freq_axis), nb_frame), dtype=bool)\\n\\n if prominence == True:\\n for t in range(nb_frame):\\n for f in range(len(tones_freqs[t])):\\n if prom[t][f] == True:\\n ind = np.argmin(np.abs(freq_axis - tones_freqs[t][f]))\\n results[ind, t] = pr[t][f]\\n promi[ind, t] = True\\n else:\\n for t in range(nb_frame):\\n for f in range(len(tones_freqs[t])):\\n ind = np.argmin(np.abs(freq_axis - tones_freqs[t][f]))\\n results[ind, t] = pr[t][f]\\n promi[ind, t] = prom[t][f]\\n\\n output = {\\n \\\"name\\\": \\\"prominence ratio\\\",\\n \\\"time\\\": time,\\n \\\"freqs\\\": freq_axis,\\n \\\"values\\\": results,\\n \\\"prominence\\\": promi,\\n \\\"global value\\\": t_pr,\\n }\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d981270b716db45ec7dd988c4889158\",\n \"score\": \"0.53150016\",\n \"text\": \"def MSE(pred_signals, true_signals):\\n\\n dim, n = np.shape(true_signals)\\n pred_mean = np.mean(pred_signals,axis=1).reshape(dim, 1)\\n pred_std = np.std(pred_signals,axis=1).reshape(dim, 1)\\n true_mean = np.mean(true_signals,axis=1).reshape(dim, 1)\\n true_std = np.std(true_signals,axis=1).reshape(dim, 1)\\n #scale data to have unit variance 1/n*y@y.T=1\\n pred_signals = np.divide((pred_signals - pred_mean), pred_std)\\n true_signals = np.divide((true_signals - true_mean), true_std)\\n MSE = np.zeros(dim)\\n MSE_matrix1 = np.zeros((dim, dim))\\n MSE_matrix2 = np.zeros((dim, dim))\\n\\n #calculate MSE between all estimated signals with changed sign and true signals and store in array\\n for i in range(0, dim):\\n for j in range(0, dim):\\n MSE_matrix1[i, j] = mean_squared_error(true_signals[j],pred_signals[i])\\n #calculate MSE between all estimated and true signals and store in array\\n for i in range(0, dim):\\n for j in range(0, dim):\\n MSE_matrix2[i, j] = mean_squared_error(true_signals[j],-pred_signals[i])\\n \\n #find minima of all possible MSE values\\n array_min1 = MSE_matrix1.min(axis=1)\\n array_min2 = MSE_matrix2.min(axis=1)\\n\\n #store minima in separate array\\n for i in range(dim):\\n if(array_min1[i]>array_min2[i]):\\n MSE[i] = array_min2[i]\\n else:\\n MSE[i] = array_min1[i]\\n\\n return MSE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7127eb6f05cd675f434d14a9ef15bd82\",\n \"score\": \"0.53097785\",\n \"text\": \"def evaluate(self):\\r\\n self.evals, self.evects = ln.eigh(self.A)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4289c7e61101b80ae13d42841f9f1813\",\n \"score\": \"0.53085506\",\n \"text\": \"def pv(flows, r):\\n dates=flows.index\\n discounts=discount(dates, r)\\n return discounts.multiply(flows, axis=0).sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcddd84e3f3d07df539687f555afaa24\",\n \"score\": \"0.52983016\",\n \"text\": \"def exp(self) -> Series:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a6f85d19d8b3dda854bcf07cb730af8\",\n \"score\": \"0.5295143\",\n \"text\": \"def calculate_signals(self, event):\\n if event.type == EventType.BAR:\\n self._set_correct_time_and_price(event)\\n \\n # Only trade if we have both observations\\n if all(self.latest_prices > -1.0):\\n # Create the observation matrix of the latest prices\\n # of TLT and the intercept value (1.0) as well as the\\n # scalar value of the latest price from IEI\\n F = np.asarray([self.latest_prices[0], 1.0]).reshape((1, 2))\\n y = self.latest_prices[1]\\n \\n # The prior value of the states \\\\theta_t is\\n # distributed as a multivariate Gaussian with\\n # mean a_t and variance-covariance R_t\\n if self.R is not None:\\n self.R = self.C + self.wt\\n else:\\n self.R = np.zeros((2, 2))\\n\\n # Calculate the Kalman Filter update\\n # ----------------------------------\\n # Calculate prediction of new observation\\n # as well as forecast error of that prediction\\n yhat = F.dot(self.theta)\\n et = y - yhat\\n\\n # Q_t is the variance of the prediction of\\n # observations and hence \\\\sqrt{Q_t} is the\\n # standard deviation of the predictions\\n Qt = F.dot(self.R).dot(F.T) + self.vt\\n sqrt_Qt = np.sqrt(Qt)\\n\\n # The posterior value of the states \\\\theta_t is\\n # distributed as a multivariate Gaussian with mean\\n # m_t and variance-covariance C_t\\n At = self.R.dot(F.T) / Qt\\n self.theta = self.theta + At.flatten() * et\\n self.C = self.R - At * F.dot(self.R)\\n \\n # Finally we generate the trading signals based on the values of etet and Qt−−√Qt. To do this we need to check what the \\\"invested\\\" status is - either \\\"long\\\", \\\"short\\\" or \\\"None\\\". \\n # Notice how we need to adjust the cur_hedge_qty current hedge quantity when we go long or short as the slope θt0 is constantly adjusting in time:\\n \\n # Only trade if days is greater than a \\\"burn in\\\" period\\n if self.days > 1:\\n # If we're not in the market...\\n if self.invested is None:\\n if et < -sqrt_Qt:\\n # Long Entry\\n print(\\\"LONG: %s\\\" % event.time)\\n self.cur_hedge_qty = int(floor(self.qty*self.theta[0]))\\n self.events_queue.put(SignalEvent(self.tickers[1], \\\"BOT\\\", self.qty))\\n self.events_queue.put(SignalEvent(self.tickers[0], \\\"SLD\\\", self.cur_hedge_qty))\\n self.invested = \\\"long\\\"\\n elif et > sqrt_Qt:\\n # Short Entry\\n print(\\\"SHORT: %s\\\" % event.time)\\n self.cur_hedge_qty = int(floor(self.qty*self.theta[0]))\\n self.events_queue.put(SignalEvent(self.tickers[1], \\\"SLD\\\", self.qty))\\n self.events_queue.put(SignalEvent(self.tickers[0], \\\"BOT\\\", self.cur_hedge_qty))\\n self.invested = \\\"short\\\"\\n # If we are in the market...\\n if self.invested is not None:\\n if self.invested == \\\"long\\\" and et > -sqrt_Qt:\\n print(\\\"CLOSING LONG: %s\\\" % event.time)\\n self.events_queue.put(SignalEvent(self.tickers[1], \\\"SLD\\\", self.qty))\\n self.events_queue.put(SignalEvent(self.tickers[0], \\\"BOT\\\", self.cur_hedge_qty))\\n self.invested = None\\n elif self.invested == \\\"short\\\" and et < sqrt_Qt:\\n print(\\\"CLOSING SHORT: %s\\\" % event.time)\\n self.events_queue.put(SignalEvent(self.tickers[1], \\\"BOT\\\", self.qty))\\n self.events_queue.put(SignalEvent(self.tickers[0], \\\"SLD\\\", self.cur_hedge_qty))\\n self.invested = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6946de9557bdc512640937b2d86db275\",\n \"score\": \"0.52889144\",\n \"text\": \"def calcularPrecio(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6946de9557bdc512640937b2d86db275\",\n \"score\": \"0.52889144\",\n \"text\": \"def calcularPrecio(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99ebcde69abad3e3eafe40a8f0a228eb\",\n \"score\": \"0.52832043\",\n \"text\": \"def compute(self):\\n self.Fi = self.FI(self.X, self.eta, self.nu)\\n self.pmf = self.Pv(self.L)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52fbe8f19348def0fc1ce0d70e49844f\",\n \"score\": \"0.52728665\",\n \"text\": \"def _calculate_p_value(self, U):\\n K = len(U)\\n p_value = 2 * np.exp(\\n (-6 * np.power(np.max(np.abs(U)), 2)) / (np.power(K, 2) + np.power(K, 3))\\n )\\n\\n return p_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36243b482d740c465be81a12aeef1adb\",\n \"score\": \"0.5260407\",\n \"text\": \"def detAcceptanceRateValues(self):\\n if(not(self.weight)):\\n for nodej in self.g.nodes():\\n WeightedSum = len(self.g.in_edges(nodej))\\n for edge in self.g.in_edges(nodej):\\n self.A[edge[0] + \\\"-\\\"+edge[1]] = (1.0/self.NormA[nodej])\\n else:\\n for nodej in self.g.nodes():\\n for edge in self.g.in_edges(nodej):\\n self.A[edge[0] + \\\"-\\\"+edge[1]] = (self.g.get_edge_data(edge[0],edge[1]) + 0.0)/self.NormA[nodej]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbf7aa94bc00a27f8909b581c8022685\",\n \"score\": \"0.5259184\",\n \"text\": \"def PoP(data, **kwargs):\\n \\n # Correspondance tables\\n CORRES_B = {'D':1, 'M':21, 'Q':63, 'H':130, 'A':262} #business days\\n CORRES_D = {'D':1, 'M':30, 'Q':91, 'H':182, 'A':365} #calendar days\\n CORRES_M = {'M':1, 'Q':3, 'H':6, 'A':12}\\n CORRES_Q = {'Q':1, 'H':2, 'A':4}\\n CORRES_LABEL = {'D':'DoD', 'M':'MoM', 'Q':'QoQ', 'H':'HoH', 'A':'YoY'}\\n\\n if \\\"per\\\" in kwargs:\\n period = kwargs[\\\"per\\\"].upper()\\n else:\\n period = 'A'\\n \\n if \\\"freq\\\" in kwargs:\\n frequency = kwargs[\\\"freq\\\"].upper()\\n else:\\n frequency = 'D'\\n\\n #map correspondance tables\\n if frequency == 'D':\\n CORRES = CORRES_D\\n elif frequency == 'B':\\n CORRES = CORRES_B\\n elif frequency == 'M':\\n CORRES = CORRES_M\\n elif frequency == 'Q':\\n CORRES = CORRES_Q\\n \\n meth = 'ratio'\\n if \\\"method\\\" in kwargs:\\n if kwargs[\\\"method\\\"] == 'diff':\\n meth = kwargs[\\\"method\\\"]\\n \\n #Converts series to DataFrame if necessary\\n if isinstance(data, pd.Series):\\n data = data.to_frame(data.name)\\n \\n # Aligns frequency and shift the second series by the period\\n data = data.asfreq(frequency, method='pad')\\n data_per = data.shift(CORRES[period])\\n if meth == 'ratio':\\n result = data/data_per-1\\n elif meth == 'diff':\\n result = data - data_per\\n result.columns = [list(result)[0] + ' ' + CORRES_LABEL[period]]\\n # Returns the period over period transformation\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fccbc7b444b581ab06c04ed20f0959de\",\n \"score\": \"0.5256896\",\n \"text\": \"def calculate(self):\\n scorer_class_name = self.get_root().scorer_class.name\\n\\n if len(self.labels) == 0:\\n raise ValueError(\\n \\\"No data present across all \\\"\\n \\\"sequencing libraries [{}]\\\".format(self.name)\\n )\\n\\n for label in self.labels:\\n self.merge_counts_unfiltered(label)\\n self.filter_counts(label)\\n\\n if self.is_barcodevariant() or self.is_barcodeid():\\n self.combine_barcode_maps()\\n\\n self.ensure_main_count_tables_exist_and_populated()\\n self.timepoints_contain_variants()\\n\\n if \\\"Demo\\\" in scorer_class_name:\\n raise ValueError(\\n 'Invalid scoring method \\\"{}\\\" '\\n \\\"[{}]\\\".format(scorer_class_name, self.name)\\n )\\n\\n if \\\"Regression\\\" in scorer_class_name and len(self.timepoints) <= 2:\\n raise ValueError(\\n \\\"Regression-based scoring \\\" \\\"requires three or more time points.\\\"\\n )\\n\\n scorer = self.get_root().scorer_class(\\n store_manager=self, options=self.get_root().scorer_class_attrs\\n )\\n scorer.run()\\n\\n # TODO: Write outlier computation as a plugin?\\n non_allowed_methods = (\\\"Counts Only\\\", \\\"Demo\\\")\\n scoring_method = scorer_class_name\\n\\n if scoring_method not in non_allowed_methods and self.component_outliers:\\n if self.is_barcodevariant() or self.is_barcodeid():\\n self.calc_outliers(\\\"barcodes\\\")\\n if self.is_coding():\\n self.calc_outliers(\\\"variants\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f2edfc1bbde94d815ef108a7855afcd\",\n \"score\": \"0.52566755\",\n \"text\": \"def _evalue_RR(self, est, lo=None, hi=None, no_effect_baseline=1):\\n if est < 0:\\n raise ValueError(\\\"Risk/Rate Ratio cannot be negative\\\")\\n if no_effect_baseline < 0:\\n raise ValueError(\\\"no_effect_baseline value is impossible\\\")\\n if no_effect_baseline != 1:\\n self.logger.info(\\n 'You are calculating a \\\"non-null\\\" E-value, i.e., an E-value for the minimum amount of unmeasured '\\n \\\"confounding needed to move the estimate and confidence interval to your specified no_effect_baseline value \\\"\\n \\\"rather than to the null value.\\\"\\n )\\n if lo is not None and hi is not None:\\n if lo > hi:\\n raise ValueError(\\\"Lower confidence limit should be less than upper confidence limit\\\")\\n if lo is not None and est < lo:\\n raise ValueError(\\\"Point estimate should be inside confidence interval\\\")\\n if hi is not None and est > hi:\\n raise ValueError(\\\"Point estimate should be inside confidence interval\\\")\\n\\n e_est = self._threshold(est, no_effect_baseline=no_effect_baseline)\\n e_lo = self._threshold(lo, no_effect_baseline=no_effect_baseline)\\n e_hi = self._threshold(hi, no_effect_baseline=no_effect_baseline)\\n\\n # if CI crosses null, set its E-value to 1\\n null_CI = False\\n if est > no_effect_baseline and lo is not None:\\n null_CI = lo < no_effect_baseline\\n if est < no_effect_baseline and hi is not None:\\n null_CI = hi > no_effect_baseline\\n if null_CI:\\n e_lo = np.float64(1)\\n e_hi = np.float64(1)\\n\\n # only report E-value for CI limit closer to null\\n if lo is not None or hi is not None:\\n if est > no_effect_baseline:\\n e_hi = None\\n else:\\n e_lo = None\\n\\n return {\\n \\\"converted_estimate\\\": est,\\n \\\"converted_lower_ci\\\": lo,\\n \\\"converted_upper_ci\\\": hi,\\n \\\"evalue_estimate\\\": e_est,\\n \\\"evalue_lower_ci\\\": e_lo,\\n \\\"evalue_upper_ci\\\": e_hi,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e5a456bf36237fe05f66878a584a056\",\n \"score\": \"0.52539456\",\n \"text\": \"def set_rsi(self):\\n day_range = self.day_ranges.get('rsi', [14])\\n dict = {}\\n mtm = self.momentum[[f'{t}_momentum_1d' for t in self.tickers]]\\n up = np.maximum(mtm, 0)\\n down = -np.minimum(mtm, 0)\\n for d in day_range:\\n count_ups = (up > 0).rolling(window=d).sum()\\n count_downs = (down > 0).rolling(window=d).sum()\\n up_avg = up.rolling(window=d).sum() / count_ups\\n down_avg = down.rolling(window=d).sum() / count_downs\\n dict[d] = up_avg / (up_avg + down_avg)\\n self.rsi = self.get_dataframe_from_dict(dict, 'rsi')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69713d3a408f8f71087b5f3ec4592ff3\",\n \"score\": \"0.5253733\",\n \"text\": \"def sipm_enf(x, spe, spe_sigma, opct, pap, dap):\\n sap = spe_sigma # Assume the sigma of afterpulses is the same\\n\\n pe_signal = np.zeros(x.size)\\n\\n # Loop over the possible total number of cells fired\\n for k in range(1, 250):\\n pk = (1-opct) * pow(opct, k-1)\\n\\n # Consider probability of afterpulses\\n papk = pow(1 - pap, k)\\n p0ap = pk * papk\\n pap1 = pk * (1-papk) * papk\\n\\n # Combine spread of pedestal and pe (and afterpulse) peaks\\n pe_sigma = np.sqrt(k * spe_sigma ** 2)\\n ap_sigma = np.sqrt(k * sap ** 2)\\n\\n # Evaluate probability at each value of x\\n pe_signal += (\\n p0ap * norm.pdf(x, k * spe, pe_sigma) +\\n pap1 * norm.pdf(x, k * spe * (1 - dap), ap_sigma)\\n )\\n\\n return pe_signal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68d8900ee63fca39075decc55671304a\",\n \"score\": \"0.5247868\",\n \"text\": \"def evaluate( self, tick, value, lastTick, lastValue ):\\n for resample in self.resamples:\\n resample.evaluate( tick, value, lastTick, lastValue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9adabd60833c423964af2657fe4d22f\",\n \"score\": \"0.523737\",\n \"text\": \"def calculation_and_save(self):\\r\\n # deallocate memory.\\r\\n gc.collect()\\r\\n\\r\\n prob_data = np.zeros((self.out_column, self.out_row), np.float64)\\r\\n ln_prob_data = np.zeros((self.out_column, self.out_row), np.float64)\\r\\n\\r\\n for j in range(int(np.log(self.out_column) / np.log(2)) + 1):\\r\\n # calculate the first probability matrix.\\r\\n if j == 0:\\r\\n (prob_data, ln_prob_data) = self._calculate_the_probability(self.data, (1, 1))\\r\\n # calculate other probability based on the previous matrix.\\r\\n elif j > 0:\\r\\n (prob_data, ln_prob_data) = self._calculate_from_old_prob_data(self.prob_data_list[j - 1][0],(2, 2))\\r\\n self.prob_data_list.append([prob_data, ln_prob_data])\\r\\n\\r\\n #### calculate multifractal parameters\\r\\n self.parameters, self.parameters_calculation, self.miu_q_list_list = self._calculate_the_spectrum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"482a9a01bd7535f41ffc8145d128bac3\",\n \"score\": \"0.52369905\",\n \"text\": \"def calculate_signals(self, event):\\n sym = self.symbol_list[0]\\n dt = self.datetime_now\\n\\n if event.type == 'Market':\\n self.bar_index += 1\\n if self.bar_index >5:\\n lags = self.bars.get_latest_bars_value(\\n self.symbol_list[0], \\\"returns\\\", N=3\\n )\\n pred_series = pd.Series(\\n {\\n 'Lag1': lags[1]*100.0,\\n 'Lag2': lags[2]*100.0\\n }\\n )\\n pred = self.model.predict(pred_series)\\n if pred > 0 and not self.long_market:\\n self.long_market = True\\n signal = ASignalEvent(1, sym, dt, 'LONG', 1.0)\\n self.events.put(signal)\\n\\n if pred < 0 and self.long_market:\\n self.long_market = False\\n signal = SignalEvent(1, sym, dt, 'EXIT', 1.0)\\n self.events.put(signal)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f70ade2a39767a02c4cf6187be746b7c\",\n \"score\": \"0.5228082\",\n \"text\": \"def mirr(values,finance_rate,reinvest_rate):\\n \\n \\n return float()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cef5da13462f5e26e95fcf593fa61ff\",\n \"score\": \"0.522527\",\n \"text\": \"def momentum_average_and_standard_deviation(self):\\r\\n F = np.fft.fft(self.x)\\r\\n prob = np.abs(F)**2\\r\\n if np.max(prob) != 0.0:\\r\\n prob = prob/np.sum(prob)\\r\\n freq = np.fft.fftfreq(self.N, d=self.dx)\\r\\n p = 2*np.pi*freq*self.hbar/self.L\\r\\n\\r\\n expval = np.sum(np.dot(p, prob))\\r\\n expval2 = np.sum(np.dot(p**2, prob))\\r\\n sigma = np.sqrt(expval2 - expval**2)\\r\\n return (expval, sigma)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c22f2110345eb960e9d39f86f484017e\",\n \"score\": \"0.52177155\",\n \"text\": \"def calculate_ensquared_energy(PSF_model, wave, N_trials, N_rms,\\n rms_amplitude, nominal_scale, spaxel_scales, rescale_coeff=None):\\n\\n N_zern = PSF_model.N_coef\\n ensquared_results = []\\n\\n a_min = 1.0 if rescale_coeff is None else rescale_coeff\\n\\n print(\\\"Calculating Ensquared Energy\\\")\\n for scale in spaxel_scales: # Loop over each Spaxel Scale [mas]\\n print(\\\"%d mas spaxel scale\\\" % scale)\\n\\n data = np.zeros((2, N_trials * N_rms))\\n amplitudes = np.linspace(0.0, rms_amplitude, N_rms)\\n i = 0\\n p0, s0 = PSF_model.compute_PSF(np.zeros(N_zern)) # Nominal PSF\\n # Calculate the EE for the nominal PSF so that you can compare\\n EE0 = ensquared_one_pix(p0, pix_scale=nominal_scale, new_scale=2*scale, plot=False)\\n\\n # 60x120\\n # EE0 = ensquared_rectangle(p0, pix_scale=nominal_scale, new_scale=60, plot=False)\\n\\n for amp in amplitudes: # Loop over coefficient strength\\n\\n for k in range(N_trials): # For each case, repeat N_trials\\n\\n c_act = np.random.uniform(-amp, amp, size=N_zern)\\n rescale = np.linspace(1, a_min, N_zern)\\n c_act *= rescale\\n phase_flat = np.dot(PSF_model.model_matrix_flat, c_act)\\n rms = wave * 1e3 * np.std(phase_flat)\\n p, s = PSF_model.compute_PSF(c_act)\\n EE = ensquared_one_pix(p, pix_scale=nominal_scale, new_scale=2*scale, plot=False)\\n # EE = ensquared_rectangle(p, pix_scale=nominal_scale, new_scale=60, plot=False)\\n dEE = EE / EE0 * 100\\n data[:, i] = [rms, dEE]\\n i += 1\\n\\n ensquared_results.append(data)\\n\\n return ensquared_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0318516352678fc935c8e852419e69e8\",\n \"score\": \"0.5211374\",\n \"text\": \"def average(self):\\n n,S = 0,0\\n for yi in (self.y):\\n n = n + 1\\n S = S + yi\\n return S/n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eabe5526ca1fe5076643b90534da2c7\",\n \"score\": \"0.5200672\",\n \"text\": \"def sens(self):\\n print(f'Sensitivity is {self.tp / (self.tp + self.fn)}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e979e07d334e57cf388208a6430a1b1\",\n \"score\": \"0.5196765\",\n \"text\": \"def ipmt(rate,per,nper,pv,fv,when):\\n \\n \\n return ndarray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c03588f476c22ddb06ffa942462d8a72\",\n \"score\": \"0.5186421\",\n \"text\": \"def calc_stats(self):\\n self.happiness += (self.time_delta * self.happiness_rate)\\n if self.happiness > self.happiness_max:\\n self.happiness = self.happiness_max\\n elif self.happiness < 0:\\n self.happiness = 0\\n\\n self.satiation += (self.time_delta * self.satiation_rate)\\n if self.satiation > self.satiation_max:\\n self.satiation = self.satiation_max\\n elif self.satiation < 0:\\n self.satiation = 0\\n\\n self.energy += (self.time_delta * self.energy_rate)\\n if self.energy > self.energy_max:\\n self.energy = self.energy_max\\n elif self.energy < 0:\\n self.energy = 0\\n\\n self.health += (self.time_delta * self.health_rate)\\n if self.health > self.health_max:\\n self.health = self.health_max\\n elif self.health < 0:\\n self.health = 0\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ffb565258728f618fe9cdcd836df45\",\n \"score\": \"0.51852655\",\n \"text\": \"def Pv(self, l=None, e=None, n=None, Q=None):\\n if l is None:\\n l = self.L\\n if e is None:\\n e = self.eta\\n if n is None:\\n n = self.nu\\n if Q is not None:\\n #print(\\\"Before quantization:\\\", len(self.data))\\n \\\"\\\"\\\"\\n warnings.filterwarnings('error')\\n try:\\n print(\\\"On try\\\")\\n div = self.data / Q\\n print(\\\"div\\\")\\n diq = (div).astype('int')\\n print(\\\"diq\\\")\\n mask = ((div - diq).astype('bool') * -1 + 1).astype('bool')\\n print(\\\"mask\\\")\\n self.data = self.data[mask]\\n print(\\\"new_data\\\")\\n except RuntimeWarning as w:\\n print(w)\\n warnings.filterwarnings('ignore')\\n div = self.data / Q\\n diq = (div).astype('int')\\n mask = ((div - diq).astype('bool') * -1 + 1).astype('bool')\\n self.data = self.data[mask]\\n print( len( self.data[ np.isnan(self.data) == True ] ) )\\n print(\\\"After quantization:\\\", len(self.data))\\n\\n warnings.filterwarnings('default')\\n \\\"\\\"\\\"\\n #print(\\\"Q:\\\", Q)\\n #print(\\\"self.Q:\\\", self.Q)\\n div = self.data / Q\\n #print(div)\\n diq = (div).astype('int')\\n #print(diq)\\n mask = ((div - diq).astype('bool') * -1 + 1).astype('bool')\\n #print(mask)\\n self.data = self.data[mask]\\n self.lower_bound = int(np.floor( np.min(self.data) /2 ) * 2)\\n self.upper_bound = int(np.floor( np.max(self.data) /2 ) * 2 +1)\\n self.L = np.arange(self.lower_bound, self.upper_bound+1).astype('int') # an interval of integer values. Used to compute the pmf\\n #print(self.data)\\n \\n return self._pv(l, e, n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a23ebc86256acecb6326674a6483616f\",\n \"score\": \"0.5184817\",\n \"text\": \"def detRejectanceRateValues(self):\\n if(not(self.weight)):\\n for nodej in self.g.nodes():\\n for edge in self.g.out_edges(nodej):\\n self.R[edge[0] + \\\"-\\\"+edge[1]] = (1.0/self.NormR[nodej])\\n else:\\n for nodej in self.g.nodes():\\n for edge in self.g.out_edges(nodej):\\n self.R[edge[0] + \\\"-\\\"+edge[1]] = (1.0 - self.g.get_edge_data(edge[0],edge[1]) )/self.NormR[nodej]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b824d100fa94f2a751f79972e81db869\",\n \"score\": \"0.51749194\",\n \"text\": \"def calculate_stats(self):\\n tps = []\\n tns = []\\n fps = []\\n fns = []\\n y_ = np.copy(self.y_)\\n thresholds = np.linspace(0, 1.01, 1000)\\n for thresh in thresholds:\\n tps.append(self.true_positive(y_, thresh))\\n tns.append(self.true_negative(y_, thresh))\\n fps.append(self.false_positive(y_, thresh))\\n fns.append(self.false_negative(y_, thresh))\\n tps = np.array(tps)\\n tns = np.array(tns)\\n fps = np.array(fps)\\n sensitivity = tps / (tps + fns)\\n specificity = tns / (tns + fps)\\n return sensitivity, specificity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"127adaf3507b9e6ca66c3d07190a3c72\",\n \"score\": \"0.5173262\",\n \"text\": \"def computeQValueFromValues(self, state, action):\\n \\\"*** YOUR CODE HERE ***\\\"\\n transisions = self.mdp.getTransitionStatesAndProbs(state,action)\\n\\n QV=0\\n\\n for (new_state,prob) in transisions:\\n QV+=prob*(self.mdp.getReward(state,action,new_state)+self.discount*self.values[new_state])\\n return QV\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bc6e27e447e85785ba57d3f80e0acdb\",\n \"score\": \"0.5170065\",\n \"text\": \"def value(self) -> float:\\n return (\\n self.__food_quantity + self.__food_quality +\\n self.__service_quality + self.__price\\n ) / self.__criterias\",\n \"title\": \"\"\n },\n {\n \"docid\": \"829bd5fbec73b05ef70b77eef62e8ddf\",\n \"score\": \"0.5166319\",\n \"text\": \"def calculateOutput(self):\\r\\n result=0.0\\r\\n total=0.0\\r\\n \\r\\n # adds up all the inputs\\r\\n i=0\\r\\n while i < len(self.input_values):\\r\\n total += self.input_values[i]*self.weights[i]\\r\\n i+=1\\r\\n # includes the baseline value\\r\\n total += self.weights[-1]\\r\\n\\r\\n # determines how close to the edges of the range the inputs reached\\r\\n # one in the positve direction and one in the negative depending on the sign of the total\\r\\n # pulls the total back into the range of -1 to 1\\r\\n if total >= 0:\\r\\n # returns a positive\\r\\n # makes output closer to 0\\r\\n total = total / self.sum_of_weights_pos\\r\\n else:\\r\\n # returns a negative -> - (-t / -w)\\r\\n # makes output closer to 1\\r\\n total = -total / self.sum_of_weights_neg\\r\\n\\r\\n # the sigmoid function\\r\\n result = 1/(1+2.0**(total*self.sigmoid_multiplier))\\r\\n\\r\\n self.output=result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17a1b1f9554027d1490a53ef44ccf719\",\n \"score\": \"0.51648015\",\n \"text\": \"def CalculateForAllDataPoints(self, params):\\n self.params.update(params)\\n\\n varsByCalc = self.GetExperimentCollection().GetVarsByCalc()\\n self.calcVals = self.GetCalculationCollection().Calculate(varsByCalc, \\n params)\\n return self.calcVals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f662f5a2c5e70cedea34e6aa3eaaeee9\",\n \"score\": \"0.5162285\",\n \"text\": \"def estimate(self, values):\\n\\n pdf = zeros_like(values)\\n\\n Ns = len(self.samples) \\n\\n # Could perform convolution with FFT.\\n for m in range(Ns):\\n pdf += self.weights[m] * exp(-(values - self.samples[m])**2 / (2 * self.sigmak**2)) / (self.sigmak * sqrt(2 * pi)) / Ns\\n\\n pdf /= trapz(pdf, values)\\n return pdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e50299435b6ca9f301f6e345ac1a46b9\",\n \"score\": \"0.5161663\",\n \"text\": \"def calculate_diversification_metrics(self):\\n # create a dataframe\\n cols = ['Undiv. VaR', 'Div. VaR', 'Div. Effect ($)', 'Div. Effect (%)', 'Div. Ratio']\\n self.diversification = pd.DataFrame(None, [''], cols)\\n\\n # get the diversified VaR\\n div_var = self.var.loc['VaR', 'Portfolio']\\n self.diversification['Div. VaR'] = div_var\\n\\n # compute the undiversified VaR\\n undiv_var = self.var.loc['VaR', :].drop('Portfolio').sum()\\n self.diversification['Undiv. VaR'] = undiv_var\\n \\n # compute the diversification effects ($)\\n self.diversification['Div. Effect ($)'] = undiv_var - div_var\\n\\n # compute the diversification effects (%)\\n self.diversification['Div. Effect (%)'] = (undiv_var - div_var) / undiv_var\\n\\n # compute the diversification ratio\\n self.diversification['Div. Ratio'] = div_var / undiv_var\\n\\n self.log(self.diversification, 'Diversification Measures', index=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16d27c037b135eabc26c70a6457f000c\",\n \"score\": \"0.5159992\",\n \"text\": \"def eval(self):\\n # remove np.spacing(1)\\n pixAcc = 1.0 * self.total_correct / (2.220446049250313e-16 + self.total_label)\\n IoU = 1.0 * self.total_inter / (2.220446049250313e-16 + self.total_union)\\n mIoU = IoU.mean().item()\\n return pixAcc, mIoU\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cc0ab11dcacbf5787b7ce42b860bc77\",\n \"score\": \"0.5156758\",\n \"text\": \"def sigma(self):\\n return self._count_rates['flux_ratio'] / self.snr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b03afda83a5bbc794c0e0a619965803\",\n \"score\": \"0.5153938\",\n \"text\": \"def volatility(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79ba4a110ff0d29a797db36781c4ff48\",\n \"score\": \"0.5151857\",\n \"text\": \"def computeAndScaleArraySums(self):\\n self.sumRT()\\n self.scaleRT()\\n self.sumA()\\n self.scaleA()\\n self.Fluence()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ce34b54002d260887e3c1125c6e6369\",\n \"score\": \"0.5151038\",\n \"text\": \"def ss_rate(pwf,pe,rw,re,k,h,mu,B):\\n return (7.08*10**(-3))*(k*h/(mu*B))*(pe-pwf)/(np.log(re/rw))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3e8926fe1c2c7ce24eda0dfa89ff7ae\",\n \"score\": \"0.51452446\",\n \"text\": \"def singal_measure(self): \\n k1_data = math.sqrt(self.__k1_data)\\n k2_data = math.sqrt(self.__k2_data)\\n b_data = (k2_data - k1_data)/(k2_data + k1_data) \\n a_data = 2.95494514*pow(b_data,1) + 0.17671943*pow(b_data,3) + 0.09230694*pow(b_data,5)\\n amp_data = (k1_data + k2_data)*(3.20976143+0.9187393*pow(a_data,2)+0.14734229*pow(a_data,4))/8192\\n amp_data = amp_data/4096\\n freq_data = (0.5+a_data+self.__k1_index)*(self.__sample_rate/8192)\\n self.__base_frequency = freq_data\\n return(amp_data,'V',freq_data,'Hz')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e66cafcced081c7b2dc352092086f493\",\n \"score\": \"0.51419634\",\n \"text\": \"def readSensor(self):# -> float:\\n self.__RS_Calc = ((self.__VOLT_RESOLUTION * self.__RL ) / self.__sensor_volt ) - self.__RL\\n \\n if(self.__RS_Calc < 0): \\n self.__RS_Calc = 0\\n\\n self.__ratio = self.__RS_Calc / self.__R0\\n \\n if(self.__ratio <= 0): \\n self.__ratio = 0\\n \\n if(self.__regressionMethod == 1): \\n self.__PPM = self.__a * math.pow( self.__ratio , self.__b )\\n \\n else:\\n ppm_log = (math.log10( self.__ratio ) - self.__b ) / self.__a\\n self.__PPM = math.pow(10, ppm_log)\\n \\n if( self.__PPM < 0): \\n self.__PPM = 0\\n \\n return self.__PPM\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4e8d90e794506bc11f66c908d37fa61\",\n \"score\": \"0.5141406\",\n \"text\": \"def results(self):\\n res = super(TrackedSIR, self).results()\\n\\n self.hook.finalize_results(initial_results=self.timeseries_results, final_results=res, params=self.params)\\n\\n # TODO: Fix this dependency issue\\n di = res['daily_infections']\\n ci = res['currently_infected']\\n\\n # Calculate the average length of an infection\\n n = self._g.nodes\\n mil = np.mean([n[x]['days_infected'] for x in n if 'days_infected' in n[x]])\\n res['mean_infection_length'] = mil\\n\\n res['daily_r'] = [((di[x + 1] / ci[x]) if ci[x] != 0 else 0) * mil for x in range(len(ci) - 1)]\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5688967c99e623a99b4c2263c14301ba\",\n \"score\": \"0.5136812\",\n \"text\": \"def calculate(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f84dc65cdf62c3bbaafde61ab93fc6e\",\n \"score\": \"0.5136497\",\n \"text\": \"def Evaluate():\\n # Retrieve dataset files\\n data_fls, ref_fls = LoadTroikaDataset()\\n errs, confs = [], []\\n for data_fl, ref_fl in zip(data_fls, ref_fls):\\n # Run the pulse rate algorithm on each trial in the dataset\\n errors, confidence = RunPulseRateAlgorithm(data_fl, ref_fl)\\n errs.append(errors)\\n confs.append(confidence)\\n # Compute aggregate error metric\\n errs = np.hstack(errs)\\n confs = np.hstack(confs)\\n return AggregateErrorMetric(errs, confs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c77f68eaf7669325ca3461523bc02c\",\n \"score\": \"0.5134388\",\n \"text\": \"def ratio_p(self):\\n ga = self.ga\\n Ms = self.Ms\\n return (2*ga*Ms**2 - (ga-1))/(ga+1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bcfa1e6e96cbd23d34740eab5dafa0c\",\n \"score\": \"0.51284164\",\n \"text\": \"def GlobalScale(self):\\n\\t\\tprint \\\"Scale following the order of importance (peaks)\\\"\\n\\t\\tP = self.GlobalPeaks()[:,0]\\n\\t\\tself.Echelle = []\\n\\t\\tfor i in range(0,len(self.GP)):\\n\\t\\t\\tnb_ext = len(self.melodies)\\n\\t\\t\\t#tonique = self.melodies[nb_ext-1].tonique()[1]\\n\\t\\t\\ttonique = self.GetTheTonic()\\n\\t\\t\\tself.Echelle.append(log10(P[i]/tonique)*1000)\\n\\t\\treturn self.Echelle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c04ce0eb342ee13eb12197902fb0dd0\",\n \"score\": \"0.5128311\",\n \"text\": \"def calculate_flows(self):\\n e = self.alpha_star**(-1) / (1 - self.alpha_star**(-self.n_e))\\n s = self.alpha_star**(-1) / (self.alpha_star**(self.n_s+1) - 1)\\n e_sum = (e*self.xf / (e+s)).sum()\\n s_sum = (s*self.xf / (e+s)).sum()\\n\\n p = self.user_f * e_sum\\n f = self.user_p / e_sum\\n\\n if p < self.user_p:\\n self.p = p\\n self.f = self.user_f\\n else:\\n self.f = f\\n self.p = self.user_p\\n self.t = self.f * s_sum\\n\\n self.swu = self.get_swu()\\n if self.swu > self.user_swu:\\n self.swu = self.user_swu\\n\\n self.f = self.swu / (self.value_function(self.xp)*e_sum\\n + self.value_function(self.xt)*s_sum\\n - self.value_function(self.xf))\\n self.p = self.f * e_sum\\n self.t = self.f * s_sum\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34281037e58f3024e2253c1e8909b337\",\n \"score\": \"0.5126956\",\n \"text\": \"def run(self):\\n self.report.chapter('Historical Simulation Value-at-Risk')\\n self.calculate_shifted_levels()\\n self.get_full_revaluation_pnl()\\n self.get_ptf_pnl()\\n self.bootstrap_pnls()\\n self.calculate_var()\\n self.calculate_sub_ptf_pnl()\\n self.calculate_sub_ptf_var()\\n self.calculate_component_var()\\n self.calculate_incremental_var()\\n self.calculate_diversification_metrics()\\n self.plot_hist()\\n self.waterfall_plots()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fe6757d77a3b9d36fab5477f05b06e9\",\n \"score\": \"0.51256603\",\n \"text\": \"def f(ts):\\n # This method is nicer than an inverse fourier transform because\\n # results are consistant for differing ranges of ts around the same\\n # time. The price payed is that the fft would be an order of\\n # magnitude faster, but not reproducible for a slightly different\\n # time array.\\n values = sum(amp * np.cos(2*np.pi*freq * ts + phase)\\n for freq, amp, phase\\n in zip(self.freqs, self.amps, self.phases))\\n\\n # Normalization calculated by guess-and-check,\\n # but seems to work fine\\n # normalization = np.sqrt(2/len(self.freqs))\\n values *= np.sqrt(2/len(self.freqs))\\n\\n # So far, the units of the values are V/V_rms, so multiply by the\\n # rms voltage:\\n values *= self.rms\\n\\n return values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d3ab37474c5bdad03e54e309012aa38\",\n \"score\": \"0.5123594\",\n \"text\": \"def production():\\r\\n # kp is the interval considered\\r\\n kp = np.linspace(4*10**(-3),16*10**(-3),1000)\\r\\n \\r\\n # Aggregate fraction calculated analytically\\r\\n a_n = 3*k1_n*kp/(kc_n*(kc_n+k1_n))\\r\\n # Changing units to uM\\r\\n a_n = a_n*1000\\r\\n kp = kp * 1000\\r\\n \\r\\n # Plotting figure\\r\\n fig, ax = plt.subplots()\\r\\n fig.subplots_adjust(left=.15, bottom=.2, right=.95, top=.97) \\r\\n ax.set_ylabel(r'Aggregated $A\\\\beta_{42}$ in ISF (pM)')\\r\\n ax.set_xlabel('Production rate ($pM.h^{-1}$)') \\r\\n \\r\\n plt.plot(kp, a_n, 'black', linewidth = 1.0)\\r\\n ax.axvline(x=kp_n*1000, color = 'g', linestyle='dashed', alpha=0.5)\\r\\n ax.text(x=kp_n*1000 + 0.2, y=150, s='Normal', alpha=1, color='g', fontsize = 6)\\r\\n \\r\\n ax.axvline(x=kp_FAD*1000, color = 'r', linestyle='dashed', alpha=0.5)\\r\\n ax.text(x=kp_FAD*1000 + 0.2, y=150, s='FAD', alpha=1, color='r', fontsize = 6)\\r\\n\\r\\n # Save and display image \\r\\n fig.set_size_inches(width, height)\\r\\n fig.savefig('Variations in production rate.png', dpi=1200)\\r\\n plt.show()\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d0e5a63f7dbf72458d4d8cb9ae05d23\",\n \"score\": \"0.5117035\",\n \"text\": \"def pv(flows, r):\\n dates = flows.index\\n discounts = discount(dates, r)\\n return discounts.multiply(flows, axis=\\\"rows\\\").sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"945e198e9811ac8aa693449d804faeb4\",\n \"score\": \"0.51122\",\n \"text\": \"def calculate_var(self):\\n # create a dataframe for the MC VaR\\n self.var = pd.DataFrame(columns=self.position_names, index=['VaR', 'Correlation'])\\n\\n # iterate over the positions\\n ptf_pnl = self.ptf_pnl['P&L ($)']\\n for asset in self.position_names:\\n asset_pnl = self.full_revaluation_pnl[asset]\\n \\n # calculate the individual VaR\\n self.var.loc['VaR', asset] = np.percentile(asset_pnl, (1 - self.confidence) * 100)\\n\\n # compute the correlation between the asset P&L and the portfolio P&L\\n self.var.loc['Correlation', asset] = pearsonr(asset_pnl, ptf_pnl)[0]\\n\\n # compute the MC n-day portfolio VaR\\n self.var.loc['VaR', 'Portfolio'] = np.percentile(ptf_pnl, (1 - self.confidence) * 100)\\n \\n self.log(self.var, 'Value at Risk')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72310e6faec92e55844761cef1671695\",\n \"score\": \"0.5109994\",\n \"text\": \"def pv(flows, r):\\n dates = flows.index\\n discounts = discount(dates, r)\\n return discounts.multiply(flows, axis='rows').sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5b41a06461174665a0d90c84cfc9563\",\n \"score\": \"0.5109505\",\n \"text\": \"def rsi(df, s, period) -> ReturnType:\\n\\n delta = df['close'].diff().to_numpy()\\n\\n # gain\\n U = (np.absolute(delta) + delta) / 2.\\n # loss\\n D = (np.absolute(delta) - delta) / 2.\\n\\n smma_u = calc_smma(U, period)\\n smma_d = calc_smma(D, period)\\n\\n return 100 - 100 / (1. + smma_u / smma_d), period\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53ae70211db2b39bcf9175c09d8946a2\",\n \"score\": \"0.51083857\",\n \"text\": \"def calculate_values(eq_model, eq_year):\\n with open(\\\"res/api-response.json\\\", \\\"r\\\") as resp:\\n data = json.load(resp)\\n\\n cost = data[eq_model][\\\"saleDetails\\\"][\\\"cost\\\"]\\n market_ratio = data[eq_model][\\\"schedule\\\"][\\\"years\\\"][eq_year][\\\"marketRatio\\\"]\\n auction_ratio = data[eq_model][\\\"schedule\\\"][\\\"years\\\"][eq_year][\\\"auctionRatio\\\"]\\n\\n return {\\\"marketValue\\\": cost * market_ratio,\\n \\\"auctionValue\\\": cost * auction_ratio\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58fc550ecf042d256546e602f715ef15\",\n \"score\": \"0.5105003\",\n \"text\": \"def __call__(self, values):\\n r = self.residuals(values)\\n return getattr(r, 'SSE', 1e9)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aedcd63e22b87624222c89f92773198\",\n \"score\": \"0.5103577\",\n \"text\": \"def testing(predicted_price):\\r\\n global y_test\\r\\n test1 = list(range(0,len(y_test)))\\r\\n test2 = list(range(0,len(y_test)))\\r\\n a=int()\\r\\n\\r\\n for a in range(0,len(y_test)):\\r\\n test1[a] = y_test[a] - predicted_price['Linear'][a]\\r\\n predicted_price['Linear'][a] = predicted_price['Linear'][a] + test1[a]\\r\\n test2[a] = y_test[a] - predicted_price['RBF'][a]\\r\\n predicted_price['RBF'][a] = predicted_price['RBF'][a] + test2[a]\\r\\n\\r\\n avr1 = sum(test1)/len(test1)\\r\\n avr2 = sum(test2)/len(test2)\\r\\n\\r\\n for a in range(len(y_test),len(predicted_price['Linear'])):\\r\\n predicted_price['Linear'][a] = predicted_price['Linear'][a] + avr1\\r\\n predicted_price['RBF'][a] = predicted_price['RBF'][a] + avr2\\r\\n\\r\\n return int(avr1), int(avr2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9dc83190f57a8f83668584c20e3bd24\",\n \"score\": \"0.51033914\",\n \"text\": \"def test_get_returns_and_pl_average_return(self):\\n all_prices = [[1., 2.], [3., 4.], [5., 6.]]\\n signals = [['buy', 'buy', 'sell'],\\n [None, None, None]]\\n returns, p_and_l = stockdata.get_returns_and_pl(all_prices, signals)\\n self.assertEqual(returns, [0, -0.38077249551779302])\\n self.assertEqual(p_and_l, [0, 0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ae17ca4995597d52aec52653bc471eb\",\n \"score\": \"0.5100543\",\n \"text\": \"def get_measures(self):\\n precision = 0\\n recall = 0\\n f1_score = 0\\n if self.t_p > 0:\\n precision = self.t_p / (self.t_p + self.f_p)\\n recall = self.t_p / (self.t_p + self.f_n)\\n if (precision + recall) > 0:\\n f1_score = 2*(precision * recall / (precision + recall))\\n return precision, recall, f1_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cd0d543787085fd904f9830537bfa4e\",\n \"score\": \"0.51004606\",\n \"text\": \"def pv(l,r):\\r\\n dates=l.index\\r\\n discounts = discount(dates,r)\\r\\n return (discounts*l).sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afcabc478b2d8a8bc373f11317c777d7\",\n \"score\": \"0.5099384\",\n \"text\": \"def value(self) -> float:\\n if self._value is None:\\n self.lr.fit(self._train[\\\"V^2\\\"].values.reshape(-1, 1), self._train[\\\"Fa\\\"])\\n self._value = self.lr.coef_[0]\\n return self._value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68453bbd8c48b38f16255b6ad75d39e1\",\n \"score\": \"0.50984603\",\n \"text\": \"def calculate_signals(self, tick_event):\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d62b154ba0ad6d9d99473f074aabd4fa\",\n \"score\": \"0.5096261\",\n \"text\": \"def weighted_prediction(scores, preds):\\n\\n if len(scores) == 1: # only one method, return the series\\n return preds[0]\\n else:\\n n = len(scores)\\n # first compute total error by series\\n total_error = {}\\n for i in range(45):\\n score = 0\\n for j in range(n):\\n score += scores[j][i]\\n\\n total_error[i] = score\\n\\n # now total_error is a dict with total_error[i] the total error\\n # of all methods for the serie-(i+1)\\n\\n # create array of weight by method and series, being proportional\\n # to the error\\n normalizer = n-1\\n weights = np.zeros(scores.shape)\\n for i in range(n):\\n for j in range(45):\\n weights[i][j] = (1 / float(normalizer)) * (1 - scores[i][j]/total_error[j])\\n\\n # now weights[i][j] is the weights for serie j+1 given by method i\\n\\n # create a series to accumulate the mean of all methods\\n df = pd.DataFrame(np.zeros(preds[0].shape), index=preds[0].index)\\n\\n # note that this series does not have the good name for the columns\\n # we change that now\\n rn = {}\\n for i in range(45):\\n rn[i] = 'series-' + str(i+1)\\n df = df.rename(columns=rn)\\n # now df has the good shape, the good name for the columns and zeros\\n # everywhere\\n\\n for i in range(n): # for each method\\n for j in range(45): # for each column\\n column = 'series-' + str(j+1)\\n\\n # df[column] receives the weighted sum of values\\n df[column] = df[column] + preds[i][column] * weights[i][j]\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47de7f2a6ef88e17daf820992c0f211a\",\n \"score\": \"0.5094053\",\n \"text\": \"def rsi_predictions(prices, rsi):\\n days_observed = 14\\n true_positive = 0\\n false_positive = 0\\n true_negative = 0\\n false_negative = 0\\n try:\\n while days_observed < len(prices) - 5:\\n change = array(prices[days_observed + 1: days_observed + 6]).mean()\\n is_overbought, is_oversold = bought_status(rsi[days_observed])\\n if is_oversold:\\n if change > prices[days_observed]:\\n true_positive += 1\\n else:\\n false_negative += 1\\n elif is_overbought:\\n if change <= prices[days_observed]:\\n true_negative += 1\\n else:\\n false_positive += 1\\n days_observed += 1\\n return calculate_predictions(true_positive, false_positive, true_negative, false_negative)\\n except IndexError as error:\\n print(error, len(rsi), len(prices))\\n return 0.5, 0.5, 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"233cc3ef9c44ae91a03aff95d4eab3e9\",\n \"score\": \"0.5089559\",\n \"text\": \"def performGetValue(self, quant, options={}):\\n # proceed depending on quantity\\n if 'Waveform' in quant.name:\\n # Always update dt\\n sampling_rate = self.getValue('Sampling Rate')\\n self.dt = 1 / sampling_rate\\n\\n # Make all of our waveform checks and update what we need to\\n if self.basis_rotation_generators_are_stale:\\n self.update_basis_rotation_generators()\\n self.update_state_preparation_sequence()\\n self.update_measurement_basis_sequence()\\n self.basis_rotation_generators_are_stale = False\\n self.state_prep_sequence_is_stale = False\\n self.measurement_basis_sequence_is_stale = False\\n\\n if self.state_prep_sequence_is_stale:\\n self.update_state_preparation_sequence()\\n self.state_prep_sequence_is_stale = False\\n \\n if self.process_sequence_is_stale:\\n self.update_process_sequence()\\n self.process_sequence_is_stale = False\\n\\n if self.measurement_basis_sequence_is_stale:\\n self.update_measurement_basis_sequence()\\n self.measurement_basis_sequence_is_stale = False\\n\\n if self.readout_is_stale:\\n self.update_readout()\\n self.readout_is_stale = False\\n\\n if self.getValue('Process X - G-E') is not None:\\n self.update_process_sequence()\\n\\n # Plot and return the appropriate trace dict:\\n if quant.name == 'Waveform - X Signal':\\n ge_frequency = 1e-3*self.getValue('G-E Frequency')\\n ef_frequency = 1e-3*self.getValue('E-F Frequency')\\n percent_of_sequence = self.getValue('Percent of Sequence') / 100\\n x_ge_signal = dsp_utils.modulate_signal(self.x_ge.build_sequence(percent_of_sequence),\\n self.dt,\\n ge_frequency,\\n np.pi/2) \\\\\\n + dsp_utils.modulate_signal(self.y_ge.build_sequence(percent_of_sequence),\\n self.dt,\\n ge_frequency,\\n np.pi/2) # sine phase\\n x_ef_signal = dsp_utils.modulate_signal(self.x_ef.build_sequence(percent_of_sequence),\\n self.dt,\\n ef_frequency,\\n np.pi/2) \\\\\\n + dsp_utils.modulate_signal(self.y_ef.build_sequence(percent_of_sequence),\\n self.dt,\\n ef_frequency,\\n np.pi/2) \\n signal = x_ge_signal + x_ef_signal\\n trace = quant.getTraceDict(signal, t0=0.0, dt=self.dt)\\n elif quant.name == 'Waveform - Y Signal':\\n ge_frequency = 1e-3*self.getValue('G-E Frequency')\\n ef_frequency = 1e-3*self.getValue('E-F Frequency')\\n percent_of_sequence = self.getValue('Percent of Sequence') / 100\\n y_ge_signal = dsp_utils.modulate_signal(self.y_ge.build_sequence(percent_of_sequence),\\n self.dt,\\n ge_frequency,\\n 0) \\\\\\n + dsp_utils.modulate_signal(self.x_ge.build_sequence(percent_of_sequence),\\n self.dt,\\n ge_frequency,\\n 0)\\n y_ef_signal = dsp_utils.modulate_signal(self.y_ef.build_sequence(percent_of_sequence),\\n self.dt,\\n ef_frequency,\\n 0) \\\\\\n + dsp_utils.modulate_signal(self.x_ef.build_sequence(percent_of_sequence),\\n self.dt,\\n ef_frequency,\\n 0)\\n signal = y_ge_signal + y_ef_signal\\n trace = quant.getTraceDict(signal, t0=0.0, dt=self.dt)\\n elif quant.name == 'Waveform - Z Signal':\\n percent_of_sequence = self.getValue('Percent of Sequence') / 100\\n signal = self.z.build_sequence(percent_of_sequence)\\n trace = quant.getTraceDict(signal, t0=0.0, dt=self.dt)\\n elif quant.name == 'Waveform - Readout I':\\n percent_of_sequence = self.getValue('Percent of Sequence') / 100\\n signal = self.readout_i.build_sequence(percent_of_sequence)\\n trace = quant.getTraceDict(signal, t0=0.0, dt=self.dt)\\n elif quant.name == 'Waveform - Readout Q':\\n percent_of_sequence = self.getValue('Percent of Sequence') / 100\\n signal = self.readout_q.build_sequence(percent_of_sequence)\\n trace = quant.getTraceDict(signal, t0=0.0, dt=self.dt)\\n elif quant.name == 'Waveform - Readout Trigger':\\n percent_of_sequence = self.getValue('Percent of Sequence') / 100\\n signal = self.readout_trigger.build_sequence(percent_of_sequence)\\n trace = quant.getTraceDict(signal, t0=0.0, dt=self.dt)\\n return trace\\n\\n elif 'QB' in quant.name:\\n demodulated_signal = self.demodulate()\\n return demodulated_signal\\n\\n else: \\n # for other quantities, just return current value of control\\n return quant.getValue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d4cfacfad586eb180e6877bceeb5057\",\n \"score\": \"0.5085563\",\n \"text\": \"def negative_predictive_values(self):\\n return self.tn() / (self.fn() + self.tn())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bc00ffcc6633b79e548a8addd2ab41f\",\n \"score\": \"0.50805104\",\n \"text\": \"def _calc_p(self):\\n assert hasattr(self, '_v_void')\\n self.pdf.update_pdf(v_void=self._v_void,\\n f=self._f.max(),\\n rt_mean=self._v_void / self._f.max())\\n self._p = self.pdf.get_p()\\n # Log.\\n self.log.d_data(self._log_tree, 'p', self._p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"999b261229b66c0308821615748258f0\",\n \"score\": \"0.5075779\",\n \"text\": \"def computeQValueFromValues(self, state, action):\\n \\\"*** YOUR CODE HERE ***\\\"\\n qValue = 0\\n for nextState, prob in self.mdp.getTransitionStatesAndProbs(state,action):\\n qValue += prob*(self.mdp.getReward(state,action,nextState)+self.discount*self.values[nextState])\\n # print(qValue)\\n return qValue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be721d7112fc1839aedb29bd4bb6b678\",\n \"score\": \"0.5072971\",\n \"text\": \"def get_statistic(self):\\n return self.valdiffs / self.get_se()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"280c0f420c48d26b6838ee8c2358804c\",\n \"score\": \"0.5072796\",\n \"text\": \"def mse(poll_df, prediction_df):\\n # TODO: Have data_dict as parameter here? Or hand over data_dict['allensbach'] directly?\\n # TODO: Refactor this once the model is wrapped in a class.\\n mse = 0\\n for party in parties:\\n true_results = poll_df[party]\\n predicted_results = prediction_df[party][1:1+len(true_results)] # first point is prediction into the future, do not use it\\n mse += np.mean((true_results - predicted_results)**2)\\n return mse / len(parties)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":489,"cells":{"query_id":{"kind":"string","value":"ac3c81a73e77f650ede7a49991f02b27"},"query":{"kind":"string","value":"Set up all dataset config options."},"positive_passages":{"kind":"list like","value":[{"docid":"6a018c9b4bf9649f104ecf5b3e637a9e","score":"0.0","text":"def configure(self, repo_dir, inner_size, scale_range, do_transforms,\n rgb, shuffle, set_name, subset_pct, macro,\n contrast_range, aspect_ratio):\n assert (subset_pct > 0 and subset_pct <= 100), (\n 'subset_pct must be between 0 and 100')\n assert(set_name in ['train', 'validation'])\n self.set_name = set_name if set_name == 'train' else 'val'\n\n self.repo_dir = repo_dir\n self.inner_size = inner_size\n if isinstance(scale_range, int):\n self.scale_range = (scale_range, scale_range)\n else:\n self.scale_range = scale_range\n self.minibatch_size = self.be.bsz\n\n self.center = not do_transforms\n self.flip = do_transforms\n self.contrast_range = contrast_range if do_transforms else (100, 100)\n self.aspect_ratio = aspect_ratio if do_transforms else 0\n if not do_transforms:\n self.scale_range = (self.scale_range[0], self.scale_range[0])\n if do_transforms:\n assert (self.aspect_ratio == 0 or self.aspect_ratio > 100), (\n 'bad value for aspect_ratio augmentation')\n\n self.shuffle = shuffle if do_transforms else False\n\n self.rgb = rgb\n self.start_idx = 0\n self.macro = macro\n self.batch_prefix = \"macrobatch_\"\n\n if not macro:\n self.filename = os.path.join(repo_dir, 'filelist.txt')\n if not os.path.exists(self.filename):\n raise IOError('Cannot find %s' % self.filename)\n filelist = np.genfromtxt(self.filename, dtype=str)\n self.ndata = int(len(filelist) * subset_pct / 100.)\n assert self.ndata != 0\n self.macro_start = 0\n self.nlabels = 1\n self.nclass = 1\n self.global_mean = None\n self.img_size = inner_size\n return\n\n # Load from repo dataset_cache:\n cache_filepath = os.path.join(repo_dir, self.batch_prefix + 'meta')\n try:\n dataset_cache = dict()\n with open(cache_filepath, 'r') as f:\n for line in f:\n (k, v) = line.split()\n dataset_cache[k] = float(v) if k.endswith('mean') else int(v)\n rgbmean = [[dataset_cache[c + '_mean']] for c in ['B', 'G', 'R']]\n dataset_cache['global_mean'] = np.array(rgbmean, dtype=np.float32)\n except IOError:\n raise IOError(\"Cannot find '%s'. Run batch_writer to preprocess the \"\n \"data and create batch files for imageset\" % (cache_filepath))\n\n # Should have following defined:\n req_attributes = ['global_mean', 'nclass', 'val_start', 'train_start',\n 'train_nrec', 'val_nrec',\n 'item_max_size', 'label_size']\n\n for r in req_attributes:\n if r not in dataset_cache:\n raise ValueError(\n 'Dataset cache missing required attribute %s' % (r))\n\n self.__dict__.update(dataset_cache)\n self.filename = os.path.join(repo_dir, self.batch_prefix)\n\n self.label = 'l_id'\n if isinstance(self.nclass, dict):\n self.nclass = self.nclass[self.label]\n\n self.recs_available = getattr(self, self.set_name + '_nrec')\n self.macro_start = getattr(self, self.set_name + '_start')\n self.ndata = int(self.recs_available * subset_pct / 100.)","title":""}],"string":"[\n {\n \"docid\": \"6a018c9b4bf9649f104ecf5b3e637a9e\",\n \"score\": \"0.0\",\n \"text\": \"def configure(self, repo_dir, inner_size, scale_range, do_transforms,\\n rgb, shuffle, set_name, subset_pct, macro,\\n contrast_range, aspect_ratio):\\n assert (subset_pct > 0 and subset_pct <= 100), (\\n 'subset_pct must be between 0 and 100')\\n assert(set_name in ['train', 'validation'])\\n self.set_name = set_name if set_name == 'train' else 'val'\\n\\n self.repo_dir = repo_dir\\n self.inner_size = inner_size\\n if isinstance(scale_range, int):\\n self.scale_range = (scale_range, scale_range)\\n else:\\n self.scale_range = scale_range\\n self.minibatch_size = self.be.bsz\\n\\n self.center = not do_transforms\\n self.flip = do_transforms\\n self.contrast_range = contrast_range if do_transforms else (100, 100)\\n self.aspect_ratio = aspect_ratio if do_transforms else 0\\n if not do_transforms:\\n self.scale_range = (self.scale_range[0], self.scale_range[0])\\n if do_transforms:\\n assert (self.aspect_ratio == 0 or self.aspect_ratio > 100), (\\n 'bad value for aspect_ratio augmentation')\\n\\n self.shuffle = shuffle if do_transforms else False\\n\\n self.rgb = rgb\\n self.start_idx = 0\\n self.macro = macro\\n self.batch_prefix = \\\"macrobatch_\\\"\\n\\n if not macro:\\n self.filename = os.path.join(repo_dir, 'filelist.txt')\\n if not os.path.exists(self.filename):\\n raise IOError('Cannot find %s' % self.filename)\\n filelist = np.genfromtxt(self.filename, dtype=str)\\n self.ndata = int(len(filelist) * subset_pct / 100.)\\n assert self.ndata != 0\\n self.macro_start = 0\\n self.nlabels = 1\\n self.nclass = 1\\n self.global_mean = None\\n self.img_size = inner_size\\n return\\n\\n # Load from repo dataset_cache:\\n cache_filepath = os.path.join(repo_dir, self.batch_prefix + 'meta')\\n try:\\n dataset_cache = dict()\\n with open(cache_filepath, 'r') as f:\\n for line in f:\\n (k, v) = line.split()\\n dataset_cache[k] = float(v) if k.endswith('mean') else int(v)\\n rgbmean = [[dataset_cache[c + '_mean']] for c in ['B', 'G', 'R']]\\n dataset_cache['global_mean'] = np.array(rgbmean, dtype=np.float32)\\n except IOError:\\n raise IOError(\\\"Cannot find '%s'. Run batch_writer to preprocess the \\\"\\n \\\"data and create batch files for imageset\\\" % (cache_filepath))\\n\\n # Should have following defined:\\n req_attributes = ['global_mean', 'nclass', 'val_start', 'train_start',\\n 'train_nrec', 'val_nrec',\\n 'item_max_size', 'label_size']\\n\\n for r in req_attributes:\\n if r not in dataset_cache:\\n raise ValueError(\\n 'Dataset cache missing required attribute %s' % (r))\\n\\n self.__dict__.update(dataset_cache)\\n self.filename = os.path.join(repo_dir, self.batch_prefix)\\n\\n self.label = 'l_id'\\n if isinstance(self.nclass, dict):\\n self.nclass = self.nclass[self.label]\\n\\n self.recs_available = getattr(self, self.set_name + '_nrec')\\n self.macro_start = getattr(self, self.set_name + '_start')\\n self.ndata = int(self.recs_available * subset_pct / 100.)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"efb231ed2b6b2f1ce3aa15434c6d4fb4","score":"0.7039832","text":"def dataset_setup(self):\n settings = self.settings\n if settings.crowd_dataset == CrowdDataset.ucf_qnrf:\n self.dataset_class = UcfQnrfFullImageDataset\n self.train_dataset = UcfQnrfTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\n seed=settings.labeled_dataset_seed,\n number_of_examples=settings.labeled_dataset_size,\n map_directory_name=settings.map_directory_name)\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\n pin_memory=self.settings.pin_memory,\n num_workers=settings.number_of_data_workers)\n self.unlabeled_dataset = UcfQnrfTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\n seed=settings.labeled_dataset_seed,\n number_of_examples=settings.unlabeled_dataset_size,\n map_directory_name=settings.map_directory_name,\n examples_start=settings.labeled_dataset_size)\n self.unlabeled_dataset_loader = DataLoader(self.unlabeled_dataset, batch_size=settings.batch_size,\n pin_memory=self.settings.pin_memory,\n num_workers=settings.number_of_data_workers)\n self.validation_dataset = UcfQnrfTransformedDataset(dataset='test', seed=101,\n map_directory_name=settings.map_directory_name)\n elif settings.crowd_dataset == CrowdDataset.shanghai_tech:\n self.dataset_class = ShanghaiTechFullImageDataset\n self.train_dataset = ShanghaiTechTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\n seed=settings.labeled_dataset_seed,\n number_of_examples=settings.labeled_dataset_size,\n map_directory_name=settings.map_directory_name)\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\n pin_memory=self.settings.pin_memory,\n num_workers=settings.number_of_data_workers)\n self.unlabeled_dataset = ShanghaiTechTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\n seed=100,\n number_of_examples=settings.unlabeled_dataset_size,\n map_directory_name=settings.map_directory_name)\n self.unlabeled_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\n pin_memory=self.settings.pin_memory,\n num_workers=settings.number_of_data_workers)\n self.validation_dataset = ShanghaiTechTransformedDataset(dataset='test', seed=101,\n map_directory_name=settings.map_directory_name)\n\n elif settings.crowd_dataset == CrowdDataset.world_expo:\n self.dataset_class = WorldExpoFullImageDataset\n self.train_dataset = WorldExpoTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\n seed=settings.labeled_dataset_seed,\n number_of_cameras=settings.number_of_cameras,\n number_of_images_per_camera=settings.number_of_images_per_camera)\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\n pin_memory=self.settings.pin_memory,\n num_workers=settings.number_of_data_workers)\n self.unlabeled_dataset = WorldExpoTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\n seed=settings.labeled_dataset_seed,\n number_of_cameras=settings.number_of_cameras,\n number_of_images_per_camera=settings.number_of_images_per_camera)\n self.unlabeled_dataset_loader = DataLoader(self.unlabeled_dataset, batch_size=settings.batch_size,\n pin_memory=self.settings.pin_memory,\n num_workers=settings.number_of_data_workers)\n self.validation_dataset = WorldExpoTransformedDataset(dataset='validation', seed=101)\n if self.settings.batch_size > self.train_dataset.length:\n self.settings.batch_size = self.train_dataset.length","title":""},{"docid":"3576e67e571d4b2bb3f0cf7892c47e71","score":"0.6755877","text":"def test_dataset_config(self):\n dataset = fluid.core.Dataset(\"MultiSlotDataset\")\n dataset.set_thread_num(12)\n dataset.set_filelist([\"a.txt\", \"b.txt\", \"c.txt\"])\n dataset.set_trainer_num(4)\n dataset.set_hdfs_config(\"my_fs_name\", \"my_fs_ugi\")\n dataset.set_download_cmd(\"./read_from_afs my_fs_name my_fs_ugi\")\n dataset.set_enable_pv_merge(False)\n\n thread_num = dataset.get_thread_num()\n self.assertEqual(thread_num, 12)\n\n filelist = dataset.get_filelist()\n self.assertEqual(len(filelist), 3)\n self.assertEqual(filelist[0], \"a.txt\")\n self.assertEqual(filelist[1], \"b.txt\")\n self.assertEqual(filelist[2], \"c.txt\")\n\n trainer_num = dataset.get_trainer_num()\n self.assertEqual(trainer_num, 4)\n\n name, ugi = dataset.get_hdfs_config()\n self.assertEqual(name, \"my_fs_name\")\n self.assertEqual(ugi, \"my_fs_ugi\")\n\n download_cmd = dataset.get_download_cmd()\n self.assertEqual(download_cmd, \"./read_from_afs my_fs_name my_fs_ugi\")","title":""},{"docid":"ecec69c5351f6100df68003d178a48c0","score":"0.66378176","text":"def initSetConfig(self):\n pass","title":""},{"docid":"09880be1ce3bb56b50a7dc27a8e7597d","score":"0.6620552","text":"def set_defaults(config={}):\n if 'seg_data' not in list(config.keys()):\n config['seg_data'] = 'vz_predict_dataset.csv.gz'\n if 'concern' not in list(config.keys()):\n config['concern'] = ''\n if 'atr' not in list(config.keys()):\n config['atr'] = ''\n if 'tmc' not in list(config.keys()):\n config['tmc'] = ''\n if 'f_cont' not in list(config.keys()):\n config['f_cont'] = ['width']\n if 'f_cat' not in list(config.keys()):\n config['f_cat'] = ['lanes', 'hwy_type', 'osm_speed', 'oneway',\n 'signal']\n\n # Add features for additional data sources\n if 'data_source' in config and config['data_source']:\n for source in config['data_source']:\n config[source['feat']].append(source['name'])\n\n if 'process' not in list(config.keys()):\n config['process'] = True\n if 'time_target' not in list(config.keys()):\n config['time_target'] = [15, 2017]\n if 'weeks_back' not in list(config.keys()):\n config['weeks_back'] = 1\n if 'name' not in list(config.keys()):\n config['name'] = 'boston'\n if 'level' not in list(config.keys()):\n config['level'] = 'week'","title":""},{"docid":"c4aa53dd289e88ee9df10a3b5d11813e","score":"0.6555243","text":"def __init__(self, data_path):\n config = self.load_options(data_path)\n for i in config:\n setattr(self, '{}'.format(i), '{}'.format(config[i]))","title":""},{"docid":"a6484a9ad97261f26351e08a8e9db916","score":"0.64702314","text":"def setup(args):\n cfg = get_cfg()\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n\n if cfg.EVAL_ONLY:\n cfg.DATASETS.TEST = (\"smokenet_test\",)\n test_annotation_file = os.path.join(cfg.DATA_DIR_PATH, \"test.json\")\n # Register into DatasetCatalog\n register_imagenet_instances(cfg.DATASETS.TEST[0], {}, test_annotation_file)\n\n else:\n train_annotation_file = os.path.join(cfg.DATA_DIR_PATH, \"train.json\")\n val_annotation_file = os.path.join(cfg.DATA_DIR_PATH, \"val.json\")\n # Register into DatasetCatalog\n register_imagenet_instances(\"smokenet_train\", {}, train_annotation_file)\n register_imagenet_instances(\"smokenet_val\", {}, val_annotation_file)\n\n cfg.freeze()\n default_setup(cfg, args)\n\n return cfg","title":""},{"docid":"93b1babbfc9dc7b6e1489669b33ed4bf","score":"0.64027584","text":"def set_dataset_paths(args):\n if not args.train_path:\n args.train_path = 'datasets/%s/train' % (args.dataset)\n if not args.val_path:\n if args.dataset == 'imagenet' or args.dataset == 'places':\n args.val_path = 'datasets/%s/val' % (args.dataset)\n else:\n args.val_path = 'datasets/%s/test' % (args.dataset)","title":""},{"docid":"4fba0cbbc75905319a8e95f65732a29a","score":"0.639718","text":"def manage_options():\n parser = OptionParser(usage=\"usage: %prog [options] dataset_file\", version=\"%prog 1.0\")\n\n parser.add_option(\"-s\", \"--seed\", dest=\"seed\", default=42, help=\"Pseudo-random seed for replicability\", type=\"int\")\n parser.add_option(\"-t\", \"--test_size\", dest=\"test_size\", default=.3, help=\"Test set size in percentage [0,1]\")\n parser.add_option(\"-n\", \"--normalize\", dest=\"normalize\", default=1, help=\"Whether the instances has to be normalized or not (default:1) - 0:No, 1:MinMax standardization, 2:L2 normalization\", type=\"int\")\n parser.add_option(\"-c\", \"--config_file\", dest=\"config_file\", default=\"./config/config.json\", help=\"Configuration file\")\n parser.add_option(\"-v\", \"--verbose\", dest=\"verbose\", default=False, help=\"Verbose output\", action=\"store_true\")\n\n (options, args) = parser.parse_args()\n if len(args) == 0:\n parser.error(\"Wrong arguments\")\n\n out_dict = vars(options)\n out_dict[\"dataset\"] = args[0]\n return out_dict","title":""},{"docid":"dd8ddecb029d2e213f7d0f0a1667c129","score":"0.6343363","text":"def set_dataset(self):\n\n fpath = os.path.join(os.path.dirname(os.path.dirname(__file__)), \"splits\", self.opt.split, \"{}_files.txt\")\n test_fpath = os.path.join(os.path.dirname(os.path.dirname(__file__)), \"splits\", \"eigen\", \"test_files.txt\")\n train_filenames = readlines(fpath.format(\"train\"))\n val_filenames = readlines(test_fpath)\n\n train_dataset = KittiDataset(\n self.opt.data_path, self.opt.gt_path, train_filenames, self.opt.height, self.opt.width,\n crph=self.opt.crph, crpw=self.opt.crpw, is_train=True, predang_path=self.opt.predang_path,\n semanticspred_path=self.opt.semanticspred_path\n )\n\n val_dataset = KittiDataset(\n self.opt.data_path, self.opt.val_gt_path, val_filenames, self.opt.height, self.opt.width,\n crph=self.opt.crph, crpw=self.opt.crpw, is_train=False, predang_path=self.opt.predang_path,\n semanticspred_path=self.opt.semanticspred_path\n )\n\n self.train_loader = DataLoader(\n train_dataset, self.opt.batch_size, shuffle=True,\n num_workers=self.opt.num_workers, pin_memory=True, drop_last=True)\n self.val_loader = DataLoader(\n val_dataset, self.opt.batch_size, shuffle=False,\n num_workers=self.opt.num_workers, pin_memory=True, drop_last=True)\n\n self.train_num = train_dataset.__len__()\n self.val_num = val_dataset.__len__()\n self.num_total_steps = self.train_num // self.opt.batch_size * self.opt.num_epochs","title":""},{"docid":"8bdbb0c84b292ff5d9ca46df7ef67483","score":"0.6332738","text":"def setup_config(self):\n \n pass","title":""},{"docid":"639b85d214507e0ebda3b0f7961fc135","score":"0.63219446","text":"def set_data_config(univ):\n data_config = DataSet()\n data_config.meta_data_root = './meta_data/universe_meta_data.json'\n data_config.symbols_list = univ\n\n return data_config","title":""},{"docid":"439d4e4c36d1933e5e57d08030c501ad","score":"0.63144433","text":"def initialize(self):\n self.options.declare('driver_uid')\n self.options.declare('cmdows_path')\n self.options.declare('kb_path')\n self.options.declare('data_folder')\n self.options.declare('base_xml_file')\n self.options.declare('show_model', default=False)","title":""},{"docid":"42f8a6e8f6443775f9d92577ff20c8ac","score":"0.62298006","text":"def addDataset(config, datasetName, **settings):\n datasetInstance = getattr(config.Datasets, datasetName, None)\n if datasetInstance == None:\n defaultInstance = getattr(config.Datasets, \"Default\", None)\n\n if defaultInstance != None:\n datasetInstance = copy.deepcopy(defaultInstance)\n datasetInstance._internal_name = datasetName\n setattr(config.Datasets, datasetName, datasetInstance)\n else:\n datasetInstance = config.Datasets.section_(datasetName)\n\n datasetInstance.Name = datasetName\n datasetInstance.Scenario = settings.get(\"scenario\", \"collision\")\n\n datasetInstance.section_(\"Repack\")\n datasetInstance.section_(\"Reco\")\n datasetInstance.section_(\"Alca\")\n datasetInstance.section_(\"WMBSPublish\")\n datasetInstance.section_(\"DQM\")\n\n default_proc_ver = settings.get(\"default_proc_ver\", None)\n\n datasetInstance.Repack.ProcessingVersion = settings.get(\"repack_proc_ver\",\n default_proc_ver)\n \n datasetInstance.Reco.DoReco = settings.get(\"do_reco\", False)\n datasetInstance.Reco.GlobalTag = settings.get(\"global_tag\", None)\n datasetInstance.Reco.CMSSWVersion = settings.get(\"reco_version\", \"Undefined\")\n datasetInstance.Reco.ConfigURL = settings.get(\"reco_configuration\",\n None)\n datasetInstance.Reco.ProcessingVersion = settings.get(\"reco_proc_ver\",\n default_proc_ver)\n\n datasetInstance.Alca.DoAlca = settings.get(\"do_alca\", False)\n datasetInstance.Alca.CMSSWVersion = settings.get(\"alca_version\", \"Undefined\")\n datasetInstance.Alca.ConfigURL = settings.get(\"alca_configuration\",\n None)\n datasetInstance.Alca.ProcessingVersion = settings.get(\"alca_proc_ver\",\n default_proc_ver)\n\n datasetInstance.WMBSPublish.DoWMBSPublish = settings.get(\"do_wmbs_publish\", False)\n datasetInstance.WMBSPublish.DataTiersTo = settings.get(\"wmbs_publish_data_tiers_to\", [] )\n\n datasetInstance.DQM.DoDQM = settings.get(\"do_dqm\", False)\n datasetInstance.DQM.CMSSWVersion = settings.get(\"dqm_version\", \"Undefined\")\n datasetInstance.DQM.ConfigURL = settings.get(\"dqm_configuration\", None)\n datasetInstance.DQM.ProcessingVersion = settings.get(\"dqm_proc_ver\",\n default_proc_ver)\n\n datasetInstance.CustodialNode = settings.get(\"custodial_node\", None)\n datasetInstance.ArchivalNode = settings.get(\"archival_node\", None)\n datasetInstance.CustodialPriority = settings.get(\"custodial_priority\",\n \"high\")\n datasetInstance.CustodialAutoApprove = settings.get(\"custodial_auto_approve\",\n False)\n\n datasetInstance.Tier1Skims = [] \n return","title":""},{"docid":"897595289fb6afd31cabf7530c3a7f82","score":"0.6225225","text":"def data_config():\n print(\"Data Config\\n\\n\")\n #pandas_sample(\"/data/datafinal_60-16.json\")\n utils.text_features_grouped(\"/data/datafinal_60-16.json\")","title":""},{"docid":"595a8cb3a99e03013cb593b7f3df49ae","score":"0.62173253","text":"def setup(self) -> None:\n self.conf.setup_seed(self.system_conf.train.seed)\n\n self._setup_datasets()\n logging.info(\"Train Data: %d\" % (len(self.train_dataset)))\n logging.info(\"Val Data: %d\" % (len(self.val_dataset)))\n\n self._net = self._setup_network().to(self.device)\n logging.info(\"Network: %s\" % str(self.net))\n\n self._crit = self._setup_crit()\n logging.info(\"Criterion: %s\" % str(self.crit))\n\n self._optim = self._setup_optim()\n logging.info(\"Optimizer: %s\" % str(self.optim))\n\n self._max_epoch = self.system_conf.train.max_epoch\n\n self._metrics = self._setup_metrics()\n logging.info(\"Metrics: %s\", str(self.metrics))\n\n self._setup_extra()\n\n if not osp.exists(self.output_path):\n os.mkdir(self.output_path)","title":""},{"docid":"6fa0547da979a18ecfd06dcada49a389","score":"0.62071174","text":"def set_defaults(args):\n # Check critical files exist\n data_dir = os.path.join(args.data_dir, args.dataset_name)\n if not args.only_test:\n train_src = os.path.join(data_dir, args.train_src)\n train_tgt = os.path.join(data_dir, args.train_tgt)\n if not os.path.isfile(train_src):\n raise IOError('No such file: %s' % train_src)\n if not os.path.isfile(train_tgt):\n raise IOError('No such file: %s' % train_tgt)\n train_src_tag = os.path.join(data_dir, args.train_src_tag)\n if not os.path.isfile(train_src_tag):\n raise IOError('No such file: %s' % train_src_tag)\n\n args.train_src_file = train_src\n args.train_tgt_file = train_tgt\n args.train_src_tag_file = train_src_tag\n\n dev_src = os.path.join(data_dir, args.dev_src)\n dev_tgt = os.path.join(data_dir, args.dev_tgt)\n if not os.path.isfile(dev_src):\n raise IOError('No such file: %s' % dev_src)\n if not os.path.isfile(dev_tgt):\n raise IOError('No such file: %s' % dev_tgt)\n dev_src_tag = os.path.join(data_dir, args.dev_src_tag)\n if not os.path.isfile(dev_src_tag):\n raise IOError('No such file: %s' % dev_src_tag)\n\n args.dev_src_file = dev_src\n args.dev_tgt_file = dev_tgt\n args.dev_src_tag_file = dev_src_tag\n\n return args","title":""},{"docid":"e32c3d2677821b54f65040d033d7a8e2","score":"0.6203413","text":"def prepare_data(self):\n self.dataset_class(\n self.hparams.data_dir, stage=self.train_stage,\n transform=self.train_transform, pre_transform=self.pre_transform,\n on_device_transform=self.on_device_train_transform, **self.kwargs)\n\n self.dataset_class(\n self.hparams.data_dir, stage=self.val_stage,\n transform=self.val_transform, pre_transform=self.pre_transform,\n on_device_transform=self.on_device_val_transform, **self.kwargs)\n\n self.dataset_class(\n self.hparams.data_dir, stage='test',\n transform=self.test_transform, pre_transform=self.pre_transform,\n on_device_transform=self.on_device_test_transform, **self.kwargs)","title":""},{"docid":"361871ad0dd1d6b9a68e98b17f6b5ff2","score":"0.6199397","text":"def dataset_settings(init_context) -> DatasetsSettings:\n return DatasetsSettings(**init_context.resource_config)","title":""},{"docid":"84a5b255a6591955af4470d1eff16b33","score":"0.61761314","text":"def initialize_options(self):\n self.test_module = None\n self.branch = False\n self.erase = False\n self.no_annotate = False\n self.no_html = False\n self.omit_prefixes = ['dendropy/test']","title":""},{"docid":"7ea4734f603530c5133ba7cd8a9eb26c","score":"0.61267513","text":"def initialize_options(self):","title":""},{"docid":"135e3e32af2dcd6946411957b57c4e68","score":"0.6121959","text":"def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n self.dir_ct = os.path.join(opt.dataroot, opt.phase) # create a path '/path/to/data/ct'\n # self.dir_mr = os.path.join(opt.dataroot, opt.phase + 'mr') # create a path '/path/to/data/mr'\n # self.dir_ct_label = os.path.join(opt.dataroot, 'trainct_lables') # create a path '/path/to/data/ct'\n # self.dir_mr_label = os.path.join(opt.dataroot, 'trainmr_lables') # create a path '/path/to/data/mr'\n #\n self.nii_paths = sorted(make_dataset(self.dir_ct, opt.max_dataset_size)) # load images from '/path/to/data/ct'\n # self.mr_paths = sorted(make_dataset(self.dir_mr, opt.max_dataset_size)) # load images from '/path/to/data/mr'\n # self.nii_paths_label = sorted(make_dataset(self.dir_ct_label, opt.max_dataset_size)) # load images from '/path/to/data/ct'\n # self.mr_paths_label = sorted(make_dataset(self.dir_mr_label, opt.max_dataset_size)) # load images from '/path/to/data/mr'\n #\n self.ct_size = len(self.nii_paths) # get the size of dataset ct\n # self.mr_size = len(self.mr_paths) # get the size of dataset mr\n # self.ct_size_label = len(self.nii_paths_label) # get the size of dataset ct\n # self.mr_size_label = len(self.mr_paths_label) # get the size of dataset mr\n mrtoct = self.opt.direction == 'mrtoct'\n input_nc = self.opt.output_nc if mrtoct else self.opt.input_nc # get the number of channels of input image\n output_nc = self.opt.input_nc if mrtoct else self.opt.output_nc # get the number of channels of output image\n # self.transform_ct = get_transform(self.opt)\n # self.transform_mr = get_transform(self.opt)","title":""},{"docid":"85c9cfef9956720172b07b0cc8e89e2a","score":"0.6118664","text":"def configure(self, options, conf):","title":""},{"docid":"1150774acded49446ae921ed58f3003b","score":"0.60992885","text":"def prepare_datasets(self):\n # download precomputed zca components and mean for CIFAR10\n #urllib.urlretrieve(\"http://cs.virginia.edu/~pcascante/zca_components.npy\", \"zca_components.npy\")\n #urllib.urlretrieve(\"http://cs.virginia.edu/~pcascante/zca_mean.npy\", \"zca_mean.npy\")\n\n # load data\n data = dataloaders.load_data_subsets(self.args.augPolicy, self.args.dataset, self.args.data_dir)\n self.set_data(data)\n\n # load zca for cifar10\n zca_components = np.load('zca_components.npy')\n zca_mean = np.load('zca_mean.npy')\n self.args.zca_components = zca_components\n self.args.zca_mean = zca_mean\n\n # get randomized set for training\n loaders = dataloaders.get_train_dataloaders(self.args.dataset, self.args.train_data, self.args.train_data_noT, self.args.batch_size, self.args.n_cpus, self.args.num_labeled, self.args.num_valid_samples, self.args.seed, self.args.set_labeled_classes, self.args.set_unlabeled_classes, ordered=False)\n self.set_loaders(loaders)\n\n # get test set if in debug mode and for final evaluation\n testloader = dataloaders.get_test_dataloader(self.args.test_data, self.args.batch_size, self.args.n_cpus)\n self.args.testloader = testloader","title":""},{"docid":"ed99b82fb40b8c617989188477577853","score":"0.6089538","text":"def config():\n num_workers = 4 # number of workers\n num_train = 0.8 # (rel.) number of training data\n num_val = 0.1 # (rel.) number of validation data\n batch_size = 128 # batch size\n mean = None # dict with property means of dataset\n stddev = None # dict with property stddev of dataset","title":""},{"docid":"d5cda168c9e0ee82a54bfa7ff106c61b","score":"0.60384506","text":"def setUp(self):\n with open(os.path.join(os.path.dirname(__file__),\n './data/oecd-canada.json')) as data_file:\n self.oecd_datasets = json.load(data_file,\n object_pairs_hook=OrderedDict)\n with open(os.path.join(os.path.dirname(__file__),\n './data/ons.json')) as data_file:\n self.ons_datasets = json.load(data_file,\n object_pairs_hook=OrderedDict)\n with open(os.path.join(os.path.dirname(__file__),\n './data/galicia-ds.json')) as data_file:\n self.galicia_dataset = json.load(data_file,\n object_pairs_hook=OrderedDict)\n with open(os.path.join(os.path.dirname(__file__),\n './data/galicia-2.0.json')) as data_file:\n self.galicia_2_dataset = json.load(data_file,\n object_pairs_hook=OrderedDict)\n with open(os.path.join(os.path.dirname(__file__),\n './data/QS104EW.json')) as data_file:\n self.uk_dataset = json.load(data_file,\n object_pairs_hook=OrderedDict)\n with open(os.path.join(os.path.dirname(__file__),\n './data/sample_data.json')) as data_file:\n self.sample_dataset = json.load(data_file,\n object_pairs_hook=OrderedDict)\n with open(os.path.join(os.path.dirname(__file__),\n './data/us-labor-ds.json')) as data_file:\n self.uslabor_dataset = json.load(data_file,\n object_pairs_hook=OrderedDict)\n with open(os.path.join(os.path.dirname(__file__),\n './data/statswe.json')) as data_file:\n self.sweden_dataset = json.load(data_file,\n object_pairs_hook=OrderedDict)\n with open(os.path.join(os.path.dirname(__file__),\n './data/A02Level.json')) as data_file:\n self.ons_dataset = json.load(data_file,\n object_pairs_hook=OrderedDict)\n with open(os.path.join(os.path.dirname(__file__),\n './data/CPI15.json')) as data_file:\n self.ons_cpi_dataset = json.load(data_file,\n object_pairs_hook=OrderedDict)\n with open(os.path.join(os.path.dirname(__file__),\n './data/collection.json')) as data_file:\n self.collection = json.load(data_file,\n object_pairs_hook=OrderedDict)\n with open(os.path.join(os.path.dirname(__file__),\n './data/cantabria.json')) as data_file:\n self.cantabria = json.load(data_file,\n object_pairs_hook=OrderedDict)\n with open(os.path.join(os.path.dirname(__file__),\n './data/dimension.json')) as data_file:\n self.dimension = json.load(data_file,\n object_pairs_hook=OrderedDict)","title":""},{"docid":"648dfc930bae8022a47d123f117ed435","score":"0.60320264","text":"def set_dataset_dirs(self):\n if self.verbose:\n print('==> Setup directories to store the data files.')\n\n self.save_data_dir = self.set_download_data_dir()\n self.save_cache_dir = self.set_download_cache_dir()","title":""},{"docid":"d6d64987e1216fe82eb812416d929806","score":"0.6015843","text":"def SetDataFlags(self):\n self.parser.add_option('--hostname', action='store', dest='hostname',\n help='String of hostname', metavar='hostname',\n default=None)\n self.AddFlagRule('hostname', required=True)\n self.parser.add_option('-z', '--zone-name', action='store',\n dest='zone_name', help='String of the zone name.',\n metavar='<zone-name>', default=None)\n self.SetAllFlagRule('zone_name', required=True)\n self.parser.add_option('-v', '--view-name', action='store',\n dest='view_name',\n help=('String of the view name <view-name>. '\n 'Example: \"internal\"'), metavar='<view-name>',\n default=None)\n self.SetAllFlagRule('view_name', required=True)\n self.parser.add_option('-r', '--recursive', action='store_true',\n dest='recursive', help='Use recursion during lookup',\n metavar='<recursive>', default=False)\n self.SetAllFlagRule('recursive', required=False)","title":""},{"docid":"3baeeaf9bdca35d14cb915d4207a4484","score":"0.60121846","text":"def _patch_cls_datasets(config: Config):\n\n assert \"data\" in config\n for subset in (\"train\", \"val\", \"test\", \"unlabeled\"):\n cfg = config.data.get(subset, None)\n if not cfg:\n continue\n cfg.type = \"OTXActionClsDataset\"\n cfg.otx_dataset = None\n cfg.labels = None","title":""},{"docid":"04897b630989af43d14a3f4950376ef6","score":"0.6010168","text":"def setOptions(self):\n self.parser.add_option('--outputpath',\n dest='outputpath',\n default=None,\n help='Where the files retrieved will be stored. Defaults to the results/ directory.',\n metavar='URL')\n\n self.parser.add_option('--dump',\n dest='dump',\n default=False,\n action='store_true',\n help='Instead of performing the transfer, dump the source URLs.')\n\n self.parser.add_option('--xrootd',\n dest='xroot',\n default=False,\n action='store_true',\n help='Give XrootD url for the file.')\n\n self.parser.add_option('--jobids',\n dest='jobids',\n default=None,\n help='Ids of the jobs you want to retrieve. Comma separated list of integers.',\n metavar='JOBIDS')\n self.parser.add_option('--checksum',\n dest='checksum',\n default='yes',\n help='Set it to yes if needed. It will use ADLER32 checksum' +\\\n 'Allowed values are yes/no. Default is yes.')\n self.parser.add_option('--command',\n dest='command',\n default=None,\n help='A command which to use. Available commands are LCG or GFAL.')","title":""},{"docid":"476ee9a160c8fa05d06febc9f02eb612","score":"0.5998177","text":"def initialize_options(self):\n self.include_extras = None\n self.include_all_extras = None\n self.extra_pkgs = []\n self.dependencies = []","title":""},{"docid":"579b7e4197bfa33717de9efbe81278d6","score":"0.59928983","text":"def set_configs(self):\n for config_item in ['tag_creds', 'encrypt_tag' ]:\n define(config_item)\n # Get configuration values from configuration filescript_name = path.basename(__file__)\n script_path = path.abspath(path.realpath(__file__))\n script_dir = path.split(script_path)[0]\n config_path = path.abspath(path.join(script_dir, 'model/EmsgEncrypt.conf'))\n options.parse_config_file(config_path)","title":""},{"docid":"9b0d02ca79a36d360f0feccd0c54aa80","score":"0.5978503","text":"def configure(self, **kwargs):","title":""},{"docid":"a1ad16369959a538e0df51f491bf1684","score":"0.5977355","text":"def __init__(self, config=None, save=False, store=True):\n \n if config:\n make_dataset(config, dataset=self, save=save, store=store)\n return","title":""},{"docid":"cb94cef3ba79e55937fcd091bf330001","score":"0.5949031","text":"def setup(self):\n\n # Create a dataset first\n data = {\n 'name': self.dataset_name,\n 'title': self.dataset_title,\n 'notes': self.dataset_notes\n }\n if not self.dataset_owner is None:\n data['owner_org'] = self.dataset_owner\n\n response = requests.post(\n '{0}/api/action/package_create'.format(self.ckan_url),\n data=json.dumps(data),\n headers={'Content-type': 'application/json',\n 'Authorization': self.api_key},\n verify=self.ssl_verify)\n\n if response.status_code != 200:\n exit('Error creating dataset: {0}'.format(response.content))\n\n dataset_id = response.json()['result']['id']\n\n # Then create a resource, empty at the beginning\n records = []\n\n # Manually set the field types to ensure they are handled properly\n fields = []\n for item in self.STRUCTURE:\n field = {\n 'id': item['id'],\n 'type': item['type']\n }\n fields.append(field)\n\n # Push the records to the DataStore table. This will create a resource\n # of type datastore.\n data = {\n 'resource': {\n 'package_id': dataset_id,\n 'name': self.resource_name,\n 'format': 'csv',\n 'notes': self.resource_notes\n },\n 'records': records,\n 'fields': fields,\n 'primary_key': self.PRIMARY_KEYS,\n }\n\n response = requests.post(\n '{0}/api/action/datastore_create'.format(self.ckan_url),\n data=json.dumps(data),\n headers={'Content-type': 'application/json',\n 'Authorization': self.api_key},\n verify=self.ssl_verify)\n\n if response.status_code != 200:\n exit('Error: {0}'.format(response.content))\n\n resource_id = response.json()['result']['resource_id']\n print('''\nDataset and DataStore resource successfully created with {0} records.\nPlease add the resource id to your ini file:\n\n[{2}]\nresource_id={1}\n '''.format(len(records), resource_id, self.CONFIG_SECTION))","title":""},{"docid":"dc819a6948fcb3aaadd381b6645841bc","score":"0.5931906","text":"def _setup_data(self):\r\n input = self.dataset\r\n pnt_attr, cell_attr = get_all_attributes(input)\r\n\r\n self._setup_data_arrays(cell_attr, 'cell')\r\n self._setup_data_arrays(pnt_attr, 'point')","title":""},{"docid":"a3508baacd0188bd0ec735325c3df5be","score":"0.5902139","text":"def configuration(self, dataset=None):\n\n dataset = dataset if dataset else self.dataset\n if dataset is None:\n raise Exception(\"dataset in configuration() and self.dataset couldn't be all None\")\n print \"Dataset being used is: \", dataset\n\n params_dict = {\"type\":\"configuration\", \"dataset\":dataset}\n\n return self.easy_response(params_dict, echo=True)","title":""},{"docid":"9f990afb8b62fa9eac5db6ddbd265485","score":"0.5896834","text":"def setup(args):\n cfg = get_cfg()\n add_deeplab_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n default_setup(cfg, args)\n return cfg","title":""},{"docid":"f69799c977e7e128b2c01740ab7acf86","score":"0.58842874","text":"def loadDatasetConfig ( self, dataset ):\n sql = \"SELECT ximagesize, yimagesize, startslice, endslice, zoomlevels, zscale, startwindow, endwindow, starttime, endtime from {} where dataset = \\'{}\\'\".format( ocpcaprivate.datasets, dataset )\n\n\n with closing(self.conn.cursor()) as cursor:\n try:\n cursor.execute ( sql )\n except MySQLdb.Error, e:\n\n logger.error (\"Could not query ocpca datasets database %d: %s. sql=%s\" % (e.args[0], e.args[1], sql))\n raise OCPCAError (\"Could not query ocpca datasets database %d: %s. sql=%s\" % (e.args[0], e.args[1], sql))\n\n # get the project information \n row = cursor.fetchone()\n\n # if the project is not found. error\n if ( row == None ):\n logger.warning ( \"Dataset %s not found.\" % ( dataset ))\n raise OCPCAError ( \"Dataset %s not found.\" % ( dataset ))\n\n [ ximagesz, yimagesz, startslice, endslice, zoomlevels, zscale, startwindow, endwindow, starttime, endtime ] = row\n return OCPCADataset ( int(ximagesz), int(yimagesz), int(startslice), int(endslice), int(zoomlevels), float(zscale), int(startwindow), int(endwindow), int(starttime), int(endtime) )","title":""},{"docid":"ba1018b1394c328561e6e2f8e7e15480","score":"0.58803207","text":"def configure(self, **kw):","title":""},{"docid":"3dd253dc9459c998a9d8ea0393ce31cf","score":"0.5868556","text":"def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n self.A_paths = sorted(make_dataset(opt.dataroot, opt.max_dataset_size))\n self.B_paths = sorted(make_dataset(opt.dataroot_style, opt.max_dataset_size))\n self.A_size = len(self.A_paths) # get the size of dataset A\n self.B_size = len(self.B_paths) # get the size of dataset B\n transform_list = []\n\n transform_list += [transforms.ToTensor()]\n transform_list += [transforms.Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])]\n self.transform = transforms.Compose(transform_list)","title":""},{"docid":"4abbe31fe7dcf08b0ad4383537989856","score":"0.5868543","text":"def setup(self, stage=None):\n # Load data and split into train and validation sets here\n # Assign train/val datasets for use in dataloaders\n if stage == \"fit\":\n self.tr_dataset = SeqDataDataset(\n self._tr_x, self._tr_yseq, self._tr_ys, self.normalization\n )\n if self.data_val is not None:\n self.val_dataset = SeqDataDataset(\n self._val_x, self._val_yseq, self._val_ys, self.normalization\n )\n else:\n raise NotImplementedError(\"We didn't implement for stage not `fit`\")","title":""},{"docid":"d53bb85f7069b772588b5b30b99a53a2","score":"0.5862461","text":"def SetDataFlags(self):\n self.parser.add_option('-v', '--view-name', action='store',\n dest='view_name',\n help=('String of the view name <view-name>. '\n 'Example: \"internal\"'), metavar='<view-name>',\n default=None)\n self.SetAllFlagRule('view_name', required=True)\n self.parser.add_option('-d', '--domain', action='store', dest='base_domain',\n help='String of the base domain.', default=None)\n self.SetAllFlagRule('base_domain', required=True)","title":""},{"docid":"a3c34d3ed95fb93b653890148126799b","score":"0.5855423","text":"def apply_options(self):\n\n Application.apply_options(self)\n\n self.conf = self.parse_config(self.args.config_file)","title":""},{"docid":"79652e4c0c1b993887037574c3d9f21f","score":"0.58543384","text":"def test_config(self):\n dataset = fluid.InMemoryDataset()\n dataset.set_parse_ins_id(True)\n dataset.set_parse_content(True)\n dataset._set_trainer_num(1)\n self.assertTrue(dataset.parse_ins_id)\n self.assertTrue(dataset.parse_content)\n self.assertEqual(dataset.trainer_num, 1)","title":""},{"docid":"269186104f91e9fba9bebd6ce52faf81","score":"0.58300835","text":"def update_config_(args):\n\n # increment the seed at each run\n args.seed = args.seed + args.run\n\n # multiply the number of examples by a factor size. Used to have number of examples depending\n # on number of labels. Usually factor is 1.\n args.datasize.n_examples = args.datasize.factor * args.datasize.n_examples\n\n if args.datasize.n_examples_test == \"train\":\n # use same number of train and test examples\n args.datasize.n_examples_test = args.datasize.n_examples\n\n if args.is_precompute_trnsf and args.train.trnsf_kwargs.is_train:\n # if training transformer then paths need to agree\n assert args.paths[\"trnsf_dirnames\"][0] == args.paths[\"chckpnt_dirnames\"][0]\n\n # monitor training when you randomize the labels because validation does not mean anything\n if args.dataset.kwargs.is_random_targets:\n args.train.trnsf_kwargs.monitor_best = \"train_loss_best\"\n args.train.clf_kwargs.monitor_best = \"train_loss_best\"\n\n if not args.train.is_tensorboard:\n args.paths[\"tensorboard_curr_dir\"] = None\n\n if args.experiment == \"gap\":\n # dib with Q++\n if args.model.name == \"vib\":\n args.model.loss.beta = args.model.loss.beta * 40\n\n elif args.model.name == \"cdibL\":\n args.model.loss.beta = args.model.loss.beta / 100\n\n elif args.model.name == \"cdibS\":\n args.model.loss.beta = args.model.loss.beta * 30\n\n if \"dibL\" in args.model.name:\n # dib with Q++\n args.model.Q_zx.hidden_size = args.model.Q_zy.hidden_size * 64\n\n if \"dibS\" in args.model.name:\n # dib with Q--\n args.model.Q_zx.hidden_size = args.model.Q_zy.hidden_size // 64\n\n if \"dibXS\" in args.model.name:\n # dib with Q------\n args.model.Q_zx.hidden_size = 1\n\n if \"dibXL\" in args.model.name:\n # dib with Q++++++++\n args.model.Q_zx.hidden_size = 8192\n\n short_long_monitor = dict(\n vloss=\"valid_loss_best\", tloss=\"train_loss_best\", vacc=\"valid_acc_best\"\n )\n\n # use short version for name of file\n args.train.monitor_best = invert_dict(short_long_monitor).get(\n args.train.monitor_best, args.train.monitor_best\n )\n\n hyperparam_path = hyperparam_to_path(args.hyperparameters)\n args.paths.merge_with(\n OmegaConf.create(\n format_container(args.paths, dict(hyperparam_path=hyperparam_path))\n )\n )\n # every change that should not modify the name of the file should go below this\n # ----------------------------------------------------------------------------\n\n # use long version in code\n args.train.monitor_best = short_long_monitor.get(\n args.train.monitor_best, args.train.monitor_best\n )\n args.train.trnsf_kwargs.monitor_best = short_long_monitor.get(\n args.train.trnsf_kwargs.monitor_best, args.train.trnsf_kwargs.monitor_best\n )\n args.train.clf_kwargs.monitor_best = short_long_monitor.get(\n args.train.clf_kwargs.monitor_best, args.train.clf_kwargs.monitor_best\n )\n\n if not args.is_precompute_trnsf:\n logger.info(\"Not precomputing the transformer so setting train=False.\")\n args.train.trnsf_kwargs.is_train = False\n args.train.kwargs.lr = args.train.lr_clf # ! DEV\n else:\n if args.model.name == \"wdecayBob\":\n args.train.weight_decay = 1e-4\n\n if args.model.name == \"dropoutBob\":\n args.encoder.architecture.dropout = 0.5\n\n if not args.datasize.is_valid_all_epochs and \"train\" in args.train.monitor_best:\n # don't validate all epochs when validation >>> training and you only look at training\n rm_valid_epochs_()\n\n if args.model.is_joint:\n args.model.gamma_force_generalization = 1\n\n if \"distractor\" in args.clfs.name and not args.is_precompute_trnsf:\n args.dataset.is_use_distractor = True\n\n if \"random\" in args.clfs.name and not args.is_precompute_trnsf:\n # if you want random dataset for classifier then make sure you are not randomizing for encoder\n args.dataset.kwargs.is_random_targets = True\n args.train.clf_kwargs.monitor_best = \"train_loss_best\" # don't monitor val\n\n if isinstance(args.train.kwargs.lr, str) and \"|\" in args.train.kwargs.lr:\n lr, lr_factor_zx = args.train.kwargs.lr.split(\"|\")\n args.train.kwargs.lr = float(lr)\n args.train.lr_factor_zx = float(lr_factor_zx)\n\n if args.model.name == \"vibL\":\n # keep alice the same but increase bob view of alice\n # vib with better approx of I[Z,Y] Q++\n args.model.Q_zy.hidden_size = args.model.Q_zy.hidden_size * 16\n\n if args.model.name == \"wdecay\":\n args.train.weight_decay = 1e-4\n\n if \"correlation\" in args.experiment:\n if args.train.optim == \"rmsprop\":\n if args.train.weight_decay == 0.0005:\n args.train.weight_decay = 0.0003\n\n elif args.train.optim == \"sgd\":\n args.train.kwargs.lr = args.train.kwargs.lr * 50\n\n if \"perminvcdib\" in args.model.name:\n args.encoder.architecture.hidden_size = [1024]\n args.model.architecture.z_dim = 1024\n args.model.Q_zy.hidden_size = 256\n args.model.Q_zy.n_hidden_layers = 1","title":""},{"docid":"4ba99130ce0a7fff3e2621bc00f6c426","score":"0.58281314","text":"def SetDataFlags(self):\n self.parser.add_option('--commit', action='store_true', dest='commit',\n help='Commits changes of hosts file without '\n 'confirmation.', default=False)\n self.parser.add_option('--no-commit', action='store_true', dest='no_commit',\n help='Suppresses changes of hosts file.',\n default=False)\n self.AddFlagRule(('no_commit', 'commit'), required=False,\n flag_type='independent_args')\n self.parser.add_option('-z', '--zone-name', action='store',\n dest='zone_name', help='String of the zone name.',\n metavar='<zone-name>', default=None)\n self.SetAllFlagRule('zone_name', required=True)\n self.parser.add_option('-v', '--view-name', action='store',\n dest='view_name',\n help=('String of the view name <view-name>. '\n 'Example: '\n '\"internal\"'), metavar='<view-name>',\n default='None')\n self.SetAllFlagRule('view_name', required=True)\n\n self.parser.add_option('-f', '--file', action='store', dest='file',\n help='File name of hosts file to write to database.',\n metavar='<file-name>', default='hosts_out')\n self.SetAllFlagRule('file', required=True)","title":""},{"docid":"4b5b1a0e46186531ab5207796d6c9535","score":"0.5824765","text":"def _load_options(self, opts):\n self.opts = opts\n self.options = opts.get(CONFIG_DATA_SECTION, {})\n\n # Validate required fields in app.config are set\n required_fields = [\"base_url\", \"username\", \"password\", \"query_limit\", \"query_ticket_grouping_types\",\n \"polling_interval\"]\n validate_fields(required_fields, self.options)\n\n # Create Secureworks client\n self.scwx_client = SCWXClient(self.opts, self.options)","title":""},{"docid":"176b8afc4112aebd8db23f9ce0803ed9","score":"0.58237094","text":"def setup(self, **kwargs):\n if self.data_dir is not None:\n path = os.path.join(self.data_dir, self.file_name)\n self.dataset = from_pickle(path)\n else:\n # Generate data set here\n X = []\n y = []\n t = []\n for init_cond in self.init_conditions:\n x0, p0, t0, t1, steps = init_cond\n q_p, dq_dp, _, ts = SHM_1D_model(x0=x0, p0=p0, t0=t0, t1=t1, steps=steps)\n X.append(q_p)\n y.append(dq_dp)\n t.append(ts)\n X = torch.cat(X, dim=0)\n y = torch.cat(y, dim=0)\n t = torch.cat(t, dim=0)\n self.dataset = TensorDataset(X, y, t)\n\n # save the dataset\n path = os.path.join(\"../../data/dataset\", self.file_name) # TODO: ugly af path, fix this sometime\n to_pickle(self.dataset, path)\n\n # Split the data set\n portion = [0.9, 0.1, 0.0]\n splits = [int(x * len(self.dataset)) for x in portion]\n if sum(splits) != len(self.dataset):\n splits[1] += len(self.dataset) - sum(splits)\n self.train_set, self.val_set, self.test_set = random_split(self.dataset, splits)","title":""},{"docid":"dce919713ec7a66e13650c49a093a55f","score":"0.5822433","text":"def init():\n global OPTIONS\n file_path = config_file()\n config = ConfigParser.ConfigParser()\n config.read(file_path)\n OPTIONS = { 'username': config.get(FLAGS.config_section, 'username'),\n 'password': config.get(FLAGS.config_section, 'password'),\n 'resource': config.get(FLAGS.config_section, 'resource'),\n 'server': config.get(FLAGS.config_section, 'server'),\n 'debug': config.getboolean(FLAGS.config_section, 'debug'),\n 'path': config.get(FLAGS.config_section, 'dds-path'),\n 'log_file': config.get(FLAGS.config_section, 'log') }\n if FLAGS.debug:\n OPTIONS['debug'] = FLAGS.debug\n if FLAGS.dds_path != '/':\n OPTIONS['path'] = FLAGS.dds_path\n if FLAGS.log_file:\n OPTIONS['log_file'] = FLAGS.log_file","title":""},{"docid":"ced7d23e13b2c08530cbb4c2b096dcdf","score":"0.58214647","text":"def setUp(self):\n Dataset.objects.create(title='Penguins of Antarctica', dataset_key='7b657080-f762-11e1-a439-00145eb45e9a',\n download_on=dateutil.parser.parse('2010-08-06T13:05:40.000+0000'))\n Dataset.objects.create(title='Antarctic, Sub-Antarctic and cold temperate echinoid database',\n dataset_key='d8b06df0-81b3-41c9-bcf8-6ba5242e2b95',\n download_on=dateutil.parser.parse('2110-04-06T13:05:40.000+0000'))","title":""},{"docid":"417f98a3cdb124bff69dca7fbbf39c7e","score":"0.58187026","text":"def setOptions(self):\n self.parser.add_option('--site',\n dest='sitename',\n default=None,\n help='The PhEDEx node name of the site to be checked.')\n self.parser.add_option('--lfn',\n dest='userlfn',\n default=None,\n help='A user lfn address.')\n self.parser.add_option('--checksum',\n dest='checksum',\n default='no',\n help='Set it to yes if needed. It will use ADLER32 checksum' +\\\n 'Allowed values are yes/no. Default is no.')\n self.parser.add_option('--command',\n dest='command',\n default=None,\n help='A command which to use. Available commands are LCG or GFAL.')","title":""},{"docid":"251da479f626b20aa41538b68dee11b6","score":"0.5818446","text":"def update_config_datasets_(args, datasets):\n args.datasize.n_examples = len(datasets[\"train\"]) # store as an integer\n steps_per_epoch = len(datasets[\"train\"]) // args.datasize.batch_size\n args.model.loss.warm_Q_zx = steps_per_epoch * args.model.loss.warm_Q_zx\n\n count_targets = datasets[\"train\"].count_targets()\n with omegaconf.open_dict(args):\n args.model.loss.n_per_target = {\n str(k): int(v) for k, v in count_targets.items()\n }","title":""},{"docid":"7e15ba5686390d0ad67491ea8e89891b","score":"0.5818113","text":"def setup_task(cls, args, **kwargs):\n args.left_pad_source = options.eval_bool(args.left_pad_source)\n args.left_pad_target = options.eval_bool(args.left_pad_target)\n if getattr(args, 'raw_text', False):\n utils.deprecation_warning('--raw-text is deprecated, please use --dataset-impl=raw')\n args.dataset_impl = 'raw'\n elif getattr(args, 'lazy_load', False):\n utils.deprecation_warning('--lazy-load is deprecated, please use --dataset-impl=lazy')\n args.dataset_impl = 'lazy'\n\n paths = args.data.split(':')\n assert len(paths) > 0\n # find language pair automatically\n if args.source_lang is None or args.target_lang is None:\n raise Exception('Could not infer language pair, please provide it explicitly') # See comment below?\n # TRY ADDING: --source-lang true_w,true_p --target-lang reco_w\n # OR ENABLING BELOW HARDCODED DEFAULTS.\n # args.source_lang = \"true_w,true_p\"\n # args.target_lang = \"reco_w\"\n # args.source_lang, args.target_lang = data_utils.infer_language_pair(paths[0])\n\n assert type(args.source_lang) == str\n if ',' not in args.source_lang:\n raise Exception(\"source-lang is \" + args.source_lang + \" source-lang needs to contain two comma separated strings\")\n # load dictionaries\n src_lang1, src_lang2 = args.source_lang.split(',')\n src_dict1 = cls.load_dictionary(os.path.join(paths[0], 'dict.{}.txt'.format(src_lang1)))\n src_dict2 = cls.load_dictionary(os.path.join(paths[0], 'dict.{}.txt'.format(src_lang2)))\n if ',' in args.target_lang:\n assert args.criterion == 'cross_entropy_dual'\n target_lang, target_lang_extra = args.target_lang.split(',')\n tgt_dict = cls.load_dictionary(os.path.join(paths[0], 'dict.{}.txt'.format(target_lang)))\n tgt_dict_extra = cls.load_dictionary(os.path.join(paths[0], 'dict.{}.txt'.format(target_lang_extra)))\n dual_decoder = True\n else:\n tgt_dict = cls.load_dictionary(os.path.join(paths[0], 'dict.{}.txt'.format(args.target_lang)))\n dual_decoder = False\n tgt_dict_extra = None\n\n assert src_dict1.pad() == tgt_dict.pad()\n assert src_dict1.eos() == tgt_dict.eos()\n assert src_dict1.unk() == tgt_dict.unk()\n assert src_dict2.pad() == tgt_dict.pad()\n assert src_dict2.eos() == tgt_dict.eos()\n assert src_dict2.unk() == tgt_dict.unk()\n print('| [{}] dictionary: {} types'.format(src_lang1, len(src_dict1)))\n print('| [{}] dictionary: {} types'.format(src_lang2, len(src_dict2)))\n\n if dual_decoder:\n print('| [{}] dictionary: {} types'.format(target_lang, len(tgt_dict)))\n print('| [{}] dictionary: {} types'.format(target_lang_extra, len(tgt_dict_extra)))\n else:\n print('| [{}] dictionary: {} types'.format(args.target_lang, len(tgt_dict)))\n return cls(args, src_dict1, src_dict2, tgt_dict, tgt_dict_extra)","title":""},{"docid":"bc0666efd09345cbba308492506d3049","score":"0.5815576","text":"def setup(config, *args, **kwargs):\n cfg_rest = config.get('rest',{}).get('datasets',{})\n db_cfg = cfg_rest.get('database',{})\n\n # add indexes\n db = pymongo.MongoClient(**db_cfg).datasets\n if 'dataset_id_index' not in db.datasets.index_information():\n db.datasets.create_index('dataset_id', name='dataset_id_index', unique=True)\n\n handler_cfg = RESTHandlerSetup(config, *args, **kwargs)\n handler_cfg.update({\n 'database': motor.motor_tornado.MotorClient(**db_cfg).datasets,\n })\n\n return [\n (r'/datasets', MultiDatasetHandler, handler_cfg),\n (r'/datasets/(?P<dataset_id>\\w+)', DatasetHandler, handler_cfg),\n (r'/datasets/(?P<dataset_id>\\w+)/description', DatasetDescriptionHandler, handler_cfg),\n (r'/datasets/(?P<dataset_id>\\w+)/status', DatasetStatusHandler, handler_cfg),\n (r'/datasets/(?P<dataset_id>\\w+)/priority', DatasetPriorityHandler, handler_cfg),\n (r'/datasets/(?P<dataset_id>\\w+)/jobs_submitted', DatasetJobsSubmittedHandler, handler_cfg),\n (r'/dataset_summaries/status', DatasetSummariesStatusHandler, handler_cfg),\n ]","title":""},{"docid":"8942ed4edf5da3d5025a84b2e11af2be","score":"0.5805535","text":"def configure_options(self):\n super(MRKmeansStep, self).configure_options()\n self.add_file_option('--prot')","title":""},{"docid":"4f59d041f51b621960766ae1a0ffa9e3","score":"0.5804469","text":"def _init_all_settings(self): # noqa: C901\n self.config = self.trainer.config\n if self.trainer.hps and self.trainer.hps.get('trainer'):\n self.config.from_json(self.trainer.hps.get('trainer'))\n self.trainer._init_distributed_setting()\n if self.config.cuda:\n self.trainer._init_cuda_setting()\n self.epochs = self.trainer.epochs\n self.distributed = self.trainer.distributed\n self.trainer.model = self._init_model()\n self.model = self.trainer.model\n self.use_syncbn = self.config.syncbn\n self.trainer.use_syncbn = self.use_syncbn\n if self.use_syncbn:\n self.trainer.model = apex.parallel.convert_syncbn_model(self.trainer.model)\n self.trainer.optimizer = self._init_optimizer()\n self.use_ema = hasattr(self.config, 'model_ema')\n if self.use_ema:\n self.model_ema = self._init_model_ema()\n self.trainer.lr_scheduler = self._init_lr_scheduler()\n self.trainer.loss = self._init_loss()\n if self.distributed:\n self.trainer._init_horovod_setting()\n self.use_amp = self.config.amp\n if self.use_amp:\n self.trainer.model, self.trainer.optimizer = amp.initialize(self.trainer.model,\n self.trainer.optimizer,\n opt_level='O1')\n self._init_dataloader()\n self.trainer.valid_metrics = self.trainer._init_metrics(None)\n self.trainer.callbacks._set_params(self.trainer)\n\n # self.trainer.has_built = True","title":""},{"docid":"037920f09c9fda6c8d2319cff3359cd4","score":"0.58026177","text":"def _init_distributed_setting(self):\n context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True)\n set_algo_parameters(elementwise_op_strategy_follow=True)\n context.set_auto_parallel_context(all_reduce_fusion_config=self.config.all_reduce_fusion_config)","title":""},{"docid":"f0b933ceb5b2f7aa85158e2f2d6f0e54","score":"0.5801967","text":"def set_spark_configs(d):\n global __init_configs_spark\n __init_configs_spark.update(d)","title":""},{"docid":"23e97844b77ada7ca3be915c95f5d49e","score":"0.5799616","text":"def __init__(self,config):\n self.backbone_model = config[\"BackboneClassifier\"]\n self.model_input_size = config[\"ModelInputSize\"]\n self.processed_dataset_folder = config[\"ProcessedDatasetFolder\"]\n self.scale = config[\"Model\"][\"Scale\"]\n self.aspect_ratio = config[\"Model\"][\"AspectRatio\"]\n self.dataset = []\n\n self.load_dataset()","title":""},{"docid":"16d79fab1606d7c0048dd45fe3e94d64","score":"0.57891893","text":"def __init__(self, config_manager):\n config = config_manager.get_config()\n raw_filepath = config['GENERAL']['dataset_path']\n assert os.path.isdir(raw_filepath), \\\n \"The dataset filepath provided is not valid.\"\n self._raw_filepath = raw_filepath\n self._mappings = None\n self._validate_data()\n self._port = config.getint(\"BLOCKCHAIN\", \"http_port\")\n self._frac = float(config['DATASET_MANAGER']['sample_fraction'])\n self._ipfs_client = None\n self._db_client = None\n self.classification = config['DATASET_MANAGER']['category']","title":""},{"docid":"85cae6c42b4846bef2e45b30111971bb","score":"0.57816124","text":"def prepare_dataset(self):\n self.ctx.log(\"Checking dataset...\")\n selected = self.ctx.config.get('dataset', None)\n if selected is None:\n if DATASET_NAME in self._get_datasets():\n # try to select existing\n AugerConfig(self.ctx).set_data_set(\n DATASET_NAME, '').set_experiment(None)\n self.dataset = DataSet(self.ctx, self.project, DATASET_NAME)\n else:\n # or create new\n self.ctx.log(\"No dataset found, creating the first one...\")\n source = self.ctx.config.get('source', None)\n dataset = DataSet(self.ctx, self.project).create(source)\n AugerConfig(self.ctx).set_data_set(\n dataset.name, source).set_experiment(None)\n self.dataset = DataSet(self.ctx, self.project, dataset.name)\n else:\n self.dataset = DataSet(self.ctx, self.project, selected)\n self.ctx.log(\"Currently selected: %s\" % selected)","title":""},{"docid":"9b03d68dca762028cdbdfb4fba89a315","score":"0.57779884","text":"def initialize_options(self):\n build_ext.initialize_options(self)\n self.gen_compiledb = None","title":""},{"docid":"32338252892318ac753ebf3bc1894131","score":"0.5775403","text":"def prepare_data(self):\n self.generic_dataset = LIDCNodulesDataset(**self.dataset_params.params)\n log.info(f\"DATASET SIZE: {len(self.generic_dataset)}\")\n\n self.tensor_dataset_path = self.__prepare_tensor_dataset()\n\n self.aug_transform = transforms.Compose([T.FlipNodule3D(), T.RotNodule3D()])\n self.dataset = DatasetFolder(\n self.tensor_dataset_path, torch.load, (\"pt\"), transform=self.__data_transform\n )\n self.dataset.norm = self.generic_dataset.norm\n\n train_inds, val_inds, test_inds = H.train_val_holdout_split(\n self.dataset, ratios=[0.85, 0.14, 0.01]\n )\n self.train_sampler = SubsetRandomSampler(train_inds)\n self.val_sampler = SubsetRandomSampler(val_inds)\n self.test_subset = Subset(self.dataset, test_inds)","title":""},{"docid":"4b638322e2623e701768a00af7dbc729","score":"0.5774466","text":"def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n self.sub_list = os.listdir(opt.dataroot)\n self.opt = opt\n\n self.input_nc = self.opt.input_nc\n self.output_nc = self.opt.output_nc","title":""},{"docid":"d733b46ea79ecbd913d57f221312701f","score":"0.57649636","text":"def configure(**config):","title":""},{"docid":"305326bce27463db1ff1340000465d08","score":"0.57591677","text":"def setup(self, **kwargs):\n if self.data_dir is not None:\n path = os.path.join(self.data_dir, self.file_name)\n self.dataset = from_pickle(path)\n else:\n # Generate data set here\n X = []\n y = []\n t = []\n for init_cond in self.init_conditions:\n x0, p0, t0, t1, steps = init_cond\n q_p, _, _, ts = SHM_1D_model(x0=x0, p0=p0, t0=t0, t1=t1, steps=steps) # TODO: merge acceleration\n ddq = -1. / 1. * q_p[:, 0]\n\n X.append(q_p) # for k=1, m=1 -> p = dq\n y.append(ddq)\n t.append(ts)\n\n X = torch.cat(X, dim=0)\n y = torch.cat(y, dim=0)\n t = torch.cat(t, dim=0)\n self.dataset = TensorDataset(X, y, t)\n\n # save the dataset\n path = os.path.join(\"../../data/dataset\", self.file_name) # TODO: ugly af path, fix this sometime\n to_pickle(self.dataset, path)\n\n # Split the data set\n portion = [0.9, 0.1, 0.0]\n splits = [int(x * len(self.dataset)) for x in portion]\n if sum(splits) != len(self.dataset):\n splits[1] += len(self.dataset) - sum(splits)\n self.train_set, self.val_set, self.test_set = random_split(self.dataset, splits)","title":""},{"docid":"45ab4f4b977270c52ea7170a69452946","score":"0.57569444","text":"def setup(self, **kwargs):\n self.configured = True","title":""},{"docid":"4da6a542d4145defa684de097a410c80","score":"0.57546705","text":"def setUpTestData(cls):\n\n dataset_create_data = {\n \"verbose_name\": \"0307-全键盘-0号麦克风-2000个样本点\",\n \"dataset_name\": \"20210307\",\n \"data_type\": \"all-0micro\",\n \"project_type\": \"single\",\n \"description\": \"cxx_0307_1__0306_123\",\n \"description_more\": \"0307-30键训练集，其余为测试集，0号麦克风，2000个样本点\",\n \"length\": 2000,\n \"fs\": 48000,\n }\n\n cls.dataset_obj = models.Dataset(**dataset_create_data)\n cls.dataset_obj.save()\n\n model_inst_create_data = {\n \"dataset_id\": cls.dataset_obj.id,\n \"algorithm\": \"blstm\",\n \"save_path\": os.path.join(settings.MODEL_INST_ROOT, \"dataset_id:10:algorithm:blstm/inst_id:7\"),\n \"extra_info\": {\"input_shape_list\": [4, 14], \"max_label_num\": 29},\n \"is_ready\": True,\n \"is_latest\": True,\n }\n cls.model_inst_obj = models.AlgorithmModelInst(**model_inst_create_data)\n cls.model_inst_obj.save()\n\n cls.dataset_unit = load.load_dataset_unit(\n save_path=os.path.join(settings.LIB_ROOT, \"preprocessed_data/2250.pickle\")\n )\n\n super().setUpTestData()","title":""},{"docid":"1e960d29c1acc31b10266df4650ec99b","score":"0.57419044","text":"def init_dataset(arguments):\n if arguments.method == 'train':\n return DataSetTrain()\n else:\n return DataSetTest()","title":""},{"docid":"13a26e6d4a9baaaab00566c05b761988","score":"0.57379067","text":"def config(self, **kw):\n self.cfg_fixture.config(**kw)","title":""},{"docid":"87306fb100f6faa2896cf7c156c31496","score":"0.57375973","text":"def configure(self):\n pass","title":""},{"docid":"87306fb100f6faa2896cf7c156c31496","score":"0.57375973","text":"def configure(self):\n pass","title":""},{"docid":"87306fb100f6faa2896cf7c156c31496","score":"0.57375973","text":"def configure(self):\n pass","title":""},{"docid":"87306fb100f6faa2896cf7c156c31496","score":"0.57375973","text":"def configure(self):\n pass","title":""},{"docid":"fc0d8e116065f043c61c92a457e6c012","score":"0.57354635","text":"def __config_init(self):\n ini_path = os.path.join('app', 'common', 'oracle_sql.ini')\n ini_container = 'oracle_sql'\n self.sql_data = get_all_data_from_ini_file(ini_path, ini_container)","title":""},{"docid":"84c705b6bdc8db6f23dcf91809dd0684","score":"0.5712451","text":"def configure(self):\r\n pass","title":""},{"docid":"315dd1255f21dcb80630fa1a33a535a5","score":"0.5712033","text":"def setup_class(cls):\n cod.get_data_jhu(data_type=\"all\", region=\"global\", update=True)\n cod.get_data_jhu(data_type=\"all\", region=\"us\", update=True)\n\n cod.get_data_nyt(data_type=\"all\", counties=False, update=True)\n cod.get_data_nyt(data_type=\"all\", counties=True, update=True)","title":""},{"docid":"f10a61fe63d5616d5cc284d2c5156940","score":"0.57063013","text":"def set_config(self):\n\n self.parser['CONNECTION'] = strings.get_config_connection()\n self.parser['DATABASE'] = strings.get_config_db()\n self.parser['METRICS'] = strings.get_config_metrics()\n\n with open(strings.get_config_path(), 'w') as file:\n self.parser.write(file)","title":""},{"docid":"ebd8368c0cbe6209283b2c1bd611d90f","score":"0.5705125","text":"def initialize_config(self):\n return {\n \"model_type\": \"bert_query\",\n \"name\": \"test_bert_query\",\n \"model_path\": os.path.join(self.model_path, \"test_bert_query\"),\n \"setup_args\": {\n \"threshold\": 0.8,\n \"seed\": [\n \"This is a cat\",\n \"Arya is a hungry cat.\",\n ],\n \"infer_config\": {\n \"k\": 2,\n \"segment_config\": {\"window_size\": 5, \"step_size\": 3},\n },\n },\n }","title":""},{"docid":"c37f5744959a1e3cc85e2a90e50fc18a","score":"0.5704601","text":"def initialize_from_config(self):","title":""},{"docid":"0be632c8f9133b049eb8f68a25d7bb8d","score":"0.56914246","text":"def setup(self, stage):\n trainval_test_split = cfg['data']['trainval_test_split']\n graph_train_val, self.graph_test = self.graph_data.split_data(test_size=trainval_test_split)\n\n train_val_split = cfg['data']['train_val_split']\n self.graph_train, self.graph_val = graph_train_val.split_data(test_size=train_val_split)","title":""},{"docid":"4a37839be163787057d5725023934369","score":"0.5689095","text":"def _init_dataloader(self):\n if self.distributed and hvd.local_rank() == 0 and 'remote_data_dir' in self.config.dataset:\n FileOps.copy_folder(self.config.dataset.remote_data_dir, self.config.dataset.data_dir)\n if self.distributed:\n hvd.join()\n args = self.config.dataset\n train_dir = os.path.join(self.config.dataset.data_dir, 'train')\n dataset_train = Dataset(train_dir)\n world_size, rank = None, None\n if self.distributed:\n world_size, rank = hvd.size(), hvd.rank()\n self.trainer.train_loader = create_loader(\n dataset_train,\n input_size=tuple(args.input_size),\n batch_size=args.batch_size,\n is_training=True,\n use_prefetcher=self.config.prefetcher,\n rand_erase_prob=args.reprob,\n rand_erase_mode=args.remode,\n rand_erase_count=args.recount,\n color_jitter=args.color_jitter,\n auto_augment=args.aa,\n interpolation='random',\n mean=tuple(args.mean),\n std=tuple(args.std),\n num_workers=args.workers,\n distributed=self.distributed,\n world_size=world_size,\n rank=rank\n )\n valid_dir = os.path.join(self.config.dataset.data_dir, 'val')\n dataset_eval = Dataset(valid_dir)\n self.trainer.valid_loader = create_loader(\n dataset_eval,\n input_size=tuple(args.input_size),\n batch_size=4 * args.batch_size,\n is_training=False,\n use_prefetcher=self.config.prefetcher,\n interpolation=args.interpolation,\n mean=tuple(args.mean),\n std=tuple(args.std),\n num_workers=args.workers,\n distributed=self.distributed,\n world_size=world_size,\n rank=rank\n )\n self.trainer.batch_num_train = len(self.trainer.train_loader)\n self.trainer.batch_num_valid = len(self.trainer.valid_loader)","title":""},{"docid":"7f0a32ab2cf50c419bde50c74f591b95","score":"0.56888485","text":"def _create_default_config(self):\n\t\tparser = self._parser\n\n\t\tparser.remove_section(\"world\")\n\t\tparser.add_section(\"world\")\n\t\tparser.set(\"world\", \"class\", \"portage.sets.base.DummyPackageSet\")\n\t\tparser.set(\"world\", \"packages\", \"@selected @system\")\n\n\t\tparser.remove_section(\"selected\")\n\t\tparser.add_section(\"selected\")\n\t\tparser.set(\"selected\", \"class\", \"portage.sets.files.WorldSelectedSet\")\n\n\t\tparser.remove_section(\"system\")\n\t\tparser.add_section(\"system\")\n\t\tparser.set(\"system\", \"class\", \"portage.sets.profiles.PackagesSystemSet\")\n\n\t\tparser.remove_section(\"usersets\")\n\t\tparser.add_section(\"usersets\")\n\t\tparser.set(\"usersets\", \"class\", \"portage.sets.files.StaticFileSet\")\n\t\tparser.set(\"usersets\", \"multiset\", \"true\")\n\t\tparser.set(\"usersets\", \"directory\", \"%(PORTAGE_CONFIGROOT)setc/portage/sets\")\n\t\tparser.set(\"usersets\", \"world-candidate\", \"true\")\n\n\t\tparser.remove_section(\"live-rebuild\")\n\t\tparser.add_section(\"live-rebuild\")\n\t\tparser.set(\"live-rebuild\", \"class\", \"portage.sets.dbapi.VariableSet\")\n\t\tparser.set(\"live-rebuild\", \"variable\", \"INHERITED\")\n\t\tparser.set(\"live-rebuild\", \"includes\", \"bzr cvs darcs git git-2 mercurial subversion tla\")\n\n\t\tparser.remove_section(\"module-rebuild\")\n\t\tparser.add_section(\"module-rebuild\")\n\t\tparser.set(\"module-rebuild\", \"class\", \"portage.sets.dbapi.OwnerSet\")\n\t\tparser.set(\"module-rebuild\", \"files\", \"/lib/modules\")\n\n\t\tparser.remove_section(\"preserved-rebuild\")\n\t\tparser.add_section(\"preserved-rebuild\")\n\t\tparser.set(\"preserved-rebuild\", \"class\", \"portage.sets.libs.PreservedLibraryConsumerSet\")\n\n\t\tparser.remove_section(\"x11-module-rebuild\")\n\t\tparser.add_section(\"x11-module-rebuild\")\n\t\tparser.set(\"x11-module-rebuild\", \"class\", \"portage.sets.dbapi.OwnerSet\")\n\t\tparser.set(\"x11-module-rebuild\", \"files\", \"/usr/lib/xorg/modules\")\n\t\tparser.set(\"x11-module-rebuild\", \"exclude-files\", \"/usr/bin/Xorg\")","title":""},{"docid":"633037df3a479ad464a73f033a299a85","score":"0.56801337","text":"def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n self.dir = os.path.join(opt.dataroot, opt.phase)\n self.AB_paths = [e[1] for e in sorted(make_numbering_dataset(self.dir, opt.max_dataset_size), key=lambda idx: idx[0])]\n assert(opt.input_nc == 1 and opt.output_nc == 2 and opt.direction == 'AtoB')\n self.transform = get_transform(self.opt, convert=False)\n\n with open(opt.captions, 'rb') as f:\n x = pickle.load(f)\n train_captions, test_captions = x[0], x[1]\n self.captions = train_captions if opt.phase == \"train\" else test_captions\n self.ixtoword, self.wordtoix = x[2], x[3]\n del x, train_captions, test_captions\n self.n_words = len(self.ixtoword)\n print('Load from: ', opt.captions)\n self.captions_per_image = opt.captions_per_image\n self.text_words_num = opt.text_words_num","title":""},{"docid":"8c84239b35e972b729c9bc01483c8ef1","score":"0.5676815","text":"def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n self.dir_x = os.path.join(opt.dataroot, opt.phase + '_x') \n self.dir_yi = os.path.join(opt.dataroot, opt.phase + '_yi') \n self.dir_yj = os.path.join(opt.dataroot, opt.phase + '_yj') \n\n self.x_paths = [os.path.join(self.dir_x,s) for s in sorted(os.listdir(self.dir_x))]\n self.yi_paths = [os.path.join(self.dir_yi,s) for s in sorted(os.listdir(self.dir_yi))]\n self.yj_paths = [os.path.join(self.dir_yj,s) for s in sorted(os.listdir(self.dir_yj))]\n\n self.dataset_size = len(self.x_paths) # get the size of dataset \n\n self.transform = transforms.Compose([\n # transforms.Resize((132, 100)), \n # transforms.RandomCrop((128, 96)), # basic augmentation\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n ])","title":""},{"docid":"4cd8fc301a87cbd29a67e725911a4680","score":"0.5671616","text":"def setup(args):\n cfg = get_cfg()\n add_kd_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n default_setup(cfg, args)\n return cfg","title":""},{"docid":"8b210d74c700b39f10a44632902daf08","score":"0.5665934","text":"def update_mart_config_from_args(\r\n config: Dict, args: argparse.Namespace, *, verbose: bool = True) -> Dict[str, Any]:\r\n if args.debug:\r\n config[\"debug\"] = True\r\n if verbose:\r\n print(f\" Change config: Set debug to True\")\r\n if args.dataset_max is not None:\r\n assert args.dataset_max > 0, \"--dataset_max must be positive int.\"\r\n config[\"dataset_train\"][\"max_datapoints\"] = args.dataset_max\r\n config[\"dataset_val\"][\"max_datapoints\"] = args.dataset_max\r\n if verbose:\r\n print(f\" Change config: Set dataset_(train|val).max_datapoints to {args.dataset_max}\")\r\n if args.preload:\r\n config[\"dataset_train\"][\"preload\"] = True\r\n config[\"dataset_val\"][\"preload\"] = True\r\n if verbose:\r\n print(f\" Change config: Set dataset_(train|val).preload to True\")\r\n if args.no_preload or args.validate:\r\n config[\"dataset_train\"][\"preload\"] = False\r\n config[\"dataset_val\"][\"preload\"] = False\r\n if verbose:\r\n print(f\" Change config: Set dataset_(train|val).preload to False (--no_preload or --validate)\")\r\n return config","title":""},{"docid":"e356167dd2993ae85357d6569ba88e41","score":"0.5659186","text":"def initialize_data(cls, host_override, directory_override, depth_override):\n cls.HOST = test.select_host(config.HOST, host_override)\n cls.DATA = test.select_directory(config.DATA, directory_override)\n cls.DEPTH = test.select_depth(config.DEPTH, depth_override)\n cls.BASE_URL = test.create_base_url(cls.HOST, cls.PORT)\n cls.JSON_FILE = test.create_json_reference(cls.DATA, cls.JSON_NAME)","title":""},{"docid":"5fc63dffb31d932eb1ad9d628eeced26","score":"0.56516856","text":"def config(self, **kwargs: Any) -> None:","title":""},{"docid":"5fc63dffb31d932eb1ad9d628eeced26","score":"0.56516856","text":"def config(self, **kwargs: Any) -> None:","title":""},{"docid":"97b37b76b3b44ffd7f424f34ad8d386a","score":"0.5646425","text":"def __init__(self, config):\n # this will store the training data headers for value ordering\n self.data_headers = None\n # attribute pre-selection (remove class attribute from select_attr)\n self.class_attr = config['class_attr']\n self.select_attr = config.get('select_attr', [])\n if self.class_attr in self.select_attr:\n self.select_attr.remove(self.class_attr)\n # part of the training data to be used\n self.train_part = config.get('train_part', 1)","title":""},{"docid":"6e717025eb1177f42fedff0e414f3b4c","score":"0.5639404","text":"def setUp(self):\n self.options = Options()\n test_path = os.path.dirname(os.path.realpath(__file__))\n self.group_dir = os.path.join(test_path, 'fitbenchmarking_results')\n os.mkdir(self.group_dir)\n self.table_names = {\"compare\": \"compare_table_name.\",\n \"runtime\": \"runtime_table_name.\"}\n self.table_descriptions = {\"compare\": \"compare table descriptions\",\n \"runtime\": \"runtime table descriptions\",\n \"both\": \"both table descriptions\"}\n self.group_name = \"random_name\"","title":""},{"docid":"5856fd48318caf4ef2dcc562a8e174e2","score":"0.56270903","text":"def __init__(self, opt):\n #super(BasicDataset).__init__(self, opt)\n self.dir = os.path.join(opt.dataroot, opt.phase) # create a path '/path/to/data/train'\n\n self.paths = sorted(self.make_dataset(self.dir, opt.max_dataset_size)) # load images from '/path/to/data/train'\n self.size = len(self.paths) # get the size of dataset A\n self.input_nc = opt.input_nc # get the number of channels of input image\n self.transform = self.get_transform(opt, grayscale=(self.input_nc == 1))","title":""},{"docid":"352a1b5ea00063885d197137c0d14d32","score":"0.5625312","text":"def setup(self, **kwargs):\n self.log('Setting up...', self.LOG_USER)\n\n self.set_read_all(write_log=True)\n self.set_defaults_pv(write_log=True)\n\n # The per band configs. May want to make available per-band values.\n smurf_init_config = self.config.get('init')\n bands = smurf_init_config['bands']\n for b in bands:\n self.set_iq_swap_in(b, smurf_init_config['iqSwapIn'], \n write_log=True, **kwargs)\n self.set_iq_swap_out(b, smurf_init_config['iqSwapOut'], \n write_log=True, **kwargs)\n self.set_ref_phase_delay(b, smurf_init_config['refPhaseDelay'], \n write_log=True, **kwargs)\n self.set_ref_phase_delay_fine(b, \n smurf_init_config['refPhaseDelayFine'], write_log=True, \n **kwargs)\n self.set_tone_scale(b, smurf_init_config['toneScale'], \n write_log=True, **kwargs)\n self.set_analysis_scale(b, smurf_init_config['analysisScale'], \n write_log=True, **kwargs)\n self.set_feedback_enable(b, smurf_init_config['feedbackEnable'],\n write_log=True, **kwargs)\n self.set_feedback_gain(b, smurf_init_config['feedbackGain'], \n write_log=True, **kwargs)\n self.set_lms_gain(b, smurf_init_config['lmsGain'], \n write_log=True, **kwargs)\n\n self.set_feedback_limit_khz(b, 225) # why 225?\n\n self.set_feedback_polarity(b, smurf_init_config['feedbackPolarity'], \n write_log=True, **kwargs)\n # self.set_band_center_mhz(b, smurf_init_config['bandCenterMHz'],\n # write_log=True, **kwargs)\n self.set_synthesis_scale(b, smurf_init_config['synthesisScale'],\n write_log=True, **kwargs)\n\n # This should be part of exp.cfg\n if b == 2:\n self.set_data_out_mux(6, \"UserData\", write_log=True, \n **kwargs)\n self.set_data_out_mux(7, \"UserData\", write_log=True, \n **kwargs)\n self.set_iq_swap_in(b, 1, write_log=True, **kwargs)\n self.set_iq_swap_out(b, 0, write_log=True, **kwargs)\n elif b ==3 :\n self.set_data_out_mux(8, \"UserData\", write_log=True, \n **kwargs)\n self.set_data_out_mux(9, \"UserData\", write_log=True, \n **kwargs)\n self.set_iq_swap_in(b, 0, write_log=True, **kwargs)\n self.set_iq_swap_out(b, 0, write_log=True, **kwargs)\n\n self.set_dsp_enable(b, smurf_init_config['dspEnable'], \n write_log=True, **kwargs)\n\n # Make band dictionaries\n self.freq_resp[b] = {}\n\n self.set_cpld_reset(0, write_log=True)\n\n for i in np.arange(1,5):\n self.set_att_uc(i, 0, write_log=True)\n self.set_att_dc(i, 0, write_log=True)\n\n self.cpld_toggle()","title":""},{"docid":"bde3b5360ad8604a853ebdeb89454b46","score":"0.5624851","text":"def initialize_options(self):\n self.orc_version = \"1.6.5\"\n self.output_dir = \"deps/\"\n self.source_url = \"https://www-us.apache.org/dist/orc/\"\n self.build_type = \"debug\"","title":""},{"docid":"fdc46717e986035b03ad0cdf1a653d51","score":"0.5624445","text":"def setup(cls, config):","title":""},{"docid":"dd4f3d7e502b04fc70387c1bfa7f511b","score":"0.56242675","text":"def set_options (self, data):\r\n if data.get(\"debug\") is not None:\r\n self.debug.setChecked(data[\"debug\"])\r\n if data.get(\"verbose\") is not None:\r\n self.verbose.setChecked(data[\"verbose\"])\r\n if data.get(\"recursionlevel\") is not None:\r\n self.recursionlevel.setValue(data[\"recursionlevel\"])\r\n if data.get(\"warninglines\") is not None:\r\n self.warninglines.setPlainText(data[\"warninglines\"])\r\n if data.get(\"ignorelines\") is not None:\r\n self.ignorelines.setPlainText(data[\"ignorelines\"])","title":""},{"docid":"9fd6e570cbbd289186a7ccfb299c75df","score":"0.561642","text":"def dataset_ids(self, dataset_ids):\n self.configuration['dataset_ids'] = dataset_ids","title":""},{"docid":"95cd6fe0dec48cce9df1ae8e84fd4738","score":"0.5615151","text":"def setup(self, conda_data):\n self.set_widgets_enabled(False)\n conda_processed_info = conda_data.get('processed_info')\n environments = conda_processed_info.get('__environments')\n packages = conda_data.get('packages')\n self.current_prefix = conda_processed_info.get('default_prefix')\n self.set_environments(environments)\n self.set_packages(packages)","title":""},{"docid":"3769b554422c9a57ddd75250003c84c4","score":"0.5612766","text":"def set_config_values(self, args):\n\n # Config file has been specified\n if 'config_file' in args:\n yaml_config = self._load_config_file(args.config_file)\n else:\n yaml_config = None\n for key, value in self._fields.items():\n self._fields[key].set_name(key)\n if key in args:\n self._fields[key].set_value(getattr(args, key))\n elif yaml_config is not None and key in yaml_config:\n self._fields[key].set_value(yaml_config[key])\n elif value.default_value() is not None:\n self._fields[key].set_value(value.default_value())\n elif value.required():\n flags = ', '.join(value.flags())\n raise TritonModelAnalyzerException(\n f'Config for {value.name()} is not specified. You need to specify it using the YAML config file or using the {flags} flags in CLI.'\n )\n self._setup_logger()\n self._preprocess_and_verify_arguments()\n self._autofill_values()","title":""},{"docid":"848d15f8c0e54408aef74b57ebbea7e5","score":"0.561176","text":"def setUp(self):\n self.use_data_loader = True\n self.epoch_num = 10\n self.drop_last = False","title":""}],"string":"[\n {\n \"docid\": \"efb231ed2b6b2f1ce3aa15434c6d4fb4\",\n \"score\": \"0.7039832\",\n \"text\": \"def dataset_setup(self):\\n settings = self.settings\\n if settings.crowd_dataset == CrowdDataset.ucf_qnrf:\\n self.dataset_class = UcfQnrfFullImageDataset\\n self.train_dataset = UcfQnrfTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\\n seed=settings.labeled_dataset_seed,\\n number_of_examples=settings.labeled_dataset_size,\\n map_directory_name=settings.map_directory_name)\\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\\n pin_memory=self.settings.pin_memory,\\n num_workers=settings.number_of_data_workers)\\n self.unlabeled_dataset = UcfQnrfTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\\n seed=settings.labeled_dataset_seed,\\n number_of_examples=settings.unlabeled_dataset_size,\\n map_directory_name=settings.map_directory_name,\\n examples_start=settings.labeled_dataset_size)\\n self.unlabeled_dataset_loader = DataLoader(self.unlabeled_dataset, batch_size=settings.batch_size,\\n pin_memory=self.settings.pin_memory,\\n num_workers=settings.number_of_data_workers)\\n self.validation_dataset = UcfQnrfTransformedDataset(dataset='test', seed=101,\\n map_directory_name=settings.map_directory_name)\\n elif settings.crowd_dataset == CrowdDataset.shanghai_tech:\\n self.dataset_class = ShanghaiTechFullImageDataset\\n self.train_dataset = ShanghaiTechTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\\n seed=settings.labeled_dataset_seed,\\n number_of_examples=settings.labeled_dataset_size,\\n map_directory_name=settings.map_directory_name)\\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\\n pin_memory=self.settings.pin_memory,\\n num_workers=settings.number_of_data_workers)\\n self.unlabeled_dataset = ShanghaiTechTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\\n seed=100,\\n number_of_examples=settings.unlabeled_dataset_size,\\n map_directory_name=settings.map_directory_name)\\n self.unlabeled_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\\n pin_memory=self.settings.pin_memory,\\n num_workers=settings.number_of_data_workers)\\n self.validation_dataset = ShanghaiTechTransformedDataset(dataset='test', seed=101,\\n map_directory_name=settings.map_directory_name)\\n\\n elif settings.crowd_dataset == CrowdDataset.world_expo:\\n self.dataset_class = WorldExpoFullImageDataset\\n self.train_dataset = WorldExpoTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\\n seed=settings.labeled_dataset_seed,\\n number_of_cameras=settings.number_of_cameras,\\n number_of_images_per_camera=settings.number_of_images_per_camera)\\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,\\n pin_memory=self.settings.pin_memory,\\n num_workers=settings.number_of_data_workers)\\n self.unlabeled_dataset = WorldExpoTransformedDataset(middle_transform=data.RandomHorizontalFlip(),\\n seed=settings.labeled_dataset_seed,\\n number_of_cameras=settings.number_of_cameras,\\n number_of_images_per_camera=settings.number_of_images_per_camera)\\n self.unlabeled_dataset_loader = DataLoader(self.unlabeled_dataset, batch_size=settings.batch_size,\\n pin_memory=self.settings.pin_memory,\\n num_workers=settings.number_of_data_workers)\\n self.validation_dataset = WorldExpoTransformedDataset(dataset='validation', seed=101)\\n if self.settings.batch_size > self.train_dataset.length:\\n self.settings.batch_size = self.train_dataset.length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3576e67e571d4b2bb3f0cf7892c47e71\",\n \"score\": \"0.6755877\",\n \"text\": \"def test_dataset_config(self):\\n dataset = fluid.core.Dataset(\\\"MultiSlotDataset\\\")\\n dataset.set_thread_num(12)\\n dataset.set_filelist([\\\"a.txt\\\", \\\"b.txt\\\", \\\"c.txt\\\"])\\n dataset.set_trainer_num(4)\\n dataset.set_hdfs_config(\\\"my_fs_name\\\", \\\"my_fs_ugi\\\")\\n dataset.set_download_cmd(\\\"./read_from_afs my_fs_name my_fs_ugi\\\")\\n dataset.set_enable_pv_merge(False)\\n\\n thread_num = dataset.get_thread_num()\\n self.assertEqual(thread_num, 12)\\n\\n filelist = dataset.get_filelist()\\n self.assertEqual(len(filelist), 3)\\n self.assertEqual(filelist[0], \\\"a.txt\\\")\\n self.assertEqual(filelist[1], \\\"b.txt\\\")\\n self.assertEqual(filelist[2], \\\"c.txt\\\")\\n\\n trainer_num = dataset.get_trainer_num()\\n self.assertEqual(trainer_num, 4)\\n\\n name, ugi = dataset.get_hdfs_config()\\n self.assertEqual(name, \\\"my_fs_name\\\")\\n self.assertEqual(ugi, \\\"my_fs_ugi\\\")\\n\\n download_cmd = dataset.get_download_cmd()\\n self.assertEqual(download_cmd, \\\"./read_from_afs my_fs_name my_fs_ugi\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecec69c5351f6100df68003d178a48c0\",\n \"score\": \"0.66378176\",\n \"text\": \"def initSetConfig(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09880be1ce3bb56b50a7dc27a8e7597d\",\n \"score\": \"0.6620552\",\n \"text\": \"def set_defaults(config={}):\\n if 'seg_data' not in list(config.keys()):\\n config['seg_data'] = 'vz_predict_dataset.csv.gz'\\n if 'concern' not in list(config.keys()):\\n config['concern'] = ''\\n if 'atr' not in list(config.keys()):\\n config['atr'] = ''\\n if 'tmc' not in list(config.keys()):\\n config['tmc'] = ''\\n if 'f_cont' not in list(config.keys()):\\n config['f_cont'] = ['width']\\n if 'f_cat' not in list(config.keys()):\\n config['f_cat'] = ['lanes', 'hwy_type', 'osm_speed', 'oneway',\\n 'signal']\\n\\n # Add features for additional data sources\\n if 'data_source' in config and config['data_source']:\\n for source in config['data_source']:\\n config[source['feat']].append(source['name'])\\n\\n if 'process' not in list(config.keys()):\\n config['process'] = True\\n if 'time_target' not in list(config.keys()):\\n config['time_target'] = [15, 2017]\\n if 'weeks_back' not in list(config.keys()):\\n config['weeks_back'] = 1\\n if 'name' not in list(config.keys()):\\n config['name'] = 'boston'\\n if 'level' not in list(config.keys()):\\n config['level'] = 'week'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4aa53dd289e88ee9df10a3b5d11813e\",\n \"score\": \"0.6555243\",\n \"text\": \"def __init__(self, data_path):\\n config = self.load_options(data_path)\\n for i in config:\\n setattr(self, '{}'.format(i), '{}'.format(config[i]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6484a9ad97261f26351e08a8e9db916\",\n \"score\": \"0.64702314\",\n \"text\": \"def setup(args):\\n cfg = get_cfg()\\n cfg.merge_from_file(args.config_file)\\n cfg.merge_from_list(args.opts)\\n\\n if cfg.EVAL_ONLY:\\n cfg.DATASETS.TEST = (\\\"smokenet_test\\\",)\\n test_annotation_file = os.path.join(cfg.DATA_DIR_PATH, \\\"test.json\\\")\\n # Register into DatasetCatalog\\n register_imagenet_instances(cfg.DATASETS.TEST[0], {}, test_annotation_file)\\n\\n else:\\n train_annotation_file = os.path.join(cfg.DATA_DIR_PATH, \\\"train.json\\\")\\n val_annotation_file = os.path.join(cfg.DATA_DIR_PATH, \\\"val.json\\\")\\n # Register into DatasetCatalog\\n register_imagenet_instances(\\\"smokenet_train\\\", {}, train_annotation_file)\\n register_imagenet_instances(\\\"smokenet_val\\\", {}, val_annotation_file)\\n\\n cfg.freeze()\\n default_setup(cfg, args)\\n\\n return cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93b1babbfc9dc7b6e1489669b33ed4bf\",\n \"score\": \"0.64027584\",\n \"text\": \"def set_dataset_paths(args):\\n if not args.train_path:\\n args.train_path = 'datasets/%s/train' % (args.dataset)\\n if not args.val_path:\\n if args.dataset == 'imagenet' or args.dataset == 'places':\\n args.val_path = 'datasets/%s/val' % (args.dataset)\\n else:\\n args.val_path = 'datasets/%s/test' % (args.dataset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fba0cbbc75905319a8e95f65732a29a\",\n \"score\": \"0.639718\",\n \"text\": \"def manage_options():\\n parser = OptionParser(usage=\\\"usage: %prog [options] dataset_file\\\", version=\\\"%prog 1.0\\\")\\n\\n parser.add_option(\\\"-s\\\", \\\"--seed\\\", dest=\\\"seed\\\", default=42, help=\\\"Pseudo-random seed for replicability\\\", type=\\\"int\\\")\\n parser.add_option(\\\"-t\\\", \\\"--test_size\\\", dest=\\\"test_size\\\", default=.3, help=\\\"Test set size in percentage [0,1]\\\")\\n parser.add_option(\\\"-n\\\", \\\"--normalize\\\", dest=\\\"normalize\\\", default=1, help=\\\"Whether the instances has to be normalized or not (default:1) - 0:No, 1:MinMax standardization, 2:L2 normalization\\\", type=\\\"int\\\")\\n parser.add_option(\\\"-c\\\", \\\"--config_file\\\", dest=\\\"config_file\\\", default=\\\"./config/config.json\\\", help=\\\"Configuration file\\\")\\n parser.add_option(\\\"-v\\\", \\\"--verbose\\\", dest=\\\"verbose\\\", default=False, help=\\\"Verbose output\\\", action=\\\"store_true\\\")\\n\\n (options, args) = parser.parse_args()\\n if len(args) == 0:\\n parser.error(\\\"Wrong arguments\\\")\\n\\n out_dict = vars(options)\\n out_dict[\\\"dataset\\\"] = args[0]\\n return out_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd8ddecb029d2e213f7d0f0a1667c129\",\n \"score\": \"0.6343363\",\n \"text\": \"def set_dataset(self):\\n\\n fpath = os.path.join(os.path.dirname(os.path.dirname(__file__)), \\\"splits\\\", self.opt.split, \\\"{}_files.txt\\\")\\n test_fpath = os.path.join(os.path.dirname(os.path.dirname(__file__)), \\\"splits\\\", \\\"eigen\\\", \\\"test_files.txt\\\")\\n train_filenames = readlines(fpath.format(\\\"train\\\"))\\n val_filenames = readlines(test_fpath)\\n\\n train_dataset = KittiDataset(\\n self.opt.data_path, self.opt.gt_path, train_filenames, self.opt.height, self.opt.width,\\n crph=self.opt.crph, crpw=self.opt.crpw, is_train=True, predang_path=self.opt.predang_path,\\n semanticspred_path=self.opt.semanticspred_path\\n )\\n\\n val_dataset = KittiDataset(\\n self.opt.data_path, self.opt.val_gt_path, val_filenames, self.opt.height, self.opt.width,\\n crph=self.opt.crph, crpw=self.opt.crpw, is_train=False, predang_path=self.opt.predang_path,\\n semanticspred_path=self.opt.semanticspred_path\\n )\\n\\n self.train_loader = DataLoader(\\n train_dataset, self.opt.batch_size, shuffle=True,\\n num_workers=self.opt.num_workers, pin_memory=True, drop_last=True)\\n self.val_loader = DataLoader(\\n val_dataset, self.opt.batch_size, shuffle=False,\\n num_workers=self.opt.num_workers, pin_memory=True, drop_last=True)\\n\\n self.train_num = train_dataset.__len__()\\n self.val_num = val_dataset.__len__()\\n self.num_total_steps = self.train_num // self.opt.batch_size * self.opt.num_epochs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bdbb0c84b292ff5d9ca46df7ef67483\",\n \"score\": \"0.6332738\",\n \"text\": \"def setup_config(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"639b85d214507e0ebda3b0f7961fc135\",\n \"score\": \"0.63219446\",\n \"text\": \"def set_data_config(univ):\\n data_config = DataSet()\\n data_config.meta_data_root = './meta_data/universe_meta_data.json'\\n data_config.symbols_list = univ\\n\\n return data_config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"439d4e4c36d1933e5e57d08030c501ad\",\n \"score\": \"0.63144433\",\n \"text\": \"def initialize(self):\\n self.options.declare('driver_uid')\\n self.options.declare('cmdows_path')\\n self.options.declare('kb_path')\\n self.options.declare('data_folder')\\n self.options.declare('base_xml_file')\\n self.options.declare('show_model', default=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42f8a6e8f6443775f9d92577ff20c8ac\",\n \"score\": \"0.62298006\",\n \"text\": \"def addDataset(config, datasetName, **settings):\\n datasetInstance = getattr(config.Datasets, datasetName, None)\\n if datasetInstance == None:\\n defaultInstance = getattr(config.Datasets, \\\"Default\\\", None)\\n\\n if defaultInstance != None:\\n datasetInstance = copy.deepcopy(defaultInstance)\\n datasetInstance._internal_name = datasetName\\n setattr(config.Datasets, datasetName, datasetInstance)\\n else:\\n datasetInstance = config.Datasets.section_(datasetName)\\n\\n datasetInstance.Name = datasetName\\n datasetInstance.Scenario = settings.get(\\\"scenario\\\", \\\"collision\\\")\\n\\n datasetInstance.section_(\\\"Repack\\\")\\n datasetInstance.section_(\\\"Reco\\\")\\n datasetInstance.section_(\\\"Alca\\\")\\n datasetInstance.section_(\\\"WMBSPublish\\\")\\n datasetInstance.section_(\\\"DQM\\\")\\n\\n default_proc_ver = settings.get(\\\"default_proc_ver\\\", None)\\n\\n datasetInstance.Repack.ProcessingVersion = settings.get(\\\"repack_proc_ver\\\",\\n default_proc_ver)\\n \\n datasetInstance.Reco.DoReco = settings.get(\\\"do_reco\\\", False)\\n datasetInstance.Reco.GlobalTag = settings.get(\\\"global_tag\\\", None)\\n datasetInstance.Reco.CMSSWVersion = settings.get(\\\"reco_version\\\", \\\"Undefined\\\")\\n datasetInstance.Reco.ConfigURL = settings.get(\\\"reco_configuration\\\",\\n None)\\n datasetInstance.Reco.ProcessingVersion = settings.get(\\\"reco_proc_ver\\\",\\n default_proc_ver)\\n\\n datasetInstance.Alca.DoAlca = settings.get(\\\"do_alca\\\", False)\\n datasetInstance.Alca.CMSSWVersion = settings.get(\\\"alca_version\\\", \\\"Undefined\\\")\\n datasetInstance.Alca.ConfigURL = settings.get(\\\"alca_configuration\\\",\\n None)\\n datasetInstance.Alca.ProcessingVersion = settings.get(\\\"alca_proc_ver\\\",\\n default_proc_ver)\\n\\n datasetInstance.WMBSPublish.DoWMBSPublish = settings.get(\\\"do_wmbs_publish\\\", False)\\n datasetInstance.WMBSPublish.DataTiersTo = settings.get(\\\"wmbs_publish_data_tiers_to\\\", [] )\\n\\n datasetInstance.DQM.DoDQM = settings.get(\\\"do_dqm\\\", False)\\n datasetInstance.DQM.CMSSWVersion = settings.get(\\\"dqm_version\\\", \\\"Undefined\\\")\\n datasetInstance.DQM.ConfigURL = settings.get(\\\"dqm_configuration\\\", None)\\n datasetInstance.DQM.ProcessingVersion = settings.get(\\\"dqm_proc_ver\\\",\\n default_proc_ver)\\n\\n datasetInstance.CustodialNode = settings.get(\\\"custodial_node\\\", None)\\n datasetInstance.ArchivalNode = settings.get(\\\"archival_node\\\", None)\\n datasetInstance.CustodialPriority = settings.get(\\\"custodial_priority\\\",\\n \\\"high\\\")\\n datasetInstance.CustodialAutoApprove = settings.get(\\\"custodial_auto_approve\\\",\\n False)\\n\\n datasetInstance.Tier1Skims = [] \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"897595289fb6afd31cabf7530c3a7f82\",\n \"score\": \"0.6225225\",\n \"text\": \"def data_config():\\n print(\\\"Data Config\\\\n\\\\n\\\")\\n #pandas_sample(\\\"/data/datafinal_60-16.json\\\")\\n utils.text_features_grouped(\\\"/data/datafinal_60-16.json\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595a8cb3a99e03013cb593b7f3df49ae\",\n \"score\": \"0.62173253\",\n \"text\": \"def setup(self) -> None:\\n self.conf.setup_seed(self.system_conf.train.seed)\\n\\n self._setup_datasets()\\n logging.info(\\\"Train Data: %d\\\" % (len(self.train_dataset)))\\n logging.info(\\\"Val Data: %d\\\" % (len(self.val_dataset)))\\n\\n self._net = self._setup_network().to(self.device)\\n logging.info(\\\"Network: %s\\\" % str(self.net))\\n\\n self._crit = self._setup_crit()\\n logging.info(\\\"Criterion: %s\\\" % str(self.crit))\\n\\n self._optim = self._setup_optim()\\n logging.info(\\\"Optimizer: %s\\\" % str(self.optim))\\n\\n self._max_epoch = self.system_conf.train.max_epoch\\n\\n self._metrics = self._setup_metrics()\\n logging.info(\\\"Metrics: %s\\\", str(self.metrics))\\n\\n self._setup_extra()\\n\\n if not osp.exists(self.output_path):\\n os.mkdir(self.output_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fa0547da979a18ecfd06dcada49a389\",\n \"score\": \"0.62071174\",\n \"text\": \"def set_defaults(args):\\n # Check critical files exist\\n data_dir = os.path.join(args.data_dir, args.dataset_name)\\n if not args.only_test:\\n train_src = os.path.join(data_dir, args.train_src)\\n train_tgt = os.path.join(data_dir, args.train_tgt)\\n if not os.path.isfile(train_src):\\n raise IOError('No such file: %s' % train_src)\\n if not os.path.isfile(train_tgt):\\n raise IOError('No such file: %s' % train_tgt)\\n train_src_tag = os.path.join(data_dir, args.train_src_tag)\\n if not os.path.isfile(train_src_tag):\\n raise IOError('No such file: %s' % train_src_tag)\\n\\n args.train_src_file = train_src\\n args.train_tgt_file = train_tgt\\n args.train_src_tag_file = train_src_tag\\n\\n dev_src = os.path.join(data_dir, args.dev_src)\\n dev_tgt = os.path.join(data_dir, args.dev_tgt)\\n if not os.path.isfile(dev_src):\\n raise IOError('No such file: %s' % dev_src)\\n if not os.path.isfile(dev_tgt):\\n raise IOError('No such file: %s' % dev_tgt)\\n dev_src_tag = os.path.join(data_dir, args.dev_src_tag)\\n if not os.path.isfile(dev_src_tag):\\n raise IOError('No such file: %s' % dev_src_tag)\\n\\n args.dev_src_file = dev_src\\n args.dev_tgt_file = dev_tgt\\n args.dev_src_tag_file = dev_src_tag\\n\\n return args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e32c3d2677821b54f65040d033d7a8e2\",\n \"score\": \"0.6203413\",\n \"text\": \"def prepare_data(self):\\n self.dataset_class(\\n self.hparams.data_dir, stage=self.train_stage,\\n transform=self.train_transform, pre_transform=self.pre_transform,\\n on_device_transform=self.on_device_train_transform, **self.kwargs)\\n\\n self.dataset_class(\\n self.hparams.data_dir, stage=self.val_stage,\\n transform=self.val_transform, pre_transform=self.pre_transform,\\n on_device_transform=self.on_device_val_transform, **self.kwargs)\\n\\n self.dataset_class(\\n self.hparams.data_dir, stage='test',\\n transform=self.test_transform, pre_transform=self.pre_transform,\\n on_device_transform=self.on_device_test_transform, **self.kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"361871ad0dd1d6b9a68e98b17f6b5ff2\",\n \"score\": \"0.6199397\",\n \"text\": \"def dataset_settings(init_context) -> DatasetsSettings:\\n return DatasetsSettings(**init_context.resource_config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84a5b255a6591955af4470d1eff16b33\",\n \"score\": \"0.61761314\",\n \"text\": \"def initialize_options(self):\\n self.test_module = None\\n self.branch = False\\n self.erase = False\\n self.no_annotate = False\\n self.no_html = False\\n self.omit_prefixes = ['dendropy/test']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ea4734f603530c5133ba7cd8a9eb26c\",\n \"score\": \"0.61267513\",\n \"text\": \"def initialize_options(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"135e3e32af2dcd6946411957b57c4e68\",\n \"score\": \"0.6121959\",\n \"text\": \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n self.dir_ct = os.path.join(opt.dataroot, opt.phase) # create a path '/path/to/data/ct'\\n # self.dir_mr = os.path.join(opt.dataroot, opt.phase + 'mr') # create a path '/path/to/data/mr'\\n # self.dir_ct_label = os.path.join(opt.dataroot, 'trainct_lables') # create a path '/path/to/data/ct'\\n # self.dir_mr_label = os.path.join(opt.dataroot, 'trainmr_lables') # create a path '/path/to/data/mr'\\n #\\n self.nii_paths = sorted(make_dataset(self.dir_ct, opt.max_dataset_size)) # load images from '/path/to/data/ct'\\n # self.mr_paths = sorted(make_dataset(self.dir_mr, opt.max_dataset_size)) # load images from '/path/to/data/mr'\\n # self.nii_paths_label = sorted(make_dataset(self.dir_ct_label, opt.max_dataset_size)) # load images from '/path/to/data/ct'\\n # self.mr_paths_label = sorted(make_dataset(self.dir_mr_label, opt.max_dataset_size)) # load images from '/path/to/data/mr'\\n #\\n self.ct_size = len(self.nii_paths) # get the size of dataset ct\\n # self.mr_size = len(self.mr_paths) # get the size of dataset mr\\n # self.ct_size_label = len(self.nii_paths_label) # get the size of dataset ct\\n # self.mr_size_label = len(self.mr_paths_label) # get the size of dataset mr\\n mrtoct = self.opt.direction == 'mrtoct'\\n input_nc = self.opt.output_nc if mrtoct else self.opt.input_nc # get the number of channels of input image\\n output_nc = self.opt.input_nc if mrtoct else self.opt.output_nc # get the number of channels of output image\\n # self.transform_ct = get_transform(self.opt)\\n # self.transform_mr = get_transform(self.opt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85c9cfef9956720172b07b0cc8e89e2a\",\n \"score\": \"0.6118664\",\n \"text\": \"def configure(self, options, conf):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1150774acded49446ae921ed58f3003b\",\n \"score\": \"0.60992885\",\n \"text\": \"def prepare_datasets(self):\\n # download precomputed zca components and mean for CIFAR10\\n #urllib.urlretrieve(\\\"http://cs.virginia.edu/~pcascante/zca_components.npy\\\", \\\"zca_components.npy\\\")\\n #urllib.urlretrieve(\\\"http://cs.virginia.edu/~pcascante/zca_mean.npy\\\", \\\"zca_mean.npy\\\")\\n\\n # load data\\n data = dataloaders.load_data_subsets(self.args.augPolicy, self.args.dataset, self.args.data_dir)\\n self.set_data(data)\\n\\n # load zca for cifar10\\n zca_components = np.load('zca_components.npy')\\n zca_mean = np.load('zca_mean.npy')\\n self.args.zca_components = zca_components\\n self.args.zca_mean = zca_mean\\n\\n # get randomized set for training\\n loaders = dataloaders.get_train_dataloaders(self.args.dataset, self.args.train_data, self.args.train_data_noT, self.args.batch_size, self.args.n_cpus, self.args.num_labeled, self.args.num_valid_samples, self.args.seed, self.args.set_labeled_classes, self.args.set_unlabeled_classes, ordered=False)\\n self.set_loaders(loaders)\\n\\n # get test set if in debug mode and for final evaluation\\n testloader = dataloaders.get_test_dataloader(self.args.test_data, self.args.batch_size, self.args.n_cpus)\\n self.args.testloader = testloader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed99b82fb40b8c617989188477577853\",\n \"score\": \"0.6089538\",\n \"text\": \"def config():\\n num_workers = 4 # number of workers\\n num_train = 0.8 # (rel.) number of training data\\n num_val = 0.1 # (rel.) number of validation data\\n batch_size = 128 # batch size\\n mean = None # dict with property means of dataset\\n stddev = None # dict with property stddev of dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5cda168c9e0ee82a54bfa7ff106c61b\",\n \"score\": \"0.60384506\",\n \"text\": \"def setUp(self):\\n with open(os.path.join(os.path.dirname(__file__),\\n './data/oecd-canada.json')) as data_file:\\n self.oecd_datasets = json.load(data_file,\\n object_pairs_hook=OrderedDict)\\n with open(os.path.join(os.path.dirname(__file__),\\n './data/ons.json')) as data_file:\\n self.ons_datasets = json.load(data_file,\\n object_pairs_hook=OrderedDict)\\n with open(os.path.join(os.path.dirname(__file__),\\n './data/galicia-ds.json')) as data_file:\\n self.galicia_dataset = json.load(data_file,\\n object_pairs_hook=OrderedDict)\\n with open(os.path.join(os.path.dirname(__file__),\\n './data/galicia-2.0.json')) as data_file:\\n self.galicia_2_dataset = json.load(data_file,\\n object_pairs_hook=OrderedDict)\\n with open(os.path.join(os.path.dirname(__file__),\\n './data/QS104EW.json')) as data_file:\\n self.uk_dataset = json.load(data_file,\\n object_pairs_hook=OrderedDict)\\n with open(os.path.join(os.path.dirname(__file__),\\n './data/sample_data.json')) as data_file:\\n self.sample_dataset = json.load(data_file,\\n object_pairs_hook=OrderedDict)\\n with open(os.path.join(os.path.dirname(__file__),\\n './data/us-labor-ds.json')) as data_file:\\n self.uslabor_dataset = json.load(data_file,\\n object_pairs_hook=OrderedDict)\\n with open(os.path.join(os.path.dirname(__file__),\\n './data/statswe.json')) as data_file:\\n self.sweden_dataset = json.load(data_file,\\n object_pairs_hook=OrderedDict)\\n with open(os.path.join(os.path.dirname(__file__),\\n './data/A02Level.json')) as data_file:\\n self.ons_dataset = json.load(data_file,\\n object_pairs_hook=OrderedDict)\\n with open(os.path.join(os.path.dirname(__file__),\\n './data/CPI15.json')) as data_file:\\n self.ons_cpi_dataset = json.load(data_file,\\n object_pairs_hook=OrderedDict)\\n with open(os.path.join(os.path.dirname(__file__),\\n './data/collection.json')) as data_file:\\n self.collection = json.load(data_file,\\n object_pairs_hook=OrderedDict)\\n with open(os.path.join(os.path.dirname(__file__),\\n './data/cantabria.json')) as data_file:\\n self.cantabria = json.load(data_file,\\n object_pairs_hook=OrderedDict)\\n with open(os.path.join(os.path.dirname(__file__),\\n './data/dimension.json')) as data_file:\\n self.dimension = json.load(data_file,\\n object_pairs_hook=OrderedDict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"648dfc930bae8022a47d123f117ed435\",\n \"score\": \"0.60320264\",\n \"text\": \"def set_dataset_dirs(self):\\n if self.verbose:\\n print('==> Setup directories to store the data files.')\\n\\n self.save_data_dir = self.set_download_data_dir()\\n self.save_cache_dir = self.set_download_cache_dir()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6d64987e1216fe82eb812416d929806\",\n \"score\": \"0.6015843\",\n \"text\": \"def SetDataFlags(self):\\n self.parser.add_option('--hostname', action='store', dest='hostname',\\n help='String of hostname', metavar='hostname',\\n default=None)\\n self.AddFlagRule('hostname', required=True)\\n self.parser.add_option('-z', '--zone-name', action='store',\\n dest='zone_name', help='String of the zone name.',\\n metavar='<zone-name>', default=None)\\n self.SetAllFlagRule('zone_name', required=True)\\n self.parser.add_option('-v', '--view-name', action='store',\\n dest='view_name',\\n help=('String of the view name <view-name>. '\\n 'Example: \\\"internal\\\"'), metavar='<view-name>',\\n default=None)\\n self.SetAllFlagRule('view_name', required=True)\\n self.parser.add_option('-r', '--recursive', action='store_true',\\n dest='recursive', help='Use recursion during lookup',\\n metavar='<recursive>', default=False)\\n self.SetAllFlagRule('recursive', required=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3baeeaf9bdca35d14cb915d4207a4484\",\n \"score\": \"0.60121846\",\n \"text\": \"def _patch_cls_datasets(config: Config):\\n\\n assert \\\"data\\\" in config\\n for subset in (\\\"train\\\", \\\"val\\\", \\\"test\\\", \\\"unlabeled\\\"):\\n cfg = config.data.get(subset, None)\\n if not cfg:\\n continue\\n cfg.type = \\\"OTXActionClsDataset\\\"\\n cfg.otx_dataset = None\\n cfg.labels = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04897b630989af43d14a3f4950376ef6\",\n \"score\": \"0.6010168\",\n \"text\": \"def setOptions(self):\\n self.parser.add_option('--outputpath',\\n dest='outputpath',\\n default=None,\\n help='Where the files retrieved will be stored. Defaults to the results/ directory.',\\n metavar='URL')\\n\\n self.parser.add_option('--dump',\\n dest='dump',\\n default=False,\\n action='store_true',\\n help='Instead of performing the transfer, dump the source URLs.')\\n\\n self.parser.add_option('--xrootd',\\n dest='xroot',\\n default=False,\\n action='store_true',\\n help='Give XrootD url for the file.')\\n\\n self.parser.add_option('--jobids',\\n dest='jobids',\\n default=None,\\n help='Ids of the jobs you want to retrieve. Comma separated list of integers.',\\n metavar='JOBIDS')\\n self.parser.add_option('--checksum',\\n dest='checksum',\\n default='yes',\\n help='Set it to yes if needed. It will use ADLER32 checksum' +\\\\\\n 'Allowed values are yes/no. Default is yes.')\\n self.parser.add_option('--command',\\n dest='command',\\n default=None,\\n help='A command which to use. Available commands are LCG or GFAL.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"476ee9a160c8fa05d06febc9f02eb612\",\n \"score\": \"0.5998177\",\n \"text\": \"def initialize_options(self):\\n self.include_extras = None\\n self.include_all_extras = None\\n self.extra_pkgs = []\\n self.dependencies = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"579b7e4197bfa33717de9efbe81278d6\",\n \"score\": \"0.59928983\",\n \"text\": \"def set_configs(self):\\n for config_item in ['tag_creds', 'encrypt_tag' ]:\\n define(config_item)\\n # Get configuration values from configuration filescript_name = path.basename(__file__)\\n script_path = path.abspath(path.realpath(__file__))\\n script_dir = path.split(script_path)[0]\\n config_path = path.abspath(path.join(script_dir, 'model/EmsgEncrypt.conf'))\\n options.parse_config_file(config_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b0d02ca79a36d360f0feccd0c54aa80\",\n \"score\": \"0.5978503\",\n \"text\": \"def configure(self, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1ad16369959a538e0df51f491bf1684\",\n \"score\": \"0.5977355\",\n \"text\": \"def __init__(self, config=None, save=False, store=True):\\n \\n if config:\\n make_dataset(config, dataset=self, save=save, store=store)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb94cef3ba79e55937fcd091bf330001\",\n \"score\": \"0.5949031\",\n \"text\": \"def setup(self):\\n\\n # Create a dataset first\\n data = {\\n 'name': self.dataset_name,\\n 'title': self.dataset_title,\\n 'notes': self.dataset_notes\\n }\\n if not self.dataset_owner is None:\\n data['owner_org'] = self.dataset_owner\\n\\n response = requests.post(\\n '{0}/api/action/package_create'.format(self.ckan_url),\\n data=json.dumps(data),\\n headers={'Content-type': 'application/json',\\n 'Authorization': self.api_key},\\n verify=self.ssl_verify)\\n\\n if response.status_code != 200:\\n exit('Error creating dataset: {0}'.format(response.content))\\n\\n dataset_id = response.json()['result']['id']\\n\\n # Then create a resource, empty at the beginning\\n records = []\\n\\n # Manually set the field types to ensure they are handled properly\\n fields = []\\n for item in self.STRUCTURE:\\n field = {\\n 'id': item['id'],\\n 'type': item['type']\\n }\\n fields.append(field)\\n\\n # Push the records to the DataStore table. This will create a resource\\n # of type datastore.\\n data = {\\n 'resource': {\\n 'package_id': dataset_id,\\n 'name': self.resource_name,\\n 'format': 'csv',\\n 'notes': self.resource_notes\\n },\\n 'records': records,\\n 'fields': fields,\\n 'primary_key': self.PRIMARY_KEYS,\\n }\\n\\n response = requests.post(\\n '{0}/api/action/datastore_create'.format(self.ckan_url),\\n data=json.dumps(data),\\n headers={'Content-type': 'application/json',\\n 'Authorization': self.api_key},\\n verify=self.ssl_verify)\\n\\n if response.status_code != 200:\\n exit('Error: {0}'.format(response.content))\\n\\n resource_id = response.json()['result']['resource_id']\\n print('''\\nDataset and DataStore resource successfully created with {0} records.\\nPlease add the resource id to your ini file:\\n\\n[{2}]\\nresource_id={1}\\n '''.format(len(records), resource_id, self.CONFIG_SECTION))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc819a6948fcb3aaadd381b6645841bc\",\n \"score\": \"0.5931906\",\n \"text\": \"def _setup_data(self):\\r\\n input = self.dataset\\r\\n pnt_attr, cell_attr = get_all_attributes(input)\\r\\n\\r\\n self._setup_data_arrays(cell_attr, 'cell')\\r\\n self._setup_data_arrays(pnt_attr, 'point')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3508baacd0188bd0ec735325c3df5be\",\n \"score\": \"0.5902139\",\n \"text\": \"def configuration(self, dataset=None):\\n\\n dataset = dataset if dataset else self.dataset\\n if dataset is None:\\n raise Exception(\\\"dataset in configuration() and self.dataset couldn't be all None\\\")\\n print \\\"Dataset being used is: \\\", dataset\\n\\n params_dict = {\\\"type\\\":\\\"configuration\\\", \\\"dataset\\\":dataset}\\n\\n return self.easy_response(params_dict, echo=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f990afb8b62fa9eac5db6ddbd265485\",\n \"score\": \"0.5896834\",\n \"text\": \"def setup(args):\\n cfg = get_cfg()\\n add_deeplab_config(cfg)\\n cfg.merge_from_file(args.config_file)\\n cfg.merge_from_list(args.opts)\\n default_setup(cfg, args)\\n return cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f69799c977e7e128b2c01740ab7acf86\",\n \"score\": \"0.58842874\",\n \"text\": \"def loadDatasetConfig ( self, dataset ):\\n sql = \\\"SELECT ximagesize, yimagesize, startslice, endslice, zoomlevels, zscale, startwindow, endwindow, starttime, endtime from {} where dataset = \\\\'{}\\\\'\\\".format( ocpcaprivate.datasets, dataset )\\n\\n\\n with closing(self.conn.cursor()) as cursor:\\n try:\\n cursor.execute ( sql )\\n except MySQLdb.Error, e:\\n\\n logger.error (\\\"Could not query ocpca datasets database %d: %s. sql=%s\\\" % (e.args[0], e.args[1], sql))\\n raise OCPCAError (\\\"Could not query ocpca datasets database %d: %s. sql=%s\\\" % (e.args[0], e.args[1], sql))\\n\\n # get the project information \\n row = cursor.fetchone()\\n\\n # if the project is not found. error\\n if ( row == None ):\\n logger.warning ( \\\"Dataset %s not found.\\\" % ( dataset ))\\n raise OCPCAError ( \\\"Dataset %s not found.\\\" % ( dataset ))\\n\\n [ ximagesz, yimagesz, startslice, endslice, zoomlevels, zscale, startwindow, endwindow, starttime, endtime ] = row\\n return OCPCADataset ( int(ximagesz), int(yimagesz), int(startslice), int(endslice), int(zoomlevels), float(zscale), int(startwindow), int(endwindow), int(starttime), int(endtime) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba1018b1394c328561e6e2f8e7e15480\",\n \"score\": \"0.58803207\",\n \"text\": \"def configure(self, **kw):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dd253dc9459c998a9d8ea0393ce31cf\",\n \"score\": \"0.5868556\",\n \"text\": \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n self.A_paths = sorted(make_dataset(opt.dataroot, opt.max_dataset_size))\\n self.B_paths = sorted(make_dataset(opt.dataroot_style, opt.max_dataset_size))\\n self.A_size = len(self.A_paths) # get the size of dataset A\\n self.B_size = len(self.B_paths) # get the size of dataset B\\n transform_list = []\\n\\n transform_list += [transforms.ToTensor()]\\n transform_list += [transforms.Normalize(mean=[0.485, 0.456, 0.406],\\n std=[0.229, 0.224, 0.225])]\\n self.transform = transforms.Compose(transform_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4abbe31fe7dcf08b0ad4383537989856\",\n \"score\": \"0.5868543\",\n \"text\": \"def setup(self, stage=None):\\n # Load data and split into train and validation sets here\\n # Assign train/val datasets for use in dataloaders\\n if stage == \\\"fit\\\":\\n self.tr_dataset = SeqDataDataset(\\n self._tr_x, self._tr_yseq, self._tr_ys, self.normalization\\n )\\n if self.data_val is not None:\\n self.val_dataset = SeqDataDataset(\\n self._val_x, self._val_yseq, self._val_ys, self.normalization\\n )\\n else:\\n raise NotImplementedError(\\\"We didn't implement for stage not `fit`\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d53bb85f7069b772588b5b30b99a53a2\",\n \"score\": \"0.5862461\",\n \"text\": \"def SetDataFlags(self):\\n self.parser.add_option('-v', '--view-name', action='store',\\n dest='view_name',\\n help=('String of the view name <view-name>. '\\n 'Example: \\\"internal\\\"'), metavar='<view-name>',\\n default=None)\\n self.SetAllFlagRule('view_name', required=True)\\n self.parser.add_option('-d', '--domain', action='store', dest='base_domain',\\n help='String of the base domain.', default=None)\\n self.SetAllFlagRule('base_domain', required=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c34d3ed95fb93b653890148126799b\",\n \"score\": \"0.5855423\",\n \"text\": \"def apply_options(self):\\n\\n Application.apply_options(self)\\n\\n self.conf = self.parse_config(self.args.config_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79652e4c0c1b993887037574c3d9f21f\",\n \"score\": \"0.58543384\",\n \"text\": \"def test_config(self):\\n dataset = fluid.InMemoryDataset()\\n dataset.set_parse_ins_id(True)\\n dataset.set_parse_content(True)\\n dataset._set_trainer_num(1)\\n self.assertTrue(dataset.parse_ins_id)\\n self.assertTrue(dataset.parse_content)\\n self.assertEqual(dataset.trainer_num, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"269186104f91e9fba9bebd6ce52faf81\",\n \"score\": \"0.58300835\",\n \"text\": \"def update_config_(args):\\n\\n # increment the seed at each run\\n args.seed = args.seed + args.run\\n\\n # multiply the number of examples by a factor size. Used to have number of examples depending\\n # on number of labels. Usually factor is 1.\\n args.datasize.n_examples = args.datasize.factor * args.datasize.n_examples\\n\\n if args.datasize.n_examples_test == \\\"train\\\":\\n # use same number of train and test examples\\n args.datasize.n_examples_test = args.datasize.n_examples\\n\\n if args.is_precompute_trnsf and args.train.trnsf_kwargs.is_train:\\n # if training transformer then paths need to agree\\n assert args.paths[\\\"trnsf_dirnames\\\"][0] == args.paths[\\\"chckpnt_dirnames\\\"][0]\\n\\n # monitor training when you randomize the labels because validation does not mean anything\\n if args.dataset.kwargs.is_random_targets:\\n args.train.trnsf_kwargs.monitor_best = \\\"train_loss_best\\\"\\n args.train.clf_kwargs.monitor_best = \\\"train_loss_best\\\"\\n\\n if not args.train.is_tensorboard:\\n args.paths[\\\"tensorboard_curr_dir\\\"] = None\\n\\n if args.experiment == \\\"gap\\\":\\n # dib with Q++\\n if args.model.name == \\\"vib\\\":\\n args.model.loss.beta = args.model.loss.beta * 40\\n\\n elif args.model.name == \\\"cdibL\\\":\\n args.model.loss.beta = args.model.loss.beta / 100\\n\\n elif args.model.name == \\\"cdibS\\\":\\n args.model.loss.beta = args.model.loss.beta * 30\\n\\n if \\\"dibL\\\" in args.model.name:\\n # dib with Q++\\n args.model.Q_zx.hidden_size = args.model.Q_zy.hidden_size * 64\\n\\n if \\\"dibS\\\" in args.model.name:\\n # dib with Q--\\n args.model.Q_zx.hidden_size = args.model.Q_zy.hidden_size // 64\\n\\n if \\\"dibXS\\\" in args.model.name:\\n # dib with Q------\\n args.model.Q_zx.hidden_size = 1\\n\\n if \\\"dibXL\\\" in args.model.name:\\n # dib with Q++++++++\\n args.model.Q_zx.hidden_size = 8192\\n\\n short_long_monitor = dict(\\n vloss=\\\"valid_loss_best\\\", tloss=\\\"train_loss_best\\\", vacc=\\\"valid_acc_best\\\"\\n )\\n\\n # use short version for name of file\\n args.train.monitor_best = invert_dict(short_long_monitor).get(\\n args.train.monitor_best, args.train.monitor_best\\n )\\n\\n hyperparam_path = hyperparam_to_path(args.hyperparameters)\\n args.paths.merge_with(\\n OmegaConf.create(\\n format_container(args.paths, dict(hyperparam_path=hyperparam_path))\\n )\\n )\\n # every change that should not modify the name of the file should go below this\\n # ----------------------------------------------------------------------------\\n\\n # use long version in code\\n args.train.monitor_best = short_long_monitor.get(\\n args.train.monitor_best, args.train.monitor_best\\n )\\n args.train.trnsf_kwargs.monitor_best = short_long_monitor.get(\\n args.train.trnsf_kwargs.monitor_best, args.train.trnsf_kwargs.monitor_best\\n )\\n args.train.clf_kwargs.monitor_best = short_long_monitor.get(\\n args.train.clf_kwargs.monitor_best, args.train.clf_kwargs.monitor_best\\n )\\n\\n if not args.is_precompute_trnsf:\\n logger.info(\\\"Not precomputing the transformer so setting train=False.\\\")\\n args.train.trnsf_kwargs.is_train = False\\n args.train.kwargs.lr = args.train.lr_clf # ! DEV\\n else:\\n if args.model.name == \\\"wdecayBob\\\":\\n args.train.weight_decay = 1e-4\\n\\n if args.model.name == \\\"dropoutBob\\\":\\n args.encoder.architecture.dropout = 0.5\\n\\n if not args.datasize.is_valid_all_epochs and \\\"train\\\" in args.train.monitor_best:\\n # don't validate all epochs when validation >>> training and you only look at training\\n rm_valid_epochs_()\\n\\n if args.model.is_joint:\\n args.model.gamma_force_generalization = 1\\n\\n if \\\"distractor\\\" in args.clfs.name and not args.is_precompute_trnsf:\\n args.dataset.is_use_distractor = True\\n\\n if \\\"random\\\" in args.clfs.name and not args.is_precompute_trnsf:\\n # if you want random dataset for classifier then make sure you are not randomizing for encoder\\n args.dataset.kwargs.is_random_targets = True\\n args.train.clf_kwargs.monitor_best = \\\"train_loss_best\\\" # don't monitor val\\n\\n if isinstance(args.train.kwargs.lr, str) and \\\"|\\\" in args.train.kwargs.lr:\\n lr, lr_factor_zx = args.train.kwargs.lr.split(\\\"|\\\")\\n args.train.kwargs.lr = float(lr)\\n args.train.lr_factor_zx = float(lr_factor_zx)\\n\\n if args.model.name == \\\"vibL\\\":\\n # keep alice the same but increase bob view of alice\\n # vib with better approx of I[Z,Y] Q++\\n args.model.Q_zy.hidden_size = args.model.Q_zy.hidden_size * 16\\n\\n if args.model.name == \\\"wdecay\\\":\\n args.train.weight_decay = 1e-4\\n\\n if \\\"correlation\\\" in args.experiment:\\n if args.train.optim == \\\"rmsprop\\\":\\n if args.train.weight_decay == 0.0005:\\n args.train.weight_decay = 0.0003\\n\\n elif args.train.optim == \\\"sgd\\\":\\n args.train.kwargs.lr = args.train.kwargs.lr * 50\\n\\n if \\\"perminvcdib\\\" in args.model.name:\\n args.encoder.architecture.hidden_size = [1024]\\n args.model.architecture.z_dim = 1024\\n args.model.Q_zy.hidden_size = 256\\n args.model.Q_zy.n_hidden_layers = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ba99130ce0a7fff3e2621bc00f6c426\",\n \"score\": \"0.58281314\",\n \"text\": \"def SetDataFlags(self):\\n self.parser.add_option('--commit', action='store_true', dest='commit',\\n help='Commits changes of hosts file without '\\n 'confirmation.', default=False)\\n self.parser.add_option('--no-commit', action='store_true', dest='no_commit',\\n help='Suppresses changes of hosts file.',\\n default=False)\\n self.AddFlagRule(('no_commit', 'commit'), required=False,\\n flag_type='independent_args')\\n self.parser.add_option('-z', '--zone-name', action='store',\\n dest='zone_name', help='String of the zone name.',\\n metavar='<zone-name>', default=None)\\n self.SetAllFlagRule('zone_name', required=True)\\n self.parser.add_option('-v', '--view-name', action='store',\\n dest='view_name',\\n help=('String of the view name <view-name>. '\\n 'Example: '\\n '\\\"internal\\\"'), metavar='<view-name>',\\n default='None')\\n self.SetAllFlagRule('view_name', required=True)\\n\\n self.parser.add_option('-f', '--file', action='store', dest='file',\\n help='File name of hosts file to write to database.',\\n metavar='<file-name>', default='hosts_out')\\n self.SetAllFlagRule('file', required=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b5b1a0e46186531ab5207796d6c9535\",\n \"score\": \"0.5824765\",\n \"text\": \"def _load_options(self, opts):\\n self.opts = opts\\n self.options = opts.get(CONFIG_DATA_SECTION, {})\\n\\n # Validate required fields in app.config are set\\n required_fields = [\\\"base_url\\\", \\\"username\\\", \\\"password\\\", \\\"query_limit\\\", \\\"query_ticket_grouping_types\\\",\\n \\\"polling_interval\\\"]\\n validate_fields(required_fields, self.options)\\n\\n # Create Secureworks client\\n self.scwx_client = SCWXClient(self.opts, self.options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"176b8afc4112aebd8db23f9ce0803ed9\",\n \"score\": \"0.58237094\",\n \"text\": \"def setup(self, **kwargs):\\n if self.data_dir is not None:\\n path = os.path.join(self.data_dir, self.file_name)\\n self.dataset = from_pickle(path)\\n else:\\n # Generate data set here\\n X = []\\n y = []\\n t = []\\n for init_cond in self.init_conditions:\\n x0, p0, t0, t1, steps = init_cond\\n q_p, dq_dp, _, ts = SHM_1D_model(x0=x0, p0=p0, t0=t0, t1=t1, steps=steps)\\n X.append(q_p)\\n y.append(dq_dp)\\n t.append(ts)\\n X = torch.cat(X, dim=0)\\n y = torch.cat(y, dim=0)\\n t = torch.cat(t, dim=0)\\n self.dataset = TensorDataset(X, y, t)\\n\\n # save the dataset\\n path = os.path.join(\\\"../../data/dataset\\\", self.file_name) # TODO: ugly af path, fix this sometime\\n to_pickle(self.dataset, path)\\n\\n # Split the data set\\n portion = [0.9, 0.1, 0.0]\\n splits = [int(x * len(self.dataset)) for x in portion]\\n if sum(splits) != len(self.dataset):\\n splits[1] += len(self.dataset) - sum(splits)\\n self.train_set, self.val_set, self.test_set = random_split(self.dataset, splits)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dce919713ec7a66e13650c49a093a55f\",\n \"score\": \"0.5822433\",\n \"text\": \"def init():\\n global OPTIONS\\n file_path = config_file()\\n config = ConfigParser.ConfigParser()\\n config.read(file_path)\\n OPTIONS = { 'username': config.get(FLAGS.config_section, 'username'),\\n 'password': config.get(FLAGS.config_section, 'password'),\\n 'resource': config.get(FLAGS.config_section, 'resource'),\\n 'server': config.get(FLAGS.config_section, 'server'),\\n 'debug': config.getboolean(FLAGS.config_section, 'debug'),\\n 'path': config.get(FLAGS.config_section, 'dds-path'),\\n 'log_file': config.get(FLAGS.config_section, 'log') }\\n if FLAGS.debug:\\n OPTIONS['debug'] = FLAGS.debug\\n if FLAGS.dds_path != '/':\\n OPTIONS['path'] = FLAGS.dds_path\\n if FLAGS.log_file:\\n OPTIONS['log_file'] = FLAGS.log_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ced7d23e13b2c08530cbb4c2b096dcdf\",\n \"score\": \"0.58214647\",\n \"text\": \"def setUp(self):\\n Dataset.objects.create(title='Penguins of Antarctica', dataset_key='7b657080-f762-11e1-a439-00145eb45e9a',\\n download_on=dateutil.parser.parse('2010-08-06T13:05:40.000+0000'))\\n Dataset.objects.create(title='Antarctic, Sub-Antarctic and cold temperate echinoid database',\\n dataset_key='d8b06df0-81b3-41c9-bcf8-6ba5242e2b95',\\n download_on=dateutil.parser.parse('2110-04-06T13:05:40.000+0000'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"417f98a3cdb124bff69dca7fbbf39c7e\",\n \"score\": \"0.58187026\",\n \"text\": \"def setOptions(self):\\n self.parser.add_option('--site',\\n dest='sitename',\\n default=None,\\n help='The PhEDEx node name of the site to be checked.')\\n self.parser.add_option('--lfn',\\n dest='userlfn',\\n default=None,\\n help='A user lfn address.')\\n self.parser.add_option('--checksum',\\n dest='checksum',\\n default='no',\\n help='Set it to yes if needed. It will use ADLER32 checksum' +\\\\\\n 'Allowed values are yes/no. Default is no.')\\n self.parser.add_option('--command',\\n dest='command',\\n default=None,\\n help='A command which to use. Available commands are LCG or GFAL.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"251da479f626b20aa41538b68dee11b6\",\n \"score\": \"0.5818446\",\n \"text\": \"def update_config_datasets_(args, datasets):\\n args.datasize.n_examples = len(datasets[\\\"train\\\"]) # store as an integer\\n steps_per_epoch = len(datasets[\\\"train\\\"]) // args.datasize.batch_size\\n args.model.loss.warm_Q_zx = steps_per_epoch * args.model.loss.warm_Q_zx\\n\\n count_targets = datasets[\\\"train\\\"].count_targets()\\n with omegaconf.open_dict(args):\\n args.model.loss.n_per_target = {\\n str(k): int(v) for k, v in count_targets.items()\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e15ba5686390d0ad67491ea8e89891b\",\n \"score\": \"0.5818113\",\n \"text\": \"def setup_task(cls, args, **kwargs):\\n args.left_pad_source = options.eval_bool(args.left_pad_source)\\n args.left_pad_target = options.eval_bool(args.left_pad_target)\\n if getattr(args, 'raw_text', False):\\n utils.deprecation_warning('--raw-text is deprecated, please use --dataset-impl=raw')\\n args.dataset_impl = 'raw'\\n elif getattr(args, 'lazy_load', False):\\n utils.deprecation_warning('--lazy-load is deprecated, please use --dataset-impl=lazy')\\n args.dataset_impl = 'lazy'\\n\\n paths = args.data.split(':')\\n assert len(paths) > 0\\n # find language pair automatically\\n if args.source_lang is None or args.target_lang is None:\\n raise Exception('Could not infer language pair, please provide it explicitly') # See comment below?\\n # TRY ADDING: --source-lang true_w,true_p --target-lang reco_w\\n # OR ENABLING BELOW HARDCODED DEFAULTS.\\n # args.source_lang = \\\"true_w,true_p\\\"\\n # args.target_lang = \\\"reco_w\\\"\\n # args.source_lang, args.target_lang = data_utils.infer_language_pair(paths[0])\\n\\n assert type(args.source_lang) == str\\n if ',' not in args.source_lang:\\n raise Exception(\\\"source-lang is \\\" + args.source_lang + \\\" source-lang needs to contain two comma separated strings\\\")\\n # load dictionaries\\n src_lang1, src_lang2 = args.source_lang.split(',')\\n src_dict1 = cls.load_dictionary(os.path.join(paths[0], 'dict.{}.txt'.format(src_lang1)))\\n src_dict2 = cls.load_dictionary(os.path.join(paths[0], 'dict.{}.txt'.format(src_lang2)))\\n if ',' in args.target_lang:\\n assert args.criterion == 'cross_entropy_dual'\\n target_lang, target_lang_extra = args.target_lang.split(',')\\n tgt_dict = cls.load_dictionary(os.path.join(paths[0], 'dict.{}.txt'.format(target_lang)))\\n tgt_dict_extra = cls.load_dictionary(os.path.join(paths[0], 'dict.{}.txt'.format(target_lang_extra)))\\n dual_decoder = True\\n else:\\n tgt_dict = cls.load_dictionary(os.path.join(paths[0], 'dict.{}.txt'.format(args.target_lang)))\\n dual_decoder = False\\n tgt_dict_extra = None\\n\\n assert src_dict1.pad() == tgt_dict.pad()\\n assert src_dict1.eos() == tgt_dict.eos()\\n assert src_dict1.unk() == tgt_dict.unk()\\n assert src_dict2.pad() == tgt_dict.pad()\\n assert src_dict2.eos() == tgt_dict.eos()\\n assert src_dict2.unk() == tgt_dict.unk()\\n print('| [{}] dictionary: {} types'.format(src_lang1, len(src_dict1)))\\n print('| [{}] dictionary: {} types'.format(src_lang2, len(src_dict2)))\\n\\n if dual_decoder:\\n print('| [{}] dictionary: {} types'.format(target_lang, len(tgt_dict)))\\n print('| [{}] dictionary: {} types'.format(target_lang_extra, len(tgt_dict_extra)))\\n else:\\n print('| [{}] dictionary: {} types'.format(args.target_lang, len(tgt_dict)))\\n return cls(args, src_dict1, src_dict2, tgt_dict, tgt_dict_extra)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0666efd09345cbba308492506d3049\",\n \"score\": \"0.5815576\",\n \"text\": \"def setup(config, *args, **kwargs):\\n cfg_rest = config.get('rest',{}).get('datasets',{})\\n db_cfg = cfg_rest.get('database',{})\\n\\n # add indexes\\n db = pymongo.MongoClient(**db_cfg).datasets\\n if 'dataset_id_index' not in db.datasets.index_information():\\n db.datasets.create_index('dataset_id', name='dataset_id_index', unique=True)\\n\\n handler_cfg = RESTHandlerSetup(config, *args, **kwargs)\\n handler_cfg.update({\\n 'database': motor.motor_tornado.MotorClient(**db_cfg).datasets,\\n })\\n\\n return [\\n (r'/datasets', MultiDatasetHandler, handler_cfg),\\n (r'/datasets/(?P<dataset_id>\\\\w+)', DatasetHandler, handler_cfg),\\n (r'/datasets/(?P<dataset_id>\\\\w+)/description', DatasetDescriptionHandler, handler_cfg),\\n (r'/datasets/(?P<dataset_id>\\\\w+)/status', DatasetStatusHandler, handler_cfg),\\n (r'/datasets/(?P<dataset_id>\\\\w+)/priority', DatasetPriorityHandler, handler_cfg),\\n (r'/datasets/(?P<dataset_id>\\\\w+)/jobs_submitted', DatasetJobsSubmittedHandler, handler_cfg),\\n (r'/dataset_summaries/status', DatasetSummariesStatusHandler, handler_cfg),\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8942ed4edf5da3d5025a84b2e11af2be\",\n \"score\": \"0.5805535\",\n \"text\": \"def configure_options(self):\\n super(MRKmeansStep, self).configure_options()\\n self.add_file_option('--prot')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f59d041f51b621960766ae1a0ffa9e3\",\n \"score\": \"0.5804469\",\n \"text\": \"def _init_all_settings(self): # noqa: C901\\n self.config = self.trainer.config\\n if self.trainer.hps and self.trainer.hps.get('trainer'):\\n self.config.from_json(self.trainer.hps.get('trainer'))\\n self.trainer._init_distributed_setting()\\n if self.config.cuda:\\n self.trainer._init_cuda_setting()\\n self.epochs = self.trainer.epochs\\n self.distributed = self.trainer.distributed\\n self.trainer.model = self._init_model()\\n self.model = self.trainer.model\\n self.use_syncbn = self.config.syncbn\\n self.trainer.use_syncbn = self.use_syncbn\\n if self.use_syncbn:\\n self.trainer.model = apex.parallel.convert_syncbn_model(self.trainer.model)\\n self.trainer.optimizer = self._init_optimizer()\\n self.use_ema = hasattr(self.config, 'model_ema')\\n if self.use_ema:\\n self.model_ema = self._init_model_ema()\\n self.trainer.lr_scheduler = self._init_lr_scheduler()\\n self.trainer.loss = self._init_loss()\\n if self.distributed:\\n self.trainer._init_horovod_setting()\\n self.use_amp = self.config.amp\\n if self.use_amp:\\n self.trainer.model, self.trainer.optimizer = amp.initialize(self.trainer.model,\\n self.trainer.optimizer,\\n opt_level='O1')\\n self._init_dataloader()\\n self.trainer.valid_metrics = self.trainer._init_metrics(None)\\n self.trainer.callbacks._set_params(self.trainer)\\n\\n # self.trainer.has_built = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"037920f09c9fda6c8d2319cff3359cd4\",\n \"score\": \"0.58026177\",\n \"text\": \"def _init_distributed_setting(self):\\n context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True)\\n set_algo_parameters(elementwise_op_strategy_follow=True)\\n context.set_auto_parallel_context(all_reduce_fusion_config=self.config.all_reduce_fusion_config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0b933ceb5b2f7aa85158e2f2d6f0e54\",\n \"score\": \"0.5801967\",\n \"text\": \"def set_spark_configs(d):\\n global __init_configs_spark\\n __init_configs_spark.update(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23e97844b77ada7ca3be915c95f5d49e\",\n \"score\": \"0.5799616\",\n \"text\": \"def __init__(self,config):\\n self.backbone_model = config[\\\"BackboneClassifier\\\"]\\n self.model_input_size = config[\\\"ModelInputSize\\\"]\\n self.processed_dataset_folder = config[\\\"ProcessedDatasetFolder\\\"]\\n self.scale = config[\\\"Model\\\"][\\\"Scale\\\"]\\n self.aspect_ratio = config[\\\"Model\\\"][\\\"AspectRatio\\\"]\\n self.dataset = []\\n\\n self.load_dataset()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16d79fab1606d7c0048dd45fe3e94d64\",\n \"score\": \"0.57891893\",\n \"text\": \"def __init__(self, config_manager):\\n config = config_manager.get_config()\\n raw_filepath = config['GENERAL']['dataset_path']\\n assert os.path.isdir(raw_filepath), \\\\\\n \\\"The dataset filepath provided is not valid.\\\"\\n self._raw_filepath = raw_filepath\\n self._mappings = None\\n self._validate_data()\\n self._port = config.getint(\\\"BLOCKCHAIN\\\", \\\"http_port\\\")\\n self._frac = float(config['DATASET_MANAGER']['sample_fraction'])\\n self._ipfs_client = None\\n self._db_client = None\\n self.classification = config['DATASET_MANAGER']['category']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85cae6c42b4846bef2e45b30111971bb\",\n \"score\": \"0.57816124\",\n \"text\": \"def prepare_dataset(self):\\n self.ctx.log(\\\"Checking dataset...\\\")\\n selected = self.ctx.config.get('dataset', None)\\n if selected is None:\\n if DATASET_NAME in self._get_datasets():\\n # try to select existing\\n AugerConfig(self.ctx).set_data_set(\\n DATASET_NAME, '').set_experiment(None)\\n self.dataset = DataSet(self.ctx, self.project, DATASET_NAME)\\n else:\\n # or create new\\n self.ctx.log(\\\"No dataset found, creating the first one...\\\")\\n source = self.ctx.config.get('source', None)\\n dataset = DataSet(self.ctx, self.project).create(source)\\n AugerConfig(self.ctx).set_data_set(\\n dataset.name, source).set_experiment(None)\\n self.dataset = DataSet(self.ctx, self.project, dataset.name)\\n else:\\n self.dataset = DataSet(self.ctx, self.project, selected)\\n self.ctx.log(\\\"Currently selected: %s\\\" % selected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b03d68dca762028cdbdfb4fba89a315\",\n \"score\": \"0.57779884\",\n \"text\": \"def initialize_options(self):\\n build_ext.initialize_options(self)\\n self.gen_compiledb = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32338252892318ac753ebf3bc1894131\",\n \"score\": \"0.5775403\",\n \"text\": \"def prepare_data(self):\\n self.generic_dataset = LIDCNodulesDataset(**self.dataset_params.params)\\n log.info(f\\\"DATASET SIZE: {len(self.generic_dataset)}\\\")\\n\\n self.tensor_dataset_path = self.__prepare_tensor_dataset()\\n\\n self.aug_transform = transforms.Compose([T.FlipNodule3D(), T.RotNodule3D()])\\n self.dataset = DatasetFolder(\\n self.tensor_dataset_path, torch.load, (\\\"pt\\\"), transform=self.__data_transform\\n )\\n self.dataset.norm = self.generic_dataset.norm\\n\\n train_inds, val_inds, test_inds = H.train_val_holdout_split(\\n self.dataset, ratios=[0.85, 0.14, 0.01]\\n )\\n self.train_sampler = SubsetRandomSampler(train_inds)\\n self.val_sampler = SubsetRandomSampler(val_inds)\\n self.test_subset = Subset(self.dataset, test_inds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b638322e2623e701768a00af7dbc729\",\n \"score\": \"0.5774466\",\n \"text\": \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n self.sub_list = os.listdir(opt.dataroot)\\n self.opt = opt\\n\\n self.input_nc = self.opt.input_nc\\n self.output_nc = self.opt.output_nc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d733b46ea79ecbd913d57f221312701f\",\n \"score\": \"0.57649636\",\n \"text\": \"def configure(**config):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"305326bce27463db1ff1340000465d08\",\n \"score\": \"0.57591677\",\n \"text\": \"def setup(self, **kwargs):\\n if self.data_dir is not None:\\n path = os.path.join(self.data_dir, self.file_name)\\n self.dataset = from_pickle(path)\\n else:\\n # Generate data set here\\n X = []\\n y = []\\n t = []\\n for init_cond in self.init_conditions:\\n x0, p0, t0, t1, steps = init_cond\\n q_p, _, _, ts = SHM_1D_model(x0=x0, p0=p0, t0=t0, t1=t1, steps=steps) # TODO: merge acceleration\\n ddq = -1. / 1. * q_p[:, 0]\\n\\n X.append(q_p) # for k=1, m=1 -> p = dq\\n y.append(ddq)\\n t.append(ts)\\n\\n X = torch.cat(X, dim=0)\\n y = torch.cat(y, dim=0)\\n t = torch.cat(t, dim=0)\\n self.dataset = TensorDataset(X, y, t)\\n\\n # save the dataset\\n path = os.path.join(\\\"../../data/dataset\\\", self.file_name) # TODO: ugly af path, fix this sometime\\n to_pickle(self.dataset, path)\\n\\n # Split the data set\\n portion = [0.9, 0.1, 0.0]\\n splits = [int(x * len(self.dataset)) for x in portion]\\n if sum(splits) != len(self.dataset):\\n splits[1] += len(self.dataset) - sum(splits)\\n self.train_set, self.val_set, self.test_set = random_split(self.dataset, splits)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45ab4f4b977270c52ea7170a69452946\",\n \"score\": \"0.57569444\",\n \"text\": \"def setup(self, **kwargs):\\n self.configured = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4da6a542d4145defa684de097a410c80\",\n \"score\": \"0.57546705\",\n \"text\": \"def setUpTestData(cls):\\n\\n dataset_create_data = {\\n \\\"verbose_name\\\": \\\"0307-全键盘-0号麦克风-2000个样本点\\\",\\n \\\"dataset_name\\\": \\\"20210307\\\",\\n \\\"data_type\\\": \\\"all-0micro\\\",\\n \\\"project_type\\\": \\\"single\\\",\\n \\\"description\\\": \\\"cxx_0307_1__0306_123\\\",\\n \\\"description_more\\\": \\\"0307-30键训练集，其余为测试集，0号麦克风，2000个样本点\\\",\\n \\\"length\\\": 2000,\\n \\\"fs\\\": 48000,\\n }\\n\\n cls.dataset_obj = models.Dataset(**dataset_create_data)\\n cls.dataset_obj.save()\\n\\n model_inst_create_data = {\\n \\\"dataset_id\\\": cls.dataset_obj.id,\\n \\\"algorithm\\\": \\\"blstm\\\",\\n \\\"save_path\\\": os.path.join(settings.MODEL_INST_ROOT, \\\"dataset_id:10:algorithm:blstm/inst_id:7\\\"),\\n \\\"extra_info\\\": {\\\"input_shape_list\\\": [4, 14], \\\"max_label_num\\\": 29},\\n \\\"is_ready\\\": True,\\n \\\"is_latest\\\": True,\\n }\\n cls.model_inst_obj = models.AlgorithmModelInst(**model_inst_create_data)\\n cls.model_inst_obj.save()\\n\\n cls.dataset_unit = load.load_dataset_unit(\\n save_path=os.path.join(settings.LIB_ROOT, \\\"preprocessed_data/2250.pickle\\\")\\n )\\n\\n super().setUpTestData()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e960d29c1acc31b10266df4650ec99b\",\n \"score\": \"0.57419044\",\n \"text\": \"def init_dataset(arguments):\\n if arguments.method == 'train':\\n return DataSetTrain()\\n else:\\n return DataSetTest()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13a26e6d4a9baaaab00566c05b761988\",\n \"score\": \"0.57379067\",\n \"text\": \"def config(self, **kw):\\n self.cfg_fixture.config(**kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87306fb100f6faa2896cf7c156c31496\",\n \"score\": \"0.57375973\",\n \"text\": \"def configure(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87306fb100f6faa2896cf7c156c31496\",\n \"score\": \"0.57375973\",\n \"text\": \"def configure(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87306fb100f6faa2896cf7c156c31496\",\n \"score\": \"0.57375973\",\n \"text\": \"def configure(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87306fb100f6faa2896cf7c156c31496\",\n \"score\": \"0.57375973\",\n \"text\": \"def configure(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc0d8e116065f043c61c92a457e6c012\",\n \"score\": \"0.57354635\",\n \"text\": \"def __config_init(self):\\n ini_path = os.path.join('app', 'common', 'oracle_sql.ini')\\n ini_container = 'oracle_sql'\\n self.sql_data = get_all_data_from_ini_file(ini_path, ini_container)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84c705b6bdc8db6f23dcf91809dd0684\",\n \"score\": \"0.5712451\",\n \"text\": \"def configure(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"315dd1255f21dcb80630fa1a33a535a5\",\n \"score\": \"0.5712033\",\n \"text\": \"def setup_class(cls):\\n cod.get_data_jhu(data_type=\\\"all\\\", region=\\\"global\\\", update=True)\\n cod.get_data_jhu(data_type=\\\"all\\\", region=\\\"us\\\", update=True)\\n\\n cod.get_data_nyt(data_type=\\\"all\\\", counties=False, update=True)\\n cod.get_data_nyt(data_type=\\\"all\\\", counties=True, update=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f10a61fe63d5616d5cc284d2c5156940\",\n \"score\": \"0.57063013\",\n \"text\": \"def set_config(self):\\n\\n self.parser['CONNECTION'] = strings.get_config_connection()\\n self.parser['DATABASE'] = strings.get_config_db()\\n self.parser['METRICS'] = strings.get_config_metrics()\\n\\n with open(strings.get_config_path(), 'w') as file:\\n self.parser.write(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd8368c0cbe6209283b2c1bd611d90f\",\n \"score\": \"0.5705125\",\n \"text\": \"def initialize_config(self):\\n return {\\n \\\"model_type\\\": \\\"bert_query\\\",\\n \\\"name\\\": \\\"test_bert_query\\\",\\n \\\"model_path\\\": os.path.join(self.model_path, \\\"test_bert_query\\\"),\\n \\\"setup_args\\\": {\\n \\\"threshold\\\": 0.8,\\n \\\"seed\\\": [\\n \\\"This is a cat\\\",\\n \\\"Arya is a hungry cat.\\\",\\n ],\\n \\\"infer_config\\\": {\\n \\\"k\\\": 2,\\n \\\"segment_config\\\": {\\\"window_size\\\": 5, \\\"step_size\\\": 3},\\n },\\n },\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c37f5744959a1e3cc85e2a90e50fc18a\",\n \"score\": \"0.5704601\",\n \"text\": \"def initialize_from_config(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0be632c8f9133b049eb8f68a25d7bb8d\",\n \"score\": \"0.56914246\",\n \"text\": \"def setup(self, stage):\\n trainval_test_split = cfg['data']['trainval_test_split']\\n graph_train_val, self.graph_test = self.graph_data.split_data(test_size=trainval_test_split)\\n\\n train_val_split = cfg['data']['train_val_split']\\n self.graph_train, self.graph_val = graph_train_val.split_data(test_size=train_val_split)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a37839be163787057d5725023934369\",\n \"score\": \"0.5689095\",\n \"text\": \"def _init_dataloader(self):\\n if self.distributed and hvd.local_rank() == 0 and 'remote_data_dir' in self.config.dataset:\\n FileOps.copy_folder(self.config.dataset.remote_data_dir, self.config.dataset.data_dir)\\n if self.distributed:\\n hvd.join()\\n args = self.config.dataset\\n train_dir = os.path.join(self.config.dataset.data_dir, 'train')\\n dataset_train = Dataset(train_dir)\\n world_size, rank = None, None\\n if self.distributed:\\n world_size, rank = hvd.size(), hvd.rank()\\n self.trainer.train_loader = create_loader(\\n dataset_train,\\n input_size=tuple(args.input_size),\\n batch_size=args.batch_size,\\n is_training=True,\\n use_prefetcher=self.config.prefetcher,\\n rand_erase_prob=args.reprob,\\n rand_erase_mode=args.remode,\\n rand_erase_count=args.recount,\\n color_jitter=args.color_jitter,\\n auto_augment=args.aa,\\n interpolation='random',\\n mean=tuple(args.mean),\\n std=tuple(args.std),\\n num_workers=args.workers,\\n distributed=self.distributed,\\n world_size=world_size,\\n rank=rank\\n )\\n valid_dir = os.path.join(self.config.dataset.data_dir, 'val')\\n dataset_eval = Dataset(valid_dir)\\n self.trainer.valid_loader = create_loader(\\n dataset_eval,\\n input_size=tuple(args.input_size),\\n batch_size=4 * args.batch_size,\\n is_training=False,\\n use_prefetcher=self.config.prefetcher,\\n interpolation=args.interpolation,\\n mean=tuple(args.mean),\\n std=tuple(args.std),\\n num_workers=args.workers,\\n distributed=self.distributed,\\n world_size=world_size,\\n rank=rank\\n )\\n self.trainer.batch_num_train = len(self.trainer.train_loader)\\n self.trainer.batch_num_valid = len(self.trainer.valid_loader)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f0a32ab2cf50c419bde50c74f591b95\",\n \"score\": \"0.56888485\",\n \"text\": \"def _create_default_config(self):\\n\\t\\tparser = self._parser\\n\\n\\t\\tparser.remove_section(\\\"world\\\")\\n\\t\\tparser.add_section(\\\"world\\\")\\n\\t\\tparser.set(\\\"world\\\", \\\"class\\\", \\\"portage.sets.base.DummyPackageSet\\\")\\n\\t\\tparser.set(\\\"world\\\", \\\"packages\\\", \\\"@selected @system\\\")\\n\\n\\t\\tparser.remove_section(\\\"selected\\\")\\n\\t\\tparser.add_section(\\\"selected\\\")\\n\\t\\tparser.set(\\\"selected\\\", \\\"class\\\", \\\"portage.sets.files.WorldSelectedSet\\\")\\n\\n\\t\\tparser.remove_section(\\\"system\\\")\\n\\t\\tparser.add_section(\\\"system\\\")\\n\\t\\tparser.set(\\\"system\\\", \\\"class\\\", \\\"portage.sets.profiles.PackagesSystemSet\\\")\\n\\n\\t\\tparser.remove_section(\\\"usersets\\\")\\n\\t\\tparser.add_section(\\\"usersets\\\")\\n\\t\\tparser.set(\\\"usersets\\\", \\\"class\\\", \\\"portage.sets.files.StaticFileSet\\\")\\n\\t\\tparser.set(\\\"usersets\\\", \\\"multiset\\\", \\\"true\\\")\\n\\t\\tparser.set(\\\"usersets\\\", \\\"directory\\\", \\\"%(PORTAGE_CONFIGROOT)setc/portage/sets\\\")\\n\\t\\tparser.set(\\\"usersets\\\", \\\"world-candidate\\\", \\\"true\\\")\\n\\n\\t\\tparser.remove_section(\\\"live-rebuild\\\")\\n\\t\\tparser.add_section(\\\"live-rebuild\\\")\\n\\t\\tparser.set(\\\"live-rebuild\\\", \\\"class\\\", \\\"portage.sets.dbapi.VariableSet\\\")\\n\\t\\tparser.set(\\\"live-rebuild\\\", \\\"variable\\\", \\\"INHERITED\\\")\\n\\t\\tparser.set(\\\"live-rebuild\\\", \\\"includes\\\", \\\"bzr cvs darcs git git-2 mercurial subversion tla\\\")\\n\\n\\t\\tparser.remove_section(\\\"module-rebuild\\\")\\n\\t\\tparser.add_section(\\\"module-rebuild\\\")\\n\\t\\tparser.set(\\\"module-rebuild\\\", \\\"class\\\", \\\"portage.sets.dbapi.OwnerSet\\\")\\n\\t\\tparser.set(\\\"module-rebuild\\\", \\\"files\\\", \\\"/lib/modules\\\")\\n\\n\\t\\tparser.remove_section(\\\"preserved-rebuild\\\")\\n\\t\\tparser.add_section(\\\"preserved-rebuild\\\")\\n\\t\\tparser.set(\\\"preserved-rebuild\\\", \\\"class\\\", \\\"portage.sets.libs.PreservedLibraryConsumerSet\\\")\\n\\n\\t\\tparser.remove_section(\\\"x11-module-rebuild\\\")\\n\\t\\tparser.add_section(\\\"x11-module-rebuild\\\")\\n\\t\\tparser.set(\\\"x11-module-rebuild\\\", \\\"class\\\", \\\"portage.sets.dbapi.OwnerSet\\\")\\n\\t\\tparser.set(\\\"x11-module-rebuild\\\", \\\"files\\\", \\\"/usr/lib/xorg/modules\\\")\\n\\t\\tparser.set(\\\"x11-module-rebuild\\\", \\\"exclude-files\\\", \\\"/usr/bin/Xorg\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"633037df3a479ad464a73f033a299a85\",\n \"score\": \"0.56801337\",\n \"text\": \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n self.dir = os.path.join(opt.dataroot, opt.phase)\\n self.AB_paths = [e[1] for e in sorted(make_numbering_dataset(self.dir, opt.max_dataset_size), key=lambda idx: idx[0])]\\n assert(opt.input_nc == 1 and opt.output_nc == 2 and opt.direction == 'AtoB')\\n self.transform = get_transform(self.opt, convert=False)\\n\\n with open(opt.captions, 'rb') as f:\\n x = pickle.load(f)\\n train_captions, test_captions = x[0], x[1]\\n self.captions = train_captions if opt.phase == \\\"train\\\" else test_captions\\n self.ixtoword, self.wordtoix = x[2], x[3]\\n del x, train_captions, test_captions\\n self.n_words = len(self.ixtoword)\\n print('Load from: ', opt.captions)\\n self.captions_per_image = opt.captions_per_image\\n self.text_words_num = opt.text_words_num\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c84239b35e972b729c9bc01483c8ef1\",\n \"score\": \"0.5676815\",\n \"text\": \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n self.dir_x = os.path.join(opt.dataroot, opt.phase + '_x') \\n self.dir_yi = os.path.join(opt.dataroot, opt.phase + '_yi') \\n self.dir_yj = os.path.join(opt.dataroot, opt.phase + '_yj') \\n\\n self.x_paths = [os.path.join(self.dir_x,s) for s in sorted(os.listdir(self.dir_x))]\\n self.yi_paths = [os.path.join(self.dir_yi,s) for s in sorted(os.listdir(self.dir_yi))]\\n self.yj_paths = [os.path.join(self.dir_yj,s) for s in sorted(os.listdir(self.dir_yj))]\\n\\n self.dataset_size = len(self.x_paths) # get the size of dataset \\n\\n self.transform = transforms.Compose([\\n # transforms.Resize((132, 100)), \\n # transforms.RandomCrop((128, 96)), # basic augmentation\\n transforms.ToTensor(),\\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cd8fc301a87cbd29a67e725911a4680\",\n \"score\": \"0.5671616\",\n \"text\": \"def setup(args):\\n cfg = get_cfg()\\n add_kd_config(cfg)\\n cfg.merge_from_file(args.config_file)\\n cfg.merge_from_list(args.opts)\\n cfg.freeze()\\n default_setup(cfg, args)\\n return cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b210d74c700b39f10a44632902daf08\",\n \"score\": \"0.5665934\",\n \"text\": \"def update_mart_config_from_args(\\r\\n config: Dict, args: argparse.Namespace, *, verbose: bool = True) -> Dict[str, Any]:\\r\\n if args.debug:\\r\\n config[\\\"debug\\\"] = True\\r\\n if verbose:\\r\\n print(f\\\" Change config: Set debug to True\\\")\\r\\n if args.dataset_max is not None:\\r\\n assert args.dataset_max > 0, \\\"--dataset_max must be positive int.\\\"\\r\\n config[\\\"dataset_train\\\"][\\\"max_datapoints\\\"] = args.dataset_max\\r\\n config[\\\"dataset_val\\\"][\\\"max_datapoints\\\"] = args.dataset_max\\r\\n if verbose:\\r\\n print(f\\\" Change config: Set dataset_(train|val).max_datapoints to {args.dataset_max}\\\")\\r\\n if args.preload:\\r\\n config[\\\"dataset_train\\\"][\\\"preload\\\"] = True\\r\\n config[\\\"dataset_val\\\"][\\\"preload\\\"] = True\\r\\n if verbose:\\r\\n print(f\\\" Change config: Set dataset_(train|val).preload to True\\\")\\r\\n if args.no_preload or args.validate:\\r\\n config[\\\"dataset_train\\\"][\\\"preload\\\"] = False\\r\\n config[\\\"dataset_val\\\"][\\\"preload\\\"] = False\\r\\n if verbose:\\r\\n print(f\\\" Change config: Set dataset_(train|val).preload to False (--no_preload or --validate)\\\")\\r\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e356167dd2993ae85357d6569ba88e41\",\n \"score\": \"0.5659186\",\n \"text\": \"def initialize_data(cls, host_override, directory_override, depth_override):\\n cls.HOST = test.select_host(config.HOST, host_override)\\n cls.DATA = test.select_directory(config.DATA, directory_override)\\n cls.DEPTH = test.select_depth(config.DEPTH, depth_override)\\n cls.BASE_URL = test.create_base_url(cls.HOST, cls.PORT)\\n cls.JSON_FILE = test.create_json_reference(cls.DATA, cls.JSON_NAME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fc63dffb31d932eb1ad9d628eeced26\",\n \"score\": \"0.56516856\",\n \"text\": \"def config(self, **kwargs: Any) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fc63dffb31d932eb1ad9d628eeced26\",\n \"score\": \"0.56516856\",\n \"text\": \"def config(self, **kwargs: Any) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b37b76b3b44ffd7f424f34ad8d386a\",\n \"score\": \"0.5646425\",\n \"text\": \"def __init__(self, config):\\n # this will store the training data headers for value ordering\\n self.data_headers = None\\n # attribute pre-selection (remove class attribute from select_attr)\\n self.class_attr = config['class_attr']\\n self.select_attr = config.get('select_attr', [])\\n if self.class_attr in self.select_attr:\\n self.select_attr.remove(self.class_attr)\\n # part of the training data to be used\\n self.train_part = config.get('train_part', 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e717025eb1177f42fedff0e414f3b4c\",\n \"score\": \"0.5639404\",\n \"text\": \"def setUp(self):\\n self.options = Options()\\n test_path = os.path.dirname(os.path.realpath(__file__))\\n self.group_dir = os.path.join(test_path, 'fitbenchmarking_results')\\n os.mkdir(self.group_dir)\\n self.table_names = {\\\"compare\\\": \\\"compare_table_name.\\\",\\n \\\"runtime\\\": \\\"runtime_table_name.\\\"}\\n self.table_descriptions = {\\\"compare\\\": \\\"compare table descriptions\\\",\\n \\\"runtime\\\": \\\"runtime table descriptions\\\",\\n \\\"both\\\": \\\"both table descriptions\\\"}\\n self.group_name = \\\"random_name\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5856fd48318caf4ef2dcc562a8e174e2\",\n \"score\": \"0.56270903\",\n \"text\": \"def __init__(self, opt):\\n #super(BasicDataset).__init__(self, opt)\\n self.dir = os.path.join(opt.dataroot, opt.phase) # create a path '/path/to/data/train'\\n\\n self.paths = sorted(self.make_dataset(self.dir, opt.max_dataset_size)) # load images from '/path/to/data/train'\\n self.size = len(self.paths) # get the size of dataset A\\n self.input_nc = opt.input_nc # get the number of channels of input image\\n self.transform = self.get_transform(opt, grayscale=(self.input_nc == 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"352a1b5ea00063885d197137c0d14d32\",\n \"score\": \"0.5625312\",\n \"text\": \"def setup(self, **kwargs):\\n self.log('Setting up...', self.LOG_USER)\\n\\n self.set_read_all(write_log=True)\\n self.set_defaults_pv(write_log=True)\\n\\n # The per band configs. May want to make available per-band values.\\n smurf_init_config = self.config.get('init')\\n bands = smurf_init_config['bands']\\n for b in bands:\\n self.set_iq_swap_in(b, smurf_init_config['iqSwapIn'], \\n write_log=True, **kwargs)\\n self.set_iq_swap_out(b, smurf_init_config['iqSwapOut'], \\n write_log=True, **kwargs)\\n self.set_ref_phase_delay(b, smurf_init_config['refPhaseDelay'], \\n write_log=True, **kwargs)\\n self.set_ref_phase_delay_fine(b, \\n smurf_init_config['refPhaseDelayFine'], write_log=True, \\n **kwargs)\\n self.set_tone_scale(b, smurf_init_config['toneScale'], \\n write_log=True, **kwargs)\\n self.set_analysis_scale(b, smurf_init_config['analysisScale'], \\n write_log=True, **kwargs)\\n self.set_feedback_enable(b, smurf_init_config['feedbackEnable'],\\n write_log=True, **kwargs)\\n self.set_feedback_gain(b, smurf_init_config['feedbackGain'], \\n write_log=True, **kwargs)\\n self.set_lms_gain(b, smurf_init_config['lmsGain'], \\n write_log=True, **kwargs)\\n\\n self.set_feedback_limit_khz(b, 225) # why 225?\\n\\n self.set_feedback_polarity(b, smurf_init_config['feedbackPolarity'], \\n write_log=True, **kwargs)\\n # self.set_band_center_mhz(b, smurf_init_config['bandCenterMHz'],\\n # write_log=True, **kwargs)\\n self.set_synthesis_scale(b, smurf_init_config['synthesisScale'],\\n write_log=True, **kwargs)\\n\\n # This should be part of exp.cfg\\n if b == 2:\\n self.set_data_out_mux(6, \\\"UserData\\\", write_log=True, \\n **kwargs)\\n self.set_data_out_mux(7, \\\"UserData\\\", write_log=True, \\n **kwargs)\\n self.set_iq_swap_in(b, 1, write_log=True, **kwargs)\\n self.set_iq_swap_out(b, 0, write_log=True, **kwargs)\\n elif b ==3 :\\n self.set_data_out_mux(8, \\\"UserData\\\", write_log=True, \\n **kwargs)\\n self.set_data_out_mux(9, \\\"UserData\\\", write_log=True, \\n **kwargs)\\n self.set_iq_swap_in(b, 0, write_log=True, **kwargs)\\n self.set_iq_swap_out(b, 0, write_log=True, **kwargs)\\n\\n self.set_dsp_enable(b, smurf_init_config['dspEnable'], \\n write_log=True, **kwargs)\\n\\n # Make band dictionaries\\n self.freq_resp[b] = {}\\n\\n self.set_cpld_reset(0, write_log=True)\\n\\n for i in np.arange(1,5):\\n self.set_att_uc(i, 0, write_log=True)\\n self.set_att_dc(i, 0, write_log=True)\\n\\n self.cpld_toggle()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde3b5360ad8604a853ebdeb89454b46\",\n \"score\": \"0.5624851\",\n \"text\": \"def initialize_options(self):\\n self.orc_version = \\\"1.6.5\\\"\\n self.output_dir = \\\"deps/\\\"\\n self.source_url = \\\"https://www-us.apache.org/dist/orc/\\\"\\n self.build_type = \\\"debug\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdc46717e986035b03ad0cdf1a653d51\",\n \"score\": \"0.5624445\",\n \"text\": \"def setup(cls, config):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd4f3d7e502b04fc70387c1bfa7f511b\",\n \"score\": \"0.56242675\",\n \"text\": \"def set_options (self, data):\\r\\n if data.get(\\\"debug\\\") is not None:\\r\\n self.debug.setChecked(data[\\\"debug\\\"])\\r\\n if data.get(\\\"verbose\\\") is not None:\\r\\n self.verbose.setChecked(data[\\\"verbose\\\"])\\r\\n if data.get(\\\"recursionlevel\\\") is not None:\\r\\n self.recursionlevel.setValue(data[\\\"recursionlevel\\\"])\\r\\n if data.get(\\\"warninglines\\\") is not None:\\r\\n self.warninglines.setPlainText(data[\\\"warninglines\\\"])\\r\\n if data.get(\\\"ignorelines\\\") is not None:\\r\\n self.ignorelines.setPlainText(data[\\\"ignorelines\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fd6e570cbbd289186a7ccfb299c75df\",\n \"score\": \"0.561642\",\n \"text\": \"def dataset_ids(self, dataset_ids):\\n self.configuration['dataset_ids'] = dataset_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95cd6fe0dec48cce9df1ae8e84fd4738\",\n \"score\": \"0.5615151\",\n \"text\": \"def setup(self, conda_data):\\n self.set_widgets_enabled(False)\\n conda_processed_info = conda_data.get('processed_info')\\n environments = conda_processed_info.get('__environments')\\n packages = conda_data.get('packages')\\n self.current_prefix = conda_processed_info.get('default_prefix')\\n self.set_environments(environments)\\n self.set_packages(packages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3769b554422c9a57ddd75250003c84c4\",\n \"score\": \"0.5612766\",\n \"text\": \"def set_config_values(self, args):\\n\\n # Config file has been specified\\n if 'config_file' in args:\\n yaml_config = self._load_config_file(args.config_file)\\n else:\\n yaml_config = None\\n for key, value in self._fields.items():\\n self._fields[key].set_name(key)\\n if key in args:\\n self._fields[key].set_value(getattr(args, key))\\n elif yaml_config is not None and key in yaml_config:\\n self._fields[key].set_value(yaml_config[key])\\n elif value.default_value() is not None:\\n self._fields[key].set_value(value.default_value())\\n elif value.required():\\n flags = ', '.join(value.flags())\\n raise TritonModelAnalyzerException(\\n f'Config for {value.name()} is not specified. You need to specify it using the YAML config file or using the {flags} flags in CLI.'\\n )\\n self._setup_logger()\\n self._preprocess_and_verify_arguments()\\n self._autofill_values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"848d15f8c0e54408aef74b57ebbea7e5\",\n \"score\": \"0.561176\",\n \"text\": \"def setUp(self):\\n self.use_data_loader = True\\n self.epoch_num = 10\\n self.drop_last = False\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":490,"cells":{"query_id":{"kind":"string","value":"39a9a2507b6189f125ecdcb37d61cb15"},"query":{"kind":"string","value":"create roi of the liver"},"positive_passages":{"kind":"list like","value":[{"docid":"25b582771929b6f1e3b27a9f7c715656","score":"0.0","text":"def find_ROI_segmentation(self):\n\n seg = self.__IsolateBody()\n seg_lung = self.__isolate_lung(seg)\n box = self.__built_box_roi(seg_lung)\n return box, seg_lung","title":""}],"string":"[\n {\n \"docid\": \"25b582771929b6f1e3b27a9f7c715656\",\n \"score\": \"0.0\",\n \"text\": \"def find_ROI_segmentation(self):\\n\\n seg = self.__IsolateBody()\\n seg_lung = self.__isolate_lung(seg)\\n box = self.__built_box_roi(seg_lung)\\n return box, seg_lung\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"47a1aeb5cb060a014f8725ac38518d33","score":"0.7261228","text":"def get_roi(self):\n start = self.image.roi.pos()\n size = self.image.roi.size()\n angle = self.image.roi.angle()\n self.ROI = [start[0],start[0] + size[0],\n start[1], start[1] + size[1], angle]\n self.plot_options.set_roi(self.ROI)","title":""},{"docid":"ee057b3599684b8e3ad9f9465c07bc8f","score":"0.68806237","text":"def set_roi(self, roi):\n with h5py.File(self.data_file, 'r+') as f:\n if 'roi' not in f:\n roigrp = f.create_group('roi')\n else:\n roigrp = f['roi']\n roigrp.create_dataset('roi{}'.format(self._next_roi_idx), data=np.asarray(roi), compression='lzf')","title":""},{"docid":"9ae0e7c1d66702d26e613f2d087db7e3","score":"0.6866821","text":"def button_setroi_clicked(self):\r\n original_workspace = DataModel.g.current_workspace\r\n roi_start = self.roi_start.value()\r\n roi_end = self.roi_end.value()\r\n roi = [\r\n roi_start[0],\r\n roi_start[1],\r\n roi_start[2],\r\n roi_end[0],\r\n roi_end[1],\r\n roi_end[2],\r\n ]\r\n\r\n roi_name = (\r\n DataModel.g.current_workspace\r\n + \"_roi_\"\r\n + str(roi[0])\r\n + \"_\"\r\n + str(roi[3])\r\n + \"_\"\r\n + str(roi[1])\r\n + \"_\"\r\n + str(roi[4])\r\n + \"_\"\r\n + str(roi[2])\r\n + \"_\"\r\n + str(roi[5])\r\n )\r\n\r\n cfg.ppw.clientEvent.emit({\"source\": \"panel_gui\", \"data\": \"make_roi_ws\", \"roi\": roi})\r\n self.add_roi(roi_name, original_workspace, roi)\r\n\r\n cfg.ppw.clientEvent.emit({\"source\": \"panel_gui\", \"data\": \"faster_refresh\", \"value\": None})","title":""},{"docid":"7db344acec9f2030da086eeb59eb145a","score":"0.6757338","text":"def getROI(self) -> retval:\n ...","title":""},{"docid":"bed4dbe4a2a572801aebe75858d4a17c","score":"0.67048454","text":"def add_roi(self, roi_fname, original_workspace, roi):\r\n if self.annotations_source.value():\r\n original_level = str(self.annotations_source.value().rsplit(\"/\", 1)[-1])\r\n else:\r\n original_level = \"None\"\r\n params = dict(\r\n workspace=original_workspace,\r\n roi_fname=roi_fname,\r\n roi=roi,\r\n original_workspace=original_workspace,\r\n original_level=original_level,\r\n )\r\n result = Launcher.g.run(\"roi\", \"create\", **params)\r\n if result:\r\n rid = result[\"id\"]\r\n rname = result[\"name\"]\r\n self._add_roi_widget(rid, rname, True)\r\n cfg.ppw.clientEvent.emit({\"source\": \"panel_gui\", \"data\": \"refresh\", \"value\": None})","title":""},{"docid":"8020c663269e9fe8c61ff71fb898a6c1","score":"0.67033744","text":"def create_roi(subject_id, subjects_dir, fs_dir, parcellation_name):\n iflogger.info(\"Create the ROIs:\")\n output_dir = op.abspath(op.curdir)\n fs_dir = op.join(subjects_dir, subject_id)\n cmp_config = cmp.configuration.PipelineConfiguration()\n cmp_config.parcellation_scheme = \"Lausanne2008\"\n log = cmp_config.get_logger()\n parval = cmp_config._get_lausanne_parcellation('Lausanne2008')[parcellation_name]\n pgpath = parval['node_information_graphml']\n aseg = nb.load(op.join(fs_dir, 'mri', 'aseg.nii.gz'))\n asegd = aseg.get_data()\n\n iflogger.info(\"Working on parcellation: \")\n iflogger.info(cmp_config._get_lausanne_parcellation('Lausanne2008')[parcellation_name])\n iflogger.info(\"========================\")\n pg = nx.read_graphml(pgpath)\n # each node represents a brain region\n # create a big 256^3 volume for storage of all ROIs\n rois = np.zeros( (256, 256, 256), dtype=np.int16 )\n\n count = 0\n for brk, brv in pg.nodes_iter(data=True):\n count = count + 1\n iflogger.info(brv)\n iflogger.info(brk)\n if brv['dn_hemisphere'] == 'left':\n hemi = 'lh'\n elif brv['dn_hemisphere'] == 'right':\n hemi = 'rh'\n if brv['dn_region'] == 'subcortical':\n iflogger.info(brv)\n iflogger.info(\"---------------------\")\n iflogger.info(\"Work on brain region: %s\" % (brv['dn_region']))\n iflogger.info(\"Freesurfer Name: %s\" % brv['dn_fsname'])\n iflogger.info(\"Region %s of %s \" % (count, pg.number_of_nodes()))\n iflogger.info(\"---------------------\")\n # if it is subcortical, retrieve roi from aseg\n idx = np.where(asegd == int(brv['dn_fs_aseg_val']))\n rois[idx] = int(brv['dn_correspondence_id'])\n\n elif brv['dn_region'] == 'cortical':\n iflogger.info(brv)\n iflogger.info(\"---------------------\")\n iflogger.info(\"Work on brain region: %s\" % (brv['dn_region']))\n iflogger.info(\"Freesurfer Name: %s\" % brv['dn_fsname'])\n iflogger.info(\"Region %s of %s \" % (count, pg.number_of_nodes()))\n iflogger.info(\"---------------------\")\n\n labelpath = op.join(output_dir, parval['fs_label_subdir_name'] % hemi)\n # construct .label file name\n\n fname = '%s.%s.label' % (hemi, brv['dn_fsname'])\n\n # execute fs mri_label2vol to generate volume roi from the label file\n # store it in temporary file to be overwritten for each region\n\n mri_cmd = 'mri_label2vol --label \"%s\" --temp \"%s\" --o \"%s\" --identity' % (op.join(labelpath, fname),\n op.join(fs_dir, 'mri', 'orig.mgz'), op.join(output_dir, 'tmp.nii.gz'))\n runCmd( mri_cmd, log )\n\n tmp = nb.load(op.join(output_dir, 'tmp.nii.gz'))\n tmpd = tmp.get_data()\n\n # find voxel and set them to intensityvalue in rois\n idx = np.where(tmpd == 1)\n rois[idx] = int(brv['dn_correspondence_id'])\n\n # store volume eg in ROI_scale33.nii.gz\n out_roi = op.join(output_dir, 'ROI_%s.nii.gz' % parcellation_name)\n\n # update the header\n hdr = aseg.get_header()\n hdr2 = hdr.copy()\n hdr2.set_data_dtype(np.uint16)\n\n log.info(\"Save output image to %s\" % out_roi)\n img = nb.Nifti1Image(rois, aseg.get_affine(), hdr2)\n nb.save(img, out_roi)\n\n iflogger.info(\"[ DONE ]\")","title":""},{"docid":"efbad9db36a42640b12611cb690c9ff6","score":"0.66888976","text":"def roi(self):\r\n return self._roi_tool.roi()","title":""},{"docid":"dc9825ee374eadef8a96b3897a57ff5c","score":"0.668095","text":"def create_FBROI(builder,my_ROI):\n UID_str = my_ROI.image_UID()\n roi_coords = my_ROI.contour()\n if UID_str not in nodule_map:\n nodule_map[UID_str] = {}\n nodule_map[UID_str]['roi_count'] = 1\n else:\n nodule_map[UID_str]['roi_count'] += 1\n roi_count = nodule_map[UID_str]['roi_count']\n roi_uid = 'roi_'+str(roi_count)\n\n contour_lst = []\n FBROI.RegionOfInterestStartContourVector(builder,len(roi_coords))\n for coord in my_ROI.contour():\n builder.PrependUOffsetTRelative(FBPoint.CreatePoint(builder,coord[0],coord[1]))\n contour_lst.append([coord[0],coord[1]])\n nodule_map[UID_str][roi_uid] = contour_lst\n contour = builder.EndVector(len(roi_coords))\n imageUID = builder.CreateString(my_ROI.image_UID())\n FBROI.RegionOfInterestStart(builder)\n FBROI.RegionOfInterestAddImageSOPUID(builder, imageUID)\n FBROI.RegionOfInterestAddInclusion(builder,my_ROI.inclusion())\n FBROI.RegionOfInterestAddContour(builder,contour)\n ROI = FBROI.RegionOfInterestEnd(builder)\n return ROI","title":""},{"docid":"8bda34fe3159095513526076530d7fa2","score":"0.66760564","text":"def _add_roi(self):\n with self.debug_output:\n cat = self._roi_cat_sel.value\n r_dict = self._copy_normalized_rois()\n r_dict[cat].append(None)\n self.rois = self._unnormalize_rois(r_dict)\n self._roi_multi_sel.index = len(self._roi_multi_sel.options) - 1","title":""},{"docid":"2cbbe9db0b1888485b9e660dabedac07","score":"0.66606545","text":"def get_roi(self):\n hstart=int(self.get_value(\"SUBARRAY HPOS\"))\n hend=hstart+int(self.get_value(\"SUBARRAY HSIZE\"))\n vstart=int(self.get_value(\"SUBARRAY VPOS\"))\n vend=vstart+int(self.get_value(\"SUBARRAY VSIZE\"))\n bin=int(self.get_value(\"BINNING\"))\n return (hstart,hend,vstart,vend,bin)","title":""},{"docid":"3f397b98ea3de302a50ce573e95edf64","score":"0.6649359","text":"def _draw_rois(self, *_):\n self._last_draw_rois = Event.now()\n\n # Clear old ROIs\n self.canvas.delete(TAG_ROI)\n self.canvas.delete(TAG_ROI_NAME)\n\n # If there are no ROIs to draw, we’re done here\n roi_collections = self.stack.rois\n if not self.show_rois_var.get() or not roi_collections:\n return\n\n # Get ROIs and display\n for roi_col in roi_collections.values():\n rois = None\n try:\n rois = roi_col[self.i_frame]\n except KeyError:\n rois = None\n if rois is None:\n try:\n rois = roi_col[Ellipsis]\n except KeyError:\n rois = None\n if rois is None:\n continue\n\n col_color = roi_col.color\n if col_color is None:\n col_color = 'yellow'\n col_stroke_width = roi_col.stroke_width\n if col_stroke_width is None:\n col_stroke_width = 1\n\n for roi in rois:\n if not roi.visible and not roi.name_visible:\n continue\n\n color = roi.color\n if color is None:\n color = col_color\n\n if roi.name:\n # user-specified ROI name is base64-encoded to avoid issues due to bad format\n name_tag = f'{TAG_ROI_ID}{base64.b64encode(roi.name.encode()).decode()}'\n else:\n name_tag = None\n\n if roi.visible:\n if name_tag:\n tags = (TAG_ROI, name_tag)\n else:\n tags = TAG_ROI\n\n stroke_width = roi.stroke_width\n if stroke_width is None:\n stroke_width = col_stroke_width\n\n roi_key = roi.key()[0]\n if roi_key == 'raw':\n contour = roi.contour\n if self.scale is not None:\n contour = contour * self.scale\n self.canvas.create_polygon(*contour[:, ::-1].flat, tags=tags,\n fill='', outline=color, width=stroke_width)\n elif roi_key == 'rect':\n corners = roi.corners\n if self.scale is not None:\n corners = corners * self.scale\n self.canvas.create_polygon(*corners[:, ::-1].flat, tags=tags,\n fill='', outline=color, width=stroke_width)\n else:\n print(f\"Undefined ROI type: '{roi_key}'\") #DEBUG\n\n if roi.name and roi.name_visible:\n txtpos = roi.centroid.flat[::-1]\n if self.scale is not None:\n txtpos = txtpos * self.scale\n if name_tag:\n tags = (TAG_ROI_NAME, name_tag)\n else:\n tags = TAG_ROI_NAME\n self.canvas.create_text(*txtpos.flat,\n fill=color, text=roi.name, tags=tags)","title":""},{"docid":"8266c5b5c5b74cbe73e7d8c98b19cefa","score":"0.6610116","text":"def set_roi(self, roi):\r\n if roi is not None:\r\n if len(roi) != 4:\r\n raise ValueError(\"ROI must be a list of four integers\")\r\n for x in roi:\r\n if not (isinstance(x, int) and x >= 0):\r\n raise ValueError(\"ROI must be a (x, y, w, h) tuple\")\r\n self.roi = roi","title":""},{"docid":"66d0f1746618ccb71fa6a3948694f300","score":"0.66022646","text":"def setRoi(self,roi, profileWidths = (1, 1)):\n if (self.img is None):\n print(\"Error - need to call setImg before setRoi\")\n return(-1)\n\n if ((roi[X_ORIGIN] >= 0) and (roi[X_ORIGIN]<self.imgSizeX)):\n self.roi[X_ORIGIN] = int(roi[X_ORIGIN])\n else:\n print(\"WARNING: ROI[X_ORIGIN] %d Out of Range - using zero\" %\n roi[X_ORIGIN])\n self.roi[X_ORIGIN] = 0\n\n if ((roi[Y_ORIGIN] >= 0) and (roi[Y_ORIGIN]<self.imgSizeY)):\n self.roi[Y_ORIGIN] = int(roi[Y_ORIGIN])\n else:\n print(\"WARNING: ROI[Y_ORIGIN] %d Out of Range - using zero\" %\n roi[Y_ORIGIN])\n self.roi[Y_ORIGIN] = 0\n\n if ((roi[X_ORIGIN] + roi[X_SIZE]) <= self.imgSizeX):\n self.roi[X_SIZE] = int(roi[X_SIZE])\n else:\n self.roi[X_SIZE] = self.imgSizeX - self.roi[X_ORIGIN]\n print(\"WARNING: ROI[X_SIZE] %d Out of Range - using %d\" %\n (roi[X_SIZE], self.roi[X_SIZE]))\n\n if ((roi[Y_ORIGIN] + roi[Y_SIZE]) <= self.imgSizeY):\n self.roi[Y_SIZE] = int(roi[Y_SIZE])\n else:\n self.roi[Y_SIZE] = self.imgSizeY - self.roi[Y_ORIGIN]\n print(\"WARNING: ROI[Y_SIZE] %d Out of Range - using %d\" %\n (roi[Y_SIZE], self.roi[Y_SIZE]))\n\n self.xProfileWidth = profileWidths[0]\n self.yProfileWidth = profileWidths[1]\n self.setProfiles(self.xProfileWidth, self.yProfileWidth)","title":""},{"docid":"5cdcad4c387feb7303b4d6179f9f3a37","score":"0.6533895","text":"def build_roi_box_head():\n return ROIBoxHead()","title":""},{"docid":"55a3be74e812b460059c53b570124058","score":"0.65069735","text":"def roi(self, xmin, xsize, ymin, ysize, zmin, zsize, tmin=0, tsize=-1):\n self.__args.extend(('-roi',\n xmin, xsize, ymin, ysize,\n zmin, zsize, tmin, tsize))\n return self","title":""},{"docid":"f3dd372b85b16aecbb29dacdf9cb2994","score":"0.64917934","text":"def set_roi(self, roi):\n with self._sem:\n self.roi = numpy.ascontiguousarray(roi, numpy.int8)\n self.buffers[\"ROI\"] = pyopencl.array.to_device(self.queue, self.roi)","title":""},{"docid":"c9f4c010d16156b78b46d9f114bd2564","score":"0.64815354","text":"def setROI(self,ROI=[]):\n\t\t\n\t\tif not ROI:\n\t\t\t\"\"\"\n\t\t\tUser selection of the ROI\n\t\t\t\n\t\t\t\"\"\"\t\n\t\t\tROI = []\n\t\t\tdef onClick(event):\n\t\t\t\tROI.append([int(event.ydata),int(event.xdata)])\n\t\t\t\tplt.scatter([event.xdata],[event.ydata],c='r')\n\t\t\t\tplt.draw()\n\t\t\t\n\t\t\tfig = plt.figure()\n\t\t\tcid = fig.canvas.mpl_connect('button_press_event', onClick)\n\t\t\tplt.imshow(self[0])\n\t\t\tplt.title(\"Please select ROI coordinates\")\n\t\t\tplt.xlim(0,self[0].shape[1])\n\t\t\tplt.ylim(self[0].shape[0],0)\n\t\t\tplt.show()\n\t\t\t\n\t\t\tprint \"\\n ROI Coordinates: \\n\",ROI\n\t\t\t\n\t\tself.ROI = np.array(ROI)","title":""},{"docid":"f9314e072c90a29f8c25dab9246155ed","score":"0.6480321","text":"def __init__(self, *args, **kwargs):\n super(PolygonROI, self).__init__(*args, **kwargs)\n rect = self.imageItem.boundingRect() # type: QRectF\n positions = [\n (rect.bottomLeft().x(), rect.bottomLeft().y()),\n (rect.bottomRight().x(), rect.bottomRight().y()),\n (rect.topRight().x(), rect.topRight().y()),\n (rect.topLeft().x(), rect.topLeft().y()),\n ]\n self._roiItem = BetterPolyLineROI(positions=positions, closed=True, scaleSnap=True, translateSnap=True)\n self.addItem(self._roiItem)","title":""},{"docid":"4c7397298610b93c2f50aa0fb92c6de2","score":"0.6459811","text":"def segment_ROI_image(Parameters, ROIs, root_dir, sub_path):\n ch = Parameters[\"channels\"]\n subimage_height = Parameters[\"subimage_height\"] \n subimage_width = Parameters[\"subimage_width\"]\n\n\n print (root_dir, sub_path)\n print(\"Segmenting around the ROIs image.... :)\") \n\n \n \n \n filepath = root_dir/sub_path\n annotations_dir = root_dir/'Annotations'/sub_path.parent\n image_dir = root_dir/'JPEGImages'/sub_path.parent\n \n print (\"IM\", image_dir)\n \n \n for dirs in [annotations_dir, image_dir]:\n if (not os.path.isdir(dirs)):\n os.makedirs(dirs)\n \n \n #Preprocess the image\n \n reader = imageio.get_reader(str(filepath))\n image_open = np.asarray(reader.get_data(ch[0]))\n image_para = np.asarray(reader.get_data(ch[1]))\n image_perp = np.asarray(reader.get_data(ch[2]))\n \n \n img = np.dstack([image_open, image_para, image_perp])\n img = img/np.amax(img) \n img = img*255\n img = img.astype('uint8')\n plt.imshow(img)\n \n height, width, depth = img.shape\n print (\"Shape\", img.shape)\n print (ROIs)\n \n \n for i in range(len(ROIs)):\n x_min = int(ROIs.loc[i, 'xmin'])\n x_max = int(ROIs.loc[i, 'xmax'])\n y_min = int(ROIs.loc[i, 'ymin'])\n y_max = int(ROIs.loc[i, 'ymax'])\n \n \n \n \n #x_length = x_max - x_min\n #y_length = y_max - y_min\n \n \n #Padding can be negative!\n #x_pad = (subimage_width - x_length)//2\n #y_pad = (subimage_height - y_length)//2\n \n x_centroid = (x_max + x_min)//2\n y_centroid = (y_max + y_min)//2\n \n print (f\"Stats: X:{x_min}, {x_max}, {x_centroid} Y:{y_min}, {y_max}, {y_centroid}\")\n\n xmin = max(0, (x_centroid - subimage_width//2))\n xmax = min(width, (x_centroid + subimage_width//2))\n ymin = max(0, (y_centroid - subimage_height//2))\n ymax = min(height, (y_centroid + subimage_height//2))\n \n subimage = img[ymin:ymax, xmin:xmax, :]\n\n subROIs = ROIs[(ROIs['X']>xmin) & \n (ROIs['X']<xmax) & \n (ROIs['Y']>ymin) & \n (ROIs['Y']<ymax)].copy()\n\n\n print (\"Stats:\", \"X\", xmin, xmax, \"Y\", ymin, ymax, subimage.shape, len(subROIs))\n\n #If ROI list is not empty \n if len(subROIs)>0:\n\n #mod ROIs to fit the new size\n subROIs['xmin'] = subROIs['xmin'] - xmin\n subROIs['xmax'] = subROIs['xmax'] - xmin\n subROIs['ymin'] = subROIs['ymin'] - ymin\n subROIs['ymax'] = subROIs['ymax'] - ymin\n\n #Check for any truncations\n subROIs['Truncated'] = ((subROIs['xmin']<0) | (subROIs['xmax']>xmax) | \n (subROIs['ymin']<0) | (subROIs['ymax']>ymax))\n\n\n #print (i, j, xmin, xmax, ymin, ymax, len(subROIs))\n print (subROIs)\n\n #Save the jpeg files\n JPEG_filename = image_dir/sub_path.name.replace('.ome.tif', f'{i}.jpg')\n imageio.imwrite(str(JPEG_filename), subimage)\n \n\n #Output the labels\n labels_filename = annotations_dir/sub_path.name.replace('.ome.tif', f'{i}--labels.xml')\n labels = {'Height': subimage.shape[0], \n 'Width': subimage.shape[1], \n 'Filename' : (sub_path.name.replace('.ome.tif', f'{i}.jpg')) , 'Folder': str(sub_path.parent)} \n output_labels (labels, subROIs, labels_filename)\n \n \n return(None)","title":""},{"docid":"e5e1f3f28458b151418e05c18a42c11f","score":"0.6402772","text":"def drawROI(self, image, roi):\n # draw a rectangle around the faces\n for (x, y, w, h) in roi:\n cv2.rectangle(\n image,\n (x, y), # lower left\n (x+w, y+h), # upper right\n (0, 255, 0), # border color\n 2 # line thickness\n )","title":""},{"docid":"eb796bb52c05694eab4adf3b5f12d464","score":"0.6394815","text":"def get_roi(image, wname):\n global REFPT, CROPPING, SRC_COORDS, T_COORDS\n clone = image.copy()\n param = [wname, image]\n cv2.namedWindow(wname)\n cv2.setMouseCallback(wname, click_and_crop, param)\n cv2.imshow(wname, image)\n cv2.waitKey(0)\n\n # if there are two reference points, then set the region of interest\n # of the image and display it\n if len(REFPT) == 2:\n if wname == \"SOURCE\":\n SRC_COORDS = REFPT\n if wname == \"TARGET\":\n T_COORDS = REFPT\n\n #print(REFPT) # for debugging\n cv2.destroyAllWindows()","title":""},{"docid":"274bfea1f4a6efbc42e4a2953ca451d1","score":"0.63507354","text":"def _fcn_build_roi_list(self):\n # Select volume :\n self.volume.select_volume(str(self._roiDiv.currentText()))\n # Clear widget list and add ROIs :\n self._roiToAdd.clear()\n self._roiToAdd.addItems(self.volume.roi_labels)\n # By default, uncheck items :\n for num in range(self._roiToAdd.count()):\n item = self._roiToAdd.item(num)\n item.setFlags(item.flags() | QtCore.Qt.ItemIsUserCheckable)\n item.setCheckState(QtCore.Qt.Unchecked)","title":""},{"docid":"839d6389281e3135a87bb6648699c32a","score":"0.6344049","text":"def make_roi_grid(self, toggle=True, method=''):\n method = method if method else self.sender().text()\n pos, shape = self.rh.ROIs[0].roi.pos(), self.rh.ROIs[0].roi.size()\n if method == 'Single ROI':\n for r in self.rh.ROIs:\n r.resize(*map(int, [pos[0], pos[1], shape[0], shape[1]]))\n elif method == 'Square grid':\n n = len(self.rh.ROIs) # number of ROIs\n d = int((n - 1)**0.5 + 1) # number of ROIs per row\n X = int(self.rh.shape[0] / d) # horizontal distance between ROIs\n Y = int(self.rh.shape[1] / int((n - 3/4)**0.5 + 0.5)) # vertical distance\n for i in range(n): # ID of ROI\n try:\n newx, newy = int(X * (i%d + 0.5)), int(Y * (i//d + 0.5))\n if any([newx//self.rh.shape[0], newy//self.rh.shape[1]]):\n warning('Tried to set square ROI grid with (xc, yc) = (%s, %s)'%(newx, newy)+\n ' outside of the image')\n newx, newy = 0, 0\n self.rh.ROIs[i].resize(*map(int, [newx, newy, 1, 1]))\n except ZeroDivisionError as e:\n error('Invalid parameters for square ROI grid: '+\n 'x - %s, y - %s, pic size - %s, roi size - %s.\\n'%(\n pos[0], pos[1], self.rh.shape[0], (shape[0], shape[1]))\n + 'Calculated width - %s, height - %s.\\n'%(X, Y) + str(e))\n elif method == '2D Gaussian masks':\n try: \n im = self.im_canvas.image.copy() - self.rh.bias\n if np.size(np.shape(im)) == 2:\n for r in self.rh.ROIs:\n r.create_gauss_mask(im) # fit 2D Gaussian to max pixel region\n # then block that region out of the image\n try:\n im[r.x-r.w : r.x+r.w+1, r.y-r.h:r.y+r.h+1] = np.zeros((2*r.w+1, 2*r.h+1)) + np.min(im)\n except (IndexError, ValueError): pass\n except AttributeError: pass","title":""},{"docid":"175773cf544079a2053d90f255009b57","score":"0.6317071","text":"def __set_roi(self, event, x, y, flags, params):\r\n if event==cv2.EVENT_LBUTTONDOWN:\r\n self.ix = x\r\n self.iy = y\r\n self.drawing = True\r\n if event==cv2.EVENT_MOUSEMOVE:\r\n if self.drawing:\r\n self.x = x\r\n self.y = y\r\n self.img = self.current_frame.copy()\r\n cv2.rectangle(self.img, (self.ix, self.iy), (x, y,), (0, 0, 255), 3)\r\n if event==cv2.EVENT_LBUTTONUP:\r\n self.drawing = False","title":""},{"docid":"f51ac614c51ac4c262c16c6176bc61e0","score":"0.6294511","text":"def click(event):\n ROIsize = self.settings['ROI_size']\n Ly =self.img.shape[-1]\n Lx =self.img.shape[-2]\n \n if self.settings['selectROI'] and (Lx,Ly)!=(ROIsize,ROIsize):\n event.accept() \n pos = event.pos()\n x = int(pos.x()) #pyqtgraph is transposed\n y = int(pos.y())\n x = max(min(x, Lx-ROIsize//2 ),ROIsize//2 )\n y = max(min(y, Ly-ROIsize//2 ),ROIsize//2 )\n self.settings['roiX']= x\n self.settings['roiY']= y\n if hasattr(self, 'roi'):\n self.imv.removeItem(self.roi) \n self.roi = pg.RectROI([x-ROIsize//2,y-ROIsize//2], [ROIsize,ROIsize])\n self.imv.addItem(self.roi)\n \n self.settings['selectROI'] = False","title":""},{"docid":"907a0595962eb8c87eced18c267928b7","score":"0.6285353","text":"def SetRoiBoxSelect(self):\n #Check that a Data set has been loaded\n if self.fname == None:\n return\n\n if self.imageType != 'la':\n print 'Function only valid for linear array probes. '\n return\n\n from matplotlib.widgets import RectangleSelector\n current_ax = plt.subplot(111) # make a new plotingrangej\n\n def on_select(eclick, erelease):\n self.roiX = [0,0]\n self.roiY = [0,0]\n\n self.roiX[0] = int(erelease.xdata/self.deltaX)\n self.roiX[1] = int(eclick.xdata/self.deltaX)\n self.roiX.sort()\n\n self.roiY[0] = int(eclick.ydata/self.deltaY)\n self.roiY[1] = int(erelease.ydata/self.deltaY)\n self.roiY.sort()\n\n # drawtype is 'box' or 'line' or 'none'\n rectprops = dict(facecolor='red', edgecolor = 'red',\n alpha=0.5, fill=False)\n \n rs = RectangleSelector(current_ax, on_select,\n drawtype='box', useblit=True,\n button=[1,3], # don't use middle button\n minspanx=0, minspany=0,\n spancoords='data',\n rectprops = rectprops)\n\n #could be image sequence or just a 2-D image\n import types\n if type(self.data) == types.NoneType:\n self.ReadFrame(0)\n temp = self.data\n\n\n from scipy.signal import hilbert\n from numpy import log10\n bMode = log10(abs(hilbert(temp, axis = 0)))\n bMode = bMode - bMode.max()\n bMode[bMode < -3] = -3\n\n #import matplotlib and create plot\n import matplotlib.cm as cm\n\n plt.imshow(bMode, cmap = cm.gray, extent = [0, self.fovX, self.fovY, 0])\n plt.show()","title":""},{"docid":"a59be2fad99acc7a2ba8e6c23a7e36d1","score":"0.6275901","text":"def roi_target(self,rois,gt_box,label,pos_thresh=cfg.rcnn_pos_thresh,\n neg_thresh_lo=cfg.rcnn_neg_thresh_lo,\n neg_thresh_hi=cfg.rcnn_neg_thresh_hi):\n assert rois.shape[1]==4, \"please remove the img_id\"\n rois=torch.cat([rois,gt_box],dim=0) # [a+b,4]\n \n ious=t_box_iou(rois,gt_box) # [a+b,b]\n max_ious,idx=ious.max(dim=1)\n \n # parameterizd box\n gt_loc=encode_box(gt_box[idx],rois)\n\n # assign the neg:\n assign=torch.full([len(rois)],-1).long().type_as(label)\n\n neg_mask=(max_ious>neg_thresh_lo)*(max_ious<neg_thresh_hi)\n # if neg_mask.sum() == 0:\n # tqdm.write(\"Warning: neg_roi for fast r-cnn is zero\",end=\" \")\n # neg_mask=(max_ious<neg_thresh_hi)\n # raise ValueError(\"there is no negative roi for fast r-cnn\")\n assign[neg_mask]=0\n \n # assign the pos:\n pos_mask=max_ious>pos_thresh\n\n # plus one since 0 denotes the neg, we must begin from the 1\n assign[pos_mask]=label[idx][pos_mask].long()+1 \n\n # normalize?\n mean=self.mean # [4]\n std=self.std # [4]\n\n mean=mean[None].expand_as(gt_loc).type_as(gt_loc)\n std=std[None].expand_as(gt_loc).type_as(gt_loc)\n\n gt_loc-=mean\n gt_loc=gt_loc/std\n\n return rois,gt_loc,assign","title":""},{"docid":"8d67f9685f4abf4db6878699a3409800","score":"0.62747985","text":"def selectROI(windowName, img, showCrosshair=..., fromCenter=...) -> retval:\n ...","title":""},{"docid":"b44d8d642d88fedfb5e042adb194c018","score":"0.6271857","text":"def add_rois(self, rois):\r\n # Load objects\r\n objects_id = str(self.boxes_source.value().rsplit(\"/\", 1)[-1])\r\n # str(self.boxes_source.value())\r\n\r\n logger.debug(f\"Get objects {objects_id}\")\r\n objects_src = DataModel.g.dataset_uri(objects_id, group=\"objects\")\r\n params = dict(workpace=True, src=objects_src, entity_type=\"boxes\")\r\n\r\n result = Launcher.g.run(\"objects\", \"get_entities\", **params)\r\n\r\n if result:\r\n entities_arr = decode_numpy(result)\r\n\r\n rois = entities_arr[:, 4:]\r\n print(rois)\r\n\r\n # Iterate through ROIs and add them to the ROI list\r\n original_workspace = DataModel.g.current_workspace\r\n\r\n for roi in rois:\r\n roi_list = list(roi)\r\n roi_list = [int(el) for el in roi_list]\r\n # reorder to z_st, z_end, x_st, x_end, y_st, y_end\r\n roi_list = [\r\n roi_list[0],\r\n roi_list[3],\r\n roi_list[1],\r\n roi_list[4],\r\n roi_list[2],\r\n roi_list[5],\r\n ]\r\n\r\n roi_name = (\r\n DataModel.g.current_workspace\r\n + \"_roi_\"\r\n + str(roi[0])\r\n + \"_\"\r\n + str(roi[3])\r\n + \"_\"\r\n + str(roi[1])\r\n + \"_\"\r\n + str(roi[4])\r\n + \"_\"\r\n + str(roi[2])\r\n + \"_\"\r\n + str(roi[5])\r\n )\r\n cfg.ppw.clientEvent.emit(\r\n {\"source\": \"panel_gui\", \"data\": \"make_roi_ws\", \"roi\": roi_list}\r\n )\r\n self.add_roi(roi_name, original_workspace, roi_list)","title":""},{"docid":"cabdf45b195176cf9a3d597d7ad953b9","score":"0.62654203","text":"def show( self ):\n source = cv.LoadImage( self.files[self.index] )\n width, height = cv.GetSize(source)\n \n center = (width/2) + self.offset;\n \n cv.Line( source, (center,0), (center,height), (0,255,0), 1)\n\n\n if self.roi:\n x,y,a,b = self.roi;\n \n print self.roi\n \n width, height = ((a - x), (b - y))\n mask = cv.CreateImage( (width, height), cv.IPL_DEPTH_8U, 1)\n \n cv.SetImageROI( source, (x, y, width, height)) \n cv.Split( source, None, None, mask, None );\n \n gray = cv.CloneImage( mask );\n \n cv.InRangeS( mask, self.thresholdMin, self.thresholdMax, mask );\n cv.And( mask, gray, gray ); \n \n line = [];\n points = []; \n \n for i in range(0,height-1):\n row = cv.GetRow( gray, i)\n \n minVal,minLoc,maxLoc,maxVal = cv.MinMaxLoc(row);\n \n y = i;\n x = maxVal[0]\n point = (0, 0, height-i)\n \n if x > 0:\n line.append((x,y));\n \n s = x / sin(radians(self.camAngle))\n x = s * cos(self.angles[self.index]) \n z = height - y\n y = s * sin(self.angles[self.index])\n \n point = (round(x,2),round(y,2),z);\n \n points.append(point)\n \n \n cv.PolyLine( source, [line], False, (255,0,0), 2, 8)\n cv.ResetImageROI( source )\n x,y,a,b = self.roi;\n cv.Rectangle( source, (int(x), int(y)), (int(a), int(b)), (255.0, 255, 255, 0) );\n\n if self.roi:\n x,y,a,b = self.roi;\n \n width, height = ((a - x), (b - y))\n mask = cv.CreateImage( (width, height), cv.IPL_DEPTH_8U, 1)\n \n cv.SetImageROI( source, (x-width, y, width, height)) # moves roi to the left\n cv.Split( source, None, None, mask, None );\n \n gray = cv.CloneImage( mask );\n \n cv.InRangeS( mask, self.thresholdMin, self.thresholdMax, mask );\n cv.And( mask, gray, gray ); \n \n line = [];\n points2 = []; \n \n for i in range(0,height-1):\n row = cv.GetRow( gray, i)\n \n minVal,minLoc,maxLoc,maxVal = cv.MinMaxLoc(row);\n \n y = i;\n x = maxVal[0]\n point = (0, 0, height-i)\n \n if x > 0:\n line.append((x,y));\n \n x = width - x; # left to the x-axis\n \n s = x / sin(radians(self.camAngle))\n \n x = s * cos(self.angles[self.index]) \n z = height - y# 500 higher then the other.\n y = s * sin(self.angles[self.index])\n \n a = radians(300)\n \n nx = ( cos(a) * x ) - ( sin(a) * y )\n ny = ( sin(a) * x ) + ( cos(a) * y )\n \n point = (nx,ny,z);\n \n points2.append(point)\n \n cv.PolyLine( source, [line], False, (255,0,0), 2, 8)\n cv.ResetImageROI( source )\n x,y,a,b = self.roi;\n cv.Rectangle( source, (int(x), int(y)), (int(a), int(b)), (255.0, 255, 255, 0) );\n\n if self.mode == 'mask':\n cv.ShowImage( 'preview', mask )\n return\n\n if self.mode == 'record' and self.roi:\n font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX,0.5,0.5,1)\n cv.PutText( source, \"recording %d\" % self.index, (20,20), font, (0,0,255))\n self.points.extend(points);\n self.points2.extend(points2);\n #self.colors.extend(colors);\n\n\n\n cv.ShowImage( 'preview', source )","title":""},{"docid":"9a1017cb14597a541268df3a7fe80ccf","score":"0.6260349","text":"def setup_roi_fields(self):\r\n apply_roi_button = QPushButton(\"Apply ROI\")\r\n reset_button = QPushButton(\"Reset ROI\")\r\n roi_fields = QGroupBox(\"Select Region of Interest:\")\r\n roi_layout = QGridLayout()\r\n roi_layout.addWidget(QLabel(\"Drag a box in the image window or type manually\"), 0, 0, 1, 3)\r\n roi_layout.addWidget(QLabel(\"Axis\"), 1, 0)\r\n roi_layout.addWidget(QLabel(\"Start Value:\"), 1, 1)\r\n roi_layout.addWidget(QLabel(\"End Value:\"), 1, 2)\r\n roi_layout.addWidget(apply_roi_button, 1, 3)\r\n roi_layout.addWidget(reset_button, 2, 3)\r\n roi_layout.addWidget(QLabel(\"x:\"), 2, 0)\r\n self.xstart_linedt = QLineEdit(\"0\")\r\n self.xstart_linedt.textChanged.connect(self.on_roi_param_changed)\r\n roi_layout.addWidget(self.xstart_linedt, 2, 1)\r\n self.xend_linedt = QLineEdit(\"0\")\r\n self.xend_linedt.textChanged.connect(self.on_roi_param_changed)\r\n roi_layout.addWidget(self.xend_linedt, 2, 2)\r\n roi_layout.addWidget(QLabel(\"y:\"), 3, 0)\r\n self.ystart_linedt = QLineEdit(\"0\")\r\n self.ystart_linedt.textChanged.connect(self.on_roi_param_changed)\r\n roi_layout.addWidget(self.ystart_linedt, 3, 1)\r\n self.yend_linedt = QLineEdit(\"0\")\r\n self.yend_linedt.textChanged.connect(self.on_roi_param_changed)\r\n roi_layout.addWidget(self.yend_linedt, 3, 2)\r\n roi_layout.addWidget(QLabel(\"z:\"), 4, 0)\r\n self.zstart_linedt = QLineEdit(\"0\")\r\n self.zstart_linedt.textChanged.connect(self.on_roi_param_changed)\r\n roi_layout.addWidget(self.zstart_linedt, 4, 1)\r\n self.zend_linedt = QLineEdit(\"0\")\r\n self.zend_linedt.textChanged.connect(self.on_roi_param_changed)\r\n roi_layout.addWidget(self.zend_linedt, 4, 2)\r\n roi_layout.addWidget(QLabel(\"Downsample Factor:\"), 5, 0)\r\n self.downsample_spinner = QSpinBox()\r\n self.downsample_spinner.setRange(1, 10)\r\n self.downsample_spinner.setSpecialValueText(\"None\")\r\n self.downsample_spinner.setMaximumWidth(60)\r\n self.downsample_spinner.valueChanged.connect(self.on_roi_param_changed)\r\n roi_layout.addWidget(self.downsample_spinner, 5, 1)\r\n roi_layout.addWidget(QLabel(\"Estimated datasize (MB):\"), 5, 3)\r\n self.data_size_label = QLabel(\"0\")\r\n roi_layout.addWidget(self.data_size_label, 5, 4)\r\n roi_fields.setLayout(roi_layout)\r\n apply_roi_button.clicked.connect(self.on_roi_apply_clicked)\r\n reset_button.clicked.connect(self.on_roi_reset_clicked)\r\n return roi_fields","title":""},{"docid":"cf3785dff72358282be07f0065e60a3a","score":"0.6255171","text":"def enableROIselection(self):\n def click(event):\n \"\"\"\n Resizes imageRaw on click event, to the specified size 'ROI_size'\n around the clicked point.\n \"\"\"\n ROIsize = self.settings['ROI_size']\n Ly =self.img.shape[-1]\n Lx =self.img.shape[-2]\n \n if self.settings['selectROI'] and (Lx,Ly)!=(ROIsize,ROIsize):\n event.accept() \n pos = event.pos()\n x = int(pos.x()) #pyqtgraph is transposed\n y = int(pos.y())\n x = max(min(x, Lx-ROIsize//2 ),ROIsize//2 )\n y = max(min(y, Ly-ROIsize//2 ),ROIsize//2 )\n self.settings['roiX']= x\n self.settings['roiY']= y\n if hasattr(self, 'roi'):\n self.imv.removeItem(self.roi) \n self.roi = pg.RectROI([x-ROIsize//2,y-ROIsize//2], [ROIsize,ROIsize])\n self.imv.addItem(self.roi)\n \n self.settings['selectROI'] = False\n \n self.imv.getImageItem().mouseClickEvent = click\n self.settings['selectROI'] = True","title":""},{"docid":"a8e5505d17ed27644b6a99092349a374","score":"0.62447166","text":"def selectROIs(windowName, img, showCrosshair=..., fromCenter=...) -> boundingBoxes:\n ...","title":""},{"docid":"6a348dea5fa2bd00eef486e7c0526feb","score":"0.6225949","text":"def asROI(self):\n return PolygonRoi(\n FloatPolygon(*map(list, zip(*map(Ray.getOrigin, self.points)))), # Unpacking x and y coordinates of rays in two lists\n Roi.NORMAL\n )","title":""},{"docid":"e329d10695fded0cfbbdfd7664d1056e","score":"0.6217549","text":"def set_rois(self, ROIlist):\n self.rh.create_rois(len(ROIlist))\n self.rh.resize_rois(ROIlist)\n self.display_rois()","title":""},{"docid":"c9a98ecddd77bb70f4bba805f038c7f1","score":"0.6217537","text":"def runDefineCropROI(self, cropParam):\n vol \t\t= slicer.mrmlScene.GetNodeByID(cropParam.GetInputVolumeNodeID()\t)\n volBounds\t= [0,0,0,0,0,0]\n vol.GetRASBounds(volBounds)\n logging.info(volBounds)\n\n #Find Dimensions of Image\n volDim\t\t= [ (volBounds[1]-volBounds[0]),\n \t\t\t (volBounds[3]-volBounds[2]),\n \t\t\t (volBounds[5]-volBounds[4]) ]\n roi\t\t\t= slicer.mrmlScene.GetNodeByID(cropParam.GetROINodeID())\n\n #Find Center of Image\n volCenter \t= [ ((volBounds[0]+volBounds[1])/2),\n \t\t\t ((volBounds[2]+volBounds[3])/2),\n ((volBounds[4]+volBounds[5])/2) ]\n\n roi.SetXYZ(volCenter)\n roi.SetRadiusXYZ(volDim[0]/2, volDim[1]/2, volDim[2]/2 )\n return roi","title":""},{"docid":"ccca5a22455665021add321a61b723cd","score":"0.6213413","text":"def create_roi(array, loc, kx=3, ky=3, edge_wrap=False):\r\n\r\n dims = array.shape\r\n if (len(dims) != 2):\r\n raise Exception(\"Array Must Be 2 Dimensional!\")\r\n\r\n # Kernel size squared\r\n kx2 = kx**2\r\n ky2 = ky**2\r\n\r\n # Kernel offsets\r\n xoff = kx / 2\r\n yoff = ky / 2\r\n\r\n # Find the seed's neighbours\r\n x = numpy.arange(kx2) % kx + (seed[1] - xoff)\r\n y = numpy.arange(ky2) / ky + (seed[0] - yoff)\r\n roi = (y,x)\r\n\r\n # If the ROI is outside the array bounds it will be set to the min/max array bounds\r\n # otherwise it will be wrap around the edges of the array if edge_wrap is set to True\r\n if not edge_wrap:\r\n # Check if any parts of the roi are outside the image\r\n bxmin = numpy.where(roi[1] < 0)\r\n bymin = numpy.where(roi[0] < 0)\r\n bxmax = numpy.where(roi[1] >= dims[1])\r\n bymax = numpy.where(roi[0] >= dims[0])\r\n\r\n # Change if roi co-ordinates exist outside the image domain.\r\n roi[1][bxmin] = 0\r\n roi[0][bymin] = 0\r\n roi[1][bxmax] = dims[1]-1\r\n roi[0][bymax] = dims[0]-1\r\n\r\n return roi","title":""},{"docid":"1fba9108f0922e840c479b2b06a95426","score":"0.6209862","text":"def getRoiImg(self):\n roiImg = cv2.cvtColor(self.img,cv2.COLOR_GRAY2RGB)\n cv2.rectangle(roiImg,\n (self.roi[X_ORIGIN], self.roi[Y_ORIGIN]),\n (self.roi[X_ORIGIN] + self.roi[X_SIZE],\n self.roi[Y_ORIGIN] + self.roi[Y_SIZE]),\n (65535,0,0),\n 3)\n return(roiImg)","title":""},{"docid":"ebcc5929151cf697cd4b92d0953746c1","score":"0.6206967","text":"def roi(self):\n w, h, x, y = c_int32(), c_int32(), c_int32(), c_int32()\n cco, packet_size = color_coding_t(), c_int32()\n dll.dc1394_format7_get_roi(self._cam, self._mode_id, pointer(cco),\n byref(packet_size),\n byref(x), byref(y), byref(w), byref(h))\n return ((w.value, h.value), (x.value, y.value),\n color_codings[cco.value], packet_size.value)","title":""},{"docid":"155a1dc6e9674817d64e245dbf31b7d6","score":"0.617504","text":"def select_roi(self, mm_generator_kw={}, **rv_kw):\n y0,x0 = int(np.floor(self.motion_borders.ymin)), int(np.floor(self.motion_borders.xmin))\n y1,x1 = int(np.ceil(self.motion_borders.ymax)), int(np.ceil(self.motion_borders.xmax))\n\n def mm_mean_subtracted(downsample=5, mean_src='maxmov', equalize=True):\n \"\"\"\n mean_src : 'maxmov' or 'all'\n \"\"\"\n with h5py.File(self.data_file, 'r+') as f:\n if 'maxmov' in f:\n mean = np.mean(f['maxmov'], axis=0)\n n = len(f['maxmov'])\n else:\n raise Exception('No maxmov available.')\n\n if mean_src == 'maxmov':\n pass\n elif mean_src == 'all':\n mean = self.mean(axis=0)\n \n for i in range(n//downsample):\n with h5py.File(self.data_file, 'r+') as f:\n fr = np.max( f['maxmov'][i*downsample : i*downsample+downsample], axis=0 )\n fr = fr-mean\n if equalize:\n fr = equalize_adapthist((fr-fr.min())/(fr.max()-fr.min()))\n\n minn = np.nanmin(fr)\n\n fr[:y0] = minn\n fr[y1:] = minn\n fr[:x0] = minn\n fr[x1:] = minn\n yield fr\n \n inst = mm_mean_subtracted(**mm_generator_kw)\n self.roiview = ROIView(next(inst), iterator=inst, **rv_kw)\n print('Remember to set roi using Data.set_roi(roi_view.roi).\\nIf you forgot to store roi_view, it is saved in object as Data.roiview.')\n\n return self.roiview","title":""},{"docid":"e25012b4e70cfae0b1304d3a2a44c080","score":"0.61721134","text":"def roi(img):\n # Region of interest\n imshape = img.shape\n roi = np.array([[\n (100,100),\n (600, 100), \n (600, 400), \n (100, 400)\n ]], dtype=np.int32)\n\n #defining a blank mask to start with\n mask = np.zeros_like(img) \n \n #defining a 3 channel or 1 channel color to fill the mask with depending on the input image\n if len(img.shape) > 2:\n channel_count = img.shape[2] # i.e. 3 or 4 depending on your image\n ignore_mask_color = (255,) * channel_count\n else:\n ignore_mask_color = 255\n \n #filling pixels inside the polygon defined by \"vertices\" with the fill color \n cv2.fillPoly(mask, roi, ignore_mask_color)\n \n #returning the image only where mask pixels are nonzero\n masked_image = cv2.bitwise_and(img, mask)\n\n return masked_image","title":""},{"docid":"cf270a56671529e2999c56421c9e05ba","score":"0.6164488","text":"def user_roi(self, roi):\n # find which ROI was dragged\n for r in self.rh.ROIs:\n if r.roi == roi:\n break\n x0, y0 = roi.pos() # lower left corner of bounding rectangle\n w, h = map(int, roi.size()) # width, height\n xc, yc = int(x0 + w//2), int(y0 + h//2) # centre of ROI\n r.w, r.h = w, h\n r.label.setPos(x0, y0)\n r.translate_mask(xc, yc)\n for key, val in zip(r.edits.keys(), [xc, yc, w, h]):\n r.edits[key].setText(str(val))","title":""},{"docid":"09a8159ff24ae4894343687daa28ec4e","score":"0.61622715","text":"def display_rois(self, n=''):\n if n:\n self.rh.create_rois(int(n))\n viewbox = self.im_canvas.getViewBox()\n for item in viewbox.allChildren(): # remove unused ROIs\n if ((type(item) == pg.graphicsItems.ROI.ROI or \n type(item) == pg.graphicsItems.TextItem.TextItem) and \n item not in [r.roi for r in self.rh.ROIs] + [r.label for r in self.rh.ROIs]):\n viewbox.removeItem(item)\n layout = self.centre_widget.layout()\n k = np.sqrt(len(self.plots))\n for i, r in enumerate(self.rh.ROIs):\n if r.roi not in viewbox.allChildren():\n reset_slot(r.roi.sigRegionChangeFinished, self.user_roi, True) \n reset_slot(r.threshedit.textEdited, self.update_plots, True)\n r.roi.setZValue(10) # make sure the ROI is drawn above the image\n viewbox.addItem(r.roi)\n viewbox.addItem(r.label)\n try:\n self.plots[i]['plot'].setTitle('ROI '+str(r.id))\n for j, label in enumerate(list(r.edits.values())+[r.threshedit, r.autothresh]):\n layout.addWidget(label, (i//k)*3, 7+(i%k)*6+j, 1,1)\n except IndexError as e: pass # warning('Atom Checker has more plots than ROIs')","title":""},{"docid":"99553b92ce7df7c8efd6b73ea679dfd8","score":"0.6157534","text":"def roi(self, data):\r\n x, y = self._event_xdata, self._event_ydata\r\n return contour_to_roi(x, y, data)","title":""},{"docid":"97ab9a035b1d90312864dedfc18f9c74","score":"0.609071","text":"def load_MPII_annotation(self, index):\n\n \n roi_rec = dict()\n roi_rec['image'] = os.path.join(self.image_path,self.annotation[index]['img_paths'])\n #size = cv2.imread(roi_rec['image']).shape\n #roi_rec['height'] = size[0]\n #roi_rec['width'] = size[1]\n roi_rec['height'] = self.annotation[index]['img_height']\n roi_rec['width'] = self.annotation[index]['img_width']\n\n \n numOtherPerson=self.annotation[index]['numOtherPeople']\n otherPersonJoints=[]\n if numOtherPerson >0:\n if numOtherPerson>1:\n otherPersonJoints=otherPersonJoints+self.annotation[index]['joint_others']\n else:\n otherPersonJoints.append(self.annotation[index]['joint_others'])\n mainPersonJoints=self.annotation[index]['joint_self']\n allPerson=otherPersonJoints+[mainPersonJoints]\n num_objs = len(allPerson)\n\n poses = np.zeros((num_objs, 28), dtype=np.uint16)\n gt_classes = np.zeros((num_objs), dtype=np.int32)\n overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n\n class_to_index = dict(zip(self.classes, range(self.num_classes)))\n # Load object bounding boxes into a data frame.\n for ix, obj in enumerate(allPerson):\n \n cls = class_to_index['person']\n po=np.zeros((16,3),dtype=np.float32)\n po[0:len(obj),:]=np.array(obj,dtype=np.float32)\n assert po.shape[0] ==16,'the image is wrong'\n\n poses[ix, :] = po[self.index2index[1:],:-1].ravel() ### obj must [14,2]\n gt_classes[ix] = cls\n overlaps[ix, cls] = 1.0\n\n roi_rec.update({'poses': poses,\n 'gt_classes': gt_classes,\n 'gt_overlaps': overlaps,\n 'max_classes': overlaps.argmax(axis=1),\n 'max_overlaps': overlaps.max(axis=1),\n 'flipped': False})\n return roi_rec","title":""},{"docid":"3d6154a1bb42664e34819c26bbdfe0fc","score":"0.606863","text":"def __init__(self, roi, points, frame_no):\n # assign next object id\n self.id = Object.counter\n Object.counter += 1\n self.roi = roi\n self.roi_valid = True # whether the RoI is acquired from object detection or predicted\n self.roi_history = {frame_no: roi}\n self.points = set(points)\n # Add random color to the object for visualization\n self.color = colorsys.hsv_to_rgb(random(), 1, 1)","title":""},{"docid":"43d05be7642aaafb6b2c3530cab1874c","score":"0.605366","text":"def __init__(self, maglim, roi):\n self.roi = roi\n mask = maglim*np.ones(hp.nside2npix(self.roi.config['coords']['nside_pixel']))\n self.nside = hp.npix2nside(len(mask))\n # Sparse maps of pixels in various ROI regions\n self.mask_roi_sparse = mask[self.roi.pixels]","title":""},{"docid":"7d5b49218197279100d84f4a623bbe11","score":"0.6052589","text":"def set_roi(self, mcaroi, name='OCR', min_x=1, size_x=4095):\n mcaroi.configure_mcaroi(\n roi_name=name,\n min_x=min_x,\n size_x=size_x \n )\n mcaroi.name = name\n mcaroi.total_rbv.name = name","title":""},{"docid":"474e81ce28edd8b2f7a4e64386569ca9","score":"0.604833","text":"def FindAdaptiveROI(image, center_ROI, aspr_ROI,displayImages, debug = True):\n #inputfilename = 'img6.png'\n #outputfilename = 'edge2.png'\n #nucleation_down = 1 # 0 for nucleation up\n #center_ROI = (511,672) #center of the object to be identified\n #aspr_ROI = 2/3 # x_width/y_width for ROI. This is found by TRAINING\n #debug = True # flag to output ERRRORs\n #remove the strip at the bottom\n #cropsequence = ((0,44),(0,0))\n #img = ReadImage(inputfilename)\n #img = CropImage(img,cropsequence,0)\n #to mainain the aspect ratio of roi to be same as that of image, set the aspect ratio\n #asp_ratio = int(1344/(1066-44))\n #list of pad sizes to be removed along x axis\n array_x_ROI = np.array([100,200,300,400,500,600,700,800,902])\n array_y_ROI = (array_x_ROI*aspr_ROI).astype(int)\n n = array_x_ROI.size\n optimum_x_ROI = 0\n optimum_y_ROI = 0\n #set the array for relative strengths and maxima positions for the unimodal or bimodal distributions.\n array_rel_strength = np.zeros(n)\n array_maximum = np.zeros((n,2))\n displayImages = 0\n for i in np.arange(n):\n x_width = array_x_ROI[i]\n y_width = array_y_ROI[i]\n #set up the cropsequence so that pads are removed centered around the center of the image.\n cropsequence = CropSequenceGenerate(image,(center_ROI,(x_width,y_width)))\n cropimg = CropImage(image,cropsequence,0)\n imgbyte = Img2Ubyte(cropimg,0)\n img_med = MedianFilter(imgbyte,displayImages)\n maximum,rel_strength = modal_analysis(img_med,displayImages,debug) #strength is zero if distribution is unimodal and close to zero if the foreground is very small compared to background or vice versa\n array_rel_strength[i] = rel_strength \n array_maximum[i] = maximum\n if displayImages==1:\n #plot the relative strength variation and choose the appropriate ROI\n plt.figure(),plt.title(\"Finding Optimum ROI by varying xROI\"),plt.plot(array_x_ROI,array_rel_strength)\n #if all are unimodal distributions, then there either is no object to be found or object is beyond the ROI. This means that we need to check for bigger ROIs with progressive increase in y axis width\n max_rel_strength = np.max(array_rel_strength)\n if debug: print(\"maximum relative strength is \" + str(max_rel_strength))\n if max_rel_strength < 0.001:\n optimum_x_ROI = 1000\n else:\n #find the optimum ROI from maximum of the relative strength vs ROI variation\n optimum_x_ROI = array_x_ROI[array_rel_strength.argsort()[-1]]\n optimum_y_ROI = array_y_ROI[array_rel_strength.argsort()[-1]]\n print(\"optimum_x_ROI is \" + str(optimum_x_ROI))\n\n #if optimum ROI is less than 1000, then it probably means that the object is not occluded and search for the ROI is completed. If the ROI is not optimized then we can increase the y_width of ROI further keeping the x_width to be constant at 1022\n if optimum_x_ROI == 1000:\n array_y_ROI = np.array([800,900,1000,1100])\n n = array_y_ROI.size\n array_x_ROI = np.ones(n,dtype = np.int32)*902\n #set the array for relative strengths and maxima positions for the unimodal or bimodal distributions.\n array_rel_strength = np.zeros(n)\n array_maximum = np.zeros((n,2))\n displayImages = 0\n for i in np.arange(n):\n x_width = array_x_ROI[i]\n y_width = array_y_ROI[i]\n #set up the cropsequence so that pads are removed across y axis around the center of the image.\n cropsequence = CropSequenceGenerate(image,(center_ROI,(x_width,y_width)))\n cropimg = CropImage(image,cropsequence,0)\n imgbyte = Img2Ubyte(cropimg,0)\n img_med = MedianFilter(imgbyte,displayImages)\n maximum,rel_strength = modal_analysis(img_med,displayImages,debug) #strength is zero if distribution is unimodal and close to zero if the foreground is very small compared to background or vice versa\n array_rel_strength[i] = rel_strength \n array_maximum[i] = maximum\n displayImages = 1\n if displayImages == 1:\n #plot the relative strength variation and choose the appropriate ROI\n plt.figure(),plt.title(\"Finding Optimum ROI by varying yROI\"),plt.plot(array_y_ROI,array_rel_strength)\n max_rel_strength = np.max(array_rel_strength)\n if max_rel_strength == 0:\n optimum_x_ROI = 0\n optimum_y_ROI = 0\n if debug: print(\"This image needs to be discarded\")\n #find the optimum ROI from maximum of the relative strength vs ROI variation\n optimum_x_ROI = array_x_ROI[array_rel_strength.argsort()[-1]]\n optimum_y_ROI = array_y_ROI[array_rel_strength.argsort()[-1]]\n if optimum_y_ROI == 1300:\n #so the whole image needs to be used for further processing\n optimum_x_ROI = 1022\n optimum_y_ROI = 1344\n #proceed with further processing with optimum ROI\n optimum_ROI = (optimum_x_ROI,optimum_y_ROI)\n if debug: print(\"Optimum ROI is \",optimum_ROI)\n return optimum_ROI","title":""},{"docid":"62bfcb8619b7f5deec11d3d41dec04a3","score":"0.60479146","text":"def create_pipeline_nii_to_subj_ROI(\n main_path, filter_gm_threshold=0.9, pipeline_name=\"nii_to_subj_ROI\",\n background_val=-1.0, plot=True, reslice=False, resample=False,\n min_BOLD_intensity=50, percent_signal=0.5):\n if reslice and resample:\n print(\"Only reslice OR resample can be true, setting reslice to False\")\n reslice = False\n\n pipeline = pe.Workflow(name=pipeline_name)\n pipeline.base_dir = main_path\n\n inputnode = pe.Node(niu.IdentityInterface(fields=[\n 'nii_4D_file', 'ROI_mask_file', 'gm_anat_file', 'ROI_coords_file',\n 'ROI_MNI_coords_file', 'ROI_labels_file']), name='inputnode')\n\n # reslice gm\n if reslice:\n\n reslice_gm = pe.Node(interface=spmu.Reslice(), name='reslice_gm')\n pipeline.connect(inputnode, 'ROI_mask_file',\n reslice_gm, 'space_defining')\n pipeline.connect(inputnode, 'gm_anat_file', reslice_gm, 'in_file')\n\n if resample:\n\n resample_gm = pe.Node(interface=RegResample(), name='resample_gm')\n pipeline.connect(inputnode, 'ROI_mask_file', resample_gm, 'ref_file')\n pipeline.connect(inputnode, 'gm_anat_file', resample_gm, 'flo_file')\n\n # Preprocess pipeline,\n filter_ROI_mask_with_GM = pe.Node(\n interface=IntersectMask(), name='filter_ROI_mask_with_GM')\n\n filter_ROI_mask_with_GM.inputs.filter_thr = filter_gm_threshold\n filter_ROI_mask_with_GM.inputs.background_val = background_val\n\n pipeline.connect(inputnode, 'ROI_mask_file',\n filter_ROI_mask_with_GM, 'indexed_rois_file')\n pipeline.connect(inputnode, 'ROI_coords_file',\n filter_ROI_mask_with_GM, 'coords_rois_file')\n pipeline.connect(inputnode, 'ROI_MNI_coords_file',\n filter_ROI_mask_with_GM, 'MNI_coords_rois_file')\n pipeline.connect(inputnode, 'ROI_labels_file',\n filter_ROI_mask_with_GM, 'labels_rois_file')\n\n if reslice:\n pipeline.connect(reslice_gm, 'out_file',\n filter_ROI_mask_with_GM, 'filter_mask_file')\n\n elif resample:\n pipeline.connect(resample_gm, 'out_file',\n filter_ROI_mask_with_GM, 'filter_mask_file')\n\n else:\n pipeline.connect(inputnode, 'gm_anat_file',\n filter_ROI_mask_with_GM, 'filter_mask_file')\n\n # Nodes version: use min_BOLD_intensity and\n # return coords where signal is strong enough\n extract_mean_ROI_ts = pe.Node(interface=ExtractTS(\n plot_fig=plot), name='extract_mean_ROI_ts')\n\n extract_mean_ROI_ts.inputs.percent_signal = percent_signal\n extract_mean_ROI_ts.inputs.min_BOLD_intensity = min_BOLD_intensity\n\n pipeline.connect(inputnode, 'nii_4D_file', extract_mean_ROI_ts, 'file_4D')\n pipeline.connect(filter_ROI_mask_with_GM, 'filtered_indexed_rois_file',\n extract_mean_ROI_ts, 'indexed_rois_file')\n pipeline.connect(filter_ROI_mask_with_GM, 'filtered_MNI_coords_rois_file',\n extract_mean_ROI_ts, 'MNI_coord_rois_file')\n pipeline.connect(filter_ROI_mask_with_GM, 'filtered_coords_rois_file',\n extract_mean_ROI_ts, 'coord_rois_file')\n pipeline.connect(filter_ROI_mask_with_GM, 'filtered_labels_rois_file',\n extract_mean_ROI_ts, 'label_rois_file')\n\n return pipeline","title":""},{"docid":"8a1cff0eea214f649e3e331ef1326f1c","score":"0.60477114","text":"def test_apply_roi_2d(self):\r\n client = self.create_client_with_image()\r\n\r\n roi = core.roi.PolygonalROI(vx=[10, 20, 20, 10],\r\n vy=[10, 10, 20, 20])\r\n client.apply_roi(roi)\r\n roi2 = self.im.edit_subset.subset_state.roi\r\n state = self.im.edit_subset.subset_state\r\n\r\n assert roi2.to_polygon()[0] == roi.to_polygon()[0]\r\n assert roi2.to_polygon()[1] == roi.to_polygon()[1]\r\n assert state.xatt is self.im.get_pixel_component_id(1)\r\n assert state.yatt is self.im.get_pixel_component_id(0)","title":""},{"docid":"7d7a77121bd54ce667e3aa5233e16e32","score":"0.6046832","text":"def getRoi(self):\n roi = self.img[self.roi[Y_ORIGIN] : \n self.roi[Y_ORIGIN] + self.roi[Y_SIZE],\n self.roi[X_ORIGIN] : \n self.roi[X_ORIGIN] + self.roi[X_SIZE]]\n return roi","title":""},{"docid":"703b707ddce0d52b847e84b7b1527ce3","score":"0.6044786","text":"def test_roi(self):\n import_file = os.path.join(\"..\", \"mlx75027.csv\")\n mlx75027 = True\n self.assertTrue(os.path.isfile(import_file))\n reg_dict = mlx.csv_import(import_file)\n\n row_end = 480\n col_end = 640\n row_offset = 1\n col_offset = 1\n mlx.set_roi(reg_dict, col_offset, col_end,\n row_offset, row_end, mlx75027)\n\n cs, ce, rs, re = mlx.calc_roi(reg_dict)\n self.assertEqual(row_offset, rs)\n self.assertEqual(col_offset, cs)\n self.assertEqual(row_end, re)\n self.assertEqual(col_end, ce)\n\n row_offset = 0\n with self.assertRaises(RuntimeError):\n mlx.set_roi(reg_dict, col_offset, col_end,\n row_offset, row_end, mlx75027)\n\n row_offset = 51\n row_end = 240\n col_offset = 50\n col_end = 150\n mlx.set_roi(reg_dict, col_offset, col_end,\n row_offset, row_end, mlx75027)\n cs, ce, rs, re = mlx.calc_roi(reg_dict)\n self.assertEqual(row_offset, rs)\n self.assertEqual(col_offset, cs)\n self.assertEqual(row_end, re)\n self.assertEqual(col_end, ce)\n return","title":""},{"docid":"7cc0129888165c7f7ca238798056105c","score":"0.6009475","text":"def roi(self):\n return SheetOverlay([el.get_roi(self.roi_bounds) for el in self.data],\n bounds=self.roi_bounds if self.roi_bounds else self.bounds,\n metadata=self.metadata)","title":""},{"docid":"61ebe7559db5e3b6d665ea50cdb99a26","score":"0.60076815","text":"def FindAdaptiveROIversion2(image, center_ROI, aspr_ROI, array_ROI, displayImages, debug = True):\n #inputfilename = 'img6.png'\n #outputfilename = 'edge2.png'\n #nucleation_down = 1 # 0 for nucleation up\n #center_ROI = (511,672) #center of the object to be identified\n #aspr_ROI = 2/3 # x_width/y_width for ROI. This is found by TRAINING\n #debug = True # flag to output ERRRORs\n #remove the strip at the bottom\n #cropsequence = ((0,44),(0,0))\n #img = ReadImage(inputfilename)\n #img = CropImage(img,cropsequence,0)\n #to mainain the aspect ratio of roi to be same as that of image, set the aspect ratio\n #asp_ratio = int(1344/(1066-44))\n #list of pad sizes to be removed along x axis\n array_x_ROI = array_ROI\n array_y_ROI = (array_x_ROI*aspr_ROI).astype(int)\n n = array_x_ROI.size\n optimum_x_ROI = 0\n optimum_y_ROI = 0\n #set the array for relative strengths and maxima positions for the unimodal or bimodal distributions.\n array_rel_strength = np.zeros(n)\n array_maximum = np.zeros((n,2))\n #displayImages = 0\n for i in np.arange(n):\n x_width = array_x_ROI[i]\n y_width = array_y_ROI[i]\n #set up the cropsequence so that pads are removed centered around the center of the image.\n cropsequence = CropSequenceGenerate(image,(center_ROI,(x_width,y_width)))\n cropimg = CropImage(image,cropsequence,0)\n imgbyte = Img2Ubyte(cropimg,0)\n img_med = MedianFilter(imgbyte,displayImages)\n maximum,rel_strength = modal_analysis(img_med,displayImages,debug) #strength is zero if distribution is unimodal and close to zero if the foreground is very small compared to background or vice versa\n array_rel_strength[i] = rel_strength \n array_maximum[i] = maximum\n #displayImages = 1\n if displayImages==1:\n #plot the relative strength variation and choose the appropriate ROI\n plt.figure(),plt.title(\"Finding Optimum ROI by varying xROI\"),plt.plot(array_x_ROI,array_rel_strength)\n #if all are unimodal distributions, then there either is no object to be found or object is beyond the ROI. This means that we need to check for bigger ROIs with progressive increase in y axis width\n max_rel_strength = np.max(array_rel_strength)\n if debug: print(\"maximum relative strength is \" + str(max_rel_strength))\n if max_rel_strength < 0.001:\n optimum_x_ROI = 902\n else:\n #find the optimum ROI from maximum of the relative strength vs ROI variation\n optimum_x_ROI = array_x_ROI[array_rel_strength.argsort()[-1]]\n optimum_y_ROI = array_y_ROI[array_rel_strength.argsort()[-1]]\n #proceed with further processing with optimum ROI\n optimum_ROI = (optimum_x_ROI,optimum_y_ROI)\n if debug: print(\"Optimum ROI is \",optimum_ROI)\n return optimum_ROI","title":""},{"docid":"806207f2d69b6bbdf242c9f5a3e52c63","score":"0.60006034","text":"def test_get_roi_ranges(self):\n pass","title":""},{"docid":"fa2e4bd00560fa389cffa10bd1321a48","score":"0.59888995","text":"def create_vecs(self,roi):\n y = np.arange(roi[0],roi[1],1)\n x = np.arange(roi[2],roi[3], 1)\n X,Y = np.meshgrid(x,y)\n return X,Y","title":""},{"docid":"940eb0279dd735e81abe2f3bf38bae6c","score":"0.5977656","text":"def roi_mask(image):\n imshape = image.shape\n ''' \n y_max = imshape[0]-70\n y_min = 11*imshape[0]/18\n x_min = 0\n x_1 = 9*imshape[1]/20\n x_2 = 11*imshape[1]/20\n x_max = imshape[1]\n '''\n y_max = imshape[0]-70\n y_min = imshape[0]/10\n x_min = 0 + 80\n x_1 = 5*imshape[1]/20\n x_2 = 15*imshape[1]/20\n x_max = imshape[1] - 80\n \n \n vertices = np.array([[(x_min,y_max), (x_1, y_min), (x_2, y_min),(x_max,y_max)]], dtype=np.int32)\n #defining a blank mask to start with\n mask = np.zeros_like(image) \n \n #defining a 3 channel or 1 channel color to fill the mask with depending on the input image\n if len(imshape) > 2:\n channel_count = imshape[2] # i.e. 3 or 4 depending on your image\n ignore_mask_color = (255,) * channel_count\n else:\n ignore_mask_color = 255\n \n #filling pixels inside the polygon defined by \"vertices\" with the fill color \n cv2.fillPoly(mask, [vertices], ignore_mask_color)\n \n #returning the image only where mask pixels are nonzero\n masked_image = cv2.bitwise_and(image, mask)\n return masked_image","title":""},{"docid":"7e665061d55116e2795386b105752774","score":"0.59717214","text":"def _roi_drawn(self, change=None):\n idx = self.image_selector.index\n if idx is not None:\n self.set_rois(idx, self.roi_selector_module.rois)","title":""},{"docid":"459b8764a2b3f6fdca9ea61e11ba07a2","score":"0.59713686","text":"def init():\n \n \n im.set_data(np.empty((para.cell_num, para.cell_num)))\n point.set_data([], [])\n text.set_text('')\n x = lsr.get_xdata() \n y = lsr.get_ydata()\n lsr.set_data(x, y)\n ltr.set_text('')\n x = lsw.get_xdata()\n y = lsw.get_ydata()\n lsw.set_data(x, y)\n ltw.set_text('')\n \n return im,","title":""},{"docid":"0ea0da0ee1ff2a4f22edb0385fe270e6","score":"0.59615475","text":"def __init__(self, x_res, y_res):\n self._x_res = x_res\n self._y_res = y_res\n self._roi = (0, self._y_res // 2), (self._x_res - 1, self._y_res - 1)\n self._x_size = 64\n self._y_size = 64\n self._x_step = 16\n self._y_step = 16\n self._horizon = 409","title":""},{"docid":"5c211daba039207156c8f1063bf63ea6","score":"0.5960826","text":"def set_roi(self, hstart=0, hend=None, vstart=0, vend=None, bin=1):\n with self._reset_buffers():\n self.set_value(\"SUBARRAY MODE\",2)\n hend=hend or self.properties[\"SUBARRAY HSIZE\"].max\n vend=vend or self.properties[\"SUBARRAY VSIZE\"].max\n min_roi,max_roi=self.get_roi_limits()\n if bin==3:\n bin=2\n self.set_value(\"SUBARRAY HSIZE\",min_roi[2])\n self.set_value(\"SUBARRAY HPOS\",(hstart//4)*4)\n self.set_value(\"SUBARRAY HSIZE\",(max(hend-hstart,min_roi[2])//4)*4)\n self.set_value(\"SUBARRAY VSIZE\",min_roi[3])\n self.set_value(\"SUBARRAY VPOS\",(vstart//4)*4)\n self.set_value(\"SUBARRAY VSIZE\",(max(vend-vstart,min_roi[3])//4)*4)\n self.set_value(\"BINNING\",min(bin,max_roi[4]))\n return self.get_roi()","title":""},{"docid":"c0c24818e2f62af5dd80f7f589b102e5","score":"0.5957256","text":"def roi(self):\n return (self._slice_0.start,\n self._slice_0.stop,\n self._slice_1.start,\n self._slice_1.stop)","title":""},{"docid":"d47c3e232540ad61b69f47a3c24c62ee","score":"0.59546846","text":"def set_image(self, path, roi=None):\r\n self.img = cv2.imread(path)\r\n if self.img is None or self.img.size == 0:\r\n raise IOError(\"Failed to read image %s\" % path)\r\n if roi and len(roi) != 4:\r\n raise ValueError(\"ROI must be a list of four integers\")\r\n\r\n # If a ROI was provided, use only that region.\r\n if roi:\r\n y, y2, x, x2 = roi\r\n self.img = self.img[y:y2, x:x2]\r\n\r\n # Reset image related variables so one instance can be used for multiple\r\n # images.\r\n self.path = path\r\n self.mask = None\r\n self.bin_mask = None\r\n\r\n return self.img","title":""},{"docid":"884317ecc428fb3f438b59817f24d0d4","score":"0.5953904","text":"def image_load_ROI(name,ROI):\n x0,y0,w = ROI\n xmin,xmax = x0-(w-1)/2,x0+(w-1)/2+1\n ymin,ymax = y0-(w-1)/2,y0+(w-1)/2+1\n I = array(Image.open(name).convert(\"I\"),float32).T[xmin:xmax,ymin:ymax]\n return I","title":""},{"docid":"fc77859867ecc31362ac5862ed169d76","score":"0.5938262","text":"def genIm(self, crop=False):\r\n if not hasattr(self.s, 'actor'):\r\n self.s.addActor()\r\n if not hasattr(self.m, 'actor'):\r\n self.m.addActor()\r\n # Generate a renderer window\r\n win = vtkRenWin()\r\n # Set the number of viewports\r\n win.setnumViewports(1)\r\n # Set the background colour\r\n win.setBackground([1,1,1])\r\n # Set camera projection \r\n # Set camera projection \r\n win.setView([0, -1, 0], 0)\r\n win.SetSize(512, 512)\r\n win.Modified()\r\n win.OffScreenRenderingOn()\r\n self.s.actor.setColor([1.0, 0.0, 0.0])\r\n self.s.actor.setOpacity(0.5)\r\n self.m.actor.setColor([0.0, 0.0, 1.0])\r\n self.m.actor.setOpacity(0.5)\r\n win.renderActors([self.s.actor, self.m.actor])\r\n win.Render()\r\n win.rens[0].GetActiveCamera().Azimuth(0)\r\n win.rens[0].GetActiveCamera().SetParallelProjection(True)\r\n win.Render()\r\n im = win.getImage()\r\n if crop is True:\r\n mask = np.all(im == 1, axis=2)\r\n mask = ~np.all(mask, axis=1)\r\n im = im[mask, :, :]\r\n mask = np.all(im == 1, axis=2)\r\n mask = ~np.all(mask, axis=0)\r\n im = im[:, mask, :]\r\n return im, win","title":""},{"docid":"969c6f592f48d8eafa0c2b55a57aef68","score":"0.5929706","text":"def autoROISize(self, filename='', imshape=(1024,1280), widget=None):\n self.fitImage(filename, imshape=imshape)\n for i in reversed(range(5)): # bad ROI size, try another\n if (self.df.isin([np.inf,-np.inf]).values.any() or self.df.isnull().values.any()\n or self.check_outlier('w') or self.check_outlier('h')):\n self._dx = int((i+1)**2 * 5)\n self._dy = int((i+1)**2 * 5)\n try:\n self.fitImage()\n except: pass\n else: break\n # check if ROIs overlap\n overx, overy = 0, 0\n xmins = np.array(sorted(self.df['xc'] - self._dx))\n xmaxs = np.array(sorted(self.df['xc'] + self._dx))\n ymins = np.array(sorted(self.df['yc'] - self._dy))\n ymaxs = np.array(sorted(self.df['yc'] + self._dy))\n # if any boxes are outside camera ROI\n if len(xmins[xmins < 0]):\n overx = np.abs(np.min(xmins[xmins<0]))\n if len(ymins[ymins < 0]):\n overy = np.abs(np.min(ymins[ymins<0]))\n if len(xmaxs[xmaxs > imshape[1]]):\n overx = max(overx, np.max(xmaxs[xmaxs > imshape[1]]) - imshape[1])\n if len(ymaxs[ymaxs > imshape[0]]):\n overy = max(overy, np.max(ymaxs[ymaxs > imshape[0]]) - imshape[0])\n # look at first row, compare first and second column\n if self._s[0] > 1:\n ox = (xmins[1] - xmaxs[0])/2.\n if ox < 0:\n overx = max(overx, np.abs(ox))\n # look at first column, compare first and second row\n if self._s[1] > 1:\n oy = (ymins[1] - ymaxs[0])/2.\n if oy < 0:\n overy = max(overy, np.abs(oy))\n # set new width, height of ROI \n self._dx -= int(round(overx))\n self._dy -= int(round(overy))\n self.fitImage()\n info(\"imageArray fitter reset ROI width, height to %s, %s\"%(self._dy, self._dx))\n xmins, xmaxs = self.df['xc'] - self._dx, self.df['xc'] + self._dx\n ymins, ymaxs = self.df['yc'] - self._dy, self.df['yc'] + self._dy\n # image could be cropped to: [xmin,ymin,xmax,ymax]\n bounds = list(map(int, [np.floor(min(xmins)), np.floor(min(ymins)), \n np.ceil(max(xmaxs)), np.ceil(max(ymaxs))]))\n # plot the bounds\n if widget:\n viewbox = self.plotContours(widget)\n s = pg.ROI((min(xmins), self._imvals.shape[0]-max(ymaxs)), # origin is bottom-left\n (max(xmaxs) - min(xmins), max(ymaxs) - min(ymins)),\n movable=False, pen='w') # rotatable=False, resizable=False, \n viewbox.addItem(s)\n return bounds","title":""},{"docid":"79f8702c0259525f6b06a0606ba11f9b","score":"0.59073627","text":"def roi(self, reg=None, wh=None):\n\n # interpret reg\n if reg is not None and wh is not None:\n # reg = getself.__class__(reg) # 2x2?\n wh = argcheck.getvector(wh)\n\n xc = reg[0]\n yc = reg[1]\n if len(wh) == 1:\n w = np.round(wh/2)\n h = w\n else:\n w = np.round(wh[0]/2)\n h = np.round(wh[1]/2)\n left = xc - w\n right = xc + w\n top = yc - h\n bot = yc + h\n\n elif reg is not None and wh is None:\n # reg = getself.__class__(reg)\n\n left = reg[0, 0]\n right = reg[0, 1]\n top = reg[1, 0]\n bot = reg[1, 1]\n\n else:\n raise ValueError(reg, 'reg cannot be None')\n\n # TODO check row/column ordering, and ndim check\n out = []\n for im in self:\n roi = im.image[top:bot, left:right, :]\n o = namedtuple('roi', ['roi',\n 'left',\n 'right',\n 'top',\n 'bot'])(self.__class__(roi),\n left,\n right,\n top,\n bot)\n out.append(o)\n\n return out","title":""},{"docid":"44bf71e8718f9315c053120a164263b4","score":"0.5904946","text":"def __init__(self, roi: RegionOfInterest, axis: int,\n left: ROITree, right: ROITree):\n assert axis >= 0 and axis < 3 \n self.roi = roi\n self.axis = axis\n self.left = left\n self.right = right","title":""},{"docid":"4a67cb700f65d1303c5169e30cac2e20","score":"0.58968097","text":"def reset_roi_fields(self):\r\n self.xstart_linedt.setText(\"0\")\r\n self.xend_linedt.setText(str(self.data_shape[2]))\r\n self.ystart_linedt.setText(\"0\")\r\n self.yend_linedt.setText(str(self.data_shape[1]))\r\n self.zstart_linedt.setText(\"0\")\r\n self.zend_linedt.setText(str(self.data_shape[0]))\r\n self.roi_changed = False","title":""},{"docid":"097c7bc7ee23bc1bef7766980b18c662","score":"0.5888198","text":"def roi_processing(numpy_frame: np.ndarray, roi_points: list):\n final_images = list()\n if len(roi_points) == 0:\n print(\"Incorrect number of Region of Interest points selected... Exiting\")\n sys.exit(1)\n\n base_masked_out = roi_extraction(roi_points=roi_points, numpy_frame=numpy_frame)\n\n x_intervals = round(numpy_frame.shape[1] / 20)\n y_intervals = round(numpy_frame.shape[0] / 30)\n x_start_interval = 0\n y_start_interval = 0\n\n for x_index in range(0, LOCAL_X_AXIS_ITERATIONS):\n x_start_interval, generated_images = roi_rotation(\n masked_image=base_masked_out,\n x_start_interval=x_start_interval,\n y_start_interval=y_start_interval,\n x_intervals=x_intervals,\n y_intervals=y_intervals\n )\n final_images.extend(generated_images)\n\n return final_images","title":""},{"docid":"8270e8a24f553c4d8223e12af95e9bc1","score":"0.5878978","text":"def str2roi(self,roiStr):\n originStr, sizeStr = roiStr.split(\":\")\n #print(\"originStr=\",originStr,\" sizeStr=\",sizeStr)\n xOrigin = int(originStr.split(\",\")[0])\n yOrigin = int(originStr.split(\",\")[1])\n xSize = int(sizeStr.split(\",\")[0])\n ySize = int(sizeStr.split(\",\")[1])\n return((xOrigin, yOrigin, xSize, ySize))","title":""},{"docid":"732fffb503966e4f2b6db66cea0564ab","score":"0.5863586","text":"def _roi_pooling():\n boxes = np.array([[10, 10, 20, 20], [-10, 10, -20, 30]])\n rois = tf.cast(boxes, tf.float32)\n x1 = rois[..., 0]\n y1 = rois[..., 1]\n x2 = rois[..., 2]\n y2 = rois[..., 3]\n\n rois = rois / 10\n\n x1 = tf.expand_dims(x1, axis=-1)\n y1 = tf.expand_dims(y1, axis=-1)\n x2 = tf.expand_dims(x2, axis=-1)\n y2 = tf.expand_dims(y2, axis=-1)\n\n # rois = tf.concatenate([x1, y1, x2, y2], axis=-1)\n rois = tf.concat([y1, x1, y2, x2], axis=-1)\n rois = tf.reshape(rois, (-1, 4))\n\n rois /=10\n print(rois)","title":""},{"docid":"dd92a6861faeedd8de15f7f90c6c169f","score":"0.5841793","text":"def updateROIState(self, v):\n st = {}\n st['angle'] = 0\n st['pos'] = pg.Point(v[0], v[1])\n st['size'] = pg.Point(v[2] - v[0], v[3] - v[1])\n self.roi.setState(st)","title":""},{"docid":"63a99de292d90c331e732ce12b5ede04","score":"0.5832336","text":"def find_ROI_segmentation(self):\n pass","title":""},{"docid":"3f1da64f0f9e95a3fde6ed8bfac0da60","score":"0.5820037","text":"def get_best_roi(self):\n yvals = np.linspace(0, 100, num=11)*7.2\n w = 20 # half width of the ROI\n roi = []\n for y in yvals:\n roi.append([self.best_fit_p(y)-w, y])\n for y in yvals[::-1]:\n roi.append([self.best_fit_p(y)+w, y])\n return roi","title":""},{"docid":"04cbb0b016818f300f0edcda6fabff5f","score":"0.5807676","text":"def roi_at(self, p):\n return self.rois[p - self.pstart]","title":""},{"docid":"d28805a86159eea42088299329941750","score":"0.5803839","text":"def project_roi(self, roi, frame_id=None):\n response = self.project_rois(rois=[roi]).points[0]\n\n # Convert to VectorStamped\n result = VectorStamped(x=response.point.x, y=response.point.y, z=response.point.z,\n frame_id=response.header.frame_id)\n\n # If necessary, transform the point\n if frame_id is not None:\n print \"Transforming roi to {}\".format(frame_id)\n result = result.projectToFrame(frame_id=frame_id, tf_listener=self.tf_listener)\n\n # Return the result\n return result","title":""},{"docid":"5cacd9c05816e2bde1d373d5d5f8f917","score":"0.5801246","text":"def partir_4(roi):\n\n aux1 = int(roi[2]/2)\n aux2 = int(roi[3]/2)\n \n roi2=[[roi[0]+(i*aux1),roi[1]+(j*aux2),aux1,aux2] for i in [0,1] for j in [0,1]]\n return roi2","title":""},{"docid":"ed9c1929e912703efd30b68a3bc54078","score":"0.5798317","text":"def add_roi_contour_sequence(self, structure, index, coordinates):\n roi_contour_sequence = self.ds_rs.ROIContourSequence[index]\n roi_contour_sequence.ReferencedROINumber = structure[\"ReferencedROINumber\"]\n roi_contour_sequence.ROIDisplayColor = random.sample(range(0, 255), 3)\n\n roi_contour_sequence.add_new('ContourSequence', 'SQ', Sequence())\n\n for i, (sop_instance_uid, coordinate) in enumerate(coordinates):\n roi_contour_sequence.ContourSequence.append(Dataset())\n contour_sequence = roi_contour_sequence.ContourSequence[-1]\n contour_sequence.ContourGeometricType = \"CLOSED_PLANAR\"\n contour_sequence.NumberOfContourPoints = int(len(coordinate) / 3)\n contour_sequence.ContourNumber = i\n contour_sequence.ContourData = coordinate\n\n contour_sequence.add_new('ContourImageSequence', 'SQ', Sequence())\n contour_sequence.ContourImageSequence.append(Dataset())\n contour_image_sequence = contour_sequence.ContourImageSequence[-1]\n contour_image_sequence.ReferencedSOPClassUID = UID('1.2.840.10008.5.1.4.1.1.2')\n contour_image_sequence.ReferencedSOPInstanceUID = sop_instance_uid","title":""},{"docid":"94fba564ff4c73cec8196e8c2267f44c","score":"0.5791027","text":"def __init__ ( self,\n source = \"CxiDs1-0|Cspad-0\",\n input = \"Something\", \n output = \"SomethingElse\",\n roi = \"0,1800,0,1800\" ):\n self.source = source\n self.input = input\n self.output = output\n self.roi = map(int,roi.split(',')) \n self.n_std = 3 # n standard deviations (vertical scale region of interest)","title":""},{"docid":"63d36f4a80ee2911e7ab9d04e7789d0b","score":"0.5783857","text":"def __built_box_roi(self, seg_lung):\n # create box and roi of left lung\n box_seg = np.zeros((self.aorta_mat.shape))\n seg_left_lung = np.zeros((self.aorta_mat.shape))\n\n # find bounding box for Aorta and lung\n min_sa, min_ca, min_aa, max_sa, max_ca, max_aa = \\\n self.__found_bounding_box(self.aorta_mat)\n seg_left_lung[max_sa:, :, :] = seg_lung[max_sa:, :, :]\n min_s_l, min_c_l, min_a_l, max_s_l, max_c_l, max_a_l = \\\n self.__found_bounding_box(seg_left_lung)\n\n # first box\n ss1 = max_s_l - (((max_s_l - min_s_l) * (S_CON-1)) // S_CON)\n se1 = max_s_l - ((max_s_l - min_s_l) // (S_CON+2))\n cs1 = min_c_l + ((max_c_l - min_c_l) // (S_CON+1))\n ce1 = min_c_l + (((max_c_l - min_c_l) * (S_CON+1)) // (S_CON+2))\n xs1 = min_a_l - ((max_aa - min_aa) // AXIAL_CON*2)\n xe1 = min_a_l + ((max_aa - min_aa) // AXIAL_CON*3)\n\n # second_box\n ss2 = max_s_l - ((max_s_l - min_s_l) // S_CON)\n se2 = max_s_l - ((max_s_l - min_s_l) // (S_CON+1))\n cs2 = min_c_l + ((max_c_l - min_c_l) // (S_CON+1))\n ce2 = min_c_l + (((max_c_l - min_c_l) * (S_CON+1)) // (S_CON+2))\n xs2 = min_aa + (((max_aa - min_aa) * 1) // (S_CON+1))\n xe2 = min_a_l - ((max_aa - min_aa) // AXIAL_CON*3)\n\n # third_box\n ss3 = max_sa - ((max_sa - min_sa) // S_CON)\n se3 = max_sa\n cs3 = min_ca\n ce3 = min_ca + (((max_ca - min_ca) * 1) // S_CON)\n xs3 = min_aa + (((max_aa - min_aa) * ((AXIAL_CON//2) - 1) ) // (AXIAL_CON - 1))\n xe3 = min_aa + (((max_aa - min_aa) * (AXIAL_CON//2)) // (AXIAL_CON - 1))\n\n # update boxes\n box_seg[ss1: se1, cs1:ce1, xs1: xe1] = 1\n box_seg[ss2: se2, cs2:ce2, xs2: xe2] = 1\n box_seg[ss3: se3, cs3:ce3, xs3: xe3] = 1\n\n # delete soft tissues and hard tissues\n box_seg[np.where((self.ct_mat < SOFT_TISSUE) | (self.ct_mat > BONES))] = 0\n\n return box_seg","title":""},{"docid":"bce14242850f5f7eb36a929dd09b8b2f","score":"0.57798356","text":"def roi(lista,imagen):\n return imagen[lista[0]:lista[0]+lista[2],lista[1]:lista[1]+lista[3]]","title":""},{"docid":"48d320e689f9c70989486862a0872e45","score":"0.5764892","text":"def clear_ROI(self):\n self.camera.OffsetX.SetValue(self.camera.OffsetX.Min)\n self.camera.OffsetY.SetValue(self.camera.OffsetY.Min)\n self.camera.Width.SetValue(self.camera.Width.Max)\n self.camera.Height.SetValue(self.camera.Height.Max)","title":""},{"docid":"4fb0f7f14b6146e04cdf13b1c9fd0aa4","score":"0.5762916","text":"def pare_roi(self, roi, min_area=10):\n\n # remove anything outside motion borders\n y0,x0 = int(np.floor(self.motion_borders.ymin)), int(np.floor(self.motion_borders.xmin))\n y1,x1 = int(np.ceil(self.motion_borders.ymax)), int(np.ceil(self.motion_borders.xmax))\n if roi.ndim == 2:\n roi = np.array([roi])\n roi = roi.astype(bool)\n roi[:,:y0,:] = False\n roi[:,y1:,:] = False\n roi[:,:,:x0] = False\n roi[:,:,x1:] = False\n\n roi = roi[np.any(roi, axis=(1,2))]\n\n # remove rois below size threshold\n thresh = self.pixels_per_micron * min_area # pixels \n areas = np.sum(roi, axis=(1,2))\n roi = roi[areas>thresh]\n \n return roi","title":""},{"docid":"f2d1bcf7ffc7a7b379602f0f024eac8b","score":"0.5729884","text":"def _build_roi_click_callback(self, func):\n def callback(event):\n nonlocal self, func\n x, y, = event.x, event.y\n d = 3\n items = self.canvas.find_overlapping(x-d, y-d, x+d, y+d)\n names = set()\n for it in items:\n for tag in self.canvas.gettags(it):\n if not tag.startswith(TAG_ROI_ID):\n continue\n name64 = tag[len(TAG_ROI_ID):]\n names.add(base64.b64decode(name64).decode())\n break\n func(event=event, names=names)\n return callback","title":""},{"docid":"5196c15d8bd315e042f9a932da2f9a3d","score":"0.571907","text":"def getROImask(self,ROI=None):\t\n\t\tif ROI is None:\n\t\t\tROI = self.ROI\n\t\n\t\tdef point_inside_ROI(point,ROI):\n\n\t\t\tn = ROI.shape[0]\n\t\t\t\n\t\t\tinside = 0\n\t\t\tx,y = point\n\t\t\tp1x = ROI[0,0]\n\t\t\tp1y = ROI[0,1]\n\t\t\tfor i in range(n+1):\n\t\t\t\tp2x = ROI[i % n,0]\n\t\t\t\tp2y = ROI[i % n,1]\n\t\t\t\tif y > min(p1y,p2y) and y <= max(p1y,p2y) and x <= max(p1x,p2x):\n\t\t\t\t\tif p1y != p2y:\n\t\t\t\t\t\txinters = (y-p1y)*(p2x-p1x)/(p2y-p1y)+p1x\n\t\t\t\t\tif p1x == p2x or x <= xinters:\n\t\t\t\t\t\tinside = (inside + 1) % 2\n\t\t\t\t\t\n\t\t\t\tp1x,p1y = p2x,p2y\n\t\t\t\t\t\n\t\t\treturn inside\n\t\t\n\t\tnx = np.max(ROI[:,0]) - np.min(ROI[:,0]) + 1\n\t\tny = np.max(ROI[-1,1]) - np.min(ROI[0,1]) + 1\n\t\t\n\t\t\n\t\txpoints = np.arange(nx) + np.min(ROI[:,0])\n\t\typoints = np.arange(ny) + np.min(ROI[:,1])\n\t\t\n\t\tpointsinROI = np.zeros(self[0].shape,dtype=np.uint8)\n\t\tpointsinROI[...] = False\n\t\tfor x in xpoints:\n\t\t\tfor y in ypoints:\n\t\t\t\tpointsinROI[x,y] = point_inside_ROI((x,y),ROI)\n\t\t\n\t\treturn np.uint8(pointsinROI)","title":""},{"docid":"e75d4391fe2646fed26e3c16b9f05236","score":"0.5714905","text":"def generate_ROI(volume):\n\n volume_gpu = cp.asarray(volume)\n\n idx = cp.where(volume_gpu > 0)\n\n roiVolume = cp.zeros(volume_gpu.shape, dtype=cp.float32)\n\n roiVolume[idx] = 1\n\n return roiVolume.get()","title":""},{"docid":"7d475c9188fb9d3f055205c52d558998","score":"0.5704831","text":"def apply_roi(self, roi):\r\n cmd = command.ApplyROI(client=self.client, roi=roi)\r\n self._session.command_stack.do(cmd)","title":""},{"docid":"07d4eba1b5e01c7e1f5e4d232fe35d26","score":"0.5697929","text":"def get_roi_image(original_image, h1, h2, w1, w2):\n return original_image[h1: h2, w1: w2]","title":""},{"docid":"3409dca63adb16bf0a22339cb367a6c2","score":"0.56953436","text":"def from_roi(cls, roi: RegionOfInterest, factor: Cartesian,\n atomic_block_size: Cartesian, atomic_voxel_size: Cartesian):\n pass\n\n # assert roi.voxel_size % atomic_voxel_size == Cartesian(0, 0, 0)\n # assert roi.voxel_size // atomic_voxel_size % factor == Cartesian(0, 0, 0)\n \n # if roi.voxel_size == atomic_voxel_size:\n # # this is the leaf roi/block\n # return cls(roi, None, None, None)\n\n # # find the relatively longest axis to split\n # children_voxel_size = roi.voxel_size // factor\n # block_nums = roi.physical_size / (children_voxel_size * )\n # block_nums = np.ceil(block_nums)\n # axis = np.argmax(block_nums)\n\n # # split along axis\n # left_start = roi.start * factor\n # left_block_nums = \n # left_stop = left_start + \n # left_roi = RegionOfInterest()\n # left = cls.from_roi(left_roi, factor, atomic_block_size, atomic_voxel_size)","title":""},{"docid":"d9c289467dd1d1f4f5af4e0a5a9b7bcd","score":"0.5691518","text":"def main():\r\n filepath = askopenfilename(\r\n filetypes=[(\"Text Files\", \"*.txt\"), (\"All Files\", \"*.*\")]\r\n )\r\n \r\n window.title(f\"CRAR - {filepath}\")\r\n image=cv2.imread(filepath) #read in the image\r\n\r\n image=cv2.resize(image,(1300,800)) #resizing because opencv does not work well with bigger images\r\n orig=image.copy()\r\n gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) #RGB To Gray Scal\r\n '''\r\ncv2.imshow(\"Title\",gray) '''\r\n blurred=cv2.GaussianBlur(gray,(5,5),0) #(5,5) is the kernel size and 0 is sigma that determines the amount of blur\r\n '''\r\ncv2.imshow(\"Blur\",blurred)'''\r\n edged=cv2.Canny(blurred,30,50) #30 MinThreshold and 50 is the MaxThreshold\r\n '''\r\ncv2.imshow(\"Canny\",edged) '''\r\n contours,hierarchy=cv2.findContours(edged,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) #retrieve the contours as a list, with simple apprximation model\r\n contours=sorted(contours,key=cv2.contourArea,reverse=True)\r\n #the loop extracts the boundary contours of the page\r\n for c in contours:\r\n p=cv2.arcLength(c,True)\r\n approx=cv2.approxPolyDP(c,0.02*p,True)\r\n if len(approx)==4:\r\n target=approx\r\n break\r\n approx=mapper.mapp(target) #find endpoints of the sheet\r\n pts=np.float32([[0,0],[800,0],[800,800],[0,800]]) #map to 800*800 target window\r\n op=cv2.getPerspectiveTransform(approx,pts) #get the top or bird eye view effect\r\n dst=cv2.warpPerspective(orig,op,(800,800))\r\n cv2.imwrite(\"IMAGE_NAME.png\", dst)\r\n cv2.imshow(\"Scanned\",dst)\r\n # creating an image object\r\n image1=cv2.imread(\"IMAGE_NAME.png\")\r\n # loading the pixel data of the image'''\r\n im = Image.open(\"IMAGE_NAME.png\")\r\n # Creating coordinates of the pixel (x,y)\r\n C=['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']\r\n C[0]= x, y = 700,340\r\n C[1]= x, y = 700,180\r\n C[2]= x, y = 700,220\r\n C[3]= x, y = 700,260\r\n C[4]= x, y = 700,300\r\n C[5]= x, y = 700,500\r\n C[6]= x, y = 700,540\r\n C[7]= x, y = 700,580\r\n C[8]= x, y = 700,620\r\n C[9]= x, y = 660,180\r\n C[10]= x, y = 660,220\r\n C[11]= x, y = 660,260\r\n C[12]= x, y = 660,300\r\n C[13]= x, y = 660,380\r\n C[14]= x, y = 660,420\r\n C[15]= x, y = 660,500\r\n h=['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']\r\n h[0]=im.getpixel(C[0])\r\n h[1]=im.getpixel(C[1])\r\n h[2]=im.getpixel(C[2])\r\n h[3]=im.getpixel(C[3])\r\n h[4]=im.getpixel(C[4])\r\n h[5]=im.getpixel(C[5])\r\n h[6]=im.getpixel(C[6])\r\n h[7]=im.getpixel(C[7])\r\n h[8]=im.getpixel(C[8])\r\n h[9]=im.getpixel(C[9])\r\n h[10]=im.getpixel(C[10])\r\n h[11]=im.getpixel(C[11])\r\n h[12]=im.getpixel(C[12])\r\n h[13]=im.getpixel(C[13])\r\n h[14]=im.getpixel(C[14])\r\n h[15]=im.getpixel(C[15])\r\n a=['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\r\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']\r\n for i in range(0,243):\r\n if(str(h[i])==\"(255, 0, 0)\"):\r\n a[i]=\"A\"\r\n elif(str(h[i])==\"(0, 255, 0)\"):\r\n a[i]=\"B\"\r\n elif(str(h[i])==\"(0, 0, 255)\"):\r\n a[i]=\"C\"\r\n elif(str(h[i])==\"(255, 255, 0)\"):\r\n a[i]=\"D\"\r\n elif(str(h[i])==\"(0, 255, 255)\"):\r\n a[i]=\"E\"\r\n elif(str(h[i])==\"(255, 0, 255)\"):\r\n a[i]=\"F\"\r\n elif(str(h[i])==\"(192, 192, 192)\"):\r\n a[i]=\"G\"\r\n elif(str(h[i])==\"(128, 128, 128)\"):\r\n a[i]=\"H\"\r\n elif(str(h[i])==\"(128, 0, 0)\"):\r\n a[i]=\"I\"\r\n elif(str(h[i])==\"(128, 128, 0)\"):\r\n a[i]=\"J\"\r\n elif(str(h[i])==\"(0, 128, 0)\"):\r\n a[i]=\"K\"\r\n elif(str(h[i])==\"(128, 0, 128)\"):\r\n a[i]=\"L\"\r\n elif(str(h[i])==\"(0, 128, 128)\"):\r\n a[i]=\"M\"\r\n elif(str(h[i])==\"(0, 0, 128)\"):\r\n a[i]=\"N\"\r\n elif(str(h[i])==\"(255, 140, 0)\"):\r\n a[i]=\"O\" #'''darkorange'''\r\n elif(str(h[i])==\"(255, 215, 0)\"):\r\n a[i]=\"P\" #'''gold'''\r\n elif(str(h[i])==\"(85, 107, 47)\"):\r\n a[i]=\"Q\" #'''dark olive green'''\r\n elif(str(h[i])==\"(173, 255, 47)\"):\r\n a[i]=\"R\" #'''green yellow'''\r\n elif(str(h[i])==\"(50, 205, 50)\"):\r\n a[i]=\"S\" #''' lime green'''\r\n elif(str(h[i])==\"(0, 250, 154)\"):\r\n a[i]=\"T\" #'''medium spring green'''\r\n elif(str(h[i])==\"(47, 79, 79)\"):\r\n a[i]=\"U\" #'''dark slate gray'''\r\n elif(str(h[i])==\"(0, 206, 209)\"):\r\n a[i]=\"V\" #'''dark turquoise'''\r\n elif(str(h[i])==\"(100, 149, 237)\"):\r\n a[i]=\"W\" #'''corn flower blue'''\r\n elif(str(h[i])==\"(0, 191, 255)\"):\r\n a[i]=\"X\" #'''dep sky blue'''\r\n elif(str(h[i])==\"(127, 255, 212)\"):\r\n a[i]=\"Y\" #''' aqua marine'''\r\n elif(str(h[i])==\"(0, 0, 205)\"):\r\n a[i]=\"Z\" #''' medium blue'''\r\n elif(str(h[i])==\"(138, 43, 226)\"):\r\n a[i]=\"a\" #''' blue violet'''\r\n elif(str(h[i])==\"(123, 104, 238)\"):\r\n a[i]=\"b\" # ''' medium slate blue'''\r\n elif(str(h[i])==\"(148, 0, 211)\"):\r\n a[i]=\"c\" #'''dark violet'''\r\n elif(str(h[i])==\"(139, 0, 139)\"):\r\n a[i]=\"d\" #''' dark mafneta'''\r\n elif(str(h[i])==\"(75, 0, 130)\"):\r\n a[i]=\"e\" #''' indigo'''\r\n elif(str(h[i])==\"(128, 0, 128)\"):\r\n a[i]=\"f\" #''' purple'''\r\n elif(str(h[i])==\"(238, 130, 238)\"):\r\n a[i]=\"g\" #'''violet'''\r\n elif(str(h[i])==\"(199, 21, 133)\"):\r\n a[i]=\"h\" #''' medium violet red'''\r\n elif(str(h[i])==\"(250, 235, 215)\"):\r\n a[i]=\"i\" #''' antique white'''\r\n elif(str(h[i])==\"(139, 69, 19)\"):\r\n a[i]=\"j\" #''' saddle brown'''\r\n elif(str(h[i])==\"(210, 105, 30)\"):\r\n a[i]=\"k\" #''' cholate '''\r\n elif(str(h[i])==\"(244, 164, 96)\"):\r\n a[i]=\"l\" #''' sandy brown '''\r\n elif(str(h[i])==\"(188, 143, 143)\"):\r\n a[i]=\"m\" #''' rosy brown'''\r\n elif(str(h[i])==\"(176, 196, 222)\"):\r\n a[i]=\"n\" #''' light steel vlue'''\r\n elif(str(h[i])==\"(240, 255, 240)\"):\r\n a[i]=\"o\" #'''honey dew'''\r\n elif(str(h[i])==\"(189, 183, 107)\"):\r\n a[i]=\"p\" #''' dark khaki'''\r\n elif(str(h[i])==\"(34, 139, 34)\"):\r\n a[i]=\"q\" #''' forest green'''\r\n elif(str(h[i])==\"(60, 179, 113)\"):\r\n a[i]=\"r\" #'' 'medium sea green'''\r\n elif(str(h[i])==\"(255, 127, 80)\"):\r\n a[i]=\"s\" #''' coral'''\r\n elif(str(h[i])==\"(255, 99, 71)\"):\r\n a[i]=\"t\" #''' tomato'''\r\n elif(str(h[i])==\"(240, 128, 128)\"):\r\n a[i]=\"u\" #''' light coral'''\r\n elif(str(h[i])==\"(255, 160, 122)\"):\r\n a[i]=\"v\" #''' light salmon'''\r\n elif(str(h[i])==\"(70, 130, 180)\"):\r\n a[i]=\"w\" #''' steel blue'''\r\n elif(str(h[i])==\"(176, 224, 230)\"):\r\n a[i]=\"x\" #''' powder blue'''\r\n elif(str(h[i])==\"(30, 144, 255)\"):\r\n a[i]=\"y\" #''' doger blue'''\r\n elif(str(h[i])==\"(230, 230, 250)\"):\r\n a[i]=\"z\" #''' lavender'''\r\n elif(str(h[i])==\"(255, 250, 205)\"):\r\n a[i]=\"0\" #'''lemon chiffon'''\r\n elif(str(h[i])==\"(233, 150, 122)\"):\r\n a[i]=\"1\" #''' dark salmon '''\r\n elif(str(h[i])==\"(255, 105, 180)\"):\r\n a[i]=\"2\" # ''' hot pink'''\r\n elif(str(h[i])==\"(205, 133, 63)\"):\r\n a[i]=\"3\" #''' rosy brown'''\r\n elif(str(h[i])==\"(222, 184, 135)\"):\r\n a[i]=\"4\" #''' burly wood'''\r\n elif(str(h[i])==\"(255, 228, 181)\"):\r\n a[i]=\"5\" #''' mocassin'''\r\n elif(str(h[i])==\"(46, 139, 87)\"):\r\n a[i]=\"6\" #''' sea green'''\r\n elif(str(h[i])==\"(60, 179, 113)\"):\r\n a[i]=\"7\" #''' medium sea green'''\r\n elif(str(h[i])==\"(107, 142, 35)\"):\r\n a[i]=\"8\" #''' dark olive drab'''\r\n elif(str(h[i])==\"(205, 92, 92)\"):\r\n a[i]=\"9\" #''' indian red'''\r\n elif(str(h[i])==\"(147, 112, 219)\"):\r\n a[i]=\"+\" #''' medium purple'''\r\n elif(str(h[i])==\"(245, 222, 179)\"):\r\n a[i]=\"/\" #''' wheat'''\r\n elif(str(h[i])==\"(240, 255, 240)\"):\r\n a[i]=\"=\" #''' honeydew'''\r\n elif(str(h[i])==\"(255, 250, 250)\"):\r\n a[i]=\".\"\r\n else:\r\n a[i]=\"\"\r\n print(h)\r\n print(a)\r\n def listToString(s):\r\n # initialize an empty string\r\n str1 = \"\"\r\n #traverse in the string\r\n for ele in s:\r\n str1 += ele\r\n # return string\r\n return str1 \r\n # Driver code\r\n f=listToString(a)\r\n print(listToString(f))\r\n sd=f.split(\".\",1)[0]\r\n hop=str(sd)\r\n print (sd)\r\n if(sd==\"///Lox///\"):\r\n goth=\"models\\\\fox.obj\"\r\n else:\r\n print(\"Errorcode\")\r\n\r\n homography = None\r\n # matrix of camera parameters (made up but works quite well for me)\r\n camera_parameters = np.array([[800, 0, 320], [0, 800, 240], [0, 0, 1]])\r\n # create ORB keypoint detector\r\n orb = cv2.ORB_create()\r\n # create BFMatcher object based on hamming distance\r\n bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\r\n # load the reference surface that will be searched in the video stream\r\n dir_name = os.getcwd()\r\n model = cv2.imread(os.path.join(dir_name, filepath), 0)\r\n # Compute model keypoints and its descriptors\r\n kp_model, des_model = orb.detectAndCompute(model, None)\r\n # Load 3D model from OBJ file\r\n obj = OBJ(os.path.join(dir_name, goth), swapyz=True)\r\n # init video capture\r\n cap = cv2.VideoCapture(0)\r\n while True:\r\n # read the current frame\r\n ret, frame = cap.read()\r\n if not ret:\r\n print(\"Unable to capture video\")\r\n return\r\n # find and draw the keypoints of the frame\r\n kp_frame, des_frame = orb.detectAndCompute(frame, None)\r\n # match frame descriptors with model descriptors\r\n matches = bf.match(des_model, des_frame)\r\n # sort them in the order of their distance\r\n # the lower the distance, the better the match\r\n matches = sorted(matches, key=lambda x: x.distance)\r\n # compute Homography if enough matches are found\r\n if len(matches) > MIN_MATCHES:\r\n # differenciate between source points and destination points\r\n src_pts = np.float32([kp_model[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)\r\n dst_pts = np.float32([kp_frame[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)\r\n # compute Homography\r\n homography, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)\r\n if args.rectangle:\r\n # Draw a rectangle that marks the found model in the frame\r\n h, w = model.shape\r\n pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)\r\n # project corners into frame\r\n dst = cv2.perspectiveTransform(pts, homography)\r\n # connect them with lines\r\n frame = cv2.polylines(frame, [np.int32(dst)], True, 255, 3, cv2.LINE_AA)\r\n # if a valid homography matrix was found render cube on model plan\r\n if homography is not None:\r\n try:\r\n # obtain 3D projection matrix from homography matrix and camera parameters\r\n projection = projection_matrix(camera_parameters, homography)\r\n # project cube or model\r\n frame = render(frame, obj, projection, model, False)\r\n #frame = render(frame, model, projection)\r\n except:\r\n pass\r\n # draw first 10 matches.\r\n if args.matches:\r\n frame = cv2.drawMatches(model, kp_model, frame, kp_frame, matches[:5], 0, flags=2)\r\n # show result\r\n cv2.imshow('frame', frame)\r\n if cv2.waitKey(1) & 0xFF == ord('q'):\r\n break\r\n else:\r\n print(\"Not enough matches found - %d/%d\" % (len(matches), MIN_MATCHES))\r\n cap.release()\r\n cv2.destroyAllWindows()\r\n return 0","title":""},{"docid":"17f976240490cd35c0f206cc4e3169d2","score":"0.56675357","text":"def cmd_show_roi(uri):\n\n show_roi(make_tracker_conn(), uri)","title":""},{"docid":"cd40564815358032e342d9db7a7a97c8","score":"0.5665808","text":"def add_roi_observation_sequence(self, structure, index):\n roi_observation_sequence = self.ds_rs.RTROIObservationsSequence[index]\n roi_observation_sequence.ObservationNumber = structure[\"ObservationNumber\"]\n roi_observation_sequence.ReferencedROINumber = structure[\"ReferencedROINumber\"]\n roi_observation_sequence.ROIObservationLabel = structure[\"ROIObservationLabel\"]\n roi_observation_sequence.RTROIInterpretedType = structure[\"RTROIInterpretedType\"]\n roi_observation_sequence.ROIInterpreter = structure[\"ROIInterpreter\"]","title":""},{"docid":"8a719412b91970a93feb8aa0824f76c7","score":"0.56491363","text":"def __init__(self, infiles, roi):\n self.roi = roi\n mask = ugali.utils.skymap.readSparseHealpixMaps(infiles, field='COVERAGE')\n self.nside = hp.npix2nside(len(mask))\n # Sparse maps of pixels in various ROI regions\n self.mask_roi_sparse = mask[self.roi.pixels]","title":""},{"docid":"e13741463f14fffb20497da7ced677fa","score":"0.564547","text":"def build_roi_heads(cfg, input_shape):\n name = cfg.MODEL.ROI_HEADS.NAME\n return ROI_HEADS_REGISTRY.get(name)(cfg, input_shape)","title":""},{"docid":"597acc8f6d20a6a89417955e65ae2890","score":"0.56221104","text":"def get_crops(self):\n\n cfg = get_cfg()\n\n valid_augs_list = [load_obj(i['class_name'])(**i['params']) for i in cfg['augmentation']['valid']['augs']]\n valid_bbox_params = OmegaConf.to_container((cfg['augmentation']['valid']['bbox_params']))\n valid_augs = A.Compose(valid_augs_list, bbox_params=valid_bbox_params)\n \n test_dataset = ImgDataset(None,\n 'test',\n self.imageDir,\n cfg,\n valid_augs)\n \n test_loader = DataLoader(test_dataset,\n batch_size=cfg.data.batch_size,\n num_workers=cfg.data.num_workers,\n shuffle=False,\n collate_fn=collate_fn)\n \n device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') \n model = torch.load(os.path.dirname(os.path.abspath(__file__))+f\"/{str(self.imageDir).lower().split('/')[-1]}/model.pth\", \n map_location=device)\n\n detection_threshold = 0.5\n results = []\n model.eval()\n\n hparams = flatten_omegaconf(cfg)\n\n lit_model = LitImg(hparams=hparams, \n cfg=cfg, \n model=model)\n\n self.results = eval_model(test_loader, \n results, \n detection_threshold, \n device, \n lit_model)\n \n for i in range(len(self.results)):\n if self.results[i]['image_id']+'.JPG' == self.imageList[self.cur-1].split('/')[-1]:\n self.mainPanel.create_rectangle(int(int(self.results[i]['x1'])*self.scale), \n int(int(self.results[i]['y1'])*self.scale),\n int(int(self.results[i]['x2'])*self.scale),\n int(int(self.results[i]['y2'])*self.scale),\n width=2,\n outline='red')\n \n self.text_label.config(text='Crop: \\n'+str(self.imageDir)[40:]+'\\nTotal: \\n'+str(len(self.results)))\n\n self.sub_button.config(state='disabled')","title":""},{"docid":"62a4208559bb856f1deeab82d3aae3c4","score":"0.561656","text":"def render_lanes_on_image(self,data,img, calib, img_width, img_height,figg):\n\n print('data in lane_image fucntion',len(data))\n proj_velo2cam2 = self.project_velo_to_cam2(calib)\n fig,ax = plt.subplots(1)\n ax.set_aspect('equal')\n \n \n # for i in range(data.shape[2]):\n # d=data[:,:,i]\n for d in data:\n pts_2d = self.project_to_image(d.transpose(), proj_velo2cam2)\n inds = np.where((pts_2d[0, :] < img_width) & (pts_2d[0, :] > 0) &\n (pts_2d[1, :] < img_height) & (pts_2d[1,:]>0) )[0]\n\n # print(inds)\n\n # Filter out pixels points\n imgfov_pc_pixel = pts_2d[:, inds]\n\n # Retrieve depth from lidar\n imgfov_pc_velo = d[inds, :]\n # imgfov_pc_velo = np.hstack((imgfov_pc_velo, np.ones((imgfov_pc_velo.shape[0], 1))))\n imgfov_pc_cam2 = proj_velo2cam2 @ imgfov_pc_velo.transpose()\n # Create a figure. Equal aspect so circles look circular \n # Show the image\n ax.imshow(img)\n ax.plot(imgfov_pc_pixel[0],imgfov_pc_pixel[1],color='red',linewidth=8)\n \n plt.savefig('video/'+figg+'.png')\n \n # return imgfov_pc_pixel[0], imgfov_pc_pixel[1]","title":""},{"docid":"6cd2ea01ba0b826e37d0d2e99924b70f","score":"0.5607524","text":"def test_get_roi_ranges_v2(self):\n pass","title":""},{"docid":"ba5771359d7dd1c21e4d405a5d7bb54f","score":"0.5607298","text":"def get_roi(self, idx=None):\n if idx is None:\n idx = self._latest_roi_idx\n if idx is None:\n return None\n\n with h5py.File(self.data_file, 'r') as f:\n roigrp = f['roi']\n _roi = ROI(roigrp['roi{}'.format(int(idx))])\n\n return _roi","title":""},{"docid":"002fe04f2661868d1fcecdbfa4ee3f8e","score":"0.56045556","text":"def preprocess_roi(roi):\n roi = cv2.resize(roi, (224, 224))\n roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)\n processed_roi = cv2.GaussianBlur(roi, (13, 13), 0)\n processed_roi = cv2.equalizeHist(processed_roi)\n return roi, processed_roi","title":""}],"string":"[\n {\n \"docid\": \"47a1aeb5cb060a014f8725ac38518d33\",\n \"score\": \"0.7261228\",\n \"text\": \"def get_roi(self):\\n start = self.image.roi.pos()\\n size = self.image.roi.size()\\n angle = self.image.roi.angle()\\n self.ROI = [start[0],start[0] + size[0],\\n start[1], start[1] + size[1], angle]\\n self.plot_options.set_roi(self.ROI)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee057b3599684b8e3ad9f9465c07bc8f\",\n \"score\": \"0.68806237\",\n \"text\": \"def set_roi(self, roi):\\n with h5py.File(self.data_file, 'r+') as f:\\n if 'roi' not in f:\\n roigrp = f.create_group('roi')\\n else:\\n roigrp = f['roi']\\n roigrp.create_dataset('roi{}'.format(self._next_roi_idx), data=np.asarray(roi), compression='lzf')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae0e7c1d66702d26e613f2d087db7e3\",\n \"score\": \"0.6866821\",\n \"text\": \"def button_setroi_clicked(self):\\r\\n original_workspace = DataModel.g.current_workspace\\r\\n roi_start = self.roi_start.value()\\r\\n roi_end = self.roi_end.value()\\r\\n roi = [\\r\\n roi_start[0],\\r\\n roi_start[1],\\r\\n roi_start[2],\\r\\n roi_end[0],\\r\\n roi_end[1],\\r\\n roi_end[2],\\r\\n ]\\r\\n\\r\\n roi_name = (\\r\\n DataModel.g.current_workspace\\r\\n + \\\"_roi_\\\"\\r\\n + str(roi[0])\\r\\n + \\\"_\\\"\\r\\n + str(roi[3])\\r\\n + \\\"_\\\"\\r\\n + str(roi[1])\\r\\n + \\\"_\\\"\\r\\n + str(roi[4])\\r\\n + \\\"_\\\"\\r\\n + str(roi[2])\\r\\n + \\\"_\\\"\\r\\n + str(roi[5])\\r\\n )\\r\\n\\r\\n cfg.ppw.clientEvent.emit({\\\"source\\\": \\\"panel_gui\\\", \\\"data\\\": \\\"make_roi_ws\\\", \\\"roi\\\": roi})\\r\\n self.add_roi(roi_name, original_workspace, roi)\\r\\n\\r\\n cfg.ppw.clientEvent.emit({\\\"source\\\": \\\"panel_gui\\\", \\\"data\\\": \\\"faster_refresh\\\", \\\"value\\\": None})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db344acec9f2030da086eeb59eb145a\",\n \"score\": \"0.6757338\",\n \"text\": \"def getROI(self) -> retval:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bed4dbe4a2a572801aebe75858d4a17c\",\n \"score\": \"0.67048454\",\n \"text\": \"def add_roi(self, roi_fname, original_workspace, roi):\\r\\n if self.annotations_source.value():\\r\\n original_level = str(self.annotations_source.value().rsplit(\\\"/\\\", 1)[-1])\\r\\n else:\\r\\n original_level = \\\"None\\\"\\r\\n params = dict(\\r\\n workspace=original_workspace,\\r\\n roi_fname=roi_fname,\\r\\n roi=roi,\\r\\n original_workspace=original_workspace,\\r\\n original_level=original_level,\\r\\n )\\r\\n result = Launcher.g.run(\\\"roi\\\", \\\"create\\\", **params)\\r\\n if result:\\r\\n rid = result[\\\"id\\\"]\\r\\n rname = result[\\\"name\\\"]\\r\\n self._add_roi_widget(rid, rname, True)\\r\\n cfg.ppw.clientEvent.emit({\\\"source\\\": \\\"panel_gui\\\", \\\"data\\\": \\\"refresh\\\", \\\"value\\\": None})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8020c663269e9fe8c61ff71fb898a6c1\",\n \"score\": \"0.67033744\",\n \"text\": \"def create_roi(subject_id, subjects_dir, fs_dir, parcellation_name):\\n iflogger.info(\\\"Create the ROIs:\\\")\\n output_dir = op.abspath(op.curdir)\\n fs_dir = op.join(subjects_dir, subject_id)\\n cmp_config = cmp.configuration.PipelineConfiguration()\\n cmp_config.parcellation_scheme = \\\"Lausanne2008\\\"\\n log = cmp_config.get_logger()\\n parval = cmp_config._get_lausanne_parcellation('Lausanne2008')[parcellation_name]\\n pgpath = parval['node_information_graphml']\\n aseg = nb.load(op.join(fs_dir, 'mri', 'aseg.nii.gz'))\\n asegd = aseg.get_data()\\n\\n iflogger.info(\\\"Working on parcellation: \\\")\\n iflogger.info(cmp_config._get_lausanne_parcellation('Lausanne2008')[parcellation_name])\\n iflogger.info(\\\"========================\\\")\\n pg = nx.read_graphml(pgpath)\\n # each node represents a brain region\\n # create a big 256^3 volume for storage of all ROIs\\n rois = np.zeros( (256, 256, 256), dtype=np.int16 )\\n\\n count = 0\\n for brk, brv in pg.nodes_iter(data=True):\\n count = count + 1\\n iflogger.info(brv)\\n iflogger.info(brk)\\n if brv['dn_hemisphere'] == 'left':\\n hemi = 'lh'\\n elif brv['dn_hemisphere'] == 'right':\\n hemi = 'rh'\\n if brv['dn_region'] == 'subcortical':\\n iflogger.info(brv)\\n iflogger.info(\\\"---------------------\\\")\\n iflogger.info(\\\"Work on brain region: %s\\\" % (brv['dn_region']))\\n iflogger.info(\\\"Freesurfer Name: %s\\\" % brv['dn_fsname'])\\n iflogger.info(\\\"Region %s of %s \\\" % (count, pg.number_of_nodes()))\\n iflogger.info(\\\"---------------------\\\")\\n # if it is subcortical, retrieve roi from aseg\\n idx = np.where(asegd == int(brv['dn_fs_aseg_val']))\\n rois[idx] = int(brv['dn_correspondence_id'])\\n\\n elif brv['dn_region'] == 'cortical':\\n iflogger.info(brv)\\n iflogger.info(\\\"---------------------\\\")\\n iflogger.info(\\\"Work on brain region: %s\\\" % (brv['dn_region']))\\n iflogger.info(\\\"Freesurfer Name: %s\\\" % brv['dn_fsname'])\\n iflogger.info(\\\"Region %s of %s \\\" % (count, pg.number_of_nodes()))\\n iflogger.info(\\\"---------------------\\\")\\n\\n labelpath = op.join(output_dir, parval['fs_label_subdir_name'] % hemi)\\n # construct .label file name\\n\\n fname = '%s.%s.label' % (hemi, brv['dn_fsname'])\\n\\n # execute fs mri_label2vol to generate volume roi from the label file\\n # store it in temporary file to be overwritten for each region\\n\\n mri_cmd = 'mri_label2vol --label \\\"%s\\\" --temp \\\"%s\\\" --o \\\"%s\\\" --identity' % (op.join(labelpath, fname),\\n op.join(fs_dir, 'mri', 'orig.mgz'), op.join(output_dir, 'tmp.nii.gz'))\\n runCmd( mri_cmd, log )\\n\\n tmp = nb.load(op.join(output_dir, 'tmp.nii.gz'))\\n tmpd = tmp.get_data()\\n\\n # find voxel and set them to intensityvalue in rois\\n idx = np.where(tmpd == 1)\\n rois[idx] = int(brv['dn_correspondence_id'])\\n\\n # store volume eg in ROI_scale33.nii.gz\\n out_roi = op.join(output_dir, 'ROI_%s.nii.gz' % parcellation_name)\\n\\n # update the header\\n hdr = aseg.get_header()\\n hdr2 = hdr.copy()\\n hdr2.set_data_dtype(np.uint16)\\n\\n log.info(\\\"Save output image to %s\\\" % out_roi)\\n img = nb.Nifti1Image(rois, aseg.get_affine(), hdr2)\\n nb.save(img, out_roi)\\n\\n iflogger.info(\\\"[ DONE ]\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efbad9db36a42640b12611cb690c9ff6\",\n \"score\": \"0.66888976\",\n \"text\": \"def roi(self):\\r\\n return self._roi_tool.roi()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc9825ee374eadef8a96b3897a57ff5c\",\n \"score\": \"0.668095\",\n \"text\": \"def create_FBROI(builder,my_ROI):\\n UID_str = my_ROI.image_UID()\\n roi_coords = my_ROI.contour()\\n if UID_str not in nodule_map:\\n nodule_map[UID_str] = {}\\n nodule_map[UID_str]['roi_count'] = 1\\n else:\\n nodule_map[UID_str]['roi_count'] += 1\\n roi_count = nodule_map[UID_str]['roi_count']\\n roi_uid = 'roi_'+str(roi_count)\\n\\n contour_lst = []\\n FBROI.RegionOfInterestStartContourVector(builder,len(roi_coords))\\n for coord in my_ROI.contour():\\n builder.PrependUOffsetTRelative(FBPoint.CreatePoint(builder,coord[0],coord[1]))\\n contour_lst.append([coord[0],coord[1]])\\n nodule_map[UID_str][roi_uid] = contour_lst\\n contour = builder.EndVector(len(roi_coords))\\n imageUID = builder.CreateString(my_ROI.image_UID())\\n FBROI.RegionOfInterestStart(builder)\\n FBROI.RegionOfInterestAddImageSOPUID(builder, imageUID)\\n FBROI.RegionOfInterestAddInclusion(builder,my_ROI.inclusion())\\n FBROI.RegionOfInterestAddContour(builder,contour)\\n ROI = FBROI.RegionOfInterestEnd(builder)\\n return ROI\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bda34fe3159095513526076530d7fa2\",\n \"score\": \"0.66760564\",\n \"text\": \"def _add_roi(self):\\n with self.debug_output:\\n cat = self._roi_cat_sel.value\\n r_dict = self._copy_normalized_rois()\\n r_dict[cat].append(None)\\n self.rois = self._unnormalize_rois(r_dict)\\n self._roi_multi_sel.index = len(self._roi_multi_sel.options) - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cbbe9db0b1888485b9e660dabedac07\",\n \"score\": \"0.66606545\",\n \"text\": \"def get_roi(self):\\n hstart=int(self.get_value(\\\"SUBARRAY HPOS\\\"))\\n hend=hstart+int(self.get_value(\\\"SUBARRAY HSIZE\\\"))\\n vstart=int(self.get_value(\\\"SUBARRAY VPOS\\\"))\\n vend=vstart+int(self.get_value(\\\"SUBARRAY VSIZE\\\"))\\n bin=int(self.get_value(\\\"BINNING\\\"))\\n return (hstart,hend,vstart,vend,bin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f397b98ea3de302a50ce573e95edf64\",\n \"score\": \"0.6649359\",\n \"text\": \"def _draw_rois(self, *_):\\n self._last_draw_rois = Event.now()\\n\\n # Clear old ROIs\\n self.canvas.delete(TAG_ROI)\\n self.canvas.delete(TAG_ROI_NAME)\\n\\n # If there are no ROIs to draw, we’re done here\\n roi_collections = self.stack.rois\\n if not self.show_rois_var.get() or not roi_collections:\\n return\\n\\n # Get ROIs and display\\n for roi_col in roi_collections.values():\\n rois = None\\n try:\\n rois = roi_col[self.i_frame]\\n except KeyError:\\n rois = None\\n if rois is None:\\n try:\\n rois = roi_col[Ellipsis]\\n except KeyError:\\n rois = None\\n if rois is None:\\n continue\\n\\n col_color = roi_col.color\\n if col_color is None:\\n col_color = 'yellow'\\n col_stroke_width = roi_col.stroke_width\\n if col_stroke_width is None:\\n col_stroke_width = 1\\n\\n for roi in rois:\\n if not roi.visible and not roi.name_visible:\\n continue\\n\\n color = roi.color\\n if color is None:\\n color = col_color\\n\\n if roi.name:\\n # user-specified ROI name is base64-encoded to avoid issues due to bad format\\n name_tag = f'{TAG_ROI_ID}{base64.b64encode(roi.name.encode()).decode()}'\\n else:\\n name_tag = None\\n\\n if roi.visible:\\n if name_tag:\\n tags = (TAG_ROI, name_tag)\\n else:\\n tags = TAG_ROI\\n\\n stroke_width = roi.stroke_width\\n if stroke_width is None:\\n stroke_width = col_stroke_width\\n\\n roi_key = roi.key()[0]\\n if roi_key == 'raw':\\n contour = roi.contour\\n if self.scale is not None:\\n contour = contour * self.scale\\n self.canvas.create_polygon(*contour[:, ::-1].flat, tags=tags,\\n fill='', outline=color, width=stroke_width)\\n elif roi_key == 'rect':\\n corners = roi.corners\\n if self.scale is not None:\\n corners = corners * self.scale\\n self.canvas.create_polygon(*corners[:, ::-1].flat, tags=tags,\\n fill='', outline=color, width=stroke_width)\\n else:\\n print(f\\\"Undefined ROI type: '{roi_key}'\\\") #DEBUG\\n\\n if roi.name and roi.name_visible:\\n txtpos = roi.centroid.flat[::-1]\\n if self.scale is not None:\\n txtpos = txtpos * self.scale\\n if name_tag:\\n tags = (TAG_ROI_NAME, name_tag)\\n else:\\n tags = TAG_ROI_NAME\\n self.canvas.create_text(*txtpos.flat,\\n fill=color, text=roi.name, tags=tags)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8266c5b5c5b74cbe73e7d8c98b19cefa\",\n \"score\": \"0.6610116\",\n \"text\": \"def set_roi(self, roi):\\r\\n if roi is not None:\\r\\n if len(roi) != 4:\\r\\n raise ValueError(\\\"ROI must be a list of four integers\\\")\\r\\n for x in roi:\\r\\n if not (isinstance(x, int) and x >= 0):\\r\\n raise ValueError(\\\"ROI must be a (x, y, w, h) tuple\\\")\\r\\n self.roi = roi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66d0f1746618ccb71fa6a3948694f300\",\n \"score\": \"0.66022646\",\n \"text\": \"def setRoi(self,roi, profileWidths = (1, 1)):\\n if (self.img is None):\\n print(\\\"Error - need to call setImg before setRoi\\\")\\n return(-1)\\n\\n if ((roi[X_ORIGIN] >= 0) and (roi[X_ORIGIN]<self.imgSizeX)):\\n self.roi[X_ORIGIN] = int(roi[X_ORIGIN])\\n else:\\n print(\\\"WARNING: ROI[X_ORIGIN] %d Out of Range - using zero\\\" %\\n roi[X_ORIGIN])\\n self.roi[X_ORIGIN] = 0\\n\\n if ((roi[Y_ORIGIN] >= 0) and (roi[Y_ORIGIN]<self.imgSizeY)):\\n self.roi[Y_ORIGIN] = int(roi[Y_ORIGIN])\\n else:\\n print(\\\"WARNING: ROI[Y_ORIGIN] %d Out of Range - using zero\\\" %\\n roi[Y_ORIGIN])\\n self.roi[Y_ORIGIN] = 0\\n\\n if ((roi[X_ORIGIN] + roi[X_SIZE]) <= self.imgSizeX):\\n self.roi[X_SIZE] = int(roi[X_SIZE])\\n else:\\n self.roi[X_SIZE] = self.imgSizeX - self.roi[X_ORIGIN]\\n print(\\\"WARNING: ROI[X_SIZE] %d Out of Range - using %d\\\" %\\n (roi[X_SIZE], self.roi[X_SIZE]))\\n\\n if ((roi[Y_ORIGIN] + roi[Y_SIZE]) <= self.imgSizeY):\\n self.roi[Y_SIZE] = int(roi[Y_SIZE])\\n else:\\n self.roi[Y_SIZE] = self.imgSizeY - self.roi[Y_ORIGIN]\\n print(\\\"WARNING: ROI[Y_SIZE] %d Out of Range - using %d\\\" %\\n (roi[Y_SIZE], self.roi[Y_SIZE]))\\n\\n self.xProfileWidth = profileWidths[0]\\n self.yProfileWidth = profileWidths[1]\\n self.setProfiles(self.xProfileWidth, self.yProfileWidth)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cdcad4c387feb7303b4d6179f9f3a37\",\n \"score\": \"0.6533895\",\n \"text\": \"def build_roi_box_head():\\n return ROIBoxHead()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55a3be74e812b460059c53b570124058\",\n \"score\": \"0.65069735\",\n \"text\": \"def roi(self, xmin, xsize, ymin, ysize, zmin, zsize, tmin=0, tsize=-1):\\n self.__args.extend(('-roi',\\n xmin, xsize, ymin, ysize,\\n zmin, zsize, tmin, tsize))\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3dd372b85b16aecbb29dacdf9cb2994\",\n \"score\": \"0.64917934\",\n \"text\": \"def set_roi(self, roi):\\n with self._sem:\\n self.roi = numpy.ascontiguousarray(roi, numpy.int8)\\n self.buffers[\\\"ROI\\\"] = pyopencl.array.to_device(self.queue, self.roi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9f4c010d16156b78b46d9f114bd2564\",\n \"score\": \"0.64815354\",\n \"text\": \"def setROI(self,ROI=[]):\\n\\t\\t\\n\\t\\tif not ROI:\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tUser selection of the ROI\\n\\t\\t\\t\\n\\t\\t\\t\\\"\\\"\\\"\\t\\n\\t\\t\\tROI = []\\n\\t\\t\\tdef onClick(event):\\n\\t\\t\\t\\tROI.append([int(event.ydata),int(event.xdata)])\\n\\t\\t\\t\\tplt.scatter([event.xdata],[event.ydata],c='r')\\n\\t\\t\\t\\tplt.draw()\\n\\t\\t\\t\\n\\t\\t\\tfig = plt.figure()\\n\\t\\t\\tcid = fig.canvas.mpl_connect('button_press_event', onClick)\\n\\t\\t\\tplt.imshow(self[0])\\n\\t\\t\\tplt.title(\\\"Please select ROI coordinates\\\")\\n\\t\\t\\tplt.xlim(0,self[0].shape[1])\\n\\t\\t\\tplt.ylim(self[0].shape[0],0)\\n\\t\\t\\tplt.show()\\n\\t\\t\\t\\n\\t\\t\\tprint \\\"\\\\n ROI Coordinates: \\\\n\\\",ROI\\n\\t\\t\\t\\n\\t\\tself.ROI = np.array(ROI)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9314e072c90a29f8c25dab9246155ed\",\n \"score\": \"0.6480321\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super(PolygonROI, self).__init__(*args, **kwargs)\\n rect = self.imageItem.boundingRect() # type: QRectF\\n positions = [\\n (rect.bottomLeft().x(), rect.bottomLeft().y()),\\n (rect.bottomRight().x(), rect.bottomRight().y()),\\n (rect.topRight().x(), rect.topRight().y()),\\n (rect.topLeft().x(), rect.topLeft().y()),\\n ]\\n self._roiItem = BetterPolyLineROI(positions=positions, closed=True, scaleSnap=True, translateSnap=True)\\n self.addItem(self._roiItem)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c7397298610b93c2f50aa0fb92c6de2\",\n \"score\": \"0.6459811\",\n \"text\": \"def segment_ROI_image(Parameters, ROIs, root_dir, sub_path):\\n ch = Parameters[\\\"channels\\\"]\\n subimage_height = Parameters[\\\"subimage_height\\\"] \\n subimage_width = Parameters[\\\"subimage_width\\\"]\\n\\n\\n print (root_dir, sub_path)\\n print(\\\"Segmenting around the ROIs image.... :)\\\") \\n\\n \\n \\n \\n filepath = root_dir/sub_path\\n annotations_dir = root_dir/'Annotations'/sub_path.parent\\n image_dir = root_dir/'JPEGImages'/sub_path.parent\\n \\n print (\\\"IM\\\", image_dir)\\n \\n \\n for dirs in [annotations_dir, image_dir]:\\n if (not os.path.isdir(dirs)):\\n os.makedirs(dirs)\\n \\n \\n #Preprocess the image\\n \\n reader = imageio.get_reader(str(filepath))\\n image_open = np.asarray(reader.get_data(ch[0]))\\n image_para = np.asarray(reader.get_data(ch[1]))\\n image_perp = np.asarray(reader.get_data(ch[2]))\\n \\n \\n img = np.dstack([image_open, image_para, image_perp])\\n img = img/np.amax(img) \\n img = img*255\\n img = img.astype('uint8')\\n plt.imshow(img)\\n \\n height, width, depth = img.shape\\n print (\\\"Shape\\\", img.shape)\\n print (ROIs)\\n \\n \\n for i in range(len(ROIs)):\\n x_min = int(ROIs.loc[i, 'xmin'])\\n x_max = int(ROIs.loc[i, 'xmax'])\\n y_min = int(ROIs.loc[i, 'ymin'])\\n y_max = int(ROIs.loc[i, 'ymax'])\\n \\n \\n \\n \\n #x_length = x_max - x_min\\n #y_length = y_max - y_min\\n \\n \\n #Padding can be negative!\\n #x_pad = (subimage_width - x_length)//2\\n #y_pad = (subimage_height - y_length)//2\\n \\n x_centroid = (x_max + x_min)//2\\n y_centroid = (y_max + y_min)//2\\n \\n print (f\\\"Stats: X:{x_min}, {x_max}, {x_centroid} Y:{y_min}, {y_max}, {y_centroid}\\\")\\n\\n xmin = max(0, (x_centroid - subimage_width//2))\\n xmax = min(width, (x_centroid + subimage_width//2))\\n ymin = max(0, (y_centroid - subimage_height//2))\\n ymax = min(height, (y_centroid + subimage_height//2))\\n \\n subimage = img[ymin:ymax, xmin:xmax, :]\\n\\n subROIs = ROIs[(ROIs['X']>xmin) & \\n (ROIs['X']<xmax) & \\n (ROIs['Y']>ymin) & \\n (ROIs['Y']<ymax)].copy()\\n\\n\\n print (\\\"Stats:\\\", \\\"X\\\", xmin, xmax, \\\"Y\\\", ymin, ymax, subimage.shape, len(subROIs))\\n\\n #If ROI list is not empty \\n if len(subROIs)>0:\\n\\n #mod ROIs to fit the new size\\n subROIs['xmin'] = subROIs['xmin'] - xmin\\n subROIs['xmax'] = subROIs['xmax'] - xmin\\n subROIs['ymin'] = subROIs['ymin'] - ymin\\n subROIs['ymax'] = subROIs['ymax'] - ymin\\n\\n #Check for any truncations\\n subROIs['Truncated'] = ((subROIs['xmin']<0) | (subROIs['xmax']>xmax) | \\n (subROIs['ymin']<0) | (subROIs['ymax']>ymax))\\n\\n\\n #print (i, j, xmin, xmax, ymin, ymax, len(subROIs))\\n print (subROIs)\\n\\n #Save the jpeg files\\n JPEG_filename = image_dir/sub_path.name.replace('.ome.tif', f'{i}.jpg')\\n imageio.imwrite(str(JPEG_filename), subimage)\\n \\n\\n #Output the labels\\n labels_filename = annotations_dir/sub_path.name.replace('.ome.tif', f'{i}--labels.xml')\\n labels = {'Height': subimage.shape[0], \\n 'Width': subimage.shape[1], \\n 'Filename' : (sub_path.name.replace('.ome.tif', f'{i}.jpg')) , 'Folder': str(sub_path.parent)} \\n output_labels (labels, subROIs, labels_filename)\\n \\n \\n return(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e1f3f28458b151418e05c18a42c11f\",\n \"score\": \"0.6402772\",\n \"text\": \"def drawROI(self, image, roi):\\n # draw a rectangle around the faces\\n for (x, y, w, h) in roi:\\n cv2.rectangle(\\n image,\\n (x, y), # lower left\\n (x+w, y+h), # upper right\\n (0, 255, 0), # border color\\n 2 # line thickness\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb796bb52c05694eab4adf3b5f12d464\",\n \"score\": \"0.6394815\",\n \"text\": \"def get_roi(image, wname):\\n global REFPT, CROPPING, SRC_COORDS, T_COORDS\\n clone = image.copy()\\n param = [wname, image]\\n cv2.namedWindow(wname)\\n cv2.setMouseCallback(wname, click_and_crop, param)\\n cv2.imshow(wname, image)\\n cv2.waitKey(0)\\n\\n # if there are two reference points, then set the region of interest\\n # of the image and display it\\n if len(REFPT) == 2:\\n if wname == \\\"SOURCE\\\":\\n SRC_COORDS = REFPT\\n if wname == \\\"TARGET\\\":\\n T_COORDS = REFPT\\n\\n #print(REFPT) # for debugging\\n cv2.destroyAllWindows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"274bfea1f4a6efbc42e4a2953ca451d1\",\n \"score\": \"0.63507354\",\n \"text\": \"def _fcn_build_roi_list(self):\\n # Select volume :\\n self.volume.select_volume(str(self._roiDiv.currentText()))\\n # Clear widget list and add ROIs :\\n self._roiToAdd.clear()\\n self._roiToAdd.addItems(self.volume.roi_labels)\\n # By default, uncheck items :\\n for num in range(self._roiToAdd.count()):\\n item = self._roiToAdd.item(num)\\n item.setFlags(item.flags() | QtCore.Qt.ItemIsUserCheckable)\\n item.setCheckState(QtCore.Qt.Unchecked)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"839d6389281e3135a87bb6648699c32a\",\n \"score\": \"0.6344049\",\n \"text\": \"def make_roi_grid(self, toggle=True, method=''):\\n method = method if method else self.sender().text()\\n pos, shape = self.rh.ROIs[0].roi.pos(), self.rh.ROIs[0].roi.size()\\n if method == 'Single ROI':\\n for r in self.rh.ROIs:\\n r.resize(*map(int, [pos[0], pos[1], shape[0], shape[1]]))\\n elif method == 'Square grid':\\n n = len(self.rh.ROIs) # number of ROIs\\n d = int((n - 1)**0.5 + 1) # number of ROIs per row\\n X = int(self.rh.shape[0] / d) # horizontal distance between ROIs\\n Y = int(self.rh.shape[1] / int((n - 3/4)**0.5 + 0.5)) # vertical distance\\n for i in range(n): # ID of ROI\\n try:\\n newx, newy = int(X * (i%d + 0.5)), int(Y * (i//d + 0.5))\\n if any([newx//self.rh.shape[0], newy//self.rh.shape[1]]):\\n warning('Tried to set square ROI grid with (xc, yc) = (%s, %s)'%(newx, newy)+\\n ' outside of the image')\\n newx, newy = 0, 0\\n self.rh.ROIs[i].resize(*map(int, [newx, newy, 1, 1]))\\n except ZeroDivisionError as e:\\n error('Invalid parameters for square ROI grid: '+\\n 'x - %s, y - %s, pic size - %s, roi size - %s.\\\\n'%(\\n pos[0], pos[1], self.rh.shape[0], (shape[0], shape[1]))\\n + 'Calculated width - %s, height - %s.\\\\n'%(X, Y) + str(e))\\n elif method == '2D Gaussian masks':\\n try: \\n im = self.im_canvas.image.copy() - self.rh.bias\\n if np.size(np.shape(im)) == 2:\\n for r in self.rh.ROIs:\\n r.create_gauss_mask(im) # fit 2D Gaussian to max pixel region\\n # then block that region out of the image\\n try:\\n im[r.x-r.w : r.x+r.w+1, r.y-r.h:r.y+r.h+1] = np.zeros((2*r.w+1, 2*r.h+1)) + np.min(im)\\n except (IndexError, ValueError): pass\\n except AttributeError: pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"175773cf544079a2053d90f255009b57\",\n \"score\": \"0.6317071\",\n \"text\": \"def __set_roi(self, event, x, y, flags, params):\\r\\n if event==cv2.EVENT_LBUTTONDOWN:\\r\\n self.ix = x\\r\\n self.iy = y\\r\\n self.drawing = True\\r\\n if event==cv2.EVENT_MOUSEMOVE:\\r\\n if self.drawing:\\r\\n self.x = x\\r\\n self.y = y\\r\\n self.img = self.current_frame.copy()\\r\\n cv2.rectangle(self.img, (self.ix, self.iy), (x, y,), (0, 0, 255), 3)\\r\\n if event==cv2.EVENT_LBUTTONUP:\\r\\n self.drawing = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f51ac614c51ac4c262c16c6176bc61e0\",\n \"score\": \"0.6294511\",\n \"text\": \"def click(event):\\n ROIsize = self.settings['ROI_size']\\n Ly =self.img.shape[-1]\\n Lx =self.img.shape[-2]\\n \\n if self.settings['selectROI'] and (Lx,Ly)!=(ROIsize,ROIsize):\\n event.accept() \\n pos = event.pos()\\n x = int(pos.x()) #pyqtgraph is transposed\\n y = int(pos.y())\\n x = max(min(x, Lx-ROIsize//2 ),ROIsize//2 )\\n y = max(min(y, Ly-ROIsize//2 ),ROIsize//2 )\\n self.settings['roiX']= x\\n self.settings['roiY']= y\\n if hasattr(self, 'roi'):\\n self.imv.removeItem(self.roi) \\n self.roi = pg.RectROI([x-ROIsize//2,y-ROIsize//2], [ROIsize,ROIsize])\\n self.imv.addItem(self.roi)\\n \\n self.settings['selectROI'] = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"907a0595962eb8c87eced18c267928b7\",\n \"score\": \"0.6285353\",\n \"text\": \"def SetRoiBoxSelect(self):\\n #Check that a Data set has been loaded\\n if self.fname == None:\\n return\\n\\n if self.imageType != 'la':\\n print 'Function only valid for linear array probes. '\\n return\\n\\n from matplotlib.widgets import RectangleSelector\\n current_ax = plt.subplot(111) # make a new plotingrangej\\n\\n def on_select(eclick, erelease):\\n self.roiX = [0,0]\\n self.roiY = [0,0]\\n\\n self.roiX[0] = int(erelease.xdata/self.deltaX)\\n self.roiX[1] = int(eclick.xdata/self.deltaX)\\n self.roiX.sort()\\n\\n self.roiY[0] = int(eclick.ydata/self.deltaY)\\n self.roiY[1] = int(erelease.ydata/self.deltaY)\\n self.roiY.sort()\\n\\n # drawtype is 'box' or 'line' or 'none'\\n rectprops = dict(facecolor='red', edgecolor = 'red',\\n alpha=0.5, fill=False)\\n \\n rs = RectangleSelector(current_ax, on_select,\\n drawtype='box', useblit=True,\\n button=[1,3], # don't use middle button\\n minspanx=0, minspany=0,\\n spancoords='data',\\n rectprops = rectprops)\\n\\n #could be image sequence or just a 2-D image\\n import types\\n if type(self.data) == types.NoneType:\\n self.ReadFrame(0)\\n temp = self.data\\n\\n\\n from scipy.signal import hilbert\\n from numpy import log10\\n bMode = log10(abs(hilbert(temp, axis = 0)))\\n bMode = bMode - bMode.max()\\n bMode[bMode < -3] = -3\\n\\n #import matplotlib and create plot\\n import matplotlib.cm as cm\\n\\n plt.imshow(bMode, cmap = cm.gray, extent = [0, self.fovX, self.fovY, 0])\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a59be2fad99acc7a2ba8e6c23a7e36d1\",\n \"score\": \"0.6275901\",\n \"text\": \"def roi_target(self,rois,gt_box,label,pos_thresh=cfg.rcnn_pos_thresh,\\n neg_thresh_lo=cfg.rcnn_neg_thresh_lo,\\n neg_thresh_hi=cfg.rcnn_neg_thresh_hi):\\n assert rois.shape[1]==4, \\\"please remove the img_id\\\"\\n rois=torch.cat([rois,gt_box],dim=0) # [a+b,4]\\n \\n ious=t_box_iou(rois,gt_box) # [a+b,b]\\n max_ious,idx=ious.max(dim=1)\\n \\n # parameterizd box\\n gt_loc=encode_box(gt_box[idx],rois)\\n\\n # assign the neg:\\n assign=torch.full([len(rois)],-1).long().type_as(label)\\n\\n neg_mask=(max_ious>neg_thresh_lo)*(max_ious<neg_thresh_hi)\\n # if neg_mask.sum() == 0:\\n # tqdm.write(\\\"Warning: neg_roi for fast r-cnn is zero\\\",end=\\\" \\\")\\n # neg_mask=(max_ious<neg_thresh_hi)\\n # raise ValueError(\\\"there is no negative roi for fast r-cnn\\\")\\n assign[neg_mask]=0\\n \\n # assign the pos:\\n pos_mask=max_ious>pos_thresh\\n\\n # plus one since 0 denotes the neg, we must begin from the 1\\n assign[pos_mask]=label[idx][pos_mask].long()+1 \\n\\n # normalize?\\n mean=self.mean # [4]\\n std=self.std # [4]\\n\\n mean=mean[None].expand_as(gt_loc).type_as(gt_loc)\\n std=std[None].expand_as(gt_loc).type_as(gt_loc)\\n\\n gt_loc-=mean\\n gt_loc=gt_loc/std\\n\\n return rois,gt_loc,assign\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d67f9685f4abf4db6878699a3409800\",\n \"score\": \"0.62747985\",\n \"text\": \"def selectROI(windowName, img, showCrosshair=..., fromCenter=...) -> retval:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b44d8d642d88fedfb5e042adb194c018\",\n \"score\": \"0.6271857\",\n \"text\": \"def add_rois(self, rois):\\r\\n # Load objects\\r\\n objects_id = str(self.boxes_source.value().rsplit(\\\"/\\\", 1)[-1])\\r\\n # str(self.boxes_source.value())\\r\\n\\r\\n logger.debug(f\\\"Get objects {objects_id}\\\")\\r\\n objects_src = DataModel.g.dataset_uri(objects_id, group=\\\"objects\\\")\\r\\n params = dict(workpace=True, src=objects_src, entity_type=\\\"boxes\\\")\\r\\n\\r\\n result = Launcher.g.run(\\\"objects\\\", \\\"get_entities\\\", **params)\\r\\n\\r\\n if result:\\r\\n entities_arr = decode_numpy(result)\\r\\n\\r\\n rois = entities_arr[:, 4:]\\r\\n print(rois)\\r\\n\\r\\n # Iterate through ROIs and add them to the ROI list\\r\\n original_workspace = DataModel.g.current_workspace\\r\\n\\r\\n for roi in rois:\\r\\n roi_list = list(roi)\\r\\n roi_list = [int(el) for el in roi_list]\\r\\n # reorder to z_st, z_end, x_st, x_end, y_st, y_end\\r\\n roi_list = [\\r\\n roi_list[0],\\r\\n roi_list[3],\\r\\n roi_list[1],\\r\\n roi_list[4],\\r\\n roi_list[2],\\r\\n roi_list[5],\\r\\n ]\\r\\n\\r\\n roi_name = (\\r\\n DataModel.g.current_workspace\\r\\n + \\\"_roi_\\\"\\r\\n + str(roi[0])\\r\\n + \\\"_\\\"\\r\\n + str(roi[3])\\r\\n + \\\"_\\\"\\r\\n + str(roi[1])\\r\\n + \\\"_\\\"\\r\\n + str(roi[4])\\r\\n + \\\"_\\\"\\r\\n + str(roi[2])\\r\\n + \\\"_\\\"\\r\\n + str(roi[5])\\r\\n )\\r\\n cfg.ppw.clientEvent.emit(\\r\\n {\\\"source\\\": \\\"panel_gui\\\", \\\"data\\\": \\\"make_roi_ws\\\", \\\"roi\\\": roi_list}\\r\\n )\\r\\n self.add_roi(roi_name, original_workspace, roi_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cabdf45b195176cf9a3d597d7ad953b9\",\n \"score\": \"0.62654203\",\n \"text\": \"def show( self ):\\n source = cv.LoadImage( self.files[self.index] )\\n width, height = cv.GetSize(source)\\n \\n center = (width/2) + self.offset;\\n \\n cv.Line( source, (center,0), (center,height), (0,255,0), 1)\\n\\n\\n if self.roi:\\n x,y,a,b = self.roi;\\n \\n print self.roi\\n \\n width, height = ((a - x), (b - y))\\n mask = cv.CreateImage( (width, height), cv.IPL_DEPTH_8U, 1)\\n \\n cv.SetImageROI( source, (x, y, width, height)) \\n cv.Split( source, None, None, mask, None );\\n \\n gray = cv.CloneImage( mask );\\n \\n cv.InRangeS( mask, self.thresholdMin, self.thresholdMax, mask );\\n cv.And( mask, gray, gray ); \\n \\n line = [];\\n points = []; \\n \\n for i in range(0,height-1):\\n row = cv.GetRow( gray, i)\\n \\n minVal,minLoc,maxLoc,maxVal = cv.MinMaxLoc(row);\\n \\n y = i;\\n x = maxVal[0]\\n point = (0, 0, height-i)\\n \\n if x > 0:\\n line.append((x,y));\\n \\n s = x / sin(radians(self.camAngle))\\n x = s * cos(self.angles[self.index]) \\n z = height - y\\n y = s * sin(self.angles[self.index])\\n \\n point = (round(x,2),round(y,2),z);\\n \\n points.append(point)\\n \\n \\n cv.PolyLine( source, [line], False, (255,0,0), 2, 8)\\n cv.ResetImageROI( source )\\n x,y,a,b = self.roi;\\n cv.Rectangle( source, (int(x), int(y)), (int(a), int(b)), (255.0, 255, 255, 0) );\\n\\n if self.roi:\\n x,y,a,b = self.roi;\\n \\n width, height = ((a - x), (b - y))\\n mask = cv.CreateImage( (width, height), cv.IPL_DEPTH_8U, 1)\\n \\n cv.SetImageROI( source, (x-width, y, width, height)) # moves roi to the left\\n cv.Split( source, None, None, mask, None );\\n \\n gray = cv.CloneImage( mask );\\n \\n cv.InRangeS( mask, self.thresholdMin, self.thresholdMax, mask );\\n cv.And( mask, gray, gray ); \\n \\n line = [];\\n points2 = []; \\n \\n for i in range(0,height-1):\\n row = cv.GetRow( gray, i)\\n \\n minVal,minLoc,maxLoc,maxVal = cv.MinMaxLoc(row);\\n \\n y = i;\\n x = maxVal[0]\\n point = (0, 0, height-i)\\n \\n if x > 0:\\n line.append((x,y));\\n \\n x = width - x; # left to the x-axis\\n \\n s = x / sin(radians(self.camAngle))\\n \\n x = s * cos(self.angles[self.index]) \\n z = height - y# 500 higher then the other.\\n y = s * sin(self.angles[self.index])\\n \\n a = radians(300)\\n \\n nx = ( cos(a) * x ) - ( sin(a) * y )\\n ny = ( sin(a) * x ) + ( cos(a) * y )\\n \\n point = (nx,ny,z);\\n \\n points2.append(point)\\n \\n cv.PolyLine( source, [line], False, (255,0,0), 2, 8)\\n cv.ResetImageROI( source )\\n x,y,a,b = self.roi;\\n cv.Rectangle( source, (int(x), int(y)), (int(a), int(b)), (255.0, 255, 255, 0) );\\n\\n if self.mode == 'mask':\\n cv.ShowImage( 'preview', mask )\\n return\\n\\n if self.mode == 'record' and self.roi:\\n font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX,0.5,0.5,1)\\n cv.PutText( source, \\\"recording %d\\\" % self.index, (20,20), font, (0,0,255))\\n self.points.extend(points);\\n self.points2.extend(points2);\\n #self.colors.extend(colors);\\n\\n\\n\\n cv.ShowImage( 'preview', source )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a1017cb14597a541268df3a7fe80ccf\",\n \"score\": \"0.6260349\",\n \"text\": \"def setup_roi_fields(self):\\r\\n apply_roi_button = QPushButton(\\\"Apply ROI\\\")\\r\\n reset_button = QPushButton(\\\"Reset ROI\\\")\\r\\n roi_fields = QGroupBox(\\\"Select Region of Interest:\\\")\\r\\n roi_layout = QGridLayout()\\r\\n roi_layout.addWidget(QLabel(\\\"Drag a box in the image window or type manually\\\"), 0, 0, 1, 3)\\r\\n roi_layout.addWidget(QLabel(\\\"Axis\\\"), 1, 0)\\r\\n roi_layout.addWidget(QLabel(\\\"Start Value:\\\"), 1, 1)\\r\\n roi_layout.addWidget(QLabel(\\\"End Value:\\\"), 1, 2)\\r\\n roi_layout.addWidget(apply_roi_button, 1, 3)\\r\\n roi_layout.addWidget(reset_button, 2, 3)\\r\\n roi_layout.addWidget(QLabel(\\\"x:\\\"), 2, 0)\\r\\n self.xstart_linedt = QLineEdit(\\\"0\\\")\\r\\n self.xstart_linedt.textChanged.connect(self.on_roi_param_changed)\\r\\n roi_layout.addWidget(self.xstart_linedt, 2, 1)\\r\\n self.xend_linedt = QLineEdit(\\\"0\\\")\\r\\n self.xend_linedt.textChanged.connect(self.on_roi_param_changed)\\r\\n roi_layout.addWidget(self.xend_linedt, 2, 2)\\r\\n roi_layout.addWidget(QLabel(\\\"y:\\\"), 3, 0)\\r\\n self.ystart_linedt = QLineEdit(\\\"0\\\")\\r\\n self.ystart_linedt.textChanged.connect(self.on_roi_param_changed)\\r\\n roi_layout.addWidget(self.ystart_linedt, 3, 1)\\r\\n self.yend_linedt = QLineEdit(\\\"0\\\")\\r\\n self.yend_linedt.textChanged.connect(self.on_roi_param_changed)\\r\\n roi_layout.addWidget(self.yend_linedt, 3, 2)\\r\\n roi_layout.addWidget(QLabel(\\\"z:\\\"), 4, 0)\\r\\n self.zstart_linedt = QLineEdit(\\\"0\\\")\\r\\n self.zstart_linedt.textChanged.connect(self.on_roi_param_changed)\\r\\n roi_layout.addWidget(self.zstart_linedt, 4, 1)\\r\\n self.zend_linedt = QLineEdit(\\\"0\\\")\\r\\n self.zend_linedt.textChanged.connect(self.on_roi_param_changed)\\r\\n roi_layout.addWidget(self.zend_linedt, 4, 2)\\r\\n roi_layout.addWidget(QLabel(\\\"Downsample Factor:\\\"), 5, 0)\\r\\n self.downsample_spinner = QSpinBox()\\r\\n self.downsample_spinner.setRange(1, 10)\\r\\n self.downsample_spinner.setSpecialValueText(\\\"None\\\")\\r\\n self.downsample_spinner.setMaximumWidth(60)\\r\\n self.downsample_spinner.valueChanged.connect(self.on_roi_param_changed)\\r\\n roi_layout.addWidget(self.downsample_spinner, 5, 1)\\r\\n roi_layout.addWidget(QLabel(\\\"Estimated datasize (MB):\\\"), 5, 3)\\r\\n self.data_size_label = QLabel(\\\"0\\\")\\r\\n roi_layout.addWidget(self.data_size_label, 5, 4)\\r\\n roi_fields.setLayout(roi_layout)\\r\\n apply_roi_button.clicked.connect(self.on_roi_apply_clicked)\\r\\n reset_button.clicked.connect(self.on_roi_reset_clicked)\\r\\n return roi_fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf3785dff72358282be07f0065e60a3a\",\n \"score\": \"0.6255171\",\n \"text\": \"def enableROIselection(self):\\n def click(event):\\n \\\"\\\"\\\"\\n Resizes imageRaw on click event, to the specified size 'ROI_size'\\n around the clicked point.\\n \\\"\\\"\\\"\\n ROIsize = self.settings['ROI_size']\\n Ly =self.img.shape[-1]\\n Lx =self.img.shape[-2]\\n \\n if self.settings['selectROI'] and (Lx,Ly)!=(ROIsize,ROIsize):\\n event.accept() \\n pos = event.pos()\\n x = int(pos.x()) #pyqtgraph is transposed\\n y = int(pos.y())\\n x = max(min(x, Lx-ROIsize//2 ),ROIsize//2 )\\n y = max(min(y, Ly-ROIsize//2 ),ROIsize//2 )\\n self.settings['roiX']= x\\n self.settings['roiY']= y\\n if hasattr(self, 'roi'):\\n self.imv.removeItem(self.roi) \\n self.roi = pg.RectROI([x-ROIsize//2,y-ROIsize//2], [ROIsize,ROIsize])\\n self.imv.addItem(self.roi)\\n \\n self.settings['selectROI'] = False\\n \\n self.imv.getImageItem().mouseClickEvent = click\\n self.settings['selectROI'] = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8e5505d17ed27644b6a99092349a374\",\n \"score\": \"0.62447166\",\n \"text\": \"def selectROIs(windowName, img, showCrosshair=..., fromCenter=...) -> boundingBoxes:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a348dea5fa2bd00eef486e7c0526feb\",\n \"score\": \"0.6225949\",\n \"text\": \"def asROI(self):\\n return PolygonRoi(\\n FloatPolygon(*map(list, zip(*map(Ray.getOrigin, self.points)))), # Unpacking x and y coordinates of rays in two lists\\n Roi.NORMAL\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e329d10695fded0cfbbdfd7664d1056e\",\n \"score\": \"0.6217549\",\n \"text\": \"def set_rois(self, ROIlist):\\n self.rh.create_rois(len(ROIlist))\\n self.rh.resize_rois(ROIlist)\\n self.display_rois()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9a98ecddd77bb70f4bba805f038c7f1\",\n \"score\": \"0.6217537\",\n \"text\": \"def runDefineCropROI(self, cropParam):\\n vol \\t\\t= slicer.mrmlScene.GetNodeByID(cropParam.GetInputVolumeNodeID()\\t)\\n volBounds\\t= [0,0,0,0,0,0]\\n vol.GetRASBounds(volBounds)\\n logging.info(volBounds)\\n\\n #Find Dimensions of Image\\n volDim\\t\\t= [ (volBounds[1]-volBounds[0]),\\n \\t\\t\\t (volBounds[3]-volBounds[2]),\\n \\t\\t\\t (volBounds[5]-volBounds[4]) ]\\n roi\\t\\t\\t= slicer.mrmlScene.GetNodeByID(cropParam.GetROINodeID())\\n\\n #Find Center of Image\\n volCenter \\t= [ ((volBounds[0]+volBounds[1])/2),\\n \\t\\t\\t ((volBounds[2]+volBounds[3])/2),\\n ((volBounds[4]+volBounds[5])/2) ]\\n\\n roi.SetXYZ(volCenter)\\n roi.SetRadiusXYZ(volDim[0]/2, volDim[1]/2, volDim[2]/2 )\\n return roi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccca5a22455665021add321a61b723cd\",\n \"score\": \"0.6213413\",\n \"text\": \"def create_roi(array, loc, kx=3, ky=3, edge_wrap=False):\\r\\n\\r\\n dims = array.shape\\r\\n if (len(dims) != 2):\\r\\n raise Exception(\\\"Array Must Be 2 Dimensional!\\\")\\r\\n\\r\\n # Kernel size squared\\r\\n kx2 = kx**2\\r\\n ky2 = ky**2\\r\\n\\r\\n # Kernel offsets\\r\\n xoff = kx / 2\\r\\n yoff = ky / 2\\r\\n\\r\\n # Find the seed's neighbours\\r\\n x = numpy.arange(kx2) % kx + (seed[1] - xoff)\\r\\n y = numpy.arange(ky2) / ky + (seed[0] - yoff)\\r\\n roi = (y,x)\\r\\n\\r\\n # If the ROI is outside the array bounds it will be set to the min/max array bounds\\r\\n # otherwise it will be wrap around the edges of the array if edge_wrap is set to True\\r\\n if not edge_wrap:\\r\\n # Check if any parts of the roi are outside the image\\r\\n bxmin = numpy.where(roi[1] < 0)\\r\\n bymin = numpy.where(roi[0] < 0)\\r\\n bxmax = numpy.where(roi[1] >= dims[1])\\r\\n bymax = numpy.where(roi[0] >= dims[0])\\r\\n\\r\\n # Change if roi co-ordinates exist outside the image domain.\\r\\n roi[1][bxmin] = 0\\r\\n roi[0][bymin] = 0\\r\\n roi[1][bxmax] = dims[1]-1\\r\\n roi[0][bymax] = dims[0]-1\\r\\n\\r\\n return roi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fba9108f0922e840c479b2b06a95426\",\n \"score\": \"0.6209862\",\n \"text\": \"def getRoiImg(self):\\n roiImg = cv2.cvtColor(self.img,cv2.COLOR_GRAY2RGB)\\n cv2.rectangle(roiImg,\\n (self.roi[X_ORIGIN], self.roi[Y_ORIGIN]),\\n (self.roi[X_ORIGIN] + self.roi[X_SIZE],\\n self.roi[Y_ORIGIN] + self.roi[Y_SIZE]),\\n (65535,0,0),\\n 3)\\n return(roiImg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebcc5929151cf697cd4b92d0953746c1\",\n \"score\": \"0.6206967\",\n \"text\": \"def roi(self):\\n w, h, x, y = c_int32(), c_int32(), c_int32(), c_int32()\\n cco, packet_size = color_coding_t(), c_int32()\\n dll.dc1394_format7_get_roi(self._cam, self._mode_id, pointer(cco),\\n byref(packet_size),\\n byref(x), byref(y), byref(w), byref(h))\\n return ((w.value, h.value), (x.value, y.value),\\n color_codings[cco.value], packet_size.value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"155a1dc6e9674817d64e245dbf31b7d6\",\n \"score\": \"0.617504\",\n \"text\": \"def select_roi(self, mm_generator_kw={}, **rv_kw):\\n y0,x0 = int(np.floor(self.motion_borders.ymin)), int(np.floor(self.motion_borders.xmin))\\n y1,x1 = int(np.ceil(self.motion_borders.ymax)), int(np.ceil(self.motion_borders.xmax))\\n\\n def mm_mean_subtracted(downsample=5, mean_src='maxmov', equalize=True):\\n \\\"\\\"\\\"\\n mean_src : 'maxmov' or 'all'\\n \\\"\\\"\\\"\\n with h5py.File(self.data_file, 'r+') as f:\\n if 'maxmov' in f:\\n mean = np.mean(f['maxmov'], axis=0)\\n n = len(f['maxmov'])\\n else:\\n raise Exception('No maxmov available.')\\n\\n if mean_src == 'maxmov':\\n pass\\n elif mean_src == 'all':\\n mean = self.mean(axis=0)\\n \\n for i in range(n//downsample):\\n with h5py.File(self.data_file, 'r+') as f:\\n fr = np.max( f['maxmov'][i*downsample : i*downsample+downsample], axis=0 )\\n fr = fr-mean\\n if equalize:\\n fr = equalize_adapthist((fr-fr.min())/(fr.max()-fr.min()))\\n\\n minn = np.nanmin(fr)\\n\\n fr[:y0] = minn\\n fr[y1:] = minn\\n fr[:x0] = minn\\n fr[x1:] = minn\\n yield fr\\n \\n inst = mm_mean_subtracted(**mm_generator_kw)\\n self.roiview = ROIView(next(inst), iterator=inst, **rv_kw)\\n print('Remember to set roi using Data.set_roi(roi_view.roi).\\\\nIf you forgot to store roi_view, it is saved in object as Data.roiview.')\\n\\n return self.roiview\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e25012b4e70cfae0b1304d3a2a44c080\",\n \"score\": \"0.61721134\",\n \"text\": \"def roi(img):\\n # Region of interest\\n imshape = img.shape\\n roi = np.array([[\\n (100,100),\\n (600, 100), \\n (600, 400), \\n (100, 400)\\n ]], dtype=np.int32)\\n\\n #defining a blank mask to start with\\n mask = np.zeros_like(img) \\n \\n #defining a 3 channel or 1 channel color to fill the mask with depending on the input image\\n if len(img.shape) > 2:\\n channel_count = img.shape[2] # i.e. 3 or 4 depending on your image\\n ignore_mask_color = (255,) * channel_count\\n else:\\n ignore_mask_color = 255\\n \\n #filling pixels inside the polygon defined by \\\"vertices\\\" with the fill color \\n cv2.fillPoly(mask, roi, ignore_mask_color)\\n \\n #returning the image only where mask pixels are nonzero\\n masked_image = cv2.bitwise_and(img, mask)\\n\\n return masked_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf270a56671529e2999c56421c9e05ba\",\n \"score\": \"0.6164488\",\n \"text\": \"def user_roi(self, roi):\\n # find which ROI was dragged\\n for r in self.rh.ROIs:\\n if r.roi == roi:\\n break\\n x0, y0 = roi.pos() # lower left corner of bounding rectangle\\n w, h = map(int, roi.size()) # width, height\\n xc, yc = int(x0 + w//2), int(y0 + h//2) # centre of ROI\\n r.w, r.h = w, h\\n r.label.setPos(x0, y0)\\n r.translate_mask(xc, yc)\\n for key, val in zip(r.edits.keys(), [xc, yc, w, h]):\\n r.edits[key].setText(str(val))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09a8159ff24ae4894343687daa28ec4e\",\n \"score\": \"0.61622715\",\n \"text\": \"def display_rois(self, n=''):\\n if n:\\n self.rh.create_rois(int(n))\\n viewbox = self.im_canvas.getViewBox()\\n for item in viewbox.allChildren(): # remove unused ROIs\\n if ((type(item) == pg.graphicsItems.ROI.ROI or \\n type(item) == pg.graphicsItems.TextItem.TextItem) and \\n item not in [r.roi for r in self.rh.ROIs] + [r.label for r in self.rh.ROIs]):\\n viewbox.removeItem(item)\\n layout = self.centre_widget.layout()\\n k = np.sqrt(len(self.plots))\\n for i, r in enumerate(self.rh.ROIs):\\n if r.roi not in viewbox.allChildren():\\n reset_slot(r.roi.sigRegionChangeFinished, self.user_roi, True) \\n reset_slot(r.threshedit.textEdited, self.update_plots, True)\\n r.roi.setZValue(10) # make sure the ROI is drawn above the image\\n viewbox.addItem(r.roi)\\n viewbox.addItem(r.label)\\n try:\\n self.plots[i]['plot'].setTitle('ROI '+str(r.id))\\n for j, label in enumerate(list(r.edits.values())+[r.threshedit, r.autothresh]):\\n layout.addWidget(label, (i//k)*3, 7+(i%k)*6+j, 1,1)\\n except IndexError as e: pass # warning('Atom Checker has more plots than ROIs')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99553b92ce7df7c8efd6b73ea679dfd8\",\n \"score\": \"0.6157534\",\n \"text\": \"def roi(self, data):\\r\\n x, y = self._event_xdata, self._event_ydata\\r\\n return contour_to_roi(x, y, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97ab9a035b1d90312864dedfc18f9c74\",\n \"score\": \"0.609071\",\n \"text\": \"def load_MPII_annotation(self, index):\\n\\n \\n roi_rec = dict()\\n roi_rec['image'] = os.path.join(self.image_path,self.annotation[index]['img_paths'])\\n #size = cv2.imread(roi_rec['image']).shape\\n #roi_rec['height'] = size[0]\\n #roi_rec['width'] = size[1]\\n roi_rec['height'] = self.annotation[index]['img_height']\\n roi_rec['width'] = self.annotation[index]['img_width']\\n\\n \\n numOtherPerson=self.annotation[index]['numOtherPeople']\\n otherPersonJoints=[]\\n if numOtherPerson >0:\\n if numOtherPerson>1:\\n otherPersonJoints=otherPersonJoints+self.annotation[index]['joint_others']\\n else:\\n otherPersonJoints.append(self.annotation[index]['joint_others'])\\n mainPersonJoints=self.annotation[index]['joint_self']\\n allPerson=otherPersonJoints+[mainPersonJoints]\\n num_objs = len(allPerson)\\n\\n poses = np.zeros((num_objs, 28), dtype=np.uint16)\\n gt_classes = np.zeros((num_objs), dtype=np.int32)\\n overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\\n\\n class_to_index = dict(zip(self.classes, range(self.num_classes)))\\n # Load object bounding boxes into a data frame.\\n for ix, obj in enumerate(allPerson):\\n \\n cls = class_to_index['person']\\n po=np.zeros((16,3),dtype=np.float32)\\n po[0:len(obj),:]=np.array(obj,dtype=np.float32)\\n assert po.shape[0] ==16,'the image is wrong'\\n\\n poses[ix, :] = po[self.index2index[1:],:-1].ravel() ### obj must [14,2]\\n gt_classes[ix] = cls\\n overlaps[ix, cls] = 1.0\\n\\n roi_rec.update({'poses': poses,\\n 'gt_classes': gt_classes,\\n 'gt_overlaps': overlaps,\\n 'max_classes': overlaps.argmax(axis=1),\\n 'max_overlaps': overlaps.max(axis=1),\\n 'flipped': False})\\n return roi_rec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d6154a1bb42664e34819c26bbdfe0fc\",\n \"score\": \"0.606863\",\n \"text\": \"def __init__(self, roi, points, frame_no):\\n # assign next object id\\n self.id = Object.counter\\n Object.counter += 1\\n self.roi = roi\\n self.roi_valid = True # whether the RoI is acquired from object detection or predicted\\n self.roi_history = {frame_no: roi}\\n self.points = set(points)\\n # Add random color to the object for visualization\\n self.color = colorsys.hsv_to_rgb(random(), 1, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d05be7642aaafb6b2c3530cab1874c\",\n \"score\": \"0.605366\",\n \"text\": \"def __init__(self, maglim, roi):\\n self.roi = roi\\n mask = maglim*np.ones(hp.nside2npix(self.roi.config['coords']['nside_pixel']))\\n self.nside = hp.npix2nside(len(mask))\\n # Sparse maps of pixels in various ROI regions\\n self.mask_roi_sparse = mask[self.roi.pixels]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d5b49218197279100d84f4a623bbe11\",\n \"score\": \"0.6052589\",\n \"text\": \"def set_roi(self, mcaroi, name='OCR', min_x=1, size_x=4095):\\n mcaroi.configure_mcaroi(\\n roi_name=name,\\n min_x=min_x,\\n size_x=size_x \\n )\\n mcaroi.name = name\\n mcaroi.total_rbv.name = name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"474e81ce28edd8b2f7a4e64386569ca9\",\n \"score\": \"0.604833\",\n \"text\": \"def FindAdaptiveROI(image, center_ROI, aspr_ROI,displayImages, debug = True):\\n #inputfilename = 'img6.png'\\n #outputfilename = 'edge2.png'\\n #nucleation_down = 1 # 0 for nucleation up\\n #center_ROI = (511,672) #center of the object to be identified\\n #aspr_ROI = 2/3 # x_width/y_width for ROI. This is found by TRAINING\\n #debug = True # flag to output ERRRORs\\n #remove the strip at the bottom\\n #cropsequence = ((0,44),(0,0))\\n #img = ReadImage(inputfilename)\\n #img = CropImage(img,cropsequence,0)\\n #to mainain the aspect ratio of roi to be same as that of image, set the aspect ratio\\n #asp_ratio = int(1344/(1066-44))\\n #list of pad sizes to be removed along x axis\\n array_x_ROI = np.array([100,200,300,400,500,600,700,800,902])\\n array_y_ROI = (array_x_ROI*aspr_ROI).astype(int)\\n n = array_x_ROI.size\\n optimum_x_ROI = 0\\n optimum_y_ROI = 0\\n #set the array for relative strengths and maxima positions for the unimodal or bimodal distributions.\\n array_rel_strength = np.zeros(n)\\n array_maximum = np.zeros((n,2))\\n displayImages = 0\\n for i in np.arange(n):\\n x_width = array_x_ROI[i]\\n y_width = array_y_ROI[i]\\n #set up the cropsequence so that pads are removed centered around the center of the image.\\n cropsequence = CropSequenceGenerate(image,(center_ROI,(x_width,y_width)))\\n cropimg = CropImage(image,cropsequence,0)\\n imgbyte = Img2Ubyte(cropimg,0)\\n img_med = MedianFilter(imgbyte,displayImages)\\n maximum,rel_strength = modal_analysis(img_med,displayImages,debug) #strength is zero if distribution is unimodal and close to zero if the foreground is very small compared to background or vice versa\\n array_rel_strength[i] = rel_strength \\n array_maximum[i] = maximum\\n if displayImages==1:\\n #plot the relative strength variation and choose the appropriate ROI\\n plt.figure(),plt.title(\\\"Finding Optimum ROI by varying xROI\\\"),plt.plot(array_x_ROI,array_rel_strength)\\n #if all are unimodal distributions, then there either is no object to be found or object is beyond the ROI. This means that we need to check for bigger ROIs with progressive increase in y axis width\\n max_rel_strength = np.max(array_rel_strength)\\n if debug: print(\\\"maximum relative strength is \\\" + str(max_rel_strength))\\n if max_rel_strength < 0.001:\\n optimum_x_ROI = 1000\\n else:\\n #find the optimum ROI from maximum of the relative strength vs ROI variation\\n optimum_x_ROI = array_x_ROI[array_rel_strength.argsort()[-1]]\\n optimum_y_ROI = array_y_ROI[array_rel_strength.argsort()[-1]]\\n print(\\\"optimum_x_ROI is \\\" + str(optimum_x_ROI))\\n\\n #if optimum ROI is less than 1000, then it probably means that the object is not occluded and search for the ROI is completed. If the ROI is not optimized then we can increase the y_width of ROI further keeping the x_width to be constant at 1022\\n if optimum_x_ROI == 1000:\\n array_y_ROI = np.array([800,900,1000,1100])\\n n = array_y_ROI.size\\n array_x_ROI = np.ones(n,dtype = np.int32)*902\\n #set the array for relative strengths and maxima positions for the unimodal or bimodal distributions.\\n array_rel_strength = np.zeros(n)\\n array_maximum = np.zeros((n,2))\\n displayImages = 0\\n for i in np.arange(n):\\n x_width = array_x_ROI[i]\\n y_width = array_y_ROI[i]\\n #set up the cropsequence so that pads are removed across y axis around the center of the image.\\n cropsequence = CropSequenceGenerate(image,(center_ROI,(x_width,y_width)))\\n cropimg = CropImage(image,cropsequence,0)\\n imgbyte = Img2Ubyte(cropimg,0)\\n img_med = MedianFilter(imgbyte,displayImages)\\n maximum,rel_strength = modal_analysis(img_med,displayImages,debug) #strength is zero if distribution is unimodal and close to zero if the foreground is very small compared to background or vice versa\\n array_rel_strength[i] = rel_strength \\n array_maximum[i] = maximum\\n displayImages = 1\\n if displayImages == 1:\\n #plot the relative strength variation and choose the appropriate ROI\\n plt.figure(),plt.title(\\\"Finding Optimum ROI by varying yROI\\\"),plt.plot(array_y_ROI,array_rel_strength)\\n max_rel_strength = np.max(array_rel_strength)\\n if max_rel_strength == 0:\\n optimum_x_ROI = 0\\n optimum_y_ROI = 0\\n if debug: print(\\\"This image needs to be discarded\\\")\\n #find the optimum ROI from maximum of the relative strength vs ROI variation\\n optimum_x_ROI = array_x_ROI[array_rel_strength.argsort()[-1]]\\n optimum_y_ROI = array_y_ROI[array_rel_strength.argsort()[-1]]\\n if optimum_y_ROI == 1300:\\n #so the whole image needs to be used for further processing\\n optimum_x_ROI = 1022\\n optimum_y_ROI = 1344\\n #proceed with further processing with optimum ROI\\n optimum_ROI = (optimum_x_ROI,optimum_y_ROI)\\n if debug: print(\\\"Optimum ROI is \\\",optimum_ROI)\\n return optimum_ROI\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62bfcb8619b7f5deec11d3d41dec04a3\",\n \"score\": \"0.60479146\",\n \"text\": \"def create_pipeline_nii_to_subj_ROI(\\n main_path, filter_gm_threshold=0.9, pipeline_name=\\\"nii_to_subj_ROI\\\",\\n background_val=-1.0, plot=True, reslice=False, resample=False,\\n min_BOLD_intensity=50, percent_signal=0.5):\\n if reslice and resample:\\n print(\\\"Only reslice OR resample can be true, setting reslice to False\\\")\\n reslice = False\\n\\n pipeline = pe.Workflow(name=pipeline_name)\\n pipeline.base_dir = main_path\\n\\n inputnode = pe.Node(niu.IdentityInterface(fields=[\\n 'nii_4D_file', 'ROI_mask_file', 'gm_anat_file', 'ROI_coords_file',\\n 'ROI_MNI_coords_file', 'ROI_labels_file']), name='inputnode')\\n\\n # reslice gm\\n if reslice:\\n\\n reslice_gm = pe.Node(interface=spmu.Reslice(), name='reslice_gm')\\n pipeline.connect(inputnode, 'ROI_mask_file',\\n reslice_gm, 'space_defining')\\n pipeline.connect(inputnode, 'gm_anat_file', reslice_gm, 'in_file')\\n\\n if resample:\\n\\n resample_gm = pe.Node(interface=RegResample(), name='resample_gm')\\n pipeline.connect(inputnode, 'ROI_mask_file', resample_gm, 'ref_file')\\n pipeline.connect(inputnode, 'gm_anat_file', resample_gm, 'flo_file')\\n\\n # Preprocess pipeline,\\n filter_ROI_mask_with_GM = pe.Node(\\n interface=IntersectMask(), name='filter_ROI_mask_with_GM')\\n\\n filter_ROI_mask_with_GM.inputs.filter_thr = filter_gm_threshold\\n filter_ROI_mask_with_GM.inputs.background_val = background_val\\n\\n pipeline.connect(inputnode, 'ROI_mask_file',\\n filter_ROI_mask_with_GM, 'indexed_rois_file')\\n pipeline.connect(inputnode, 'ROI_coords_file',\\n filter_ROI_mask_with_GM, 'coords_rois_file')\\n pipeline.connect(inputnode, 'ROI_MNI_coords_file',\\n filter_ROI_mask_with_GM, 'MNI_coords_rois_file')\\n pipeline.connect(inputnode, 'ROI_labels_file',\\n filter_ROI_mask_with_GM, 'labels_rois_file')\\n\\n if reslice:\\n pipeline.connect(reslice_gm, 'out_file',\\n filter_ROI_mask_with_GM, 'filter_mask_file')\\n\\n elif resample:\\n pipeline.connect(resample_gm, 'out_file',\\n filter_ROI_mask_with_GM, 'filter_mask_file')\\n\\n else:\\n pipeline.connect(inputnode, 'gm_anat_file',\\n filter_ROI_mask_with_GM, 'filter_mask_file')\\n\\n # Nodes version: use min_BOLD_intensity and\\n # return coords where signal is strong enough\\n extract_mean_ROI_ts = pe.Node(interface=ExtractTS(\\n plot_fig=plot), name='extract_mean_ROI_ts')\\n\\n extract_mean_ROI_ts.inputs.percent_signal = percent_signal\\n extract_mean_ROI_ts.inputs.min_BOLD_intensity = min_BOLD_intensity\\n\\n pipeline.connect(inputnode, 'nii_4D_file', extract_mean_ROI_ts, 'file_4D')\\n pipeline.connect(filter_ROI_mask_with_GM, 'filtered_indexed_rois_file',\\n extract_mean_ROI_ts, 'indexed_rois_file')\\n pipeline.connect(filter_ROI_mask_with_GM, 'filtered_MNI_coords_rois_file',\\n extract_mean_ROI_ts, 'MNI_coord_rois_file')\\n pipeline.connect(filter_ROI_mask_with_GM, 'filtered_coords_rois_file',\\n extract_mean_ROI_ts, 'coord_rois_file')\\n pipeline.connect(filter_ROI_mask_with_GM, 'filtered_labels_rois_file',\\n extract_mean_ROI_ts, 'label_rois_file')\\n\\n return pipeline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a1cff0eea214f649e3e331ef1326f1c\",\n \"score\": \"0.60477114\",\n \"text\": \"def test_apply_roi_2d(self):\\r\\n client = self.create_client_with_image()\\r\\n\\r\\n roi = core.roi.PolygonalROI(vx=[10, 20, 20, 10],\\r\\n vy=[10, 10, 20, 20])\\r\\n client.apply_roi(roi)\\r\\n roi2 = self.im.edit_subset.subset_state.roi\\r\\n state = self.im.edit_subset.subset_state\\r\\n\\r\\n assert roi2.to_polygon()[0] == roi.to_polygon()[0]\\r\\n assert roi2.to_polygon()[1] == roi.to_polygon()[1]\\r\\n assert state.xatt is self.im.get_pixel_component_id(1)\\r\\n assert state.yatt is self.im.get_pixel_component_id(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d7a77121bd54ce667e3aa5233e16e32\",\n \"score\": \"0.6046832\",\n \"text\": \"def getRoi(self):\\n roi = self.img[self.roi[Y_ORIGIN] : \\n self.roi[Y_ORIGIN] + self.roi[Y_SIZE],\\n self.roi[X_ORIGIN] : \\n self.roi[X_ORIGIN] + self.roi[X_SIZE]]\\n return roi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"703b707ddce0d52b847e84b7b1527ce3\",\n \"score\": \"0.6044786\",\n \"text\": \"def test_roi(self):\\n import_file = os.path.join(\\\"..\\\", \\\"mlx75027.csv\\\")\\n mlx75027 = True\\n self.assertTrue(os.path.isfile(import_file))\\n reg_dict = mlx.csv_import(import_file)\\n\\n row_end = 480\\n col_end = 640\\n row_offset = 1\\n col_offset = 1\\n mlx.set_roi(reg_dict, col_offset, col_end,\\n row_offset, row_end, mlx75027)\\n\\n cs, ce, rs, re = mlx.calc_roi(reg_dict)\\n self.assertEqual(row_offset, rs)\\n self.assertEqual(col_offset, cs)\\n self.assertEqual(row_end, re)\\n self.assertEqual(col_end, ce)\\n\\n row_offset = 0\\n with self.assertRaises(RuntimeError):\\n mlx.set_roi(reg_dict, col_offset, col_end,\\n row_offset, row_end, mlx75027)\\n\\n row_offset = 51\\n row_end = 240\\n col_offset = 50\\n col_end = 150\\n mlx.set_roi(reg_dict, col_offset, col_end,\\n row_offset, row_end, mlx75027)\\n cs, ce, rs, re = mlx.calc_roi(reg_dict)\\n self.assertEqual(row_offset, rs)\\n self.assertEqual(col_offset, cs)\\n self.assertEqual(row_end, re)\\n self.assertEqual(col_end, ce)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cc0129888165c7f7ca238798056105c\",\n \"score\": \"0.6009475\",\n \"text\": \"def roi(self):\\n return SheetOverlay([el.get_roi(self.roi_bounds) for el in self.data],\\n bounds=self.roi_bounds if self.roi_bounds else self.bounds,\\n metadata=self.metadata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61ebe7559db5e3b6d665ea50cdb99a26\",\n \"score\": \"0.60076815\",\n \"text\": \"def FindAdaptiveROIversion2(image, center_ROI, aspr_ROI, array_ROI, displayImages, debug = True):\\n #inputfilename = 'img6.png'\\n #outputfilename = 'edge2.png'\\n #nucleation_down = 1 # 0 for nucleation up\\n #center_ROI = (511,672) #center of the object to be identified\\n #aspr_ROI = 2/3 # x_width/y_width for ROI. This is found by TRAINING\\n #debug = True # flag to output ERRRORs\\n #remove the strip at the bottom\\n #cropsequence = ((0,44),(0,0))\\n #img = ReadImage(inputfilename)\\n #img = CropImage(img,cropsequence,0)\\n #to mainain the aspect ratio of roi to be same as that of image, set the aspect ratio\\n #asp_ratio = int(1344/(1066-44))\\n #list of pad sizes to be removed along x axis\\n array_x_ROI = array_ROI\\n array_y_ROI = (array_x_ROI*aspr_ROI).astype(int)\\n n = array_x_ROI.size\\n optimum_x_ROI = 0\\n optimum_y_ROI = 0\\n #set the array for relative strengths and maxima positions for the unimodal or bimodal distributions.\\n array_rel_strength = np.zeros(n)\\n array_maximum = np.zeros((n,2))\\n #displayImages = 0\\n for i in np.arange(n):\\n x_width = array_x_ROI[i]\\n y_width = array_y_ROI[i]\\n #set up the cropsequence so that pads are removed centered around the center of the image.\\n cropsequence = CropSequenceGenerate(image,(center_ROI,(x_width,y_width)))\\n cropimg = CropImage(image,cropsequence,0)\\n imgbyte = Img2Ubyte(cropimg,0)\\n img_med = MedianFilter(imgbyte,displayImages)\\n maximum,rel_strength = modal_analysis(img_med,displayImages,debug) #strength is zero if distribution is unimodal and close to zero if the foreground is very small compared to background or vice versa\\n array_rel_strength[i] = rel_strength \\n array_maximum[i] = maximum\\n #displayImages = 1\\n if displayImages==1:\\n #plot the relative strength variation and choose the appropriate ROI\\n plt.figure(),plt.title(\\\"Finding Optimum ROI by varying xROI\\\"),plt.plot(array_x_ROI,array_rel_strength)\\n #if all are unimodal distributions, then there either is no object to be found or object is beyond the ROI. This means that we need to check for bigger ROIs with progressive increase in y axis width\\n max_rel_strength = np.max(array_rel_strength)\\n if debug: print(\\\"maximum relative strength is \\\" + str(max_rel_strength))\\n if max_rel_strength < 0.001:\\n optimum_x_ROI = 902\\n else:\\n #find the optimum ROI from maximum of the relative strength vs ROI variation\\n optimum_x_ROI = array_x_ROI[array_rel_strength.argsort()[-1]]\\n optimum_y_ROI = array_y_ROI[array_rel_strength.argsort()[-1]]\\n #proceed with further processing with optimum ROI\\n optimum_ROI = (optimum_x_ROI,optimum_y_ROI)\\n if debug: print(\\\"Optimum ROI is \\\",optimum_ROI)\\n return optimum_ROI\",\n \"title\": \"\"\n },\n {\n \"docid\": \"806207f2d69b6bbdf242c9f5a3e52c63\",\n \"score\": \"0.60006034\",\n \"text\": \"def test_get_roi_ranges(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa2e4bd00560fa389cffa10bd1321a48\",\n \"score\": \"0.59888995\",\n \"text\": \"def create_vecs(self,roi):\\n y = np.arange(roi[0],roi[1],1)\\n x = np.arange(roi[2],roi[3], 1)\\n X,Y = np.meshgrid(x,y)\\n return X,Y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"940eb0279dd735e81abe2f3bf38bae6c\",\n \"score\": \"0.5977656\",\n \"text\": \"def roi_mask(image):\\n imshape = image.shape\\n ''' \\n y_max = imshape[0]-70\\n y_min = 11*imshape[0]/18\\n x_min = 0\\n x_1 = 9*imshape[1]/20\\n x_2 = 11*imshape[1]/20\\n x_max = imshape[1]\\n '''\\n y_max = imshape[0]-70\\n y_min = imshape[0]/10\\n x_min = 0 + 80\\n x_1 = 5*imshape[1]/20\\n x_2 = 15*imshape[1]/20\\n x_max = imshape[1] - 80\\n \\n \\n vertices = np.array([[(x_min,y_max), (x_1, y_min), (x_2, y_min),(x_max,y_max)]], dtype=np.int32)\\n #defining a blank mask to start with\\n mask = np.zeros_like(image) \\n \\n #defining a 3 channel or 1 channel color to fill the mask with depending on the input image\\n if len(imshape) > 2:\\n channel_count = imshape[2] # i.e. 3 or 4 depending on your image\\n ignore_mask_color = (255,) * channel_count\\n else:\\n ignore_mask_color = 255\\n \\n #filling pixels inside the polygon defined by \\\"vertices\\\" with the fill color \\n cv2.fillPoly(mask, [vertices], ignore_mask_color)\\n \\n #returning the image only where mask pixels are nonzero\\n masked_image = cv2.bitwise_and(image, mask)\\n return masked_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e665061d55116e2795386b105752774\",\n \"score\": \"0.59717214\",\n \"text\": \"def _roi_drawn(self, change=None):\\n idx = self.image_selector.index\\n if idx is not None:\\n self.set_rois(idx, self.roi_selector_module.rois)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"459b8764a2b3f6fdca9ea61e11ba07a2\",\n \"score\": \"0.59713686\",\n \"text\": \"def init():\\n \\n \\n im.set_data(np.empty((para.cell_num, para.cell_num)))\\n point.set_data([], [])\\n text.set_text('')\\n x = lsr.get_xdata() \\n y = lsr.get_ydata()\\n lsr.set_data(x, y)\\n ltr.set_text('')\\n x = lsw.get_xdata()\\n y = lsw.get_ydata()\\n lsw.set_data(x, y)\\n ltw.set_text('')\\n \\n return im,\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea0da0ee1ff2a4f22edb0385fe270e6\",\n \"score\": \"0.59615475\",\n \"text\": \"def __init__(self, x_res, y_res):\\n self._x_res = x_res\\n self._y_res = y_res\\n self._roi = (0, self._y_res // 2), (self._x_res - 1, self._y_res - 1)\\n self._x_size = 64\\n self._y_size = 64\\n self._x_step = 16\\n self._y_step = 16\\n self._horizon = 409\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c211daba039207156c8f1063bf63ea6\",\n \"score\": \"0.5960826\",\n \"text\": \"def set_roi(self, hstart=0, hend=None, vstart=0, vend=None, bin=1):\\n with self._reset_buffers():\\n self.set_value(\\\"SUBARRAY MODE\\\",2)\\n hend=hend or self.properties[\\\"SUBARRAY HSIZE\\\"].max\\n vend=vend or self.properties[\\\"SUBARRAY VSIZE\\\"].max\\n min_roi,max_roi=self.get_roi_limits()\\n if bin==3:\\n bin=2\\n self.set_value(\\\"SUBARRAY HSIZE\\\",min_roi[2])\\n self.set_value(\\\"SUBARRAY HPOS\\\",(hstart//4)*4)\\n self.set_value(\\\"SUBARRAY HSIZE\\\",(max(hend-hstart,min_roi[2])//4)*4)\\n self.set_value(\\\"SUBARRAY VSIZE\\\",min_roi[3])\\n self.set_value(\\\"SUBARRAY VPOS\\\",(vstart//4)*4)\\n self.set_value(\\\"SUBARRAY VSIZE\\\",(max(vend-vstart,min_roi[3])//4)*4)\\n self.set_value(\\\"BINNING\\\",min(bin,max_roi[4]))\\n return self.get_roi()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0c24818e2f62af5dd80f7f589b102e5\",\n \"score\": \"0.5957256\",\n \"text\": \"def roi(self):\\n return (self._slice_0.start,\\n self._slice_0.stop,\\n self._slice_1.start,\\n self._slice_1.stop)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d47c3e232540ad61b69f47a3c24c62ee\",\n \"score\": \"0.59546846\",\n \"text\": \"def set_image(self, path, roi=None):\\r\\n self.img = cv2.imread(path)\\r\\n if self.img is None or self.img.size == 0:\\r\\n raise IOError(\\\"Failed to read image %s\\\" % path)\\r\\n if roi and len(roi) != 4:\\r\\n raise ValueError(\\\"ROI must be a list of four integers\\\")\\r\\n\\r\\n # If a ROI was provided, use only that region.\\r\\n if roi:\\r\\n y, y2, x, x2 = roi\\r\\n self.img = self.img[y:y2, x:x2]\\r\\n\\r\\n # Reset image related variables so one instance can be used for multiple\\r\\n # images.\\r\\n self.path = path\\r\\n self.mask = None\\r\\n self.bin_mask = None\\r\\n\\r\\n return self.img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"884317ecc428fb3f438b59817f24d0d4\",\n \"score\": \"0.5953904\",\n \"text\": \"def image_load_ROI(name,ROI):\\n x0,y0,w = ROI\\n xmin,xmax = x0-(w-1)/2,x0+(w-1)/2+1\\n ymin,ymax = y0-(w-1)/2,y0+(w-1)/2+1\\n I = array(Image.open(name).convert(\\\"I\\\"),float32).T[xmin:xmax,ymin:ymax]\\n return I\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc77859867ecc31362ac5862ed169d76\",\n \"score\": \"0.5938262\",\n \"text\": \"def genIm(self, crop=False):\\r\\n if not hasattr(self.s, 'actor'):\\r\\n self.s.addActor()\\r\\n if not hasattr(self.m, 'actor'):\\r\\n self.m.addActor()\\r\\n # Generate a renderer window\\r\\n win = vtkRenWin()\\r\\n # Set the number of viewports\\r\\n win.setnumViewports(1)\\r\\n # Set the background colour\\r\\n win.setBackground([1,1,1])\\r\\n # Set camera projection \\r\\n # Set camera projection \\r\\n win.setView([0, -1, 0], 0)\\r\\n win.SetSize(512, 512)\\r\\n win.Modified()\\r\\n win.OffScreenRenderingOn()\\r\\n self.s.actor.setColor([1.0, 0.0, 0.0])\\r\\n self.s.actor.setOpacity(0.5)\\r\\n self.m.actor.setColor([0.0, 0.0, 1.0])\\r\\n self.m.actor.setOpacity(0.5)\\r\\n win.renderActors([self.s.actor, self.m.actor])\\r\\n win.Render()\\r\\n win.rens[0].GetActiveCamera().Azimuth(0)\\r\\n win.rens[0].GetActiveCamera().SetParallelProjection(True)\\r\\n win.Render()\\r\\n im = win.getImage()\\r\\n if crop is True:\\r\\n mask = np.all(im == 1, axis=2)\\r\\n mask = ~np.all(mask, axis=1)\\r\\n im = im[mask, :, :]\\r\\n mask = np.all(im == 1, axis=2)\\r\\n mask = ~np.all(mask, axis=0)\\r\\n im = im[:, mask, :]\\r\\n return im, win\",\n \"title\": \"\"\n },\n {\n \"docid\": \"969c6f592f48d8eafa0c2b55a57aef68\",\n \"score\": \"0.5929706\",\n \"text\": \"def autoROISize(self, filename='', imshape=(1024,1280), widget=None):\\n self.fitImage(filename, imshape=imshape)\\n for i in reversed(range(5)): # bad ROI size, try another\\n if (self.df.isin([np.inf,-np.inf]).values.any() or self.df.isnull().values.any()\\n or self.check_outlier('w') or self.check_outlier('h')):\\n self._dx = int((i+1)**2 * 5)\\n self._dy = int((i+1)**2 * 5)\\n try:\\n self.fitImage()\\n except: pass\\n else: break\\n # check if ROIs overlap\\n overx, overy = 0, 0\\n xmins = np.array(sorted(self.df['xc'] - self._dx))\\n xmaxs = np.array(sorted(self.df['xc'] + self._dx))\\n ymins = np.array(sorted(self.df['yc'] - self._dy))\\n ymaxs = np.array(sorted(self.df['yc'] + self._dy))\\n # if any boxes are outside camera ROI\\n if len(xmins[xmins < 0]):\\n overx = np.abs(np.min(xmins[xmins<0]))\\n if len(ymins[ymins < 0]):\\n overy = np.abs(np.min(ymins[ymins<0]))\\n if len(xmaxs[xmaxs > imshape[1]]):\\n overx = max(overx, np.max(xmaxs[xmaxs > imshape[1]]) - imshape[1])\\n if len(ymaxs[ymaxs > imshape[0]]):\\n overy = max(overy, np.max(ymaxs[ymaxs > imshape[0]]) - imshape[0])\\n # look at first row, compare first and second column\\n if self._s[0] > 1:\\n ox = (xmins[1] - xmaxs[0])/2.\\n if ox < 0:\\n overx = max(overx, np.abs(ox))\\n # look at first column, compare first and second row\\n if self._s[1] > 1:\\n oy = (ymins[1] - ymaxs[0])/2.\\n if oy < 0:\\n overy = max(overy, np.abs(oy))\\n # set new width, height of ROI \\n self._dx -= int(round(overx))\\n self._dy -= int(round(overy))\\n self.fitImage()\\n info(\\\"imageArray fitter reset ROI width, height to %s, %s\\\"%(self._dy, self._dx))\\n xmins, xmaxs = self.df['xc'] - self._dx, self.df['xc'] + self._dx\\n ymins, ymaxs = self.df['yc'] - self._dy, self.df['yc'] + self._dy\\n # image could be cropped to: [xmin,ymin,xmax,ymax]\\n bounds = list(map(int, [np.floor(min(xmins)), np.floor(min(ymins)), \\n np.ceil(max(xmaxs)), np.ceil(max(ymaxs))]))\\n # plot the bounds\\n if widget:\\n viewbox = self.plotContours(widget)\\n s = pg.ROI((min(xmins), self._imvals.shape[0]-max(ymaxs)), # origin is bottom-left\\n (max(xmaxs) - min(xmins), max(ymaxs) - min(ymins)),\\n movable=False, pen='w') # rotatable=False, resizable=False, \\n viewbox.addItem(s)\\n return bounds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f8702c0259525f6b06a0606ba11f9b\",\n \"score\": \"0.59073627\",\n \"text\": \"def roi(self, reg=None, wh=None):\\n\\n # interpret reg\\n if reg is not None and wh is not None:\\n # reg = getself.__class__(reg) # 2x2?\\n wh = argcheck.getvector(wh)\\n\\n xc = reg[0]\\n yc = reg[1]\\n if len(wh) == 1:\\n w = np.round(wh/2)\\n h = w\\n else:\\n w = np.round(wh[0]/2)\\n h = np.round(wh[1]/2)\\n left = xc - w\\n right = xc + w\\n top = yc - h\\n bot = yc + h\\n\\n elif reg is not None and wh is None:\\n # reg = getself.__class__(reg)\\n\\n left = reg[0, 0]\\n right = reg[0, 1]\\n top = reg[1, 0]\\n bot = reg[1, 1]\\n\\n else:\\n raise ValueError(reg, 'reg cannot be None')\\n\\n # TODO check row/column ordering, and ndim check\\n out = []\\n for im in self:\\n roi = im.image[top:bot, left:right, :]\\n o = namedtuple('roi', ['roi',\\n 'left',\\n 'right',\\n 'top',\\n 'bot'])(self.__class__(roi),\\n left,\\n right,\\n top,\\n bot)\\n out.append(o)\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44bf71e8718f9315c053120a164263b4\",\n \"score\": \"0.5904946\",\n \"text\": \"def __init__(self, roi: RegionOfInterest, axis: int,\\n left: ROITree, right: ROITree):\\n assert axis >= 0 and axis < 3 \\n self.roi = roi\\n self.axis = axis\\n self.left = left\\n self.right = right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a67cb700f65d1303c5169e30cac2e20\",\n \"score\": \"0.58968097\",\n \"text\": \"def reset_roi_fields(self):\\r\\n self.xstart_linedt.setText(\\\"0\\\")\\r\\n self.xend_linedt.setText(str(self.data_shape[2]))\\r\\n self.ystart_linedt.setText(\\\"0\\\")\\r\\n self.yend_linedt.setText(str(self.data_shape[1]))\\r\\n self.zstart_linedt.setText(\\\"0\\\")\\r\\n self.zend_linedt.setText(str(self.data_shape[0]))\\r\\n self.roi_changed = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"097c7bc7ee23bc1bef7766980b18c662\",\n \"score\": \"0.5888198\",\n \"text\": \"def roi_processing(numpy_frame: np.ndarray, roi_points: list):\\n final_images = list()\\n if len(roi_points) == 0:\\n print(\\\"Incorrect number of Region of Interest points selected... Exiting\\\")\\n sys.exit(1)\\n\\n base_masked_out = roi_extraction(roi_points=roi_points, numpy_frame=numpy_frame)\\n\\n x_intervals = round(numpy_frame.shape[1] / 20)\\n y_intervals = round(numpy_frame.shape[0] / 30)\\n x_start_interval = 0\\n y_start_interval = 0\\n\\n for x_index in range(0, LOCAL_X_AXIS_ITERATIONS):\\n x_start_interval, generated_images = roi_rotation(\\n masked_image=base_masked_out,\\n x_start_interval=x_start_interval,\\n y_start_interval=y_start_interval,\\n x_intervals=x_intervals,\\n y_intervals=y_intervals\\n )\\n final_images.extend(generated_images)\\n\\n return final_images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8270e8a24f553c4d8223e12af95e9bc1\",\n \"score\": \"0.5878978\",\n \"text\": \"def str2roi(self,roiStr):\\n originStr, sizeStr = roiStr.split(\\\":\\\")\\n #print(\\\"originStr=\\\",originStr,\\\" sizeStr=\\\",sizeStr)\\n xOrigin = int(originStr.split(\\\",\\\")[0])\\n yOrigin = int(originStr.split(\\\",\\\")[1])\\n xSize = int(sizeStr.split(\\\",\\\")[0])\\n ySize = int(sizeStr.split(\\\",\\\")[1])\\n return((xOrigin, yOrigin, xSize, ySize))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"732fffb503966e4f2b6db66cea0564ab\",\n \"score\": \"0.5863586\",\n \"text\": \"def _roi_pooling():\\n boxes = np.array([[10, 10, 20, 20], [-10, 10, -20, 30]])\\n rois = tf.cast(boxes, tf.float32)\\n x1 = rois[..., 0]\\n y1 = rois[..., 1]\\n x2 = rois[..., 2]\\n y2 = rois[..., 3]\\n\\n rois = rois / 10\\n\\n x1 = tf.expand_dims(x1, axis=-1)\\n y1 = tf.expand_dims(y1, axis=-1)\\n x2 = tf.expand_dims(x2, axis=-1)\\n y2 = tf.expand_dims(y2, axis=-1)\\n\\n # rois = tf.concatenate([x1, y1, x2, y2], axis=-1)\\n rois = tf.concat([y1, x1, y2, x2], axis=-1)\\n rois = tf.reshape(rois, (-1, 4))\\n\\n rois /=10\\n print(rois)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd92a6861faeedd8de15f7f90c6c169f\",\n \"score\": \"0.5841793\",\n \"text\": \"def updateROIState(self, v):\\n st = {}\\n st['angle'] = 0\\n st['pos'] = pg.Point(v[0], v[1])\\n st['size'] = pg.Point(v[2] - v[0], v[3] - v[1])\\n self.roi.setState(st)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63a99de292d90c331e732ce12b5ede04\",\n \"score\": \"0.5832336\",\n \"text\": \"def find_ROI_segmentation(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f1da64f0f9e95a3fde6ed8bfac0da60\",\n \"score\": \"0.5820037\",\n \"text\": \"def get_best_roi(self):\\n yvals = np.linspace(0, 100, num=11)*7.2\\n w = 20 # half width of the ROI\\n roi = []\\n for y in yvals:\\n roi.append([self.best_fit_p(y)-w, y])\\n for y in yvals[::-1]:\\n roi.append([self.best_fit_p(y)+w, y])\\n return roi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04cbb0b016818f300f0edcda6fabff5f\",\n \"score\": \"0.5807676\",\n \"text\": \"def roi_at(self, p):\\n return self.rois[p - self.pstart]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d28805a86159eea42088299329941750\",\n \"score\": \"0.5803839\",\n \"text\": \"def project_roi(self, roi, frame_id=None):\\n response = self.project_rois(rois=[roi]).points[0]\\n\\n # Convert to VectorStamped\\n result = VectorStamped(x=response.point.x, y=response.point.y, z=response.point.z,\\n frame_id=response.header.frame_id)\\n\\n # If necessary, transform the point\\n if frame_id is not None:\\n print \\\"Transforming roi to {}\\\".format(frame_id)\\n result = result.projectToFrame(frame_id=frame_id, tf_listener=self.tf_listener)\\n\\n # Return the result\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cacd9c05816e2bde1d373d5d5f8f917\",\n \"score\": \"0.5801246\",\n \"text\": \"def partir_4(roi):\\n\\n aux1 = int(roi[2]/2)\\n aux2 = int(roi[3]/2)\\n \\n roi2=[[roi[0]+(i*aux1),roi[1]+(j*aux2),aux1,aux2] for i in [0,1] for j in [0,1]]\\n return roi2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed9c1929e912703efd30b68a3bc54078\",\n \"score\": \"0.5798317\",\n \"text\": \"def add_roi_contour_sequence(self, structure, index, coordinates):\\n roi_contour_sequence = self.ds_rs.ROIContourSequence[index]\\n roi_contour_sequence.ReferencedROINumber = structure[\\\"ReferencedROINumber\\\"]\\n roi_contour_sequence.ROIDisplayColor = random.sample(range(0, 255), 3)\\n\\n roi_contour_sequence.add_new('ContourSequence', 'SQ', Sequence())\\n\\n for i, (sop_instance_uid, coordinate) in enumerate(coordinates):\\n roi_contour_sequence.ContourSequence.append(Dataset())\\n contour_sequence = roi_contour_sequence.ContourSequence[-1]\\n contour_sequence.ContourGeometricType = \\\"CLOSED_PLANAR\\\"\\n contour_sequence.NumberOfContourPoints = int(len(coordinate) / 3)\\n contour_sequence.ContourNumber = i\\n contour_sequence.ContourData = coordinate\\n\\n contour_sequence.add_new('ContourImageSequence', 'SQ', Sequence())\\n contour_sequence.ContourImageSequence.append(Dataset())\\n contour_image_sequence = contour_sequence.ContourImageSequence[-1]\\n contour_image_sequence.ReferencedSOPClassUID = UID('1.2.840.10008.5.1.4.1.1.2')\\n contour_image_sequence.ReferencedSOPInstanceUID = sop_instance_uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94fba564ff4c73cec8196e8c2267f44c\",\n \"score\": \"0.5791027\",\n \"text\": \"def __init__ ( self,\\n source = \\\"CxiDs1-0|Cspad-0\\\",\\n input = \\\"Something\\\", \\n output = \\\"SomethingElse\\\",\\n roi = \\\"0,1800,0,1800\\\" ):\\n self.source = source\\n self.input = input\\n self.output = output\\n self.roi = map(int,roi.split(',')) \\n self.n_std = 3 # n standard deviations (vertical scale region of interest)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63d36f4a80ee2911e7ab9d04e7789d0b\",\n \"score\": \"0.5783857\",\n \"text\": \"def __built_box_roi(self, seg_lung):\\n # create box and roi of left lung\\n box_seg = np.zeros((self.aorta_mat.shape))\\n seg_left_lung = np.zeros((self.aorta_mat.shape))\\n\\n # find bounding box for Aorta and lung\\n min_sa, min_ca, min_aa, max_sa, max_ca, max_aa = \\\\\\n self.__found_bounding_box(self.aorta_mat)\\n seg_left_lung[max_sa:, :, :] = seg_lung[max_sa:, :, :]\\n min_s_l, min_c_l, min_a_l, max_s_l, max_c_l, max_a_l = \\\\\\n self.__found_bounding_box(seg_left_lung)\\n\\n # first box\\n ss1 = max_s_l - (((max_s_l - min_s_l) * (S_CON-1)) // S_CON)\\n se1 = max_s_l - ((max_s_l - min_s_l) // (S_CON+2))\\n cs1 = min_c_l + ((max_c_l - min_c_l) // (S_CON+1))\\n ce1 = min_c_l + (((max_c_l - min_c_l) * (S_CON+1)) // (S_CON+2))\\n xs1 = min_a_l - ((max_aa - min_aa) // AXIAL_CON*2)\\n xe1 = min_a_l + ((max_aa - min_aa) // AXIAL_CON*3)\\n\\n # second_box\\n ss2 = max_s_l - ((max_s_l - min_s_l) // S_CON)\\n se2 = max_s_l - ((max_s_l - min_s_l) // (S_CON+1))\\n cs2 = min_c_l + ((max_c_l - min_c_l) // (S_CON+1))\\n ce2 = min_c_l + (((max_c_l - min_c_l) * (S_CON+1)) // (S_CON+2))\\n xs2 = min_aa + (((max_aa - min_aa) * 1) // (S_CON+1))\\n xe2 = min_a_l - ((max_aa - min_aa) // AXIAL_CON*3)\\n\\n # third_box\\n ss3 = max_sa - ((max_sa - min_sa) // S_CON)\\n se3 = max_sa\\n cs3 = min_ca\\n ce3 = min_ca + (((max_ca - min_ca) * 1) // S_CON)\\n xs3 = min_aa + (((max_aa - min_aa) * ((AXIAL_CON//2) - 1) ) // (AXIAL_CON - 1))\\n xe3 = min_aa + (((max_aa - min_aa) * (AXIAL_CON//2)) // (AXIAL_CON - 1))\\n\\n # update boxes\\n box_seg[ss1: se1, cs1:ce1, xs1: xe1] = 1\\n box_seg[ss2: se2, cs2:ce2, xs2: xe2] = 1\\n box_seg[ss3: se3, cs3:ce3, xs3: xe3] = 1\\n\\n # delete soft tissues and hard tissues\\n box_seg[np.where((self.ct_mat < SOFT_TISSUE) | (self.ct_mat > BONES))] = 0\\n\\n return box_seg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bce14242850f5f7eb36a929dd09b8b2f\",\n \"score\": \"0.57798356\",\n \"text\": \"def roi(lista,imagen):\\n return imagen[lista[0]:lista[0]+lista[2],lista[1]:lista[1]+lista[3]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48d320e689f9c70989486862a0872e45\",\n \"score\": \"0.5764892\",\n \"text\": \"def clear_ROI(self):\\n self.camera.OffsetX.SetValue(self.camera.OffsetX.Min)\\n self.camera.OffsetY.SetValue(self.camera.OffsetY.Min)\\n self.camera.Width.SetValue(self.camera.Width.Max)\\n self.camera.Height.SetValue(self.camera.Height.Max)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb0f7f14b6146e04cdf13b1c9fd0aa4\",\n \"score\": \"0.5762916\",\n \"text\": \"def pare_roi(self, roi, min_area=10):\\n\\n # remove anything outside motion borders\\n y0,x0 = int(np.floor(self.motion_borders.ymin)), int(np.floor(self.motion_borders.xmin))\\n y1,x1 = int(np.ceil(self.motion_borders.ymax)), int(np.ceil(self.motion_borders.xmax))\\n if roi.ndim == 2:\\n roi = np.array([roi])\\n roi = roi.astype(bool)\\n roi[:,:y0,:] = False\\n roi[:,y1:,:] = False\\n roi[:,:,:x0] = False\\n roi[:,:,x1:] = False\\n\\n roi = roi[np.any(roi, axis=(1,2))]\\n\\n # remove rois below size threshold\\n thresh = self.pixels_per_micron * min_area # pixels \\n areas = np.sum(roi, axis=(1,2))\\n roi = roi[areas>thresh]\\n \\n return roi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d1bcf7ffc7a7b379602f0f024eac8b\",\n \"score\": \"0.5729884\",\n \"text\": \"def _build_roi_click_callback(self, func):\\n def callback(event):\\n nonlocal self, func\\n x, y, = event.x, event.y\\n d = 3\\n items = self.canvas.find_overlapping(x-d, y-d, x+d, y+d)\\n names = set()\\n for it in items:\\n for tag in self.canvas.gettags(it):\\n if not tag.startswith(TAG_ROI_ID):\\n continue\\n name64 = tag[len(TAG_ROI_ID):]\\n names.add(base64.b64decode(name64).decode())\\n break\\n func(event=event, names=names)\\n return callback\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5196c15d8bd315e042f9a932da2f9a3d\",\n \"score\": \"0.571907\",\n \"text\": \"def getROImask(self,ROI=None):\\t\\n\\t\\tif ROI is None:\\n\\t\\t\\tROI = self.ROI\\n\\t\\n\\t\\tdef point_inside_ROI(point,ROI):\\n\\n\\t\\t\\tn = ROI.shape[0]\\n\\t\\t\\t\\n\\t\\t\\tinside = 0\\n\\t\\t\\tx,y = point\\n\\t\\t\\tp1x = ROI[0,0]\\n\\t\\t\\tp1y = ROI[0,1]\\n\\t\\t\\tfor i in range(n+1):\\n\\t\\t\\t\\tp2x = ROI[i % n,0]\\n\\t\\t\\t\\tp2y = ROI[i % n,1]\\n\\t\\t\\t\\tif y > min(p1y,p2y) and y <= max(p1y,p2y) and x <= max(p1x,p2x):\\n\\t\\t\\t\\t\\tif p1y != p2y:\\n\\t\\t\\t\\t\\t\\txinters = (y-p1y)*(p2x-p1x)/(p2y-p1y)+p1x\\n\\t\\t\\t\\t\\tif p1x == p2x or x <= xinters:\\n\\t\\t\\t\\t\\t\\tinside = (inside + 1) % 2\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\tp1x,p1y = p2x,p2y\\n\\t\\t\\t\\t\\t\\n\\t\\t\\treturn inside\\n\\t\\t\\n\\t\\tnx = np.max(ROI[:,0]) - np.min(ROI[:,0]) + 1\\n\\t\\tny = np.max(ROI[-1,1]) - np.min(ROI[0,1]) + 1\\n\\t\\t\\n\\t\\t\\n\\t\\txpoints = np.arange(nx) + np.min(ROI[:,0])\\n\\t\\typoints = np.arange(ny) + np.min(ROI[:,1])\\n\\t\\t\\n\\t\\tpointsinROI = np.zeros(self[0].shape,dtype=np.uint8)\\n\\t\\tpointsinROI[...] = False\\n\\t\\tfor x in xpoints:\\n\\t\\t\\tfor y in ypoints:\\n\\t\\t\\t\\tpointsinROI[x,y] = point_inside_ROI((x,y),ROI)\\n\\t\\t\\n\\t\\treturn np.uint8(pointsinROI)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e75d4391fe2646fed26e3c16b9f05236\",\n \"score\": \"0.5714905\",\n \"text\": \"def generate_ROI(volume):\\n\\n volume_gpu = cp.asarray(volume)\\n\\n idx = cp.where(volume_gpu > 0)\\n\\n roiVolume = cp.zeros(volume_gpu.shape, dtype=cp.float32)\\n\\n roiVolume[idx] = 1\\n\\n return roiVolume.get()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d475c9188fb9d3f055205c52d558998\",\n \"score\": \"0.5704831\",\n \"text\": \"def apply_roi(self, roi):\\r\\n cmd = command.ApplyROI(client=self.client, roi=roi)\\r\\n self._session.command_stack.do(cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07d4eba1b5e01c7e1f5e4d232fe35d26\",\n \"score\": \"0.5697929\",\n \"text\": \"def get_roi_image(original_image, h1, h2, w1, w2):\\n return original_image[h1: h2, w1: w2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3409dca63adb16bf0a22339cb367a6c2\",\n \"score\": \"0.56953436\",\n \"text\": \"def from_roi(cls, roi: RegionOfInterest, factor: Cartesian,\\n atomic_block_size: Cartesian, atomic_voxel_size: Cartesian):\\n pass\\n\\n # assert roi.voxel_size % atomic_voxel_size == Cartesian(0, 0, 0)\\n # assert roi.voxel_size // atomic_voxel_size % factor == Cartesian(0, 0, 0)\\n \\n # if roi.voxel_size == atomic_voxel_size:\\n # # this is the leaf roi/block\\n # return cls(roi, None, None, None)\\n\\n # # find the relatively longest axis to split\\n # children_voxel_size = roi.voxel_size // factor\\n # block_nums = roi.physical_size / (children_voxel_size * )\\n # block_nums = np.ceil(block_nums)\\n # axis = np.argmax(block_nums)\\n\\n # # split along axis\\n # left_start = roi.start * factor\\n # left_block_nums = \\n # left_stop = left_start + \\n # left_roi = RegionOfInterest()\\n # left = cls.from_roi(left_roi, factor, atomic_block_size, atomic_voxel_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9c289467dd1d1f4f5af4e0a5a9b7bcd\",\n \"score\": \"0.5691518\",\n \"text\": \"def main():\\r\\n filepath = askopenfilename(\\r\\n filetypes=[(\\\"Text Files\\\", \\\"*.txt\\\"), (\\\"All Files\\\", \\\"*.*\\\")]\\r\\n )\\r\\n \\r\\n window.title(f\\\"CRAR - {filepath}\\\")\\r\\n image=cv2.imread(filepath) #read in the image\\r\\n\\r\\n image=cv2.resize(image,(1300,800)) #resizing because opencv does not work well with bigger images\\r\\n orig=image.copy()\\r\\n gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) #RGB To Gray Scal\\r\\n '''\\r\\ncv2.imshow(\\\"Title\\\",gray) '''\\r\\n blurred=cv2.GaussianBlur(gray,(5,5),0) #(5,5) is the kernel size and 0 is sigma that determines the amount of blur\\r\\n '''\\r\\ncv2.imshow(\\\"Blur\\\",blurred)'''\\r\\n edged=cv2.Canny(blurred,30,50) #30 MinThreshold and 50 is the MaxThreshold\\r\\n '''\\r\\ncv2.imshow(\\\"Canny\\\",edged) '''\\r\\n contours,hierarchy=cv2.findContours(edged,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) #retrieve the contours as a list, with simple apprximation model\\r\\n contours=sorted(contours,key=cv2.contourArea,reverse=True)\\r\\n #the loop extracts the boundary contours of the page\\r\\n for c in contours:\\r\\n p=cv2.arcLength(c,True)\\r\\n approx=cv2.approxPolyDP(c,0.02*p,True)\\r\\n if len(approx)==4:\\r\\n target=approx\\r\\n break\\r\\n approx=mapper.mapp(target) #find endpoints of the sheet\\r\\n pts=np.float32([[0,0],[800,0],[800,800],[0,800]]) #map to 800*800 target window\\r\\n op=cv2.getPerspectiveTransform(approx,pts) #get the top or bird eye view effect\\r\\n dst=cv2.warpPerspective(orig,op,(800,800))\\r\\n cv2.imwrite(\\\"IMAGE_NAME.png\\\", dst)\\r\\n cv2.imshow(\\\"Scanned\\\",dst)\\r\\n # creating an image object\\r\\n image1=cv2.imread(\\\"IMAGE_NAME.png\\\")\\r\\n # loading the pixel data of the image'''\\r\\n im = Image.open(\\\"IMAGE_NAME.png\\\")\\r\\n # Creating coordinates of the pixel (x,y)\\r\\n C=['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']\\r\\n C[0]= x, y = 700,340\\r\\n C[1]= x, y = 700,180\\r\\n C[2]= x, y = 700,220\\r\\n C[3]= x, y = 700,260\\r\\n C[4]= x, y = 700,300\\r\\n C[5]= x, y = 700,500\\r\\n C[6]= x, y = 700,540\\r\\n C[7]= x, y = 700,580\\r\\n C[8]= x, y = 700,620\\r\\n C[9]= x, y = 660,180\\r\\n C[10]= x, y = 660,220\\r\\n C[11]= x, y = 660,260\\r\\n C[12]= x, y = 660,300\\r\\n C[13]= x, y = 660,380\\r\\n C[14]= x, y = 660,420\\r\\n C[15]= x, y = 660,500\\r\\n h=['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']\\r\\n h[0]=im.getpixel(C[0])\\r\\n h[1]=im.getpixel(C[1])\\r\\n h[2]=im.getpixel(C[2])\\r\\n h[3]=im.getpixel(C[3])\\r\\n h[4]=im.getpixel(C[4])\\r\\n h[5]=im.getpixel(C[5])\\r\\n h[6]=im.getpixel(C[6])\\r\\n h[7]=im.getpixel(C[7])\\r\\n h[8]=im.getpixel(C[8])\\r\\n h[9]=im.getpixel(C[9])\\r\\n h[10]=im.getpixel(C[10])\\r\\n h[11]=im.getpixel(C[11])\\r\\n h[12]=im.getpixel(C[12])\\r\\n h[13]=im.getpixel(C[13])\\r\\n h[14]=im.getpixel(C[14])\\r\\n h[15]=im.getpixel(C[15])\\r\\n a=['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','',\\r\\n '','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']\\r\\n for i in range(0,243):\\r\\n if(str(h[i])==\\\"(255, 0, 0)\\\"):\\r\\n a[i]=\\\"A\\\"\\r\\n elif(str(h[i])==\\\"(0, 255, 0)\\\"):\\r\\n a[i]=\\\"B\\\"\\r\\n elif(str(h[i])==\\\"(0, 0, 255)\\\"):\\r\\n a[i]=\\\"C\\\"\\r\\n elif(str(h[i])==\\\"(255, 255, 0)\\\"):\\r\\n a[i]=\\\"D\\\"\\r\\n elif(str(h[i])==\\\"(0, 255, 255)\\\"):\\r\\n a[i]=\\\"E\\\"\\r\\n elif(str(h[i])==\\\"(255, 0, 255)\\\"):\\r\\n a[i]=\\\"F\\\"\\r\\n elif(str(h[i])==\\\"(192, 192, 192)\\\"):\\r\\n a[i]=\\\"G\\\"\\r\\n elif(str(h[i])==\\\"(128, 128, 128)\\\"):\\r\\n a[i]=\\\"H\\\"\\r\\n elif(str(h[i])==\\\"(128, 0, 0)\\\"):\\r\\n a[i]=\\\"I\\\"\\r\\n elif(str(h[i])==\\\"(128, 128, 0)\\\"):\\r\\n a[i]=\\\"J\\\"\\r\\n elif(str(h[i])==\\\"(0, 128, 0)\\\"):\\r\\n a[i]=\\\"K\\\"\\r\\n elif(str(h[i])==\\\"(128, 0, 128)\\\"):\\r\\n a[i]=\\\"L\\\"\\r\\n elif(str(h[i])==\\\"(0, 128, 128)\\\"):\\r\\n a[i]=\\\"M\\\"\\r\\n elif(str(h[i])==\\\"(0, 0, 128)\\\"):\\r\\n a[i]=\\\"N\\\"\\r\\n elif(str(h[i])==\\\"(255, 140, 0)\\\"):\\r\\n a[i]=\\\"O\\\" #'''darkorange'''\\r\\n elif(str(h[i])==\\\"(255, 215, 0)\\\"):\\r\\n a[i]=\\\"P\\\" #'''gold'''\\r\\n elif(str(h[i])==\\\"(85, 107, 47)\\\"):\\r\\n a[i]=\\\"Q\\\" #'''dark olive green'''\\r\\n elif(str(h[i])==\\\"(173, 255, 47)\\\"):\\r\\n a[i]=\\\"R\\\" #'''green yellow'''\\r\\n elif(str(h[i])==\\\"(50, 205, 50)\\\"):\\r\\n a[i]=\\\"S\\\" #''' lime green'''\\r\\n elif(str(h[i])==\\\"(0, 250, 154)\\\"):\\r\\n a[i]=\\\"T\\\" #'''medium spring green'''\\r\\n elif(str(h[i])==\\\"(47, 79, 79)\\\"):\\r\\n a[i]=\\\"U\\\" #'''dark slate gray'''\\r\\n elif(str(h[i])==\\\"(0, 206, 209)\\\"):\\r\\n a[i]=\\\"V\\\" #'''dark turquoise'''\\r\\n elif(str(h[i])==\\\"(100, 149, 237)\\\"):\\r\\n a[i]=\\\"W\\\" #'''corn flower blue'''\\r\\n elif(str(h[i])==\\\"(0, 191, 255)\\\"):\\r\\n a[i]=\\\"X\\\" #'''dep sky blue'''\\r\\n elif(str(h[i])==\\\"(127, 255, 212)\\\"):\\r\\n a[i]=\\\"Y\\\" #''' aqua marine'''\\r\\n elif(str(h[i])==\\\"(0, 0, 205)\\\"):\\r\\n a[i]=\\\"Z\\\" #''' medium blue'''\\r\\n elif(str(h[i])==\\\"(138, 43, 226)\\\"):\\r\\n a[i]=\\\"a\\\" #''' blue violet'''\\r\\n elif(str(h[i])==\\\"(123, 104, 238)\\\"):\\r\\n a[i]=\\\"b\\\" # ''' medium slate blue'''\\r\\n elif(str(h[i])==\\\"(148, 0, 211)\\\"):\\r\\n a[i]=\\\"c\\\" #'''dark violet'''\\r\\n elif(str(h[i])==\\\"(139, 0, 139)\\\"):\\r\\n a[i]=\\\"d\\\" #''' dark mafneta'''\\r\\n elif(str(h[i])==\\\"(75, 0, 130)\\\"):\\r\\n a[i]=\\\"e\\\" #''' indigo'''\\r\\n elif(str(h[i])==\\\"(128, 0, 128)\\\"):\\r\\n a[i]=\\\"f\\\" #''' purple'''\\r\\n elif(str(h[i])==\\\"(238, 130, 238)\\\"):\\r\\n a[i]=\\\"g\\\" #'''violet'''\\r\\n elif(str(h[i])==\\\"(199, 21, 133)\\\"):\\r\\n a[i]=\\\"h\\\" #''' medium violet red'''\\r\\n elif(str(h[i])==\\\"(250, 235, 215)\\\"):\\r\\n a[i]=\\\"i\\\" #''' antique white'''\\r\\n elif(str(h[i])==\\\"(139, 69, 19)\\\"):\\r\\n a[i]=\\\"j\\\" #''' saddle brown'''\\r\\n elif(str(h[i])==\\\"(210, 105, 30)\\\"):\\r\\n a[i]=\\\"k\\\" #''' cholate '''\\r\\n elif(str(h[i])==\\\"(244, 164, 96)\\\"):\\r\\n a[i]=\\\"l\\\" #''' sandy brown '''\\r\\n elif(str(h[i])==\\\"(188, 143, 143)\\\"):\\r\\n a[i]=\\\"m\\\" #''' rosy brown'''\\r\\n elif(str(h[i])==\\\"(176, 196, 222)\\\"):\\r\\n a[i]=\\\"n\\\" #''' light steel vlue'''\\r\\n elif(str(h[i])==\\\"(240, 255, 240)\\\"):\\r\\n a[i]=\\\"o\\\" #'''honey dew'''\\r\\n elif(str(h[i])==\\\"(189, 183, 107)\\\"):\\r\\n a[i]=\\\"p\\\" #''' dark khaki'''\\r\\n elif(str(h[i])==\\\"(34, 139, 34)\\\"):\\r\\n a[i]=\\\"q\\\" #''' forest green'''\\r\\n elif(str(h[i])==\\\"(60, 179, 113)\\\"):\\r\\n a[i]=\\\"r\\\" #'' 'medium sea green'''\\r\\n elif(str(h[i])==\\\"(255, 127, 80)\\\"):\\r\\n a[i]=\\\"s\\\" #''' coral'''\\r\\n elif(str(h[i])==\\\"(255, 99, 71)\\\"):\\r\\n a[i]=\\\"t\\\" #''' tomato'''\\r\\n elif(str(h[i])==\\\"(240, 128, 128)\\\"):\\r\\n a[i]=\\\"u\\\" #''' light coral'''\\r\\n elif(str(h[i])==\\\"(255, 160, 122)\\\"):\\r\\n a[i]=\\\"v\\\" #''' light salmon'''\\r\\n elif(str(h[i])==\\\"(70, 130, 180)\\\"):\\r\\n a[i]=\\\"w\\\" #''' steel blue'''\\r\\n elif(str(h[i])==\\\"(176, 224, 230)\\\"):\\r\\n a[i]=\\\"x\\\" #''' powder blue'''\\r\\n elif(str(h[i])==\\\"(30, 144, 255)\\\"):\\r\\n a[i]=\\\"y\\\" #''' doger blue'''\\r\\n elif(str(h[i])==\\\"(230, 230, 250)\\\"):\\r\\n a[i]=\\\"z\\\" #''' lavender'''\\r\\n elif(str(h[i])==\\\"(255, 250, 205)\\\"):\\r\\n a[i]=\\\"0\\\" #'''lemon chiffon'''\\r\\n elif(str(h[i])==\\\"(233, 150, 122)\\\"):\\r\\n a[i]=\\\"1\\\" #''' dark salmon '''\\r\\n elif(str(h[i])==\\\"(255, 105, 180)\\\"):\\r\\n a[i]=\\\"2\\\" # ''' hot pink'''\\r\\n elif(str(h[i])==\\\"(205, 133, 63)\\\"):\\r\\n a[i]=\\\"3\\\" #''' rosy brown'''\\r\\n elif(str(h[i])==\\\"(222, 184, 135)\\\"):\\r\\n a[i]=\\\"4\\\" #''' burly wood'''\\r\\n elif(str(h[i])==\\\"(255, 228, 181)\\\"):\\r\\n a[i]=\\\"5\\\" #''' mocassin'''\\r\\n elif(str(h[i])==\\\"(46, 139, 87)\\\"):\\r\\n a[i]=\\\"6\\\" #''' sea green'''\\r\\n elif(str(h[i])==\\\"(60, 179, 113)\\\"):\\r\\n a[i]=\\\"7\\\" #''' medium sea green'''\\r\\n elif(str(h[i])==\\\"(107, 142, 35)\\\"):\\r\\n a[i]=\\\"8\\\" #''' dark olive drab'''\\r\\n elif(str(h[i])==\\\"(205, 92, 92)\\\"):\\r\\n a[i]=\\\"9\\\" #''' indian red'''\\r\\n elif(str(h[i])==\\\"(147, 112, 219)\\\"):\\r\\n a[i]=\\\"+\\\" #''' medium purple'''\\r\\n elif(str(h[i])==\\\"(245, 222, 179)\\\"):\\r\\n a[i]=\\\"/\\\" #''' wheat'''\\r\\n elif(str(h[i])==\\\"(240, 255, 240)\\\"):\\r\\n a[i]=\\\"=\\\" #''' honeydew'''\\r\\n elif(str(h[i])==\\\"(255, 250, 250)\\\"):\\r\\n a[i]=\\\".\\\"\\r\\n else:\\r\\n a[i]=\\\"\\\"\\r\\n print(h)\\r\\n print(a)\\r\\n def listToString(s):\\r\\n # initialize an empty string\\r\\n str1 = \\\"\\\"\\r\\n #traverse in the string\\r\\n for ele in s:\\r\\n str1 += ele\\r\\n # return string\\r\\n return str1 \\r\\n # Driver code\\r\\n f=listToString(a)\\r\\n print(listToString(f))\\r\\n sd=f.split(\\\".\\\",1)[0]\\r\\n hop=str(sd)\\r\\n print (sd)\\r\\n if(sd==\\\"///Lox///\\\"):\\r\\n goth=\\\"models\\\\\\\\fox.obj\\\"\\r\\n else:\\r\\n print(\\\"Errorcode\\\")\\r\\n\\r\\n homography = None\\r\\n # matrix of camera parameters (made up but works quite well for me)\\r\\n camera_parameters = np.array([[800, 0, 320], [0, 800, 240], [0, 0, 1]])\\r\\n # create ORB keypoint detector\\r\\n orb = cv2.ORB_create()\\r\\n # create BFMatcher object based on hamming distance\\r\\n bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\\r\\n # load the reference surface that will be searched in the video stream\\r\\n dir_name = os.getcwd()\\r\\n model = cv2.imread(os.path.join(dir_name, filepath), 0)\\r\\n # Compute model keypoints and its descriptors\\r\\n kp_model, des_model = orb.detectAndCompute(model, None)\\r\\n # Load 3D model from OBJ file\\r\\n obj = OBJ(os.path.join(dir_name, goth), swapyz=True)\\r\\n # init video capture\\r\\n cap = cv2.VideoCapture(0)\\r\\n while True:\\r\\n # read the current frame\\r\\n ret, frame = cap.read()\\r\\n if not ret:\\r\\n print(\\\"Unable to capture video\\\")\\r\\n return\\r\\n # find and draw the keypoints of the frame\\r\\n kp_frame, des_frame = orb.detectAndCompute(frame, None)\\r\\n # match frame descriptors with model descriptors\\r\\n matches = bf.match(des_model, des_frame)\\r\\n # sort them in the order of their distance\\r\\n # the lower the distance, the better the match\\r\\n matches = sorted(matches, key=lambda x: x.distance)\\r\\n # compute Homography if enough matches are found\\r\\n if len(matches) > MIN_MATCHES:\\r\\n # differenciate between source points and destination points\\r\\n src_pts = np.float32([kp_model[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)\\r\\n dst_pts = np.float32([kp_frame[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)\\r\\n # compute Homography\\r\\n homography, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)\\r\\n if args.rectangle:\\r\\n # Draw a rectangle that marks the found model in the frame\\r\\n h, w = model.shape\\r\\n pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)\\r\\n # project corners into frame\\r\\n dst = cv2.perspectiveTransform(pts, homography)\\r\\n # connect them with lines\\r\\n frame = cv2.polylines(frame, [np.int32(dst)], True, 255, 3, cv2.LINE_AA)\\r\\n # if a valid homography matrix was found render cube on model plan\\r\\n if homography is not None:\\r\\n try:\\r\\n # obtain 3D projection matrix from homography matrix and camera parameters\\r\\n projection = projection_matrix(camera_parameters, homography)\\r\\n # project cube or model\\r\\n frame = render(frame, obj, projection, model, False)\\r\\n #frame = render(frame, model, projection)\\r\\n except:\\r\\n pass\\r\\n # draw first 10 matches.\\r\\n if args.matches:\\r\\n frame = cv2.drawMatches(model, kp_model, frame, kp_frame, matches[:5], 0, flags=2)\\r\\n # show result\\r\\n cv2.imshow('frame', frame)\\r\\n if cv2.waitKey(1) & 0xFF == ord('q'):\\r\\n break\\r\\n else:\\r\\n print(\\\"Not enough matches found - %d/%d\\\" % (len(matches), MIN_MATCHES))\\r\\n cap.release()\\r\\n cv2.destroyAllWindows()\\r\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17f976240490cd35c0f206cc4e3169d2\",\n \"score\": \"0.56675357\",\n \"text\": \"def cmd_show_roi(uri):\\n\\n show_roi(make_tracker_conn(), uri)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd40564815358032e342d9db7a7a97c8\",\n \"score\": \"0.5665808\",\n \"text\": \"def add_roi_observation_sequence(self, structure, index):\\n roi_observation_sequence = self.ds_rs.RTROIObservationsSequence[index]\\n roi_observation_sequence.ObservationNumber = structure[\\\"ObservationNumber\\\"]\\n roi_observation_sequence.ReferencedROINumber = structure[\\\"ReferencedROINumber\\\"]\\n roi_observation_sequence.ROIObservationLabel = structure[\\\"ROIObservationLabel\\\"]\\n roi_observation_sequence.RTROIInterpretedType = structure[\\\"RTROIInterpretedType\\\"]\\n roi_observation_sequence.ROIInterpreter = structure[\\\"ROIInterpreter\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a719412b91970a93feb8aa0824f76c7\",\n \"score\": \"0.56491363\",\n \"text\": \"def __init__(self, infiles, roi):\\n self.roi = roi\\n mask = ugali.utils.skymap.readSparseHealpixMaps(infiles, field='COVERAGE')\\n self.nside = hp.npix2nside(len(mask))\\n # Sparse maps of pixels in various ROI regions\\n self.mask_roi_sparse = mask[self.roi.pixels]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e13741463f14fffb20497da7ced677fa\",\n \"score\": \"0.564547\",\n \"text\": \"def build_roi_heads(cfg, input_shape):\\n name = cfg.MODEL.ROI_HEADS.NAME\\n return ROI_HEADS_REGISTRY.get(name)(cfg, input_shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"597acc8f6d20a6a89417955e65ae2890\",\n \"score\": \"0.56221104\",\n \"text\": \"def get_crops(self):\\n\\n cfg = get_cfg()\\n\\n valid_augs_list = [load_obj(i['class_name'])(**i['params']) for i in cfg['augmentation']['valid']['augs']]\\n valid_bbox_params = OmegaConf.to_container((cfg['augmentation']['valid']['bbox_params']))\\n valid_augs = A.Compose(valid_augs_list, bbox_params=valid_bbox_params)\\n \\n test_dataset = ImgDataset(None,\\n 'test',\\n self.imageDir,\\n cfg,\\n valid_augs)\\n \\n test_loader = DataLoader(test_dataset,\\n batch_size=cfg.data.batch_size,\\n num_workers=cfg.data.num_workers,\\n shuffle=False,\\n collate_fn=collate_fn)\\n \\n device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') \\n model = torch.load(os.path.dirname(os.path.abspath(__file__))+f\\\"/{str(self.imageDir).lower().split('/')[-1]}/model.pth\\\", \\n map_location=device)\\n\\n detection_threshold = 0.5\\n results = []\\n model.eval()\\n\\n hparams = flatten_omegaconf(cfg)\\n\\n lit_model = LitImg(hparams=hparams, \\n cfg=cfg, \\n model=model)\\n\\n self.results = eval_model(test_loader, \\n results, \\n detection_threshold, \\n device, \\n lit_model)\\n \\n for i in range(len(self.results)):\\n if self.results[i]['image_id']+'.JPG' == self.imageList[self.cur-1].split('/')[-1]:\\n self.mainPanel.create_rectangle(int(int(self.results[i]['x1'])*self.scale), \\n int(int(self.results[i]['y1'])*self.scale),\\n int(int(self.results[i]['x2'])*self.scale),\\n int(int(self.results[i]['y2'])*self.scale),\\n width=2,\\n outline='red')\\n \\n self.text_label.config(text='Crop: \\\\n'+str(self.imageDir)[40:]+'\\\\nTotal: \\\\n'+str(len(self.results)))\\n\\n self.sub_button.config(state='disabled')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62a4208559bb856f1deeab82d3aae3c4\",\n \"score\": \"0.561656\",\n \"text\": \"def render_lanes_on_image(self,data,img, calib, img_width, img_height,figg):\\n\\n print('data in lane_image fucntion',len(data))\\n proj_velo2cam2 = self.project_velo_to_cam2(calib)\\n fig,ax = plt.subplots(1)\\n ax.set_aspect('equal')\\n \\n \\n # for i in range(data.shape[2]):\\n # d=data[:,:,i]\\n for d in data:\\n pts_2d = self.project_to_image(d.transpose(), proj_velo2cam2)\\n inds = np.where((pts_2d[0, :] < img_width) & (pts_2d[0, :] > 0) &\\n (pts_2d[1, :] < img_height) & (pts_2d[1,:]>0) )[0]\\n\\n # print(inds)\\n\\n # Filter out pixels points\\n imgfov_pc_pixel = pts_2d[:, inds]\\n\\n # Retrieve depth from lidar\\n imgfov_pc_velo = d[inds, :]\\n # imgfov_pc_velo = np.hstack((imgfov_pc_velo, np.ones((imgfov_pc_velo.shape[0], 1))))\\n imgfov_pc_cam2 = proj_velo2cam2 @ imgfov_pc_velo.transpose()\\n # Create a figure. Equal aspect so circles look circular \\n # Show the image\\n ax.imshow(img)\\n ax.plot(imgfov_pc_pixel[0],imgfov_pc_pixel[1],color='red',linewidth=8)\\n \\n plt.savefig('video/'+figg+'.png')\\n \\n # return imgfov_pc_pixel[0], imgfov_pc_pixel[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cd2ea01ba0b826e37d0d2e99924b70f\",\n \"score\": \"0.5607524\",\n \"text\": \"def test_get_roi_ranges_v2(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba5771359d7dd1c21e4d405a5d7bb54f\",\n \"score\": \"0.5607298\",\n \"text\": \"def get_roi(self, idx=None):\\n if idx is None:\\n idx = self._latest_roi_idx\\n if idx is None:\\n return None\\n\\n with h5py.File(self.data_file, 'r') as f:\\n roigrp = f['roi']\\n _roi = ROI(roigrp['roi{}'.format(int(idx))])\\n\\n return _roi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"002fe04f2661868d1fcecdbfa4ee3f8e\",\n \"score\": \"0.56045556\",\n \"text\": \"def preprocess_roi(roi):\\n roi = cv2.resize(roi, (224, 224))\\n roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)\\n processed_roi = cv2.GaussianBlur(roi, (13, 13), 0)\\n processed_roi = cv2.equalizeHist(processed_roi)\\n return roi, processed_roi\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":491,"cells":{"query_id":{"kind":"string","value":"10073b23c2a4f916a7b6da4d1f04804b"},"query":{"kind":"string","value":"Test config entries are reloaded when new info is set."},"positive_passages":{"kind":"list like","value":[{"docid":"8e1f5233977f8257c1946dfb2fbc1ce9","score":"0.6789433","text":"async def test_new_info_reload_config_entries(hass, init_integration, mock_dashboard):\n assert init_integration.state == ConfigEntryState.LOADED\n\n with patch(\"homeassistant.components.esphome.async_setup_entry\") as mock_setup:\n await dashboard.async_set_dashboard_info(hass, \"test-slug\", \"test-host\", 6052)\n\n assert len(mock_setup.mock_calls) == 1\n assert mock_setup.mock_calls[0][1][1] == init_integration\n\n # Test it's a no-op when the same info is set\n with patch(\"homeassistant.components.esphome.async_setup_entry\") as mock_setup:\n await dashboard.async_set_dashboard_info(hass, \"test-slug\", \"test-host\", 6052)\n\n assert len(mock_setup.mock_calls) == 0","title":""}],"string":"[\n {\n \"docid\": \"8e1f5233977f8257c1946dfb2fbc1ce9\",\n \"score\": \"0.6789433\",\n \"text\": \"async def test_new_info_reload_config_entries(hass, init_integration, mock_dashboard):\\n assert init_integration.state == ConfigEntryState.LOADED\\n\\n with patch(\\\"homeassistant.components.esphome.async_setup_entry\\\") as mock_setup:\\n await dashboard.async_set_dashboard_info(hass, \\\"test-slug\\\", \\\"test-host\\\", 6052)\\n\\n assert len(mock_setup.mock_calls) == 1\\n assert mock_setup.mock_calls[0][1][1] == init_integration\\n\\n # Test it's a no-op when the same info is set\\n with patch(\\\"homeassistant.components.esphome.async_setup_entry\\\") as mock_setup:\\n await dashboard.async_set_dashboard_info(hass, \\\"test-slug\\\", \\\"test-host\\\", 6052)\\n\\n assert len(mock_setup.mock_calls) == 0\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"cec03cd3d4109cf17a5d260e3e613182","score":"0.71862674","text":"def test_update_configuration_file(self):\n pass","title":""},{"docid":"29be3542c9b40c6e9f7ae7e822ab3b52","score":"0.71835166","text":"def test_update_default_configuration(self):\n pass","title":""},{"docid":"10333df80818da208a0612b862cf567f","score":"0.7136592","text":"def config_test(self): # type: ignore","title":""},{"docid":"5e2cd748a741ae396f685369d35d6669","score":"0.70737314","text":"def test_config(self):\n self.maxDiff = None\n test_config = config.expand(self.before_config)\n self.assertDictEqual(test_config, self.after_config)","title":""},{"docid":"e463b2eb37251d4a3cd69014c74db7d6","score":"0.69686246","text":"def test_configurations_put(self):\n pass","title":""},{"docid":"93a4f6cfc8f69375ebafd2214ccc534d","score":"0.6806966","text":"async def test_config_migration(hass: HomeAssistant) -> None:\n\n old_entry_data: dict[str, Any] = {\n \"slots\": 5,\n \"corona_filter\": True,\n \"regions\": {\"083350000000\": \"Aach, Stadt\"},\n }\n\n old_conf_entry: MockConfigEntry = MockConfigEntry(\n domain=DOMAIN, title=\"NINA\", data=old_entry_data\n )\n\n old_conf_entry.add_to_hass(hass)\n\n await hass.config_entries.async_setup(old_conf_entry.entry_id)\n await hass.async_block_till_done()\n\n assert dict(old_conf_entry.data) == ENTRY_DATA","title":""},{"docid":"068adc002f97416764ad2b6549584094","score":"0.6795898","text":"def test_update_analysis_configuration(self):\n pass","title":""},{"docid":"639ec052199080c80a7b7f8d0247d3c2","score":"0.6768208","text":"async def test_load_and_unload(\n hass: HomeAssistant,\n climacell_config_entry_update: pytest.fixture,\n) -> None:\n data = _get_config_schema(hass)(MIN_CONFIG)\n config_entry = MockConfigEntry(\n domain=DOMAIN,\n data=data,\n unique_id=_get_unique_id(hass, data),\n version=1,\n )\n config_entry.add_to_hass(hass)\n assert await hass.config_entries.async_setup(config_entry.entry_id)\n await hass.async_block_till_done()\n assert len(hass.states.async_entity_ids(WEATHER_DOMAIN)) == 1\n\n assert await hass.config_entries.async_remove(config_entry.entry_id)\n await hass.async_block_till_done()\n assert len(hass.states.async_entity_ids(WEATHER_DOMAIN)) == 0","title":""},{"docid":"9964b249c2bf3803d7bb684b5f5ff2bd","score":"0.6704383","text":"def setUp(self):\n main.app.config.update(\n {\n 'DATA_CSV': TEST_DATA_CSV,\n 'DATA_XML': TEST_DATA_XML\n }\n )","title":""},{"docid":"27225cc8282fb4233886564932c32a20","score":"0.66697","text":"def test_post_config(self):\n pass","title":""},{"docid":"4e66d191ab3604a9959ccf9a658dcb84","score":"0.66383064","text":"def set_config_for_test(testcase, data):\n set_config_data(data)\n testcase.addCleanup(set_config_data, {})","title":""},{"docid":"832a22dab07369bb5c62d2eea21de9c9","score":"0.66349006","text":"def setUp(self):\n self.config = TestConfig(show_section_numbers=0)","title":""},{"docid":"832a22dab07369bb5c62d2eea21de9c9","score":"0.66349006","text":"def setUp(self):\n self.config = TestConfig(show_section_numbers=0)","title":""},{"docid":"498b9fe8f68fdcd963768ef8f71bf335","score":"0.6628223","text":"def test_edit_application_configuration(self):\n pass","title":""},{"docid":"d3987259aedc3d82ec7333fbf8772eb6","score":"0.65842843","text":"def test_config_loaded():\n eq_(settings.environment, 'test')\n eq_(settings.database, 'test_database')\n eq_(settings.amqp, 'test_amqp')","title":""},{"docid":"78b8165ca3db085ebd8cefd8d000c4fb","score":"0.65570647","text":"async def help_test_reloadable(\n hass: HomeAssistant,\n mqtt_client_mock: MqttMockPahoClient,\n domain: str,\n config: ConfigType,\n) -> None:\n # Set up with empty config\n config = copy.deepcopy(config[mqtt.DOMAIN][domain])\n # Create and test an old config of 2 entities based on the config supplied\n old_config_1 = copy.deepcopy(config)\n old_config_1[\"name\"] = \"test_old_1\"\n old_config_2 = copy.deepcopy(config)\n old_config_2[\"name\"] = \"test_old_2\"\n\n old_config = {\n mqtt.DOMAIN: {domain: [old_config_1, old_config_2]},\n }\n # Start the MQTT entry with the old config\n entry = MockConfigEntry(domain=mqtt.DOMAIN, data={mqtt.CONF_BROKER: \"test-broker\"})\n entry.add_to_hass(hass)\n mqtt_client_mock.connect.return_value = 0\n with patch(\"homeassistant.config.load_yaml_config_file\", return_value=old_config):\n await entry.async_setup(hass)\n\n assert hass.states.get(f\"{domain}.test_old_1\")\n assert hass.states.get(f\"{domain}.test_old_2\")\n assert len(hass.states.async_all(domain)) == 2\n\n # Create temporary fixture for configuration.yaml based on the supplied config and\n # test a reload with this new config\n new_config_1 = copy.deepcopy(config)\n new_config_1[\"name\"] = \"test_new_1\"\n new_config_2 = copy.deepcopy(config)\n new_config_2[\"name\"] = \"test_new_2\"\n new_config_extra = copy.deepcopy(config)\n new_config_extra[\"name\"] = \"test_new_3\"\n\n new_config = {\n mqtt.DOMAIN: {domain: [new_config_1, new_config_2, new_config_extra]},\n }\n with patch(\"homeassistant.config.load_yaml_config_file\", return_value=new_config):\n # Reload the mqtt entry with the new config\n await hass.services.async_call(\n \"mqtt\",\n SERVICE_RELOAD,\n {},\n blocking=True,\n )\n await hass.async_block_till_done()\n\n assert len(hass.states.async_all(domain)) == 3\n\n assert hass.states.get(f\"{domain}.test_new_1\")\n assert hass.states.get(f\"{domain}.test_new_2\")\n assert hass.states.get(f\"{domain}.test_new_3\")","title":""},{"docid":"577593c9cf755fe1880059828e436c49","score":"0.6556689","text":"def test_config_dict_set(self):\n self.assertNotEqual(CONFIG.config, {})","title":""},{"docid":"4f774736639dcb2d17d22b7751ffa2e4","score":"0.6549595","text":"def test_002_changed(self):\n conf_test = make_temp_file(dedent(\"\"\"\n [task.default]\n stderr: true\n stdout_msgtree: false\n stderr_msgtree: false\n engine: select\n port_qlimit: 1000\n auto_tree: false\n local_workername: none\n distant_workername: pdsh\n\n [task.info]\n debug: true\n fanout: 256\n grooming_delay: 0.5\n connect_timeout: 12.5\n command_timeout: 30.5\"\"\"))\n self.defaults = Defaults(filenames=[conf_test.name])\n self.assertTrue(self.defaults.stderr)\n self.assertFalse(self.defaults.stdout_msgtree)\n self.assertFalse(self.defaults.stderr_msgtree)\n self.assertEqual(self.defaults.engine, 'select')\n self.assertEqual(self.defaults.port_qlimit, 1000)\n self.assertFalse(self.defaults.auto_tree)\n self.assertEqual(self.defaults.local_workername, 'none')\n self.assertEqual(self.defaults.distant_workername, 'pdsh')\n # task_info\n self.assertTrue(self.defaults.debug)\n self.assertEqual(self.defaults.fanout, 256)\n self.assertEqual(self.defaults.grooming_delay, 0.5)\n self.assertEqual(self.defaults.connect_timeout, 12.5)","title":""},{"docid":"cb964b1152404b5e6b49671649b84fd5","score":"0.64850193","text":"def test_reload(self):\n pass","title":""},{"docid":"0576d1d0217357f9e1a177285ba6dd4a","score":"0.6464505","text":"def update_config(self, config):","title":""},{"docid":"1218a320554dba5b23b2faebd708ad8d","score":"0.63927615","text":"def tearDown(self):\n\n self.config = configurator.Config()\n self.config.set_config()","title":""},{"docid":"aa689ba6eb9afd61a6b18136b4d8ac1d","score":"0.6380534","text":"def override_config() -> None:\n cfg.data_path = Path(\"tests\", \"test_data\")\n cfg.wow = io.reader(name=\"test_dot_pricer\", ftype=\"json\")","title":""},{"docid":"dae2dd9714cb10268ebf7a50c2c8c5a8","score":"0.63724375","text":"async def test_v3_load_and_unload(\n hass: HomeAssistant,\n climacell_config_entry_update: pytest.fixture,\n) -> None:\n data = _get_config_schema(hass)(API_V3_ENTRY_DATA)\n config_entry = MockConfigEntry(\n domain=DOMAIN,\n data=data,\n unique_id=_get_unique_id(hass, data),\n version=1,\n )\n config_entry.add_to_hass(hass)\n assert await hass.config_entries.async_setup(config_entry.entry_id)\n await hass.async_block_till_done()\n assert len(hass.states.async_entity_ids(WEATHER_DOMAIN)) == 1\n\n assert await hass.config_entries.async_remove(config_entry.entry_id)\n await hass.async_block_till_done()\n assert len(hass.states.async_entity_ids(WEATHER_DOMAIN)) == 0","title":""},{"docid":"8a33bd37880173a89dafa4b14473e243","score":"0.63598603","text":"def test_caching(self):\n data = utils.get_data()\n main.app.config.update(\n {\n 'DATA_CSV': TEST_DATA_CSV_2,\n }\n )\n new_data = utils.get_data()\n self.assertDictEqual(data, new_data)","title":""},{"docid":"bcf3bafde73389e12c8665c6e732d460","score":"0.63354975","text":"def test_configurations_get(self):\n pass","title":""},{"docid":"1b7ac51d851e8b6a486ad2a641091813","score":"0.6271766","text":"def test_upate(self): \n config = self.config_config()\n\n config.path_text = 'teste_text 2'\n config.path_video = 'teste_video 2'\n config.version = '2'\n config.Update()\n\n self.clean_config(config) \n\n config.Select()\n \n assert config.path_text == 'teste_text 2'\n assert config.path_video == 'teste_video 2'\n assert config.version == '2'","title":""},{"docid":"1530242965675402674c3ee29278b9c4","score":"0.62656087","text":"def setUp(self) -> None:\n\n self.our_config = copy.deepcopy(pyf_test_dataset.DEFAULT_CONFIG)","title":""},{"docid":"3e16005647a3de21a87d28fce10b0ae3","score":"0.6258549","text":"async def test_setup_update_unload_entry(hass):\n\n hass.config_entries.async_forward_entry_setups = AsyncMock()\n with patch.object(hass.config_entries, \"async_update_entry\") as p:\n config_entry = MockConfigEntry(\n domain=DOMAIN, data=ADVANCED_USER_INPUT, entry_id=\"test\", unique_id=None\n )\n await hass.config_entries.async_add(config_entry)\n assert p.called\n\n assert await async_setup_entry(hass, config_entry)\n assert DOMAIN in hass.data and config_entry.entry_id in hass.data[DOMAIN]\n\n # check user input is in config\n for key in ADVANCED_USER_INPUT:\n if key in hass.data[DOMAIN][config_entry.entry_id]:\n assert (\n hass.data[DOMAIN][config_entry.entry_id][key]\n == ADVANCED_USER_INPUT[key]\n )\n\n hass.config_entries.async_forward_entry_setups.assert_called_with(\n config_entry, PLATFORMS\n )\n\n # ToDo test hass.data[DOMAIN][config_entry.entry_id][UPDATE_LISTENER]\n\n hass.config_entries.async_reload = AsyncMock()\n assert await async_update_options(hass, config_entry) is None\n hass.config_entries.async_reload.assert_called_with(config_entry.entry_id)\n\n # Unload the entry and verify that the data has been removed\n assert await async_unload_entry(hass, config_entry)\n assert config_entry.entry_id not in hass.data[DOMAIN]","title":""},{"docid":"e7905e45eb48a2b81429251d35a1382d","score":"0.6248757","text":"def test_override_config(self):\n processor = Processor()\n self.assertIsNone(processor.config.groups)\n self.assertIsNone(processor.config.policies)\n\n processor.config = Config(GROUPS, POLICIES_ALLOW)\n self.assertEqual(len(processor.config.groups), 1)\n self.assertEqual(len(processor.config.policies), 1)","title":""},{"docid":"1c9337a762826e8652502155e64aa8ba","score":"0.6238477","text":"def setUp(self):\n super(TestCOTInjectConfig, self).setUp()\n self.config_file = self.sample_cfg","title":""},{"docid":"f3bb166c3dfdbefe3bb4eeaa4c2e9f2e","score":"0.6235656","text":"def test_setup_from_config(self):\n self._test_setup({\"selfheal\": {\"interval\": 30.0}}, 30.0)","title":""},{"docid":"58c0a3045b24f5862bcbd04393c77b47","score":"0.62166053","text":"def test_site_config_load():\n clear_site_config()\n for i, path in enumerate(reversed(get_conf_paths())):\n exp_value = 100*(1 + i)\n content = '[main]\\nmemory_usage/timeout = ' + str(exp_value) + '\\n'\n\n conf_fpath = os.path.join(path, 'spyder.ini')\n with open(conf_fpath, 'w') as fh:\n fh.write(content)\n\n config = ConfigurationManager()\n config.reset_to_defaults()\n value = config.get('main', 'memory_usage/timeout')\n\n print(path, value, exp_value)\n\n assert value == exp_value\n clear_site_config()","title":""},{"docid":"b89fd8fe1bca987bbb30599c696bf408","score":"0.6211188","text":"def test_configuration(self):\n return False","title":""},{"docid":"919178d9fdb2b2a3664015957d71e66b","score":"0.62096184","text":"def setUp(self):\n jsonfig_patcher = mock.patch('otter.tap.api.jsonfig')\n self.jsonfig = jsonfig_patcher.start()\n self.addCleanup(jsonfig_patcher.stop)\n\n self.jsonfig.from_path.return_value = test_config","title":""},{"docid":"f3128ee4cebf5620979fdd54fc1db2d6","score":"0.6185549","text":"def setUp(self):\n self.fake_config = {'foo': 'bar'}\n self.stack_count = 0","title":""},{"docid":"75fa6a069ab54d1379b07123c3a7f25a","score":"0.61517274","text":"def test_dev_config(self):\n self.assertEqual()","title":""},{"docid":"f91fed02ac6a785913d8869bb9098ba8","score":"0.61512685","text":"def test_settings():","title":""},{"docid":"5f75fa031139fd972449182660181439","score":"0.6129881","text":"def test_projects_ini_update_config():\n k = project_ini.load_config('tests/data/project_ini/project.ini')\n project_ini.update_config(k, {'app': {'name': 'pytest'}})\n assert 'version' in k['app'], '\"app\" section missing from config'\n assert k['app']['name'] == 'pytest', '\"version\" option missing from \"app\" section'","title":""},{"docid":"77d1bade50b33d1e27f6ae38643fed79","score":"0.61083764","text":"def test_update_notification_settings(self):\n pass","title":""},{"docid":"9de6390b37a0239b1f21906b7fd3f5bb","score":"0.6108358","text":"def test_config_interface(mock_save):\n config = Config(EXAMPLE_CONFIG_FILE)\n\n with pytest.raises(KeyError):\n config[\"slow_start\"][\"second_season\"] = None\n\n config[\"slow_start\"] = {}\n config[\"slow_start.second_season\"] = \"When?\"\n assert config[\"slow_start\"][\"second_season\"] == \"When?\"\n assert mock_save.call_count == 2\n\n config[\"slow_start.second_season\"] = \"When?\"\n assert mock_save.call_count == 2","title":""},{"docid":"0e4ba17f53309acd1e20f92707828367","score":"0.6085126","text":"def testUpdateNothing(self):\n cfg = config_dict.ConfigDict()\n cfg.x = 5\n cfg.y = 9\n cfg.update()\n self.assertLen(cfg, 2)\n self.assertEqual(cfg.x, 5)\n self.assertEqual(cfg.y, 9)","title":""},{"docid":"2b46026763f9e337eec9b1a8b525b913","score":"0.6082148","text":"def test_insert(self): \n config = self.config_config()\n\n self.clean_config(config) \n\n config.Select()\n\n self.compare_config(config)","title":""},{"docid":"a444dba4f48e0a17b1522d35e0e33db3","score":"0.6072898","text":"def test_real_config(self):\n with patch('gstorage.apps.checks.register') as mock_method:\n app = GStorageConfig.create('gstorage')\n app.ready()\n assert mock_method.call_count == 1","title":""},{"docid":"89b6832db4f2e99f8c7efe0c13443ee0","score":"0.60726607","text":"def test_reloading_config_reuses_client(self, mock_close_clients):\n # load a config with a client and connect it\n self.zoidberg.load_config('./tests/etc/zoidberg.yaml')\n client = self.zoidberg.config.gerrits['master']['client']\n self.zoidberg.connect_client(self.zoidberg.config.gerrits['master'])\n client.marker = 'CheckForThisMarker'\n # reload the same config\n self.zoidberg.load_config('./tests/etc/zoidberg.yaml')\n # check the client has not changed\n client = self.zoidberg.config.gerrits['master']['client']\n self.assertEqual('CheckForThisMarker', client.marker)\n\n # reload config with different connection details\n self.zoidberg.load_config('./tests/etc/zoidberg2.yaml')\n # check the client has changed\n client = self.zoidberg.config.gerrits['master']['client']\n self.assertEqual(None, getattr(client, 'marker', None))","title":""},{"docid":"82f58ab24810e52974f220a99a1f5b3b","score":"0.6065014","text":"def test_show_config(self):\n pass","title":""},{"docid":"60765b757c49265b57ce78ba6dff6ff8","score":"0.6064447","text":"def test_projects_ini_update_config_empty_dict():\n k = project_ini.load_config('tests/data/project_ini/project.ini')\n project_ini.update_config(k, {})\n assert 'version' in k['app'], '\"app\" section missing from config'\n assert k['app']['name'] == 'fcs-etl', 'wrong name'","title":""},{"docid":"9a666a452bd515dbcd10a013ca4142cc","score":"0.6046669","text":"def load_config(self):\r\n self.config.reload()\r\n self.validate_config()","title":""},{"docid":"b43dd23350fcb59fb73dca5b4f2e39e6","score":"0.6029484","text":"def setup_test_data(self, test_data_config: Union[DictConfig, Dict]):\n raise NotImplementedError()","title":""},{"docid":"cf5f6d55286712c7a761e78727518a25","score":"0.60251236","text":"def test_custom_config():\n config = Config(EXAMPLE_CONFIG_FILE)\n\n assert config[\"heartwarming_anime.name\"] == \"Slow Start\"\n assert \"cute_girls\" in config\n assert \"pesky_boys\" not in config\n assert os.path.expanduser(\"~\") in config[\"data_dir\"]\n assert \"{0}slow_start\".format(os.path.sep) in config[\"local_config_file\"]","title":""},{"docid":"aee435be569f88459171e0b412c1b58e","score":"0.60246325","text":"def test_config_expand1():\n test_config = config.expand(fixtures.expand1.before_config)\n assert test_config == fixtures.expand1.after_config","title":""},{"docid":"f6d8d139de7b0ac4aab51426f34522b7","score":"0.5996789","text":"def test_900_restart_on_config_change(self):\n # Config file affected by juju set config change\n conf_file = '/etc/nova/nova.conf'\n\n # Make config change, check for service restarts\n logging.info('Changing debug config on nova-cloud-controller')\n self.restart_on_changed_debug_oslo_config_file(\n conf_file,\n self.services)","title":""},{"docid":"15f5ee0fe3a0c974f2a1d0ba00c3b596","score":"0.59943527","text":"def test_init_settings(self, **kwargs):\n # check by default url setting\n self.assertEqual(atxcf.get_setting(\"program_url\"),\n atxcf.get_default_program_url())\n os.remove(atxcf.get_settings_filename())","title":""},{"docid":"e71c11e1ad58989788746663730d507d","score":"0.5986337","text":"def setUp(self):\n\n CONFIG.parse(PROJECT + '/examples/config.yaml')","title":""},{"docid":"764fae636c3a9c1dac031a3b1dc11bcf","score":"0.5981106","text":"def after_init_config(self):","title":""},{"docid":"764fae636c3a9c1dac031a3b1dc11bcf","score":"0.5981106","text":"def after_init_config(self):","title":""},{"docid":"671fe283385de42cdbbc13f5dae54065","score":"0.59794176","text":"def test_config(self, mock_requests):\n app_id = self.create_app()\n\n # check to see that an initial/empty config was created\n url = \"/v2/apps/{app_id}/config\".format(**locals())\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200, response.data)\n self.assertIn('values', response.data)\n self.assertEqual(response.data['values'], {})\n config1 = response.data\n\n # set an initial config value\n body = {'values': json.dumps({'NEW_URL1': 'http://localhost:8080/'})}\n response = self.client.post(url, body)\n self.assertEqual(response.status_code, 201, response.data)\n config2 = response.data\n self.assertNotEqual(config1['uuid'], config2['uuid'])\n self.assertIn('NEW_URL1', response.data['values'])\n\n # read the config\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200, response.data)\n config3 = response.data\n self.assertEqual(config2, config3)\n self.assertIn('NEW_URL1', response.data['values'])\n\n # set an additional config value\n body = {'values': json.dumps({'NEW_URL2': 'http://localhost:8080/'})}\n response = self.client.post(url, body)\n self.assertEqual(response.status_code, 201, response.data)\n config3 = response.data\n self.assertNotEqual(config2['uuid'], config3['uuid'])\n self.assertIn('NEW_URL1', response.data['values'])\n self.assertIn('NEW_URL2', response.data['values'])\n\n # read the config again\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200, response.data)\n config4 = response.data\n self.assertEqual(config3, config4)\n self.assertIn('NEW_URL1', response.data['values'])\n self.assertIn('NEW_URL2', response.data['values'])\n\n # unset a config value\n body = {'values': json.dumps({'NEW_URL2': None})}\n response = self.client.post(url, body)\n self.assertEqual(response.status_code, 201, response.data)\n config5 = response.data\n self.assertNotEqual(config4['uuid'], config5['uuid'])\n self.assertNotIn('NEW_URL2', json.dumps(response.data['values']))\n\n # unset all config values\n body = {'values': json.dumps({'NEW_URL1': None})}\n response = self.client.post(url, body)\n self.assertEqual(response.status_code, 201, response.data)\n self.assertNotIn('NEW_URL1', json.dumps(response.data['values']))\n\n # set a port and then unset it to make sure validation ignores the unset\n body = {'values': json.dumps({'PORT': '5000'})}\n response = self.client.post(url, body)\n self.assertEqual(response.status_code, 201, response.data)\n self.assertIn('PORT', response.data['values'])\n\n body = {'values': json.dumps({'PORT': None})}\n response = self.client.post(url, body)\n self.assertEqual(response.status_code, 201, response.data)\n self.assertNotIn('PORT', response.data['values'])\n\n # disallow put/patch/delete\n response = self.client.put(url)\n self.assertEqual(response.status_code, 405, response.data)\n response = self.client.patch(url)\n self.assertEqual(response.status_code, 405, response.data)\n response = self.client.delete(url)\n self.assertEqual(response.status_code, 405, response.data)\n return config5","title":""},{"docid":"62d6886dacfc39e791a76285626e9a65","score":"0.5972372","text":"def test_update_fsa_settings(self):\n pass","title":""},{"docid":"a61237f646c0eb78c74a720917b8c544","score":"0.5970906","text":"def test_900_restart_on_config_change(self):\n # Config file affected by juju set config change\n conf_file = '/etc/nova/nova.conf'\n\n # Make config change, check for service restarts\n logging.info('Changing the debug config on nova-compute')\n self.restart_on_changed_debug_oslo_config_file(\n conf_file,\n ['nova-compute'])","title":""},{"docid":"afbfbadc31a1e2a612ea29570a2cf90b","score":"0.59632045","text":"def mock_config_entry_data():\n return {\n \"product_name\": \"Product Name\",\n \"product_type\": \"product_type\",\n \"serial\": \"aabbccddeeff\",\n \"name\": \"Product Name\",\n CONF_IP_ADDRESS: \"1.2.3.4\",\n }","title":""},{"docid":"e1492313b97d4a0eb815f8dad2784f31","score":"0.594567","text":"def setUp(self):\n super(SysChangeMonTestCase, self).setUp()\n self.app.storage = TestStorage()\n self.app.config = TestConfigHandler()\n self.app.log = TestLogHandler()","title":""},{"docid":"4bdf5213a8a44eaee6d99541efab7b4d","score":"0.5939667","text":"def reload_config(self):\n\n self.config = load_script_config(\n script='data_taker',\n config=self.gui.config.text(),\n logger=self.log\n )","title":""},{"docid":"e172cd7a3f1f507f5f8af350a8037329","score":"0.5927022","text":"def setUp(self) -> None:\n\n self.config_loader = ConfigLoader()\n\n self.tempfile = tempfile.NamedTemporaryFile()\n\n self.our_dataset = {\n \"chrome\": {\n \"linux\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 \"\n \"(KHTML, like Gecko) Chrome/77.0.3865.116 \"\n \"Safari/537.36 Edg/77.11.4.5118\",\n \"macosx\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) \"\n \"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4364.0 \"\n \"Safari/537.36\",\n \"win10\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) \"\n \"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4361.0 \"\n \"Safari/537.36\",\n },\n \"edge\": {\n \"linux\": None,\n \"macosx\": None,\n \"win10\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) \"\n \"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.140 \"\n \"Safari/537.36 Edge/18.17763/5.9.7 (Linux;Android 10) \"\n \"ExoPlayerLib/2.9.6\",\n },\n \"firefox\": {\n \"linux\": \"Mozilla/5.0 (Linux x86_64; en-US) Gecko/20130401 \"\n \"Firefox/82.4\",\n \"macosx\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_0; \"\n \"en-US) Gecko/20100101 Firefox/74.7\",\n \"win10\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:84.0) \"\n \"Gecko/20100101 Firefox/84.0/8mqDiPuL-36\",\n },\n \"ie\": {\n \"linux\": None,\n \"macosx\": None,\n \"win10\": \"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 10.0; \"\n \"Win64; x64; Trident/7.0; .NET4.0C; .NET4.0E; .NET CLR \"\n \"2.0.50727; .NET CLR 3.0.30729; .NET CLR 3.5.30729; Tablet \"\n \"PC 2.0; wbx 1.0.0; wbxapp 1.0.0)\",\n },\n \"opera\": {\n \"linux\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 \"\n \"(KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36 \"\n \"OPR/73.0.3856.284\",\n \"macosx\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_3) \"\n \"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.198 \"\n \"Safari/537.36 OPR/72.0.3815.400\",\n \"win10\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) \"\n \"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 \"\n \"Safari/537.36 OPR/73.0.3856.284 (Edition avira-2)\",\n },\n \"safari\": {\n \"linux\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 \"\n \"(KHTML, like Gecko) Version/4.0 Chrome/70.0.3538.110 \"\n \"Safari/537.36 SputnikBrowser/1.2.5.158\",\n \"macosx\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) \"\n \"AppleWebKit/600.8.9 (KHTML, like Gecko) Version/9.0.3 \"\n \"Safari/601.4.4\",\n \"win10\": None,\n },\n }\n\n self.tempfile.write(json.dumps(self.our_dataset).encode())\n self.tempfile.seek(0)\n\n self.user_agent_dataset = UserAgentDataset()\n self.user_agent_dataset.source_file = self.tempfile.name\n\n self.get_content_patch = unittest.mock.patch.object(DatasetBase, \"get_content\")\n self.mock_get_content = self.get_content_patch.start()\n self.mock_get_content.return_value = copy.deepcopy(self.our_dataset)","title":""},{"docid":"aaa443dea22a55c15285d8c7a2e727af","score":"0.5913581","text":"def test_update_defaults(self):\n configuration = DiscussionsConfiguration.objects.get(context_key=self.course_key_with_defaults)\n configuration.enabled = False\n configuration.plugin_configuration = {\n 'url': 'http://localhost',\n }\n configuration.provider_type = 'legacy'\n configuration.save()\n configuration = DiscussionsConfiguration.objects.get(context_key=self.course_key_with_defaults)\n assert configuration is not None\n assert not configuration.enabled\n assert configuration.lti_configuration is None\n assert configuration.plugin_configuration['url'] == 'http://localhost'\n assert configuration.provider_type == 'legacy'","title":""},{"docid":"5ec65e4e41d28cc67e65cd569987d4fe","score":"0.5912548","text":"def test_220_nova_metadata_propagate(self):\n if self.current_release < self.BIONIC_ROCKY:\n logging.info(\"Feature didn't exist before Rocky. Nothing to test\")\n return\n\n # Expected default and alternate values\n current_value = zaza.model.get_application_config(\n 'nova-cloud-controller')['vendor-data-url']['value']\n new_value = 'http://some-other.url/vdata'\n\n set_default = {'vendor-data-url': current_value}\n set_alternate = {'vendor-data-url': new_value}\n default_entry = {'api': {\n 'vendordata_dynamic_targets': [current_value]}}\n alternate_entry = {'api': {'vendordata_dynamic_targets': [new_value]}}\n\n # Config file affected by juju set config change\n conf_file = '/etc/nova/nova.conf'\n\n # Make config change, check for service restarts\n logging.info(\n 'Setting config on nova-cloud-controller to {}'.format(\n set_alternate))\n self.restart_on_changed(\n conf_file,\n set_default,\n set_alternate,\n default_entry,\n alternate_entry,\n self.services)","title":""},{"docid":"070d6a8493354003596b344fe9b4fb23","score":"0.5895899","text":"def _update_infobase_config():\r\n # update infobase configuration\r\n from infogami.infobase import server\r\n if not config.get(\"infobase\"):\r\n config.infobase = {}\r\n # This sets web.config.db_parameters\r\n server.update_config(config.infobase)","title":""},{"docid":"d842f67ba9fe20e33c575d5f9ddc0c87","score":"0.58910453","text":"def reload_config(self):\n from samuraix import config # TODO?\n self.dispatch_event('on_load_config', config)","title":""},{"docid":"e37efdbc4a6f74462041208a763045df","score":"0.5889935","text":"def test_corrupted_config_data(self):\n self.managed_list.zk_config_manager._data = \"[dasdsad\"\n self.managed_list._reload_config_data()\n self.assertEqual([], self.managed_list.get_list())\n\n self.managed_list.zk_config_manager._data = \"{}\"\n self.managed_list._reload_config_data()\n self.assertEqual([], self.managed_list.get_list())","title":""},{"docid":"c8451b7310582a17e7aaa841385f1dda","score":"0.5887346","text":"async def test_manual_configuration_update_configuration(\n hass: HomeAssistant, mock_config_entry, mock_vapix_requests\n) -> None:\n assert mock_config_entry.data[CONF_HOST] == \"1.2.3.4\"\n\n result = await hass.config_entries.flow.async_init(\n AXIS_DOMAIN, context={\"source\": SOURCE_USER}\n )\n\n assert result[\"type\"] == FlowResultType.FORM\n assert result[\"step_id\"] == \"user\"\n\n mock_vapix_requests(\"2.3.4.5\")\n result = await hass.config_entries.flow.async_configure(\n result[\"flow_id\"],\n user_input={\n CONF_HOST: \"2.3.4.5\",\n CONF_USERNAME: \"user\",\n CONF_PASSWORD: \"pass\",\n CONF_PORT: 80,\n },\n )\n await hass.async_block_till_done()\n\n assert result[\"type\"] == FlowResultType.ABORT\n assert result[\"reason\"] == \"already_configured\"\n assert mock_config_entry.data[CONF_HOST] == \"2.3.4.5\"","title":""},{"docid":"aede172042886a07b247fa637318ad96","score":"0.58785474","text":"def test_config():\n assert not create_app().testing\n assert create_app({\"TESTING\": True}).testing","title":""},{"docid":"5e751f2145364cf0ad9d852b42cb16b6","score":"0.58688074","text":"def test_config_with_defaults(self):\n\n with MockConsulKV():\n app = self.create_app()\n consul = Consul(app)\n consul.apply_remote_config()\n\n self.assertEqual(app.config['cfg_1'], 'consul_1')\n self.assertEqual(app.config['cfg_3'], 'consul_3')\n self.assertEqual(app.config['cfg_4'], {'inner': 'value'})","title":""},{"docid":"d6500ea495b8e6ec354d7e3690a52f78","score":"0.5866945","text":"def test_read_config_file_old(self):\n es_url = 'http://127.0.0.1:9200'\n # Build\n file_obj = FileTestObj()\n file_obj.write_config(file_obj.args['configfile'], YAMLCONFIG.format(es_url))\n # Test\n build_obj = Builder(configfile=file_obj.args['configfile'])\n assert build_obj.client_args.hosts[0] == es_url\n # Teardown\n file_obj.teardown()","title":""},{"docid":"9ad84a816505064e75b943cf0c49c748","score":"0.5866806","text":"def setUp(self):\n self.config = trial.Options()\n self.savedModules = dict(sys.modules)","title":""},{"docid":"5fb8d2fc87cf2091bdd6c702e4ef51b4","score":"0.58650434","text":"def test_set_in_config(self):\n options = self._test_options(config={\"option\": \"foo\"})\n self.assertEqual(options.test_option, \"foo\")\n\n options = self._test_options(config={\"option\": \"bar\"})\n self.assertEqual(options.test_option, \"bar\")","title":""},{"docid":"4b026be5e011f9ad664b40c6662f8051","score":"0.5862787","text":"async def test_unload_config_entry(\n hass: HomeAssistant, aioclient_mock: AiohttpClientMocker\n) -> None:\n entry = await init_integration(hass, aioclient_mock)\n assert hass.data[DOMAIN]\n await hass.config_entries.async_unload(entry.entry_id)\n await hass.async_block_till_done()\n assert not hass.data.get(DOMAIN)","title":""},{"docid":"20c25555ab7c88e2071674939b0228cd","score":"0.5859502","text":"def testFixConfig(self):\n \n tarball = self.recipe.get_tarball(self.download_url, self.download_dir)\n \n extracted = self.recipe.install_tarball(self.download_dir, tarball, self.destination)\n \n options = {}\n options['admin_username'] = \"foo\"\n options['admin_password'] = \"foo\"\n options['adminserver_hostport'] = \"foo\"\n options['process_uid'] = \"foo\"\n options['process_gid'] = \"foo\"\n options['licenseinfo'] = \"foo\"\n options['httpd_enabled'] = \"foo\"\n options['hostport'] = \"foo\"\n options['live_dir'] = \"foo\"\n options['vod_common_dir'] = \"foo\"\n options['vod_dir'] = \"foo\"\n options['appsdir'] = \"foo\"\n options['js_scriptlibpath'] = \"foo\"\n \n locations = self.recipe.create_config(self.destination, options)\n \n for loc in locations:\n f = open(loc).read()\n self.assertTrue('foo' in f)","title":""},{"docid":"a23d50e982ec4e0f90275287f458141f","score":"0.5856261","text":"def test_correct_new_naming_scrape(self):\n # Given\n c = config({\n 'scrap_start_urls': False\n })\n\n config_loaded = ConfigLoader(c)\n\n assert config_loaded.scrape_start_urls == False\n assert config_loaded.scrap_start_urls == config_loaded.scrape_start_urls","title":""},{"docid":"a5d55a50082ac0622529f7fd00754ab5","score":"0.5852883","text":"def test():\n c = Config()\n c.defaults()\n c.print_config()","title":""},{"docid":"1d8289aefa98a164c0a99316b18387c6","score":"0.5851456","text":"def config_entry_fixture():\n return MockConfigEntry(domain=DOMAIN, title=\"Sonos\")","title":""},{"docid":"ee92e4c04faf05a3eccdb550f7da7ee8","score":"0.5847191","text":"def setUp(self):\n self.config = trial.Options()","title":""},{"docid":"082166328d0a75b158a7a8625039097f","score":"0.5846698","text":"def test_create_default_configuration(self):\n pass","title":""},{"docid":"9ddfa3cb600b8dd557896e5a9e83fb46","score":"0.5841066","text":"def _on_config_changed(self, _):\n current = self.config[\"thing\"]\n if current not in self._stored.things:\n logger.debug(\"found a new thing: %r\", current)\n self._stored.things.append(current)","title":""},{"docid":"9ae3caeb47c00efdb21c4a803d94d9a8","score":"0.5838629","text":"def setUpTestData(cls):\r\n cfg.TESTING = True\r\n cfg.IGNORE_SYNC_DURING_CONNECT = False\r\n fake_initialize()","title":""},{"docid":"9b7bf8dc5f92f419a90194935b302df5","score":"0.5834541","text":"def test_config_router_put(self):\n pass","title":""},{"docid":"d65e94cdb1db4e3fdadfeda05d5d9c74","score":"0.5833664","text":"def reload(self):\n # Load newest config\n self.__loader.load_config()\n self.__properties = self.__loader.properties\n self.load_config_objects()","title":""},{"docid":"df3305fb844848a66d578c75568ae597","score":"0.58320844","text":"def setUp(self):\n self.test_app = create_app()\n self.test_app.config.from_object(\"project.config.TestingConfig\")","title":""},{"docid":"74ab7ed6a342387a013326873ebdb50e","score":"0.5821723","text":"def test_conf(self):\n self.TESTED_UNIT = 'ceph-fs/0'\n\n def _get_conf():\n \"\"\"get/parse ceph daemon response into dict.\n\n :returns dict: Current configuration of the Ceph MDS daemon\n :rtype: dict\n \"\"\"\n cmd = \"sudo ceph daemon mds.$HOSTNAME config show\"\n conf = model.run_on_unit(self.TESTED_UNIT, cmd)\n return json.loads(conf['Stdout'])\n\n @retry(wait=wait_exponential(multiplier=1, min=4, max=10),\n stop=stop_after_attempt(10))\n def _change_conf_check(mds_config):\n \"\"\"Change configs, then assert to ensure config was set.\n\n Doesn't return a value.\n \"\"\"\n model.set_application_config('ceph-fs', mds_config)\n results = _get_conf()\n self.assertEqual(\n results['mds_cache_memory_limit'],\n mds_config['mds-cache-memory-limit'])\n self.assertAlmostEqual(\n float(results['mds_cache_reservation']),\n float(mds_config['mds-cache-reservation']))\n self.assertAlmostEqual(\n float(results['mds_health_cache_threshold']),\n float(mds_config['mds-health-cache-threshold']))\n\n # ensure defaults are set\n mds_config = {'mds-cache-memory-limit': '4294967296',\n 'mds-cache-reservation': '0.05',\n 'mds-health-cache-threshold': '1.5'}\n _change_conf_check(mds_config)\n\n # change defaults\n mds_config = {'mds-cache-memory-limit': '8589934592',\n 'mds-cache-reservation': '0.10',\n 'mds-health-cache-threshold': '2'}\n _change_conf_check(mds_config)\n\n # Restore config to keep tests idempotent\n mds_config = {'mds-cache-memory-limit': '4294967296',\n 'mds-cache-reservation': '0.05',\n 'mds-health-cache-threshold': '1.5'}\n _change_conf_check(mds_config)","title":""},{"docid":"d0f25b51b32bc79a9cb119514ff57cb4","score":"0.58194387","text":"def init_test(cls):\n cls.set_configuration()\n cls._first_read_id = cls._insert_readings_data()\n cls._insert_readings_data()\n payload = PayloadBuilder()\\\n .WHERE(['id', '>=', cls._first_read_id]) \\\n .ORDER_BY(['id', 'ASC']) \\\n .payload()\n readings = cls._readings.query(payload)\n cls._raw_data = readings['rows']\n\n yield\n # Delete all test data from readings and configuration\n cls._storage_client.delete_from_tbl(\"readings\", {})\n payload = PayloadBuilder().WHERE([\"key\", \"=\", cls._CONFIG_CATEGORY_NAME]).payload()\n cls._storage_client.delete_from_tbl(\"configuration\", payload)","title":""},{"docid":"1f27f6258b7429a26a9901bea6d615fd","score":"0.58063304","text":"def test_test_config(self):\n app = create_app('appname.config.TestingConfig', env='testing')\n\n assert app.config['DEBUG'] is False","title":""},{"docid":"e24023b611c82117bd20f68a1d1cca7f","score":"0.580564","text":"def test_update_value(self):\n fixes.fixes = {}\n old_fixes = fixes.fixes\n fixes._load_file(join_data_path('fixes.py'))\n self.assertIs(fixes.fixes, old_fixes)","title":""},{"docid":"e9ad9f61e71f1b1d8f693b1c6f299b1e","score":"0.579319","text":"def test_config_set_same_key(self, mock_requests):\n app_id = self.create_app()\n url = \"/v2/apps/{app_id}/config\".format(**locals())\n\n # set an initial config value\n body = {'values': json.dumps({'PORT': '5000'})}\n response = self.client.post(url, body)\n self.assertEqual(response.status_code, 201, response.data)\n self.assertIn('PORT', response.data['values'])\n\n # reset same config value\n body = {'values': json.dumps({'PORT': '5001'})}\n response = self.client.post(url, body)\n self.assertEqual(response.status_code, 201, response.data)\n self.assertIn('PORT', response.data['values'])\n self.assertEqual(response.data['values']['PORT'], '5001')","title":""},{"docid":"4bdb7dfe598c20dcfb58133fdd20b858","score":"0.5790044","text":"def test_save_config_by_urls(self):\n\n # our key to use\n key = 'test_save_config_by_urls'\n\n # GET returns 405 (not allowed)\n response = self.client.get('/add/{}'.format(key))\n assert response.status_code == 405\n\n # no data\n response = self.client.post('/add/{}'.format(key))\n assert response.status_code == 400\n\n # No entries specified\n response = self.client.post(\n '/add/{}'.format(key), {'urls': ''})\n assert response.status_code == 400\n\n # Added successfully\n response = self.client.post(\n '/add/{}'.format(key), {'urls': 'mailto://user:pass@yahoo.ca'})\n assert response.status_code == 200\n\n # No URLs loaded\n response = self.client.post(\n '/add/{}'.format(key),\n {'config': 'invalid content', 'format': 'text'})\n assert response.status_code == 400\n\n # Test a case where we fail to load a valid configuration file\n with patch('apprise.AppriseConfig.add', return_value=False):\n response = self.client.post(\n '/add/{}'.format(key),\n {'config': 'garbage://', 'format': 'text'})\n assert response.status_code == 400\n\n with patch('os.remove', side_effect=OSError):\n # We will fail to remove the device first prior to placing a new\n # one; This will result in a 500 error\n response = self.client.post(\n '/add/{}'.format(key), {\n 'urls': 'mailto://user:newpass@gmail.com'})\n assert response.status_code == 500\n\n # URL is actually not a valid one (invalid Slack tokens specified\n # below)\n response = self.client.post(\n '/add/{}'.format(key), {'urls': 'slack://-/-/-'})\n assert response.status_code == 400\n\n # Test with JSON\n response = self.client.post(\n '/add/{}'.format(key),\n data=json.dumps({'urls': 'mailto://user:pass@yahoo.ca'}),\n content_type='application/json',\n )\n assert response.status_code == 200\n\n # Test with JSON (and no payload provided)\n response = self.client.post(\n '/add/{}'.format(key),\n data=json.dumps({}),\n content_type='application/json',\n )\n assert response.status_code == 400\n\n # Test with XML which simply isn't supported\n response = self.client.post(\n '/add/{}'.format(key),\n data='<urls><url>mailto://user:pass@yahoo.ca</url></urls>',\n content_type='application/xml',\n )\n assert response.status_code == 400\n\n # Invalid JSON\n response = self.client.post(\n '/add/{}'.format(key),\n data='{',\n content_type='application/json',\n )\n assert response.status_code == 400\n\n # Test the handling of underlining disk/write exceptions\n with patch('os.makedirs') as mock_mkdirs:\n mock_mkdirs.side_effect = OSError()\n # We'll fail to write our key now\n response = self.client.post(\n '/add/{}'.format(key),\n data=json.dumps({'urls': 'mailto://user:pass@yahoo.ca'}),\n content_type='application/json',\n )\n\n # internal errors are correctly identified\n assert response.status_code == 500\n\n # Test the handling of underlining disk/write exceptions\n with patch('gzip.open') as mock_open:\n mock_open.side_effect = OSError()\n # We'll fail to write our key now\n response = self.client.post(\n '/add/{}'.format(key),\n data=json.dumps({'urls': 'mailto://user:pass@yahoo.ca'}),\n content_type='application/json',\n )\n\n # internal errors are correctly identified\n assert response.status_code == 500","title":""},{"docid":"a1262b915a262e2d12626f58a6e9b929","score":"0.57868624","text":"async def test_load_unload_config_entry(\n hass: HomeAssistant,\n mock_config_entry: MockConfigEntry,\n mock_open_meteo: AsyncMock,\n) -> None:\n mock_config_entry.add_to_hass(hass)\n await hass.config_entries.async_setup(mock_config_entry.entry_id)\n await hass.async_block_till_done()\n\n assert mock_config_entry.state is ConfigEntryState.LOADED\n\n await hass.config_entries.async_unload(mock_config_entry.entry_id)\n await hass.async_block_till_done()\n\n assert not hass.data.get(DOMAIN)\n assert mock_config_entry.state is ConfigEntryState.NOT_LOADED","title":""},{"docid":"dbeda210a750f8ee4c4509c7db44d1d7","score":"0.578367","text":"def test_test_config(self):\n with open(TEST_DATA_PATH / \"test_config.yaml\") as f:\n self.schema.load(yaml.safe_load(f))","title":""},{"docid":"279e6341fa673c0e89954886516219e8","score":"0.577966","text":"def test_default_config(mock_load):\n config = Config()\n config.load()\n\n assert \"data_dir\" in config\n assert \"local_config_file\" in config\n assert \"Slow Start\" in config[\"reddit.user_agent\"]\n assert mock_load.call_count == 1","title":""},{"docid":"d32cf09b13497e3691433f288c818aa6","score":"0.57740635","text":"def test_update(self):\n pass","title":""},{"docid":"d32cf09b13497e3691433f288c818aa6","score":"0.57740635","text":"def test_update(self):\n pass","title":""},{"docid":"bce1d8fde9c6047e29c2de59953cbe73","score":"0.57730144","text":"def test_config_whitelist_put(self):\n pass","title":""},{"docid":"e669f5b95793507c27da42c1e0b99296","score":"0.5764217","text":"async def test_reauth_multiple_config_entries(\n hass: HomeAssistant, oauth, setup_platform, config_entry\n) -> None:\n await setup_platform()\n\n old_entry = MockConfigEntry(\n domain=DOMAIN,\n data={\n **config_entry.data,\n \"extra_data\": True,\n },\n )\n old_entry.add_to_hass(hass)\n\n entries = hass.config_entries.async_entries(DOMAIN)\n assert len(entries) == 2\n\n orig_subscriber_id = config_entry.data.get(\"subscriber_id\")\n\n # Invoke the reauth flow\n result = await oauth.async_reauth(config_entry)\n\n await oauth.async_oauth_web_flow(result)\n\n await oauth.async_finish_setup(result)\n\n # Only reauth entry was updated, the other entry is preserved\n entries = hass.config_entries.async_entries(DOMAIN)\n assert len(entries) == 2\n entry = entries[0]\n assert entry.unique_id == PROJECT_ID\n entry.data[\"token\"].pop(\"expires_at\")\n assert entry.data[\"token\"] == {\n \"refresh_token\": \"mock-refresh-token\",\n \"access_token\": \"mock-access-token\",\n \"type\": \"Bearer\",\n \"expires_in\": 60,\n }\n assert entry.data.get(\"subscriber_id\") == orig_subscriber_id # Not updated\n assert not entry.data.get(\"extra_data\")\n\n # Other entry was not refreshed\n entry = entries[1]\n entry.data[\"token\"].pop(\"expires_at\")\n assert entry.data.get(\"token\", {}).get(\"access_token\") == \"some-token\"\n assert entry.data.get(\"extra_data\")","title":""},{"docid":"6bb853ab716fa9c0b6ca3f7987db35c3","score":"0.57627","text":"def testReferences(self):\n cfg = _get_test_config_dict()\n cfg.dict_ref = cfg.dict\n\n self.assertEqual(cfg.dict_ref, cfg.dict)","title":""},{"docid":"149321352d7f9526ce11705e5a3c4c40","score":"0.5761119","text":"async def test_name_already_configured(hass, api):\n entry = MockConfigEntry(\n domain=transmission.DOMAIN,\n data=MOCK_ENTRY,\n options={CONF_SCAN_INTERVAL: DEFAULT_SCAN_INTERVAL},\n )\n entry.add_to_hass(hass)\n\n mock_entry = MOCK_ENTRY.copy()\n mock_entry[CONF_HOST] = \"0.0.0.0\"\n result = await hass.config_entries.flow.async_init(\n transmission.DOMAIN,\n context={\"source\": config_entries.SOURCE_USER},\n data=mock_entry,\n )\n\n assert result[\"type\"] == \"form\"\n assert result[\"errors\"] == {CONF_NAME: \"name_exists\"}","title":""}],"string":"[\n {\n \"docid\": \"cec03cd3d4109cf17a5d260e3e613182\",\n \"score\": \"0.71862674\",\n \"text\": \"def test_update_configuration_file(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29be3542c9b40c6e9f7ae7e822ab3b52\",\n \"score\": \"0.71835166\",\n \"text\": \"def test_update_default_configuration(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10333df80818da208a0612b862cf567f\",\n \"score\": \"0.7136592\",\n \"text\": \"def config_test(self): # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e2cd748a741ae396f685369d35d6669\",\n \"score\": \"0.70737314\",\n \"text\": \"def test_config(self):\\n self.maxDiff = None\\n test_config = config.expand(self.before_config)\\n self.assertDictEqual(test_config, self.after_config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e463b2eb37251d4a3cd69014c74db7d6\",\n \"score\": \"0.69686246\",\n \"text\": \"def test_configurations_put(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93a4f6cfc8f69375ebafd2214ccc534d\",\n \"score\": \"0.6806966\",\n \"text\": \"async def test_config_migration(hass: HomeAssistant) -> None:\\n\\n old_entry_data: dict[str, Any] = {\\n \\\"slots\\\": 5,\\n \\\"corona_filter\\\": True,\\n \\\"regions\\\": {\\\"083350000000\\\": \\\"Aach, Stadt\\\"},\\n }\\n\\n old_conf_entry: MockConfigEntry = MockConfigEntry(\\n domain=DOMAIN, title=\\\"NINA\\\", data=old_entry_data\\n )\\n\\n old_conf_entry.add_to_hass(hass)\\n\\n await hass.config_entries.async_setup(old_conf_entry.entry_id)\\n await hass.async_block_till_done()\\n\\n assert dict(old_conf_entry.data) == ENTRY_DATA\",\n \"title\": \"\"\n },\n {\n \"docid\": \"068adc002f97416764ad2b6549584094\",\n \"score\": \"0.6795898\",\n \"text\": \"def test_update_analysis_configuration(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"639ec052199080c80a7b7f8d0247d3c2\",\n \"score\": \"0.6768208\",\n \"text\": \"async def test_load_and_unload(\\n hass: HomeAssistant,\\n climacell_config_entry_update: pytest.fixture,\\n) -> None:\\n data = _get_config_schema(hass)(MIN_CONFIG)\\n config_entry = MockConfigEntry(\\n domain=DOMAIN,\\n data=data,\\n unique_id=_get_unique_id(hass, data),\\n version=1,\\n )\\n config_entry.add_to_hass(hass)\\n assert await hass.config_entries.async_setup(config_entry.entry_id)\\n await hass.async_block_till_done()\\n assert len(hass.states.async_entity_ids(WEATHER_DOMAIN)) == 1\\n\\n assert await hass.config_entries.async_remove(config_entry.entry_id)\\n await hass.async_block_till_done()\\n assert len(hass.states.async_entity_ids(WEATHER_DOMAIN)) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9964b249c2bf3803d7bb684b5f5ff2bd\",\n \"score\": \"0.6704383\",\n \"text\": \"def setUp(self):\\n main.app.config.update(\\n {\\n 'DATA_CSV': TEST_DATA_CSV,\\n 'DATA_XML': TEST_DATA_XML\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27225cc8282fb4233886564932c32a20\",\n \"score\": \"0.66697\",\n \"text\": \"def test_post_config(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e66d191ab3604a9959ccf9a658dcb84\",\n \"score\": \"0.66383064\",\n \"text\": \"def set_config_for_test(testcase, data):\\n set_config_data(data)\\n testcase.addCleanup(set_config_data, {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"832a22dab07369bb5c62d2eea21de9c9\",\n \"score\": \"0.66349006\",\n \"text\": \"def setUp(self):\\n self.config = TestConfig(show_section_numbers=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"832a22dab07369bb5c62d2eea21de9c9\",\n \"score\": \"0.66349006\",\n \"text\": \"def setUp(self):\\n self.config = TestConfig(show_section_numbers=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"498b9fe8f68fdcd963768ef8f71bf335\",\n \"score\": \"0.6628223\",\n \"text\": \"def test_edit_application_configuration(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3987259aedc3d82ec7333fbf8772eb6\",\n \"score\": \"0.65842843\",\n \"text\": \"def test_config_loaded():\\n eq_(settings.environment, 'test')\\n eq_(settings.database, 'test_database')\\n eq_(settings.amqp, 'test_amqp')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78b8165ca3db085ebd8cefd8d000c4fb\",\n \"score\": \"0.65570647\",\n \"text\": \"async def help_test_reloadable(\\n hass: HomeAssistant,\\n mqtt_client_mock: MqttMockPahoClient,\\n domain: str,\\n config: ConfigType,\\n) -> None:\\n # Set up with empty config\\n config = copy.deepcopy(config[mqtt.DOMAIN][domain])\\n # Create and test an old config of 2 entities based on the config supplied\\n old_config_1 = copy.deepcopy(config)\\n old_config_1[\\\"name\\\"] = \\\"test_old_1\\\"\\n old_config_2 = copy.deepcopy(config)\\n old_config_2[\\\"name\\\"] = \\\"test_old_2\\\"\\n\\n old_config = {\\n mqtt.DOMAIN: {domain: [old_config_1, old_config_2]},\\n }\\n # Start the MQTT entry with the old config\\n entry = MockConfigEntry(domain=mqtt.DOMAIN, data={mqtt.CONF_BROKER: \\\"test-broker\\\"})\\n entry.add_to_hass(hass)\\n mqtt_client_mock.connect.return_value = 0\\n with patch(\\\"homeassistant.config.load_yaml_config_file\\\", return_value=old_config):\\n await entry.async_setup(hass)\\n\\n assert hass.states.get(f\\\"{domain}.test_old_1\\\")\\n assert hass.states.get(f\\\"{domain}.test_old_2\\\")\\n assert len(hass.states.async_all(domain)) == 2\\n\\n # Create temporary fixture for configuration.yaml based on the supplied config and\\n # test a reload with this new config\\n new_config_1 = copy.deepcopy(config)\\n new_config_1[\\\"name\\\"] = \\\"test_new_1\\\"\\n new_config_2 = copy.deepcopy(config)\\n new_config_2[\\\"name\\\"] = \\\"test_new_2\\\"\\n new_config_extra = copy.deepcopy(config)\\n new_config_extra[\\\"name\\\"] = \\\"test_new_3\\\"\\n\\n new_config = {\\n mqtt.DOMAIN: {domain: [new_config_1, new_config_2, new_config_extra]},\\n }\\n with patch(\\\"homeassistant.config.load_yaml_config_file\\\", return_value=new_config):\\n # Reload the mqtt entry with the new config\\n await hass.services.async_call(\\n \\\"mqtt\\\",\\n SERVICE_RELOAD,\\n {},\\n blocking=True,\\n )\\n await hass.async_block_till_done()\\n\\n assert len(hass.states.async_all(domain)) == 3\\n\\n assert hass.states.get(f\\\"{domain}.test_new_1\\\")\\n assert hass.states.get(f\\\"{domain}.test_new_2\\\")\\n assert hass.states.get(f\\\"{domain}.test_new_3\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"577593c9cf755fe1880059828e436c49\",\n \"score\": \"0.6556689\",\n \"text\": \"def test_config_dict_set(self):\\n self.assertNotEqual(CONFIG.config, {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f774736639dcb2d17d22b7751ffa2e4\",\n \"score\": \"0.6549595\",\n \"text\": \"def test_002_changed(self):\\n conf_test = make_temp_file(dedent(\\\"\\\"\\\"\\n [task.default]\\n stderr: true\\n stdout_msgtree: false\\n stderr_msgtree: false\\n engine: select\\n port_qlimit: 1000\\n auto_tree: false\\n local_workername: none\\n distant_workername: pdsh\\n\\n [task.info]\\n debug: true\\n fanout: 256\\n grooming_delay: 0.5\\n connect_timeout: 12.5\\n command_timeout: 30.5\\\"\\\"\\\"))\\n self.defaults = Defaults(filenames=[conf_test.name])\\n self.assertTrue(self.defaults.stderr)\\n self.assertFalse(self.defaults.stdout_msgtree)\\n self.assertFalse(self.defaults.stderr_msgtree)\\n self.assertEqual(self.defaults.engine, 'select')\\n self.assertEqual(self.defaults.port_qlimit, 1000)\\n self.assertFalse(self.defaults.auto_tree)\\n self.assertEqual(self.defaults.local_workername, 'none')\\n self.assertEqual(self.defaults.distant_workername, 'pdsh')\\n # task_info\\n self.assertTrue(self.defaults.debug)\\n self.assertEqual(self.defaults.fanout, 256)\\n self.assertEqual(self.defaults.grooming_delay, 0.5)\\n self.assertEqual(self.defaults.connect_timeout, 12.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb964b1152404b5e6b49671649b84fd5\",\n \"score\": \"0.64850193\",\n \"text\": \"def test_reload(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0576d1d0217357f9e1a177285ba6dd4a\",\n \"score\": \"0.6464505\",\n \"text\": \"def update_config(self, config):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1218a320554dba5b23b2faebd708ad8d\",\n \"score\": \"0.63927615\",\n \"text\": \"def tearDown(self):\\n\\n self.config = configurator.Config()\\n self.config.set_config()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa689ba6eb9afd61a6b18136b4d8ac1d\",\n \"score\": \"0.6380534\",\n \"text\": \"def override_config() -> None:\\n cfg.data_path = Path(\\\"tests\\\", \\\"test_data\\\")\\n cfg.wow = io.reader(name=\\\"test_dot_pricer\\\", ftype=\\\"json\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dae2dd9714cb10268ebf7a50c2c8c5a8\",\n \"score\": \"0.63724375\",\n \"text\": \"async def test_v3_load_and_unload(\\n hass: HomeAssistant,\\n climacell_config_entry_update: pytest.fixture,\\n) -> None:\\n data = _get_config_schema(hass)(API_V3_ENTRY_DATA)\\n config_entry = MockConfigEntry(\\n domain=DOMAIN,\\n data=data,\\n unique_id=_get_unique_id(hass, data),\\n version=1,\\n )\\n config_entry.add_to_hass(hass)\\n assert await hass.config_entries.async_setup(config_entry.entry_id)\\n await hass.async_block_till_done()\\n assert len(hass.states.async_entity_ids(WEATHER_DOMAIN)) == 1\\n\\n assert await hass.config_entries.async_remove(config_entry.entry_id)\\n await hass.async_block_till_done()\\n assert len(hass.states.async_entity_ids(WEATHER_DOMAIN)) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a33bd37880173a89dafa4b14473e243\",\n \"score\": \"0.63598603\",\n \"text\": \"def test_caching(self):\\n data = utils.get_data()\\n main.app.config.update(\\n {\\n 'DATA_CSV': TEST_DATA_CSV_2,\\n }\\n )\\n new_data = utils.get_data()\\n self.assertDictEqual(data, new_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcf3bafde73389e12c8665c6e732d460\",\n \"score\": \"0.63354975\",\n \"text\": \"def test_configurations_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b7ac51d851e8b6a486ad2a641091813\",\n \"score\": \"0.6271766\",\n \"text\": \"def test_upate(self): \\n config = self.config_config()\\n\\n config.path_text = 'teste_text 2'\\n config.path_video = 'teste_video 2'\\n config.version = '2'\\n config.Update()\\n\\n self.clean_config(config) \\n\\n config.Select()\\n \\n assert config.path_text == 'teste_text 2'\\n assert config.path_video == 'teste_video 2'\\n assert config.version == '2'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1530242965675402674c3ee29278b9c4\",\n \"score\": \"0.62656087\",\n \"text\": \"def setUp(self) -> None:\\n\\n self.our_config = copy.deepcopy(pyf_test_dataset.DEFAULT_CONFIG)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e16005647a3de21a87d28fce10b0ae3\",\n \"score\": \"0.6258549\",\n \"text\": \"async def test_setup_update_unload_entry(hass):\\n\\n hass.config_entries.async_forward_entry_setups = AsyncMock()\\n with patch.object(hass.config_entries, \\\"async_update_entry\\\") as p:\\n config_entry = MockConfigEntry(\\n domain=DOMAIN, data=ADVANCED_USER_INPUT, entry_id=\\\"test\\\", unique_id=None\\n )\\n await hass.config_entries.async_add(config_entry)\\n assert p.called\\n\\n assert await async_setup_entry(hass, config_entry)\\n assert DOMAIN in hass.data and config_entry.entry_id in hass.data[DOMAIN]\\n\\n # check user input is in config\\n for key in ADVANCED_USER_INPUT:\\n if key in hass.data[DOMAIN][config_entry.entry_id]:\\n assert (\\n hass.data[DOMAIN][config_entry.entry_id][key]\\n == ADVANCED_USER_INPUT[key]\\n )\\n\\n hass.config_entries.async_forward_entry_setups.assert_called_with(\\n config_entry, PLATFORMS\\n )\\n\\n # ToDo test hass.data[DOMAIN][config_entry.entry_id][UPDATE_LISTENER]\\n\\n hass.config_entries.async_reload = AsyncMock()\\n assert await async_update_options(hass, config_entry) is None\\n hass.config_entries.async_reload.assert_called_with(config_entry.entry_id)\\n\\n # Unload the entry and verify that the data has been removed\\n assert await async_unload_entry(hass, config_entry)\\n assert config_entry.entry_id not in hass.data[DOMAIN]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7905e45eb48a2b81429251d35a1382d\",\n \"score\": \"0.6248757\",\n \"text\": \"def test_override_config(self):\\n processor = Processor()\\n self.assertIsNone(processor.config.groups)\\n self.assertIsNone(processor.config.policies)\\n\\n processor.config = Config(GROUPS, POLICIES_ALLOW)\\n self.assertEqual(len(processor.config.groups), 1)\\n self.assertEqual(len(processor.config.policies), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c9337a762826e8652502155e64aa8ba\",\n \"score\": \"0.6238477\",\n \"text\": \"def setUp(self):\\n super(TestCOTInjectConfig, self).setUp()\\n self.config_file = self.sample_cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3bb166c3dfdbefe3bb4eeaa4c2e9f2e\",\n \"score\": \"0.6235656\",\n \"text\": \"def test_setup_from_config(self):\\n self._test_setup({\\\"selfheal\\\": {\\\"interval\\\": 30.0}}, 30.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c0a3045b24f5862bcbd04393c77b47\",\n \"score\": \"0.62166053\",\n \"text\": \"def test_site_config_load():\\n clear_site_config()\\n for i, path in enumerate(reversed(get_conf_paths())):\\n exp_value = 100*(1 + i)\\n content = '[main]\\\\nmemory_usage/timeout = ' + str(exp_value) + '\\\\n'\\n\\n conf_fpath = os.path.join(path, 'spyder.ini')\\n with open(conf_fpath, 'w') as fh:\\n fh.write(content)\\n\\n config = ConfigurationManager()\\n config.reset_to_defaults()\\n value = config.get('main', 'memory_usage/timeout')\\n\\n print(path, value, exp_value)\\n\\n assert value == exp_value\\n clear_site_config()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b89fd8fe1bca987bbb30599c696bf408\",\n \"score\": \"0.6211188\",\n \"text\": \"def test_configuration(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"919178d9fdb2b2a3664015957d71e66b\",\n \"score\": \"0.62096184\",\n \"text\": \"def setUp(self):\\n jsonfig_patcher = mock.patch('otter.tap.api.jsonfig')\\n self.jsonfig = jsonfig_patcher.start()\\n self.addCleanup(jsonfig_patcher.stop)\\n\\n self.jsonfig.from_path.return_value = test_config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3128ee4cebf5620979fdd54fc1db2d6\",\n \"score\": \"0.6185549\",\n \"text\": \"def setUp(self):\\n self.fake_config = {'foo': 'bar'}\\n self.stack_count = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75fa6a069ab54d1379b07123c3a7f25a\",\n \"score\": \"0.61517274\",\n \"text\": \"def test_dev_config(self):\\n self.assertEqual()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f91fed02ac6a785913d8869bb9098ba8\",\n \"score\": \"0.61512685\",\n \"text\": \"def test_settings():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f75fa031139fd972449182660181439\",\n \"score\": \"0.6129881\",\n \"text\": \"def test_projects_ini_update_config():\\n k = project_ini.load_config('tests/data/project_ini/project.ini')\\n project_ini.update_config(k, {'app': {'name': 'pytest'}})\\n assert 'version' in k['app'], '\\\"app\\\" section missing from config'\\n assert k['app']['name'] == 'pytest', '\\\"version\\\" option missing from \\\"app\\\" section'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d1bade50b33d1e27f6ae38643fed79\",\n \"score\": \"0.61083764\",\n \"text\": \"def test_update_notification_settings(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9de6390b37a0239b1f21906b7fd3f5bb\",\n \"score\": \"0.6108358\",\n \"text\": \"def test_config_interface(mock_save):\\n config = Config(EXAMPLE_CONFIG_FILE)\\n\\n with pytest.raises(KeyError):\\n config[\\\"slow_start\\\"][\\\"second_season\\\"] = None\\n\\n config[\\\"slow_start\\\"] = {}\\n config[\\\"slow_start.second_season\\\"] = \\\"When?\\\"\\n assert config[\\\"slow_start\\\"][\\\"second_season\\\"] == \\\"When?\\\"\\n assert mock_save.call_count == 2\\n\\n config[\\\"slow_start.second_season\\\"] = \\\"When?\\\"\\n assert mock_save.call_count == 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e4ba17f53309acd1e20f92707828367\",\n \"score\": \"0.6085126\",\n \"text\": \"def testUpdateNothing(self):\\n cfg = config_dict.ConfigDict()\\n cfg.x = 5\\n cfg.y = 9\\n cfg.update()\\n self.assertLen(cfg, 2)\\n self.assertEqual(cfg.x, 5)\\n self.assertEqual(cfg.y, 9)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b46026763f9e337eec9b1a8b525b913\",\n \"score\": \"0.6082148\",\n \"text\": \"def test_insert(self): \\n config = self.config_config()\\n\\n self.clean_config(config) \\n\\n config.Select()\\n\\n self.compare_config(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a444dba4f48e0a17b1522d35e0e33db3\",\n \"score\": \"0.6072898\",\n \"text\": \"def test_real_config(self):\\n with patch('gstorage.apps.checks.register') as mock_method:\\n app = GStorageConfig.create('gstorage')\\n app.ready()\\n assert mock_method.call_count == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89b6832db4f2e99f8c7efe0c13443ee0\",\n \"score\": \"0.60726607\",\n \"text\": \"def test_reloading_config_reuses_client(self, mock_close_clients):\\n # load a config with a client and connect it\\n self.zoidberg.load_config('./tests/etc/zoidberg.yaml')\\n client = self.zoidberg.config.gerrits['master']['client']\\n self.zoidberg.connect_client(self.zoidberg.config.gerrits['master'])\\n client.marker = 'CheckForThisMarker'\\n # reload the same config\\n self.zoidberg.load_config('./tests/etc/zoidberg.yaml')\\n # check the client has not changed\\n client = self.zoidberg.config.gerrits['master']['client']\\n self.assertEqual('CheckForThisMarker', client.marker)\\n\\n # reload config with different connection details\\n self.zoidberg.load_config('./tests/etc/zoidberg2.yaml')\\n # check the client has changed\\n client = self.zoidberg.config.gerrits['master']['client']\\n self.assertEqual(None, getattr(client, 'marker', None))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f58ab24810e52974f220a99a1f5b3b\",\n \"score\": \"0.6065014\",\n \"text\": \"def test_show_config(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60765b757c49265b57ce78ba6dff6ff8\",\n \"score\": \"0.6064447\",\n \"text\": \"def test_projects_ini_update_config_empty_dict():\\n k = project_ini.load_config('tests/data/project_ini/project.ini')\\n project_ini.update_config(k, {})\\n assert 'version' in k['app'], '\\\"app\\\" section missing from config'\\n assert k['app']['name'] == 'fcs-etl', 'wrong name'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a666a452bd515dbcd10a013ca4142cc\",\n \"score\": \"0.6046669\",\n \"text\": \"def load_config(self):\\r\\n self.config.reload()\\r\\n self.validate_config()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b43dd23350fcb59fb73dca5b4f2e39e6\",\n \"score\": \"0.6029484\",\n \"text\": \"def setup_test_data(self, test_data_config: Union[DictConfig, Dict]):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf5f6d55286712c7a761e78727518a25\",\n \"score\": \"0.60251236\",\n \"text\": \"def test_custom_config():\\n config = Config(EXAMPLE_CONFIG_FILE)\\n\\n assert config[\\\"heartwarming_anime.name\\\"] == \\\"Slow Start\\\"\\n assert \\\"cute_girls\\\" in config\\n assert \\\"pesky_boys\\\" not in config\\n assert os.path.expanduser(\\\"~\\\") in config[\\\"data_dir\\\"]\\n assert \\\"{0}slow_start\\\".format(os.path.sep) in config[\\\"local_config_file\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aee435be569f88459171e0b412c1b58e\",\n \"score\": \"0.60246325\",\n \"text\": \"def test_config_expand1():\\n test_config = config.expand(fixtures.expand1.before_config)\\n assert test_config == fixtures.expand1.after_config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6d8d139de7b0ac4aab51426f34522b7\",\n \"score\": \"0.5996789\",\n \"text\": \"def test_900_restart_on_config_change(self):\\n # Config file affected by juju set config change\\n conf_file = '/etc/nova/nova.conf'\\n\\n # Make config change, check for service restarts\\n logging.info('Changing debug config on nova-cloud-controller')\\n self.restart_on_changed_debug_oslo_config_file(\\n conf_file,\\n self.services)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15f5ee0fe3a0c974f2a1d0ba00c3b596\",\n \"score\": \"0.59943527\",\n \"text\": \"def test_init_settings(self, **kwargs):\\n # check by default url setting\\n self.assertEqual(atxcf.get_setting(\\\"program_url\\\"),\\n atxcf.get_default_program_url())\\n os.remove(atxcf.get_settings_filename())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e71c11e1ad58989788746663730d507d\",\n \"score\": \"0.5986337\",\n \"text\": \"def setUp(self):\\n\\n CONFIG.parse(PROJECT + '/examples/config.yaml')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"764fae636c3a9c1dac031a3b1dc11bcf\",\n \"score\": \"0.5981106\",\n \"text\": \"def after_init_config(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"764fae636c3a9c1dac031a3b1dc11bcf\",\n \"score\": \"0.5981106\",\n \"text\": \"def after_init_config(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"671fe283385de42cdbbc13f5dae54065\",\n \"score\": \"0.59794176\",\n \"text\": \"def test_config(self, mock_requests):\\n app_id = self.create_app()\\n\\n # check to see that an initial/empty config was created\\n url = \\\"/v2/apps/{app_id}/config\\\".format(**locals())\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200, response.data)\\n self.assertIn('values', response.data)\\n self.assertEqual(response.data['values'], {})\\n config1 = response.data\\n\\n # set an initial config value\\n body = {'values': json.dumps({'NEW_URL1': 'http://localhost:8080/'})}\\n response = self.client.post(url, body)\\n self.assertEqual(response.status_code, 201, response.data)\\n config2 = response.data\\n self.assertNotEqual(config1['uuid'], config2['uuid'])\\n self.assertIn('NEW_URL1', response.data['values'])\\n\\n # read the config\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200, response.data)\\n config3 = response.data\\n self.assertEqual(config2, config3)\\n self.assertIn('NEW_URL1', response.data['values'])\\n\\n # set an additional config value\\n body = {'values': json.dumps({'NEW_URL2': 'http://localhost:8080/'})}\\n response = self.client.post(url, body)\\n self.assertEqual(response.status_code, 201, response.data)\\n config3 = response.data\\n self.assertNotEqual(config2['uuid'], config3['uuid'])\\n self.assertIn('NEW_URL1', response.data['values'])\\n self.assertIn('NEW_URL2', response.data['values'])\\n\\n # read the config again\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200, response.data)\\n config4 = response.data\\n self.assertEqual(config3, config4)\\n self.assertIn('NEW_URL1', response.data['values'])\\n self.assertIn('NEW_URL2', response.data['values'])\\n\\n # unset a config value\\n body = {'values': json.dumps({'NEW_URL2': None})}\\n response = self.client.post(url, body)\\n self.assertEqual(response.status_code, 201, response.data)\\n config5 = response.data\\n self.assertNotEqual(config4['uuid'], config5['uuid'])\\n self.assertNotIn('NEW_URL2', json.dumps(response.data['values']))\\n\\n # unset all config values\\n body = {'values': json.dumps({'NEW_URL1': None})}\\n response = self.client.post(url, body)\\n self.assertEqual(response.status_code, 201, response.data)\\n self.assertNotIn('NEW_URL1', json.dumps(response.data['values']))\\n\\n # set a port and then unset it to make sure validation ignores the unset\\n body = {'values': json.dumps({'PORT': '5000'})}\\n response = self.client.post(url, body)\\n self.assertEqual(response.status_code, 201, response.data)\\n self.assertIn('PORT', response.data['values'])\\n\\n body = {'values': json.dumps({'PORT': None})}\\n response = self.client.post(url, body)\\n self.assertEqual(response.status_code, 201, response.data)\\n self.assertNotIn('PORT', response.data['values'])\\n\\n # disallow put/patch/delete\\n response = self.client.put(url)\\n self.assertEqual(response.status_code, 405, response.data)\\n response = self.client.patch(url)\\n self.assertEqual(response.status_code, 405, response.data)\\n response = self.client.delete(url)\\n self.assertEqual(response.status_code, 405, response.data)\\n return config5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62d6886dacfc39e791a76285626e9a65\",\n \"score\": \"0.5972372\",\n \"text\": \"def test_update_fsa_settings(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a61237f646c0eb78c74a720917b8c544\",\n \"score\": \"0.5970906\",\n \"text\": \"def test_900_restart_on_config_change(self):\\n # Config file affected by juju set config change\\n conf_file = '/etc/nova/nova.conf'\\n\\n # Make config change, check for service restarts\\n logging.info('Changing the debug config on nova-compute')\\n self.restart_on_changed_debug_oslo_config_file(\\n conf_file,\\n ['nova-compute'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afbfbadc31a1e2a612ea29570a2cf90b\",\n \"score\": \"0.59632045\",\n \"text\": \"def mock_config_entry_data():\\n return {\\n \\\"product_name\\\": \\\"Product Name\\\",\\n \\\"product_type\\\": \\\"product_type\\\",\\n \\\"serial\\\": \\\"aabbccddeeff\\\",\\n \\\"name\\\": \\\"Product Name\\\",\\n CONF_IP_ADDRESS: \\\"1.2.3.4\\\",\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1492313b97d4a0eb815f8dad2784f31\",\n \"score\": \"0.594567\",\n \"text\": \"def setUp(self):\\n super(SysChangeMonTestCase, self).setUp()\\n self.app.storage = TestStorage()\\n self.app.config = TestConfigHandler()\\n self.app.log = TestLogHandler()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bdf5213a8a44eaee6d99541efab7b4d\",\n \"score\": \"0.5939667\",\n \"text\": \"def reload_config(self):\\n\\n self.config = load_script_config(\\n script='data_taker',\\n config=self.gui.config.text(),\\n logger=self.log\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e172cd7a3f1f507f5f8af350a8037329\",\n \"score\": \"0.5927022\",\n \"text\": \"def setUp(self) -> None:\\n\\n self.config_loader = ConfigLoader()\\n\\n self.tempfile = tempfile.NamedTemporaryFile()\\n\\n self.our_dataset = {\\n \\\"chrome\\\": {\\n \\\"linux\\\": \\\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 \\\"\\n \\\"(KHTML, like Gecko) Chrome/77.0.3865.116 \\\"\\n \\\"Safari/537.36 Edg/77.11.4.5118\\\",\\n \\\"macosx\\\": \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) \\\"\\n \\\"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4364.0 \\\"\\n \\\"Safari/537.36\\\",\\n \\\"win10\\\": \\\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) \\\"\\n \\\"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4361.0 \\\"\\n \\\"Safari/537.36\\\",\\n },\\n \\\"edge\\\": {\\n \\\"linux\\\": None,\\n \\\"macosx\\\": None,\\n \\\"win10\\\": \\\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) \\\"\\n \\\"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.140 \\\"\\n \\\"Safari/537.36 Edge/18.17763/5.9.7 (Linux;Android 10) \\\"\\n \\\"ExoPlayerLib/2.9.6\\\",\\n },\\n \\\"firefox\\\": {\\n \\\"linux\\\": \\\"Mozilla/5.0 (Linux x86_64; en-US) Gecko/20130401 \\\"\\n \\\"Firefox/82.4\\\",\\n \\\"macosx\\\": \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_0; \\\"\\n \\\"en-US) Gecko/20100101 Firefox/74.7\\\",\\n \\\"win10\\\": \\\"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:84.0) \\\"\\n \\\"Gecko/20100101 Firefox/84.0/8mqDiPuL-36\\\",\\n },\\n \\\"ie\\\": {\\n \\\"linux\\\": None,\\n \\\"macosx\\\": None,\\n \\\"win10\\\": \\\"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 10.0; \\\"\\n \\\"Win64; x64; Trident/7.0; .NET4.0C; .NET4.0E; .NET CLR \\\"\\n \\\"2.0.50727; .NET CLR 3.0.30729; .NET CLR 3.5.30729; Tablet \\\"\\n \\\"PC 2.0; wbx 1.0.0; wbxapp 1.0.0)\\\",\\n },\\n \\\"opera\\\": {\\n \\\"linux\\\": \\\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 \\\"\\n \\\"(KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36 \\\"\\n \\\"OPR/73.0.3856.284\\\",\\n \\\"macosx\\\": \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_3) \\\"\\n \\\"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.198 \\\"\\n \\\"Safari/537.36 OPR/72.0.3815.400\\\",\\n \\\"win10\\\": \\\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) \\\"\\n \\\"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 \\\"\\n \\\"Safari/537.36 OPR/73.0.3856.284 (Edition avira-2)\\\",\\n },\\n \\\"safari\\\": {\\n \\\"linux\\\": \\\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 \\\"\\n \\\"(KHTML, like Gecko) Version/4.0 Chrome/70.0.3538.110 \\\"\\n \\\"Safari/537.36 SputnikBrowser/1.2.5.158\\\",\\n \\\"macosx\\\": \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) \\\"\\n \\\"AppleWebKit/600.8.9 (KHTML, like Gecko) Version/9.0.3 \\\"\\n \\\"Safari/601.4.4\\\",\\n \\\"win10\\\": None,\\n },\\n }\\n\\n self.tempfile.write(json.dumps(self.our_dataset).encode())\\n self.tempfile.seek(0)\\n\\n self.user_agent_dataset = UserAgentDataset()\\n self.user_agent_dataset.source_file = self.tempfile.name\\n\\n self.get_content_patch = unittest.mock.patch.object(DatasetBase, \\\"get_content\\\")\\n self.mock_get_content = self.get_content_patch.start()\\n self.mock_get_content.return_value = copy.deepcopy(self.our_dataset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaa443dea22a55c15285d8c7a2e727af\",\n \"score\": \"0.5913581\",\n \"text\": \"def test_update_defaults(self):\\n configuration = DiscussionsConfiguration.objects.get(context_key=self.course_key_with_defaults)\\n configuration.enabled = False\\n configuration.plugin_configuration = {\\n 'url': 'http://localhost',\\n }\\n configuration.provider_type = 'legacy'\\n configuration.save()\\n configuration = DiscussionsConfiguration.objects.get(context_key=self.course_key_with_defaults)\\n assert configuration is not None\\n assert not configuration.enabled\\n assert configuration.lti_configuration is None\\n assert configuration.plugin_configuration['url'] == 'http://localhost'\\n assert configuration.provider_type == 'legacy'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ec65e4e41d28cc67e65cd569987d4fe\",\n \"score\": \"0.5912548\",\n \"text\": \"def test_220_nova_metadata_propagate(self):\\n if self.current_release < self.BIONIC_ROCKY:\\n logging.info(\\\"Feature didn't exist before Rocky. Nothing to test\\\")\\n return\\n\\n # Expected default and alternate values\\n current_value = zaza.model.get_application_config(\\n 'nova-cloud-controller')['vendor-data-url']['value']\\n new_value = 'http://some-other.url/vdata'\\n\\n set_default = {'vendor-data-url': current_value}\\n set_alternate = {'vendor-data-url': new_value}\\n default_entry = {'api': {\\n 'vendordata_dynamic_targets': [current_value]}}\\n alternate_entry = {'api': {'vendordata_dynamic_targets': [new_value]}}\\n\\n # Config file affected by juju set config change\\n conf_file = '/etc/nova/nova.conf'\\n\\n # Make config change, check for service restarts\\n logging.info(\\n 'Setting config on nova-cloud-controller to {}'.format(\\n set_alternate))\\n self.restart_on_changed(\\n conf_file,\\n set_default,\\n set_alternate,\\n default_entry,\\n alternate_entry,\\n self.services)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"070d6a8493354003596b344fe9b4fb23\",\n \"score\": \"0.5895899\",\n \"text\": \"def _update_infobase_config():\\r\\n # update infobase configuration\\r\\n from infogami.infobase import server\\r\\n if not config.get(\\\"infobase\\\"):\\r\\n config.infobase = {}\\r\\n # This sets web.config.db_parameters\\r\\n server.update_config(config.infobase)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d842f67ba9fe20e33c575d5f9ddc0c87\",\n \"score\": \"0.58910453\",\n \"text\": \"def reload_config(self):\\n from samuraix import config # TODO?\\n self.dispatch_event('on_load_config', config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e37efdbc4a6f74462041208a763045df\",\n \"score\": \"0.5889935\",\n \"text\": \"def test_corrupted_config_data(self):\\n self.managed_list.zk_config_manager._data = \\\"[dasdsad\\\"\\n self.managed_list._reload_config_data()\\n self.assertEqual([], self.managed_list.get_list())\\n\\n self.managed_list.zk_config_manager._data = \\\"{}\\\"\\n self.managed_list._reload_config_data()\\n self.assertEqual([], self.managed_list.get_list())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8451b7310582a17e7aaa841385f1dda\",\n \"score\": \"0.5887346\",\n \"text\": \"async def test_manual_configuration_update_configuration(\\n hass: HomeAssistant, mock_config_entry, mock_vapix_requests\\n) -> None:\\n assert mock_config_entry.data[CONF_HOST] == \\\"1.2.3.4\\\"\\n\\n result = await hass.config_entries.flow.async_init(\\n AXIS_DOMAIN, context={\\\"source\\\": SOURCE_USER}\\n )\\n\\n assert result[\\\"type\\\"] == FlowResultType.FORM\\n assert result[\\\"step_id\\\"] == \\\"user\\\"\\n\\n mock_vapix_requests(\\\"2.3.4.5\\\")\\n result = await hass.config_entries.flow.async_configure(\\n result[\\\"flow_id\\\"],\\n user_input={\\n CONF_HOST: \\\"2.3.4.5\\\",\\n CONF_USERNAME: \\\"user\\\",\\n CONF_PASSWORD: \\\"pass\\\",\\n CONF_PORT: 80,\\n },\\n )\\n await hass.async_block_till_done()\\n\\n assert result[\\\"type\\\"] == FlowResultType.ABORT\\n assert result[\\\"reason\\\"] == \\\"already_configured\\\"\\n assert mock_config_entry.data[CONF_HOST] == \\\"2.3.4.5\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aede172042886a07b247fa637318ad96\",\n \"score\": \"0.58785474\",\n \"text\": \"def test_config():\\n assert not create_app().testing\\n assert create_app({\\\"TESTING\\\": True}).testing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e751f2145364cf0ad9d852b42cb16b6\",\n \"score\": \"0.58688074\",\n \"text\": \"def test_config_with_defaults(self):\\n\\n with MockConsulKV():\\n app = self.create_app()\\n consul = Consul(app)\\n consul.apply_remote_config()\\n\\n self.assertEqual(app.config['cfg_1'], 'consul_1')\\n self.assertEqual(app.config['cfg_3'], 'consul_3')\\n self.assertEqual(app.config['cfg_4'], {'inner': 'value'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6500ea495b8e6ec354d7e3690a52f78\",\n \"score\": \"0.5866945\",\n \"text\": \"def test_read_config_file_old(self):\\n es_url = 'http://127.0.0.1:9200'\\n # Build\\n file_obj = FileTestObj()\\n file_obj.write_config(file_obj.args['configfile'], YAMLCONFIG.format(es_url))\\n # Test\\n build_obj = Builder(configfile=file_obj.args['configfile'])\\n assert build_obj.client_args.hosts[0] == es_url\\n # Teardown\\n file_obj.teardown()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ad84a816505064e75b943cf0c49c748\",\n \"score\": \"0.5866806\",\n \"text\": \"def setUp(self):\\n self.config = trial.Options()\\n self.savedModules = dict(sys.modules)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fb8d2fc87cf2091bdd6c702e4ef51b4\",\n \"score\": \"0.58650434\",\n \"text\": \"def test_set_in_config(self):\\n options = self._test_options(config={\\\"option\\\": \\\"foo\\\"})\\n self.assertEqual(options.test_option, \\\"foo\\\")\\n\\n options = self._test_options(config={\\\"option\\\": \\\"bar\\\"})\\n self.assertEqual(options.test_option, \\\"bar\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b026be5e011f9ad664b40c6662f8051\",\n \"score\": \"0.5862787\",\n \"text\": \"async def test_unload_config_entry(\\n hass: HomeAssistant, aioclient_mock: AiohttpClientMocker\\n) -> None:\\n entry = await init_integration(hass, aioclient_mock)\\n assert hass.data[DOMAIN]\\n await hass.config_entries.async_unload(entry.entry_id)\\n await hass.async_block_till_done()\\n assert not hass.data.get(DOMAIN)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20c25555ab7c88e2071674939b0228cd\",\n \"score\": \"0.5859502\",\n \"text\": \"def testFixConfig(self):\\n \\n tarball = self.recipe.get_tarball(self.download_url, self.download_dir)\\n \\n extracted = self.recipe.install_tarball(self.download_dir, tarball, self.destination)\\n \\n options = {}\\n options['admin_username'] = \\\"foo\\\"\\n options['admin_password'] = \\\"foo\\\"\\n options['adminserver_hostport'] = \\\"foo\\\"\\n options['process_uid'] = \\\"foo\\\"\\n options['process_gid'] = \\\"foo\\\"\\n options['licenseinfo'] = \\\"foo\\\"\\n options['httpd_enabled'] = \\\"foo\\\"\\n options['hostport'] = \\\"foo\\\"\\n options['live_dir'] = \\\"foo\\\"\\n options['vod_common_dir'] = \\\"foo\\\"\\n options['vod_dir'] = \\\"foo\\\"\\n options['appsdir'] = \\\"foo\\\"\\n options['js_scriptlibpath'] = \\\"foo\\\"\\n \\n locations = self.recipe.create_config(self.destination, options)\\n \\n for loc in locations:\\n f = open(loc).read()\\n self.assertTrue('foo' in f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a23d50e982ec4e0f90275287f458141f\",\n \"score\": \"0.5856261\",\n \"text\": \"def test_correct_new_naming_scrape(self):\\n # Given\\n c = config({\\n 'scrap_start_urls': False\\n })\\n\\n config_loaded = ConfigLoader(c)\\n\\n assert config_loaded.scrape_start_urls == False\\n assert config_loaded.scrap_start_urls == config_loaded.scrape_start_urls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5d55a50082ac0622529f7fd00754ab5\",\n \"score\": \"0.5852883\",\n \"text\": \"def test():\\n c = Config()\\n c.defaults()\\n c.print_config()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d8289aefa98a164c0a99316b18387c6\",\n \"score\": \"0.5851456\",\n \"text\": \"def config_entry_fixture():\\n return MockConfigEntry(domain=DOMAIN, title=\\\"Sonos\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee92e4c04faf05a3eccdb550f7da7ee8\",\n \"score\": \"0.5847191\",\n \"text\": \"def setUp(self):\\n self.config = trial.Options()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"082166328d0a75b158a7a8625039097f\",\n \"score\": \"0.5846698\",\n \"text\": \"def test_create_default_configuration(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ddfa3cb600b8dd557896e5a9e83fb46\",\n \"score\": \"0.5841066\",\n \"text\": \"def _on_config_changed(self, _):\\n current = self.config[\\\"thing\\\"]\\n if current not in self._stored.things:\\n logger.debug(\\\"found a new thing: %r\\\", current)\\n self._stored.things.append(current)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae3caeb47c00efdb21c4a803d94d9a8\",\n \"score\": \"0.5838629\",\n \"text\": \"def setUpTestData(cls):\\r\\n cfg.TESTING = True\\r\\n cfg.IGNORE_SYNC_DURING_CONNECT = False\\r\\n fake_initialize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b7bf8dc5f92f419a90194935b302df5\",\n \"score\": \"0.5834541\",\n \"text\": \"def test_config_router_put(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d65e94cdb1db4e3fdadfeda05d5d9c74\",\n \"score\": \"0.5833664\",\n \"text\": \"def reload(self):\\n # Load newest config\\n self.__loader.load_config()\\n self.__properties = self.__loader.properties\\n self.load_config_objects()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df3305fb844848a66d578c75568ae597\",\n \"score\": \"0.58320844\",\n \"text\": \"def setUp(self):\\n self.test_app = create_app()\\n self.test_app.config.from_object(\\\"project.config.TestingConfig\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74ab7ed6a342387a013326873ebdb50e\",\n \"score\": \"0.5821723\",\n \"text\": \"def test_conf(self):\\n self.TESTED_UNIT = 'ceph-fs/0'\\n\\n def _get_conf():\\n \\\"\\\"\\\"get/parse ceph daemon response into dict.\\n\\n :returns dict: Current configuration of the Ceph MDS daemon\\n :rtype: dict\\n \\\"\\\"\\\"\\n cmd = \\\"sudo ceph daemon mds.$HOSTNAME config show\\\"\\n conf = model.run_on_unit(self.TESTED_UNIT, cmd)\\n return json.loads(conf['Stdout'])\\n\\n @retry(wait=wait_exponential(multiplier=1, min=4, max=10),\\n stop=stop_after_attempt(10))\\n def _change_conf_check(mds_config):\\n \\\"\\\"\\\"Change configs, then assert to ensure config was set.\\n\\n Doesn't return a value.\\n \\\"\\\"\\\"\\n model.set_application_config('ceph-fs', mds_config)\\n results = _get_conf()\\n self.assertEqual(\\n results['mds_cache_memory_limit'],\\n mds_config['mds-cache-memory-limit'])\\n self.assertAlmostEqual(\\n float(results['mds_cache_reservation']),\\n float(mds_config['mds-cache-reservation']))\\n self.assertAlmostEqual(\\n float(results['mds_health_cache_threshold']),\\n float(mds_config['mds-health-cache-threshold']))\\n\\n # ensure defaults are set\\n mds_config = {'mds-cache-memory-limit': '4294967296',\\n 'mds-cache-reservation': '0.05',\\n 'mds-health-cache-threshold': '1.5'}\\n _change_conf_check(mds_config)\\n\\n # change defaults\\n mds_config = {'mds-cache-memory-limit': '8589934592',\\n 'mds-cache-reservation': '0.10',\\n 'mds-health-cache-threshold': '2'}\\n _change_conf_check(mds_config)\\n\\n # Restore config to keep tests idempotent\\n mds_config = {'mds-cache-memory-limit': '4294967296',\\n 'mds-cache-reservation': '0.05',\\n 'mds-health-cache-threshold': '1.5'}\\n _change_conf_check(mds_config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f25b51b32bc79a9cb119514ff57cb4\",\n \"score\": \"0.58194387\",\n \"text\": \"def init_test(cls):\\n cls.set_configuration()\\n cls._first_read_id = cls._insert_readings_data()\\n cls._insert_readings_data()\\n payload = PayloadBuilder()\\\\\\n .WHERE(['id', '>=', cls._first_read_id]) \\\\\\n .ORDER_BY(['id', 'ASC']) \\\\\\n .payload()\\n readings = cls._readings.query(payload)\\n cls._raw_data = readings['rows']\\n\\n yield\\n # Delete all test data from readings and configuration\\n cls._storage_client.delete_from_tbl(\\\"readings\\\", {})\\n payload = PayloadBuilder().WHERE([\\\"key\\\", \\\"=\\\", cls._CONFIG_CATEGORY_NAME]).payload()\\n cls._storage_client.delete_from_tbl(\\\"configuration\\\", payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f27f6258b7429a26a9901bea6d615fd\",\n \"score\": \"0.58063304\",\n \"text\": \"def test_test_config(self):\\n app = create_app('appname.config.TestingConfig', env='testing')\\n\\n assert app.config['DEBUG'] is False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e24023b611c82117bd20f68a1d1cca7f\",\n \"score\": \"0.580564\",\n \"text\": \"def test_update_value(self):\\n fixes.fixes = {}\\n old_fixes = fixes.fixes\\n fixes._load_file(join_data_path('fixes.py'))\\n self.assertIs(fixes.fixes, old_fixes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ad9f61e71f1b1d8f693b1c6f299b1e\",\n \"score\": \"0.579319\",\n \"text\": \"def test_config_set_same_key(self, mock_requests):\\n app_id = self.create_app()\\n url = \\\"/v2/apps/{app_id}/config\\\".format(**locals())\\n\\n # set an initial config value\\n body = {'values': json.dumps({'PORT': '5000'})}\\n response = self.client.post(url, body)\\n self.assertEqual(response.status_code, 201, response.data)\\n self.assertIn('PORT', response.data['values'])\\n\\n # reset same config value\\n body = {'values': json.dumps({'PORT': '5001'})}\\n response = self.client.post(url, body)\\n self.assertEqual(response.status_code, 201, response.data)\\n self.assertIn('PORT', response.data['values'])\\n self.assertEqual(response.data['values']['PORT'], '5001')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bdb7dfe598c20dcfb58133fdd20b858\",\n \"score\": \"0.5790044\",\n \"text\": \"def test_save_config_by_urls(self):\\n\\n # our key to use\\n key = 'test_save_config_by_urls'\\n\\n # GET returns 405 (not allowed)\\n response = self.client.get('/add/{}'.format(key))\\n assert response.status_code == 405\\n\\n # no data\\n response = self.client.post('/add/{}'.format(key))\\n assert response.status_code == 400\\n\\n # No entries specified\\n response = self.client.post(\\n '/add/{}'.format(key), {'urls': ''})\\n assert response.status_code == 400\\n\\n # Added successfully\\n response = self.client.post(\\n '/add/{}'.format(key), {'urls': 'mailto://user:pass@yahoo.ca'})\\n assert response.status_code == 200\\n\\n # No URLs loaded\\n response = self.client.post(\\n '/add/{}'.format(key),\\n {'config': 'invalid content', 'format': 'text'})\\n assert response.status_code == 400\\n\\n # Test a case where we fail to load a valid configuration file\\n with patch('apprise.AppriseConfig.add', return_value=False):\\n response = self.client.post(\\n '/add/{}'.format(key),\\n {'config': 'garbage://', 'format': 'text'})\\n assert response.status_code == 400\\n\\n with patch('os.remove', side_effect=OSError):\\n # We will fail to remove the device first prior to placing a new\\n # one; This will result in a 500 error\\n response = self.client.post(\\n '/add/{}'.format(key), {\\n 'urls': 'mailto://user:newpass@gmail.com'})\\n assert response.status_code == 500\\n\\n # URL is actually not a valid one (invalid Slack tokens specified\\n # below)\\n response = self.client.post(\\n '/add/{}'.format(key), {'urls': 'slack://-/-/-'})\\n assert response.status_code == 400\\n\\n # Test with JSON\\n response = self.client.post(\\n '/add/{}'.format(key),\\n data=json.dumps({'urls': 'mailto://user:pass@yahoo.ca'}),\\n content_type='application/json',\\n )\\n assert response.status_code == 200\\n\\n # Test with JSON (and no payload provided)\\n response = self.client.post(\\n '/add/{}'.format(key),\\n data=json.dumps({}),\\n content_type='application/json',\\n )\\n assert response.status_code == 400\\n\\n # Test with XML which simply isn't supported\\n response = self.client.post(\\n '/add/{}'.format(key),\\n data='<urls><url>mailto://user:pass@yahoo.ca</url></urls>',\\n content_type='application/xml',\\n )\\n assert response.status_code == 400\\n\\n # Invalid JSON\\n response = self.client.post(\\n '/add/{}'.format(key),\\n data='{',\\n content_type='application/json',\\n )\\n assert response.status_code == 400\\n\\n # Test the handling of underlining disk/write exceptions\\n with patch('os.makedirs') as mock_mkdirs:\\n mock_mkdirs.side_effect = OSError()\\n # We'll fail to write our key now\\n response = self.client.post(\\n '/add/{}'.format(key),\\n data=json.dumps({'urls': 'mailto://user:pass@yahoo.ca'}),\\n content_type='application/json',\\n )\\n\\n # internal errors are correctly identified\\n assert response.status_code == 500\\n\\n # Test the handling of underlining disk/write exceptions\\n with patch('gzip.open') as mock_open:\\n mock_open.side_effect = OSError()\\n # We'll fail to write our key now\\n response = self.client.post(\\n '/add/{}'.format(key),\\n data=json.dumps({'urls': 'mailto://user:pass@yahoo.ca'}),\\n content_type='application/json',\\n )\\n\\n # internal errors are correctly identified\\n assert response.status_code == 500\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1262b915a262e2d12626f58a6e9b929\",\n \"score\": \"0.57868624\",\n \"text\": \"async def test_load_unload_config_entry(\\n hass: HomeAssistant,\\n mock_config_entry: MockConfigEntry,\\n mock_open_meteo: AsyncMock,\\n) -> None:\\n mock_config_entry.add_to_hass(hass)\\n await hass.config_entries.async_setup(mock_config_entry.entry_id)\\n await hass.async_block_till_done()\\n\\n assert mock_config_entry.state is ConfigEntryState.LOADED\\n\\n await hass.config_entries.async_unload(mock_config_entry.entry_id)\\n await hass.async_block_till_done()\\n\\n assert not hass.data.get(DOMAIN)\\n assert mock_config_entry.state is ConfigEntryState.NOT_LOADED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbeda210a750f8ee4c4509c7db44d1d7\",\n \"score\": \"0.578367\",\n \"text\": \"def test_test_config(self):\\n with open(TEST_DATA_PATH / \\\"test_config.yaml\\\") as f:\\n self.schema.load(yaml.safe_load(f))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"279e6341fa673c0e89954886516219e8\",\n \"score\": \"0.577966\",\n \"text\": \"def test_default_config(mock_load):\\n config = Config()\\n config.load()\\n\\n assert \\\"data_dir\\\" in config\\n assert \\\"local_config_file\\\" in config\\n assert \\\"Slow Start\\\" in config[\\\"reddit.user_agent\\\"]\\n assert mock_load.call_count == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d32cf09b13497e3691433f288c818aa6\",\n \"score\": \"0.57740635\",\n \"text\": \"def test_update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d32cf09b13497e3691433f288c818aa6\",\n \"score\": \"0.57740635\",\n \"text\": \"def test_update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bce1d8fde9c6047e29c2de59953cbe73\",\n \"score\": \"0.57730144\",\n \"text\": \"def test_config_whitelist_put(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e669f5b95793507c27da42c1e0b99296\",\n \"score\": \"0.5764217\",\n \"text\": \"async def test_reauth_multiple_config_entries(\\n hass: HomeAssistant, oauth, setup_platform, config_entry\\n) -> None:\\n await setup_platform()\\n\\n old_entry = MockConfigEntry(\\n domain=DOMAIN,\\n data={\\n **config_entry.data,\\n \\\"extra_data\\\": True,\\n },\\n )\\n old_entry.add_to_hass(hass)\\n\\n entries = hass.config_entries.async_entries(DOMAIN)\\n assert len(entries) == 2\\n\\n orig_subscriber_id = config_entry.data.get(\\\"subscriber_id\\\")\\n\\n # Invoke the reauth flow\\n result = await oauth.async_reauth(config_entry)\\n\\n await oauth.async_oauth_web_flow(result)\\n\\n await oauth.async_finish_setup(result)\\n\\n # Only reauth entry was updated, the other entry is preserved\\n entries = hass.config_entries.async_entries(DOMAIN)\\n assert len(entries) == 2\\n entry = entries[0]\\n assert entry.unique_id == PROJECT_ID\\n entry.data[\\\"token\\\"].pop(\\\"expires_at\\\")\\n assert entry.data[\\\"token\\\"] == {\\n \\\"refresh_token\\\": \\\"mock-refresh-token\\\",\\n \\\"access_token\\\": \\\"mock-access-token\\\",\\n \\\"type\\\": \\\"Bearer\\\",\\n \\\"expires_in\\\": 60,\\n }\\n assert entry.data.get(\\\"subscriber_id\\\") == orig_subscriber_id # Not updated\\n assert not entry.data.get(\\\"extra_data\\\")\\n\\n # Other entry was not refreshed\\n entry = entries[1]\\n entry.data[\\\"token\\\"].pop(\\\"expires_at\\\")\\n assert entry.data.get(\\\"token\\\", {}).get(\\\"access_token\\\") == \\\"some-token\\\"\\n assert entry.data.get(\\\"extra_data\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bb853ab716fa9c0b6ca3f7987db35c3\",\n \"score\": \"0.57627\",\n \"text\": \"def testReferences(self):\\n cfg = _get_test_config_dict()\\n cfg.dict_ref = cfg.dict\\n\\n self.assertEqual(cfg.dict_ref, cfg.dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"149321352d7f9526ce11705e5a3c4c40\",\n \"score\": \"0.5761119\",\n \"text\": \"async def test_name_already_configured(hass, api):\\n entry = MockConfigEntry(\\n domain=transmission.DOMAIN,\\n data=MOCK_ENTRY,\\n options={CONF_SCAN_INTERVAL: DEFAULT_SCAN_INTERVAL},\\n )\\n entry.add_to_hass(hass)\\n\\n mock_entry = MOCK_ENTRY.copy()\\n mock_entry[CONF_HOST] = \\\"0.0.0.0\\\"\\n result = await hass.config_entries.flow.async_init(\\n transmission.DOMAIN,\\n context={\\\"source\\\": config_entries.SOURCE_USER},\\n data=mock_entry,\\n )\\n\\n assert result[\\\"type\\\"] == \\\"form\\\"\\n assert result[\\\"errors\\\"] == {CONF_NAME: \\\"name_exists\\\"}\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":492,"cells":{"query_id":{"kind":"string","value":"052b79f70712e6d9033814bffb508da3"},"query":{"kind":"string","value":"Use this decorator to mark you ploogin function as a handler to call upon teardown."},"positive_passages":{"kind":"list like","value":[{"docid":"d19096ed85842fc259165a9fd2f7ad0a","score":"0.7959539","text":"def upon_teardown(f: Callable):\n return PlooginEventHandler(event=PlooginEvents.TEARDOWN, f=f)","title":""}],"string":"[\n {\n \"docid\": \"d19096ed85842fc259165a9fd2f7ad0a\",\n \"score\": \"0.7959539\",\n \"text\": \"def upon_teardown(f: Callable):\\n return PlooginEventHandler(event=PlooginEvents.TEARDOWN, f=f)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"122522864231496b572e2050164568f0","score":"0.70912457","text":"def teardown_func():\r\n pass","title":""},{"docid":"b304946a999ba13baed34b64e8f6f7fe","score":"0.6499198","text":"def teardown_appcontext(self, f):\r\n self.teardown_appcontext_funcs.append(f)\r\n return f","title":""},{"docid":"8bfab0117a997d4c00b8fcac9c9eef31","score":"0.63808227","text":"def removes_return():\n\n def decorator(fn):\n fn._sa_instrument_after = \"fire_remove_event\"\n return fn\n\n return decorator","title":""},{"docid":"f62c594e656418483cae9f66e51d4b15","score":"0.63546515","text":"def teardown_method(self, method):","title":""},{"docid":"c6824430490ee51c8554ac87fa30f5aa","score":"0.63046306","text":"def teardown_method(self):","title":""},{"docid":"9e28b4fd46a02ef78e7bb53df4606602","score":"0.62905","text":"def global_teardown():","title":""},{"docid":"799e85de31556bf5ade6b6fb734ff19f","score":"0.6266707","text":"def teardown_request(self, f):\r\n self.teardown_request_funcs.setdefault(None, []).append(f)\r\n return f","title":""},{"docid":"1407bd08bd4a0720d8d5c863b8ea3eef","score":"0.6244014","text":"def teardown_method(self):\n pass","title":""},{"docid":"7d22dd7492c2352edc502eea1964ff29","score":"0.6233653","text":"def teardown_function(function):\n print(\"\\nTEAR DOWN <==\")","title":""},{"docid":"272bdd019cf78c57a02d76dece41ecbf","score":"0.61997914","text":"def test_teardown(self):\n assert self.default_handler.teardown() is None\n self.assert_quantity_in_outbox(0)","title":""},{"docid":"0dc36fa9a96303b41df6b08092f37669","score":"0.6186899","text":"def event(name):\n def decorator(fun):\n unsafe_hook_event(name, fun)\n @plugins.finalizer\n def finalizer():\n unsafe_unhook_event(name, fun)\n return fun\n return decorator","title":""},{"docid":"fd3c9f93161b2d2285c6f4f83c07771e","score":"0.613534","text":"def teardown(self):\n if getattr(self.obj, 'im_self', None): \n name = 'teardown_method' \n else: \n name = 'teardown_function' \n obj = self.parent.obj \n meth = getattr(obj, name, None)\n if meth is not None: \n return meth(self.obj)","title":""},{"docid":"bf4dbf9fc42c42f671302c137c8f451b","score":"0.60999477","text":"def removes(arg):\n\n def decorator(fn):\n fn._sa_instrument_before = (\"fire_remove_event\", arg)\n return fn\n\n return decorator","title":""},{"docid":"ec171fd49c43d34afeff7ead9a224051","score":"0.60797954","text":"def test_teardown(test):","title":""},{"docid":"cca4a866fcbd1b6a7874e67d10483296","score":"0.6051324","text":"def process_func(request, resource_handler):\n if request.node.get_closest_marker(\"setup_func\"):\n print(\"SetUp function {0}\".format(request.function.__name__))\n print(request.keywords.node.funcargs.keys())\n resource_handler.clear_result()\n\n yield\n\n if request.node.get_closest_marker(\"setup_func\"):\n print(\"TearDown function {0}\".format(request.function.__name__))\n print(request.keywords.node.funcargs.keys())\n resource_handler.clear_result()","title":""},{"docid":"b62d17afc6bd583791e1bf7784068158","score":"0.60283476","text":"def early_teardown():","title":""},{"docid":"9da38c8914547f2c018070df7d55a563","score":"0.60210234","text":"def teardown():","title":""},{"docid":"936c217419dc476bb5fee4f59b0b6f00","score":"0.6016588","text":"def teardown(self) -> None:","title":""},{"docid":"b99862f8d76c7ba5a0e4cd0069059992","score":"0.6006935","text":"def post_register_handler(self, func):\n return self.hook.post_register_handler(func)","title":""},{"docid":"5f3b6108703ab4def60f5e087fd4a4d5","score":"0.6006008","text":"def teardown_class(self):","title":""},{"docid":"51c5288f747ef901c42bb38055d315a5","score":"0.6005165","text":"def shutdown(self, f):\n self.on_shutdown = f","title":""},{"docid":"0a09d28455627f09cfb6be0c94daca86","score":"0.5996199","text":"def teardown_method(self, method):\n pass","title":""},{"docid":"761470949271a19227a3ad6d88f39c76","score":"0.5993585","text":"def addCleanup(self, function, *args, **kwargs):\n pass","title":""},{"docid":"fa7dd48c444260e5cc0d0cafee636a9d","score":"0.5960369","text":"def unregister_handler(self, func):\n return self.hook.unregister_handler(func)","title":""},{"docid":"8b47a3f0ff39e9c16615ca9049751af5","score":"0.5933463","text":"def teardown_backend(self, method):\n pass","title":""},{"docid":"6503edbdd5978a82376935b92fd3835b","score":"0.5925772","text":"def addCleanup(self, function, *args, **kwargs):\n if gen.is_coroutine_function(function):\n return self._async_cleanups.append((function, args, kwargs))\n\n return super(BaseTestCase, self).addCleanup(function, *args, **kwargs)","title":""},{"docid":"410fcbccb6f62055bef61a8e2e8f7803","score":"0.59216607","text":"def register(hook_fn, *args, **kwargs):\n entry = (hook_fn, args, kwargs)\n _teardown_hooks.append(entry)","title":""},{"docid":"5bc707d25ff5b4825e045055b9aef197","score":"0.5912825","text":"def test_teardown(self):\n assert self.http_handler.teardown() is None\n self.assert_quantity_in_outbox(0)","title":""},{"docid":"5e94f1b7ce75decc0ae84bf29a7e2615","score":"0.58993286","text":"def teardown(self):","title":""},{"docid":"5e94f1b7ce75decc0ae84bf29a7e2615","score":"0.58993286","text":"def teardown(self):","title":""},{"docid":"5e94f1b7ce75decc0ae84bf29a7e2615","score":"0.58993286","text":"def teardown(self):","title":""},{"docid":"5e94f1b7ce75decc0ae84bf29a7e2615","score":"0.58993286","text":"def teardown(self):","title":""},{"docid":"5e94f1b7ce75decc0ae84bf29a7e2615","score":"0.58993286","text":"def teardown(self):","title":""},{"docid":"fa3d2ea79b735e744116b6fcd129a3f9","score":"0.58323044","text":"def unregister(env, test_type, func, *targs, **kargs):\r\n exithandlers = \"exithandlers__%s\" % test_type\r\n if env.data.get(exithandlers):\r\n env.data[exithandlers].remove((func, targs, kargs))\r\n return func","title":""},{"docid":"d77faca1fb981bcad31cd3a08d4cbc93","score":"0.5792379","text":"def handler(event):\n\n def decorator(fn):\n def apply(cls):\n event.connect(fn, sender=cls)\n return cls\n\n fn.apply = apply\n return fn\n\n return decorator","title":""},{"docid":"38b19715c3cc46a5ac54c8d7110cf985","score":"0.57874817","text":"def teardown_method(self):\n if self.ae:\n self.ae.shutdown()","title":""},{"docid":"38b19715c3cc46a5ac54c8d7110cf985","score":"0.57874817","text":"def teardown_method(self):\n if self.ae:\n self.ae.shutdown()","title":""},{"docid":"38b19715c3cc46a5ac54c8d7110cf985","score":"0.57874817","text":"def teardown_method(self):\n if self.ae:\n self.ae.shutdown()","title":""},{"docid":"38b19715c3cc46a5ac54c8d7110cf985","score":"0.57874817","text":"def teardown_method(self):\n if self.ae:\n self.ae.shutdown()","title":""},{"docid":"38b19715c3cc46a5ac54c8d7110cf985","score":"0.57874817","text":"def teardown_method(self):\n if self.ae:\n self.ae.shutdown()","title":""},{"docid":"38b19715c3cc46a5ac54c8d7110cf985","score":"0.57874817","text":"def teardown_method(self):\n if self.ae:\n self.ae.shutdown()","title":""},{"docid":"c8677ee6db6542be0feca3ce2db6d1cc","score":"0.5783382","text":"def teardown(self) -> None:\n pass","title":""},{"docid":"8edec6a5489fd343bc75592c24e24e9c","score":"0.57828677","text":"def handler(event):\r\n\r\n def decorator(fn):\r\n def apply(cls):\r\n event.connect(fn, sender=cls)\r\n return cls\r\n\r\n fn.apply = apply\r\n return fn\r\n\r\n return decorator","title":""},{"docid":"5669a3f47f28a3d0e4691342a5f74b72","score":"0.57804215","text":"def teardown_method(self, method):\n self.hass.stop()","title":""},{"docid":"5669a3f47f28a3d0e4691342a5f74b72","score":"0.57804215","text":"def teardown_method(self, method):\n self.hass.stop()","title":""},{"docid":"e392cf0bdb54ab68a8461c198df8680f","score":"0.5763199","text":"def after(f, name=None):\n return Interceptor(\n name=name or _interceptor_func_name(f),\n leave=lambda ctx: f(ctx))","title":""},{"docid":"ae95ca2fa8bfce52554b607cc82f8fb1","score":"0.5745509","text":"def atexit_register(func):\n try:\n import uwsgi\n\n orig = getattr(uwsgi, \"atexit\", None)\n\n def uwsgi_atexit():\n if callable(orig):\n orig()\n func()\n\n uwsgi.atexit = uwsgi_atexit\n except ImportError:\n atexit.register(func)","title":""},{"docid":"53817cd8cbc6aa3a180120a28f49f907","score":"0.5720094","text":"def teardown(self):\n pass","title":""},{"docid":"53817cd8cbc6aa3a180120a28f49f907","score":"0.5720094","text":"def teardown(self):\n pass","title":""},{"docid":"53817cd8cbc6aa3a180120a28f49f907","score":"0.5720094","text":"def teardown(self):\n pass","title":""},{"docid":"53817cd8cbc6aa3a180120a28f49f907","score":"0.5720094","text":"def teardown(self):\n pass","title":""},{"docid":"53817cd8cbc6aa3a180120a28f49f907","score":"0.5720094","text":"def teardown(self):\n pass","title":""},{"docid":"53817cd8cbc6aa3a180120a28f49f907","score":"0.5720094","text":"def teardown(self):\n pass","title":""},{"docid":"e8a56e292dc5a03ca8729c483c984e0c","score":"0.57088655","text":"def teardown(self) -> None:\n raise NotImplementedError # pragma: nocover","title":""},{"docid":"9175735e90047c4930fe248f5b189375","score":"0.5702149","text":"def disable_during_tests(f):\n\n @functools.wraps(f)\n def outer(*args, **kwargs):\n if settings.TESTING:\n return\n return f(*args, **kwargs)\n\n return outer","title":""},{"docid":"24efb8965b07ef67a12f5f2d24c9cec5","score":"0.56644285","text":"def teardown():\n print(\"TEAR DOWN!\")","title":""},{"docid":"44c4a58de2993be7893494ff90631641","score":"0.5651762","text":"def teardown_method(self, method):\n self.patcher.stop()","title":""},{"docid":"8870770df8985ade760c61ef5758d431","score":"0.5647366","text":"def teardown_class(klass):","title":""},{"docid":"df59f615d28b73b19a0e28a5f0a31091","score":"0.5635772","text":"def teardown_method(self):\n\n del self.const, self.instruments, self.in_kwargs, self.ref_time\n return","title":""},{"docid":"2e1e8c82572cbad4a6dcd1cc2eee41a5","score":"0.5634583","text":"def test_teardown(self):\n assert self.oef_search_handler.teardown() is None\n self.assert_quantity_in_outbox(0)","title":""},{"docid":"c914acadfe979ecfdc93389813bc52f6","score":"0.5626401","text":"def exit_handler():\r\n print(\"Test Ended\")\r\n end_test()","title":""},{"docid":"01ef302f9bfb72c5499b374bea90c7ac","score":"0.56203854","text":"def unspy(self, unregister=True):\n if hasattr(self.orig_func, 'im_self'):\n setattr(self.owner, self.func_name, self.orig_func)\n else:\n assert hasattr(self.orig_func, 'spy')\n del FunctionSpy._code_maps[self.orig_func.func_code]\n del self.orig_func.spy\n self.orig_func.func_code = self._old_code\n\n if unregister:\n self.agency.spies.remove(self)","title":""},{"docid":"fabfd6061e736e9eb43e0e92b8f320c4","score":"0.56188613","text":"def _register_cleanup_func(self, func):\n self._cleanup_funcs.append(func)","title":""},{"docid":"c9eeb173757014da941d2ceef29cb0a1","score":"0.56151485","text":"def remove_handler(self, function):\n try:\n self.handlers.remove(function)\n except ValueError: # handler wasn't in the list, pretend we don't notice\n pass","title":""},{"docid":"d63d1d92479b9e2240bdcc21f435ce0a","score":"0.5593257","text":"def register_after(self, handler):\n self._middlewares['after'].append(handler)","title":""},{"docid":"0ff21c6c357fc160e1e4715cde32f87d","score":"0.55899626","text":"def thread_shutdown_hook(func): # pragma: no cover\n thread_shutdown_hooks.append(func)\n return func","title":""},{"docid":"d748505956dd3100122c70d89a17b2eb","score":"0.5589789","text":"def teardown() -> None:\r\n file_path: str = expression_file_util.EVENT_HANDLER_SCOPE_COUNT_FILE_PATH\r\n file_util.remove_file_if_exists(file_path=file_path)","title":""},{"docid":"8755777020559874cb772e58d7df7047","score":"0.55887985","text":"def teardown_class(cls):","title":""},{"docid":"8755777020559874cb772e58d7df7047","score":"0.55887985","text":"def teardown_class(cls):","title":""},{"docid":"1802dd1c524cb47e2a3ac187a7e13b48","score":"0.5583274","text":"def teardown_test_class(duthost):\n yield\n config_reload(duthost)","title":""},{"docid":"51e7cf4d65de823bf9e60291fda4d19d","score":"0.5540833","text":"def register_finalize_hook(fn):\n _FINALIZE_HOOKS.append(fn)\n return fn","title":""},{"docid":"154f2f376f0feed958d5226db9e43724","score":"0.55214787","text":"def teardown(self):\n # delete pass after you implement.\n pass","title":""},{"docid":"154f2f376f0feed958d5226db9e43724","score":"0.55214787","text":"def teardown(self):\n # delete pass after you implement.\n pass","title":""},{"docid":"154f2f376f0feed958d5226db9e43724","score":"0.55214787","text":"def teardown(self):\n # delete pass after you implement.\n pass","title":""},{"docid":"9cb35e394d9058966a81889512cd7f62","score":"0.5512","text":"def teardown_function(function):\n if (resource('ska_mid/tm_subarray_node/1').get(\"State\") == \"ON\"):\n the_waiter = waiter()\n the_waiter.set_wait_for_tearing_down_subarray()\n SubArray(1).deallocate()\n the_waiter.wait()\n LOGGER.info(the_waiter.logs)\n if (resource('ska_mid/tm_subarray_node/1').get(\"State\") == \"OFF\"):\n the_waiter = waiter()\n the_waiter.set_wait_for_going_to_standby()\n SKAMid().standby()\n the_waiter.wait()\n LOGGER.info(the_waiter.logs)","title":""},{"docid":"b9e5ff114a527731860035975f1209e8","score":"0.55105096","text":"def __exit__(self, exc_type, exc_val, exc_tb) -> None:\n self.spec[self.func_name] = self.func","title":""},{"docid":"67e85ac7e4044c42ad522dc46668ae5f","score":"0.54970384","text":"def handler_decorator(_func: AnyFunc) -> AnyFunc:\n\n inner = get_inner(_func)\n signature = inspect.signature(inner)\n parameters = signature.parameters.items()\n converters = get_converters(inner.__name__, parameters, partial(reduce, apply))\n\n target = partial( # type: ignore\n _parse_and_call,\n func=inner,\n converters=converters,\n silent=silent,\n error_handler=error_handler,\n )\n\n if inspect.iscoroutinefunction(inner):\n\n @wraps(inner)\n async def wrapper(request: Request = None, *args, **kwargs) -> Response:\n \"\"\"Entry point to async intent handler\"\"\"\n\n return await target(*(request, *args), **kwargs)\n\n else:\n\n @wraps(inner)\n def wrapper(request: Request = None, *args, **kwargs) -> Response:\n \"\"\"Entry point to sync handler\"\"\"\n\n return target(*(request, *args), **kwargs)\n\n setattr(wrapper, \"__intent_handler__\", True)\n return wrapper","title":""},{"docid":"c33e12d43a755dbbaafeb36feb16bca0","score":"0.5489948","text":"def safe_fixture(request):\n print(\"\\n(Begin setting up safe_fixture)\")\n request.addfinalizer(safe_cleanup)\n risky_function()","title":""},{"docid":"19e2ae18c031c7017f533e89a7fb3f07","score":"0.5484392","text":"def test_unspy_with_slippery_bound_method(self):\n obj = SlipperyFuncObject()\n spy = self.agency.spy_on(obj.my_func, owner=obj)\n spy.unspy()\n\n self.assertFalse(hasattr(obj.my_func, 'spy'))\n self.assertNotIn('my_func', obj.__dict__)\n\n # Make sure the old behavior has reverted.\n slippery_func.count = 0\n\n func1 = obj.my_func\n func2 = obj.my_func\n self.assertIsNot(func1, func2)\n\n # These will have already had their values set, so we should get\n # stable results again.\n self.assertEqual(func1(), 0)\n self.assertEqual(func1(), 0)\n self.assertEqual(func2(), 1)\n self.assertEqual(func2(), 1)\n\n # These will trigger new counter increments when re-generating the\n # function in the decorator.\n self.assertEqual(obj.my_func(), 2)\n self.assertEqual(obj.my_func(), 3)","title":""},{"docid":"d54629b4974a20a248854a0d0a605bf7","score":"0.5481681","text":"def test_wiring_callable_works(self):\n self.w.this.wire(self.returns_42)\n self.addCleanup(self.w.this.unwire, self.returns_42)","title":""},{"docid":"1381f4ca1a2b3295ed8bbbf1105d1d50","score":"0.5480568","text":"def teardown_method(self) -> None:\n self.gql_patcher.stop()","title":""},{"docid":"7c894502e12f53ad3f3fb1345d8eb08a","score":"0.54796493","text":"def setUp(self):\n self.ResetHandler()","title":""},{"docid":"1f06c63254d3b677496aaca740947bea","score":"0.54755324","text":"def to_do_this(self, func):\n def to_do_func(original):\n \"\"\"\n Return supplied function\n \"\"\"\n del original\n return func\n self.__action__ = to_do_func","title":""},{"docid":"93c0f97a0a59c6a29fa33bb9648f8821","score":"0.545279","text":"def testTearDown():","title":""},{"docid":"c96874671a416a63db92b783e86f6a8b","score":"0.54483306","text":"def register_exit(func):\n\n CLEANUP_LAMBDAS.append(func)","title":""},{"docid":"f76fab3b6d5c206a89089c07effd836a","score":"0.54418224","text":"def teardown_method(self, method):\n self.sc.stop()","title":""},{"docid":"f76fab3b6d5c206a89089c07effd836a","score":"0.54418224","text":"def teardown_method(self, method):\n self.sc.stop()","title":""},{"docid":"f76fab3b6d5c206a89089c07effd836a","score":"0.54418224","text":"def teardown_method(self, method):\n self.sc.stop()","title":""},{"docid":"678c3acff38c88a533e417195ccf9825","score":"0.5436341","text":"def teardown_method(self):\n\n del self.py_inst, self.inst_time\n return","title":""},{"docid":"9acee0ceb4163849a843c9a5c061c9ad","score":"0.5430923","text":"def unsubscribe(self, f):\r\n self.add_handler(f, type='unsubscribe')\r\n return f","title":""},{"docid":"72041a5359f053692afec9bbf52231d2","score":"0.5426463","text":"def setup_handler(self, __setup_handler: SetupHandler) -> None:\r\n self._setup_handler = __setup_handler","title":""},{"docid":"0a81c5006d030d53e4fb1c72961a7db1","score":"0.54232126","text":"def teardown_method(self):\n del self.py_inst, self.inst_time\n return","title":""},{"docid":"328d7c3a515f92eacf12d31f02a2974d","score":"0.5422609","text":"def upon_setup(f: Callable):\n return PlooginEventHandler(event=PlooginEvents.SETUP, f=f)","title":""},{"docid":"b8cdd0063745ecbe048b156c47720694","score":"0.54214203","text":"def _mock_handler(self):\n\n return object()","title":""},{"docid":"0a83163e1b6d07b70f41c68ff815a20e","score":"0.5399992","text":"def intercept(func):\n name = func.func_name\n def dec(cls, path, *args, **kwargs):\n for callback in cls.callbacks[name]:\n callback(path, *args, **kwargs)\n return func(cls, path, *args, **kwargs)\n return dec","title":""},{"docid":"60c8d6d4cd1b85a672c967ea7eaff8a6","score":"0.5397453","text":"def replaces(arg):\n\n def decorator(fn):\n fn._sa_instrument_before = (\"fire_append_event\", arg)\n fn._sa_instrument_after = \"fire_remove_event\"\n return fn\n\n return decorator","title":""},{"docid":"f4b0b73cb5439f738cb349a7630379d3","score":"0.539728","text":"def teardown_method(self):\n del self.test_inst, self.start_time, self.data_list, self.meta, self.out\n return","title":""},{"docid":"1175bd977e6a1a3c89fec7fd85a1fc6c","score":"0.5396883","text":"def __del__(self):\n yield self.logout()","title":""},{"docid":"ab4bfb3e47d75db795ff97166ce2f0a0","score":"0.53966373","text":"def add_stop_hook(self, handler):\n self.stop_hooks.append(handler)","title":""},{"docid":"89ff09c0d4b5d4b30dedfcc2064e2429","score":"0.5369737","text":"def test_tear_down(self):\n ctx = context.Context(self.vm)\n\n with patch.object(ctx, '__exit__', autospec=True) as r:\n ctx.tear_down()\n r.assert_called_once_with()","title":""}],"string":"[\n {\n \"docid\": \"122522864231496b572e2050164568f0\",\n \"score\": \"0.70912457\",\n \"text\": \"def teardown_func():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b304946a999ba13baed34b64e8f6f7fe\",\n \"score\": \"0.6499198\",\n \"text\": \"def teardown_appcontext(self, f):\\r\\n self.teardown_appcontext_funcs.append(f)\\r\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bfab0117a997d4c00b8fcac9c9eef31\",\n \"score\": \"0.63808227\",\n \"text\": \"def removes_return():\\n\\n def decorator(fn):\\n fn._sa_instrument_after = \\\"fire_remove_event\\\"\\n return fn\\n\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f62c594e656418483cae9f66e51d4b15\",\n \"score\": \"0.63546515\",\n \"text\": \"def teardown_method(self, method):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6824430490ee51c8554ac87fa30f5aa\",\n \"score\": \"0.63046306\",\n \"text\": \"def teardown_method(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e28b4fd46a02ef78e7bb53df4606602\",\n \"score\": \"0.62905\",\n \"text\": \"def global_teardown():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"799e85de31556bf5ade6b6fb734ff19f\",\n \"score\": \"0.6266707\",\n \"text\": \"def teardown_request(self, f):\\r\\n self.teardown_request_funcs.setdefault(None, []).append(f)\\r\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1407bd08bd4a0720d8d5c863b8ea3eef\",\n \"score\": \"0.6244014\",\n \"text\": \"def teardown_method(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d22dd7492c2352edc502eea1964ff29\",\n \"score\": \"0.6233653\",\n \"text\": \"def teardown_function(function):\\n print(\\\"\\\\nTEAR DOWN <==\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"272bdd019cf78c57a02d76dece41ecbf\",\n \"score\": \"0.61997914\",\n \"text\": \"def test_teardown(self):\\n assert self.default_handler.teardown() is None\\n self.assert_quantity_in_outbox(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dc36fa9a96303b41df6b08092f37669\",\n \"score\": \"0.6186899\",\n \"text\": \"def event(name):\\n def decorator(fun):\\n unsafe_hook_event(name, fun)\\n @plugins.finalizer\\n def finalizer():\\n unsafe_unhook_event(name, fun)\\n return fun\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd3c9f93161b2d2285c6f4f83c07771e\",\n \"score\": \"0.613534\",\n \"text\": \"def teardown(self):\\n if getattr(self.obj, 'im_self', None): \\n name = 'teardown_method' \\n else: \\n name = 'teardown_function' \\n obj = self.parent.obj \\n meth = getattr(obj, name, None)\\n if meth is not None: \\n return meth(self.obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf4dbf9fc42c42f671302c137c8f451b\",\n \"score\": \"0.60999477\",\n \"text\": \"def removes(arg):\\n\\n def decorator(fn):\\n fn._sa_instrument_before = (\\\"fire_remove_event\\\", arg)\\n return fn\\n\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec171fd49c43d34afeff7ead9a224051\",\n \"score\": \"0.60797954\",\n \"text\": \"def test_teardown(test):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cca4a866fcbd1b6a7874e67d10483296\",\n \"score\": \"0.6051324\",\n \"text\": \"def process_func(request, resource_handler):\\n if request.node.get_closest_marker(\\\"setup_func\\\"):\\n print(\\\"SetUp function {0}\\\".format(request.function.__name__))\\n print(request.keywords.node.funcargs.keys())\\n resource_handler.clear_result()\\n\\n yield\\n\\n if request.node.get_closest_marker(\\\"setup_func\\\"):\\n print(\\\"TearDown function {0}\\\".format(request.function.__name__))\\n print(request.keywords.node.funcargs.keys())\\n resource_handler.clear_result()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b62d17afc6bd583791e1bf7784068158\",\n \"score\": \"0.60283476\",\n \"text\": \"def early_teardown():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9da38c8914547f2c018070df7d55a563\",\n \"score\": \"0.60210234\",\n \"text\": \"def teardown():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"936c217419dc476bb5fee4f59b0b6f00\",\n \"score\": \"0.6016588\",\n \"text\": \"def teardown(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b99862f8d76c7ba5a0e4cd0069059992\",\n \"score\": \"0.6006935\",\n \"text\": \"def post_register_handler(self, func):\\n return self.hook.post_register_handler(func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f3b6108703ab4def60f5e087fd4a4d5\",\n \"score\": \"0.6006008\",\n \"text\": \"def teardown_class(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c5288f747ef901c42bb38055d315a5\",\n \"score\": \"0.6005165\",\n \"text\": \"def shutdown(self, f):\\n self.on_shutdown = f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a09d28455627f09cfb6be0c94daca86\",\n \"score\": \"0.5996199\",\n \"text\": \"def teardown_method(self, method):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"761470949271a19227a3ad6d88f39c76\",\n \"score\": \"0.5993585\",\n \"text\": \"def addCleanup(self, function, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa7dd48c444260e5cc0d0cafee636a9d\",\n \"score\": \"0.5960369\",\n \"text\": \"def unregister_handler(self, func):\\n return self.hook.unregister_handler(func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b47a3f0ff39e9c16615ca9049751af5\",\n \"score\": \"0.5933463\",\n \"text\": \"def teardown_backend(self, method):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6503edbdd5978a82376935b92fd3835b\",\n \"score\": \"0.5925772\",\n \"text\": \"def addCleanup(self, function, *args, **kwargs):\\n if gen.is_coroutine_function(function):\\n return self._async_cleanups.append((function, args, kwargs))\\n\\n return super(BaseTestCase, self).addCleanup(function, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"410fcbccb6f62055bef61a8e2e8f7803\",\n \"score\": \"0.59216607\",\n \"text\": \"def register(hook_fn, *args, **kwargs):\\n entry = (hook_fn, args, kwargs)\\n _teardown_hooks.append(entry)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bc707d25ff5b4825e045055b9aef197\",\n \"score\": \"0.5912825\",\n \"text\": \"def test_teardown(self):\\n assert self.http_handler.teardown() is None\\n self.assert_quantity_in_outbox(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e94f1b7ce75decc0ae84bf29a7e2615\",\n \"score\": \"0.58993286\",\n \"text\": \"def teardown(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e94f1b7ce75decc0ae84bf29a7e2615\",\n \"score\": \"0.58993286\",\n \"text\": \"def teardown(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e94f1b7ce75decc0ae84bf29a7e2615\",\n \"score\": \"0.58993286\",\n \"text\": \"def teardown(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e94f1b7ce75decc0ae84bf29a7e2615\",\n \"score\": \"0.58993286\",\n \"text\": \"def teardown(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e94f1b7ce75decc0ae84bf29a7e2615\",\n \"score\": \"0.58993286\",\n \"text\": \"def teardown(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3d2ea79b735e744116b6fcd129a3f9\",\n \"score\": \"0.58323044\",\n \"text\": \"def unregister(env, test_type, func, *targs, **kargs):\\r\\n exithandlers = \\\"exithandlers__%s\\\" % test_type\\r\\n if env.data.get(exithandlers):\\r\\n env.data[exithandlers].remove((func, targs, kargs))\\r\\n return func\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d77faca1fb981bcad31cd3a08d4cbc93\",\n \"score\": \"0.5792379\",\n \"text\": \"def handler(event):\\n\\n def decorator(fn):\\n def apply(cls):\\n event.connect(fn, sender=cls)\\n return cls\\n\\n fn.apply = apply\\n return fn\\n\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b19715c3cc46a5ac54c8d7110cf985\",\n \"score\": \"0.57874817\",\n \"text\": \"def teardown_method(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b19715c3cc46a5ac54c8d7110cf985\",\n \"score\": \"0.57874817\",\n \"text\": \"def teardown_method(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b19715c3cc46a5ac54c8d7110cf985\",\n \"score\": \"0.57874817\",\n \"text\": \"def teardown_method(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b19715c3cc46a5ac54c8d7110cf985\",\n \"score\": \"0.57874817\",\n \"text\": \"def teardown_method(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b19715c3cc46a5ac54c8d7110cf985\",\n \"score\": \"0.57874817\",\n \"text\": \"def teardown_method(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b19715c3cc46a5ac54c8d7110cf985\",\n \"score\": \"0.57874817\",\n \"text\": \"def teardown_method(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8677ee6db6542be0feca3ce2db6d1cc\",\n \"score\": \"0.5783382\",\n \"text\": \"def teardown(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8edec6a5489fd343bc75592c24e24e9c\",\n \"score\": \"0.57828677\",\n \"text\": \"def handler(event):\\r\\n\\r\\n def decorator(fn):\\r\\n def apply(cls):\\r\\n event.connect(fn, sender=cls)\\r\\n return cls\\r\\n\\r\\n fn.apply = apply\\r\\n return fn\\r\\n\\r\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5669a3f47f28a3d0e4691342a5f74b72\",\n \"score\": \"0.57804215\",\n \"text\": \"def teardown_method(self, method):\\n self.hass.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5669a3f47f28a3d0e4691342a5f74b72\",\n \"score\": \"0.57804215\",\n \"text\": \"def teardown_method(self, method):\\n self.hass.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e392cf0bdb54ab68a8461c198df8680f\",\n \"score\": \"0.5763199\",\n \"text\": \"def after(f, name=None):\\n return Interceptor(\\n name=name or _interceptor_func_name(f),\\n leave=lambda ctx: f(ctx))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae95ca2fa8bfce52554b607cc82f8fb1\",\n \"score\": \"0.5745509\",\n \"text\": \"def atexit_register(func):\\n try:\\n import uwsgi\\n\\n orig = getattr(uwsgi, \\\"atexit\\\", None)\\n\\n def uwsgi_atexit():\\n if callable(orig):\\n orig()\\n func()\\n\\n uwsgi.atexit = uwsgi_atexit\\n except ImportError:\\n atexit.register(func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53817cd8cbc6aa3a180120a28f49f907\",\n \"score\": \"0.5720094\",\n \"text\": \"def teardown(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53817cd8cbc6aa3a180120a28f49f907\",\n \"score\": \"0.5720094\",\n \"text\": \"def teardown(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53817cd8cbc6aa3a180120a28f49f907\",\n \"score\": \"0.5720094\",\n \"text\": \"def teardown(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53817cd8cbc6aa3a180120a28f49f907\",\n \"score\": \"0.5720094\",\n \"text\": \"def teardown(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53817cd8cbc6aa3a180120a28f49f907\",\n \"score\": \"0.5720094\",\n \"text\": \"def teardown(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53817cd8cbc6aa3a180120a28f49f907\",\n \"score\": \"0.5720094\",\n \"text\": \"def teardown(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8a56e292dc5a03ca8729c483c984e0c\",\n \"score\": \"0.57088655\",\n \"text\": \"def teardown(self) -> None:\\n raise NotImplementedError # pragma: nocover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9175735e90047c4930fe248f5b189375\",\n \"score\": \"0.5702149\",\n \"text\": \"def disable_during_tests(f):\\n\\n @functools.wraps(f)\\n def outer(*args, **kwargs):\\n if settings.TESTING:\\n return\\n return f(*args, **kwargs)\\n\\n return outer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24efb8965b07ef67a12f5f2d24c9cec5\",\n \"score\": \"0.56644285\",\n \"text\": \"def teardown():\\n print(\\\"TEAR DOWN!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c4a58de2993be7893494ff90631641\",\n \"score\": \"0.5651762\",\n \"text\": \"def teardown_method(self, method):\\n self.patcher.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8870770df8985ade760c61ef5758d431\",\n \"score\": \"0.5647366\",\n \"text\": \"def teardown_class(klass):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df59f615d28b73b19a0e28a5f0a31091\",\n \"score\": \"0.5635772\",\n \"text\": \"def teardown_method(self):\\n\\n del self.const, self.instruments, self.in_kwargs, self.ref_time\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e1e8c82572cbad4a6dcd1cc2eee41a5\",\n \"score\": \"0.5634583\",\n \"text\": \"def test_teardown(self):\\n assert self.oef_search_handler.teardown() is None\\n self.assert_quantity_in_outbox(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c914acadfe979ecfdc93389813bc52f6\",\n \"score\": \"0.5626401\",\n \"text\": \"def exit_handler():\\r\\n print(\\\"Test Ended\\\")\\r\\n end_test()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01ef302f9bfb72c5499b374bea90c7ac\",\n \"score\": \"0.56203854\",\n \"text\": \"def unspy(self, unregister=True):\\n if hasattr(self.orig_func, 'im_self'):\\n setattr(self.owner, self.func_name, self.orig_func)\\n else:\\n assert hasattr(self.orig_func, 'spy')\\n del FunctionSpy._code_maps[self.orig_func.func_code]\\n del self.orig_func.spy\\n self.orig_func.func_code = self._old_code\\n\\n if unregister:\\n self.agency.spies.remove(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fabfd6061e736e9eb43e0e92b8f320c4\",\n \"score\": \"0.56188613\",\n \"text\": \"def _register_cleanup_func(self, func):\\n self._cleanup_funcs.append(func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9eeb173757014da941d2ceef29cb0a1\",\n \"score\": \"0.56151485\",\n \"text\": \"def remove_handler(self, function):\\n try:\\n self.handlers.remove(function)\\n except ValueError: # handler wasn't in the list, pretend we don't notice\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d63d1d92479b9e2240bdcc21f435ce0a\",\n \"score\": \"0.5593257\",\n \"text\": \"def register_after(self, handler):\\n self._middlewares['after'].append(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ff21c6c357fc160e1e4715cde32f87d\",\n \"score\": \"0.55899626\",\n \"text\": \"def thread_shutdown_hook(func): # pragma: no cover\\n thread_shutdown_hooks.append(func)\\n return func\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d748505956dd3100122c70d89a17b2eb\",\n \"score\": \"0.5589789\",\n \"text\": \"def teardown() -> None:\\r\\n file_path: str = expression_file_util.EVENT_HANDLER_SCOPE_COUNT_FILE_PATH\\r\\n file_util.remove_file_if_exists(file_path=file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8755777020559874cb772e58d7df7047\",\n \"score\": \"0.55887985\",\n \"text\": \"def teardown_class(cls):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8755777020559874cb772e58d7df7047\",\n \"score\": \"0.55887985\",\n \"text\": \"def teardown_class(cls):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1802dd1c524cb47e2a3ac187a7e13b48\",\n \"score\": \"0.5583274\",\n \"text\": \"def teardown_test_class(duthost):\\n yield\\n config_reload(duthost)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51e7cf4d65de823bf9e60291fda4d19d\",\n \"score\": \"0.5540833\",\n \"text\": \"def register_finalize_hook(fn):\\n _FINALIZE_HOOKS.append(fn)\\n return fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"154f2f376f0feed958d5226db9e43724\",\n \"score\": \"0.55214787\",\n \"text\": \"def teardown(self):\\n # delete pass after you implement.\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"154f2f376f0feed958d5226db9e43724\",\n \"score\": \"0.55214787\",\n \"text\": \"def teardown(self):\\n # delete pass after you implement.\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"154f2f376f0feed958d5226db9e43724\",\n \"score\": \"0.55214787\",\n \"text\": \"def teardown(self):\\n # delete pass after you implement.\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cb35e394d9058966a81889512cd7f62\",\n \"score\": \"0.5512\",\n \"text\": \"def teardown_function(function):\\n if (resource('ska_mid/tm_subarray_node/1').get(\\\"State\\\") == \\\"ON\\\"):\\n the_waiter = waiter()\\n the_waiter.set_wait_for_tearing_down_subarray()\\n SubArray(1).deallocate()\\n the_waiter.wait()\\n LOGGER.info(the_waiter.logs)\\n if (resource('ska_mid/tm_subarray_node/1').get(\\\"State\\\") == \\\"OFF\\\"):\\n the_waiter = waiter()\\n the_waiter.set_wait_for_going_to_standby()\\n SKAMid().standby()\\n the_waiter.wait()\\n LOGGER.info(the_waiter.logs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9e5ff114a527731860035975f1209e8\",\n \"score\": \"0.55105096\",\n \"text\": \"def __exit__(self, exc_type, exc_val, exc_tb) -> None:\\n self.spec[self.func_name] = self.func\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67e85ac7e4044c42ad522dc46668ae5f\",\n \"score\": \"0.54970384\",\n \"text\": \"def handler_decorator(_func: AnyFunc) -> AnyFunc:\\n\\n inner = get_inner(_func)\\n signature = inspect.signature(inner)\\n parameters = signature.parameters.items()\\n converters = get_converters(inner.__name__, parameters, partial(reduce, apply))\\n\\n target = partial( # type: ignore\\n _parse_and_call,\\n func=inner,\\n converters=converters,\\n silent=silent,\\n error_handler=error_handler,\\n )\\n\\n if inspect.iscoroutinefunction(inner):\\n\\n @wraps(inner)\\n async def wrapper(request: Request = None, *args, **kwargs) -> Response:\\n \\\"\\\"\\\"Entry point to async intent handler\\\"\\\"\\\"\\n\\n return await target(*(request, *args), **kwargs)\\n\\n else:\\n\\n @wraps(inner)\\n def wrapper(request: Request = None, *args, **kwargs) -> Response:\\n \\\"\\\"\\\"Entry point to sync handler\\\"\\\"\\\"\\n\\n return target(*(request, *args), **kwargs)\\n\\n setattr(wrapper, \\\"__intent_handler__\\\", True)\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c33e12d43a755dbbaafeb36feb16bca0\",\n \"score\": \"0.5489948\",\n \"text\": \"def safe_fixture(request):\\n print(\\\"\\\\n(Begin setting up safe_fixture)\\\")\\n request.addfinalizer(safe_cleanup)\\n risky_function()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e2ae18c031c7017f533e89a7fb3f07\",\n \"score\": \"0.5484392\",\n \"text\": \"def test_unspy_with_slippery_bound_method(self):\\n obj = SlipperyFuncObject()\\n spy = self.agency.spy_on(obj.my_func, owner=obj)\\n spy.unspy()\\n\\n self.assertFalse(hasattr(obj.my_func, 'spy'))\\n self.assertNotIn('my_func', obj.__dict__)\\n\\n # Make sure the old behavior has reverted.\\n slippery_func.count = 0\\n\\n func1 = obj.my_func\\n func2 = obj.my_func\\n self.assertIsNot(func1, func2)\\n\\n # These will have already had their values set, so we should get\\n # stable results again.\\n self.assertEqual(func1(), 0)\\n self.assertEqual(func1(), 0)\\n self.assertEqual(func2(), 1)\\n self.assertEqual(func2(), 1)\\n\\n # These will trigger new counter increments when re-generating the\\n # function in the decorator.\\n self.assertEqual(obj.my_func(), 2)\\n self.assertEqual(obj.my_func(), 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d54629b4974a20a248854a0d0a605bf7\",\n \"score\": \"0.5481681\",\n \"text\": \"def test_wiring_callable_works(self):\\n self.w.this.wire(self.returns_42)\\n self.addCleanup(self.w.this.unwire, self.returns_42)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1381f4ca1a2b3295ed8bbbf1105d1d50\",\n \"score\": \"0.5480568\",\n \"text\": \"def teardown_method(self) -> None:\\n self.gql_patcher.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c894502e12f53ad3f3fb1345d8eb08a\",\n \"score\": \"0.54796493\",\n \"text\": \"def setUp(self):\\n self.ResetHandler()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f06c63254d3b677496aaca740947bea\",\n \"score\": \"0.54755324\",\n \"text\": \"def to_do_this(self, func):\\n def to_do_func(original):\\n \\\"\\\"\\\"\\n Return supplied function\\n \\\"\\\"\\\"\\n del original\\n return func\\n self.__action__ = to_do_func\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93c0f97a0a59c6a29fa33bb9648f8821\",\n \"score\": \"0.545279\",\n \"text\": \"def testTearDown():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96874671a416a63db92b783e86f6a8b\",\n \"score\": \"0.54483306\",\n \"text\": \"def register_exit(func):\\n\\n CLEANUP_LAMBDAS.append(func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f76fab3b6d5c206a89089c07effd836a\",\n \"score\": \"0.54418224\",\n \"text\": \"def teardown_method(self, method):\\n self.sc.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f76fab3b6d5c206a89089c07effd836a\",\n \"score\": \"0.54418224\",\n \"text\": \"def teardown_method(self, method):\\n self.sc.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f76fab3b6d5c206a89089c07effd836a\",\n \"score\": \"0.54418224\",\n \"text\": \"def teardown_method(self, method):\\n self.sc.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"678c3acff38c88a533e417195ccf9825\",\n \"score\": \"0.5436341\",\n \"text\": \"def teardown_method(self):\\n\\n del self.py_inst, self.inst_time\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9acee0ceb4163849a843c9a5c061c9ad\",\n \"score\": \"0.5430923\",\n \"text\": \"def unsubscribe(self, f):\\r\\n self.add_handler(f, type='unsubscribe')\\r\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72041a5359f053692afec9bbf52231d2\",\n \"score\": \"0.5426463\",\n \"text\": \"def setup_handler(self, __setup_handler: SetupHandler) -> None:\\r\\n self._setup_handler = __setup_handler\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a81c5006d030d53e4fb1c72961a7db1\",\n \"score\": \"0.54232126\",\n \"text\": \"def teardown_method(self):\\n del self.py_inst, self.inst_time\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"328d7c3a515f92eacf12d31f02a2974d\",\n \"score\": \"0.5422609\",\n \"text\": \"def upon_setup(f: Callable):\\n return PlooginEventHandler(event=PlooginEvents.SETUP, f=f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8cdd0063745ecbe048b156c47720694\",\n \"score\": \"0.54214203\",\n \"text\": \"def _mock_handler(self):\\n\\n return object()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a83163e1b6d07b70f41c68ff815a20e\",\n \"score\": \"0.5399992\",\n \"text\": \"def intercept(func):\\n name = func.func_name\\n def dec(cls, path, *args, **kwargs):\\n for callback in cls.callbacks[name]:\\n callback(path, *args, **kwargs)\\n return func(cls, path, *args, **kwargs)\\n return dec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60c8d6d4cd1b85a672c967ea7eaff8a6\",\n \"score\": \"0.5397453\",\n \"text\": \"def replaces(arg):\\n\\n def decorator(fn):\\n fn._sa_instrument_before = (\\\"fire_append_event\\\", arg)\\n fn._sa_instrument_after = \\\"fire_remove_event\\\"\\n return fn\\n\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4b0b73cb5439f738cb349a7630379d3\",\n \"score\": \"0.539728\",\n \"text\": \"def teardown_method(self):\\n del self.test_inst, self.start_time, self.data_list, self.meta, self.out\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1175bd977e6a1a3c89fec7fd85a1fc6c\",\n \"score\": \"0.5396883\",\n \"text\": \"def __del__(self):\\n yield self.logout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab4bfb3e47d75db795ff97166ce2f0a0\",\n \"score\": \"0.53966373\",\n \"text\": \"def add_stop_hook(self, handler):\\n self.stop_hooks.append(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89ff09c0d4b5d4b30dedfcc2064e2429\",\n \"score\": \"0.5369737\",\n \"text\": \"def test_tear_down(self):\\n ctx = context.Context(self.vm)\\n\\n with patch.object(ctx, '__exit__', autospec=True) as r:\\n ctx.tear_down()\\n r.assert_called_once_with()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":493,"cells":{"query_id":{"kind":"string","value":"ae1c483eb54cca9c8de99dade4522368"},"query":{"kind":"string","value":"load all plugins, warn if a plugin couldn't be loaded."},"positive_passages":{"kind":"list like","value":[{"docid":"5e96272094c0dc55ffcd58db3de76cb8","score":"0.6897457","text":"def load_all(self):\n # get the names of all required plugins\n names = samuraix.config.get('core.plugins', [])\n log.debug('loading %s', names)\n # get all available entrypoints and create a dictionary mapping\n # an entrypoint's name to an entrypoint.\n entrypoints = list(pkg_resources.iter_entry_points('samuraix.plugin'))\n dct = dict((ep.name, ep) for ep in entrypoints)\n\n for name in names:\n try:\n ep = dct[name]\n except KeyError:\n log.error(\"The plugin '%s' couldn't be found!\" % name)\n else:\n log.info(\"Loading plugin '%s'...\", name)\n cls = ep.load()\n self[cls.key] = cls(self.app)","title":""}],"string":"[\n {\n \"docid\": \"5e96272094c0dc55ffcd58db3de76cb8\",\n \"score\": \"0.6897457\",\n \"text\": \"def load_all(self):\\n # get the names of all required plugins\\n names = samuraix.config.get('core.plugins', [])\\n log.debug('loading %s', names)\\n # get all available entrypoints and create a dictionary mapping\\n # an entrypoint's name to an entrypoint.\\n entrypoints = list(pkg_resources.iter_entry_points('samuraix.plugin'))\\n dct = dict((ep.name, ep) for ep in entrypoints)\\n\\n for name in names:\\n try:\\n ep = dct[name]\\n except KeyError:\\n log.error(\\\"The plugin '%s' couldn't be found!\\\" % name)\\n else:\\n log.info(\\\"Loading plugin '%s'...\\\", name)\\n cls = ep.load()\\n self[cls.key] = cls(self.app)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a0187f0401cfa952954bc543f1edc71f","score":"0.75690514","text":"def load_plugins():","title":""},{"docid":"b85d1c938ae197871c603b0950307892","score":"0.73343587","text":"def LoadPlugins(self):\n\t\tfor curfile in [f for f in glob.glob(os.path.join('plugins', self.PluginPath, '*.py'))\n\t\t\t\t\tif not os.path.basename(f) in ['__init__.py']]:\n\t\t\tLoadPluginFile(curfile)","title":""},{"docid":"593ce4902165ee83444aed2589435f1d","score":"0.71911377","text":"def load_plugins(self, plugins):\r\n\r\n # prints an info message about the import operation\r\n # that is going to be performed in the plugin\r\n self.info(\"Loading plugins (importing %d main module files)...\" % len(plugins))\r\n\r\n # iterates over all the plugins requested for loading and\r\n # runs the import operation for each of them in case that\r\n # operation is required by module inexistence\r\n for plugin in plugins:\r\n # in case the plugin module is already loaded continues\r\n # the loop as no loading is required for it, otherwise\r\n # runs the proper loading process for the plugin logging\r\n # an error in case an exception occurs in the importing\r\n if plugin in sys.modules: continue\r\n try: __import__(plugin)\r\n except Exception as exception:\r\n self.error(\"Problem importing module %s: %s\" % (plugin, legacy.UNICODE(exception)))\r\n\r\n # prints an info message about the fact that the loading\r\n # operation for all of the requested plugin has finished\r\n self.info(\"Finished loading plugins\")","title":""},{"docid":"dc9757ef6ead2d10aa3e85e7faef073c","score":"0.7007953","text":"def _load_plugins(self):\n plugins = ait.config.get('server.plugins')\n\n if plugins is None:\n log.warn('No plugins specified in config.')\n else:\n for index, p in enumerate(plugins):\n try:\n plugin = self._create_plugin(p['plugin'])\n self.plugins.append(plugin)\n log.info('Added plugin {}'.format(plugin))\n\n except Exception:\n exc_type, value, tb = sys.exc_info()\n log.error('{} creating plugin {}: {}'.format(exc_type,\n index,\n value))\n if not self.plugins:\n log.warn('No valid plugin configurations found. No plugins will be added.')","title":""},{"docid":"06de2f29a24d1c02d4b296800386dabc","score":"0.6884243","text":"def load_plugins(self):\n plugin_manager = PluginManager()\n self.__fs_plugins = plugin_manager.load_filesystem_plugins()","title":""},{"docid":"41f17f756dbe7fa787bc0fe219585060","score":"0.68089104","text":"def load():\n global _loaded\n if _loaded:\n return\n log.log.debug(\"Loading plugins\")\n _loaded = True\n\n for folder, sub, files in os.walk(\"nab/plugins/\"):\n sys.path.insert(0, folder)\n for f in files:\n fname, ext = os.path.splitext(f)\n if ext == '.py' and fname != \"__init__\":\n log.log.debug(fname)\n importlib.import_module(fname)","title":""},{"docid":"e7c296bc9610964381613d30a8337554","score":"0.6773787","text":"def load_plugins(self):\n # note: need to update if the setup.py module names change\n MODULE_NAME = \"floss\"\n req = pkg_resources.Requirement.parse(MODULE_NAME)\n requested_directory = os.path.join(MODULE_NAME, \"plugins\")\n try:\n plugins_path = pkg_resources.resource_filename(req, requested_directory)\n\n plugnplay.plugin_dirs = [plugins_path]\n plugnplay.load_plugins(logging.getLogger(\"plugin_loader\"))\n except pkg_resources.DistributionNotFound as e:\n self.i(\"failed to load extra plugins: %s\", e)","title":""},{"docid":"0708b4fd3248e392ca6417a85d0fc89f","score":"0.6760845","text":"def loadHandlers(tag):\n\n def pr (*args,**keys):\n if not g.app.unitTesting:\n g.es_print(*args,**keys)\n\n plugins_path = g.os_path_abspath(g.os_path_join(g.app.loadDir,\"..\",\"plugins\"))\n files = glob.glob(g.os_path_join(plugins_path,\"*.py\"))\n files = [g.os_path_abspath(theFile) for theFile in files]\n\n s = g.app.config.getEnabledPlugins()\n if not s: return\n\n if not g.app.silentMode:\n pr('@enabled-plugins found in %s' % (\n g.app.config.enabledPluginsFileName),color='blue')\n\n enabled_files = getEnabledFiles(s,plugins_path)\n\n # Load plugins in the order they appear in the enabled_files list.\n if files and enabled_files:\n for theFile in enabled_files:\n if theFile in files:\n loadOnePlugin(theFile)\n\n # Note: g.plugin_signon adds module names to g.app.loadedPlugins\n if 0:\n if g.app.loadedPlugins:\n pr(\"%d plugins loaded\" % (len(g.app.loadedPlugins)), color=\"blue\")","title":""},{"docid":"7d4c9386e45cce1c12e362683fd84987","score":"0.6735198","text":"def init_plugins (self):\n self.load_plugins()\n for p in self.plugins:\n p.init()","title":""},{"docid":"8d4a8b252a64c06f381d34e5809f0e4c","score":"0.67192745","text":"def load_plugins(self):\n if self.base:\n codecs = self.base + '.plugins.codecs'\n compressions = self.base + '.plugins.compressions'\n assemblies = self.base + '.plugins.assemblies'\n hashs = self.base + '.plugins.hashs'\n formatters = self.base + '.plugins.formatters'\n misc = self.base + '.plugins.misc'\n else:\n codecs = 'deen.plugins.codecs'\n compressions = 'deen.plugins.compressions'\n assemblies = 'deen.plugins.assemblies'\n hashs = 'deen.plugins.hashs'\n formatters = 'deen.plugins.formatters'\n misc = 'deen.plugins.misc'\n codecs = importlib.import_module(codecs)\n self.codecs = self._get_plugin_classes_from_module(codecs)\n compressions = importlib.import_module(compressions)\n self.compressions = self._get_plugin_classes_from_module(compressions)\n assemblies = importlib.import_module(assemblies)\n self.assemblies = self._get_plugin_classes_from_module(assemblies)\n hashs = importlib.import_module(hashs)\n self.hashs = self._get_plugin_classes_from_module(hashs)\n formatters = importlib.import_module(formatters)\n self.formatters = self._get_plugin_classes_from_module(formatters)\n misc = importlib.import_module(misc)\n self.misc = self._get_plugin_classes_from_module(misc)","title":""},{"docid":"ea6e8895e06fdcaaa41cff9117e86010","score":"0.66606915","text":"def load_plugins():\n loaded_plugins = []\n plugins_info = get_plugins_info()\n for plugin_info in plugins_info:\n plugin_module = imp.load_module(main_module, *plugin_info[\"info\"])\n loaded_plugins.append(plugin_module)\n return loaded_plugins","title":""},{"docid":"eff480252ce0c401fcaa83e44a23ccfb","score":"0.6649144","text":"def notify_load_complete_loaded_plugins(self):\r\n\r\n # retrieves the loaded plugins list\r\n loaded_plugins_list = self.get_all_loaded_plugins()\r\n\r\n # iterates over all the loaded plugins\r\n for loaded_plugin in loaded_plugins_list:\r\n # notifies the plugin about the load complete\r\n loaded_plugin.init_complete()","title":""},{"docid":"7c9868d3b0ce0ec0a4f7aa23b8de00bf","score":"0.66406846","text":"def _import_plugins(self):\n if self.detected:\n return\n\n # In some cases, plugin scanning may start during a request.\n # Make sure there is only one thread scanning for plugins.\n self.scanLock.acquire()\n if self.detected:\n return # previous threaded released + completed\n\n try:\n _import_apps_submodule(\"content_plugins\")\n self.detected = True\n finally:\n self.scanLock.release()","title":""},{"docid":"e68366fc43e018fe946db719ed03f550","score":"0.6628261","text":"def import_plugins(self):\n if self.imported:\n return\n for plugin_name in settings.PLUGINS:\n module_name, cls = plugin_name.rsplit(\".\", 1)\n module = import_module(module_name)\n plugin = getattr(module, cls)\n self.plugin_classes.append(plugin)","title":""},{"docid":"36a8168789dfbd1fd8d02231891ea390","score":"0.66164017","text":"def register_plugins(self):\n\t\tif self.plugin_whitelist:\n\t\t\tplugin_list = self.plugin_whitelist\n\t\telse:\n\t\t\tplugin_list = plugins.__all__\n\t\tfor plugin in plugin_list:\n\t\t\tif plugin in plugins.__all__:\n\t\t\t\tself.registerPlugin(plugin, self.plugin_config.get(plugin, {}), False)\n\t\t\telse:\n\t\t\t\traise NameError(\"No plugin by the name of %s listed in plugins.__all__.\" % plugin)\n\t\t# run post_init() for cross-plugin interaction\n\t\tfor plugin in self.plugin:\n\t\t\tself.plugin[plugin].post_init()","title":""},{"docid":"a35ce8a68001acd50841081ebcca242b","score":"0.65868634","text":"def reload_plugins(self):\n self.plugins = []\n self.seen_paths = []\n logger.info(f'Looking for plugins under package {self.plugin_package}')\n self.walk_package(self.plugin_package)","title":""},{"docid":"6bc5daf3337b90ecce8b21a44bf99f5a","score":"0.6573317","text":"def load_plugins(self):\r\n\r\n for plugin_path in settings.PLUGINS:\r\n try:\r\n try:\r\n pg_module, pg_classname = plugin_path.rsplit('.', 1)\r\n except ValueError, e:\r\n raise exceptions.ImproperlyConfigured('%s isn\\'t a plugin module' % plugin_path)\r\n\r\n try:\r\n mod = importlib.import_module(pg_module)\r\n except ImportError, e:\r\n raise exceptions.ImproperlyConfigured('Error importing plugin %s: \"%s\"' % (pg_module, e))\r\n\r\n try:\r\n pg_class = getattr(mod, pg_classname)\r\n except AttributeError, e:\r\n raise exceptions.ImproperlyConfigured('Plugins module \"%s\" does not define a \"%s\" class' % (pg_module, pg_classname))\r\n\r\n try:\r\n pg_instance = pg_class(handler=self, protocol=self.protocol)\r\n except TypeError, e:\r\n raise exceptions.InvalidPlugin('Plugins class \"%s\" does not provide a command: %s' % (pg_class, e))\r\n\r\n if hasattr(pg_instance, 'command'):\r\n if isinstance(pg_instance.command, str):\r\n self._command_plugins[pg_instance.command] = pg_instance\r\n else:\r\n for command in pg_instance.command:\r\n self._command_plugins[command] = pg_instance\r\n else:\r\n self._generic_plugins.append(pg_instance)\r\n except exceptions.JeevesException, e:\r\n if pg_classname:\r\n print \"%s: %s\" % (pg_classname, e)\r\n else:\r\n print e","title":""},{"docid":"499980f31a450fedd65b30aaf87bc78a","score":"0.65441203","text":"def find_plugins(self):\n if len(self.plugins):\n # only let this happen once per launch\n return\n\n if config:\n path = os.path.abspath(os.path.join(os.path.dirname(config.config.plugin_dir), API_PLUGINS_FOLDER))\n for item in sorted(os.listdir(path)):\n loader_file = os.path.join(path, item, API_PLUGIN_LOADER)\n if os.path.exists(loader_file):\n print(\"Found plugin file: {}\".format(loader_file))\n loader_dir = os.path.join(path, item)\n orig_path = list(sys.path)\n try:\n if loader_dir not in sys.path:\n sys.path.append(loader_dir)\n plugin_module = importlib.machinery.SourceFileLoader(\n 'plugin_{}'.format(item.encode(\n encoding='ascii',\n errors='replace').decode('utf-8').replace('.', '_')),\n loader_file).load_module()\n if \"__plugin__\" in dir(plugin_module):\n plugin = plugin_module.__plugin__\n if not isinstance(plugin, EDMCPluginBase):\n raise ValueError(\"{} from {} is not a subclass of EDMCPluginBase\".format(\n str(plugin), item))\n print(\"Loaded plugin {} from module {}\".format(str(plugin), item))\n self.plugins.append(plugin)\n\n else:\n raise AttributeError(\"plugin {} lacks __plugin__\".format(loader_dir))\n except Exception as err:\n print(\"Error attempting to discover plugin {} : {}\".format(loader_file, err))\n sys.path = orig_path","title":""},{"docid":"076cc8c86b7ea3a7a2cd5c9dac0356a1","score":"0.65420604","text":"def load_plugins(self):\n\n ocio_plugins = self.config.get('ocio_plugins', default=dict())\n if not ocio_plugins:\n LOGGER.warning('No OCIO plugins found in configuration file: \"{}\"'.format(self.config.get_path()))\n return\n\n for ocio_plugin_name, ocio_plugin_info in ocio_plugins.items():\n plugin_name = ocio_plugin_info.get('plugin_name', None)\n if not plugin_name:\n LOGGER.warning(\n 'Impossible to load \"{}\" OCIO Plugin because its plugin_name is not defined!'.format(\n ocio_plugin_name))\n continue\n\n if not tp.Dcc.is_plugin_loaded(plugin_name):\n LOGGER.info('Loading OCIO Plugin: \"{}\"'.format(plugin_name))\n try:\n tp.Dcc.load_plugin(plugin_name, quiet=True)\n except Exception as exc:\n LOGGER.error(\n 'Error while loading OCIO Plugin: \"{}\" | {} | {}'.format(\n plugin_name, exc, traceback.format_exc()))\n continue\n\n LOGGER.info('OCIO Plugin \"{}\" loaded successfully!'.format(plugin_name))\n self.__class__._available_plugins.append(ocio_plugin_name)","title":""},{"docid":"ed8e9dc0d50db8bc5cb66061df16c85b","score":"0.65240896","text":"def load_plugin_classes(self):\n c.world_load_plugin_classes(self.world)","title":""},{"docid":"57702d14121a171e55d822faa6c25b7e","score":"0.6510039","text":"def plugins():\n pass","title":""},{"docid":"2b741b2266d3000680a80836c366666b","score":"0.6471247","text":"def _check_plugins(self, name: str) -> None:\n if name not in self._plugins and name not in self._plugin_status:\n raise WechatyPluginError(f'plugins <{name}> not exist')","title":""},{"docid":"4e1662bf6bbaca5c8de8cf0abfd3a8d5","score":"0.6442013","text":"def _locate_plugins() -> None:\n path = (\n Path(sys.executable).parent\n if hasattr(sys, \"frozen\")\n else REPOSITORY_DIRECTORY\n )\n current_system = get_current_system()\n\n # This is for debugging purposes\n if current_system.DEBUG_MODE:\n console.print(f\"[bold green]Loading plugins from {path}[/]\")\n console.print(\"[bold green]Plugins found:[/]\")\n\n for plugin in Path(__file__).parent.glob(\"plugins/*_plugin.py\"):\n python_path = plugin.relative_to(path).with_suffix(\"\")\n\n # This is for debugging purposes\n if current_system.DEBUG_MODE:\n console.print(f\" [bold red]{plugin.name}[/]\")\n console.print(f\" [bold yellow]{python_path}[/]\")\n console.print(f\" [bold bright_cyan]{__package__}[/]\")\n console.print(f\" [bold magenta]{python_path.parts}[/]\")\n console.print(\n f\" [bold bright_magenta]{'.'.join(python_path.parts)}[/]\"\n )\n\n module = importlib.import_module(\n \".\".join(python_path.parts), package=__package__\n )\n for _, obj in inspect.getmembers(module):\n if (\n inspect.isclass(obj)\n and issubclass(obj, (PltTA))\n and obj != PlotlyTA.__class__\n ) and obj not in PlotlyTA.plugins:\n PlotlyTA.plugins.append(obj)","title":""},{"docid":"45954d365c4be3e1e436c1ba223e0c90","score":"0.6440719","text":"def start_plugins(self):\n good_plugins = []\n for plugin in list(self.plugins):\n print(\"Starting plugin: {}\".format(plugin))\n try:\n plugin.plugin_start()\n good_plugins.append(plugin)\n except Exception as err:\n print(\"Error starting plugin {}\".format(err))\n self.plugins = good_plugins","title":""},{"docid":"14e5755eb839b29fcbb0ad560f91210f","score":"0.6426013","text":"def discover():\n if CFG[\"plugins\"][\"autoload\"]:\n experiment_plugins = CFG[\"plugins\"][\"experiments\"].value\n for exp_plugin in experiment_plugins:\n try:\n importlib.import_module(exp_plugin)\n except ImportError as import_error:\n LOG.error(\"Could not find '%s'\", exp_plugin)\n LOG.error(\"ImportError: %s\", import_error.msg)","title":""},{"docid":"7cb930da2ac32e8f69910a6939ef39a0","score":"0.6419173","text":"def load_plugins(self, filter=None, use_blacklist=True):\n log.debug('Starting plugin loading.')\n count = len(list(self._plugins.keys()))\n\n # Start loading plugins.\n for name in self.list_plugins(filter):\n # Don't load plugins twice.\n if name in self._plugins:\n continue\n\n # Get the PluginInfo for this.\n info = self._infos.get(name)\n if not info:\n raise KeyError('Tried to load unknown plugin: %s' % name)\n\n # If this is blacklisted, and we care, skip it.\n if use_blacklist and isblacklisted(name):\n log.debug('Skipping blacklisted plugin: %s' % info.nice_name)\n continue\n\n # Try loading it now.\n try:\n self._load_plugin(name, use_blacklist)\n except DependencyError as err:\n log.error('Unable to load plugin: %s\\n%s' %\n (info.nice_name, err))\n except (ImportError, VersionError):\n log.exception('Unable to load plugin: %s' % info.nice_name)\n\n # Count up and log the number of loaded plugins.\n count = len(list(self._plugins.keys())) - count\n log.info('Loaded %d plugins.' % count)","title":""},{"docid":"1cdc35869f5b46ba8f960605f6353486","score":"0.6406636","text":"def ready_plugins(self):\n for plugin, plugin_cls in zip(self.plugins, self.plugin_classes):\n logger.info(\"calling ready for plugin %s\", repr(plugin_cls))\n plugin.do_ready()","title":""},{"docid":"1cf4c702bb1a9622a0e6bf5b667ffa7d","score":"0.6365202","text":"def load_plugins():\n return load_dynamic_modules(pathfinder.plugins_path(), abstractplugin.AbstractPlugin)","title":""},{"docid":"d8e7a1b11f1957dc1d5fbc9c4777d1be","score":"0.6350461","text":"def load_execute_file_plugins():\n textutils.output_info('Executing ' + str(len(file.__all__)) + ' file plugins')\n for plugin_name in file.__all__:\n plugin = __import__ (\"plugins.file.\" + plugin_name, fromlist=[plugin_name])\n if hasattr(plugin , 'execute'):\n plugin.execute()","title":""},{"docid":"aa34ded51019788fa75d90b9acf81f5b","score":"0.63288945","text":"def importPlugins(plugins):\n global loaded\n for type_ in plugins:\n # typeMods will be a module reference containing all the modules for type_\n # Eg. module xcluster. Then typeMods.xcluster will be available.\n typeMods = __import__(''.join(['plugins.', type_]), fromlist=plugins[type_])\n for plugin in plugins[type_]:\n try:\n loaded[type_].append(eval('.'.join(['typeMods',plugin])))\n except Exception, err:\n sys.stderr.write(\"PLUGIN IMPORT ERROR:\\n%s\" % str(err))","title":""},{"docid":"ff40c7976203610c9f74eff9cbf61d7b","score":"0.6314812","text":"def reloadPlugins(self):\r\n return 0","title":""},{"docid":"c3e393b9d15b6063efbf077505d2237f","score":"0.6308202","text":"def _load_plugins(plugins, debug=True):\r\n plugs = []\r\n for plugin in plugins:\r\n setup_class = plugin.get('setup_class')\r\n plugin_name = plugin.get('__name__').split()[-1]\r\n mod_name = '.'.join(setup_class.split('.')[:-1])\r\n class_name = setup_class.split('.')[-1]\r\n try:\r\n mod = __import__(mod_name, globals(), locals(), [class_name])\r\n except SyntaxError, e:\r\n raise exception.PluginSyntaxError(\r\n \"Plugin %s (%s) contains a syntax error at line %s\" %\r\n (plugin_name, e.filename, e.lineno))\r\n except ImportError, e:\r\n raise exception.PluginLoadError(\r\n \"Failed to import plugin %s: %s\" %\r\n (plugin_name, e[0]))\r\n klass = getattr(mod, class_name, None)\r\n if not klass:\r\n raise exception.PluginError(\r\n 'Plugin class %s does not exist' % setup_class)\r\n if not issubclass(klass, clustersetup.ClusterSetup):\r\n raise exception.PluginError(\r\n \"Plugin %s must be a subclass of \"\r\n \"starcluster.clustersetup.ClusterSetup\" % setup_class)\r\n args, kwargs = utils.get_arg_spec(klass.__init__, debug=debug)\r\n config_args = []\r\n missing_args = []\r\n for arg in args:\r\n if arg in plugin:\r\n config_args.append(plugin.get(arg))\r\n else:\r\n missing_args.append(arg)\r\n if debug:\r\n log.debug(\"config_args = %s\" % config_args)\r\n if missing_args:\r\n raise exception.PluginError(\r\n \"Not enough settings provided for plugin %s (missing: %s)\"\r\n % (plugin_name, ', '.join(missing_args)))\r\n config_kwargs = {}\r\n for arg in kwargs:\r\n if arg in plugin:\r\n config_kwargs[arg] = plugin.get(arg)\r\n if debug:\r\n log.debug(\"config_kwargs = %s\" % config_kwargs)\r\n try:\r\n plug_obj = klass(*config_args, **config_kwargs)\r\n except Exception as exc:\r\n log.error(\"Error occured:\", exc_info=True)\r\n raise exception.PluginLoadError(\r\n \"Failed to load plugin %s with \"\r\n \"the following error: %s - %s\" %\r\n (setup_class, exc.__class__.__name__, exc.message))\r\n if not hasattr(plug_obj, '__name__'):\r\n setattr(plug_obj, '__name__', plugin_name)\r\n plugs.append(plug_obj)\r\n return plugs","title":""},{"docid":"8f3598c8156386020240ffa0bd065995","score":"0.63020456","text":"def load_plugins():\n _plugins = {}\n for plugin_id, plugin_data in PLUGINS.items():\n plugin_module_name = f\"tgcf_{plugin_id}\"\n plugin_class_name = f\"Tgcf{plugin_id.title()}\"\n try:\n plugin_module = import_module(plugin_module_name)\n plugin_class = getattr(plugin_module, plugin_class_name)\n plugin = plugin_class(plugin_data)\n assert plugin.id_ == plugin_id\n\n except ModuleNotFoundError:\n logging.error(f\"Could not find plugin for {plugin_id}\")\n except AttributeError:\n logging.error(f\"Found plugin {plugin_id}, but failed to load.\")\n else:\n print(f\"Loaded plugin {plugin_id}\")\n _plugins.update({plugin.id_: plugin})\n return _plugins","title":""},{"docid":"92f407c27bb3f65845bc497a59e5605f","score":"0.62988406","text":"def test_load_no_plugins(self, no_config_mock):\n modules = plugin.load_plugin_modules([])\n assert modules == []","title":""},{"docid":"286c8f96f25fe42f3c00ff11c1a2a4f7","score":"0.62756485","text":"def load_plugins(plugin_dir=None):\n\n if len(loaded_plugins):\n # prevent loading plugins multiple times\n return\n\n plugin_dirs = [\n plugin_dir,\n os.path.join(os.getcwd(), 'scap', 'plugins'),\n os.path.join(os.path.expanduser('~'), '.scap', 'plugins')\n ]\n\n plugins = find_plugins(plugin_dirs)\n if len(plugins) < 1:\n return\n\n # Turn off those obnoxious *.pyc files for plugins so we don't litter\n maybe_write_bytecode = sys.dont_write_bytecode\n sys.dont_write_bytecode = True\n\n # import each of the plugin modules\n for plugin in plugins:\n # module path relative to scap.plugins:\n plugin_module = \".%s\" % plugin\n try:\n mod = importlib.import_module(plugin_module, \"scap.plugins\")\n # find classes in mod which extend scap.cli.Application\n for objname in dir(mod):\n obj = getattr(mod, objname)\n if type(obj) is type and issubclass(obj, Application):\n if objname in loaded_plugins:\n # duplicate: another plugin already used the same name\n msg = 'Duplicate plugin named %s, skipping.'\n logging.getLogger().warning(msg, objname)\n continue\n # copy the class into the scap.plugins namespace\n setattr(this_module, objname, obj)\n loaded_plugins[objname] = obj\n __all__.append(objname)\n except Exception as e:\n msg = 'Problem loading plugins from module: scap.plugins.%s (%s)'\n err_msg = type(e).__name__ + ':' + str(e)\n logger = logging.getLogger()\n logger.warning(msg % (plugin, err_msg))\n\n # Restore the original setting\n sys.dont_write_bytecode = maybe_write_bytecode","title":""},{"docid":"47d51b69dba04d0982452ac109d29454","score":"0.6275517","text":"def load_execute_host_plugins():\n textutils.output_info('Executing ' + str(len(host.__all__)) + ' host plugins')\n for plugin_name in host.__all__:\n plugin = __import__ (\"plugins.host.\" + plugin_name, fromlist=[plugin_name])\n if hasattr(plugin , 'execute'):\n plugin.execute()","title":""},{"docid":"1a6bcf3e4f8d82a2849f24a51aca2f58","score":"0.627525","text":"def get_plugins():\n pass","title":""},{"docid":"9bd08ccb33b2b113965c591b39637367","score":"0.62589246","text":"def find_plugins(self):\n\n for loader in iter_entry_points(group='vvv', name=None):\n\n try:\n # Construct the plug-in instance\n name = loader.name\n klass = loader.load()\n instance = klass()\n logger.debug(\"Loaded plug-in: %s\", name)\n self.plugins[name] = instance\n except Exception as e:\n logger.error(\"Could not load plug-in: %s\", loader)\n raise e","title":""},{"docid":"e422b7cc4d5f6e26a5e1e2a64f3f03d1","score":"0.6235418","text":"def load_plugin_manager_plugins(self):\r\n\r\n for plugin in self.plugin_instances:\r\n if not PLUGIN_MANAGER_EXTENSION_TYPE in plugin.capabilities: continue\r\n self._load_plugin(plugin, loading_type = PLUGIN_MANAGER_EXTENSION_TYPE)","title":""},{"docid":"0178898fe7c664d0b612a732e36343c3","score":"0.6128111","text":"def load(self):\n self._pm.load_plugins(self.path)","title":""},{"docid":"3753a6d9cde87292d257e6602ce31b2a","score":"0.6127432","text":"def loader(module):\n\n\t\tplugins = []\n\t\t# Return empty plugins list when do not have plugin\n\t\t# interface\n\t\ttry:\n\t\t\tmodule.countPlugins()\n\t\texcept Exception as e:\n\t\t\tlogger.warning(\"Module: {} in plugin directory is not a plugin\".format(module.__name__))\n\t\t\treturn plugins\n\t\tfor i in range(0, module.countPlugins()):\n\t\t\tpi = module.createPlugin(i, **kwargs)\n\t\t\tif (pi):\n\t\t\t\tplugins.append(pi)\n\t\t\t\tlogger.info(\"Plugin: {} is loaded\".format(pi.namePlugin()) )\n\t\treturn plugins","title":""},{"docid":"ffb30c37a9440315ba2efc19a404cbe1","score":"0.61070305","text":"def get_plugins():","title":""},{"docid":"ac5c4bba7e2269126450aa6fa3e90c3c","score":"0.6105481","text":"def test_import_plugin(self):\n self.assertEqual(len(loader.import_plugins(['monitoring'])), 1)","title":""},{"docid":"c4bcdae6cceb75ad1b8c39c3fe10d2f1","score":"0.6092951","text":"def testPluginsLoaded(self):\r\n sources, targets = self.pl.load_sources_and_targets(self.filename, True)\r\n self.assertEqual(len(sources), 3)\r\n self.assertEqual(len(targets), 2)","title":""},{"docid":"6f674f01a16b67037c9f1c362ee7e131","score":"0.60923755","text":"def loadPlugins(path, catalogue):\n pluginsRoot, _ = xmlUtils.loadToTree(catalogue)\n for pluginNode in pluginsRoot:\n name = pluginNode.find('name').text\n location = pluginNode.find('location').text\n if location is None:\n raise PluginError('Installation is corrupted for plugin \"{}\". Check raven/plugins/plugin_directory.xml and try reinstalling using raven/scripts/intall_plugin')\n if name is None:\n name = os.path.basename(location)\n print('Loading plugin \"{}\" at {}'.format(name, location))\n module = loadPluginModule(name, location)\n loadEntities(name, module)","title":""},{"docid":"acaed84ed521d4058c292b4b59f3a9fe","score":"0.6090377","text":"def setup_all():\n # Check there is plugin manager instance\n if PluginManager.__instance is None:\n PluginManager()\n if PluginManager.__instance is None:\n return False\n retval = True\n # For each plugin, setup the plugin dependencies\n for name in PluginManager.get_names():\n retval = PluginManager.setup(name) and retval\n return retval","title":""},{"docid":"3cfd572af47c196625bc4d5bf0f7dd9b","score":"0.6089976","text":"def test_import_plugin_not_found(self):\n self.assertEqual(len(loader.import_plugins(['bad_name'])), 0)","title":""},{"docid":"c4ce46bc018037d5fdfa621610034ab1","score":"0.6080716","text":"def get_all_loaded_plugins(self):\r\n\r\n # creates the loaded plugins instances list\r\n loaded_plugins_instances = []\r\n\r\n # iterates over all the plugin instances\r\n for plugin_instance in self.plugin_instances:\r\n # in case the plugin instance is loaded\r\n if plugin_instance.is_loaded():\r\n # adds the plugin instance to the loaded plugins instances list\r\n loaded_plugins_instances.append(plugin_instance)\r\n\r\n # returns the loaded plugins instances\r\n return loaded_plugins_instances","title":""},{"docid":"7e4517add6f6163c323f7403d7a726b1","score":"0.6054527","text":"def external_plugins_loaded(plugins):\n\n for p in plugins:\n command = None\n ready = False\n for app_command in sublime_plugin.application_command_classes:\n if app_command.__name__ == p:\n command = app_command\n break\n if command is not None:\n ready = command.is_ready()\n else:\n # Command isn't found in list, so just return ready\n ready = True\n return ready","title":""},{"docid":"98f254f32f57d13093a2354e0b243735","score":"0.60352504","text":"def _initializePlugins(self, dirname, plugins, plugintype, name=None):\n ret = []\n syspathset = set(sys.path)\n\n dirname = os.path.abspath(dirname)\n self.logger.debug(\"looking for plugin(s) in {}\".format(dirname))\n if not os.path.isdir(dirname):\n self.logger.debug(\n \"directory {} does not exist ... moving on\".format(dirname)\n )\n return ret\n\n # Include all plugin directories in sys.path for includes\n if dirname not in sys.path:\n syspathset.add(dirname)\n sys.path = list(syspathset)\n\n # Loop through all files in passed dirname looking for plugins\n for filename in os.listdir(dirname):\n filename = dirname + os.sep + filename\n\n # If the file exists\n if os.path.isfile(filename):\n # Split file into a base name plus extension\n basename = os.path.basename(filename)\n base, extension = os.path.splitext(basename)\n\n # If we're a python file and we don't start with _\n # if extension == \".py\" and not basename.startswith(\"_\"):\n # APPPERF-263: If name param is supplied, only attempt to load\n # {name}.py from {app}/bin directory\n if extension == \".py\" and (\n (name is None and not basename.startswith(\"_\")) or base == name\n ):\n self.logger.debug(\"Searching for plugin in file '%s'\" % filename)\n try:\n # Import the module\n # module = imp.load_source(base, filename)\n\n mod_name, mod_path, mod_desc = imp.find_module(base, [dirname])\n # TODO: Probably need to adjust module.load() to be added later so this can be pickled.\n module = imp.load_module(base, mod_name, mod_path, mod_desc)\n plugin = module.load()\n\n # spec = importlib.util.spec_from_file_location(base, filename)\n # plugin = importlib.util.module_from_spec(spec)\n # spec.loader.exec_module(plugin)\n\n # set plugin to something like output.file or generator.default\n pluginname = plugintype + \".\" + base\n plugins[pluginname] = plugin\n\n # Return is used to determine valid configs, so only return the base name of the plugin\n ret.append(base)\n\n self.logger.debug(\n \"Loading module '%s' from '%s'\" % (pluginname, basename)\n )\n\n # 12/3/13 If we haven't loaded a plugin right or we haven't initialized all the variables\n # in the plugin, we will get an exception and the plan is to not handle it\n if \"validSettings\" in dir(plugin):\n self.config._validSettings.extend(plugin.validSettings)\n if \"defaultableSettings\" in dir(plugin):\n self.config._defaultableSettings.extend(\n plugin.defaultableSettings\n )\n if \"intSettings\" in dir(plugin):\n self.config._intSettings.extend(plugin.intSettings)\n if \"floatSettings\" in dir(plugin):\n self.config._floatSettings.extend(plugin.floatSettings)\n if \"boolSettings\" in dir(plugin):\n self.config._boolSettings.extend(plugin.boolSettings)\n if \"jsonSettings\" in dir(plugin):\n self.config._jsonSettings.extend(plugin.jsonSettings)\n if \"complexSettings\" in dir(plugin):\n self.config._complexSettings.update(plugin.complexSettings)\n except ValueError:\n self.logger.error(\n \"Error loading plugin '%s' of type '%s'\"\n % (base, plugintype)\n )\n except ImportError as ie:\n self.logger.warning(\n \"Could not load plugin: %s, skipping\" % base\n )\n self.logger.exception(ie)\n except Exception as e:\n self.logger.exception(str(e))\n raise e\n return ret","title":""},{"docid":"0a1f2c58bd11b3956b4fd62b22c2d080","score":"0.6033774","text":"def unloadAll(self):\n for p in self.__loadedPlugins:\n p.unload()\n self.__loadedPlugins = []","title":""},{"docid":"0d890e45598128c112c936a328361310","score":"0.6014353","text":"def _setup():\n poll_plugins = []\n hook_plugins = []\n possible_plugins = set(os.listdir(_PLUGIN_FOLDER)).difference(_BLACKLIST)\n\n # Find plugins\n for i in possible_plugins:\n # Check that this is a valid module\n if not _validate_module_name(i):\n continue\n\n # Import module\n module_name = _extract_module_name(i)\n module = importlib.import_module(\".\" + module_name, package=__name__)\n\n # Module sorting and validation\n try:\n if \"poll\" in module.TYPE:\n if module.API_VERSION >= _MIN_POLL_API_VERSION:\n poll_plugins.append(module)\n else:\n print(_ERROR_TEMPLATES[\"BAD_API\"].format(module_name))\n if \"hook\" in module.TYPE:\n if module.API_VERSION >= _MIN_HOOK_API_VERSION:\n hook_plugins.append(module)\n else:\n print(_ERROR_TEMPLATES[\"BAD_API\"].format(module_name))\n except AttributeError:\n print(_ERROR_TEMPLATES[\"BAD_HEADER\"].format(module_name))\n except SyntaxError:\n print(_ERROR_TEMPLATES[\"BAD_HEADER\"].format(module_name))\n\n return poll_plugins, hook_plugins","title":""},{"docid":"cbfbeec82cd6846e74e35c99ace52edb","score":"0.59802026","text":"def plugin_loaded():\r\n\r\n persist.plugin_is_loaded = True\r\n persist.settings.load()\r\n persist.printf('debug mode:', 'on' if persist.debug_mode() else 'off')\r\n util.create_tempdir()\r\n\r\n for linter in persist.linter_classes.values():\r\n linter.initialize()\r\n\r\n plugin = SublimeLinter.shared_plugin()\r\n queue.start(plugin.lint)\r\n\r\n util.generate_menus()\r\n util.generate_color_scheme(from_reload=False)\r\n util.install_syntaxes()\r\n\r\n persist.settings.on_update_call(SublimeLinter.on_settings_updated)\r\n\r\n # This ensures we lint the active view on a fresh install\r\n window = sublime.active_window()\r\n\r\n if window:\r\n plugin.on_activated(window.active_view())","title":""},{"docid":"8d9a341fbbfece3159f7cc8fec68cbfc","score":"0.59794676","text":"def get_plugins(self):\n modules = self._get_modules()\n\n # load plugins again only if a plugin was added or removed\n if len(modules) == len(self._plugins):\n return self._plugins\n\n self._plugins = []\n\n for plugin_module in modules:\n modname, fp, path, desc = plugin_module\n\n try:\n # load the module and look for classes\n module = imp.load_module(modname, fp, path, desc)\n except Exception as e:\n error_msg = \"{exception}: {message}\".format(\n exception=e.__class__.__name__,\n message=unicode(e))\n self._errors[fp.name] = error_msg\n continue\n\n classes_in_module = inspect.getmembers(module, inspect.isclass)\n\n for cls in classes_in_module:\n cls = cls[1]\n\n # ignore if it's the base plugin class\n if cls.__name__ == self.plugin_type.base_class.__name__:\n continue\n\n # make sure that the class is a subclass of the plugin's\n # base class\n if self.plugin_type.base_class in cls.__bases__:\n self.register_plugin(cls)\n\n return self._plugins","title":""},{"docid":"e7c8c91212ce59e819da0e42cfc0cdf6","score":"0.597294","text":"def command(self, bot, comm, groups):\n plugins = PluginUtils.get_plugins(bot)\n names = ', '.join(p.name for p in plugins)\n bot.reply(comm, 'Loaded Plugins: {0}.'.format(names))\n return True","title":""},{"docid":"e442dfc446f845e9a43f124e885d65ec","score":"0.59726703","text":"def test_raises_when_loading_default_plugins_without_allow_qualified(self):\n default_plugin_qualnames = plugin.get_qualified_module_names(\n _repobee.ext.defaults\n )\n\n with pytest.raises(exception.PluginLoadError) as exc_info:\n plugin.load_plugin_modules(default_plugin_qualnames)\n\n assert \"failed to load plugin module\" in str(exc_info.value)","title":""},{"docid":"198ef8a052e161ec2e62d56b9ad57674","score":"0.5961389","text":"def load_startup_plugins(self):\r\n\r\n # iterates over all the plugin instances\r\n for plugin in self.plugin_instances:\r\n # searches for the startup type in the plugin capabilities\r\n # in case the plugins contains such capability must load\r\n # it because it's considered to be a startup plugin\r\n if STARTUP_TYPE in plugin.capabilities: self._load_plugin(plugin, loading_type = STARTUP_TYPE)","title":""},{"docid":"373057132c7785d8325862d8e9450550","score":"0.5956981","text":"def register_core_plugins():\n register_plugin(rho_bot_scheduler)\n register_plugin(rho_bot_configuration)\n register_plugin(rho_bot_roster)\n register_plugin(rho_bot_storage_client)\n register_plugin(rho_bot_rdf_publish)\n register_plugin(rho_bot_representation_manager)\n register_plugin(rho_bot_get_or_lookup)\n register_plugin(rho_bot_get_or_create)\n register_plugin(export_configuration)\n register_plugin(import_configuration)\n register_plugin(reset_configuration)","title":""},{"docid":"67590941dcbd13a979a5519c331f7a31","score":"0.59513134","text":"def start_plugin_polling(self):\r\n for plugin in [key for key in self.config.application.keys()\r\n if key not in self.IGNORE_KEYS]:\r\n LOGGER.info('Enabling plugin: %s', plugin)\r\n plugin_class = None\r\n\r\n # If plugin is part of the core agent plugin list\r\n if plugin in plugins.available:\r\n plugin_class = self._get_plugin(plugins.available[plugin])\r\n\r\n # If plugin is in config and a qualified class name\r\n elif '.' in plugin:\r\n plugin_class = self._get_plugin(plugin)\r\n\r\n # If plugin class could not be imported\r\n if not plugin_class:\r\n LOGGER.error('Enabled plugin %s not available', plugin)\r\n continue\r\n\r\n self.poll_plugin(plugin, plugin_class,\r\n self.config.application.get(plugin))","title":""},{"docid":"77b401dc24a5abf43bfc99e79d5f468f","score":"0.5948454","text":"def populate(self, modules):\n for module in modules:\n try:\n m = importlib.import_module(module)\n except SyntaxError as e:\n print(\"module '{}' failed loading with message {}\".format(module,e.msg))\n continue\n\n if hasattr(m, 'Plugin'):\n self.add_plugin(m.Plugin)\n continue\n\n for name, plugin_class in inspect.getmembers(m):\n if 'Plugin' in name:\n p = plugin_class()\n self.add_plugin(p)","title":""},{"docid":"9bd49c8ff5dd8ed2fd4ee3d31d48bdf0","score":"0.5943103","text":"def start_plugins(self):\r\n\r\n # retrieves all the plugin classes available\r\n self.plugin_classes = self.get_all_plugin_classes()\r\n\r\n # iterates over all the available plugin classes\r\n for plugin in self.plugin_classes:\r\n # retrieves the plugin id\r\n plugin_id = plugin.id\r\n\r\n # sets the plugin class in the plugin classes map\r\n self.plugin_classes_map[plugin_id] = plugin\r\n\r\n # starts the plugin (creating the singleton) in\r\n # case the plugin is not currently loaded\r\n if not plugin in self.loaded_plugins: self.start_plugin(plugin)","title":""},{"docid":"6a35618734fcaa8dc12034dadfc29fc0","score":"0.5905988","text":"def get_plugins(self):\n pass","title":""},{"docid":"b2f67c40ab372888a54f9f0fdf175604","score":"0.5901993","text":"def testEnablePlugins(self):\n parser = interface.BaseParser()\n\n parser.EnablePlugins([])\n self.assertEqual(len(parser._plugins_per_name), 0)\n\n parser.EnablePlugins(parser.ALL_PLUGINS)\n self.assertEqual(len(parser._plugins_per_name), 0)","title":""},{"docid":"e8e747338361824281ed449e6332803f","score":"0.5860712","text":"def rescan_plugins(self):\n component.get(\"CorePluginManager\").scan_for_plugins()","title":""},{"docid":"e952c39df7cffa1323fd6876a9a4cf41","score":"0.5859997","text":"def activate_plugins(plugin_names):","title":""},{"docid":"e88833b7328b31ddd0034c3a331da67e","score":"0.58597225","text":"def load(conf):\n global modules, config\n\n config.read(conf)\n flist = sortmodule([f[:-3] for f in os.listdir(basedefs.DIR_PLUGINS) if f[-3:] == \".py\"])\n \n for sec, fname in flist:\n try:\n #modules.append(loadplugins(sec))\n modules[sec] = loadplugins(fname)\n except Exception as e:\n logging.error(e)\n logging.error(traceback.format_exc())\n raise InstallError(\"failed when load plugins\")\n\n return modules","title":""},{"docid":"1c967ee8a8c20d2e1c2020a3dd02859f","score":"0.58570623","text":"def get_available_plugins():\n print(\"Available plugins:\\n\")\n\n for instrument, class_name in _available_plugins.iteritems():\n\n print(\"%s for %s\" % (class_name, instrument))","title":""},{"docid":"b4a5447ac807cf56e40fb508b68ec1d6","score":"0.58551806","text":"def test_load_plugins_multiple(self, load_config):\n mock_config = MagicMock()\n mock_config.get.return_value = 'lunch, todo'\n load_config.return_value = mock_config\n plugins = PluginManager().load_plugins()\n\n self.assertEqual(2, len(plugins))\n self.assertEqual(\n 'ashaw_notes.plugins.lunch',\n plugins[0].__module__\n )\n self.assertEqual(\n 'ashaw_notes.plugins.todo',\n plugins[1].__module__\n )","title":""},{"docid":"af18a7121862896cd99c62f473f2a7fa","score":"0.5849801","text":"def discoverable_plugins():\n discovered_plugins = {}\n for entry_point in iter_entry_points(\"framework\"):\n try:\n ep = entry_point.load()\n discovered_plugins[entry_point.name] = ep\n except (DistributionNotFound, ImportError):\n logging.exception(f\"Plugin {entry_point.name} not found\")\n return discovered_plugins","title":""},{"docid":"2047b0e96b8046ccb42c36fe1f036b4c","score":"0.58488655","text":"def load_default_plugins_auto(self, load_default_plugins):\n yield","title":""},{"docid":"61cc98fc936279ea4593dba22a7ae302","score":"0.58452135","text":"def _plugins(args):\n\n # plugins\n retval = 0\n for plugin in plugins.PLUGINS:\n if plugins.install(plugin, args) == 1:\n retval = 1\n\n return retval","title":""},{"docid":"71fcc32d5ac4938f29490688d8d108f5","score":"0.5836939","text":"def discoverPlugins():\n availablePlugins = {}\n pluginDir = 'plugins'\n #basePluginPath = sys.path[0]\n if sys.platform == 'win32':\n basePluginPath = sys.path[0].split('library.zip')[0]\n if sys.platform == 'darwin':\n basePluginPath = sys.path[0].split('find.app')[0]\n \n # make sure the plugins folder exists\n if not os.path.exists(os.path.join(basePluginPath, pluginDir)):\n raise error.PluginError(\"The \\\"plugins\\\" directory is missing, please place in the same folder as the FIND program file.\")\n return\n \n if not basePluginPath in sys.path:\n sys.path.insert(0, basePluginPath)\n \n #TODO: look at recursive descent for module discovery\n # Accumulate available plugin modules\n errMsg = []\n for type_ in pluginTypes:\n path = os.path.join(basePluginPath, pluginDir, type_)\n if not os.path.exists(path):\n errMsg.append(\"The \\\"%s\\\" plugins directory is missing.\" % type_)\n else:\n availablePlugins[type_] = [fname[:-3] for fname in os.listdir(path) \n if fname.endswith(\".py\") and \n not fname.startswith(\"__init__\")]\n \n # raise error with all missing plugin subdirectories\n if len(errMsg) > 0:\n raise error.PluginError('\\n\\t'.join(errMsg))\n return\n \n return availablePlugins","title":""},{"docid":"6f111ebbda9a8aa2ce3e2927e1ed4fe1","score":"0.5824811","text":"async def load_extensions(self) -> None:\n for extension in self.extension_list:\n try:\n self.load_extension(extension)\n logger.debug(f\"Cog {extension} loaded.\")\n except Exception as e:\n logger.error(f\"Cog {extension} failed to load with {type(e)}: {e}\")","title":""},{"docid":"a2615ad584fca8a37ed7c93d3ce6b5c4","score":"0.58019894","text":"def discover():\n if CFG[\"plugins\"][\"autoload\"].value():\n log = logging.getLogger('benchbuild')\n experiment_plugins = CFG[\"plugins\"][\"experiments\"].value()\n for ep in experiment_plugins:\n try:\n importlib.import_module(ep)\n log.debug(\"Found experiment: {0}\".format(ep))\n except ImportError as ie:\n log.error(\"Could not find '{0}'\".format(ep))\n log.error(\"ImportError: {0}\".format(ie.msg))","title":""},{"docid":"eed22a941ddea5d5c5f72026b9b8a75d","score":"0.5801577","text":"def register_plugins(bus=None):\n from ..plugins import RunnablePlugin\n\n for plugin in get_enabled_plugins().values():\n if isinstance(plugin, RunnablePlugin):\n plugin.bus = bus\n plugin.start()","title":""},{"docid":"4cc6ce98447d2a4204a98706ea0e1ecd","score":"0.5796698","text":"def init_plugins(self, disabled_glob=(), enable_plugins=(), cli=None):\n # :api\n if self.conf.plugins:\n self._plugins._load(self.conf, disabled_glob, enable_plugins)\n self._plugins._run_init(self, cli)","title":""},{"docid":"adb3dd35836d0bd0be00bc914d8ea6ef","score":"0.57896024","text":"def finishLoadPlugin(name):\n if name not in _delayedPlugins:\n #Try to find module in system (such as from pip install)\n #Note that loadEntities (below) checks if there is a subclass of\n # PluginBase.PluginBase before using it, so this is safe.\n spec = importlib.util.find_spec(name)\n if spec is None:\n return False\n plugin = importlib.util.module_from_spec(spec)\n _delayedPlugins[name] = (spec, plugin, False)\n spec, plugin, loaded = _delayedPlugins[name]\n if not loaded:\n spec.loader.exec_module(plugin)\n loadEntities(name, plugin)\n #Set loaded flag to true to prevent reloading\n _delayedPlugins[name] = (spec, plugin, True)\n return True","title":""},{"docid":"5d3b2c565665ef24d1f508ce0197f5ef","score":"0.57852995","text":"def loadedPlugins(self):\n return self.__loadedPlugins","title":""},{"docid":"1434668d47af9a45a3cc7971c75aa1b9","score":"0.57738394","text":"def load_plugins(config):\n plugin = {}\n for p in ('retrieval', 'decryption', 'verification', 'scrubbing', 'reporting'):\n try:\n module_name = 'erawan.plugins.{}.{}'.format(p, config['plugins'][p]['name'])\n except KeyError:\n logger.critical('Could not import %s plugin; is it set in config?', p)\n sys.exit(1)\n try:\n plugin[p] = importlib.import_module(module_name)\n except ImportError:\n logger.critical('Unable to load plugin \"%s\"', module_name)\n sys.exit(1)\n return plugin","title":""},{"docid":"ca103e644f66671feac38b2f5232642f","score":"0.57731414","text":"def loadPlugins(pluginfldr,hivesdict):\n pluginmanager = PluginManager()\n pluginmanager.setPluginPlaces([pluginfldr])\n pluginmanager.collectPlugins()\n\n print \"\"\n logging.info(\"[PLUGINS IDENTIFIED: %s]\" % len(pluginmanager.getAllPlugins()))\n\n compat_plugins = list()\n incompat_plugins = list()\n for plugin in pluginmanager.getAllPlugins():\n compat = 1\n for req in plugin.plugin_object.getRequirements():\n if (hivesdict[req][0] <> 1):\n compat = 0\n break\n if compat:\n compat_plugins.append(plugin.name)\n else:\n incompat_plugins.append(plugin.name)\n\n logging.info(\" [%s] Compatible Plugins:\" % len(compat_plugins))\n for plugin in compat_plugins:\n logging.info(\" - %s\" % plugin)\n logging.info(\" [%s] Incompatible Plugins:\" % len(incompat_plugins))\n for plugin in incompat_plugins:\n logging.info(\" - %s\" % plugin)\n\n return pluginmanager, compat_plugins","title":""},{"docid":"d86c186eb48e0358d8b7c2f56f51dec0","score":"0.57704806","text":"def load_py_files():\n def _load_py_files(env, search_path, auto_enable=None):\n for path in search_path:\n plugin_files = locate(\"*.py\", path)\n for plugin_file in plugin_files:\n try:\n plugin_name = os.path.basename(plugin_file[:-3])\n module_name = _get_module_name(plugin_file)\n import_module(module_name)\n _enable_plugin(env, plugin_name)\n except NotImplementedError, e:\n #print \"Cant Implement This\"\n pass\n\n\n return _load_py_files","title":""},{"docid":"92a08a26d0e9538024234c209ca11eb1","score":"0.57653815","text":"def init_plugins(self):\n\n assert self.project_path\n assert os.path.exists(self.project_path)\n\n for plugin_id, instance in self.plugins.items():\n\n try:\n\n plugin_installation = os.path.join(self.installation, plugin_id)\n\n instance.init(\n plugin_id=plugin_id,\n main=self,\n reporter=self.reporter,\n options=self.options_data,\n files=self.files_data,\n installation_path=plugin_installation,\n walker=self.walker,\n project_path=self.project_path\n )\n\n instance.setup_options()\n except Exception as e:\n logger.error(\"Could not initialize plug-in: %s\", plugin_id)\n raise e","title":""},{"docid":"d85918a9bc5eee3652fbfca95d1b746b","score":"0.57314444","text":"def loaded_plugins(self):\n return self.__plugins.keys()","title":""},{"docid":"ed219590e4d56d2d6a1e3106f28c98fe","score":"0.56982917","text":"def load_modules():\n\n # logger = logging.getLogger(__name__)\n locations = [marvin.support.path.PLUGIN_PATH]\n modules = []\n for finder, name, ispkg in pkgutil.walk_packages(locations):\n try:\n loader = finder.find_module(name)\n mod = loader.load_module(name)\n except:\n Log.warn(\"Skipped loading module '{0}' due to an error.\", name)\n else:\n # if hasattr(mod, 'WORDS'):\n modules.append(mod)\n # else:\n # Log.warn(\"Skipped loading module '{0}' because it misses \" +\n # \"the WORDS constant.\", name)\n modules.sort(key=lambda mod: mod.PRIORITY if hasattr(mod, 'PRIORITY')\n else 0, reverse=True)\n return modules","title":""},{"docid":"2f86f70acff17b995cbd67be5b3e67b4","score":"0.5694283","text":"def load_main_plugins(self):\r\n\r\n # iterates over all the plugin instances\r\n for plugin in self.plugin_instances:\r\n # searches for the main type in the plugin capabilities\r\n # in case the plugins contains such capability must load\r\n # it because it's considered to be a main plugin\r\n if MAIN_TYPE in plugin.capabilities: self._load_plugin(plugin, loading_type = MAIN_TYPE)","title":""},{"docid":"5b9dfcb1572fc614237e168f57979d76","score":"0.5691813","text":"def start_plugin_manager_plugins(self):\r\n\r\n # retrieves all the plugin manager plugin classes available\r\n self.plugin_classes = self.get_all_plugin_classes(PluginManagerPlugin)\r\n\r\n # iterates over all the available plugin manager plugin classes\r\n for plugin in self.plugin_classes:\r\n # retrieves the plugin id\r\n plugin_id = plugin.id\r\n\r\n # sets the plugin class in the plugin classes map\r\n self.plugin_classes_map[plugin_id] = plugin\r\n\r\n # tests the plugin for loading, verifying that it's\r\n # current loaded and then starts the plugin\r\n if not plugin in self.loaded_plugins: continue\r\n self.start_plugin(plugin)","title":""},{"docid":"a4d7bf7cf95dad7aec3324c161b162ff","score":"0.5677742","text":"def veerify_installed_plugins(self):\n cmd = \"feature:list\"\n error_flag = 0\n for plugin in config.ODL_PLUGINS:\n pattern = \"{0}\\s+\\|.*\\|\\s*x\\s+\\|\".format(plugin)\n if self.execute_command(cmd=cmd,exp_out=pattern):\n continue\n else:\n self.log_handler.writeTolog(msg=\"****** PLUGIN is NOT INSTALLED Properly, Verification FAILED ******\")\n error_flag += 1 \n return False if error_flag > 0 else True","title":""},{"docid":"cc71a6579b2ce27854d627fe356afc6f","score":"0.5674591","text":"def get_plugins(cls):\n return []","title":""},{"docid":"c10d2804bc3eda8526fd37d1c68fd9f1","score":"0.5656471","text":"def check_plugins(package):\n if not package.cmake:\n return\n defined_macros = package.source_code.search_for_pattern(PLUGIN_RE)\n existing_plugins = plugin_xml_by_package(package)\n defined_plugins = package.manifest.get_plugin_xmls()\n build_rules = package.cmake.get_source_build_rules('add_library', resolve_target_name=True)\n\n for rel_fn, plugin_info in defined_macros.items():\n library = lookup_library(build_rules, rel_fn)\n # pkg2/name2 is the parent class\n for pkg1, name1, pkg2, name2 in plugin_info:\n # Create file if needed\n if pkg2 not in existing_plugins:\n xml_filename = '%s_plugins.xml' % pkg2\n print('\\tCreating %s' % xml_filename)\n p_xml = PluginXML(xml_filename, os.path.join(package.root, xml_filename))\n package.plugin_configs.append(p_xml)\n existing_plugins[pkg2] = [p_xml]\n\n # Make sure plugins are properly exported\n for plugin_xml in existing_plugins[pkg2]:\n if plugin_xml.rel_fn not in defined_plugins[pkg2]:\n ex_el = package.manifest.add_plugin_export(pkg2, plugin_xml.rel_fn)\n enforce_tabbing_helper(package.manifest, ex_el, 2)\n\n # Make sure the class is in the files\n if not contains_library(existing_plugins[pkg2], library, pkg1, name1):\n # insert into first\n xml = existing_plugins[pkg2][0]\n xml.insert_new_class(library, pkg1, name1, pkg2, name2)","title":""},{"docid":"3c67082771f00c8ced6d0e6a0f44ea51","score":"0.56513125","text":"def register_plugins(self):\n self.plugin_manager.register(BasePlugin())\n self.plugin_manager.register(SubcmdPlugin())","title":""},{"docid":"4abac434c9ce320fcaef9200f2a39092","score":"0.5645184","text":"def get_plugins():\n installed_plugins = []\n for ep in pr.iter_entry_points(group='gaia.plugins'):\n try:\n module = ep.load()\n importlib.import_module(module.__name__)\n installed_plugins.append(module)\n if hasattr(module, 'get_config'):\n config.update(module.get_config())\n except ImportError:\n logger.error('Could not load module: {}'.format(\n traceback.print_exc()))\n return installed_plugins","title":""},{"docid":"a239dc76fad6393ecf996e606f714d12","score":"0.5637939","text":"def load_plugins(config, plugin_kwargs):\n installed_plugins = _gather_installed_plugins()\n metrics_plugin = _get_metrics_plugin(config, installed_plugins)\n if metrics_plugin:\n plugin_kwargs['metrics'] = metrics_plugin\n\n active_plugins = _get_activated_plugins(config, installed_plugins)\n if not active_plugins:\n return [], [], [], None\n plugin_namespaces = _get_plugin_config_keys(active_plugins)\n plugin_configs = _load_plugin_configs(plugin_namespaces, config)\n plugin_names, plugins, errors = _init_plugins(\n active_plugins, installed_plugins, plugin_configs, plugin_kwargs)\n return plugin_names, plugins, errors, plugin_kwargs","title":""},{"docid":"437487fa3ee2fe5cd1186b54bc1d4dbf","score":"0.56353956","text":"def configure_plugins(self):\n # :api\n self._plugins._run_config()","title":""},{"docid":"5b7ec78dcd859e4daae914b6be8c23d4","score":"0.56248754","text":"def load(self, ignore_blacklist=False):\n if self.is_loaded:\n log.warning('Trying to load already loaded plugin %r.' %\n self.data['name'])\n return\n\n if not ignore_blacklist and self.is_blacklisted:\n raise addons.DependencyError('Plugin %r is blacklisted.' % \n self.data['name'])\n\n # Check our dependencies for safety.\n addons.check_dependencies(self)\n\n # Load our dependencies.\n for name in self.requires.iterkeys():\n if name == '__app__' or (':' in name and not\n name.startswith('plugin:')):\n continue\n dep = addons.get('plugin', name)\n\n # Make sure we're needed.\n if not self.name in dep.needed_by:\n dep.needed_by.append(self.name)\n\n # If it's loaded, just continue.\n if dep.is_loaded:\n continue\n dep.load()\n\n # Okay, now load!\n self._do_load()","title":""},{"docid":"dd151a0874df861566182b10974c604f","score":"0.56133425","text":"def command(self, bot, comm, groups):\n name = groups[0]\n plugins = PluginUtils.get_plugins(bot)\n matched_plugins = [p for p in plugins if p.name == name]\n if len(matched_plugins) != 0:\n bot.reply(comm, \"%s is already loaded.\" % name)\n return False\n\n # Fun fact: the fresh thing is just a dummy. It just can't be None\n new_plugin = twisted.plugin.retrieve_named_plugins(\n IPlugin, [name], 'hamper.plugins', {'fresh': True})[0]\n\n bot.factory.loader.registerPlugin(new_plugin)\n bot.reply(comm, 'Loading {0}.'.format(new_plugin))\n return True","title":""},{"docid":"a998050f7e268c9bfd16890e7a7d7a95","score":"0.5605278","text":"def load_extra_plugins(pathspec):\n # Collect names of loaded plugins so we have something to test\n # success/failure against\n loaded_plugins = []\n\n paths = pathspec.split(':')\n for path in paths:\n loaded_plugins.extend(_load_plugins_from_dir(path))\n\n return loaded_plugins","title":""},{"docid":"416cb6b3eeda012ff007371776b39107","score":"0.5588129","text":"def loadCheckedLoadables(self):\n self.loadableTable.updateCheckstate()\n loadableCount = 0\n for plugin in self.loadablesByPlugin:\n for loadable in self.loadablesByPlugin[plugin]:\n if loadable.selected:\n loadableCount += 1\n self.progress = qt.QProgressDialog(self.window)\n self.progress.minimumDuration = 0\n self.progress.show()\n self.progress.setValue(0)\n self.progress.setMaximum(loadableCount)\n step = 0\n for plugin in self.loadablesByPlugin:\n for loadable in self.loadablesByPlugin[plugin]:\n if self.progress.wasCanceled:\n break\n slicer.app.processEvents()\n self.progress.setValue(step)\n slicer.app.processEvents()\n if loadable.selected:\n self.progress.labelText = '\\nLoading %s' % loadable.name\n slicer.app.processEvents()\n if not plugin.load(loadable):\n qt.QMessageBox.warning(slicer.util.mainWindow(), \n 'Load', 'Could not load: %s as a %s' % (loadable.name,plugin.loadType))\n step += 1\n self.progress.setValue(step)\n slicer.app.processEvents()\n self.progress.close()\n self.progress = None\n self.close()","title":""},{"docid":"f5a0d9b86475fe88e5385586184e960e","score":"0.5579736","text":"def load_plugins_dir(dirname):","title":""},{"docid":"a30c64138abb6bdce55323859d29eb77","score":"0.55797166","text":"def get_plugin_manager_plugins_loaded(self):\r\n\r\n return self.plugin_manager_plugins_loaded","title":""},{"docid":"db59f48dbaa04346f601e5f6abab9cd0","score":"0.55631167","text":"def load_py_files():\r\n def _load_py_files(env, search_path, auto_enable=None):\r\n for path in search_path:\r\n plugin_files = glob(os.path.join(path, '*.py'))\r\n for plugin_file in plugin_files:\r\n try:\r\n plugin_name = os.path.basename(plugin_file[:-3])\r\n env.log.debug('Loading file plugin %s from %s' % \\\r\n (plugin_name, plugin_file))\r\n if plugin_name not in sys.modules:\r\n module = imp.load_source(plugin_name, plugin_file)\r\n if path == auto_enable:\r\n _enable_plugin(env, plugin_name)\r\n except Exception as e:\r\n env.log.error('Failed to load plugin from %s: %s',\r\n plugin_file,\r\n exception_to_unicode(e, traceback=True))\r\n\r\n return _load_py_files","title":""}],"string":"[\n {\n \"docid\": \"a0187f0401cfa952954bc543f1edc71f\",\n \"score\": \"0.75690514\",\n \"text\": \"def load_plugins():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b85d1c938ae197871c603b0950307892\",\n \"score\": \"0.73343587\",\n \"text\": \"def LoadPlugins(self):\\n\\t\\tfor curfile in [f for f in glob.glob(os.path.join('plugins', self.PluginPath, '*.py'))\\n\\t\\t\\t\\t\\tif not os.path.basename(f) in ['__init__.py']]:\\n\\t\\t\\tLoadPluginFile(curfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"593ce4902165ee83444aed2589435f1d\",\n \"score\": \"0.71911377\",\n \"text\": \"def load_plugins(self, plugins):\\r\\n\\r\\n # prints an info message about the import operation\\r\\n # that is going to be performed in the plugin\\r\\n self.info(\\\"Loading plugins (importing %d main module files)...\\\" % len(plugins))\\r\\n\\r\\n # iterates over all the plugins requested for loading and\\r\\n # runs the import operation for each of them in case that\\r\\n # operation is required by module inexistence\\r\\n for plugin in plugins:\\r\\n # in case the plugin module is already loaded continues\\r\\n # the loop as no loading is required for it, otherwise\\r\\n # runs the proper loading process for the plugin logging\\r\\n # an error in case an exception occurs in the importing\\r\\n if plugin in sys.modules: continue\\r\\n try: __import__(plugin)\\r\\n except Exception as exception:\\r\\n self.error(\\\"Problem importing module %s: %s\\\" % (plugin, legacy.UNICODE(exception)))\\r\\n\\r\\n # prints an info message about the fact that the loading\\r\\n # operation for all of the requested plugin has finished\\r\\n self.info(\\\"Finished loading plugins\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc9757ef6ead2d10aa3e85e7faef073c\",\n \"score\": \"0.7007953\",\n \"text\": \"def _load_plugins(self):\\n plugins = ait.config.get('server.plugins')\\n\\n if plugins is None:\\n log.warn('No plugins specified in config.')\\n else:\\n for index, p in enumerate(plugins):\\n try:\\n plugin = self._create_plugin(p['plugin'])\\n self.plugins.append(plugin)\\n log.info('Added plugin {}'.format(plugin))\\n\\n except Exception:\\n exc_type, value, tb = sys.exc_info()\\n log.error('{} creating plugin {}: {}'.format(exc_type,\\n index,\\n value))\\n if not self.plugins:\\n log.warn('No valid plugin configurations found. No plugins will be added.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06de2f29a24d1c02d4b296800386dabc\",\n \"score\": \"0.6884243\",\n \"text\": \"def load_plugins(self):\\n plugin_manager = PluginManager()\\n self.__fs_plugins = plugin_manager.load_filesystem_plugins()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41f17f756dbe7fa787bc0fe219585060\",\n \"score\": \"0.68089104\",\n \"text\": \"def load():\\n global _loaded\\n if _loaded:\\n return\\n log.log.debug(\\\"Loading plugins\\\")\\n _loaded = True\\n\\n for folder, sub, files in os.walk(\\\"nab/plugins/\\\"):\\n sys.path.insert(0, folder)\\n for f in files:\\n fname, ext = os.path.splitext(f)\\n if ext == '.py' and fname != \\\"__init__\\\":\\n log.log.debug(fname)\\n importlib.import_module(fname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7c296bc9610964381613d30a8337554\",\n \"score\": \"0.6773787\",\n \"text\": \"def load_plugins(self):\\n # note: need to update if the setup.py module names change\\n MODULE_NAME = \\\"floss\\\"\\n req = pkg_resources.Requirement.parse(MODULE_NAME)\\n requested_directory = os.path.join(MODULE_NAME, \\\"plugins\\\")\\n try:\\n plugins_path = pkg_resources.resource_filename(req, requested_directory)\\n\\n plugnplay.plugin_dirs = [plugins_path]\\n plugnplay.load_plugins(logging.getLogger(\\\"plugin_loader\\\"))\\n except pkg_resources.DistributionNotFound as e:\\n self.i(\\\"failed to load extra plugins: %s\\\", e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0708b4fd3248e392ca6417a85d0fc89f\",\n \"score\": \"0.6760845\",\n \"text\": \"def loadHandlers(tag):\\n\\n def pr (*args,**keys):\\n if not g.app.unitTesting:\\n g.es_print(*args,**keys)\\n\\n plugins_path = g.os_path_abspath(g.os_path_join(g.app.loadDir,\\\"..\\\",\\\"plugins\\\"))\\n files = glob.glob(g.os_path_join(plugins_path,\\\"*.py\\\"))\\n files = [g.os_path_abspath(theFile) for theFile in files]\\n\\n s = g.app.config.getEnabledPlugins()\\n if not s: return\\n\\n if not g.app.silentMode:\\n pr('@enabled-plugins found in %s' % (\\n g.app.config.enabledPluginsFileName),color='blue')\\n\\n enabled_files = getEnabledFiles(s,plugins_path)\\n\\n # Load plugins in the order they appear in the enabled_files list.\\n if files and enabled_files:\\n for theFile in enabled_files:\\n if theFile in files:\\n loadOnePlugin(theFile)\\n\\n # Note: g.plugin_signon adds module names to g.app.loadedPlugins\\n if 0:\\n if g.app.loadedPlugins:\\n pr(\\\"%d plugins loaded\\\" % (len(g.app.loadedPlugins)), color=\\\"blue\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d4c9386e45cce1c12e362683fd84987\",\n \"score\": \"0.6735198\",\n \"text\": \"def init_plugins (self):\\n self.load_plugins()\\n for p in self.plugins:\\n p.init()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d4a8b252a64c06f381d34e5809f0e4c\",\n \"score\": \"0.67192745\",\n \"text\": \"def load_plugins(self):\\n if self.base:\\n codecs = self.base + '.plugins.codecs'\\n compressions = self.base + '.plugins.compressions'\\n assemblies = self.base + '.plugins.assemblies'\\n hashs = self.base + '.plugins.hashs'\\n formatters = self.base + '.plugins.formatters'\\n misc = self.base + '.plugins.misc'\\n else:\\n codecs = 'deen.plugins.codecs'\\n compressions = 'deen.plugins.compressions'\\n assemblies = 'deen.plugins.assemblies'\\n hashs = 'deen.plugins.hashs'\\n formatters = 'deen.plugins.formatters'\\n misc = 'deen.plugins.misc'\\n codecs = importlib.import_module(codecs)\\n self.codecs = self._get_plugin_classes_from_module(codecs)\\n compressions = importlib.import_module(compressions)\\n self.compressions = self._get_plugin_classes_from_module(compressions)\\n assemblies = importlib.import_module(assemblies)\\n self.assemblies = self._get_plugin_classes_from_module(assemblies)\\n hashs = importlib.import_module(hashs)\\n self.hashs = self._get_plugin_classes_from_module(hashs)\\n formatters = importlib.import_module(formatters)\\n self.formatters = self._get_plugin_classes_from_module(formatters)\\n misc = importlib.import_module(misc)\\n self.misc = self._get_plugin_classes_from_module(misc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea6e8895e06fdcaaa41cff9117e86010\",\n \"score\": \"0.66606915\",\n \"text\": \"def load_plugins():\\n loaded_plugins = []\\n plugins_info = get_plugins_info()\\n for plugin_info in plugins_info:\\n plugin_module = imp.load_module(main_module, *plugin_info[\\\"info\\\"])\\n loaded_plugins.append(plugin_module)\\n return loaded_plugins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eff480252ce0c401fcaa83e44a23ccfb\",\n \"score\": \"0.6649144\",\n \"text\": \"def notify_load_complete_loaded_plugins(self):\\r\\n\\r\\n # retrieves the loaded plugins list\\r\\n loaded_plugins_list = self.get_all_loaded_plugins()\\r\\n\\r\\n # iterates over all the loaded plugins\\r\\n for loaded_plugin in loaded_plugins_list:\\r\\n # notifies the plugin about the load complete\\r\\n loaded_plugin.init_complete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c9868d3b0ce0ec0a4f7aa23b8de00bf\",\n \"score\": \"0.66406846\",\n \"text\": \"def _import_plugins(self):\\n if self.detected:\\n return\\n\\n # In some cases, plugin scanning may start during a request.\\n # Make sure there is only one thread scanning for plugins.\\n self.scanLock.acquire()\\n if self.detected:\\n return # previous threaded released + completed\\n\\n try:\\n _import_apps_submodule(\\\"content_plugins\\\")\\n self.detected = True\\n finally:\\n self.scanLock.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e68366fc43e018fe946db719ed03f550\",\n \"score\": \"0.6628261\",\n \"text\": \"def import_plugins(self):\\n if self.imported:\\n return\\n for plugin_name in settings.PLUGINS:\\n module_name, cls = plugin_name.rsplit(\\\".\\\", 1)\\n module = import_module(module_name)\\n plugin = getattr(module, cls)\\n self.plugin_classes.append(plugin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36a8168789dfbd1fd8d02231891ea390\",\n \"score\": \"0.66164017\",\n \"text\": \"def register_plugins(self):\\n\\t\\tif self.plugin_whitelist:\\n\\t\\t\\tplugin_list = self.plugin_whitelist\\n\\t\\telse:\\n\\t\\t\\tplugin_list = plugins.__all__\\n\\t\\tfor plugin in plugin_list:\\n\\t\\t\\tif plugin in plugins.__all__:\\n\\t\\t\\t\\tself.registerPlugin(plugin, self.plugin_config.get(plugin, {}), False)\\n\\t\\t\\telse:\\n\\t\\t\\t\\traise NameError(\\\"No plugin by the name of %s listed in plugins.__all__.\\\" % plugin)\\n\\t\\t# run post_init() for cross-plugin interaction\\n\\t\\tfor plugin in self.plugin:\\n\\t\\t\\tself.plugin[plugin].post_init()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a35ce8a68001acd50841081ebcca242b\",\n \"score\": \"0.65868634\",\n \"text\": \"def reload_plugins(self):\\n self.plugins = []\\n self.seen_paths = []\\n logger.info(f'Looking for plugins under package {self.plugin_package}')\\n self.walk_package(self.plugin_package)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bc5daf3337b90ecce8b21a44bf99f5a\",\n \"score\": \"0.6573317\",\n \"text\": \"def load_plugins(self):\\r\\n\\r\\n for plugin_path in settings.PLUGINS:\\r\\n try:\\r\\n try:\\r\\n pg_module, pg_classname = plugin_path.rsplit('.', 1)\\r\\n except ValueError, e:\\r\\n raise exceptions.ImproperlyConfigured('%s isn\\\\'t a plugin module' % plugin_path)\\r\\n\\r\\n try:\\r\\n mod = importlib.import_module(pg_module)\\r\\n except ImportError, e:\\r\\n raise exceptions.ImproperlyConfigured('Error importing plugin %s: \\\"%s\\\"' % (pg_module, e))\\r\\n\\r\\n try:\\r\\n pg_class = getattr(mod, pg_classname)\\r\\n except AttributeError, e:\\r\\n raise exceptions.ImproperlyConfigured('Plugins module \\\"%s\\\" does not define a \\\"%s\\\" class' % (pg_module, pg_classname))\\r\\n\\r\\n try:\\r\\n pg_instance = pg_class(handler=self, protocol=self.protocol)\\r\\n except TypeError, e:\\r\\n raise exceptions.InvalidPlugin('Plugins class \\\"%s\\\" does not provide a command: %s' % (pg_class, e))\\r\\n\\r\\n if hasattr(pg_instance, 'command'):\\r\\n if isinstance(pg_instance.command, str):\\r\\n self._command_plugins[pg_instance.command] = pg_instance\\r\\n else:\\r\\n for command in pg_instance.command:\\r\\n self._command_plugins[command] = pg_instance\\r\\n else:\\r\\n self._generic_plugins.append(pg_instance)\\r\\n except exceptions.JeevesException, e:\\r\\n if pg_classname:\\r\\n print \\\"%s: %s\\\" % (pg_classname, e)\\r\\n else:\\r\\n print e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"499980f31a450fedd65b30aaf87bc78a\",\n \"score\": \"0.65441203\",\n \"text\": \"def find_plugins(self):\\n if len(self.plugins):\\n # only let this happen once per launch\\n return\\n\\n if config:\\n path = os.path.abspath(os.path.join(os.path.dirname(config.config.plugin_dir), API_PLUGINS_FOLDER))\\n for item in sorted(os.listdir(path)):\\n loader_file = os.path.join(path, item, API_PLUGIN_LOADER)\\n if os.path.exists(loader_file):\\n print(\\\"Found plugin file: {}\\\".format(loader_file))\\n loader_dir = os.path.join(path, item)\\n orig_path = list(sys.path)\\n try:\\n if loader_dir not in sys.path:\\n sys.path.append(loader_dir)\\n plugin_module = importlib.machinery.SourceFileLoader(\\n 'plugin_{}'.format(item.encode(\\n encoding='ascii',\\n errors='replace').decode('utf-8').replace('.', '_')),\\n loader_file).load_module()\\n if \\\"__plugin__\\\" in dir(plugin_module):\\n plugin = plugin_module.__plugin__\\n if not isinstance(plugin, EDMCPluginBase):\\n raise ValueError(\\\"{} from {} is not a subclass of EDMCPluginBase\\\".format(\\n str(plugin), item))\\n print(\\\"Loaded plugin {} from module {}\\\".format(str(plugin), item))\\n self.plugins.append(plugin)\\n\\n else:\\n raise AttributeError(\\\"plugin {} lacks __plugin__\\\".format(loader_dir))\\n except Exception as err:\\n print(\\\"Error attempting to discover plugin {} : {}\\\".format(loader_file, err))\\n sys.path = orig_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"076cc8c86b7ea3a7a2cd5c9dac0356a1\",\n \"score\": \"0.65420604\",\n \"text\": \"def load_plugins(self):\\n\\n ocio_plugins = self.config.get('ocio_plugins', default=dict())\\n if not ocio_plugins:\\n LOGGER.warning('No OCIO plugins found in configuration file: \\\"{}\\\"'.format(self.config.get_path()))\\n return\\n\\n for ocio_plugin_name, ocio_plugin_info in ocio_plugins.items():\\n plugin_name = ocio_plugin_info.get('plugin_name', None)\\n if not plugin_name:\\n LOGGER.warning(\\n 'Impossible to load \\\"{}\\\" OCIO Plugin because its plugin_name is not defined!'.format(\\n ocio_plugin_name))\\n continue\\n\\n if not tp.Dcc.is_plugin_loaded(plugin_name):\\n LOGGER.info('Loading OCIO Plugin: \\\"{}\\\"'.format(plugin_name))\\n try:\\n tp.Dcc.load_plugin(plugin_name, quiet=True)\\n except Exception as exc:\\n LOGGER.error(\\n 'Error while loading OCIO Plugin: \\\"{}\\\" | {} | {}'.format(\\n plugin_name, exc, traceback.format_exc()))\\n continue\\n\\n LOGGER.info('OCIO Plugin \\\"{}\\\" loaded successfully!'.format(plugin_name))\\n self.__class__._available_plugins.append(ocio_plugin_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed8e9dc0d50db8bc5cb66061df16c85b\",\n \"score\": \"0.65240896\",\n \"text\": \"def load_plugin_classes(self):\\n c.world_load_plugin_classes(self.world)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57702d14121a171e55d822faa6c25b7e\",\n \"score\": \"0.6510039\",\n \"text\": \"def plugins():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b741b2266d3000680a80836c366666b\",\n \"score\": \"0.6471247\",\n \"text\": \"def _check_plugins(self, name: str) -> None:\\n if name not in self._plugins and name not in self._plugin_status:\\n raise WechatyPluginError(f'plugins <{name}> not exist')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e1662bf6bbaca5c8de8cf0abfd3a8d5\",\n \"score\": \"0.6442013\",\n \"text\": \"def _locate_plugins() -> None:\\n path = (\\n Path(sys.executable).parent\\n if hasattr(sys, \\\"frozen\\\")\\n else REPOSITORY_DIRECTORY\\n )\\n current_system = get_current_system()\\n\\n # This is for debugging purposes\\n if current_system.DEBUG_MODE:\\n console.print(f\\\"[bold green]Loading plugins from {path}[/]\\\")\\n console.print(\\\"[bold green]Plugins found:[/]\\\")\\n\\n for plugin in Path(__file__).parent.glob(\\\"plugins/*_plugin.py\\\"):\\n python_path = plugin.relative_to(path).with_suffix(\\\"\\\")\\n\\n # This is for debugging purposes\\n if current_system.DEBUG_MODE:\\n console.print(f\\\" [bold red]{plugin.name}[/]\\\")\\n console.print(f\\\" [bold yellow]{python_path}[/]\\\")\\n console.print(f\\\" [bold bright_cyan]{__package__}[/]\\\")\\n console.print(f\\\" [bold magenta]{python_path.parts}[/]\\\")\\n console.print(\\n f\\\" [bold bright_magenta]{'.'.join(python_path.parts)}[/]\\\"\\n )\\n\\n module = importlib.import_module(\\n \\\".\\\".join(python_path.parts), package=__package__\\n )\\n for _, obj in inspect.getmembers(module):\\n if (\\n inspect.isclass(obj)\\n and issubclass(obj, (PltTA))\\n and obj != PlotlyTA.__class__\\n ) and obj not in PlotlyTA.plugins:\\n PlotlyTA.plugins.append(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45954d365c4be3e1e436c1ba223e0c90\",\n \"score\": \"0.6440719\",\n \"text\": \"def start_plugins(self):\\n good_plugins = []\\n for plugin in list(self.plugins):\\n print(\\\"Starting plugin: {}\\\".format(plugin))\\n try:\\n plugin.plugin_start()\\n good_plugins.append(plugin)\\n except Exception as err:\\n print(\\\"Error starting plugin {}\\\".format(err))\\n self.plugins = good_plugins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e5755eb839b29fcbb0ad560f91210f\",\n \"score\": \"0.6426013\",\n \"text\": \"def discover():\\n if CFG[\\\"plugins\\\"][\\\"autoload\\\"]:\\n experiment_plugins = CFG[\\\"plugins\\\"][\\\"experiments\\\"].value\\n for exp_plugin in experiment_plugins:\\n try:\\n importlib.import_module(exp_plugin)\\n except ImportError as import_error:\\n LOG.error(\\\"Could not find '%s'\\\", exp_plugin)\\n LOG.error(\\\"ImportError: %s\\\", import_error.msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cb930da2ac32e8f69910a6939ef39a0\",\n \"score\": \"0.6419173\",\n \"text\": \"def load_plugins(self, filter=None, use_blacklist=True):\\n log.debug('Starting plugin loading.')\\n count = len(list(self._plugins.keys()))\\n\\n # Start loading plugins.\\n for name in self.list_plugins(filter):\\n # Don't load plugins twice.\\n if name in self._plugins:\\n continue\\n\\n # Get the PluginInfo for this.\\n info = self._infos.get(name)\\n if not info:\\n raise KeyError('Tried to load unknown plugin: %s' % name)\\n\\n # If this is blacklisted, and we care, skip it.\\n if use_blacklist and isblacklisted(name):\\n log.debug('Skipping blacklisted plugin: %s' % info.nice_name)\\n continue\\n\\n # Try loading it now.\\n try:\\n self._load_plugin(name, use_blacklist)\\n except DependencyError as err:\\n log.error('Unable to load plugin: %s\\\\n%s' %\\n (info.nice_name, err))\\n except (ImportError, VersionError):\\n log.exception('Unable to load plugin: %s' % info.nice_name)\\n\\n # Count up and log the number of loaded plugins.\\n count = len(list(self._plugins.keys())) - count\\n log.info('Loaded %d plugins.' % count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cdc35869f5b46ba8f960605f6353486\",\n \"score\": \"0.6406636\",\n \"text\": \"def ready_plugins(self):\\n for plugin, plugin_cls in zip(self.plugins, self.plugin_classes):\\n logger.info(\\\"calling ready for plugin %s\\\", repr(plugin_cls))\\n plugin.do_ready()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cf4c702bb1a9622a0e6bf5b667ffa7d\",\n \"score\": \"0.6365202\",\n \"text\": \"def load_plugins():\\n return load_dynamic_modules(pathfinder.plugins_path(), abstractplugin.AbstractPlugin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8e7a1b11f1957dc1d5fbc9c4777d1be\",\n \"score\": \"0.6350461\",\n \"text\": \"def load_execute_file_plugins():\\n textutils.output_info('Executing ' + str(len(file.__all__)) + ' file plugins')\\n for plugin_name in file.__all__:\\n plugin = __import__ (\\\"plugins.file.\\\" + plugin_name, fromlist=[plugin_name])\\n if hasattr(plugin , 'execute'):\\n plugin.execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa34ded51019788fa75d90b9acf81f5b\",\n \"score\": \"0.63288945\",\n \"text\": \"def importPlugins(plugins):\\n global loaded\\n for type_ in plugins:\\n # typeMods will be a module reference containing all the modules for type_\\n # Eg. module xcluster. Then typeMods.xcluster will be available.\\n typeMods = __import__(''.join(['plugins.', type_]), fromlist=plugins[type_])\\n for plugin in plugins[type_]:\\n try:\\n loaded[type_].append(eval('.'.join(['typeMods',plugin])))\\n except Exception, err:\\n sys.stderr.write(\\\"PLUGIN IMPORT ERROR:\\\\n%s\\\" % str(err))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff40c7976203610c9f74eff9cbf61d7b\",\n \"score\": \"0.6314812\",\n \"text\": \"def reloadPlugins(self):\\r\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3e393b9d15b6063efbf077505d2237f\",\n \"score\": \"0.6308202\",\n \"text\": \"def _load_plugins(plugins, debug=True):\\r\\n plugs = []\\r\\n for plugin in plugins:\\r\\n setup_class = plugin.get('setup_class')\\r\\n plugin_name = plugin.get('__name__').split()[-1]\\r\\n mod_name = '.'.join(setup_class.split('.')[:-1])\\r\\n class_name = setup_class.split('.')[-1]\\r\\n try:\\r\\n mod = __import__(mod_name, globals(), locals(), [class_name])\\r\\n except SyntaxError, e:\\r\\n raise exception.PluginSyntaxError(\\r\\n \\\"Plugin %s (%s) contains a syntax error at line %s\\\" %\\r\\n (plugin_name, e.filename, e.lineno))\\r\\n except ImportError, e:\\r\\n raise exception.PluginLoadError(\\r\\n \\\"Failed to import plugin %s: %s\\\" %\\r\\n (plugin_name, e[0]))\\r\\n klass = getattr(mod, class_name, None)\\r\\n if not klass:\\r\\n raise exception.PluginError(\\r\\n 'Plugin class %s does not exist' % setup_class)\\r\\n if not issubclass(klass, clustersetup.ClusterSetup):\\r\\n raise exception.PluginError(\\r\\n \\\"Plugin %s must be a subclass of \\\"\\r\\n \\\"starcluster.clustersetup.ClusterSetup\\\" % setup_class)\\r\\n args, kwargs = utils.get_arg_spec(klass.__init__, debug=debug)\\r\\n config_args = []\\r\\n missing_args = []\\r\\n for arg in args:\\r\\n if arg in plugin:\\r\\n config_args.append(plugin.get(arg))\\r\\n else:\\r\\n missing_args.append(arg)\\r\\n if debug:\\r\\n log.debug(\\\"config_args = %s\\\" % config_args)\\r\\n if missing_args:\\r\\n raise exception.PluginError(\\r\\n \\\"Not enough settings provided for plugin %s (missing: %s)\\\"\\r\\n % (plugin_name, ', '.join(missing_args)))\\r\\n config_kwargs = {}\\r\\n for arg in kwargs:\\r\\n if arg in plugin:\\r\\n config_kwargs[arg] = plugin.get(arg)\\r\\n if debug:\\r\\n log.debug(\\\"config_kwargs = %s\\\" % config_kwargs)\\r\\n try:\\r\\n plug_obj = klass(*config_args, **config_kwargs)\\r\\n except Exception as exc:\\r\\n log.error(\\\"Error occured:\\\", exc_info=True)\\r\\n raise exception.PluginLoadError(\\r\\n \\\"Failed to load plugin %s with \\\"\\r\\n \\\"the following error: %s - %s\\\" %\\r\\n (setup_class, exc.__class__.__name__, exc.message))\\r\\n if not hasattr(plug_obj, '__name__'):\\r\\n setattr(plug_obj, '__name__', plugin_name)\\r\\n plugs.append(plug_obj)\\r\\n return plugs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f3598c8156386020240ffa0bd065995\",\n \"score\": \"0.63020456\",\n \"text\": \"def load_plugins():\\n _plugins = {}\\n for plugin_id, plugin_data in PLUGINS.items():\\n plugin_module_name = f\\\"tgcf_{plugin_id}\\\"\\n plugin_class_name = f\\\"Tgcf{plugin_id.title()}\\\"\\n try:\\n plugin_module = import_module(plugin_module_name)\\n plugin_class = getattr(plugin_module, plugin_class_name)\\n plugin = plugin_class(plugin_data)\\n assert plugin.id_ == plugin_id\\n\\n except ModuleNotFoundError:\\n logging.error(f\\\"Could not find plugin for {plugin_id}\\\")\\n except AttributeError:\\n logging.error(f\\\"Found plugin {plugin_id}, but failed to load.\\\")\\n else:\\n print(f\\\"Loaded plugin {plugin_id}\\\")\\n _plugins.update({plugin.id_: plugin})\\n return _plugins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92f407c27bb3f65845bc497a59e5605f\",\n \"score\": \"0.62988406\",\n \"text\": \"def test_load_no_plugins(self, no_config_mock):\\n modules = plugin.load_plugin_modules([])\\n assert modules == []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"286c8f96f25fe42f3c00ff11c1a2a4f7\",\n \"score\": \"0.62756485\",\n \"text\": \"def load_plugins(plugin_dir=None):\\n\\n if len(loaded_plugins):\\n # prevent loading plugins multiple times\\n return\\n\\n plugin_dirs = [\\n plugin_dir,\\n os.path.join(os.getcwd(), 'scap', 'plugins'),\\n os.path.join(os.path.expanduser('~'), '.scap', 'plugins')\\n ]\\n\\n plugins = find_plugins(plugin_dirs)\\n if len(plugins) < 1:\\n return\\n\\n # Turn off those obnoxious *.pyc files for plugins so we don't litter\\n maybe_write_bytecode = sys.dont_write_bytecode\\n sys.dont_write_bytecode = True\\n\\n # import each of the plugin modules\\n for plugin in plugins:\\n # module path relative to scap.plugins:\\n plugin_module = \\\".%s\\\" % plugin\\n try:\\n mod = importlib.import_module(plugin_module, \\\"scap.plugins\\\")\\n # find classes in mod which extend scap.cli.Application\\n for objname in dir(mod):\\n obj = getattr(mod, objname)\\n if type(obj) is type and issubclass(obj, Application):\\n if objname in loaded_plugins:\\n # duplicate: another plugin already used the same name\\n msg = 'Duplicate plugin named %s, skipping.'\\n logging.getLogger().warning(msg, objname)\\n continue\\n # copy the class into the scap.plugins namespace\\n setattr(this_module, objname, obj)\\n loaded_plugins[objname] = obj\\n __all__.append(objname)\\n except Exception as e:\\n msg = 'Problem loading plugins from module: scap.plugins.%s (%s)'\\n err_msg = type(e).__name__ + ':' + str(e)\\n logger = logging.getLogger()\\n logger.warning(msg % (plugin, err_msg))\\n\\n # Restore the original setting\\n sys.dont_write_bytecode = maybe_write_bytecode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47d51b69dba04d0982452ac109d29454\",\n \"score\": \"0.6275517\",\n \"text\": \"def load_execute_host_plugins():\\n textutils.output_info('Executing ' + str(len(host.__all__)) + ' host plugins')\\n for plugin_name in host.__all__:\\n plugin = __import__ (\\\"plugins.host.\\\" + plugin_name, fromlist=[plugin_name])\\n if hasattr(plugin , 'execute'):\\n plugin.execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a6bcf3e4f8d82a2849f24a51aca2f58\",\n \"score\": \"0.627525\",\n \"text\": \"def get_plugins():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bd08ccb33b2b113965c591b39637367\",\n \"score\": \"0.62589246\",\n \"text\": \"def find_plugins(self):\\n\\n for loader in iter_entry_points(group='vvv', name=None):\\n\\n try:\\n # Construct the plug-in instance\\n name = loader.name\\n klass = loader.load()\\n instance = klass()\\n logger.debug(\\\"Loaded plug-in: %s\\\", name)\\n self.plugins[name] = instance\\n except Exception as e:\\n logger.error(\\\"Could not load plug-in: %s\\\", loader)\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e422b7cc4d5f6e26a5e1e2a64f3f03d1\",\n \"score\": \"0.6235418\",\n \"text\": \"def load_plugin_manager_plugins(self):\\r\\n\\r\\n for plugin in self.plugin_instances:\\r\\n if not PLUGIN_MANAGER_EXTENSION_TYPE in plugin.capabilities: continue\\r\\n self._load_plugin(plugin, loading_type = PLUGIN_MANAGER_EXTENSION_TYPE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0178898fe7c664d0b612a732e36343c3\",\n \"score\": \"0.6128111\",\n \"text\": \"def load(self):\\n self._pm.load_plugins(self.path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3753a6d9cde87292d257e6602ce31b2a\",\n \"score\": \"0.6127432\",\n \"text\": \"def loader(module):\\n\\n\\t\\tplugins = []\\n\\t\\t# Return empty plugins list when do not have plugin\\n\\t\\t# interface\\n\\t\\ttry:\\n\\t\\t\\tmodule.countPlugins()\\n\\t\\texcept Exception as e:\\n\\t\\t\\tlogger.warning(\\\"Module: {} in plugin directory is not a plugin\\\".format(module.__name__))\\n\\t\\t\\treturn plugins\\n\\t\\tfor i in range(0, module.countPlugins()):\\n\\t\\t\\tpi = module.createPlugin(i, **kwargs)\\n\\t\\t\\tif (pi):\\n\\t\\t\\t\\tplugins.append(pi)\\n\\t\\t\\t\\tlogger.info(\\\"Plugin: {} is loaded\\\".format(pi.namePlugin()) )\\n\\t\\treturn plugins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffb30c37a9440315ba2efc19a404cbe1\",\n \"score\": \"0.61070305\",\n \"text\": \"def get_plugins():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac5c4bba7e2269126450aa6fa3e90c3c\",\n \"score\": \"0.6105481\",\n \"text\": \"def test_import_plugin(self):\\n self.assertEqual(len(loader.import_plugins(['monitoring'])), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4bcdae6cceb75ad1b8c39c3fe10d2f1\",\n \"score\": \"0.6092951\",\n \"text\": \"def testPluginsLoaded(self):\\r\\n sources, targets = self.pl.load_sources_and_targets(self.filename, True)\\r\\n self.assertEqual(len(sources), 3)\\r\\n self.assertEqual(len(targets), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f674f01a16b67037c9f1c362ee7e131\",\n \"score\": \"0.60923755\",\n \"text\": \"def loadPlugins(path, catalogue):\\n pluginsRoot, _ = xmlUtils.loadToTree(catalogue)\\n for pluginNode in pluginsRoot:\\n name = pluginNode.find('name').text\\n location = pluginNode.find('location').text\\n if location is None:\\n raise PluginError('Installation is corrupted for plugin \\\"{}\\\". Check raven/plugins/plugin_directory.xml and try reinstalling using raven/scripts/intall_plugin')\\n if name is None:\\n name = os.path.basename(location)\\n print('Loading plugin \\\"{}\\\" at {}'.format(name, location))\\n module = loadPluginModule(name, location)\\n loadEntities(name, module)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acaed84ed521d4058c292b4b59f3a9fe\",\n \"score\": \"0.6090377\",\n \"text\": \"def setup_all():\\n # Check there is plugin manager instance\\n if PluginManager.__instance is None:\\n PluginManager()\\n if PluginManager.__instance is None:\\n return False\\n retval = True\\n # For each plugin, setup the plugin dependencies\\n for name in PluginManager.get_names():\\n retval = PluginManager.setup(name) and retval\\n return retval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cfd572af47c196625bc4d5bf0f7dd9b\",\n \"score\": \"0.6089976\",\n \"text\": \"def test_import_plugin_not_found(self):\\n self.assertEqual(len(loader.import_plugins(['bad_name'])), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4ce46bc018037d5fdfa621610034ab1\",\n \"score\": \"0.6080716\",\n \"text\": \"def get_all_loaded_plugins(self):\\r\\n\\r\\n # creates the loaded plugins instances list\\r\\n loaded_plugins_instances = []\\r\\n\\r\\n # iterates over all the plugin instances\\r\\n for plugin_instance in self.plugin_instances:\\r\\n # in case the plugin instance is loaded\\r\\n if plugin_instance.is_loaded():\\r\\n # adds the plugin instance to the loaded plugins instances list\\r\\n loaded_plugins_instances.append(plugin_instance)\\r\\n\\r\\n # returns the loaded plugins instances\\r\\n return loaded_plugins_instances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e4517add6f6163c323f7403d7a726b1\",\n \"score\": \"0.6054527\",\n \"text\": \"def external_plugins_loaded(plugins):\\n\\n for p in plugins:\\n command = None\\n ready = False\\n for app_command in sublime_plugin.application_command_classes:\\n if app_command.__name__ == p:\\n command = app_command\\n break\\n if command is not None:\\n ready = command.is_ready()\\n else:\\n # Command isn't found in list, so just return ready\\n ready = True\\n return ready\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98f254f32f57d13093a2354e0b243735\",\n \"score\": \"0.60352504\",\n \"text\": \"def _initializePlugins(self, dirname, plugins, plugintype, name=None):\\n ret = []\\n syspathset = set(sys.path)\\n\\n dirname = os.path.abspath(dirname)\\n self.logger.debug(\\\"looking for plugin(s) in {}\\\".format(dirname))\\n if not os.path.isdir(dirname):\\n self.logger.debug(\\n \\\"directory {} does not exist ... moving on\\\".format(dirname)\\n )\\n return ret\\n\\n # Include all plugin directories in sys.path for includes\\n if dirname not in sys.path:\\n syspathset.add(dirname)\\n sys.path = list(syspathset)\\n\\n # Loop through all files in passed dirname looking for plugins\\n for filename in os.listdir(dirname):\\n filename = dirname + os.sep + filename\\n\\n # If the file exists\\n if os.path.isfile(filename):\\n # Split file into a base name plus extension\\n basename = os.path.basename(filename)\\n base, extension = os.path.splitext(basename)\\n\\n # If we're a python file and we don't start with _\\n # if extension == \\\".py\\\" and not basename.startswith(\\\"_\\\"):\\n # APPPERF-263: If name param is supplied, only attempt to load\\n # {name}.py from {app}/bin directory\\n if extension == \\\".py\\\" and (\\n (name is None and not basename.startswith(\\\"_\\\")) or base == name\\n ):\\n self.logger.debug(\\\"Searching for plugin in file '%s'\\\" % filename)\\n try:\\n # Import the module\\n # module = imp.load_source(base, filename)\\n\\n mod_name, mod_path, mod_desc = imp.find_module(base, [dirname])\\n # TODO: Probably need to adjust module.load() to be added later so this can be pickled.\\n module = imp.load_module(base, mod_name, mod_path, mod_desc)\\n plugin = module.load()\\n\\n # spec = importlib.util.spec_from_file_location(base, filename)\\n # plugin = importlib.util.module_from_spec(spec)\\n # spec.loader.exec_module(plugin)\\n\\n # set plugin to something like output.file or generator.default\\n pluginname = plugintype + \\\".\\\" + base\\n plugins[pluginname] = plugin\\n\\n # Return is used to determine valid configs, so only return the base name of the plugin\\n ret.append(base)\\n\\n self.logger.debug(\\n \\\"Loading module '%s' from '%s'\\\" % (pluginname, basename)\\n )\\n\\n # 12/3/13 If we haven't loaded a plugin right or we haven't initialized all the variables\\n # in the plugin, we will get an exception and the plan is to not handle it\\n if \\\"validSettings\\\" in dir(plugin):\\n self.config._validSettings.extend(plugin.validSettings)\\n if \\\"defaultableSettings\\\" in dir(plugin):\\n self.config._defaultableSettings.extend(\\n plugin.defaultableSettings\\n )\\n if \\\"intSettings\\\" in dir(plugin):\\n self.config._intSettings.extend(plugin.intSettings)\\n if \\\"floatSettings\\\" in dir(plugin):\\n self.config._floatSettings.extend(plugin.floatSettings)\\n if \\\"boolSettings\\\" in dir(plugin):\\n self.config._boolSettings.extend(plugin.boolSettings)\\n if \\\"jsonSettings\\\" in dir(plugin):\\n self.config._jsonSettings.extend(plugin.jsonSettings)\\n if \\\"complexSettings\\\" in dir(plugin):\\n self.config._complexSettings.update(plugin.complexSettings)\\n except ValueError:\\n self.logger.error(\\n \\\"Error loading plugin '%s' of type '%s'\\\"\\n % (base, plugintype)\\n )\\n except ImportError as ie:\\n self.logger.warning(\\n \\\"Could not load plugin: %s, skipping\\\" % base\\n )\\n self.logger.exception(ie)\\n except Exception as e:\\n self.logger.exception(str(e))\\n raise e\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a1f2c58bd11b3956b4fd62b22c2d080\",\n \"score\": \"0.6033774\",\n \"text\": \"def unloadAll(self):\\n for p in self.__loadedPlugins:\\n p.unload()\\n self.__loadedPlugins = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d890e45598128c112c936a328361310\",\n \"score\": \"0.6014353\",\n \"text\": \"def _setup():\\n poll_plugins = []\\n hook_plugins = []\\n possible_plugins = set(os.listdir(_PLUGIN_FOLDER)).difference(_BLACKLIST)\\n\\n # Find plugins\\n for i in possible_plugins:\\n # Check that this is a valid module\\n if not _validate_module_name(i):\\n continue\\n\\n # Import module\\n module_name = _extract_module_name(i)\\n module = importlib.import_module(\\\".\\\" + module_name, package=__name__)\\n\\n # Module sorting and validation\\n try:\\n if \\\"poll\\\" in module.TYPE:\\n if module.API_VERSION >= _MIN_POLL_API_VERSION:\\n poll_plugins.append(module)\\n else:\\n print(_ERROR_TEMPLATES[\\\"BAD_API\\\"].format(module_name))\\n if \\\"hook\\\" in module.TYPE:\\n if module.API_VERSION >= _MIN_HOOK_API_VERSION:\\n hook_plugins.append(module)\\n else:\\n print(_ERROR_TEMPLATES[\\\"BAD_API\\\"].format(module_name))\\n except AttributeError:\\n print(_ERROR_TEMPLATES[\\\"BAD_HEADER\\\"].format(module_name))\\n except SyntaxError:\\n print(_ERROR_TEMPLATES[\\\"BAD_HEADER\\\"].format(module_name))\\n\\n return poll_plugins, hook_plugins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbfbeec82cd6846e74e35c99ace52edb\",\n \"score\": \"0.59802026\",\n \"text\": \"def plugin_loaded():\\r\\n\\r\\n persist.plugin_is_loaded = True\\r\\n persist.settings.load()\\r\\n persist.printf('debug mode:', 'on' if persist.debug_mode() else 'off')\\r\\n util.create_tempdir()\\r\\n\\r\\n for linter in persist.linter_classes.values():\\r\\n linter.initialize()\\r\\n\\r\\n plugin = SublimeLinter.shared_plugin()\\r\\n queue.start(plugin.lint)\\r\\n\\r\\n util.generate_menus()\\r\\n util.generate_color_scheme(from_reload=False)\\r\\n util.install_syntaxes()\\r\\n\\r\\n persist.settings.on_update_call(SublimeLinter.on_settings_updated)\\r\\n\\r\\n # This ensures we lint the active view on a fresh install\\r\\n window = sublime.active_window()\\r\\n\\r\\n if window:\\r\\n plugin.on_activated(window.active_view())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d9a341fbbfece3159f7cc8fec68cbfc\",\n \"score\": \"0.59794676\",\n \"text\": \"def get_plugins(self):\\n modules = self._get_modules()\\n\\n # load plugins again only if a plugin was added or removed\\n if len(modules) == len(self._plugins):\\n return self._plugins\\n\\n self._plugins = []\\n\\n for plugin_module in modules:\\n modname, fp, path, desc = plugin_module\\n\\n try:\\n # load the module and look for classes\\n module = imp.load_module(modname, fp, path, desc)\\n except Exception as e:\\n error_msg = \\\"{exception}: {message}\\\".format(\\n exception=e.__class__.__name__,\\n message=unicode(e))\\n self._errors[fp.name] = error_msg\\n continue\\n\\n classes_in_module = inspect.getmembers(module, inspect.isclass)\\n\\n for cls in classes_in_module:\\n cls = cls[1]\\n\\n # ignore if it's the base plugin class\\n if cls.__name__ == self.plugin_type.base_class.__name__:\\n continue\\n\\n # make sure that the class is a subclass of the plugin's\\n # base class\\n if self.plugin_type.base_class in cls.__bases__:\\n self.register_plugin(cls)\\n\\n return self._plugins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7c8c91212ce59e819da0e42cfc0cdf6\",\n \"score\": \"0.597294\",\n \"text\": \"def command(self, bot, comm, groups):\\n plugins = PluginUtils.get_plugins(bot)\\n names = ', '.join(p.name for p in plugins)\\n bot.reply(comm, 'Loaded Plugins: {0}.'.format(names))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e442dfc446f845e9a43f124e885d65ec\",\n \"score\": \"0.59726703\",\n \"text\": \"def test_raises_when_loading_default_plugins_without_allow_qualified(self):\\n default_plugin_qualnames = plugin.get_qualified_module_names(\\n _repobee.ext.defaults\\n )\\n\\n with pytest.raises(exception.PluginLoadError) as exc_info:\\n plugin.load_plugin_modules(default_plugin_qualnames)\\n\\n assert \\\"failed to load plugin module\\\" in str(exc_info.value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"198ef8a052e161ec2e62d56b9ad57674\",\n \"score\": \"0.5961389\",\n \"text\": \"def load_startup_plugins(self):\\r\\n\\r\\n # iterates over all the plugin instances\\r\\n for plugin in self.plugin_instances:\\r\\n # searches for the startup type in the plugin capabilities\\r\\n # in case the plugins contains such capability must load\\r\\n # it because it's considered to be a startup plugin\\r\\n if STARTUP_TYPE in plugin.capabilities: self._load_plugin(plugin, loading_type = STARTUP_TYPE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"373057132c7785d8325862d8e9450550\",\n \"score\": \"0.5956981\",\n \"text\": \"def register_core_plugins():\\n register_plugin(rho_bot_scheduler)\\n register_plugin(rho_bot_configuration)\\n register_plugin(rho_bot_roster)\\n register_plugin(rho_bot_storage_client)\\n register_plugin(rho_bot_rdf_publish)\\n register_plugin(rho_bot_representation_manager)\\n register_plugin(rho_bot_get_or_lookup)\\n register_plugin(rho_bot_get_or_create)\\n register_plugin(export_configuration)\\n register_plugin(import_configuration)\\n register_plugin(reset_configuration)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67590941dcbd13a979a5519c331f7a31\",\n \"score\": \"0.59513134\",\n \"text\": \"def start_plugin_polling(self):\\r\\n for plugin in [key for key in self.config.application.keys()\\r\\n if key not in self.IGNORE_KEYS]:\\r\\n LOGGER.info('Enabling plugin: %s', plugin)\\r\\n plugin_class = None\\r\\n\\r\\n # If plugin is part of the core agent plugin list\\r\\n if plugin in plugins.available:\\r\\n plugin_class = self._get_plugin(plugins.available[plugin])\\r\\n\\r\\n # If plugin is in config and a qualified class name\\r\\n elif '.' in plugin:\\r\\n plugin_class = self._get_plugin(plugin)\\r\\n\\r\\n # If plugin class could not be imported\\r\\n if not plugin_class:\\r\\n LOGGER.error('Enabled plugin %s not available', plugin)\\r\\n continue\\r\\n\\r\\n self.poll_plugin(plugin, plugin_class,\\r\\n self.config.application.get(plugin))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77b401dc24a5abf43bfc99e79d5f468f\",\n \"score\": \"0.5948454\",\n \"text\": \"def populate(self, modules):\\n for module in modules:\\n try:\\n m = importlib.import_module(module)\\n except SyntaxError as e:\\n print(\\\"module '{}' failed loading with message {}\\\".format(module,e.msg))\\n continue\\n\\n if hasattr(m, 'Plugin'):\\n self.add_plugin(m.Plugin)\\n continue\\n\\n for name, plugin_class in inspect.getmembers(m):\\n if 'Plugin' in name:\\n p = plugin_class()\\n self.add_plugin(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bd49c8ff5dd8ed2fd4ee3d31d48bdf0\",\n \"score\": \"0.5943103\",\n \"text\": \"def start_plugins(self):\\r\\n\\r\\n # retrieves all the plugin classes available\\r\\n self.plugin_classes = self.get_all_plugin_classes()\\r\\n\\r\\n # iterates over all the available plugin classes\\r\\n for plugin in self.plugin_classes:\\r\\n # retrieves the plugin id\\r\\n plugin_id = plugin.id\\r\\n\\r\\n # sets the plugin class in the plugin classes map\\r\\n self.plugin_classes_map[plugin_id] = plugin\\r\\n\\r\\n # starts the plugin (creating the singleton) in\\r\\n # case the plugin is not currently loaded\\r\\n if not plugin in self.loaded_plugins: self.start_plugin(plugin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a35618734fcaa8dc12034dadfc29fc0\",\n \"score\": \"0.5905988\",\n \"text\": \"def get_plugins(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2f67c40ab372888a54f9f0fdf175604\",\n \"score\": \"0.5901993\",\n \"text\": \"def testEnablePlugins(self):\\n parser = interface.BaseParser()\\n\\n parser.EnablePlugins([])\\n self.assertEqual(len(parser._plugins_per_name), 0)\\n\\n parser.EnablePlugins(parser.ALL_PLUGINS)\\n self.assertEqual(len(parser._plugins_per_name), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8e747338361824281ed449e6332803f\",\n \"score\": \"0.5860712\",\n \"text\": \"def rescan_plugins(self):\\n component.get(\\\"CorePluginManager\\\").scan_for_plugins()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e952c39df7cffa1323fd6876a9a4cf41\",\n \"score\": \"0.5859997\",\n \"text\": \"def activate_plugins(plugin_names):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e88833b7328b31ddd0034c3a331da67e\",\n \"score\": \"0.58597225\",\n \"text\": \"def load(conf):\\n global modules, config\\n\\n config.read(conf)\\n flist = sortmodule([f[:-3] for f in os.listdir(basedefs.DIR_PLUGINS) if f[-3:] == \\\".py\\\"])\\n \\n for sec, fname in flist:\\n try:\\n #modules.append(loadplugins(sec))\\n modules[sec] = loadplugins(fname)\\n except Exception as e:\\n logging.error(e)\\n logging.error(traceback.format_exc())\\n raise InstallError(\\\"failed when load plugins\\\")\\n\\n return modules\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c967ee8a8c20d2e1c2020a3dd02859f\",\n \"score\": \"0.58570623\",\n \"text\": \"def get_available_plugins():\\n print(\\\"Available plugins:\\\\n\\\")\\n\\n for instrument, class_name in _available_plugins.iteritems():\\n\\n print(\\\"%s for %s\\\" % (class_name, instrument))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4a5447ac807cf56e40fb508b68ec1d6\",\n \"score\": \"0.58551806\",\n \"text\": \"def test_load_plugins_multiple(self, load_config):\\n mock_config = MagicMock()\\n mock_config.get.return_value = 'lunch, todo'\\n load_config.return_value = mock_config\\n plugins = PluginManager().load_plugins()\\n\\n self.assertEqual(2, len(plugins))\\n self.assertEqual(\\n 'ashaw_notes.plugins.lunch',\\n plugins[0].__module__\\n )\\n self.assertEqual(\\n 'ashaw_notes.plugins.todo',\\n plugins[1].__module__\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af18a7121862896cd99c62f473f2a7fa\",\n \"score\": \"0.5849801\",\n \"text\": \"def discoverable_plugins():\\n discovered_plugins = {}\\n for entry_point in iter_entry_points(\\\"framework\\\"):\\n try:\\n ep = entry_point.load()\\n discovered_plugins[entry_point.name] = ep\\n except (DistributionNotFound, ImportError):\\n logging.exception(f\\\"Plugin {entry_point.name} not found\\\")\\n return discovered_plugins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2047b0e96b8046ccb42c36fe1f036b4c\",\n \"score\": \"0.58488655\",\n \"text\": \"def load_default_plugins_auto(self, load_default_plugins):\\n yield\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61cc98fc936279ea4593dba22a7ae302\",\n \"score\": \"0.58452135\",\n \"text\": \"def _plugins(args):\\n\\n # plugins\\n retval = 0\\n for plugin in plugins.PLUGINS:\\n if plugins.install(plugin, args) == 1:\\n retval = 1\\n\\n return retval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71fcc32d5ac4938f29490688d8d108f5\",\n \"score\": \"0.5836939\",\n \"text\": \"def discoverPlugins():\\n availablePlugins = {}\\n pluginDir = 'plugins'\\n #basePluginPath = sys.path[0]\\n if sys.platform == 'win32':\\n basePluginPath = sys.path[0].split('library.zip')[0]\\n if sys.platform == 'darwin':\\n basePluginPath = sys.path[0].split('find.app')[0]\\n \\n # make sure the plugins folder exists\\n if not os.path.exists(os.path.join(basePluginPath, pluginDir)):\\n raise error.PluginError(\\\"The \\\\\\\"plugins\\\\\\\" directory is missing, please place in the same folder as the FIND program file.\\\")\\n return\\n \\n if not basePluginPath in sys.path:\\n sys.path.insert(0, basePluginPath)\\n \\n #TODO: look at recursive descent for module discovery\\n # Accumulate available plugin modules\\n errMsg = []\\n for type_ in pluginTypes:\\n path = os.path.join(basePluginPath, pluginDir, type_)\\n if not os.path.exists(path):\\n errMsg.append(\\\"The \\\\\\\"%s\\\\\\\" plugins directory is missing.\\\" % type_)\\n else:\\n availablePlugins[type_] = [fname[:-3] for fname in os.listdir(path) \\n if fname.endswith(\\\".py\\\") and \\n not fname.startswith(\\\"__init__\\\")]\\n \\n # raise error with all missing plugin subdirectories\\n if len(errMsg) > 0:\\n raise error.PluginError('\\\\n\\\\t'.join(errMsg))\\n return\\n \\n return availablePlugins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f111ebbda9a8aa2ce3e2927e1ed4fe1\",\n \"score\": \"0.5824811\",\n \"text\": \"async def load_extensions(self) -> None:\\n for extension in self.extension_list:\\n try:\\n self.load_extension(extension)\\n logger.debug(f\\\"Cog {extension} loaded.\\\")\\n except Exception as e:\\n logger.error(f\\\"Cog {extension} failed to load with {type(e)}: {e}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2615ad584fca8a37ed7c93d3ce6b5c4\",\n \"score\": \"0.58019894\",\n \"text\": \"def discover():\\n if CFG[\\\"plugins\\\"][\\\"autoload\\\"].value():\\n log = logging.getLogger('benchbuild')\\n experiment_plugins = CFG[\\\"plugins\\\"][\\\"experiments\\\"].value()\\n for ep in experiment_plugins:\\n try:\\n importlib.import_module(ep)\\n log.debug(\\\"Found experiment: {0}\\\".format(ep))\\n except ImportError as ie:\\n log.error(\\\"Could not find '{0}'\\\".format(ep))\\n log.error(\\\"ImportError: {0}\\\".format(ie.msg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eed22a941ddea5d5c5f72026b9b8a75d\",\n \"score\": \"0.5801577\",\n \"text\": \"def register_plugins(bus=None):\\n from ..plugins import RunnablePlugin\\n\\n for plugin in get_enabled_plugins().values():\\n if isinstance(plugin, RunnablePlugin):\\n plugin.bus = bus\\n plugin.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cc6ce98447d2a4204a98706ea0e1ecd\",\n \"score\": \"0.5796698\",\n \"text\": \"def init_plugins(self, disabled_glob=(), enable_plugins=(), cli=None):\\n # :api\\n if self.conf.plugins:\\n self._plugins._load(self.conf, disabled_glob, enable_plugins)\\n self._plugins._run_init(self, cli)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adb3dd35836d0bd0be00bc914d8ea6ef\",\n \"score\": \"0.57896024\",\n \"text\": \"def finishLoadPlugin(name):\\n if name not in _delayedPlugins:\\n #Try to find module in system (such as from pip install)\\n #Note that loadEntities (below) checks if there is a subclass of\\n # PluginBase.PluginBase before using it, so this is safe.\\n spec = importlib.util.find_spec(name)\\n if spec is None:\\n return False\\n plugin = importlib.util.module_from_spec(spec)\\n _delayedPlugins[name] = (spec, plugin, False)\\n spec, plugin, loaded = _delayedPlugins[name]\\n if not loaded:\\n spec.loader.exec_module(plugin)\\n loadEntities(name, plugin)\\n #Set loaded flag to true to prevent reloading\\n _delayedPlugins[name] = (spec, plugin, True)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d3b2c565665ef24d1f508ce0197f5ef\",\n \"score\": \"0.57852995\",\n \"text\": \"def loadedPlugins(self):\\n return self.__loadedPlugins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1434668d47af9a45a3cc7971c75aa1b9\",\n \"score\": \"0.57738394\",\n \"text\": \"def load_plugins(config):\\n plugin = {}\\n for p in ('retrieval', 'decryption', 'verification', 'scrubbing', 'reporting'):\\n try:\\n module_name = 'erawan.plugins.{}.{}'.format(p, config['plugins'][p]['name'])\\n except KeyError:\\n logger.critical('Could not import %s plugin; is it set in config?', p)\\n sys.exit(1)\\n try:\\n plugin[p] = importlib.import_module(module_name)\\n except ImportError:\\n logger.critical('Unable to load plugin \\\"%s\\\"', module_name)\\n sys.exit(1)\\n return plugin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca103e644f66671feac38b2f5232642f\",\n \"score\": \"0.57731414\",\n \"text\": \"def loadPlugins(pluginfldr,hivesdict):\\n pluginmanager = PluginManager()\\n pluginmanager.setPluginPlaces([pluginfldr])\\n pluginmanager.collectPlugins()\\n\\n print \\\"\\\"\\n logging.info(\\\"[PLUGINS IDENTIFIED: %s]\\\" % len(pluginmanager.getAllPlugins()))\\n\\n compat_plugins = list()\\n incompat_plugins = list()\\n for plugin in pluginmanager.getAllPlugins():\\n compat = 1\\n for req in plugin.plugin_object.getRequirements():\\n if (hivesdict[req][0] <> 1):\\n compat = 0\\n break\\n if compat:\\n compat_plugins.append(plugin.name)\\n else:\\n incompat_plugins.append(plugin.name)\\n\\n logging.info(\\\" [%s] Compatible Plugins:\\\" % len(compat_plugins))\\n for plugin in compat_plugins:\\n logging.info(\\\" - %s\\\" % plugin)\\n logging.info(\\\" [%s] Incompatible Plugins:\\\" % len(incompat_plugins))\\n for plugin in incompat_plugins:\\n logging.info(\\\" - %s\\\" % plugin)\\n\\n return pluginmanager, compat_plugins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d86c186eb48e0358d8b7c2f56f51dec0\",\n \"score\": \"0.57704806\",\n \"text\": \"def load_py_files():\\n def _load_py_files(env, search_path, auto_enable=None):\\n for path in search_path:\\n plugin_files = locate(\\\"*.py\\\", path)\\n for plugin_file in plugin_files:\\n try:\\n plugin_name = os.path.basename(plugin_file[:-3])\\n module_name = _get_module_name(plugin_file)\\n import_module(module_name)\\n _enable_plugin(env, plugin_name)\\n except NotImplementedError, e:\\n #print \\\"Cant Implement This\\\"\\n pass\\n\\n\\n return _load_py_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92a08a26d0e9538024234c209ca11eb1\",\n \"score\": \"0.57653815\",\n \"text\": \"def init_plugins(self):\\n\\n assert self.project_path\\n assert os.path.exists(self.project_path)\\n\\n for plugin_id, instance in self.plugins.items():\\n\\n try:\\n\\n plugin_installation = os.path.join(self.installation, plugin_id)\\n\\n instance.init(\\n plugin_id=plugin_id,\\n main=self,\\n reporter=self.reporter,\\n options=self.options_data,\\n files=self.files_data,\\n installation_path=plugin_installation,\\n walker=self.walker,\\n project_path=self.project_path\\n )\\n\\n instance.setup_options()\\n except Exception as e:\\n logger.error(\\\"Could not initialize plug-in: %s\\\", plugin_id)\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d85918a9bc5eee3652fbfca95d1b746b\",\n \"score\": \"0.57314444\",\n \"text\": \"def loaded_plugins(self):\\n return self.__plugins.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed219590e4d56d2d6a1e3106f28c98fe\",\n \"score\": \"0.56982917\",\n \"text\": \"def load_modules():\\n\\n # logger = logging.getLogger(__name__)\\n locations = [marvin.support.path.PLUGIN_PATH]\\n modules = []\\n for finder, name, ispkg in pkgutil.walk_packages(locations):\\n try:\\n loader = finder.find_module(name)\\n mod = loader.load_module(name)\\n except:\\n Log.warn(\\\"Skipped loading module '{0}' due to an error.\\\", name)\\n else:\\n # if hasattr(mod, 'WORDS'):\\n modules.append(mod)\\n # else:\\n # Log.warn(\\\"Skipped loading module '{0}' because it misses \\\" +\\n # \\\"the WORDS constant.\\\", name)\\n modules.sort(key=lambda mod: mod.PRIORITY if hasattr(mod, 'PRIORITY')\\n else 0, reverse=True)\\n return modules\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f86f70acff17b995cbd67be5b3e67b4\",\n \"score\": \"0.5694283\",\n \"text\": \"def load_main_plugins(self):\\r\\n\\r\\n # iterates over all the plugin instances\\r\\n for plugin in self.plugin_instances:\\r\\n # searches for the main type in the plugin capabilities\\r\\n # in case the plugins contains such capability must load\\r\\n # it because it's considered to be a main plugin\\r\\n if MAIN_TYPE in plugin.capabilities: self._load_plugin(plugin, loading_type = MAIN_TYPE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b9dfcb1572fc614237e168f57979d76\",\n \"score\": \"0.5691813\",\n \"text\": \"def start_plugin_manager_plugins(self):\\r\\n\\r\\n # retrieves all the plugin manager plugin classes available\\r\\n self.plugin_classes = self.get_all_plugin_classes(PluginManagerPlugin)\\r\\n\\r\\n # iterates over all the available plugin manager plugin classes\\r\\n for plugin in self.plugin_classes:\\r\\n # retrieves the plugin id\\r\\n plugin_id = plugin.id\\r\\n\\r\\n # sets the plugin class in the plugin classes map\\r\\n self.plugin_classes_map[plugin_id] = plugin\\r\\n\\r\\n # tests the plugin for loading, verifying that it's\\r\\n # current loaded and then starts the plugin\\r\\n if not plugin in self.loaded_plugins: continue\\r\\n self.start_plugin(plugin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d7bf7cf95dad7aec3324c161b162ff\",\n \"score\": \"0.5677742\",\n \"text\": \"def veerify_installed_plugins(self):\\n cmd = \\\"feature:list\\\"\\n error_flag = 0\\n for plugin in config.ODL_PLUGINS:\\n pattern = \\\"{0}\\\\s+\\\\|.*\\\\|\\\\s*x\\\\s+\\\\|\\\".format(plugin)\\n if self.execute_command(cmd=cmd,exp_out=pattern):\\n continue\\n else:\\n self.log_handler.writeTolog(msg=\\\"****** PLUGIN is NOT INSTALLED Properly, Verification FAILED ******\\\")\\n error_flag += 1 \\n return False if error_flag > 0 else True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc71a6579b2ce27854d627fe356afc6f\",\n \"score\": \"0.5674591\",\n \"text\": \"def get_plugins(cls):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c10d2804bc3eda8526fd37d1c68fd9f1\",\n \"score\": \"0.5656471\",\n \"text\": \"def check_plugins(package):\\n if not package.cmake:\\n return\\n defined_macros = package.source_code.search_for_pattern(PLUGIN_RE)\\n existing_plugins = plugin_xml_by_package(package)\\n defined_plugins = package.manifest.get_plugin_xmls()\\n build_rules = package.cmake.get_source_build_rules('add_library', resolve_target_name=True)\\n\\n for rel_fn, plugin_info in defined_macros.items():\\n library = lookup_library(build_rules, rel_fn)\\n # pkg2/name2 is the parent class\\n for pkg1, name1, pkg2, name2 in plugin_info:\\n # Create file if needed\\n if pkg2 not in existing_plugins:\\n xml_filename = '%s_plugins.xml' % pkg2\\n print('\\\\tCreating %s' % xml_filename)\\n p_xml = PluginXML(xml_filename, os.path.join(package.root, xml_filename))\\n package.plugin_configs.append(p_xml)\\n existing_plugins[pkg2] = [p_xml]\\n\\n # Make sure plugins are properly exported\\n for plugin_xml in existing_plugins[pkg2]:\\n if plugin_xml.rel_fn not in defined_plugins[pkg2]:\\n ex_el = package.manifest.add_plugin_export(pkg2, plugin_xml.rel_fn)\\n enforce_tabbing_helper(package.manifest, ex_el, 2)\\n\\n # Make sure the class is in the files\\n if not contains_library(existing_plugins[pkg2], library, pkg1, name1):\\n # insert into first\\n xml = existing_plugins[pkg2][0]\\n xml.insert_new_class(library, pkg1, name1, pkg2, name2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c67082771f00c8ced6d0e6a0f44ea51\",\n \"score\": \"0.56513125\",\n \"text\": \"def register_plugins(self):\\n self.plugin_manager.register(BasePlugin())\\n self.plugin_manager.register(SubcmdPlugin())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4abac434c9ce320fcaef9200f2a39092\",\n \"score\": \"0.5645184\",\n \"text\": \"def get_plugins():\\n installed_plugins = []\\n for ep in pr.iter_entry_points(group='gaia.plugins'):\\n try:\\n module = ep.load()\\n importlib.import_module(module.__name__)\\n installed_plugins.append(module)\\n if hasattr(module, 'get_config'):\\n config.update(module.get_config())\\n except ImportError:\\n logger.error('Could not load module: {}'.format(\\n traceback.print_exc()))\\n return installed_plugins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a239dc76fad6393ecf996e606f714d12\",\n \"score\": \"0.5637939\",\n \"text\": \"def load_plugins(config, plugin_kwargs):\\n installed_plugins = _gather_installed_plugins()\\n metrics_plugin = _get_metrics_plugin(config, installed_plugins)\\n if metrics_plugin:\\n plugin_kwargs['metrics'] = metrics_plugin\\n\\n active_plugins = _get_activated_plugins(config, installed_plugins)\\n if not active_plugins:\\n return [], [], [], None\\n plugin_namespaces = _get_plugin_config_keys(active_plugins)\\n plugin_configs = _load_plugin_configs(plugin_namespaces, config)\\n plugin_names, plugins, errors = _init_plugins(\\n active_plugins, installed_plugins, plugin_configs, plugin_kwargs)\\n return plugin_names, plugins, errors, plugin_kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437487fa3ee2fe5cd1186b54bc1d4dbf\",\n \"score\": \"0.56353956\",\n \"text\": \"def configure_plugins(self):\\n # :api\\n self._plugins._run_config()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b7ec78dcd859e4daae914b6be8c23d4\",\n \"score\": \"0.56248754\",\n \"text\": \"def load(self, ignore_blacklist=False):\\n if self.is_loaded:\\n log.warning('Trying to load already loaded plugin %r.' %\\n self.data['name'])\\n return\\n\\n if not ignore_blacklist and self.is_blacklisted:\\n raise addons.DependencyError('Plugin %r is blacklisted.' % \\n self.data['name'])\\n\\n # Check our dependencies for safety.\\n addons.check_dependencies(self)\\n\\n # Load our dependencies.\\n for name in self.requires.iterkeys():\\n if name == '__app__' or (':' in name and not\\n name.startswith('plugin:')):\\n continue\\n dep = addons.get('plugin', name)\\n\\n # Make sure we're needed.\\n if not self.name in dep.needed_by:\\n dep.needed_by.append(self.name)\\n\\n # If it's loaded, just continue.\\n if dep.is_loaded:\\n continue\\n dep.load()\\n\\n # Okay, now load!\\n self._do_load()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd151a0874df861566182b10974c604f\",\n \"score\": \"0.56133425\",\n \"text\": \"def command(self, bot, comm, groups):\\n name = groups[0]\\n plugins = PluginUtils.get_plugins(bot)\\n matched_plugins = [p for p in plugins if p.name == name]\\n if len(matched_plugins) != 0:\\n bot.reply(comm, \\\"%s is already loaded.\\\" % name)\\n return False\\n\\n # Fun fact: the fresh thing is just a dummy. It just can't be None\\n new_plugin = twisted.plugin.retrieve_named_plugins(\\n IPlugin, [name], 'hamper.plugins', {'fresh': True})[0]\\n\\n bot.factory.loader.registerPlugin(new_plugin)\\n bot.reply(comm, 'Loading {0}.'.format(new_plugin))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a998050f7e268c9bfd16890e7a7d7a95\",\n \"score\": \"0.5605278\",\n \"text\": \"def load_extra_plugins(pathspec):\\n # Collect names of loaded plugins so we have something to test\\n # success/failure against\\n loaded_plugins = []\\n\\n paths = pathspec.split(':')\\n for path in paths:\\n loaded_plugins.extend(_load_plugins_from_dir(path))\\n\\n return loaded_plugins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"416cb6b3eeda012ff007371776b39107\",\n \"score\": \"0.5588129\",\n \"text\": \"def loadCheckedLoadables(self):\\n self.loadableTable.updateCheckstate()\\n loadableCount = 0\\n for plugin in self.loadablesByPlugin:\\n for loadable in self.loadablesByPlugin[plugin]:\\n if loadable.selected:\\n loadableCount += 1\\n self.progress = qt.QProgressDialog(self.window)\\n self.progress.minimumDuration = 0\\n self.progress.show()\\n self.progress.setValue(0)\\n self.progress.setMaximum(loadableCount)\\n step = 0\\n for plugin in self.loadablesByPlugin:\\n for loadable in self.loadablesByPlugin[plugin]:\\n if self.progress.wasCanceled:\\n break\\n slicer.app.processEvents()\\n self.progress.setValue(step)\\n slicer.app.processEvents()\\n if loadable.selected:\\n self.progress.labelText = '\\\\nLoading %s' % loadable.name\\n slicer.app.processEvents()\\n if not plugin.load(loadable):\\n qt.QMessageBox.warning(slicer.util.mainWindow(), \\n 'Load', 'Could not load: %s as a %s' % (loadable.name,plugin.loadType))\\n step += 1\\n self.progress.setValue(step)\\n slicer.app.processEvents()\\n self.progress.close()\\n self.progress = None\\n self.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5a0d9b86475fe88e5385586184e960e\",\n \"score\": \"0.5579736\",\n \"text\": \"def load_plugins_dir(dirname):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a30c64138abb6bdce55323859d29eb77\",\n \"score\": \"0.55797166\",\n \"text\": \"def get_plugin_manager_plugins_loaded(self):\\r\\n\\r\\n return self.plugin_manager_plugins_loaded\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db59f48dbaa04346f601e5f6abab9cd0\",\n \"score\": \"0.55631167\",\n \"text\": \"def load_py_files():\\r\\n def _load_py_files(env, search_path, auto_enable=None):\\r\\n for path in search_path:\\r\\n plugin_files = glob(os.path.join(path, '*.py'))\\r\\n for plugin_file in plugin_files:\\r\\n try:\\r\\n plugin_name = os.path.basename(plugin_file[:-3])\\r\\n env.log.debug('Loading file plugin %s from %s' % \\\\\\r\\n (plugin_name, plugin_file))\\r\\n if plugin_name not in sys.modules:\\r\\n module = imp.load_source(plugin_name, plugin_file)\\r\\n if path == auto_enable:\\r\\n _enable_plugin(env, plugin_name)\\r\\n except Exception as e:\\r\\n env.log.error('Failed to load plugin from %s: %s',\\r\\n plugin_file,\\r\\n exception_to_unicode(e, traceback=True))\\r\\n\\r\\n return _load_py_files\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":494,"cells":{"query_id":{"kind":"string","value":"d2de2d49a99e311fe7785f9523cffc0f"},"query":{"kind":"string","value":"Remove a child handler."},"positive_passages":{"kind":"list like","value":[{"docid":"85b819a21028347d4916f20c7b44ca3d","score":"0.7669957","text":"def removeHandler(handler):","title":""}],"string":"[\n {\n \"docid\": \"85b819a21028347d4916f20c7b44ca3d\",\n \"score\": \"0.7669957\",\n \"text\": \"def removeHandler(handler):\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"63c1c16a7834019e5cad6290cb3e4810","score":"0.7600718","text":"def remove_handler(self, handler):\n self.handlers.remove(handler)","title":""},{"docid":"3a2d370956a7b1319092fbc4335fbb48","score":"0.7523634","text":"def remove_handler(self, fd):","title":""},{"docid":"a11df2d526635964abcd4376be1ed29f","score":"0.73864824","text":"def remove_hanlder(self, handler):\n if handler in self.handlers:\n self.handlers.remove(handler)","title":""},{"docid":"f18e7aaef78981bbbab2216cf289b0fc","score":"0.7103593","text":"def __remove_handler(self, handler):\n self.h.remove(handler)\n self._logger.removeHandler(handler)","title":""},{"docid":"fa27ba739ff4455767f34eff0cbb1796","score":"0.6946388","text":"def removeHandler(self, handler):\n self.streamManager.removeHandler(handler)","title":""},{"docid":"9648c4b6328e5472cb3f67e03b648d97","score":"0.68827665","text":"def remove_handler(self, handler=None, request=None):\n code = 0\n if request != None:\n code = codes.codes[request].data()\n\n with self._handlers_lock:\n # Remove Handler\n if code in self._handlers: \n if handler != None and handler in self._handlers[code]:\n self._handlers[code].remove(handler)\n elif handler == None:\n self._handlers[code] = []","title":""},{"docid":"f9adaffb5e7c7b3f8c59f65767df2ce0","score":"0.6882579","text":"def child_removed(self, child):\n pass","title":""},{"docid":"a02845085b98432fcfdd562cecd42c53","score":"0.67751646","text":"def delete_handler(self):\n if self._hypervisor_handler:\n self._hypervisor_handler.close()\n self._hypervisor_handler = None","title":""},{"docid":"d9a2f22e90e05d0039c1d4788e1343f8","score":"0.67251134","text":"def removeHandler(self, handler_name):\n\n if not self.handler_exists(handler_name):\n raise Exception(\"Cannot remove handler {}! No handler with that name found!\".format(handler_name))\n\n super().removeHandler(self.handlers[handler_name])\n self.inheritor_handlers.pop(handler_name)","title":""},{"docid":"a175023370e471b1d82b76a80d9e6039","score":"0.672014","text":"def test_removeHandler(self):\n handler = XMPPHandler()\n handler.setHandlerParent(self.factory.streamManager)\n handler.disownHandlerParent(self.factory.streamManager)\n self.assertNotIn(handler, self.factory.streamManager)\n self.assertIdentical(None, handler.parent)","title":""},{"docid":"ea9ad8f3f60f4a7cfab5278d2bee9ae6","score":"0.6605218","text":"def remove(self) -> None:\n handler = self.handler()\n engine = self.engine()\n\n if handler is None or engine is None:\n return\n\n if hasattr(handler, \"_parent\"):\n handler = handler._parent()\n if handler is None:\n raise RuntimeError(\n \"Internal error! Please fill an issue on https://github.com/pytorch/ignite/issues \"\n \"if encounter this error. Thank you!\"\n )\n\n if isinstance(self.event_name, EventsList):\n for e in self.event_name:\n if engine.has_event_handler(handler, e):\n engine.remove_event_handler(handler, e)\n else:\n if engine.has_event_handler(handler, self.event_name):\n engine.remove_event_handler(handler, self.event_name)","title":""},{"docid":"d1f30479635ef881430cd1da6c2897e0","score":"0.6560829","text":"def add_handler(self, handler, handler_name, replace = False, ignore_duplicate = False, quiet = False):\n super()._add_child(handler, handler_name, replace = replace, ignore_duplicate = ignore_duplicate,\n quiet = quiet)\n return","title":""},{"docid":"916d10ae1190b2579a8d3c79551d967f","score":"0.65423375","text":"def removeHandler(self, handler):\n self._logger.removeHandler(handler)","title":""},{"docid":"b4cbfc64a653f319884d7009a54756a4","score":"0.6493946","text":"def __isub__(self, handler):\n self.__receivers.remove(handler)\n return self","title":""},{"docid":"cf2282576b9d15bb04bbcdda53648725","score":"0.6450347","text":"def remove_handler(self, is_input, index: int) -> None:\n if is_input:\n self.remove_input(index)\n else:\n self.remove_output(index)","title":""},{"docid":"c45942cf8d6dff24924f6e7bf4a04113","score":"0.64359504","text":"def remove_handler(self, *handlers):\n handlers = list(set(itertools.chain(*[self._to_handler(h) for h in handlers])))\n for h in handlers:\n h.remove_file(self.name)\n self._update(remove = handlers)","title":""},{"docid":"61cab82df9ed93686ef65f7d55395174","score":"0.6349693","text":"def removeHandler(self):\n self.writers[-1].endDocument()\n del self.writers[-1]\n return","title":""},{"docid":"07ea9b9ed86798f928653fc03aeba1b4","score":"0.6344045","text":"def remove_handler(key, handler):\n if type(key) is Predicate:\n key = key.name\n getattr(Q, key).remove_handler(handler)","title":""},{"docid":"708a9443eda5047f1b75f9e2ed17c261","score":"0.63343525","text":"def remove_child(self, child):\r\n self.children.remove(child)","title":""},{"docid":"d0f6160910a267a201268dfbfe856fb1","score":"0.6327482","text":"def child_removed(self, child: WebComponent):\n w = self.widget\n if w is not None:\n w.remove(child.widget)","title":""},{"docid":"3d61132e67fb6fe9dc83fdab8abea9fd","score":"0.6306453","text":"def child_removed(self, child):\n if isinstance(child, QtMenu):\n self.widget().removeAction(child.widget().menuAction())\n elif isinstance(child, QtAction):\n self.widget().removeAction(child.widget())\n elif isinstance(child, QtActionGroup):\n self.widget().removeActions(child.actions())","title":""},{"docid":"89586c9c05cc291ef4a39b9420c7b819","score":"0.6234144","text":"def remove_handler(self, id):\n handlers = self._handlers\n id = unicode(id)\n if id in handlers:\n handler, logger_name = handlers.pop(id)\n logger = logging.getLogger(logger_name)\n logger.removeHandler(handler)\n for filter_id in self._filters.keys():\n infos = self._filters[filter_id]\n if infos[1] == id:\n del self._filters[filter_id]\n\n self.filter_ids = self._filters.keys()\n self.handler_ids = self._handlers.keys()","title":""},{"docid":"548b7a79813ed0343a24618d325abae3","score":"0.6232785","text":"def disownHandlerParent(parent):","title":""},{"docid":"ed30af7004209efaf975812095704e03","score":"0.6229243","text":"def remove_handler(self, exception):\n self._exception_handlers.pop(exception, None)","title":""},{"docid":"5d1eebcca932623b8fdd71432b9ea0e5","score":"0.6176159","text":"def delete_child(self, child: ParserNode) -> None:\n return # pragma: no cover","title":""},{"docid":"9a06f3fac300aa7ceb71d23b27531f86","score":"0.613862","text":"async def _on_child_removed(self, child):\n _L.debug(f'Child removed: {child.pid}, {child.origin}, {child.argv}')","title":""},{"docid":"7d3a31568215953e34fa490e4abdcf83","score":"0.6090841","text":"def _remove_handlers(mcs, logger: logging.Logger, file_handler: logging.FileHandler):\n\n logger.removeHandler(file_handler)","title":""},{"docid":"14d0d1b2afdab3c4d2e7e3aed1410057","score":"0.60696393","text":"def child_removed(self, child):\n super(NodeContent, self).child_removed(child)","title":""},{"docid":"9692f133fff3369281af4e3c0c2c07e1","score":"0.6059061","text":"def unregister_handler(self, handle):\n self.__handler.unregister_handler(handle)","title":""},{"docid":"87b08530eacf4d2290aca0a3ff38f4e1","score":"0.6041732","text":"def remove_handlers(self, uavcan_type):\n self._handler_dispatcher.remove_handlers(uavcan_type)","title":""},{"docid":"38d9308501655eacf21338b4cf065f97","score":"0.6018849","text":"def del_handler(\n obj: logging.Logger,\n hname: str = \"\",\n htype: logging.Handler = None,\n traverse: bool = True,\n) -> Dict[str, List[logging.Handler]]:\n found = find_handlers(obj=obj, hname=hname, htype=htype, traverse=traverse)\n for name, handlers in found.items():\n for handler in handlers:\n logging.getLogger(name).removeHandler(handler)\n return found","title":""},{"docid":"a697bcc6dd1a9c080587ddaa3b0c4b65","score":"0.5975939","text":"def del_slix_event_handler(self, module, event_name, handler):\n self.core.xmpp.del_event_handler(event_name, handler)","title":""},{"docid":"5c671a21c7f2e47bf840a5ed2cd54701","score":"0.5962276","text":"def childRemoved(self, childResource:Resource, originator:str) -> None:\n\t\tCSE.notification.checkSubscriptions(self, NotificationEventType.deleteDirectChild, childResource)","title":""},{"docid":"c7d1cf734b69d5d385af27ac746873eb","score":"0.5951292","text":"def delete(self):\n\n # TODO find a way to remove this when sub-classing in HCRoot\n self.parent.del_child(self)","title":""},{"docid":"66f04b1af3ada443fd9d3334fb210a5e","score":"0.59508365","text":"def del_event_handler(self, event_name, handler):\n return self.api.del_event_handler(event_name, handler)","title":""},{"docid":"a9771b4f8a8d553ada2db1f9086048f5","score":"0.59346044","text":"def _unRegisterChild(self, fd):\n \n del self._activeProcesses[fd]","title":""},{"docid":"c0d49a94b0aa94818bccb879f0b4b1fe","score":"0.58867985","text":"def remove_handler_by_name(self, handler_name: str):\n for h in self._logger.handlers:\n if h.get_name() == handler_name:\n self._logger.removeHandler(h)\n break","title":""},{"docid":"7955ac4b206718b128f16d1294c58b26","score":"0.58552915","text":"def remove_child(self, index):\n\t\tdel self.child[index]","title":""},{"docid":"bdd87e886cd05860ff87af4391fd0d06","score":"0.5829237","text":"def removeChild(self, node):\r\n raise NotImplementedError","title":""},{"docid":"a512a25d9699ae37ddf017012dfc6ee3","score":"0.5819003","text":"def removeEventHandler(self, eventType, eventHandler):\n self.EventHandlerMap[eventType].remove(eventHandler)","title":""},{"docid":"adb920fede2717a59b5cd9eb476f5ae4","score":"0.57805306","text":"def remove_child_vault(self, vault_id, child_id):\n pass","title":""},{"docid":"ac58df37d3563b42a1ada689fa5dede5","score":"0.5776085","text":"def _remove_child(self, node: Node, child: Node):\n node.remove_child(child)\n del self._node_store[child.path]","title":""},{"docid":"1668a4273bb188e80dbcb2c2f1561990","score":"0.57349205","text":"def child_removed(self, child):\n super(ToolkitObject, self).child_removed(child)\n if isinstance(child, ToolkitObject) and self.proxy_is_active:\n self.proxy.child_removed(child.proxy)","title":""},{"docid":"94fc157a75efc3a48507283ae956ded3","score":"0.5718932","text":"def del_event_handler(self, name, pointer):\n\t\tif not name in self.event_handlers:\n\t\t\treturn\n\t\t\n\t\t# Need to keep handlers that do not use\n\t\t# the given function pointer\n\t\tdef filter_pointers(handler):\n\t\t\treturn handler[0] != pointer\n\n\t\tself.event_handlers[name] = filter(filter_pointers, \n\t\t\t\t\t\t self.event_handlers[name])","title":""},{"docid":"a768fef9038570ca5fa98f5e57190dcb","score":"0.5718361","text":"def remove_child(self, value):\n if value in self.children:\n del self.children[value]","title":""},{"docid":"7123da8c13e53c13e432b70d38a666f2","score":"0.57093704","text":"def _remove_child(self, widget):\n widget.p.parent = None","title":""},{"docid":"aead265fea9e05f046cea6b16e65dac6","score":"0.5701246","text":"def remove(self, thischild, delete=False):\n self.psi = None\n if thischild in self.children:\n thischild.parent = None\n self.children.remove(thischild)\n if delete is True:\n thischild.deleteLater()\n else:\n for child in self.children:\n if isinstance(child, QTrapGroup):\n child.remove(thischild, delete=delete)\n if ((len(self.children) == 0) and isinstance(self.parent, QTrapGroup)):\n self.parent.remove(self)","title":""},{"docid":"8b1e98e84bd6160c783cd7fee203a4d7","score":"0.56933457","text":"def del_event_handler(self, name: str, callback: Callable):\n if not name:\n for callbacks in self.events.values():\n for priority in callbacks.values():\n for entry in priority[:]:\n if entry == callback:\n priority.remove(callback)\n else:\n callbacks = self.events[name]\n for priority in callbacks.values():\n for entry in priority[:]:\n if entry == callback:\n priority.remove(callback)","title":""},{"docid":"2928fd383701d28d6565a0a574cf9303","score":"0.5681991","text":"def remove(self, handler):\n return bool()","title":""},{"docid":"2928fd383701d28d6565a0a574cf9303","score":"0.5681991","text":"def remove(self, handler):\n return bool()","title":""},{"docid":"8c0905f92ae3e865bb3490a3f7170065","score":"0.56665117","text":"def clear_exit_event_handler(self, handler):\n self.current.exit_event.clear_handler(handler)","title":""},{"docid":"50fc1de3319f30e213b9a41f091b1222","score":"0.5659567","text":"def pop_handlers(self):\n return getattr(self, '_handler_stack', []).pop()","title":""},{"docid":"dc92a643b1c1f75981c9e800428d3447","score":"0.5641592","text":"def remove_child(self, node, child):\n self._nodes[node].discard(child)","title":""},{"docid":"8f7f20e3ec52398473b7535256b891ac","score":"0.56173235","text":"def on_remove(self):\n pass","title":""},{"docid":"bb4b84130831562e5011952072ad7a4d","score":"0.5594565","text":"def unhook(callback):\n _listener.remove_handler(callback)","title":""},{"docid":"34de54ffa0b1947dfc8992d9a56f80c3","score":"0.5548653","text":"def _remove_form_handler(self, form_handler_name, form_handler_data,\n wait=WAIT_FOR):\n # This part is necessary, since we need to activate the\n # tab with form handlers. Otherwise Selenium raises\n # an exception about non-visible element on the page\n # that we're trying to fetch.\n form_handlers_tab_link = self.driver.find_element_by_xpath(\n \"\"\"//a[@href=\"#tab-form-handlers\"]\"\"\"\n )\n form_handlers_tab_link.click()\n\n # Get the label of the given form element in order to delete it later\n # from the form.\n delete_form_handler_label = self.driver.find_element_by_xpath(\n \"\"\"//td[contains(text(), '{0}')]\"\"\".format(form_handler_name)\n )\n\n # Get the parent of the label\n delete_form_handler_label_parent_container = \\\n delete_form_handler_label.find_element_by_xpath('..')\n\n # Click the add form element button to add a new form element to the\n # form.\n delete_form_handler_link = \\\n delete_form_handler_label_parent_container \\\n .find_element_by_partial_link_text(\n 'Delete'\n )\n delete_form_handler_link.click()\n\n logger.debug(form_handler_name)\n\n # Wait until the fobi page opens with the form element in.\n WebDriverWait(self.driver, timeout=TIMEOUT).until(\n lambda driver: driver.find_element_by_xpath(\n \"\"\"//div[contains(text(), 'The form handler plugin \"{0}\" \"\"\"\n \"\"\"was deleted successfully.') \"\"\"\n \"\"\"and contains(@class, \"alert-info\")]\"\"\".format(\n form_handler_name\n )\n )\n )","title":""},{"docid":"e31891a5595a62c5ce1ef7aae021fd11","score":"0.55190015","text":"def remove(self, child):\n self.xmlnode.remove(child.xmlnode)","title":""},{"docid":"3313b90e7ac18c8f2a8601418bde9126","score":"0.5507837","text":"def removeChild(*args):\n return _coin.SoVRMLLOD_removeChild(*args)","title":""},{"docid":"24dc5f46e43dfdc4c62fc6e0eec15a4c","score":"0.5507475","text":"def sig_handler(cls, sig_num, stack_frame):\n cls.cleanup()","title":""},{"docid":"e4ca215c0f4d32ed44ae6a14ed186192","score":"0.5506857","text":"def del_event_handler(self, module, *args, **kwargs):\n return self.plugin_manager.del_event_handler(module, *args, **kwargs)","title":""},{"docid":"6168896018b6c0adf3b81b58d57ca7ed","score":"0.549339","text":"def __sub__(self, callback):\r\n\r\n self.callbacks.remove(callback)\r\n return self","title":""},{"docid":"8ac76e7846b604c42fccf171fd3f8c51","score":"0.5488947","text":"def delete_child(self):\r\n\r\n child = self.children[-1]\r\n del self.children[-1]\r\n return child","title":""},{"docid":"5bf594700d85a157c024a9a3f4cb13d6","score":"0.5488658","text":"def removeChild(*args):\n return _coin.SoVRMLParent_removeChild(*args)","title":""},{"docid":"e6c64f80c0285d3e461f4580e2a725e5","score":"0.54857415","text":"def _teardown_collection_handlers(self):\n base_msg_type = self.message.msg_type\n self.bus.remove(base_msg_type + '.handling', self._register_handler)\n self.bus.remove(base_msg_type + '.response', self._receive_response)","title":""},{"docid":"7a7a1e9df5091b16238cf9de66d9477f","score":"0.54792094","text":"def removeChild(*args):\n return _coin.SoVRMLSwitch_removeChild(*args)","title":""},{"docid":"d1ca9319f079cabec581a922c0f029ff","score":"0.54777","text":"def remove_word_listener(word_or_handler):\n _word_listeners[word_or_handler]()","title":""},{"docid":"af83d76a77c6d0bfedefa745115e9b52","score":"0.54432476","text":"def add_handler(self, handler):\n if handler not in self.handlers:\n self.handlers.append(handler)","title":""},{"docid":"4885ae8089d1987d46d1647aba5266eb","score":"0.54353815","text":"def removeChild(*args):\n return _coin.SoGroup_removeChild(*args)","title":""},{"docid":"3fac9d0335364881cf95224aeb370559","score":"0.54325205","text":"def exposed_delete_chunk(self, chunk_id):\n del self.__class__.handle_table[chunk_id]","title":""},{"docid":"97f82e162bf9a551001d2abdc31f68b3","score":"0.54276466","text":"def remove(self, h, handle):\n record_id, data = self._find(h)\n if record_id is None:\n return\n handles = self._unmarshal(data, just_handles=True)\n handles.remove(handle)\n if len(handles) == 0:\n self.block.delete(record_id)\n else:\n self.block.put(record_id, self._marshal(h, handles))","title":""},{"docid":"c74d57c721f0d72ea51062688c620e29","score":"0.5422727","text":"def remove_event(reactor, event):\r\n reactor.remove(event)","title":""},{"docid":"6c625709ce7bda506877de28543c0749","score":"0.5419543","text":"def rename_handlers(self):\n for handler_name, handler in self.handlers.items():\n handler_operation = handler_name.decode()\n handler_operation = handler_operation.split('.')[-1].encode()\n self.handlers[b'repl:' + self.name + b'.' + handler_operation] = handler\n self.redis_connection.execute_command('DEL', handler_name)","title":""},{"docid":"439f239b53fab15436c559c61ec10792","score":"0.54055494","text":"def remove_word_listener(word_or_handler):\n _remove_named_hook(word_or_handler, _word_listeners)","title":""},{"docid":"28d5b61586c7cec3a2c7f23f9e32ad31","score":"0.53964347","text":"def tearDown(self):\n self.log.removeHandler(self.handler)\n self.handler.close()","title":""},{"docid":"27a84976b9ef22ec7ff6bb953554a34e","score":"0.5374946","text":"def removing(self): # API (gets overwritten) TODO rename to remove_event(), it would make more sense I guess\n pass","title":""},{"docid":"64bb139459f936f4e3b532ac66c07f94","score":"0.53707385","text":"def unregister_at_level(self, level):\n self.handler.unregister_at_level(level, dispatcher)","title":""},{"docid":"338876a1b4c635698ef572d78867fcb8","score":"0.53624785","text":"def _uninstall_signal_handlers(self):\n loop = asyncio.get_event_loop()\n loop.remove_signal_handler(signal.SIGINT)\n loop.remove_signal_handler(signal.SIGTERM)","title":""},{"docid":"2cba3c09a05c54553acd72dfb5a99d0b","score":"0.5362352","text":"def remove(target, identifier, fn):\r\n _event_key(target, identifier, fn).remove()","title":""},{"docid":"16551ab6f66206dc3dd72a314839d520","score":"0.5353773","text":"def add_exit_event_handler(self, handler):\n self.current.exit_event.add_handler(handler)","title":""},{"docid":"6e6e5209688b700be7ab010561815aae","score":"0.53493893","text":"def unhook(remove):\n _hooks[remove]()","title":""},{"docid":"efbfc7319fe72c40be50f7b59aaac86b","score":"0.5332615","text":"def removeChild(self,child):\n self.pv_cdomlette.removeChild(child.pv_cdomlette)\n for i in range(0,len(self.childNodes)-1):\n if id(self.childNodes[i]) == id(child):\n toto = self.childNodes[i]\n toto.pv_deleted = True\n self.notify_observers(toto)\n del self.childNodes[i]\n print \"coucou %s\" % toto\n self.notify_observers(self)","title":""},{"docid":"807410c2641efabffc586ca46dcd60f0","score":"0.53256506","text":"def remove_child_qualifier(self, qualifier_id, child_id):\n pass","title":""},{"docid":"8675cc6474c8d17829f609fcfffaceb7","score":"0.53240865","text":"def kill(self):\n for handler in self.logger.handlers[:]:\n self.logger.removeHandler(handler)","title":""},{"docid":"e953f8d8eba4ddc9d59185945154c79c","score":"0.5312111","text":"def delete_event_callback(self, event):","title":""},{"docid":"dac6789dd46eefb6abc1f87e943a782b","score":"0.5303661","text":"def clear(self):\n\n if not self._handler:\n raise ValueError(\"No handler configured\")\n\n self._handler.clear()","title":""},{"docid":"ef47db25ea38d2efd4350e381633f100","score":"0.5300582","text":"def signal_unbind(self, signal_handler):\n try:\n handlers = self._signals[\n signal_handler.signal_name]\n except KeyError:\n pass\n else:\n signal_handler.function = None\n try:\n handlers.remove(signal_handler)\n except IndexError:\n pass","title":""},{"docid":"277477aaec1aa82d3e9d230e22a5605c","score":"0.52999586","text":"def remove_child(self, child, class_name = None):\n if class_name is None: class_name = self.__class__.__name__\n ## if child_name provided\n if isinstance(child, str):\n if child in self._children: self._remove_child(child)\n else: raise UnknownChild( (f\"Cannot remove {self._child_adj}: '{child}' is not a\"\n f\" known {self._child_alias} name\") )\n ## if Child object provided\n else:\n ## if Child obj is in self.children, retrieve key\n inv_children = dict((v, k) for k, v in self._children.items())\n if child in inv_children: self._remove_child(inv_children[child])\n else: raise UnknownChild( (f\"Cannot remove {self._child_adj}: The {self._child_obj} object\"\n f\" provided is not known to this {class_name}\") )\n return","title":""},{"docid":"e3f64971c111162ae10e8f6b6421c71d","score":"0.52982634","text":"def remove() -> None:","title":""},{"docid":"ddffbb04569e5ddbc1684409b150f641","score":"0.52908236","text":"def unregister_error_handler() -> None:\n Hub.handle_error = ORIGINAL_ERROR_HANDLER","title":""},{"docid":"2e921d60aab2652531909eacdb97ca6b","score":"0.52737355","text":"def remove_child(self, child_name):\n self._update_json()\n put_data = self.json.copy()\n for obj_key in ['links', 'metadata']:\n put_data.pop(obj_key)\n obj_children = put_data.pop('objects')\n put_data['objects'] = []\n child_type = ''\n for obj_child in obj_children:\n if obj_child['name'] != child_name:\n put_data['objects'].append(obj_child)\n else:\n child_type = obj_child['type']\n if child_type:\n logging.warning(\"Removing {} child object {} from parent {}!\".format(child_type, child_name, self.name))\n self.update(put_data)\n else:\n logging.error(\"Child {} not found inside parent {}!\".format(child_name, self.name))","title":""},{"docid":"a6feec5067a9850c6d67f1869e2aee18","score":"0.5272715","text":"def unregisterPacketHandler(self, packetType):\n pass","title":""},{"docid":"3af39222a28b9a7395e6374c258b8ccd","score":"0.5271657","text":"def remove_hook(self, name, fun):\n\t\tself.hooks[name].remove(fun)","title":""},{"docid":"256f268e566e8d1f9bf1075746200bc6","score":"0.526498","text":"def remove(self, widget):\n self._detach_child(widget)\n del self._children[widget]\n self._repack_and_regroup_children()","title":""},{"docid":"8cd4c5e5b999e00b539da04836032f43","score":"0.5257587","text":"def callbackWrapper(self):\n self.callback(*self.args)\n self.methodscalled.remove(self)","title":""},{"docid":"f938ddd4a9d844fbb00109182932e6cf","score":"0.5240832","text":"def _remove_named_hook(name_or_handler, names):\n if callable(name_or_handler):\n handler = name_or_handler\n try:\n name = next(n for n, h in names.items() if h == handler)\n except StopIteration:\n raise ValueError('This handler is not associated with any name.')\n unhook(handler)\n del names[name]\n else:\n name = name_or_handler\n try:\n handler = names[name]\n except KeyError as e:\n raise ValueError('No such named listener: ' + repr(name), e)\n unhook(names[name])\n del names[name]\n\n return name","title":""},{"docid":"a738913a0eafb0af7f2a8ef6d646c856","score":"0.52406603","text":"def off(self, event_name, handler):\n # todo: make this thread safe!!!\n if event_name in self.listeners and handler in self.listeners[event_name]:\n self.listeners[event_name].remove(handler)","title":""},{"docid":"052b4bad8d2a5ccd936c6bb5f7b44485","score":"0.52362853","text":"def sig_handler(sig,frame):\n ioloop.IOLoop.instance().add_callback(shutdown)","title":""},{"docid":"052b4bad8d2a5ccd936c6bb5f7b44485","score":"0.52362853","text":"def sig_handler(sig,frame):\n ioloop.IOLoop.instance().add_callback(shutdown)","title":""},{"docid":"71ce42a89e2181b3e1163f07c1d3dc59","score":"0.5233347","text":"def terminate(self) -> None:\n self.unregister_endpoint(self.handler)","title":""},{"docid":"cc820064bbe1d9f53d9acf7832ce88a3","score":"0.52318746","text":"def remove(self, widget):\n self._detach_child(widget)\n self._children.remove(widget)\n del self._sizes[widget]\n self._repack_and_regroup_children()","title":""}],"string":"[\n {\n \"docid\": \"63c1c16a7834019e5cad6290cb3e4810\",\n \"score\": \"0.7600718\",\n \"text\": \"def remove_handler(self, handler):\\n self.handlers.remove(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2d370956a7b1319092fbc4335fbb48\",\n \"score\": \"0.7523634\",\n \"text\": \"def remove_handler(self, fd):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a11df2d526635964abcd4376be1ed29f\",\n \"score\": \"0.73864824\",\n \"text\": \"def remove_hanlder(self, handler):\\n if handler in self.handlers:\\n self.handlers.remove(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f18e7aaef78981bbbab2216cf289b0fc\",\n \"score\": \"0.7103593\",\n \"text\": \"def __remove_handler(self, handler):\\n self.h.remove(handler)\\n self._logger.removeHandler(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa27ba739ff4455767f34eff0cbb1796\",\n \"score\": \"0.6946388\",\n \"text\": \"def removeHandler(self, handler):\\n self.streamManager.removeHandler(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9648c4b6328e5472cb3f67e03b648d97\",\n \"score\": \"0.68827665\",\n \"text\": \"def remove_handler(self, handler=None, request=None):\\n code = 0\\n if request != None:\\n code = codes.codes[request].data()\\n\\n with self._handlers_lock:\\n # Remove Handler\\n if code in self._handlers: \\n if handler != None and handler in self._handlers[code]:\\n self._handlers[code].remove(handler)\\n elif handler == None:\\n self._handlers[code] = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9adaffb5e7c7b3f8c59f65767df2ce0\",\n \"score\": \"0.6882579\",\n \"text\": \"def child_removed(self, child):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a02845085b98432fcfdd562cecd42c53\",\n \"score\": \"0.67751646\",\n \"text\": \"def delete_handler(self):\\n if self._hypervisor_handler:\\n self._hypervisor_handler.close()\\n self._hypervisor_handler = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9a2f22e90e05d0039c1d4788e1343f8\",\n \"score\": \"0.67251134\",\n \"text\": \"def removeHandler(self, handler_name):\\n\\n if not self.handler_exists(handler_name):\\n raise Exception(\\\"Cannot remove handler {}! No handler with that name found!\\\".format(handler_name))\\n\\n super().removeHandler(self.handlers[handler_name])\\n self.inheritor_handlers.pop(handler_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a175023370e471b1d82b76a80d9e6039\",\n \"score\": \"0.672014\",\n \"text\": \"def test_removeHandler(self):\\n handler = XMPPHandler()\\n handler.setHandlerParent(self.factory.streamManager)\\n handler.disownHandlerParent(self.factory.streamManager)\\n self.assertNotIn(handler, self.factory.streamManager)\\n self.assertIdentical(None, handler.parent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea9ad8f3f60f4a7cfab5278d2bee9ae6\",\n \"score\": \"0.6605218\",\n \"text\": \"def remove(self) -> None:\\n handler = self.handler()\\n engine = self.engine()\\n\\n if handler is None or engine is None:\\n return\\n\\n if hasattr(handler, \\\"_parent\\\"):\\n handler = handler._parent()\\n if handler is None:\\n raise RuntimeError(\\n \\\"Internal error! Please fill an issue on https://github.com/pytorch/ignite/issues \\\"\\n \\\"if encounter this error. Thank you!\\\"\\n )\\n\\n if isinstance(self.event_name, EventsList):\\n for e in self.event_name:\\n if engine.has_event_handler(handler, e):\\n engine.remove_event_handler(handler, e)\\n else:\\n if engine.has_event_handler(handler, self.event_name):\\n engine.remove_event_handler(handler, self.event_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1f30479635ef881430cd1da6c2897e0\",\n \"score\": \"0.6560829\",\n \"text\": \"def add_handler(self, handler, handler_name, replace = False, ignore_duplicate = False, quiet = False):\\n super()._add_child(handler, handler_name, replace = replace, ignore_duplicate = ignore_duplicate,\\n quiet = quiet)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"916d10ae1190b2579a8d3c79551d967f\",\n \"score\": \"0.65423375\",\n \"text\": \"def removeHandler(self, handler):\\n self._logger.removeHandler(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4cbfc64a653f319884d7009a54756a4\",\n \"score\": \"0.6493946\",\n \"text\": \"def __isub__(self, handler):\\n self.__receivers.remove(handler)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf2282576b9d15bb04bbcdda53648725\",\n \"score\": \"0.6450347\",\n \"text\": \"def remove_handler(self, is_input, index: int) -> None:\\n if is_input:\\n self.remove_input(index)\\n else:\\n self.remove_output(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c45942cf8d6dff24924f6e7bf4a04113\",\n \"score\": \"0.64359504\",\n \"text\": \"def remove_handler(self, *handlers):\\n handlers = list(set(itertools.chain(*[self._to_handler(h) for h in handlers])))\\n for h in handlers:\\n h.remove_file(self.name)\\n self._update(remove = handlers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61cab82df9ed93686ef65f7d55395174\",\n \"score\": \"0.6349693\",\n \"text\": \"def removeHandler(self):\\n self.writers[-1].endDocument()\\n del self.writers[-1]\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07ea9b9ed86798f928653fc03aeba1b4\",\n \"score\": \"0.6344045\",\n \"text\": \"def remove_handler(key, handler):\\n if type(key) is Predicate:\\n key = key.name\\n getattr(Q, key).remove_handler(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708a9443eda5047f1b75f9e2ed17c261\",\n \"score\": \"0.63343525\",\n \"text\": \"def remove_child(self, child):\\r\\n self.children.remove(child)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f6160910a267a201268dfbfe856fb1\",\n \"score\": \"0.6327482\",\n \"text\": \"def child_removed(self, child: WebComponent):\\n w = self.widget\\n if w is not None:\\n w.remove(child.widget)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d61132e67fb6fe9dc83fdab8abea9fd\",\n \"score\": \"0.6306453\",\n \"text\": \"def child_removed(self, child):\\n if isinstance(child, QtMenu):\\n self.widget().removeAction(child.widget().menuAction())\\n elif isinstance(child, QtAction):\\n self.widget().removeAction(child.widget())\\n elif isinstance(child, QtActionGroup):\\n self.widget().removeActions(child.actions())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89586c9c05cc291ef4a39b9420c7b819\",\n \"score\": \"0.6234144\",\n \"text\": \"def remove_handler(self, id):\\n handlers = self._handlers\\n id = unicode(id)\\n if id in handlers:\\n handler, logger_name = handlers.pop(id)\\n logger = logging.getLogger(logger_name)\\n logger.removeHandler(handler)\\n for filter_id in self._filters.keys():\\n infos = self._filters[filter_id]\\n if infos[1] == id:\\n del self._filters[filter_id]\\n\\n self.filter_ids = self._filters.keys()\\n self.handler_ids = self._handlers.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"548b7a79813ed0343a24618d325abae3\",\n \"score\": \"0.6232785\",\n \"text\": \"def disownHandlerParent(parent):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed30af7004209efaf975812095704e03\",\n \"score\": \"0.6229243\",\n \"text\": \"def remove_handler(self, exception):\\n self._exception_handlers.pop(exception, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d1eebcca932623b8fdd71432b9ea0e5\",\n \"score\": \"0.6176159\",\n \"text\": \"def delete_child(self, child: ParserNode) -> None:\\n return # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a06f3fac300aa7ceb71d23b27531f86\",\n \"score\": \"0.613862\",\n \"text\": \"async def _on_child_removed(self, child):\\n _L.debug(f'Child removed: {child.pid}, {child.origin}, {child.argv}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d3a31568215953e34fa490e4abdcf83\",\n \"score\": \"0.6090841\",\n \"text\": \"def _remove_handlers(mcs, logger: logging.Logger, file_handler: logging.FileHandler):\\n\\n logger.removeHandler(file_handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14d0d1b2afdab3c4d2e7e3aed1410057\",\n \"score\": \"0.60696393\",\n \"text\": \"def child_removed(self, child):\\n super(NodeContent, self).child_removed(child)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9692f133fff3369281af4e3c0c2c07e1\",\n \"score\": \"0.6059061\",\n \"text\": \"def unregister_handler(self, handle):\\n self.__handler.unregister_handler(handle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87b08530eacf4d2290aca0a3ff38f4e1\",\n \"score\": \"0.6041732\",\n \"text\": \"def remove_handlers(self, uavcan_type):\\n self._handler_dispatcher.remove_handlers(uavcan_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38d9308501655eacf21338b4cf065f97\",\n \"score\": \"0.6018849\",\n \"text\": \"def del_handler(\\n obj: logging.Logger,\\n hname: str = \\\"\\\",\\n htype: logging.Handler = None,\\n traverse: bool = True,\\n) -> Dict[str, List[logging.Handler]]:\\n found = find_handlers(obj=obj, hname=hname, htype=htype, traverse=traverse)\\n for name, handlers in found.items():\\n for handler in handlers:\\n logging.getLogger(name).removeHandler(handler)\\n return found\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a697bcc6dd1a9c080587ddaa3b0c4b65\",\n \"score\": \"0.5975939\",\n \"text\": \"def del_slix_event_handler(self, module, event_name, handler):\\n self.core.xmpp.del_event_handler(event_name, handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c671a21c7f2e47bf840a5ed2cd54701\",\n \"score\": \"0.5962276\",\n \"text\": \"def childRemoved(self, childResource:Resource, originator:str) -> None:\\n\\t\\tCSE.notification.checkSubscriptions(self, NotificationEventType.deleteDirectChild, childResource)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7d1cf734b69d5d385af27ac746873eb\",\n \"score\": \"0.5951292\",\n \"text\": \"def delete(self):\\n\\n # TODO find a way to remove this when sub-classing in HCRoot\\n self.parent.del_child(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66f04b1af3ada443fd9d3334fb210a5e\",\n \"score\": \"0.59508365\",\n \"text\": \"def del_event_handler(self, event_name, handler):\\n return self.api.del_event_handler(event_name, handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9771b4f8a8d553ada2db1f9086048f5\",\n \"score\": \"0.59346044\",\n \"text\": \"def _unRegisterChild(self, fd):\\n \\n del self._activeProcesses[fd]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d49a94b0aa94818bccb879f0b4b1fe\",\n \"score\": \"0.58867985\",\n \"text\": \"def remove_handler_by_name(self, handler_name: str):\\n for h in self._logger.handlers:\\n if h.get_name() == handler_name:\\n self._logger.removeHandler(h)\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7955ac4b206718b128f16d1294c58b26\",\n \"score\": \"0.58552915\",\n \"text\": \"def remove_child(self, index):\\n\\t\\tdel self.child[index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdd87e886cd05860ff87af4391fd0d06\",\n \"score\": \"0.5829237\",\n \"text\": \"def removeChild(self, node):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a512a25d9699ae37ddf017012dfc6ee3\",\n \"score\": \"0.5819003\",\n \"text\": \"def removeEventHandler(self, eventType, eventHandler):\\n self.EventHandlerMap[eventType].remove(eventHandler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adb920fede2717a59b5cd9eb476f5ae4\",\n \"score\": \"0.57805306\",\n \"text\": \"def remove_child_vault(self, vault_id, child_id):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac58df37d3563b42a1ada689fa5dede5\",\n \"score\": \"0.5776085\",\n \"text\": \"def _remove_child(self, node: Node, child: Node):\\n node.remove_child(child)\\n del self._node_store[child.path]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1668a4273bb188e80dbcb2c2f1561990\",\n \"score\": \"0.57349205\",\n \"text\": \"def child_removed(self, child):\\n super(ToolkitObject, self).child_removed(child)\\n if isinstance(child, ToolkitObject) and self.proxy_is_active:\\n self.proxy.child_removed(child.proxy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94fc157a75efc3a48507283ae956ded3\",\n \"score\": \"0.5718932\",\n \"text\": \"def del_event_handler(self, name, pointer):\\n\\t\\tif not name in self.event_handlers:\\n\\t\\t\\treturn\\n\\t\\t\\n\\t\\t# Need to keep handlers that do not use\\n\\t\\t# the given function pointer\\n\\t\\tdef filter_pointers(handler):\\n\\t\\t\\treturn handler[0] != pointer\\n\\n\\t\\tself.event_handlers[name] = filter(filter_pointers, \\n\\t\\t\\t\\t\\t\\t self.event_handlers[name])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a768fef9038570ca5fa98f5e57190dcb\",\n \"score\": \"0.5718361\",\n \"text\": \"def remove_child(self, value):\\n if value in self.children:\\n del self.children[value]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7123da8c13e53c13e432b70d38a666f2\",\n \"score\": \"0.57093704\",\n \"text\": \"def _remove_child(self, widget):\\n widget.p.parent = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aead265fea9e05f046cea6b16e65dac6\",\n \"score\": \"0.5701246\",\n \"text\": \"def remove(self, thischild, delete=False):\\n self.psi = None\\n if thischild in self.children:\\n thischild.parent = None\\n self.children.remove(thischild)\\n if delete is True:\\n thischild.deleteLater()\\n else:\\n for child in self.children:\\n if isinstance(child, QTrapGroup):\\n child.remove(thischild, delete=delete)\\n if ((len(self.children) == 0) and isinstance(self.parent, QTrapGroup)):\\n self.parent.remove(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b1e98e84bd6160c783cd7fee203a4d7\",\n \"score\": \"0.56933457\",\n \"text\": \"def del_event_handler(self, name: str, callback: Callable):\\n if not name:\\n for callbacks in self.events.values():\\n for priority in callbacks.values():\\n for entry in priority[:]:\\n if entry == callback:\\n priority.remove(callback)\\n else:\\n callbacks = self.events[name]\\n for priority in callbacks.values():\\n for entry in priority[:]:\\n if entry == callback:\\n priority.remove(callback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2928fd383701d28d6565a0a574cf9303\",\n \"score\": \"0.5681991\",\n \"text\": \"def remove(self, handler):\\n return bool()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2928fd383701d28d6565a0a574cf9303\",\n \"score\": \"0.5681991\",\n \"text\": \"def remove(self, handler):\\n return bool()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c0905f92ae3e865bb3490a3f7170065\",\n \"score\": \"0.56665117\",\n \"text\": \"def clear_exit_event_handler(self, handler):\\n self.current.exit_event.clear_handler(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50fc1de3319f30e213b9a41f091b1222\",\n \"score\": \"0.5659567\",\n \"text\": \"def pop_handlers(self):\\n return getattr(self, '_handler_stack', []).pop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc92a643b1c1f75981c9e800428d3447\",\n \"score\": \"0.5641592\",\n \"text\": \"def remove_child(self, node, child):\\n self._nodes[node].discard(child)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f7f20e3ec52398473b7535256b891ac\",\n \"score\": \"0.56173235\",\n \"text\": \"def on_remove(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb4b84130831562e5011952072ad7a4d\",\n \"score\": \"0.5594565\",\n \"text\": \"def unhook(callback):\\n _listener.remove_handler(callback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34de54ffa0b1947dfc8992d9a56f80c3\",\n \"score\": \"0.5548653\",\n \"text\": \"def _remove_form_handler(self, form_handler_name, form_handler_data,\\n wait=WAIT_FOR):\\n # This part is necessary, since we need to activate the\\n # tab with form handlers. Otherwise Selenium raises\\n # an exception about non-visible element on the page\\n # that we're trying to fetch.\\n form_handlers_tab_link = self.driver.find_element_by_xpath(\\n \\\"\\\"\\\"//a[@href=\\\"#tab-form-handlers\\\"]\\\"\\\"\\\"\\n )\\n form_handlers_tab_link.click()\\n\\n # Get the label of the given form element in order to delete it later\\n # from the form.\\n delete_form_handler_label = self.driver.find_element_by_xpath(\\n \\\"\\\"\\\"//td[contains(text(), '{0}')]\\\"\\\"\\\".format(form_handler_name)\\n )\\n\\n # Get the parent of the label\\n delete_form_handler_label_parent_container = \\\\\\n delete_form_handler_label.find_element_by_xpath('..')\\n\\n # Click the add form element button to add a new form element to the\\n # form.\\n delete_form_handler_link = \\\\\\n delete_form_handler_label_parent_container \\\\\\n .find_element_by_partial_link_text(\\n 'Delete'\\n )\\n delete_form_handler_link.click()\\n\\n logger.debug(form_handler_name)\\n\\n # Wait until the fobi page opens with the form element in.\\n WebDriverWait(self.driver, timeout=TIMEOUT).until(\\n lambda driver: driver.find_element_by_xpath(\\n \\\"\\\"\\\"//div[contains(text(), 'The form handler plugin \\\"{0}\\\" \\\"\\\"\\\"\\n \\\"\\\"\\\"was deleted successfully.') \\\"\\\"\\\"\\n \\\"\\\"\\\"and contains(@class, \\\"alert-info\\\")]\\\"\\\"\\\".format(\\n form_handler_name\\n )\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e31891a5595a62c5ce1ef7aae021fd11\",\n \"score\": \"0.55190015\",\n \"text\": \"def remove(self, child):\\n self.xmlnode.remove(child.xmlnode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3313b90e7ac18c8f2a8601418bde9126\",\n \"score\": \"0.5507837\",\n \"text\": \"def removeChild(*args):\\n return _coin.SoVRMLLOD_removeChild(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24dc5f46e43dfdc4c62fc6e0eec15a4c\",\n \"score\": \"0.5507475\",\n \"text\": \"def sig_handler(cls, sig_num, stack_frame):\\n cls.cleanup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4ca215c0f4d32ed44ae6a14ed186192\",\n \"score\": \"0.5506857\",\n \"text\": \"def del_event_handler(self, module, *args, **kwargs):\\n return self.plugin_manager.del_event_handler(module, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6168896018b6c0adf3b81b58d57ca7ed\",\n \"score\": \"0.549339\",\n \"text\": \"def __sub__(self, callback):\\r\\n\\r\\n self.callbacks.remove(callback)\\r\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac76e7846b604c42fccf171fd3f8c51\",\n \"score\": \"0.5488947\",\n \"text\": \"def delete_child(self):\\r\\n\\r\\n child = self.children[-1]\\r\\n del self.children[-1]\\r\\n return child\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf594700d85a157c024a9a3f4cb13d6\",\n \"score\": \"0.5488658\",\n \"text\": \"def removeChild(*args):\\n return _coin.SoVRMLParent_removeChild(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6c64f80c0285d3e461f4580e2a725e5\",\n \"score\": \"0.54857415\",\n \"text\": \"def _teardown_collection_handlers(self):\\n base_msg_type = self.message.msg_type\\n self.bus.remove(base_msg_type + '.handling', self._register_handler)\\n self.bus.remove(base_msg_type + '.response', self._receive_response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a7a1e9df5091b16238cf9de66d9477f\",\n \"score\": \"0.54792094\",\n \"text\": \"def removeChild(*args):\\n return _coin.SoVRMLSwitch_removeChild(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1ca9319f079cabec581a922c0f029ff\",\n \"score\": \"0.54777\",\n \"text\": \"def remove_word_listener(word_or_handler):\\n _word_listeners[word_or_handler]()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af83d76a77c6d0bfedefa745115e9b52\",\n \"score\": \"0.54432476\",\n \"text\": \"def add_handler(self, handler):\\n if handler not in self.handlers:\\n self.handlers.append(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4885ae8089d1987d46d1647aba5266eb\",\n \"score\": \"0.54353815\",\n \"text\": \"def removeChild(*args):\\n return _coin.SoGroup_removeChild(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fac9d0335364881cf95224aeb370559\",\n \"score\": \"0.54325205\",\n \"text\": \"def exposed_delete_chunk(self, chunk_id):\\n del self.__class__.handle_table[chunk_id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97f82e162bf9a551001d2abdc31f68b3\",\n \"score\": \"0.54276466\",\n \"text\": \"def remove(self, h, handle):\\n record_id, data = self._find(h)\\n if record_id is None:\\n return\\n handles = self._unmarshal(data, just_handles=True)\\n handles.remove(handle)\\n if len(handles) == 0:\\n self.block.delete(record_id)\\n else:\\n self.block.put(record_id, self._marshal(h, handles))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c74d57c721f0d72ea51062688c620e29\",\n \"score\": \"0.5422727\",\n \"text\": \"def remove_event(reactor, event):\\r\\n reactor.remove(event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c625709ce7bda506877de28543c0749\",\n \"score\": \"0.5419543\",\n \"text\": \"def rename_handlers(self):\\n for handler_name, handler in self.handlers.items():\\n handler_operation = handler_name.decode()\\n handler_operation = handler_operation.split('.')[-1].encode()\\n self.handlers[b'repl:' + self.name + b'.' + handler_operation] = handler\\n self.redis_connection.execute_command('DEL', handler_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"439f239b53fab15436c559c61ec10792\",\n \"score\": \"0.54055494\",\n \"text\": \"def remove_word_listener(word_or_handler):\\n _remove_named_hook(word_or_handler, _word_listeners)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d5b61586c7cec3a2c7f23f9e32ad31\",\n \"score\": \"0.53964347\",\n \"text\": \"def tearDown(self):\\n self.log.removeHandler(self.handler)\\n self.handler.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27a84976b9ef22ec7ff6bb953554a34e\",\n \"score\": \"0.5374946\",\n \"text\": \"def removing(self): # API (gets overwritten) TODO rename to remove_event(), it would make more sense I guess\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64bb139459f936f4e3b532ac66c07f94\",\n \"score\": \"0.53707385\",\n \"text\": \"def unregister_at_level(self, level):\\n self.handler.unregister_at_level(level, dispatcher)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"338876a1b4c635698ef572d78867fcb8\",\n \"score\": \"0.53624785\",\n \"text\": \"def _uninstall_signal_handlers(self):\\n loop = asyncio.get_event_loop()\\n loop.remove_signal_handler(signal.SIGINT)\\n loop.remove_signal_handler(signal.SIGTERM)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cba3c09a05c54553acd72dfb5a99d0b\",\n \"score\": \"0.5362352\",\n \"text\": \"def remove(target, identifier, fn):\\r\\n _event_key(target, identifier, fn).remove()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16551ab6f66206dc3dd72a314839d520\",\n \"score\": \"0.5353773\",\n \"text\": \"def add_exit_event_handler(self, handler):\\n self.current.exit_event.add_handler(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e6e5209688b700be7ab010561815aae\",\n \"score\": \"0.53493893\",\n \"text\": \"def unhook(remove):\\n _hooks[remove]()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efbfc7319fe72c40be50f7b59aaac86b\",\n \"score\": \"0.5332615\",\n \"text\": \"def removeChild(self,child):\\n self.pv_cdomlette.removeChild(child.pv_cdomlette)\\n for i in range(0,len(self.childNodes)-1):\\n if id(self.childNodes[i]) == id(child):\\n toto = self.childNodes[i]\\n toto.pv_deleted = True\\n self.notify_observers(toto)\\n del self.childNodes[i]\\n print \\\"coucou %s\\\" % toto\\n self.notify_observers(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"807410c2641efabffc586ca46dcd60f0\",\n \"score\": \"0.53256506\",\n \"text\": \"def remove_child_qualifier(self, qualifier_id, child_id):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8675cc6474c8d17829f609fcfffaceb7\",\n \"score\": \"0.53240865\",\n \"text\": \"def kill(self):\\n for handler in self.logger.handlers[:]:\\n self.logger.removeHandler(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e953f8d8eba4ddc9d59185945154c79c\",\n \"score\": \"0.5312111\",\n \"text\": \"def delete_event_callback(self, event):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dac6789dd46eefb6abc1f87e943a782b\",\n \"score\": \"0.5303661\",\n \"text\": \"def clear(self):\\n\\n if not self._handler:\\n raise ValueError(\\\"No handler configured\\\")\\n\\n self._handler.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef47db25ea38d2efd4350e381633f100\",\n \"score\": \"0.5300582\",\n \"text\": \"def signal_unbind(self, signal_handler):\\n try:\\n handlers = self._signals[\\n signal_handler.signal_name]\\n except KeyError:\\n pass\\n else:\\n signal_handler.function = None\\n try:\\n handlers.remove(signal_handler)\\n except IndexError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"277477aaec1aa82d3e9d230e22a5605c\",\n \"score\": \"0.52999586\",\n \"text\": \"def remove_child(self, child, class_name = None):\\n if class_name is None: class_name = self.__class__.__name__\\n ## if child_name provided\\n if isinstance(child, str):\\n if child in self._children: self._remove_child(child)\\n else: raise UnknownChild( (f\\\"Cannot remove {self._child_adj}: '{child}' is not a\\\"\\n f\\\" known {self._child_alias} name\\\") )\\n ## if Child object provided\\n else:\\n ## if Child obj is in self.children, retrieve key\\n inv_children = dict((v, k) for k, v in self._children.items())\\n if child in inv_children: self._remove_child(inv_children[child])\\n else: raise UnknownChild( (f\\\"Cannot remove {self._child_adj}: The {self._child_obj} object\\\"\\n f\\\" provided is not known to this {class_name}\\\") )\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3f64971c111162ae10e8f6b6421c71d\",\n \"score\": \"0.52982634\",\n \"text\": \"def remove() -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddffbb04569e5ddbc1684409b150f641\",\n \"score\": \"0.52908236\",\n \"text\": \"def unregister_error_handler() -> None:\\n Hub.handle_error = ORIGINAL_ERROR_HANDLER\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e921d60aab2652531909eacdb97ca6b\",\n \"score\": \"0.52737355\",\n \"text\": \"def remove_child(self, child_name):\\n self._update_json()\\n put_data = self.json.copy()\\n for obj_key in ['links', 'metadata']:\\n put_data.pop(obj_key)\\n obj_children = put_data.pop('objects')\\n put_data['objects'] = []\\n child_type = ''\\n for obj_child in obj_children:\\n if obj_child['name'] != child_name:\\n put_data['objects'].append(obj_child)\\n else:\\n child_type = obj_child['type']\\n if child_type:\\n logging.warning(\\\"Removing {} child object {} from parent {}!\\\".format(child_type, child_name, self.name))\\n self.update(put_data)\\n else:\\n logging.error(\\\"Child {} not found inside parent {}!\\\".format(child_name, self.name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6feec5067a9850c6d67f1869e2aee18\",\n \"score\": \"0.5272715\",\n \"text\": \"def unregisterPacketHandler(self, packetType):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3af39222a28b9a7395e6374c258b8ccd\",\n \"score\": \"0.5271657\",\n \"text\": \"def remove_hook(self, name, fun):\\n\\t\\tself.hooks[name].remove(fun)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"256f268e566e8d1f9bf1075746200bc6\",\n \"score\": \"0.526498\",\n \"text\": \"def remove(self, widget):\\n self._detach_child(widget)\\n del self._children[widget]\\n self._repack_and_regroup_children()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd4c5e5b999e00b539da04836032f43\",\n \"score\": \"0.5257587\",\n \"text\": \"def callbackWrapper(self):\\n self.callback(*self.args)\\n self.methodscalled.remove(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f938ddd4a9d844fbb00109182932e6cf\",\n \"score\": \"0.5240832\",\n \"text\": \"def _remove_named_hook(name_or_handler, names):\\n if callable(name_or_handler):\\n handler = name_or_handler\\n try:\\n name = next(n for n, h in names.items() if h == handler)\\n except StopIteration:\\n raise ValueError('This handler is not associated with any name.')\\n unhook(handler)\\n del names[name]\\n else:\\n name = name_or_handler\\n try:\\n handler = names[name]\\n except KeyError as e:\\n raise ValueError('No such named listener: ' + repr(name), e)\\n unhook(names[name])\\n del names[name]\\n\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a738913a0eafb0af7f2a8ef6d646c856\",\n \"score\": \"0.52406603\",\n \"text\": \"def off(self, event_name, handler):\\n # todo: make this thread safe!!!\\n if event_name in self.listeners and handler in self.listeners[event_name]:\\n self.listeners[event_name].remove(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"052b4bad8d2a5ccd936c6bb5f7b44485\",\n \"score\": \"0.52362853\",\n \"text\": \"def sig_handler(sig,frame):\\n ioloop.IOLoop.instance().add_callback(shutdown)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"052b4bad8d2a5ccd936c6bb5f7b44485\",\n \"score\": \"0.52362853\",\n \"text\": \"def sig_handler(sig,frame):\\n ioloop.IOLoop.instance().add_callback(shutdown)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71ce42a89e2181b3e1163f07c1d3dc59\",\n \"score\": \"0.5233347\",\n \"text\": \"def terminate(self) -> None:\\n self.unregister_endpoint(self.handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc820064bbe1d9f53d9acf7832ce88a3\",\n \"score\": \"0.52318746\",\n \"text\": \"def remove(self, widget):\\n self._detach_child(widget)\\n self._children.remove(widget)\\n del self._sizes[widget]\\n self._repack_and_regroup_children()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":495,"cells":{"query_id":{"kind":"string","value":"fe2b6ed15d7c01e5da08eb0482b1fda2"},"query":{"kind":"string","value":"Look for currency code."},"positive_passages":{"kind":"list like","value":[{"docid":"5fa7adbf3f0a8e6d3acd86e2f51ea3b6","score":"0.6849101","text":"def look(query): \n filename = resource_filename(__name__, 'data/currency_code.json')\n with open(filename, 'r') as file:\n currency_code = json.load(file)\n \n def lookname(word):\n import re\n query = re.compile(word, flags=re.I)\n ret = []\n for it in currency_code:\n if re.search(query, it['name']):\n ret.append(it)\n return ret\n \n results = lookname(query)\n if len(results) == 0:\n click.echo('Error: Currency not found.')\n else:\n for r in results:\n currency, code = r['name'], r['code']\n message = \"{1} ({0})\".format(currency, code)\n click.echo(message)","title":""}],"string":"[\n {\n \"docid\": \"5fa7adbf3f0a8e6d3acd86e2f51ea3b6\",\n \"score\": \"0.6849101\",\n \"text\": \"def look(query): \\n filename = resource_filename(__name__, 'data/currency_code.json')\\n with open(filename, 'r') as file:\\n currency_code = json.load(file)\\n \\n def lookname(word):\\n import re\\n query = re.compile(word, flags=re.I)\\n ret = []\\n for it in currency_code:\\n if re.search(query, it['name']):\\n ret.append(it)\\n return ret\\n \\n results = lookname(query)\\n if len(results) == 0:\\n click.echo('Error: Currency not found.')\\n else:\\n for r in results:\\n currency, code = r['name'], r['code']\\n message = \\\"{1} ({0})\\\".format(currency, code)\\n click.echo(message)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ee6e587f59dfd4d74745f86a071eb09a","score":"0.68294126","text":"def currency_code(self) -> str:\n return pulumi.get(self, \"currency_code\")","title":""},{"docid":"2ed1c8c097a4e983214ff610ddcc960f","score":"0.67247885","text":"def __getitem__(self, value: Union[int, str]) -> Currency:\n if not value:\n raise CurrencyNotFound(f'Currency \"{value}\" not found.')\n\n item = self._format_num_code(value)\n item = item.lower()\n\n try:\n currency = self.currencies[item]\n except KeyError:\n raise CurrencyNotFound()\n\n return currency","title":""},{"docid":"ee981fc5e08ac786f0b21976e6ff79a1","score":"0.66217786","text":"def currency_code(self):\n code = self.currency\n\n if code not in CURRENCIES:\n code = common.settings.currency_code_default()\n\n return code","title":""},{"docid":"346b9a130ddde5662cf80d6ad2a1827a","score":"0.6579701","text":"def lookup_currency(self): # pragma: no cover\n return NotImplemented","title":""},{"docid":"927cf6ded6020a7a2d43200bf5cba98b","score":"0.6526249","text":"def get_currency(self, currency):\n\n if currency not in self._currency_addresses:\n self._currency_addresses = self.get_currencies()\n\n res = None\n if currency[:2] == '0x':\n for token, c in self._currency_addresses.items():\n if c['address'] == currency:\n res = c\n break\n # check if we found the currency\n if res is None:\n raise IdexCurrencyNotFoundException(currency)\n else:\n if currency not in self._currency_addresses:\n raise IdexCurrencyNotFoundException(currency)\n res = self._currency_addresses[currency]\n\n return res","title":""},{"docid":"4bcb2ceace3b3829b9b5730403019f54","score":"0.6213957","text":"def get_currency(input_country) -> str:\n file = open(\"country_conversion.json\", \"r\")\n data = json.load(file)\n file.close()\n for cntry in data:\n if cntry['Country'] == input_country:\n input_country = cntry['Code']\n break\n return input_country","title":""},{"docid":"e3d06049a4efb4befb2d1b6cd5ff3695","score":"0.61457765","text":"def test_find_currency_data():\n result = dtypes.find_currency_data()\n expected = {\n '＄': 'ARS', '﹩': 'ARS', '$': 'ARS', '￡': 'FKP', '₭': 'LAK',\n '£': 'FKP', '￦': 'KPW', '￥': 'CNY', '﷼': 'IRR', '₾': 'GEL',\n '₽': 'RUB', '₼': 'AZN', '₺': 'TRY', '₹': 'INR', '₸': 'KZT',\n '₵': 'GHS', '₴': 'UAH', '₲': 'PYG', '₱': 'CUP', '₮': 'MNT',\n '€': 'EUR', '₫': 'VND', '₪': 'ILS', '₩': 'KPW', '₨': 'LKR',\n '₦': 'NGN', '₡': 'CRC', '៛': 'KHR', '฿': 'THB', '৳': 'BDT',\n '؋': 'AFN', '֏': 'AMD', '¥': 'CNY', '¢': 'GHS'\n }\n assert result == expected","title":""},{"docid":"1cb11866dd97e23093c9e2bdeb9da83e","score":"0.60134774","text":"def string_to_currency(self, s):\n return # osid.financials.Currency","title":""},{"docid":"a9f088a9e36f3d4471f2a0516b7ccd7a","score":"0.59600127","text":"def guess_currency_from_address(address):\n if is_py2:\n fixer = lambda x: int(x.encode('hex'), 16)\n else:\n fixer = lambda x: x # does nothing\n\n first_byte = fixer(b58decode_check(address)[0])\n hits = []\n for currency, data in crypto_data.items():\n if hasattr(data, 'get'): # skip incomplete data listings\n version = data.get('address_version_byte', None)\n if version is not None and first_byte == version:\n hits.append([currency, data['name']])\n\n if hits:\n return hits\n\n raise ValueError(\"Unknown Currency with first byte: %s\" % first_byte)","title":""},{"docid":"da5ce1d29e2b499ada2b3b643218edbc","score":"0.5934486","text":"def test_is_currency_code_format(self):\r\n\r\n self.assertTrue(is_currency_code_format(\"USD\"))\r\n self.assertFalse(is_currency_code_format(\"US!\"))\r\n self.assertFalse(is_currency_code_format(\"USSR\"))\r\n self.assertFalse(is_currency_code_format(\" D\"))","title":""},{"docid":"7f6e9b4f82a4e0c8983f61974351e49e","score":"0.5933416","text":"def find_by_currency(currency):\n\n\tcountries_info = get_response(\"/currency/\" + currency)\n\treturn get_country_objects(countries_info)","title":""},{"docid":"070304ac4bd019f62116617e4b61070c","score":"0.5826321","text":"def test_is_real_currency_code(self):\r\n\r\n self.assertTrue(is_real_currency_code('USD'))\r\n self.assertFalse(is_real_currency_code('ZZZ'))","title":""},{"docid":"b259f1d2c53e28a01027287e1eaca8c6","score":"0.57734346","text":"def is_stock_cn(code):\n code = str(code)\n if code[0] in ['5', '6', '9'] or \\\n code[:3] in [\"009\", \"126\", \"110\", \"201\", \"202\", \"203\", \"204\", '688'] or \\\n (code.startswith('XSHG')) or \\\n (code.endswith('XSHG')):\n if (code.startswith('XSHG')) or \\\n (code.endswith('XSHG')):\n if (len(code.split('.')) > 1):\n try_split_codelist = code.split('.')\n if (try_split_codelist[0] == 'XSHG') and (len(try_split_codelist[1]) == 6):\n code = try_split_codelist[1]\n elif (try_split_codelist[1] == 'XSHG') and (len(try_split_codelist[0]) == 6):\n code = try_split_codelist[0]\n if (code[:5] in [\"00000\"]) or \\\n (code[:3] in [\"000\"]):\n return True, QA.MARKET_TYPE.INDEX_CN, 'SH', '上交所指数'\n if code.startswith('60') == True:\n return True, QA.MARKET_TYPE.STOCK_CN, 'SH', '上交所A股'\n elif code.startswith('688') == True:\n return True, QA.MARKET_TYPE.STOCK_CN, 'SH', '上交所科创板'\n elif code.startswith('900') == True:\n return True, QA.MARKET_TYPE.STOCK_CN, 'SH', '上交所B股'\n elif code.startswith('50') == True:\n return True, QA.MARKET_TYPE.FUND_CN, 'SH', '上交所传统封闭式基金'\n elif code.startswith('51') == True:\n return True, QA.MARKET_TYPE.FUND_CN, 'SH', '上交所ETF基金'\n else:\n print(code, True, None, 'SH', '上交所未知代码')\n return True, None, 'SH', '上交所未知代码'\n elif code[0] in ['0', '2', '3'] or \\\n code[:3] in ['000', '001', '002', '200', '300', '159'] or \\\n (code.startswith('XSHE')) or \\\n (code.endswith('XSHE')):\n if (code.startswith('000') == True) or \\\n (code.startswith('001') == True):\n if (code in ['000003', '000112', '000300', '000132', '000133']):\n return True, QA.MARKET_TYPE.INDEX_CN, 'SH', '中证指数'\n else:\n return True, QA.MARKET_TYPE.STOCK_CN, 'SZ', '深交所主板'\n if code.startswith('002') == True:\n return True, QA.MARKET_TYPE.STOCK_CN, 'SZ', '深交所中小板'\n elif code.startswith('003') == True:\n return True, QA.MARKET_TYPE.STOCK_CN, 'SZ', '中广核？？'\n elif code.startswith('159') == True:\n return True, QA.MARKET_TYPE.FUND_CN, 'SZ', '深交所ETF基金'\n elif code.startswith('200') == True:\n return True, QA.MARKET_TYPE.STOCK_CN, 'SZ', '深交所B股'\n elif code.startswith('399') == True:\n return True, QA.MARKET_TYPE.INDEX_CN, 'SZ', '中证指数'\n elif code.startswith('300') == True:\n return True, QA.MARKET_TYPE.STOCK_CN, 'SZ', '深交所创业板'\n elif (code.startswith('XSHE')) or \\\n (code.endswith('XSHE')):\n pass\n else:\n print(code, True, None, 'SZ', '深交所未知代码')\n return True, None, 'SZ', '深交所未知代码'\n else:\n print(code, '不知道')\n return False, None, None, None","title":""},{"docid":"3006564fc4ea5475cb662f5573eb246e","score":"0.57121426","text":"def getCurrencyRate(self,code):\n currencyrate=self.currencyratedict[code]\n return currencyrate.getCurrencyRate()","title":""},{"docid":"a2171d7de63c5885abb9874d88d535c2","score":"0.56446785","text":"def _get_currency(self):\n user = self.env['res.users'].browse(self._uid)\n if user.company_id:\n return user.company_id.currency_id.id\n else:\n return self.env['res.currency'].search(\n [('rate', '=', 1.0)])[0]","title":""},{"docid":"a31e5c700d7aea261fe7ed86117b9f9d","score":"0.56382793","text":"def find_by_code(cls, code):\n\n formatted_code = code.upper()\n\n coupon = Coupon.query.filter(Coupon.redeemable, Coupon.code == formatted_code).first()\n\n return coupon","title":""},{"docid":"31dda01bdc976b63e6a03a9b74516226","score":"0.5622921","text":"def search_exchange():\n\tdolar = soup.find(\"div\", {\"class\": \"dolar\"})\n\tfor li in dolar.findAll('li'):\n\t\tcurrency, amount = li.text.split('RD$')\n\t\treturn (currency, amount)","title":""},{"docid":"64e8cf1846dd2ba185091ca081f3a21e","score":"0.5618524","text":"def find_by_code(cls, code):\n formatted_code = code.upper()\n coupon = Coupon.query.filter(Coupon.redeemable,\n Coupon.code == formatted_code).first()\n\n return coupon","title":""},{"docid":"34bcfdc0842c24acdf06b88d814122b2","score":"0.5607418","text":"def find_currency(df):\n doc_currency = \"\"\n # euros = \"Euro|€|EUR\"\n # dollars = \"Dollar|\\$|USD|dols\"\n # pounds = \"£|GBP|BP\"\n currencies = {\"euro\": EUROS, \"dollar\": DOLLARS, \"pound\": POUNDS}\n\n for key, value in currencies.items():\n if df.apply(lambda x: x.str.contains(value, case=False).any(), axis=1).any(\n axis=None\n ):\n # If doc_currency is set two times => set undefined\n if doc_currency:\n doc_currency = \"euro\" # \"undefined\"\n break\n # Set doc currency\n else:\n doc_currency = key\n\n # Create column for unit in df marking the token which contains unit\n df.loc[:, \"currency\"] = False\n for key, value in currencies.items():\n df.loc[df[\"text\"].str.contains(value, case=False), \"currency\"] = True\n # Set default unit to 1\n if not doc_currency:\n doc_currency = \"euro\"\n return doc_currency","title":""},{"docid":"4b951e8bedf117c911b482d92923c1ac","score":"0.5592632","text":"def parse_currency(self):\n price = re.search('(\\d\\.*)+', self.pq_form('.price').text())\n p = [float(i) for i in price.group().strip().split(' ')]\n\n return \"%.2f\" % max(p) if len(p) == 2 else \"%.2f\" % p[0]","title":""},{"docid":"a81dfae6f8a698e39ce77fe32c017525","score":"0.5579692","text":"def ekat_to_gnc_Currency(GNCBook, EkatCurrency):\n CommodityTable = GNCBook.get_table()\n return CommodityTable.lookup(\"CURRENCY\", str(EkatCurrency))","title":""},{"docid":"b9ccbe027e15cc8282435257c9ec6e43","score":"0.55710196","text":"def get_coupon_code_price_in_checkout():\n coupon_code_price_text = Element(\"checkout\", \"coupon_code_price\").get_attribute(\"text\")\n coupon_code_price = coupon_code_price_text[coupon_code_price_text.find(\"$\")+1:]\n return float(coupon_code_price)","title":""},{"docid":"c0dff2676b71692bef34c656d98e3649","score":"0.5542053","text":"def currency(self) -> pulumi.Input[Union[str, 'Currency']]:\n return pulumi.get(self, \"currency\")","title":""},{"docid":"e473aaa456d221d68da322d3b068db4d","score":"0.5538454","text":"def get_denomination(s):\n\n if is_money_string(s):\n denomination_match = re.search(rf'({_SUPPORTED_DENOMINATIONS})', s)\n if denomination_match:\n denomination = denomination_match.group(0)\n if denomination == '$':\n denomination = 'USD'\n return denomination\n return None","title":""},{"docid":"47344aede6290e298bc5d710217f9ef9","score":"0.55297154","text":"def get_rate(self, currency_code): \n return Decimal(self.rates[currency_code])","title":""},{"docid":"e69e46cf09939d9a6734f33014517df0","score":"0.55219483","text":"def getCurrency(self):\n return self.base.get(\"currency\", [])","title":""},{"docid":"12e62d6aebf33efc457369adbd74e749","score":"0.54618835","text":"def get_currency_type(self):\n return # osid.type.Type","title":""},{"docid":"a6abc535c447c218f08b1a0048668d38","score":"0.53959584","text":"def get_currency_type(self):\n return # osid.type.Type","title":""},{"docid":"4ff2f0efdfcef9ef4f33132f9982daba","score":"0.53625256","text":"def get_company_code(self):\n code_df = pd.read_html('http://kind.krx.co.kr/corpgeneral/corpList.do?method=download&searchType=13', header=0)[0] \n # convert into 6 digits code\n code_df.종목코드 = code_df.종목코드.map('{:06d}'.format) \n # remove unnecessary columns \n code_df = code_df[['회사명', '종목코드']] \n # change Korean to English\n code_df = code_df.rename(columns={'회사명': 'name', '종목코드': 'code'})\n self.code = code_df.query(\"name=='{}'\".format(self.item_name))['code'].to_string(index=False).lstrip()\n print('Item Code:',self.code)\n return self.code","title":""},{"docid":"c96fc91befb6d6daffe6a9c3efea18ea","score":"0.5346923","text":"def clean_currency(currency):\n if \"TDM\" in currency:\n currency = \"TDM\"\n elif \"Mio\" in currency and \"DM\" in currency:\n currency = \"Mio DM\"\n elif \"Mrd\" in currency and \"DM\" in currency:\n currency = \"Mrd DM\"\n elif \"DM\" in currency:\n currency = \"DM\"\n elif \"Thlf\" in currency:\n currency = \"Thlf\"\n elif \"hlf\" in currency:\n currency = \"hlf\"\n elif \"mögen\" in currency:\n currency = \"DM\"\n return currency","title":""},{"docid":"4acb2108c1ef6b47cc9f376817d769bd","score":"0.5315113","text":"def record_currency(self, record):\n return record.currency_id","title":""},{"docid":"672c3a07646cc6f85d86036e78f25e7f","score":"0.52842635","text":"def test_convert_currency(self):\r\n\r\n self.assertEqual(convert_currency('USD', 'USD', '50'), 'US$50.0')","title":""},{"docid":"ce36fe58516565b4bba094857a6469ed","score":"0.5282381","text":"def get_cur_by_id(cur_id):\n sql = \"\"\"\n SELECT currency\n FROM currencies\n WHERE id = %s;\n \"\"\"\n args = (cur_id,)\n try:\n query_result = Currency._make_select(sql, args)\n except IntegrityError:\n return False\n return query_result[0]['currency']","title":""},{"docid":"aa88d30e95a9addc5792fd13dfd3064b","score":"0.52776617","text":"def test_creates_currency(self):\n Currency('EUR')","title":""},{"docid":"15562f6459e92fd2cd75611fa5a36d47","score":"0.52687216","text":"async def test_currency(self) -> None:\n nanites, asp_tokens = await self.character.currency()\n self.assertIsInstance(nanites, int)\n self.assertIsInstance(asp_tokens, int)","title":""},{"docid":"e6d65da97522012b7991b84c015de7bf","score":"0.5256139","text":"def get_exchange_rate(currency):\n response = requests.get('https://bitpay.com/api/rates')\n resp_obj = json.loads(response.content)\n result = '<N/A>'\n for elem in resp_obj:\n if elem['code'] == currency:\n result = str(elem['rate'])\n return result","title":""},{"docid":"79243ec60ac490e7e21a78aad59a979a","score":"0.525378","text":"def get_source_currency(self, obj: CurrencyExchangeRate) -> str:\n return obj.source_currency.code","title":""},{"docid":"441d19f1ad753cf78c1d00c6b145e7d9","score":"0.5250038","text":"def search_okcicredit(cursor, contract_num):\n try:\n cursor.execute(\n \"\"\"\n SELECT\n tc.id,\n tc.client_id,\n CONCAT(\n REPLACE(tper.name3,\"'\", '\"'), ' ',\n LEFT(tper.name , 1), '. ',\n LEFT(tper.name2 , 1), '.'),\n tc.vnoska,\n ts.status,\n tc.egn,\n dwh.GetDolgBody(tc.id, date(now()))+\n dwh.GetDolgFine(tc.id, date(now()))+\n dwh.GetDolgPrc(tc.id, date(now()))\n FROM\n mbank.tcredits tc\n join mbank.tstatuses ts on ts.credit_id = tc.id\n and ts.is_last = 1\n left join mbank.tpersons tper on tper.id = tc.client_id\n WHERE tc.contract_num = {0}\n and ts.status = 59;\n \"\"\".format(contract_num)\n )\n except Exception:\n return None\n\n credit = cursor.fetchall()\n\n if credit:\n return credit\n\n return None","title":""},{"docid":"5765a27fc1d71f6e40e1d92f5f3948ec","score":"0.52460676","text":"def get_currency_format_type(self):\n return # osid.type.Type","title":""},{"docid":"8de5e2d0e651ed0703855a491e8cae71","score":"0.5234212","text":"def test_retreive_miscapitalized_currency(client):\n\n res = client.get('/v1/currencies/uSd')\n assert res.status_code == 404","title":""},{"docid":"4ddce66c0a37d8ebff9476e709bda5e5","score":"0.52306783","text":"def isContainsDollar(entry):\n return entry[1].find('$')!=-1","title":""},{"docid":"a7d9178acced0c0df2b491ede2785eb5","score":"0.5221874","text":"def get_source_currency_type(self):\n return # osid.type.Type","title":""},{"docid":"455b6cb0ecd72e95ee19c7948bea71d0","score":"0.5203276","text":"def get_currency(self):\n return self.currency","title":""},{"docid":"455b6cb0ecd72e95ee19c7948bea71d0","score":"0.5203276","text":"def get_currency(self):\n return self.currency","title":""},{"docid":"79be60e07bc0158716409b8567cd8101","score":"0.51875174","text":"def commodity_code(self) -> str:\n return pulumi.get(self, \"commodity_code\")","title":""},{"docid":"2e00b98328b78f993aed9983555cdbc5","score":"0.51707137","text":"def get_country_code(country_name):\n for country_code, country in COUNTRIES.items():\n if country_name.lower() == country.lower():\n return country_code\n elif 'congo, dem.' in country_name.lower():\n return 'cd'\n elif 'hong kong' in country_name.lower():\n return 'hk'\n elif 'macao' in country_name.lower():\n return 'mo'\n elif 'egypt' in country_name.lower():\n return 'eg'\n elif 'gambia' in country_name.lower():\n return 'gm'\n elif 'yemen' in country_name.lower():\n return 'ye'\n else:\n return None","title":""},{"docid":"1dbbeb612d99c5533f0e1366ed83b8c4","score":"0.5159652","text":"def iscurrency(currency):\n strout = currency_response(\"USD\", currency, 1)\n boolnum = has_noerror(strout)\n return boolnum","title":""},{"docid":"c86bc7a0d9fd0fc39eeffc8b1dac9ad7","score":"0.5142611","text":"def currency(self):\n return self._currency","title":""},{"docid":"c86bc7a0d9fd0fc39eeffc8b1dac9ad7","score":"0.5142611","text":"def currency(self):\n return self._currency","title":""},{"docid":"c86bc7a0d9fd0fc39eeffc8b1dac9ad7","score":"0.5142611","text":"def currency(self):\n return self._currency","title":""},{"docid":"c86bc7a0d9fd0fc39eeffc8b1dac9ad7","score":"0.5142611","text":"def currency(self):\n return self._currency","title":""},{"docid":"1784c18e6bdda1ae4e1688d3a36fb826","score":"0.5138435","text":"def find_cntry(line):\n match = COUNTRY_REGEX.match(line)\n if match:\n country = match.group(1)\n if \"De facto\" in country:\n return None\n if \"|\" in country:\n return country.partition(\"|\")[2]\n return country\n return None","title":""},{"docid":"8ea7b2be2f5f1a9a7bc0f938900b1195","score":"0.5131995","text":"def _money(text,rex=re.compile('(-?)([0-9]*)\\.?([0-9]*)')):\n return rex.match(text).groups()","title":""},{"docid":"662deb19db1c60083daa880fdb9c5b2b","score":"0.5127136","text":"def erc20_value_search(coins):\n url2 = \"http://api.binance.com/api/v3/ticker/price\"\n response2 = requests.get(url2)\n rate_list = response2.json()\n pair_conversions = defaultdict(lambda : 0)\n\n for coin in coins:\n for rate in rate_list:\n exchange_coin = rate['symbol']\n exchange_price = rate['price']\n if coin + 'ETH' == exchange_coin:\n pair_conversions[exchange_coin] = exchange_price\n else:\n pass\n \n return pair_conversions","title":""},{"docid":"c65c5d15400f8e8e1d9aa8290ce2297c","score":"0.51143295","text":"def _weatherSymbol(self, code):\n\n table = {'partlycloudy': '~☁',\n 'cloudy': '☁',\n 'tstorms': '⚡',\n 'sunny': '☀',\n 'snow': '❄',\n 'sleet': '☄',\n 'rain': '☔',\n 'mostlysunny': '~☀',\n 'mostlycloudy': '~☁',\n 'hazy': '♒',\n 'fog': '♒',\n 'flurries': '❄',\n 'clear': '☼',\n 'chanceflurries': '?❄',\n 'chancerain': '?☔',\n 'chancesleet': '?❄',\n 'chancesnow': '?❄',\n 'chancetstorms': '?☔'}\n # return symbol from table.\n try:\n return table[code]\n except KeyError:\n return \"unknown\"","title":""},{"docid":"8d9721f057827b87b15cbfd1a3ac2500","score":"0.5104792","text":"def validate_currency(currency):\n if not currency:\n return False\n\n if not re.search('^\\d+$', currency):\n return False\n\n if not re.search('^0', currency):\n return False\n\n try:\n decimal.Decimal(currency)\n except:\n return False\n\n return True","title":""},{"docid":"daf402fb2b1f3a9252d1337a6b960b29","score":"0.5099933","text":"def find_by_callingcode(code_num):\n\tcountries_info = get_response(\"/callingcode/\" + code_num)\n\treturn get_country_objects(countries_info)","title":""},{"docid":"597486b56082becc2c6da756f55c48d2","score":"0.5079034","text":"def search_credit(cursor, contract_num):\n try:\n cursor.execute(\n \"\"\"\n SELECT\n tc.id,\n tc.client_id,\n CONCAT(REPLACE(tper.name3,\"'\", '\"'), ' ', LEFT(tper.name , 1), '. ', LEFT(tper.name2 , 1), '.'),\n tc.vnoska,\n ts.status,\n tc.egn,\n dwh.GetDolgBody(tc.id, date(now()))+\n dwh.GetDolgFine(tc.id, date(now()))+\n dwh.GetDolgPrc(tc.id, date(now()))\n FROM\n mbank.tcredits tc\n join mbank.tstatuses ts on ts.credit_id = tc.id\n and ts.is_last = 1\n left join mbank.tpersons tper on tper.id = tc.client_id\n WHERE tc.contract_num = {0}\n and (ts.status = 5 or ts.status = 55 or ts.status = 555);\n \"\"\".format(contract_num)\n )\n except Exception:\n return None\n\n credit = cursor.fetchall()\n\n if credit:\n return credit\n\n return None","title":""},{"docid":"db39390e78aa9d3a1fed693bbdb72298","score":"0.5078839","text":"def _find_payment(data):\n transaction_id = data[\"google-order-number\"]\n for Cart in CART_TYPES:\n try:\n return Cart.payment_class.objects.select_related('cart').filter(transaction_id=transaction_id)[0]\n except:\n pass","title":""},{"docid":"051c346c43b8b85215ee9c3b7f29e063","score":"0.5062812","text":"def set_currency_symbol(self):\n is_set = None\n try:\n self.logger.info('Start: set currency symbol')\n self._business_admin_page.set_currency_symbol()\n is_set = True\n except WebDriverException as exp:\n is_set = False\n self.logger.error(exp.msg)\n raise\n finally:\n self.logger.info('End: set currency symbol')\n return is_set","title":""},{"docid":"1a92f63c20b03123295110132d514454","score":"0.50594723","text":"def valid_currencies():\n return [currency for currency in Money.VALID_CURRENCIES]","title":""},{"docid":"87e19d5a139b383840b7e07816fd0e58","score":"0.505653","text":"def get_currency_unit(value):\n currency = \"\"\n unit = \"1\"\n if \"TEUR\" in value:\n currency = \"EUR\"\n unit = \"1000\"\n elif \"TDM\" in value:\n currency = \"DM\"\n unit = \"1000\"\n elif \"Mio\" in value:\n unit = \"1000000\"\n currency = value[value.find(\"Mio\")+3:]\n elif \"Mrd\" in value:\n unit = \"1000000000\"\n currency = value[value.find(\"Mrd\")+3:]\n else:\n numbers = []\n lcidx = 0\n for cidx, char in enumerate(value):\n if char.isdigit():\n numbers.append(char)\n lcidx = cidx\n unit = \"\".join(numbers)\n currency = value[lcidx + 1:].strip()\n currency = clean_currency(currency)\n return currency, unit","title":""},{"docid":"1f2a54b10d945c9151241cf9d658f0ae","score":"0.5055238","text":"def remove_currency(raw_price):\n return raw_price.split(\" \")[-1]","title":""},{"docid":"2721c4525ac8eb941edc2647fc12c80e","score":"0.5054427","text":"def quote_currency(self):\n\t\treturn self.__quote_currency","title":""},{"docid":"dec2ca60ee3871e62408f5f7b7018a5f","score":"0.50301546","text":"def findCode(self, COMP_NAME, TARGET): \n\t\tMIN_ERR=1.0e20\n\t\tBEST_CODE=-1\n\t\t\n\t\tif (COMP_NAME=='C1'):\n\t\t\tm=self.calcC1\n\t\telif (COMP_NAME=='C2'):\n\t\t\tm=self.calcC2\n\t\telif (COMP_NAME=='R2'):\n\t\t\tm=self.calcR2\n\t\telif (COMP_NAME=='C3'):\n\t\t\tm=self.calcC3\n\t\telif (COMP_NAME=='R3'):\n\t\t\tm=self.calcR3\n\t\telse:\n\t\t\tprint \"Error: Wrong Name of the LMS8001 IC Loop Filter Component. Exiting. Returning -1.\"\n\t\t\treturn BEST_CODE\n\t\t\n\t\tfor CODE in range(0,16):\n\t\t\tABS_ERR=abs(m(CODE)-TARGET)\n\t\t\tif (ABS_ERR<MIN_ERR):\n\t\t\t\tMIN_ERR=ABS_ERR\n\t\t\t\tBEST_CODE=CODE\n\n\t\treturn BEST_CODE","title":""},{"docid":"134f8f5a73940fa989981cfb694e81eb","score":"0.5020025","text":"def fetch_key_bcp_lang_code(lang):\n for r in query_omw(\"\"\"SELECT id\n FROM lang\n WHERE (bcp47 = ?)\"\"\", [lang]):\n return r['id']","title":""},{"docid":"47d9f80a4bc708ada05613465e18ade1","score":"0.5015696","text":"def get_currency():\n if default_currency:\n \t#\n # url calling format = 'http://apilayer.net/api/live?access_key={}¤cies=EUR,CNY,GBP,MXN&format=1'\n #\n qs = Currency.objects.all().values_list('code', flat=True)\n url = 'http://apilayer.net/api/live?access_key={}¤cies={}&format=1'.format(settings.API_LAYER_ACCESS_KEY, ','.join(qs))\n print (url)\n\n r = requests.get(url)\n\n result = json.loads(r.text)\n\n for q in result['quotes']:\n code = q[3:]\n currency = Currency.objects.filter(code=code).first()\n currency.rate = result['quotes'][q]\n currency.save()","title":""},{"docid":"7a0696364920f4f3646e20f6f3d20f49","score":"0.5005908","text":"def get_code(self):\r\n with open('data/county-fips-to-name.csv', 'r') as csvfile:\r\n csvdata = csv.reader(csvfile)\r\n for row in csvdata:\r\n if row[1] in self.county:\r\n code = row[0]\r\n break\r\n\r\n # county fips code has to be length of 5\r\n if len(code) == 4:\r\n code = '0' + code\r\n\r\n return code","title":""},{"docid":"30affd6f50b6dbe3237da1843ce32487","score":"0.50012606","text":"def _currency_symbols():\n current = locale.getlocale()\n locales = list(locale.locale_alias.values())\n symbols = set()\n\n for loc in locales:\n try:\n locale.setlocale(locale.LC_MONETARY, locale.normalize(loc))\n currency = \"{int_curr_symbol}\".format(**locale.localeconv())\n if currency != \"\":\n symbols.add(currency.strip())\n except (locale.Error, UnicodeDecodeError):\n continue\n\n locale.setlocale(locale.LC_MONETARY, current)\n return list(symbols)","title":""},{"docid":"297d6d4da94fd2cccc12617bb854a105","score":"0.49998227","text":"def get_currency(self, userid):\n self.cursor.execute(\"SELECT currency, userid FROM users WHERE userid = ?\", (userid,))\n currencyfetch = self.cursor.fetchone()\n if currencyfetch is not None:\n currency = currencyfetch[0]\n else:\n currency = 0\n return currency","title":""},{"docid":"df8b6f2edc6da5167e1d56ca38de42c4","score":"0.49984938","text":"def country_code(cls, valid):\n return 1 if valid else \"FAKE\"","title":""},{"docid":"413f3ee5a44707f0be5a18ce93275577","score":"0.4996388","text":"def test_remove_dollar_symbols(self):\n item = '3773.49'\n result = self.open_csv.remove_dollar_symbols(item)\n\n print 'item: %s' % item\n print 'result: %s' % result\n\n self.assertNotIn('$', result)","title":""},{"docid":"71d6250f09304775b0cb5630eb6d2420","score":"0.49881542","text":"def code(self):\n return self._bank_code","title":""},{"docid":"2e549861eb4bcffc07c364b91c27721f","score":"0.49851966","text":"def parse_currency(s):\n if not isinstance(s, text_type):\n return float('nan')\n s = re.sub(u'[\\$\\u20AC\\u00A3\\uFFE1\\u00A5\\uFFE5]|(EUR)', '', s)\n s = s.replace(',', '')\n try:\n return float(s)\n except:\n return float('nan')","title":""},{"docid":"d431064777b66734e060344c7e2b62fa","score":"0.4984376","text":"def currency(self):\n return self.properties.get('currency', CurrencyColumn())","title":""},{"docid":"86ecbfef3b55058fbc31d11a240cbfa7","score":"0.4983195","text":"def _extractCurrencies(self, numbers_node):\n currs_nodes = numbers_node.getElementsByTagName('currencies')\n if not currs_nodes:\n return\n\n currencies = InheritingDictionary()\n for curr_node in currs_nodes[0].getElementsByTagName('currency'):\n type = curr_node.getAttribute('type')\n currency = LocaleCurrency(type)\n\n nodes = curr_node.getElementsByTagName('symbol')\n if nodes:\n currency.symbol = self._getText(nodes[0].childNodes)\n currency.symbolChoice = \\\n nodes[0].getAttribute('choice') == u'true' \n\n nodes = curr_node.getElementsByTagName('displayName')\n if nodes:\n currency.displayName = self._getText(nodes[0].childNodes)\n\n currencies[type] = currency\n\n return currencies","title":""},{"docid":"0602c7cc52171f745e91273fc6a4addb","score":"0.4977636","text":"def _get_carrier(self, number):\n number = self.number\n if not self.prefix or not self.country:\n return None\n try:\n mod = importlib.import_module(\n 'plnumbers.{}'.format(self.country)\n )\n except ImportError:\n return None\n\n self._NUMBERTYPES = mod.LANE_TYPE\n match = None\n for pattern, value in mod.LOOKUPS:\n if re.search(pattern, number):\n match = mod.CARRIERS[value]\n break\n\n if match:\n self.numbertype = self._NUMBERTYPES.get(match[1])\n return match[0]\n else:\n self.numbertype = self._NUMBERTYPES.get('u')\n return None","title":""},{"docid":"2efd2934cd989281be4558bc4fcea4ff","score":"0.49642444","text":"def search_skycredit(cursor, contract_num):\n try:\n cursor.execute(\n \"\"\"\n SELECT\n tc.id,\n tc.client_id,\n CONCAT(REPLACE(tper.name3,\"'\", '\"'), ' ', LEFT(tper.name , 1), '. ', LEFT(tper.name2 , 1), '.'),\n tc.vnoska,\n ts.status,\n tc.egn,\n dwh.GetDolgBody(tc.id, date(now()))+\n dwh.GetDolgFine(tc.id, date(now()))+\n dwh.GetDolgPrc(tc.id, date(now()))\n FROM\n mbank.tcredits tc\n join mbank.tstatuses ts on ts.credit_id = tc.id\n and ts.is_last = 1\n left join mbank.tpersons tper on tper.id = tc.client_id\n WHERE tc.contract_num = {0}\n and (ts.status = 55 or ts.status = 555);\n \"\"\".format(contract_num)\n )\n except Exception:\n return None\n\n credit = cursor.fetchall()\n\n if credit:\n return credit\n\n return None","title":""},{"docid":"ebd2f845dc6b8c1b1ed9db2023624901","score":"0.49468762","text":"def currency_code(self, currency_code):\n\n self._currency_code = currency_code","title":""},{"docid":"ebd2f845dc6b8c1b1ed9db2023624901","score":"0.49468762","text":"def currency_code(self, currency_code):\n\n self._currency_code = currency_code","title":""},{"docid":"34dd4b341635f85a6d38546c29469839","score":"0.49370578","text":"def account_available(currency: str) -> int:\n if currency in sc.AVAILABLE_CURRENCY_ACCOUNTS:\n return -1\n else:\n return 1","title":""},{"docid":"972344c48da4e0dd81189ee3f43c3569","score":"0.4933475","text":"def get_currencies(self):\n\n endpoint = self.__endpoints.get(\"currencies\")\n payload = {\"accessToken\": self.__api_key}\n\n response = self.__request(endpoint, payload)\n if response[\"success\"]:\n return response\n raise KolletErrors(response[\"message\"])","title":""},{"docid":"072b9471f95d3c886425c65af7080cc8","score":"0.49302197","text":"def verse_num_extractor(data):\n if re.search('॥ [०१२३४५६७८९]+ ॥\\n', data):\n num = re.sub(r'.*॥ ([०१२३४५६७८९]+) ॥\\n', r'\\g<1>', data)\n num = transliterate(num, 'devanagari', 'slp1')\n num = int(num)\n return num\n else:\n return False","title":""},{"docid":"9a07b1f20a23d728d5cdf4ed91850e83","score":"0.49299568","text":"def _parse(self, data: Tuple[bytes, bytes]) -> TypeCurrencyIndex:\n currencies = {}\n counter = 0\n\n LOG.debug('Parsing data ...')\n\n index = self._index_currency\n format_num = self._format_num_code\n\n for sub_data in data:\n root = ElementTree.fromstring(sub_data)\n\n for child in root:\n props = {}\n for prop in child:\n props[prop.tag] = prop.text\n\n num = props['ISO_Num_Code'] or None\n if num:\n # ISO numeric code like '036' is loaded like '36', so it needs to be formatted into ISO 4217,\n # also data from the Bank of Russia contains replaced currencies that do not have ISO attributes.\n # additional If-statement was added to exclude format None\n num = format_num(num)\n\n currency = Currency(\n id=child.attrib['ID'],\n name_eng=props['EngName'],\n name_ru=props['Name'],\n code=props['ISO_Char_Code'],\n num=num,\n par=Decimal(props['Nominal']),\n )\n\n counter += 1\n currencies.update(index(currency))\n\n LOG.debug(f\"Parsed: {counter} currencies\")\n return currencies","title":""},{"docid":"ddef1e8a27fb0f9e23a3018308992692","score":"0.49265334","text":"def test_get_currency():\n response = client.get(\"/util/currency?from_curr=USD&to_curr=INR&amt=1\")\n assert response.status_code == 200\n assert response.json() >= 70","title":""},{"docid":"e1f313f626fa43dead65fe37818e2eec","score":"0.49244246","text":"def convert_to(self, currency):\n return None # TODO","title":""},{"docid":"404441da833e468aa19ffea6e5d8e644","score":"0.49193448","text":"def return_currency_instance_from_exchange_pk(exchange_pk, exchange_instance):\n exchange_pk_instance = None\n try:\n exchange_pk_instance = CurrencyExchangePK.objects.get(\n exchange=exchange_instance, key=exchange_pk)\n except CurrencyExchangePK.DoesNotExist:\n pass\n\n if exchange_pk_instance:\n return exchange_pk_instance.currency\n\n return None","title":""},{"docid":"85cdbef879c617ace2731e20e41ece74","score":"0.49176356","text":"def get_target_currency_type(self):\n return # osid.type.Type","title":""},{"docid":"75b54ab89489867ae2f8610a70e2b401","score":"0.49009168","text":"def get_base_symbols(self):\n with self._lock:\n self._update_mkt_info()\n return list(set([mkt[\"BaseCurrencyCode\"] for mkt in self._mkt_info[\"result\"]]))","title":""},{"docid":"a973a44bd00bf6402a3655763da91451","score":"0.48923814","text":"def country(self, key):\n\n return key[:2]","title":""},{"docid":"cbf8ad54a8de0f71fe4a073be7cc90c9","score":"0.48863983","text":"def symbol_lookup(self):\n try: \n url = SYMBOL_LOOKUP_URL + self.stock.symbol\n result = urllib2.urlopen(url).url\n cboe_id = result.split(\"?\")[1]\n self.stock.cboe_id = cboe_id\n except urllib2.URLError, e:\n cboe_id = \"\"\n\n return cboe_id","title":""},{"docid":"01e9c1c938432b79a1524311c8f9a74a","score":"0.48590714","text":"def currency_db():\n # type: () -> List[Text]\n return [\"vnd\", \"usd\", \"euro\"]","title":""},{"docid":"730dfc990e898be6b3af1fcf7c77dadf","score":"0.48570985","text":"def CodeLookup(self, item: QtWidgets.QTreeWidgetItem, codelist: QtWidgets.QTreeWidget, gid: str):\n # Initialize vars\n wlist = [w.widget() for w in self.mdi.subWindowList() if isinstance(w.widget(), Database)\n or isinstance(w.widget(), CodeList) and w.widget().TreeWidget is not codelist]\n lsplt = re.split('[ \\n]', item.text(1))\n totalen = len(lsplt)\n\n # Begin search!\n for widget in wlist:\n\n # Mark code matches from different game ids with an additional asterisk\n regmatch = int(not(bool(widget.gameID == gid))) + 1\n\n # Process the widget's tree\n for child in filter(lambda x: x.text(1) and 'Unknown Code' not in x.text(0),\n widget.TreeWidget.findItems('', Qt.MatchContains | Qt.MatchRecursive)):\n matches = 0\n\n # For each code, check each line of the code we're looking a name for\n for line in lsplt:\n if line in child.text(1):\n matches += 1\n\n # If more than 2/3rds of the code match, we found the code we were looking for\n if matches / totalen >= 2 / 3:\n item.setText(0, child.text(0) + '*' * regmatch)\n item.setText(2, child.text(2)) # Copy comment\n item.setText(4, child.text(4)) # Copy author\n return","title":""},{"docid":"7fc56a8261e1fbd46de10a6bd80e45f1","score":"0.485295","text":"def get_currency_name(self):\n try:\n self.logger.info('Start: get currency name')\n return self._business_admin_page.get_currency_name()\n except WebDriverException as exp:\n self.logger.error(exp.msg)\n raise\n finally:\n self.logger.info('End: get currency name')","title":""},{"docid":"d7719b598269ec478586b369cb16ee4e","score":"0.48456493","text":"def country_code(self) -> str:\n return pulumi.get(self, \"country_code\")","title":""},{"docid":"d7719b598269ec478586b369cb16ee4e","score":"0.48456493","text":"def country_code(self) -> str:\n return pulumi.get(self, \"country_code\")","title":""},{"docid":"1765af00ce468af9c89fdfd0cae26c48","score":"0.4832439","text":"def get_current_exchange_rate(src_currency, tgt_currency):\n\timport urllib2\n\n\tresponse = urllib2.urlopen('http://www.google.com/ig/calculator?hl=en&q=1%s=?%s' % (src_currency, tgt_currency))\n\tdata = response.read()\n\t\n\t# Returns hash: {lhs: \"1 Euro\",rhs: \"1.3118 U.S. dollars\",error: \"\",icc: true}\n\tprint(data)\n\n\tindex = data.find('rhs: \"') + 6\n\tworking_str = data[index:]\n\tprint(working_str)\n\texchange_rate, discard_remainder = working_str.split(' ', 1)\n\tprint(exchange_rate)\n\treturn float(exchange_rate)","title":""},{"docid":"a71885ff21b27e994c4d569f0eca35e2","score":"0.4832284","text":"def test_euro(self):\n self.assertTrue(ord(unicodedata.lookup('EURO SIGN')) in self.font)","title":""},{"docid":"5c2850e3664a7dba21be74b2854f380e","score":"0.4831148","text":"def process_coin(amt, moneyIn):\n if moneyIn == 'quarter' or moneyIn == 'quarters':\n return amt * .25\n elif moneyIn == 'dime' or moneyIn == 'dimes':\n return amt * .10\n elif moneyIn == 'nickel' or moneyIn == 'nickels':\n return amt * .05\n elif moneyIn == 'penny' or moneyIn == 'pennies':\n return amt * .01","title":""},{"docid":"ab01337328b42be31dcfe6fd2b9daf91","score":"0.48303366","text":"def deck_flag_convert(country_code):\n cc_dict = {\n \"en\": \"gb\",\n \"ko\": \"kr\",\n \"ja\": \"jp\",\n \"zh\": \"cn\",\n \"hi\": \"in\",\n \"sr\": \"rs\",\n \"la\": \"va\",\n \"el\": \"gr\",\n }\n\n # Only return a value different from country_code if an alternative \n # is specified in cc_dict\n if country_code in cc_dict:\n return cc_dict[country_code]\n\n return country_code","title":""}],"string":"[\n {\n \"docid\": \"ee6e587f59dfd4d74745f86a071eb09a\",\n \"score\": \"0.68294126\",\n \"text\": \"def currency_code(self) -> str:\\n return pulumi.get(self, \\\"currency_code\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ed1c8c097a4e983214ff610ddcc960f\",\n \"score\": \"0.67247885\",\n \"text\": \"def __getitem__(self, value: Union[int, str]) -> Currency:\\n if not value:\\n raise CurrencyNotFound(f'Currency \\\"{value}\\\" not found.')\\n\\n item = self._format_num_code(value)\\n item = item.lower()\\n\\n try:\\n currency = self.currencies[item]\\n except KeyError:\\n raise CurrencyNotFound()\\n\\n return currency\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee981fc5e08ac786f0b21976e6ff79a1\",\n \"score\": \"0.66217786\",\n \"text\": \"def currency_code(self):\\n code = self.currency\\n\\n if code not in CURRENCIES:\\n code = common.settings.currency_code_default()\\n\\n return code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"346b9a130ddde5662cf80d6ad2a1827a\",\n \"score\": \"0.6579701\",\n \"text\": \"def lookup_currency(self): # pragma: no cover\\n return NotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"927cf6ded6020a7a2d43200bf5cba98b\",\n \"score\": \"0.6526249\",\n \"text\": \"def get_currency(self, currency):\\n\\n if currency not in self._currency_addresses:\\n self._currency_addresses = self.get_currencies()\\n\\n res = None\\n if currency[:2] == '0x':\\n for token, c in self._currency_addresses.items():\\n if c['address'] == currency:\\n res = c\\n break\\n # check if we found the currency\\n if res is None:\\n raise IdexCurrencyNotFoundException(currency)\\n else:\\n if currency not in self._currency_addresses:\\n raise IdexCurrencyNotFoundException(currency)\\n res = self._currency_addresses[currency]\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcb2ceace3b3829b9b5730403019f54\",\n \"score\": \"0.6213957\",\n \"text\": \"def get_currency(input_country) -> str:\\n file = open(\\\"country_conversion.json\\\", \\\"r\\\")\\n data = json.load(file)\\n file.close()\\n for cntry in data:\\n if cntry['Country'] == input_country:\\n input_country = cntry['Code']\\n break\\n return input_country\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3d06049a4efb4befb2d1b6cd5ff3695\",\n \"score\": \"0.61457765\",\n \"text\": \"def test_find_currency_data():\\n result = dtypes.find_currency_data()\\n expected = {\\n '＄': 'ARS', '﹩': 'ARS', '$': 'ARS', '￡': 'FKP', '₭': 'LAK',\\n '£': 'FKP', '￦': 'KPW', '￥': 'CNY', '﷼': 'IRR', '₾': 'GEL',\\n '₽': 'RUB', '₼': 'AZN', '₺': 'TRY', '₹': 'INR', '₸': 'KZT',\\n '₵': 'GHS', '₴': 'UAH', '₲': 'PYG', '₱': 'CUP', '₮': 'MNT',\\n '€': 'EUR', '₫': 'VND', '₪': 'ILS', '₩': 'KPW', '₨': 'LKR',\\n '₦': 'NGN', '₡': 'CRC', '៛': 'KHR', '฿': 'THB', '৳': 'BDT',\\n '؋': 'AFN', '֏': 'AMD', '¥': 'CNY', '¢': 'GHS'\\n }\\n assert result == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cb11866dd97e23093c9e2bdeb9da83e\",\n \"score\": \"0.60134774\",\n \"text\": \"def string_to_currency(self, s):\\n return # osid.financials.Currency\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9f088a9e36f3d4471f2a0516b7ccd7a\",\n \"score\": \"0.59600127\",\n \"text\": \"def guess_currency_from_address(address):\\n if is_py2:\\n fixer = lambda x: int(x.encode('hex'), 16)\\n else:\\n fixer = lambda x: x # does nothing\\n\\n first_byte = fixer(b58decode_check(address)[0])\\n hits = []\\n for currency, data in crypto_data.items():\\n if hasattr(data, 'get'): # skip incomplete data listings\\n version = data.get('address_version_byte', None)\\n if version is not None and first_byte == version:\\n hits.append([currency, data['name']])\\n\\n if hits:\\n return hits\\n\\n raise ValueError(\\\"Unknown Currency with first byte: %s\\\" % first_byte)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da5ce1d29e2b499ada2b3b643218edbc\",\n \"score\": \"0.5934486\",\n \"text\": \"def test_is_currency_code_format(self):\\r\\n\\r\\n self.assertTrue(is_currency_code_format(\\\"USD\\\"))\\r\\n self.assertFalse(is_currency_code_format(\\\"US!\\\"))\\r\\n self.assertFalse(is_currency_code_format(\\\"USSR\\\"))\\r\\n self.assertFalse(is_currency_code_format(\\\" D\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f6e9b4f82a4e0c8983f61974351e49e\",\n \"score\": \"0.5933416\",\n \"text\": \"def find_by_currency(currency):\\n\\n\\tcountries_info = get_response(\\\"/currency/\\\" + currency)\\n\\treturn get_country_objects(countries_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"070304ac4bd019f62116617e4b61070c\",\n \"score\": \"0.5826321\",\n \"text\": \"def test_is_real_currency_code(self):\\r\\n\\r\\n self.assertTrue(is_real_currency_code('USD'))\\r\\n self.assertFalse(is_real_currency_code('ZZZ'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b259f1d2c53e28a01027287e1eaca8c6\",\n \"score\": \"0.57734346\",\n \"text\": \"def is_stock_cn(code):\\n code = str(code)\\n if code[0] in ['5', '6', '9'] or \\\\\\n code[:3] in [\\\"009\\\", \\\"126\\\", \\\"110\\\", \\\"201\\\", \\\"202\\\", \\\"203\\\", \\\"204\\\", '688'] or \\\\\\n (code.startswith('XSHG')) or \\\\\\n (code.endswith('XSHG')):\\n if (code.startswith('XSHG')) or \\\\\\n (code.endswith('XSHG')):\\n if (len(code.split('.')) > 1):\\n try_split_codelist = code.split('.')\\n if (try_split_codelist[0] == 'XSHG') and (len(try_split_codelist[1]) == 6):\\n code = try_split_codelist[1]\\n elif (try_split_codelist[1] == 'XSHG') and (len(try_split_codelist[0]) == 6):\\n code = try_split_codelist[0]\\n if (code[:5] in [\\\"00000\\\"]) or \\\\\\n (code[:3] in [\\\"000\\\"]):\\n return True, QA.MARKET_TYPE.INDEX_CN, 'SH', '上交所指数'\\n if code.startswith('60') == True:\\n return True, QA.MARKET_TYPE.STOCK_CN, 'SH', '上交所A股'\\n elif code.startswith('688') == True:\\n return True, QA.MARKET_TYPE.STOCK_CN, 'SH', '上交所科创板'\\n elif code.startswith('900') == True:\\n return True, QA.MARKET_TYPE.STOCK_CN, 'SH', '上交所B股'\\n elif code.startswith('50') == True:\\n return True, QA.MARKET_TYPE.FUND_CN, 'SH', '上交所传统封闭式基金'\\n elif code.startswith('51') == True:\\n return True, QA.MARKET_TYPE.FUND_CN, 'SH', '上交所ETF基金'\\n else:\\n print(code, True, None, 'SH', '上交所未知代码')\\n return True, None, 'SH', '上交所未知代码'\\n elif code[0] in ['0', '2', '3'] or \\\\\\n code[:3] in ['000', '001', '002', '200', '300', '159'] or \\\\\\n (code.startswith('XSHE')) or \\\\\\n (code.endswith('XSHE')):\\n if (code.startswith('000') == True) or \\\\\\n (code.startswith('001') == True):\\n if (code in ['000003', '000112', '000300', '000132', '000133']):\\n return True, QA.MARKET_TYPE.INDEX_CN, 'SH', '中证指数'\\n else:\\n return True, QA.MARKET_TYPE.STOCK_CN, 'SZ', '深交所主板'\\n if code.startswith('002') == True:\\n return True, QA.MARKET_TYPE.STOCK_CN, 'SZ', '深交所中小板'\\n elif code.startswith('003') == True:\\n return True, QA.MARKET_TYPE.STOCK_CN, 'SZ', '中广核？？'\\n elif code.startswith('159') == True:\\n return True, QA.MARKET_TYPE.FUND_CN, 'SZ', '深交所ETF基金'\\n elif code.startswith('200') == True:\\n return True, QA.MARKET_TYPE.STOCK_CN, 'SZ', '深交所B股'\\n elif code.startswith('399') == True:\\n return True, QA.MARKET_TYPE.INDEX_CN, 'SZ', '中证指数'\\n elif code.startswith('300') == True:\\n return True, QA.MARKET_TYPE.STOCK_CN, 'SZ', '深交所创业板'\\n elif (code.startswith('XSHE')) or \\\\\\n (code.endswith('XSHE')):\\n pass\\n else:\\n print(code, True, None, 'SZ', '深交所未知代码')\\n return True, None, 'SZ', '深交所未知代码'\\n else:\\n print(code, '不知道')\\n return False, None, None, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3006564fc4ea5475cb662f5573eb246e\",\n \"score\": \"0.57121426\",\n \"text\": \"def getCurrencyRate(self,code):\\n currencyrate=self.currencyratedict[code]\\n return currencyrate.getCurrencyRate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2171d7de63c5885abb9874d88d535c2\",\n \"score\": \"0.56446785\",\n \"text\": \"def _get_currency(self):\\n user = self.env['res.users'].browse(self._uid)\\n if user.company_id:\\n return user.company_id.currency_id.id\\n else:\\n return self.env['res.currency'].search(\\n [('rate', '=', 1.0)])[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a31e5c700d7aea261fe7ed86117b9f9d\",\n \"score\": \"0.56382793\",\n \"text\": \"def find_by_code(cls, code):\\n\\n formatted_code = code.upper()\\n\\n coupon = Coupon.query.filter(Coupon.redeemable, Coupon.code == formatted_code).first()\\n\\n return coupon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31dda01bdc976b63e6a03a9b74516226\",\n \"score\": \"0.5622921\",\n \"text\": \"def search_exchange():\\n\\tdolar = soup.find(\\\"div\\\", {\\\"class\\\": \\\"dolar\\\"})\\n\\tfor li in dolar.findAll('li'):\\n\\t\\tcurrency, amount = li.text.split('RD$')\\n\\t\\treturn (currency, amount)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64e8cf1846dd2ba185091ca081f3a21e\",\n \"score\": \"0.5618524\",\n \"text\": \"def find_by_code(cls, code):\\n formatted_code = code.upper()\\n coupon = Coupon.query.filter(Coupon.redeemable,\\n Coupon.code == formatted_code).first()\\n\\n return coupon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34bcfdc0842c24acdf06b88d814122b2\",\n \"score\": \"0.5607418\",\n \"text\": \"def find_currency(df):\\n doc_currency = \\\"\\\"\\n # euros = \\\"Euro|€|EUR\\\"\\n # dollars = \\\"Dollar|\\\\$|USD|dols\\\"\\n # pounds = \\\"£|GBP|BP\\\"\\n currencies = {\\\"euro\\\": EUROS, \\\"dollar\\\": DOLLARS, \\\"pound\\\": POUNDS}\\n\\n for key, value in currencies.items():\\n if df.apply(lambda x: x.str.contains(value, case=False).any(), axis=1).any(\\n axis=None\\n ):\\n # If doc_currency is set two times => set undefined\\n if doc_currency:\\n doc_currency = \\\"euro\\\" # \\\"undefined\\\"\\n break\\n # Set doc currency\\n else:\\n doc_currency = key\\n\\n # Create column for unit in df marking the token which contains unit\\n df.loc[:, \\\"currency\\\"] = False\\n for key, value in currencies.items():\\n df.loc[df[\\\"text\\\"].str.contains(value, case=False), \\\"currency\\\"] = True\\n # Set default unit to 1\\n if not doc_currency:\\n doc_currency = \\\"euro\\\"\\n return doc_currency\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b951e8bedf117c911b482d92923c1ac\",\n \"score\": \"0.5592632\",\n \"text\": \"def parse_currency(self):\\n price = re.search('(\\\\d\\\\.*)+', self.pq_form('.price').text())\\n p = [float(i) for i in price.group().strip().split(' ')]\\n\\n return \\\"%.2f\\\" % max(p) if len(p) == 2 else \\\"%.2f\\\" % p[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a81dfae6f8a698e39ce77fe32c017525\",\n \"score\": \"0.5579692\",\n \"text\": \"def ekat_to_gnc_Currency(GNCBook, EkatCurrency):\\n CommodityTable = GNCBook.get_table()\\n return CommodityTable.lookup(\\\"CURRENCY\\\", str(EkatCurrency))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9ccbe027e15cc8282435257c9ec6e43\",\n \"score\": \"0.55710196\",\n \"text\": \"def get_coupon_code_price_in_checkout():\\n coupon_code_price_text = Element(\\\"checkout\\\", \\\"coupon_code_price\\\").get_attribute(\\\"text\\\")\\n coupon_code_price = coupon_code_price_text[coupon_code_price_text.find(\\\"$\\\")+1:]\\n return float(coupon_code_price)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0dff2676b71692bef34c656d98e3649\",\n \"score\": \"0.5542053\",\n \"text\": \"def currency(self) -> pulumi.Input[Union[str, 'Currency']]:\\n return pulumi.get(self, \\\"currency\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e473aaa456d221d68da322d3b068db4d\",\n \"score\": \"0.5538454\",\n \"text\": \"def get_denomination(s):\\n\\n if is_money_string(s):\\n denomination_match = re.search(rf'({_SUPPORTED_DENOMINATIONS})', s)\\n if denomination_match:\\n denomination = denomination_match.group(0)\\n if denomination == '$':\\n denomination = 'USD'\\n return denomination\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47344aede6290e298bc5d710217f9ef9\",\n \"score\": \"0.55297154\",\n \"text\": \"def get_rate(self, currency_code): \\n return Decimal(self.rates[currency_code])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e69e46cf09939d9a6734f33014517df0\",\n \"score\": \"0.55219483\",\n \"text\": \"def getCurrency(self):\\n return self.base.get(\\\"currency\\\", [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e62d6aebf33efc457369adbd74e749\",\n \"score\": \"0.54618835\",\n \"text\": \"def get_currency_type(self):\\n return # osid.type.Type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6abc535c447c218f08b1a0048668d38\",\n \"score\": \"0.53959584\",\n \"text\": \"def get_currency_type(self):\\n return # osid.type.Type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ff2f0efdfcef9ef4f33132f9982daba\",\n \"score\": \"0.53625256\",\n \"text\": \"def get_company_code(self):\\n code_df = pd.read_html('http://kind.krx.co.kr/corpgeneral/corpList.do?method=download&searchType=13', header=0)[0] \\n # convert into 6 digits code\\n code_df.종목코드 = code_df.종목코드.map('{:06d}'.format) \\n # remove unnecessary columns \\n code_df = code_df[['회사명', '종목코드']] \\n # change Korean to English\\n code_df = code_df.rename(columns={'회사명': 'name', '종목코드': 'code'})\\n self.code = code_df.query(\\\"name=='{}'\\\".format(self.item_name))['code'].to_string(index=False).lstrip()\\n print('Item Code:',self.code)\\n return self.code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96fc91befb6d6daffe6a9c3efea18ea\",\n \"score\": \"0.5346923\",\n \"text\": \"def clean_currency(currency):\\n if \\\"TDM\\\" in currency:\\n currency = \\\"TDM\\\"\\n elif \\\"Mio\\\" in currency and \\\"DM\\\" in currency:\\n currency = \\\"Mio DM\\\"\\n elif \\\"Mrd\\\" in currency and \\\"DM\\\" in currency:\\n currency = \\\"Mrd DM\\\"\\n elif \\\"DM\\\" in currency:\\n currency = \\\"DM\\\"\\n elif \\\"Thlf\\\" in currency:\\n currency = \\\"Thlf\\\"\\n elif \\\"hlf\\\" in currency:\\n currency = \\\"hlf\\\"\\n elif \\\"mögen\\\" in currency:\\n currency = \\\"DM\\\"\\n return currency\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4acb2108c1ef6b47cc9f376817d769bd\",\n \"score\": \"0.5315113\",\n \"text\": \"def record_currency(self, record):\\n return record.currency_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"672c3a07646cc6f85d86036e78f25e7f\",\n \"score\": \"0.52842635\",\n \"text\": \"def test_convert_currency(self):\\r\\n\\r\\n self.assertEqual(convert_currency('USD', 'USD', '50'), 'US$50.0')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce36fe58516565b4bba094857a6469ed\",\n \"score\": \"0.5282381\",\n \"text\": \"def get_cur_by_id(cur_id):\\n sql = \\\"\\\"\\\"\\n SELECT currency\\n FROM currencies\\n WHERE id = %s;\\n \\\"\\\"\\\"\\n args = (cur_id,)\\n try:\\n query_result = Currency._make_select(sql, args)\\n except IntegrityError:\\n return False\\n return query_result[0]['currency']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa88d30e95a9addc5792fd13dfd3064b\",\n \"score\": \"0.52776617\",\n \"text\": \"def test_creates_currency(self):\\n Currency('EUR')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15562f6459e92fd2cd75611fa5a36d47\",\n \"score\": \"0.52687216\",\n \"text\": \"async def test_currency(self) -> None:\\n nanites, asp_tokens = await self.character.currency()\\n self.assertIsInstance(nanites, int)\\n self.assertIsInstance(asp_tokens, int)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6d65da97522012b7991b84c015de7bf\",\n \"score\": \"0.5256139\",\n \"text\": \"def get_exchange_rate(currency):\\n response = requests.get('https://bitpay.com/api/rates')\\n resp_obj = json.loads(response.content)\\n result = '<N/A>'\\n for elem in resp_obj:\\n if elem['code'] == currency:\\n result = str(elem['rate'])\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79243ec60ac490e7e21a78aad59a979a\",\n \"score\": \"0.525378\",\n \"text\": \"def get_source_currency(self, obj: CurrencyExchangeRate) -> str:\\n return obj.source_currency.code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"441d19f1ad753cf78c1d00c6b145e7d9\",\n \"score\": \"0.5250038\",\n \"text\": \"def search_okcicredit(cursor, contract_num):\\n try:\\n cursor.execute(\\n \\\"\\\"\\\"\\n SELECT\\n tc.id,\\n tc.client_id,\\n CONCAT(\\n REPLACE(tper.name3,\\\"'\\\", '\\\"'), ' ',\\n LEFT(tper.name , 1), '. ',\\n LEFT(tper.name2 , 1), '.'),\\n tc.vnoska,\\n ts.status,\\n tc.egn,\\n dwh.GetDolgBody(tc.id, date(now()))+\\n dwh.GetDolgFine(tc.id, date(now()))+\\n dwh.GetDolgPrc(tc.id, date(now()))\\n FROM\\n mbank.tcredits tc\\n join mbank.tstatuses ts on ts.credit_id = tc.id\\n and ts.is_last = 1\\n left join mbank.tpersons tper on tper.id = tc.client_id\\n WHERE tc.contract_num = {0}\\n and ts.status = 59;\\n \\\"\\\"\\\".format(contract_num)\\n )\\n except Exception:\\n return None\\n\\n credit = cursor.fetchall()\\n\\n if credit:\\n return credit\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5765a27fc1d71f6e40e1d92f5f3948ec\",\n \"score\": \"0.52460676\",\n \"text\": \"def get_currency_format_type(self):\\n return # osid.type.Type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8de5e2d0e651ed0703855a491e8cae71\",\n \"score\": \"0.5234212\",\n \"text\": \"def test_retreive_miscapitalized_currency(client):\\n\\n res = client.get('/v1/currencies/uSd')\\n assert res.status_code == 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ddce66c0a37d8ebff9476e709bda5e5\",\n \"score\": \"0.52306783\",\n \"text\": \"def isContainsDollar(entry):\\n return entry[1].find('$')!=-1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7d9178acced0c0df2b491ede2785eb5\",\n \"score\": \"0.5221874\",\n \"text\": \"def get_source_currency_type(self):\\n return # osid.type.Type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"455b6cb0ecd72e95ee19c7948bea71d0\",\n \"score\": \"0.5203276\",\n \"text\": \"def get_currency(self):\\n return self.currency\",\n \"title\": \"\"\n },\n {\n \"docid\": \"455b6cb0ecd72e95ee19c7948bea71d0\",\n \"score\": \"0.5203276\",\n \"text\": \"def get_currency(self):\\n return self.currency\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79be60e07bc0158716409b8567cd8101\",\n \"score\": \"0.51875174\",\n \"text\": \"def commodity_code(self) -> str:\\n return pulumi.get(self, \\\"commodity_code\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e00b98328b78f993aed9983555cdbc5\",\n \"score\": \"0.51707137\",\n \"text\": \"def get_country_code(country_name):\\n for country_code, country in COUNTRIES.items():\\n if country_name.lower() == country.lower():\\n return country_code\\n elif 'congo, dem.' in country_name.lower():\\n return 'cd'\\n elif 'hong kong' in country_name.lower():\\n return 'hk'\\n elif 'macao' in country_name.lower():\\n return 'mo'\\n elif 'egypt' in country_name.lower():\\n return 'eg'\\n elif 'gambia' in country_name.lower():\\n return 'gm'\\n elif 'yemen' in country_name.lower():\\n return 'ye'\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dbbeb612d99c5533f0e1366ed83b8c4\",\n \"score\": \"0.5159652\",\n \"text\": \"def iscurrency(currency):\\n strout = currency_response(\\\"USD\\\", currency, 1)\\n boolnum = has_noerror(strout)\\n return boolnum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c86bc7a0d9fd0fc39eeffc8b1dac9ad7\",\n \"score\": \"0.5142611\",\n \"text\": \"def currency(self):\\n return self._currency\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c86bc7a0d9fd0fc39eeffc8b1dac9ad7\",\n \"score\": \"0.5142611\",\n \"text\": \"def currency(self):\\n return self._currency\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c86bc7a0d9fd0fc39eeffc8b1dac9ad7\",\n \"score\": \"0.5142611\",\n \"text\": \"def currency(self):\\n return self._currency\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c86bc7a0d9fd0fc39eeffc8b1dac9ad7\",\n \"score\": \"0.5142611\",\n \"text\": \"def currency(self):\\n return self._currency\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1784c18e6bdda1ae4e1688d3a36fb826\",\n \"score\": \"0.5138435\",\n \"text\": \"def find_cntry(line):\\n match = COUNTRY_REGEX.match(line)\\n if match:\\n country = match.group(1)\\n if \\\"De facto\\\" in country:\\n return None\\n if \\\"|\\\" in country:\\n return country.partition(\\\"|\\\")[2]\\n return country\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ea7b2be2f5f1a9a7bc0f938900b1195\",\n \"score\": \"0.5131995\",\n \"text\": \"def _money(text,rex=re.compile('(-?)([0-9]*)\\\\.?([0-9]*)')):\\n return rex.match(text).groups()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"662deb19db1c60083daa880fdb9c5b2b\",\n \"score\": \"0.5127136\",\n \"text\": \"def erc20_value_search(coins):\\n url2 = \\\"http://api.binance.com/api/v3/ticker/price\\\"\\n response2 = requests.get(url2)\\n rate_list = response2.json()\\n pair_conversions = defaultdict(lambda : 0)\\n\\n for coin in coins:\\n for rate in rate_list:\\n exchange_coin = rate['symbol']\\n exchange_price = rate['price']\\n if coin + 'ETH' == exchange_coin:\\n pair_conversions[exchange_coin] = exchange_price\\n else:\\n pass\\n \\n return pair_conversions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c65c5d15400f8e8e1d9aa8290ce2297c\",\n \"score\": \"0.51143295\",\n \"text\": \"def _weatherSymbol(self, code):\\n\\n table = {'partlycloudy': '~☁',\\n 'cloudy': '☁',\\n 'tstorms': '⚡',\\n 'sunny': '☀',\\n 'snow': '❄',\\n 'sleet': '☄',\\n 'rain': '☔',\\n 'mostlysunny': '~☀',\\n 'mostlycloudy': '~☁',\\n 'hazy': '♒',\\n 'fog': '♒',\\n 'flurries': '❄',\\n 'clear': '☼',\\n 'chanceflurries': '?❄',\\n 'chancerain': '?☔',\\n 'chancesleet': '?❄',\\n 'chancesnow': '?❄',\\n 'chancetstorms': '?☔'}\\n # return symbol from table.\\n try:\\n return table[code]\\n except KeyError:\\n return \\\"unknown\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d9721f057827b87b15cbfd1a3ac2500\",\n \"score\": \"0.5104792\",\n \"text\": \"def validate_currency(currency):\\n if not currency:\\n return False\\n\\n if not re.search('^\\\\d+$', currency):\\n return False\\n\\n if not re.search('^0', currency):\\n return False\\n\\n try:\\n decimal.Decimal(currency)\\n except:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daf402fb2b1f3a9252d1337a6b960b29\",\n \"score\": \"0.5099933\",\n \"text\": \"def find_by_callingcode(code_num):\\n\\tcountries_info = get_response(\\\"/callingcode/\\\" + code_num)\\n\\treturn get_country_objects(countries_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"597486b56082becc2c6da756f55c48d2\",\n \"score\": \"0.5079034\",\n \"text\": \"def search_credit(cursor, contract_num):\\n try:\\n cursor.execute(\\n \\\"\\\"\\\"\\n SELECT\\n tc.id,\\n tc.client_id,\\n CONCAT(REPLACE(tper.name3,\\\"'\\\", '\\\"'), ' ', LEFT(tper.name , 1), '. ', LEFT(tper.name2 , 1), '.'),\\n tc.vnoska,\\n ts.status,\\n tc.egn,\\n dwh.GetDolgBody(tc.id, date(now()))+\\n dwh.GetDolgFine(tc.id, date(now()))+\\n dwh.GetDolgPrc(tc.id, date(now()))\\n FROM\\n mbank.tcredits tc\\n join mbank.tstatuses ts on ts.credit_id = tc.id\\n and ts.is_last = 1\\n left join mbank.tpersons tper on tper.id = tc.client_id\\n WHERE tc.contract_num = {0}\\n and (ts.status = 5 or ts.status = 55 or ts.status = 555);\\n \\\"\\\"\\\".format(contract_num)\\n )\\n except Exception:\\n return None\\n\\n credit = cursor.fetchall()\\n\\n if credit:\\n return credit\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db39390e78aa9d3a1fed693bbdb72298\",\n \"score\": \"0.5078839\",\n \"text\": \"def _find_payment(data):\\n transaction_id = data[\\\"google-order-number\\\"]\\n for Cart in CART_TYPES:\\n try:\\n return Cart.payment_class.objects.select_related('cart').filter(transaction_id=transaction_id)[0]\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"051c346c43b8b85215ee9c3b7f29e063\",\n \"score\": \"0.5062812\",\n \"text\": \"def set_currency_symbol(self):\\n is_set = None\\n try:\\n self.logger.info('Start: set currency symbol')\\n self._business_admin_page.set_currency_symbol()\\n is_set = True\\n except WebDriverException as exp:\\n is_set = False\\n self.logger.error(exp.msg)\\n raise\\n finally:\\n self.logger.info('End: set currency symbol')\\n return is_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a92f63c20b03123295110132d514454\",\n \"score\": \"0.50594723\",\n \"text\": \"def valid_currencies():\\n return [currency for currency in Money.VALID_CURRENCIES]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87e19d5a139b383840b7e07816fd0e58\",\n \"score\": \"0.505653\",\n \"text\": \"def get_currency_unit(value):\\n currency = \\\"\\\"\\n unit = \\\"1\\\"\\n if \\\"TEUR\\\" in value:\\n currency = \\\"EUR\\\"\\n unit = \\\"1000\\\"\\n elif \\\"TDM\\\" in value:\\n currency = \\\"DM\\\"\\n unit = \\\"1000\\\"\\n elif \\\"Mio\\\" in value:\\n unit = \\\"1000000\\\"\\n currency = value[value.find(\\\"Mio\\\")+3:]\\n elif \\\"Mrd\\\" in value:\\n unit = \\\"1000000000\\\"\\n currency = value[value.find(\\\"Mrd\\\")+3:]\\n else:\\n numbers = []\\n lcidx = 0\\n for cidx, char in enumerate(value):\\n if char.isdigit():\\n numbers.append(char)\\n lcidx = cidx\\n unit = \\\"\\\".join(numbers)\\n currency = value[lcidx + 1:].strip()\\n currency = clean_currency(currency)\\n return currency, unit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f2a54b10d945c9151241cf9d658f0ae\",\n \"score\": \"0.5055238\",\n \"text\": \"def remove_currency(raw_price):\\n return raw_price.split(\\\" \\\")[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2721c4525ac8eb941edc2647fc12c80e\",\n \"score\": \"0.5054427\",\n \"text\": \"def quote_currency(self):\\n\\t\\treturn self.__quote_currency\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dec2ca60ee3871e62408f5f7b7018a5f\",\n \"score\": \"0.50301546\",\n \"text\": \"def findCode(self, COMP_NAME, TARGET): \\n\\t\\tMIN_ERR=1.0e20\\n\\t\\tBEST_CODE=-1\\n\\t\\t\\n\\t\\tif (COMP_NAME=='C1'):\\n\\t\\t\\tm=self.calcC1\\n\\t\\telif (COMP_NAME=='C2'):\\n\\t\\t\\tm=self.calcC2\\n\\t\\telif (COMP_NAME=='R2'):\\n\\t\\t\\tm=self.calcR2\\n\\t\\telif (COMP_NAME=='C3'):\\n\\t\\t\\tm=self.calcC3\\n\\t\\telif (COMP_NAME=='R3'):\\n\\t\\t\\tm=self.calcR3\\n\\t\\telse:\\n\\t\\t\\tprint \\\"Error: Wrong Name of the LMS8001 IC Loop Filter Component. Exiting. Returning -1.\\\"\\n\\t\\t\\treturn BEST_CODE\\n\\t\\t\\n\\t\\tfor CODE in range(0,16):\\n\\t\\t\\tABS_ERR=abs(m(CODE)-TARGET)\\n\\t\\t\\tif (ABS_ERR<MIN_ERR):\\n\\t\\t\\t\\tMIN_ERR=ABS_ERR\\n\\t\\t\\t\\tBEST_CODE=CODE\\n\\n\\t\\treturn BEST_CODE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"134f8f5a73940fa989981cfb694e81eb\",\n \"score\": \"0.5020025\",\n \"text\": \"def fetch_key_bcp_lang_code(lang):\\n for r in query_omw(\\\"\\\"\\\"SELECT id\\n FROM lang\\n WHERE (bcp47 = ?)\\\"\\\"\\\", [lang]):\\n return r['id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47d9f80a4bc708ada05613465e18ade1\",\n \"score\": \"0.5015696\",\n \"text\": \"def get_currency():\\n if default_currency:\\n \\t#\\n # url calling format = 'http://apilayer.net/api/live?access_key={}¤cies=EUR,CNY,GBP,MXN&format=1'\\n #\\n qs = Currency.objects.all().values_list('code', flat=True)\\n url = 'http://apilayer.net/api/live?access_key={}¤cies={}&format=1'.format(settings.API_LAYER_ACCESS_KEY, ','.join(qs))\\n print (url)\\n\\n r = requests.get(url)\\n\\n result = json.loads(r.text)\\n\\n for q in result['quotes']:\\n code = q[3:]\\n currency = Currency.objects.filter(code=code).first()\\n currency.rate = result['quotes'][q]\\n currency.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a0696364920f4f3646e20f6f3d20f49\",\n \"score\": \"0.5005908\",\n \"text\": \"def get_code(self):\\r\\n with open('data/county-fips-to-name.csv', 'r') as csvfile:\\r\\n csvdata = csv.reader(csvfile)\\r\\n for row in csvdata:\\r\\n if row[1] in self.county:\\r\\n code = row[0]\\r\\n break\\r\\n\\r\\n # county fips code has to be length of 5\\r\\n if len(code) == 4:\\r\\n code = '0' + code\\r\\n\\r\\n return code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30affd6f50b6dbe3237da1843ce32487\",\n \"score\": \"0.50012606\",\n \"text\": \"def _currency_symbols():\\n current = locale.getlocale()\\n locales = list(locale.locale_alias.values())\\n symbols = set()\\n\\n for loc in locales:\\n try:\\n locale.setlocale(locale.LC_MONETARY, locale.normalize(loc))\\n currency = \\\"{int_curr_symbol}\\\".format(**locale.localeconv())\\n if currency != \\\"\\\":\\n symbols.add(currency.strip())\\n except (locale.Error, UnicodeDecodeError):\\n continue\\n\\n locale.setlocale(locale.LC_MONETARY, current)\\n return list(symbols)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"297d6d4da94fd2cccc12617bb854a105\",\n \"score\": \"0.49998227\",\n \"text\": \"def get_currency(self, userid):\\n self.cursor.execute(\\\"SELECT currency, userid FROM users WHERE userid = ?\\\", (userid,))\\n currencyfetch = self.cursor.fetchone()\\n if currencyfetch is not None:\\n currency = currencyfetch[0]\\n else:\\n currency = 0\\n return currency\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df8b6f2edc6da5167e1d56ca38de42c4\",\n \"score\": \"0.49984938\",\n \"text\": \"def country_code(cls, valid):\\n return 1 if valid else \\\"FAKE\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"413f3ee5a44707f0be5a18ce93275577\",\n \"score\": \"0.4996388\",\n \"text\": \"def test_remove_dollar_symbols(self):\\n item = '3773.49'\\n result = self.open_csv.remove_dollar_symbols(item)\\n\\n print 'item: %s' % item\\n print 'result: %s' % result\\n\\n self.assertNotIn('$', result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d6250f09304775b0cb5630eb6d2420\",\n \"score\": \"0.49881542\",\n \"text\": \"def code(self):\\n return self._bank_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e549861eb4bcffc07c364b91c27721f\",\n \"score\": \"0.49851966\",\n \"text\": \"def parse_currency(s):\\n if not isinstance(s, text_type):\\n return float('nan')\\n s = re.sub(u'[\\\\$\\\\u20AC\\\\u00A3\\\\uFFE1\\\\u00A5\\\\uFFE5]|(EUR)', '', s)\\n s = s.replace(',', '')\\n try:\\n return float(s)\\n except:\\n return float('nan')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d431064777b66734e060344c7e2b62fa\",\n \"score\": \"0.4984376\",\n \"text\": \"def currency(self):\\n return self.properties.get('currency', CurrencyColumn())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86ecbfef3b55058fbc31d11a240cbfa7\",\n \"score\": \"0.4983195\",\n \"text\": \"def _extractCurrencies(self, numbers_node):\\n currs_nodes = numbers_node.getElementsByTagName('currencies')\\n if not currs_nodes:\\n return\\n\\n currencies = InheritingDictionary()\\n for curr_node in currs_nodes[0].getElementsByTagName('currency'):\\n type = curr_node.getAttribute('type')\\n currency = LocaleCurrency(type)\\n\\n nodes = curr_node.getElementsByTagName('symbol')\\n if nodes:\\n currency.symbol = self._getText(nodes[0].childNodes)\\n currency.symbolChoice = \\\\\\n nodes[0].getAttribute('choice') == u'true' \\n\\n nodes = curr_node.getElementsByTagName('displayName')\\n if nodes:\\n currency.displayName = self._getText(nodes[0].childNodes)\\n\\n currencies[type] = currency\\n\\n return currencies\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0602c7cc52171f745e91273fc6a4addb\",\n \"score\": \"0.4977636\",\n \"text\": \"def _get_carrier(self, number):\\n number = self.number\\n if not self.prefix or not self.country:\\n return None\\n try:\\n mod = importlib.import_module(\\n 'plnumbers.{}'.format(self.country)\\n )\\n except ImportError:\\n return None\\n\\n self._NUMBERTYPES = mod.LANE_TYPE\\n match = None\\n for pattern, value in mod.LOOKUPS:\\n if re.search(pattern, number):\\n match = mod.CARRIERS[value]\\n break\\n\\n if match:\\n self.numbertype = self._NUMBERTYPES.get(match[1])\\n return match[0]\\n else:\\n self.numbertype = self._NUMBERTYPES.get('u')\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2efd2934cd989281be4558bc4fcea4ff\",\n \"score\": \"0.49642444\",\n \"text\": \"def search_skycredit(cursor, contract_num):\\n try:\\n cursor.execute(\\n \\\"\\\"\\\"\\n SELECT\\n tc.id,\\n tc.client_id,\\n CONCAT(REPLACE(tper.name3,\\\"'\\\", '\\\"'), ' ', LEFT(tper.name , 1), '. ', LEFT(tper.name2 , 1), '.'),\\n tc.vnoska,\\n ts.status,\\n tc.egn,\\n dwh.GetDolgBody(tc.id, date(now()))+\\n dwh.GetDolgFine(tc.id, date(now()))+\\n dwh.GetDolgPrc(tc.id, date(now()))\\n FROM\\n mbank.tcredits tc\\n join mbank.tstatuses ts on ts.credit_id = tc.id\\n and ts.is_last = 1\\n left join mbank.tpersons tper on tper.id = tc.client_id\\n WHERE tc.contract_num = {0}\\n and (ts.status = 55 or ts.status = 555);\\n \\\"\\\"\\\".format(contract_num)\\n )\\n except Exception:\\n return None\\n\\n credit = cursor.fetchall()\\n\\n if credit:\\n return credit\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd2f845dc6b8c1b1ed9db2023624901\",\n \"score\": \"0.49468762\",\n \"text\": \"def currency_code(self, currency_code):\\n\\n self._currency_code = currency_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd2f845dc6b8c1b1ed9db2023624901\",\n \"score\": \"0.49468762\",\n \"text\": \"def currency_code(self, currency_code):\\n\\n self._currency_code = currency_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34dd4b341635f85a6d38546c29469839\",\n \"score\": \"0.49370578\",\n \"text\": \"def account_available(currency: str) -> int:\\n if currency in sc.AVAILABLE_CURRENCY_ACCOUNTS:\\n return -1\\n else:\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972344c48da4e0dd81189ee3f43c3569\",\n \"score\": \"0.4933475\",\n \"text\": \"def get_currencies(self):\\n\\n endpoint = self.__endpoints.get(\\\"currencies\\\")\\n payload = {\\\"accessToken\\\": self.__api_key}\\n\\n response = self.__request(endpoint, payload)\\n if response[\\\"success\\\"]:\\n return response\\n raise KolletErrors(response[\\\"message\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"072b9471f95d3c886425c65af7080cc8\",\n \"score\": \"0.49302197\",\n \"text\": \"def verse_num_extractor(data):\\n if re.search('॥ [०१२३४५६७८९]+ ॥\\\\n', data):\\n num = re.sub(r'.*॥ ([०१२३४५६७८९]+) ॥\\\\n', r'\\\\g<1>', data)\\n num = transliterate(num, 'devanagari', 'slp1')\\n num = int(num)\\n return num\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a07b1f20a23d728d5cdf4ed91850e83\",\n \"score\": \"0.49299568\",\n \"text\": \"def _parse(self, data: Tuple[bytes, bytes]) -> TypeCurrencyIndex:\\n currencies = {}\\n counter = 0\\n\\n LOG.debug('Parsing data ...')\\n\\n index = self._index_currency\\n format_num = self._format_num_code\\n\\n for sub_data in data:\\n root = ElementTree.fromstring(sub_data)\\n\\n for child in root:\\n props = {}\\n for prop in child:\\n props[prop.tag] = prop.text\\n\\n num = props['ISO_Num_Code'] or None\\n if num:\\n # ISO numeric code like '036' is loaded like '36', so it needs to be formatted into ISO 4217,\\n # also data from the Bank of Russia contains replaced currencies that do not have ISO attributes.\\n # additional If-statement was added to exclude format None\\n num = format_num(num)\\n\\n currency = Currency(\\n id=child.attrib['ID'],\\n name_eng=props['EngName'],\\n name_ru=props['Name'],\\n code=props['ISO_Char_Code'],\\n num=num,\\n par=Decimal(props['Nominal']),\\n )\\n\\n counter += 1\\n currencies.update(index(currency))\\n\\n LOG.debug(f\\\"Parsed: {counter} currencies\\\")\\n return currencies\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddef1e8a27fb0f9e23a3018308992692\",\n \"score\": \"0.49265334\",\n \"text\": \"def test_get_currency():\\n response = client.get(\\\"/util/currency?from_curr=USD&to_curr=INR&amt=1\\\")\\n assert response.status_code == 200\\n assert response.json() >= 70\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1f313f626fa43dead65fe37818e2eec\",\n \"score\": \"0.49244246\",\n \"text\": \"def convert_to(self, currency):\\n return None # TODO\",\n \"title\": \"\"\n },\n {\n \"docid\": \"404441da833e468aa19ffea6e5d8e644\",\n \"score\": \"0.49193448\",\n \"text\": \"def return_currency_instance_from_exchange_pk(exchange_pk, exchange_instance):\\n exchange_pk_instance = None\\n try:\\n exchange_pk_instance = CurrencyExchangePK.objects.get(\\n exchange=exchange_instance, key=exchange_pk)\\n except CurrencyExchangePK.DoesNotExist:\\n pass\\n\\n if exchange_pk_instance:\\n return exchange_pk_instance.currency\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85cdbef879c617ace2731e20e41ece74\",\n \"score\": \"0.49176356\",\n \"text\": \"def get_target_currency_type(self):\\n return # osid.type.Type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75b54ab89489867ae2f8610a70e2b401\",\n \"score\": \"0.49009168\",\n \"text\": \"def get_base_symbols(self):\\n with self._lock:\\n self._update_mkt_info()\\n return list(set([mkt[\\\"BaseCurrencyCode\\\"] for mkt in self._mkt_info[\\\"result\\\"]]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a973a44bd00bf6402a3655763da91451\",\n \"score\": \"0.48923814\",\n \"text\": \"def country(self, key):\\n\\n return key[:2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbf8ad54a8de0f71fe4a073be7cc90c9\",\n \"score\": \"0.48863983\",\n \"text\": \"def symbol_lookup(self):\\n try: \\n url = SYMBOL_LOOKUP_URL + self.stock.symbol\\n result = urllib2.urlopen(url).url\\n cboe_id = result.split(\\\"?\\\")[1]\\n self.stock.cboe_id = cboe_id\\n except urllib2.URLError, e:\\n cboe_id = \\\"\\\"\\n\\n return cboe_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01e9c1c938432b79a1524311c8f9a74a\",\n \"score\": \"0.48590714\",\n \"text\": \"def currency_db():\\n # type: () -> List[Text]\\n return [\\\"vnd\\\", \\\"usd\\\", \\\"euro\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"730dfc990e898be6b3af1fcf7c77dadf\",\n \"score\": \"0.48570985\",\n \"text\": \"def CodeLookup(self, item: QtWidgets.QTreeWidgetItem, codelist: QtWidgets.QTreeWidget, gid: str):\\n # Initialize vars\\n wlist = [w.widget() for w in self.mdi.subWindowList() if isinstance(w.widget(), Database)\\n or isinstance(w.widget(), CodeList) and w.widget().TreeWidget is not codelist]\\n lsplt = re.split('[ \\\\n]', item.text(1))\\n totalen = len(lsplt)\\n\\n # Begin search!\\n for widget in wlist:\\n\\n # Mark code matches from different game ids with an additional asterisk\\n regmatch = int(not(bool(widget.gameID == gid))) + 1\\n\\n # Process the widget's tree\\n for child in filter(lambda x: x.text(1) and 'Unknown Code' not in x.text(0),\\n widget.TreeWidget.findItems('', Qt.MatchContains | Qt.MatchRecursive)):\\n matches = 0\\n\\n # For each code, check each line of the code we're looking a name for\\n for line in lsplt:\\n if line in child.text(1):\\n matches += 1\\n\\n # If more than 2/3rds of the code match, we found the code we were looking for\\n if matches / totalen >= 2 / 3:\\n item.setText(0, child.text(0) + '*' * regmatch)\\n item.setText(2, child.text(2)) # Copy comment\\n item.setText(4, child.text(4)) # Copy author\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fc56a8261e1fbd46de10a6bd80e45f1\",\n \"score\": \"0.485295\",\n \"text\": \"def get_currency_name(self):\\n try:\\n self.logger.info('Start: get currency name')\\n return self._business_admin_page.get_currency_name()\\n except WebDriverException as exp:\\n self.logger.error(exp.msg)\\n raise\\n finally:\\n self.logger.info('End: get currency name')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7719b598269ec478586b369cb16ee4e\",\n \"score\": \"0.48456493\",\n \"text\": \"def country_code(self) -> str:\\n return pulumi.get(self, \\\"country_code\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7719b598269ec478586b369cb16ee4e\",\n \"score\": \"0.48456493\",\n \"text\": \"def country_code(self) -> str:\\n return pulumi.get(self, \\\"country_code\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1765af00ce468af9c89fdfd0cae26c48\",\n \"score\": \"0.4832439\",\n \"text\": \"def get_current_exchange_rate(src_currency, tgt_currency):\\n\\timport urllib2\\n\\n\\tresponse = urllib2.urlopen('http://www.google.com/ig/calculator?hl=en&q=1%s=?%s' % (src_currency, tgt_currency))\\n\\tdata = response.read()\\n\\t\\n\\t# Returns hash: {lhs: \\\"1 Euro\\\",rhs: \\\"1.3118 U.S. dollars\\\",error: \\\"\\\",icc: true}\\n\\tprint(data)\\n\\n\\tindex = data.find('rhs: \\\"') + 6\\n\\tworking_str = data[index:]\\n\\tprint(working_str)\\n\\texchange_rate, discard_remainder = working_str.split(' ', 1)\\n\\tprint(exchange_rate)\\n\\treturn float(exchange_rate)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a71885ff21b27e994c4d569f0eca35e2\",\n \"score\": \"0.4832284\",\n \"text\": \"def test_euro(self):\\n self.assertTrue(ord(unicodedata.lookup('EURO SIGN')) in self.font)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c2850e3664a7dba21be74b2854f380e\",\n \"score\": \"0.4831148\",\n \"text\": \"def process_coin(amt, moneyIn):\\n if moneyIn == 'quarter' or moneyIn == 'quarters':\\n return amt * .25\\n elif moneyIn == 'dime' or moneyIn == 'dimes':\\n return amt * .10\\n elif moneyIn == 'nickel' or moneyIn == 'nickels':\\n return amt * .05\\n elif moneyIn == 'penny' or moneyIn == 'pennies':\\n return amt * .01\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab01337328b42be31dcfe6fd2b9daf91\",\n \"score\": \"0.48303366\",\n \"text\": \"def deck_flag_convert(country_code):\\n cc_dict = {\\n \\\"en\\\": \\\"gb\\\",\\n \\\"ko\\\": \\\"kr\\\",\\n \\\"ja\\\": \\\"jp\\\",\\n \\\"zh\\\": \\\"cn\\\",\\n \\\"hi\\\": \\\"in\\\",\\n \\\"sr\\\": \\\"rs\\\",\\n \\\"la\\\": \\\"va\\\",\\n \\\"el\\\": \\\"gr\\\",\\n }\\n\\n # Only return a value different from country_code if an alternative \\n # is specified in cc_dict\\n if country_code in cc_dict:\\n return cc_dict[country_code]\\n\\n return country_code\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":496,"cells":{"query_id":{"kind":"string","value":"1be57fd6b647dff48b37a3519962bba8"},"query":{"kind":"string","value":"Assign groups to the usernames provided in the data"},"positive_passages":{"kind":"list like","value":[{"docid":"8ab869b356ec5f6a72bf0fdc936d85c1","score":"0.7481766","text":"def assign_user_groups(self, group_name: str, rows: list[tuple[str, str]]) -> None:\n group = Group.objects.get(name=group_name)\n\n for username, _ in rows:\n user = User.objects.get(username=username)\n user.groups.add(group)","title":""}],"string":"[\n {\n \"docid\": \"8ab869b356ec5f6a72bf0fdc936d85c1\",\n \"score\": \"0.7481766\",\n \"text\": \"def assign_user_groups(self, group_name: str, rows: list[tuple[str, str]]) -> None:\\n group = Group.objects.get(name=group_name)\\n\\n for username, _ in rows:\\n user = User.objects.get(username=username)\\n user.groups.add(group)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4195b1546c4302f61e324df7124c0277","score":"0.65661025","text":"def set_user_groups(self):\n # Notice that we only add or remove groups that we had\n # offered as options on the form, and don't touch any other\n # groups that the user might belong to.\n for value, label in self.fields['help_desk_group'].choices:\n if value:\n group = Group.objects.get(name=value)\n if value in self.cleaned_data['help_desk_group']:\n self.instance.groups.add(group)\n else:\n self.instance.groups.remove(group)","title":""},{"docid":"e3171e6ddd397ed7b992ec276b6af7fe","score":"0.6380261","text":"def group_user(self, registrant):\n if self.groups is not None:\n groups_qs = Group.objects.filter(name__in=self.groups.split(','))\n group_list = [group for group in groups_qs]\n registrant.groups.add(*group_list)","title":""},{"docid":"1f97adb20039af67c5a1d77f6beac656","score":"0.6095477","text":"def NameGroups(data_arr,id_key):\n\t\tnew_data_arr = []\n\t\tfor data in data_arr:\n\t\t\ttry:\n\t\t\t\tdata_arr[id_key] = clc._GROUP_MAPPING[data[id_key]]\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\tnew_data_arr.append(data)\n\t\tif clc.args: clc.v1.output.Status(\"ERROR\",2,\"Group name conversion not yet implemented\")\n\t\treturn(new_data_arr)","title":""},{"docid":"fd4bdfb1386cfecc2aac63bb4bac72ad","score":"0.60787326","text":"def assign_user_to_group(global_token, regional_token, contractid, region,\n username, groupname):\n try:\n # if user exists return its id otherwise return 'None'\n userid = get_itemid(get_keystoneobject_list(\n regional_token, region, contractid, 'users'), username, 'users')\n # if group exists return its id otherwise return 'None'\n groupid = get_itemid(get_keystoneobject_list(\n regional_token, region, contractid, 'groups'), groupname, 'groups')\n region = 'gls'\n identityURL = 'https://identity.' + region + \\\n '.cloud.global.fujitsu.com/v3/groups/' + groupid + '/users/' + userid\n # make the put rest request\n response = requests.put(identityURL,\n headers={'X-Auth-Token': global_token,\n 'Content-Type': 'application/json'})\n return response\n except:\n return 'Failed to assign user to group'","title":""},{"docid":"77fab95e1d3a628d6830039ce6162d45","score":"0.6037697","text":"def sync_groups_and_permissions(self, data):\n def get_perm(p):\n \"\"\"\n ``p`` format: '<ct_app_label>.<ct_model>.<p_codename>'\n \"\"\"\n try:\n ct_app_label, ct_model, p_codename = p.split('.')\n except ValueError:\n raise ValueError(u'Value must be in format \"<ct_app_label>.<ct_model>.<p_codename>\". Got \"%s\"' % p)\n try:\n return Permission.objects.get(content_type__app_label = ct_app_label,\n content_type__model = ct_model,\n codename = p_codename)\n except Permission.DoesNotExist:\n raise Permission.DoesNotExist(u'Permission \"%s\" does not exist.' % p)\n\n for group_name, perms in data.items():\n group, created = Group.objects.get_or_create(name=group_name)\n for p in perms:\n try:\n perm = get_perm(p)\n group.permissions.add(perm)\n except Permission.DoesNotExist as e:\n print(e)","title":""},{"docid":"d0dd2b53855ea048220b4c1147cd09a9","score":"0.60220426","text":"def GroupUsers(self, Group, Count):","title":""},{"docid":"98ed61d7985a739980ea979c1f2b67ea","score":"0.6008645","text":"def servergroupassign(value):\n\n SERVERGROUP[\"members\"].append(value)","title":""},{"docid":"2568020385b7669bb418a6088f65188a","score":"0.5981344","text":"async def set_(self, ctx, group_name: str.title):\n valid_groups = ('Rosso', 'Verde', 'Blu')\n if group_name not in valid_groups:\n await ctx.send('Gruppo invalido. Scegline uno tra: ' + ', '.join(valid_groups))\n else:\n await self.db.find_one_and_update(\n {'user_id': str(ctx.author.id)},\n {'$set': {'group': group_name}},\n upsert=True\n )\n await ctx.send(f'Benvenuto in {group_name}!')","title":""},{"docid":"f5aa61b241b424c021a24b6f307a7163","score":"0.596635","text":"def name_mapping_unix_group_add_user(self, group_name, user_name):\n return self.request( \"name-mapping-unix-group-add-user\", {\n 'group_name': [ group_name, 'group-name', [ basestring, 'None' ], False ],\n 'user_name': [ user_name, 'user-name', [ basestring, 'None' ], False ],\n }, {\n } )","title":""},{"docid":"2f3f2c71cbff64c15f437d93d8a81aac","score":"0.59597236","text":"def create_user_groups(sender, **kwargs):\n verbosity = kwargs.get(\"verbosity\")\n if verbosity > 0:\n print \"Initializing data post_syncdb\"\n for group in dashboard_group_permissions:\n role, created = Group.objects.get_or_create(name=group)\n if verbosity > 1 and created:\n print \"Creating group {0}\".format(group)\n for perm in dashboard_group_permissions[group]:\n role.permissions.add(Permission.objects.get(codename=perm))\n if verbosity > 1:\n print \"Permitting {0} to {1}\".format(group, perm)\n role.save()","title":""},{"docid":"6f2fbde5b7830238d109b73b9344dc40","score":"0.59519976","text":"def create(self, validated_data):\n user = User(username=validated_data['username'])\n user.set_password(validated_data['password'])\n user.save()\n for item in validated_data['groups']:\n user.groups.add(Group.objects.get(name=item))\n return user","title":""},{"docid":"fae5df138c2ca4197f6b5297cb1f0210","score":"0.59002745","text":"def test_get_user_groups(self):\n pass","title":""},{"docid":"3b92c7ecd945d875f2d56d9075f7d434","score":"0.5897225","text":"def get_users_in_group(df_groups, group_id):\n df = df_groups[(df_groups[0] == group_id)][[1,2]]\n df = [{'name' : list(df[1])[i].lower(), 'id' : list(df[2])[i]} for i in range(len(df))]\n for d in range(len(df)):\n if 't-' in df[d]['name']:\n df[d]['name'] = df[d]['name'][4:(len(df[d]['name'])-1)]\n return df","title":""},{"docid":"3b92c7ecd945d875f2d56d9075f7d434","score":"0.5897225","text":"def get_users_in_group(df_groups, group_id):\n df = df_groups[(df_groups[0] == group_id)][[1,2]]\n df = [{'name' : list(df[1])[i].lower(), 'id' : list(df[2])[i]} for i in range(len(df))]\n for d in range(len(df)):\n if 't-' in df[d]['name']:\n df[d]['name'] = df[d]['name'][4:(len(df[d]['name'])-1)]\n return df","title":""},{"docid":"4e5c4ed80c78da600662359d8fed5ec2","score":"0.5861429","text":"def update_groups(self, props, **kws):\n new_groups = props.get('group', None)\n if new_groups is not None:\n if isinstance(new_groups, str):\n new_groups = new_groups,\n [self._group.add(g) for g in new_groups]\n if self._family is not None:\n self._group.add(self._family)","title":""},{"docid":"39e4cf10ad8f2a4e50f1334dc4ab7ea4","score":"0.58612275","text":"def set_members(self, groups, **kwargs):\n status, data = self.run_gerrit_command('set-members', groups, **kwargs)\n\n return status, data","title":""},{"docid":"076eea61693b6884d106e63f42f0ed6e","score":"0.5826847","text":"def setgroup(self, group_name, members):\n is_valid_redis_key(group_name)\n if 'all' in group_name:\n raise ValueError('Choose another group name, reserverd word: \"all\"')\n\n group_name = group_name.lower()\n try:\n self.getgroup(group_name)\n except ValueError:\n # If does not exists, move to the next step\n pass\n else:\n raise GroupError('Group already exists')\n\n if type(members) is not list:\n raise TypeError('Expect a list. Found: %s' % type(members))\n\n if 'any' in members:\n raise ValueError('Wrong member identified, reserverd word: \"any\"')\n \n gmembers, ipmembers = [], []\n for member in members:\n data = isvalidtype(member)\n if type(data) is IPNetwork:\n ipmembers.append(str(data))\n else:\n gmembers.append(member)\n\n key = self.namespace(group_name)\n ipkey = self.ipnamespace(group_name)\n with self.redis.pipeline() as pipe:\n if gmembers:\n pipe.sadd(key, *gmembers)\n if ipmembers:\n pipe.sadd(ipkey, *ipmembers)\n pipe.sadd(':'.join(('list', 'groups')), key)\n pipe.execute()","title":""},{"docid":"23aae09bcd96b6fa9b2adbde36a7f4bd","score":"0.5807303","text":"def fetch_data(self, connection: oracledb.Connection, group_name: str) -> None:\n\n self.stdout.write(f\"Adding users to {group_name} group\")\n query = GROUPS_TO_ROLES[group_name]\n\n with connection.cursor() as cursor:\n cursor.execute(query)\n\n while True:\n rows = cursor.fetchmany(1000)\n\n if not rows:\n break\n\n if group_name in (\"Importer User\", \"Exporter User\"):\n self.assign_org_permissions(group_name, rows)\n elif group_name == \"Constabulary Contact\":\n self.assign_constabulary_contacts(rows)\n else:\n self.assign_user_groups(group_name, rows)","title":""},{"docid":"bc525004378bb0ff19488838511638f1","score":"0.57904255","text":"def create():\n\n if request.method == \"POST\":\n # Get the data on the user creating the group\n current_user = list(users.find({\"_id\": session[\"user_id\"]}))[0]\n users_list = [current_user['username']]\n for key in request.form:\n if \"username\" in key:\n if not check_valid_user(request.form[key]):\n return apology(\"Invalid Username for Member\")\n users_list.append(request.form[key])\n # Create the group and add it to the database\n new_group = [{\n \"name\": request.form.get(\"name\"),\n \"users\": users_list,\n \"expenses\": []\n }]\n id = groups.insert_many(new_group)\n # Redirect the user back to homepage\n return redirect(\"/\")\n else:\n return render_template(\"create.html\")","title":""},{"docid":"52cc63773fe3c97c919c2e2fca0103a8","score":"0.5774964","text":"def _apply_groups(self, gdb):\n for chk_grp in self._groups:\n if not chk_grp in gdb:\n gdb[chk_grp] = Group(chk_grp, set([self.handle,]))\n logger.info ('%s: Created new group, %r', self.handle, chk_grp)\n # ensure membership in existing groups\n group = gdb[chk_grp]\n if not self.handle in group.members:\n group.add (self.handle)\n group.save ()\n for gname, group in gdb.iteritems():\n if gname not in self._groups and self.handle in group.members:\n group.remove (self.handle)\n group.save ()","title":""},{"docid":"b4486681e9579d89a9a5aaa47f8fa2e9","score":"0.57730323","text":"def groups_add(self, name):\n group, _ = Group.objects.get_or_create(name=name)\n group.user_set.add(self)\n\n return self","title":""},{"docid":"82967eb91ecc9bb3d5dbca34408bd7da","score":"0.57272804","text":"def add_groups(self, *group_names):\n self._used_groups |= set(group_names)\n\n self.invalidate_members()","title":""},{"docid":"1e04fa212a845efea66b1ad3be2f0f7a","score":"0.571146","text":"def add2group(user, group):\n # usermod -a G \n proc_usermod = subprocess.run(\n [\"usermod\", \"-a\", \"-G\", group, user]\n )\n if proc_usermod.returncode:\n raise ValueError(\"usermod returned non-zero!\")","title":""},{"docid":"0f4715cf613244cd0c5c77ad7ec44d92","score":"0.5691425","text":"def lti_to_askup_user(self):\n askup_user, _ = User.objects.get_or_create(username=self.user_id)\n askup_user.groups = [Group.objects.get(name='Student')]\n askup_user.save()\n self.askup_user = askup_user\n self.save()","title":""},{"docid":"6d71b56ebe217cc2df406d87acaee6e2","score":"0.5683542","text":"async def acl_groups(self, user_identity):\n pass # pragma: no cover","title":""},{"docid":"cbceb9f35698e6674e1d19191cd8376d","score":"0.5655622","text":"def groups(self, groups):\n \n self._groups = groups","title":""},{"docid":"e6da21aec076fa354456d417addf3d49","score":"0.5629212","text":"def test_changing_user_profile_of_a_user_should_update_groups_of_user(self):\n self.user.user_profile_id = self.user_profile2\n for group in self.user_profile2.groups_id:\n self.assertIn(\n group, self.user.groups_id,\n 'The user has not the new dependence!')","title":""},{"docid":"fc790896ae703e954748014441723676","score":"0.5621036","text":"def get_members(group_list):\n username=[]\n for groupname in group_list:\n try: \n group=grp.getgrnam(groupname)\n username.extend(group.gr_mem)\n except:\n pass\n \n return username","title":""},{"docid":"b547b643aab57837bd015745fdf6b49c","score":"0.5616088","text":"def group(self, irc, msg, args, name):\n group = name\n \n try:\n l = ldap.open(\"ldap.geeksoc.org\")\n l.protocol_version = ldap.VERSION3\n except ldap.LDAPError, e:\n irc.reply('Error getting info for user: \"%s\"' % name)\n return\n \n baseDN = \"cn=%s,ou=Groups, dc=geeksoc, dc=org\" %(group)\n searchFilter = \"(memberUid=*)\"\n \n try:\n results = l.search_s(baseDN, ldap.SCOPE_SUBTREE, searchFilter)\n string = ''\n for dn, entry in results:\n for u in entry['memberUid']:\n string += u + ' '\n irc.reply(\"Group: %s, Members: %s\" % (group, string))\n\n except ldap.LDAPError, e:\n irc.reply(e)","title":""},{"docid":"505a75d78ccf546d612ec984be735a2c","score":"0.5597802","text":"def update_account_groups(self, user, account, groups, replace=False):\n return","title":""},{"docid":"9fe72c2ca227d20ef4dff35cae235d8f","score":"0.5594665","text":"def add_group(self, username, group):\n doc = self.__get_user(username)\n groups = doc['user']['groups']\n if group not in groups:\n groups.append(group)\n\n self._db.save(doc)","title":""},{"docid":"c811253b5f88dd55ec85feb14e481f3e","score":"0.55838674","text":"def _manage_groups(self):\n if len(self._argsdict) == 0:\n self._logger.error('usage: proctor.py group {create, append} [-h]')\n self._logger.error(\n \"proctor.py group: error: command must include subcommand 'create' or 'append'. Try proctor.py -h.\")\n sys.exit(-1)\n\n subcommand = sys.argv[2]\n if subcommand == 'create':\n self._create_server_group(self._args.groupname)\n return\n if subcommand == 'append':\n self._add_users_to_server_group(self._args.groupname, self._args.emails)\n else:\n raise ValueError(f'Unknown group subcommand: {subcommand}')","title":""},{"docid":"e4469e556bfe69a36c0e795bef45162e","score":"0.55825984","text":"def assign_group_numbers_to_data(data_list:list) -> list:\n list_size = len(data_list)\n for i in range(list_size):\n data_list[i] = data_list[i].assign(separate_num=i)\n return data_list","title":""},{"docid":"c2366be983de91640910a0ed92081683","score":"0.55739546","text":"def set_group_name(self, name):\n self.groupname = name","title":""},{"docid":"bb0e37022327253915179938cada26c8","score":"0.5558407","text":"def shutil_setuid(user = None, group = None, xgroups = None):\n if group:\n gid = grp.getgrnam(group).gr_gid\n os.setgid(gid)\n logg.debug(\"setgid %s for %s\", gid, strQ(group))\n groups = [gid]\n try:\n os.setgroups(groups)\n logg.debug(\"setgroups %s < (%s)\", groups, group)\n except OSError as e: # pragma: no cover (it will occur in non-root mode anyway)\n logg.debug(\"setgroups %s < (%s) : %s\", groups, group, e)\n if user:\n pw = pwd.getpwnam(user)\n gid = pw.pw_gid\n gname = grp.getgrgid(gid).gr_name\n if not group:\n os.setgid(gid)\n logg.debug(\"setgid %s for user %s\", gid, strQ(user))\n groupnames = [g.gr_name for g in grp.getgrall() if user in g.gr_mem]\n groups = [g.gr_gid for g in grp.getgrall() if user in g.gr_mem]\n if xgroups:\n groups += [g.gr_gid for g in grp.getgrall() if g.gr_name in xgroups and g.gr_gid not in groups]\n if not groups:\n if group:\n gid = grp.getgrnam(group).gr_gid\n groups = [gid]\n try:\n os.setgroups(groups)\n logg.debug(\"setgroups %s > %s \", groups, groupnames)\n except OSError as e: # pragma: no cover (it will occur in non-root mode anyway)\n logg.debug(\"setgroups %s > %s : %s\", groups, groupnames, e)\n uid = pw.pw_uid\n os.setuid(uid)\n logg.debug(\"setuid %s for user %s\", uid, strQ(user))\n home = pw.pw_dir\n shell = pw.pw_shell\n logname = pw.pw_name\n return {\"USER\": user, \"LOGNAME\": logname, \"HOME\": home, \"SHELL\": shell}\n return {}","title":""},{"docid":"a027d668730a828cd37febf3486162aa","score":"0.5551757","text":"def addGroupMember(user,group):\n group.members.append(user)\n db.session.commit()","title":""},{"docid":"e84c45b39f957d34875a30c792d93f8a","score":"0.5541557","text":"def _add_to_db(self, principalUID, members):\n for member in members:\n yield self.execute(\n \"\"\"\n insert into GROUPS (GROUPNAME, MEMBER)\n values (:1, :2)\n \"\"\", (principalUID.decode(\"utf-8\"), member,)\n )","title":""},{"docid":"2382bc274127528f780d5a746e3d368a","score":"0.5526822","text":"def assign_users(G,participants,breakouts):\n assign = {}\n i=0\n for p in participants:\n assign_p = {}\n for l in p[1]['learn']:\n if l in breakouts:\n assign_p[l] = 'breakout'\n else:\n edges = G.edges(p[0], data=True)\n common = []\n \n for e in edges:\n if (l in e[2]['common']) and (l!=''):\n common.append(e[1])\n else: \n pass\n \n if common != []:\n assign_p[l] = random.choice(common) \n \n assign[p[0]] = assign_p\n i+=1\n \n return assign","title":""},{"docid":"52568660abad3114b7dc5ea6a89c6ad0","score":"0.55113107","text":"def create_person_group(self):\n url = self.base_url + \"persongroups/\" + self.pg_name\n response = requests.put(url, headers=self.headers, json={\"name\" : self.pg_name})\n if response.status_code == 200 :\n print(\"added \" + self.pg_name + \" person group\")\n else:\n print(response.json())","title":""},{"docid":"2942802e325cec573289c0a39f4e7480","score":"0.5499307","text":"def _add_users_to_server_group(self, group_name, emails_file_name):\n email_list = self._get_emails_from_file(emails_file_name)\n self._server.add_users_to_group(group_name, email_list)","title":""},{"docid":"b356c9a7b912dbe4fbf4ca98c6f65cbf","score":"0.5491542","text":"def add_group(username, group_name, logger, client):\n client.users.add_to_group(username, group_name)\n logger.info('User `{0}` added successfully to group '\n '`{1}`'.format(username, group_name))","title":""},{"docid":"6b7489873492418b0040dbfa39379695","score":"0.54899526","text":"def groups(self, groups):\n\n self._groups = groups","title":""},{"docid":"6b7489873492418b0040dbfa39379695","score":"0.54899526","text":"def groups(self, groups):\n\n self._groups = groups","title":""},{"docid":"6b7489873492418b0040dbfa39379695","score":"0.54899526","text":"def groups(self, groups):\n\n self._groups = groups","title":""},{"docid":"6b7489873492418b0040dbfa39379695","score":"0.54899526","text":"def groups(self, groups):\n\n self._groups = groups","title":""},{"docid":"6b7489873492418b0040dbfa39379695","score":"0.54899526","text":"def groups(self, groups):\n\n self._groups = groups","title":""},{"docid":"ec2723071fc401ad36197d6cc2f83f3b","score":"0.5487621","text":"def userGroup(self, _):\n logging.info(\"Querying registered groups ...\")\n status, data = self._httpRequest(\"GET\", \"/reqmgr/reqMgr/group\")\n groups = json.loads(data)\n logging.info(data)\n logging.info(\"Querying registered users ...\")\n status, data = self._httpRequest(\"GET\", \"/reqmgr/reqMgr/user\")\n logging.info(data)\n logging.info(\"Querying groups membership ...\")\n for group in groups:\n status, data = self._httpRequest(\"GET\", \"/reqmgr/reqMgr/group/%s\" % group)\n logging.info(\"Group: '%s': %s\" % (group, data))","title":""},{"docid":"b32c0afc9f0eb98da8b3f77f35d6f8e3","score":"0.54645133","text":"def _parse_data_to_users(self):\n while True:\n dn, status, label, value = yield\n user = self._parse_dn(dn)\n\n if user not in self.users:\n u = User(user)\n self.users[user] = u\n else:\n u = self.users[user]\n u.add_data({'Status': status, label: value})","title":""},{"docid":"414f34fbbb8bd679a9d2e0a78078e6e7","score":"0.5460207","text":"def setGroup(self, node):\n\t\tself.group = node.getGroup()\n\t\tself.groupName = node.getGroupName()","title":""},{"docid":"d863b6794e09c27ed41cb8c007fac0b3","score":"0.5457117","text":"def set(self, group_name: str, tags: List[str]) -> None:\n self.set_group[group_name] = tags","title":""},{"docid":"d863b6794e09c27ed41cb8c007fac0b3","score":"0.5457117","text":"def set(self, group_name: str, tags: List[str]) -> None:\n self.set_group[group_name] = tags","title":""},{"docid":"72708b75875ad7ea17c522f4749c30d5","score":"0.5441494","text":"def setUp(self):\n self.groups = GroupProfile.objects.all()\n for gr in GroupProfile.objects.all():\n self.members[gr.name] = User.objects.filter(groups=gr.group)\n # create 2 groups\n #self.users.append(create_user(\"__user1__\", \"Joe\", \"Smith\"))\n #self.users.append(create_user(\"__user2__\", \"Alice\", \"Jones\"))\n #self.users.append(create_user(\"__user3__\", \"Mary\", \"Willson\"))\n #self.users.append(create_user(\"__user4__\", \"Mary\", \"Willson\"))\n #self.users.append(create_user(\"__user5__\", \"Mary\", \"Willson\"))\n #self.users.append(create_user(\"__user6__\", \"Mary\", \"Willson\"))\n #self.members[\"__group1__\"] = [self.users[0], self.users[3],]\n #self.members[\"__group2__\"] = [self.users[1], self.users[2], self.users[3], self.users[4],]\n #self.groups.append(create_group(\"__group1__\", self.users[0], self.members[\"__group1__\"]))\n #self.groups.append(create_group(\"__group2__\", self.users[1], self.members[\"__group2__\"]))\n # create 3 goals\n #self.goals[\"__group1__\"] = [create_goal(\"__goal1.1__\", self.groups[0], self.users[0]),\n # create_goal(\"__goal1.2__\", self.groups[0], self.users[0]),\n # create_goal(\"__goal1.3__\", self.groups[0], self.users[2]),\n # ]\n #self.goals[\"__group2__\"] = [create_goal(\"__goal2.1__\", self.groups[1], self.users[1]),\n # create_goal(\"__goal2.2__\", self.groups[1], self.users[2]),\n # ]","title":""},{"docid":"841fda894e959596e9156215325e0aa0","score":"0.54232115","text":"def save(self, *args, **kwargs):\n\t\tbase_group = Group.objects.get_or_create(name='usuario_base')[0]\n\t\tself.user.groups.add(base_group)\n\t\treturn super(BaseUser, self).save(*args, **kwargs)","title":""},{"docid":"a08180febd23e08d900ea795cafad2e9","score":"0.5408937","text":"def get_groups(name):\r\n groups = {}\r\n isa_structure = [\"Project\", \"Investigation\", \"Study\", \"Assay\"]\r\n groupname = name.split('/')[-1]\r\n groups[isa_structure[len(name.split('/'))-1]] = groupname","title":""},{"docid":"0c1113417c495c49ed6fd7b5a1473f74","score":"0.54006624","text":"def test_add_mft_user_group(self):\n pass","title":""},{"docid":"b5189cbb611d0cb00f6617611ddfb5b4","score":"0.53828937","text":"async def add_group_user(request: web.Request):\n user_id = request[RQT_USERID_KEY]\n gid = request.match_info[\"gid\"]\n new_user_in_group = await request.json()\n\n assert \"uid\" in new_user_in_group or \"email\" in new_user_in_group # nosec\n\n new_user_id = new_user_in_group[\"uid\"] if \"uid\" in new_user_in_group else None\n new_user_email = (\n parse_obj_as(LowerCaseEmailStr, new_user_in_group[\"email\"])\n if \"email\" in new_user_in_group\n else None\n )\n\n await api.add_user_in_group(\n request.app,\n user_id,\n gid,\n new_user_id=new_user_id,\n new_user_email=new_user_email,\n )\n raise web.HTTPNoContent","title":""},{"docid":"0496cf3dcf5cc19e37002ea22f8ed59d","score":"0.5369904","text":"def process_mesh_vertex_groups(mesh_data, obj_user):\n mesh_data[\"vertex_groups\"] = []\n\n if obj_user and obj_user.vertex_groups:\n for vertex_group in obj_user.vertex_groups:\n vertex_group_data = OrderedDict()\n vertex_group_data[\"name\"] = vertex_group.name\n vertex_group_data[\"index\"] = vertex_group.index\n\n mesh_data[\"vertex_groups\"].append(vertex_group_data)","title":""},{"docid":"ae6942e3b6855109bf33966566e2727a","score":"0.5366977","text":"def test_get_mft_user_groups(self):\n pass","title":""},{"docid":"d5aba429aa69ac2885b09652b9b0b319","score":"0.53580564","text":"def test_put_groups_id(self):\n pass","title":""},{"docid":"3736bfbf09e33862d5d524cd4cdc4848","score":"0.5339213","text":"def assign_org_permissions(self, group_name: str, rows: list[tuple[str, str, int]]) -> None:\n for username, roles, org_id in rows:\n user = User.objects.get(username=username)\n\n if group_name == \"Importer User\":\n org = Importer.objects.get(pk=org_id)\n else:\n org = Exporter.objects.get(pk=org_id)\n\n assign_manage = \":AGENT_APPROVER\" in roles\n\n organisation_add_contact(org, user, assign_manage)\n\n # Check user should have view permissions\n if \":VIEW\" not in roles:\n obj_perms = get_org_obj_permissions(org)\n remove_perm(obj_perms.view, user, org)\n\n # Check user should have edit permissions\n if \":EDIT_APP\" not in roles and \":VARY_APP\" not in roles:\n obj_perms = get_org_obj_permissions(org)\n remove_perm(obj_perms.edit, user, org)","title":""},{"docid":"717b958d5b1bcad7f756ea3ecd336425","score":"0.533714","text":"def set_default_names(self):\n for i, group in enumerate(self.groups):\n group.name = f'{self.name}-{i + 1}'","title":""},{"docid":"94a3adf877e471b484d755a7e87374f9","score":"0.53289646","text":"def assign(self):\r\n for symbolData in self.symbolData:\r\n symbolData.assign(self.group)","title":""},{"docid":"cd6892bdcadfafbb86815bc9b5fde913","score":"0.5322197","text":"def groups(user):\n return [group.name for group in user.groups.all()]","title":""},{"docid":"2145982f72284691b8d0411466ffff25","score":"0.5309958","text":"def group_create(name, gid=None):\n\toptions = []\n\tif gid:\n\t\toptions.append(\"-g '%s'\" % (gid))\n\tsudo(\"groupadd %s '%s'\" % (\" \".join(options), name))","title":""},{"docid":"0f7da911a16a0a57a0bd1b070f662ecc","score":"0.5302245","text":"def test_update_mft_user_group(self):\n pass","title":""},{"docid":"2789cfba02137174c5782ac4a5f9ac26","score":"0.5300633","text":"def group2db(user, user_group):\n group = Group()\n group.user_group = user\n group.gitlab_id = user_group.id\n group.name = user_group.name\n group.description = user_group.description\n group.visibility = user_group.visibility\n group.ssh_url_to_repo = user_group.ssh_url_to_repo\n group.http_url_to_repo = user_group.http_url_to_repo\n group.web_url = user_group.web_url\n group.name_with_namespace = user_group.name_with_namespace\n group.path = user_group.path\n group.path_with_namespace = user_group.path_with_namespace\n group.created_at = user_group.created_at\n group.last_activity_at = user_group.last_activity_at\n\n return group","title":""},{"docid":"81c13fbd0b39aaaedae553ce70ece968","score":"0.5298014","text":"def load_groups() -> dict:\n dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n os.chdir(dir)\n os.chdir('data')\n os.chdir('server_data')\n with open('groups.csv', mode='r') as infile:\n reader = csv.reader(infile)\n group = {rows[0].upper(): [rows[1].upper(), rows[2].upper(), rows[3].upper(), rows[4].upper(),\n rows[5].upper(), rows[6].upper()] for rows in reader}\n\n for key in group.keys():\n group[key] = list(filter(None, group[key]))\n\n return group","title":""},{"docid":"b60082d7537b9b30fb2354c5c63a1abf","score":"0.5295297","text":"def _set_user_permissions_for_volumes(users, volumes):\n\n group_name = 'volumes'\n\n user_data_script_section = f\"\"\"\ngroupadd {group_name}\n\"\"\"\n\n for user in users:\n user_data_script_section += f\"\"\"\nusermod -a -G {group_name} {user.login}\n\"\"\"\n for volume in volumes:\n user_data_script_section += f\"\"\"\nchgrp -R {group_name} {volume.mount}\nchmod -R 2775 {volume.mount} \n\"\"\"\n\n return user_data_script_section","title":""},{"docid":"81aba641c992f48111b2dfb07da610b0","score":"0.52936935","text":"def test_update_users_of_a_group(self):\n profile = self.env['res.users'].create(\n {'name': 'P', 'login': 'p_login', 'is_user_profile': True})\n user = self.env['res.users'].create(\n {'name': 'U', 'login': 'u_login', 'user_profile_id': profile.id})\n self.group1.with_context(use_pdb=True).users |= profile\n self.assertIn(\n self.group1, user.groups_id,\n 'User U should have Group 1 in dependencies!')","title":""},{"docid":"800fd3d88b5063cd87449f3e8edd85e6","score":"0.52934253","text":"def add_user_to_group(sender, instance, created, **kwargs):\n\n try:\n if created:\n if instance.role is 'instructor':\n instance.groups.add(Group.objects.get(pk=3))\n else:\n instance.groups.add(Group.objects.get(pk=4))\n except Group.DoesNotExist:\n pass","title":""},{"docid":"046de7ce7a8f16a32ebe2e6ff137f468","score":"0.5292615","text":"def get_all_group_name(self, username):\n return self.fetch_to_list(self.fetch_data(\"select distinct Group_name from Task where username = '{}';\".format(username)))","title":""},{"docid":"794f46ba0f19aa7047f9292b677d8132","score":"0.52788293","text":"def set_group(self, group):\n if type(group) in (list, tuple):\n self.groups = group\n else:\n self.groups = (group,)","title":""},{"docid":"492a2123572f8acfb83bd63edf84d096","score":"0.5275229","text":"def save(self, *args, **kwargs):\n # Save the group instance\n group = super(CSVGroupCreationForm, self).save(*args, **kwargs)\n\n # Add users to the group\n group.user_set.add(*self.__initial_users)","title":""},{"docid":"0496bed4c4120e0f6f4deb600b61dff7","score":"0.52697587","text":"def _link_groups_and_permissions(self, user: CustomUser) -> CustomUser:\n\n groups, permissions = self.data_groups, self.data_user_permissions\n groups, permissions = [groups] if isinstance(groups, str) else groups, [permissions] if isinstance(permissions, str) else permissions\n\n try:\n if groups is not None:\n if len(groups) > 0 and len(groups[0]) > 0:\n user.groups.set([Group.objects.get_by_natural_key(group) for group in groups])\n else:\n user.groups.set([])\n\n if permissions is not None:\n if len(permissions) > 0 and len(permissions[0]) > 0:\n user.user_permissions.set([Permission.objects.get(codename=permission) for permission in permissions])\n else:\n user.user_permissions.set([])\n\n except ObjectDoesNotExist as error:\n raise ObjectNotFound(_(str(error)))\n\n user.save()\n return user","title":""},{"docid":"a472ce3f2d630da3e873c74e7e1aab39","score":"0.5267011","text":"def change_user_group(self, user_group_id, user_group_data):\r\n return self.put(\r\n 'usergroups/{}'.format(user_group_id),\r\n payload=user_group_data).json()","title":""},{"docid":"949ebc48dfeadba08e7460e15c7ec6e4","score":"0.5263152","text":"def test_changing_group_dependencies_should_update_groups_of_users(self):\n self.group1.implied_ids = [(4, self.group2.id)]\n self.assertIn(\n self.group2, self.user.groups_id,\n 'The user has not the new dependence!')","title":""},{"docid":"a389cfaf84cdf2963673a4b25d96377f","score":"0.5262883","text":"def create_grp(conn, id, name):\n cur = conn.cursor()\n try:\n info = (id, name)\n cur.execute(d.sql_cmd['createGrp'], info)\n except Error as e:\n print(e)","title":""},{"docid":"411ee83f11e7fc621648da4cc9f73c20","score":"0.5262673","text":"def group_members(self, group_name):\n if group_name not in self._group_members:\n members = self.rest.get(\n f'/a/groups/{self.group(group_name)[\"group_id\"]}/members').json\n self._group_members[group_name] = [{\n \"id\": str(member['_account_id']),\n \"name\": member.get('name', ''),\n \"username\": member.get('username'),\n \"github_id\": str(self.oauth_id(member['_account_id'])),\n \"github_username\":\n str(self.oauth_id(member['_account_id'])),\n \"email\": member['email'] if 'email' in member else ''\n } for member in members]\n return self._group_members[group_name]","title":""},{"docid":"bbe9c70f99026eff468bc4adecae349d","score":"0.5248733","text":"def groupfinder(userid, request) -> List[str]:\n user = request.user\n groups = None\n if user:\n groups = []\n for group in user.groups:\n groups.append(\"group:%s\" % group.name)\n groups.append(\"user:%s\" % user.id)\n return groups","title":""},{"docid":"ed0789646c53fc3f88ba92497a9dc88d","score":"0.5246923","text":"def addGroup(name):\n group = Group()\n group.name = name\n group.members.append(current_user)\n db.session.add(group)\n db.session.commit()\n return group.id","title":""},{"docid":"13ddc95537ce62988e09b5b0efdb069c","score":"0.5245989","text":"def _get_groups(self, username):\n\n groups = set([username])\n for provider in self.group_providers:\n for group in provider.get_permission_groups(username):\n groups.add(group)\n \n # TODO: Necessary? What is this for?\n perms = PermissionSystem(self.env).get_all_permissions()\n repeat = True\n while repeat:\n repeat = False\n for subject, action in perms:\n if subject in groups and action.islower() and action not in groups:\n groups.add(action)\n repeat = True \n \n return groups","title":""},{"docid":"51cc622f8d4bb17d15f4266aa6690397","score":"0.5234191","text":"def add_group(self, path):\n self.pwd.create_group[path]","title":""},{"docid":"adbded0c3dbf92d87fbc5763f24179b0","score":"0.5225458","text":"def parse_group_file(group_file):\n\n user2groups = {}\n groups_encountered = set()\n\n with group_file:\n for index, group_data in enumerate(group_file.read().splitlines()):\n group_data = group_data.split(\":\")\n try:\n validate_group_format(group_data, index + 1, groups_encountered)\n except Exception as e:\n sys.exit(str(e))\n\n group_name = group_data[GroupFieldEnum.GROUPNAME]\n groups_encountered.add(group_name)\n users = group_data[GroupFieldEnum.USERS]\n if users == '':\n continue\n for user in users.split(\",\"):\n if user not in user2groups:\n user2groups[user] = []\n\n user2groups[user].append(group_name)\n\n return user2groups","title":""},{"docid":"23ff2db1bf879c76aefbc7abad2ab861","score":"0.5221914","text":"def default_groups(database):\n return create_default_groups()","title":""},{"docid":"b7cd5fe7c0355d4a7defbd2519278c72","score":"0.52143776","text":"def add_to_team(self, user, groups=None, **kw):\n # TODO: user argument should be renamed to userid for clarity\n # however doing so now would break backwards compatibility\n data = kw.copy()\n data[\"user\"] = user\n if groups is not None:\n data[\"groups\"] = groups = set(groups)\n members = self.members\n if not user or user not in members:\n data[\"UID\"] = uid = getUtility(IUUIDGenerator)()\n key = user or uid\n data[\"_mtime\"] = DateTime()\n if groups is None:\n data[\"groups\"] = groups = set()\n members[key] = data\n for name, func in self.counters:\n if func(data):\n if name not in self.context._counters:\n self.context._counters[name] = Length()\n self.context._counters[name].change(1)\n membership = self.membership_factory(self, data)\n membership.handle_added()\n membership._update_groups(set(), groups)\n notify(TeamMemberAddedEvent(self.context, membership))\n self.context.reindexObject(idxs=[\"workspace_members\", \"workspace_leaders\"])\n else:\n membership = self.membership_factory(self, self.members[user])\n membership.update(data)\n return membership","title":""},{"docid":"6f9e9835ead6b52622802ac6a80fc4b9","score":"0.5209074","text":"def add_to_group(switch, member, group):\n if switch not in GroupManager.group_to_member:\n GroupManager.group_to_member[switch] = {}\n\n if group not in GroupManager.group_to_member[switch]:\n GroupManager.group_to_member[switch][group] = []\n\n if member not in GroupManager.group_to_member[switch].get(group, []):\n GroupManager.group_to_member[switch].get(group).append(member)","title":""},{"docid":"aad5949c3a50ecd22346e533de342dc2","score":"0.52085155","text":"def editgroup(self, group_name, members):\n is_valid_redis_key(group_name)\n group_name = group_name.lower()\n group, groupips = self.getgroup(group_name)\n if not group and not groupips:\n raise GroupError('Group \"%s\" does not exists' % group_name)\n if type(members) is not list:\n raise TypeError('Expect a list. Found: %s' % type(members))\n\n gmembers, ipmembers = [], []\n for member in members:\n data = isvalidtype(member)\n if type(data) is IPNetwork:\n ipmembers.append(str(data))\n else:\n gmembers.append(member)\n\n if 'any' in members:\n raise ValueError('Could not add \"any\" type members')\n\n key = self.namespace(group_name)\n ipkey = self.ipnamespace(group_name)\n\n with self.redis.pipeline() as pipe:\n if gmembers:\n pipe.sadd(key, *gmembers)\n if ipmembers:\n pipe.sadd(ipkey, *ipmembers)\n pipe.sadd(':'.join(('list', 'groups')), key)\n pipe.execute()","title":""},{"docid":"a08480f5ab64dbd5e55cdb6b54d41ba2","score":"0.52025557","text":"def switch_user_type(self):\n self.user.groups.all().delete()\n user_type = self.get_user_type()\n if user_type == 'performer':\n self.user.groups.add(Group.objects.get_or_create(name=\"Clients\")[0])\n else:\n self.user.groups.add(Group.objects.get_or_create(name=\"Performers\")[0])","title":""},{"docid":"efdbd3138fd3a0dcd8d2aa2d380dd219","score":"0.51879734","text":"def set_perms(header):\n payload = [\n {'group_id': '36124', 'permission': 'admin'},\n {'group_id': '41497', 'permission': 'write'},\n {'group_id': '45777', 'permission': 'read'},\n {'group_id': '36180', 'permission': 'write'}\n ]\n for group_perms in payload:\n try:\n r = requests.post(base_url + \"/repositories/\" + org + \"/\" + new_repo + \"/groups/\", json=group_perms, headers=header)\n r.raise_for_status()\n except requests.exceptions.HTTPError as err:\n print red(\"Setting perms failed: {}\").format(err)\n sys.exit(1)\n print green(\"All perms set.\")","title":""},{"docid":"e809cff4c7d178bfc464bd4130b4d1c4","score":"0.5174541","text":"def write_groups(out_file, groupname):\r\n print(\"To create a single group please just enter the main group name i.e. Group Name\")\r\n print('To create a subgroup to an exisitng group, please enter /Group Name/Subgroup Name/etc/etc/')\r\n print() \r\n attributes = {}\r\n print(\"Enter attributes for\", groupname)\r\n meta = input(\"Is there a metadata file? (Y/N): \")\r\n if meta == \"Y\" or meta == \"y\":\r\n metapath = input(\"Enter metadata file path: \")\r\n with open(metapath, 'r') as metafile:\r\n for line in metafile:\r\n line = line.split('\\t')\r\n item = line[0].strip('\\n')\r\n value = line[-1].strip('\\n')\r\n if item in attributes.keys():\r\n attributes[item].append(value)\r\n else:\r\n attributes[item] = [value]\r\n else:\r\n input_attributes = input(\"Enter an attribute followed by a value. i.e. Project Name: iknowit, Date: 04-11-2019: \")\r\n for attribute in input_attributes.split(','):\r\n attribute = attribute.split(':')\r\n attributes[attribute[0].strip(' ')] = attribute[1].strip(' ')\r\n data_file = h5py.File(out_file, 'a')\r\n dset = data_file.create_group(groupname)\r\n for k, v in attributes.items():\r\n dset.attrs[k] = v","title":""},{"docid":"f2735c4bb13c4d07c7d21299c9de6526","score":"0.5172792","text":"def _group(self, data, group_assignments):\n groups = np.unique(group_assignments)\n group_to_data = defaultdict(dict)\n for group in groups:\n for k, v in data.items():\n if isinstance(v, np.ndarray):\n assert len(group_assignments) == len(v), \\\n f'group_assignments and \"{k}\" must be the same length'\n group_to_data[group][k] = v[group_assignments == group]\n return group_to_data","title":""},{"docid":"07c14e4dd0b87e3c36d397faf69ddbbd","score":"0.51625884","text":"def _create_server_group(self, group_name):\n self._server.create_group(group_name)","title":""},{"docid":"c414d67009ccf48d55a2d1b2c47dbf37","score":"0.5162409","text":"def save(self, *args, **kwargs):\n\t\tadmin_group = Group.objects.get_or_create(name='administrator')[0]\n\t\tself.user.groups.add(admin_group)\t\t\n\t\treturn super(AdminUser, self).save(*args, **kwargs)","title":""},{"docid":"483b86fe6581bed3dc5d5a7631019a97","score":"0.5162018","text":"def set_groups(self):\n self.groups = self.sub_data.groupby([self.name]) # List of grouped data by this attribute\n\n # For every distinct value, create new sub attribute object\n for i in range(len(self.values)):\n data = self.groups.get_group((self.values[i])) # Sub data set for the value\n\n # New object\n new_sub_att = sa.SubAttribute(self.values[i], data, self.classes)\n\n # Add new sub attribute to the list\n self.sub_values.append(new_sub_att)","title":""},{"docid":"07036db69edd89a7df5c6deafff57d8a","score":"0.51615137","text":"def __get_groups(self):\n search_filter = '(objectClass=group)'\n try:\n raw_result = self.ldap_con.search_s(\n settings.AUTH_LDAP_GROUP_SEARCH, ldap.SCOPE_SUBTREE, search_filter)\n except ldap.NO_SUCH_OBJECT as e:\n raise\n\n for group in raw_result:\n group_cn = group[1]['cn'][0].lower()\n try:\n alphanumeric_dash_underscore(group_cn)\n except:\n logging.warn(\n 'group %s is not valid for the name of a mailing list' %\n group_cn)\n continue\n self.ldap_groups[group_cn] = group[1]['member']","title":""},{"docid":"29f02e5130953452b5de5091f50010ee","score":"0.5155799","text":"def registerServerGroup(self, name, hosts):\n pgcCmd = PGC_HOME + os.sep + \"pgc register GROUP --json \\\"\" + name + \"\\\"\"\n pgcProcess = subprocess.Popen(pgcCmd, stdout=subprocess.PIPE, shell=True)\n pgcData = pgcProcess.communicate()\n yield self.session.publish('com.bigsql.onRegisterServerGroup', pgcData)\n import util\n util.map_group_hosts(name, hosts, p_group_id=0, isJson=False, printMsg=False)","title":""},{"docid":"f3c80c42887a4bab920bc73b599236a3","score":"0.5152189","text":"def test_get_groups(self):\n pass","title":""},{"docid":"679ae5d67e6b2ad605aa0480dc1502d0","score":"0.51420146","text":"def test_jenkins_groups(User, JenkinsRoleDefaults):\n jenkins_name = JenkinsRoleDefaults[\"jenkins_name\"]\n jenkins_groups = User(jenkins_name).groups\n assert set([\"adm\", \"sudo\", \"jenkins\"]).issubset(jenkins_groups)","title":""},{"docid":"de970ca1a6a9942d9270d95f2d5641af","score":"0.5141631","text":"def addgrp(user=None, grp=None):\n if not grp:\n print(\"No group name provided.. exiting.\")\n sys.exit()\n logger.scriptlog(\"\\n%s\" % env.host)\n addgroup = sudo(\"groupadd %s\" % grp)\n if addgroup.return_code == 0:\n logger.scriptlog(\"Group added %s\" % grp)\n elif addgroup.return_code == 9:\n logger.scriptlog(\"Group already exists: %s\" % grp)\n else:\n logger.scriptlog(\"An error occured while adding group: %s\" % grp)\n try:\n sudo(\"usermod -aG %s %s\" % (grp, user))\n logger.scriptlog(\"Added %s to group: %s\" % (user, grp))\n except Exception as e:\n logger.error(e)","title":""},{"docid":"da5c85a627016a1dcd73db9987144ca8","score":"0.51397866","text":"def addGroupToDevice(self):\n groups = self.groups\n if self.groupname not in groups:\n groups.append(self.groupname)\n groups.sort()\n self.device.setGroups(groups)\n commit()","title":""}],"string":"[\n {\n \"docid\": \"4195b1546c4302f61e324df7124c0277\",\n \"score\": \"0.65661025\",\n \"text\": \"def set_user_groups(self):\\n # Notice that we only add or remove groups that we had\\n # offered as options on the form, and don't touch any other\\n # groups that the user might belong to.\\n for value, label in self.fields['help_desk_group'].choices:\\n if value:\\n group = Group.objects.get(name=value)\\n if value in self.cleaned_data['help_desk_group']:\\n self.instance.groups.add(group)\\n else:\\n self.instance.groups.remove(group)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3171e6ddd397ed7b992ec276b6af7fe\",\n \"score\": \"0.6380261\",\n \"text\": \"def group_user(self, registrant):\\n if self.groups is not None:\\n groups_qs = Group.objects.filter(name__in=self.groups.split(','))\\n group_list = [group for group in groups_qs]\\n registrant.groups.add(*group_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f97adb20039af67c5a1d77f6beac656\",\n \"score\": \"0.6095477\",\n \"text\": \"def NameGroups(data_arr,id_key):\\n\\t\\tnew_data_arr = []\\n\\t\\tfor data in data_arr:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tdata_arr[id_key] = clc._GROUP_MAPPING[data[id_key]]\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tpass\\n\\t\\t\\tnew_data_arr.append(data)\\n\\t\\tif clc.args: clc.v1.output.Status(\\\"ERROR\\\",2,\\\"Group name conversion not yet implemented\\\")\\n\\t\\treturn(new_data_arr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd4bdfb1386cfecc2aac63bb4bac72ad\",\n \"score\": \"0.60787326\",\n \"text\": \"def assign_user_to_group(global_token, regional_token, contractid, region,\\n username, groupname):\\n try:\\n # if user exists return its id otherwise return 'None'\\n userid = get_itemid(get_keystoneobject_list(\\n regional_token, region, contractid, 'users'), username, 'users')\\n # if group exists return its id otherwise return 'None'\\n groupid = get_itemid(get_keystoneobject_list(\\n regional_token, region, contractid, 'groups'), groupname, 'groups')\\n region = 'gls'\\n identityURL = 'https://identity.' + region + \\\\\\n '.cloud.global.fujitsu.com/v3/groups/' + groupid + '/users/' + userid\\n # make the put rest request\\n response = requests.put(identityURL,\\n headers={'X-Auth-Token': global_token,\\n 'Content-Type': 'application/json'})\\n return response\\n except:\\n return 'Failed to assign user to group'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77fab95e1d3a628d6830039ce6162d45\",\n \"score\": \"0.6037697\",\n \"text\": \"def sync_groups_and_permissions(self, data):\\n def get_perm(p):\\n \\\"\\\"\\\"\\n ``p`` format: '<ct_app_label>.<ct_model>.<p_codename>'\\n \\\"\\\"\\\"\\n try:\\n ct_app_label, ct_model, p_codename = p.split('.')\\n except ValueError:\\n raise ValueError(u'Value must be in format \\\"<ct_app_label>.<ct_model>.<p_codename>\\\". Got \\\"%s\\\"' % p)\\n try:\\n return Permission.objects.get(content_type__app_label = ct_app_label,\\n content_type__model = ct_model,\\n codename = p_codename)\\n except Permission.DoesNotExist:\\n raise Permission.DoesNotExist(u'Permission \\\"%s\\\" does not exist.' % p)\\n\\n for group_name, perms in data.items():\\n group, created = Group.objects.get_or_create(name=group_name)\\n for p in perms:\\n try:\\n perm = get_perm(p)\\n group.permissions.add(perm)\\n except Permission.DoesNotExist as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0dd2b53855ea048220b4c1147cd09a9\",\n \"score\": \"0.60220426\",\n \"text\": \"def GroupUsers(self, Group, Count):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98ed61d7985a739980ea979c1f2b67ea\",\n \"score\": \"0.6008645\",\n \"text\": \"def servergroupassign(value):\\n\\n SERVERGROUP[\\\"members\\\"].append(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2568020385b7669bb418a6088f65188a\",\n \"score\": \"0.5981344\",\n \"text\": \"async def set_(self, ctx, group_name: str.title):\\n valid_groups = ('Rosso', 'Verde', 'Blu')\\n if group_name not in valid_groups:\\n await ctx.send('Gruppo invalido. Scegline uno tra: ' + ', '.join(valid_groups))\\n else:\\n await self.db.find_one_and_update(\\n {'user_id': str(ctx.author.id)},\\n {'$set': {'group': group_name}},\\n upsert=True\\n )\\n await ctx.send(f'Benvenuto in {group_name}!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5aa61b241b424c021a24b6f307a7163\",\n \"score\": \"0.596635\",\n \"text\": \"def name_mapping_unix_group_add_user(self, group_name, user_name):\\n return self.request( \\\"name-mapping-unix-group-add-user\\\", {\\n 'group_name': [ group_name, 'group-name', [ basestring, 'None' ], False ],\\n 'user_name': [ user_name, 'user-name', [ basestring, 'None' ], False ],\\n }, {\\n } )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f3f2c71cbff64c15f437d93d8a81aac\",\n \"score\": \"0.59597236\",\n \"text\": \"def create_user_groups(sender, **kwargs):\\n verbosity = kwargs.get(\\\"verbosity\\\")\\n if verbosity > 0:\\n print \\\"Initializing data post_syncdb\\\"\\n for group in dashboard_group_permissions:\\n role, created = Group.objects.get_or_create(name=group)\\n if verbosity > 1 and created:\\n print \\\"Creating group {0}\\\".format(group)\\n for perm in dashboard_group_permissions[group]:\\n role.permissions.add(Permission.objects.get(codename=perm))\\n if verbosity > 1:\\n print \\\"Permitting {0} to {1}\\\".format(group, perm)\\n role.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f2fbde5b7830238d109b73b9344dc40\",\n \"score\": \"0.59519976\",\n \"text\": \"def create(self, validated_data):\\n user = User(username=validated_data['username'])\\n user.set_password(validated_data['password'])\\n user.save()\\n for item in validated_data['groups']:\\n user.groups.add(Group.objects.get(name=item))\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fae5df138c2ca4197f6b5297cb1f0210\",\n \"score\": \"0.59002745\",\n \"text\": \"def test_get_user_groups(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b92c7ecd945d875f2d56d9075f7d434\",\n \"score\": \"0.5897225\",\n \"text\": \"def get_users_in_group(df_groups, group_id):\\n df = df_groups[(df_groups[0] == group_id)][[1,2]]\\n df = [{'name' : list(df[1])[i].lower(), 'id' : list(df[2])[i]} for i in range(len(df))]\\n for d in range(len(df)):\\n if 't-' in df[d]['name']:\\n df[d]['name'] = df[d]['name'][4:(len(df[d]['name'])-1)]\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b92c7ecd945d875f2d56d9075f7d434\",\n \"score\": \"0.5897225\",\n \"text\": \"def get_users_in_group(df_groups, group_id):\\n df = df_groups[(df_groups[0] == group_id)][[1,2]]\\n df = [{'name' : list(df[1])[i].lower(), 'id' : list(df[2])[i]} for i in range(len(df))]\\n for d in range(len(df)):\\n if 't-' in df[d]['name']:\\n df[d]['name'] = df[d]['name'][4:(len(df[d]['name'])-1)]\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e5c4ed80c78da600662359d8fed5ec2\",\n \"score\": \"0.5861429\",\n \"text\": \"def update_groups(self, props, **kws):\\n new_groups = props.get('group', None)\\n if new_groups is not None:\\n if isinstance(new_groups, str):\\n new_groups = new_groups,\\n [self._group.add(g) for g in new_groups]\\n if self._family is not None:\\n self._group.add(self._family)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39e4cf10ad8f2a4e50f1334dc4ab7ea4\",\n \"score\": \"0.58612275\",\n \"text\": \"def set_members(self, groups, **kwargs):\\n status, data = self.run_gerrit_command('set-members', groups, **kwargs)\\n\\n return status, data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"076eea61693b6884d106e63f42f0ed6e\",\n \"score\": \"0.5826847\",\n \"text\": \"def setgroup(self, group_name, members):\\n is_valid_redis_key(group_name)\\n if 'all' in group_name:\\n raise ValueError('Choose another group name, reserverd word: \\\"all\\\"')\\n\\n group_name = group_name.lower()\\n try:\\n self.getgroup(group_name)\\n except ValueError:\\n # If does not exists, move to the next step\\n pass\\n else:\\n raise GroupError('Group already exists')\\n\\n if type(members) is not list:\\n raise TypeError('Expect a list. Found: %s' % type(members))\\n\\n if 'any' in members:\\n raise ValueError('Wrong member identified, reserverd word: \\\"any\\\"')\\n \\n gmembers, ipmembers = [], []\\n for member in members:\\n data = isvalidtype(member)\\n if type(data) is IPNetwork:\\n ipmembers.append(str(data))\\n else:\\n gmembers.append(member)\\n\\n key = self.namespace(group_name)\\n ipkey = self.ipnamespace(group_name)\\n with self.redis.pipeline() as pipe:\\n if gmembers:\\n pipe.sadd(key, *gmembers)\\n if ipmembers:\\n pipe.sadd(ipkey, *ipmembers)\\n pipe.sadd(':'.join(('list', 'groups')), key)\\n pipe.execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23aae09bcd96b6fa9b2adbde36a7f4bd\",\n \"score\": \"0.5807303\",\n \"text\": \"def fetch_data(self, connection: oracledb.Connection, group_name: str) -> None:\\n\\n self.stdout.write(f\\\"Adding users to {group_name} group\\\")\\n query = GROUPS_TO_ROLES[group_name]\\n\\n with connection.cursor() as cursor:\\n cursor.execute(query)\\n\\n while True:\\n rows = cursor.fetchmany(1000)\\n\\n if not rows:\\n break\\n\\n if group_name in (\\\"Importer User\\\", \\\"Exporter User\\\"):\\n self.assign_org_permissions(group_name, rows)\\n elif group_name == \\\"Constabulary Contact\\\":\\n self.assign_constabulary_contacts(rows)\\n else:\\n self.assign_user_groups(group_name, rows)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc525004378bb0ff19488838511638f1\",\n \"score\": \"0.57904255\",\n \"text\": \"def create():\\n\\n if request.method == \\\"POST\\\":\\n # Get the data on the user creating the group\\n current_user = list(users.find({\\\"_id\\\": session[\\\"user_id\\\"]}))[0]\\n users_list = [current_user['username']]\\n for key in request.form:\\n if \\\"username\\\" in key:\\n if not check_valid_user(request.form[key]):\\n return apology(\\\"Invalid Username for Member\\\")\\n users_list.append(request.form[key])\\n # Create the group and add it to the database\\n new_group = [{\\n \\\"name\\\": request.form.get(\\\"name\\\"),\\n \\\"users\\\": users_list,\\n \\\"expenses\\\": []\\n }]\\n id = groups.insert_many(new_group)\\n # Redirect the user back to homepage\\n return redirect(\\\"/\\\")\\n else:\\n return render_template(\\\"create.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52cc63773fe3c97c919c2e2fca0103a8\",\n \"score\": \"0.5774964\",\n \"text\": \"def _apply_groups(self, gdb):\\n for chk_grp in self._groups:\\n if not chk_grp in gdb:\\n gdb[chk_grp] = Group(chk_grp, set([self.handle,]))\\n logger.info ('%s: Created new group, %r', self.handle, chk_grp)\\n # ensure membership in existing groups\\n group = gdb[chk_grp]\\n if not self.handle in group.members:\\n group.add (self.handle)\\n group.save ()\\n for gname, group in gdb.iteritems():\\n if gname not in self._groups and self.handle in group.members:\\n group.remove (self.handle)\\n group.save ()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4486681e9579d89a9a5aaa47f8fa2e9\",\n \"score\": \"0.57730323\",\n \"text\": \"def groups_add(self, name):\\n group, _ = Group.objects.get_or_create(name=name)\\n group.user_set.add(self)\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82967eb91ecc9bb3d5dbca34408bd7da\",\n \"score\": \"0.57272804\",\n \"text\": \"def add_groups(self, *group_names):\\n self._used_groups |= set(group_names)\\n\\n self.invalidate_members()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e04fa212a845efea66b1ad3be2f0f7a\",\n \"score\": \"0.571146\",\n \"text\": \"def add2group(user, group):\\n # usermod -a G \\n proc_usermod = subprocess.run(\\n [\\\"usermod\\\", \\\"-a\\\", \\\"-G\\\", group, user]\\n )\\n if proc_usermod.returncode:\\n raise ValueError(\\\"usermod returned non-zero!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f4715cf613244cd0c5c77ad7ec44d92\",\n \"score\": \"0.5691425\",\n \"text\": \"def lti_to_askup_user(self):\\n askup_user, _ = User.objects.get_or_create(username=self.user_id)\\n askup_user.groups = [Group.objects.get(name='Student')]\\n askup_user.save()\\n self.askup_user = askup_user\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d71b56ebe217cc2df406d87acaee6e2\",\n \"score\": \"0.5683542\",\n \"text\": \"async def acl_groups(self, user_identity):\\n pass # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbceb9f35698e6674e1d19191cd8376d\",\n \"score\": \"0.5655622\",\n \"text\": \"def groups(self, groups):\\n \\n self._groups = groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6da21aec076fa354456d417addf3d49\",\n \"score\": \"0.5629212\",\n \"text\": \"def test_changing_user_profile_of_a_user_should_update_groups_of_user(self):\\n self.user.user_profile_id = self.user_profile2\\n for group in self.user_profile2.groups_id:\\n self.assertIn(\\n group, self.user.groups_id,\\n 'The user has not the new dependence!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc790896ae703e954748014441723676\",\n \"score\": \"0.5621036\",\n \"text\": \"def get_members(group_list):\\n username=[]\\n for groupname in group_list:\\n try: \\n group=grp.getgrnam(groupname)\\n username.extend(group.gr_mem)\\n except:\\n pass\\n \\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b547b643aab57837bd015745fdf6b49c\",\n \"score\": \"0.5616088\",\n \"text\": \"def group(self, irc, msg, args, name):\\n group = name\\n \\n try:\\n l = ldap.open(\\\"ldap.geeksoc.org\\\")\\n l.protocol_version = ldap.VERSION3\\n except ldap.LDAPError, e:\\n irc.reply('Error getting info for user: \\\"%s\\\"' % name)\\n return\\n \\n baseDN = \\\"cn=%s,ou=Groups, dc=geeksoc, dc=org\\\" %(group)\\n searchFilter = \\\"(memberUid=*)\\\"\\n \\n try:\\n results = l.search_s(baseDN, ldap.SCOPE_SUBTREE, searchFilter)\\n string = ''\\n for dn, entry in results:\\n for u in entry['memberUid']:\\n string += u + ' '\\n irc.reply(\\\"Group: %s, Members: %s\\\" % (group, string))\\n\\n except ldap.LDAPError, e:\\n irc.reply(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"505a75d78ccf546d612ec984be735a2c\",\n \"score\": \"0.5597802\",\n \"text\": \"def update_account_groups(self, user, account, groups, replace=False):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe72c2ca227d20ef4dff35cae235d8f\",\n \"score\": \"0.5594665\",\n \"text\": \"def add_group(self, username, group):\\n doc = self.__get_user(username)\\n groups = doc['user']['groups']\\n if group not in groups:\\n groups.append(group)\\n\\n self._db.save(doc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c811253b5f88dd55ec85feb14e481f3e\",\n \"score\": \"0.55838674\",\n \"text\": \"def _manage_groups(self):\\n if len(self._argsdict) == 0:\\n self._logger.error('usage: proctor.py group {create, append} [-h]')\\n self._logger.error(\\n \\\"proctor.py group: error: command must include subcommand 'create' or 'append'. Try proctor.py -h.\\\")\\n sys.exit(-1)\\n\\n subcommand = sys.argv[2]\\n if subcommand == 'create':\\n self._create_server_group(self._args.groupname)\\n return\\n if subcommand == 'append':\\n self._add_users_to_server_group(self._args.groupname, self._args.emails)\\n else:\\n raise ValueError(f'Unknown group subcommand: {subcommand}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4469e556bfe69a36c0e795bef45162e\",\n \"score\": \"0.55825984\",\n \"text\": \"def assign_group_numbers_to_data(data_list:list) -> list:\\n list_size = len(data_list)\\n for i in range(list_size):\\n data_list[i] = data_list[i].assign(separate_num=i)\\n return data_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2366be983de91640910a0ed92081683\",\n \"score\": \"0.55739546\",\n \"text\": \"def set_group_name(self, name):\\n self.groupname = name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb0e37022327253915179938cada26c8\",\n \"score\": \"0.5558407\",\n \"text\": \"def shutil_setuid(user = None, group = None, xgroups = None):\\n if group:\\n gid = grp.getgrnam(group).gr_gid\\n os.setgid(gid)\\n logg.debug(\\\"setgid %s for %s\\\", gid, strQ(group))\\n groups = [gid]\\n try:\\n os.setgroups(groups)\\n logg.debug(\\\"setgroups %s < (%s)\\\", groups, group)\\n except OSError as e: # pragma: no cover (it will occur in non-root mode anyway)\\n logg.debug(\\\"setgroups %s < (%s) : %s\\\", groups, group, e)\\n if user:\\n pw = pwd.getpwnam(user)\\n gid = pw.pw_gid\\n gname = grp.getgrgid(gid).gr_name\\n if not group:\\n os.setgid(gid)\\n logg.debug(\\\"setgid %s for user %s\\\", gid, strQ(user))\\n groupnames = [g.gr_name for g in grp.getgrall() if user in g.gr_mem]\\n groups = [g.gr_gid for g in grp.getgrall() if user in g.gr_mem]\\n if xgroups:\\n groups += [g.gr_gid for g in grp.getgrall() if g.gr_name in xgroups and g.gr_gid not in groups]\\n if not groups:\\n if group:\\n gid = grp.getgrnam(group).gr_gid\\n groups = [gid]\\n try:\\n os.setgroups(groups)\\n logg.debug(\\\"setgroups %s > %s \\\", groups, groupnames)\\n except OSError as e: # pragma: no cover (it will occur in non-root mode anyway)\\n logg.debug(\\\"setgroups %s > %s : %s\\\", groups, groupnames, e)\\n uid = pw.pw_uid\\n os.setuid(uid)\\n logg.debug(\\\"setuid %s for user %s\\\", uid, strQ(user))\\n home = pw.pw_dir\\n shell = pw.pw_shell\\n logname = pw.pw_name\\n return {\\\"USER\\\": user, \\\"LOGNAME\\\": logname, \\\"HOME\\\": home, \\\"SHELL\\\": shell}\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a027d668730a828cd37febf3486162aa\",\n \"score\": \"0.5551757\",\n \"text\": \"def addGroupMember(user,group):\\n group.members.append(user)\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e84c45b39f957d34875a30c792d93f8a\",\n \"score\": \"0.5541557\",\n \"text\": \"def _add_to_db(self, principalUID, members):\\n for member in members:\\n yield self.execute(\\n \\\"\\\"\\\"\\n insert into GROUPS (GROUPNAME, MEMBER)\\n values (:1, :2)\\n \\\"\\\"\\\", (principalUID.decode(\\\"utf-8\\\"), member,)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2382bc274127528f780d5a746e3d368a\",\n \"score\": \"0.5526822\",\n \"text\": \"def assign_users(G,participants,breakouts):\\n assign = {}\\n i=0\\n for p in participants:\\n assign_p = {}\\n for l in p[1]['learn']:\\n if l in breakouts:\\n assign_p[l] = 'breakout'\\n else:\\n edges = G.edges(p[0], data=True)\\n common = []\\n \\n for e in edges:\\n if (l in e[2]['common']) and (l!=''):\\n common.append(e[1])\\n else: \\n pass\\n \\n if common != []:\\n assign_p[l] = random.choice(common) \\n \\n assign[p[0]] = assign_p\\n i+=1\\n \\n return assign\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52568660abad3114b7dc5ea6a89c6ad0\",\n \"score\": \"0.55113107\",\n \"text\": \"def create_person_group(self):\\n url = self.base_url + \\\"persongroups/\\\" + self.pg_name\\n response = requests.put(url, headers=self.headers, json={\\\"name\\\" : self.pg_name})\\n if response.status_code == 200 :\\n print(\\\"added \\\" + self.pg_name + \\\" person group\\\")\\n else:\\n print(response.json())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2942802e325cec573289c0a39f4e7480\",\n \"score\": \"0.5499307\",\n \"text\": \"def _add_users_to_server_group(self, group_name, emails_file_name):\\n email_list = self._get_emails_from_file(emails_file_name)\\n self._server.add_users_to_group(group_name, email_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b356c9a7b912dbe4fbf4ca98c6f65cbf\",\n \"score\": \"0.5491542\",\n \"text\": \"def add_group(username, group_name, logger, client):\\n client.users.add_to_group(username, group_name)\\n logger.info('User `{0}` added successfully to group '\\n '`{1}`'.format(username, group_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b7489873492418b0040dbfa39379695\",\n \"score\": \"0.54899526\",\n \"text\": \"def groups(self, groups):\\n\\n self._groups = groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b7489873492418b0040dbfa39379695\",\n \"score\": \"0.54899526\",\n \"text\": \"def groups(self, groups):\\n\\n self._groups = groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b7489873492418b0040dbfa39379695\",\n \"score\": \"0.54899526\",\n \"text\": \"def groups(self, groups):\\n\\n self._groups = groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b7489873492418b0040dbfa39379695\",\n \"score\": \"0.54899526\",\n \"text\": \"def groups(self, groups):\\n\\n self._groups = groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b7489873492418b0040dbfa39379695\",\n \"score\": \"0.54899526\",\n \"text\": \"def groups(self, groups):\\n\\n self._groups = groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec2723071fc401ad36197d6cc2f83f3b\",\n \"score\": \"0.5487621\",\n \"text\": \"def userGroup(self, _):\\n logging.info(\\\"Querying registered groups ...\\\")\\n status, data = self._httpRequest(\\\"GET\\\", \\\"/reqmgr/reqMgr/group\\\")\\n groups = json.loads(data)\\n logging.info(data)\\n logging.info(\\\"Querying registered users ...\\\")\\n status, data = self._httpRequest(\\\"GET\\\", \\\"/reqmgr/reqMgr/user\\\")\\n logging.info(data)\\n logging.info(\\\"Querying groups membership ...\\\")\\n for group in groups:\\n status, data = self._httpRequest(\\\"GET\\\", \\\"/reqmgr/reqMgr/group/%s\\\" % group)\\n logging.info(\\\"Group: '%s': %s\\\" % (group, data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b32c0afc9f0eb98da8b3f77f35d6f8e3\",\n \"score\": \"0.54645133\",\n \"text\": \"def _parse_data_to_users(self):\\n while True:\\n dn, status, label, value = yield\\n user = self._parse_dn(dn)\\n\\n if user not in self.users:\\n u = User(user)\\n self.users[user] = u\\n else:\\n u = self.users[user]\\n u.add_data({'Status': status, label: value})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"414f34fbbb8bd679a9d2e0a78078e6e7\",\n \"score\": \"0.5460207\",\n \"text\": \"def setGroup(self, node):\\n\\t\\tself.group = node.getGroup()\\n\\t\\tself.groupName = node.getGroupName()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d863b6794e09c27ed41cb8c007fac0b3\",\n \"score\": \"0.5457117\",\n \"text\": \"def set(self, group_name: str, tags: List[str]) -> None:\\n self.set_group[group_name] = tags\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d863b6794e09c27ed41cb8c007fac0b3\",\n \"score\": \"0.5457117\",\n \"text\": \"def set(self, group_name: str, tags: List[str]) -> None:\\n self.set_group[group_name] = tags\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72708b75875ad7ea17c522f4749c30d5\",\n \"score\": \"0.5441494\",\n \"text\": \"def setUp(self):\\n self.groups = GroupProfile.objects.all()\\n for gr in GroupProfile.objects.all():\\n self.members[gr.name] = User.objects.filter(groups=gr.group)\\n # create 2 groups\\n #self.users.append(create_user(\\\"__user1__\\\", \\\"Joe\\\", \\\"Smith\\\"))\\n #self.users.append(create_user(\\\"__user2__\\\", \\\"Alice\\\", \\\"Jones\\\"))\\n #self.users.append(create_user(\\\"__user3__\\\", \\\"Mary\\\", \\\"Willson\\\"))\\n #self.users.append(create_user(\\\"__user4__\\\", \\\"Mary\\\", \\\"Willson\\\"))\\n #self.users.append(create_user(\\\"__user5__\\\", \\\"Mary\\\", \\\"Willson\\\"))\\n #self.users.append(create_user(\\\"__user6__\\\", \\\"Mary\\\", \\\"Willson\\\"))\\n #self.members[\\\"__group1__\\\"] = [self.users[0], self.users[3],]\\n #self.members[\\\"__group2__\\\"] = [self.users[1], self.users[2], self.users[3], self.users[4],]\\n #self.groups.append(create_group(\\\"__group1__\\\", self.users[0], self.members[\\\"__group1__\\\"]))\\n #self.groups.append(create_group(\\\"__group2__\\\", self.users[1], self.members[\\\"__group2__\\\"]))\\n # create 3 goals\\n #self.goals[\\\"__group1__\\\"] = [create_goal(\\\"__goal1.1__\\\", self.groups[0], self.users[0]),\\n # create_goal(\\\"__goal1.2__\\\", self.groups[0], self.users[0]),\\n # create_goal(\\\"__goal1.3__\\\", self.groups[0], self.users[2]),\\n # ]\\n #self.goals[\\\"__group2__\\\"] = [create_goal(\\\"__goal2.1__\\\", self.groups[1], self.users[1]),\\n # create_goal(\\\"__goal2.2__\\\", self.groups[1], self.users[2]),\\n # ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"841fda894e959596e9156215325e0aa0\",\n \"score\": \"0.54232115\",\n \"text\": \"def save(self, *args, **kwargs):\\n\\t\\tbase_group = Group.objects.get_or_create(name='usuario_base')[0]\\n\\t\\tself.user.groups.add(base_group)\\n\\t\\treturn super(BaseUser, self).save(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a08180febd23e08d900ea795cafad2e9\",\n \"score\": \"0.5408937\",\n \"text\": \"def get_groups(name):\\r\\n groups = {}\\r\\n isa_structure = [\\\"Project\\\", \\\"Investigation\\\", \\\"Study\\\", \\\"Assay\\\"]\\r\\n groupname = name.split('/')[-1]\\r\\n groups[isa_structure[len(name.split('/'))-1]] = groupname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c1113417c495c49ed6fd7b5a1473f74\",\n \"score\": \"0.54006624\",\n \"text\": \"def test_add_mft_user_group(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5189cbb611d0cb00f6617611ddfb5b4\",\n \"score\": \"0.53828937\",\n \"text\": \"async def add_group_user(request: web.Request):\\n user_id = request[RQT_USERID_KEY]\\n gid = request.match_info[\\\"gid\\\"]\\n new_user_in_group = await request.json()\\n\\n assert \\\"uid\\\" in new_user_in_group or \\\"email\\\" in new_user_in_group # nosec\\n\\n new_user_id = new_user_in_group[\\\"uid\\\"] if \\\"uid\\\" in new_user_in_group else None\\n new_user_email = (\\n parse_obj_as(LowerCaseEmailStr, new_user_in_group[\\\"email\\\"])\\n if \\\"email\\\" in new_user_in_group\\n else None\\n )\\n\\n await api.add_user_in_group(\\n request.app,\\n user_id,\\n gid,\\n new_user_id=new_user_id,\\n new_user_email=new_user_email,\\n )\\n raise web.HTTPNoContent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0496cf3dcf5cc19e37002ea22f8ed59d\",\n \"score\": \"0.5369904\",\n \"text\": \"def process_mesh_vertex_groups(mesh_data, obj_user):\\n mesh_data[\\\"vertex_groups\\\"] = []\\n\\n if obj_user and obj_user.vertex_groups:\\n for vertex_group in obj_user.vertex_groups:\\n vertex_group_data = OrderedDict()\\n vertex_group_data[\\\"name\\\"] = vertex_group.name\\n vertex_group_data[\\\"index\\\"] = vertex_group.index\\n\\n mesh_data[\\\"vertex_groups\\\"].append(vertex_group_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae6942e3b6855109bf33966566e2727a\",\n \"score\": \"0.5366977\",\n \"text\": \"def test_get_mft_user_groups(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5aba429aa69ac2885b09652b9b0b319\",\n \"score\": \"0.53580564\",\n \"text\": \"def test_put_groups_id(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3736bfbf09e33862d5d524cd4cdc4848\",\n \"score\": \"0.5339213\",\n \"text\": \"def assign_org_permissions(self, group_name: str, rows: list[tuple[str, str, int]]) -> None:\\n for username, roles, org_id in rows:\\n user = User.objects.get(username=username)\\n\\n if group_name == \\\"Importer User\\\":\\n org = Importer.objects.get(pk=org_id)\\n else:\\n org = Exporter.objects.get(pk=org_id)\\n\\n assign_manage = \\\":AGENT_APPROVER\\\" in roles\\n\\n organisation_add_contact(org, user, assign_manage)\\n\\n # Check user should have view permissions\\n if \\\":VIEW\\\" not in roles:\\n obj_perms = get_org_obj_permissions(org)\\n remove_perm(obj_perms.view, user, org)\\n\\n # Check user should have edit permissions\\n if \\\":EDIT_APP\\\" not in roles and \\\":VARY_APP\\\" not in roles:\\n obj_perms = get_org_obj_permissions(org)\\n remove_perm(obj_perms.edit, user, org)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"717b958d5b1bcad7f756ea3ecd336425\",\n \"score\": \"0.533714\",\n \"text\": \"def set_default_names(self):\\n for i, group in enumerate(self.groups):\\n group.name = f'{self.name}-{i + 1}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a3adf877e471b484d755a7e87374f9\",\n \"score\": \"0.53289646\",\n \"text\": \"def assign(self):\\r\\n for symbolData in self.symbolData:\\r\\n symbolData.assign(self.group)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd6892bdcadfafbb86815bc9b5fde913\",\n \"score\": \"0.5322197\",\n \"text\": \"def groups(user):\\n return [group.name for group in user.groups.all()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2145982f72284691b8d0411466ffff25\",\n \"score\": \"0.5309958\",\n \"text\": \"def group_create(name, gid=None):\\n\\toptions = []\\n\\tif gid:\\n\\t\\toptions.append(\\\"-g '%s'\\\" % (gid))\\n\\tsudo(\\\"groupadd %s '%s'\\\" % (\\\" \\\".join(options), name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f7da911a16a0a57a0bd1b070f662ecc\",\n \"score\": \"0.5302245\",\n \"text\": \"def test_update_mft_user_group(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2789cfba02137174c5782ac4a5f9ac26\",\n \"score\": \"0.5300633\",\n \"text\": \"def group2db(user, user_group):\\n group = Group()\\n group.user_group = user\\n group.gitlab_id = user_group.id\\n group.name = user_group.name\\n group.description = user_group.description\\n group.visibility = user_group.visibility\\n group.ssh_url_to_repo = user_group.ssh_url_to_repo\\n group.http_url_to_repo = user_group.http_url_to_repo\\n group.web_url = user_group.web_url\\n group.name_with_namespace = user_group.name_with_namespace\\n group.path = user_group.path\\n group.path_with_namespace = user_group.path_with_namespace\\n group.created_at = user_group.created_at\\n group.last_activity_at = user_group.last_activity_at\\n\\n return group\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81c13fbd0b39aaaedae553ce70ece968\",\n \"score\": \"0.5298014\",\n \"text\": \"def load_groups() -> dict:\\n dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\\n os.chdir(dir)\\n os.chdir('data')\\n os.chdir('server_data')\\n with open('groups.csv', mode='r') as infile:\\n reader = csv.reader(infile)\\n group = {rows[0].upper(): [rows[1].upper(), rows[2].upper(), rows[3].upper(), rows[4].upper(),\\n rows[5].upper(), rows[6].upper()] for rows in reader}\\n\\n for key in group.keys():\\n group[key] = list(filter(None, group[key]))\\n\\n return group\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b60082d7537b9b30fb2354c5c63a1abf\",\n \"score\": \"0.5295297\",\n \"text\": \"def _set_user_permissions_for_volumes(users, volumes):\\n\\n group_name = 'volumes'\\n\\n user_data_script_section = f\\\"\\\"\\\"\\ngroupadd {group_name}\\n\\\"\\\"\\\"\\n\\n for user in users:\\n user_data_script_section += f\\\"\\\"\\\"\\nusermod -a -G {group_name} {user.login}\\n\\\"\\\"\\\"\\n for volume in volumes:\\n user_data_script_section += f\\\"\\\"\\\"\\nchgrp -R {group_name} {volume.mount}\\nchmod -R 2775 {volume.mount} \\n\\\"\\\"\\\"\\n\\n return user_data_script_section\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81aba641c992f48111b2dfb07da610b0\",\n \"score\": \"0.52936935\",\n \"text\": \"def test_update_users_of_a_group(self):\\n profile = self.env['res.users'].create(\\n {'name': 'P', 'login': 'p_login', 'is_user_profile': True})\\n user = self.env['res.users'].create(\\n {'name': 'U', 'login': 'u_login', 'user_profile_id': profile.id})\\n self.group1.with_context(use_pdb=True).users |= profile\\n self.assertIn(\\n self.group1, user.groups_id,\\n 'User U should have Group 1 in dependencies!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800fd3d88b5063cd87449f3e8edd85e6\",\n \"score\": \"0.52934253\",\n \"text\": \"def add_user_to_group(sender, instance, created, **kwargs):\\n\\n try:\\n if created:\\n if instance.role is 'instructor':\\n instance.groups.add(Group.objects.get(pk=3))\\n else:\\n instance.groups.add(Group.objects.get(pk=4))\\n except Group.DoesNotExist:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"046de7ce7a8f16a32ebe2e6ff137f468\",\n \"score\": \"0.5292615\",\n \"text\": \"def get_all_group_name(self, username):\\n return self.fetch_to_list(self.fetch_data(\\\"select distinct Group_name from Task where username = '{}';\\\".format(username)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"794f46ba0f19aa7047f9292b677d8132\",\n \"score\": \"0.52788293\",\n \"text\": \"def set_group(self, group):\\n if type(group) in (list, tuple):\\n self.groups = group\\n else:\\n self.groups = (group,)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"492a2123572f8acfb83bd63edf84d096\",\n \"score\": \"0.5275229\",\n \"text\": \"def save(self, *args, **kwargs):\\n # Save the group instance\\n group = super(CSVGroupCreationForm, self).save(*args, **kwargs)\\n\\n # Add users to the group\\n group.user_set.add(*self.__initial_users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0496bed4c4120e0f6f4deb600b61dff7\",\n \"score\": \"0.52697587\",\n \"text\": \"def _link_groups_and_permissions(self, user: CustomUser) -> CustomUser:\\n\\n groups, permissions = self.data_groups, self.data_user_permissions\\n groups, permissions = [groups] if isinstance(groups, str) else groups, [permissions] if isinstance(permissions, str) else permissions\\n\\n try:\\n if groups is not None:\\n if len(groups) > 0 and len(groups[0]) > 0:\\n user.groups.set([Group.objects.get_by_natural_key(group) for group in groups])\\n else:\\n user.groups.set([])\\n\\n if permissions is not None:\\n if len(permissions) > 0 and len(permissions[0]) > 0:\\n user.user_permissions.set([Permission.objects.get(codename=permission) for permission in permissions])\\n else:\\n user.user_permissions.set([])\\n\\n except ObjectDoesNotExist as error:\\n raise ObjectNotFound(_(str(error)))\\n\\n user.save()\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a472ce3f2d630da3e873c74e7e1aab39\",\n \"score\": \"0.5267011\",\n \"text\": \"def change_user_group(self, user_group_id, user_group_data):\\r\\n return self.put(\\r\\n 'usergroups/{}'.format(user_group_id),\\r\\n payload=user_group_data).json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"949ebc48dfeadba08e7460e15c7ec6e4\",\n \"score\": \"0.5263152\",\n \"text\": \"def test_changing_group_dependencies_should_update_groups_of_users(self):\\n self.group1.implied_ids = [(4, self.group2.id)]\\n self.assertIn(\\n self.group2, self.user.groups_id,\\n 'The user has not the new dependence!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a389cfaf84cdf2963673a4b25d96377f\",\n \"score\": \"0.5262883\",\n \"text\": \"def create_grp(conn, id, name):\\n cur = conn.cursor()\\n try:\\n info = (id, name)\\n cur.execute(d.sql_cmd['createGrp'], info)\\n except Error as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"411ee83f11e7fc621648da4cc9f73c20\",\n \"score\": \"0.5262673\",\n \"text\": \"def group_members(self, group_name):\\n if group_name not in self._group_members:\\n members = self.rest.get(\\n f'/a/groups/{self.group(group_name)[\\\"group_id\\\"]}/members').json\\n self._group_members[group_name] = [{\\n \\\"id\\\": str(member['_account_id']),\\n \\\"name\\\": member.get('name', ''),\\n \\\"username\\\": member.get('username'),\\n \\\"github_id\\\": str(self.oauth_id(member['_account_id'])),\\n \\\"github_username\\\":\\n str(self.oauth_id(member['_account_id'])),\\n \\\"email\\\": member['email'] if 'email' in member else ''\\n } for member in members]\\n return self._group_members[group_name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe9c70f99026eff468bc4adecae349d\",\n \"score\": \"0.5248733\",\n \"text\": \"def groupfinder(userid, request) -> List[str]:\\n user = request.user\\n groups = None\\n if user:\\n groups = []\\n for group in user.groups:\\n groups.append(\\\"group:%s\\\" % group.name)\\n groups.append(\\\"user:%s\\\" % user.id)\\n return groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed0789646c53fc3f88ba92497a9dc88d\",\n \"score\": \"0.5246923\",\n \"text\": \"def addGroup(name):\\n group = Group()\\n group.name = name\\n group.members.append(current_user)\\n db.session.add(group)\\n db.session.commit()\\n return group.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13ddc95537ce62988e09b5b0efdb069c\",\n \"score\": \"0.5245989\",\n \"text\": \"def _get_groups(self, username):\\n\\n groups = set([username])\\n for provider in self.group_providers:\\n for group in provider.get_permission_groups(username):\\n groups.add(group)\\n \\n # TODO: Necessary? What is this for?\\n perms = PermissionSystem(self.env).get_all_permissions()\\n repeat = True\\n while repeat:\\n repeat = False\\n for subject, action in perms:\\n if subject in groups and action.islower() and action not in groups:\\n groups.add(action)\\n repeat = True \\n \\n return groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51cc622f8d4bb17d15f4266aa6690397\",\n \"score\": \"0.5234191\",\n \"text\": \"def add_group(self, path):\\n self.pwd.create_group[path]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adbded0c3dbf92d87fbc5763f24179b0\",\n \"score\": \"0.5225458\",\n \"text\": \"def parse_group_file(group_file):\\n\\n user2groups = {}\\n groups_encountered = set()\\n\\n with group_file:\\n for index, group_data in enumerate(group_file.read().splitlines()):\\n group_data = group_data.split(\\\":\\\")\\n try:\\n validate_group_format(group_data, index + 1, groups_encountered)\\n except Exception as e:\\n sys.exit(str(e))\\n\\n group_name = group_data[GroupFieldEnum.GROUPNAME]\\n groups_encountered.add(group_name)\\n users = group_data[GroupFieldEnum.USERS]\\n if users == '':\\n continue\\n for user in users.split(\\\",\\\"):\\n if user not in user2groups:\\n user2groups[user] = []\\n\\n user2groups[user].append(group_name)\\n\\n return user2groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23ff2db1bf879c76aefbc7abad2ab861\",\n \"score\": \"0.5221914\",\n \"text\": \"def default_groups(database):\\n return create_default_groups()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7cd5fe7c0355d4a7defbd2519278c72\",\n \"score\": \"0.52143776\",\n \"text\": \"def add_to_team(self, user, groups=None, **kw):\\n # TODO: user argument should be renamed to userid for clarity\\n # however doing so now would break backwards compatibility\\n data = kw.copy()\\n data[\\\"user\\\"] = user\\n if groups is not None:\\n data[\\\"groups\\\"] = groups = set(groups)\\n members = self.members\\n if not user or user not in members:\\n data[\\\"UID\\\"] = uid = getUtility(IUUIDGenerator)()\\n key = user or uid\\n data[\\\"_mtime\\\"] = DateTime()\\n if groups is None:\\n data[\\\"groups\\\"] = groups = set()\\n members[key] = data\\n for name, func in self.counters:\\n if func(data):\\n if name not in self.context._counters:\\n self.context._counters[name] = Length()\\n self.context._counters[name].change(1)\\n membership = self.membership_factory(self, data)\\n membership.handle_added()\\n membership._update_groups(set(), groups)\\n notify(TeamMemberAddedEvent(self.context, membership))\\n self.context.reindexObject(idxs=[\\\"workspace_members\\\", \\\"workspace_leaders\\\"])\\n else:\\n membership = self.membership_factory(self, self.members[user])\\n membership.update(data)\\n return membership\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f9e9835ead6b52622802ac6a80fc4b9\",\n \"score\": \"0.5209074\",\n \"text\": \"def add_to_group(switch, member, group):\\n if switch not in GroupManager.group_to_member:\\n GroupManager.group_to_member[switch] = {}\\n\\n if group not in GroupManager.group_to_member[switch]:\\n GroupManager.group_to_member[switch][group] = []\\n\\n if member not in GroupManager.group_to_member[switch].get(group, []):\\n GroupManager.group_to_member[switch].get(group).append(member)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aad5949c3a50ecd22346e533de342dc2\",\n \"score\": \"0.52085155\",\n \"text\": \"def editgroup(self, group_name, members):\\n is_valid_redis_key(group_name)\\n group_name = group_name.lower()\\n group, groupips = self.getgroup(group_name)\\n if not group and not groupips:\\n raise GroupError('Group \\\"%s\\\" does not exists' % group_name)\\n if type(members) is not list:\\n raise TypeError('Expect a list. Found: %s' % type(members))\\n\\n gmembers, ipmembers = [], []\\n for member in members:\\n data = isvalidtype(member)\\n if type(data) is IPNetwork:\\n ipmembers.append(str(data))\\n else:\\n gmembers.append(member)\\n\\n if 'any' in members:\\n raise ValueError('Could not add \\\"any\\\" type members')\\n\\n key = self.namespace(group_name)\\n ipkey = self.ipnamespace(group_name)\\n\\n with self.redis.pipeline() as pipe:\\n if gmembers:\\n pipe.sadd(key, *gmembers)\\n if ipmembers:\\n pipe.sadd(ipkey, *ipmembers)\\n pipe.sadd(':'.join(('list', 'groups')), key)\\n pipe.execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a08480f5ab64dbd5e55cdb6b54d41ba2\",\n \"score\": \"0.52025557\",\n \"text\": \"def switch_user_type(self):\\n self.user.groups.all().delete()\\n user_type = self.get_user_type()\\n if user_type == 'performer':\\n self.user.groups.add(Group.objects.get_or_create(name=\\\"Clients\\\")[0])\\n else:\\n self.user.groups.add(Group.objects.get_or_create(name=\\\"Performers\\\")[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efdbd3138fd3a0dcd8d2aa2d380dd219\",\n \"score\": \"0.51879734\",\n \"text\": \"def set_perms(header):\\n payload = [\\n {'group_id': '36124', 'permission': 'admin'},\\n {'group_id': '41497', 'permission': 'write'},\\n {'group_id': '45777', 'permission': 'read'},\\n {'group_id': '36180', 'permission': 'write'}\\n ]\\n for group_perms in payload:\\n try:\\n r = requests.post(base_url + \\\"/repositories/\\\" + org + \\\"/\\\" + new_repo + \\\"/groups/\\\", json=group_perms, headers=header)\\n r.raise_for_status()\\n except requests.exceptions.HTTPError as err:\\n print red(\\\"Setting perms failed: {}\\\").format(err)\\n sys.exit(1)\\n print green(\\\"All perms set.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e809cff4c7d178bfc464bd4130b4d1c4\",\n \"score\": \"0.5174541\",\n \"text\": \"def write_groups(out_file, groupname):\\r\\n print(\\\"To create a single group please just enter the main group name i.e. Group Name\\\")\\r\\n print('To create a subgroup to an exisitng group, please enter /Group Name/Subgroup Name/etc/etc/')\\r\\n print() \\r\\n attributes = {}\\r\\n print(\\\"Enter attributes for\\\", groupname)\\r\\n meta = input(\\\"Is there a metadata file? (Y/N): \\\")\\r\\n if meta == \\\"Y\\\" or meta == \\\"y\\\":\\r\\n metapath = input(\\\"Enter metadata file path: \\\")\\r\\n with open(metapath, 'r') as metafile:\\r\\n for line in metafile:\\r\\n line = line.split('\\\\t')\\r\\n item = line[0].strip('\\\\n')\\r\\n value = line[-1].strip('\\\\n')\\r\\n if item in attributes.keys():\\r\\n attributes[item].append(value)\\r\\n else:\\r\\n attributes[item] = [value]\\r\\n else:\\r\\n input_attributes = input(\\\"Enter an attribute followed by a value. i.e. Project Name: iknowit, Date: 04-11-2019: \\\")\\r\\n for attribute in input_attributes.split(','):\\r\\n attribute = attribute.split(':')\\r\\n attributes[attribute[0].strip(' ')] = attribute[1].strip(' ')\\r\\n data_file = h5py.File(out_file, 'a')\\r\\n dset = data_file.create_group(groupname)\\r\\n for k, v in attributes.items():\\r\\n dset.attrs[k] = v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2735c4bb13c4d07c7d21299c9de6526\",\n \"score\": \"0.5172792\",\n \"text\": \"def _group(self, data, group_assignments):\\n groups = np.unique(group_assignments)\\n group_to_data = defaultdict(dict)\\n for group in groups:\\n for k, v in data.items():\\n if isinstance(v, np.ndarray):\\n assert len(group_assignments) == len(v), \\\\\\n f'group_assignments and \\\"{k}\\\" must be the same length'\\n group_to_data[group][k] = v[group_assignments == group]\\n return group_to_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07c14e4dd0b87e3c36d397faf69ddbbd\",\n \"score\": \"0.51625884\",\n \"text\": \"def _create_server_group(self, group_name):\\n self._server.create_group(group_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c414d67009ccf48d55a2d1b2c47dbf37\",\n \"score\": \"0.5162409\",\n \"text\": \"def save(self, *args, **kwargs):\\n\\t\\tadmin_group = Group.objects.get_or_create(name='administrator')[0]\\n\\t\\tself.user.groups.add(admin_group)\\t\\t\\n\\t\\treturn super(AdminUser, self).save(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"483b86fe6581bed3dc5d5a7631019a97\",\n \"score\": \"0.5162018\",\n \"text\": \"def set_groups(self):\\n self.groups = self.sub_data.groupby([self.name]) # List of grouped data by this attribute\\n\\n # For every distinct value, create new sub attribute object\\n for i in range(len(self.values)):\\n data = self.groups.get_group((self.values[i])) # Sub data set for the value\\n\\n # New object\\n new_sub_att = sa.SubAttribute(self.values[i], data, self.classes)\\n\\n # Add new sub attribute to the list\\n self.sub_values.append(new_sub_att)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07036db69edd89a7df5c6deafff57d8a\",\n \"score\": \"0.51615137\",\n \"text\": \"def __get_groups(self):\\n search_filter = '(objectClass=group)'\\n try:\\n raw_result = self.ldap_con.search_s(\\n settings.AUTH_LDAP_GROUP_SEARCH, ldap.SCOPE_SUBTREE, search_filter)\\n except ldap.NO_SUCH_OBJECT as e:\\n raise\\n\\n for group in raw_result:\\n group_cn = group[1]['cn'][0].lower()\\n try:\\n alphanumeric_dash_underscore(group_cn)\\n except:\\n logging.warn(\\n 'group %s is not valid for the name of a mailing list' %\\n group_cn)\\n continue\\n self.ldap_groups[group_cn] = group[1]['member']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29f02e5130953452b5de5091f50010ee\",\n \"score\": \"0.5155799\",\n \"text\": \"def registerServerGroup(self, name, hosts):\\n pgcCmd = PGC_HOME + os.sep + \\\"pgc register GROUP --json \\\\\\\"\\\" + name + \\\"\\\\\\\"\\\"\\n pgcProcess = subprocess.Popen(pgcCmd, stdout=subprocess.PIPE, shell=True)\\n pgcData = pgcProcess.communicate()\\n yield self.session.publish('com.bigsql.onRegisterServerGroup', pgcData)\\n import util\\n util.map_group_hosts(name, hosts, p_group_id=0, isJson=False, printMsg=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c80c42887a4bab920bc73b599236a3\",\n \"score\": \"0.5152189\",\n \"text\": \"def test_get_groups(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679ae5d67e6b2ad605aa0480dc1502d0\",\n \"score\": \"0.51420146\",\n \"text\": \"def test_jenkins_groups(User, JenkinsRoleDefaults):\\n jenkins_name = JenkinsRoleDefaults[\\\"jenkins_name\\\"]\\n jenkins_groups = User(jenkins_name).groups\\n assert set([\\\"adm\\\", \\\"sudo\\\", \\\"jenkins\\\"]).issubset(jenkins_groups)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de970ca1a6a9942d9270d95f2d5641af\",\n \"score\": \"0.5141631\",\n \"text\": \"def addgrp(user=None, grp=None):\\n if not grp:\\n print(\\\"No group name provided.. exiting.\\\")\\n sys.exit()\\n logger.scriptlog(\\\"\\\\n%s\\\" % env.host)\\n addgroup = sudo(\\\"groupadd %s\\\" % grp)\\n if addgroup.return_code == 0:\\n logger.scriptlog(\\\"Group added %s\\\" % grp)\\n elif addgroup.return_code == 9:\\n logger.scriptlog(\\\"Group already exists: %s\\\" % grp)\\n else:\\n logger.scriptlog(\\\"An error occured while adding group: %s\\\" % grp)\\n try:\\n sudo(\\\"usermod -aG %s %s\\\" % (grp, user))\\n logger.scriptlog(\\\"Added %s to group: %s\\\" % (user, grp))\\n except Exception as e:\\n logger.error(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da5c85a627016a1dcd73db9987144ca8\",\n \"score\": \"0.51397866\",\n \"text\": \"def addGroupToDevice(self):\\n groups = self.groups\\n if self.groupname not in groups:\\n groups.append(self.groupname)\\n groups.sort()\\n self.device.setGroups(groups)\\n commit()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":497,"cells":{"query_id":{"kind":"string","value":"98152f6bc8440d2b9e45186f6f94403b"},"query":{"kind":"string","value":"GIVEN a flask application WHEN user makes advanced search with specified search inputs THEN return recipes satisfy search parameters"},"positive_passages":{"kind":"list like","value":[{"docid":"2613a22a3e801f9c6d7ed8349eb75375","score":"0.67837054","text":"def test_advanced_search_results_applies_filter_to_recipes(self, test_client, db, search_term, allergies, diet,\n cal_range, time):\n response = advanced_search_function(test_client, search_term=search_term, diet_type=diet, cal_range=cal_range,\n time=time)\n assert response.status_code == 200\n assert search_term.encode() in response.data\n\n min_cal = int(cal_range.split(',')[0])\n max_cal = int(cal_range.split(',')[1])\n user_allergies = list(map(int, allergies))\n\n response_recipe_ids = get_recipe_ids(test_client, response)\n\n from app.models import Recipes\n recipes = db.session.query(Recipes).filter(Recipes.recipe_id.in_(response_recipe_ids)).all()\n\n for recipe in recipes:\n assert recipe.diet_type[0].diet_type_id >= diet\n assert search_term in recipe.recipe_name.lower()\n assert min_cal <= int(recipe.nutrition_values.calories) <= max_cal\n assert recipe.total_time <= time\n\n # recipe_allergies = [allergy.allergy_id for allergy in recipe.allergies]\n # for allergy in user_allergies:\n # assert allergy not in recipe_allergies","title":""}],"string":"[\n {\n \"docid\": \"2613a22a3e801f9c6d7ed8349eb75375\",\n \"score\": \"0.67837054\",\n \"text\": \"def test_advanced_search_results_applies_filter_to_recipes(self, test_client, db, search_term, allergies, diet,\\n cal_range, time):\\n response = advanced_search_function(test_client, search_term=search_term, diet_type=diet, cal_range=cal_range,\\n time=time)\\n assert response.status_code == 200\\n assert search_term.encode() in response.data\\n\\n min_cal = int(cal_range.split(',')[0])\\n max_cal = int(cal_range.split(',')[1])\\n user_allergies = list(map(int, allergies))\\n\\n response_recipe_ids = get_recipe_ids(test_client, response)\\n\\n from app.models import Recipes\\n recipes = db.session.query(Recipes).filter(Recipes.recipe_id.in_(response_recipe_ids)).all()\\n\\n for recipe in recipes:\\n assert recipe.diet_type[0].diet_type_id >= diet\\n assert search_term in recipe.recipe_name.lower()\\n assert min_cal <= int(recipe.nutrition_values.calories) <= max_cal\\n assert recipe.total_time <= time\\n\\n # recipe_allergies = [allergy.allergy_id for allergy in recipe.allergies]\\n # for allergy in user_allergies:\\n # assert allergy not in recipe_allergies\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d34d8d2e127a11f836cd282e7367d401","score":"0.7443953","text":"def search_recipes():\n if request.method == 'POST':\n cuisine = request.form.get(\"cuisine_name_filter\")\n allergen = request.form.get(\"allergen_name_filter\")\n meal = request.form.get(\"meal_type_filter\")\n search_item = request.form.get('search_item')\n \n \n query = {}\n if cuisine and allergen and meal:\n filter_recipes = mongo.db.recipes.find({\"cuisine_name\":cuisine, \"allergen_name\":allergen, \"meal_type\":meal})\n query = {cuisine,allergen,meal}\n \n elif cuisine and allergen:\n filter_recipes = mongo.db.recipes.find({\"cuisine_name\":cuisine, \"allergen_name\":allergen})\n query = {cuisine,allergen}\n\n elif cuisine and meal:\n filter_recipes = mongo.db.recipes.find({\"cuisine_name\":cuisine, \"meal_type\":meal})\n query = {cuisine,meal}\n\n elif allergen and meal:\n filter_recipes = mongo.db.recipes.find({\"allergen_name\":allergen, \"meal_type\":meal})\n query = {allergen,meal}\n\n elif cuisine:\n filter_recipes=mongo.db.recipes.find({\"cuisine_name\":cuisine})\n query = {cuisine}\n\n elif allergen:\n filter_recipes = mongo.db.recipes.find({\"allergen_name\":allergen})\n query = {allergen}\n\n elif meal:\n filter_recipes = mongo.db.recipes.find({\"meal_type\":meal})\n query = {meal}\n \n else:\n # create the index\n filter_recipes = mongo.db.recipes.create_index( [(\"$**\", 'text')] )\n page, per_page, offset = get_page_args(page_parameter = 'page',\n per_page_parameter = 'per_page')\n\n # search with the search term that came through the form\n cursor = mongo.db.recipes.find({ \"$text\": { \"$search\": search_item } })\n total_filter_recipes = cursor.count()\n pagination = Pagination(page = page,\n per_page = ITEMS_PER_PAGE,\n total = total_filter_recipes,\n record_name = 'Recipes')\n recipes = [recipe for recipe in cursor]\n \n # send recipes to page\n return render_template('search.html', \n recipes = recipes, \n query = search_item,\n page = page,\n per_page = per_page,\n pagination = pagination)\n \n \n page, per_page, offset = get_page_args(page_parameter = 'page',\n per_page_parameter = 'per_page')\n total_filter_recipes = filter_recipes.count()\n recipes_fetched = filter_recipes.skip((page - 1)*ITEMS_PER_PAGE).limit(ITEMS_PER_PAGE)\n pagination = Pagination(page = page,\n per_page = ITEMS_PER_PAGE,\n total = total_filter_recipes,\n record_name = 'Recipes')\n \n return render_template('search.html',\n recipes = recipes_fetched,\n page = page,\n query = query,\n per_page = per_page,\n pagination = pagination)","title":""},{"docid":"0e342c8a03e29122482602bab0253101","score":"0.72639793","text":"def search():\n # Search Buttons function\n query = request.form.get(\"query\")\n recipes = list(mongo.db.recipes.find({\"$text\": {\"$search\": query}}))\n return render_template(\"recipes.html\", recipes=recipes)","title":""},{"docid":"8e245e87fbc36fd84f22a94e0a8498b9","score":"0.7207813","text":"def search_by_recipe(recipe_name):\n # recipe_name_list=ingredient_name.split(\" \")\n # recipe_name_format=\"+\".join(recipe_name_list)\n \n searched_recipes=get_search_by_recipe(recipe_name)\n \n return render_template('searches.html',recipes=searched_recipes)","title":""},{"docid":"a9ea5e1a1b3cd7d4a945b44adad9775d","score":"0.7062741","text":"def search():\n query = request.form.get(\"query\")\n recipes = list(mongo.db.recipes.find({\"$text\": {\"$search\": query}}))\n return render_template(\"recipes.html\", recipes=recipes)","title":""},{"docid":"5989decddcf5552f4e4ebc7f3e631f0f","score":"0.7018663","text":"def search():\n query = request.form.get(\"query\")\n recipes = mongo.db.recipes.find({\"$text\": {\"$search\": query}})\n return render_template('recipes.html', recipes=recipes,\n categories=mongo.db.categories.find())","title":""},{"docid":"f2322ac395b81a7064b5ff49a6f0cad1","score":"0.6964389","text":"def search():\n # Wildcard text search index\n mongo.db.recipes.create_index([(\"$**\", pymongo.TEXT)])\n # Results per page\n p_limit = 9\n current_page = int(request.args.get('current_page', 1))\n # Input term for search query\n word_search = request.args.get('word_search')\n # Results for search sorted by ID\n results = mongo.db.recipes.find({'$text': {'$search': str(word_search)}}, {\"score\": {\"$meta\": 'textScore'}}).sort('_id', pymongo.ASCENDING).skip((current_page -1)*p_limit).limit(p_limit)\n # Pagination\n results_count = mongo.db.recipes.find({'$text': {'$search': str(word_search)}}).count()\n results_pages = range(1, int(math.ceil(results_count / p_limit)) + 1)\n total_page_no = int(math.ceil(results_count/p_limit))\n \n # Most Popular recipes - appear when there are no results to the user's query\n recommended = mongo.db.recipes.find().sort(\"favourite_count\", pymongo.DESCENDING).limit(3)\n \n return render_template(\"search.html\", \n p_limit = p_limit,\n current_page=current_page, \n results_count=results_count,\n word_search=word_search,\n results=results,\n results_pages=results_pages,\n total_page_no=total_page_no,\n recommended=recommended)","title":""},{"docid":"4c87406c5498045544ca25cbc0bd939b","score":"0.6890836","text":"def search_by_ingredients(ingredient_name):\n # recipe_name_list=ingredient_name.split(\" \")\n # recipe_name_format=\"+\".join(recipe_name_list)\n searched_recipes=get_search_by_ingredients(ingredient_name)\n return render_template('search.html',recipes=searched_recipes)","title":""},{"docid":"7a7112f1b549bfc777a2049ae2ce605a","score":"0.6860355","text":"def search():\n search = request.form.get(\"search\")\n types = list(mongo.db.type.find().sort(\"type_name\", 1))\n recipes = list(mongo.db.recipes.find({\"$text\": {\"$search\": search}}))\n flash(\"{} Results for '{}'\".format(len(recipes), search))\n return render_template(\"recipes.html\", recipes=recipes, types=types)","title":""},{"docid":"edec680426b6b94882a250d4b5993ea7","score":"0.6691776","text":"def search_recipes(query):\n # search Recipes function\n if search:\n recipes = list(\n mongo.db.recipes.find({\"category_name\": query}))\n\n return render_template(\"recipes.html\", recipes=recipes)","title":""},{"docid":"bd82c3e788872091ac7e6bfb70961775","score":"0.6485085","text":"def view_recipes_with_ingredients():\r\n\r\n ingredient_list = request.args.get('ingredients')\r\n ing_list = crud.string_to_list(ingredient_list)\r\n ing_id_list = []\r\n for ingr in ing_list:\r\n ingred_by_name = crud.get_ingredient_by_name(ingr)\r\n ing_id_list.append(ingred_by_name.ingredient_id)\r\n recipe_search_results = crud.get_recipes_by_multiple_ing(ing_id_list)\r\n\r\n return render_template('ing_recipe_search_results.html', see_recipes=recipe_search_results)","title":""},{"docid":"eb1ee04354501f9af87e6391d489f999","score":"0.64551157","text":"def test_search_recipes(self):\n nutrients = {'maxCarbs': '100'}\n ingredients = ['potatoes']\n diet = 'vegetarian'\n intolerances = ['dairy']\n recipe_list = RecipeRecommender.search_recipes(ingredients, nutrients,\n diet, intolerances)\n self.assertIsInstance(recipe_list, list)\n if len(recipe_list) > 0:\n self.assertIsInstance(recipe_list[0], Recipe)","title":""},{"docid":"b9e2b75a19daf7b318b88bffe21389ab","score":"0.64290017","text":"def search(self, **kwargs):","title":""},{"docid":"50254ace5f6e7f0d82864ce49c642b1e","score":"0.6413538","text":"def search_gear():\n\n gear_keyword = request.form.get(\"gear_name\")\n category = request.form.get(\"category\")\n brand = request.form.get(\"brand\")\n # zipcode = request.form.get(\"zipcode\")\n if category == \"All Categories\" and brand == \"All Brands\":\n equipSearch = Equipment.query.filter(Equipment.gear_name.contains(gear_keyword), Equipment.available.is_(True)).all()\n elif category != \"All Categories\" and brand == \"All Brands\":\n equipSearch = Equipment.query.filter(Equipment.category == category, Equipment.gear_name.contains(gear_keyword), Equipment.available.is_(True)).all()\n elif category == \"All Categories\" and brand != \"All Brands\":\n equipSearch = Equipment.query.filter(Equipment.brand == brand, Equipment.gear_name.contains(gear_keyword), Equipment.available.is_(True)).all()\n else:\n equipSearch = Equipment.query.filter(Equipment.category == category, Equipment.brand == brand, Equipment.gear_name.contains(gear_keyword), Equipment.available.is_(True)).all()\n\n equipment = []\n\n # print equipSearch\n\n for e in equipSearch:\n d = {\n 'gear_id' : e.gear_id,\n 'gear_name': e.gear_name,\n 'category' : e.category,\n 'brand' : e.brand,\n 'lender_email' : e.lender_email,\n 'gear_photo_url' : e.gear_photo_url,\n 'zipcode' : e.zipcode\n }\n equipment.append(d)\n\n return jsonify(equipment)","title":""},{"docid":"6e2c3c45116018faa04088cc58b7c0b6","score":"0.63533044","text":"def get_recipes(request):\n if not request.body:\n return JsonResponse({})\n\n # parse json string to list of ingredient names\n ing = request.body.decode(\"utf-8\")\n ingredients_to_search_by = ing[1:-1].replace('\"', \"\").split(',')\n # save ingredients for the future\n # get user id, if applicable. Else default to 0\n usr_id = 0\n if request.user:\n save_ingredients_to_user(request.user, ingredients_to_search_by, request.session)\n usr_id = request.user.id\n # send ingredients to search algorithm\n values = IngredientUtils(usr_id).find_recipes(ingredients_to_search_by)\n # convert queryset to JSON!!! (Now made serializable in the find_recipes call)\n for recipe in values:\n recipe['appliances'] = list(Appliance.objects.filter(recipe=recipe['id']).values('name'))\n\n return JsonResponse({'results': values})","title":""},{"docid":"4800436b60324eebca1d49b9a4c93ec7","score":"0.6332947","text":"def search_books():\n \n # Retrieve input prompted by the user\n search_input = request.args.get(\"search\")\n\n if search_input == \"\":\n return render_template(\"search.html\", search=search_input)\n\n # Fetch database\n database_response = db.execute(\"SELECT * FROM books WHERE title iLIKE :entry OR author iLIKE :entry OR isbn iLIKE :entry\", {'entry': \"%\" + search_input + \"%\"}).fetchall()\n\n return render_template(\"search.html\", results=database_response, search=search_input)","title":""},{"docid":"7096118bc097e9c224f60b1784c7f405","score":"0.6324785","text":"def search():\n\n search_this_string = request.form['search']\n cursor = gear.find({'$text': {'$search': search_this_string}})\n return render_template('searchresults.html', result=list(cursor),\n categories=list(categories.find()))","title":""},{"docid":"3caf852ab6929412009e0660220b69c3","score":"0.632282","text":"def search():\n drug = request.args.get('drug').lower()\n location = request.args.get('location')\n\n # get DB\n db = get_db()\n cur = db.cursor()\n\n # store search\n now = now_int()\n cur.execute('INSERT INTO Searches VALUES(?,?)', (drug, now))\n db.commit()\n\n # find drug in DB\n cur.execute('SELECT drug_id FROM DrugNames WHERE Name = ?', (drug,))\n exact_row = cur.fetchone()\n found = exact_row is not None\n\n # find variants\n no_spaces = re.sub('\\\\s+', '', drug)\n id_counts = dict()\n for idx in range(len(no_spaces)-2):\n trigram = no_spaces[idx:(idx+3)]\n for drug_id in cur.execute(\"\"\"SELECT drug_id\n FROM DrugNameParts\n WHERE Part = ?\n LIMIT 100\"\"\", (trigram,)):\n id_counts[drug_id[0]] = id_counts.get(drug_id[0], 0) + 1\n\n sorted_id_counts = sorted(list(id_counts.items()),\n key=lambda tup: -1 * tup[1])\n\n result = {'known': found}\n\n if found:\n drug_id = exact_row[0]\n result['drug-id'] = drug_id\n\n # TODO get location using https://bcgov.github.io/ols-devkit/ale/\n loc_lat = 49.28202\n loc_long = -123.11875\n\n # record request\n cur.execute('INSERT INTO DrugRequests VALUES (?,?,?,?)',\n (drug_id, now, loc_lat, loc_long))\n db.commit()\n\n # fetch availability\n pharmacies = list()\n # TODO: Order pharmacies by distance\n \"\"\"\n for row in cur.execute(\n '''SELECT pharma_id, (lat - ?) * (lat - ?) + (long - ?) * (long - ?) AS distance\n FROM PharmaLoc\n ORDER BY distance ASC\n LIMIT 10''', (loc_lat, loc_lat, loc_long, loc_long)):\n pharma_id = row[0]\n pharmacies.append({'id': pharma_id})\n \"\"\"\n\n for row in cur.execute(\n ''' SELECT pharma_id, availability, when_reported\n FROM Availabilities\n WHERE drug_id = ? AND availability = 1\n ORDER BY when_reported ''', (drug_id,)):\n pharma_id = row[0]\n pharmacies.append({'id': pharma_id})\n\n for index in range(len(pharmacies)):\n pharma_id = pharmacies[index]['id']\n pharmacy = cur.execute('SELECT *, rowid FROM PharmaDoc WHERE rowid = ?', (pharma_id,)).fetchone()\n pharmacies[index] = {\n 'name': pharmacy[0],\n 'address': pharmacy[1],\n 'phone': pharmacy[2],\n 'fax': pharmacy[3],\n 'manager': pharmacy[4],\n 'hours': pharmacy[5],\n 'latitude': pharmacy[6],\n 'longitude': pharmacy[7],\n 'pharma_id': pharmacy[8]\n }\n\n\n if not pharmacies:\n #If pharmacies list is empty, there are no pharmacies that have the medication\n result['unavailableAtPharmacies'] = True\n result['pharmacies'] = pharmacies\n\n variants = []\n for variant_id, count in sorted_id_counts[:10]:\n cur.execute('SELECT name FROM DrugNames WHERE drug_id = ?',\n (variant_id,))\n variants.append({\n 'id': variant_id,\n 'name': cur.fetchone()[0]\n })\n\n result['other-drugs'] = variants\n\n return jsonify(result)","title":""},{"docid":"8a0307aa13ebf3479143b33f5c05112a","score":"0.622523","text":"def test_search_function_applies_preferences_with_logged_in_user(self, test_client, user, logged_in_user, db, itr,\n search_term):\n # User details as stored in db\n user_diet_name = user.diet_preferences[0].diet_type.diet_name\n user_diet_id = user.diet_preferences[0].diet_type.diet_type_id\n user_allergy_names = [allergy.allergy.allergy_name for allergy in user.allergies]\n user_allergy_ids = [allergy.allergy.allergy_id for allergy in user.allergies]\n\n # User details as presented in front-end, through config global variables\n diet_name = str((config.DIET_CHOICES[user_diet_id - 1])[1]).lower()\n allergy_names = [str((config.ALLERGY_CHOICES[i - 1])[1]).lower() for i in user_allergy_ids]\n\n response = search_function(test_client, search_term)\n assert b'Based on saved user preferences, we have applied the following filters:' in response.data\n # Check the Flash message\n assert b'Diet type: ' + diet_name.encode() in response.data\n assert b'Allergies: ' in response.data\n for allergy_name in allergy_names:\n assert allergy_name.encode() in response.data\n\n \"\"\"\n GIVEN a flask application and registered user (with randomly generated diet type and food preferences)\n WHEN user requests to view all recipes\n THEN returned recipes satisfies user's diet preferences and allergies\n \"\"\"\n # Pull the recipe ids that are returned in on the response page (as a list). See conftest helper function.\n # We will compare these recipe ids in a db query to ensure that the filters have been applied\n response_recipe_ids = get_recipe_ids(test_client, response)\n\n from app.models import Recipes, RecipeDietTypes, RecipeAllergies\n\n for id in response_recipe_ids: # for all recipes that are returned in results\n # Query all recipe ids which have the user's allergies\n blacklist = db.session.query(RecipeAllergies.recipe_id) \\\n .filter(RecipeAllergies.allergy_id.in_(user_allergy_ids)) \\\n .distinct().subquery()\n\n # Use outerjoin to exclude blacklisted recipes in query\n query = db.session.query(Recipes) \\\n .outerjoin(blacklist, Recipes.recipe_id == blacklist.c.recipe_id) \\\n .join(RecipeDietTypes) \\\n .join(RecipeAllergies) \\\n .filter(Recipes.recipe_id == id) \\\n .filter(RecipeDietTypes.diet_type_id >= user_diet_id)\n # db recipe id should = recipe id on page\n # db diet type should be >= user's saved diet preference\n # This query should be not None (i.e. it should return something) if the filters have been applied\n assert query is not None","title":""},{"docid":"bfc3514a140a19cfbe36bf25600d0da2","score":"0.62131435","text":"def search(**kwargs):\n filter_list = []\n if 'main_search_term' in kwargs:\n term = kwargs['main_search_term']\n filter_list.append(or_(\n Listing.name.ilike('%{}%'.format(str(term))),\n Listing.product_id.ilike('%{}%'.format(str(term))),\n Listing.description.ilike('%{}%'.format(str(term))))\n )\n if 'strict_name_search' in kwargs:\n term = kwargs['strict_name_search']\n filter_list.append(Listing.name.like('%{}%'.format(str(term))))\n if 'name_search_term' in kwargs and kwargs['name_search_term']:\n term = kwargs['name_search_term']\n vendors = Vendor.query.filter(Vendor.company_name.ilike('%{}%'.format(str(term)))).all()\n vendor_ids = [vendor.id for vendor in vendors]\n filter_list.append(Listing.vendor_id.in_(vendor_ids))\n\n # used by vendors to filter by availability\n if 'avail' in kwargs:\n avail_criteria = kwargs['avail']\n format(avail_criteria)\n if avail_criteria == \"non_avail\":\n filter_list.append(Listing.available == False)\n if avail_criteria == \"avail\":\n filter_list.append(Listing.available == True)\n if avail_criteria == \"both\":\n filter_list.append(or_(Listing.available == True,\n Listing.available == False))\n\n # used by merchants to filter by availability\n if 'available' in kwargs:\n filter_list.append(Listing.available == True)\n\n if 'fav_vendor' in kwargs and kwargs['fav_vendor']:\n bookmarked_vendor_ids = BookmarkVendor.query.filter_by(merchant_id=current_user.id).all()\n ids = []\n for bookmark in bookmarked_vendor_ids:\n ids.append(bookmark.vendor_id)\n filter_list.append(Listing.vendor_id.in_(ids))\n\n if 'favorite' in kwargs and kwargs['favorite']:\n bookmarked_vendor_ids = Bookmark.query.filter_by(merchant_id=current_user.id).all()\n ids = []\n for bookmark in bookmarked_vendor_ids:\n ids.append(bookmark.listing_id)\n filter_list.append(Listing.id.in_(ids))\n\n if 'min_price' in kwargs and kwargs['min_price']:\n filter_list.append(Listing.price >= kwargs['min_price'])\n\n if 'max_price' in kwargs and kwargs['max_price']:\n filter_list.append(Listing.price <= kwargs['max_price'])\n\n filtered_query = Listing.query.filter(*filter_list)\n print Listing.query.filter(*filter_list)\n if 'sort_by' in kwargs and kwargs['sort_by']:\n sort = kwargs['sort_by']\n format(sort)\n else:\n sort = None\n\n if sort == \"low_high\":\n sorted_query = filtered_query.order_by(Listing.price)\n elif sort == \"high_low\":\n sorted_query = filtered_query.order_by(desc(Listing.price))\n elif sort == \"alphaAZ\":\n sorted_query = filtered_query.order_by(func.lower(Listing.name))\n elif sort == \"alphaZA\":\n sorted_query = filtered_query.order_by(desc(func.lower(Listing.name)))\n else: # default sort\n sorted_query = filtered_query.order_by(Listing.price)\n return sorted_query","title":""},{"docid":"d958957bc44e58ea62a899c111a260c5","score":"0.61862475","text":"def search(self):","title":""},{"docid":"f17d676912eef501c2927bdea9c3694e","score":"0.61715937","text":"def search(self, *args, **kwargs):","title":""},{"docid":"f17d676912eef501c2927bdea9c3694e","score":"0.61715937","text":"def search(self, *args, **kwargs):","title":""},{"docid":"39225a18a2d33d748b4987938888d014","score":"0.61298484","text":"def search_main():\n form = SearchForm()\n user = http_auth.username()\n if form.validate_on_submit():\n query_terms = form.search_terms.data.split(' ')\n answer = OrderedDict()\n for query in query_terms:\n result = str(searcher.query(query))\n answer[query] = result.split(' ', 1)[1]\n return render_template('search_results.html', title='Search', answer=answer, user=user)\n return render_template('search.html', title='Search', zone=FORWARD_ZONE, form=form, user=user)","title":""},{"docid":"000894a50f9672cc8b408ac34d4623e6","score":"0.61075556","text":"def getResults(ingredient):\n puppy = Puppy()\n recipelist = puppy.search_recipe(ingredient)\n\n return recipelist","title":""},{"docid":"4abf100a357f8c541475efb313d4715d","score":"0.6101594","text":"def search():\n search_string = str(flask.request.args['search_val'])\n \n min_rating = flask.request.args['min_rating']\n max_rating = flask.request.args['max_rating']\n\n page = int(flask.request.args['page'])\n page_offset = (page - 1) * 10\n\n genres = flask.request.args['genres']\n genres_list = json.loads(genres)\n\n # TODO: actually store genres in a separate table to avoid this terrible regex hack\n # for now, this is reasonably performant at this scale\n query_regex = \".*\" + \".*|.*\".join(genres_list) + \".*\"\n\n\n count_query = (\"SELECT \" \n \"count(*) \"\n \"FROM \"\n \"movies \"\n \"INNER JOIN avg_ratings ON movies.id = avg_ratings.movie_id \"\n \"WHERE \"\n \"avg_ratings.avg_rating >= %s and avg_ratings.avg_rating <= %s \"\n \"AND \"\n \"movies.genres ~ %s \")\n\n with db.cursor(cursor_factory = psycopg2.extras.DictCursor) as cur:\n cur.execute(count_query,\n (float(min_rating), float(max_rating), query_regex, )\n )\n result = cur.fetchone()\n count = result[0]\n\n # This query retrieves data for the movie list view using a view previously calculated for average rating\n # It also uses\n\n base_query = (\"SELECT \" \n \"movies.id, movies.title, movies.genres, avg_ratings.avg_rating \"\n \"FROM \"\n \"movies \"\n \"INNER JOIN avg_ratings ON movies.id = avg_ratings.movie_id \"\n \"WHERE \"\n \"avg_ratings.avg_rating >= %s and avg_ratings.avg_rating <= %s \"\n \"AND \"\n \"movies.genres ~ %s \")\n\n # order by rating if the search query is empty\n if search_string == \"\":\n with db.cursor(cursor_factory = psycopg2.extras.DictCursor) as cur:\n cur.execute(\n base_query +\n \"ORDER BY avg_ratings.avg_rating DESC, movies.id DESC LIMIT 10 OFFSET %s;\",\n (float(min_rating), float(max_rating), query_regex, page_offset,)\n )\n result = cur.fetchmany(10)\n return flask.json.dumps(search_results_to_dict(result, count), default=decimal_default)\n # otherwise, order by search closeness\n else:\n with db.cursor(cursor_factory = psycopg2.extras.DictCursor) as cur:\n cur.execute(\n base_query +\n \"ORDER BY SIMILARITY(title, %s) DESC, movies.id DESC LIMIT 10 OFFSET %s;\",\n (float(min_rating), float(max_rating), query_regex, search_string, page_offset,)\n )\n result = cur.fetchmany(10)\n\n return flask.json.dumps(search_results_to_dict(result, count), default=decimal_default)","title":""},{"docid":"45fbe39b2e72f8e470a3101018855f8b","score":"0.607085","text":"def search(json, session):\n reload(logic)\n return logic.search(json, collection, session)","title":""},{"docid":"98e9186fbbc43f49c4f7106a3c4fc826","score":"0.60681784","text":"def search_saved():\n user = session.get('user', 'Guest')\n search = request.form.get(\"search\")\n types = list(mongo.db.type.find().sort(\"type_name\", 1))\n recipes = list(mongo.db.recipes.find({\"$text\": {\"$search\": search}}))\n saved_list = list(mongo.db.users.distinct(\n \"saved_recipes\", {\"user_name\": user}))\n # clears any empty entries\n check_list = []\n for item in saved_list:\n if item == \"\":\n continue\n else:\n check_list.append(item)\n # length of the list shows the rating count\n size = len(check_list)\n return render_template(\"saved_recipes.html\", recipes=recipes, types=types,\n check_list=check_list, size=size, user=user)","title":""},{"docid":"21d2078f3b4d86f4337b5f5dd60ce340","score":"0.60452926","text":"def search(query, **parameters):","title":""},{"docid":"21d2078f3b4d86f4337b5f5dd60ce340","score":"0.60452926","text":"def search(query, **parameters):","title":""},{"docid":"892d55a87d19cd4c3cc9923acfa488c6","score":"0.60349613","text":"def searchDocuments(self, REQUEST):\n if self.onSearch:\n # Manually generate a result set\n try:\n results = self.runFormulaScript(\n 'from_%s_onsearch' % self.id,\n self,\n self.onSearch)\n except PlominoScriptException, e:\n if self.REQUEST:\n e.reportError('Search event failed.')\n return self.OpenForm(searchresults=[])\n else:\n # Allow Plomino to filter by view, default query, and formula\n db = self.getParentDatabase()\n searchview = db.getView(self.getSearchView())\n\n #index search\n index = db.getIndex()\n query = {'PlominoViewFormula_'+searchview.getViewName(): True}\n\n for f in self.getFormFields(\n includesubforms=True,\n request=REQUEST):\n fieldname = f.id\n #if fieldname is not an index -> search doesn't matter and returns all\n submittedValue = REQUEST.get(fieldname)\n if submittedValue:\n submittedValue = asUnicode(submittedValue)\n # if non-text field, convert the value\n if f.getFieldType() == \"NUMBER\":\n settings = f.getSettings()\n if settings.type == \"INTEGER\":\n v = long(submittedValue)\n elif settings.type == \"FLOAT\":\n v = float(submittedValue)\n elif settings.type == \"DECIMAL\":\n v = decimal(submittedValue)\n elif f.getFieldType() == \"DATETIME\":\n # if datetime widget, the request param contains an object\n if getattr(REQUEST.get(fieldname), 'year'):\n submittedValue = \"%(year)s-%(month)s-%(day)s %(hour)s:%(minute)s\" % self.REQUEST.get('birthDate')\n v = StringToDate(submittedValue, format='%Y-%m-%d %H:%M')\n else:\n v = submittedValue\n # rename Plomino_SearchableText to perform full-text\n # searches on regular SearchableText index\n if fieldname == \"Plomino_SearchableText\":\n fieldname = \"SearchableText\"\n query[fieldname] = v\n\n sortindex = searchview.getSortColumn()\n if not sortindex:\n sortindex = None\n results = index.dbsearch(\n query,\n sortindex=sortindex,\n reverse=searchview.getReverseSorting())\n\n #filter search with searchformula\n searchformula = self.getSearchFormula()\n if searchformula:\n filteredResults = []\n try:\n for doc in results:\n valid = self.runFormulaScript(\n SCRIPT_ID_DELIMITER.join(['form', self.id, 'searchformula']),\n doc.getObject(),\n self.SearchFormula)\n if valid:\n filteredResults.append(doc)\n except PlominoScriptException, e:\n e.reportError('Search formula failed')\n results = filteredResults\n\n return self.OpenForm(searchresults=results)","title":""},{"docid":"b44e0b7310731adcde15d418e63a7f66","score":"0.60249716","text":"def newquerysearch():\n id = request.form[\"id\"]\n search = request.form[\"search\"]\n if (len(search) >= 2):\n results = []\n res2 = []\n try:\n r = requests.post(\"https://www.zefix.ch/ZefixREST/api/v1/firm/search.json\", json={\"name\": search,\"languageKey\":\"de\",\"maxEntries\":4})\n x = r.json()[\"list\"]\n for y in x:\n name = y[\"name\"]\n uid = y[\"uid\"]\n che = uid[0:3] + \"-\" + uid[3:6] + \".\" + uid[6:9] + \".\" + uid[9:12]\n if len(res2) < 1:\n res = query(\"SELECT * FROM liquidations WHERE che='{}'\".format(che))\n if len(res) > 0:\n res2.append(res)\n else:\n results.append([name, che])\n else:\n results.append([name, che])\n except:\n pass\n if len(res2) > 0:\n results.append(res2[0])\n if len(results) == 0:\n search = [id, search, [], False, 0]\n return render_template(\"section3.html\", search=search)\n if isinstance(results[-1][0], str):\n r = False\n else:\n r = True\n search = [id, search, results, r, len(results)]\n else:\n search = [id, search, [], False, 0]\n return render_template(\"section3.html\", search=search)","title":""},{"docid":"90f50215d680daaba55133f7137cd380","score":"0.5978783","text":"def query_criteria():","title":""},{"docid":"53f5bf88f37e7ac3813379aca7ed712c","score":"0.59494305","text":"def test_search_find_one_result_by_name(self):\n make_recipe(\n \"legalaid.case\",\n reference=\"ref1\",\n personal_details__full_name=\"xyz\",\n personal_details__postcode=\"123\",\n **self.get_extra_search_make_recipe_kwargs()\n )\n\n self.resource.personal_details.full_name = \"abc\"\n self.resource.personal_details.postcode = \"123\"\n self.resource.personal_details.save()\n self.resource.reference = \"ref2\"\n self.resource.save()\n\n response = self.client.get(\n self.list_url, data={\"search\": \"abc\"}, HTTP_AUTHORIZATION=self.get_http_authorization()\n )\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(1, len(response.data[\"results\"]))\n self.assertCaseEqual(response.data[\"results\"][0], self.resource)","title":""},{"docid":"c13b98cbbb643da3d3546f938e885068","score":"0.59477454","text":"def test_search_page(self):\n res = self.client.get('/search', follow_redirects=True, data={\n 'recipe_name': 'Pumpkin rice',\n 'recipe_description': 'From brownies and pancakes to veggie-packed curries, stir-fries and salads, these vegan recipes are vibrant and delicious.',\n 'recipe_instructions': 'Add all of the ingredients',\n 'recipe_keywords': 'vegan, pumpkin, rice, butternut squash'\n })\n data = res.data.decode('utf-8')\n assert res.status == '200 OK'\n assert 'vegan' in data\n assert 'Pumpkin' in data\n assert 'delicious' in data\n assert 'butternut' in data","title":""},{"docid":"72b05eb19781242644ca48149f75afa6","score":"0.5934002","text":"def search(self, event):\r\n filter_choice = self.categories.GetValue()\r\n keyword = self.search_ctrl.GetValue()\r\n self.skeleton_results = controller.search_records(\r\n self.session, filter_choice, keyword)\r\n self.update_skeleton_results()","title":""},{"docid":"b9cc9081fe156f407138c25695500d69","score":"0.59256864","text":"def search():\n search_query = request.args.get(\"q\")\n\n if search_query is None or len(search_query) < 1:\n response_json = {\"status\": 422}\n if search_query is None:\n response_json[\"message\"] = \"Search query not found. Use ?q=searchquery.\"\n else:\n response_json[\"message\"] = \"Search query length must be at least 1.\"\n response = jsonify(response_json)\n response.status_code = 422\n return response\n\n search_query = search_query.lower()\n response_json = {\"status\": 200}\n\n model_queries = {\n \"cities\": City.query,\n \"doctors\": Doctor.query,\n \"specialties\": Specialty.query,\n }\n for model_name in [\"cities\", \"doctors\", \"specialties\"]:\n response_json[model_name] = search_model(\n search_query,\n model_queries[model_name],\n SEARCH_ATTRIBUTES[model_name],\n SERIALIZE_ATTRIBUTES[model_name],\n )\n response_json[f\"num_{model_name}\"] = len(response_json[model_name])\n\n response_json[\"num_results\"] = sum(\n response_json[attr] for attr in [\"num_cities\", \"num_doctors\", \"num_specialties\"]\n )\n\n response_json[\"page\"] = 1\n response_json[\"total_pages\"] = 1\n response = jsonify(response_json)\n response.status_code = 200\n return response","title":""},{"docid":"dbac4911c206691027a4166cf627df86","score":"0.59028435","text":"def recommend(ingredienten, nietappliance):\n\n # make the query\n begin = \"\"\"PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\nPREFIX rp: <http://www.groepsproject.com/recipes/>\nSELECT DISTINCT ?title ?link ?linktitle ?description\nWHERE {\n ?recipe rdf:type rp:Recipe.\n \"\"\"\n # remove the recipes with other ingredients\n for ingredient in range(len(ingredienten)):\n begin += \"\"\"FILTER NOT EXISTS {?recipe rp:hasIngredient ?ingredient%i.\n ?ingredient%i rdf:type rp:%s}\n \"\"\" % (ingredient, ingredient, ingredienten[ingredient])\n\n # remove the recipes for which you don't have the appliance\n for appliance in nietappliance:\n begin += \"\"\"FILTER NOT EXISTS {?recipe rp:needsAppliance rp:%s}\n \"\"\" % appliance\n\n # get the recipes data\n begin += \"\"\"?recipe rp:hasTitle ?title;\n rp:hasLink ?link;\n rp:hasDescription ?description;\n rp:hasLinkTitle ?linktitle.\n} ORDER BY DESC(?link)\"\"\"\n\n # display the query in terminal\n print(begin)\n\n # fill in your own reposity here\n sparql = SPARQLWrapper(\"http://localhost:7200/repositories/project\")\n\n # set the right format, and type of request method\n sparql.setReturnFormat(JSON)\n sparql.setMethod(POST)\n\n # run the query\n sparql.setQuery(begin)\n results = sparql.query()\n # set max colwidth off\n pd.set_option('display.max_colwidth', -1)\n # load as pandas dataframe\n processed_results = json.load(results.response)\n cols = processed_results['head']['vars']\n out = []\n for row in processed_results['results']['bindings']:\n item = []\n for c in cols:\n item.append(row.get(c, {}).get('value'))\n out.append(item)\n df = pd.DataFrame(out, columns=cols)\n df.reindex(columns=['linktitle', 'link', 'description', 'title']).to_html(\"templates/test.html\")\n with open(\"templates/test.html\", \"r\") as f1, open(\"templates/results.html\", \"w\", encoding=\"utf-8\") as f2:\n f2.write(\"\"\"<head>\n <link href= {{ url_for('static', filename = 'css-custom.css') }} rel=\"stylesheet\">\n <script src=\"https://code.jquery.com/jquery-3.3.1.slim.min.js\" integrity=\"sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo\" crossorigin=\"anonymous\"></script>\n <script>\n $(document).ready(function(){\n $('td').each(function(){\n var content = $(this).html();\n var Rexp = /((http|https|ftp):\\/\\/[\\w?=&.\\/-;#~%-]+(?![\\w\\s?&.\\/;#~%\"=-]*>))/g;\n content = content.replace(Rexp, \"<a href='$1' target='_blank'>$1</a>\");\n $(this).html(content);\n });\n });\n</script>\n</head>\n\"\"\")\n for line in f1:\n f2.write(line)\n os.remove(\"templates/test.html\")\n return True","title":""},{"docid":"4300f62b4505ed3c3c9545b7073a9c05","score":"0.5902526","text":"def user_search():\n search = request.form.get(\"search\")\n types = list(mongo.db.type.find().sort(\"type_name\", 1))\n recipes = list(mongo.db.recipes.find().sort(\"recipe_name\", 1))\n users = list(mongo.db.users.find({\"$text\": {\"$search\": search}}))\n products = list(\n mongo.db.products.find().sort(\"product_name\", 1))\n tools = list(mongo.db.tools.find().sort(\"name\", 1))\n count = len(users)\n # checks if no users match search\n if count == 0:\n flash(\"NO USERS FOUND\")\n return render_template(\"management.html\", users=users, types=types,\n recipes=recipes, count=count, search=search)\n # returns results that match\n else:\n flash(\"Search results: {} for '{}'\".format(len(users), search))\n return render_template(\"management.html\", users=users, types=types,\n recipes=recipes, count=count, search=search,\n tools=tools, products=products)","title":""},{"docid":"c3876bbf72d9d3ff2b8ce175b0a97e02","score":"0.58976126","text":"def search():\n\n search_term = request.values.get(\"search_term\")\n category = request.values.get(\"category\")\n todo_list = collection.find({category: search_term})\n\n return render_template('search.html', todo_list=todo_list)","title":""},{"docid":"f5c349001c9881fa824305799cfacd06","score":"0.5897531","text":"def search():\n query = request.args.get('query', '')\n results = search_client.search(query)\n return jsonify(results)","title":""},{"docid":"23d20b3f9223921230e80b490020e86c","score":"0.5896862","text":"def vegan_filter():\n types = list(mongo.db.type.find().sort(\"type_name\", 1))\n recipes = list(mongo.db.recipes.find({\"vegan\": \"Yes\"}))\n\n return render_template(\"recipes.html\", types=types,\n recipes=recipes)","title":""},{"docid":"26af26d25d80c12b687933c2b644e511","score":"0.58917093","text":"def search():\n # request.form = {\"form_name\": \"hi\"}\n form_name = request.form.get(\"form_name\")\n if form_name == \"search\":\n checked = ['checked=\"true\"', '', '']\n keys = request.form.get('key_word', \"\")\n category = request.form.get('search_field')\n print(\"from search.controllers.search: category hasn't been added as a search option, will do later\")\n session['checked'] = checked\n session['keys'] = keys\n else:\n if session.get(\"checked\") is None or session.get(\"keys\") is None:\n print('unrecognized form named {}'.format(form_name))\n return error_page()\n else:\n checked = session['checked']\n keys = session['keys']\n\n if is_valid_keys(keys):\n flag, page, docid = searchidlist(keys)\n session['page'] = page\n session['s_flag'] = flag\n session['doc_id'] = docid\n\n if flag == 0: return error_page()\n\n docs = cut_page(page, 0, docid)\n\n return render_template('high_search.html', checked=checked, key=keys, docs=docs, page=page,\n nonempty=True, welcome=False, browse_categories=browse_categories)\n else:\n return redirect(url_for('main'))","title":""},{"docid":"5b2f7e986422895ef3132d6bb76f75b5","score":"0.5891332","text":"def api_search():\n category = request.args.get('category')\n search = request.args.get('search')\n return jsonify(get_search_results(category, search, current_user=current_user))","title":""},{"docid":"ee7ceda59a188d4d517c1e45b8b8b193","score":"0.5882391","text":"def search_results():\n terms = request.args.get('q')\n\n return render_template('search.html', terms=terms)","title":""},{"docid":"9405d118b77f363a99424be2dae6cad9","score":"0.58684224","text":"def search(self):\r\n pass","title":""},{"docid":"ed8e74e2da111cc3fc92acf2c9ab0d15","score":"0.5853514","text":"def search():\n metadata = METADATA.copy()\n\n # extract search term from the HTML form\n search_term = request.form['search']\n\n if search_term != '':\n # put search term in same vector space as data model was trained on\n search_transformed = PIPELINE.transform([search_term])\n # put search in topic space\n search_topics = TOPIC_MODEL.transform(search_transformed)\n\n # calculate cosine similarity matrix\n doc_topic_matrix = metadata[metadata.columns[6:]].values\n similarity = cosine_similarity(doc_topic_matrix, search_topics)\n top_10_idx = np.argsort(similarity.flatten())[-10:]\n metadata = metadata.iloc[list(top_10_idx)]\n\n metadata = apply_threshold_to_topics(metadata)\n\n return render_template('index.html',\n comedy_info=metadata.values.tolist(),\n dropdown_options=['Observational', 'Black Culture', 'British & Australian',\n 'Political', 'Immigrant Upbringing', 'Relationships & Sex'],\n search_text=search_term)","title":""},{"docid":"a5a6f2ada8bafdaf43e45f61be876581","score":"0.5838428","text":"def test_ingredient_search(self):\n pass","title":""},{"docid":"302da5baf997582a6f3c5ca20e166f37","score":"0.5821525","text":"def process_search(self, request):\n cat = self.cleaned_data['cat']\n search_query = self.cleaned_data['q']\n if cat != '' or search_query != '':\n self.set_search_query_set(request)\n if cat != '' and search_query != '':\n self.search_category_and_query()\n elif cat != '':\n self.search_category_only()\n else:\n self.search_query_string_only()\n coupons = self.intersect_index_and_db_results(request)\n else:\n coupons = self.no_query_found(request)\n return coupons, self.suggestion","title":""},{"docid":"e6d923c09d171e62803313ef0725ef72","score":"0.5811273","text":"def search_restaurant():\n\n\tlocation = request.form.get('location')\n\tterm = request.form.get('term')\n \n \n\tresults = yelp.get_results(location=location, term=term)\n\t\n \n\n\treturn jsonify(results=results)","title":""},{"docid":"e3117a08ac8a588968575e9fbfa0c23d","score":"0.58079803","text":"def search():\n context = make_context()\n\n return make_response(render_template('search.html', **context))","title":""},{"docid":"fe76696fced27b2c2c9c4833b711d933","score":"0.57921505","text":"def search():\n pattern = flask.request.args.get('pattern', \"*\").strip().lower()\n\n # if the pattern contains \"in:<collection>\" (eg: in:builtin),\n # filter results to only that (or those) collections\n # This was kind-of hacked together, but seems to work well enough\n collections = [c[\"name\"].lower() for c in current_app.kwdb.get_collections()]\n words = []\n filters = []\n if pattern.startswith(\"name:\"):\n pattern = pattern[5:].strip()\n mode = \"name\"\n else:\n mode=\"both\"\n\n for word in pattern.split(\" \"):\n if word.lower().startswith(\"in:\"):\n filters.extend([name for name in collections if name.startswith(word[3:])])\n else:\n words.append(word)\n pattern = \" \".join(words)\n\n keywords = []\n for keyword in current_app.kwdb.search(pattern, mode):\n kw = list(keyword)\n collection_id = kw[0]\n collection_name = kw[1].lower()\n if len(filters) == 0 or collection_name in filters:\n url = flask.url_for(\".doc_for_library\", collection_id=kw[0], keyword=kw[2])\n row_id = \"row-%s.%s\" % (keyword[1].lower(), keyword[2].lower().replace(\" \",\"-\"))\n keywords.append({\"collection_id\": keyword[0],\n \"collection_name\": keyword[1],\n \"name\": keyword[2],\n \"synopsis\": keyword[3],\n \"version\": __version__,\n \"url\": url,\n \"row_id\": row_id\n })\n\n keywords.sort(key=lambda kw: kw[\"name\"])\n return flask.render_template(\"search.html\",\n data={\"keywords\": keywords,\n \"version\": __version__,\n \"pattern\": pattern\n })","title":""},{"docid":"73af425a18f76c5f126622e6c2a3db0c","score":"0.57903445","text":"def test_search_query_string_with_booleans(workbook, es_testapp):\n search = '/search/?type=Biosource&q=GM12878'\n res_stem = es_testapp.get(search).json\n assert len(res_stem['@graph']) > 1\n bios_uuids = [r['uuid'] for r in res_stem['@graph'] if 'uuid' in r]\n swag_bios = '331111bc-8535-4448-903e-854af460b888'\n assert swag_bios in bios_uuids\n # assert induced_stem_uuid not in not_induced_uuids\n # now search for stem +induced (AND is now \"+\")\n search_and = '/search/?type=Biosource&q=swag+%2BGM12878'\n res_both = es_testapp.get(search_and).json\n both_uuids = [r['uuid'] for r in res_both['@graph'] if 'uuid' in r]\n assert len(both_uuids) == 1\n assert swag_bios in both_uuids\n # search with OR (\"|\")\n search_or = '/search/?type=Biosource&q=swag+%7CGM12878'\n res_or = es_testapp.get(search_or).json\n or_uuids = [r['uuid'] for r in res_or['@graph'] if 'uuid' in r]\n assert len(or_uuids) > 1\n assert swag_bios in or_uuids\n # search with NOT (\"-\")\n search_not = '/search/?type=Biosource&q=GM12878+-swag'\n res_not = es_testapp.get(search_not).json\n not_uuids = [r['uuid'] for r in res_not['@graph'] if 'uuid' in r]\n assert swag_bios not in not_uuids","title":""},{"docid":"2bd286c31b0b2164f4db540bca304209","score":"0.57748353","text":"def findRecipe(keywords=\"Calhacks\"):\n \n apiKey = \"dvxI9e588cOKNvbOzd24EXsVRW9Y2OW8\"\n\n # Base URL for Big Oven API\n url = \"http://api.bigoven.com/recipes?pg=0&rpp=1\"\n\n # Searching for the input Keywords\n url += \"&title_kw=\" + keywords.replace(\" \", \"+\")\n\n # Adding the developer's API Key\n url += \"&api_key=\" + apiKey\n\n recipeCount = parsePage(url, ['ResultCount'])[1][0]\n \n # No recipes are found. \n if recipeCount == \"0\":\n return keywords, set([\"No such recipe found\"]), \"\", \"\"\n\n randomRecipe = randint(1, int(recipeCount))\n url = url.replace(\"pg=0\", \"pg=\" + str(randomRecipe))\n\n xml, [recipeId] = parsePage(url, ['RecipeID'])\n \n try:\n cuisineType = parseList(xml, 'Cuisine')[0].firstChild.nodeValue\n except (IndexError, AttributeError):\n cuisineType = \"\"\n \n # Return the list of ingredients in the Recipe\n recipeUrl = \"http://api.bigoven.com/recipe/\" + recipeId\n recipeUrl += \"?api_key=\" + apiKey\n \n xml, [title, inst] = parsePage(recipeUrl, ['Title', 'Instructions'])\n\n recipeList = set()\n try:\n for el in parseList(xml, 'Name'):\n recipeList.add(el.firstChild.nodeValue)\n except AttributeError:\n recipeList.add(\"Ingredients are in the Instructions\")\n\n return title, recipeList, cuisineType, inst","title":""},{"docid":"5deff366a389cb2b4bb3c80786d39a02","score":"0.57731175","text":"def search_for_outcome():\n try:\n # matching input to variable: https://stackoverflow.com/questions/14105452/what-is-the-cause-of-the-bad-request-error-when-submitting-form-in-flask-applica\n req = request.form\n print(req)\n query = db.session.query(COPA_Case_Attributes)\n logger.info(\"Query initialized\")\n field_list = [\"police_shooting\", \"race_complainants\", \"sex_complainants\", \"age_complainants\",\n \"race_officers\",\"sex_involved_officers\", \"age_officers\", \"excessive_force\",\n \"years_on_force_officers\"]\n field_label = {\"police_shooting\":\"Police shooting: \", \"race_complainants\":\"Race of complainant: \", \"sex_complainants\": \"Gender of complainant: \",\n \"age_complainants\": \"Age of complainant: \",\n \"race_officers\": \"Race of officer: \", \"sex_involved_officers\": \"Gender of officer: \",\n \"age_officers\": \"Age of officer: \", \"excessive_force\": \"Excessive force: \",\n \"years_on_force_officers\": \"Years on force of officer: \"}\n messages = [\"Showing: Predictions displayed based on selection - \"]\n detail = \"\"\n # build query dynamically: https://stackoverflow.com/questions/37336520/sqlalchemy-dynamic-filter\n # build query dynamically: https://stackoverflow.com/questions/7604967/sqlalchemy-build-query-filter-dynamically-from-dict\n # get query attr rather than string: https://stackoverflow.com/questions/10251724/how-to-give-column-name-dynamically-from-string-variable-in-sql-alchemy-filter\n for f in field_list:\n if request.form[f]:\n query = query.filter(getattr(COPA_Case_Attributes, f) == request.form[f])\n detail = detail+field_label[f]+request.form[f]+\". \"\n if detail == \"\":\n detail = \"None\"\n logger.info(\"Perform query\")\n cases = query.limit(app.config[\"MAX_ROWS\"]).all()\n logger.info(\"Query results: {}\".format(cases))\n messages.append(detail)\n return render_template('index.html', cases=cases, messages = messages)\n except Exception as e:\n logger.error(e)\n logger.warning(\"Not able to display search, error page returned\")\n return render_template('error.html')","title":""},{"docid":"4cb968761c90b686d9e59e383661abda","score":"0.5769029","text":"def get_recipes(recipe):\n\n selectlist_recipes, selectlist_styles, selectlist_user, sel_user_styles = get_selectlists(session.get(\"user_id\"))\n deleteable = False\n if Recipe.query.filter_by(name=recipe).one().user_id == session.get(\"user_id\"):\n deleteable = True\n recipe, color = get_recipe_instructions(recipe)\n color = color_conversion(color)\n return render_template(\"explore_brews.html\", selectlist_recipes=selectlist_recipes,\n selectlist_styles=selectlist_styles, recipe=recipe,\n color=color, deleteable=deleteable)","title":""},{"docid":"5223ab9b566325ba09bdab1c73a4297f","score":"0.57479477","text":"def search(query, restaurants):\n # BEGIN Question 10\n \"*** REPLACE THIS LINE ***\"\n # END Question 10","title":""},{"docid":"c54c47aaf9f52e2661012975537b8ae8","score":"0.57413507","text":"def search(self):\n logger.info(f'Received a request to search. Search type: {self.search_type}')\n\n queried_store, security = self.get_db_specific_sections()\n search, query, skip, limit = self.arg_parser()\n\n # Pipeline initially made use of 'project', I've opted to instead\n # do all post processing in 'post_processing()' for simplicity.\n pipeline = search + query + security\n start = time.time()\n full_search_res = list(queried_store.aggregate(pipeline))\n logger.info(f'Got search response in {time.time() - start} seconds.'\\\n f'Pipeline: {pipeline} First two results: {full_search_res[0:1]}')\n final_result = self.post_processing(full_search_res, skip, limit)\n return final_result","title":""},{"docid":"10d82fdbad0cca70bb8ee3b66bd59ac3","score":"0.57355696","text":"def find_restaurants(request, list):\n return matching(request, {}, list)","title":""},{"docid":"70c7a6d4fb4e121269e8c2480cc251ce","score":"0.57247204","text":"def search(request):\n logger.info(\"__ input_forms search __\")\n\n term = request.GET[\"term\"]\n results = []\n\n provider_list = EndpointGroup.objects.all().order_by(\"name\")\n for provider in provider_list:\n\n provider_instance = endpoint_provider.get_provider_instance_from_group(provider.id)\n provider_instance.apply_filter(\"Name\", term)\n endpoints_list = provider_instance.get_page(0, 128)\n\n for endpoint in endpoints_list:\n r = dict()\n r[\"value\"] = str(provider.id) + \":\" + str(endpoint.get('id', 0))\n r[\"label\"] = provider.name + \"/\" + endpoint.get('name', 'unknown')\n results.append(r)\n\n return HttpResponse(json.dumps(results), content_type=\"application/json\")","title":""},{"docid":"ae4d1f976957f156d57111d361c8afb9","score":"0.5721357","text":"def get_queryset(self):\n\n name = self.request.query_params.get('name')\n queryset = Recipe.objects.all()\n\n if name:\n queryset = queryset.filter(name__icontains=name)\n\n return queryset","title":""},{"docid":"faac51fdcada1a00243487bc4181977e","score":"0.5710637","text":"def search():\n query = bt.request.query.q\n t = bt.request.query.t\n mode, model, count = (\n (\"games\", Game, 10) if t == \"categories\" else (\"users\", Streamer, 5)\n )\n search_results = Helix.get(f\"search/{t}?query={query}&first={count}\")\n ids = {int(result[\"id\"]) for result in search_results}\n\n asyncio.run(Db.cache(ids, mode=mode))\n if t == \"categories\":\n results = model.select().where(model.id.in_(ids))\n else:\n results = [\n Result(result, model.get_by_id(int(result[\"id\"])))\n for result in search_results\n ]\n return bt.template(\"search.tpl\", query=query, mode=mode, results=results)","title":""},{"docid":"bbc4d7cbc7e65843d2cfed4782280c20","score":"0.56858754","text":"def FoodSearch(self, **dico):\n if not 'input' in dico:\n #TODO: APIError\n raise Exception(\"input is missing\") \n return self.oauth.ApiCall(\"foods/search\", \"GET\", dico)","title":""},{"docid":"22d281c15fb2028a398b7b9e8bcec1f1","score":"0.5680111","text":"def search():\n\n fuzzy_term = None\n if 'search' in request.args:\n fuzzy_term = request.args['search']\n session['search'] = fuzzy_term\n\n items = []\n\n searchable_classes = [Document, User]\n for cls in searchable_classes:\n items.extend(\n map(\n lambda x: (x, cls.__name__.lower()),\n cls.fuzzy_search(fuzzy_term)\n )\n )\n\n print('Found these items for term \"{}\":'.format(fuzzy_term))\n print(items)\n\n return items","title":""},{"docid":"f41827a56cd90552e5f1cbe6d93dd72d","score":"0.5675769","text":"def check_ingredients():\n query = request.args.get(\"q\")\n print(f\"Query recieved: {query}\")\n ingredients = db.check_ingredient_from_db(query)\n return jsonify(ingredients)","title":""},{"docid":"b592e615c9b7f10b0474d98ef4bbffff","score":"0.56677955","text":"def test_autocomplete_ingredient_search(self):\n pass","title":""},{"docid":"fbb69dcc4a5a9f621bc073885f7a8623","score":"0.56562763","text":"def search_cupcake():\n search = request.args[\"searchTerm\"]\n\n cupcakes = Cupcake.query.filter(Cupcake.flavor.ilike(f\"%{search}%\")).all()\n\n serialized = [c.serialize() for c in cupcakes]\n\n return (jsonify(cupcakes=serialized), 200)","title":""},{"docid":"35d4b1b4ae0b890d96a80ab06e5beb40","score":"0.5648273","text":"def search():\n global searching\n global searchQuery\n\n if request.method == \"POST\":\n query = request.form.get(\"query\")\n searching = True\n searchQuery = query\n\n films = list(mongo.db.film_list.find({\"$text\": {\"$search\": query}}))\n return render_template(\n \"get_films.html\", films=films, search=searching,\n searchQuery=searchQuery)\n\n return get_films()","title":""},{"docid":"dfcb3768490aff1fbf14975ea87037de","score":"0.56399745","text":"def artists_search(mode: str, form: FlaskForm, simple_search_term: str = None) -> dict:\n return ncsg_search(mode, form, get_entity(ARTIST_TABLE), simple_search_term=simple_search_term)","title":""},{"docid":"b6d475728fd1a4ceead5b6a2e8e903cf","score":"0.56354815","text":"def searchProgram():\n return","title":""},{"docid":"8504852f423a62b8c6dc87b7048fcb04","score":"0.5621537","text":"def search_book():\n if not g.user:\n flash('You have to Login')\n return redirect('/')\n term = request.args['term']\n \n b = Book.query.filter(Book.title.ilike(f'%{term}%')).all()\n\n results = [book.serialize() for book in b]\n \n return jsonify(books=results)","title":""},{"docid":"258e5c4deee79cd2db6eb64847dc5ee3","score":"0.5621454","text":"def handle_cloth_search(request):\n searched_name = request.GET.get('cloth_name')\n context = dict()\n \n if searched_name:\n search_result = Cloth.objects.filter(name__icontains=searched_name)\n context = {'search_result': search_result}\n \n return render(request, 'clothing/search_result_list.html', context)","title":""},{"docid":"a65dcf26dc253551dea0b68e79fd99c4","score":"0.5612832","text":"def search(items, target):\n # TODO: Fill this in.\n return","title":""},{"docid":"5394cd29e5329e4366e086fa14b3aea1","score":"0.560931","text":"def search():\n # Search google \n search_google()","title":""},{"docid":"e295fddc82333156dd4a279832d7cc97","score":"0.5592493","text":"def search():\n term = request.args[\"term\"]\n sort = request.args[\"sort\"]\n return f\"<h1>Search Results For: {term}</h1> <p>Sorting by: {sort}</p>\"","title":""},{"docid":"29f49f76f32be498d896d89670acb718","score":"0.55910236","text":"def get_queryset(self):\n self.queryset = Recipe.objects.all()\n name = self.request.query_params.get('name', None)\n ingredients = self.request.query_params.get('ingredients')\n\n if name:\n return self.queryset.filter(name__startswith=name)\n\n if ingredients:\n ingredient_ids = self._params_to_ints(ingredients)\n print(ingredient_ids)\n self.queryset = queryset.filter(ingredients__name__in=ingredients)\n\n\n return self.queryset","title":""},{"docid":"9ed037ae98a92d047b7f2c7ad4335c87","score":"0.5580274","text":"def get_search():\n getreq_url = SEARCH_BASE_URL + request.form.get('search')\n r = requests.get(url = getreq_url)\n data = r.json()\n\n return render_template('search_results.html', books=data)","title":""},{"docid":"e5c3cdd7e48c11d43ceca2bd36ad2677","score":"0.5579418","text":"def test_search_find_one_result_by_postcode(self):\n make_recipe(\n \"legalaid.case\",\n personal_details__postcode=\"123\",\n personal__details__full_name=\"abc\",\n **self.get_extra_search_make_recipe_kwargs()\n )\n\n response = self.client.get(\n self.list_url,\n data={\"search\": self.resource.personal_details.postcode},\n HTTP_AUTHORIZATION=self.get_http_authorization(),\n )\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(1, len(response.data[\"results\"]))\n self.assertCaseEqual(response.data[\"results\"][0], self.resource)","title":""},{"docid":"fababc0cca564f36a5dd42425a0a8b50","score":"0.55777663","text":"def search(self, query):\n # SO, let's create our results\n results = None\n for shelf in self.library:\n # First shelf we're running the search on\n if results is None:\n results = shelf.search(query)\n # We use upgrade_and_extend, because multiple hits can happen.\n else:\n results.upgrade_and_extend(shelf.search(query))\n\n return results","title":""},{"docid":"b8840b500c07bd06ed5c1925492d9632","score":"0.5576955","text":"def post(self):\n text = self.get_argument(\"text\")\n __type = self.get_argument(\"type\")\n\n\n if __type == \"dish\":\n \"\"\"\n It might be a possibility that the user enetered the dish which wasnt in autocomplete\n then we have to search exact dish name of seach on Laveneshtein algo\n \"\"\"\n ##search in ES for dish name \n\n result = list()\n __result = ElasticSearchScripts.get_dish_match(text)\n for dish in __result:\n eatery_id = dish.get(\"eatery_id\")\n __eatery_details = r_clip_eatery.find_one({\"eatery_id\": eatery_id})\n for e in [\"eatery_highlights\",\n \"eatery_cuisine\", \"eatery_trending\", \"eatery_known_for\", \"eatery_type\", \"_id\"]:\n try:\n __eatery_details.pop(e)\n except Exception as e:\n print e\n pass\n dish.update({\"eatery_details\": __eatery_details})\n result.append(dish)\n\n elif __type == \"cuisine\":\n ##gives out the restarant for cuisine name\n print \"searching for cuisine\"\n result = list()\n __result = ElasticSearchScripts.eatery_on_cuisines(text)\n print __result\n for eatery in __result:\n __result= short_eatery_result_collection.find_one({\"__eatery_id\": eatery.get(\"__eatery_id\")}, {\"_id\": False, \"food\": True, \"ambience\": True, \\\n \"cost\":True, \"service\": True, \"menu\": True, \"overall\": True, \"location\": True, \"eatery_address\": True, \"eatery_name\": True, \"__eatery_id\": True})\n\n eatery.update({\"eatery_details\": __result})\n result.append(eatery)\n\n elif __type == \"eatery\":\n \n ##TODO : Some issue with the restaurant chains for example\n ##big chills at different locations, DOnt know why ES\n ##not returning multiple results\n ##TODO: dont know why dropped nps are still in result.\n result = r_eateries.find_one({\"eatery_name\": text},\n {\"_id\": False, \"eatery_known_for\": False,\n \"droppped_nps\": False,\n \"eatery_trending\": False,\n \"eatery_highlights\": False})\n \n print result.get(\"eatery_id\")\n __result = process_result(result)\n \n pictures = result.pop(\"pictures\")\n result.update({\"pictures\": pictures[0:2]})\n \n result.update(__result)\n result = [result]\n\n elif not __type:\n print \"No type defined\"\n\n else:\n print __type\n self.write({\"success\": False,\n\t\t\t \"error\": True,\n\t\t\t \"messege\": \"Maaf kijiyega, Yeh na ho paayega\",\n\t\t\t })\n self.finish()\n return \n print result\n self.write({\"success\": True,\n\t\t\t \"error\": False,\n\t\t\t \"result\": result,\n\t\t\t})\n self.finish()\n return","title":""},{"docid":"d396f9be5eceab3cbb62987c1686e7c4","score":"0.5575648","text":"def search_app(q='', fq=None, app=True, **kw):\n from allura.model import ArtifactReference\n from allura.lib.security import has_access\n\n history = kw.pop('history', None)\n if app and kw.pop('project', False):\n # Used from app's search controller. If `project` is True, redirect to\n # 'entire project search' page\n redirect(c.project.url() + 'search/?' +\n urlencode(dict(q=q, history=history)))\n search_comments = kw.pop('search_comments', None)\n limit = kw.pop('limit', None)\n page = kw.pop('page', 0)\n default = kw.pop('default', 25)\n allowed_types = kw.pop('allowed_types', [])\n parser = kw.pop('parser', None)\n sort = kw.pop('sort', 'score desc')\n fq = fq if fq else []\n search_error = None\n results = []\n count = 0\n matches = {}\n limit, page, start = g.handle_paging(limit, page, default=default)\n if not q:\n q = ''\n else:\n # Match on both `title` and `text` by default, using 'dismax' parser.\n # Score on `title` matches is boosted, so title match is better than body match.\n # It's 'fuzzier' than standard parser, which matches only on `text`.\n if search_comments:\n allowed_types += ['Post']\n if app:\n fq = [\n 'project_id_s:%s' % c.project._id,\n 'mount_point_s:%s' % c.app.config.options.mount_point,\n '-deleted_b:true',\n 'type_s:(%s)' % ' OR '.join(\n ['\"%s\"' % t for t in allowed_types])\n ] + fq\n search_params = {\n 'qt': 'dismax',\n 'qf': 'title^2 text',\n 'pf': 'title^2 text',\n 'fq': fq,\n 'hl': 'true',\n 'hl.simple.pre': '#ALLURA-HIGHLIGHT-START#',\n 'hl.simple.post': '#ALLURA-HIGHLIGHT-END#',\n 'sort': sort,\n }\n if not history:\n search_params['fq'].append('is_history_b:False')\n if parser == 'standard':\n search_params.pop('qt', None)\n search_params.pop('qf', None)\n search_params.pop('pf', None)\n try:\n results = search(\n q, short_timeout=True, ignore_errors=False,\n rows=limit, start=start, **search_params)\n except SearchError as e:\n search_error = e\n if results:\n count = results.hits\n matches = results.highlighting\n\n def historize_urls(doc):\n if doc.get('type_s', '').endswith(' Snapshot'):\n if doc.get('url_s'):\n doc['url_s'] = doc['url_s'] + \\\n '?version=%s' % doc.get('version_i')\n return doc\n\n def add_matches(doc):\n m = matches.get(doc['id'], {})\n title = h.get_first(m, 'title')\n text = h.get_first(m, 'text')\n if title:\n title = (markupsafe.escape(title)\n .replace('#ALLURA-HIGHLIGHT-START#', markupsafe.Markup('<strong>'))\n .replace('#ALLURA-HIGHLIGHT-END#', markupsafe.Markup('</strong>')))\n if text:\n text = (markupsafe.escape(text)\n .replace('#ALLURA-HIGHLIGHT-START#', markupsafe.Markup('<strong>'))\n .replace('#ALLURA-HIGHLIGHT-END#', markupsafe.Markup('</strong>')))\n doc['title_match'] = title\n doc['text_match'] = text or h.get_first(doc, 'text')\n return doc\n\n def paginate_comment_urls(doc):\n if doc.get('type_s', '') == 'Post':\n artifact = doc['_artifact']\n if artifact:\n doc['url_paginated'] = artifact.url_paginated()\n return doc\n\n def filter_unauthorized(doc):\n aref = ArtifactReference.query.get(_id=doc.get('id'))\n # cache for paginate_comment_urls to re-use\n doc['_artifact'] = aref and aref.artifact\n # .primary() necessary so that a ticket's comment for example is checked with the ticket's perms\n if doc['_artifact'] and not has_access(doc['_artifact'].primary(), 'read', c.user):\n return None\n else:\n return doc\n\n filtered_results = [_f for _f in map(filter_unauthorized, results) if _f]\n count -= len(results) - len(filtered_results)\n results = filtered_results\n results = map(historize_urls, results)\n results = map(add_matches, results)\n results = map(paginate_comment_urls, results)\n\n # Provide sort urls to the view\n score_url = 'score desc'\n date_url = 'mod_date_dt desc'\n try:\n field, order = sort.split(' ')\n except ValueError:\n field, order = 'score', 'desc'\n sort = ' '.join([field, 'asc' if order == 'desc' else 'desc'])\n if field == 'score':\n score_url = sort\n elif field == 'mod_date_dt':\n date_url = sort\n params = request.GET.copy()\n params.update({'sort': score_url})\n score_url = url(request.path, params=params)\n params.update({'sort': date_url})\n date_url = url(request.path, params=params)\n return dict(q=q, history=history, results=list(results) or [],\n count=count, limit=limit, page=page, search_error=search_error,\n sort_score_url=score_url, sort_date_url=date_url,\n sort_field=field)","title":""},{"docid":"f3ff9eab02b3a9cb4280c54363cece15","score":"0.5573637","text":"def test_search_find_one_result_by_ref(self):\n make_recipe(\n \"legalaid.case\",\n personal_details__full_name=\"abc\",\n personal_details__postcode=\"123\",\n **self.get_extra_search_make_recipe_kwargs()\n )\n\n response = self.client.get(\n self.list_url, data={\"search\": self.resource.reference}, HTTP_AUTHORIZATION=self.get_http_authorization()\n )\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(1, len(response.data[\"results\"]))\n self.assertCaseEqual(response.data[\"results\"][0], self.resource)","title":""},{"docid":"f82c245bbbbd336d3d782f23d8c82c6a","score":"0.5571029","text":"def search_by_query(self):\n\n if self.uri:\n self.user_query = input('\\n\\t9) What do you want to search ?\\n\\t')\n\n print('\\n\\tSearch results...\\n\\t{result_data}'.format(\n result_data=self.pyro_obj.get_by_query(self.user_query))\n )","title":""},{"docid":"bd88235e0591068ceebc9aaccf12d7e9","score":"0.55677366","text":"def run_keyword_search_process():\n\n print(\"This will search for matches in the title and notes fields.\")\n search_term = input(\"Enter a search term: \").strip()\n\n contains_search_term = ((Time_Entry.title.contains(search_term))\n | (Time_Entry.notes.contains(search_term)))\n\n matching_entries = Time_Entry.select().where(contains_search_term)\n\n clear_screen()\n\n if len(matching_entries) == 0:\n print(\"No matching entries found.\")\n dummy = input(\"Press enter to continue. \")\n else:\n run_options_loop(matching_entries)","title":""},{"docid":"5aef14ba8df317f5a827902d64eb63f8","score":"0.55642","text":"def genre_search(genre):\n global searching\n global searchQuery\n\n searching = True\n searchQuery = genre\n\n films = list(mongo.db.film_list.find({\"$text\": {\"$search\": genre}}))\n return render_template(\n \"get_films.html\", films=films, search=searching,\n searchQuery=searchQuery)","title":""},{"docid":"ff6c1b05cdd6222288a0a684a6442dbc","score":"0.5560062","text":"def search_wines():\n\n title_keywords = request.args.get('title_keywords')\n title_keywords = title_keywords.title()\n wines = crud.search_wines(title_keywords)\n\n if not wines:\n title_keywords = title_keywords.upper()\n wines = crud.search_wines(title_keywords)\n\n if 'user_id' in session:\n user = crud.get_user_by_id(session['user_id'])\n return render_template('search_results.html', wines=wines, user=user)\n else:\n return render_template('search_results.html', wines=wines)\n\n if 'user_id' in session:\n user = crud.get_user_by_id(session['user_id'])\n return render_template('search_results.html', wines=wines, user=user)\n else:\n return render_template('search_results.html', wines=wines)","title":""},{"docid":"8965ad2d058519370db92cdd744a095f","score":"0.5551595","text":"def search(self, query, maxresults=20):\n pass","title":""},{"docid":"47b15b40ab4e89e6a497a59b17552d85","score":"0.55505663","text":"def search(self, **kwargs):\n resource = self.resource + 'search'\n required = ['q']\n metadata, response = self.__get__(resource, kwargs) # use metadata for something else.\n return response","title":""},{"docid":"b51c612202c0eab6a1df6c2dbb8793af","score":"0.55419123","text":"def search():\n\n # Make sure that the search bar is not empty\n if not request.form.get(\"keyword\"):\n return apology(\"search criteria must not be empty\", 403)\n\n keyword = request.form.get(\"keyword\")\n\n # Add in the wildcard characters for the SQL queries\n like_keyword = \"%\" + keyword + \"%\"\n\n # Get all posts that match the keyword(s)\n posts = db.execute(\n \"SELECT title, datetime, posts.id AS post_id, user_id, username FROM posts INNER JOIN users ON posts.user_id = users.id WHERE title LIKE ? OR contents LIKE ? ORDER BY datetime DESC\", (like_keyword), (like_keyword))\n\n if len(posts) == 0:\n no_posts = True\n else:\n no_posts = False\n\n # Get all people that match the keyword(s)\n people = db.execute(\"SELECT id AS user_id, username FROM users WHERE username LIKE ?\", (like_keyword))\n\n if len(people) == 0:\n no_people = True\n else:\n no_people = False\n\n return render_template(\"search.html\", posts=posts, no_posts=no_posts, people=people, no_people=no_people)","title":""},{"docid":"59dc767c55a5cb20d0031a9b6c23661d","score":"0.5539616","text":"def search(request, **kwargs):\r\n return SearchView.as_view(**kwargs)(request)","title":""},{"docid":"9eaf1e4c914f16a3d0281bb5531e8b58","score":"0.55371684","text":"async def search(self, *args: Any, **kwargs: Any) -> List[dict]:\n await self.wait_for(self.connection.search(*args, **kwargs))\n return self.connection.response","title":""},{"docid":"70e1d787867d2d87a194956dc557d9e9","score":"0.553495","text":"def find_recipe(input_text, recipes, p2v_our_emoji):\n\tprint(input_text)\n\ttokens = tokenizer.tokenize(input_text)\t\n\tprint(tokens)\n\tvector = np.sum([p2v_our_emoji[t] for t in tokens], axis=0) / len(tokens)\n\t\n\tkey, value = min(recipes.items(), key=lambda kv: (distance.euclidean(vector, np.array(kv[1]['recipe vector']))))\n\n\tresult = {}\n\tresult['title'] = value['title']\n\tresult['instructions'] = value['instructions']\n\n\treturn json.dumps(result)","title":""},{"docid":"cfa0f3a32da4a4e4e943c3fbaa93c17f","score":"0.55348986","text":"def search(self, search):\n return self.backend.search(search)","title":""},{"docid":"2d678bbbaeb1c1705e495fb6ee1fc45e","score":"0.55339485","text":"def search_route():\n\n with sqlite3.connect(DBPATH) as conn:\n query = request.get_json().get(\"query\")\n res = conn.execute(\n \"select id, title from answers where title like ? \", [f\"%{query}%\"],\n )\n answers = [{\"id\": r[0], \"title\": r[1]} for r in res]\n print(query, \"--> \")\n pprint(answers)\n return jsonify(answers), 200","title":""},{"docid":"add902afc70ea874d20a8a1244441f25","score":"0.55326116","text":"def search():\n if request.args.get('q'):\n g.search_form.q.data = request.args.get('q')\n q = g.search_form.q.data if g.search_form.q.data else None\n\n if q is None:\n return render_template('404.html'), 404\n\n page = request.args.get('page', 1, type=int)\n per_page = app.config['PER_PAGE']\n\n paginated_papers, total = Paper.search(q, page, per_page)\n\n href=\"search?q={}\".format(q) + '&page={0}' ##customizing to include search query parameter\n pagination = Pagination(href=href, page=page, per_page=per_page, total=total, record_name='papers',format_total=True, format_number=True)\n # print(pagination.__dict__)\n\n return render_template('papers.html', papers=paginated_papers, pagination=pagination, per_page=per_page, categories=CATEGORY_LIST, icondict=icondict)","title":""},{"docid":"c87e446336cfff2ed89d5cb1d990f022","score":"0.55319804","text":"def do_search(request):\n products = Product.objects.filter(name__icontains=request.Get['q'])\n return render(request, \"products.html\", {\"products\": products})","title":""},{"docid":"0e46ab899a626dbf93cb43a7ce62c44a","score":"0.5531373","text":"def search(request):\n # r = requests.get(\"http://api.angel.co/1/jobs\")\n # response_data = r.json()\n # final_jobs = []\n # for job in response_data[\"jobs\"]:\n # job_tags = job['tags']\n # job_match = True\n # for tag in job_tags:\n # if tag['tag_type'] is \"SkillTag\" and not business_search(tag['name']):\n # job_match = False\n # if tag['tag_type'] is \"LocationTag\" and not location_search(location, tag['name']):\n # job_match = False\n # # if category matches business and location matches search, check for desc and add it to list.\n # if job_match and (keyword in job['description']):\n # final_jobs.append(job)\n # context = {'jobs': final_jobs}\n # return render(request, 'search/search.html', context)\n return HttpResponse(\"Works for now\")","title":""},{"docid":"510daa725373e06e01db50bd2c307089","score":"0.5529226","text":"def search_criteria():\n global search_term\n global location\n\n raw_search_term = input(\"What would you like to find?\")\n split_search = raw_search_term.split(\" \")\n if len(split_search) > 1:\n search_term = \"+\".join(split_search)\n else:\n search_term = raw_search_term\n raw_location = input(\"What city would you like to search in?\")\n split_location = raw_location.split(\" \")\n if len(split_location) > 1:\n location = \"+\".join(split_location)\n else:\n location = raw_location","title":""},{"docid":"0eaeace753c6815c28529c2ef29c9a91","score":"0.55195177","text":"def search_db():\n search_term = request.args.get(\"search-text\")\n search_term_formatted = '%' + search_term + '%'\n \n search_results = Truck_schedule.query.filter(or_(Truck_schedule.truck_name.ilike(search_term_formatted), Truck_schedule.extra_text.ilike(search_term_formatted))).distinct(Truck_schedule.truck_id).all()\n\n if search_results:\n pass\n else:\n search_results = None\n\n return render_template(\"search_results.html\", search_term=search_term, search_results=search_results)","title":""},{"docid":"a83ffa094a3467a0e0ea58166ca767b3","score":"0.55110025","text":"def npo_search():\n tag = request.form[\"tag\"]\n orders = Order.query.filter_by(user_id=current_user.id)\n # details = OrderDetails.query.all()\n if not tag:\n flash(\"Missing keyword\")\n return redirect(url_for(\"views.dashboard\", user=current_user, username=current_user.username))\n\n search = \"%{}%\".format(tag)\n location = User.query.filter(User.location.like(search)).all()\n food = Food.query.all()\n\n if location is not None:\n for i in location:\n filtered = Food.query.filter_by(users_id=i.id).all()\n\n return render_template(\n 'npo.html',\n businessname=current_user.businessname,\n food=food,\n filtered=filtered,\n users=location,\n tag=tag,\n orders=orders,\n user=current_user)\n\n businessname = User.query.filter(User.businessname.like(search)).all()\n\n if businessname is not None:\n for i in businessname:\n filtered = Food.query.filter_by(users_id=i.id).all()\n return render_template(\n 'npo.html',\n businessname=current_user.businessname,\n food=food,\n filtered=filtered,\n users=businessname,\n tag=tag,\n orders=orders,\n user=current_user)\n\n flash(\"Not found\")\n return render_template(\n 'npo.html',\n businessname=current_user.businessname,\n food=food,\n user=current_user,\n tag=tag\n )","title":""},{"docid":"c61fc536630739a75ab74a8c72c1fb94","score":"0.55088633","text":"def search(request, template_name=\"aggregator/search.html\", template_loader=loader, extra_context=None, context_processors=None, mimetype=None):\n # Create search terms list.\n if request.GET.__contains__('search'):\n search_string = request.GET['search']\n \n # First, split on double-quotes to extract any multi-word terms\n search_terms = search_string.split('\"')\n cleaned_search_terms = []\n \n # Then, remove any unnecessary whitespace at the beginings or ends of the terms\n for item in search_terms:\n if not item.startswith(' ') and not item.endswith(' ') and not item == '':\n cleaned_search_terms.append(item)\n if item.startswith(' '):\n cleaned_search_terms.append(item[1:])\n if item.endswith(' '):\n cleaned_search_terms.append(item[:-1])\n \n \n # Filtering by model\n model_query = request.GET.get('models', 'default')\n model_query_list = request.GET.getlist('models')\n \n if model_query == \"default\":\n content_types = get_aggregator_content_types(type_set=\"default\")\n model_list = [ ct.app_label + \".\" + ct.model for ct in content_types]\n model_string = \"default\"\n elif model_query == \"all\":\n model_string = \"all\"\n content_types = get_aggregator_content_types(type_set=\"all\")\n else:\n model_string = \",\".join(model_query_list)\n model_list = model_query_list\n content_types = []\n for item in model_list:\n for model in settings.AGGREGATOR_MODELS:\n if item == model['model']:\n app_label = item.split('.')[0]\n model = item.split('.')[1]\n content_type = ContentType.objects.get(app_label=app_label, model=model)\n content_types.append(content_type)\n \n # Set up a list to put results into\n search_results = []\n\n # Search each model for the search terms\n for content_type in content_types:\n content_type_string = content_type.app_label + \".\" + content_type.model\n model = content_type.model_class()\n manager = model._default_manager\n opts = model._meta\n query = Q()\n queries = []\n for model in settings.AGGREGATOR_MODELS:\n if content_type_string == model['model']:\n search_fields = model['search_fields']\n\n for term in cleaned_search_terms:\n for field in search_fields:\n lookup = field + \"__icontains\"\n kwargs = { lookup: term }\n q = Q(**kwargs)\n queries.append(q)\n\n for q in queries:\n query = query | q\n\n results = manager.filter(query)\n \n # Create a list of primary keys for each item in the results, then use that list to get a \n # QuerySet of associated ContentItem objects. Append the results of that QuerySet to the\n # search_results list.\n result_pks = [ i['pk'] for i in results.values('pk') ]\n result_content_items = ContentItem.objects.filter(content_type=content_type, object_id__in=result_pks)\n search_results.extend(result_content_items)\n items = dictsortreversed(search_results, 'timestamp')\n else:\n cleaned_search_terms = []\n search_string = \"\"\n items = []\n content_types = []\n model_string = \"\"\n hits = 0\n # Build the context\n context = RequestContext(request, {\n \"search_terms\" : cleaned_search_terms,\n \"search_string\" : search_string,\n \"items\" : items,\n \"all_content_types\" : get_aggregator_content_types(type_set=\"all\"),\n \"content_types\" : content_types,\n \"model_string\" : model_string,\n \"hits\" : len(items),\n }, context_processors)\n if extra_context:\n for key, value in extra_context.items():\n if callable(value):\n context[key] = value()\n else:\n context[key] = value\n \n # Load, render, and return\n t = template_loader.get_template(template_name)\n return HttpResponse(t.render(context), mimetype=mimetype)","title":""}],"string":"[\n {\n \"docid\": \"d34d8d2e127a11f836cd282e7367d401\",\n \"score\": \"0.7443953\",\n \"text\": \"def search_recipes():\\n if request.method == 'POST':\\n cuisine = request.form.get(\\\"cuisine_name_filter\\\")\\n allergen = request.form.get(\\\"allergen_name_filter\\\")\\n meal = request.form.get(\\\"meal_type_filter\\\")\\n search_item = request.form.get('search_item')\\n \\n \\n query = {}\\n if cuisine and allergen and meal:\\n filter_recipes = mongo.db.recipes.find({\\\"cuisine_name\\\":cuisine, \\\"allergen_name\\\":allergen, \\\"meal_type\\\":meal})\\n query = {cuisine,allergen,meal}\\n \\n elif cuisine and allergen:\\n filter_recipes = mongo.db.recipes.find({\\\"cuisine_name\\\":cuisine, \\\"allergen_name\\\":allergen})\\n query = {cuisine,allergen}\\n\\n elif cuisine and meal:\\n filter_recipes = mongo.db.recipes.find({\\\"cuisine_name\\\":cuisine, \\\"meal_type\\\":meal})\\n query = {cuisine,meal}\\n\\n elif allergen and meal:\\n filter_recipes = mongo.db.recipes.find({\\\"allergen_name\\\":allergen, \\\"meal_type\\\":meal})\\n query = {allergen,meal}\\n\\n elif cuisine:\\n filter_recipes=mongo.db.recipes.find({\\\"cuisine_name\\\":cuisine})\\n query = {cuisine}\\n\\n elif allergen:\\n filter_recipes = mongo.db.recipes.find({\\\"allergen_name\\\":allergen})\\n query = {allergen}\\n\\n elif meal:\\n filter_recipes = mongo.db.recipes.find({\\\"meal_type\\\":meal})\\n query = {meal}\\n \\n else:\\n # create the index\\n filter_recipes = mongo.db.recipes.create_index( [(\\\"$**\\\", 'text')] )\\n page, per_page, offset = get_page_args(page_parameter = 'page',\\n per_page_parameter = 'per_page')\\n\\n # search with the search term that came through the form\\n cursor = mongo.db.recipes.find({ \\\"$text\\\": { \\\"$search\\\": search_item } })\\n total_filter_recipes = cursor.count()\\n pagination = Pagination(page = page,\\n per_page = ITEMS_PER_PAGE,\\n total = total_filter_recipes,\\n record_name = 'Recipes')\\n recipes = [recipe for recipe in cursor]\\n \\n # send recipes to page\\n return render_template('search.html', \\n recipes = recipes, \\n query = search_item,\\n page = page,\\n per_page = per_page,\\n pagination = pagination)\\n \\n \\n page, per_page, offset = get_page_args(page_parameter = 'page',\\n per_page_parameter = 'per_page')\\n total_filter_recipes = filter_recipes.count()\\n recipes_fetched = filter_recipes.skip((page - 1)*ITEMS_PER_PAGE).limit(ITEMS_PER_PAGE)\\n pagination = Pagination(page = page,\\n per_page = ITEMS_PER_PAGE,\\n total = total_filter_recipes,\\n record_name = 'Recipes')\\n \\n return render_template('search.html',\\n recipes = recipes_fetched,\\n page = page,\\n query = query,\\n per_page = per_page,\\n pagination = pagination)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e342c8a03e29122482602bab0253101\",\n \"score\": \"0.72639793\",\n \"text\": \"def search():\\n # Search Buttons function\\n query = request.form.get(\\\"query\\\")\\n recipes = list(mongo.db.recipes.find({\\\"$text\\\": {\\\"$search\\\": query}}))\\n return render_template(\\\"recipes.html\\\", recipes=recipes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e245e87fbc36fd84f22a94e0a8498b9\",\n \"score\": \"0.7207813\",\n \"text\": \"def search_by_recipe(recipe_name):\\n # recipe_name_list=ingredient_name.split(\\\" \\\")\\n # recipe_name_format=\\\"+\\\".join(recipe_name_list)\\n \\n searched_recipes=get_search_by_recipe(recipe_name)\\n \\n return render_template('searches.html',recipes=searched_recipes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9ea5e1a1b3cd7d4a945b44adad9775d\",\n \"score\": \"0.7062741\",\n \"text\": \"def search():\\n query = request.form.get(\\\"query\\\")\\n recipes = list(mongo.db.recipes.find({\\\"$text\\\": {\\\"$search\\\": query}}))\\n return render_template(\\\"recipes.html\\\", recipes=recipes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5989decddcf5552f4e4ebc7f3e631f0f\",\n \"score\": \"0.7018663\",\n \"text\": \"def search():\\n query = request.form.get(\\\"query\\\")\\n recipes = mongo.db.recipes.find({\\\"$text\\\": {\\\"$search\\\": query}})\\n return render_template('recipes.html', recipes=recipes,\\n categories=mongo.db.categories.find())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2322ac395b81a7064b5ff49a6f0cad1\",\n \"score\": \"0.6964389\",\n \"text\": \"def search():\\n # Wildcard text search index\\n mongo.db.recipes.create_index([(\\\"$**\\\", pymongo.TEXT)])\\n # Results per page\\n p_limit = 9\\n current_page = int(request.args.get('current_page', 1))\\n # Input term for search query\\n word_search = request.args.get('word_search')\\n # Results for search sorted by ID\\n results = mongo.db.recipes.find({'$text': {'$search': str(word_search)}}, {\\\"score\\\": {\\\"$meta\\\": 'textScore'}}).sort('_id', pymongo.ASCENDING).skip((current_page -1)*p_limit).limit(p_limit)\\n # Pagination\\n results_count = mongo.db.recipes.find({'$text': {'$search': str(word_search)}}).count()\\n results_pages = range(1, int(math.ceil(results_count / p_limit)) + 1)\\n total_page_no = int(math.ceil(results_count/p_limit))\\n \\n # Most Popular recipes - appear when there are no results to the user's query\\n recommended = mongo.db.recipes.find().sort(\\\"favourite_count\\\", pymongo.DESCENDING).limit(3)\\n \\n return render_template(\\\"search.html\\\", \\n p_limit = p_limit,\\n current_page=current_page, \\n results_count=results_count,\\n word_search=word_search,\\n results=results,\\n results_pages=results_pages,\\n total_page_no=total_page_no,\\n recommended=recommended)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c87406c5498045544ca25cbc0bd939b\",\n \"score\": \"0.6890836\",\n \"text\": \"def search_by_ingredients(ingredient_name):\\n # recipe_name_list=ingredient_name.split(\\\" \\\")\\n # recipe_name_format=\\\"+\\\".join(recipe_name_list)\\n searched_recipes=get_search_by_ingredients(ingredient_name)\\n return render_template('search.html',recipes=searched_recipes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a7112f1b549bfc777a2049ae2ce605a\",\n \"score\": \"0.6860355\",\n \"text\": \"def search():\\n search = request.form.get(\\\"search\\\")\\n types = list(mongo.db.type.find().sort(\\\"type_name\\\", 1))\\n recipes = list(mongo.db.recipes.find({\\\"$text\\\": {\\\"$search\\\": search}}))\\n flash(\\\"{} Results for '{}'\\\".format(len(recipes), search))\\n return render_template(\\\"recipes.html\\\", recipes=recipes, types=types)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edec680426b6b94882a250d4b5993ea7\",\n \"score\": \"0.6691776\",\n \"text\": \"def search_recipes(query):\\n # search Recipes function\\n if search:\\n recipes = list(\\n mongo.db.recipes.find({\\\"category_name\\\": query}))\\n\\n return render_template(\\\"recipes.html\\\", recipes=recipes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd82c3e788872091ac7e6bfb70961775\",\n \"score\": \"0.6485085\",\n \"text\": \"def view_recipes_with_ingredients():\\r\\n\\r\\n ingredient_list = request.args.get('ingredients')\\r\\n ing_list = crud.string_to_list(ingredient_list)\\r\\n ing_id_list = []\\r\\n for ingr in ing_list:\\r\\n ingred_by_name = crud.get_ingredient_by_name(ingr)\\r\\n ing_id_list.append(ingred_by_name.ingredient_id)\\r\\n recipe_search_results = crud.get_recipes_by_multiple_ing(ing_id_list)\\r\\n\\r\\n return render_template('ing_recipe_search_results.html', see_recipes=recipe_search_results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb1ee04354501f9af87e6391d489f999\",\n \"score\": \"0.64551157\",\n \"text\": \"def test_search_recipes(self):\\n nutrients = {'maxCarbs': '100'}\\n ingredients = ['potatoes']\\n diet = 'vegetarian'\\n intolerances = ['dairy']\\n recipe_list = RecipeRecommender.search_recipes(ingredients, nutrients,\\n diet, intolerances)\\n self.assertIsInstance(recipe_list, list)\\n if len(recipe_list) > 0:\\n self.assertIsInstance(recipe_list[0], Recipe)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9e2b75a19daf7b318b88bffe21389ab\",\n \"score\": \"0.64290017\",\n \"text\": \"def search(self, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50254ace5f6e7f0d82864ce49c642b1e\",\n \"score\": \"0.6413538\",\n \"text\": \"def search_gear():\\n\\n gear_keyword = request.form.get(\\\"gear_name\\\")\\n category = request.form.get(\\\"category\\\")\\n brand = request.form.get(\\\"brand\\\")\\n # zipcode = request.form.get(\\\"zipcode\\\")\\n if category == \\\"All Categories\\\" and brand == \\\"All Brands\\\":\\n equipSearch = Equipment.query.filter(Equipment.gear_name.contains(gear_keyword), Equipment.available.is_(True)).all()\\n elif category != \\\"All Categories\\\" and brand == \\\"All Brands\\\":\\n equipSearch = Equipment.query.filter(Equipment.category == category, Equipment.gear_name.contains(gear_keyword), Equipment.available.is_(True)).all()\\n elif category == \\\"All Categories\\\" and brand != \\\"All Brands\\\":\\n equipSearch = Equipment.query.filter(Equipment.brand == brand, Equipment.gear_name.contains(gear_keyword), Equipment.available.is_(True)).all()\\n else:\\n equipSearch = Equipment.query.filter(Equipment.category == category, Equipment.brand == brand, Equipment.gear_name.contains(gear_keyword), Equipment.available.is_(True)).all()\\n\\n equipment = []\\n\\n # print equipSearch\\n\\n for e in equipSearch:\\n d = {\\n 'gear_id' : e.gear_id,\\n 'gear_name': e.gear_name,\\n 'category' : e.category,\\n 'brand' : e.brand,\\n 'lender_email' : e.lender_email,\\n 'gear_photo_url' : e.gear_photo_url,\\n 'zipcode' : e.zipcode\\n }\\n equipment.append(d)\\n\\n return jsonify(equipment)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e2c3c45116018faa04088cc58b7c0b6\",\n \"score\": \"0.63533044\",\n \"text\": \"def get_recipes(request):\\n if not request.body:\\n return JsonResponse({})\\n\\n # parse json string to list of ingredient names\\n ing = request.body.decode(\\\"utf-8\\\")\\n ingredients_to_search_by = ing[1:-1].replace('\\\"', \\\"\\\").split(',')\\n # save ingredients for the future\\n # get user id, if applicable. Else default to 0\\n usr_id = 0\\n if request.user:\\n save_ingredients_to_user(request.user, ingredients_to_search_by, request.session)\\n usr_id = request.user.id\\n # send ingredients to search algorithm\\n values = IngredientUtils(usr_id).find_recipes(ingredients_to_search_by)\\n # convert queryset to JSON!!! (Now made serializable in the find_recipes call)\\n for recipe in values:\\n recipe['appliances'] = list(Appliance.objects.filter(recipe=recipe['id']).values('name'))\\n\\n return JsonResponse({'results': values})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4800436b60324eebca1d49b9a4c93ec7\",\n \"score\": \"0.6332947\",\n \"text\": \"def search_books():\\n \\n # Retrieve input prompted by the user\\n search_input = request.args.get(\\\"search\\\")\\n\\n if search_input == \\\"\\\":\\n return render_template(\\\"search.html\\\", search=search_input)\\n\\n # Fetch database\\n database_response = db.execute(\\\"SELECT * FROM books WHERE title iLIKE :entry OR author iLIKE :entry OR isbn iLIKE :entry\\\", {'entry': \\\"%\\\" + search_input + \\\"%\\\"}).fetchall()\\n\\n return render_template(\\\"search.html\\\", results=database_response, search=search_input)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7096118bc097e9c224f60b1784c7f405\",\n \"score\": \"0.6324785\",\n \"text\": \"def search():\\n\\n search_this_string = request.form['search']\\n cursor = gear.find({'$text': {'$search': search_this_string}})\\n return render_template('searchresults.html', result=list(cursor),\\n categories=list(categories.find()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3caf852ab6929412009e0660220b69c3\",\n \"score\": \"0.632282\",\n \"text\": \"def search():\\n drug = request.args.get('drug').lower()\\n location = request.args.get('location')\\n\\n # get DB\\n db = get_db()\\n cur = db.cursor()\\n\\n # store search\\n now = now_int()\\n cur.execute('INSERT INTO Searches VALUES(?,?)', (drug, now))\\n db.commit()\\n\\n # find drug in DB\\n cur.execute('SELECT drug_id FROM DrugNames WHERE Name = ?', (drug,))\\n exact_row = cur.fetchone()\\n found = exact_row is not None\\n\\n # find variants\\n no_spaces = re.sub('\\\\\\\\s+', '', drug)\\n id_counts = dict()\\n for idx in range(len(no_spaces)-2):\\n trigram = no_spaces[idx:(idx+3)]\\n for drug_id in cur.execute(\\\"\\\"\\\"SELECT drug_id\\n FROM DrugNameParts\\n WHERE Part = ?\\n LIMIT 100\\\"\\\"\\\", (trigram,)):\\n id_counts[drug_id[0]] = id_counts.get(drug_id[0], 0) + 1\\n\\n sorted_id_counts = sorted(list(id_counts.items()),\\n key=lambda tup: -1 * tup[1])\\n\\n result = {'known': found}\\n\\n if found:\\n drug_id = exact_row[0]\\n result['drug-id'] = drug_id\\n\\n # TODO get location using https://bcgov.github.io/ols-devkit/ale/\\n loc_lat = 49.28202\\n loc_long = -123.11875\\n\\n # record request\\n cur.execute('INSERT INTO DrugRequests VALUES (?,?,?,?)',\\n (drug_id, now, loc_lat, loc_long))\\n db.commit()\\n\\n # fetch availability\\n pharmacies = list()\\n # TODO: Order pharmacies by distance\\n \\\"\\\"\\\"\\n for row in cur.execute(\\n '''SELECT pharma_id, (lat - ?) * (lat - ?) + (long - ?) * (long - ?) AS distance\\n FROM PharmaLoc\\n ORDER BY distance ASC\\n LIMIT 10''', (loc_lat, loc_lat, loc_long, loc_long)):\\n pharma_id = row[0]\\n pharmacies.append({'id': pharma_id})\\n \\\"\\\"\\\"\\n\\n for row in cur.execute(\\n ''' SELECT pharma_id, availability, when_reported\\n FROM Availabilities\\n WHERE drug_id = ? AND availability = 1\\n ORDER BY when_reported ''', (drug_id,)):\\n pharma_id = row[0]\\n pharmacies.append({'id': pharma_id})\\n\\n for index in range(len(pharmacies)):\\n pharma_id = pharmacies[index]['id']\\n pharmacy = cur.execute('SELECT *, rowid FROM PharmaDoc WHERE rowid = ?', (pharma_id,)).fetchone()\\n pharmacies[index] = {\\n 'name': pharmacy[0],\\n 'address': pharmacy[1],\\n 'phone': pharmacy[2],\\n 'fax': pharmacy[3],\\n 'manager': pharmacy[4],\\n 'hours': pharmacy[5],\\n 'latitude': pharmacy[6],\\n 'longitude': pharmacy[7],\\n 'pharma_id': pharmacy[8]\\n }\\n\\n\\n if not pharmacies:\\n #If pharmacies list is empty, there are no pharmacies that have the medication\\n result['unavailableAtPharmacies'] = True\\n result['pharmacies'] = pharmacies\\n\\n variants = []\\n for variant_id, count in sorted_id_counts[:10]:\\n cur.execute('SELECT name FROM DrugNames WHERE drug_id = ?',\\n (variant_id,))\\n variants.append({\\n 'id': variant_id,\\n 'name': cur.fetchone()[0]\\n })\\n\\n result['other-drugs'] = variants\\n\\n return jsonify(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a0307aa13ebf3479143b33f5c05112a\",\n \"score\": \"0.622523\",\n \"text\": \"def test_search_function_applies_preferences_with_logged_in_user(self, test_client, user, logged_in_user, db, itr,\\n search_term):\\n # User details as stored in db\\n user_diet_name = user.diet_preferences[0].diet_type.diet_name\\n user_diet_id = user.diet_preferences[0].diet_type.diet_type_id\\n user_allergy_names = [allergy.allergy.allergy_name for allergy in user.allergies]\\n user_allergy_ids = [allergy.allergy.allergy_id for allergy in user.allergies]\\n\\n # User details as presented in front-end, through config global variables\\n diet_name = str((config.DIET_CHOICES[user_diet_id - 1])[1]).lower()\\n allergy_names = [str((config.ALLERGY_CHOICES[i - 1])[1]).lower() for i in user_allergy_ids]\\n\\n response = search_function(test_client, search_term)\\n assert b'Based on saved user preferences, we have applied the following filters:' in response.data\\n # Check the Flash message\\n assert b'Diet type: ' + diet_name.encode() in response.data\\n assert b'Allergies: ' in response.data\\n for allergy_name in allergy_names:\\n assert allergy_name.encode() in response.data\\n\\n \\\"\\\"\\\"\\n GIVEN a flask application and registered user (with randomly generated diet type and food preferences)\\n WHEN user requests to view all recipes\\n THEN returned recipes satisfies user's diet preferences and allergies\\n \\\"\\\"\\\"\\n # Pull the recipe ids that are returned in on the response page (as a list). See conftest helper function.\\n # We will compare these recipe ids in a db query to ensure that the filters have been applied\\n response_recipe_ids = get_recipe_ids(test_client, response)\\n\\n from app.models import Recipes, RecipeDietTypes, RecipeAllergies\\n\\n for id in response_recipe_ids: # for all recipes that are returned in results\\n # Query all recipe ids which have the user's allergies\\n blacklist = db.session.query(RecipeAllergies.recipe_id) \\\\\\n .filter(RecipeAllergies.allergy_id.in_(user_allergy_ids)) \\\\\\n .distinct().subquery()\\n\\n # Use outerjoin to exclude blacklisted recipes in query\\n query = db.session.query(Recipes) \\\\\\n .outerjoin(blacklist, Recipes.recipe_id == blacklist.c.recipe_id) \\\\\\n .join(RecipeDietTypes) \\\\\\n .join(RecipeAllergies) \\\\\\n .filter(Recipes.recipe_id == id) \\\\\\n .filter(RecipeDietTypes.diet_type_id >= user_diet_id)\\n # db recipe id should = recipe id on page\\n # db diet type should be >= user's saved diet preference\\n # This query should be not None (i.e. it should return something) if the filters have been applied\\n assert query is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc3514a140a19cfbe36bf25600d0da2\",\n \"score\": \"0.62131435\",\n \"text\": \"def search(**kwargs):\\n filter_list = []\\n if 'main_search_term' in kwargs:\\n term = kwargs['main_search_term']\\n filter_list.append(or_(\\n Listing.name.ilike('%{}%'.format(str(term))),\\n Listing.product_id.ilike('%{}%'.format(str(term))),\\n Listing.description.ilike('%{}%'.format(str(term))))\\n )\\n if 'strict_name_search' in kwargs:\\n term = kwargs['strict_name_search']\\n filter_list.append(Listing.name.like('%{}%'.format(str(term))))\\n if 'name_search_term' in kwargs and kwargs['name_search_term']:\\n term = kwargs['name_search_term']\\n vendors = Vendor.query.filter(Vendor.company_name.ilike('%{}%'.format(str(term)))).all()\\n vendor_ids = [vendor.id for vendor in vendors]\\n filter_list.append(Listing.vendor_id.in_(vendor_ids))\\n\\n # used by vendors to filter by availability\\n if 'avail' in kwargs:\\n avail_criteria = kwargs['avail']\\n format(avail_criteria)\\n if avail_criteria == \\\"non_avail\\\":\\n filter_list.append(Listing.available == False)\\n if avail_criteria == \\\"avail\\\":\\n filter_list.append(Listing.available == True)\\n if avail_criteria == \\\"both\\\":\\n filter_list.append(or_(Listing.available == True,\\n Listing.available == False))\\n\\n # used by merchants to filter by availability\\n if 'available' in kwargs:\\n filter_list.append(Listing.available == True)\\n\\n if 'fav_vendor' in kwargs and kwargs['fav_vendor']:\\n bookmarked_vendor_ids = BookmarkVendor.query.filter_by(merchant_id=current_user.id).all()\\n ids = []\\n for bookmark in bookmarked_vendor_ids:\\n ids.append(bookmark.vendor_id)\\n filter_list.append(Listing.vendor_id.in_(ids))\\n\\n if 'favorite' in kwargs and kwargs['favorite']:\\n bookmarked_vendor_ids = Bookmark.query.filter_by(merchant_id=current_user.id).all()\\n ids = []\\n for bookmark in bookmarked_vendor_ids:\\n ids.append(bookmark.listing_id)\\n filter_list.append(Listing.id.in_(ids))\\n\\n if 'min_price' in kwargs and kwargs['min_price']:\\n filter_list.append(Listing.price >= kwargs['min_price'])\\n\\n if 'max_price' in kwargs and kwargs['max_price']:\\n filter_list.append(Listing.price <= kwargs['max_price'])\\n\\n filtered_query = Listing.query.filter(*filter_list)\\n print Listing.query.filter(*filter_list)\\n if 'sort_by' in kwargs and kwargs['sort_by']:\\n sort = kwargs['sort_by']\\n format(sort)\\n else:\\n sort = None\\n\\n if sort == \\\"low_high\\\":\\n sorted_query = filtered_query.order_by(Listing.price)\\n elif sort == \\\"high_low\\\":\\n sorted_query = filtered_query.order_by(desc(Listing.price))\\n elif sort == \\\"alphaAZ\\\":\\n sorted_query = filtered_query.order_by(func.lower(Listing.name))\\n elif sort == \\\"alphaZA\\\":\\n sorted_query = filtered_query.order_by(desc(func.lower(Listing.name)))\\n else: # default sort\\n sorted_query = filtered_query.order_by(Listing.price)\\n return sorted_query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d958957bc44e58ea62a899c111a260c5\",\n \"score\": \"0.61862475\",\n \"text\": \"def search(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f17d676912eef501c2927bdea9c3694e\",\n \"score\": \"0.61715937\",\n \"text\": \"def search(self, *args, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f17d676912eef501c2927bdea9c3694e\",\n \"score\": \"0.61715937\",\n \"text\": \"def search(self, *args, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39225a18a2d33d748b4987938888d014\",\n \"score\": \"0.61298484\",\n \"text\": \"def search_main():\\n form = SearchForm()\\n user = http_auth.username()\\n if form.validate_on_submit():\\n query_terms = form.search_terms.data.split(' ')\\n answer = OrderedDict()\\n for query in query_terms:\\n result = str(searcher.query(query))\\n answer[query] = result.split(' ', 1)[1]\\n return render_template('search_results.html', title='Search', answer=answer, user=user)\\n return render_template('search.html', title='Search', zone=FORWARD_ZONE, form=form, user=user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"000894a50f9672cc8b408ac34d4623e6\",\n \"score\": \"0.61075556\",\n \"text\": \"def getResults(ingredient):\\n puppy = Puppy()\\n recipelist = puppy.search_recipe(ingredient)\\n\\n return recipelist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4abf100a357f8c541475efb313d4715d\",\n \"score\": \"0.6101594\",\n \"text\": \"def search():\\n search_string = str(flask.request.args['search_val'])\\n \\n min_rating = flask.request.args['min_rating']\\n max_rating = flask.request.args['max_rating']\\n\\n page = int(flask.request.args['page'])\\n page_offset = (page - 1) * 10\\n\\n genres = flask.request.args['genres']\\n genres_list = json.loads(genres)\\n\\n # TODO: actually store genres in a separate table to avoid this terrible regex hack\\n # for now, this is reasonably performant at this scale\\n query_regex = \\\".*\\\" + \\\".*|.*\\\".join(genres_list) + \\\".*\\\"\\n\\n\\n count_query = (\\\"SELECT \\\" \\n \\\"count(*) \\\"\\n \\\"FROM \\\"\\n \\\"movies \\\"\\n \\\"INNER JOIN avg_ratings ON movies.id = avg_ratings.movie_id \\\"\\n \\\"WHERE \\\"\\n \\\"avg_ratings.avg_rating >= %s and avg_ratings.avg_rating <= %s \\\"\\n \\\"AND \\\"\\n \\\"movies.genres ~ %s \\\")\\n\\n with db.cursor(cursor_factory = psycopg2.extras.DictCursor) as cur:\\n cur.execute(count_query,\\n (float(min_rating), float(max_rating), query_regex, )\\n )\\n result = cur.fetchone()\\n count = result[0]\\n\\n # This query retrieves data for the movie list view using a view previously calculated for average rating\\n # It also uses\\n\\n base_query = (\\\"SELECT \\\" \\n \\\"movies.id, movies.title, movies.genres, avg_ratings.avg_rating \\\"\\n \\\"FROM \\\"\\n \\\"movies \\\"\\n \\\"INNER JOIN avg_ratings ON movies.id = avg_ratings.movie_id \\\"\\n \\\"WHERE \\\"\\n \\\"avg_ratings.avg_rating >= %s and avg_ratings.avg_rating <= %s \\\"\\n \\\"AND \\\"\\n \\\"movies.genres ~ %s \\\")\\n\\n # order by rating if the search query is empty\\n if search_string == \\\"\\\":\\n with db.cursor(cursor_factory = psycopg2.extras.DictCursor) as cur:\\n cur.execute(\\n base_query +\\n \\\"ORDER BY avg_ratings.avg_rating DESC, movies.id DESC LIMIT 10 OFFSET %s;\\\",\\n (float(min_rating), float(max_rating), query_regex, page_offset,)\\n )\\n result = cur.fetchmany(10)\\n return flask.json.dumps(search_results_to_dict(result, count), default=decimal_default)\\n # otherwise, order by search closeness\\n else:\\n with db.cursor(cursor_factory = psycopg2.extras.DictCursor) as cur:\\n cur.execute(\\n base_query +\\n \\\"ORDER BY SIMILARITY(title, %s) DESC, movies.id DESC LIMIT 10 OFFSET %s;\\\",\\n (float(min_rating), float(max_rating), query_regex, search_string, page_offset,)\\n )\\n result = cur.fetchmany(10)\\n\\n return flask.json.dumps(search_results_to_dict(result, count), default=decimal_default)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45fbe39b2e72f8e470a3101018855f8b\",\n \"score\": \"0.607085\",\n \"text\": \"def search(json, session):\\n reload(logic)\\n return logic.search(json, collection, session)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e9186fbbc43f49c4f7106a3c4fc826\",\n \"score\": \"0.60681784\",\n \"text\": \"def search_saved():\\n user = session.get('user', 'Guest')\\n search = request.form.get(\\\"search\\\")\\n types = list(mongo.db.type.find().sort(\\\"type_name\\\", 1))\\n recipes = list(mongo.db.recipes.find({\\\"$text\\\": {\\\"$search\\\": search}}))\\n saved_list = list(mongo.db.users.distinct(\\n \\\"saved_recipes\\\", {\\\"user_name\\\": user}))\\n # clears any empty entries\\n check_list = []\\n for item in saved_list:\\n if item == \\\"\\\":\\n continue\\n else:\\n check_list.append(item)\\n # length of the list shows the rating count\\n size = len(check_list)\\n return render_template(\\\"saved_recipes.html\\\", recipes=recipes, types=types,\\n check_list=check_list, size=size, user=user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21d2078f3b4d86f4337b5f5dd60ce340\",\n \"score\": \"0.60452926\",\n \"text\": \"def search(query, **parameters):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21d2078f3b4d86f4337b5f5dd60ce340\",\n \"score\": \"0.60452926\",\n \"text\": \"def search(query, **parameters):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"892d55a87d19cd4c3cc9923acfa488c6\",\n \"score\": \"0.60349613\",\n \"text\": \"def searchDocuments(self, REQUEST):\\n if self.onSearch:\\n # Manually generate a result set\\n try:\\n results = self.runFormulaScript(\\n 'from_%s_onsearch' % self.id,\\n self,\\n self.onSearch)\\n except PlominoScriptException, e:\\n if self.REQUEST:\\n e.reportError('Search event failed.')\\n return self.OpenForm(searchresults=[])\\n else:\\n # Allow Plomino to filter by view, default query, and formula\\n db = self.getParentDatabase()\\n searchview = db.getView(self.getSearchView())\\n\\n #index search\\n index = db.getIndex()\\n query = {'PlominoViewFormula_'+searchview.getViewName(): True}\\n\\n for f in self.getFormFields(\\n includesubforms=True,\\n request=REQUEST):\\n fieldname = f.id\\n #if fieldname is not an index -> search doesn't matter and returns all\\n submittedValue = REQUEST.get(fieldname)\\n if submittedValue:\\n submittedValue = asUnicode(submittedValue)\\n # if non-text field, convert the value\\n if f.getFieldType() == \\\"NUMBER\\\":\\n settings = f.getSettings()\\n if settings.type == \\\"INTEGER\\\":\\n v = long(submittedValue)\\n elif settings.type == \\\"FLOAT\\\":\\n v = float(submittedValue)\\n elif settings.type == \\\"DECIMAL\\\":\\n v = decimal(submittedValue)\\n elif f.getFieldType() == \\\"DATETIME\\\":\\n # if datetime widget, the request param contains an object\\n if getattr(REQUEST.get(fieldname), 'year'):\\n submittedValue = \\\"%(year)s-%(month)s-%(day)s %(hour)s:%(minute)s\\\" % self.REQUEST.get('birthDate')\\n v = StringToDate(submittedValue, format='%Y-%m-%d %H:%M')\\n else:\\n v = submittedValue\\n # rename Plomino_SearchableText to perform full-text\\n # searches on regular SearchableText index\\n if fieldname == \\\"Plomino_SearchableText\\\":\\n fieldname = \\\"SearchableText\\\"\\n query[fieldname] = v\\n\\n sortindex = searchview.getSortColumn()\\n if not sortindex:\\n sortindex = None\\n results = index.dbsearch(\\n query,\\n sortindex=sortindex,\\n reverse=searchview.getReverseSorting())\\n\\n #filter search with searchformula\\n searchformula = self.getSearchFormula()\\n if searchformula:\\n filteredResults = []\\n try:\\n for doc in results:\\n valid = self.runFormulaScript(\\n SCRIPT_ID_DELIMITER.join(['form', self.id, 'searchformula']),\\n doc.getObject(),\\n self.SearchFormula)\\n if valid:\\n filteredResults.append(doc)\\n except PlominoScriptException, e:\\n e.reportError('Search formula failed')\\n results = filteredResults\\n\\n return self.OpenForm(searchresults=results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b44e0b7310731adcde15d418e63a7f66\",\n \"score\": \"0.60249716\",\n \"text\": \"def newquerysearch():\\n id = request.form[\\\"id\\\"]\\n search = request.form[\\\"search\\\"]\\n if (len(search) >= 2):\\n results = []\\n res2 = []\\n try:\\n r = requests.post(\\\"https://www.zefix.ch/ZefixREST/api/v1/firm/search.json\\\", json={\\\"name\\\": search,\\\"languageKey\\\":\\\"de\\\",\\\"maxEntries\\\":4})\\n x = r.json()[\\\"list\\\"]\\n for y in x:\\n name = y[\\\"name\\\"]\\n uid = y[\\\"uid\\\"]\\n che = uid[0:3] + \\\"-\\\" + uid[3:6] + \\\".\\\" + uid[6:9] + \\\".\\\" + uid[9:12]\\n if len(res2) < 1:\\n res = query(\\\"SELECT * FROM liquidations WHERE che='{}'\\\".format(che))\\n if len(res) > 0:\\n res2.append(res)\\n else:\\n results.append([name, che])\\n else:\\n results.append([name, che])\\n except:\\n pass\\n if len(res2) > 0:\\n results.append(res2[0])\\n if len(results) == 0:\\n search = [id, search, [], False, 0]\\n return render_template(\\\"section3.html\\\", search=search)\\n if isinstance(results[-1][0], str):\\n r = False\\n else:\\n r = True\\n search = [id, search, results, r, len(results)]\\n else:\\n search = [id, search, [], False, 0]\\n return render_template(\\\"section3.html\\\", search=search)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90f50215d680daaba55133f7137cd380\",\n \"score\": \"0.5978783\",\n \"text\": \"def query_criteria():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53f5bf88f37e7ac3813379aca7ed712c\",\n \"score\": \"0.59494305\",\n \"text\": \"def test_search_find_one_result_by_name(self):\\n make_recipe(\\n \\\"legalaid.case\\\",\\n reference=\\\"ref1\\\",\\n personal_details__full_name=\\\"xyz\\\",\\n personal_details__postcode=\\\"123\\\",\\n **self.get_extra_search_make_recipe_kwargs()\\n )\\n\\n self.resource.personal_details.full_name = \\\"abc\\\"\\n self.resource.personal_details.postcode = \\\"123\\\"\\n self.resource.personal_details.save()\\n self.resource.reference = \\\"ref2\\\"\\n self.resource.save()\\n\\n response = self.client.get(\\n self.list_url, data={\\\"search\\\": \\\"abc\\\"}, HTTP_AUTHORIZATION=self.get_http_authorization()\\n )\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(1, len(response.data[\\\"results\\\"]))\\n self.assertCaseEqual(response.data[\\\"results\\\"][0], self.resource)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c13b98cbbb643da3d3546f938e885068\",\n \"score\": \"0.59477454\",\n \"text\": \"def test_search_page(self):\\n res = self.client.get('/search', follow_redirects=True, data={\\n 'recipe_name': 'Pumpkin rice',\\n 'recipe_description': 'From brownies and pancakes to veggie-packed curries, stir-fries and salads, these vegan recipes are vibrant and delicious.',\\n 'recipe_instructions': 'Add all of the ingredients',\\n 'recipe_keywords': 'vegan, pumpkin, rice, butternut squash'\\n })\\n data = res.data.decode('utf-8')\\n assert res.status == '200 OK'\\n assert 'vegan' in data\\n assert 'Pumpkin' in data\\n assert 'delicious' in data\\n assert 'butternut' in data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72b05eb19781242644ca48149f75afa6\",\n \"score\": \"0.5934002\",\n \"text\": \"def search(self, event):\\r\\n filter_choice = self.categories.GetValue()\\r\\n keyword = self.search_ctrl.GetValue()\\r\\n self.skeleton_results = controller.search_records(\\r\\n self.session, filter_choice, keyword)\\r\\n self.update_skeleton_results()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9cc9081fe156f407138c25695500d69\",\n \"score\": \"0.59256864\",\n \"text\": \"def search():\\n search_query = request.args.get(\\\"q\\\")\\n\\n if search_query is None or len(search_query) < 1:\\n response_json = {\\\"status\\\": 422}\\n if search_query is None:\\n response_json[\\\"message\\\"] = \\\"Search query not found. Use ?q=searchquery.\\\"\\n else:\\n response_json[\\\"message\\\"] = \\\"Search query length must be at least 1.\\\"\\n response = jsonify(response_json)\\n response.status_code = 422\\n return response\\n\\n search_query = search_query.lower()\\n response_json = {\\\"status\\\": 200}\\n\\n model_queries = {\\n \\\"cities\\\": City.query,\\n \\\"doctors\\\": Doctor.query,\\n \\\"specialties\\\": Specialty.query,\\n }\\n for model_name in [\\\"cities\\\", \\\"doctors\\\", \\\"specialties\\\"]:\\n response_json[model_name] = search_model(\\n search_query,\\n model_queries[model_name],\\n SEARCH_ATTRIBUTES[model_name],\\n SERIALIZE_ATTRIBUTES[model_name],\\n )\\n response_json[f\\\"num_{model_name}\\\"] = len(response_json[model_name])\\n\\n response_json[\\\"num_results\\\"] = sum(\\n response_json[attr] for attr in [\\\"num_cities\\\", \\\"num_doctors\\\", \\\"num_specialties\\\"]\\n )\\n\\n response_json[\\\"page\\\"] = 1\\n response_json[\\\"total_pages\\\"] = 1\\n response = jsonify(response_json)\\n response.status_code = 200\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbac4911c206691027a4166cf627df86\",\n \"score\": \"0.59028435\",\n \"text\": \"def recommend(ingredienten, nietappliance):\\n\\n # make the query\\n begin = \\\"\\\"\\\"PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\\nPREFIX rp: <http://www.groepsproject.com/recipes/>\\nSELECT DISTINCT ?title ?link ?linktitle ?description\\nWHERE {\\n ?recipe rdf:type rp:Recipe.\\n \\\"\\\"\\\"\\n # remove the recipes with other ingredients\\n for ingredient in range(len(ingredienten)):\\n begin += \\\"\\\"\\\"FILTER NOT EXISTS {?recipe rp:hasIngredient ?ingredient%i.\\n ?ingredient%i rdf:type rp:%s}\\n \\\"\\\"\\\" % (ingredient, ingredient, ingredienten[ingredient])\\n\\n # remove the recipes for which you don't have the appliance\\n for appliance in nietappliance:\\n begin += \\\"\\\"\\\"FILTER NOT EXISTS {?recipe rp:needsAppliance rp:%s}\\n \\\"\\\"\\\" % appliance\\n\\n # get the recipes data\\n begin += \\\"\\\"\\\"?recipe rp:hasTitle ?title;\\n rp:hasLink ?link;\\n rp:hasDescription ?description;\\n rp:hasLinkTitle ?linktitle.\\n} ORDER BY DESC(?link)\\\"\\\"\\\"\\n\\n # display the query in terminal\\n print(begin)\\n\\n # fill in your own reposity here\\n sparql = SPARQLWrapper(\\\"http://localhost:7200/repositories/project\\\")\\n\\n # set the right format, and type of request method\\n sparql.setReturnFormat(JSON)\\n sparql.setMethod(POST)\\n\\n # run the query\\n sparql.setQuery(begin)\\n results = sparql.query()\\n # set max colwidth off\\n pd.set_option('display.max_colwidth', -1)\\n # load as pandas dataframe\\n processed_results = json.load(results.response)\\n cols = processed_results['head']['vars']\\n out = []\\n for row in processed_results['results']['bindings']:\\n item = []\\n for c in cols:\\n item.append(row.get(c, {}).get('value'))\\n out.append(item)\\n df = pd.DataFrame(out, columns=cols)\\n df.reindex(columns=['linktitle', 'link', 'description', 'title']).to_html(\\\"templates/test.html\\\")\\n with open(\\\"templates/test.html\\\", \\\"r\\\") as f1, open(\\\"templates/results.html\\\", \\\"w\\\", encoding=\\\"utf-8\\\") as f2:\\n f2.write(\\\"\\\"\\\"<head>\\n <link href= {{ url_for('static', filename = 'css-custom.css') }} rel=\\\"stylesheet\\\">\\n <script src=\\\"https://code.jquery.com/jquery-3.3.1.slim.min.js\\\" integrity=\\\"sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo\\\" crossorigin=\\\"anonymous\\\"></script>\\n <script>\\n $(document).ready(function(){\\n $('td').each(function(){\\n var content = $(this).html();\\n var Rexp = /((http|https|ftp):\\\\/\\\\/[\\\\w?=&.\\\\/-;#~%-]+(?![\\\\w\\\\s?&.\\\\/;#~%\\\"=-]*>))/g;\\n content = content.replace(Rexp, \\\"<a href='$1' target='_blank'>$1</a>\\\");\\n $(this).html(content);\\n });\\n });\\n</script>\\n</head>\\n\\\"\\\"\\\")\\n for line in f1:\\n f2.write(line)\\n os.remove(\\\"templates/test.html\\\")\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4300f62b4505ed3c3c9545b7073a9c05\",\n \"score\": \"0.5902526\",\n \"text\": \"def user_search():\\n search = request.form.get(\\\"search\\\")\\n types = list(mongo.db.type.find().sort(\\\"type_name\\\", 1))\\n recipes = list(mongo.db.recipes.find().sort(\\\"recipe_name\\\", 1))\\n users = list(mongo.db.users.find({\\\"$text\\\": {\\\"$search\\\": search}}))\\n products = list(\\n mongo.db.products.find().sort(\\\"product_name\\\", 1))\\n tools = list(mongo.db.tools.find().sort(\\\"name\\\", 1))\\n count = len(users)\\n # checks if no users match search\\n if count == 0:\\n flash(\\\"NO USERS FOUND\\\")\\n return render_template(\\\"management.html\\\", users=users, types=types,\\n recipes=recipes, count=count, search=search)\\n # returns results that match\\n else:\\n flash(\\\"Search results: {} for '{}'\\\".format(len(users), search))\\n return render_template(\\\"management.html\\\", users=users, types=types,\\n recipes=recipes, count=count, search=search,\\n tools=tools, products=products)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3876bbf72d9d3ff2b8ce175b0a97e02\",\n \"score\": \"0.58976126\",\n \"text\": \"def search():\\n\\n search_term = request.values.get(\\\"search_term\\\")\\n category = request.values.get(\\\"category\\\")\\n todo_list = collection.find({category: search_term})\\n\\n return render_template('search.html', todo_list=todo_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c349001c9881fa824305799cfacd06\",\n \"score\": \"0.5897531\",\n \"text\": \"def search():\\n query = request.args.get('query', '')\\n results = search_client.search(query)\\n return jsonify(results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d20b3f9223921230e80b490020e86c\",\n \"score\": \"0.5896862\",\n \"text\": \"def vegan_filter():\\n types = list(mongo.db.type.find().sort(\\\"type_name\\\", 1))\\n recipes = list(mongo.db.recipes.find({\\\"vegan\\\": \\\"Yes\\\"}))\\n\\n return render_template(\\\"recipes.html\\\", types=types,\\n recipes=recipes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26af26d25d80c12b687933c2b644e511\",\n \"score\": \"0.58917093\",\n \"text\": \"def search():\\n # request.form = {\\\"form_name\\\": \\\"hi\\\"}\\n form_name = request.form.get(\\\"form_name\\\")\\n if form_name == \\\"search\\\":\\n checked = ['checked=\\\"true\\\"', '', '']\\n keys = request.form.get('key_word', \\\"\\\")\\n category = request.form.get('search_field')\\n print(\\\"from search.controllers.search: category hasn't been added as a search option, will do later\\\")\\n session['checked'] = checked\\n session['keys'] = keys\\n else:\\n if session.get(\\\"checked\\\") is None or session.get(\\\"keys\\\") is None:\\n print('unrecognized form named {}'.format(form_name))\\n return error_page()\\n else:\\n checked = session['checked']\\n keys = session['keys']\\n\\n if is_valid_keys(keys):\\n flag, page, docid = searchidlist(keys)\\n session['page'] = page\\n session['s_flag'] = flag\\n session['doc_id'] = docid\\n\\n if flag == 0: return error_page()\\n\\n docs = cut_page(page, 0, docid)\\n\\n return render_template('high_search.html', checked=checked, key=keys, docs=docs, page=page,\\n nonempty=True, welcome=False, browse_categories=browse_categories)\\n else:\\n return redirect(url_for('main'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b2f7e986422895ef3132d6bb76f75b5\",\n \"score\": \"0.5891332\",\n \"text\": \"def api_search():\\n category = request.args.get('category')\\n search = request.args.get('search')\\n return jsonify(get_search_results(category, search, current_user=current_user))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee7ceda59a188d4d517c1e45b8b8b193\",\n \"score\": \"0.5882391\",\n \"text\": \"def search_results():\\n terms = request.args.get('q')\\n\\n return render_template('search.html', terms=terms)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9405d118b77f363a99424be2dae6cad9\",\n \"score\": \"0.58684224\",\n \"text\": \"def search(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed8e74e2da111cc3fc92acf2c9ab0d15\",\n \"score\": \"0.5853514\",\n \"text\": \"def search():\\n metadata = METADATA.copy()\\n\\n # extract search term from the HTML form\\n search_term = request.form['search']\\n\\n if search_term != '':\\n # put search term in same vector space as data model was trained on\\n search_transformed = PIPELINE.transform([search_term])\\n # put search in topic space\\n search_topics = TOPIC_MODEL.transform(search_transformed)\\n\\n # calculate cosine similarity matrix\\n doc_topic_matrix = metadata[metadata.columns[6:]].values\\n similarity = cosine_similarity(doc_topic_matrix, search_topics)\\n top_10_idx = np.argsort(similarity.flatten())[-10:]\\n metadata = metadata.iloc[list(top_10_idx)]\\n\\n metadata = apply_threshold_to_topics(metadata)\\n\\n return render_template('index.html',\\n comedy_info=metadata.values.tolist(),\\n dropdown_options=['Observational', 'Black Culture', 'British & Australian',\\n 'Political', 'Immigrant Upbringing', 'Relationships & Sex'],\\n search_text=search_term)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5a6f2ada8bafdaf43e45f61be876581\",\n \"score\": \"0.5838428\",\n \"text\": \"def test_ingredient_search(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"302da5baf997582a6f3c5ca20e166f37\",\n \"score\": \"0.5821525\",\n \"text\": \"def process_search(self, request):\\n cat = self.cleaned_data['cat']\\n search_query = self.cleaned_data['q']\\n if cat != '' or search_query != '':\\n self.set_search_query_set(request)\\n if cat != '' and search_query != '':\\n self.search_category_and_query()\\n elif cat != '':\\n self.search_category_only()\\n else:\\n self.search_query_string_only()\\n coupons = self.intersect_index_and_db_results(request)\\n else:\\n coupons = self.no_query_found(request)\\n return coupons, self.suggestion\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6d923c09d171e62803313ef0725ef72\",\n \"score\": \"0.5811273\",\n \"text\": \"def search_restaurant():\\n\\n\\tlocation = request.form.get('location')\\n\\tterm = request.form.get('term')\\n \\n \\n\\tresults = yelp.get_results(location=location, term=term)\\n\\t\\n \\n\\n\\treturn jsonify(results=results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3117a08ac8a588968575e9fbfa0c23d\",\n \"score\": \"0.58079803\",\n \"text\": \"def search():\\n context = make_context()\\n\\n return make_response(render_template('search.html', **context))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe76696fced27b2c2c9c4833b711d933\",\n \"score\": \"0.57921505\",\n \"text\": \"def search():\\n pattern = flask.request.args.get('pattern', \\\"*\\\").strip().lower()\\n\\n # if the pattern contains \\\"in:<collection>\\\" (eg: in:builtin),\\n # filter results to only that (or those) collections\\n # This was kind-of hacked together, but seems to work well enough\\n collections = [c[\\\"name\\\"].lower() for c in current_app.kwdb.get_collections()]\\n words = []\\n filters = []\\n if pattern.startswith(\\\"name:\\\"):\\n pattern = pattern[5:].strip()\\n mode = \\\"name\\\"\\n else:\\n mode=\\\"both\\\"\\n\\n for word in pattern.split(\\\" \\\"):\\n if word.lower().startswith(\\\"in:\\\"):\\n filters.extend([name for name in collections if name.startswith(word[3:])])\\n else:\\n words.append(word)\\n pattern = \\\" \\\".join(words)\\n\\n keywords = []\\n for keyword in current_app.kwdb.search(pattern, mode):\\n kw = list(keyword)\\n collection_id = kw[0]\\n collection_name = kw[1].lower()\\n if len(filters) == 0 or collection_name in filters:\\n url = flask.url_for(\\\".doc_for_library\\\", collection_id=kw[0], keyword=kw[2])\\n row_id = \\\"row-%s.%s\\\" % (keyword[1].lower(), keyword[2].lower().replace(\\\" \\\",\\\"-\\\"))\\n keywords.append({\\\"collection_id\\\": keyword[0],\\n \\\"collection_name\\\": keyword[1],\\n \\\"name\\\": keyword[2],\\n \\\"synopsis\\\": keyword[3],\\n \\\"version\\\": __version__,\\n \\\"url\\\": url,\\n \\\"row_id\\\": row_id\\n })\\n\\n keywords.sort(key=lambda kw: kw[\\\"name\\\"])\\n return flask.render_template(\\\"search.html\\\",\\n data={\\\"keywords\\\": keywords,\\n \\\"version\\\": __version__,\\n \\\"pattern\\\": pattern\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73af425a18f76c5f126622e6c2a3db0c\",\n \"score\": \"0.57903445\",\n \"text\": \"def test_search_query_string_with_booleans(workbook, es_testapp):\\n search = '/search/?type=Biosource&q=GM12878'\\n res_stem = es_testapp.get(search).json\\n assert len(res_stem['@graph']) > 1\\n bios_uuids = [r['uuid'] for r in res_stem['@graph'] if 'uuid' in r]\\n swag_bios = '331111bc-8535-4448-903e-854af460b888'\\n assert swag_bios in bios_uuids\\n # assert induced_stem_uuid not in not_induced_uuids\\n # now search for stem +induced (AND is now \\\"+\\\")\\n search_and = '/search/?type=Biosource&q=swag+%2BGM12878'\\n res_both = es_testapp.get(search_and).json\\n both_uuids = [r['uuid'] for r in res_both['@graph'] if 'uuid' in r]\\n assert len(both_uuids) == 1\\n assert swag_bios in both_uuids\\n # search with OR (\\\"|\\\")\\n search_or = '/search/?type=Biosource&q=swag+%7CGM12878'\\n res_or = es_testapp.get(search_or).json\\n or_uuids = [r['uuid'] for r in res_or['@graph'] if 'uuid' in r]\\n assert len(or_uuids) > 1\\n assert swag_bios in or_uuids\\n # search with NOT (\\\"-\\\")\\n search_not = '/search/?type=Biosource&q=GM12878+-swag'\\n res_not = es_testapp.get(search_not).json\\n not_uuids = [r['uuid'] for r in res_not['@graph'] if 'uuid' in r]\\n assert swag_bios not in not_uuids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bd286c31b0b2164f4db540bca304209\",\n \"score\": \"0.57748353\",\n \"text\": \"def findRecipe(keywords=\\\"Calhacks\\\"):\\n \\n apiKey = \\\"dvxI9e588cOKNvbOzd24EXsVRW9Y2OW8\\\"\\n\\n # Base URL for Big Oven API\\n url = \\\"http://api.bigoven.com/recipes?pg=0&rpp=1\\\"\\n\\n # Searching for the input Keywords\\n url += \\\"&title_kw=\\\" + keywords.replace(\\\" \\\", \\\"+\\\")\\n\\n # Adding the developer's API Key\\n url += \\\"&api_key=\\\" + apiKey\\n\\n recipeCount = parsePage(url, ['ResultCount'])[1][0]\\n \\n # No recipes are found. \\n if recipeCount == \\\"0\\\":\\n return keywords, set([\\\"No such recipe found\\\"]), \\\"\\\", \\\"\\\"\\n\\n randomRecipe = randint(1, int(recipeCount))\\n url = url.replace(\\\"pg=0\\\", \\\"pg=\\\" + str(randomRecipe))\\n\\n xml, [recipeId] = parsePage(url, ['RecipeID'])\\n \\n try:\\n cuisineType = parseList(xml, 'Cuisine')[0].firstChild.nodeValue\\n except (IndexError, AttributeError):\\n cuisineType = \\\"\\\"\\n \\n # Return the list of ingredients in the Recipe\\n recipeUrl = \\\"http://api.bigoven.com/recipe/\\\" + recipeId\\n recipeUrl += \\\"?api_key=\\\" + apiKey\\n \\n xml, [title, inst] = parsePage(recipeUrl, ['Title', 'Instructions'])\\n\\n recipeList = set()\\n try:\\n for el in parseList(xml, 'Name'):\\n recipeList.add(el.firstChild.nodeValue)\\n except AttributeError:\\n recipeList.add(\\\"Ingredients are in the Instructions\\\")\\n\\n return title, recipeList, cuisineType, inst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5deff366a389cb2b4bb3c80786d39a02\",\n \"score\": \"0.57731175\",\n \"text\": \"def search_for_outcome():\\n try:\\n # matching input to variable: https://stackoverflow.com/questions/14105452/what-is-the-cause-of-the-bad-request-error-when-submitting-form-in-flask-applica\\n req = request.form\\n print(req)\\n query = db.session.query(COPA_Case_Attributes)\\n logger.info(\\\"Query initialized\\\")\\n field_list = [\\\"police_shooting\\\", \\\"race_complainants\\\", \\\"sex_complainants\\\", \\\"age_complainants\\\",\\n \\\"race_officers\\\",\\\"sex_involved_officers\\\", \\\"age_officers\\\", \\\"excessive_force\\\",\\n \\\"years_on_force_officers\\\"]\\n field_label = {\\\"police_shooting\\\":\\\"Police shooting: \\\", \\\"race_complainants\\\":\\\"Race of complainant: \\\", \\\"sex_complainants\\\": \\\"Gender of complainant: \\\",\\n \\\"age_complainants\\\": \\\"Age of complainant: \\\",\\n \\\"race_officers\\\": \\\"Race of officer: \\\", \\\"sex_involved_officers\\\": \\\"Gender of officer: \\\",\\n \\\"age_officers\\\": \\\"Age of officer: \\\", \\\"excessive_force\\\": \\\"Excessive force: \\\",\\n \\\"years_on_force_officers\\\": \\\"Years on force of officer: \\\"}\\n messages = [\\\"Showing: Predictions displayed based on selection - \\\"]\\n detail = \\\"\\\"\\n # build query dynamically: https://stackoverflow.com/questions/37336520/sqlalchemy-dynamic-filter\\n # build query dynamically: https://stackoverflow.com/questions/7604967/sqlalchemy-build-query-filter-dynamically-from-dict\\n # get query attr rather than string: https://stackoverflow.com/questions/10251724/how-to-give-column-name-dynamically-from-string-variable-in-sql-alchemy-filter\\n for f in field_list:\\n if request.form[f]:\\n query = query.filter(getattr(COPA_Case_Attributes, f) == request.form[f])\\n detail = detail+field_label[f]+request.form[f]+\\\". \\\"\\n if detail == \\\"\\\":\\n detail = \\\"None\\\"\\n logger.info(\\\"Perform query\\\")\\n cases = query.limit(app.config[\\\"MAX_ROWS\\\"]).all()\\n logger.info(\\\"Query results: {}\\\".format(cases))\\n messages.append(detail)\\n return render_template('index.html', cases=cases, messages = messages)\\n except Exception as e:\\n logger.error(e)\\n logger.warning(\\\"Not able to display search, error page returned\\\")\\n return render_template('error.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cb968761c90b686d9e59e383661abda\",\n \"score\": \"0.5769029\",\n \"text\": \"def get_recipes(recipe):\\n\\n selectlist_recipes, selectlist_styles, selectlist_user, sel_user_styles = get_selectlists(session.get(\\\"user_id\\\"))\\n deleteable = False\\n if Recipe.query.filter_by(name=recipe).one().user_id == session.get(\\\"user_id\\\"):\\n deleteable = True\\n recipe, color = get_recipe_instructions(recipe)\\n color = color_conversion(color)\\n return render_template(\\\"explore_brews.html\\\", selectlist_recipes=selectlist_recipes,\\n selectlist_styles=selectlist_styles, recipe=recipe,\\n color=color, deleteable=deleteable)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5223ab9b566325ba09bdab1c73a4297f\",\n \"score\": \"0.57479477\",\n \"text\": \"def search(query, restaurants):\\n # BEGIN Question 10\\n \\\"*** REPLACE THIS LINE ***\\\"\\n # END Question 10\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c54c47aaf9f52e2661012975537b8ae8\",\n \"score\": \"0.57413507\",\n \"text\": \"def search(self):\\n logger.info(f'Received a request to search. Search type: {self.search_type}')\\n\\n queried_store, security = self.get_db_specific_sections()\\n search, query, skip, limit = self.arg_parser()\\n\\n # Pipeline initially made use of 'project', I've opted to instead\\n # do all post processing in 'post_processing()' for simplicity.\\n pipeline = search + query + security\\n start = time.time()\\n full_search_res = list(queried_store.aggregate(pipeline))\\n logger.info(f'Got search response in {time.time() - start} seconds.'\\\\\\n f'Pipeline: {pipeline} First two results: {full_search_res[0:1]}')\\n final_result = self.post_processing(full_search_res, skip, limit)\\n return final_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10d82fdbad0cca70bb8ee3b66bd59ac3\",\n \"score\": \"0.57355696\",\n \"text\": \"def find_restaurants(request, list):\\n return matching(request, {}, list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70c7a6d4fb4e121269e8c2480cc251ce\",\n \"score\": \"0.57247204\",\n \"text\": \"def search(request):\\n logger.info(\\\"__ input_forms search __\\\")\\n\\n term = request.GET[\\\"term\\\"]\\n results = []\\n\\n provider_list = EndpointGroup.objects.all().order_by(\\\"name\\\")\\n for provider in provider_list:\\n\\n provider_instance = endpoint_provider.get_provider_instance_from_group(provider.id)\\n provider_instance.apply_filter(\\\"Name\\\", term)\\n endpoints_list = provider_instance.get_page(0, 128)\\n\\n for endpoint in endpoints_list:\\n r = dict()\\n r[\\\"value\\\"] = str(provider.id) + \\\":\\\" + str(endpoint.get('id', 0))\\n r[\\\"label\\\"] = provider.name + \\\"/\\\" + endpoint.get('name', 'unknown')\\n results.append(r)\\n\\n return HttpResponse(json.dumps(results), content_type=\\\"application/json\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae4d1f976957f156d57111d361c8afb9\",\n \"score\": \"0.5721357\",\n \"text\": \"def get_queryset(self):\\n\\n name = self.request.query_params.get('name')\\n queryset = Recipe.objects.all()\\n\\n if name:\\n queryset = queryset.filter(name__icontains=name)\\n\\n return queryset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faac51fdcada1a00243487bc4181977e\",\n \"score\": \"0.5710637\",\n \"text\": \"def search():\\n query = bt.request.query.q\\n t = bt.request.query.t\\n mode, model, count = (\\n (\\\"games\\\", Game, 10) if t == \\\"categories\\\" else (\\\"users\\\", Streamer, 5)\\n )\\n search_results = Helix.get(f\\\"search/{t}?query={query}&first={count}\\\")\\n ids = {int(result[\\\"id\\\"]) for result in search_results}\\n\\n asyncio.run(Db.cache(ids, mode=mode))\\n if t == \\\"categories\\\":\\n results = model.select().where(model.id.in_(ids))\\n else:\\n results = [\\n Result(result, model.get_by_id(int(result[\\\"id\\\"])))\\n for result in search_results\\n ]\\n return bt.template(\\\"search.tpl\\\", query=query, mode=mode, results=results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbc4d7cbc7e65843d2cfed4782280c20\",\n \"score\": \"0.56858754\",\n \"text\": \"def FoodSearch(self, **dico):\\n if not 'input' in dico:\\n #TODO: APIError\\n raise Exception(\\\"input is missing\\\") \\n return self.oauth.ApiCall(\\\"foods/search\\\", \\\"GET\\\", dico)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22d281c15fb2028a398b7b9e8bcec1f1\",\n \"score\": \"0.5680111\",\n \"text\": \"def search():\\n\\n fuzzy_term = None\\n if 'search' in request.args:\\n fuzzy_term = request.args['search']\\n session['search'] = fuzzy_term\\n\\n items = []\\n\\n searchable_classes = [Document, User]\\n for cls in searchable_classes:\\n items.extend(\\n map(\\n lambda x: (x, cls.__name__.lower()),\\n cls.fuzzy_search(fuzzy_term)\\n )\\n )\\n\\n print('Found these items for term \\\"{}\\\":'.format(fuzzy_term))\\n print(items)\\n\\n return items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f41827a56cd90552e5f1cbe6d93dd72d\",\n \"score\": \"0.5675769\",\n \"text\": \"def check_ingredients():\\n query = request.args.get(\\\"q\\\")\\n print(f\\\"Query recieved: {query}\\\")\\n ingredients = db.check_ingredient_from_db(query)\\n return jsonify(ingredients)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b592e615c9b7f10b0474d98ef4bbffff\",\n \"score\": \"0.56677955\",\n \"text\": \"def test_autocomplete_ingredient_search(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbb69dcc4a5a9f621bc073885f7a8623\",\n \"score\": \"0.56562763\",\n \"text\": \"def search_cupcake():\\n search = request.args[\\\"searchTerm\\\"]\\n\\n cupcakes = Cupcake.query.filter(Cupcake.flavor.ilike(f\\\"%{search}%\\\")).all()\\n\\n serialized = [c.serialize() for c in cupcakes]\\n\\n return (jsonify(cupcakes=serialized), 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35d4b1b4ae0b890d96a80ab06e5beb40\",\n \"score\": \"0.5648273\",\n \"text\": \"def search():\\n global searching\\n global searchQuery\\n\\n if request.method == \\\"POST\\\":\\n query = request.form.get(\\\"query\\\")\\n searching = True\\n searchQuery = query\\n\\n films = list(mongo.db.film_list.find({\\\"$text\\\": {\\\"$search\\\": query}}))\\n return render_template(\\n \\\"get_films.html\\\", films=films, search=searching,\\n searchQuery=searchQuery)\\n\\n return get_films()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfcb3768490aff1fbf14975ea87037de\",\n \"score\": \"0.56399745\",\n \"text\": \"def artists_search(mode: str, form: FlaskForm, simple_search_term: str = None) -> dict:\\n return ncsg_search(mode, form, get_entity(ARTIST_TABLE), simple_search_term=simple_search_term)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6d475728fd1a4ceead5b6a2e8e903cf\",\n \"score\": \"0.56354815\",\n \"text\": \"def searchProgram():\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8504852f423a62b8c6dc87b7048fcb04\",\n \"score\": \"0.5621537\",\n \"text\": \"def search_book():\\n if not g.user:\\n flash('You have to Login')\\n return redirect('/')\\n term = request.args['term']\\n \\n b = Book.query.filter(Book.title.ilike(f'%{term}%')).all()\\n\\n results = [book.serialize() for book in b]\\n \\n return jsonify(books=results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"258e5c4deee79cd2db6eb64847dc5ee3\",\n \"score\": \"0.5621454\",\n \"text\": \"def handle_cloth_search(request):\\n searched_name = request.GET.get('cloth_name')\\n context = dict()\\n \\n if searched_name:\\n search_result = Cloth.objects.filter(name__icontains=searched_name)\\n context = {'search_result': search_result}\\n \\n return render(request, 'clothing/search_result_list.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a65dcf26dc253551dea0b68e79fd99c4\",\n \"score\": \"0.5612832\",\n \"text\": \"def search(items, target):\\n # TODO: Fill this in.\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5394cd29e5329e4366e086fa14b3aea1\",\n \"score\": \"0.560931\",\n \"text\": \"def search():\\n # Search google \\n search_google()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e295fddc82333156dd4a279832d7cc97\",\n \"score\": \"0.5592493\",\n \"text\": \"def search():\\n term = request.args[\\\"term\\\"]\\n sort = request.args[\\\"sort\\\"]\\n return f\\\"<h1>Search Results For: {term}</h1> <p>Sorting by: {sort}</p>\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29f49f76f32be498d896d89670acb718\",\n \"score\": \"0.55910236\",\n \"text\": \"def get_queryset(self):\\n self.queryset = Recipe.objects.all()\\n name = self.request.query_params.get('name', None)\\n ingredients = self.request.query_params.get('ingredients')\\n\\n if name:\\n return self.queryset.filter(name__startswith=name)\\n\\n if ingredients:\\n ingredient_ids = self._params_to_ints(ingredients)\\n print(ingredient_ids)\\n self.queryset = queryset.filter(ingredients__name__in=ingredients)\\n\\n\\n return self.queryset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ed037ae98a92d047b7f2c7ad4335c87\",\n \"score\": \"0.5580274\",\n \"text\": \"def get_search():\\n getreq_url = SEARCH_BASE_URL + request.form.get('search')\\n r = requests.get(url = getreq_url)\\n data = r.json()\\n\\n return render_template('search_results.html', books=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5c3cdd7e48c11d43ceca2bd36ad2677\",\n \"score\": \"0.5579418\",\n \"text\": \"def test_search_find_one_result_by_postcode(self):\\n make_recipe(\\n \\\"legalaid.case\\\",\\n personal_details__postcode=\\\"123\\\",\\n personal__details__full_name=\\\"abc\\\",\\n **self.get_extra_search_make_recipe_kwargs()\\n )\\n\\n response = self.client.get(\\n self.list_url,\\n data={\\\"search\\\": self.resource.personal_details.postcode},\\n HTTP_AUTHORIZATION=self.get_http_authorization(),\\n )\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(1, len(response.data[\\\"results\\\"]))\\n self.assertCaseEqual(response.data[\\\"results\\\"][0], self.resource)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fababc0cca564f36a5dd42425a0a8b50\",\n \"score\": \"0.55777663\",\n \"text\": \"def search(self, query):\\n # SO, let's create our results\\n results = None\\n for shelf in self.library:\\n # First shelf we're running the search on\\n if results is None:\\n results = shelf.search(query)\\n # We use upgrade_and_extend, because multiple hits can happen.\\n else:\\n results.upgrade_and_extend(shelf.search(query))\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8840b500c07bd06ed5c1925492d9632\",\n \"score\": \"0.5576955\",\n \"text\": \"def post(self):\\n text = self.get_argument(\\\"text\\\")\\n __type = self.get_argument(\\\"type\\\")\\n\\n\\n if __type == \\\"dish\\\":\\n \\\"\\\"\\\"\\n It might be a possibility that the user enetered the dish which wasnt in autocomplete\\n then we have to search exact dish name of seach on Laveneshtein algo\\n \\\"\\\"\\\"\\n ##search in ES for dish name \\n\\n result = list()\\n __result = ElasticSearchScripts.get_dish_match(text)\\n for dish in __result:\\n eatery_id = dish.get(\\\"eatery_id\\\")\\n __eatery_details = r_clip_eatery.find_one({\\\"eatery_id\\\": eatery_id})\\n for e in [\\\"eatery_highlights\\\",\\n \\\"eatery_cuisine\\\", \\\"eatery_trending\\\", \\\"eatery_known_for\\\", \\\"eatery_type\\\", \\\"_id\\\"]:\\n try:\\n __eatery_details.pop(e)\\n except Exception as e:\\n print e\\n pass\\n dish.update({\\\"eatery_details\\\": __eatery_details})\\n result.append(dish)\\n\\n elif __type == \\\"cuisine\\\":\\n ##gives out the restarant for cuisine name\\n print \\\"searching for cuisine\\\"\\n result = list()\\n __result = ElasticSearchScripts.eatery_on_cuisines(text)\\n print __result\\n for eatery in __result:\\n __result= short_eatery_result_collection.find_one({\\\"__eatery_id\\\": eatery.get(\\\"__eatery_id\\\")}, {\\\"_id\\\": False, \\\"food\\\": True, \\\"ambience\\\": True, \\\\\\n \\\"cost\\\":True, \\\"service\\\": True, \\\"menu\\\": True, \\\"overall\\\": True, \\\"location\\\": True, \\\"eatery_address\\\": True, \\\"eatery_name\\\": True, \\\"__eatery_id\\\": True})\\n\\n eatery.update({\\\"eatery_details\\\": __result})\\n result.append(eatery)\\n\\n elif __type == \\\"eatery\\\":\\n \\n ##TODO : Some issue with the restaurant chains for example\\n ##big chills at different locations, DOnt know why ES\\n ##not returning multiple results\\n ##TODO: dont know why dropped nps are still in result.\\n result = r_eateries.find_one({\\\"eatery_name\\\": text},\\n {\\\"_id\\\": False, \\\"eatery_known_for\\\": False,\\n \\\"droppped_nps\\\": False,\\n \\\"eatery_trending\\\": False,\\n \\\"eatery_highlights\\\": False})\\n \\n print result.get(\\\"eatery_id\\\")\\n __result = process_result(result)\\n \\n pictures = result.pop(\\\"pictures\\\")\\n result.update({\\\"pictures\\\": pictures[0:2]})\\n \\n result.update(__result)\\n result = [result]\\n\\n elif not __type:\\n print \\\"No type defined\\\"\\n\\n else:\\n print __type\\n self.write({\\\"success\\\": False,\\n\\t\\t\\t \\\"error\\\": True,\\n\\t\\t\\t \\\"messege\\\": \\\"Maaf kijiyega, Yeh na ho paayega\\\",\\n\\t\\t\\t })\\n self.finish()\\n return \\n print result\\n self.write({\\\"success\\\": True,\\n\\t\\t\\t \\\"error\\\": False,\\n\\t\\t\\t \\\"result\\\": result,\\n\\t\\t\\t})\\n self.finish()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d396f9be5eceab3cbb62987c1686e7c4\",\n \"score\": \"0.5575648\",\n \"text\": \"def search_app(q='', fq=None, app=True, **kw):\\n from allura.model import ArtifactReference\\n from allura.lib.security import has_access\\n\\n history = kw.pop('history', None)\\n if app and kw.pop('project', False):\\n # Used from app's search controller. If `project` is True, redirect to\\n # 'entire project search' page\\n redirect(c.project.url() + 'search/?' +\\n urlencode(dict(q=q, history=history)))\\n search_comments = kw.pop('search_comments', None)\\n limit = kw.pop('limit', None)\\n page = kw.pop('page', 0)\\n default = kw.pop('default', 25)\\n allowed_types = kw.pop('allowed_types', [])\\n parser = kw.pop('parser', None)\\n sort = kw.pop('sort', 'score desc')\\n fq = fq if fq else []\\n search_error = None\\n results = []\\n count = 0\\n matches = {}\\n limit, page, start = g.handle_paging(limit, page, default=default)\\n if not q:\\n q = ''\\n else:\\n # Match on both `title` and `text` by default, using 'dismax' parser.\\n # Score on `title` matches is boosted, so title match is better than body match.\\n # It's 'fuzzier' than standard parser, which matches only on `text`.\\n if search_comments:\\n allowed_types += ['Post']\\n if app:\\n fq = [\\n 'project_id_s:%s' % c.project._id,\\n 'mount_point_s:%s' % c.app.config.options.mount_point,\\n '-deleted_b:true',\\n 'type_s:(%s)' % ' OR '.join(\\n ['\\\"%s\\\"' % t for t in allowed_types])\\n ] + fq\\n search_params = {\\n 'qt': 'dismax',\\n 'qf': 'title^2 text',\\n 'pf': 'title^2 text',\\n 'fq': fq,\\n 'hl': 'true',\\n 'hl.simple.pre': '#ALLURA-HIGHLIGHT-START#',\\n 'hl.simple.post': '#ALLURA-HIGHLIGHT-END#',\\n 'sort': sort,\\n }\\n if not history:\\n search_params['fq'].append('is_history_b:False')\\n if parser == 'standard':\\n search_params.pop('qt', None)\\n search_params.pop('qf', None)\\n search_params.pop('pf', None)\\n try:\\n results = search(\\n q, short_timeout=True, ignore_errors=False,\\n rows=limit, start=start, **search_params)\\n except SearchError as e:\\n search_error = e\\n if results:\\n count = results.hits\\n matches = results.highlighting\\n\\n def historize_urls(doc):\\n if doc.get('type_s', '').endswith(' Snapshot'):\\n if doc.get('url_s'):\\n doc['url_s'] = doc['url_s'] + \\\\\\n '?version=%s' % doc.get('version_i')\\n return doc\\n\\n def add_matches(doc):\\n m = matches.get(doc['id'], {})\\n title = h.get_first(m, 'title')\\n text = h.get_first(m, 'text')\\n if title:\\n title = (markupsafe.escape(title)\\n .replace('#ALLURA-HIGHLIGHT-START#', markupsafe.Markup('<strong>'))\\n .replace('#ALLURA-HIGHLIGHT-END#', markupsafe.Markup('</strong>')))\\n if text:\\n text = (markupsafe.escape(text)\\n .replace('#ALLURA-HIGHLIGHT-START#', markupsafe.Markup('<strong>'))\\n .replace('#ALLURA-HIGHLIGHT-END#', markupsafe.Markup('</strong>')))\\n doc['title_match'] = title\\n doc['text_match'] = text or h.get_first(doc, 'text')\\n return doc\\n\\n def paginate_comment_urls(doc):\\n if doc.get('type_s', '') == 'Post':\\n artifact = doc['_artifact']\\n if artifact:\\n doc['url_paginated'] = artifact.url_paginated()\\n return doc\\n\\n def filter_unauthorized(doc):\\n aref = ArtifactReference.query.get(_id=doc.get('id'))\\n # cache for paginate_comment_urls to re-use\\n doc['_artifact'] = aref and aref.artifact\\n # .primary() necessary so that a ticket's comment for example is checked with the ticket's perms\\n if doc['_artifact'] and not has_access(doc['_artifact'].primary(), 'read', c.user):\\n return None\\n else:\\n return doc\\n\\n filtered_results = [_f for _f in map(filter_unauthorized, results) if _f]\\n count -= len(results) - len(filtered_results)\\n results = filtered_results\\n results = map(historize_urls, results)\\n results = map(add_matches, results)\\n results = map(paginate_comment_urls, results)\\n\\n # Provide sort urls to the view\\n score_url = 'score desc'\\n date_url = 'mod_date_dt desc'\\n try:\\n field, order = sort.split(' ')\\n except ValueError:\\n field, order = 'score', 'desc'\\n sort = ' '.join([field, 'asc' if order == 'desc' else 'desc'])\\n if field == 'score':\\n score_url = sort\\n elif field == 'mod_date_dt':\\n date_url = sort\\n params = request.GET.copy()\\n params.update({'sort': score_url})\\n score_url = url(request.path, params=params)\\n params.update({'sort': date_url})\\n date_url = url(request.path, params=params)\\n return dict(q=q, history=history, results=list(results) or [],\\n count=count, limit=limit, page=page, search_error=search_error,\\n sort_score_url=score_url, sort_date_url=date_url,\\n sort_field=field)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ff9eab02b3a9cb4280c54363cece15\",\n \"score\": \"0.5573637\",\n \"text\": \"def test_search_find_one_result_by_ref(self):\\n make_recipe(\\n \\\"legalaid.case\\\",\\n personal_details__full_name=\\\"abc\\\",\\n personal_details__postcode=\\\"123\\\",\\n **self.get_extra_search_make_recipe_kwargs()\\n )\\n\\n response = self.client.get(\\n self.list_url, data={\\\"search\\\": self.resource.reference}, HTTP_AUTHORIZATION=self.get_http_authorization()\\n )\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(1, len(response.data[\\\"results\\\"]))\\n self.assertCaseEqual(response.data[\\\"results\\\"][0], self.resource)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f82c245bbbbd336d3d782f23d8c82c6a\",\n \"score\": \"0.5571029\",\n \"text\": \"def search_by_query(self):\\n\\n if self.uri:\\n self.user_query = input('\\\\n\\\\t9) What do you want to search ?\\\\n\\\\t')\\n\\n print('\\\\n\\\\tSearch results...\\\\n\\\\t{result_data}'.format(\\n result_data=self.pyro_obj.get_by_query(self.user_query))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd88235e0591068ceebc9aaccf12d7e9\",\n \"score\": \"0.55677366\",\n \"text\": \"def run_keyword_search_process():\\n\\n print(\\\"This will search for matches in the title and notes fields.\\\")\\n search_term = input(\\\"Enter a search term: \\\").strip()\\n\\n contains_search_term = ((Time_Entry.title.contains(search_term))\\n | (Time_Entry.notes.contains(search_term)))\\n\\n matching_entries = Time_Entry.select().where(contains_search_term)\\n\\n clear_screen()\\n\\n if len(matching_entries) == 0:\\n print(\\\"No matching entries found.\\\")\\n dummy = input(\\\"Press enter to continue. \\\")\\n else:\\n run_options_loop(matching_entries)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aef14ba8df317f5a827902d64eb63f8\",\n \"score\": \"0.55642\",\n \"text\": \"def genre_search(genre):\\n global searching\\n global searchQuery\\n\\n searching = True\\n searchQuery = genre\\n\\n films = list(mongo.db.film_list.find({\\\"$text\\\": {\\\"$search\\\": genre}}))\\n return render_template(\\n \\\"get_films.html\\\", films=films, search=searching,\\n searchQuery=searchQuery)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6c1b05cdd6222288a0a684a6442dbc\",\n \"score\": \"0.5560062\",\n \"text\": \"def search_wines():\\n\\n title_keywords = request.args.get('title_keywords')\\n title_keywords = title_keywords.title()\\n wines = crud.search_wines(title_keywords)\\n\\n if not wines:\\n title_keywords = title_keywords.upper()\\n wines = crud.search_wines(title_keywords)\\n\\n if 'user_id' in session:\\n user = crud.get_user_by_id(session['user_id'])\\n return render_template('search_results.html', wines=wines, user=user)\\n else:\\n return render_template('search_results.html', wines=wines)\\n\\n if 'user_id' in session:\\n user = crud.get_user_by_id(session['user_id'])\\n return render_template('search_results.html', wines=wines, user=user)\\n else:\\n return render_template('search_results.html', wines=wines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8965ad2d058519370db92cdd744a095f\",\n \"score\": \"0.5551595\",\n \"text\": \"def search(self, query, maxresults=20):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47b15b40ab4e89e6a497a59b17552d85\",\n \"score\": \"0.55505663\",\n \"text\": \"def search(self, **kwargs):\\n resource = self.resource + 'search'\\n required = ['q']\\n metadata, response = self.__get__(resource, kwargs) # use metadata for something else.\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b51c612202c0eab6a1df6c2dbb8793af\",\n \"score\": \"0.55419123\",\n \"text\": \"def search():\\n\\n # Make sure that the search bar is not empty\\n if not request.form.get(\\\"keyword\\\"):\\n return apology(\\\"search criteria must not be empty\\\", 403)\\n\\n keyword = request.form.get(\\\"keyword\\\")\\n\\n # Add in the wildcard characters for the SQL queries\\n like_keyword = \\\"%\\\" + keyword + \\\"%\\\"\\n\\n # Get all posts that match the keyword(s)\\n posts = db.execute(\\n \\\"SELECT title, datetime, posts.id AS post_id, user_id, username FROM posts INNER JOIN users ON posts.user_id = users.id WHERE title LIKE ? OR contents LIKE ? ORDER BY datetime DESC\\\", (like_keyword), (like_keyword))\\n\\n if len(posts) == 0:\\n no_posts = True\\n else:\\n no_posts = False\\n\\n # Get all people that match the keyword(s)\\n people = db.execute(\\\"SELECT id AS user_id, username FROM users WHERE username LIKE ?\\\", (like_keyword))\\n\\n if len(people) == 0:\\n no_people = True\\n else:\\n no_people = False\\n\\n return render_template(\\\"search.html\\\", posts=posts, no_posts=no_posts, people=people, no_people=no_people)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59dc767c55a5cb20d0031a9b6c23661d\",\n \"score\": \"0.5539616\",\n \"text\": \"def search(request, **kwargs):\\r\\n return SearchView.as_view(**kwargs)(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eaf1e4c914f16a3d0281bb5531e8b58\",\n \"score\": \"0.55371684\",\n \"text\": \"async def search(self, *args: Any, **kwargs: Any) -> List[dict]:\\n await self.wait_for(self.connection.search(*args, **kwargs))\\n return self.connection.response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70e1d787867d2d87a194956dc557d9e9\",\n \"score\": \"0.553495\",\n \"text\": \"def find_recipe(input_text, recipes, p2v_our_emoji):\\n\\tprint(input_text)\\n\\ttokens = tokenizer.tokenize(input_text)\\t\\n\\tprint(tokens)\\n\\tvector = np.sum([p2v_our_emoji[t] for t in tokens], axis=0) / len(tokens)\\n\\t\\n\\tkey, value = min(recipes.items(), key=lambda kv: (distance.euclidean(vector, np.array(kv[1]['recipe vector']))))\\n\\n\\tresult = {}\\n\\tresult['title'] = value['title']\\n\\tresult['instructions'] = value['instructions']\\n\\n\\treturn json.dumps(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfa0f3a32da4a4e4e943c3fbaa93c17f\",\n \"score\": \"0.55348986\",\n \"text\": \"def search(self, search):\\n return self.backend.search(search)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d678bbbaeb1c1705e495fb6ee1fc45e\",\n \"score\": \"0.55339485\",\n \"text\": \"def search_route():\\n\\n with sqlite3.connect(DBPATH) as conn:\\n query = request.get_json().get(\\\"query\\\")\\n res = conn.execute(\\n \\\"select id, title from answers where title like ? \\\", [f\\\"%{query}%\\\"],\\n )\\n answers = [{\\\"id\\\": r[0], \\\"title\\\": r[1]} for r in res]\\n print(query, \\\"--> \\\")\\n pprint(answers)\\n return jsonify(answers), 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"add902afc70ea874d20a8a1244441f25\",\n \"score\": \"0.55326116\",\n \"text\": \"def search():\\n if request.args.get('q'):\\n g.search_form.q.data = request.args.get('q')\\n q = g.search_form.q.data if g.search_form.q.data else None\\n\\n if q is None:\\n return render_template('404.html'), 404\\n\\n page = request.args.get('page', 1, type=int)\\n per_page = app.config['PER_PAGE']\\n\\n paginated_papers, total = Paper.search(q, page, per_page)\\n\\n href=\\\"search?q={}\\\".format(q) + '&page={0}' ##customizing to include search query parameter\\n pagination = Pagination(href=href, page=page, per_page=per_page, total=total, record_name='papers',format_total=True, format_number=True)\\n # print(pagination.__dict__)\\n\\n return render_template('papers.html', papers=paginated_papers, pagination=pagination, per_page=per_page, categories=CATEGORY_LIST, icondict=icondict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c87e446336cfff2ed89d5cb1d990f022\",\n \"score\": \"0.55319804\",\n \"text\": \"def do_search(request):\\n products = Product.objects.filter(name__icontains=request.Get['q'])\\n return render(request, \\\"products.html\\\", {\\\"products\\\": products})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e46ab899a626dbf93cb43a7ce62c44a\",\n \"score\": \"0.5531373\",\n \"text\": \"def search(request):\\n # r = requests.get(\\\"http://api.angel.co/1/jobs\\\")\\n # response_data = r.json()\\n # final_jobs = []\\n # for job in response_data[\\\"jobs\\\"]:\\n # job_tags = job['tags']\\n # job_match = True\\n # for tag in job_tags:\\n # if tag['tag_type'] is \\\"SkillTag\\\" and not business_search(tag['name']):\\n # job_match = False\\n # if tag['tag_type'] is \\\"LocationTag\\\" and not location_search(location, tag['name']):\\n # job_match = False\\n # # if category matches business and location matches search, check for desc and add it to list.\\n # if job_match and (keyword in job['description']):\\n # final_jobs.append(job)\\n # context = {'jobs': final_jobs}\\n # return render(request, 'search/search.html', context)\\n return HttpResponse(\\\"Works for now\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"510daa725373e06e01db50bd2c307089\",\n \"score\": \"0.5529226\",\n \"text\": \"def search_criteria():\\n global search_term\\n global location\\n\\n raw_search_term = input(\\\"What would you like to find?\\\")\\n split_search = raw_search_term.split(\\\" \\\")\\n if len(split_search) > 1:\\n search_term = \\\"+\\\".join(split_search)\\n else:\\n search_term = raw_search_term\\n raw_location = input(\\\"What city would you like to search in?\\\")\\n split_location = raw_location.split(\\\" \\\")\\n if len(split_location) > 1:\\n location = \\\"+\\\".join(split_location)\\n else:\\n location = raw_location\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eaeace753c6815c28529c2ef29c9a91\",\n \"score\": \"0.55195177\",\n \"text\": \"def search_db():\\n search_term = request.args.get(\\\"search-text\\\")\\n search_term_formatted = '%' + search_term + '%'\\n \\n search_results = Truck_schedule.query.filter(or_(Truck_schedule.truck_name.ilike(search_term_formatted), Truck_schedule.extra_text.ilike(search_term_formatted))).distinct(Truck_schedule.truck_id).all()\\n\\n if search_results:\\n pass\\n else:\\n search_results = None\\n\\n return render_template(\\\"search_results.html\\\", search_term=search_term, search_results=search_results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a83ffa094a3467a0e0ea58166ca767b3\",\n \"score\": \"0.55110025\",\n \"text\": \"def npo_search():\\n tag = request.form[\\\"tag\\\"]\\n orders = Order.query.filter_by(user_id=current_user.id)\\n # details = OrderDetails.query.all()\\n if not tag:\\n flash(\\\"Missing keyword\\\")\\n return redirect(url_for(\\\"views.dashboard\\\", user=current_user, username=current_user.username))\\n\\n search = \\\"%{}%\\\".format(tag)\\n location = User.query.filter(User.location.like(search)).all()\\n food = Food.query.all()\\n\\n if location is not None:\\n for i in location:\\n filtered = Food.query.filter_by(users_id=i.id).all()\\n\\n return render_template(\\n 'npo.html',\\n businessname=current_user.businessname,\\n food=food,\\n filtered=filtered,\\n users=location,\\n tag=tag,\\n orders=orders,\\n user=current_user)\\n\\n businessname = User.query.filter(User.businessname.like(search)).all()\\n\\n if businessname is not None:\\n for i in businessname:\\n filtered = Food.query.filter_by(users_id=i.id).all()\\n return render_template(\\n 'npo.html',\\n businessname=current_user.businessname,\\n food=food,\\n filtered=filtered,\\n users=businessname,\\n tag=tag,\\n orders=orders,\\n user=current_user)\\n\\n flash(\\\"Not found\\\")\\n return render_template(\\n 'npo.html',\\n businessname=current_user.businessname,\\n food=food,\\n user=current_user,\\n tag=tag\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c61fc536630739a75ab74a8c72c1fb94\",\n \"score\": \"0.55088633\",\n \"text\": \"def search(request, template_name=\\\"aggregator/search.html\\\", template_loader=loader, extra_context=None, context_processors=None, mimetype=None):\\n # Create search terms list.\\n if request.GET.__contains__('search'):\\n search_string = request.GET['search']\\n \\n # First, split on double-quotes to extract any multi-word terms\\n search_terms = search_string.split('\\\"')\\n cleaned_search_terms = []\\n \\n # Then, remove any unnecessary whitespace at the beginings or ends of the terms\\n for item in search_terms:\\n if not item.startswith(' ') and not item.endswith(' ') and not item == '':\\n cleaned_search_terms.append(item)\\n if item.startswith(' '):\\n cleaned_search_terms.append(item[1:])\\n if item.endswith(' '):\\n cleaned_search_terms.append(item[:-1])\\n \\n \\n # Filtering by model\\n model_query = request.GET.get('models', 'default')\\n model_query_list = request.GET.getlist('models')\\n \\n if model_query == \\\"default\\\":\\n content_types = get_aggregator_content_types(type_set=\\\"default\\\")\\n model_list = [ ct.app_label + \\\".\\\" + ct.model for ct in content_types]\\n model_string = \\\"default\\\"\\n elif model_query == \\\"all\\\":\\n model_string = \\\"all\\\"\\n content_types = get_aggregator_content_types(type_set=\\\"all\\\")\\n else:\\n model_string = \\\",\\\".join(model_query_list)\\n model_list = model_query_list\\n content_types = []\\n for item in model_list:\\n for model in settings.AGGREGATOR_MODELS:\\n if item == model['model']:\\n app_label = item.split('.')[0]\\n model = item.split('.')[1]\\n content_type = ContentType.objects.get(app_label=app_label, model=model)\\n content_types.append(content_type)\\n \\n # Set up a list to put results into\\n search_results = []\\n\\n # Search each model for the search terms\\n for content_type in content_types:\\n content_type_string = content_type.app_label + \\\".\\\" + content_type.model\\n model = content_type.model_class()\\n manager = model._default_manager\\n opts = model._meta\\n query = Q()\\n queries = []\\n for model in settings.AGGREGATOR_MODELS:\\n if content_type_string == model['model']:\\n search_fields = model['search_fields']\\n\\n for term in cleaned_search_terms:\\n for field in search_fields:\\n lookup = field + \\\"__icontains\\\"\\n kwargs = { lookup: term }\\n q = Q(**kwargs)\\n queries.append(q)\\n\\n for q in queries:\\n query = query | q\\n\\n results = manager.filter(query)\\n \\n # Create a list of primary keys for each item in the results, then use that list to get a \\n # QuerySet of associated ContentItem objects. Append the results of that QuerySet to the\\n # search_results list.\\n result_pks = [ i['pk'] for i in results.values('pk') ]\\n result_content_items = ContentItem.objects.filter(content_type=content_type, object_id__in=result_pks)\\n search_results.extend(result_content_items)\\n items = dictsortreversed(search_results, 'timestamp')\\n else:\\n cleaned_search_terms = []\\n search_string = \\\"\\\"\\n items = []\\n content_types = []\\n model_string = \\\"\\\"\\n hits = 0\\n # Build the context\\n context = RequestContext(request, {\\n \\\"search_terms\\\" : cleaned_search_terms,\\n \\\"search_string\\\" : search_string,\\n \\\"items\\\" : items,\\n \\\"all_content_types\\\" : get_aggregator_content_types(type_set=\\\"all\\\"),\\n \\\"content_types\\\" : content_types,\\n \\\"model_string\\\" : model_string,\\n \\\"hits\\\" : len(items),\\n }, context_processors)\\n if extra_context:\\n for key, value in extra_context.items():\\n if callable(value):\\n context[key] = value()\\n else:\\n context[key] = value\\n \\n # Load, render, and return\\n t = template_loader.get_template(template_name)\\n return HttpResponse(t.render(context), mimetype=mimetype)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":498,"cells":{"query_id":{"kind":"string","value":"4e02ba312122b710df2ee54808e84bad"},"query":{"kind":"string","value":"Displays UI for a download error"},"positive_passages":{"kind":"list like","value":[{"docid":"0e70bafff92bfcb6561858673ec8d1cb","score":"0.80256706","text":"def show_download_error(self, name, err):\n self.kodiUi.show_error(30952, self.language(30953).format(name, err))","title":""}],"string":"[\n {\n \"docid\": \"0e70bafff92bfcb6561858673ec8d1cb\",\n \"score\": \"0.80256706\",\n \"text\": \"def show_download_error(self, name, err):\\n self.kodiUi.show_error(30952, self.language(30953).format(name, err))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0583d83d4d08c67fa3f074b1be0bc052","score":"0.70555574","text":"def download_message(self):\n\n print('Failed to find any DR %s files'% self.subset)\n print('')","title":""},{"docid":"9b23a29c0c13ae8fd955349fe1e02077","score":"0.67040294","text":"def _url_error(self, err):\n\n self.info_state_lbl.setText(\"\")\n self.progress_lbl.hide()\n self.frame_select_insta.show()\n\n error_pop_up = QMessageBox(self)\n error_pop_up.setIcon(QMessageBox.Critical)\n error_pop_up.setStyleSheet(style_sheets.qmsg_box_style)\n if err == \"url\":\n error_pop_up.setText('Invalid instagram post url')\n if err == \"timeout\":\n error_pop_up.setText(\"Timeout, couldn't get a response for this url\")\n if err == \"connection\":\n error_pop_up.setText(\"Can't establish a connection to the requested url,\"\n \"\\ncheck your connection\")\n error_pop_up.show()","title":""},{"docid":"6deb02ef56a95ed1f2f79e7eb685002c","score":"0.654053","text":"def download_message(self):\n\n print('Failed to find any Flowers %s files'% self.subset)\n print('')\n print('If you have already downloaded and processed the data, then make '\n 'sure to set --data_dir to point to the directory containing the '\n 'location of the sharded TFRecords.\\n')\n print('Please see README.md for instructions on how to build '\n 'the flowers dataset using download_and_preprocess_flowers.\\n')","title":""},{"docid":"a7f12a75740c836bb95ec5e58a214a1f","score":"0.6322234","text":"def error(self, req):\r\n import cgitb\r\n req.stdout.write('Content-Type: text/html\\r\\n\\r\\n' +\r\n cgitb.html(sys.exc_info()))","title":""},{"docid":"55f479c6257a784fb3451e045185dcae","score":"0.6170256","text":"def showError(self, err):\n self.beginResetModel()\n self.HEADER = self.ERROR\n self.rows= [PyRExTV.ResRow([None, err])]\n self.endResetModel()","title":""},{"docid":"d85010b6985d874124812d390e069f7e","score":"0.6166488","text":"def _display_error(self, title, message):\n message = 'ERROR: ' + message\n self._set_status_var_text(message)\n tkMessageBox.showerror(title, message)","title":""},{"docid":"5329a7b9c683c2663844702dcc149a3e","score":"0.6147564","text":"def print_err_message():\n err = \"[-] Something went wrong!\"\n text_progress.configure(state='normal')\n text_progress.delete(1.0, END)\n text_progress.insert(INSERT, err)\n text_progress.configure(state='disabled')","title":""},{"docid":"05f1d5f82431eff36e3a6663c1e070e6","score":"0.61249125","text":"def test_static_web_ui__error(self, _):\n resp, code = self.api_server.static_web_ui('kytos', 'napp', 'filename')\n\n self.assertEqual(resp, '')\n self.assertEqual(code, 404)","title":""},{"docid":"dd20cf1d6f2231c2db7457202dd69a8e","score":"0.61151296","text":"def showErrorDialogue(self, e):\n\n self.errorDialogue.show()\n self.errorLabel[\"text\"] = str(e)","title":""},{"docid":"274bbbf644d908e1a9579aaa4d58d941","score":"0.60758865","text":"def Write404Error(self):\n self.error(404)\n self.response.out.write('Error 404, file not found')","title":""},{"docid":"ca31c7fda8825080b136068a01a3e948","score":"0.60434794","text":"def _MWSfileOpenErrorDialog(self):\n\n title = \"Error in opening a MWSDeck file\"\n msg = \"Specified file contains no card data or is corrupted\"\n QtGui.QMessageBox.warning(self, title, msg)","title":""},{"docid":"07fddf193d6462af6d3ddcb66a00416b","score":"0.6027234","text":"def error(e):\n\n #return repr(e)\n return render_template(\"error.html\", titel=\"Error 404\", text=\"Sidan du sökte hittades inte, vänligen testa igen.\")","title":""},{"docid":"52d2098fa52f5ba3d19475bab6beb225","score":"0.5993623","text":"def show_missing_extractor_error(self):\n self.kodiUi.show_error(30952, 30954, time=10000)","title":""},{"docid":"3e1b14550b18363df5492d27e6bc6771","score":"0.5990825","text":"def _txtFileOpenErrorDialog(self):\n\n title = \"Error in opening a text file\"\n msg = \"Specified file contains no card data or is corrupted\"\n QtGui.QMessageBox.warning(self, title, msg)","title":""},{"docid":"3ae5d5ed98670ae8fd90c0992ae152e1","score":"0.5983925","text":"def showError(self, error):\n self.model.showError(error)","title":""},{"docid":"f2e48e6ff5b6dec99d38ebe0faa4a7e4","score":"0.59500605","text":"def write_error(self, status_code, **kwargs):\n print 'In get_error_html. status_code: ', status_code\n try:\n # handle exception here\n # get request and look up self.resources\n # if status_code in [403, 404, 500, 503]:\n error_class, message, traceback_object = kwargs[\"exc_info\"]\n except Exception, e:\n message = \"Woha, %s\" % e\n\n self.write('%s Error, message: %s' % (status_code, message))","title":""},{"docid":"8730d402c7a29dbd3181c17a7bae8a1c","score":"0.594727","text":"def show_error(self, message=\"\", *args, **nargs):\n # update attribute with data-status=\"error\"\n self.attribs['data-status'] = 'error'\n if message:\n self._error = message\n self.show = True","title":""},{"docid":"514125b184d77a68faf78dcf34ee70b0","score":"0.59212035","text":"def show_error(self, pHeading, pMessage):\n self.kodiUi.show_error(pHeading, pMessage)","title":""},{"docid":"3e07fea7075775aded8ffc0843df4801","score":"0.59114474","text":"def error(self, message):\n self.print_usage(sys.stdout)\n self.exit(3, gettext('%s: error: %s\\n') % (self.prog, message))","title":""},{"docid":"0a6772d2b5268d553893758b34246f4f","score":"0.59029454","text":"def ShowError(self, error):\n self.SetOrientation() # Always show text \"right side up\".\n lcd.font(lcd.FONT_DejaVu18, rotate=0, transparent=True)\n lcd.fill(RED)\n print('ShowError: error=%r' % error)\n self.print_exception(error)\n lcd.print('%s' % error, 10, 10, WHITE)","title":""},{"docid":"df22af9899e09925a075e3a9a50f79e9","score":"0.5902446","text":"def error_page(self):\n return content","title":""},{"docid":"2c0607456d9298981485c647d9cf3f88","score":"0.58983326","text":"def display_error(msg: str) -> None:\r\n messagebox.showerror(\"Error\", msg)","title":""},{"docid":"00e5e13208f9853802ccfcce436e1231","score":"0.5882246","text":"def _error(self):\n self.redirect(self.error_action)","title":""},{"docid":"e7cf63f39e090ffc8e3040292abebb25","score":"0.58759266","text":"def show_error(self, msg):\n self.send_event(bridge.Command.ERROR, msg)","title":""},{"docid":"0e6957a56e1a19da9efc10b755107284","score":"0.5871805","text":"def error_message_view():\n\n print(\"\\nDésolé mais votre réponse ne décrit pas une action possible.\\n\"\n \"Veuillez essayer de nouveau\\n\")","title":""},{"docid":"69b94a9037123bcf4f574951b4fda92f","score":"0.5854621","text":"def _display_error(error: BaseException):\n print(\"***** Error\")\n print(error)","title":""},{"docid":"26fc490341cbbec5cdb8cc76c0d10345","score":"0.5849313","text":"def on_error(self, status):\n print('Error status code : ' + str(status))","title":""},{"docid":"57307f270d81d4143f814a413d920be4","score":"0.584909","text":"def __showError(self, out):\n self.errorGroup.show()\n self.errors.insertPlainText(out)\n self.errors.ensureCursorVisible()","title":""},{"docid":"57307f270d81d4143f814a413d920be4","score":"0.584909","text":"def __showError(self, out):\n self.errorGroup.show()\n self.errors.insertPlainText(out)\n self.errors.ensureCursorVisible()","title":""},{"docid":"b504e2abd8d827a4fe3c55d424ca0599","score":"0.58407325","text":"def on_error(self, status):\r\n print(status)","title":""},{"docid":"f9d2ec11fed66c95c2e4d386e1b3f6bf","score":"0.58246607","text":"def playstatus_error(self, updater, exception: Exception) -> None:\n print(self.formatter(output(False, exception=exception)), flush=True)","title":""},{"docid":"0d54a66b16ebc618b540ce887eb048d3","score":"0.5817636","text":"def error(self, message):\n self.success = False\n print(f'Error in {self.filename}: {message}')","title":""},{"docid":"82a637c019b6bfa9e68c309ab8e67625","score":"0.58109105","text":"def error (self, error_string):\n self.set_value('error', error_string)\n self.render('error.html')","title":""},{"docid":"e60f98e4c3b07090b15b6c8dc95ecd41","score":"0.5806683","text":"def server_error_500(error):\n logging.exception('An error occurred during a request..' + str(error))\n return render_template(\"filemanager/failure.html\")","title":""},{"docid":"59e3df5b481cfa423297933aefbc993d","score":"0.5792314","text":"def show_error(self, msg=\"\"):\n\n self.show_error_signal.emit(msg)","title":""},{"docid":"59e3df5b481cfa423297933aefbc993d","score":"0.5792314","text":"def show_error(self, msg=\"\"):\n\n self.show_error_signal.emit(msg)","title":""},{"docid":"f98b5bdd52b00604685091e845943e3a","score":"0.5772071","text":"def result_err(failure):\n print \"{0}: failure... {1!r}\".format(self._name, failure)\n # failure.raiseException()\n if USE_GUI:\n title = self.tr(\"Operation Error\")\n msg = self.tr(\"{0}: your operation failed.\").format(self._name)\n QtGui.QMessageBox.critical(None, title, msg)","title":""},{"docid":"380cc1327720a7d5eeaef397c6db2b09","score":"0.5763218","text":"def error_page(request, error):\n return default_render('error.html')","title":""},{"docid":"c2ff76bcedbdeb462b88612d3294ddc0","score":"0.57516384","text":"def error(self, msg):\n click.echo(click.style(msg, bg='red', fg='white'))\n exit()","title":""},{"docid":"0afbbccbd4b0fad0ee4f01125d4db48f","score":"0.5741481","text":"def show_error(self):\n self.input = tkMessageBox.showerror(self.name, self.message)\n return","title":""},{"docid":"4f4853de125f65e32501af888d8d641d","score":"0.5723974","text":"def error(self, message):\n\n sys.stderr.write('error: %s\\n' % message)\n self.print_help()\n sys.exit(2)","title":""},{"docid":"abd9dd7de6d3959383d49f951618e33f","score":"0.5723259","text":"def generate_summary(self):\n message = self.result_description\n if message is None:\n message = \"unknown error... contact administrator.\"\n return f\"HTML/URL Renderer: {message}\"","title":""},{"docid":"6f58941998f38dcb734618fa8afa0d59","score":"0.5720473","text":"def Start_Download(self):\n url = self.lineEdit.text()\n save_location = self.lineEdit_2.text()\n\n try:\n\n urreq.urlretrieve(url, save_location, self.Handel_Progress)\n\n except Exception:\n\n QMessageBox.warning(self, 'Download Error', 'The Download Filed')\n return\n\n QMessageBox.information(self, 'Download Completed', 'The Download Finished')\n\n self.progressBar.setValue(0)\n self.lineEdit.setText('')\n self.lineEdit_2.setText('')","title":""},{"docid":"6b8761af94a28dce915d994a99290809","score":"0.57128114","text":"def renderError(self, error_code):\n\n self.error(error_code)\n self.response.write(\"Oops! Something went wrong.\")","title":""},{"docid":"6b8761af94a28dce915d994a99290809","score":"0.57128114","text":"def renderError(self, error_code):\n\n self.error(error_code)\n self.response.write(\"Oops! Something went wrong.\")","title":""},{"docid":"0b4b9aacd6369314a36ca8bc80ae0996","score":"0.57080424","text":"def show_error_box(msg):\n Ui_MainWindow.show_message(\"There's a problem...\", msg, QMessageBox.Critical)","title":""},{"docid":"43ebddc432525adf0d9a68b85157d44a","score":"0.5699492","text":"def error(self, msg, *args):\n self.log(click.style(\"## ERROR - %s\" % msg, *args, fg='red'))","title":""},{"docid":"90b460005399771431987a29c7e46d24","score":"0.56919736","text":"def displayError(self, msg):\n dialog = Gtk.MessageDialog(\n self, Gtk.DialogFlags.MODAL, Gtk.MessageType.ERROR,\n Gtk.ButtonsType.CLOSE, msg)\n dialog.run()\n dialog.destroy()","title":""},{"docid":"4b68879eccf67c6cf36d7290ca57c9eb","score":"0.5680849","text":"def error(error_msg):\n return render_template('error.html', error=error_msg, title=\"ERROR\")","title":""},{"docid":"16cddf90356f80c6698432b32be0ac6a","score":"0.56776005","text":"def draw_error_message(self, context):\n self.layout.label(text=bpy.context.window_manager.send2ue.error_message)","title":""},{"docid":"1976c96691ab6e9ff5001abcb5e941a5","score":"0.5667852","text":"def show_download_progress(self):\n self.kodiUi.show_progress_dialog(30955)","title":""},{"docid":"bfd04a91e0f147fcac4725b628992fa4","score":"0.566438","text":"def write_error(self, status_code, **kwargs):\n if self.settings.get(\"serve_traceback\") and \"exc_info\" in kwargs:\n # in debug mode, try to send a traceback\n self.set_header('Content-Type', 'text/plain')\n for line in traceback.format_exception(*kwargs[\"exc_info\"]):\n self.write(line)\n self.finish()\n else:\n if status_code == 404:\n # 重载404页面\n self.render(\"404.html\")\n\n self.finish(\"<html><title>%(code)d: %(message)s</title>\"\n \"<body>%(code)d: %(message)s</body></html>\" % {\n \"code\": status_code,\n \"message\": self._reason,\n })","title":""},{"docid":"213c0bc8c6178b05106e3373789641c0","score":"0.5660347","text":"def show_error(parent, title, text):\n return show_dialog_with_icon(parent=parent, title=title, text=text,\n buttons=RESPONSES_OK, icon_name=\"qubes-info\")","title":""},{"docid":"d359ea32ecc9783097a39c006a5c3445","score":"0.5657757","text":"def alerterror(title, message):\n messagebox.showerror(title, message)","title":""},{"docid":"b96d39c055b33cd368339910dac49753","score":"0.5646884","text":"def error_page(self):\n return None","title":""},{"docid":"2c1d01c8af030f6d3250a4eec30d7992","score":"0.5642748","text":"def on_error(self, status_code, data):\n print status_code, data\n #self.disconnect()","title":""},{"docid":"3fff0ef73b8a7945040338ce94500b30","score":"0.56341285","text":"def errorMessages(self,index):\n msgBox = QMessageBox()\n msgBox.setIcon(QMessageBox.Critical)\n\n if index == 0:\n msgBox.setText(\"Error: It was not possible to load the bag file!\"\n \"Reason: topic incompatibility.\")\n elif index == 1:\n msgBox.setText(\"Error: It was not possible to load the annotation file!\"\n \"Reason: topic incompatibility.\")\n elif index == 2:\n msgBox.setText(\"Error: You must select the topics you are interested.\")\n elif index == 3:\n msgBox.setText(\"Error: You must load a bag file and/or an annotation file!\")\n elif index == 4:\n msgBox.setText(\"Error: Error when opening the bag file!\")\n elif index == 5:\n msgBox.setText(\"Error: Error when opening the annotation json file!\")\n msgBox.resize(100,40)\n msgBox.exec_()","title":""},{"docid":"3a99e176d3db725358cb91650456ff2f","score":"0.5625095","text":"def message(self, request):\n return \"An exception error occurred.\"","title":""},{"docid":"eb42735ef5bfc7d4ead7b5dc6e5ad626","score":"0.56234473","text":"def error(self, message) -> None:\n sys.stderr.write('error: %s\\n' % message)\n self.print_help()\n sys.exit(2)","title":""},{"docid":"98b126421bc2dd06f77aeafaaa481321","score":"0.56231016","text":"def display_import_result(self):\n reply = QtGui.QMessageBox.question(self, 'Import Result',\n \"Imported Cache successfully\")","title":""},{"docid":"fdc333ce0ae8c6ba8918690ed07bc5f1","score":"0.56229734","text":"def query_failure():\n writer = codecs.getwriter('utf-8')(sys.stdout.buffer)\n response_html_dir = os.path.join(\n os.path.split(\n os.path.split(\n os.path.abspath(__file__))[0])[0], \n \"query_failure.html\")\n response_html = open(response_html_dir).read()\n #HTTP Headers\n writer.write(\"Content-Type: text/html; charset=utf-8\" + '\\r\\n')\n writer.write(\"Content-Length: \" + str(len(response_html)) + '\\r\\n')\n # Seperator between header and HTML\n writer.write('\\r\\n')\n #HTML\n writer.write(response_html)","title":""},{"docid":"e760b2495c4b18ee2389af589819bc05","score":"0.5619451","text":"def renderError(self, error_code):\n\n\t\tself.error(error_code)\n\t\tif (error_code == 404):\n\t\t\tself.response.write(str(error_code) + \": The resource could not be found\")\n\t\telse:\n\t\t\tself.response.write(\"Oops! Something went wrong.\")","title":""},{"docid":"d8ee045fa03e428bba53e73170e34293","score":"0.5614987","text":"def error(self, message: str):\n sys.stderr.write(f\"Error {message}\\n\")\n self.print_help()\n sys.exit(1)","title":""},{"docid":"e34208438f46a8f39f1eaa6512ccaad9","score":"0.5603017","text":"def errorBox(self, msg: str):\n exit(f'{self.__getTime()}{self.__getError(msg)}')","title":""},{"docid":"a7542139420bba2366ea5fa77340c11b","score":"0.55986756","text":"def ShowError(self, msg):\n acm.UX().Dialogs().MessageBox(self.m_fuxDlg.Shell(), 'Error', \"Error: \" + msg,\n 'Cancel', None, None, 'Button1', 'Button1')","title":""},{"docid":"5110bf4a62455e3062806be4de195140","score":"0.55972624","text":"def display_warning(self):\n reply = QtGui.QMessageBox.question(self, 'Error', \"No Object Selected\")\n\n # updates text representing write directory","title":""},{"docid":"c8666ffac29597285dce0028a6905d38","score":"0.5595954","text":"def error(self, exit_code):\n print('error: command exited with status %s\\n' % exit_code)","title":""},{"docid":"e99d9139d405855b6d717a4245b35f24","score":"0.55914426","text":"def error_msg(self,error): \n self.error = error\n print \"\"\n print \"ERROR: \"+self.error\n print \"\"","title":""},{"docid":"b6f641ab70e7150fa6bff24031b26a9a","score":"0.5588419","text":"def error(msg):\n click.echo(click.style(msg, bold=True, fg=\"red\"), err=True)","title":""},{"docid":"6bad5f5899147376cdb5a53413e6e24c","score":"0.55665463","text":"def on_download_page_fail(self, f, host):\n error_msg = f.getErrorMessage()\n log.debug(\"Error downloading page: %s\", error_msg)\n d = f\n if f.check(PageRedirect):\n # Handle redirect errors\n location = urljoin(self.host_to_url(host), error_msg.split(\" to \")[1])\n self.redirects[host] = url_to_host(location)\n d = self.download_page(host, url=location)\n d.addCallbacks(self.on_download_page_complete, self.on_download_page_fail,\n errbackArgs=(host,))\n\n return d","title":""},{"docid":"0657970672b98b3ca39cfa08b223922e","score":"0.55642885","text":"def download_page(self):\n try:\n logging.info('Downloading page %s ....', self.download_data.url)\n html = network.download_html(self.download_data.url)\n self.comic_html = html\n except Exception as e:\n self.page_download_status = WebComicDownloadStatus.FAILED.name\n logging.debug('Download failed on URL, %s because of %s', self.download_data.url, str(e) )","title":""},{"docid":"84f0cb330cbe50abc31f366e6ae87455","score":"0.5558545","text":"def default_error_handler(status_code, successful, failed, errors):\n msg = \"%d points out of %d had errors\\n\" \\\n \"HTTP status returned from Apptuit: %d\\n\" \\\n \"Detailed error messages: %s\\n\" % \\\n (failed, successful + failed, status_code, str(errors))\n sys.stderr.write(msg)","title":""},{"docid":"55c6c1167d8b4b3e26f690209a4a1c0c","score":"0.5558469","text":"def on_login_failed(browser_instance, gui):\n showerror('Ok', 'Login Unsuccessful')","title":""},{"docid":"69a67c8a3f1b7b0c0a17a1e486262679","score":"0.55516905","text":"def displayError(parent, message):\n\n messageDialog = wx.MessageDialog(parent, message, \"Error\",\n wx.OK|wx.CENTRE|wx.ICON_ERROR)\n messageDialog.Center(wx.BOTH)\n messageDialog.ShowModal()","title":""},{"docid":"7199fca9368ef37ea03e09a515660309","score":"0.55443347","text":"def display_error(self, msg, type = \"Error\"):\n mbox.showerror(type, msg)","title":""},{"docid":"b04f4c35f3094ed547d352c152712253","score":"0.55437934","text":"def error_page(self, vr):\n return jinja.get_template('error.html').render(\n errors=vr.errors,\n warnings=vr.warnings\n )","title":""},{"docid":"31223b08fa0ea4bb0751013aff5a54ff","score":"0.55414","text":"def _show_error(self):\n\n sublime.error_message(\n \"An error occurred installing Package Control\\n\\n\"\n \"Please check the Console for details\\n\\n\"\n \"Visit https://packagecontrol.io/installation for manual instructions\")","title":""},{"docid":"b75ab9fdef7eab7b2eeb3ccd4e084049","score":"0.55318207","text":"def _handle_error(self, reason, request):\n reason.printTraceback()","title":""},{"docid":"ff1c45405c35e294db9b812bfe45ba4a","score":"0.55314916","text":"def error(self, msg):\n pass","title":""},{"docid":"59bf79228619071a93034652b80981d1","score":"0.5531016","text":"def error(string):\n click.secho('ERROR: {}'.format(string), fg='red', err=True)","title":""},{"docid":"5090d867ea8767151e63ebf1f4d8c4d0","score":"0.55282885","text":"def _error(self, msg):\n sys.stderr.write(\"\\nerror: \" + msg + \"\\n\")\n sys.stdout.write(\"\\n\")","title":""},{"docid":"d2dd204595502c37ebbea525e371e53a","score":"0.5528245","text":"def display_errors(self, error_db):\n error_report = error_db.report_errors(command_line=False,\n file_output=False)\n translated_report = wx.GetTranslation(error_report)\n window = wx.MessageBox(error_report,\n caption=_('Errors logged in error_report.txt'))","title":""},{"docid":"ed84588e565baffdede0fdbcd4358b56","score":"0.552524","text":"def err_back(self, failure):\n\n file_error_urls = open(ERROR_DIRECTION_URLS + str(self.now.day) + str(self.now.hour) + str(self.now.minute), 'a+')\n file_error_proxy = open(ERROR_DIRECTION_PROXY + str(self.now.day) + str(self.now.hour) + str(self.now.minute), 'a+')\n\n if failure.check(HttpError):\n # these exceptions come from HttpError spider middleware\n # you can get the non-200 response\n response = failure.value.response\n self.logger.error(\"!!HttpError on %s\", response.url)\n file_error_urls.write(response.url + \"\\n\")\n file_error_proxy.write(response.meta[\"proxy\"] + \"\\n\")\n\n elif failure.check(DNSLookupError):\n # this is the original request\n request = failure.request\n self.logger.error(\"!!DNSLookupError on %s\", request.url)\n file_error_urls.write(request.url + \"\\n\")\n file_error_proxy.write(request.meta[\"proxy\"] + \"\\n\")\n\n elif failure.check(TimeoutError, TCPTimedOutError):\n request = failure.request\n self.logger.error(\"!!TimeoutError on %s\", request.url)\n file_error_urls.write(request.url + \"\\n\")\n file_error_proxy.write(request.meta[\"proxy\"] + \"\\n\")\n\n file_error_urls.close()\n file_error_proxy.close()","title":""},{"docid":"22a21f6dccb7b8cd878189d8f2a43cac","score":"0.5523169","text":"def error(self, s):\n print(\" ERROR: '%s', %s\" % (self.src_id, s), file=sys.stderr)","title":""},{"docid":"d5c534b1e3adf3883e7081ff384d31cb","score":"0.55210364","text":"def error(self):\n pass","title":""},{"docid":"5137840a894ff230b7ce278a0fbb4c9a","score":"0.55150735","text":"def show_database_error(self, err):\n self.kodiUi.show_error(30951, '{}'.format(err))","title":""},{"docid":"1eb9a2a744c95987f60983dbcc3215de","score":"0.5509607","text":"def _ExceptionResponse(self):\n\n status = '500 Server Error'\n response_headers = [('Content-type', 'text/html; charset=utf-8')]\n self.start_response(status, response_headers)\n return target_errors.ExcInfoAsHtml()","title":""},{"docid":"d62ced1a085bf7b994a491f988739b33","score":"0.5492359","text":"def __refresh_failed_slot(self, filename):\n self.send_message_signal.emit(\"Refresh of \" + filename + \" has started unsuccessfully\")","title":""},{"docid":"42837a8c57b84f58c951d068bba1b393","score":"0.54920834","text":"def print_login_error(self, message):\n label = Label(self.error_frame, text=message, fg=\"red\", anchor=CENTER, justify=CENTER,\n bg=self.controller.color_1)\n label.grid(column=0, row=0, sticky=EW)","title":""},{"docid":"64d33bababac488de6dddd22a946ec35","score":"0.54873234","text":"def buildErrorGUI(self):\n\n self.errorDialogue = DirectDialog(frameSize = (-0.8, 0.8, -0.4, 0.4),\n frameColor = (0.2, 0.3, 0.7, 1),\n fadeScreen = 0.4,\n image = None,\n geom = None,\n relief = DGG.FLAT)\n\n self.errorTitle = DirectLabel(text = \"Error!\",\n scale = 0.09,\n text_align = TextNode.ACenter,\n text_fg = (1, 1, 1, 1),\n parent = self.errorDialogue,\n pos = (0, 0, 0.3),\n relief = None)\n self.errorLabel = DirectLabel(text = \"<Error text here>\",\n scale = 0.07,\n text_align = TextNode.ACenter,\n text_fg = (1, 1, 1, 1),\n parent = self.errorDialogue,\n pos = (0, 0, 0.1),\n relief = None)\n\n self.errorDoneBtn = DirectButton(text = \"Close\", command = self.hideErrorDialogue,\n scale = 0.05,\n text_align = TextNode.ACenter,\n pos = (0, 0, -0.35),\n parent = self.errorDialogue,\n text_bg = (0.1, 0.8, 0.2, 1))","title":""},{"docid":"1ed2b57be47413d1c08586a5fd4aa9a0","score":"0.5485057","text":"def display_error_message(self):\n\n self._error_message.setVisible(True)","title":""},{"docid":"758fe71a554951f7fc2ceb7dd7b7ef92","score":"0.5481646","text":"def error_message(self):\n pass","title":""},{"docid":"6f5df56c6f586734960f5a21d1d74d6e","score":"0.54746085","text":"def handle_error(err, halt=True):\n print('{}{}{}'.format(c.Style.BRIGHT, c.Fore.RED, err))\n if halt:\n sys.exit(1)","title":""},{"docid":"45102d15cf919e707046179902c49cda","score":"0.5467163","text":"def display_export_result(self):\n reply = QtGui.QMessageBox.question(self, 'Export Result',\n \"Files saved to Directory: %s\" % self.file_location)","title":""},{"docid":"2541857d4f34a6173a5af6d93b92272e","score":"0.54597694","text":"def error_404(error):\n return 'Sorry, nothing at this URL.'","title":""},{"docid":"07c52ea3a9688fec7f296714da2ce422","score":"0.5458576","text":"def show_error_message(self, msg):\n msgbox = QMessageBox()\n msgbox.critical(dialog, \"Error\", msg)","title":""},{"docid":"2c525949f47ba9fdff84a5eefc115387","score":"0.54544574","text":"def display_error_msg():\n print(\"\\n !! mauvaise saisie !!\\n\")","title":""},{"docid":"c616695ae4cd395babca5148f88b9888","score":"0.545296","text":"def print_http_error(url, status_code, response):\n error(\"FAILED!\\nStatus code: {}\\nResponse: {}\\nURL: {}\".format(\n status_code, response, url))","title":""},{"docid":"344b648ab0184da724cb78ba21b010e4","score":"0.5452114","text":"def error_404(error):\n return 'Sorry, Nothing at this URL.'","title":""},{"docid":"8ecddb1065e7b686db09438d379400ba","score":"0.54326767","text":"def send_error_page(request, error):\n return response_ajax_or_not(request, {'htmlname': 'error.html',\n 'message': error})","title":""}],"string":"[\n {\n \"docid\": \"0583d83d4d08c67fa3f074b1be0bc052\",\n \"score\": \"0.70555574\",\n \"text\": \"def download_message(self):\\n\\n print('Failed to find any DR %s files'% self.subset)\\n print('')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b23a29c0c13ae8fd955349fe1e02077\",\n \"score\": \"0.67040294\",\n \"text\": \"def _url_error(self, err):\\n\\n self.info_state_lbl.setText(\\\"\\\")\\n self.progress_lbl.hide()\\n self.frame_select_insta.show()\\n\\n error_pop_up = QMessageBox(self)\\n error_pop_up.setIcon(QMessageBox.Critical)\\n error_pop_up.setStyleSheet(style_sheets.qmsg_box_style)\\n if err == \\\"url\\\":\\n error_pop_up.setText('Invalid instagram post url')\\n if err == \\\"timeout\\\":\\n error_pop_up.setText(\\\"Timeout, couldn't get a response for this url\\\")\\n if err == \\\"connection\\\":\\n error_pop_up.setText(\\\"Can't establish a connection to the requested url,\\\"\\n \\\"\\\\ncheck your connection\\\")\\n error_pop_up.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6deb02ef56a95ed1f2f79e7eb685002c\",\n \"score\": \"0.654053\",\n \"text\": \"def download_message(self):\\n\\n print('Failed to find any Flowers %s files'% self.subset)\\n print('')\\n print('If you have already downloaded and processed the data, then make '\\n 'sure to set --data_dir to point to the directory containing the '\\n 'location of the sharded TFRecords.\\\\n')\\n print('Please see README.md for instructions on how to build '\\n 'the flowers dataset using download_and_preprocess_flowers.\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7f12a75740c836bb95ec5e58a214a1f\",\n \"score\": \"0.6322234\",\n \"text\": \"def error(self, req):\\r\\n import cgitb\\r\\n req.stdout.write('Content-Type: text/html\\\\r\\\\n\\\\r\\\\n' +\\r\\n cgitb.html(sys.exc_info()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55f479c6257a784fb3451e045185dcae\",\n \"score\": \"0.6170256\",\n \"text\": \"def showError(self, err):\\n self.beginResetModel()\\n self.HEADER = self.ERROR\\n self.rows= [PyRExTV.ResRow([None, err])]\\n self.endResetModel()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d85010b6985d874124812d390e069f7e\",\n \"score\": \"0.6166488\",\n \"text\": \"def _display_error(self, title, message):\\n message = 'ERROR: ' + message\\n self._set_status_var_text(message)\\n tkMessageBox.showerror(title, message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5329a7b9c683c2663844702dcc149a3e\",\n \"score\": \"0.6147564\",\n \"text\": \"def print_err_message():\\n err = \\\"[-] Something went wrong!\\\"\\n text_progress.configure(state='normal')\\n text_progress.delete(1.0, END)\\n text_progress.insert(INSERT, err)\\n text_progress.configure(state='disabled')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05f1d5f82431eff36e3a6663c1e070e6\",\n \"score\": \"0.61249125\",\n \"text\": \"def test_static_web_ui__error(self, _):\\n resp, code = self.api_server.static_web_ui('kytos', 'napp', 'filename')\\n\\n self.assertEqual(resp, '')\\n self.assertEqual(code, 404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd20cf1d6f2231c2db7457202dd69a8e\",\n \"score\": \"0.61151296\",\n \"text\": \"def showErrorDialogue(self, e):\\n\\n self.errorDialogue.show()\\n self.errorLabel[\\\"text\\\"] = str(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"274bbbf644d908e1a9579aaa4d58d941\",\n \"score\": \"0.60758865\",\n \"text\": \"def Write404Error(self):\\n self.error(404)\\n self.response.out.write('Error 404, file not found')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca31c7fda8825080b136068a01a3e948\",\n \"score\": \"0.60434794\",\n \"text\": \"def _MWSfileOpenErrorDialog(self):\\n\\n title = \\\"Error in opening a MWSDeck file\\\"\\n msg = \\\"Specified file contains no card data or is corrupted\\\"\\n QtGui.QMessageBox.warning(self, title, msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07fddf193d6462af6d3ddcb66a00416b\",\n \"score\": \"0.6027234\",\n \"text\": \"def error(e):\\n\\n #return repr(e)\\n return render_template(\\\"error.html\\\", titel=\\\"Error 404\\\", text=\\\"Sidan du sökte hittades inte, vänligen testa igen.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52d2098fa52f5ba3d19475bab6beb225\",\n \"score\": \"0.5993623\",\n \"text\": \"def show_missing_extractor_error(self):\\n self.kodiUi.show_error(30952, 30954, time=10000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e1b14550b18363df5492d27e6bc6771\",\n \"score\": \"0.5990825\",\n \"text\": \"def _txtFileOpenErrorDialog(self):\\n\\n title = \\\"Error in opening a text file\\\"\\n msg = \\\"Specified file contains no card data or is corrupted\\\"\\n QtGui.QMessageBox.warning(self, title, msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae5d5ed98670ae8fd90c0992ae152e1\",\n \"score\": \"0.5983925\",\n \"text\": \"def showError(self, error):\\n self.model.showError(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2e48e6ff5b6dec99d38ebe0faa4a7e4\",\n \"score\": \"0.59500605\",\n \"text\": \"def write_error(self, status_code, **kwargs):\\n print 'In get_error_html. status_code: ', status_code\\n try:\\n # handle exception here\\n # get request and look up self.resources\\n # if status_code in [403, 404, 500, 503]:\\n error_class, message, traceback_object = kwargs[\\\"exc_info\\\"]\\n except Exception, e:\\n message = \\\"Woha, %s\\\" % e\\n\\n self.write('%s Error, message: %s' % (status_code, message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8730d402c7a29dbd3181c17a7bae8a1c\",\n \"score\": \"0.594727\",\n \"text\": \"def show_error(self, message=\\\"\\\", *args, **nargs):\\n # update attribute with data-status=\\\"error\\\"\\n self.attribs['data-status'] = 'error'\\n if message:\\n self._error = message\\n self.show = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"514125b184d77a68faf78dcf34ee70b0\",\n \"score\": \"0.59212035\",\n \"text\": \"def show_error(self, pHeading, pMessage):\\n self.kodiUi.show_error(pHeading, pMessage)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e07fea7075775aded8ffc0843df4801\",\n \"score\": \"0.59114474\",\n \"text\": \"def error(self, message):\\n self.print_usage(sys.stdout)\\n self.exit(3, gettext('%s: error: %s\\\\n') % (self.prog, message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a6772d2b5268d553893758b34246f4f\",\n \"score\": \"0.59029454\",\n \"text\": \"def ShowError(self, error):\\n self.SetOrientation() # Always show text \\\"right side up\\\".\\n lcd.font(lcd.FONT_DejaVu18, rotate=0, transparent=True)\\n lcd.fill(RED)\\n print('ShowError: error=%r' % error)\\n self.print_exception(error)\\n lcd.print('%s' % error, 10, 10, WHITE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df22af9899e09925a075e3a9a50f79e9\",\n \"score\": \"0.5902446\",\n \"text\": \"def error_page(self):\\n return content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c0607456d9298981485c647d9cf3f88\",\n \"score\": \"0.58983326\",\n \"text\": \"def display_error(msg: str) -> None:\\r\\n messagebox.showerror(\\\"Error\\\", msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e5e13208f9853802ccfcce436e1231\",\n \"score\": \"0.5882246\",\n \"text\": \"def _error(self):\\n self.redirect(self.error_action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7cf63f39e090ffc8e3040292abebb25\",\n \"score\": \"0.58759266\",\n \"text\": \"def show_error(self, msg):\\n self.send_event(bridge.Command.ERROR, msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e6957a56e1a19da9efc10b755107284\",\n \"score\": \"0.5871805\",\n \"text\": \"def error_message_view():\\n\\n print(\\\"\\\\nDésolé mais votre réponse ne décrit pas une action possible.\\\\n\\\"\\n \\\"Veuillez essayer de nouveau\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69b94a9037123bcf4f574951b4fda92f\",\n \"score\": \"0.5854621\",\n \"text\": \"def _display_error(error: BaseException):\\n print(\\\"***** Error\\\")\\n print(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26fc490341cbbec5cdb8cc76c0d10345\",\n \"score\": \"0.5849313\",\n \"text\": \"def on_error(self, status):\\n print('Error status code : ' + str(status))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57307f270d81d4143f814a413d920be4\",\n \"score\": \"0.584909\",\n \"text\": \"def __showError(self, out):\\n self.errorGroup.show()\\n self.errors.insertPlainText(out)\\n self.errors.ensureCursorVisible()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57307f270d81d4143f814a413d920be4\",\n \"score\": \"0.584909\",\n \"text\": \"def __showError(self, out):\\n self.errorGroup.show()\\n self.errors.insertPlainText(out)\\n self.errors.ensureCursorVisible()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b504e2abd8d827a4fe3c55d424ca0599\",\n \"score\": \"0.58407325\",\n \"text\": \"def on_error(self, status):\\r\\n print(status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9d2ec11fed66c95c2e4d386e1b3f6bf\",\n \"score\": \"0.58246607\",\n \"text\": \"def playstatus_error(self, updater, exception: Exception) -> None:\\n print(self.formatter(output(False, exception=exception)), flush=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d54a66b16ebc618b540ce887eb048d3\",\n \"score\": \"0.5817636\",\n \"text\": \"def error(self, message):\\n self.success = False\\n print(f'Error in {self.filename}: {message}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82a637c019b6bfa9e68c309ab8e67625\",\n \"score\": \"0.58109105\",\n \"text\": \"def error (self, error_string):\\n self.set_value('error', error_string)\\n self.render('error.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e60f98e4c3b07090b15b6c8dc95ecd41\",\n \"score\": \"0.5806683\",\n \"text\": \"def server_error_500(error):\\n logging.exception('An error occurred during a request..' + str(error))\\n return render_template(\\\"filemanager/failure.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59e3df5b481cfa423297933aefbc993d\",\n \"score\": \"0.5792314\",\n \"text\": \"def show_error(self, msg=\\\"\\\"):\\n\\n self.show_error_signal.emit(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59e3df5b481cfa423297933aefbc993d\",\n \"score\": \"0.5792314\",\n \"text\": \"def show_error(self, msg=\\\"\\\"):\\n\\n self.show_error_signal.emit(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f98b5bdd52b00604685091e845943e3a\",\n \"score\": \"0.5772071\",\n \"text\": \"def result_err(failure):\\n print \\\"{0}: failure... {1!r}\\\".format(self._name, failure)\\n # failure.raiseException()\\n if USE_GUI:\\n title = self.tr(\\\"Operation Error\\\")\\n msg = self.tr(\\\"{0}: your operation failed.\\\").format(self._name)\\n QtGui.QMessageBox.critical(None, title, msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"380cc1327720a7d5eeaef397c6db2b09\",\n \"score\": \"0.5763218\",\n \"text\": \"def error_page(request, error):\\n return default_render('error.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2ff76bcedbdeb462b88612d3294ddc0\",\n \"score\": \"0.57516384\",\n \"text\": \"def error(self, msg):\\n click.echo(click.style(msg, bg='red', fg='white'))\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0afbbccbd4b0fad0ee4f01125d4db48f\",\n \"score\": \"0.5741481\",\n \"text\": \"def show_error(self):\\n self.input = tkMessageBox.showerror(self.name, self.message)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f4853de125f65e32501af888d8d641d\",\n \"score\": \"0.5723974\",\n \"text\": \"def error(self, message):\\n\\n sys.stderr.write('error: %s\\\\n' % message)\\n self.print_help()\\n sys.exit(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abd9dd7de6d3959383d49f951618e33f\",\n \"score\": \"0.5723259\",\n \"text\": \"def generate_summary(self):\\n message = self.result_description\\n if message is None:\\n message = \\\"unknown error... contact administrator.\\\"\\n return f\\\"HTML/URL Renderer: {message}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f58941998f38dcb734618fa8afa0d59\",\n \"score\": \"0.5720473\",\n \"text\": \"def Start_Download(self):\\n url = self.lineEdit.text()\\n save_location = self.lineEdit_2.text()\\n\\n try:\\n\\n urreq.urlretrieve(url, save_location, self.Handel_Progress)\\n\\n except Exception:\\n\\n QMessageBox.warning(self, 'Download Error', 'The Download Filed')\\n return\\n\\n QMessageBox.information(self, 'Download Completed', 'The Download Finished')\\n\\n self.progressBar.setValue(0)\\n self.lineEdit.setText('')\\n self.lineEdit_2.setText('')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b8761af94a28dce915d994a99290809\",\n \"score\": \"0.57128114\",\n \"text\": \"def renderError(self, error_code):\\n\\n self.error(error_code)\\n self.response.write(\\\"Oops! Something went wrong.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b8761af94a28dce915d994a99290809\",\n \"score\": \"0.57128114\",\n \"text\": \"def renderError(self, error_code):\\n\\n self.error(error_code)\\n self.response.write(\\\"Oops! Something went wrong.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b4b9aacd6369314a36ca8bc80ae0996\",\n \"score\": \"0.57080424\",\n \"text\": \"def show_error_box(msg):\\n Ui_MainWindow.show_message(\\\"There's a problem...\\\", msg, QMessageBox.Critical)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43ebddc432525adf0d9a68b85157d44a\",\n \"score\": \"0.5699492\",\n \"text\": \"def error(self, msg, *args):\\n self.log(click.style(\\\"## ERROR - %s\\\" % msg, *args, fg='red'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90b460005399771431987a29c7e46d24\",\n \"score\": \"0.56919736\",\n \"text\": \"def displayError(self, msg):\\n dialog = Gtk.MessageDialog(\\n self, Gtk.DialogFlags.MODAL, Gtk.MessageType.ERROR,\\n Gtk.ButtonsType.CLOSE, msg)\\n dialog.run()\\n dialog.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b68879eccf67c6cf36d7290ca57c9eb\",\n \"score\": \"0.5680849\",\n \"text\": \"def error(error_msg):\\n return render_template('error.html', error=error_msg, title=\\\"ERROR\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16cddf90356f80c6698432b32be0ac6a\",\n \"score\": \"0.56776005\",\n \"text\": \"def draw_error_message(self, context):\\n self.layout.label(text=bpy.context.window_manager.send2ue.error_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1976c96691ab6e9ff5001abcb5e941a5\",\n \"score\": \"0.5667852\",\n \"text\": \"def show_download_progress(self):\\n self.kodiUi.show_progress_dialog(30955)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfd04a91e0f147fcac4725b628992fa4\",\n \"score\": \"0.566438\",\n \"text\": \"def write_error(self, status_code, **kwargs):\\n if self.settings.get(\\\"serve_traceback\\\") and \\\"exc_info\\\" in kwargs:\\n # in debug mode, try to send a traceback\\n self.set_header('Content-Type', 'text/plain')\\n for line in traceback.format_exception(*kwargs[\\\"exc_info\\\"]):\\n self.write(line)\\n self.finish()\\n else:\\n if status_code == 404:\\n # 重载404页面\\n self.render(\\\"404.html\\\")\\n\\n self.finish(\\\"<html><title>%(code)d: %(message)s</title>\\\"\\n \\\"<body>%(code)d: %(message)s</body></html>\\\" % {\\n \\\"code\\\": status_code,\\n \\\"message\\\": self._reason,\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"213c0bc8c6178b05106e3373789641c0\",\n \"score\": \"0.5660347\",\n \"text\": \"def show_error(parent, title, text):\\n return show_dialog_with_icon(parent=parent, title=title, text=text,\\n buttons=RESPONSES_OK, icon_name=\\\"qubes-info\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d359ea32ecc9783097a39c006a5c3445\",\n \"score\": \"0.5657757\",\n \"text\": \"def alerterror(title, message):\\n messagebox.showerror(title, message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b96d39c055b33cd368339910dac49753\",\n \"score\": \"0.5646884\",\n \"text\": \"def error_page(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c1d01c8af030f6d3250a4eec30d7992\",\n \"score\": \"0.5642748\",\n \"text\": \"def on_error(self, status_code, data):\\n print status_code, data\\n #self.disconnect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fff0ef73b8a7945040338ce94500b30\",\n \"score\": \"0.56341285\",\n \"text\": \"def errorMessages(self,index):\\n msgBox = QMessageBox()\\n msgBox.setIcon(QMessageBox.Critical)\\n\\n if index == 0:\\n msgBox.setText(\\\"Error: It was not possible to load the bag file!\\\"\\n \\\"Reason: topic incompatibility.\\\")\\n elif index == 1:\\n msgBox.setText(\\\"Error: It was not possible to load the annotation file!\\\"\\n \\\"Reason: topic incompatibility.\\\")\\n elif index == 2:\\n msgBox.setText(\\\"Error: You must select the topics you are interested.\\\")\\n elif index == 3:\\n msgBox.setText(\\\"Error: You must load a bag file and/or an annotation file!\\\")\\n elif index == 4:\\n msgBox.setText(\\\"Error: Error when opening the bag file!\\\")\\n elif index == 5:\\n msgBox.setText(\\\"Error: Error when opening the annotation json file!\\\")\\n msgBox.resize(100,40)\\n msgBox.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a99e176d3db725358cb91650456ff2f\",\n \"score\": \"0.5625095\",\n \"text\": \"def message(self, request):\\n return \\\"An exception error occurred.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb42735ef5bfc7d4ead7b5dc6e5ad626\",\n \"score\": \"0.56234473\",\n \"text\": \"def error(self, message) -> None:\\n sys.stderr.write('error: %s\\\\n' % message)\\n self.print_help()\\n sys.exit(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98b126421bc2dd06f77aeafaaa481321\",\n \"score\": \"0.56231016\",\n \"text\": \"def display_import_result(self):\\n reply = QtGui.QMessageBox.question(self, 'Import Result',\\n \\\"Imported Cache successfully\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdc333ce0ae8c6ba8918690ed07bc5f1\",\n \"score\": \"0.56229734\",\n \"text\": \"def query_failure():\\n writer = codecs.getwriter('utf-8')(sys.stdout.buffer)\\n response_html_dir = os.path.join(\\n os.path.split(\\n os.path.split(\\n os.path.abspath(__file__))[0])[0], \\n \\\"query_failure.html\\\")\\n response_html = open(response_html_dir).read()\\n #HTTP Headers\\n writer.write(\\\"Content-Type: text/html; charset=utf-8\\\" + '\\\\r\\\\n')\\n writer.write(\\\"Content-Length: \\\" + str(len(response_html)) + '\\\\r\\\\n')\\n # Seperator between header and HTML\\n writer.write('\\\\r\\\\n')\\n #HTML\\n writer.write(response_html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e760b2495c4b18ee2389af589819bc05\",\n \"score\": \"0.5619451\",\n \"text\": \"def renderError(self, error_code):\\n\\n\\t\\tself.error(error_code)\\n\\t\\tif (error_code == 404):\\n\\t\\t\\tself.response.write(str(error_code) + \\\": The resource could not be found\\\")\\n\\t\\telse:\\n\\t\\t\\tself.response.write(\\\"Oops! Something went wrong.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8ee045fa03e428bba53e73170e34293\",\n \"score\": \"0.5614987\",\n \"text\": \"def error(self, message: str):\\n sys.stderr.write(f\\\"Error {message}\\\\n\\\")\\n self.print_help()\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e34208438f46a8f39f1eaa6512ccaad9\",\n \"score\": \"0.5603017\",\n \"text\": \"def errorBox(self, msg: str):\\n exit(f'{self.__getTime()}{self.__getError(msg)}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7542139420bba2366ea5fa77340c11b\",\n \"score\": \"0.55986756\",\n \"text\": \"def ShowError(self, msg):\\n acm.UX().Dialogs().MessageBox(self.m_fuxDlg.Shell(), 'Error', \\\"Error: \\\" + msg,\\n 'Cancel', None, None, 'Button1', 'Button1')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5110bf4a62455e3062806be4de195140\",\n \"score\": \"0.55972624\",\n \"text\": \"def display_warning(self):\\n reply = QtGui.QMessageBox.question(self, 'Error', \\\"No Object Selected\\\")\\n\\n # updates text representing write directory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8666ffac29597285dce0028a6905d38\",\n \"score\": \"0.5595954\",\n \"text\": \"def error(self, exit_code):\\n print('error: command exited with status %s\\\\n' % exit_code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e99d9139d405855b6d717a4245b35f24\",\n \"score\": \"0.55914426\",\n \"text\": \"def error_msg(self,error): \\n self.error = error\\n print \\\"\\\"\\n print \\\"ERROR: \\\"+self.error\\n print \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6f641ab70e7150fa6bff24031b26a9a\",\n \"score\": \"0.5588419\",\n \"text\": \"def error(msg):\\n click.echo(click.style(msg, bold=True, fg=\\\"red\\\"), err=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bad5f5899147376cdb5a53413e6e24c\",\n \"score\": \"0.55665463\",\n \"text\": \"def on_download_page_fail(self, f, host):\\n error_msg = f.getErrorMessage()\\n log.debug(\\\"Error downloading page: %s\\\", error_msg)\\n d = f\\n if f.check(PageRedirect):\\n # Handle redirect errors\\n location = urljoin(self.host_to_url(host), error_msg.split(\\\" to \\\")[1])\\n self.redirects[host] = url_to_host(location)\\n d = self.download_page(host, url=location)\\n d.addCallbacks(self.on_download_page_complete, self.on_download_page_fail,\\n errbackArgs=(host,))\\n\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0657970672b98b3ca39cfa08b223922e\",\n \"score\": \"0.55642885\",\n \"text\": \"def download_page(self):\\n try:\\n logging.info('Downloading page %s ....', self.download_data.url)\\n html = network.download_html(self.download_data.url)\\n self.comic_html = html\\n except Exception as e:\\n self.page_download_status = WebComicDownloadStatus.FAILED.name\\n logging.debug('Download failed on URL, %s because of %s', self.download_data.url, str(e) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84f0cb330cbe50abc31f366e6ae87455\",\n \"score\": \"0.5558545\",\n \"text\": \"def default_error_handler(status_code, successful, failed, errors):\\n msg = \\\"%d points out of %d had errors\\\\n\\\" \\\\\\n \\\"HTTP status returned from Apptuit: %d\\\\n\\\" \\\\\\n \\\"Detailed error messages: %s\\\\n\\\" % \\\\\\n (failed, successful + failed, status_code, str(errors))\\n sys.stderr.write(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55c6c1167d8b4b3e26f690209a4a1c0c\",\n \"score\": \"0.5558469\",\n \"text\": \"def on_login_failed(browser_instance, gui):\\n showerror('Ok', 'Login Unsuccessful')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a67c8a3f1b7b0c0a17a1e486262679\",\n \"score\": \"0.55516905\",\n \"text\": \"def displayError(parent, message):\\n\\n messageDialog = wx.MessageDialog(parent, message, \\\"Error\\\",\\n wx.OK|wx.CENTRE|wx.ICON_ERROR)\\n messageDialog.Center(wx.BOTH)\\n messageDialog.ShowModal()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7199fca9368ef37ea03e09a515660309\",\n \"score\": \"0.55443347\",\n \"text\": \"def display_error(self, msg, type = \\\"Error\\\"):\\n mbox.showerror(type, msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b04f4c35f3094ed547d352c152712253\",\n \"score\": \"0.55437934\",\n \"text\": \"def error_page(self, vr):\\n return jinja.get_template('error.html').render(\\n errors=vr.errors,\\n warnings=vr.warnings\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31223b08fa0ea4bb0751013aff5a54ff\",\n \"score\": \"0.55414\",\n \"text\": \"def _show_error(self):\\n\\n sublime.error_message(\\n \\\"An error occurred installing Package Control\\\\n\\\\n\\\"\\n \\\"Please check the Console for details\\\\n\\\\n\\\"\\n \\\"Visit https://packagecontrol.io/installation for manual instructions\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b75ab9fdef7eab7b2eeb3ccd4e084049\",\n \"score\": \"0.55318207\",\n \"text\": \"def _handle_error(self, reason, request):\\n reason.printTraceback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff1c45405c35e294db9b812bfe45ba4a\",\n \"score\": \"0.55314916\",\n \"text\": \"def error(self, msg):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59bf79228619071a93034652b80981d1\",\n \"score\": \"0.5531016\",\n \"text\": \"def error(string):\\n click.secho('ERROR: {}'.format(string), fg='red', err=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5090d867ea8767151e63ebf1f4d8c4d0\",\n \"score\": \"0.55282885\",\n \"text\": \"def _error(self, msg):\\n sys.stderr.write(\\\"\\\\nerror: \\\" + msg + \\\"\\\\n\\\")\\n sys.stdout.write(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2dd204595502c37ebbea525e371e53a\",\n \"score\": \"0.5528245\",\n \"text\": \"def display_errors(self, error_db):\\n error_report = error_db.report_errors(command_line=False,\\n file_output=False)\\n translated_report = wx.GetTranslation(error_report)\\n window = wx.MessageBox(error_report,\\n caption=_('Errors logged in error_report.txt'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed84588e565baffdede0fdbcd4358b56\",\n \"score\": \"0.552524\",\n \"text\": \"def err_back(self, failure):\\n\\n file_error_urls = open(ERROR_DIRECTION_URLS + str(self.now.day) + str(self.now.hour) + str(self.now.minute), 'a+')\\n file_error_proxy = open(ERROR_DIRECTION_PROXY + str(self.now.day) + str(self.now.hour) + str(self.now.minute), 'a+')\\n\\n if failure.check(HttpError):\\n # these exceptions come from HttpError spider middleware\\n # you can get the non-200 response\\n response = failure.value.response\\n self.logger.error(\\\"!!HttpError on %s\\\", response.url)\\n file_error_urls.write(response.url + \\\"\\\\n\\\")\\n file_error_proxy.write(response.meta[\\\"proxy\\\"] + \\\"\\\\n\\\")\\n\\n elif failure.check(DNSLookupError):\\n # this is the original request\\n request = failure.request\\n self.logger.error(\\\"!!DNSLookupError on %s\\\", request.url)\\n file_error_urls.write(request.url + \\\"\\\\n\\\")\\n file_error_proxy.write(request.meta[\\\"proxy\\\"] + \\\"\\\\n\\\")\\n\\n elif failure.check(TimeoutError, TCPTimedOutError):\\n request = failure.request\\n self.logger.error(\\\"!!TimeoutError on %s\\\", request.url)\\n file_error_urls.write(request.url + \\\"\\\\n\\\")\\n file_error_proxy.write(request.meta[\\\"proxy\\\"] + \\\"\\\\n\\\")\\n\\n file_error_urls.close()\\n file_error_proxy.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22a21f6dccb7b8cd878189d8f2a43cac\",\n \"score\": \"0.5523169\",\n \"text\": \"def error(self, s):\\n print(\\\" ERROR: '%s', %s\\\" % (self.src_id, s), file=sys.stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5c534b1e3adf3883e7081ff384d31cb\",\n \"score\": \"0.55210364\",\n \"text\": \"def error(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5137840a894ff230b7ce278a0fbb4c9a\",\n \"score\": \"0.55150735\",\n \"text\": \"def show_database_error(self, err):\\n self.kodiUi.show_error(30951, '{}'.format(err))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eb9a2a744c95987f60983dbcc3215de\",\n \"score\": \"0.5509607\",\n \"text\": \"def _ExceptionResponse(self):\\n\\n status = '500 Server Error'\\n response_headers = [('Content-type', 'text/html; charset=utf-8')]\\n self.start_response(status, response_headers)\\n return target_errors.ExcInfoAsHtml()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d62ced1a085bf7b994a491f988739b33\",\n \"score\": \"0.5492359\",\n \"text\": \"def __refresh_failed_slot(self, filename):\\n self.send_message_signal.emit(\\\"Refresh of \\\" + filename + \\\" has started unsuccessfully\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42837a8c57b84f58c951d068bba1b393\",\n \"score\": \"0.54920834\",\n \"text\": \"def print_login_error(self, message):\\n label = Label(self.error_frame, text=message, fg=\\\"red\\\", anchor=CENTER, justify=CENTER,\\n bg=self.controller.color_1)\\n label.grid(column=0, row=0, sticky=EW)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64d33bababac488de6dddd22a946ec35\",\n \"score\": \"0.54873234\",\n \"text\": \"def buildErrorGUI(self):\\n\\n self.errorDialogue = DirectDialog(frameSize = (-0.8, 0.8, -0.4, 0.4),\\n frameColor = (0.2, 0.3, 0.7, 1),\\n fadeScreen = 0.4,\\n image = None,\\n geom = None,\\n relief = DGG.FLAT)\\n\\n self.errorTitle = DirectLabel(text = \\\"Error!\\\",\\n scale = 0.09,\\n text_align = TextNode.ACenter,\\n text_fg = (1, 1, 1, 1),\\n parent = self.errorDialogue,\\n pos = (0, 0, 0.3),\\n relief = None)\\n self.errorLabel = DirectLabel(text = \\\"<Error text here>\\\",\\n scale = 0.07,\\n text_align = TextNode.ACenter,\\n text_fg = (1, 1, 1, 1),\\n parent = self.errorDialogue,\\n pos = (0, 0, 0.1),\\n relief = None)\\n\\n self.errorDoneBtn = DirectButton(text = \\\"Close\\\", command = self.hideErrorDialogue,\\n scale = 0.05,\\n text_align = TextNode.ACenter,\\n pos = (0, 0, -0.35),\\n parent = self.errorDialogue,\\n text_bg = (0.1, 0.8, 0.2, 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ed2b57be47413d1c08586a5fd4aa9a0\",\n \"score\": \"0.5485057\",\n \"text\": \"def display_error_message(self):\\n\\n self._error_message.setVisible(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"758fe71a554951f7fc2ceb7dd7b7ef92\",\n \"score\": \"0.5481646\",\n \"text\": \"def error_message(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f5df56c6f586734960f5a21d1d74d6e\",\n \"score\": \"0.54746085\",\n \"text\": \"def handle_error(err, halt=True):\\n print('{}{}{}'.format(c.Style.BRIGHT, c.Fore.RED, err))\\n if halt:\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45102d15cf919e707046179902c49cda\",\n \"score\": \"0.5467163\",\n \"text\": \"def display_export_result(self):\\n reply = QtGui.QMessageBox.question(self, 'Export Result',\\n \\\"Files saved to Directory: %s\\\" % self.file_location)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2541857d4f34a6173a5af6d93b92272e\",\n \"score\": \"0.54597694\",\n \"text\": \"def error_404(error):\\n return 'Sorry, nothing at this URL.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07c52ea3a9688fec7f296714da2ce422\",\n \"score\": \"0.5458576\",\n \"text\": \"def show_error_message(self, msg):\\n msgbox = QMessageBox()\\n msgbox.critical(dialog, \\\"Error\\\", msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c525949f47ba9fdff84a5eefc115387\",\n \"score\": \"0.54544574\",\n \"text\": \"def display_error_msg():\\n print(\\\"\\\\n !! mauvaise saisie !!\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c616695ae4cd395babca5148f88b9888\",\n \"score\": \"0.545296\",\n \"text\": \"def print_http_error(url, status_code, response):\\n error(\\\"FAILED!\\\\nStatus code: {}\\\\nResponse: {}\\\\nURL: {}\\\".format(\\n status_code, response, url))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"344b648ab0184da724cb78ba21b010e4\",\n \"score\": \"0.5452114\",\n \"text\": \"def error_404(error):\\n return 'Sorry, Nothing at this URL.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ecddb1065e7b686db09438d379400ba\",\n \"score\": \"0.54326767\",\n \"text\": \"def send_error_page(request, error):\\n return response_ajax_or_not(request, {'htmlname': 'error.html',\\n 'message': error})\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":499,"cells":{"query_id":{"kind":"string","value":"0bcbb126ae5223f3d1f5ca2950034367"},"query":{"kind":"string","value":"Validates if the output is not the same as input"},"positive_passages":{"kind":"list like","value":[{"docid":"75fdf2e321d1a07332176bae092a4632","score":"0.60977846","text":"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\n\tif not inputVolumeNode:\n\t logging.debug('isValidInputOutputData failed: no input volume node defined')\n\t return False\n\tif not outputVolumeNode:\n\t logging.debug('isValidInputOutputData failed: no output volume node defined')\n\t return False\n\tif inputVolumeNode.GetID()==outputVolumeNode.GetID():\n\t logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\n\t return False\n\treturn True","title":""}],"string":"[\n {\n \"docid\": \"75fdf2e321d1a07332176bae092a4632\",\n \"score\": \"0.60977846\",\n \"text\": \"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\\n\\tif not inputVolumeNode:\\n\\t logging.debug('isValidInputOutputData failed: no input volume node defined')\\n\\t return False\\n\\tif not outputVolumeNode:\\n\\t logging.debug('isValidInputOutputData failed: no output volume node defined')\\n\\t return False\\n\\tif inputVolumeNode.GetID()==outputVolumeNode.GetID():\\n\\t logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\\n\\t return False\\n\\treturn True\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4435bb4b59cd7ac285ad925231272dda","score":"0.7131176","text":"def check_output(output, expected_output):\r\n o = copy.deepcopy(output) # so that we don't mutate input\r\n e = copy.deepcopy(expected_output) # so that we don't mutate input\r\n\r\n o.sort()\r\n e.sort()\r\n return o == e","title":""},{"docid":"75e1998a0bc2c0379c6edb2c60d13706","score":"0.7067986","text":"def check_output(output, expected_output):\n o = copy.deepcopy(output) # so that we don't mutate input\n e = copy.deepcopy(expected_output) # so that we don't mutate input\n \n o.sort()\n e.sort()\n return o == e","title":""},{"docid":"282980064d519f21e29c23c9cf51d43b","score":"0.6973755","text":"def check_output(output, expected_output):\n print(output)\n o = copy.deepcopy(output) # so that we don't mutate input\n e = copy.deepcopy(expected_output) # so that we don't mutate expected output\n \n o.sort()\n e.sort()\n return o == e","title":""},{"docid":"c49093131ad982787b01caf821630388","score":"0.65401703","text":"def not_equal_to(self, input1, input2, output, callback=None):\n args = []\n args.append(\"--input1='{}'\".format(input1))\n args.append(\"--input2='{}'\".format(input2))\n args.append(\"--output='{}'\".format(output))\n return self.run_tool('not_equal_to', args, callback) # returns 1 if error","title":""},{"docid":"0e74f1e8c7b92e717d8c23fe85990428","score":"0.62993574","text":"def test_wtf_positive(input_1, input_2, output):\n assert wtf(input_1, input_2) == output","title":""},{"docid":"44c86d85c8da7ede9a8f069dee724c78","score":"0.62793255","text":"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\n if not inputVolumeNode:\n logging.debug('isValidInputOutputData failed: no input volume node defined')\n return False\n if not outputVolumeNode:\n logging.debug('isValidInputOutputData failed: no output volume node defined')\n return False\n if inputVolumeNode.GetID()==outputVolumeNode.GetID():\n logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\n return False\n return True","title":""},{"docid":"44c86d85c8da7ede9a8f069dee724c78","score":"0.62793255","text":"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\n if not inputVolumeNode:\n logging.debug('isValidInputOutputData failed: no input volume node defined')\n return False\n if not outputVolumeNode:\n logging.debug('isValidInputOutputData failed: no output volume node defined')\n return False\n if inputVolumeNode.GetID()==outputVolumeNode.GetID():\n logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\n return False\n return True","title":""},{"docid":"44c86d85c8da7ede9a8f069dee724c78","score":"0.62793255","text":"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\n if not inputVolumeNode:\n logging.debug('isValidInputOutputData failed: no input volume node defined')\n return False\n if not outputVolumeNode:\n logging.debug('isValidInputOutputData failed: no output volume node defined')\n return False\n if inputVolumeNode.GetID()==outputVolumeNode.GetID():\n logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\n return False\n return True","title":""},{"docid":"44c86d85c8da7ede9a8f069dee724c78","score":"0.62793255","text":"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\n if not inputVolumeNode:\n logging.debug('isValidInputOutputData failed: no input volume node defined')\n return False\n if not outputVolumeNode:\n logging.debug('isValidInputOutputData failed: no output volume node defined')\n return False\n if inputVolumeNode.GetID()==outputVolumeNode.GetID():\n logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\n return False\n return True","title":""},{"docid":"44c86d85c8da7ede9a8f069dee724c78","score":"0.62793255","text":"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\n if not inputVolumeNode:\n logging.debug('isValidInputOutputData failed: no input volume node defined')\n return False\n if not outputVolumeNode:\n logging.debug('isValidInputOutputData failed: no output volume node defined')\n return False\n if inputVolumeNode.GetID()==outputVolumeNode.GetID():\n logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\n return False\n return True","title":""},{"docid":"660df1c7a069772253febe3bb689f890","score":"0.62490493","text":"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\r\n if not inputVolumeNode:\r\n logging.debug('isValidInputOutputData failed: no input volume node defined')\r\n return False\r\n if not outputVolumeNode:\r\n logging.debug('isValidInputOutputData failed: no output volume node defined')\r\n return False\r\n if inputVolumeNode.GetID()==outputVolumeNode.GetID():\r\n logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\r\n return False\r\n return True","title":""},{"docid":"39fd04a6aeee9b62960ee36d515c0fa7","score":"0.6244626","text":"def testCheckInputNegative2(self):\n self.assertListEqual(self.result2, self.expected2)","title":""},{"docid":"65fdd432fddd8050ff5fd2043394d824","score":"0.6186744","text":"def testCheckInputNegative1(self):\n self.assertListEqual(self.result1, self.expected1)","title":""},{"docid":"7f748fa4fb9604bc09e04b43637792fb","score":"0.6133478","text":"def compare_output(expected_output, received_output):\r\n\r\n for i, val in enumerate(expected_output):\r\n if val != 2:\r\n msg = \"received and expected output does not match at postion {}\".format(i)\r\n assert received_output[i] == val, msg","title":""},{"docid":"ef0c98264bfb54049aff27bfc9f847ea","score":"0.6129218","text":"def validate(self, out):\n\n outlines = self.stdout.strip().split(\"\\n\")\n testlines = out.strip().split(\"\\n\")\n\n if len(outlines) > len(testlines):\n raise ExcessiveOutput()\n elif len(outlines) < len(testlines):\n raise MissingOutput()\n\n for i in range(len(outlines)):\n if outlines[i] != testlines[i]:\n raise WrongOutput()","title":""},{"docid":"56e82f53709bdc6d8b16b5ee4c9c3f80","score":"0.60609865","text":"def check_output(self, out):\n # Output should follow a uniform distribution in [0, 1<<32)\n expected_avg = 1 << 31\n expected_std = (1 << 32) / np.sqrt(12)\n rtol = 0.05 # given enough iterations\n np.testing.assert_allclose(out.mean(), expected_avg, rtol=rtol)\n np.testing.assert_allclose(out.std(), expected_std, rtol=rtol)","title":""},{"docid":"5aeb6f945aef3891ef41761cc5d719f7","score":"0.60038656","text":"def _checkDuplicate(self, input):\n if \"red_team\" not in input or \"blue_team\" not in input:\n # siamo nell'update..non sono campi obbligatori\n return\n if input.get(\"red_team\") == input.get(\"blue_team\"):\n raise GraphQLError(\n u\"Non si può impostare due volte la stessa squadra per un match.\" # noqa\n )","title":""},{"docid":"6183bbadbd1d6f2aece2ee0d3aa05706","score":"0.60011446","text":"def test_part_2(problem_input, output):\n assert part_2.solution(problem_input) == output","title":""},{"docid":"b2ea70baa886b85ae6b00395bee7b98a","score":"0.59514016","text":"def isconsistent(self, other) -> bool:","title":""},{"docid":"90e8ba8c1a34ea315346707bfad6ef15","score":"0.58970046","text":"def validate(self, input):","title":""},{"docid":"a018fc8dfed76655820027b03a1c967e","score":"0.5868509","text":"def err_no_ordered(res, outputs):\n this_outs = np.array(outputs, copy=True).T\n this_res = np.resize(np.array(res, copy=True).T, this_outs.shape)\n\n return np.sum(this_res != this_outs)","title":""},{"docid":"98f1dcce0708401bfba2dd1c59fe4c69","score":"0.5866357","text":"def testComplementOfEquivalence(self):\n self.assertOutput(\n original=\"Process.pid != 10\",\n output=\"Process.pid != 10\")","title":""},{"docid":"5f6d07772759616b6a707ae1b8a9c6c8","score":"0.5821444","text":"def test_part_1(problem_input, output):\n assert part_1.solution(problem_input) == output","title":""},{"docid":"5f6d07772759616b6a707ae1b8a9c6c8","score":"0.5821444","text":"def test_part_1(problem_input, output):\n assert part_1.solution(problem_input) == output","title":""},{"docid":"22aa3a843fbaf43c6926ff15fab1710e","score":"0.580891","text":"def __eq__(self, other):\r\n return other.outputs == self.outputs","title":""},{"docid":"cdda169727196925126521ca8b2353d7","score":"0.5807647","text":"def check_output_duplicates(self):\n seen = dict()\n idx = None\n for name, value in self.output._allitems():\n if name is None:\n if idx is None:\n idx = 0\n else:\n idx += 1\n if value and value in seen:\n raise WorkflowError(\n \"Duplicate output file pattern in rule {}. First two \"\n \"duplicate for entries {} and {}.\".format(\n self.name, seen[value], name or idx\n )\n )\n seen[value] = name or idx","title":""},{"docid":"0fd07527cccf7b00d4859b64dda1fa45","score":"0.579142","text":"def validValidity():","title":""},{"docid":"0fd07527cccf7b00d4859b64dda1fa45","score":"0.579142","text":"def validValidity():","title":""},{"docid":"48f5069ca1cda746deb7223eb070b84d","score":"0.5776022","text":"def test():\n in_out = [([], 0),\n ([15], 15),\n ([15,30], 30),\n ([30,15], 30),\n ([15,15,15], 30),\n ([15,150,15], 150),\n ([30, 15, 60, 75, 45, 15, 15, 45], 180),\n ([30, 15, 60, 75, 45, 45, 15, 45], 195),\n ([30, 45, 60, 90, 45, 15, 15, 45], 195)]\n \n for input, output in in_out:\n try:\n assert f1(input) == output\n except:\n print(input,f1(input),output)","title":""},{"docid":"4cbc29a4e977a88e59a21b2c8ae56922","score":"0.5759873","text":"def test_mismatch(self) -> None:\n result = object()\n other = object()\n self.assertThat(\n returns(Is(result)).match(lambda: other),\n Not(Is(None)),\n )","title":""},{"docid":"3600d66c96b8bd5ac5f8f684fc2ee9ff","score":"0.57452494","text":"def testEquality(self):\n self.assertEqual(self.bin1, self.bin2, \"Unequal bins with the same outcome repeated\")","title":""},{"docid":"fd393d4e4cf705c8e054cc4e832b44cd","score":"0.57303685","text":"def test__validate_syncing__0():\n for input_value, expected_output in (\n (True, True),\n (False, False)\n ):\n output = validate_syncing(input_value)\n vampytest.assert_eq(output, expected_output)","title":""},{"docid":"e197a3733580292e2e3d827eb99e6f55","score":"0.5711529","text":"def check_output(gold_mr, out_mr, fix_type='all'):\n # count the errors in the output, looking at the MR\n added, missing, valerr, repeated = 0, 0, 0, 0\n diff = {}\n for slot in set(gold_mr.keys()) | set(out_mr.keys()):\n if slot in gold_mr and slot not in out_mr:\n if fix_type != 'all' and 'missing' not in fix_type: # ignore missing stuff -- adjust out_mr\n out_mr[slot] = dict(gold_mr[slot])\n else:\n missing += sum(gold_mr[slot].values())\n diff[slot] = {val: -count for val, count in gold_mr[slot].items()}\n elif slot not in gold_mr and slot in out_mr:\n if fix_type != 'all' and 'added' not in fix_type: # ignore added stuff -- adjust out_mr\n del out_mr[slot]\n else:\n added += sum(out_mr[slot].values())\n diff[slot] = out_mr[slot]\n else:\n # remove repeated first (check if MR has same val less than out + same value more than 1x)\n for val in out_mr[slot].keys():\n if val in gold_mr[slot] and gold_mr[slot][val] < out_mr[slot][val]:\n repeated += out_mr[slot][val] - gold_mr[slot][val]\n out_mr[slot][val] = gold_mr[slot][val]\n # now compute the diff in the # of value occurrences\n slot_diff = {val: gold_mr[slot].get(val, 0) - out_mr[slot].get(val, 0)\n for val in set(gold_mr[slot].keys()) | set(out_mr[slot].keys())}\n if fix_type != 'all':\n for val, val_diff in list(slot_diff.items()):\n if 'missing' not in fix_type and val_diff > 0: # ignore missing stuff -- adjust out_mr\n out_mr[slot][val] = out_mr[slot].get(val, 0) + val_diff\n del slot_diff[val]\n if 'added' not in fix_type and val_diff < 0: # ignore added stuff -- adjust out_mr\n out_mr[slot][val] = out_mr[slot].get(val, 0) - val_diff\n del slot_diff[val]\n diff[slot] = {val: -count for val, count in slot_diff.items() if count != 0}\n # diffs both ways\n mr_not_out = sum([count for count in slot_diff.values() if count > 0])\n out_not_mr = - sum([count for count in slot_diff.values() if count < 0])\n # value errors up to the same # of values\n valerr += min(mr_not_out, out_not_mr)\n # others fall under missing & added\n missing += max(mr_not_out - out_not_mr, 0)\n added += max(out_not_mr - mr_not_out, 0)\n\n diff = json.dumps({slot: vals for slot, vals in diff.items() if vals})\n return added, missing, valerr, repeated, diff, out_mr","title":""},{"docid":"87733e706297acda0c0cf150ae9cfbf6","score":"0.5707366","text":"def test_valid_inputs(self):\n for input_value, expected_output in get_items(self.valid_inputs):\n initial_input_value = copy.deepcopy(input_value)\n\n serializer = self.serializer_class(data=input_value)\n serializer.is_valid()\n\n assert serializer.initial_data == initial_input_value\n assert self.field.run_validation(initial_input_value) == expected_output","title":""},{"docid":"6f30ede80f9a9be6525140017f26c6c1","score":"0.56929","text":"def check_input(self, input):\n pass","title":""},{"docid":"8e696a5e2699def0183a0863bf457205","score":"0.568187","text":"def _numpy_checker(x, y):\n x, y = x[0], y[0]\n if (x.dtype != y.dtype or x.shape != y.shape\n or numpy.any(numpy.abs(x - y) > 1e-10)):\n raise Exception(\"Output mismatch.\", {'performlinker': x, 'clinker': y})","title":""},{"docid":"adfab7077ce81d9ca0f8b4d8426a20b3","score":"0.56721437","text":"def result_should_be(self, expected):\n if self._result != expected:\n raise AssertionError('%s != %s' % (self._result, expected))","title":""},{"docid":"9144536b77f19fa9699d1e9aeb25a2a6","score":"0.5655695","text":"def test_input_unchanged(self):\n matrix_1 = [[6, 6], [3, 1]]\n matrix_2 = [[1, 2], [3, 4]]\n matrix_1_original = deepcopy(matrix_1)\n matrix_2_original = deepcopy(matrix_2)\n add(matrix_1, matrix_2)\n self.assertEqual(matrix_1, matrix_1_original)\n self.assertEqual(matrix_2, matrix_2_original)","title":""},{"docid":"b2ede05d0fa7cbc699d67556e2bdb814","score":"0.56550705","text":"def verify(self, testInput, testOutput):\n expOutput = self.get_expected_result(testInput)\n info = 'Input: {}, Size of input: {}\\nOutput: {}, \\nExpected: {}'.format(\n testInput, len(testInput), testOutput, expOutput)\n\n # Test correct inversions\n self.assert_equal(expOutput, testOutput, info)","title":""},{"docid":"b8127c50bad105bf4250a70a7e60ad35","score":"0.56527394","text":"def test_not_equal_on_not_equal_unique_identifier(self):\n a = objects.EncryptionKeyInformation(\n unique_identifier=\"100182d5-72b8-47aa-8383-4d97d512e98a\"\n )\n b = objects.EncryptionKeyInformation(\n unique_identifier=\"00000000-1111-2222-3333-444444444444\"\n )\n\n self.assertTrue(a != b)\n self.assertTrue(b != a)","title":""},{"docid":"3a51e12a73b2739801872442407bcfa3","score":"0.5620477","text":"def failIfDifferent(self, first_file, second_file):\n diff_cmd = ['diff', first_file, second_file]\n if subprocess.call(diff_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) != 0:\n self.fail('Output does not match!')","title":""},{"docid":"d3fe5cb32ac07f5345159d8539c4c728","score":"0.5611779","text":"def test_input_output(self):\n ovf = OVF(self.input_ovf, self.working_dir, self.temp_file)\n ovf.write()\n self.check_diff('')\n\n # Filename output too\n ovf = OVF(self.input_ovf, self.working_dir, self.temp_file + \".a.b.c\")\n ovf.write()\n self.check_diff('', file2=(self.temp_file + \".a.b.c\"))","title":""},{"docid":"61296a7cf76fcf941bc9a3cf483d4707","score":"0.56083167","text":"def validate_output(self):\n for f_out, nifti_mrs_img in zip(self.fileoutNames, self.imageOut):\n nifti_mrs_img.validate()","title":""},{"docid":"5c5632f57391261d278013ca88b3b3a8","score":"0.5585658","text":"def test_validate_outputs_invalid_case(hrm):\n output_dict = {\"num_beats\": 10000, \"duration\": 60, \"voltage_extremes\": (\n 0, 200)}\n\n with pytest.raises(ValueError):\n hrm.validate_outputs(output_dict)","title":""},{"docid":"e4472e935e7da0a84b8f0773879f376f","score":"0.5583051","text":"def test_validity_function_invalid(capsys):\n # get auth_tool object\n auth_tool = get_auth_tool_object(Namespace(json=None))\n # define a correct value\n correct = \"a\"\n incorrect = \"b\"\n hint = \"Test hint\"\n # run function with patched input\n with patch('builtins.input', side_effect=[incorrect, correct]):\n # run validity function\n auth_tool._ensure_valid_value_for(\n \"test\", lambda x: x == correct, hint=hint\n )\n out, err = capsys.readouterr()\n assert not bool(out)\n assert err == hint","title":""},{"docid":"992e68f4b36c8c3445ecf11baa85978e","score":"0.55691046","text":"def testCheckInputPositive(self):\n self.assertListEqual(self.result3, self.expected3)","title":""},{"docid":"b6cbf39ff494bffe79af394688eec788","score":"0.5550888","text":"def user_input_validate(print_statement):\n print(print_statement)\n first_value = input('\\r - Enter: ')\n second_value = input('\\r - Re-enter: ')\n if first_value == second_value:\n return first_value\n else:\n colour_print(f'(colour_warning)\\r # The values entered did not match, please try again.(colour_clear)')\n return False","title":""},{"docid":"6b7d4afa42c33544264008d236badb06","score":"0.5540823","text":"def shape_check(output: torch.Tensor, target: torch.Tensor) -> None:\n output_shape = list(output.shape)\n output_shape.pop(1)\n if output_shape != list(target.shape):\n raise ValueError(\n \"Got {} shape for output and {} for target. \"\n \"Shape should be equal except for 1 dimension of output (specifying classes).\".format(\n output.shape, target.shape\n )\n )","title":""},{"docid":"32ef6f06b05710c514e8f7c0a6c5fa7e","score":"0.5534121","text":"def is_inconsistent(self) -> bool:\n return False","title":""},{"docid":"f403d7c34f9ae824510495a878144249","score":"0.55231065","text":"def same_output_than(self, other):\n for t in [t for t in self.tables if t.type.name == \"output\"]:\n is_same = False\n for t2 in [t2 for t2 in other.tables if t2.type.name == \"output\"]:\n if t.model == t2.model and t.tablename == t2.tablename:\n is_same = True\n break\n if not is_same:\n return False\n\n for f in [f for f in self.files if f.type.name == \"input\"]:\n is_same = False\n for f2 in [f2 for f2 in other.files if f2.type.name == \"input\"]:\n if (f.name == f2.name and\n f.path == f2.path):\n is_same = True\n break\n if not is_same:\n return False\n return True","title":""},{"docid":"ba358fb98aa4c62372610eb3e978847a","score":"0.5521292","text":"def compare_output(self, input, output, expected):\n if isinstance(input, unicode):\n input = input.encode('raw_unicode_escape')\n if sys.version_info > (3,):\n # API difference: Python 3's node.__str__ doesn't escape\n #assert expected is None or isinstance(expected, unicode)\n if isinstance(expected, bytes):\n expected = expected.decode('utf-8')\n if isinstance(output, bytes):\n output = output.decode('utf-8')\n else:\n if isinstance(expected, unicode):\n expected = expected.encode('raw_unicode_escape')\n if isinstance(output, unicode):\n output = output.encode('raw_unicode_escape')\n # Normalize line endings:\n if expected:\n expected = '\\n'.join(expected.splitlines())\n if output:\n output = '\\n'.join(output.splitlines())\n try:\n self.assertEquals(output, expected)\n except AssertionError, error:\n print >>sys.stderr, '\\n%s\\ninput:' % (self,)\n print >>sys.stderr, input\n try:\n comparison = ''.join(self.compare(expected.splitlines(1),\n output.splitlines(1)))\n print >>sys.stderr, '-: expected\\n+: output'\n print >>sys.stderr, comparison\n except AttributeError: # expected or output not a string\n # alternative output for non-strings:\n print >>sys.stderr, 'expected: %r' % expected\n print >>sys.stderr, 'output: %r' % output\n raise error","title":""},{"docid":"e33bb90afcd9067e40a55c370304e6dc","score":"0.5476065","text":"def test_equality(self):\n pass","title":""},{"docid":"31fec2a4a86c2ebefb77e8dcc64a669e","score":"0.5474954","text":"def __ne__(self, other: 'ResourceRecordInputRdataRdataAaaaRecord') -> bool:\n return not self == other","title":""},{"docid":"ac05c306755d65c964f0dd08b1d14bbe","score":"0.54709154","text":"def not_equal(x1, x2, out=None):\n if isinstance(x1, numeric_types) and isinstance(x2, numeric_types):\n return _np.not_equal(x1, x2, out=out)\n return _api_internal.not_equal(x1, x2, out)","title":""},{"docid":"91f8192c136330fc7bd7113f25e045d5","score":"0.54685175","text":"def test_isscalar(input, output):\n assert output == utils.isscalar(input)","title":""},{"docid":"ddd8f35f2920a36fdebc337c4e95b683","score":"0.54648757","text":"def test_equal_on_not_equal_unique_identifier(self):\n a = objects.EncryptionKeyInformation(\n unique_identifier=\"100182d5-72b8-47aa-8383-4d97d512e98a\"\n )\n b = objects.EncryptionKeyInformation(\n unique_identifier=\"00000000-1111-2222-3333-444444444444\"\n )\n\n self.assertFalse(a == b)\n self.assertFalse(b == a)","title":""},{"docid":"b8b4641aeceab8e773d46ac3f28069b0","score":"0.54492074","text":"def test_eq_false(self):\n loc = Location(\"SLO\", 35.3, -120.7)\n loc1 = Location(\"LA\", -30.3, -131.21)\n loc2 = Location(\"SJ\", 23.4, -110.9)\n self.assertNotEqual(loc,loc1)\n self.assertNotEqual(loc1,loc2)\n self.assertNotEqual(loc2,loc)","title":""},{"docid":"e0530d00c0cb99b10874a4da13c2edcb","score":"0.54431087","text":"def _almost_equal(current, new):\n if current is None or new is None:\n return False\n return abs(current - new) / current < 1e-03","title":""},{"docid":"1ca04a4fc5f18bdd30c16f0075c02050","score":"0.5442778","text":"def __ne__(self, other: 'ResourceRecordUpdateInputRdataRdataAaaaRecord') -> bool:\n return not self == other","title":""},{"docid":"dd080dc2f4704f7ab3a007e213779d2e","score":"0.54427606","text":"def same_gen_check():\r\n print('-' * 5 + 'same_gen_check' + '-' * 5)\r\n a = test_gen()\r\n b = test_gen()\r\n\r\n print('a == b : ', a==b)\r\n print('a is b : ', a is b)","title":""},{"docid":"7c75a7528bd1d17935af9164db727e15","score":"0.5442165","text":"def test_card_not_equal_other(self):\n card = Card(Suit.SPADE, 2)\n for bad_value in (\"a\", \"1\", \"2\", 2, 20.0, Card):\n self.assertNotEqual(card, bad_value)","title":""},{"docid":"8e46f73aa43a0a606b5b19c0764f1a89","score":"0.5439961","text":"def test_not_equal_on_not_equal_unique_identifier(self):\n a = objects.MACSignatureKeyInformation(\n unique_identifier=\"100182d5-72b8-47aa-8383-4d97d512e98a\"\n )\n b = objects.MACSignatureKeyInformation(\n unique_identifier=\"00000000-1111-2222-3333-444444444444\"\n )\n\n self.assertTrue(a != b)\n self.assertTrue(b != a)","title":""},{"docid":"c548e9a3bc682343904412f052a3da19","score":"0.54387414","text":"def __ne__(self, other: 'ResourceRecordInputRdataRdataTxtRecord') -> bool:\n return not self == other","title":""},{"docid":"669c60b31bb6657e88ec711c4cc524ec","score":"0.5436216","text":"def test_equality_of_mana_when_they_are_not_equal():\n mana = ertai.Mana(\"Black\", \"Blue\", \"Red\", \"Red\")\n other_mana = ertai.Mana(\"Black\", \"Blue\", \"Red\")\n assert mana != other_mana","title":""},{"docid":"4d9bca8af462f6df5297aa872777bbca","score":"0.54290247","text":"def test_not_equal_on_type_mismatch(self):\n a = objects.MACSignatureKeyInformation()\n b = 'invalid'\n\n self.assertTrue(a != b)\n self.assertTrue(b != a)","title":""},{"docid":"2c6c704ce4b8bd887a680d348441fadc","score":"0.5428968","text":"def __ne__(self, other: 'ResourceRecordUpdateInputRdataRdataTxtRecord') -> bool:\n return not self == other","title":""},{"docid":"6fad971253de5babe8ce7013643e7709","score":"0.54234815","text":"def __ne__(self, other: 'ResourceRecordInputRdataRdataMxRecord') -> bool:\n return not self == other","title":""},{"docid":"fa1d482e501daac7a7a5b348d1292f0d","score":"0.54188776","text":"def check_same_states(result_1: Result, result_2: Result) -> None:\n\n # Get all state of result_1\n states_result_1 = []\n for sample in result_1:\n states_result_1.append(sample.state.int)\n\n # Get all state of result_2\n states_result_2 = []\n for sample in result_2:\n states_result_2.append(sample.state.int)\n\n states_result_1.sort()\n states_result_2.sort()\n\n # Check content equality\n assert states_result_1 == states_result_2","title":""},{"docid":"94fff62b936026879aa966e6b1e8276a","score":"0.5417261","text":"def test_not_equal_on_type_mismatch(self):\n a = objects.EncryptionKeyInformation()\n b = 'invalid'\n\n self.assertTrue(a != b)\n self.assertTrue(b != a)","title":""},{"docid":"dff16808bff9a13fe7c05513a15d8956","score":"0.5400954","text":"def test_assertNotEqual_unequal(self):\n for first, second in self.unequal_pairs:\n try:\n self.assertNotEqual(first, second)\n except:\n raise AssertionError, \\\n \"unittests.assertNotEqual failed on input %s and %s\" \\\n % (`first`, `second`)","title":""},{"docid":"844f7ee721e6243404bf3e27db57b407","score":"0.5398239","text":"def _validate_source(self, other_replica_uid, other_generation,\n other_transaction_id):\n (old_generation,\n old_transaction_id) = yield self._get_replica_gen_and_trans_id(\n other_replica_uid)\n if other_generation < old_generation:\n raise InvalidGeneration\n if other_generation > old_generation:\n return\n if other_transaction_id == old_transaction_id:\n return\n raise InvalidTransactionId","title":""},{"docid":"8e0701bfc5105a6cd477f88404a93c42","score":"0.5387769","text":"def test_error_on_wrong_update(inp1, inp2):\n metric = LearnedPerceptualImagePatchSimilarity()\n with pytest.raises(ValueError, match=\"Expected both input arguments to be normalized tensors .*\"):\n metric(inp1, inp2)","title":""},{"docid":"e38acc7ea08fd2e9dd562c8df85f5e90","score":"0.5381053","text":"def check_results_equality(result_1: Result, result_2: Result, amplitude: bool = True) -> None:\n\n # First we check the two results have the same states\n check_same_states(result_1, result_2)\n\n # Then we check the two results have the same probabilities\n check_same_probabilities(result_1, result_2)\n\n # Finally we check the two results have the same stats properties\n if amplitude:\n check_same_state_properties(result_1, result_2)\n else:\n check_same_state_properties(result_1, result_2, amplitude)","title":""},{"docid":"0972941ab7a2fccb2ccef33add61bd21","score":"0.53801984","text":"def __eq__(self, other):\n return (isinstance(other, self.__class__) and\n self.same_files(other, \"input\") and\n self.same_tables(other, \"input\") and\n self.same_files(other, \"output\") and\n self.same_tables(other, \"output\") and\n self.same_options(other))","title":""},{"docid":"37f6b33e5cd3be14fd7689b4f6269bb5","score":"0.53799206","text":"def test_onestep_identical(inputs):\n seq_x, seq_y = inputs\n\n output = onestep.onestep(seq_x, seq_y, order=2, verbose=False, check=True)\n\n assert output is None","title":""},{"docid":"3f17459c3337ae4cff1f309bfd2798b6","score":"0.5377264","text":"def testEquivalent(self):\r\n a = Vector3(1, 2, 3)\r\n b = Vector3(1, 2, 3)\r\n c = Vector3(3, 2, 1)\r\n self.assertEqual(a, b)\r\n self.assertNotEqual(a, c)","title":""},{"docid":"031fb79bf5377b6a817a73d9cb045590","score":"0.53721464","text":"def _inner_consistency_check(self, other):\n # chack mesh\n if self.mesh is not other.mesh:\n raise Exception(\"Mesh of the forms do not match\")","title":""},{"docid":"29de9d0bcbcdf74d3bf2978f0a486bd6","score":"0.53686476","text":"def test_not_match(self):\n put_in_stdin('x\\ny')\n with redirect_stdout() as output:\n res = click.prompt(\"\", type=InputValidator(converters=[YES, NO]))\n self.assertTrue(res.kind_of(YES))\n self.assertTrue(\"Error: x. Help: y - yes; n - no\" in output.getvalue())","title":""},{"docid":"66b01fdfea5c6ccf99437d401f114300","score":"0.5365897","text":"def test_not_equal_on_not_equal_mac_signatures(self):\n a = objects.KeyWrappingData(mac_signature=b'\\x01')\n b = objects.KeyWrappingData(mac_signature=b'\\x10')\n\n self.assertTrue(a != b)\n self.assertTrue(b != a)","title":""},{"docid":"ee33d5401fea0d4797bf13f5c9389740","score":"0.5364966","text":"def __ne__(self, other: 'ResourceRecordUpdateInputRdataRdataMxRecord') -> bool:\n return not self == other","title":""},{"docid":"a7f0dc294bce0cac5665078240fd32a6","score":"0.53580844","text":"def test_sameas(value, other):\r\n return value is other","title":""},{"docid":"3ae64d092af5146f3fad262ec6dc633e","score":"0.53479916","text":"def __ne__(self, other: 'ResourceRecordInputRdataRdataARecord') -> bool:\n return not self == other","title":""},{"docid":"ae8600cd52e03de7e6f50667bd0c1d62","score":"0.534541","text":"def assertEqual_(self, output, expected):\n self.assertEqual(output, expected, 'Expected \"%s\". Got \"%s\".' % (expected, output))","title":""},{"docid":"102bc05291914f285e698aeae237be48","score":"0.53439385","text":"def test_equality_of_mana_when_not_comparing_the_same_objects():\n mana = ertai.Mana(\"Black\", \"Blue\", \"Red\", \"Red\")\n assert mana != \"A string\"","title":""},{"docid":"773136141f3341a6c9a90ed17ef9416a","score":"0.53420055","text":"def test_not_equal_on_type_mismatch(self):\n a = objects.KeyWrappingData()\n b = 'invalid'\n\n self.assertTrue(a != b)\n self.assertTrue(b != a)","title":""},{"docid":"bcc2a31ce637f9b0ca481d01a60fdee1","score":"0.5341564","text":"def _expected_not_equal_to_actual(eq: EquipmentSinglePhase):\n if (eq.state is None) or (eq.expected_state is None):\n return False\n else:\n return eq.expected_state != eq.state","title":""},{"docid":"f45a917695f33a1ae1f5193ca7320862","score":"0.53412175","text":"def test_ne(self):\n self.assertTrue(self.oc1 != self.oc3)\n self.assertFalse(self.oc1 != self.oc2)","title":""},{"docid":"ef801b8d2007b27d9eab1f4bf42f3492","score":"0.5335848","text":"def check_placeholder_output(self, prog, outputs_name):\n block = prog[\"main\"]\n input_name = [x.name for x in list(block.inputs.values())]\n with block:\n new_outputs = []\n for output, output_name in zip(block.outputs, outputs_name):\n if output.name not in input_name or output.name == output_name:\n new_output = output\n else:\n new_output = mb.identity(x=output, name=output_name)\n new_outputs.append(new_output)\n block.set_outputs(new_outputs)","title":""},{"docid":"c0a2fbe1725941edfb4fb841f8a6d484","score":"0.5334173","text":"def equal_to(self, input1, input2, output, callback=None):\n args = []\n args.append(\"--input1='{}'\".format(input1))\n args.append(\"--input2='{}'\".format(input2))\n args.append(\"--output='{}'\".format(output))\n return self.run_tool('equal_to', args, callback) # returns 1 if error","title":""},{"docid":"35b99b18beb52498cad5db57464f5dd4","score":"0.53287077","text":"def _assert_output_vs_expected(output, expected):\n import unittest\n if isinstance(output, dict):\n testcase = unittest.TestCase('__init__')\n try:\n testcase.assertDictEqual(expected, output,\n msg=\"\\n\\nUnfortunately, the output is *not* correct..\")\n except AssertionError as error:\n print(error)\n return 0\n print(\"\\nWell done! Output is correct!\")\n return 1\n print(\"\\n\\nUnfortunately, the output is *not* a dictionary!\")\n return 0","title":""},{"docid":"3053d582e09491948d969ec69e5b20bf","score":"0.53269976","text":"def test_wtf_negative(input_1, input_2, output):\n\n with pytest.raises(output):\n wtf(input_1, input_2)","title":""},{"docid":"0da1bfe5d9a3b408acdcf3d02dab3b44","score":"0.532426","text":"def __ne__(self, other):\n return self._num != other._num and self._denom != other._denom","title":""},{"docid":"37e82cc40fd82503bf9871ca27470f8e","score":"0.532312","text":"def test__validate_old_attributes__0():\n for input_value, expected_output in (\n (None, {}),\n ({}, {}),\n ({'a': 'b'}, {'a': 'b'})\n ):\n output = validate_old_attributes(input_value)\n vampytest.assert_eq(expected_output, output)","title":""},{"docid":"bb61c65d9695700849551a820b2dc15a","score":"0.5318516","text":"def test_output_is_input(self):\n cl_object = processed_config_line('', self.args)\n expect = ''\n actual = cl_object.result()\n self.assertEqual(expect, actual)","title":""},{"docid":"de0610778f3ee013cd25cf9389aedf81","score":"0.53174376","text":"def __ne__(self, other: 'ResourceRecordUpdateInputRdataRdataARecord') -> bool:\n return not self == other","title":""},{"docid":"147f52296cbdaaec02b830d227f1a520","score":"0.5312722","text":"def transfer_id_same():\n\n print(\"Target account ID cannot be same as user account ID\")","title":""},{"docid":"64f0df94bb81705a3783f8667ad4d5c5","score":"0.5306945","text":"def is_correct(self, result):\n pass","title":""},{"docid":"6821d13a4192e399342a1fb84324967c","score":"0.5299344","text":"def __ne__(self, other):\n if not isinstance(other, VmDiskOrderByInput):\n return True\n\n return self.to_dict() != other.to_dict()","title":""},{"docid":"49955bfb108a6287ab78bb7f429aacef","score":"0.5297024","text":"def validate_unique(self):\n pass","title":""},{"docid":"25f9b130d1bd1789e7d4b8cddd52c4bc","score":"0.52968687","text":"def test_model_output_between_zero_and_one():\n\n np.testing.assert_array_less(0.0, result)\n\n np.testing.assert_array_less(result, 1.0)","title":""}],"string":"[\n {\n \"docid\": \"4435bb4b59cd7ac285ad925231272dda\",\n \"score\": \"0.7131176\",\n \"text\": \"def check_output(output, expected_output):\\r\\n o = copy.deepcopy(output) # so that we don't mutate input\\r\\n e = copy.deepcopy(expected_output) # so that we don't mutate input\\r\\n\\r\\n o.sort()\\r\\n e.sort()\\r\\n return o == e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75e1998a0bc2c0379c6edb2c60d13706\",\n \"score\": \"0.7067986\",\n \"text\": \"def check_output(output, expected_output):\\n o = copy.deepcopy(output) # so that we don't mutate input\\n e = copy.deepcopy(expected_output) # so that we don't mutate input\\n \\n o.sort()\\n e.sort()\\n return o == e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"282980064d519f21e29c23c9cf51d43b\",\n \"score\": \"0.6973755\",\n \"text\": \"def check_output(output, expected_output):\\n print(output)\\n o = copy.deepcopy(output) # so that we don't mutate input\\n e = copy.deepcopy(expected_output) # so that we don't mutate expected output\\n \\n o.sort()\\n e.sort()\\n return o == e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c49093131ad982787b01caf821630388\",\n \"score\": \"0.65401703\",\n \"text\": \"def not_equal_to(self, input1, input2, output, callback=None):\\n args = []\\n args.append(\\\"--input1='{}'\\\".format(input1))\\n args.append(\\\"--input2='{}'\\\".format(input2))\\n args.append(\\\"--output='{}'\\\".format(output))\\n return self.run_tool('not_equal_to', args, callback) # returns 1 if error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e74f1e8c7b92e717d8c23fe85990428\",\n \"score\": \"0.62993574\",\n \"text\": \"def test_wtf_positive(input_1, input_2, output):\\n assert wtf(input_1, input_2) == output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c86d85c8da7ede9a8f069dee724c78\",\n \"score\": \"0.62793255\",\n \"text\": \"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\\n if not inputVolumeNode:\\n logging.debug('isValidInputOutputData failed: no input volume node defined')\\n return False\\n if not outputVolumeNode:\\n logging.debug('isValidInputOutputData failed: no output volume node defined')\\n return False\\n if inputVolumeNode.GetID()==outputVolumeNode.GetID():\\n logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c86d85c8da7ede9a8f069dee724c78\",\n \"score\": \"0.62793255\",\n \"text\": \"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\\n if not inputVolumeNode:\\n logging.debug('isValidInputOutputData failed: no input volume node defined')\\n return False\\n if not outputVolumeNode:\\n logging.debug('isValidInputOutputData failed: no output volume node defined')\\n return False\\n if inputVolumeNode.GetID()==outputVolumeNode.GetID():\\n logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c86d85c8da7ede9a8f069dee724c78\",\n \"score\": \"0.62793255\",\n \"text\": \"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\\n if not inputVolumeNode:\\n logging.debug('isValidInputOutputData failed: no input volume node defined')\\n return False\\n if not outputVolumeNode:\\n logging.debug('isValidInputOutputData failed: no output volume node defined')\\n return False\\n if inputVolumeNode.GetID()==outputVolumeNode.GetID():\\n logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c86d85c8da7ede9a8f069dee724c78\",\n \"score\": \"0.62793255\",\n \"text\": \"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\\n if not inputVolumeNode:\\n logging.debug('isValidInputOutputData failed: no input volume node defined')\\n return False\\n if not outputVolumeNode:\\n logging.debug('isValidInputOutputData failed: no output volume node defined')\\n return False\\n if inputVolumeNode.GetID()==outputVolumeNode.GetID():\\n logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c86d85c8da7ede9a8f069dee724c78\",\n \"score\": \"0.62793255\",\n \"text\": \"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\\n if not inputVolumeNode:\\n logging.debug('isValidInputOutputData failed: no input volume node defined')\\n return False\\n if not outputVolumeNode:\\n logging.debug('isValidInputOutputData failed: no output volume node defined')\\n return False\\n if inputVolumeNode.GetID()==outputVolumeNode.GetID():\\n logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"660df1c7a069772253febe3bb689f890\",\n \"score\": \"0.62490493\",\n \"text\": \"def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\\r\\n if not inputVolumeNode:\\r\\n logging.debug('isValidInputOutputData failed: no input volume node defined')\\r\\n return False\\r\\n if not outputVolumeNode:\\r\\n logging.debug('isValidInputOutputData failed: no output volume node defined')\\r\\n return False\\r\\n if inputVolumeNode.GetID()==outputVolumeNode.GetID():\\r\\n logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')\\r\\n return False\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39fd04a6aeee9b62960ee36d515c0fa7\",\n \"score\": \"0.6244626\",\n \"text\": \"def testCheckInputNegative2(self):\\n self.assertListEqual(self.result2, self.expected2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65fdd432fddd8050ff5fd2043394d824\",\n \"score\": \"0.6186744\",\n \"text\": \"def testCheckInputNegative1(self):\\n self.assertListEqual(self.result1, self.expected1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f748fa4fb9604bc09e04b43637792fb\",\n \"score\": \"0.6133478\",\n \"text\": \"def compare_output(expected_output, received_output):\\r\\n\\r\\n for i, val in enumerate(expected_output):\\r\\n if val != 2:\\r\\n msg = \\\"received and expected output does not match at postion {}\\\".format(i)\\r\\n assert received_output[i] == val, msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef0c98264bfb54049aff27bfc9f847ea\",\n \"score\": \"0.6129218\",\n \"text\": \"def validate(self, out):\\n\\n outlines = self.stdout.strip().split(\\\"\\\\n\\\")\\n testlines = out.strip().split(\\\"\\\\n\\\")\\n\\n if len(outlines) > len(testlines):\\n raise ExcessiveOutput()\\n elif len(outlines) < len(testlines):\\n raise MissingOutput()\\n\\n for i in range(len(outlines)):\\n if outlines[i] != testlines[i]:\\n raise WrongOutput()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56e82f53709bdc6d8b16b5ee4c9c3f80\",\n \"score\": \"0.60609865\",\n \"text\": \"def check_output(self, out):\\n # Output should follow a uniform distribution in [0, 1<<32)\\n expected_avg = 1 << 31\\n expected_std = (1 << 32) / np.sqrt(12)\\n rtol = 0.05 # given enough iterations\\n np.testing.assert_allclose(out.mean(), expected_avg, rtol=rtol)\\n np.testing.assert_allclose(out.std(), expected_std, rtol=rtol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aeb6f945aef3891ef41761cc5d719f7\",\n \"score\": \"0.60038656\",\n \"text\": \"def _checkDuplicate(self, input):\\n if \\\"red_team\\\" not in input or \\\"blue_team\\\" not in input:\\n # siamo nell'update..non sono campi obbligatori\\n return\\n if input.get(\\\"red_team\\\") == input.get(\\\"blue_team\\\"):\\n raise GraphQLError(\\n u\\\"Non si può impostare due volte la stessa squadra per un match.\\\" # noqa\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6183bbadbd1d6f2aece2ee0d3aa05706\",\n \"score\": \"0.60011446\",\n \"text\": \"def test_part_2(problem_input, output):\\n assert part_2.solution(problem_input) == output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ea70baa886b85ae6b00395bee7b98a\",\n \"score\": \"0.59514016\",\n \"text\": \"def isconsistent(self, other) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90e8ba8c1a34ea315346707bfad6ef15\",\n \"score\": \"0.58970046\",\n \"text\": \"def validate(self, input):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a018fc8dfed76655820027b03a1c967e\",\n \"score\": \"0.5868509\",\n \"text\": \"def err_no_ordered(res, outputs):\\n this_outs = np.array(outputs, copy=True).T\\n this_res = np.resize(np.array(res, copy=True).T, this_outs.shape)\\n\\n return np.sum(this_res != this_outs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98f1dcce0708401bfba2dd1c59fe4c69\",\n \"score\": \"0.5866357\",\n \"text\": \"def testComplementOfEquivalence(self):\\n self.assertOutput(\\n original=\\\"Process.pid != 10\\\",\\n output=\\\"Process.pid != 10\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f6d07772759616b6a707ae1b8a9c6c8\",\n \"score\": \"0.5821444\",\n \"text\": \"def test_part_1(problem_input, output):\\n assert part_1.solution(problem_input) == output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f6d07772759616b6a707ae1b8a9c6c8\",\n \"score\": \"0.5821444\",\n \"text\": \"def test_part_1(problem_input, output):\\n assert part_1.solution(problem_input) == output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22aa3a843fbaf43c6926ff15fab1710e\",\n \"score\": \"0.580891\",\n \"text\": \"def __eq__(self, other):\\r\\n return other.outputs == self.outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdda169727196925126521ca8b2353d7\",\n \"score\": \"0.5807647\",\n \"text\": \"def check_output_duplicates(self):\\n seen = dict()\\n idx = None\\n for name, value in self.output._allitems():\\n if name is None:\\n if idx is None:\\n idx = 0\\n else:\\n idx += 1\\n if value and value in seen:\\n raise WorkflowError(\\n \\\"Duplicate output file pattern in rule {}. First two \\\"\\n \\\"duplicate for entries {} and {}.\\\".format(\\n self.name, seen[value], name or idx\\n )\\n )\\n seen[value] = name or idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fd07527cccf7b00d4859b64dda1fa45\",\n \"score\": \"0.579142\",\n \"text\": \"def validValidity():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fd07527cccf7b00d4859b64dda1fa45\",\n \"score\": \"0.579142\",\n \"text\": \"def validValidity():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48f5069ca1cda746deb7223eb070b84d\",\n \"score\": \"0.5776022\",\n \"text\": \"def test():\\n in_out = [([], 0),\\n ([15], 15),\\n ([15,30], 30),\\n ([30,15], 30),\\n ([15,15,15], 30),\\n ([15,150,15], 150),\\n ([30, 15, 60, 75, 45, 15, 15, 45], 180),\\n ([30, 15, 60, 75, 45, 45, 15, 45], 195),\\n ([30, 45, 60, 90, 45, 15, 15, 45], 195)]\\n \\n for input, output in in_out:\\n try:\\n assert f1(input) == output\\n except:\\n print(input,f1(input),output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cbc29a4e977a88e59a21b2c8ae56922\",\n \"score\": \"0.5759873\",\n \"text\": \"def test_mismatch(self) -> None:\\n result = object()\\n other = object()\\n self.assertThat(\\n returns(Is(result)).match(lambda: other),\\n Not(Is(None)),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3600d66c96b8bd5ac5f8f684fc2ee9ff\",\n \"score\": \"0.57452494\",\n \"text\": \"def testEquality(self):\\n self.assertEqual(self.bin1, self.bin2, \\\"Unequal bins with the same outcome repeated\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd393d4e4cf705c8e054cc4e832b44cd\",\n \"score\": \"0.57303685\",\n \"text\": \"def test__validate_syncing__0():\\n for input_value, expected_output in (\\n (True, True),\\n (False, False)\\n ):\\n output = validate_syncing(input_value)\\n vampytest.assert_eq(output, expected_output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e197a3733580292e2e3d827eb99e6f55\",\n \"score\": \"0.5711529\",\n \"text\": \"def check_output(gold_mr, out_mr, fix_type='all'):\\n # count the errors in the output, looking at the MR\\n added, missing, valerr, repeated = 0, 0, 0, 0\\n diff = {}\\n for slot in set(gold_mr.keys()) | set(out_mr.keys()):\\n if slot in gold_mr and slot not in out_mr:\\n if fix_type != 'all' and 'missing' not in fix_type: # ignore missing stuff -- adjust out_mr\\n out_mr[slot] = dict(gold_mr[slot])\\n else:\\n missing += sum(gold_mr[slot].values())\\n diff[slot] = {val: -count for val, count in gold_mr[slot].items()}\\n elif slot not in gold_mr and slot in out_mr:\\n if fix_type != 'all' and 'added' not in fix_type: # ignore added stuff -- adjust out_mr\\n del out_mr[slot]\\n else:\\n added += sum(out_mr[slot].values())\\n diff[slot] = out_mr[slot]\\n else:\\n # remove repeated first (check if MR has same val less than out + same value more than 1x)\\n for val in out_mr[slot].keys():\\n if val in gold_mr[slot] and gold_mr[slot][val] < out_mr[slot][val]:\\n repeated += out_mr[slot][val] - gold_mr[slot][val]\\n out_mr[slot][val] = gold_mr[slot][val]\\n # now compute the diff in the # of value occurrences\\n slot_diff = {val: gold_mr[slot].get(val, 0) - out_mr[slot].get(val, 0)\\n for val in set(gold_mr[slot].keys()) | set(out_mr[slot].keys())}\\n if fix_type != 'all':\\n for val, val_diff in list(slot_diff.items()):\\n if 'missing' not in fix_type and val_diff > 0: # ignore missing stuff -- adjust out_mr\\n out_mr[slot][val] = out_mr[slot].get(val, 0) + val_diff\\n del slot_diff[val]\\n if 'added' not in fix_type and val_diff < 0: # ignore added stuff -- adjust out_mr\\n out_mr[slot][val] = out_mr[slot].get(val, 0) - val_diff\\n del slot_diff[val]\\n diff[slot] = {val: -count for val, count in slot_diff.items() if count != 0}\\n # diffs both ways\\n mr_not_out = sum([count for count in slot_diff.values() if count > 0])\\n out_not_mr = - sum([count for count in slot_diff.values() if count < 0])\\n # value errors up to the same # of values\\n valerr += min(mr_not_out, out_not_mr)\\n # others fall under missing & added\\n missing += max(mr_not_out - out_not_mr, 0)\\n added += max(out_not_mr - mr_not_out, 0)\\n\\n diff = json.dumps({slot: vals for slot, vals in diff.items() if vals})\\n return added, missing, valerr, repeated, diff, out_mr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87733e706297acda0c0cf150ae9cfbf6\",\n \"score\": \"0.5707366\",\n \"text\": \"def test_valid_inputs(self):\\n for input_value, expected_output in get_items(self.valid_inputs):\\n initial_input_value = copy.deepcopy(input_value)\\n\\n serializer = self.serializer_class(data=input_value)\\n serializer.is_valid()\\n\\n assert serializer.initial_data == initial_input_value\\n assert self.field.run_validation(initial_input_value) == expected_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f30ede80f9a9be6525140017f26c6c1\",\n \"score\": \"0.56929\",\n \"text\": \"def check_input(self, input):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e696a5e2699def0183a0863bf457205\",\n \"score\": \"0.568187\",\n \"text\": \"def _numpy_checker(x, y):\\n x, y = x[0], y[0]\\n if (x.dtype != y.dtype or x.shape != y.shape\\n or numpy.any(numpy.abs(x - y) > 1e-10)):\\n raise Exception(\\\"Output mismatch.\\\", {'performlinker': x, 'clinker': y})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adfab7077ce81d9ca0f8b4d8426a20b3\",\n \"score\": \"0.56721437\",\n \"text\": \"def result_should_be(self, expected):\\n if self._result != expected:\\n raise AssertionError('%s != %s' % (self._result, expected))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9144536b77f19fa9699d1e9aeb25a2a6\",\n \"score\": \"0.5655695\",\n \"text\": \"def test_input_unchanged(self):\\n matrix_1 = [[6, 6], [3, 1]]\\n matrix_2 = [[1, 2], [3, 4]]\\n matrix_1_original = deepcopy(matrix_1)\\n matrix_2_original = deepcopy(matrix_2)\\n add(matrix_1, matrix_2)\\n self.assertEqual(matrix_1, matrix_1_original)\\n self.assertEqual(matrix_2, matrix_2_original)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ede05d0fa7cbc699d67556e2bdb814\",\n \"score\": \"0.56550705\",\n \"text\": \"def verify(self, testInput, testOutput):\\n expOutput = self.get_expected_result(testInput)\\n info = 'Input: {}, Size of input: {}\\\\nOutput: {}, \\\\nExpected: {}'.format(\\n testInput, len(testInput), testOutput, expOutput)\\n\\n # Test correct inversions\\n self.assert_equal(expOutput, testOutput, info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8127c50bad105bf4250a70a7e60ad35\",\n \"score\": \"0.56527394\",\n \"text\": \"def test_not_equal_on_not_equal_unique_identifier(self):\\n a = objects.EncryptionKeyInformation(\\n unique_identifier=\\\"100182d5-72b8-47aa-8383-4d97d512e98a\\\"\\n )\\n b = objects.EncryptionKeyInformation(\\n unique_identifier=\\\"00000000-1111-2222-3333-444444444444\\\"\\n )\\n\\n self.assertTrue(a != b)\\n self.assertTrue(b != a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a51e12a73b2739801872442407bcfa3\",\n \"score\": \"0.5620477\",\n \"text\": \"def failIfDifferent(self, first_file, second_file):\\n diff_cmd = ['diff', first_file, second_file]\\n if subprocess.call(diff_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) != 0:\\n self.fail('Output does not match!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3fe5cb32ac07f5345159d8539c4c728\",\n \"score\": \"0.5611779\",\n \"text\": \"def test_input_output(self):\\n ovf = OVF(self.input_ovf, self.working_dir, self.temp_file)\\n ovf.write()\\n self.check_diff('')\\n\\n # Filename output too\\n ovf = OVF(self.input_ovf, self.working_dir, self.temp_file + \\\".a.b.c\\\")\\n ovf.write()\\n self.check_diff('', file2=(self.temp_file + \\\".a.b.c\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61296a7cf76fcf941bc9a3cf483d4707\",\n \"score\": \"0.56083167\",\n \"text\": \"def validate_output(self):\\n for f_out, nifti_mrs_img in zip(self.fileoutNames, self.imageOut):\\n nifti_mrs_img.validate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c5632f57391261d278013ca88b3b3a8\",\n \"score\": \"0.5585658\",\n \"text\": \"def test_validate_outputs_invalid_case(hrm):\\n output_dict = {\\\"num_beats\\\": 10000, \\\"duration\\\": 60, \\\"voltage_extremes\\\": (\\n 0, 200)}\\n\\n with pytest.raises(ValueError):\\n hrm.validate_outputs(output_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4472e935e7da0a84b8f0773879f376f\",\n \"score\": \"0.5583051\",\n \"text\": \"def test_validity_function_invalid(capsys):\\n # get auth_tool object\\n auth_tool = get_auth_tool_object(Namespace(json=None))\\n # define a correct value\\n correct = \\\"a\\\"\\n incorrect = \\\"b\\\"\\n hint = \\\"Test hint\\\"\\n # run function with patched input\\n with patch('builtins.input', side_effect=[incorrect, correct]):\\n # run validity function\\n auth_tool._ensure_valid_value_for(\\n \\\"test\\\", lambda x: x == correct, hint=hint\\n )\\n out, err = capsys.readouterr()\\n assert not bool(out)\\n assert err == hint\",\n \"title\": \"\"\n },\n {\n \"docid\": \"992e68f4b36c8c3445ecf11baa85978e\",\n \"score\": \"0.55691046\",\n \"text\": \"def testCheckInputPositive(self):\\n self.assertListEqual(self.result3, self.expected3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6cbf39ff494bffe79af394688eec788\",\n \"score\": \"0.5550888\",\n \"text\": \"def user_input_validate(print_statement):\\n print(print_statement)\\n first_value = input('\\\\r - Enter: ')\\n second_value = input('\\\\r - Re-enter: ')\\n if first_value == second_value:\\n return first_value\\n else:\\n colour_print(f'(colour_warning)\\\\r # The values entered did not match, please try again.(colour_clear)')\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b7d4afa42c33544264008d236badb06\",\n \"score\": \"0.5540823\",\n \"text\": \"def shape_check(output: torch.Tensor, target: torch.Tensor) -> None:\\n output_shape = list(output.shape)\\n output_shape.pop(1)\\n if output_shape != list(target.shape):\\n raise ValueError(\\n \\\"Got {} shape for output and {} for target. \\\"\\n \\\"Shape should be equal except for 1 dimension of output (specifying classes).\\\".format(\\n output.shape, target.shape\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32ef6f06b05710c514e8f7c0a6c5fa7e\",\n \"score\": \"0.5534121\",\n \"text\": \"def is_inconsistent(self) -> bool:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f403d7c34f9ae824510495a878144249\",\n \"score\": \"0.55231065\",\n \"text\": \"def same_output_than(self, other):\\n for t in [t for t in self.tables if t.type.name == \\\"output\\\"]:\\n is_same = False\\n for t2 in [t2 for t2 in other.tables if t2.type.name == \\\"output\\\"]:\\n if t.model == t2.model and t.tablename == t2.tablename:\\n is_same = True\\n break\\n if not is_same:\\n return False\\n\\n for f in [f for f in self.files if f.type.name == \\\"input\\\"]:\\n is_same = False\\n for f2 in [f2 for f2 in other.files if f2.type.name == \\\"input\\\"]:\\n if (f.name == f2.name and\\n f.path == f2.path):\\n is_same = True\\n break\\n if not is_same:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba358fb98aa4c62372610eb3e978847a\",\n \"score\": \"0.5521292\",\n \"text\": \"def compare_output(self, input, output, expected):\\n if isinstance(input, unicode):\\n input = input.encode('raw_unicode_escape')\\n if sys.version_info > (3,):\\n # API difference: Python 3's node.__str__ doesn't escape\\n #assert expected is None or isinstance(expected, unicode)\\n if isinstance(expected, bytes):\\n expected = expected.decode('utf-8')\\n if isinstance(output, bytes):\\n output = output.decode('utf-8')\\n else:\\n if isinstance(expected, unicode):\\n expected = expected.encode('raw_unicode_escape')\\n if isinstance(output, unicode):\\n output = output.encode('raw_unicode_escape')\\n # Normalize line endings:\\n if expected:\\n expected = '\\\\n'.join(expected.splitlines())\\n if output:\\n output = '\\\\n'.join(output.splitlines())\\n try:\\n self.assertEquals(output, expected)\\n except AssertionError, error:\\n print >>sys.stderr, '\\\\n%s\\\\ninput:' % (self,)\\n print >>sys.stderr, input\\n try:\\n comparison = ''.join(self.compare(expected.splitlines(1),\\n output.splitlines(1)))\\n print >>sys.stderr, '-: expected\\\\n+: output'\\n print >>sys.stderr, comparison\\n except AttributeError: # expected or output not a string\\n # alternative output for non-strings:\\n print >>sys.stderr, 'expected: %r' % expected\\n print >>sys.stderr, 'output: %r' % output\\n raise error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e33bb90afcd9067e40a55c370304e6dc\",\n \"score\": \"0.5476065\",\n \"text\": \"def test_equality(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31fec2a4a86c2ebefb77e8dcc64a669e\",\n \"score\": \"0.5474954\",\n \"text\": \"def __ne__(self, other: 'ResourceRecordInputRdataRdataAaaaRecord') -> bool:\\n return not self == other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac05c306755d65c964f0dd08b1d14bbe\",\n \"score\": \"0.54709154\",\n \"text\": \"def not_equal(x1, x2, out=None):\\n if isinstance(x1, numeric_types) and isinstance(x2, numeric_types):\\n return _np.not_equal(x1, x2, out=out)\\n return _api_internal.not_equal(x1, x2, out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91f8192c136330fc7bd7113f25e045d5\",\n \"score\": \"0.54685175\",\n \"text\": \"def test_isscalar(input, output):\\n assert output == utils.isscalar(input)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddd8f35f2920a36fdebc337c4e95b683\",\n \"score\": \"0.54648757\",\n \"text\": \"def test_equal_on_not_equal_unique_identifier(self):\\n a = objects.EncryptionKeyInformation(\\n unique_identifier=\\\"100182d5-72b8-47aa-8383-4d97d512e98a\\\"\\n )\\n b = objects.EncryptionKeyInformation(\\n unique_identifier=\\\"00000000-1111-2222-3333-444444444444\\\"\\n )\\n\\n self.assertFalse(a == b)\\n self.assertFalse(b == a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8b4641aeceab8e773d46ac3f28069b0\",\n \"score\": \"0.54492074\",\n \"text\": \"def test_eq_false(self):\\n loc = Location(\\\"SLO\\\", 35.3, -120.7)\\n loc1 = Location(\\\"LA\\\", -30.3, -131.21)\\n loc2 = Location(\\\"SJ\\\", 23.4, -110.9)\\n self.assertNotEqual(loc,loc1)\\n self.assertNotEqual(loc1,loc2)\\n self.assertNotEqual(loc2,loc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0530d00c0cb99b10874a4da13c2edcb\",\n \"score\": \"0.54431087\",\n \"text\": \"def _almost_equal(current, new):\\n if current is None or new is None:\\n return False\\n return abs(current - new) / current < 1e-03\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ca04a4fc5f18bdd30c16f0075c02050\",\n \"score\": \"0.5442778\",\n \"text\": \"def __ne__(self, other: 'ResourceRecordUpdateInputRdataRdataAaaaRecord') -> bool:\\n return not self == other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd080dc2f4704f7ab3a007e213779d2e\",\n \"score\": \"0.54427606\",\n \"text\": \"def same_gen_check():\\r\\n print('-' * 5 + 'same_gen_check' + '-' * 5)\\r\\n a = test_gen()\\r\\n b = test_gen()\\r\\n\\r\\n print('a == b : ', a==b)\\r\\n print('a is b : ', a is b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c75a7528bd1d17935af9164db727e15\",\n \"score\": \"0.5442165\",\n \"text\": \"def test_card_not_equal_other(self):\\n card = Card(Suit.SPADE, 2)\\n for bad_value in (\\\"a\\\", \\\"1\\\", \\\"2\\\", 2, 20.0, Card):\\n self.assertNotEqual(card, bad_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e46f73aa43a0a606b5b19c0764f1a89\",\n \"score\": \"0.5439961\",\n \"text\": \"def test_not_equal_on_not_equal_unique_identifier(self):\\n a = objects.MACSignatureKeyInformation(\\n unique_identifier=\\\"100182d5-72b8-47aa-8383-4d97d512e98a\\\"\\n )\\n b = objects.MACSignatureKeyInformation(\\n unique_identifier=\\\"00000000-1111-2222-3333-444444444444\\\"\\n )\\n\\n self.assertTrue(a != b)\\n self.assertTrue(b != a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c548e9a3bc682343904412f052a3da19\",\n \"score\": \"0.54387414\",\n \"text\": \"def __ne__(self, other: 'ResourceRecordInputRdataRdataTxtRecord') -> bool:\\n return not self == other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"669c60b31bb6657e88ec711c4cc524ec\",\n \"score\": \"0.5436216\",\n \"text\": \"def test_equality_of_mana_when_they_are_not_equal():\\n mana = ertai.Mana(\\\"Black\\\", \\\"Blue\\\", \\\"Red\\\", \\\"Red\\\")\\n other_mana = ertai.Mana(\\\"Black\\\", \\\"Blue\\\", \\\"Red\\\")\\n assert mana != other_mana\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d9bca8af462f6df5297aa872777bbca\",\n \"score\": \"0.54290247\",\n \"text\": \"def test_not_equal_on_type_mismatch(self):\\n a = objects.MACSignatureKeyInformation()\\n b = 'invalid'\\n\\n self.assertTrue(a != b)\\n self.assertTrue(b != a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c6c704ce4b8bd887a680d348441fadc\",\n \"score\": \"0.5428968\",\n \"text\": \"def __ne__(self, other: 'ResourceRecordUpdateInputRdataRdataTxtRecord') -> bool:\\n return not self == other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fad971253de5babe8ce7013643e7709\",\n \"score\": \"0.54234815\",\n \"text\": \"def __ne__(self, other: 'ResourceRecordInputRdataRdataMxRecord') -> bool:\\n return not self == other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa1d482e501daac7a7a5b348d1292f0d\",\n \"score\": \"0.54188776\",\n \"text\": \"def check_same_states(result_1: Result, result_2: Result) -> None:\\n\\n # Get all state of result_1\\n states_result_1 = []\\n for sample in result_1:\\n states_result_1.append(sample.state.int)\\n\\n # Get all state of result_2\\n states_result_2 = []\\n for sample in result_2:\\n states_result_2.append(sample.state.int)\\n\\n states_result_1.sort()\\n states_result_2.sort()\\n\\n # Check content equality\\n assert states_result_1 == states_result_2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94fff62b936026879aa966e6b1e8276a\",\n \"score\": \"0.5417261\",\n \"text\": \"def test_not_equal_on_type_mismatch(self):\\n a = objects.EncryptionKeyInformation()\\n b = 'invalid'\\n\\n self.assertTrue(a != b)\\n self.assertTrue(b != a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dff16808bff9a13fe7c05513a15d8956\",\n \"score\": \"0.5400954\",\n \"text\": \"def test_assertNotEqual_unequal(self):\\n for first, second in self.unequal_pairs:\\n try:\\n self.assertNotEqual(first, second)\\n except:\\n raise AssertionError, \\\\\\n \\\"unittests.assertNotEqual failed on input %s and %s\\\" \\\\\\n % (`first`, `second`)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"844f7ee721e6243404bf3e27db57b407\",\n \"score\": \"0.5398239\",\n \"text\": \"def _validate_source(self, other_replica_uid, other_generation,\\n other_transaction_id):\\n (old_generation,\\n old_transaction_id) = yield self._get_replica_gen_and_trans_id(\\n other_replica_uid)\\n if other_generation < old_generation:\\n raise InvalidGeneration\\n if other_generation > old_generation:\\n return\\n if other_transaction_id == old_transaction_id:\\n return\\n raise InvalidTransactionId\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e0701bfc5105a6cd477f88404a93c42\",\n \"score\": \"0.5387769\",\n \"text\": \"def test_error_on_wrong_update(inp1, inp2):\\n metric = LearnedPerceptualImagePatchSimilarity()\\n with pytest.raises(ValueError, match=\\\"Expected both input arguments to be normalized tensors .*\\\"):\\n metric(inp1, inp2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e38acc7ea08fd2e9dd562c8df85f5e90\",\n \"score\": \"0.5381053\",\n \"text\": \"def check_results_equality(result_1: Result, result_2: Result, amplitude: bool = True) -> None:\\n\\n # First we check the two results have the same states\\n check_same_states(result_1, result_2)\\n\\n # Then we check the two results have the same probabilities\\n check_same_probabilities(result_1, result_2)\\n\\n # Finally we check the two results have the same stats properties\\n if amplitude:\\n check_same_state_properties(result_1, result_2)\\n else:\\n check_same_state_properties(result_1, result_2, amplitude)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0972941ab7a2fccb2ccef33add61bd21\",\n \"score\": \"0.53801984\",\n \"text\": \"def __eq__(self, other):\\n return (isinstance(other, self.__class__) and\\n self.same_files(other, \\\"input\\\") and\\n self.same_tables(other, \\\"input\\\") and\\n self.same_files(other, \\\"output\\\") and\\n self.same_tables(other, \\\"output\\\") and\\n self.same_options(other))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37f6b33e5cd3be14fd7689b4f6269bb5\",\n \"score\": \"0.53799206\",\n \"text\": \"def test_onestep_identical(inputs):\\n seq_x, seq_y = inputs\\n\\n output = onestep.onestep(seq_x, seq_y, order=2, verbose=False, check=True)\\n\\n assert output is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f17459c3337ae4cff1f309bfd2798b6\",\n \"score\": \"0.5377264\",\n \"text\": \"def testEquivalent(self):\\r\\n a = Vector3(1, 2, 3)\\r\\n b = Vector3(1, 2, 3)\\r\\n c = Vector3(3, 2, 1)\\r\\n self.assertEqual(a, b)\\r\\n self.assertNotEqual(a, c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"031fb79bf5377b6a817a73d9cb045590\",\n \"score\": \"0.53721464\",\n \"text\": \"def _inner_consistency_check(self, other):\\n # chack mesh\\n if self.mesh is not other.mesh:\\n raise Exception(\\\"Mesh of the forms do not match\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29de9d0bcbcdf74d3bf2978f0a486bd6\",\n \"score\": \"0.53686476\",\n \"text\": \"def test_not_match(self):\\n put_in_stdin('x\\\\ny')\\n with redirect_stdout() as output:\\n res = click.prompt(\\\"\\\", type=InputValidator(converters=[YES, NO]))\\n self.assertTrue(res.kind_of(YES))\\n self.assertTrue(\\\"Error: x. Help: y - yes; n - no\\\" in output.getvalue())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66b01fdfea5c6ccf99437d401f114300\",\n \"score\": \"0.5365897\",\n \"text\": \"def test_not_equal_on_not_equal_mac_signatures(self):\\n a = objects.KeyWrappingData(mac_signature=b'\\\\x01')\\n b = objects.KeyWrappingData(mac_signature=b'\\\\x10')\\n\\n self.assertTrue(a != b)\\n self.assertTrue(b != a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee33d5401fea0d4797bf13f5c9389740\",\n \"score\": \"0.5364966\",\n \"text\": \"def __ne__(self, other: 'ResourceRecordUpdateInputRdataRdataMxRecord') -> bool:\\n return not self == other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7f0dc294bce0cac5665078240fd32a6\",\n \"score\": \"0.53580844\",\n \"text\": \"def test_sameas(value, other):\\r\\n return value is other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae64d092af5146f3fad262ec6dc633e\",\n \"score\": \"0.53479916\",\n \"text\": \"def __ne__(self, other: 'ResourceRecordInputRdataRdataARecord') -> bool:\\n return not self == other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae8600cd52e03de7e6f50667bd0c1d62\",\n \"score\": \"0.534541\",\n \"text\": \"def assertEqual_(self, output, expected):\\n self.assertEqual(output, expected, 'Expected \\\"%s\\\". Got \\\"%s\\\".' % (expected, output))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"102bc05291914f285e698aeae237be48\",\n \"score\": \"0.53439385\",\n \"text\": \"def test_equality_of_mana_when_not_comparing_the_same_objects():\\n mana = ertai.Mana(\\\"Black\\\", \\\"Blue\\\", \\\"Red\\\", \\\"Red\\\")\\n assert mana != \\\"A string\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"773136141f3341a6c9a90ed17ef9416a\",\n \"score\": \"0.53420055\",\n \"text\": \"def test_not_equal_on_type_mismatch(self):\\n a = objects.KeyWrappingData()\\n b = 'invalid'\\n\\n self.assertTrue(a != b)\\n self.assertTrue(b != a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcc2a31ce637f9b0ca481d01a60fdee1\",\n \"score\": \"0.5341564\",\n \"text\": \"def _expected_not_equal_to_actual(eq: EquipmentSinglePhase):\\n if (eq.state is None) or (eq.expected_state is None):\\n return False\\n else:\\n return eq.expected_state != eq.state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f45a917695f33a1ae1f5193ca7320862\",\n \"score\": \"0.53412175\",\n \"text\": \"def test_ne(self):\\n self.assertTrue(self.oc1 != self.oc3)\\n self.assertFalse(self.oc1 != self.oc2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef801b8d2007b27d9eab1f4bf42f3492\",\n \"score\": \"0.5335848\",\n \"text\": \"def check_placeholder_output(self, prog, outputs_name):\\n block = prog[\\\"main\\\"]\\n input_name = [x.name for x in list(block.inputs.values())]\\n with block:\\n new_outputs = []\\n for output, output_name in zip(block.outputs, outputs_name):\\n if output.name not in input_name or output.name == output_name:\\n new_output = output\\n else:\\n new_output = mb.identity(x=output, name=output_name)\\n new_outputs.append(new_output)\\n block.set_outputs(new_outputs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0a2fbe1725941edfb4fb841f8a6d484\",\n \"score\": \"0.5334173\",\n \"text\": \"def equal_to(self, input1, input2, output, callback=None):\\n args = []\\n args.append(\\\"--input1='{}'\\\".format(input1))\\n args.append(\\\"--input2='{}'\\\".format(input2))\\n args.append(\\\"--output='{}'\\\".format(output))\\n return self.run_tool('equal_to', args, callback) # returns 1 if error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35b99b18beb52498cad5db57464f5dd4\",\n \"score\": \"0.53287077\",\n \"text\": \"def _assert_output_vs_expected(output, expected):\\n import unittest\\n if isinstance(output, dict):\\n testcase = unittest.TestCase('__init__')\\n try:\\n testcase.assertDictEqual(expected, output,\\n msg=\\\"\\\\n\\\\nUnfortunately, the output is *not* correct..\\\")\\n except AssertionError as error:\\n print(error)\\n return 0\\n print(\\\"\\\\nWell done! Output is correct!\\\")\\n return 1\\n print(\\\"\\\\n\\\\nUnfortunately, the output is *not* a dictionary!\\\")\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3053d582e09491948d969ec69e5b20bf\",\n \"score\": \"0.53269976\",\n \"text\": \"def test_wtf_negative(input_1, input_2, output):\\n\\n with pytest.raises(output):\\n wtf(input_1, input_2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0da1bfe5d9a3b408acdcf3d02dab3b44\",\n \"score\": \"0.532426\",\n \"text\": \"def __ne__(self, other):\\n return self._num != other._num and self._denom != other._denom\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37e82cc40fd82503bf9871ca27470f8e\",\n \"score\": \"0.532312\",\n \"text\": \"def test__validate_old_attributes__0():\\n for input_value, expected_output in (\\n (None, {}),\\n ({}, {}),\\n ({'a': 'b'}, {'a': 'b'})\\n ):\\n output = validate_old_attributes(input_value)\\n vampytest.assert_eq(expected_output, output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb61c65d9695700849551a820b2dc15a\",\n \"score\": \"0.5318516\",\n \"text\": \"def test_output_is_input(self):\\n cl_object = processed_config_line('', self.args)\\n expect = ''\\n actual = cl_object.result()\\n self.assertEqual(expect, actual)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de0610778f3ee013cd25cf9389aedf81\",\n \"score\": \"0.53174376\",\n \"text\": \"def __ne__(self, other: 'ResourceRecordUpdateInputRdataRdataARecord') -> bool:\\n return not self == other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147f52296cbdaaec02b830d227f1a520\",\n \"score\": \"0.5312722\",\n \"text\": \"def transfer_id_same():\\n\\n print(\\\"Target account ID cannot be same as user account ID\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64f0df94bb81705a3783f8667ad4d5c5\",\n \"score\": \"0.5306945\",\n \"text\": \"def is_correct(self, result):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6821d13a4192e399342a1fb84324967c\",\n \"score\": \"0.5299344\",\n \"text\": \"def __ne__(self, other):\\n if not isinstance(other, VmDiskOrderByInput):\\n return True\\n\\n return self.to_dict() != other.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49955bfb108a6287ab78bb7f429aacef\",\n \"score\": \"0.5297024\",\n \"text\": \"def validate_unique(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25f9b130d1bd1789e7d4b8cddd52c4bc\",\n \"score\": \"0.52968687\",\n \"text\": \"def test_model_output_between_zero_and_one():\\n\\n np.testing.assert_array_less(0.0, result)\\n\\n np.testing.assert_array_less(result, 1.0)\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":4,"numItemsPerPage":100,"numTotalItems":994482,"offset":400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc5MDUzODQ0NCwianRpIjoiMDRlNTYxOTAtMGY3Zi00YjgwLTg3MGQtM2M4YWZkMTIxMWI0Iiwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc5MDU0MjA0NCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.GJ74n2njGelbUfjfqHx9YSAi0rXpmkotpNM4RQb8JOZmMk0vqcxhDF2Ga1PI47BN5fYhn2fBrRmMTUWI-xPXCA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":994482,"numBytesParquet":23972089656}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
90da290424ff6c0b0ec779d425a34e69 | Tells if the cover is closed or not. | [
{
"docid": "94be06c29ccedfac3b40d3085291a36e",
"score": "0.7914196",
"text": "def is_closed(self) -> bool | None:\n return self.coordinator.get_cover_state(self._device.endpoint_id).position == 0",
"title": ""
}
] | [
{
"docid": "6bc50b64b9fd4d10972a8ac37d487483",
"score": "0.83833516",
"text": "def is_closed(self) -> bool:\n return self.current_cover_position == 0",
"title": ""
},
{
"docid": "5b2d57a7fa5eef813ddc71cf7b48801e",
"score": "0.8137329",
"text": "def is_closing(self) -> bool | N... |
71c049b254e653c937237ed6a9da8422 | getVocabCount returns the number of unique keys within a master dictionary. | [
{
"docid": "f67ac3977e8f08ef4acbe84d187a2d7b",
"score": "0.81111103",
"text": "def getVocabCount(data):\r\n return len(data.keys())",
"title": ""
}
] | [
{
"docid": "f33fcc86d003bd7cd3980d7071d16b9b",
"score": "0.703855",
"text": "def vocab_count(corpus):\n\n flatten_sequence=[i for j in corpus for i in j]\n vocab_counter=Counter(flatten_sequence)\n return vocab_counter",
"title": ""
},
{
"docid": "12139c8bea7b2e2b2212431dd452c485",
... |
cb2bee471fe9022f5ccc69799cb945ac | Visualizes the board by printing the contents of the cells ++ |B T| < That is a cell; T represents the item on top ++ B represents the item on the bottom | [
{
"docid": "9652f13837aa4d3ed343007b1b611e42",
"score": "0.77480453",
"text": "def draw(self):\n border = \"+---+\"\n item = \"|{} {}|\"\n\n def draw_all_border():\n \"\"\"\n prints '+---+' times the number of cols\n specified in NUM_COLS\n ... | [
{
"docid": "83d45ba96060002fd66381146b94dcc9",
"score": "0.79420996",
"text": "def render_board(self):\n\n # this prints the top and bottom boundaries,\n # an in between renders '*' + row cells + '*'\n # for each row on the board\n print('*'*(self.width+2))\n for row... |
74553c2a9c9e131ccdb7c35572865c13 | Exit when the Python version is too low. | [
{
"docid": "0e40d912964040f4de81dfc0a64f14d6",
"score": "0.81366885",
"text": "def check_python_version():\n if sys.version_info < MINIMUM_PYTHON_VERSION:\n sys.exit(\"Python {}.{}+ is required.\".format(*MINIMUM_PYTHON_VERSION))",
"title": ""
}
] | [
{
"docid": "be7a125e9493d1fb55458bf7b214668a",
"score": "0.8035704",
"text": "def verify_python_version(major_version: int, minor_revision: int)-> None:\n if (major_version, minor_revision) > sys.version_info:\n message: str = f\"This program can only be run on Python {major_version}\" \\\n ... |
1de74d3420e875c832d2504b4f8f75b4 | send request to gateway and see what happens | [
{
"docid": "819dd0c5c231a4caec4b1d38d80b0574",
"score": "0.5543378",
"text": "def _send_request(gateway_port, protocol, request_size=1):\n c = Client(host='localhost', port=gateway_port, protocol=protocol)\n res = c.post('/foo', inputs=DocumentArray.empty(2), request_size=request_size)\n assert... | [
{
"docid": "d7599c2466337c11497f121f43b7029e",
"score": "0.68202937",
"text": "def send(self, request : str):\n pass",
"title": ""
},
{
"docid": "2102a2b11d82925d33626cb88db9f718",
"score": "0.66711557",
"text": "def _send_in_request(self):\n try:\n req_param... |
ce64364992c6bc20f154f3764ce0f11e | prints info about the state | [
{
"docid": "9b891587e8aaf264f0bf683b7daea014",
"score": "0.0",
"text": "def print_info(self, as_str: bool = False, file: TextIO = None) -> str:",
"title": ""
}
] | [
{
"docid": "6b63db4d1dd56ad5a154cab28942a6a9",
"score": "0.8899063",
"text": "def print_state():\n ...",
"title": ""
},
{
"docid": "a2ad2d1917f408c900da9218030ba604",
"score": "0.84972256",
"text": "def print_state(self):\n print(self._state)",
"title": ""
},
{
... |
bcc11dc5d3ed124862ba585c4e4fd20e | Should answers if the single point (x, y) is on black. | [
{
"docid": "7e9f388570f8d4b23da6bdcf5714d9a2",
"score": "0.0",
"text": "def onBlack(self, p, path=None):",
"title": ""
}
] | [
{
"docid": "e049a1e2d55a6aa4ff11eeaa97d59aa3",
"score": "0.6627004",
"text": "def IsByColor(self) -> bool:",
"title": ""
},
{
"docid": "e049a1e2d55a6aa4ff11eeaa97d59aa3",
"score": "0.6627004",
"text": "def IsByColor(self) -> bool:",
"title": ""
},
{
"docid": "6ba50191159d... |
8141ad562afe95aa356d6a6fbfb33095 | Return the index of the last occurrence of obj in seq | [
{
"docid": "252c38dee4f187c3fdc4cbe11e021502",
"score": "0.86840785",
"text": "def lastIndexOf(obj, seq):\n try:\n if __version__ < 2.6:\n for index in (i for i in xrange(len(seq) - 1, -1, -1) if seq[i] == obj):\n return index\n else:\n return next((... | [
{
"docid": "6d108c14eef57a621782cb5c243aeb17",
"score": "0.6792807",
"text": "def indexOf(obj, seq):\n try:\n if __version__ < 2.6:\n for index in (i for i in xrange(len(seq)) if seq[i] == obj):\n return index\n else:\n return next((i for i in xrange... |
4b94c59b0a2448d4d51330e6a6f17910 | Cuts the string at the last dot '.' . The return has a proper ending. | [
{
"docid": "ecbea9709a6f94d312c1fe08e166c133",
"score": "0.7466881",
"text": "def endSentence(string): \n\tind = string.rfind('.')\n\treturn string if ind == -1 else string[0:ind+1]",
"title": ""
}
] | [
{
"docid": "91a5a97e7735f1fa13c411c46141835d",
"score": "0.69080025",
"text": "def get_suffix(filename, has_dot=False):\n\tpos = filename.rfind('.')\n\tif 0 < pos < len(filename) - 1:\n\t\tindex = pos if has_dot else pos + 1\n\t\treturn filename[index:]\n\telse:\n\t\treturn ''",
"title": ""
},
{... |
768a424a7fbaf8e532540aedfefb3526 | Load rules from database | [
{
"docid": "df8400c592d652b7f4d401077a1a95a7",
"score": "0.6805147",
"text": "def load_rules(self):\n self.logger.debug(\"Loading rules\")\n self.rules = {}\n self.back_rules = {}\n nr = 0\n nbr = 0\n for c in EventClass.objects.all():\n if c.disposit... | [
{
"docid": "a468dffbce4892037f26c4c154e990e6",
"score": "0.6632937",
"text": "def load_rules(self, force_reload=False):\n self.enforcer.load_rules(force_reload)",
"title": ""
},
{
"docid": "7042a162636269b04044cde3ba146696",
"score": "0.66135895",
"text": "def handle_rules():\... |
1bf5a427d9751c3d3ea5fc7078d6316d | Initial method for PFEMElementCompressible | [
{
"docid": "1c4bd45509485b8c3ba8ce1f0be94cbf",
"score": "0.0",
"text": "def __init__(self, osi, ele_nodes, rho, mu, b1, b2, thickness, kappa):\n self.osi = osi\n self.ele_node_tags = [x.tag for x in ele_nodes]\n self.ele_nodes = ele_nodes\n self.rho = float(rho)\n self... | [
{
"docid": "44a31e94dea9ad3f6f0a37de5a8aeb34",
"score": "0.6386662",
"text": "def compress(self, *args, **kwargs): # real signature unknown\n pass",
"title": ""
},
{
"docid": "6f4c1934e03e72884567ccec72b36dec",
"score": "0.6303826",
"text": "def TransformCompress(deck, entity... |
36aed014ebb5272053f13d612b4487c6 | A header with the contents of headerName must exist. The match takes place whether or not the request's header has a value. | [
{
"docid": "290bfb9ada89baa1712c649d5090e93b",
"score": "0.0",
"text": "def present_match(self) -> Optional[bool]:\n return pulumi.get(self, \"present_match\")",
"title": ""
}
] | [
{
"docid": "a03941438ea712611c36539805b0d33a",
"score": "0.7539977",
"text": "def verify_header(headers, header_name, header_value):\n matching_headers = list()\n\n for header in headers:\n if header[0] == header_name:\n matching_headers.append(header)\n\n # Hmmm....\n if l... |
bad8a2eb03ed3c9eac0d82fdf186d7d7 | Passes XML file to get_hosts_details_list(), expects a dictionary. For nodes which sgeexecd isn't running, np_load_avg is '', this function checks for such instances. | [
{
"docid": "a5b253859bb36d436f01120784621e21",
"score": "0.5170468",
"text": "def seek_ooc_nodes(xmlfile):\n\n # Passes XML file get_hosts_details_list() for parsing.\n hosts_lists = get_hosts_details_list(xmlfile)\n\n # Dictionary of out of circulation nodes.\n ooc_nodes = {}\n\n for nod... | [
{
"docid": "4df47758b7d2ee9b8c0d07074529331c",
"score": "0.62492794",
"text": "def get_hosts_details_list(xmlfile):\n\n findall_hosts = []\n\n try:\n dom = ElementTree.parse(xmlfile)\n except Exception, e:\n print(e)\n else:\n # Find all hosts in dom.\n findall_ho... |
4260c860a2adc3fb1078bafeeb794104 | transfer time_str to seconds | [
{
"docid": "b2e90d66a3adcda65cc4efdb98bc1a62",
"score": "0.751273",
"text": "def to_second(time_str):\n format_time_str = \"%s-%s-%s %s:%s:%s\"%(\n time_str[0:4],\n time_str[4:6],\n time_str[6:8],\n time_str[8... | [
{
"docid": "f14a4ed4850f99c6f69590e97d5ffd04",
"score": "0.8461396",
"text": "def seconds_from_string(time_str):\n h, m, s = time_str.split(\":\")\n return int(h) * 3600 + int(m) * 60 + int(s)",
"title": ""
},
{
"docid": "57a8ff0914734f63ca6a05afa09ad6dc",
"score": "0.79744... |
bd7aea159980b3602622f3dfae95db5f | Ensure the storage directories has the same tags as specified for the table in src_table_tags. Location for storage is looked up in hive server. | [
{
"docid": "2b4e011e0066b1b71fda618179c169c4",
"score": "0.61331445",
"text": "def sync_table_storage_tags(self, src_table_tags, clear_not_listed=False):\n self.worklog = {}\n run = 0\n while True:\n try:\n run += 1\n self.ensure_tags_in_atla... | [
{
"docid": "3c70b31b4047e9d45943652a4e2857e5",
"score": "0.7110176",
"text": "def _sync_tags_for_one_tables_storage(self, schema, table, expected_tags):\n storage_url = self.hive_client.get_location(schema, table)\n if storage_url is not None:\n guid = self.atlas_client.add_hdfs... |
7c446390108b5b78d1dda6462a45bbe7 | import_list takes an uploaded file ad inserts it into a previously created list. It requires an account id, list id, fields, data id, delimiter and if using an excel file, the sheetname. | [
{
"docid": "fa93b532e1ea93d2c7fab4b532fb56de",
"score": "0.81622994",
"text": "def import_list(accountId,listId,fields,dataId,delimiter,sheetName='0'):\n\tn = 1\n\tglobal importRequestBody\n\timportRequestBody = '<Parameters><DataId>'+dataId+'</DataId><FieldMappings>'\n\tfor field in fields:\n\t\timport... | [
{
"docid": "402435b65c3e242f259e34c97077a19b",
"score": "0.706584",
"text": "def list_from_file(accountId,fileName,fileFormat):\n\tglobal fields\n\tglobal listId\n\tglobal listName\n\tglobal delimiter\n\tlistName = fileName.split(\"/\")\n\tlistName = listName[1].rstrip('.csvxtl')\n\tif fileFormat == 'cs... |
64445d11317cada3dd6527bb5d7d08db | Change the theme of the UI. Settings are saved in settings,prop `Press Ctrl + D` | [
{
"docid": "82e3f70bf59b9b6b496daf00c2ccbbb8",
"score": "0.75060755",
"text": "def changeTheme(self):\n dump(not dark, \"settings.prop\")\n self.__init__()",
"title": ""
}
] | [
{
"docid": "c51fe62d907454b4e0ecd0cc6a0ca4b7",
"score": "0.7390407",
"text": "def themes_button_pushed():\n theme = self.settings.value(\"theme\", \"dark\")\n if theme == \"dark\":\n theme = \"light\"\n self.settings.setValue(\"theme\", theme)\n ... |
e1a9830fb6a965c312254b2ffd48274b | convert v at DH grid points to v in SH | [
{
"docid": "a56c130749e423fb5b19c7cbab86ff38",
"score": "0.7078962",
"text": "def v2vSH(self, v, l_max=None):\n lm = (v.shape[0]-2)//2\n if l_max is not None:\n lm = min(l_max, lm)\n return _sht.shtools.SHExpandDH(v, norm=1, sampling=2, csphase=1)[:,:lm+1,:lm+1]",
"ti... | [
{
"docid": "363f89814917abaaee26ce35a157690e",
"score": "0.69157463",
"text": "def vSH2v(self, vSH, l_max=None, Nth=None):\n if Nth is None and l_max is None:\n l_max = self._get_lmax(vSH)\n Nth = l_max * 2 + 2\n lm = l_max\n elif Nth is None:\n ... |
6e0f3a9a52edcea73856d83f939359f1 | Hit the CKAN REST API for an ISO 19139 XML representation of a package with data uploaded into the datastore. | [
{
"docid": "6ecbc6fab3063de593ebbb7655738681",
"score": "0.5573738",
"text": "def get_record(context, repo, ckan_url, ckan_id, ckan_info):\n query = ckan_url + 'package_iso/object/%s'\n url = query % ckan_info['id']\n response = requests.get(url)\n try:\n xml = etr... | [
{
"docid": "983b2cf26fb95740b519f798a2f78874",
"score": "0.5985983",
"text": "def package(request, name):\n raw_data = api_data.data_cve_circl(name=name)\n fields_names = ['id', 'summary', 'cvss']\n extracted_data = api_data.extract_fields(raw_data, fields_names)\n\n return HttpResponse(json... |
7193ad479e923837663da261e3082151 | Create and return a new event_book | [
{
"docid": "f7d629676eb2c844f672df6e3a2c4758",
"score": "0.8405833",
"text": "def create_event_book(event, book):\n\n event_book = EventBook(isbn=book.isbn, event_id=event.id)\n\n db.session.add(event_book)\n db.session.commit()\n\n return event_book",
"title": ""
}
] | [
{
"docid": "0d292365bac8582a6c8931c41917df02",
"score": "0.6702231",
"text": "def create_book(self, title):\n new_thing = Book(NumbID.new_id(), title)\n self.item_list.add_item(new_thing)\n pass",
"title": ""
},
{
"docid": "0d7fc8fb973dd13dfc3a4eff6b4d5358",
"score":... |
c8fc14f8dd58de6ef45b4f8ce90e979c | Fetches a list of all networks for a tenant | [
{
"docid": "f7cd8cb047bcf67a3e404398ccc8b010",
"score": "0.7156412",
"text": "def list_networks(self):\n return self.do_request(\"GET\", self.networks_path)",
"title": ""
}
] | [
{
"docid": "5484cad849024a381fec2e39ae0ed61d",
"score": "0.72490734",
"text": "def networks_get(self, max_retries=REST_API_MAX_RETRIES):\n return self.__get(\"%s/networks\" % self.__base_mgmt_url,\n max_retries=max_retries)",
"title": ""
},
{
"docid": "0081c7e4551977b... |
b1e368a6bd73daaa5b5b06167e0ebb87 | Return the character c asis, unless it is a metacharacter, in which case return it preceded by a backslash | [
{
"docid": "1bfd2d0c84b7972ab5a4ab6000d7accc",
"score": "0.77703065",
"text": "def escape(c):\n return RE.backslash + c if c in RE.metacharacters else c",
"title": ""
}
] | [
{
"docid": "75fbacca7dae2d88620bdcaad170b4ea",
"score": "0.6689678",
"text": "def parse_escaped_string_char(c):\n if not c:\n fatal_error(\"found EOF while reading a string\")\n if c == \"\\\\\":\n result = \"\\\\\"\n elif c == '\"':\n result = '\"'\n elif c == \"b\":\n r... |
59229feb1e25dc424b9d82333814ddb8 | Select and operate a set of commands | [
{
"docid": "ab9587793bb4325face4533f44adb4f5",
"score": "0.6718301",
"text": "def send_select_and_operate_command_set(self, command_set, callback=opendnp3.PrintingCommandResultCallback.Get(),\n config=opendnp3.TaskConfig.Default()):\n self.master.SelectA... | [
{
"docid": "2df849345075724f9350c6a92d703750",
"score": "0.6681098",
"text": "def __execute_cmd(self, selection):\n \n if selection == 1: \n self.__cmd_display_vehicle_types()\n elif selection == 2:\n self.__cmd_display_vehicle_costs()\n elif selection ... |
f29e15f6b34b7bdcb12e1ab3ecd86024 | gets the last pressed state and resets it | [
{
"docid": "1e1f3947c6998210f9efe61fc0149b7b",
"score": "0.73674",
"text": "def getLastPressedState(self):\n theLastPressedState = self.lastPressedState\n self.lastPressedState = self.ButtonPressStates.NOTPRESSED\n return theLastPressedState",
"title": ""
}
] | [
{
"docid": "e1d14403759ef02d19bb64bfa64507ee",
"score": "0.69121516",
"text": "def checkLastPressedState(self):\n return self.lastPressedState",
"title": ""
},
{
"docid": "e4a783ab340dec881532a8b02984d7cc",
"score": "0.6591248",
"text": "def _changeInactive(self):\n key... |
c9fc8d8d5b393f521beefd7d79414612 | Check if this LogicSigAccount has been delegated to another account with a signature. | [
{
"docid": "a894643b41f72a73529af925ad7ad05b",
"score": "0.6835205",
"text": "def is_delegated(self) -> bool:\n return bool(self.lsig.sig or self.lsig.msig)",
"title": ""
}
] | [
{
"docid": "e172a971baae36258682374bfad7d59d",
"score": "0.575139",
"text": "def is_delegated(self):\n return self.assignee is not None",
"title": ""
},
{
"docid": "b3343719ba5f73dc3fc53a6dc830f486",
"score": "0.5476073",
"text": "def is_delegate(self, user):\n if not u... |
6f7ce6571a86f179bb4246d10c8acc4f | Returns a set of value names that pass the criterion. | [
{
"docid": "e03dc704da494c380ed9af2336a1f730",
"score": "0.0",
"text": "def filter_value_to_type_binding(self, binding):\n named_field_list = [\n (field_value.name, field_value.field)\n for field_value in binding.value_list]\n return self._filter(named_field_list)",
... | [
{
"docid": "0954cc11dbfe4d93f4f957957eafcd7f",
"score": "0.64739496",
"text": "def get_values(self, attr_name):\n ret = set(self._attr_value_cdist[attr_name].keys()\n + self._attr_value_counts[attr_name].keys()\n + self._branches.keys())\n return ret",
... |
9ad8828fd1d1d0cab49175a179fc50d1 | Return a list of port names. | [
{
"docid": "ab5f584700a931b565950f95db72a7b6",
"score": "0.7265796",
"text": "def keys(self):\n return [port.name for port in self]",
"title": ""
}
] | [
{
"docid": "4477977bfbec33537b48c10c569677ff",
"score": "0.83994097",
"text": "def get_ports(self):\r\n return self.namelist",
"title": ""
},
{
"docid": "b4ccc8436a78d6a7f02a88b434b05fda",
"score": "0.80273235",
"text": "def ports(self) -> Sequence[str]:\n return pulumi... |
2e3756a1e27c85a27925bd99fe1d47a3 | Initialize a new network | [
{
"docid": "da32c712e706dee654950e6a6ce22ddd",
"score": "0.0",
"text": "def __init__(self, feature_sizes, continuous_field_size, embedding_size=4,\n hidden_dims=[128, 128], use_fm1=False, num_classes=10, dropout=[0.5, 0.5],\n use_cuda=True, verbose=False):\n super(... | [
{
"docid": "86cca404ef0f66cb8aeddc2e1a86b18d",
"score": "0.856658",
"text": "def init_network(self):\n self.net = network.Network([784, 30, 10])\n return(\"made a network yo\")",
"title": ""
},
{
"docid": "66a311e8cd13327d8498e7017d8bf118",
"score": "0.8541491",
"text":... |
27df6b814fe3dd43223050a9a8f7231b | Memoize an expensive computation as a property of an object | [
{
"docid": "9336f3465d63239701943e1ee62370fc",
"score": "0.65158",
"text": "def memoized_property(name=None):\n def memoized_decorator(f):\n @property\n @wraps(f)\n def wrapper(self):\n cached_name = name\n if name is None:\n cached_name = \"_... | [
{
"docid": "c6d11056d507385cd7ed343e3a13b958",
"score": "0.7218838",
"text": "def memoize(obj):\n cache = obj._cache = {}\n\n @functools.wraps(obj)\n def memoizer(*args, **kwargs):\n key = str(args) + str(kwargs)\n if key not in cache:\n cache[key] = obj(*args, **kwargs... |
934b19fce364275d066951c97d409937 | Tests the default credential values, and also the AttributeError mechanism. | [
{
"docid": "77e6af9a04b26d3bd156e277a5cd3c28",
"score": "0.79476535",
"text": "def test_credential_default_values():\n creds = Credentials()\n assert creds.url is None\n assert creds.token is None\n assert creds.org_key is None\n assert creds.ssl_verify\n assert creds.ssl_verify_hostna... | [
{
"docid": "922fa2958aad9658ef8a568eeae6abc2",
"score": "0.7252466",
"text": "def test_attribute_defaults(self):\n creds = NokiaCredentials()\n self.assertEqual(creds.access_token, None)\n self.assertEqual(creds.token_expiry, None)\n self.assertEqual(creds.token_type, None)\n... |
ee1068b325a01087d8763130e600b894 | Perform data extraction, geocoding, analysis, and rendering. | [
{
"docid": "a0905a75696c2d5f05df9afb5332cecb",
"score": "0.0",
"text": "def aorta(filename, attribute, maxl, minl, v1, v2, v3, lb, sb):\r\n global start\r\n global stop\r\n lb.configure(text='Reading File')\r\n df = pd.read_csv(filename)\r\n df = df.drop(0)\r\n\r\n df['Date'] = pd.to_d... | [
{
"docid": "284d7694dea5621aaa51c881c1394f8b",
"score": "0.67977667",
"text": "def run(self):\n self.prepare()\n self.extract()\n self.transform()\n self.load()\n self.finalize()\n pass",
"title": ""
},
{
"docid": "c476f77c1cdbb522f954f429c6a30600",
... |
d9cf02e19c112c1bbb11bbb69e3a7402 | GET /reports/new/ must return status code 200 | [
{
"docid": "63920f5807fbde4b4725d59f0dcc6378",
"score": "0.0",
"text": "def test_get(self):\n self.assertEqual(200, self.response.status_code)",
"title": ""
}
] | [
{
"docid": "b03f4adc79057fbdaa7d623898943a07",
"score": "0.63913965",
"text": "def test_create_invalid_report_admin(self):\n request = self.factory.post(\n '/api/inventories/', {})\n force_authenticate(request, user=self.test_admin)\n response = InventoryReportListCreateV... |
eb453cae7da383dd193905d275001909 | View the reputation points leaderboard. | [
{
"docid": "29930feaccab07b2952f3ab60de65a3e",
"score": "0.7601583",
"text": "async def leaderboard(self, ctx):\n leaderboard = await self.bot.database.get_top_reps()\n embed = discord.Embed(colour=EMBED_ACCENT_COLOUR)\n embed.set_author(\n name=\"Reputation Leaderboard\"... | [
{
"docid": "6375733d6284707ba6aaf4b11d8578d3",
"score": "0.70809627",
"text": "def show_leaderboard():\n leaderboard_data = get_sorted_scores()\n return render_template(\"leaderboard.html\", leaderboard_data=leaderboard_data)",
"title": ""
},
{
"docid": "437d7c5565d4948aa3c19c5e239cc8f... |
0f56ae144be87b4aecaf10b5786b8608 | Transform ref_point using same transforms as those applied to data. | [
{
"docid": "9289eeb4c4a6ca5e01b8cee83ee0a20b",
"score": "0.74598885",
"text": "def _transform_ref_point(\n self, ref_point: Dict[str, float], padding_obs_data: ObservationData\n ) -> Dict[str, float]:\n metric_names = list(self._metric_names or [])\n objective_metric_names = list... | [
{
"docid": "aafeb46b7f285a3e23a27911fa1f244f",
"score": "0.7291867",
"text": "def TransformPoint(self):\r\n pass",
"title": ""
},
{
"docid": "80496a8057adef353ffb0a6fb1d02cc9",
"score": "0.7044938",
"text": "def TransformPoints(self):\r\n pass",
"title": ""
},
{... |
eda41d01a55a56d65e9d5b161b5a42cc | Put entry in dct. If already there, check it's the same. | [
{
"docid": "aac336579641b8bb8a0b9fd44be61b14",
"score": "0.8130835",
"text": "def put(dct, entry):\r\n id = int(entry['id'])\r\n if id in dct:\r\n if entry == dct[id]:\r\n pass\r\n else:\r\n print entry\r\n print dct[id]\r\n assert False\r\... | [
{
"docid": "74c66a751f436991c28c48de00e0304b",
"score": "0.60856813",
"text": "def addEntry(self, key, dictVal):\n hashBucket = self.buckets[key%self.numBuckets]\n for i in range(len(hashBucket)):\n if hashBucket[i][0] == key:\n hashBucket[i] = (key, dictVal)\n ... |
7637cc26122219aaeb441ef46942a6d2 | Easy Train Test Split call. | [
{
"docid": "07a292fa1648fecd88b751277960cde6",
"score": "0.6491326",
"text": "def TrainTestSplit(X, Y, R=0, test_size=0.2):\n return train_test_split(X, Y, test_size=test_size, random_state=R)",
"title": ""
}
] | [
{
"docid": "338cdcf7bb94999d0d6357a807bab52c",
"score": "0.7243466",
"text": "def patched_train_test_split(*args, **kwargs):\n # pylint: disable=no-method-argument\n original = gorilla.get_original_attribute(model_selection, 'train_test_split')\n\n def execute_inspections(op_id, cal... |
1ef65124cda4457e6d1bae66faf6be05 | Asserts un usuario no puede ver la lista de comentarios de un album privado | [
{
"docid": "f930d48d8b63a1bc7c65f228b0c9fa7f",
"score": "0.7511498",
"text": "def test_given_existing_user_when_list_comment_of_private_album_then_fail(self):\n # Arrange\n user = UsuarioFixture().create()\n \n album_fixture = AlbumFixture()\n album_fixture.default_acc... | [
{
"docid": "1ceeb5fb1d2d91f655dfb3b4bfce0bb5",
"score": "0.67601097",
"text": "def test_given_existing_user_when_list_comments_of_public_album_then_see_comments_belong_given_album_success(self):\n # Arrange\n user = UsuarioFixture().create()\n \n album_fixture = AlbumFixture(... |
334f6cdf19b6c8625c88452985f78908 | The Huffman compression algorithm which compresses the symbols "000" to "111" based on their frequencies. | [
{
"docid": "0c789003af40ce578fb302bf36ff424f",
"score": "0.66966075",
"text": "def HuffmanCompression(D: Union[np.ndarray, Dict[str, int]]) -> Tuple[Dict[str, str], DefaultDict[str, int]]:\n compressedDB = defaultdict(int)\n chars = freq = None\n # If the database is a dictionary, process it ap... | [
{
"docid": "a28069afcd22e3460f263258dc2433c7",
"score": "0.7296862",
"text": "def encode(symb2freq):\r\n huffCode = namedtuple('huffCode', ' symbol code')\r\n lista = []\r\n heap = [[wt, [sym, \"\"]] for sym, wt in symb2freq.items()]\r\n heapify(heap)\r\n while len(heap) > 1:\r\n l... |
31d033cc82d32e22921df7903354c64b | Method that generates the dataset and performs augmentation in case enabled | [
{
"docid": "70c0eb3495fba542eedac61fb1518c1d",
"score": "0.0",
"text": "def get_dataset(config):\n # dataset statistics: https://discuss.pytorch.org/t/normalization-in-the-mnist-example/457\n data_mean = 0.1307\n data_stddev = 0.3081\n\n transforms = [torchvision.transforms.ToTensor(),\n ... | [
{
"docid": "07a991037dd21221b0577b5aaca8428f",
"score": "0.6980123",
"text": "def test_data_augmentation():\n data = bz2.BZ2File('src/tests/dataprocessing/affectNet_sample.pbz2', 'rb')\n df = cPickle.load(data)\n\n x, y = affect.preprocess_data(df)\n x, y = affect.clean_data_and_normalize(x,... |
66ac20af48f8b5e02bd54d9a86c45a5a | active_thread_priority(cfi_gen_sptr self) > int | [
{
"docid": "a33e52697845d5ead872ccc46c11c439",
"score": "0.85378665",
"text": "def active_thread_priority(self):\n return _my_lte_swig.cfi_gen_sptr_active_thread_priority(self)",
"title": ""
}
] | [
{
"docid": "b55d3d743ab2f15b11d8c6a1e248a910",
"score": "0.83537924",
"text": "def active_thread_priority(self):\n return _my_lte_swig.dci_gen_sptr_active_thread_priority(self)",
"title": ""
},
{
"docid": "c417f577ec9e7dadad8ca80f5405b4de",
"score": "0.8266334",
"text": "def a... |
b1d2b42da53429a0ea3c80e7d9fac1a1 | the sum of ratio must equal to 1 | [
{
"docid": "7d1557dc3a5a1bf229ca9f2e5bc8a903",
"score": "0.0",
"text": "def split_Train_Test_Data(data_dir, ratio):\r\n dataset = ImageFolder(data_dir) # data_dir精确到分类目录的上一级\r\n character = [[] for i in range(len(dataset.classes))]\r\n for x, y in dataset.samples: # 将数据按类标存放\r\n charac... | [
{
"docid": "e540e26756b22115e2f811ea4aad52c7",
"score": "0.7472728",
"text": "def ratio(self):\n pass",
"title": ""
},
{
"docid": "e540e26756b22115e2f811ea4aad52c7",
"score": "0.7472728",
"text": "def ratio(self):\n pass",
"title": ""
},
{
"docid": "e540e267... |
cb561c571a88b19c636e2904f461b665 | Get value of key in metadata file dict. | [
{
"docid": "1d06cbe8af841874ef8e3fe11e35fef5",
"score": "0.76708704",
"text": "def get_from_metadata_file(cls, dirpath, key):\n fullpath = os.path.join(dirpath, cls.metadata_filename)\n if os.path.exists(fullpath):\n with open(fullpath, 'rb') as ifh:\n d = pickle.... | [
{
"docid": "fdcf88d1de45ebc3a51e78a7326b9fcf",
"score": "0.7239693",
"text": "def _get_metadata(self, key):\n try:\n val = self.pod['metadata'][key]\n except (KeyError, TypeError):\n _log.debug('No %s found in pod %s', key, self.pod)\n return None\n ... |
5c5ed9a97b3694924465816aef676480 | Check if all files are available before going deeper | [
{
"docid": "e6972172325e0f574f4e7a7133e53c0b",
"score": "0.59060025",
"text": "def check_before_run(self):\n if not osp.exists(self.dataset_dir):\n raise RuntimeError('\"{}\" is not available'.format(self.dataset_dir))\n if not osp.exists(self.train_dir):\n raise Runt... | [
{
"docid": "62ef37979051d2c0dbd9aa09ffc24a8c",
"score": "0.72828716",
"text": "def dir_ready(path): \n files = get_top_level_files(path)\n return len(files) > 0",
"title": ""
},
{
"docid": "d9a328575ad702a323f7305f026f4507",
"score": "0.6978389",
"text": "def scan(self):\n ... |
ca42f6eef5fe72059e7c74868c838763 | Check for known face. Return True and name if a face is recognized after 10 seconds otherwise return False, this method is also able to take images with regular webcams | [
{
"docid": "e19a957c878eb2968ea10a72cd00597c",
"score": "0.7239366",
"text": "def known_face(use_nao=True, timeout=True):\n # this stuff was in the main so I put it here\n # fileDir = os.path.dirname(os.path.realpath(__file__))\n fileDir = os.path.join(os.path.dirname(__file__), '')\n modelD... | [
{
"docid": "9653a7cf0dd78e76556db5bbdf2a997d",
"score": "0.7215693",
"text": "def face_detection(self) -> bool:\n cascade = CascadeClassifier(data.haarcascades + \"haarcascade_frontalface_default.xml\")\n for _ in range(20):\n ignore, image = self.validation_video.read() # read... |
7105b31703cb96fb21a04f9bcca0e3b7 | Add track to playlist | [
{
"docid": "15a26a618a8e29fa4482209cf5c63ceb",
"score": "0.7280744",
"text": "def _add_track(self, token, playlist_name, playlist_id):\n category_id = db.session.query(Category).filter_by(name=playlist_name).first().id\n playlist_id_db = db.session.query(Playlist).filter_by(name=playlist_n... | [
{
"docid": "5a2b9c76e830e3fd25acca6023db3f2e",
"score": "0.82097757",
"text": "def addToPlaylist(self, track):\n #playlist must already exist - TODO: Write code to generate playlist\n playlist = self.data.find(\"/PLAYLISTS/NODE[@TYPE='FOLDER']/SUBNODES/NODE[@TYPE='PLAYLIST'][@NAME='Auto-Cu... |
9fd26661050b991b6f943d1839838ff4 | create spark session singleton | [
{
"docid": "45cdbd6311a89744c2d5a8886058da2e",
"score": "0.75396127",
"text": "def create_spark_session():\r\n spark = SparkSession \\\r\n .builder \\\r\n .config(\"spark.jars.packages\", \"org.apache.hadoop:hadoop-aws:2.7.0\") \\\r\n .getOrCreate()\r\n return spark",
"tit... | [
{
"docid": "554b2f15e15e9ec5beee4734dc8b4df0",
"score": "0.8047757",
"text": "def get_session():\n return SparkSession.builder.config(conf=SparkConf()).getOrCreate()",
"title": ""
},
{
"docid": "d6ae01809c2fbcd7f876bc0b068dd333",
"score": "0.8015288",
"text": "def create_spark_ses... |
dbe20b962bcf7dac313794a3b2d4e57f | A utility function that tests whether the value of the next token label equals a given token label. This method consumes a token from the lexer if and only if there is a match. Either way, a boolean is returned indicating the match status. If `consume` is false then no tokens will ever be consumed. Otherwise, and by de... | [
{
"docid": "51d2ad81eb6f6f9334d17ef83afa5c9e",
"score": "0.75924224",
"text": "def match_next(self, token_label_to_match, peeklevel=1, consume=True,\n raise_on_fail=False, raise_on_success=False,\n err_msg_tokens=3):\n # TODO: Consider a way for users to define... | [
{
"docid": "9605df346bee22f91cce408c8e5a75e9",
"score": "0.501809",
"text": "def __consume(self, expected: TokenType, err_msg: str):\n if self.__match(expected):\n token = self.__peek()\n self.__advance()\n return token\n else:\n error_handler.parse_error(self.__peek(), err_msg... |
06f5b2af157552051cd0dfb4628f5923 | Method to run subprocesses. Calling this will capture the `stderr` and `stdout`, please call `subprocess.run` manually in case you would like for them not to be captured. | [
{
"docid": "eb107510d71332148c82e42f766e07cc",
"score": "0.5504604",
"text": "def run_subprocess(\n command: Union[str, List[str]],\n folder: Optional[Union[str, Path]] = None,\n check=True,\n **kwargs,\n) -> subprocess.CompletedProcess:\n if isinstance(command, str):\n command = c... | [
{
"docid": "f5688d99c31b258c8534ce6ac571aa0c",
"score": "0.7337682",
"text": "def captured_run(*args, **kwargs):\n proc = subprocess.run(\n *args, **kwargs, stdout=subprocess.PIPE, stderr=subprocess.PIPE\n )\n return proc.returncode, proc.stdout, proc.stderr",
"title": ""
},
{
... |
1f4d4179cd968853da4e5fb2a6f4fb8d | Returns a logger object so that a given file can log its activity. If two loggers are created with the same name, they will output 2x to the same file. | [
{
"docid": "004bc6477ad75f3b157d32bd866799bb",
"score": "0.6979247",
"text": "def get(name, path='activity.log', is_debug_log=False):\n # SOURCE: http://stackoverflow.com/questions/7621897/python-logging-module-globally\n\n # formatter = IndentFormatter(\"%(asctime)s [%(levelname)8s] %(module)30s:... | [
{
"docid": "e56dcc006cd3c4b8ef1ffba27df2b49e",
"score": "0.8057711",
"text": "def get_logger(file_name:str):\n logger = logging.getLogger(file_name)\n formatter = logging.Formatter(\n \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n )\n ... |
c472743f52030deaf5de990780590f7a | Imports string as if CSV file. This is used in unit tests | [
{
"docid": "0b8aa7ad592fff084c01e1e19b36f1ec",
"score": "0.6923848",
"text": "def import_csv_string(self, csv_string):\n csv.register_dialect('spreadsheet', delimiter=',', quoting=csv.QUOTE_NONE)\n csv_reader = csv.reader(StringIO.StringIO(csv_string), 'spreadsheet')\n for row_key, ... | [
{
"docid": "d6070672033524f9ce53baf99a3704c0",
"score": "0.662369",
"text": "def __importCsv(self, event):\n self.__importFile(\",\", \"csv files (*.csv)|*.csv\", \"Import CSV file\")",
"title": ""
},
{
"docid": "33e993137b4edcc3fbd1690710fd7b72",
"score": "0.6558638",
"text":... |
5ad97563f6b71b5c046bd4bf03b2a5ee | Tests whether the parser is able to remove accents in a sentence. | [
{
"docid": "e454df3d7ba40f6d5044264afe1d6097",
"score": "0.74372214",
"text": "def test_parser_can_remove_accents(self):\n parser = Parser(\n \"bienvenue à lès brignoles\",\n STOPWORDS,\n ACCENTS,\n QUESTIONS\n )\n result = parser.remove_a... | [
{
"docid": "89fa645e87c860f8c72a5b0778d44014",
"score": "0.7088505",
"text": "def test_remove_all_accent_string_with_accent(self):\r\n assert Parser().remove_all_accent(\"clélio favoccià\") \\\r\n == \"clelio favoccia\"",
"title": ""
},
{
"docid": "dd1fa717b226a27361066e... |
acc7a63206020ff5d6bb00ecbe4b662b | Returns a static stroke object for testing | [
{
"docid": "dbd8f386dcf0a1a9e6e9c93e5f877671",
"score": "0.67312616",
"text": "def get_test_stroke():\n from freestyle.types import Stroke, Interface0DIterator, StrokeVertexIterator, SVertex, Id, StrokeVertex\n # points for our fake stroke\n points = (Vector((1.0, 5.0, 3.0)), Vector((1.0, 2.0, ... | [
{
"docid": "26e01ae5b10c2afe2c53fe297785f838",
"score": "0.6220379",
"text": "def _get_stroke(self):\n return self._stroke",
"title": ""
},
{
"docid": "09db3748c30cbedf546cd383e9fa9b06",
"score": "0.6145492",
"text": "def getDrawing1():\n\n D = Drawing(400, 200)\n D.add(... |
02dd63739c339d90408253e7a3b72ff9 | test user cant register twice | [
{
"docid": "671b34155f4e7e9e3f48771f946fc9e0",
"score": "0.77129304",
"text": "def test_user_only_registers_once(self):\n\n\t\tuser_data = {'username': 'test', 'password': 'test1023'}\n\t\tresult = self.app.post('/api/v1/auth/register', data = json.dumps(user_data), content_type = 'application/json')\n\... | [
{
"docid": "6cc28e7b43510fbd583a6120a419a875",
"score": "0.79305637",
"text": "def test_already_registered_user(self):\n\n res = self.client.post('/auth/register', data=self.user_data)\n self.assertEqual(res.status_code, 201)\n second_res = self.client.post('/auth/register', data=se... |
3fb46fe5c6fa1ad110ecaa85dafd6cef | Returns the DulmageMendelsohn partition of the incidence graph of the provided variables and constraints. | [
{
"docid": "b73a53526604bd10459f4662f4c284fc",
"score": "0.7901976",
"text": "def dulmage_mendelsohn(self, variables=None, constraints=None):\n variables, constraints = self._validate_input(variables, constraints)\n matrix = self._extract_submatrix(variables, constraints)\n\n row_pa... | [
{
"docid": "657d43145eaa2baf31afc3fd98ae445c",
"score": "0.57001877",
"text": "def undiGraphToMarkovNetwork(graph,variables=dict(),domainSize=2):\r\n nodes=graph.nodes()\r\n cliques=getAllMaximalCliquesUndiGraph(graph)\r\n definedVariables=variables.keys()\r\n mn=gum.MarkovNet()\r\n for n... |
8b6239c4aff78f55d89d8985bbe1f02d | Canonicallize the element to a uniform string | [
{
"docid": "ccd7c06bcf8e2c94ec2333f1f448446e",
"score": "0.0",
"text": "def canon(self, e):\n s = \"\"\n # return strings based on what type the object is\n if isinstance(e, dict):\n return self._wrap_dict(self._process_dict(s, e))\n elif isinstance(e, list):\n ... | [
{
"docid": "13a46b4160cfe2ea93dbb236f8134e0b",
"score": "0.6361481",
"text": "def canonical(self) -> str:\n return self._canonical",
"title": ""
},
{
"docid": "19df0ba24defec0f26bc8ea0963ac6e5",
"score": "0.6322695",
"text": "def stringify_affiliation_rec(node):\n parts = _... |
573e059c748a01c6b64a8b842d0f319b | compute the connection time based on speed and distance | [
{
"docid": "74dec35c36ad2ebb90f4a0ec67e53438",
"score": "0.7062803",
"text": "def _compute_rsu_connection_time(self):\n\n self.contact_time = []\n for s in self.car_speed:\n speed_in_ms = s/3.6\n distance_in_m = 1000*self.rsu_distance\n self.contact_time.ap... | [
{
"docid": "6481d0852a6287332ce706d3aaafdd06",
"score": "0.68263894",
"text": "def _get_distance_time(self):\n try:\n\n ds = my_dist_class.return_distance(self.ozone, self.dzone)\n # ds = np.ceil(\n # # self.DIST_MAT.query(\n # # \"PULocatio... |
ca961428184200e1478e260d1fffe2fb | Partial pipeline for preprocessing raw doc 1. tokenize using wordbreak 2. remove punctuation, whitespace 3. lowercase | [
{
"docid": "af00f352315e097ec49060a2ad363845",
"score": "0.75026083",
"text": "def _doc_pre_pipeline(self, doc):\n\n for token in words(doc):\n token = token.translate(self._pw_remove_table)\n token = token.lower()\n if not token:\n continue\n ... | [
{
"docid": "acb41e359bfd7490763ec6f40b44b52b",
"score": "0.72116965",
"text": "def preprocess(self, text):\n # Split by white space\n text = text.lower()\n #words = text.split()\n\n # Do preprocessing by removing replacing tokens with empty strings\n # or changing toke... |
62c4d12bee7a750060e0852cacd22721 | Creates a convolutional layer for the network | [
{
"docid": "12b31323bf75ffe525543ae1f187d4a8",
"score": "0.6913519",
"text": "def create_conv2d(self, x, w, b, stride = 1, name = None):\n x = tf.nn.conv2d(x, w, strides=[1, stride, stride, 1], padding='VALID', name = name)\n x = tf.nn.bias_add(x, b)\n return tf.nn.relu(x)",
"title": ""
}... | [
{
"docid": "9b18c57393e3ffa0fdd332093dad271f",
"score": "0.79682297",
"text": "def createConvolutionLayer(inputLayer, kernelHeight, kernelWidth, channelSize, kernelCount, strideX, strideY):\r\n \r\n \r\n weights = tf.Variable(tf.truncated_normal([kernelHeight, kernelWidth, channelSize, kernelCo... |
acf117360a21a1bbc9a3c05a5cf6051f | Add new remote server IP address as additional location, can be used for running dhcp server From all added locations all files on clean up will be downloaded to specific local location | [
{
"docid": "fbc6e2a3d0c8f6fd90bec7e2fc231b98",
"score": "0.5117198",
"text": "def check_remote_address(remote_address):\n if remote_address not in world.f_cfg.multiple_tested_servers:\n world.f_cfg.multiple_tested_servers.append(remote_address)",
"title": ""
}
] | [
{
"docid": "8e447d7652e79472a573a51834cef2af",
"score": "0.6541148",
"text": "def config_add_path():\n # Name of the remote\n # [positional]\n name: Option\n\n # Source path\n # [positional]\n source: Option\n\n # Destination path\n # [positional]\n dest: Option\n\n cfg = g... |
11c676fc12f1e4891f99a7e1e073cb55 | Calculates needed space for widget content. | [
{
"docid": "de4f06c415bdf6dee842b9a400786d81",
"score": "0.55907434",
"text": "def sizeHint(self):\n #if not self._autosizeFlag:\n # return QSize(self.WIDTH, self.HEIGHT)\n \n self._scaleWidth = 1 # for getDistance()\n self._scaleHeight = 1\n \n ... | [
{
"docid": "1155a86a800520373ac0f383fe60deba",
"score": "0.6282941",
"text": "def compute_size(self):\n # Get current monitor number\n screen = self.window.get_screen()\n monitor_n = screen.get_monitor_at_window(self.window.get_window())\n # and its width\n scr_width =... |
0c6921aceb94749aa6b6642aaa0ea885 | x = load_data() y = load_data(filename='train2014_targets.npy') | [
{
"docid": "1072289ac577dec90c9674646ada4184",
"score": "0.7426716",
"text": "def load_data(path=data_path,filename='train2014_inputs.npy'):\n return np.load(open(path+filename,'r'))",
"title": ""
}
] | [
{
"docid": "cfa423a7262af569aa74eecca0e47e49",
"score": "0.7542153",
"text": "def load_train_data():\n X = np.load('../input/X_train.npy')\n y = np.load('../input/y_train.npy')\n\n X = X.astype(np.float32)\n X /= 255\n\n np.random.seed(seed)\n np.random.shuffle(X)\n np.random.seed(s... |
c9546f4c5d727301dbd5d933b1aa8c44 | Return True if the coordinate zoom is within the textual range. Range might look like "110" or just "5". | [
{
"docid": "5dae3fbc0dbe04dd9f431545d1a8e6dd",
"score": "0.70948666",
"text": "def in_zoom(coord, range):\r\n zooms = search(\"^(\\d+)-(\\d+)$|^(\\d+)$\", range)\r\n \r\n if not zooms:\r\n raise Core.KnownUnknown(\"Bad zoom range in a Sandwich Layer: %s\" % repr(range))\r\n \r\n mi... | [
{
"docid": "ee8bba99b5ecbbc0604f016688a089fc",
"score": "0.6414671",
"text": "def is_latlongzoom(instr):\n return (re.match(r'^\\s*[0-9.+-]+\\s*,\\s*[0-9.+-]+\\s*,\\s*[0-9.+-]+\\s*$', instr) != None)",
"title": ""
},
{
"docid": "22f45d12935bef870772e66811690144",
"score": "0.63187474",
... |
5c183dd391f4ef6a5586df5028e5cc42 | Annotate the plot with spectra line identifications. | [
{
"docid": "1eb1271c99282ddc0be6fe229f840681",
"score": "0.623306",
"text": "def annotate_lines(self, lines):\n # lines is list of tuple (obswlen, name)\n (xlow, xhigh) = self.axplot.get_xlim()\n (_, yhigh) = self.axplot.get_ylim()\n ypos = yhigh * 0.8\n ydelta = yhigh... | [
{
"docid": "517a5732970aff8a8cc25ea11fa467ec",
"score": "0.64772844",
"text": "def plt_spec_lines():\n\n for i in range(0, Molecule.species_count):\n mid_line = (Molecule.right_endpt[i] + Molecule.left_endpt[i]) / 2\n shift1 = Molecule.energy[i] - PlotParameter.energy_vshift\n sh... |
3299d307690f88859f4f8c980929956f | Helper that makes ul's of survivors. | [
{
"docid": "1221daa2f16253cbc0088a3ef29be390",
"score": "0.52719736",
"text": "def generation_html(children_list, recursion=False):\n output = '<ul>\\n'\n if children_list == []:\n return \"\"\n for child in children_list:\n... | [
{
"docid": "f12da939bd31f73d3f45c0a8db6ef9c1",
"score": "0.5558531",
"text": "def survivor(names, step):\n\n\tx = step - 1\n\tnext = step - 1\n\n\twhile len(names) > 1:\n\t\tnames.pop(next)\n\t\tnext = (next + x) % len(names)\n\treturn names[0]",
"title": ""
},
{
"docid": "a00d9d1b2b4a136f1c... |
7df19a930475e0746a2207f71f3a444d | check that detect zero parameters works | [
{
"docid": "5f0d8fb03363c103de54e05bdf38ccf2",
"score": "0.0",
"text": "def test_detect_special_parameters(self): \n \n expected = set(['I3x32', 'etaWS', 'conjg__CKM3x2', 'CKM1x2', 'WT', 'I1x32', 'I1x33', 'I1x31', 'I2x32', 'CKM3x1', 'I2x13', 'I2x12', 'I3x23', 'I3x22', 'I3x21', 'conj... | [
{
"docid": "17b43d7805ad3ca46fd86dec2647d914",
"score": "0.75550795",
"text": "def _getzero_check(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "5eb4d97ff5ea5e534d14ab8d79542583",
"score": "0.68417084",
"text": "def is_zero(self):\n raise NotImplemented... |
7758ec47526a6a5d91f6c907195b382e | Decorator to register config source. Configuration source is a callable with one required argument configuration object to populate. It may have other required and optional arguments. | [
{
"docid": "61adbc54e1fc95858d9a24d3a7aabc04",
"score": "0.72159165",
"text": "def config_source(source, config_type='dict', force=False):\n def wrapper(f):\n group = _config_sources[config_type]\n if source in group and not force:\n raise AssertionError('Already registered: ... | [
{
"docid": "4a7b79b255267a1c1647426b5c034cae",
"score": "0.6829033",
"text": "def with_config_source(self, config_source):\n self.add_config_source(config_source)\n return self",
"title": ""
},
{
"docid": "7b897da9424b7612ad6f0a34ce951dc2",
"score": "0.62327915",
"text"... |
2826709e18c7c69a2f13705c2b42a249 | Check whether given key exists in the storage. | [
{
"docid": "fa29db7f0ddd9ddbcc8d10b0643c2c42",
"score": "0.8047286",
"text": "def exists(self, key):\n raise NotImplementedError",
"title": ""
}
] | [
{
"docid": "d342848cde6652d44924809003edcf8c",
"score": "0.8223379",
"text": "def exists(self, key):\n return key in self.store",
"title": ""
},
{
"docid": "f6e7f2c042f51cced36d1e93683ef87a",
"score": "0.80804545",
"text": "def key_exists(self, key):\n return self.get_c... |
5cf519e7b3d733d205b310c40cab2f3b | Builds the ServerSet based on the string passed to the function | [
{
"docid": "56892db44aaf227b717a6ba7dee89fd5",
"score": "0.5113373",
"text": "def from_slurm_list(cls, string:str):\n servers = []\n partition = []\n regex = r\"([a-z]+)(\\[(\\d+,?|\\d+[-]\\d*)+\\]|\\d)\"\n for name, whole, _ in re.findall(regex, string):\n partiti... | [
{
"docid": "c698ba992fc665300714d3a85cffef5b",
"score": "0.53885657",
"text": "def _make_set(var):\n if var is None:\n return set()\n if not isinstance(var, list):\n if isinstance(var, str):\n var = var.split()\n else:\n var = list(var)\n return set(va... |
30ac5fa669a8ecd7bc7812f2e763041d | convert angular distance, position angle, and instrument rotator angle to position on the cold plate | [
{
"docid": "9091d304b7eb567d5fa388cb8a5d44b2",
"score": "0.0",
"text": "def _addpad2xy(ang_dist_d, p_ang_d, inr_d):\n t = 90.0-(p_ang_d-inr_d)\n x = np.cos(np.deg2rad(t))\n y = np.sin(np.deg2rad(t))\n return x, y",
"title": ""
}
] | [
{
"docid": "5a178f5d0c1537269618fad5b084c085",
"score": "0.6049731",
"text": "def translate_and_rotate(detection_coverage,center,index,angle):\n lobe = detection_coverage[index].lobe\n return translate(rotate(lobe,angle),center)",
"title": ""
},
{
"docid": "3a8815282d60d9031b5edccbf093... |
0b92ad0a1411b245bfc5ee1adb466918 | Sets the orph_acode of this AllClinicalEntityInner. | [
{
"docid": "95cd10116ad403c0000388a633f69d17",
"score": "0.8471786",
"text": "def orph_acode(self, orph_acode: int):\n\n self._orph_acode = orph_acode",
"title": ""
}
] | [
{
"docid": "070880d3e8e73536ab931ec7c6ecbd7b",
"score": "0.68790334",
"text": "def orph_acode(self) -> int:\n return self._orph_acode",
"title": ""
},
{
"docid": "1e1c422fd06122e03b7e28f3701dd8b8",
"score": "0.58737314",
"text": "def alcohol_nambca_code(self, alcohol_nambca_co... |
270edec675db2162eb577ddf0a3627a6 | Processes a PHEME tweet topic | [
{
"docid": "efd8bf64eae990a5ff2be51184961626",
"score": "0.0",
"text": "def processCategory(path):\n return [processTweetFolder(path + '\\\\' + tweetFolder, tweetFolder)\n for tweetFolder in os.listdir(path)]",
"title": ""
}
] | [
{
"docid": "ec2d35081a91dc4331f1f408c47b8ac5",
"score": "0.6758134",
"text": "def process_tweet(tweet):\n data = {'cmd': None,\n 'player': None,\n 'id': None,\n 'flag': None}\n text = tweet.full_text.lower()\n parts = text.split()\n\n # if the tweet has more ... |
687306ade1c3e9045e854107329a2b13 | Solves the linear equation system ax = b for x. Inputs | [
{
"docid": "3e2a107766d9ac334518820327d3047d",
"score": "0.58857983",
"text": "def linearsolver(a, b):\n n, k = a.shape \n augmented = np.c_[a, b] # augment coeff matrix with constants\n # ranks\n rank_a = np.linalg.matrix_rank(a)\n rank_augmented = np.linalg.matrix_rank(augmented)\n ... | [
{
"docid": "2d9985fbf8b50443a132f1b5e0cef9bb",
"score": "0.8001566",
"text": "def LinearEquationSolver(A, b):\n return solve(A,b)",
"title": ""
},
{
"docid": "6aa2401580e0cacb84c3e48056587f51",
"score": "0.78103673",
"text": "def linear_equation_solver(A, b):\n A = np.array(A)\... |
4c4319526216d75cad864ec588286e07 | Initialize and archive a directory. | [
{
"docid": "32f305018df589536570c77793b3707f",
"score": "0.7136352",
"text": "def _zip_directory():\n # compress directory\n shutil.make_archive(path, ZIP, path)\n\n # delete uncompressed directory\n if remove:\n shutil.rmtree(path)",
"title": ""
}
] | [
{
"docid": "1962984ac69b2fae8fc56421507535c0",
"score": "0.66676486",
"text": "def _unzip_directory():\n if not os.path.exists(archive_path):\n os.mkdir(archive_path + \"/\")\n shutil.unpack_archive(\"%s.%s\" % (archive_path, ZIP), extract_dir=archive_path)",
"title": ""
}... |
20caadb329100a99690e943e8f6c8a1c | r"""Creates a new ConsumerGroup in a given project and location. | [
{
"docid": "83b634c97e7afe7f1c7783396b29643e",
"score": "0.0",
"text": "def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)",
"title": ""
}
] | [
{
"docid": "26ed96200cd06ef37e551ffd0f266087",
"score": "0.66833925",
"text": "def create_group(self, name, description=None):",
"title": ""
},
{
"docid": "4dcac98d45f9863597f62c26f4108fc2",
"score": "0.632166",
"text": "def create_rg(self):\n azure_cli_run(\"group create -l {... |
9d4222829eb58cdfc1cf654e4354aac9 | Calculate Brightness Temperature Again, you'll have to access appropriate metadata variables by their index number. | [
{
"docid": "5846bf56d5212a0821deb9e3554955c7",
"score": "0.0",
"text": "def bt_calc(rad, var_list):\n bt = (var_list[3] / np.log(var_list[2]/rad) + 1) - 273.15\n return bt\n #plt.imshow(bt, cmap='RdYlGn')\n #plt.colorbar()",
"title": ""
}
] | [
{
"docid": "b6e43d70f458cfb622bd4efcd671dbb0",
"score": "0.6685459",
"text": "def temperature(self):\n # perform one measurement in high res, forced mode\n self._write_register_byte(_BMP280_REGISTER_CONTROL, 0xFE)\n\n # Wait for conversion to complete\n while (self._read_byte... |
cffab3920c4d83557a98391a400c30c3 | Returns a queryset of users who joined within a given timeframe | [
{
"docid": "b8a9691c85d8128d7a7b7c46ce33ac75",
"score": "0.5213686",
"text": "def user_ids(self):\n return just_joined(\n minutes=self.minutes_since_signup,\n days=self.days_since_signup\n )",
"title": ""
}
] | [
{
"docid": "2aa2474f62b89a7863b309674faace28",
"score": "0.73507196",
"text": "def users_registered_within_period(users, window=1):\n threshold = utcnow() - datetime.timedelta(hours=window)\n filtered = users.filter(\n date_joined__gte=threshold\n ).order_by(\n '-date_joined'\n ... |
ac3d2581b95010523b87ccb2cb547941 | Make message from multiple sensor readings for device key. | [
{
"docid": "5ce38a831fc692b171186a567589b81f",
"score": "0.6766938",
"text": "def make_sensor_readings_message(\n self,\n device_key: str,\n sensor_readings: List[SensorReading],\n timestamp: int = None,\n ) -> Message:\n topic = self.SENSOR_READING + self.DEVICE_PA... | [
{
"docid": "b17e67957534721525784ecdcbb173d8",
"score": "0.60640067",
"text": "def make_sensor_reading_message(\n self, device_key: str, sensor_reading: SensorReading\n ) -> Message:\n topic = (\n self.SENSOR_READING\n + self.DEVICE_PATH_PREFIX\n + devic... |
34aba6c22545ff88c6c765471d69fd2a | A shortand to create an original Article and its translation. | [
{
"docid": "5d2e225981e4df530ec9820e1b8a9211",
"score": "0.6733866",
"text": "def multilingual_article(**kwargs):\n langs = set(kwargs.pop(\"langs\", []))\n contents = kwargs.pop(\"contents\", {})\n translations = {}\n\n # Create original object\n original = ArticleFactory(**kwargs)\n ... | [
{
"docid": "a13a1997c353eade0d83338d86918ad0",
"score": "0.6235401",
"text": "def maketrans(self, *args, **kwargs): # real signature unknown\n pass",
"title": ""
},
{
"docid": "d1713bd4c92bd95b2575278cf87f58e5",
"score": "0.6148261",
"text": "def create_translation_entry(self,... |
f5f611dc1fa49edc0903b5086772d214 | Combine multiple generators into one | [
{
"docid": "cbfb857bc40adbf0cbc3a1fca182edd7",
"score": "0.7115147",
"text": "def multiplex(sources):\n return it.chain.from_iterable(sources)",
"title": ""
}
] | [
{
"docid": "c7a09ddd2a1ad01359b94e02267b5713",
"score": "0.73177344",
"text": "def combine_generators_no_random_crop(sharp_generator, blur_generator):\n while True:\n sharp_batch = sharp_generator.next()\n blur_batch = blur_generator.next()\n\n res = [sharp_batch, blur_batch]\n\n... |
96dd970d4d2f402366f34f7bc5e38ac1 | This function tokenize, remove stop words and apply lower case for every word within the text | [
{
"docid": "0e412cf780cdd57714e1ba1ad43bc1c9",
"score": "0.0",
"text": "def parse_sentence(self, text):\n\n final_list = []\n list_of_words = self.clean_text(text.split())\n i = 0\n plus = 1\n while i < len(list_of_words):\n if len(list_of_words[i]) < 2 and ... | [
{
"docid": "a9c24e059ba0d629375783d4818e3a20",
"score": "0.77378315",
"text": "def tokenize(txt: str):\n return [w.lower() for w in txt.split(' ')]",
"title": ""
},
{
"docid": "c5959a44e97cef37db948692cef781c8",
"score": "0.7713063",
"text": "def tokenize(self, text):\n wor... |
b63d48e3c4fe997cca7ed55c2e825fdf | Converts all schemes in a given object to its proper swagger representation. | [
{
"docid": "8f948b5ea876011ccdf22b5f90657f56",
"score": "0.5604471",
"text": "def _extract_schemas(self, obj):\n definitions = {}\n if isinstance(obj, list):\n for i, o in enumerate(obj):\n obj[i], definitions_ = self._extract_schemas(o)\n definitio... | [
{
"docid": "7f31adca643df64f561772f8c5bcdf1d",
"score": "0.6359533",
"text": "def swaggerish(self):\n\n # Better chosen dinamically from endpoint.py\n schemes = ['http']\n if self._customizer._production:\n schemes = ['https']\n\n # A template base\n output ... |
1eeb69bd9b832fc54ecb2ace1e7e5086 | Checks if the username and password are correct. Returns True if it is, False otherwise. | [
{
"docid": "fc87a52a3502d066792596aed153d276",
"score": "0.0",
"text": "def authenticate_account(self, username, password):\n\n self.session = validate_session(self.session)\n account = self.session.query(User).filter(User.username == username).first()\n # Check if account/username ... | [
{
"docid": "446a6f1c2a454e86e0924b6a75fccf40",
"score": "0.835001",
"text": "def check_auth(username, password):\n un = username == blp.login_credentials['user']\n pw = password == blp.login_credentials['pass']\n return un and pw",
"title": ""
},
{
"docid": "45aaaa68062e19e8cefcf511... |
ddabbd7b41297deab1ffbb5f91311cdb | Destroy all the stores. | [
{
"docid": "f9ebd72c0ef3741a5c880058e8813d2e",
"score": "0.81792104",
"text": "def destroy_stores(self):\n shutil.rmtree(os.path.join(DATA_STORE, self.name))",
"title": ""
}
] | [
{
"docid": "9f429cbeb9566b1fe28e72709e16eb8b",
"score": "0.7163623",
"text": "def cleanupStore(self):\n if self.store:\n self.store.cleanup()",
"title": ""
},
{
"docid": "ec3f31f5e70bd594f86b9c285dbaf905",
"score": "0.64436644",
"text": "def clean(self):\n lo... |
c41095549bd4ed7e3560b4e62f9c5644 | Toogle the feature /autosave | [
{
"docid": "3d88796a7e042d014f8f188692779a26",
"score": "0.6832002",
"text": "def autosave(connection):\r\n if not connection.protocol.autosave:\r\n connection.protocol.autosave = True\r\n return \"Autosave enabled.\"\r\n else:\r\n connection.protocol.autosave = False\r\n ... | [
{
"docid": "95232758d83058425927fc755b481bdc",
"score": "0.7093163",
"text": "def autosave_handler(self):\n self.autosave_timer_waiting = False\n if self.editor.document().isModified():\n self.autosave()",
"title": ""
},
{
"docid": "cbae35afd4db48fd9c88b845cb79de50",... |
edd8209b451c7f174cedf83f811e6ded | Builds appropriate phrase for app | [
{
"docid": "2ee64393237424f2194cb6e240785cf0",
"score": "0.0",
"text": "def _is_it_taps_aff(self, main_weather_data: MainWeatherData) -> str:\n self.temp = round(main_weather_data.temp, 1)\n\n if \".0\" in \"{:.1f}\".format(self.temp):\n self.temp = int(self.temp)\n\n if ... | [
{
"docid": "5068d9b7315d7647fd277a2378690c05",
"score": "0.6656243",
"text": "def gen_phrase_string(self):\n return self.post_process(self.gen_phrase())",
"title": ""
},
{
"docid": "f4c38d3360b740fb302558e517c732aa",
"score": "0.6161804",
"text": "def translate(self, phrase):\... |
599789f663255a4386290c952ba2936e | Removes points that are outside the interval determined by [min_percentile, max_percentile]. The percentiles are computed and applied to each axis (x,y,z) individually. | [
{
"docid": "4799caa088d6eb0a3b02e5d1a31d3f83",
"score": "0.70688254",
"text": "def remove_distant_and_close_points(cloud:np.ndarray, min_percentile:int=1, max_percentile:int=99)->np.ndarray:\n cloud = cloud.reshape(-1,3)\n\n min_x = np.percentile(cloud[:,0], min_percentile, interpolation='nearest'... | [
{
"docid": "4b77098adb812cbb946d3a783f6e7601",
"score": "0.6658416",
"text": "def test_exclude_percentile(self):\n data = np.copy(DATA)\n data[0:50, 0:50] = np.nan\n with pytest.warns(AstropyUserWarning,\n match='Input data contains invalid values'):\n ... |
8e02496a6c8282245427a4927c688a28 | Returns analytical solution for different moments of lognormal size distr | [
{
"docid": "b2866e65341a2e864981cd5a55805301",
"score": "0.6666449",
"text": "def analytic_moms_for_lognormal(mom, n_tot, mean_r, gstdev):\n if mom == 0:\n ret = n_tot\n else:\n ret = n_tot * mean_r**mom * math.exp((mom**2)/2. * math.pow(math.log(gstdev), 2))\n return ret",
"t... | [
{
"docid": "8d0eeec394c6cdc872ab50fcf4dab135",
"score": "0.65942085",
"text": "def lognormvariate(self, mu, sigma):\n pass",
"title": ""
},
{
"docid": "3d8a95f64a721e2186597f80c6ebf099",
"score": "0.6581352",
"text": "def lognormvariate(self, mu, sigma):\n pass",
"title": ""
... |
8dea5d2418572c602a423cc45cb5061a | Test to start the specified service | [
{
"docid": "bea8ebe3b44f298935e6ce5406c87de9",
"score": "0.7395312",
"text": "def test_start():\n mock_true = MagicMock(return_value=True)\n mock_false = MagicMock(return_value=False)\n mock_info = MagicMock(side_effect=[{\"Status\": \"Running\"}])\n\n with patch.object(win32serviceutil, \"S... | [
{
"docid": "06347100c5d25a23c13c075938520e7a",
"score": "0.7975998",
"text": "def start_service(service):\n subprocess.call([\"service\", service, \"start\"])",
"title": ""
},
{
"docid": "4502c4639ce33b3e8797f182e1151e8d",
"score": "0.78600645",
"text": "def test_stop_start(self):... |
3d4824220e2b76fe41c979db0d89d648 | Calculate valuation signals such as P/E and P/Sales ratios. | [
{
"docid": "8f6ca49ea6fe4c1d2298b36e8c742fc1",
"score": "0.0",
"text": "def val_signals(self, variant='daily', func=None,\n shares_index=SHARES_DILUTED):\n\n # Load the required datasets.\n # This is only really necessary if the cache-file needs refreshing,\n # bu... | [
{
"docid": "35f81c10fe1b2d0507b135e909721a2d",
"score": "0.6021242",
"text": "def npv(rate,values):\n \n \n return float()",
"title": ""
},
{
"docid": "51c6470dfd89042b82c41307358c6aea",
"score": "0.5861876",
"text": "def results( self ):\n A = AA = 0.0\n x = xx = 0.0\... |
ac3c81a73e77f650ede7a49991f02b27 | Set up all dataset config options. | [
{
"docid": "6a018c9b4bf9649f104ecf5b3e637a9e",
"score": "0.0",
"text": "def configure(self, repo_dir, inner_size, scale_range, do_transforms,\n rgb, shuffle, set_name, subset_pct, macro,\n contrast_range, aspect_ratio):\n assert (subset_pct > 0 and subset_pct <= ... | [
{
"docid": "efb231ed2b6b2f1ce3aa15434c6d4fb4",
"score": "0.7039832",
"text": "def dataset_setup(self):\n settings = self.settings\n if settings.crowd_dataset == CrowdDataset.ucf_qnrf:\n self.dataset_class = UcfQnrfFullImageDataset\n self.train_dataset = UcfQnrfTransfo... |
39a9a2507b6189f125ecdcb37d61cb15 | create roi of the liver | [
{
"docid": "25b582771929b6f1e3b27a9f7c715656",
"score": "0.0",
"text": "def find_ROI_segmentation(self):\n\n seg = self.__IsolateBody()\n seg_lung = self.__isolate_lung(seg)\n box = self.__built_box_roi(seg_lung)\n return box, seg_lung",
"title": ""
}
] | [
{
"docid": "47a1aeb5cb060a014f8725ac38518d33",
"score": "0.7261228",
"text": "def get_roi(self):\n start = self.image.roi.pos()\n size = self.image.roi.size()\n angle = self.image.roi.angle()\n self.ROI = [start[0],start[0] + size[0],\n start[1], start[1] ... |
10073b23c2a4f916a7b6da4d1f04804b | Test config entries are reloaded when new info is set. | [
{
"docid": "8e1f5233977f8257c1946dfb2fbc1ce9",
"score": "0.6789433",
"text": "async def test_new_info_reload_config_entries(hass, init_integration, mock_dashboard):\n assert init_integration.state == ConfigEntryState.LOADED\n\n with patch(\"homeassistant.components.esphome.async_setup_entry\") as ... | [
{
"docid": "cec03cd3d4109cf17a5d260e3e613182",
"score": "0.71862674",
"text": "def test_update_configuration_file(self):\n pass",
"title": ""
},
{
"docid": "29be3542c9b40c6e9f7ae7e822ab3b52",
"score": "0.71835166",
"text": "def test_update_default_configuration(self):\n ... |
052b79f70712e6d9033814bffb508da3 | Use this decorator to mark you ploogin function as a handler to call upon teardown. | [
{
"docid": "d19096ed85842fc259165a9fd2f7ad0a",
"score": "0.7959539",
"text": "def upon_teardown(f: Callable):\n return PlooginEventHandler(event=PlooginEvents.TEARDOWN, f=f)",
"title": ""
}
] | [
{
"docid": "122522864231496b572e2050164568f0",
"score": "0.70912457",
"text": "def teardown_func():\r\n pass",
"title": ""
},
{
"docid": "b304946a999ba13baed34b64e8f6f7fe",
"score": "0.6499198",
"text": "def teardown_appcontext(self, f):\r\n self.teardown_appcontext_funcs.app... |
ae1c483eb54cca9c8de99dade4522368 | load all plugins, warn if a plugin couldn't be loaded. | [
{
"docid": "5e96272094c0dc55ffcd58db3de76cb8",
"score": "0.6897457",
"text": "def load_all(self):\n # get the names of all required plugins\n names = samuraix.config.get('core.plugins', [])\n log.debug('loading %s', names)\n # get all available entrypoints and create a dictio... | [
{
"docid": "a0187f0401cfa952954bc543f1edc71f",
"score": "0.75690514",
"text": "def load_plugins():",
"title": ""
},
{
"docid": "b85d1c938ae197871c603b0950307892",
"score": "0.73343587",
"text": "def LoadPlugins(self):\n\t\tfor curfile in [f for f in glob.glob(os.path.join('plugins', ... |
d2de2d49a99e311fe7785f9523cffc0f | Remove a child handler. | [
{
"docid": "85b819a21028347d4916f20c7b44ca3d",
"score": "0.7669957",
"text": "def removeHandler(handler):",
"title": ""
}
] | [
{
"docid": "63c1c16a7834019e5cad6290cb3e4810",
"score": "0.7600718",
"text": "def remove_handler(self, handler):\n self.handlers.remove(handler)",
"title": ""
},
{
"docid": "3a2d370956a7b1319092fbc4335fbb48",
"score": "0.7523634",
"text": "def remove_handler(self, fd):",
"... |
fe2b6ed15d7c01e5da08eb0482b1fda2 | Look for currency code. | [
{
"docid": "5fa7adbf3f0a8e6d3acd86e2f51ea3b6",
"score": "0.6849101",
"text": "def look(query): \n filename = resource_filename(__name__, 'data/currency_code.json')\n with open(filename, 'r') as file:\n currency_code = json.load(file)\n \n def lookname(word):\n import re\n ... | [
{
"docid": "ee6e587f59dfd4d74745f86a071eb09a",
"score": "0.68294126",
"text": "def currency_code(self) -> str:\n return pulumi.get(self, \"currency_code\")",
"title": ""
},
{
"docid": "2ed1c8c097a4e983214ff610ddcc960f",
"score": "0.67247885",
"text": "def __getitem__(self, val... |
1be57fd6b647dff48b37a3519962bba8 | Assign groups to the usernames provided in the data | [
{
"docid": "8ab869b356ec5f6a72bf0fdc936d85c1",
"score": "0.7481766",
"text": "def assign_user_groups(self, group_name: str, rows: list[tuple[str, str]]) -> None:\n group = Group.objects.get(name=group_name)\n\n for username, _ in rows:\n user = User.objects.get(username=username... | [
{
"docid": "4195b1546c4302f61e324df7124c0277",
"score": "0.65661025",
"text": "def set_user_groups(self):\n # Notice that we only add or remove groups that we had\n # offered as options on the form, and don't touch any other\n # groups that the user might belong to.\n for val... |
98152f6bc8440d2b9e45186f6f94403b | GIVEN a flask application WHEN user makes advanced search with specified search inputs THEN return recipes satisfy search parameters | [
{
"docid": "2613a22a3e801f9c6d7ed8349eb75375",
"score": "0.67837054",
"text": "def test_advanced_search_results_applies_filter_to_recipes(self, test_client, db, search_term, allergies, diet,\n cal_range, time):\n response = advanced_se... | [
{
"docid": "d34d8d2e127a11f836cd282e7367d401",
"score": "0.7443953",
"text": "def search_recipes():\n if request.method == 'POST':\n cuisine = request.form.get(\"cuisine_name_filter\")\n allergen = request.form.get(\"allergen_name_filter\")\n meal = request.form.get(\"meal_type_f... |
4e02ba312122b710df2ee54808e84bad | Displays UI for a download error | [
{
"docid": "0e70bafff92bfcb6561858673ec8d1cb",
"score": "0.80256706",
"text": "def show_download_error(self, name, err):\n self.kodiUi.show_error(30952, self.language(30953).format(name, err))",
"title": ""
}
] | [
{
"docid": "0583d83d4d08c67fa3f074b1be0bc052",
"score": "0.70555574",
"text": "def download_message(self):\n\n print('Failed to find any DR %s files'% self.subset)\n print('')",
"title": ""
},
{
"docid": "9b23a29c0c13ae8fd955349fe1e02077",
"score": "0.67040294",
"text": "def _u... |
0bcbb126ae5223f3d1f5ca2950034367 | Validates if the output is not the same as input | [
{
"docid": "75fdf2e321d1a07332176bae092a4632",
"score": "0.60977846",
"text": "def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):\n\tif not inputVolumeNode:\n\t logging.debug('isValidInputOutputData failed: no input volume node defined')\n\t return False\n\tif not outputVolumeNode:\n... | [
{
"docid": "4435bb4b59cd7ac285ad925231272dda",
"score": "0.7131176",
"text": "def check_output(output, expected_output):\r\n o = copy.deepcopy(output) # so that we don't mutate input\r\n e = copy.deepcopy(expected_output) # so that we don't mutate input\r\n\r\n o.sort()\r\n e.sort()\r\n ... |